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University of Groningen
Missed opportunities? Germany and the transatlantic labor-productivity gap, 1900-1940Veenstra, Joost
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Missed Opportunities?
Joost Veenstra
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Publisher: University of Groningen, Groningen, The Netherlands
Printer: Ipskamp Drukkers B.V.
ISBN: 978–90–367–6791–0 / 978–90–367–6790–3 (eBook)
c©2014 Joost Veenstra
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system of any nature, or transmitted in any form or by any means, electronic, mechan-
ical, now known or hereafter invented, including photocopying or recording, without
prior written permission of the publisher.
This document was prepared using LATEX.
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Missed Opportunities?Germany and the Transatlantic Labor-Productivity
Gap, 1900–1940
PhD thesis
to obtain the degree of PhD at theUniversity of Groningenon the authority of the
Rector Magnificus, Prof. E. Sterkenand in accordance with
the decision by the College of Deans.
This thesis will be defended in public on
Thursday 20 February 2014 at 11.00 hours
by
Joost Veenstra
born on 23 March 1984in Leiden
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Supervisors:Prof. H.J. de JongProf. M.P. Timmer
Assessment committee:Prof. S.N. BroadberryProf. J. StrebProf. J.L. van Zanden
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Contents
List of Tables v
List of Figures vii
Acknowledgement ix
1 Introduction 1
2 Catching-Up with the Global Labor-Productivity Leader? 15
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 The transatlantic labor-productivity gap . . . . . . . . . . . . . . . . . . 28
2.5 Labor-productivity growth in interwar Germany . . . . . . . . . . . . . 36
2.6 Drivers of growth and catch-up . . . . . . . . . . . . . . . . . . . . . . . 40
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.A Representativeness of the industrial surveys . . . . . . . . . . . . . . . . 51
2.B Adjustment of the employment census . . . . . . . . . . . . . . . . . . . 54
3 The Yanks of Europe? 57
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.5 Technology in German manufacturing . . . . . . . . . . . . . . . . . . . 81
3.6 The long-term perspective . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.A Distance function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.B Global best-practice frontiers . . . . . . . . . . . . . . . . . . . . . . . . 95
3.C Labor-productivity growth at the frontier . . . . . . . . . . . . . . . . . 98
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ii Missed Opportunities?
3.D Robustness check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4 Industrial Output Growth in Pre-WW2 Germany 103
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.2 The time-series debate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.A German/UK comparative labor productivity in 1936 . . . . . . . . . . . 130
4.B Indices of German industrial output . . . . . . . . . . . . . . . . . . . . 132
5 Did a European Convergence Club Exist Before World War 1? 133
5.A Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
5.3 Purchasing power parities for pre-WW1 European countries . . . . . . . 141
5.4 Comparative productivity around 1910 . . . . . . . . . . . . . . . . . . . 145
5.5 Change of comparative labor productivity, 1870–1910 . . . . . . . . . . 150
5.6 Manufacturing and convergence at the country level . . . . . . . . . . . 154
5.7 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
5.A Sweden’s relative performance . . . . . . . . . . . . . . . . . . . . . . . . 163
5.B Value added estimates Germany . . . . . . . . . . . . . . . . . . . . . . 165
A Data Appendix 167
A.1 Pre-WW1 labor-productivity growth in German industries . . . . . . . . 167
A.2 Labor-productivity levels pre-WW1 Germany . . . . . . . . . . . . . . . 171
A.3 Labor-productivity levels interwar Germany . . . . . . . . . . . . . . . . 173
A.4 Labor-productivity levels pre-WW1 US . . . . . . . . . . . . . . . . . . 175
A.5 Labor-productivity levels interwar US . . . . . . . . . . . . . . . . . . . 177
A.6 Purchasing power parities (GER36/US35) . . . . . . . . . . . . . . . . . 178
A.7 Coverage and number of UVRs (GER36/US35) . . . . . . . . . . . . . . 179
A.8 Unit-value ratios GER09/US09 . . . . . . . . . . . . . . . . . . . . . . . 180
A.9 Unit-value ratios GER09/GER36 . . . . . . . . . . . . . . . . . . . . . . 185
A.10 Unit-value ratios GER36/US35 . . . . . . . . . . . . . . . . . . . . . . . 189
A.11 Value added (per employee) pre-WW1 Germany . . . . . . . . . . . . . 201
A.12 Value added (per employee) interwar Germany . . . . . . . . . . . . . . 203
A.13 Value added (per employee) pre-WW1 US . . . . . . . . . . . . . . . . . 205
A.14 Value added (per employee) pre-WW1 UK . . . . . . . . . . . . . . . . . 206
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Contents iii
References 207
Official publications 223
Nederlandse samenvatting 227
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List of Tables
2.1 Average number of employees per establishment . . . . . . . . . . . . . . 24
2.2 Purchasing power parities . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.3 Coverage and number of UVRs . . . . . . . . . . . . . . . . . . . . . . . 30
2.4 German/US comparative labor productivity in 1909 and 1936/35 . . . . 31
2.5 German/US comparative labor productivity: sample vs. full coverage . . 33
2.6 German average annual labor-productivity growth . . . . . . . . . . . . 37
2.7 Employment shares Germany and US . . . . . . . . . . . . . . . . . . . 39
2.8 Share of workers employed in large establishments . . . . . . . . . . . . 42
2.9 Distribution of employment over establishment-size classes . . . . . . . . 43
2.10 Representativeness of coverage statistical quarterlies . . . . . . . . . . . 53
2.11 Employment occupational census covered by the industrial surveys . . . 56
3.1 Electrification rates in German and US manufacturing . . . . . . . . . . 69
3.2 Horse power per 1,000 hours worked in manufacturing . . . . . . . . . . 75
3.3 Annual labor-productivity growth at the frontier . . . . . . . . . . . . . 77
3.4 Decomposition of the 1936/39 German/US labor-productivity gap . . . 80
3.5 Created labor-productivity potential at the frontier . . . . . . . . . . . . 99
3.6 Decomposition of the 1936/39 German/US labor-productivity gap . . . 100
4.1 Benchmark estimates of comparative labor productivity . . . . . . . . . 110
4.2 Unit-root test (augmented Dicky-Fuller) . . . . . . . . . . . . . . . . . . 117
4.3 Estimates of the state-space model . . . . . . . . . . . . . . . . . . . . . 120
4.4 Backward projections of comparative labor productivity . . . . . . . . . 124
5.1 Purchasing power parities . . . . . . . . . . . . . . . . . . . . . . . . . . 141
5.2 Idem, compared to other work . . . . . . . . . . . . . . . . . . . . . . . 142
5.3 Fisher purchasing power parities . . . . . . . . . . . . . . . . . . . . . . 144
5.4 Number of matched products . . . . . . . . . . . . . . . . . . . . . . . . 144
5.5 Comparative labor productivity . . . . . . . . . . . . . . . . . . . . . . . 146
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vi Missed Opportunities?
5.6 Laspeyres, Paasche and Fischer comparative labor productivity . . . . . 147
5.7 Employment share of manufacturing branches . . . . . . . . . . . . . . . 148
5.8 Comparative labor productivity in northwestern Europe . . . . . . . . . 149
5.9 Idem, compared to other studies . . . . . . . . . . . . . . . . . . . . . . 149
5.10 Comparative GDP per capita in northwestern Europe . . . . . . . . . . 155
5.11 Comparative labor productivity in sectors of the economy . . . . . . . . 157
5.12 Comparative labor productivity, alternative estimate . . . . . . . . . . . 163
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List of Figures
1.1 Comparative German/US labor productivity in manufacturing . . . . . 2
1.2 GDP per capita levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1 Peak and census years, 1900–1913 . . . . . . . . . . . . . . . . . . . . . 27
3.1 Estimating the frontier for industrial chemicals . . . . . . . . . . . . . . 63
3.2 Decomposition techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.3 Change of the frontier in industrial chemicals . . . . . . . . . . . . . . . 72
3.4 Distribution of manufacturing employment over capital-labor ratios . . . 74
3.5 Labor-productivity vs. machine-intensity differences . . . . . . . . . . . 81
3.6 Labor-productivity catch-up in two sequential steps . . . . . . . . . . . 88
3.7 German catch-up in manufacturing after WW2 . . . . . . . . . . . . . . 90
3.8 Frontiers for the years 1909, 1919, 1929 and 1939 . . . . . . . . . . . . . 95
3.9 Decomposition of growth potential . . . . . . . . . . . . . . . . . . . . . 98
4.1 Time series of output in German industry . . . . . . . . . . . . . . . . . 108
4.2 Backward projection of labor productivity (LP) . . . . . . . . . . . . . . 109
4.3 Logarithms of output series with breaking trend . . . . . . . . . . . . . 118
4.4 The state series, observed series and observation disturbance . . . . . . 121
4.5 Reconciliation with 1907 German/UK benchmarks . . . . . . . . . . . . 125
4.6 Idem, alternative 1936/35 labor-productivity benchmark . . . . . . . . . 130
5.1 Comparative labor productivity before WW1 . . . . . . . . . . . . . . . 151
5.2 Initial performance and subsequent labor-productivity growth . . . . . . 152
5.3 Dispersion of comparative labor productivity before WW1 . . . . . . . . 153
5.4 Dispersion of comparative GDP per capita before WW1 . . . . . . . . . 156
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Acknowledgement
This dissertation is a product of my own labor as well as of those who have supported
me in the process of writing it. I would like to use this opportunity to thank several
people who in various ways have offered encouragement and assistance. Without their
help I could not have written this thesis.
First and foremost, I am indebted to my supervisors, Prof. Herman de Jong and
Prof. Marcel Timmer. They created a friendly environment that inspired me with the
confidence to explore new research avenues. Originally trained as a historian, at the
outset of the project I was not familiar with the research methods that I ultimately
applied in this study. Even though the prospect of learning to understand new tools of
analysis daunted from time to time, I have always felt able to face this challenge under
the guidance of my supervisors. My gratitude goes out to Dr. Jan Jacobs, too, for
encouraging me to use econometric analysis to study history and for patiently showing
me the way.
The time working on my dissertation has been made truly special by Jop Wolter,
with whom I shared the office. Over the years Jop has become one of my closest friends
and has proved instrumental in my development as a person and researcher. Because of
his willingness to listen to and answer my questions, I often think of him as my unofficial
third supervisor. My beautiful wife, Laurie Reijnders, deserves special mention. It would
not have been possible to bring this project to a successful end without her loving care
and kind understanding. Returning home to Laurie is a profound joy each day. Also, I
owe a large debt to my family for their unwavering belief in my ability to overcome the
difficulties I encountered in the process of writing this dissertation.
In addition, I would like to acknowledge the financial support from the Netherlands
Organisation for Scientific Research as well as the Faculty of Economics and Business
at the University of Groningen.∗ My appreciation goes out to the SOM Graduate
School and the N.W. Posthumus Institute for providing the administrative, scholarly
and scientific setting needed for my research to thrive. Furthermore, I would like to
* NWO Grant no. 360-53-102.
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x Missed Opportunities?
express my sincere gratitude to the assessment committee, Prof. Stephen Broadberry,
Prof. Jochen Streb and Prof. Jan Luiten van Zanden, for honoring me by reading and
commenting on my thesis.
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Chapter 1Introduction
This dissertation explores Germany’s inability to match American levels of manufac-
turing labor productivity in the pre-WW1 and pre-WW2 period. There is still much to
learn about labor-productivity growth and technological change during this period. In
the case of Germany, the need for more research is illustrated by an online discussion in
2007 among prominent scholars of German history on the question ‘Do we need a new
economic history of Germany?’.1 At the outset of the discussion the concern was raised
that in German historiography “economic processes are usually taken as a background
to social, political, demographic, and cultural transformations of greater immediate in-
terest to the profession”.2 In a contribution to this debate, Albrecht Ritschl noted in
similar vein that “in Germany, making the boring numbers speak is still a young and
less than well established tradition. In contrast to the US, German economic history
never experienced a Cliometric revolution”.3 With regard to the early half of the twen-
tieth century, a scarcity of reliable information discouraged quantitative research and
Ritschl argues that “the macroeconomic history of Imperial Germany has traditionally
been plagued by an abundance of low-quality data”.4
Despite the data difficulties, the growth trajectory of Germany is not a black box
entirely. For manufacturing, Stephen Broadberry shows that labor-productivity growth
between 1900–1980 comprised two phases.5 Figure 1.1, which plots Germany’s compar-
ative labor-productivity development in manufacturing vis-a-vis the US over the period
1900–1980, demonstrates a widening gap before WW2. This long period of divergence
1. W. Gray, “Forum: Do We Need a New Economic History of Germany?,” H-Net Online, June2007, www.h-net.msu.edu.
2. ibid.3. A. Ritschl, “Do We Need a New Economic History of Germany?,” H-Net Online, July 2007,
www.h-net.msu.edu.4. ibid.5. S.N. Broadberry, The Productivity Race: British Manufacturing in International Perspective,
1850–1990 (Cambridge: Cambridge University Press, 1997), 43–45.
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2 Missed Opportunities?
Figure 1.1: Comparative German/US labor productivity in manufacturing (US = 1.0)
0.0
0.2
0.4
0.6
0.8
1.0
1900 1910 1920 1930 1940 1950 1960 1970 1980
WW1 WW2
Divergence Convergence
Sources: Broadberry, The Productivity Race.
lasted up to the late 1940s. It was not before 1947 that the dynamics reversed and Ger-
many managed to close in on American levels of labor productivity. Other European
countries shared Germany’s relative backwardness before WW2 and across the Atlantic
a large labor-productivity gap persisted from the late nineteenth century up until the
post-WW2 period.
Figure 1.2 shows that the transatlantic labor-productivity gap manifested also at the
country level. As such, it is a main feature of economic development in the early twenti-
eth century. The persistence, widening even, of the gap is striking and points at the pres-
ence of systematic growth determinants that long-lastingly influenced economic devel-
opment. Of particular interest in this respect is the timing of the labor-productivity gap;
the emergence of the gap coincided with a period of rapid technological development,
a time also referred to as the second industrial revolution.6 If the “Great Inventions”
of the second industrial revolution determined the growth dynamics of the post-1870
period, differences between countries in the adoption of new technologies may explain
the pattern of diverging development.7 Manufacturing industries, employing about 30%
6. R. Lipsey, K. Carlaw, and C. Bekar, Economic Transformations: General Purpose Technologiesand Long-Term Economic Growth (Oxford University Press, 2005).
7. R. Gordon, “Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds,”
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Chapter 1. Introduction 3
of the labor force in developed countries during the first half of the twentieth century,
proved particularly receptive to technological change.8 Because many new technologies
were embodied in capital, manufacturing industries could reap the labor-productivity
benefits associated with innovation by adopting modern machinery.9
Figure 1.2: GDP per capita levels (1,000 $1990)
0
2
4
6
8
10
12
14
1870 1913 1929 1938 1950
Europe US
Sources: Bolt and van Zanden, “The First Update of the Maddison Project.” Europe is calculated on
the basis of British, French and German data.
The comparatively high pace of growth in America suggests that the US success-
fully caught the winds of change, while Europe spilled them. This notion of missed
opportunities implies a latent growth potential that Germany failed to fully explore.
In its 2007 Global Economic Prospects report, the World Bank hints at such a latent
and partially unused capacity for growth in Europe.10 The World Bank examined the
historical growth record of G-5 countries and on the basis of GDP data for the thirty
years running up to 1900 ‘predicted’ economic growth for the 50 following years. The
predicted rate of GDP growth turned out significantly higher than the G-5 countries
Centre for Economic Policy Research Policy Insight No. 63 (2012): 5.8. For employment shares, see B. Mitchell, International Historical Statistics. Europe 1850–2005.
Sixth Edition (London: Macmillan, 1951), 153–164 and M. O’Mahony, Britain’s Productivity Perfor-mance, 1950–1996; An International Perspective (National Institute of Economic / Social Research,1999), 12.
9. H. Jerome, Mechanization in Industry (National Bureau Economic Research, 1934); S. Schurr etal., Electricity in the American Economy. Agent of Technological Progress (Greenwood Press, 1990);W. Devine, “From Shafts to Wires: Historical Perspective on Electrification,” Journal of EconomicHistory Vol. 43, No. 2 (1983): 347–372.10. The World Bank, “World Bank Report: Challenge of Geopolitical Shifts for Long-Term Economic
Forecasts: Lessons of History,” Global Economic Perspectives. Managing the Next Wave of Globaliza-tion (2007): 55.
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4 Missed Opportunities?
actually realized during the first half of the twentieth century, a failure fully attributable
to a slowdown in Europe. As the prediction essentially projects the nature of annual
shocks between 1870–1899 on the years afterward, the discrepancy between forecasted
and realized growth implies that after 1900 Europe deviated from its long-run devel-
opment path. Because the forecasted growth trajectory captures the latent production
capacity, the deviation from it suggests that part of that potential remained unrealized.
Had Europe managed to continue after 1900 as before, the gap to the US would have
turned out considerably smaller in the early twentieth century.
For the case of Germany, the notion of missed opportunities does not correspond
well with the conventional outlook on historical development. The long phase of labor-
productivity divergence before WW2 defies the traditional, and largely qualitative, lit-
erature that attributed special features to the German growth experience. Adam Tooze
articulates this tension between the two strands of literature as follows:
“Was there anything peculiar about Germany’s experience of economic
growth? This seems to me to be a question that though obvious and once a
classic topic for student essays is in fact in need of reassessment. Certainly
in many accounts of Germany’s uneven modernization there was a strong
assumption that the modernity of its economy at least was not in ques-
tion. Indeed, in some interpretations of Europe’s economic development,
claims were made for a peculiar sophistication of the German economy. And
yet from a vantage point at the end of the twentieth century Germany’s
long-run economic trajectory surely looks less distinctive than previously
thought. During the era of steel, chemicals and heavy electrical engineering
German industrialism was no doubt surrounded by a formidable aura. And
it certainly was a considerable industrial competitor. However, even then
these dramatic elements of industrialism formed only a part of economic life
in Germany. And their status as defining elements of economic modernity
was not set to last.”11
The question, then, is how the paradoxical lack of fast labor-productivity growth
at a time of fast technological change ought to be perceived. Did the widening labor-
productivity gap result from a German failure to successfully ride the waves of techno-
logical change? With regard to German manufacturing in the early twentieth century,
this question is addressed in the present study.
11. A. Tooze, “Do We Need a New Economic History of Germany?,” H-Net Online, June 2007,www.h-net.msu.edu.
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Chapter 1. Introduction 5
Two issues receive particular attention. First, given the ‘plague of low-quality data’,
attention goes out to an accurate measurement of German labor-productivity levels
in manufacturing on the level of industries. This involves a critical review of both
the data and the measurement techniques traditionally used by researchers studying
historical labor-productivity patterns. Secondly, the contribution of technological
change to Germany’s labor-productivity performance is studied and its contribution to
the widening German/US labor-productivity gap quantified.
Chapter 2 sets the stage for this study by measuring German/US comparative
labor productivity in manufacturing industries for the years 1909 and 1936/35. As
the quality of the time series provided by the German Historical National Accounts
has been called into question, new estimates of labor-productivity growth in Ger-
man manufacturing are necessary.12 For this purpose state-of-the-art techniques are
employed to construct benchmarks of comparative labor productivity. To allow for
effects of composition and to capture inter-industry variation in performance, the
labor-productivity comparisons constructed in this study apply the industry-of-origin
approach, which breaks down the manufacturing sector in manufacturing industries.
Moreover, following the literature, German and US output values are converted to
a common currency using industry-specific purchasing power parities to enable an
international comparison.13 These measurement techniques have previously been
applied only to bilateral comparisons between the US/UK and Germany/UK for years
prior to WW1 and WW2, but never for a study of German/US labor-productivity
differences in periods before 1950.14
12. W.G. Hoffmann, Das Wachstum der Deutschen Wirtschaft Seit der Mitte des 19. Jahrhunderts(Berlin: Springer-Verlag, 1965); R. Fremdling, “German National Accounts for the 19th and Early 20thCentury: A Critical Assessment,” Vierteljahrschrift fur Sozial- und Wirtschaftsgeschichte Vol. 75, no. 3(1988): 339–357; A. Ritschl, “Spurious Growth in German Output Data, 1913–1938,” European Reviewof Economic History Vol. 8 (2004): 201–223.13. A. Maddison and B. van Ark, “Comparison of Real Output in Manufacturing,” Policy, Planning
and Research Working Papers Vol. 5 (1988): 1–33; B. van Ark, International Comparisons of Out-put and Productivity: Manufacturing Productivity Performance of Ten Countries from 1950 to 1990(Groningen: Groningen Growth / Development Centre, 1993), 1–233; B. van Ark and M.P. Timmer,“The ICOP Manufacturing Database: International Comparisons of Productivity Levels,” Interna-tional Productivity Monitor No. 3 (2001): 44–51; R. Inklaar and M. Timmer, “GGDC ProductivityLevel Database: International Comparisons of Output, Input and Productivity at the Industry Level.,”GGDC Research Memorandum No. 104 (2008): 1–81.14. Broadberry, The Productivity Race; S.N. Broadberry and D. Irwin, “Labor Productivity in the
United States and the United Kingdom During the Nineteenth Century,” Explorations in EconomicHistory Vol. 43 (2006): 257–279; S.N. Broadberry and C. Burhop, “Comparative Productivity in Britishand German Manufacturing Before World War II: Reconciling Direct Benchmark Estimates and TimeSeries Projections,” The Journal of Economic History Vol. 67 (2007): 315–349; R. Fremdling, H.J.de Jong, and M.P. Timmer, “British and German Manufacturing Productivity Compared: A NewBenchmark for 1935/36 Based on Double Deflated Value Added,” The Journal of Economic HistoryVol. 67, no. 2 (2007): 350–378; H.J. de Jong and P.J. Woltjer, “Depression Dynamics: a New Estimate
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6 Missed Opportunities?
The benchmarks uncover a large labor-productivity gap in both periods at the level
of total manufacturing, confirming earlier studies by, among others, Broadberry.15 The
variation of comparative performance on the industry level is substantial, however, and
shows that the diverging trend for total manufacturing described in figure 1.1 fails to
do justice to the dynamics in several underlying industries. This applies particularly to
German chemicals, textiles and primary metals, which displayed a labor-productivity
performance close to the level of their American counterpart. Not surprisingly, general
theories as regards the German-American productivity gap have difficulty accounting
for the cross-industry variation. There are nonetheless some patterns recognizable. For
one, production in the strong performing industries involves mainly goods used as in-
termediates in other industries. For instance, the pig iron obtained in primary metals is
further processed by fabricated-metals and transportation-equipment industries, while
the yarn and thread produced in spinning industries function as inputs for weaving in-
dustries. European markets have been associated with heterogeneous demand patterns,
which discouraged the adoption of standardized production processes, but industries
involved in the production of mainly basic goods may not have suffered from this.16
The comparatively strong performing German manufacturing industries share an-
other characteristic as well. There appears to be an overlap between German industries
with relatively high labor-productivity levels and those associated in the literature with
relatively large establishment size and a high degree of vertical integration. Both phe-
nomena are associated with economies of scale, which possibly endowed industries with
relatively high labor-productivity levels.17 Although in general the scale of production
and the degree of vertical integration was much smaller in Germany compared to the
US, the relatively strong-performing German industries lagged only little behind.18 Nev-
ertheless, these are necessary but not sufficient conditions for catch-up and conceivably
of the Anglo-American Manufacturing Productivity Gap in the Interwar Period,” Economic HistoryReview Vol. 64 (2011): 472–492.15. Broadberry, The Productivity Race.16. L. Rostas, “Industrial Production, Productivity and Distribution in Britain, Germany and the
United States,” The Economic Journal Vol. 53, no. 1 (1943): 39–54, 58-59; A. Chandler, Scale andScope: the dynamics of industrial capitalism (Harvard: Belknap Press of Harvard University Press,1990), 1–780, 47; D.S. Landes, The Unbound Prometheus: Technological Change and Industrial Devel-opment in Western Europe From 1750 to the Present (Cambridge University Press, 1969), 247; S.N.Broadberry, “Technological Leadership and Productivity Leadership in Manufacturing Since the Indus-trial Revolution: Implications for the Convergence Debate,” The Economic Journal Vol. 104 (1994):291–302, 291.17. L. Hannah, “The American Mircale, 1875–1950, and After: A View in the Europan Mirror,”
Business and Economic History Vol. 24, no. 2 (1995): 197–220; L. Hannah, “Logistics, Market Size,and Giant Plants in the Early Twentieth Century: A Global View,” Journal of Economic History Vol.68, no. 1 (2008): 46–78.18. J. Kinghorn and J. Nye, “The Scale of Production in Western Economic Development: A Com-
parison of Official Industry Statistics in the United States, Britain, France, and Germany, 1905-193,”Journal of Economic History Vol. 56, no. 1 (1996): 90–112.
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Chapter 1. Introduction 7
explain some, but certainly not all the observed variation in comparative labor produc-
tivity. For instance, the paper and cement industries, both of which can be argued to
produce standard goods, failed to match the high labor-productivity level vis-a-vis the
US displayed by textiles.
Other factors must have been at work also. Perhaps most importantly in this respect
are differences in the mix of factor inputs employed in production. A possible and
frequently-used explanation for observed labor-productivity differences between the US
and the UK in the nineteenth century has been put forward in the Rothbarth-Habakkuk
thesis, which emphasizes the importance of factor endowments for the capital-labor ratio
at which countries choose to operate.19 In the US a scarcity of skilled labor and an
abundance of natural resources provided an incentive to substitute machinery for labor.
This minimized costs and led to a capital-intensive production process. The supply
of factor inputs faced by European producers differed, which induced the adoption of
less capital-intensive technology. As some determinants of relative factor costs, such
as the availability of natural resources or the size and density of the population, are
exogenous to the production process, a country’s initial conditions influence the choice
of technology. In the extreme, if one assumes that these fixed initial conditions fully
determine the relative factor costs and the choice of factor-input mix, the existence of
different technological paths across the Atlantic was foreordained.20
If technological progress is directed toward the technology currently used by coun-
tries, differences in relative factor costs lead to technological lock-in.21 This begs the
question whether such path dependencies effectively blocked the traditional channels
of labor productivity catch-up described by standard neo-classical growth theories?
Provided that the necessary capabilities and resources are available (Gerschenkron’s
idea of ‘appropriate’ economic institutions and Abramovitz’ ‘social capabilities’)
countries distanced far away from the technological frontier can catch-up quickly by
importing or imitating technologies that are already in use in developed countries.22
19. E. Rothbarth, “Causes of the Superior Efficiency of U.S.A. Industry as Compared with BritishIndustry,” The Economic Journal Vol. 56, no. 223 (1946): 383–390; M Abramovitz, “Resource andOutput Trend in the United States Since 1870,” American Economic Review Vol. 63, No. 2 (1956):5–23; H.J. Habakkuk, American and British Technology in the Nineteenth Century. The Search forLabour-saving Inventions (Cambridge: Cambridge University Press, 1962), 1–222.20. N. Rosenberg, “Why in America?,” in Exploring the Black Box. Technology, Economics, and
History (Cambridge University Press, 1994), 109–120.21. P. David, Technical Choice, Innovation and Economic Growth. Essays on American and British
Experience in the Nineteenth Century (Cambridge: Cambridge University Press, 1975), 1–334. Theclassic reference for path dependency is P. David, “Clio and the Economics of QWERTY,” AmericanEconomic Review Vol. 75, no. 2 (1985): 332–327.22. A. Gerschenkron, Economic Backwardness in Historical Perspective; A Book of Essays (Cam-
bridge University Press, 1962); M. Abramovitz, “Catching-up, Forging Ahead and Falling Behind,”Journal of Economic History Vol. 46 (1986): 385–406.
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8 Missed Opportunities?
Also the recent economic-growth literature has often emphasized the importance of
investment-based strategies for follower countries.23 Yet if follower countries refrain
from adopting advanced technology because of different relative factor costs, Europe
may have been trapped on a labor-intensive and low-productive technological path.
For the case of Germany, recent studies have concluded otherwise, though. Re-
search on the machine-tool industry during the interwar years revealed a process of
technology adoption on the part of Germany and finds that at the outbreak of WW2
capital-intensity levels were as high as in the US.24 The tradition of copying and
adopting American machinery contests the notion of technological lock-in. But how
can large labor-productivity differences coexist with a diminishing machine-intensity
gap? Chapter 3 addresses this paradox by specifically accounting for the contribution
of machine-intensity differences to the German/US labor-productivity gap in 1936/39.
For this purpose I use data envelopment analysis techniques, which offer several
advantages over traditional Solow-based level accounting exercises. First, the analysis
allows for localized innovation, a main feature of technological progress ever since
the first industrial revolution.25 Second, the data envelopment analysis involves a
non-parametric approach and, as such, does not require information on capital and
labor prices to proxy the marginal factor returns.26 Third, the analysis uses horse
power per hour worked, which offers a more accurate indicator of machine intensity
than the total capital stock per employee statistics conventionally employed.27
The data envelopment analysis applied here estimates a global best-practice frontier
and the change thereof between 1899–1939. This best-practice frontier indicates for each
point in the range of operated capital-labor ratios the highest labor-productivity level
contemporaneously or previously attained. Positioning German and American manu-
facturing industries in relation to the global best-practice frontier permits a decompo-
sition of the labor-productivity gap in components of capital intensity and efficiency.
23. P. Aghion, “Higher Education and Innovation,” Perspektiven der Wirtschaftspolitik Vol. 9 (2008):28–45; Daron Acemoglu, “Directed Technical Change,” The Review of Economic Studies Vol. 68, no. 4(2002): 781–809; J. Vandenbussche, P. Aghion, and C. Meghir, “Growth, Distance to the Frontier andComposition of Human Capital,” Journal of Economic Growth Vol. 11 (2006): 97–127.24. C. Ristuccia and A. Tooze, “Machine Tool and Mass Production in the Armaments Boom: Ger-
many and the United States, 1929–44,” Economic History Review Vol. 66, no. 4 (2013): 953–974.25. R.C. Allen, “Technology and the Great Divergence: Global Economic Development Since 1820,”
Explorations in Economic History 49 (2012): 1–16.26. S. Kumar and R. Russell, “Technological Change, Technological Catch-up, and Capital Deepening:
Relative Contributions to Growth and Convergence,” The American Economic Review Vol. 92, no. 3(2002): 527–548; M.P. Timmer and B. Los, “Localized Innovation and Productivity Growth in Asia:An Intertemporal DEA Approach,” Journal of Productivity Analysis Vol. 23 (2005): 47–64.27. A.J. Field, “On the Unimportance of Machinery,” Explorations in Economic History Vol. 22
(1985): 378–401.
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Chapter 1. Introduction 9
The former element captures the difference between the labor-productivity level at
the frontier for German and American industries. As such, it measures the difference in
labor-productivity potential between machine-intensity levels operated in Germany and
the US. This difference in labor-productivity potential does not fully account for the
observed variation in labor-productivity levels for the reason that industries exploited
their labor-productivity potential only partly. The extent to which an industry manages
to exhaust its labor-productivity potential is expressed by the second component, i.e.
efficiency. The difference between the efficiency level in Germany and the US accounts
for the remainder of the labor-productivity gap that is left unexplained by variation
in the labor-productivity potential at the level of machine intensity explored in both
countries.
The labor-productivity gap decomposition shows that it was not a low machine-
intensity level that refrained German industries from matching the labor-productivity
performance of their American counterparts. Rather, a relatively low level of efficiency
accounts for more than two-thirds of the labor-productivity gap. The limited impor-
tance of machine-intensity differences can be attributed to a process of machine in-
tensification in Germany during the 1920s and 1930s.28 The rapid move toward high
capital-labor ratios in German manufacturing aligns well with theoretical models of
appropriate technology in which new production knowledge is appropriate only for one
capital-labor ratio; if innovation takes place exclusively at high capital-labor ratios, “fol-
lower countries” must adopt capital-labor ratios already explored by leader countries in
the past to prevent a further widening of the labor-productivity gap.29
However, as much of what one needs to know to employ new production knowledge
is implicit and not available from handbooks, it takes time to assimilate and operate
machinery at the level displayed by countries exploring that capital-labor ratio before,
an effect that possibly explains part of the initially low efficiency levels in German
manufacturing.30 Other factors came into play as well. The findings of chapter 2
hinted at the positive influence that economies of scale exerted on labor-productivity
levels. A relatively large establishment size, for instance, could have made possible
a labor-productivity performance that was otherwise unattainable at a particular
level of machine intensity. Especially interesting in this respect is the below average
28. R. Richter, “Technology and Knowledge Transfer in the Machine Tool Industry. The UnitedStates and Germany, 1870–1930,” Essays in Economic & Business History Vol. 26 (2008): 173–188;R. Richter and J. Streb, “Catching-Up and Falling Behind: Knowledge Spillover from American toGerman Machine Toolmakers,” Journal of Economic History Vol. 71, no. 4 (2011): 1006–1031.29. S. Basu and D. Weil, “Appropriate Technology and Growth,” The Quarterly Journal of Economics
Vol. 113 (1998): 1025–1054.30. B. Los and M. P. Timmer, “The ‘Appropriate Technology’ Explanation of Productivity Growth
Differentials: An Emperical Approach,” Journal of Development Economics Vol. 77 (2005): 517–531.
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10 Missed Opportunities?
scope for labor-productivity catch-up through enhanced efficiency levels in textiles and
primary metals, which were both identified in chapter 2 as relatively strong performing
German manufacturing industries. This suggests that the on average smaller scale of
production in the other German manufacturing industries restrained the full efficient
use of machinery, a notion also advanced by Cristiano Ristuccia and Adam Tooze.31
Chapter 4 moves on to critically assess the measurement techniques conven-
tionally employed in long-run economic history by addressing the debate on German
output growth over WW1. Studies on long-run economic growth are often plagued by
limited data availability, particularly for early periods. In the absence of, for instance,
production data, the unobserved output change can be proxied by the behavior of
correlates. However, the correlation between the proxy and target series is never
perfect, which introduces inaccuracy to the estimates and may spark off a debate
concerning the appropriateness of different proxies. This scenario unfolded in the debate
on industrial output growth in pre-WW2 Germany, leading to different time-series
estimates.32 The choice between output proxies carries important implications for the
assessment of Germany’s growth experience; when used to calculate labor productivity,
the different output estimates indicate a German performance prior to WW1, i.e. 1907,
either equal to or well above the British level. Because the exact fit between proxy
and target variables cannot be determined when the latter is unobservable, choosing
between proxies proceeds on the basis of circumstantial evidence. Given the historical
questions at stake, the debate would benefit from a less conjectural approach.
In chapter 4 I apply a new approach to this old debate. Instead of choosing between
the different estimates, I acknowledge that all series are based on correlates of output.
Consequently, the dynamic properties of each observed series must be captured by the
same component, i.e. output change, while the deviation between the series reflects the
different accuracy of the correlates in capturing the unobserved change in output. Us-
ing state space time series analysis, I filter this common component from the output
series.33 This way, I do not discard any data and thus make full and efficient use of all
information. In a second step, the filtered output series is combined with employment
data to derive an index of German labor-productivity change, which, expressed relative
31. Ristuccia and Tooze, “Machine Tool and Mass Production,” 9.32. R. Wagenfuhr, “Die Industriewirtschaft. Entwicklungstendenzen der deutschen und interna-
tionalen Industrieproduktion 1860 bis 1932,” in Vierteljahrsheftte zur Konjunkturforschung, vol. (Son-derheft) 31 (Berlin: Verlag von Reimar Hobbing, 1933); Hoffmann, Das Wachstum; Ritschl, “SpuriousGrowth in German Output Data.”33. J. Commandeur and S.J. Koopman, An Introduction to State Space Time Series Analysis (Ox-
ford University Press Inc., 2007); J. Durbin and S.J. Koopman, Time Series Analysis by State SpaceMethods, vol. 24, Oxford Statistical Science Series (Oxford University Press Inc., 2001).
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Chapter 1. Introduction 11
to its British counterpart, is extrapolated backward from a known German/UK com-
parative level of labor productivity in 1936/35.34 This exercise is repeated twice, using
the point and interval estimates of the filtered common component, respectively. The
former attributes a 15% lead to Germany in 1907, while the latter indicates a range of
about 11% around the point estimate that contains the estimated parameter with 95%
certainty.
This finding takes on significance for the reconciliation between time-series projec-
tions and benchmark estimates. Faced with the different time series of output presented
in the literature, scholars have previously employed 1907 labor-productivity benchmarks
to test the accuracy of the time series estimates.35 As with the latter, however, vari-
ous benchmark estimates are presented, which ascribe a lead to Germany of either 5%
or 25%. Previously, criteria for the fit between benchmark estimates and time series
projections were loosely defined and the procedure applied in this chapter moves the
debate forward by providing a statistical framework to quantify the margin for error.36
Although the benchmarks presented in the literature deviate markedly, they all fall
within the tails of the confidence interval around my time-series projections. All esti-
mates can therefore be reconciled. This suggests that when benchmarks are used as a
check upon time series, taking into account the measurement error leads to a different
assessment of the fit between both measures as compared to the exclusive use of point
estimates.
Of course, this raises the question if such a broad range of German labor-
productivity levels obtained by the methodology advanced in this chapter renders
impossible a concise assessment of Germany’s comparative performance? Paradoxically,
my answer to this question is that working with confidence intervals actually increases
the reliability of the conclusions regarding historical economic development. Any
conclusion drawn from the filtered time-series estimates are explicitly founded on
a solid statistical basis, which provides an increased certainty compared to studies
employing point estimates only. I can confidently infer that, first, Germany had
overtaken Britain in terms of labor productivity already before WW1, yet by a small
margin only. Second, over WW1 there was a statistically significant change in labor
productivity leadership with Germany dropping below the UK. And, third, given
Fremdling, de Jong and Timmer’s 1936/35 German/UK benchmark comparison,
34. Fremdling, de Jong and Timmer, 2007.35. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing”;
A. Ritschl, “The Anglo-German Industrial Productivity Puzzle, 1895-1935: A Restatement and APossible Resolution,” Journal of Economic History Vol. 68, no. 2 (2008): 535–565; S.N. Broadberryand C. Burhop, “Resolving the Anglo-German Industrial Productivity Puzzle, 1895–1935: A Responseto Professor Ritschl,” Journal of Economic History Vol. 68, Nr. 3 (2008): 930–934.36. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing,” 326.
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12 Missed Opportunities?
Britain’s lead evaporated again in the 1930s and both countries performed roughly on
par shortly before WW2.
In chapter 5 I explore the possibility of a European convergence club in manu-
facturing before WW1. Several of the potential constraints to productivity growth
in pre-WW1 Britain and Germany, e.g. small domestic markets and relative factor
costs less favorable to capital-intensive production in comparison to the US, are easily
extended to other European countries, too. This invites the question whether, or to
what extent, the condition of being ‘European’ determined the growth experience
of countries.37 The notion of convergence in manufacturing labor-productivity levels
is particularly relevant for the pre-WW1 era, as the period in between 1870–1913 is
characterized by openness to trade and globalization. The relative openness to trade
potentially promoted the convergence between European manufacturing industries
toward a common level of performance, as trade theory suggests that differences in
relative factor prices and thus in the mix of factor inputs used in production iron out
under conditions of free trade.38 Chapter 5 explores this possibility by constructing
benchmarks of comparative labor productivity for the US, UK, Germany, France, the
Netherlands and Sweden around the year 1910.
Despite the openness to trade, the benchmarks show that the level of labor produc-
tivity had not converged between European countries before WW1 and marked differ-
ences persisted, both for total manufacturing and manufacturing branches. Moreover,
backward extrapolation of comparative labor productivity to 1870 points out that the
dispersion of performance hovered around a constant level throughout the period and
showed no signs of convergence. These findings are in sharp contrast to total-economy
developments; GDP per capita levels converged steadily between 1870–1913.39 This
finding aligns well with Broadberry’s notion that convergence at the country level was
fueled mainly by changes in the structure of the economy rather than labor-productivity
developments in manufacturing.40 At the same time, it may also mean that the pre-
37. For conditional convergence, see R. Barro, “Economic Growth in a Cross Section of Countries,”The Quarterly Journal of Economics Vol. 106, No. 2 (1991): 407–443; R. Barro and X. Sala-I-Martin,“Convergence,” Journal of Political Economy Vol. 100 (1992): 223–258; J. Fagerberg, “Technologyand International Differences in Growth Rates,” Journal of Economic Literature Vol. 32, no. 3 (1994):1147–1175.38. E. Heckscher, “The Effect of Foreign Trade on the Distribution of Income,” Ekonomisk Tidskrift
(1919): 497–512; B. Ohlin, Interregional and International Trade (Cambridge, Mass.: Harvard Univer-sity Press, 1983); P. Samuelson, “International Trade and the Equalization of Factor Prices,” EconomicJournal (1948): 165–184; P. Samuelson, “International Factor-Price Equalization Once Again,” Eco-nomic Journal (1949): 181–197.39. J. Williamson, “Globalization, Convergence, and History,” Journal of Economic History Vol. 56,
no. 2 (1996): 277–306.40. S.N. Broadberry, “Manufacturing and the Convergence Hypothesis: What the Long-Run Data
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Chapter 1. Introduction 13
WW1 period did not witness convergence of capital-labor ratios in manufacturing. For
instance, Abramovitz argued that the social competence necessary to exploit the new
technology was limited before WW1.41 Indeed, within Europe the level of machine
intensity differed considerably.42
As chapter 3 revealed that Germany operated before WW1 at a relative machine-
intensity level vis-a-vis the US much lower than during the 1930s, I am inclined to
attach more importance to differences in capital-labor ratios in an explanation of the
transatlantic labor-productivity gap before WW1 than at the end of the interwar pe-
riod. This makes the first half of the twentieth century a period of transition in which
German manufacturing gradually moved toward capital-labor ratios that promised a
considerable scope for labor-productivity growth. The stationarity, even deterioration,
of Germany’s comparative labor-productivity performance between 1909–1936 relative
to the US did not reflect the lack of technological progress, but an incomplete adop-
tion of new technology hampered by learning effects. This transition phase is enclosed
on both ends by periods that arguably display very different dynamics. The relatively
low levels of machine intensity in pre-WW1 German manufacturing suggests that the
labor-productivity gap to the US in the period before 1900 was driven largely by the
use of different technology, while the post-WW2 era witnessed a rapid decrease of both
the labor-productivity and capital-intensity gap to the US.43 This process of capital-
intensity convergence, however, had already set in during the interwar years. While it
failed to bring German labor-productivity levels closer to the US in the short run, it
formed the necessary first step on the road to catch-up and may partly explain the
German growth miracle in the post-WW2 period.
Show,” Journal of Economic History Vol. 53, no. 4 (1993): 772–795.41. Abramovitz, “Catching-up,” 395.42. Hannah, “Logistics, Market Size, and Giant Plants,” 71.43. van Ark, International Comparisons of Output and Productivity.
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Chapter 2Catching-Up with the Global Labor-Productivity
Leader? German and US Industrial Labor Productivity
Compared Before and After WW1
2.1 Introduction
Germany’s rapid economic development from the late nineteenth century onwards has
traditionally been described as a typical example of catch-up growth.1 In particular the
rapid transformation of the new, science-based industries, such as engineering, chemical
production, and metal manufacturing during the second industrial revolution has re-
ceived much attention.2 To which degree these developments propelled Germany to the
vanguard of industrial development is still a topic of debate, as demonstrated by the
discussion recently held between Broadberry & Burhop and Ritschl, who fail to reach
consensus on the question whether or not Germany had surpassed Britain by the turn
of the twentieth century.3 It has not been attempted to compare Germany with the US,
which is a surprise given the latter’s well-established lead over Europe in manufactur-
ing; if German growth genuinely resulted from a catch-up process, i.e. the “benefits” of
lagging behind, than the universal productivity leader – and not Britain – seems the
appropriate point of reference.4
This chapter presents a German/US comparison of labor productivity in mining
and manufacturing for two benchmark years, i.e. 1909 and 1936/35. The results of
1. See for instance: Gerschenkron, Economic backwardness, 16; Landes, The Unbound Prometheus,236.
2. H.J. Braun, The German Economy in the Twentieth Century: the German Reich and the FederalRepublic (London: Routledge, 1990), 20.
3. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing”;Ritschl, “The Anglo-German Industrial Productivity Puzzle.”
4. America’s superiority in manufacturing is clearly demonstrated in Broadberry, The ProductivityRace.
15
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16 Missed Opportunities?
this exercise are confronted with two strands of literature, each highlighting a different
aspect of the German growth experience. First, the preconditions for growth may have
been less favorable in Germany than in the US. It has been argued that relative-factor
costs in Europe discouraged the substitution of machinery for skilled labor, which in
turn constrained the adoption of labor-productivity enhancing technology.5 Moreover,
the literature has suggested that the small scale of European production negatively
affected labor productivity. Large-scale production and standardization was only limited
applicable, because producers faced a small domestic market characterized by a demand
for customized goods.6 Although these conditions have been ascribed to Europe as a
whole, evidence in support of such theories are based mainly on the case of Britain. The
question, then, is whether the British constraints to labor-productivity growth applied
also to Germany?
Arthur Shadwell, a British contemporary, who traveled the UK, Germany and the
US shortly after the turn of the twentieth century in order to compare the qualities of
industrial life in these countries, took note of Germany’s remarkable success in the face
of circumstances potentially detrimental to development:
“Not a rich country, possessing no exceptional resources or facilities, no
extensive and convenient seaboard, with no tide of skilled immigrant labour
to make things easy, and with enemies in arms on both sides of her, she
has yet within the space of thirty years, and while bearing the burden of
an enormous system of military defense, built up from comparatively small
beginnings a great edifice of manufacturing industry which for variety and
quality of output can compete in any market with most of the finest products
of Great Britain. That is no exaggeration but a plain statement of facts, and
it can be said of no other country.”7
Having matched British performance, did Germany subsequently encounter the same
barriers for further growth that prevented the UK from catching-up to the US? This
was not necessarily the case, given that Chandler has likened several elements of the
German system of manufacturing to the US, rather than to the UK.8 Also, the unique
institutional setting in which German producers operated, in particular the cartel-tariff
5. Habakkuk, American and British Technology; David, Technical Choice, 66; P. Temin, “LabourScarcity in America,” Journal of Interdisciplinary History Vol. 1 (1971): 251–264, 162;Field, “On theUnimportance of Machinery,” 379.
6. Rostas, “Industrial Production, Productivity and Distribution,” 58-59; Chandler, Scale and Scope,47; Landes, The Unbound Prometheus, 247; Broadberry, “Technological Leadership,” 291.
7. A. Shadwell, Industrial Efficiency (Longmans, Green, / Co., 1906), 14-15.8. A. Chandler, “Organizational Capabilities and the Economic History of the Industrial Entreprise,”
Journal of Economic Perspectives Vol. 6, no. 3 (1992): 79–100.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 17
system, has been associated with labor-productivity benefits, which may have counter-
acted some of the ails that European countries suffered from.9
Second, in addition to questions of relative standing and catch-up, this chapter
addresses the issue of labor-productivity in Germany, too. The US is known for its
unprecedented growth spurt during the interwar period and by placing Germany’s labor-
productivity performance in relation to its American counterpart the growth record of
German industries between 1909 and 1936 may easily be underrated.10 In the presence
of rapid growth in the US, a stagnant level of comparative labor productivity reflects
fast growth in Germany, too. Conversely, a relative standing close to the US does not
by necessity imply a rapid development on the part of Germany.
A focus solely on German growth is called for also because doubt has been cast
upon the reliability of the output and employment indices of the German Historical
National Accounts (HNA) constructed during the 1960s under supervision of Walther
Hoffmann. The critique on Hoffmann’s series is directed toward his use of income data
to estimate output growth. Because of the increased bargaining power of labor unions
over WW1, the productivity to wage ratio changed and using the latter as a proxy for
output leads to spurious growth.11 As the quality of the time series has been called
into question, extrapolating backwards from known labor-productivity levels in the
interwar period could potentially lead to inaccurate estimates. New estimates of labor-
productivity growth in German manufacturing are therefore necessary.12
In response to the first issue, i.e. relative standing, this chapter presents a comparison
of labor-productivity levels between Germany and the US in 1909 and 1936/35. The
second question, i.e. growth in Germany, is subsequently addressed by an exclusively
German inter-temporal comparison of labor-productivity levels between 1909 and 1936.
In both cases the latest methodological developments for constructing productivity
comparisons are taken on board to allow for the most accurate analysis possible. This
involves the application of an industry-of-origin approach to the benchmark estimates,
which, among other things, entails a break down of manufacturing in industries to
provide the detail needed to map out an economy’s productivity profile, i.e. its strong
and weak elements. Having set out the methodology and data in sections 2.2 and 2.3,
the results, which are presented in sections 2.4 and 2.5, are finally positioned in the
literature in section 2.6.
9. Hannah, “The American Mircale,” 207–208; Kinghorn and Nye, “The Scale of Production,” 109;M.Levenstein and V. Suslow, “What Determines Cartel Success,” Journal of Economic Literature Vol.44, no. 1 (2006): 43–95, 85.10. A.J. Field, “The Most Technologically Progressive Decade of the Century,” The American Eco-
nomic Review Vol. 93, no. 4 (2003): 1399–1413.11. Ritschl, “Spurious Growth in German Output Data.”12. Hoffmann, Das Wachstum; Fremdling, “German National Accounts.”
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18 Missed Opportunities?
2.2 Methodology
Most research focusing on productivity comparisons is conducted on the total-economy
level, as, for instance, the well-known long-run series provided by Maddison.13 As a
result, Maddison’s time series outline major trends of economic development, but the
dynamics that play out within the economy go by unnoticed. As such, the drivers be-
hind the observed total-economy growth patterns remain hidden. For instance, studies
focusing on technological development conducted on the basis of total-economy data
may miss important historical developments, as effects of efficiency-increasing innova-
tions tend to be underestimated when their impact is measured by their contribution to
GDP. In fact, Paul David used this argument to criticize the conclusions of Robert Fo-
gel’s seminal work on the impact of railroads on American economic growth.14 Another
example that stresses the importance of research on the disaggregated level concerns
Broadberry’s analysis of Anglo-American GDP-per-capita difference, which he ascribes
to compositional effects. When employment shifts from low productivity to high produc-
tivity sectors, output per worker on the total-economy level increases and such a change
in employment structure was crucial for America’s growth spurt during the nineteenth
century.15
To allow for effects of composition and to capture inter-industry variance in per-
formance, the labor-productivity comparisons constructed in this study employ the
industry-of-origin approach, which dissects the manufacturing sector in its underlying
components, i.e. manufacturing industries. By accurately measuring the state of man-
ufacturing in a particular year, the industry-of-origin benchmark provides the starting
point for further research that aims to explain a country’s growth experience. In addi-
tion, a benchmark for the pre-WW1 period supplies a check upon time-series projections
extrapolated backward from more recent benchmark estimates. As it is difficult for back-
ward extrapolations to accurately allow for changes in the structure of an economy (or
manufacturing), especially when the projection covers periods characterized by turbu-
lence and rapid change, such as the World Wars or the Great Depression, problems
occur when time series are projected into the distant past.16 A large deviation between
13. A. Maddison, Phases of Capitalist Development (Oxford: Oxford University Press, 1982); A.Maddison, Dynamic Forces in Capitalist Development: A Long-Run Comparative View (Oxford: Ox-ford University Press, 1991), 1–333; A. Maddison, Monitoring the World Economy 1820–1992 (Paris:Organisation for Economic Cooperation / Development, 1995), 1–255.14. P. David, “Transport Innovation and Economic Growth: Professor Fogel On and Off the Rails,”
Economic History Review Vol. 22, no. 3 (1969): 506–525.15. Broadberry, The Productivity Race.16. A. Gerschenkron, “Soviet Heavy Industry: A Dollar Index of Soviet Machinery Output, 1927–28
to 1937,” The Review of Economics and Statistics Vol. 37, no. 2 (1955): 120–130.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 19
time-series projections and direct level estimates may indicate a degree of inaccuracy
on the part of the former.17 An additional advantage of the benchmark over time-series
projections is that because the level estimates of productivity produced for the former
refer to one year, data on output and employment can be obtained from a single primary
source, which guarantees a consistency between the output and input measures.18
The importance and appeals of the industry-of-origin approach were recognized by
the late 1940s. The first industry-of-origin benchmark was constructed by Rostas in
1948.19 In an attempt to assess the state of the British and American economies, he
broke down the manufacturing sector and pinpointed the comparative performance of
UK industries in relation to their US counterparts. Since then, the industry-of-origin
approach has been adopted by other scholars, most notably among which Stephen
Broadberry, to address the debate on (historic) patterns of convergence and divergence.
The research conducted here follows in this tradition and builds upon the work of these
early pioneers.
Although a productivity comparison on an industry level is a simple and straight-
forward concept, such a comparison can be made in a variety of ways. The methods
used here are refinements of the basic methodologies of comparison set out by Rostas,
Paige & Bombach and Broadberry. The first industry-of-origin benchmarks obtained
productivity figures by taking the ratio between output volume and employment on
the industry level. When output is expressed in volumes an international comparison is
straightforward, since the unit of measurement is the same for all countries, for instance
produced tons of coke per employee. However, as noted by Inklaar and Timmer, the
direct comparison of physical units of output for the measurement of productivity is
only possible for a specified product or a closely related group of products.20 Conse-
quently, this limits the ability to estimate productivity for industries producing a wide
array of heterogeneous goods, which is always the case when comparing productivity at
the industry or total-economy level. In view of these limitations, it is more practical to
compare output values, rather than output volumes.
Unfortunately, when the value approach is applied, the advantage of directly com-
paring productivity levels between countries is lost. While hectolitres and kilograms are
the same in the US and Germany, US$ and German Goldmark cannot be compared
directly. Therefore, a conversion factor is necessary to express the output value of dif-
ferent countries in a common currency. The exchange rate is not an optimal conversion
17. Chapter 4 provides a detailed discussion of this issue.18. van Ark and Timmer, “The ICOP Manufacturing Database.”19. L. Rostas, Comparative Productivity in British and American Industry (Cambridge: Cambridge
University Press, 1948), 1–263; Rostas, “Industrial Production, Productivity and Distribution.”20. Inklaar and Timmer, “GGDC Productivity Level Database,” 6-8.
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20 Missed Opportunities?
factor for this purpose, since it only signifies the price relation between internationally
traded goods. Moreover, the exchange rate is a particularly inconvenient instrument
for this research, as the countries under comparison operated under different monetary
regimes, at times. Both countries were on the gold standard at the start of the twen-
tieth century. Exchange rates were effectively fixed and domestic price movement was
determined by a country’s gold supply. However, by the late 1930s the US had abolished
payments stipulated in gold, while Germany still adhered to the gold standard. Finally,
the exchange rate is a total-economy measure of the price ratio that does not allow for
variation thereof between different sectors of the economy.
A more appropriate alternative to the exchange rate is an industry-specific conver-
sion rate based on producer prices. This technique has been set out by van Ark and is
referred to as the International Comparisons of Output and Productivity (henceforth,
ICOP) methodology.21 The building blocks of the conversion rates are formed by prod-
uct prices. As these prices are seldom available in the statistical records, they have to
be derived from data on the produced value and quantity of products. In a bilateral
country comparison, these product prices – referred to as unit values – are computed
for both countries as in equation (1).
pij =vijqij
(2.1)
Where pij is the unit value of product i in country j, vij the output value of that
product and qij the corresponding produced volume. Subsequently, identical products
are selected and matched between the two countries involved in the comparison. The
ratio between the unit value of the same commodity in both countries captures the
product-specific relative price expressed in terms of country n’s currency per unit of
the base country o’s currency, as in equation (2).
uvrio =pinpio
(2.2)
With uvrio as the unit value ratio (henceforth, UVR) of product i, which represents
the relative unit value in country n (pin) compared to the unit value in country o
(pio). In order to derive an industry-level conversion factor, a weighted average is taken
of the product-specific price ratios classified in the same industry group. The weights
allotted to the UVRs for the purpose of aggregation reflect the product’s share in
total industrial output (vi/∑
vi). The aggregated UVRs are traditionally referred to
as purchasing power parities (henceforth, PPPs). The process of aggregation proceeds
21. Maddison and van Ark, “Comparison of Real Output in Manufacturing”; van Ark, InternationalComparisons of Output and Productivity; van Ark and Timmer, “The ICOP Manufacturing Database.”
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 21
in three sequential steps, as described by equations (3), (4) and (5). The UVRs are
aggregated, first, using base country o’s output weights and, second, using the weights
of the numerator country n to get a Laspeyres (Lgo) and Paasche (P go) gross output
PPP, respectively:
Lgo =
∑(vio · pin
pio
)∑
vio=
∑(vin · uvrio)∑
vio(2.3)
P go =
∑vin∑(
vin · pio
pin
) =
∑vin∑
(vin/uvrio)(2.4)
In a third step the geometric average of the Laspeyres and Paasche PPPs is taken (F go),
which is used throughout this chapter to convert industrial output:
F go =√Lgo · P go (2.5)
Equations (1)–(5) provide the tools needed to express German and US gross output
in a common currency. Labor productivity in country j (yj) is then expressed as in
equation (6), where goij denotes gross output of product i in country j and lij the
labor input employed in the production process.
yj =
∑goij∑lij
(2.6)
In the following analysis labor is defined initially as the number of employees involved
in production and subsequently as total annual hours worked. The adjustment for hours
worked takes on significance mainly for the interwar comparison, as the first half of the
twentieth century saw a rapidly decreasing length of the working week, especially in the
US.22 Combining equations (5) and (6), the level of labor productivity in country n as
compared to base country o is expressed as in equation (7) below:
LP =yn/F
go
yo(2.7)
2.3 Data
As mentioned, one of the advantages of comparing levels of labor productivity is the
possibility for each country to draw data on production and labor from the same pri-
mary source, ensuring consistency between the output and input measures. Generally,
I employ in this study the censuses of production published by the statistical offices of
22. Jong and Woltjer, “Depression Dynamics.”
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22 Missed Opportunities?
Germany and the US. The pre-WW1 analysis for the US is based on the Thirteenth Cen-
sus of the United States published by the US Bureau of Commerce.23 For 1935 US I rely
primarily on the Biennial Census of Manufactures 1935 and the Sixteenth Decennial
Census of the United States.24 The US censuses provide an extensive and consistent cov-
erage of the American manufacturing sector in both years. For interwar Germany I use
the comprehensive archival records of the German production census published in Die
deutsche Industrie: Gesamtergebnisse der amtlichen Produktionsstatistik (henceforth,
production census of 1936). This is the first official German census of manufactures
and is available in two forms; a published edition and the original archival records. The
former has been to set up to hide particular manufacturing activities that were related
to the war effort. The archival records contain considerably more detailed and accurate
information and is used in this study.25
Collecting data to calculate labor productivity for pre-WW1 Germany was less
straightforward. The statistical offices of the US and the UK published a census of
manufactures already before WW1. For manufacturing industries these censuses re-
port data on output, employment, installed capital, etc. and as such are ideally suited
for constructing benchmarks. Because the first German census of manufacturing was
not published until 1936, I have to rely on other sources for the prewar period. The
Kaiserlichen Statistischen Amte (henceforth, Imperial Statistical Office) monitored the
economy in a variety of ways from the turn of the twentieth century onwards. Using
a combination of official statistical publications the industry-level data needed for the
construction of benchmarks is obtained. Because it forms the weakest link in the chain
of benchmarks presented here, the computation of German labor-productivity levels
before WW1 requires further elaboration.
Labor productivity in pre-WW1 Germany
To calculate German labor-productivity levels for the prewar period, I mainly rely
on information obtained from the Vierteljahrshefte zur Statistik des deutschen Reichs
(henceforth, statistical quarterlies). In the statistical quarterlies of 1913 the results of
23. United States Department of Commerce: Bureau of the Census, Thirteenth Census of the UnitedStates Taken in the Year 1910, vol. VIII: Manufactures (Washington D.C.: United States GovernmentPrinting Office, 1913). For mining, United States Department of the Interior, United States GeologicalSurvey 1910.24. United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1935 ;
United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1940 (I);United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1940 (II).25. Reichsamt fur Wehrwirtschaftliche Planung, Die Deutsche Industrie 1936 ; for a detailed discus-
sion of this source see: R. Fremdling, H.J. de Jong, and M.P. Timmer, “Censuses Compared: A NewBenchmark for British and German Manufacturing 1935/1936,” Groningen Growth and DevelopmentCentre Memorandum no. 90 (2007): 1–36.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 23
industrial surveys for the years between 1907 and 1911 are published.26 The surveys
report output and employment data for a number of industries. For those industries
that are included, the surveys do not provide full coverage. Instead, the production of
a sample of firms is reported. Partly this is due to the fact that the surveys are only
sent to firms affiliated with the national health-insurance scheme for workers (Gewerbe-
Unfallversicherungsgesetze). The smallest workplaces are in effect not covered and the
scope of the surveys may be limited to the larger firms in German industries. This
could lead to compatibility problems when comparing Germany with the US. The US
census of manufactures provides almost full coverage as only household industries and
establishments with an annual output lower than $500 are excluded.27
If due to scale advantages labor productivity is higher in large establishments as
compared to small establishments, the benchmark results based on data obtained from
the statistical quarterlies could potentially overestimate the productivity performance
of German industries. Table 2.1 captures the employment coverage of the industrial
surveys. On the left side of the table the average number of employees working in estab-
lishments included in the industrial surveys is reported. On the right side, I included the
same statistic for comparable industries obtained from the Berufs- und Betriebszahlung
published in 1907 (henceforth, occupational census), which has full-employment cover-
age.28 The nomenclature does not match perfectly between both sources. Nevertheless,
the fit is close enough to be reasonably sure that the classification of the occupational
census refers to the same manufacturing activities. For all industries the comparison of
average establishment size shows that the statistical quarterlies report data on relatively
large establishments. This creates a potential bias in favor of Germany.
In order to quantify the potential bias, I need to know which establishment-size
classes are represented by the establishments included in the industrial surveys. If,
for instance, it turns out that the surveys exclude establishments with less than 10
employees, the part of employment covered by those establishments is not represented
by the surveys, which introduces an upward bias in my estimates. Note that I estimate
the representativeness of the surveys and not their coverage. I use the representativeness
of the surveys for two reasons. First, as for some industries the nomenclature between the
industrial surveys and the occupational census differs, a comparison between the number
26. Kaiserlichen Statistischen Amte, “Ergebnisse der deutschen Produktionserhebungen 1913”;Kaiserlichen Statistischen Amte, “Ergebnisse der deutschen Produktionserhebungen 1914.”27. United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1910
(VIII), 19. In the case of the British census of manufactures (1907) household industries, one-personestablishments, and establishments with less than 10 employees are excluded. As a consequence, about25% of employment is not covered. See: Board of Trade, UK Census of Production 1907, 8.28. Kaiserlichen Statistischen Amte, “Gewerbliche Betriebsstatistik,” Abteilung II, Heft 1, Tabelle 8,
1–27.
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24 Missed Opportunities?
Table 2.1: Average number of employees per establishment ( lini) in German
manufacturing industries, 1907/09
Statistical Quarterlies Employment Census
Description lini
Description lini
Kraftfahrzeug- und Hilfsindustrie 244 Fabrikation Kraftfahrzeugen 58
Eisenverarbeitungsindustrie 445 Grosseisen- und -Stahlindustrie 270
Eisen- und Stahlgiessereien 79 Eisengiesserie und -Emaillierung 79
Silber-/Blei-/Kupfer-/Zinkhutten 310 Silber-/Blei-/Kupfer-/Zink-/Zinnhutten 140
Schwefelsaure 69 Chemische Grossindustrie 36
Teer Destill. und Petroleum raff. 43 Kohlenteerschwelerei, Petroleumraff. 30
Kokereien 143 Verkokungsanstalten 131
Zementindustrie 166 Zement- und Trassfabrikation 81
Sources: Kaiserlichen Statistischen Amte, “Gewerbliche Betriebsstatistik,” in Berufs– undBetriebszahlung, Statistik des deutschen Reichs (Berlin, 1907); Kaiserlichen Statistischen Amte,“Erganzungsheft zu die Ergebnisse der deutschen Produktionserhebungen,” in Vierteljahrsheftezur Statistik des deutschen Reichs: Erganzungsheft, vol. Vol. 22, no. 3 (Berlin, 1913).
of employees reported by the surveys and the census, which covers total employment,
introduce a degree of inaccuracy. The compared statistics may not refer to exactly the
same unit of production. Second, even in cases where this is not a problem, the coverage
of the surveys does not provide information on the size of the establishments included
in the surveys. For instance, if 70% of an industry’s employment is covered by the
surveys, it is not clear whether the excluded 30% are employed in relatively small or
large establishments. Therefore, the sign of the bias associated with the surveys remains
unclear, too. Instead, the representativeness of the surveys, i.e. the establishment-size
classes represented by the surveyed establishments, does provide a tool to assess the
bias in the survey’s results.
Using a combination of the average firm size reported by the industrial surveys
and information obtained from the occupational census, I have estimated, first, which
establishment-size classes are represented by the surveys and, second, the share of total-
industry employment that is covered by these establishment-size classes.29 The results
indicate that in most industries the surveys represent all but the smallest establishment-
size classes. As, in general, between 95% and 100% of the manufacturing labor force is
employed in establishments-size classes represented in the surveys, there is no reason
to think that the surveys introduce a structural upward bias in the German labor-
productivity estimates.
29. See appendix 2.A for more detail and the results of this exercise.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 25
Additional sources for the pre-WW1 period
Another potential drawback of the statistical quarterlies is that several manufacturing
industries are not included in the surveys. Data on, for instance, the food industry,
electrical and mechanical engineering, or the instrument industry are not reported.
For some industries important activities are omitted as well. The chemical industry
is poorly represented by coal-tar distillations, potash and sulfuric acid: information
on inorganic chemicals is unavailable. Furthermore, the industrial surveys only report
output in the textile industry, but no employment, making it impossible to calculate
labor-productivity levels. Lastly, due to their incomplete coverage, the industrial surveys
do not provide a complete output structure of the manufacturing sector. Additional
sources are needed to provide the weights necessary to aggregate the UVRs and PPPs
for an analysis on the level of total manufacturing.
To fill the gaps in the data of the statistical quarterlies, three other publications of
the Imperial Statistical Office are used here. First, the Statistisches Jahrbuch fur das
Deutschen Reich (henceforth, statistical yearbook) provides annual data on a limited
number of industries (mostly the production of mines and blast furnaces).30 To a large
extent these industries are in more detail covered by the industrial surveys. However, the
statistical yearbook includes data on the production of taxable goods, such as sugar,
tobacco, and alcoholic beverages and thereby provides information on output in the
food & kindred industry, which remained outside the scope of the industrial surveys.
Unfortunately, the yearbook reports physical quantities only. Hence, labor-productivity
levels for sugar and tobacco are expressed in produced tons per employee.31 For alcoholic
beverages, output is measured in hectoliters. In contrast to output data, the number
of employees working in the food & kindred industry is not reported by the statistical
yearbook. As with the textile industry, for which only output is reported in the industrial
surveys, an additional source is needed to find employment data necessary to calculate
labor productivity. For this purpose the occupational census is used, both in the case of
textiles and food & kindred. The number of workers in the textile industry derived from
30. Kaiserlichen Statistischen Amte, Statistisches Jahrbuch fur das deutschen Reich (Berlin, 1909–1912), 52–133.31. The obtained comparative labor-productivity level is subsequently projected on the US nominal
level of labor productivity (in US$), to obtain the German level of labor productivity expressed in US$.Because the statistical quarterlies report information on output value and volume for some products,starch mainly, it was possible to construct a PPP for this industry, which is then used to convert laborproductivity from US$ to German Goldmark. Of course, the comparative level of German/US laborproductivity has not changed in any sense, but expressing the comparison in output value enables anaggregation scheme along the lines of equation (2.6), which would not have been possible when outputis expressed in volumes. For tobacco, the same procedure has been followed with the difference that noindustry-specific PPP could be obtained and I relied on the total-manufacturing PPP, instead.
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26 Missed Opportunities?
the occupation census is adjusted in line with the coverage of the industrial surveys.32
Secondly, the labor-productivity estimates for paper and glass production are based
on reports of trade unions, which were collected during the 1920s and used to recon-
struct developments on the industry level. These reports were subsequently published in
the Ausschuss zur Untersuchung der Erzeugungs- und Absatzbedingungen der deutschen
Wirtschaft.33 Finally, the estimates for tire production (rubber) are obtained from the
Industrielle Produktionsstatistik, a special edition of the Wirtschaft und Statsitik pub-
lished by the Statischen Reichsamt.34 Although these publications report predominantly
production figures for the period since 1925, the interwar data is sometimes comple-
mented with information on years before WW1 for purpose of comparison.
At this point, I am able to calculate labor-productivity levels for many manufac-
turing industries in pre-WW1 Germany. In most cases, the data employed to calculate
labor productivity does not cover an industry’s total output and employment. For the
purpose of aggregation, however, it is recommendable to allot total-industry weights
to the industry-level labor-productivity estimates. This way, the composition of manu-
facturing is properly taken into account. Because total-industry output is not reported
for Germany, but total employment is (by the occupational census), I have estimated
total output by multiplying the German nominal labor-productivity level by total em-
ployment. Essentially, the labor-productivity estimates are thus reweighted according
to industry-employment shares derived from the occupational census. Earlier research
already pointed out that this procedure underestimates the share of high-productivity
industries, but to a small extent only and is unlikely to affect the results substantially.35
This is confirmed by the 1936/35 German/US benchmark. Using the product of nominal
labor productivity and total-industry employment as a proxy for total-industry output
produces the same result as obtained by the use of actual total-industry output.
A potential problem is that most labor-productivity levels calculated on the basis of
the industrial surveys do not refer to the same year as the weighting scheme, i.e. 1907. In
fact, except for the textile and food & kindred industries, all productivity data refer to
either 1908, 1909 or 1910. If the results are to be interpreted as representative for 1907,
labor-productivity levels must have remained constant over this period, which seems
unlikely. In this study I pursue a less stringent approach by choosing the year for which
32. See appendix 2.B for more detail.33. Verhandlungen und Berichte des Unterausschusses fur allgemeine Wirtschaftsstruktur, “Die
deutsche Zellstof-, Holzschliff-, Papier- und Pappenindustries”; Verhandlungen und Berichte des Un-terausschusses fur allgemeine Wirtschaftsstruktur, “Die deutsche Glasindustrie.”34. Kaiserlichen Statistischen Amte, “Industrielle Produktionsstatistik”; Kaiserlichen Statistischen
Amte, “Industrielle Produktionsstatistik”; Kaiserlichen Statistischen Amte, “Industrielle Produktion-sstatistik.”35. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing,” 320.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 27
Figure 2.1: Peak and census years, 1900–1913
United States
0
2
4
6
8
10
12
14
16
realG
DP
(1913
=100)
1900 1902 1904 1906 1908 1910 1912 191450
60
70
80
90
100
110
0
2
4
6
8
10
12
unem
plo
ym
ent
rate
(%)
Germany
1900 1902 1904 1906 1908 1910 1912 191450
60
70
80
90
100
Real GDPa
Trend growth GDPc
Unemployment rateb
Census years
a Sources: Angus Maddison, “Historical Statistics of the World Economy: 1–2008 AD,”Groningen Growth and Development Centre, 2008, http://www.ggdc.net/maddison/, Table 2:GDP Levels, retrieved: 23 March 2011.
b Sources: [US] D.R. Weir, “A Century of U.S. Unemployment, 1890–1990,” Research inEconomic History Vol. 14 (1992): 301–346, 341–343; [GER] T. Pierenkemper, “The Standardof Living and Employment in Germany, 1850-1960: An Overview,” Journal of EuropeanEconomic History Vol. 16 (1987): 51–73, 58–59.
c The basic long-run trend growth is fitted as a least-squares polynomial of degree 2, for theperiod 1870–1913.
the most output data is available, i.e. 1908 or 1909, as the basis for the benchmark. This
setting assumes that the composition of the manufacturing labor force has remained
unaltered between 1907 and 1909. As the employment structure is much less volatile
than the movement of productivity levels, projecting the 1907 structure on either 1908
or 1909 does not give cause for concerns.36 As the prewar benchmark is used for a
comparison with America and the latter’s census of manufactures refers to 1909, I
designated 1909 as base for the German benchmark.
The choice of 1909 as the prewar benchmark-year was further strengthened by move-
ments of the business cycle. Whenever possible, I took care to avoid years which are
at a peak or in a through of the cycle. Figure 2.1 shows that the level of real GDP
at the selected census years for both countries was above the long-run trend, and that
36. On the basis of the industrial surveys I am able to calculate the annual change in labor productivitybetween 1907 and 1911 for several industries, see appendix A.1. In almost all of these industries laborproductivity increased (rapidly) over the years 1908–1911 (Δ LP). Assuming that labor productivitydid not change, even in this short period, is therefore problematic. Instead, the employment share ofthese industries changed little.
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28 Missed Opportunities?
the unemployment rate at that point in time was relatively low or stable. This is an
essential requirement for my analysis, as I strive to determine the level of potential pro-
ductivity differentials between the countries under comparison. I thus want to exclude
the effects of business cycles and capacity under-utilization as much as possible; which,
I am convinced, is the case for the selected benchmark year.37 Consequently, all German
labor-productivity estimates originally based on data from other years are adjusted to
a 1909-basis using Hoffmann’s industry-level time series of output and employment.
2.4 The transatlantic labor-productivity gap
The methodology and data described in the previous chapters enables me to compare
labor-productivity levels between German and US manufacturing industries. This is
necessary because the extent to which Germany lagged behind the global productivity
leader is not immediately evident from other studies. In the literature German/UK and
US/UK comparisons are presented for prewar and interwar years. Until now, direct Ger-
man/US comparisons were not available and the productivity gap between Germany
and the US could only be obtained indirectly using the German/UK and US/UK com-
parisons. The quality of an indirect German/US estimate depends on the consistency in
the applied methodology and the coverage of industries between the German/UK and
US/UK comparisons, which is never perfect.
Moreover, such a procedure is in particular problematic for the pre-WW1 period,
because the size of the gap between Germany and the UK before WW1 is not undis-
puted. Both Steven Broadberry & Carsten Burhop (henceforth, B&B) and Albrecht
Ritschl have presented German/UK benchmarks for 1907, reporting a productivity ra-
tio in manufacturing of 1.08 and 1.28, respectively.38 Contingent on the choice between
these benchmarks, Broadberry & Irwin’s estimate of a 2:1 American lead over Britain
in 1909/10 implies a German/US productivity ratio of either 0.54 (via B&B) or 0.63
(via Ritschl); a difference of about 15%, which is sizable for this type of research.39
As described in section 2.2, the industry-of-origin approach compares the gross out-
put by industries between countries using an industry-specific conversion factor or
PPP. The inter-industry variation illustrated by table 2.2 highlights the importance
of industry-specific conversion factors. The listed Laspeyres, Paasche, and Fischer gross
37. See Jong and Woltjer, “Depression Dynamics” for an elaborate discussion of the business cycleand capacity utilization effects and a sensitivity analysis for the interwar period.38. Ritschl, “The Anglo-German Industrial Productivity Puzzle,” 549; S.N. Broadberry, R. Fremdling,
and P. Solar, “European Industry, 1700–1870,” Jahrbuch fur Wirtschaftsgeschichte Vol. 2 (2008): 141–171, 93239. Broadberry and Irwin, “Labor Productivity in the United States and the United Kingdom,” 261.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 29
Table 2.2: Purchasing power parities
Industry PPP (GER/US)
1909 1936/35
Exchange rate 4.20 2.48
Lasp. Paas. Fisch. Lasp. Paas. Fisch.
Mining 7.18 6.19 6.66 5.36 5.44 5.40
Manufacturing 4.33 3.49 3.89 3.84 3.00 3.39
Food and kindred products 4.19 2.87 3.47 3.31 3.83 3.56
Textile mill products 2.85 2.85 2.85 3.29 3.29 3.29
Tobacco manufactures . . . . . . . . . 2.72 2.65 2.69
Paper and allied products 4.59 4.53 4.56 3.78 3.51 3.64
Chemical and allied products 3.64 2.83 3.21 3.09 2.63 2.85
Petroleum and coal products 6.63 6.05 6.33 3.96 2.04 2.84
Rubber products 7.71 7.71 7.71 4.99 3.53 4.20
Leather and leather products 4.86 5.42 5.13 4.22 4.25 4.23
Stone, clay, and glass products 4.13 4.12 4.12 3.14 2.78 2.95
Primary metal products 3.58 3.22 3.39 2.89 2.74 2.81
Transportation equipment 4.82 5.03 4.92 4.83 3.84 4.31
Sources: see text, section 2.3. For UVRs: see appendix A.8 and A.10.
output PPPs differ markedly between manufacturing industries and the exchange rate
functions poorly for the purpose of converting industrial output. On the aggregated
level, however, the 1909 Fischer PPP for industry closely resembles the official ex-
change rate, which signals that the latter reflects fairly accurately the average price
ratio between Germany and the US. This no longer holds for the interwar period, when
the official exchange rate overvalued the Reichsmark with considerable margin. In this
case, the use of the exchange rate to convert Reichsmark to US Dollar would introduce
a bias in the productivity comparison in favor of Germany.
In addition, in view of the high coverage of the UVRs, listed in table 2.3, the PPPs
constructed here provide a reliable as well as methodologically appropriate alternative
to the official exchange rate. On average, the output that is matched between pre-WW1
Germany and the US in the construction of UVRs covers, respectively, 61% and 50%
of the output that is compared in the labor-productivity benchmark. For the interwar
period the coverage of compared output is similar. In contrast, the number of product
matches is much larger for 1936/35 than it is for 1909. The fact that a substantially
larger number of matches does not lead to a correspondingly larger share of covered
output reflects the increased complexity and product diversity of manufacturing. The
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30 Missed Opportunities?
Table 2.3: Coverage and number of UVRs
Industry Share in compared output No.
1909 1936/35 1909 1935
GER US GER US
Mining 94 52 60 27 7 5
Manufacturing 61 50 74 48 74 125
Food and kindred products 4 8 70 47 6 9
Tobacco manufactures . . . . . . 57 118 1
Textile mill products 58 30 148 114 11 7
Paper and allied products 100 60 43 25 3 12
Chemical and allied products 143 38 94 21 15 33
Petroleum and coal products 86 86 59 9 8 3
Rubber products 64 98 69 73 1 3
Leather and leather products 79 120 133 251 5 7
Stone, clay, and glass products 43 42 70 57 2 7
Primary metal products 74 58 76 46 18 27
Transportation equipment 68 57 47 57 5 16
chemicals and primary-metals industries are a case in point. In both situations an
increased number of matches did not produce a higher coverage of output.
For some industries, the UVRs cover more than the output included in the labor-
productivity comparison. Products designated to a SIC category are sometimes classi-
fied in the ‘wrong’ industry group in the primary source. Although in such cases the
UVRs of these products are reclassified here in the ‘correct’ industry group, their out-
put is usually not included in the labor-productivity comparison. For instance, leather
gloves are classified as a product of the apparel industry in the historical statistics,
but, according to the SIC, belong to leather production. As such, the UVR of leather
gloves are included in this study in the PPP for the leather industry, but their output
cannot be included in the labor-productivity comparison for the leather industry when
the corresponding labor employed in the production of leather gloves is not separately
reported in the primary source.
Looking at the PPPs presented in table 2.2, for several industries the Laspeyres
and Paasche PPPs vary substantially, specifically in 1936/35. This is an indication of
structural differences between the countries under comparison. The deviation between
the Laspeyres and Paasche PPPs stems from the use of, respectively, base-country (US)
or non base-country (German) weights for the process of aggregation and variation
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 31
between the two is evidence of dissimilar production structures. A clear example is the
case of the petroleum and coal industry in 1936/35; the early adoption of petroleum-
based production techniques in the US led to a large gap in output prices relative to
Germany. That is, US petroleum refining was more cost efficient and when assigned a
large share in industrial output the industry PPP (Laspeyres) takes on a high value
reflecting the relatively low production cost in America. Reversely, the specialized coal-
based chemical industry in Germany was still able to produce cost efficient against its
American counterpart. As in Germany coke production took on high importance relative
to petroleum refining – exactly the opposite of the American case – using German
output weights (Paasche PPP) produces a PPP very different from the conversion factor
obtained through application of US output shares.
Table 2.4: German/US comparative labor productivity (US = 100%),Single-deflated gross output per employee/hour
Description Per employee Per hour
1909 1936/35 1909 1936/35
Mining 40 29 44 26
Manufacturing 57 52 56 47
Food and kindred products 55 43 55 41
Tobacco manufactures 30 35 30 34
Textile mill products 90 111 86 103
Paper and allied products 53 52 53 46
Chemical and allied products 80 105 82 96
Petroleum and coal products 42 55 43 50
Rubber products 50 46 51 43
Leather and leather products 66 57 65 55
Stone, clay, and glass products 51 54 51 48
Primary metal products 67 103 64 88
Transportation equipment 30 24 30 21
Sources: see text, section 2.3. For output and employment data: seeappendices A.2, A.3, A.4 and A.5.
The conversion of industrial output with the PPPs reported in table 2.2 enables
a comparison of German and US labor-productivity levels in 1909 and 1936/35. The
results of these comparisons are listed in table 2.4. Because of the data constraints
discussed in section 2.3 above, comparative productivity for 12 pre-WW1 industries
could be calculated (11 manufacturing industries and mining). Although for 1936/35
it was possible to provide full-manufacturing coverage, for reasons of consistency and
comparability table 2.4 includes an estimate based on the same selection of industries as
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32 Missed Opportunities?
studied for the year 1909. The employment coverage of the prewar comparison amounts
to 34% for Germany and 47% for the US. In 1936/35 the coverage of the prewar industry-
sample was 32% and 33%, respectively.40
The lack of full-coverage data for the pre-WW1 period may introduce a bias in
the estimates. Indeed, on the aggregate level, a comparison between the sample and
full-coverage results for 1936/35, presented in table 2.5 below, shows that the former
overstate Germany’s performance by about 10%. The difference is accounted for by
two effects. First, the performance in some German industries is overestimated by the
sample data. For textiles, chemicals and, to a lesser extent, primary metals the total-
industry results show a much poorer performance on the part of Germany. This indicates
that the production activities covered by the 1909 sample displayed an a-typically high
performance level, a finding that helps explain the strong performance in some parts of
German manufacturing, an issue to which I later return. Second, several industries are
excluded by the sample data and these tended to perform relatively weak. Including
these industries thus drags down the overall level of German labor productivity. Then
again, even after downward adjusting the performance in some industries, the main
characteristics of German manufacturing remain unaltered, as do the conclusions drawn
on the basis of the comparison.
With regard to these conclusions, the top row of tables 2.4 and 2.5 reports the com-
parative performance on the aggregate level and shows that Germany tracked America
at considerable distance, both in 1909 and 1936/35. If I accept the idea that countries
lagging behind look to the universal productivity frontier for catch-up growth, as is
often suggested in the literature, Germany had yet a long way to go by 1909.41 De-
spite this large potential for catch-up growth, at the end of the interwar period German
and American levels of productivity had not converged. Instead, the US extended its
lead and the German/US productivity ratio dropped from 57% to 52%, which might
not come as surprise given the many calamities since 1914. Still, the distance to the
US before WW1 was larger for the UK than it was for Germany. Given Britain’s edge
over Continental Europe all through the nineteenth century, this change in European
productivity leadership signifies a success on the part of Germany in modernizing the
manufacturing sector since the second industrial revolution.
Up till this point I have looked at gross output per employee in Germany and the US.
40. For Germany 1909 95% of mining employment is covered. All employment is taken into accountfor US 1909, US 1935 and Germany 1936.41. The idea of catch-up is old and can be found in the works of, for instance, Gerschenkron, Economic
backwardness, 113, 116 and Abramovitz, “Catching-up,” 387. For more recent frontier analysis see,for example, Acemoglu, “Directed Technical Change,” 39 and Vandenbussche, Aghion, and Meghir,“Growth, Distance to the Frontier and Composition of Human Capital,” 98.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 33
Table 2.5: German/US comparative labor productivity(US = 100%), sample and full-coverage data
Description Per employee Per hour
Sample All Sample All
Mining 29 29 26 26
Manufacturing 52 46 47 41
Food and kindred products 43 45 41 44
Tobacco manufactures 35 35 34 34
Textile mill products 111 74 103 69
Apparel products . . . 49 . . . 39
Lumber and wood products . . . 49 . . . 46
Paper and allied products 52 52 46 46
Chemical and allied products 105 72 96 66
Petroleum and coal products 55 56 50 51
Rubber products 46 41 43 39
Leather and leather products 57 50 55 48
Stone, clay, and glass products 54 48 50 43
Primary metal products 103 93 88 79
Fabricated metal products . . . 48 . . . 42
Machinery (excl. electrical) . . . 49 . . . 40
Electrical machinery . . . 49 . . . 43
Transportation equipment 24 25 23 22
Sources: see text, section 2.3. The coverage and number of the UVRs: see(for sample) table 2.3 and (for full coverage) appendix A.6 and A.7
There are, however, good reasons to adjust for working hours. Over the interwar period
both the US and European countries saw a rapid drop of hours worked per year. The
increased bargaining power of labor unions, but also the effects of the Great Depression
led to a shortening of the working week and an increasing number of holidays.42 Since
the change in hours worked was larger for the US, adjusting for hours will affect the
labor-productivity comparisons. For the US, total annual hours worked plummeted from
2,718 in 1909 to 1,817 in 1935; a drop of 33%. The corresponding figures for Germany
42. The correction for hours worked is based on data from M. Huberman, “Working Hours of theWorld Unite? New International Evidence of Worktime, 1870–1913,” Journal of Economic HistoryVol. 64, no. 4 (2004): 964–1000 and M. Huberman and C. Minns, “The Times They are not Changin’:Days and Hours of Work in Old and New Worlds, 1870-2000,” Explorations in Economic HistoryVol. 44, no. 4 (2007): 538–567. In addition, several primary sources have been used, i.e. KaiserlichenStatistischen Amte, Statistisches Jahrbuch fur das deutschen Reich, Hoffmann, Das Wachstum, UnitedStates Department of Commerce: Bureau of the Census, US Census of Manufactures 1910 (VIII),International Labour Office, Year Book of Labour Statistics 1939 (Geneva: International Labour Office,1939) and R. Matthews, C.H. Feinstein, and J.C. Odling-Smee, British Economic Growth, 1856–1973(Oxford: Clarendon Press, 1982), 1–712.
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34 Missed Opportunities?
are 2,723 hours in 1909 and 2,073 in 1936, which signifies a sharp decrease of hours
worked as well, but the reduction was not as pronounced as in the US. Table 2.4 reports
the comparative labor-productivity levels corrected for hours worked. Compared to the
output per employee results the correction for differences in working hours matters
hardly for the year 1909. In contrast, it makes a big difference for the interwar period
and places Germany even further at the back foot.
The benchmark results call for a moderate revision of German industry’s competi-
tiveness relative to the US. First, before WW1 German industry was somewhat stronger
than implicitly indicated by B&B, but certainly not as strong as suggested by Ritschl.
Second, the considerable drop in comparative performance over the interwar period
when labor input is measured by hours worked questions the stationary 2:1 ratio at-
tributed to the transatlantic productivity gap in Broadberry’s work, a conclusion also
drawn by de Jong & Woltjer for the case of the US and the UK.43 Nevertheless, the
findings on the aggregate level broadly align with the traditional view on the ‘produc-
tivity race’, in which the US enjoyed a commanding lead over Europe throughout the
first half of the twentieth century.44 So far as the contribution of the comparisons is
concerned, their value mainly derives from the new information provided on the disag-
gregated rather than the aggregated level. Productivity estimates on the level of total
manufacturing hide industry-specific dynamics and aggregate results do not always ef-
fectively capture the growth experience of underlying industries. Previous benchmark
studies have frequently found considerable inter-industry differences in comparative
labor-productivity levels. Such is also the case for the German/US comparison. Whereas
German manufacturing on average dropped far behind the US, comparative performance
in manufacturing industries ranged from very poor to impressively strong.
Variation in comparative performance between industries can be explained as an
economy’s productivity profile. Broadberry, for instance, has pointed at Britain’s char-
acteristic comparative advantage in light industries, where the productivity gap with
the US was smaller than on the level of total manufacturing.45 There is more than one
story to be told for German manufacturing industries, too. Classified according to their
distance to the frontier, German industries can be grouped in two categories. First,
many industries failed to keep-up with the US and performed at a level half that of
their American counterparts, or even less. At the low end of this group are tobacco
manufacturing and the transportation-equipment industry, while paper production and
leather performed somewhat better. A second group is formed by industries that man-
43. Jong and Woltjer, “Depression Dynamics.”44. Broadberry, The Productivity Race, 34.45. ibid., 26-27.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 35
aged to keep up with the US. This was a small group constituted by textiles, chemicals
and primary metals.
When we probe deeper and dissect the manufacturing sector on the SIC 3-digit
level, the pronounced variation between comparative levels of performance persists.46
For instance, a break down of primary metals in 1909 points out that the iron & steel
industries in Germany were not at all inferior to the US. A low efficiency in nonfer-
rous metals, however, depresses the productivity level for German primary metals as
a whole. Similarly, the large gap between Germany and the US in petroleum & coal
production was caused by a low level of productivity in German petroleum refining. In
coke production Germany was no less efficient than America. The comparative perfor-
mance of chemical industries in 1936/35 varied, too; while the German paint production
performed at a third of the US level, Germany enjoyed an advantage over America of
about 2:1 in the fertilizer industry. In short, the range of German industrial performance
relative to the American frontier was large.
In spite of the observed variation in comparative labor productivity between in-
dustries, the pattern of strong versus weak performers persisted over time. Industries
already performing distinctively weak or strong before 1909 did likewise in 1936/35. Es-
pecially industries at the lower end of the performance scale were predominantly station-
ary. Leather manufacturing, paper production, petroleum refining, and transportation-
equipment industries all persistently trailed the US at a large distance. At the opposite
end of the spectrum textiles, primary metals and chemicals, all of which already did
well in 1909, improved their comparative performance. Still, table 2.5 suggests that
the industries included in the sample performed a-typically strong compared to total-
industry comparative labor productivity. In particular, the spinning activities studied
in the sample does much better in relation to the US than the textile industry on the
whole. The same goes for chemicals and, to a lesser extent, primary metals. Even so,
between the sample- and full-coverage comparison the top-three German industries dis-
playing the strongest comparative performance is the same, only their relative levels
drop from parity to about two-thirds the level of the US. The recurrence in 1936/35 of
a productivity profile similar to the case of 1909 suggests that the level of comparative
performance was dictated by long-run growth determinants, rather than the turbulence
of the period.
With respect to this persistent productivity profile, an identifying trade mark for all
strong or weak performing German industries, such as the aforementioned distinction
between light versus heavy industries in the case of the UK, is not directly evident. Yet
46. Compare the data underlying the 2-digit labor-productivity levels in appendices A.2 and A.4. Forthe UVRs needed to express both output values in a common currency, see appendix A.8.
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36 Missed Opportunities?
there is, I think, a common denominator shared by all three of the industries that man-
aged to approach US levels of labor productivity: the well-performing industries produce
mainly basic, standardized goods. A large part of the primary-metals industry’s output
is processed further in the fabricated-metals, (electrical-) machinery or transportation-
equipment industries. The textile industries studied for 1909 concern spinning activities
producing yarn and thread, which is subsequently used in weaving or apparel industries.
The chemical products included in the prewar sample, in particular sulfuric acid and
potassium compounds, form the intermediate inputs needed for the production of fertil-
izers. Indeed, for textiles, chemicals as well as primary metals the 1936/35 comparison
shows that the prewar industry sample displays a level of labor productivity well above
the average for the industry as a whole (see table 2.5). Conversely, many of the indus-
tries facing a particular large gap to the US involved the production of predominantly
consumer goods, the food & drink and transportation equipment industries being prime
examples.
2.5 Labor-productivity growth in interwar Germany
In spite of the large productivity gaps in both 1909 and 1936/35, German industries
did not necessarily lack progress. Conditional on the rate of productivity growth in the
US, a German industry could rapidly increase productivity levels and still fail to catch-
up. Table 2.6 provides an overview of productivity growth in German manufacturing
industries between 1909 and 1936. As with the German/US comparisons, the results are
obtained through application of the ICOP methodology where industry-specific PPPs
are constructed on the basis of UVRs. Because it concerns a single-country intertemporal
comparison, the PPP is interpreted as a price index and used to convert nominal to real
output, i.e. 1909 Goldmark into 1936 Reichsmark. The labor-productivity difference
between 1909 and 1936 is expressed in average per annum growth to get a accurate
estimate of the pace of change.
The growth rates measured at the industry level can be contrasted with Hoffmann’s
time-series estimates constructed in the German HNA. As mentioned in the introduc-
tion, the output and employment indices presented in the HNA are contested and doubts
have been cast upon the reliability of these series. My intertemporal benchmark com-
parison does not suffer from the problems associated with the time series and, therefore,
offers a convenient alternative.47. Table 2.6 also reports the average annual growth rate
of output per employee in German industries between 1909 and 1936 calculated on
47. See chapter 4 for an elaborate discussion of the problems associated with Hoffmann’s time series
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 37
Table 2.6: German average annual labor-productivity growth (%)
Description PPP Annual lab.-prod. growth
09/36 This study Hoffmann
Employees Hours Employees
Mining 0.94 2.2 2.6 2.2
Manufacturing 0.94 0.6 1.7
Food and kindred products 0.66 -1.2 -0.1 } -0.3Tobacco manufactures . . . 1.7 2.8
Textile mill products 0.76 -0.5 0.8 -0.5
Paper and allied products 1.11 1.5 2.4 1.9
Chemicals and allied products 1.16 1.7 2.8 } 2.1Petroleum and coal products 0.85 0.8 1.9
Rubber products 2.01 1.6 2.8
Leather and leather products 0.99 -0.5 0.6 -0.3
Stone, clay, and glass products 1.01 2.4 3.5 1.5
Primary metals products 0.91 0.7 1.7 0.6
Transportation equipment 2.07 5.7 6.8
Sources: see text, section 2.3. For UVRs: see appendix A.9. For output andemployment data: see appendix A.2 and A.3.
the basis of Hoffmann’s data. The intertemporal benchmark results correspond reason-
ably well with the Hoffmann estimates. The growth rates observed for mining, textiles,
leather and metal production differ little between the calculations of Hoffmann and my
own. Other industries deviate more, but the gap is nowhere large. An exception is the
case of Hoffmann’s building materials industry, which set out against the stone, clay,
and glass industry shows a relatively low rate of growth. This discrepancy is driven by
the composition of the industries, which differs between Hoffmann’s and my classifi-
cation. The growth of the stone, clay, and glass industry is driven mainly by cement
production, a process that underwent rapid change over the interwar years.
A second advantage of the intertemporal benchmark is that it provides more detail
as compared to Hoffmann’s estimates. For example, whereas in case of the latter the
food & kindred industry is combined with tobacco, the benchmark separates the two
and shows that the -0.3 average annual growth rate displayed by Hoffmann’s series is the
result of a decline in output per employee levels in food & kindred, which is in turn partly
offset by an increase in tobacco production. The same applies to chemical industries;
whereas the benchmark distinguishes between chemical and petroleum production, the
time series do not allow for such a break up. Moreover, transportation equipment and
rubber industries are not covered in the NHA, although they do include data on wood
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38 Missed Opportunities?
production and printing activities; industries for which the data required by the ICOP
methodology is not available.
Third and final, the intertemporal benchmark presented here corrects for differences
in working hours between the prewar and interwar period, which Hoffmann’s series do
not. As noted above, in Germany the total number of hours worked on an annual basis
decreased by 25%. Taking account of the reduction in labor input leads to an upward
adjustment of labor-productivity growth. As a result, the food, textile and leather in-
dustries no longer display a negative rate of labor-productivity growth, as implied by
Hoffmann. Rather, these industries stagnated or experienced little growth only. The
correction for hours increases the average annual growth rate in industries by about 1
percentage point across the board, with the exception of mining for which the adjust-
ment makes a small difference only. The reduction of hours worked was considerably
smaller in mining, mostly because the time spent below ground was already relatively
low in 1909.
Table 2.6 shows that on the aggregate level German industry realized a moderate
rate of labor-productivity growth, with mining doing better than manufacturing. On
the level of industries, the growth experience varied considerable. Some industries dis-
played rapid growth while others appeared to stagnate or even decline. With respect
to the former, the common denominator of fast-growing industries appears to have
been maturity. In particular ‘young’ industries developed rapidly, the transportation
equipment industry being a prime example. The latter’s fast-paced growth is reflected
by the price ratio between 1909 and 1935. The price level dropped sharply between
1909 and 1936, a characteristic feature of rapidly developing industries. Rubber, which
through the production of tires was closely related to the motor-vehicles industry, and
chemicals & allied belong to this category, too. Industries born (chemicals, motor ve-
hicles, tires, petroleum)48 or extensively modified (primary metals, tobacco)49 during
the late nineteenth century succeeded in raising productivity levels. In contrast, none
of the stagnated industries (e.g. food & kindred, textiles and leather) can be plausibly
typecast as young.50
The results presented in table 2.6 shed new light on the comparative German/US
productivity levels. Germany’s outstanding comparative performance in textiles over
the interwar period suddenly looks less impressive knowing that the relatively small
productivity gap resulted from a lack of any significant progress in both countries. To
48. Landes, The Unbound Prometheus, 234.49. ibid., 235.50. Although these industries modernized, too. In textiles, for instance, the ring spindle gradually
replaced the mule and the food industries witnessed the introduction of new techniques that conservedproducts for a longer time. Broadberry, Fremdling, and Solar, “European Industry,” 158, 161.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 39
all appearances, it involves a matured industry already well past its growth stage. On
the other hand, Germany’s success in keeping-up with the US in emerging industries,
e.g. chemicals & allied, and industries still in development, such as primary metals,
stands out more firmly. Furthermore, the large and persistent productivity gap in the
transportation-equipment industry was, perhaps, not the failure it at first seems to have
been; the persistent gap between Germany and the US might be understood best as a
success on the part of the latter rather than a failure of the former.
So despite an increasingly large gap to the US, Germany did not lack development.
This development is reflected by the fast labor-productivity growth experienced in sev-
eral industries in table 2.6, but can also be deduced from changes in the employment
structure. Table 2.7 reports the employment shares on the industry level in 1909 and
1936/35 for both Germany and the US. In Germany the combined share of typically
modern industries – i.e. (petro)chemicals & rubber and machinery & engineering – al-
most tripled from 9% in 1909 to 26% in 1936. In the US the same industries employed
12% of labor in 1909 and, like Germany, 26% in 1936. Moreover, in Germany the share
of food & kindred, a low-productive industry, rapidly declined, while several other ma-
tured industries, such as wood production, developed along similar lines. The combined
share of textiles and apparel remained stable over the years (from 24% in 1909 to 23%
in 1936), but it did likewise in the US (21% and 22%, respectively). Even though the
move of labor toward modern industries did not lead to catch-up growth, the German
manufacturing sector was restructuring between 1909–1936 in a fashion not dissimilar
to the US.
Table 2.7: Employment shares Germany and US (%)
Description GER US
1907 1936 1909 1935
Manufacturing 100 100 100 100
Food & tobacco 16 8 12 12
Textiles & apparel, leather 31 24 26 26
Wood & furniture 9 5 14 7
Paper & printing 5 6 8 9
(Petro)chemicals & rubber 2 6 5 9
Metals 14 17 18 14
Machinery & engineering 7 20 7 17
Miscellaneous 17 12 10 6
May not sum to total due to rounding. Sources: see textsection 2.3.
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40 Missed Opportunities?
From the results presented in this section, I draw three conclusions. First, the Ger-
man manufacturing sector was characterized by high cross-industry variation in com-
parative performance, a feature not captured by total-industry estimates. Although on
average German manufacturing persistently lagged behind, several industries performed
on par with their US counterparts. There is no evidence of convergence, as most indus-
tries faced an increasingly large gap to the US. Secondly, despite a relative decline in
competitiveness, German industry did not stagnate. From the intertemporal productiv-
ity comparison it is clear that several industries realized fast growth. However, there is
no relation between the rate of growth over time and the distance toward the US frontier.
For example, labor productivity in the transportation-equipment industry increased at
an unprecedented rate, but proved not enough to close on its American counterpart. In
contrast, the textile industry failed to improve labor-productivity levels over time, yet
was able to deliver a strong comparative performance all through the period of study.
Thirdly, from this I infer that the drivers of growth and catch-up were not necessarily
the same. Related to this, I tentatively suggest that a strong performance as compared
to the US coincides with the production of basic, standardized goods, while fast growth
over the interwar period appears to depend on an industry’s maturity mainly.
2.6 Drivers of growth and catch-up
Given the political and social mayhem of the period, the increasing productivity gap
between Germany and the US on the aggregate level is, perhaps, not much of a surprise.
And, as Hannah puts it, “it seems obtuse to seek the reasons for these standings [in
comparative productivity levels] in the traditional subject matter of business history.
The laggards spent much of the first half of the twentieth century killing one another”.51
Yet the benchmark comparisons show a number of German industries that either man-
aged to close-in on the US or displayed fast growth over the interwar period. These
industries stand out sharply against the backdrop of comparative failure or stagnation
in German manufacturing and the stark contrast between successful development and
backwardness requires further examining. Moreover, the wide-spread range of compar-
ative industrial performance suggests that factors other than the period’s turbulence
in general were at play, a belief that is strengthened by the recurrence of a similar
productivity profile in 1909 and 1936/35. The latter’s persistence fuels the notion that
comparative performance was determined by factors present throughout the entire pe-
riod of study.
51. Hannah, “The American Mircale,” 209–210.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 41
Based on the literature I develop in this section an argument that helps explain
the benchmark results. Central to the line of argument pursued here is the idea that
large-scale production induces efficiency advantages.52 It has often been suggested that
the scale of production was relatively large in the US compared to Europe. As a result,
the former adopted standardization and high-throughput production technology, usu-
ally associated with high levels of capital intensity, more widely than the latter.53 It is
a popular belief that US producers were able to realize large-scale production because,
among other things, they enjoyed a large domestic market characterized by homogenous
demand. European markets, in contrast, were smaller and typically specialized in cus-
tomized production.54 From this line of reasoning it follows that in particular German
manufactures of consumer goods must have suffered from heterogeneous demand, not
producers of basic goods. Given that the strong performing German industries have been
associated with the production of basic goods, the relative scale of production between
Germany and the US may help understand the comparative productivity findings.
Establishment size
With the framework set out above in mind, the first question to answer is whether
the scale of production actually differed between the US and Germany. There are two
ways in which scale is related to production efficiency and, hence, measured. First, if
establishments (factories) are large, efficiency advantages can be obtained by standard-
izing production lines. Second, large firms that incorporate several (or all) stages of the
production chain, i.e. firms that are vertically integrated, may enjoy efficiency benefits
through a reduction of transaction costs, especially when markets function poorly.
Looking at the former measure first, the average establishment size in German man-
ufacturing was indeed smaller than in the US, as Kinghorn and Nye show for the
pre-WW1 period.55 Table 2.8 reports for several manufacturing industries the share of
industrial employment working in establishments with over 50 employees. The picture
is the same across the board; the employment share working in large-scale establish-
ments is in each and every case lower in Germany than it is in the US. Nevertheless, the
size of the gap varied between industries. The difference is almost nonexistent in iron
& steel and quite small for chemicals as well. In contrast, if the share of employment
working in large-scale establishments provides an indicator of industrial development,
52. Chandler, Scale and Scope, 23.53. Rostas, “Industrial Production, Productivity and Distribution,” 58-59; Chandler, Scale and Scope,
47; Landes, The Unbound Prometheus, 247.54. Broadberry, “Technological Leadership,” 291.55. Kinghorn and Nye, “The Scale of Production,” 99.
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42 Missed Opportunities?
the German textile, lumber, leather, and food industries lagged substantially behind; in
these industries the share of workers employed in establishments with over 50 workers
was 3, 4, or – in the case of the food industry – even 5 times smaller than in the US.
Table 2.8: Share of workers employed in establishmentsemploying >50 workers (%)
Description US 1909 Germany 1907
Textiles 93 38
Paper and printing 67 51
Lumber 81 22
Leather 90 25
Iron and steel 99 98
Food 67 13
Ceramics 85 55
Chemicals 85 70
Sources: J. Kinghorn and J. Nye, “The Scale of Production inWestern Economic Development: A Comparison of OfficialIndustry Statistics in the United States, Britain, France, andGermany, 1905-193,” Journal of Economic History Vol. 56, no.1 (1996): 90–112, 99.
For iron & steel and chemicals the small difference in average establishment size coin-
cides with a correspondingly small difference in labor-productivity levels. I am inclined
to relate Germany’s emphasis on large-scale production in these industries to Hannah’s
observations regarding giant plants (>1,000 workers) in Germany and the US. Hannah
states that giant production units were particularly representative for “modern” in-
dustries and in chemicals, shipbuilding, and electrical manufacturing Germany counted
more giant plants than the US.56 The opposite conclusion applies to tobacco and auto-
mobiles.57 With the exception of electrical engineering, the presence of giant plants or
the lack thereof corresponds well to the comparative productivity levels presented in this
research; chemicals performed on par with the US, while the transportation-equipment
industry and tobacco manufacturing trailed the American frontier at considerable dis-
tance. Moreover, following Kinghorn and Nye, Hannah underlines Germany’s overall
smaller average establishment size in manufacturing and suggests it might have been
the bulk of small workshops that drove Germany’s low overall labor productivity.58
So far as the data of Kinghorn and Nye go (table 2.8), the small share of employment
working in large-scale establishments reported for the German textile industry is difficult
56. Hannah, “Logistics, Market Size, and Giant Plants,” 68.57. ibid., 69.58. ibid., 72.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 43
Table 2.9: Distribution of employment over establishment-size classes (%) inGerman manufacturing industries, 1907
SIC Industry ≤ 50 51–1,000 ≥ 1, 001
Textiles Totala 28 72 0
Cotton spinning 31 69 0
Linen spinning 18 82 0
Jute spinning 7 93 0
Silk spinning 51 49 0
Chemicals General chemicals 29 58 13
Petroleum and coal Totala 21 79 0
Petroleum 66 34 0
Coke 18 82 0
Stone, clay and glass Cement 12 84 4
Primary metals Totala 15 59 27
Iron & steel 5 55 40
Cast iron 30 60 11
Nonferrous metals 9 82 9
Transportation equipment Motor vehicles 21 50 29a Industry totals are a weighted average calculated using employment weights. Foremployment data, see appendix A.2.May not sum to total due to rounding. Sources: Kaiserlichen Statistischen Amte,“Gewerbliche Betriebsstatistik,” in Berufs– und Betriebszahlung, Statistik des deutschenReichs (Berlin, 1907).
to reconcile with the strong labor-productivity performance it delivered in both 1909 and
1936/35. This puzzle can be explained by table 2.9, which reports for several industries
covered by the labor-productivity comparisons the distribution of employment over
establishment-size classes. For 1909 the textile industries included in the comparison
concern spinning activities and table 2.9 shows that in these industries the employment
share working in establishments with over 50 employees was much higher than the 38%
reported by Kinghorn and Nye for the whole of textiles. In cotton spinning, which was
the largest spinning industry in terms of employment, this share amounted to 69%. The
other textile industries, i.e. jute, linen and silk spinning, employed 93, 82 and 49% of
total labor in large-scale establishments, respectively. Clearly, the spinning industries
not only displayed above average labor-productivity levels, as the 1936/35 comparison
testifies, they were also characterized by relatively large establishments. For the other
two strong performers, i.e. iron & steel and chemicals, the employment share working
in large establishments differs not between the industry sample of the comparisons and
Kinghorn and Nye’s data. So each of the three German strong-performing industries
produced on a scale not much smaller than their American counterparts.
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44 Missed Opportunities?
Vertical integration: firms versus cartels
The establishment-size data provides some empirical support for the notion that in
textile spinning, iron & steel, and chemicals Germany faced little problems when it
comes to production scale. If these industries were indeed involved in the production of
predominantly basic goods and did not face a demand for customized consumer goods,
the comparable establishment size in both countries suggests that technical constraints
to standardized production should be no less in Germany than in the US. However,
the efficiency effect of establishment size may be offset by differences between German
and US firm size. Optimal firm size is partly determined by the relative costs of market
transactions and if markets function poorly these transaction costs can be lowered by
integrating several (or all) stages of the production chain within a single firm. As the
size of firms was typically larger in the US than in Europe, this potentially endowed
American producers with an advantage over their German competitors.59
But there is a problem with this argument. In the case of firm size, being larger is not
always better. More specifically, as optimal firm size is determined by transaction costs,
the smaller firm in Germany might simply reflect a well-integrated market that reduced
the incentive for firms to extent their control over more stages of the production chain.
Country-specific conditions conducive to low transaction costs can thus limit the size
of firms. Cartels – a much favored model of industrial organization in Germany – could
have provided such conditions and the smaller firm size in Germany need not have been
a sign of backwardness.60 Cartels offered an alternative way to attain a reduction of
transaction costs. Through the control exerted by the cartel over different stages of the
production chain, coordination problems could be addressed efficiently without having
to integrate these production stages in one firm. Related to this, the stability offered
by cartels potentially induced higher rates of investment, leading to capital deepening
and productivity growth.61
Although cartels are associated with a reduced intensity of competition, moving Ger-
many away from competitive capitalism, the literature on Germany is strikingly positive
about the effect of cartels on economic development.62 If German cartels tended toward
a monopoly control of the market, they could have closed the door on technological
development, yet Burhop and Lubbers conclude that in the case of German coal-mining
corporations productivity was not significantly affected by cartel membership.63 Over
59. Chandler, “Organizational Capabilities,” 83.60. Kinghorn and Nye, “The Scale of Production,” 109; Hannah, “The American Mircale,” 207–208.61. Levenstein and Suslow, “What Determines Cartel Success,” 85.62. J. Kocka, “Entrepreneurs and Managers in German Industrialization,” The Cambridge Economic
History of Europe Vol. 7 (1978): 492–589, 564.63. C. Burhop and L. Lubers, “Cartels, Magerial Incentives, and Productive Efficiency in German
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 45
the period 1881–1913 there is no evidence that the Rheinisch-Westphalien Coal Syndi-
cate, one of the longest-lasting cartels, adversely influenced levels of technical efficiency.
In similar vein, Kinghorn argues that German coal and iron & steel cartels around
the turn of the century did not lead to true monopoly power, yet they did allow firm
members to use more efficient production technologies.64 Strikingly, the top three of
industries with the largest number of cartels included iron & steel, chemicals and tex-
tiles, i.e. precisely those industries that the benchmark comparisons showed to deliver
a strong performance relative to the US.65
Vertical integration: protectionist policy
Vertical integration was encouraged not only by the cartel system, but also by the pro-
tectionist policy that Germany maintained to restrict foreign competition. The case-
studies in the work of Webb are particularly useful in this respect. Being the leading
advocates of protective tariffs, the iron foundries, cotton-spinning mills, and large-scale
agriculturalists are centrally placed in Webb’s research. To interpret the impact of tariffs
on domestic production accurately, he measures the effective rate of protection, which
captures the tariff-instigated percentage increase in value added and thereby takes into
account the price change in both intermediate inputs and finished products.66 Webb
concludes that, together with cartels, protection encouraged vertical integration; as tar-
iffs raised domestic market prices above the world level, backward integration ensured
purchase of intermediate inputs against cost, rather than market prices.67 Moreover,
by stimulating vertical integration, the tariff system reinforced the stability of prices
already encouraged by cartels, which – as noted above – reduced the riskiness of invest-
ment in capital-intensive technologies.68 Smaller, non-vertically integrated firms faced
market prices above world level and, therefore, did not gain from protection. As a result,
trade tariffs favored the large-scale, more politically powerful enterprises.69
It is hard to say how the increased costs of intermediate inputs affected comparative
productivity between Germany and the US. As in the case of iron & steel and textiles –
Coal Mining, 1881–1913,” Journal of Economic History Vol. 69, no.2 (2009): 500–527, 502.64. J. Kinghorn, “Kartell or Cartel? Evidence from Turn of the Century German Coal, Iron and Steel
Industries,” Journal of Economic History Vol. 56, no. 2 (1996): 491–492, 492.65. Kocka, “Entrepreneurs and Managers,” 564.66. S. Webb, “Tariff Protection for the Iron Industry, Cotton Textiles, and Agriculture in Germany,
1879–1914,” Jahrbucher fur Nationalokonomie und Statistik Vol. 192 (1977): 336–357, 337.67. S. Webb, “Tariffs, Cartels, Technology, and Growth in the German Steel Industry,” Journal of
Economic History Vol. 40, no. 2 (1980): 309–330, 328.68. Additionally, for the pre-1950 period higher tariffs are associated with lower relative capital-
good prices and because the latter are negatively related to the rate of investment protection canstimulate growth through increased investment. W. Collins and J. Williamson, “Capital-Goods Pricesand Investment, 1870–1950,” Journal of Economic History Vol. 61, no. 1 (2001): 59–94, 80, 81.69. Webb, “Tariff Protection,” 353; Webb, “Tariffs, Cartels, Technology, and Growth,” 323.
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46 Missed Opportunities?
i.e. the industries studied by Webb – protectionist policy confers certain cost advantages
to vertically-integrated and large-scale firms. However, these advantages are relative
to other, small-scale firms in German industry. This goes to show that trade tariffs
hurt the competitiveness of small establishments in particular, while larger firms could
only hope to avoid the backlash of protectionism and stand their ground relative to
the US through vertical integration. It is not evident how protectionism improved the
comparative performance of German industries.
Then again, even though domestic firms faced prices above market level as a conse-
quence of tariff walls, protectionist policy may have secured the survival of developing
industries by shutting out foreign competition. Given that many of the German modern
industries were outperformed by their American counterparts, as the comparisons show,
such an infant-industry approach suited these industries well. Indeed, it has been sug-
gested in the literature that in contrast to the post-1950 period, in which – by and large
– growth benefited from free trade, trade tariffs around the turn of the century were pos-
itively correlated with growth.70 As all through the first half of the twentieth century
manufacturing products entering Germany were (increasingly) tariffed, protectionist
policy may explain the growth captured by the German intertemporal benchmark.71
It should be noted, however, that the existence of a ‘tariff-growth paradox’ has been
called into question. Many scholars have used regression analysis to test the hypoth-
esis that economic growth was a function of protection, but different specifications of
the model have led to results both confirming (O’Rourke, 2000; Jacks, 2006) and re-
futing (Capie, 1983; Schularik and Solomou, 2011) the tariff-growth paradox.72 In any
case, the cry for protectionism was fueled in Germany by notions much different than
those set out by the infant-industry argument. The textile industry is a case in point;
not only provided tariffs protection for the strong (instead of the weak), cotton spin-
ning can hardly be described as an emerging industry. To take another example, when
70. P. Bairoch, “Free Trade and European Economic Development in the 19th Century,” EuropeanEconomic Review Vol. 3, no. 2 (1972): 211–245, 242; J.A. Frankel and D. Romer, “Does trade causegrowth?,” American Economic Review Vol. 89, no. 3 (1999): 379–399, 394; K. O’Rourke, “Tariffs andGrowth in the Late 19th Century,” Economic J Vol. 110, no. 463 (2000): 456–483, 473; D. Jacks,“New Results on the Tariff-Growth Paradox,” European Review of Economic History Vol. 10 (2006):205–230, 221.71. V. Hentschel, “German Economic and Social Policy, 1815–1939,” in The Cambridge Economic
History of Europe, ed. P. Mathias and S. Pollard, vol. Vol. 8 (1989), 752–813, 786;C.P. Kindleberger,“Commercial Policy between the Wars,” in The Cambridge Economic History of Europe, ed. P. Math-ias and S. Pollard, vol. Vol. 8 (Cambridge University Press, 1989), 161–196, 180; Broadberry, TheProductivity Race, 141.72. F. Capie, Tariffs and Growth; Some Insights from the World Economy, 1850–1940 (Manchester
University Press, 1994), 42; M. Schularick and S. Solomou, “Tariffs and Economic Growth in the FirstEra of Globalization,” Journal of Economic Growth Vol. 16, no. 1 (2011): 33–70 49, 56. Schularick andSolomou claim that the real paradox is not that free trade was bad for growth, but that changes ininternational economic policies seems to have mattered little to countries’ growth trajectories.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 47
cheap foreign grain threatened domestic agricultural production, the politically power-
ful landowners successfully lobbied for tariffs.73 And to add insult to injury, because
tariffs on agricultural imports were on balance higher than for manufacturing goods,
the landowners effectively slowed down the rate of GDP per capita growth by delaying
the shift of employment toward high productive industries.74 Clearly, if protectionism
induces growth, it was an unintended byproduct of an otherwise strictly conservative
policy.
Relative factor costs
Apart from the differences in industrial organization between Germany and the US
described above, Europe’s inability to catch-up in general has been explained by the
Rothbarth-Habakkuk thesis. In Europe, factor and resource endowments as well as de-
mand patterns are said to have favored a labor-intensive way of production.75 Natural
resources were scarce and skilled labor was in ample supply, which provided an incentive
to economize on fixed capital in the form of machinery.76 In contrast, the US was well
endowed with natural resources, while skilled labor was relatively expensive. Machinery
was substituted for skilled labor, resulting in the use of capital-intensive production
techniques. This way, local circumstances determined the initial choice of technology.
Technological progress is subsequently directed toward the particular technological path
a country has chosen, leading to lock-in effects.77 As capital-intensive production tech-
niques are associated with higher labor-productivity levels, Europe could not catch-up
with the US.
A study of capital-intensity levels lies outside the scope of this paper. Chapter 3
returns to this issue and provides a thorough analysis of technological development in
both countries. Nevertheless, some remarks are in place here. The benchmark results
do not fit the Rothbarth-Habakkuk thesis exceptionally well. The German industries
that performed on par with their US counterparts challenge the deterministic nature
of the initial-conditions approach. Some scholars – most notably Rosenberg – have de-
scribed America’s lead as foreordained; US resource endowments acted as a benevolent
73. P. Bairoch, “European Trade Policy, 1815–1914,” in The Cambridge Economic History of Europe,ed. P. Mathias and S. Pollard, vol. Vol. 8 (Cambridge University Press, 1989), 1–160, 76; C.P. Kindle-berger, “The Rise of Free Trade in Western Europe, 1820–1875,” Journal of Economic History Vol.35, no. 1 (1975): 20–55, 46.74. S.N. Broadberry, “How Did the United States and Germany Overtake Britain? A Sectoral Analysis
of Comparative Productivity Levels, 1870-1990,” The Journal of Economic History Vol. 58 (1998): 375–407, 386; Hannah, “The American Mircale,” 201.75. Habakkuk, American and British Technology.76. Temin, “Labour Scarcity,” 162;Field, “On the Unimportance of Machinery,” 379.77. David, Technical Choice, 66
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48 Missed Opportunities?
Providence, inevitably setting the stage for America’s edge over Europe.78 However, the
strong German performance in textiles, primary-metals manufacturing, and chemicals
suggests that in these industries either similar production techniques (i.e. capital-to-
labor ratios) were employed by both countries or that higher levels of capital intensity do
not necessarily translate into higher labor-productivity levels. Either way, initial condi-
tions (whatever those were) did not prevent these particular industries from catching-up
and the Rothbarth-Habakkuk thesis – originally suggested as a possible explanation for
19th century Anglo-American, rather than 20th century German-American productivity
differences – sits uncomfortably with the case of Germany.
Some of the cross-industry differences in performance might be explained by the
variance in the degree to which industries rely on raw materials and capital-intensive
production techniques. However, the importance of natural resources in, for instance,
the iron & steel and the textile industries is undeniable, both in the form of raw materials
and as combustibles. A more likely explanation is that factor costs in the ‘successful’
German industries deviated only little from those in the US. In a case study on the pre-
WW1 iron & steel industry, Bob Allen accounted for price differences between German,
American, and British iron products by studying the costs of materials used and the
efficiency of production. Allen finds that in 1910 the price of used raw materials (ore
and scrap) was actually lower in Germany than in both the US and UK.79 Fuel (blast
furnace coke) was more expensive as compared to the US, but cheaper than in Britain.
In line with the benchmark results, Allen shows that productivity in Germany was
comparable to the US and higher than in the UK.80 Iron production in Germany was
characterized by low material costs and high efficiency levels.
Apparently, the costs of using capital in the primary-metals industry did not dif-
fer much between Germany and the US. Does this mean that both countries operated
similar production techniques? In the late nineteenth century, the Bessemer process for
the mass-production of steel from molten pig iron revolutionized the iron & steel indus-
try. Although the large-scale application of the Bessemer process was introduced first
in Britain, the technology was swiftly improved upon in the US so that by the 1880s
the coke-fueled blast furnaces developed in America formed the pinnacle of available
production techniques.81 Hyde shows that when American steel-producing technologies
78. Rosenberg, “Why in America?” 112.79. R.C. Allen, “International Competition in Iron and Steel, 1850–1913,” Journal of Economic His-
tory Vol. 39 (1979): 911–937 932. British iron ore mined in the East Midlands and Cleveland was atleast as cheap as the German ore from West-Phalia, but for some reason Britain mainly used the moreexpensive Spanish ore.80. ibid. 931.81. C. Hyde, “Iron and Steel Technologies Moving Between Europe and the United States, Before
1914,” in International Technology Transfer. Europe, Japan and the USA, 1700-1914, ed. D.J. Jeremy
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 49
proved their superiority European manufacturers started to copy American designs.82
Adoption of American technology by German entrepreneurs is observed in other turn-of-
the-century industries, too. For instance, Richter and Streb present evidence of transat-
lantic technology transfer in the machine-tool industry, a tradition that continued well
into the twentieth century. They quote contemporary industry periodicals, which report
a good many cases where German manufacturers imported American machinery and
incorporated these technologies in their own production process without the slightest
adjustment.83 The implementation of American technology in German industries seems
difficult to reconcile with the idea of technological lock-in driven by local circumstances.
2.7 Conclusion
A more compelling advocate of research on the disaggregated level than the case of Ger-
many is hard to imagine. In the literature the German growth experience has been de-
scribed in different manners; traditionally, emphasis is placed on the success of German
industries during the second industrial revolution, but more recent research struggled to
find quantitative evidence of German catch-up growth. Contingent on the level of aggre-
gation, the German/US productivity comparisons presented here justify both stories.
There are no signs of convergence at the level of total manufacturing. Zooming in on the
performance of underlying industries, however, several clear-cut German successes are
observable, most notably in the production of chemicals, textiles, and primary metals.
The stark contrast between success and failure returns when manufacturing industries
are dissected even further and the focus shifts from the SIC 2-digit to the 3-digit level;
in primary metals, for instance, iron & steel industries performed comparatively strong,
while non-ferrous metal production failed to keep-up with the US.
Not surprisingly, general theories as regards to the German-American productivity
gap have a difficult time accounting for the cross-industry variation. There are nonethe-
less some recurrent patterns recognizable. First of all, while in general the scale of
production was smaller than in the US, there is a striking overlap between the German
industries listed in the literature as having relatively many large factories or even giant
plants, many cartels, and high tariffs and those that performed strong in comparison
to the US. According to the literature the cartel-tariff system had the potential of rais-
ing efficiency levels by encouraging large-scale production, lowering transaction costs
(Aldershot: Edward Elgar, 1991), 51–73 52.82. ibid. 68.83. R. Richter and J. Streb, “Catching-up and Falling Behind. Knowledge Spillover from American
to German Machine Tool Makers,” FZID Discussion Paper (2009): 1–24, 1-2.
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50 Missed Opportunities?
through vertical integration, and creating a low-risk environment for investment.
The notion that these institutions provided German industries with a competitive
edge certainly sits well with the benchmark results. As to why specifically these German
industries managed to upscale production, a possible explanation concerns the nature
of the manufactured products. It has been suggested that the smaller establishment
size in Germany resulted from a domestic demand for customized goods, which ham-
pered standardized production. However, textile spinning, iron & steel and chemical
industries produced predominantly basic, rather than consumer goods. Unconstrained
by heterogeneous demand patterns, large-scale production was attainable.
Looking at productivity growth in German industries between 1909 and 1936,
though, the maturity of industries takes on importance. To name an example, trans-
portation equipment, i.e. the fastest-growing German manufacturing industry, neither
had particularly many cartels nor comparatively large plants. However, ‘born’ around
the turn of the century, it was a typically modern industry. Emerging industries, such as
motor-vehicles and chemicals production, and industries extensively revised in the late
nineteenth century, for instance tobacco and primary-metals manufacturing, displayed
fast growth rates, too. But, as Landes notes, ‘there is a tendency to concentrate on
the most striking examples of German achievement’ and the overall success of German
manufacturing should not be exaggerated: even when German industries grew fast, it
was mostly too slow for catch-up with the US84.
Although the drivers of growth and catch-up seem different (an industry’s maturity
and its scale of production, respectively) they can be reconciled. As demonstrated by the
German industries that are associated with large establishments, the scale of production
serves to exhaust a technology’s potential. However, when technological development
in industries has come to a standstill, labor productivity can increase only through
more efficient exploitation of the technology in use. If scale is a factor in this and
the average establishment size was fairly large already, the scope for growth was little
indeed. Such was the case for textiles. In contrast, when industries experienced rapid
technological change, the level of labor productivity increased, even when the scale of
production was suboptimal. However, if for each new technology large-scale production
is associated with higher labor-productivity levels, industries that attained a sizable
scope of production benefited more from technological change. From this perspective,
the rapid productivity growth between 1909 and 1936 signifies fast technological change,
while the increasingly large gap to the US reflects the inability to benefit fully from new
developments.
84. Landes, The Unbound Prometheus, 317.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 51
2.A Representativeness of the industrial surveys
To calculate German labor-productivity levels for the prewar period, I mainly rely
on information obtained from the Vierteljahrshefte zur Statistik des deutschen Reichs
(henceforth, statistical quarterlies). In the statistical quarterlies of 1913 the results of
industrial surveys for the years between 1907 and 1911 are published. The surveys re-
port output and employment data for a number of industries. For those industries that
are included, the surveys do not provide full coverage. Instead, the production of a
sample of firms is reported. Partly this is due to the fact that the surveys are only
sent to firms affiliated with the national health-insurance scheme for workers (Gewerbe-
Unfallversicherungsgesetze). Small workplaces are in effect not covered and the scope
of the surveys is thereby limited to the larger firms in German industries. This could
lead to compatibility problems when comparing Germany with the US. The US census
of manufactures provides almost full coverage as only household industries and estab-
lishments with an annual output lower than $500 are excluded.
In order to quantify the potential bias, I need to know which establishment-size
classes are represented by the establishments included in the industrial surveys. If,
for instance, it turns out that the surveys exclude establishments with less than 10
employees, the part of employment covered by those establishments are not represented
by the surveys, which introduces an upward bias in my estimates. Table 2.10 shifts the
cut-off point in the occupational census upwards in a series of steps – i.e. it increases
the number of excluded establishment-size classes – and thereby raises the average
establishment size up till the point that it equals the average establishment size of the
industrial surveys. When that point is reached, I have an approximation of the survey’s
cut-off point and the establishment-size classes that are represented. The occupational
census reports the share of employment working in the represented establishment-size
classes, which helps to estimate the margin for bias. If the employment coverage of not-
represented establishment-size classes is low, the bias in my results is correspondingly
low as well, and vice versa.
Table 2.10 points out, for instance, that the establishments of the motor-vehicle
industry reported by the industrial surveys have on average 244 employees. According
to the occupational census, establishments in this industry have on average 58 employ-
ees. If establishments with less than 21 workers are omitted, that number rises to 198.
Raising the cut-off point even higher – covering all establishments with 51 employees
or more – the average establishment size in the motor-vehicle industry increases to 313.
Somewhere in between lies the average establishment size reported by the occupational
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52 Missed Opportunities?
census, i.e. 244. From this I conclude that the cut-off point of the statistical quarter-
lies for this particular industry is lower than establishment-size class ≥51, but higher
than establishment-size class ≥21. The occupational census reports that the share of
employment working in establishments with more than 51 workers amounts to 86%.
The coverage of establishments larger than 21 employees is 93%. The sample of estab-
lishments included in the industrial surveys is thus representative for at least 86% but
less than 93% of total employment in the motor-vehicle industry. Column l∗i reports the
arithmetic average of these lower and upper bound estimates.
Table 2.10 suggests that the cut-off point of the industrial surveys is high, especially
for the nonferrous-metals, secondary-metals, coke, and motor-vehicle industries. Estab-
lishments with less than 21 employees appear to not have been surveyed. In contrast,
the coverage of the cement, sulfuric-acid, and coal-tar distillation industries extents to
establishments from 6 employees and more. Yet even when the cut-off point is rather
high, the share of employment not covered by the relatively large establishments is very
small. In most industries about 90% of total employment works in establishments with
51 employees or more, as a consequence of which the surveys represent establishment-
size classes that cover almost all employment. Furthermore, because the employment
figures presented in the surveys of statistical quarterly are derived from insurance data
and report the total insured hours expressed in the number of full-time equivalent work-
ers, the actual number of employees in the surveyed industries was likely to be higher
and the average establishment size lower, which increases the establishment-size classes
included in the sample. Thus, although the industrial surveys cover only a small share
of total employment, the average establishment size suggests that the establishments in-
cluded in the industrial surveys cover a wide range of establishment-size classes. In turn,
the employment census points out that nearly all employees work in the establishment-
size classes covered by the industrial surveys, which leads me to conclude that there is
little evidence of a systematic bias toward large establishments in the latter.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 53
Tab
le2.10:Representativenessofcoverage(l
∗ i)statisticalquarterlies
StatisticalQuarterlies
EmploymentCen
sus
l i ni
l i nifordifferentcut-offpoints
Description
≥1≥6
≥11
≥21
≥51
≥101
l∗ i
Kraftfahrzeu
g-undHilfsindustrie
244
5898
143
198
313
428
89%
Eisenverarbeitungsindustrie
445
270
306
334
366
433
511
96%
Eisen
-undStahlgiessereien
79
79
9110
212
318
627
510
0%
Silber-/Blei-/K
upfer-/Z
inkhutten
310
140
158
175
215
270
324
94%
Schwefelsaure
6936
80
9913
320
730
699
%
TeerDestill.undPetroleumraff.
4330
36
45
6410
818
196
%
Kok
ereien
143
131
134
134
141
165
209
99%
Zem
entindustrie
166
81153
186
219
271
313
98%
Sources:Kaiserlich
enStatistisch
enAmte,“Gew
erblich
eBetrieb
sstatistik,”
inBerufs–und
Betriebszahlung,
Statistik
des
deu
tsch
enReich
s(B
erlin,1907);
Kaiserlich
enStatistisch
enAmte,
“Erganzu
ngsh
eftzu
die
Ergeb
nisse
der
deu
tsch
enProduktionserh
ebungen
,”in
Vierteljahrshefte
zur
Statistik
des
deu
tsch
enReich
s:Erganzu
ngsheft,
vol.Vol.22,no.3(B
erlin,1913).
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54 Missed Opportunities?
2.B Adjustment of the employment census
In contrast to output data, the number of employees working in the food & kindred
industry is not reported by the statistical yearbook. As with the textile industry, for
which only output is reported in the industrial surveys, an additional source is needed
to find employment data necessary to calculate labor productivity. For this purpose
the occupational census is used, both in the case of textiles and food & kindred. The
number of workers in the textile industry derived from the occupation census is adjusted
in line with the coverage of the industrial surveys. This is done according to the number
of establishments included in the surveys. As mentioned, the number of establishments
per establishment-size class are reported by the occupational census. Assuming that the
industrial surveys cover the largest establishments only, I have subtracted the number
of establishments in the largest establishment-size class of the occupational census from
the number of establishments reported by the surveys. In table 2.11 this process is
repeated for all subsequent establishment-size classes until all establishments reported
by the industrial surveys are accounted for. If the assumption that the industrial surveys
capture only the largest establishments in textile industries is violated, my computation
overestimates employment needed for the reported production and, thus, underestimates
labor productivity.
As the share of employment working in small establishments is usually not very
large, the adjustment of the employment number of the occupational census is lim-
ited, too, as can be seen in table 2.11 (in the column headedl∗iLi). Notable exceptions
are the silk and woolen (worsted) industries, where about 25% of total employment
works in establishments with less than 50 employees and which are not accounted for
by the industrial surveys.85 The reliability of these estimates depends on (a) the fit of
the nomenclature between both sources and (b) the assumption that the surveys only
capture the largest establishments being correct. For the linen industry (Flachspinnerei
und -zwirnerei) I can check the accurateness of the estimates, as for the year 1908 em-
ployment is also reported by the industrial surveys. The adjusted estimate, based on
the occupational census, overestimates actual employment by 5.2%, which is reassur-
ingly close. In fact, the difference dissolves entirely when I take into account that the
employment number reported by the industrial surveys refers to 1908 and not 1907,
which is the year for which I calculate labor productivity. Between 1907 and 1908 out-
put in the linen industry increased by 5.5%. If I assume that labor productivity did not
change between 1907 and 1908 (and 5.5% is subtracted from the employment number
85. See table 2.9 for the spread of employment over establishment-size classes in textile industries.
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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 55
of 1908 to offset the effects of output growth), the difference between employment as
reported by the surveys and as estimated by me on the basis of the occupational census
amounts to only 0.3%. For employment in the food & kindred industries I have also
used the occupational census. However, in this case no adjustments were needed as the
output data were obtained from the statistical yearbook and thus do not suffer from
the coverage problems associated with the industrial surveys.
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56 Missed Opportunities?
Tab
le2.11
:Employmentoccupational
censuscoveredbytheindustrial
surveys
EmploymentCensus(1907)
IndustrialSurveys(1907)
Description
l iDescription
l∗ il∗ i Li
Bau
mwollspinnerei
98,746
Bau
mwollspinnerei
und-zwirnerei
97,625
98.9%
Seiden
spinnerei
7,41
3Seiden
spinnerei
und-zwirnerei
5,71
277
.1%
Flachshechelei
und-Spinnerei
18,586
Flachsspinnerei
und-zwirnerei
16,439
88.5%
Jute-undZellstoffspinerei
12,868
Jutespinnerei
und-zwirnerei
12,866
100.0%
Wollspinnerei
58,498
Kam
mgarnspinnerei
und-zwirnerei
43,042
73.6%
EmploymentCensus(1907)
StatisticalYearbook
(1907)
Brauerei
111,779
Biergew
innung
111,779
100.0%
Ruben
zuckerfabrikation
37,380
Gew
innungvon
Roh
-und
37,380
100.0%
undZuckerraffinerie
Verbrauchszucker
Starkezuckerfabrikation
und
2,72
2Gew
innungvon
Starkezucker
2,722
100.0%
Melassenverarbeitung
Tab
akfabrikation
61,162
Roh
tabak
infabrikation
sreifem
61,162
100.0%
Zustan
de
Sources:Kaiserlich
enStatistisch
enAmte,“Gew
erblich
eBetrieb
sstatistik,”
inBerufs–undBetriebszahlung,
Statistik
des
deu
tsch
enReich
s(B
erlin,1907),
52–59;Kaiserlich
enStatistisch
enAmte,“Erganzu
ngsh
eftzu
die
Ergeb
nisse
der
deu
tsch
enProduktionserh
ebungen
,”in
Vierteljahrshefte
zurStatistik
des
deu
tsch
enReich
s:Erganzu
ngsheft,
vol.Vol.22,no.3(B
erlin,1913),
68–75;Kaiserlich
enStatistisch
enAmte,Statistisch
esJahrbuch
furdasdeu
tsch
enReich
(Berlin,1909–1912),
1909:‘V
erbrauch
rech
nungen
’–103,107,275.
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Chapter 3The Yanks of Europe? Labor Productivity and
Technology in German and US Manufacturing, 1899–1939
3.1 Introduction
The pattern of divergence between German and American manufacturing uncovered
in the previous chapter roughly aligns with the 2:1 ratio suggested by Broadberry for
the transatlantic labor-productivity gap in the early twentieth century.1 Traditionally,
Europe’s inability to catch-up has been partly ascribed to local circumstances, i.e. factor
and resource endowments as well as demand patterns, which favored labor-intensive
production.2 In Europe, natural resources were scarce, while skilled labor was in ample
supply, the combination of which provided an incentive to economize on fixed capital in
the form of machinery.3 In comparison, the US was well endowed with natural resources,
while skilled labor was relatively expensive. Therefore, machinery was substituted for
skilled labor, resulting in a capital-intensive production process.
Furthermore, as the American demand for goods was more homogenous and given
the size of the US domestic market, manufacturers could standardize production, imple-
ment high throughput systems, and thereby raise productivity levels.4 This advantage
was denied to European countries, which faced heterogeneous markets characterized by
a demand for customized goods. Thus, local circumstances determined the initial choice
of capital-labor ratios. If technological progress is directed towards the capital-labor ra-
tios currently in use, local circumstances can lead to technological lock-in.5 Assuming
that the increase of labor productivity achieved at high capital-intensity levels surpass
1. Broadberry, The Productivity Race, 3; Broadberry and Irwin, “Labor Productivity in the UnitedStates and the United Kingdom,” 265.
2. Habakkuk, American and British Technology.3. Temin, “Labour Scarcity,” 162; Field, “On the Unimportance of Machinery,” 379.4. Broadberry, “Technological Leadership,” 291.5. David, Technical Choice, 66.
57
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58 Missed Opportunities?
those realized at low capital-labor ratios, labor-productivity levels will differ across the
Atlantic.
However, three pillars of this explicative model have recently been called into ques-
tion. First, the notion of technological lock-in flies in the face of widespread transatlantic
technology transfer recorded during the early twentieth century. Contemporary industry
periodicals report a good many cases where German manufacturers imported Ameri-
can machinery and incorporated these in domestic production lines.6 Apart from a new
coat of paint, imported American machinery was often installed in its original form; ev-
idence that contradicts the development of dichotomous technological paths.7 Second,
case studies reveal a process of rapid capital deepening over the interwar period in the
German machine-tool industry.8 By the late 1930s the number of machines installed on
the factory floor available per worker was comparable between the US and Germany.
The potential external benefits may be substantial, as developments in the machine-tool
industry spill over to other manufacturing industries that extensively use machinery.
Third, the stereotypical US high-throughput model has been downplayed lately. Only
a minority of American industries actually employed thoroughgoing mass-production
systems; a much larger share of manufacturing focused on specialized, European-type
production processes.9 This begs the question whether the Germans ought to be seen
as ‘the Yankees of Europe’?10
In addition to these case-studies, quantitative research on the level of total manufac-
turing also failed to confirm the alleged importance of capital-intensity differences for the
labor-productivity gap. Decomposing the US/UK and German/UK labor-productivity
gap for years between 1870 and 1950 in effects of comparative total-factor productivity
and comparative capital intensity, Broadberry finds the latter component to explain lit-
tle of the observed labor-productivity differences.11 Using Broadberry’s data and decom-
position framework, only about 25% of the German/US labor-productivity gap in both
1909 and 1937 is explained by differences in capital intensity. Yet in spite of the modest
contribution assigned by the decomposition to the role of capital-intensity differences,
this finding has not led to a reinterpretation of the German/US labor-productivity gap.
Instead, the lack of strong empirical evidence in support of the supposed significance
6. Richter and Streb, “Catching-up and Falling Behind,” 1–2.7. Richter, “Technology and Knowledge Transfer.”8. C. Ristuccia and J. Tooze, “The Cutting Edge of Modernity: Machine Tools in the United States
and Germany 1930–1945,” Cambridge Working Papers in Economics No. 0342 (2003): 1–48.9. P. Scranton, Endless Novelty. Specialty Production and American Industrialisation 1865–1925
(Princeton University Press, 1997).10. Quote obtained from Kindleberger. See C. Kindleberger, Economic Response: Comparative Stud-
ies in Trade, Finance, and Growth (Cambridge, Mass.: Harvard University Press, 1978), 188.11. Broadberry, The Productivity Race, 105,106; Broadberry, “Manufacturing and the Convergence
Hypothesis,” 784.
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Chapter 3. The Yanks of Europe? 59
of variation in capital-labor ratios has been attributed to methodological weaknesses
of the level-accounting exercise based on the standard Solow model.12 Three problems
stand out in this respect.
A first deficit relates to the nature of technological change. In the conventional
Solow-based decomposition framework the effect of technological change is proportion-
ate at any level of capital intensity. New production knowledge increases the labor-
productivity potential by the same factor everywhere along the production possibility
frontier. Technological change was, however, not factor neutral, but localized and capital
biased; Allen shows that ever since the first industrial revolution innovation took place
at the highest capital-labor ratios in use, while low capital-intensive technology saw
little or no improvement.13 This has consequences for the impact of variation in capital
intensity on labor-productivity differences. If innovation was indeed localized, countries
operating at capital-labor ratios unaffected by technological change faced a widening
labor-productivity gap relative to countries that did enjoy the benefits of innovation.
A second inadequacy, related to the implementation of the level-accounting exercise,
concerns the use of total capital-stock data per worker as a measure for the capital
intensity of production. The size of the capital stock is determined largely by stocks
of buildings and inventories and the value of the machinery and equipment stock is
low in comparison.14 Because America’s alleged capital-intensity advantage pertains to
machinery, rather than to buildings, it is inappropriate to use total capital-stock data
to calculate capital intensity for the question addressed here, as I will show later on.
A measure of machine intensity is much better suited for the purpose and more apt to
capture the dynamics in German manufacturing as described above for the machine-tool
industry.
Thirdly, a Solow-based decomposition framework requires information on the shares
of capital and labor in output to proxy the marginal factor returns. Using a weighted
average of Germany, the UK and the US for 1975, Broadberry attributes a weight of 0.23
to capital.15 The capital-intensity gap between Germany and the US must be incredibly
large in this setting to make a substantial contribution to the labor-productivity gap.
Compared to 1975, wage levels were relatively low and capital costs relatively high in
the early twentieth century and, consequently, the effect of capital intensity may be
severely underestimated. Without additional factor price information these shares are
notoriously difficult to pin down.
12. Broadberry, The Productivity Race, 106–109.13. Allen, “Technology and the Great Divergence,” 6.14. Field, “On the Unimportance of Machinery.”15. Broadberry, The Productivity Race, 105.
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60 Missed Opportunities?
This chapter provides a decomposition of German-American labor-productivity dif-
ferences that does not suffer from these problems by employing a method that allows
for factor-biased technological change, focuses on the machine intensity of production
and does not rely on fixed and exogenously-given rates of marginal factor productivity.
Following Kumar and Russell, a global best-practice production frontier is constructed
through application of a data envelopment analysis.16 The data envelopment analysis
provides a non-parametric approach, which allows for localized innovation by letting
the empirical data dictate the shape of the frontier and, as a consequence, requires
minimal assumptions of functional form. This data-driven analysis is applied on the
disaggregated level and the effect of localized innovation on the shape of the frontier is
permitted to vary between manufacturing industries. Moreover, instead of using total
capital-stock data, I rely on applied horse-power statistics, a more direct measure of
machinery. Because this new approach allows for the localized nature of technological
change, requires minimal assumptions with regard to the functional form of the frontier
and employs an appropriate measure of capital intensity, it supplies a more sophisticated
instrument to analyze labor-productivity differences than conventional level-accounting
techniques.
The data envelopment analysis draws a global best-practice production frontier on
the basis of observed labor-productivity to capital-intensity ratios in manufacturing in-
dustries. The frontier indicates the maximum labor-productivity performance contem-
poraneously or previously attained over the full range of capital-labor ratios that are
currently in use or have been explored in the past. As a consequence, the frontier is based
only on best-practice observations, which assigns other observations a position below
the frontier. This enables me to calculate the difference between the labor-productivity
level actually realized by industries and the best-practice labor-productivity level indi-
cated by the frontier for the capital-labor ratio at which this industry operates. The
discrepancy between realized and potential labor-productivity levels can be interpreted
as the level of efficiency at which the machinery stock is operated and enables a richer
decomposition of the labor-productivity gap than possible with conventional level ac-
counting. It follows that differences in labor productivity are attributed to components
of, first, comparative machine intensity and, second, comparative efficiency.17 This way,
the part of the labor-productivity gap not accounted for by differences in capital-labor
ratios can be ascribed to a suboptimal utilization of the machinery stock, rather than
to different technological paths as in traditional decomposition techniques.
Although the production frontier analysis is purely data driven, requiring a mini-
16. Kumar and Russell, “Technological Change,” 530–531.17. ibid., 531,532.
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Chapter 3. The Yanks of Europe? 61
mum of assumptions, the decomposition framework itself is firmly grounded in theo-
retical growth models. Particularly relevant in this respect is Basu and Weil’s model
of appropriate technology in which new production knowledge is appropriate only for
a limited range of capital-labor ratios. This setting identifies two channels for labor-
productivity growth depending on a country’s initial position; through innovation for
“leader countries” and for “follower countries” by adopting capital-labor ratios already
explored by leader countries in the past.18 When innovation is confined to high capital-
labor ratios, as Allen demonstrates for my period of study, low-end countries must strive
for higher levels of capital intensity or face an ever increasing labor-productivity gap.19
Yet the process of rapid capital intensification can come at a cost, as empirical studies
point out.20 As much of what one needs to know to employ new production knowledge
is implicit and not available from handbooks, it takes time to assimilate and operate
machinery at the level displayed by countries exploring that capital-labor ratio before.21
According to Los and Timmer, these findings suggest a sequence in which developing
countries first create scope for labor-productivity growth by adopting high capital-labor
ratios and, subsequently, ‘learn’ to operate efficiently at that capital-intensity level, from
which point onwards the latent labor-productivity gains materialize.22
The two sources of labor-productivity growth set out by the model, i.e. capital inten-
sity and efficiency, provide a framework that possibly explains the paradoxical lack of
labor-productivity catch up at a time of capital-intensity convergence between German
and US manufacturing. If innovation was localized and took place at high capital-labor
ratios only, Germany – a follower country – faced a strong incentive to increase capital-
intensity levels, but the associated labor-productivity gains may not have materialized
in the short run as German industries struggled to learn how to operate efficiently at the
the new capital-labor ratios, a process that required time. The next section discusses
the analytical framework necessary to test whether such dynamics can be identified for
German manufacturing over the interwar period. Subsequently, section 3.3 describes the
data, while the results are presented in section 3.4. After positioning these results in the
more qualitative literature on German historical economic development in section 3.5,
the next section (3.6) adopts a long-term perspective and positions the findings of this
chapter in long-run developments. Finally, section 3.7 concludes.
18. Basu and Weil, “Appropriate Technology,” 1036.19. ibid., 1043–1045.20. Los and Timmer, “The ’Appropriate Technology’ Explanation,” 519.21. A. Atkinson and J. Stiglitz, “A New View of Technological Change,” The Economic Journal Vol.
79 (1969): 573–578; David, Technical Choice, 59–60.22. Los and Timmer, “The ’Appropriate Technology’ Explanation,” 529.
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62 Missed Opportunities?
3.2 Methodology
In order to see if the model set out above applies to German manufacturing in the early
twentieth century, I follow a two-stage research strategy. First, I look at the level of
capital intensity in German manufacturing in 1909 and 1936 and measure the rate of
labor-productivity growth if machinery was operated at full efficiency throughout the
entire period. In order to do so, I need to know what the maximum labor-productivity
potential for explored capital-labor ratios was. This requires a global best-practice labor-
productivity frontier that captures the highest attainable levels of labor productivity
over the full range of explored capital-labor ratios by any country in the world and
allows for localized innovation. Secondly, I decompose the labor-productivity gap in
manufacturing industries between Germany in 1936 and the US in 1939. This gap is
attributed to differences in both capital intensity and the efficiency at which machinery
is operated. Both components can be measured only by knowing what the full labor-
productivity potential is at the capital-labor ratios adopted by Germany and the US,
for which purpose the global best-practice frontier is used again.
The first part of the analysis quantifies the potential return to the adoption of high
capital-labor ratios and illustrates the (ex-post) incentive for capital-intensification in
German manufacturing. The second part measures how efficiently the machinery stock
was operated in German manufacturing by the late 1930s, following a period of capital
intensification, and indicates the degree to which the transatlantic labor-productivity
gap was sustained by differences in efficiency levels between Germany and the US.
Because both steps in the analysis require a global best-practice frontier, the estimation
of this frontier provides the basis for the analysis of labor-productivity differences.
Therefore, this section discusses the data envelopment analysis (henceforth, DEA) used
to construct the frontier first, before describing the decomposition of the German/US
labor-productivity gap in 1936/39.
The global best-practice frontier
Using DEA-techniques the global best-practice frontier is estimated in four sequential
steps. A first step involves collecting data on the level of capital intensity and labor
productivity for manufacturing industries. Subsequently, manufacturing industries that
produce similar products are sorted into groups. For this purpose the standard industrial
classification of 1945 (henceforth, SIC) is used.23 Thirdly, all industries classified in
23. For a detailed overview of the SIC, see United States Department of Commerce: Bureau of theCensus, Census of Manufactures 1947, vol. Industry Description (Washington: United States Govern-
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Chapter 3. The Yanks of Europe? 63
the same group are placed in 〈k, y〉 space, where k is capital intensity and y is labor
productivity. In a final step, the global best-practice frontier is drawn by enveloping
the data in the tightest-fitting convex cone using linear line segments.24 The upper
boundary of the envelop represents the global best-practice frontier.25 This exercise is
then repeated for several periods to also obtain the movement of the frontier over time.
Figure 3.1: Estimating the frontier for industrial chemicals, 1899–1939
0
1
2
3
4
5 10
y
k
(a) Observations up to and incl. 1909
0
1
2
3
4
5 10
y
k
(b) Observations up to and incl. 1939
0
1
2
3
4
5 10
F(’09)
F(’39)
y
k
(c) The frontiers in 1909 and 1939
0
2
4
5 10
F(’09)
F(’39)
(’09)
(’36)
y
k
ya
yb
(d) German chemical industry, 1909 and 1936
Figures 3.1a until 3.1c capture the procedure described above for the case of indus-
trial chemicals. The top-left pane draws the frontier for 1909 based on data from the
US, Germany and the UK. Although not truly global, the frontier contains the three
leading industrial nations, both in terms of size and labor-productivity levels, of the
ment Printing Office, 1949).24. Kumar and Russell, “Technological Change,” 530.25. For best-practice industries, see W. Salter, Productivity and Technical Change, second edition
(Cambridge University Press, 1966), 1–220.
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64 Missed Opportunities?
early twentieth century. The data on which the frontier for 1909 is based includes all
current and past observations. By including observations from earlier years, knowledge
previously generated is ‘remembered’ over time and remains accessible in the current
period.26 In practice, this means that the cloud of observations in figure 3.1a contains
data from 1899, 1905 and 1909 for the US, 1907 for the UK, and 1909 for Germany.
This information is derived from the published production censuses or other statistical
sources. Section 3.3 provides more detail on the dataset constructed for the DEA.
The top-right pane of the figure shows how the frontier changes over time, in this
case between 1909 and 1939. All observations already included in the frontier estimation
for 1909 are exported to figure 3.1b and show up as the gray dots. In addition, the
figure plots all available observations for the period afterward up to and including
1939, which, as before, are collected from the production censuses. This includes data
on 1936 for Germany, 1930 for the UK and all census years since 1909 for the US.
Subsequently, the frontier is drawn. Leaving out the observations that are not part of
the frontier, figure 3.1c clearly shows the upward shift of the frontier between 1909
and 1939. Throughout the rest of this chapter this outward movement of the frontier is
referred to as technological change. As the frontier captures the highest achieved levels
of labor productivity for contemporaneously or previously explored capital-labor ratios,
the vertical distance between an industry observation and the frontier determines the
efficiency level at which technology is operated. For instance, figure 3.1d positions the
German chemical industry relative to the frontier in 1909 and 1936. Clearly, the level of
efficiency declined with the increase of the distance to the frontier. Linear programming
techniques are used to accurately calculate an industry’s vertical distance to the best-
practice frontier.27
An appeal of the DEA approach is that the shape of the frontier can be revealed
without imposing a specific functional form on technology.28 The convexity of the en-
velop poses the only restriction on the functional form of the frontier. As the param-
eters of the production frontier are obtained from the data, rather than presupposed,
the DEA allows for any form of localized technical change.29 That is, an increase in
the labor-productivity potential at particular capital-intensity levels through innova-
26. The unlikeness of an ‘imploding frontier’ was already noted by Basu and Weil, “AppropriateTechnology,” 1031, 1036 and Kumar and Russell, “Technological Change,” 540, but the first to formallyexclude the possibility of technological degradation were Los and Timmer (Los and Timmer, “The’Appropriate Technology’ Explanation”).27. R. Fare, S. Grosskopf, and K. Lovell, Production Frontiers (Cambridge: Cambridge University
Press, 1994), 1–296; Kumar and Russell, “Technological Change,” 531. For the linear programmingtechniques, see appendix 3.A.28. Fare, Grosskopf, and Lovell, Production Frontiers, 12.29. Los and Timmer, “The ’Appropriate Technology’ Explanation,” 522.
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Chapter 3. The Yanks of Europe? 65
tion may shift the frontier upward only for a limited range of capital-labor ratios, while
leaving other parts of the frontier unchanged. In the example of industrial chemicals,
in spite of substantial technological change at high levels of capital intensity, the first
line segment of the frontier remains unaltered throughout the entire period. Because of
its non-parametric nature, the DEA is particularly suited for the issue at hand.
The global best-practice frontier and the change thereof over time permits the first
part of the analysis introduced above, i.e. measuring the rate of labor-productivity
growth at the frontier for German capital-intensity levels in 1909 and 1936. Using fig-
ure 3.1d as example again, I measure the labor-productivity level at the frontier for the
capital-labor ratios at which the German chemical industry operated, i.e. ya for 1909
and yb for 1936. Subsequently, the growth rate of labor productivity at the frontier
can be computed between 1909 and 1936. This is an annual growth rate of 3.4% in
this particular case, which can be interpreted as the created potential for growth as
a result of adopting higher capital-labor ratios between 1909 and 1936. Also evident
from figure 3.1d is Germany’s inability to exploit the full potential of the machinery
it operated in 1936, as testified by the vertical distance between the German chemical
industry and the frontier. The question remains to what degree this low efficiency level
contributed to the labor-productivity gap between Germany and the US in the late
1930s. A decomposition of the labor-productivity gap in components of relative capital
intensity and relative efficiency is necessary to answer that question.
The decomposition of labor-productivity gap
Having constructed the frontier, the labor-productivity gap between Germany and the
US can be decomposed into two elements. The stylized figure 3.2 illustrates these compo-
nents graphically. A German industry in 1936 and its American counterpart in 1939 are
positioned relative to the global best-practice frontier. The observed labor-productivity
gap between the two countries (y1/y0) is attributable, first, to the use of different
capital-labor ratios, because the labor-productivity potential (ya and yb) at the capital-
intensity levels of Germany and the US (ka and kb, respectively) differs. If both countries
operate at their respective capital-intensity levels with full efficiency, the US enjoys a
labor-productivity lead over Germany on account of the American capital-labor ratio
having a larger labor-productivity potential. Secondly, the labor-productivity gap is
determined by the efficiency level of Germany (y0/ya) relative to the US (y1/yb). If
Germany exploits relatively little of its labor-productivity potential compared to the
US, this contributes to the observed labor-productivity gap, too.
In equation 3.1 the combined effect of differences in capital intensity and efficiency
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66 Missed Opportunities?
Figure 3.2: Decomposition techniques
F(’39)
(GER’36)
(US’39)
y
0 k
y0
ya
y1
yb
ka
kb
explains the German/US labor-productivity gap. If the efficiency component takes on a
value of 1, the productivity gap surely results from the use of different capital-labor ra-
tios, a scenario much in line with the notion of technological lock-in mentioned above.
However, if the efficiency component takes on a value lower than 1, which indicates
that Germany exploits relatively little of the labor-productivity potential associated
with the capital-labor ratio at which it operates, the productivity gap cannot be under-
stood to reflect simply the use of different capital-labor ratios. If the first part of the
analysis indicates that Germany experienced a process of capital intensification over
the interwar period, the framework proposed by Los and Timmer suggests that the
labor-productivity gap decomposition may uncover low levels of efficiency in German
manufacturing in 1936; adoption of high capital-labor ratios creates growth potential,
which remains unexplored in the short run because it takes time to efficiently assimilate
production knowledge.
y0y1
=
(yayb
)︸ ︷︷ ︸
technology
·(y0/yay1/yb
)︸ ︷︷ ︸efficiency
(3.1)
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Chapter 3. The Yanks of Europe? 67
3.3 Data
As described in the previous section, for the DEA and the labor-productivity decom-
position industry-level data is required on labor input, capital input and output. The
complete data set used here entails approximately 105 separate industries, which are
classified in 28 SIC industry groups, and in total consists of nearly 1,200 observed
input-output combinations, including US, UK and German observations for years be-
tween 1899 and 1939. US data is available for 1899, 1905, 1909, 1914, 1919, 1929 and
1939, omitting only the census year 1935 on account of unreported capital data.30 For
the UK data is obtained for 1907 and 1930, while the data set includes German ob-
servations for 1909 and 1936.31 Thus, German manufacturing industries in 1936 must
be compared to their American counterparts in 1939 and the labor-productivity gap is
decomposed using the 1939 frontier.
Capital input
The level of technology is measured by capital intensity, but instead of using total
capital-stock estimates, as traditional level-accounting exercises have done for this pe-
riod, I rely on a measure of machine intensity.32 Much of the production knowledge
gained since the second industrial revolution was contained in tangible capital in the
form of machinery installed on the factory floor. So a measure of capital that captures
the stock of machinery is needed. Because total capital-stock data contains other invest-
ment components besides equipment, i.e. inventories and buildings, they do not accu-
rately capture the level of machine intensity; the share of machinery in the total capital
stock is typically small and conclusions regarding the process of machine intensification
drawn from total capital-stock data are bound to mislead.33 Indeed, it has been shown
for the period 1960–1985 that the correlation with GDP growth was much stronger for
changes in equipment than for any other component of investment.34 Although data on
30. United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1910(VIII); United States Department of Commerce: Bureau of the Census, US Census of Manufactures1914 ; United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1920(VIII); United States Department of Commerce: Bureau of the Census, US Census of Manufactures1929 (II); United States Department of Commerce: Bureau of the Census, US Census of Manufactures1935 ; United States Department of Commerce: Bureau of the Census, US Census of Manufactures1940 (II).31. For the UK, Board of Trade, UK Census of Production 1907 ; Board of Trade, UK Census of
Production 1930. For Germany, see chapter 2.32. Broadberry, The Productivity Race, 105, 106.33. Field, “On the Unimportance of Machinery.”34. B. de Long and L. Summers, “Equipment Investment and Economic Growth,” The Quarterly
Journal of Economics Vol. 106, No. 2 (1991): 445–502; B. de Long and L. Summers, “EquipmentInvestment and Economic Growth: How Strong is the Nexus?,” Brookings Papers on Economic Activity
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68 Missed Opportunities?
investment in equipment is available from the historical national accounts of Germany
and the US, they do not provide the level of detail needed for an analysis on the industry
level. As data on the value of installed machinery is unobtainable, I rely on horsepower
statistics instead.35
Is applied horse power an accurate proxy for installed machinery? A possible concern
in this respect is the relation between the unobserved level of machine intensity and
the observed power generated by machinery per unit of labor. To produce the same
output, new machinery often applies less horsepower, i.e. machinery is increasingly
energy-efficient. This applies in particular to the introduction of electrical apparatus, the
application of which became widespread since the 1920s. Because electrical machinery
no longer relied on a single drive shaft, the efficiency of both machinery and factory-floor
design improved considerably.36 The reported horse power applied during production
thus tends to be high for countries employing predominantly steam engines relative to
users of mainly electric motors. Even though the former country applies more horse
power, it would be wrong to conclude that it operates at higher capital-labor ratios. If
the electrification rate in Germany lies well below the US, using horsepower as a proxy
for installed machinery might overestimate the machine-intensity level in Germany.
In practice, however, German and American manufacturing industries are charac-
terized by very similar electrification rates, as table 3.1 shows. Over WW1 the share
of electrical machinery in total applied horsepower increased rapidly in both countries.
Therefore, I feel comfortable using horse power per hour worked as a proxy for ma-
chine intensity. Moreover, as the US substituted electricity for steam power in a process
mirrored by Germany, table 3.1 does not furnish evidence suggesting the latter was
slow adopting new technology. Only in food, drink & tobacco and miscellaneous indus-
tries Germany proved unable to match American electrification rates. In contrast, the
difference between both countries was nonexistent in most modern industries, such as
chemicals & allied, petroleum & coke, (electrical) machinery and transportation equip-
ment.
A different source of potential worry when using horse-power statistics concerns the
danger of ‘double counting’. The horse power applied on the factory floor is supplied
by machinery running on either non-electric or electric power. In case of the latter, the
electricity needed to operate the machinery can be internally generated in the factory
(by electricity generators) or purchased from an electrical power network to which the
No. 2 (1992): 157–211.35. As reported by the Census of Manufactures for the US, the Census of Production for the UK,
and the Gewerbliche Betriebszahlung/Gewerbliche Betriebsstatistik for Germany.36. Schurr et al., Electricity in the American Economy, 32; Jerome, Mechanization in Industry, 250,
253.
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Chapter 3. The Yanks of Europe? 69
Table 3.1: Electrification rates in German and US manufacturing industries, ca. 1910and ca. 1930
Industry Share electric HP in total HP (%)
ca. 1910 ca. 1930
GER US GERUS GER US GER
US
Food, drink and tobacco 15.2 15.2 1.00 54.9 75.4 0.73
Textiles and apparel 11.1 20.1 0.55 72.3 77.4 0.94
Lumber and furniture 17.4 5.8 2.98 66.2 58.6 1.13
Paper and printing 22.3 21.4 1.04 80.3 74.9 1.07
Chemicals, petroleum, coke and rubber 25.1 29.4 0.85 76.0 78.3 0.97
Primary metals 21.4 27.9 0.77 80.1 87.7 0.91
Fabricated metal products 30.3 21.1 1.44 85.5 93.6 0.91
Machinery (incl. electric) 40.6 46.9 0.87 94.3 96.6 0.98
Transportation equipment 31.6 40.1 0.79 92.5 93.8 0.99
Miscellaneous 25.9 22.4 1.15 74.6 91.4 0.82
MANUFACTURING 20.3 22.2 0.92 75.9 81.9 0.93
Sources: see text.
factory is connected. When the electricity is internally generated, the horse power used
by electricity generators should be excluded in the analysis as it does not contribute
directly to the fabrication of goods. Only for pre-WW1 Germany the data does not
allow a correction for double counting.37 This is not a source of major concern, because
the share of electricity in total horse power was modest before the 1920s and the part of
electric power internally generated even smaller. If anything, the bias provides a lower
bound estimate of machine intensification between 1909 and 1936 and underestimates
the created potential for labor-productivity growth.
A final worry concerns the horse-power data for interwar Germany. The 1936
machine-intensity level is indicated by horse-power data obtained from the employment
census of 1933, a procedure that introduces a bias in the capital-intensity estimates.38
In 1933 unemployment in Germany stood at 36.2%, only slightly lower than the all-time
high level of 43.8% the year before.39 By 1936 the unemployment rate had decreased to
12.0% and the employed labor force in manufacturing was 38% larger than in 1933.40
As 1936 was the first year that saw employment levels equal to those of before the Great
37. Hoffmann, Das Wachstum, 263–264.38. Statistik des Deutschen Reichs, “Gewerbliche Betriebszahlung,” in Volks-, Berugs- und Be-
triebszahlung vom 1933 (Berlin: Verlag fur Sozialpolitik, Wirtschaft und Statistik, 1933).39. T. Pierenkemper, “The Standard of Living and Employment in Germany, 1850-1960: An
Overview,” Journal of European Economic History Vol. 16 (1987): 51–73, 59.40. ibid., 59; Hoffmann, Das Wachstum, 199.
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70 Missed Opportunities?
Depression, a part of the installed horse power reported for 1933 may have stood idle or
underutilized on the factory floor. Although the horse-power statstics only report the
frequently used horse power on the factory floor, capital-labor ratios may be spuriously
high.
Alternatively, horse-power data is available for 1938 as well, but these suffer from
three problems and cannot be used. First, the coverage of industries is low as compared
to the 1933 data. Second, by 1938 the German statistical publications hid production
activities related to the war effort. And, third, the build-up for war affected the employ-
ment structure of German manufacturing. Although these impairments have a limited
impact on total manufacturing, the distortions can be quite pronounced on the industry
level and very difficult to identify. Given the importance for this study of data on the
disaggregated level, I prefer the use of the 1933 horse-power statistics. Nevertheless, to
check the sensitivity of the analysis, the decomposition of the labor-productivity gap is
done using German machine-intensity data of both 1933 and 1938. The results do not
differ in any major way (see appendix 3.D) and the findings are robust to variation in
the German level of machine intensity.
Output and labor input
Output is measured by value added as reported by the statistical publications of the
US, the UK and Germany.41 This is necessary to avoid movements of the frontier that
are driven by changes in input prices, rather than improvements of the production
process. German and British output is expressed in US$ using industry-specific output
PPPs.42 Subsequently, nominal value added in US$ is converted to constant prices (with
a 1929 base) by applying price deflators at the industry level. Deflators are calculated
on the basis of Fabricant’s indices of physical output and nominal output series.43
After reclassification to fit the SIC, the modifications and extensions to the indices
of production proposed by Kendrick are incorporated, too.44 Labor input is expressed
in terms of hours worked. The necessity of the hours adjustment has been stressed
41. For the US the Census of Manufactures. For the UK the Census of Production (1907 and 1930)and for Germany the industrial surveys (1909, see chapter 2) and the first industrial census (1936, seechapter 2).42. For Germany/US the PPPs constructed in chapter 2 are used. For UK/US the PPPs are used
of de Jong and Woltjer (interwar period) as well as Veenstra and Woltjer (pre-WW1 period). SeeJong and Woltjer, “Depression Dynamics” and J. Veenstra and P.J. Woltjer, “The Yanks of Europe?Technological Change and Labor Productivity in German Manufacturing, 1909–1936,” XVIth WorldEconomic History Congress (2012).43. S. Fabricant, The Output of Manufacturing Industries, 1899–1937 (New York: National Bureau
Economic Analysis, 1940), 123–321; 605–639.44. J.W. Kendrick, Productivity Trends in the United States (Princeton N.J.: National Bureau Eco-
nomic Research, 1961), 416–421; 467–475.
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Chapter 3. The Yanks of Europe? 71
in chapter 2 and by de Jong & Woltjer in their study on US/UK labor-productivity
differences.45
3.4 Results
The main findings of this chapter can be summarized in four points. First, technological
change at the global production frontier over the period 1899–1939 was decidedly non-
neutral and biased toward capital. Second, in terms of machine-intensity levels Germany
gradually converged on the US over the interwar period. Third, due to the bias in
technological change, the process of adopting machine-intensive technology markedly
increased the scope for labor-productivity growth in German manufacturing. Fourth,
the German/US labor-productivity gap in the late 1930s was mainly due to a relatively
low level of efficiency in factor use, rather than different capital-labor ratios. The created
potential for growth was not realized before WW2 and Germany failed to efficiently
assimilate new production knowledge in the short run.
The global best-practice production frontier between 1899–1939
The movement of the global best-practice production frontier between 1899 and 1939,
as measured by the DEA, contains a bias toward machine-intensive technology. Tech-
nological change did not shift the frontier by the same proportional amount at all
capital-labor ratios. Instead, innovation was localized at the machine-intensive side of
the production frontier.
This finding aligns well with the DEA-literature discussed before. Using similar
techniques, Kumar and Russell concluded for the period 1965–1990 that technological
change has been decidedly nonneutral; outward movement of the frontier was localized
at predominantly high levels of capital intensity.46 Timmer and Los also uncover very
similar dynamics for a broad sample of OECD countries in the last quarter of the
twentieth century; innovation was highly localized and skewed toward the higher capital
intensities.47 Allen shows that the capital bias in technological change was not restricted
to the post-WW2 period. Since the first industrial revolution the global production
frontier shifted upward only at the highest capital-labor ratios in use, while low capital
45. The interwar period saw a substantial drop in the average hours of work for the interwar period. Asthe decrease in hours of work was more pronounced in the US relative to Europe, adjusting for hourswidens the labor-productivity gap between the US and Europe. See Jong and Woltjer, “DepressionDynamics,” 485–488.46. Kumar and Russell, “Technological Change,” 529, 538.47. Timmer and Los, “Localized Innovation,” 55.
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72 Missed Opportunities?
Figure 3.3: Change of the frontier in industrial chemicals
0
y
k
‘09
1
3
2 4
(a) 1909
0
y
‘29
‘09
1
3
k2 4
(b) 1909–1929
0
y ‘39
‘29
‘09
1
3
k2 4
(c) 1909–1939
intensities saw little or no improvement.48 For the first half of the twentieth century,
my findings confirm Allen’s conclusions. Under these conditions a country benefits from
technological change only if it adopts high capital-labor ratios.
The localized nature of and the capital bias in technological change are shown in
figure 3.3 for the case of industrial chemicals, an industry that was already introduced
earlier to exemplify the DEA-technique.49 The figure displays the global best-practice
frontier for three years, i.e. 1909, 1929 and 1939. In each year the labor-productivity
potential was largest for high capital-labor ratios. Industries moving along the produc-
tion frontier would thus increase labor-productivity levels, even if the frontier had not
changed as time progressed. However, the frontier did change and looking at the shifts of
the frontier from year to year, the largest gains in labor-productivity levels are observed
for high capital-labor ratios.
The dashed vertical lines capture the localized nature of change by indicating the
lowest capital-labor ratio at which the frontier shifted during a period of time. Between
1909–1929 the first line segment of the frontier remained unaffected by innovation at
other levels of capital intensity. The vertical line is placed further to the right for the
period 1929–1939, including the first two line segments, which implies that between the
period 1909–1929 and 1929–1939 innovation applied to increasingly machine-intensive
technology. In order to enjoy benefits from technological change, industries had to adopt
capital-labor ratios beyond the threshold indicated by the vertical lines. Industries that
48. Allen, “Technology and the Great Divergence,” 6.49. See figures 3.1a, 3.1b, 3.1c and 3.1d on page 63.
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Chapter 3. The Yanks of Europe? 73
failed to acquire a level of machine intensity in the range for which new production
knowledge was appropriate were inevitably left behind in the labor-productivity race.
Machine intensification in German manufacturing between 1909–1936
In view of the localized nature of and capital bias in technological change countries
faced a strong incentive to adopt high-end technology. The traditional notion of a static
technology gap between the US and Germany until the post-WW2 period, however,
suggests the prevalence of lock-in effects. This is certainly untrue in the narrowest
sense. Even if the relative machine-intensity level in German manufacturing lagged
equally far behind the US throughout the first half of the twentieth century, Germany
must have increased capital-labor ratios just as fast as the US did. Additionally, and
more importantly, a constant technology gap is observable only with total capital-stock
data. The horse-power definition of capital employed here uncovers different dynamics.
Although German manufacturing industries faced a very large machine-intensity gap
before WW1, the distance to the US narrowed over the interwar period. The increase
in machine-intensity levels in Germany proceeded at about 1.5 times the speed of the
US and in 1936/39 the machine-intensity gap was almost half its size in 1909. While
Germany still lagged well behind the US, the interwar period was a time of convergence
in capital-labor ratios, rather than technological lock-in.
The narrowing machine-intensity gap is captured by figure 3.4. Two Kernel-density
plots are drawn, each of which displays the distribution of manufacturing employment
over available capital-labor ratios in Germany and the US based on observations of
machine-intensity levels and employment shares at the industry level. Starting with
the plot for 1909, it can be seen that before WW1 German manufacturing produced
in a much less capital-intensive way than the US. However, the overlap of the US
and German distributions in the late 1930s demonstrates that machine-intensity levels
in Germany were increasingly similar to the US. The speed of convergence differed
between manufacturing industries, but the gap declined across the board. Looking at
table 3.2, the reduction of the machine-intensity gap in several industries stands out.
For instance, the German machinery industry more than halved the gap to the US.
Even more rapid was the machine-intensity convergence in electrical machinery. In 1936
German machine-intensity levels stood at about 90% the level of its direct American
counterpart. As will be discussed later, other industries displayed a remarkable catch-
up process, too. For instance, textiles, which closed the gap to an even greater extent
than the machinery branch, and chemicals. Other industries were less successful in this
respect. A noteworthy example is the slight increase of the gap in the primary-metals
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74 Missed Opportunities?
Figure 3.4: Distribution of manufacturing employment overcapital-labor ratios, US and Germany in 1909 and 1936/39
0.06 0.25 1.00 4.00 16.00
k
Kern
el
Densit
y
(a) 1909
0.06 0.25 1.00 4.00 16.00
k
Kern
el
Densit
y
(b) 1936/39
US GER Overlap
industry.
The improved fit of Germany’s machine-intensity profile with its American equiva-
lent in the 1930s suggests that Germany trailed the US by several years in an otherwise
very similar development. This leads me to conclude that while before WW1 both coun-
tries tracked different technological paths, such a distinction is no longer evident for the
interwar period. What is more, the comparatively high rate of machine intensification
in Germany implies that initial conditions did not stand in the way of machine-intensive
production. This could signify that the pattern of relative factor costs in German indus-
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Chapter 3. The Yanks of Europe? 75
Table 3.2: Horse power per 1,000 hours worked in manufacturing,Germany and the US in 1909 and 1936/39
Industry 1909 1936/39
GER US GERUS GER US GER
US
Food, drink and tobacco 0.48 1.15 41 1.81 2.63 69
Textiles and apparel 0.18 0.56 32 0.86 1.09 79
Lumber and furniture 0.30 1.23 25 1.51 3.01 50
Paper and printing 0.53 1.23 43 1.82 3.22 56
Chemicals, petroleum, coke and rubber 0.59 1.24 48 3.47 5.89 59
Primary metals 0.77 1.92 40 3.33 9.52 35
Fabricated metal products 0.24 0.69 34 1.05 1.89 56
Machinery 0.27 0.82 33 1.27 2.14 59
Electrical machinery 0.30 0.96 31 1.52 1.67 91
Transportation equipment 0.24 0.64 38 1.09 2.58 42
Miscellaneous 0.24 1.06 23 1.34 3.00 45
Total manufacturing 0.34 1.07 32 1.58 2.98 53
Sources: see text.
tries deviated only little from those in the US. Or if they did, it did not deter German
entrepreneurs from acquiring higher levels of machine intensity.
Nevertheless, in spite of the rapid increase in machine-intensity levels, German man-
ufacturing still lagged behind its American counterpart. With the exception of electrical
machinery, German industries failed to fully close the machine-intensity gap. The short
time between the hyperinflation and the Great Depression offered only so much room
for extensive revisions to the production process. When the depression hit Europe in
1929 Germany had enjoyed relative stability for less than a decade and many long-term
projects slowed down, stalled, or were canceled all together.50 With the exception of
the machine-tool industry, Germany never reached the level of mechanization displayed
by the forerunners of American industrial development, such as Ford.
It was an often entertained notion that due to labor unions’ increased bargaining
power after WW1 investment was constrained by rising wage costs.51 Although the
consensus view now is that labor costs failed to harm investment worse than it did before
1914, the “roaring twenties” in Germany are broadly agreed to have been confined to
50. M. Nolan, Visions of Modernity. American Business and the Modernization of Germany (OxfordUniversity Press, 1994), 132.51. K. Borchardt, “Zwangslagen und Handlungsspielrame in der großen Wirtschaft der fruhen
dreißiger Jahre,” Jahrbuch der Bayerischen Akademie der Wissenschaften (1979): 85–132; K. Bor-chardt, Perspectives on Modern German Economic History and Policy (1991); A. Ritschl, “Zu hoheLohne in der Weimarer Republik? Eine Auseinandersetzung mit Holtfrerichs Berechnungen zur Lohn-position der Arbeiterschaft 1925–1932,” Geschichte und Gesellschaft Vol. 16 (1990): 375–402.
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76 Missed Opportunities?
the brief period between hyperinflation and depression, i.e. between 1924–1928.52 As the
alleged investment boom associated with the armament race in the late 1930s effectively
crowded out investment in both the private and public sectors, all together, the window
of opportunity for capital deepening in interwar Germany was rather small, which helps
understand the lack of full catch-up in machine intensity.53
Creating potential for labor-productivity growth
The specific focus on capital in the form of machinery unveils a tradition of machine
intensification in German manufacturing. As the labor-productivity potential increases
with machine intensity this process created substantial scope for labor-productivity
growth in German manufacturing. Table 3.3 illustrates this potential for growth that
German industries realized as a result of increased capital intensity. The first column
lists the average annual labor-productivity growth at the frontier over the interwar
period at the capital-labor ratios operated by German industries in 1909. As such it
reports the increase in labor-productivity potential had German manufacturing failed
to increase machine-intensity levels after 1909 and provides the counter-factual scenario
of technological lock-in in the strictest sense. The second column captures the average
annual labor-productivity growth at the frontier as a result of the actual change in
machine-intensity levels in German manufacturing industries. The difference between
the columns can be interpreted as the created potential for labor-productivity growth
through machine intensification in Germany.
The first column clearly shows the potential danger of lock-in. For many industries
the frontier changed only little at the capital-labor ratios displayed in 1909, a conse-
quence of the localized and capital-biased nature of technological change. Innovation
and introduction of new technology on the frontier, in this case only by US industries,
took place chiefly at high machine-intensity levels. In the cases of, for instance, metals
and machinery there was practically no movement of the frontier at all at these low
capital-labor ratios. To increase the potential for labor-productivity growth, machine
intensification was a necessity in these industries. Even in textiles, in which technological
change did manifest at low capital-labor ratios, frontier movements were much greater
at higher levels of machine intensity. The second column lists the created potential for
labor-productivity growth at the frontier as a result of the actually realized increase in
machine-intensity levels between 1909 and 1936. The reported growth rates are much
52. H.J. Voth, “With a Bang, not a Whimper: Pricking Germany’s “Stock Market Bubble” in 1927and the Slide into Depression,” Journal of Economic History Vol. 63, no. 1 (2003): 66.53. J. Scherner, “‘Armament in Depth’ or ‘Armament in Breadt’? German Investment Pattern and
Rearmament during the Nazi Period,” Economic History Review Vol. 66, no. 2 (2013): 13.
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Chapter 3. The Yanks of Europe? 77
Table 3.3: Annual labor-productivity growth (ln %) at the frontierbetween 1909–1939 for German capital-labor ratios
Industry At machine intensity of
1909 1909–36
Food, drink and tobacco 0.5 4.7
Textiles and apparel 1.2 3.2
Paper and printing 0.8 3.4
Chemicals, petroleum, coke and rubber 0.6 4.3
Stone, clay and glass 0.7 3.2
Primary and fabricated metals 0.3 3.3
Machinery (incl. electric) 0.1 1.7
Transportation equipment 0.9 6.1
Miscellaneous 0.3 2.9
Total manufacturing 0.6 3.4
For more detail, see appendix 3.C. Sources: see text, section 2.3.
higher than those under the first column, indicating that the potential reward to capi-
tal deepening was very large. If German industries operated fully efficient, the increase
of machine intensity recorded in section 3.4 would have pushed up labor-productivity
levels in manufacturing at an average annual rate of 3.4% (ln), more than five times as
fast as in the counter-factual situation of stagnant machine-intensity levels. For several
industries, such as food, chemicals and transportation equipment, the potential gains
were even larger.
The growth rates in table 3.3 reflect the dynamics of the frontier at German levels
of machine intensity and, as such, capture the effects of innovation appropriate for
German industries. An improved understanding of the displayed patterns can therefore
be obtained by looking at the history of technological change over the interwar period.
Take for instance the transportation equipment industry. Table 3.3 reports almost no
change of the frontier at low levels of machine intensity, but rapid change at high levels
of machine intensity. This aligns with the literature, which has put emphasis on the key
position that the process of mechanization claimed in the development of this industry,
as illustrated by the introduction of Ford’s assembly line in the early 1920s.54 The
movement of the frontier corroborates the notion of rapid labor-productivity growth
induced by increasingly high levels of machine intensity.
The capital bias is less pronounced in the textile industry. Although adoption of
54. R.R. Nelson and G. Wright, “The Rise and Fall of American Technological Leadership: ThePostwar Era in Historical Perspective,” Journal of Economic Literature Vol. 30 (1992): 1931–1964,1944–45.
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78 Missed Opportunities?
high machine-intensity levels created substantial additional scope for labor-productivity
growth, the frontier moved outward for low-end technology, too. This development pat-
tern may be explained by the lack of major technological breakthroughs during the
interwar period. The latest technological revolution experienced in textiles dated from
the late nineteenth century with the introduction of the ring spindle, which replaced the
less productive self-acting spinning mule.55 Over the interwar period the ring spindle
was adopted widely and subsequent productivity gains derived from further improve-
ments of the spindle, such as an increase of the spindle’s rate of revolutions.56 This
suggests a tradition of learning-by-doing in textiles, as a result of which the labor-
productivity potential of the ring-spindle technology was exploited to an increasing
extent. Nevertheless, the first half of the twentieth century saw a reduction in the rate
of labor-productivity growth, which suggests that the gains derived from learning-by-
doing fell short of those obtained through the switch from the old self-acting mule to
the ring spindle at the end of the nineteenth century.57
As with the transportation equipment industry, in chemicals technological develop-
ment took place at predominantly high machine-intensity levels. It has been noted in
the literature that in chemicals the subsequent stages of production are closely linked,
resulting in a continuous production line that combines different steps of the produc-
tion chain.58 This promoted not only vertical integration and large-scale production, it
also encouraged mechanization and automation, which accounts for the capital bias in
chemicals. Moreover, the fast pace of change pertains to new technology introduced in
the early 1920s, such as the production of synthetic fuels, rubber, and artificial resins.59
Other industries experienced technological change as well. For example, in primary
metals the open-hearth furnace – a late-nineteenth century innovation – was widely
adopted, in food industries conservation methods revolutionized, and paper machines
were both widened to increase the surface of paper under process and equipped with
multiple engines to improve performance.60 Apart from these industry-specific techno-
logical changes, the whole of manufacturing enjoyed the benefits from electrification.
Although electricity was introduced already in the late nineteenth century, it was the
55. J. Radkau, Technik in Deutschland vom 18. Jahrhundert bis zur Gegenwart (1989), 185–86.56. G. Egbers, “Innovation, Know-How, Rationalization, and Investment in the German Textile In-
dustry During the Period 1871–1935,” Zeitschrift fur Unternehmensgeschichte Beiheft 22 (1982): 234–256, 243.57. ibid., 242 (diagram 3).58. R. Berthold, ed., Produktivkrafte in Deutschland, 1917/18 bis 1945 (Akademie-Verlag Berlin,
1988), 126.59. ibid., 125.60. U. Wengenroth, Enterprise and Technology. The German and British Steel Industries, 1865–
1895 (Cambridge University Press, 1994), 195; Berthold, Produktivkrafte in Deutschland, 141 andibid., 137–38.
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Chapter 3. The Yanks of Europe? 79
first half of the twentieth century that witnessed the widespread application of elec-
tricity in manufacturing industries.61 Among its many advantages, electricity allowed
for a more efficient (and flexible) lay out of factory-floor design as machinery no longer
relied on a single drive shaft for propulsion.62 Production processes characterized by
high machine-intensity by nature profited most from the productivity gains associated
with electricity.
Decomposition of the labor-productivity gap in 1936/39
It is clear that the reduction of the German/US machine-intensity gap over the
interwar period, although far from complete, created a large potential for labor-
productivity growth in German manufacturing. Moreover, this created potential for
labor-productivity growth was larger in Germany than in the US, a necessary condi-
tion for catch-up.63 But the increase of machine-intensity levels proved insufficient to
close the labor-productivity gap. This section presents the labor-productivity gap de-
composition for 1936/‘39, which shows that the potential for catch-up growth created
in German manufacturing was not fully realized, at least not in the short run. A large
German/US labor-productivity gap is still observed at the end of the 1930s, but the
bulk of the gap is ascribed to the inability of German industries to operate machinery
at American levels of efficiency and not to a lack of machine-intensive technology in
German manufacturing as suggested in the literature.64
Table 3.4 reports the results of the decomposition along the lines of equation (3.1)
on page 66. Germany’s labor-productivity gap to the US is decomposed in two elements,
i.e. machine-intensity differences and relative efficiency levels. The table demonstrates
that it was not the choice of capital-labor ratios that kept Germany from catching-up
with America. At the level of total manufacturing, Germany had to increase labor pro-
ductivity by 86% to match the performance of its American counterpart. If Germany
operated machinery at US levels of efficiency, labor productivity would have risen by
62% (which covers 72% of the labor-productivity gap). The complete closing of the
machine-intensity gap would augment German labor productivity by 24% only (which
covers the remaining 28% of the labor-productivity gap), a small increase only in com-
parison with the potential gains attainable through an improved efficiency level.
The relatively small effect of machine-intensity differences may surprise given that
Germany still employed capital-labor ratios about half the level in the US. This lim-
61. Nelson and Wright, “The Rise and Fall,” 1945.62. Schurr et al., Electricity in the American Economy, 32.63. For a comparison between the created growth potential in Germany and the US, see appendix 3.C.64. Broadberry, The Productivity Race, 108, 109.
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80 Missed Opportunities?
Table 3.4: Decomposition of the increase (in %) of Germanlabor-productivity levels needed to catch-up with the US, 1936/39
Industry Total Obtainable through:
Needed for Technical Machine
catch-up efficiency intensity
Food, drink and tobacco 99 74 25
Textiles, apparel and leather 55 19 36
Paper and printing 101 48 54
Chemicals, petroleum and rubber 74 58 21
Stone, clay, and glass products 97 68 29
Primary and fabricated metals 74 53 21
Machinery (including electrical) 97 94 3
Transportation equipment 101 75 26
Miscellaneous 120 96 24
Total manufacturing 86 62 24
Sources: see section 3.3.
ited impact on the labor-productivity gap can be explained by the convex shape of
the frontier. The returns to further increases in machine intensity diminish sharply at
the highest ranges of capital-labor ratios of the frontier. Industries that are positioned
beyond that point may enjoy a large machine-intensity lead, which does not necessarily
translate into a markedly different labor-productivity potential. This is still a rational
choice, however, if innovation takes place at high ranges of machine intensity. In fact,
this is exactly how Basu and Weil perceive the behavior of pioneer countries. In or-
der to grow, they acquire as yet unexplored capital-labor ratios and in time increase
the labor-productivity potential through a process of learning-by-doing.65 This is also
perfectly in line with Allen’s empirical frontier analysis on the total-economy level; pi-
oneer economies first invent technology that is more capital intensive and subsequently
improve labor productivity as the new technology is perfected.66
The example of industrial chemicals in figure 3.5 illustrates this mechanism and
shows that the labor-productivity gap between the US and German industry is dispro-
portionally small as compared to the machine-intensity gap. The American chemical
industry appears to have ‘overshot’ by acquiring a capital-labor ratio beyond k∗, but
may move the frontier outward in the next period.
So the machine-intensity contribution to the labor-productivity gap depends on the
65. Basu and Weil, “Appropriate Technology,” 1030.66. Allen, “Technology and the Great Divergence,” 11.
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Chapter 3. The Yanks of Europe? 81
Figure 3.5: Labor-productivity vs. machine-intensitydifferences in industrial chemicals
0
F(’39)
(GER ‘36)
ya
yb
(US ‘39)
ka
kb
k*
degree to which capital-labor ratios differ between both countries as well as the shape
of the frontier. For instance, the lack of catch-up potential by means of machine in-
tensification for machinery industries reflects a relatively small machine-intensity gap.
Then again, in textiles, apparel and leather, machine-intensity differences were rela-
tively small, too, but even so there was substantial scope for labor-productivity in-
crease through adopting higher capital-labor ratios. This is explained by the strong
capital bias of the frontier for apparel and leather, which attributes considerable labor-
productivity gains to an increase in machine intensity.67 This also applies to paper and
printing. Apart from these exceptions, however, in all other industries the bulk of the
labor-productivity gap is ascribed to low efficiency levels, rather than differences in
machine-intensity levels.
3.5 Technology in German manufacturing
The DEA and the subsequent decomposition of the labor-productivity gap demonstrate
that German industries acquired new growth possibilities through machine intensifica-
tion. The gained potential for labor-productivity growth provides in hindsight a jus-
tification for Germany’s rapid move toward machine-intensive production technology.
67. See appendix 3.B for the global best-practice frontiers.
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82 Missed Opportunities?
Yet these possibilities for labor-productivity growth remained largely unrealized. This
raises two questions. First, did German entrepreneurs purposefully create potential for
labor-productivity growth through capital deepening? An awareness of frontier devel-
opments is prerequisite to technological spillover and Germany could expect to create
additional labor-productivity potential only when new production knowledge was imme-
diately available to countries not on the frontier.68 Secondly, if German entrepreneurs
were aware of the potential gains associated with adopting high capital-labor ratios,
what obstructed the efficient use of new machinery stock? In this section I turn to the
literature for an understanding of these issues.
Frontier awareness
In the case of Germany, ‘frontier awareness’ translates to an understanding and ap-
preciation of American production technology among German industrialists. Such an
America-centered orientation is well documented in the literature on interwar Germany.
In a study on German modernization, Nolan notes that American influences on German
entrepreneurship were limited before the 1920s. From the 1890s onwards, the scientific
management of labor as proposed by Frederick Taylor gained a strong foothold in the
minds of American producers. Proponents of Taylorism traveled to Germany, too, but
found their message difficult to sell; partly because of working-class opposition for fear
of reform at the cost of the laborer and partly because Germany’s successful industrial
development before 1914 did not create a necessity for new concepts.69
In the 1920s the situation was different. WW1, the reparation payments demanded
at Versailles, and the hyperinflation of the early 1920s had left the German economy
weakened in general and technological backward in particular. Change was needed and
by that time an attractive alternative to Taylorism was offered by Ford’s achievements in
the Detroit motor-vehicle industry. Rather than improving performance by rationalizing
on the factor input labor only, Fordism stressed the importance of both labor and
technology in the production process.70 As a consequence, the Fordist approach to
production appealed strongly to German entrepreneurs and set the example for future
development in Germany:
“With the end of Germany’s acute postwar dependency and instability,
America came to be seen as an economic model. In the words of one ob-
68. N. Rosenberg, “Economic Development and the Transfer of Technology: Some Historical Perspec-tives,” in The Economics of Technical Change, ed. E. Mansfield and E. Mansfield (Aldershot: EdwardElgar Publishing Limited, 1993), 380, 397.69. Nolan, Visions of Modernity, 45.70. ibid., 48.
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Chapter 3. The Yanks of Europe? 83
server: ‘One seeks to learn from her, to study her organization, management,
and technology.”’71
The economic-growth literature has often emphasized the importance of investment-
based strategies for follower countries.72 Provided that the necessary capabilities and
resources are available (Gerschenkron’s idea of ‘appropriate’ economic institutions
and Abramovitz’ ‘social capabilities’) countries distanced far away from the frontier
can catch-up quickly by importing or imitating advanced technologies.73 Such an
investment-based catch-up strategy helps explain the strong German orientation on
America. Apart from the theoretical notions of modern economic-growth models, how-
ever, perhaps a more decisive incentive to follow the American example was provided
by the realities of the interwar period. In 1924, after the damage suffered by the econ-
omy during WW1 and the subsequent problems in the Weimar Republic had become
visible, industrialists and entrepreneurs sought ways to recover. At the time America
showed an unprecedented growth record.74 The growth experience of the US was not
just a theoretical possibility discussed in academic debate. More than anything, the
US demonstrated the feasibility of fast economic growth. What better way forward for
Germany than to follow the American example?
“For industrialists, (...) Fordist productivism offered a possible solution to
the economic problems of low productivity, inefficient technology, lack of
standardization, and the resulting high costs that plagued the economy as a
whole. (...) Rationalization, at least in the first instance, was defined by all
in technological and productivist terms. A shared perception of the prob-
lems of German production led to a shared belief that there was no better
place to start learning alternative production methods than from Ford, the
embodiment of American technological leadership, efficiency, and cost cut-
ting.”75
Many German entrepreneurs traveled to the US to study first hand the organization
of American manufacturing industries. Although the extensive application of machinery
and the high level of efficiency at which American manufacturing operated never failed
to impress the visitors, many Germans felt that the American example could not be
71. ibid., 23–24.72. Aghion, “Higher Education and Innovation,” 31; Acemoglu, “Directed Technical Change,” 39;
Vandenbussche, Aghion, and Meghir, “Growth, Distance to the Frontier and Composition of HumanCapital,” 98.73. Gerschenkron, Economic backwardness, 113, 116; Abramovitz, “Catching-up,” 387.74. Field, “The Most Technologically Progressive Decade.”75. Nolan, Visions of Modernity, 38.
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84 Missed Opportunities?
repeated in Germany. Market size, demand patterns, and wage structures differed just
too much between the US and Germany. Nevertheless, it was argued that the principles
of American production technology could be isolated and implemented in Germany as
well.76
Given the widespread enthusiasm about American production technology, it does
not surprise that in the 1920s German manufacturing industries deployed imitating
activities to catch-up with their American competitors.77 Well-known examples of both
imitating strategies and direct technology transfer concern the German machine-tool
industry. Richter and Streb, for instance, quote contemporary sources reporting that
American machine tools were copied by German engineers without any modification to
the original design:
“Information coming from Germany indicates that a number of American
machine-tools are (...) made without the slightest alteration.”78
But there were countless more examples of imitation by German machine-tool man-
ufactures. In the mid-1920s, the American trade commissioner listed over sixty US
machine-tool producers whose export suffered from German firms duplicating their
products.79 They suffered mainly because the changes implemented by the Germans on
American designs were negligible:
“But so far as the central idea and the means of carrying it our [were]
concerned, these tools [were] simply American out and out.”80
Richter concludes that not only thousands of American machine tools were in use in
Germany, but also the same amount or even more German copies of these tools.81 This
invites the question why Germany needed that many American machine tools if the
German production system was locked-in on a technological path essentially different
from the US, as traditionally has been uphold in the literature?82
In a recent paper, Ristuccia and Tooze quantify the prevalence of technology transfer
and imitating activities in the machine-tool industry. Although they base their analysis
on the number of purchased machines in Germany and the US, their conclusion aligns
76. Nolan, Visions of Modernity, 38.77. For a discussion on the channels of technology transfer between Germany and the US, see H.J.
Braun, “The National Association of German-American Technologists and Technology Transfer Be-tween Germany and the United States,” in History of Technology, ed. N. Smith (Mansell PublishingLimited, 1984), 15–35.78. Richter and Streb, “Catching-Up and Falling Behind,” 1007.79. Richter and Streb, “Catching-up and Falling Behind,” 17.80. Richter, “Technology and Knowledge Transfer,” 179.81. ibid., 180.82. ibid., 177.
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Chapter 3. The Yanks of Europe? 85
with my horse-power statistics; Germany converged on the US in terms of machine
intensity and by the 1930s both countries operated very similar production technology.83
Moreover, with respect to the nature of capital, they find no evidence to suggest that
Germany hoarded old machinery; additions to the machinery stock consisted of new
technologies not unlike those in America.
On the basis of this evidence, Ristuccia and Tooze reject the notion of dichotomous
technological paths across the Atlantic and note that “Germany by the late 1930s
showed all the signs of an economy tooling up for the mass production in internal
combustion engines on the lines pioneered by the US in the 1920s”.84 Furthermore, as
Scranton and Richter did before, they downplay the importance of mass production in
US manufacturing.85 Mass production was only one element in a much broader range
of technologies employed in America.86
As Fordist production methods cannot be taken as representative for the American
national technology system, so should the machine-tool industry not be interpreted as
archetypical to the whole of German manufacturing. My data show that only in the
electrical machinery industry Germany closed the machine-intensity gap with the US
(see table 3.2). Other industries still lagged behind and quite considerably in some cases.
Nevertheless, the broad pattern of capital deepening displayed most prominently by the
machine-tool industry are observed in a wide range of other manufacturing industries,
too, although to a lesser extent.
The adoption of American technology proceeded in two waves. As early as between
1870 and 1914 machinery was acquired in America, yet the practice of technology adop-
tion was limited to large and modern German establishments, mostly in metal produc-
ing and metal processing industries.87 In this first wave, the American technologies
were met with reservation concerning its applicability in Germany and implementation
was frequently deemed feasible only once machinery was modified to suit local condi-
tions.88 The second wave of Americanization took place in the 1920s and was much
more widespread.89 Small firms participated in imitating activities, too, as it was rec-
ognized that copying American production technology improved competitiveness and
reduced development costs.90 The tradition of technology transfer thus continued into
the interwar period, but on a much grander scale than before WW1.
83. Ristuccia and Tooze, “Machine Tool and Mass Production,” 13, 18.84. ibid., 11.85. Scranton, Endless Novelty, 341–43; Richter, “Technology and Knowledge Transfer,” 178.86. Ristuccia and Tooze, “Machine Tool and Mass Production,” 9.87. Radkau, Technik in Deutschland, 177.88. ibid., 179.89. ibid., 181, 269, 275.90. Richter, “Technology and Knowledge Transfer,” 181.
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86 Missed Opportunities?
In general, the process of Americanization was characterized by increased standard-
ization and mass production. In the primary metals industry, for instance, Germany
adopted the American method of positioning rolling mills in line to not have to move
metal in different directions or between different machines during the stages of produc-
tion.91 In iron production Germany took the Siemens-Martin process from America,
too. Although the latter was introduced as early as the 1870s, early designs were unable
to process pig iron with a high phosphor content, as in Germany.92 When this problem
was overcome, the open-heart furnace was readily adopted and spread over Germany
during the first half of the twentieth century.93
German constraints to high efficiency
Given the numerous examples of technology transfer it is difficult to maintain for the
early twentieth century the notion that Germany was stuck on a technology path very
different from the US. Then what prevented Germany from operating this new technol-
ogy at efficiency levels displayed by the US? Going back to table 3.4, which demonstrates
that low levels of efficiency formed the major impediment to labor-productivity catch-
up, the industry-level results provide some clues. Take for instance the transportation
equipment industry. Germany could have raised labor-productivity levels by 75% if it
improved its efficiency to the level displayed by its US counterpart, an increase three
times larger than the 26% obtainable through capital deepening. It has been suggested
that the industrial organization of the German motor-vehicle industry lay at the root
of the comparatively low level of labor productivity. Keck writes that before the turn of
the twentieth century German firms were among the early technical leaders, but failed
to turn that into a commercial lead, mainly due to the difficulties German automobile
producers encountered adopting mass-production technology.94
This reluctance of German producers to fully embrace the American system has
been ascribed to different causes – mostly associated with demand patterns, such as a
relatively small domestic market, a demand for heavy-built, custom-made and expensive
cars or a fluctuating demand for automobiles which encouraged flexible production –
and resulted in low productivity levels; over the year 1921 Daimler produced less cars
than Ford did in one day.95 Still, production was increasingly standardized in Germany,
91. Radkau, Technik in Deutschland, 122.92. Wengenroth, Enterprise and Technology, 195, 243.93. G. Milkereit, “Innovation, Know-How, Rationalization and Investments in the German Mining and
Metal-Producing Industries, Including the Iron and Steelmaking Industry, 1868/71–1930,” Zeitschriftfur Unternehmensgeschichte Beiheft 22 (1982): 159.94. O. Keck, “The National System for Technical Innovation in Germany,” in National Innovation
Systems. A Comparative Analysis, ed. R. Nelson (Oxford University Press, 1993), 129, 131.95. Radkau, Technik in Deutschland, 275, 278.
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Chapter 3. The Yanks of Europe? 87
but instead of using a single production line, as in America, German car assembly
remained divided into different production stages, each with its own assembly belt to
allow for flexible production.96 German industrial organization was aimed at minimizing
operating costs of machinery, rather than maximizing output with respect to labor. So
Germany failed to exploit the full potential of the technology in use and approach
American performance.
The notion that low levels of efficiency prevented convergence is illustrated most con-
vincingly by machine-producing industries. Table 3.4 reveals that the labor-productivity
gap in these industries was fully attributable to a lack of efficiency on the part of Ger-
many. Indeed, as shown in table 3.2 and more extensively described by Ristuccia and
Tooze, the level of capital intensity was similar between Germany and the US.97 So
differences in labor-productivity performance can only be ascribed to the level of effi-
ciency at which technology is operated. As with the transportation-equipment industry,
it has been noted that German machine-tool industries focused more on flexible produc-
tion than on high-throughput systems.98 The capital intensity did not differ between
both countries, but the composition of installed machinery did. High volume, autom-
atized machinery was underrepresented in Germany, which may explain the relatively
low levels of output per unit of labor input in this industry.99
In addition to these industry-specific factors that kept Germany from exploiting the
full potential of their technology stock, Ristuccia and Tooze suggest that the labor-
productivity gap stemmed from general differences between Germany and the US, such
as the latter’s cheap energy sources and larger scale of production.100 Also, by the
1930s it became evident that due to the emphasis on production and productivity, i.e.
supply-side factors, the productive capacity of industries had expanded much faster than
demand. In effect, many industries were overcapitalized and had excess capacity that
was left unused.101 As the new direction of technological development and industrial
organization was ill-matched to meet demand patterns, the success of the modernization
process was less than what was hoped for. Furthermore, autarkic policies related to the
build-up to WW2 may have acted as a barrier to efficiency, too. For instance, the food
industry saw major changes in the 1930s by government decree to suit the needs of
a country preparing for war.102 Together with protectionist policies that reduced the
incentive to improve efficient production, it may explain the low efficiency levels.
96. ibid., 278, 280.97. Ristuccia and Tooze, “Machine Tool and Mass Production.”98. ibid., 9; Radkau, Technik in Deutschland, 277.99. Ristuccia and Tooze, “Machine Tool and Mass Production,” 9.
100. Ristuccia and Tooze, “The Cutting Edge of Modernity,” 9.101. Nolan, Visions of Modernity, 132.102. Berthold, Produktivkrafte in Deutschland, 143–144.
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88 Missed Opportunities?
3.6 The long-term perspective
The labor-productivity gap decomposition points out that the gains of modernization
were limited, in the short run at least. I do not necessarily interpret the lack of catch-up
growth as a failure on the part of Germany. Previous applications of the DEA-approach
in the field of development economics led to findings not dissimilar to mine. That is,
fast capital deepening does not necessarily translate directly into correspondingly fast
labor-productivity growth. For a sample of countries in the period between 1975–1992,
Timmer and Los show that the created potential for labor-productivity growth due to
capital deepening was large, not unlike interwar Germany.103 Moreover, the country
that created the largest potential, i.e. Korea, experienced an increase of its distance to
the global best-practice frontier over time. Korea grew at just above halve the growth
potential it had created. Instead of interpreting the declining value for efficiency as
a failure, Timmer and Los conclude that these findings suggest a sequence in which
countries first create opportunities for growth by rapidly increasing capital intensities
and subsequently learn to operate the new technology at its full potential.104
Figure 3.6: Labor-productivity catch-up in two sequential steps
0
(pre-WW1)
(pre-WW2)
y
k
(post-WW2)
??
(2) ‘Learning’ and operating
technology efficiently
(1) Creating potential through
capital intensification
Timmer and Los’ interpretation of the Korean growth experience is a two-stepped
approach to catch-up. Follower countries (or industries) go through two sequential
phases of development in order to close the gap with the frontier, as depicted in fig-
ure 3.6. If the initial phase of catch-up – the adoption of high capital-labor ratios –
103. Timmer and Los, “Localized Innovation,” 58.104. ibid., 60.
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Chapter 3. The Yanks of Europe? 89
involves an extensive transformation of the production process, efficiency levels may
be low in the short run. Only after the economy has adjusted to the new situation
and has ‘learned’ to operate technology at its full potential, the labor-productivity gap
to the frontier narrows. The time lag between creating potential and moving toward
the frontier may therefore depend on the speed of capital deepening. For the case of
Germany, this implies that the implementation problems that German engineers and
industrialists encountered in the 1920s and 1930s were not necessarily signs of failed
industrialization. Instead, they were features of progress and inextricably linked to the
initial phase of catch-up growth.
By the 1930s Germany had created a large potential for labor-productivity growth
and, in theory, it should cash in this latent capacity in a later period, i.e. the 1940s, by
means of ‘learning’. It falls outside the scope of this chapter to extend the analysis to
include the post-WW2 period, mostly because the postwar data on capital is not di-
rectly compatible with the horse power measure employed here. Nevertheless, Germany
was not in the position to realize its growth potential during the period 1939–1945 for
obvious reasons. Equally well-documented is Germany’s change of fortunes after 1946,
from which year onwards it rapidly closed the productivity gap with America. In 1980
(West) German levels of labor productivity stood at about 80% of those in the US.105
Furthermore, the process of capital deepening picked up again after 1950 and contin-
ued until the early 1970s, when America had almost lost its lead over Germany.106
Figure 3.7 sets out German/US relative machine intensity against German/US rela-
tive labor productivity for both the pre-WW2 period, based on data from this study,
and the post-WW2 period, based on data from O’Mahony. Both series are not directly
compatible, as the capital data in this chapter refers to machinery, while O’Mahony
measures the total capital stock. Nevertheless, the figure suggests that the unexploited
potential for labor-productivity growth was gradually realized after 1950.
The unprecedented rate of labor-productivity growth in Germany during the early
postwar years has been explained partly by reconstruction dynamics; Vonyo demon-
strates that wartime destruction and dislocation left much of the capacity for growth
unrealized, a potential which was exploited during the late 1940s and early 1950s.107 In
similar vein, Wolf argues that Germany’s direct productive capacity was not severely
damaged during the war, as a result of which the postwar productive capability consid-
erable exceeded actual production.108 The ensuing rebound growth – or “soft growth”,
105. O’Mahony, Britain’s Productivity Performance, 16.106. ibid., 24, 25.107. Tamas Vonyo, “Postwar Reconstruction and the Golden Age of Economic Growth,” EconomicHistory Review Vol. 12, no. 2 (2008): 235, 239.108. H. Wolf, “Post-War Germany in the European Context: Domestic and External Determinants of
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90 Missed Opportunities?
Figure 3.7: German catch-up in manufacturing after WW2
0.20
0.40
0.60
0.80
1.00
0.40 0.60 0.80 1.00
19091936
1950
1973
1979
US = 1.00
US
= 1
.00
This study
O’M
ahony (1
999)
Com
para
tive l
abor
pro
ducti
vit
y
Comparative capital intensity
as Wolf calls it – explains most of Germany’s labor-productivity growth up till 1955,
while by 1960 this potential had been exhausted.109
To these explanations I add that rebound growth in postwar Germany was driven not
exclusively by the potential left unrealized as a result of wartime dislocations, but also
by the capacity for growth already build-up during the interwar period. Shortly after
1950 German industrial production had recovered up to the pre-WW2 level of 1938.
Therefore, according to Wolf, “hard growth” – i.e. expansion of the full-employment
production level – sets in from the 1950s onwards.110 The decomposition presented
here demonstrates that also at 1936 production levels the capacity for labor-productivity
growth had not yet been exhausted. While the postwar reconstruction dynamics were
limited mainly to the late 1940s, the realization of the growth potential left unused
before WW2 possibly provided some of the fuel for the growth spurt during the 1950s.
Indeed, Ristuccia and Tooze suggest that the postwar growth miracle was prepared by
the process of capital deepening in the machine-tool industry during the 1930s.111
It is not implied here that the high rate of capital deepening over the interwar
period provided the foundation for the German postwar growth miracle. Too much has
happened between the 1930s and 1950s to make such a connection. I do like to point out,
Growth,” in Europe’s Post-War Recovery, ed. B. Eichengreen (Cambridge, Mass.: Harvard UniversityPress, 1995), 326.109. Wolf, “Post-War Germany,” 328.110. ibid.111. Ristuccia and Tooze, “Machine Tool and Mass Production,” 20.
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Chapter 3. The Yanks of Europe? 91
however, that the emphasis in the reconstruction literature on rebound growth during
the late 1940s and early 1950s fits conceptually well within the framework proposed
here; with the only difference that the latent capacity for growth present after the war,
in my view, results from technological inefficiency incurred by both wartime dislocations
and rapid machine intensification in the interwar period.
3.7 Conclusion
This paper studied the growth experience of German manufacturing over the interwar
period. Qualitative evidence on technological change in Germany is difficult to align
with models of path-dependent technological progress used so far to explain the per-
sistent transatlantic labor-productivity gap between 1850 and 1950. Labor-abundant
and resource-scarce European countries were supposedly trapped on a labor-intensive
technological path that limited the scope for productivity growth. However, recent case
studies unveil a tradition of imitation and technology transfer in German manufac-
turing, particularly in the machine-tool industry. During the 1920s German industries
actively copied and duplicated American, capital-intensive, technology. This contradicts
the notion of a Germany-specific technological path.
This paper reassesses the productivity dynamics in German manufacturing and
adopted as point of departure Basu and Weil’s framework of appropriate technology
that predicts convergence in light of capital deepening. My findings show that, first,
over the interwar period Germany increased machine intensity at a rate higher than in
the US, as a result of which the machine-intensity gap between both countries narrowed.
This convergence does not align with models that imply stable capital-intensity levels or
gaps over the interwar period. Secondly, using DEA-techniques, I show that the change
of the global technology frontier was localized and biased toward capital. Consequently,
Germany’s process of capital deepening created a large potential for labor-productivity
growth. Third, and lastly, the decomposition of the labor-productivity gap between in-
terwar Germany and the US revealed that this potential for growth remained partly
unrealized, as Germany operated at low levels of efficiency. If efficiency levels equaled
those in the US, the labor-productivity gap would be a quarter of the size it actually
was.
These findings confirm the anecdotal evidence reported by case studies and reject
the notion of a dichotomous technological development across the Atlantic during the
first half of the twentieth century. The convergence in machine intensity occurred at a
time when German entrepreneurs and industrialists increasingly looked to America as
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92 Missed Opportunities?
the example for industrial development. Frontier awareness, i.e. knowledge of produc-
tion technology used at the frontier, was a prerequisite to technological spillover and
abundantly present among German industrialists in the late 1920s and early 1930s.
In spite of the infatuation with the US, reality could not match the pro-America
rhetoric employed by German engineers, industrialists, and entrepreneurs. Yet these
small gains in labor productivity were not a failure on the part of Germany. Following
Los and Timmer, the decrease of relative efficiency is understood as a feature of progress
inextricably linked to the first phase of catch-up growth, which is creating potential by
capital deepening. Only after an economy has adjusted to the new situation and exhausts
the full potential of the new technology, the labor-productivity gap to the frontier can be
narrowed. Indeed, a review of the literature shows that German entrepreneurs struggled
to fully embrace American industrial design, especially the adoption systems of mass
production.
In view of these findings, I see the interwar period as a time of transition in German
manufacturing. This transition phase is enclosed on both ends by periods that arguably
display very different dynamics. The relatively low levels of machine intensity in pre-
WW1 German manufacturing suggests that the labor-productivity gap to the US in the
period before 1900 was driven largely by the use of different technology, while the post-
WW2 era witnessed a rapid decrease of both the labor-productivity and capital-intensity
gap to the US. Yet the dynamics that propelled Germany to the frontier after the 1950s
should, perhaps, not necessarily be understood as a development strictly confined to
the postwar period, but as a process of technology catch-up that was already set in
motion in the 1920s and 1930s. Or, as Nelson and Wright note with regard to the
interwar period, the “global diffusion and adaption of American methods would surely
have continued, (...) either by imitation or by direct foreign investment, if it had not
been interrupted by World War II.”112
112. Nelson and Wright, “The Rise and Fall.”
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Chapter 3. The Yanks of Europe? 93
3.A Distance function
In this paper I emphasize the role of technological change as a driver behind the wave
of modernization that marked the interwar period and stress the importance of effi-
ciency behind the German productivity dynamics of the 1920s and 1930s, particularly
in relation to the US. By adopting a data envelopment analysis (DEA), which ap-
plies non-parametric linear programming techniques, I can decompose TFP into two
components: changes in technological efficiency and shifts in technology over time. In
addition, as the DEA does not require the imposition of a particular functional form
on the production frontier, it allows for any type of technological change, be it biased
or factor-neutral.113
In this appendix I will summarize the basic framework behind the DEA, based
primarily on the work of Fare, Grosskopf and Lovell.114 They illustrate that a distance
function can be used to determine the Farrell efficiency indices of a production set
for any number of inputs or outputs. On the basis of the efficiency scores, a (global)
production frontier can be constructed, which in turn allows me to determine the change
in technology over time.115 In this basic example I assume that all inputs and output
quantities are non-negative and that, for each time period t = 1, . . . , T , the production
technology St models the transformation of N inputs, xt ∈ RN+ , into M outputs, yt ∈
RM+ ,
St ={(xt, yt) : xt can produce yt
}(3.2)
The input distance function Dti(x
t, yt) at time t is defined as
Dti(x
t, yt) = min{θ : (θxt, yt) ∈ St
}(3.3)
For the constant returns to scale case and a technology set St, the input distance
113. The main advantage of the Data Envelopment Analysis technique is its flexibility and adaptability.A DEA allows for multiple inputs and outputs, does not require input- or output-prices and does notrequire behavioral assumptions such as cost minimization or profit maximization.114. Fare, Grosskopf, and Lovell, Production Frontiers.115. R. Fare et al., “Productivity Growth, Technical Progress, and Efficiency Change in IndustrializedCountries,” American Economic Review Vol. 84, no. 1 (1994): 68–69.
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94 Missed Opportunities?
function for production (xj,t, yj,t) can be specified as
min θ subject toθ,λ1,...,λJ
yj,t ≤∑k
λkyk,t (3.4)
θxj,t ≥∑k
λkxk,t
λk ≥ 0 ∀ k.
The solution to the linear program for the intensity vector λ∗ and efficiency index θ∗
can be interpreted as follows. There is a (hypothetical) composite producer formed as a
non-negative linear combination of all J observations using the components of λ∗. This
composite producer consumes no more than θ∗ times observation j’s inputs, while still
producing j’s output. The composite producer thus represents a fully efficient producer
who is located on the global production frontier at j’s output level, while θ∗ represents
the ratio between both the inputs of the composite producer and xtjrespectively. Note
that if (xt, yt) ∈ St, the Farrell efficiency index θ will take on a value between 0 and 1,
where a value of 1 implies full efficiency.
The observations for which the input distance function returns a θ equal to 1 together
determine the position and shape of the production frontier. The frontier is formed
by tightly enveloping the fully efficient observations, or ‘best practice’ activities, with
linear segments; as illustrated in figures 3.1a and 3.1b on page 63. The frontier is thus
a subset of all feasible techniques that attain the highest labor productivity for the
capital intensity levels they correspond to.116
116. Timmer and Los, “Localized Innovation,” 52.
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Chapter 3. The Yanks of Europe? 95
3.B Global best-practice frontiers
Figure 3.8: Frontiers for the years 1909, 1919, 1929 and 1939
0 2 4 6 8k
0
1
2
3
4
y
(a) Industry 20
0 0.2 0.4 0.6 0.8 1.0 1.2k
0
2
4
6
8
y
(b) Industry 21
0 0.5 1.0 1.5 2.0 2.5 3.0k
0
0.4
0.8
1.2
1.6
2.0
y
(c) Industry 22x
0 1 2 3 4k
0
1.5
3.0
4.5
y
(d) Industry 227
0 0.06 0.12 0.18k
0
0.5
1.0
1.5
2.0
2.5
3.0
y
(e) Industry 23
0 0.6 1.2 1.8k
0
0.4
0.8
1.2
1.6
y
(f) Industry 24t5
0 0.4 0.8 1.2 1.6 2.0 2.4k
0
0.4
0.8
1.2
1.6
2.0
2.4
y
(g) Industry 26x
0 4 8 12 16k
0
0.4
0.8
1.2
1.6
2.0
y
(h) Industry 261
0 0.3 0.6 0.9k
0
0.5
1.0
1.5
2.0
2.5
3.0
y
(i) Industry 27
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96 Missed Opportunities?
0 1 2 3 4k
0
1
2
3
4
y
(a) Industry 28x
0 1 2 3 4 5k
0
1
2
3
4
y
(b) Industry 281t2
0 0.2 0.4 0.6 0.8 1.0 1.2k
0
1.5
3.0
4.5
y
(c) Industry 283
0 0.5 1.0 1.5 2.0 2.5 3.0k
0
1
2
3
4
5
y
(d) Industry 284
0 3 6 9k
0
1
2
3
4
y
(e) Industry 2287t8
0 2 4 6 8 10k
0
1.5
3.0
4.5
y
(f) Industry 29
0 1 2 3 4 5k
0
0.5
1.0
1.5
2.0
2.5
3.0
y
(g) Industry 30
0 0.4 0.8 1.2 1.6k
0
1
2
3
4
y
(h) Industry 31
0 5 10 15 20 25 30k
0
0.5
1.0
1.5
2.0
2.5
3.0
y
(i) Industry 32
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Chapter 3. The Yanks of Europe? 97
0 1 2 3 4 5 6k
0
0.4
0.8
1.2
1.6
2.0
y
(a) Industry 33
0 0.6 1.2 1.8k
0
0.4
0.8
1.2
1.6
2.0
2.4
y
(b) Industry 34
0 0.4 0.8 1.2 1.6 2.0k
0
0.4
0.8
1.2
1.6
2.0
y
(c) Industry 35x
0 0.1 0.2 0.3 0.4 0.5k
0
0.6
1.2
1.8
y
(d) Industry 357t9
0 0.4 0.8 1.2 1.6 2.0k
0
0.4
0.8
1.2
1.6
2.0
y
(e) Industry 36
0 1 2 3 4k
0
0.4
0.8
1.2
1.6
y
(f) Industry 37x
0 0.4 0.8 1.2 1.6k
0
0.5
1.0
1.5
2.0
2.5
3.0
y
(g) Industry 371n25
0 0.6 1.2 1.8k
0
1
2
3
4
y
(h) Industry 38
0 0.2 0.4 0.6 0.8 1.0 1.2k
0
0.4
0.8
1.2
1.6
2.0
2.4
y
(i) Industry 39
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98 Missed Opportunities?
3.C Labor-productivity growth at the frontier
Section 3.4 described how the adoption of high capital-labor ratios in German manufac-
turing created a large potential for labor-productivity growth. The labor-productivity
growth at the frontier for the German change in capital-intensity levels between 1909
and 1936, presented in the second column of table 3.3, can be further decomposed in
two elements; pure technological change and capital intensification. Both effects are
captured by figure 3.9. The highest attainable level of labor productivity for German
capital-labor ratios in 1909 is ya and in 1936 yd. The growth at the frontier from ya to
yd can be ascribed to (1) a move along the frontier as a result of capital intensification
and (2) an upward shift of the frontier over time by means of technological change.
Figure 3.9: Decomposition of growth potential
F(’09)
F(’39)
y
0 k
ya
yb
yc
yd
kGER’09
kGER ‘36
The contribution of capital intensification is calculated as the geometric average of
the labor-productivity increase due to a move along the frontier in each period, i.e.
yb/ya for 1909 and yd/yc for 1936. The effect of technological change is then measured
as the geometric average of the change in labor-productivity at capital-intensity levels
of 1909 and 1936, i.e. yc/ya and yd/yb, respectively. Equation (3.5) explains the total
growth of labor productivity at the frontier over the period 1909–1936 by the combined
contribution of both elements.
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Chapter 3. The Yanks of Europe? 99
ybya
=
(ybya
· ydyc
)0.5
︸ ︷︷ ︸capital deepening
·(ycya
· ydyb
)0.5
︸ ︷︷ ︸technological change
(3.5)
Table 3.5 reports the results of this decomposition, for Germany as well as the US.
The upshot is clear; both Germany and the US created a large potential for labor-
productivity growth, but whereas the former achieved this mainly through capital in-
tensification, the latter did it by means of technological change. To be more precise, for
total manufacturing 59% of total labor-productivity growth at the frontier for German
capital-labor ratios is ascribed to capital intensification. In the US only 29% of the
total labor-productivity gains were realized through this channel. So America enjoyed
an increase in labor-productivity potential mainly as a result of technological change.
These results imply that innovation took place at capital-labor ratios displayed by the
US. Or, in other words, innovations was mainly US based during the interwar period.
Germany experienced a different path of development and created potential by adopting
capital-intensive technology already explored by forerunners.
Table 3.5: Created labor-productivity potential (ln %) in Germany and the USdecomposed in elements of technological change and capital intensity
Industry Created potential Contribution (%) of
Annual ln(%) Techn. change Capital int.
GER US GER US GER US
Food, etc. 4.67 3.25 29 53 71 47
Textiles, etc. 3.15 2.75 57 76 43 24
Paper, etc. 3.44 2.81 32 65 68 35
Chemicals, etc. 4.26 3.76 46 72 54 28
Stones, etc. 3.18 3.03 42 77 58 23
Metals, etc. 3.29 2.73 34 76 66 24
Machinery, etc. 1.66 1.22 34 88 66 12
Transportation equipment 6.12 5.21 48 73 52 27
Miscellaneous 2.90 2.18 29 77 71 23
Manufacturing 3.40 2.77 41 71 59 29
Sources: see text, section 2.3.
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100 Missed Opportunities?
3.D Robustness check
In the discussion of the data in section 3.3, it was suggested that the use of 1933 horse-
power statistics as a proxy for 1936 potentially overestimates machine-intensity levels
in German manufacturing. Because labor was laid off during the Great Depression,
the machine stock was underutilized and may have stood partly idle on the factory
floor in 1933. An alternative strategy is to take 1938 horse-power statistics for German
manufacturing. In 1938, however, factor utilization differed from 1936, too, although in
exactly the opposite way compared to 1933. Whereas the unemployment rate in 1936
was smaller than in 1933 by a factor three, it was larger than in 1938 by exactly the
same factor.117 I already justified my choice for the 1933 data on the grounds that the
1938 statistics suffer from a lower coverage and distortions due to the war effort that are
impossible to correct. Nevertheless, as a robustness check, it is possible to decompose the
labor-productivity gap using 1938 horse-power data for Germany, as done in table 3.6.
Table 3.6: Decomposition of the German labor-productivity increase(in %) needed to catch-up with the US, 1936/39
(German 1938 machine-intensity levels)
Industry Total Obtainable through:
Needed for Technical Machine
catch-up efficiency intensity
Food, drink and tobacco 99 55 45
Textiles, apparel and leather 55 18 37
Paper and printing 101 52 50
Chemicals, petroleum and rubber 74 57 17
Stone, clay, and glass products 97 51 45
Primary and fabricated metals 74 42 32
Machinery (including electrical) 97 86 11
Transportation equipment 101 64 37
Miscellaneous 120 86 34
Total manufacturing 86 55 31
Sources: see section 3.3.
The results in table 3.6 are obtained on the basis of a lower bound estimate of capital-
intensity levels in German manufacturing and, hence, an upper bound estimate of the
catch-up potential by means of capital intensification. At the same time, it increases
industries’ efficiency level as the frontier assigns a relatively small labor-productivity
117. Pierenkemper, “The Standard of Living and Employment,” 59.
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Chapter 3. The Yanks of Europe? 101
potential to relatively low capital-labor ratios. Consequently, the indicated potential
for labor-productivity catch-up by means of improving of technical efficiency presents
a lower bound estimate. It is argued in chapter 3 that standing in the way of German
labor-productivity catch-up was a relatively low level of technical efficiency, rather than
the use of different capital-labor ratios. The use of a lower bound estimate for machine
intensity in table 3.6 thus offers a check upon the strength of this argument. Looking
at the results, it is clear that the conclusions remain unaltered. Although, as expected,
the catch-up scope through machine intensification increases marginally, it is clear even
here that the bulk of the German/US labor-productivity gap is attributable to a low
technical efficiency, rather than a lack of sufficiently high capital-labor ratios.
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Chapter 4Industrial Output Growth in Pre-WW2 Germany.
A Reinterpretation of Time-Series Evidence∗
4.1 Introduction
The necessity of re-assessing the state of the German economy, as repeatedly stressed in
the previous chapters, stems for a large part from the difficulty encountered by previous
studies to construct a reliable time series of industrial output in pre-WW2 Germany.
Several series have been proposed, yet a final solution for the issue is not available. The
confusion surrounding the quality of these historical indices invites discussion concerning
the development of Germany’s performance around the turn of the twentieth century.
In particular Germany’s comparative performance in industry relative to the UK prior
to WW1, a time when the effects of modernization were not yet diluted by the economic
dislocations of the World Wars, has been the topic of debate.1
At stake in this debate is the labor-productivity leadership in Europe; if Germany
had already surpassed Britain – the labor-productivity leader of old – this would suggest
a failure on the latter’s part to benefit from the opportunities offered by the innovations
of the second industrial revolution as much as the former did. This line of reasoning
suggests that the barriers to growth frequently attributed to the UK, such as the relative
costs of factor inputs or market conditions discussed earlier, acted less as a constraint on
Germany’s development. The relative standing within Europe may carry implications,
too, for the growth process. If follower countries develop through catch-up mechanisms,
i.e. copying technology operated at the global productivity frontier, Germany could
no longer look to the UK for future growth. A further interest concerns the period
* This chapter is based on joint work with Jan P.A.M. Jacobs (University of Groningen).1. Ritschl, “Spurious Growth in German Output Data”; Broadberry and Burhop, “Comparative Pro-
ductivity in British and German Manufacturing”; Ritschl, “The Anglo-German Industrial ProductivityPuzzle”; Broadberry and Burhop, “Resolving.” Section 4.2 visits the debate in detail.
103
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104 Missed Opportunities?
after WW1. Prerequisite for understanding the impact of the war and the subsequent
upheaval in the Weimar Republic is an assessment of the state of the economy before
these shocks occurred.
The estimates so far presented in the literature differ to such a degree that two
stories can be told of Germany’s comparative performance before WW1 that are in fact
incompatible; Germany either performed on par with the UK or it outperformed the
UK by roughly 25%.2 With an eye to the historical questions touched upon above, the
ambiguity is unsatisfactory. This begs the question whether it is possible to confidently
draw conclusions regarding Germany’s historical growth record in the face of conflicting
data? I think it is and I arrive at that conclusion through application of a new approach
to this debate.
Much of the deviation between the various output series suggested in the literature
results from the use of different output proxies, which are used because data on value
added is unobtainable for the period before WW2. New releases of the German output
series are therefore valued at the accuracy of the proxies applied to estimate industrial
production. In the absence of value added data to evaluate the accuracy of the prox-
ies, the choice between alternative versions of the output index is not straightforward.
Nevertheless, once a revision is deemed more appropriate as a measure of value added
change, it has been custom to discard all other, older alternatives of the output index.
This chapter sets out to solve the time-series issue by casting the debate in a new
framework. Instead of choosing between different output proxies, I acknowledge that
all series estimate output change by studying variables that are assumed to correlate
strongly, but not perfectly with value added. It follows that the behavior of all series is
largely determined by the same underlying component, i.e. value added change, while
deviations in the observed series are contingent on the different correlation between
the employed output proxies and actual output growth. Using state space time series
analysis, I estimate value added change by filtering from all available data an unob-
served common component.3 This way, the analysis makes full and efficient use of all
information available, rather than choosing for one particular alternative only.
A second aim of the chapter is to shed light on the statistical error associated with the
estimation process. Due to incomplete information the estimates of output change are
essentially based on sample data, so the estimates are inaccurate to some extent. In the
debate on German output growth, however, indicators of statistical dispersion are not
2. Broadberry and Burhop, “Resolving,” 932; S.N. Broadberry and C. Burhop, “Resolving the Anglo-German Industrial Productivity Puzzle, 1895–1935: A Response to Professor Ritschl,” Warwick eco-nomic research papers Vol. 848 (2008): 16. See also table 4.1 on page 110.
3. Commandeur and Koopman, An Introduction; Durbin and Koopman, Time Series Analysis.
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 105
provided and point estimates are implicitly treated as true values. As such, important
properties of the data are omitted. By providing an indication of the statistical error
in my estimates, this chapter follows in the tradition of Charles Feinstein and Mark
Thomas, who argued that any new statistical series should be accompanied by a guide
to the associated margins of error.4
The statistical error is important for the reconciliation between time series and
benchmark estimates, i.e direct level estimates. If the output level is known for a par-
ticular year, the index of industrial production can be used to obtain output levels in
other years through extrapolation. The discrepancy between output levels obtained in-
directly through time series and directly by benchmarks is a frequently used measure of
the former’s accuracy. Because benchmark studies calculate output levels for particular
years, these snap shots of industrial performance are a popular measurement technique
in the debate on German output growth; new releases of the output index that do not
reconcile with benchmark estimates are ill-received. Yet conditional on the width of
the confidence interval, the lack of perfect reconciliation between the output index and
benchmark estimates does not necessarily disqualify the fit between both measures.
The innovative feature of my approach is the decomposition of the observed data in
an unobserved value added component and a noise factor resulting from the use of prox-
ies. As state space analysis is designed to uncover the dynamic evolution of time series
when these properties are not directly observable from the data, it is an appropriate tool
of analysis. In my case, the system of observed time series is modeled as a function of
an unobserved common process plus an irregular component containing index-specific
noise. By casting the debate in state space form, I offer a formal framework to sta-
tistically assess the similarity and dissimilarity between output series presented in the
literature. Moreover, because all information available is used, my analysis transcends
earlier contributions to the debate on German output growth by the application of an
integrated, rather than exclusive, approach.
4.2 The time-series debate
Hoffmann’s Historical National Accounts
In the early 1960s, a team of researchers under the auspices of Walther Hoffmann
constructed the German historical national accounts, as a part of which an output index
4. C.H. Feinstein and M. Thomas, “A Plea for Errors,” Historical Methods Vol. 35, no. 4 (2002):155; C.H. Feinstein and M. Thomas, “A Plea for Errors,” University of Oxford Discussion Papers inEconomic and Social History No. 41 (2001): 3.
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106 Missed Opportunities?
for German industry was produced.5 Hoffmann’s output index is a weighted average
of the estimated change in output in twelve manufacturing and utility industries for
the period between 1870 and 1938. As for the pre-WW1 period data on value added
change in German industries is not available, output proxies are used instead. Output
change in the majority of the industries is estimated using physical indicators, usually
manufactured tons of goods. In contrast, for metal processing – an industry class that
contains machine building, shipbuilding and electrical engineering – Hoffmann chose
to rely on labor-income data as a proxy of output change. The aggregate time series
for industry is subsequently constructed weighting the twelve industry indices by their
share of value added (the compound output index is plotted in figure 4.1a).
Hoffmann’s index of industrial production has received severe criticism and its reli-
ability has been called into question, in particular by Rainer Fremdling.6 The problems
associated with the Hoffmann series concern two issues. First, using the annual wage-
bill to proxy output change presumes a constant wage-productivity ratio. However,
Borchardt (1979) argued that after WW1 wages rose as a consequence of labor unions’
increased bargaining power, rather than raised labor-productivity levels.7 In light of
Borchardt’s thesis, the assumption of a constant wage-productivity ratio might not be
innocuous. More to the point, the dichotomous development between wages and la-
bor productivity leads to an upward bias in Hoffmann’s output estimates for metal
processing.
Second, value-added data for manufacturing industries is available for 1936 only
(based on the first German census of production). To construct value-added weights
Hoffmann multiplied the level of labor productivity in 1936 by employment in 1933
(derived from the employment census). Although the resulting value-added shares might
function as a weighting scheme for the interwar period, it cannot reasonably be imposed
on periods before WW1. Hoffmann ‘solved’ this problem by using proxy value-added
weights; that is, he multiplied the level of labor productivity in 1936 by employment
in 1882 and 1907 to obtain value-added shares for the years 1871-1895 and 1895-1913,
respectively. However, this assumes comparative levels of value added per employee
across German industries to have remained unchanged over the period 1870-1938, which
it did not.
5. Hoffmann, Das Wachstum.6. Fremdling, “German National Accounts”; R. Fremdling, “German Industrial Employment 1925,
1933, 1936 and 1939. A New Benchmark for 1936 and a Note on Hoffmann’s Tales,” Sonderdruck aus:Jahrbuch fur Wirtschaftsgeschichte Vol. 2 (2007): 171–195.
7. Ritschl, “Spurious Growth in German Output Data,” 202; Borchardt, “Zwangslagen und Hand-lungsspielrame.”
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 107
Revisions of Hoffmann’s output series
As the problems associated with the weighting scheme are not easily solved in the
absence of value-added data, the wage-bill issue has been discussed most extensively in
the literature. The first to address Hoffmann’s output index for metal processing was
Albrecht Ritschl.8 In a comprehensive overview of already existing German time series
he wondered why Hoffmann chose to use the wage bill as a proxy for production in
metal processing while other, possibly less problematic, proxies are readily available for
the interwar period. In fact, production indices of various metal processing industries
were presented in 1933 by Wagenfuhr of the Institut fur Konjunkturforschung (IfK,
see figure 4.1a).9 When Hoffmann’s metal processing time series is compared with the
official production data provided by the IfK, deviations in output growth are manifest
mainly for the machine-building industry, which is part of metal processing. For this
reason Ritschl uses sales data of the Verband Deutscher Machinen- und Anlagenbau (the
German machinery producers’ association) to reassess output change in the machine-
building industry and he records an output growth of only half the magnitude suggested
by Hoffmann. Since metal processing has a weight of 17 percent in Hoffmann’s compound
index of industrial production, the revised data on machine building moderates German
output growth considerably. Ritschl’s revision reports a relatively low rate of growth
particularly over WW1, as can be seen in figure 4.1a.
The modified output index evoked a reaction from Stephen Broadberry and Carsten
Burhop for Ritschl’s proposed changes imply a revision of Germany’s performance rel-
ative to the UK that does not sit well with previous research.10 Combining Ritschl’s
output index with Hoffmann’s time series of employment to obtain the change in la-
bor productivity, the level of labor productivity in 1936 can be extrapolated backward,
as illustrated by figure 4.2. Adjusted to a manufacturing basis (i.e. excluding mining,
construction and utility industries), the extrapolated productivity levels suggest a com-
manding German lead over the UK in the pre-WW1 period, a performance on the
part of Germany much stronger than previously.11 A snap shot of comparative labor-
productivity levels for 1907 constructed by Broadberry & Burhop (table 4.1, first row)
points at an equality in performance between both countries rather than a distinct
German lead; a result seemingly at odds with the extrapolated labor-productivity lev-
els obtained using Ritschl’s output series. Hoffmann’s original index of output, on the
8. Ritschl, “Spurious Growth in German Output Data.”9. Wagenfuhr, “Die Industriewirtschaft.”
10. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing.”11. R. Fremdling, “Productivity Comparison Between Great Britain and Germany, 1855–1913,” Scan-
dinavian Economic History Review Vol. 1 (1991): 37. See also table 4.1.
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108 Missed Opportunities?
Figure 4.1: Time series of output in German industry, 1913 = 100
0
20
40
60
80
100
120
140
160
180
1880 1890 1900 1910 1920 1930
Ritschl Hoffmann Wagenfuhr
WW1
(a) Output levels in industry
-.4
-.2
.0
.2
.4
.6
1880 1890 1900 1910 1920 1930
Ritschl Hoffmann Wagenfuhr
WW1
(b) Annual growth rates of output
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 109
other hand, does fit nicely with the benchmark results, which Broadberry & Burhop
interpret as proof of the superior quality of Hoffmann’s series.
Figure 4.2: Backward projection of labor productivity (LP)
Slow growth
Fast growth
LP
level
1907 1936 Time
High
Low
Known LP
level
Reconciliation between time series and benchmarks
Because Ritschl’s revised time-series estimate is rejected on the basis of the reconcili-
ation between time series and benchmarks, a sine qua non for Broadberry & Burhop,
the intrinsic quality of the modified index remains largely undiscussed in Broadberry &
Burhop. Consequently, Ritschl observes a trade off in Broadberry & Burhop between
the quality of the employed time series and the quality of its fit with the benchmark
estimates.12 After once more highlighting the potential bias in Hoffmann’s index of
industrial production, he concludes that any time-series projection of comparative pro-
ductivity has to deal with these problems. Nevertheless, Ritschl accepts Broadberry &
Burhop’s reconciliation principle – the notion that (point) estimates derived through
application of benchmark and time-series analysis must resemble – and, therefore, re-
works the 1907 benchmark, which leads to results in line with his modified time series
of industrial output (table 4.1, second row). In response, Broadberry & Burhop reject
Ritschl’s proposed changes to the benchmark and adhere to their own, slightly modified,
estimates. Furthermore, they resolve the reconciliation issue by combining a downward
adjusted version of Ritschl’s output index with a new employment series, proposed by
12. Ritschl, “The Anglo-German Industrial Productivity Puzzle.”
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110 Missed Opportunities?
Fremdling; this procedure conveniently leads to a German level of labor productivity
prior to WW1 in line with Broadberry & Burhop’s benchmark of comparative perfor-
mance (latest benchmark revisions; table 4.1, third row).13
Table 4.1: Benchmark estimates of comparative labor productivity
Source GER/UK labor productivity
Indus. Manuf.
1907
Broadberry & Burhop (2007) 1.02 1.05
Ritschl 1.25 1.28
Broadberry & Burhop (2008) 1.05 1.08
Fremdling 0.74 . . .
1935/36Broadberry . . . 1.02
Fremdling, de Jong, Timmer . . . 1.07
Sources: see text, section 4.2.
In short, the debate has been fueled to a large extent by the notion that point esti-
mates obtained by benchmarks and time-series analysis should reconcile. However, this
notion defies the literature that emphasized several causes for deviation between both
measures. This topic has been debated in relation with long-span time series projec-
tions and the problems associated with reconciling historical time series with benchmark
estimates are well documented.14 In general, deviations stem from methodological dif-
ferences between both measures. At the root of this inconsistency lies the difference
between weight structures employed in bilateral benchmarks and time series, a problem
solvable only by application of a single aggregation scheme for both spatial and tempo-
ral comparisons.15 As the structural composition of economies changes over time, the
13. Broadberry and Burhop, “Resolving”; Fremdling, “German Industrial Employment.”14. I. Kravis, A. Heston, and R. Summers, “World Product and Income: International Comparisons of
Real Gross Products,” World Bank Report (1982): 326; R. Summers and A. Heston, “The Penn WorldTable (Mark 5): An Expanded Set of International Comparisons, 1950–1988,” The Quarterly Journalof Economics Vol. 106 (1991): 327–368; A. Heston, R. Summers, and B. Aten, “Price Structure, theQuality Factor and Chaining,” 2001, http://www.oecd.org/std/prices-ppp/2425050.pdf and A.Deaton and A. Heston, “Understanding PPPs and PPP-based National Accounts,” American EconomicJournal: Macroeconomics Vol. 2 (2010): 1–35. In the field of economic history this issue has beenaddressed by M. Ward and J. Devereux, “Measuring British Decline: Direct Versus Long-Span IncomeMeasures,” The Journal of Economic History Vol. 63 (2003): 826–851; S.N. Broadberry, “Relative PerCapita Income Levels in the United Kingdom and the United States since 1870: Reconciling Time-SeriesProjections and Direct-Benchmark Estimates,” The Journal of Economic History Vol. 63 (2003): 852–863 and M. Ward and J. Devereux, “Relative U.K./U.S. Output Reconsidered: A Reply to ProfessorBroadberry,” The Journal of Economic History Vol. 64 (2004): 879–891.15. H.J. de Jong and P.J. Woltjer, “A Comparison of Real Output and Productivity for British and
American Manufacturing in 1935,” Groningen Growth and Development Centre Memorandum No. 108(2009): 16; G. Szilagyi, “Procedures for Updating the Results of International Comparisons,” Reviewof Income and Wealth Vol. 30 (1984): 156–157.
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 111
weighting scheme of time series requires updating, which leads to inconsistency with
benchmark comparisons. While forcing a single weighting scheme on time series en-
sures consistency over time and across space, it renders the interpretation of the results
difficult and is therefore undesirable.16
So a perfect fit between time series and benchmarks cannot be expected nor de-
manded, and the time-series revision proposed by Ritschl does not necessarily provide
sufficient grounds to reject Broadberry & Burhop’s 1907 benchmark, or vice versa. In-
deed, the differences between Ritschl’s revised output index and Hoffmann’s original
are, in general, limited at the level of total manufacturing. Even though the data un-
derlying figure 4.1a displays a compound annual growth rate over the period between
1907–1936 of 1.20% for Ritschl’s index versus 1.93% for Hoffmann’s series, which is
substantial because small variations between annual growth rates can and indeed do
lead to large deviations in output levels in the long run, the difference between both
estimates can be almost fully ascribed to the period 1913–1925, while before and after
the annual growth rate hardly differs between the series. Figure 4.1b shows for both
series the compound annual growth rate over this period; whereas Hoffmann’s series
suggest a decade of (small) growth, Ritschl’s data indicate a continuous decline in out-
put. But over the other periods the annual growth rates are very similar. The question
is, then, if the inconsistency between benchmarks and time series can be accounted for
by index-number factors.
This effect can be quantified if the benchmarks and time series are constructed
exclusively on the basis of price and quantity data. This is impossible for pre-WW2
Germany, as the necessary data are not always obtainable; price information is often
unavailable and so are quantities for some industries, in which case proxies are used. The
nature of the data not only hinders a measure of the index-number induced deviation,
by itself it also presents a second source of inconsistency. The proxies employed in the
time series are associated with measurement errors, which introduce inconsistency with
the benchmarks.
Moreover, both the benchmarks and the time series suffer to a different degree from
a lack of representativeness in the data used, as the industry coverage varies between
both measures, an issue already briefly touched upon in chapter 2. The output se-
ries studied here apply to industry and include manufacturing, mining, construction
and some utility industries. Although the 1907 benchmarks include manufacturing and
16. E. Dalgaard and H. Sørensen, “Consistency Between PPP Benchmarks and National Price andVolume Indices,” in 27th General Conference of the International Association for Research in Incomeand Wealth (Stockholm: Sweden, 2002), 4; Jong and Woltjer, “A Comparison of Real Output andProductivity,” 16.
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112 Missed Opportunities?
mining, the other two sectors are not captured. This introduces a bias. Ideally, the
construction and utility industries are taken out of the time-series sample, but this pro-
cedure is rendered impossible by the lack of industry weights for Wagenfuhr’s series.17
Dalgaard and Sørenson note that the ensuing discrepancies cannot be accounted for by
index-number formulas and, therefore, pose genuine problems of consistency.18 It is the
inconsistency attributable to these genuine factors that arbitrates the quality of the fit
between benchmark estimates and time-series projections.
Using the state-space form I can assess this fit between both measures. A break down
of the inconsistency between benchmarks and time series in genuine and non-genuine
components is impossible here, but perhaps not necessary. As the model estimates a
common component containing the dynamic properties of the three observed time series,
the different benchmarks presented in the literature are confronted only with my filtered
time-series estimate. Given that all 1907 benchmarks use the same weighting scheme, i.e.
the employment structure obtained from the 1907 census, the deviation with the filtered
time series that is explained by index-number related factors is the same for each match
between benchmark and time series. It follows that variation in inconsistency between
my time series and the presented benchmarks traces back to genuine factors. Assuming,
first, that the estimated time series captures the change in output and, second, the 1936
benchmark from which the time series is extrapolated backward accurately measures
the level of output, the 1907 benchmark that shows the closest fit with the backward
projections suffers least from these genuine consistency problems.
This still leaves the question how much inconsistency one is willing to allow for and
not reject the fit between the filtered time series and the benchmarks? The uncertainty
associated with estimating the unobserved common component provides a yardstick of
measurement error in the time series (although not in the benchmarks). Using the vari-
ance of the model I construct a confidence interval to indicate a range around the point
estimates that contains the true value of the estimated parameter with high probability.
In case a benchmark estimate falls inside that range, the inconsistency can be explained
by measurement error in the time series and while both measures do not reconcile the
fit cannot be rejected. If a benchmark estimate falls outside the confidence interval,
the unexplained inconsistency is caused by additional genuine factors originating in the
benchmark, such as its measurement error or industry coverage, that introduce further
noise and thus impair the quality of the fit with the time-series projections.
17. In the absence of industry weights it is not feasible to aggregate the industry series to the levelof total manufacturing.18. Dalgaard and Sørensen, “Consistency Between PPP Benchmarks and National Price and Volume
Indices,” 9–10.
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 113
4.3 Methodology
The purpose of state space time series analysis is to uncover the dynamic evolution
of observations measured over time when the dynamic properties cannot be directly
observed from the data.19 As I am interested in the unobserved change of industrial
output, which is assumed to determine the behavior of the observed time series, state
space modeling provides a tool of analysis particularly suited to my design. By using
the state-space form, I build upon a literature that has used such models before in the
field of economic history, in particular the research of Lee & Anderson, Crafts & Mills
and Pfister, Riedel & Uebele.20 All three study the interaction between economic and
demographic variables in early-modern times (the former for England and the latter
for Germany), using the state-space form to estimate the dynamics of, for instance,
technological change, the demand for labor or weather and disease prevalence, none of
which is observed.21
These analyses are all univariate, though, and the application of the state-space
form to filter a common state from multiple observed time series is new to the field of
economic history. Although not applied in the state-space form before, I am not the first
to estimate common components from different time series. When in the face of data
restrictions the dynamics of a particular variable can be extracted from the behavior
of other data that are assumed to relate with the variable of interest, such a procedure
provides a useful research strategy for periods characterized by poor data coverage.
This avenue has been explored by Sarfarez and Uebele, who ‘track down’ business-cycle
movements in Germany before WW1, a period that suffers from data scarcity, through
application of dynamic-factor analysis.22 Similarly, in a paper on market integration,
Uebele employs comparable techniques to estimate a common price change for regional,
national and international markets from multiple time series.23
In my case, using the state-space form to estimate a common component has several
advantages. As explained, by casting the time series of industrial output presented
in the literature in state-space form, I am able to estimate an unobserved dynamic
19. Commandeur and Koopman, An Introduction; Durbin and Koopman, Time Series Analysis.20. R. Lee and M. Anderson, “Malthus in State Space: Macro Economic-Demographic Relations in
English History,” Journal of Population Economics Vol. 15 (2002): 195–220; N. Crafts and T. Mills,“From Malthus to Solow: How did the Malthusian Economy Really Evolve?,” Journal of Macroeco-nomics Vol. 31 (2009): 68–93; U. Pfister, J. Riedel, and M. Uebele, “Real Wages and the Origins ofModern Economic Growth in Germany, 16th to 19th Centuries,” EHES Working Papers in EconomicHistory No. 17 (2012): 1–27.21. Crafts and Mills, “From Malthus to Solow,” 82; Pfister, Riedel, and Uebele, “Real Wages,” 13.22. S. Sarferaz and M. Uebele, “Tracking Down the Business Cycle: A Dynamic Factor Model for
Germany, 1820–1913,” Explorations in Economic History Vol. 46, no. 3 (2009): 368–387.23. M Uebele, “National and International Market Integration in the 19th Century: Evidence from
Comovement,” Explorations in Economic History Vol. 48, no. 2 (2011): 226–242.
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114 Missed Opportunities?
process. Moreover, in a multivariate setting information from multiple time series can
be used to improve the estimate of the target series, output in this case, by assuming
that the unobserved component is common to all observed series.24 A second appeal
of the state-space form is that stationarity of the time series is not required, because
it concerns a structural time series model in which the trend, seasonal and error terms
are explicitly modeled. This is an advantage, given that most real series in the field
of economics are non-stationary.25 Thirdly, the state-space framework can deal with
missing observations with relative ease; as the years covering WW1 are not accounted
for in the output indices, this is a benefit, too.26 In short, the state-space form provides
a flexible and easy to work with instrument to analyze the German output series.
Specification of the model
Using matrix notation, all multivariate state-space models can be written in the gen-
eral format of equations (4.1) and (4.2). The model contains two equations. First, the
observed series (yt) are modeled by the measurement (or observation) equation, which
defines the series by two components, i.e. the unobserved dynamic process called the
state (αt) and a disturbance term (εt). Second, the state equation models the unob-
served dynamic process as a function of its value in previous periods plus a disturbance
term (ηt). Both disturbance terms are normally and independently distributed (NID)
around a mean of zero with a variance of σε2 and ση2 , respectively.
yt = Ztαt + εt, εt ∼ NID(0, Ht) (4.1)
αt+1 = Ttαt +Rtηt, ηt ∼ NID(0, Qt) (4.2)
The specification used here is a local linear trend model, which is a special case of the
general state-space framework presented in the set of equations (4.1) and (4.2). Each
of the observed series is modeled as a function of a common state component and an
index-specific observation disturbance. In case of Wagenfuhr’s and Hoffmann’s series,
however, the common state is weighted by a coefficient a, because the literature has
credited Ritschl’s time-series revision with the highest reliability.27 My special case of
24. A. Harvey and C. Chung, “Estimating the Underlying Change in Unemployment in the UK,”Journal of the Royal Statistical Society Vol. 163, No. 3 (2000): 305, 314–315.25. Commandeur and Koopman, An Introduction, 134.26. ibid., 103.27. Broadberry & Burhop accepted Ritschl’s (adjusted) revisions to Hoffmann’s output series, but
combined it with a different employment series, i.e. Fremdling’s instead of Hoffmann’s, to reconcile thebackward extrapolation of labor productivity with their own 1907 German/UK benchmark estimate.See Broadberry and Burhop, “Resolving,” 933.
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 115
the state-space form is then defined as:
yt =
⎛⎜⎜⎝
y(1)t
y(2)t
y(3)t
⎞⎟⎟⎠ , αt =
(μt
vt
), ηt =
(ξt
ζt
), Tt =
[1 1
1 0
]
Rt =
[0 0
0 1
], Zt =
⎡⎢⎢⎣
1 0
a1 0
a2 0
⎤⎥⎥⎦ , εt =
⎡⎢⎢⎣ε(1)t
ε(2)t
ε(3)t
⎤⎥⎥⎦ , Ht =
⎡⎢⎢⎣σ2ε(1)
0 0
0 σ2ε(2)
0
0 0 σ2ε(3)
⎤⎥⎥⎦ (4.3)
Qt =
[σ2ξ 0
0 σ2ζ
]
where y(1)t , y
(2)t and y
(3)t refer to Ritschl’s, Wagenfuhr’s and Hoffmann’s output series,
respectively. Writing out these components in scalar notation, this yields the following
measurement equations:
ln(Ritschl) = μt + ε(1)t
ln(Wagenfuhr) = a1μt + ε(2)t (4.4)
ln(Hoffmann) = a2μt + ε(3)t
For the state equation, I get:
μt+1 = μt + vt (4.5)
vt+1 = vt + ζt (4.6)
In this structural model the trend is captured by the common state component. The
dynamics of the state is determined by two trend components; a level μt and slope vt.
With respect to the former, the level component can be regarded as the equivalent of the
intercept in a classical regression model, with the difference that in state-space form the
intercept may be treated stochastically, in which case the level component is allowed to
change over time and its dynamics are contained by the level disturbance ξt.28 However,
from Rt it follows that I have modeled the level component deterministically by setting
the disturbance term at zero, as in a classical regression model, so the level disturbance
ξt does not return in equation (4.5), where the system of equations (4.3) is written out
in scalar notation. As with the level component, the slope vt can be conceived of as the
equivalent of a regression coefficient, but in contrast to the classical regression model
the value of the slope coefficient may differ from period to period. Therefore, the slope
28. Commandeur and Koopman, An Introduction, 9.
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116 Missed Opportunities?
is also referred to as the drift.29 The behavior of the slope is determined by the slope
disturbance ζt.
The values of the two hyperparameters, i.e. the measurement and state disturbances,
cannot be obtained analytically and the model is therefore estimated using maximum
likelihood based inference. The likelihood function associated with the model is ob-
tained through the application of an algorithm called the Kalman filter.30 In my case,
the estimated unobserved common component refers to the filtered state, which is the
estimate of the state vector based on all past observations and the current observation.
This means that the estimation process involves only a forward pass through the data.
Alternatively, I could have smoothed the state by also performing a backward pass and
thereby using all observations (i.e. past, current and future observations) to estimate
the state vector. As the name suggests, such a procedure effectively smooths the dy-
namics of the state. However, when ‘corners are cut’ the state series takes on a value
for the years before WW1 lower than of the observed series, because the output drop
over the war is already taken into account before it actually happened. With an eye to
the historical context to which the state vector refers, it does not make sense for shocks
to have backward effects and, therefore, I use the filtered state.
The unknown parameters are estimated using the log-likelihood function in Eviews,
which corresponds to the definitions of Durbin and Koopman.31 Estimation involves a
numerical search procedure that starts by choosing a set of starting values for the un-
known parameters and calculating the corresponding value of the log-likelihood func-
tion. Subsequently, the process is repeated, selecting different parameter values that
improve the log-likelihood function. These iterations are executed up to the point that
no further improvements are obtained and the log-likelihood function is optimized.
However, due to the multivariate nature of the model, the optimization process may
produce either a suboptimal or no solution for particular starting values. Following
Van den Bossche, I use a multiple random start procedure that runs the optimization
algorithm repeatedly, each time starting from a different set of initial values for the
unknown parameters.32 The whole estimation procedure is repeated 1,000 times and
the solution reported with the highest log-likelihood value is used henceforth.33
29. Commandeur and Koopman, An Introduction, 21.30. R.E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Journal of Basic
Engineering Vol. 82 (1960): 35–45.31. Quantitative Micro Software, Eviews 6 User’s Guide II (2007), 387; Durbin and Koopman, Time
Series Analysis, 138; A. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter(Cambridge University Press, 1989), 126; F van den Bossche, “Fitting State Space Models with Eviews,”Journal of Statistical Software Vol. 41, no. 8 (2011): 3.32. ibid., 10.33. Eviews provides different optimization procedures, i.e. Marquardt and Berndt-Hall-Hausman. I
used the former first derivative technique. For further specification of the program, see appendix.
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 117
Data
It has been noted that before carrying out any estimation, it is important to deter-
mine the nature of the time series in hand.34 It is in particular crucial to examine the
properties of the observed series’ trend and establish whether it is deterministic (sta-
tionary) or stochastic (nonstationary). If the trend’s nature of one of the three series
studied here differs from the others, there is no common trend to estimate. Therefore,
before running the analysis, I test for stationarity using augmented Dicky Fuller (ADF)
tests. If the ADF shows that the series has a unit root, this points in the direction of a
non-stationary trend. Yet the results of a unit-root test do not provide definitive proof
of stationarity or the lack thereof. In the presence of structural breaks, unit root has
difficulty distinguishing stationarity from nonstationarity.35
Table 4.2: Unit-root test (augmented Dicky-Fuller)
Output series Adjusted sample τ -Statistic ρ
Hoffmann (1965) 1872–1938 -1.29 0.92
Ritschl (2004) 1872–1938 -1.10 0.93
Wagenfuhr (1933) 1872–1931 -2.59 0.84ρ Coefficient on the lagged dependent variable.* Significant at either the 0.10, 0.05 or 0.01 level.
A worry in this respect is the inclusion of WW1 in my period of study, as structural
breaks in the twentieth century often occurred at times of war.36 Figures 4.3a, 4.3b
and 4.3c show the logarithms of the observed series fitted with a linear breaking trend
function, where I allow both the intercept and the slope of the linear trend to change
after 1914. All series are clearly upward trending, hinting at the presence of unit roots.
Looking at the regression coefficients, in the case of Hoffmann’s and Ritschl’s series no
trend breaks are detected over WW1. Wagenfuhr’s series, on the other hand, displays
a significant decrease of the intercept at the 1% level and a significant increase of the
slope at the 5% level. This result is driven primarily by the coverage of the series; in
contrast to the other two series, the reconstruction phase after WW1 is included, while
the slump during 1930s is omitted. Nevertheless, as the slope of Wagenfuhr’s series
increases, there is no evidence of a break at which the generating process switched from
nonstationary to stationary. Indeed, table 4.2 suggests that a common trend can be
filtered from the three series.
34. J.P.A.M. Jacobs and J.P. Smits, “Historical Time Series Analysis: An Introduction and SomeApplications,” Jahrbuch fur Wirtschaftsgeschichte / Economic History Yearbook (2006): 5.35. ibid., 7.36. ibid., 10.
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118 Missed Opportunities?
Figure 4.3: Logarithms of output series with breaking trend
-.6
-.4
-.2
.0
.2
.4
3.0
3.5
4.0
4.5
5.0
5.5
1875 1890 1905 1920 1935
(a) Hoffmann
-.6
-.4
-.2
.0
.2
.4
3.2
3.6
4.0
4.4
4.8
5.2
1875 1890 1905 1920 1935
(b) Ritschl
-.4
-.2
.0
.2
.4
2.8
3.2
3.6
4.0
4.4
4.8
1875 1890 1905 1920
(c) Wagenfuhr
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 119
4.4 Results
A common trend
The solution of the model obtained by Eviews is presented in table 4.3 below and fig-
ure 4.4a displays the filtered state with a 99% confidence interval. Some of the estimation
results reported in table 4.3 require further elaboration. First, although the coefficients
on each of the three observation disturbances are significantly different from zero at the
1% level, the variance of the error term associated with Wagenfuhr’s index is somewhat
larger than those of Ritschl and, in particular, Hoffmann. Figures 4.4b, 4.4c and 4.4d
provide an explanation. These graphs plot the observed series, the filtered state and
the difference between the two captured by the residuals. With respect to the latter,
the linear breaking trend functions in figures 4.3a, 4.3b and 4.3c already showed the
increased volatility of the output series after WW1, a feature which makes it difficult
for the filtered state to produce a tight fit with the series. Although all observed series
suffer from this, especially Wagenfuhr’s index shows a large variation in the disturbance
term between 1919–1925.
The state disturbance of the slope is significantly different from zero, too, which
together with the lagged value of the slope models the rate of change in the state series.
Moreover, the positive final value of the slope component signifies an increase of the
state series over the last period, as indeed can be seen in figure 4.4a. The steep angle
of the output estimate is explained by the build-up to WW2; German industry had
shifted gear and operated at full speed to meet the demands of the armaments race. All
output series presented in the literature agree in this respect, although they deviate for
earlier periods. Overall, the filtered state lies in between the observed output series and
over the period 1907–1936 displays a compound annual growth rate of 1.63%, which is
slower than the pace of growth set by Hoffmann’s series, but faster than in Ritschl’s
index. Over WW1 the state trails Wagenfuhr’s index, because the other series provide
no information on output change until 1925. At the time all three observed series pick
up, the state displays a level of output slightly above Ritschl, but well below Hoffmann.
The manner in which the filtered state, plotted in figure 4.4a, bridges the data gap of
WW1 provides a graphic account of how the estimation process functions; in the absence
of current-year information, the state is forecast on the basis of past observations only.
The forecast is driven purely by the change in the slope component, which takes on the
value of the previous period. Given that the pace of growth was high in the years prior
to WW1, the filtered state predicts a substantial increase of output levels over the war,
too. As can be seen in figure 4.4a, the growth of the state between 1913–1914 is linearly
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120 Missed Opportunities?
Table 4.3: Estimates of the state-space model
Parameter Description Estimate
Variance coefficients
ε(1) Obs. disturbance Ritschl 0.011*
ε(2) Obs. disturbance Wagenfuhr 0.013*
ε(3) Obs. disturbance Hoffmann 0.007*
ζ State (slope) disturbance 0.005*
Coefficients on common level
a1 Wagenfuhr 0.988*
a2 Hoffmann 0.997*
Final states
μ Level 5.183*
v Slope 0.106
Fit of the model
Log likelihood 76.999
Akaike information criterion -2.254* Significant at the 1% level.
extrapolated up till 1918, pushing the output estimate far above the level in 1914.
From 1919 onwards current-year data is available again. The estimation process
is updated and the estimated state vector instantly drops toward the output level at
which Wagenfuhr’s index picks up after the war. Nevertheless, while for years before
and after the war the filtered state estimate (in contrast to the smoothed state) does
not suffer from the data gap between 1914–1918, it should be born to mind that the
forecast for the missing observations does not contain interpretable information about
output change during the war. The economy was anything but in equilibrium over these
years and a forecast based on peace-time dynamics can impossibly account for war-time
shocks, as the exploding confidence limits attest.
Interval estimates and comparative labor productivity
In figures 4.4b, 4.4c and 4.4d vertical lines are drawn at two points in time, 1907 and
1936, to see how the observed series and the filtered state bridge the interwar period in
different ways. The level differences between the series are fairly limited for 1907 and
somewhat larger for 1936, with the state in between the series of Hoffmann and Ritschl.
It should be noted that the estimated latent output change depicted in figure 4.4a is
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 121
Figure 4.4: The state series, observed series and observation disturbance
2.8
3.2
3.6
4.0
4.4
4.8
5.2
5.6
6.0
6.4
1875 1890 1905 1920 1935
(a) Filtered state and 99% confidence interval
-1
0
1
2
3
3.0
3.5
4.0
4.5
5.0
1875 1890 1905 1920 1935
(b) Hoffmann
-2
-1
0
1
2
3.0
3.5
4.0
4.5
5.0
1875 1890 1905 1920 1935
(c) Ritschl
-2
-1
0
1
2
3
3.0
3.5
4.0
4.5
5.0
1875 1890 1905 1920 1935
(d) Wagenfuhr
Observed series
Standardized residuals
Filtered state
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122 Missed Opportunities?
bound to be a weighted average of sorts of the three observed series and the fact that
the state lies in between the observations of Ritschl and Hoffmann should not come as a
surprise. Therefore, of prime interest is not the obtained dynamics of the state. Rather,
the measure of uncertainty associated with the state estimation contains the innovative
element of my research design.
As the state estimation error is a linear function of the initial state error and the
variance of both the measurement and state disturbances, it follows that the uncertainty
associated with the estimated state – and thus the confidence interval in figure 4.4a –
is determined partly by the deviations between the series of Wagenfuhr, Hoffmann and
Ritschl.37 Given that the latter are based on output proxies, the estimation uncertainty
reflects the variation in the accuracy with which the different proxies capture the latent
change of output. In practice, this means that the absence of output data in the available
historical sources introduces uncertainty to my estimates. Although this is an intuitive
notion, my approach has the benefit of quantifying the degree of uncertainty.
Traditionally, in the debate on German output growth, indicators of statistical dis-
persion are not provided and point estimates are implicitly treated as true values. Look-
ing at the upper and lower confidence limits, however, the margin for error can be quite
large: using a 99% confidence level, as in figure 4.4a, I find a range of over 10% around
my point estimate of output change. Actually, for most of the post-WW1 period all
observed series fall within the confidence bounds around the state. When measurement
error is allowed for the differences between the series of Hoffmann, Ritschl and Wa-
genfuhr with regard to the magnitude of output decline in German industry over WW1
– the issue which sparked off the debate in the first place, as explained in section 4.2 –
are not that dramatic.
The interval estimates are relevant in particular for the debate on labor productivity.
Ritschl’s output index was initially meant as a contribution to the discussion on German
historical growth developments, but the ensuing debate took shape mostly through
the (unintended) implications of the revision for comparative labor-productivity levels.
Combined with employment data and extrapolated backwards from a known level of
labor productivity in 1936, Ritschl’s revised output index translates into a level of labor
productivity in 1907 higher than in Britain; a result that calls into question the parity in
productivity performance between Germany and the UK (before WW1) pointed out by
previous research of Broadberry.38 To extrapolate backward German/UK comparative
labor productivity from 1936, I use equation (4.7).
37. Durbin and Koopman, Time Series Analysis, 15.38. See the previous section addressing the time-series discussion.
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 123
ygetyukt
=
(yget /yge36yukt /yuk36
)· y
ge36
yuk36(4.7)
with yt as a country’s level of labor productivity in year t and yt/y36 the change of labor
productivity between year t and the base year 1936. As the level of labor productivity
is unobtainable, except for the benchmark year, the change of labor productivity yt/y36
is derived on the basis of the change in output and employment:
yty36
=ot/o36lt/l36
(4.8)
where ot/o36 captures the output change between year t and base year 1936, respectively,
and lt/l36 measures the change of employment over this period. By inserting different
values for ot/o36 as indicated by the output series of Hoffmann, Ritschl and my filtered
state, and keeping all other variables constant, it is possible to measure the implications
of the various indices for the German/UK labor-productivity gap in years before WW1.
Table 4.4 reports the results of this exercise. The data used for the UK comes from
Broadberry, while I obtain German employment from Fremdling’s latest estimates. Sub-
sequently, the change in labor productivity in the UK and Germany is calculated and in
case of the latter three versions are presented (Hoffmann, Ritschl and the filtered state).
In a next step, German/UK comparative labor productivity is computed in five vari-
ants. In addition to the estimates derived using the output series of Hoffmann, Ritschl
and the filtered state, I introduce the notion of measurement error, too, and include an
estimate of comparative labor productivity using the lower and upper confidence lim-
its of the filtered state. Lastly, as already mentioned, the relative series are projected
backward from a benchmark estimate in 1936.
On the basis of earlier research, I can choose between two benchmark estimates
of comparative German/UK labor productivity in 1935/36, i.e. between Broadberry &
Fremdling and Fremdling, de Jong and Timmer (see table 4.4. Since the former uses less
reliable data and less advanced methods, I have opted for the latter.39 Using this ratio of
105.4 in 1936, Germany outperformed the UK prior to WW1 by a margin of 15.5%. The
lower bound of the interval estimate reports a comparative labor-productivity level of
101.4 while the upper bound indicates a level of 131.5. I conclude from this that between
Germany and Britain the former attained the highest level of labor productivity in 1907,
although it is improbable that Germany’s lead extended to a margin over 125% or below
105% the level of the UK. Moreover, after WW1 the UK regained the upper hand again
39. See chapter 2 for more detail.
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124 Missed Opportunities?
and held on to that until at least the mid-1930s. Shortly before WW2 industry in
both countries performed approximately on par once more, although the width of the
interval estimate renders it impossible to say with certainty whether the real value of
comparative labor productivity favored Germany or the UK.
Table 4.4: Backward projections of comparative labor productivity
Variable Source 1907 1925 1933 1936
United Kingdom (1913=100%)
Employment } Broadberry (1997)93.0 93.4 89.4 101.1
Output 88.8 111.8 119.6 155.7
Germany (1913=100%)
Employment Fremdling (2007) 92.6 111.8 77.6 100.5
Output Hoffmann (1965) 78.7 103.4 83.1 137.1
Ritschl (2004) 80.7 90.6 71.5 114.2
Filtered state 79.4 93.1 78.5 132.2
Comparative labor productivity (UK=100%)∗
Labor productivity Hoffmann (1965) 105.9 91.9 95.2 105.4
Ritschl (2004) 130.4 96.7 98.4 105.4
Filtered state 115.5 87.2 89.1 105.4
Idem, upper bound 131.5 100.1 103.1 . . .
Idem, lower bound 101.4 76.1 76.9 . . .∗Extrapolated backward from a German/UK comparative level of 105.4, obtained fromFremdling, de Jong, and Timmer, “British and German Manufacturing ProductivityCompared,” 353.Sources: see text, section 4.2.
In a final step I take these estimates to the issue of reconciling time series and
benchmark estimates. As described in section 4.2, the different sides in the debate on
German/UK comparative labor productivity tried to ensure a close fit between their
time series projections and benchmark comparisons. However, the confidence interval
around the filtered state, and thus around the levels of comparative performance, already
showed that point estimates of time series estimates are associated with considerable
uncertainty. So I move away from the notion that benchmarks and time series estimates
need to align closely. Instead, I let the measurement error of my estimated value-added
change determine the deviation between both measures that I am willing to allow for.
The question is, then, which of the benchmarks presented in the literature (if any at all)
I am compelled to reject on the basis of the uncertainty associated with the estimation
process.
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 125
Figure 4.5: Reconciliation with 1907 German/UK benchmarks ofcomparative labor productivity (UK=100%)
60
70
80
90
100
110
120
130
140
1907 1925 1933
________Ritschl (2008)
________B&B (2007)________B&B (2008)
________Fremdling (1991)
(a) Measurement error in time series(99% confidence level)
60
70
80
90
100
110
120
130
140
1907 1925 1933
________Ritschl (2008)
________B&B (2008)________B&B (2007)
________Fremdling (1991)
(b) Measurement error in time series(95% confidence level)
In answer to that question, I have combined the backward projections with direct
estimates of comparative labor productivity for 1907 in figures 4.5a and 4.5b (for the
levels indicated by the benchmarks included in the figures, see table 4.1 on page 110).
Under the assumption that the time-series estimate captures the true value of compar-
ative labor productivity, a 1907 German/UK benchmark estimate that falls outside the
confidence interval must represent something else instead. The message conveyed by ta-
ble 4.4 is simple; a deviation between both measures of a margin up till about 10% does
not imply a disqualification of the fit between both measures. Indeed, figure 4.5a shows
that all 1907 benchmark estimates can be reconciled with my time-series projections
when the state estimation error is taken into account.
I am willing to accept for 1907 a broad range of benchmark estimates, because the
uncertainty associated with the time-series’ estimation procedure is fairly large. Since
the confidence interval encapsulates all benchmark estimates, from my point of view, I
cannot exclude the possibility that the benchmark estimates are different draws from
the same probability distribution. Thus, they may well refer to the same parameter, even
though the estimates differ substantially. The message to take away from this is that
measurement error must be considered when benchmarks are used to check the accuracy
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126 Missed Opportunities?
of time series. In this case the estimation error of the time series is sizable partly because
output change is unobservable. While this may differ in other cases, my research suggests
that, in general, conclusions should not be based on differences between point estimates
only. In the particular case of the debate on German/UK comparative performance, the
confidence interval around the time series projection includes more than one benchmark
estimate.
Having said that, the benchmarks of Broadberry & Burhop (2007 vintage) lies almost
on top of the lower confidence limit. The probability of observing such a value is very low,
i.e. about 1%. From this, I derive that although in the extreme all benchmark estimates
can be reconciled with the time-series projection, the likelihood of such an event is
extremely small. Indeed, if I decrease the confidence level to 95%, as in figure 4.5b, this
first benchmark of Broadberry & Burhop falls well outside the confidence interval. Also,
I have difficulty accounting for Fremdling’s results. If my time series projection captures
the true value of comparative labor productivity, Fremdling’s benchmark must measure
something else. In contrast, at both 99% and 95% confidence levels neither Burhop &
Broadberry’s revised estimate (2008 vintage) nor Ritschl’s benchmark falls outside the
interval, which leads me to conclude that none of these estimates can be rejected on
the basis of the fit with the time-series projections.
In some respects, the finding that I cannot reject neither Broadberry & Burhop’s nor
Ritschl’s benchmark takes me back to where I started. That is, the uncertainty intro-
duced in my estimates by the absence of output data in the available historical sources
makes it impossible to choose between the 1907 German/UK benchmark estimates.
Then again, this is in itself an important conclusion, as it implies that the reconcili-
ation principle employed in the debate may not have been appropriate in the face of
the large uncertainty associated with the time series. Rather, broad margins should be
taken into account in the backward extrapolations. Of course, this begs the question
if such a broad range of German labor-productivity levels obtained by the method-
ology advanced in this chapter renders impossible a concise assessment of Germany’s
comparative performance?
Paradoxically, my answer to this question is that working with confidence intervals
around my point estimates actually increases the reliability of the conclusions drawn
with regard to historical economic development. Any conclusion drawn from the filtered
time-series estimates are explicitly founded on a solid statistical basis, which provides
an increased certainty compared to studies employing point estimates only. So looking
at figures 4.5a and 4.5b I can confidently infer that, first, Germany had overtaken
Britain in terms of labor productivity already before WW1, yet by a small margin only.
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 127
Second, over WW1 there was a statistically significant change in labor productivity
leadership with Germany dropping below the UK. And, third, given Fremdling, de Jong
and Timmer’s 1936/35 German/UK benchmark comparison, Britain’s lead evaporated
again in the 1930s and both countries performed roughly on par shortly before WW2.
In view of earlier research, these findings confirm the trend over the last two decades
in the literature on German-Anglo productivity differences. Fremdling’s estimate of a
German/British comparative level of 74% seems very low now, but deviated much less
from other estimations presented in the literature in the 1980s and 1990s. For instance,
Bairoch (1973) placed Germany at 93% the level of Britain, Crafts (1983) suggested a
German performance of 87%, Dormois & Bardini (1994) found a comparative level of
82% and Burger (1994) indicated a level of 79%.40 Compared to these earlier works,
the findings of Broadberry & Burhop and Ritschl correspond much better with the
contemporary perspective on German-Anglo industrial relations. Arthur Shadwell, who
traveled the UK, Germany and the US shortly after the turn of the twentieth century in
order to compare the qualities of industrial life in the three leading industrial countries
of the time, wrote that:
“[Germany] built up (. . . ) a great edifice of manufacturing industry which
for variety and quality of output can compete in any market with most of
the finest products of Great Britain.”41
4.5 Conclusion
Several attempts have been made in the literature to quantify output and labor-
productivity growth in German industry for the period before 1950. Given that on
the basis of different time series of output two incompatible stories can be told of
Germany’s comparative performance before WW1, the uncertainty is uncomfortable.
This begs the question whether it is possible to confidently draw conclusions regarding
Germany’s historical growth record in the face of these conflicting data?
I contribute to this debate by casting the time-series discussion in a new framework.
All output series presented in the literature set out to measure the change in output,
but value-added data is not obtainable in which case proxies are used to estimate
output change. While output proxies are assumed to correlate strongly with value-
added change, they cannot do so perfectly. The underlying data used to construct the
40. J. Dormois, “The Impact of Late-Nineteenth Century Tariffs on the Productivity of EuropeanIndustries, 1870–1930,” in Classical Trade Protection, 1815–1914, ed. J. Dormois and P. Lains (London:Routledge, 2006), 179.41. Shadwell, Industrial Efficiency, 14-15.
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128 Missed Opportunities?
output indices differs little between the series, but for some industries different output
proxies are employed, which drives the deviation between the series.
Because in the absence of output data it is impossible to determine which of the
proxies captures output growth best, the choice between time series is arbitrary to some
extent. Therefore, I argue that it is inappropriate to choose between the series, discard-
ing information provided by the rejected series. As all series employ proxies that are
correlated with value-added change, the dynamic properties of the three observed series
must be contained by a common unobserved component. Using time-series analysis, in
a first step I filtered this common component from the series, which is then interpreted
as the actual change in value added.
In a second step I looked at the uncertainty associated with the process of estimating
the common component. This means that I look at point as well as interval estimates.
Although this seems obvious, it has been tradition in the literature addressed here to
exclude information on statistical error and implicitly treat the point estimates as the
‘true’ value of the parameter. Using an upper and lower confidence limit, I indicate
a range around the estimated common component which contains the true value of
value-added with 99% certainty.
In a third step the estimated change in output is combined with data on employ-
ment to get the change of German labor productivity, which I then compared with its
British counterpart. Extrapolated backward from a robust benchmark of comparative
labor productivity in 1935/36, the level of comparative labor productivity in 1907 is
obtained. This exercise is repeated thrice, replacing the filtered common component
with the upper and lower confidence bound, respectively. This way, I identify a range
of comparative labor productivity containing the true value of the estimated parameter
with a high probability.
With this approach I deviate from the traditional notion that benchmarks and time
series estimates need to align closely. Faced with the different time series of output pre-
sented in the literature, scholars have previously employed the 1907 labor-productivity
benchmarks to test the accuracy of the time series estimates. The idea is simple; if
the benchmark estimate does not provide a tight fit with the backward projections,
the latter must be flawed. Criteria for the fit between benchmark estimates and time
series projections are loosely defined and not supported by a theoretical justification
thereof.42
In this chapter, I move away from that notion. Instead, I let the measurement error
42. Broadberry suggests a range of 10% around the point estimates. See: Broadberry and Burhop,“Comparative Productivity in British and German Manufacturing,” 326 in which the authors refer toBroadberry, “Manufacturing and the Convergence Hypothesis.”
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 129
of my estimated value-added change determine the deviation between both measures
that I am willing to allow for. The question is, then, which of the benchmarks presented
in the literature (if any at all) I am compelled to reject on the basis of the uncertainty
associated with the estimation process. Using a conservative 99% confidence level, all
benchmarks fall within the interval around my point estimate, while at the 95% level
Broadberry and Burhop’s 2007 benchmarks falls outside the confidence bounds. These
findings suggest a comparatively strong performance on the part of Germany.
The interval around the point estimates are fairly large and I am willing to accept a
broad range of German/UK comparative labor-productivity levels. The message to take
away from this is that measurement error must be considered when benchmarks are used
to check the accuracy of time series. Still, if I project the margin of error around the point
estimate (at the 95% confidence level) on the reliability scheme of Chapman, my series
falls into the B-category of “good estimates”.43 Moreover, the width of the intervals
dooes not prevent me from drawing conclusions regarding Germany’s comparative labor-
productivity development during the first half of the twentieth century. If anything, I
draw such conclusions with increased confidence. It is clear that Germany had a lead
over the UK before WW1 around the range of 10%–20%. The situation reversed over
WW1, when Germany fell behind. Although the German economy managed to catch-up
again by the late 1930s, it did not regain the advantage over the UK enjoyed before
WW1.
43. A. Chapman, Wages and Salaries in the United Kingdom, 1920–1938 (Cambridge: CambridgeUniversity Press, 1953), 231; Feinstein and Thomas, “A Plea for Errors,” 158; Feinstein and Thomas,“A Plea for Errors,” 16.
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130 Missed Opportunities?
4.A German/UK comparative labor productivity in
1936
In figures 4.5a and 4.5a comparative labor productivity is extrapolated backward from
a German/UK relative level of 105.4 in 1936/35. This level is directly obtained from
Fremdling, de Jong and Timmer (2007) and is based on a single-deflated value-added
measure of output. Alternatively, Fremdling, de Jong and Timmer also provide an esti-
mate based on output measured by double-deflated value added. This level of compar-
ative performance takes into account differences in the German/UK price relation be-
tween intermediate inputs and final outputs and raises Germany’s performance slightly
to a level 106.8% of Britain.
Figure 4.6: Reconciliation with 1907 German/UK benchmarks(UK = 100%)
60
70
80
90
100
110
120
130
140
1907 1925 1933
________B&B (2008)
________Ritschl (2008)
________B&B (2007)
________Fremdling (1991)
(a) Measurement error in time series(99% confidence level)
60
70
80
90
100
110
120
130
140
1907 1925 1933
________Ritschl (2004)
________B&B (2008)________B&B (2007)
________Fremdling (1991)
(b) Measurement error in time series(95% confidence level)
I have opted for the single-deflate output measure, as the 1907 German/UK bench-
marks are based on singel-deflated output, too. But if we extrapolate backward from a
1936 level of 106.8%, comparative productivity in 1907 increases from 115.5% to 117.0%,
as is depicted in figure 4.6. Looking at the interval estimate, the conclusions do not
change at the 99% confidence level. At the 95% confidence level, however, the interval
estimate envelopes only Ritschl’s 1907 benchmark, while both vintages of Broadberry
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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 131
& Burhop’s benchmark drop below the lower confidence limit.
Possibly more problematic is the fact that Fremdling, de Jong and Timmer’s esti-
mate measures comparative labor productivity in manufacturing. As the filtered state
estimated in this chapter captures output change in German industry, which includes
manufacturing, mining, construction and some utility industries (e.g. electricity pro-
duction), the state series ought to be tied to a level estimate for 1936 based on the same
selection of industries. Clearly, this is not the case, because a 1936 estimate for total
industry is not available. The 1907 German/UK benchmarks (see table 4.1) suggest
that German performance in industry is slightly worse than for manufacturing only. If
this applies to 1936, too, then the level of 105.4 should be adjusted downward.
By how much exactly, however, is impossible to say without re-estimating Fremdling,
de Jong and Timmer’s benchmark to include mining and construction. In all likeli-
hood, the adjustment will be small only. For instance, in the case of the 1936/35 labor-
productivity comparison between Germany and the US, presented in chapter 2, is 4 per-
centage points only. In this margin is projected on the 1936/35 estimate of Fremdling,
de Jong and Timmer, extrapolating the filtered state backward to 1907 leads to a com-
parative German/UK performance of about 111%. Correspondingly, the 99% upper and
lower confidence limits drop to 127% and 98%, respectively, a range that encompasses
all 1907 German/UK benchmark estimates. At the 95% level, then, Ritschl’s bench-
mark estimate of 124.5 falls just outside the upper confidence limit, while Broadberry
& Burhop’s 2007 estimate of 101.8 is very close to the lower confidence limit. This is
pure conjunction, however.
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132 Missed Opportunities?
4.B Indices of German industrial output
(1913 = 100)
Year State HM65 RL04 WF33 Year State HM65 RL04 WF33
1880 27.3 26.1 32.3 24.6 1910 85.5 85.5 87.8 88.6
1881 28.1 27.2 33.5 26.8 1911 91.5 90.7 93.8 96.0
1882 28.4 27.1 32.2 28.4 1912 97.1 97.2 98.3 98.9
1883 30.3 29.3 35.0 30.4 1913 100.0 100.0 100.0 100.0
1884 32.0 30.4 37.2 31.4 1914 104.0 . . . . . . . . .
1885 32.9 30.7 38.4 32.2 1915 108.1 . . . . . . . . .
1886 33.5 30.8 40.7 32.7 1916 112.4 . . . . . . . . .
1887 35.3 33.4 41.8 35.0 1917 116.9 . . . . . . . . .
1888 36.9 35.2 42.7 36.0 1918 121.5 . . . . . . . . .
1889 39.9 38.7 46.2 38.6 1919 39.6 . . . . . . 37.8
1890 41.5 39.9 46.2 40.3 1920 47.4 . . . . . . 55.1
1891 42.3 40.8 46.7 41.4 1921 60.3 . . . . . . 66.3
1892 42.8 41.7 48.3 40.0 1922 71.5 . . . . . . 71.4
1893 44.3 43.1 51.1 42.4 1923 57.1 . . . . . . 46.9
1894 47.0 45.4 55.0 44.9 1924 66.0 . . . . . . 70.4
1895 50.4 48.9 58.8 47.6 1925 90.9 103.4 90.6 82.7
1896 53.1 49.9 60.7 52.9 1926 90.1 93.7 81.9 79.6
1897 55.2 52.5 60.9 55.9 1927 106.6 118.8 105.6 100.0
1898 58.3 55.8 63.5 60.4 1928 112.8 119.1 107.5 102.0
1899 60.5 58.0 63.5 63.7 1929 113.7 121.4 106.5 103.1
1900 62.1 61.4 62.3 64.7 1930 100.3 106.1 88.7 90.8
1901 61.0 58.7 61.5 64.9 1931 80.1 85.1 70.4 73.5
1902 62.0 60.2 62.6 68.7 1932 67.1 72.8 61.6 . . .
1903 66.3 64.8 69.0 72.9 1933 72.0 83.1 71.5 . . .
1904 70.5 67.5 73.0 77.7 1934 89.3 103.1 88.0 . . .
1905 73.4 70.0 75.8 79.4 1935 109.7 121.2 102.9 . . .
1906 76.1 73.0 76.4 84.3 1936 127.0 137.1 114.2 . . .
1907 79.5 78.7 80.7 82.9 1937 142.0 152.9 127.0 . . .
1908 79.4 78.0 82.0 78.8 1938 156.0 168.1 140.4 . . .
1909 81.3 81.4 84.1 81.3
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Chapter 5Did a European Convergence Club Exist Before
World War 1? Comparative Labor Productivity in
Northwestern Europe, 1875–1913
5.A Introduction
Looking back on the previous chapters, much of the presented and discussed evidence
hints at the possibility of a common growth experience for European countries in the
period before WW1. First, chapter 2 showed that the US enjoyed a commanding labor-
productivity lead in manufacturing over Europe. This finding broadly aligns with other
manufacturing benchmarks presented in the literature to the degree that they demon-
strate an inability on the part of Europe to close in on America.1 Subsequently, in
chapter 3 it was noted that on the basis of capital-intensity data for the pre-WW1 pe-
riod the possibility could not be ruled out that Europe’s backwardness resulted from the
use of relatively labor-intensive technology, possibly induced by a skilled-labor abun-
dance. Furthermore, chapter 4 demonstrated that within Europe Germany and the UK
operated at roughly similar levels of labor productivity.
These findings suggest that while the preconditions for growth in the period run-
ning up to 1910 differed across the Atlantic, they may have been similar between Eu-
ropean countries. Indeed, according to Stephen Broadberry, the US’s substantial lead
over Europe in manufacturing labor productivity showed a great degree of stationarity
of comparative performance in manufacturing, which suggests the prevalence of dif-
ferent long-run growth paths across the Atlantic.2 This begs the question whether in
1. US/UK: Broadberry and Irwin, “Labor Productivity in the United States and the United King-dom”; Jong and Woltjer, “Depression Dynamics.” Germany/UK: Broadberry and Burhop, “Compar-ative Productivity in British and German Manufacturing”; Ritschl, “The Anglo-German IndustrialProductivity Puzzle”; Broadberry and Burhop, “Resolving.”
2. Broadberry, “Manufacturing and the Convergence Hypothesis,” 788.
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134 Missed Opportunities?
the decades leading up to WW1 European countries converged on a common level of
manufacturing performance?
Although the incapability of the UK and Germany to match US performance levels
has received most attention, several of the arguments presented in the literature aimed
at explaining the transatlantic labor-productivity gap are in principal easily extended
to include other countries as well. First, America’s advantage over Europe has been as-
sociated with its uniquely abundant supply of industrial mineral supplies and a scarcity
of skilled labor, the combination of which favored capital-intensive production.3 The
analysis in chapter 3 indeed uncovered a large capital-intensity gap in the pre-WW1
period in line with David’s and Broadberry’s view regarding differences in the choice of
technology. Given America’s unique supply of natural resources, all European countries
suffered from the same disadvantage as the UK and Germany did. The same holds for ar-
guments concerning market size and demand preferences; if these prevented Britain and
Germany from catching-up, they may well have constrained labor-productivity growth
in other European countries in a similar fashion.4
If European countries were indeed similarly affected by these local conditions in the
period before WW1, convergence with the US was unattainable for all. Instead, until
the catch-up mechanism described in chapter 3 kicked in after WW1, at least in the
case of Germany, countries may well have followed a European labor-productivity path,
characterized by low levels of performance as compared to the US. The notion of such
conditional convergence was introduced in the literature in response to the lack of em-
pirical support for unconditional convergence, as originally suggested by Solow.5 Since
the preconditions for unconditional convergence – i.e. countries are identical in levels of
technological knowledge, savings rates, population growth and depreciation rates – exist
in theory only, Solow’s model provides a bad fit with observed historical growth pat-
terns.6 However, controlling for differences across countries with respect to particular
parameters, such as the savings rate, human-capital formation or government consump-
tion, research revealed an inverse relation between initial per capita levels of income and
3. N. Crafts, “Forging Ahead and Falling Behind: The Rise and Relative Decline of the First Indus-trial Nation,” Journal of Economic Perspectives Vol. 12, No. 2 (1998): 202–203. See also chapter 3 fora more detailed discussion.
4. The lack of large-scale production has featured prominently, for instance, in explanations for theslow development in Dutch industry during a large part of the nineteenth century. See J.P. Smits,“The Determinants of Productivity Growth in Dutch Manufacturing, 1815–1913,” European Review ofEconomic History No. 2 (2000): 223–246.
5. R. Solow, “A Contribution to the Theory of Economic Growth,” Quarterly Journal of EconomicsVol. 70 (1956): 65–94.
6. L. Pritchett, “Divergence, Big Time,” Journal of Economic Perspectives Vol. 11 (1997): 1034–1052; W. Easterly and R. Levine, “It’s Not Factor Accumulation: Stylized Facts and Growth Models,”The World Bank Economic Review Vol. 15, no. 2 (2001): 177–219
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 135
subsequent rates of growth predicted by Solow.7 If the conditions under which countries
develop are similar, which may be argued for turn-of-the-century Europe, convergence
is expected.
A factor that promoted similar conditions for growth in Europe at the start of
the twentieth century concerns the relative openness of European economies between
1870–1913.8 Trade theory (Heckscher-Ohlin-Samuelson) predicts that differences in rel-
ative factor prices and thus in the mix of factor inputs used in production disappear
over time under conditions of free trade.9 Openness to trade and perfect competition
induces a country to specialize in the commodities whose production requires the inten-
sive use of the country’s relatively abundant, and thus cheap, production factor. When
engaging in international trade, a labor-abundant country specializes in labor-intensive
production processes. Consequently, the demand for labor increases, wages rise and the
wage/interest ratio goes up, too, which in turn erodes the the country’s comparative ad-
vantage in the production of labor-intensive commodities. As capital-abundant countries
experience a change of the wage/interest ratio in the opposite direction, relative factor
costs equalize between countries. For these dynamics to occur, barriers to trade ought
to be minimal. Around 1900, Europe showed the potential for such a well-integrated
market.10
In fact, Williamson already documented strong patterns of convergence in Europe
in the period 1870–1913 on the total-economy level. Using Maddison’s GDP-per-capita
data as well as his own data on real wages, Williamson shows that in the pre-WW1
period present OECD countries converged at a steady pace, a pattern particularly dis-
tinct when the US and Canada are left out of the sample.11 Broadberry notes, however,
that in the case of Germany, Britain and the US convergence was stronger on the
total-economy level than for manufacturing only.12 This suggests that convergence was
fueled mainly by compositional effects (reallocation of labor from agriculture to either
industry or services) rather than driven by the use of increasingly similar production
techniques induced by relative factor-cost equalization between countries. This might
be because the equalization of relative factor costs was, perhaps, thwarted; although
European economies were relatively open, trade tariffs did persist throughout the pe-
7. Barro, “Economic Growth”; Barro and Sala-I-Martin, “Convergence”; Fagerberg, “Technology.”8. For globalization and catch-up, see Williamson, “Globalization,” 295. For globalization in general,
see K. O’Rourke and J.G. Williamson, Globalization and History: The Evolution of Nineteenth CenturyAtlantic Economy (Cambridge, 1999).
9. Heckscher, “The Effect of Foreign Trade”; Ohlin, Interregional and International Trade; Samuel-son, “International Trade”; Samuelson, “International Factor-Price Equalization.”10. Hannah, “Logistics, Market Size, and Giant Plants.”11. Williamson, “Globalization,” 284.12. Broadberry, “Manufacturing and the Convergence Hypothesis,” 780–781.
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136 Missed Opportunities?
riod 1870–1913, particularly in countries such as Germany and France.13. Alternatively,
even if relative factor costs differed little between countries, they may not have oper-
ated the same technology. Because the social competence necessary to exploit the most
advanced technology was still limited in the period before WW1, notes Abramovitz,
technology transfer left a weak mark on convergence.14 Therefore, the strong conver-
gence measured on the total-economy level before 1914 might not be visible when the
focus is on manufacturing only.
This chapter studies (sigma) convergence in manufacturing between five northwest-
ern European countries, i.e. the UK, Germany, France, the Netherlands and Sweden, in
the period leading up to WW1. First, I look at levels of manufacturing labor produc-
tivity in 1910 by constructing five bilateral industry-of-origin benchmarks. These are
needed because the time series of long-run productivity performance suffer from the
drawback that they do not adequately account for shifts in sectoral output and changes
in product prices, particularly when they are projected from a certain benchmark-year
into distant periods. In recent years, economic historians have stressed the need for new,
more detailed, comparisons of welfare and productivity for earlier periods, particularly
for the pre-WW1 era.15 As the previous chapters have emphasized, direct benchmark
comparisons between countries are a much wanted addition to the long-span projections.
Moreover, the best-known comparisons of long-run performance, i.e. those of Maddi-
son, are unsuited for the purpose of this chapter, as they capture developments at the
total-economy level only.16 Although I am not the first to measure comparative labor
productivity between pre-WW1 European countries, previous studies deviate from the
approach applied here in that they do not use the ICOP-technique to convert output
of different countries in a common currency.17
13. Dormois, “The Impact of Late-Nineteenth Century Tariffs,” 187.14. Abramovitz, “Catching-up,” 395. Williamson underlines this point, too, and ranks technology
transfer as a minor player in nineteenth-century convergence. See Williamson, “Globalization,” 299.15. E. Frankema, J.P. Smits, and P. Woltjer, “Comparing Productivity in the Netherlands, France,
UK and US, ca. 1910: A New PPP benchmark and its Implications for Changing,” Groningen Growthand Development Centre Memorandum no. 113 (2010): 1–34; L. Prados de la Escosura, “InternationalComparisons of Real Product, 1820–1990,” Explorations in Economic History Vol. 37 (2000): 1–41;K. Fukao, D. Ma, and T. Yuan, “Real GDP in Pre-War Asia: A 1934-36 Benchmark PurchasingPower Parity Comparison with the U.S.,” Review of Income and Wealth Vol. 53 (2007): 503–537; J.van Zanden, “Rich and Poor Before the Industrial Revolution. A Comparison Between Java and theNetherlands at the Beginning of the Nineteenth Century,” Explorations in Economic History Vol. 40(2003): 1–23.16. Maddison, Phases of Capitalist Development ; Maddison, Dynamic Forces in capitalist develop-
ment ; Maddison, Monitoring the World Economy.17. J. Dormois and C. Bardini, “Branch Comparisons of Manufacturing Labour Productivity for Eight
European Countries, Ca. 1910–1913,” Paper for N.W. Posthumus seminar on comparative historicalnational accounts for Europe in the 19th and 20th centuries (1994): 1–29; Dormois, “The Impactof Late-Nineteenth Century Tariffs”; J. Dormois, La Defense du Travail National? L’Incidence duProtectionnisme sur l’Industrie en Europe, 1870–1914 (Presses de l’Universite Paris-Sorbonne, 2009).
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 137
In a second step, I study the growth of manufacturing labor productivity in these
northwestern countries over the period 1870–1913. The notion of convergence implies a
time dimension and it is necessary not only to look at comparative levels of performance
in 1910, but also at the increase or decrease of labor-productivity differences over time.
If the level comparison constructed in the first step demonstrates very similar levels
of performance between these countries, labor productivity in all probability converged
in the period running up to WW1. In case of widely different labor-productivity levels
in 1910, the time dimension may still provide evidence of a decreasing variation in
performance levels, but – in the light of Broadberry’s findings – it is not necessarily
anticipated. In addition, as in the case of America versus Europe, between European
countries the conditions for growth differed also. While the five countries studied here
may be the same in that they all faced less favorable conditions for labor-productivity
growth as compared to the US, although arguably to a different degree, dissimilarity
prevailed in many other respects.
5.2 Methodology
For the construction of benchmarks this chapter employs the approach set out in chap-
ter 2. The only differences are that, first, output is measured by value added (rather
than gross output) and, second, employment is not corrected for hours worked. With
respect to the latter, this choice is induced by the inability to find the necessary data
on hours worked on the industry level for all countries studied here. This is, however,
unlikely to introduce a bias in the results as chapter 2 already showed that such an
adjustment makes little difference for the pre-WW1 period. This means that relative
productivity at the industry level is estimated by the value of net output per employee
(in national currency), translated into a common currency with an industry-specific
PPP-adjusted price ratio based on factory-gate data.18
Previous work on economic performance in pre-WW1 European countries has been
conducted along different lines. The choice for a different strategy has been fueled by
the lack of data on the early twentieth century. In some cases, the limited availability of
data for the pre-WW1 period introduces difficulty implementing the analysis along the
lines of the ICOP approach. With the exception of the US and the UK, no other coun-
18. The benchmark method is formally defined in section 2.2 on page 20 of chapter 2. See also: D.Paige and G. Bombach, A Comparison of National Output and Productivity of the United Kingdomand the United States (Paris: Organisation for European Economic Co-operation, 1959), 1–245; vanArk and Timmer, “The ICOP Manufacturing Database”; Fremdling, de Jong, and Timmer, “Britishand German Manufacturing Productivity Compared”; Fremdling, de Jong, and Timmer, “CensusesCompared”; Jong and Woltjer, “Depression Dynamics”; Jong and Woltjer, “A Comparison of RealOutput and Productivity.”
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138 Missed Opportunities?
try published a census of manufactures and the quantity and quality of the data made
available through other sources, such as statistical yearbooks, lack full-manufacturing
coverage, as already demonstrated for Germany. Consequently, sometimes even the con-
struction of output conversion factors based on factory-gate prices can be difficult. In
such circumstances, one may proceed in different ways to convert output. First, as
is done here, PPPs can be constructed using the factory-gate prices that are available,
which possibly provide poor coverage of the output that is compared between countries.
Alternatively, price information can be obtained from other sources, for instance
from wholesale, retail or trade data. This approach perhaps increases coverage, but it
introduces an unknown bias in the PPPs, as these expenditure prices are determined
partly by factors outside the production process. Third and finally, when no price infor-
mation is available or the quality of the data is ill-regarded, the official exchange rate
presents a last resort. There are arguments against and in favor of each of these three
options and the choice of technique primarily depends on the type of question that is
confronted with the data. As the benchmarks presented here are valued for their break-
down of manufacturing in underlying industries, the exchange rate, which captures a
total-economy average relative price level, provides a poor instrument of analysis in this
case. As a result, either factory-gate or expenditure PPPs should be used.
Both measures of relative price do not necessarily return the same value and as
the PPPs form a main ingredient in the calculation of comparative labor productivity,
the choice between factory-gate or expenditure PPPs may affect our understanding of
historical development. This sensitivity of comparative labor productivity with regard to
relative price levels is clearly demonstrated by the debate between Broadberry and Ward
& Devereux in the Journal of Economic History. Ward & Devereux have constructed
expenditure PPPs – in line with the methods applied by scholars such as Gilbert, Kravis
and Maddison in the United Nations International Comparison Project – to obtain seven
benchmark estimates of US and UK income per capita and output per worker between
1872 and 1930.19 Their expenditure PPPs deviate markedly from conventional estimates
of US/UK relative price levels and imply a revision of America’s overtaking of Britain
in GDP-per-capita levels. While traditionally the view was held that the UK pertained
a lead up till 1900, the results obtained by Ward & Devereux suggest that the US had
overtaken Britain already before the 1870s.20 In this instance, the new PPPs change
our perception of the past.
19. Ward and Devereux, “Measuring British Decline”; Ward and Devereux, “Relative U.K./U.S. Out-put Reconsidered”; A. Maddison, The World Economy: a Millennial Perspective (Paris: Organisationfor Economic Cooperation / Development, 2001), 1–383.20. Broadberry, “Relative Per Capita Income Levels.”
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 139
As the expenditure PPPs establish a direct link between comparative income levels
and consumption possibilities, those estimates are particularly suited for international
comparisons of income and living standards, as in the case of Ward & Devereux. How-
ever, for international comparisons of productivity and economic performance in gen-
eral, which is the purpose of this research, a direct comparison of output at an industry
level is preferable.21 Whereas expenditure PPPs take the impact on consumer prices
of imports, trade margins, transport costs and taxes into account, factory-gate PPPs
exclude such factors and thus produce a more refined comparison of labor productivity
levels. This is not to say that a factory-gate approach is a superior methodology, it is
suggested only that the choice for expenditure or factory-gate prices primarily depends
on one’s research objective: living standards as measured by real income or economic
performance as measured by real value added? Given that the focus in the current
chapter is on the latter, factory-gate prices are favored.
Previously, however, researchers studying comparative labor productivity in pre-
WW1 Europe have opted for one of the two alternative strategies for the purpose of
converting output values. Most relevant in this respect is the research conducted by the
French economic historian Jean-Pierre Dormois, who released several vintages of a pre-
WW1 European labor-productivity comparison, also on the disaggregated level. His first
attempt, together with Carlo Bardini, employed the price comparison approach using
PPP-adjusted price ratios to convert output, as advocated here. The PPPs, however,
are based on expenditure prices obtained from either wholesale, retail or export data
and calculated on the level of total manufacturing only.22 The problems with these
PPPs, duly acknowledged by the authors, not only concern the nature of expenditure
prices and the lack of detail on the industry level, but also the selection bias of the
commodities included in the basket of goods compared between countries; Dormois and
Bardini selected semi-finished products only, while finished goods are left out. They do
so because semi-finished products are of universal quality and thus easily comparable
between countries.23
In more recent years, Dormois introduced a new release of his European labor-
productivity comparison. Here, the construction of PPPs involves a two-step approach.
First, he takes the crude ‘real’ exchange rates published by the Economic and Financial
Department of the League of Nations in 1926, based on factory-gate prices, which in
a next step are extrapolated backward to 1910 using country-specific price indices.24
21. van Ark, International Comparisons of Output and Productivity; van Ark and Timmer, “TheICOP Manufacturing Database.”22. Dormois and Bardini, “Branch Comparisons,” 7.23. ibid., 8.24. Dormois, “The Impact of Late-Nineteenth Century Tariffs,” 177.
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140 Missed Opportunities?
Although this takes into account the difference between expenditure and factory-gate
prices, such a procedure suffers from two drawbacks. The use of price indices is haz-
ardous given the volatility of exchange rates in the post-WW1 period and, just as in his
previous work, the obtained conversion factor refers to the level of total manufacturing,
which prevents a further refinement of the analysis on the disaggregated level. In a third
and last comparison, Dormois simply uses an exchange-rate based conversion factor.25
Again, this procedure neither allows for inter-industry variation, nor does it take the
exchange rate’s bias into account.
Although Dormois is fully aware of the shortcomings associated with his approach,
it has certain advantages, also. Expenditure prices are relatively easy to come by, which
helps provide a broad data basis. Indeed, Dormois’ sample of countries is impressive,
truly representative of Europe. Hence, there is a tradeoff between the appropriateness
of the PPPs and the coverage of the data. Whereas Dormois placed more importance
on the latter, my concern goes out to the former. The data limitations mainly concern
the lack of manufacturing-wide censuses of production. In fact, only the US and the UK
published such a census. For several other countries, information on output volumes and
values is available, but these data usually have limited coverage. This inevitably limits
the scope of research, which is restricted to countries exclusively from northwestern
Europe.
Factory-gate prices in this study are based on the volumes and values of the items
reported in official statistical publications.26 These surveys contain detailed information
on produced items, average prices, gross output, intermediate input and employment,
enabling me to construct labor-productivity comparisons bottom-up. For the United
States the analysis is based on the Thirteenth Census of the United States taken in the
year 1910, published by the Bureau of the Census of the U.S. Department of Commerce.
For the United Kingdom I rely primarily on the First Census of Production of 1907
published under the census of production act of 1906. The data for the Netherlands
was taken from the Statistiek van de Voortbrenging en het Verbruik der Nederlandsche
Nijverheid in 1913 en 1916 published by the National Statistical Office (Centraal Bureau
voor de Statistiek). For France I employed on the Evaluation de la Production published
by the Chambers of Commerce (1910) and the Statistiques Administratives (1912). In
addition the Annuaire Statistique de la France for 1908 and the summary tables of
1966 are used, too. The Swedish data are obtained from Prado’s newly constructed
benchmarks, while for Germany I rely on the same sources as in chapter 2.
25. Dormois, La Defense du Travail National?, 187.26. The data are collected by me for Germany, Prado for Sweden and primary data For the UK,
France and the Netherlands comes from Frankema, Smits, and Woltjer, “Comparing Productivity.”
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 141
5.3 Purchasing power parities for pre-WW1 Euro-
pean countries
This chapter’s main contribution to the literature is the application of the ICOP ap-
proach to construct industry-of-origin benchmarks for pre-WW1 European countries.
The PPPs constructed for the countries studied here are reported in table 5.1. The
table shows the official exchange rate and three PPP-variants; the Laspeyres, Paasche
and Fisher PPPs. Respectively, these refer to PPPs obtained by use of base-country
weights, non base-country weights and the geometric average thereof. Comparing the
Fisher PPP with the exchange rate, some deviation is observed, but in all cases to a
small extent only. For the UK and Germany, the official exchange rate slightly overes-
timates the domestic currency’s strength, while the reverse applies in all other cases.
The fact that the manufacturing PPPs resemble the exchange rate fairly closely signifies
that the former heavily influences the latter. Given that the exchange rate captures,
by and large, the relative price of traded goods, it suggests that trade consisted of
manufacturing products in the main, which – indeed – it did.27
Table 5.1: Purchasing power parities for total manufacturing, ca. 1910
UK GER FRA NL SWE
(£/$) (Mark/$) (Ffr/$) (Dfl/$) (Skr/$)
Exchange rate 0.21 4.20 5.18 2.49 3.73
PPP – Laspeyres 0.22 4.33 5.44 2.66 4.17
PPP – Paasche 0.18 3.49 5.60 1.99 3.98
PPP – Fisher 0.20 3.89 5.52 2.30 4.07
Sources: see section 5.2.
As compared to other pre-WW1 star comparisons presented in the literature, the
use of factory-gate prices in the construction of PPPs sets this study apart from earlier
work. So how do my results compare to the conversion factors used by others? Table 5.2
reports the PPPs used here and those introduced before in the research discussed in the
previous section. The first two rows of table 5.2 sets out my results against Dormois’
latest PPPs. As already explained, this last batch of PPPs by Dormois are obtained
by taking the relative factory-gate prices in the 1920s and then extrapolating these
27. By 1913, northwestern Europe was a net exporter of manufactured goods and a net importer ofprimary products, such as food and raw agricultural materials. Moreover, the bulk of the trade betweennorthwestern European countries studied here (imports plus exports) involved manufactured products,while primary goods were imported from other, less-developed parts of the world. See O’Rourke andWilliamson, Globalization and History, 412.
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142 Missed Opportunities?
backward to the pre-WW1 period using the price indices of the countries compared.
Of the several conversion factors proposed by Dormois, this latest batch comes closest
to mine with respect to the way they are constructed; both rely on factory-gate prices.
Indeed, the difference between both measures is very small on the total-manufacturing
level. For each country pair, the PPP reported by Dormois lies well within a 5% range
around the conversion factors constructed here.
The last three rows of table 5.2 report my PPPs and those of research conducted
during the early 1990s, which rely on expenditure prices. In contrast to the upper
two rows, the conversion factors in these studies are used to express foreign currency
into British pound, rather than US dollar. To compare these with the US-based PPPs
applied in this study, I calculated for each country the indirect UK-based PPP. To
obtain the latter, all US-based Fisher PPPs reported in table 5.1 are divided by the
price ratio of the UK relative to the US. For instance, dividing the German/US Fisher
PPP of 3.89 by the UK/US Fisher PPP of 0.20 leads to a German/UK relative price of
19.68. This procedure does not provide the most accurate estimate of a country’s price
level relative to the UK for two reasons. First, the weights to calculate Laspeyres and
Paasche PPPs differ between, for instance, a German/US and German/UK comparison,
an effect which is not taken into account in my short-cut procedure. Second, to stick
with the German example, the coverage of matched products is assumed to be identical
between the German/US and the UK/US PPPs, which is not the case in reality.
Table 5.2: Purchasing power parities of this study compared to other work
UK GER FRA NL SWE
(£/$) (Mark/$) (Ffr/$) (Dfl/$) (Skr/$)
Dormois (2006) 0.20 4.07 5.43 n.a. n.a.
This study 0.20 3.89 5.52 2.30 4.07
(£/£) (Mark/£) (Ffr/£) (Dfl/£) (Skr/£)
Dormois & Bardini (1994) 1.00 24.18 29.80 12.84 22.24
Burger (1994) 1.00 22.93 29.70 12.74 n.a.
This study 1.00 19.68 27.93 11.65 20.63
Sources: J. Dormois, “The Impact of Late-Nineteenth Century Tariffs on the Productivity ofEuropean Industries, 1870–1930,” in Classical Trade Protection, 1815–1914, ed. J. Dormoisand P. Lains (London: Routledge, 2006), 178, J. Dormois and C. Bardini, “BranchComparisons of Manufacturing Labour Productivity for Eight European Countries, Ca.1910–1913,” Paper for N.W. Posthumus seminar on comparative historical national accountsfor Europe in the 19th and 20th centuries (1994): 9 and A. Burger, “A Five CountryComparison of Industrial Labour Productivity, 1850–1990,” Paper for N.W. Posthumusseminar on comparative historical national accounts for Europe in the 19th and 20thcenturies (1994): 5.
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 143
Nevertheless, the deviation between my indirect UK-based PPPs and those reported
in other literature is fairly small, Germany being the exception. Whereas for France,
the Netherlands and Sweden the difference between the PPPs of different studies never
exceeds the 10% margin, the German/UK conversion factor presented in this study
takes on a value well below previous estimates. As compared to my PPP, the use of
either Dormois & Bardini’s or Burger’s relative price level to convert output value in
a common currency will lead to a substantial underestimation of German comparative
labor-productivity performance. This implies that the labor-productivity comparisons
presented in the next section are expected to display a German performance that is
much stronger than reported in earlier research.
Overall, the cases of Germany, France, the Netherlands and Sweden appear to sug-
gest that expenditure PPPs overestimate the price level of manufacturing goods relative
to the UK. Indeed, in all cases the expenditure PPPs are higher than my factory-gate
PPPs and the difference between both measures directly translates to an underestima-
tion of output to the same degree. In the case of the Netherlands, for instance, the
expenditure PPPs of Dormois & Bardini and Burger are about 10% higher than my
factory-gate PPP, which means that the former underestimate the value of Dutch man-
ufacturing output expressed in units of British pound by about 10%, too. This affects
the comparative performance of the Netherlands correspondingly.
However, other than in the case of Germany these differences between the old ex-
penditure PPPs and my new factory-gate PPPs are quite limited. But then again, the
total-manufacturing results do not contain the innovative feature of this research. This
chapter’s contribution to the literature lies in its ability to breakdown manufacturing
into underlying branches by introducing branch-specific PPPs, which, to the best of
my knowledge, has never been done before in this way for the pre-WW1 period. These
branch-level PPPs allow for variation in relative price levels within manufacturing and
thereby provide a more refined analysis of comparative labor productivity. The results
are reported in table 5.3, which clearly shows the diversity between branch-level PPPs.
The degree of variation suggests that a uniform currency converter on the level of total
manufacturing will not generate accurate productivity comparisons at the branch level
as it rules out the possibility of inter-industry relative-price differences.
Looking at the PPPs in table 5.3, for each country branches can be identified that
enjoyed conditions favorable to participation on the international market and thus con-
ducive to specialization. In general, a PPP below average or below the formal exchange
rate indicates that products are produced at relatively low costs and therefore may be
internationally competitive. Such is the case, for instance, in Britain for the textiles,
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144 Missed Opportunities?
Table 5.3: Fisher purchasing power parities for manufacturing branches, ca. 1910
UK GER FRA NL SWE
Branch (£/$) (Mark/$) (Ffr/$) (Dfl/$) (Skr/$)
Food, drink & tobacco 0.19 3.47 6.18 1.92 4.15
Textiles, leather & clothing 0.16 3.68 3.81 2.07 4.20
Chemicals 0.21 5.28 7.68 3.39 3.10
Metals & machinery 0.22 3.46 5.83 3.86 n.a.
Miscellaneous 0.20 4.46 5.39 1.90 3.89
Exchange rate 0.21 4.20 5.18 2.49 3.73
Sources: see section 5.2.
leather & clothing branch and – to a lesser extent – for food, drink & tobacco as well.
In Germany, particularly chemicals is characterized by high price levels. This could
be misleading, though, because several industries – i.e. general chemicals, petroleum
& coke and rubber – are grouped in this branch. Chapter 2 already observed that in
particular the rubber and petroleum & coke industries displayed price levels well above
average, while the opposite applied to general chemicals. In France the textiles, leather
& clothing branch shows relatively low prices, while chemicals did not. In terms of rela-
tive price levels, Dutch manufacturing appears to be split in two. Relatively low prices
levels are found in food, drink & tobacco as well as in textiles, leather & clothing, while
heavy industries faced high production costs. The reverse applies to Sweden, where only
the chemical branch explicitly displays low relative prices.
Table 5.4: Number of matched products
Branch UK GER FRA NL SWE
Food, drink & tobacco 20 6 7 11 6
Textiles, leather & clothing 24 16 3 12 4
Chemicals 23 24 4 14 3
Metals & machinery 30 23 2 7 0
Miscellaneous 14 5 2 6 2
Total 111 74 18 50 15
Coefficient of variation 0.10 0.16 0.20 0.30 0.10
Sources: see section 5.2.
Although the chemical industry is covered for Sweden, no product matches could be
made for metals & machinery, which means that a branch-specific conversion factor is
unobtainable. This point is illustrated by table 5.4, which shows the number of product
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 145
matches for each bilateral comparison. In general, a high number of product matches
indicates a broad coverage of production and ensures that the PPP is representative
for the branch it applies to. In contrast, when industry PPPs rely on a few product
matches only, the ensuing PPP may not reflect accurately the relative price level of a
branch, especially when the products included in a branch are highly divers, such as in
chemicals. With this in mind, table 5.4 invokes confidence in the British, German and
Dutch comparisons, as they provide a much higher coverage of products as compared
to, for instance, the earlier star comparisons of Dormois & Bardini and Burger.28 The
product coverage of the French and Swedish comparisons is similar to that in these
earlier studies.
Yet there are mitigating circumstances for the comparisons with few product
matches only. The coefficient of variation, which captures the spread between the branch
PPPs of a country, reported in the last row of table 5.4, is reassuringly low for France
and Sweden. Although in combination with a low number of matches, a variation of rel-
ative prices larger than for countries with better coverage may suggest that the branch
PPPs are based on an unrepresentative sample of products, the spread of the branch
PPPs in neither France nor Sweden points in that direction. Even if we adjust the co-
efficient of variation for all countries to exclude metals & machinery, as in the case of
Sweden, the country displaying the largest variation is the Netherlands.29 Given the
high number of product matches for the Dutch/US comparison, I am fairly confident
that this reflects actual differences in relative price levels between branches.
This belief is strengthened by the fact that manufacturing branches by 1910 were, in
terms of product variation, much less complex than in later periods and a large share of
total output was covered by fewer products. This means that a low number of matches
does not necessarily pose problems concerning the reliability of the comparison. Lastly,
both in the case of France and Sweden, the total-manufacturing PPP relate to the
expenditure PPPs presented before in the literature and the formal exchange rate in a
manner very similar to the countries with much higher coverage.
5.4 Comparative productivity around 1910
Using the PPPs introduced above to convert the labor-productivity data of the countries
studied here to a common currency, I obtain a measure of comparative performance
28. Dormois and Bardini, “Branch Comparisons”; A. Burger, “A Five Country Comparison of Indus-trial Labour Productivity, 1850–1990,” Paper for N.W. Posthumus seminar on comparative historicalnational accounts for Europe in the 19th and 20th centuries (1994): 1–27.29. Excluding metals & machinery the coefficient of variation for the UK, Germany, France and the
Netherlands is, respectively, 0.09, 0.16, 0.22 and 0.24.
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146 Missed Opportunities?
relative to the US. Table 5.5 reports the comparison of single deflated value added
per employee.30 Clearly, none of the European countries were able to catch-up with
America, a finding which does not come as a surprise. Germany approached American
productivity levels closest, but still faced a big gap. Moreover, the German/US gross
output per employee comparison presented in chapter 2 attributed a stronger relative
performance to Germany, i.e. a comparative level of 57%, which suggests that the share
of intermediate inputs in gross output was larger for Germany than for the US. The
country lagging behind furthest was the Netherlands, which attained a performance
of only a third the level realized across the Atlantic, while the other three countries,
i.e. France, the UK and Sweden, were evenly spaced in between these two European
extremes.31
Table 5.5: Comparative labor productivity (US = 100%), ca. 1910single deflated value added per employee
Branch UK GER FRA NL SWE
Food, drink & tobacco 47 33 38 40 38
Textiles, leather & clothing 48 75 46 29 44
Chemicals 49 54 32 10 39
Metals & machinery 38 72 45 18 36
Miscellaneous 42 51 49 40 44
Manufacturing 41 50 38 32 36
Sources: see section 5.2.
The size of the US lead differed between manufacturing branches, with each Euro-
pean country having relatively strong and weak points. The most pronounced differ-
ences in comparative performance between manufacturing branches are observed for the
Netherlands. The Dutch economy displayed extremely low levels of labor productivity
in heavy industries, a finding which was already anticipated by the PPPs reported in
table 5.3. The high PPPs for these industries indicate that the Netherlands proved un-
able to produce at low costs, which, among other things, could result from low levels of
productive efficiency. The Dutch performance in more traditional and light industries
was much stronger. Germany showed a mixed experience, too, which has already been
pointed out in chapter 2. The UK, France and Sweden are characterized by less di-
verging levels of comparative performance, although even in these cases branches with
30. In case of single deflation, the purchasing power parities are based on final products only andnot corrected for possible deviations between German/US price relations of intermediate and finalproducts. See also section 2.2 in chapter 3.31. Sweden’s relative distance to the US has been calculated using a two-step procedure. See ap-
pendix 5.A for the details.
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 147
relatively strong and weak performances are easily recognized.
Looking at the difference between the level of comparative labor productivity mea-
sured using the Laspeyres and Paasche PPP, it appears likely that the inter-industry
variation in performance relative to the US stimulated specialization in European coun-
tries. Table 5.6 reports these statistics and shows that for all countries except France
the use of the Paasche PPP leads to the highest estimates of comparative labor produc-
tivity. Given that the Laspeyres and Paasche PPPs are constructed using base-country
weights (always the US) and non base-country weights (European countries), respec-
tively, the gap between the two measures reflects deviations in the production structure
of the countries compared, which is known as Gerschenkron effects. The more favorable
outcome for European countries when the Paasche PPP is applied reveals an emphasis
on the strong-performing branches in the manufacturing composition of these countries.
Projecting the American structure of production on these European countries, as the
Laspeyres PPP does, leads to a decrease of comparative performance.
Table 5.6: Laspeyres, Paasche and Fischer comparativelabor productivity (US = 100%)
UK GER FRA NL SWE
Laspeyres 38 45 38 27 35
Paasche 45 56 37 37 37
Fisher 41 50 38 32 36
Sources: see section 5.2.
It follows that when a country is heavily specialized in the production of particular
goods, the industrial structure of manufacturing deviates markedly from countries with
a different specialization. However, the gap between the Laspeyres and Paasche indices
provides an imperfect measure of compositional differences, as they are calculated using
the matched value of output in the process of aggregation. Alternatively, one can reweigh
the branch-level PPPs with the share of total output of that branch in manufacturing.
This essentially assumes that the price ratios of the matched items are representative
for the whole branch. In view of the limited product coverage for some countries, I have
chosen not to do so. Table 5.7 offers a more accurate description of compositional differ-
ences by reporting the employment share of branches in total manufacturing.32 Much
of the manufacturing labor force in the Netherlands and, in particular, France was con-
centrated in textiles, leather & clothing, while heavy industries, such as chemicals and
metals, employed a relatively small part of the manufacturing labor force as compared
32. Sweden is not included due to a lack of full employment coverage by the data.
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148 Missed Opportunities?
to other countries. Furthermore, the emphasis on textiles in the UK is evident, while
the US and Germany had a broadly similar distribution of labor over manufacturing
branches that showed no pronounced specialization patterns.
Table 5.7: Employment share (%) of manufacturing branches, ca. 1910
US UK GER FRA NL
Food, drink & tobacco 12 10 15 7 23
Textiles, leather & clothing 27 40 31 46 35
Chemicals 5 3 3 1 2
Metals & machinery 25 29 20 18 20
Miscellaneous 32 18 31 28 20
Sources: see section 5.2.
Ranked according to their distance to the US, the indirect comparative performance
between the European countries can be derived from table 5.5. Although in all bilateral
comparisons the US is set as base country, the reference country can be changed. For
instance, a relative productivity level of 50% for Germany/US and 41% for UK/US im-
plies a German/UK comparative performance of 122%. Setting the UK as the reference
country for the other comparisons, too, I obtain the relative levels reported in table 5.8.
Examining these figures with the possibility of a European convergence club in mind, it
cannot be concluded that the northwestern European countries studied here performed
at similar levels of labor productivity by 1910. All countries are contained in a range
of roughly 20% above and below UK levels of labor productivity, which means that
marked differences existed between the best and worst performers. With respect to the
latter, i.e. the Netherlands, it operated at a productivity level 64% of the former, i.e.
Germany. So the gap between Germany and the Netherlands was not much smaller than
between Germany and the US. In comparison, the deviation between the UK, France
and Sweden was relatively small.
Set out against previous star comparisons for Europe, as is done in table 5.9, my
results show a relatively strong performance of European countries across the board. As
expected, Germany does much better than indicated by Dormois. The previous section
already showed that the use of factory-gate PPPs increased Germany’s performance by
about 10% as compared to Dormois’ expenditure PPPs. The rest of the difference is
caused by the fact that Dormois relies on Hoffmann’s (adjusted) time series of output.
With an eye to the problems associated with Hoffmann’s series identified in chapter 4,
my comparison relies on level estimates directly obtained from contemporary statistical
sources. In a bilateral setting this point has already been stressed by Broadberry &
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 149
Table 5.8: Comparative labor productivity in northwestern Europe(UK = 100%), ca. 1910
Branch GER FRA NL SWE CoV
Food, drink & tobacco 71 82 87 81 0.11
Textiles, leather & clothing 155 96 61 91 0.30
Chemicals 111 66 20 80 0.42
Metals & machinery 188 118 47 94 0.42
Miscellaneous 122 117 97 107 0.09
Manufacturing 122 91 77 88 0.16
Sources: see section 5.2.
Burhop and Ritschl, who both attribute Germany with a lead over Britain.
For other countries, the differences originate mainly in the use of new PPPs. For
France, for instance, the value-added data is derived from Dormois, which means that
the deviation between his and my estimates traces back to other origins. In this case,
the relatively weak performance of France in Dormois (2006) stems not only from the
different PPPs, but also his use of census-definition coverage for the UK. As the cut-off
point of the British census is very high, and a large, low-productive part of manufac-
turing remains unaccounted for, using census data increases British performance. The
comparisons presented here use full coverage, which help explain why France does better
relative to Britain as compared to previous work.
Table 5.9: Comparative labor productivity in northwesternEurope (UK = 100%) compared to other studies, ca.1910
GER FRA NL SWE
Dormois & Bardini (1994) 71 63 n.a. 77
Burger (1994) 86 79 n.a. n.a.
Dormois & Bardini (1995) 78 75 n.a. 67
Dormois (2004) 97 79 n.a. n.a.
Broadberry (1997) 116 71 n.a. 77
This study 122 91 77 88
A similar story applies to Sweden’s relative performance. Svante Prado estimated a
Swedish labor-productivity performance of 69% of the British level, which is substan-
tially lower than my indirect estimate of 88% presented here. His Swedish/US estimate
of 42%, on the other hand, aligns well with the figures presented above. Given that
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150 Missed Opportunities?
Prado’s figures imply a UK/US level of 61%, which seems unreasonably high set out
against the direct estimates composed by Broadberry & Irwin and Woltjer, he seems to
overstate the British performance prior to WW1. This results partly from Prado’s use
of census-definition UK data. My results are closer to Broadberry’s estimates, which
put Sweden and the UK on parity in 1913 and points at a Swedish performance of about
80% the level in Britain by 1909.
5.5 Change of comparative labor productivity, 1870–
1910
The notion of convergence implies a time element and a study of the levels of relative
performance in one year only cannot answer the question whether European countries
gravitated toward a common path typified by a performance about half the level of
the US. Even though the previous section recorded substantial differences between the
manufacturing performance of European countries, the spread of comparative labor-
productivity levels may still have been less by 1910 than in periods before. Having
established the relative levels of labor productivity for European countries around 1910,
these can be extrapolated backward using time series of output and employment for
each country. This is done by, first, calculating the change in labor productivity per
country and, second, multiplying the relative change of labor productivity between two
countries by the level of comparative labor productivity in a base year, which is the
benchmark year 1909 in this case, as described in equation (5.1):
yeurt
yust=
(yeurt /yeur09
yust /yus09
)· y
eur09
yus09(5.1)
with yt as a country’s level of labor productivity in period t and yt/y09 the change
of labor productivity between period t and base-year period 1909. As the level of la-
bor productivity is unobtainable, except for the benchmark year, the change of labor
productivity is derived from the change in output and employment:
yty09
=ot/o09lt/l09
(5.2)
where ot and o09 capture output in period t and base-year period 1909, respectively,
while lt and l09 refer to employment in these periods. The time-series data necessary
for this exercise is taken from the existing literature.33
33. UK and US: Broadberry, The Productivity Race – SWE: S Prado, Aspiring to a Higher Rank:Swedish Factor Prices and Productivity in International Perspective, 1860-1950 (University of Gothen-
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 151
Figure 5.1: Comparative labor productivity, 1870–1909 (US=1.0)
.2
.3
.4
.5
.6
1870 1875 1880 1885 1890 1895 1900 1905
UK (Broadberry) Germany France
Netherlands Sweden
Sources: see section 5.2.
Figure 5.1 plots the projections. It does not reveal obvious signs of convergence. At
the start of the 1870s, the northwestern European countries studied here were divided
in two groups. The UK and France performed at slightly less than half the level of the
US, while the Netherlands and Sweden trailed further behind, operating at roughly a
third of American performance levels. For this period no information on Germany is
available. Shortly after 1880, when Germany does enter the sample, matters had started
to change in Europe. France’s comparative performance steadily declined on account of
a stagnant level of labor productivity at home. During this process France fell behind
of the UK and by 1890 joined ranks with the Netherlands and Sweden. Germany, which
in 1882 still trails behind the UK, appears to catch up with Britain around 1890 and,
particularly in the period 1900–1905, managed to move away, but by a small margin
only. During the same years, Sweden started to take over first the Netherlands and then
burg, 2008) – FRA: J.P. Dormois, “Tracking the Elusive French Productivity Lag in Industry, 1840–1973,” Hi-Stat Discussion Paper Series No. 152 (2006): 1–41 – NL: J.P. Smits, E. Horlings, and J.L.van Zanden, Dutch GNP and its Components, 1800–1913, GGDC Monograph Series 5 (Groningen:Groningen Growth / Development Centre, 2000), 1–246.
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152 Missed Opportunities?
France in a sudden surge of labor-productivity increase. As a result of Swedish catch-
up and the UK falling back on the level of Germany, a divide of countries in groups
of relatively strong and weak performers, as in the early 1870s, is no longer evident.
Instead, the performance of European countries was spread out at irregular intervals
between levels relative to the US of 51% and 32%.
Figure 5.2: Comparative labor-productivity in 1885 andsubsequent labor-productivity growth
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
30 32 34 36 38 40 42 44
Comparative productivity, 1885 (US=100%)
Co
effi
cien
t o
n l
inea
r tr
end
, 1
88
5-1
90
9
Sweden
Germany
Netherlands
France UK
Sources: see section 5.2.
There is little evidence of Solow-type convergence mechanisms in the comparative
growth dynamics captured by figure 5.1. The lack of a late-comer advantage is illustrated
in figure 5.2. For each European country the relation is plotted between the labor-
productivity gap to the US in 1885 and a measure of growth over the subsequent 25
years. The employed measure of labor-productivity growth over the period 1885–1909
reflects the slope coefficient on the fitted linear trend of labor-productivity growth.
Thus, the measure of growth is country specific and not expressed in relative terms to
the US. In case of catch-up growth, the countries displaying the largest distance to the
US in 1885 are expected to subsequently experience the highest growth rates. There
is no strong evidence in support of this notion. Although the regression line slopes
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 153
downward, this negative relation between initial performance and subsequent growth is
driven by Sweden only. If Sweden is left out of the sample, the relation actually reverses.
In short, there is no clear pattern of catch-up growth in northwestern Europe between
1885–1909.
Figure 5.3: Dispersion of comparative labor productivity, 1875–1909(coefficient of variation)
.00
.05
.10
.15
.20
.25
1875 1880 1885 1890 1895 1900 1905
Average
Sources: see section 5.2.Countries: UK, Germany, France, the Netherlands and Sweden.
To press home the point, figure 5.3 plots the spread of comparative labor produc-
tivity in Europe over the period 1875–1909, as measured by the coefficient of variation.
Both at the start and the end of the period, the spread in performance is close to
the total-period average. In between, the coefficient of variation takes a dip first, then
steadily rises during the decade 1885–1895 before slowly sinking back again to the 1875
level at which it stabilizes after 1900. The convergence between 1875 and 1885 is driven
by a modest increase in Dutch comparative performance, a small drop in Britain’s posi-
tion relative to the US and France’s gradual decline. These co-occurring events brought
the performance of European countries closer together at first. However, because later
on the French plunge continued and the distance between the UK and the Netherlands
increased again, divergence set in. Over the entire period, these episodes of convergence
and divergence cancel out and the change of the coefficient of variation does not contain
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154 Missed Opportunities?
a trend.34 In contrast to total-economy developments, the first era of globalization saw
no convergence in manufacturing labor productivity between northwestern European
countries.
On the disaggregated level it was not possible to extrapolate the benchmark levels
backward. The data necessary for such an exercise are not available.35 Nevertheless,
looking at the dispersion of labor productivity at the industry level between European
countries reported in the last column of table 5.8, it seems unlikely that a break down of
the aggregate time series would show different results. Only in food, drink & tobacco the
dispersion is lower than on the total manufacturing level. Because the composition of
miscellaneous differs between the countries, the CoV thereof is difficult to interpret and
may not reflect the dispersion of performance between similar industries. That leaves
textiles, chemicals and metals, all of which show a high degree of variation. There is no
reason to expect industry-level patterns different from the lack of convergence observed
for total manufacturing. Rather, the disaggregated results clearly reject the notion of a
similar labor-productivity path across countries in northwestern Europe before WW1.
5.6 Manufacturing and convergence at the country
level
The time-series extrapolations demonstrate a stationarity of Europe’s comparative per-
formance relative to the US in the long run, a conclusion very much in line with Broad-
berry’s earlier work on the US, UK and Germany. Although the ranking of European
countries according to their comparative performance changed from time to time, e.g.
France’s relative decline between 1870–1890 and Sweden’s growth spurt after 1900, the
dispersion of performance in manufacturing remained unaltered on average. With re-
gard to a European convergence club, no evidence was found of a common long-run
equilibrium for northwestern European countries. The stationarity of European manu-
facturing performance applied to productivity differences both relative to the US and
between European countries.
At the same time convergence did take place on the total economy level. Table 5.10
reports for the five European countries included in this study the levels of GDP per
capita relative to the US and the dispersion of productivity across countries measured
34. The slope coefficient on the fitted linear trend is not statistically different from zero. Moreover,augmented Dicky Fuller tests do not suggest unit root, so the series appears stationary.35. For the US, the UK, Germany and the Netherlands time-series evidence is available at the level of
industries, for France and Sweden it is not. It may be hazardous to draw conclusions regarding Europe’sgrowth experience on an even further reduced sample of European countries (only 3 countries).
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 155
Table 5.10: Comparative GDP per capita in northwestern Europe (US = 100%)
Country 1870 1880 1890 1900 1910
United Kingdom 131 109 118 110 93
Germany 75 63 72 73 67
France 77 67 70 70 60
Netherlands 113 92 94 81 76
Sweden 57 48 50 53 53
CoV 0.30 0.26 0.26 0.21 0.17
Sources: Bolt and van Zanden, “The First Update of the Maddison Project.”
by the coefficient of variation. The table clearly illustrates the relative decline of the
UK and the Netherlands, as well as France to a lesser extent. Germany and Sweden lost
less ground relative to the US, but still faced a large gap throughout the entire period.
Looking at the dispersion of GDP-per-capita levels, convergence is evident particularly
when the focus is on the club of European countries, as Williamson also noted for the
larger group of OECD countries.36 Much of the convergence took place since the 1890s,
during the last two decades of this era of globalization.
Comparing table 5.10 with the benchmark results presented in table 5.8, it is clear
that at first, around 1870, the spread of GDP-per-capita levels between northwestern
European countries was much larger than the spread of manufacturing labor produc-
tivity. By 1909 the dispersion of GDP per capita had declined, but it had not for
manufacturing labor productivity. As a result, the spread of performance between both
productivity measures turned out quite similar prior to WW1. Figure 5.4 captures this
pattern of convergence. The coefficient of variation of GDP per capita and manufactur-
ing labor productivity converged because the latter fluctuated around a constant level,
while the former decreased over the period 1875–1909. Whereas around 1880 the coef-
ficient of variation of GDP-per-capita levels more than doubled the dispersion of man-
ufacturing labor productivity, the gap had closed all but entirely by 1909. The lack of
convergence within manufacturing by implication means that the convergence observed
on the country level was a consequence of either compositional effects or a decreased
dispersion of productivity in services and agriculture between European countries.
With regard to developments outside manufacturing, table 5.11 lists the level of com-
parative labor productivity in agriculture, mining and services as reported by Frankema,
Smits and Woltjer.37 As compared to manufacturing performance, for all countries the
36. Williamson, “Globalization,” 284.37. Frankema, Smits, and Woltjer, “Comparing Productivity,” 13.
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156 Missed Opportunities?
Figure 5.4: Dispersion of comparative performance, 1875–1909(coefficient of variation)
.12
.16
.20
.24
.28
1875 1880 1885 1890 1895 1900 1905
Manuf. labor productivity GDP per capita
Average (GDPcap)
Average (manuf. lp)
Countries: UK, Germany, France, the Netherlands and Sweden.Sources GDP per capita: J. Bolt and J.L. van Zanden, “The First Update of the MaddisonProject; Re-Estimating Growth Before 1820,” Maddison Project Working Paper 4 (2013).Sources manufacturing labor productivity: this study.
gap to the US was much smaller in services. Moreover, particularly in the UK compara-
tive levels of labor productivity in agriculture were higher than in other sectors. Also in
the Netherlands agriculture had not fallen as far behind the US as manufacturing, even
though agriculture had experienced a relative decline in the decades running up to 1910.
Midway the nineteenth century the Dutch level of labor productivity in agriculture was
at 85% of the British level.38 And in services this figure was even as high as 92% (es-
pecially due to the strong performance of the Dutch trade sector, which had a level of
labor productivity which was 30% higher than in the UK). Both agriculture and services
witnessed a steady decline in comparative productivity rates vis-a-vis the United States
as well as the United Kingdom throughout the second half of the nineteenth century.39
In France, agriculture operated at low labor-productivity levels relative to other
38. Frankema, Smits, and Woltjer, “Comparing Productivity,” 21.39. ibid.
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 157
Table 5.11: Comparative labor productivity in sectors of the economy(US = 100%), ca. 1910
UK FRA NL
Agriculture 56 37 47
Mining 38 39 10
Manufacturing 41 38 32
Services 84 68 85
Source: Frankema, Smits, and Woltjer, “Comparing Productivity.”
productive activities, including manufacturing. Surprisingly, during the phase of indus-
trialization after the 1850s France maintained a large labor force in agriculture. The
limited migration from rural to urban areas has been ascribed to a persistent cultural
belief in and adherence to small, traditional farming, which defied modernization and
suppressed agricultural labor productivity until well into the twentieth century.40 In
contrast, agriculture in the UK attained high levels of performance already early in the
nineteenth century. Whereas in France the move out of agriculture was delayed until
after the turn of the century, the share of agricultural employment was comparatively
small in Britain. These different dynamics help explain why the gap to the US in terms
of GDP-per-capita levels was much smaller than the manufacturing labor-productivity
gap for the UK and the Netherlands, but less so for France.
The comparative productivity levels obtained in this study also carry implications
for our understanding of the period after 1909 and may answer questions concerning
economic development in the interwar period. Van Ark’s data show that by 1950 the
UK, Germany, France and the Netherlands operated much closer together in terms
of manufacturing labor productivity than before WW1. Furthermore, over the period
1950–1989 the dispersion thereof did not reduce further.41 Given the lack of convergence
(or, for that matter, divergence) between 1875–1910, forces must have been active during
the period 1910–1950 that drove together levels of manufacturing performance between
European countries. An example of which is the Netherlands, which showed the lowest
levels of labor productivity before WW1, but closed the gap to Germany entirely over
the interwar years.42 In view of the findings in chapter 4, it seems likely that such
40. J.P. Dormois, The French Economy in the Twentieth Century (Cambridge: Cambridge UniversityPress, 2004), 102.41. Van Ark reports two series of comparative labor productivity (Sweden is not included), taking
first the US and then the UK as the base country. The CoV of the former increased between 1950–1989from 0.09 to 0.12, while in the latter’s case it remained constant at 0.10. See van Ark, InternationalComparisons of Output and Productivity, 290–291.42. H.J. de Jong, Catching Up Twice: the Nature of Dutch Industrial Growth During the Twentieth
Century in a Comparative Perspective (Berlin: Akademie Verlag, 2003), 66.
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158 Missed Opportunities?
convergence during the early twentieth century was fueled by a decrease of Germany’s
lead over the other countries, which resulted from setbacks encountered by the former,
rather than a catch-up process on the part of the latter.
Here again, the development in manufacturing differs from the patterns observed
on the total-economy level. The dispersion of GDP per capita levels in 1910 and 1950
were much the same for the sample of European countries studied here, while it de-
creased rapidly between 1950–1989.43 It is clear that the driving forces behind labor-
productivity development differed considerably between manufacturing and the total
economy. With respect to the former, the northwestern-European context in which the
UK, Germany, France, the Netherlands and Sweden operated did not provide a suf-
ficient condition for convergence in manufacturing labor productivity over the period
1875–1910.
5.7 Concluding remarks
The lack of labor-productivity convergence in manufacturing between 1875–1910 raises
questions regarding the role of technology in these developments. Chapter 3 argued
that the contribution of capital-intensity differences to the large German/US labor-
productivity gap in 1936/39 was comparatively small relative to the effect of tech-
nological efficiency. Does the same line of reasoning apply to the labor-productivity
differences observed for the period before WW1? For an answer to that question I rely
on the existing literature, because a study of capital intensity and its implications for
labor productivity lies outside the scope of this chapter. The remainder of this section
positions my labor-productivity findings in the literature and the results of the previous
chapters. Several elements of the analysis are based on conjecture and call for further
research.
Technology and labor productivity before WW1
With respect to the size of the distance European countries trailed behind the US,
it is not unlikely that differences in machine intensity played a more important role
before WW1 than at the end of the interwar period. For one, chapter 3 revealed a
German/US machine-intensity gap in 1909 much larger than in 1936/‘39. Before WW1
German manufacturing operated at a capital-labor ratio three times lower than the US.
Manufacturing industries in the UK and France faced a large machine-intensity gap with
the US, too, around 1910; Britain employed less than half as much horse power per unit
43. For 1910: Maddison, see table 5.10. For 1950 and 1989: Maddison.
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 159
of labor as the US and France about a sixth only.44 These pronounced differences at the
start of the twentieth century between European countries and America suggest that
variation in capital-labor ratios contributed substantially to the US’s lead over Europe.
France is a case in point. According to Dormois, in the traditional consumer indus-
tries – which employed a large part of the French manufacturing labor force – it failed
to achieve a strong labor-productivity performance because of a limited adoption of
newly available technology, a feature common to much of French manufacturing.45 The
relatively weak Dutch performance during the late nineteenth century has also been
explained by the slow adoption of steam power.46 Traditional sources of energy, like
wind, water and peat prevailed. These technologies had remained unchanged from the
seventeenth century until about the 1850s.47 Levels of aggregate domestic demand were
so low that traditional types of production (i.e. based on the use of wind- and water
power) retained their cost advantage over the introduction of steam engines, a process
that induces high initial fixed costs.48
So among other factors, the Netherlands retained obsolete technology because it
was cost efficient, a point which has also been made repeatedly for nineteenth-century
Britain.49 Broadberry argues that for Britain, the increased competition from abroad
between 1870–1914, particularly from America and Germany, led to an efficiency in-
crease in flexible and labor-intensive production.50 It was a rational response to compe-
tition to cut-back on relatively expensive factor inputs in an attempt to minimize pro-
duction costs. In the face of small domestic markets, heterogeneous demand patterns,
scarcity of natural resources and abundance of skilled labor, this process of competition-
induced cost minimization discouraged the UK from acquiring machine-intensive tech-
nology and encouraged the further improvement of the technology already in use.
“Most industries were characterized by a high degree of competition, which
acted as a spur to efficiency, with existing rivals or new entrants ready to
44. Hannah, “Logistics, Market Size, and Giant Plants,” 71.45. Dormois, The French Economy, 14; Dormois, “Tracking the Elusive French Productivity Lag,” 8.46. Smits, “The Determinants of Productivity Growth in Dutch Manufacturing, 1815–1913,” 239–240.47. M. Jansen, De Industriele Ontwikkeling in Nederland (Amsterdam NEHA, 2000).48. Smits, “The Determinants of Productivity Growth in Dutch Manufacturing, 1815–1913,” 235–
238; Horlings and Smits point at the importance of demand constraints in the Dutch economy and itsimpact on the timing of modern economic growth, see: E. Horlings and J. Smits, “Private ConsumerExpenditure in the Netherlands, 1800–1913,” Economic and Social History in the Netherlands No. 7(1996): 15–40.49. See also: D. McCloskey, Economic Maturity and Entrepreneurial Decline: British Iron and Steel
(Cambridge, Mass.: Harvard University Press, 1973), C.K. Harley, “Skilled Labour and the Choiceof Technique in Edwardian Industry,” Explorations in Economic History Vol. 11 (1974): 391–414, L.Sandberg, Lancashire in Decline (Columbus, 1974).50. Broadberry, The Productivity Race, 158–159.
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160 Missed Opportunities?
take up opportunities neglected by incumbent producers.”51
Other than considerations of cost advantages, it has been suggested that countries
may refrain from adopting advanced technology due to a lack of necessary social ca-
pabilities.52 Yet before WW1 educational attainment differed little between countries
in the developed world, although some deviations were evident. Germany enjoyed a
lead over the US, UK and France in terms of average years of secondary schooling,
while the percentage of population gaining access to a college education was largest
in America.53 However, given the small share of science-based industries in manufac-
turing before WW1, the modest differences in educational attainment were of limited
consequence for convergence.54 What appears to have mattered more, at least on the
total-manufacturing level, are specialization patterns. An emphasis on mature industries
delays the wide-spread adoption of technologies introduced in modern industries. For
instance, in the industries in which the Dutch economy had strongly specialized, such
as food processing, the use of steam power proved difficult for technological reasons.55
The upshot of this literature is clear; instead of adopting high capital-labor ratios,
European countries improved their competitiveness by increasing the labor-productivity
performance of the technology already in use. It implies that variation in labor-
productivity levels within Europe stemmed from the degree to which countries success-
fully explored or even enhanced the potential of the machine-intensity level at which
they operated. This provides a useful perspective to address questions of relative stand-
ing that remained unanswered by a study of capital-intensity differences only. How could
Germany attain higher labor-productivity levels than the UK with a lower capital-labor
ratio? Why did France trail the UK at relatively close distance only, while it employed
half as much horse power per unit of labor?56 What prevented Sweden, which enjoyed a
machine-intensity level not dissimilar from the US, from outperforming all other Euro-
pean countries?57 If European countries indeed experienced labor-productivity growth
through a process of learning-by-doing, rather than by slavishly copying advanced tech-
51. Broadberry, The Productivity Race, 209.52. Abramovitz, “Catching-up,” 395.53. Nelson and Wright, “The Rise and Fall,” 1947–1948.54. ibid., 1942, 1949.55. W. Lintsen, Geschiedenis van de Techniek in Nederland. De Wording van een Moderne Samen-
leving, 1800–1890 (Zutphen: Walburg Pers, 1992), 269–271.56. Broadberry reports a French machine-intensity level of 77% of the UK. As, according to Broad-
berry, machine intensity in the UK stood at a level 47% of the US, France’s comparative machine-intensity relative to the US was 36%, i.e. similar to Germany’s level of machine intensity. Moreover,he reports a France/UK comparative labor-productivity level of 65%, which is much lower than myestimate of 91%. Broadberry, The Productivity Race, 109. Without going into detail about the differ-ences in the estimates, Broadberry’s figures imply a different question: why did France trail Germanyat considerable distance, while it employed just as much horse power per unit of labor?57. Hannah, “Logistics, Market Size, and Giant Plants,” 71.
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 161
nology, the labor-productivity gaps between pre-WW1 European countries can, perhaps,
be understood best in terms of efficiency differences.
The above suggests that while machine-intensity differences take on importance for
the transatlantic labor-productivity gap before WW1 in particular, they fail to properly
explain variation in labor productivity within Europe. For the latter, it appears that
factors determining the labor-productivity performance of the technology in use play a
prominent role. Chapter 2 already discussed several potential efficiency augmenters, e.g.
a large establishment size or a high degree of vertical integration, and suggested that
the presence thereof in German industries coincided with a strong labor-productivity
performance. This is sill mostly conjecture and additional research is needed to shed
light on these issues. Especially because the efficiency component in labor-productivity
differences provides a measure of ignorance; a great many factors influence the labor-
productivity performance realized at particular machine-intensity levels and further
research is necessary to identify the main determinants thereof.
Technology and labor productivity over WW1
A next question concerns the changing dynamics over WW1. Tying the results of this
chapter to my previous findings, I am strengthened in the belief that the period 1900-
1940 was as a phase of transition in which European countries adopted increasingly high
levels of capital intensity, but operated machinery in conditions less favorable for labor-
productivity growth. Over WW2 there was a continuity in capital deepening, which after
the war coincided with a gradual improvement of technological efficiency. The dynamics
of the pre-1910 and post-1950 period are very dissimilar, while the period in between
witnessed a build-up of catch-up potential on account of European countries starting to
explore increasingly high capital-labor ratios. But the question remains why countries
like Germany changed their strategy for labor-productivity growth over WW1. Did the
barriers to technology adoption before WW1 disappear during the interwar years?
This question is not necessary limited to the German growth experience only. The
case of the Netherlands provides another example. In the nineteenth century, both a
delayed adoption of advanced technology and other factors, such as the lack of large-
scale production, constrained labor-productivity growth.58 At the end of the nineteenth
century the increase in trade lifted some of the barriers to large-scale production, but
even in the interwar period the average plant size was in many, if not most, industries
still smaller than in the UK and Germany.59 The machine-intensity level, on the other
58. Smits, “The Determinants of Productivity Growth in Dutch Manufacturing, 1815–1913,” 235.59. ibid., 240 and Jong, Catching Up Twice, 99.
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162 Missed Opportunities?
hand, was rapidly increasing and by 1930 relatively high in most Dutch industries as
compared to their British and German counterparts.60 Clearly, there was a continuity
over the interwar period for some constraints to labor-productivity growth, but machine
intensity does not appear to be one of them.
A possible explanation for the change over WW1 relates to the disintegration of mar-
kets. The decision to not adopt American production technology before WW1 seems
related to competition, which forced countries to produce cost efficiently, even though
innovation took place at predominantly high capital-labor ratios and an awareness of
these frontier movements provided an incentive for capital intensification. The outbreak
of WW1 ended the long period of openness and globalization between 1870–1914 and
countries resorted to protectionist policies after WW1. International markets disinte-
grated rapidly.61 As protectionist policies raise domestic prices above the world-market
level, they relieve the downward pressure on production costs. Firms are allowed to
produce with some degree of inefficiency without losing their domestic market share
to foreign competitors, which was not possible before WW1. Under these condition in-
dustries can adopt new technology, even when it is initially operated at low efficiency
levels.
This line of reasoning presents a variation on the infant-industry argument and
may shed new light on the differences in accumulation strategies over WW1.62 But
this perspective is at odds with the literature that associates the move away from
competition during the interwar years with a distortion to ‘adjustment mechanisms’,
which provided the possibility to retain old technology longer than would have been
feasible under conditions of competition.63 These adjustment mechanisms, however,
prevented capital intensification in the period before WW1 and, once blocked, may have
created the necessary opportunity for a move toward high machine-intensity levels. But
this assertion requires supporting empirical evidence, which calls for further research.
60. Jong, Catching Up Twice, 79.61. Broadberry, The Productivity Race, 210.62. H.J. Chang, Kicking Away the Ladder. Development Strategy in Historical Perspective (London:
Anthem Press, 2002).63. Broadberry, The Productivity Race, 291.
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 163
5.A Sweden’s relative performance
Although Sweden’s comparative performance reported in table 5.8 (on page 149) is
based on a direct comparison with the US, the results have been adjusted. The third
column in table 5.12 presents the original numbers of the direct Swedish/US compari-
son. It shows that Sweden performed on 45% the level of America. If we compare this
figure with Britain’s distance to the US as measured by the value-added per employee
comparison in table 5.8, i.e. 41%, Sweden appears to have had a 10% lead over the UK
in 1909. This finding seems odd given that previous estimates have always pointed out
the reverse (see table 5.9). The data used here are obtained from Prado, who has con-
structed both a Swedish/US and a Swedish/UK comparison for 1909. My Swedish/US
estimate is close to Prado’s own figures (45% and 42%, respectively). In contrast, the im-
plicit Swedish/UK relative level presented in table 5.9 is not; Prado’s direct comparison
demonstrates a Swedish performance of 69% the level of Britain.
Table 5.12: Comparative labor productivity (US = 100%), ca. 1910single deflated gross output per employee
US=100% UK=100%
Branch UK GER SWE US GER SWE
Food, drink & tobacco 50 56 32 200 112 81
Textiles, leather & clothing 64 86 58 157 134 91
Chemicals 59 50 49 169 84 80
Metals & machinery 45 60 43 220 133 94
Miscellaneous 46 44 39 216 95 107
Manufacturing 51 57 45 198 113 88
The deviation between my indirect estimate of Swedish/UK comparative labor pro-
ductivity and the direct estimate of Prado stems from the fact that the Swedish data
refers only partially to value added. Most of the output value used in the comparisons
concerns gross output.64 As the Swedish/US comparison reflects to a large extent the
comparative level of gross output per employee, deriving an implicit Swedish/UK level
via a value-added based UK/US benchmark is a hazardous procedure. Given that much
of the underlying data of the Swedish/US comparison refers to gross output, it may be
more appropriate to use a gross-output UK/US comparison for the purpose of deriving
64. 59% of the covered output value for the Swedish/US comparison and only 25% for the UK/Swedishbenchmark. See Prado, 96.
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164 Missed Opportunities?
an implicit Swedish/UK level. Table 5.12 presents such a gross-output based compari-
son for the UK, Germany and Sweden. For France and the Netherlands no gross-output
data is available. Clearly, adjusting our output definition from value added to gross out-
put has a substantial effect on the comparative performance of the UK. The increased
British performance suggests that the share of intermediate inputs in gross output is
far lower in the US than in the UK.
If I now calculate for Sweden indirect levels of comparative productivity relative
to the UK (shown in the last three columns of table 5.12), Sweden’s performance is
adjusted downward from 110% to 88%.65 The Swedish performance is still substan-
tially higher than Prado’s estimates, but his figures imply a UK/US level of 61%, which
seems suspiciously high set out against the direct estimates composed by other re-
search.66 As with the case of France, the difference between Prado’s direct Swedish/UK
benchmark and the implicit relative level indicated here results mainly from Prado’s
use of census-definition UK data, which overestimates British performance. Also, the
PPPs constructed here favor Sweden. Sweden’s comparative performance reported in
table 5.8, is then obtained by projecting the Swedish/UK ratio presented above on the
UK/US relative levels of table 5.8.
65. For Germany I have included in table 5.12 an indirect productivity level relative to the UK usinggross-output definitions, too, which turns out slightly lower than in the 22% German advantage overBritain obtained using value added (see table 5.8).66. Broadberry and Irwin, “Labor Productivity in the United States and the United Kingdom”;
Frankema, Smits, and Woltjer, “Comparing Productivity”; Jong and Woltjer, “Depression Dynamics.”
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Chapter 5. Did a European Convergence Club Exist Before World War 1? 165
5.B Value added estimates Germany
Estimating value added in German manufacturing is less than straightforward. For
some industries the value of processed intermediate inputs is either not obtainable
from the sources or under-reported. In case of the latter, a value-added based labor-
productivity comparison presents a bias in Germany’s favor. The available data for
pre-WW1 Germany is listed in table A.11. Looking at the reported data, it is clear that
for tobacco, paper, rubber and stone, clay & glass VA/GO ratios need to be obtained
differently. In case of tobacco and rubber because no intermediate inputs can be derived
from the sources, while the reports for paper and stone, clay & glass clearly overestimate
the share of value added in gross output. For these industries the data from the German
census of 1936 are used, which are presented in table A.12.
Alternatively, I could have opted to use either US or UK VA/GO ratios as a substi-
tute (see tables A.13 and A.14. The advantage would be that for both countries data is
available for the same period, i.e. before WW1. Over the interwar VA/GO ratios may
very well have changed in Germany. On the other hand, using US VA/GO ratios means
that I do not correct for intermediate inputs at all, as the German/US comparison would
lead to exactly the same results as the gross-output estimate presented in chapter 2.
Moreover, given the emphasis both in the literature and in the findings of this chapter
on differences in factor costs between the countries studied here, it is unwise to force
Britain’s input-output structure on Germany. For these reasons I have chosen to use
German VA/GO ratios from the interwar period.
To see what impact the choice of different VA/GO ratios has on the aggregate
relative level of German/US labor productivity, for those industries for which I have
no (or no reliable) pre-WW1 German data on value added I have used in turn the
VA/GO ratio of Germany in 1936, the US in 1909 and the UK in 1907. Apart from
the four industries listed above (tobacco, paper, rubber and stone, clay & glass), I also
included the food industry in this exercise, because the VA/GO ratio of Germany in
1909 is based only on the starch industry. This may not be representative for the whole
of the food industry. Using for these industries the VA/GO ratio of Germany in 1936,
the US in 1909 and the UK in 1907 leads to a German/US comparative performance
of, respectively, 0.50, 0.61 and 0.57. Clearly, the data used in the chapter provides a
lower-bound estimate of Germany’s comparative labor-productivity performance.
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Appendix AData Appendix
A.1 Pre-WW1 labor-productivity growth in German
industries
Description Year LPa UVb Δ LPc Δ LPd
Manufacturing
Iron and steel 1908 11,854 88.70
1909 12,475 87.85 1.06 1.05
1910 13,241 87.32 1.07 1.06
1911 14,244 89.93 1.04 1.08
Zinc 1908 7,310 391.39
1909 8,540 420.22 1.09 1.17
1910 9,191 427.11 1.06 1.08
1911 10,614 453.21 1.09 1.15
Lead, silver, and copper 1908 24,358 554.41
1909 26,741 544.46 1.12 1.10
1910 27,869 541.78 1.05 1.03
1911 34,364 609.20 1.10 1.23
Sulfuric acid 1908 13,026 44.27
1909 15,948 42.98 1.26 1.22
1910 16,369 41.22 1.07 1.03
1911 17,878 43.74 1.03 1.09
Tin 1908 37,198 705.52
1909 45,151 1,128.55 0.76 1.21
1910 60,174 1,526.07 0.99 1.33
1911 77,399 1,495.63 1.31 1.29
Nickel, cobalt, bismuth, and ar-
senic
1908 19,026 2,280.86
Continued on next page. . .
167
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168 Missed Opportunities?
Table A.1 – Continued
Description Year LPa UVb Δ LPc Δ LPd
1909 19,800 2,229.72 1.06 1.04
1910 21,725 2,260.75 1.08 1.10
1911 23,632 2,273.42 1.08 1.09
Coal-tar distillations 1908 11,954 44.45
1909 14,348 43.01 1.24 1.20
1910 14,446 41.41 1.05 1.01
1911 14,989 41.44 1.04 1.04
Lignite-tar distillations 1908 13,444 186.65
1909 12,604 166.68 1.05 0.94
1910 13,891 164.61 1.12 1.10
1911 13,501 158.42 1.01 0.97
Petroleum refining 1908 23,324 166.20
1909 23,424 147.41 1.13 1.00
1910 26,590 153.28 1.09 1.14
1911 28,278 154.94 1.05 1.06
Coke 1908 18,932 19.79
1909 18,621 18.33 1.06 0.98
1910 19,462 18.16 1.05 1.05
1911 20,253 n.a. 1.04
Motor vehicles 1907 4,537 5,542.41
1908 4,294 5,272.01 0.99 0.95
1909 4,179 5,203.07 0.99 0.97
1910 5,426 5,580.26 1.21 1.30
1911 5,681 6,293.17 0.93 1.05
Cement 1910 5,666 19.74
1911 6,357 20.05 1.10 1.12
Mining
Coal 1908 2,806 10.80
1909 2,608 10.41 0.96 0.93
1910 2,591 10.16 1.02 0.99
1911 2,622 9.93 1.04 1.01
Briquette 1908 24,558 14.22
1909 25,144 13.67 1.07 1.02
1910 25,587 13.21 1.05 1.02
1911 24,952 12.64 1.02 0.98
Lignite 1908 2,755 2.33
1909 2,623 2.33 0.95 0.95
1910 2,720 2.29 1.06 1.04
1911 2,782 2.24 1.05 1.02
Iron ore 1908 2,128 4.48
1909 2,132 4.01 1.12 1.00
1910 2,300 4.02 1.08 1.08
Continued on next page. . .
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Appendix A. Data Appendix 169
Table A.1 – Continued
Description Year LPa UVb Δ LPc Δ LPd
1911 2,421 4.06 1.04 1.05
Lead, silver, and zinc ore 1908 1,434 12.74
1909 1,676 14.74 1.01 1.17
1910 1,905 15.43 1.09 1.14
1911 2,044 16.00 1.03 1.07
Arsenic and copper ore 1908 1,322 29.59
1909 1,461 28.65 1.14 1.11
1910 1,680 27.81 1.18 1.15
1911 1,791 27.79 1.07 1.07
Pyrite 1908 2,162 7.21
1909 2,288 7.40 1.03 1.06
1910 2,154 7.41 0.94 0.94
1911 2,274 7.62 1.03 1.06
Uranium, tin, cobalt, nickel, bis-
muth, vitriol ore, and bauxite
1908 1,000 21.84
1909 1,048 23.86 0.96 1.05
1910 1,039 20.06 1.18 0.99
1911 1,251 19.98 1.21 1.20
Petroleum 1908 3,589 69.69
1909 4,896 67.67 1.40 1.36
1910 5,276 68.38 1.07 1.08
1911 5,022 68.97 0.94 0.95
Asphalt rock 1908 3,391 9.09
1909 3,242 8.34 1.04 0.96
1910 3,699 8.46 1.12 1.14
1911 3,497 7.56 1.06 1.04
Raw graphite 1908 1,087 45.57
1909 1,349 36.36 1.56 1.24
1910 1,293 33.18 1.05 0.96
1911 1,345 29.03 1.19 1.04
Salt (fine) 1908 4,818 30.05
1909 4,892 30.38 1.00 1.02
1910 5,046 30.51 1.03 1.03
1911 4,595 29.27 0.95 0.91
Salt (crude) 1909 8,023 16.66
1910 8,880 16.15 1.14 1.11
1911 9,315 16.40 1.03 1.05
Notes on next page. . .
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170 Missed Opportunities?
Notes to table A.1:a Output values are measured in Goldmarks.b Weighted average of the UVs of products produced in that industry.c Output is deflated using industry-specific PPPs; comparison based on real output values.d Output is not deflated; comparison based on nominal output values.
Sources: see section 2.3.
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Appendix A. Data Appendix 171
A.2 Labor-productivity levels pre-WW1 Germany
Description Year Output Empl. LP
FOOD AND DRINKS – 157,556 –
Starch 1911 90,466,000 6,248 14,479
Glucose (starch) 1907 14,303,641 2,149 6,656
Brewing 1907 69,535,000a 111,779 622
Raw sugar 1907 2,138,731b 37,380 57
TOBACCO MANUFACTURES 97,390,000c 203,224 479d
Tobacco 1907 97,390,000c 203,224 479d
TEXTILES 1,134,115,084 175,216 6,473
Cotton spinning 1907 698,824,785 97,975 7,133
Silk spinning 1907 28,420,113 5,712 4,976
Woolen (worsted) 1907 263,273,841 43,042 6,117
Jute 1907 78,787,997 12,866 6,124
Linen 1907 64,808,348 15,622 4,149
PAPER 750,000,000 94,197 7,962
Paper, cardboard, and wood pulp 1913 750,000,000 94,197 7,962
CHEMICALS 233,542,686 21,560 10,832
Sulfuric acid 1909 92,481,257 5,799 15,948
Coal-tar distillations 1909 39,471,000 2,751 14,348
Potassium compounds 1909 85,591,429 12,058 7,430
PETROLEUM REFINING AND COKE 488,367,000 25,830 18,907
Petroleum refining 1909 36,073,000 1,540 23,424
Coke 1909 452,294,000 24,290 18,621
RUBBER 140,046,000 8,975 15,604
Tires 1912 140,046,000 8,975 15,604
LEATHER 656,507,000 42,750 15,357
Leather tanning and dressing 1910 656,507,000 42,750 15,357
STONE, CLAY, AND GLASS 402,846,000 114,228 3,527
Cement 1910 126,846,000 22,386 5,666
Glass 1910 276,000,000 91,842 3,005
PRIMARY METALS 4,176,161,000 325,661 12,824
Cast iron and steel 1909 467,564,000 125,057 3,739
Pig iron, wrought iron, etc. 1909 3,309,537,000 177,709 18,623
Zinc 1909 101,249,000 11,856 8,540
Lead, silver, and copper 1909 258,208,000 9,656 26,741
Tin 1909 21,763,000 482 45,151
TRANSPORTATION EQUIPMENT 80,325,000 19,211 4,181
Motor vehicles 1909 80,325,000 19,211 4,181
Notes on next page. . .
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172 Missed Opportunities?
Notes to table A.2:a Output is measured in hectoliter.b Output is measured in metric ton.c Output is measured in kilogram.d Output is the quantity of raw tobacco used in the production of tobacco.
Sources: see section 2.3.
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Appendix A. Data Appendix 173
A.3 Labor-productivity levels interwar Germany
Description Year Output Empl. LP
FOOD AND DRINKS – 142,401 –
Starch 1936 109,604,000 5,828 18,806
Brewing 1936 33,856,000a 76,376 443
Sugar 1936 2,527,600b 60,197 42
TOBACCO MANUFACTURES 91,900c 154,255 596d
Tobacco products 1936 91,900c 154,255 596d
TEXTILES 1,651,310,000 217,540 7,591
Cotton spinning 1936 828,023,000 109,116 7,588
Artificial silk spinning 1936 275,492,000 36,955 7,455
Woolen (worsted) 1936 422,149,000 48,269 8,746
Jute 1936 66,741,000 10,927 6,108
Linen 1936 58,905,000 12,273 4,800
PAPER 819,204,000 76,291 10,738
Paper, cardboard, and wood pulp 1936 819,204,000 76,291 10,738
CHEMICALS 471,382,000 32,450 14,526
Sulfuric acid 1936 67,914,000 4,990 16,610
Coal tar distillations 1936 215,567,000 9,703 22,217
Potash and potassium compounds 1936 187,901,000 17,757 10,582
PETROLEUM REFINING AND COKE 1,080,337,000 39,630 27,261
Petroleum refining 1936 370,641,000 16,089 23,037
Coke 1936 709,696,000 23,541 30,147
RUBBER 195,922,000 14,878 13,169
Tires 1936 195,922,000 14,878 13,169
LEATHER 608,541,000 44,747 13,600
Leather tanning and dressing 1936 608,541,000 44,747 13,600
STONE, CLAY, AND GLASS 535,938,000 79,967 6,702
Cement 1936 267,552,000 20,030 13,358
Glass 1936 268,386,000 59,937 4,478
PRIMARY METALS 6,441,727,000 374,713 17,191
Cast iron and steel 1936 879,762,000 152,022 5,787
Pig iron (blast furnaces) 1936 847,973,000 27,495 30,841
Wrought iron 1936 2,393,000 226 10,588
Ingots 1936 1,533,646,000 46,253 33,158
Rolling works 1936 2,607,873,000 131,693 19,803
Zinc 1936 38,372,000 4,552 8,430
Copper refining 1936 183,232,000 1,825 100,401
Copper, lead, and silver 1936 184,738,000 8,197 22,537
Gold and silver refining 1936 129,005,000 701 184,030
Tin 1936 15,384,000 712 21,607
Continued on next page. . .
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174 Missed Opportunities?
Table A.3 – Continued
Description Year Output Empl. LP
Nickel, cobalt 1936 19,349,000 1,037 18,659
TRANSPORTATION EQUIPMENT 2,599,877,000 284,462 9,140
Motor vehicles 1936 1,445,742,000 111,261 12,994
Trailers 1936 266,254,000 37,991 7,008
Aircraft engines 1936 276,097,000 35,139 7,857
Aircraft 1936 611,784,000 100,071 6,113
Notes to table A.3:a Output is measured in hectoliter.b Output is measured in metric ton.c Output is measured in kilogram.d Output is the quantity of raw tobacco used in the production of tobacco.
Sources: see section 2.3.
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Appendix A. Data Appendix 175
A.4 Labor-productivity levels pre-WW1 US
Description Year Output Empl. LP
FOOD AND DRINKS – 84,665 –
Starch 1909 15,868,393 1,925 8,243
Glucose (starch) 1909 32,930,918 2,848 11,563
Brewing 1909 66,176,940a 66,725 992
Sugar 1909 751,639b 12,536 60
TOBACCO MANUFACTURES 236,405,000c 197,637 1,196
Tobaccod 1909 236,405,000c 197,637 1,196
TEXTILES 841,021,276 318,061 2,644
Cotton: yarns and threads 1909 494,070,173 175,359e 2,817
Silk: throwing and winding mills 1909 17,145,992 17,646 972
Woolen: worsted goods 1909 312,624,663 114,422 2,732
Jute 1909 10,795,230 6,901 1,564
Linen 1909 6,385,218 3,733 1,710
PAPER 267,656,964 81,473 3,285
Paper, cardboard, and wood pulp 1909 267,656,964 81,473 3,285
CHEMICALS 128,393,253 2,773 4,204
Sulfuric acid 1909 9,884,057 2,582 3,828
Coal-tar products 1905 820,309 170 4,825
Potash 1909 88,940 21 4,235
PETROLEUM REFINING AND COKE 332,694,281 47,866 6,951
Petroleum refining 1909 236,997,659 16,640 14,243
Coke 1909 95,696,622 31,226 3,065
RUBBER 128,435,747 31,284 4,105
Tiresf 1909 128,435,747 31,284 4,105
LEATHER 327,874,187 67,100 4,886
Leather products 1909 327,874,187 67,100 4,886
STONE, CLAY, AND GLASS 155,300,658 102,084 1,521
Cement 1909 63,205,455 29,511 2,142
Glass 1909 92,095,203 72,573 1,269
PRIMARY METALS 2,113,637,262 362,351 5,833
Pig iron 1909 391,429,283 43,061 9,090
Steel works and rolling mills 1909 985,722,534 260,762 3,780
Wire 1909 84,486,518 19,945 4,236
Tin plate and terneplate 1909 47,969,645 5,846 8,206
Zinc: smelting and refining 1909 34,205,894 7,156 4,780
Lead: smelting and refining 1909 167,405,650 8,059 20,773
Copper: smelting and refining 1909 378,805,974 16,832 22,505
Gold and silver refining 1909 23,611,764 690 34,220
TRANSPORTATION EQUIPMENT 259,900,642 90,376 2,876
Continued on next page. . .
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176 Missed Opportunities?
Description Year Output Empl. LP
Motor vehicles 1909 259,900,642 90,376 2,876
Notes to table A.4:a Output is measured in hectoliter.b Output is measured in metric ton.c Output is measured in kilogram.d Output data is derived from the ’Report of commissioner of internal revenue’. The numbers
of cigars, cigarettes and other tobacco products are converted to the input of raw tobacco.
This was feasible as the internal revenue reports the number of pounds of tobacco needed for
the production of 1,000 cigars or cigarettes or other products. Subsequently, the use of raw
tobacco for the production of cigars, cigarettes and other tobacco manufactures are summed.
Employment is obtained from the census of manufactures 1909 and represents the total number
of people working in the tobacco manufactures industry (so both workers and proprietors).e For cotton yarn and thread production, only the number of spinners are reported by the
census. Here, I added to the spinners an estimate of wage earners also working in this industry.f These data refer to ‘Rubber, not elsewhere classified’. Tire production is the main activity
of this industry. For 1909 I do not have information about the share of the value of tires in
total production. For 1914, tires formed 65% of the total output value of ‘Rubber, n.e.c.’.
Sources: see section 2.3.
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Appendix A. Data Appendix 177
A.5 Labor-productivity levels interwar US
Description Year Output Empl. LP
FOOD AND DRINKS 1,545,169,173 129,983 11,887
Starch 1935 103,631,751 8,616 12,028
Sugar 1935 498,654,860 29,775 16,747
Beverages 1935 942,883,102 91,592 10,294
TOBACCO MANUFACTURES 615,518,450 95,589 6,439
Tobacco 1935 615,518,450 95,589 6,439
TEXTILES 217,935,924 85,881 2,538
Yarn and thread mills 1935 66,243,746 33,938 1,952
Throwing and spinning together 1935 46,181,765 25,124 1,838
Worsted yarn 1935 83,608,832 19,846 4,213
Jute 1935 16,294,140 5,008 3,254
Linen 1935 5,607,441 1,965 2,854
PAPER 1,529,829,650 268,742 5,693
Paper 1935 1,529,829,650 268,742 5,693
CHEMICALS 944,823,119 154,157 6,129
Inorganic chemicals 1935 944,823,119 154,157 6,129
PETROLEUM REFINING AND COKE 2,423,292,920 137,898 17,573
Petroleum refining 1935 2,184,589,237 119,461 18,287
Coke 1935 238,703,683 18,437 12,947
RUBBER 446,091,602 65,715 6,788
Tires 1935 446,091,602 65,715 6,788
LEATHER 308,344,763 54,823 5,624
Tanning and finishing 1935 308,344,763 54,823 5,624
STONE, CLAY, AND GLASS 404,342,190 96,661 4,183
Cement 1935 120,417,129 23,311 5,166
Glass 1935 283,925,061 73,350 3,871
PRIMARY METALS 3,682,794,765 622,659 5,915
Blast furnaces and steel mills 1935 2,305,969,590 406,137 5,678
Iron and steel foundries 1935 288,330,311 112,087 2,572
Primary nonferrous metals 1935 533,867,769 79,168 6,743
Nonferrous metal rolling and drawing 1935 554,627,095 25,267 21,951
TRANSPORTATION EQUIPMENT 4,128,372,408 475,043 8,691
Motor vehicles and equipment 1935 3,942,014,123 425,045 9,274
Aircraft and parts 1935 45,347,030 14,931 3,037
Railroad equipment 1935 117,925,622 29,294 4,026
Motorcycles and bicycles 1935 23,085,633 5,773 3,999
Sources: see section 2.3.
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178 Missed Opportunities?
A.6 Purchasing power parities (GER36/US35)
Industry PPP (GER/US)
Sample All
Exchange rate 2.48 2.48
Lasp. Paas. Fisch. Lasp. Paas. Fisch.
Mining 5.36 5.44 5.40 5.36 5.44 5.40
Manufacturing 3.84 3.00 3.39 3.11 4.86 3.89
3.78 3.06 3.48
3.52 3.01 3.59 4.15 3.21 3.65
Food and kindred products 3.31 3.83 3.56 4.71 4.01 4.35
Tobacco 3.29 3.29 3.29 3.29 3.29 3.29
Textile mill products 2.72 2.65 2.69 4.05 2.97 3.47
Apparel and related 4.11 3.11 3.58
Lumber and wood 6.46 6.06 6.26
Paper and allied products 3.78 3.51 3.64 3.78 3.51 3.64
Chemical and allied products 3.09 2.63 2.85 3.57 3.21 3.39
Petroleum and coal products 3.96 2.04 2.84 3.96 2.04 2.84
Rubber products 4.99 3.53 4.20 4.99 3.53 4.20
Leather and leather products 4.22 4.25 4.23 4.22 4.25 4.23
Stone, clay, and glass products 3.14 2.78 2.95 3.06 2.64 2.84
Primary metal products 2.89 2.74 2.81 2.89 2.74 2.81
Fabricated metal products 4.29 2.96 3.56
Machinery 3.48 3.61 3.55
Electrical machinery 3.83 3.40 3.61
Transportation equipment 4.83 3.84 4.31 4.76 3.34 3.99
Instruments and related 5.36 5.41 5.39
Sources: see section 2.3.
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Appendix A. Data Appendix 179
A.7 Coverage and number of UVRs (GER36/US35)
Industry Share in compared output No.
Sample All Sample All
GER US GER US
Mining 60 27 60 27 5 5
Manufacturing 74 48 43 38 125 202
Food and kindred products 70 47 67 45 9 26
Tobacco manufactures 57 118 57 118 1 1
Textile mill products 148 114 58 35 7 13
Apparel and related 55 59 10
Wood and lumber 71 60 2
Paper and allied products 43 25 43 25 12 12
Chemical and allied products 94 21 21 24 33 47
Petroleum and coal products 59 9 39 8 3 3
Rubber products 69 73 28 48 3 3
Leather and leather products 133 251 50 63 7 7
Stone, clay, and glass products 70 57 39 28 7 13
Primary metal products 76 46 58 43 27 27
Fabricated metal products 12 7 9
Machinery 3 2 3
Electrical machinery 9 16 5
Transportation equipment 47 57 37 56 16 19
Instruments and related 7 3 2
Sources: see section 2.3.
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180 Missed Opportunities?
A.8
Unit-valuera
tiosGER09/US09
Pro
duct
Unit
GER
1909
US
1909
UVR
GO
Vol.
GO
Vol.
Mining
2,017,384,000
654,109,829
Coalandlignite
Short
ton
1,685,365,000
235,505,173
405,486,777
379,744,257
6.70
Ironore
Longton
80,781,000
19,811,943
110,290,596
51,294,271
1.90
Pyrite
Longton
1,579,000
209,914
1,028,157
247,070
1.81
Uranium
ore,tinore,cobalt
ore,nickel
ore,bismuth
ore,
vitriolore
andbauxite
Longton
673,000
27,758
679,447
129,101
4.61
Petroleum
Barrel
9,297,000
987,919
128,248,783
182,134,274
13.36
Raw
graphite
Short
ton
224,000
6,790
32,238
5,096
5.21
Salt
Barrel
239,465,000
109,078,633
8,343,831
30,117,646
7.92
Manufacturing
6,765,599,461
2,824,473,286
Starch,potato
Lbs.
41,739,000
516,653,311
925,570
26,582,595
2.32
Starch,maize
Lbs.
5,176,000
52,866,850
15,962,916
638,825,366
3.92
Starch,wheat
Lbs.
11,775,000
63,765,623
626,337
12,127,686
3.58
Starch,glucose
sirups
Lbs.
13,044,000
124,333,661
17,922,514
769,660,210
4.51
Starch,dextrin
Lbs.
5,903,000
49,310,794
610,999
16,148,931
3.16
Starch,sugar
Lbs.
2,311,000
21,940,625
3,620,816
159,060,478
4.63
Total
79,948,000
39,669,152
Cotton,yarn
Lbs.
608,939,000
814,529,360
109,314,953
470,370,995
3.22
Cotton,spinningwaste
Lbs.
11,096,000
64,047,263
10,874,380
310,513,348
4.95
Cotton,thread:
Lbs.
36,698,000
26,053,185
20,516,269
23,706,957
1.63
Linen
,yarn
Lbs.
53,890,000
71,100,962
982,742
5,486,891
4.23
Continued
onnextpage...
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Appendix A. Data Appendix 181
Table
A.8
–Continued
Pro
duct
Unit
GER
1909
US
1909
UVR
GO
Vol.
GO
Vol.
Linen
,thread
Lbs.
4,589,000
3,622,960
3,407,008
6,530,503
2.43
Jute,yarn
Lbs.
62,769,000
294,848,170
4,361,550
62,512,247
3.05
Silk
Lbs.
20,371,235
3,611,584
2,104,066
779,462
2.09
Twistedraw
silk
Lbs.
11,633,000
1,016,117
6,341,719
1,088,780
1.97
Woolen,worstedthread
Lbs.
410,817,000
229,965,656
93,701,641
131,430,238
2.51
Twine
Lbs.
29,357,000
51,365,311
2,417,391
13,715,771
3.24
Ropes
andcables
Lbs.
11,359,000
27,602,232
1,164,526
7,603,907
2.69
Total
1,261,518,235
255,186,245
Pulp
Short
ton
230,000,000
1,668,238
30,177,366
910,846
4.16
Paper
Short
ton
460,000,000
1,775,824
101,654,400
1,870,459
4.77
Cardboard
Short
ton
60,000,000
407,855
29,497,735
883,088
4.40
Total
750,000,000
161,329,501
Copper
sulfate
Lbs.
1,958,000
11,677,886
1,569,200
37,357,501
3.99
Zincsulfate
Lbs.
303,000
11,951,259
472,302
25,054,213
1.34
Sulfuricacid
Short
ton
43,306,000
1,348,209
5,629,496
683,588
3.90
Pyrite
(iron(III)ox
ide)
Short
ton
5,779,257
590,079
6,807,265
1,217,401
1.75
Zincox
ide
Short
ton
42,775,000
418,720
953,467
12,360
1.32
Sulfurousacid(liquid)
Short
ton
484,000
6,398
476,135
31,349
4.98
Tar,
pitch
Short
ton
14,391,000
499,591
800,862
61,100
2.20
Coaltar,
distilled
Gallon
3,332,000
20,176,706
22,704
577,750
4.20
Coaltar,
creosote
Gallon
9,986,000
54,697,349
17,546
288,817
3.01
Ammonia,aqua
Lbs.
2,000
24,251
839,820
20,983,476
2.06
Ammonia,sulfuricacid
Lbs.
61,706,000
619,377,702
3,675,771
127,982,211
3.47
Potash
fertilizer
Short
ton
57,547,000
1,114,482
15,438,167
1,089,495
3.64
Potassium
chloride
Short
ton
68,097,000
545,936
6,497,364
177,372
3.41
Continued
onnextpage...
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182 Missed Opportunities?
Table
A.8
–Continued
Pro
duct
Unit
GER
1909
US
1909
UVR
GO
Vol.
GO
Vol.
Potassium
sulfate
(over
42%
K2O,i.e.
potassium
oxide)
Short
ton
19,984,000
133,963
1,684,998
39,232
3.47
Kainite(patentkali)
Short
ton
5,083,000
69,385
3,939,263
448,885
8.35
Total
334,733,257
48,824,360
Coke
Short
ton
368,023,000
25,999,789
89,965,483
39,315,065
6.19
Tarandtarcompounds
Gallon
15,326,000
164,645,847
1,408,611
60,126,006
3.97
Ben
zine
Barrel
18,710,000
853,271
39,771,959
10,806,550
5.96
Kerosene
Barrel
3,797,000
173,848
94,547,010
33,495,798
7.74
Lube,
liquid
paraffin,
and
fuel
oils
Barrel
11,543,000
475,156
38,884,236
10,745,885
6.71
Alkaneoil,lignitetardistil-
lation
Barrel
3,989,000
281,209
9,473,975
3,239,230
4.85
Paraffin
Barrel
4,097,000
76,409
9,388,812
946,830
5.41
Ammonium
sulfate
(from
cokeov
ens)
Lbs.
420,000
4,027,846
3,227,316
123,111,197
3.98
Total
425,905,000
286,667,402
Tires
Number
78,076,000
1,283,000
125,780,035
15,928,722
7.71
Total
78,076,000
125,780,035
Leather,sole
Sides
194,683,000
8,618,321
306,476,720
35,610,504
2.62
Leather,upper
Sides
256,828,000
3,614,148
32,540,720
10,652,060
23.26
Leather,utensils
Sides
41,088,000
1,124,271
39,069,476
5,345,077
5.00
Leather,machinery
Sides
33,892,000
990,284
6,995,133
1,042,070
5.10
Leather,split
Sides
24,551,000
1,750,764
7,410,740
8,134,229
15.39
Total
551,042,000
392,492,789
Window
glass
50-footbox
es30,000,000
4,413,203
11,742,959
6,921,611
4.01
Continued
onnextpage...
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Appendix A. Data Appendix 183
Table
A.8
–Continued
Pro
duct
Unit
GER
1909
US
1909
UVR
GO
Vol.
GO
Vol.
Cem
ent
Barrel
125,857,000
37,546,400
52,797,973
65,399,889
4.15
Total
155,857,000
64,540,932
Pig
iron
Short
ton
633,541,000
12,540,434
391,429,283
25,651,798
3.31
Ingots
Short
ton
919,414,000
12,546,252
3,593,726
142,745
2.91
Steel
castings
Short
ton
41,334,000
147,784
38,862,448
504,856
3.63
Railway
-track
material
Short
ton
173,732,000
1,646,891
83,811,312
2,964,951
3.73
Beams
Short
ton
143,817,000
1,435,764
14,488,412
396,911
2.74
Ironbars
Short
ton
296,702,000
3,020,421
121,488,423
3,784,248
3.06
Hoops
Short
ton
39,922,000
336,802
10,429,681
341,043
3.88
Wirerods
Short
ton
101,148,000
954,239
61,947,958
2,295,279
3.93
Sheets
Short
ton
192,690,000
1,526,109
133,272,393
3,332,733
3.16
Tinplate
Lbs.
17,557,000
122,063,341
38,259,885
1,123,968,875
4.23
Tubes
Short
ton
108,572,000
434,975
75,109,011
1,386,605
4.61
Railway
rollingstock
Short
ton
52,733,000
263,880
3,831,344
102,348
5.34
Zinc
Short
ton
95,500,000
241,493
24,864,300
230,225
3.66
Lead
Short
ton
47,462,000
195,682
30,460,168
354,188
2.82
Silver
Troyounce
51,272,000
23,521,229
28,455,200
28,050,600
2.15
Gold
Troyounce
92,090,000
1,060,878
99,673,400
4,821,701
4.20
Copper
Lbs.
45,041,000
82,426,431
142,083,711
1,092,951,624
4.20
Tin
Short
ton
21,281,000
9,040
12,896
17
3.10
Total
3,073,808,000
1,302,073,551
Motorcycles
Number
2,246,797
3,703
2,985,866
18,496
3.76
Tricars
Number
2,050,586
936
30,122
132
9.60
Cars
Number
40,532,198
6,682
141,433,667
112,318
4.82
Trucks(deliverywagons)
Number
8,503,599
636
3,165,512
1,366
5.77
Continued
onnextpage...
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184 Missed Opportunities?
Table
A.8
–Continued
Pro
duct
Unit
GER
1909
US
1909
UVR
GO
Vol.
GO
Vol.
Engines
formotorb
oats
Number
1,378,789
1,739
294,152
2,796
7.54
Total
54,711,969
147,909,319
Sources:seesection2.3.
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Appendix A. Data Appendix 185
A.9
Unit-valuera
tiosGER09/GER36
Pro
duct
Unit
GER
1909
GER
1936
UVR
GO
Vol.
GO
Vol.
Mining
2,029,694,000
2,683,195,000
Coal
Metricton
1,530,224,000
146,964,199
1,687,606,000
158,282,800
1.02
Lignite
Metricton
155,141,000
66,682,500
396,629,000
161,396,700
1.06
Briquette
Metricton
70,429,000
5,151,849
97,573,000
6,044,300
1.18
Lignitebriquettes
Metricton
132,289,000
14,601,690
367,596,000
36,074,500
1.12
Ironore
Metricton
80,781,000
20,129,863
23,581,000
3,542,500
1.66
Copper
ore
Metricton
23,098,000
797,408
11,827,000
1,149,700
0.36
Pyrite
Metricton
1,579,000
213,282
2,903,000
286,200
1.37
Petroleum
Metricton
9,297,000
137,382
46,491,000
443,000
1.55
Potash
rock
Metricton
6,941,000
1,391,738
23,594,000
2,378,400
1.99
Bituminousrock
Metricton
642,000
76,964
666,000
108,800
0.73
Refi
ned
rock
salt
Metricton
19,273,000
634,399
24,729,000
580,900
1.40
Manufacturing
7,281,327,205
9,176,445,813
Potato
starch
Dz
41,739,000
2,343,500
27,485,000
1,058,000
1.46
Maizestarch
Dz
5,176,000
239,800
23,491,000
680,000
1.60
Maizestarch,sirup
Dz
13,044,000
563,968
17,429,000
475,000
1.59
Total
59,959,000
68,405,000
Woolencarded
Kilogram
234,332,000
92,777,466
274,356,000
86,436,000
1.26
Cottonyarn
Kilogram
750,014,000
401,119,094
580,316,600
283,882,000
1.09
Flax
Kilogram
69,157,000
35,368,751
57,008,677
19,078,000
1.53
Jute
Kilogram
85,886,000
146,978,945
62,567,000
113,527,000
0.94
Woolweaving
Kilogram
774,509,000
113,355,019
820,908,213
86,756,000
1.38
Continued
onnextpage...
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186 Missed Opportunities?
Table
A.9
–Continued
Pro
duct
Unit
GER
1909
GER
1936
UVR
GO
Vol.
GO
Vol.
Worstedcombed
Kilogram
410,817,000
104,310,667
413,667,000
59,519,000
1.76
Total
2,324,715,000
2,208,823,490
Paper
Metricton
460,000,000
1,611,000
278,090,904
1,158,326
0.84
Cardboard
Metricton
60,000,000
370,000
22,231,468
85,071
1.61
Total
520,000,000
300,322,372
Sulfuricacid
Metricton
43,306,000
1,223,075
26,413,000
1,087,114
0.69
Sphalerite
Metricton
42,775,000
379,856
606,770
11,904
0.45
Ammonia
sulphate
Metricton
420,000
1,827
80,506,935
535,471
0.65
Ammonia
water
Metricton
2,000
11
43,013,525
176,464
1.34
Ben
zene
Metricton
2,097,000
19,122
90,841,000
449,064
1.84
Toluol
Metricton
585,000
2,791
12,588,704
24,189
2.48
Phen
ol
Metricton
1,505,000
2,211
5,755,880
6,853
1.23
Coaltar
Metricton
3,332,000
91,520
112,232,513
2,972,765
1.04
Tar(pitch
)Metricton
14,391,000
453,221
24,838,815
637,826
1.23
Total
108,413,000
396,797,142
Kerosene
Metricton
3,797,000
26,025
1,529,827
12,425
0.84
Bezine
Metricton
18,710,000
118,050
138,668,237
752,705
1.16
Lubricatingoils
Metricton
11,543,000
75,431
78,310,098
400,835
1.28
Total
34,050,000
218,508,162
Tires,motorveh
icles
Number
78,076,000
1,283,000
105,701,691
3,333,215
0.52
Tires,bicycles
Number
45,584,000
15,066,000
29,413,626
21,745,000
0.45
Total
123,660,000
135,115,317
Sole
leather
Kilogram
194,683,000
70,741,274
151,708,073
51,079,000
1.08
Upper
leather
Kilogram
256,828,000
29,665,808
125,868,281
15,572,000
0.93
Continued
onnextpage...
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“prnt˙thesis” — 2014/1/22 — 12:20 — page 187 — #201�
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Appendix A. Data Appendix 187
Table
A.9
–Continued
Pro
duct
Unit
GER
1909
GER
1936
UVR
GO
Vol.
GO
Vol.
Leather
utensils
Kilogram
41,088,000
9,228,288
17,147,000
3,432,000
1.12
Total
492,599,000
294,723,354
Window
glass
Square
meter
30,000,000
20,500,000
28,453,701
24,770,453
0.78
Mirrorglass
Square
meter
22,000,000
1,700,000
19,645,020
1,986,097
0.76
Cem
ent(dry)
Metricton
121,917,000
5,867,088
256,220,000
11,612,582
1.06
Total
173,917,000
304,318,721
Pig
iron,foundry
Metricton
123,593,000
2,222,661
54,531,000
1,007,078
0.97
Pig
iron,Thomas
Metricton
367,685,000
6,985,507
541,885,000
10,363,123
0.99
Pig
iron,Martin
Metricton
83,350,000
1,202,215
150,364,000
2,650,799
0.82
Ingots:Thomas
Metricton
504,847,000
6,679,807
500,945,000
7,877,558
0.84
Ingots:Martin
(standard)
Metricton
356,039,000
4,313,673
803,779,000
10,395,653
0.94
Ironbars
Metricton
296,702,000
2,740,080
518,834,000
4,169,134
1.15
Hoops
Metricton
39,922,000
305,542
111,101,000
800,622
1.06
Wirerods
Metricton
101,148,000
865,671
142,932,000
1,175,248
1.04
Wheels
andaxes
Metricton
52,733,000
239,388
25,108,000
111,672
1.02
Forgings
Metricton
53,962,000
140,825
124,482,000
304,554
1.07
Scrap
Metricton
93,608,000
2,178,505
149,855,000
3,641,210
0.96
Cast
ironandsteel
Metricton
467,564,000
2,419,360
634,975,805
2,209,813
1.49
Railway
-track
materials
Metricton
173,732,000
1,494,034
75,975,558
224,572
2.91
Railstraps
Metricton
143,817,000
1,302,503
14,525,947
91,437
1.44
Sheets
(thick)
Metricton
94,899,000
768,789
45,179,885
311,752
1.17
Sheets
(thin)
Metricton
97,791,000
615,674
30,388,765
92,721
2.06
Matte
Metricton
639,000
1,266
26,866,000
82,752
0.64
Soft
lead
Metricton
42,289,000
161,985
27,475,000
120,253
0.88
Silver
Metricton
45,483,000
564
45,483,000
564
1.00
Continued
onnextpage...
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188 Missed Opportunities?
Table
A.9
–Continued
Pro
duct
Unit
GER
1909
GER
1936
UVR
GO
Vol.
GO
Vol.
Casted
copper
Metricton
20,039,000
16,152
101,230,000
75,510
1.08
Rolled
copper
Metricton
17,056,000
13,635
64,365,000
85,458
0.60
Gold
Metricton
92,090,000
33
61,989,000
22
1.01
Raw
zinc
Metricton
70,998,000
162,250
1,602,000
8,425
0.43
Tin
Metricton
21,281,000
8,201
6,278,000
2,372
1.02
Tin
ash
Metricton
21,000
55
4,139,000
14,589
0.74
Nickel
Metricton
11,297,000
3,779
13,308,000
5,427
0.82
Total
3,372,585,000
4,277,596,960
Aircraft
engines
Units
173,753
20
2,199,034
193
1.31
Motorcycleen
gines
Units
161,000
733
3,496,933
23,837
0.67
Caren
gines
Units
1,217,789
1,006
21,120,479
25,675
0.68
Total
1,552,543
26,816,446
Motorcycles
Units
2,246,797
3,703
82,123,390
140,844
0.96
Cars
Units
40,532,198
6,682
514,588,459
205,713
0.41
Goodsveh
icles
Units
8,503,599
636
166,087,000
30,739
0.40
Chasis
Units
18,594,069
2,126
182,220,000
30,810
0.68
Total
69,876,662
945,018,849
Sources:seesection2.3.
�
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“prnt˙thesis” — 2014/1/22 — 12:20 — page 189 — #203�
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Appendix A. Data Appendix 189
A.10
Unit-valuera
tiosGER36/US35
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Mining
1,739,485,000
775,471,000
Coal
Short
ton
1,687,606,000
174,476,921
658,063,000
372,373,000
5.47
Ironore
Longton
23,581,000
3,486,552
83,035,000
33,426,000
2.72
Pyrite
Longton
2,903,000
281,680
1,583,000
514,192
3.35
Bituminousrock
Longton
666,000
107,082
10,952,000
3,042,000
1.73
Refi
ned
rock
salt:table
salt
Short
ton
24,729,000
640,333
21,838,000
7,927,000
14.02
Manufacturing
22,665,097,391
15,644,790,417
Flour:
rye
Metricton
380,910,000
1,625,600
5,914,056
134,168
5.32
Flour:
wheat
Metricton
851,830,000
2,773,200
664,567,583
9,097,311
4.20
Bread
Metricton
256,971,000
685,300
706,897,740
4,221,847
2.24
Biscu
its
Metricton
126,104,000
87,300
179,601,710
600,123
4.83
Cocao:pow
dered
Metricton
31,146,000
18,500
10,240,865
57,356
9.43
Chocolate:bars
andblocks
Metricton
157,313,000
73,800
43,937,504
123,467
5.99
Confectionery:ch
ocolate
Metricton
103,865,000
46,900
120,619,679
325,750
5.98
Confectionery:hard
candy,
caramel,etc.
Metricton
83,673,000
67,200
66,914,834
294,558
5.48
Curedfish:herring
Metricton
133,783,000
178,584
1,614,018
8,364
3.88
Curedmeat:bacon,sm
oked
-
andpickledpork
Metricton
107,341,000
64,071
393,507,389
892,718
3.80
Curedmeat:
cooked
hams
Metricton
93,567,000
40,509
35,581,320
49,132
3.19
Cans:
soup
Metricton
31,376,000
24,300
47,208,754
312,780
8.55
Sausages
andmeatpuddings
Metricton
126,301,000
45,217
233,077,321
613,847
7.36
Continued
onnextpage...
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190 Missed Opportunities?
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Fresh
meat:
beefandveal
Metricton
113,479,000
56,440
655,532,197
2,453,131
7.52
Starch:potato
Metricton
27,485,000
105,800
883,315
15,321
4.51
Starch:corn
Metricton
23,491,000
68,000
26,637,504
343,043
4.45
Syrup:corn
Metricton
17,429,000
47,500
31,252,948
452,313
5.31
Anim
alfeed
s:poultry,cat-
tle,
andpigs
Metricton
148,974,000
691,100
222,698,070
5,644,475
5.46
Sugar:
unrefined
Metricton
350,647,000
999,900
16,297,431
243,996
5.25
Sugar:
refined
Metricton
673,696,000
1,527,700
95,916,815
1,150,737
5.29
Milk:
conden
sed,
evapo-
rated,andpow
dered
Metricton
87,298,000
92,311
152,656,076
1,253,180
7.76
Margarine
Metricton
320,650,000
437,079
47,256,720
176,399
2.74
Beer
Liters
873,305,000
3,008,500,000
336,490,207
3,815,434,600
3.29
Malt
Metricton
134,692,000
357,870
205,618,081
915,138
1.68
Brandy
Liters
62,491,000
21,836,600
6,299,741
21,227,336
9.64
Spirit,rectified
Liters
14,174,000
24,692,000
8,671,744
29,963,771
1.98
Total
5,331,991,000
4,315,893,622
Cigarettes
Thousands
655,083,000
38,470,701
723,249,455
139,903,223
3.29
Total
655,083,000
723,249,455
Yarn:cotton,single
Metricton
580,317,000
283,882
133,568,824
176,918
2.71
Yarn:cotton,mixed
Metricton
18,216,000
7,180
2,667,574
1,553
1.48
Woven
products:
cotton
Metricton
977,585,000
228,471
561,231,966
712,534
5.43
Yarn:wool,mohair,etc.
Metricton
1,508,931,000
232,711
101,595,298
44,428
2.84
Yarn:rayon
Metricton
222,251,000
51,537
3,198,407
1,452
1.96
Woven
products:
silk
Metricton
332,819,000
23,685
77,417,722
13,384
2.43
Yarn:flax
Metricton
51,856,000
16,576
778,756
706
2.83
Continued
onnextpage...
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Appendix A. Data Appendix 191
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Upholstery
filling:jute
and
flax
Metricton
5,152,000
2,502
22,143,047
97,133
9.03
Yarn:jute
Metricton
36,163,000
73,762
2,616,723
10,077
1.89
Yarn:jute
andflax,mixed
Metricton
26,404,000
39,765
3,692,084
13,294
2.39
Knittedfabric:
wool
Metricton
41,759,000
5,417
10,649,845
3,796
2.75
Hosiery:cotton,wool,
silk
andartificialsilk
Pairs
267,546,000
423,240,000
262,674,297
1,052,064,444
2.53
Gloves:
cotton,
wool,
silk
andartificialsilk
Pairs
47,266,000
60,000,000
6,470,974
13,933,728
1.70
Total
4,116,265,000
1,188,705,517
Suits
and
uniform
s:men
’s
andboy
s’
Number
161,297,000
5,291,251
330,087,375
23,576,405
2.18
Overcoats:men
’sandboy
s’Number
113,203,000
3,614,252
78,791,685
6,248,793
2.48
Dressinggow
nsandrobes
Number
24,175,000
1,780,489
21,478,983
6,457,057
4.08
Pants
and
trousers:men
’s
andboy
s’
Number
54,964,345
9,823,211
59,547,289
46,753,141
4.39
Workers
apparel,
aprons:
men
’sandboy
s’
Number
74,705,000
21,737,191
68,885,890
110,258,472
5.50
Coats:
women
’sand
chil-
drens’
Number
189,222,000
7,684,283
153,503,872
15,016,375
2.41
Suits
and
ensembles:
women
’sandch
ildren’s
Number
30,326,000
1,079,399
85,822,242
9,703,145
3.18
Dresses:women
’sand
chil-
dren’s
Number
118,811,000
8,096,232
517,467,190
209,438,322
5.94
Undergarm
ents:men
’sand
boy
s’
Number
91,984,000
25,358,419
147,567,857
225,434,508
5.54
Continued
onnextpage...
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192 Missed Opportunities?
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Hats
andcaps
Number
63,034,000
26,087,008
175,087,243
233,255,796
3.22
Total
921,721,345
1,638,239,626
Lumber:softwoodandhard-
wood
Cubic
meter
547,776,017
10,825,260
399,182,036
46,106,271
5.84
Dressed
lumber
Cubic
meter
104,307,738
1,447,408
225,766,751
23,617,635
7.54
Total
652,083,755
624,948,787
Woodpulp:mechanical
Metricton
75,418,800
878,317
24,283,573
1,203,407
4.26
Woodpulp:fiber,bleached
Metricton
66,398,972
338,830
24,852,071
457,653
3.61
Wood
pulp:
fiber,
un-
bleached
Metricton
112,024,430
722,815
63,222,883
2,016,208
4.94
Paper:new
sprint
Metricton
79,424,525
473,041
33,353,967
859,754
4.33
Paper:
free
from
ground
wood
Metricton
92,210,992
216,794
86,829,954
868,704
4.26
Paper:
containing
ground
wood
Metricton
106,455,387
468,491
14,717,265
205,189
3.17
Paper:wrapping
Metricton
114,297,519
557,287
103,893,419
1,304,077
2.57
Paper:glassine
Metricton
40,000,295
94,321
8,011,249
33,880
1.79
Cardboard:raw
Metricton
22,231,468
85,071
8,768,545
93,488
2.79
Paper:ch
romo
Metricton
21,137,000
49,413
2,705,905
39,381
6.23
Paper:waxing
Metricton
13,165,000
18,959
5,217,626
41,856
5.57
Paper:parchment
Metricton
12,566,000
18,335
1,812,675
12,847
4.86
Total
755,330,388
377,669,132
Acid:sulphuric
Metricton
26,413,190
959,907
31,907,994
2,542,961
2.19
Sulphate:sodium
Metricton
3,849,000
122,528
4,262,546
251,021
1.85
Sodium
silicate,solid
Metricton
1,629,000
21,165
1,066,387
27,702
2.00
Continued
onnextpage...
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Appendix A. Data Appendix 193
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Sodium
silicate,liquid
Metricton
6,556,000
62,697
6,607,204
142,579
2.26
Acid:boric
Metricton
2,526,000
5,348
1,245,874
13,035
4.94
Acid:tartaric
Metricton
4,871,000
4,161
1,609,027
3,124
2.27
Acid:citric
Metricton
1,291,000
980
2,768,377
4,760
2.26
Sodium
carb
onate
Metricton
56,171,000
706,105
31,357,132
1,733,697
4.40
Sodium
bicarb
onate
Metricton
2,163,000
25,453
3,658,321
123,882
2.88
Chloride:
ammonium
Metricton
7,622,000
46,354
1,583,613
15,814
1.64
Hydroxide:
sodium
Metricton
19,580,000
276,827
28,134,175
653,364
1.64
Hydroxide:
potassium
Metricton
12,139,000
61,343
1,260,031
8,635
1.36
Chlorine
Metricton
17,127,000
137,261
7,961,186
188,132
2.95
Hydrogen
gas
Metricton
7,775,000
2,817,000
1,556,658
1,494,959
2.65
Carb
onbisulphide
Metricton
6,939,000
40,038
3,384,851
53,414
2.73
Ammonia
Metricton
47,789,000
259,331
6,914,878
73,797
1.97
Acid:nitric
Metricton
10,797,000
64,782
2,142,817
22,226
1.73
Fertilizer:
nitrogen
ous
Metricton
110,804,000
1,140,718
93,091,644
3,811,610
3.98
Fertilizer:
superphosphate
Metricton
30,571,000
743,250
19,778,461
1,758,243
3.66
Acid:phosphoric
Metricton
14,226,000
36,416
1,333,702
10,293
3.02
Calcium
carbide
Metricton
73,070,000
412,750
6,234,380
133,440
3.79
Methanol
Metricton
16,437,000
50,384
5,075,683
56,817
3.65
Butanol
Metricton
6,146,000
6,478
2,601,983
16,274
5.93
Acetone
Metricton
4,517,000
5,603
2,642,149
31,262
9.54
Acetate:ethyl
Metricton
3,393,000
5,895
2,679,195
18,890
4.06
Ether
Metricton
4,543,000
4,664
1,305,459
3,590
2.68
Drugs:
ephed
rine
Metricton
1,040,000
5153,040
57.80
Sodium
bisulphite
Metricton
6,735,000
8,644
2,650,638
6,839
2.01
Acid:boric
Metricton
1,124,000
2,746
1,245,874
13,035
4.28
Continued
onnextpage...
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194 Missed Opportunities?
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Sodium
borate
(borax)
Metricton
3,212,000
17,182
3,693,129
96,280
4.87
Sulphate:aluminum
Metricton
7,868,000
92,785
8,007,782
320,434
3.39
Chloride:
aluminum
Metricton
2,439,000
2,893
397,295
2,122
4.50
Sulphate:copper
Metricton
3,706,000
13,986
2,002,099
24,838
3.29
Vitreousen
amels
Metricton
1,688,000
2,417
4,399,947
33,293
5.28
Cellulose
acetate
Metricton
20,267,000
6,978
7,986,489
4,715
1.71
Acid:acetic
Metricton
5,252,000
6,506
5,455,362
46,040
6.81
Explosive:
nitroglycerin
Metricton
24,335,000
8,752
818,748
1,126
3.83
Paint:
whitelead
Metricton
13,107,000
32,000
12,937,249
76,943
2.44
Paint:
zincox
ide
Metricton
1,920,000
3,485
143,819
566
2.17
Paint:
chem
icalcolors
Metricton
15,754,000
20,116
9,812,458
38,349
3.06
Varnish:non-cellulose
Metricton
80,794,000
88,040
50,025,338
215,428
3.95
Varnish:cellulose
Metricton
40,039,000
19,523
38,252,340
95,706
5.13
Linseed
oil
Metricton
24,854,000
67,596
43,271,858
219,097
1.86
Soap:toilet
Metricton
49,792,000
39,257
53,324,747
160,107
3.81
Soap:bar
Metricton
55,086,000
112,018
51,340,273
514,403
4.93
Soap:pow
der
Metricton
143,905,000
234,945
60,717,053
433,522
4.37
Soap:flakes
andliquid
Metricton
27,671,000
72,441
44,686,244
259,629
2.22
Total
1,029,532,190
673,485,509
Sulphate:ammonium
Metricton
4,313,000
94,060
2,073,258
87,298
1.93
Lubricatingoils
Metricton
78,310,098
400,835
186,533,605
4,046,171
4.24
Coke
Metricton
551,493,000
37,656,197
23,283,543
3,020,417
1.90
Total
634,116,098
211,890,406
Tires:passen
ger
car
Number
48,832,656
2,301,623
221,555,480
42,479,133
4.07
Tires:trucksandbus
Number
51,536,061
433,103
99,852,136
6,003,338
7.15
Continued
onnextpage...
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Appendix A. Data Appendix 195
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Tires:cycleandmotor
Number
34,746,600
22,341,489
3,418,907
4,006,476
1.82
Total
135,115,317
324,826,523
Sole
leather:tanned
Metricton
184,629,073
56,312
89,678,756
156,956
5.74
Upper
leather:cattle
Metricton
18,426,000
4,537
51,928,960
66,816
5.23
Harness
and
saddlery
leather
Metricton
17,147,000
3,432
4,844,697
7,866
8.11
Gloves:leather,men
’sand
boy
s’
Pairs
12,054,126
3,056,112
14,576,068
21,935,256
5.94
Gloves:
leather,
women
’s
andch
ildren’s
Pairs
20,268,864
6,008,820
9,543,121
9,419,004
3.33
Belts:leather
Number
13,695,189
10,529,189
9,384,052
50,258,670
6.97
Boots
andshoes:leather
Pairs
543,218,000
79,574,000
593,391,703
330,714,911
3.80
Total
809,438,252
773,347,357
Asb
estos:
millboard
Metricton
2,726,000
6,036
398,934
3,846
4.35
Asb
estos:
yarn
Metricton
2,372,000
1,294
1,843,979
3,647
3.63
Asb
estos:
textiles
Metricton
703,000
398
2,700,488
5,902
3.86
Bricks:
common
Number
266,870,000
9,021,000,000
25,249,116
2,283,928,000
2.68
Tiles:floor
Metricton
25,875,000
180,292
1,083,771
21,411
2.84
Tiles:wall
Metricton
32,288,000
128,293
7,959,499
47,098
1.49
Cem
ent
Metricton
256,220,000
11,612,582
113,504,670
12,831,997
2.49
Glass:window
Square
meters
28,453,000
24,770,453
18,180,053
39,849,677
2.52
Glass:globes
Metricton
11,914,000
14,227
494,516
5,251
8.89
Bottles:
beer
Metricton
29,057,737
150,566
3,707,702
82,388
4.29
Bottles:
transparent
Metricton
8,159,200
30,912
29,355,858
509,436
4.58
Bottles:
med
icinaland
per-
fumery
Metricton
25,096,000
61,809
30,345,232
355,348
4.75
Continued
onnextpage...
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196 Missed Opportunities?
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Bottles:
bottlesandjars
Metricton
16,096,000
63,934
34,806,621
382,334
2.77
Total
705,829,937
269,630,439
Pig-iron
Metricton
788,898,000
14,779,136
314,164,787
20,887,926
3.55
Steel:ingots,blooms,billets,
andslabs
Metricton
1,304,724,000
18,273,211
143,505,275
4,339,025
2.16
Steel:
sheet
and
tin-plate
bars
Metricton
406,739,000
4,621,549
75,799,384
2,607,089
3.03
Steel:bars
Metricton
518,834,000
4,169,134
155,641,438
2,896,416
2.32
Wirerods
Metricton
142,932,000
1,175,248
35,980,306
834,644
2.82
Iron
and
steel:
flats,strips
andhoops
Metricton
267,763,654
1,318,708
86,657,854
1,598,814
3.75
Steel:platesandsheets
Metricton
189,058,000
1,545,000
288,156,378
5,096,508
2.16
Ironandsteel:scrap
Metricton
149,855,000
3,641,210
17,315,365
1,394,006
3.31
Ironandsteel:pipes
Metricton
66,246,798
445,335
25,185,189
556,765
3.29
Steel:castings
Metricton
56,092,156
119,299
67,441,571
367,389
2.56
Iron:castings
Metricton
63,359,784
105,500
50,540,532
393,712
4.68
Wireproducts,
ferrous:
ca-
bles,
ropeandstrands
Metricton
37,353,542
58,936
29,717,968
91,822
1.96
Wire
products,
ferrous:
drawnwire,
plain
Metricton
125,538,042
563,360
74,598,302
941,414
2.81
Ingots
andpigs:
lead
Metricton
27,475,000
120,253
11,428,581
117,491
2.35
Ingots
andpigs:
copper
Metricton
27,283,000
50,283
4,935,130
26,563
2.92
Ingots
andpigs:
zinc
Metricton
1,602,000
8,425
3,044,215
28,175
1.76
Ingots
andpigs:
tin
Metricton
6,278,000
2,372
2,756,568
2,532
2.43
Ingots
andpigs:
solder,soft
Metricton
3,253,000
2,750
18,729,891
39,583
2.50
Continued
onnextpage...
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Appendix A. Data Appendix 197
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Plates,
sheets
and
tubes:
nickel
andnickel
alloy
s
Metricton
13,308,000
5,427
17,541,193
23,848
3.33
Ingots
and
pigs:
aluminum
andaluminum
alloy
s
Metricton
201,863,000
76,162
16,315,105
44,213
7.18
Plates,
sheets
and
tubes:
copper
Metricton
64,365,000
85,458
73,959,607
272,639
2.78
Plates,
sheets
and
tubes:
brass
andbronze
Metricton
210,088,000
244,931
144,332,364
453,110
2.69
Castings:
magnesium
alloy,
elektron
Metricton
20,643,000
3,706
1,748,977
1,242
3.95
Ingots
and
pigs:
white-base
alloy
s
Metricton
4,254,000
3,274
5,164,519
10,967
2.76
Platesandsheets:zinc
Metricton
23,677,000
73,861
8,130,743
45,297
1.79
Castings:
aluminum
and
aluminum
alloy
s
Metricton
88,449,000
26,743
32,428,531
42,175
4.30
Castings:
copper
Metricton
101,230,000
75,510
1,414,823
3,032
2.87
Total
4,911,161,976
1,706,634,596
Wire
products,
ferrous:
barb
edwire
Metricton
16,963,002
81,768
10,772,272
177,805
3.42
Wireproducts,
ferrous:
wire
netting
Metricton
39,627,465
94,395
42,498,252
372,648
3.68
Iron
and
steel:
machine
screws
Metricton
36,681,739
53,853
7,379,550
21,221
1.96
Wireproducts,
ferrous:
cut
andwirenails
Metricton
34,942,050
135,694
30,621,003
434,560
3.65
Tools:filesandrasps
Metricton
17,628,184
6,863
8,522,655
20,588
6.20
Continued
onnextpage...
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198 Missed Opportunities?
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Razorblades
Number
8,441,995
101,982,755
17,733,193
1,241,383,291
5.79
Ironandsteel:woodscrews
Metricton
37,587,390
78,813
4,995,807
17,091
1.63
Wire
products,
ferrous:
other
chains
Metricton
11,378,699
28,586
4,926,322
23,698
1.91
Metalfoils:
other
thangold
foil
Metricton
40,458,000
10,194
14,216,043
28,848
8.05
Total
243,708,524
141,665,097
Business
machines:
type-
writers
Metricton
58,115,000
4,729
19,674,453
6,540
4.08
Pipe
and
fittings:
gas
and
water
Metricton
43,359,000
54,105
6,269,736
55,141
7.05
Electricalmach
ines:va
cuum
cleaners
Number
33,237,000
651,304
22,635,103
871,934
1.97
Total
134,711,000
48,579,292
Electricity
meters
Number
35,135,000
1,649,575
11,832,835
1,107,895
1.99
Radio
apparatus:
receiving
sets
Number
106,548,000
1,292,600
129,109,032
5,569,562
3.56
Incandescent
light
bulb:
large
Number
62,926,000
91,368,000
51,046,338
387,914,279
5.23
Incandescent
light
bulb:
small
Number
6,611,000
70,816,000
9,744,816
241,779,372
2.32
Incandescentlightbulb:car-
bon
Number
4,021,000
4,503,000
189,997
1,030,546
4.84
Total
215,241,000
201,923,018
Railway
wheels
andaxles
Metricton
25,108,000
111,672
10,951,005
113,878
2.34
Continued
onnextpage...
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Appendix A. Data Appendix 199
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Motorveh
icle
parts:
veh
icle
chains
Metricton
20,543,284
17,989
7,258,405
30,039
4.73
Railway
material
Metricton
75,975,558
224,572
39,111,888
890,674
7.70
Vessels:steam
Gross
tonnage
65,312,088
130,120
37,610,218
168,727
2.25
Vessels:motor
Gross
tonnage
89,943,056
237,913
19,147,362
71,568
1.41
Vessels:lighters,scow
sand
barges
Gross
tonnage
4,787,154
39,088
7,714,131
131,146
2.08
Bicycles
Number
63,732,944
1,249,947
12,059,867
656,828
2.78
Motorscooters
Number
16,734,000
65,350
482,348
4,896
2.60
Priva
tecars
Number
514,588,459
205,713
1,752,794,114
3,212,835
4.59
Trucks:
capacity
exceed
ing
1.5
short
tons(30cw
ts.)
Number
166,087,000
30,739
43,808,889
34,814
4.29
Trailers
Number
65,238,066
22,059
15,918,524
22,951
4.26
Bodies:
cars
Number
84,702,000
81,687
351,682,014
2,083,205
6.14
Bodies:
busses
andtrucks
Number
42,789,000
28,150
39,479,304
200,863
7.73
Railway
equipmen
t:locomo-
tives
Metricton
69,551,000
53,366
20,245,112
25,425
1.64
Railway
equipmen
t:car-
riages
Number
37,237,000
691
4,318,363
142
1.77
Railway
equipmen
t:freight
cars
Number
23,706,000
4,132
20,990,050
8,805
2.41
Trams
Number
2,097,000
132
3,233,346
229
1.13
Engines:airplanes
Number
2,199,000
193
15,661,237
4,119
3.00
Motor-veh
icle
parts:
alu-
minum
and
aluminum
alloy
s
Metricton
7,724,000
2,202
13,400,209
18,890
4.94
Continued
onnextpage...
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200 Missed Opportunities?
Table
A.10–Continued
Pro
duct
Unit
GER
1936
US
1935
UVR
GO
Vol.
GO
Vol.
Total
1,378,054,609
2,415,866,386
Watches:pocket
Number
7,340,000
2,723,165
2,364,350
3,996,654
4.56
Watches:wrist
Number
28,374,000
3,923,095
5,871,305
4,615,406
5.69
Total
35,714,000
8,235,655
Sources:seesection2.3.
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Appendix A. Data Appendix 201
A.11 Value added (per employee) pre-WW1 Ger-
many
Description VA VA/GO LP
FOOD AND DRINKS (184,139,574) 0.37 1,128
Brewing . . . . . . . . .
Raw sugar . . . . . . . . .
Glucose (starch) . . . . . . . . .
Starch 33,166,000 0.37 5,308
TOBACCO MANUFACTURES . . . . . . . . .
Tobacco . . . . . . . . .
TEXTILES (766,327,839) 0.36 (2,330)
Cotton spinning 225,433,762 0.32 2,301
Silk spinning . . . . . . . . .
Woolen (worsted) . . . . . . . . .
Jute 21,418,368 0.27 1,665
Linen 53,425,129 0.82 3,250
PAPER 556,000,000 0.74 5,903
Paper, cardboard, and wood pulp 556,000,000 0.74 5,903
CHEMICALS 108,830,924 0.42 5,048
Sulfuric acid 36,861,257 0.40 6,356
Coal-tar distillations 14,648,000 0.37 5,325
Lignite-tar distillations 8,150,000 0.68 1,127
Potash 49,171,667 0.55 1,286
PETROLEUM REF. & COKE (145,477,672) 0.29 (5,632)
Petroleum refining . . . . . . . . .
Coke 131,655,000 0.30 5,420
RUBBER . . . . . . . . .
Tires . . . . . . . . .
LEATHER 139,551,000 0.21 3,264
Leather tanning and dressing 139,551,000 0.21 3,264
STONE, CLAY, AND GLASS (365,391,450) 0.91 (3,199)
Cement 115,018,000 0.91 5,138
Glass . . . . . . . . .
PRIMARY METALS (1,259,417,314) 0.30 (3,867)
Cast iron and steel 303,941,000 0.65 2,430
Pig iron, wrought iron, ingots, and
rolling works
915,533,000 0.28 5,152
Zinc 37,010,000 0.37 3,122
Lead, silver, and copper 27,855,000 0.11 2,885
Tin 2,914,000 0.13 6,046
Nickel, cobalt, bismuth, and arsenic . . . . . . . . .
Continued on next page. . .
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202 Missed Opportunities?
Table A.11 – Continued
TRANSPORTATION EQUIPMENT 40,590,000 0.51 2,112
Motor vehicles 40,590,000 0.51 2,112
Notes to table A.11:
Estimates of value added and value added per employee that are derived on the basis of an
industry’s average value-added/gross-output ratio are in parentheses. Only in case value added
for some of the underlying 3-digit industries is unobtainable.
Sources: see section 2.3.
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Appendix A. Data Appendix 203
A.12 Value added (per employee) interwar Germany
Description VA VA/GO LP
FOOD AND DRINKS 1,404,606,000 0.49 8,197
Brewing 1,020,115,000 0.55 9,685
Sugar 330,696,000 0.29 5,494
Starch 53,795,000 0.49 9,230
TOBACCO MANUFACTURES 702,719,000 0.61 4,554
Tobacco 702,719,000 0.61 4,554
TEXTILES 696,231,000 0.42 3,200
Cotton spinning 319,188,000 0.39 2,925
Artificial silk spinning 166,405,000 0.60 4,503
Woolen (worsted) 156,513,000 0.37 3,243
Jute 29,387,000 0.44 2,689
Linen 24,738,000 0.42 2,016
PAPER 315,198,000 0.38 4,132
Paper, cardboard, and wood pulp 315,198,000 0.38 4,132
CHEMICALS 98,039,000 0.35 6,672
Sulfuric acid 39,220,000 0.58 7,860
Coal-tar distillations 58,819,000 0.27 6,062
Metal salts, chemicals 66,150,000 0.44 7,382
PETROLEUM REF. & COKE 265,697,000 0.25 6,704
Petroleum refining 111,512,000 0.30 6,931
Coke 154,185,000 0.22 6,550
RUBBER 98,617,000 0.50 6,628
Tires 98,617,000 0.50 6,628
LEATHER 270,437,000 0.44 6,044
Leather tanning and dressing 270,437,000 0.44 6,044
STONE, CLAY, AND GLASS 342,779,000 0.64 4,287
Cement 152,882,000 0.57 7,633
Glass 189,897,000 0.71 3,168
PRIMARY METALS 1,912,360,000 0.30 5,104
Cast iron and steel 592,893,000 0.67 3,900
Pig iron (blast furnaces) 212,657,000 0.25 7,734
Wrought iron 797,000 0.33 3,527
Ingots 304,582,000 0.20 6,585
Rolling works 722,006,000 0.28 5,482
Zinc 14,954,000 0.39 3,285
Copper refining 15,057,000 0.08 8,250
Copper, lead, and silver 28,765,000 0.16 3,509
Gold and silver refining 11,461,000 0.09 16,350
Tin 4,055,000 0.26 5,695
Continued on next page. . .
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204 Missed Opportunities?
Table A.12 – Continued
Description VA VA/GO LP
Nickel, cobalt 5,133,000 0.27 4,950
TRANSPORTATION EQUIPMENT 1,302,448,000 0.50 4,579
Motor vehicles 635,272,000 0.44 5,710
Trailers 140,469,000 0.53 3,697
Aircraft engines 157,751,000 0.57 4,489
Aircraft 368,956,000 0.60 3,687
Sources: Reichsamt fur Wehrwirtschaftliche Planung, Die Deutsche Industrie 1936.
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Appendix A. Data Appendix 205
A.13 Value added (per employee) pre-WW1 US
Description VA VA/GO LP
FOOD AND DRINKS 312,558,521 0.67 3,692
Brewing 278,134,000 0.74 4,168
Sugar 30,183,107 0.38 2,292
Starch 4,241,414 0.27 2,203
TOBACCO MANUFACTURES 239,509,000 0.57 1,212
Tobacco 239,509,000 0.57 1,212
TEXTILES 236,956,506 0.29 771
Cotton: yarns and threads 123,060,703 0.25 702
Silk: throwing and winding mills 9,058,076 0.53 513
Woolen: worsted goods 104,837,727 0.34 916
Jute . . . . . . . . .
Linen . . . . . . . . .
PAPER 102,214,623 0.38 1,255
Paper, cardboard, and wood pulp 102,214,623 0.38 1,255
CHEMICALS 4,779,921 0.45 1,737
Sulfuric acid 4,498,229 0.46 1,742
Coal-tar distillations 281,692 0.34 1,657
PETROLEUM REF. & COKE 69,396,352 0.21 1,450
Petroleum refining 37,724,257 0.16 2,267
Coke 31,672,095 0.33 1,014
RUBBER 46,243,926 0.36 1,478
Tires 46,243,926 0.36 1,478
LEATHER 79,595,254 0.24 1,186
Leather products 79,595,254 0.24 1,186
STONE, CLAY, AND GLASS 93,837,368 0.60 919
Cement 33,861,664 0.54 1,147
Glass 59,975,704 0.65 826
PRIMARY METALS 500,357,782 0.24 1,381
Pig iron 70,791,394 0.18 1,644
Steel works and rolling mills 328,221,678 0.33 1,259
Wire 23,943,587 0.28 1,200
Tin plate and terneplate 6,080,211 0.13 1,040
Zinc: smelting and refining 8,975,893 0.26 1,254
Lead: smelting and refining 15,442,628 0.09 1,916
Copper: smelting and refining 45,274,336 0.12 2,690
Gold and silver refining 1,628,055 0.07 2,360
TRANSPORTATION EQUIPMENT 123,172,337 0.47 1,363
Motor vehicles 123,172,337 0.47 1,363
Sources: United States Department of Commerce: Bureau of Foreign andDomestic Commerce, Statistical Abstract of the United States, 1913.
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206 Missed Opportunities?
A.14 Value added (per employee) pre-WW1 UK
Description VA VA/GO LP
FOOD AND DRINKS 44,512,000 0.61 487
Brewing 41,221,000 0.27 485
Sugar & molasses 3,291,000 0.56 506
TOBACCO MANUFACTURES 5,817,000 0.24 155
Tobacco 5,817,000 0.24 155
TEXTILES 56,221,000 0.27 74
Cotton 45,007,000 0.26 79
Silk 1,762,000 0.34 55
Jute, hemp, and linen 9,452,000 0.29 61
PAPER 4,542,000 0.33 111
Paper and allied 4,542,000 0.33 111
CHEMICALS 9,568,000 0.40 183
Chemicals, coal-tar products and
drugs
9,568,000 0.40 183
PETROLEUM REF. & COKE 4,037,000 0.29 254
Shale oil 777,000 0.33 229
Coke 2,993,000 0.30 273
Manufactured fuels 267,000 0.22 174
RUBBER 2,976,000 0.33 124
India rubber 2,976,000 0.33 124
LEATHER 3,385,000 0.19 117
Leather tanning and dressing 3,385,000 0.19 117
STONE, CLAY, AND GLASS 1,955,000 0.52 132
Cement 1,955,000 0.52 132
PRIMARY METALS 49,679,000 0.22 109
Iron and steel 30,048,000 0.29 115
Wrought iron and steel tubing 2,189,000 0.33 108
Wire 2,120,000 0.32 116
Tin plate 2,009,000 0.22 97
Lead, tin, and zinc 1,097,000 0.12 133
Copper and brass 2,930,000 0.17 137
Gold and silver refining 431,000 0.01 197
Anchor, chain, nail, bolts, etc. 2,314,000 0.41 83
Galvanized sheet, hardware, etc. 6,541,000 0.41 87
TRANSPORTATION EQUIPMENT 5,901,000 0.51 109
Motor vehicles 5,901,000 0.51 109
Sources: Board of Trade, UK Census of Production 1907.
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6. Berlin: Verlag von Reimar Hobbing, 1929.
. “Industrielle Produktionsstatistik. Sammlung von Ergebnissen der
Produktions- und Vorratsstatistik bis Mitte 1934.” In Sonderhefte zu Wirtschaft
und Statistik. No. 13. Berlin: Verlag von Reimar Hobbing, 1934.
223
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224 Missed Opportunities?
Reichsamt fur Wehrwirtschaftliche Planung. Die deutsche Industrie. Gesamtergeb-
nisse der amtlichen Productionsstatistik. Schriftenreiche des Reichsamt fur
wehrwritschaftliche Planung, Heft 1. Berlin: Verlag fur Sozialpolitik, Wirtschaft
und Statistik, 1939.
Statistik des Deutschen Reichs. “Gewerbliche Betriebszahlung.” In Volks-, Berugs- und
Betriebszahlung vom 1933. Berlin: Verlag fur Sozialpolitik, Wirtschaft und Statis-
tik, 1933.
United States Department of Commerce: Bureau of the Census. Thirteenth Census
of the United States Taken in the Year 1910. Washington D.C.: United States
Government Printing Office, 1913.
. Abstract of the Census of Manufactures 1914. Washington D.C.: United States
Government Printing Office, 1917.
. Fourteenth Census of the United States Taken in the Year 1920. Washington
D.C.: United States Government Printing Office, 1923.
. Fifteenth Decennial Census of the United States: Manufactures. Washington
D.C.: United States Government Printing Office, 1933.
. Biennial Census of Manufactures 1935. Washington D.C.: United States Gov-
ernment Printing Office, 1938.
. Sixteenth Decennial Census of the United States: Manufactures. Washington
D.C.: United States Government Printing Office, 1942.
. Sixteenth Decennial Census of the United States: Manufactures. Washington
D.C.: United States Government Printing Office, 1942.
. Census of Manufactures 1947. Washington: United States Government Printing
Office, 1949.
United States Department of the Interior. United States Geological Survey: Mineral
Resources of the United States 1910. Washington D.C.: United States Government
Printing Office, 1911.
Verhandlungen und Berichte des Unterausschusses fur allgemeine Wirtschaftsstruk-
tur. “Die deutsche Zellstof-, Holzschliff-, Papier- und Pappenindustries.” In Auss-
chuss zur Untersuchung der Erzeugungs- und Absatzbedingungen der deutschen
Wirtschaft. Berlin: Verlag E.S. Mittler & Sohn, 1931.
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. “Die deutsche Glasindustrie.” In Ausschuss zur Untersuchung der Erzeugungs-
und Absatzbedingungen der deutschen Wirtschaft. Berlin: Verlag E.S. Mittler &
Sohn, 1931.
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Nederlandse samenvatting
Na 1870 bracht een groeispurt van het welvaartsniveau, gemeten in bruto binnenlands
product per hoofd van de bevolking, de Verenigde Staten (VS) aan de vooravond van de
twintigste eeuw op gelijke hoogte met het Verenigd Koninkrijk (VK). Na de eeuwwisse-
ling zette de snelle Amerikaanse groei door en de VS namen het economisch leiderschap
over van het VK. Sindsdien hebben de VS die voorsprong niet meer afgestaan en het
transatlantisch productiviteitsgat vormt daardoor een kenmerkende eigenschap van eco-
nomische ontwikkeling in de moderne tijd. Deze divergentie werd voor het eerst zicht-
baar tijdens een periode van snelle technologische ontwikkeling, de tweede industriele
revolutie. Nieuwe technologieen creeerden ruimte voor arbeidsproductiviteitsgroei. De
relatief snelle Amerikaanse welvaartsgroei roept de vraag op of de VS de groeipotentie
van nieuwe technologie beter exploiteerden dan Europese landen?
Dit is de vraag die centraal staat in deze studie. In het bijzonder gaat de aandacht
uit naar de positie van de Duitse industrie in het transatlantisch productiviteitsver-
schil. Met name in de industrie, waarin tot halverwege de twintigste eeuw ongeveer
eenderde van de beroepsbevolking actief was, leidden de nieuwe productietechnologieen
tot grote verandering. Een studie naar de ontwikkeling van de industrie is bovenal
interessant voor Duitsland, dat gezien haar prominente positie binnen Europa onder-
vertegenwoordigd is in de kwantitatieve literatuur. Voor een deel volgt de onderbelichte
positie van Duitsland uit een gebrek aan consistente data voor de vroege twintigste
eeuw. De ontoereikendheid van de data compliceert de berekening van het Duitse in-
dustriele arbeidsproductiviteitsniveau en beperkt het zicht op de Duitse positie binnen
het transatlantisch divergentieproces.
Na een inleidend hoofdstuk zal in hoofdstuk 2 de prestatie van de Duitse indu-
strie ten opzichte van de VS worden geanalyseerd door middel van nieuwe niveauschat-
tingen van comparatieve arbeidsproductiviteit voor de steekjaren 1909 en 1936/35. Deze
analyse geeft in elk steekjaar een doorsnede van de industrie en brengt daarmee de
verschillen in comparatieve arbeidsproductiviteit tussen industriele bedrijfstakken in
kaart. Om de Duitse en Amerikaanse productiewaarden te vergelijken moeten ze in een
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228 Missed Opportunities?
gemeenschappelijke munteenheid worden uitgedrukt. De officiele wisselkoers, die een ge-
middelde prijsratio uitdrukt tussen Duitsland en Amerika, doet in dit geval geen recht
aan het gedetailleerde aggregatieniveau van deze studie. In plaats daarvan zijn aan de
hand van prijsinformatie industrie-specifieke conversiefactoren, koopkrachtpariteiten,
berekend.
De resultaten laten zien dat het arbeidsproductiviteitsverschil tussen de Duitse en
Amerikaanse industrie in 1909 weliswaar groot was, maar kleiner dan de literatuur aan-
geeft voor het verschil tussen het VK en de VS in dezelfde periode. De Duitse industrie
bereikte destijds een niveau van ongeveer 60% van de VS. Tijdens het interbellum ver-
slechterde Duitsland’s positie en de relatieve arbeidsproductiviteit daalde in 1936/35
tot een niveau onder de 50% van de VS. Echter, op het niveau van de afzonderlijke in-
dustriele bedrijfstakken blijken er grote verschillen te zijn. Lang niet alle bedrijfstakken
keken tegen een grote achterstand op. Bijvoorbeeld, de textiel, chemie en ijzer & staal
industrieen kwamen heel dicht bij het Amerikaanse arbeidsproductiviteitsniveau.
De geobserveerde verschillen in relatieve arbeidsproductiviteit staan op gespannen
voet met theorieen die de transatlantische verschillen verklaren aan de hand van al-
gemene land-gebonden kenmerken. Een prominent voorbeeld van het laatstgenoemde
is de Rothbarth-Habakkuk thesis. Deze stelling verklaart productiviteitsverschillen aan
de hand van verschillen in de inzet van productiefactoren. De schaarste aan geschoolde
arbeid en de relatieve overvloed aan natuurijke voorraden in de VS stimuleerden de
substitutie van kapitaal voor arbeid en leidden tot een kapitaalintensief productiepro-
ces gekenmerkt door een snellere arbeidproductiviteitsgroei dan in Europa. Maar de
resultaten van hoofdstuk 2 trekken deze stelling in twijfel, aangezien de nationale con-
text waarbinnen de Duitse industrie functioneerde leidde tot allesbehalve een uniform
niveau van relatieve arbeidsproductiviteit.
Het gegeven dat sommige Duitse industrieen het Amerikaans prestatieniveau dicht
benaderden suggereert dat verschillen in productietechnologie afwezig waren of geen
doorslaggevend effect hadden op de comparatieve arbeidsproductiviteit. Hoofdstuk 3
bestudeert dit vraagstuk. Hierin schat ik niveaus van kapitaalintensiteit in Duitsland
en de VS. Voor het steekjaar 1936 koppel ik de kapitaalintensiteit aan de niveaus van
arbeidsproductiviteit gemeten in hoofdstuk 2. Deze gegevens stellen mij in staat om
‘best-practice frontiers’ te schatten die voor elke bekende combinatie van de productie-
factoren kapitaal en arbeid het potentieel haalbare prestatieniveau aanduidt. Aan de
hand van deze best-practice frontiers ontleed ik arbeidsproductiviteitsverschillen tussen
Duitsland en de VS in twee componenten. Een eerste component wordt gedreven door
het ongelijke arbeidsproductiviteitspotentieel van de verschillende kapitaalintensiteit
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Official publications 229
waarmee de landen produceren. Het resterende, tweede, component vormt een indicatie
van de technische efficientie waarmee de productiefactoren ingezet werden.
De decompositie verwerpt de Rothbarth-Habakkuk thesis als een mogelijke verkla-
ring voor het arbeidsproductiviteitsverschil tussen de Duitse en Amerikaans industrie
in de vroege twintigste eeuw. Verschillen in kapitaalintensiteit verklaren slechts een-
derde van het arbeidsproductiviteitsverschil, terwijl tweederde wordt gedreven door een
relatief laag efficientieniveau in Duitsland. In het licht van de snelle inhaalslag in kapi-
taalintensiteit die Duitsland maakte tussen 1909 en 1936 verbaast de beperkte bijdrage
van kapitaalintensiteitsverschillen aan de Duitse arbeidsproductiviteitsachterstand niet.
De best-practice frontiers laten zien dat het arbeidsproductiviteitspotentieel van kapi-
taalintensieve productietechnologieen het snelst toenam. Met andere woorden, Duitse
industrieen zagen zich gedwongen om net als de VS te investeren in kapitaalintensieve
technologieen om niet verder achterop te raken.
Dientengevolge droeg de ontwikkeling in Duitsland de belofte van substantiele ar-
beidsproductiviteitsgroei met zich mee, maar die belofte werd niet tijdens het inter-
bellum ingelost. De literatuur suggereert dat dit effect onlosmakelijk verbonden is met
snelle verandering. Een efficiente adoptie van moderne productietechnologie vergt tijd,
tijd om te leren en het productieproces aan de nieuwe situatie aan te passen. De relatief
lage efficientie in de Duitse industrie wijst niet op een gebrek aan ontwikkeling, maar
was een bijwerking van modernisering en een eerste, noodzakelijke, stap op weg naar
Amerikaanse niveaus van arbeidsproductiviteit in de periode na de Tweede Wereloorlog.
In hoofdstuk 4 verschuift de aandacht naar de eerder genoemde schaarste aan
Duitse data. Het gebrek aan informatie wreekt zich met name bij tijdreeksanalyse, aan-
gezien voor enkele Duitse industrieen data met jaarlijkse frequentie ontbreken. In deze
gevallen kan de toe- en afname van de productie alleen afgeleid worden uit andere, ge-
correleerde, proxyvariabelen. Inmiddels hebben onderzoekers voor dit doel verschillende
proxyvariabelen gebruikt en zijn er meerdere productiereeksen voorgesteld. Deze reeksen
laten over de periode 1914–1925 een andere groeidynamiek zien, wat leidt tot uiteenlo-
pende arbeidsproductiviteitsschattingen. Bijgevolg is het onduidelijk of Duitsland met
haar snelle industriele arbeidsproductiviteitsgroei in de late negentiende eeuw er wel of
niet in was geslaagd de traditionele Engelse hegemonie in Europa te doorbreken.
Hoofdstuk 4 gebruikt een nieuwe analysemethode om een antwoord op deze vraag te
geven. Alle in de literatuur voorgestelde reeksen zijn gecorreleerd met industriele pro-
ductie, maar meten de verandering daarvan met een afwijking die wordt veroorzaakt
door de imperfecte correlatie tussen de proxyvariabelen en productie. Met behulp van
tijdreeksanalyse schat ik de productieverandering door uit de beschikbare tijdreeksen
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230 Missed Opportunities?
een component te filteren dat het gemeenschappelijke dynamisch gedrag van alle reeksen
beschrijft. De geschatte productiegroei impliceert een niveau van Duitse arbeidsproduc-
tiviteit voor het jaar 1907 van ongeveer 15% boven het VK.
Hoewel Duitsland nog voor het uitbreken van de Eerste Wereldoorlog het VK had
gepasseerd, was de marge klein vergeleken met de grote achterstand ten opzichte van
de VS waartegen beiden landen aankeken. Dit roept de vraag op of de industrie in
Europese landen in de periode 1870–1914 convergeerde naar een gemeenschappelijk ar-
beidsproductiviteitsniveau? Convergentie tussen Europese landen werd in de periode
1870–1914 gestimuleerd door een mondiale globalisering en sterke integratie van Eu-
ropese markten. Klassieke handelstheorie voorspelt dat in deze situatie verschillen in
relatieve factorkosten, en dus de keuze van productietechnologie, verdwijnen. Daarnaast
leden Europese landen mogelijkerwijs onder vergelijkbare groeibarrieres, zoals een ge-
brek aan schaalvoordelen door een heterogene voorkeur voor producten.
Hoofdstuk 5 bestudeert de mogelijkheid van convergentie binnen Europa door het
industriele arbeidsproductiviteitsniveau te vergelijken tussen 6 geındustrialiseerde lan-
den, namelijk Amerika, Duitsland, Engeland, Franrijk, Nederland en Zweden, in het
steekjaar 1909. Daaruit blijkt dat alle Europese landen opereerden op een niveau ver
onder dat van Amerika, maar binnen Europa wees niets op een gemeenschappelijk ar-
beidsproductiviteitsniveau. Wanneer aan de hand van tijdreeksen de comparatieve in-
dustriele arbeidsproductiviteitv an de verschillende landen wordt teruggetrokken blijkt
dat de variatie in prestatieniveaus rond een constant niveau schommelde in de periode
1870–1909. In deze periode groeiden Europese welvaartsniveaus, gemeten in bruto bin-
nenlands product per hoofd van de bevolking, wel naar elkaar toe. Gezien het gebrek
aan convergentie in industriele arbeidsproductiviteit werd de afnemende variatie in het
welvaartsniveau tussen Europese landen klaarblijkelijk gedreven door andere factoren.