Catching up or Falling behind? Economic Performance and Regional Trajectories in the "New Europe"

Clark University

Catching up or Falling behind? Economic Performance and Regional Trajectories in the "NewEurope"Author(s): Mick Dunford and Adrian SmithSource: Economic Geography, Vol. 76, No. 2 (Apr., 2000), pp. 169-195Published by: Clark UniversityStable URL: http://www.jstor.org/stable/144552 .

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Catching Up or Falling Behind? Economic Performance and Regional Trajectories in the "New Europe"*

Mick Dunford School of European Studies, University of Sussex,

Brighton BN1 9QN, United Kingdom m.f. dunford@sussex. ac. uk

Adrian Smith School of Social Sciences, University of Sussex,

Brighton BN1 9QN, United Kingdom a.m. smith @sussex. ac. uk

Abstract: This paper examines the trajectories of economic development of European national and regional economies in light of the pressures for greater integration and enlargement of the European Union. Using a variety of data sets, we demonstrate that there are significant variations in the speed and direction of change in per capita income and in productivity and employment rates across countries and a sample of European regions, and that falling behind (divergence) occurs as well as catching up (convergence). Making sense of spatial development therefore requires, we argue, that attention be paid to processes of differentiation and, in particular, to the falling behind experienced by less developed areas in East Central Europe and the forging ahead of the most developed, as well as to processes of catch-up. The paper also contributes to an assessment of the appro- priateness of interpretations of growth and spatial development through counter- ing the dominant discourse of convergence in neoclassical and neoliberal formula- tions and by suggesting that integration brings with it a number of important territorial "costs" associated with increasing inequality.

Key words: convergence, divergence, uneven development, Europe, Eastern Europe, European Union, productivity, employment, transition.















Do economies that are less developed tend to grow more rapidly than those that are more developed, and do differences in levels of economic prosperity and well- being have a tendency to be ironed out?

*Previous versions of this paper were presented at the workshop on "Regional Inequality in an Enlarged Europe: Regional Performance and Policy Responses" held at the Centre on European Political Economy, Sussex European Institute, March 1998, and at the annual con- ference of the University Association for Contemporary European Studies, Lincoln, September 1998. We would like to thank Michael Bradshaw, School of Geography, University of Birmingham, for supplying the Russian gross regional product and employment data and the three anonymous reviewers for their useful comments. Research for this paper





Answers to these questions, frequently posed in economic geography and econom- ics, have varied. In recent years, during a period in which neoliberal economic ideas have held sway, expectations of catch-up or convergence have predominated. Claims that convergence will occur have predominated particularly in discussions of the effects of globalization and in discourses over European economic integration and

was undertaken as part of a project entitled "Regional Economic Performance, Governance and Cohesion in an Enlarged Europe," funded by the U.K.'s Economic and Social Research Council under its "One Europe or Several?" research program (award no. L213252028). Further details about this project can be found at the project web site: http://www.geog.susx. ac.uk/research/changing_europe/regional.html.





169 169 169



ECONOMIC GEOGRAPHY ECONOMIC GEOGRAPHY ECONOMIC GEOGRAPHY

the consequences of the collapse of state socialism. The globalization literature, for example, suggests that convergence will stem from the adoption by companies of similar technologies, from a closer alignment of consumption patterns and life- styles, and from the alignment of national government policies as a result of the influ- ence of global financial markets. Similarly, one of the reasons why the countries of East Central Europe (ECE) embarked on a series of"transitions" from state socialism in the late 1980s and early 1990s was the belief that "economic opening" would enable these countries to adopt economic structures that would lead to greater prosperity and convergence on the living standards of Western economies. At the beginning of the twenty-first century, then, questions of convergence are at the center of analyses of regional economic performance in Europe.

Expectations of convergence are in part a consequence of the explicit or implicit acceptance of certain underlying economic ideas. In the first part of the paper, then, we explore briefly the economic geography literature that, on the one hand, predicts and, on the other, questions the likelihood of convergence. In subsequent parts of the paper we examine national and regional economic disparities in the European Union (EU), in Western Europe, and in East Central Europe. In particular, we focus on the roles of productivity and employment rate differentials in shaping this map of inequality. We also chart the changing nature of these disparities over time, and we address the extent to which different trajectories of regional change are identifiable. We highlight the significant "development divide" between the EU and the countries of ECE and suggest that the competitive position of ECE economies has been further weakened by the pur- suance of a neoliberal policy agenda since 1989-91. We also assess the level of regional inequality in ECE states through an analysis of the Slovak, Hungarian, and Russian cases and argue that there is an important divergence at a subnational level





across ECE, reflecting a broader experience of geographic uneven development in the region. Finally, we suggest how economic geographers might make concep- tual, geographic, and historical sense of the complex set of trajectories identified.

Convergence, Market

Integration, and Theories of

Spatial Development

Contemporary expectations of convergence are a result of the dominance of neoclassical models of regional economic development. As we shall show, recent empirical evidence does not fit easily with the expectations of these theories. In this section, our aim therefore is to set these theories in their context and to identify some competing theories that may make better sense of the recent economic trajectories of Europe's constituent countries and regions.

Of the classical economists and their critics, who were essentially interested in growth and distribution, Malthus and Marx saw economic growth as a process that was inherently uneven and that could lead to cumulative growth and decline, whereas Ricardo envisaged a smooth process of growth leading to a stationary state with zero growth and no institutional or technological change because of decreasing marginal returns in agriculture (Boyer 1997). With the early development of capitalism, therefore, there were conflicting expectations between proponents of convergence and uneven development. The neoclassical revolution led to a shift away from questions concerning growth to those concerning the allocation of a given volume of resources. Growth questions reemerged in the 1940s, however, when some of Keynes's followers sought to extend his ideas to the long-run and developed models which indicated that market forces act- ing on their own would not lead to long- term, full employment (Harrod 1939; Domar 1946; Robinson 1956). According to these neo-Keynesian authors the


Convergence, Market


Spatial Development




Convergence, Market


Spatial Development



170 170 170



CATCHING UP OR FALLING BEHIND? CATCHING UP OR FALLING BEHIND? CATCHING UP OR FALLING BEHIND?

dynamic interaction of consumption and investment decisions would result in an unstable macroeconomic development path: "either the economy experienced explosive growth, or it was trapped in a cumulative and self-defeating depression" (Boyer 1997, 34). Developed in a period of economic crisis, these theories, along with subsequent Myrdalian models of circular and cumulative causation (Myrdal 1959), suggested considerable pessimism about the likelihood of market-induced convergence.

To neoclassical economists writing in the postwar "golden age" these neo-Keynesian models rested on restrictive assumptions, of which the most important was that of a fixed relation between the factors of production. Encouraged by the regular character of Western postwar expansion, neo- classicists such as Solow and Swan developed alternative models that predicted a much more peaceful and regular growth process. Such models eliminated Keynesian problems of long-run unemployment and economic instability. If all markets are competitive, if the same technology is available in each economy, and if rates of investment are similar, every economy would grow at the same long-term rate, a rate determined by the speed of exogenous technical change and population growth. Under these idealized conditions neoclassical theory provides a simple rationale for an absolute economic convergence of growth rates. Because production functions display constant returns to scale, and because there are diminishing returns to capital, economies with a relatively small capital stock have higher marginal productivity and will catch up with more developed areas. Absolute convergence will occur, however, only if structural conditions are similar, or as long as the economies concerned comprise a convergence club with similar initial conditions. If structural conditions differ, their long-run growth rates will differ, and while their speed of growth will be greater the more distant they are from their long-term growth rates (conditional convergence),





levels of gross domestic product (GDP) per head will not converge.

Contemporary expectations of catch-up rest on the implicit acceptance of models of absolute convergence and, in particular, on the assumption that Western European and ECE economies that adopt market reforms comprise a "convergence club." Attempts to estimate regional growth regression equations centered on these models demonstrate, however, that not all national and regional economies are converging on the same long-term growth rate (so that hypotheses of absolute convergence are unsustainable) and that in economies that do converge observed convergence in per capita GDP is slow (1-2% per year) (see, e.g., Martin and Sunley 1998).

These deficiencies have given impetus to other approaches that produce more quali- fied results but that are not adequately reflected in political discourse. The slow speed of convergence lends credence, for example, to the ideas that returns to capital and labor are perhaps not diminishing; that technology is not a "public good"; and that interregional spillovers of capital, labor, and technology are less than expected. Some of these ideas are embodied in new endogenous growth models (in which technology and human capital are made endogenous). A second approach, associated with the work of Quah (1993), argues that convergence models are of limited value, in that they only relate a region's growth to its own history, and not to the interregional system of which it is a part. Yet others have argued that what requires analysis is the time path of economies and not the relationship between annual growth rates and initial GDP per head at an arbitrary starting date (see Dunford 1993).

In "radical" economic geography, by contrast, there has been a long-term emphasis on capitalist uneven development and on episodic theories of economic evolution (Harvey 1982; Smith 1984; Dunford 1988; Dunford and Perrons 1983). Making sense of the historical record of regional uneven development









171 171 171




involves, proponents argue, recognizing that two divergent tendencies always coexist within capitalist economies. At every point in time forces leading toward an equalization of the conditions of production and exchange coexist with forces leading to a differentiation of such conditions. In capitalist economies competition is a force which leads those enterprises whose productivity is less than average and whose costs are above average to close the gap on the market leaders or to disappear, though the forces for convergence are rarely pow- erful enough to homogenize economic structures and performance. At the same time, the quest for surplus profits by enterprises leads to a constant differentiation of production and exchange. The outcome of these two counteracting tendencies depends on the balance between these forces and the conditions in which they unfold, and will vary over time and space. The result is a complex mosaic of economic wealth, unevenly distributed between places. While some have conceived of these processes of uneven development in terms of a seesaw effect (Smith 1984), others have argued that the coexistence of forces leading to differentiation and equalization result in convergence and divergence at different spatial scales over similar time periods (Dunford 1993, 1996; Smith 1998).

While neoclassical, endogenous, and "radical" theories of convergence and uneven development have considered the experience of "advanced" capitalist economies, there has been a parallel debate about the nature of regional and national inequality in the Eastern European countries that established (to varying degrees) state socialist development trajectories. The empirical evidence on the process of planned industrialization in peripheral areas in ECE suggests that partial regional convergence was achieved at both national and subnational scales. Significant regional differences persisted, however, and the process of industrialization created a form of structurally unsustainable regional dependency upon large,





integrated production plants (see Smith 1998; Koropeckyj 1972; Zaniewski 1992). In addition, due to structural rigidities in the state socialist development model, the early gains of ECE in catching up with Western economies were lost as divergence set in and productivity and output growth slowed (Dunford 1998; Smith 1998).

With the attempt to introduce market mechanisms and capitalist development strategies in ECE and with the closer alignment of Eastern and Western European economies after 1989, the prospect of catch-up within ECE and between ECE and Western Europe has once again been raised. National and regional economies on the eastern periphery of Europe are now being opened to the global economy in a way that raises significant questions about how strategies of global market engage- ment intersect with the legacies of state socialist development models (Grabher and Stark 1998; Smith and Pickles 1998). The post-1989 changes also raise questions concerning the effect that the "transition to capitalism" will have upon the nature of uneven development in the region.

Drawing on these theoretical ideas and recent debates about transition, this paper aims to show that within the post-1989 European economy there are significant fluctuations in the speed and direction of change in Europe's national and subnational economies relative to one another. We argue that simultaneous and sequential falling behind (divergence) is occurring as well as catching up (convergence) (Abramovitz 1986). In light of these results, we argue that an understanding of uneven development in Europe requires greater sensitivity to the historically and geograph- ically differentiated trajectories of change characteristic of different parts of the con- tinent. The paper therefore aims to counter the suggestion that market reforms and market integration will automatically result in greater "cohesion" between the various territories of Europe.







172 172 172




The Map of Economic Inequality in Contemporary Europe

Measuring disparities in economic performance' and the extent to which convergence or cohesion2 exist between the member states of the EU and the countries of ECE is a complicated and problematic task. Official reports from the World Bank (1996), the European Bank for Reconstruction and Development (EBRD 1996), the United Nations Economic Commission for Europe (UNECE 1997), and the European Union use estimates of performance derived from national accounts statistics collected by national sta-

1 "Economic performance" is defined here as the speed and regularity with which a country or region increases incomes for the owners of its economic assets and for its population and is measured by per capita GDP growth at purchasing power standards (PPS). Measurements in PPS make allowances for differences in the price of goods and services in different countries. It is also therefore a measure of relative living standards. By focusing upon per capita GDP we are thus interested in the economic performance of places and the claims that people living in those places have over that wealth. The analysis therefore has a social significance in the sense that the distribution of wealth has implications for the life-styles and consumption patterns that develop in places. However, we are not directly concerned with the issue of whether or not there has been convergence in life-style and consumption in Europe. There is some evidence to suggest that the "new rich" of Eastern Europe have increasing access to patterns of consumption similar to those of the wealthy in the West. At the same time, however, impoverishment and employment loss for many has meant recourse to strategies of survival such as domestic food production and the informal economy (see Smith 2000 and Bridger and Pine 1998).

2 "Cohesion" depends on the degree of equal- ity in the distribution of GDP per head and the extent to which there are processes of catch-up, in which less developed countries and regions and lower-income groups enjoy faster rates of income growth than more developed areas or richer groups.









tistical offices, increasingly on the basis of common principles. Data of a comparable quality are nonetheless difficult to obtain, especially over any length of time. Furthermore, as Altvater (1998, 599) reminds us, official ECE data on GDP do not fully record the size of the under- ground or shadow economies. While the shadow economy is important in all national economies, it has become central to the development of economic survival strategies throughout ECE since 1989 (Bridger and Pine 1998; Clarke 1999; Smith 2000). For the EU, a European Commission communication estimated that the "black" economy accounted for between 2 and 4 percent of GDP in Finland and from 29 to 35 percent in Greece and for 7 to 19 percent of total declared EU employment, with most undeclared work carried out by people with regular jobs (CEC 1998). For ECE, Altvater estimates that the size of the shadow economy in Russia is at least 40 percent of GDP. The role of the shadow economy is less significant in Central European countries, but it may still account for between 15 and 25 percent of national GDP, therefore suggesting that official GDP figures underestimate the "true" level of economic activity (Altvater 1998). As a consequence, any firm conclu- sions about relative wealth should be treated with caution and the measures reported here should be regarded as orders of magnitude rather than as precise estimates. Quite significant components of new, often private, sometimes illegal and unregistered business activity and more informal economic activity, such as domestic food production and street selling, are therefore not captured in some of these data.3 Nevertheless, these data are all that

3 It should be remembered, however, that household food production of significant proportions was widely practiced prior to 1989 in the countries of ECE. While there may have been a deepening of such practices in the face of employment and income loss, the importance of this activity under state socialism suggests





173 173 173




we have to begin to map the quite considerable extent of national and regional inequality between east and west in Europe, and the problem of underreporting is found across Europe. In this section, and keeping in mind these limitations, we first examine disparities between the national economies of Europe, followed by a consideration of subnational inequalities.

National Economic Disparities in Europe

Figure 1 shows national disparities in GDP per head (measured in purchasing power standards (PPS)), relative to the EU average in 1996, for various regional subdivisions in Europe (see also Dunford 1993).4 While the range of national disparities is greatest within the current 15 EU member states (ranging from 66% of the EU average in Greece to 168% in Luxembourg), it is clear that the countries of ECE, including those states which have embarked on accession negotiations with the EU,5 lie well below even the poorest member states.

Within the EU, four main clusters of member states are identifiable. Luxem- bourg clearly performs strongest in terms of per capita income. A second group includes (in descending rank order) Belgium, Denmark, Austria, Germany, the

also that communist and Western governmental estimates of economic output may have been understated.

4 The data set used in this national-level analysis is the World Bank's "World Development Indicators" (World Bank 1997). We include parts of the former Soviet Union (such as the Central Asian states) for illustrative purposes only, as they are not part of our focus on "European" dynamics.

5 Currently ten states have applied to join the EU from ECE: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia. The EU is actively negotiating terms with all of these ECE states, although five of these countries have only recently begun the process: Bulgaria, Latvia, Lithuania, Romania, and Slovakia.















Netherlands, France, Italy, and Sweden, with a per capita GDP ranging from just above average (101% in the case of Sweden) to 114 percent (Belgium). Clearly comprising a core of strong national economies, these countries lie at the heart of EU wealth generation. A third group lies just below the EU average and includes Finland, the United Kingdom, and Ireland,6 although over the last ten years there has been significant improvement in the position of Ireland and a decline in the position of the United Kingdom. A final group, which originally comprised four countries and now includes three-all but one of the poorest "cohesion countries"7- lies between 77 percent (Spain) and 66 percent (Greece) of the average.

All ECE states recorded per capita GDP levels in 1995 below 60 percent of the EU average-that is, below the lowest level for any of the 15 member states. The wealthiest (Slovenia) recorded a GDP per head 59 percent of the average and the Czech Republic recorded 54 percent of the average, while the poorest ECE country (Romania) recorded a level of 23 percent. A cluster of ECE countries exists between 36 percent and 23 percent of the average. The wealthiest of the EU-applicant states from the former Soviet Union (Estonia) recorded a similar GDP per head to that of Romania (23%), while the poorest, Latvia, recorded a level of 18 percent of the EU average. Russia, which is not an applicant

6 The data from World Bank (1997) sources and from EUROSTAT (1997) differ slightly as to GDP per head in EU member states and the rank ordering of countries. The main difference is that in the World Bank data set the United Kingdom is above the average and is above Sweden and Finland, while in the (more up-to- date) EUROSTAT data the United Kingdom lies below average and below those two countries.

7 The cohesion countries are Greece, Ireland, Spain, and Portugal. As the least developed of the 15 member states, they are eligible for addi- tional economic assistance from the EU's Cohesion Fund.









174 174 174




GDP per head relative to EU average (PPS, EU15=100)

180

LuxemboL rg 160

140

Switzerland

120 1 Belgium Norway

*Iceland

100 * yprls

Ireland

80 Spain

Portugal * Malta Greece 60Gree Slovenia 60 9

Czech Republic

?~~~~~~~40 ,Slovakia Hungary Poland

2Bulgaria ithuania ~~~~20n~~ ^~Romania

Croatia Estonia

Russia


180

LuxemboL rg 160

140

Switzerland


*Iceland

100 * yprls

Ireland

80 Spain


Czech Republic



Croatia Estonia

Russia


180

LuxemboL rg 160

140

Switzerland


*Iceland

100 * yprls

Ireland

80 Spain


Czech Republic



Croatia Estonia

Russia

Os Os Os

Figure 1. National inequalities in Europe, 1996. Source: elaborated from World Bank (1997). Figure 1. National inequalities in Europe, 1996. Source: elaborated from World Bank (1997). Figure 1. National inequalities in Europe, 1996. Source: elaborated from World Bank (1997).

state, recorded a GDP per head of 23 percent of the EU average, although its position is likely to have been significantly worsened as a result of the 1998 financial crisis and the further devaluation of the ruble.



The large disparities between the EU and the countries of ECE in 1996 lie not

only in the failure of the state socialist

development model to convergefully with the levels of development found in parts of Western Europe (as is conventionally







175 175 175




argued), but also in the immediate impacts of transition in the region. The countries of ECE have seen a remarkable collapse of economic output in the early 1990s, and only recently has there been a return to positive growth, although by no means uni- versally. By 1996, official GDP had dropped to well below 1988 levels throughout ECE (see Dunford 1998; Smith 1998) (Table 1, column 2). There have been clear national variations in this process of economic decline: Polish GDP stood at nearly 97 percent of its 1988 level, Russian GDP at 55 percent, and Georgian at only 22 percent. Comparing 1996 GDP with 1988 implies unrealistically, however, that growth would have ended without transition in 1989 (Dunford 1998). Column 3 in Table 1 therefore records 1996 GDP as a percentage of what it would have been if growth rates in the late 1980s had continued.s Consistently, the loss of GDP is seen to be greater by several percentage points than the estimates of the level of GDP foregone derived without taking potential growth into account (column 1). Column 4 in Table 1 provides an indication of the net present value of GDP which has been foregone under the assumption that no growth would have occurred after 1988, and column 5 reports the same measure of foregone GDP assuming that growth rates in the 1980s had continued into the 1990s. The losses are dramatic. Russia, for example, has lost between 118 and 198 percent of its 1988 GDP. Much lower but still significant levels of collapse were experienced in Central Europe. Slovakia, which has seen one of the highest economic growth rates up until 1999, witnessed a level of

8 Caution must be taken in using national growth rates for ECE during the 1980s and extrapolating these forward in comparison with more recent estimates given the different accounting methods used in these economies before 1989 to measure material product. The estimates of economic growth in the 1980s are taken from World Bank sources and adjusted as far as possible for differences in accounting pro- cedures.





foregone GDP of between 99 and 274 percent of 1988 levels, and Poland, which is at the forefront of negotiations to join the EU, lost between 69 and 107 percent.

All of these figures, however, give no real impression of how much output has to be recouped in order to return to levels of economic development of the late 1980s. Rollo and Stern (1992) have estimated, for example, that it could take until at least 2000 before per capita gross national product (GNP) levels return to those of 1988 in most transition economies. Dunford (1998, 86) has argued that "net progress (in the sense of a larger present value of GDP) will occur only if exceptionally high rates of growth are sustained over very long periods of time." For example, Table 1, column 6, reports the year in which the present value of cumulative transition and nontransition (assuming zero growth after 1988) GDP will coincide for a select group of countries. These estimates rest on two assumptions: the first is that without transition there would have been no further growth after 1988; the second is that growth after 1995 will average 5 percent per year. Under these assumptions, there will be no net gain until 2006 in the Czech Republic, 2007 in Poland, and 2025 in Romania. Dunford (1998, 88) therefore concludes that "the crash of the early 1990s represents in quantitative terms an extraor- dinary reversal and much more than a 'tran- sitional recession.'

Regional Economic Disparities in Europe

There are also wide disparities in economic development between regions within the EU and ECE, and these differences must be seen as a factor standing in the way of greater cohesion and regional convergence. Figure 2 indicates the scale of these disparities in 1995 by plotting PPS estimates of regional per capita GDP relative to the EU average for NUTS III9

9 European Union member states are divided territorially into five levels of aggregation, or NUTS (Nomenclature of Territorial Units for











176 176 176



Table 1 Table 1 Table 1

GDP and Transition in East Central Europe Net Present Value of Net Present Value of Year in Which

Foregone GDP with Foregone GDP at Cumulative Average Annual 1996 GDP as a No Growth as a 1980-89 Growth Transition and

Pretransition GDP 1996 GDP as a Percentage of 1996 Percentage of 1988 Rates as a Percentage Nontransition GDP Growtha Percentage of 1988 Nontransition GDPe GDPf of 1988 GDPg Coincideh

(1) (2) (3) (4) (5) (6) Armenia 3.0 38 30 -196 -263 Azerbaijan 2.4d 28 23 -229 -282 Belarus 5.9d 63 40 -77 -220 Bulgaria 5.7 61 39 -144 -282 Czech Republic 5.2b 96 64 -59 -183 2006 Estonia 4.9" 63 43 -135 -251 Georgia 2.8 22 18 -280 -344 Hungary 2.1 85 72 -71 -119 2012 Kazakhstan 5.2a 48 32 -148 -273 Kyrgyz Republic 15.6d 55 17 -111 -585 Latvia 4.0 49 36 -152 -246 Lithuania 13.9d 68 24 -104 -509 Poland 1.7 97 85 -69 -107 2007 Romania 2.0 78 67 -129 -173 2025 Russian Federation 3.5 55 41 -118 -198 Slovak Republic 7.0b 83 48 -99 -274 2017 Tajikistan 12.3? 30 12 -201 -547 Turkmenistan 13.6" 52 19 -126 -520 Ukraine 4.7d 40 28 -142 -254 Uzbekistan 11.0" 85 37 -51 -351 2010

Source: World Bank (1997). a Growth of real GDP at purchasing power standards (in 1987 $s) in 1980-88; b in 1984-88; in 1986-88; d in 1987-88. e Assuming that 1980-88 rates of growth would have continued. f Assuming that without transition there would have been no further growth, and discounted at 10 percent per year.

g Assuming that without transition growth would have continued at pretransition rates, and discounted at 10 percent per year. h Discounted at 10 percent per year,
















regions in 15 EU member states and for the three ECE countries for which data are available.10 The countries are ranked from left to right according to their national GDP per head. Output per head in regions in these countries varied enormously, from 353 percent of the EU average in Frank- furt am Main to 4 percent in the Ingush Republic in the North Caucasus of Russia (although we must keep in mind the potential underreporting of economic activity in the "grey" economies of all regions). Clearly a large number of Russian regions lie in the poorest income range. The poorest regions within the two EU-applicant states of Slovakia and Hungary are found in the former, which has much greater levels of internal regional disparities than does Hungary. Within EU member states, 19 percent of the population of the EU lived in NUTS III areas with a per capita GDP of less than 75 percent of the European Union average.1l Included were all but three NUTS III regions in Greece; all of Portugal outside of Lisbon and two other NUTS III regions; large areas of the former German Democratic Republic; over half of the 50 Spanish NUTS III regions;

Statistics). NUTS II regions are called Basic Administrative Units and NUTS III regions are subdivisions of these units. NUTS II data are used for Italy, though Valle d'Aosta is also a NUTS III area.

10 The regional divisions adopted for ECE countries in Figure 2 are not NUTS III equivalent. At present there is no parallel unit for regional statistical analysis in ECE countries, although a system is currently being imple- mented in the applicant countries. The regions used are as follows: Slovakia, 38 districts; Hungary, 20 counties; Russia, 89 regions. Data for Hungarian counties are for 1996. The sources of data are national statistical offices and other estimates. Consequently, the data are not standardized. At the time of going to press, EUROSTAT released regional GDP data for the applicant states of ECE. We have not, however, been able to use these data in this analysis. 11 75% of the average EU per capita GDP is used as the basis for identifying Objective 1 regions at the NUTS II level for Structural Fund assistance.







six of the NUTS II regions of the Italian Mezzogiorno; parts of south and east Austria, including Burgenland; three of the eight Irish NUTS III regions; and South Yorkshire, Merseyside, the Scottish Highlands, various Welsh and southern regions in the United Kingdom. In a sub- stantial number of these areas unemployment was high; the share of income from low-productivity agricultural sectors was large; and/or high levels of industrial decline in mining, steel, textiles, and ship- building had been experienced.

Among the three ECE countries presented in Figure 2, by far the majority of regions fall below 75 percent of EU per capita GDP. No Hungarian regions appear above 66 percent of the EU average, not even the capital city region of Budapest. In Slovakia, the capital region of Bratislava12 (144% of the average) and the industrial- ized region of Kosice in the east (78%) lie above the 75 percent threshold, but the distribution of regional per capita GDP is significantly more polarized than in Hungary. The relative strength of these regions is in part due to the concentration of comparatively high value-added industrial activity, with Bratislava and Ko?ice alone accounting for 35 percent of Slovak industrial output (Smith 1998). These core regions have also seen a high rate of average annual per capita GDP growth (6%) between 1993 and 1995, suggesting that they are increasingly able to forge a growth trajectory away from more marginal areas. Another factor contributing to the high per capita wealth of Bratislava and other city regions is the fact that there is significant net in-commuting, so that large numbers of

12 The figure for Bratislava should be treated with some caution. While it is reasonable to argue that the capital city region dominates the rest of the Slovak space economy (Smith 1998), the level of per capita GDP recorded places the city on roughly the same level as Greater London. This is due largely to the relatively small resident population of the city, as well as to significant in-commuting, leading to a high income-per-head figure.







178 178 178




GDP per head relative to EU average in 1995 (PPS, EU15=100) 400

350 *

300

250

200

0

150 -


350 *

300

250

200

0

150 -


350 *

300

250

200

0

150 -

1? s A F6 \a ell eb. ll,

j 4s~ c s. V?, e <,-"


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j 4s~ c s. V?, e <,-"

\(l~i b 3( \ q \ \?a p " 4< 4\~

6jQ

\(l~i b 3( \ q \ \?a p " 4< 4\~

6jQ

\(l~i b 3( \ q \ \?a p " 4< 4\~

6jQ

Figure 2. Regional inequalities in Europe at NUTS III level, 1995. Source: EUROSTAT (1997); Karasz, Rencko, and Pauhofova (1996); Hungarian Central Statistical Office (1998); Goskomstat

Rossii (1997).


Rossii (1997).


Rossii (1997).

people who contribute to output do not reside in the area. In both, Hungary and Slovakia there is also a large development divide between the capital city region and the rest of the country. In Hungary, for



example, Budapest recorded per capita GDP that was 26 percentage points above the next region of Gy6r-Moson-Sopron on the border with Austria. In Russia, no

region lies above 75 percent of the average





179 179 179




per capita income. Tyumen' in West Siberia (72%) and the Sakha Republic in the Russian Far East (42%) record the highest levels of per capita GDP. These two regions are areas with major resource extraction industries-oil and gas in Tyumen' and diamonds in the Sakha Republic-which together accounted for approximately 47 percent of Russia's exports in 1995 (Bradshaw, Stenning, and Sutherland 1998, 160). They also have relatively small populations. However, aside from Tyumen', the regional distribution of per capita income in Russia places the second "wealthiest" region roughly alongside the poorest East German regions in Sachsen, although such precise compar- isons are difficult given the inability of regional GDP estimates to pick up all informal economic activity.

At the opposite extreme, within the EU a significant number of German regions and two French regions were located above 200 percent of the average EU per capita income. Included here were a number of central city regions whose per capita GDP scores are somewhat inflated by the scale of net inward commuting: Frankfurt am Main (353%), Munich (260%), Darmstadt (247%), and Wolfsburg (242%) in Germany, and Paris (304%) and Hauts de Seine (268%) in France. Indeed, the data for France clearly draw out the con- tinuing salience of the notion of a divide between the Ile de France and the "French desert" in accounting for the polarization of the French space economy (Gravier 1947; Veltz 1996). More generally, a large share of the regions located at more than 125 percent above the average were West German, and most were metropolitan economies clustered around an axis (the so-called "blue banana") extending from Greater London through Belgium and the Netherlands along the Rhine and into Lombardy and Emilia Romagna in the north of Italy.13

13 To be in this top range of regional per capita output does not imply, however, that a household or individual is rich. It is average out-







A clear development divide character- izes, then, the economic geography of an increasingly integrated and enlarged Europe. How are these disparities likely to change in the future? Are we likely to witness the convergence of regional economies as predicted by mainstream theories of transition and regional development? Modeling work undertaken at the World Bank has estimated that convergence to 75 percent of EU per capita income (the threshold up to which areas are eligible for Objective I status under current EU Structural Fund rules)14 will take between 15 years (in the case of the Czech Republic) and 91 years (in the case of Slovenia) if current national economic growth rates continue (Table 2) (Barbone and Zalduendo 1997). Under this growth scenario, Poland will never converge and Hungary and Slovakia will take 41 years each. If the average growth rates of these five economies are used, convergence rates will take from 28 years (in the case of the Czech Republic) to 50 years (in the case of Poland). If a rate of growth equivalent to the average EU level is achieved, then the period for convergence increases slightly; if low rates of EU growth are considered, convergence will never occur.

These calculations suggest that the likelihood of convergence in the near future in the wider Europe is doubtful. Analyses of the relative situation and of development tendencies in the two parts of Europe rein- force this conclusion. There are obvious and significant east-west technological gaps that will be difficult to close. More important, perhaps, is the evidence within ECE of a profound process of regional uneven development. On the one hand capital accumulation is increasingly centered in

put that is large, and household income depends, first, on whether the income associated with a region's output of goods and services accrues to the region's inhabitants and, second, on the personal distribution of income within the region.

14 The estimates are based on the assumption that the EU continues to perform at the present EU average levels.









180 180 180



181 181 181 CATCHING UP OR FALLING BEHIND?

Table 2

CATCHING UP OR FALLING BEHIND?

Table 2

CATCHING UP OR FALLING BEHIND?

Table 2

Estimates of Years Required for Per Capita GNP Convergence to 75 Percent of EU Level Current National Average ECE Low EU Average EU High EU

Growth Rate Growth Rate Growth Rate Growth Rate Growth Rate

Czech Republic 15 28 a 31 12

Hungary 41 46 - 50 22 Poland -50 - 54 24 Slovakia 41 44 47 20 Slovenia 91 45 49 21

Source: Barbone and Zalduendo (1997, 4). a

Convergence will not occur.













core areas, such as capital city regions and western border areas (Smith 1998; Dun- ford and Smith 1998). On the other hand, capitalist development strategies and mar- ketization are leading to the peripheraliza- tion of more marginal regions that are increasingly "left behind." Indeed, it is possible that we are beginning to witness the emergence of complex patterns of international and subnational uneven development in ECE and between the EU and former Communist countries. The recent positive growth occurring in Central Europe has been accompanied by continued decline in much of the former Soviet Union and parts of the former Yugoslavia and by sharp fluctuations in economic for- tunes in countries such as Bulgaria. Indeed, so marked are these differentials that they may well be re-creating the old European east-west division of labor and development divide. We return to this point later. In other words, then, it is possible to identify processes that suggest both convergence and divergence at different geographic scales occurring simultane- ously, rather than those solely of growth and catch-up.

Components of Territorial Disparities in Europe

To help identify the causes of uneven economic development and disparities in wealth creation, differentials in development can be divided into two elements: an







element that depends on productivity differentials; and an element that depends on employment rate (the percentage of the population employed) differentials (Dun- ford 1996). More formally,

Gross Domestic Product Resident Population

Gross Domestic Product

Employed Population

Employed Population Resident Population

An examination of the geography of these elements plays an important role in identifying the factors that underpin Europe's uneven development.

Figure 3 plots productivity and employment rates in 1996 for Western European and ECE countries. As with the output data used for ECE countries, the employment data may also underestimate actual employment levels due to the importance of unrecorded economic activity. Some caution needs to be exercised, then, in interpreting employment rates, particularly for ECE but also in EU countries. This analysis uses the best internationally comparable data, derived from the International Labour Organization (ILO). In most cases the underlying sources of these data are national labor force surveys, many of which have a relatively standardized form and have been developed under ILO guidance. Given that these data are




Employed Population







Employed Population






Log of Productivity (GDP/Employment) $; A



* lu

f~it be

~ C. cy nl deat no

"i ~ 'fi ?e a ?o . ch

\~ '* se'dk ? is -...^^^*gr

15000 ECU

* hr sl cz

Do * hu 10000 ECU

bl It' et ? ro 5000 ECU

*Iv Iuz

*kg

az

* lu

f~it be

~ C. cy nl deat no

"i ~ 'fi ?e a ?o . ch

\~ '* se'dk ? is -...^^^*gr

15000 ECU

* hr sl cz

Do * hu 10000 ECU


*Iv Iuz

*kg

az

* lu

f~it be

~ C. cy nl deat no

"i ~ 'fi ?e a ?o . ch

\~ '* se'dk ? is -...^^^*gr

15000 ECU

* hr sl cz

Do * hu 10000 ECU


*Iv Iuz

*kg

az

1.6 1.6 1.6 Emplovment Rate (Emplovment/Pooulation) Emplovment Rate (Emplovment/Pooulation) Emplovment Rate (Emplovment/Pooulation)

1.7 1.7 1.7

at Austria az Azerbaijan be Belarus be Belgium bu Bulgaria hr Croatia cy Cyprus cz Czech Republic



dk Denmark et Estonia fi Finland fr France de Germany gr Greece hu Hungary is Iceland



ie Ireland it Italy kz Kazakhstan ky Kyrgyz Republic Iv Latvia It Uthuania lu Luxembourg ma Malta



mo Moldova nl Netherlands no Norway po Poland pt Portugal ro Romania ru Russia sl Slovakia



sv Slovenia es Spain se Sweden ch Switzerland uk Ukraine uk United Kingdom uz Uzbekistan



Figure 3. Productivity and employment rates in European economies, 1996. Source: elaborated from World Bank (1997).



4.8

4.6

4.4

4.2

4.0

3.8

3.6

4.8

4.6

4.4

4.2

4.0

3.8

3.6

4.8

4.6

4.4

4.2

4.0

3.8

3.6

3.4 1

3.4 1

3.4 1 .3 .3 .3 1.4 1.4 1.4

Log of Log of Log of 1.5 1.5 1.5 1.8 1.8 1.8

o.u o.u o.u




derived from surveys of individuals rather than enterprise returns, the scope for underreporting employment levels is further reduced. The data in Figure 3 are plotted using logarithms of the rates of productivity and employment. A particular level of GDP per head is therefore repre- sented by a straight line sloping downward from left to right. We include three such lines in Figure 3, representing values of GDP per capita in ECU (at PPS) of 5,000 (U.S. $6,200), 10,000 ($12,400), and 15,000 ($18,600).15

Clear productivity and employment rate divides exist in Europe. First, there is a profound divide in productivity rates between virtually all ECE countries and all Western European countries, although the scale of this divide is distorted by differences in the degree to which output and employment are underrecorded in the different parts of Europe.16 Productivity rates in Western Europe ranged from 62 percent of the EU average in Portugal to 170 percent in Luxembourg, while in ECE the highest rate was 53 percent in Slovenia. Second, within Western Europe and ECE productivity and employment play quite different roles. Taking Western Europe first, four main groups can be identified:

1) Within Switzerland, the Nordic countries (except Finland) and Austria, Germany, the United Kingdom, and the Netherlands, productivity in 1996 was around the average (86-100% of the EU average), but the rate of employment was relatively high (43-54%).

2) Within one of the four EU cohesion countries (Ireland) and Italy, Finland, Cyprus, France, Belgium, and Luxembourg, productivity tended to be equivalent to this first group or

15 The ECU to U.S. $ exchange rate is based on that for June 1996, which was $1.24 U.S. to 1 ECU.

16 The partial exception is Slovenia, which approaches the productivity rate of Portugal.













much higher (97-170%), whereas employment rates tended to be much lower (34-39%).

3) In Portugal and Greece (two of the other cohesion countries) productivity rates were lower than the Western European norm (62-71%) and employment rates were low to average (37-45%).

4) In Spain, while productivity was comparable to that of Germany and the Netherlands (100%), the employment rate and the mobilization of human resources were much lower than in all other Western European economies (31%).

Within the ECE countries plotted in Figure 3, four main groups can be identified:

1) In the two "wealthiest" economies (Slovenia and the Czech Republic), relatively high (in comparison to other ECE countries) productivity and employment rates (43-53% of the average and 44-49% respectively) are evident.

2) In Slovakia, Hungary, and Poland productivity and employment rates are lower than in this first group (30-36% and 38-42% respectively).

3) In the majority of Commonwealth of Independent States (CIS) members, the Baltic States, and Romania, productivity rates are very low (14-20%) but employment rates are toward the high end for ECE countries (43-49%). Lower productivity in all other Central European states is a key determinant of poor performance, although it is somewhat offset by relatively high employment rates. In the poorest EU-applicant countries, then, low productivity is a key factor in determining poor performance.

4) In Bulgaria (one of the poorest EU- applicant states) and Croatia employment rates are extremely low (23%), while productivity is comparable to the Czech Republic (43%). In

















183 183 183




Bulgaria, poor economic performance, related to the complexity of political-economic change and strug- gles over the nature of change (Begg and Pickles 1998; Smith and Pickles 1998), is in part the result of a low employment rate. The low employment rate is, in part, reflected in the relatively high unemployment levels experienced in Bulgaria, although there has also been a mass withdrawal from the formal labor market (Smith 2000).

Clearly, these data indicate that the relatively poor economic position of ECE countries as measured by per capita GDP has quite different causes, as do the differential levels of development identifiable in Western European economies. The relatively "strong" economies of ECE-the Czech Republic and Slovenia-benefit from relatively high employment rates (by ECE standards) and a relatively strong mobilization of their human potential. In Bulgaria and Croatia, productivity is relatively high but is achieved on the basis of a very low degree of mobilization of these countries' human potential in the formal economy.

However, these significant national differences mask important subnational variations in the roles of employment and productivity rates. Figure 4 plots productivity and employment rates in 1995 for the NUTS1 regions of the 15 EU member states and the broadly equivalent regions of Hungary, Slovakia, and Russia.17 It is clear that productivity and employment rates play quite different roles in different European regions. First, there is again a clear divide between east and west. While

17 For Hungary we use 7 regional aggregations of counties identified by the Hungarian Statistical Office. For Slovakia 8 regional aggregations of the 38 districts are used, correspond- ing to the new territorial map introduced in 1996. In Russia, we use the 11 standard economic regions plus the Moscow city region.









the employment rates of Hungarian and Russian regions were comparable to those of most French, German, and Austrian regions, levels of productivity differed dramatically. In all ECE regions productivity rates were below those of EU regions, and productivity differentials are a key feature in explaining the dynamics of uneven development in Europe. However, within the ECE regions there were also significant differences between the roles of employment and productivity rates. In Russia, a relatively high employment rate, due to labor retention in enterprises (Clarke 1998), was matched with a lower level of productivity in most regions than in the other two ECE countries, due in part to out-migration in the early 1990s (Bradshaw, Stenning, and Sutherland 1998). (The exceptions are West Siberia and the North.) In Slovakia, by contrast, and excluding the capital city region of Bratislava (s112), which had an extraordinarily high employment rate (93%), attributable to large-scale in-commuting from neighboring regions, employment rates tended to be low (comparable to those in parts of southern Europe and the result of increasing mass unemployment (Smith 1998)), while productivity was on the whole higher than in Russia. Hungarian regions occupied a middle ground between these two positions, although the capital city region of Budapest (Central Hungary: hul) had a much stronger employment and productivity rate. Figure 4 suggests then that productivity differences provide a more important mechanism for determining regional uneven development in Russia, while in Slovakia, and to a lesser extent in Hungary, the differential ability of regional economies to mobilize their human potential is more important.

Second, the two determinants of regional GDP per head play quite contrast- ing roles in different parts of the EU. In most cases all the NUTS 1 regions in a particular member state were clustered in a particular part of the graph, although there are exceptions, with quite wide regional differences in some member states. The areas with the lowest levels of GDP per





184 184 184



Log of Productivity (GDP/Employment) R n



bel

lu de6 fri

AQ i t it it3 2 it de7

de5 x. de7

f3 es3 A A be2 ita itb fr3 ? es2 be3 it5' dec e2ea _ 3 del

es6 A es4 fr8it7 es5 f fi1r7 n12 4 de 3 dk

sl71* , hu x6 fi 2 n de4 ptl ded *deg

+

+pt2 pt3 dee de8 s2 fr 8 slls

hul s144 s186 s153

vs65 hu7 hu2 hu3 hu6 x hu4 s126 -

s135 h V ru9 s177 v h5

rul v rua 0

rub *ru8

* ru3 ru6 0

ru5 * u2 * ru4

ru7 ruc s s

bel

lu de6 fri


de5 x. de7




+


hul s144 s186 s153


s135 h V ru9 s177 v h5

rul v rua 0

rub *ru8

* ru3 ru6 0

ru5 * u2 * ru4

ru7 ruc s s

bel

lu de6 fri


de5 x. de7




+


hul s144 s186 s153


s135 h V ru9 s177 v h5

rul v rua 0

rub *ru8

* ru3 ru6 0

ru5 * u2 * ru4

ru7 ruc s s

1.4 1.4 1.4 1.5 1.6 Log of Employment Rate (Employment/Population)

1.5 1.6 Log of Employment Rate (Employment/Population)

1.5 1.6 Log of Employment Rate (Employment/Population)

1.7 1.7 1.7

- at Austria - fr France --- po Portugal -- be Belgium -- gr Greece - se Sweden - de Germany -- ie Ireland -.--- uk UK A- dk Denmark --- it Italy--+ hu Hungary

-v- es Spain -- lu Luxembourg - sl Slovakia +- fi Finland -*- nl Netherlands-- ru Russia





Figure 4. Productivity and employment rate differentials in European regions, 1995. Source: EUROSTAT (1997); Karasz, Rencko, and Pauhofova (1996); Hungarian Central Statistical Office

(1998); Goskomstat Rossii (1997). Note: Bratislava and Budapest are placed at the righthand side of the graph, but lie farther right (at 1.97 and 1.70).





4.8

4.6

4.4

4.2

4.0

3.8

3.6

4.8

4.6

4.4

4.2

4.0

3.8

3.6

4.8

4.6

4.4

4.2

4.0

3.8

3.6

Uj.J Uj.J Uj.J



186

Abbreviations to Figure 4

186


186



atl Ostoesterreich at2 Suedoesterreich at3 Westoesterreich bel Reg.Bruxelles-

Cap./Brussels Hfdst.Gew.

be2 Vlaams Gewest be3 Region

Wallonne del Baden-

Wuerttemberg de2 Bayern de3 Berlin de4 Brandenburg de5 Bremen de6 Hamburg de7 Hessen de8 Mecklenburg-

Vorpommern de9 Niedersachsen dea Nordrhein-

Westfalen deb Rheinland-Pfalz dec Saarland ded Sachsen dee Sachsen-Anhalt def Schleswig-

Holstein deg Thueringen dkO Danmark esl Noroeste es2 Noreste es3 Madrid




Wallonne del Baden-








Wallonne del Baden-





es4 Centro (E) es5 Este es6 Sur es7 Canarias fil Manner-Suomi fi2 Ahvenanmaa/

Aaland frl Ile De France fr2 Bassin Parisien fr3 Nord-Pas-De-

Calais fr4 Est fr5 Ouest fr6 Sud-Ouest fr7 Centre-Est fr8 Mediterranee fr9 Departements

D'Outre-Mer grl Voreia Ellada gr2 Kentriki Ellada gr3 Attiki gr4 Nisia Aigaiou,

Kriti hul Central Hungary hu2 Central

Transdanubia hu3 Western

Transdanubia hu4 Southern

Transdanubia hu5 Northern

Hungary









Hungary









Hungary

hu6 Northern Great Plain

hu7 Southern Great Plain

ie0 Ireland itl Nord Ovest it2 Lombardia it3 Nord Est it4 Emilia-Romagna it5 Centro (I) it6 Lazio it7 Abruzzo-Molise it8 Campania it9 Sud ita Sicilia itb Sardegna lu0 Luxembourg

(Grand-Duche) nll Noord-

Nederland nl2 Oost-Nederland n13 West-Nederland n14 Zuid-Nederland ptl Continente pt2 Acores pt3 Madeira rul North ru2 North West ru3 Central ru4 Volga-Vyatka ru5 Central

Chemozem ru6 Volga






Chemozem ru6 Volga






Chemozem ru6 Volga

ru7 ru8 ru9 rua rub ruc seO s112 s126 s135 s144 s153 s165 s177 s186 ukl uk2

uk3 uk4 uk5 uk6

uk7 uk8

uk9 uka ukb


uk3 uk4 uk5 uk6

uk7 uk8

uk9 uka ukb


uk3 uk4 uk5 uk6

uk7 uk8

uk9 uka ukb

North Caucasus Urals West Siberia East Siberia Far East Kaliningrad Sverige Bratislava Banska Bystrica Nitra Trnava Trencin Zilina Presov Kosice North Yorkshire and Humberside East Midlands East Anglia South East (UK) South West (UK) West Midlands North West (UK) Wales Scotland Northern Ireland



head were in Greece, Portugal, and parts of East Germany.8s What is clear in the case of Greece (with the exception of Kentriki Ellada in the west), mainland Portugal, and East Germany, however, is the fact that their employment rates were close to the EU average. Yet their levels of productivity were low by EU standards. Indeed, these regions are located mid-way between the less-productive Eastern European regions and those with higher levels of productivity elsewhere in the EU.

18 Data were not available for the French overseas departements, which also had particularly low levels of GDP per head.





The situation in parts of Spain (particularly the islands, the northeast, and the center) and southern Italy (Campania (it8), Sud (it9), Sicilia (ita), and Sardegna (itb)) was rather different. Generally speaking, employment rates were low, lying beneath most of the poorest-performing regions in Slovakia. The rates of productivity were much closer to the EU norm than in the cases of Greece, Portugal, and East Germany. However, other parts of Italy were located near economically strong EU

regions. The different positions of the two parts of the country graphically confirm the existence of two different economic and social worlds in Italy (Dunford 1988).








The position of the United Kingdom is also particularly striking. Generally speaking, UK productivity is relatively low, lying only slightly above that of Greek regions. Employment rates, on the other hand, were much more varied. Overall, comparatively high rates of employment partially compensate for low productivity.

Regions in the Nordic countries are associated with substantially higher rates of employment, while productivity rates did not differ significantly from those in parts of Spain and Italy. GDP per head seems therefore to have been relatively high largely as a result of the high degree of mobilization of the human potential of the Nordic countries. A similar situation pre- vails in most Austrian regions and some parts of Germany. In Germany, Hamburg (de6), Hessen (de7), which contains the city of Frankfurt, and to a lesser extent Bremen (de5) had high productivity and employment rates, although the situations in Hamburg and Bremen are difficult to interpret due to the significance of commuting. In the rest of Germany, employment rates were relatively high (46 and 47%) in the southern Lander of Baden- Wiirttemberg (del) and Bayern (de2). Rates of productivity in these two Lander were close to those in the northern Rhinelands (110%), but the latter was characterized by a lower employment rate, of around 41 percent.

With the striking exception of Ile de France (frl), the French and Belgian regions displayed close-to-average productivity rates but less-than-average employment rates. French provincial productivity rates ranged from 90 percent in the Ouest (fr5) to 114 percent in Nord-Pas-de-Calais (fr3). Employment rates ranged from 31 percent in the old industrial region of Nord-Pas-de-Calais to 40 percent in the Centre-Est (Rhone-Alpes and Auvergne: fr7). The Ile de France stood out from the rest of France as a consequence of its higher employment rate (43%) and in particular of its rate of productivity (153%). Belgium was characterized by high rates of productivity and rates of employment that







were on average low (96%). In Belgium, therefore, the strong positive impact of productivity on relative GDP per head is in part offset by the downward pressure exerted by comparatively low rates of employment.

It is clear from this discussion that at different geographic scales there are wide differences in the proximate determinants of income differentials across Europe, as well as in income levels themselves. In ECE, the productivity gap with Western Europe is dramatic, but there is also no one-to-one relationship with employment rates, although we should bear in mind the limits to the official data used and the inability of these data to "capture" the dynamics of the informal economy. Within Western Europe productivity differentials are less acute but variations in employment rates are dramatic.

Trends in Inequality in the "New Europe"

So far we have mapped out the contemporary situation and suggested that a profound set of barriers and divides stands in the way of convergence and greater cohesion. How have these disparities changed over time? Is there any evidence of a longer-term convergence of differentials in Europe? Data on long-run national trends in income inequality between European countries are set out in Table 3. As there are missing values in the data, Table 3 also records the number of observations. In the period immediately following the Second World War there was significant convergence, after 130 years of divergence between 1820 and 1950, when rapid industrialization and growth in parts of Western Europe was accompanied by sluggish growth in peripheral areas. Convergence lasted until the late 1970s, when the postwar trend was reversed and divergence set in again. In 1989-92 the divergence trend intensified.

The period of postwar convergence was the result of the implementation of two











187 187 187



188 188 188 ECONOMIC GEOGRAPHY

Table 3

ECONOMIC GEOGRAPHY

Table 3

ECONOMIC GEOGRAPHY

Table 3

Trends in International Inequality in Europe and the New World, 1820-1992

Europe and the Western, Southern, Western and New World and Eastern Europe Southern Europe Western Europe

Year CVa Number CV Number CV Number CV Number

1820 23.7 18 24.1 15 21.5 13 21.6 11 1870 39.7 22 35.4 18 32.6 15 31.0 12 1900 37.4 22 37.1 18 33.1 15 30.6 12 1913 43.2 25 40.7 21 33.4 16 25.1 12 1929 45.7 27 46.2 23 33.7 16 25.2 12 1938 43.6 26 44.7 22 33.1 16 21.1 12 1950 52.8 27 51.1 23 40.8 16 28.9 12 1973 37.6 27 39.0 23 24.5 16 16.5 12 1979 35.8 27 36.8 23 21.8 16 10.7 12 1989 38.7 27 40.3 23 20.6 16 8.8 12 1992 44.2 27 47.5 23 19.3 16 9.6 12

Source: elaborated from data in Maddison (1995, 110-266). a Coefficient of variation of real GDP per head at purchasing power standards.




1820 23.7 18 24.1 15 21.5 13 21.6 11 1870 39.7 22 35.4 18 32.6 15 31.0 12 1900 37.4 22 37.1 18 33.1 15 30.6 12 1913 43.2 25 40.7 21 33.4 16 25.1 12 1929 45.7 27 46.2 23 33.7 16 25.2 12 1938 43.6 26 44.7 22 33.1 16 21.1 12 1950 52.8 27 51.1 23 40.8 16 28.9 12 1973 37.6 27 39.0 23 24.5 16 16.5 12 1979 35.8 27 36.8 23 21.8 16 10.7 12 1989 38.7 27 40.3 23 20.6 16 8.8 12 1992 44.2 27 47.5 23 19.3 16 9.6 12





1820 23.7 18 24.1 15 21.5 13 21.6 11 1870 39.7 22 35.4 18 32.6 15 31.0 12 1900 37.4 22 37.1 18 33.1 15 30.6 12 1913 43.2 25 40.7 21 33.4 16 25.1 12 1929 45.7 27 46.2 23 33.7 16 25.2 12 1938 43.6 26 44.7 22 33.1 16 21.1 12 1950 52.8 27 51.1 23 40.8 16 28.9 12 1973 37.6 27 39.0 23 24.5 16 16.5 12 1979 35.8 27 36.8 23 21.8 16 10.7 12 1989 38.7 27 40.3 23 20.6 16 8.8 12 1992 44.2 27 47.5 23 19.3 16 9.6 12


general models of growth: a Keynesian or Fordist development model in Western Europe; and a model of state socialist industrialization in ECE (see Smith (1998) for a treatment of ECE). The first countries to converge with Western Europe were the Soviet Union in the 1930s and the state socialist countries of ECE in the 1950s and 1960s. "South" European countries started to make up for their relatively slow growth later, after 1960. As the south did not suffer a relative slowdown of the same magnitude as that experienced in the state socialist world in the late 1970s and 1980s and did not suffer the dramatic collapse that followed the fall of Communism, southern European economies had restored their nineteenth-century lead over the east by 1992. After the mid-1980s there was a renewal of catch-up by some southern countries. The overall divergence that has occurred since 1989 has been the result of the profound shedding of economic capacity in ECE.

More detailed annual data for the period 1980-96 on GDP disparities in European countries indicate the extent of this more recent divergence. Figure 5 presents three measures of inequality: the coefficients of variation based on the population-weighted absolute deviation (CV(WMAD/MEAN)) and the population-weighted standard





deviation (CV(WSD/MEAN)) and the Theil coefficient for a variable number of European countries.19 Until 1986 there was a slight fall in the level of disparities, suggesting limited convergence. Between 1986 and 1987 inequality jumped markedly, although this jump is related to an increase in the number of countries for which data are available (from 30 to 39). However, the trend since the late 1980s for the 39 countries for which there were data has been for inequalities to increase by approximately 28 percentage points on the coefficient of variation of the weighted standard deviation.

A Disaggregation of Trends in Output per Head

To what extent are these recent changes in disparities a result of convergence or divergence in rates of productivity and in

19 For reasons of data availability and the redrawing of political boundaries, the number of countries reported varies and leads to some problems of interpretation, discussed in the text. The number of countries included is as follows: between 1980 and 1983, 27 countries; in 1984 and 1985, 29 countries; in 1986, 30 countries; between 1987 and 1994, 39 countries; in 1995, 38 countries; and in 1996, 35 countries.












Coefficient of variation (WMAD/MEAN, WSD/MEAN) Coefficient of variation (WMAD/MEAN, WSD/MEAN) Coefficient of variation (WMAD/MEAN, WSD/MEAN)

80

70

60

50

40

30

20

10

0

80

70

60

50

40

30

20

10

0

80

70

60

50

40

30

20

10

0

Theil coefficient Theil coefficient Theil coefficient

CV(WSD/MEAN) CV(WSD/MEAN) CV(WSD/MEAN)

U --/-'-a_"-- /CV (WMAD/I * 1

U --/-'-a_"-- /CV (WMAD/I * 1

U --/-'-a_"-- /CV (WMAD/I * 1

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00 1980 1982 1984 1986 1988 1990 1992 1994 1996 1980 1982 1984 1986 1988 1990 1992 1994 1996 1980 1982 1984 1986 1988 1990 1992 1994 1996

Figure 5. Changes in economic inequality in Europe, 1980-1996. Source: elaborated from World Bank (1997).



employment rates? Are areas that are economically weaker comparatively unsuc- cessful in their attempts to redeploy people who lose their jobs as a result of structural change or to provide alternative employ-



ment possibilities for new generations entering the job market for the first time? To answer some of these questions Figure 6 presents trends in the rates of productivity and employment between 1986 and



189 189 189

r-l", r-l", r-l",




1996. Not all European countries are plotted in Figure 6, in order to maintain clarity; those that do appear have been chosen as representative of particular trajectories.



It is clear from Figure 6 that EU member states, ECE, and other European countries are developing along different trajectories, with most (except perhaps



Log of GDP per person employed Log of GDP per person employed Log of GDP per person employed 4.8

4.6

4.4

4.2

4.0

3.8

3.6

3.4

4.8

4.6

4.4

4.2

4.0

3.8

3.6

3.4

4.8

4.6

4.4

4.2

4.0

3.8

3.6

3.4 1.3 1.3 1.3 1.4 1.5 1.6 1.7

Log of employment rate 1.4 1.5 1.6 1.7

Log of employment rate 1.4 1.5 1.6 1.7

Log of employment rate 1.8 1.8 1.8

Figure 6. Employment and productivity rate dynamics in Europe and the former Soviet Union, 1986-1996. Source: elaborated from World Bank (1997).



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Switzerland and Greece), at any point in time, either seeing improved rates of productivity and worsened rates of employment or vice versa. There is strong evidence in the poorest EU countries of a trade-off between productivity and employment rates. In Spain, for example, in 1988-90 employment rates improved significantly, after which productivity increases were accompanied by a worsening of employment rates, until recently when the relationship was reversed. In Ireland, until 1993 productivity rates improved, with relatively slow increases in employment rates (except in 1989-90); after which both elements have tended to increase, resulting in one of the few cases where productivity and employment have converged at one and the same time. In Portugal until 1991 the employment rate increased, while productivity first increased and then declined. Since then increases in productivity have occurred at the expense of Portugal's employment rate.

In the wealthiest EU economy, Luxembourg, a significant improvement in productivity has been accompanied by a worsening of the employment rate. Within the Nordic countries reported a clear pat- tern, similar to that in France, can be identified, associated with the erosion of the Scandinavian model of welfarism. After 1990 there has been a significant worsening of employment rates (particularly in Finland), while productivity has increased.

Within Western Europe, then, there are few signs of an overall virtuous cycle of convergence in which rates of productivity and employment increase. Instead there is evidence that the tendencies toward increased uneven development identified earlier are a result of a trade-off in which gains in productivity are achieved at the expense of employment, and vice versa. Though productivity growth rates have been low, equaling just 1.8 and 0.7 percent per year in 1979-89 and 1989-95, growth has also been slow, and employment growth has not been sufficiently fast to allow catch-up to combine simultaneous







improvements in relative employment and productivity.

Within the ECE countries reported in Figure 6 there appears to be a similar trade-off between employment and productivity, but there has also been a combined fall of employment rates and productivity. The magnitude of the changes in ECE are much greater than those in Western Europe, and in some cases reflect a virtual collapse of formal economic activity, although as we have previously noted informal activity plays an increasing role. Two main dynamics can be identified. First, there is a group of countries that has experienced a combined decline in productivity and employment rates, although productivity has tended to drop at a much faster rate than employment. In Russia and Latvia, as well as in other CIS and Baltic states not presented in Figure 6, these changes have been dramatic and underpin the widespread collapse of formal economic activity. However, the relatively slower reduction in employment rates means that mass open unemployment has not emerged to a significant extent, although this does not mean that labor mobility and flexibility are not a feature of these economies (see Clarke 1998). Second, there is a group of countries in which there has been an overall tendency for productivity rates to increase at the expense of employment rates. In Bulgaria, for example, this trade-off has been dramatic, with a massive fall in employment rates (signaled by mass unemployment and a falling participation rate) and a marginal increase in productivity. In Hungary and Poland these changes have been less dramatic than in Bulgaria, but still of importance. In Hungary employment rates have fallen, with some increase in productivity, whereas in Poland productivity rates increased between 1986 and 1988, fell between 1988 and 1991, and increased again after 1991. There has been a constant fall in employment rates over this period, which has been translated in part (as in Bulgaria and Hungary) into mass unemployment (Smith 2000). In the Czech





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Republic productivity rates have also fluc- tuated, although they have increased strongly since 1994, while overall employment rates have fallen since 1991.

Clearly, then, as in the EU, there is little evidence of an overall increase in both productivity and employment rates leading to economic growth and convergence in the formal economies of ECE. In several cases, there appears to be a trade-off between productivity improvements (albeit often at very low levels) at the expense of employment, or at best with marginal change in relative employment. In other cases, particularly in the former Soviet Union, the erosion of economic and human capacity has been dramatic. In addition, the relatively high economic growth rates in some applicant countries are now beginning to slow, and the marginal increases in employment rates that have been seen in some cases have largely failed to significantly reduce mass unemployment. In other words, it is possible to identify processes of uneven development at different geographic scales, in which the two determinants of output are moving in different directions over time. This suggests that analyses of uneven development in Europe need to be sensitive to the geo- graphically complex articulation of productivity and employment and to recognize that convergence along one dimension may be accompanied by divergence along the other.

Divergence and Differentiation in the "New Europe": Peripherality in a Historical Context

This analysis of trends in uneven development in Europe indicates that after a rapid phase of convergence there was a mid-1970s turning point when growth slowed and divergence occurred, and in ECE since the late 1980s a dramatic collapse of output has occurred. Since the early 1980s catch-up resumed at a EU member state level, but divergence pre-









dominated between regions within EU member states, and most dramatically between Western and Eastern European economies, with a particularly pronounced collapse of formal economic output in the former Soviet Union. Clearly, then, at different geographic scales processes of differentiation and divergence coexist with those of equalization and convergence to form the complex economic geographies of an increasingly integrated European space economy. Within the limits of the data available, this analysis suggests that the neoliberal and neoclassical expectation that integration brings catch-up is false, and that our attention should be focused more on the complex and differentiated mosaics of uneven development in Europe at the start of the twenty-first century.

Indeed, evidence of a more unequal distribution of employment and of a negative trade-off between productivity and employment rate growth suggest a funda- mental dilemma facing Europe. There is a realization that the gains of increasing integration require that the resources released as a result of restructuring be reemployed in activities where their contribution to output is greater. Greater competition would normally result in a reduction in prices relative to incomes so that the con- sumers and users of goods and services gain. In so far, however, as the producers released as a result of restructuring (or the new generations that join the labor market) are not reemployed in activities in which their contribution to output is greater than in the activities they (or their predecessors) left, gains will be offset by greater welfare expenditures and a greater burden on government social security finances. Potential output will be lost, and the multiplier effects of reduced incomes and expenditures of those who do not find new employment will have a depressive effect on output and employment. As adjustment is not instantaneous, increases in competition in sectors that were formerly protected and in activities in which productivity growth exceeds the rate of output growth will result in a short-term increase in unem-





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ployment and nonemployment. Such short-term effects can easily become translated into long-term structural problems, and in ECE the increasing duration of long-term unemployment suggests that "adjustment" is not occurring sufficiently to reintegrate those who have lost work (Smith 2000).

These disparities therefore raise important questions in relation to the long-term trajectories of economic development in which the countries of ECE have been located on the eastern periphery of a European division of labor. While we do not have space to fully explore the complexity of such historical influences, a brief discussion is important because it points to the significance of the historically situated understanding of pathways of economic transformation that underpin the dual dynamics of convergence and divergence. Indeed, the broad national and regional disparities in development between east and west that we have identified in this paper have their roots, at least in part, in the last two centuries of the region's economic history. For example, Table 4 presents per capita GDP data for 1913 for some of the ECE economies. These data clearly suggest that the area that became Czechoslovakia ranked closest to the European average, with Hungary, Poland, and Romania all appearing in the upper ranks of this distribution earlier in the previous century. Bulgaria, Yugoslavia, and

Table 4

GNP per Capita in Selected Eastern and Central European Countries, 1913

GNP per Capita (1960 U.S.$s)

European average 534 Czechoslovakia 524

Hungary 372 Romania 320 Poland 301

Yugoslavia 284 Bulgaria 263

Source: Aldcroft and Morewood (1995, 84).



Table 4









Table 4







Russia lay, as they do today, in a more peripheral position. However, throughout the eighteenth century the only major growth in per capita GNP occurred in Britain (Bairoch 1976). Elsewhere in Europe, per capita GNP stagnated around growth rates of 0.2 to 0.3 percent per annum. Furthermore, using Bairoch's estimates, Berend and Ranki (1982) have argued that during the nineteenth century Eastern Europe and Austria-Hungary were consistently at the lower end of economic development league tables in Europe, as Britain consolidated its economic power and parts of Western Europe began to experience industrialization. As Berend and Ranki (1982, 18) argue, "from the middle of the nineteenth century on, then, Europe became much more divided than [it] . . . had earlier been, the differences that development and backwardness implied having grown considerably more acute. Clearly, the road opened up by the dual revolution, the path to modernization taken by Western Europe, was by no means one that was open to the countries of the periphery in the second half of the nineteenth century." Berend and Ranki go on to argue that the continued peripheral- ization of Eastern Europe resulted from the "refeudalization" of the region through a "second serfdom," which developed in a number of ways. Foremost among these was through the "pull" of industrial development in Western Europe, as a consequence of which Eastern Europe was increasingly forced into a role in the European division of labor as a producer of agricultural products and raw materials for Western European markets. Concomitant with this was the reinscribing of serfdom and tithing to feudal lords, particularly in Russia and Romania.

The contemporary picture of EU-ECE disparities presented in this paper is clearly different from this historical record, in that it involves the reestablishment of a variety of forms of "capitalism" in ECE. Yet, this historical analysis does suggest that we may be witnessing a reworking of an East-West European division of labor and a develop-





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ment divide of quite significant and endur- ing proportions. With this historical context in mind, the extent to which increased integration at the beginning of the twenty- first century will lead to convergence remains an open question. What is clear, however, is that differentiation is more apparent than convergence, and that, as in the EU, convergence will only occur through faster improvement in peripheral territories and for marginalized peoples. This suggests the need for a political com- mitment to increasing demand in ECE through reintegrating marginalized spaces in the "new Europe." It also suggests that much more research is required on how the wealth that is created in different parts of a more integrated Europe becomes distributed, and on who the winners and losers in this process are.

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Catching up or Falling behind? Economic Performance and Regional Trajectories in the "New Europe"