Long Term Freight Market Index and Inferences*

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The Asian Journal of Shipping and Logistics ● Volume 27 Number 3 December 2011 pp. 405-422 ●

Long Term Freight Market Index and Inferences*

Okan DURU ** · Shigeru YOSHIDA***

Contents

I . IntroductionII. Life expectancy as a long- run leading indicatorIII. Long term freight index

IV. Modeling dry cargo seaborne tradeV. Treatment of spurious regression drawbacksVI. Conclusion

Abstract

This paper proposes to establish a long term shipping freight index for dry cargo transportation and investigates its particulars among the cyclic fluctuations. Many scholars investigated dry cargo shipping markets and some of them attempted to construct a composite index of freight rates. Although, several critiques are indicated about the method of composition, these indices depicted long term movements in general. This paper also presents models for long term freight rates and seaborne trade with the recent data. A novel contribution is derived from using life expectancy as a long-run leading indicator.

Key words : Dry cargo freight rates, Life expectancy, Long term analysis.

* This paper is previously published in the Journal of Japan Society of Logistics and Shipping Economics (Vol.44) and selected for International Exchange award. By the copyright permission of the Japan Society of Logistics and Shipping Economics, this paper is reviewed and republished in the AJSL.** Research Fellow, Istanbul Technical University, Turkey, E-mail: [email protected]*** Professor of Maritime Sciences, Kobe University, Japan, E-mail: [email protected]

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Long Term Freight Market Index and Inferences

I. Introduction

Index measures are widely used in economic and financial institutions for defining a specific system. The main purpose of indices is to calculate composite effects of a system (probably an economic market) and in some cases it is used to combine several individual series which recorded in different time spans. In shipping economics literature, various measures of freight index is proposed and presented (See table 1). Several institutions also calculate similar indices for dry cargo and tanker markets (i.e. Baltic Exchange, Tramp Data Co., J.E. HYDE).

Veenstra and Dalen reviewed freight index issue and calculated various alternative indices according to route characteristics and qualitative measures of shipments such as age of ships.1) This study uses monthly fixtures data of eight years including cargo, port and ship particulars. In the short run, substantial differences are reported when comparing the Lloyd’s Shipping Economics TTC index and Maritime Research Inc. index. However, they indicated that there is no particular difference on the long run evaluation (annual base series). Different methods of calculation conclude similar long run fluctuations while disparity exists in short run results.

The recent evidences reveal that the previous and current index measures can prove fluctuations in long run and probably simultaneous results. This paper attempts to construct a composite freight index based on averaging percentage changes year-by-year. The long term composite freight index (LFI) is investigated according to anterior theories of a number of studies and finally it is executed for building a single equation econometric model of dry cargo seaborne trade. Because of the lack of proper data, econometric model is based on the 2nd half of 1900s. However, the sample period is almost larger than previous works.2)

The proposed models originally introduce the series of life expectancy as a long term indicator in shipping trade. The following section reviews why and how life expectancy influence world merchandise trade and shipping business in long-run.

The present paper reviews the existing freight index measures and proposes

1) Veenstra and Dalen (2008).2) Beenstock and Vergottis (1989, 1993); Hale and Vanags (1989).

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a long term continuous freight index series by the combination of them. The critiques based on representative accuracy of indices are also investigated. Finally, inferences of long term index are performed by univariate and multivariate analysis.

II. Life expectancy as a long-run leading indicator

The role of population particulars is indicated by many scholars. The number of consumers is a key factor on production industry and it influences price of goods and service accordingly. Shipping freights represent a considerable proportion of the price of finished goods, so shipping freights and wholesale prices have a strong relationship in the global economy.3)

The effect of population is somewhat complicated since many high population countries can not contribute to developing economic system. The definition of consuming population is crucial. Consuming particulars of population depend on wealth and quality of life among the whole members of community. Increasing life quality is followed by increasing life expectancy on every levels of age. Particularly, its impacts are expected to be in long run.

Life expectancy is used for many econometric models and it defines several economic dynamics. Fogel pointed out effects of decreasing mortality and increasing life expectancy on economic growth.4) Life expectancy is frequently used for long term modeling and analysis of economic growth.5) Bloom, Canning and Sevilla presented a model of economic growth and it is reported that life expectancy is a statistically significant driver of increase in production output among 104 countries.6)

Bloom and Canning express four main reasons for defining life expectancy as an economic indicator:7)

“Productivity. Healthier populations tend to have higher labor productivity, because their workers are physically more energetic and mentally more robust.

3) Metaxas (1971).4) Fogel (1994).5) Barro (1996), Sachs and Warner (1997) and Bloom, Canning and Sevilla (2004) among others.6) Bloom, Canning and Sevilla (2004).7) Bloom and Canning (1999).

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Education. Healthier people who live longer have stronger incentives to invest in developing their skills, because they expect to reap the benefits of such investments over longer periods.

Investment in physical capital. Improvements in longevity create a greater need for people to save for their retirement. Insofar as increased savings lead to increased investment, workers will have access to more capital and their incomes will rise.

Demographic dividend. The transition from high to low rates of both mortality and fertility has been dramatic and rapid in many developing countries in recent decades. As this happens, income per capita can rise dramatically, provided the broader policy environment permits the new workers to be absorbed into productive employment.“

III. Long term freight index

Analysis of freight indices is conducted by many scholars for several terms of shipping history (Table 1). Among these studies, different parts of the last three century presented. Table 1 shows details of freight rates and index series for various sources. Deflators which are used in the present paper are also presented in Table 1. The series of freight rates and indices cover a part of long term data and overlap each other in some periods. For instance, the series of Harley8) and American Export Freight rate index overlap each other in the period of 1869-1872, or U.K. Chamber of Shipping freight index and Norwegian Shipping News (NSN) voyage freight rate indices overlap between 1948 and 1969. These connections ensure to compare correlations of consequent series. Particularly after 1869, series have high correlation coefficients (more than 0.80). For instance, Isserlis index, American Export Freight Index, Economist’s index and U.K. Chamber of Shipping index indicate usually same fluctuations in pre-World War I and post-WWI periods (See Fig. 1). The series of the second half of 1900s are highly correlated (over 0.90 corr. coefficient in general). Most of the indices of before 1950 consist of a large number of routes because of the lesser size of shipments.

8) Harley (1988).

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However, the recent industry indices are originated from the large shipments which cover most of the world dry bulk seaborne trade.

<Figure 1> ISSCI, AEFRI, ECONI and UKCSV between 1850 and 1970.

All these indices are turned to ratio-to-change data which denotes percentage fluctuations in itself. The beginning of the series (1741) is defined as ‘100’ and subsequent years are calculated as year-to-year average changes of available series for the year t. The long term freight index, LFI, is calculated as follows;

(eq.1)

where Δ is the differencing operator for index series, xnt, for year t. The first differences of indices are divided to xt-1 value for calculating percentage change. The average value of ratio-to-change is applied to previous year LFIt-1. LFI series is deflated by Brown-Hopkins composite price index for 1741-1954 periods and by retail price index (RPI) of U.K. for 1954-2008 periods

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(Deflators are presented in Table 1).9)

The LFI series indicates two important super cycles which existed in the term of World War I (WWI) and in the recent market conditions. Although, LFI series is a deflated data, freight markets seem to be affected more than price fluctuations. A long term cycle appears in 1741 and the beginning of 1900s which is around 160 years. The peak of this cycle is around 1810s and after a sharp decline, a long term period of continuous decreasing market is followed till WWI.

Nobel Laureate Douglass North reported a seminal work about interactions of transportation cost and world economics.10) One of the critical evidence of that study is the shipping transportation costs are incurred a long term decline because of the productivity gains sourced from backhaul shipments. Shipping transportation was based on one-way traffic throughout centuries, and after the Industrial Revolution of Europe and America, backhaul cargoes become available. Although, seaborne trade dramatically increases, ships commence to carry cargoes in both direction brought a huge productivity rather than a long ballast voyages for returning back to home port. Productivity gains affected shipping freights till beginning of 1900s between U.K. – North America, U.K.-East India among others.

Another biggest effect was arisen from the technological improvement of ship propulsion. By 1800s, world merchant fleet starts to convert to steam powered ships. Steam power brought decreasing number of crew and crew qualification and high navigating speeds as well. Increasing cargo carrying capacity was another critical improvement.11)

In the beginning of 1900s, shipping markets are influenced by WWI and also two-way traffic probably reached its capacities. One of the most improving technology, diesel engines, applied to commercial ships, but in the long term there was no considerable decline on freight rates. Although, several warfare affected world economics in the last century, the biggest effect has been arisen from increasing innovations and merchandise trade. In various places of world, civilizations improved higher growth rates, higher living quality and increasing population. Baby boom of the second half of 1900s exposed larger consuming economics. While ship technology improved massive developments on propulsion, crew size, cargo capacity etc, world merchandise trade also grow up enormously.

9) The LFI data is previously published in Duru et al (2010). Interested researchers can found the complete series of LFI till 2008.10) North (1958).11) Harley (1988, 1989); Mohammed and Williamson (2004).

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In 1983, when Douglas North takes Nobel Prize, he shared that prize with Robert Fogel who is a population economist and one of the founder of the scientific field, ’cliometrics’. One of the most valuable studies of Fogel is about the long term interactions of life expectancy as an indicator of living quality and economic fluctuations. Fogel indicates that life expectancy and physical size of humankind affect various issues in economics.12) Fig. 2 shows LFI series together with life expectancy data at age 10 in period of 1741 and 2002 for U.S. population. Similarities of long term fluctuations of two series are remarkable.

<Table 1 > Data used in this study.Term Description Code Source

Freight rates & indices

1741-1872 Tyne - London Coal route freight rate series. TLCH Harley (1988)

1741-1872 U.S. - British Grain route freight rate series. USGH Harley (1988)

1790-1815 British Import Freight Rate Index series. BIFRI North(1958)Nobel Laureate

1814-1910 American Export Freight Rate Index series. AEFRI North (1958)

1869-1936 Isserlis Composite Index series. ISSCI Isserlis (1938)

1869-1913 New UK Index series. NUKFI Klovland (2002)

1898-1913 Economist’s Freight Index series. ECONI Yoshimura (1942)

1921-1939 Economist’s Freight Index series. ECONI Yoshimura (1942)

1920-1969 UK Chamber of Shipping Index series. UKCSV Isserlis (1938)Hummels (1999)

1948-1997 Norwegian Shipping News Spot Freight Index series. NSNVI Hummels (1999)

1948-1990 Norwegian Shipping News Time Charter Index series. NSNTI Hummels (1999)

1952-1989 UK Chamber of Shipping Time Charter Index series. UKCST Hummels (1999)

1986-2008 Baltic Freight Index / Baltic Dry Index series. BFI/BDI Baltic Exchange Co.Hummels (1999)

1988-1996 German Ministry of Transport T/C Index series. GMTTI Hummels (1999)

1991-2007 Lloyd’s Shipping Economist (LSE) Tramp Index series. LSEFI LSE Magazine various issues.

Deflator series

1741-1954 Price of Composite Unit of Consumables. PUCON Brown & Hopkins(1956)

1954-2008 RPI: Retail Price Index of U.K.RPIUK Office for national

statistics, U.K.(www.statistics.gov.uk).

12) Fogel (1986).

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<Figure 2> LFI and Life expectancy (at age 10, U.S.) between 1741 and 2002(Fogel13); U.N. Population Information Network-POPIN, http://www.un.org/popin).

IV. Modeling Dry cargo seaborne trade

Econometric modeling of shipping industry is investigated by several studies and relationship between freight markets, fleet capacity and seaborne trade volume is frequently reported. Beenstock and Vergottis presented models for dry cargo and tanker segment shipping markets.14) The modeling period of these studies included 25 years in most (between 1960 and 1985) and the measure of goodness of fit, R squared value, is not over 0.7 in many of the models. 25 years and over periods are illustrated as a typical long term modeling studies.

The opinion of dynamics between life expectancy and economics was a pioneering step in economic history and later the novel research field, cliometrics. In this paper, a possible combination of long term dynamics of population and seaborne trade itself is attempted to establish a wider econometric model of dry cargo shipping. The seaborne trade model of Beenstock and Vergottis15) is modified and life expectancy data at age 10 in U.S. is incorporated. The LFI is used to expose freight market effects.

13) Fogel (1986).14) Beenstock and Vergottis (1989, 1993).15) Supra note 12.

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Therefore, theoretical model is: + + + ST= f (LFI, BF, E10, Pure judgmental factors) (eq.2)

where ST is dry cargo seaborne trade in ton-miles; LFI is the long term composite freight index which is proposed in this paper; BF is the dry cargo (mainly bulker) fleet in dwt and E10 is the life expectancy at age 10 of U.S. population. For freight market model, effects of fleet capacity should be negative.

Therefore, theoretical model is: + - -/+ LFI = f (ST, BF, E10, Pure judgmental factors) (eq.3)

Interpretation of life expectancy is somewhat complicated. Fig. 1 indicates a long wave co-movement among the freight rates and life expectancy. However, in shorter terms (10-15 years), relationship is reversing. Increasing life expectancy is an indicator of forthcoming recession in freight markets. Increasing life expectancy will be followed by a slowdown and it means economic reduction. The econometric model is estimated by Ordinary Least Squares method and residual white noise tests are performed by investigating auto correlations, heteroscedasticity and Durbin-Watson statistics (D.W.).16) Since the normality of residuals is confirmed by Jarque-Bera17) statistics (J-B: 2.40, p: 0.30) and no autoregressive variable is included, the D.W. indication is consistent. t-test values of coefficients are reported in parenthesis under each of them. All coefficients which are indicated in models are statistically consistent under 95% confidence level (p < 0.05).

Freight rate elasticity of seaborne trade is 0.14 which is almost same as the calculation of Beenstock and Vergottis (eq. 4). Elasticity of life expectancy on seaborne trade points out that a 1-year increase on life expectancy before 11 years raises seaborne trade about 7% in current values. On account of absolute average of yearly variations of seaborne trade is about 6% in period of 1960-2007 and 1 year increase on life expectancy takes about 5 years in the last century, about 1.5% rise on seaborne trade is probably triggered by demographic changes.

16) Durbin and Watson (1950, 1951).17) Jarque and Bera (1980).

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Model of Dry cargo seaborne trade (ST)

Model of Dry cargo freight rate (LFI)

In Eq. 5, fleet size and life expectancy has negative coefficients as discussed before. 1% increases of life expectancy (in 11 year’s time lags; around 0.7 years) supports 18% decrease of freight levels.

V. Treatment of spurious regression drawbacks Although, many scholars used non-stationary variables as both dependent

and independent variables or there is no reported test of stationarity,21) the modern econometrics consists of a standard test of stationarity in variables. Tsolakis also indicated that several ex ante studies are based on a mix of non-stationary and stationary datasets which is explicitly violates assumptions of

18) Ljung-Box (1978) Q statistics for auto correlations of residuals.19) Akaike Information Criterion (Akaike, 1973).20) Schwarz Information Criterion (Schwarz, 1978).21) Norman (1979); Beenstock and Vergottis (1989) among others.

18)

19) 20)

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the classical linear regression model (CLRM).22) In these conditions, spurious regressions are possible.23) Therefore, amendments are performed to confirm stationarity of intended data series. Rather than traditional differencing method, the present paper applies percentage changes as a transformation method. The selection of the percentage changing transformation method lies on significance of estimated equations and its scale-free advantages.

Augmented Dickey-Fuller test is performed to confirm stationarity of series.24) All data series are stationary at 95% level of confidence after percentage change transformation (See table 2). ADF tests are performed with intercept and trend configurations except BF percentage change series (no intercept, no trend).

<Table 2 > Augmented Dickey-Fuller (ADF) unit root tests.

Results Critical Values (p: 0.05)27)

Level % change Level % change

ST 2.28 (c, t)a -5.70 (c, t) -3.51 -3.51

LFI -0.36 (c, t) -7.35 (c, t) -3.50 -3.50

BF -1.91 (c, t) -2.72 (none)b -3.52 -1.94

E10 -1.39 (c, t) -3.53 (c, t) -3.51 -3.51a (c, t) means ADF test based on intercept and trend.b (none) means ADF test based on no intercept and no trend.

By using amended data series, econometric models of seaborne trade and freight market are established as in Eq. 6 and Eq. 7.

22) Tsolakis (2005).23) Granger and Newbold (1974, 1977).24) Dickey and Fuller (1979).25) MacKinnon (1996).

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Model of Dry cargo seaborne trade

Model of Dry cargo freight rate (LFI)

Treatment of spurious regressions induced lower coefficient of determination (R-squared statistics), but residual statistics are well recorded as normally distributed (5% confidence level), white noise (as mean and variation) and no significant autocorrelation. Evaluation of ratio-to-change model results differs than the log-linear model. 1% increase of E10 %∆

t-14 term provides 9.38% additional growth of seaborne trade in Eq. 6. Effects of fleet size and current freight rates are weak as it is recorded in previous studies. In Eq. 7, 1% increase of life expectancy in 18 years time lag (E10 %∆

t-18) develop 49% raise of freight rate.

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V. Conclusion The present research provides a long term freight rate index and reviews

previous econometric models of dry cargo freight rate and seaborne trade. Price elasticity of seaborne trade is estimated very close to evidences of Beenstock and Vergottis (inelastic). Life expectancy is found a significant indicator for both seaborne trade and dry cargo freight rates.

Time lags between freight rates, seaborne trade and life expectancy is defined in several years. However, the log-linear model is only reference and not a significant model because of the spurious regression prospects. After data transformation, for seaborne trade it is about 9-14 years and for freight rates it is about 18-21 years.

By the reference result of the model results and graphical review, effects of life expectancy is found negative in short run (less than 15 years; Juglar-type cycles), and positive for longer periods (15-25 years; Kuznet-type cycles).*

* Date of Contribution ; Oct. 30, 2011 Date of Acceptance ; Dec. 5, 2011

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Acknowledgement

Authors are thankful to Koichiro Tezuka and two anonymous referees for their constructive comments and suggestions on earlier version of this paper.

Symbols & Data sourceST : Seaborne trade in ton-miles (dry cargo). Fearnleys reviews, Maritime transportation statistics handbook, Japan Shipowners Association.ST %∆ : Seaborne trade in ton-miles (dry cargo) (percentage change).LFI : Long term composite freight index.LFI %∆ : Long term composite freight index (percentage change).BF : Bulk carrier fleet (dwt). Fearnleys reviews, Maritime transportation statistics handbook, Japan Shipowners Association. BF %∆ : Bulk carrier fleet (dwt) (percentage change).E10

t-n : Life expectation at age 10 (n years lagged) (U.S. population). Fogel (1986), U.N. Population Information Network-POPIN, http://www.un.org/popin/ .E10 %∆

t-n: Life expectation at age 10 (n years lagged) (percentage change).

AbbreviationsOLS : Ordinary Least SquaresD.W. : Durbin-Watson statistics (1950, 1951)ACL : Auto correlation of residuals in L lags.

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Long Term Freight Market Index and Inferences*