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Distortions, structural transformation and the Europe-US income gap

  • El-hadj Bah EMAIL logo
Veröffentlicht/Copyright: 26. September 2013
The B.E. Journal of Macroeconomics
Aus der Zeitschrift The B.E. Journal of Macroeconomics Band 13 Heft 1

Abstract

This paper uses a model of structural transformation with sectoral distortions to study the convergence of income between Western Europe and the United States (US). It shows that in addition to income effects and growth differentials in sectoral total factor productivities (TFP), sectoral distortions are important for understanding the reallocation of resources across sectors. Allowing for a growing wedge between the marginal revenue product of labor relative to capital can explain the rapid decline in the manufacturing share of hours for the US that started in the 1980s. The distortions are also important for explaining the paths of sectoral hours for many European countries. However, the distortions lead to a misallocation of resources across sectors, which helps explain why the rapid Europe-US income convergence that started in the 1950s stopped in the early 1980s.

Keywords: European catch-up; policy distortions; sectoral productivity; structural transformation; JEL Classification: E2; O1; O4
Corresponding author: El-hadj Bah, Department of Economics, University of Auckland, Auckland, New Zealand, e-mail:
  1. 1

    The countries are Austria, Belgium, Denmark, Finland, France, Italy, the Netherlands, Spain and the United Kingdom (UK).

  2. 2

    The processes of structural transformation may differ across countries (Bah 2011; McMillan and Rodrik 2012).

  3. 3

    Echevarria (1997), Kongsamut, Rebelo and Xie (2001), among others, emphasize income effects, while Baumol (1967), Ngai and Pissarides (2007) emphasize relative price differentials. Gollin, Parente and Rogerson (2002, 2007), Rogerson (2008) and Duarte and Restuccia (2010) combine both channels.

  4. 4

    See, for instance, Cordoba and Ripoll (2008), Restuccia, Yang and Zhu (2008), Caselli (2005), Adamopoulos and Akyol (2009), Chanda and Dalgaard (2008), Vollrath (2009).

  5. 5

    In this paper, industry and manufacturing are used interchangeably. The exact definition of a sector is presented in the data appendix.

  6. 6

    Studies in the literature use both income effects (Echevarria 1997; Laitner 2000; Caselli and Coleman II 2001; Kongsamut, Rebelo, and Xie 2001; Gollin, Parente, and Rogerson 2002) and price effects (Baumol 1967; Ngai and Pissarides 2007) as key drivers of structural transformation. Acemoglu and Guerrieri (2008), instead, introduce differences in factor shares and capital deepening as the main mechanisms for resource reallocation. For a more detailed discussion of the main mechanisms of structural transformation, see Herrendorf, Rogerson and Valentinyi (2009), Herrendorf, Rogerson and Valentinyi (2013).

  7. 7

    While land is important for agriculture, it is often assumed to be constant and is normalized to 1. Most studies in the literature employ this convenient formulation and treat land as being included in physical capital.

  8. 8

    See the discussions about this assumption presented in Section 5.

  9. 9

    As their derivation shows, we cannot separate the effects of the distortions to capital from the effects of the distortions to labor. Therefore, my distortions here are on labor relative to capital.

  10. 10

    See Herrendorf, Rogerson, and Valentinyi (2013) for a detailed discussion of this issue.

  11. 11

    See the Appendix for additional details.

  12. 12

    The corresponding values in Herrendorf, Rogerson, and Valentinyi (2009) are 0.01–0.02 and 0.15–0.18, respectively.

  13. 13

    The data series has been filtered to focus on low frequency time series.

  14. 14

    I use the prices in 1950 to compute GDP per capita for all periods.

  15. 15

    Bah and Brada (2009) use a similar model to assess the productivity catch-up in 10 transition countries in Eastern Europe.

  16. 16

    Details concerning the data and computation methods are presented in the appendix.

  17. 17

    Such discrepancies are also present for other countries.

  18. 18

    The US sectoral prices in 1950 are used to compute the GDP per capita for all countries.

  19. 19

    If their initial estimates include distortions, they will appear here. However, these are the best estimates available.

  20. 20

    The simulations for Finland, Italy and the UK begin in 1971, but in this graph, I provide a common starting date to facilitate the comparison.

  21. 21

    Note that errors in the estimates of sectoral TFP in the data will affect the magnitude of the distortions inferred from the model.

  22. 22

    The fit for the counterfactuals below is worse, indicating that the distortions do not hinder the capital reallocation.

  23. 23

    The series has been normalized to one in 1995 to focus on the growth of the wedge.

  24. 24

    See, for example, Matsuyama (2009), Coleman II (2007), Uy, Yi, and Zhang (2013), Tombe (2012), Huang (2011), Ungor (2011), among others.

  25. 25

    Large exporters, such as Germany, Japan and Korea, also have large manufacturing labor shares.

I thank Richard Rogerson for his invaluable advice and guidance on this and other projects. I am grateful to Berthold Herrendorf, Josef C. Brada and Professor Edward C. Prescott for their advice. I have also benefited from comments and suggestions by seminar participants at numerous conferences and institutions. I am grateful to the editor, Diego Restuccia, and two anonymous referees for their important comments and suggestions.

Appendix

Data sources

The analysis in this paper required data collection for 10 countries: the US (1950–2007), Austria (1981–2007), Belgium (1981–2006), Denmark (1981–2007), Finland (1971–2007), France (1981–2007), Italy (1971–2007), the Netherlands (1981–2007), Spain (1981–2007) and the UK (1971–2007). The selection of countries was driven by the data availability. From the Penn World Tables (PWT), version 7.1 (Heston, Summers, and Aten 2011), I obtained real GDP per capita (rgdpl), population (POP) and investment as a share of GDP in constant dollars (ki). After applying the HP filter, I calculated capital stocks using the perpetual inventory method, capital-output ratio and GDP per capita relative to the US. The sectoral data come from the Groningen Growth and Development Centre (GGDC), EU KLEMS and the Bureau of Economic Analysis (BEA) (Bureau of Economic Analysis 2012). The three sectors are defined as follows: Agriculture consists of agriculture, forestry, fishing and hunting. Industry is composed of mining, manufacturing, utilities and construction. The rest are grouped into services. To compute the sectoral TFPs for the US, I obtained capital stocks for the major NAIC sectors from the BEA (Table 3.2ES. Chain-Type Quantity Indexes for Net Stock of Private Fixed Assets by Industry). I used the chain-type indexes and nominal values for 2005 (Table 3.1ES. Current-Cost Net Stock of Private Fixed Assets by Industry and Table 7.1B. Current-Cost Net Stock of Government Fixed Assets) to obtain series in constant dollars. I used the shares in value added to aggregate the sectors into agriculture, industry and services. Value added in constant prices and hours worked by sectors were taken from the Groningen 10-sector database (Timmer and de Vries 2009). I also used this database to compute the prices of agriculture and services relative to industry for the US economy. The average investment rate for the US was taken from the NIPA tables (Table 5.1. Saving and investment by sector). I also used the NIPA tables (Table 1.1. Current-Cost Net Stock of Fixed Assets and Consumer Durable Goods) to compute the capital-GDP ratio in 1950. The growth rates of sectoral labor productivity, defined as valued added per hour, were calculated with data from the 10-sector database. For the European countries, I obtained sectoral shares of hours from the EU KLEMS database. I used the GGDC Productivity Level database (Inklaar and Timmer 2008) to compute the sectoral TFP relative to the US in 1997. The database disaggregates economies into lower-digit SIC codes, and I used the shares of value added to aggregate them into the three main sectors studied here. This database complements the EU KLEMS data, which were used to calculate the sectoral TFP growth rates. From the average growth rates, I calculated γ1–γ2 and γ2–γ3, which that were then used to compute the paths of sectoral TFPs. The average growth rates relative to the US from 1971 or 1981 to 1997 were used to compute the sectoral TFPs in the initial period. The initial sectoral TFPs were then scaled up or down such that the model matched GDP per capita relative to the US in the initial period. The sectoral labor productivity growth rates were taken from the EU KLEMS database (variable LP_I).

The wage data for the US were taken from the Bureau of Labor Statistics. From the OECD STAN database for structural analysis (OECD 2012), I obtained total labor costs and sectoral hours for eight European countries for the 1995–2006 period. I used these series to compute labor costs per hour. Trade balance as a share of GDP was obtained from the World Development Indicators 2012 (World Bank 2012). Most data series have been filtered using the HP filter to focus on low frequency trends. The filter was applied before any ratios were taken.

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Published Online: 2013-09-26
Published in Print: 2013-01-01

©2013 by Walter de Gruyter Berlin Boston

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https://doi.org/10.1515/bejm-2012-0147
Schlagwörter für diesen Artikel
European catch-up; policy distortions; sectoral productivity; structural transformation; JEL Classification: E2; O1; O4

Artikel in diesem Heft

  1. Masthead
  2. Masthead
  3. Advances
  4. How have global shocks impacted the real effective exchange rates of individual euro area countries since the euro’s creation?
  5. Employment by age, education, and economic growth: effects of fiscal policy composition in general equilibrium
  6. Overeducation and skill-biased technical change
  7. Strategic wage bargaining, labor market volatility, and persistence
  8. Households’ uncertainty about Medicare policy
  9. Contributions
  10. Deconstructing shocks and persistence in OECD real exchange rates1)
  11. A contribution to the empirics of welfare growth
  12. Development accounting with wedges: the experience of six European countries
  13. Implementation cycles, growth and the labor market
  14. International technology adoption, R&D, and productivity growth
  15. Bequest taxes, donations, and house prices
  16. Business cycle accounting of the BRIC economies
  17. Privately optimal severance pay
  18. Small business loan guarantees as insurance against aggregate risks
  19. Output growth and unexpected government expenditures
  20. International business cycles and remittance flows
  21. Effects of productivity shocks on hours worked: UK evidence
  22. A prior predictive analysis of the effects of Loss Aversion/Narrow Framing in a macroeconomic model for asset pricing
  23. Exchange rate pass-through and fiscal multipliers
  24. Credit demand, credit supply, and economic activity
  25. Distortions, structural transformation and the Europe-US income gap
  26. Monetary policy shocks and real commodity prices
  27. Topics
  28. News-driven international business cycles
  29. Business cycle dynamics across the US states
  30. Required reserves as a credit policy tool
  31. The macroeconomic effects of the 35-h workweek regulation in France
  32. Productivity and resource misallocation in Latin America1)
  33. Information and communication technologies over the business cycle
  34. In search of lost time: the neoclassical synthesis
  35. Divorce laws and divorce rate in the US
  36. Is the “Great Recession” really so different from the past?
  37. Monetary business cycle accounting for Sweden
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