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Capital’s grabbing hand? A cross-industry analysis of the decline of the labor share in OECD countries

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Abstract

We examine the determinants of the within-industry decline of the labor share, using industry-level annual data for 25 OECD countries, 20 business-sector industries and covering up to 28 years. We find that total factor productivity growth—which captures (albeit imprecisely) capital-augmenting or labor-replacing technical change—and capital deepening jointly account for as much as 80 % of the within-industry contraction of the labor share. We also find that another factor explaining the aggregate decline of the labor share is the increased international competition, with higher import penetration causing a contraction of the share of labour-intensive industries in total value added. However, the fraction of the drop of the labor share explained by international competition remains limited.

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Notes

  1. For an extended version of this paper with country by country data see Bassanini and Manfredi (2012).

  2. This is consistent with capital and labor being gross substitutes as found in a number of studies based on aggregate data (see for example Masanjala and Papageorgiou 2004). The seminal paper of Berndt (1976) also finds elasticities of substitution greater than 1, although insignificantly. More generally, however, estimated elasticities of substitution reported in the literature can vary from significantly smaller to significantly larger than 1 (see e.g. Antras 2004).

  3. A different strand of literature has also introduced the “Skill Biased Organizational Change” hypothesis, according to which the increasing diffusion of new organizational practices within firms plays a role in the increasing demand for skilled workers (see e.g. Caroli and van Reenen 2001; Caroli et al. 2001). Piva et al. (2005) show that upskilling is more a function of reorganizational strategy than a consequence of technological change alone.

  4. Australia, Austria, Belgium, Denmark, Germany, Finland, France, Italy, Japan, the Netherlands, Spain, the United Kingdom and the United States.

  5. Data on industry output, capital services and productivity are from EUKLEMS and associated databases. By contrast, most trade variables are from OECD STAN (import penetration and trade exposure) or OECD (2008) and are restricted to manufacturing industries for which long time series are available. Descriptive statistics and detailed data definitions and sources are available in the "Appendix".

  6. These correspond to Czech Republic, Estonia, Greece, Hungary, Korea, Poland and Portugal.

  7. The sample includes Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, the Netherlands, Slovenia, Spain, Sweden, the United Kingdom and the United States.

  8. TFP and the capital-labor ratio are available only for a subset of high-wage countries. Therefore, when they are included, the sample is de facto restricted to high-wage countries only.

  9. Caution is required, however, about results in Panel B, since there are many other, possibly relevant interactions that are not included in the specification and differences across industry groups might turn out insignificant in a more refined model. This is issue is, nevertheless, beyond the scope of this paper (see Bassanini and Duval 2009, for a discussion).

  10. On average, it was 12 and 6.5 percentage points in high and low-wage-share industries, respectively, for the countries reported in Fig. 1 The above rates fall to 9 and 5.5 percentage points, respectively, if Belgium and the Netherlands, the clear outliers in Fig. 1 are excluded.

  11. As the managing director of a US leading manufacturer of prototypes put it, “people don’t seem to come in with the right skill sets to work in modern manufacturing. It seems as if technology has evolved faster than people. [… The advantage of capital equipment is that] you don’t have to train machines” (New York Times, June 10th, 2011).

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Author information

Authors and Affiliations

  1. OECD, Paris, France

    Andrea Bassanini & Thomas Manfredi

  2. IZA, Bonn, Germany

    Andrea Bassanini

Authors
  1. Andrea Bassanini
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  2. Thomas Manfredi
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Corresponding author

Correspondence to Andrea Bassanini.

Additional information

This paper draws on an unpublished background paper prepared by the authors for the 2012 edition of the OECD Employment Outlook. Nevertheless, the views expressed here are those of the authors and cannot be attributed to the OECD or its member countries. The authors would like to gratefully acknowledge the skilful research assistance provided by Agnès Puymoyen and the comments received from Mark Keese, Pascal Marianna, John Martin, Stefano Scarpetta, Paul Swaim and, especially, Ann Vourc’h as well as from the participants at the April 2012 meeting of the ELSAC Working Party on Employment.

Appendix: data construction, sources and descriptive statistics

Appendix: data construction, sources and descriptive statistics

Earnings and hourly wage data refer to total gross annual earnings and average hourly wages, respectively of wage and salary employees. Employment and hours worked refer to annual averages for wage and salary employees. Real value added is obtained by deflating nominal value added in each industry with the industry-specific double deflator. TFP data are also constructed using double-deflated value added. All these data as well as capital stocks and 1997 USD purchasing power parities data are from EUKLEMS and the associated GGCD productivity database (Inklaar and Timmer 2008). EUKLEMS data obtained through interpolation and/or estimated on the basis of conjectures were removed from the sample, following the criteria detailed in OECD (2011b).

For the computation of the labor share in each industry, average hourly compensation of self-employed is assumed to be equal to the average hourly wage of the industry. 1997 USD purchasing power parities data, drawn from EUKLEMS, are used for the definition of high-wage countries. The distributions by educational attainment of earnings, wage, and hours also come from the EUKLEMS Database. Education is divided into three categories: low-education (less than upper secondary); medium education (upper secondary); and high education (more than upper secondary). The business sector, in this case, is partitioned in nine industries for reasons of data reliability (following the criteria detailed in OECD (2011b).

Import-weighted real exchange rates are defined as follows:

$$ x_{ikt} = \sum\limits_{i = 1}^{I} {\sum\limits_{l = 1}^{L} {m_{{iklt_{0} }} e_{klt} p_{lt} /p_{kt} } } $$

where x stands for the import-weighted real exchange rate, m is to the import share from country l in industry i of country k at a fixed time period t 0 (early 1980s in these data)—the import weights thus vary across industries and countries but are constant over time—e is the nominal bilateral exchange rate between countries k and l at time t—which varies across partner countries and time, but not across industries—the p variables refer to price levels, as approximated by the GDP deflator, in countries l and k respectively. An increase in the industry-specific exchange rate represents a real depreciation in the price of output produced in industry i of country k relative to its trading partners (weighted by import shares). Put differently, an increase in the industry-specific exchange rate represents an improvement in the terms of trade in industry i for country k. The source is OECD (2007).

Import penetration is defined as the ratio imports to apparent demand (imports plus output minus exports). Trade exposure is the sum of import penetration and export propensity, the latter defined as the ratio of exports to domestic output. The source of both variables is the OECD STAN Database.

OECD industry-specific indicators on regulatory barriers to inward FDI concern foreign equity limits, screening and approval, restrictions on top foreign personnel, and other restrictions concerning notably reciprocity rules and profit/capital repatriation. For each of these components the indicator vary between 0 and 1 from the least to the most restrictive. They are available between 1997 and 2006 at approximately five-year intervals. In the regressions, missing 2007 data are replaced with 2006 data. All components, except restrictions on top foreign personnel, were lumped together by simple addition. The source is Kalinova et al. (2010).

Table 7 reports descriptive statistics of main variables.

Table 7 Descriptive statistics of the main variables
Full size table

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Bassanini, A., Manfredi, T. Capital’s grabbing hand? A cross-industry analysis of the decline of the labor share in OECD countries. Eurasian Bus Rev 4, 3–30 (2014). https://doi.org/10.1007/s40821-014-0004-y

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