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Industry Concentration and Wage Inequality: a Directed Technical Change Approach

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Abstract

Despite several theoretical approaches linking rising market power to more income inequality, a theoretical-based empirical quantification of this relationship has not been made. We devised a directed technical change model and characterize this relationship. To test our model, we calculate concentration indexes and relate them with skill-premium using industry data per country for 40 countries from 1995 to 2011. In general, we show a negative and robust relationship between the market power index and wage inequality. Additional evidence shows that results tend to be different for countries with different income levels and for different initial values of skill-premium and market power.

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Notes

  1. see Remarks by the President in State of the Union Address (Jan. 25, 2015).

  2. The si is the share of industry i on the output, that is, \(HHI={{\sum }_{i}^{n}} {s_{i}^{2}}\), where n is the number of industries in the economy.

  3. To classify countries by income groups we use the GNI per capita calculated using the World Bank Atlas method and its respective thresholds.

  4. Figures 2 and 3 show the average weight and skill-premium by sectors for the set of 40 countries included in our sample and the period 1995-2011.

  5. We cannot exclude certain forms of reverse causality that could be potentially suggested by alternative theoretical relationships.

  6. To compute the employment share we use the number of persons engaged and the population data from the PWT 9.0 and, to compute the country trade openness we use the imports and exports share also from the PWT 9.0.

  7. A positive relationship between income inequality and openness was also obtained in Barro (2000).

  8. Bucci et al. (2003) use the efficiency wages argument to explain the negative relationship between market concentration and wage inequality. In the research market there are monitoring problems that imply that the firms with lower markups have to set a higher efficiency wage, An alternative explanation may be the labor unions strength.

  9. Introduction of the lagged value and first-differences help to prevent potential reverse causality to be a source of empirical endogeneity. Introduction of time and sector-country dummies helps to consider heterogeneity effects or idiosyncratic shocks by sector-country, time effects and of course, omitted variables.

  10. For example, Duffy and Papagiourgiou (2000) present estimates for σ using a panel database for 82 countries over a 28-year period. Nonlinear estimations for σ oscillate between 1.2 and 2.3, while linear estimations oscillate around 1.4.

  11. Note that our panel is large in N and small in T being robust standard errors the best option to lead with heteroskedasticity and/or autocorrelation, which is already made in the baseline regressions presented in Tables 3 to 6.

  12. We thank a referee this interesting discussion.

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Acknowledgements

We acknowledge the useful suggestions and comments of the Editor and three anonymous referees which greatly contributed to improve the paper. The usual disclaimer applies.

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Authors and Affiliations

  1. Universidade Portucalense, Porto and CEFAGE-UBI, Covilhã, Portugal

    Manuela Magalhães

  2. Departamento de Gestão e Economia and CEFAGE-UBI, Universidade da Beira Interior, Avenida Marquês d’Avila e Bolama, 6201-001, Covilhã, Portugal

    Tiago Sequeira

  3. Faculdade de Economia, and CEF.UP, Universidade do Porto, Porto, Portugal

    Óscar Afonso

Authors
  1. Manuela Magalhães
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  2. Tiago Sequeira
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  3. Óscar Afonso
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Corresponding author

Correspondence to Tiago Sequeira.

Appendices

Appendix A: Country Sample

Table 8 WIOD countries, regional aggregation and income classification
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Appendix B: List of Sectors

Table 9 List of sectors at two-digit level
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Appendix C: HH Index Map in 2011 and 1995

Fig. 4
figure 4

HHI 2011

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Fig. 5
figure 5

HHI 1995

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Magalhães, M., Sequeira, T. & Afonso, Ó. Industry Concentration and Wage Inequality: a Directed Technical Change Approach. Open Econ Rev 30, 457–481 (2019). https://doi.org/10.1007/s11079-018-9513-0

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