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Labor Market Flexibility and Unemployment: New Empirical Evidence of Static and Dynamic Effects

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Abstract

The aim of this paper is to analyze the relationship between labor market flexibility and unemployment outcomes. Using a panel of 97 countries from 1985 to 2008, the results of the paper suggest that improvements in labor market flexibility have a statistically and significant negative impact on unemployment outcomes (over unemployment, youth unemployment, and long-term unemployment). Among the different labor market flexibility indicators analyzed, hiring and firing regulations and hiring costs are found to have the strongest effect.

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Notes

  1. For example, Nickell (1998), Elmeskov et al. (1998) and Nunziata (2002) find robust evidence that the level and the duration of unemployment benefits increase the level of unemployment. Belot and Van Ours (2004) and Nickell (1997) find that high labor taxes tend to increase unemployment rates. Botero et al. (2004) find that more rigid employment laws are associated with high unemployment, especially for the young. See Bassanini and Duval (2006) for a detailed review.

  2. Data for labor market flexibility are available for 140 countries over the period 1980–2008. In particular, data for labor market flexibility are available every 5 years from 1980 to 2000, and annually over the period 2001–2008. Limited data availability for unemployment and our measure of output gap shortens the sample to 97 countries over the period 1985–2008. See the Annex for a list of countries and years included in the sample.

  3. It is interesting to note that this specification is able to capture most of the time and cross-country variation of unemployment (the associated R2 is about 0.8).

  4. For example, Bassanini and Duval (2006) conclude that changes in labor market institutions seem to explain nearly two-thirds of non-cyclical unemployment changes in OECD countries.

  5. In detail, equation 1 has been re-estimated over the following time samples: (i) 1980–1990; (ii) 1980–1995; 1980–2000; (iii) 1980–2001; (iv) 1980–2002; (v) 1980–2003; (vi) 1980–2004; (vii) 1980–2005; (viii) 1980–2006; (ix) 1980–2007; (x) 1980–2008.

  6. Note that long-term unemployment is measured as the percentage of total unemployment that is of long term. That is, if one half of total unemployment is of long term, then the corresponding figure would be 50%.

  7. In contrast, Bassanini and Duval (2006) find that high centralization in wage bargaining is associated with lower unemployment rates in OECD countries.

  8. The two-step GMM-system estimates (with Windmeijer standard errors) are computed using the xtabond2 Stata command developed by Roodman (2009a). All explanatory variables are considered as endogenous (instrumented using up to 2 lags). The significance of the results is robust to different choices of instruments and predetermined variables.

References

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Acknowledgements

The authors would like to thank Josef Brada, Joël Toujas-Bernaté, Saul Estrin, and other participants to the Conference on Crises, Institutions and Labor Market Performance: Comparing Evidence and Policies (Perugia) the IMF-MCD Department Seminars and the World Bank MNA Seminars for useful comments and discussions. The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy.

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Authors and Affiliations

  1. University of Chicago, 1126 E. 59th Street, Chicago, 60637, IL, USA

    Lorenzo E Bernal-Verdugo

  2. International Monetary Fund and University of Palermo, IMF, 700 19th Street, NW, Washington DC, 20431, USA

    Davide Furceri

  3. International Monetary Fund, 700 19th Street NW, Washington DC, 20431, USA

    Dominique Guillaume

Authors
  1. Lorenzo E Bernal-Verdugo
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  2. Davide Furceri
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  3. Dominique Guillaume
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Appendix

Appendix

Data Description

The dependent and control variables included in the analysis belong to one of several categories, namely:

  1. 1)

    Unemployment

    • Unemployment rate (lur, from WEO): Percentage of the total labor force that is currently unemployed.

    • Youth unemployment rate (unempyouth, from WDI): Percentage of the total labor force of ages 15–24, that is, currently unemployed.

    • Long-term unemployment (unemplong, from WDI): Fraction (in percentage) of the unemployment rate that is of long term.

  2. 2)

    Macroeconomic variables

    • GDP per capita (rgdpl, from WEO): Purchasing power parity (PPP) converted GDP per capita (with the Laspeyres methodology), derived from growth rates of private consumption, government expenditures, and investment at 2005 constant prices.

    • Demand pressure (gap_growth_n): Gap in the current real GDP per capita growth with respect to a moving average of n years, centered at the current period.

    • Government size (lncg, from PWT): (log) Government consumption share of PPP converted GDP per capita at current prices, in percentage.

    • Openness (lnopenk, from PWT): (log) Openness at 2005 constant prices, in percentage.

  3. 3)

    Demographic variables

    • Population size (lnpop, from PWT): (log) Total population (in thousands).

    • Urbanization (lnurbpop, from WDI): (log) Urban population, as percentage of total population.

    • Density (lnpopdens, from WDI): (log) Population density, measured by the number of people per km2 of land area.

  4. 4)

    Financial crisis

    • Financial crisis indicator (crisis): This dummy variable assigns a value of 1 to years in which a country was going through a financial crisis according to Laeven and Valencia (2010), and 0 otherwise.

Table A1

Table A1 Data sample
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Bernal-Verdugo, L., Furceri, D. & Guillaume, D. Labor Market Flexibility and Unemployment: New Empirical Evidence of Static and Dynamic Effects. Comp Econ Stud 54, 251–273 (2012). https://doi.org/10.1057/ces.2012.3

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