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A replication note on downward nominal and real wage rigidity: survey evidence from European firms

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Abstract

This note shows that the models estimated in Babecký et al. (Scand J Econ 112(4):884–910, 2010) do not allow identifying the determinants of downward nominal wage rigidity and provides new empirical evidence on the importance of downward nominal wage rigidity and its determinants in several European countries.

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Notes

  1. See, for instance, Card and Hyslop (1997), Kahn (1997), Altonji and Devereux (2000), Fehr and Goette (2005), Goette et al. (2007), Dickens et al. (2007) and Knoppik and Beissinger (2009).

  2. See, for instance, Dickens and Goette (2006) and Dickens et al. (2007).

  3. In rigour, the authors estimate a bivariate probit model to account for the interdependence between their measures of DNWR and DRWR which are investigated together in their paper. This, however, is not relevant for the point made in this note which concerns the sample used and not the type of model estimated. Moreover, as we shall show below, the empirical results are basically the same when a univariate or a bivariate model is used.

  4. As in Babecký et al. (2010), in this note the union coverage is computed as the proportion of workers in the firm covered by collective wage agreements. In rigour, however, for each firm, the union coverage should be defined as the proportion of workers covered by collective wage agreements out of those scheduled for a wage cut. This limitation stemming from the lack of appropriate data may be expected to have implications for the parameters of the models estimated below. For a discussion, see Dias et al. (2012).

  5. Our dataset matches closely the one used in Babecký et al. (2010), though they differ in some respects. Ours is an updated version of the original dataset, whose major difference is the inclusion of Cyprus, which was not available to be used in Babecký et al. (2010), and the exclusion of Greece, because the survey conducted in this country has no information on base-wage cuts, which is a variable of interest for us. We also excluded the firms that have not answered one of the two questions on wage freezes or wage cuts. The final set of countries includes Austria, Belgium, Cyprus, Czech Republic, Estonia, France, Hungary, Ireland, Italy, Lithuania, Netherlands, Poland, Portugal, Slovenia and Spain. For further details on the design of the survey, see Druant et al. (2009) or Babecký et al. (2010).

  6. Some of our figures on the incidence of wage freezes differ from the ones presented in Babecký et al. (2010). We believe that the main source of divergence stems from the fact that figures in Babecký et al. (2010) are employment-weighted, while our figures in Table 1 are not. The fact that we are using a slightly different sample may also help explaining the differences.

  7. Note that the estimates of DNWR are based on firm-level data and not on worker-level data, i.e. it is implicitly assumed that all the workers in the firms involved are scheduled for a wage cut, regardless of the share of workers covered by the wage cuts or freezes. Moreover, because they are based on dnwr1, i.e. do not exclude the workers whose wages would have been frozen in the absence of DNWR, they are likely to overestimate the importance of DNWR.

  8. The estimates of DNWR in Dickens et al. (2007) use the statistic dnwr1 [see Eq. (1)] based on worker-level data taken from households surveys or administrative data on individuals.

  9. For a review of the literature underlying such regressors, see Babecký et al. (2010).

  10. The four regressions include country dummies to account for fixed effects whose estimated coefficients are not reported in Table 2. These country dummies enable us to control for variations in any country-specific omitted factor, such as differences in the survey design across countries, different degrees of employment protection legislation, different inflation rates, etc.

  11. In the survey about 0.7 % of the firms answered that they had both frozen and cut wages over the last 5 years. For estimation purposes, we assumed that wages in such firms are flexible, so that they enter in the estimated probit models with \(y_{i}=0\).

  12. It is well known that the estimators of the parameters in probit or logit models are biased in finite samples. Moreover, King and Zeng (2001) show that these biases become especially acute and the conventional variance estimators significantly magnified in the presence of rare events, i.e. when Prob(\(y_{i}=1\)) (or Prob(\(y_{i}=0\))) is very small. In our case, the small proportion of wage cuts in the population of firms whose workers were scheduled for a wage cut (around 20 %), is likely also to make it more difficult to get unbiased and statistically significant coefficients for the parameters of the model.

References

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Acknowledgments

The authors would like to thank Claudia Duarte and Pedro Portugal for helpful discussions and useful suggestions.

Author information

Authors and Affiliations

  1. Department of Economics, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Daniel A. Dias

  2. CEMAPRE, Lisbon, Portugal

    Daniel A. Dias

  3. Research Department, Banco de Portugal, Lisbon, Portugal

    Carlos R. Marques & Fernando Martins

  4. ISEG, Technical University of Lisbon, Lisbon, Portugal

    Fernando Martins

  5. Universidade Lusíada de Lisboa, Lisbon, Portugal

    Fernando Martins

Authors
  1. Daniel A. Dias
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  2. Carlos R. Marques
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  3. Fernando Martins
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Corresponding author

Correspondence to Fernando Martins.

Additional information

The views expressed in this paper are those of the authors and do not necessarily reflect those of the Banco de Portugal or the Eurosystem.

Appendix: Variable definitions

Appendix: Variable definitions

In this appendix, we describe the covariates used in the probit models whose results are presented in Sect. 4. The details are as follows:

  • Low-skilled blue-collar—Proportion of low-skilled blue-collar workers on firm’s total employment.

  • High-skilled blue-collar—Proportion of high-skilled blue-collar workers on firm’s total employment.

  • Low-skilled white-collar—Proportion of low-skilled white-collar workers on firm’s total employment.

  • Labour cost share—Proportion of labour costs on total costs.

  • Permanent workers—Proportion of workers with permanent contracts on the firm’s total workforce.

  • Only firm-level agreement—Dummy variable which equals 1 if the firm applies only an agreement concluded inside the firm.

  • Only outside agreement—Dummy variable which equals 1 if the firm applies only an agreement concluded outside the firm.

  • Both agreements—Dummy variable which equals 1 if the firm applies both firm-level and outside wage agreements.

  • Coverage—Proportion or workers covered by collective wage agreements.

  • Tenure 1–5 years—Proportion of workers with tenure between 1 and 5 years.

  • Tenure above 5 years—Proportion of workers with tenure above 5 years.

  • High competition—Dummy variable equal to one if the firm answers in the survey that it will likely or very likely decrease its price in reaction to a decrease in the price of its main competitor.

  • Size = 20–49—Dummy variable equal to one if the number of employees is between 20 and 49.

  • Size = 50–199—Dummy variable equal to one if the number of employees is between 50 and 199.

  • Size = 200+—Dummy variable equal to one if the number of employees is equal or larger than 200.

  • Construction—Dummy variable equal to one if the firm operates in the Construction sector.

  • Trade—Dummy variable equal to one if the firm operates in the Trade sector.

  • Other services—Dummy variable equal to one if the firm operates in any other sector.

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Dias, D.A., Marques, C.R. & Martins, F. A replication note on downward nominal and real wage rigidity: survey evidence from European firms. Empir Econ 49, 1143–1152 (2015). https://doi.org/10.1007/s00181-014-0902-y

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