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Public–private wage differentials in euro-area countries: evidence from quantile decomposition analysis


We evaluate the public–private wage differential for men in ten euro-area countries in the period 2004–2007. Using the most recent methodologies on a Mincerian equation, we assess how much of the differential depends on differences in endowments and how much on differences in the remuneration of such skills. For the first time, we look at the contribution of specific covariates at different quantiles of the wage distribution and decompose the variance into an explained and an unexplained component. We find that the pay gap is often decreasing over the distribution and that it is mostly determined by higher endowments in the upper tail of the wage distribution and by higher returns of such endowments at the low tail, with considerable heterogeneity across countries. We further find that the wage distribution in the public sector is more compressed than in the private sector in some countries. This is the result, for all countries, of more dispersed distributions of endowments in the public sector and of returns in the private sector.

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  1. For a comprehensive review of the literature on public–private wage gap in these countries see Giordano et al. (2011).

  2. The method proposed by Mata and Machado (2005) is based on quantile regressions (Koenker and Bassett 1978) for each possible quantile and a simulation procedure. It does not allow for the investigation of covariate specific contributions. An additional drawback of this method is that it is slow. However, Melly (2005a) has suggested a faster algorithm.

  3. A limit of this approach is related to the (strong) assumption of rank preserving of the individuals across the two groups (e.g. an individual who ranks third in the observed group 0 will rank third in the counterfactual group 1).

  4. The IF is defined as \(IF\!=\!IF(y;\nu ,F)\!=\!\lim _{\rightarrow \epsilon _0} (\nu (F_{\epsilon })-\nu (F)))/\epsilon \). Hence, by definition, \(\int \text{ IF } \hbox {d}F(y)\!=\!0\).

  5. The conditional distribution \(F(y|x_0)\) is estimated by regressing each possible value of the dependent variable through a link function \(\Lambda (\cdot )\), whereas the counterfactual \(\hat{F}_{y_0^c}(y)\) is obtained as \(\hat{F}_{y_0^c}(y)=\frac{1}{N_1}\sum _{i \in 1 } \Lambda (x_i \hat{\alpha _0(y)})\), where \(\hat{\alpha _0(y)})\) is the vector of coefficients that makes it possible to estimate proportions (i.e. the CDF) and all the other features of the distribution function.

  6. While Chernozhukov et al. (2013) globally inverts quantiles and proportions, the analysis by Firpo et al. (2009), Fortin et al. (2011) is performed locally. Hence, when the relationship between counterfactual proportions and counterfactual quantiles is locally linear the two methods are equivalent.

  7. The results using the alternative method, not shown here but available from the authors upon request, do not significantly differ from those discussed later in this paper.

  8. Recent contributions suggest that logarithm can be misleading in the presence of heteroscedasticity (see Blackburn 2007). We have estimated the same models with the level of wage rather than its logarithm. While numerical differences arise, the ratio between the unexplained part and the overall difference is rather stable across the two definitions. The results are available from the authors upon request.

  9. A thorough discussion of the findings of the decomposition analysis at the mean and various quantiles can be found in Depalo et al. (2013).

  10. In order to apply the RIF method described in Sect. 3, recall that for a quantile \(\tau \) the \(RIF(y;Q_{\tau })=Q_{\tau } + \frac{\tau - 1(y \le Q_{\tau })}{f_y(Q_{\tau })}\) where \(Q_{\tau }\) is the \(\tau \)-th quantile of log hourly wage, 1() is an indicator function and \(f()\) is the density of the marginal distribution of wage.

  11. The RIF is \(RIF(y;\sigma ^2)=(y-\mu )^2\), for the variance, and \(RIF(y;GC)=1+2A_G+C_G\), for the Gini coefficient, where \(A_G=2 \mu ^{-1}R(F)\), \(C_G=-2 \mu ^{-1}[y(1-p(y))+GL]\), \(p(y)\) is a proportion of weights and GL is the Generalized Lorenz curve.

  12. For decompositions with sample selection, Fortin et al. (2011) suggest a simple approach for the mean, whereas Albrecht et al. (2009) present a quantile regression.

  13. This is the case of transportation and some social services. The latter, when provided by the private sector, are included in the category “other”. See Table 2 for detailed documentation about the definitions.


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We would like to thank the referee and the editor, Francesco Caprioli, David Card, Stephen Hall, Francesco Manaresi, Franco Peracchi, Pietro Rizza, Alfonso Rosolia and Emmanuel Saez for helpful comments and suggestions. All the routines will be available at the webpage: The views expressed in this paper are those of the authors and do not imply any responsibility of their institutions.

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Correspondence to Domenico Depalo.

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Depalo, D., Giordano, R. & Papapetrou, E. Public–private wage differentials in euro-area countries: evidence from quantile decomposition analysis. Empir Econ 49, 985–1015 (2015).

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