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Gender differences in productivity rewards: the role of human capital

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Abstract

In this paper, we evaluate the gender wage gap component due to differences in characteristics’ rewards in Italy. The main focus is on the relationship between human capital characteristics and gender differences in rewards. We propose a methodology that combines the quantile regression analysis with non-parametric procedures for the estimation of the probability density functions of reward differentials in order to evaluate the evolution of the gap due to human capital characteristics. The analysis is carried out on Italian data taken from the latest available cross-section of the European Community Household Panel (2001). Our study suggests that education can be a good productivity signal and helps reduce the range of the gap; furthermore, highly educated women experience lesser gender-based pay differences as the length of the employment relationship increases.

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Notes

  1. The main contribution of the traditional Oaxaca and Blinder approach (Oaxaca 1973; Blinder 1973) can be summarised with the idea that the wage gap can be decomposed into two terms: a first term representing productivity differences explained by individual characteristics, and a second term explaining earnings gaps in terms of differences in the remuneration of those characteristics.

  2. The same method is applied in the present article and it will be discussed in the methodological section of the paper.

  3. The bootstrapping procedure allows us to test whether coefficients of different quantile regressions are significantly different.

  4. Albrecht et al. (2003) adopt a simplified version of the methodology proposed by Machado and Mata in a mimeo that was later published in the Journal of Applied Econometrics (Machado and Mata 2005).

  5. To estimate the joint density function, we use a Gaussian product kernel with bandwidths chosen optimally according to Silverman (1986).

  6. In this, we follow the procedure originally suggested by Quah (1996). As an alternative, the marginal distribution is often estimated directly using a univariate kernel. However, as pointed out by Overman and Ioannides (2001), the two estimators have identical asymptotic statistical properties, and produce very similar results in practice.

  7. Under regularity conditions, this represents a consistent estimator for conditional distribution (Rosenblatt 1971; Silverman 1986; Quah 1996; Chen et al. 2001).

  8. This is the most recent available wave, referring to the year 2001.

  9. A summary of the statistics of the variables used in the estimates is to be found in the Appendix (Table 2).

  10. Given the unbalanced presence of Italian women and men in part-time work, we prefer to exclude part-time workers from the sample, in keeping with most of the literature on the analysis of wage differentials.

  11. The wage rate (hourly wage) is calculated following the procedure generally exploited when using the ECHP dataset: we divide (gross) monthly current wage and salary earnings from the main job by the number of weekly hours worked (in the main job), multiplied by the monthly standard number of weeks (4.3).

  12. As we shall explain later in the paper, ECHP data contain a continuous variable measuring tenure only for periods shorter than 15 years. Therefore, we are forced to represent experience accumulated within a firm as a set of dummy variables.

  13. All descriptive statistics and results reported in the paper are calculated on samples of 15–65 year-old individuals, employed full-time.

  14. We also include dummies for macro-economic sectors.

  15. We used 16 occupational categories, taking “elementary occupations in sales and services” as a benchmark.

  16. The questionnaire asks the individual if s/he supervises or co-ordinates the work of any personnel and, if so, whether s/he has any say in their pay or promotion. On the base of those questions, the database defines a categorical variable with value zero if the worker declares not to have any supervisory or co-ordination position in the business, value 1 if s/he answers positively to the first question, but negatively to the second, and value 2 if the interviewee answers positively to both questions. On the basis of this information, we construct two dummies to be included in the empirical model: one for individuals with an intermediate degree of supervision, and a second for those with a higher supervisory role.

  17. In the category “other type of contract”, we summarise the categories defined by the ECHP as “casual work with no contract” and “other arrangement”.

  18. Throughout the paper, for the sake of simplicity, we will refer to the extent of log wage gaps (at different deciles) as percentages, although we are aware that, for example, a 0.15 log wage difference corresponds to a 16.18% gap.

  19. This is consistent with the existence of vertical occupational segregation by gender in Italy (Rosti 2006).

  20. It could be argued that experience and tenure are positively correlated and, therefore, we carried out some correlation analysis. An initial check was carried out on the continuous variables, for the subsample of workers with less than 15 years of company service; correlation levels in that case amount to 0.5 both for females and males when highly-educated, falling to 0.25 for low-educated women and to 0.15 for low-educated men. The correlation evaluated between experience and tenure dummies (the specification we use in the estimates) is mainly negative, and in absolute value is lower than 0.5 for both sexes, independently of educational levels. Correlation becomes positive and assumes values around 0.6 only for women and men with tenure longer than 15 years; still, it is not a high level. In any case, even if such characteristics were correlated it would not necessarily imply that these characteristics are rewarded in a similar fashion. Indeed, the evidence reported in the paper suggests that remaining within the same firm is rather penalising for female workers.

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Correspondence to Donata Favaro.

Appendix

Appendix

See Tables 2, 3, 4 and 5.

Table 2 Sample descriptive statistics
Table 3 Type of occupation in current job. Distribution by education and gender (%)
Table 4 Quantile regressions—Highly educated workers
Table 5 Quantile regressions—Low-educated workers

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Addabbo, T., Favaro, D. & Magrini, S. Gender differences in productivity rewards: the role of human capital. Int Rev Econ 59, 81–110 (2012). https://doi.org/10.1007/s12232-011-0142-9

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