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Empirical Economics

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01.05.2014

The trend over time of the gender wage gap in Italy

verfasst von: Chiara Mussida, Matteo Picchio

Erschienen in: Empirical Economics | Ausgabe 3/2014

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Abstract

We analyse gender wage gaps in Italy in the mid-1990s and in the mid-2000s. In this period, important labour market developments took place and they could have had a gender asymmetric impact on wages. We identify the time trends of different components of the gender wage gap across all the wage distribution. Although the unconditional gender wage gap remained roughly constant over time, we find that the component of the gap due to different rewards of similar characteristics deteriorated women’s relative wage. We show that especially women at the centre-top of the wage distribution swam against the tide: while the trend in female qualifications slightly reduced the gender wage gap, the gender-relative trends in the wage structure significantly increased it.

Vorheriger Artikel Parametric versus nonparametric methods in risk scoring: an application to microcredit

Nächster Artikel Regional unemployment and norm-induced effects on life satisfaction

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In Italy the average GDP annual growth rate was around 1.5 % in this period (Eurostat).

For an assessment of the Treu reform, see Sciulli (2006a, b) and Schindler (2009).

Up to the second half of the 1990s, the Italian standard work arrangement had traditionally been full time, open ended, and characterized by one of the strictest employment protection legislations, mostly against dismissals, in the OECD area (Lazear 1990; Kugler and Pica 2008).

These figures are available in Internet at http://epp.eurostat.ec.europa.eu.

More information about the ECHP and SILC is available in Internet at http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home and http://epp.eurostat.ec.europa.eu/portal/page/portal/microdata/eu_silc, respectively.

Considering that on average there are 4.345 weeks in a month, the hourly wage in the mid-1990s (mid-2000s) is computed as follows: \(w= \text{ PI111 }/(\#\,\text{ of } \text{ months } \text{ at } \text{ work }\times \text{ PE005 }\times 4.345) (w= \text{ PY010N }/(\#\,\text{ of } \text{ months } \text{ at } \text{ work }\times \text{ PL060 }\times 4.345)\)).

Employee earning is defined as the total remuneration payable by an employer to an employee in return for work done by the latter during the income reference period. The income reference period is the previous calendar or tax year. Information on earning at time \(t\) (current year) therefore refers to time \(t-1\) (income reference or previous year).

The definitions of both employment and not employment are based on self-declared economic status and therefore do not match the ILO criteria.

The deflator is the Consumer Price Index (CPI), provided by ISTAT.

For the overall sample, we also consider a dummy indicator for the presence of children younger than 12 years and the number of household components. Indeed, even if exclusion restrictions are not needed for model identification (for details see Picchio and Mussida 2011), these two variables will be exploited to explain the selection equation, but will not enter the specification of the wage distributions.

The conditional probability of full-time employment is therefore given by \(\Pr (y_t=1|z_t,\varepsilon )=\exp [-\exp (z_{t}^{\prime }\delta +\varepsilon )]\). As a consequence, an increase in a variable with a positive coefficient results in the decrease in the probability of full-time employment. The estimation results of the gompit selection equation into employment by gender and time period are reported in Table 10 and commented in Appendix A.

A piecewise constant function is constant within predefined intervals. We divided the wage support into \(J=72\) intervals \(I_j=[w_{j-1}, w_j)\), where \(j=1,\ldots ,J, w_0<w_1<\cdots <w_J, w_0=0\), and \(w_J=\infty \). \(w_1\) and \(w_{J-1}\) correspond to the first and last percentiles of the wage distribution in each time period. We chose the width of the other 70 wage baseline segments by dividing the wage support between \(w_1\) and \(w_{J-1}\) in 70 equally spaced intervals. Our choice of the number of the baseline segments is somewhat arbitrary. Nevertheless, it returns a narrow segment width (0.22€ and 0.28€ for the mid-1990s and the mid-2000s, respectively) and it is thereby suitable for flexibly approximating all possible wage hazard functions.

We tried to increase the number of support points. The decomposition analyses reported below are robust to this kind of sensitivity analysis.

Parameter estimates without sample selection are not reported in the paper, but available upon request.

As stressed by the job satisfaction literature (see e.g. Chevalier 2007), men and women might have different tastes and preferences, leading to different choices in the labour market.

The estimation results and the gender wage gap decompositions without sample selection are available upon request from the authors.

The unexplained component of the gender pay gap is often viewed as discrimination by the literature (Chevalier 2007). Discriminatory behaviours are nonetheless difficult to observe. Only very detailed information might help finding evidence of discrimination for the process under investigation, e.g. employment discrimination in hiring (Goldin and Rouse 2000).

As the reliability of these simulations depends on the capacity of our model to predict the realized wage distributions, we compute goodness-of-fit checks of the estimated model and report them in Appendix B.

Barbieri and Sestito (2008) find that in Italy temporary work increases future chances of having a “satisfactory employment” especially for women.

For example, Cockx and Picchio (2012) find that for Belgian youth, short-lived jobs are stepping stones to long-lasting jobs especially for more disadvantaged individuals, e.g. the lower educated and those living in areas where the unemployment rate is higher.

Overeducated workers have a job requiring lower qualifications than their educational level.

For detailed statistics on overeducation by gender and educational level, see ISTAT (2005, 2009).

Under the Gompertz assumption on the distribution of the error term \(u_t\) in Eq. (1), the probability of workforce participation is indeed given by \(\exp \{-\exp (z^\prime _t \delta +\varepsilon )\}\).

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Titel: The trend over time of the gender wage gap in Italy
verfasst von: Chiara Mussida
Matteo Picchio
Publikationsdatum: 01.05.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 3/2014
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-013-0710-9

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