| Summary: | In the first chapter of this thesis, we investigate the impact of human capital and wage structure on the gender pay in a panel of European countries using a newly available and appropriate database for cross-country comparisons and a comparable methodology for each country.
Our first question is : What role do certain individual characteristics and choices of working men and women play in shaping the cross-country differences in the gender pay gap? What is the exact size of the gender pay gap using the “more appropriate” database available for our purpose? Giving that there are mainly only two harmonized data-sets for comparing gender pay gap throughout Europe: the European Community Household Panel (ECHP) and the European Structure of Earning Survey (ESES). Each database having its shortages: the main weakness of the ECHP is the lack of perfect reliability of the data in general and of wages in particular. However the main advantage of this database is the panel-data dimension and the information on both households and individuals. The data of the ESES is, on the contrary, of a very high standard but it only covers the private sector and has a cross-sectional dimension. Furthermore only few countries are currently available : Denmark, Belgium, Spain, Ireland and Italy.
We use the European Structure of Earning Survey (ESES) to analyse international differences in gender pay gaps in the private sector based on a sample of five European economies: Belgium, Denmark, Ireland, Italy and Spain. Using different methods, we examine how wage structures, differences in the distribution of measured characteristics and occupational segregation contribute to and explain the pattern of international differences. Furthermore, we take account of the fact that indirect discrimination may influence female occupational distributions. We find these latter factors to have a significant impact on gender wage differentials. However, the magnitude of their effect varies across countries.
In the second chapter, we analyse the persistence of the gender pay differentials over time in Europe and better test the productivity hypothesis by taking into account unobserved heterogeneity.
Our second question is : What is the evolution of the pay differential between men and women over a period of time in Europe? And what is the impact of unobserved heterogeneity?
The researcher here provides evidence on the effects of unobserved individual heterogeneity on estimated gender pay differentials. Using the European Community Household Panel (ECHP), we present a cross-country comparison of the evolution of unadjusted and adjusted gender pay gaps using both cross-section and panel-data estimation techniques. The analysed countries differ greatly with respect to labour market legislation, bargaining practices structure of earnings and female employment rates. On adjusting for unobserved heterogeneity, we find a narrowed male-female pay differential, as well as significantly different rates of return on individual characteristics. In particularly, the adjusted wage differential decreases by 7 per cent in Belgium, 14 per cent in Ireland, between 20-30 per cent Germany, Italy, the Netherlands and Spain and of 41 per cent and 54 per cent in the UK and in Denmark respectively.
In the third chapter, we investigate causes of the gender pay gap beyond the gender differences in observed and unobserved productive characteristics or simply the sex. Explanations of the gender pay gap may be the penalty women face for having children. Obviously, the motherhood wage penalty is relevant to larger issues of gender inequality given that most women are mothers and that childrearing remains a women’s affair. Thus, any penalty associated with motherhood but not with fatherhood affects many women and as such contributes to gender inequalities as the gender pay gap. Furthermore, the motherhood wage effect may be different along the wage distribution as women with different earnings may not be equal in recognising opportunities to reconcile their mother’s and earner’s role. This brings us to our third question.
Our third question is : What is the wage effect for mothers of young children in the household? And does it vary along the wage distribution of women?
This chapter provides more insight into the effect of the presence of young children on women’s wages. We use individual data from the ECHP (1996-2001) and both a generalised linear model (GLM) and quantile regression (QR) techniques to estimate the wage penalty/bonus associated with the presence of children under the age of sixteen for mothers in ten EU Member States. We also correct for potential selection bias using the Heckman (1979) correction term in the GLM (at the mean) and a selectivity correction term in the quantile regressions. To distinguish between mothers according to their age at the time of their first birth, wage estimations are carried out, separately, for mothers who had their first child before the age of 25 (‘young mothers’) and mothers who had their first child after the age of 25 (‘old mothers’). Our results suggest that on average young mothers earn less than non-mothers while old mothers obtain a gross wage bonus in all countries. These wage differentials are mainly due to differences in human capital, occupational segregation and, to a lesser extent, sectoral segregation between mothers and non-mothers. This overall impact of labour market segregation, suggests a “crowding” explanation of the family pay gap – pay differential between mothers and non-mothers. Nevertheless, the fact that we still find significant family pay gaps in some countries after we control for all variables of our model suggests that we cannot reject the “taste-based” explanation of the family gap in these countries. Our analysis of the impact of family policies on the family pay gap across countries has shown that parental leave and childcare policies tend to decrease the pay differential between non-mothers and mothers. Cash and tax benefits, on the contrary, tend to widen this pay differential. Sample selection also affects the level of the mother pay gap at the mean and throughout the wage distribution in most countries. Furthermore, we find that in most countries inter-quantile differences in pay between mothers and non-mothers are mainly due to differences in human-capital. Differences in their occupational and sectoral segregation further shape these wage differentials along the wage distribution in the UK, Germany and Portugal in our sample of young mothers and in Spain in the sample of old mothers.
In the fourth chapter, we analyse the combined effect of motherhood and the family status on women’s wage.
Our fourth question is : Is there a lone motherhood pay gap in Europe? And does it vary along the wage distribution of mothers?
Substantial research has been devoted to the analysis of poverty and income gaps between households of different types. The effects of family status on wages have been studied to a lesser extent. In this chapter, we present a selectivity corrected quantile regression model for the lone motherhood pay gap – the differential in hourly wage between lone mothers and those with partners. We used harmonized data from the European Community Household Panel and present results for a panel of European countries. We found evidence of lone motherhood penalties and bonuses. In our analysis, most countries presented higher wage disparities at the top of the wage distribution rather than at the bottom or at the mean. Our results suggest that cross-country differences in the lone motherhood pay gap are mainly due to differences in observed and unobserved characteristics between partnered mothers and lone mothers, differences in sample selection and presence of young children in the household. We also investigated other explanations for these differences such as the availability and level of childcare arrangements, the provision of gender-balanced leave and the level of child benefits and tax incentives. As expected, we have found significant positive relationship between the pay gap between lone and partnered mothers and the childcare, take-up and cash and tax benefits policies. Therefore improving these family policies would reduce the raw pay gap observed.
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