The parenthood pay gap is not fully explained by human capital depreciation and unobserved heterogeneity. Endogenous worker-firm matching could also account for such wage differences. This hypothesis is tested thanks to linked employer-employee data on the French private sector between 1995 and 2011. Childbirth penalties are estimated for women and for men from hourly wage equations including firm- and worker-fixed effects on top of usual measures of human capital. Though worker-firm matching explains none of the motherhood wage penalty, it plays a role in the case of fathers who do not experience any wage loss after childbirth, but do not enjoy any premium either; there is evidence of an erosion of this premium since the end of the 1990s. In a counterfactual where women do not incur any penalty after childbirth, the gender gap still amounts to 2/3 of the one that currently prevails.
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Royal is currently Minister of Ecology and Sustainable Development, while Hollande is President of France.
The author of that quotation ignores probably that more than two centuries went by since Mary Wollstonecraft condemned the restrictive domestic sphere to which women were confined. In Wollstonecraft (1792), she praised for the same level of education for men and women and, implicitly, for no other discrimination than capacities. Ironically, the posterity has focused on her private life as storied post mortem by her husband William Godwin in Godwin (1798). Mary Wollstonecraft was (also) the mother of the writer Mary Shelley.
One could argue that working part-time constitutes a negative signal that individuals send to their employers by reducing voluntarily their activity. However, this explanation does not belong to “human capital” theory but rather to a competing explanation, the “signaling” theory proposed by Spence (1973).
Changes in both men and women’s employment after childbirth have been widely documented in Pailhé and Solaz (2007).
The SIRET is a concatenation of the SIREN, a firm identifier, and of an establishment identifier.
The absence of a DADS as well as incorrect or missing answers are punished by law with fines.
At the exclusion of contractual civil unions called PACS that have emerged in France since 1999. However, even if the number of PACS has raised dramatically since then, interestingly the number of marriages has not fallen accordingly but has rather been stable over the period, which indicates that PACS is not a perfect substitute for marriage, and that the content of marriage has been rather the same.
Yet individuals born abroad are missing from the EDP.
For instance, some of them were not born in October.
By definition, years of absence from the DADS file cannot be characterized.
As time passes, the entry condition will become less restrictive in terms of age–hence the selection will be less drastic in future works relying on the same source.
To the best of my knowledge, the Australian case is not an outlier with respect to the issue of part-time employment.
In what follows, I will not distinguish P i t from the other covariates in X i t .
These characteristics are attached to an individual’s main employment. It does not contain any information on location or distance work-home for instance. Exhaustive information on marital history is also missing.
In an abuse of terminology, size refers to all firms with a size belonging to one of the 12 previously mentioned size categories.
I thank a referee for this suggestion.
In the presence of age and individual fixed effects, the slope of potential experience is not identified due to collinearity, as potential experience is defined here as the difference between the current year and the year an individual first appears in the panel.
This might also help explain the erosion of the fatherhood premium at the end of the 1990s. The introduction of a short paternity leave (11 days) in France dates to 2002.
I proceed to robustness checks with respect to the 80 % threshold in Section 5.3.
Missing years (1990, 1994, 2003–2005) for which the quality of the data on wages is questionable have been reintegrated into the sample in order to construct meaningful statistics.
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I am grateful to the editor, Erdal Tekin, to three anonymous referees for helpful suggestions as well as to Miriam Beblo, Richard Blundell, Élise Coudin, Laurent Gobillon, Francis Kramarz, Fabrice Lenglart, Laurent Linnemer, Thierry Magnac, Sophie Ponthieux, Johannes Spinnewijn, Michael Visser, and Andrea Weber for their insightful comments. I am especially indebted to Dominique Meurs and Sébastien Roux for their stimulating discussions. I also thank attendees at Insee (Paris) seminars, at the Fourth SOLE/EALE World Conference (Montréal) and at the European Winter Meeting of the Econometric Society 2014 (Madrid). All errors and opinions are mine. The author declares that he has no conflict of interest.
Responsible editor: Erdal Tekin
Appendix A : data
Appendix A : data
I proceed to some cleaning of the DADS panel. First, I recode the age variable as the difference between the current year and the year of birth. The former age variable exhibits some errors due to scan problems before the numerical DADS was introduced. Second, département codes are sometimes one-digit instead of being two-digit; other département or region codes are missing. In that case, I rely on other observations in the whole database in order to recover that information.
In the EDP database, I eliminate observations for which days or months of marriage or birth are equal either to 00 or 99, as well as observations for which the year of birth is 0000.
I restrict my attention to individuals born on October of even-numbered years: careers of individuals born on October of odd-numbered years is unknown before 2002. The most important selection is dictated by the necessity of measuring experience properly (see infra): I focus on individuals who entered the panel after 1995, which leaves me with 46,280 individuals (338,879 observations at the individual-year level and 489,852 observations at the individual-firm-year level). We eliminate further individuals whose net annual earnings are missing or less than 10 euros in 2011 terms. I also restrict my sample to individuals aged 16 to 65, working at least 10 h a year, whose job duration is consistent with worked hours (for instance, the ratio of the latter over the former must be less than 24), which leaves me with 45,483 individuals (317,476 individual-year observations). After trimming observations with a hourly wage that is smaller than 80 % of the legal minimum wage,Footnote 20 and after dropping years 2003 to 2005, my estimation sample is composed of 41,531 individuals (212,189 individual-year observations and 301,079 individual-firm-year observations). Among those individuals, 19,932 are women while 21,599 are men. Last but not least, I define time-varying variables for marriage (parenthood) as the fact of being married (experiencing a childbirth) before time t for individual i.
In this sample, 62 % (69.5 %) of women (men) do not have any child yet, against 36.3 % (43.9 %) in the DADS-EDP database, the difference being mainly due to the youth of individuals in the estimation sample, and being at the source of the main empirical limit of the current analysis. Among the women in the working sample,Footnote 21 29.2 % leave after first birth; among the remaining mothers, 34.5 % leave after second birth, this rate being roughly the same for subsequent births. The estimation sample does not seem gender-biased with respect to the DADS-EDP database: for instance, 54.6 % of non-parents are women in our sample, against 57.5 % in the DADS-EDP; about 47 % of parents of either one child or two children are women in our sample, against about 50 % in the DADS-EDP. Finally, individuals who work in small firms are relatively less likely in the estimation sample: in 2011, 42 % of individuals present in the DADS-EDP and working in the private sector belong to a firm with less than 20 employees, against 33 % in the estimation sample.
1.3 Definition of main employment
Aggregating data at the individual-year level requires to define for each individual her main employment in the year. I select the employment with (in successive order) the highest number of working days, the highest wage, a full-time position (if any), and the highest number of worked hours. If there are still ties after applying those criteria, I choose the job with the last SIREN in lexicographical order—to keep the code deterministic. Finally, if several observations resisted to the last iteration, I would consider them as authentic doubles and eliminate them—which does not happen here. We define job characteristics (private/public sector, industry, geographic location, firm’s size, full-time/part-time, but also seniority) at the individual-year level as being related to the main employment. I sum wages and working hours, and define working days as the minimum of 360 (the annual number of working days in the DADS by convention) and the sum of working days over the whole year.
1.4 Computation of experience
Mincer (1958) demonstrated how important it is to control properly for experience and seniority in wage equations. I devote much attention to compute these variables as precisely as possible. Seniority is defined as the difference between the current date and the first appearance of a pair (individual, firm). Thanks to the comprehensive nature of the DADS panel, it is possible to reconstitute the whole salaried career of an individual, hence to compute his experience from observed working times. Experience will thus be defined as closely as possible as the amount of salaried time spent on the labor market. Since worked hours have been available from 1995 onwards only, I restrict my attention to individuals who entered the panel after 1995. I consider that workers increase their full-time/part-time experience variable every year by their share of working hours expressed in full-time units (FTU).
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Wilner, L. Worker-firm matching and the parenthood pay gap: Evidence from linked employer-employee data. J Popul Econ 29, 991–1023 (2016). https://doi.org/10.1007/s00148-016-0597-9
- High dimensional fixed effects
- Worker-firm matching
- Parenthood pay gap
- Gender inequalities
- Linked employer-employee data