Abstract
In this paper, we exploit rich cross-country survey data covering 15 European countries over the period 2000–2015 to investigate the relationship between the gender of the immediate supervisor (i.e. having a male or a female “boss”) and perceived gender discrimination at the workplace. We show that a female boss is associated with reduced gender discrimination, with positive spillovers mainly on female subordinates, in jobs where female presence is also higher and where work organization is more complex. The presence of more flexible work schedules and a better balance between work and life, further contributes to reinforce the mitigating effect of female leadership on discrimination. Results are shown to be consistent with available evidence on gender differentials in pay and career advancement, as well as being robust to a number of sensitivity checks.
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Notes
In an analysis of the propensity to hire and retain females among athletic directors, Bednar and Gicheva (2014), find instead no evidence that gender is strongly predictive of a supervisor’s female-friendliness.
Goldin (2014) shows that saturating a traditional Mincerian wage equation with 3-digit occupational dummies, or weighting equally male and females across occupations, the residual gender pay inequality is reduced by less than 1/3, meaning that the other 2/3 depends on other factors. A relevant part of the residual gender inequality is shown to be related to how the work is organized and rewarded in firms, and how the tasks and responsibilities are allocated across gender.
European Working Conditions Survey Integrated Data File, 1991–2015 [computer file] 3rd Edition, February 2017. UK Data Service. Data are publicly available at http://www.eurofound.europa.eu - SN: 7363, https://doi.org/10.5255/UKDA-SN-7363-3.
We restrict our analysis to EU15 (Austria, Belgium, Denmark, Germany, Greece, Finland, France, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and the UK), since these are the only countries that have been surveyed in all waves. The whole empirical analysis is carried out using either country level post-stratification weight or cross-national weights (Eurofound, 2010).
Retired individuals, students in full-time education, the self-employed and employees in the armed forces have been excluded. We also removed all observations in which the respondent replied “Don’t know” or “Refusal”.
The exact wording of the question is, my job offers good prospects for career advancement and respondents had to agree or disagree (on a 5-point scale, from strongly agree to strongly disagree) with the statement. We recoded the variable as a dummy taking value one if the respondent agreed or strongly agreed and zero otherwise.
Harassment is measured through a dummy variable that takes value 1 if, “over the past 12 months, during the course of work” the individual has been subjected to bullying/harassment or sexual harassment.
Cardoso and Winter-Ebmer (2007) used administrative data from the Ministry of Employment in Portugal; Flabbi et al. (2019) matched the Italian social security archive with two company surveys; Bertrand et al. (2018) used data from the Norwegian Registry Archives merged with the Register of Business Enterprises and the Register of Company Accounts. Datta Gupta and Eriksson (2012) and Gagliarducci and Paserman (2015) were able to match their employer–employee panel data (the first from Statistic Denmark, the second from IAB and social security data for Germany) with ad hoc workplace surveys with information on management and work organization practices similar to our own. Kato and Kodama (2015) used firm-level data from Japan.
Notice that, since we are modeling a “rare” event (i.e., with a large number of zeros), linear probability models can be problematic due to the large number of out-of-bounds predictions.
Female share is the (weighted) average share of female employees computed by occupation, firm size, country, and year in the sample. We also experimented an alternative specification where femaleshare is computed by industrial sector, firm size, country, and year, and we obtained very similar results.
Note that the Oster (2019) procedure can only be performed in linear models.
Our baseline estimates were also replicated on an extended sample covering 33 countries using the last two waves (EWCS 2010–2015). The main set of results on the extended sample confirms most of our findings above (more detailed results are available upon request with the authors).
Coefficients estimates for the full specification can be found in Table 11 in the Appendix.
Notice that the relatively small values of McFadden’s pseudo-R-squared can also depend to the presence of (classical) measurement error.
A back-of-the envelope calculation suggests that a woman with average tenure and a female boss (i.e., approximately 8.5 years in our sample) over her working career has a lower probability of reporting gender discrimination of about 14 percent.
It accounts for more than 23% of the total variance and has positive loadings on all variables, larger for workloads and absence of work–life balance.
The Stata command (inteff) developed by Norton et al. (2004) provides the correct marginal effect of interaction terms for logit and probit models, as well as the correct standard errors.
Experimentation with slightly different thresholds produces similar results.
The exact wording of the question is: “At your place of work are workers with the same job title as you … (Mostly women/Mostly men/More or less equal numbers of men and women)”.
For each model we also report the Wald-χ2 test for the joint significance of all predictors.
Detailed information on variables’ specification can be found in the Appendix.
Recent literature in political and social sciences has raised the issue of explaining and predicting rare events (i.e., binary dependent variables with fewer ones than zeroes) with binary choice models. Besides the bias due to small samples, recent studies (King & Zeng, 2001) have argued that in rare events data, the biases in probabilities can be meaningful even with big sample sizes and that these biases result in an underestimation of event probabilities. To address these concerns, we also experimented with penalized likelihood methods. Results (not reported) are virtually unchanged.
We selected two questions available in each wave of the EWCS: “Do you think your health or safety is at risk because of your work?” and “dDoes your work affect your health?”
Altonji et al. (2005) provide a test statistic (valid under the null of a zero treatment effect) for the degree of selection on unobservables that fully confounds the estimate, but do not detail how to estimate a bias-adjusted treatment effect. Moreover, the baseline assumption that the inclusion of the unobservables would produce an R-squared of 1 is likely to understate the robustness of results, especially when there is measurement error in the outcome (Oster, 2019).
As β in Eq. (2) is not identified in the case of omitted variables, the set of parameters (\(\widetilde \beta ,\,\dot \beta ,\,\widetilde R,\,\dot R\)), and additional inputs (δ, and Rmax) are needed to implement the procedure. In particular, \(\dot \beta\) and \(\dot R\) come from an uncontrolled regression of the outcome on the treatment (bosswoman dummy) without additional explanatory variables, while \(\widetilde \beta\) and \(\widetilde R\) are obtained from a control regression, which includes the full set of observable controls. The bias-adjusted coefficient β* is then defined as \(\beta ^ \ast \approx \widetilde \beta - \delta \left[ {\dot \beta - \widetilde \beta } \right]\frac{{R_{max} - \widetilde R}}{{\widetilde R - \dot R}}\), when δ = 1. Oster (2019) suggests a heuristic approach with \(R_{max} = 1.3\widetilde R\), based on a sample of randomized trials (90% of the trial results are robust to this value, while only 45% of results from a sample of non-randomized studies survive). In our empirical exercise we also consider a more restrictive value of \(R_{max} = 2\widetilde R\), that allow 80% of randomized results to survive. We also evaluate whether the bounds of the identified set lie within the confidence interval of \(\widetilde \beta\), especially if the estimated coefficient does not move towards zero when including additional explanatory variables. Further details on the method can be found in Oster (2019).
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Lucifora, C., Vigani, D. What if your boss is a woman? Evidence on gender discrimination at the workplace. Rev Econ Household 20, 389–417 (2022). https://doi.org/10.1007/s11150-021-09562-x
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DOI: https://doi.org/10.1007/s11150-021-09562-x