We analyse the wage effects following employment breaks of women who enter motherhood using a novel matching approach where mothers’ wages upon return to work are compared to those of their female colleagues within the same establishment. Using an administrative German data set, we apply a fixed-effects propensity score matching based on information two years before birth of the first child. Our results yield new insights into the nature of the wage penalty associated with motherhood: when matching with establishment-specific effects we find that first births reduce women's wages by 19%, whereas ignoring the identifier and matching across all establishments would yield a wage cut of 26%. We therefore conclude that selection into establishments is an important explanatory factor for the family pay gap.

Although mentoring programs for female STEM students are often carried out with a great deal of passion on the part of program managers and mentors, robust results on their effects are often missing. However, regular evaluations are indispensable for an efficient allocation of resources towards gender balances in STEM. To accomplish this requirement, empirically valid and easy-to-use evaluation concepts are needed. We therefore develop an evaluation concept which corresponds to a Logic Chart, capturing three levels of expected effects (output—outcome—impact). On each level of impact, we derive a set of success indicators that can be measured by qualitative methods. A major advantage of our evaluation design is that the effect of a mentoring program can be observed directly after the end of the program. Furthermore, the results provide information about different channels of impact (e.g., reduced stereotypes or increased self-efficacy) and hence offer concrete indications for the further development of the program.

Nowadays, visibility is playing an increasingly important role in science communication. The topic is particularly significant for female experts, as their visibility not only inspires other women but also challenges gender stereotypes in academia. This article provides the first insights into the actual public visibility of scientific experts in Germany and presents potential factors that influence visibility. The analysis is based on the theoretical concept of doing visibility and identifies factors that influence the decision to increase visibility via social media. Our complex dataset comprises 139 experts (59 from computer science and 80 from social sciences), who took part in our online survey about visibility and personal information. Additionally, we tracked the respondent’s online footprint to ascertain their actual visibility in social media and press. The study reveals significant differences in creating visibility and the perception of visibility by sex and discipline. Computer scientists are more active on social media, while male social scientists are more visible in the press. Male computer scientists (57%) post their work on social media most frequently, followed by female computer scientists (35%), female social scientists (24%), and male social scientists (17%). Furthermore, the engagement on social media depends on the discipline, age, and media affinity of the experts. Overall, female experts gain less visibility on LinkedIn and in the press, although the gender differences in IT are smaller in this respect. Based on the results, we discuss potential reasons for the unequal distribution of visibility and suggest targeted interventions to close the gender visibility gap, such as (social) media or interview training. In addition, organisations and media representatives should be trained to actively contribute to breaking down gender stereotypes.