Reject and Resubmit: A Formal Analysis of Gender Differences in Reapplication and Their Contribution to Women’s Presence in Talent Pipelines

A common explanation for women’s underrepresentation in many economic contexts is that women exit talent pipelines at higher rates than men. Recent empirical findings reveal that, in male-dominated selection contexts, women are less likely than men to reapply after being rejected for an opportunity. We examine the conditions under which this gender difference contributes to women’s underrepresentation in talent pipelines over time. We formally model and analyze the population dynamics of a generic selection context, which we then ground using three distinct empirical settings. We show that gender differences in reapplication are an important mechanism of gender segregation in some selection contexts but negligible in others. The extent to which gender differences in reapplication contribute to women’s underrepresentation is driven in part by the rejection rate. Higher rejection rates increase the stock of rejected applicants, which in turn enables gender differences in reapplication to disproportionally reduce women’s representation. The results demonstrate that interactions between individuals’ choices on the supply side and screeners’ behavior on the demand side may have consequences for gender inequality, even if we were able to fully eliminate demand-side biases. We discuss the theoretical and policy implications of our research for understanding women’s underrepresentation in talent pipelines. We also interrogate the effectiveness of common interventions focused on encouraging women to apply for opportunities in male-dominated domains.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1635 .

Empowerment of women's entrepreneurship in family business through Twitter

Purpose

The main purpose is to provide a global understanding of the role of women in entrepreneurship and family businesses, enabling the evaluation of the impact and the sentiment their role generates. To this end, empowerment and businesswomen's positioning through user-generated content (UCG) on Twitter is assessed.

Design/methodology/approach

The research is carried out from a quantitative and qualitative perspective through the evaluation of UGC from the social platform Twitter. A total of 37,852 tweets have been collected and subsequently analysed about the role of entrepreneurial women. For that purpose, a set of supervised machine learning algorithms have been developed for sentiment analysis, as a natural language processing (NLP) technique, outlining random forest as the one with the highest accuracy. Finally, social network analysis (SNA) techniques and graph theory are applied to a generated text-to-network, which enables the identification of the most relevant topics in the discussion.

Findings

The results revealed a positive relationship in the sentiment of the generated content in relation to women entrepreneurs and leaders. An increasing trend was evidenced in the number of published tweets, as well as in the identified topics, highlighting the needs and challenges faced by women in the business environment as the most widely discussed.

Research limitations/implications

The study develops both theoretical and practical implications so that the findings result in applications in academia and society. The performed analysis creates consciousness about the challenges of women in society, specifically in entrepreneurship.

Originality/value

The study contributes to further enriching the literature on women's entrepreneurship by addressing UGC via Twitter around the role of women, entrepreneurship and power positions.

Do New Firms Recruit Employees From Small or Large Firms, and Do Small or Large Firms Recruit Employees From Firms That Cease to Operate?

Panel data of Norwegian industries show that when they increase in the number of firms, firm size inequality in employees decreases. Decreasing firm size inequality implies that large firms become smaller in employees, and an increasing number of firms in an industry implies that more new firms are established than closed, i.e., ceasing to operate and going out of business. Thus, new firms chiefly recruit employees from large firms. Similarly, the data show that when industries decrease in the number of firms, firm size inequality in employees increases. Increasing firm size inequality implies that large firms become larger in employees, and a decreasing number of firms in an industry implies that more firms are closed than established. Thus, large firms chiefly recruit employees from firms that cease to operate. An implication of our findings is that large firms are crucial in recruiting employees to new firms and in recruiting employees from firms that cease to operate.