Gender imbalances in work environments have been a long-standing concern. Identifying the existence of such imbalances is key to designing policies to help overcome them. In this work, we study gender trends in employment across various dimensions in the United States. This is done by analyzing anonymous, aggregate statistics that were extracted from LinkedIn's advertising platform. The data contain the number of male and female LinkedIn users with respect to (i) location, (ii) age, (iii) industry and (iv) certain skills. We studied which of these categories correlate the most with high relative male or female presence on LinkedIn. In addition to examining the summary statistics of the LinkedIn data, we model the gender balance as a function of the different employee features using linear regression. Our results suggest that the gender gap varies across all feature types, but the differences are most profound among industries and skills. A high correlation between gender ratios of people in our LinkedIn data set and data provided by the US Bureau of Labor Statistics serves as external validation for our results.
Gender imbalances in access to opportunities such as education, work or career development have been a long-standing concern. International organizations have developed indicators for measuring gender equality. According to most of these indicators, the progress on gender equality has been relatively slow. According to the World Economic Forum 2017 report on Gender gap, 82 countries have their overall gender gap compared to last year @cite_6 . Using digital data to understand gender gaps is important in order to inform policy that can mitigate such gaps. Understanding and quantifying gender gaps has been an active area of research for a few decades @cite_3 . In particular, there has been a lot of effort into understanding gender gaps in employment and hiring practices and the role they play in creating a balanced work environment @cite_1 @cite_5 .
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The low participation by women authors in research is an important equity issue in Computer Science Research (CSR). There are various parameters and methodologies that can be used to measure the gender imbalance. In this paper, we present a study on gender gap, imbalance and women participation in CSR. We conduct our experiments on DBLP bibliographical database and analyze several years of publication dataset across various domains of CSR. We perform Exploratory Data Analysis on the bibliographical dataset and study the trend of gender imbalance over several years. We propose eight research questions across various facets and our results shows a significant gender imbalance in different sub-fields within CSR and low rate of women participation across various regions of world. This paper studies the gender compensation gap among high-level executives in US corpora- tions. We use the ExecuComp data set that contains information on total compensation for the top five highest paid executives of a large group of US firms over the period 1992-1997. About 2.5 of the executives in the sample are women. These women earn about 45 less than their male counterparts. As much as 75 of this gap can be accounted for by the fact that women manage smaller companies and are less likely to be CEO; Chair, or President of their company. The unexplained gender gap can be reduced to less than 5 when one further accounts for the fact that female executives are younger and have less seniority than male executives. Over the period under study, women have nearly tripled their participation in the top executive ranks and have also strongly improved their relative compensation, mostly by gaining representation into the larger corporations. While the absence of a significant conditional gender gap implies that women and men who hold similar jobs in firms of similar size receive fairly equal treatment in terms of compensation, it does not rule out the possibility of discrimination in terms of gender segregation or promotion. Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world’s knowledge from a neutral point of view. Though everyone can participate it is well known that the editor community has a narrow diversity, with a majority of white male editors. While this participatory gender gap has been studied extensively in the literature, this work sets out to assess potential gender inequalities in Wikipedia articles along different dimensions: notability, topical focus, linguistic bias, structural properties, and meta-data presentation. We find that (i) women in Wikipedia are more notable than men, which we interpret as the outcome of a subtle glass ceiling effect; (ii) family-, gender-, and relationship-related topics are more present in biographies about women; (iii) linguistic bias manifests in Wikipedia since abstract terms tend to be used to describe positive aspects in the biographies of men and negative aspects in the biographies of women; and (iv) there are structural differences in terms of meta-data and hyperlinks, which have consequences for information-seeking activities. While some differences are expected, due to historical and social contexts, other differences are attributable to Wikipedia editors. The implications of such differences are discussed having Wikipedia contribution policies in mind. We hope that the present work will contribute to increased awareness about, first, gender issues in the content of Wikipedia, and second, the different levels on which gender biases can manifest on the Web. The growth of the "gig" economy generates worker flexibility that, some have speculated, will favor women. We explore one facet of the gig economy by examining labor supply choices and earnings among more than a million rideshare drivers on Uber in the U.S. Perhaps most surprisingly, we find that there is a roughly 7 gender earnings gap among drivers. The uniqueness of our data - knowing exactly the production and compensation functions - permits us to completely unpack the underlying determinants of the gender earnings gap. We find that the entire gender gap is caused by three factors: experience on the platform (learning-by-doing), preferences over where when to work, and preferences for driving speed. This suggests that, as the gig economy grows and brings more flexibility in employment, women's relatively high opportunity cost of non-paid-work time and gender-based preference differences can perpetuate a gender earnings gap even in the absence of discrimination.