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.Comment: Accepted at a poster at ICWSM 2018. Please cite the ICWSM versio
