Professional Gender Gaps Across US Cities

TL;DR

This study analyzes gender employment disparities across US cities using LinkedIn data, revealing significant variations across industries and skills, and validates findings with US Bureau of Labor Statistics data.

Contribution

It introduces a novel analysis of gender gaps across multiple dimensions using LinkedIn aggregate data and models these disparities with linear regression.

Findings

Gender gaps vary significantly across industries and skills.

High correlation between LinkedIn gender ratios and US Bureau of Labor Statistics data.

Gender imbalance patterns differ by location, age, industry, and skills.

Abstract

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.