Gender Disparities in Higher Educational Attainment and Connections to Local Economic Conditions

Authors: Bonnie Bounds*, The Ohio State University
Topics: Economic Geography, Women, Rural Geography
Keywords: higher education, educational attainment, local economies, educational segregation by gender
Session Type: Paper
Day: 4/5/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Council Room, Omni, West
Presentation File: No File Uploaded

Since the 1980s, women in the US have entered college and earned bachelor’s degrees at higher rates than men, and today most holders of bachelor’s degrees are female. However, relatively little research has been done to understand how the gender (im)balance of a place’s college-educated population connects to local economic conditions. The potential connection between gender (im)balance and economic conditions may be more salient in rural areas than in urban areas, since rural areas tend to have smaller college-educated populations (in size and in proportion) than do urban areas. Rural and urban labor markets are also structured differently, since rural economies tend to rely on low-skilled jobs in natural resource extraction and manufacturing (traditionally male occupations) and in the service sector (traditionally female occupations), whereas high-skilled jobs are concentrated in urban areas. My preliminary research on this subject indicates that both rural and urban counties with disproportionately female college-educated populations also tend to experience worse economic conditions than counties with more gender-balanced college-educated populations (although the direction of causality remains unclear). In this paper, I break the college-educated population of rural US counties into 10-year age cohorts and examine how differences in gender balance within age cohorts connect to local economic conditions. Using socioeconomic and demographic data from the American Community Survey, I will construct a linear regression model to examine how variations in gender balance across age cohorts predict local median incomes, unemployment rates, poverty rates, and other indicators of economic health.

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