Cross-national Differences in Self-Employment Participation and Earnings

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

This project examines cross-national differences in entrepreneurship, among both men and women, as measured by self-employment. The project investigates the contexts where women's self-employment is more frequent and more profitable, and offers insights on the policies for work-family reconciliation that promote female entrepreneurship. Past research shows US women's self-employment is profoundly shaped by their family responsibilities. The research seeks to understand whether policies for universal publicly-funded childcare or universal maternity leave would help to close gender gaps in entrepreneurship and pay. Understanding how work-family supports may encourage specific forms of female self-employment offers a potentially transformative understanding regarding how to promote women's entrepreneurship. This study takes an innovative approach to understanding how work-family reconciliation policies shape women's entrepreneurship across different welfare state contexts. The project uses data from 15 westernized countries to examine differences in women's self-employment and work-family policies cross-nationally. Using social policy data, cultural indicators, and longitudinal panel data from these locales, this project deploys event history analysis, fixed effects regression, and multi-level modeling to estimate policy effects net of controls for individual factors associated with self-employment, labor force participation, and earnings. The study's broader impacts are tied to understanding ways to encourage entrepreneurship in the US, and the project expands the scientific understanding of the individual and national level factors leading to women's engagement in self-employment. This project has direct application for increased economic competitiveness of the US. Self-employment is a driving force behind new job creation in the US. The findings will be disseminated to media as well as scholarly outlets. The project also includes extensive training, including in advanced techniques of data analysis, for involved graduate students.