Date of Award

August 2024

Degree Type

Thesis

Degree Name

Master of Arts

Department

Sociology

First Advisor

Aki Roberts

Committee Members

Rebecca Konkel, Yuka Doherty

Keywords

feminist theory, gender inequality, rape, sex trafficking, United States

Abstract

Feminist and symbolic interactionist gender-based perspectives theoretically link gender inequality and sex trafficking. Previous research has explored the relationship at the country level; however, the relationship between sex trafficking and gender inequality across numerous U.S. cities has yet to be investigated. The primary purpose of this master’s thesis is to explore the relationship between gender inequality and reported sex trafficking offenses, using data from 72 cities with a population of 100,000 or more. Five indicators of gender inequality were selected with guidance from the United Nations Development Program (2017) and previous research. The number of sex trafficking offenses reported to the Uniform Crime Reporting Program in 2019 was the dependent measure, and criminology theories were used to substantiate control variables. A second model was also included, replacing sex trafficking, the dependent variable, with rape rate across the same 72 cities. Since sex trafficking and rape are both gender-based crimes, the same theoretical framework seems to apply to both. Multiple gender inequality indicators were significant predictors of sex trafficking within a log-rate Poisson regression model. Adolescent birth rate, an indicator of gender inequality demonstrated a positive relationship, in line with a liberal feminist framework and gender-based symbolic interactionist perspective. Continually, education and median income measures showed a negative relationship, in line with a radical feminist perspective. Finally, the other two measures of gender inequality, percentage of women in government and measure of health insurance were nonsignificant. Within the model testing gender inequality and rape, gender inequality was not a significant predictor, contrary to previous research.

Available for download on Wednesday, September 03, 2025

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