Is Pink the New White?: A Textual and Economic Analysis on Gender in White-Collar Crimes

David Arturo Guzmán Illescas

Master's thesis

Abstract:

This thesis examined the intricate relationship between economic indicators and their impact on decision-making processes regarding white-collar crime. Gender segregation was crucial to understand how men and women respond differently to these indicators, and cases involving both genders as accomplices were also analyzed. The study used cases litigations published by the SEC as a proxy for white-collar crime data, employing web crawling and data scraping techniques for data collection. The parsed data underwent filtering and cleaning procedures to ensure focus on relevant litigations, the resulting litigations were later classified by the nature of crime, and the offenders were assigned a gender identity. The initial analysis revealed a gender gap in white-collar criminals, with men comprising the majority of offenders, and primarily engaging solo. As for crime types, fraud and securities fraud represented the most frequent type. Subsequent analysis explored the relationships between economic indicators and litigation numbers, including linear, lagged and Granger-causal analyses. The findings pointed to diverse associations: a complex response of men and women working together to unemployment, men’s engaging in white-collar crime as response to GDP growth, a lagged relationship between inflation and complicit men and women, men committing more white-collar crimes when income inequality is perceived, and a weak positive relationship by women and white-collar crime as a result of diminishing gender inequality. While significant relationships were detected, some may have occurred by chance, and others only emerged after time delays.

Abstract:

This thesis examined the intricate relationship between economic indicators and their impact on decision-making processes regarding white-collar crime. Gender segregation was crucial to understand how men and women respond differently to these indicators, and cases involving both genders as accomplices were also analyzed. The study used cases litigations published by the SEC as a proxy for white-collar crime data, employing web crawling and data scraping techniques for data collection. The parsed data underwent filtering and cleaning procedures to ensure focus on relevant litigations, the resulting litigations were later classified by the nature of crime, and the offenders were assigned a gender identity. The initial analysis revealed a gender gap in white-collar criminals, with men comprising the majority of offenders, and primarily engaging solo. As for crime types, fraud and securities fraud represented the most frequent type. Subsequent analysis explored the relationships between economic indicators and litigation numbers, including linear, lagged and Granger-causal analyses. The findings pointed to diverse associations: a complex response of men and women working together to unemployment, men’s engaging in white-collar crime as response to GDP growth, a lagged relationship between inflation and complicit men and women, men committing more white-collar crimes when income inequality is perceived, and a weak positive relationship by women and white-collar crime as a result of diminishing gender inequality. While significant relationships were detected, some may have occurred by chance, and others only emerged after time delays.

Keywords

Data Scrapping, White-collar Crime, Longitudinal Analysis, Web Crawling

Keywords

White-collar Crime, Longitudinal Analysis, Web Crawling, Data Scrapping

Language used: English

Date on which the thesis was submitted / produced: 29. 6. 2023

Thesis defence

• Date of defence: 23. 8. 2023
• Supervisor: Ivana Malá
• Reader: Adam Čabla

Vysoká škola ekonomická v Praze

Master programme / field:
Economic Data Analysis / Data Analysis and Modeling