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Structural roles and gender disparities in corruption networks.
Arthur A B Pessa1, Alvaro F Martins1, Mônica V Prates1
1Departamento de Física, Universidade Estadual de Maringá, Maringá, PR, 87020-900, Brazil.
Scientific Reports
|April 24, 2025
Summary
Gender disparities persist in corruption networks, with males dominating highly central roles. While network resilience is unaffected by gender, male overrepresentation in scandals is notable, though explained by random network models.
Area of Science:
- Socioeconomics
- Network Analysis
- Criminology
Background:
- Criminal activities disproportionately involve males, with lower female participation, especially in serious offenses.
- Female roles in organized crime and corruption networks are less understood.
- Existing research suggests females are less tolerant of illegal practices.
Purpose of the Study:
- To analyze gender differences in corruption networks using data from political scandals in Brazil and Spain.
- To investigate the representation, centrality, and network behavior of males and females in corruption.
Main Methods:
- Analysis of political scandal data from Brazil and Spain.
- Network analysis to assess gender representation, centrality, and resilience.
- Comparison with a null network model to account for random associations.
Main Results:
- Females constitute 10% (Brazil) and 20% (Spain) of agents in corruption networks, a stable proportion over time and scandal size.
- Males are overrepresented among highly central individuals in corruption networks.
- Gender does not significantly impact network resilience to attacks or dismantling.
- Males are more frequently involved in multiple scandals; female-dominated scandals are rare.
Conclusions:
- While females are underrepresented in corruption networks, their centrality is comparable to males, except for highly central roles occupied by males.
- Network resilience is not gender-dependent.
- Underrepresentation of females partially explains homophily, but male-to-male connections in Spain exceed null model expectations.
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