Uncovering Hidden Populations: A Network-Based Approach to Estimate Child Trafficking Prevalence
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Measuring hidden populations, such as child trafficking victims, is difficult: victims are hard to reach and are often reluctant to disclose their experiences, so direct surveys understate prevalence. We evaluate network elicitation as an alternative, comparing estimates based on reports about close friends, about acquaintances, and self-reports. We field all three in a single nationally representative household survey of 1,474 Jamaican children aged 12 to 17, who report on themselves and on their networks, and we measure seven locally common forms of trafficking. Self-reports show that forced hazardous work (3.2 percent) and domestic servitude (2.1 percent) are the most prevalent forms, while forced criminality in gangs (0.4 percent) and commercial sexual exploitation (0.04 percent) are the least prevalent, despite the latter's public discourse prominence. Prevalence estimation based on acquaintance networks, a popular approach to estimating hidden populations, performed poorly, returning estimates falling well below direct estimates. By contrast, reports about close friends, which to our knowledge have not previously been used to estimate trafficking prevalence, adjusted for information transmission rates, yield estimates that are 1.7 to 2.8 times larger than self-reported estimates for six of the seven forms. We conclude that close-friend reports are the more defensible approach: unlike self-reports, they are less affected by reluctance to disclose victimization, and unlike acquaintance reports, they rely on a measurable transmission rate and on respondents' closer knowledge of their friends.
Institution: University of California, Berkeley
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225144097