Abstract

Effective police intervention strategies and national crime strategies to
combat human trafficking for child sexual exploitation material
production require accurate prevalence estimates. In this talk, I will
describe the results of a survey method called the network scale-up
method (NSUM), where survey respondents were asked how many
people do you know who traffic children to produce CSAM. This means
they can provide information about those in their social network without
disclosing personally identify information or their own experiences. NSUM
models often necessitate standalone surveys for each geographic
region, escalating costs, and complexity. I will introduce a partially pooled
NSUM model, using a hierarchical Bayesian framework that efficiently
aggregates and utilizes data across multiple regions without increasing
sample sizes. I developed this model for a novel national survey dataset
from the Philippines and I will demonstrate its ability to produce detailed
municipal-level prevalence estimates of trafficking for CSEM production.
Our results not only underscore the model’s precision in estimating
hidden populations but also highlight its potential for broader application
in other areas of social science and public health research, offering
significant implications for resource allocation and intervention planning.