Small Area Estimation of Child Trafficking Proportions in Chiefdoms of Sierra Leone and Overall Ranking of the Chiefdoms

Abstract Human trafficking and child trafficking are serious violations of human rights. Child trafficking is a despicable form of crime inflicted on the most vulnerable segment of the society. Availability of reliable estimates of the prevalence of child trafficking for various subpopulations is the first priority in tackling this problem. In our study, subpopulations are children of age 5–17 years living in small geographic regions (or chiefdoms) in Sierra Leone. We develop improved estimates of the true rates of prevalence and identify the most adversely affected regions by estimating the unknown true ranks. These estimates shed light on the severity of the problem and bring attention to the critically affected regions (i.e., chiefdoms). Using household survey data from Sierra Leone, we propose a unit-level hierarchical Bayes probit regression model to reliably estimate the prevalence rates of trafficking in the chiefdoms. Using Markov chain Monte Carlo generated samples of the small area characteristics from the posterior distribution of the hierarchical Bayes model, we compute point and interval estimates of the prevalence rates, and a set of probability distributions for the unknown true ranks of the small areas in terms of their prevalence rates.