Estimating Labor Trafficking Among Farmworkers

An Inverse Sampling Strategy Based on Reliable Housing Predictions

Dados Bibliográficos

ID2186145
AutoresWayne J Pitts (0000-0003-4428-2356, RTI International, autor correspondente), K Barrick (0000-0001-7955-509X, RTI International), Sheldon X Zhang (0000-0002-7983-0554, San Diego State University), Pamela K Lattimore (0000-0001-9870-8258, RTI International)
Ano2015
Volume1
Fascículo2
Páginas117-135
Data de publicação2015-04-03
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoJournal of Human Trafficking (JOURNAL)
Identificadores do periódicoISSN: 2332-2705 • E-ISSN: 2332-2713
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/23322705.2014.977092
OpenAlexW1486606934
IdiomaEN
Citações recebidas1
Referências citadas17

The lack of knowledge on the magnitude of the human-trafficking problem regionally and internationally remains the weakest link in the current countertrafficking movement and has given rise to estimates and unsubstantiated claims. However, difficult-to-count populations such as trafficking victims pose significant sampling challenges for social scientists because a sampling frame cannot be reliably established. As a result, field researchers often choose to accept nonparametric strategies such as convenience sampling at the cost of weakened generalizability and inferential potential. We launched a pilot in 2013 to test whether a new sampling frame enumeration strategy, based on Global Positioning System (GPS)-enabled field inspections, would be economically feasible and methodologically reliable. The pilot study included 543 dwellings randomly drawn from U.S. Census block and block clusters in four North Carolina counties. Findings support the rationale behind our field enumeration strategies and its cost effectiveness in establishing a sound sampling frame for our target population. We believe that this sampling strategy may offer considerable value for future fieldwork among the difficult-to-count migrant farmworker population

Census · Econometrics · Economics · Environmental health · Generalizability theory · Geography · Population · Sampling frame · Statistics · Telecommunications · Computer Science · HIV, Drug Use, Sexual Risk · Mathematics · Medicine · Sex work and related issues · Sexuality, Behavior, and Technology