Indicators of Sex Trafficking in Online Escort Ads

Principal Investigator(s):  View help for Principal Investigator(s) Kristina Lugo-Graulich, Justice Research and Statistics Association

Version:  View help for Version V1

Name File Type Size Last Modified
Ad data cleaning file_Mar 2023_No Massage.dta application/x-stata-dta 621.7 KB 10/24/2023 05:46:AM
Quantitative Ad Data Codebook.pdf application/pdf 1.1 MB 10/24/2023 05:46:AM
Quantitative Ad Data Do File 10-2-2022.do text/plain 21.1 KB 10/24/2023 05:46:AM

Project Citation: 

Lugo-Graulich, Kristina. Indicators of Sex Trafficking in Online Escort Ads. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-10-24. https://doi.org/10.3886/E194682V1

Project Description

Summary:  View help for Summary Although law enforcement and researchers have shared certain escort ad characteristics to detect sex trafficking, such as looking “young” or specific language cues, these indicators do not necessarily predict trafficking when tested against the counterfactual. We examined over 250 indicators in 1,263 ads from four states for which case outcomes were known (positive and negative for trafficking) and held focus groups with trafficking survivors, investigators, and non-trafficked sex workers on indicator meanings and ad posting practices. We found that language signaling a provider is trustworthy, use of an obscured phone number, language suggesting youth, and specification of the provider’s ethnicity were statistically significant predictors of trafficking. Additionally, and importantly for practice, photos in which the subject “appears young” or that displayed visible tattoos were associated more with ads that did not belong to a trafficking case than ads that were related to trafficking.

Investigators can apply these results in practice immediately to increase precision in victim identification, and resource allocations can shift to cases most likely to be associated with trafficking, despite the difficulties in accessing such ads because of SESTA/FOSTA and the shutdown of Backpage. Policy recommendations to improve the laws governing advertising websites are also made.
Funding Sources:  View help for Funding Sources United States Department of Justice. Office of Justice Programs. National Institute of Justice (2017-MU-CX-0005)

Scope of Project

Subject Terms:  View help for Subject Terms Sex Trafficking; Online; escort ads; criminal investigations; prosecution
Geographic Coverage:  View help for Geographic Coverage U.S.
Time Period(s):  View help for Time Period(s) 2013 – 2021
Collection Date(s):  View help for Collection Date(s) 2018 – 2021
Universe:  View help for Universe Sex trafficking and commercial sex activity that advertises on the internet.
Data Type(s):  View help for Data Type(s) event/transaction data

Methodology

Response Rate:  View help for Response Rate N/A
Sampling:  View help for Sampling

Case file sampling

Case data from closed cases were eligible for inclusion based on several criteria: (1) case year (year the investigation began) was 2013 or later; (2) case falls into the categories of sex trafficking or trafficking-adjacent activities (e.g., prostitution, promoting prostitution); (3) case must involve online escort ads; (4) ads associated with the case were not ads for recruitment of victims; and (5) case detail must be available such as police report(s), prosecutorial files, indictments, and/or interview transcripts upon which to base a determination of trafficking. The research team used these case details to identify elements of trafficking that comport with the federal definition of human trafficking (see below), so that determination of positive and negative case status did not rely solely on charging decisions that can be impacted by outside factors such as plea agreements. For this study, a single case may contain multiple related perpetrators and/or victims even if the defendants were prosecuted separately. If selecting all available cases from fieldwork sites or all ads associated with each case was a possibility, this option was chosen. In instances where this was not possible, a process as close to random sampling as possible was employed with the ads, with some purposive oversampling of cases to ensure representation of important subcategories of trafficking victims and of cases negative for trafficking.

Ad sampling within cases

While most cases were associated with 10 or fewer ads, a few cases returned hundreds of related ads. Therefore, sampling of ads within the outlier cases was undertaken to reduce potential skew. First, we conducted a manual deduplication process in which identical ads that had been copied and reposted were removed. Next, a power analysis was conducted to determine the appropriate number of ads per case to achieve statistical power, while reducing potential skew. Based on a general linear model, which includes logistic regressions, a random sample of 30 ads for each outlier case was selected to reach a power of 0.8. Since the independent variables are binary, normality measures of skewness and kurtosis are not required (Seltman, 2018).             The final case file sample included 114 cases investigated in four states (California, Georgia, Texas, and Nebraska), involving 1,263 unduplicated ads. The ads within these cases were posted in 35 states and Ontario, Canada, indicating extensive travel involved in most cases. Seventy-six (76) percent of ads were classified as trafficking cases, 9% were negative for trafficking, and 15% of ads could not be classified due to insufficient case file information.
Data Source:  View help for Data Source Case file data from five jurisdictions in four states. Online escort ads collected from three web scraper archives.
Collection Mode(s):  View help for Collection Mode(s) coded on-site observation; face-to-face interview; mixed mode; telephone interview; web scraping
Weights:  View help for Weights State weights were calculated to mitigate selection bias at the criminal case level and must be used in the analysis.
Unit(s) of Observation:  View help for Unit(s) of Observation cases, ads within cases
Geographic Unit:  View help for Geographic Unit State

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V1 [2023-10-24]

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