Jobs for Justice: The Impact of Stable Employment in Expediting Financial Wellbeing for Survivors of Human Trafficking

ABSTRACT

Financial wellbeing – broadly characterized as a liveable income, savings, and autonomy over financial decisions – has been shown to influence physical and psychological health and is therefore an important element of holistic wellbeing. The present study examines the factors that impact the financial wellbeing of survivors of human trafficking in the United States. Using survey data from a sample of trafficking survivors in the U.S. we find that both temporal distance from the exploitative experience and a stable source of income significantly predict financial wellbeing for trafficking survivors. Importantly, however, stable work that provides predictable income mitigates the impact of time in establishing financial wellbeing. The implications of this research are significant for policy and programs aimed at improving the livelihoods of trafficking survivors.

KEYWORDS:

• Trafficking
• Livelihood
• Financial Wellbeing
• Regression Analysis
• Participatory Action Research

Acknowledgments

The authors would like to thank individual donors, corporations, and foundations who contribute to Polaris’ unrestricted fund, including partial funding for the National Survivor Study from IHG Hotels & Resorts, Match Group, PayPal, and United Way Worldwide. The authors would also like to thank the National Survivor Study core research team and community advisory group, including: Lara Powers, Katherine T. Bright, Tristan Call, Michael Chen, Hazel Fasthorse, Tawana Bandy Fattah, La Toya Gix, Elizabeth Jacobs, Forrest Jacobs, Ashley Maha’a, Erin Marsh, Namrita S. Singh, Karen Snyder, Lauren Vollinger, Charity Watters, Wade Arvizu, Marlene Carson, Harold D’Souza, Wang Fen, and Eric Harris.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/23322705.2023.2257126

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

¹ For example, women, children, racial and ethnic minorities, LGBTQ+, disabled and migrants have all been groups identified as being vulnerable or at higher risk to trafficking victimization and/or are disproportionately represented among trafficking victims compared to their numbers in the general population (see, for example, IOM et al., 2022; Jagoe et al., 2022; Latham-Sprinkle et al., 2019; UNODC, 2020; Whitbeck et al., 2004).

² While we expect there to be a relationship between stable work and financial wellbeing, when one does not feel competent in their work, does not feel adequately supported, or the environment becomes unduly demanding, overall wellbeing may be jeopardized, which could have negative implications for financial wellbeing. While it is beyond the scope of the present study, we acknowledge that the quality of the stable employment may be just as important as the stable employment itself.

³ The survey instrument is available upon request.

⁴ The survey and sampling methodology received ethical approval from the Biomedical Research Alliance of New York (BRANY) Institutional Review Board (IRB). The study also received Certificate of Confidentiality (CoC) through the National Institutes of Health (NIH), which protects the privacy of research participants by prohibiting disclosure of identifiable, sensitive information to anyone not connected to the research team.

⁵ In addition to running these models with the imputed dataset, we also ran all of the models with the original non-imputed dataset. The results were very similar across all of the models making a strong case for the accuracy of our imputation method. Output for these models is available in the appendix.

⁶ There is ample cross-disciplinary precedent for using factor scores as dependent variables. See, for example, Grasso and Simons (2011), Salcioglu et al. (2007), and Vogel et al. (2019).

⁷ There were 13 respondents who answered that they had no income, making the denominator in this equation zero. To avoid this, we recoded $0 as $1000 in income per year.

⁸ To impute missing data for the financial wellbeing dependent variable, which was created from the composite of three factors, two separate imputation procedures were investigated. The first was to directly impute missing data of the underlying factor variables and then create the composite variable. The second was to create the composite variable first and then impute the missing data in the composite variable. It was found that the first approach resulted in a greater imputation accuracy score and was therefore used for imputing the missing entries.

⁹ There were not enough non-binary responses to treat gender as non-binary for the purpose of the statistical analysis. Therefore, we include gender in the model as a binary rather than categorical variable.

¹⁰ Termed “the motherhood penalty,” the financial impact of children is greater for females than males (e.g., Budig & England, 2001; Budig & Hodges, 2010; Gangl & Ziefle, 2009). Further, single women of children are more deeply impacted financially than men and married women (Bartfeld, 2000; De Vaus et al., 2017; Leopold, 2018; Lichtenstein & Johnson, 2019).

¹¹ In addition to running these models with the composite measure of financial wellbeing as the dependent variable, we also ran all of the models with each of the factor variables as the dependent variable. The results were very similar across all of the models making a strong case for the financial wellbeing composite measure. Output for these models is available in the appendix.

¹² The marginal means plots provided are for the most common profile in the sample; however, we provide additional marginal means plots in the Appendix for minority females without children and without a disability for comparison.

¹³ We did not find consistently or strong statistically significant results based on age, race, or gender.