Financial Capability and Sociodemographic Factors among Survivors of Human Trafficking

Abstract

Improving the economic well-being of the girls and women is a key to reducing re-trafficking and in providing stability that survivors can use to rebuild their lives. The study looks at how various sociodemographic traits affected the financial capability of n = 144 women and girls who received intervention at a residential care facility in Ghana, West Africa. Three domain of financial capability are assessed in this, i.e., financial risk, financial planning, and financial saving. A scaled likelihood ratio test (chi-square difference test) was used to evaluate the significance of each direct covariate effect(%). Each of the overall goodness-of-fit indices suggested that the initial CFA model fit the data well, χ = 31.45, p = 0.04, RMSEA = 0.067 (90% CI: 0.017-0.108), TLI = 0.923, CFI = 0.948. Older women reported lower levels of financial savings than younger women. We found that women with secondary school education or higher reported significantly higher financial risk than women with less education. Women with children reported lower levels of financial saving than women without children. Married women indicated significantly more financial saving than single women. There was a significant negative effect of time spent in trafficking conditions on financial saving, indicating the highest average level of financial savings at intervention and decreased thereafter. Programs and policies in resource-scarce contexts that aim to assist trafficking survivors must go beyond providing psychosocial counseling and focus also on economic development opportunities.

Financial capability: a systematic conceptual review, extension and synthesis

“…Previous research also shows consumer background differences in financial capability. Sociodemographic factors are associated with the financial capability of survivors of human trafficking (Okech et al. , 2018).…”

“…Previous research also shows consumer background differences in financial capability. Sociodemographic factors are associated with the financial capability of survivors of human trafficking (Okech et al, 2018). Gender moderates associations among financial literacy, Birkenmaier and Fu (2020), Henager and Wilmarth (2018), Huang et al (2015a), Huang et al (2016), Johnson and Lamdin (2015), Khan and Akhtar (2020), Luo and Zeng (2020), Luo et al (2021), McGarity et al (2020), Montalto et al (2019, Mouna and Jarboui (2015), Ponchio et al (2019), Ranta and Salmela-Aro (2018), Ranta et al (2020), Reyers (2019), Riitsalu and Murakas (2019), Robb et al (2019), Sabri and Zakaria (2015), Seay et al (2015), Tharp et al (2020), von Stumm et al (2013, Xiao and O'Neil (2018b) A review of financial capability money attitudes, financial strains and financial capability in Malaysia (Sabri et al, 2017).…”

“…financial behaviors: Lu ci c et al (2021),Mokhtar et al (2020) Background differences in financial knowledge:Banerjee (2016),Chen and Garand (2018),Khan et al (2021),Overton and Fox O'Mahony (2018),Yakoboski et al (2020) Background differences in financial capability:Khan et al (2017),Kim and Xiao (2021),Lazar et al (2016), O'Donnell and Keeney (2010),Okech et al (2018),Raj (2020),Robson and Peetz (2020),Sabri et al (2017),Silva et al (2010),Xiao and O'Neill (2022), Xiao et al (2015), Xiao et al (2020) P2: Background differences in consumer well-being Background and financial wellbeing: Cui et al (2019), Fan and Babiarz (2019), Nam et al (2016), Nejad and O'Connor (2016) Background and other domain wellbeing: Taylor et al (2011), West and Cull (2020) P3: Intervention to financial capability Financial socialization: Collins and Odders-White (2021), Jorgensen et al (2017b), LeBaron et al (2018), Marchant and Harrison (2020), Shim et al (2013), Wu et al (2017), Zhao and Zhang (2020) Financial education: Doran and Bagdasaryan (2018), Friedline and West (2016), Harvey (2019), Kim and Mountain (2019), Loke et al (2015), Rochelle et al (2017), Sherraden, MS et al (2011), Wagner (2019), Xiao and O'Neill (2016), Xiao and Porto (2019), Zhu (2020) Other interventions: Birkenmaier et al (2021b), Bunnell et al (2020), Collins and Nafziger (2019), Huang et al (2013), Huang et al (2022) Capacity building: Birkenmaier et al (2021a), Frey et al (2015), Loke et al (2017), Luukkanen and Uusitalo (2014) P4: Intervention to consumer well-being Public policy to wellbeing: Brackertz (2014), Despard et al (2020a, b), Huang et al (2015b) Financial socialization to wellbeing: Shim et al (2010), Fan and Chatterjee (2019) Financial education to wellbeing: Farnsworth et al (2011), Xiao and Porto (2017) P5: Financial Capability factors interactions Knowledge to behavior: Birkenmaier and Fu (2016), Chatterjee (2013),Despard and Chowa (2014),Despard et al (2020a, b),Kim and Lee (2018),Kim and Yuh (2018),,Lamb (2016),Loke (2017),Lusardi and Mitchell (2011), Luukkanen and Uusitalo (2019), Pellinen et al (2011), Roa et al (2019), Robb and Woodyard (2011), Seay et al (2017), Zhao and Zhang (2021) Knowledge to capability: Kagotho et al (2018) Behavior correlations: Bialowolski et al (2021a, b), Çera et al (2021), Fan (2021), Jorgensen et al (2017a), Lee and Kim (2020), Moreland (2018), Nam and Loibl (2021), Xiao and O'Neill (...…”

NASN Position Statement: Human Trafficking

“…Any person, regardless of gender, race, or age, may succumb to human trafficking (NHTH, 2019). Major victim risk factors driving the trafficking industry are poverty, social injustice, natural disasters, substance abuse, family breakdown, and homelessness (Okech et al, 2018; UNODC, 2020; Wolfe et al, 2018).…”

Assessing financial capability among people with disabilities

“…The dataset from the NFCS was first tested against the proposed path model for adequacy (i.e., goodness-of-fit). Multiple fit-indices were examined to determine the adequacy of these data for this model, including the root mean square error of approximation (RMSEA), the Tucker-Lewis Index (TLI), and the Comparative Fit Index (CFI; Muthe´n & Muthe´n, 2007;Okech, McGarity, Hansen, & Howard, 2018). In order to determine if the model was substantiated by the data, acceptable model fit was established across multiple fit indices.…”