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. 2021 Feb 5:bcab008. doi: 10.1093/bjsw/bcab008

Financial Challenges and Capacity among African Refugees in the Southern USA: A Study of Socio-demographic Differences

Youn Kyoung Kim 1,✉, Arati Maleku 2, Younghee Lim 3, Njeri Kagotho 2, Jennifer Scott 1, McKenzie M Ketchum 1
PMCID: PMC7929184

Abstract

Refugees’ successful integration into US society requires adaptation to economic, financial and social norms. Despite the importance of considering financial challenges (financial stress and financial anxiety) and financial capacity (financial literacy and financial self-efficacy) in reaching personal financial goals, literature examining the relationship between financial challenges and capacity—critical in refugee resettlement and integration—is sparse and fragmented. This study explored financial challenges and capacity amongst resettled African refugees (N = 130) in the southern USA using data from a larger community-based participatory research study that used a mixed-methods approach. We explored socio-demographic differences in financial stress, financial anxiety, financial literacy and financial self-efficacy across African refugee subpopulation groups. Our study highlights the importance of social work advocacy for data disaggregation, which helps establish the scope of the problem, unmask subpopulation differences and make vulnerable groups more visible to facilitate the development of tailored programmes and services to reach economic integration goals. We provide social work implications for data disaggregation in the current corona virus context, which will leave long-term financial scars on refugee subpopulations.

Keywords: Africa, data disaggregation, financial anxiety, financial challenges and capacity, financial literacy, financial self-efficacy, financial stress, immigrants, refugees

Introduction

Financial self-sufficiency and economic integration of resettled refugees are the stated goals of the US refugee resettlement programme (U.S. Department of Health and Human Services, 2013). Upon arrival in the USA, refugees often encounter economic hardships—lower employment rates, lower incomes and higher rates of welfare reliance, compared with US citizens and legal permanent residents (Fix et al., 2017). Financial assistance available to refugees upon arrival through refugee resettlement programmes is limited in quantity and duration (Capps et al., 2012) and, thus, is often insufficient to alleviate this economic hardship. Studies examining refugees’ economic hardship have pointed to challenges in finding stable employment; effects of frequent job changes on families’ well-being (Maleku et al., 2020; Jamil et al., 2012); obstacles in access and utilisation of financial services (Rhine and Greene, 2006; Zhan et al., 2013) and difficulties associated with compounded experiences of trauma and lack of connection with family members at home or abroad (Lutheran Immigration and Refugee Service [LIRS], 2012).

Studies have highlighted the importance of mainstream financial services to immigrant and refugees’ economic and social integration (Zhan et al., 2013). However, immigrants and refugees are less likely than native-born citizens to use financial services (Rhine and Greene, 2006), credit or other financing tools (Newberger et al., 2004). Refugees have also been shown to have lower levels of wealth accumulation (Osili and Paulson, 2014), savings and investments (Zhan et al., 2013) and to be more vulnerable to predatory lending practices (Paulson et al., 2006). Financial stress, which refers to the psychological or emotional effects associated with the inability to meet financial obligations (Heckman et al., 2014), and financial anxiety, which refers to the negative emotions associated with finances (Shapiro and Burchell, 2012), are important constructs to understand how financial challenges affect individuals and families. It is equally important to explore financial capacity, which is often conceptualised as the combination of financial literacy—comprehension of financial concepts such as borrowing, saving and protection (Huston, 2010)—and financial self-efficacy—confidence in one’s capacity to make sound financial decisions for financial well-being (LIRS, 2012)—in the refugee context. Because social workers advocate for resettlement support for refugees, these financial constructs are crucial for understanding how financial capacity affects economic integration across refugee subpopulations. It is noteworthy that differential experiences of financial challenges and capacity are based on socio-demographic characteristics, not limited to age, sex, education, language proficiency and employment status (Griffiths and Loy, 2019). Although there is established literature on the overall financial well-being of refugees (Ting, 2010), literature exploring refugees’ financial well-being at the intersection of financial challenges and capacity is limited. Additionally, investigations often discuss refugees as a singular monolithic cultural group. In particular, the increasing national diversity of African refugee population in the USA, whose members currently hail from fifty-four countries (Anderson, 2017), suggests increasing heterogeneity in financial challenges and capacity, with direct implications for financial self-sufficiency and financial service landscape. Given the evident socio-demographic diversity amongst the African refugees, there could be hidden differences across subpopulation groups. Disaggregation of data—dividing the larger population by subpopulation groups based on socio-demographic characteristics—will therefore help establish the scope of the problem and make subpopulation groups hidden within the larger population, more visible. This visibility is crucial to unmasking discrepancies and devising tailored financial programmes and services, central to bolstering efforts towards economic inclusion. Disaggregation of data is critical for resource allocation, policy making and combating disparities across racial and ethnic minority populations (Rubin et al., 2018). Visibility of these unique needs, strengths and life experiences of minority groups is a social justice imperative, fundamental to social workers. Our study, therefore, provides a comprehensive profile of financial challenges and capacity across key demographic and social characteristics amongst the diverse African refugee sample.

African refugees in the USA

African immigrants have been growing at the fastest rate across all immigrant groups in the USA (Anderson, 2017; Echeverria-Estrada and Batalova, 2019). Although not all African immigrants are refugees, most come to the USA as refugees or gain asylum after arrival (Capps et al., 2012). In 2009, African refugees from five countries (Ethiopia, Somalia, Liberia, Sudan and Eritrea) accounted for almost 30 per cent of all Black immigrants (Capps et al., 2012).

Financial challenges and capacity

African refugees move to a new country to flee persecution. Upon arrival to their new home, they face challenges such as learning a new culture and way of life whilst fulfilling obligations to family members and communities they left behind (Stoll and Johnson, 2007; Maleku et al., 2020). The inability to meet household financial obligations in their new home and the psychological effects caused by the gaps to fulfil financial demands from their original homes create financial stress (Heckman et al., 2014). Additionally, African refugees often face challenges in seeking employment (Allen, 2006), retaining food security, achieving English proficiency and becoming financially self-sufficient (Hadley et al., 2007). Even if employed, they face major challenges with upward mobility (Perera et al., 2013). Furthermore, despite higher education levels amongst African immigrants compared with other immigrant groups, they earn less (Capps et al., 2012). These trends point to systematic racial discrimination and oppressive practices, often faced by Black, Indigenous and people of colour.

Utilisation of financial services

Although studies that examine utilisation of financial services amongst African refugees are limited, research on how immigrants utilise and understand financial services might provide relevant insights. Utilisation of mainstream financial services is crucial to the economic and social integration of immigrants (Zhan et al., 2013). However, immigrants are less likely than native-born individuals in the USA to have checking and saving accounts (Paulson et al., 2006); underestimate minimum account balance requirements (Suro et al., 2002); and lack knowledge about bank account management and loan requirements (Schoenholtz and Stanton, 2001), and sceptical attitudes about bank use (Zhan et al., 2006). Immigrants’ underutilisation of financial services and disengagement from financial institutions can increase financial challenges and decrease financial capacity, making them more susceptible to predatory financial practices (Fuentes, 2009). In addition to employment and income, sociocultural factors can also influence financial behaviour and capacity (Government Accountability Office [GAO], 2010). Specifically, lack of familiarity with financial institutions, differing cultural norms, attitudes about money management, mistrust of financial systems and income and education levels can influence financial literacy (Northwood and Rhine, 2018). Immigrants from countries whose financial systems and consumer bank participation rates are comparable to those in the USA, however, have greater financial capacity (Northwood and Rhine, 2018).

Socio-demographic characteristics of refugees in the USA

Kuhlman’s (1991) economic adaption model postulate six dimensions—demographic characteristics, premigration-related factors, dynamics of the host society, postmigration residency factors and the policy environment—key to refugee integration. Amongst others, socio-demographic factors such as education, language, sex and marital status have the highest predictive impact on refugee economic integration (Potocky-Tripodi, 2003).

Education and language

Immigrants from sub-Saharan Africa have high education levels compared with other immigrant groups, where Nigerians, South Africans and Kenyans report the highest educational attainment (Echeverria-Estrada and Batalova, 2019). Higher education is associated with stronger economic well-being (Potocky-Tripodi, 2001). However, unlike native-born Americans, foreign-born immigrants, especially refugees, are often unable to receive a commensurate economic return on education received outside the USA (Painter, 2013). Particularly, those who migrate in later life are more likely to have received their education in their home countries and thus face more barriers to maximise their education. Closely associated with educational attainment is English language proficiency, which is a crucial determinant of successful refugee integration in US society (Shaw and Poulin, 2015). Immigrants, who have gained education postmigration, are more likely to have stronger language skills (Zeng and Xie, 2004). Immigrants with poor English proficiency tend to earn lower incomes, attain lower education levels, face issues related to financial statements and fees and have less access to financial education (GAO, 2010).

Sex

Biological sex is a powerful demographic indicator that can influence social structure and socialisation. Gender roles tied to biological sex can overwhelmingly alter migration and resettlement experiences based on sociocultural norms and individuals’ premigration socialisation (Acevedo-Garcia et al., 2012).

Marital status

Structural position, such as marital status, is correlated with physical, mental, social and financial well-being (Mookherjee, 1997). Recent evidence shows that people who are married have higher financial literacy compared with people who are not married (Brown and Graf, 2013). The impact of marital status suggests that differences in structural position yield different economic resources (Potrich et al., 2015).

Length of stay

Length of stay, which is the duration of US residency, is a predictor of economic well-being (Kuhlman, 1991). Evidence suggests that length of stay is also an important indicator for utilisation of programmes, cultural adaptation and increased labour market knowledge amongst refugees (Hilmet et al., 2012). Evidence suggests that refugee well-being scores related to finances, education and housing improved over time (Shaw and Poulin, 2015). With increased length of stay in the USA, refugees were also less likely to need financial support services, such as coordination with workforce services and assistance with food stamps, Medicaid, cash assistance, banking and mail (Shaw and Poulin, 2015). Refugee populations with longer US residence generally showed higher levels of labour force participation, English language proficiency and home ownership (Kallick and Mathema, 2016). However, these results varied when disaggregated by length of stay and country of origin, where amongst the four refugee groups in the study (Hmong, Burmese, Bosnian and Somali), Somali refugees showed lower home ownership rates, even after living in the USA for more than ten years (Kallick and Mathema, 2016).

Geopolitical factors

For decades, refugees from sub-Saharan Africa have been fleeing armed conflict, tribal violence and drought. As of 2017, nearly 1.5 million sub-Saharan immigrants lived in the USA, making them one of the country’s fastest-growing immigrant populations (Connor, 2018). Although refugee resettlement declined in the USA after the Trump administration’s restrictions on refugee admittance (Connor and Krogstad, 2018), most refugees had come from the Middle East and Africa as of the end of fiscal year 2017 (Radford, 2017). More than 9,377 refugees were admitted to the USA from the Democratic Republic of Congo by the end of 2017; they also represented the nationality with the largest number of refugees in fifteen US states (Radford, 2017). Although studies have shown that immigrants from sub-Saharan Africa are often relatively well educated and come from moderate socio-economic backgrounds, it is worth noting that the sub-Saharan region is a large conglomeration of forty-six nationalities with diverse linguistic and cultural backgrounds (Anderson and Connor, 2018).

Conceptual framework

Building on the dimensions of the refuge economic integration model (Kuhlman, 1991), we drew on the financial capability framework (FCF; Johnson and Sherraden, 2007) as the conceptual lens for this study. Financial knowledge and financial inclusion are the key constructs of FCF. Financially capable individuals are proficient in financial concepts and principles that facilitate their ability to navigate personal finances. Financial skills attained through socialisation and education help individuals make the best financial decisions (Johnson and Sherraden, 2007). However, when immigrants come to a new country, their financial values, attitudes and behaviours might not be in sync with the financial systems of their host country. So immigrants and refugees are often at a disadvantage, largely due to unfamiliarity with financial services systems. One strength of the FCF is its inclusion of both internal and external conditions that affect an individual’s financial well-being. Individual and external factors associated with lower financial participation amongst immigrant populations include being unbanked in their home country (Porto, 2016), previous negative experiences with financial institutions (Osili and Paulson, 2008, 2014), fear of being asked to provide valid immigration documentation (Suro et al., 2002) and experiences with predatory lending practices (Fuentes, 2009). Financial inclusion, which focuses on access to financial institutions, is integral to individuals’ capacity to act and make decisions that are in their best financial interests. When individuals lack the internal capacity and institutional supports to navigate their financial environment, they are at increased risk of experiencing financial challenges, such as financial anxiety and financial stress. In the African refugee context, financial stress due to the inability to meet financial obligations and the financial anxiety linked with making financial decisions have dire impacts on the overall well-being of African refugees (Kim et al., 2020; Maleku et al., 2020).

Study purpose

In an effort to understand differences in economic integration in the African refugee population, we explored financial challenges (financial anxiety and financial stress) and financial capacity (financial self-efficacy and financial literacy) across socio-demographic characteristics (age, sex, length of stay, marital status, employment status, African subregion, education and English language proficiency). By exploring subpopulation differences across key socio-demographic characteristics, we aimed to (i) establish the scope of financial challenges and capacity at the centre of refugees’ lives and (ii) unmask discrepancies and increase visibility of subpopulation groups in the larger refugee population to inform tailored financial programmes for more vulnerable subpopulation groups.

Methods

Research approach and data collection

This study is part of a larger community-based participatory research study that explored the resettlement challenges and capacity of African refugees in the southern USA using a mixed-methods research approach (Maleku et al., 2020). Community leaders, collaborators and stakeholders were active partners in the study; they were involved in the conceptualisation of the study, refinement of study protocol, data collection and dissemination of research findings. This study drew on the quantitative data focussed on financial challenges and capacity. Institutional review board approval was received at the study site, which determined fulfilment of ethical considerations for participant recruitment and data collection.

Community members were trained in research protocols and employed for data collection and analysis, which bolstered community participation in the research and contributed to refinement of research protocol centred on cultural humility. Four bilingual members from the local African refugee community—fluent in English, Kiswahili, French and Arabic—conducted the in-person survey interviews. Using respondent-driven sampling, which uses linkages in underlying social networks to increase the sample amongst hard-to-reach populations (Gile and Handcock, 2010), we recruited refugee participants aged eighteen years or older. Recruitment took place at formal and informal community events hosted by two community-based organisations.

Measures

Demographic variables

Eight socio-demographic variables—age, sex, length of stay, marital status, employment status, African subregion, education and English language proficiency—were used. There were five response options for age, coded as follows: 1 = twenty years or younger; 2 = twenty-one to thirty years; 3 = thirty-one to forty years; 4 = forty-one to fifty years; and 5 = fifty-one or older. Response categories for sex included female (coded as 1), male (coded as 2) and other. The ‘other’ category was excluded from the analyses given the relative low frequency (f = 2), which could not be used in t-test analyses. Demographic categories were coded as follows: marital status (1 = single, 2 = married and 3 = other), education (1 = no education, 2= high school or less, 3 = GED or vocational training, and 4 = college or more) and employment (1 = unemployed, 2 = part-time and 3 = full-time). The countries of origin were grouped by African subregions (coded 1 = central, 2 = western, 3 = eastern and 4 = northern). Although subregional grouping is not the lowest level of data disaggregation, this decision was made to garner meaningful interpretation, which would have otherwise been limited by the smaller sample size across original nineteen African countries. There was no representation from the southern Africa region in the sample. Length of stay was recoded (1 = less than five years, 2 = six to ten years, 3 = eleven to fifteen years and 4 = sixteen or more years). We gathered information on respondents perceived English language proficiency based on self-reported speaking, listening, reading and writing abilities on a five-point Likert scale ranging from 1 (poor) to excellent (5). We combined these four variables into a single English proficiency variable, with scores ranging from 4 to 20. A dichotomous variable was then created; scores below the mean of 15.9 were categorised as poor (coded as 1), and those equal to or above the mean were categorised as good (coded as 2).

Financial self-efficacy

To assess levels of financial self-efficacy, we used the Financial Efficacy Scale developed by Dietz et al. (2003), which included three items from the Pearlin Mastery Scale (Pearlin and Schooler, 1978). Participants were asked to indicate the degree to which they agreed with three questions: ‘I have little control over financial things that happen to me’, ‘I often feel helpless in dealing with the money problems of life’ and There is little I can do to change many of the important money issues in my life’. Responses were measured on a four-point Likert scale ranging from 1 (strongly agree) to 4 (strongly disagree). The items were reverse coded. The total possible scores ranged from 3 to 12, with higher scores indicating higher levels of financial self-efficacy (Cronbach’s alpha = 0.80).

Financial literacy

Three questions assessing financial knowledge and skills were used to construct a three-item scale (Lusardi and Mitchell, 2011). Participants were asked to indicate the degree to which they agreed with the following three statements: ‘I am good at dealing with day-to-day financial matters, such as checking accounts, credit and debit cards, and tracking expenses’, ‘I am pretty good at math’ and ‘Overall, I feel knowledgeable enough about money, checking accounts, credit/debit cards, tracking expenses, etc. to manage my financial responsibilities on an everyday basis’. A seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with higher scores indicating higher financial literacy, was used (Cronbach’s alpha = 0.89).

Financial anxiety

Using a four-point Likert scale, respondents were asked to indicate the degree to which they agreed with the following seven items from the Financial Anxiety Scale (Archuleta et al., 2013): I feel anxious about my financial situation’, ‘I have difficulty sleeping because of my financial situation’, ‘I have difficulty concentrating on my school or work because of my financial situation’, ‘I am irritable because of my financial situation’, ‘I have difficulty controlling worries about my financial situation’, ‘My muscles feel tense because of worries about my financial situation’ and ‘I feel fatigued because I worry about my financial situation’. Response options ranged from 1 (strongly disagree) to 4 (strongly agree), with higher scores indicating higher levels of financial anxiety (Cronbach’s alpha = 0.89).

Financial stress

We conceptualised financial stress as a negative financial circumstance and used a shortened five-item scale (Heckman et al., 2014), originally used with a sample of college students in Ohio. We omitted two items (current college debt and student loan debt) not relevant to our refugee sample. This resulted in a scale consisting of three statements: ‘I have enough money to participate in most of the same activities as my peers do’, ‘I regularly spend more than I have by using credit or borrowing’ and ‘I pay my bills on time every month’. Respondents were asked to rate their agreement using a four-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree). The first and third items were reverse coded, where higher scores indicated higher levels of financial stress. Although the Cronbach’s alpha for this variable suggested a weaker internal consistency (0.50), it has also been suggested that lower Cronbach’s alpha can be applicable in some behavioural research (Raykov, 2004). Given the unavailability of financial stress scales that capture the lived experiences of refugee populations, we proceeded to use the measure.

Data analysis

We first conducted descriptive analyses to describe the overall characteristics of our sample. Secondly, we examined associations between financial challenges (financial stress and financial anxiety) and financial capacity (financial literacy and financial self-efficacy) using Pearson’s correlation analyses. We then examined variations in financial challenges and capacity across demographic characteristics. Using SPSS 24 statistical analysis software, we conducted independent-samples t-tests and analysis of variance with Tukey’s test for post hoc assessment to compare financial outcomes across demographic subgroups.

Results

Our sample (N = 130; Table 1) represented seventeen African countries: Congo (33.8 per cent), Liberia (23.8 per cent); South Sudan (11.5 per cent); Sudan (10 per cent); 37.7 per cent of participants had emigrated from Central African Republic, Congo and Guinea; 33.1 per cent from Liberia, Sierra Leone, Mali, Togo, Nigeria, Côte d’Ivoire, Ghana; 19.2 per cent from South Sudan, Rwanda, Ethiopia, Somalia, Uganda, Kenya; and 10 per cent from Sudan. Most of the participants were between twenty-one and thirty years old (40 per cent). Our sample had more females (57.2 per cent) than males (42.2 per cent). Participants self-identified as single (53.5 per cent); married (34.1 per cent) and other (12.4 per cent). Education levels ranged from no formal education (5.9 per cent) to college education (27.5 per cent). Employment status included full-time employment (60.3 per cent), part-time employment (22.2 per cent) or no employment (17.5 per cent). Approximately 25.8 per cent had lived in the USA for less than five years and 28.1 per cent for sixteen years or longer.

Table 1.

Sample characteristics (N = 130)

Variable Per cent (n)
Age
 20 or younger 12.2 (16)
 21–30 40.0 (52)
 31–40 20.8 (27)
 41–50 16.2 (21)
 51 or older 10.8 (14)
Sex
 Female 57.2 (74)
 Male 42.2 (54)
Length of stay
 Under 5 years 25.8 (33)
 6–10 years 21.1 (27)
 11–15 years 25.0 (32)
 16 years and over 28.1 (36)
Marital status
 Single 53.5 (69)
 Married 34.1 (44)
 Other 12.4 (17)
Employment
 Full-time 60.3 (76)
 Part-time 22.2 (28)
 Not employed 17.5 (22)
Subregions of Africa
 Central Africa (Central African Republic, Congo, Guinea) 37.7 (49)

 Western Africa (Liberia, Sierra Leone, Mali, Togo,

Nigeria, Côte d'Ivoire, Ghana)

 33.1 (43)
 Eastern Africa (South Sudan, Rwanda, Ethiopia, Somalia, Uganda, Kenya) 19.2 (25)
 Northern Africa (Sudan) 10 (13)
Education
 None 5.9 (7)
 High school or less 66.6 (85)
 College or plus 27.5 (35)
English proficiency
 Poor 33.8 (44)
 Good 66.2 (86)
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Note. Actual n varies based on missing values.

Correlations

Pearson’s correlations were conducted to determine the statistical significance of the relationships between all financial variables (financial stress, financial anxiety, financial literacy and financial self-efficacy). As shown in Table 2, positive correlations were observed between financial self-efficacy and financial literacy (r = 0.34, p < 0.001) and between financial anxiety and financial stress (r = 0.47, p < 0.001). Results also showed significant negative correlations between financial self-efficacy and financial anxiety (r = –0.67, p < 0.001), financial self-efficacy and financial stress (r = –0.40, p < 0.001), financial literacy and financial anxiety (r = –0.28, p < 0.01) and financial literacy and financial stress (r = –0.48, p < 0.001).

Table 2.

Correlations: financial challenges and capacity

M (range) SD 1 2 3 4
Financial self-efficacy 8.36 (4–12) 2.09 1
Financial literacy 14.76 (3 –21) 4.55 0.34*** 1
Financial anxiety 15.25 (7–26) 4.96 –0.67*** –0.28** 1
Financial stress 6.91 (3–11) 1.82 –0.40*** –0.48*** 0.47*** 1
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**

p < 0.01.

***

p < 0.001.

Financial challenges and financial capacity across subgroups

Independent-samples t-tests and analyses of variance showed significant variation in financial challenges (financial anxiety and financial stress) and financial capacity (financial self-efficacy and financial literacy) across demographic and socio-economic characteristics (age, sex, length of stay, marital status, employment, subregions of Africa and English proficiency).

Financial self-efficacy

As shown in Table 3, financial self-efficacy was distinguished by marital status (F = 6.69, p < 0.01), employment (F = 6.80, p < 0.01) and English proficiency (t = –3.41, p < 0.001). Specifically, Tukey’s post hoc test revealed that African refugees who were single (M = 8.26) or other (M = 6.94) reported lower levels of financial self-efficacy compared with their married counterparts (M = 9.05). Moreover, post hoc test showed that African refugees who had full-time employment had higher levels of financial self-efficacy (M = 8.84) compared with those with part-time employment (M = 7.50) or who were not employed (M = 7.52). African refugees with good English proficiency had higher levels of financial self-efficacy (M = 8.79) compared with the group with lower English proficiency (M = 7.51). Age and education were not significantly associated with financial self-efficacy.

Table 3.

Financial challenges and capacity: differences across key socio-demographic variables (N = 130)

Financial capacity
 Financial challenges
Financial self-efficacy
 Financial literacy
 Financial anxiety
 Financial stress
Mean (SD) F/t Mean (SD) F/t Mean (SD) F/t Mean (SD) F/t
Age, years
 ≤20 7.81 (1.80) 1.10 13 (3.76) 1.60 16.81 (4.51) 0.69 7.87 (1.51) 1.65
 21–30 8.73 (2.28) 15.33 (4.29) 14.84 (5.51) 6.56 (1.82)
 31–40 8.33 (2.27) 16.04 (4.08) 14.85 (5.23) 6.89 (2.14)
 41–50 8.38 (1.83) 13.74 (5.09) 14.90 (3.82) 6.90 (1.51)
 ≥50 7.62 (1.50) 13.86 (5.59) 16.31 (4.40) 7.23 (1.74)
Sex
 Male 8.58 (1.97) 1.03 16.31 (3.57) 3.18** 14.41 (4.36) –1.54 6.58 (1.60) –1.73
 Female 8.20 (2.13) 13.67 (4.89) 15.80 (5.32) 7.15 (1.96)
Length of stay, years
 Under 5 7.78 (2.09) 1.13 13.40 (4.36) 2.99* 16.50 (5.02) 0.92 7.16 (1.27) 0.83
 6–10 8.41 (2.04) 13.48 (4.68) 14.96 (3.84) 7.08 (1.83)
 11–15 8.66 (2.04) 15.88 (4.50) 14.53 (4.52) 7.09 (1.75)
 ≥16 8.52 (2.11) 15.97 (4.20) 15.38 (5.71) 6.56 (2.18)
Marital status
 Single 8.26 (2.19) 6.69** 14.34 (4.36) 1.77 15.67 (5.26) 2.90 6.91 (1.86) 1.18
 Married 9.05 (1.92) 15.80 (4.39) 13.95 (4.35) 6.68 (1.76)
 Other 6.94 (1.18) 13.71 (5.31) 17.06 (4.61) 7.50 (1.83)
Employment
 Full time 8.84 (1.99) 6.80** 16.0 (3.80) 7.15*** 14.35 (4.71) 5.11** 6.62 (1.80) 2.79
 Part time 7.50 (2.10) 14.50 (4.45) 16.04 (4.79) 6.93 (1.94)
 Unemployed 7.52 (1.69) 12.0 (1.13) 17.95 (4.75) 7.67 (1.63)
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*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

Financial literacy

As shown in Table 4, financial literacy levels were significantly different by length of stay (F = 2.99, p < 0.05), employment (F = 7.15, p < 0.001), subregion (F = 2.92, p < 0.05) and English language proficiency (t = –4.33, p < 0.001). Male African refugees showed higher levels of financial literacy (M = 16.31) than female African refugees (M = 13.67). In addition, African refugees whose length of stay in the USA exceeded fifteen years showed higher levels of financial literacy (M = 15.97) compared with refugees between 11 and 15 years (M = 15.88), 6 and 10 years (M = 13.48), or 5 years and shorter (M = 13.40). However, the post hoc results revealed no statistically significant differences between the four groups. In addition, the post hoc results demonstrated that African refugees with full-time employment had higher levels of financial literacy (M = 16.0) than those with part-time employment (M = 14.50) or who were not employed (M = 12.0). Interestingly, African refugees from eastern and central Africa showed higher levels of financial literacy (M = 15.86 and M = 15.46, respectively) than those from western (M = 13.88) and northern (M = 14.76) Africa. Last, African refugees with good English proficiency demonstrated higher levels of financial literacy (M = 16.03) than those with poor English proficiency (M = 12.46). Age and education were not significantly associated with financial literacy.

Table 4.

Financial challenges and capacity: differences across key socio-demographic variables (N = 130)

Financial self-efficacy
 Financial literacy
 Financial anxiety
 Financial stress
Mean (SD) F/t Mean (SD) F/t Mean (SD) F/t Mean (SD) F/t
Subregions of Africa
 Central 8.46 (2.17) 2.04 15.46 (4.10) 2.92* 14.94 (4.97) 1.62 6.26 (1.71) 3.91**
 Western 8.79 (2.03) 13.88 (5.61) 14.35 (4.58) 7.30 (1.86)
 Eastern 8.00 (2.02) 15.86 (2.99) 16.71 (5.03) 7.00 (1.83)
 Northern 7.31 (1.84) 14.76 (4.55) 16.69 (5.63) 7.77 (1.48)
Education
 None 8.43 (2.70) 0.32 11.67 (5.99) 2.51 14.43 (6.73) 0.78 6.57 (1.72) 0.14
 High school or less 8.29 (2.01) 14.63 (4.22) 15.77 (4.88) 6.93 (1.87)
 College or plus 8.63 (2.07) 15.94 (4.54) 14.61 (4.92) 6.97 (1.84)
English proficiency
 Poor 7.51 (1.94) –3.41*** 12.46 (4.49) –4.33*** 17.14 (4.80) 3.13** 7.33 (1.52) 2.02*
 Good 8.79 (2.04) 16.03 (4.07) 14.32 (4.79) 6.69 (1.93)
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*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

Financial anxiety

Financial anxiety levels were significantly different by employment (F = 5.11, p < 0.01) and English proficiency (t = 3.13, p < 0.01). African refugees with full-time employment had lower levels of financial anxiety (M = 14.35) than those with part-time employment (M = 16.04) and those who were not employed (M = 17.95), according to the post hoc results. Similarly, African refugees with good English proficiency showed lower levels of financial anxiety (M = 14.32) than those with poor English proficiency (M = 17.14).

Financial stress

Financial stress levels were significantly different by subregion of Africa (F = 3.91, p < 0.01) and English proficiency (t = 2.02, p < 0.05). African refugees from northern Africa exhibited higher levels of financial stress (M = 7.77) than those from western (M = 7.30), eastern (M = 7.00) or central (M = 6.26) Africa. In addition, African refugees with good English proficiency showed lower levels of financial stress (M = 6.69) than those with poor English proficiency (M = 7.33).

Discussion

We found significant differences in financial challenges (measured by financial stress and financial anxiety) and financial capacity (measured by financial literacy and financial self-efficacy) across six socio-demographic variables: sex, marital status, English language proficiency, length of stay in the USA, employment status and geographic region of origin amongst African refugees. Prior research contended that current refugee services fail to meet the basic self-sufficiency goals of refugee resettlement (Shaw and Poulin, 2015). Financial decisions made by refugees have ripple effects not only in their communities, but also in the broader American society through higher charges for financial products and deviation of economic resources (Lusardi, 2010). Therefore, financial capacity amongst refugees is crucial to the overall well-being of the larger society (Lusardi, 2010).

Consistent with prior studies that found women have lower financial literacy levels than men (Lusardi and Mitchell, 2011; Potrich et al., 2015), our findings suggest that African men have higher levels of financial literacy compared with African women. Women’s financial decisions greatly affect the financial status of their families and, consequently, the larger society. Further, our finding also provides implications for empowerment programmes for refugee women, which should keep financial literacy at the centre of economic empowerment and integration for refugee women. Findings affirm that marital status was positively correlated with financial self-efficacy. Married African refugees had higher financial self-efficacy than single African refugees. Because people who are single cannot draw on the financial resources of their spouse, they can be more vulnerable to financial risks (Gorman, 2000). In terms of English language proficiency, study findings show that African refugees with good English proficiency had higher levels of financial literacy and financial self-efficacy. They also showed lower levels of financial anxiety and financial stress compared with African refugees with poor English proficiency. Findings suggest that English language proficiency is associated with knowledge in financial matters, control over personal finances and confidence with financial issues and decision making. Prior research has established that language training is a continual need amongst refugee populations (Shaw and Poulin, 2015). Investment in effective English language training at federal, state and local levels should, therefore, be a continual part of refugee inclusion efforts. Although English language proficiency is crucial to bolstering financial self-efficacy amongst refugee populations, linguistic diversity must also be valued as an integral component of cultural heritage and identity (Maleku et al., 2019). Linguistically accessible financial services at the community level, in tandem with English language classes, could create a platform for increasing financial participation amongst African refugees. Community programmes that incentivise and encourage native-born US individuals to become English tutors to refugee families might not only empower refugee communities but also create opportunities to increase social cohesion across US society.

In contrast to prior studies that showed increased financial literacy with age and educational level (Lusardi et al., 2010) in US sample, our study showed no significant relationship between financial challenges and capacity, age or education. The lack of variability in age and education distribution in our African refuge sample compared with US studies could have contributed to this inconsistency. Almost 77 per cent of our study participants were between eighteen and forty years old, whereas 35.8 per cent of the US population is between eighteen and forty-four years old (MarketingCharts, 2020). Similarly, 72.5 per cent of our study participants had either no education or only a high-school diploma, compared with only 11 per cent of the US population over twenty-five years old with a high-school diploma or less (Schmidt, 2018). Further, collective refugee experience amongst our general African refugee sample could have indiscriminately affected their financial well-being. Further analysis is warranted to investigate the effects of age and education level using a larger African refugee sample with greater variability.

Findings corroborate prior studies that have demonstrated positive association between longer periods of US residence and higher financial literacy amongst African refugees (Hilmet et al., 2012). Extended residence allows more exposure to the US financial system and increased financial knowledge such as opening a bank account or remitting money (Consumer Financial Protection Bureau, 2015). Further, our findings show that African refugees employed full time have higher levels of financial self-efficacy and financial literacy and lower levels of financial anxiety. Level of self-efficacy can continuously develop throughout the lifespan as individuals continually integrate information from performance and imagined experiences, verbal persuasion, physiological and emotional states (Maddux and Kleiman, 2021). Full-time employment may have afforded African refugees with better opportunities to acquire financial literacy as they experience more salient financial issues, such as retirement security. Calculating retirement needs may result in higher retirement savings (Mayer et al., 2011) and other positive financial behaviours (e.g. spending less than income) that foster financial self-efficacy and reduce financial anxiety.

Findings showed that African refugees from northern Africa (Sudan) had higher levels of financial stress compared with refugees from western Africa (Liberia, Sierra Leone, Mali, Togo, Nigeria, Côte d’Ivoire, Ghana), eastern Africa (South Sudan, Rwanda, Ethiopia, Somalia, Uganda, Kenya) or central Africa (Central African Republic, Congo, Guinea). Interestingly, study findings show that refugees from eastern and central Africa had higher levels of financial literacy than refugees from western and northern Africa. Evidence suggests that refugees from two north-eastern African countries—Somalia and Eritrea—have the greatest need for financial services, including coordination with public benefits offices and assistance with food stamps, bank setup, bills and mail (Shaw and Poulin, 2015). Although limited, the crude explanation of leading economic performance indicators in central and eastern Africa compared with other regions could point to well-developed financial systems and infrastructure that could have translated to higher levels of financial literacy in these regions, newly independent countries such as Sudan in the northern region that survived decades of conflict could indicate the impact of geopolitical situations and lingering financial stress (African Development Bank Group, n.d.). Future studies should explore country-level disaggregation for a more comprehensive examination.

Limitations

Our study has several limitations. The cross-sectional research design used in the study precludes any inferences being made concerning the temporal order of occurrences or conditions. Longitudinal research designs are more suitable for monitoring temporal changes in the financial behaviour of African refugees. Given relatively small sample size, generalisation of study findings to other African refugee populations must be undertaken with caution. Further, financial literacy, financial self-efficacy, financial stress and financial anxiety measures were not validated with the African refugee population. We utilised self-reported data, and thus, participants may have under- or over-reported the frequency or severity of their financial behaviours. Although the exploration of subpopulation differences was a major motive for data disaggregation, the regional grouping of countries did not allow for the lowest level of disaggregation. This grouping may have obfuscated country-specific differences, as in the case of financial literature based on regional differences. Although the use of a bivariate statistical approach was appropriate for our study’s descriptive exploratory purpose, future studies could use multivariate approaches to uncover specific socio-demographic characteristics to predict financial outcomes amongst the African refugee population.

Social work implications

In a world of ‘big data’, social workers, especially those working with marginalised populations such as diverse refugee groups, are best positioned to recognise the unique needs of subpopulation groups. Further, refugees are especially at risk in times of crisis, such as the current COVID-19 pandemic, because they are less likely to receive formal support and more likely to rely on social networks (Hayward et al., 2019). Emerging US data on the impact of COVID-19 on the financial health of populations show that the financial shocks have hit Blacks and Hispanics the hardest (Lopez et al., 2020), largely due to racial discrimination and oppressive practices faced by ethnic minorities. Therefore, by advocating for disaggregated data amongst subpopulation groups, social workers will be adept at efficiently increasing the visibility of hidden subpopulation groups in the post-COVID-19 context. Further, understanding the disruptive role of migration and resettlement stressors on the mental well-being of refugee subpopulations, compounded by the pandemic, will be crucial to informing tailored programmes and services.

Social workers continue to play a unique role in refugee resettlement. In the post-COVID-19 era, social work researchers and practitioners will need to provide leadership in developing, communicating and scaling-targeted approaches across subpopulations to mitigate the financial scars of the COVID-19 pandemic, assist refugees achieve financial capacity and improve their overall well-being. More broadly, the use of disaggregated data in the refugee context can provide important information regarding advocacy for financial inclusion policies, offer evidence for targeted funding opportunities, and help assess emerging patterns in and amongst refugee subpopulations. In the post-pandemic context, it will be even more important to assess systematic efforts for financial inclusion and economic integration of refugees in the USA. Tailored interventions that acknowledge the vulnerabilities of different demographic subgroups are therefore crucial to strengthening the African refugee population’s financial well-being. This understanding will be the first step in creating entry points towards a financially inclusive and cohesive society that prioritises the economic well-being of all refugee subpopulations.

References

  1. Acevedo-Garcia D., Sanchez-Vaznaugh E., Viruell-Fuentes E. A., Almeida J. (2012) ‘ Integrating social epidemiology into immigrant health research: A cross-national framework’, Social Science & Medicine, 75, pp. 2060–2068. [DOI] [PubMed] [Google Scholar]
  2. Allen R. (2006) Employment and Earnings Outcomes for Recently Arrived Refugees in Portland, Maine, Augusta, Maine Department of Labor. [Google Scholar]
  3. Anderson M. (2017) ‘African immigrant population in the U.S. steadily climbs’, available online at https://www.pewresearch.org/fact-tank/2017/02/14/african-immigrant-population-in-u-s-steadily-climbs/ (accessed June 13, 2019).
  4. Anderson M., Connor P. (2018) ‘Sub-Saharan African immigrants in the U.S. are often more educated than those in top European destinations’, available online at https://www.pewresearch.org/global/2018/04/24/sub-saharan-african-immigrants-in-the-u-s-are-often-more-educated-than-those-in-top-european-destinations/ (accessed June 13, 2019).
  5. Archuleta K. L., Dale A., Spann S. M. (2013) ‘ College students and financial distress: Exploring debt, financial satisfaction, and financial anxiety’, Journal of Financial Counseling and Planning, 24(2), pp. 50–62. [Google Scholar]
  6. Brown M., Graf R. (2013) ‘ Financial literacy and retirement planning in Switzerland’, Numeracy, 6(2), pp. 1–21. [Google Scholar]
  7. Capps R., Mccabe K., Fix M. (2012) ‘Diverse streams: African migration to the United States’, available online at https://www.migrationpolicy.org/research/CBI-african-migration-united-states (accessed December 13, 2019).
  8. Connor P. (2018) ‘International migration from Sub-Saharan Africa has grown dramatically since 2010’, available online at https://www.pewresearch.org/fact-tank/2018/02/28/international-migration-from-sub-saharan-africa-has-grown-dramatically-since-2010/ (accessed December 13, 2019).
  9. Connor P., Krogstad J. M. (2018) ‘For the first time, U.S. resettles fewer refugees than the rest of the world’, available online at https://www.pewresearch.org/fact-tank/2018/07/05/for-the-first-time-u-s-resettles-fewer-refugees-than-the-rest-of-the-world/ (accessed December 13, 2019).
  10. Consumer Financial Protection Bureau (2015). ‘Financial Literacy Annual Report’, available online at https://files.consumerfinance.gov/f/201510_cfpb_financial-literacy-annual-report.pdf (accessed December 15, 2019).
  11. Dietz B. E., Carrozza M., Ritchey P. N. (2003) ‘ Does financial self-efficacy explain gender differences in retirement saving strategies?’, Journal of Women & Aging, 15(4), pp. 83–96. [DOI] [PubMed] [Google Scholar]
  12. Echeverria-Estrada C., Batalova J. (2019) ‘Sub-Saharan African immigrants in the United States’, available online at https://www.migrationpolicy.org/article/sub-saharan-african-immigrants-united-states-2018 (accessed September 23, 2020).
  13. Fix M., Hooper K., Zong J. (2017). ‘ How are Refuges Faring? Integration at U.S. and State Levels’, available online at https://www.migrationpolicy.org/sites/default/files/publications/TCM-Asylum-USRefugeeIntegration-FINAL.pdf (accessed December 8, 2019).
  14. Fuentes N. L. (2009) ‘ Defrauding the American dream: Predatory lending in Latino communities and reform of California’s lending law’, California Law Review, 97, 1279. [Google Scholar]
  15. Gile K. J., Handcock M. S. (2010) ‘ Respondent-driven sampling: An assessment of current methodology’, Sociological Methodology, 40(1), pp. 285–327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gorman E. H. (2000) ‘ Marriage and money: The effect of marital status on attitudes toward pay and finances’, Work and Occupations, 27(1), pp. 64–88. [Google Scholar]
  17. Government Accountability Office (2010) ‘Consumer finance: Factors affecting the financial literacy of individuals with limited English proficiency’, available online at https://www.gao.gov/assets/310/304561.pdf (accessed July 15, 2019).
  18. Griffiths D., Loy C. (2019) ‘ Primary integration outcomes in a newly resettled Bhutanese refugee community’, International Social Work, 62(4), pp. 1213–29. [Google Scholar]
  19. Hadley C., Zodhiates A., Sellen D. W. (2007) ‘ Acculturation, economics, and food security among refugees resettled in the USA: A case study of west African refugees’, Public Health Nutrition, 10(4), pp. 405–12. [DOI] [PubMed] [Google Scholar]
  20. Hayward R. A., Morris Z., Otero Ramos Y., Silva Díaz A. (2019) ‘ “Todo ha sido a pulmon”: Community organizing after disaster in Puerto Rico’, Journal of Community Practice, 27 (3–4), pp. 249–59. [Google Scholar]
  21. Heckman S., Lim H., Montalto C. (2014) ‘ Factors related to financial stress among college students’, Journal of Financial Therapy, 5(1), pp. 19–39. [Google Scholar]
  22. Hilmet J., Kanno S., Abo-Shasha R., Alsaqa M. M., Fakhouri M., Arnetz B. B. (2012) ‘ Promoters and barriers to work: A comparative study of refugees versus immigrants in the United States’, New Iraqi Journal of Medicine, 8(2), pp. 19–28. [PMC free article] [PubMed] [Google Scholar]
  23. Huston S. J. (2010) ‘ Measuring financial literacy’, The Journal of Consumer Affairs, 44(2), pp. 296–316. [Google Scholar]
  24. Jamil H., Aldhalimi A., Arnetz B. B. (2012) ‘ Employment satisfaction and health outcomes among professional Iraqi refugees as compared to immigrant’, Journal of Immigrant & Refugee Studies, 10(4), pp. 395–406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Johnson E., Sherraden M. S. (2007) ‘ From financial literacy to financial capability among youth’, Journal of Sociology and Social Welfare, 34, 119. [Google Scholar]
  26. Kallick D. D., Mathema S. (2016) ‘Refugee integration in the United States’, available online at https://www.americanprogress.org/issues/immigration/reports/2016/06/16/139551/refugee-integration-in-the-united-states/ (accessed December 13, 2019).
  27. Kim Y. K., Lim Y., Maleku A., Kagotho N., Kang M. Y. (2020) ‘ Financial stress and depression among African refugees in the southern United States: A serial multiple mediation analysis of financial self-efficacy and financial anxiety’, Journal of Immigrant & Refugee Studies, 18(2), pp. 151–71. [Google Scholar]
  28. Kuhlman T. (1991) ‘ The economic integration of refugees in developing countries: A research model’, Journal of Refugee Studies, 4(1), pp. 1–20. [Google Scholar]
  29. Lopez M.H., Rainie L., Budiman A. (2020). ‘Financial and health impacts of COVID-19 vary widely by race and ethnicity’, available online at https://www.pewresearch.org/fact-tank/2020/05/05/financial-and-health-impacts-of-covid-19-vary-widely-by-race-and-ethnicity/ (accessed December 9, 2020).
  30. Lusardi A. (2010) Financial Capability in the United States: Consumer Decision-Making and the Role of Social Security, Ann Arbor, Michigan Retirement Research Center. [Google Scholar]
  31. Lusardi A., Mitchell O. (2011) ‘ Financial literacy and retirement planning in the United States’, Journal of Pension Economics and Finance, 10(4), pp. 509–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lutheran Immigration and Refugee Service (LIRS). (2012) ‘Financial literacy for newcomers: Weaving immigrant needs into financial education’, available online at http://www.higheradvantage.org/wp-ontent/uploads/2012/05/rw_financial_literacy.pdf (accessed July 12, 2019).
  33. Maddux J. E., Kleiman E. M. (2021) ‘Self-efficacy: The power of believing you can’, Chapter 31, pp. 443–452. In Snyder C. R., Lopez S. J., Marques S.C., Edwards L.M. (Eds). ‘The Oxford Handbook of Positive Psychology’ (3rd edition), Oxford University Press, New York. [Google Scholar]
  34. Maleku A., Kim Y. K., Lee G. (2019) ‘ Social cohesion & immigrant health: Does language efficacy matter’, International Journal of Migration, Health and Social Care, 15(1), pp. 17–30. [Google Scholar]
  35. Maleku A., Kim Y. K., Kagotho N., Lim Y. (2020) ‘ Expanding the transformative explanatory sequential mixed methods design archetype in a cross-cultural context: The polemics of African refugee livelihoods in places of resettlement’, Journal of Mixed Methods Research. DOI: 10.1177/1558689820936378. Published July 1, 2020. [Google Scholar]
  36. MarketingCharts. (2020) ‘So how many millennials are there in the US, anyway?’, available online at https://www.marketingcharts.com/featured-30401 (accessed January 27, 2021).
  37. Mookherjee H. N. (1997) ‘ Marital status, gender, and perception of well-being’, The Journal of Social Psychology, 137(1), pp. 95–105. [DOI] [PubMed] [Google Scholar]
  38. Mayer R. N., Zick C. Z., Marsden M. (2011) ‘ Does calculating retirement needs boost retirement savings?’ The Journal of Consumer Affairs, 45(2), pp. 175–200. [Google Scholar]
  39. Newberger R., Rhine S. L. W., Chiu S. (2004) ‘ Immigrant financial market participation: Defining the research questions’, Chicago Fed Letter, 199, 1–4. [Google Scholar]
  40. Northwood J. M., Rhine S. L. W. (2018) ‘ Use of bank and nonbank financial services: Financial decision making by immigrants and native born’, The Journal of Consumer Affairs, 52(2), pp. 317–48. [Google Scholar]
  41. Osili U. O., Paulson A. (2008) ‘ Institutions and financial development: Evidence from international migrants in the United States’, The Review of Economics and Statistics, 90(3), pp. 498–517. [Google Scholar]
  42. Osili U. O., Paulson A. (2014) ‘ Crises and confidence: Systemic banking crises and depositor behavior’, Journal of Financial Economics, 111(3), pp. 646–60. [Google Scholar]
  43. Painter M. A. II. (2013) ‘ Immigrant and native financial well-being: The roles of place of education and race/ethnicity’, Social Science Research, 42(5), pp. 1375–89. [DOI] [PubMed] [Google Scholar]
  44. Paulson A., Singer A., Newberger R., Smith J. (2006) ‘Financial access for immigrants: Lessons from diverse perspectives’, available online at https://www.brookings.edu/research/financial-access-for-immigrants-lessons-from-diverse-perspectives/ (accessed December 8, 2019).
  45. Pearlin L. I., Schooler C. (1978) ‘ The structure of coping’, Journal of Health and Social Behavior, 19(1), pp. 2–21. [PubMed] [Google Scholar]
  46. Perera S., Gavian M., Frazier P., Johnson D., Spring M., Westermeyer J., Butcher J., Halcon L., Robertson C., Savik K., Jaranson J. (2013) ‘ A longitudinal study of demographic factors associated with stressors and symptoms in African refugees’, American Journal of Orthopsychiatry, 83(4), pp. 472–82. [DOI] [PubMed] [Google Scholar]
  47. Potocky-Tripodi M. (2001) ‘ Micro and macro determinants of refugee economic status’, Journal of Social Service Research, 27(4), pp. 33–60. [Google Scholar]
  48. Potocky-Tripodi M. (2003) ‘ Refugee economic adaptation: Theory, evidence, and implications for policy and practice’, Journal of Social Service Research, 30(1), pp. 63–91. [Google Scholar]
  49. Potrich A. C. G., Vieira K. M., Kirch G. (2015) ‘ Determinant of financial literacy: Analysis of the influence of socioeconomic and demographic variables’, Revista Contabilidade & Finanças, 26(69), pp. 1060–70. [Google Scholar]
  50. Radford J. (2017) ‘How U.S. refugee resettlement in each state has shifted since 2002’, available online at https://www.pewresearch.org/fact-tank/2017/11/02/how-u-s-refugee-resettlement-shifted-in-states-since-2002/ (accessed December 8, 2019).
  51. Raykov T. (2004) ‘ Behavioral scale reliability and measurement invariance evaluation using latent variable modeling’, Behavior Therapy, 35(2), pp. 299–331. [Google Scholar]
  52. Rhine S., Greene W. H. (2006) ‘ The determinants of being unbanked for U.S. immigrants’, The Journal of Consumer Affairs, 40(1), pp. 21–40. [Google Scholar]
  53. Rubin V., Ngo D., Ross A., Butler D., Balaram N. (2018). ‘Counting a diverse nation: Disaggregating data on race and ethnicity to advance a culture of health’, available online at https://www.policylink.org/sites/default/files/Counting_a_Diverse_Nation_08_15_18.pdf (accessed July 17, 2019).
  54. Schmidt E.P. (2018) ‘Postsecondary enrollment before, during, and since the Great Recession’, available online at https://files.eric.ed.gov/fulltext/ED591095.pdf (accessed January 27, 2021).
  55. Schoenholtz A., Stanton K. (2001) Reaching the Immigrant Market: Creating Home Ownership Opportunities for New Americans, Washington, DC, Georgetown University Institute for the Study of International Migration and the Fannie Mae Foundation. [Google Scholar]
  56. Shapiro G. K., Burchell B. J. (2012) ‘ Measuring financial anxiety’, Journal of Neuroscience, Psychology, and Economics, 5(2), pp. 92–103. [Google Scholar]
  57. Shaw S. A., Poulin P. (2015) ‘ Findings from an extended case management U.S. refugee resettlement program’, Journal of International Migration and Integration, 16(4), pp. 1099–120. [Google Scholar]
  58. Stoll K., Johnson P. (2007) ‘ Determinants of the psychosocial adjustment of Southern Sudanese men’, Journal of Refugee Studies, 20(4), pp. 621–40. [Google Scholar]
  59. Suro R., Bendixen S., Lowell B. L., Benavides D. C. (2002) ‘Billions in motion: Latino immigrants, remittances, and banking’, available online at https://publications.iadb.org/en/publication/billions-motion-latino-immigrants-remittances-and-banking (accessed July 17, 2019).
  60. Ting L. (2010) ‘ Out of Africa: Coping strategies of African immigrant women survivors of intimate partner violence’, Health Care for Women International, 31(4), pp. 345–64. [DOI] [PubMed] [Google Scholar]
  61. U.S. Department of Health and Human Services. (2013) ‘Office of refugee resettlement: What we do’, available online at http://www.acf.hhs.gov/programs/orr/about/what-we-do (accessed July 16, 2019).
  62. Zeng Z., Xie Y. (2004) ‘ Asian-Americans’ earnings disadvantage reexamined: The role of place of education’, American Journal of Sociology, 109(5), pp. 1075–108. [Google Scholar]
  63. Zhan M., Anderson S. G., Scott J. (2006) ‘ Financial management knowledge of the low-income population’, Journal of Social Service Research, 33(1), pp. 93–106. [Google Scholar]
  64. Zhan M., Anderson S. G., Scott J. (2013) ‘Improving financial capacity among low-income immigrants’ effects of a financial education program’, in Birkenmaier J., Sherraden M., Curley J. (eds), Financial Capability and Asset Development: Research, Education, Policy and Practice, New York, Oxford University Press. [Google Scholar]

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