Financial well-being and overall health are significantly linked, especially among those in poverty who have been exposed to violence and suffer from unaddressed trauma. Yet existing public assistance programs fail to address the presence or impact of trauma and adversity. Built specifically for families living in poverty who experience adversity, the Building Wealth and Health Network (the Network) provides a space for families to heal from the effects of trauma while also building social networks and economic security. The sample for this study was primarily Black (91%) women (92%) in Philadelphia with at least one child. A repeated measures linear regression model was performed via a Difference-in-Differences approach to test differences in financial well-being scores between two groups (full participation vs. low/no participation) at two time points (baseline vs 3 months, and baseline vs 6 months). We use this program as a field study to better understand the financial well-being of program participants who took part in fewer or more program sessions. Those who participated in more sessions reported greater increases in two measures of financial well-being at three months and six months post baseline, when compared to those with low or no participation.
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Trauma, adversity and income poverty have long been understood to be interconnected causes of poor health (Braveman & Gottlieb, 2014; Braveman et al., 2018; Brisson et al., 2020; Felitti et al., 1998; Gartland et al., 2019). Indeed, prior studies have shown financial well-being and overall health are significantly linked, especially among those who have been exposed to violence and suffer from unaddressed trauma (Frank et al., 2010; Galbraith et al., 2005; Smith, 1999; Sun et al., 2016; Sun et al., 2017). Yet the primary program meant to directly assist with income poverty, the Temporary Assistance for Needy Families (TANF) program, fails to take into account the health impacts of adversity and violence exposure.
Most TANF programs have promoted a “work first” approach, which demands that caregivers enter the workforce quickly and simultaneously incentivizes state administrators to push someone into the workforce in order to reduce welfare participation rolls (Schott, 2012). This is despite knowledge that major barriers to work among TANF participants include disability and other major social and behavioral adversities (Bloom et al., 2011; Ziliak, 2009). This programmatic failure is an example of how systems supposedly meant to help people in poverty, may be ultimately designed to keep them there, especially if they are Black and brown (Ford & Airhihenbuwa, 2018). As an example, states with the lowest TANF grant amounts have the highest percentages of Black TANF recipients (Chilton & Halperin-Goldstein, 2020; Hahn et al., 2017; Schott et al., 2015) and caregivers return to TANF or become “disconnected” from public assistance supports due to poor health or lack of success in the workforce (Cancian et al., 2014; Loprest, 2011). Despite the many problems with the TANF program, over 400,000 adults and 1.4 million children participate in TANF, and there are still avenues for improvement, especially in the area of healing from adversity and improving financial skills. TANF-eligible families have low financial literacy, limited credit history, few or no assets, and are unbanked or under-banked (Anderson et al., 2007).
Asset building and financial health or well-being are important aspects of daily life critical for health (Weida et al., 2020; Phojanakong et al., 2020). Savings and other tangible assets help sustain families through unexpected financial hardships (Sherraden et al., 2015). Further, building assets can improve health, increase civic engagement, and reduce stress associated with maternal depression (Shanks & Robinson, 2011; Sherraden et al., 2015). Financial health or well-being is a comprehensive assessment of finances that includes the ability to support meeting basic needs, as well as opportunities to save and build wealth. It also underlies all facets of daily living such as securing food and paying for housing and is inextricably tied to health (O’Neill et al., 2006; O’Neill et al., 2005; O’Neill et al., 2016) and mental health outcomes (Wilkinson, 2016). Additionally, several studies have examined links between health and financial literacy as well as financial stress and mental and physical health outcomes (Braun et al., 2009).
Overall, public assistance programs, long thought to be a support to minimize the social determinants of health, are devoid of effective solutions to promote long-term economic security, and fall short on appropriate administration and funding to support economic well-being (Chilton et al., 2019). Opportunities have emerged to change this, through programs such as the Building Wealth and Health Network (The Network). The goals of the Building Wealth and Health Network are to improve health and increase wealth through building resilience and supporting financial freedom. Created for families with low to no earned income, The Network provides a space for families to heal from the effects of trauma while also building social networks and economic security. The Network is built into the TANF program in Philadelphia and uses trauma-informed financial empowerment programming to improve the wealth and health of low-income caregivers via financial incentives, financial education, and peer support. Launched as a Randomized Controlled Trial, the Network reduced economic hardship and depressive symptoms (Sun et al., 2016), increase self-efficacy (Welles et al., 2017), as well as improve mental health and coping strategies (Dugan et al., 2019), and household food security (Sun et al., 2016; Weida et al., 2020; Phojanakong et al., 2020).
To build on this work, we study the association between greater participation in Network classes and two measures of financial well-being: financial behaviors, knowledge and efficacy, and financial capability. We hypothesize that participation in The Network will be associated with an increase in scores on all aforementioned measures of financial well-being, sustained at multiple time points post baseline.
Methods
Participants
The study sample (N = 369) is derived from The Building Wealth and Health Network program. The program has undergone a variety of program innovations. This analysis evaluates the Phase II implementation of the Network program which followed the initial Randomized Control Trial mentioned above. This implementation took place from October 2015 to May 2017 and participants were observed for a year, with the last cohort finishing in June 2018.
The Building Wealth and Health Network (The Network)
As an operating framework, The Network utilized the Sanctuary Model® (Bloom & Sreedhar, 2008), a trauma-informed and evidence-supported operating system for human services organizations designed to foster an organizational culture that transforms the effects of trauma. Network members participated in The Network’s three components: financial self-empowerment classes, financial incentives in which The Network encourages members to continue to save for the future by matching money deposited in the account up to $20/month for a full year, and one-on-one support through optional individualized financial coaching and social work referral. The Network curriculum was developed primarily for individuals with low or no income who have experienced financial adversity and interpersonal, familial, or societal violence. It combined financial literacy education (learning modules on financial education that included strategies for banking, improving credit scores, reducing debt, and increasing income) and trauma-informed emotional support into 16 topics delivered in 3-h interactive sessions once a week for 16 weeks in an adult-based peer-support model. The empowerment-focused curriculum emphasized sharing of resources, ideas, and experiences among the group, rather than relying on a coach to teach the curriculum, and dialogs were fostered to strengthen social support. The curriculum was based on empirical evidence that financial insecurity and poor mental health are inextricably linked by exposure to interpersonal, social and systemic violence (Braveman et al., 2018; Brisson et al., 2020; Ford & Airhihenbuwa, 2018; Gee & Ford, 2011; Paradies et al., 2015). This is why simply teaching someone financial information and skills, without addressing personal, emotional, and social barriers to their goals, can be ineffective. For more details on methods please see previous works (Weida et al., 2020; Phojanakong et al., 2020).
Recruitment
Participants in The Network are referred to as “members”. Members were recruited through local County Assistance Offices (CAOs) as well as through community flyers. Members were eligible if they were (1) a primary caregiver of a child aged <6; (2) able to speak English; (3) self-reported participants in at least 1 public assistance program, including TANF, SNAP, WIC, Medicaid, or housing subsidies (Section 8/Housing Choice, or living in a Philadelphia Housing Authority home); and (4) a Philadelphia resident. Recruitment occurred one week before the start of sessions and continued up through the third week of sessions. Each cohort was composed of 25–35 members, with 10–15 members attending weekly sessions on average. Transportation costs for participation and childcare support were provided by the TANF program for those recruited from CAOs. Those recruited in community settings received transportation passes for participation. Members were enrolled from October 2015 to May 2017 and observed for a year, with the last cohort finishing in June 2018.
Data Collection Procedures
Through the use of Audio Computer Assisted Survey Interviews (ACASI), Network members completed surveys at baseline and 3-month intervals (a total of 5 surveys) on several measures including those on physical and mental health, well-being, finances, and social support. Across 11 cohorts, 369 individuals consented to participate in the program and completed a baseline survey. At month 12, 208 of the 369 participants completed the full 12-month follow-up survey.
Measures
Outcome: Financial Well-being
Two measures were used to assess financial well-being: The Financial Behaviors, Knowledge and Efficacy Scale (Danes et al., 1999), and the Financial Capability Scale (Collins & O’Rourke, 2013).
The Financial Behaviors, Knowledge and Efficacy scale (FBKE; Danes et al., 1999) was created to evaluate financial programming. The scale is a 12-item retrospective Likert scale survey asking respondents to rate how often in the last six months they engaged in a range of financial: behaviors (e.g., “I compared prices when I shopped”), knowledge (e.g., “I knew the cost of buying on credit”), and efficacy (e.g., “I felt confident about making decisions that dealt with money”). Response options ranged from “Almost Always” to “Almost Never”. The scale can be separated into three separate subscales (7 financial behavior items, 3 financial knowledge items and 2 financial self-efficacy items) or summed. For this analysis, the scale was summed for a final FBKE sum scale with possible range 0–48, with higher scores indicating greater positive financial behaviors, knowledge, and self-efficacy. Chronbach’s alpha for this scale for this sample was 0.442.
The Financial Capability Scale (FCS; Collins & O’Rourke, 2013) was developed to evaluate financial programs by assessing seven aspects (through seven measures) of respondents’ current financial situations typically targeted in financial programming, including goal setting behaviors (“Do you currently have at least one financial goal?”), planning for unexpected shocks or events (“If you had an unexpected expense…how confident are you that your family could come up with money to make ends meet within a month?”) and budgeting (“Over the past month would you say your family’s spending on living expenses was less than its total income?”). The scale is summed, with higher scores indicating higher financial capability (the final question is reverse coded: “Over the last 2 months have you paid a late fee on a loan or bill?”). The scale ranges 0–9. Chronbach’s alpha for this scale for this sample was 0.867.
Program Participation
Per previous analyses (Dugan et al., 2019; Weida et al., 2020; Phojanakong et al., 2020) we assessed differences by comparing two groups: low/no participation and full participation. The low/no participation group consisted of participants who attended <4 sessions and the full-participation group consisted of participants who attended ≥4 sessions. Four sessions were selected as the attendance group cutoff because participants attending less than 4 sessions had very low exposure to the trauma-informed peer support curriculum, did not establish a peer relationship with program coaches and other participants in their respective cohorts, and were ineligible to participate in opening a bank account. This minimal exposure to the curriculum and other activities made the low/no participation group an appropriate comparison group with those who were exposed to the intervention. Written informed consent was obtained and the Drexel University Institutional Review Board approved this study.
Analysis
A repeated measures linear regression model using data from the Phase II (baseline N = 369; 3 month = 253; 6 month = 226) samples was performed via a Difference-in-Differences approach to test differences in financial well-being scores between the two groups (full participation vs. low/no participation) at two time points (baseline vs 3 months, and baseline vs 6 months). The Difference-in-Differences approach allows for the comparison of changes over time “while accounting for changes in secular trends and controlling for both measured and unmeasured confounding” (Warton et al., 2016). Additionally, this method helps to infer causation as it keeps time-order (baseline to 3 months, for example) for both groups, and as it uses repeated measures for each individual, thus minimizing issues of unmeasured confounding (i.e., differences that arise from comparing two separate individuals at two separate time points).
Covariates were collected at baseline and included demographic factors (age, gender, marital status, number of children in the home, and education level) as well as current employment status and receipt of public benefits.
Table 1 below provides baseline characteristics by participation group. Since the vast majority (91%) of participants self-identified as non-Hispanic Black/African American, race/ethnicity was not included as a variable. The two groups were not statistically significantly different from each other on any of the baseline characteristics, with the exception of education level: a larger proportion of those in the full participation had attended technical school or some college than those in the low/no participation group. Education level was thus controlled for in all analyses.
Table 1
Preliminary Overview of Baseline Characteristics by Participation Group
Low/no Participation (N = 127)
Full Participation (N = 242)
p-value (b/w group)
Variable
% or M
N or SD
% or M
N or SD
Demographic Variables
Age of caregivera
26.88
12.19
28.68
10.93
0.151
Raceb
0.485
Black
91.1
339
92.7
281
White
2.4
9
1.3
4
Other
6.5
11
2.3
7
Genderb
0.241
Female
91.34
116
95.45
231
Marital Statusb
0.281
Partner in home
22.05
28
20.22
50
# of children in homeb
0.481
1 child
35.43
45
42.15
102
2 children
32.28
41
26.45
64
3+ children
29.92
38
30.17
73
Educationb
0.004
Some HS/grade school
33.86
43
21.07
51
HS or GED
46.46
59
45.87
111
Technical school or some college +
19.68
25
32.69
80
Currently Employedb
15.75
20
17.84
43
0.306
Household Food Secureb
43.31
55
48.76
118
0.159
Housing Secureb
40.16
51
36.78
89
0.483
Energy Secureb
59.84
76
66.94
162
0.093
WICb
54.33
69
55.42
133
0.406
SNAPb
96.85
123
96.67
232
0.463
TANFb
78.15
93
76.44
172
0.360
Financial Well-being (Outcome Variables)
Financial Behavior, Knowledge and Efficacy Full Sum Scalea
17.64
9.68
17.60
9.69
0.967
Financial Behaviora
11.91
6.45
11.30
6.59
0.395
Financial Knowledgea
2.07
2.76
2.32
2.97
0.426
Financial Efficacya
3.66
2.30
3.99
2.29
0.193
Financial Capability Scalea
4.63
1.72
4.40
1.54
0.192
p-values are from chi-square tests for categorical variables and t-tests for continuous variables
aindicates M, SD for continuous variables
bindicates %, n for categorical variables
Results
When compared to members who have low or not participation in the Network, members who participated showed significant improvements in financial behaviors, knowledge, efficacy and financial capability at both the three month and six-month follow-up timepoints. Table 2 below shows the difference-in-difference estimates of the association between levels of participation in The Building Wealth and Health Network and two measures of financial well-being: financial behaviors, knowledge and efficacy (FBKE) and financial capability scale (FCS). Figures 1–4 illustrate the D-I-D for each scale and each time point.
Table 2
Difference-in-Difference Estimates of Association between levels of Network participation and Financial Well-being Outcomes
Low/No Participation
Full Participation
Unadjusted Difference-in-Differences
Adjusted Difference-in-Differences
Outcome
Pre-Program
Post-Program
Pre-Program
Post-Program
Estimate
P value
Estimate
P value
FBKE score
3 months
18.39
32.04
19.2
35.83
2.95
0.071
2.99
0.078
6 months
20.1
23.42
19.16
26.11
3.88
0.023
3.63
0.031
FCS score
3 months
4.78
5.85
4.27
6.64
1.29
<0.0001
1.30
<0.0001
6 months
4.86
5.11
4.49
5.54
0.79
0.007
0.81
0.006
Adjusted difference-in-differences estimates were adjusted for demographic factors (race, age, gender, marital status, education and number of children in their care) as well as current employment status and receipt of public benefits
Difference-in-Differences for Financial Behavior, Knowledge and Efficacy (FBKE) Sum Score at 3 Months. Note: This graph shows the Difference-in-Difference estimates of association between levels of Network participation and Financial Behavior, Knowledge and Efficacy Sum Scores at two time points: baseline and 3 months. Adjusted difference-in-differences estimates were adjusted for demographic factors (race, age, gender, marital status, education and number of children in their care) as well as current employment status and receipt of public benefits
Fig. 2
Difference-in-Differences for Financial Behavior, Knowledge and Efficacy (FBKE) Sum Score at 6 Months. Note: This graph shows the Difference-in-Difference estimates of association between levels of Network participation and Financial Behavior, Knowledge and Efficacy Sum Scores at two time points: baseline and 6 months. Adjusted difference-in-differences estimates were adjusted for demographic factors (race, age, gender, marital status, education and number of children in their care) as well as current employment status and receipt of public benefit
Fig. 3
Difference-in-Differences for Financial Capability Scale (FCS) Sum Score at 3 Months. Note: This graph shows the Difference-in-Difference estimates of association between levels of Network participation and Financial Capability Scale Sum Scores at two time points: baseline and 3 months. Adjusted difference-in-differences estimates were adjusted for demographic factors (race, age, gender, marital status, education and number of children in their care) as well as current employment status and receipt of public benefits
Fig. 4
Difference-in-Differences for Financial Capability Scale (FCS) Sum Score at 6 Months. Note: This graph shows the Difference-in-Difference estimates of association between levels of Network participation and Financial Capability Scale Sum Scores at two time points: baseline and 6 months. Adjusted difference-in-differences estimates were adjusted for demographic factors (race, age, gender, marital status, education and number of children in their care) as well as current employment status and receipt of public benefits
×
×
×
×
Financial Behavior, Knowledge and Efficacy Scale
At 3 months (N = 253), before adjusting for any covariates, individuals who participated in four or more classes (full participation; n = 198) in the Network on average experienced a change in FBKE score that was 2.95 points higher than the change experienced in the low to no participation group (n = 55) (p = 0.071). After adjusting for demographic factors (age, gender, marital status, education and number of children in their care) as well as current employment status, those in the full participation group experienced an average change that was 2.99 points higher on the FBKE scale at 3 months, compared to the change in the low to no participation group (p = 0.078), indicating that the inclusion of covariates did not have a strong effect on the estimated association of program participation. Figure 1 above demonstrates the adjusted difference in differences between the two groups at the two separate time points. As is shown in the figure, the slope of the increase in FBKE scores from baseline to 3 months for the full participation group is steeper than the slope of the increase for the low/no participation group after accounting for relevant covariates (p = 0.078).
Results after 6 months (N = 226) were similar to what was seen at the 3-month mark, such that in the unadjusted model, individuals who participated in four or more classes (full participation; n = 172) in the Network on average experienced a change of 3.88 points higher on the FBKE scale at 6 months, compared to the change experienced in the low to no participation group (n = 54) (p = 0.023). In the adjusted model, individuals who participated in four or more classes (full participation) in the Network on average experienced a change of 3.63 points higher on the FBKE scale at 6 months, compared to the change in the low to no participation group (p = 0.031). As shown in Fig. 2, the slope of the increase in FBKE scores from baseline to 6 months for the full participation group is significantly steeper (p = 0.031) than the slope of the increase for the low/no participation group, after accounting for relevant covariates.
Financial Capability Scale
A similar pattern was seen for the Financial Capability Scale (FCS) at both 3 and 6 months. Before adjusting for any covariates, individuals who participated in the Network on average experienced a change of 1.29 points higher on the FCS at 3 months, compared to those in the low to no participation group (p < 0.0001). After adjusting for the same covariates as above (age, gender, marital status, education and number of children in their care) as well as current employment status and receipt of public benefits, those in the full participation group experienced a change of on average 1.30 points higher on the FCS scale at 3 months, compared to those in the low to no participation group (p < 0.0001). As shown in Fig. 3, the slope of the increase in FCS scores from baseline to 3 months for the full participation group is significantly steeper (p < 0.0001) than the slope of the increase for the low/no participation group, after accounting for relevant covariates.
At 6 months, the unadjusted model showed that individuals who participated in four or more classes (full participation; n = 172) in the Network on average experienced a change of 0.79 points higher on the FCS scale, compared to those in the low to no participation group (n = 54) (p = 0.007). The adjusted model shows individuals who participated in four or more classes (full participation; n = 172) in the Network on average experienced a change of 0.81 points higher on the FCS scale at 6 months, compared to those in the low to no participation group (n = 54) (p = 0.006). Figure 4 illustrates the DID for FCS at 6 months.
Discussion
Results demonstrate that participation in a trauma-informed, healing-centered financial empowerment program is associated with significant improvements in financial well-being, and that this association persists after program completion.
There are several potential reasons for these positive results. First, The Network’s curriculum was designed specifically for and co-created with families living in poverty, and explicitly addresses racialized trauma. By utilizing a framework that takes into account the context of lived experiences as well as systemic intersectional racism, financial skills are not addressed in isolation but rather with the understanding of the realistic complexities experienced by people participating in public assistance. Addressing social and emotional barriers as well as providing a space for peer learning and support might also explain why positive associations with financial well-being were found not only at 3 months, but also sustained by the 6-month time point. For the Financial Behaviors, Knowledge and Self-Efficacy scale, some of the components of financial self-efficacy were included in the curriculum, in addition to financial knowledge. For example, addressing other areas of confidence and empowerment in life (i.e., friendships, relationships) might have a spillover effect into financial efficacy, in addition to the increased social support.
Additionally, financial empowerment was intentionally embedded into the trauma-informed aspects of the program, and financial skill building along with access to opening bank accounts likely played a key role as it allowed for members to practice financial behaviors learned in the curriculum in real time, while simultaneously providing small cash infusions. Evidence has shown that pairing financial education with opening bank accounts with small cash infusions is more effective for sustained financial well-being (Grinstein-Weiss et al., 2015) than financial education alone, as families with low-incomes face compounding financial barriers such as lack of access to health care, job volatility and higher likelihood of disability or caring for someone with a disability (Hannagan & Morduch, 2015). The average amount of matched savings over the 12 months was $43.81. A majority of members were saving until four months when there was a drop off (this was also around the time of program completion), however around 20% of members were still saving at 12 months. Although the amount of matched savings is not substantial, it provided opportunities for members to utilize skills gained in the program in real-time. The incentives and savings were supportive of program success as evidenced by other positive outcomes after 12 months including increased household food security (Weida et al., 2020; Phojanakong et al., 2020).
There is also the possibility that some of the sustained improvements in financial well-being were due to the optional, additional one-on-one coaching support provided. Another program found that individualized financial coaching of single mothers had a positive impact on their children’s school outcomes (Fuji et al., 2024), while another large study found that financial coaching had some positive effects on financial confidence and savings behaviors (Theodos et al., 2015). As our program was created with coaching as an important but optional additional support, the program’s positive associations are likely a result of all factors considered together, rather than simply those who attended the coaching sessions, since there were no members who attended only coaching sessions and not the rest of the integral components of the program.
Given the inextricable link between financial well-being and overall health (O’Neill et al., 2006; O’Neill et al., 2005; O’Neill et al., 2016; Wilkinson, 2016; Wilkinson & Marmot, 2003), the importance of comprehensive financial programs that address all aspects of a person, including the systemic and interpersonal trauma as barriers to their success, cannot be overstated. Previous studies have demonstrated that participation in The Network has resulted in numerous positive outcomes including reduced economic hardship, reduced depressive symptoms, increased self-efficacy, as well as improved mental health and coping strategies (Dugan et al., 2019), and food security (Weida et al., 2020; Phojanakong et al., 2020). This study adds to this literature by demonstrating that participation in a financial empowerment program is associated with aspects of improved financial well-being: financial behaviors, knowledge and efficacy and financial capability.
Limitations
All measures used in this study relied on self-report, and respondents may underreport financial adversity, or exaggerate their financial capacity. Additionally, there was loss to follow up for surveys and some program attrition, introducing the possibility of selection bias (those who chose to fill out the surveys might be systematically different than those who did not). We anticipated these potential differences prior to analysis (see Table 1) by comparing participation groups on demographic and outcome variables at baseline and found only education to be significantly different. For this reason it was included as a covariate. Selection bias is minimized through the Differences in Differences approach, however, due to the nonrandom recruitment methods, no analytic approach introduced after the fact can eliminate this bias entirely. Furthermore, selection bias is common among financial education programs (Kaiser et al., 2022) and was likely the case for our program as well. Another limitation related to selection bias is the inability for us to determine certain unobservable differences between those who decided to attend more session versus those who attended less or none, such as motivation. Motivation could be a driving factor in seeing the differences between the two participation groups as those who are more motivated to attend more classes might also be more motivated to improve their finances. Additionally, difference-in-difference analysis typically requires that outcomes between the two groups are constant over time if not for the intervention, usually achieved by multiple baseline or pre-test measures. We were not able to see multiple baseline measurements as our study design only had one baseline assessment.
Despite these limitations, this study of a trauma-informed, financial empowerment program shows how participation levels are associated with financial well-being for families with very low incomes; a population often left out of financial well-being studies.
Conclusion
These findings are consistent with other positive results found from participation in the trauma-informed program The Building Wealth and Health Network. In order for programs or policies to achieve their intended solution of reducing poverty, providing financial empowerment in a trauma-informed peer learning setting can be effective over time.
Compliance with ethical standards
Conflict of interest
The authors declare no competing interests.
Ethical approval
The questionnaires and methodology for this study was approved by Drexel University’s Institutional Review Board. All procedures performed were in accordance with the ethical standards of the IRB and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent
Written informed consent was obtained from all individual participants included in the study.
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