Abstract
Objective:
Research has shown that psychosocial well-being in veterans, including financial status, is related to better clinical outcomes after leaving military service. The current study examines variables linking financial well-being to physical health in veterans and non-veterans and identifies financial variables related to veteran status.
Methods:
We analyzed data from the nationally representative 2021 Survey of Household Economics and Decisionmaking conducted by the U.S. Federal Reserve Board and compared the responses of veterans (N = 1176) to a non-veteran sample matched by age, sex, education, race, ethnicity, and geographic region (N = 1176).
Results:
Multivariable analyses revealed that although veterans and non-veterans were similar in many financial domains, veterans were more likely to spend money on the lottery and gambling, pay overdraft fees on bank accounts, and take out payday or pawn shop loans. Analyses showed over one-third (35%) of veterans reported credit card debt, significantly higher than non-veterans. In veterans and non-veterans, higher physical health ratings were related to higher income, lacking medical debt, living in a community of greater economic advantage, and having a rainy-day fund in case of financial emergencies. Ratings of one’s credit score were also significantly associated with ratings of one’s physical health, in both veterans and non-veterans.
Conclusions:
The data pinpoint specific financial domains to inform policy, education, and outreach aimed at improving veterans’ psychosocial well-being. The results also reveal that individual and environment-level financial variables were related to physical health in this national survey, demonstrating the value of assessing financial well-being in the context of medical care, for both veteran and non-veteran patients.
Key Words: psychosocial well-being, financial well-being, veterans, debt, health
Research confirms that psychosocial well-being in work, health, social, and financial domains relates to greater success transitioning to the community among veterans after leaving military service as well as lower odds of adverse social or medical outcomes.1–3 Financial strain specifically is related to poorer health outcomes among veterans, as in the general population.4 Among Vietnam Era veterans, lower income was found to be associated with worse health-related quality of life 2 decades later.5 Unemployment has been shown to relate to greater days with poorer physical health in veterans.6 After 9/11, veterans meeting the criteria for posttraumatic stress disorder, traumatic brain injury, or major depressive disorder were less likely to have money to cover basic needs.2,7 Co-occurring posttraumatic stress disorder and alcohol misuse also predicted higher financial strain.8 Research during the COVID pandemic showed financial stress was significantly associated with alcohol and cannabis use in veterans.9
Homelessness and suicide are linked to financial strain in veterans, too. A longitudinal study demonstrated after 9/11, veterans with money management problems (eg, not paying bills, being sent to a collection agency, and being financial scam victims) had quadrupled the rate of homelessness in the following year, controlling for mental disorders and income.10 Studies also show suicide risk is increased among veterans with financial problems.11 In a sample of active-duty soldiers experiencing suicidal crises, 23% reported a financial stressor in the 24 hours before their crisis.12 Among National Guard members, another study found that: (1) income decreases and difficulty making ends meet were associated with suicidal thinking; and (2) recent credit problems were strongly associated with suicide attempts.13 An analysis of Veterans Affairs (VA) electronic health records found veterans with financial problems were at greater risk for suicidality, after adjusting for mental health diagnoses.14 Veterans lacking money to cover basic needs (eg, food, clothes, shelter, transportation, and medical care) were three times more likely to endorse suicidal ideation 1 year later compared with veterans with money to cover basic needs (22% vs 7%).15
The transition from the military to civilian life can pose challenges to achieving financial well-being.16,17 When in the military, service members’ basic needs are generally met (eg, meal plans, housing allowances, and health care), whereas, in the transition to civilian life, many new veterans find themselves financially independent for the first time in their mid-to-late 20s or even 30s, a decade after most civilians do.7 Furthermore, after separation, many military personnel require reeducation18 and training to learn skills appropriate for civilian work.19,20 Data indicate predatory lenders have historically targeted service members and veterans: higher concentrations of payday lenders have been found in zip codes near military bases.21–23 As a result, veterans who may have taken out such unsecured loans while they are active duty could be vulnerable to lower credit ratings in the future after military discharge.
Despite the aforementioned challenges veterans might face after leaving the military that can be affecting their financial well-being, relatively little is known about how veterans compare to non-veterans concerning money management. Data suggest active-duty service members are more likely than civilians to take out payday loans, though whether this has long-term adverse effects on military service members is not clear.24 Data from the 2009 and 2012 National Financial Capability Studies revealed that military households have equivalent use of financial services but more problematic credit card behaviors than civilians,25 which continued in 2018.26 Recent data from the Consumer Financial Protection Bureau revealed that many young veterans in the first year after military separation become delinquent on debt payments, particularly those regarding auto loans, credit cards, and personal or retail installment loans.27 Analysis of over 10 million administrative bankruptcy records revealed bankruptcy filers were more likely to be veterans than non-veterans.28 National data showed veterans with longer-term unemployment (over 27 weeks) had significantly more days with poor physical health than non-veterans.6
To our knowledge, there have been no comparisons of veterans to non-veterans regarding financial well-being using the matched sampling statistical technique, which is important given demographics differ substantially between the two (eg, veterans are more likely to be older and male). Scant research has focused on veterans’ financial well-being since the COVID-19 pandemic.9 Only a few analyses examining financial well-being and health have enrolled national samples; specifically, unemployment has been associated with lower physical well-being4 and household net wealth, assets, and debt “are important determinants of overall life satisfaction and financial well-being.”29 The current study aims to address these gaps in the literature by: (1) determining whether veterans are vulnerable to specific money management problems compared with non-veterans; and (2) examining the link between financial status and physical health in veterans and non-veterans.
METHODS
Sample and Procedure
The 2021 Survey of Household Economics and Decisionmaking (2021 SHED) was conducted from October 29 through November 22, 2021.30 In total, 67 staff of the Federal Reserve Board wrote survey questions consulting with other Federal Reserve System staff, outside academics, and professional survey experts. A private consumer research firm, Ipsos, administered the survey using KnowledgePanel, a nationally representative probability-based online panel.
The Federal Reserve conducted the 2021 SHED to increase survey participation and completion among hard-to-reach demographic groups,30 using targeted communication with monetary incentives. Target groups—young adults ages 18 to 29, adults with less than a high school degree, adults with household income under $50,000 under age 60, and non-White, non-Hispanic adults—received extra email reminders and text messages and additional monetary incentives. Survey respondents not in a target group received a $5 incentive payment after survey completion, whereas those in the target groups received a $15 incentive. Incentives offered to some targeted individuals increased to $25 to boost motivation for completing the survey.
Participation depended on the following decisions made by respondents. First, respondents agreed to participate in Ipsos’ KnowledgePanel. According to Ipsos, 10.1% of individuals contacted to join KnowledgePanel agreed to participate in this study. Second, respondents completed an initial profile survey. 61.3% completed this initial demographic profile survey. Third, selected panel members agreed to complete the 2021 SHED. Of the 18,322 panel members contacted to take the 2021 SHED, 11,965 participated and completed the survey, yielding a final-stage completion rate of 65.3%. Taking all third stages, the cumulative response rate was 4.0%. After removing those with high refusal rates or completing the survey too quickly, the 2021 SHED enrolled 11,874 respondents.30
Of these, a total of 1176 participants reported prior service in the U.S. Armed Forces, comprising the veteran sample analyzed. Active-duty service members were excluded to ensure a matched comparison of veterans and non-veterans. Also, because veterans and non-veterans differ in demographics (eg, veterans are more likely to be male than non-veterans), we used statistical procedures to ensure equivalence and matched a non-veteran sample on age, sex, education, race, ethnicity, and geographic region with the same sample size (n = 1176).
Measures
Demographics included age (years), sex (female = 0 vs male = 1), race (White = 0 vs non-White = 1), ethnicity (non-Hispanic = 0 vs Hispanic = 1), education (high school or below = 0 vs post-high school = 1), and marital status (0 = not married; 1 = married). Annual household income was divided at the median (0 = <$50,000; 1 = ≥$50,000). Employment was coded as 0 = not working and 1 = working full-time/part-time. The urban-rural classification was scored (0 = non-metro/rural; 1 = metro). Geographic region (South, West, North, and Midwest) and community economic conditions were measured, the latter with “In your community—How would you rate economic conditions today?” scored from 1 = poor to 4 = excellent.
Debt information included: (1) medical debt: Do you currently have any debt from medical care you or your family members have received? (0 = no; 1 = yes), (2) legal debt: “Do you or someone in your immediate family currently have any unpaid legal expenses, fines fees, or court costs?” (0 = no; 1 = yes), and (3) housing instability was coded (0 = no; 1 = yes) if respondents indicated being evicted or receiving an eviction notice, the city condemned respondent’s property, a bank planned to take or took possession of one’s home in foreclosure, missed mortgage payments, or owed money for bank rent or fees.
Credit information included: (1) payday/pawnshop loans endorsed either “In the past 12 months, did you and/or your spouse or partner take out a payday loan or payday advance? or “In the past 12 months, did you and/or your spouse or partner out a pawn shop loan or an auto title loan? (0 = no; 1 = yes), (2) credit card debt: “Do you currently have any outstanding unpaid credit card debt?” (0 = no; 1 = yes), (3) unpaid balance: “In the past 12 months, how frequently have you carried an unpaid balance on one or more of your credit cards?” (1 = most or all of the time; 0 = other), (4) buy now pay later: “In the past year, have you used a “buy now pay later” service to buy something?” (0 = no; 1 = yes), and (5) credit score: “Where do you think your credit score falls?” (1 = very poor to 5 = Excellent).
Government benefit data included: Supplemental Nutrition Assistance Program (0 = no; 1 = yes), food pantry (0 = no; 1 = yes), and section 8 housing (0 = no; 1 = yes).
Financial behavior data included: (1) respondent spent money in the past year on the lottery, for example, buying Mega Millions, Powerball, or scratch tickets (0 = no; 1 = yes), or (2) spent money in the past year on gambling in person or online (0 = no; 1 = yes). Respondents were asked if they paid overdraft fees: “In the past 12 months, did you and/or spouse or partner pay an overdraft fee on a bank account?” (0 = no; 1 = yes) and if they had rainy-day funds: “If you were to lose your main source of income (eg, job or government benefits), could you cover your expenses for 3 months by borrowing money, using savings, or selling assets?” (0 = no; 1 = yes).
Physical health was measured by the following: “In general, would you say your physical health is…?” We reverse-coded original scoring so higher scores indicated higher perceived health (1 = poor and 5 = excellent).
Statistical Analysis
Statistical analyses were conducted using SAS v 9.4. We conducted an analysis in which we compared veterans to non-veterans on financial variables using the PROC PSMATCH procedure in SAS for an optimal match on propensity scores. We created an equal-sized sample of non-veterans with the same age, sex, education, race, ethnicity, and geographic region as the veteran sample. We then ran χ2 analyses to identify commonalities and differences in demographic and financial variables.
After, multivariable logistic regression models in which veteran status served as the independent variable and the above financial variables served as the dependent variables. These models were each run twice, once with unadjusted estimates for veteran status on financial variables and a second adjusted for age, sex, race, ethnicity, education, marital status, urban versus rural, income, employment, and geographic region. Parallel linear regressions were conducted for continuous dependent variables (credit score and community economic conditions).
Finally, multiple linear regression analyses were conducted for the entire sample of veterans and non-veterans with complete data on the measures previously, in which physical health ratings served as the dependent variable and in which veteran status, demographics, and financial variables served as the independent variables (all included in the multivariable model together simultaneously).
RESULTS
The demographic and financial characteristics of the sample are presented in Table 1, which includes χ2 analyses examining differences between veteran and non-veteran status on demographic and financial variables. The table demonstrates a good match between subsamples on sex, race, ethnicity, education, and geographic region; the mean age for the veteran subsample was 65.37 years (SD = 13.77), and for the non-veteran subsample was 64.87 years (SD = 13.35), also indicating a good match between subsamples.
TABLE 1.
Veteran Versus Non-Veteran Comparison on Demographic and Financial Variables
| Factors | Non-Veterans (N = 1176); n (%) | Veterans (N = 1176); n (%) | χ2 | P |
|---|---|---|---|---|
| Demographics | ||||
| Sex (M) | 1061 (90.2) | 1061 (90.2) | 0.00 | 1.00 |
| Race (non-White) | 186 (15.82) | 186 (15.82) | 0.00 | 1.00 |
| Ethnicity (Hispanic) | 104 (8.84) | 104 (8.84) | 0.00 | 1.00 |
| Education (post-high school) | 885 (75.3) | 885 (75.3) | 0.00 | 1.00 |
| Region | ||||
| South | 489 (41.6) | 502 (42.7) | 5.17 | 0.16 |
| West | 318 (27.0) | 274 (23.3) | — | — |
| Northeast | 166 (14.1) | 170 (14.5) | — | — |
| Midwest | 203 (17.3) | 230 (19.6) | — | — |
| Income (above median) | 727 (61.8) | 664 (56.5) | 6.98 | 0.008 |
| Married | 868 (73.8) | 871 (74.1) | 0.02 | 0.888 |
| Employed | 553 (47.0) | 510 (43.4) | 3.17 | 0.075 |
| Setting of residence (metro) | 1304 (87.93) | 992 (84.35) | 6.28 | 0.012 |
| Debt | ||||
| Medical debt | 110 (9.35) | 128 (10.88) | 1.51 | 0.218 |
| Legal debt | 22 (1.87) | 34 (2.89) | 2.63 | 0.105 |
| Housing instability | 25 (2.13) | 25 (2.13) | 0.00 | 1.000 |
| Credit | ||||
| Payday/pawn shop loans | 39 (3.32) | 80 (6.80) | 14.88 | <0.001 |
| Credit card debt* | 319 (27.13) | 410 (34.86) | 16.46 | <0.001 |
| Unpaid credit card balance* | 167 (14.20) | 218 (18.54) | 8.08 | 0.005 |
| Buy now pay later | 61 (5.19) | 77 (6.55) | 1.97 | 0.160 |
| Government benefits | ||||
| SNAP | 56 (4.76) | 55 (4.68) | 0.01 | 0.923 |
| Food pantry | 71 (6.04) | 72 (6.12) | 0.01 | 0.931 |
| Section 8 housing | 16 (1.36) | 20 (1.70) | 0.45 | 0.502 |
| Financial behaviors | ||||
| Bought lottery tickets | 458 (38.95) | 528 (44.90) | 8.56 | 0.003 |
| Gambling | 123 (10.46) | 176 (14.97) | 10.76 | 0.001 |
| Overdraft fees | 39 (3.32) | 80 (6.80) | 14.88 | 0.000 |
| Rainy-day funds | 889 (75.60) | 870 (73.98) | 0.81 | 0.367 |
Bold values are statistical significance P<0.05.
From the subset of the 2021 Survey of Household Economics and Decisionmaking sample with these credit items.
Veterans and non-veterans were found to differ in income, urban versus rural, payday/pawnshop loans, credit card debt, unpaid credit card balance, paying overdraft fees, purchasing lottery tickets, and gambling. To illustrate, veterans reported higher credit card debt than non-veterans (35% vs 27%: χ2 = 16.46; df = 1; P < 0.001) and more outstanding unpaid credit card debt balances (19% vs 14%: χ2 = 8.08; df = 1; P = 0.005).
Multivariable logistic analyses are depicted in Table 2. Adjusting for age, sex, race, ethnicity, education, marital status, urban versus rural, income, employment, and geographic region, it was found that veterans were more likely to report payday/pawnshop loans, credit card debt, unpaid credit card balance, paying overdraft fees, purchasing lottery tickets, and gambling.
TABLE 2.
Association of Veteran Status With Financial Variables, Unadjusted and Adjusted ORs From Logistic Regression Analyses
| Unadjusted ORs | Adjusted ORs | |||
|---|---|---|---|---|
| Factors | OR (95% CI) | P | OR (95% CI) | P |
| Debt | ||||
| Medical debt | 1.18 (0.91–1.55) | 0.22 | 1.15 (0.87–1.53) | 0.33 |
| Legal debt | 1.56 (0.91–2.69) | 0.10 | 1.52 (0.87–2.64) | 0.14 |
| Housing Instability | 1.00 (0.57–1.75) | 1.00 | 0.92 (0.50–1.69) | 0.78 |
| Credit | ||||
| Payday/pawn shop loans | 1.87 (1.06–3.29) | 0.03 | 1.83 (1.01–3.32) | 0.047 |
| Credit card debt | 1.46 (1.22–1.75) | <0.001 | 1.47 (1.22–1.77) | <0.001 |
| Unpaid credit card balance | 1.38 (1.11–1.73) | 0.004 | 1.40 (1.11–1.76) | 0.004 |
| Buy now pay later | 1.28 (0.91–1.81) | 0.16 | 1.31 (0.92–1.88) | 0.14 |
| Government benefits | ||||
| SNAP | 0.98 (0.67–1.44) | 0.92 | 1.00 (0.65–1.53) | 1.00 |
| Food pantry | 1.02 (0.72–1.42) | 0.93 | 0.99 (0.69–1.44) | 0.97 |
| Section 8 housing | 1.25 (0.65–2.43) | 0.50 | 1.25 (0.62–2.51) | 0.53 |
| Financial behaviors | ||||
| Bought lottery tickets | 1.28 (1.08–1.51) | 0.004 | 1.26 (1.09–1.52) | 0.003 |
| Gambling | 1.51 (1.18–1.93) | 0.001 | 1.56 (1.22–2.00) | <0.001 |
| Overdraft fees | 2.11 (1.43–3.12) | <0.001 | 2.10 (1.40–3.15) | <0.001 |
| Rainy-day funds | 0.92 (0.76–1.11) | 0.37 | 0.93 (0.76–1.15) | 0.50 |
Bold values are statistical significance P<0.05.
Adjusted odds ratios control for age, sex, race, ethnicity, education, marital status, urban vs. rural, income, employment, and geographic region. Odds ratios >1 indicate that the variable is higher for veterans. ORs <1 indicate that the variable is higher for non-veterans. To illustrate, the table shows that veterans are more likely to report paying for overdraft fees than non-veterans, 2.11 times more often when unadjusted and 2.10 times more often when adjusted.
OR indicates odds ratio.
Linear regression models not shown were adjusted for the same demographic variables. Ratings of credit scores did not differ between the two groups. Although unadjusted analyses indicated that veterans reported worse community economic conditions (parameter estimate = −0.07, t = −2.38, P = 0.02), when adjusted for demographics, this finding was no longer statistically significant (parameter estimate = −0.05, t = −1.88, P = 0.06).
Multivariable linear regression analyses (Table 3) revealed higher ratings of physical health were significantly associated (P < 0.05) with the following variables: higher educational attainment, higher income, being male, being married, being employed, better community economic conditions, a higher rating of credit score, not having medical debt, not purchasing lottery tickets, and reporting having rainy-day funds in the event of a financial emergency. Veteran status was unrelated to physical health ratings. While missing data on credit cards noted in Table 1 affected sample size in the linear regression model, post hoc linear regression analysis of the entire combined veteran and non-veteran samples (N = 2352) run without credit card variables yielded the same aforementioned factors associated with ratings of physical health.
TABLE 3.
Multivariable Linear Regression of Financial Factors Associated With Physical Health
| Factors | Parameter estimate | t | P |
|---|---|---|---|
| Demographics | |||
| Veteran status | 0.034 | 0.88 | 0.38 |
| Age | −0.0006 | −0.35 | 0.73 |
| Sex (M) | 0.18 | 2.71 | 0.007 |
| Race (non-White) | −0.02 | −0.34 | 0.73 |
| Ethnicity (Hispanic) | −0.03 | −0.41 | 0.68 |
| Education (post-high school) | 0.11 | 2.16 | 0.03 |
| Income | 0.04 | 2.87 | 0.004 |
| Married | 0.11 | 2.27 | 0.02 |
| Employed | 0.23 | 4.88 | <0.001 |
| Setting of residence (metro) | −0.02 | −0.27 | 0.79 |
| Community economic conditions | 0.11 | 4.11 | <0.001 |
| Debt | |||
| Medical debt | −0.17 | −2.25 | 0.03 |
| Legal debt | −0.18 | −1.15 | 0.25 |
| Housing instability | 0.31 | 1.74 | 0.08 |
| Credit | |||
| Payday/pawn shop loans | −0.06 | −0.32 | 0.75 |
| Credit card debt | 0.003 | 0.08 | 0.94 |
| Unpaid credit card balance | −0.002 | −0.03 | 0.98 |
| Buy now pay later | 0.04 | 0.41 | 0.69 |
| Credit score | 0.18 | 5.66 | <0.001 |
| Government benefits | |||
| SNAP | −0.21 | −1.51 | 0.13 |
| Food pantry | 0.01 | 0.12 | 0.91 |
| Section 8 housing | 0.18 | 0.82 | 0.41 |
| Financial behaviors | |||
| Bought lottery tickets | −0.20 | −4.98 | <0.001 |
| Gambling | −0.02 | −0.33 | −0.74 |
| Overdraft fees | −0.09 | −0.89 | 0.37 |
| Rainy-day funds | 0.17 | 3.12 | 0.002 |
Bold values are statistical significance P<0.05.
All variables were entered into a multivariable model that includes the entire sample of veteran and non-veteran participants (n = 1913) with complete data on all variables.
DISCUSSION
Financial well-being is a core component of an individual’s overall psychosocial well-being,1,4,6,29 and research has shown that for military veterans, financial strain is linked to worse clinical outcomes.3,9–11 To our knowledge, this is the first study of the financial well-being of veterans and non-veterans using nationally representative data and employing statistical matching techniques to compare the two groups. This method helps clarify extant literature not using these match sampling techniques; for example, one report described veterans as generally better off financially compared with other Americans,31 whereas another report indicated that veterans using the VA tended to be less well-off than their non-VA veteran counterparts.32
In the current multivariable analyses, we found veterans and non-veterans were the same across many categories of financial status. That housing instability did not differ between veterans and non-veterans is a testament to programming by the Department of VA to reduce homelessness among veterans, who traditionally constituted a disproportionate proportion of individuals experiencing homelessness.33 In addition, veterans reported equivalent levels of having rainy day funds attests to increased financial literacy training in the military, along with the creation of the Department of Defense (DoD) Office of Financial Readiness, which provides financial education and links to certified financial planners.2 Finally, veterans and non-veterans did not differ concerning medical and legal debt or using government benefits, which is encouraging data to support that citizens who select to serve in the military will be just as well off financially as their civilian peers.
Nevertheless, the SHED national survey did reveal some critical differences that should inform policy at the DOD, VA, as well as other government and non-government agencies that work with military veterans. By using statistical techniques that created a sample of non-veterans that matched veterans for age, sex, ethnicity, race, education, and geographic region, the analyses revealed that veterans were more likely than non-veterans to have credit card debt and to engage in financial behaviors like gambling. This is consistent with research from the last decade finding that military families report more problematic credit card behaviors than civilians.25,27,31
How can these mixed results be understood? Conceptualizations of financial well-being make a distinction between one’s financial situation, such as employment, having stable housing, or being the recipient of government benefits, versus financial behaviors, such as resisting impulsive spending or paying one’s credit card balance.34 From this perspective, the current data from the SHED revealed that veterans and non-veterans demonstrated generally similar financial situations with respect to housing stability, employment, and accessing government benefits. At the same time, the analyses suggest that veterans reported more difficulty than non-veterans with respect to financial behaviors, including in domains of credit management (payday/pawnshop loans, credit card debt, and unpaid credit card balance) and spending (lottery tickets, gambling, and paying overdraft fees).
As a result, both DoDand VA can be informed new SHED findings because they point to specific vulnerabilities that exist for veterans with respect to financial behavior. Research confirms that financial hardship, such as that could result from being in debt, relates to stress among recently discharged veterans,2 suggesting financial education during this period could be beneficial.17 Findings are especially relevant to the DoD Transition Assistance Program in the 365 days before military separation35 and the VA Solid Start program in the 365 days after military separation.2,36 The current analysis indicates that staff interacting with veterans should evaluate if transitioning service members report credit card debt, spend money on gambling, or pay overdraft fees on bank accounts.
Another reason the current results are valuable for DOD and VA policy is that through the SHED, we uncovered that financial well-being predicted physical health in veterans and non-veterans, above and beyond demographics. The set of findings in Table 3 indicates that social determinants of health, such as financial status, are critical to incorporate into policy and clinical work with adults in the general population. These financial variables reflected both individual-level (eg, income and credit score) and environment-level (eg, economic conditions of one’s neighborhood) dimensions.
Traditionally, broad policies have been aimed at addressing financial literacy in schools, places of work, and society at large. But the results suggest another critical avenue for addressing financial literacy: health care policy. Given current findings, medical providers and social workers could address financial well-being in the context of physical well-being. Consider how financial strain could interfere with medical care. One patient may lack money to afford transportation to get to their doctor regularly. Another patient may lack stable employment to pay for treatment required for chronic health problems like diabetes or hypertension. Other patients may struggle to adhere to their medication regimen because they live in neighborhoods of greater economic disadvantage. One could envision many ways in which financial well-being affects physical health, and vice versa. These findings provide national data pinpointing specific facets of financial well-being that can adversely impact physical health.
There are several limitations to consider. First, as with all surveys, data are based on respondents’ self-report which may be inaccurate. Second, because the data are cross-sectional, it is not possible to determine causal associations (eg, does financial well-being cause physical health problems?). Third, the variable for physical health is broad, and more detailed assessments of physical health appear warranted from the current preliminary findings. Fourth, there were no measures of mental health in the 2021 SHED,30 which could have bolstered findings. Financial stress has been linked with mental health problems, which in turn could have affected physical health. Fifth, there was limited information about military history; thus, future studies should examine variables, such as combat experience, deployments, the branch of service, type of discharge (honorable vs other), and length of military service, to ascertain more nuanced findings about veterans and financial well-being.
CONCLUSION
The analyses of a nationally representative 2021 SHED dataset showed that veterans and non-veterans were similar across many financial domains. Still, compared with non-veterans, veterans were likelier to report credit card debt, spend money on gambling, and pay overdraft fees on bank accounts. Across the entire sample, we discovered significant links between financial well-being and physical health: higher credit scores, post-high school education, working, living in a neighborhood of greater economic advantage, and having a rainy-day fund as a safety net to cover unexpected expenses. Results help inform policy and clinical interventions to improve the financial well-being of veterans by pointing to specific areas of vulnerability. At the same time, the findings reveal that financial well-being relates significantly to physical health for both veterans and non-veterans, demonstrating that assessment of social determinants such as financial well-being is critical to incorporate into health policy and medical treatment of both veterans and non-veterans.
ACKNOWLEDGMENTS
The authors thank the Board of Governors of the U.S. Federal Reserve for conducting the Survey of Household Economics and Decisionmaking.
Footnotes
The authors declare no conflict of interest.
Contributor Information
Eric B. Elbogen, Email: eric.elbogen@duke.edu.
Bethzaida N. Serrano, Email: bethzaida.serrano@duke.edu.
Jovin Huang, Email: jovin.huang@duke.edu.
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