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Assessing the Short-Term Stability of Financial Well-Being in Low- and Moderate-Income Households


Much of the literature on household finance tends to focus on relatively objective measures of financial security (e.g., savings, income, financial knowledge), and there has been less research on measures of subjective financial well-being. This gap is due in part to the absence of a common understanding on defining and measuring subjective financial well-being. The Consumer Financial Protection Bureau has recently developed a new Financial Well-Being Scale that provides a comprehensive way to measure this construct. The research on this scale is still scarce and little is known about how subjective financial well-being evolves over time. This paper uses a two-wave survey of low- and moderate-income tax filers to present among the first longitudinal analyses of this scale. Through descriptive analysis and lagged dependent variable regressions, we assess (1) the stability of financial well-being over a six-month period; (2) the extent to which relatively stable household characteristics predict volatility in subjective financial well-being; and (3) the relationship between adverse financial events, including financial shocks and material hardships, and subjective financial well-being. We find that financial well-being scores are extremely stable over the short-term, and that relatively stable household characteristics are not strong predictors of subjective financial well-being changes. We also find that, while adverse financial events like job loss are significantly associated with lower subjective financial well-being scores, the magnitude of these relationships is not large. These results have implications for the use of the financial well-being scale in evaluations of financial security interventions.

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  1. The goal of developing the CFPB’s Financial Well-Being Scale was to provide a more accurate measure of subjective financial well-being. The scale was developed using large samples of survey respondents and applying rigorous statistical methods such as item response theory techniques. In addition, the CFPB validated the scale against other similar concepts (e.g., financial satisfaction). For more details on scale development and validation, see

  2. Earlier versions of the results presented in this current study were featured in two research briefs (Bufe et al., 2019; Sun et al., 2019).

  3. The response rate between the waves was 31%. In the Appendix, Table 7 compares characteristics of respondents who dropped out after Wave 1 with respondents who completed both survey waves (i.e., those who constitute our analytical sample). The comparison of weighted samples suggests attrited and non-attrited individuals were similar on observable characteristics.

  4. We restricted the 2017 ACS sample to adults with incomes at 200% of the federal poverty line (FPL) or lower, and developed inverse probability weights based on the respondents’ age, age squared, education, student status, gender, race/ethnicity, and the presence of children in the household. For more details on this weighting process, see Solon et al. (2015). Table 8 (see Appendix) compares the observable characteristics of our sample with those of adult respondents to the 2017 ACS whose incomes did not exceed 200% of the FPL. The comparison shows our weighted sample was quite similar to the population of U.S. adults who are at or below 200% of the FPL.

  5. The one exception to this is the question on eviction, which we ask over the prior 12 months at Wave 1 and over the prior 6 months at Wave 2.

  6. We also estimated these models including controls for state of residence and the date of survey completion. These state and date controls did not appreciably change our estimates.

  7. In the models with financial characteristics, we include the tax-related variables highlighted in Table 1, including whether a household received a refund, the amount of the refund received, and the amount of taxes owed. Because these variables were not central to our analysis, they are not included in the results presented here; however, estimates for these variables are available upon request.

  8. Regressing the Wave 1 financial well-being quartile on the change in financial well-being scores confirms these results. The change in financial well-being scores for the first and fourth quartiles, respectively, was significantly different from the change in all other quartiles, whereas change in financial well-being scores for those in the second quartile did not differ significantly from the change for those in the third quartile (and vice versa).

  9. To appropriately estimate the relationships between shocks and hardships and financial well-being, our models require sufficient intraperson variation in the experience of these shocks and hardships across the two survey waves. Our analysis shows that the proportion of households that either experienced a shock in Wave 2 but not in Wave 1 or experienced a shock in Wave 1 but not in Wave 2 ranged from a low of 3.5% for evictions (n = 115) to a high of 27.5% for auto repairs (n = 924). The proportion of households that either experience a hardship in Wave 2 but not in Wave 1 or vice versa ranges from a low of 8.8% for skipped rent payments (n = 293) to a high of 19.2% for skipping any medical care (n = 638). These results indicate that the amount of intraperson variation in the experience of shocks and hardships in our sample is sufficient to estimate the relationships between these adverse events and financial well-being scores.

  10. Each of the regression models in the main analysis control for the baseline level of subjective financial well-being. This approach enables us to estimate the average subjective financial well-being at Wave 2 across a variety of household characteristics, independent of initial subjective financial well-being levels. As a robustness check, we re-estimated the full OLS models in our main analysis using first differences regression models that did not control for baseline subjective financial well-being. In these models, the dependent variable was the difference between Wave 2 and Wave 1 financial well-being scores, and all household characteristics, shocks, and hardships were measured as in the main analysis. Generally, this estimation strategy did not notably change the results from the main analysis. The vast majority of household characteristics remained unassociated with significant changes in subjective financial well-being, and the relationships between adverse financial events and financial well-being scores exhibited similar coefficient patterns, though the relationships were slightly more attenuated than those in the main analysis. This finding speaks to the validity of using models that control for baseline subjective financial well-being, as in the main analysis, to estimate the relationship between household and individual characteristics and downstream subjective financial well-being. The results from the first differences estimation are available upon request.


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Correspondence to Stephen Roll.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board of Washington University in St. Louis (IRB ID: 201801040) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.


Statistical compilations disclosed in this document relate directly to the bona fide research of, and public policy discussions concerning, financial security of individuals and households as it relates to the tax filing process and more generally. Compilations follow Intuit's protocols to help ensure the privacy and confidentiality of customer tax data.

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See Tables7, 8, 9

Table 7 Descriptive Statistics, by Sample Attrition
Table 8 Descriptive Statistics, 2018 ACS and Analytical Sample
Table 9 Tetrachoric Correlations Between Shocks and Hardships

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Roll, S., Kondratjeva, O., Bufe, S. et al. Assessing the Short-Term Stability of Financial Well-Being in Low- and Moderate-Income Households. J Fam Econ Iss 43, 100–127 (2022).

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  • Financial well-being
  • Income volatility
  • Financial security
  • Material hardship
  • Panel data analysis