Method: A subset of respondents aged 55 and older (N = 9,888) from the 2015 National Financial Capability Survey was selected. We used factor mixture modeling (FMM) to identify FC profiles and regression analyses to assess sociodemographic factors and financial outcomes associated with FC profiles. First, a confirmatory factor analysis (CFA) was used to evaluate the model fit for three latent factors: (1) financial literacy (ability, confidence, and knowledge), (2) financial access (checking, saving, investment, retirement, and credit card), and (3) financial functioning (precautionary saving, planning ahead, and long-term financial goals). These latent factors were subsequently modeled using FMM to explore the number of latent classes for FC with a series of model testing. Regression analyses were conducted to test the relationship between sociodemographic characteristics (age, gender, marital status, education, and employment), FC profiles, and financial outcomes (financial satisfaction, self-rated credit score, and bank overdraft).
Results: The CFA results indicated the model fitted the data well (CFI = 0.94, TLI = 0.92). Four FC profiles were identified using FMM: High-FC (N = 3678, 37.20%; reference group), Moderate-FC (N = 3036, 30.70%), Low-FC (N = 1282, 12.97%), and High Financial Literacy Only (N =1892, 19.3%). Multinomial logistic regression results showed that age, gender, marital status, education, and employment were associated with FC profiles. Those who were younger, female, non-white, not married, and not employed were more likely to have lower- to moderate-level of FC. OLS and logistic regression showed that, compared to the High-FC group, the High Financial Literacy Only, Low-FC, and Moderate-FC groups had lower financial satisfaction (b = -1.34 to -4.04) and self-rated credit score (b = -0.29 to -1.73) as well as higher odds to experience bank overdraft (OR =2.63-10.46).
Conclusion: Heterogeneity exists in financial capability among older adults. Clearly there are vulnerable groups—Low-FC and High Financial Literacy Only—and High-FC group that may represent the vision of “financially healthy.” Race, gender, and socioeconomic disparities manifest in FCAP among older persons, which in turn, leads to disparities in financial outcomes in later life. Programs and policies should target on vulnerable FC groups in order to reduce economic inequality at the older age.