Financial Capability Profiles in Later Life: Do Neighborhood Characteristics Matter?

Background: Financial capability represents how individuals’ financial functioning could be influenced by their ability to act (i.e., financial literacy) and opportunity to act (i.e., financial access). These financial attributes could be developed over the life course and shaped by the macro neighborhood characteristics that individuals are embedded in. The current evidence tends to focus on the single aspect of financial capability, with little attention paid to the older population and the neighborhood effects. However, the places where older adults live may affect involvement in financial issues and, in turn, result in diverse profiles in financial capability. Guided by the financial capability framework, this study examines whether and how neighborhoods affect financial capability in later life, investigating the associations between neighborhood clusters and the financial capability profiles of older adults.

Methods: Neighborhood socioeconomic data (e.g., population density, consumer price index [CPI], gross domestic product [GDP] per capita, income per capita, education, GINI coefficients, poverty rates, and internet access), measured at the census tract level, was extracted from the American Community Survey and linked with the zip code within the 2018 National Financial Capability Study. A total of 5,512 older Americans aged 65+ residing in 3,612 census tracts were selected. Factor mixture modeling (FMM) and latent class analysis (LCA) via Mplus were used to explore the profiles of financial capability (based on latent factors of financial literacy, access, and functioning) and clusters of neighborhood characteristics. The multilevel multinomial logistic model via Stata was conducted to examine the associations while correcting the potential cluster effects. Covariates included age, gender, race, marital status, education, employment, number of children, and income.

Results: Four financial capability profiles (statistics presented in individual-level) using FMM (financially inactive [n=891, 16.18%], financially constrained [n=1,151, 20.90%], financially capable [n=2,706, 49.13%], and financially stable [n=760, 13.80%)] and five neighborhood clusters (statistics presented in census tract level) using LCA (congested & underprivileged [n=442, 12.24%], desolated & marginalized [n=539, 14.92%], increased economic inequality [n=524, 14.51%], prosperous [n=1,161, 32.14%], and moderate [n=946, 26.19%] areas), were identified. Using financially inactive as the reference group, the multilevel model results showed that, compared to those living in prosperous areas, older adults living in socioeconomically disadvantaged areas (i.e., congested & underprivileged [RRR=0.55-0.58] or desolated & marginalized areas [RRR=0.51-0.61]) were 42-45% and 39-49% less likely to be financially capable or financially stable, respectively.

Conclusions: The significant relationships between neighborhood clusters and financial capability profiles of older adults support the idea that where individuals live matters for the development of financial capability over the life course. As neighborhood characteristics are proxies of access, resources, and opportunities for better financial services and infrastructure, improving the social and economic structures could promote the development of financial capability and create financially inclusive communities. Policies related to continued financial education, efforts to address economic inequality and institutional saving schemes, financial inclusive initiates for all ages and incomes, and investments for physical/virtual financial systems and affordable, accessible, and appropriate mainstream financial products could be promoted as strategies to build financial capability and assets for all.