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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2023 Dec 29;79(4):gbad194. doi: 10.1093/geronb/gbad194

Banking on the Neighborhood? Inequalities in Older Adults’ Access to Local Banking and Neighborhood Perceptions

Alyssa W Goldman 1,✉, Megan Doherty Bea 2
Editor: Kenzie Latham-Mintus3
PMCID: PMC10923209  PMID: 38155541

Abstract

Objectives

Access to local banking represents an understudied dimension of neighborhood-based inequalities that could significantly influence older adults’ perceptions of their neighborhood spaces in ways that matter for disparities in well-being. We evaluate disparities in banking access and then examine how local banking access informs older adults’ perceptions of neighborhood collective efficacy and danger, above and beyond other neighborhood socioeconomic characteristics.

Methods

We use nationally representative data from older adults in the United States who were interviewed at Round 3 of the National Social Life, Health, and Aging Project, linked with data on banks in respondents’ residential and surrounding census tracts from the National Establishment Time-Series database, in a series of bivariate and multivariable regression analyses.

Results

White older adults and those with higher levels of education have significantly greater local banking access than Black and Hispanic older adults and those with lower levels of education. Higher rates of local banking institutions are associated with significantly lower perceptions of neighborhood danger, but not with perceived collective efficacy. This finding emerges when accounting for neighborhood concentrated disadvantage and physical disorder.

Discussion

Local banks may represent neighborhood investment and the broader economic vitality of a community, as well as the ability of communities to meet older adults’ everyday needs in ways that enhance older residents’ feelings of safety. Increasing access to local financial institutions may help attenuate neighborhood-based contributors to inequalities in health and well-being among the older adult population.

Keywords: Banking, Collective efficacy, Neighborhood danger, Spatial inequality, Third places

As an increasing number of individuals in the United States are “aging-in-place”—that is, aging independently in their communities—older adults’ perceptions of their neighborhood environments have emerged as significant social-contextual issues for understanding inequality in aging. Among the most significant dimensions of neighborhood evaluations are perceptions of neighborhood safety and collective efficacy—that is, a sense of social cohesion, trust, and a willingness to intervene on behalf of the common good among neighbors (Sampson et al., 1997). Lower perceptions of neighborhood safety are associated with myriad adverse health outcomes, including depression (Muhammad et al., 2021; Wilson-Genderson & Pruchno, 2013), functional decline (Sun et al., 2012), higher body mass index (Fish et al., 2010), and lower levels of psychological health (Choi & Matz-Costa, 2018). Higher levels of neighborhood collective efficacy are linked with a lower likelihood of obesity (Cohen et al., 2006), better overall self-rated health (Browning & Cagney, 2002), and a lower risk of major depression (Ahern & Galea, 2011). These findings underscore individuals’ perceptions of their neighborhood environments as key dimensions of structural inequalities in well-being (Diez Roux & Mair, 2010; Yen et al., 2009), and have prompted increased policy attention toward improving older adults’ evaluations of their local environments (Beard et al., 2016).

Prior research documents that perceptions of neighborhood safety and collective efficacy are shaped by individual and social–environmental characteristics, including personal levels of social integration and length of neighborhood residency, as well as neighborhood concentrated disadvantage, physical disorder, features of the built environment (e.g., parks and green spaces), and the presence of local “third places” where people can connect and interact outside of home and work (Alidoust et al., 2019; Baba & Austin, 1989; Cohen et al., 2008; Kleinhans & Bolt, 2014; Rosenbaum, 2006). Within the literature on the social environment and neighborhood perceptions, there is less attention to the role of local banking institutions.

Banks are an important resource for families and households that also materially support community growth and development (Hegerty, 2020). Indeed, recent research documents that banking-related activities such as community foreclosures can adversely affect neighborhood perceptions in ways that contribute to psychological distress and cognitive decline (Friedman et al., 2021). Yet, the presence of local banking institutions is unequally distributed across communities (e.g., Faber, 2019), generating potential inequalities in older adults’ access to financial institutions and related resources. In this study, we consider that the presence of banks themselves may play a role in older adults’ evaluations of their neighborhoods in ways that are distinct from other neighborhood characteristics, and that may have implications for inequality in aging and well-being.

Banking and Neighborhood Perceptions

The presence of local banks can shape the extent to which older adults feel that their neighborhoods meet their needs. Local bank branches provide access to savings and credit mechanisms that serve as buffers in times of need and that can be important ways of building wealth over time, especially for lower-income families. In-person bank use is a primary method of banking access for many lower-income households (FDIC, 2020, 2022). Furthermore, access to a local branch in a community improves the quantity and quality (i.e., interest rates) of mortgage originations for low- and middle-income families (Ergungor, 2010). More broadly, greater local bank branch density mitigates income inequality (D’Onofrio et al., 2019).

Even with the increasing use of mobile banking platforms, physical proximity to a bank branch is essential for older adults to access financial services and manage accounts (Dahl & Franke, 2017). In 2019, 83% of households with a bank account visited a branch in person at least once, with older adult households more likely to visit a branch 10 or more times throughout the year (FDIC, 2020). Nearly 40% of households with an adult aged 65 or older reported using in-person banking as their primary method of banking, compared to 26% of these households that reported online banking as their primary method.

Local banking establishments can also provide a host of economic benefits for their communities, including investment in affordable housing, local businesses, education, health care, and the built environment (Jutte et al., 2021). Local lending fosters new businesses and sustains small businesses in times of need, providing financing for neighborhood services and amenities to thrive (Cortés et al., 2020). In these ways, the presence of local banks may be a lynchpin in catalyzing the broader socioeconomic vitality of a place, supporting older residents’ favorable perceptions of the safety and collective efficacy of their neighborhoods. This in turn can have ripple effects on the labor market (Chen et al., 2017) and broader community development (Jutte et al., 2021), in ways that support those neighborhood characteristics that positively inform perceptions of neighborhood safety and collective efficacy.

Finally, like other neighborhood third places, banks can serve a social function. In-person visits to a local branch can be opportunities to engage in informal interaction with other local residents, which may foster a sense of belonging and community. More so than younger age groups, older adults place a high value on face-to-face interaction when engaging in routine activities like banking and grocery shopping (Grougiou & Pettigrew, 2011; Strobl et al., 2016). Branch visits also draw older adults outside of the home environment, and may lead to more time spent in other local places, such as nearby parks, coffee shops, and senior centers. Collectively, these activities can positively shape evaluations of neighborhood characteristics (Cohen et al., 2008).

Racial Inequality in Neighborhood Banking Access

Access to banking services is unequal across neighborhoods, and federal efforts to address these spatial inequalities have had limited success. A key law, the Community Reinvestment Act (CRA), requires evaluations of banking institutions' efforts “to help meet the credit needs of the communities in which they do business, including low- and moderate-income neighborhoods” (Federal Reserve, 2023). Though its creation in the 1970s was motivated in part to address redlining—the discriminatory practice of denying financial services to individuals in neighborhoods considered “risky” based on race and income—the CRA does not explicitly address racial discrimination by banks, and its enforcement in low- and moderate-income areas has been uneven (Park & Quercia, 2020). Thus, despite the CRA, the historical links between banking access and redlining have had enduring effects on broader neighborhood (dis)investment and access to local financial services in predominately Black and lower-income neighborhoods (Aaronson et al., 2021; Faber, 2019; Park & Quercia, 2020).

Branches in the United States continue to be disproportionately located in predominately White and higher-income neighborhoods (Hegerty, 2020; Small et al., 2021). Branch closures in recent decades have occurred more frequently in Black, Latino, and lower-income neighborhoods (Richardson et al., 2017), with unequal consequences. For example, bank closures after the Great Recession resulted in a decline in the likelihood of an individual having a bank account, with sharper declines among Black, Hispanic, and middle-income individuals (Blanco et al., 2022). Other studies have found that minority-owned small businesses have more difficulty in obtaining credit from banks compared to their White counterparts (Bates & Robb, 2016; Prieger, 2023), with implications for spatial inequalities in business growth. Any positive influence of local financial services on neighborhood perceptions may therefore be stronger among older adults who live in historically marginalized communities, wherein banking establishments represent an important yet relatively less available means of household and neighborhood investment that can catalyze support for other neighborhood resources and amenities (e.g., Friedman et al., 2021). Disparities in local banking have been further aggravated by recent branch closures in the wake of the COVID-19 pandemic, which were more likely to occur in predominately Black and Hispanic communities, rural, and lower-income neighborhoods (Edlebi et al., 2022), which were also less likely to have robust access to banking before the pandemic.

The Present Study

The goals of our study are twofold. First, we describe how access to local banking institutions in the United States varies across sociodemographic subgroups of older adults using nationally representative survey data. Second, we examine how older adults’ perceptions of their neighborhood environments are shaped by the presence of banking institutions. We focus on banking institutions given their importance in supporting families and local businesses in managing financial needs and their relatively understudied role in the everyday well-being and neighborhood experiences of U.S. older adults.

We focus on older adults for several reasons. First, the projected growth of the older adult population in the United States calls for a deeper understanding of how place-based factors structure inequalities in aging (Wiles et al., 2012). Second, financial exploitation is a significant and prevalent social problem. Recent estimates suggest that one in 20 older adults encounter financial exploitation, making it the most common form of older adult abuse (Peterson et al., 2014; Wood & Lichtenberg, 2017). Access to financial service professionals is a key policy recommendation to support older adults’ awareness and understanding of and response to financial exploitation attempts and incidents (Wood & Lichtenberg, 2017). Inequality in access to local banks, therefore, has implications for inequality in financial exploitation. Finally, the physical presence of bank branches is important for addressing the “digital divide” in the adoption of mobile banking platforms, for which older adults lag behind other age groups (Choudrie et al., 2018).

Data and Methods

We use nationally representative data from Round 3 of the National Social Life, Health, and Aging Project (NSHAP) (Waite et al., 2017). The NSHAP is a nationally representative panel study of community-dwelling adults aged 50 and older with the goal of better understanding how the social context intersects with health and well-being as individuals age. The NSHAP includes in-home interviews with respondents, followed by a leave-behind questionnaire that respondents are asked to return by mail. To date, there are three rounds of data available, collected in 2005/2006 (Round 1), 2010/2011 (Round 2), and 2015/2016 (Round 3). Round 1 respondents (N = 3,005) were born between 1920 and 1947. The NSHAP attempted to reinterview all Round 1 respondents at subsequent rounds. Round 2 (N = 3,377) and Round 3 (N = 4,777) also included interviews with respondents’ coresident partners. Additionally, Round 3 introduced a new cohort (Cohort 2) of respondents born between 1948 and 1965 (aged 50–67) to supplement the original sample (Cohort 1). In this study, we use data from Round 3, which includes the largest sample and the most recent portrait of local banking access among U.S. adults aged 50 and older.

Perceived Neighborhood Characteristics

Within the leave-behind questionnaire, respondents were asked several questions about their residential areas, which we used to create two measures of neighborhood perceptions. First, we created a measure of perceived collective efficacy. Respondents were asked to rate how strongly they agreed with the following statements (1 = “strongly disagree,” 5 = “strongly agree”): “This is a close-knit area,” “People around here are willing to help their neighbors,” “People in this area generally don’t get along with each other,” “People in this area don’t share the same values,” and “People in this area can be trusted.” Additionally, respondents were asked to rate how often (0 = “never,” 3 = “often”): “… do you and people in this area visit in each other’s homes or when you meet on the street?” “… do you and other people in this area do favors for each other?” “… do you and other people in this area ask each other for advice about personal things?” We standardized and then averaged responses to these eight items to create a perceived collective efficacy scale (alpha = 0.79; York Cornwell & Goldman, 2021).

Next, we created a measure of perceived neighborhood danger. Respondents were asked to rate how strongly they agreed with the following statements (1 = “strongly disagree,” 5 = “strongly agree”): “Many people in this area are afraid to go out at night,” “There are places in this area where everyone knows ‘trouble’ is expected,” and “You’re taking a big chance if you walk in this area alone after dark.” We averaged responses to these three items to create a scale of perceived neighborhood danger (alpha = 0.81).

Local Banking Institutions

Our main predictor of interest is the density of banking establishments in respondents’ residential areas. We use data on the presence of banking establishments from the National Establishment Time-Series (NETS) database, made publicly available by U.S. census tracts through the National Neighborhood Data Archive at the Inter-university Consortium for Political and Social Research (Finlay, Li, et al., 2022). We linked Round 3 of the NSHAP with data on the presence of banking establishments from the 2015 NETS by respondents’ residential census tracts. We considered that census tracts represent somewhat arbitrary boundaries with respect to residents’ access to local establishments and local exposures, particularly because we do not have precise information about respondents’ home locations within their census tract. Therefore, to better account for potential access and exposure to local banking, our focal spatial unit is the area encompassed by respondents’ residential census tract and their neighboring census tracts. We used data on census tract adjacency from the Diversity and Disparities component of the American Communities Project at Brown University (https://s4.ad.brown.edu/Projects/Diversity/).

The NETS includes all banking establishments classified under the North American Industry Classification System category 5221, which refers to all institutions that accept deposits and make loans. This includes three subcategories, which represent all bank and credit union locations: commercial banks (code #522110), savings and loan banks (code #522120), and credit unions (code #522130). Of note, other types of financial service institutions that are often considered to be predatory, including payday lenders and check-cashers, are not included in this measure. To measure local banking density, we summed the number of banking establishments across all three subcategories in respondents’ residential and contiguous census tracts, divided this sum by the total population in the residential and contiguous census tracts, and multiplied this result by 1,000.

Covariates

Our models adjust for respondent age in years at Round 3, as well as self-reported gender, race/ethnicity, educational attainment, whether the respondent was employed, and household income in the prior year, coded categorically. Because older adults who have lived in their neighborhood for a longer period may experience higher levels of personal comfort and familiarity with the local area than newer residents, we control for whether the respondent had lived in their current neighborhood for more than 20 years (= 1). We also adjust for whether the respondent was married or living with a partner, and the number of respondents’ noncoresident social network members who lived in their neighborhood (range: 0–5) who may represent a source of local social integration outside of the household context.

Other neighborhood (residential tract) characteristics may be associated with older adults’ neighborhood perceptions as well as the presence of local banks. As physical disorder is associated with neighborhood assessments (Ross & Jang, 2000), we created a measure to capture this construct based on NSHAP field interviewer assessments of respondents’ neighborhood characteristics (litter, noise, building density, traffic, odor/pollution, and condition of respondents’ residence and other buildings on the block). Items were standardized and averaged (alpha = 0.77). We used data from the 2011 to 2015 American Community Survey (ACS) to create a measure of tract-level concentrated disadvantage by standardizing and averaging the percentage of the population with incomes below poverty, the percentage with less than a high school degree, the percentage of households receiving public assistance, and the median household income (reversed) (alpha = 0.86). Higher scores represent greater concentrated disadvantage. Finally, given the enduring linkages between redlining practices and neighborhood investment, such that neighborhoods with lower banking density could reflect longstanding neighborhood disinvestment in Black and Latino communities, we used a measure of tract racial composition from the ACS in stratified models to consider whether the relationship between banking establishment density and neighborhood perceptions differed for older adults living in neighborhoods with different racial compositions.

Analysis

We first examine descriptive statistics of the main variables in our analyses. Given our conceptualization of local banking as a dimension of neighborhood inequality that could contribute to disparities in other measures of personal, social, and economic well-being, we also examine how access to local banking is patterned across social groups. Next, we use multivariable linear regression models to test how older adults’ perceptions of neighborhood collective efficacy and neighborhood danger are a function of local banking density. Next, we stratify our analytic sample by whether respondents live in residential tracts that are either above or at or below the median proportion of White residents (median = 0.75) in the residential tracts of the analytic sample. This approach allows us to test whether local banking, in the context of all other model covariates, has different associations with neighborhood perceptions for older adults living in neighborhoods that may be more or less affected by racially exclusionary banking and community disinvestment practices.

We use data from 3,498 Round 3 respondents who had nonmissing data on the variables used in our models. Of the 4,607 respondents aged 50 and older, the largest source of missing data (17%) comes from the questions about neighborhood perceptions (our dependent variables), which were asked in the leave-behind questionnaire and which some respondents did not return. Of the remaining respondents, approximately 6% did not provide income information and an additional 4% had missing values on other covariates.

Unweighted t tests indicated that respondents excluded due to missing data lived in areas with significantly lower rates of banking establishments compared to those included in our sample (t = 2.42, p < .05). To account for the possibility that respondents in our analytic sample systematically differ from those excluded, we implement an inverse probability weighting procedure. We first used a logit model to predict all respondents’ inclusion in the final analytic sample, using sociodemographic, health, and neighborhood measures as predictor variables. From this logit model, we derived predicted probabilities and then multiplied the inverse of these probabilities by the respondent-level weights included in the NSHAP. We apply these adjusted weights to our analytic models to give greater weight to those respondents who resemble those excluded with respect to the covariates used in the logit model (Morgan & Todd, 2008). All models adjust for the NSHAP survey design. Analyses were conducted using Stata 17.

Results

We begin by examining the descriptive statistics shown in Table 1. Across the overall analytic sample, respondents perceived relatively low levels of neighborhood danger, with average scores falling between “disagree” and “neither agree nor disagree” (M = 2.293, SD = 0.908). Our collective efficacy measure represents the average of standardized items. Examining the distribution of select scale items suggests that respondents tended to perceive, on average, that they can trust others in their local area (between “neither agree nor disagree” and “agree”), but do not necessarily believe that they share the same values (between “disagree” and “neither agree nor disagree”). On average, respondents in our analytic sample live in areas with banking establishment densities of 0.423, or 0.423 banks per 1,000 residents in the residential and surrounding (contiguous) census tracts. There is substantial heterogeneity in banking densities across the sample, as shown by the standard deviation (0.298).

Table 1.

Descriptive Statistics of Key Variables Used in the Analysis (N = 3,498)a

Variable Mean (SD) Percentage
Perceived neighborhood characteristics
 Perceived neighborhood collective efficacy (Range: −2.22 to 1.79) 0.001 (0.665)
 Perceived neighborhood danger (Range: 1–5) 2.293 (0.908)
Neighborhood characteristics
 Banking establishment densityb 0.423 (0.298)
 Residential tract concentrated disadvantagec −0.099 (0.796)
 Physical disorder −0.032 (0.661)
 Proportion White residents in residential tractc 0.676 (0.284)
Respondent characteristics
 Female 55%
 Age (in years) 63.758 (9.685)
 Race/ethnicity
  White 71%
  Black 14%
  Hispanic 11%
  Another racial/ethnic group 3%
 Educational attainment
  Less than high school 13%
  High school or equivalent 24%
  Some college 35%
  Bachelor’s or more 28%
 Household income
  Less than $25,000 23%
  $25,000–$49,999 26%
  $50,000–$99,999 30%
  $100,000 or higher 21%
 Currently working (= 1) 36%
 Married or living with a partner (= 1) 70%
 Number of local (noncoresident) network members 0.737 (1.025)
 Lived in neighborhood for more than 20 years (= 1) 49%
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Notes:

NETS = National Establishment Time-Series; NSHAP = National Social Life, Health, and Aging Project.

aMeans are weighted using respondent-level weights and are adjusted for the NSHAP survey design.

bBanking establishment density represents the number of establishments per 1,000 residents in respondents’ residential and surrounding (contiguous) census tracts taken from the NETS database for 2015.

cMeasures are based on variables from the 2011 to 2015 ACS.

Turning to respondent characteristics, just over half of respondents are women (55%), with the majority identifying as White (71%), and the average age is 64 years old. The majority (63%) have at least some college education, with 51% reporting annual household incomes of at least $50,000. Most respondents were not currently employed (64%) and were married or living with a partner (70%). Just under half of the sample (49%) had lived in their neighborhood for more than 20 years, and the majority reported having zero or one social network member who lived in their local neighborhood.

Figure 1 shows the mean rates of local banking establishments by dimensions of structural inequality. Older adults with less than a high school degree live in areas with significantly lower rates of banking establishments (M = 0.339) compared to older adults with a Bachelor’s or more (M = 0.467; p < .001 in adjusted Wald tests). We also observe disparities by race/ethnicity. White older adults reside in areas with significantly higher densities of banking establishments (M = 0.455) compared to Black (M = 0.330) and Hispanic (M = 0.293) older adults (p < .001 in both adjusted Wald tests). Put differently, the gap in mean local banking density between White older adults and Black and Hispanic older adults is comparable in size to the gap between those of the highest and lowest levels of educational attainment. Respondents in the highest income bracket have more banks per 1,000 residents (M = 0.477) than those in the lowest income category (M = 0.395), as do older adults residing in tracts in the lowest quartile of concentrated disadvantage (M = 0.458) compared to those residing in tracts in the highest quartile of concentrated disadvantage (M = 0.356, p < .05). These descriptive findings underscore that older adults’ access to local banking institutions is stratified by race/ethnicity and social class.

Figure 1.

Figure 1.

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Mean rates of local banking establishments (per 1,000 residents) across social groups.

Turning to our multivariable results, Models 1 and 4 of Table 2 report the bivariate associations between banking density and perceived collective efficacy and danger, respectively. Banking density is not significantly associated with perceived collective efficacy; however, more banks per capita are associated with significantly lower perceptions of neighborhood danger (b = −0.412, p < .001). When adjusting for respondent characteristics in Models 2 and 5, banking density remains negatively associated with perceived danger (b = −0.179, p < .01). Across both outcomes, neighborhood assessments are patterned by dimensions of social inequality. Black and Hispanic older adults report significantly lower levels of local collective efficacy (b = −0.137, p < .05; b = −0.173, p < .01, respectively) and higher levels of danger compared to White respondents (b = 0.349, p < .001; b = 0.317, p < .001, respectively). Those with a Bachelor’s degree also report significantly more favorable neighborhood perceptions compared to those with a high school degree or less (p < .001 for both outcomes), as do those in the highest income bracket compared to those who earned $25,000 or less (p < .001 for both outcomes). Whereas collective efficacy does not appear to be shaped by local banking density, we find that older adults with more local social network members (b = 0.105, p < .001) and those who have lived in their neighborhood for more than 20 years (b = 0.116, p < .001) perceive significantly higher levels of neighborhood collective efficacy.

Table 2.

Unstandardized Coefficients from Linear Regression Models Predicting Older Adults’ Perceived Neighborhood Collective Efficacy and Danger as a Function of Local Banking Establishment Density (N = 3,498)

Variables Perceived neighborhood collective efficacy Perceived neighborhood danger
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Neighborhood characteristics
 Banking establishment density 0.088 −0.012 −0.015 −0.412*** −0.179** −0.151**
(0.063) (0.060) (0.059) (0.103) (0.065) (0.053)
 Concentrated disadvantage −0.040 0.267***
(0.023) (0.028)
 Physical disorder −0.138*** 0.213***
(0.031) (0.032)
Respondent characteristics
 Female 0.010 −0.004 0.050 0.081**
(0.023) (0.022) (0.029) (0.029)
 Age (in years) 0.004* 0.002 −0.003 0.001
(0.002) (0.002) (0.002) (0.002)
 Race/ethnicity (Ref = White)
  Black −0.137* −0.063 0.349*** 0.134*
(0.056) (0.054) (0.064) (0.054)
  Hispanic −0.173** −0.144* 0.317*** 0.224***
(0.057) (0.056) (0.068) (0.061)
  Another racial/ethnic group −0.062 −0.022 0.098 0.043
(0.076) (0.071) (0.087) (0.088)
 Education (Ref = less than high school)
  High school or equivalent 0.084* 0.064 −0.191* −0.131*
(0.041) (0.039) (0.073) (0.066)
  Some college 0.145** 0.107* −0.338*** −0.219***
(0.048) (0.046) (0.069) (0.065)
  Bachelor’s or more 0.222*** 0.168*** −0.562*** −0.367***
(0.042) (0.039) (0.068) (0.068)
 Household income (Ref = less than $25,000)
  $25,000–$49,999 0.028 −0.002 −0.227*** −0.151**
(0.043) (0.043) (0.049) (0.050)
  $50,000–$99,999 0.102* 0.039 −0.398*** −0.235***
(0.047) (0.047) (0.056) (0.049)
  $100,000 or higher 0.167*** 0.063 −0.615*** −0.316***
(0.050) (0.056) (0.060) (0.058)
 Currently working 0.010 0.010 −0.052 −0.049
(0.028) (0.027) (.036) (.033)
 Married or living with a partner 0.065* 0.050 −0.047 −0.029
(0.027) (0.027) (0.039) (0.035)
 Number of local (noncoresident) network members 0.105*** 0.105*** 0.033 0.028
(0.013) (0.012) (0.018) (0.017)
 Neighborhood tenure (more than 20 y) 0.116*** 0.122*** −0.033 −0.054
(0.026) (0.025) (0.038) (0.036)
Constant −0.036 −0.611*** −0.412** 2.467*** 3.107*** 2.636***
(0.034) (0.130) (0.127) (0.052) (0.176) (0.185)
R-squared 0.002 0.087 0.105 0.018 0.229 0.297
F (df) 1.99
(1, 95)		 17.14***
(16, 80)		 17.59***
(18, 78)		 16.11***
(1, 95)		 39.94***
(16, 80)		 54.99***
(18, 78)
N 3,498 3,498 3,498 3,498 3,498 3,498
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Notes: All models are weighted using respondent-level weights that adjust for attrition and selection and account for the National Social Life, Health, and Aging Project survey design. Robust standard errors in parentheses.

*p < .05; **p < .01; ***p < .001 (two-sided tests).

Models 3 and 6 adjust for other neighborhood characteristics that could influence banking density and neighborhood perceptions. The association between banking density and perceived danger is robust to the inclusion of concentrated disadvantage and physical neighborhood disorder (b = −0.151, p < .01). Notably, the banking density coefficient when predicting neighborhood danger is over 10 times the magnitude of that when predicting collective efficacy in Model 3. Additional neighborhood measures are also relevant. Higher levels of concentrated disadvantage and neighborhood physical disorder are associated with greater perceived danger (b = 0.267 and b = 0.213, p < .001 for both). Whereas concentrated disadvantage does not appear to shape collective efficacy, higher levels of physical disorder are associated with significantly lower levels of this outcome (b = −0.138, p < .001). Neighborhood tenure and number of local network members remain positively associated with collective efficacy (p < .001), net of accounting for these other neighborhood characteristics. To further understand the magnitude of these associations, we also ran the models using standardized versions of all variables. Comparisons of standardized coefficients revealed that the effect of local banking density is approximately 0.32 the size of the effect of physical disorder and approximately 0.20 the size of the effect of concentrated disadvantage on perceived neighborhood danger.

Given the intersection of the historical and enduring effects of redlining and the importance of banking in social–spatial inequality, we further consider whether the effects of local banking and other key covariates on neighborhood perceptions may differ for older adults residing in tracts with different racial compositions. In Table 3, we examine models predicting neighborhood perceptions that stratify the sample at the median proportion of White residents in the residential tracts represented in our analytic sample, effectively interacting all model coefficients with whether the respondent resides above or at or below the 50th percentile of proportion White residents. Consistent with the findings in Table 2, local banking density is not significantly associated with perceived collective efficacy in either subsample. Higher rates of local banking are associated with significantly lower levels of perceived local danger among respondents who reside in tracts at or below the median proportion of White residents (b = −0.168, p < .05). These findings suggest that the negative association between local banking density and perceived danger emerges in the sample of older adults who reside in more racially diverse tracts. Banking density is nonsignificant in models restricted to respondents who reside in tracts above the median proportion of White residents, although in both subsamples we observe positive and statistically significant associations between concentrated disadvantage and physical disorder and perceived neighborhood danger. Tests of equality do not indicate statistically significant differences in the magnitude of the coefficients for banking density across the stratified models.

Table 3.

Unstandardized Coefficients from Linear Regression Models Predicting Older Adults’ Perceived Neighborhood Collective Efficacy and Danger as a Function of Local Banking Establishment Density, Stratified by Residence Above and at/below Median Proportion White Residents in Residential Tract (N = 3,498)

Variables Perceived neighborhood collective efficacy Perceived neighborhood danger
At or below median proportion White Above median proportion White At or below median proportion White Above median proportion White
Banking establishment density −0.068 0.055 −0.168* −0.054
(0.076) (0.080) (0.080) (0.070)
Concentrated disadvantage −0.003 −0.071 0.262*** 0.220***
(0.031) (0.036) (0.035) (0.041)
Physical disorder −0.076* −0.22*** 0.167*** 0.219***
(0.038) (0.048) (0.037) (0.052)
Constant −0.462* −0.322 2.639*** 2.786***
(0.195) (0.178) (0.268) (0.252)
R-squared 0.091 0.123 0.288 0.224
F (df) 7.59***
(18, 76)		 9.25***
(18, 72)		 28.70***
(18, 76)		 27.88***
(18. 72)
N 1,748 1,750 1,748 1,750
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Notes: All models are weighted using respondent-level weights that adjust for attrition and selection and account for the National Social Life, Health, and Aging Project survey design. All models adjust for the full set of covariates shown in Models 4 and 6 of Table 2.

* p < .05;

** p < .01;

*** p < .001 (two-sided tests). Robust standard errors in parentheses.

Of note, associations between banking density and perceived neighborhood danger are sensitive to different categorizations of neighborhood racial composition. Supplemental models (not shown) that stratify the sample at the mean proportion of White residents (0.63) and, separately, at 0.50 White residents rather than at the sample median (0.75) demonstrate marginally negative associations between banking density and perceived neighborhood danger among those respondents who live in tracts with a lower proportion of White residents, although these associations are explained by concentrated disadvantage and physical disorder.

Discussion

Our study makes two primary contributions. First, we document that access to local banking is unequally distributed among older adults in the United States, which could contribute to inequalities in older adults’ sense of security, quality of life, and overall well-being as they age. In particular, Black and Hispanic older adults, as well as older adults with lower levels of education, are less likely than White and more educated older adults to have access to local financial institutions. These findings resonate with prior literature on the segregation of local financial services (Small et al., 2021). Our examination of this topic with an older adult sample has particular implications for addressing financial exploitation. Older adults are at higher risk of financial exploitation than other age groups. Our findings suggest that this risk may be patterned by dimensions of social inequality due to more limited proximal access to banking services and professionals. Indeed, financial services are increasingly needed to serve as “frontline providers” to assist with financial education, fraud detection, and financial decision-making among a growing older population that is aging-in-place, who are at higher risk for financial exploitation, and an increasing number of whom face cognitive and other impairments (Wood & Lichtenberg, 2017). Efforts to increase equitable access to local banking—and reduce bank branch closures—may especially benefit older adults who are aging-in-place.

Second, we find that higher levels of local bank density are associated with significantly lower levels of perceived neighborhood danger, even when accounting for local physical disorder and concentrated disadvantage, which are both strongly linked with perceptions of fear and risk (Sampson et al., 1997; Scarborough et al., 2010). Local bank branches may signal neighborhood investment and the broader economic vitality of a community, including local businesses, housing developments, and municipal resources that support the built environment in ways that enhance feelings of safety (e.g., Branas et al., 2018) and perceptions of neighborhoods as low-risk communities that are supported by financial investment (Jutte et al., 2021). Perceived collective efficacy, on the other hand, appears to be shaped by older adults’ personal connections to and familiarity with their neighborhood, which may directly foster a sense of mutual trust and willingness to intervene for the common good (Sampson et al., 1997), with no significant effect of banking establishments.

Importantly, these results add nuance to our understanding of how “third places” shape neighborhood perceptions. Whereas banks, like other “third places,” represent local nonwork and nonresidential locations, their null association with collective efficacy suggests that these institutions do not directly contribute to a perceived sense of community and social engagement—mechanisms that are often implicated in research on the contributions of “third places” to everyday social life and well-being (Alidoust et al., 2019; Rosenbaum, 2006). More broadly, our findings prompt consideration of the possibility that not all “third places” function similarly with respect to the types of resources they provide or their influence on neighborhood perceptions. More widely studied “third places” such as coffee shops, eateries, libraries, and senior centers could have direct implications for perceived collective efficacy by virtue of creating spaces for older adults to socialize and form and maintain local social ties that allow for the development of local trust, shared values, and cohesion. We hypothesize that local institutions such as banks, post offices, health care clinics, and social service offices are functionally distinct, providing access to critical professional services and expertise that are implicated in residents’ quality of life and self-efficacy, but that ultimately carry greater implications for beliefs about security, community investment, and meeting residents' immediate needs, than for social characteristics of the local environment. Future studies of “third places” may unpack general assumptions about the roles of categories of establishments in residents’ daily lives to better understand how the presence and closure of different domains of establishments could affect neighborhood-related inequalities.

Though we find more limited evidence of moderation by neighborhood racial composition, the disproportionate access to local banks across racial/ethnic and socioeconomic subgroups that is revealed in our descriptive analyses still suggests that local banking could contribute to disparities in well-being given the effects of perceived local danger on physical and mental health (e.g., Choi & Matz-Costa, 2018; Diez Roux & Mair, 2010; Sun et al., 2012). Investment in local banking in more racially diverse or historically segregated areas could be an especially important pathway toward equity in credit-building opportunities, protection from financial risk, and perceptions of local safety.

Whereas Round 3 of the NSHAP was collected in 2015/2016, it is also possible that racial/ethnic differences in the observed associations have been exacerbated in the wake of the COVID-19 pandemic given disproportionate bank closures that have occurred in Black, Hispanic, and lower-income neighborhoods (Edlebi et al., 2022). Our focus in this study is on the role of local banking access in shaping older residents’ beliefs about their neighborhoods, but bank establishments’ decisions to open, maintain, or close branch locations can also be informed by industry beliefs about neighborhoods and their impact on revenue-generating streams (e.g., Adams & Amel, 2016). Future data sets that include measures of financial industry-level beliefs about neighborhoods, in addition to residents’ own neighborhood perceptions, can be leveraged in longitudinal frameworks to assess the magnitude and directionality of associations between banking density and neighborhood assessments, better accounting for potential unmeasured confounding.

Several limitations are important to note. Our study uses census tracts to define neighborhoods, but future research should consider other delineations, such as census block groups and respondent-provided neighborhood boundaries, which could represent areas that older adults frequently visit. Relatedly, access to in-person banking may be affected by individuals’ knowledge of bank branch locations and access to vehicle or public transit options, which may be captured in other data sets. Linking the NSHAP with NETS allowed us to examine the collective role of credit unions, commercial, and savings and loan banks, but different types of banking establishments could have different effects on residents’ neighborhood perceptions (e.g., local versus globally based institutions). Future research can disaggregate types of financial institutions, and also consider the role of nontraditional financial services (e.g., payday lenders and check cashiers) that could shape neighborhood perceptions and are disproportionately present in lower-income and non-White neighborhoods (Faber, 2019).

Although the NETS is one of the most comprehensive annual data sources for businesses in operation across the United States (Finlay et al., 2019), coverage is not perfect. Discrepancies in some industries have been noted when comparing to government records (e.g., manufacturing; Barnatchez et al., 2017) although its coverage of financial institutions, including banking, is largely in line with U.S. Census and Bureau of Labor Statistics establishment and employment estimates for this industry (Barnatchez et al., 2017; see, e.g., Table 3 in Barnatchez et al. on p. 24). Still, some nonbank establishments may be miscoded with banking industry identifiers and some bank branches may be miscoded with another industry identifier when industry classification takes place (Finlay et al., 2019). Therefore, our data source provides a reasonable but imperfect estimate of banking density in the United States. Whereas our models used a linear measure of banking density, we also note that using a dichotomous measure of whether respondents lived in an area with banking density equal to or less than one standard deviation below the mean banking density of the analytic sample yielded results that were substantively similar to those presented here. Finally, future research is needed to better understand the mechanisms that underly this association, including the extent to which local banks influence other aspects of local social infrastructure that facilitate a sense of neighborhood safety.

Conclusion

Neighborhood amenities and institutions play a critical role in shaping the well-being of older adults who are aging in their communities (Finlay et al., 2019; Finlay, Esposito, et al., 2022), and for whom subjective assessments of neighborhood environments have numerous health implications (e.g., Choi & Matz-Costa, 2018). Our findings call for greater attention to the role of local financial institutions, which influence older adults’ assessments of neighborhood danger in ways that are distinct from more widely used neighborhood social and economic indicators. The historical and enduring role of banking institutions in income and wealth inequality is inextricably linked to place-based factors. Although the presence of banking establishments alone does not exclude the possibility of customer-level discrimination, our study indicates that policy-driven investment in access to local financial institutions may be a pathway toward attenuating neighborhood-based contributors to inequalities in health and well-being among the older adult population.

Acknowledgments

We thank Ami Campbell for her research assistance. We also thank the researchers at the National Neighborhood Data Archive for making the banking data available.

Contributor Information

Alyssa W Goldman, Department of Sociology, Boston College, Chestnut Hill, Massachusetts, USA.

Megan Doherty Bea, Department of Consumer Science, University of Wisconsin—Madison, Madison, Wisconsin, USA.

Kenzie Latham-Mintus, (Social Sciences Section).

Funding

Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Numbers K01AG078406 (A.W.G.) and P30AG066619 (A.W.G.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The National Social Life, Health, and Aging Project is supported by the National Institute on Aging of the National Institutes of Health (R01AG043538, R01AG048511, R37AG030481).

Conflict of Interest

None.

Author Contributions

A.W.G. and M.D.B. contributed to the study design, manuscript development, and revisions.

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