Content uploaded by Mathieu R. Despard
Author content
All content in this area was uploaded by Mathieu R. Despard on Feb 03, 2017
Content may be subject to copyright.
February 1, 2017
A Geographic Investigation of Brick-and-Mortar Financial Services
and Households’ Financial Health
Terri Friedline, Mathieu Despard, & Stacia West
Acknowledgments
We are grateful to MetLife Foundation for financially supporting this research, which is part of
the Mapping Financial Opportunity (MFO) project.
We also appreciate the use of data from the 2014 Consumer Financial Health Study (CFHS),
which was provided by the Center for Financial Services Innovation (CFSI). Aliza Gutman and
James Schintz of CFSI were instrumental in accessing and interpreting data from the 2014
CFHS.
We extend additional thanks to many others whose supports have made this research possible,
including Evelyn Stark at MetLife Foundation; Steven Maynard Moody, Xan Wedel, and Travis
Weller at the Institute for Policy & Social Research (IPSR); William Elliott at the Center for
Assets, Education, and Inclusion (AEDI); and Justin King and Rachel Black at New America.
Invaluable research assistance was also provided by students at the Universities of Kansas and
Michigan, including Daniel Barrera, Rachael Eastlund, Joel Gallegos, Cassie Peters, Ivan Ray,
Kevin Refior, Nikolaus Schuetz, and Ashley Williamson. Expert editorial assistance was
provided by Benjamin Friedline.
The Mapping Financial Opportunity (MFO) project benefitted from the input, thoughts, and
feedback from numerous colleagues, and we especially appreciate the time and considerations of
Leigh Phillips at EARN; Jonathan Mintz and David Rothstein at CFE Fund; Ann Solomon at the
National Federation of Community Development Federal Credit Unions; Andrea Luquetta at the
California Reinvestment Coalition; Lindsay Daniels at the National Council of La Raza; Mehrsa
Baradaran at the University of Georgia; Sarah Dewees at First Nations Development Institute;
Keith Ernst at the Federal Deposit Insurance Corporation (FDIC); and Lisa Servon at the
University of Pennsylvania.
The report’s cover photo is courtesy of Gavin St. Ours via Flickr Creative Commons.
Finally, the quality and accuracy of the research presented in this brief report are the sole
responsibilities of the authors, and the aforementioned individuals and organizations do not
necessarily agree with the report’s findings or conclusions.
1
About the Authors
Terri Friedline is the Faculty Director of Financial Inclusion at the Center on Assets, Education,
and Inclusion, a Research Fellow at New America, and an Assistant Professor at The University
of Kansas School of Social Welfare. She can be contacted by email at tfriedline@ku.edu or
followed on Twitter @TerriFriedline.
Mathieu Despard is an Assistant Professor at the University of Michigan School of Social Work
and a faculty associate with the Center on Assets, Education, and Inclusion at The University of
Kansas School of Social Welfare and the Center for Social Development at Washington
University in St. Louis. He can be contacted by email at mdespard@umich.edu or followed on
Twitter @DespardMat.
Stacia West is an Assistant Professor at the University of Tennessee College of Social Work and a
faculty associate with the Center on Assets, Education, and Inclusion at The University of
Kansas School of Social Welfare. She can be contacted via email at swest11@utk.edu.
Recommended Citation
Friedline, T., Despard, M., & West, S. (2017). Investing in the future: A geographic
investigation of brick-and-mortar financial services and households’ financial health.
Lawrence, KS: University of Kansas, Center on Assets, Education, & Inclusion (AEDI).
2
Overview
Households need access to financial services that enhance their long-term financial health by
providing opportunities to accumulate assets and build credit. Under this purview, banks and
credit unions can be used for future investment, and alternative financial service (AFS)
providers have been heavily critiqued for their role in undermining households’ long-term
financial health. The types of financial services available within the community may be
associated with financial health, improving or impeding a household’s ability to invest in the
future, maintain a manageable level of debt, and achieve long-term goals.
This study used data on financial services, individual/household and community demographics
(including smartphone use), and household financial health to test whether the geographic
concentrations or densities of bank and credit union branches and AFS providers within
communities were associated with households’ financial health. We used two measures of
financial services: the numbers of financial services per 1,000 population, or densities, and the
composition of financial services densities relative to one another. We explored these
associations by income as the availability of financial services within communities varies based
on household income levels.
The findings from this study are not intended to be used for drawing clear prescriptions about
building brick-and-mortar branches in communities. Instead, these findings offer preliminary
understandings of whether the availability of financial services in communities relates to
households’ financial health, for which households, and under what conditions.
Key Findings
Financial services are almost exclusively associated with the future financial investments
of lowest-income households. These households may be the most responsive or sensitive
to the availability and composition of financial services in their communities than
households with greater income.
o The probability of lowest-income households owning investment accounts rises
by 6% for each additional bank or credit union branch per 1,000 population.
o The probability of lowest-income households maintaining their debt at a
manageable level rises by 9% for every additional bank or credit union branch or
AFS provider per 1,000 population.
The composition of financial services densities—relative to one another—appear to
matter more for lowest-income households, especially when communities have higher
densities of banks and credit unions than AFS providers.
o For lowest-income households, living in communities with densities of bank and
credit union branches that equal and outnumber those of AFS providers is
associated with a 30% rise in the rate of accumulating financial assets.
o The probability of being confident in meeting long-term savings goals rises by
16% when lowest-income households are located in communities where densities
of bank and credit union branches outnumber those of AFS providers.
3
Introduction
Financial health has been defined as being able to manage day-to-day finances, adjust to
changing financial circumstances and unexpected expenses, and plan for long-term financial
goals (Gutman, Garon, Hogarth, & Schneider, 2015). Not only must households be able to
manage day-to-day finances and adjust to changes, they also need and deserve opportunities to
plan for their long-term financial health and invest in their futures. Families and their children
may worry less about today when they know that tomorrow’s financial health is secured through
their accumulated assets and manageable debt.
Financial services set the stage for long-term financial health by offering products and services
that help households achieve long-term financial health. For example, if a household invests and
accumulates assets in the products and services offered by banks and credit unions (e.g., money
market accounts, stocks, and mutual funds), they have assets that provide a sense of financial
security in case the car breaks down or they need a new roof on their home. Banks and credit
unions can also provide a household with the credit, or debt, that they can leverage for
additional future investments. Indeed, owning financial products and services from banks and
credit unions is associated with households’ accumulated assets and collateralized debts
(Friedline & Freeman, 2016; Friedline, Johnson, & Hughes, 2014). A household accumulates
more assets in money market and retirement accounts, stocks, and mutual funds when they also
have checking and savings accounts (Friedline, Johnson, & Hughes, 2014).
In contrast, there is concern that the use of alternative financial services (AFS) may undermine a
household’s long-term financial plans and degrade their financial health. A household may pay
high interest rates tomorrow for being able to borrow the money they need today, and, in the
process, undermine their ability to invest in the future. A twenty percent interest rate on a $500,
two-week loan from an AFS provider like a payday lender can translate into an annualized
percentage rate (APR) of over 1,000%. This APR is substantially higher than the average APR of
4% on loans from banks and credit unions (Saunders & Schumacher, 2000).
1
What makes this
rate even more egregious is that this interest compounds each time the loan is renewed, which
seems to happen frequently among those who take out loans from AFS providers. Recent studies
of AFS providers suggests that 80% of borrowers who take out a loan from AFS providers renew
their loan within 14 days (Consumer Financial Protection Bureau [CFPB], 2014) and that 15% of
borrowers who renew their loan do so at least 10 times(Stegman & Faris, 2003).
The types of financial services that a household uses—and how the use of these services relates
to their investments in future financial health—may depend in part on their availability within a
community. There is some evidence for this with regard to AFS providers. For instance,
Friedline and Kepple (2016) found that when communities’ AFS densities were higher, these
services were more widely used by households from all income levels. They also found that when
1
It should be noted that the supply of small-dollar loans from banks and credit unions is limited, and the
fees that banks and credit unions charge can operate much like the high-cost interest rates charged by
AFS providers. In 2011, banks generated nearly $17 billion from overdraft fees (Borné & Smith, 2013).
Thirty percent of account holders are charged overdraft fees (Consumer Financial Protection Bureau
[CFPB], 2014), and these fees are concentrated in accounts held by lower-income households.
4
AFS densities were higher, lower-income households tended to use them more chronically than
other income groups. In other words, the increased availability of AFS within their community
helps to explain a household’s increased use of these services, with implications for their ability
to invest in the future.
The types of financial services that a household uses—and how the
use of these services relates to their financial health—may depend
in part on the availability of these services within a household’s
community.
When it comes to the relationship between the availability of financial services and a
household’s investments in the future, the research presented above provides some limited
perspective on the issue, and also raises a number of pertinent questions that require further
study. For example, is a higher concentration or density of AFS negatively associated with the
ability to maintain manageable levels of debt? Is a household’s ability to use and accumulate
assets associated with living in a community with more bank and credit union branches than
AFS providers? Is the availability of financial services associated with achieving long-term
savings goals?
We test these questions using data on financial services, community demographics, and
household financial health. Moreover, we explore these associations by income
2
given that
households may be exposed to varying densities of bank and credit union branches and AFS
providers within communities based on their income levels.
A Geographic Investigation of Financial Services
and Households’ Future Investments in Financial Health
This brief report investigates the association between the geographic availability of financial
services—the concentrations or densities of bank and credit union branches and alternative
financial service providers within communities—and households’ financial health using data
from the 2012 National Financial Capability Study (NFCS), 2014 Consumer Financial Health
Study (CFHS), US Geological Survey, FDIC, National Credit Union Administration (NCUA), Esri
Business Analyst, and US Census Bureau’s American Community Survey (ACS). Zip codes
served as a proxy for communities given that the use of geographic space (i.e., activity space) is
larger than other, smaller geographic units such as census blocks (Crawford, Jilcott Pitts,
McGuirt, Keyserling, & Ammerman, 2014).
2
The samples were divided into lowest-income households with less than $35,000 in annual income,
modest-income households with between $35,000 and $75,000 in annual income, and highest-income
households with more than $75,000 in annual income.
5
Financial services in communities were measured in two different ways. First, we examined the
densities of bank and credit union branches and AFS providers
3
as the numbers of financial
services per 1,000 population in a zip code. Density measures adjust for the population size and,
when examined as predictors, can indicate whether there is an increase or reduction in a
household’s financial health for each additional financial service within every 1,000 people.
4
Second, we examined the composition of financial services relative to one another. That is, there
may be differences in a household’s financial health if the density of bank and credit union
branches in their community is greater than the density of AFS providers. From this perspective,
the relative mix of financial services may relate to households’ financial health. Additional
information on the data and methods is available in the technical appendix.
Investing in the Future
A household that is planning for the long term is likely able to make investments in their future,
as indicated by owning investment accounts and accumulating assets. Owning investment
accounts and accumulating assets are important because they may facilitate a household’s
ability to afford college tuition, make a down payment on a home, or save to provide
inheritances to future generations. In addition, these investments also provide a more
substantial cushion for adjusting to changing financial circumstances.
When it comes to investing in one’s future, the density of financial services within communities
has associations with investing in the future for lowest-income households. More specifically,
the probability of lowest-income households owning investment accounts rises by 6% for each
additional bank or credit union branch per 1,000 population. At the same time, the probability
of owning investment accounts increases by 2% when they live in communities with densities of
bank and credit union branches that equal those of AFS providers, compared to communities
with higher densities of AFS providers. These result may indicate that lowest-income
households’ investments may be more sensitive to the resources and opportunities in the
communities in which they live than their modest- and highest-income counterparts.
Lowest-income households may accumulate more financial assets
when they live in communities with densities of bank and credit
union branches that equal or outnumber those of AFS providers.
Apart from investments, there are relationships between the composition of financial services
within a community and the amount of assets accumulated by lowest-income households. In
fact, there is almost a 30% rise in the accumulation of financial assets associated with lowest-
income households who live in communities with densities of bank and credit union branches
that equal and outnumber those of AFS providers. In this case, the separate densities of financial
3
AFS providers included auto title loan, payday loan, check cashing, tax refund, pawn shop, and rent-to-
own services.
4
Please note that the analyses used to produce the findings in this report utilized linear regression and the
relationships that were tested were correlational.
6
services are not associated with households’ accumulated assets. Instead, the composition of
financial services densities appears to matter more for lowest-income households.
Note: This figure presents findings from the correlational relationships between financial services densities and
lowest-income households’ (N = 1,451) reported accumulated liquid assets from the 2014 Consumer Financial Health
Study (CFHS). The complete analysis is available in the technical appendix.
Maintaining Manageable Debt
Maintaining debt at a manageable level is another indicator of a household’s ability to plan for
the long-term and to advance their financial health over time (Gutman et al., 2015). Debt can be
used in productive ways that can promote financial health by building credit and improving
financial standing (Dwyer, McCloud, & Hodson, 2011). Mortgage debt undertaken on a home is
one example of this. A borrower who makes regular mortgage payments has the benefits of
improving their credit score and investing in a type of debt that may eventually increase wealth
via home equity.
5
However, to advance financial health, the amount of debt should be
manageable. This means that a borrower should be able to make regular, timely payments and
maintain a debt-to-income ratio of approximately 40 percent (Bricker, Dettling, Henriques,
Hsu, et al., 2014; Federal Housing Authority, 2016).
The densities of financial services within lowest-income households’ communities relate to their
ability to maintain manageable debt. Again, these relationships only emerge among lowest-
income households and not their modest- and highest-income peers. The probability of lowest-
income households maintaining their debt at a manageable level rises by 9% for every additional
bank or credit union branch or AFS provider per 1,000 population. Similarly, the probability of
lowest-income households maintaining manageable debt increases by 1% when they live in
5
While secured debt is not always associated with improved financial health—as was the case during the
Great Recession when equity on some home mortgages was negative and many households found
themselves overleveraged (Ferreira, Gyourko, & Tracy, 2010)—its collateralized nature allows borrowers
to leverage existing assets and bend credit markets to their advantage (Campbell & Hercowitz, 2005).
7
communities with densities of bank and credit union branches that outnumber those of AFS
providers, compared to the opposite composition of densities.
The probability of lowest-income households maintaining their
debt at a manageable level rises by 9% for every additional bank or
credit union branch per 1,000 population.
Note: This figure presents findings from the correlational relationships between financial services densities and
lowest-income households’ (N = 8,586) reported manageable debt from the 2012 National Financial Capability Study
(NFCS). The complete analysis is available in the technical appendix.
Meeting Long-Term Financial Goals
A household should be better equipped to meet their long-term financial goals when they invest
in the future and maintain a manageable debt level. A household’s belief in their ability to meet
these goals gives some indication as to whether they can lead their preferred financial lives over
the long term and, perhaps, the extent to which they perceive that they have control over their
future.
The probability of lowest-income households’ confidence in meeting
long-term savings goals rises by 16% when they live in
communities with greater densities of bank and credit union
branches.
In this case, the composition of financial services densities relates to lowest-income households’
confidence in meeting long-term savings goals, while the densities of AFS providers—
irrespective of bank and credit union branch densities—appear to be important for modest-
income households. The probability of being confident in their ability to meet long-term savings
goals rises by 16% when lowest-income households are located in communities where densities
of bank and credit union branches outnumber those of AFS providers, when compared to living
in communities with higher densities of AFS providers. Among modest-income households, the
8
probability of being confident about meeting long-term savings goals falls by 4% for each
additional AFS provider per 1,000 population.
Discussion
In this brief, we present findings on the relationship between the presence of financial services
within communities and multiple indicators of households’ long-term financial health. There are
two notable findings. The first is that financial services are almost exclusively associated with
the future financial investments of lowest-income households. Lowest-income households may
be more responsive or sensitive to the availability and composition of financial services in their
communities than modest- or highest-income households.
The second finding is that lowest-income households may be better able to improve their long-
term financial health when they live in communities with higher densities of banks and credit
unions than AFS providers. When they live in communities with a greater presence of banks and
credit unions than AFS providers, lowest-income households tend to be more likely to own
investment accounts, accumulate more financial assets, keep debt at manageable levels, and
meet their long-term savings goals.
Limitations
Readers should be aware of certain limitations concerning data and findings in this brief. First,
these findings should not be interpreted as causal. That is, an association between availability of
financial services and household financial health does not mean, for example, that having a
certain density of banks in a household’s community means that the household will accumulate
more assets. Other factors not available in the data are likely at play, such as whether these
financial services are used and whether the products themselves are affordable. Factors that
affect the use of financial services and the affordability of their products can include having
checking accounts closed due to overdrafting (Campbell, Jerez, & Tufano, 2008) and insufficient
funds to meet minimum monthly account balance requirements (FDIC, 2016).
Second, though the household financial data are drawn from nationally representative samples,
zip code-level data on financial services densities differ somewhat from the data for the nation
as a whole. For example, the average bank and credit union density for zip codes in the NFCS
data is .19 per 1,000 population, which is somewhat lower than the average bank and credit
union density for all zip codes, which is .33 per 1,000 population.
Finally, concerning AFS, data were available for 2015 and not matched to the years that
household financial data were collected in 2012 and 2014. The available data also do not allow
us to make a distinction between credit-related AFS like payday loans, and transaction-related
AFS like check cashing. The data do not allow us to consider this distinction, even though
payday loans are potentially more damaging to household financial health than check cashing.
9
Conclusion
This geographic investigation provides some evidence that financial services within households’
communities—particularly for households with the lowest incomes—may be important for their
future investments. A geographic investigation does not refute the potential of mobile banking
for expanding financial access since it is not confined to a community or specific geographic
space. Instead, this investigation helps us to further understand how households make use of
the financial services that are available to them in their communities, whether any investments
into communities’ financial services availability are warranted, and which households might
experience the greatest benefits from these investments. This research is only a first step toward
considering these possibilities.
10
Technical Appendix
Data Sources
This study used several sources of data to test associations between the financial services within
individuals’ and/or households’ residential communities and their financial health, including
the 2012 National Financial Capability Study (NFCS), 2014 Consumer Financial Health Study
(CFHS), Federal Deposit of Insurance Corporation (FDIC), National Credit Union Association
(NCUA), Esri Business Analyst, and US Census Bureau’s American Community Survey (ACS).
Zip codes served as a proxy for communities given that zip codes are units defined by the US
Postal Service and that use of geographic space (i.e., activity space) is larger than smaller
geographic units such as census blocks (Crawford, Jilcott Pitts, McGuirt, Keyserling, &
Ammerman, 2014).
Financial health data were drawn from the 2012 NFCS and 2014 CFHS. The 2012 NFCS was
commissioned by the FINRA Investor Education Foundation and was completed online by a
sample of 25,509 adults in the United States between July and October 2012. Additional
information regarding the 2012 NFCS is available from the FINRA Investor Education
Foundation. The 2014 CFHS was commissioned by the Center for Financial Services Innovation
(CFSI) and was completed in partnership with GfK by a sample of 7,152 adults in the United
States between June and August 2014. Additional information regarding the 2014 CFHS is
available in a published report by CFSI (Gutman, Garon, Hogarth, & Schneider, 2015).
Measures
Financial services density. Financial services data were collected through several sources.
The FDIC and NCUA provided data for bank and credit union branch locations, including their
street addresses and zip codes. Bank branch locations were collected through the FDIC’s
summary of deposits, which provided quarterly information on all bank and bank branch
locations. Credit union branch locations were collected through the NCUA call reports, which
provided quarterly information on all credit union and credit union branch locations. Bank and
credit union branch location data were retrieved from the first quarters in 2012 and 2014.
Branch location data from 2012 were used with the 2012 NFCS and data from 2014 were used
with the 2014 CFHS.
Data by zip code on alternative financial service locations and market potential were collected
from 2015 Esri Business Analyst Geographic Information System (GIS). Unfortunately, Esri
Business Analyst only maintains current year data, meaning that it was not possible to collect
archived AFS data from 2012 or 2014 that corresponded with the timing of the NFCS or CFHS
survey data collection. Information on changes in AFS locations between 2012, 2014, and 2015
was unavailable; however, there is reason to believe that any changes during these years would
have been small and would not have substantially altered our results. Major state regulatory
policies that could have impacted AFS locations were implemented during the preceding decade
before our data were collected (Bhutta, 2014). Moreover, substantial changes have typically
11
occurred over longer time periods such as 10 years or more and any dramatic changes were
likely confined to the Great Recession through 2011 (Agarwal, Gross, & Mazumder, 2016).
Twelve codes from the North American Industry Classification Systems (NAICS) were used to
identify alternative financial services and included auto title loan, payday loan, check cashing,
tax refund, pawn shop, and rent-to-own services.
Density measures were calculated by aggregating the locations of bank and credit union
branches and alternative financial services within zip codes and calculating their total numbers
of locations per 1,000 population. Zip codes with no matching density measure were considered
to not have any post offices, bank and credit union branches, or alternative financial services
within their communities. Densities were capped at the 99th percentile. Density measures were
merged with household financial health data using zip codes. In the NFCS data, there were
10,207 zip codes (32% of all residential zip codes in the US), and an average of 2.5 households
per zip code (SD = 3.21; range: 1 to 54). In the CFHS data, there were 5,298 zip codes (17% of all
residential zip codes in the US), and an average of 1.4 households per zip code (SD = 0.68;
range: 1 to 6).
State regulation of payday lenders. Given that regulation may have played a role in the
density of AFS within a zip code and a household’s use of these services (Bhutta, 2014; Melzer,
2011), the states in which individual respondents lived were coded for their regulation of payday
lenders in 2011 (no regulation = 0; light or heavy regulation = 1; prohibited regulation = 2). The
measure for a community’s density of AFS was more comprehensive than just payday lending
services, also including auto title loans, check cashers, tax refunds, pawn shops, and rent-to-own
stores that may not have been affected by payday lending regulation. However, in some cases
individuals have been found to adjust their use of AFS depending on the regulatory environment
(Friedline & Kepple, 2016), and perhaps rely more often on auto title loans or pawn shops where
payday lenders are prohibited (Carter, 2015; McKernan, Ratcliffe, & Kuehn, 2013).
Individual and/or household demographics. Individual and/or household demographic
variables previously found to have associations with financial health were taken from the 2012
NFCS and 2014 CFHS and controlled in the analyses. These variables included age, gender, race,
gender, presence of children in the household, marital status, education level, employment
status, annual household income (lowest < $35,000 N = 9,250; modest $35,000 to < $75,000 N
= 8,616; highest ≥ $75,000 N = 7,643), financial literacy, and bank account ownership.
Community demographics. Additional community demographic data were collected from
the US Census Bureau American Community Survey’s (ACS) 2010 to 2014 five-year estimates
and Esri Business Analyst. These data provided aggregate population estimates by Census
Bureau zip code tabulation areas (ZCTAs), which were cross-walked to zip codes. Population
density equaling 1,000 residents per square mile was controlled in order to account for the
variation in geographic size across zip codes. These variables also measured the percent of the
population that was of different racial groups, was unemployed, and was living in poverty. For
example, the US Census Bureau calculated the unemployment rate dividing the total number of
the unemployed by the total number of the population ages 16 years and older who reported
12
participating in the labor force. These data also included whether the zip code was located
within urban clusters or towns.
The market potential or local consumption rate of savings accounts and smartphones were
included, which were collected from 2015 Esri Business Analyst. Zip codes’ market potential was
defined as the expected number of consumers who had savings accounts or used smartphones
divided by the total number of adults. The use of smartphones served as a proxy for the potential
of mobile banking within a household’s community.
Analysis Plan
Linear regression was the primary analytic tool used to assess statistical significance for the
relationship between densities of financial services and financial health. Logistic, multiple, and
zero-inflated negative binomial regression in Stata version 12 were used to predict financial
health outcomes. Regression coefficients and predicted probabilities using Stata’s .margins,
atmeans command were used to report statistical significance.
Propensity score weighting was used for analyses of financial health based on whether
households were in one of three types of communities, where (1) bank and credit union branch
density < alternative financial services density; (2) bank and credit union branch density =
alternative financial services density; and (3) bank and credit union branch density > alternative
financial services density. Financial health may differ based on the relative availability of
different types of financial services in one's community. To examine this possibility, we used
propensity score weighting to adjust for differences in household characteristics among these
three types of communities that otherwise might explain differences in financial health (Guo &
Fraser, 2010; Imbens, 2000). First, we examined differences in household characteristics for the
three types of communities. Next, we used multinomial logit regression to predict the
probablities of living in each of the three communities based on differences in household
characteristics that were statistically significant (Guo & Fraser, 2010). Lastly, we used these
predicted probabilities to calculate average treatment-effect-for-the-treated (ATT) propensity
score weights which were used in analyses to balance the three types of communities. Models
incorporated robust standard errors to adjust for correlations among households in the same zip
code.
Results
A summary of the results is provided here and complete results are available upon request.
Investing in the future. With regard to investing in the future, data from the 2012 NFCS and
2014 CFHS were used (see Tables 1 and 2). In measuring ownership of investment accounts,
participants responded to a question in the 2012 NFCS that asked whether or not they owned or
held any investments in stocks, bonds, mutual funds, or other types of securities (see Table 1).
Among lowest-income households (n = 7,354), bank and credit union density was positively
associated with owning investment accounts (β = .602; SE = .143; Pr = .060; p < .05). Compared
to AFS densities that outnumbered those of banks and credit unions, having equal and greater
13
densities of banks and credit unions was positively related to lowest-income households’
ownership of investment accounts (respectively, β = .507; SE = .130; Pr = .023; p < .01 and β =
.229; SE = .101; Pr = .010; p < .05). There were no significant associations among modest-
income (n = 7,787) or highest-income (n = 7,118) households.
In measuring accumulated financial assets, participants responded to a question in the 2014
CFHS that asked how much money their households had in checking, savings, money market
accounts, stocks, and bonds (see Table 2). Among lowest-income households (n = 1,451), the
composition of financial services densities were related to the amount of accumulated assets.
Compared to AFS densities that outnumbered those of banks and credit unions, having equal
and greater densities of banks and credit unions was positively related to lowest-income
households’ amount of accumulated assets (respectively, β = .296; SE = .155; p < .10 and β =
.281; SE = .137; p < .05). There were no significant associations among modest-income (n =
1,301) or highest-income (n = 2,310) households.
Table 1. Owning Investment Accounts
Lowest-Income
Individuals
Modest-Income
Individuals
Highest-Income
Individuals
β (SE)
Pr
β (SE)
β (SE)
Bank and credit union density
.602* (.143)
.060
.979 (.157)
1.207 (.217)
AFS density
.695 (.219)
1.034 (.250)
.683 (.195)
Model
.034*** (.016)
.031*** (.012)
.052*** (.022)
Psuedo R2
.078
.085
.093
Financial services density
(Reference: Banks and credit
unions < AFS)
Banks and credit unions = AFS
.507** (.130)
.023
.102 (.105)
.115 (.105)
Banks and credit unions > AFS
.229* (.101)
.010
.083 (.077)
.083 (.080)
Model
–4.681*** (.579)
–5.060*** (.595)
–4.053*** (.576)
Psuedo R2
.351
.194
.115
N
7,354
7,787
7,118
Source: Data from the 2012 National Financial Capability Study (NFCS).
Notes: Participants responded to a question that asked whether or not they owned or held any
investments in stocks, bonds, mutual funds, or other types of securities. Logistic regression analyses
controlled for community and individual demographics and state regulation of payday lenders. Models with
categorizations of financial service density were weighted using the average treatment-effect-for-the-
treated (ATT) propensity score weights to adjust for observed selection. Analyses only undertaken with
NFCS data. β = regression coefficients. Robust SE = robust standard error. Pr = predicted probability. * p
< .05; ** p < .01; *** p < .001; † p < .10
Table 2: Total Financial Assets
Lowest-Income
Individuals
Modest-Income
Individuals
Highest-Income
Individuals
Any Assets
Asset
Amount
Any Assets
Asset
Amount
Any Assets
Asset
Amount
β (LSE)
β (LSE)
β (LSE)
β (LSE)
β (LSE)
β (LSE)
14
Bank and credit union density
.455 (.347)
.527 (.156)
–1.055 (.895)
–.087 (.231)
–.417 (.904)
.009 (.134)
AFS density
1.556 (.289)
–.156 (.231)
–.244 (.984)
–.219 (.267)
.484 (.823)
–.006 (.164)
Model
–1.999 (3.411)
–2.227**(.678)
–2.308 (.389)
.549 (.476)
2.356 (1.451)
.614* (.307)
Ln α
.397*** (.106)
–1.309***(.159)
–1.643***(.104)
Financial services density
(Reference: Banks and credit
unions < AFS)
Banks and credit unions = AFS
–.116 (.347)
.296† (.155)
.470 (.427)
–.093 (.119)
.162 (.362)
–.089 (.059)
Banks and credit unions > AFS
–.087 (.289)
.281* (.137)
.531 (.452)
–.032 (.094)
.220 (.281)
–.009 (.043)
Model
–4.441**(1.639)
–.143 (.874)
.324 (3.561)
.475 (.743)
–3.222 (2.030)
.812* (.407)
Ln α
.668* (.313)
1.090*** (.232)
1.642*** (.177)
N
1,451
1,301
2,310
Source: Data from the 2014 Consumer Financial Health Study (CFHS).
Notes: Participants responded to a question that asked how much money their households had in
checking, savings, money market accounts, stocks, and bonds. The question excluded money held in
retirement accounts. Zero-inflated negative binomial (ZINB) regression analyses separately modeled the
presence of any assets ($0; > $0) and the amount of assets that participants had accumulated (> $0),
while controlling for community and individual demographics and state regulation of payday lenders.
Models with categorizations of financial service density were weighted using the average treatment-effect-
for-the-treated (ATT) propensity score weights to adjust for observed selection. Analyses only undertaken
with CFHS data; a corresponding variable measuring asset amounts is not available in NFCS data. β =
regression coefficients. LSE = linearized standard error. * p < .05; ** p < .01; *** p < .001; † p < .10
Maintaining manageable debt. With regard to keeping their debt at a manageable level,
data from the 2012 NFCS were used (see Table 3). Participants responded to a seven-point
Likert scale question that asked their report on having too much debt. Their responses were
dichotomized to measure whether or not they believed their debt was at manageable levels.
Among lowest-income households (n = 8,586), bank and credit union density and AFS density
were positively associated with maintaining manageable debt (respectively, β = 1.444; SE =
.207; Pr = .089; p < .01 and β = .691; SE = .128; Pr = .089; p < .05). Compared to AFS densities
that outnumbered those of banks and credit unions, having greater densities of banks and credit
unions was positively related at trend level to lowest-income households’ manageable debt (β =
.103; SE = .062; Pr = .011; p < .10 There were no significant associations among modest-income
(n = 8,176) or highest-income (n = 7,312) households.
Table 3. Debt at Manageable Levels
Lowest-Income
Individuals
Modest-Income
Individuals
Highest-Income
Individuals
β (SE)
Pr
β (SE)
β (SE)
Bank and credit union density
1.444** (.207)
.089
1.224 (.186)
1.199 (.216)
AFS density
.691* (.128)
.089
1.220 (.267)
1.185 (.335)
Model
1.042 (.296)
.916 (.300)
.297** (.122)
Psuedo R2
.022
.047
.081
15
Financial services density
(Reference: Banks and credit
unions < AFS)
Banks and credit unions = AFS
.131 (.081)
–.036 (.096)
–.052 (.103)
Banks and credit unions > AFS
.103† (.062)
.011
.046 (.069)
–.026 (.079)
Model
–.023 (.370)
.391 (.496)
1.588 (.557)
Psuedo R2
.424
.115
.087
N
8,586
8,176
7,312
Source: Data from the 2012 National Financial Capability Study (NFCS).
Notes: Participants responded to a seven-point Likert scale question that asked their report on having too
much debt. Their responses were dichotomized to measure whether or not they believed their debt was at
manageable levels. Logistic regression analyses controlled for community and individual demographics
and state regulation of payday lenders. Models with categorizations of financial service density were
weighted using the average treatment-effect-for-the-treated (ATT) propensity score weights to adjust for
observed selection. Analyses undertaken with CFHS data did not reveal any significant relationships
between financial services densities and debt outcomes; however, CFHS’ debt measurements were
different than those in the NFCS, such as calculating total debt payments per month and debt-to-income
ratios. β = regression coefficients. Robust SE = robust standard error. Pr = predicted probability. * p <
.05; ** p < .01; *** p < .001; † p < .10
Meeting long-term financial goals. With regard to meeting long-term financial goals, data
from the 2014 CFHS were used (see Table 4). Participants responded to a five-point Likert scale
question that asked them to rate their confidence in meeting long-term saving goals. Among
lowest-income households (n = 1,478), neither bank and credit union density nor AFS density
was associated with confidence in meeting short-term savings goals. However, compared to AFS
densities that outnumbered those of banks and credit unions, having a greater density of banks
and credit unions was positively related to lowest-income households’ confidence in meeting
these long-term goals (β = .156; SE = .079; p < .05). Among modest-income households (n =
1,343), there was some evidence that AFS density was negatively associated with their confidence
in meeting long-term financial goals (β = ─.432; SE = .252; p < .10). There were no significant
associations among highest-income (n = 2,379) households.
Table 4. Long-Term Savings Goals
Lowest-Income
Individuals
Modest-Income
Individuals
Highest-Income
Individuals
β (SE)
β (SE)
β (SE)
Bank and credit union density
.054 (.247)
.278 (.227)
–.071 (.163)
AFS density
–.395 (.247)
–.432† (.252)
.093 (.194)
Model
1.648*** (.378)
1.370** (.415)
2.227*** (.342)
R2
.030
.037
.025
Financial services density
(Reference: Banks and credit
unions < AFS)
Banks and credit unions = AFS
.130 (.098)
.031 (.096)
–.028 (.068)
Banks and credit unions > AFS
.156* (.079)
.077 (.070)
.033 (.057)
Model
1.790** (.561)
1.448 (.505)
2.030 (.429)
R2
.088
.056
.052
16
N
1,478
1,343
2,379
Source: Data from the 2014 Consumer Financial Health Study (CFHS).
Notes: Participants responded to a five-point Likert scale question that asked them to rate their
confidence in meeting long-term saving goals. Multiple regression analyses modeled the continuous
responses and controlled for community and individual demographics and state regulation of payday
lenders. Models with categorizations of financial service density were weighted using the average
treatment-effect-for-the-treated (ATT) propensity score weights to adjust for observed selection. Analyses
only undertaken with CFHS data; a corresponding variable is not available in NFCS data. β = regression
coefficients. Robust SE = robust standard error. * p < .05; ** p < .01; *** p < .001; † p < .10
17
References
Agarwal, S., Gross, T., & Mazumder, B. (2016). How did the Great Recession affect payday
loans? Chicago, IL: The Federal Reserve Bank of Chicago.
Bhutta, N. (2014). Payday loans and consumer financial health. Journal of Banking & Finance,
47, 230-242. doi:10.1016/j.jbankfin.2014.04.024
Borné, R., & Smith, P. (2013). High-cost overdraft practices: The state of lending in America &
its impact on US households. Durham, NC: Center for Responsible Lending. Retrieved from
http://www.responsiblelending.org/state-of-lending/reports/8-Overdrafts.pdf.
Bricker, J., Dettling, L., Henriques, A., Hsu, J., et al. (2014). Changes in U.S. family finances
from 2010 to 2013: Evidence from the Survey of Consumer Finances. Federal Reserve Bulletin,
100(4), 1-41. Retrieved from http://www.federalreserve.gov/pubs/bulletin/2014/pdf/scf14.pdf
Campbell, J., & Hercowitz, Z. (2005). The role of collateralized household debt in
macroeconomic stabilization (NBER Working Paper No. 11330). Cambridge, MA: National
Bureau of Economic Research. Retrieved from
http://users.nber.org/~jrc/workingpapers/chz2aug06.pdf
Campbell, D., Jerez, A. M., & Tufano, P. (2008). Bouncing out of the banking system: An
empirical analysis of involuntary bank account closures. Retrieved from
http://joinbankon.org/resources/bouncing_out_of_the_banking_system_an_empirical_analy
sis_of_involuntary_bank_account_closures
Carter, S. (2015). Payday loan and pawnshop usage: The impact of allowing payday loan
rollovers. Journal of Consumer Affairs, 49(2), 436-456. doi:10.1111/joca.12072
Consumer Financial Protection Bureau. (2014). CFPB data point: Payday lending. Washington,
DC: Consumer Financial Protection Bureau. Retrieved from
http://www.consumerfinance.gov/newsroom/cfpb-finds-four-out-of-five-payday-loans-are-
rolled-over-or-renewed/
Crawford, T., Jilcott Pitts, S., McGuirt, J., Keyserling, T., & Ammerman, A. (2014).
Conceptualizing and comparing neighborhood and activity space measures for food
environment research. Health & Place, 30, 215-225. doi:10.1016/j.healthplace.2014.09.007
Dwyer, R., McCloud, L., & Hodson, R. (2011). Youth debt, mastery, and self-esteem: Class-
stratified effects of indebtedness on self-concept. Social Science Research, 40(3), 727-741. doi:
10.1016/j.ssresearch.2011.02.001
Federal Deposit Insurance Corporation. (2016). 2015 National survey of unbanked and
underbanked households. Washington, DC: FDIC. Retrieved from
https://www.fdic.gov/householdsurvey/2015/2015report.pdf
Federal Housing Authority. (2016). FHA mortgage lending limits. Washington, DC: FHA.
Retrieved from https://www.fha.com/fha_requirements_debt
18
Ferreira, F., Gyourko, J., & Tracy, J. (2010). Housing busts and household mobility. Journal of
Urban Economics, 68(1), 34-45. doi:10.1016/j.jue.2009.10.007
Friedline, T., Johnson, P., & Hughes, R. (2014). Toward healthy balance sheets: Are savings
accounts a gateway to young adults' asset diversification and accumulation? Federal Reserve
Bank of St. Louis Review, 96(4), 359-389.
Friedline, T., & Kepple, N. (2016). Does community access to alternative financial services relate
to individuals' use of these services? Beyond individual explanations. Journal of Consumer
Policy. doi:10.1007/s10603-016-9331-y
Guo, S., & Fraser, W. M. (2010). Propensity score analysis: Statistical methods and
applications. Thousand Oaks: CA, Sage Publications, Inc.
Gutman, A., Garon, T., Hogarth, J., & Schneider, R. (2015). Understanding and improving
consumer financial health in America. Chicago, IL: CFSI.
Imbens, G. W. (2000). The role of the propensity score in estimating dose–response functions.
Biometrika, 87(3), 706-710. doi:10.1093/biomet/87.3.706
McKernan, S-M., Ratcliffe, C., & Kuehn, D. (2013). Prohibitions, price caps, and disclosures: A
look at state policies and alternative financial product use. Journal of Economic Behavior and
Organization, 95, 207-223. doi:10.1016/j.jebo.2013.05.012
Melzer, B. (2011). The real costs of credit access: Evidence from the payday lending market. The
Quarterly Journal of Economics, 126(1), 517-555. doi:10.1093/qje/qjq009
Saunders, A., & Schumacher, L. (2000). The determinants of bank interest rate margins: An
international study. Journal of International Money and Finance, 19(6), 813-832.
doi:10.1016/S0261-5606(00)00033-4
Stegman, M., & Faris, R. (2003). Payday lending: A business model that encourages chronic
borrowing. Economic Development Quarterly, 17, 8-23. doi:10.1177/0891242402239196
