You Can Take It To The Bank: Demographic, Socioeconomic...
Transcript of You Can Take It To The Bank: Demographic, Socioeconomic...
You Can Take It To The Bank:
Demographic, Socioeconomic, and Financial Knowledge of the Unbanked and
Underbanked
Draft
Elizabeth Breitbach
University of Nebraska
10/19/2012
Abstract
Checking and savings accounts are frequently used financial instruments by U.S. households. As household
transactions shift from cash toward e-money, these financial accounts become even more important for economic
well-being. Households without these accounts may be able to participate fully in the economy, but may incur
additional costs by using alternative financial instruments. The focus of this research is to study the segment of the
U.S. population who are considered unbanked and underbanked. The study draws on three large, national data
sets: (1) a survey of Financial Capability in the United States by the Financial Industry Regulatory Authority
(FINRA), a National Survey of Unbanked and Underbanked Households by the Federal Deposit Insurance
Corporation (FDIC), and, (3) the Survey of Consumer Finances by the Federal Reserve System. The study
investigates the economic and demographic characteristics of unbanked and underbanked households across the
three surveys to explain which households have a low level of banking participation.
Keywords: Checking, Bank, Consumer, Finance
JEL Code: Personal Finance (D140), Banks; Other Depository Institutions; Mico Finance Institutions; Mortgages
(G210)
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Introduction:
As with much of the world, individual financial decisions in the U.S. are involved at some level with
the banking industry, even if an individual chooses to avoid banking institutions altogether. These banking
relationships are significant for several reasons. Individuals with traditional transaction accounts are
found to have higher levels of savings than their unbanked counterparts. Not only do bank accounts
promote increased saving, they often offer check cashing and bill paying services at a lower cost than
alternative financial products such as non-bank money orders or bill pay and non-bank check cashing
services. For most households it would seem impossible not to have a transaction account to make day-
to-day financial payments, obtain cash for purchases, or deposit a paycheck and other checks. For
approximately 7.5% of U.S. households, however, access or use of banking for transactions purpose never
occurs because they do not have a checking or savings account.
Holding some form of transaction account is an essential tool of day to day life. Often utility
companies and other billing agencies require payment to be made by check, online, or by cash at the
office location. Having a checking account offers easier payment since it does not require the customer to
drive to the corporate location, during business hours, to pay in cash. If a household has a traditional
checking account, they can mail in a check, sign up for automatic bill pay, or make online payments. The
ease in payment with a transaction account reduces the cost bore by the individual, and with automatic
bill pay, it can be easily and immediately used to prevent late fees from accruing.
Not only is bill paying easier with a transaction account, budgeting and determining areas where
expenses may be cut can be better examined and is more likely to occur if the household has an account
(Hogarth & Anguelov 2004). Creating a budget and tracking how well the household is sticking to their
goals can be easier with a monthly statement of transactions, typically provided with a checking account.
Without a transaction account, keeping receipts and combining income and expenses into a monthly
statement can be difficult and time consuming. Utilizing a bank prepared statement ensures all income
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and expenses made with the account are included and the totals can be seen with little effort by the
household. Unnecessary or frivolous expenses can be tracked using monthly statements of usage.
Carefully analyzing one’s monthly statement can ensure a household is aware of where their money is
going each month and can influence future purchasing behavior. For example, after examining a bank
statement the household may become aware of the total monthly purchases at coffee shops and could
find ways to decrease this expense, either by purchasing a coffee machine or limiting their purchases to
fewer times per week. This increased awareness is one way statements can help reduce frivolous
spending and, in turn, increase savings.
A transaction account also offers a proof of payment. A check or other form of bank-provided
proof can verify that an individual made a payment and the date it went through the banking system.
Proof can be important in financial transactions that are affected by legality or timing. For example,
verifying the date a check was written and cashed can help avoid late fees, along with offering proof of
payment in the secondary market. Cash payments do not have this benefit, it can be difficult to prove
that a payment was made and when it occurred.
Receiving income also is more efficient with a transaction account. With the increasing popularity
of e-transactions, many employers and other income sources have come to prefer direct deposits to
payroll checks (Anguelov, et al 2004). The Federal Government is also following this trend; the U.S.
Treasury Department enacted a voluntary program called Electronic Transfers Accounts (ETA) for anyone
who receives a payment from the Federal Government. An ETA is a low cost bank account that allows for
the federal payment to be directly deposited each month (Department of the U.S. Treasury 2012).
If a household has a transaction account where income can be deposited, it can be received faster
and with less hassle than going to a bank or other business to cash the check. When an individual cashes a
check at a non-bank business they are likely to pay for the service, either with a flat fee or as a percentage
of the check amount. These transactions costs can become a relatively large percentage of a typical
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household’s income if the transactions are frequently made (CCCS 2010). These additional costs can
contribute to income constraints that force households to choose between paying bills and other
necessities.
Alternatives have arisen due to individuals lacking traditional bank accounts, whether by choice or
allowance. To pay bills, non-bank money orders and prepaid cards have evolved to meet the needs of
unbanked households. To meet the demand for non-bank check cashing, some grocery and other retail
stores cash checks for a fee, as well as specific businesses that solely cash checks. These services can be
very costly, especially when transactions are completed on a reoccurring basis (Bell 2011). When
managed properly, a traditional checking account can have very low to no fees. These cost savings can
help lower income households meet debt obligations or accumulate funds that accrue interest.
The advantages of a checking account so far have focused on the ease and low cost of this service.
Another reason for using a transaction account is increased safety over holding cash or a prepaid card. It
is not safe to hold a significant amount of cash in a home or on person because the individual generally
assumes the risks associated with loss from theft, fire, and misplacement. Even when insurance does
cover some of these costs, it may only be a portion of the total loss. By contrast, a bank or financial
institutions assume a certain degree of risk for checking accounts that protect the customer. For
example, if a checkbook is stolen or lost it can be closed or flagged so the bank is aware the account
holder is not the individual writing checks. This becomes more difficult when the household primarily
relies on cash or prepaid cards. It is nearly impossible to track the use of cash to make purchases. If a
prepaid card is lost or stolen, it is possible to shut the account down, although a reactivation fee and new
card fee may be incurred.
Another benefit of holding a transaction account at a banking institution is federal insurance
through the Federal Deposit Insurance Corporation (FDIC). Even if a bank goes out of business individuals
will receive their funds up to the insured amount. Most prepaid cards are not federally insured and do not
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offer the same protection if the corporation goes out of business. This creates an additional risk with
these services as many of them are relatively new entities that rely heavily on partnerships with retail
outlets (Bell 2012).
A traditional bank account used responsibly not only has the advantages discussed above, but it
can also lead to future positive outcomes. Studies have shown that households with a transaction
account are more likely to have savings accounts than their unbanked counterparts (Beverly, et al. 2004).
Additional savings can be a benefit for low income households who have trouble meeting unexpected
expenses. Holding a rainy day fund can help a household avoid having to secure a loan to meet
unexpected expenses, which can be especially costly if the household has a low credit rating. Payday
loans are typically short-term loans that have high annual percentage rates (APRs), which in extreme fees
can be in excess of 400%. Having the funds to address debt obligations when they arise will lead to
further cost savings. Furthermore, an account can also have a positive effect on an individual’s credit
score, increasing household access lower cost loans.
A well-managed transaction account not only leads to greater savings, but other ‘good’ financial
decisions as well. Not only is saving for unexpected expenses important, but retirement savings, savings
for durable goods, and savings to improve one’s education can also be helpful to further increase the
future income of a household. This can improve not only the respondent’s current financial well-being,
but their future wellness as well. Having additional savings can serve as a cushion for unexpected
expenses that arise or can offer a way for households to purchase relatively expensive durable items
typically purchased with credit. Using money to purchase these items can lead to further savings by
avoiding interest payments and other fees incurred when using credit. Future well-being must also be
considered, as savings for additional education or retirement can put the household in a better financial
position. With the uncertain future of Social Security and Medicare, savings may be vital as a household
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moves toward retirement. Increased wealth can occur not only through current savings, but investing in
one’s human capital to grow future earning potential.
In addition to households who avoid traditional banking services, there are households who use
these services while supplementing them with costly alternatives. Approximately 18% of households fall
into this category of underbanked. Although these households do receive the benefits of a transaction
account, they do not seem to take full advantage of all the services a bank account offers. This behavior
warrants further discussion due to the additional costs incurred by the supplemental services.
These alternatives include frequent use of non-bank money orders or check cashing services,
which is particularly concerning. Most traditional transaction accounts include checks or a debit card as a
form of payment method from the account. The use of non-bank money orders indicates these
households are not fully aware of the services an account offers. As mentioned, the fees on check-cashing
can be a significant portion of a household’s income if used frequently on a reoccurring basis; most
banking institutions offer free check cashing services to customers and direct deposit is encouraged.
The other services that are used in determining whether a household is underbanked is the use of
non-bank short term loans, including payday loans, pawnshops, tax anticipated refund loans, and rent-to-
own services. Compared to both traditional bank loans and credit cards, these loans are less than ideal
due to their high interest rates and service fees.
While there has been research examining who the unbanked are and why they do not have an
account, literature on the underbanked is nearly nonexistent, other than research on a specific
alternative service used to define them. To improve on current literature in this area, multiple data sets
from recent years will be used: the 2009 FINRA National Capabilities Survey, State-by-State (FINRA), the
2009 FDIC Survey of Unbanked and Underbanked Households (FDIC), and the 2010 Federal Reserve’s
Survey of Consumer Finances (SCF). The extensive information included in these data sets is relevant
since previous literature has not had as much information on the financial knowledge of the household or
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their use of alternative services. This analysis will offer a more complete understanding of what these
households are using to make day-to-day transactions and whether information and knowledge on the
benefits of bank accounts can move these households toward a higher level of banking participation.
It is first crucial to discuss who the unbanked and underbanked are by examining their
demographic and socioeconomic characteristics, along with their financial knowledge. Describing their
characteristics is important in understanding which subgroups of the population hold this level of banking
participation. If these households are not aware of the benefits, marketing tools can be used to move
these households to a higher level of participation. Financial knowledge is also expected to have an
impact on banking participation. If a household does not have a traditional bank account, or supplements
it with alternatives, it may be the case that providing more financial education will help in properly
managing an account and improve the likelihood of utilization.
Households with low levels of banking participation are more likely to be Hispanic or African
American, young, and low income. This analysis will further look at the effect of income by not only
exploring the level of income, but whether the household has experienced a change in income. This
question will offer insight into the effect a recession can have on banking participation and indicate the
potential effect a savings account can have. An indicator for the financial knowledge of a respondent can
be obtained in the FINRA data set. Using this information it can be seen whether it is the demographic
characteristics of a household alone that determine banking participation or if education has an effect. It
is the hope that financial knowledge does have an impact so proper education and information can lead
households to a higher level of banking participation.
There are obvious benefits to having a transaction account, raising the question of why some
households would choose to avoid their services. Understanding which households are choosing to be
unbanked and underbanked is an important piece in comprehending this decision. Exploring which
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households have a low level of involvement is essential if it is the hope of policy makers and banking
institutions to move these households toward a higher level of banking participation.
Literature Review Unbanked
The first question to be answered is what it means to be unbanked. Grimes, et al. (2010) define
unbanked as not having “any type of commercial bank account.” Hogarth, et al. (2005) define unbanked
as individuals not having a “transaction account.” They cite that a transaction account as including
“checking, savings, money market accounts at depository institutions and brokerage firms and call
accounts.” Rhine and Greene (2006a) and Rhine, et al. (2006b) define unbanked, similar to Grimes, et al.,
as lack of a checking or savings account, whereas Paulson and Rhine (2008) separate checking and saving
accounts and explore banking participation at the individual account level. A review of the literature
presents few discrepancies in defining unbanked, which enables ease in comparing results of different
studies.
Not only is it important to discuss how these papers describe an unbanked household, it is
essential to compare the data sets that are used. Some data sets focus on a subset of the United States
population, like those in the Chicago metropolitan area, or a specific ethnic group, such as Hispanics.
These differences are important to note when making comparisons across surveys. Grimes, et al. (2010)
uses the Council for Economic Education’s National Financial Services Survey, conducted in 2008. This
data set includes 1,759 respondents from the United States. Hogarth, et al. (2005) employs the Survey of
Consumer Finances from 1989 to 2001. When looking at the unbanked over time, the authors use the
surveys separated by year and as a time series to show differences in banking participation across time.
The discussion in this paper will focus on the full sample, which includes information on nearly 16,000
households following the U.S. Census population of the United States.
The focus of Rhine and Greene’s (2006a) study is exploring the banking participation of U.S.
immigrants. To get information on the length of time the immigrant was in the United States and other
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related information the authors used the Survey of Income Program Participation. This survey includes
information on both U.S. born households and Immigrant households. For better comparison, the results
presented in the literature review will focus on the U.S. born households. Amuedo-Dorantes and Bansak
(2006) also explore banking participation focusing on immigrants, specifically Mexican immigrants. The
primary purpose of the paper is to determine what factors affect the amount of money the immigrant
transfers home. One expected determinant of the aggregate transferred is whether the respondent
opened a bank account while in the United States, and the authors run regressions on banking
participation to better understand the decision to hold an account. The data set used to analyze these
questions is the Mexican Migration Project from 1970 to 2004. For the banking participation regression,
there are 2,978 observations. Rhine, et al. (2006b) explored racial and ethnic differences in banking
participation using the Metro Chicago Information Center and the Federal Reserve Bank of Chicago’s
annual survey. A total of 2,339 respondents were included in the banking analysis. The final data set that
will be extensively explored in this section is by Paulson and Rhine (2008). This paper focuses on an even
more specific ethnic group in the United States, Hmong immigrants living in Minnesota. The data set
includes information on 202 respondents from this subset of the United States and 202 control
respondents from similar neighborhoods.
After defining what it means to be unbanked, it is necessary to explore who is unbanked, including
demographic and socio-economic characteristics, along with the amount of credit the household has
access to and level of assets they hold. Gender has had both mixed results in its significance and its sign.
Rhine and Greene (2006a) found that female immigrants are less likely to be unbanked, relative to
immigrant males. However, in another article, Paulson and Rhine (2008) found that male head of
households were less likely to be unbanked relative to situations where females were the head of
household. While these studies show conflicting significant results, others have found no significant
relationship between gender and banking participation (Amuedo-Dorantes and Bansak 2006, Grimes, et
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al. 2010, Rhine, et al. 2006b). Hogarth, et al. (2005) explored gender and banking participation over time.
They found that both single males and single females have increased their banking participation between
1989 and 2001. However, over the period males maintained a slightly higher banking participation rate
than females, with a gap of about 4%.
Race and ethnicity are also expected to be determinants of whether or not a household has a bank
account. It has been suggested that if an individual does not speak English as a first language, they may
feel intimidated by the banking system (Rhine and Greene, 2006a). Amuedo-Dorantes and Bansak (2006)
explored Mexican Immigrants who had plans to return to Mexico. While in the U.S., these immigrants
often sent money home through the use of money transfers. When looking at the issue of banked versus
unbanked, the authors found that undocumented workers and those who were in the United States for
only a short time were more likely to be unbanked. Holding a bank account can make this process easier
and less costly. Spader, et al. (2009) also looked at the issue of Hispanics and banking. To increase the
percentage of Hispanics with bank accounts, a television show was developed to create a more favorable
opinion of banks and the services they offer. The show had positive effects on the participant’s opinions
of banks and banking services, but little change was shown in behavior (Spader, et al. 2009). This is an
important result, since a large percentage of the unbanked do not have an account because they dislike
dealing with the institution. Most results have found black and Hispanic households are significantly more
likely to be unbanked, relative to Caucasian households (Hogarth, et al. 2005, Rhine and Greene 2006a,
Rhine et al. 2006b). Grimes, et al. (2010) finds a similar trend but the result is not as significant (significant
at the 10% level).
Grimes, et al. (2010) found that a significantly negative indictor of being unbanked is age. While
this result is significant, .at 08% it is not large in magnitude. This may be due to the fact that the authors
included it as a continuous variable and few changes occur from year to year, whereas more significant
changes take place from decade to decade. Amuedo-Dorantes and Bansak (2006) also included age as a
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continuous variable; their results were also small in magnitude, but not significant. Paulson and Rhine
(2008) included age as a continuous variable and added age squared to better understand the
relationship. They found that as age increased the respondent was less likely to hold a checking or savings
account, although it was at a decreasing rate. Rhine and Greene (2006a) found that U.S. born individuals
between 18 and 25 are 3% more likely to be unbanked relative to all other age groups. Rhine, et al
(2006b) found a coefficient of similar magnitude and significance. Hogarth, et al. (2005) broke households
in four age cohorts. Relative to 18 to 34 year olds, households in the 50 to 64 years and 65 and over
cohorts are significantly more likely to be banked.
When looking at married versus single households, Rhine and Greene (2006a) and Rhine, et al.
(2006b) found that those who are married are less likely to be unbanked, however, Grimes, et al. (2010)
found it to be insignificant. Hogarth, et al. (2005) separated single male and female households; relative
to married households, single female households are more likely to be banked, while the result for males
is insignificant. Family size, or number of dependents, is another variable that has been included in
analysis of banking participation. Hogarth, et al. (2005) has explored the effect of dependent children in
the household and found that households with dependents are less likely to be banked, but the mean
difference is not significant. Paulson and Rhine (2008) also found that household size did not significantly
impact a households banking participation, while, Rhine and Green (2006a) found that a larger family size
was significantly more likely to be unbanked.
Education is also found to be a significant factor in indicating whether or not an individual has a
bank account. Hogarth, et al. (2005) uses a set of dummy variables to specify the education level of
respondents. They find that, relative to those with a high school degree, individuals with a high school
degree or less are significantly less likely to be banked, and those with some college or more are
significantly more likely to be banked. Grimes, et al. (2010) includes a dummy variable for whether the
respondent has any post-secondary education. Relative to those with a high school degree or less, these
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individuals are significantly less likely to be unbanked. Rhine and Greene (2006a) and Rhine, et al. (2006b)
both find that those with a high school degree or less are significantly more likely, relative to those with
at least some college education, to be unbanked. Paulson and Rhine (2008) found that, relative to those
with less than a high school degree, respondents with a high school degree, some college experience, and
those with a college degree or higher are significantly more likely to be banked. Amuedo-Dorantes and
Bansak (2006) did not find significant results for education, but this may be due to the fact that education
was included as a continuous variable.
Work force participation is another possible determinant of banking participation. Previous
studies that used a dummy variable to indicate if the respondent was in the work force and has a work
commitment have found mixed results. Rhine and Greene (2006a) included work commitment as a
continuous variable equal to the number of hours worked. As work commitment increased respondents
were less likely to be unbanked, but the result was not significant. Grimes, et al. (2010) included a dummy
variable for whether the respondent was full time, part time or self-employed. Being employed lead to a
lesser likelihood of being unbanked, but the result was not significant. Hogarth, et al. (2005) took a more
in depth look at work status. Variables were included for working, retired, unemployed – looking, and
unemployed – not looking. Relative to head of households who are unemployed – not looking, those who
are working and retired are significantly more likely be banked, while unemployed – looking are
significantly more likely to be unbanked.
Many studies have concluded that having low income is not only a significant factor effecting
banking participation, but it is one of the primary determinants in predicting if households are unbanked
(Grimes, et al. 2010, Hogarth et al. 2005, Paulson and Rhine 2008, Rhine, et al. 2006b, Rhine and Greene
2006a). Amuedo-Dorantes and Bansak (2006) include variables for living standards instead of income,
these variables are not significant indicators of banking participation.
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It is not only income, but a household’s overall financial situation that affects banking
participation. The inclusion of net worth and access to credit as controls for banking participation vary
across studies. Rhine and Greene (2006a) included a set of variables to indicate net worth of the
respondent. Relative to households reporting no net worth, those with positive net worth, and even
those with negative net worth, are significantly less likely to be unbanked. Hogarth, et al. (2005) includes
net worth as a set of dummy variables with similar results. Hogarth also includes variables on whether the
respondent is a home owner and a vehicle owner. Results indicate that homeowners and those owning a
car, both newer and older, are significantly more likely to be banked. Grimes et al. (2010) and Rhine, et al.
(2006b) include a dummy variable for whether the respondent owns their home, results are consistent
with those found by Hogarth, et al. (2005).
Grimes, et al. (2010) includes a dummy variable for whether or not the household owns a credit
card. They find that households holding at least one credit card are significantly less likely to be
unbanked. Hogarth, et al. (2005) includes access to credit as a dummy variable for whether the
respondent has been rejected or obtained a lesser amount of credit than requested. These households
are significantly more likely to be banked, relative to those who have not been rejected. While this result
is not expected, it may be explained by unbanked households not making an attempt to apply for credit.
If this is true, it is mainly banked individuals attempting to access credit, consequently they are the
individuals being rejected.
Underbanked
The majority of current literature is in the area of unbanked individuals, however, there is another
category of individuals who are not unbanked but seem to be making costly financial decisions that
warrant further investigation. The FDIC defines an underbanked household as “those that have a checking
or savings account but rely on alternative financial services. Specifically, underbanked households have
used non-bank money orders, non-bank check-cashing services, payday loans, rent-to-own agreements,
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or pawnshops at least once or twice a year or refund anticipation loans at least once in the past five
years.” There has been little research done specifically in the area of underbanked, but use of the
alternative services that define the underbanked have been explored to various degrees.
The first two alternative services that make up the underbanked are the use of money orders and
non-bank check cashing services. Schuh & Stavins (2011) found that 11% of check users had also used
money orders. The authors also reported that consumers using money orders paid a higher percentage of
their total transactions in cash rather than bank account deductions or online bill paying. Paulson and
Rhine (2008) explored the use of non-bank money orders and check cashing services together. They
found that low income households are significantly more likely to use these services, and as use
significantly increases so does household size. Rhine, et al. (2006b) explored obtaining financial services
from currency exchanges, including cashing checks, purchasing money orders, paying bills, and wire
transferring money. The authors found that low income households were nearly 40% more likely to use
these services. Those who are 25 years or younger, are black or Hispanic, or have a high school degree or
less, are more likely to use these services as well. An indicator for whether the household was unbanked
was included in the analysis. Households without a bank account were significantly more likely to use
financial services from currency exchanges.
With low income being a significant determinant of usage for both money orders and check
cashing services, a look at the fees associated with these services is warranted. Some check cashing
outlets provide money orders for free when another service, such as check cashing, is purchased. Fox and
Woodall (2006) report that the average fee for a $100 money order was $1.08 and ranged from 50 cents
to $16. They report the fee has increased by 8% since 1997. The authors also compared outlets’ fees to
those at the United Postal Services, which charge 95 cents per money order. Fox and Woodall (2006) also
explored the fees on check cashing services. The cost of cashing the check, and whether the outlet would
even cash the check, depended on the type of check. For a government benefit check, 94% of outlets
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cashed the checks with an average fee of 2.44%. The authors reported an increase in the cost of cashing
a Social Security check; in 1997 the fee was 2.11% of the check value, increasing to 2.44% in 2006.
Paychecks are also widely accepted in check cashing outlets, 93% were willing to cash them. The average
fee for cashing these checks was 2.52% and ranged from 1% to 5%. The average fee increased if the check
was a hand-written paycheck (4.11%) and ranged from 1% to 10%. The most expensive type of check to
cash is a personal check, with only half of check cashing outlets willing to cash them. The average fee for a
cashing a personal check was 8.77%, with fees ranging from 2% to 15%, a drop from 9.36% in 1997.
Accompanying this decrease was and increase in outlets willing to cash personal checks.
The high cost of check cashing services has provoked some state governments to intervene in the
market, capping the fees that businesses can charge. Governments have also worked to lower the cost of
banking by capping the fees banks charge for minimum balances and number of checks written.
Washington (2006) found that these efforts resulted in a three to four percentage point decrease in the
number of low-income minority households that were unbanked. The results became stronger the longer
the regulation was in place, but due to the lack of immediate success of these programs, some were
cancelled, bringing back the original problem of high costs. Washington cites two years as the time frame
when results of the regulation begin to show, which she attributed to the lack of advertisement by banks
of the new accounts.
Payday loans and other high interest short-term loans are relatively new to the market, so little
research has been done in the area. There were virtually no payday loan businesses in 1990, but by 2001
the number was approximately 12,000 to 14,000 (Consumer Federation of America & U.S. Public Interest
Research Group 2001). Most of the research done in the area of payday loans and other alternative
services explores access to the loans (Melzer 2011, Skiba and Tobacman 2007) and effects of policy and
regulations on the use of these services (Carter 2012, Avery 2011, Peterson 2007, Stoesz 2012, Hill, et al.
1998, Edmiston 2011).
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One study that investigated payday loan customers is a 2008 study by Lawrence and Elliehausen.
When looking at a mean comparison between payday advance customers and all adults, those who have
used payday loans were more likely to have incomes between $25,000 and $49,999. Individuals under 35
represented a higher percentage of payday users. Both these results were confirmed by Elliehausen
(2009), who also found that payday borrowers had a limited amount of liquid assets, had a high school
diploma or some college education, and had experienced credit limitations in the past 5 years. Chatterjee,
et al. explored the use of high interest loans to meet short term need. The authors found that older
individuals, males, whites and those without children under the age of 18 were less likely to use
alternative banking options. Educational attainment and income were also negatively associated,
specifically with payday borrowing. Stegman and Faris (2003) focused on low income individuals in North
Carolina and found that African American and younger households were more likely to use payday loans.
The authors also included variables for whether the respondent received welfare and whether the
respondent had a savings account.
Fox and Woodall (2006) also explored payday loans. They reported that in order to qualify for a
payday loan a customer needs a bank account and a source of income. The authors found that the
average maximum loan size was $696, ranging from $250 to $5,000. To determine the cost of a payday
loan, the authors inquired about borrowing $300 for two weeks. The average cost of this short term loan
was $46.85, or 406% APR. The highest fees were charged in states that did not have caps on the interest
rate. Under the Truth in Lending Act, payday lenders are required to quote the cost “as an interest rate if
any cost in quoted,” in attempt to help the consumer understand the actual cost of the loan. Fox and
Woodall report that three fourths of the clerks reported the cost for the entire loan amount or the loan
cost per $100 borrowed. Another 8% of the clerks refused to disclose the cost and only 17% reported the
cost as an interest rate.
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Those households who choose to purchase or rent items from Rent-to-Own (RTO) stores and have
a transaction account are also considered to be underbanked. RTOs can be more expensive than other
forms of short term credit provided by banking institutions. Anderson and Jaggia (2012) explored three
different categories of using RTO. First, the customer can choose to return, meaning “payments cease and
the merchandise is returned to the store, perhaps involuntarily.” The second option is purchase,
“ownership is transferred to the customer, possibly through the exercise of an early payment option.”
Skip is the final category, meaning “payments prematurely stop but the merchandise cannot be recovered
by the store for some reason – ‘the customer skipped with it.’” The authors then combine all these to
explore all RTO customers. The authors found that nearly 60% of RTO customers were under the age of
25, 25% were male, and 25% were married. The authors also found that 45% were repeat customers.
Among the three categories 62.2% of customers returned the good, 20.1% purchased and 3% skipped.
The other category, which is left out for this analysis, is the 14.7% of contracts that remain open. Looking
at the authors’ regression analysis, it was found that older and repeat customers are significantly less
likely to return or skip, and those who are unemployed are significantly less likely to return the item;
whereas married households are significantly less likely to skip.
In an earlier paper, Anderson and Jaggia (2009) explored various types of goods that can be
purchased/rented at a RTO store. They focused on appliances, electronics, and furniture. They found that
older, married customers were more likely to rent furniture. Those receiving a form of government aid
are significantly less likely to rent electronics and furniture. McKernan, et al. (2003) also explored use of
RTOs. They reported that households who do not own their home, have an income of $15,000 to $24,999,
are separated or African American, and those with a high school degree or lower are more likely to use
RTOs. Individuals who were older and retired were significantly less likely to use them. Geography had an
impact on use as well; those in nonmetropolitan areas were more likely to use RTOs, as were households
in the Midwest and South.
18
When check cashing outlets first emerged, the pawn broking business began to decline. It then
rebounded in the mid-1970s and continued to grow rapidly throughout the ‘90s (Caskey 1994). While
there is relatively little current research done on pawn shops in the United States, there was a survey
done in 2010 on pawn broking in the UK. The report finds that pawn broking customers tend to be
women with families. Three-fourths of the customers were between the ages of 20 and 49. A small
percentage, 20% owned their home, either with a mortgage or outright. Work statuses of the customers
were also explored; 25% of the customers were unemployed and looking for work, 27% were full time
employees, and 13% were part time employees. Another interesting finding was that 53% of customers
lived in a household with no one working; however, most of these households were comprised of single
parents or adults living alone. Banking status was also reported, with 11% of customers considered
unbanked. Unbanked was defined as not having a traditional bank account or a Post Office Card Account
(Collard and Hayes 2010).
The final alternative use that makes up the underbanked is consumers who use tax anticipated
refund loans. To be considered underbanked by the FDIC, a household must have used this service at
least once in the past 5 years. Elliehausen (2005) explored the use of refund anticipation loans. The
majority of refund anticipation loans are used by $15,000 to $24,999 and $25,000 to$39,999 income
groups. The users of refund loans also tend to be younger, married with children, and have high
consumer debt payment to monthly income ratio.
Financial Literacy
Financial literacy has been an important topic during the current financial crisis. Many experts
believe it is a lack of financial knowledge that leads individuals to make costly financial decisions
(Bernheim and Garrett 2003, Fox, Bartholomae and Lee 2005). However, the focus of the current financial
crisis on improving financial knowledge has been met with skepticism and criticism (Willis, 2011). There
are many studies that have been published showing little to no improvements in knowledge after an
19
intervention has been made, although these findings may be the result of flaws in the transfer of
knowledge (Cole and Shastry 2008, Hathaway and Khatiwada 2008, Carswell 2009).
The question of what determines banking behavior and other financial behaviors may not be
completely based upon our demographic and socioeconomic characteristics. If our behaviors were solely
determined by characteristics we are unable to alter, changing our behavior or preventing ‘bad’ financial
behavior from occurring would be difficult. It is the hope of researchers and policy makers that something
can be done to modify financial behavior, most believe this is financial literacy and financial knowledge.
Before going more in depth into the discussion of financial literacy and behavior, it is important to
address the question of what financial literacy is and how it is determined if someone is financially
literate. As researchers begin to characterize someone as financially literate or illiterate, distinction
between vocabularies must be clear. In Huston’s 2010 article, she raises issue with the fact that experts
are using terms such as financial knowledge, financial literacy, and financial education interchangeably.
Though these terms have been used synonymously in the past, they may not hold the same meaning to
all experts, especially in different fields. To ensure these terms are used efficiently, common definitions
needs to be established.
Huston offers a suggestion on how we should define financial literacy: “To be financially literate,
individuals must demonstrate knowledge and skills needed to make choices within a financial
marketplace that all consumers face regardless of their particular characteristics.” She stresses two parts
of this definition: knowledge and skills. Huston also points out the difference between financial literacy
and financial education. She defines financial education as targeted toward “improving a person’s level of
knowledge and/or ability, can and should be tailored to suit different demographics, life stages, and
learning styles.” She believes the difference between financial literacy and financial education is that
financial education is used to teach individuals to become financially literate (Huston 2010).
20
An alternative definition for financial literacy comes from Remund in his 2010 article: “Financial
literacy is a measure of the degree to which one understands key financial concepts and possesses the
ability and confidence to manage personal finances through appropriate, short-term decision making and
sound, long-range financial planning, while mindful of life events and changing economic conditions.”
Remund includes the two components that Huston stresses while adding a time horizon and changing
economic conditions. His definition seems to be stronger than Huston’s, but he does not offer clear
descriptions of many ideas he presents. He fails to explain what he believes to be the key concepts and
what determine sound decisions. Huston’s definition allows for knowledge without action while
Remund’s requires knowledge and action (Remund 2010).
Defining financial literacy is an important task not only for providing more consistent research, but
also in allowing for better comparison across studies. A better definition of financial literacy comes from
the Jump$tart Coalition (2007), which defines financial literacy as “the ability to use knowledge and skills
to manage financial resources effectively for a lifetime of financial wellbeing.” They also state that
“financial literacy is not an absolute state…(it) refers to an evolving state of competency that enables
each individual to respond effectively to ever-changing personal and economic circumstances.” This is an
improved definition of financial literacy because it addresses several issues the above definitions do not.
For example, the Jump$tart definition includes a time dimension, stating that financial literacy is a
“lifetime of financial wellbeing.” This is important because it recognizes that financial literacy is not just
knowing about your finances today but also knowing how choices today impact future financial wellbeing.
Another important element that is included in this definition is the recognition that financial
markets change over time. The financial decisions made by an individual’s grandparents are not
necessarily the same decisions that are relevant today. The introduction of new financial instruments and
changing government involvement will introduce new choices and opportunities. Additionally, this
definition is superior to others that have been presented in that it allows for individuals to make decisions
21
that may be seen as less than ideal as long as an individual is “responding effectively” to their personal
circumstance.
It has been stated previously that education has an effect on the banking participation of
respondents. Many studies have found that those with higher levels of education are more likely to be
financially literate (Lusardi and Mitchell 2008, Van Rooij, et al. 2011a, Fonesca, et al. 2012, Worthington
2006). There are two possible reasons for this; first, those who have higher levels of education may learn
financial knowledge in their extra years of schooling. The other possible reason is a self-selection issue,
those who have higher levels of financial knowledge may self-select to attend higher levels of education
because they understand the financial benefits better than those who choose not to attend.
While there has been little done on the impact of financial literacy on banking participation,
literacy has been shown to have an effect on other financial behaviors. High levels of financial knowledge
have been found to lead to more responsible credit card behavior (Robb 2011, Wickramasinghe and
Gurugamage 2012), increased patience (Hastings and Mitchell 2011), planning for retirement and wealth
accumulation (Behrman, et al. 2012, Fernandez et al. 2010, Lusardi and Mitchell 2007, Lusardi and
Mitchell 2008, Van Rooij, et al. 2011a), and participating in the stock market (Abreu and Mendes 2010,
Van Rooij, et al. 2011b). Since many other financial behaviors are impacted by financial literacy, it is
expected that it will also have an effect on banking participation.
One study that explores the effect of financial literacy on banking participation is Grimes, et al.
The authors measured financial literacy in a number of ways. First, the authors explored financial literacy
by using the number of correct answers on a set of financial literacy questions. The set of questions can
be found in Appendix 1. The authors found that, out of 7 questions, the average percentage answered
correctly was 48.16%, or approximately 3 questions.
A second measure of financial literacy was through the use of two dummy variables indicating
whether the respondent had ever taken an economics course, business course, or a personal finance
22
course in high school. The authors used these variables in a broad definition of economics to indicate if
the respondent had any access to financial education. It was reported that 35.58%, 29.67%, and 10.10%
of respondents had taken an economics, business, or personal finance course, respectively. When
combined into the broad definition, 55.63% had taken one or more of the courses.
The authors first completed a mean comparison, comparing financial knowledge of unbanked and
banked respondents. It was found that banked individuals were significantly more likely to take a business
course. The results were not statistically different for the other courses individually; however, the broad
definition of economics was significant, with more banked individuals taking one or more of the courses.
The other financial literacy indicator, score on the set of financial literacy questions, was also statistically
different. Banked individuals scored, on average, 49.60% compared to the unbanked respondents’ score
of 36.04%.
Using a probit regression, the authors explored the unbanked, controlling for a variety of
demographic, socioeconomic, and geographical factors. Four models were used with each differing on
how courses are included; all included the score on the financial literacy questions. Across all regressions
the sign and significance of the score variable remains the same, higher levels of financial knowledge
leads to less likelihood of being unbanked. When the different dummy variables for courses were
included, controlling for exposure to economics, all had a negative effect on being unbanked. However,
taking at least one or more economic, business or finance course (broad definition of economics) and
taking at least one business course were significant.
Data
The first data set used in this paper is the Financial Capability in the United States –State-by-State
Survey created by the Financial Industry Regulatory Authority (FINRA). This data set is part of a set of
three surveys in the Financial Capabilities Study. The others are the National Survey and Military Survey
(FINRA Investor Education Foundation 2012). All data sets collected information from a unique set of
23
respondents. The national survey was a telephone survey where approximately 1,500 households
responded. The survey was completed between May and July of 2009. The military survey included
information on 800 military service members and spouses. Due to the small sample size of these surveys
these data sets will not be included in the analysis. The State-by-State data set was chosen for this
analysis due to large number of observations, approximately 28,146 American adults. A sample of at least
500 respondents from each state and the District of Columbia was obtained by an online survey between
June and October of 2009. The questionnaire is similar across surveys, so comparisons can be easily done.
All results presented in this paper were confirmed using the National survey.
The primary purpose of these surveys was to evaluate the financial capabilities of adults in the
United States. The main content areas covered by the FINRA surveys include: financial capabilities,
financial literacy measures, financial behaviors, financial attitudes, and standard demographic
characteristics. For the purpose of this study, variables from all sections will be used (Applied Research
and Consulting LLC 2009).
In order to compare results across studies in this paper the state data set will be weighted to
match 2008 American Community Survey (ACS). Since the sample distributions are initially by state, the
weights will adjust distributions, by age category to match gender, race/ethnicity, and level of education.
There are three stated goals of the FINRA surveys, with the first being to benchmark key measures
of financial capabilities. Key financial capabilities of interest are listed as “banked” status, access and
participation in retirement savings, and debt burden. The next objective is to understand the
characteristics of relevant households, such as demographic characteristics, financial knowledge, and
behavioral traits. The final goal is to inform public policy based on the results of this study. Using the
characteristics found to be significant, it is the hope of the survey creators that public policies promoting
financial capabilities are put in place (Applied Research and Consulting LLC 2009).
24
The State-by-State Survey was chosen as the primary data set due to the financial knowledge
variables that can be obtained. This data set asked respondents a set of five financial literacy questions to
estimate their financial understanding. Since banking participation requires a relatively low level of
financial knowledge, it is expected that the respondents’ score will have a small but significant effect on
participation. While it is expected that financial literacy will have a small effect in general, the magnitude
of the effect may get larger as we move to a comparison of the underbanked and fully banked. For a
household to be underbanked they must use alternative services that are often more costly than their
traditional counterparts. Having a knowledge that these services tend to be more costly may incentivize
the household to choose traditional services. In addition to the respondent’s total score, individual
questions, which vary in difficulty, will be explored. It is predicted that the relatively easy questions will
have the greatest impact on banking participation due to low level of financial involvement. Given the
richness of the financial literacy variables, the FINRA survey will be primary data set used in the analysis.
The second data set used to analyze differences in the characteristics across banking levels is the
FDIC National Survey of Unbanked and Underbanked Households. This survey was a supplement to the
January 2009 Census Bureau’s Current Population Survey (CPS). Since this data set is linked to the CPS,
there is more information concerning the work status of the respondents than with the FINRA data set.
The full CPS data set includes information on 54,000 households, with nearly 47,000 respondents
completing the supplemental FDIC survey. While this was the number of respondents who began the
survey, the number of questions in the survey varied based on the responses given. If the respondent was
not aware whether the household had a checking or saving account, or refused to answer the question,
the survey ended. The survey was also terminated If the respondent reported that they were “not at all”
involved in making financial decisions, or that they did not know or refused to answer their level of
participation in the decision making process. After these drops were made, the number of observations
used was 45,875. All households that reported knowing whether they had a checking or saving account
25
were included in the unbanked analysis. However, when moving to the underbanked analysis, households
for which the survey was terminated, due to their involvement in making financial decisions, were not
included.
The FDIC Executive Summary states the purpose of the survey is to “address a gap in reliable data
on the underbanked and underbanked households in the United States.” Under the Federal Deposit
Insurance Reform Conforming Amendments Act of 2005 (Reform Act) the FDIC must conduct ongoing
surveys to determine the efforts of banks to serve the underbanked, this survey is conducted in order to
comply with that law.
The sampling method of the CPS is complex. The first step, based on the 2000 census information,
created just over 2,000 geographical areas called “primary sampling units” (PSU) for the entire United
States. These PSUs are formed into strata, by themselves and within each state. A total of 842 PSUs are
sampled. The second step was to choose households within these PSUs to survey. Around 72,000
households are chosen each month, but due to unoccupied households and those who do not respond
because they are absent or refuse to answer, the data set usually falls to around 57,000 households. The
CPS then collects data on the members of the household, applying household responses to all members.
In a given month, information is obtained on approximately 112,000 individuals age 15 years or older,
31,000 children (0-14 years of age), and about 450 individuals in the Armed Forces.
As with most national data sets, the CPS does oversample some groups, requiring the use of
weights to complete an analysis. The first weight included in the data set is the “inverse of the probability
of the person being in the sample.” This weight is fairly consistent for individuals living within the same
state but can differ greatly across states. The CPS also includes weight for non-interviewed households
and ratio estimates. The ratio estimate is a weight that accounts for differences between the sample and
the actual population. The characteristics that are considered are “age, race, sex, and state of residence.”
This is primarily purpose of these weights are for analysis of work force participation. When looking at
26
banking participation the household weight will be applied to all descriptive statistics and regression
analysis.
One limitation of the FDIC survey is that it does not include information on the respondents’
financial knowledge. While the FDIC lacks this information, there are several benefits of the survey. The
first is the large sample size, the FDIC data set has nearly 46,000 observations. Another benefit is the
significant number of banking participation questions asked of respondents. The FINRA survey has a
limited number of questions on why a household is unbanked and no questions on why a household is
underbanked. The FDIC survey has many questions concerning both these “why” questions. Knowing why
a household chooses a given level of participation can offer insight into whether the household is
unbanked by choice or result of refusal. These indicators allow for a more in-depth analysis of
demographic and socioeconomic characteristics and differences on why individuals do not hold a bank
account. The FDIC survey also includes information on whether the household ever had a bank account
and if they plan to open one in the near future. These questions offer interesting distinctions within the
subset of unbanked households.
The final data set that will be used in this analysis is the 2010 Survey of Consumer Finances which
is sponsored by the Federal Reserve Board in cooperation with the Department of the Treasury. The data
was collected between May and December and includes a sample size of 6,492. The purpose of the SCF is
to track changes in the financial situations and participation over time. The SCF has been conducted
triennially since 1983, with panel surveys being completed in 1983-1989 and 2007-2009. The purpose of
the most recent panel data set was to explore the effect the current recession has had on consumer
finances.
The SCF also oversamples select segments of the population to obtain a more accurate picture of
the population. The sample design consists of obtaining “a standard, geographically based random
sample and a special oversample of relatively wealthy families.” Keeping consistent with previous
27
literature that has used the SCF, the descriptive statistics will be weighted, but the regression analysis will
use a repeated imputation inference (RII) technique that addresses the issue of missing observations.
While this paper will use this method, an in-depth discussion will not be included. (For more information
on the RII method see (Montalto and Sung 1996, Kennickell 1998)).
The SCF does have some benefits over the previously presented data sets. The first benefit is that
a previous analysis of the unbanked has been completed using the SCF (Hogarth, et al. 2005). This is
particularly important to compare current results to previous studies ensuring that questions are similar
across the data sets. Another benefit of the SCF is the time trend, it has been given every three years
since 1983. These time factors can be used to look at banking participation over time, and of particular
interest, how banking participation has changed during periods of expansions and recessions. One
limitation of the SCF data set is that it does not include information on underbanked households. There is
one question included concerning the use of payday loans. However, due to the small percentage of total
households, particularly banked households, using this service, determining which households are
underbanked is difficult. Since the underbanked cannot be identified using this data set, it will be left out
and only the previous two data sets will be used in the underbanked analysis.
Since there are three data sets that will be used to analyze banking participation a comparison of
variables across surveys is important to ensure that terms are well defined and discrepancies are pointed
out. For a complete comparison of the variables used in this paper see Appendix 2.
The first comparison to discuss is how the survey chose the respondent. For the FINRA survey the
respondent was selected at random, there was no targeting of “heads of households or primary financial
decision makers” (Applied Research and Consulting LLC 2009). As mentioned above, the FDIC survey is a
supplement to the CPS. The “reference person” for the FDIC survey is the “person who owns or rents the
home” (FDIC 2009). The SCF, like the FINRA survey, does not target the head of household, but the
28
respondent is also not chosen at random. The respondent of the SCF is the “most financially
knowledgeable person in the household (Lindamood, et al. 2007).
The variables that warrant the most discussion are the dependent variables unbanked and
underbanked. The definition of unbanked is fairly consistent across the three data sets. The FINRA data
set requires two questions to determine whether the respondent is unbanked. First they are asked if they
or their household has a checking account. The second relevant question is whether they or their
household has a “saving account, money market account, or CDs.” If a respondent answered yes to at
least one of these questions, they are considered to be banked. If a household reported they did not
know/refused to answer one (and did not hold the other account) or both they were dropped from the
analysis1. The FDIC survey asks one question to get at the same unbanked variable; “Do you or does
anyone in your household currently have a checking or saving account?” The SCF treats the banking
questions similar to the FINRA data sets, asking whether the respondent had a checking account, a saving
account of some type. A difference that should be noted is the inconsistency between previous work
using the SCF and this study. Previous work using the SCF has defined unbanked as not having a
transaction account, including a checking account, savings or money market account, or a call account.
Since the comparison to the other data sets used in this analysis is more important than comparing to
previous literature, the call accounts will not be included in the definition of unbanked2.
The next dependent variable is whether the household is underbanked. As previously mentioned,
the FDIC defines underbanked households as “those that have a checking or savings account but rely on
alternative financial services. Specifically, underbanked households have used non-bank money orders,
non-bank check-cashing services, payday loans, rent-to-own agreements, or pawnshops at least once or
twice a year or refund anticipation loans at least once in the past five years.” This is the definition that will
be used to build the underbanked variable. Due to the newness of the definition different questions were
1 Determining whether 373 households or 1.3% of households were unbanked was not possible due to don’t know/refused responses.
2 There are nine households that have call accounts, but no other transaction account. These households would be considered banked under
the Hogarth, et al. (2005) definition.
29
asked about alternative financial services used by the unbanked. This leads to slight inconsistencies in the
definition.
Since the FDIC developed the definition, the FDIC survey includes all relevant questions to
determine whether a household is underbanked. The difference will come in the FINRA definition of
underbanked. The first discrepancy will be use of non-bank money orders and check cashing services. The
FINRA survey did not ask banked households about their usage of these services, which may
underestimate the number of underbanked households3. Another difference is the inclusion of an auto
title loan. While the FDIC data set does not include this service in its definition, it can be considered an
alternative to traditional banking loans so will be included in FINRA definition of underbanked4. The
FINRA data sets do not include information about frequency of use, leading to the final difference
between definitions. Distinguishing between frequent and infrequent users is not possible, so
underbanked households are those that have taken out or used these services5. As previously mentioned,
the SCF does not include enough information to determine if a household is underbanked. The only
question asked concerning alternative financial services is whether the respondent uses payday loan
services. While this variable will not be used for any analysis purpose, the descriptive statistics will be
reported for comparison purposes.
Most demographic and socioeconomic variables were found in all data sets. The data was
combined in a manner that was consistent with the FINRA data sets. For example, age was included as a
categorical variable in the FINRA data set, and was used as such for all data sets. While most of the
controls were found in all surveys, there were a couple variables that were not common across data sets.
Race/Ethnicity variables varied slightly across surveys. The FINRA and FDIC surveys include similar race
3 If households who use solely money orders and/or check cashing services were excluded from the FDIC definition of underbanked the
percentage of banked households would fall from 20.3% to 7.0% (of banked households). 4 Excluding households who only use auto title loans from the underbanked the percentage of underbanked households fall from 23.2% to
20.1% (of banked households). 5 When infrequent users of alternative services are considered underbanked 34.2% of banked households would fall into that category, an
increase of 14%. If those only using money orders and/or check cashing services are excluded from that percentage there is an increase from 7.0% to 10.7%.
30
breakdowns including: Caucasian/White (non-Hispanic), African American/Black (non-Hispanic), Hispanic,
Asian, Native American/Native Alaskan, and other (primarily composed of respondents reporting more
than one race). The SCF includes a slightly different breakdown, White-Caucasian (non-Hispanic), Black or
African American (non-Hispanic), Hispanic, or other. The other category includes Asians, American
Indians/Alaskans, Native Hawaiian, and others. This is not an ideal breakdown of race since the
differences between Asians and Native Americans are significant (Fernanadez 1996).
Employment status is another area with slight discrepancies between the data sets. The FINRA
data set includes information on whether the household is self-employed, full time employed, part time
employed, a homemaker, student, disabled, unemployed or temporarily laid off, or retired. The FDIC has
a similar breakdown but with self-employed households combined with full time or part time employed
based on the number of hours worked. Another difference worth noting is the indicator for whether a
household is a student. The FINRA survey asks the respondent if they are a student as one option for
work status, whereas the FDIC survey asks the respondent their work status, then an additional question
on whether they are enrolled in school full or part time. For the FDIC survey a respondent is considered a
student if they are not in the workforce, retired, or disabled and report being enrolled in school full or
part time. The breakdown for the SCF is similar to the FINRA but defined slightly different. Respondents
of the SCF were given a few more options as work status choices: volunteer and other reason for not
being in the labor force. To keep results as consistent as possible, these responses where combined with
homemakers.
The FDIC also has two important variables that are not able to be determined based on the data
available. The first is if the respondent has a credit card. Controlling for whether or not the respondent
has a credit card is important because it is an indicator of access to credit. If a household has access to
credit, along with a greater need for a transaction account, their access to traditional short term loans
may make them less likely to use alternatives. The second variable that cannot be obtained from the FDIC
31
data set is the drop in income variable. The FINRA and SCF surveys include questions asking whether the
respondent has experienced a drop in income in the past 12 months. If a household has experienced the
drop in income, it may have an effect on their banking participation and use of alternative services. The
need for short term loans may be greater for these respondents due to a lack of funds to meet short term
debt obligations.
While there are some differences across surveys, overall they are very similar and comparisons
can be made with a few notes for the variations. Using the three data sets will create stronger results due
to the individual and combined strengths.
Who are the Unbanked?
To examine who is unbanked, first a mean comparison will be completed using the three data
sets. The comparisons are presented in Tables 1a through 1c, which report the descriptive statistics from
the FINRA, FDIC, and SCF, respectively. All descriptive statistics have been weighted so the results
represent characteristics of the population of the United States.
Previous literature has indicated that the percentage of unbanked is around 10% (Grimes, et al.
2010, Hogarth, et al. 1998). Table 1a presents the FINRA data set, with results indicating a much lower
percentage of unbanked, 5.3%. This percentage is slightly lower than other surveys that will be explored
in this paper. Both the FDIC and the SCF find that 7.5% of households are unbanked. While the FINRA data
set does report a smaller percentage of unbanked household, it is still in line with the other results.
The focus of this paper is to determine the characteristics of unbanked households, and
demographic variables have been found to be different across banking participation levels. Gender is one
variable that will be included in the analysis, but the impact is expected to vary across surveys. This is
because the respondent is chosen in different ways across surveys. The gender variable, in the case
where the head of household or the most financially knowledgeable is interviewed, is more interesting
since it will determine whether males running a household have different banking participation than their
32
female counterparts. As expected, the FINRA data set indicates no difference in banking participation by
gender. However, when the interviewed household member is not chosen at random, the results indicate
that a higher percentage of females fall into the unbanked category.
Age is another demographic variable that was expected to have an effect on whether or not an
individual is unbanked. A set of dummy variables is used to compare the effects of banking participation
for different age groups. The dummies include young adults (18-34 years of age), middle aged (35-54
years of age), and mature adults (55 years of age or older). It was expected that as age increases the level
of banking will increase as well. A mean comparison further confirms this hypothesis. All data sets seem
to follow the trend that as age increases banking participation increases. The strongest results come from
the State survey, the full sample has a nearly 33% breakdown of all age groups. When exploring the age
breakdown within the unbanked category 52% fall into the youngest cohort and only 10% fall into the
oldest.
It was also expected that there will be differences in banking across race and ethnicity. One reason
may be language barriers that are present. If English is not the first language, some individuals may feel
uncomfortable engaging in banking services. It was expected that relative to Caucasian/white non-
Hispanics, African American/Black, Hispanic, Native American/Alaskan, and the “other” category are more
likely to be unbanked, while Asians will be less likely. Race/ethnicity variables vary slightly across surveys.
The FINRA comparison shows strong statistically different results for all race categories with the
exception of the other category. These results indicate that higher percentages of Caucasian/white non-
Hispanics and Asians are banked, while African American/blacks, Hispanics, and other represent a higher
percentage of the unbanked. These trends follow through to the FDIC data. The SCF finds similar trends,
however there is no significant difference in the other category, most likely due to the breakdown issue
stated in the previous section.
33
Marital status is the next demographic variable of interest. It was expected that, relative to
married respondents, single, never married, individuals will be more likely to be unbanked. As the
number of adults in a household increases, it was expected that their finances will become more
complicated and their need for an account will increase. It will also be the case that their income will
grow and they may be more likely to meet minimum balance requirements and avoid various fees that
may be associated with low balances and overdrafts. The expected sign of divorced and widowed
individuals is unknown. Previous results indicate married households are more likely to be banked while
single households are more likely to be unbanked. Initial analysis also appears to indicate a greater
percentage of divorced or separated households fall into the unbanked status, while the reverse is true
for widowed.
It was expected that the presence of children will decrease the likelihood that a household is
banked. The extra expense of having a dependent child will make meeting minimum balances harder and
will be more likely to make bank hours and locations inconvenient. While the data sets have information
on number of children as a continuous variable, it was expected that it is the presence of at least one
child that will have an impact on banking participation. For this reason a dummy variable has been
created to account for at least one dependent child being present in the household. Across all surveys, a
higher percentage of unbanked households have at least one dependent child present. Not only is the
result significant but the magnitude is large. The FDIC survey finds the largest spread with 42% of
unbanked households having a dependent child present, compared to 29% of banked households.
Whether or not an individual is banked will likely be affected by their education level as well. As an
individual’s education increases they are more likely to have a higher paying job, creating a greater need
for an account. As education increases it was also expected that an individual will become more aware of
the additional expenses associated with not having an account, decreasing their use of alternative
services and, in turn, increasing their banking participation. When looking at the mean difference
34
comparison the initial hypothesis is confirmed. Those with a high school degree or less are significantly
more likely to be unbanked, while those with some college or more are significantly more likely to be
banked. These results are consistent across all surveys.
The next variable of interest is employment status. It was expected that work status will have an
effect on banking participation. If an individual is employed, cashing a paycheck is much easier if they
have a bank account. It is predicted that, relative to full time workers, individuals who are part-time
employed, permanently sick or disabled, and unemployed or temporarily laid off will be more likely to be
unbanked. Exploring the results of the three data sets shows consistent results. Across all surveys,
respondents who are employed self-employed, full time or retired represent a higher percentage of
banked households, while those who are employed part time, homemakers, disabled, and unemployed
are unbanked.
As previous literature has indicated, income was expected to be one of the most significant
determinants of being unbanked. Most unbanked respondents describe lack of money as the chief reason
they are unbanked. It was expected that, relative to middle income, lower income households will be
more likely to be unbanked. Across all data sets, the percentage of unbanked households that fall into the
lowest income category is approximately 85%.
If a household has experienced a decrease in income, it is also to be an important indicator of
whether a household is unbanked. It was expected that households who have been confronted with a fall
in income would be less likely to meet minimum balance requirements and therefore, less likely to hold a
transaction account. Both the FINRA and SCF show there is a significant difference between the
percentage of unbanked and banked households who report experiencing a drop in income. The FINRA
survey shows that 56% of unbanked households experience the drop in income, while only 40% of banked
households report the same.
35
The next set of variables that are of interest to this study are indicators for the household’s access
to credit and their level of assets. Access is an important determinant of banking participation since it is
an indicator of involvement with other financial institutions. The first variable of this subset is a dummy
variable for whether or not the respondent owns their residence. It was expected that this variable will be
negatively associated with being unbanked. Homeowners are likely to have, or have had, a mortgage,
therefore a greater need for a transaction account to make payments. The breakdown between levels of
banking indicates a higher percentage of banked households are homeowners. Using the FINRA data, only
17% of unbanked households own a home, compared to 62% of banked households. The mean difference
in homeownership between the unbanked and banked is statistically significant at the 1% level. The FDIC
and SCF report results of similar magnitude and significance.
Credit cards are another example of a respondent’s involvement with financial institutions.
Obtaining a credit card requires credit, which can be built by holding a transaction account. Also, paying
for a credit card is easier if the household has a checking account. While the FINRA data set does offer
information on the number of credit cards the respondent has, it is the act of holding at least once credit
card that was expected to have an impact on banking participation. For this reason, an indicator for
whether the respondent has at least one credit card will be included in the analysis. The SCF analysis will
include a similar dummy variable. As expected, only a small percentage of the unbanked hold at least one
credit card, 20%, compared to 78% of banked households. Results are even stronger using the SCF data,
only 10% of unbanked households report having at least one credit card, while 73% of banked households
do. Both sets of results are significant at the 1% level.
The final set of variables that will be used to determine who the unbanked are is financial
knowledge or financial literacy. While there are mixed results on whether or not financial literacy affects
banking participation, it is important to explore potential differences in the level of literacy between the
banked and unbanked. It was expected that financial literacy will not have a significant effect on banking
36
participation in this study for two reasons. First, the questions asked of respondents were general
financial knowledge and had little to do directly with the choice of having a bank account. Since the
knowledge is not directly associated with bank account holdership, the effect will be smaller than if the
financial questions dealt directly with common misconceptions of bank accounts and alternative financial
services. The second reason that financial literacy is predicted to have little to no effect on account
ownership is that most respondents’ cite the reason they do not have an account as “not enough money”
or “no need or want of an account.” If most respondents did not have an account due to high fees or
other high cost complaints, it may be more likely that financial literacy would have an impact.
The FINRA questions used in this analysis can be found in Appendix 3. Statistics on financial
literacy can be found in Table 2. The average number of questions answered correctly by a respondent
was three, with the difference between the number of questions answered correctly by the unbanked
and banked being significantly different. Unbanked respondents, on average, answered 2.67 questions
correctly, while banked respondents answered 3.20 correct. The number of don’t know/refused
responses are also of interest because it is an indicator of the respondent acknowledging they are not
financially knowledgeable about the specific topic. This should be differentiated from respondents who
answered incorrectly. On average, unbanked households responded don’t know/refused slightly more
often, but the difference is not large in magnitude.
Not only is it important to look at the financial literacy score as an aggregate, but individually as
well. The first two questions inquire about savings and interest rates, with the second adding an inflation
component. These questions are used to determine the numeracy skills of the respondent. Both
questions were answered correctly by 79% and 67% of respondents, respectively, indicating that they
were fairly easy questions. The third question, the bond question, is the most difficult, with only 29% of
respondents answering correctly. It was also the question where the most individuals reported they “did
not know” the answer. This question may differentiate respondents with basic knowledge from those
37
with sophisticated financial knowledge. The fourth question concerns length of a mortgage and the
principal payments and total amount of the loan. This question is also considered relatively easy, and was
answered correctly by 78% of individuals. The final question of the set concerns stock diversification and
risk. This question was fairly difficult in that only 55% of respondents answered the question correctly.
Regression Results
To explore the different levels of banking participation, probit regression will be used. The
dependent variable, unbanked, will be coded 1 if the responded does not have any type of transaction
account and 0 otherwise. The first set of regressions includes controls for demographic and
socioeconomic characteristics, as well as the respondents’ access to credit and assets. This is the basic
model which includes a fairly consistent set of variables included in all three surveys. The results for these
regressions can be seen in Tables 3a through 3c.
The results seem to indicate that females are less likely to be unbanked, relative to males. The
results are significant for all surveys except the FINRA survey. This may be a result of a difference in the
role of the respondent in the household. The FINRA survey asked to speak with the individual in the
household whose birthday was closest, so any significance would be described as differences in reporting;
women respond differently than males to the banking questions. Since the results are not significant, it
appears that women are not more likely to report being unbanked than men. The FDIC focuses on the
head of household and the respondent for the SCF is the most financially knowledgeable, so the fact that
gender does play a significant role in these cases is more noteworthy than the FINRA results. While these
results are significant the results are small in magnitude, with less than a 1% difference.
It was expected that as age increased, finances became more complex and need for an account
increased. The results for this variable are consistent across surveys; the oldest households are
significantly less likely to be unbanked, relative to those in the middle cohort. This result is significant but
38
not as large in magnitude as expected, as those in the oldest cohort are approximately 1% less likely to be
unbanked, relative to those in the middle cohort.
Race was also found to be a significant determinant of banking participation. Consistent across all
data sets, relative to Caucasian/Whites, African American/Black and Hispanic households were more
likely to be unbanked, with the result being significant in most cases. While the results are similar in sign
across the surveys, the magnitude varies slightly. The FDIC analysis indicates that African
Americans/Blacks are 5% more likely to be unbanked and Hispanics are 3%. The FINRA and SCF
percentages are closer to 1%.
The mean difference analysis indicated that a higher percentage of married households were
banked. These results only follow through to the probit analysis, results for all data sets indicate that
single, never married and divorced/separated households are significantly more likely to unbanked. These
magnitudes remain fairly small and hover around 1%.
Including an indicator for dependent children present in the household resulted in a significantly
positive coefficient. Households with children under 18 present are also significantly more likely to be
unbanked, but the result is not significant. This effect may explained by the additional expenses children
bring to a household. With the additional expenses, households may be unable to meet the minimum
requirements to hold a bank account, or simply may not have a need for an account due to lack of
funding. It is also possible that the presence of children makes traditional banking more inconvenient
than the alternatives.
Education was expected to have a strong influence on level of banking participation, it was
predicted that not only do increases in education lead to higher incomes and more complex finances, but
also more knowledge that may lead to increased bank participation. This expectation was confirmed by
all three data sets, which find the levels of education to be significantly associated with banking
participation. Those with less than a high school degree, relative to respondents with a high school
39
degree, were 2% more likely to be unbanked. A respondent with some college education, or greater, is 1
to 2% less likely to be unbanked, relative to a high school graduate.
Based on the mean difference analysis, it was expected that full time workers would be more
likely to be banked. This regression omitted the full time dummy variable, so results are reported relative
to full time workers. Since the variables are coded in this manner, the expected sign is positive, indicating
a greater likelihood of being unbanked for other work statuses. The FINRA and SCF results are the
expected sign, with most positive or near zero. The FDIC survey finds that students are significantly less
likely to be unbanked, though this coefficient is not large in magnitude. The coefficient on students for
the other studies is not significant and is near 0. Respondents who report being homemakers are
significantly more likely to be unbanked, the same is also true of disabled and unemployed households.
A set of income variables are the next controls included in the regression analysis, both income
level and changes in income (when available) were included. It was expected that income would be a
primary motivator of whether a household held a transaction account. This result was confirmed by all
studies, low income households are significantly more likely to be unbanked. Both the FDIC and SCF also
find that those that fall into the highest income bracket are significantly less likely to unbanked, whereas
the result was near 0 and insignificant in the FINRA analysis.
Not only was the level of income expected to have an effect, but the change in income was
included to control for households that felt they no longer needed an account or could no longer afford it
due to the loss of funds. This variable is of particular interest because of the time period in which the
survey was administered, 2009 and 2010. In both surveys that included this variable, the result was in the
expected direction: households who experienced a drop in income were more likely to be unbanked. The
result, however, is not large in magnitude and is only significant in the FINRA analysis. One potential
reason this variable does not have the impact expected is due to its correlation with the unemployed
status of the respondent.
40
The final set of variables included in this regression set is controls for a household’s access to
credit and assets. Homeowners are significantly less likely to be unbanked in both the FINRA and FDIC
analysis. This was expected since homeownership generally requires some interaction with financial
institutions and is an indicator of wealth. A homeowner is 2% less likely to be unbanked; this result is
similar in magnitude and significant across all surveys. It was also expected credit cards would lead to a
less likelihood of being unbanked, since acquiring that form of credit typically requires an account and
creates a greater need for the account. The FINRA and SCF both find that respondents holding at least
one credit card are 4% less likely to be unbanked.
Financial literacy is the final set of variables that will be explored in terms of banking participation.
Financial literacy will be controlled for in several different ways. First, the number of questions answered
correctly will be included. All respondents were given the option to refuse or answer “do not know” to
each question. These responses, along with incorrect responses, were given a value of 0. A correct
response was given a value of 1. The sum was then taken, the highest possible score on this value is 5 and
the lowest is 0. The regression results for this analysis are presented in Column 1 of Table 4.1. The more
questions the respondent answered correctly, the less likely the household was unbanked. While this
result is significant, it is not large in magnitude.
The second way that financial literacy will be analyzed is by including each question individually.
These variables are coded similar to the first regression, but the results are not aggregated, and rather
used as separate indicators. It was expected, since banking is a low level of financial involvement,
relatively easy questions would have the strongest impact on banking participation. All questions, with
the exception of the question on the relationship between bond prices and interest rates, are the
expected signs and most significant. The coefficients are not large in magnitude, but this may be a result
of the correlation between questions.6
6 Correlations between questions range from 0.13 to 0.36.
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The final set of financial literacy regressions includes each question separately, with results found
in Table 4.2. Indicators for whether the respondent answered the question correctly and whether they
responded “don’t know” are included. Results are presented relative to those who answered the question
incorrectly. The expected sign of answering a given question correctly is negative, meaning those
households are less likely to be unbanked. This result is found across all questions, except the bond price
question which has a coefficient of 0. The results are also significant, answering the question correctly
leads to a nearly 1% decrease in the likelihood the household is unbanked.
Who are the Underbanked?
When exploring banking participation we see several significant differences between households
that are banked and unbanked. However, thinking that these are the only two levels of banking would be
erroneous. There are different levels of participation within the banked category that can be explored.
This portion of the paper will focus on those who have a transaction account and also use alternative
financial services to finance short term loans, referred to as the underbanked. It was expected that the
underbanked are more similar to the unbanked because they use more costly financial services. For this
reason, it was expected many of the results will be similar to the unbanked analysis. In this section of the
paper only households who have a transaction account will be analyzed. The sample size will not match
those who are banked in the previous analysis because the level of participation is unknown for some
banked households. Respondents that don’t know/refused to disclose their level of participation, or for
which the survey was terminated before their status was determined have been dropped.7 For the FINRA
data set, this decreases the sample size from 26,544 to 26,146 households, of which 23% are
underbanked. The FDIC data suggests that 20% of banked households are underbanked. The sample size
using the FDIC sample has decreased to 41,813 households, from 43,514. The SCF will be left out of this
analysis due to the lack of information on the variables that define the underbanked.
7 Households who answered yes to at least one alternative service are considered underbanked, even if they did not know/refused other
services. If the respondent answered they did not use any of the alternatives but didn’t respond or refused one question they were dropped from the analysis.
42
Table 5a and 5b present the descriptive statistics for the FINRA and FDIC full sample of banked and
a breakdown of the underbanked and fully banked. Exploring the demographic variables is the first step in
determining the difference between underbanked and fully banked households. A mean analysis
indicates that there are a slightly larger percentage of females falling into the underbanked category than
the fully banked.
Age is another demographic variable expected to influence banking participation, it is
hypothesized that older respondents are less likely to be underbanked. This was expected as this cohort
may have more complex finances and more credit experience that will allow them to obtain traditional
financial services over the alternatives. Both the FINRA and FDIC data sets show a trend of greater
banking participation as age increases. There are a higher percentage of young and middle age adults
who are underbanked, while the reverse is true for the older cohort.
The mean difference in race variables when comparing the unbanked to the banked was highly
significant. The differences are not as clear for the comparison of the underbanked to the fully banked.
Both the FINRA and FDIC survey indicate that Caucasian/White and Asian respondents represent a
significantly higher percentage of fully banked households. Respondents who are African American/Black,
Hispanic, Native American/Alaskan, and report multiple races/ethnicity compose a higher percentage of
underbanked households. The most drastic result is for African American respondents, which make up
just fewer than 10% of all banked respondents. When looking at the race breakdown by banking
participation, Blacks represent more than 20% of underbanked households and only 7% of fully banked
households.
Both mean analyses also show a significant difference in banking participation based on marital
status. Single, never married, and divorced/separated households represent a significantly greater
percentage of the underbanked. Married households and widowed households therefore make up a
greater percentage of fully banked households.
43
It was expected that households with dependent children are more likely to require alternative
financial services due to the additional expenses children bring to the household. Meeting short term
debt obligations may become more difficult with children present in the household. For this reason, it
was expected that households with children present are more likely to be underbanked. An initial
analysis using the FINRA and FDIC indicates this prediction is true, 53% and 36% of underbanked
households report having at least one dependent child present, respectively.
As with the unbanked, it was expected that underbanked households will be less educated than
fully banked households. As an individual becomes more educated they become more aware of the
additional expenses of alternative financial services, leading to decreased use. The results are as expected
for respondents with a college degree or higher; there are significantly more fully banked compared to
underbanked. The FDIC data indicates that both those with less than a high school and those that hold a
high school degree or equivalent are more likely to be underbanked.
As discussed above, it was expected the reason underbanked households use alternative services
is to meet short term debt obligations. Respondents who are unemployed or disabled may be more likely
to need the additional money to meet these needs. It is also hypothesized that retired households are
less likely to be underbanked because they have more complex financial needs and have established
credit throughout their working career. Both sets of data confirm these results. Unemployed households
make up 4.9% of all banked households in the FDIC survey. When moving to the breakdown of
underbanked and fully banked households, unemployed respondents represent 8.3% of the
underbanked. The reverse is found for retired respondents, the FINRA results show retired respondents
make up 8.3% of the underbanked, but over 20% of the fully banked.
A household’s level of income was expected to be the primary determinant of whether the
household had a transaction account. Unlike the unbanked, it was expected that experiencing a drop in
income will have a larger effect on whether a respondent is underbanked. Both the FINRA and FDIC
44
results show significant differences in the mean percentage of households who are underbanked versus
those fully banked for the lowest and highest income levels in the expected directions. An indicator for
drop in income is included in the FINRA analysis. Results show that 54% of underbanked households have
experienced an unexpected drop in income, while only 35% of their fully banked counterparts have
experienced a similar change in income.
Access to credit and level of assets are also expected to have an impact on whether or not a
household chooses to use alternative financial services in addition to traditional bank accounts. It was
expected that homeowners and those holding credit cards will be less likely to use the alternative
financial services because they are able to obtain traditional forms of credit that are relatively less
expensive. If an individual is a homeowner, they most likely have taken out a mortgage to pay for their
home. This can improve a homeowner’s credit rating, allowing them to obtain other loans, including a
home equity loan. Traditional loans generally allow these individuals to borrow funds at a lower rate. This
is also true of credit cards; obtaining credit cards can increase credit scores and allow users to obtain
other forms of credit. Credit cards can also be seen as substitutes for other short term loan alternatives.
These hypotheses seem to be confirmed by the mean difference analysis. The FDIC data indicates that
53% of underbanked households are homeowners, compared to 77% of fully banked households. These
results are slightly different in magnitude, but confirmed with the FINRA data. The FINRA results also
explore the use of credit cards by the two groups, 61% of underbanked households have at least one
credit card, while 84% of fully banked households do.
The final set of variables that will be used to better understand the underbanked are a set of
financial literacy variables. It was expected that the decision on whether to have a bank account would
not be strongly impacted by the respondent’s financial literacy. Since obtaining an alternative financial
loan or using alternatives to traditional accounts can be very costly decisions and require more
involvement, it is projected that financial knowledge will have a greater effect. The reason most
45
households cite as being unbanked is that they do not have enough money to have an account. It does
not seem, based on this reason, that financial knowledge can significantly influence banking behavior. It is
the lack of funds to meet current debt obligations that was predicted to be the reason a household is
underbanked. If this is a result of mismanagement of money, education can be beneficial.
Table 6 shows the descriptive statistics for the financial literacy variables of all banked households,
along with underbanked and fully banked subsets. Looking at the first indicator of financial literacy, the
number of correct answers given on the set of five questions, a significant difference between the
number answered correctly by the underbanked and the fully banked households is shown. Underbanked
households answer 2.7 questions correctly while fully banked households answer 3.2 correct. While this
difference does not seem large, due to the small number of questions the result is significantly different.
Underbanked respondents were slightly more likely to respond don’t know/refuse a response.
Due to the varying difficulty of questions, an exploration of individual questions to determine the
rates underbanked households answered them correctly is also of interest. Fully banked households
answered all questions correctly significantly more often than their underbanked counterparts. The
inflation question had the largest difference between the percentage of underbanked who answered
correctly compared to fully banked, with a spread of 15.1%. This is particularly concerning since it is a
numeracy question that deals with inflation rates and interest rates. If a respondent does not answer this
question correctly, it may be the case they do not fully understand interest rates. This may be an
indication that financial education would be beneficial in helping the respondent understand what an
interest rate is and the effect it has on income. The second largest spread was found in the stock
diversification and riskiness question. 44% of underbanked households answered the question correctly,
compared to 59% of fully banked households. It may be the case that this question differentiates
between individuals with simple or sophisticated levels of financial involvement. Since an individual with
complex finances would be expected to be fully banked, this may be the effect the question is picking up.
46
The bond price question was the most difficult question, and there is nearly a 7% difference in the
number of underbanked and fully banked households that answered the question correctly. It is
hypothesized that relatively difficult questions will be better at differentiating between the underbanked
and fully banked respondents, compared to relatively easy questions.
Regression Results
Table 7a reports results for the FINRA data set and Table 7b for the FDIC data. The dependent
variable of interest is underbanked, with a dummy variable equal to 1 if the household uses at least one
alternative banking service and 0 otherwise. The first regression includes information on the respondents’
demographic and socioeconomic characteristics that are expected to impact banking participation.
Gender is first of the demographic variables included in the underbanked analysis. The FINRA
results show that females are significantly less likely to be underbanked, but the coefficient is not large in
magnitude. This result follows to the FDIC survey, but the result is not significant.
The expectations of age on banking participation were confirmed. Looking at the FINRA data,
those in the youngest cohort are 5% more likely to be underbanked, relative to those in the middle aged
cohort. As was expected, respondents in the oldest cohort were nearly 8% less likely to be underbanked.
The FDIC results tell a similar story, but the coefficients are not as large in magnitude and only the oldest
cohort effect is significant.
The effect of race/ethnicity is much larger than expected. Both the FINRA and FDIC results report
African Americans/Blacks, relative to Caucasian/Whites, are 12% and 21% more likely to be underbanked,
respectively. These results are significant at the 1% level. The Native American/Alaskan effect is also large
in magnitude, the FDIC results indicate this group is 19% more likely to be underbanked. Hispanics are
also significantly more likely to be underbanked, but the result is not as large in magnitude as the
previous race variables. While these races/ethnicities are more likely to be underbanked, the reverse is
47
true for Asians, who are significantly less likely to be underbanked; results ranging from 10% to 4% are
found.
The effect of marital status is not very strong, only divorced/separated households are
significantly more likely to be underbanked in both surveys. These respondents are approximately 3%
more likely to be underbanked relative to married households.
It was expected that those households where dependent children are present will have more
expenses than households where there are no children under the age of 18. With greater expenses there
may be greater need for short term loans to meet debt obligations. This result is confirmed by the
significance of the indicator for children present in the household in both the FINRA and FDIC analysis.
Relative to those with no dependent children, these households are 10% and 4% more likely to be
underbanked.
Education was also predicted to be a significant determinant of whether or not a household was
underbanked. As education increases, an individual was expected to become more aware of the costs
associated with alternative financial services and in turn, use them less. The results found in both the
FINRA and FDIC analysis confirm this hypothesis. Those who have less than a high school degree are more
likely to be underbanked, relative to those with a high school degree or equivalent. However, the result is
only significant in the FIDC regression. For all levels of education above a high school degree, respondents
are less likely to be underbanked. Having some college education decreases the likelihood by 2% and
respondents with a college degree or higher are 10% less likely to be underbanked.
It was anticipated that since the majority of services that define the underbanked are related to
short term loans, those who are unemployed and temporarily disabled may be more likely to fall into this
category. This prediction is confirmed by the FDIC analysis. Relative to respondents employed full time,
those who are unemployed are 6% more likely to be underbanked while those who are disabled are 5%
more likely. The conflicting result comes from the FINRA survey. Results confirm that disabled
48
respondents are 5% more likely to be underbanked, whereas unemployed households are 2% less likely.
While this result was not expected it may be explained by the inclusion of the drop in income variable.
Households who have experienced an unexpected drop in income are 9% more likely to be underbanked.
Due to the correlation between these variables, the change in income variable may take some of the
explanatory power of unemployment.8
While income was expected to have large effect on whether a household had a transaction
account, it was not expected to be as strong of a determinant in whether the household is underbanked.
Although the prediction was income would not have as large of an effect, the results are still significant
and in the expected direction. Households in the lowest income bracket are 1% and 3% more likely to be
underbanked, while those in the highest bracket are 6% and 8% less likely to be underbanked, in the
FINRA and FDIC analysis, respectively.
The final controls in the first set regression are indicators for assets and credit. Homeownership
and holding at least one credit card are both negatively associated with being underbanked. A
homeowner is nearly 10% less likely to be underbanked with both the FINRA and FDIC data. This is the
expected result due to the fact that homeowners have access to more affordable short term loan options,
such as a home equity loan. Households that have at least one credit card are 12% less likely to be
underbanked. This was the hypothesized result since it is assumed credit cards are substitutes for many
of the alternative loans that define the underbanked.
Tables 8.1 and 8.2 show the impact financial literacy has on the banking status of households with
transaction accounts. The first regression includes financial literacy as the number of questions answered
correctly out of five. This result is significant and in the expected direction: the higher number of
questions answered correctly the less likely the household is underbanked. The inclusion of the financial
knowledge variable leads to some changes in the demographic and socioeconomic controls; slightly
8 The coefficient on unemployed becomes positive if the control for a household experiencing a drop in income is not included in the
regression.
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decreasing the impact of the race/ethnicity variable. While this result was unexpected, it suggests that
race alone is not the main determinant of whether or not a household uses the alternative services. The
change in income variable is still a significant indicator and remains relatively large in magnitude.
The second regression separates the questions into individual controls to determine the impact of
each question on its own. All coefficients are in the expected direction except the mortgage question.
While this question is in the unexpected direction, the result is not significant. The largest effect comes
from the inflation and stock diversification questions. These questions had the largest spread between
the percentages of underbanked and fully banked answering the question correctly. The results show that
if a respondent answered the savings, inflation, and stock diversification questions correctly they are 2%,
3%, and 3% less likely to be underbanked, respectively.
The final set of regressions includes each question separately. An indicator for whether the
respondent answered the question correctly and don’t know/refused to answer are included, with the
omitted group responding incorrectly. Results show that all signs on the coefficients for answering the
questions correctly are significant and in the expected direction. The question with the largest impact is
the stock diversification question. Answering that question correctly leads to an 8% less likelihood the
respondent is underbanked. Another interesting result from these regressions are the signs and
significance of the coefficients on the don’t know/refused responses. Those who didn’t know or refused
to answer are significantly less likely to be underbanked for all individual questions.
Conclusion
The idea that 7% of households in the United States are unbanked and an additional 18% are
underbanked may be hard to believe for most Americans. As nearly all day to day transactions are easier
when a checking account is held. Households who do not use these accounts or underutilize them may
not be minimizing their expenses or theirinvested time. Understanding who these individuals are is
important to ensure they are making a fully informed decision by remaining at a low level of participation.
50
An analysis across three data sets has shown that unbanked individuals tend to be African
American, Hispanic, and Native American/Alaskan. This subset of individuals may not feel as
comfortable/welcomed in banking institutions or have other reasons for avoiding traditional accounts.
Unemployed and disabled individuals were also more likely to not have a transaction account; it is
expected these households choose to avoid banking services due to lack of income, making it difficult to
meet minimum balances, or they may feel there is less need for an account. As with previous research,
income had a significant effect on banking participation. Households earning less than $35,000 were
significantly more likely to be unbanked. Those who experienced an unexpected drop in income were also
more likely to avoid transaction accounts. While these were the expected results, the coefficients were
not as large in magnitude as previous literature. This may be due to the fact that both the level of income
and change in income were included in the same regression. Previously, the change in income variable
had not been included. The final set of variables, homeownership and credit card possession, lead to
significantly less likelihood the household was unbanked. These results were expected since they are
indicators of wealth and access to credit.
The second section dropped the unbanked from the investigation and included only those
households who reported holding a transaction account. Determining which households were
underbanked, relative to fully banked, was the intended objective. While results were in similar directions
as the unbanked analysis, the degree of significance was slightly different. The race/ethnicity effect is still
strongly present in the underbanked analysis; African American/Blacks, Hispanics, and Native
American/Alaskans are significantly more likely to be underbanked. Those with dependent children are
also more likely to be underbanked, which was the expected result since these households may require
short term loans to meet unanticipated expenses. The education coefficients found in the underbanked
analysis were larger in magnitude compared to the unbanked; additional education, beyond high school,
was found to decrease the likelihood a household was underbanked. It was expected the effect on the
51
underbanked would be stronger since it is a more complicated financial decision to employ alternative
financial services. Using short term loans that have high interest rates and using non-bank services
(generally offered for free with a transaction account), indicate these individuals have a lack of
information and financial knowledge. Improving education on budgeting and services offered by
traditional bank accounts may improve banking participation among the low educated.
Work status did not seem to be a significant determinant of whether a household choose to have
a bank account, but was a good indicator for whether they were underbanked. Those who were
unemployed and disabled were particularly more likely to use alternatives to supplement their bank
account. This was the expected result, as these individuals may have experienced a decrease in income
that led to greater need for funds to meet short term debt obligations. This is further confirmed by the
significance of a change in the level of income, experiencing an unexpected drop in income leads to a 10%
greater likelihood a household is underbanked.
The results suggest, demographic and socioeconomic characteristics are not the sole determinant
of a household’s banking participation level, financial knowledge also has an effect. This result was larger
for the underbanked than the unbanked, but the conclusion was as expected. The primary reason
unbanked households do not have an account is lack of money, whereas the need to meet short term
debt obligations is the primary reason the underbanked use alternative services. The strong underbanked
result indicates these households may lack budgeting skills or have a high cost of budgeting. These
households decide to pay a premium, in the form of high fees and service charges on alternative loans, to
avoid budgeting and money management. It may be the case that money management education could
move these households toward a higher level of participation.
The cross study comparison of results yielded very similar results in terms of significance and
magnitude of coefficients. This was the expected result since the surveys were nationally representative
52
and completed by reputable agencies. The slight discrepancies found across the analyses can be mostly
explained by differences in the wording of questions and information available.
Now that these individuals have been defined, a better understanding of why unbanked and
underbanked households partake in particular levels of banking participation and how they make day to
day transactions is appropriate. First, it must be taken into consideration that these households do not
have a bank account because a bank does not allow them to hold one. Some individuals are not allowed
to hold an account after fraud or extensive fees have been accrued. If this is the case, a different
approach must be taken as compared to a situation where the respondent does not have an account by
choice. Understanding these details can lead to better insight into whether education and access to
information would benefit these individuals. The alternatives households use in place of an account is
also important to understand, since it may be the case they are paying a premium to avoid traditional
services. It may be the case they find these services to be more convenient or welcoming, in which
education and information may not lead to a better outcome. However, if households use alternatives
because they believe banks do not offer these services or do so for a higher price, education will lead
consumers to change their behavior toward increased banking participation.
Much understanding of the unbanked and underbanked is necessary to determine whether these
households are making a decision that is least costly to them. It is important to not assume that holding a
transaction account would make them better off, since they may be willing to pay a premium to avoid
banking institutions. Further insight into the reasons households have a low level of banking, and if they
are fully informed in their decision-making, can help both institution owners and policy makers. If bank
managers understand why these households are avoiding their services, they may be able to increase
their customer base by implementing minor changes. Policy makers, who often target low income
individuals and unemployed households (those who avoid traditional services the most), can amend and
modify programs and processes in their attempts to improve the financial situation of thousands.
53
Appendices
Appendix 1: Economic Understanding Questions: National Financial Services Survey
Each question included a Don’t Know/Not Sure and a Refused option. 1. What is the current national unemployment rate?
a) one percent or less b) between 1 percent and 10 percent c) over 10 percent
2. What is the current annual rate of inflation?
a) one percent or less b) between 1 percent and 10 percent c) over 10 percent
3. Is the main purpose of the Federal Reserve:
a) to set interest rates and monetary policy b) to set tax rates and government spending
4. There is a deficit in the Federal Budget when:
a) government spending is greater than tax revenues b) US imports are greater than US exports c) the total demand for money is greater than the total supply of money
5. The purchasing power of people’s income is MOST affected by the:
a) inflation rate b) trade deficit c) balance of payments
6. In a competitive market, the prices of most products are determined by:
a) The government b) business monopolies c) supply and demand d) the Consumer Price Index
7. Does setting quotas on foreign goods imported into the US increase the number of jobs for American
workers in the next 5 to 10 years? a) Yes b) No
54
Appendix 2: Comparison of Variables across Data Sets Categories Variables FINRA State-by-State FDIC SCF
Dependent Variable:
Unbanked Unbanked
(Do you/Does your household) have a checking account
and (Do you/Does your
household) have a saving account, money market
account, or CDs?
(Do you/Does anyone in your household) currently have a
checking or savings account?
Do you (or anyone in your family living here) have any
checking accounts at any type of institution?
and Do you (or anyone in your
family living here) have any savings or money market
accounts?
Underbanked Variables/
Alternative Loans Check Cashing
Do you or your spouse sometimes go to a check
cashing store to cash checks?
AND Do you or your spouse
sometimes cash checks at a grocery store or
supermarket?
Have you or anyone in your household ever gone to a place other than a bank, a savings
and loan or a credit union to cash a check
that was received from someone else?
NA
Money Order Do you or your spouse
sometimes pay your bills with money orders?
Have you or anyone in your household ever purchased a money
order at a place other than a bank, a savings
and loan or a credit union?
NA
Payroll Card NA
Do you/Does anyone in your household receive payment for wages by having the employer
deposit the salary onto a payroll card instead of
paying via cash or check?
NA
Payday Loans In the past 5 years: Have
you taken out a short term “payday” loan?
Have you or anyone in your household ever used payday loan or
payday advance services?
During the past year, have you (or anyone living here) taken out a “payday loan,”
that is borrowed money that was supposed to be repaid in full out of your
next paycheck?
Pawn Shop In the past 5 years: Have you used a pawn shop?
Have you or anyone in your household ever sold items at a pawn
shop?
NA
Tax
Anticipation Loan
In the past 5 years: Have you gotten an advance on
your tax refund? This is sometimes called a
“refund anticipation loan” or “Rapid Refund”
In the past 5 years, have you or anyone in your household taken out a tax refund anticipation
loan?
NA
Rent to Own In the past 5 years: Have you used a rent-to-own?
NA NA
Auto Title Loan In the past 5 years: Have Have you or anyone in NA
55
you taken out an auto title loan?
your household ever rented or leased
anything from a rent-to-own store because you couldn’t get financing
any other way?
Control Variables:
Gender Male, Female Male, Female Male, Female
Age 18-34, 35-54, 55+ 18-34, 35-54, 55+ 18-34, 35-54, 55+
Race/Ethnicity
Caucasian/White, African American/Black, Hispanic,
Asian, Native American/Alaskan, Other
Caucasian/White, African American/Black, Hispanic, Asian, Native
American/Alaskan, Other
Caucasian/White, African American/Black, Hispanic,
Other
Marital Status
Married, Single (never married),
Divorced/Separated, Widow
Married, Single (never married),
Divorced/Separated, Widow
Married, Single (never married),
Divorced/Separated, Widow
Dependents Presence of Children
under 18 Presence of Children
under 18 Presence of Children under
18
Education
Less than High School Degree, High School
Degree or equivalent, Some College Education,
College Degree, Post College Education
Less than High School Degree, High School
Degree or equivalent, College Degree, Post
College Education
Less than High School Degree, High School Degree
or equivalent, College Degree, Post College
Education
Work Status
Self Employed, Full Time, Part Time, Homemaker,
Student Disabled, Unemployed/Laid-off,
Retired
*Full Time, Part Time, Homemaker, Student
Disabled, Unemployed/Laid-off,
Retired
Self Employed, Full Time, Part Time, Homemaker,
Student Disabled, Unemployed/Laid-off,
Retired
Income Level Less than $35,000,
Between $35,000 and $75,000, Over $75,000
Less than $35,000, Between $35,000 and $75,000, Over $75,000
Less than $35,000, Between $35,000 and $75,000, Over
$75,000
Change in
Income
In the past 12 months (have you/has your
household) experienced a large drop in income
which you did not expect?
NA
Is this income unusually high or low compared to
what you would expect in a "normal" year?
Homeowner Do you (or your
spouse/partner) currently own your home?
Are your living quarters (a) owned or being
bought by a household member?
Do you (and your family living here) own this (house
and lot/apartment/ranch/farm)?
Credit Card
How many credit cards do you have? Please include
store and gas station credit cards but NOT
debit cards.
NA
Do you (or anyone in your family living here) have any
credit cards or charge cards?
Financial Literacy
See Appendix 3 NA NA
*Unlike the other surveys, the FDIC survey asks whether the respondent is a student in a question separate from work force participation. For this survey students are defined as those respondents who report not being in the work force, but enrolled in school part or full time. Individuals who are in the work force or retired and report being a student are not considered a student, but are reported as their other work status.
56
Appendix 3: Economic Understanding Questions: FINRA
Each question included a Don’t Know/Not Sure and a Refused option.
1. Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?
a) More than $102 b) Exactly $102 c) Less than $102
2. Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account?
a) More than today b) Exactly the same c) Less than today
3. If interest rates rise, what will typically happen to bond prices? a) They will rise b) They will fall c) They will stay the same d) There is no relationship between bond prices and the interest rate
4. A 15-year mortgage typically requires higher monthly payments than a 30-year mortgage, but the total interest paid over the life of the loan will be less.
a) True b) False
5. Buying a single company’s stock usually provides a safer return than a stock mutual fund. a) True b) False
57
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62
Tables
Obs. Mean S.D. Obs. Mean S.D. Obs. Mean S.D.
Unbanked 27773 0.053 0.223 - - - - - -
Banked 27773 0.947 0.223 - - - - - -
Gender Female 28146 0.513 0.500 1229 0.514 0.500 26544 0.512 0.500
Age Cohort 18-34*** 28146 0.306 0.461 1229 0.521 0.500 26544 0.293 0.455
35-54 28146 0.379 0.485 1229 0.377 0.485 26544 0.379 0.485
55+*** 28146 0.315 0.465 1229 0.101 0.302 26544 0.327 0.469
Race White, Non-Hispanic*** 28146 0.685 0.464 1229 0.519 0.500 26544 0.697 0.460
Black, Non-Hispanic*** 28146 0.115 0.319 1229 0.227 0.419 26544 0.107 0.309
Hispanic*** 28146 0.134 0.341 1229 0.212 0.409 26544 0.129 0.335
Asian*** 28146 0.046 0.210 1229 0.019 0.138 26544 0.047 0.212
Native American/Alaskan** 28146 0.016 0.127 1229 0.024 0.152 26544 0.016 0.126
More than One Race 28146 0.008 0.092 1229 0.008 0.088 26544 0.009 0.092
Marital Status Married*** 28146 0.534 0.499 1229 0.235 0.424 26544 0.551 0.497
Single*** 28146 0.282 0.450 1229 0.543 0.498 26544 0.267 0.443
Divorced*** 28146 0.140 0.347 1229 0.200 0.400 26544 0.136 0.343
Widow*** 28146 0.044 0.205 1229 0.022 0.146 26544 0.045 0.208
Dependents Dependent Children*** 28146 0.384 0.486 1229 0.425 0.495 26544 0.382 0.486
Education Less than High School*** 28146 0.035 0.183 1229 0.175 0.380 26544 0.027 0.161
High School Degree*** 28146 0.293 0.455 1229 0.462 0.499 26544 0.282 0.450
Some College*** 28146 0.419 0.493 1229 0.305 0.461 26544 0.427 0.495
College Degree*** 28146 0.159 0.365 1229 0.048 0.215 26544 0.166 0.372
Post College Education*** 28146 0.094 0.292 1229 0.009 0.096 26544 0.099 0.298
Work Status Self Employed** 28146 0.081 0.272 1229 0.069 0.253 26544 0.081 0.273
Full Time Employed*** 28146 0.361 0.480 1229 0.172 0.377 26544 0.373 0.484
Part Time 28146 0.098 0.297 1229 0.105 0.307 26544 0.097 0.296
Homemaker*** 28146 0.089 0.285 1229 0.116 0.321 26544 0.087 0.282
Student*** 28146 0.058 0.234 1229 0.076 0.266 26544 0.057 0.232
Disabled*** 28146 0.042 0.201 1229 0.074 0.261 26544 0.040 0.197
Unemployed*** 28146 0.098 0.297 1229 0.339 0.474 26544 0.084 0.278
Retired*** 28146 0.172 0.378 1229 0.049 0.216 26544 0.179 0.384
Income Less than $35K*** 28146 0.407 0.491 1229 0.843 0.364 26544 0.380 0.485
$35K to $75K*** 28146 0.349 0.477 1229 0.123 0.328 26544 0.363 0.481
$75K or more*** 28146 0.244 0.430 1229 0.035 0.184 26544 0.257 0.437
Unexpected Drop in Income*** 27585 0.406 0.491 1180 0.563 0.496 26134 0.397 0.489
Access to Credit and Assets Homeowner*** 27808 0.591 0.492 1205 0.166 0.372 26330 0.615 0.486
Credit Card*** 27369 0.748 0.434 1202 0.204 0.403 25948 0.781 0.413
* significantly different at the 10% level
** 5% level
*** 1% level
Demographics
Bank Participation
Full Sample
Table 1a: Banked versus Unbanked
FINRA State By State Financial Capability Study
Descriptive Statistics
Unbanked Banked
63
Obs. Mean S.D. Obs. Mean S.D. Obs. Mean S.D.
Unbanked 46547 0.075 0.263 - - - - - -
Banked 46547 0.925 0.263 - - - - - -
Gender Female*** 46547 0.489 0.500 3033 0.562 0.496 43514 0.483 0.500
Age Cohort 18-34*** 46547 0.221 0.415 3033 0.372 0.483 43514 0.209 0.407
35-54*** 46547 0.397 0.489 3033 0.423 0.494 43514 0.395 0.489
55+*** 46547 0.382 0.486 3033 0.204 0.403 43514 0.396 0.489
Race Caucasian*** 46547 0.717 0.450 3033 0.320 0.467 43514 0.749 0.433
Black*** 46547 0.116 0.320 3033 0.333 0.471 43514 0.098 0.298
Hispanic*** 46547 0.112 0.315 3033 0.295 0.456 43514 0.097 0.296
Asian*** 46547 0.038 0.191 3033 0.020 0.139 43514 0.039 0.194
Native American/Alaskan*** 46547 0.006 0.077 3033 0.018 0.134 43514 0.005 0.070
Other* 46547 0.011 0.105 3033 0.014 0.117 43514 0.011 0.104
Marital Status Married*** 46547 0.521 0.500 3033 0.278 0.448 43514 0.540 0.498
Single*** 46547 0.205 0.404 3033 0.389 0.488 43514 0.191 0.393
Divorced*** 46547 0.175 0.380 3033 0.259 0.438 43514 0.168 0.374
Widow*** 46547 0.099 0.299 3033 0.074 0.261 43514 0.101 0.301
Dependents Dependent Children*** 46547 0.300 0.458 3033 0.416 0.493 43514 0.290 0.454
Education Less than High School*** 46547 0.125 0.331 3033 0.403 0.491 43514 0.102 0.303
High School Degree*** 46547 0.293 0.455 3033 0.369 0.483 43514 0.287 0.452
College Degree*** 46547 0.195 0.396 3033 0.038 0.192 43514 0.207 0.405
Post College Education*** 46547 0.107 0.309 3033 0.009 0.094 43514 0.115 0.319
Work Status Full Time Employed*** 46350 0.537 0.499 3032 0.354 0.478 43318 0.552 0.497
Part Time* 46350 0.079 0.270 3032 0.086 0.280 43318 0.079 0.269
Homemaker*** 46350 0.071 0.257 3032 0.174 0.379 43318 0.063 0.243
Student 46350 0.006 0.079 3032 0.006 0.074 43318 0.006 0.079
Disabled*** 46350 0.054 0.227 3032 0.165 0.371 43318 0.045 0.208
Unemployed*** 46350 0.055 0.229 3032 0.139 0.346 43318 0.049 0.215
Retired*** 46350 0.197 0.398 3032 0.076 0.266 43318 0.206 0.405
Income Less than $35K*** 39907 0.391 0.488 2594 0.886 0.317 37313 0.351 0.477
$35K to $75K*** 39907 0.334 0.472 2594 0.103 0.304 37313 0.352 0.478
$75K or more*** 39907 0.275 0.447 2594 0.011 0.104 37313 0.297 0.457
Access to Credit and Assets Homeowner*** 46547 0.681 0.466 3033 0.238 0.426 43514 0.717 0.450
* significantly different at the 10% level
** 5% level
*** 1% level
Demographics
Bank Participation
Full Sample
Table 1b: Banked versus Unbanked
FDIC Survey of Unbanked and Underbanked Households
Descriptive Statistics
Unbanked Banked
64
Obs. Mean S.D. Obs. Mean S.D. Obs. Mean S.D.
Unbanked 6482 0.075 0.263 - - - - - -
Banked 6482 0.925 0.263 - - - - - -
Gender Female*** 6482 0.271 0.445 461 0.382 0.486 6021 0.262 0.440
Age Cohort 18-34*** 6482 0.210 0.407 461 0.308 0.462 6021 0.202 0.401
35-54** 6482 0.393 0.488 461 0.438 0.496 6021 0.389 0.488
55+*** 6482 0.397 0.489 461 0.254 0.435 6021 0.409 0.492
Race Caucasian*** 6482 0.708 0.455 461 0.366 0.482 6021 0.736 0.441
Black*** 6482 0.138 0.345 461 0.352 0.478 6021 0.121 0.326
Hispanic*** 6482 0.108 0.310 461 0.240 0.427 6021 0.097 0.296
Other 6482 0.046 0.210 461 0.041 0.198 6021 0.047 0.211
Marital Status Married*** 6482 0.505 0.500 461 0.243 0.429 6021 0.526 0.499
Single*** 6482 0.210 0.408 461 0.409 0.492 6021 0.194 0.396
Divorced*** 6482 0.193 0.394 461 0.275 0.447 6021 0.186 0.389
Widow* 6482 0.092 0.289 461 0.074 0.262 6021 0.093 0.291
Dependent Children** 6482 0.435 0.496 461 0.480 0.500 6021 0.432 0.495
Education Less than High School*** 6482 0.120 0.325 461 0.360 0.480 6021 0.100 0.300
High School Degree*** 6482 0.276 0.447 461 0.338 0.473 6021 0.271 0.445
College Degree*** 6482 0.242 0.428 461 0.024 0.154 6021 0.260 0.438
Post College Education*** 6482 0.127 0.333 461 0.009 0.096 6021 0.136 0.343
Work Status Self Employed*** 6482 0.114 0.318 461 0.079 0.270 6021 0.117 0.321
Full time Employed*** 6482 0.490 0.500 461 0.329 0.470 6021 0.504 0.500
Part Time Employed*** 6482 0.047 0.212 461 0.096 0.295 6021 0.043 0.203
Homemaker** 6482 0.015 0.122 461 0.031 0.173 6021 0.014 0.117
Student 6482 0.016 0.124 461 0.013 0.113 6021 0.016 0.125
Disabled*** 6482 0.070 0.255 461 0.205 0.404 6021 0.059 0.236
Unemployed*** 6482 0.069 0.254 461 0.191 0.393 6021 0.060 0.237
Retired*** 6482 0.193 0.395 461 0.083 0.277 6021 0.202 0.402
Income Less than $35K*** 6482 0.398 0.490 461 0.866 0.340 6021 0.360 0.480
$35K to $75K*** 6482 0.325 0.469 461 0.124 0.329 6021 0.342 0.474
$75K or more*** 6482 0.276 0.447 461 0.010 0.099 6021 0.298 0.457
Drop in Income*** 6482 0.253 0.435 461 0.372 0.483 6021 0.244 0.429
Access to Credit and Assets Homeowner*** 6482 0.601 0.490 461 0.165 0.371 6021 0.636 0.481
Credit Card*** 6482 0.680 0.467 461 0.095 0.293 6021 0.727 0.446
* significantly different at the 10% level** 5% level
*** 1% level
Demographics
Table 1c: Banked versus Unbanked
Survey of Consumer Finances
Bank Participation
Descriptive Statistics
BankedUnbankedFull Sample
65
Obs. Mean S.D. Obs. Mean S.D. Obs. Mean S.D.
Unbanked 27773 0.053 0.223 - - - - - -
Banked 27773 0.947 0.223 - - - - - -
# of Questions Correct*** 28146 2.989 1.443 1229 2.026 1.445 26544 3.062 1.412
Don't Know/Refused*** 28146 1.280 1.420 1229 1.982 1.669 26544 1.221 1.376
Savings Question*** 28146 0.777 0.416 1229 0.619 0.486 26544 0.791 0.407
Inflation Question*** 28146 0.645 0.478 1229 0.408 0.492 26544 0.662 0.473
Bond Price Question*** 28146 0.276 0.447 1229 0.195 0.396 26544 0.283 0.450
Mortgage Question*** 28146 0.756 0.429 1229 0.498 0.500 26544 0.775 0.417
Stock Diversification Q.*** 28146 0.534 0.499 1229 0.306 0.461 26544 0.550 0.497
* difference at the 10% level
** the 5% level
*** the 1% level
Table 2: Banked versus Unbanked subset of Financial Literacy Variables
Actual Knowledge
FINRA State By State Financial Capability Study
Bank Participation
Descriptive Statistics
Full Sample Unbanked Banked
66
Dependent Variable
Coefficient P-Value *
Gender (Male) Female -0.001 0.647
Age Cohort (35-55 years) 18-34 0.001 0.683
55+ -0.007 0.002 *
Race (White Non Hispanic) Black 0.008 0.000 *
Hispanic 0.004 0.140
Asian -0.005 0.246
Native American/Alaskan -0.003 0.454
Multiple Races -0.001 0.818
Marital Status (Married) Single 0.010 0.000 *
Divorced 0.011 0.000 *
Widow -0.002 0.625
Presence of Children Dependent Children 0.003 0.047 *
Education (High School Degree) Less than High School 0.025 0.000 *
Some College -0.009 0.000 *
College Degree -0.010 0.000 *
Post College Education -0.011 0.000 *
Work Status (Full Time) Self Employed 0.011 0.003 *
Part Time 0.004 0.186
Homemaker 0.015 0.000 *
Student 0.001 0.785
Disabled 0.009 0.019 *
Unemployed 0.030 0.000 *
Retired 0.000 0.892
Income ($35K to $75K) Less than $35K 0.015 0.000 *
$75K or more 0.000 0.928
Change in income Unexpected Drop in Income 0.003 0.089 *
Homeowner -0.016 0.000 *
Credit Card -0.046 0.000 *
Observations 26585
Pseudo R2 0.305
Observed P 0.051
Predicted P 0.013
Access to Credit and Assets
Table 3a: Unbanked Households Relative to All Banked
Demographics
Variables
Probit Regression - Marginal Effects Reported
FINRA State By State Financial Capability Study
Unbanked
67
Dependent Variable
Coefficient P-Value *
Gender (Male) Female -0.004 0.001 *
Age Cohort (35-55 years) 18-34 0.001 0.528
55+ -0.011 0.000 *
Race (White Non Hispanic) Black 0.050 0.000 *
Hispanic 0.031 0.000 *
Asian 0.004 0.382
Native American/Alaskan 0.077 0.000 *
Other 0.008 0.133
Marital Status (Married) Single 0.013 0.000 *
Divorced 0.013 0.000 *
Widow 0.006 0.034 *
Presence of Children Dependent Children 0.008 0.000 *
Education (High School Degree) Less than High School 0.020 0.000 *
Some College -0.012 0.000 *
College Degree -0.017 0.000 *
Post College Education -0.016 0.000 *
Work Status (Full Time) Part Time 0.004 0.042 *
Homemaker 0.029 0.000 *
Student -0.012 0.001 *
Disabled 0.032 0.000 *
Unemployed 0.024 0.000 *
Retired -0.004 0.055 *
Income ($35K to $75K) Less than $35K 0.033 0.000 *
$75K or more -0.015 0.000 *
Access to Credit and Assets Homeowner -0.025 0.000 *
Observations 39731
Pseudo R2 0.355
Observed P 0.074953
Predicted P 0.015675
Table 3b: Unbanked Households Relative to All Banked
Demographics
Variables
Probit Regression - Marginal Effects Reported
FDIC Survey of Unbanked and Underbanked Households
Unbanked
68
Dependent Variable
Coefficient P-Value *
Gender (Male) Female -0.005 0.017 *
Age Cohort (35-55 years) 18-34 -0.002 0.222
55+ -0.004 0.075 *
Race (White Non Hispanic) Black 0.022 0.002 *
Hispanic 0.015 0.013 *
Other 0.014 0.144
Marital Status (Married) Single 0.007 0.077 *
Divorced 0.005 0.134
Widow 0.001 0.734
Presence of Children Dependent Children 0.002 0.296
Education (High School Degree) Less than High School 0.013 0.009 *
College Degree -0.008 0.008 *
Post College Education -0.006 0.075 *
Work Status (Full Time) Self Employed 0.005 0.229
Part Time 0.007 0.181
Homemaker 0.016 0.265
Student -0.005 0.175
Disabled 0.016 0.021 *
Unemployed 0.016 0.012 *
Retired 0.000 0.914
Income ($35K to $75K) Less than $35K 0.014 0.004 *
$75K or more -0.007 0.072 *
Change in income Unexpected Drop in Income 0.003 0.183
Homeowner -0.013 0.001 *
Credit Card -0.041 0.000 *
Observations 6482
Pseudo R2 0.426
Observed P 0.075
Predicted P 0.009
Access to Credit and Assets
Table 3c: Unbanked Households Relative to All Banked
Federal Reserve Survey of Consumer Finances
Probit Regression - Marginal Effects Reported
Unbanked
Variables
Demographics
69
Dependent Variable
Coefficient P-Value * Coefficient P-Value *
# of Questions Correct -0.003 0.000 * - -
Savings Question - - -0.004 0.026 *
Inflation Question - - -0.003 0.088 *
Bond Price Question - - 0.002 0.356
Mortgage Question - - -0.006 0.000 *
Stock Diversification Q. - - -0.001 0.434
Gender (Male) Female -0.002 0.174 -0.002 0.200
Age Cohort (35-55 years) 18-34 0.000 0.951 0.000 0.856
55+ -0.007 0.003 * -0.007 0.003 *
Race (White Non Hispanic) Black 0.007 0.002 * 0.006 0.003 *
Hispanic 0.003 0.198 0.003 0.250
Asian -0.006 0.196 -0.006 0.166
Native American/Alaskan -0.002 0.542 -0.002 0.564
Multiple Races -0.001 0.781 -0.001 0.732
Marital Status (Married) Single 0.009 0.000 * 0.009 0.000 *
Divorced 0.011 0.000 * 0.011 0.000 *
Widow -0.003 0.517 -0.003 0.503
Presence of Children Dependent Children 0.003 0.058 * 0.003 0.065 *
Education (High School Degree) Less than High School 0.022 0.000 * 0.022 0.000 *
Some College -0.007 0.000 * -0.007 0.000 *
College Degree -0.009 0.000 * -0.009 0.000 *
Post College Education -0.010 0.001 * -0.010 0.001 *
Work Status (Full Time) Self Employed 0.011 0.002 * 0.011 0.002 *
Part Time 0.004 0.177 0.004 0.178
Homemaker 0.013 0.000 * 0.013 0.000 *
Student 0.001 0.718 0.001 0.815
Disabled 0.008 0.033 * 0.008 0.032 *
Unemployed 0.028 0.000 * 0.028 0.000 *
Retired 0.000 0.941 0.000 0.937
Income ($35K to $75K) Less than $35K 0.014 0.000 * 0.014 0.000 *
$75K or more 0.000 0.931 0.000 0.939
Change in income Unexpected Drop in Income 0.003 0.056 * 0.003 0.051 *
Homeowner -0.015 0.000 * -0.014 0.000 *
Credit Card -0.044 0.000 * -0.044 0.000 *
Observations 26585 26585
Pseudo R2 0.309 0.310
Observed P 0.051 0.051
Predicted P 0.013 0.013
Access to Credit and Assets
Table 4.1: Unbanked Households Relative to All Banked - Controlling for Financial Literacy
Probit Regression - Marginal Effects Reported
Unbanked
Demographics
Variables
Knowledge
Unbanked
FINRA State By State Financial Capability Study
70
Dependent Variable
Coefficient P-Value * Coefficient P-Value * Coefficient P-Value * Coefficient P-Value * Coefficient P-Value *
Savings Question Correct -0.005 0.029 * - - - - - - - -
Don't Know/Refused 0.002 0.387 - - - - - - - -
Inflation Question Correct - - -0.004 0.044 * - - - - - -
Don't Know/Refused - - 0.002 0.403 - - - - - -
Bond Price Question Correct - - - - 0.000 0.872 - - - -
Don't Know/Refused - - - - -0.001 0.583 - - - -
Mortgage Question Correct - - - - - - -0.009 0.000 * - -
Don't Know/Refused - - - - - - -0.001 0.730 - -
Stock Diversification Q. Correct - - - - - - - - -0.007 0.014 *
Don't Know/Refused - - - - - - - - -0.004 0.138
Gender (Male) Female -0.001 0.375 -0.002 0.298 -0.001 0.687 -0.001 0.427 -0.001 0.516
Age Cohort (35-55 years) 18-34 0.001 0.677 0.000 0.945 0.001 0.690 0.001 0.648 0.000 0.792
55+ -0.007 0.002 * -0.007 0.003 * -0.007 0.002 * -0.007 0.002 * -0.007 0.003 *
Race (White Non Hispanic) Black 0.008 0.000 * 0.007 0.001 * 0.008 0.000 * 0.007 0.002 * 0.008 0.000 *
Hispanic 0.003 0.150 0.003 0.171 0.003 0.145 0.003 0.228 0.003 0.162
Asian -0.006 0.198 -0.005 0.232 -0.005 0.245 -0.006 0.183 -0.005 0.248
Native American/Alaskan -0.002 0.523 -0.002 0.487 -0.003 0.451 -0.002 0.501 -0.003 0.475
Multiple Races -0.001 0.785 -0.001 0.824 -0.001 0.813 -0.001 0.750 -0.001 0.775
Marital Status (Married) Single 0.010 0.000 * 0.010 0.000 * 0.010 0.000 * 0.009 0.000 * 0.010 0.000 *
Divorced 0.011 0.000 * 0.011 0.000 * 0.011 0.000 * 0.011 0.000 * 0.011 0.000 *
Widow -0.002 0.575 -0.002 0.590 -0.002 0.631 -0.003 0.526 -0.002 0.602
Presence of Children Dependent Children 0.003 0.057 * 0.003 0.061 * 0.003 0.049 * 0.003 0.049 * 0.003 0.047
Education (High School Degree) Less than High School 0.023 0.000 * 0.024 0.000 * 0.025 0.000 * 0.024 0.000 * 0.024 0.000 *
Some College -0.008 0.000 * -0.008 0.000 * -0.009 0.000 * -0.008 0.000 * -0.008 0.000 *
College Degree -0.010 0.000 * -0.010 0.000 * -0.010 0.000 * -0.010 0.000 * -0.010 0.000 *
Post College Education -0.011 0.000 * -0.010 0.000 * -0.011 0.000 * -0.010 0.000 * -0.011 0.000 *
Work Status (Full Time) Self Employed 0.011 0.003 * 0.011 0.002 * 0.011 0.003 * 0.011 0.003 * 0.011 0.002 *
Part Time 0.004 0.164 0.004 0.187 0.004 0.189 0.004 0.211 0.004 0.171
Homemaker 0.014 0.000 * 0.014 0.000 * 0.015 0.000 * 0.014 0.000 * 0.014 0.000 *
Student 0.001 0.790 0.001 0.758 0.001 0.790 0.001 0.839 0.001 0.740
Disabled 0.008 0.027 * 0.008 0.023 * 0.009 0.019 * 0.008 0.025 * 0.009 0.020 *
Unemployed 0.029 0.000 * 0.029 0.000 * 0.030 0.000 * 0.028 0.000 * 0.030 0.000 *
Retired 0.000 0.939 0.000 0.917 0.000 0.901 0.000 0.903 0.000 0.898
Income ($35K to $75K) Less than $35K 0.015 0.000 * 0.015 0.000 * 0.015 0.000 * 0.014 0.000 * 0.015 0.000 *
$75K or more 0.000 0.993 0.000 0.982 0.000 0.918 0.000 0.991 0.000 0.935
Change in income Unexpected Drop in Income 0.003 0.070 * 0.003 0.077 * 0.003 0.095 * 0.003 0.057 * 0.003 0.089 *
Homeowner -0.015 0.000 * -0.015 0.000 * -0.016 0.000 * -0.015 0.000 * -0.016 0.000 *
Credit Card -0.046 0.000 * -0.046 0.000 * -0.046 0.000 * -0.045 0.000 * -0.046 0.000 *
Observations 26585 26585 26585 26585 26585
Pseudo R2 0.307 0.307 0.305 0.309 0.306
Observed P 0.051 0.051 0.051 0.051 0.051
Predicted P 0.013 0.013 0.013 0.013 0.013
Access to Credit and Assets
Unbanked
Table 4.2: Unbanked Households Relative to All Banked - Controlling for Financial Literacy
FINRA State By State Financial Capability Study
Probit Regression - Marginal Effects Reported
Variables
UnbankedUnbankedUnbanked
Demographics
Knowledge
Unbanked
71
Obs. Mean S.D. Obs. Mean S.D. Obs. Mean S.D.
Underbanked 26146 0.232 0.422 - - - - - -
Fully Banked 26146 0.768 0.422 - - - - - -
Gender Female* 26146 0.512 0.500 5894 0.520 0.500 20252 0.510 0.500
Age Cohort 18-34*** 26146 0.290 0.454 5894 0.413 0.492 20252 0.252 0.434
35-54*** 26146 0.381 0.486 5894 0.421 0.494 20252 0.369 0.483
55+*** 26146 0.329 0.470 5894 0.166 0.372 20252 0.379 0.485
Race White, Non-Hispanic*** 26146 0.698 0.459 5894 0.593 0.491 20252 0.730 0.444
Black, Non-Hispanic*** 26146 0.107 0.309 5894 0.181 0.385 20252 0.085 0.279
Hispanic*** 26146 0.128 0.334 5894 0.163 0.370 20252 0.118 0.322
Asian*** 26146 0.047 0.211 5894 0.034 0.180 20252 0.051 0.219
Native American/Alaskan** 26146 0.016 0.126 5894 0.027 0.163 20252 0.013 0.112
More than One Race* 26146 0.008 0.092 5894 0.010 0.099 20252 0.008 0.089
Marital Status Married*** 26146 0.552 0.497 5894 0.475 0.499 20252 0.575 0.494
Single*** 26146 0.265 0.441 5894 0.327 0.469 20252 0.247 0.431
Divorced*** 26146 0.137 0.344 5894 0.163 0.369 20252 0.129 0.336
Widow*** 26146 0.046 0.209 5894 0.036 0.185 20252 0.049 0.215
Dependent Children*** 26146 0.382 0.486 5894 0.525 0.499 20252 0.339 0.473
Education Less than High School*** 26146 0.026 0.160 5894 0.045 0.207 20252 0.021 0.142
High School Degree*** 26146 0.282 0.450 5894 0.348 0.476 20252 0.262 0.440
Some College*** 26146 0.427 0.495 5894 0.450 0.498 20252 0.420 0.494
College Degree*** 26146 0.166 0.372 5894 0.114 0.318 20252 0.181 0.385
Post College Education*** 26146 0.100 0.299 5894 0.043 0.203 20252 0.117 0.321
Work Status Self Employed 26146 0.081 0.274 5894 0.078 0.269 20252 0.082 0.275
Full Time Employed** 26146 0.374 0.484 5894 0.384 0.486 20252 0.370 0.483
Part Time 26146 0.096 0.295 5894 0.096 0.295 20252 0.096 0.295
Homemaker*** 26146 0.087 0.282 5894 0.108 0.311 20252 0.081 0.272
Student*** 26146 0.056 0.230 5894 0.063 0.243 20252 0.054 0.226
Disabled*** 26146 0.041 0.198 5894 0.062 0.241 20252 0.034 0.182
Unemployed*** 26146 0.084 0.278 5894 0.126 0.332 20252 0.072 0.258
Retired*** 26146 0.181 0.385 5894 0.083 0.276 20252 0.210 0.407
Income Less than $35K*** 26146 0.378 0.485 5894 0.505 0.500 20252 0.340 0.474
$35K to $75K 26146 0.363 0.481 5894 0.365 0.482 20252 0.362 0.481
$75K or more*** 26146 0.259 0.438 5894 0.130 0.336 20252 0.298 0.457
Unexpected Drop in Income*** 25779 0.397 0.489 5821 0.541 0.498 19958 0.353 0.478
Homeowner*** 25988 0.616 0.486 5855 0.414 0.493 20133 0.677 0.468
Credit Card*** 25619 0.782 0.413 5813 0.608 0.488 19806 0.835 0.372
* difference at the 10% level
** the 5% level
*** the 1% level
a. 398 households responded that they did not know or refused to report if they used alternative financial services
Underbanked Fully Banked
Table 5a: Underbanked versus Fully Banked
Bank Participation
Access to Credit and Assets
Demographics
FINRA State By State Financial Capability Study
Descriptive Statistics
All Bankeda
72
Obs. Mean S.D. Obs. Mean S.D. Obs. Mean S.D.
Underbanked 41813 0.203 0.402 - - - - - -
Fully Banked 41813 0.797 0.402 - - - - - -
Gender Female*** 41813 0.484 0.500 8110 0.507 0.500 33703 0.478 0.500
Age Cohort 18-34*** 41813 0.208 0.406 8110 0.287 0.452 33703 0.188 0.390
35-54*** 41813 0.395 0.489 8110 0.443 0.497 33703 0.383 0.486
55+*** 41813 0.397 0.489 8110 0.270 0.444 33703 0.429 0.495
Race Caucasian*** 41813 0.753 0.431 8110 0.601 0.490 33703 0.792 0.406
Black*** 41813 0.098 0.297 8110 0.206 0.404 33703 0.070 0.256
Hispanic*** 41813 0.095 0.293 8110 0.150 0.357 33703 0.081 0.273
Asian*** 41813 0.038 0.192 8110 0.016 0.125 33703 0.044 0.205
Native American/Alaskan*** 41813 0.005 0.071 8110 0.010 0.100 33703 0.004 0.061
Other*** 41813 0.011 0.104 8110 0.018 0.132 33703 0.009 0.096
Marital Status Married*** 41813 0.540 0.498 8110 0.452 0.498 33703 0.563 0.496
Single*** 41813 0.189 0.392 8110 0.257 0.437 33703 0.172 0.378
Divorced*** 41813 0.169 0.375 8110 0.221 0.415 33703 0.156 0.363
Widow*** 41813 0.101 0.301 8110 0.070 0.255 33703 0.109 0.311
Dependent Children*** 41813 0.291 0.454 8110 0.361 0.480 33703 0.273 0.445
Education Less than High School*** 41813 0.101 0.301 8110 0.155 0.362 33703 0.087 0.281
High School Degree*** 41813 0.286 0.452 8110 0.340 0.474 33703 0.273 0.445
College Degree*** 41813 0.208 0.406 8110 0.121 0.326 33703 0.231 0.421
Post College Education*** 41813 0.116 0.320 8110 0.052 0.222 33703 0.132 0.339
Work Status Full Time Employed*** 41625 0.551 0.497 8064 0.567 0.495 33561 0.547 0.498
Part Time 41625 0.079 0.270 8064 0.081 0.273 33561 0.079 0.269
Homemaker*** 41625 0.063 0.243 8064 0.077 0.266 33561 0.059 0.236
Student 41625 0.006 0.077 8064 0.006 0.079 33561 0.006 0.076
Disabled*** 41625 0.045 0.208 8064 0.076 0.266 33561 0.037 0.190
Unemployed*** 41625 0.049 0.215 8064 0.083 0.276 33561 0.040 0.196
Retired*** 41625 0.207 0.405 8064 0.109 0.312 33561 0.232 0.422
Income Less than $35K*** 36194 0.351 0.477 7365 0.483 0.500 28829 0.315 0.464
$35K to $75K 36194 0.352 0.477 7365 0.353 0.478 28829 0.351 0.477
$75K or more*** 36194 0.298 0.457 7365 0.164 0.370 28829 0.334 0.472
Homeowner*** 41813 0.718 0.450 8110 0.530 0.499 33703 0.765 0.424
* difference at the 10% level
** the 5% level
*** the 1% level
a. 1,029 households responded that they did not know or refused to report if they used alternative financial services. An additional
672 households did not answer these questions
Table 5b: Underbanked versus Fully Banked
Bank Participation
Access to Credit and Assets
Demographics
FDIC Survey of Unbanked and Underbanked Households
Descriptive Statistics
All Bankeda Underbanked Fully Banked
73
Obs. Mean S.D. Obs. Mean S.D. Obs. Mean S.D.
Underbanked 26146 0.232 0.422 - - - - - -
Fully Banked 26146 0.768 0.422 - - - - - -
# of Questions Correct*** 26146 3.078 1.403 5894 2.673 1.356 20252 3.200 1.393
Don't Know/Refused*** 26146 1.203 1.360 5894 1.387 1.401 20252 1.147 1.342
Savings Question*** 26146 0.794 0.404 5894 0.734 0.442 20252 0.812 0.390
Inflation Question*** 26146 0.666 0.472 5894 0.550 0.498 20252 0.701 0.458
Bond Price Question*** 26146 0.285 0.451 5894 0.232 0.422 20252 0.301 0.459
Mortgage Question*** 26146 0.779 0.415 5894 0.719 0.450 20252 0.797 0.402
Stock Diversification Q.*** 26146 0.554 0.497 5894 0.439 0.496 20252 0.589 0.492
* difference at the 10% level
** the 5% level
*** the 1% level
Table 6: Underbanked versus Fully Banked subset of Financial Literacy Variables
Bank Participation
Actual Knowledge
FINRA State By State Financial Capability Study
Descriptive Statistics
All Banked Underbanked Fully Banked
74
Dependent Variable
Coefficient P-Value *
Gender (Male) Female -0.014 0.035 *
Age Cohort (35-55 years) 18-34 0.051 0.000 *
55+ -0.076 0.000 *
Race (White Non Hispanic) Black 0.121 0.000 *
Hispanic 0.007 0.530
Asian -0.036 0.056 *
Native American/Alaskan 0.073 0.002 *
Multiple Races 0.017 0.470
Marital Status (Married) Single -0.015 0.122
Divorced 0.025 0.012 *
Widow 0.041 0.025 *
Presence of Children Dependent Children 0.099 0.000 *
Education (High School Degree) Less than High School 0.033 0.134
Some College -0.021 0.007 *
College Degree -0.071 0.000 *
Post College Education -0.088 0.000 *
Work Status (Full Time) Self Employed -0.021 0.092 *
Part Time -0.043 0.000 *
Homemaker -0.014 0.238
Student -0.067 0.000 *
Disabled 0.048 0.006 *
Unemployed -0.022 0.061 *
Retired -0.052 0.000 *
Income ($35K to $75K) Less than $35K 0.009 0.296
$75K or more -0.075 0.000 *
Change in income Unexpected Drop in Income 0.091 0.000 *
Homeowner -0.102 0.000 *
Credit Card -0.120 0.000 *
Observations 25174
Pseudo R2 0.149
Observed P 0.234
Predicted P 0.198
Access to Credit and Assets
Demographics
Table 7a: Underbanked Households Relative to Fully Banked
FINRA State By State Financial Capability Study
Probit Regression - Marginal Effects Reported
Variables
Underbanked
75
Dependent Variable
Coefficient P-Value *
Gender (Male) Female -0.002 0.734
Age Cohort (35-55 years) 18-34 0.008 0.251
55+ -0.049 0.000 *
Race (White Non Hispanic) Black 0.209 0.000 *
Hispanic 0.062 0.000 *
Asian -0.097 0.000 *
Native American/Alaskan 0.192 0.000 *
Other 0.116 0.000 *
Marital Status (Married) Single 0.007 0.343
Divorced 0.036 0.000 *
Widow -0.009 0.388
Presence of Children Dependent Children 0.035 0.000 *
Education (High School Degree) Less than High School 0.044 0.000 *
Some College -0.015 0.016 *
College Degree -0.101 0.000 *
Post College Education -0.099 0.000 *
Work Status (Full Time) Part Time -0.013 0.130
Homemaker -0.005 0.619
Student -0.075 0.005 *
Disabled 0.051 0.000 *
Unemployed 0.064 0.000 *
Retired -0.075 0.000 *
Income ($35K to $75K) Less than $35K 0.025 0.000 *
$75K or more -0.055 0.000 *
Access to Credit and Assets Homeowner -0.105 0.000 *
Observations 36024
Pseudo R2 0.123
Observed P 0.214
Predicted P 0.185
Demographics
Table 7b: Underbanked Households Relative to Fully Banked
FDIC Survey of Unbanked and Underbanked Households
Probit Regression - Marginal Effects Reported
Variables
Underbanked
76
Coefficient P-Value * Coefficient P-Value *
# of Questions Correct -0.015 0.000 * - -
Savings Question - - -0.018 0.044 *
Inflation Question - - -0.029 0.000 *
Bond Price Question - - -0.009 0.225
Mortgage Question - - 0.012 0.154
Stock Diversification Q. - - -0.025 0.001 *
Gender (Male) Female -0.023 0.001 * -0.024 0.000 *
Age Cohort (35-55 years) 18-34 0.047 0.000 * 0.045 0.000 *
55+ -0.074 0.000 * -0.073 0.000 *
Race (White Non Hispanic) Black 0.113 0.000 * 0.114 0.000 *
Hispanic 0.004 0.703 0.005 0.678
Asian -0.040 0.035 * -0.038 0.041 *
Native American/Alaskan 0.073 0.001 * 0.073 0.001 *
Multiple Races 0.016 0.478 0.017 0.457
Marital Status (Married) Single -0.017 0.085 * -0.016 0.104 *
Divorced 0.025 0.014 * 0.025 0.013 *
Widow 0.040 0.028 * 0.041 0.022 *
Presence of Children Dependent Children 0.097 0.000 * 0.097 0.000 *
Education (High School Degree) Less than High School 0.028 0.202 0.028 0.204
Some College -0.014 0.085 * -0.013 0.096 *
College Degree -0.061 0.000 * -0.060 0.000 *
Post College Education -0.078 0.000 * -0.076 0.000 *
Work Status (Full Time) Self Employed -0.020 0.111 -0.019 0.121
Part Time -0.043 0.000 * -0.042 0.000 *
Homemaker -0.017 0.164 -0.016 0.176
Student -0.066 0.000 * -0.064 0.000 *
Disabled 0.045 0.010 * 0.045 0.010 *
Unemployed -0.023 0.049 * -0.022 0.062 *
Retired -0.052 0.000 * -0.051 0.000 *
Income ($35K to $75K) Less than $35K 0.005 0.549 0.006 0.476
$75K or more -0.072 0.000 * -0.071 0.000 *
Change in income Unexpected Drop in Income 0.092 0.000 * 0.091 0.000 *
Homeowner -0.099 0.000 * -0.100 0.000 *
Credit Card -0.117 0.000 * -0.118 0.000 *
Observations 25174 25174
Pseudo R2 0.151 0.151
Observed P 0.234 0.234
Predicted P 0.197 0.197
Table 8.1: Underbanked Households Relative to Fully Banked - Controlling for Financial Literacy
FINRA State By State Financial Capability Study
Probit Regression - Marginal Effects Reported
Variables
Access to Credit and Assets
Demographics
Knowledge
Underbanked Underbanked
77
Dependent Variable
Coefficient P-Value * Coefficient P-Value * Coefficient P-Value * Coefficient P-Value * Coefficient P-Value *
Savings Question Correct -0.054 0.000 * - - - - - - - -
Don't Know/Refused -0.047 0.000 * - - - - - - - -
Inflation Question Correct - - -0.059 0.000 * - - - - - -
Don't Know/Refused - - -0.036 0.001 * - - - - - -
Bond Price Question Correct - - - - -0.032 0.000 * - - - -
Don't Know/Refused - - - - -0.035 0.000 * - - - -
Mortgage Question Correct - - - - - - -0.024 0.038 * - -
Don't Know/Refused - - - - - - -0.033 0.016 * - -
Stock Diversification Q. Correct - - - - - - - - -0.084 0.000 *
Don't Know/Refused - - - - - - - - -0.058 0.000 *
Gender (Male) Female -0.016 0.017 * -0.018 0.008 * 0.007 0.511 -0.014 0.044 * -0.017 0.011 *
Age Cohort (35-55 years) 18-34 0.051 0.000 * 0.044 0.000 * 0.009 0.289 0.051 0.000 * 0.048 0.000 *
55+ -0.076 0.000 * -0.074 0.000 * 0.009 0.330 -0.077 0.000 * -0.075 0.000 *
Race (White Non Hispanic) Black 0.118 0.000 * 0.114 0.000 * 0.012 0.106 0.120 0.000 * 0.115 0.000 *
Hispanic 0.006 0.603 0.004 0.731 0.011 0.128 0.007 0.542 0.005 0.626
Asian -0.037 0.049 * -0.038 0.042 * 0.018 0.046 * -0.035 0.063 * -0.037 0.047 *
Native American/Alaskan 0.071 0.002 * 0.071 0.002 * 0.025 0.016 * 0.072 0.002 * 0.072 0.002 *
Multiple Races 0.016 0.495 0.016 0.503 0.024 0.008 * 0.016 0.501 0.014 0.536
Marital Status (Married) Single -0.016 0.107 -0.016 0.093 * 0.010 0.262 -0.015 0.126 -0.016 0.101 *
Divorced 0.025 0.013 * 0.025 0.013 * 0.010 0.138 0.025 0.014 * 0.025 0.012 *
Widow 0.041 0.022 * 0.042 0.021 * 0.019 0.046 * 0.040 0.027 * 0.040 0.028 *
Presence of Children Dependent Children 0.098 0.000 * 0.097 0.000 * 0.008 0.385 0.099 0.000 * 0.098 0.000 *
Education (High School Degree) Less than High School 0.032 0.142 0.031 0.158 0.023 0.026 * 0.033 0.136 0.032 0.152
Some College -0.019 0.018 * -0.018 0.027 * 0.008 0.427 -0.021 0.007 * -0.017 0.033 *
College Degree -0.068 0.000 * -0.065 0.000 * 0.008 0.167 -0.070 0.000 * -0.065 0.000 *
Post College Education -0.085 0.000 * -0.082 0.000 * 0.009 0.101 * -0.088 0.000 * -0.082 0.000 *
Work Status (Full Time) Self Employed -0.021 0.090 * -0.020 0.106 0.012 0.082 * -0.021 0.095 * -0.020 0.111
Part Time -0.043 0.000 * -0.043 0.000 * 0.011 0.096 * -0.043 0.000 * -0.042 0.000 *
Homemaker -0.014 0.227 -0.014 0.226 0.012 0.086 * -0.014 0.233 -0.015 0.217
Student -0.067 0.000 * -0.065 0.000 * 0.013 0.055 * -0.067 0.000 * -0.066 0.000 *
Disabled 0.047 0.007 * 0.047 0.007 * 0.018 0.040 * 0.049 0.006 * 0.048 0.006 *
Unemployed -0.022 0.060 * -0.022 0.062 * 0.011 0.084 * -0.022 0.058 * -0.021 0.068 *
Retired -0.052 0.000 * -0.051 0.000 * 0.011 0.181 -0.051 0.000 * -0.052 0.000 *
Income ($35K to $75K) Less than $35K 0.008 0.318 0.007 0.371 0.008 0.375 0.009 0.297 0.006 0.497
$75K or more -0.073 0.000 * -0.073 0.000 * 0.008 0.261 -0.075 0.000 * -0.073 0.000 *
Change in income Unexpected Drop in Income 0.090 0.000 * 0.091 0.000 * 0.007 0.398 0.091 0.000 * 0.090 0.000 *
Homeowner -0.102 0.000 * -0.102 0.000 * 0.008 0.616 -0.102 0.000 * -0.101 0.000 *
Credit Card -0.120 0.000 * -0.120 0.000 * 0.009 0.784 -0.120 0.000 * -0.118 0.000 *
Observations 25174 25174 25174 25174 25174
Pseudo R2 0.150 0.151 0.150 0.149 0.151
Observed P 0.234 0.234 0.234 0.234 0.234
Predicted P 0.198 0.198 0.198 0.198 0.197
Variables
Knowledge
Demographics
Access to Credit and Assets
Table 8.2: Underbanked Households Relative to Fully Banked - Controlling for Financial Literacy
FINRA State By State Financial Capability Study
Probit Regression - Marginal Effects Reported
Underbanked Underbanked Underbanked Underbanked Underbanked