1. The Effect of Financial Literacy on Payday Loan Usage
Econ 4980
Kurt Kunzler

2. ABSTRACT
The purpose of this study is to examine the relationship between financial literacy and the
use of payday lending services. This question is important because many people
throughout the world are becoming victim of these "predatory" loan programs. Typical
loans charge an upward of 400% APR for their services and the already financially
strapped single mothers taking out these loans (highest demographic) are propelled into
an endless debt cycle leading to bankruptcy. An intercept survey method was used to
acquire individual data on payday lending use, financial literacy, and other economic
and demographic indicators. The survey was conducted outside a discount food market,
located within one block of Check City, a popular payday lender. Results show that
payday lending use is inversely related to financial literacy. The study illustrates some of
the first use of utility theory and budget constraints as it relates to financial literacy. As
financial knowledge increases, high interest loan usage decreases. This suggests the
importance for public offerings of financial literacy courses at community centers or
public libraries. If nothing else, the results confirm the efforts made by the State of Utah
for one financial literacy course to be taught in all high schools.

3. The purpose of this research is to examine the relationship between financial
literacy and payday loan usage. Specifically, I will directly examine the customers of
“payday lenders” or “small dollar loans”. My question is thus:
Does basic financial literacy decrease one’s likelihood of taking out a “payday”
loan?
This question is important because many people throughout the world are
becoming victims of “predatory” loan programs. Typical payday loans charge an upward
of 400% APR for their services and financially strapped single mothers taking out these
loans (highest demographic) are propelled into an endless cycle of debt, eventually
leading to bankruptcy. As most have noticed if they live in Ogden, these loan
establishments are everywhere! Check City being the most notorious franchise. I have
had a difficult time understanding why anyone would take a loan with such obvious risk
involved and so I will be conducting a survey to examine how these establishments are
gaining customers.
A little background on the industry is important. Access to affordable credit is not
a problem for most Americans, however it is for some. Whether medical bills, job loss,
spiraling debt, or whatever the cause, many Americans are not able to make ends meet. In
a preliminary survey I conducted online, 80% of payday loans went to paying off existing
bills. The majority of payday loan users are white females between 25 and 44 years old
(PEW Charitable Trust). However, African Americans without college degrees earning
less than $40,000 per year are more likely to use payday loans. Many military families

4. are also falling prey to these establishments, however, discussing military pay is not for
this paper . PEW found that 69% use the loans to cover recurring bills and only 16% for
unexpected expenses. Twelve million Americans borrow a $375 payday loan an average
of 8 times per year, paying $520 in interest each year. 85% do not have a college degree,
which led me to wonder if lack of financial knowledge really plays a role in consumer
demand or if these users are really choosing to maximize their utility. Remember, APR is
upwards of 400% for payday loans compared to 16% for credit cards.
Literature Review
The following paragraphs deal with what research has taught us so far about the
effect of financial literacy on payday loan usage. First of all, there has not been a lot of
research on this subject. Most of the research on the payday loan market seeks to examine
the relationship between payday loans and: government policy, bank overdraft fees, and
competition among financial institutions. However, these really do not get to the root of
the problem with payday lending, namely, that 12 million Americans are using payday
loans that charge interest upwards of 400% APR, PEW (2012), how can they afford
something like this? The issue at hand is not whether banks are competing with payday
lenders using overdraft fees; rather it is how can we reduce the debt cycles caused by
these exorbitant loans. The fact that so many users do not have college degrees has led
myself, as well as others, to evaluate the likelihood that the real problem boils down to
consumer information and education. The following studies illustrate the current

5. knowledge about the benefits of financial literacy with respect to the payday lending
market.
Causes of Default
Dobbie and Skiba (2013) find that payday loan users who take out larger loans
actually have lower default rates. However, those who explicitly choose to borrow larger
amounts actually have higher default rates. This is an interesting observation due to the
fact that it alludes to the assumption that adverse selection is in play during these
transactions. The authors find that a $50 increase in a customers credit line decreases
their probability of default by 4-6 percentage points overall, which is a 22-33% decrease
when you take into account that the average rate of default in their study was 19%. Using
regression kink analysis the authors find a similar result, a 2-5 percentage point reduction
in the default rate. This suggests that moral hazard is not present in these transactions,
which is surprising given that there is strong evidence of it in lending markets according
to the authors (Adams et al. 2009). The authors do find evidence of adverse selection
because borrowers who chose a $50 larger loan increased their probability of default by
5-9 percentage points overall which is a 28-44% increase from the average default rate.
Similar results were found using regression kink.
Essentially what this comes down to is that borrowers, who know that they are
going to default, or know they have a high chance of default, are more likely to choose a
larger loan. If they are likely to default anyway, why not reap the benefit of a larger loan?

6. However there is evidence to show that the default rate could be increasing because
people who need larger loans are usually very illiquid, Dobbie and Skiba (2013). This
study does not provide enough evidence to say what the welfare effect of decreasing
information asymmetry would be, however it does highlight the need for examination of
information asymmetry for both the lender and the borrower.
Information Availability
In pondering about the need for payday lending I kept coming back to the
question of financial literacy. Why would anyone take out a high interest, short-term loan
such as a payday loan? Do they think it is a good deal? Most certainly not, it must come
down to desperation. There are no other alternatives, right? If that is the case, it doesn’t
matter what the terms of the loan are, they need to receive the cash. Any additional
information would not bias their decision to take out a payday loan, or at least, it
wouldn’t alter the dollar amount they choose to borrow. These thoughts are along the
lines of the assumptions made by Bertrand and Morse (2011). They assume that
individuals are fully aware of the benefits and consequences when taking out a payday
loan. This being said, additional information should not sway their decision to take out
the loan since they are already well informed of the costs. The authors performed a
randomized test to see whether this assumption holds. Using three treatments, the authors
provide information that the borrower is most likely not aware of.

7. The first is APR. Bertrand and Morse provide information on the APR that these
borrowers have to pay and compare it to alternative forms of credit such as: car loans,
credit cards, and mortgages. The second piece of information given is the total dollar
amount of the accumulated fees for having a $300 payday loan for: 2 weeks, 1 month, 2
months, or 3 months. The information is compared to equivalent fees on a credit card.
Lastly, information on refinancing is given to the borrowers. This is essentially the
frequency of payments and how long it will take them to repay their payday loan. The
results are as follows:
• APR Treatment: 16% less likely to borrow than control group.
• Dollar Treatment: 23% less likely to borrow than control group.
• Refinance Treatment: 12% less likely to borrow than control group.
The study provides significant evidence that payday loan transparency, thought to
be the most important factor as evidenced by information listing requirement regulations,
is actually not as relevant as financial understanding. Payday loans are typically
transparent due to federal laws. However, if consumers don’t understand what the
information means, the point of the policy is moot. This is why financial literacy is
important to reduce the negative impacts of payday loans and all high interest loans.

8. Financial literacy
Chatterjee, Goetz & Palmer (2009) evaluate the characteristics of payday loan
users. Similar to the PEW Charitable Trust’s findings, these authors find a negative
relationship between payday lending and educational attainment. To be more specific
about what type of education plays a role in decreasing payday loan usage, Disney &
Gathergood (2013) ask whether financial literacy effects credit choice. Not to anyone’s
surprise, it does. This is especially true of high interest loans such as payday loans.
Disney and Gathergood survey 3000 households in the UK using
YOUGOV.UK’s Debt Transparency Survey. Using principles characterized by Hung et
al. (2009) about how to measure actual financial knowledge rather than perceived
knowledge, the authors developed the following questions:
Simple interest question
1. ‘‘Cheryl owes £1000 on her bank overdraft and the interest rate she is charged is
15% per year. If she didn’t pay anything off, at this interest rate, how much
money would she owe on her overdraft after 1 year?’’ £850 / £1000 / £1150 /
£1500 / Do not know.
Interest compounding question
2. ‘‘Sarah owes £1000 on her credit card and the interest rate she is charged is 20%
per year compounded annually. If she didn’t pay anything off, at this interest rate,
how many years would it take for the amount she owes to double?’’ Less than 5
years / Between 5 and 10 years / More than 10 years / Do not know.
Minimum payments question

9. 3. ‘‘David has a credit card debt of £3000 at an Annual Percentage Rate of 12% (or
1% per month). He makes payments of £30 per month and does not gain any
charges or additional spending on the card. How long will it take him to pay off
this debt?’’ Less than 5 years / Between 5 and 10 years / More than 10 years /
None of the above, he will continue to be in debt / Do not know.
They find that individuals who borrow consumer credit are 13% less likely to
answer the compound interest question correctly and 23% less likely to answer the
minimum payments question correctly. Those in the top 20% of debt-to-income ratio are
50% less likely to answer the questions correctly. Also those with lower financial literacy
scores are more likely to hold high interest forms of credit, like payday loans. This
evidence supports the hypothesis that improving financial literacy will decrease the use of
payday loans. Thus coinciding with Bertrand et al. (2011), that consumers are still not
fully aware of what they are doing when taking out a payday loan. Many studies find that
excessive or harmful debt in general is closely related to financial literacy, Varum et. al.
(2014).
Theory
The basic theory illustrated in this paper is that of Utility Maximization. Utility is
defined as: "The satisfaction that a person receives from his or her economic
activities”(Nicholson et al., 2010). Utility is often a comparison of two goods, however,
one of those goods can be “all other things” so the models can be broad. Indifference

10. curves are often used to illustrate this principle. In general, holding all else constant,
economists assume that people will make the utility maximizing choice, the one that
brings them the most satisfaction. This theory as it related to payday lending is not very
prevalent in the literature. The underlying theory is present of course but is never clearly
displayed by the authors.
Bertrand and Morse (2011) assume that individuals will be fully aware of the
benefits and consequences when taking out a payday loan and additional information
should not sway their decisions to take out a payday loan. This implies that individuals
are maximizing their utility in every choice. However, the results do show that
individuals are actually not as informed as the authors assumed. When exposed to better
information about the true costs of these loans, they are significantly less likely to take
out a payday loan. This illustrated the utility principle with a constraint to information.
People are maximizing their utility, as far as they know.
Webster et al. (2012) argue that although many view these loans are predatory and
ill advised, users actually benefit from them. This is to say the users actually maximize
their utility when faced with other alternatives, i.e. not eating or having their electricity
shut off. The authors explain that in times of economic trouble, there really aren’t any
other alternatives to choose from. This is consistent with Bertrand and Morse’s first
assumptions. They too thought that people would not use these high interest loans if there
were a reasonable alternative. However, they found that assumption to be false.
Webster’s argument that there is no alternative may not be completely destroyed by
Bertrand because when people were given full information about the cost of their loans,
80% of people were still as likely to use a payday loan. This illustrated that there is

11. definitely a utility-based decision going on for the majority of users. The 20% that fall off
may not be in dire need of the loans.
To illustrate the need for an information constraint in utility maximization of
payday loans, think of the traditional budget constraint. You maximize your utility based
on the resources given at a particular time. Information in this case, is the resource given
that constrains how much a person can maximize their utility. Disney & Gathergood
(2013) illustrate the need for financial literacy, or financial information. When consumers
are more informed, they are less likely to practice poor financial habits such as taking out
a payday loan. Financial literacy is the constraint to which an individual’s utility can be
maximized. I hypothesize that increasing ones financial literacy actually shifts this
constraint outward (see graph 4) allowing users to receive a greater amount of utility than
was otherwise possible.
Data and Methods
This research is ultimately aimed at determining how financial literacy impacts
one’s decision to use a “Payday” or “Title” loan. The sample comes from random
individuals surveyed throughout the Ogden Utah area both online and in person. The
survey consists of 25 questions, which includes a financial literacy quiz throughout the
survey. The quiz consists of the following 6 questions:
• What is APR?
• What is the typical interest rate on a car loan?
• Which credit score is best?

12. • Simple Interest Question
• Compound Interest Question
• Number of Payments Question
(See table 2)
The participants are graded using a total score method and a weighted score
method. The total score method is simply the percentage of correct answers. The
weighted score method assigns percentage weights to the questions in order to account
for the more challenging financial literacy questions. Answering the first three questions
correctly and scoring a total score of 50% is not nearly as impressive as answering the
last three questions correctly and scoring 50%, thus a weighted quiz score is necessary.
The weights assigned are as follows:
Q1 – 5%, Q2 – 10%, Q3 – 10%, Q4 – 15%, Q5 – 30%, Q6 – 30%
This weighting accounts for the better measures of financial literacy, which are
the compound interest question and the number of payments question. These questions
are most likely to be found in the consumer credit world and thus are a more valuable
representation of reality. An evaluation of this financial literacy quiz is included in table
2 and the results section of the paper. Simple interest is useful, however it is very
impractical due to the fact that almost all loans incorporate some sort of compounding.
We do not throw out the usefulness of simple interest knowledge, no, instead we have
assigned it a lower weight to account for impracticality. The first three questions are

13. really to gage whether the user has a basic understanding of credit and current interest
rates. The quiz includes questions from one mentioned previously that was issued to three
thousand United Kingdom households by Disney & Gathergood (2013). In modeling this
research it was imperative to use a consistent measure of financial literacy to that of the
research. However, I do recognize the objective nature of the weights.
The rest of the survey consists of demographic information and gauging financial
stress as well as finding our dependent variable, payday loan use. These variables along
with the rest of the descriptive statistics are found in Table 1. The sample consists of 35
males and 61 females, 14 of which indicated the number of loans borrowed this year,
while 17 indicated that they “use” payday loans at least yearly. Of the 96 observations, 67
are white and 16 are Hispanic. The survey was designed to collect enough variables about
the subjects to rule out the issues that arise from omitted variable bias. However, our
sample size is small enough that it will be difficult to find significant results about the
payday loan users. The coefficients should at least provide preliminary results and
indicate the correct directions of the economic effects.
The difficulties of doing a paper survey caused a loss of about 10
observations due to incomplete answers from the respondents. However, we ended with a
total of 96 complete responses, which, is sufficient for this paper. The data is not
necessarily representative of the population however. In order to have true representation,
we would have only 4% of survey takers that use payday loans, consistent with Pew
Charitable Trust’s findings . For my research purposes and with the time limitations
present, I decided to do targeted sampling. Using Facebook advertising, I ran an ad
campaign for a $20 amazon gift card drawing. The users could enter by completing the

14. survey and leaving their email address. We targeted people in Ogden who are interested
in payday loans, debt counseling or other types of financial instruction. This sample was
fairly consistent with our second sample of in-person surveys. The in-person surveys
were issued outside of a discount grocery store, during the month of November. I set up a
booth with a poster begging the question, “Do you want a dollar?”. There were printouts
of dollar bills on the poster as well. I’m not sure which attracted individuals to the table,
the poster or me standing there with a stack of $1 bills. To my surprise, only 70% of
people who took the survey actually accepted the dollar, the other 30% decided they
didn’t need it. Either way, it provided some incentive for individuals to take the survey.
The reason for the discount store was that I assumed I could find a higher number of
individuals who may have financial troubles and thus a higher percentage of payday loan
users. This assumption proved to be true and I was very fortunate to increase the
percentage of users to about 18%.
Dummy variables are used to control for things like home ownership,
financial stress, marriage status, employment status, and education. Normal OLS
estimation is coupled with a Logit model and Poisson regression in order to evaluate the
robustness of my findings. The Logit model is useful when the dependent variable is
binary, 1 or 0, which is the case for the variable “users” (see table 1). Poisson is
especially useful for count data, which is the dependent variable “loans”. OLS is also
used with the dependent variable “loans” which for purposes of OLS estimation, is
treated as a continuous random variable.

15. Econometric Methods
The models used in this paper incorporate two forms of the dependent
variable, “users” and “loans”. Reported results were different between the collected
surveys. The reason that some had used 0 payday loans this year but stated that they use
payday loans yearly or monthly is not well understood. For that reason I have decided on
the following six models that will explain the effect of financial literacy on payday loan
usage.
Model 1:
loans = β0 + β1 wscore + β2age + β3male + β4hispanic + β5otherrace + β6couple
+ β7divorced + β8single + β9other + β10educ + β11rent + β12havesavings +
β13HHincome + u
In this model the frequency of payday loan use was regressed against the control
variable, “wscore” or weighted score, controlling for demographic variables such as age,
race, sex, income, education and the like. White married females are the base group in
this regression. Also rent is used to compare against the base group of homeowners or
those living in a home without payment of rent. Lastly the “havesavings” variable is a
binary variable that is true if a person indicated that they had savings above $500, which
is about the average loan size. One would naturally assume that you would not take out a
high interest loan if you have savings equal to that loan.

16. Model 2:
loans = β0 + β1 wscore + β2age + β3male + β4hispanic + β5otherrace + β6couple
+ β7divorced + β8single + β9other + β10educ + β11rent + β12havesavings +
β13HHincome + β14stress + u
Model 2a(poisson):
loans = β0 + β1 wscore + β2age + β3male + β4hispanic + β5otherrace + β6couple
+ β7divorced + β8single + β9other + β10educ + β11rent + β12havesavings +
β13HHincome + β14stress + u
Model two includes the variable “stress” which accounts for the self-reported
level of financial stress on a scale of 1 to 5, with 5 being the highest level of stress. This
variable became important to control for in order to allow certain variables like income to
become more significant. Also Norvilitis et al. (2006) found that stress is very closely
related to debt. I recognize the issues of multicollinearity that arise in this regression,
however, there only seems to be sufficient correlation between household income and
whether a person rents or not. However, not controlling for income causes more problems
due to omitted variable bias. These simple OLS estimations are not really sufficient for
our model due to the small sample of payday loan users and the fact that “loans” is really
count data. For these reasons a Poisson model, 2a, has been used as a better estimator.

17. Model 3:
users = β0 + β1 wscore + β2age + β3male + β4hispanic + β5otherrace + β6couple
+ β7divorced + β8single + β9other + β10educ + β11rent + β12havesavings +
β13HHincome + β14stress + u
Model 4:
users = β0 + β1 wpass + β2age + β3male + β4hispanic + β5otherrace + β6couple +
β7divorced + β8single + β9other + β10educ + β11rent + β12havesavings +
β13HHincome + β14stress + u
Model 4a (poisson):
loans = β0 + β1 wpass + β2age + β3male + β4hispanic + β5otherrace + β6couple +
β7divorced + β8single + β9other + β10educ + β11rent + β12havesavings +
β13HHincome + β14stress + u
Models three and four use logit models, which are a more preferred
estimator of the coefficients for binary dependent variables. They also use the variable
“users” due to the higher number of observations for that variable. Model four includes a
new variable for the financial literacy score, “wpass’” this means that the individual has a
weighted score of 60% or higher. This was incorporated due to the low financial literacy
scores shown throughout the entire sample (see table 2). Model 4a again is used as a
better estimator and this time is using the more relaxed measure of financial literacy
“wpass”, essentially pass or fail.

18. These methods of estimation are someone complex but do not include
interaction effects, elasticity models or quadratic effects. This does not mean that those
types of models would not apply to the payday loan market, rather, I found after
estimating those models that they provided no further results and instead complicated my
findings. Had the sample size been larger, I think some of those models could have been
very effective. However, taking the log of payday loans would result in taking the log of
zero, which would cause error in your estimation. Therefore elasticity models do not
seem like a viable option. It is sufficient to say that they are outside of the scope of this
paper. Heteroskedasticity is present due to the small sample size and thus our OLS
regression is testing for robust standard errors.
It useful to note that a Poisson regression of equation 2 found much more
statistically significant results than the previous regressions. Poisson regression actually
found results that are consistent with many of the a priori expectations that I had in
writing this paper. Poisson is useful for count data especially when the dependent
variable takes on relatively few values. The side-by-side results of the Poisson
regressions can be found in tables 9-1and 9-2. Overall, Poisson regression seems to have
the greatest interpretation for the coefficients.
The literature on financial literacy find that females have lower financial literacy
levels than their male counterparts, significant at the .01% level Hung et. al. (2009). They
also find that college students have higher levels of financial literacy than those who did
not graduate college. In keeping with historical evidence, the findings in this paper
confirm those of Hung et. al., which is good evidence that our sample is sufficient for at
least preliminary results.

19. Results
The results will follow in order of appearance in this paper, thus we will first
discuss the results of the financial literacy quiz found in table 2. Payday loan users were
found to have lower total and weighted financial literacy scores that non-users. Users
scored an average of 38.9% and 28.6% on the total and weighted quizzes respectively.
Contrasting that which was found for non-users who scored 43.1% and 35.9%
respectively. This is evidence that payday loan users do in fact have lower financial
literacy that non-users. An interesting result was that individuals with more than 2 credit
cards actually scored the highest on both quizzes. This could be due to the fact that they
are granted credit because of their good payment history, which ultimately stems from an
understanding of budgeting and other financial practices. Also it could be the case that
these individuals have more experience with interest rates than those who do not
regularly use consumer credit. Thus they are able to answer the interest rate questions.
The later is more likely in this sample. Also we see that education plays a role in
financial literacy. Those with higher levels of education had higher scores on both
measures of the quiz. Lastly, Hispanics had lower scores on both measures of the quiz as
compared to all other races, whites having the highest scores.
If you look at graphs 1-3 you can see some interesting comparisons as well.
Graph 1 shows a scatter diagram of the number of payday loans and their weighted quiz
score. Though the observations are few, it is apparent that individuals who use the
majority of loans have a weighted score under 40% with half of them being under 20%.
Graph 2 shows how the level of financial stress is related to the number of payday loans
used this year. There is a slight upward trend in this data but to say that one causes the

20. other is difficult just by eyeballing a graph. Financial stress could certainly lead to a
payday loan and the opposite is true as well. But, it is important to know that the
relationship exists. Graph 3 illustrates the relationship between payday loans and
educational attainment. The most interesting finding is that there are no payday loans
taken out after 15 years of education, which is considered as “some college”. Those with
a bachelors degree or higher did not use payday loans, similar to Roche (2012) findings.
Our sample contained 18 people who received a bachelors degree or higher, 43 who had
“some college” and 35 with a high school diploma or less. It is well known that education
increases income and completing your bachelors is pertinent in order to be competitive in
the workforce. Those that have not, are more likely to earn lower wages and run into
financial trouble.
Summary results for each model are found in tables 3-8. Model 1 had mostly
insignificant results and the financial literacy score was completely statistically
insignificant with a p-value of .839 and a t-statistic of 0.20. However there were a few
statistically significant findings. The first was that going from not renting to renting leads
a person on average to use .79 more loans than someone who is not renting. A person
obviously cannot have a fraction of a loan, they either have a loan or they don’t but since
OLS estimates are averages, the result is sufficient and the signs on the coefficients are
consistent with the literature, Chatterjee et. al. (2009) and Disney et. al. (2013). Having
savings above $500 reduced payday loans by 0.83, which makes sense because payday
loans are on average, $500 loans. “HHincome” is not significant do to the tight range of
incomes in the sample. Model 2 controls for the level of perceived financial stress and the
results are almost identical and financial stress is statistically significant.

21. Model 2a and 4a will be discussed last. The third and fourth models used logit
regression and the dependent variable was “users”. Both showed the correct coefficients
for the financial literacy scores but still there was no statistical significance. Even model
four, which uses “wpass” (a more relaxed measure of literacy) as the focus variable,
shows no statistical significance. “Rent” became less significant and “havesavings”,
“HHincome”, and “stress” were still statistically significant estimates of payday loan
usage, however, household income is not consistent with the literature or logic.
Model 2a and 4a using Poisson regression are the most consistent with the
literature and my a priori expectations. Model 2a using the “wscore” focus variable
shows no statistical significance for financial literacy, but we do find more significant
results. Males on average use 1.18 more loans than their female counterparts significant
at the 5% level. Hispanics on average use 3.13 more loans as compared to other races,
significant at 1%. Members of unmarried couples use 1 more payday loan on average
than their counterparts, significant at 10%. Education had a significant positive
relationship with payday loans but graph 3 shows that this is only for those without 4-
year college degrees. It seems that those who had completed “some college” actually
were the most likely to use a payday loan in this sample. This could be due to financial
problems, which rendered them unable to pay for college and finish. It could be due to
lack of motivation, which has effected their wages, or a number other factors that will not
be discussed in this paper.
Model 4a showed similar results except “wpass” became statistically significant at
the 10% level. This is the only model that shows a statistically significant result for the
effect of financial literacy on payday loan usage. Those who pass the weighted financial

22. literacy quiz with at least a 60% are 51% less likely to use payday loans than those who
fail, on average, 51% was found using the equation 100*[exp(-.714)-1] where exp is the
cumulative exponential distribution and -.714 is the coefficient on “wpass”. The rest of
the findings are almost identical to Model 2a. Poisson distribution is the best way to
interpret the dependent variable “loans” because it is count data where many of the
responses are zero. It makes sense that lack of financial knowledge would lead to
increased payday loans, especially since we know it leads to increased debt, Norvilitis et.
al. (2006).
To my knowledge these results expand the literature because no one has directly
interpreted the effects of financial literacy on payday loan usage with a Poisson model,
though some have come close with consumer credit, Disney et. al. (2013). Based on the
Poisson I reject my null hypothesis that financial literacy does not lead to a decrease in
payday loan usage and conclude that financial literacy does in fact reduce payday loan
usage. Increased financial literacy may in fact increase the amount of utility that
consumers can enjoy do to greater information ability and greater diversity of choices.
However further research needs to be done in order to explicitly examine the effects. It
would appear that information asymmetry is in fact hurting consumers that have low
incomes and savings less than $500. Payday lenders have an advantage over their
customers. I do recommend that a more thorough measure of financial literacy be used in
the future. The reason being: a score of 60% is not a great indication of financial literacy.

23. Conclusion
As stated before, the purpose of this research is to examine the relationship
between financial literacy payday loan usage. Due to budget and time constraints, the
sample size was good but having at least 50 payday loan users would have provided
much better results. However, due to the small population of payday loan users, it is
difficult to obtain such a sample size. With the population being at about 4% one would
need to collect 1250 surveys to find 50 that have used payday loans, assuming you
sampled completely randomly. Using my targeted sampling method I would need to
increase my sample size 2.5x in order to reach 50 users. I also recommend using a better
test of financial literacy than I used in my research. Though I borrowed questions from
previous research, six questions is not a sufficient measure of one’s financial knowledge.
This research can be applied to the growing concern for high school financial
education. Utah has been pushing for better financial education of high school students
and many other states are following suit. Further research could expand on the financial
benefits that arise from becoming fiscally literate and learning to avoid high interest
consumer loans. Further research could also evaluate how payday loan users break the
debt cycle and make ends meet without using payday loans. That research could be
utilized by debt consulting companies and non-profits to better relieve their clients from
the burdens of debt. Also further research could examine the relationship between
financial security and happiness. Those who have stable finances could be happier and
stable finances may stem from a better understanding of finance. Remember, there are
twelve million Americans that use payday loans, with an average of 400% interest, in
order to pay their bills and make ends meet; education can certainly help.

24. References
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Payday Borrowing. Journal Of Finance, 66(6), 1865-1893.
Chatterjeez, S., Goetz, J., & Palmer, L. (2009). An Examination of Short-Term
Borrowing in the United States. Global Journal Of Business Research, 3(2), 1-8.
Disney, R., & Gathergood, J. (2013). Financial Literacy and Consumer Credit
Portfolios. Journal Of Banking And Finance, 37(7), 2246-2254.
doi:http://dx.doi.org.hal.weber.edu:2200/10.1016/j.jbankfin.2013.01.013
Dobbie, W., & Skiba, P. M. (2013). Information Asymmetries in Consumer
Credit Markets: Evidence from Payday Lending. American Economic Journal: Applied
Economics, 5(4), 256-282. doi:http://dx.doi.org.hal.weber.edu:2200/10.1257/app.5.4.256
Edmiston, K. D. (2011). Could Restrictions on Payday Lending Hurt Consumers?.
Federal Reserve Bank Of Kansas City Economic Review, 96(1), 31-61.
Edmiston, K. D. (2011). Could Restrictions on Payday Lending Hurt Consumers?.
Federal Reserve Bank Of Kansas City Economic Review, 96(1), 31-61.
Harris, G. A. (2011). Charlatans on the Move. Public Integrity, 13(4), 353-370.
Li, M., Mumford, K. J., & Tobias, J. L. (2012). A Bayesian Analysis of Payday
Loans and Their Regulation. Journal Of Econometrics, 171(2), 205-216.
doi:http://dx.doi.org.hal.weber.edu:2200/10.1016/j.jeconom.2012.06.010
Melzer, B. T., & Morgan, D. P. (2015). Competition in a Consumer Loan Market:
Payday Loans and Overdraft Credit. Journal Of Financial Intermediation, 24(1), 25-44.
doi:http://dx.doi.org.hal.weber.edu:2200/10.1016/j.jfi.2014.07.001
Morgan, D. P., Strain, M. R., & Seblani, I. (2012). How Payday Credit Access
Affects Overdrafts and Other Outcomes. Journal Of Money, Credit, And Banking, 44(2-
3), 519-531
Morgan, D. P., Strain, M. R., & Seblani, I. (2012). How Payday Credit Access
Affects Overdrafts and Other Outcomes. Journal Of Money, Credit, And Banking, 44(2-
3), 519-531.
Nicholson, W., & Snyder, C. (2010). Utility and Choice. In Intermediate
microeconomics and its application (11th ed.). Mason, Ohio: South-Western/Cengage
Learning.
Norvilitis, J. M., Merwin, M. M., Osberg, T. M., Roehling, P. V., Young, P., &
Kamas, M. M. (2006). Personality Factors, Money Attitudes, Financial Knowledge, and

25. Credit-Card Debt in College Students. Journal Of Applied Social Psychology, 36(6),
1395-1413. doi:10.1111/j.0021-9029.2006.00065.x
Roche, T. (2012). Payday Lending In America: Who Borrows, Where They
Borrow, and Why. Retrieved October 1, 2015, from
http://www.pewtrusts.org/~/media/legacy/uploadedfiles/pcs_assets/2012/pewpaydaylendi
ngreportpdf.pdf
Stango, V. (2012). Some New Evidence on Competition in Payday Lending
Markets. Contemporary Economic Policy, 30(2), 149-161.
Varum, C., & Kolyban, A. (2014). Wealth and Credit Compliance: Does
Economic Literacy Matter?. Financial Services Review, 23(4), 325-339.
Webster IV, W. M. (2012). Payday Loan Prohibitions: Protecting Financially
Challenged Consumers or Pushing Them over the Edge?. Washington & Lee Law
Review, 69(2), 1051-1092.
Wood, W. C. (2013). Payday Lending in Virginia: An Empirical Study of
Customers. Virginia Economic Journal, 1883-90.

26. Graph 1: Weighted Score and Payday Loans
Graph 2: Financial stress and Payday Loans
0246810
#ofpaydayloans
0 .2 .4 .6 .8 1
Weighted Average Score on financial literacy quiz
0246810
#ofpaydayloans
1 2 3 4 5
Financial stress level 1-5 (5 being worst)

27. Graph 3: Education and Payday Loans
Graph 4: Utility Theory
0246810
#ofpaydayloans
10 15 20
Number of years of education

28. Table 1: Descriptive Statistics
Variable
Obs
Mean
Std.
Dev.
Min
Max
variable
label
loans
96
0.489583
1.615678
0
10
Number
of
payday
loans
users
96
0.177083
0.383743
0
1
Those
who
use
payday
loans
at
least
yearly
tscore
96
0.421875
0.230627
0
0.83
Total
Score
on
financial
literacy
quiz
wscore
96
0.347917
0.258326
0
0.9
Weighted
Average
Score
on
financial
literacy
wpass
96
0.270833
0.446723
0
1
Received
a
weighted
score
of
60%
or
better.
age
96
43.854170
14.880560
17
80
Age
male
96
0.364583
0.483840
0
1
Male=1
hispanic
96
0.166667
0.374634
0
1
Hispanic=1
otherrace
96
0.135417
0.343964
0
1
Otherrace=1
couple
96
0.114583
0.320191
0
1
Couple=1
(members
of
unmarried
couple)
divorced
96
0.239583
0.429070
0
1
Divorced=1
single
96
0.104167
0.307080
0
1
Single=1
other
96
0.041667
0.200875
0
1
Other=1
(Not
single,
married,
divorced,
couple)
educ
96
14.687500
2.381121
9
21
Number
of
years
of
education
rent
96
0.416667
0.495595
0
1
Renting=1
havesavings
96
0.479167
0.502188
0
1
Savings
above
$500
HHincome
96
44.218750
31.231970
5
100
Household
income
stress
96
1.927083
1.180943
1
5
Financial
stress
level
1-­‐5
(5
being
worst)

29. Table 2: Financial Literacy Scores
Observations
Mean
Std.
Dev.
Min
Max
Average
Total
Score
on
Financial
Literacy
Quiz:
42.2%
23.1%
0%
83.3%
Payday
Loan
Users
14
36.9%
22.80%
0%
66.7%
Non
-­‐
Users
82
43.1%
23.10%
0%
83.3%
Credit
Cards
over
2
18
50.0%
21.40%
16.7%
83.3%
White
67
46.7%
21.8%
0%
83.3%
Hispanic
16
26.0%
20.2%
0%
66.7%
Other
Race
13
38.5%
24.8%
0%
66.7%
No
High
School
Diploma
7
16.7%
16.7%
0%
50.0%
High
School
Diploma
28
37.5%
22.0%
0%
83.3%
At
least
some
college
55
47.6%
21.9%
0%
83.3%
Average
Weighted
Score
on
Financial
Literacy
Quiz
34.8%
25.8%
0%
90.0%
Payday
Loan
Users
14
28.6%
24.2%
0%
80.0%
Non
-­‐
Users
82
35.9%
26.1%
0%
90.0%
Credit
Cards
over
2
18
41.4%
26.1%
5.0%
90.0%
White
67
38.8%
26.3%
0%
90.0%
Hispanic
16
20.0%
17.1%
0%
55.0%
Other
Race
13
32.3%
26.8%
0%
80.0%
No
High
School
Diploma
7
14.3%
17.2%
0%
45.0%
High
School
Diploma
28
31.8%
24.5%
0%
90.0%
At
least
some
college
55
38.5%
25.9%
0%
90.0%

30. Table 3: Results from Model 1 Regression.
loans Coef. Robust Std. Err. t P>t
wscore .1284501 .6307222 0.20 0.839
age -.0050483 .009752 -0.52 0.606
male .1940773 .2683657 0.72 0.472
hispanic -.133499 .2731538 -0.49 0.626
otherrace -.3201888 .373529 -0.86 0.394
couple .2752899 .8613347 0.32 0.750
divorced .4656581 .576714 0.81 0.422
single -.2277323 .2758246 -0.83 0.411
other -.7451603 .4710154 -1.58 0.117
educ .0097302 .0511428 0.19 0.850
rent .7904011 .3453791 2.29 0.025**
havesavings -.8249051 .3666932 -2.25 0.027**
HHincome .0093961 .0050881 1.85 0.068*
_cons .0803333 .987025 0.08 0.935
*Significant at 10%
**Significant at 5%
***Significant at 1%

31. Table 4: Results from Model 2 Regression.
loans Coef. Std. Err. t P>t
wscore .093594 .6184284 0.15 0.880
age -.0013039 .0087588 -0.15 0.882
male .1420787 .2735449 0.52 0.605
hispanic -.0000908 .2804741 -0.00 1.000
otherrace -.3325161 .3328528 -1.00 0.321
couple .1886932 .8561039 0.22 0.826
divorced .3133731 .5098808 0.61 0.541
single -.091072 .3434448 -0.27 0.792
other -.5408869 .476386 -1.14 0.260
educ .0185609 .0488897 0.38 0.705
rent .7833999 .3667544 2.14 0.036**
havesavings -.6461 .3253605 -1.99 0.050**
HHincome .0108724 .0055077 1.97 0.052*
stress .336012 .1576812 2.13 0.036**
_cons -.9749578 1.162497 -0.84 0.404
*Significant at 10%
**Significant at 5%
***Significant at 1%

32. Table 5: Results from Model 2a Poisson Regression.
loans Coef. Std. Err. z P>z
wscore -.8018114 .6817524 -1.18 0.240
age .0183029 .0287714 0.64 0.525
male 1.187317 .4828306 2.46 0.014**
hispanic 3.124997 1.025171 3.05 0.002***
otherrace -.4096028 .6220489 -0.66 0.510
couple 1.002904 .5987437 1.68 0.094*
divorced 1.312155 .6924423 1.89 0.058*
single -15.78358 1635.172 -0.01 0.992
other -18.28019 2968.094 -0.01 0.995
educ .2010598 .1150564 1.75 0.081*
rent 5.041175 1.09549 4.60 0.000***
havesavings -3.285934 .9100885 -3.61 0.000***
HHincome .0761584 .0163543 4.66 0.000***
stress .7287848 .1835314 3.97 0.000***
_cons -13.54259 3.667535 -3.69 0.000
*Significant at 10%
**Significant at 5%
***Significant at 1%

33. Table 6: Results from Model 3 Logit regression.
users Coef. Std. Err. z P>z
wscore -1.465685 1.375551 -1.07 0.287
age -.0313975 .0399215 -0.79 0.432
male .6345953 .8494726 0.75 0.455
hispanic 1.227149 1.129557 1.09 0.277
otherrace -.0642869 1.120551 -0.06 0.954
couple -1.065947 1.142875 -0.93 0.351
divorced .4528169 1.070372 0.42 0.672
single 0 (omitted)
other 0 (omitted)
educ -.1848514 .2040137 -0.91 0.365
rent 1.66734 1.081328 1.54 0.123
havesavings -2.0191 1.044729 -1.93 0.053*
HHincome .0320265 .0178886 1.79 0.073*
stress .5303389 .3075693 1.72 0.085*
_cons -.1376731 3.887793 -0.04 0.972
*Significant at 10%
**Significant at 5%
***Significant at 1%

34. Table 7: Results from Model 4 Logit regression.
users Coef. Std. Err. z P>z
wpass -.9112385 .8709053 -1.05 0.295
age -.0319794 .0401788 -0.80 0.426
male .6867019 .8615914 0.80 0.425
hispanic 1.371034 1.110058 1.24 0.217
otherrace .0254866 1.131885 0.02 0.982
couple -1.21855 1.191711 -1.02 0.307
divorced .466878 1.080251 0.43 0.666
single 0 (omitted)
other 0 (omitted)
educ -.1708687 .2067398 -0.83 0.409
rent 1.711811 1.105447 1.55 0.121
havesavings -2.196499 1.052074 -2.09 0.037**
HHincome .0337341 .0182965 1.84 0.065*
stress .5601235 .3113054 1.80 0.072*
_cons -.7289626 3.909641 -0.19 0.852
*Significant at 10%
**Significant at 5%
***Significant at 1%

35. Table 8: Results from Model 4a with Poisson.
loans Coef. Std. Err. z P>z
wpass -.7143583 .438107 -1.63 0.103*
age .0137152 .0301511 0.45 0.649
male 1.197271 .4920748 2.43 0.015**
hispanic 3.48784 1.11229 3.14 0.002***
otherrace -.526182 .6382667 -0.82 0.410
couple .7169961 .6263652 1.14 0.252
divorced 1.448731 .7300281 1.98 0.047**
single -15.89528 1640.958 -0.01 0.992
other -18.43843 2952.335 -0.01 0.995
educ .2417627 .1215486 1.99 0.047**
rent 5.433828 1.222337 4.45 0.000***
havesavings -3.596435 .982168 -3.66 0.000***
HHincome .0810639 .0174831 4.64 0.000***
stress .7540255 .1881375 4.01 0.000***
_cons -14.59342 3.900248 -3.74 0.000
*Significant at 10%
**Significant at 5%
***Significant at 1%

36.
Table
9-­‐1:
Logit
to
Poission
Comparison
Results from Model 4 Logit regression. Results from Model 4a Poisson.
users Coef. P>z Coef. P>z
wpass -0.9112385 0.295 -0.7143583 0.103*
age -0.0319794 0.426 0.0137152 0.649
male 0.6867019 0.425 1.197271 0.015**
hispanic 1.3710340 0.217 3.48784 0.002***
otherrace 0.0254866 0.982 -0.526182 0.41
couple -1.2185500 0.307 0.7169961 0.252
divorced 0.4668780 0.666 1.448731 0.047**
single 0.0 -15.89528 0.992
other 0.0 -18.43843 0.995
educ -0.1708687 0.409 0.2417627 0.047**
rent 1.7118110 0.121 5.433828 0.000***
havesavings -2.1964990 0.037** -3.596435 0.000***
HHincome 0.0337341 0.065* 0.0810639 0.000***
stress 0.5601235 0.072* 0.7540255 0.000***
_cons -0.7289626 0.852 -14.59342 0
*Significant at 10%
**Significant at 5%
***Significant at 1%

37. Table
9-­‐2:
Logit
to
Poission
Comparison
Results from Model 2 Regression. Results from Model 2a Poisson
loans Coef. P>t Coef. P>z
wscore 0.0935940 0.88 -0.8018114 0.24
age -0.0013039 0.882 0.0183029 0.525
male 0.1420787 0.605 1.187317 0.014**
hispanic -0.0000908 1 3.124997 0.002***
otherrace -0.3325161 0.321 -0.4096028 0.51
couple 0.1886932 0.826 1.002904 0.094*
divorced 0.3133731 0.541 1.312155 0.058*
single -0.0910720 0.792 -15.78358 0.992
other -0.5408869 0.26 -18.28019 0.995
educ 0.0185609 0.705 0.2010598 0.081*
rent 0.7833999 0.036** 5.041175 0.000***
havesavings -0.6461000 0.050** -3.285934 0.000***
HHincome 0.0108724 0.052* 0.0761584 0.000***
stress 0.3360120 0.036** 0.7287848 0.000***
_cons -0.9749578 0.404 -13.54259 0
*Significant at 10%
**Significant at 5%
***Significant at 1%