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2
2016

How did the Great Recession
affect payday loans?
Sumit Agarwal, Tal Gross, and Bhashkar Mazumder

Introduction and summary

1

The sharp decline in the U.S. economy that began in 2007, commonly referred to as the Great Recession,
made it very difficult for many Americans to borrow. According to the Senior Loan Officer Survey conducted by the Board of Governors of the Federal Reserve System, banks across the country dramatically
tightened credit card standards during the first two years of the Great Recession. From 2008 to 2010, the
average number of credit cards per person fell from roughly 2.2 to 1.7, and the total limit on all credit card
balances fell from around $25,000 to $21,000.1 Given this sharp contraction in consumer credit, an obvious
question is whether borrowers responded by shifting from conventional borrowing to more unconventional
sources of credit. In particular, did Americans turn to payday lenders as an alternative? Payday lenders
provide short-term, unsecured loans, typically of relatively small amounts of money at relatively high
rates of interest. The typical payday loan is $300 for two weeks at an annualized interest rate of more than
350 percent. Payday lending is a controversial practice. Nearly all states regulate the industry, 13 states
have made payday lending effectively illegal, and an additional five states have imposed severe restrictions
on the interest that can be charged on payday loans.
In this article, we aim to answer two important questions: first, whether payday borrowing rose during the
Great Recession; and second, whether the use of payday loans expanded beyond low-income borrowers to
include more middle-income borrowers. In 2008, Senator Elizabeth Warren, at the time a law professor at
Harvard University, argued that “as the economy has worsened … payday loans have increasingly become
crutches for those higher up the economic scale” (Christensen, 2008). Many articles in the popular press
in recent years have voiced the same concern (for example, White, 2013; Popper and Thompson, 2011;
and Marshall, 2015).

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To our knowledge, there has been relatively little research documenting trends in payday borrowing across
different subgroups. Therefore, it is not clear whether payday lending is growing overall or whether it is
being used by a broader swath of the population than in the past. To answer these questions, we analyze
both publicly available data and confidential payday borrowing records. Using the Survey of Consumer
Finances (SCF, conducted by the Board of Governors of the Federal Reserve System), we find that from
2007 to 2009 there was a notable increase in payday borrowing, but that payday utilization then remained
flat from 2009 through 2013. Unfortunately the SCF does not measure payday borrowing prior to 2007,
so we have relatively limited data on trends prior to the Great Recession. Therefore, it is not clear whether
the increase from 2007 to 2009 was simply a continuation of a secular trend (that is, unrelated to the business
cycle) or a cyclical phenomenon (directly related to the business cycle). The SCF data also suggest that
more middle-income borrowers have in fact been using payday loan services since 2007, as Elizabeth
Warren and others have long speculated.
We also study several alternative data sources to identify time trends in payday borrowing. Those analyses lead
to a different conclusion: that payday borrowing did not change dramatically during the Great Recession.
First, we find that the revenues of payday lending firms grew steadily from 2006 through 2013 but that
there was no break in trend during the financial crisis. Second, we use data from Google searches and find no
evidence that more Internet users were searching for online payday loans during the economic downturn.
Third, we find no evidence of an increase in licensed payday lenders in California during the recession.
In fact, if anything, there appears to have been a decline. Fourth, we use proprietary data on payday loan
applications in Nevada, a state that was severely affected by the housing downturn, and find no relationship
between foreclosure rates and payday lending. Fifth, we use proprietary data from a bricks-and-mortar payday
lender in the Midwest and find no evidence of a change in loan volume during the economic downturn.
However, there is suggestive evidence from this lender that more middle-income borrowers have been
turning to payday loans since the recession began.

2

Overall, our findings are mixed. On the one hand, we find some evidence from survey data of an increase
in payday borrowing but only in the first two years after the onset of the Great Recession with no further
increase. On the other hand, a variety of alternative approaches suggest that the impacts of the Great
Recession on payday borrowing have been minimal.
In the next section, we provide some background on payday borrowing. We then discuss the evidence
based on publicly available data and present our analysis of actual lending data from payday lenders.

Background on payday lending
To take out a payday loan, a customer visits a payday lender with his or her most recent paycheck stub and
bank statement. Because of these requirements, the unbanked typically cannot take out a payday loan.
Payday lenders typically operate by requiring that a customer write a personal check for the amount being
borrowed plus a fee. The lender cashes the check once the loan has matured. For example, in the case of a
loan for $350, the borrower would write a check or authorize a bank to draw $400, post-dating the check
to the next payday, usually ten to 14 days hence. The cost of payday borrowing is described to the consumer
as a fee: a fixed-dollar cost per $100 in borrowing. The implied annual interest rate is usually over 400 percent,
which is disclosed to the consumer in the loan paperwork. The payday lender verifies the borrower’s employment and bank information but does not run a formal credit check. On payday, if the borrower is not able
to cover the check, he or she may return to the lender and refinance the loan, incurring another $50 fee,
which is paid in cash (Morse, 2011).2

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The payday loan industry did not exist a few decades ago (Caskey, 2005), but as of 2013 there were approximately 18,000 payday lending stores, and total loan volume in the industry was approximately $45 billion
(Hecht, 2014). Given how costly the loans are, why are they so popular? One explanation is that credit-constrained
households are unable to borrow through traditional means (banks or credit cards) and instead resort to
borrowing from payday lenders when faced with a sudden income shock or unexpected expense. Several
studies provide descriptive evidence suggesting that credit constraints are an important factor.3 Bhutta, Skiba,
and Tobacman (2015) find that those who turn to payday loans typically have no other forms of credit
available. Other survey research suggests that misperceptions among consumers concerning the costs of
payday loans are also an important factor. For example, Martin (2010) finds that most payday borrowers
are not able to accurately describe the annual percentage rate (APR) on payday loans or predict the total
dollar cost of the loans.4
Consumers also use payday loans much more frequently than might be expected. According to Martin
(2010), 63 percent of payday borrowers reported using the loans for regular, recurring monthly bills and
expenses. Kenneth (2008) finds that convenience factors draw consumers to particular payday lenders.
Unlike qualifying for other forms of credit, obtaining cash through payday loans is fast and relatively easy.
Payday loans may also help individuals avoid the stigma of admitting to friends, family, or a financial
institution that they are financially constrained. Many studies show that repeat customers make up the vast
majority of payday customers.5

3

The literature is mixed on whether payday lending enhances or reduces consumer welfare. The simplest
neoclassical model suggests that additional forms of credit should weakly improve consumer welfare.
This notion is supported by several empirical studies that show that restrictions on payday borrowing lead
to worse outcomes. Morgan and Strain (2008), for instance, find that bans on payday lending increase the
rate at which consumers bounce checks or file for bankruptcy. Indeed, Morgan, Strain, and Seblani (2012)
find that returned check numbers and overdraft fee income at banks increase after payday credit bans. Similarly,
Zinman (2010) finds that restrictions on payday loans in Oregon led to a reduction in the overall financial
condition of Oregon households. Finally, Morse (2011) found that greater access to payday credit reduces
the number of foreclosures that follow natural disasters in California.
Other studies, however, have found that access to payday loans may reduce welfare. Carrell and Zinman
(2014) find a decline in job performance and readiness among U.S. Air Force personnel stationed in areas
with payday loan availability. Skiba and Tobacman (2011) find that access to payday loans leads to more
bankruptcy filings. Melzer (2011) finds that access to payday loans leads to increased self-reported difficulty
in paying bills and causes individuals to delay expenditures on needed health care.

Survey data on payday lending use
We begin our analysis with the Survey of Consumer Finances (SCF). This is the only survey we know of
that measures how payday borrowing evolved through the Great Recession. Although the SCF has been
running since the 1980s, the 2007 to 2009 panel of the SCF was the first survey to ask specifically about
payday loans. Because it is a panel, it also allows us to compare the same households at two points in time.
The survey asks respondents: “During the past year, have you (or anyone in your family living here) taken
out a ‘payday loan,’ that is, borrowed money that was supposed to be repaid in full out of your next paycheck?”
The same question was repeated in 2010 and 2013 but to entirely different sets of households in each year.
In table 1 and figure 1, we show the fraction of respondents who reported taking out a payday loan in
2007, 2009, 2010, and 2013. Table 1 also shows the same results broken down by age, race, education,
and income quartile.

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TABLE 1

	
	
	

Probability of household head having a payday loan last year
Full sample
Age
	<35
	35–44
	45–54
	55–64
	65+
Race/ethnicity
	White
	Black
	Hispanic
	Other
Education level
	 No high school diploma/GED
	 High school diploma/GED

4

	 Some college
	 College degree

2007
0.026
(0.001)

2009
0.041
(0.002)

2010
0.039
(0.001)

2013
0.042
(0.001)

0.052
(0.004)
0.028
(0.003)
0.024
(0.003)
0.017
(0.003)
0.003
(0.001)

0.073
(0.005)
0.059
(0.005)
0.039
(0.004)
0.021
(0.003)
0.007
(0.002)

0.057
(0.003)
0.051
(0.003)
0.040
(0.003)
0.034
(0.003)
0.013
(0.002)

0.063
(0.003)
0.058
(0.003)
0.036
(0.003)
0.040
(0.003)
0.017
(0.002)

0.019
(0.001)
0.047
(0.005)
0.048
(0.007)
0.019
(0.006)

0.031
(0.002)
0.102
(0.007)
0.046
(0.006)
0.023
(0.006)

0.030
(0.001)
0.082
(0.004)
0.038
(0.003)
0.043
(0.006)

0.030
(0.001)
0.095
(0.005)
0.055
(0.004)
0.021
(0.004)

0.029
(0.004)
0.030
(0.003)
0.038
(0.004)
0.014
(0.002)

0.065
(0.006)
0.047
(0.003)
0.060
(0.005)
0.018
(0.002)

0.050
(0.004)
0.043
(0.002)
0.059
(0.003)
0.021
(0.001)

0.031
(0.003)
0.050
(0.002)
0.077
(0.004)
0.021
(0.001)

0.041
(0.003)
0.027
(0.003)
0.006
(0.002)
0
(.)

0.055
(0.003)
0.048
(0.003)
0.020
(0.002)
0.001
(0.001)

0.052
(0.002)
0.053
(0.002)
0.026
(0.002)
0.004
(0.001)

0.054
(0.002)
0.050
(0.003)
0.033
(0.002)
0.005
(0.001)

Total household income quartile
	 1st quartile
	 2nd quartile
	 3rd quartile
	 4th quartile

Notes: This table presents estimates for the probability of having a payday loan in the previous year stratified by survey
year (2007, 2009, 2010, and 2013) and head of household characteristics. Standard errors are reported in parentheses
unless otherwise denoted. Total household income refers to income from all sources in the previous year before taxes and
other deductions. These estimates are weighted using revised Kennickell–Woodburn consistent weights. 	
Sources: Data are from the Board of Governors of the Federal Reserve System, 2007–09, 2010, and 2013 Survey of
Consumer Finances (SCF). For 2007 and 2009, head of household characteristics are from the 2007 data.

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FIGURE 1

Probability of having a payday loan last year

	
	

A. Overall
0.040
0.030
0.020
0.010
0.000

2007

’08

’09

’10

’11

’12

’13

B. By age

C. By race/ethnicity

0.08

0.12
0.10
0.08
0.06
0.04
0.02
0.00

0.06
0.04
0.02
0.00

2007

’09

<35

35–44

’10
45–54

’13
55–64

65+

White

D. By education

E. By total income

0.10

0.06
0.05
0.04
0.03
0.02
0.01
0.00

0.08
0.06
0.04
0.02
0.00

5

2007

2007

’09
No HS diploma/GED
Some college

’10

’13

HS diploma/GED
College degree

2007
1st quartile

’09
Black

’09
2nd quartile

’10

’13

Hispanic

’10

Other

’13

3rd quartile

4th quartile

Notes: These figures display the estimates for the probability of having a payday loan in the past year stratified by survey
year (2007, 2009, 2010, and 2013) and head of household characteristics from table 1. Total household income refers
to income from all sources in the previous year before taxes and other deductions. These estimates are weighted using
revised Kennickell–Woodburn consistent weight that accounts for systematic deviations from estimates of homeownership
by racial/ethnic groups based on the U.S. Bureau of Labor Statistics, Current Population Survey.
Sources: 2007–09, 2010, and 2013 Survey of Consumer Finances (SCF). For the years 2007 and 2009, head of household
characteristics are from the 2007 data.

The table suggests that the fraction of SCF respondents with a payday loan increased from 2.6 percent in
2007 to 4.1 percent in 2009. This 1.5 percentage point rise represents a 61 percent increase and is statistically
significant. However, the fraction remains relatively flat thereafter, with estimates of 3.9 percent in 2010 and
4.2 percent in 2013. Since the increase from 2007 to 2009 was very large, it seems reasonable to assume that the
economic downturn must have played a role. However, we cannot disentangle how much of this rise was a continuation of a pre-existing secular trend as opposed to an effect of the recession. Unfortunately, the SCF did not
measure payday borrowing before 2007, and we are aware of no other nationally representative estimates.
When we look by subgroups of the population, we see some interesting patterns.6 First, we consider differences
by age, dividing the sample into those under the age of 35, between 35 and 44, between 45 and 54, between
55 and 64, and 65 and older. Payday borrowing tends to decline with age, with the highest borrowing
rates among those under 35. When we examine trends, however, there appears to have been a narrowing in the age
gradient. While all age groups experienced an increase from 2007 to 2009, those between the ages of 55

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Federal Reserve Bank of Chicago

and 64 continued to increase their payday borrowing in 2010 and 2013. Indeed in 2013, the estimate of
payday use by 55–64 year olds was actually higher than that of 45–54 year olds.
The SCF data show that blacks have a higher rate of payday borrowing than Hispanics and whites, and their
use of payday loans more than doubled from 4.7 percent in 2007 to 10.1 percent in 2009. In contrast, payday
borrowing rates for Hispanics were flat over the same period. Hispanics did experience a notable rise in
payday use from 3.8 percent in 2010 to 5.5 percent in 2013. The time pattern for whites is largely in line
with the overall pattern for the population.
The results by education level also suggest some interesting patterns that have evolved over time. As might
be expected, payday borrowing is lowest among those with a college degree. However, when examining
changes from 2007 through 2013, payday borrowing rates for those with some college roughly doubled
from 3.8 percent in 2007 to 7.7 percent in 2013. In contrast, for those without a high school diploma, payday
borrowing was only a bit higher in 2013 at 3.0 percent than it was in 2007 at 2.9 percent.
Finally, when we look at income quartiles, as one might expect, we find payday borrowing is highest for
the two lowest quartiles of the income distribution. However, when we look at the time patterns, there does
appear to be clear evidence of payday loans increasingly being used by those in the third and second quartiles
of the income distribution. This is consistent with the speculation by Warren and numerous writers in the
popular press about the middle class increasingly turning to payday loans.

Other sources of public data

6

We also consider several publicly available sources
of data that can indirectly address whether consumers
became more interested in payday borrowing during
the Great Recession. We first study the revenue of
publicly traded payday lenders. By talking to financial
executives, we identified five publicly traded firms
that provide a large number of payday loans.7
Figure 2 presents the total revenue of those five
firms from 2006 through 2012 versus the revenue
of all corporations in the Compustat database. As
is well known, corporate revenue plummeted from
late 2007 through early 2009. In contrast, the revenue
of these five publicly traded fringe banks seemed
unaffected by the recession. While it is true that the
pattern of no cyclical sensitivity among payday
lenders is different from what other corporations
experienced, our hypothesis is that they would have
seen revenues grow if there had been a notable rise
in payday usage as the SCF results imply. Instead,
the revenues of these five companies were on a
similar trajectory throughout the entire period.8

FIGURE 2

Quarterly revenue of
publicly traded fringe bankers
			
All corporations ($mil.)
Fringe bankers ($mil.)
1,600

4,000,000

1,400

3,800,000

1,200

3,600,000

1,000

3,400,000

800

3,200,000

600

3,000,000

400

2006

’07

’08

’09

Fringe banks

’10

’11

’12

2,800,000

All corporations

Notes: The figure presents total corporate revenue for
five publicly traded firms that offer payday loans. The
stock tickers of those firms are CSH, DLLR, EZPW,
FCFS, and WRLD. The dashed line plots total corporate
revenue in the United States, based on publicly traded
corporations in the Compustat database.
Source: Compustat.

Another source of public data on payday lenders
comes from online web searches. Much of payday borrowing occurs online and, presumably, much of
that borrowing begins with a Google search. Figure 3 plots Google search volume for the phrase “payday

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FIGURE 3

Google searches
for phrase “payday loan”
National unemployment rate
10

Relative search intensity
40

	 	
	 	
	 	

FIGURE 4

Number of new payday lenders
in California by quarter
80
60

30

9

20

8

10

7

0

6

–10

5

40

–20
2006

4
’07

’08

’09

Google searches

’10

’11

’12

0

2006

’07

’08

’09

’10

Note: The figure presents number of new, licensed
payday lending locations, opened each quarter
in California.
Source: California Department of Corporations.

Unemployment rate

Notes: The figure presents relative search intensity
for the phrase “payday loan” versus the national
unemployment rate. Search intensity consists of
numbers supplied by Google, which capture relative
search intensity, but do not measure the absolute
level. We subtract from search intensity a fixed effect
for each month to eliminate the effect of seasonality.
Source: Google Trends data
(https://www.google.com/trends/).

7

20

loan” versus the unemployment rate. The figure
suggests that the unemployment rate rose dramatically
in 2008 and 2009. Meanwhile, the relative number
of Google searches for the phrase “payday loan”
increased only slightly. This does not suggest a
sudden increase in interest for payday loans.

FIGURE 5

Median family income in zip codes
of new payday lenders in California
			
50,000
45,000

40,000

35,000

2006

’07

’08

’09

’10

Note: The figure presents the median family income
based on the 2000 decennial census in the ZIP
codes of all new California payday lenders.
Sources: California Department of Corporations
and 2000 Census.

Figure 3 also suggests that Google searches for the
phrase “payday loan” rose dramatically in August
2007. We believe that that one outlier was driven by
a public debate about the regulation of payday lenders in Washington, DC. Still, beyond that short-lived
interest in payday loans, it appears that interest online held steady throughout this period.

As an additional indirect measure of payday lending over time, we study the number of licensed payday
lenders in California. The state of California requires all payday lenders to have a “deferred deposit transaction” license. The state provides data on the number and location of licensees. Figure 4 plots the total
number of new licensees each quarter. It suggests a secular decrease. In 2006, there were roughly 40 new
payday lenders opening each quarter; that number had decreased to roughly 20 by 2009.
Figure 4 thus suggests that, if anything, there was a decrease in the number of new stores opening during
this period. That fact is important in light of the next section, in which we study the number of loans per
store at one lender. But a remaining question is whether, as the recession began, new stores began to open in
different areas. Figure 5 explores this possibility. Each new store is matched to the median family income

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Federal Reserve Bank of Chicago

in that zip code, as measured in the 2000 decennial census. The figure then plots the average income for
new stores in each quarter. The figure suggests no clear pattern in that time series. Overall, these patterns
don’t suggest any dramatic change in payday borrowing during the Great Recession.

Data from payday lenders
Next, we examine trends based on two proprietary data sets of payday loans. The first data set comprises
online payday loans. We purchased a random sample of online payday loan applications by Nevada homeowners. We focused on Nevada homeowners because Nevada was hit especially hard by the foreclosure
crisis, and homeowners there may therefore have been especially credit constrained. For each month from
2007 through 2012, we purchased a sample of 500 loan applications.
In order to quantify the effect of the Great Recession over time, we compiled counts of foreclosures by
zip code.9 We view those data as a proxy for the extent to which homeowners in Nevada were in financial
distress. We merge the counts of foreclosures with counts of payday loan applications from each zip code.
Table 2 presents regression estimates of the log of payday loan applications on the log of foreclosures.
The first column presents the simple bivariate association and shows that roughly a 10 percent increase in
foreclosures is associated with a 4 percent increase in payday loan applications. The coefficient is statistically
significant at conventional levels. The estimate and statistical significance of the association does not
diminish when we include fixed effects for each month.

8

But such a cross-sectional relationship does not prove that the foreclosure crisis led to an increase in payday
loan applications. It could be that zip codes in which many homeowners are interested in payday loans
also happen to be zip codes with many foreclosures. That is, the association could simply be evidence of
omitted variables rather than a causal relationship. To test whether that is the case, we exploit the panel
nature of the data: We observe zip codes over time in the data. When we include zip code fixed effects
(column 3), however, the point estimate becomes negative. Finally, when we include both zip code fixed
effects and month fixed effects, the relationship becomes statistically insignificant (column 4). This final
regression suggests that foreclosures have no effect on payday loan applications once one accounts for
time-invariant characteristics of each zip code and the overall time trend in foreclosures.
TABLE 2

Association between foreclosures and payday applications in Nevada
Dependent variable: The logarithm of payday loans by zip code and month
(1)
Logarithm of
foreclosure filings

(2)

(4)

0.387

0.412

–0.051

–0.010

(0.033)

(0.033)

(0.021)

(0.020)

[0.000]

[0.000]

[0.016]

[0.600]

✓

✓

Zip code fixed effects
Month fixed effects
R2

(3)

✓
0.261

0.337

✓
0.602

0.669

Notes: N = 3,726. The sample consists of zip code month-level counts of payday loans issued by an online lender to
homeowners in Nevada. Standard errors in parentheses are robust to auto correlation between observations based on
the same zip code. Associated p-values are in brackets.
Sources: Authors’ calculations based on payday application data from Datastream Group Inc., and foreclosure data
provided by Neale Mahoney.

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FIGURE 6

	 	
	 	
	

Total loan volume
	

230,000

FIGURE 7

Number of new payday borrowers
20,000
18,000

210,000

16,000

190,000

14,000

170,000

12,000
150,000

10,000

130,000
2007

’08

’09

’10

Note: The figure plots monthly counts of payday
loans at one bricks-and-mortar payday loan chain.
Source: Payday lender with locations throughout
the Midwest.

8,000
2007

’08

’09

’10

Note: The figure plots monthly counts of new
payday borrowers at one bricks-and-mortar
payday loan chain.
Source: Payday lender with locations throughout
the Midwest.

Overall, we take this exercise as suggestive rather than definitive. It only allows us to study online payday
loans and only for a select group of applicants in one state. That said, the Great Recession was most severe
in Nevada, and this is one area in which we would expect to find a relationship.
We address some of these shortcomings by examining a second proprietary data set, one based on a bricksand-mortar payday lender in the Midwest.10 The data set consists of over 17 million payday loans serviced
by a chain of lenders with locations throughout the Midwest. In order to focus on a consistent sample, we
restrict the data set to 230 physical locations that opened before 2007. In what follows, we test whether
the number of borrowers or the type of borrowers changed as the Great Recession began.

9

Figure 6 plots the total number of loans over time. This chain of payday lenders serviced roughly 200,000
loans during this period. The figure suggests some seasonal variation in that volume of loans. But, overall,
we see no clear pattern in total volume. The number of loans did decrease in the beginning of 2009, but
had recovered fully by the end of the year.
Figure 7 presents the number of new payday borrowers, borrowers that appear in the database for the first
time. Between 10,000 and 20,000 new borrowers appear in the data set each month. The trend may exhibit
some seasonal variation, but it does not suggest an influx of new borrowers as the recession began.
Finally, we study the socioeconomic status of the new borrowers each month. For each payday loan, we
observe the zip code of residence of the borrower. We merge that variable with the median household income from the 2000 decennial census. Figure 8 then plots the average of those values for all new borrowers.
Here, there is arguably some evidence of a rise in the household income of new borrowers that would be
consistent with what was found in the SCF.

Discussion
Our analysis of data from the SCF suggests that there was a sharp increase in payday borrowing from 2007
to 2009, but that there was no subsequent change through 2013. We are also unsure how much of the 2007
to 2009 increase reflects a continuation of a secular trend versus a cyclical effect of the financial crisis.
The SCF data suggest some shifting demographic patterns with older borrowers, black borrowers, those

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with some college education, and those in the middle
of the income distribution increasingly turning to payday loans. We also use other public and proprietary
data sources and conduct a series of exercises that
might help uncover trends in demand for payday loans.
Each of the exercises, on its own, paints a small
picture of the overall market for payday loans. But,
taken together, the results suggest that demand for
payday loans was largely unaffected by the Great
Recession. Overall, we conclude that there was
only a modest change in payday borrowing that was
heavily concentrated in the 2007 to 2009 period.

FIGURE 8

	
	
	

Socioeconomic status
of new payday borrowers
42,500
42,000
41,500
41,000
40,500
2007

’08

’09

’10

Note: The figure plots average median family income

in the zip codes of new payday borrowers at one
We can only speculate as to why this is the case. It
bricks-and-mortar payday loan chain.
is worth emphasizing that those without a paycheck
Source: Payday lender with locations throughout the
typically cannot procure payday loans, and there was
Midwest and 2000 Census.
a huge increase in unemployment and the duration
of unemployment during the recession. Thus, some
of the newly unemployed during this period may have, if anything, reduced their payday borrowing. On
the other hand, there were also large expansions in unemployment insurance, and many individuals were
able to receive partial unemployment insurance while still working.11 It is also possible that the underlying
demand for payday loans did rise during this period but that the many regulatory changes in the industry prevented an increase in the supply of loans. Supply might also have been constrained if sources of
credit to the payday loan industry declined during this period. These are important avenues for future
research. Our main contribution is to show that despite the fact that the Great Recession led to a general
increase in the number of financially constrained consumers, there was not a dramatic shift in the use of
payday loans during this period.

10

NOTES
Authors’ calculations based on the Federal Reserve Bank of New York’s Consumer Credit Panel.

1

The numbers we list here are typical. Caskey (2005) provides a more detailed description of the process.

2

For example, Elliehausen and Lawrence (2001) survey payday borrowers and estimate that nearly 73 percent of payday customers
responded that they had been turned down for a loan, compared with just 21.8 percent of all adults. Further, roughly 61 percent of
respondents report being constrained by their credit card borrowing limit. Stegman and Faris (2003) find that avoiding overdrafts
and problems with debt collection are commonly cited by payday credit users as primary drivers of their demand for payday loans.
3

Almost 60 percent did not know the APR on their loan. Respondents who thought they knew their APR claimed that it was in the
range of 18 percent to 96 percent when, in fact, the APRs were between 417 and 587 percent. Consumers also appear to misperceive
the relative costs of credit cards and payday loans. Among the 30 percent who reported having a credit card, 15 percent said they
would not use it because they thought it should only be used for emergencies.
4

In an analysis of data from Oklahoma, Pew (2012) reports that more borrowers had at least 17 loans in a year than just one. A study
by the Center for Responsible Lending (Ernst, Farris, and King 2004), using data from North Carolina regulators, reports that 91 percent
of loans were made to borrowers with five or more loans per year. Chessin (2005) studied Colorado borrowers and found that about
65 percent of loan volume in that state comes from customers who borrow more than 12 times per year.
5

These patterns are unchanged if we look only at individuals who have a checking account or only those who are employed.

6

The stock tickers of those five firms are CSH, DLLR, EZPW, FCFS, and WRLD. Some of these lenders also supply pawn loans.

7

We find a similar pattern for the market capitalization of these five firms versus the total market capitalization of all publicly
traded firms. There appears to be no change in trend for these payday lenders during the Great Recession.
8

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The data on foreclosures consist of counts of foreclosure-related filings. The data are part of the public record and stored by each
county office. We are grateful to Neale Mahoney for providing us with an extract of the data.
9

We are grateful to Will Dobbie for granting us access to those data. This data set was studied earlier by Dobbie and Skiba (2013).

10

There is evidence suggesting that some lenders may provide loans against an unemployment insurance check.
See: http://articles.latimes.com/2010/mar/01/business/la-fi-payday1-2010mar01.
11

REFERENCES
Bhutta, Neil, Paige Marta Skiba, and Jeremy Tobacman, 2015, “Payday loan choices and consequences,”
Journal of Money, Credit and Banking, Vol. 47, Nos. 2–3, March–April, pp. 223–260.
Carrell, Scott, and Jonathan Zinman, 2014, “In harm’s way? Payday loan access and military personnel
performance,” Review of Financial Studies, Vol. 27, No. 9, September, pp. 2805–2840.
Caskey, John P., 2005, “Fringe banking and the rise of payday lending,” in Credit Markets for the Poor,
Patrick Bolton and Howard Rosenthal (eds.), New York: Russell Sage Foundation, pp. 17–45.
Chessin, Paul, 2005, “Borrowing from Peter to pay Paul: A statistical analysis of Colorado’s Deferred
Deposit Loan Act,” Denver University Law Review, Vol. 83, No. 2, pp. 387–423.
Christensen, Kim, 2008, “A middle-class move to payday lenders,” Los Angeles Times, December 24,
http://articles.latimes.com/2008/dec/24/business/fi-payday24.
Dobbie, Will, and Paige Marta Skiba, 2013, “Information asymmetries in consumer credit markets:
Evidence from payday lending,” American Economic Journal: Applied Economics, Vol. 5, No. 4, October,
pp. 256–282.
Elliehausen, Gregory, and Edward C. Lawrence, 2001, “Payday advance credit in America: An analysis
of customer demand,” Georgetown University, McDonough School of Business, Credit Research Center,
monograph, No. 35, April.

11

Ernst, Keith, John Farris, and Uriah King, 2004, “Quantifying the economic cost of predatory payday
lending,” Center for Responsible Lending, report, revised February 24.
Hecht, John, 2014, “Alternative financial services: Innovating to meet customer needs in an evolving
regulatory framework,” presentation at the 14th Annual Meeting and Conference, Community Financial
Services Association of America, Orlando, FL, February 27, http://cfsaa.com/Portals/0/cfsa2014_
conference/Presentations/CFSA2014_THURSDAY_GeneralSession_JohnHecht_Stephens.pdf.
Kenneth, Michael, 2008, “Payday lending: Can ‘reputable’ banks end cycles of debt?,” University of San
Francisco Law Review, Vol. 42, No. 3, pp. 659–714.
Marshall, Carolyn, 2015, “Payday loans: A short-term fix that can turn into a long-term debt trap,” Sacramento
Bee, May 29, http://www.safebee.com/money/payday-loans-short-term-fix-can-turn-long-term-debt-trap.
Martin, Nathalie, 2010, “1,000% interest—good while supplies last: A study of payday loan practices
and solutions,” Arizona Law Review, Volume 52, No. 3, pp. 563–622.
Melzer, Brian T., 2011, “The real costs of credit access: Evidence from the payday lending market,” Quarterly
Journal of Economics, Vol. 126, No. 1, February, pp. 517–555.
Morgan, Donald P., and Michael R. Strain, 2008, “Payday holiday: How households fare after payday
credit bans,” Federal Reserve Bank of New York, staff report, No. 309, revised February.

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Morgan, Donald P., Michael R. Strain, and Ihab Seblani, 2012, “How payday credit access affects
overdrafts and other outcomes,” Journal of Money, Credit and Banking, Vol. 44, Nos. 2–3, March–April,
pp. 519–531.
Morse, Adair, 2011, “Payday lenders: Heroes or villains?,” Journal of Financial Economics, Vol. 102,
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Pew Charitable Trusts, 2012, “Payday lending in America: Who borrows, where they borrow, and why,”
Payday Lending in America, Washington, DC, July.
Popper, Margaret, and Mary Thompson, 2011, “Lack of credit leads some borrowers to controversial
payday lenders,” CNBC, January 13, http://www.cnbc.com/id/41056493.
Skiba, Paige Marta, and Jeremy Tobacman, 2011, “Do payday loans cause bankruptcy?,” Vanderbilt
University Law School, working paper, No. 11-13, February 23.
Stegman, Michael A., and Robert Faris, 2003, “Payday lending: A business model that encourages
chronic borrowing,” Economic Development Quarterly, Vol. 17, No. 1, February, pp. 8–32.
White, Martha C., 2013, “Meet the new payday loan customer: Middle-class, well-educated,” Time,
October 2, http://business.time.com/2013/10/02/meet-the-new-payday-loan-customer-middle-class-well-educated/.
Zinman, Jonathan, 2010, “Restricting consumer credit access: Household survey evidence on effects
around the Oregon rate cap,” Journal of Banking and Finance, Vol. 34, No. 3, pp. 546–556.

12

Sumit Agarwal is the vice dean of research and the
Low Tuck Kwong Professor at the National University
of Singapore; Tal Gross is an assistant professor in the
Department of Health Policy and Management at
Columbia University; and Bhashkar Mazumder is a
senior economist and research advisor in the Economic
Research Department and executive director of the Chicago
Census Research Data Center at the Federal Reserve
Bank of Chicago. The authors gratefully acknowledge
funding from the Russell Sage Foundation’s special
initiative on the Social Effects of the Great Recession,
Project No. 92-12-05. They also thank Leonard Nakamura,
Mel Stephens, Gene Amromin, and various seminar
participants for helpful comments. They are especially
grateful to Neale Mahoney and Will Dobbie for access to
data on foreclosures and payday loans.
© 2016 Federal Reserve Bank of Chicago
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