March 2014

CFPB Data Point:

Payday Lending

The CFPB Office of Research
2 CFPB DATA POINT: PAYDAY LENDING

 Kathleen Burke

 Jonathan Lanning

 Jesse Leary

 Jialan Wang

This is the first in an occasional series of publications from the Consumer Financial Protection 

Bureau’s Office of Research. These publications are intended to further the Bureau’s objective of 

providing an evidence-based perspective on consumer financial markets, consumer behavior, 

and regulations to inform the public discourse.
3 CFPB DATA POINT: PAYDAY LENDING

Table of contents

Table of contents.........................................................................................................3

1. Introduction...........................................................................................................4

2. Data........................................................................................................................6

3. Loan sequences....................................................................................................7

3.1 Loan sequence definitions and state roll-over restrictions...................... 7

3.2 Sequence duration and loan volume...................................................... 10

3.3 Loan size and amortization .................................................................... 16

4. Annual loan usage..............................................................................................20

4.1 Numbers of sequences and loans ...........................................................22

4.2 Repayers, renewers, defaulters .............................................................. 25

Appendix A: Discussion and Analysis of Annual Loan Usage 

Using Alternate Sampling Methods ..................................................................29
4 CFPB DATA POINT: PAYDAY LENDING

1. Introduction

In this Data Point we present the results of several analyses of consumers’ use of payday loans. 

The focus of the analyses is loan sequences, the series of loans borrowers often take out 

following a new loan. 

Key findings of this report include:

 Over 80% of payday loans are rolled over or followed by another loan within 14 days (i.e., 

renewed). Same-day renewals are less frequent in states with mandated cooling-off 

periods, but 14-day renewal rates in states with cooling-off periods are nearly identical to 

states without these limitations. We define loan sequence as a series of loans taken out 

within 14 days of repayment of a prior loan.

 While many loan sequences end quickly, 15% of new loans are followed by a loan 

sequence at least 10 loans long. Half of all loans are in a sequence at least 10 loans long.

 Few borrowers amortize, or have reductions in principal amounts, between the first and 

last loan of a loan sequence. For more than 80% of the loan sequences that last for more 

than one loan, the last loan is the same size as or larger than the first loan in the 

sequence. Loan size is more likely to go up in longer loan sequences, and principal 

increases are associated with higher default rates.

 Monthly borrowers are disproportionately likely to stay in debt for 11 months or longer. 

Among new borrowers (i.e., those who did not have a payday loan at the beginning the 

year covered by the data) 22% of borrowers paid monthly averaged at least one loan per 

pay period. The majority of monthly borrowers are government benefits recipients.

 Most borrowing involves multiple renewals following an initial loan, rather than multiple 

distinct borrowing episodes separated by more than 14 days. Roughly half of new 
5 CFPB DATA POINT: PAYDAY LENDING

borrowers (48%) have one loan sequence during the year. Of borrowers who neither 

renewed nor defaulted during the year, 60% took out only one loan.

The next section describes the data used in the analysis; subsequent sections describe the 

specific analyses and results exploring sequence durations, loan sizes and amortization, and 

loan usage over the year. An appendix discusses sampling issues and provides results from 

different sampling approaches.
6 CFPB DATA POINT: PAYDAY LENDING

2. Data

The Bureau obtained data from a number of storefront payday lenders through the supervisory 

process. These are the same data from which the Bureau drew a sample for use in the analysis 

described in the CFPB Payday Loans and Deposit Advance Products White Paper (April 2013), 

hereafter referred to as the “White Paper.”1 The data provide information on all payday loans 

extended by each lender over a period of at least 12 months. The dataset contains an anonymous 

customer ID that allows us to link all loans made to the same consumer by a given lender during 

the observed time period. 

The data provided by different lenders contain differing levels of detail. In addition to 

information on origination and maturity dates of the loans, the majority of the analysis 

presented in this Data Point requires that we determine whether a borrower defaulted on a loan. 

The information on which we base our analysis of defaults is only available for a subset of the 

lenders.2 In addition, while some of the lenders provided more than 12 months of data, most did 

not. Therefore, we limit our analysis to 12 months for each lender so that the period of time over 

which we measure borrowing at each lender is consistent across lenders. The dataset used in the 

detailed analysis of borrowing patterns includes information on over 12 million loans in 30

states, and covers 12-month windows in 2011 and 2012. We describe further details of our 

sample and methodology in each section.

 

1 CFPB, “Payday Loans and Deposit Advance Products, a White Paper of Initial Data Findings,” available at 

http://files.consumerfinance.gov/f/201304_cfpb_payday-dap-whitepaper.pdf . 

2 Portions of the analysis can be run without using information about loan defaults. We have run those portions of the 

analysis for a broader set of lenders, and the results are very similar.
7 CFPB DATA POINT: PAYDAY LENDING

3. Loan sequences

In this section, we describe patterns of borrowing following an initial payday loan. A primary 

driver of the cost of using payday loans is the extent to which borrowers roll loans over or 

engage in re-borrowing within a short period of time after repaying a loan. We use the term 

“renewal” to describe both paying additional fees to roll over a loan and re-borrowing within a 

given time period after repaying a loan.

One way borrowers could mitigate the cost of renewing, if they have difficulty paying off the 

initial loan in full, would be to borrow less each time they take out a new loan, and thereby 

effectively amortize the debt through a series of smaller and smaller balloon loans. Therefore, in 

addition to describing the duration of loan sequences, we study whether consumers’ loan 

amounts change over the course of a loan sequence. 

3.1 Loan sequence definitions and state rollover restrictions

We define a loan sequence as an initial payday loan plus the series of subsequent loans that are 

renewed within 14 days of repayment of the prior loan. For most consumers, re-borrowing 

within 14 days means borrowing prior to receiving another paycheck after repaying the prior 

loan. In addition, a number of states place restrictions on rolling loans over or on taking out a 

new loan within some short window after repaying a loan. A definition of loan sequence that 

only includes direct roll-overs or loans taken out on the same day that another is repaid could 

therefore understate the true duration over which consumers re-borrow before they fully repay 

an initial need.
8 CFPB DATA POINT: PAYDAY LENDING

Figure 1 shows the share of loans that are renewed within three different timeframes. These 

results are for all loans made during 12-month windows for a set of lenders for which we have 

information on loan origination and maturity dates.3 The categories are based on the state of 

the storefront where the loans were taken out, with same-day, 7-day, and 14-day renewal rates 

for groups of states based on regulatory restrictions around roll-overs and waiting periods, and 

for all states represented in the sample. 

The first category includes five states with no effective limitations on roll-overs: Kansas, Ohio, 

Nevada, Utah and Texas.4 The second category consists of some of the many states that prohibit 

roll-overs but do not prevent lenders from making a loan to a borrower on the same day that a 

prior loan is repaid. The states in this category are California, Iowa, Kentucky, Michigan, 

Mississippi, Nebraska, New Mexico, South Carolina, and Tennessee. The final category includes 

states that impose a waiting period of at least one day after a loan is repaid before a lender can 

make another loan to the same borrower. These states are Alabama, Florida, Virginia and 

Wisconsin.5 Note that waiting periods may not be applicable for every loan – in Wisconsin and 

Alabama, they are triggered only after two consecutive loans – and therefore we observe some 

same-day renewals in these states. The "All" category includes all of the above states, as well as 

states with intermediate restrictions that do not fit neatly into the other categories: Alaska, 

Idaho, Illinois, Louisiana, Missouri, Oklahoma, Rhode Island and Washington. 

 

3 We have also conducted this analysis for the subset of lenders that are used in the detailed loan sequence analysis –

those lenders for whom we have the information that we use to analyze defaults – and the results are very similar.

4 While the deferred presentment statute in Texas imposes some restrictions on roll-overs, nearly all payday loans 

during our sample period were being made under the Credit Services Organization (CSO) model, which carries few 

restrictions. Licensed Ohio lenders operate under the state’s mortgage loan and small loan statutes, so the 

limitations of its deferred presentment statutes do not apply to the loans in our sample.

5 Alabama’s law specifies that borrowers must wait until the next business day to take out a loan after two consecutive 

loans are paid in full. Florida’s law specifies a 24-hour cooling-off period after each loan. Virginia law forbids 

lenders from making a new loan on the same day a borrower repays a previous one. Wisconsin allows one renewal 

and requires a 24 hour waiting period after that renewal. 
9 CFPB DATA POINT: PAYDAY LENDING

FIGURE 1: PAYDAY LOAN RENEWAL RATES BY STATE ROLL-OVER RESTRICTION

Figure 1 shows that while waiting periods are associated with a much lower level of same-day 

renewals, states with waiting periods or rollover restrictions have seven-day and 14-day renewal 

rates that are nearly identical to other states. Over all states in the sample, 82% of loans are 

renewed within fourteen days, and this percentage varies by only three percentage points across 

the three groups of states. 
10 CFPB DATA POINT: PAYDAY LENDING

3.2 Sequence duration and loan volume

In this section we use data from the subset of lenders for whom we have sufficient information 

to analyze defaults. For the purposes of this analysis, a borrower is considered to have defaulted 

on a loan if the loan had not been repaid at the time the data were provided to the CFPB, or 30 

days after the maturity date of the loan, whichever is later. Using this definition, loans with 

maturity dates within 30 days of the end of the 12-month sample period cannot be considered to 

have defaulted, and are included in the analysis as loans on which the borrower did not default.6

The loan sequences included in this analysis are those that began in the second month of a 

lender’s 12-month sample period. 7 We restrict the analysis to these sequences so that we can 

ensure we are able to observe the first loan in a sequence and thus accurately measure sequence 

duration.8 Using sequences that begin in the second month gives us the longest window to 

observe sequence length. As our dataset contains at most one year of loan usage for a given 

consumer, we are able to describe the pattern of sequence lengths up to roughly 11 months, but 

we cannot observe how long sequences may extend beyond 11 months.

Figure 2 shows the distribution of loan sequence lengths. The darker portion of the bar shows 

sequences that did not end in default, the lighter portion shows sequences that did end in 

default. Figure 2 shows that 36% of new sequences end with the initial loan being repaid; on 4%

 

6 We have conducted the analysis dropping these loans and the results are essentially unchanged.

7 If the distribution of sequence lengths is stable over time, then using a cohort of sequences that begin in the same 

time period will provide an unbiased estimate of the length of all loan sequences. See, e.g., Carlson and Horrigan,

“Measures of Unemployment Duration as Guides to Research and Policy: Comment,” The American Economic 

Review, Vol. 73, No. 5 (Dec., 1983), pp. 1143-1150.

8 Supplemental Security Income (SSI) benefits are normally disbursed on the first of the month, unless the first falls 

on a holiday or weekend, in which case they are disbursed on the prior business day. The exact timing of the “second 

month” is adjusted forward by a day if the first month of a lender’s data period does not include a day on which 

(SSI) benefits were disbursed. This is done to ensure that loans to SSI recipients in the second month are truly new 

loans. We have also conducted the analysis for episodes that began in the third month, and the results are very 

similar.
11 CFPB DATA POINT: PAYDAY LENDING

of new sequences the borrower defaults on the first loan. More than half of new sequences do 

not go past a single renewal. On the other hand, 22% of sequences extend for seven or more 

loans, and 15% of sequences extend for 10 or more loans.

FIGURE 2: DURATION OF PAYDAY LOAN SEQUENCES
12 CFPB DATA POINT: PAYDAY LENDING

FIGURE 3: SHARE OF PAYDAY LOANS ORIGINATED IN PAYDAY LOAN SEQUENCES OF DIFFERENT 

LENGTHS

Though most loan sequences are short, most loans are part of long sequences. Figure 3 shows 

that half of all loans are in sequences of 10 or more loans; 62% are in sequences of seven or more 

loans.

Because the duration of payday loans are typically determined by the borrower’s pay frequency, 

borrowers that are paid bi-weekly typically take out shorter loans than borrowers that are paid 

monthly (or who receive benefits on a monthly basis). Consumer paid bi-weekly, therefore, 

could potentially take out many more loans over the course of 11 months than could borrowers

who are paid monthly. The dataset contains information on the source and frequency of the 

income that a consumer reported to the lender when applying for a loan. To further explore the 

duration of loan sequences, we separately analyze borrowers with different pay frequencies.

Figure 4 shows the distribution of sequence lengths, and whether those sequences ended in 

default, separately for borrowers who are paid weekly, bi-weekly, or twice a month, and 

borrowers who are paid monthly. In our sample, 14% of borrowers are paid weekly, 47% are paid 

bi-weekly, 9% are paid twice a month, and 30% are paid monthly.
13 CFPB DATA POINT: PAYDAY LENDING

FIGURE 4: DURATION OF PAYDAY LOAN SEQUENCES BY BORROWER PAY FREQUENCY

PANEL A: BORROWERS PAID WEEKLY, BI-WEEKLY, AND TWICE PER MONTH

PANEL B: BORROWERS PAID MONTHLY
14 CFPB DATA POINT: PAYDAY LENDING

Panel A of Figure 4 shows the distribution of loan sequence length for borrowers that are paid 

more frequently than monthly. For these borrowers, 38% of new sequences end with the initial 

loan being repaid; on 5% of new sequences the borrower defaults on the initial loan. The 

majority of loan sequences to these borrowers end after a single renewal, but 12% of loan 

sequences span 11 or more loans. 

Panel B of Figure 4 shows loan sequence lengths for borrowers who are paid monthly or receive 

monthly benefits. The majority of these borrowers, 58%, receive monthly government benefits, 

including Supplemental Security Income (SSI) and Social Security Disability or Retirement 

benefits. Panel B shows a lower rate of successful initial repayment; only 30% of initial loans are 

repaid without renewal and these borrowers default on 4% of initial loans. There is not the same 

long tail of sequences with a very high number of loans to these borrowers, as there is not time 

in an 11 month period for borrowers who are paid monthly to take out that many loans, but 16% 

of these sequences last for 11 or more loans.

Figure 5 shows the distribution of loans by sequence length for borrowers that are paid more 

frequently than monthly (Panel A) and for borrowers who are paid monthly or receive monthly 

benefits (Panel B). The large portion of loans accounted for by long sequences is particularly 

important because the number of loans is proportional to the amount of fees incurred by 

borrowers, and by definition, also the fee revenues earned by lenders. Panel B, which shows 

results for monthly borrowers, is particularly striking. It shows that over 40% of all loans to 

these borrowers were in sequences that, once begun, persisted for the rest of the year for which 

data were available. 
15 CFPB DATA POINT: PAYDAY LENDING

FIGURE 5: SHARE OF PAYDAY LOANS ORIGINATED IN PAYDAY LOAN SEQUENCES OF DIFFERENT 

LENGTHS

PANEL A: BORROWERS PAID WEEKLY, BI-WEEKLY, AND TWICE PER MONTH

PANEL B: BORROWERS PAID MONTHLY
16 CFPB DATA POINT: PAYDAY LENDING

3.3 Loan size and amortization

One way borrowers could mitigate the expense of a sequence of payday loans would be to reduce 

the principal amounts over time. On the other hand, some borrowers may take out larger and 

larger loans over time, either to cover the fees associated with the loans or to cover additional 

needs.9 To find the frequency with which each of these phenomena occurs, we analyzed loan size 

over the course of loan sequences. 

Figure 6 shows the distribution of loan sequences by whether the principal increases, stays the 

same, or decreases between the first and last loan of the sequence, separately by borrower pay 

frequency. It shows that most borrowers do not self-amortize their payday loans. For both nonmonthly (panel A) and monthly (panel B) borrowers, over 80% (47%/57% and 59%/63%, 

respectively) of loan sequences that last more than one loan have either no change or an 

increase in loan amount. 

Figure 7 shows these proportions for loan sequences of different lengths, separately by borrower 

pay frequency and by whether the sequence ended in default. Each bar in this figure shows the 

proportion of loan sequences of that length that had an increase, no change, or a decrease in the 

amount of the loan between the first and last loan of the sequence. The figure shows two key 

patterns. Longer loan sequences and loan sequences that end in default are both more likely to 

have larger final loans than initial loans. 

Further analysis indicates that the share of loans that show a reduction in the amount borrowed 

is fairly constant throughout a sequence. Increases in loan amounts, on the other hand, are more 

likely to occur early in a loan sequence. For example, each bar in Figure 8 shows loan amount 

changes, if any, for subsequent loans over the course of sequences that are seven loans long. 

This pattern suggests that increases in loan amount do not seem to be caused by snowballing 

loan amounts across the sequence. It may also reflect borrowers quickly reaching the maximum 

loan size allowable by law or permitted by the lender. Patterns of amortization within sequences 

of other lengths follow both of these consistent patterns, and are omitted for brevity.

 

9 The extent to which consumers may elect to decrease or increase principal amounts over the course of a sequence 

may be limited by state loan size restrictions and lenders’ willingness to lend larger amounts. Some lenders may 

restrict new consumers to loan amounts below the maximum allowable by state law, and then permit increases after 

consumers demonstrate successful payment.
17 CFPB DATA POINT: PAYDAY LENDING

FIGURE 6: SHARE OF PAYDAY LOAN SEQUENCES BY CHANGE IN LOAN AMOUNT BETWEEN FIRST AND 

LAST LOAN

PANEL A: BORROWERS PAID WEEKLY, BI-WEEKLY, AND TWICE PER MONTH

PANEL B: BORROWERS PAID MONTHLY
18 CFPB DATA POINT: PAYDAY LENDING

FIGURE 7: SHARE OF PAYDAY LOAN SEQUENCES BY CHANGE IN LOAN AMOUNT BETWEEN FIRST AND 

LAST LOAN, BY SEQUENCE LENGTH

PANEL A: BORROWERS PAID WEEKLY, BI-WEEKLY, AND TWICE PER MONTH

PANEL B: BORROWERS PAID MONTHLY
19 CFPB DATA POINT: PAYDAY LENDING

FIGURE 8: SHARE OF PAYDAY LOANS WITH CHANGE IN LOAN AMOUNT FROM PRIOR LOAN IN 

SEQUENCE FOR SEVEN-LOAN SEQUENCES 

PANEL A: BORROWERS PAID WEEKLY, BI-WEEKLY, AND TWICE PER MONTH

PANEL B: BORROWERS PAID MONTHLY
20 CFPB DATA POINT: PAYDAY LENDING

4. Annual loan usage 

In the previous section, we described sequences of payday loans following a new loan. The 

experiences of payday loan borrowers depend not only on what happens with individual loan 

sequences, but patterns of borrowing across multiple sequences. In this section we measure the 

number of loan sequences and loans borrowers have in a year.10 We also look at the extent to 

which borrowers repay all of their loans without renewing, versus renew or default on some 

loans.

There are a number of different ways to define the set of consumers whose annual usage we 

might measure. The approach taken in the White Paper was to measure the usage of borrowers 

who took out loans in the first month of the data period. This provides the longest possible 

window for measuring usage and, given the short duration of most loans, is similar to measuring 

the usage of borrowers in a lender’s portfolio at a point in time. As noted in the White Paper, 

using this approach will lead to a higher proportion of heavy users of payday loans, as compared 

to other approaches. Another approach is to attempt to determine which loans were made to

new payday borrowers who took out a loan early in the time period, and measure subsequent 

borrowing by those consumers. This also allows for a relatively long time window for measuring 

usage, but limits the analysis by excluding the majority of payday borrowers in the data. This 

approach also leads to a lower proportion of heavy users of payday loans than other methods, 

since all borrowers with continuous usage in a year are excluded. A third approach is to measure 

the usage of any borrower who took out a loan during the sample period. This approach is the 

most inclusive across borrowers, but borrowers who take out their first loan late in the period 

can only be observed for a limited time.

 

10 To the extent borrowers take out loans from multiple payday lenders, the results in this section will understate total 

borrowing.
21 CFPB DATA POINT: PAYDAY LENDING

Here we provide results for the “new borrowers” approach, and we show results for the “White 

Paper” approach and the “all borrowers” approach in an appendix. New borrowers are defined 

as borrowers who took out a loan in the second month of the data period and did not take out a 

loan in the first month.11 Given the limitations of the data we cannot ensure that these 

borrowers are new to payday borrowing, but can ensure that they are not in the midst of an 

ongoing loan sequence with the lender making the “new loan” that we observe.

The findings are generally similar across the approaches, except with regard to the number of 

loans borrowers take out over the year. The “White Paper” approach includes more borrowers 

who are in long sequences, and therefore the median number of loans, 11, is higher than for the 

other two approaches. The “new borrower” approach, which tends to exclude borrowers in very 

long sequences, leads to a median number of loans of six over an 11-month period; the “all 

borrowers” approach gives a median number of six loans over a year, which includes borrowers 

who enter the data late in the time period. 

 

11 As in the definition of new sequences, the exact timing of the “first month” and “second month” is adjusted forward 

by a day if the first month of a lender’s data period does not include a day on which SSI benefits were disbursed. 

This is done to ensure that SSI recipients who borrowed in the second month were not repaying a prior loan on the 

same day.
22 CFPB DATA POINT: PAYDAY LENDING

4.1 Numbers of sequences and loans

Figure 9 shows the distribution of the number of loan sequences new borrowers have over an 11-

month period. It shows that nearly half of borrowers (48%) have a single loan sequence, and 

roughly three-quarters (74%) have no more than two sequences. This analysis shows that 

relatively few payday borrowers have multiple periods of borrowing separated by periods 

without borrowing within a given year.

FIGURE 9: NUMBER OF PAYDAY LOAN SEQUENCES DURING 11 MONTH PERIOD
23 CFPB DATA POINT: PAYDAY LENDING

Figure 10 shows the distribution of the number of loans new borrowers take out over the 11-

month period. It shows that 15% of these borrowers had only one loan during this time. The 

median number of loans per 11 months for this sample is six.

FIGURE 10: NUMBER OF PAYDAY LOANS DURING 11 MONTH PERIOD

As discussed above, loan length depends on pay frequency, so people who are paid more 

frequently can potentially take out more loans over a period of time. Figure 11 presents results 

split by pay frequency. Panel B shows that a substantial portion of monthly borrowers took out a 

payday loan in each of the 11 months; 22% of these borrowers averaged one or more loans per 

pay period following their initial loan. The majority of these borrowers, 58%, receive monthly 

government benefits, including SSI and Social Security Disability or Retirement benefits.
24 CFPB DATA POINT: PAYDAY LENDING

FIGURE 11: NUMBER OF PAYDAY LOANS DURING 11 MONTH PERIOD

PANEL A: BORROWERS PAID WEEKLY, BI-WEEKLY, AND TWICE PER MONTH

PANEL B: BORROWERS PAID MONTHLY
25 CFPB DATA POINT: PAYDAY LENDING

4.2 Repayers, renewers, defaulters

In order to further characterize loan usage over the course of a year, we categorize borrowers 

based on their default and renewal behavior. We place consumers into one of three mutually 

exclusive and exhaustive categories: defaulter, renewer, and repayer. A defaulter is a borrower 

who defaults on any loan during the year. A renewer is a borrower who did not default and who 

renewed at least one loan within a period of 14 days after repaying a prior loan. A repayer is a 

borrower who neither defaulted nor renewed at any point during the year. A repayer could have 

taken out more than one loan over the observation period but in each case would have repaid 

the loan without re-borrowing within 14 days after repaying the loan.

There are some mechanical relationships between borrower categories and total loan usage. 

Borrowers who default may not be able to borrow again from the same lender, leading to lower 

observed usage levels. Consumers who take out many new loans over the course of the year (i.e.,

loans taken out more than 14 days subsequent to repayment of the last one) have more 

opportunities to renew a loan than borrowers who only take out a single new loan. Nonetheless, 

quantifying the relative shares of each group among the overall consumer population and the 

usage patterns of these groups may provide some meaningful insights.
26 CFPB DATA POINT: PAYDAY LENDING

FIGURE 12: SHARE OF BORROWERS THAT REPAY, RENEW, OR DEFAULT ON THEIR PAYDAY LOANS 

DURING AN 11 MONTH PERIOD

Figure 12 shows the distribution of new borrowers across these three categories. It shows that 

the majority (64%) of new borrowers become renewers. Similar proportions of new borrowers 

are categorized as defaulters (20%) and repayers (15%).

Figure 13 shows the number of loan sequences per new borrower. It shows that compared with 

repayers and renewers, defaulters are more likely to have just a single sequence. As noted above,

defaulting may preclude a customer from borrowing from that lender again, so it is unsurprising 

that many defaulters have only a single sequence. 

Repayers also tend to be low-intensity users; 61% took out only one loan during the time period. 

This is another manifestation of one of the findings in the prior section: it is unusual for 

borrowers to take out a payday loan at multiple times during a year separated by periods 

without borrowing.

Most renewers experience only 1-3 sequences during the 11-month time period; 36% have only 

one sequence, 30% have two, 19% have three and only 15% have four or more sequences.
27 CFPB DATA POINT: PAYDAY LENDING

FIGURE 13: NUMBER OF PAYDAY LOAN SEQUENCES DURING 11 MONTH PERIOD FOR REPAYERS, 

RENEWERS, AND DEFAULTERS

Figure 14 shows the distribution of loans per consumer for repayers, renewers, and defaulters 

during an 11-month period. By definition, the distribution of the number of loans for repayers is 

the same as the distribution of the loan sequences. The three consumer types display strikingly 

different usage intensities. 

Defaulters are concentrated at lower intensities, with 27% defaulting on the first loan they took 

out. Substantial numbers of borrowers, however, took out a relatively large number of loans 

before defaulting.

Renewers display a wide range of usage intensities. The number of loans is fairly evenly 

distributed in the range between 2 and 12 loans, and reflects a combination of biweekly 

consumers who are able to take up to 24 loans, and monthly borrowers who typically take at 

most 11 loans in an 11-month period.
28 CFPB DATA POINT: PAYDAY LENDING

FIGURE 14: NUMBER OF PAYDAY LOANS DURING 11 MONTH PERIOD FOR REPAYERS, RENEWERS, AND 

DEFAULTERS

Taken together, the results from this section suggest that while there is a very wide variation in 

the number of loans that payday borrowers take out over the course of a year, this is related to 

differences in whether the borrowers repay versus renew those loans and how many loans they 

take out before repaying without re-borrowing. In other words, the differences in the number of 

loans come primarily from differences in the length of sequences, not the number of sequences. 
29 CFPB DATA POINT: PAYDAY LENDING

APPENDIX A: DISCUSSION AND ANALYSIS OF 

ANNUAL LOAN USAGE USING ALTERNATE SAMPLING 

METHODS

In this appendix we describe the effects of alternate sampling methods on the results discussed 

in Section 4 of the text. The approach used in Section 4 of the text is to measure the borrowing 

behavior of “new” borrowers, those who took out a loan in the second month of the dataset and 

did not have a prior loan in the dataset. In this appendix we present a subset of the results from 

Section 4 using two alternative approaches. The “all borrowers” approach includes all borrowers 

with at least one loan in the sample period, with the exception of borrowers whose first loan is 

made in the last month of the sample period (we cannot determine whether these borrowers

repaid, renewed, or defaulted on their loan). The “White Paper” method tracks 12-month usage 

for consumers who have a loan in the first month of a lender’s 12-month window.12

Figure A.1 shows the distribution of the number of loan sequences for the two alternative 

approaches. It shows that for both of the methods described above as well as the “new borrower” 

methodology portrayed in Figure 9, the distribution of the number of sequences is very similar, 

with slightly more borrowers in the “all borrower” sample having only a single loan sequence 

during the year. This at least partially reflects that the “all borrower” approach permits 

borrowers to enter the sample later in the year, when there is less time for multiple loan 

sequences.

 

12 Note that these results do not exactly replicate the results of the White Paper, since we use a different sample of 

lenders, those for which we have the information that we use to analyze defaults.
30 CFPB DATA POINT: PAYDAY LENDING

FIGURE A1: NUMBER OF PAYDAY LOAN SEQUENCES PER YEAR

PANEL A: “ALL BORROWERS”

PANEL B: “WHITE PAPER SAMPLE”
31 CFPB DATA POINT: PAYDAY LENDING

Figure A.2 shows the distribution of the number of loans during the year for the two alternative 

approaches. It demonstrates the phenomena described in the text, that using the “new 

borrower” approach excludes many borrowers in longer sequences, or, alternatively, using the 

“White Paper” approach includes a larger number of borrowers who are relatively heavy users of 

payday loans, and likely in long sequences. The “all borrower” approach shows spikes in use at 

twelve loans and 24 or more loans, like the “White Paper” approach, but also shows a similar

share to the “new borrower” approach of borrowers who only took out one loan.

Figure A.3 shows the distribution of consumers across the “repayer,” “renewer,” and “defaulter” 

borrower types for the two alternative sampling approaches. Panel A shows that the distribution 

of borrowers across types is very similar between the “all borrower” sample and the “new 

borrowers” sample described in the text. Panel B shows the distribution using the “White Paper” 

method. The share of renewers in the “White Paper” sample is significantly higher than the 

other two approaches, reflecting the fact that the borrowers in a lenders portfolio at a point in 

time are disproportionately borrowers in long loan sequences. Overall, all three methods show 

that renewers represent the majority of all borrowers, with repayers and defaulters making up 

less than half of all borrowers for any sample.
32 CFPB DATA POINT: PAYDAY LENDING

FIGURE A2: NUMBER OF PAYDAY LOANS PER YEAR

PANEL A: “ALL BORROWERS”

PANEL B: “WHITE PAPER SAMPLE”
33 CFPB DATA POINT: PAYDAY LENDING

FIGURE A3: SHARE OF BORROWERS THAT REPAY, RENEW, OR DEFAULT ON THEIR PAYDAY LOANS 

DURING A YEAR

PANEL A: “ALL BORROWERS”

PANEL B: “WHITE PAPER SAMPLE”