Cash-on-hand and demand for credit

Subprime borrowers typically have below-average credit scores and are charged with higher interest rates for loans or credit cards. Subprime borrowers are often identified by having a FICO credit score below 640, and this threshold varies slightly by lenders, across financial products, and over time.

This is a convenient way to compare the cost of short-term credit to other financial services, such as credit card and auto loans. However, using APR to measure the cost of short-term credit might not be entirely accurate. One might argue it is not reasonable to compound the charges of short-term credit over the period of a whole year.

Most subprime consumers have multiple jobs. They have incentives to report their income strategically to gain more access to credit and better loan terms. Common characteristics of subprime consumers include limited or inferior credit history, volatile income or employment history, and facing relatively high financial uncertainty. Consumer advocates often argue that small-dollar loans are too costly and should only be restricted to people who have the ability to pay. Accurate income measures and elasticity of demand for credit to income shocks would also be a key factor, when lenders build underwriting models and identify the population with sufficient ability to pay. For example, CFPB is considering imposing regulations on rollover or renewal of payday loans at national level, based on borrowers’ income or other characteristics (see proposal from CFPB at http://​www.​consumerfinance.​gov/​newsroom/​cfpb-considers-proposal-to-end-payday-debt-traps/​). Pew institute has proposed a 5% loan-to-income ratio as the benchmark for underwriting (see details from the Pew institute at http://​www.​pewtrusts.​org/​~/​media/​legacy/​uploadedfiles/​pcs_​assets/​2013/​PewPaydayOvervie​wandRecommendati​onspdf).

These papers also highlight the importance of liquidity, especially for low-income families. Related studies can also be found in sociology. For example, Sykes et al. (2015) offers detailed accounts of the use of tax refunds through in-depth interviews.

Full report is available from the CFSA at http://​cfsaa.​com/​Portals/​0/​cfsa2014_​conference/​Presentations/​CFSA2014_​THURSDAY_​GeneralSession_​JohnHecht_​Stephens.

To get a loan, a borrower gives a payday lender a postdated check (e.g., dated on the borrower’s next payday) and receives cash right away. The borrower will pay the full loan amount and fees on the next payday. The lender then holds the check until the borrower’s next payday, which generally falls anywhere from less than a week to a month later.

Lanning et al. (2014) document that the payday loans are often rolled over or followed by another loan within 14 days.

See state-level regulations on payday loans from NCSL at http://​www.​ncsl.​org/​research/​financial-services-and-commerce/​payday-lending-state-statutes.​aspx.

Source: https://​www.​law.​cornell.​edu/​uscode/​text/​15/​1601.

The annual percentage rate is the periodic interest rate applied to outstanding balances multiplied by the number of periods in a year. The finance charge is the total dollar amount of all interest payments. Other disclosures for payday loan transactions include the amount of the loan (amount financed), the total of payments (for payday loans, the check amount) and the schedule of payments.

Sources: https://​www.​law.​cornell.​edu/​uscode/​text/​15/​chapter-41/​subchapter-V.

Thirty-two states enacted safe harbor legislation for payday lenders and permit loans based on checks written from consumers’ bank accounts at triple digit interest rates, or with no rate cap at all. These states include: Alabama, Alaska, California, Delaware, Florida, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Michigan, Minnesota, Mississippi, Missouri, Nebraska, Nevada, New Mexico, North Dakota, Oklahoma, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, Wisconsin and Wyoming. See statutes from NCSL for details at http://​www.​ncsl.​org/​research/​financial-services-and-commerce/​payday-lending-state-statutes.​aspx.

In the online small-dollar credit market, lenders can operate under two different legal models. They can choose to obtain state licenses and comply with state-level regulations, or they can choose to locate and register their company at tribal areas or offshore locations and bypass any regulations. State-licensed lenders make up the most part of the industry. In the baseline analysis, I focus on state-licensed lenders and provide results including offshore or tribal lenders as robustness checks.

Size limits frequently range between $300 and $500 per advance. Some states directly limit the size of the advance. Others limit the size of the check, which includes the amount of the advance plus the finance charge. Montana has a variation on size limits that restricts advances to the lesser of $300 or 25% of the customer’s net monthly income. Nevada limits the amount of the advance to one-third of the customer’s net monthly income. Many states also limit the aggregate amount of advances to a customer at a company, which is generally the same as the size of the maximum advance. The intent of these restrictions on nonprice terms is to force consumers to use payday loans for short-term needs and to keep the consumers from falling too far into debt.

For more details on construction of commuting zones, see documentations from USDA at https://​catalog.​data.​gov/​dataset/​commuting-zones-and-labor-market-areas.

For most updated information on eligibility and generosity of EITC, see details from IRS at http://​www.​eitc.​irs.​gov/​EITC-Central/​abouteitc/​ranges.

People who file both state and federal tax returns would get both federal and state EITC benefits, if eligible. In particular, most people use paid services (for example, H&R Block), when filing tax returns. The paid tax preparation services have strong incentives to encourage people to take up and file for both federal and state EITC benefits, if eligible.

I use CZs updated in 2000 and a crosswalk between ZIP code areas and CZs to identify borrowers’ location from loan-level data.

The Brookings Institution uses original aggregated-level data from the IRS. The difference is that the Brookings Institution re-assigned residence to more accurate and consistent ZIP code areas and counties. Data from the Brookings Institution also includes some additional information on EITC-eligible tax filers. See more details at http://​www.​brookings.​edu/​research/​interactives/​eitc.

Since the tax return data only include federal tax filings, I assume that people who file federal tax returns would also file state tax returns. This is a reasonable assumption for the following reasons. First, a substantial part of the population eligible for EITC use paid tax filing services, which would mostly likely assist taxpayers to file and claim any refunds both at the federal and state level. Second, people using electronic tax filing would file for both federal and state returns automatically. All states offer some form of e-filing options for taxpayers. Third, taxpayers would have strong incentives to file for both, if eligible for any public assistance.

In this paper, I restrict data to lenders who have been reporting to this credit bureau consistently throughout the period of study.

A comprehensive report of online small-dollar credit borrowers observed in data is available at https://​www.​nonprime101.​com/​wp-content/​uploads/​2015/​02/​Profiling-Internet-Small-Dollar-Lending-Final.

The payday loan application process does not involve a traditional credit check, and payday borrowing activity is not reported to the national credit bureaus, Equifax, Experian or TransUnion. This means that payday borrowing is not a factor that directly affects one’s traditional credit score. Although some alternative credit data vendors (for example, LexisNexis) cover this segment, it is not yet widely used for underwriting in traditional financial products. Thus, usage of small-dollar credit is unlikely to affect borrowers’ subsequent access to credit from other sources. If borrowers worry about the impact on credit scores when applying for payday loans, one might be concerned that demand for small-dollar credit could be correlated with the availability of other types of liquidity from more traditional sources or demand for credit in the future, as credit score is often used for underwriting for auto loans, mortgages, etc.

There are several advantages of focusing on loans from the online credit market. First, online credit is on average more expensive than the traditional storefront lenders. People who turn to online borrowing could be relatively more credit constrained. I will capture the population who have a high demand for credit. Second, another key advantage of focusing on the online credit market is to avoid confounding factors from the supply side. People in different locations basically have the same access to credit, regardless of their residential address and distance to locations of financial services. This allows me to ignore the traveling distance to the physical lending sites. This helps with the identification strategy, which relies on discontinuity in EITC generosity across state borders.

Storefront lenders are only allowed to operate in states that allow payday loans. However, for online lenders, there are two options. They can choose to be state-licensed and obey all state-level regulations. Or lenders could be incorporated or registered in tribal or offshore areas, which would allow them to operate even in states than ban payday loans. Due to the presence of tribal or offshore lenders, I observe loans originated for borrowers living in states that ban payday loans in the dataset. However, with the presence of these two types of lenders, we would expect the market conditions in states that ban payday loans to be very different from the other states.

According to the distribution of distance to the closest state borders for 5-digit ZIP code areas, 10 km and 20 km is about 25th percentile and median of the distribution, respectively.

R-squared is high (0.86) for the state-level analysis, and lower (about 0.4) for ZIP5 level. This makes sense since there would be more confounding factors at more detailed geographic levels (ZIP5 level), which leads to a lower value for the R-squared.

The measure of default here depends on lenders’ reporting. A caveat is that lenders could report “default” status for loans differently or selectively due to accounting purpose and lag in reporting time to credit bureau.

Data are available at http://​www.​federalreserve.​gov/​releases/​housedebt/​.

To measure state income tax, I use a national representative sample from the CPS, run the sample through TAXSIM and assign state of residence as Alabama, Alaska, and so on. Then, I calculate the average tax rate for each state.

Cutoffs for the deciles of average EITC benefits (in $) are 1906, 1990, 2057, 2132, 2164, 2194, 2231, 2306, and 2476. Cutoffs for the deciles of fraction of borrowers (in %) are .16, .29, .48, .66, .81, 1.06, 1.26, 1.80, and 2.56.