This research utilized a unique way to obtain nationwide data on pay day loans supplied towards the writers by a market trade team, the Community Financial Services Association of America (CFSA). The info set consisted of this universe of pay day loans originated by five nationwide storefront payday lending chains with places all over nation. It included info on over ninety-three https://tennesseetitleloans.org/ million loans, which have been deidentified for research purposes. A default or late payment), and the ZIP code of the payday lenderвЂ™s storefront for each loan, we observed the dates that the loan was made and was due, the outcome of the loan (for example. The info set covered all loans from 2009 through the first months of 2014. Appendix display A1 maps the continuing states contained in the information set. 16
We centered on the state of California, which entered into an understanding with all the management of President Barack Obama for early county-by-county utilization of the ACAвЂ™s Medicaid expansion last year and 2012. We learned the first expansions in Ca, because our information failed to provide a time that is sufficiently long to review the 2014 expansions and supplied reasonably small information regarding loans in other very early expansion states. We compared California counties that expanded Medicaid early to counties nationwide that failed to achieve this, including four Ca counties that delayed expansion.
We aggregated the CFSA information to your county-month degree, producing loan that is aggregate, standard rates, as well as other measures of loan volumes and results in each county and thirty days combination. The aggregated information set contained 58,020 county-month observations for the time 2009вЂ“13, which covered roughly twenty-four months before and twenty-four months following the Ca Medicaid expansions. Ca rolled away Medicaid expansion over 2011 and 2012, and we utilized the times of expansion by county given by Benjamin Sommers and coauthors. 17 These times are placed in Appendix Exhibit A2, along side county-specific typical monthly payday borrowing before to expansion. 16 Appendix Exhibit A3 shows the study that is aggregate data. 16 We examined results within the 43 expansion counties in Ca, making use of as an evaluation team 920 counties in nonexpanding states and 4 Ca counties that delayed expansion.
Our main results were three measures of loan amount: the sheer number of loans, the money lent, as well as the amount of unique borrowers
We measured unique borrowers in the information every month utilizing the data setвЂ™s anonymized debtor identifiers. Medicaid expansions offer medical health insurance for uninsured grownups more youthful than age 65, therefore we stratified our results by age and centered on individuals younger than age 65. provided past research findings that Medicaid expansions disproportionately benefited those more youthful than age 50, we further examined the circulation regarding the amount of loans among nonelderly grownups by borrowerвЂ™s age (18вЂ“34, 35вЂ“49, and 50вЂ“64).
Also, we thought that we would see greater reductions in payday lending within counties with greater preexpansion stocks of low-income adults that are uninsured. We investigated this possibility by comparing counties with a top share of uninsured to individuals with a low share. Counties classified as having a higher share had been those who work in the utmost effective tercile regarding the share uninsured with incomes of not as much as 138 % associated with the federal poverty degree, based on the 2010 Census BureauвЂ™s Small region medical insurance quotes; counties classified as having a decreased share had been when you look at the base tercile.
Our outcomes that are secondary the stocks of loans that ended in standard, were repaid belated, and had been rollovers. Rollovers are loans which can be removed during the exact same time a past loan is born, that allows the debtor to give the loanвЂ™s extent without repaying the principalвЂ”in trade for spending a finance charge. We identified most most likely rollovers when you look at the information as loans that started within 2 days of a past deadline for similar debtor and exact same loan provider. 18