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Our main results had been three measures of loan amount: the sheer number of loans, how much money lent, together with wide range of unique borrowers.

We aggregated the CFSA information into the county-month degree, producing aggregate loan counts, standard rates, as well as other measures of loan volumes and results in each county and month combination. The data that are aggregated contained 58,020 county-month observations for the time scale 2009–13, which covered approximately twenty-four months before and twenty-four months after the Ca Medicaid expansions. Ca rolled down Medicaid expansion over 2011 and 2012, and the dates were used by us of expansion by county provided by Benjamin Sommers and coauthors. 17 These times are placed in Appendix Exhibit A2, along side county-specific normal monthly payday borrowing before to expansion. 16 Appendix Exhibit A3 shows the study that is aggregate data. 16 We examined results into the 43 expansion counties in Ca, making use of as an assessment team 920 counties in nonexpanding states and 4 Ca counties that delayed expansion.

We measured borrowers that are unique the information every month with the data set’s anonymized borrower identifiers.

Medicaid expansions provide medical insurance for uninsured grownups younger than age 65, therefore we stratified our results by age and dedicated to individuals more youthful than age 65. Provided previous research findings that Medicaid expansions disproportionately benefited those more youthful than age 50, we further examined the circulation associated with the amount of loans among nonelderly grownups by borrower’s age (18–34, 35–49, and 50–64).

Also, we thought that we would see greater reductions in payday lending within counties with greater preexpansion shares of low-income adults that are uninsured. We investigated this possibility by comparing counties with a higher share of uninsured to people that have a low share. Counties classified as having a share that is high those in the most notable tercile associated with share uninsured with incomes of lower than 138 % associated with the federal poverty degree, in line with the 2010 Census Bureau’s Small region medical insurance Estimates; counties classified as having a minimal share had been into the bottom tercile.

Our additional results were the stocks of loans that ended in default, were repaid late, and were rollovers.

Rollovers are loans which are removed during the exact same time a previous loan is born, makes it possible for the debtor to increase the loan’s timeframe without repaying the principal—in trade for spending a finance charge. We identified likely rollovers into the information as loans that started within 2 days of a previous deadline for similar borrower and lender that is same. 18

For both our primary and secondary results, we utilized a regular analysis that is difference-in-differences of outcomes that covered approximately twenty-four months before and twenty-four months after the 2011–2012 California Medicaid expansions. As noted above, we compared 43 California very early expansion counties to 924 nonexpansion counties (like the 4 previously mentioned nonexpansion Ca counties) when you look at the nationwide information set, with standard mistakes clustered at the county degree. We stratified our findings because of the chronilogical age of the borrower—focusing on individuals younger than age sixty-five, who does have been likely become affected by Medicaid expansion. As a sensitivity test (see Appendix display A7), 16 we examined borrowers over the age of age sixty-five and utilized a triple-differences approach in the county-month-age degree.

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