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The difference-in-differences methodology we relied on contrasted lending that is payday

By on May 19, 2021

The difference-in-differences methodology we relied on contrasted lending that is payday

For both our main and secondary results, we utilized a typical difference-in-differences analysis of county-month results that covered approximately twenty-four months before and twenty-four months following the 2011–2012 Ca Medicaid expansions. As noted above, we compared 43 Ca very early expansion counties to 924 nonexpansion counties (like the 4 mentioned before nonexpansion California counties) within the nationwide information set, with standard mistakes clustered in the county degree. We stratified our findings because of the age of the borrower—focusing on people more youthful than age sixty-five, that would have been likely become suffering from Medicaid expansion. As being a sensitiveness test (see Appendix display A7), 16 we examined borrowers more than age sixty-five and utilized a triple-differences approach in the level that is county-month-age.

Our study had not been in a position to straight connect insurance that is individual to payday borrowing; to the knowledge, the info to do so try not to exist.

To exclude systemic preexisting time trends that may have undermined our difference-in-differences approach, we estimated an “event study” regression associated with effectation of Medicaid expansion in the quantity of loans. This tested the credibility of y our presumption that payday borrowing will have had trends that are similar expansion and nonexpansion counties if none for the counties had expanded Medicaid. The regression included a set impact for each county, a hard and fast impact for each month, and indicators for four six-month durations before Medicaid expansion and three six-month durations after expansion (see Appendix Exhibit A8). 16


Additionally, although we discovered no proof of this, we’re able to perhaps not rule the possibility out that state- or county-level alterations in the legislation (or enforcement of laws) of pay day loans or other industry modifications could have took place Ca into the duration 2010–14. Nonetheless, we tested the appropriateness of y our approach in a number of methods. First, we stratified our models by generation (individuals more youthful or more than age sixty-five): Those who work in younger team could be beneficiaries associated with the Medicaid expansion, while those within the older team wouldn’t normally, given that they will be entitled to Medicare. 2nd, we examined just just how alterations in payday financing diverse aided by the share of uninsured individuals into the county before expansion: we might expect you’ll find a better lowering of payday financing in areas with greater stocks compared to areas with lower shares. Final, we carried out an “event study” regression, described above, to assess any time that is preexisting in payday financing. Our extra methodology supplied reassuring proof that our findings had been due to the Medicaid expansion.


Study Results

The difference-in-differences methodology we relied on contrasted lending that is payday and after California’s early Medicaid expansion within the state’s expansion counties versus nonexpansion counties nationwide. To regulate for confounding, time-varying facets that affect all counties at specific times (such as for example recessions, vacations, and seasonality), this method utilized nonexpansion counties, in Ca along with other states, as being a control team.

Display 1 presents quotes for the effect of Medicaid expansion in the overall number of payday financing, our main results; the table that is accompanying in Appendix Exhibit A4. 16 We discovered big general reductions in borrowing after the Medicaid expansion among individuals more youthful than age sixty-five. The amount of loans removed per thirty days declined by 790 for expansion counties, weighed against nonexpansion counties. Offered a preexpansion mean of 6,948 loans per that amounts to an 11 percent drop in the number of loans month. This lowering of loan amount equals a $172,000 decrease in borrowing per thirty days per county, from the mean of $1,644,000—a fall of 10 %. And 277 less borrowers that are unique county-month took down loans, which represents an 8 % decrease through the preexpansion mean of 3,603.

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