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 2011вЂ“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 borrowerвЂ”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 вЂњevent studyвЂќ 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 2010вЂ“14.