Payday advances and credit results by applicant gender and age, OLS estimates

Table reports OLS regression estimates for result factors written in line headings. Test of most loan that is payday. Additional control factors maybe maybe maybe not shown: received loan that is payday; controls for sex, marital status dummies (hitched, divorced/separated, solitary), web month-to-month income, month-to-month rental/mortgage re payment, amount of kiddies, housing tenure dummies (house owner without home loan, property owner with home loan, tenant), training dummies (senior school or reduced, university, college), work dummies (employed, unemployed, out from the labor pool), discussion terms between receiveing pay day loan dummy and credit rating decile. * denotes statistical significance at 5% degree, ** at 1% degree, and *** at 0.1% degree.

Payday advances and credit results by applicant age and gender, OLS estimates

Table reports OLS regression estimates for outcome factors printed in line headings. Test of all of the pay day loan applications. Additional control factors perhaps maybe perhaps not shown: gotten loan that is payday; settings for sex, marital status dummies (hitched, divorced/separated, solitary), net month-to-month income, month-to-month rental/mortgage re re payment, wide range of children, housing tenure dummies (house owner without mortgage, property owner with home loan, renter), training dummies (senior school or reduced, college, college), work dummies (employed, unemployed, from the labor pool), discussion terms between receiveing cash advance dummy and credit rating decile. * denotes statistical significance at 5% degree, ** at 1% degree, and *** at 0.1% degree.

Payday advances and credit results by applicant earnings and work status, OLS quotes

Table reports OLS regression estimates for result variables printed in line headings. Test of all of the cash advance applications. Additional control factors perhaps not shown: gotten pay day loan dummy; settings for age, age squared, sex, marital status dummies (hitched, divorced/separated, solitary), web month-to-month earnings, month-to-month rental/mortgage re payment, wide range of kids, housing tenure dummies (property owner without home loan, house owner with home loan, tenant), training dummies (senior school or reduced, college, college), employment dummies (employed, unemployed, from the work force), relationship terms between receiveing pay day loan dummy and credit history decile. * denotes statistical significance at 5% degree, ** at 1% degree, and *** at 0.1% degree.

Payday advances and credit results by applicant employment and income status, OLS quotes

Table reports OLS regression estimates for result factors written in line headings. Test of all of the loan that is payday. Additional control factors perhaps not shown: gotten cash advance dummy; settings for age, age squared, sex, marital status dummies (hitched, divorced/separated, solitary), web month-to-month earnings, month-to-month rental/mortgage re re re payment, wide range of kids, housing tenure dummies (house owner without home loan, house owner with home loan, tenant), training dummies (senior school or reduced, college, college), work dummies (employed, unemployed, out from the work force), discussion terms between receiveing pay day loan dummy and speedy cash loans coupons credit rating decile. * denotes significance that is statistical 5% degree, ** at 1% level, and *** at 0.1% degree.

2nd, none regarding the relationship terms are statistically significant for just about any associated with the other result factors, including measures of standard and credit rating. Nonetheless, this total outcome is maybe not astonishing given that these covariates enter credit scoring models, and therefore loan allocation choices are endogenous to these covariates. As an example, then restrict lending to unemployed individuals through credit scoring models if for a given loan approval, unemployment raises the likelihood of non-payment (which we would expect. Ergo we ought to never be astonished that, depending on the credit history, we find no separate information in these factors.

Overall, these outcomes claim that we see heterogeneous responses in credit applications, balances, and creditworthiness outcomes across deciles of the credit score distribution if we extrapolate away from the credit score thresholds using OLS models. Nonetheless, we interpret these outcomes to be suggestive of heterogeneous outcomes of payday advances by credit rating, once more because of the caveat why these OLS quotes are likely biased in this analysis.