Regulatory constraint and small business lending: do innovative peer-to-peer lenders have an advantage?

Financial Innovation

Table 9 Robustness results for county level data

	Total small business loan volume	Small business loan for businesses with gross revenues less than $1 million	Small business loan for businesses with gross revenues more than $1 million
Treated*DFA	− 0.591***	− 0.068***	0.001
	(− 17.942)	(− 2.867)	(0.054)
Population	0.879***	0.891***	0.973***
	(34.882)	(46.980)	(58.192)
DebtoIncome	− 0.027	0.057***	− 0.081***
	(− 1.084)	(3.460)	(− 5.242)
Income	0.657***	0.296***	1.104***
	(4.945)	(3.270)	(12.696)
Unemployment	0.002	− 0.023***	− 0.013*
	(0.193)	(− 3.182)	(− 1.830)
BRNUM	0.014	0.175***	0.146***
	(1.140)	(16.431)	(16.599)
C3	− 0.001	0.002	0.003
	(− 0.342)	(0.969)	(1.501)
HHI	0.000	− 0.039	− 0.097
	(0.001)	(− 0.583)	(− 1.485)
Domdep	− 0.006	− 0.001	0.001
	(− 1.306)	(− 0.264)	(0.234)
County FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Obs	5589	5584	5589
Adj. R²	0.739	0.842	0.867

Table 9 shows that by limiting the research period to one year before and after treatment, there is no change on county-level small business lending and the effect of Dodd-Frank Regulation is still significant except for small business loans businesses with gross revenues of more than $1 million. Standard errors are clustered at the county level and shown in parentheses. Statistical significance at the 10%, 5% and 1% levels is denoted by^*,^** and ^***, respectively.t-statistics are presented in parentheses