Table 11.

Regression results for use and importance variables

	pct_sold	vbsc_rank1	vbsc_top3
Intercept	42.812***	− 1.009	1.636*
	(8.657)	(1.065)	(0.994)
Farm characteristic
acres	− 0.299**	− 0.056	0.228
	(0.136)	(0.048)	(0.140)
gfi	− 0.074***	0.001	− 0.012***
	(0.026)	(0.004)	(0.004)
pct_farminc	− 0.038	0.000	0.005
	(0.047)	(0.005)	(0.008)
Commodities produced
meats&dairy	4.447	− 0.286	− 0.309
	(4.140)	(0.297)	(0.339)
hortcrop	− 0.187	− 1.016***	− 0.480
	(5.620)	(0.276)	(0.397)
agroncrop	− 12.434***	0.802	0.194
	(3.921)	(0.616)	(0.359)
Region (base = Midwest)
Northwest	4.580	− 0.658	− 0.185
	(4.667)	(0.534)	(0.663)
Pacific	− 5.495	− 0.470	− 0.662
	(5.448)	(0.700)	(0.630)
Northeast	1.891	− 1.046*	− 1.155
	(4.706)	(0.605)	(0.724)
Southeast	17.402 **	0.496	− 1.049*
	(7.255)	(0.620)	(0.620)
Operator characteristic
female	− 8.337**	− 0.237	− 0.643**
	(3.748)	(0.242)	(0.312)
age	− 0.147	0.024*	0.009
	(0.128)	(0.014)	(0.012)
Number of obs	225	226	226
p-value for F/χ2 test	0.000	0.000	0.000
Pseudo R-squared	0.014	0.086	0.101

*, **, and *** signify statistical significance at the 0.1, 0.05, and 0.01 levels, respectively

The numbers in parentheses are standard errors adjusted for VBSC clusters. The pct_sold equation is estimated using tobit regression with the dependent variable censored at 0 and 100. The vbsc_rank1 and vbsc_top3 equations are estimated using logit regression