Rural Credit and Farms Efficiency: Modelling Farmers Credit Allocation Decisions, Evidences from Benin

Table 6

Results of stochastic frontier with conditional mean model.

Variables	Coefficients	Standards errors
Lrevenu
cons	0.5639018	0.0789205	0.000***
lcapital	0.0258756	0.0193221	0.181
lacreage	0.3210206	0.0588239	0.000***
llabour	0.5577022	0.0300458	0.000***
linterm_input	0.0867108	0.049874	0.082*
0,5 * lcapital^2	−0.0252712	0.0144483	0.080*
0,5 * lacreage^2	0.1591446	0.0946285	0.093*
0,5 * llabour^2	−0.0645713	0.0262547	0.014
0,5 * linterm_input^2	−0.1568689	0.0810997	0.053*
Lcapital lacreage	−0.0481295	0.0354144	0.174
Lcapital llabour	0.0495199	0.0160244	0.002**
Lcapital linterm_input	−0.0241044	0.0265636	0.364
Lacreage linterm_input	0.0604558	0.080368	0.452
Lacreage llabour	0	(omitted)
linterm_input × llabour	0.035108	0.0333955	0.293
Mu
Credit	−0.0000135		0.033**
Credit^2		.	.
Credit * lcapital			0.769
Credit * lacreage			0.410
Credit * linterm_input			0.094*
Credit * llabour			0.055*
sigma_	0.1377795	0.0323337	—
sigma_	0.5679396	0.1314753	—
sigma2	0.7057191	0.1101696	—
Gamma	0.8047672	0.0680913	—
Log likelihood = −315.32666 Wald chi2(13) = 1266.53 Prob > chi2 = 0.0000

Note: *,**, ***significant at 10%, 5%, and 1%, respectively
Source: authors’ calculations.