What sets cooperative farmers apart from non-cooperative farmers? A transaction cost economics analysis of coffee farmers in Mexico

Agricultural and Food Economics

Table 3 Regression results of the farmer’s coffee cooperative membership/affiliation decision model

	Logit	Logit	MELogit
Shade-grown coffee	− 0.830**	− 0.917**	− 0.917*
	[0.3950]	[0.4590]	[0.4940]
Income from coffee	− 0.007	− 0.016	− 0.016
	[0.0115]	[0.0113]	[0.0116]
No off-farm income	0.665*	0.560	0.560
	[0.3760]	[0.3860]	[0.4770]
Coffee farming experience	0.0122	0.0172*	0.0172*
	[0.0085]	[0.0092]	[0.0103]
Low-level market competition	0.446*	0.669**	0.669**
	[0.2520]	[0.2740]	[0.2670]
Farmland size	0.599***	0.703***	0.703**
	[0.2120]	[0.2430]	[0.2950]
Farmland size squared	− 0.0337*	− 0.0415**	− 0.0415*
	[0.0190]	[0.0211]	[0.0255]
Altitude		0.00226	0.00226*
		[0.0015]	[0.0012]
Education level		− 0.248	− 0.248
		[0.2340]	[0.2300]
Private land		0.789*	0.789**
		[0.4580]	[0.4000]
Constant	− 0.649	− 1.320	− 1.320
	[1.1830]	[2.6970]	[2.4990]
N	159	154	154
Chi-square	26.88	32.31	48.31
Pseudo R-square	0.145	0.197
Clusters	No	No	42
AIC	203.8	192.8	192.8
BIC	228.4	226.2	226.2

The dependent variable equals 1 for coffee cooperative members and 0 otherwise. Robust standard errors are provided in parentheses. *p < 0.10, **p < 0.05; ***p < 0.01. Clusters represent the number of communities in which surveyed coffee farmers operate. AIC is Akaike Information Criterion, and BIC is Bayesian Information Criterion. Explanatory variables are described in Table 2