Gender differences in willingness to pay for capital-intensive agricultural technologies: the case of fish solar tent dryers in Malawi

Agricultural and Food Economics

Table 4 Estimated models for model selection tests

Variable	Tobit		Probit		Truncated regression		Heckman
Variable	Coef.	t	Coef.	z	Coef.	z	Coef.	z
Age	0.219	1.21	− 0.015**	− 2.13	0.085	2.46	0.091**	2.63
Age squared	− 0.003	− 1.54			− 0.001	− 2.38	− 0.001**	− 2.38
Female	0.358	0.39	− 0.105	− 0.60	0.055	0.41	0.039	0.25
Married			− 0.403	− 1.82*
Education attainment	0.140	0.47	− 0.019	− 0.28	0.147	2.63	0.157**	2.42
Local market	− 0.765	− 0.46	− 0.308	− 0.84	0.677	2.09	0.885**	2.31
Distant market	1.873*	1.78	0.337	1.41	0.517	2.58	0.356	1.44
Knowledge			0.557	2.38**
Own savings account	− 0.858	− 1.19	− 0.279	− 1.72*	0.036	0.27	0.169	0.99
Make decisions on income			− 0.365	− 2.15**
ln (Fishing assets)	− 0.067	− 1.08			0.022	1.84	0.024**	2.03
ln (Fish processing assets)	− 0.072	− 0.96			0.011	0.79	0.008	0.59
Madzedze	− 0.483	− 0.46	0.046	0.17	− 0.802	− 4.2	− 0.805***	− 3.56
Malembo	− 2.359**	− 2.24	− 0.438	− 1.71*	− 0.497	− 2.57	− 0.279	− 1.09
Msaka	− 2.154**	− 2.00	− 0.425	− 1.68*	− 0.814	− 4.08	− 0.598**	− 2.30
Chikombe	− 1.322	− 0.86	− 0.360	− 0.99	− 0.425	− 1.51	− 0.261	− 0.77
Lambda							− 1.128*	− 1.87
Constant	4.095	1.05	1.854	3.25***	8.054	10.44	8.240***	10.09
Number of observations	382		382		284		382
LR/Wald Chi-square	25.34**		31.61***		62.17***		51.27***
Log likelihood	− 1008.58		− 201.712		− 383.964
Pseudo R²	0.0124		0.0752

The same probit model has been used as a selection equation in the Heckman selection model and participation equation in the double hurdle model
***Denotes statistically significant at 1%
**Denotes statistically significant at 5%
*Denotes statistically significant at 10%