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 2 Estimation results of the IHS double hurdle model for willingness to pay

Variable	Participation		Level of WTP		Heteroskedasticity
Variable	Coef.	z	Coef.	z	Coef.	z
Age	− 0.015**	− 2.13	0.108***	2.88	0.005	1.29
Age squared			− 0.001***	− 2.71
Female	− 0.105	− 0.60	0.126	0.95	0.042	0.65
Married	− 0.403*	− 1.82			− 0.305	− 2.56**
Education attainment	− 0.019	− 0.28	0.165***	2.79
Local market	− 0.308	− 0.84	0.618*	1.92
Distant market	0.337	1.41	0.374*	1.72
Knowledge	0.557**	2.38
Own savings account	− 0.279*	− 1.72	− 0.005	− 0.04	0.022	0.25
ln (fishing assets)			0.021*	1.83
ln (fish processing assets)			0.015	1.12
Make decisions on income	− 0.365**	− 2.15			− 0.063	− 0.72
Madzedze	0.046	0.17	− 0.821***	− 4.90	0.269	1.93*
Malembo	− 0.438*	− 1.71	− 0.455**	− 2.64	0.292	2.14**
Msaka	− 0.425*	− 1.68	− 0.828***	− 4.57	0.277	1.88*
Chikombe	− 0.360	− 0.99	− 0.313	− 1.22	0.246	1.35
Constant	1.854***	3.25	7.572***	9.44	0.739	4.74***
Number of observations			382
Wald chi² (13)			28.89***
Log likelihood			− 581.36