Skip to main content

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

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

Variable

Participation

Level of WTP

Heteroskedasticity

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

   
  1. ***Denotes statistically significant at 1%
  2. **Denotes statistically significant at 5%
  3. *Denotes statistically significant at 10%