Skip to main content

Advertisement

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%