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Agricultural and Food Economics

Table 4 Estimated models for model selection tests

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

Variable Tobit Probit Truncated regression Heckman
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      
  1. The same probit model has been used as a selection equation in the Heckman selection model and participation equation in the double hurdle model
  2. ***Denotes statistically significant at 1%
  3. **Denotes statistically significant at 5%
  4. *Denotes statistically significant at 10%
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