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Table 3 The effect of crop diversification in Zimbabwe: joint estimation results

From: Crop diversification and livelihoods of smallholder farmers in Zimbabwe: adaptive management for environmental change

  Log_income Log_fcsdaily Log_legumeprod Log_cereal Hfood_security Hdds
Coefficient SD Coefficient SD Coefficient SD Coefficient SD Coefficient SD Coefficient SD
Crop diversification 3.498*** (0.648) 0.637*** (0.139) 0.223 (0.526) 1.181*** (0.322) 0.568 (0.571) 3.545*** (0.563)
Head of household 0.108 (0.252) 0.028 (0.056) −0.189 (0.208) 0.016 (0.086) 0.107 (0.142) 0.033 (0.236)
Gender (male=1) −0.046 (0.518) −0.128 (0.096) 0.826 (0.550) 0.194 (0.143) 0.323 (0.236) 0.148 (0.379)
Age −0.007 (0.052) −0.013 (0.011) −0.012 (0.044) 0.004 (0.018) −0.046 (0.027) −0.105* (0.051)
Age squared 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) −0.000 (0.000) 0.000 (0.000) 0.001* (0.000)
Secondary education 0.463 (0.276) −0.099 (0.054) 0.375 (0.216) 0.041 (0.095) −0.289 (0.152) −0.138 (0.277)
Married 0.572 (0.520) 0.141 (0.094) −0.519 (0.532) 0.112 (0.145) −0.242 (0.238) −0.331 (0.382)
Number of workers 0.099 (0.064) −0.027 (0.020) −0.066 (0.045) −0.027 (0.026) −0.029 (0.040) 0.112 (0.065)
Land size 0.418*** (0.099) 0.012 (0.006) 0.038 (0.022) −0.013 (0.014) −0.026 (0.026) −0.001 (0.043)
Land size squared −0.010* (0.005)           
Distance to market 0.001 (0.001) −0.000 (0.000) 0.000 (0.001) 0.002* (0.001) 0.001 (0.001) 0.000 (0.002)
Farming experience 0.074* (0.036) 0.006 (0.006) 0.055* (0.025) −0.004 (0.013) 0.004 (0.019) 0.011 (0.030)
Farming experience squared −0.001* (0.001) −0.000 (0.000) −0.001 (0.000) 0.000 (0.000) −0.000 (0.000) −0.000 (0.001)
Farmer 0.654 (0.373) 0.076 (0.082) −0.202 (0.308) 0.112 (0.151) −0.152 (0.188) −0.255 (0.325)
Household wealth             
 Quintile 2 −0.187 (0.383) −0.185* (0.079) −0.594 (0.303) −0.073 (0.146) −0.162 (0.236) −0.779* (0.344)
 Quintile 3 0.086 (0.382) −0.134 (0.079) −0.445 (0.268) −0.285* (0.139) −0.216 (0.219) −0.898* (0.375)
 Quintile 4 −0.019 (0.424) −0.150 (0.085) −0.855** (0.310) −0.075 (0.171) −0.196 (0.240) −0.672 (0.382)
 Quintile 5 0.471 (0.435) −0.216* (0.093) −0.381 (0.280) 0.214 (0.144) −0.070 (0.259) −0.910* (0.409)
Goromonzi district −0.770* (0.341) 0.124 (0.064) 0.439 (0.252) 0.236* (0.104) 0.220 (0.173) 0.057 (0.300)
Wedza district −1.977*** (0.368) 0.264*** (0.074) −1.826*** (0.360) −1.258*** (0.234) 0.283 (0.202) 1.387*** (0.404)
Mudzi district −2.586*** (0.305) 0.125 (0.068) −0.153 (0.232) −1.622*** (0.140) 0.065 (0.228) 0.382 (0.334)
Constant −1.672 (1.353) 2.226*** (0.317) 6.259*** (1.193) 6.000*** (0.488) 0.173 (0.802) 6.744*** (1.390)
Atanhrho_12 −0.823*** (0.180) −1.134*** (0.263) 0.092 (0.132) −0.773* (0.352) −0.224 (0.358) −1.002*** (0.204)
Observations 538   538   538   538   538   538  
Log-likelihood −1445.7   −558.9   −1018.7   −824.8   −482.1   −1383.2  
  1. *** Significant at 1 % level; ** significant at 5 % level; * significant at 10 % level. Robust standard errors in parentheses. Except for the model for household food security which is estimated via a probit regression model, all the other models are based on a continuous outcome variable and thus use ordinary least squares approach