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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
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