Skip to main content

Table 4 Multiple linear regression functions: the effect of crop diversity on dietary diversity

From: Does crop diversity contribute to dietary diversity? Evidence from integration of vegetables into maize-based farming systems

Variables Model 1 Model 2
Quadratic effect Interaction effect
Dependent variable: dietary diversity   
Head of the household—female (1 = yes, 0 = no) 0.0247 0.0234
(0.0900) (0.0862)
Age (years) −0.00109 −0.00186
(−0.0770) (−0.133)
Level of education for the respondent (primary level—dummy) 2.099** 2.086**
(2.509) (2.568)
Secondary level—dummy 1.181* 1.142*
(1.717) (1.740)
Higher secondary and above—dummy 0.983 1.056
(1.292) (1.456)
Household size (number of family members) −0.00954 −0.0116
(−0.144) (−0.182)
Number of times met with public extension officers in last 4 months 0.206** 0.213**
(2.472) (2.585)
Ownership of radio 0.324 0.246
(1.168) (0.882)
Ownership of mobile −0.0554 0.0164
(−0.176) (0.0529)
Own farm area (acre) 0.0167 0.0123
(1.628) (1.424)
Ln monthly food expenditure 0.358** 0.426***
(2.362) (2.695)
Ln monthly non-food expenditure 0.0154 −0.0108
(0.103) (−0.0722)
Ln revenue generated from crop sales per transaction (crop income) 3.107* 0.387*
(1.792) (1.655)
Simpson’s Index −1.207* 10.16*
(−1.957) (1.664)
Interaction effect between SI * crop income   −0.868*
  (−1.845)
Access to credit (dummy 1 = yes, 0 = no) 0.565 0.722**
(1.583) (2.058)
Kiteto −0.139 −0.141
(−0.418) (−0.415)
Kongwa −0.163 −0.170
(−0.493) (−0.504)
Square of Ln revenue generated from crop sales per transaction (crop income) −0.121*  
(−1.783)  
Constant −18.81 −4.522
(−1.623) (−1.084)
Observations 200 200
R-squared 0.180 0.181
  1. Robust t-statistics in parentheses
  2. *** p < 0.01; ** p < 0.05; * p < 0.1