Consumption of protein-rich food items: effect of cattle ownership and land-use consolidation

Figures & data

Table 1. Descriptive statistics and definitions of the study variables.

Variable	Obs	Mean (SD)	Definition
meat	9709	0.462 (1.146)	Number of days per week meat is consumed by the household
milk	9709	1.135 (2.248)	Number of days per week mil is consumed by the household
pulse	9709	5.668 (2.248)	Number of days per week pulses are consumed by the household
consolidation	7121	0.211 (0.408)	Part of the land is under land consolidation programme (1 = yes, 0 = if otherwise)
location	9709	0.161 (0.368)	Urban rural status of the household (1 = urban, 0 = rural)
food_sec	9709	3.207 (0.789)	Food situation of the household as measured by food consumption group (1 = severely food insecure, 2 = moderately food insecure, 3 = marginally food secure, and 4 = food secure)
wealth	8357	2.835 (1.363)	Household wealth index (1 = poorest, 2 = poor, 3 = medium, 4 = wealthy, and 5 = wealthiest)
age	9709	47.627 (15.318)	The age of household’s head in years
cattle	9709	0.413 (0.492)	Household owns or manages cattle (1 = yes, 0 = otherwise)
land size	9709	1.815 (1.583)	Size of the owned land, categories from 0 to 7 (0 = no land owned, …., 7 = 5 ha and above)
sex	9709	0.267 (0.442)	The gender of household head (1 = female, 0 = male)
education	9709	2.429 (1.493)	Level of education of household head (1 = no education …, 8 = completed university)
hhsize	9709	4.785 (2.08)	Number of the persons living in the household
liveli source	9709	0.798 (0.401)	Agriculture is the main source of household’s livelihood (1 = yes, 0 = otherwise)
liveli_acti	9709	2.023 (0.759)	Household livelihood activities (1 = one activities, 2 = two activities, 3 = three activities or more)
Social_network	9709	0.277 (0.448)	The head of household is a member of a cooperative or an association (1 = yes, 0 = otherwise)

Table 2. Effect of cattle ownership and land consolidation on the consumption of food items with high-value proteins.

Variable	Ordered logistic regression estimations			Heteroscedastic ordered logistic regression estimations
	Model 1 (Meat)	Model 2 (Milk)	Model 3 (Pulse)	Model 1 (Meat)	Model 2 (Milk)	Model 3 (Pulse)
	Coefficients (Std. dev.)	Coefficients (Std. dev.)	Coefficients (Sd. Dev.)	Coefficients (Std. dev.)	Coefficients (Std. dev.)	Coefficients (Sd. Dev.)
consolidation	−0.050 (0.085)	−0.045 (0.077)	0.211*** (0.073)	0.008 (0.110)	−0.013 (0.113)	0.249*** (0.085)
cattle	−0.192*** (0.073)	0.792*** (0.067)	0.105* (0.058)	−0.207** (0.102)	0.528*** (0.092)	0.105 (0.064)
age	−0.010*** (0.003)	−0.002 (0.002)	.008*** (0.002)	−0.010*** (0.003)	−0.003 (0.003)	0.008*** (0.002)
sex	−0.096 (0.097)	0.058 (0.084)	−0.087 (0.070)	−0.100 (0.097)	0.069 (0.098)	−0.086 (0.070)
education	0.115*** (0.026)	0.172*** (0.025)	−0.067*** (0.026)	0.113*** (0.026)	0.196*** (0.029)	−0.068** (0.026)
hhsize	−0.036** (0.018)	0.020 (0.016)	−0.062*** (0.015)	−0.036** (0.018)	0.018 (0.019)	−0.063*** (0.015)
land_size	0.092*** (0.026)	0.07*** (0.024)	0.030 (0.024)	0.092*** (0.026)	0.086*** (0.028)	0.032 (0.024)
wealth	0.401*** (0.031)	0.271*** (0.027)	0.013 (0.026)	0.400*** (0.032)	0.323*** (0.034)	0.013 (0.026)
food_consgroup	0.746*** (0.059)	0.739*** (0.051)	1.839*** (0.045)	0.745*** (0.063)	0.871*** (0.064)	1.852*** (0.056)
liveli_acti	0.253*** (0.049)	0.025 (0.043)	0.136*** (0.038)	0.253*** (0.049)	0.023 (0.050)	0.136*** (0.039)
liveli_source	−0.053 (0.084)	−0.179** (0.075)	−0.128** (0.065)	−0.054 (0.084)	−0.204** (0.088)	−0.129** (0.065)
social_network	0.048 (0.072)	0.029 (0.065)	0.118* (0.063)	0.049 (0.071)	0.021 (0.076)	0.121* (0.063)
location	0.367*** (0.122)	1.038*** (0.108)	0.047 (0.130)	0.366*** (0.122)	1.118*** (0.123)	0.047 (0.130)
lnsigma consolidation	–	–	–	−0.044 (0.055)	−0.045 (0.056)	0.041 (0.044)
lnsigma cattle	–	–	–	0.011 (0.048)	0.297 (0.050)	−0.001 (0.034)
/cut1	5.518 (0.274)	5.438 (0.242)	1.250 (0.204)	5.510*** (0.300)	6.062*** (0.308)	1.265*** (0.210)
/cut2	6.277 (0.277)	5.696 (0.243)	2.005 (0.195)	6.265*** (0.312)	6.371*** (0.313)	2.025*** (0.204)
/cut3	7.240 (0.283)	6.095 (0.245)	2.934 (0.192)	7.223*** (0.330)	6.851*** (0.322)	2.962*** (0.206)
/cut4	8.021 (0.290)	6.510 (0.247)	4.018 (0.194)	8.001*** (0.348)	7.355*** (0.332)	4.053*** (0.214)
/cut5	8.553 (0.299)	6.678 (0.248)	4.796 (0.197)	8.530*** (0.364)	7.560*** (0.337)	4.837*** (0.223)
/cut6	8.977 0.309)	6.787 (0.248)	5.275 (0.200)	8.952*** (0.379)	7.695*** (0.340)	5.319*** (0.230)
/cut7	9.112 (0.313)	6.827 (0.249)	5.621 (0.201)	9.086*** (0.384)	7.743*** (0.341)	5.668*** (0.234)
Observations	6486	6486	6486	6486	6486	6486
LR chi2	1223.67	1223.67	2580.80	907.37	1258.70	2581.69
Prob > chi2	0.000	0.000	0.000	0.000	0.000	0.000

*** p < 0.001, ** p < 0.05, * p < 0.10.