Impact of Awash irrigation on the welfare of smallholder farmers in Eastern Ethiopia

Figures & data

Figure 1. Conceptual framework for determinants of irrigation participation.Source: Modified from Mengistie and Kidane (2016)

Table 1. Variables of the model and their hypothesized relationships

Variable label	Description	Unit	Expected signs (ESRM)
			Expenditure	Income	Irrigate
	Household characteristics
Gender	Male headed households (1 if male = 1, otherwise = 0)	Dummy	+	+	+
Age	Age of the household head	Years	-	-	-
Age square	Age of household head square	Years	+	+	+
Adult labour	Household member adult labour	Number	+	+	+
Education	Literacy of household head (literate = 1, illiterate = 0	Dummy	+	+	+
	Asset
Land holding	Amount of land owned by household head	Hectares	+	+	-
Livestock holding	Total livestock holding	TLU	+	+	-
	Institutional characteristics
Accredit	Access to credit (yes = 1, Otherwise = 0)	Dummy	+	+	+
Extcontact	Number of extension contact in a year	Number	+	+	+
Marktdis	Distance from respondents’ live home to the nearest market	In kilometre	_	_	_
Schdis	Distance from homestead to irrigation scheme	In kilometre	_	_	_
	Output (outcome)
Consumption expenditure	Per capita consumption expenditure	In kilogram
income.	Per capita income	In kilogram
Irrigate	If the household head participate in irrigation = 1, if not = 0	Dummy

Table 2. Endogenous switching regression results for irrigation participation and impact on expenditure

Explanatory variables	Irrigation participation		Per capita consumption expenditure
			User		Non-user
	Coef.	Stt. Err.	Coef.	Stt. Err.	Coef.	Stt. Err.
Age of the farmer	−.080	.073	−.035**	.014	−.029	.031
Age square of farmer	.00073	.00075	.0003***	.0001	.0003	.0003
Gender	−.187	.499	−.020	.084	.361**	.154
Adult labour	.039*	.104	−.062***	.020	−.122***	.038
Education	.368*	.209	.042	.047	.047	.074
Land holding size	.312***	.115	−.027	.022	.039	.044
Livestock holding	−.225***	.045	−.019	.013	−.012	.010
Access to credit	.141	.236	.085	.053	.191**	.077
Number of extension contact	.034***	.006	−.001	.002	.003	.002
Nearest local market Distance	−.267***	.049	−.033**	.015	.002	.017
Access to non-farm job	−.994***	.229	.030	.054	−.063	.074
Nearest scheme distance	−.365***	.074
Constant	−.567***	.097	10.358***	.334	9.320 ***	.516
/lns1	1.318	.0561	.277***	.015	.419***	.026
/lns2	−.877	.063	.0455	.339	.385**	.165
/r1	−.199	.288
/r2	.276***	.173
sigma_1	.268***	.015
sigma_2	.416	.026
rho_1 (${\rho }_{1\mathbf{c}}$)	.197	.277
rho_2 (${\rho }_{2\mathbf{c}})$	.385**	.165
Log likelihood	−220.721
Wald chi2(11)	50.98***
LR test of independent equations χ2 (1) 3.34*

*** 1% level of significance; **5% level of significance; *10% level of significance.

Source, Own survey (2019).

Table 3. Endogenous switching regression results for irrigation participation and its impact on income

Explanatory variables	Irrigation participation		Per capita income
			User		Non-user
	Coef.	Stt. Err.	Coef.	Stt. Err.	Coef.	Stt. Err.
Age of the farmer	−.123*	.070	−.019	.023	.0008**	.0003
Age square of farmer	.001	.0007	.0002	.0002	−.0002	.0003
Gender	−.196	.401	−.068	.146	−.380 **	.176
Adult labour	.119***	.099	.068**	.034	.108**	.044
Education	.223*	.178	.075	.083	.043	.084
Land holding size	.199*	.112	−.033	.037	.056	.051
Livestock holding	−.251***	.045	−.062***	.022	.023*	.012
Access to credit	.141	.236	.442***	.087	.144	.088
Number of extension contact	.0301***	.008	.006**	.003	.006**	.003
Nearest local market distance	−.038***	.017	−.091***	.023	.013	.020
Access to non—farm job	−1.065***	.211	.191**	.083	.085	.087
Nearest scheme distance	−.368***	.075
Constant	5.771***	1.683	9.972***	.566	10.216***	.843
/lns1	−.863	.054
/lns2	−.802	.059		.
/r1	.025	.551
/r2	.036	.214
sigma_1	.506***	.039
sigma_2	.477	.028
rho_1 (${\rho }_{1\mathbf{Y}}$)	.899***	104
rho_2 (${\rho }_{1\mathbf{Y}}$)	.079	.237
Log likelihood	−314.945
Wald chi2(11)	61.55***
LR test of independent equations χ2 (1) 6.92 ***

Table 4. Impact of irrigation on expenditure and income using endogenous switching regression model

Outcome variable	Household type and treatment effects	Decision stage		Treatment effects
		Irrigation user	Non-user
Per capita consumption expenditure	Irrigation user	(a)8564.084 (112.348)	(c) 7178.786 (117.778)	$AT{T}_C$= 1385.29 *** (162.769)
	Non-user	(d) 7588.329 (91.445)	(b) 5865.995 (89.539)	$AT{U}_C$= 1722.33*** (127.982)
	Heterogeneous effects	$B{H}_{1C}$= 975.755*** (146.447)	$B{H}_{2C=}$ 1312.79*** (149.989)	$T{H}_C$= −337.04
Per capita income	Irrigation user	(e) 8824.714 (209.280)	(g) 5719.953 (146.399)	$AT{T}_Y$= 3104.761***
	Non-user	(h) 7326.186 (192.967)	(f) 5393.873 (107.124)	$AT{U}_Y$= 1932.313*** (165.358)
	Heterogeneity effects	$B{H}_{1Y}$= 1498.53*** (1317.231)	$B{H}_{2Y}$= 326.08* (184.187)	$T{H}_Y$= 1172.45

*, ** and *** represent significance at 10%, 5% and 1% levels, respectively.

The values in the parenthesis are standard error

Source: Own survey result (2019)

ATT_{c =} average treatment on treated for per consumption; ATU_c = average treatment on untreated for per consumption; TH_c = transitional heterogeneity; BH = base heterogeneity; Y subscript = per capita income