Table 7.

Robustness Checks

	Malawi		Zambia
	Attrition reweighting	Randomization inference	Attrition reweighting	Randomization inference
	(1)	(2)	(3)	(4)
Any economic activities	0.035 [0.127]	0.034 [0.197]	0.056** [0.016]	0.055** [0.020]
Agricultural work for the household (excluding livestock)	0.065** [0.020]	0.063** [0.019]	N.A.
Livestock herding for the household	0.068*** [0.000]	0.068*** [0.000]	0.038*** [0.007]	0.039*** [0.007]
Household non-agricultural business	0.001 [0.781]	0.002 [0.836]	0.034* [0.054]	0.034* [0.088]
Paid work outside the household	−0.061*** [0.001]	−0.061** [0.024]	0.003 [0.806]	0.001 [0.907]
Any chores	0.095*** [0.000]	0.097*** [0.000]	0.026 [0.119]	0.028 [0.152]
Collecting water or firewood	0.093*** [0.000]	0.095*** [0.000]	0.033* [0.072]	0.034* [0.094]
Taking care of children, cooking or cleaning	0.083*** [0.000]	0.084*** [0.001]	0.002 [0.901]	0.005 [0.761]
Taking care of elderly or sick household members	0.016 [0.229]	0.019 [0.173]	N.A.
Any economic activities or chores	0.090*** [0.000]	0.091*** [0.000]	0.030* [0.092]	0.031 [0.107]
N	5,806	5,806	3,999	3,999

Notes: In columns (1) and (3), we use the endline data and regression specification (1), adding inverse probability weights. P-values in brackets, estimated using the wild bootstrap method (parameters: 999 replications, Rademacher weights), clustering at the unit of randomization level (village cluster for Malawi, Community Welfare Assistance Committee for Zambia). In columns (2) and (4), we use randomization inference: we randomly reassigned treatment status (at the original level of randomization) and estimated the impact of this placebo treatment using specification (1), clustering standard errors at the unit of randomization level. This procedure is repeated 1,000 times to obtain p-values, which equal the proportion of reestimated impacts that are larger in absolute value than the original estimated impact (implemented applying the Stata command ‘ritest’ by Heß, 2017). The sample includes children aged 6-15 and 5-14 years at baseline, in Malawi and Zambia respectively (8-17 years at endline in both countries). Regressions include fixed effects for gender, age, stratification variable (Traditional Authority for Malawi, district for Zambia) and enumerator. For Malawi, regressions also include controls for household composition (dummy variables for whether the household includes 3 members, 4 or more members, number of members below 21 years of age, number of members between 21 and 29 years of age).

^*p<0.1

^**p<0.05

^***p<0.01.

Sources: Malawi Economic, Health and Demographic Survey (MEHDS), Zambia Multiple Category Targeting Programme (MCP).