Comparing the Productive Effects of Cash and Food Transfers in a Crisis Setting: Evidence from a Randomised Experiment in Yemen

Figures & data

Table 1. Balance and summary statistics

	Beneficiary				Nonbeneficiary				T-test
	(1)		(2)		(3)		(4)
	Food FDPs		Cash FDPs		Food FDPs		Cash FDPs		(1) − (2)	(1) − (3)	(2) − (4)	(3) − (4)
Variable	N/[FDPs]	Mean/SE	N/[FDPs]	Mean/SE	N/[FDPs]	Mean/SE	N/[FDPs]	Mean/SE	Difference
Grows any crop	617	1.00	537	0.99	379	0.99	363	0.98	0.01	0.01	0.01	0.01
	[66]	[0.00]	[67]	[0.00]	[63]	[0.01]	[59]	[0.01]
Grows a cash crop	617	0.21	537	0.26	379	0.24	363	0.29	−0.06	−0.03	−0.03	−0.05
	[66]	[0.04]	[67]	[0.03]	[63]	[0.04]	[59]	[0.04]
Grows khat	617	0.16	537	0.25	379	0.19	363	0.27	−0.09*	−0.03	−0.02	−0.07
	[66]	[0.03]	[67]	[0.03]	[63]	[0.04]	[59]	[0.04]
Grows wheat	617	0.05	537	0.06	379	0.04	363	0.04	−0.00	0.01	0.01	0.00
	[66]	[0.02]	[67]	[0.02]	[63]	[0.03]	[59]	[0.02]
Grows only sorghum	614	0.83	531	0.81	375	0.79	355	0.77	0.01	0.04	0.04	0.01
	[66]	[0.03]	[67]	[0.02]	[63]	[0.03]	[59]	[0.03]
Cattle	1000	0.47	980	0.47	682	0.39	688	0.44	0.00	0.08**	0.03	−0.05
	[67]	[0.04]	[67]	[0.04]	[66]	[0.04]	[66]	[0.04]
Sheep and goats	1000	2.44	980	2.22	682	1.65	688	1.72	0.22	0.79***	0.50*	−0.06
	[67]	[0.25]	[67]	[0.25]	[66]	[0.17]	[66]	[0.28]
Camels and donkeys	1000	0.42	980	0.45	682	0.37	688	0.45	−0.03	0.05	−0.01	−0.08
	[67]	[0.03]	[67]	[0.04]	[66]	[0.04]	[66]	[0.05]
Poultry	1000	0.67	980	0.69	682	0.34	688	0.73	−0.02	0.33***	−0.04	−0.39**
	[67]	[0.10]	[67]	[0.13]	[66]	[0.07]	[66]	[0.16]
Beehives	1000	0.10	980	0.05	682	0.02	688	0.01	0.04	0.08*	0.04	0.01
	[67]	[0.04]	[67]	[0.03]	[66]	[0.01]	[66]	[0.01]
Tropical livestock units	1000	0.89	980	0.88	682	0.72	688	0.81	0.01	0.18***	0.07	−0.10
	[67]	[0.06]	[67]	[0.06]	[66]	[0.06]	[66]	[0.07]
Total livestock value	1000	341.20	980	335.20	682	291.91	688	313.31	6.00	49.29*	21.89	−21.40
	[67]	[25.27]	[67]	[23.78]	[66]	[27.02]	[66]	[30.92]
Ag expenditure	1000	2.73	980	1.23	682	0.78	688	0.65	1.50	1.95**	0.58	0.13
	[67]	[0.88]	[67]	[0.41]	[66]	[0.38]	[66]	[0.31]
Total value of agricultural assets	1000	16.19	980	12.94	682	32.93	688	15.61	3.26*	−16.73	−2.67	17.32
	[67]	[1.23]	[67]	[1.10]	[66]	[20.67]	[66]	[3.85]
Anyone in household worked for a wage	1000	0.42	980	0.44	682	0.50	688	0.46	−0.02	−0.08***	−0.02	0.04
	[67]	[0.02]	[67]	[0.02]	[66]	[0.02]	[66]	[0.03]
Anyone in household worked in a business	1000	0.17	980	0.18	682	0.13	688	0.18	−0.01	0.03**	0.00	−0.05
	[67]	[0.02]	[67]	[0.02]	[66]	[0.02]	[66]	[0.03]
Number earning wage (primary)	1000	0.55	980	0.60	682	0.68	688	0.65	−0.05	−0.12**	−0.06	0.02
	[67]	[0.04]	[67]	[0.04]	[66]	[0.04]	[66]	[0.05]
Number in business (primary)	1000	0.20	980	0.24	682	0.16	688	0.21	−0.04	0.04	0.04	−0.05
	[67]	[0.03]	[67]	[0.04]	[66]	[0.03]	[66]	[0.04]
Hours worked off-farm per worker	1000	4.40	980	4.85	682	6.38	688	6.60	−0.45	−1.98***	−1.75***	−0.22
	[67]	[0.27]	[67]	[0.29]	[66]	[0.38]	[66]	[0.45]
Any child worked	1000	0.23	980	0.21	682	0.12	688	0.14	0.02	0.11***	0.07***	−0.02
	[67]	[0.01]	[67]	[0.02]	[66]	[0.02]	[66]	[0.02]
Woman-headed household	1000	0.08	980	0.06	682	0.12	688	0.15	0.01	−0.04***	−0.09***	−0.03
	[67]	[0.01]	[67]	[0.01]	[66]	[0.01]	[66]	[0.02]
Household head attended primary and up	1000	0.27	980	0.25	682	0.34	688	0.36	0.02	−0.08***	−0.11***	−0.01
	[67]	[0.02]	[67]	[0.02]	[66]	[0.02]	[66]	[0.02]
Household head is married	1000	0.77	980	0.82	682	0.70	688	0.71	−0.05*	0.07***	0.11***	−0.01
	[67]	[0.02]	[67]	[0.02]	[66]	[0.02]	[66]	[0.02]
Household head’s age	1000	47.59	980	47.04	682	47.49	688	46.17	0.55	0.10	0.88	1.33
	[67]	[0.61]	[67]	[0.60]	[66]	[0.78]	[66]	[0.74]
Household size	1000	8.66	980	8.89	682	7.08	688	7.07	−0.24	1.58***	1.83***	0.01
	[67]	[0.12]	[67]	[0.14]	[66]	[0.16]	[66]	[0.15]
Household members ages 0–5	1000	1.20	980	1.23	682	1.04	688	1.12	−0.03	0.17**	0.11*	−0.09
	[67]	[0.05]	[67]	[0.04]	[66]	[0.05]	[66]	[0.06]
Household members ages 6–17	1000	3.88	980	4.00	682	2.62	688	2.63	−0.12	1.26***	1.37***	−0.00
	[67]	[0.07]	[67]	[0.08]	[66]	[0.10]	[66]	[0.07]
Number of televisions	1000	0.29	980	0.27	682	0.29	688	0.25	0.02	0.01	0.02	0.03
	[67]	[0.04]	[67]	[0.04]	[66]	[0.04]	[66]	[0.04]
Number of refrigerators	1000	0.07	980	0.07	682	0.06	688	0.08	0.01	0.02	−0.01	−0.02
	[67]	[0.02]	[67]	[0.01]	[66]	[0.02]	[66]	[0.02]
Number of motor vehicles	1000	0.02	980	0.03	682	0.02	688	0.02	−0.00	0.00	0.00	−0.00
	[67]	[0.01]	[67]	[0.01]	[66]	[0.01]	[66]	[0.01]
Standardised wealth index	1000	0.07	980	−0.02	682	0.00	688	−0.01	0.10	0.07	−0.01	0.01
	[67]	[0.10]	[67]	[0.08]	[66]	[0.11]	[66]	[0.09]

Notes: Food distribution points (FDPs) represent a cluster-level of randomisation. T-tests include controls on the randomisation stratum (governorate). The total value of livestock and the value of agricultural assets are in US dollars. At baseline, US$1 = YER 214. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table 2. Impact of food and cash transfers on crop choice: unmatched estimates

Outcome	Grows any crop	Grows a cash crop	Grows khat	Grows wheat	Grows only sorghum
RDiD	0.02	0.02	0.02	0.01	0.01
	(0.01)*	(0.01)*	(0.01)**	(0.01)**	(0.01)
N	2,360	2,360	2,360	2,360	2,290
Westfall-Young p-value	0.107	0.107	0.028	0.099	0.336
ADD: food	0.01	0.03	0.03	−0.00	−0.02
	(0.01)	(0.01)**	(0.01)**	(0.01)	(0.01)*
N	2,013	2,013	2,013	2,013	1,963
Westfall-Young p-value	0.508	0.038	0.038	0.846	0.118
ADD: cash	−0.01	−0.01	−0.01	−0.02	−0.02
	(0.01)*	(0.01)	(0.01)	(0.01)**	(0.01)
N	1,858	1,858	1,858	1,858	1,790
Westfall-Young p-value	0.151	0.393	0.262	0.113	0.223
DDD	0.02	0.04	0.04	0.01	−0.00
	(0.01)**	(0.01)**	(0.02)***	(0.01)	(0.02)
N	3,871	3,871	3,871	3,871	3,753
Westfall-Young p-value	0.082	0.045	0.011	0.273	0.838

Notes: Westfall-Young p-values are listed below the number of observations (N). The calculations are based on 1,200 replications. RDiD estimates control for governorate fixed effects. All other estimates control for subdistrict (uzla) fixed effects. All estimates are restricted to those who own or operate land. Cash crops include any noncereals or pulses. Estimates for the sorghum only regressions are restricted to the sample that grows any crops. Control variables include household size and baseline values of the following: gender, educational attainment, and marital status of the household head and a standardised asset index. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table 3. Impact of food and cash transfers on crop choice: estimates with matched beneficiary and nonbeneficiary samples

Outcome	Grows any crop	Grows a cash crop	Grows khat	Grows wheat	Grows only sorghum
ADD: food	−0.00	0.02	0.03	0.00	−0.02
	(0.01)	(0.01)**	(0.01)**	(0.00)	(0.01)**
N	1,961	1,961	1,961	1,961	1,912
ADD: cash	−0.02	−0.03	−0.02	−0.01	−0.01
	(0.01)**	(0.02)	(0.01)*	(0.01)**	(0.01)
N	1,804	1,804	1,804	1,804	1,739
DDD	0.02	0.05	0.05	0.02	−0.01
	(0.01)*	(0.02)**	(0.02)***	(0.01)**	(0.02)
N	3,765	3,765	3,765	3,765	3,651

Notes: N = number of observations. All estimates control for uzla fixed effects. All estimates are restricted to those who own or operate land. Cash crops include any noncereals or pulses. Estimates for the sorghum only regressions are restricted to the sample that grows any crop. Estimates are obtained using regression adjustment after weighting the nonbeneficiary sample by kernel matching on the propensity score. The matching algorithm uses a bandwidth of .004 and a tricube kernel. The propensity score is estimated using a logit model. Matching variables are household size, numbers of children attending or not attending school, housing characteristics (floor material, water source), characteristics of the household head (gender, literacy, marital status), asset ownership (televisions, phones, sewing machines), and governorate of residence. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table 4. Impact of food and cash transfers on number of livestock: unmatched estimates

Outcome	Cattle	Sheep and goats	Camels and donkeys	Poultry	Beehives	Tropical livestock units
RDiD	−0.04	−0.61	−0.03	−0.24	−0.06	−0.12
	(0.04)	(0.31)**	(0.04)	(0.17)	(0.04)	(0.06)*
N	3,960	3,960	3,960	3,960	3,960	3,960
Westfall-Young p-value	0.401	0.036	0.401	0.195	0.262	0.036
ADD: food	−0.00	−0.23	0.03	−0.32	−0.09	−0.01
	(0.04)	(0.29)	(0.03)	(0.12)***	(0.04)**	(0.05)
N	3,364	3,364	3,364	3,364	3,364	3,364
Westfall-Young p-value	0.958	0.535	0.449	0.018	0.061	0.958
ADD: cash	0.05	0.30	0.08	0.30	−0.02	0.13
	(0.04)	(0.21)	(0.04)**	(0.15)*	(0.04)	(0.06)**
N	3,336	3,336	3,336	3,336	3,336	3,336
Westfall-Young p-value	0.223	0.205	0.023	0.037	0.406	0.023
DDD	−0.05	−0.53	−0.05	−0.62	−0.07	−0.13
	(0.05)	(0.36)	(0.05)	(0.19)***	(0.05)	(0.08)*
N	6,700	6,700	6,700	6,700	6,700	6,700
Westfall-Young p-value	0.326	0.183	0.326	0.000	0.315	0.093

Notes: Westfall-Young p-values are listed below the number of observations (N). The calculations are based on 1,200 replications. RDiD estimates control for governorate fixed effects. All other estimates control for subdistrict (uzla) fixed effects. Dependent variables in all columns, except the final column, are the number of animals. Control variables include household size and baseline values of the following: gender, educational attainment, and marital status of the household head and a standardised asset index. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table 5. Impact of food and cash transfers on number of livestock: estimates with matched beneficiary and nonbeneficiary samples

Outcome	Cattle	Sheep and goats	Camels and donkeys	Poultry	Beehives	Tropical livestock units
ADD: food	−0.02	−0.20	0.02	−0.43	−0.08	−0.02
	(0.04)	(0.31)	(0.05)	(0.13)***	(0.03)**	(0.06)
N	3,332	3,332	3,332	3,332	3,332	3,332
ADD: cash	0.02	0.30	0.09	0.24	−0.06	0.11
	(0.06)	(0.24)	(0.05)*	(0.17)	(0.05)	(0.08)
N	3,290	3,290	3,290	3,290	3,290	3,290
DDD	−0.04	−0.50	−0.07	−0.67	−0.01	−0.14
	(0.07)	(0.40)	(0.07)	(0.21)***	(0.06)	(0.10)
N	6,622	6,622	6,622	6,622	6,622	6,622

Notes: All estimates control for uzla fixed effects. The dependent variables in all columns, except the final column, are the number of animals. Estimates are obtained using regression adjustment after weighting the nonbeneficiary sample by kernel matching on the propensity score. The matching algorithm uses a bandwidth of .004 and a tricube kernel. The propensity score is estimated using a logit model. Matching variables are household size, numbers of children attending or not attending school, housing characteristics (floor material, water source), characteristics of the household head (gender, literacy, marital status), asset ownership (televisions, phones, sewing machines), and governorate of residence. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table 6. Impact of food and cash transfers on agricultural assets

Outcome	Ploughs	Small tools, such as hoes	Large tools, such as pumps, carts	Hand mills	Knapsack sprayer	Tractors	Total value of agricultural assets
RDiD	−0.05	−0.07	−0.04	0.01	−0.01	−0.00	−9.17
	(0.03)	(0.14)	(0.02)**	(0.03)	(0.01)	(0.00)	(30.92)
N	3,960	3,960	3,960	3,960	3,960	3,960	3,960
Westfall-Young p-value	0.288	0.960	0.178	0.960	0.960	0.960	0.960
ADD: food	−0.00	0.30	−0.01	0.04	0.01	−0.00	1.64
	(0.04)	(0.12)**	(0.02)	(0.03)	(0.02)	(0.00)	(2.68)
N	3,364	3,364	3,364	3,364	3,364	3,364	3,364
Westfall-Young p-value	0.978	0.106	0.964	0.418	0.823	0.418	0.964
ADD: cash	0.04	0.36	0.06	−0.03	0.01	0.00	12.67
	(0.03)	(0.13)***	(0.03)*	(0.03)	(0.01)	(0.00)	(30.76)
N	3,336	3,336	3,336	3,336	3,336	3,336	3,336
Westfall-Young p-value	0.408	0.113	0.223	0.353	0.792	0.975	0.881
DDD	−0.04	−0.06	−0.07	0.07	0.01	−0.00	−10.73
	(0.05)	(0.18)	(0.04)*	(0.04)*	(0.02)	(0.00)	(30.80)
N	6,700	6,700	6,700	6,700	6,700	6,700	6,700
Westfall-Young p-value	0.797	0.956	0.158	0.128	0.956	0.797	0.956

Notes: Westfall-Young p-values are listed below the number of observations (N). The calculations are based on 1,200 replications. RDiD estimates control for governorate fixed effects. All other estimates control for subdistrict (uzla) fixed effects. In columns 1–6, the dependent variable is the number of items. The value of agricultural assets is the self-reported replacement value and given in US dollars. Control variables include household size and baseline values of the following: gender, educational attainment, and marital status of the household head and a standardised asset index. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table 7. Impact of food and cash transfers on investment

Outcome	Ag expenditure	Any ag expenditure	Any debt expenditure	Debt expenditure
RDiD	−1.53	−0.03	−0.05	−4.76
	(1.00)	(0.01)**	(0.02)**	(3.51)
N	3,960	3,960	3,959	3,959
Westfall-Young p-value	0.072	0.020	0.017	0.072
ADD: food	−1.85	−0.03	0.00	−3.10
	(0.96)*	(0.01)*	(0.02)	(3.39)
N	3,364	3,364	3,364	3,364
Westfall-Young p-value	0.036	0.036	0.762	0.326
ADD: cash	−0.51	−0.02	0.03	0.03
	(0.52)	(0.01)*	(0.02)	(2.46)
N	3,336	3,336	3,335	3,335
Westfall-Young p-value	0.315	0.052	0.156	0.991
DDD	−1.34	−0.01	−0.02	−3.11
	(1.08)	(0.02)	(0.03)	(4.17)
N	6,700	6,700	6,699	6,699
Westfall-Young p-value	0.226	0.543	0.543	0.543

Notes: Westfall-Young p-values are listed below the number of observations (N). The calculations are based on 1,200 replications. RDiD estimates control for governorate fixed effects. All other estimates control for subdistrict (uzla) fixed effects. Expenditures are in US dollars. Agricultural expenditure includes equipment, inputs, and veterinary services, but excludes labour and livestock purchases. Control variables include household size and baseline values of the following: gender, educational attainment, and marital status of the household head and a standardised asset index. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table 8. Impact of food and cash transfers on investment in land

Outcome	Investment in any plot between rounds	Changed irrigation system	Change in size of plot, square meters
SD	0.01	−0.01	−1.22
	(0.03)	(0.01)	(0.72)*
N	1,203	1,124	1,122
Westfall-Young p-value	.572	.523	.217
EDD	0.02	−0.03	−0.01
	(0.04)	(0.01)**	(0.68)
N	1,968	1,848	1,844
Westfall-Young p-value	.774	.008	.997

Notes: Westfall-Young p-values are listed below the number of observations (N). The calculations are based on 1,200 replications. Dependent variables are taken in end line only and represent reported changes between baseline and end line. The SD estimates reflect single difference specifications that measure the relative impact of food to cash for beneficiaries only. The end line double difference (EDD) estimates difference out the nonbeneficiaries within each subdistrict. Control variables include household size and baseline values of the following: gender, educational attainment, and marital status of the household head and a standardised asset index. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table 9. Impact of food and cash transfers on labour

Outcome	Anyone in household worked for a wage	Anyone in household worked in a household business	Number earning wage (primary)	Number in business (primary)	Hours worked off-farm per worker	Any child worked
RDiD	−0.01	−0.01	0.01	0.01	0.15	−0.03
	(0.04)	(0.03)	(0.06)	(0.05)	(0.43)	(0.02)
N	3,960	3,960	3,960	3,960	3,960	3,960
Westfall-Young p-value	0.978	0.976	0.978	0.978	0.976	0.274
ADD: Food	0.13	−0.06	0.26	−0.06	1.46	−0.04
	(0.04)***	(0.03)**	(0.06)***	(0.05)	(0.48)***	(0.02)*
N	3,364	3,364	3,364	3,364	3,364	3,364
Westfall-Young p-value	0.000	0.001	0.000	0.053	0.000	0.017
ADD: Cash	0.06	0.00	0.15	0.00	1.24	−0.03
	(0.03)**	(0.03)	(0.07)**	(0.05)	(0.44)***	(0.02)
N	3,336	3,336	3,336	3,336	3,336	3,336
Westfall-Young p-value	0.009	0.951	0.008	0.951	0.000	0.232
DDD	0.06	−0.07	0.11	−0.06	0.22	−0.01
	(0.05)	(0.04)	(0.09)	(0.07)	(0.65)	(0.03)
N	6,700	6,700	6,700	6,700	6,700	6,700
Westfall-Young p-value	0.237	0.132	0.267	0.489	0.872	0.872

Notes: Westfall-Young p-values are listed below the number of observations (N). The calculations are based on 1,200 replications. RDiD estimates control for governorate fixed effects. All other estimates control for subdistrict (uzla) fixed effects. Business activity excludes on-farm work. Columns 3 and 4 count only those who report their primary jobs as wage earning or business. Child is defined as any household member age under 14. Control variables include household size and baseline values of the following: gender, educational attainment, and marital status of the household head and a standardised asset index. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table A1. Impact of food and cash transfers on agricultural assets: matched

Outcome	Ploughs	Small tools, such as hoes	Large tools, such as pumps, carts	Hand mills	Knapsack sprayer	Tractors	Total value of agricultural assets
ADD: food	0.06	0.28	−0.01	0.03	0.01	−0.01	−59.56
	(0.06)	(0.17)*	(0.01)	(0.04)	(0.02)	(0.00)	(57.18)
N	3,332	3,332	3,332	3,332	3,332	3,332	3,332
ADD: cash	0.03	0.29	0.04	0.02	−0.01	−0.00	6.50
	(0.04)	(0.15)*	(0.02)**	(0.03)	(0.01)	(0.00)	(34.07)
N	3,290	3,290	3,290	3,290	3,290	3,290	3,290
DDD	0.03	−0.01	−0.05	0.01	0.02	−0.00	−66.09
	(0.07)	(0.23)	(0.02)**	(0.06)	(0.03)	(0.00)	(66.48)
N	6,622	6,622	6,622	6,622	6,622	6,622	6,622

Notes: All estimates control for uzla fixed effects. In columns 1–6, the dependent variable is the number of items. The value of agricultural assets is the self-reported replacement value and given in US dollars. Estimates obtained using regression adjustment after weighting non-beneficiary sample by kernel matching on the propensity score. The matching algorithm uses a bandwidth of .004 and a tricube kernel. The propensity score is estimated using a logit model. The matching variables are household size, numbers of children attending or not attending school, housing characteristics (floor material, water source), characteristics of the household head (gender, literacy, marital status), asset ownership (televisions, phones, sewing machines), and governorate of residence. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table A2. Impact of food and cash transfers on investment: matched

Outcome	Ag expenditure	Any ag expenditure	Any debt expenditure	Debt expenditure
ADD: food	−1.74	−0.01	−0.02	−5.62
	(0.95)*	(0.02)	(0.02)	(3.78)
N	3,332	3,332	3,332	3,332
ADD: cash	−0.55	−0.01	0.04	2.88
	(0.48)	(0.01)	(0.02)*	(3.79)
N	3,290	3,290	3,289	3,289
DDD	−1.19	−0.00	−0.05	−8.49
	(1.06)	(0.02)	(0.03)*	(5.35)
N	6,622	6,622	6,621	6,621

Notes: All estimates control for uzla fixed effects. Expenditures in US dollars. Agricultural expenditure includes equipment, inputs, and veterinary services, but excludes labour and livestock purchases. Estimates obtained using regression adjustment after weighting non-beneficiary sample by kernel matching on the propensity score. The matching algorithm uses a bandwidth of .004 and a tricube kernel. The propensity score is estimated using a logit model. The matching variables are household size, numbers of children attending or not attending school, housing characteristics (floor material, water source), characteristics of the household head (gender, literacy, marital status), asset ownership (televisions, phones, sewing machines), and governorate of residence. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table A3. Impact of food and cash transfers on land investment: matched

Outcome	Investment in any plot between rounds	Changed irrigation system	Change in size of plot, square meters
EDD	0.01	−0.01	−1.22
	(0.03)	(0.01)	(0.72)*
N	1,203	1,124	1,122

Notes: The dependent variables are taken in end line only and represent reported changes between baseline and end line. The end line double difference (EDD) estimates difference out the nonbeneficiaries within each subdistrict. The matching algorithm uses a bandwidth of .004 and a tricube kernel. The propensity score is estimated using a logit model. The matching variables are household size, numbers of children attending or not attending school, housing characteristics (floor material, water source), characteristics of the household head (gender, literacy, marital status), asset ownership (televisions, phones, sewing machines), and governorate of residence. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table A4. Impact of food and cash transfers on labour: matched

Outcome	Anyone in household worked for a wage	Anyone in household worked in a household business	Number earning wage (primary)	Number in business (primary)	Hours worked off-farm per worker	Any child worked
ADD: food	0.16	−0.09	0.31	−0.10	1.26	0.03
	(0.04)***	(0.03)**	(0.08)***	(0.05)**	(0.50)**	(0.03)
N	3,332	3,332	3,332	3,332	3,332	3,332
ADD: cash	0.03	−0.01	0.07	−0.02	0.69	0.04
	(0.04)	(0.04)	(0.10)	(0.06)	(0.54)	(0.03)
N	3,290	3,290	3,290	3,290	3,290	3,290
DDD	0.12	−0.07	0.24	−0.09	0.58	−0.00
	(0.06)**	(0.05)	(0.13)*	(0.08)	(0.73)	(0.04)
N	6,622	6,622	6,622	6,622	6,622	6,622

Notes: All estimates control for uzla fixed effects. Business activity excludes on-farm work. Columns 3 and 4 count only those who report their primary jobs as wage earning or business. Child is defined as any household member age under 14. Estimates obtained using regression adjustment after weighting non-beneficiary sample by kernel matching on the propensity score. The matching algorithm uses a bandwidth of .004 and a tricube kernel. The propensity score is estimated using a logit model. The matching variables are household size, numbers of children attending or not attending school, housing characteristics (floor material, water source), characteristics of the household head (gender, literacy, marital status), asset ownership (televisions, phones, sewing machines), and governorate of residence. Standard errors in parenthesis are clustered at the FDP level. *p < .1; **p < .05; ***p < .01.

Table A5. Household fixed effects estimates, key outcome variables

Outcome	Grows khat	Poultry	Tropical livestock units	Ag expenditure	Small tools, such as hoes	Hours worked off-farm per worker
RDiD	0.02	−0.24	−0.12	−1.53	−0.07	0.15
	(0.01)**	(0.17)	(0.06)*	(1.00)	(0.14)	(0.43)
N	2,360	3,960	3,960	3,960	3,960	3,960
ADD: food	0.02	−0.32	−0.01	−1.85	0.30	1.46
	(0.01)**	(0.12)***	(0.05)	(0.95)*	(0.12)**	(0.48)***
N	1,948	3,364	3,364	3,364	3,364	3,364
ADD: cash	−0.01	0.30	0.13	−0.51	0.36	1.24
	(0.01)	(0.15)*	(0.05)**	(0.52)	(0.13)***	(0.44)***
N	1,764	3,336	3,336	3,336	3,336	3,336
DDD	0.03	−0.62	−0.14	−1.34	−0.06	0.22
	(0.01)***	(0.19)***	(0.08)*	(1.08)	(0.18)	(0.64)
N	3,712	6,700	6,700	6,700	6,700	6,700

Notes: RDiD estimates control for governorate fixed effects. All other estimates control for household fixed effects. See outcome variable definitions in Table 5 and Appendix A, Tables A1–A4. Control variables include household size and baseline values of the following: gender, educational attainment, and marital status of the household head and a standardised asset index. Standard errors in parenthesis are clustered at the FDP level. See the online Supplementary Materials for an additional explanatory note. *p < .1; **p < .05; ***p < .01.