Appendix B.

Online Appendix

B.1.

Online Appendix Tables
Table B.1: Balance on Secondary Outcomes
Gikuriro
Village

GD Main
Village

GD Large
Village

Control
Mean

Observations

R2

Stock of borrowing†−0.459(0.363)[0.89]−0.007(0.409)[1.00]−0.262(0.408)[1.00]

5.96

1751

0.04

Stock of saving†−0.157(0.378)[1.00]−0.665∗(0.364)[0.89]−0.269(0.421)[1.00]

5.18

1751

0.02

Health knowledge
index−0.590(0.366)[0.89]−0.119(0.412)[1.00]−0.225(0.520)[1.00]

0.19

1751

0.03

Sanitation practices
index

0.285∗(0.169)[0.89]−0.105(0.190)[1.00]−0.069(0.210)[1.00]

−0.23

1751

0.04

Productive assets†

0.281∗∗(0.125)[0.89]

0.195(0.132)[0.89]

0.231∗(0.122)[0.89]

11.41

1751

0.12

Consumption assets†

0.158(0.290)[1.00]−0.034(0.316)[1.00]

0.426(0.300)[0.89]

8.71

1751

0.08

House value†−0.042(0.059)[1.00]−0.012(0.074)[1.00]−0.067(0.066)[1.00]

13.59

1751

0.09

Housing quality index

0.018(0.112)[1.00]−0.195(0.132)[0.89]−0.014(0.198)[1.00]

0.02

1751

0.04

Pregnancy−0.018(0.025)[1.00]−0.031(0.022)[1.00]−0.021(0.024)[1.00]

0.28

2358

0.03

Live Birth−0.017(0.050)[1.00]−0.007(0.049)[1.00]

0.085(0.061)[1.00]

0.81

645

0.10

Birth in Facility

0.011(0.038)[1.00]−0.056(0.043)[1.00]−0.024(0.044)[1.00]

0.93

544

0.11

Any Vaccinations inpast year

0.009(0.019)[1.00]−0.006(0.021)[1.00]

0.001(0.030)[1.00]

0.93

1349

0.01

Completed
Vaccinations−0.015(0.037)[1.00]−0.015(0.045)[1.00]

0.017(0.042)[1.00]

0.72

1347

0.02

Disease Burden

0.030(0.040)[1.00]

0.004(0.032)[1.00]

0.007(0.043)[1.00]

0.42

1146

0.02

A. Household outcomes

B. Individual outcomes

Notes—See prior table. Indexes are unweighted sums of z-scores of their underlying components. Individual
secondary outcomes all measured as rates within respective populations.

1

Table B.2: Balance on Household Covariates
Gikuriro
Village

GD Main
Village

GD Large
Village

Control
Mean

Observations

R2

Female Headed

0.036(0.025)[1.00]

0.043∗(0.026)[0.84]−0.018(0.029)[1.00]

0.16

1751

0.06

Agricultural

0.017(0.028)[1.00]−0.027(0.029)[1.00]

0.002(0.035)[1.00]

0.85

1751

0.04

Wage Worker−0.002(0.029)[1.00]−0.063∗∗(0.031)[0.63]−0.084∗∗(0.035)[0.46]

0.25

1751

0.04

Microenterprise−0.015(0.025)[1.00]

0.008(0.024)[1.00]−0.024(0.023)[1.00]

0.13

1751

0.02

Savings Group−0.013(0.038)[1.00]−0.022(0.039)[1.00]

0.026(0.044)[1.00]

0.25

1751

0.02

Village Eligibility
Ratio−0.015(0.025)[1.00]

0.037(0.029)[1.00]

0.017(0.033)[1.00]

0.16

1751

0.50

Age of Head

2.186∗∗(1.047)[0.63]

2.868∗∗(1.200)[0.46]

1.415(1.487)[1.00]

34.16

1751

0.07

Schooling of Head

−0.006(0.005)[1.00]−0.002(0.006)[1.00]−0.005(0.004)[1.00]

0.00

1751

0.02

Dependency Ratio

0.008(0.012)[1.00]−0.007(0.012)[1.00]

0.003(0.016)[1.00]

0.59

1751

0.04

Household Size

−0.082(0.134)[1.00]−0.054(0.151)[1.00]−0.183(0.163)[1.00]

5.18

1751

0.02

Poorest Category−0.040(0.033)[1.00]−0.002(0.045)[1.00]−0.068∗(0.039)[0.84]

0.22

1751

0.05

Next Poorest Category

0.067∗(0.040)[0.84]

0.056(0.046)[1.00]

0.061(0.051)[1.00]

0.50

1751

0.12

Notes—Columns present coefficients and standard errors from a regression of baseline covariates on treatment
indicators, with fixed effects for blocks. Asterices denote significance at the 10, 5, and 1 percent levels, and are basedon clustered standard errors, in parentheses. Anderson (2008) sharpened q-values presented in brackets.

2

3-0.00436∗∗(0.00190)
0.190∗∗(0.0766)
0.147∗∗(0.0589)-0.00543∗∗∗(0.00204)0.150∗∗(0.0746)
0.165∗∗(0.0632)
0.0212(0.0812)0.0159(0.0584)-0.0460(0.0688)

Age of HH head

Poorest poverty
group

Next poorest group

HH in Agriculture

HH in Wage Work

HH in
Microenterprise
0.511
524

0.0294(0.0802)

0.0539(0.0532)

0.568
524

-0.0501(0.0604)

0.0795(0.0483)-0.0462(0.0815)0.151∗∗(0.0612)0.222∗∗∗(0.0740)-0.00153(0.00182)-0.102(0.0762)0.0679(0.0409)-0.0344(0.0448)

Farmer Training

0.480
529

0.335
529

-0.00755(0.0662)0.139∗∗∗(0.0487)

0.0994∗(0.0554)0.0194(0.0645)-0.0458(0.0711)0.211∗∗∗(0.0509)

0.238∗∗∗(0.0745)-0.00377∗∗(0.00143)-0.0472(0.0650)-0.0271(0.0301)

0.0351(0.0341)

Received Livestock-0.0895(0.0808)0.0869(0.0687)

0.162∗∗(0.0756)-0.00178(0.00182)-0.0392(0.0693)0.0806(0.0518)-0.0496(0.0551)

Farmer Harvest

Notes—Outcome is a dummy variable indicating that household received different components of Gikuriro. First three columns are indicators for receiving
training in proper nutrition (1), hygenic cooking habits (2), agricultural extension (3). Column 4 is an indicator that the household has successfully harvested
a farm garden as instructed by FFLS, and Column 5 for the receipt of livestock from Gikuriro. Regressions are run among all households defined as eligible atbaseline based on intended targeting criteria.

0.628
524

-0.00581(0.0628)0.0190(0.0691)

Female headed HH

Mean DVN

0.0894∗∗(0.0367)

0.0815∗(0.0423)

Number of Members-0.0215(0.0762)-0.0594(0.0390)-0.0639(0.0436)

Cooking Training

Number of Children

Nutrition Training

Table B.3: Determinants of Receiving GikuriroTable B.4: Anthropometric Impacts using Attrition IPW
Gikuriro

Main

Large

Mean

Obs.

R2

Height-for-age

0.051(0.045)[0.62]−0.021(0.039)[1.00]

0.091∗∗(0.046)[0.35]

−2.06

2125

0.71

Weight-for-age

0.038(0.040)[0.69]

0.010(0.034)[1.00]

0.067∗(0.036)[0.35]

−1.06

2104

0.68

Mid-upper arm
circumference

0.022(0.056)[1.00]−0.007(0.065)[1.00]

0.135∗(0.078)[0.35]

−0.58

1629

0.50

Notes—Regressions weighted using the product of standard survey weights and inverse propensity weights calculated
from the probability that a child with baseline anthropometrics attrites from the endline.

4

Table B.5: Cost Equivalent Total Causal Effects, primary outcomes
Gikuriro:Differential

Cost-equivalent
GD impact

Transfer
Cost

Control
Mean

Observations

R2

Consumption†−0.016(0.096)[0.93]−0.125(0.097)[0.81]

0.001(0.002)[0.85]

10.39

2717

0.15

Household dietary
diversity score

0.184(0.122)[0.81]−0.056(0.121)[0.85]−0.003(0.002)[0.81]

4.12

2718

0.21

Household non-land
wealth†0.169(0.248)[0.85]−0.354(0.254)[0.81]−0.001(0.004)[0.93]

13.28

2718

0.29

Height-for-Age−0.013(0.053)[1.00]−0.001(0.051)[1.00]

0.001(0.001)[1.00]

−1.75

2618

0.74

Weight-for-Age−0.057(0.047)[1.00]−0.009(0.037)[1.00]

0.000(0.001)[1.00]

−0.87

2594

0.74

Mid-Upper Arm Circ

−0.065(0.067)[1.00]−0.007(0.064)[1.00]

0.001(0.001)[1.00]

−0.61

1981

0.57

A. Household outcomes

B. Individual outcomes

Notes—Analysis pools eligible and ineligible households and is weighted to be representative of the population in
study villages. First column is a dummy for Gikuriro treatment, giving the differential effect of Gikuriro over cash at
equivalent cost. Second column is a dummy for either treatment, giving the impact of cash at the cost of Gikuriro.
Third column is the cost slope, measured as the dollar-value deviation (in hundreds of dollars) of the treatment
received from the cost of Gikuriro. Asterices denote significance at the 10, 5, and 1 percent levels, and are basedon clustered standard errors, in parentheses. Anderson (2008) sharpened q-values presented in brackets. Variables
marked with a † are in inverse hyperbolic sines.

5

Table B.6: Cost Equivalent Total Causal Effects, secondary outcomes
Gikuriro:Differential

Cost-equivalent
GD impact

Transfer
Cost

Control
Mean

Observations

R2

Stock of borrowing†

0.495(0.361)[0.93]−0.354(0.365)[1.00]

0.001(0.005)[1.00]

5.75

2715

0.11

Stock of saving†

0.264(0.363)[1.00]−0.482(0.354)[0.93]−0.004(0.005)[1.00]

5.38

2718

0.15

Health knowledge
index

0.952∗∗(0.382)[0.19]

0.497(0.373)[0.93]−0.014∗∗(0.007)[0.44]

−0.01

2718

0.06

Sanitation practices
index

0.326
(0.206)[0.93]−0.162(0.218)[1.00]

0.007(0.005)[0.93]

0.03

2718

0.07

Productive assets†

0.035(0.129)[1.00]−0.116(0.134)[1.00]

0.005∗∗(0.002)[0.19]

11.65

2718

0.30

Consumption assets†

0.054(0.227)[1.00]

0.047(0.225)[1.00]

0.004(0.004)[1.00]

9.08

2718

0.32

House value†−0.024(0.070)[1.00]

0.021(0.060)[1.00]

0.001(0.001)[1.00]

13.70

2531

0.39

Housing quality index−0.165(0.139)[1.00]−0.007(0.148)[1.00]−0.000(0.002)[1.00]

0.12

2718

0.16

Child Mortality

0.006(0.006)[1.00]−0.002(0.002)[1.00]−0.000(0.000)[1.00]

0.01

3373

0.02

Pregnancy

0.019(0.020)[1.00]

0.001(0.018)[1.00]−0.000(0.000)[1.00]

0.12

4137

0.11

Live Birth

0.053(0.088)[1.00]−0.047(0.082)[1.00]

0.000(0.001)[1.00]

0.70

594

0.13

Birth in Facility−0.071(0.052)[0.83]

0.042(0.060)[1.00]−0.001(0.001)[1.00]

0.90

416

0.17

Any Vaccinations inpast year

0.023(0.044)[1.00]

0.092∗(0.054)[0.81]

0.001(0.001)[0.81]

0.73

1479

0.31

Completed
Vaccinations

0.094∗(0.057)[0.81]

0.104∗(0.060)[0.81]

0.002∗∗(0.001)[0.81]

0.48

1479

0.17

Disease Burden

0.039(0.043)[1.00]−0.021(0.045)[1.00]

0.000(0.001)[1.00]

0.54

3366

0.06

Diarrheal Prevalence−0.026(0.021)[0.87]

0.033(0.021)[0.81]−0.000(0.000)[1.00]

0.09

3366

0.05

A. Household outcomes

B. Individual outcomes

Notes—See previous table.

6Table B.7: Heterogeneity by Baseline Malnutrition(1)
Height-for-Age-0.0416(0.0444)(2)
Weight-for-Age-0.0349(0.0619)(3)
Mid-Upper Arm Circ
0.0852(0.0564)

Baseline outcome x GD Main

-0.0247(0.0457)-0.0654(0.0445)0.0776(0.0653)

Baseline outcome x GD Large

0.0220(0.0433)

0.00599(0.0461)0.0804(0.0603)

Gikuriro

0.0434(0.0428)0.0323(0.0362)0.0253(0.0557)

GD Main-0.0252(0.0398)

0.00182(0.0357)-0.00498(0.0647)

GD Large

0.0940∗(0.0517)0.0641(0.0392)

0.135∗(0.0795)

Baseline Outcome

0.768∗∗∗(0.0336)2125-2.0310.696

0.748∗∗∗(0.0355)2104-1.0430.673

0.600∗∗∗(0.0425)1629-0.572
0.507

Baseline outcome x Gikuriro

Observations
Mean of DV
R squaredStandard errors in parentheses∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Notes—Regressions with both baseline and endline outcome measurement are ANCOVA with lagged dependent
variables as controls, run on the panel sample. Regressions include fixed effects for the randomization blocks, andare weighted to be representative of all households in study villages. Anthropometric outcomes are demeaned prior
to interaction so that the uninteracted treatment terms provide impact at average level of baseline anthro measure..

7

8

0.0779(0.0829)0.191∗∗(0.0817)-0.115(0.139)0.0105(0.106)0.115(0.119)0.246∗∗(0.105)0.141(0.148)2360-2.0310.0722

Indicator x GD Large

Gikuriro

GD Main

GD Large

Indicator-0.0101(0.142)2020-0.5720.07260.0773(0.111)-0.108(0.0905)-0.0813(0.0822)0.160(0.144)0.159(0.104)(3)
Mid-Upper Arm Circ
0.115
(0.109)-0.0102(0.254)2360-2.031
0.06990.196∗∗(0.0848)-0.0171(0.0988)0.00489(0.0833)

0.407(0.396)0.382(0.522)

Newborn(4)
Height-for-Age
0.599(0.645)0.0247(0.295)2347-1.0430.03580.185∗∗∗(0.0668)

0.00905(0.0678)0.00325(0.0629)0.729(0.469)0.594(0.495)(5)Weight-for-Age
0.251(0.505)0.177(0.270)2020-0.5720.07400.159∗∗(0.0783)-0.0346(0.0723)-0.0263(0.0690)0.304(0.281)

0.666(0.506)(6)
Mid-Upper Arm Circ
0.282
(0.491)

Notes—First three columns present an interaction with an indictor for a child in the ‘First Thousand Days’ (<33 months at endline) and the last three columns
present interactions with an indicator for ‘Newborn’ (<13 months at endline). Regressions are endline cross-sections, run on the panel sample, and do not include
the lagged outcome variable so as to be able to consider children who are newborns in R2. Regressions include fixed effects for the randomization blocks, and areweighted to be representative of eligible households in study villages.

Standard errors in parentheses∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Observations
Mean of DV
R squared

0.123(0.117)2347-1.0430.03560.0114(0.0801)-0.0159(0.122)-0.152(0.115)-0.300∗∗(0.138)

Indicator x GD Main

Indicator x Gikuriro(2)
Weight-for-Age-0.0206(0.113)First Thousand Days
(1)
Height-for-Age-0.00731(0.138)

Table B.8: Heterogeneity by Baseline Age9(2)Diet Diversity
b/se
0.37
(0.25)
0.31(0.27)-0.49(0.32)-0.037(0.32)-0.33(0.26)-0.27(0.28)-0.22(0.20)
0.34(0.27)
0.050(0.19)
0.16(0.21)
0.011(0.23)
4.16
1509
0.18
0.80
0.85

(3)
Wealth†b/se-0.34(0.39)
0.00021(0.50)
0.025(0.52)-0.19(0.52)-0.67*(0.40)-0.63(0.45)-0.041(0.25)-0.10(0.33)
0.25(0.21)
0.43(0.33)
0.23(0.49)
12.9
1509
0.22
0.52
0.95

(4)
Borrowing†
b/se
0.85
(0.77)
0.25(0.77)-0.86(0.85)-0.29(1.02)
0.89(0.64)-0.34(0.68)-0.41(0.61)
0.088(0.72)
0.20(0.47)-0.54(0.63)-0.81(0.60)
5.96
1509
0.12
0.38
0.066

(5)
Saving†b/se
1.04(0.82)
0.69(0.87)-0.57(0.87)-0.072(0.83)-0.54(0.54)-0.44(0.65)-0.98(0.70)
0.62(0.62)
1.01**(0.41)
0.69(0.60)-0.47(0.63)
5.18
1509
0.17
0.61
0.88

(6)
Healthb/se-0.34(0.68)
0.40(0.70)
0.78(0.78)-0.020(0.90)-1.51***(0.57)-0.60(0.73)-0.19(0.42)-0.27(0.60)
0.77*(0.39)
0.43(0.68)
0.14(0.69)
0.19
1509
0.049
0.35
0.22

(7)
Sanitation
b/se
0.54
(0.42)
0.68(0.47)-0.90*(0.53)
0.21(0.47)
0.28(0.39)-0.11(0.43)-0.67**(0.27)
0.094(0.38)-0.045(0.27)-0.51(0.32)-0.39(0.39)
-0.23
1509
0.079
0.77
0.38

(8)
Prod assets†b/se
0.56**(0.27)
0.56*(0.30)-0.59**(0.26)
0.11(0.33)-0.32(0.24)-0.34(0.29)-0.61***(0.17)
0.14(0.20)
0.20(0.19)-0.12(0.19)-0.080(0.22)
11.4
1509
0.32
0.98
0.93

(9)
Cons assets†
b/se
0.23
(0.42)
0.66(0.52)-1.06*(0.57)-0.86*(0.48)-0.060(0.48)-0.23(0.52)-0.67**(0.29)
1.07***(0.36)-0.16(0.27)-0.31(0.32)
0.10(0.42)
8.71
1509
0.38
0.42
0.77

Notes—Analysis excludes the GD Large arm, and uses interaction with three behavioral parameters to ask whether the effect of GD Main or Gikuriro areheterogeneous by Impatience, Time Inconsistency, or Other Control Problems. SEs clustered at the village level are in parentheses.

Control Mean
Observations
R2
Self_control_GD=GK
Other_control_GD=GK

GiveDirectly

Gikuriro

Lack Other Control

ImpatientTime Inconsistent

Lack Other Cont x GD

Lack Other Cont x GK

Impatient x GD

Impatient x GK

Inconsistent x GD

Inconsistent x GK

(1)
Consumption†
b/se
0.22
(0.20)
0.42*(0.23)-0.044(0.20)
0.30(0.22)
0.16(0.22)
0.16(0.22)-0.17(0.17)-0.18(0.16)-0.16(0.16)-0.29*(0.16)-0.33**(0.16)
10.4
1508
0.15
0.29
0.97

Table B.9: Cash versus Kind Heterogeneity by Behavioral Attributes

10

(2)
Diet Diversity
b/se
0.34
(0.30)
0.13(0.30)-0.27(0.32)-0.40(0.34)
0.11(0.33)-0.25(0.30)-0.19(0.18)
0.40*(0.23)-0.067(0.18)-0.0054(0.29)
0.47*(0.25)
4.16
1131
0.19
0.60
0.31

(3)
Wealth†b/se
0.74(0.70)
0.72(0.61)
0.27(0.76)-1.17*(0.68)-0.75(0.88)-0.028(0.55)-0.25(0.23)-0.029(0.28)
0.082(0.21)-0.35(0.69)-0.28(0.54)
12.9
1131
0.25
0.97
0.49

(4)
Borrowing†
b/se
0.097
(1.05)
0.17(0.89)-1.61(1.10)
1.23(1.06)
0.087(1.08)
0.076(0.81)-0.32(0.55)-0.064(0.61)
0.054(0.43)
0.35(0.82)-1.08*(0.64)
5.96
1131
0.16
0.95
0.99

(5)
Saving†b/se
0.48(0.91)
0.51(0.80)-0.58(1.12)-0.059(0.83)-0.27(0.71)
0.15(0.70)-0.72(0.64)
0.53(0.58)
0.86**(0.43)
0.54(0.72)
0.050(0.62)
5.18
1131
0.18
0.97
0.58

(6)
Healthb/se
0.061(0.97)-0.48(0.86)
0.047(0.80)
0.21(0.90)-0.78(0.75)
0.33(0.84)
0.22(0.42)-0.48(0.59)
0.48(0.36)
0.34(0.76)-0.029(0.65)
0.19
1131
0.060
0.61
0.26

(7)
Sanitationb/se
1.10**(0.48)
0.58(0.38)
0.47(0.48)
0.086(0.48)-0.68(0.71)-0.54(0.49)-0.73***(0.23)
0.10(0.31)
0.019(0.27)-0.26(0.51)-0.14(0.31)
-0.23
1131
0.091
0.31
0.85

(8)
Prod assets†b/se
0.77**(0.33)
1.13***(0.27)
0.49(0.56)-0.41(0.36)
0.093(0.34)-0.21(0.29)-0.81***(0.18)
0.090(0.19)
0.14(0.18)-0.20(0.29)-0.094(0.22)
11.4
1131
0.31
0.27
0.40

(9)
Cons assets†
b/se
0.96
(0.69)
1.09*(0.61)-0.42(0.77)-0.95(0.73)
0.41(0.80)-0.64(0.48)-0.61*(0.36)
0.56(0.43)-0.13(0.26)
0.15(0.61)-0.0084(0.44)
8.71
1131
0.38
0.85
0.26

Notes—Analysis uses only the control and the GD Main arm assigned to Lump Sum or Flow transfers to ask whether the impact of Lump Sum or FLowtransfers is heterogeneous by Impatience, Time Inconsistency, or Other Control Problems. SEs clustered at the village level are in parentheses. .

Control Mean
Observations
R2
Self_control_LS=Flow
Other_control_LS=Flow

GD Flow

GD Lump Sum

Lack Other Control

ImpatientTime Inconsistent

Lack Other Cont x Flow

Lack Other Cont x Lump Sum

Impatient x Flow

Impatient x Lump Sum

Inconsistent x Flow

Inconsistent x Lump Sum(1)
Consumption†
b/se
0.37
(0.25)
0.35*(0.19)
0.31(0.26)
0.18(0.23)
0.28(0.21)
0.19(0.23)-0.13(0.15)-0.15(0.17)-0.12(0.16)-0.27(0.20)-0.21(0.16)
10.4
1131
0.15
0.93
0.65

Table B.10: Lump Sum vs Flow Heterogeneity by Behavioral Attributes

11

(2)
Diet Diversity
b/se
0.075
(0.84)-0.79(0.71)
0.29(0.74)
0.18(0.79)
0.30(0.62)-0.86(0.59)-0.0070(0.86)
4.16
200
0.36

(3)
Wealth†b/se
1.93(1.41)-3.60*(1.85)
1.54(2.13)-0.24(0.71)
2.78**(1.37)-2.81(2.03)-1.50(1.15)
12.9
200
0.33

(4)
Borrowing†
b/se
-0.28
(1.86)
3.60(2.74)-0.86(2.01)
0.49(1.33)-2.92(2.21)-0.38(1.66)-1.37(1.49)
5.96
200
0.21

(5)
Saving†b/se
0.52(1.45)
0.78(2.58)
0.53(1.77)-0.039(1.30)-0.34(2.40)-0.95(1.63)-1.40(1.29)
5.18
200
0.31

(6)
Healthb/se-2.14(1.73)
1.94(1.80)-1.57(1.76)
1.76(1.30)-2.75*(1.50)
2.40*(1.42)
0.40(1.31)
0.19
200
0.25

(7)
Sanitation
b/se
-1.07
(1.06)-0.056(1.35)-1.46(0.99)
0.80(0.91)
0.59(1.14)
0.34(0.87)
1.18(0.81)
-0.23
200
0.27

(8)
Prod assets†
b/se
0.77
(0.69)-1.03(0.81)
0.12(0.78)-0.40(0.61)
0.71(0.71)-0.53(0.72)-0.75(0.66)
11.4
200
0.33

(9)
Cons assets†
b/se
1.46
(1.20)-2.85**(1.28)-0.095(1.24)-0.43(0.91)
1.84*(1.02)-1.97**(0.97)-1.39(0.96)
8.71
200
0.37

Notes—Analysis uses only the GD Main households that chose Flow transfers, and asks whether the impact of actually getting this choice (rather than being
assigned to Lump Sum) is heterogeneous by Impatience, Time Inconsistency, or Other Control Problems. SEs clustered at the village level are in parentheses.

Control Mean
Observations
R2

Got Choice in Choice Experiment

Lack Other Control

ImpatientTime Inconsistent

Lack Other Cont x Got It

Impatient x Got It

Inconsistent x Got It(1)Consumption†
b/se
0.61
(0.41)-0.32(0.46)
0.084(0.48)-0.38(0.36)
0.42(0.41)-0.22(0.37)-0.41(0.40)
10.4
200
0.27

Table B.11: Getting Flow when one Chose It, Heterogeneity by Behavioral Attributes

Online Appendix Figures
Figure B.1: Actual and Assigned Treatment Amounts

Actual Transfer Amount from GD Data
200
400
600
800
1,000

Comparing Assigned and Actual GD Transfer Amounts

0

B.2.

41.32

83.63

116.91

Horizontal lines represent intended transfer amounts

12

532

Figure B.2: Fan Regression Impacts by Age

The GD Large treatment effect on Height for Age-.1

0

Height for Age
.1
.2

.3

.4

With 95 % Confidence Intervals

0

20
40
Age in Months at Endline

60

Fan Regression for impact of the GD Large treatment, smoothed over age at endline

Figure B.3: Fan Regression Impacts by Age

The GD Large treatment effect on Child Anemia-.2

Impact on Anemia Prevalence
-.15
-.1
-.05
0

.05

With 95 % Confidence Intervals

0

20
40
Age in Months at Endline

Fan Regression for impact of the GD Large treatment, smoothed over age at endline

1360

Figure B.4: Predicted impacts on dimensions of child health outcomes are positively associated with one another(a) WAZ and HAZ(b) WAZ and MUACZ(c) HAZ and MUACZ
Notes: Figure displays associations between predicted impacts of cash relative to kind on weight-for-age z-scores,
height-for-age z-scores, and mid-upper-arm-circumference z-scores. Loess fit and associated 95 percent confidence
interval overlaid.

14

B.3.

Eligibility for the Study

The study aims to compare nutrition and health gains among poor households with young
children across the two programs and a control. We therefore used a definition of eligibility tailoredto Gikuriro’s stated target population: namely, households that contained malnourished children, orpregnant and lactating mothers. A core challenge of the benchmarking endeavor is the need to use
a measure of eligibility in a manner that can be defined identically across arms.34 As a result, weestablished a set of ‘hard’ eligibility criteria on the basis of which beneficiaries would be selected andthe survey would be stratified. Households meeting these criteria would be identified by the survey
firm, Innovations for Poverty Action (IPA), prior to sampling for the baseline study, to establish acomparable population of eligible households in all arms—including control—of the study.
CRS and USAID agreed that the following criteria represent the target population for Gikuriro:• Criteria 1. All households in a village with a malnourished child (defined by a threshold valueof weight/age) were enrolled.
– Weight/age is used because it is believed that this data is more consistently available
than data on middle-upper arm circumference (MUAC) and height/age, and because it
is used by CHWs as a basis for referring children to their local Health Centers.
– The threshold weight/age value for inclusion was determined using the Rwandan Ministryof Health standards for malnutrition. The data used to identify eligibles was based on
the Community Health Worker data from Growth Monitoring and Promotion visits.
• Criteria 2. All households in Ubudehe 1 or 2 with children under the age of 5 (Ubudehe is
the Rwandan government household-level poverty classification, with 1 being the poorest, 3
being non-poor, and rural areas containing very few of the wealthiest Ubudehe 4 households).
• Criteria 3. All households in Ubudehe 1 or 2 with a pregnant or lactating mother.
Both implementers agreed to attempt to treat all eligible households that were identified as meetingany of these criteria. CRS anticipated an average of 30 eligible households per village, and inprinciple had established a rationing rule in case that number was exceeded. As will be described
below, the number of households per village that could be identified by the survey firm as meetingthese targets turned out to be substantially lower. We did not try to impose restrictions on how
Gikuriro would target outside of the households identified by the survey firm to be eligible.
We asked IPA to identify the universe of households that they could locate who met these criteria,using three sources. First, CHW records from the national ‘Growth Monitoring and Promotion’
exercise, which is intended to provide monthly height and weight measurements for all children
under two and annual measurements for all children under five; second, government (census) recordsof household Ubudehe classifications; and finally local health facility information, which provides analternative data point on children’s nutritional status.35 Children were defined as malnourished ifthey had at least one measurement that met government thresholds for malnourishment definitionsin the past year, and households were defined as eligible if they had any individual meeting thecriteria above. In each village we recorded the number of households in each stratum and sampled
up to eight eligibles and four ineligibles for inclusion in the study. Throughout this document we
34

We did not intend the scope of the benchmarking exercise to include the implementers’ (potentially different)
ability to cost-effectively identify this target population, so as to maintain the interpretation of impacts as being
differential impacts on a consistently defined beneficiary group.
35
In practice, most children attending local clinics are referred by a CHW and so are also recorded as malnourishedin the Growth Monitoring process.

15

use the words ‘eligible’ and ‘ingeligible’ to refer to the classification made by the survey firm atbaseline.

B.4.

Study OutcomesPrimary Outcomes. The study focuses on five dimensions. Here we briefly summarize each;details of the construction of these outcomes are included in Appendix A.
1. Household monthly consumption per capita (inverse hyperbolic sine—henceforth IHS—to deal
with skewness).
2. Household Dietary Diversity, measured using hte WHO standard Household Dietary Diversity
Score.
3. Anemia: measured with a biomarker test following DHS protocols at endline only.
4. Child growth and development: measured using in height-for-age, weight-for-age and Mid
Upper Arm Circumference at baseline and endline for children under the age of 6 in eligible
households.
5. Value of household non-land net wealth. This outcome is the sum of productive and consumption assets; the value of the household’s dwelling, if owned; and the value of the stock of net
savings, less the stock of debt (IHS).
Secondary Outcomes. Three types of outcomes are selected to be secondary: proximate
outcomes of one or both interventions that do not have an intrinsic welfare interpretation (suchas borrowing and saving stocks); outcomes that have welfare weight but are not within the causal
chain of both programs (such as investments in health-seeking behavior, which Gikuriro seeks to
impact, or housing quality, which has been identified as a dimension of benefit in prior evaluationsof GiveDirectly (Haushofer and Shapiro, 2016)); or outcomes of common interest on which power islimited (such as disease burden and mortality).
1. Stock of borrowing and stock of savings (IHS).
2. Birth outcomes: the likelihood of pregnancy and likelihood of live birth within 12 months
prior to endline.
3. Health knowledge and sanitation practices.
4. Disease burden and mortality. Mortality is measured as the likelihood that an individual
member of the household from baseline has died prior to endline. Disease burden is measured
as the prevalence of fever, fever with diarrhea or vomiting, or coughing with blood at endline,
5. Health-seeking behavior/preventative care. We focus on the share of pregnancies resulting
in births in medical facilities, the share of children under two years of age with at least one
vaccination in the prior year, and the share of children under two years of age with a complete
dose of vaccines.
6. Household productive assets (IHS).
7. Housing quality. Two measures are used: the self-reported replacement cost of the current
dwelling (irrespective of ownership status, IHS), and an index of housing construction quality,
constructed from measures of wall and roof materials and from the number of rooms in thedwelling.
16

The inverse hyperbolic sine is commonly used in analysis of outcomes such as consumption, savings,
and asset values that tend to be highly right-skewed and also to contain zeros. The IHS transformation preserves the interpretation of a log (meaning that impacts can be interpreted as percent
changes) but does not drop zeros. Only outcomes that we expected to be skewed were pre-registered
to be analyzed using IHS. All non-binary outcomes are also Winsorized at the 1 percent and 99
percent level (values above the 99th percentile are overwritten with the value at the 99th percentile
to reduce skewness and increase statistical power). Because we restrict the analysis in this paper tothe pre-specified primary and secondary outcomes only, we do not correct the results for multiple
inference (Anderson, 2008).

B.5.

Pre-committed Analysis of Heterogeneity

B.5.1

Anthropometric effects by baseline malnourishment

We hypothesized in the Pre-Analysis Plan that the benefits of the treatments in terms of childanthropometrics would be largest for those who began the study most malnourished. To test this,
we run a regression with child anthropometrics (HAZ, WAZ, and MUAC) as the outcomes, using
the structure of Equation 1 above and controlling for our battery of baseline covariates, a dummy for
all three treatments (GK, GD, and GD large), the baseline biometric outcome, and the interaction
between the treatments and baseline biometrics. The hypothesis is that the interaction terms will
be negative, meaning that the programs are most effective for those who had the worst baseline
biometric outcomes. Table B.7 the results of this analysis. The interpretation of the impacts in thistable are as follows: rows 4-6 give the simple impact of the programs when the interacted term iszero (which, in this case, is at the mean). Rows 1-3 provide a test of the differential impact of the
program across baseline anthropometric measures, so the lack of significance in these rows meansthat the impacts are not heterogeneous by nutrition status at baseline. The implication is thatthe improvement in anthropometrics induced by the GD large treatment were experienced broadly
across the baseline distribution of HAZ and WAZ, and were not concentrated among those whobegan the study most malnourished.

B.6.

Selection of Control Variables.

In our pre-analysis plan, we state that control variables for the primary specification “will beselected on the basis of their ability to predict the primary outcomes”. In doing so, we seek to build
on recent developments that balance the challenge of using baseline data to select variables thatwill reduce residual variance with the danger that researcher freedom in the selection of control
variables can lead to p-hacking, in which right-hand-side variables are selected specifically on thebasis of the statistical significance of the coefficient of interest (Card and Krueger, 1995; Casey etal., 2012), thereby invalidating inference.
To balance these concerns, we follow the post-double-selection approach set forth in Belloni et al.
(2014b). Those authors advocate a two-step procedure in which, first, Lasso is used to automate theselection of control variables, and second, the post-Lasso estimator (Belloni et al., 2012) is used toestimate the coefficients of primary interest in in the ITT, effectively using Lasso as a model selection
device but not imposing the shrunken coefficients that results from the Lasso estimates directly.
Belloni et al. (2014b) demonstrate that this approach not only reduces bias in estimated treatment
effects better than alternative approaches—less a concern given the successful randomization in our
experiment—but that it may improve power while retaining uniformly valid inference.
In the first stage, model selection is undertaken by retaining control variables from the unionof those chosen either as predictive of the treatment assignment or of the outcome. This model
17

selection stage can be undertaken after residualizing to account for a set of control variables thatthe authors have a priori determined belonw in the model, as in Belloni et al. (2014a); in ourcase, we retain block fixed effects, lagged values of the outcome, and lagged values of (the inverse
hyperbolic sine of) household wealth in all specifications, per our pre-analysis plan. We modify theheteroskedasticity-robust Lasso estimator of Belloni et al. (2012) to incorporate sampling weights
consistent with our design, using the Lasso penalty is chosen as a function of the sample size andthe number of potential covariates, as in Belloni et al. (2014a).
Resulting covariates selected for each of the primary and secondary outcomes, at household andindividual level, are presented in Tables B.12 and B.13, respectively.

Table B.12: Covariates selected in Belloni et al. (2014) post-double-lasso selection procedure for household outcomes
Outcomeconsumption_asinhHousehold dietary diversity
score

wealth_asinhborrowingstock_asinh

savingsstock_asinh

Health Knowledge Index

Sanitation Practices Index

productiveassets_asinh

assetsconsumption_asinhControl set
Baseline value of consumption_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Baseline value of dietarydiversity, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Lsavingsstock_asinh3
Lconsumpti_x_Ldietarydi
Lconsumpti_x_Lproductiv
Ldietarydi_x_Lassetscon
Baseline value of wealth_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
L.Own dwelling
Baseline value of borrowingstock_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Baseline value of savingsstock_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Lconsumpti_x_Lproductiv
Lconsumpti_x_Lassetscon
Baseline value of health_knowledge, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Baseline value of sanitation_practices, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Lproductiv_x_Lassetscon
Baseline value of productiveassets_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Lconsumpti_x_Lassetscon
Baseline value of assetsconsumption_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
L.Number of rooms
L.Durables expenditure (12-month recall)
Ldietarydi_x_Lassetscon
Continued on next page18

Table B.12 (continued)
OutcomeControl set
Lproductiv_x_Lassetscon
selfcostdwell_asinh
Baseline value of selfcostdwell_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
L.Number of rooms
L.Durables expenditure (12-month recall)
Housing Quality Index
Baseline value of housing_quality, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
L.Number of rooms
Note: block fixed effects and lag of the relevant outcome included in all specifications. Specifications that include
both eligible and ineligible households include an indicator for eligibility status.

19

Table B.13: Covariates selected in Belloni et al. (2014) post-double-lasso selection procedure for individual outcomes
Outcomehaz06, Winsorized fraction.005, high only

waz06, Winsorized fraction.005, high only

muacz, Winsorized fraction.01

SampleUnder 5s

Control set
L.haz06, Winsorized fraction .005, high only

Under 5s

femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Food expenditure (weekly recall)
L.Food consumption-value own production (weekly recall)
L.waz06, Winsorized fraction .005, high only
Lconsumpti_x_Lselfcostd
L.waz06, Winsorized fraction .005, high only

Under 5s

femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Food expenditure (weekly recall)
L.Food consumption-value own production (weekly recall)
Lconsumpti_x_Lproductiv
L.muacz, Winsorized fraction .01

anemia_dummy

Under 5sanemia_dummyPregnant/lactating
women

mortality

All

Was this women pregnant at
any point in the past 12
monthsPregnant/lactating
women

femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.waz06, Winsorized fraction .005, high only
Lconsumpti_x_Lproductiv
femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
agemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
agemonths

agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Lwealth_asinh
Continued on next page20

Table B.13 (continued)
OutcomeDid pregnancy conclude inlive birthfacility_birth

SamplePregnant/lactating
women

Control set
agemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Food expenditure (weekly recall)
L.Food consumption-value own production (weekly recall)
Lconsumpti_x_Lwealth_asagemonths

Pregnant/lactating
women

anthro_vacc_year

Under 3santhro_vacc_complete

Under 3s

Any fever, diarrhea, or
coughing blood at individual/round level

Under 5s

agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
Lconsumpti_x_Lproductiv
femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
femaleUnder 5s

agemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Food consumption-value own production (weekly recall)femaleIndividual reported with diarrhea/vomiting/fever now

agemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
Note: block fixed effects and lag of the relevant outcome included in all specifications. Specifications that include
both eligible and ineligible households include an indicator for eligibility status.

21

B.7.

Flyers from GiveDirectly for Cash Beneficiaries.

GDID: _________________________PAYMENT METHOD: _______________________RECIPIENT INFORMATION REGARDING NUTRITION AND HYGIENE
GiveDirectly’s program is supported by made possible by the generous support of the American
people through USAID. The information below is approved by the Rwanda Ministry of Health.
●●●Infant Nutrition○ Infants less than 6 months old should be fed by breast only. During this period an infant
receives only breast milk and no other liquids or solids, not even water, unless medicallyindicated. A non-breastfed baby is 14 times more likely to die than an exclusively
breastfed baby in the first 6 months.
○ Infants 6 to 24 months old should continue to be fed by breast, but should also receive
complementary feeding that includes animal-source foods (meats, fish, milk products,eggs) and fruits and vegetables that are rich in vitamin A (such as mango, papaya,oranges, yellow sweet potato and carrots). Guidelines are for kids 6-24 months to eat atleast 4 food groups: fruits, vegetables and legumes, grains, meats, dairy.
■ Infants 6 to 8 months old should be fed complementary foods 2-3 times daily;■ Infants 9 to 24 months old should be fed complementary foods 3-4 times daily,plus 1-2 snacks.
Reducing Illness○ If you or your children get diarrhoea, use Oral Rehydration Salts (ORS) to replace thenutrients being lost. Typical symptoms of diarrhoea include frequent, loose, waterystools, abdominal cramps, and/or abdominal pain. If ORS is not available, a simple
solution can be prepared for drinking by mixing one liter of clean drinking water and mix
it with ½ teaspoon of salt and 6 teaspoons of sugar.
○ The government has a 6-monthly deworming program and Vitamin A supplementation
program. Ask your Community Health Worker for more information.
Dietary Diversity○ Anemia■ Anemia is a health condition, commonly caused by nutritional deficiency of ironand other nutrients (folate or vitamin B12). Around 72% of 6-8 months-olds in
Rwanda have Anemia. Anemia can be an underlying cause for maternal deathand prenatal and perinatal infant loss. Anemia among children is associatedwith low mental performance and physical development.
■ Examples of iron-rich food: fish, meat, milk products, oranges, lemons,
grapefruits, guavas, papayas, and green leafy vegetables. Breast milk for yourchild is an important source of iron, too.
○ Here are some other examples of food you can produce/buy/eat to cheaply increasenutrition:

1

22

■

■

■
■
●

●

Breeding small, inexpensive animals such as hens, rabbits and guinea pigs canprovide you and your children with important body building protein and otherimportant nutrients.
Grow kitchen gardens if you have time. You can grow different vegetables foryour family throughout the year, like amaranths, carrots, and dark-green leaves
such as spinach and dodo, all of which are important sources of body protecting
nutrients.
Consume soya beans, yogurt, avocados and dodo (which you could grow)
Eat orange-flesh rather than white-flesh sweet potatoes

Hygiene○ Handwashing with soap or wood ash can kill bacteria/viruses and prevents the spreadof disease. Handwashing with soap at critical times is estimated to reduce diarrhoea by
47%. The most important times that hands should be washed with soap and water are:■ After defecating■ After cleaning a child who has defecated■ Before eating or handling food○ Recommended practices for personal hygiene further include:■ Washing hair every week with shampoo■ Washing the face every day after sleeping■ Brushing teeth twice every day, in the morning and the night after eating○ Safe disposal of waste means defecating into a latrine, disposing into a latrine, or burial.
Inappropriate disposal of human feces, such as open defecation, facilitates thetransmission of pathogens and disease.
Birth preparedness for delivery○ Early initiation of antenatal care (ANC) can reduce common maternal complications andmaternal and perinatal mortality. Visit your nearest health facility early during pregnancy
for medical tests and more information. The World Health Organization promotes fourantenatal clinic visits, one in each trimester, during each pregnancy.

223