Appendix A

Supplementary tables and Figures
Table A.1: Process of identifying the eligible sample
SectorOrientation sign-ups

Verified eligible

Baseline completed

KaduhaKibumbwe
Kigabiro
Kiyumba
MuganoMuhaziMunyagaMunyiginya
MusangeMushishiro
Nyakariro
Nyarusange
Shyogwe

273
144
66
102
244
192
157
115
170
88
227
245
252

261
139
52
70
198
170
137
102
115
87
200
226
210

235
127
49
66
196
159
124
94
110
82
190
214
202

Total

2275

1967

1848

Notes: Table gives the number of individuals participating in each of three phases of study recruitment, for each of the 13 sectors inwhich recruitment took place.

59

Table A.2: Survey modules by instrument and round
ModuleBaseline instrument

Endline instrument

Both
Beneficiary
Beneficiary
Beneficiary
Both
Beneficiary
Both
Both
Both
Both
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
—
—
—
—
—
—
—
HouseholdHouseholdHouseholdHouseholdHouseholdHouseholdHousehold

Both——
Beneficiary
Both
Beneficiary
Beneficiary
Both
Both
Both
—
Beneficiary
Beneficiary
Beneficiary
—
—
—
—
—
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
Beneficiary
BeneficiaryHouseholdHouseholdHouseholdHouseholdHouseholdHouseholdHousehold

Identification
Social network
Firm creation and employment history
Wage employment
Microenterprise activities and assets
Time use
IncomeSavings
Borrowing
Lending
Business contacts
Private consumption
Private assets
Psychometrics
Raven’s test
Digit-span recall
Numeracy
Lottery choice
Convex time budget
Locus of control
Big Five
Aspirations
Mental healthBusiness knowledge
Business attitudes
Program participation
Gender empowerment
Household roster
Dwelling characteristics
Land use and ownership
Inter-household transfers
Consumption
Dietary diversity
Household assets

60

Table A.3: Correlates of HD Participation
Huguka Dukore stage completed. . .
Complier

Work Ready Now

Be Your Own Boss

Technical Training

Ubudehe category I

0.0106(0.0281)0.0146(0.0319)0.0128(0.0356)−0.0199(0.0327)

Beneficiary female

0.0142(0.0285)0.0146(0.0317)0.0284(0.0363)−0.0187(0.0336)

Beneficiary age

0.0120∗∗∗(0.0041)0.0100∗∗(0.0048)0.0085∗(0.0052)

0.0067(0.0042)

Beneficiary years ofeducation

0.0037(0.0061)0.0051(0.0071)

0.0050(0.0083)

0.0073(0.0073)

Household members

0.0025(0.0074)

0.0061(0.0078)0.0083(0.0085)0.0146∗(0.0079)

Employed

0.0930∗(0.0504)

0.0757(0.0555)

0.0846(0.0615)

0.0450(0.0590)

Productive hours−0.0038∗∗∗(0.0014)−0.0041∗∗∗(0.0015)−0.0047∗∗∗(0.0015)−0.0044∗∗∗(0.0014)Monthly income−0.0019(0.0045)−0.0008(0.0051)0.0004(0.0055)−0.0026(0.0050)

Productive assets−0.0001(0.0030)0.0004(0.0037)−0.0016(0.0041)−0.0027(0.0040)

HH consumption percapita0.0027(0.0134)

0.0042(0.0152)0.0103(0.0179)

0.0157(0.0154)

Beneficiary-specificconsumption−0.0092∗(0.0053)−0.0139∗∗(0.0059)−0.0109∗(0.0064)−0.0021(0.0058)

HH net non-land wealth0.0030(0.0023)0.0040∗(0.0024)0.0050∗(0.0028)

0.0026(0.0024)

Savings−0.0018(0.0033)−0.0028(0.0036)−0.0023(0.0041)−0.0030(0.0036)

Debt

0.0080∗∗(0.0034)

0.0099∗∗∗(0.0038)

0.0096∗∗(0.0042)0.0086∗∗(0.0038)

HH livestock wealth0.0025(0.0026)

0.0025(0.0030)−0.0003(0.0033)−0.0034(0.0030)

Business Knowledge−0.0129(0.0135)

0.0056(0.0154)0.0019(0.0179)−0.0151(0.0156)

Average completion rate
Observations
R2
p-value

0.86
668
0.12
0.00

0.79
668
0.16
0.00

0.69
668
0.18
0.01

0.48
668
0.47
0.00

Notes: Table estimates Linear Probability Model for four measures of progression through the Huguka Dukore program: attending
the first week of the WRN coursework and hence triggering payment to the implementer, then completing each of the three subsequent
components of the program. Rows are the baseline covariates over which we look for heterogeneity in these compliance rates. p-value
in the final row is from F-test on the joint significance of all of the covariates. Standard errors in parentheses; *=10%, **=5%, and***=1% significance61

Table A.4: Sampling of Attritors for Intensive Tracking Exercise
Intensive tracking

Control mean

Observations

R2

Ubudehe category I−0.10(0.09)[1.00]

0.38

120

0.01

Beneficiary female−0.00(0.09)[1.00]

0.62

122

0.00

Beneficiary age

1.66(0.61)[0.13]

22.64

122

0.06

Beneficiary years ofeducation

0.07(0.45)[1.00]

8.10

122

0.00

Household members−0.25(0.37)[1.00]

4.66

122

0.00

Employed−0.00(0.09)[1.00]

0.31

122

0.00

Productive hours

2.48(4.09)[1.00]

11.75

122

0.00

Monthly income−0.41(0.91)[1.00]

4.15

122

0.00

Productive assets

0.65(0.85)[1.00]

2.59

122

0.00

HH consumption percapita0.14(0.20)[1.00]

9.44

122

0.00

Beneficiary-specificconsumption

0.08(0.36)[1.00]

7.61

122

0.00

HH net non-land wealth

0.95(1.02)[1.00]

10.48

122

0.01

Savings

0.42(0.83)[1.00]

7.67

122

0.00

Debt

−0.21
(0.86)[1.00]

7.60

122

0.00

HH livestock wealth

2.22(1.09)[0.37]

5.26

122

0.04

Business Knowledge

0.34(0.18)[0.37]

−0.02

122

0.03

Notes: Table regresses a sequence of covariates on a dummy variable for having been sampled for intensive tracking, within the sample
of original attritors. Standard errors in parentheses, p-values corrected for False Discovery Rate across whole table in hard brackets;stars are based on FDR-adjusted values, *=10%, **=5%, and ***=1% significance.

62

Table A.5: Intent-to-treat analysis: Primary outcomes, aggregated specification
GiveDirectly

ControlMeanp-values
Obs.

R

2

(a)

(b)

(c)

HD

Main

Large

Combined

Employed

0.02(0.03)[0.29]

0.03(0.03)[0.24]

0.01(0.05)[0.51]

0.01(0.04)[0.51]

0.48

1770

0.16

0.89

0.52

0.46

Productive hours

2.80∗(1.57)[0.07]

4.34∗∗∗(1.65)[0.01]

1.12(2.06)[0.35]

2.33(2.03)[0.17]

18.64

1770

0.18

0.93

0.03

0.07

Monthly income

0.32(0.26)[0.16]

1.00∗∗∗(0.25)[0.00]

0.73∗∗(0.35)[0.03]

1.04∗∗∗(0.32)[0.00]

8.05

1770

0.21

0.11

0.02

0.10

Productive assets

1.54∗∗∗(0.35)[0.00]

3.86∗∗∗(0.34)[0.00]

4.02∗∗∗(0.47)[0.00]

4.42∗∗∗(0.44)[0.00]

5.61

1770

0.26

0.00

0.00

0.00

HH consumption percapita0.05(0.06)[0.23]

0.23∗∗∗(0.06)[0.00]

0.36∗∗∗(0.07)[0.00]

0.27∗∗∗(0.07)[0.00]

9.46

1737

0.33

0.05

0.64

0.17

Note: Intention to treat pooling the three smaller cash transfer amounts into a single arm, GD Main. Regressions include but do not report the lagged dependent variable, fixed
effects for randomization blocks, and a set of LASSO-selected baseline covariates, and are weighted to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at
the household level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficientestimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance. p-values in final three columns derived from F -tests of hypotheses that benefit-cost
ratios are equal between (a) GD Main and HD; (b) GD Main and GD Large; and (c) GD Main and Combined. Employed is a dummy variable for spending more than 10 hours per
week working for a wage or as primary operator of a microenterprise. Productive hours are measured over prior 7 days in all activities other than own-farm agriculture. Monthly
income, productive assets, and household consumption are winsorized at 1% and 99% and analyzed in Inverse Hyperbolic Sine, meaning that treatment effects can be interpreted aspercent changes.

Table A.6: Intent-to-treat analysis: Secondary outcomes, aggregated specification
GiveDirectly
Combined

ControlMeanp-values
Obs.

R

2

(a)

(b)

(c)

HD

Main

Large

Subjective well-being

0.19∗∗∗(0.07)[0.00]

0.47∗∗∗(0.07)[0.00]

0.55∗∗∗(0.09)[0.00]

0.41∗∗∗(0.09)[0.00]

0.00

1770

0.13

0.03

0.03

0.00

Mental health−0.04(0.07)[0.34]

0.01(0.07)[0.34]

0.11(0.10)[0.16]

0.12(0.09)[0.12]

0.00

1770

0.07

0.43

0.48

0.40

Beneficiary-specificconsumption

0.15(0.11)[0.12]

0.58∗∗∗(0.10)[0.00]

0.45∗∗∗(0.15)[0.00]

0.69∗∗∗(0.12)[0.00]

8.27

1770

0.23

0.01

0.00

0.02

HH net non-land wealth−0.17(0.40)[0.31]

0.91∗∗∗(0.35)[0.01]

1.11∗∗∗(0.41)[0.01]

0.90∗∗(0.48)[0.05]

11.28

1770

0.20

0.02

0.41

0.28

HH livestock wealth−0.00(0.37)[0.46]

2.08∗∗∗(0.35)[0.00]

2.17∗∗∗(0.47)[0.00]

2.22∗∗∗(0.45)[0.00]

7.81

1770

0.25

0.00

0.02

0.02

Savings

1.04∗∗∗(0.23)[0.00]

1.30∗∗∗(0.23)[0.00]

1.43∗∗∗(0.31)[0.00]

1.70∗∗∗(0.27)[0.00]

9.24

1770

0.20

0.40

0.04

0.15

Debt

0.40
(0.28)[0.10]−0.29(0.30)[0.19]−0.37(0.42)[0.22]

0.00(0.38)[0.46]

8.75

1770

0.20

0.02

0.80

0.34

Panel A. Beneficiary welfare

Panel B. Household wealthPanel C. Beneficiary cognitive and non-cognitive skills
Locus of control

0.06(0.06)[0.45]

0.05(0.06)[0.53]

0.08(0.08)[0.45]

0.23∗∗(0.08)[0.02]

0.00

1770

0.27

0.58

0.98

0.14

Aspirations−0.01(0.07)[1.00]

0.05(0.07)[0.54]

0.03(0.09)[1.00]

0.14(0.08)[0.20]

0.00

1770

0.08

0.52

0.60

0.63

Big Five index

0.12(0.07)[0.20]

0.07(0.07)[0.45]−0.08(0.09)[0.53]

0.02(0.09)[1.00]

0.00

1770

0.10

0.23

0.10

0.40

Business knowledge

0.65∗∗∗(0.07)[0.00]

0.08(0.07)[0.45]−0.03(0.09)[1.00]

0.63∗∗∗(0.09)[0.00]

0.00

1770

0.23

0.00

0.19

0.00

Business attitudes

0.12(0.07)[0.20]

0.16∗(0.06)[0.06]

0.06(0.09)[0.65]

0.15(0.09)[0.20]

0.00

1770

0.09

0.81

0.06

0.26

Notes: Regressions include but do not report the lagged dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates, and are weighted
to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery Rates acrossall the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.7: Breakdown of employment impacts
GiveDirectly
Combined

Control
Mean

Obs.

R2

p-valueHD

Lower

Middle

Upper

Large

Non-agricultural microenterprise

0.05(0.03)[0.11]

0.09∗(0.04)[0.06]

0.08∗(0.04)[0.08]

0.14∗∗(0.05)[0.02]

0.17∗∗∗(0.04)[0.00]

0.12∗∗(0.04)[0.02]

0.22

1770

0.12

0.00

Other microenterprise or
self-employment

0.04∗(0.02)[0.06]

0.04(0.03)[0.11]

0.11∗∗(0.03)[0.02]

0.07∗(0.03)[0.06]

0.05∗(0.03)[0.09]

0.04(0.03)[0.11]

0.07

1770

0.09

0.02

Agricultural processing or trading

0.01(0.03)[0.24]

0.13∗∗(0.04)[0.02]

0.01(0.04)[0.24]

0.07∗(0.04)[0.08]

0.06(0.04)[0.11]

0.02(0.04)[0.24]

0.17

1770

0.08

0.05

Agricultural wage labor−0.02(0.03)[0.24]−0.08∗∗(0.03)[0.03]−0.07∗(0.03)[0.06]−0.08∗∗(0.03)[0.03]−0.11∗∗(0.03)[0.02]−0.09∗∗(0.03)[0.03]

0.22

1770

0.15

0.00

Non-agricultural wage labor

0.06∗(0.03)[0.06]−0.04(0.04)[0.16]

0.01(0.04)[0.24]−0.06(0.04)[0.11]−0.07∗(0.04)[0.09]

0.01(0.04)[0.24]

0.30

1770

0.20

0.00

Employed (0 hr)

0.05(0.03)[1.00]

0.05(0.04)[1.00]

0.07(0.04)[1.00]

0.02(0.04)[1.00]

0.06(0.04)[1.00]

0.06(0.04)[1.00]

0.70

1770

0.12

0.46

Employed (10 hr)

0.02(0.03)[1.00]

0.03(0.05)[1.00]

0.05(0.05)[1.00]

0.00(0.05)[1.00]

0.01(0.05)[1.00]

0.01(0.04)[1.00]

0.48

1770

0.16

0.95

Employed (20 hr)

0.04(0.03)[1.00]

0.04(0.04)[1.00]

0.08(0.04)[1.00]

0.02(0.04)[1.00]

0.01(0.04)[1.00]

0.04(0.04)[1.00]

0.29

1770

0.17

0.62

Employed (30 hr)

0.02(0.03)[1.00]

0.03(0.04)[1.00]

0.09(0.04)[1.00]

0.04(0.04)[1.00]

0.00(0.04)[1.00]

0.06(0.04)[1.00]

0.19

1770

0.17

0.37

Employed (40 hr)

0.03(0.02)[1.00]

0.03(0.03)[1.00]

0.09(0.04)[0.70]−0.01(0.03)[1.00]

0.02(0.03)[1.00]

0.04(0.03)[1.00]

0.13

1770

0.17

0.26

Panel A. Employment composition

Panel B. Alternative hours thresholds

Notes: Panel A presents impacts on indicators for employment of any hours in the corresponding activity type in the preceding week. Panel B presents impacts on an indicator for
overall employment, using the reported threshold for minimum hours. Regressions include but do not report an indicator for lagged employment status, fixed effects for randomization
blocks, and a set of LASSO-selected baseline covariates, and are weighted to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at the household level toreflect the design effect, and p-values corrected for False Discovery Rates across outcomes in each panel are presented in hard brackets. Stars on coefficient estimates are derived fromthe FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.8: Robustness of Linearity in Primary Cost Equivalence Adjustment
Base
LinearQuadratic

Cubic

Drop
lower

Drop
mid

Drop
upper

Drop
huge

Employed−0.012(0.041)−0.022(0.067)0.072(0.143)−0.020(0.058)−0.002(0.046)−0.024(0.043)−0.022(0.055)

Productive hours−2.249(2.189)0.706(3.616)8.098(7.736)−4.722(3.054)−0.980(2.473)−2.460(2.225)−0.398(2.928)Monthly income−0.709∗∗(0.322)−0.204(0.534)−0.070(1.102)−1.005∗∗(0.419)−0.677∗(0.370)−0.654∗(0.335)−0.358(0.439)Productive assets−2.272∗∗∗(0.434)−2.499∗∗∗(0.678)−2.567∗(1.524)−2.130∗∗∗(0.603)−2.271∗∗∗(0.480)−2.292∗∗∗(0.449)−2.492∗∗∗(0.563)

HH consumption percapita−0.133∗(0.077)−0.151(0.116)0.003(0.267)−0.158(0.106)−0.110(0.084)−0.147∗(0.081)−0.157(0.097)

Notes: Table reports the coefficient on the differential effect of HD over cost-equivalent cash using seven different
specifications. Column 1 is the linear adjustment reported elsewhere. Column 2 includes a quadratic, and column 3
a quadratic and cubic term in the cost deviations from Gikuriro. Columns 4-7 leave out one of the cash treatment
arms and repeat the linear cost adjustment. Asterices denote significance at the 10, 5, and 1 percent levels, and are
based on household-clustered standard errors, in parentheses.

Table A.9: Robustness of Linearity in Secondary Cost Equivalence Adjustment
Base
LinearQuadratic

Cubic

Drop
lower

Drop
mid

Drop
upper

Drop
huge

Subjective well-being−0.229∗∗∗(0.085)−0.193(0.135)0.011(0.300)−0.288∗∗(0.119)−0.199∗∗(0.094)−0.225∗∗(0.087)−0.225∗∗(0.111)

Mental health−0.001(0.090)0.059(0.142)0.268(0.298)−0.074(0.122)

0.032(0.099)−0.009(0.092)0.048(0.117)

Beneficiary-specificconsumption−0.444∗∗∗(0.120)−0.265(0.169)−0.213(0.359)−0.573∗∗∗(0.166)−0.419∗∗∗(0.128)−0.429∗∗∗(0.124)−0.317∗∗(0.146)

HH net non-land
wealth

−0.803(0.491)0.284(0.877)0.879(1.641)−1.515∗∗∗(0.549)−0.683(0.583)−0.634(0.524)−0.169(0.713)

HH livestock wealth−1.918∗∗∗(0.453)−1.286∗(0.706)−2.520(1.559)−2.215∗∗∗(0.620)−2.109∗∗∗(0.498)−1.832∗∗∗(0.475)−1.412∗∗(0.593)

Savings−0.097(0.280)0.278(0.446)0.063(1.005)−0.308(0.390)−0.086(0.314)−0.044(0.292)0.183(0.364)

Debt

0.567
(0.394)0.233(0.642)0.675(1.374)0.743(0.556)0.660(0.442)0.480(0.401)

0.337(0.521)Panel A. Beneficiary welfare

Panel B. Household wealthPanel C. Beneficiary cognitive and non-cognitive skills
Locus of control−0.003(0.078)−0.129(0.126)−0.168(0.269)0.097(0.104)−0.027(0.088)−0.027(0.081)−0.084(0.103)

Aspirations−0.061(0.089)−0.024(0.139)−0.420(0.296)−0.035(0.117)−0.121(0.100)−0.026(0.093)−0.025(0.117)

Big Five index−0.011(0.087)0.012(0.145)0.139(0.300)−0.040(0.114)0.014(0.099)−0.008(0.091)−0.003(0.118)Business knowledge

0.536∗∗∗(0.090)0.548∗∗∗(0.142)0.539∗(0.292)

0.558∗∗∗(0.119)0.538∗∗∗(0.101)0.527∗∗∗(0.093)0.543∗∗∗(0.118)Business attitudes−0.092(0.085)−0.125(0.143)−0.004(0.290)−0.070(0.113)−0.072(0.097)−0.103(0.088)−0.129(0.116)

Notes: Table reports the coefficient on the differential effect of HD over cost-equivalent cash using seven different
specifications. Column 1 is the linear adjustment reported elsewhere. Column 2 includes a quadratic, and column 3
a quadratic and cubic term in the cost deviations from Gikuriro. Columns 4-7 leave out one of the cash treatment
arms and repeat the linear cost adjustment. Asterices denote significance at the 10, 5, and 1 percent levels, and are
based on household-clustered standard errors, in parentheses.

66

Table A.10: Heterogeneity: Gender

Employed

Productive
Hours

Monthly
IncomeProductive
AssetsConsumption

HD

0.09
(0.05)[0.30]

1.43(2.78)[0.96]

0.14(0.37)[0.96]

1.03(0.61)[0.30]

0.01(0.10)[1.00]

GD main0.05(0.05)[0.71]

4.44(2.98)[0.41]

0.79∗(0.33)[0.06]

2.97∗∗∗(0.59)[0.00]

0.29∗∗(0.10)[0.02]

GD large

0.04(0.07)[0.88]−0.08(3.62)[1.00]

0.62(0.51)[0.52]

3.18∗∗∗(0.77)[0.00]

0.36∗∗(0.12)[0.01]

Combined

0.05(0.06)[0.75]

3.55(3.67)[0.71]

0.59(0.43)[0.44]

3.50∗∗∗(0.71)[0.00]

0.37∗∗(0.13)[0.02]

HD × Female−0.12(0.07)[0.30]

2.16(3.35)[0.88]

0.02(0.50)[1.00]

0.32(0.75)[0.96]

0.11(0.13)[0.71]

GD main × Female−0.05(0.07)[0.75]−0.32(3.53)[1.00]−0.07(0.48)[1.00]

1.17(0.73)[0.33]−0.06(0.12)[0.96]

GD large × Female−0.04(0.09)[0.96]

1.95(4.29)[0.96]

0.28(0.68)[0.96]

1.02(0.99)[0.70]−0.01(0.15)[1.00]

Combined × Female−0.08(0.09)[0.71]−2.65(4.30)[0.88]

0.61(0.60)[0.70]

1.25(0.89)[0.44]−0.21(0.16)[0.49]

Female−0.16∗∗∗(0.05)[0.01]−13.87∗∗∗(2.26)[0.00]−1.80∗∗∗(0.35)[0.00]−1.32∗(0.53)[0.05]

0.03(0.09)[0.96]

Control mean
Observations
R2
p-value

0.48
1770
0.06
0.53

18.64
1770
0.10
0.84

8.05
1770
0.07
0.81

5.61
1770
0.11
0.43

9.46
1737
0.10
0.38

Notes: Table presents tests for heterogeneity of treatment effects by Gender. Uninteracted coefficients in the first
four rows give the treatment effect of the program on men, and the next four rows test for the differential effectbetween women and men. Standard errors are (in soft brackets) are clustered at the household level to reflect thedesign effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented
in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and***=1% significance. p-value in the last row from an F-test on whether treatments have a jointly differential effectby gender.

67

Table A.11: Heterogeneity: Risk aversion

Employed

Productive
Hours

Monthly
IncomeProductive
Assets∗∗∗Consumption

HD

0.01
(0.03)[0.84]

2.64(1.62)[0.23]

0.12(0.27)[0.84]

1.21(0.37)[0.01]

0.08(0.06)[0.40]

GD main

0.01(0.03)[0.84]

4.25∗∗(1.70)[0.05]

0.74∗∗(0.26)[0.02]

3.68∗∗∗(0.36)[0.00]

0.25∗∗∗(0.06)[0.00]

GD large

0.02(0.05)[0.84]

0.78(2.10)[0.84]

0.74(0.37)[0.13]

3.83∗∗∗(0.49)[0.00]

0.35∗∗∗(0.08)[0.00]

Combined

0.01(0.04)[0.84]

2.66(2.11)[0.38]

1.01∗∗∗(0.31)[0.01]

4.26∗∗∗(0.43)[0.00]

0.26∗∗∗(0.08)[0.01]

HD × Baseline risk
aversion

0.03(0.03)[0.67]

1.68(1.61)[0.50]

0.50(0.26)[0.15]−0.11(0.37)[0.84]

0.04(0.06)[0.84]

GD main × Baseline
risk aversion

0.01(0.03)[0.84]−0.51(1.71)[0.84]

0.34(0.25)[0.38]−0.06(0.35)[0.84]

0.00(0.06)[0.94]

GD large × Baseline
risk aversion

0.05(0.05)[0.40]

1.96(2.07)[0.56]

0.55(0.37)[0.30]−0.68(0.49)[0.36]−0.01(0.08)[0.84]

Combined × Baseline
risk aversion

0.07(0.04)[0.23]

3.75(2.07)[0.18]

0.75∗(0.31)[0.05]

0.12(0.43)[0.84]−0.03(0.08)[0.84]

Baseline risk aversion−0.01(0.02)[0.84]−0.38(1.10)[0.84]−0.43∗(0.19)[0.06]

0.10(0.26)[0.84]−0.05(0.04)[0.50]

Control mean
Observations
R2
p-value

0.48
1770
0.02
0.42

18.64
1770
0.03
0.27

8.05
1770
0.03
0.13

5.61
1770
0.11
0.65

9.46
1737
0.10
0.95

Notes: Table presents tests for heterogeneity of treatment effects by Risk Aversion. Risk Aversion demeaned before
interaction so first four rows give effect of treatment at average value, and next four rows test for differential treatment
effect by risk aversion. Standard errors are (in soft brackets) are clustered at the household level to reflect the design
effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hardbrackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. p-value in the last row from an F-test on whether treatments have a jointly differential effect by gender.

68

Table A.12: Heterogeneity: Baseline household consumption

Employed

Productive
Hours

Monthly
IncomeProductive
Assets∗∗∗Consumption

HD

0.01
(0.03)[0.64]

2.60(1.62)[0.18]

0.14(0.27)[0.63]

1.27(0.37)[0.00]

0.13∗(0.06)[0.09]

GD main

0.02(0.03)[0.63]

4.31∗∗(1.70)[0.04]

0.76∗∗(0.26)[0.01]

3.72∗∗∗(0.36)[0.00]

0.28∗∗∗(0.06)[0.00]

GD large

0.03(0.05)[0.63]

1.17(2.08)[0.63]

0.84∗(0.36)[0.06]

3.93∗∗∗(0.48)[0.00]

0.40∗∗∗(0.08)[0.00]

Combined

0.02(0.04)[0.64]

2.90(2.14)[0.27]

1.08∗∗∗(0.30)[0.00]

4.32∗∗∗(0.42)[0.00]

0.26∗∗∗(0.08)[0.00]

HD × Baseline HH
consumption per AE−0.04(0.03)[0.31]−1.09(1.72)[0.63]

0.03(0.25)[0.80]−0.19(0.38)[0.63]−0.02(0.07)[0.64]

GD main × Baseline
HH consumption per
AE

0.01(0.03)[0.64]

0.83(1.63)[0.63]

0.29(0.27)[0.37]−0.07(0.34)[0.76]−0.11(0.06)[0.13]

GD large × Baseline
HH consumption per
AE

0.09∗
(0.04)[0.09]

2.51(1.95)[0.29]

0.70(0.39)[0.14]

0.46(0.48)[0.45]−0.13(0.08)[0.18]

Combined × Baseline
HH consumption per
AE

−0.02
(0.04)[0.63]−1.44(2.15)[0.63]−0.69∗(0.30)[0.06]−0.68(0.41)[0.17]

0.00(0.11)[0.82]

Baseline HH
consumption per AE

0.02(0.02)[0.63]

0.43(1.05)[0.64]

0.10(0.18)[0.63]

0.51(0.27)[0.12]

0.32∗∗∗(0.04)[0.00]

Control mean
Observations
R2
p-value

0.48
1770
0.02
0.05

18.64
1770
0.03
0.43

8.05
1770
0.04
0.01

5.61
1770
0.12
0.21

9.46
1737
0.18
0.24

Notes: Table presents tests for heterogeneity of treatment effects by baseline Household Consumption. Consumption
demeaned before interaction so first four rows give effect of treatment at average value, and next four rows test fordifferential treatment effect by consumption. Standard errors are (in soft brackets) are clustered at the household
level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in thetable are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values,*=10%, **=5%, and ***=1% significance. p-value in the last row from an F-test on whether treatments have ajointly differential effect by gender.

69

Table A.13: Heterogeneity: Baseline local employment rates

Employed

Productive
Hours

Monthly
IncomeProductive
Assets∗∗∗Consumption

HD

0.01
(0.03)[1.00]

2.53(1.61)[0.37]

0.10(0.27)[1.00]

1.21(0.37)[0.01]

0.08(0.06)[0.60]

GD main

0.01(0.03)[1.00]

4.29∗∗(1.71)[0.05]

0.73∗∗(0.26)[0.02]

3.68∗∗∗(0.36)[0.00]

0.25∗∗∗(0.06)[0.00]

GD large

0.02(0.05)[1.00]

0.83(2.11)[1.00]

0.77(0.37)[0.13]

3.84∗∗∗(0.49)[0.00]

0.34∗∗∗(0.08)[0.00]

Combined

0.02(0.04)[1.00]

2.80(2.13)[0.60]

1.04∗∗∗(0.31)[0.01]

4.30∗∗∗(0.42)[0.00]

0.26∗∗∗(0.08)[0.01]

HD × Baseline cellshare employed

0.35(0.32)[0.60]

19.81(16.21)[0.60]

2.94(2.56)[0.60]

4.69(3.64)[0.60]

0.15(0.62)[1.00]

GD main × Baselinecell share employed

0.17(0.32)[1.00]−1.97(17.05)[1.00]

3.00(2.47)[0.60]

1.72(3.50)[1.00]

0.26(0.59)[1.00]

GD large × Baselinecell share employed

0.34(0.46)[1.00]

0.26(22.84)[1.00]

2.38(3.23)[1.00]

5.25(4.42)[0.60]−0.88(0.79)[0.60]

Combined × Baselinecell share employed

0.27(0.43)[1.00]−1.71(21.35)[1.00]

2.68(3.11)[1.00]

7.59(4.23)[0.23]

1.51(0.76)[0.16]

Baseline cell share
employed

0.04(0.25)[1.00]

0.29(12.35)[1.00]

1.02(1.98)[1.00]−1.41(2.87)[1.00]−0.24(0.50)[1.00]

Control mean
Observations
R2
p-value

0.48
1770
0.02
0.85

18.64
1770
0.03
0.65

8.05
1770
0.03
0.77

5.61
1770
0.11
0.35

9.46
1737
0.10
0.10

Notes: Table presents tests for heterogeneity of treatment effects by baseline Employment Rates. Employment
demeaned before interaction so first four rows give effect of treatment at average value, and next four rows testfor differential treatment effect by employment rates. Standard errors are (in soft brackets) are clustered at thehousehold level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes inthe table are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values,*=10%, **=5%, and ***=1% significance. p-value in the last row from an F-test on whether treatments have ajointly differential effect by gender.

70

Table A.14: Heterogeneity: Age 23 and over

Employed

Productive
Hours

Monthly
IncomeProductive
AssetsConsumption

HD

0.06
(0.05)[0.64]

4.32(2.52)[0.32]

0.26(0.43)[1.00]

1.17(0.56)[0.15]

0.09(0.10)[0.82]

GD main0.02(0.05)[1.00]

2.39(2.46)[0.82]

0.72(0.42)[0.32]

4.26∗∗∗(0.53)[0.00]

0.28∗∗(0.09)[0.02]

GD large

0.00(0.07)[1.00]

0.03(3.23)[1.00]

0.88(0.62)[0.47]

4.27∗∗∗(0.76)[0.00]

0.31∗(0.12)[0.05]

Combined

0.08(0.06)[0.64]

2.02(2.98)[1.00]

1.53∗∗(0.47)[0.01]

3.97∗∗∗(0.63)[0.00]

0.21(0.11)[0.30]

HD × Older than 22−0.08(0.07)[0.64]−3.01(3.25)[0.82]−0.23(0.55)[1.00]

0.08(0.74)[1.00]−0.01(0.12)[1.00]

GD main × Older
than 220.00(0.07)[1.00]

3.64(3.41)[0.71]

0.12(0.53)[1.00]−1.06(0.72)[0.47]−0.05(0.12)[1.00]

GD large × Older than
22

0.01
(0.10)[1.00]

1.04(4.23)[1.00]−0.29(0.74)[1.00]−0.76(0.99)[0.97]

0.04(0.15)[1.00]

Combined × Older
than 22−0.11(0.09)[0.64]

1.55(4.19)[1.00]−0.88(0.63)[0.47]

0.57(0.86)[1.00]

0.10(0.16)[1.00]

Older than 22

0.12∗(0.05)[0.06]

2.47(2.18)[0.64]

1.04∗(0.39)[0.05]

0.28(0.53)[1.00]

0.22∗(0.09)[0.06]

Control mean
Observations
R2
p-value

0.48
1770
0.02
0.51

18.64
1770
0.04
0.46

8.05
1770
0.04
0.57

5.61
1770
0.11
0.26

9.46
1737
0.11
0.90

Notes: Table presents tests for heterogeneity of treatment effects by age. First four rows give effect of treatment
among young, and next four rows test for differential treatment effect for those 23 and over. Standard errors are(in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected for False
Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates arederived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance. p-value in the last row from an
F-test on whether treatments have a jointly differential effect by gender.

71

Table A.15: Spillover effects: full model, employment outcome

HD

Treatment
GD Main
GD Huge

Direct effects of treatment at saturation level of zero
Direct effect−0.02(0.07)[1.00]−0.02(0.07)[1.00]

0.05(0.13)[1.00]

Spillover effects of treatment onto control individuals
Spillover to control−0.01(0.09)[1.00]−0.09(0.09)[1.00]

0.06(0.16)[1.00]

Additional effect of treatment onto individuals assigned to. . .
HD

0.00(0.11)[1.00]

0.10(0.11)[1.00]−0.10(0.19)[1.00]

GD main

0.02(0.11)[1.00]

0.15(0.12)[1.00]−0.28(0.20)[1.00]

GD large

0.03(0.22)[1.00]−0.08(0.17)[1.00]−0.23(0.42)[1.00]

Saturation mean
Saturation SD
p-value

0.36
0.23
1.00

0.36
0.23
0.55

0.09
0.13
0.49

Notes: Each column describes the direct and spillover effects of a specific treatment on Employment; all results inthe table are from a single estimation. Saturation mean and standard deviation correspond to the distribution ofsaturation rates for the treatment in question. Regressions include but do not report the lagged dependent variable,
fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates, and are weighted to reflect
intensive tracking. Standard errors are (in soft brackets) are clustered at the household level to reflect the design
effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hardbrackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. p-value in the last row corresponds to a test for whether the treatment in question has interference
effects on any arm, including control.

72

Table A.16: Spillover effects: full model, productive hours outcome

HD

Treatment
GD Main
GD Huge

Direct effects of treatment at saturation level of zero
Direct effect

2.26(3.71)[1.00]

5.60(3.67)[0.76]

9.52(6.27)[0.76]

Spillover effects of treatment onto control individuals
Spillover to control

0.81(4.39)[1.00]

1.74(4.68)[1.00]−0.12(6.56)[1.00]

Additional effect of treatment onto individuals assigned to. . .
HD

0.81(5.72)[1.00]−2.50(5.82)[1.00]−8.79(9.63)[0.87]

GD main−7.55(5.60)[0.76]

2.76(6.39)[1.00]−12.81(8.73)[0.76]

GD large−5.79(10.27)[1.00]−11.98(7.70)[0.76]−31.75(17.74)[0.76]

0.36
0.23
0.65

0.36
0.23
0.49

0.09
0.13
0.09

Saturation mean
Saturation SD
p-value

Notes: Each column describes the direct and spillover effects of a specific treatment on Productive Hours; all resultsin the table are from a single estimation. Saturation mean and standard deviation correspond to the distribution ofsaturation rates for the treatment in question. Regressions include but do not report the lagged dependent variable,
fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates, and are weighted to reflect
intensive tracking. Standard errors are (in soft brackets) are clustered at the household level to reflect the design
effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hardbrackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. p-value in the last corresponds to a test for whether the treatment in question has interference effects
on any arm, including control.

73

Table A.17: Spillover effects: full model, monthly income outcome

HD

Treatment
GD Main
GD Huge

Direct effects of treatment at saturation level of zero
Direct effect0.23(0.55)[1.00]

0.96(0.58)[1.00]

1.77(0.88)[1.00]

Spillover effects of treatment onto control individuals
Spillover to control

0.48(0.82)[1.00]−0.69(0.79)[1.00]

0.53(1.13)[1.00]

Additional effect of treatment onto individuals assigned to. . .
HD

−0.92
(0.87)[1.00]

0.91(0.82)[1.00]

0.05(1.57)[1.00]

GD main−0.28(0.91)[1.00]

0.39(0.92)[1.00]−1.10(1.40)[1.00]

GD large−1.61(1.41)[1.00]−1.75(1.40)[1.00]

2.08(3.37)[1.00]

0.36
0.23
0.66

0.36
0.23
0.28

0.09
0.13
0.88

Saturation mean
Saturation SD
p-value

Notes: Each column describes the direct and spillover effects of a specific treatment on Monthly Income (IHS);
all results in the table are from a single estimation. Saturation mean and standard deviation correspond to the
distribution of saturation rates for the treatment in question. Regressions include but do not report the lagged
dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates, and areweighted to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at the household level
to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table
are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%,**=5%, and ***=1% significance. p-value in the last row corresponds to a test for whether the treatment in question
has interference effects on any arm, including control.

74

Table A.18: Spillover effects: productive assets outcome

HD

Treatment
GD Main

GD Huge

Direct effects of treatment at saturation level of zero
Direct effect2.34∗∗∗(0.74)[0.01]

4.35∗∗∗(0.73)[0.00]

5.64∗∗∗(1.09)[0.00]

Spillover effects of treatment onto control individuals
Spillover to control

1.05(1.00)[0.36]

1.69(1.10)[0.26]

2.18(1.72)[0.34]

Additional effect of treatment onto individuals assigned to. . .
HD

−0.61
(1.32)[0.49]−1.95(1.17)[0.26]−2.85(2.10)[0.32]

GD main−2.17(1.34)[0.26]

0.23(1.11)[0.52]−1.82(1.88)[0.36]

GD large−0.88(1.90)[0.49]−2.22(1.87)[0.35]−7.88(4.10)[0.20]

0.36
0.23
0.62

0.36
0.23
0.21

0.09
0.13
0.27

Saturation mean
Saturation SD
p-value

Notes: Each column describes the direct and spillover effects of a specific treatment on Productive Assets (IHS);
all results in the table are from a single estimation. Saturation mean and standard deviation correspond to the
distribution of saturation rates for the treatment in question. Regressions include but do not report the lagged
dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates, and areweighted to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at the household level
to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table
are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%,**=5%, and ***=1% significance. p-value in the last row corresponds to a test for whether the treatment in question
has interference effects on any arm, including control.

75

Table A.19: Spillover effects: consumption

HD

Treatment
GD Main
GD Huge

Direct effects of treatment at saturation level of zero
Direct effect

0.01(0.12)[1.00]

0.29(0.11)[0.23]

0.20(0.20)[1.00]

Spillover effects of treatment onto control individuals
Spillover to control

0.05(0.16)[1.00]

0.02(0.18)[1.00]−0.36(0.25)[0.75]

Additional effect of treatment onto individuals assigned to. . .
HD

0.16(0.20)[1.00]−0.05(0.19)[1.00]−0.01(0.32)[1.00]

GD main−0.21(0.17)[1.00]−0.08(0.19)[1.00]

0.53(0.28)[0.40]

GD large−0.04(0.33)[1.00]

0.18(0.30)[1.00]

1.22(0.59)[0.37]

0.36
0.23
0.68

0.36
0.23
0.90

0.09
0.13
0.11

Saturation mean
Saturation SD
p-value

Notes: Each column describes the direct and spillover effects of a specific treatment on Household Consumption(IHS); all results in the table are from a single estimation. Saturation mean and standard deviation correspond to the
distribution of saturation rates for the treatment in question. Standard errors are (in soft brackets) are clustered at thehousehold level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes inthe table are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values,*=10%, **=5%, and ***=1% significance. p-value in the last row corresponds to a test for whether the treatment inquestion has interference effects on any arm, including control.

76

77

6.65∗∗∗(1.77)
54.03∗∗( 25.10)13.79∗∗∗(3.21)

0.12(0.13)
8.01∗∗∗(1.51)
75.78∗∗( 30.91)
9.81∗∗∗(3.46)
9.81∗∗∗(2.58)

0.20(0.15)
2.71∗∗(1.07)
12.61( 12.44)
5.57(4.38)
6.22∗∗∗(2.32)

Non-household workers

Own work days permonthMonthly customers

Daily sales

Monthly profits

12.08∗∗∗(2.65)

14.42∗∗∗(4.15)

34.68∗∗( 14.59)

11.59∗∗∗(1.70)

0.50(0.31)

0.45∗∗∗(0.12)

10.90∗∗∗(2.37)

10.22∗∗∗(2.87)

41.11∗∗∗( 15.19)

8.81∗∗∗(1.72)

18.53∗∗∗(4.97)

38.22∗∗( 14.96)

8.63∗∗∗(1.50)

0.34∗(0.19)

0.24∗(0.13)
11.37∗∗∗(1.76)

0.24∗∗(0.10)

0.62∗∗∗(0.12)

Combined0.41∗∗∗(0.10)

0.58∗∗∗(0.14)

Large

6.11

6.64

31.13

9.11

0.22

0.27

1.40

Control
Mean

1770

1770

1770

1770

1770

1770

1770

Obs.

0.03

0.02

0.02

0.07

0.02

0.03

0.09

R2

Note: Table uses data from the beneficiary enterprise survey, summing values across all businesses reported. Variables such as number of employees and customers
may be subject to double-counting across businesses. Outcomes are overwritten with zeros for beneficiaries who do not operate any business. All monetary values
are in US Dollars. Standard errors are in parentheses, clustered at the household level.

9.86∗∗∗(2.68)

0.09(0.11)

0.21∗∗(0.08)

0.21∗∗(0.09)

0.16∗∗(0.06)

Household workers

0.59∗∗∗(0.13)

0.58∗∗∗(0.13)

0.43∗∗∗(0.13)-0.01(0.09)

GiveDirectly
Middle
Upper

Lower

Number of businesses

HD

Table A.20: Impact on Business Outcomes

78

0.838
(0.471)
0.094(0.077)0.463(0.107)0.016(0.018)

Productive hours

Monthly income

Productive assets

HH consumption percapita0.050(0.020)0.998(0.118)0.192(0.092)0.699(0.593)0.008(0.012)

Lower

0.054
(0.018)0.775(0.102)0.220(0.069)1.332(0.489)0.010(0.009)Middle0.040(0.013)0.650(0.078)0.194(0.059)0.603(0.427)0.000(0.008)

Upper

0.042
(0.009)0.475(0.055)0.086(0.041)0.132(0.244)0.001(0.005)

Large

0.032
(0.008)0.526(0.052)0.124(0.038)0.275(0.241)0.001(0.005)

Combined

0.37

0.00

0.16

0.14

0.85

(a)

0.12

0.00

0.31

0.82

0.95

(b)

0.67

0.00

0.24

0.33

0.57

(c)

p-values

0.31

0.42

0.41

0.63

0.94

(d)

Note: Table gives the impact per $100 spent, which is calculated by dividing the estimated ITT impacts by the cost per arm in hundreds of dollars. The standard
errors in the table are similarly the ITT SEs divided by costs. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit ratios
are equal between: (a) joint test across all arms, (b) GD Lower and HD; (c) GD Lower and GD Large; and (d) GD Large and Combined arms.

0.007(0.010)

EmployedHD

GiveDirectly

Table A.21: Cost-Effectiveness (benefit per $100 spent), Primary outcomes

79

0.045
(0.035)

Beneficiary-specificconsumption

0.020(0.024)−0.002(0.022)0.035(0.020)0.197(0.022)0.037(0.020)

Aspirations

Big Five index

Business knowledge

Business attitudes

0.038(0.018)0.016(0.019)0.022(0.019)−0.009(0.019)0.005(0.017)0.017(0.015)0.011(0.016)0.003(0.015)0.022(0.015)0.000(0.014)−0.095(0.076)0.265(0.051)0.447(0.075)0.226(0.070)0.105(0.020)0.005(0.016)0.081(0.016)

Upper

0.074
(0.011)−0.003(0.011)0.018(0.010)0.003(0.010)−0.009(0.011)0.007(0.011)

0.016(0.009)0.027(0.009)0.000(0.045)0.202(0.032)0.263(0.053)0.106(0.057)0.082(0.014)0.014(0.011)0.048(0.010)Combined

0.003(0.010)0.010(0.010)−0.043(0.050)0.169(0.036)0.257(0.056)0.131(0.049)0.053(0.018)0.013(0.011)

0.066(0.011)

Large

0.25

0.00

0.17

0.52

0.65

0.18

0.14

0.00

0.05

0.00

0.60

0.09

(a)

0.63

0.00

0.55

0.41

0.59

0.19

0.56

0.00

0.56

0.01

0.89

0.08

(b)

0.08

0.29

0.25

0.48

0.30

0.86

0.22

0.12

0.53

0.01

0.22

0.12

(c)

p-values

0.40

0.00

0.35

0.26

0.12

0.45

0.39

0.92

0.67

0.11

0.93

0.17

(d)

Note: Table gives the impact per $100 spent, which is calculated by dividing the estimated ITT impacts by the cost per arm in hundreds of dollars. The standard
errors in the table are similarly the ITT SEs divided by costs. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit ratios
are equal between: (a) joint test across all arms, (b) GD Lower and HD; (c) GD Lower and GD Large; and (d) GD Large and Combined arms.

0.049(0.024)0.022(0.024)0.019(0.025)0.032(0.021)0.019(0.018)Locus of control

Panel C. Beneficiary cognitive and non-cognitive skills−0.046(0.088)0.122(0.084)Debt−0.024(0.105)0.311(0.070)

Savings

0.374(0.105)0.262(0.068)0.445(0.123)−0.002(0.110)

HH livestock wealth

0.244(0.089)0.125(0.027)0.014(0.019)0.107(0.020)

Middle0.264(0.080)0.041(0.150)−0.054(0.120)

HH net non-land wealthPanel B. Household wealth−0.016(0.023)−0.013(0.022)

Mental health
0.129(0.030)0.101(0.024)0.058(0.020)Lower

Subjective well-being

Panel A. Beneficiary welfare

HD

GiveDirectly

Table A.22: Cost-Effectiveness (benefit per $100 spent), Secondary outcomes

80

0.11
(0.30)[0.31]−0.33(0.36)[0.19]
0.39(0.32)[0.15]

0.90(0.44)[0.05]
1.75∗∗∗(0.57)[0.00]
0.68∗(0.42)[0.09]

∗∗

Lower
0.45
(0.44)[0.19]
2.67∗∗∗(0.55)[0.00]
1.87∗∗∗(0.48)[0.00]

Middle1.41(0.45)[0.00]
2.62∗∗∗(0.59)[0.00]
0.56(0.47)[0.15]

∗∗∗

Upper
∗

0.68
(0.42)[0.09]
3.11∗∗∗(0.53)[0.00]
0.32(0.44)[0.20]

Large
1.24(0.43)[0.01]
2.46∗∗∗(0.50)[0.00]
0.81∗(0.45)[0.08]∗∗∗

Combined

3.41

4.90

2.24

Control
Mean

1675

1704

1705

Obs.

0.15

0.17

0.15

R

2

0.63

0.00

0.10

(a)

0.20

0.60

0.18

(b)

p-values

0.36

0.33

0.27

(c)

Note: Regressions include but do not report the lagged dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates,
and are weighted to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values
corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived from the
FDR-corrected p-values, *=10%, **=5%, and ***=1% significance. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit
ratios are equal between: (a) GD Lower and HD; (b) GD Lower and GD Large; and (c) GD Large and Combined treatments.

HH gifts madeHH gifts madeHH loans madeHD

GiveDirectly

Table A.23: Program impacts on household-to-household transfers

Figure A.1: Project timeline
Tranche I follow-up

Tranche I baseline
Rolling phone surveys

Measurement

Tranche II follow-up

Tranche II baseline
2017

81

Nov

2018

Dec

Jan

2019

Feb

Mar

Apr

May

Cash transfers

HD Tranche I

HD Tranche II

Jun

Jul

Cash transfer 1

Workforce
readiness training

Skills training/
Be Your Own Boss

Workforce
readiness training

Skills training/
Be Your Own Boss

Aug

Sep

Oct

Nov

Dec

Cash transfer 2

Job placement and coaching

Job placement and coaching

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Figure A.2: CDF of Savings Stocks (IHS)

Figure A.3: CDF of Productive Hours

82

Figure A.4: Non-Ag Wage Employment, varying hours thresholds

Figure A.5: Non-Ag Self Employment, varying hours thresholds

83

Figure A.6: Ag Wage Employment, varying hours thresholds

Figure A.7: Ag Self Employment, varying hours thresholds

84

Figure A.8: Ag processing or trading Employment, varying hours thresholds

Figure A.9: Rolling Phone Survey: Monthly impacts on Productive Hours

85

Figure A.10: Rolling Phone Survey: Monthly impacts on Apprenticeship Hours

Figure A.11: Cost Equivalence on Secondary Outcomes86Figure A.12: Village-Level Treatment Saturations

87

Online AppendicesAppendix B

Outcome definitions

Appendix B.1

Defining Primary outcomes

For each of the outcomes defined below, we provide a definition, followed by an explanation of howthat measure will be constructed from survey data. Survey questions either begin with a ‘B-’ forthe beneficiary instrument or a ‘H-’ for the household instrument, followed by the two-digit section
number, followed by ‘q’ and the question number. These refer to the beneficiary and household
instruments, respectively.30
There are five primary outcomes:
1. Employment status. A binary indicator variable taking a value of one if the beneficiary
spent 10 hours or more in the prior week working in a wage job or as primary operator of a
microenterprise. The 1 week recall is per ILO definition. Defined as ’Yes’ if beneficiary spent
10 hours or more on any of the following activities:• Processing or trading of agricultural goods (B02qagroprocesshrs)• Agricultural (off farm) wage labor (B02qfarmhours)• Non-agricultural wage labor (B02qnoagrichrs)• Non-agricultural microenterprise (B02qenterphrs)• Microenterprise or other self employment (B02qsemployhrs).
2. Off-own-farm productive time use. Defined as the number of productive hours over the past
7 days. Sum of hours from questions:• Processing or trading of agricultural goods (B02qagroprocesshrs)• Agricultural (off farm) wage labor (B02qfarmhours)• Non-agricultural wage labor (B02qnoagrichrs)• Non-agricultural microenterprise (B02qenterphrs)• Microenterprise or other self employment (B02qsemployhrs)• Apprenticeship (B02qapprenticehrs)
3. Beneficiary’s (monthly) income. Defined as the sum of the following monthly recall questions:• Agricultural own-farm income (B02qagricearn)• Agricultural wage income (B02qfarmwage)• Non-agricultural wage income (B02qnoagricwage)• Microenterprise profits (B02qenterpwage + B02qsemploywage);30

In the electronic survey instrument, all variables begin with an ‘m’ prefix, but this notation does not guarantee
uniqueness across instruments. Consequently for the purposes of this PAP we adopt the ‘B-’ and ‘H-’ convention
above.

1• Livestock rearing income (B02qlivestockwage)• Agricultural processing and trading income (B02qagroprocessearn)• Apprenticeship income (B02qapprenticewage)
This outcome will be winsorized at the 1st and 99th percentile, and we will take the inverse
hyperbolic sine transformation of this as the primary measure.
4. Productive assets under beneficiary control. (Sum of asset values from beneficiary enterprisemodule that are reported as used in the beneficiary’s business, Section B05: tools, machinery,furniture, inventories, and other physical assets.) This outcome will be winsorized at the 1st
and 99th percentile, and we will take the inverse hyperbolic sine transformation of this as theprimary measure.
5. Household consumption per capita. Sum of monthly purchase values of Section H10, divided
by adult-equivalent household members. This outcome will be winsorized at the 1st and 99th
percentile, and we will take the inverse hyperbolic sine transformation of this as the primarymeasure.
The first three of these primary outcomes provide direct measures of the extent to which a study
participant is productively employed: their formal (non-farm) employment categorization, theirproductive time use, and their earnings. To the extent that these measures are potentially seasonalin nature, one might worry that interventions could differentially affect the sectoral composition of
employment, and that differential seasonality across these would tip the scales in favor of one or theother mode of intervention. More broadly, income may be more fully measured in one sector relative
to another. Such concerns are partly addressed by the inclusion of household consumption as a
primary outcome: to the extent that beneficiaries smooth consumption, household consumption
will be less susceptible to such concerns. In addition, we will include as a robustness check ananalysis of impacts on a rolling panel of employment status measures, collected over the six months
prior to the endline.
One potential challenge for the analysis of monetary outcomes (income, assets, and consumption) is that, if treatments induce migration, they may cause subjects to face different prices. Such
differences in prices could cause the study to over- (or under-)state the the real value of estimatedimpacts. On the other hand, deflating values to control-group prices is not straightforward, for atleast two reasons: study subjects may alter the quality of products purchased in ways not capturedby the study, therefore giving the appearance of price impacts; and study subjects may earn incomes in more expensive locations but intend for part of that income may be consumed—by the
subject themselves, or by family members to which they remit income—in their place of origin.
To address these concerns, we will report as a robustness check an analysis of primary outcomes(3)–(5) that uses control-group prices to deflate these values. This will be particularly importantto the interpretation of the study results if treatments have effects on migration.

2

Appendix B.2

Defining Secondary outcomes

We propose to analyze three families of secondary outcome: one which speaks to alternative measures of beneficiary welfare; a second that speaks to wealth effects that may indicate likely long-term
benefits; and a third family that highlights key mechanisms of interest.
1. Alternative measures of beneficiary welfare
Within this family, we consider the following alternative measures of beneficiary well-being:(a) Subjective well being: Index of responses to B10_swb_happiness and B10_swb_lifesatisfaction,
constructed as the average of z-scores.
(b) Mental health: Index of section B11 responses. Z-score of the simple average across allquestions for each beneficiary.
(c) Beneficiary-specific consumption expenditures (sum of values from Section B08). Thisoutcome will be winsorized at the 1st and 99th percentile, and we will take the inverse
hyperbolic sine transformation of this as the primary measure.
2. Household net wealth, and its components
Like productive assets, the accumulation and protection of household wealth. Conditional on
this, households’ access to borrowing opportunities—viewed as a measure of their financial
access—may be a mechanism through which the interventions studied are multiplied. Given
this welfare ambiguity, we propose to analyze both total household net (non-land) wealth, aswell as stocks of savings and debt, taken individually.
(a) Household net non-land wealth. Sum of values of household assets (H12), plus savings
value (H06), value of loans outstanding that are expected to be repaid (H08), less debt
value (H07). This outcome will be winsorized at the 1st and 99th percentile, and we willtake the inverse hyperbolic sine transformation of this as the primary measure.
(b) Total value of all livestock wealth. Sum of values of household livestock assets (H12).
Specifically, summing over values derived from H12_oxen through H12_ducks in thehousehold instrument. This outcome will be winsorized at the 1st and 99th percentiles,
and we will take the inverse hyperbolic sine transformation of this as the primary measure.
(c) Stock of savings. Beneficiary stock of savings, sum of values in B06. Plus householdstock of savings from analogous questions (H06). This outcome will be winsorized at the
1st and 99th percentile, and we will take the inverse hyperbolic sine transformation ofthis as the primary measure.
(d) Stock of debt. Beneficiary sum of borrowed amounts from all (formal and informal)
sources (B07), plus household borrowings from analogous questions (H07). This outcome
will be winsorized at the 1st and 99th percentile, and we will take the inverse hyperbolic
sine transformation of this as the primary measure.
3

3. Cognitive and non-cognitive skills
A specific feature of the theory of change that motivates EDC’s curriculum is that a focus
not just on specific skills, but on non-cognitive attitudes and attitudes, may make that intervention more likely to have persistent effects. At the same time, cash transfers may alsochange, inter alia, beneficiaries’ sense of control and aspirations. To test these mechanisms,
we define the following family of secondary outcomes:(a) Locus of control: Index of responses to B09. Z-score of the simple average across allquestions for each beneficiary.
(b) Aspirations: Index of responses to B13. Z-score of the simple average across all questions
for each beneficiary.
(c) Conscientiousness, agreeableness, and emotional stability from BFI (Section B12). Eachindex is the Z-score of the simple average of the questions related to the corresponding
dimension. Following EDC’s analysis of Akaze Kanoze employers,31 we will examine
program impacts on the three most highly-rated components of the Big-Five Index from
employers’ perspective: conscientiousness, agreeableness, and emotional stability.
(d) Business knowledge. Index of B14. Z-score of the simple average across all questions foreach beneficiary.
(e) Business attitudes. Index of B15. Z-score of the simple average across all questions foreach beneficiary.

Appendix C

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 tobuild on recent developments that balance the challenge of using baseline data to select variables
that will reduce residual variance in equation (1) with the danger that researcher freedom in theselection of control variables can lead to p-hacking, in which right-hand-side variables are selected
specifically on the basis of the statistical significance of the coefficient of interest (Card and Krueger,
1995; Casey et al., 2012), thereby invalidating inference.
To balance these concerns, we adapt the post-double-selection approach set forth in Belloni etal. (2014b, henceforth BCH). BCH advocate a two-step procedure in which, first, Lasso is used toautomate the selection of control variables, and second, the post-Lasso estimator (Belloni et al.,2012) is used to estimate the coefficients of primary interest in Equation (1), 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 inestimated treatment effects better than alternative approaches—less a concern given the successful
31

Povec Pagel, Olaru, Alcid, and Beauvy-Sany, 2017, “Identifying cross-cutting non-cognitive skills for positive
youth development”, Final report, Education Development Center, Inc.

4

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
selection stage can be undertaken after residualizing to account for a set of control variables thatthe authors have a priori determined below in the model, as in Belloni et al. (2014a). In our case, 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 the BCH approach for application to a randomized experiment in three ways. First,
again following (Jones et al., 2019), for each outcome we choose the Lasso penalty parameter thatminimizes the 10-fold cross-validated mean squared error. Second, to ensure that chance differencesin the leverage of observations across different covariate sets do not lead to different conclusions
about the (relative) impacts of treatment across different outcomes (Young, 2019), we take theunion of covariate sets selected to be predictive of the five primary outcomes of the study, anduse these as controls for all outcomes. And third, we modify the heteroskedasticity-robust Lasso
estimator of Belloni et al. (2012) to incorporate sampling weights consistent with our design.32
The set of potential covariates is determined as follows:• Baseline values of all primary outcomes, including the individual components of the employment status, productive time use, monthly income variables outlined in Section 2.8;
• Baseline values of all secondary outcomes,• Baseline values of all dimensions of heterogeneity pre-specified in Section 3.4.
• The number of study participants (in any arm of the study) in an individual’s village, which
is defined as the measure of network ‘degree’ for each individual in the spillover analysis of
Section 3.5.
All variables are normalized prior to inclusion in the selection routine, to have mean zero andvariance of one in the baseline sample. We include squares of all continuous variables and all
pairwise interactions among the potential covariates above, and between the potential covariates
above and the set of variables that force the routine to include without penalty To ensure thatsample size is not affected by the choice of covariates, we impute values of zero for all variablesin the potential covariate list, and for each potential covariate we include an indicator for whether
such an imputation was undertaken among the list of potential covariates to be fed into the BCH
first-stage selection procedure.
32
Specifically, we up-weight observations in our ‘intensive tracking’ endline sample by the inverse of the fraction of
not-initially-reached individuals in the follow-up survey who were then assigned to intensive tracking.

5Appendix D

Administrative Information

Appendix D.1

Funding

All research funding for this project was provided by USAID.

Appendix D.2

Details of Study Participant Selection

To meet the Huguka Dukore eligiblity criteria, participating youth must meet the following criteria:• 6-12 years of basic education (inclusive).
• Age 16-30 at enrollment.
• Drawn from Ubudehe poverty groups 1 and 2, per GiveDirectly’s remit from the Rwandan
Government to treat only the poorest households with cash transfers.
Additionally, HD in its outreach specifically targeted the following criteria for inclusion, meaningthat such youth will be specially recruited to participate:• Out of school for three consecutive years• Income of less than $1.75 per day• Youth exhibiting some form of disability (that can be accommodated in HD programming)• Women.
• Youth who have not benefited from related interventions in the past.
Hard eligibility criteria and targeted characteristics were provided to local government leaders,
who provided lists of potential candidates to EDC. Those candidates were then invited to theinformation session and formally screened for eligibility.
All listing and determination of eligibility were conducted by EDC via an ‘over-subscription’process. Under this protocol, EDC enrolled more eligible individuals than they were able to treat
with HD, in order to generate the samples for the alternate (household grants) arm and the control.
In the end we recruited 1848 study youth from approximately 250 villages in our 13 sectors, for an
average of roughly 7.4 study individuals per village.
Below, we characterize the process for (over)subscription, which delivered the sample of individuals for the baseline.
1. Sector-level meeting to discuss HD with local leaders that introduced the study. In thismeeting, sector officials were fully informed about the scope of the study, emphasizing theseparateness of the two interventions and implementers.

6

2. Announcement to the community in public places (churches, community halls) or ameeting to engage potential beneficiaries. At this point only the HD program wsa described
to beneficiaries, and with only general language about the household grants arm. Guiding
language: “We are pleased to be able to bring programming to this community that seeks
to improve the livelihoods of vulnerable youth. To this end, we are requesting the names
and contact details of youth meeting the following criteria: ¡insert eligibility criteria here¿.
Participating youth should be willing and interested to join an employment skills program,called HD, that will provide training and work experience to participants.”
3. Screening of youth by the selection committee which produced the final list of potential
beneficiaries that was passed to local implementing partners (IPs).
4. Invitation of potential beneficiaries to an orientation meeting. The language of thisinvitation reflected the fact that potential beneficiaries were not guaranteed places in HD,
and might be randomly allocated to a different program or the control. Guiding language
for official communication: “We have determined that you are eligible for the Huguka Dukore
program. There may be more eligible individuals than Huguka Dukore will treat this year, so
you are not yet guaranteed a place, though some of those not treated by Huguka Dukore will
be supported by another NGO. To find out more about the Huguka Dukore program and to
take the next step toward this opportunity, please attend an orientation meeting at XXX on
YYY date.”
5. Orientation and awareness meeting with selected youth by local IPs at which they
are given further explanation about the program. In HD’s other districts, these orientation
meetings convey information about the scope of that program, under a presumption thatthose who participate in the orientation meeting can have a place in HD should they choose
to take it up.
6. Description of the lottery for program assignment. The lottery is described during
this meeting with reference to another intervention providing livelihoods assistance that will
also be determined by the lottery. Guiding language: “Today you have learned more aboutthe Huguka Dukore program. This is one of two programs that are being delivered by distinct NGOs, in coordination with Sector and District officials, both of which seek to improve
livelihoods for vulnerable youth. If you decide that you are interested in participating in oneof these programs, there is one more step in the selection process. To participate, you must
attest that you have the time and interest required to participate in Huguka Dukore. Your
name will then be entered into a pool of applicants. There will be a public meeting in which alottery will be used to determine which of these applicants receives a place in HD. You may
attend this meeting if you wish, but you do not have to do so in order to gain a place. Not all
whose names are entered into the lottery will be placed in HD. Some of those who participate
in the lottery will be passed to a second NGO, which provides assistance to individuals seeking7

to improve their livelihoods. Those who receive a place in either program will be contacted
directly by the relevant organization after the lottery. To gain access to either program, youmust participate in this lottery. If you are willing to participate, please provide your nameand contact details in writing. Prior to the lottery, you may be contacted by an independent
research organization called Innovations for Poverty Action, who are conducting a survey ofpotential beneficiaries. You do not have to participate in this survey in order to gain accessto our program, and participation will not affect your chances of enrollment. However, we
would be grateful for your willingness to participate in an interview with IPA, which will help
us to understand the design and impacts of our work.
7. Registration for the lottery assignment. To correctly reflect the lottery process to participants, they were told when asked to enroll in the study that it is “a lottery in which you
will have a chance of receiving HD, a chance of receiving assistance from a different organization that gives household grants, and a chance that you do not receive either program.”
Individuals who do not choose to register for the study will not be excluded from receiving
HD if they are eligible & choose to participate.

8