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The impact of a multipronged approach to poverty alleviation on household outcomes
Vilas Gobin11 June 2015
Motivation The poorest of the poor often do not benefit
from poverty alleviation programs, for example: The poorest benefit the least from MFIs (Morduch,
1999; Rabbani et al., 2006). Public assistance programs and social safety nets
fail in targeting the poorest (Mukherjee, 2005; Jalan and Murgai, 2007; Banerjee et al., 2007).
There is also limited evidence that public assistance programs and social safety nets lead to any long-term sustained graduation from dependency (Independent Evaluation Group, 2011).
Motivation2
Motivation Evidence that removing credit constraints alone may
not be sufficient to alleviate poverty through microenterprises (Angelucci et al., 2014; Banerjee et al., 2014a; Banerjee et al., 2014b; de Mel et al 2012; Fafchamps et al., 2011; Karlan and Zinman 2010).
Relaxing human capital constraints alone may also not be sufficient to alleviate poverty through microenterprises (McKenzie and Woodruff, 2014).
Emerging evidence that both financial and human capital constraints need to be simultaneously addressed if microenterprises are to deliver on their transformative potential (de Mel et al., 2012; Berge et al., 2014; Bandiera et al. 2013; Banerjee et al., 2015).
Motivation3
The poverty graduation approach Challenging the Frontiers of Poverty Reduction – Targeting
the Ultra-Poor A package of interventions including: consumption support,
physical asset transfer, skills training, savings services. Ongoing support for 2 years at which time participants are
expected to graduate from extreme poverty and be able to participate in microfinance.
Reported Impacts Bandiera et al. (2013) – increase in earnings, expenditure, food security
and life satisfaction (rural Bangladesh) Ultra-Poor Poverty Graduation pilots in 6 countries:
India, Pakistan, Honduras, Peru, Ethiopia, Ghana Improvements in consumption, food security, assets, finance, income,
time use, mental health, women’s decision making Morduch et al. (2012) – no impact on income, consumption or
asset accumulation (Andhra Pradesh, India)
Poverty Graduation Models4
Study Site
Study Site5
Description of the intervention
The intervention6
Randomisation of Program Assignment 1755 eligible women identified in November
2012 across 14 locations Limited capacity to enrol all women resulted in
eligible women being split into three groups. Three groups were to be enrolled in either
March/April 2013, September/October 2013 or March/April 2014
A public lottery was used to randomly assign women to one of the three funding cycles
Research design, implementation, data
7
Funding Cycle
Sample Size
Group A Apr 2013 585
Group B Sep 2013 585
Group C Apr 2014 582
Randomisation of Program Assignment All women interviewed at baseline in
November 2012 Follow-up surveys conducted at 6 month intervals
to coincide with beginning of each funding cycle.
Research design, implementation, data
8
11/12
4/13 9/13 4/14
Baseline
survey
Group A 1stGrant
Group B 1stGrant
Group A 2ndGrant
GroupC 1stGrant
Group B 2ndGrant
Midline
survey
Endline
survey
Randomisation of Program Assignment
Interested is estimating the effect of which consists of business training, a cash grant of
USD 100, and mentoring which consists of savings training, participation in a
savings group, a cash grant of USD 50, mentoring and micro-trainings.
Research design, implementation, data
9
11/12
4/13 9/13 4/14
Baseline
survey
Group A 1stGrant
Group B 1stGrant
Group A 2ndGrant
GroupC 1stGrant
Group B 2ndGrant
Midline
survey
Endline
survey
Randomisation of Program Assignment
To estimate impact of Use midline data to compare Group A to Group B
and C
Use endline data to compare Group B to Group C
Research design, implementation, data
10
11/12
4/13 9/13 4/14
Baseline
survey
Group A 1stGrant
Group B 1stGrant
Group A 2ndGrant
GroupC 1stGrant
Group B 2ndGrant
Midline
survey
Endline
survey
Randomisation of Program Assignment
To estimate impact of Use endline data to estimate combined impact of
and by comparing Group A to Group C.
And
Research design, implementation, data
11
11/12
4/13 9/13 4/14
Baseline
survey
Group A 1stGrant
Group B 1stGrant
Group A 2ndGrant
GroupC 1stGrant
Group B 2ndGrant
Midline
survey
Endline
survey
Balance Checks
Research design, implementation, data
12
Summary Statistics and Balance Checks for the Treatment and Control Groups
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)
Monthly expenditure per capita
Monthly food
expenditure per
capita
Monthly non-food
expenditure per
capita
Monthly income per
capita
Total savings
per capita
TLU per capita
Durable asset index
Meals per day
# nights that child has gone to bed hungry
Proportion of
children in school
Household Size
# children Married
Years of educatio
n
Business Experienc
e
Benefitting from
HSNP
Participating in CARE VSLA
Panel A: T-test comparison of means of baseline characteristics between treatment and control groupsGroup A mean (standard error)
34.562(1.516)
24.182(1.188)
10.380(0.747)
21.770(0.925)
3.772(0.344)
0.683(0.030)
-0.234(0.169)
1.941(0.016)
0.549(0.027)
0.435(0.012)
5.778(0.079)
3.875(0.071)
0.800(0.017)
0.328(0.060)
0.576(0.020)
0.106(0.013)
0.089(0.012)
Group B mean (standard error)
34.480(1.402)
23.862(1.075)
10.617(0.770)
22.319(0.933)
3.920(0.328)
0.640(0.037)
0.113(0.189)
1.950(0.016)
0.576(0.029)
0.442(0.012)
5.692(0.075)
3.737(0.070)
0.831(0.016)
0.470(0.072)
0.562(0.021)
0.103(0.013)
0.106(0.013)
Group C mean (standard error)
32.825(1.215)
22.494(0.874)
10.331(0.648)
22.449(0.995)
5.123(0.598)
0.684(0.034)
0.124(0.179)
1.933(0.014)
0.576(0.029)
0.412(0.011)
5.596(0.077)
3.711(0.070)
0.773(0.017)
0.414(0.070)
0.538(0.021)
0.113(0.013)
0.108(0.013)
P-value from t-test of equality of means of group A and groups B and C combined 0.610 0.466 0.916 0.593 0.123 0.540 0.099 0.994 0.430 0.588 0.163 0.083 0.919 0.145 0.302 0.899 0.220P-value from t-test of equality of means of group B and group C 0.373 0.323 0.776 0.927 0.078 0.379 0.967 0.426 0.991 0.075 0.373 0.798 0.014 0.575 0.411 0.551 0.901P-value from t-test of equality of means of group A and group C 0.372 0.253 0.961 0.617 0.051 0.307 0.146 0.711 0.496 0.171 0.100 0.102 0.264 0.351 0.192 0.685 0.268Panel B: F-test from regression of treatment on 15 variables above (excluding (2) and (3)) Treatment group Control Group F-Stat p-value
A B and C 0.76 0.723
B C 1.18 0.283
A C 1.15 0.308
All monetary values are reported in 2014 USD, PPP terms
Regression Model
Research design, implementation, data
13
Results14
(1) (2) (3) (4) (5) (6)
Monthly expenditure per
capita
Monthly income per capita
Total savings per capita TLU per capita Durable asset index
# nights that child has gone to bed
hungry
3.669(3.127)
10.663***(2.846)
0.888(0.557)
-0.023(0.060)
0.335(0.325)
-0.110*(0.062)
Sub-location FEs
Yes Yes Yes Yes Yes Yes
Observations 1682 1682 1682 1682 1682 1597R-squared 0.282 0.106 0.116 0.332 0.318 0.260Control Group Mean 50.805 24.849 3.430 1.075 2.050 0.514
Note: Regressions include sub-location fixed effects, in addition to control variables for 1) loans taken from REAP savings groups, and 2) the number of REAP businesses in a manyatta. Robust standard errors in parentheses. Standard errors are clustered at the business group level. All monetary values are reported in 2014 USD, PPP terms*Significant at the 10% confidence level, **Significant at the 5% confidence level, ***Significant at the1% confidence level
Results15
(1) (2) (3) (4) (5) (6)
Monthly expenditure per
capita
Monthly income per capita
Total savings per capita TLU per capita Durable asset index
# nights that child has gone to bed
hungry
0.282(3.900)
7.439***(2.660)
1.680**(0.747)
0.202**(0.102)
0.700*(0.381)
-0.192**(0.077)
Sub-location FEs
Yes Yes Yes Yes Yes Yes
Observations 1117 1117 1117 1117 1117 1089R-squared 0.103 0.114 0.106 0.225 0.408 0.090Control Group Mean 57.394 25.232 4.440 1.303 2.843 0.789
Note: Regressions include sub-location fixed effects, in addition to control variables for 1) loans taken from REAP savings groups, and 2) the number of REAP businesses in a manyatta. Robust standard errors in parentheses. Standard errors are clustered at the business group level. All monetary values are reported in 2014 USD, PPP terms*Significant at the 10% confidence level, **Significant at the 5% confidence level, ***Significant at the1% confidence level
(1) (4) (5) (6) (7) (9)
Monthly expenditure per
capita
Monthly income per capita
Total savings per capita TLU per capita Durable asset index
# nights that child has gone to bed
hungry
-1.960(1.681)
3.809***(1.109)
2.868***(0.374)
0.077(0.051)
0.398**(0.174)
-0.084**(0.040)
Sub-location FEs
Yes Yes Yes Yes Yes Yes
Observations 1095 1095 1095 1095 1095 1068R-squared 0.128 0.126 0.148 0.272 0.423 0.102Control Group Mean 57.394 25.232 4.440 1.303 2.843 0.789
Note: Regressions include sub-location fixed effects, in addition to control variables for 1) loans taken from REAP savings groups, and 2) the number of REAP businesses in a manyatta. Robust standard errors in parentheses. Standard errors are clustered at the business group level. All monetary values are reported in 2014 USD, PPP terms*Significant at the 10% confidence level, **Significant at the 5% confidence level, ***Significant at the1% confidence level
(1) (2) (4) (5)
Treatment effect
[p-value]
q-value for all 6 hypotheses
Treatment effect
[p-value]
q-value for all 6 hypotheses
Treatment effect
[p-value]
q-value for all 6 hypotheses
Treatment effect
[p-value]
q-value for all 6 hypotheses
Monthly expenditure per capita
3.6687[0.241] 0.362 0.2824
[0.942] 0.942 -1.9596[0.244] 0.244 -5.6283
[0.0860] 0.172
Monthly income per capita
10.663***[0.000] 0.001 7.4387***
[0.005] 0.03 3.8089***[0.001] 0.003 -6.8541
[0.0146] 0.044
Total savings per capita
0.8882[0.111] 0.222 1.6805**
[0.025] 0.05 2.8683***[0.000] 0.001 1.9801
[0.0008] 0.005
TLU per capita -0.0226[0.704] 0.704 0.2023**
[0.047] 0.071 0.0769[0.133] 0.16 0.0995
[0.1411] 0.212
Durable asset index
0.3352[0.303] 0.364 0.7000*
[0.067] 0.081 0.3981**[0.023] 0.046 0.0629
[0.825] 0.825
# nights that child has gone to bed hungry
-0.1103*[0.073] 0.219 -0.1917**
[0.014] 0.042 -0.0837**[0.038] 0.057 0.0266
[0.7027] 0.825
q-values are estimated using the Benjamini-Hochberg step-up method
Conclusion
Results18
Providing ultra-poor women with capital and skills enables them to improve household incomes through entrepreneurial activities.
Women are also better able to plan for future shocks through the accumulation of liquid savings as well as livestock.
The poverty graduation model appears to have passed an extreme test of its ability to improve the lives of the ultra-poor Implemented in low population density region that is
prone to insecurity and extreme climatic conditions, lacks infrastructure, and has limited access to markets.
Thank You
(1) (2) (4) (5)
Treatment effect
[p-value]
q-value for all 6 hypotheses
Treatment effect
[p-value]
q-value for all 6 hypotheses
Treatment effect
[p-value]
q-value for all 6 hypotheses
Treatment effect
[p-value]
q-value for all 6 hypotheses
Monthly expenditure per capita
3.6687[0.241] 0.362 0.2824
[0.942] 0.942 -1.9596[0.244] 0.244 -5.6283
[0.0860] 0.172
Monthly income per capita
10.663***[0.000] 0.001 7.4387***
[0.005] 0.03 3.8089***[0.001] 0.003 -6.8541
[0.0146] 0.044
Total savings per capita
0.8882[0.111] 0.222 1.6805**
[0.025] 0.05 2.8683***[0.000] 0.001 1.9801
[0.0008] 0.005
TLU per capita -0.0226[0.704] 0.704 0.2023**
[0.047] 0.071 0.0769[0.133] 0.16 0.0995
[0.1411] 0.212
Durable asset index
0.3352[0.303] 0.364 0.7000*
[0.067] 0.081 0.3981**[0.023] 0.046 0.0629
[0.825] 0.825
# nights that child has gone to bed hungry
-0.1103*[0.073] 0.219 -0.1917**
[0.014] 0.042 -0.0837**[0.038] 0.057 0.0266
[0.7027] 0.825
q-values are estimated using the Benjamini-Hochberg step-up method
Survey Attrition On average, less than 2 percent of women
could not be reached for a follow-up interview in either the midline or endline rounds of data collection
Research design, implementation, data
21
Table 2: Number of individuals interviewed at baseline, midline and endline and the number of businesses they come from.
A B C # Women # Businesses # Women # Businesses # Women # Businesses
Baseline(Nov 2012)
585(100%)
195(100%)
585(100%)
195(100%)
582(100%)
194(100%)
Midline(Sep 2013)
549(93.8%)
186(95.4%)
565(96.6%)
193(99.0%)
565(97.1%)
193(99.5%)
Endline(Apr 2014)
534(91.3%)
189(96.9%)
556(95%)
192(98.5%)
561(96.4%)
190(97.9%)
Description of the intervention
The intervention22
Table 1: Summary of REAP’s graduation criteria Graduation category Criteria
1) Food security a. no family member goes to bed hungry in the last weekb. Participants consume at least 2 meals daily
2) Durable asset ownership a. Participant owns at least two of the following durable assets: mobile phone, panga, korobois/lantern, blanket, mattress, nylon, or latrine.
3) Sustainable livelihoods a. Participant’s REAP business value, or her total productive asset base (total livestock value + REAP business) is worth 125% of its value at the time of disbursementb. Participant can demonstrate at least KES 3,840 in non-BOMA monthly income.
4) Shock preparedness a. Participant is an active member in a savings group and can access KES 4,680 between her savings and share of BOMA business.*
5) Human capital investment a. Participant spends at least KES33 on school- and medical-related expenditures per capita per month.b. One of three school-aged children enrolled in school.c. Participant has participated in an Adult Literacy program since enrolment in REAP.
* An active member of a savings group is defined as a member with an attendance rate equal to or greater than 90%.
Table 10: Proportion of participants that attain REAP’s graduation criteria and indicators at endline
Graduation Criteria IndicatorProportion of participants that meet
graduation criteria at endline Group A Group B Group C1) Food security a. no family member goes to bed hungry in the last week 68.9% 70.5% 62.9%
b. Participants consume at least 2 meals daily 99.4% 99.6% 99.3%2) Durable asset ownership
a. Participant owns at least two of the following durable assets: mobile phone, panga, korobois/lantern, blanket, mattress, nylon, or latrine.
96.6% 95.3% 95.5%
3) Sustainable livelihoods
a. Participant’s REAP business value, or her total productive asset base (total livestock value + REAP business) is worth 125% of its value at the time of disbursement
88.6% 85.8% 68.3%
b. Participant can demonstrate at least KES 3,840 in non-BOMA monthly income. 50.9% 44.8% 45.8%
4) Shock preparedness
a. Participant is an active member in a savings group and can access KES 4,680 between her savings and share of BOMA business.*
87.3% 68.0% 3.6%
5) Human capital investment
a. Participant spends at least KES33 on school- and medical-related expenditures per capita per month. 64.8% 59.9% 58.3%
b. One of three school-aged children enrolled in school. 87.3% 89.2% 86.1%c. Participant has participated in an Adult Literacy program since enrolment in REAP. 1.3% 0.9% N/A
Overall Graduation Rate 81.4% 64.2% 3.2%* We do not have enough information to determine active membership in a savings group; so we focus on access to savings and share of the BOMA business.
Spillover Effects Not randomised by location so increased
likelihood of spillover effects Control households may benefit from reduced
prices in goods or increased availability of goods in location >90% enterprises are petty trade of primarily food
items Not expected to be substantial given large number of
pre-existing businesses
Research design, implementation, data
24
Spillover Effects
Research design, implementation, data
25
Table 5: Population, Villages and Number of Businesses by Location
Location Population*Number
of Villages
Pre-existent businessesBusinesses formed between 2013 and
2014
Non-Program Program
April 2013 September 2013
April 2014
1 13012 38 55 186 20 20 20
2 8357 30 54 105 10 10 10
3 7000 18 61 85 10 10 10
4 7800 11 91 136 10 10 10
5 4078 9 29 70 10 10 10
6 3300 13 10 60 10 10 10
7 10238 27 77 90 20 20 20
8 8935 15 31 100 20 20 20
9 4226 17 32 55 10 10 10
10 11220 19 119 70 20 20 20
11 3076 14 9 18 10 10 10
12 4065 13 21 35 10 10 10
13 8030 7 19 70 20 20 20
14 11223 33 119 25 15 15 14*Population numbers based on the 2009 Kenya Census.
Spillover Effects Treatment groups may also experience decreased
revenue and profits due to presence of other businesses Include business density as a control variable in
estimation of program effect Control households may benefit from access to
loans from SGs established under the program. SGs already exist in all locations prior to April 2013 We capture information on borrowing from program SGs
so can control for this effect.
Research design, implementation, data
26
Program Anticipation Participants may change behaviour in
anticipation of receiving funding Participants informed of when they will receive
funding during initial selection stage. If anticipation resulted in changes in behaviour
that affect the outcomes of interest we would expect to see differences in these outcomes between Groups B and C at midline. Group B would have anticipated receiving funding 6
months before Group C We compare means of outcome variables between
Groups B and C to check for any differences.
Research design, implementation, data
27
Research design, implementation, data
28
Table 6: Comparison of groups B and C, six months after group A is enrolled in REAP and prior to group B’s enrolment in REAP
(1) (2) (3) (4) (5) (6)
Monthly expenditure per capita
Monthly income per capita
Total savings per capita TLU per capita Durable asset index # nights that child has
gone to bed hungry
Panel A: T-test comparison of means of characteristics of group B and C
Group B mean (standard error)
49.906(1.949)
26.263(2.198)
3.683(0.408)
1.031(0.052)
2.014(0.265)
0.565(0.044)
Group C mean (standard error)
51.703(2.191)
23.437(1.354)
3.178(0.544)
1.119(0.057)
2.086(0.272)
0.463(0.058)
P-value from t-test of equality of means of group B and group C
0.540 0.274 0.458 0.254 0.848 0.163
Panel B: F-test from regression of treatment on 6 variables above
F-Stat p-value 0.99 0.432 All monetary values are reported in 2014 USD, PPP terms
Results
Results29
Table 8: Impacts of REAP on income from various sources
(1) (2) (3) (4) (5) (6) (7)
Monthly total income per capita
Monthly income from livestock per
capita
Monthly income from other
agriculture per capita
Monthly income from non-agri trade
per capita
Monthly income from labour per
capita
Monthly income from transfers per
capita
Monthly income from other sources
per capita
426.878*** 36.514 -0.075 393.472*** -7.097 -1.230 0.680(121.624) (103.661) (2.926) (56.946) (14.713) (15.148) (2.652)281.926** 78.971 4.136 173.321*** 26.610 5.185 0.814(131.318) (111.565) (4.959) (32.983) (38.679) (11.029) (1.738)255.660** 38.603 1.774 180.736*** 47.473 -8.489 1.142(107.805) (89.830) (4.947) (36.146) (44.504) (14.591) (2.714)
Time FEs Yes Yes Yes Yes Yes Yes YesLocation FEs Yes Yes Yes Yes Yes Yes YesN 5062 5062 5062 5062 5062 5062 5062R-Squared 0.049 0.043 0.034 0.096 0.026 0.070 0.014Note: Regressions include time and location fixed effects, in addition to control variables for 1) loans taken from REAP savings groups, and 2) the number of REAP businesses in a manyatta. Robust standard errors in parentheses. Standard errors are clustered at the business group level.*Significant at the 10% confidence level, **Significant at the 5% confidence level, ***Significant at the1% confidence level
Results30
Relative change in outcome compared to control group
Monthly total expenditure per capita
1.4% -1.9% -11.1%
Monthly total income per capita
42.0%*** 32.3%** 28.6%**
Savings per capita 54.9%** 61.4%** 163.0%***TLU per capita 2.1% 16.1%* 13.9%Durable asset index 29.3% 25.7%* 37.2%**Nights child has gone to bed hungry in last week
-15.2% -25.2% -16.6%
Figures displayed are percentage changes in outcome variables relative to the control group.*Significant at the 10% confidence level, **Significant at the 5% confidence level, ***Significant at the1% confidence level