The impact of a multipronged approach to poverty alleviation on household outcomes Vilas Gobin 11 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). Motivation 2

 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). Motivation 3

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 Models 4

Study Site 5

Description of the intervention The intervention 6

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 CycleSample Size Group AApr Group BSep Group CApr

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 1 st Grant Group B 1 st Grant Group A 2 nd Grant GroupC 1 st Grant Group B 2 nd Grant Midline survey Endline survey

Randomisation of Program Assignment Research design, implementation, data 9 11/12 4/13 9/13 4/14 Baseline survey Group A 1 st Grant Group B 1 st Grant Group A 2 nd Grant GroupC 1 st Grant Group B 2 nd Grant Midline survey Endline survey

Randomisation of Program Assignment Research design, implementation, data 10 11/12 4/13 9/13 4/14 Baseline survey Group A 1 st Grant Group B 1 st Grant Group A 2 nd Grant GroupC 1 st Grant Group B 2 nd Grant Midline survey Endline survey

Randomisation of Program Assignment Research design, implementation, data 11 11/12 4/13 9/13 4/14 Baseline survey Group A 1 st Grant Group B 1 st Grant Group A 2 nd Grant GroupC 1 st Grant Group B 2 nd Grant 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 expendit ure per capita Monthly food expenditur e per capita Monthly non-food expenditur e 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 Proportio n of children in school Househol d Size # children Married Years of educatio n Business Experien ce Benefitti ng from HSNP Participa ting in CARE VSLA Panel A: T-test comparison of means of baseline characteristics between treatment and control groups Group A mean (standard error) (1.516) (1.188) (0.747) (0.925) (0.344) (0.030) (0.169) (0.016) (0.027) (0.012) (0.079) (0.071) (0.017) (0.060) (0.020) (0.013) (0.012) Group B mean (standard error) (1.402) (1.075) (0.770) (0.933) (0.328) (0.037) (0.189) (0.016) (0.029) (0.012) (0.075) (0.070) (0.016) (0.072) (0.021) (0.013) (0.013) Group C mean (standard error) (1.215) (0.874) (0.648) (0.995) (0.598) (0.034) (0.179) (0.014) (0.029) (0.011) (0.077) (0.070) (0.017) (0.070) (0.021) (0.013) (0.013) P-value from t-test of equality of means of group A and groups B and C combined P-value from t-test of equality of means of group B and group C P-value from t-test of equality of means of group A and group C Panel B: F-test from regression of treatment on 15 variables above (excluding (2) and (3)) Treatment groupControl GroupF-Statp-value AB and C BC AC All monetary values are reported in 2014 USD, PPP terms

Regression Model Research design, implementation, data 13

Results 14 (1)(2)(3)(4)(5)(6) Monthly expenditure per capita Monthly income per capita Total savings per capita TLU per capitaDurable asset index # nights that child has gone to bed hungry (3.127) *** (2.846) (0.557) (0.060) (0.325) * (0.062) Sub-location FEs Yes Observations R-squared Control Group Mean 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

Results 15 (1)(2)(3)(4)(5)(6) Monthly expenditure per capita Monthly income per capita Total savings per capita TLU per capitaDurable asset index # nights that child has gone to bed hungry (3.900) 7.439*** (2.660) 1.680** (0.747) 0.202** (0.102) 0.700* (0.381) ** (0.077) Sub-location FEs Yes Observations R-squared Control Group Mean 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 capitaDurable asset index # nights that child has gone to bed hungry (1.681) 3.809*** (1.109) 2.868*** (0.374) (0.051) 0.398** (0.174) ** (0.040) Sub-location FEs Yes Observations R-squared Control Group Mean 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 [0.241] [0.942] [0.244] [0.0860] Monthly income per capita *** [0.000] *** [0.005] *** [0.001] [0.0146] Total savings per capita [0.111] ** [0.025] *** [0.000] [0.0008] TLU per capita [0.704] ** [0.047] [0.133] [0.1411] Durable asset index [0.303] * [0.067] ** [0.023] [0.825] # nights that child has gone to bed hungry * [0.073] ** [0.014] ** [0.038] [0.7027] q-values are estimated using the Benjamini-Hochberg step-up method

Conclusion Results 18  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 [0.241] [0.942] [0.244] [0.0860] Monthly income per capita *** [0.000] *** [0.005] *** [0.001] [0.0146] Total savings per capita [0.111] ** [0.025] *** [0.000] [0.0008] TLU per capita [0.704] ** [0.047] [0.133] [0.1411] Durable asset index [0.303] * [0.067] ** [0.023] [0.825] # nights that child has gone to bed hungry * [0.073] ** [0.014] ** [0.038] [0.7027] 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. ABC # 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 intervention 22 Table 1: Summary of REAP’s graduation criteria Graduation categoryCriteria 1) Food securitya. no family member goes to bed hungry in the last week b. 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 disbursement b. 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 CriteriaIndicator Proportion of participants that meet graduation criteria at endline Group AGroup BGroup C 1) Food securitya. 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 Rate81.4%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 LocationPopulation* Number of Villages Pre-existent businesses Businesses formed between 2013 and 2014 Non- Program Program April 2013 September 2013 April *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 capitaDurable 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) (1.949) (2.198) (0.408) (0.052) (0.265) (0.044) Group C mean (standard error) (2.191) (1.354) (0.544) (0.057) (0.272) (0.058) P-value from t-test of equality of means of group B and group C Panel B: F-test from regression of treatment on 6 variables above F-Statp-value All monetary values are reported in 2014 USD, PPP terms

Results 29 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 *** *** ( )( )(2.926)(56.946)(14.713)(15.148)(2.652) ** *** ( )( )(4.959)(32.983)(38.679)(11.029)(1.738) ** *** ( )(89.830)(4.947)(36.146)(44.504)(14.591)(2.714) Time FEs Yes Location FEs Yes N 5062 R-Squared Note: 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

Results 30 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 capita54.9%**61.4%**163.0%*** TLU per capita2.1%16.1%*13.9% Durable asset index29.3%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