Can unconditional cash transfers help households graduate from poverty long-term
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Ashua Handa (UNC) presented long-term evidence of the impact of cash transfers in Zambia at Oxford’s Center for the Study of African Economies Conference in March 2019.
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Can unconditional cash transfers help households graduate from poverty long-term
- 1. In search of the holy grail: Can unconditional cash transfers graduate households out of poverty? Sudhanshu Handa & Gustavo Angeles - UNC Gelson Tembo – UNZA and Palm Associates Luisa Natali - UNICEF Office of Research
- 2. Exploit reform of cash transfer system to see what happened to households who were removed • From 2004-2014 Zambian government experimented with different cash transfer models • Two new models introduced in 2010 and 2011, accompanied by RCTs • Child Grant Program (CGP): evaluation period 2010-2014 • Multiple Category Grant: evaluation period 2011-2014 • In 2014 policy decision to merge all programs into one social cash transfer program (SCT), scale-up began in 2015, case load now 550k • What happened to those not eligible for the new program?
- 3. An exciting period in Zambia for cash transfers GoZ budget contribution went from $5m to $35m in 2014 and $45m in 2015 0 50000 100000 150000 200000 250000 2003 2005 2007 2009 2011 2013 2015 2017 Households Reached by Cash Transfers in Zambia CGP MCP follow-ups CGP MCP baselines CGP MCP follow-ups GoZ take over Election
- 4. 4
- 5. 2,519 households February 2011 Treatment arm Control arm 1,153 households 1,145 households 1,221 households 1,179 households 1,221 households 1,238 households 1,197 households 1,226 households 1051 households 1087 households September-October 2014 48-month follow-up September-October 2017 84-month follow-up October-November 2010: Baseline survey First transfer in treatment communities October-November 2012: 24-month follow-up June-July 2013: 30-month follow-up October-November 2013: 36-month follow-up 797 out (75%) 841 out (77%) CGP Impact Evaluation
- 6. 6 Total consumption pc [24m] [36m] Food security scale (HFIAS) [24m] [36m] Overall asset index [24m] [36m] Relative poverty index [24m] [36m] Incomes & Revenues index (SD) [24m] [36m] Finance & Debt index (SD) [24m] [36m] Material needs index (5-17)[24m] [36m] Schooling index (11-17) [24m] [36m] Anthropometric index (11-17) [24m] [36m] -.2 0 .2 .4 .6 .8 Effect size in SDs of the control group Endlines 1&2 (24&36-months) at a glance Intent-to-Treat effects (CGP) - indices Large effects at 36m on productive and protective Domains Big implied multiplier JDE Vol. 133 (2018)
- 7. Follow-up study details • Went back to CGP households in 2017 (Kalabo, Shangombo, Kaputa) • • Kaputa: Retargeting implemented in 2015 in study sites • Shangombo & Kalabo: implemented in 2017 in study sites • But households were also being graduated due to age of child! • Mean months of exposure is 45 (Jan 2011 – Sep 2014) • Control households who were ineligible for SCT received ZM500
- 8. -Mean exposure 45 months -Last group were paid in Q1 of 2017 -Survey done Q4 2017
- 9. Retargeting and thus exposure varied across districts
- 10. Research questions • What happened to households who stopped receiving cash? • Compare original CGP households who were ineligible vs original control households who were also ineligible • What happened to original controls who became eligible? • Compare them to original treatment households who remained eligible • How does this inform broader ‘graduation from poverty’ debate? • Are initial impacts sustained by everyone? Are there high fliers who can help us understand graduation from poverty?
- 11. 11 Total consumption pc Food security scale Overall asset index Relative poverty index Incomes & Revenues index (SD) Finance & Debt index (SD) Material needs index (5-17) Schooling index (11-17) -.2 0 .2 .4 .6 .8 Effect size in SDs of the control group At 36- and 84-months Intent-to-Treat effects (CGP Ineligibles)36m 84m What happened to households who were no longer eligible? Differences no longer significant These are the purple effect sizes and Cis, all include 0
- 12. What happened to those who were removed from the CGP? Consumption, food security
- 13. What happened to those who were removed from the CGP? Subjective well-being
- 14. What happened to those who were removed from the CGP? Productive and economic indicators
- 15. Conclusions on what happened to those who were removed from the cash transfer? • Gradual convergence on protective outcomes, not a steep drop • Slight decline in original T, and slight increase in C • Remember C also received ZM500 lump-sum, which could explain their gradual increase • Convergence slower for productive indicators • Assets, agricultural input spending, area cultivated • Results not sensitive when accounting for length of exposure • Length of exposure and separating out Kaputa
- 16. How about new recipients—did they catch-up immediately? Yes! All 84m effects include 0
- 17. Comparing new SCT recipients with eligible CGP households: Complete catch-up
- 18. Do new recipients catch-up? Productive indicators
- 19. Search for the Holy Grail… • Are there some households that maintained their consumption after leaving the program? HIGH FLYERS • Who are they? What did they do? What secrets do they hold about ‘graduating’ out of poverty? • How do we define a HIGH FLYER? (Work in progress) • Two examples
- 20. Among Original T ineligibles, identify consumption trajectory suggesting ‘high flyer’
- 21. The Holy Grail
- 22. Any individual features that distinguish high flyers? High Fliers Others Household size 5.7 5.7 # of able-bodied members 1.93 1.90 Highest grade of CGP recipient 6 4 Individual FISP receipt 2.50 1.75 Distance to nearest market (km) 2.3 2.7 Impatient (never wait for future money) (%) 11 16 Life will be better in 3 years (%) 72 62
- 24. Any community level factors to explain the high fliers? 0 10 20 30 40 50 60 70 FISP Seed Support Ag Extension Microfinance Skills Training Kalabo Community features (%) High Flyer Other
- 25. Another definition of HIGH FLYER: Top quartile of consumption distribution at 36m and 84m (~10% of households fall in this category) High Fliers Others Household size 4.9 5.8 # of able-bodied members 1.8 1.9 Highest grade of CGP recipient 6 4 Individual FISP receipt 2.2 2.0 Distance to nearest market (km) 2.4 2.4 Impatient (never wait for future money) (%) 16 16 Life will be better in 3 years (%) 68 62
- 27. Any community level factors to explain the high fliers? 0 10 20 30 40 50 60 70 FISP Seed Support Ag Extension Microfinance Skills Training Kalabo Community features (%) High Flyer Other
- 29. Preliminary conclusions and next steps • What happens to those who left program? • Gradual convergence with original C group, slower for assets/productive • New entrants to SCT converge quickly to long-time recipients • Households are ultra-poor, transfer size is ~15% of consumption • Search for the Holy Grail • Use machine learning, group-based trajectory model, other definitions • See if patterns emerge on who they are, what they did, context • High flyers in the original control group too!?!
Notes de l'éditeur
- Gradual convergence, none of the differences are statistically significant at 84.
- Clear convergence, slight decline or stability in original T group, catch-up among original C group
- Here the original T do remain above C, but again, none of the differences are statistically significant. Convergence seems slower for productive indicators.
- Now look at the eligible. Did the new beneficiaries catch-up immediately? Did those who were on the CGP and now SCT continue to stay ahead? Again, despite what the graphs show, none of these differences are statistically significant. So the new beneficiaries have caught up. GELSON: FOR ARUSHA, WE NEED TO FOCUS ON HIGH FLIERS AND GRADUATION. SO THIS PART CAN BE IGNORED. I AM CUTTING THE HIGH FLIERS IN DIFFERENT WAYS AND WILL ADD RESULTS FROM ONE OTHER APPROACH
- None of these differences are statistically significant, though trend lines are negative for land cultivated
- These are ‘mixture models’ , or group-based trajectory models. The data identified four groups, group 1 is the outlier, where consumption is rising, 6% of the sample
- Are these households different in any way? Not really, they live a bit closer to market, and have slightly more schooling (complete primary)
- How about where they live, the CWACs? These variables are CWAC level indicators, there is some indication that CWACs where high fliers live have more complementary activities. Also note that these high fliers are more likely to come from Kalabo! Is this the famous road?
- Are these households different in any way? Not really, they live a bit closer to market, and have slightly more schooling (complete primary)
- How about where they live, the CWACs? These variables are CWAC level indicators, there is some indication that CWACs where high fliers live have more complementary activities. Also note that these high fliers are more likely to come from Kalabo! Is this the famous road?