Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Cash Transfers, Micro-Entrepreneurial Activity and Child Work

611 views

Published on

Evidence from Malawi, Zambia and Tanzania

Valeria Groppo, UNICEF Innocenti
September 10, 2019

Published in: Education
  • Easy and hassle free way to make money online! I have just registered with this site and straight away I was making money! It doesn't get any better than this. Thank you for taking out all the hassle and making money answering surveys as easy as possible even for non-techie guys like me! ★★★ http://t.cn/AieX2Loq
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

Cash Transfers, Micro-Entrepreneurial Activity and Child Work

  1. 1. unite for children Valeria Groppo, Universita’ degli Studi di Firenze September 10th, 2019 de Hoop, J., Groppo, V. and Handa, S. (forthcoming). Cash Transfers, Microentrepreneurial Activity, and Child Work. Evidence from Malawi and Zambia. World Bank Economic Review. Zietz, S. de Hoop, J. and Handa, S. (2018). The Role of Productive Activities in the Lives of Adolescents: Photovoice Evidence from Malawi. Children and Youth Services Review, 86, 246-255. de Hoop, J., Gichane, M. W., Groppo, V. and Simmons Zuilkowski, S. (2019). Cash Transfers, Public Works and Child Activities. Mixed-Method Evidence from Tanzania. Cash Transfers, Micro-Entrepreneurial Activity and Child Work: Evidence from Malawi, Zambia and Tanzania
  2. 2. 2 Cash Transfers in Sub-Saharan Africa Expansion: • Almost 800 million people worldwide enrolled in CTs • In 2014, 40 countries (out of 50) in SSA had some form of CT in place, up from 21 in 2010 • Close to 60+ million people across SSA reached by CTs (~20m in South Africa alone) Program characteristics: • Focus on poverty and vulnerability; role for community • Target households with few adults who are fit to work • Unconditional or soft conditions
  3. 3. 3 “Transfer Project” • Multi-organizational research initiative (UNICEF, FAO, Save the Children UK, and UNC-CH). • Objective to inform design and implementation of CT programs by national governments. • Overall evidence of positive impacts (e.g. food security, household productive activities, education).
  4. 4. 4 Conceptual framework Poor credit constrained households may invest cash transfers in: Child education and health Δ HH production technology & adult labor supply Δ child time allocation: work in HH, work for pay, chores, play, school Δ types of child work: hazards & on the job learning Δ child wellbeing: physical health, mental health, and development Household business
  5. 5. 5 Literature and contribution Extended evidence on child labor and education impacts of cash transfers and labor market programs (Dammert et al. 2018, for a review) • Conditional cash transfers mostly reduce child work • Unconditional cash transfers have more mixed effects Contribution • Detrimental forms of child work
  6. 6. 6 Research questions • Do households rely on children to expand productive activities? • If so, what are the implications for child well- being? ➢ Education ➢ Detrimental forms of child work ➢ Health
  7. 7. 7 Considered cash transfer programs: Malawi’s Social Cash Transfer Program (SCTP) Zambia’s Multiple Category Targeting Program (MCP) Tanzania’s Productive Social Safety Net (PSSN)
  8. 8. 8 Malawi SCTP Zambia MCP Tanzania PSSN Eligibility Ultra-poverty (PMT) & Labor constraints (dependency ratio 4+) • Female/elderly headed with orphans, or • Including disabled • Critical cases Extreme poverty, based on: (1) Geographical targeting (2) community-based targeting (3) PMT Type UCT UCT UCT, CCT, PWP Amount Varying with households size and n. children. Fixed. Varying with n. children • CT, max 38,000 TZS (18 USD) per month • Public Works: 2,300 TZS (1.4 USD) per day, max 60 days in 4 months. Average monthly 2,571 MKW (3.7 USD). 55 ZMW (12 USD). 19,000 TZS (8 USD). Program details
  9. 9. 9 Labor-constrained criteria select unique households: Malawi 0 .01.02.03.04 Density 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 age 0 .02.04.06 Density 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 How old is [NAME] (years)? Malawi SCT Households Rural Ultra-Poor IHS3
  10. 10. 10 Evaluation design and timeline Malawi SCTP Zambia MCP Tanzania PSSN Location Two rural districts (Salima, Mangochi) Two rural districts Eight mainland districts, one district in Zanzibar Village selection 29 villages randomly selected 92 villages 102 villages Targeting Nov 2012 - May 2013 Jan - Sept 2011 2014 - 2015 Baseline June - Oct 2013 Salima: all eligible households in each village; Mangochi: random 125 in each village. Nov - Dec 2011 Random 33 households in each village. May - July 2015 Random 15 to 18 households per village. Random assignm ent of villages November 2013 14 Treatment 15 Control December 2011 46 Treatment 46 Control August 2015 35 Treatment (CT) 26 Treatment (CT & PWP) 41 Control Endline Oct - Nov 2015 Nov - Dec 2014 Apr - Jun 2017
  11. 11. 11 Baseline balance (Malawi, household level) All Attritor Panel C T-C T-C T-C (S.D.) [p] [p] [p] (1) (2) (3) (4) Labor constrained (dependency ratio 4+) 0.882 0.026 -0.060 0.034* (0.323) [0.218] [0.190] [0.083] Natural floor (sand or mud) 0.979 0.002 0.005 0.003 (0.144) [0.795] [0.871] [0.742] Natural roof (grass or iron sheets) 0.989 -0.020 -0.045 -0.018 (0.104) [0.362] [0.441] [0.381] Natural walls (grass or mud) 0.726 -0.012 0.148** -0.026 (0.446) [0.794] [0.036] [0.570] Unimproved toilet 0.580 -0.006 -0.025 -0.004 (0.494) [0.880] [0.756] [0.925] Owns any agricultural assets 0.898 0.001 -0.008 0.002 (0.303) [0.980] [0.877] [0.927] Last year's harvested maize lasted <4 months 0.525 0.009 0.000 0.010 (0.500) [0.805] [0.998] [0.792] Members have on average only one meal a day 0.207 0.018 0.040 0.016 (0.405) [0.576] [0.401] [0.615] Received any assistance past 12 months 0.717 0.005 0.044 0.001 (0.451) [0.925] [0.442] [0.982] N households 1,456 2,759 219 2,540
  12. 12. 12 Baseline balance (Malawi, child level) All Attritor Panel C T-C T-C T-C (S.D.) [p] [p] [p] (1) (2) (3) (4) Livestock herding for the household 0.013 0.002 0.007 0.002 (0.111) [0.598] [0.323] [0.755] Household non-agricultural business 0.049 -0.008 -0.022 -0.006 (0.215) [0.766] [0.745] [0.770] Paid work outside the household 0.086 -0.001 -0.048** 0.006 (0.280) [0.933] [0.016] [0.615] Collecting water or firewood 0.434 0.008 -0.012 0.009 (0.496) [0.719] [0.721] [0.686] Taking care of children, cooking / cleaning 0.353 0.007 -0.015 0.008 (0.478) [0.837] [0.718] [0.815] Joint test of orthogonality [0.214] [0.636] [0.490] N children 6,733 927 5,806
  13. 13. 13 Baseline balance, summary (panel sample) Malawi Zambia Tanzania Number of household characteristics 25 22 26 Number of child characteristics 10 7 4 Number T-C significant 2 6 3 Joint test of orthogonality [p] [0.490] [0.256] NA N children 5,806 3,999 3,516
  14. 14. 14 Differential child attrition Dependent variable: Attrited at endline Malawi Zambia Tanzania (1) (2) (3) Treatment 0.019 0.027 0.013 [0.128] [0.166] [0.467] N 6,733 4,816 4,246 Control average 0.128 0.156 0.168 Notes: Regressions include fixed effects for the stratification variable. Standard errors are clustered at the village level. P-values in brackets, estimated clustering at the unit of randomization level (obtained with wild bootstrap method for Malawi and Zambia). *p <0.1, **p <0.05, ***p < 0.01. Sources: Malawi Economic, Health and Demographic Survey (MEHDS), Zambia Multiple Category Targeting Programme (MCP), Tanzania PSSN REPOA Evaluation.
  15. 15. 15 Econometric model Comparison of treatment to control at endline (OLS) Outcomeiv = 1 + 2Tv + 3'Xiv + iv (1) Controls: stratification variable, age, sex, enumerator (Malawi, Zambia); determinants of transfer size Precision: wild cluster bootstrap (Malawi, Zambia) Samples: • Panel children age <18 at endline • Panel households with sampled children Robustness (attrition): weights (IPW), Lee bounds, Horowitz-Manski bounds
  16. 16. 16 Outcome variables • Household and adult productive activities • Child engagement in economic activities and household chores (extensive and intensive margin) • Elements of child labor for elimination: ➢ Excessive hours (International conventions, ILO 2008) ➢ Hazardous work (MICS module, Malawi and Tanzania only) • Additional child well-being indicators: ➢ School participation ➢ Illness and injury
  17. 17. 17 Household productive activities Dependent variables: Owns any livestock Cultivated any crop last season Sold any crop Hired any person last season Any non- farm business (1) (2) (3) (4) (5) Malawi Treatment 0.339*** 0.019*** 0.107*** 0.048*** 0.115*** [0.000] [0.000] [0.001] [0.000] [0.006] Zambia Treatment 0.269*** 0.088*** 0.160*** 0.180*** 0.022 [0.000] [0.000] [0.000] [0.000] [0.382] Tanzania Treatment 0.175*** 0.044 NA NA -0.023 [0.000] [0.194] [0.324] Observations, Malawi 2,540 2,540 2,540 2,540 2,540 Observations, Zambia 1,917 1,917 1,917 1,917 1,917 Observations, Tanzania 1,307 1,307 NA NA 1,307 Control average, Malawi 0.306 0.973 0.201 0.018 0.205 Control average, Zambia 0.448 0.878 0.350 0.053 0.089 Control average, Tanzania 0.428 0.782 NA NA 0.159
  18. 18. 18 Adult work Dependent variables: Any economic activities Agricultural work Livestock herding Non- agricultural business Paid work outside the household (1) (2) (3) (4) (5) Malawi Treatment 0.022*** 0.120*** 0.125*** 0.034*** -0.119*** [0.411] [0.000] [0.000] [0.071] [0.001] Zambia Treatment 0.028*** 0.051*** 0.027*** 0.033*** -0.064** [0.025] [0.000] [0.014] [0.044] [0.014] Tanzania Treatment 0.025 0.045 0.119*** -0.010 -0.018 [0.253] [0.157] [0.000] [0.501] [0.337] Observations, Malawi 3,064 3,064 3,064 3,064 3,064 Observations, Zambia 3,381 3,381 3,381 3,381 3,381 Observations, Tanzania 2,777 2,777 2,777 2,777 2,777 Control average, Malawi 0.673 0.373 0.088 0.072 0.477 Control average, Zambia 0.909 0.0710 0.033 0.015 0.321 Control average, Tanzania 0.787 0.703 0.358 0.125 0.248
  19. 19. 19 Child work, participation (past week) Dependent variables: Any economic activities Agricultural work Livestock herding Non- agricultural business Paid work outside the household (1) (2) (3) (4) (5) Malawi Treatment 0.034 0.063** 0.068*** 0.002 -0.061*** [0.133] [0.024] [0.000] [0.715] [0.001] Zambia Treatment 0.055** NA 0.039*** 0.034* 0.001 [0.014] [0.007] [0.056] [0.889] Tanzania Treatment 0.032 0.025 0.040** 0.014 0.011 [0.180] [0.304] [0.040] [0.371] [0.500] Observations, Malawi 5,806 5,806 5,806 5,806 5,806 Observations, Zambia 3,999 NA 3,999 3,999 3,999 Observations, Tanzania 3,516 3,516 3,516 3,516 3,516 Control average, Malawi 0.302 0.144 0.038 0.019 0.188 Control average, Zambia 0.180 NA 0.029 0.139 0.045 Control average, Tanzania 0.283 0.248 0.099 0.029 0.042
  20. 20. 20 Child work, participation (past year) Dependent variables: Any economic activities Agricultural work Livestock herding Non- agricultural business Paid work outside the household (1) (2) (3) (4) (5) Malawi Treatment NA 0.072*** NA NA -0.035 [0.000] [0.153] Zambia Treatment NA 0.088*** NA NA -0.045 [0.000] [0.204] Tanzania Treatment -0.006 -0.002 0.038** -0.005 -0.019** [0.743] [0.923] [0.016] [0.016] [0.019] Observations, Malawi NA 5,806 NA NA 4,682 Observations, Zambia NA 3,999 NA NA 682 Observations, Tanzania 3,516 3,516 3,516 3,516 3,516 Control average, Malawi NA 0.607 NA NA 0.431 Control average, Zambia NA 0.710 NA NA 0.321 Control average, Tanzania 0.361 0.329 0.156 0.011 0.047
  21. 21. 21 Child work, hours (past week) Dependent variables: Any economic activities Agricultural work Livestock herding Non- agricultural business Paid work outside the household (1) (2) (3) (4) (5) Malawi Treatment 0.030 0.337* 0.146*** 0.057 -0.510*** [0.945] [0.095] [0.000] [0.122] [0.040] Zambia Treatment 0.379** NA 0.116*** 0.246* 0.018 [0.029] [0.000] [0.066] [0.804] Tanzania Treatment 0.023 0.106 -0.047 0.105** -0.068 [0.958] [0.768] [0.719] [0.033] [0.463] Observations, Malawi 5,806 5,806 5,806 5,806 5,806 Observations, Zambia 3,999 3,999 3,999 3,999 3,999 Observations, Tanzania 3,516 3,516 3,516 3,516 3,516 Control average, Malawi 2.613 0.960 0.113 0.106 1.434 Control average, Zambia 0.859 NA 0.060 0.566 0.233 Control average, Tanzania 4.183 3.072 0.787 0.078 0.296
  22. 22. 22 Schooling and child labor Dependent variables: Currently attending school Attends regularly Highest grade of education Excessive hours Any hazards (1) (2) (3) (4) (5) Malawi Treatment 0.084** 0.140*** 0.299 0.013 0.044** [0.016] [0.000] [0.122] [0.375] [0.026] Zambia Treatment 0.087*** 0.092** -0.046 0.050*** NA [0.002] [0.041] [0.522] [0.004] Tanzania Treatment 0.052** -0.003 0.174** 0.021 0.009 [0.028] [0.926] [0.044] [0.358] [0.569] Observations, Malawi 11,612 11,612 11,612 5,806 5,806 Observations, Zambia 7,998 7,642 7,998 3,999 NA Observations, Tanzania 7,032 1,900 3,516 3,516 3,516 Control average, Malawi 0.820 0.728 3.230 0.250 0.250 Control average, Zambia 0.688 0.507 3.724 0.243 NA Control average, Tanzania 0.676 0.834 2.968 0.263 0.138
  23. 23. 23 Summary of impacts (quantitative) Malawi SCTP Zambia MCP Tanzania PSSN Any economic activities No change ↑ No change Farm work for the household (livestock or non-livestock) ↑ ↑ ↑ Household non-farm business No change ↑ No change Paid work outside the household ↓ No change ↓ Excessive hours No change ↑ No change Hazardous work ↑ NA No change Illness/injuries ↓ No change No change School attendance ↑ ↑ ↑
  24. 24. 24 Participant in UNICEF Innocenti research, Malawi. Photovoice ▪ What do you See here? ▪ What is really Happening here? ▪ How does this relate to Our lives? ▪ Why does this problem / phenomenon exist? ▪ What can / should we Do about it?
  25. 25. 25 • All respondents perceive school attendance as important and identify child work as a potential deterrent to school attendance. • Caregivers prioritize work only in case of real and urgent need • Caregivers experience feelings of guilt and regret when prioritizing work “I feel guilty that I’m killing the child’s future… in March I got very sick […]. Him being the eldest at home, he was supposed to do everything alone and when it’s too much, he could miss classes.” [Mangochi V3 CG 2] Photo Credit: Participant in UNICEF Innocenti research, Malawi.
  26. 26. 26 Photo credit: Participant in UNICEF Innocenti research, Tanzania Cash transfers helped families to pay for schooling costs: When a household receives PSSN funds one of the conditions is to make sure that they spend the money in buying school uniforms, shoes, pens, exercise books and other school needs. The fact that PSSN has taken care of school requirements has reduced the burden on children. Children now get time to rest and revise what they have been taught in school. (Caregiver FGD, Tanzania)
  27. 27. 27 Photo credit: Participant in UNICEF Innocenti research, Malawi • There may be longer-term implications for child health. • Child exposure to work- related hazards was a concern across communities. • Commonly mentioned hazards: animal bites, use of sharp tools, heat and fumes. Harvesting the sweet potatoes involves the digging of the ridges that to get to the tuber crop. This make the dust and cause him to get sick with a cough. (Caregiver, Malawi)
  28. 28. 28 Photo Credit: Participant in UNICEF Innocenti research, Malawi Youth and caregivers welcomed the shift of child work from outside to inside the household: PSSN has re-shaped children’s contributions to the livelihood of the household. When I get PSSN money instead of doing wage labor with my children, I work in my own farms. To me this is a good thing because working in other people’s farm is something that we hate, but sometimes we have to do it in order to get food. (49-year-old female caregiver, Tanzania)
  29. 29. 29 Photo credit: Participant in UNICEF Innocenti research, Malawi The shift in economic activities from outside the home to within may have resulted in a safer environment for children. I have seen children abused by landlords when engaged in casual works in the farms example during weeding activities, the land lord abuses children and sometimes refuse to pay them their money after they have completed the work (Youth FGD, Tanzania)
  30. 30. 30 Conclusions • Expansion of the household enterprise  increased child engagement in farming, including some forms of harmful child labor. • Child engagement in work outside the household mostly declined. • School participation improved. • Beneficiaries do not necessarily perceive child engagement in economic activities as a negative. It can also be a source of pride and an opportunity for learning. • Heterogeneity across countries (context, program design). • Future research: child time use (sleep, play), learning.
  31. 31. 31 Policy implications • Closely monitor impacts of cash transfer programs on child time use, including not only child work but also school participation, play and sleep. • In case of impacts on household entrepreneurial activities, complementary policies should be considered to enhance positive impacts and limit potentially negative effects on child labor. ➢ Information campaigns to emphasize the importance of child schooling. ➢ We would encourage testing the effectiveness of such interventions alongside cash transfer programs in similar settings.
  32. 32. 32 • Transfer Project website: www.cpc.unc.edu/projects/transfer • UNICEF Office of Research – Innocenti: https://www.unicef- irc.org/research/273/ • Facebook: https://www.facebook.com/TransferProject • Twitter: @TransferProjct For more information Ghana, credit: Ivan Griffi

×