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Conditional cash transfers, child work and
schooling: mixed methods evidence from the
United Republic of Tanzania
WIDER Seminar Series with Valeria Groppo
Discussant Milla Nyyssölä
Throughout this seminar your
microphone will be muted, please
send questions for the discussion
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WIDER’s YouTube channel.​
Conditional Cash Transfers, Child Work and
Schooling: Mixed Methods Evidence from the
United Republic of Tanzania
J. de Hoop, M. W. Gichane, V. Groppo, S. Simmons
Zuilkowski, on behalf of Tanzania’s Productive Social Safety
Net (PSSN) Youth Evaluation Team
Valeria Groppo, WIDER Seminar Series, 20 January 2021
Outline
for every child, answers
• Introduction
• Literature & contribution
• Productive Social Safety Net (PSSN)
• Evaluation timeline
• Methodology
• Results
• Conclusions
3
Introduction
Millions
Cirillo, C., & Tebaldi, R. (2016). Social Protection in Africa: Inventory of non-contributory programmes, International Policy Centre for Inclusive
Growth, United Nations Development Programme, Brasília.
African non-contributory social protection programmes by start date
4
Introduction – Research question
Cash transfer programmes contributed to poverty
reduction and human capital development (e.g. Bastagli
et al. 2016), but can they also reduce child labour?
With funding from the United States Department of Labor,
we evaluated the impact of the United Republic of
Tanzania’s Productive Social Safety Net (PSSN).
5
Introduction - context
The prevalence of child labour is highest
in Africa, at 19.6% (ILO 2017)
• Nearly 30% of Tanzanian children engage
in child labour (ILO 2016)
Tanzania Social Action Fund (TASAF)
TASAF I: 2000-2005
TASAF II: 2005-2013
TASAF III/PSSN
• Objectives: increase income and
consumption, improve ability to cope
with shocks, improve education
• Components, during study period:
(1) cash transfer; (2) public works
• Coverage: national, 15% population (6
million people) in 2016 Tanzania, Handeni district, endline data collection, 2017.
6
Introduction - Mechanisms
Theoretically, when households receives cash transfers:
• Income effect increases schooling and reduces the demand for child work.
• However, household productive investments may also increase, which may
increase the demand for child work.
Children may be requested to work for the household, in order to
compensate for adult time spent in public works.
 The impact of PSSN on child work and schooling is a priori undetermined.
7
Literature – Cash transfers
Child work
CCTs tend to reduce both child participation and hours in economic activities and
household chores (Dammert et al. 2018, de Hoop & Rosati 2014, Fiszbein et al.
2009, Skoufias et al. 2001).
UCTs had more mixed impacts:
• Child participation in economic activities declined in some settings, e.g. Ecuador (Edmonds &
Schady 2012), but remained unchanged in others, e.g. Malawi (de Hoop et al. 2019).
• In some instances, child work increased, including hazardous work or excessive hours (de
Hoop et al. 2019).
Schooling
Both CCTs and UCTs improve school enrolment and attendance (Baird et al. 2014,
Handa et al. 2018).
8
Literature – Public works
The evidence is mixed (Dammert et al. 2018):
• Some studies finding reductions in child work (e.g. Hoddinott et al.
2009)
• Others finding increased child participation in household chores or
school absenteeism (e.g. Rosas & Sabarwal 2016).
9
Our contribution
• We use mixed methods
• Cluster Randomized Controlled Trial (cRCT)
• In-Depth Interviews and Focus Group Discussions with children and caregivers
• Our study adds to the relatively thin literature on conditional cash
transfers in Africa
• We analyze programme effects on a range of work types, including
exposure to hazards
10
The PSSN programme
11
The evaluation
(UNICEF Innocenti –
Policy Research for
Development,
REPOA)
Tanzania, Handeni district, endline data collection, 2017. 12
Timeline
2014-2015
Targeting &
location selection
• Eligibility:
extreme
poverty; ‘ability
to work’.
• Eight mainland
PAAs, plus one
in Zanzibar
• 102 villages
May-
July 2015
Baseline
Survey
(quant)
• Random
selection of
15-18
households
per village
August 2015
Random
assignment
(lottery)
• PSSN villages
(61 in total, of
which 35 cash
only, 26 cash
& public
works)
• Control
(delayed
treatment, 41
villages)
September-
October 2015
First cash
transfer in PSSN
villages
April-
June 2017
Endline
Survey
(quant)
September
-October
2017
Qualitative
data
collection
13
Sample and attrition
Children aged 3-15 years at baseline (5-17 at endline)
Of the 4,246 children observed at baseline, 3,516 (83%) were observed
at endline.
• Attrition is not significantly different in control vs treatment villages.
Most baseline characteristics are balanced in the full sample at baseline,
as well as in the reduced panel sample at endline.
• One main exception (Female indicator)
14
Baseline balance
Full baseline sample Attritor Panel
Control mean Difference T-C Difference T-C Difference T-C
(S.D.) [p-value] [p-value] [p-value]
(1) (2) (3) (4)
Panel A. Household level (determinants of transfer size)
Any child <5 years of age 0.581 -0.010 -0.004 -0.011
(0.494) [0.707] [0.956] [0.684]
Any child aged 5–17 years 0.963 -0.000 0.042 -0.004
(0.190) [0.981] [0.307] [0.665]
Number of children attending primary school 1.526 -0.105 0.105 -0.126
(1.284) [0.229] [0.547] [0.177]
Number of children attending secondary school 0.390 0.030 0.320** -0.003
(0.805) [0.581] [0.011] [0.960]
N (households) 587 1,460 153 1,307
15
Baseline balance – Cont’d
Full baseline sample Attritor Panel
Control mean Difference T-C Difference T-C Difference T-C
(S.D.) [p-value] [p-value] [p-value]
(1) (2) (3) (4)
Panel B. Household level (productive activities)
Owned/operated any land last growing season 0.751 0.042 0.083 0.038
(0.433) [0.202] [0.207] [0.268]
Owns any livestock 0.576 -0.030 0.087 -0.044
(0.495) [0.406] [0.228] [0.250]
Owned/operated any non-farm business past 12 months 0.250 0.011 0.112* 0.000
(0.434) [0.667] [0.075] [0.996]
N (households) 587 1,460 153 1,307
Panel C. Child level
Age 8.792 0.086 0.278 0.062
(3.694) [0.453] [0.416] [0.614]
Female 0.488 0.023 -0.003 0.028*
(0.500) [0.115] [0.943] [0.079]
Literacy 0.371 0.007 0.016 0.007
(0.483) [0.737] [0.681] [0.760]
Attends school 0.505 0.008 0.020 0.008
(0.500) [0.700] [0.650] [0.739]
Panel D. Child attrition
Attrited 0.168 0.013
(0.374) [0.467]
16
Methods - Quantitative
Information on most child activities is captured at endline only. So, we use cross-sectional models:
Outcomeiv = 1 + 2Pv + 3'Xiv + iv
Outcomeiv : outcome variable for child i living in village v
Pv : equal to one if PSSN village (cash transfers only, or cash transfers & public works)
Xiv : baseline control variables (gender, age, determinants of transfer size, district fixed effects)
iv : error term
2 : overall impact of the PSSN on child outcomes
OLS with robust standard errors clustered at the village level.
Given non-universal take-up, estimated impacts should be interpreted as intent-to-treat effects.
17
Methods - Qualitative
• Three purposively selected mainland PAAs, plus the one Zanzibar PAA.
• Two villages for each PAA (one from the treatment, one from control).
• IDIs and FGDs with children and with caregivers:
Children’s FGDs used a photovoice technique
In each village, six IDIs (three with children 11-17 years old, three with
caregivers, all selected from the quantitative sample) and two FGDs (one with
children, one with caregivers).
Consent provided by all caregivers, as well as from the caregivers of children
who participated in IDIs and FGDs. All children provided assent.
18
Results
Tanzania, Handeni district, endline data collection, 2017.
19
PSSN determined a change in child work type
-0.006
-0.002
0.038**
-0.005
-0.019**
-0.04
-0.02
0
0.02
0.04
Any
economic
activities
Farm work
for the
household
(excl.
livestock)
Livestock
herding for
the
household
Household
non-farm
business
Paid work
outside the
household
N = 3,516 children
aged 5-17 years
*p <0.1, **p <0.05
20
Qualitative insights on PSSN & child work
“The PSSN programme has given
children time to rest for some
days without involvement in
casual works. In the previous
time, children were forced to
work every day or every week so
as to get their needs, but now as
we are assured of providing
them with school requirement
so they may spend even a week
without working in casual
labours.”
- Caregiver FGD participant
“On one hand, I get more time
now. If I want, I can spend more
time because we have labourers
who work in our farms, my
grandfather use PSSN money to
employ casuals [day labourers]
to help us in farming. On the
other hand, [PSSN] money has
reduced my time to search for
casual works because if I fail to
get money, I can use [PSSN]
money. I was spending one day
per week for casual works before
PSSN, but after PSSN I spend one
day per month on casual works.”
- Child FGD participant
“PSSN has not changed
what I have been doing
before. I am still doing
charcoal business, herding
cattle and sometimes selling
sisal poles. The activities
have neither increased nor
decreased because of PSSN.”
- 15-year-old-boy
21
0.009
-0.002
-0.032
-0.04
-0.02
0
0.02
0.04
Any
hazard
Ever been
hurt or
suffered
from
illnesses/in
juries
Number of
days of main
activities
missed due to
most serious
illness/injury
PSSN did not affect child hazards, health
N = 3,516 children
aged 5-17 years
22
PSSN impacts on hazards, by type
-0.003
0.002
0.007
0.014
0.002 0.003*
0.001 0.001*
-0.03
-0.01
0.01
0.03
0.05
Carrying
heavy
loads
Working
with
dangerous
tools
Dusts,
fumes
or
gases
Loud
noise or
vibrations
Work at
night
Extreme
tempera
tures
Bullying,
intimidation
or violence
Bars, hotels or
places of
entertainment
N = 3,516 children
aged 5-17 years
*p <0.1, **p <0.05
23
Work outside the household is riskier
“I have seen children abused by
landlords when engaged in
casual works in the farms,
example during weeding
activities, the landlord abuses
children and sometimes refuse
to pay them their money after
they have completed the work.”
- Child FGD participant
“When children engaged in
casual work like cultivation
without supervision from their
parents, they may be influenced
by other children on bad
behaviour like theft, disobedient
or alcoholism, which is not
good.”
- Caregiver FGD participant
24
PSSN improved education outcomes
0.052** 0.048**
0.174**
-0.003
0.049**
-0.017
-0.04
0
0.04
0.08
0.12
0.16
0.2
Currently
attending
school
Can read
and write
Highest
grade
completed
Attended
school
regularly,
past week
Spent at
least one
hour
studying,
past week
Dropped out of
school between
baseline and
endline
N = 3,516 children
aged 5-17 years
*p <0.1, **p <0.05
25
Qualitative insights on PSSN & schooling
“The casual labourers that I am
employing have given a relief to
my [grand]children. As they
spend less time in farming
activities now than it used to be
before PSSN, they can use that
time to work for their own
consumption or concentrate on
studies.”
- Caregiver
“There is no such a segregation
as children from well-off
household and poor households.
All children are equal now; they
all get uniforms and are smart in
school uniforms.”
- Caregiver FGD participant
“I have to work in
grandmother’s business. I
lose concentration in
academics because I have to
spend time in the business
instead of studying. I get
tired, particularly during
examination time.”
- Child FGD participant
26
Impacts on child work, by gender
Any
economic
activities
Farm work
for the
household
(excl.
livestock)
Livestock
herding for
the
household
Household
non-farm
business
Paid work
outside the
household
N = 3,516 children
(1,728 female,
1,788 male)
aged 5-17 years
*p <0.1, **p <0.05
0.004 0.005
0.035*
-0.002
-0.008
-0.016
-0.008
0.04**
-0.007
-0.031***
-0.04
-0.02
0
0.02
0.04
0.06
Female
Male
27
Education impacts, by gender
Currently
attending
school
Can read
and write
Highest
grade
completed
Attended
school
regularly,
past week
Spent at
least one
hour
studying,
past week
Dropped out of
school between
baseline and
endline
0.031
0.05
0.158*
-0.003
0.032
-0.006
0.072 0.048
0.183**
-0.007
0.065***
-0.028
-0.06
-0.02
0.02
0.06
0.1
0.14
0.18
Female
Male
N = 3,516 children
(1,728 female,
1,788 male)
aged 5-17 years
*p <0.1, **p <0.05
28
Conclusions
for every child, answers
• PSSN had beneficial effects on child work, with substitution effects
stronger for male and older children
• PSSN improved child education
• Important to monitor unintended effects of programmes that expand
household productive capacity
• Important to use mixed methods
• Complementary interventions could be considered to enhance
education improvements
• Information to caregivers on the importance of education and the risks
related to child labour
29
Thank you!
Social Policy Specialist
UNICEF Office of Research - Innocenti
vgroppo@unicef.org
30
References
Baird, S., Ferreira, F. H. G., Özler, B., & Woolcock, M. (2014). Conditional, unconditional and everything in between: A systematic review of the
effects of cash transfer programmes on schooling outcomes. Journal of Development Effectiveness, 6(1), 1–43.
Bastagli, F., J. Hagen-Zanker, L. Harman, V. Barca, G. Sturge, T. Schmidt, & L. Pellerano. 2016. Cash Transfers: What Does the Evidence Say? A
Rigorous Review of Programme Impact and of the Role of Design and Implementation Features. Overseas Development Institute, London.
Cirillo, C., & Tebaldi, R. (2016). Social Protection in Africa: Inventory of non-contributory programmes, International Policy Centre for Inclusive
Growth, United Nations Development Programme, Brasília.
Dammert, A. C., de Hoop, J., Mvukiyehe, E., & Rosati, F. C. (2018). Effects of public policy on child labor: Current knowledge, gaps, and
implications for program design. World Development, 110, 104–123.
de Hoop, J., Groppo, V., & Handa, S. on behalf of the Malawi Social Cash Transfer Program and the Zambia Multiple Category Targeted Program
study teams (2019). Cash transfers, microentrepreneurial activity, and child work: Evidence from Malawi and Zambia. The World Bank
Economic Review.
de Hoop, J., & Rosati, F. C. (2014). Cash transfers and child labor. World Bank Research Observer, 29(2), 202–234.
Fiszbein, A., Schady, N., Ferreira, F. H. G., Grosh, M., Keleher, N., Olinto, P., & Skoufias, E. (2009). Conditional cash transfers: Reducing present
and future poverty (World Bank Policy Research Report No. 47603). World Bank, Washington, D. C.
Handa, S., Daidone, S., Peterman, A., Davis, B., Pereira, A., Palermo, T., & Yablonski, J. (2018). Myth-busting? Confronting six common
perceptions about unconditional cash transfers as a poverty reduction strategy in Africa. World Bank Research Observer, 33(2), 259–298.
International Labour Organization. (2016). Tanzania mainland national child labour survey 2014: Analytical report. ILO, Geneva.
International Labour Organization. (2017). Global estimates of child labour: Results and trends, 2012-2016, ILO, Geneva.
Schultz, T. P. (2004). School subsidies for the poor: evaluating the Mexican Progresa poverty program. Journal of Development Economics, 74(1),
199–250.
Skoufias, E., Parker, S. W., Behrman, J. R. & Pessino, C. (2001). Conditional cash transfers and their impact on child work and schooling:
Evidence from the Progresa program in Mexico. Economia, 2(1), 45–96.
31
Acknowledgments
The authors are grateful to the teams that implemented the impact evaluation in the United Republic of Tanzania and to all of the external
partners who made this work possible. The Productive Social Safety Net youth evaluation team comprises Tia Palermo (co-principal
investigator), Amber Peterman, Leah Prencipe, Lisa Hjelm, Valeria Groppo and Jacobus de Hoop (UNICEF Office of Research – Innocenti);
Flora Myamba (co-principal investigator, baseline), Blandina Kilama (co-principal investigator, endline) and Paula Tibandebage (Policy
Research for Development [REPOA]); Tumpe Lukongo and Paul Luchemba (Tanzania Social Action Fund [TASAF]); and Paul Quarles van
Ufford (UNICEF Tanzania).
The evaluation team would like to acknowledge the support of TASAF and the Tanzania Commission for AIDS (TACAIDS), in particular
Ladislaus Mwamanga, Amadeus Kamagenge and Mishael Fariji (TASAF) and Fatma Mrisho (TACAIDS) for the implementation of the
evaluation. In addition, the following members of the UNICEF Tanzania team were instrumental to the success of this evaluation: Alison
Jenkins, Beatrice Targa, Victoria Chuwa and Tulanoga Matimbwi. Thanks to Corinna Keeler (doctoral student, University of North Carolina
at Chapel Hill) for support on mapping data coordinates, and to Steven Okiya (independent consultant) for support on the SurveyCTO data
collection software. The team also thanks the enumerators and ethnographers who worked diligently in data collection. The authors are
also thankful to the children and caregivers who took part in the study. They also gratefully acknowledge the support of Kevin Hong (United
States Department of Labor) in the preparations and fieldwork for the qualitative analysis. The work benefited from comments provided by
Ana Dammert and two anonymous reviewers.
Funding for the youth evaluation was generously provided by UNICEF Tanzania, the UNICEF Eastern and Southern Africa Regional Office,
the American World Jewish Service and an anonymous donor, the Swedish International Development Cooperation Agency (Sida) and the
United Kingdom of Great Britain and Northern Ireland’s Department for International Development (DFID) through grants to the UNICEF
Office of Research – Innocenti in support of the Transfer Project and the Structural Determinants of Adolescent Well-being research
programme. Funding for the REPOA study examining impacts of the PSSN on women’s empowerment (including for household surveys,
through which child labour data were collected) was provided by the Growth and Economic Opportunities for Women (GrOW) research
programme of the International Development Research Centre (IDRC).
Additional funding at endline for the collection of qualitative data and the examination of programme impacts on education, economic
activities, household chores and child labour was provided by the US Department of Labor to the UNICEF Office of Research – Innocenti
under cooperative agreement number IL-2669414-75-K-36. This working paper does not necessarily reflect the views or policies of the US
Department of Labor, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government.
One hundred per cent of the total costs of this examination were financed with federal funds, from the total cooperative agreement of
US$1,384,468. 32

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Conditional cash transfers, child work and schooling in Tanzania

  • 1. Conditional cash transfers, child work and schooling: mixed methods evidence from the United Republic of Tanzania WIDER Seminar Series with Valeria Groppo Discussant Milla Nyyssölä Throughout this seminar your microphone will be muted, please send questions for the discussion using the Q&A button. This webinar will be recorded, and the recording may be added on UNU- WIDER’s YouTube channel.​
  • 2. Conditional Cash Transfers, Child Work and Schooling: Mixed Methods Evidence from the United Republic of Tanzania J. de Hoop, M. W. Gichane, V. Groppo, S. Simmons Zuilkowski, on behalf of Tanzania’s Productive Social Safety Net (PSSN) Youth Evaluation Team Valeria Groppo, WIDER Seminar Series, 20 January 2021
  • 3. Outline for every child, answers • Introduction • Literature & contribution • Productive Social Safety Net (PSSN) • Evaluation timeline • Methodology • Results • Conclusions 3
  • 4. Introduction Millions Cirillo, C., & Tebaldi, R. (2016). Social Protection in Africa: Inventory of non-contributory programmes, International Policy Centre for Inclusive Growth, United Nations Development Programme, Brasília. African non-contributory social protection programmes by start date 4
  • 5. Introduction – Research question Cash transfer programmes contributed to poverty reduction and human capital development (e.g. Bastagli et al. 2016), but can they also reduce child labour? With funding from the United States Department of Labor, we evaluated the impact of the United Republic of Tanzania’s Productive Social Safety Net (PSSN). 5
  • 6. Introduction - context The prevalence of child labour is highest in Africa, at 19.6% (ILO 2017) • Nearly 30% of Tanzanian children engage in child labour (ILO 2016) Tanzania Social Action Fund (TASAF) TASAF I: 2000-2005 TASAF II: 2005-2013 TASAF III/PSSN • Objectives: increase income and consumption, improve ability to cope with shocks, improve education • Components, during study period: (1) cash transfer; (2) public works • Coverage: national, 15% population (6 million people) in 2016 Tanzania, Handeni district, endline data collection, 2017. 6
  • 7. Introduction - Mechanisms Theoretically, when households receives cash transfers: • Income effect increases schooling and reduces the demand for child work. • However, household productive investments may also increase, which may increase the demand for child work. Children may be requested to work for the household, in order to compensate for adult time spent in public works.  The impact of PSSN on child work and schooling is a priori undetermined. 7
  • 8. Literature – Cash transfers Child work CCTs tend to reduce both child participation and hours in economic activities and household chores (Dammert et al. 2018, de Hoop & Rosati 2014, Fiszbein et al. 2009, Skoufias et al. 2001). UCTs had more mixed impacts: • Child participation in economic activities declined in some settings, e.g. Ecuador (Edmonds & Schady 2012), but remained unchanged in others, e.g. Malawi (de Hoop et al. 2019). • In some instances, child work increased, including hazardous work or excessive hours (de Hoop et al. 2019). Schooling Both CCTs and UCTs improve school enrolment and attendance (Baird et al. 2014, Handa et al. 2018). 8
  • 9. Literature – Public works The evidence is mixed (Dammert et al. 2018): • Some studies finding reductions in child work (e.g. Hoddinott et al. 2009) • Others finding increased child participation in household chores or school absenteeism (e.g. Rosas & Sabarwal 2016). 9
  • 10. Our contribution • We use mixed methods • Cluster Randomized Controlled Trial (cRCT) • In-Depth Interviews and Focus Group Discussions with children and caregivers • Our study adds to the relatively thin literature on conditional cash transfers in Africa • We analyze programme effects on a range of work types, including exposure to hazards 10
  • 12. The evaluation (UNICEF Innocenti – Policy Research for Development, REPOA) Tanzania, Handeni district, endline data collection, 2017. 12
  • 13. Timeline 2014-2015 Targeting & location selection • Eligibility: extreme poverty; ‘ability to work’. • Eight mainland PAAs, plus one in Zanzibar • 102 villages May- July 2015 Baseline Survey (quant) • Random selection of 15-18 households per village August 2015 Random assignment (lottery) • PSSN villages (61 in total, of which 35 cash only, 26 cash & public works) • Control (delayed treatment, 41 villages) September- October 2015 First cash transfer in PSSN villages April- June 2017 Endline Survey (quant) September -October 2017 Qualitative data collection 13
  • 14. Sample and attrition Children aged 3-15 years at baseline (5-17 at endline) Of the 4,246 children observed at baseline, 3,516 (83%) were observed at endline. • Attrition is not significantly different in control vs treatment villages. Most baseline characteristics are balanced in the full sample at baseline, as well as in the reduced panel sample at endline. • One main exception (Female indicator) 14
  • 15. Baseline balance Full baseline sample Attritor Panel Control mean Difference T-C Difference T-C Difference T-C (S.D.) [p-value] [p-value] [p-value] (1) (2) (3) (4) Panel A. Household level (determinants of transfer size) Any child <5 years of age 0.581 -0.010 -0.004 -0.011 (0.494) [0.707] [0.956] [0.684] Any child aged 5–17 years 0.963 -0.000 0.042 -0.004 (0.190) [0.981] [0.307] [0.665] Number of children attending primary school 1.526 -0.105 0.105 -0.126 (1.284) [0.229] [0.547] [0.177] Number of children attending secondary school 0.390 0.030 0.320** -0.003 (0.805) [0.581] [0.011] [0.960] N (households) 587 1,460 153 1,307 15
  • 16. Baseline balance – Cont’d Full baseline sample Attritor Panel Control mean Difference T-C Difference T-C Difference T-C (S.D.) [p-value] [p-value] [p-value] (1) (2) (3) (4) Panel B. Household level (productive activities) Owned/operated any land last growing season 0.751 0.042 0.083 0.038 (0.433) [0.202] [0.207] [0.268] Owns any livestock 0.576 -0.030 0.087 -0.044 (0.495) [0.406] [0.228] [0.250] Owned/operated any non-farm business past 12 months 0.250 0.011 0.112* 0.000 (0.434) [0.667] [0.075] [0.996] N (households) 587 1,460 153 1,307 Panel C. Child level Age 8.792 0.086 0.278 0.062 (3.694) [0.453] [0.416] [0.614] Female 0.488 0.023 -0.003 0.028* (0.500) [0.115] [0.943] [0.079] Literacy 0.371 0.007 0.016 0.007 (0.483) [0.737] [0.681] [0.760] Attends school 0.505 0.008 0.020 0.008 (0.500) [0.700] [0.650] [0.739] Panel D. Child attrition Attrited 0.168 0.013 (0.374) [0.467] 16
  • 17. Methods - Quantitative Information on most child activities is captured at endline only. So, we use cross-sectional models: Outcomeiv = 1 + 2Pv + 3'Xiv + iv Outcomeiv : outcome variable for child i living in village v Pv : equal to one if PSSN village (cash transfers only, or cash transfers & public works) Xiv : baseline control variables (gender, age, determinants of transfer size, district fixed effects) iv : error term 2 : overall impact of the PSSN on child outcomes OLS with robust standard errors clustered at the village level. Given non-universal take-up, estimated impacts should be interpreted as intent-to-treat effects. 17
  • 18. Methods - Qualitative • Three purposively selected mainland PAAs, plus the one Zanzibar PAA. • Two villages for each PAA (one from the treatment, one from control). • IDIs and FGDs with children and with caregivers: Children’s FGDs used a photovoice technique In each village, six IDIs (three with children 11-17 years old, three with caregivers, all selected from the quantitative sample) and two FGDs (one with children, one with caregivers). Consent provided by all caregivers, as well as from the caregivers of children who participated in IDIs and FGDs. All children provided assent. 18
  • 19. Results Tanzania, Handeni district, endline data collection, 2017. 19
  • 20. PSSN determined a change in child work type -0.006 -0.002 0.038** -0.005 -0.019** -0.04 -0.02 0 0.02 0.04 Any economic activities Farm work for the household (excl. livestock) Livestock herding for the household Household non-farm business Paid work outside the household N = 3,516 children aged 5-17 years *p <0.1, **p <0.05 20
  • 21. Qualitative insights on PSSN & child work “The PSSN programme has given children time to rest for some days without involvement in casual works. In the previous time, children were forced to work every day or every week so as to get their needs, but now as we are assured of providing them with school requirement so they may spend even a week without working in casual labours.” - Caregiver FGD participant “On one hand, I get more time now. If I want, I can spend more time because we have labourers who work in our farms, my grandfather use PSSN money to employ casuals [day labourers] to help us in farming. On the other hand, [PSSN] money has reduced my time to search for casual works because if I fail to get money, I can use [PSSN] money. I was spending one day per week for casual works before PSSN, but after PSSN I spend one day per month on casual works.” - Child FGD participant “PSSN has not changed what I have been doing before. I am still doing charcoal business, herding cattle and sometimes selling sisal poles. The activities have neither increased nor decreased because of PSSN.” - 15-year-old-boy 21
  • 22. 0.009 -0.002 -0.032 -0.04 -0.02 0 0.02 0.04 Any hazard Ever been hurt or suffered from illnesses/in juries Number of days of main activities missed due to most serious illness/injury PSSN did not affect child hazards, health N = 3,516 children aged 5-17 years 22
  • 23. PSSN impacts on hazards, by type -0.003 0.002 0.007 0.014 0.002 0.003* 0.001 0.001* -0.03 -0.01 0.01 0.03 0.05 Carrying heavy loads Working with dangerous tools Dusts, fumes or gases Loud noise or vibrations Work at night Extreme tempera tures Bullying, intimidation or violence Bars, hotels or places of entertainment N = 3,516 children aged 5-17 years *p <0.1, **p <0.05 23
  • 24. Work outside the household is riskier “I have seen children abused by landlords when engaged in casual works in the farms, example during weeding activities, the landlord abuses children and sometimes refuse to pay them their money after they have completed the work.” - Child FGD participant “When children engaged in casual work like cultivation without supervision from their parents, they may be influenced by other children on bad behaviour like theft, disobedient or alcoholism, which is not good.” - Caregiver FGD participant 24
  • 25. PSSN improved education outcomes 0.052** 0.048** 0.174** -0.003 0.049** -0.017 -0.04 0 0.04 0.08 0.12 0.16 0.2 Currently attending school Can read and write Highest grade completed Attended school regularly, past week Spent at least one hour studying, past week Dropped out of school between baseline and endline N = 3,516 children aged 5-17 years *p <0.1, **p <0.05 25
  • 26. Qualitative insights on PSSN & schooling “The casual labourers that I am employing have given a relief to my [grand]children. As they spend less time in farming activities now than it used to be before PSSN, they can use that time to work for their own consumption or concentrate on studies.” - Caregiver “There is no such a segregation as children from well-off household and poor households. All children are equal now; they all get uniforms and are smart in school uniforms.” - Caregiver FGD participant “I have to work in grandmother’s business. I lose concentration in academics because I have to spend time in the business instead of studying. I get tired, particularly during examination time.” - Child FGD participant 26
  • 27. Impacts on child work, by gender Any economic activities Farm work for the household (excl. livestock) Livestock herding for the household Household non-farm business Paid work outside the household N = 3,516 children (1,728 female, 1,788 male) aged 5-17 years *p <0.1, **p <0.05 0.004 0.005 0.035* -0.002 -0.008 -0.016 -0.008 0.04** -0.007 -0.031*** -0.04 -0.02 0 0.02 0.04 0.06 Female Male 27
  • 28. Education impacts, by gender Currently attending school Can read and write Highest grade completed Attended school regularly, past week Spent at least one hour studying, past week Dropped out of school between baseline and endline 0.031 0.05 0.158* -0.003 0.032 -0.006 0.072 0.048 0.183** -0.007 0.065*** -0.028 -0.06 -0.02 0.02 0.06 0.1 0.14 0.18 Female Male N = 3,516 children (1,728 female, 1,788 male) aged 5-17 years *p <0.1, **p <0.05 28
  • 29. Conclusions for every child, answers • PSSN had beneficial effects on child work, with substitution effects stronger for male and older children • PSSN improved child education • Important to monitor unintended effects of programmes that expand household productive capacity • Important to use mixed methods • Complementary interventions could be considered to enhance education improvements • Information to caregivers on the importance of education and the risks related to child labour 29
  • 30. Thank you! Social Policy Specialist UNICEF Office of Research - Innocenti vgroppo@unicef.org 30
  • 31. References Baird, S., Ferreira, F. H. G., Özler, B., & Woolcock, M. (2014). Conditional, unconditional and everything in between: A systematic review of the effects of cash transfer programmes on schooling outcomes. Journal of Development Effectiveness, 6(1), 1–43. Bastagli, F., J. Hagen-Zanker, L. Harman, V. Barca, G. Sturge, T. Schmidt, & L. Pellerano. 2016. Cash Transfers: What Does the Evidence Say? A Rigorous Review of Programme Impact and of the Role of Design and Implementation Features. Overseas Development Institute, London. Cirillo, C., & Tebaldi, R. (2016). Social Protection in Africa: Inventory of non-contributory programmes, International Policy Centre for Inclusive Growth, United Nations Development Programme, Brasília. Dammert, A. C., de Hoop, J., Mvukiyehe, E., & Rosati, F. C. (2018). Effects of public policy on child labor: Current knowledge, gaps, and implications for program design. World Development, 110, 104–123. de Hoop, J., Groppo, V., & Handa, S. on behalf of the Malawi Social Cash Transfer Program and the Zambia Multiple Category Targeted Program study teams (2019). Cash transfers, microentrepreneurial activity, and child work: Evidence from Malawi and Zambia. The World Bank Economic Review. de Hoop, J., & Rosati, F. C. (2014). Cash transfers and child labor. World Bank Research Observer, 29(2), 202–234. Fiszbein, A., Schady, N., Ferreira, F. H. G., Grosh, M., Keleher, N., Olinto, P., & Skoufias, E. (2009). Conditional cash transfers: Reducing present and future poverty (World Bank Policy Research Report No. 47603). World Bank, Washington, D. C. Handa, S., Daidone, S., Peterman, A., Davis, B., Pereira, A., Palermo, T., & Yablonski, J. (2018). Myth-busting? Confronting six common perceptions about unconditional cash transfers as a poverty reduction strategy in Africa. World Bank Research Observer, 33(2), 259–298. International Labour Organization. (2016). Tanzania mainland national child labour survey 2014: Analytical report. ILO, Geneva. International Labour Organization. (2017). Global estimates of child labour: Results and trends, 2012-2016, ILO, Geneva. Schultz, T. P. (2004). School subsidies for the poor: evaluating the Mexican Progresa poverty program. Journal of Development Economics, 74(1), 199–250. Skoufias, E., Parker, S. W., Behrman, J. R. & Pessino, C. (2001). Conditional cash transfers and their impact on child work and schooling: Evidence from the Progresa program in Mexico. Economia, 2(1), 45–96. 31
  • 32. Acknowledgments The authors are grateful to the teams that implemented the impact evaluation in the United Republic of Tanzania and to all of the external partners who made this work possible. The Productive Social Safety Net youth evaluation team comprises Tia Palermo (co-principal investigator), Amber Peterman, Leah Prencipe, Lisa Hjelm, Valeria Groppo and Jacobus de Hoop (UNICEF Office of Research – Innocenti); Flora Myamba (co-principal investigator, baseline), Blandina Kilama (co-principal investigator, endline) and Paula Tibandebage (Policy Research for Development [REPOA]); Tumpe Lukongo and Paul Luchemba (Tanzania Social Action Fund [TASAF]); and Paul Quarles van Ufford (UNICEF Tanzania). The evaluation team would like to acknowledge the support of TASAF and the Tanzania Commission for AIDS (TACAIDS), in particular Ladislaus Mwamanga, Amadeus Kamagenge and Mishael Fariji (TASAF) and Fatma Mrisho (TACAIDS) for the implementation of the evaluation. In addition, the following members of the UNICEF Tanzania team were instrumental to the success of this evaluation: Alison Jenkins, Beatrice Targa, Victoria Chuwa and Tulanoga Matimbwi. Thanks to Corinna Keeler (doctoral student, University of North Carolina at Chapel Hill) for support on mapping data coordinates, and to Steven Okiya (independent consultant) for support on the SurveyCTO data collection software. The team also thanks the enumerators and ethnographers who worked diligently in data collection. The authors are also thankful to the children and caregivers who took part in the study. They also gratefully acknowledge the support of Kevin Hong (United States Department of Labor) in the preparations and fieldwork for the qualitative analysis. The work benefited from comments provided by Ana Dammert and two anonymous reviewers. Funding for the youth evaluation was generously provided by UNICEF Tanzania, the UNICEF Eastern and Southern Africa Regional Office, the American World Jewish Service and an anonymous donor, the Swedish International Development Cooperation Agency (Sida) and the United Kingdom of Great Britain and Northern Ireland’s Department for International Development (DFID) through grants to the UNICEF Office of Research – Innocenti in support of the Transfer Project and the Structural Determinants of Adolescent Well-being research programme. Funding for the REPOA study examining impacts of the PSSN on women’s empowerment (including for household surveys, through which child labour data were collected) was provided by the Growth and Economic Opportunities for Women (GrOW) research programme of the International Development Research Centre (IDRC). Additional funding at endline for the collection of qualitative data and the examination of programme impacts on education, economic activities, household chores and child labour was provided by the US Department of Labor to the UNICEF Office of Research – Innocenti under cooperative agreement number IL-2669414-75-K-36. This working paper does not necessarily reflect the views or policies of the US Department of Labor, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government. One hundred per cent of the total costs of this examination were financed with federal funds, from the total cooperative agreement of US$1,384,468. 32

Editor's Notes

  1. Expansion of non-contributory social protection programmes in Africa between 2000 and 2015 (Cirillo & Tebaldi, 2016) Number almost tripled; 66% programs indeed launched 2000-2015 Mostly unconditional cash transfers, followed by cash-for-work, conditional cash transfers, and social support services Most common objectives: to reduce poverty and vulnerability, improve health, education, food security
  2. Expansion of non-contributory social protection programmes in Africa between 2000 and 2015 (Cirillo & Tebaldi, 2016) Number almost tripled; 66% programs indeed launched 2000-2015 Mostly unconditional cash transfers, followed by cash-for-work, conditional cash transfers, and social support services Most common objectives: to reduce poverty and vulnerability, improve health, education, food security
  3. A similar reasoning holds for public works.
  4. Three-stage targeting: geographical, (2) community-based, (3) proxy means test. PAAs were randomly selected from among 16 mainland PAAs (and 1 Zanzibar PAA) included in WB evaluation (these, in turn, had been randomly selected from the 96 PAAs of wave 4 and wave 5 scale-ups of PSSN). Women Empowerment in Agriculture Index type 1 (dual-adult) and type 2 (single-female) households were included, in the proportion of two thirds and one third respectively.
  5. information on child education and productive activities is available for children aged 5 years and older at endline; all of the sample remained children under 18 years of age at endline. We consistently used these age limits, so that each impact estimate is based on the same sample of children. In the baseline data, school attendance for children aged 3 years was recorded as zero. About 16 per cent of these youngest children had started school by endline. For consistency with estimates on labour outcomes, we opted to include these youngest children in the sample and conduct all child-level impact estimates on the same sample of children. Our results are robust to slightly changing the sample age range, for instance, considering children aged 4–15 years or 4–16 years at baseline (results available upon request).
  6. information on child education and productive activities is available for children aged 5 years and older at endline; all of the sample remained children under 18 years of age at endline. We consistently used these age limits, so that each impact estimate is based on the same sample of children. In the baseline data, school attendance for children aged 3 years was recorded as zero. About 16 per cent of these youngest children had started school by endline. For consistency with estimates on labour outcomes, we opted to include these youngest children in the sample and conduct all child-level impact estimates on the same sample of children. Our results are robust to slightly changing the sample age range, for instance, considering children aged 4–15 years or 4–16 years at baseline (results available upon request).
  7. information on child education and productive activities is available for children aged 5 years and older at endline; all of the sample remained children under 18 years of age at endline. We consistently used these age limits, so that each impact estimate is based on the same sample of children. In the baseline data, school attendance for children aged 3 years was recorded as zero. About 16 per cent of these youngest children had started school by endline. For consistency with estimates on labour outcomes, we opted to include these youngest children in the sample and conduct all child-level impact estimates on the same sample of children. Our results are robust to slightly changing the sample age range, for instance, considering children aged 4–15 years or 4–16 years at baseline (results available upon request).
  8. Mailand: Mbogwe (highest rate of child labour), Kahama and Uyui (mining and tobacco farming, respectively). Zanzibar: Unguja, to understand child labour differences vs. mainland.