Cash Transfers, Micro-Entrepreneurial Activity and Child Work

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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
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
“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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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, Tanzania 2,777 2,777 2,777 2,777 2,777
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
Child work, participation (past week)
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, Zambia 3,999 NA 3,999 3,999 3,999
Control average, Zambia 0.180 NA 0.029 0.139 0.045

20
Child work, participation (past year)
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
Control average, Malawi NA 0.607 NA NA 0.431
Control average, Zambia NA 0.710 NA NA 0.321

21
Child work, hours (past week)
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]
Control average, Zambia 0.859 NA 0.060 0.566 0.233

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, Zambia 7,998 7,642 7,998 3,999 NA
Control average, Zambia 0.688 0.507 3.724 0.243 NA

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
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
• 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
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
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
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
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
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
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
• 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

Cash Transfers, Micro-Entrepreneurial Activity and Child Work

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