This document reviews evidence on the relationship between income increases and reductions in child labor. It analyzes 128 studies on smallholder agriculture from around the world. The evidence shows: - Income increases due to higher prices or productivity sometimes reduce child labor, but not always, depending on other household and community factors. - Cash transfer programs have more consistently shown positive impacts on reducing child labor, though not in all cases, and have broader benefits to child well-being like education. - Many knowledge gaps remain around context-specific evidence for cocoa growing regions and the exact impact of achieving a living income threshold.

1. A review of evidence
LIVING INCOME AND
CHILD LABOUR

2. CHILD LABOUR: THE CHALLENGE
children in child labour in cocoa-
growing areas of Côte d’Ivoire and
Ghana
of them, over 90% are doing
hazardous work (Tulane, 2015)
2 million +
If poverty is a root cause, could a “living income” eliminate child labour?

3. PROJECT CONTEXT
KAKAO PLATFORM
Swiss Platform for Sustainable Cocoa
Living Income Working Group
International
Cocoa Initiative
International
Trade Center
Research questions:
1. When farmer incomes increase to cross a living income threshold, what is the effect
on child labour?
2. What other factors are important?
3. What practical measures could ensure that income increases translate into child
labour reduction?

4. SCOPE OF THE STUDY
Focus:
• Recent studies (since 2000)
• Smallholder agriculture, worldwide
• Income change, rather than “living income”
• Types of income change: economic shocks (positive & negative); social policies
128 48
14studies included in
bibliography ‘core studies’
analysed in detail (on sub-Saharan Africa)

5. PRICE
INCREASE
Kruger (2007) -coffee,
Brazil
Soares et al. (2012) –
coffee Brazil
Edmonds and Pavcnik
(2005) rice, Vietnam
Hou et al. (2016)
wheat Pakistan
Frempong & Stadelmann
(2019)
Uganda
AGRICULTURAL
OUTPUT
Takahashi and Barrett
(2014) rice Vietnam
CLIMATIC
EVENTS
Shah and Steinberg
(2017) India
Dumas (2013)
Tanzania
TRADE, FDI,
REMITTANCES
Edmonds et al.
(2010)
Ajefu 2018 Yang (2008) Philippines
Child Labour falls No effect
Child Labour
increases
Mixed effects
THE EVIDENCE: PRICE INCREASE DUE TO SHOCK

6. THE EVIDENCE: PRICE DECREASE DUE TO SHOCK
PRICE
DECREASE
Cogneau and Jedwab
(2012) – cocoa, Côte
d’Ivoire
AGRICULTURAL
OUTPUT
Beegle et al 2006
Tanzania
Bandara et al 2015
Tanzania
Dillon (2013) Mali
CLIMATIC
EVENTS
Shah and Steinberg
(2017) India
Dumas (2013)
Tanzania
Baez et al. (2017)
Guatemala
Cook and Beachy
(2018) Haiti
ILLNESS/
DEATH
Alam 2015, Tanzania
(mother sick= more CL,
father sick= less CL)
Dhanaraj 2016, India
(mother sick= more
CL, father sick= less
CL)
Dillon 2013, Mali
(mother sick= more CL,
father sick= less CL)
Mendolia et al.
2019, Vietnam
Dinku et al.
(2018)
Bandara (2015)
Tanzania
TRADE, FDI,
REMITTANCES
Alcazar et al. (2012)
Mexico
Edmonds et al. (2010)
Child Labour falls No effect
Child Labour
increases
Mixed effects

7. THE EVIDENCE: SOCIAL POLICIES
Child Labour falls No effect
Child Labour
increases
Mixed effects
UNCONDITIONAL
CASH (UCT)
Edmonds and
Schady (2012)
Ecuador
Handa et al. (2016)
Zambia
Covarrubias et al (2012),
Endline Impact Report
(2016), Malawi
Handa et al. (2016),
Ghana
CONDITIONAL
CASH (CCT)
Dammert 2009
Nicaragua
Galiani and McEwan
(2013) Honduras
Del Carpio et al. (2016)
Nicaragua
Skoufias and Parker 2001,
Janvry et al. (2006),
Behrman et al. (2011)
(long-term: girls) Mexico
De Hoop et al.
2017),
Philippines
IN-KIND
TRANSFER
Ravallion and
Wodon (2000)
Bangladesh
Kazianga et al. (2012)
Burkina Faso
Bandiera et al. (2017)
Bangladesh
SCHOOL
SUBSIDIES
Angrist et al (2002)
Colombia (girls)
Hoop and Rosati
(2014b) (boys)
FINANCIAL
SERVICES
Tarozzi et al. (2015)
Ethiopia
Crepon et al. 2015
Morocco
Angelucci et al. (2015)
Landmann and Frölich
(2015)
EMPLOYMENT
OPPORTUNITIES
Dinku et al. (2019)
Ethiopia
Shah and Steinberg
(2015) India
Bandiera et al. (2017)
Bangladesh

8. WHAT OTHER FACTORS ARE IMPORTANT?
CHILD
CHARACTERISTICS
Gender Age Unobservable factors
(preferences, abilities, bias)
HOUSEHOLD
CHARACTERISTICS
Parent education/
power relations
Asset ownership Access to
financial services
Siblings
COMMUNITY
CHARACTERISTICS
Infrastructure Labour market Banking Land markets

9. WHAT DID WE LEARN?
• Increases in income can result in decreased child labour, but not always.
• Mediating factors can change the direction of the effect: resilience in face of shocks,
household composition and decision-making, access to complimentary services.
• For now, strongest available evidence is around cash transfers, but even these don't
always result in lower rates of child labour (although evidence exists for positive
impacts on child wellbeing more broadly, especially around education)

10. MULTIPLE KNOWLEDGE GAPS REMAIN
For example…
• Context-specific evidence for cocoa and West Africa
• Effect of crossing the living income threshold
• Effect of different cash delivery options (when, to whom, how much, for how long?)
• Impact of other activities commonly used in cocoa-growing areas (IGAs, VSLAs,
women’s empowerment, diversification, agro-forestry, productivity investments,
price premiums…)
However, the wealth of current and planned interventions present multiple
opportunities to generate robust evidence and fill these knowledge gaps!