NORC Final Report:
Assessing Progress in Reducing Child
Labor in Cocoa Production in Cocoa
Growing Areas of Côte d’Ivoire and Ghana

OCTOBER 2020
FINAL REPORT 2018/2019
SURVEY ROUND
Funding for this report was provided by the United States
Department of Labor under Cooperative Agreement
number IL-28104-15-75-K-17. 100 percent of the total
costs of the project or program is financed with federal
funds, for a total of $3,458,681.00 dollars. The
statements found in this report do not necessarily reflect
the views or policies of United States Department ofLabor, nor does mention of trade names, commercial
products, or organizations imply endorsement by theUnited States Government.

PRESENTED BY:
Santadarshan Sadhu
Kareem Kysia
Letitia Onyango
Clifford Zinnes
Sarah Lord
Alexandre Monnard
Ingrid Rojas Arellano

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Acknowledgments
We would first like to thank the governments of Côte d’Ivoire and Ghana for their continued
support and transparency during the life of this project. In Côte d'Ivoire we start by thanking
First Lady Madame Dominique Ouattara, president of the National Oversight Committee of
Actions in the Fight against Child Trafficking, Exploitation and Child Labor (CNS). The First
Lady’s dedication to the betterment of children in Côte d'Ivoire is well known and without her
support and commitment this project would not have been possible. In Ghana we would first liketo thank the Honorable Ignatius Baffour Awuah, Minister of Employment and Labour Relations
for his strong interest in the report and ensuring Ghanaian interests were incorporated. We would
also like to thank the government of Ghana more generally, which, as a coordinated team,provided essential inputs into our study at every step in the process. Most notably, we would liketo thank the Ministry of Labor and Social Protection, through the consistent support of Elizabeth“Lizzy” Akanbombire and Emma Ofori, by acting as a central hub for the NORC team to turn tofor advice and permissions. We thank Vincent Akomea and Paul Ntim at COCOBOD whoengaged with the research team from the very beginning, providing essential insights into cocoa
production in Ghana. Lucy Twumwaah from the Ghana Statistical Service and her entire teamprovided data support and local knowledge building up to and throughout the data collection
process. Andy Tagoe from GAWU was heavily involved in discussing issues pertinent toworkers in Ghana and Kenneth Mamadu was a key partner on the ground for NORC, helping us
navigate the cocoa sector in Ghana and providing expert input on changes in the cocoa sector andinterventions aimed at child labor.
In Côte d'Ivoire, our thanks go out for the support of the Office of the First Lady and the
National Oversight Committee of Actions in the Fight against Child Trafficking, Exploitation
and Child Labor (CNS). Most notably, we would like to thank Madame Patricia Sylvie Yao who,
as the Director of the Cabinet of the First Lady and Executive Secretary of CNS, was able toprovide strong leadership and guidance not only to her staff but to the NORC research team aswell. We also thank Amany Konan and Tessy Winkelman as well as the staff members of theFirst Lady’s Cabinet. Mr. Konan is a strong advocate for the rights of children and an expert on
Ivorian legislation dealing with child labor and his guidance throughout the research process wasmuch appreciated. International consultant to the First Lady’s Cabinet, Ms. Winkelman was adiligent advocate for the perspectives and needs of the Ivorian stakeholders. Finally, we wish to
thank Dr. Martin NGuettia, of the Ministry of Employment and Social Protection, who chairs theInterministerial Committee to Combat Trafficking, Exploitation and Child Labor (CIM), for his
key input at each step of the process.
We would also like to thank the World Cocoa Foundation (WCF) through Tim McCoy andSusan Smith who both made themselves (and through them a large cross-section of theFINAL REPORT | i

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghanainternational chocolate and cocoa industry) available to the NORC team from the very beginningof the process. Key inputs and advice on the West African cocoa sector were provided by
Mathias Lange and Nick Weatherall from the International Cocoa Initiative and were very muchappreciated. The International Labour Organization, UNICEF, and Fairtrade International werealso key contributors to our understanding of labor related issues around cocoa production in
Ghana and Côte d'Ivoire. We also recognize the hard work and dedication of the entire team at
the Bureau of International Labor Affairs of the U.S. Department of Labor, which funded thisstudy and provided methodological oversight throughout the process.
Most importantly, we would like to recognize the contributions of the entire research team fromNORC and our local partner Kantar Public as this project was designed, fielded, and completed
over course of the past five years. This includes Stephen Cohen, Ruth Essuman, Jonathan Addie,
Everest Konan, Cristian Ilie, Audra Grant, Erika Keaveney, Ernst Adjei, Samantha Downey,
Ridhi Sahai, Alex Rigaux, Russell Owen, May Noureddine, Alain Otarola, Phoebe Bui, Kyle
Vincent and multiple support staff, programmers and most importantly the enumerators in
Ghana, Côte d'Ivoire, and the United States.
Finally, we thank the children, farmers, teachers, and other research participants in Côte d’Ivoire
and Ghana for their valuable time in taking part in this study. This study was supported byhundreds of key people throughout the process.

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table of Contents
1

Executive Summary .............................................................................................................. 1
1.1

Background .............................................................................................................. 1

1.2

The Present Study: Objective and Important Considerations .................................. 1
Objectives ................................................................................................ 1
Important Considerations ......................................................................... 2

1.3

Methodology ............................................................................................................. 4

1.4

Preliminary estimates, Stakeholders’ concerns and the Independent Expert
Group ....................................................................................................................... 41.5

CLCCG Stakeholder Engagement ........................................................................... 6

1.6

Definitions of Child Labor and Hazardous Work ...................................................... 8

1.7

Main Findings ......................................................................................................... 10
Recent Trends with Children in Cocoa Growing Households(2013/14-2018/19) .................................................................................. 11
1.7.1.1 Child Labor ............................................................................. 11
1.7.1.2

Hazardous Child Labor .......................................................... 11

Historic Trends with Children in All Agricultural Households(2008/09-2018/19) .................................................................................. 12
1.7.2.1 Child Labor ............................................................................. 12
1.7.2.2
1.8

Hazardous Child Labor .......................................................... 12

General Quantitative Insights ................................................................................. 13
School Attendance ................................................................................. 13
Hours of Work Exceeding Maximum Allowable Hours ........................... 13
1.8.2.1 Children in the 5-11 Age Group ............................................. 13
1.8.2.2

Children in the 12-14 Age Group ........................................... 14

1.8.2.3

Children in the 15-17 Age Group ........................................... 14

Types of Hazardous Work in Cocoa Production .................................... 15
1.9

Analytical Findings on Context ............................................................................... 15
Importance of Cocoa Agriculture ............................................................ 15
Cocoa Production Stratum ..................................................................... 16
Use of Agro-chemical Products .............................................................. 16

1.10

Assessment Findings ............................................................................................. 17
Quantitative Assessment ....................................................................... 17
Qualitative Assessment .......................................................................... 18

1.11

Conclusions ............................................................................................................ 19

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2

Stakeholder Statements ..................................................................................................... 22

3

Study Objectives ................................................................................................................. 30
3.1

Measuring the Progress Toward Achieving the Goals and Targets of the
Declaration and Framework ................................................................................... 30

3.2

Assessment of Effectiveness of Interventions Funded Toward Achieving Goals
and Targets of the Declaration and Framework ..................................................... 31

3.3

Definitions: Working Children, Child Labor and Hazardous Child Labor basedon Common and Local Definitions ......................................................................... 31
Working Children (Children in Employment) .......................................... 32
Children in Child Labor ........................................................................... 32
Children in Hazardous Child Labor: Common Definition ........................ 33Children in Hazardous Child Labor: National Definitions ....................... 34
3.3.4.1 Ghanaian National Legislation (Local Definition) and SubCategories of Hazardous Work .............................................. 35
3.3.4.2

4

Ivorian National Legislation (Local Definition) and SubCategories of Hazardous Work .............................................. 35

Methodology ........................................................................................................................ 36
4.1

Sampling Methodology and Implementation of the 2018/19 Survey Round .......... 36
Sampling Approach and Stratification .................................................... 36
Types and number of interviews completed ........................................... 37
Implementation of 2018/19 Survey Round ............................................. 38

4.2

Description of Sample ............................................................................................ 39
Comparison of Sample Sizes between 2008/09 and 2018/19 Roundof Survey ................................................................................................ 39
Respondent Characteristics ................................................................... 40
4.2.2.1 Nationality of the Child Respondents ..................................... 41
Area of Land Under Cultivation .............................................................. 41

4.3

Limitations and Methodological Consideration: Coverage of Data andComparison of Data Across Rounds ...................................................................... 41
2013/14 Data Quality Issues .................................................................. 42
2008/09 and 2018/19 Comparability Issues with Population Estimateof Counts ................................................................................................ 42
Expert Group Review of limitations on the statistical comparisons ofprevalence rates and ratios across survey rounds ................................. 445Part I: Main Findings ........................................................................................................... 46
5.1

Understanding Contextual Factors ......................................................................... 50
Cocoa Production and Cocoa Price ....................................................... 50

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana
Significance of Cocoa Production among Agricultural Household ......... 51
Use of Agro-Chemical Products ............................................................. 53
Trend in School Attendance ................................................................... 54
5.2

Main Findings: All Agricultural Households ............................................................ 55
Children’s Engagement in any Economic Activities ............................... 55
Estimate of Working Children in Agriculture ........................................... 56
Estimate of Child Labor in Agriculture .................................................... 59
Estimate of Working Children, and Average Hours of Work AmongChildren Working in Cocoa Production .................................................. 60
5.2.4.1 Estimate of Working Children in Cocoa Production ............... 61
5.2.4.2

Average Hours Worked by Children in Cocoa Production ..... 63

5.2.4.3

Activities Performed by Children in Cocoa Production........... 67

Estimate of Child Labor and Hazardous Child Labor in Cocoa
Production .............................................................................................. 68
5.2.5.1 Children’s Engagement in the Components of Hazardous
Labor in Cocoa Production .................................................... 71
5.2.5.2

Children’s Exposure to Multiple Hazardous Activities
Related to Cocoa Production ................................................. 74

5.2.5.3

Children’s Exposure to Various Components of AgroChemical Products ................................................................. 77

Injuries Suffered while Working in Agriculture in the Past 12 Months
and Health Consequences for Children ................................................. 79Children’s Engagement in Non-Cocoa Agriculture and NonAgricultural Sector .................................................................................. 83
5.3

Main Findings: Cocoa Growing Households (2013/2014 and 2018/2019) ............. 84
Cocoa Growing Households: Estimate of Working Children .................. 85
Cocoa Growing Households: Estimate of Child Labor andHazardous Child Labor .......................................................................... 86
5.3.2.1 Children’s Engagement in the Components of Hazardous
Work in Cocoa Production ..................................................... 89
5.3.2.2

Exposure to Various Components of Agro-Chemical
Product Use ........................................................................... 91

5.4

School Attendance among Children in All Agricultural Households ....................... 93

5.5

School Attendance among Children Working in Cocoa Production ....................... 95

5.6

School Attendance among Children in Child Labor and Hazardous Child Laborin Cocoa Production ............................................................................................... 97

5.7

Basic Literacy and Numeracy among Children Working in Cocoa Production ....... 99

5.8

Estimate of Children’s Work Interfering with Education among Children
Working in Cocoa Production ............................................................................... 100

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67

8

Analytic Insights: Role of Production Stratum, Agro-chemical Use & Household
Demographics ................................................................................................................... 102
6.1

Child Labor and Hazardous Work in Cocoa Production by Production Stratum .. 102

6.2

Expenditure on Agro-Chemical per ton of Cocoa Produced ................................ 104

6.3

Household Composition: Distribution of Children in Household ........................... 106

Findings Based on Country Specific Definitions of Hazardous Work (2018/19 Survey
Round)................................................................................................................................ 108
7.1

Estimate of Children Engaged in Child Labor, and Hazardous Work in Cocoa
Production in Côte d’Ivoire based on Ivoirian Legislation..................................... 108

7.2

Estimate of Children Engaged in Child Labor, and Hazardous Work in Cocoa
Production in Ghana based on Ghanaian Legislation .......................................... 109

Part II: Assessment of the Effects of Interventions on Child Labor ............................. 111
8.1

Objectives ............................................................................................................. 111

8.2

Methodological Approaches ................................................................................. 111
General Quantitative Approach ............................................................ 111
General Qualitative Approach .............................................................. 112

8.3

Research Questions, Analysis and Findings ........................................................ 112
Impact of Education and Vocational Training ....................................... 113
8.3.1.1 Children’s Attitudes towards Education................................ 114
8.3.1.2

Education Material Assistance and Child Labor in the
Cocoa Sector ....................................................................... 115

8.3.1.3

Findings from Quantitative Analysis ..................................... 116

8.3.1.4

Impact of School-Based Interventions ................................. 117

8.3.1.5

Persisting Challenges .......................................................... 120

8.3.1.6

Impact of Vocational Training Programs .............................. 121

Impact of Livelihood Services .............................................................. 123
8.3.2.1 Findings from Quantitative Analysis ..................................... 124
8.3.2.2

Findings from Qualitative Analysis ....................................... 125

Impact of Occupational Safety and Health training .............................. 127
8.3.3.1 Findings from Quantitative Analysis ..................................... 128
8.3.3.2

Findings from Qualitative Assessment ................................. 129

Impact of Awareness-raising campaigns ............................................. 130
8.3.4.1 Exposure to Child Labor Campaigns ................................... 130
8.3.4.2

Child Labor Attitudes and Practices ..................................... 131

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Emerging Themes on Intervention Effectiveness ................................. 134
8.3.5.1 Theme 1: Beneficiary and Community Ownership ............... 134
8.3.5.2

Theme 2: Potential of indirect interventions ......................... 135

8.3.5.3

Theme 3: Awareness campaigns ......................................... 136

8.3.5.4

Theme 4: Timeliness of material support ............................. 138

8.3.5.5

Theme 5: Intervention coordination ..................................... 138

Overall Sustainability of Interventions .................................................. 139
8.3.6.1 Strategies for promoting sustainability ................................. 139
8.3.6.2

Intervention Sustainability .................................................... 140

Impact of Funded Interventions on the Prevalence of Child Laborand Hazardous Child Labor in Cocoa Production ................................ 142
8.3.7.1 Findings from Quantitative Analysis ..................................... 143
8.4

Caveats and Limitations ....................................................................................... 144
Limitations of Quantitative Analyses .................................................... 145
Limitations of Qualitative Analyses ...................................................... 145

9Conclusions and Recommendations for Future Research ........................................... 146
9.1

Overview .............................................................................................................. 146

9.2

Lesson Learned and Future Research ................................................................. 148
Stakeholder engagement ..................................................................... 149
Common and National Definitions ........................................................ 149
Child Labor, Hazardous Child Labor, and the component parts ofHazardous Child Labor ........................................................................ 150
Enumerator Training ............................................................................ 150
Evaluation based on Randomized Control Trials ................................. 150
Final Remarks ...................................................................................... 150

10 Report Annexes................................................................................................................. 152
10.1

Annex I: Detailed Survey Methodology and Implementation in the 2018/19
Round ................................................................................................................... 152
Sampling design ................................................................................... 152
10.1.1.1 Sampling Method ................................................................. 152
10.1.1.2 Selection of Census Enumeration Areas ............................. 152
10.1.1.3 Selection of Households from Identified CEAs for Survey ... 153
10.1.1.4 Important Considerations ..................................................... 154
Survey methodology ............................................................................ 154
10.1.2.1 Quantitative approach .......................................................... 154

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana
10.1.2.2 Qualitative approach ............................................................ 155
Design of survey instruments ............................................................... 158
Child Questionnaire .............................................................................. 159
10.1.4.1 Household Roster ................................................................ 160
10.1.4.2 Household Head Questionnaire ........................................... 160
10.1.4.3 Community Leader Questionnaire ....................................... 161
10.1.4.4 School Questionnaire ........................................................... 161
10.1.4.5 Cocoa Shed Questionnaire .................................................. 161
Training ................................................................................................ 161
10.1.5.1 Training of Trainers .............................................................. 161
10.1.5.2 Enumerator Training ............................................................ 162
Pilot 163
Data collection ...................................................................................... 164
10.1.7.1 Methodology ........................................................................ 164
Generating the Sampling Weights ....................................................... 164
Hypothesis Testing ............................................................................... 166
10.2

Annex II: Notes on Comparability of Data and Population Estimates across
2008/09, 2013/2014 and 2018/19 Survey Rounds ............................................... 167
Incomplete documentation and implication on comparison of
population estimates between the survey rounds ................................ 167
Missing data to link the child respondents to their respective
households in 2008/09 survey round and its implication oncomparison of prevalence estimates .................................................... 169
Errors made in survey administration in Côte d’Ivoire during 2013/14
survey round and implication on comparison of population estimates
between the 2013/14 and 2018/19 survey rounds. .............................. 170

10.3

Annex III: Common Definition and Local Definitions of Child Labor &Hazardous Child Labor......................................................................................... 171
Common definition ............................................................................... 172
Cȏte d’Ivoire ......................................................................................... 173
Ghana .................................................................................................. 174

10.4

Annex IV: Supplementary Tables I ....................................................................... 176
Survey respondents ............................................................................. 176
Descriptive analysis ............................................................................. 178

10.5

Annex V: Supplementary tables II ........................................................................ 197
Comparison of exposure to Hazardous Work indicators using 7 days
vs. 12 months reference periods for 2018/19 ....................................... 197

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana
Frequency of exposure to Hazardous Work in Cocoa Production in
2018/19 over the 12 months reference periods ................................... 201
10.6

Annex VI: Additional Insights from 2018/19 Survey Round .................................. 207

10.7

Annex VII: Supplement I: Quantitative Analysis of Assessment of Effectivenessof Interventions ..................................................................................................... 213
Literature review ................................................................................... 213
10.7.1.1 References ........................................................................... 216
Modelling Child Labor and Hazardous Child Labor .............................. 217

10.8

Annex VIII: Supplement II: Quantitative Analysis of Assessment of
Effectiveness of Interventions .............................................................................. 219
Education Material Assistance and Child Labor in the Cocoa Sector .. 219
10.8.1.1 Methodology ........................................................................ 220
10.8.1.2 Data Source ......................................................................... 221
10.8.1.3 Analysis and Results ............................................................ 221
Livelihood Support and Child Labor in the Cocoa Sector .................... 228
10.8.2.1 Methodology ........................................................................ 228
10.8.2.2 Data Source ......................................................................... 230
10.8.2.3 Analysis and Results ............................................................ 230
Occupational Safety and Health Training and Hazardous Child
Labor in the Cocoa Sector ................................................................... 233
10.8.3.1 Methodology ........................................................................ 233
10.8.3.2 Data Source ......................................................................... 234
10.8.3.3 Analysis and Results ............................................................ 235
Impact of Multiple Interventions and Child Labor in the Cocoa Sector. 238
10.8.4.1 Methodology ........................................................................ 238
10.8.4.2 Data Source ......................................................................... 239
10.8.4.3 Analysis and Results ............................................................ 242

10.9

Annex IX: Qualitative Methodology and Supplemental Analysis .......................... 249
General Qualitative Approach .............................................................. 249
10.9.1.1 Key Informant Interviews ..................................................... 249
10.9.1.2 Focus Group Discussions .................................................... 249
10.9.1.3 Sampling .............................................................................. 249
10.9.1.4 Training ................................................................................ 251
Analysis ................................................................................................ 251
10.9.2.1 Education and Vocational Training ...................................... 252
10.9.2.2 Impact of Livelihood Services .............................................. 253
10.9.2.3 Occupational Safety & Health (OSH) Training ..................... 254
10.9.2.4 Awareness-Raising Campaigns ........................................... 254

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10.9.2.5 Relative Effectiveness of Interventions ................................ 255
10.9.2.6 Sustainability ........................................................................ 255
10.10 Annex X: Caveats and limitations of Quantitative Analysis .................................. 255
10.11 Annex XI: Definitions of the Child Labor Intervention Categories......................... 258
Education ............................................................................................. 258
Vocational Training .............................................................................. 259
Sensitization/awareness raising program ............................................ 259
Occupational Safety and Health program for youth of legal working
age 259
Capacity building including community, regional, and national level
initiatives .............................................................................................. 259
National, Community-based and/or supply-chain based monitoring
and remediation systems/Child Protection and Child Labor
Monitoring and Remediation Systems ................................................. 260
Community Action Plans and Community Development Plans ............ 260
Promotion of sustainable livelihood for vulnerable households............ 260
Labor saving practices ......................................................................... 261
Improving access to existing public services for families vulnerable
to child labor ......................................................................................... 261
Gender and Women’s Empowerment .................................................. 261
Research .............................................................................................. 262
Material Assistance .............................................................................. 262
Enforcement of anti-child labor regulations .......................................... 262
Compliance initiatives (Code of conduct/certification) .......................... 262
10.12 ANNEX XII: EXPERT GROUP FINDINGS ........................................................... 262
Background .......................................................................................... 262
Composition of the Expert Group ......................................................... 263
III. Mandate .......................................................................................... 263
Preliminary Remarks ............................................................................ 264
Assessment Results on the Validity of the 2018/19 Prevalence
Estimates ............................................................................................. 264
10.12.5.1 The 2018/19 survey results (ratios) are representative of
cocoa-growing areas of Côte d’Ivoire and Ghana. Is thisstatement technical sound? ................................................. 264
10.12.5.2 Were the procedures and bases that NORC followed
scientifically sound and able to generate reliable
population counts for 2018/19 within cocoa growing areasof Côte d’Ivoire and Ghana? ................................................ 267

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Assessment of the Comparability of 2018/19 Survey Round with
Prior Surveys ........................................................................................ 268
10.12.6.1 Sampling Frames: ................................................................ 268
10.12.6.2 Weighting Scheme: .............................................................. 271
Expert Group Appendix ........................................................................ 275
References ........................................................................................... 276
10.13 Answers from the Chair of the Expert Group on NORCs Response .................... 277

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Table of Figures
Figure 1: Graphic Representation of Common Definition of Child Labor ................................... 8
Figure 2: Child Labor Common Definition ................................................................................ 34
Figure 3: Measurement Framework on Child Labor in Cocoa Growing Areas ......................... 46
Figure 4: Research Questions and Analyses ......................................................................... 113
Figure 5: Range Used for Stratification of Department/Districts: 2018/19 Survey Round ...... 153
Figure 6: KII Interviews: 2018/19 Survey Round .................................................................... 156
Figure 7: Ghana FGD Communities ....................................................................................... 158
Figure 8: Cȏte d’Ivoire FGD Communities .............................................................................. 158

Table of Tables
Table 1:

Distribution of Sample*: Agricultural Households, Cocoa Growing Households
and Non-Cocoa Growing Agricultural Households, in Côte d’Ivoire and Ghana,
2018/19 ..................................................................................................................... 38

Table 2:Comparison of Sample Sizes by Survey Type, All Agricultural Households WithAt Least One Child Age 5-17, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19 .... 39

Table 3:

Respondent Characteristics: Head of Household, All Agricultural Households, inCôte d’Ivoire and Ghana, 2018/19 ............................................................................ 40

Table 4:

Respondent Characteristics: Children 5-17 Years, All Agricultural Households, inCôte d’Ivoire and Ghana, 2018/19 ............................................................................ 40

Table 5:Comparisons for Population Count and Prevalence Rate ......................................... 43

Table 6:

Population Count and Prevalence Rate Estimates for Households, Working
Children, and Children Exposed to Child Labor and Hazardous Child Labor, inCôte d’Ivoire and Ghana, 2018/19 ............................................................................ 48

Table 7:

Cocoa Production by Tons of Cocoa and Prices by USD/Ton, and thePercentage of Cocoa Growing Households, in Côte d’Ivoire and Ghana,
2008/09, 2013/14, and 2018/19 ................................................................................ 52

Table 8:Estimates of Change in Household Use of Agro-Chemicals in the Last 12
Months in Cocoa Households, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19 ... 53

Table 9:School Attendance for All Children in the Last 12 Months, All Agricultural
Households, Côte d’Ivoire and Ghana, 2008/09 and 2018/19 .................................. 54

Table 10: Estimates of Change in Children Working in the Last 12 Months and in the Last
7 Days, All Agricultural Households, 5-17 Years, in Côte d’Ivoire and Ghana,
2008/09 and 2018/19 ................................................................................................ 56
Table 11: Estimates of Children Working in Agriculture in the Last 12 Months and the Last
7 Days, All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and2018/19 ..................................................................................................................... 58

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Table 12: Estimates of Change in Children Working in Agriculture, and in Children
Engaged in Child Labor in the Last 12 Months, All Agricultural Households, 5-17
Years, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19 ........................................ 59
Table 13: Estimates of Children in Cocoa Production in the Last 12 Months and in the Last
7 Days, All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and2018/19 ..................................................................................................................... 62
Table 14: Working Hours in Any Economic Activity and Minimum Age, Children Working in
Cocoa Production, All Agricultural Households, in Côte d’Ivoire and Ghana,
2008/09 and 2018/19 ................................................................................................ 66
Table 15: Child Work Involved in Cocoa Production, All Children 5-17 Years Working in
Cocoa Production, All Agricultural Households, in Côte d’Ivoire and Ghana,
2008/09 and 2018/19 ................................................................................................ 67
Table 16: Estimates of Change in Children Working in Cocoa Production, Children
Engaged in Child Labor in Cocoa Production, and Children Engaged in
Hazardous Work in the Cocoa Sector in the Last 12 Months, 5-17 Years, All
Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19 ............ 70
Table 17: Estimates of Percentages of all Children Exposed to Hazardous Work Activities
in the Cocoa Sector, 5-17 Years, All Agricultural Households, in Côte d’Ivoire
and Ghana, 2008/09 and 2018/19* ........................................................................... 73
Table 18: Estimates of Exposure of Children Working in Cocoa Production, 5-17 Years, toMultiple Types of Hazardous Work, All Agricultural Households, in Côte d'Ivoire
and Ghana, 2008/09 and 2018/19............................................................................. 76
Table 19: Disaggregation of Exposure to Agro-Chemicals, Children Working in Cocoa
Production in the Last 12 Months, All Agricultural Households, in Côte d’Ivoire
and Ghana, 2008/09 and 2018/19............................................................................. 78
Table 20: Injuries Experienced by Children While Working in Agriculture, Children 5-17
Years, All Agricultural Households, in Côte d’Ivoire and Ghana, 2018/19 ................ 80
Table 21: Injuries Experienced by Children While Working in Agriculture by Exposure to
Hazardous Work, Children 5-17 Years, All Agricultural Households, in Côte
d’Ivoire and Ghana, 2018/19 ..................................................................................... 82
Table 22: Health Consequences from Injuries Experienced While Working in Agriculture,Children 5-17 Years Working in Cocoa Production, All Agricultural Households,
in Côte d’Ivoire and Ghana, 2018/19......................................................................... 83
Table 23: Estimates of Change in Children Working in Agriculture Other than the Cocoa
Sector, in Non-Agricultural Sector, 5-17 Years, All Agricultural Households, inthe Last 12 Months, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19 ................... 84
Table 24: Estimates of Children in Cocoa Households Working in Cocoa Production in theLast 12 Months and in the Last 7 Days, in Côte d’Ivoire and Ghana, 2013/14 and2018/19 ..................................................................................................................... 86
Table 25: Estimates of Change in Children in Cocoa Household, 5-17 Years, Working in
Cocoa Production, Children Engaged in Child Labor in Cocoa Production, andFINAL REPORT | xiii

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Children Engaged in Hazardous Work in the Cocoa Sector in the Last 12
Months, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19 ...................................... 88
Table 26: Estimates of Change in Children in Cocoa Households, 5-17 Years, Exposed to
Various Types of Hazardous Work Activities in the Cocoa Sector, in Côte
d’Ivoire and Ghana, 2013/14 and 2018/19*............................................................... 90
Table 27: Disaggregation of Exposure to Agro-Chemicals, Children in Cocoa Households
Working Cocoa Production in the Last 12 Months, in Côte d’Ivoire and Ghana,
2013/14 and 2018/19 ................................................................................................ 92
Table 28: School Attendance for Children in the Last 12 Months, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19 ............................... 94
Table 29: School Attendance for Children Working in Cocoa Production in the Last 12
Months, All Agricultural Households, by Age Group and Gender, in Côte d’Ivoire
and Ghana, 2008/09 and 2018/19............................................................................. 96
Table 30: School Attendance for Children Engaged in Child Labor in Cocoa Production
and Children Engaged in Hazardous Work in Cocoa Production, All Agricultural
Households, By Age Group, 5-17 Years, in Côte d’Ivoire and Ghana, 2008-09
and 2018-19 .............................................................................................................. 98
Table 31: Basic Literacy and Numeracy for Children Working in Cocoa Production, All
Agricultural Households, 5-17 Years, in Côte d’Ivoire and Ghana, 2018/19 ............. 99
Table 32: Estimates of Children Working in Cocoa Production Not Attending School, andWork Interferes with Schooling, 6-14 Years, All Agricultural Households, in Côte
d’Ivoire and Ghana, 2008/09 and 2018/19 .............................................................. 101
Table 33: Estimates of Change in Children Engaged in Child Labor and Exposure to
Hazardous Labor of Children Working in Cocoa Production in Areas with High,
Medium and Low Cocoa Production, All Agricultural Households, in Côte d’Ivoire
and Ghana, in 2008/09 and 2018/19 ....................................................................... 103
Table 34: Estimates of Change in Cocoa Households’ Use of Agro-Chemical Products
Expenditure Per Ton of Cocoa in USD in the Last 12 Months, in Côte d’Ivoire
and Ghana, 2013/14 and 2018/19........................................................................... 105
Table 35: Estimates of Average Number of Children, 5-17 Years, All Agricultural
Households, by Age Group, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19 .... 107
Table 36: Estimates of Children, 5-17 Years, Exposed to Various Types of Hazardous
Work Activities by the Côte d’Ivoire Country Definition, in Côte d’Ivoire, 2018/19* . 109
Table 37: Estimates in Children, 5-17 Years, Exposed to Various Types of Hazardous
Work Activities by the Ghana Country Definition, in Ghana, 2018/19* .................... 110
Table 38: Prevalence of Children Engaged in Child Labor and Hazardous Child Labor byCountry Definitions, All Agricultural Households, 5-17 Years, in Côte d’Ivoire andGhana, 2018/19....................................................................................................... 110
Table 39: Number of EAs per Stratum .................................................................................... 165
Table 40: Illustration of Weight Construction........................................................................... 166

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Table 41: Types and Numbers of Interviews Completed, by Region, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2018/19 .................................................. 176
Table 42: Household Head and Child Survey Response Rates, Côte d’Ivoire and Ghana,
All Agricultural Households, 2018/19 ...................................................................... 177
Table 43: Place of Birth and Nationality of Survey Respondents: Children, All Agricultural
Households, 5-17 Years, in Côte d'Ivoire and Ghana, 2018/19 .............................. 177
Table 44: Household Land Under Cultivation and Under Cocoa Cultivation, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2018/19 .................................................. 178
Table 45: Selected Characteristics (Age Group, Gender) of Children Engaged in Child
Labor in Agriculture, All Agricultural Households, 5-17 Years, in Côte d’Ivoire
and Ghana, 2008/09 and 2018/19........................................................................... 178
Table 46: Selected Characteristics (Age Group, School Attendance) of Children Engaged
in Child Labor in Agriculture, All Agricultural Households, 5-17 Years, in Côte
d’Ivoire and Ghana, 2008/09 and 2018/19 .............................................................. 178
Table 47: Prevalence of Child Labor By School Attendance, All Agricultural Households, 517 Years, in Côte d’Ivoire and Ghana, 2018/19 ...................................................... 179
Table 48: Working Hours and Minimum Age, Children Working in Cocoa Production, By
Sex, All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and2018/19 ................................................................................................................... 179
Table 49: Estimates of Change Children, 12-17, Engaged in Light Work and Regular Workin the Cocoa Production, All Agricultural Households, in Côte d'Ivoire andGhana, 2008/09 and 2018/19.................................................................................. 180
Table 50: Child Work Involved in Cocoa Production, All Children 5-17 Years, All
Agricultural Households, in Côte d’Ivoire and Ghana, 2008-09 and 2018-19 ......... 181
Table 51: Children Engaged in Child Labor and Hazardous Work in Cocoa Production, All
Agricultural Households, 5-17 Years, by Sex and Age Group in Côte d’Ivoire andGhana, 2008/09 and 2018/19.................................................................................. 183
Table 52: Estimates of Percentages of Children Exposed to Hazardous Work* Activities in
the Cocoa Sector, 5-17 Years, All Agricultural Households, by Age Group in
Côte d’Ivoire and Ghana, 2008/09 and 2018/19 ..................................................... 184
Table 53: Estimates of Percentages of Children Exposed to Hazardous Work* Activities in
the Cocoa Sector, 5-17 Years, By Sex, All Agricultural Households, in Côte
d’Ivoire and Ghana, 2008/09 and 2018/19 .............................................................. 186
Table 54: Exposure to Agro-Chemicals, Children Working in Cocoa Production in the Last
12 Months, All Agricultural Households, by Age Group and by Sex, in Côte
d’Ivoire and Ghana, 2008/09 and 2018/19 .............................................................. 187
Table 55: Prevalence of Children Engaged Child Labor and Hazardous Child Labor in
Cocoa and Non-Cocoa Households, 5-17 Years, in Côte d’Ivoire and Ghana,
2018/19 ................................................................................................................... 187

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Table 56: Working Hours in Any Economic Activity and Minimum Age, Children in Cocoa
Households Working in Cocoa Production, in Côte d’Ivoire and Ghana, 2013/14
and 2018/19 ............................................................................................................ 188
Table 57: Working Hours in Any Economic Activity and Minimum Age, Children in Cocoa
Households, Working in Cocoa Production, By Sex, in Côte d’Ivoire and Ghana,
2013/14 and 2018/19 .............................................................................................. 189
Table 58: Children in Cocoa Households, Engaged in Child Labor and Hazardous Child
Labor* in Cocoa Production, 5-17 Years, by Sex and Age group in Côte d’Ivoire
and Ghana, 2013/14 and 2018/19........................................................................... 190
Table 59: Estimates of Percentages of all Children in Cocoa Households, 5-17 Years, By
Age Group, Exposed to Hazardous Work* Activities in the Cocoa Sector, in Côte
d’Ivoire and Ghana, 2013/14 and 2018/19 .............................................................. 191
Table 60: Estimates of Percentages of all Children in Cocoa Households, 5-17 Years, By
Sex, Exposed to Hazardous Work* Activities in the Cocoa Sector, in Côte
d’Ivoire and Ghana, 2013/14 and 2018/19*............................................................. 193
Table 61: Estimates of Exposure of Children Working in Cocoa Production in Cocoa
Households, 5-17 Years, to Hazardous Work, by Count, in Côte d'Ivoire andGhana, 2013/14 and 2018/19.................................................................................. 194
Table 62: Disaggregation of Exposure to Agro-Chemicals, Children in Cocoa Households
Working in Cocoa Production in the Last 12 Months, by Age Group and Sex, inCôte d’Ivoire and Ghana, 2013/14 and 2018/19 ..................................................... 195
Table 63: Children in Cocoa Households, Children Engaged in Child Labor and Hazardous
Work in Cocoa Production, by School Attendance, 5-17 Years, in Côte d’Ivoire
and Ghana, 2013/14 and 2018/19........................................................................... 197
Table 64: Estimates of Change in Children Engaged in Child Labor and Exposure to
Hazardous Labor of Children Working in Agriculture By the 7 Day and 12 MonthDefinitions, in Côte d’Ivoire and Ghana, in 2018/19 ................................................ 199
Table 65: Prevalence of Children, 5-17 Years, Exposed to Various Types of Hazardous
Work Activities by the 7 Day and 12 Month Definitions, in Côte d’Ivoire andGhana, 2018/19* ..................................................................................................... 200
Table 66: Prevalence of Children, 5-17 Years, Exposed to Land Clearing Components in
Cocoa by Frequency, in Côte d’Ivoire and Ghana, 2018/19 ................................... 203
Table 67: Prevalence of Children, 5-17 Years, Exposed to Heavy Loads Components in
Cocoa by Frequency, in Côte d’Ivoire and Ghana, 2018/19 ................................... 204
Table 68: Prevalence of Children, 5-17 Years, Exposed to Agrochemicals Components in
Cocoa by Frequency, in Côte d’Ivoire and Ghana, 2018/19 ................................... 205
Table 69: Prevalence of Children, 5-17 Years, Exposed to Sharp Tools Components in
Cocoa by Frequency, in Côte d’Ivoire and Ghana, 2018/19 ................................... 206
Table 70: Estimate of Proportion of Children Engaged in Child Labor and Exposure to
Hazardous Child Labor in Cocoa by Parent and Household Characteristics, All
Agricultural Households, in Côte d’Ivoire and Ghana, 2018/19 ............................... 209

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Table 71: Estimate of Proportion of Child Labor and Exposure to Hazardous Child Labor in
Cocoa by Community Characteristics, All Agricultural Households, in Côte
d’Ivoire and Ghana, 2018/19 ................................................................................... 211
Table 72: Estimate of Proportion of Child Labor and Exposure to Hazardous Child Labor in
Cocoa by Child, School Characteristics, All Agricultural Households, in Côte
d’Ivoire and Ghana, 2018/19 ................................................................................... 213
Table 73: Ghana Entropy Balancing: Differences in Covariates Affecting Selection Before
and After Balancing ................................................................................................. 222
Table 74: Ghana, Probit Regression and Generalized Linear Regression: Education
Material Assistance and Child Labor and Hazardous Child Labor .......................... 223
Table 75: Côte d’Ivoire Entropy Balancing: Differences in Covariates Affecting Selection
Before and After Balancing ..................................................................................... 225
Table 76: Côte d’Ivoire, Probit Regression and Generalized Linear Regression: Education
Material Assistance and Child Labor and Hazardous Child Labor .......................... 226
Table 77: Côte d’Ivoire Entropy Balancing: Differences in Covariates Affecting Selection
Before and After Balancing ..................................................................................... 230
Table 78: Côte d’Ivoire, Probit regression and Generalized Linear Regression: Livelihood
Service Support and Child Labor and Hazardous Child Labor ................................ 231
Table 79: Ghana Entropy Balancing: Differences in Covariates Affecting Selection Before
and After Balancing ................................................................................................. 235
Table 80: Ghana Participation in Occupational Safety and Health Training and Likelihoodof Exposure to Hazardous Child Labor ................................................................... 236
Table 81: Distribution of communities in Côte d’Ivoire by treatment combination ................... 240
Table 82: Distribution of communities in Ghana by treatment combination ............................ 240
Table 83: Matching Balance Check: Covariates Balance Before and After Mahalanobis
Metric Matching in Côte d’Ivoire .............................................................................. 244
Table 84: Impact of Multiple Treatment on Likelihood of having Child Labor and Hazardous
Child Labor in Côte d’Ivoire: Probit Regression ...................................................... 245
Table 85: Impact of Multiple Treatment on the Household-level rate of Child Labor andHazardous Child Labor in cocoa production: Average treatment effects
estimates based on GLM in Côte d’Ivoire ............................................................... 247
Table 86: Comparison of Population Counts, Prevalence Rates with Different Sampling
Weights ................................................................................................................... 273

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Acronyms
CAPI

Computer Assisted Personal Interviewing

CCP

Cocoa Communities Project

CDI

Côte d’Ivoire

CEA

Census Enumeration Areas

CIT

Cognitive Interview Technique

CIM

Interministerial Committee to Combat Trafficking, Exploitation and Child Labour

CLChild labor

CLCCG

Child Labor Cocoa Coordinating GroupCOCOBOD

Ghana Cocoa Board

CNS

Comité National de Surveillance des Actions de Lutte contre la Traite,
l’Exploitation et le travail des Enfants

CSOs

Civil Society Organizations

DC

District of Columbia

DOL

Department of Labor

EA

Enumeration Area

ECLIC

Eliminating Child Labor in Cocoa

EG

Expert Group

ENSEA

Ecole Nationale de Statistique et d’Economie Appliquée

FGD

Focus Group Discussion

GAP

Good Agricultural Practices

GAWU

General Agricultural Workers’ Union of Ghana

GCLMS

Ghana Child Labor Monitoring System

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GDP

Gross Domestic Product

GLM

Generalized Linear Model

GSS

Ghana Statistical Service

GPS

Global Positioning Satellite

HCL

Hazardous child labor

HH

Household

ICCO

International Cocoa Organization

ICI

International Cocoa Initiative

ICLS

International Conference of Labor Statisticians

ILAB

Bureau of International Affairs, U.S. Department of Labor

ILO

International Labour Organization

INS

Insitut National de la Statistique de Côte d’Ivoire

IPEC

International Programme on Eliminating Child Labour

IRB

Institutional Review Board

ISSR

Institute of Statistical Social and Economic Research

JHS

Junior High School

KG

Kindergarten

KII

Key Informant Interviews

KM

Kilometer

MOCA

Mobilizing Community Action and Promoting Opportunities for Youth in
Ghana’s Cocoa Growing Communities

NGOs

Non-governmental organizations

NORC

NORC at the University of Chicago

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NPA

National Plan of Action for the Elimination of the Worst Forms of Child Labor

OCFT

Office of Child Labor, Forced Labor and Human Trafficking

OSH

Occupational Safety and Health

PSM

Propensity Matching Score

PSU

Primary Sampling Unit

PTA

Parent Teacher Association

SHS

Senior High School

SOSTECI

Système d'Observation et de Suivi du Travail des Enfants en Côte d’Ivoire

SSU

Secondary Sampling Unit Data Science

STATA

Statistical Software for

TOT

Training of Trainers

WCF

World Cocoa Foundation

WFCL

Worst forms of child labor

UNICEF

United Nations Children’s Fund

USD

United States Dollars

USDOL

United States Department of Labor

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1 Executive Summary
1.1

BackgroundIn 2010, in response to evidence of children working under hazardous conditions in the West
African cocoa sector, the Governments of Côte d’Ivoire and Ghana, representatives from theInternational Chocolate and Cocoa Industry (Industry), and the U.S. Department of Labor (DOL)
signed a Declaration and Framework tied to the Harkin-Engel Protocol, under which Industry
publicly acknowledged child labor in the cocoa sector and committed to take steps to address it.
In the signing of the Declaration and Framework, these partners committed to take action to
reduce child labor and the worst forms of child labor (WFCL) in cocoa production towards thegoal of achieving a 70 percent reduction in the WFCL in the cocoa sectors of the two countries in
the aggregate by 2020. The Child Labor Cocoa Coordinating Group (CLCCG) was established tocoordinate efforts among the partners working under the Declaration and Framework.
The Framework lays out multiple goals to support implementation of the Declaration and further
the aims of the original Protocol. Among those goals was the continuation of nationally
representative child labor surveys, recurring at least every 5 years. The aim of the surveys was toprovide comparable data for ongoing assessments of child labor prevalence in cocoa growing
areas of Côte d’Ivoire and Ghana and included a commitment to make publicly available therelated survey methodologies, data, and reports based on the findings of these surveys.
Under this goal, surveys were carried out by Tulane University during the 2008/09 and 2013/14
cocoa harvest seasons in Côte d’Ivoire and Ghana. In 2016, NORC at the University of Chicago(NORC) was awarded a four-year cooperative agreement by the USDOL Bureau of International
Labor Affairs 1 (ILAB) to implement the 2018/2019 Assessing Progress in Reducing Child Laborin Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana project.
1.2

The Present Study: Objective and Important Considerations
Objectives

The first objective of this study is to assess and measure changes in the prevalence of working
children, children in child labor, and children in hazardous work in the cocoa growing areas ofCôte d’Ivoire and Ghana between 2008/09 and 2018/19. The second objective of the study is toassess interventions implemented between 2008/09 and 2018/19, providing stakeholders and
1

ILAB's mission is to promote a fair global playing field for workers in the United States and around the world by
enforcing trade commitments, strengthening labor standards, and combating international child labor, forced labor,
and human trafficking. For more information please refer to the ILAB website:
https://www.dol.gov/agencies/ilab/about-us/mission

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policymakers with information on the efficacy of programming during that period. The final
objective of this project is to provide accurate point estimates of child labor and hazardous childlabor in the cocoa producing areas of Côte d’Ivoire and Ghana for 2018/19.
NORC spent the last five years working closely and collaboratively with ILAB, the governmentsof Côte d’Ivoire and Ghana, Industry (represented by the World Cocoa Foundation (WCF)),
Civil Society Organizations (CSOs), and multiple international organizations with an interest inchild labor (including UNICEF, the International Cocoa Initiative (ICI), and the International
Labour Organization (ILO)). This report is the outcome of that collaborative effort to accurately
measure and report on the issue of child labor and hazardous child labor in the cocoa growing
areas of Ghana and Côte d’Ivoire.
Important Considerations
There are several important considerations readers of this report should be aware of. First is thedifference between child labor in general and
Focus: Child Labor
forced labor (or forced child labor/child slavery).
Child Labor is defined by ILO Conventions 138
This study focuses on child labor andhazardous child labor as defined by ILO
on the Minimum Age for Admission toconventions and does not cover forced child
Employment and 182 on the Worst Forms oflabor, child slavery, or child trafficking.
2Child Labor. These key international
conventions prohibit the employment of children below a minimum age (as established bynational legislation), define the worst forms of child labor, prohibit older children who arepermitted to work from engaging in hazardous work which is likely to harm the health and safetyof children, protect all children under 18 from WFCL, and forbid hazardous unpaid household
services. Forced Labor is defined by ILO Convention 29 as “all work or service exacted fromany person under the menace of any penalty and for which the said person has not offeredhimself voluntarily” (for children under menace of penalty the idea of “voluntariness” becomesmoot and is not applicable).
This research focuses on child labor and hazardous child labor, not forced child labor/childslavery and other forms of the WFCL. Although the issue of forced child labor in cocoa
production is important and deserves attention, the research methodology required to accurately
measure forced child labor is very different from the methodology 3 adopted for the current scopeof our research which was determined at the onset of this research in 2008/09. To remain

2

Article 3(a) of Convention 182 includes forced or compulsory labor as a WFCL.
Forced child labor is, by definition, an illicit activity with a vulnerable and hard-to-reach population. Probability
based research methods would need to account for that from the outset and focus on areas where vulnerability would
be highest (for example, encampments and forest land).
3

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consistent with previous rounds of the survey, this report only speaks to the issue of child laborin cocoa production in Côte d’Ivoire and Ghana and uses hazardous child labor (HCL) as a proxy
for WFCL. A child is considered to be involved in hazardous child labor if they have participatedin any hazardous work activities. 4
A second important consideration is understanding the difference between aggregate andindividual country statistics presented in this report. Aggregate statistics combine data from thecocoa growing areas of Côte d’Ivoire and Ghana, per the Harkin-Engel Protocol, using acommon definition of child labor and hazardous child labor. Individual country statistics are
estimated using both a common definition of hazardous child labor, as well as country-specificdefinitions of hazardous child labor presented in more detail in Sections 3.3.3 and 3.3.4.
Finally, it is important to note what longitudinal data is comparable for this study. First, due toissues in survey implementation during the 2013/14 data collection round, it is not possible tocompare the data from all agricultural households 5 between 2013/14 and 2018/19. Instead, weonly compare data collected from cocoa growing households 6 during this time period. 7
Consequently, the comparison of recent trends between 2013/14 and 2018/19 is based on datafrom cocoa growing households only. 8 Additionally, the sampling frames for 2008/09 and2018/19 survey rounds were not exactly identical and thus population counts of the number ofchildren in child labor are not comparable between rounds and, for the purposes of the report andanalysis, we cannot make claims in terms of changes found in population counts. However, it ispossible to undertake the comparison of the prevalence rates (ratios between all children andchildren in child labor or hazardous child labor) between 2008/09 and 2018/19 as the differencein sampling frames are unlikely to influence the inferences made from the comparison of ratios
and thus are presented throughout the report. 9
A second issue with the comparability of data from the 2008/09 round is due to a lack of
documentation from the 2008/09 survey round. 10 Due to this lack of documentation, we were4

See Section 3.3.3 for more details on the different types of hazardous work activities considered.

5

Agricultural households are defined as households where at least one member (adult or child) was involved inwork in any type of agriculture/farming in the past 12 months.

6

Cocoa growing households are defined as households where the head of household indicated that cocoa farming
was a type of agriculture carried out by the household.

7

This report focuses on multiple levels of analysis moving from the child labor situation among all agricultural
households in the cocoa growing regions of Côte d’Ivoire and Ghana down to the child labor situation in only thosehouseholds that are active in cocoa production.

8

Please refer to the Section 4.3 for a detailed description of the issues.
Please refer to the Section 4.3 for a detailed description of the issues. Additional information on comparability canalso be found in the expert group’s finding in Annex 10.12.

910

Please refer to the Section 4.3 for a detailed description of the issues.

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unable to generate the child labor and hazardous child labor estimates separately for cocoa
growing and non-cocoa growing households. What this means for the report is that we are unable
to compare cocoa growing households in terms of prevalence rates between 2008/09 and2018/19.
To summarize, we are able to compare the estimates of prevalence rates between 2008/09 and2018/19 for all agricultural households, and between 2013/14 and 2018/19 for cocoa growing
households. Therefore, the statistics in this report are split into two segments; 2008/09-2018/19
comparisons for agricultural households and 2013/14-2018/19 for cocoa growing households.
1.3

Methodology

Towards meeting the two-fold objectives of this study, NORC conducted a sectoral survey
designed to be representative of all children age 5-17 living in agricultural households in thecocoa growing areas 11 during the 2018/19 main cocoa harvest season in Côte d’Ivoire and Ghana
in order to develop population estimates for the prevalence of child labor and hazardous childlabor in those regions. A detailed description of our methodology can be found in Section 4 andinformation on the number of surveys completed by country and comparisons to the 2008/09
round can be found in Section 4.2.
The data from the 2018/19 was then compared with the data from the previous survey rounds toassess how the main outcome indicators of interest – the prevalence of children in child labor andthe prevalence of children in hazardous work in the cocoa sector 12– changed between 2008/09
and 2018/19. The subsequent analysis of child labor and hazardous child labor rates arepresented in Chapter 4 and 5 of this report.
For addressing the second main objective of this report – assessing relative effectiveness ofvarious interventions funded by the members of the CLCCG and other stakeholders inaddressing child labor issues – NORC utilized a mixed-methods approach based on quantitativeand qualitative analysis. A detailed description of the methods used is presented in Chapter 8 ofthis report.
1.4

The Draft report, Stakeholders’ concerns and the Independent Expert GroupNORC completed preliminary analysis on the data in late 2019 and early 2020, and, as part ofthe collaborative process, shared the preliminary results with stakeholders in early 2020 in a draft
report. As the analysis in the draft report was not yet finalized, data and findings from the draft
report should not have been considered final or complete. Unfortunately, that draft report was
11

Including children working on cocoa farms, children performing other work and children who are not working.

12

Cocoa sector is defined as the cocoa growing areas of Côte d’Ivoire and Ghana

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leaked to news sources and the preliminary results were circulated beyond the intended
recipients, leading to concerns being expressed outside of that group.
To alleviate resulting stakeholder concerns on the draft report, some of which are expressedagain in the stakeholder statements that follow this Executive Summary, NORC proposed theformation of an Expert Group to review aspects of the NORC sampling methodology.
In July 2020, the Fundamental Principles and Rights at Work Branch of the International Labour
Organization graciously agreed to organize this Expert Group comprising statisticians and survey
professionals to look carefully at the sampling methodology, assess the validity of the 2018/19
prevalence estimates, and assess the comparability of the 2018/19 survey round with the priorrounds.
The Expert Group’s complete findings can be found in Annex 10.12. Their main
recommendations were:
1. Include household selection probabilities when estimating sampling weights for the 2018/19
surveys.
2. Include estimates of population counts and prevalence rates for 2018/19 using the weights
based on the household selection probabilities and construct variance estimates accordingly.
3. Attach to the major outcomes of interest (e.g. number of working children, number ofchildren in child labor, number of children in hazardous work, child labor rate, and hazardous
work rate) a precision measure (such as variance, standard error, confidence internal orcoefficient of variation) so readers can form their own conclusions on the accuracy of thedata.
4. Add clarification on the methodological limitations of comparison of prevalence rates andproportions of different variables across survey rounds due to the difference between thesampling frame used by NORC for the 2018/19 survey round and used by Tulane University
in previous survey rounds, which could lead to potential bias in the comparison of point
estimates across survey rounds.
Following the guidance provided by the Expert Group, we revised our sampling weight
construction and generated population estimates of counts, prevalence rates, and proportions for
2018/19. The corresponding updated estimates are presented in this final report.
Additionally, following the recommendations of the Expert Group, the tests of significance ofdifference between the prevalence rates/proportions of the 2008/09-2018/19 and 2013/142018/19 survey rounds used Rao-Wu bootstrap standards errors of 2018/19 survey round.
Further, we only present differences on key outcomes of interest found to be significant at a 1
percent level of significance (i.e. p-values less than or equal to 0.01). This ensures that theFINAL REPORT | 5

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conclusions of the study regarding the change in prevalence rates and proportions of different
variables across survey rounds are unlikely to be affected by methodological limitations.
Finally, given the issue of comparability of sampling frames and its implication on thecomparability of population counts within survey rounds, this report does not present population
counts of the previous survey rounds (2008/09 & 2013/14).
NORC is fully committed to research transparency. Data for this report is publicly available andestimates presented herein are fully replicable. It is our hope that researchers will use the data for
additional research on child labor and hazardous child labor in cocoa production.
1.5

CLCCG Stakeholder Engagement

There has been extensive engagement by the CLCCG stakeholders to confront child labor overthe last ten years. Given the depth and breadth of projects that may impact child labor, we areunable to list all of the projects and programs funded by the stakeholders over a ten-year period
and across two countries. Examples of this engagement include major initiatives from theGovernments of Côte d’Ivoire and Ghana, the USDOL, and the International Cocoa and
Chocolate Industry to confront the issue of child labor in cocoa supply chains. Note that theseexamples were provided by each stakeholder and not independently assessed by NORC:

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Provided by the Government of Côte d’Ivoire

Provided by the Government of Ghana

Over the past ten years, the Ivorian government has
designed three successive national action plans tofight child labor and trafficking of children, with amajor impact on reducing child labor. In 2019, Côte
d’Ivoire was one of 12 countries that received fromUSDOL the highest assessment of “Significant
Advancement” for its work on child labor in cocoa
production in addition to other efforts to address theworst forms of child labor. The country has achieved
this designation each year from 2014 to 2019.
Important changes in its education system andmassive construction of schools in rural areas
resulted in dramatic improvements in school
enrolment rates from 59 percent to 85 percent. The
government also established SOSTECI, amechanism for monitoring child labor in cocoa,
strengthened its child labor laws, and allocatedadditional financial and material resources to antichild labor police. Today traffickers are facing up to
20 years in jail. Regionally, the government signed
bilateral agreements with neighboring countries toeradicate child trafficking at its source. Since 2019,an important mechanism is gradually being
implemented nationwide by the government toestablish transparency and traceability in the cocoa
supply chain.

Initiatives by the Government of Ghana include
the Cocoa Productivity Enhancement
Programme which attempts to reducechildren’s exposure to agro-chemicals throughgovernment-funded mass spraying in highcocoa productivity regions. The Ghana Cocoa
Board Child-Education Support Programme
was designed to provide support to children of
cocoa farmers in all seven cocoa growing
regions and districts to be in school and relieve
farmers of financial encumbrances associatedwith schooling. Finally, the Government of
Ghana has had a strong focus on increasing
school attendance through multiple school
initiatives over the last 20 years. These
initiatives have led to a dramatic increase inschool attendance for children across Ghana,
which are also reflected in this study’s findings.

Provided by the USDOL

Provided by Industry

Initiatives undertaken by the United States
Department of Labor (USDOL) to combat child laborin the cocoa sectors of Ghana and Côte d’Ivoire
under the Declaration and accompanying Frameworkof Action include funding the Towards Child Labor
Free Cocoa Growing Communities in Côte d’Ivoire
and Ghana project (Cocoa Communities Project or
CCP) implemented by the International Labor
Organization (ILO), the Eliminating Child Labor in
Cocoa (ECLIC) project in Côte d’Ivoire implemented
by the International Cocoa Initiative (ICI), and the
Mobilizing Community Action and Promoting
Opportunities for Youth in Ghana’s Cocoa Growing
Communities (MOCA) projects implemented byWinrock International. These projects had a specificfocus on community mobilization through community
action plans and committees, the provision of
relevant, quality education for children engaged in or
at risk of child labor and enhanced sustainable
livelihoods for their households. USDOL also funded
all three survey rounds described in this report.

Over the past decade, Industry funded theconstruction and rehabilitation of schools andprovision of school supplies and othereducation support services in hundreds ofcommunities in Côte d’Ivoire and Ghana. In
addition to implementing child laborawareness-raising activities, Industry supported
child labor data collection and monitoring
through community and supply chain-basedchild labor monitoring and remediation
systems, including an ILO-IPEC project thatdeveloped and expanded community-basedchild labor monitoring systems. Industry
supported activities to increase the incomes ofhouseholds with at-risk children and provided
supplementary funding to the International
Cocoa Initiative to expand its work to
implement community action plans, train
community child protection committees andbuild classrooms.

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1.6

Definitions of Child Labor and Hazardous Work

The graphic below is helpful for understanding the component parts of child labor and hazardous
child labor discussed in this report. Our study defines child labor in the cocoa sector as any child
5-17 years old who works in cocoa farming and is a) economically active below the age forallowable work or b) taking part in one of the six hazardous activities listed below.
Figure 1: Graphic Representation of Common Definition of Child Labor

The current study uses a “common definition” of child labor and hazardous child labor focusing
on the “common ground” between the Ghanaian and the Ivorian legal definitions within abroader ILO framework. 13 According to the common definition, a child is categorized as
participating in child labor if they have exceeded the maximum allowable working hours fortheir age group and/or are exposed to hazardous work activities. Children are categorized as
participating in hazardous work if they are exposed to at least one subcategory of the common
definition as described below:
1. Conducting land clearing,
2. Carrying heavy loads,
3. Using agro-chemicals,
4. Using sharp tools,
5. Engaging in long working hours, or

The common definition was developed by Tulane University. For more details, please consult the study report:
Survey Research on Child Labor in West African Cocoa Growing Areas, Final Report, 2013-14, Tulane University:
https://www.dol.gov/sites/dolgov/files/ILAB/research_file_attachment/Tulane%20University%20%20Survey%20Research%20Cocoa%20Sector%20-%2030%20July%202015.pdf
13

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6. Engaging in night work.
Before moving to the main findings, it is important to present some key demographic
information from our 2018/19 sample as well as cocoa production and price dynamics to aid inunderstanding the main findings of the study:■

61 percent of child respondents (59 percent in Ghana and 64 percent in Côte d’Ivoire) werein the 5-11 year old age group with 17 percent in the 5-6 year old age group (overall age
ranges followed a distribution reflective of the population of children).

■

The majority (99 percent) of children aged 5-17 were living with their parents or anotherrelative at the time of the interview.

■

Among the children who were economically active, 78 percent (82 percent in Ghana and 74
percent in Côte d’Ivoire) were working for their own family’s farm.

■Among the children who were working in cocoa farming, 81 percent (83 percent in Ghana
and 78 percent in Côte d’Ivoire) were working for their parents (father/mother) and 94
percent (97 percent in Ghana and 90 percent in Côte d’Ivoire) reported working for either
their parents or other relatives.

■

The median farm size of households was 12.5 acres (approximately 5 hectares) in Côte
d’Ivoire and 7 acres (approximately 3 hectares) in Ghana.

Overall, these demographics indicate that mostchildren in this study were from families ofsmallholder farmers 14 conducting farming
activities as part of their household’s normal
economic activity.

Quantitative Insight
Cocoa is a key part of agriculture in the cocoa
growing areas of both countries as
demonstrated by the fact that a predominantmajority of agricultural households grow cocoa.

Over the same ten-year time period (2008/09 and 2018/19), cocoa production across Côte
d’Ivoire and Ghana, measured as estimated tons of cocoa produced, increased from 1.9 million
tons in 2008/09 to 3.1 million tons in 2018/19. 15 The global price of cocoa over the same time
period increased from $2,263 USD/ton to $2,626 USD/ton. The increase in production and price
may explain this study’s finding that the proportion of agricultural households involved in any
type of cocoa production (with or without child labor) increased from 55 percent in 2008/09 to84 percent in 2018/19 in cocoa growing areas of Côte d’Ivoire and Ghana.

14

Smallholder farmers are defined to be owning land up to 5 hectare in Côte d’Ivoire:(https://www.cgap.org/sites/default/files/Working-Paper-Survey-and-Segmentation-Smallholders-Coted%27IvoireJul-2017.pdf) and up to 3.2 hectares Ghana:
http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/37971/filename/37972.pdf).
15

According to the estimates reported by the International Cocoa Organization (ICCO).

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We present the study findings below starting with the population estimates of key outcomes ofinterest including child labor and hazardous child labor in cocoa production in 2018/19. This isfollowed by a summary of key findings on the comparison of the prevalence rates of child laborand hazardous child labor in cocoa growing areas of Côte d’Ivoire and Ghana between survey
rounds. We then present more detailed findings from the survey and the assessment ofeffectiveness of interventions implemented by the key stakeholders in both countries.
1.7

Main Findings

The 2018/19 data from agricultural households (with at least one child in the 5-17 age group) in
the cocoa growing areas of Côte d’Ivoire and Ghana indicate that approximately:■

1.56 million children were engaged in child labor in cocoa production (including
approximately 790,000 children in Côte d’Ivoire and 770,000 in Ghana).

■

1.48 million children were exposed to at least one component of hazardous child labor incocoa production (including approximately 770,000 children in Côte d’Ivoire and 710,000 in
Ghana) under the common definition.

The data on the prevalence of child labor in cocoa production (proportion of children in cocoa
growing areas age 5-17 engaged in child labor in cocoa production) indicates that in 2018/19:■

45 percent of children living in agricultural households in cocoa growing areas age 5-17 were
engaged in child labor in cocoa production in aggregate across Côte d’Ivoire and Ghana.

■

The country-specific data indicates that in cocoa growing areas 38 percent of children in Côte
d’Ivoire and 55 percent of children in Ghana living in agricultural households were engaged
in child labor in cocoa production.

The data on prevalence of hazardous child labor in cocoa production (proportion of children incocoa growing areas age 5-17 engaged in hazardous work in cocoa production) indicates that in2018/19:■43 percent of children living in agricultural households in cocoa growing areas age 5-17 were
engaged in hazardous work in cocoa production in aggregate between the two countries.

■

The country-specific data indicates that in cocoa growing areas 37 percent of children in Côte
d’Ivoire and 51 percent of children in Ghana living in agricultural households were engaged
in hazardous work in cocoa production.

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Below we present the comparison of changes in the prevalence rates over time, first focusing on
the recent trends during the past five year period (2013/14-2018/19) and then presenting thelong-term trends based on the comparison between 2008/09 and 2018/19 periods. 16
Recent Trends with Children in Cocoa Growing Households (2013/142018/19)
1.7.1.1

Child Labor

Between 2013/14 and 2018/19, the prevalence rate ofchild labor in cocoa production among cocoa growing
households of Côte d’Ivoire remained stable at around41 percent. During the same period, there was no
statistically significant change in the child labor rates incocoa production among cocoa growing households in
Ghana (remaining stable at around 58 percent).
1.7.1.2

Recent Trends: Child Labor in
Cocoa Growing Households
Amid a 14 percent increase in cocoa
production between 2013/14 and2018/19 in Côte d’Ivoire and Ghana inaggregate, the prevalence rate of childlabor in cocoa production among cocoa
growing households in each individual
country remained stable.

Hazardous Child Labor

While cocoa production during this period increased 14 percent in aggregate across Côte d’Ivoire
and Ghana, there was no significant increase in hazardous child labor in cocoa production among
cocoa growing households in Côte d’Ivoire andRecent Trends: Hazardous Child
Ghana between 2013/14 and 2018/19
Labor in Cocoa Growing Households(remaining stable at around 39 percent in Côte
Amid a 14 percent increase in cocoa production
d’Ivoire and 55 percent in Ghana). The fact that between 2013/14 and 2018/19 in Côte d’Ivoire
and Ghana in aggregate, the prevalence rate of
the percentage increase in hazardous child labor hazardous child labor in cocoa production
among cocoa growing households remained
was not statistically significant and the increasestable in each individual country.
in cocoa farming was large may indicate thepositive impact of increased interventions, including a higher priority given to reducing childlabor in the cocoa sector of Côte d’Ivoire and Ghana by the national governments, internationalorganizations, and the Industry stakeholders in recent years.

It is important to note that as per the Expert Group recommendation, while comparing the data between two
rounds, we will consider a difference to be statistically significant if the difference is found to be significant at a 1
percent level of significance (corresponding to p-value of 0.01 or smaller).

16

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Historic Trends with Children in All Agricultural Households (2008/092018/19)
As the main objective of the study, we compare the trend in prevalence rate of child labor andhazardous child labor between 2008/09 and 2018/19 using data from all agricultural householdswith at least one eligible child age 5-17 (including both cocoa growing households and otheragricultural households that did not grow cocoa).
1.7.2.1

Child LaborHistoric Trends: Child Laborin Agricultural Households
Amid a 62 percent increase in cocoa
production between 2008/09 and2018/19 in Côte d’Ivoire and Ghana
in aggregate, the prevalence of childlabor in cocoa production among all
agricultural households increased 14
percentage points.

The prevalence rate of child labor (proportion of children
age 5-17 in child labor) in cocoa production among
agricultural households in cocoa growing areas of Côte
d’Ivoire and Ghana increased by 14 percentage points
between the 2008/09 and 2018/19 survey rounds (from 31
percent in 2008/09 to 45 percent in 2018/19). It is important to consider that there was a 62
percent increase in cocoa production during this same time in Côte d’Ivoire and Ghana inaggregate.

In Côte d’Ivoire the prevalence rate of child labor in cocoa production among all agricultural
households increased from 23 percent in 2008/09 to 38 percent in 2018/19, while in Ghana theprevalence rate increased from 44 percent in 2008/09 to 55 percent in 2018/19.
1.7.2.2

Hazardous Child LaborHistoric Trends: Hazardous Child
Labor in Agricultural Households

The prevalence rate of hazardous child labor in
Amid a 62 percent increase in cocoa
cocoa production in agricultural households inproduction between 2008/09 and 2018/19 in
Côte d’Ivoire and Ghana in aggregate, thecocoa growing areas of Côte d’Ivoire and Ghana
prevalence rate of hazardous child labor inincreased by 13 percentage points in aggregatecocoa production among all agricultural
between 2008/09 and 2018/19 (from 30 percent inhouseholds increased 13 percentage points.
2008/09 to 43 percent in 2018/19). It is important toconsider that there was a 62 percent increase in cocoa production during this same period in Côte
d’Ivoire and Ghana in aggregate.
In Côte d’Ivoire the prevalence rate of hazardous child labor in cocoa production among all
agricultural households increased from 23 percent in 2008/09 to 37 percent in 2018/19, while in
Ghana the prevalence rate increased from 43 percent in 2008/09 to 51 percent in 2018/19.
Comparison of trends over time indicate that despite the efforts made by the governments,
Industry, and other key stakeholders in combating child labor and hazardous child labor during
the past 10 years, the child labor and hazardous child labor prevalence rates did not go down.

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1.8

General Quantitative Insights
School Attendance

Between 2008/09 and 2018/19, in the cocoa growing areas of Côte d’Ivoire and Ghana, school
attendance among children 5-17 increased
Quantitative Insight
significantly. In Côte d’Ivoire, school attendance
School attendance among children inamong children in agricultural households in cocoa
agricultural households increased from 58
growing areas increased by 22 percentage points (from percent to 80 in Côte d’Ivoire and from 89
to 96 percent in Ghana.
58 percent in 2008/09 to 80 percent in 2018/19) andschool attendance among Ghanaian children in cocoa
growing areas increased from 89 percent to 96 percent between 2008/09 and 2018/19. The
school attendance data suggests that reforms in both countries and a greater push for education
have led to significant gains in levels of school attendance among children in agricultural
households in the cocoa growing areas of the two countries.
School attendance among children in agricultural households:■■■

For the 5-11 age group, school attendance increased from 60 percent to 81 percent for Côte
d’Ivoire and 89 percent to 97 percent in Ghana.
For the 12-14 age group, school attendance increased from 68 percent to 88 percent in Côte
d’Ivoire and 93 percent to 98 percent in Ghana.
For the 15-17 age group, school attendance increased from 39 percent to 66 percent in Côte
d’Ivoire and remained stable at 87 percent in Ghana.
Hours of Work Exceeding Maximum Allowable Hours

1.8.2.1

Children in the 5-11 Age GroupThe ILO guidelines classify children under the minimum age for light work (in this case,children 5-11) undertaking any types of work for at least one hour per week as engaged in childlabor. The proportion of children in cocoa production in agricultural households in the 5-11 agegroup who undertook at least one hour of work per week in any economic activity increased
from 17 percent in 2008/09 to 26 percent in 2018/19 in aggregate. Average number of hours
worked in any economic activity by children in the 5-11 age group who worked in cocoa
production in the reference week before the survey decreased from 8.7 hours in 2008/09 to 6.3
hours in 2018/19.
In Côte d’Ivoire, the proportion of children engaged in cocoa production in the 5-11 age group
who undertook at least one hour of work in any economic activity during the reference weekincreased from 13 percent to 21 percent. The average hours worked in the reference week byFINAL REPORT | 13

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children working in cocoa production in this age group decreased from 11 hours in 2008/09 to 7
hours in 2018/19.
In Ghana, the proportion of children engaged in cocoa production in the 5-11 age group who
undertook at least one hour of work in any economic activity during the reference weekremained constant at around 30 percent. The average hours worked in the reference week bychildren working in cocoa production in this age group decreased from 7 hours in 2008/09 to 5
hours in 2018/19.
1.8.2.2

Children in the 12-14 Age GroupThe proportion of children engaged in cocoa production in the 12-14 age group working morethan ILO recommended hours (14 or more hours per week) in any economic activity classified as
child labor remained stable at around 14 percent in aggregate. The average number of hours
worked in any economic activity in the reference week before the survey by children working incocoa production in this age group also remained stable at around 10 hours.
In Côte d’Ivoire, the proportion of children age 12-14 working in cocoa production and working
more than the ILO recommended maximum hours per week in any economic activity increased
from 13 percent in 2008/09 to 20 percent in 2018/19. The average hours worked in the reference
week by children in this age group in cocoa production remained stable at approximately 13
hours.
In Ghana, the proportion of children in the 12-14 age group working in cocoa production andworking more than the ILO recommended maximum hours per week in any economic activity
remained stable at approximately 11 percent, and average hours worked in the reference week bychildren in this age group in cocoa production also remained stable at approximately 7 hours.
1.8.2.3

Children in the 15-17 Age GroupThe proportion of children in the 15-17 age group 17 in cocoa production working more than the
ILO recommended hours (42 hours per week) in any economic activity, which classified them aschild labor, remained stable at approximately 2 percent in aggregate. However, the average
number of hours worked in the reference week by children in this age group in cocoa production
decreased from 14 to 12 hours per week.
In Côte d’Ivoire, the average number of hours worked in any economic activity in the reference
week by children aged 15-17 who were working in cocoa production dropped by approximatelyChildren in the 15-17 age group are allowed to undertake regular work (42 hours or less) as per the ILO
framework.

17

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6 hours in the week prior to the survey (from 20 hours in 2008/09 to 14 hours in 2018/19),
although there was no statistically significant change in the proportion of children in that age
group working more than the ILO recommended maximum hours (remaining stable atapproximately 3 percent).
In Ghana, for children in the 15-17 age group working in cocoa production there was no
statistically significant difference in the proportion of children working in any economic activity
more than the ILO recommended hours (remaining stable at 2%). Average hours worked
remained stable at around 9 hours.
Types of Hazardous Work in Cocoa Production
Comparison of the six types of hazardous work among children involved in cocoa production inthis study indicate:■■■

Use of sharp tools (36 percent), carrying heavy loads (29 percent), and exposure to agrochemicals (24 percent) were the most common sources of hazardous work in 2018/19 inaggregate.
Use of sharp tools, the most commonly performed hazardous activities in cocoa agriculture,
increased by 8 percentage points between 2008/09 and 2018/19 in aggregate (from 28
percent in 2008/09 to 36 percent in 2018/19).
Exposure to agro-chemicals became more pervasive between 2008/09 and 2018/19 as the
proportion of children exposed to agro-chemical products increased by approximately fivefold between 2008/09 and 2018/19, from 5 percent to 24 percent in aggregate.
►

1.9

Similar trends were found in both countries, with exposure to agro-chemical products
increasing from 4 percent to 19 percent in Côte d’Ivoire and from 7 percent to 32 percent
in Ghana. This increase is aligned with findings showing a significant increase in agrochemical use among cocoa growing households between 2013/14 and 2018/19.
Analytical Findings on Context
Importance of Cocoa Agriculture

Comparison of survey data from 2008/09 to 2018/19 indicates that children in the study areas
shifted away from other agricultural activities to cocoa production, potentially due to theincreasing importance of cocoa farming. In the cocoa growing areas of Côte d’Ivoire and Ghana,
there was a 9 percentage points decrease (from 29% to 20%) in the proportion of children
involved in agricultural work outside of the cocoa sector between survey rounds.

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Cocoa Production Stratum
An analysis of child labor by cocoa production stratum 18 reveals important insights into how
child labor manifests within the study areas. The prevalence of child labor between 2008/09 and2018/19 did not see a statistically significant increase in the high production stratum (remaining
stable at around 43 percent), while it increased
Analytical Insight
substantially in the medium and low production strata
The prevalence rate of child labor did not(by 16 and 27 percentage points respectively). There
increase in the high cocoa production
was a similar trend in the change in prevalence ofstratum while we observe substantial
increases in prevalence within the low andhazardous child labor within each production stratum
medium production areas between 2008/09
during the same period. This may indicate theand 2018/19. There was a similar trend inimportance of focusing intervention efforts on areas
the change in prevalence of exposure tohazardous work during the same period.
where cocoa production has not been as historicallyhigh. 19
These findings suggest that as high production areas become increasingly saturated with cocoa
farming, cocoa production activities are expanding into new areas where the infrastructure is
weak, and awareness related to child labor and hazardous work is limited. Additionally,
interventions targeting child labor over the past ten years (2008 to 2018) have likely focused on
the high production areas where prevalence is more widespread and the perceived need for suchinterventions is greatest. Thus, it seems the increase in production that led to expansion into newless saturated areas may have resulted in increased child labor and exposure to hazardous workin cocoa production.
Use of Agro-chemical Products
Use of agro-chemical products by cocoa growing households increased significantly between
2013/14 and 2018/19 in both Côte d’Ivoire and Ghana. In aggregate, use of pesticides andherbicides (percentage of household reporting use of the input) each increased by around 20
percentage points and fertilizer usage increased by 10 percentage points between 2013/14 and2018/19. Household-level agro-chemical use is important because it is highly correlated withchildren’s exposure to agro-chemicals and helps explain why children’s exposure to agrochemical products increased significantly from 2013/14 to 2018/19.

18

Cocoa production stratum is determined by the amount of cocoa produced per district and department. Districts
and departments are placed into high, medium, and low production stratum with high producing the most cocoa andlow producing the least cocoa.

19Intervention data collected by NORC from the stakeholders indicate that the interventions implemented bydifferent partners between 2010- 2018 were mostly concentrated in the high production stratum with some in themedium stratum and very few in the low production stratum.

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Self-reported data from the household survey indicate an overall increasing trend in expenditures(per ton of cocoa produced) on fertilizer, pesticides, and herbicides between 2013/14 and2018/19. In aggregate, there were statistically significant increases in expenditures on fertilizer(from USD $556 to $1,254 per ton of cocoa per year), pesticides (from USD $267 to $745 per
ton of cocoa per year) and herbicides (from USD $230 to $481 per ton of cocoa per year). 20
1.10 Assessment Findings
The comparisons of descriptive statistics presented above provide a rich overview of theprevalence of child labor and hazardous child labor in the cocoa sector in Côte d’Ivoire andGhana in 2018/19 and how they compare to the situation in previous survey rounds. The second
major objective of this study is to assess the role of interventions implemented by key
stakeholders in confronting the issues of child labor and hazardous child labor in the cocoa
sector. Our assessment of the effectiveness of various interventions focuses on how different
types of interventions, rather than specific interventions implemented by any individualorganization, were effective in addressing child labor issues in general, and hazardous child laborin particular, in the two countries. NORC employed a suite of quantitative and qualitative
analytic tools to generate robust conclusions to assess whether particular categories ofinterventions implemented to address child labor and hazardous child labor were effective in
reducing child labor and hazardous child labor prevalence. 21
The quantitative analysis is based on a quasi-experimental design used to assess the impact ofdifferent categories of interventions on child labor and hazardous child labor, while thequalitative analysis uses Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs)
to provide complementary evidence and deep contextual insights.
Quantitative Assessment
The quantitative analysis considered the effectiveness of different types of interventions
including education material assistance, livelihood support, and occupational safety and healthprojects. Results derived from the quasi-experimental evaluation based on data from Côte
d’Ivoire indicate that when multiple interventions were implemented in a community, it led

The reported values of expenditures were adjusted for inflation (reported in real USD figures) and generated after
dropping the households with cocoa production in the bottom 10 percentile of the distribution to avoid overestimation of expenditure per-ton of cocoa production.

20

21

Please see Section 8.2 and Annex 10.8 for a description of the assessment methodology.

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to a statistically significant reduction in the rates of child labor and hazardous child laborin cocoa production 22:■

Households in communities with multiple interventions were 25 percentage points less likely
to have at least one child engaged in child labor and 28 percentage points less likely to haveat least one child engaged in hazardous child labor 23 than the households from comparison
communities. 24■

The rate of child labor among households in
Quantitative Insightcommunities with multiple interventions was
When multiple interventions wereapproximately 17 percentage points lower than
implemented in a community, it led to a
the rate of child labor among households in thestatistically significant reduction in the rates
25
of child labor and hazardous child labor incomparison communities. The rate ofcocoa production.
hazardous child labor among households incommunities receiving multiple interventions was approximately 17 percentage points lower
than the rates of hazardous child labor among households in the comparison communities. 26
Qualitative Assessment

Our qualitative assessment analyzed data collected from FGDs with beneficiaries and from KIIs
with a variety of stakeholders in Côte d’Ivoire and Ghana. The qualitative aspect offers
complementary insights on the effectiveness of interventions and efforts related to the reductionof child labor and hazardous child labor in the two
Qualitative Insightcountries.
The improved accessibility and affordabilityof schools allowed children who would
otherwise be working during school hours
to enroll and spend less time working.

In both countries, school-based interventions werereported to be very successful in addressing childlabor, especially when coupled with community
mobilization and deep engagement with caregivers, teachers, and community leaders. Schoolbased interventions, including school construction, feeding programs, and infrastructure22

The results presented here on multiple interventions are based on data collected from Côte d’Ivoire since theavailable sample size in Ghana was too small for undertaking the analysis of impact of multiple interventions.

23

The results were based on multivariate regression, where we control for the household, community, and school
characteristics that are expected to influence the likelihood of engaging children in child labor and in hazardous
child labor by a household.

24

Comparison communities were similar to the treatment communities in certain observable community
characteristics, but did not receive such interventions.

25

The results were based on multivariate regression, where we control for the household, community, and school
characteristics that are expected to influence rates of child labor within a household.

26

The results were based on multivariate regression, where we control for the household, community, and school
characteristics that are expected to influence the rates of hazardous child labor within a household.

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improvement facilitated an increase in school attendance in Côte d’Ivoire and Ghana. In many
communities, parents reported taking their children to the farm because they could not afford toenroll them in school or pay for costs of school materials. Parents residing in communities where
the nearest school is self-reported to be far from home also report having no choice but to taketheir children with them to the farm. The improved accessibility and affordability of schools
allowed children who would otherwise be working during school hours to enroll and spend lesstime working.
Community leaders, implementers, donors, and government officials report that in most cases,
factors related to poverty and truancy contribute to high child labor rates. Notably, respondents
thought that some of the most effective interventions were those addressing the root causes ofchild labor but not necessarily designed to support child labor prevention only. These include
school construction, good agricultural practices training, and road construction between cocoa
communities and larger communities.
While vocational training programs were notoffered frequently, when offered they enable youth
to explore post-education opportunities in theirown communities. Vocational training programs
were most impactful for girls, as they provide afoundation for future income generation.

Qualitative Insight
Respondents recommend that engaging
community leaders, including representatives
for women and youth, early in the design ofthe intervention helped promote sustainability.

Families receiving livelihood support spoke of improved cocoa yields, improved access to credit,
and improved financial outcomes leading from those interventions. Livelihood support also
facilitates off-season income generation, which in combination with improved financial
outcomes, allows households to reinvest in their farms and hire farm labor. Caregivers who wereable to hire farm laborers report engaging their children in farm work less often.
Interventions engaging community members in their design and implementation are also
considered more effective, while those taking a more top-down approach were less effective.
Interventions promoting alternative income generation and school enrollment are believed to be
most sustainable. Working groups, national action plans, and coordinated activities also holdsignificant potential for facilitating current and future sustainability, provided these groups focus
on activity coordination and resource mobilization.
1.11 Conclusions
It is important to note that the Framework of Action to Support Implementation of the HarkinEngel Protocol calls for a 70 percent reduction in the worst forms of child labor over a ten-year
period. In part, this metric is an aspirational, although important, goal to work towards. However,FINAL REPORT | 19

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

it is only one among many possible metrics useful in measuring progress in reducing the worst
forms of child labor.
Policymakers and stakeholders are often required to make decisions to confront critical issues,
such as child labor in important value chains, before rigorous research is conducted to truly
understand the scope, contextual reality, and potential interplay of factors surrounding the issue
and impacting the targets. When policymakers and stakeholders set a target without forecasting
the complex and dynamic relationship of contextual factors that may influence long-term trends,
they can underestimate the time and resources needed to achieve a particular target, such as 70
percent reduction in the worst forms of child labor over a ten-year period amid a 62 percent
increase in cocoa production.
This problem is compounded when the focus becomes realizing a metric rather thanunderstanding the scope of the problem and the interrelated contextual factors driving it.
Although reducing hazardous child labor in the cocoa sector is important, understanding what is
driving high rates of hazardous child labor and what is and is not working to improve children’s
lives in cocoa growing areas is equally important in order to focus interventions most effectively.
In addition, the targets set in the Declaration and Framework focus on a reduction in the rate of
the WFCL which requires a total withdrawal of children from all types of hazardous activities
related to cocoa production. That one metric alone may not be sufficient to document the
progress made in fighting child labor and the worst forms of child labor in cocoa production in
Côte d’Ivoire and Ghana. For example, if an intervention reduces a child’s exposure to one
component of hazardous work (defined in Section 3.3 of this report), this progress would not bereflected in the overall measured rate of hazardous child labor unless the child is involved in onlythat particular hazard. For example, if a child engages in both sharp tool use and agro-chemical
spraying, they are categorized under hazardous child labor conditions. If an intervention thenfocuses on reducing agro-chemical use, and changes behavior around that metric, the child will
still be categorized in hazardous child labor because of the sharp tool use. In essence, the targets
may overshadow what is actual working on the ground to reduce hazardous child labor andimprove the lives of children within the cocoa sector.
The next point requires taking a step back andConsideration
viewing the cocoa sector from a broader vantage
Child labor is a complex problem requiring
point. Cocoa production accounts for 10 percent
multiple interrelated interventions toof the gross domestic product (GDP) of Côte
achieve significant impacts.
d’Ivoire and 7 percent of the GDP of Ghana.
Fluctuations in the cocoa sector not only impact the lives of children but also the lives of
millions of cocoa farmers and the economies of both Côte d’Ivoire and Ghana (thus impacting
the populations of those countries regardless of their relationship to cocoa production).

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

This report hopes to provide a broader perspective by not only presenting the overall numbers ofchildren involved in child labor and in hazardous work in the cocoa sector, but also theinterrelated factors that might influence the observed prevalence rates, different initiatives bystakeholders to address the issues, and where and how interventions are effectively impacting
child labor and hazardous child labor.
In conclusion, this report makes a strong case for understanding child labor and hazardous childlabor in cocoa production as a complex problem requiring multiple complementary solutions.
Survey findings show an increase in both child labor and hazardous child labor in cocoa
production in the cocoa growing areas of Côte d’Ivoire and Ghana between 2008/09 and2018/19, while cocoa production increased significantly over the same period.
We also find child labor and hazardous child labor rates in cocoa production stabilizing withinareas with historically high cocoa production while increasing in medium and low production
areas. The increased prevalence of child labor and hazardous child labor rates in the medium andlow cocoa production areas underscores the importance of investing in child labor interventions
within medium and low production areas. This is further supported by findings looking at the
2013/14 to 2018/19 period, which show child labor and hazardous child labor rates within cocoa
growing households stabilizing even in the face of increased cocoa production.
The findings also indicate that the fight against child labor becomes effective when a suite ofinterventions targeting livelihoods, education, awareness, legislation, and community monitoringand multiple stakeholders such as governments, NGOs, CSOs, Industry stakeholders, andinternational organizations come together to confront child labor issues.
Our work in assessing the progress made from 2010 to 2020 makes the complex nature of the
problem apparent and evidence laid out in this report supports the need to take a system approachto the issue of child labor, in which multiple overlapping solutions and interventions are neededin order to address the complex issue at hand.
Throughout the report, the findings suggest the importance of continued investments focused onchild labor in cocoa production using a holistic system-based approach. Such an approach would
consider various push factors such as limited access to education and poverty in relation to pull
factors such as an increase in the global price of cocoa and increasing cocoa yields (for example
provision of low cost and easily available agricultural inputs) and how those factors relate toproduction stratum, educational opportunities, and hazardous child labor.
Although the Harkin-Engel Protocol is coming to an end, the success of the protocol in bringing
together government, international, and Industry stakeholders to address the issue of child laborand hazardous child labor in the cocoa sector can serve as a model for continued engagement by
all stakeholders.

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

2 Stakeholder Statements

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FOREWORD BY WORLD COCOA FOUNDATIONThis report by NORC at the University of Chicago culminates a five-year research effort
designed to assess and measure the prevalence of working children, children in childlabor, and children in hazardous work in the cocoa growing areas of Côte d’Ivoire andGhana. The report, which has been subject to review by an independent expert group,
provides important insights to help better understand where the sector is making
progress and where improvement or course-correction is required.
As a signatory to the landmark Harkin-Engel Protocol in 2001, the World Cocoa
Foundation (WCF) has about 100 member companies that represent more than 80% of
the global cocoa supply chain. Our vision is a thriving and sustainable cocoa sector,
where farmers prosper, communities are empowered, and the planet is healthy.
Child labor has no place in the cocoa supply chain. As this report shows, there aretoday still too many children in cocoa farming doing work for which they are too young,or work that endangers them. Child labor remains a persistent challenge in Côte d’Ivoire
and Ghana, despite major efforts by the governments, companies, cocoa-growing
communities, and development partners. These efforts include WCF member company
investments of more than $215 million since 2001 in programs at the community level inCôte d’Ivoire and Ghana, to fight child labor. Our investments have focused on boostingand diversifying farmer income, child labor monitoring and remediation throughcommunity and supply chain-based systems, gender empowerment, construction andrehabilitation of schools, provision of school supplies and other education support
services, and awareness-raising – all in close partnership with the governments andcivil society organizations.
The report indicates that earlier targets to reduce child labor were set without fully
understanding the complexity and scale of a challenge associated with poverty in rural
Africa. It nevertheless shows that government and company programs to reduce childlabor are making a difference. The report emphasizes that the more than 60 percent
increase in total cocoa production in Côte d’Ivoire and Ghana over the past 10 years did
not bring a similar surge in child labor.
A separate study by NORC commissioned by WCF demonstrates that hazardous childlabor has been reduced by one-third in communities where company programs are inplace – a finding that is consistent with results from work by the International Cocoa
Initiative (ICI), which shows that child labor can be reduced by one-half among thosechild laborers identified by company due diligence measures. In this study for WCF,NORC recommended that a package of company measures addressing child labor be
expanded: “… efforts to combat child labor and hazardous child labor in respective
supply chains should be increased given current successes.” 1 For these children
1

Assessment of Effectiveness of Cocoa Industry Interventions in Reducing Child Labor in
Cocoa Growing Areas of Côte d’Ivoire and Ghana, NORC, October 2021
https://www.worldcocoafoundation.org/report/2018-19-norc-report-sub-study
Abidjan Office: Cocody 7e Tranche Rue L 129, Lot 225, Ilot 2644, Abidjan, Côte d’Ivoire. T +225 22 50 17 41
Accra Office: East Legon Boundary Road, Adjacent Sweet Aroma, Accra, Ghana. T + 233 302 542 187
Washington Office: 1025 Connecticut Avenue NW, Suite 1205, Washington, DC 20036. T +1 202 737 7870
www.worldcocoa.org

helped by company measures, these families, and these communities, the future isbrighter.
The report stresses that we need to build on what has worked and take it to the nextlevel. Companies are already ramping up investments to accelerate results.
Our actions on the ground are supporting the implementation of the strong national
action plans recently developed by the governments of Côte d’Ivoire and Ghana incollaboration with companies, civil society organizations, development partners andothers, to eliminate child labor. In 2019, companies invested $65 million in a wide rangeof social development activities to address child labor, covering child protection,
education, community development, income diversification for vulnerable households,
and other child survival activities – about six-times higher than what was spent a year in
2001-18.
The governments of Côte d’Ivoire and Ghana, the cocoa and chocolate companies, andother stakeholders now know more about what works – and are scaling up these efforts
for impact.
•

To protect children, leading companies are planning to increase the coverage ofchild protection systems to 100% by 2025, from about 20% in 2019, in their direct
supply chains in Côte d’Ivoire and Ghana.

•

To ensure access to quality education in Côte d’Ivoire, the government intends tolaunch a $120 million pooled funding facility for primary education in partnershipwith the Jacobs Foundation, that aims to reach 5 million children, with $25 million
expected from industry.

•

To help raise farmers out of poverty, companies have supported in 2020/21 the
new Living Income Differential pricing policy of Côte d’Ivoire and Ghana that will
provide an estimated $1.2 billion in additional revenues for cocoa farmers on topof official market prices.

•

To boost household incomes and yields, leading companies are planning toreach 100% coverage by 2025 of all farmers in their direct supply chains in Côte
d’Ivoire and Ghana with training, coaching, or farm development plans on good
agricultural practices.

The report concludes by stressing the importance of a holistic system-based approachthat brings together all partners in joint action.
WCF and our member companies strongly endorse the critical role of multi-stakeholder
collaboration to drive system change. Companies are therefore working closely with the
producing governments to develop a more transformative approach to make sure
today’s generation of children are protected from child labor and hazardous labor on
cocoa farms, building on the lessons learned and scaling up the good practices from theHarkin Engel Protocol process.

Abidjan Office: Cocody 7e Tranche Rue L 129, Lot 225, Ilot 2644, Abidjan, Côte d’Ivoire. T +225 22 50 17 41
Accra Office: East Legon Boundary Road, Adjacent Sweet Aroma, Accra, Ghana. T + 233 302 542 187
Washington Office: 1025 Connecticut Avenue NW, Suite 1205, Washington, DC 20036. T +1 202 737 7870
www.worldcocoa.org

Success will require more ambitious partnerships and collaboration. Companies alone
cannot solve the problem – we are committed to supporting the development of a new
multi-stakeholder partnership led by Côte d’Ivoire and Ghana and encompassing all key
actors, to tackle the root causes of child labor.
WCF appreciates that NORC has approached the preparation of this report with astrong commitment to collaboration with all stakeholders and evidence-based analysis.
We believe the report has benefited from the review by an external and independentgroup of experts of specific methodological issues flagged by the two governments(their findings are found in Annex 10.12 of the report).
We recognize the tireless efforts of the United States Department of Labor team thathas led this work over the past five years. Thanks and appreciation also go to thegovernments of Côte d'Ivoire, including notably Ivorian First Lady Dominique Ouattara,
and of Ghana, for their partnership, leadership and steadfastness on this issue over the
past decade. We are also grateful to our valued partners at ICI, UNICEF, theInternational Labour Organization, and other partner organizations who have provided
critical leadership and implementation support.
Finally, we recognize the sustained commitment of the global cocoa and chocolate
industry to test new approaches, build new partnerships, and scale up efforts to achieve
national impact.
Child labor is heavily associated with poverty and its various socio-economic causes
and consequences. In response, the report stresses that measures to combat childlabor are particularly successful with a suite of interventions tackling multiple drivers
simultaneously. WCF and our member companies, in partnership with the governments
and other partners, plan to scale up existing actions, build broader alliances and deploy
a broader range of actions to reduce child labor in the cocoa supply chain.
Only by taking this more direct and comprehensive approach can we ensure today’s
generation of children reach their full potential and have a chance at the bright future
they deserve.

Richard Scobey
President

Abidjan Office: Cocody 7e Tranche Rue L 129, Lot 225, Ilot 2644, Abidjan, Côte d’Ivoire. T +225 22 50 17 41
Accra Office: East Legon Boundary Road, Adjacent Sweet Aroma, Accra, Ghana. T + 233 302 542 187
Washington Office: 1025 Connecticut Avenue NW, Suite 1205, Washington, DC 20036. T +1 202 737 7870
www.worldcocoa.org

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

3 Study Objectives
The cooperative agreement between USDOL and NORC had two overarching goals. Those goals
defined the work of this report and parameters of the analysis. In short, the two goals were:
1. Goal 1: Conduct a sectorally-representative survey during the 2018/2019 harvest season,
which covers the previous 12-month reference period, to develop population estimates for theprevalence of working children, child labor, and the worst forms of child labor (using
hazardous labor as a proxy) in agriculture, including the cocoa sector, in the cocoa growing
areas of Côte d’Ivoire and Ghana, and then use those estimates to measure percent changes in
the prevalence in the worst forms of child labor compared to data collected in these same
areas in 2008/09 and 2018/19.
2. Goal 2: Assess the relative effectiveness of projects contributing to reduced child labor andthe worst forms of child labor in Côte d’Ivoire and Ghana, and progress toward achievementof the goals and targets of the Declaration and Framework. The assessment also seeks toaddress a set of major research questions, developed between USDOL and NORC, on theefficacy of funded interventions.
3.1

Measuring the Progress Toward Achieving the Goals and Targets of the
Declaration and Framework

The main objective of this report is to use data from the three survey rounds to assess how themain outcome indicators of interest – the prevalence of children in child labor and the prevalenceof children in hazardous work in the cocoa sector 27– changed between 2008/09 and 2018/19.
Towards that objective, NORC conducted a survey during the 2018/19 harvest season to develop
population estimates of the prevalence of working children, child labor, and hazardous childlabor (as a proxy of the worst forms of child labor) in agricultural households, in the cocoa
growing areas of Côte d’Ivoire and Ghana. The main data collection efforts across Ghana andCôte d’Ivoire, and the subsequent analysis of child labor rates are presented in Chapter 4 and 5 ofthis report. The primary research questions associated with the data collection and analysis
include:■What is the population of economically active children in cocoa growing areas during the
2018/19 harvest seasons?

■

What is the population of children in agricultural households in cocoa growing areas in eachcountry in 2018/19 disaggregated by employment status (i.e., working, non-working)?

27

Cocoa sector is defined as the cocoa growing areas of Côte d’Ivoire and Ghana

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana■

What is the population of children working in agriculture in cocoa-growing areas
disaggregated by the sector of engagement (i.e., cocoa sector, and agriculture other than thecocoa sector)?

■

What is the population of children working in sectors other than agriculture?

■

How many children were working, were engaged in child labor, and were engaged in theworst forms of child labor (using hazardous labor as a proxy) in the cocoa sector during the
2018/19 harvest seasons?

■

What is the percentage change in the proportion of children that were working, were engaged
in child labor, and were performing hazardous work in cocoa production in cocoa growing
areas between the 2008/19 and 2018/19 main harvest seasons?

3.2

Assessment of Effectiveness of Interventions Funded Toward Achieving
Goals and Targets of the Declaration and Framework

The second main objective of this report is to assess the relative effectiveness of various
interventions funded by the members of the CLCCG and other stakeholders. Under thisobjective, we address specific research questions (see Section 8.3 for a list of the researchquestions) with the aim of understanding how different types of interventions were effective in
addressing child labor issues.
It is important to note that our analysis does not assess the effectiveness of individualinterventions or implementers. Given both the disparate types and overall number ofinterventions conducted between 2008/09 and 2018/19, it was not feasible to assess theeffectiveness of each one due to data limitations. Rather we assess the effectiveness of different
categories of interventions (such as education related interventions, livelihoods programs, andoccupational safety and health interventions, to name a few).
3.3

Definitions: Working Children, Child Labor and Hazardous Child Labor
based on Common and Local Definitions

This section presents the definitions of working children (children in employment), child labor,
and hazardous child labor used throughout the report 28. Data on working children, child labor,
and hazardous child labor were collected using both a twelve-month and seven-day reference
period to allow for comparisons of the estimates based on two reference periods commonly
reported for measuring children’s engagement in work and to understand children’s activities
during the peak harvest season versus throughout the year. While data were collected for both
28

The definitions of working children, child labor, and hazardous child labor are from the International Labour
Organization (ILO), Report III: Child Labour Statistics, 18th International Conference of Labour Statisticians,
Geneva, (November 24 – December 2008).

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reference periods, in most instances, data analysis focuses on the twelve-month reference
period to remain consistent with previous survey rounds and to address the seasonality of various
tasks performed in cocoa agriculture.
For generating aggregate estimates on hazardous work performed by children in cocoa
production, we use a common definition focusing on the “common ground” between Ghanaian
and the Ivorian definitions within a broader ILO framework. 29
Additionally, we present estimates of child labor and hazardous child labor based on Ivorian and
Ghanaian country specific definitions using individual country national legislations as the basefor defining child labor and hazardous child labor. This country specific analysis, based solely on
the 2018/19 survey round, is helpful for Ghanaian and Ivorian stakeholders addressing national
programs around child labor and is found in Chapter 6 of this report.
Working Children (Children in Employment)
Working children, as per an ILO and ICLS framework 30, are defined as children (5 – 17 years
old) who have worked at least one hour during the reference period in any economic activity,
either paid or unpaid. The research team then further differentiated within this broad category to
account for agricultural work, cocoa work, and non-agricultural work. It is important to note that,the definition of “work” by children does not include household chores within their own
households.
Children in Child Labor
The definition of child labor is also based on an ILO and ICLS framework. 3132 For the purposeof this report, children engaged in child labor are defined as (a) children working below
minimum age (if they are under 12), (b) children exceeding the number of working hours
allowable for their age group based on the ILO Convention 138 on the Minimum Age on
Admission to Employment 33 (if they are between 12-17), (c) children of any age performing

29

The common definition was developed by Tulane University. For more details, please consult the study report:
Survey Research on Child Labor in West African Cocoa Growing Areas, Final Report, 2013-14, Tulane University.

30

International Labour Organization (ILO), Report III: Child Labour Statistics, 18th International Conference ofLabour Statisticians, Geneva, (November 24 – December 2008).

31

International Labour Organization (ILO), Report of the Conference, 18th International Conference of Labour
Statisticians, (2008).

32

The research team relied on the 18th ICLS to remain consistent with previous survey rounds. The most current andup-to-date iteration of the ICLS is the 20th

33

International Labour Organization (ILO), Convention 138 Concerning Minimum Age for Admission to
Employment, (June 26, 1973).

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hazardous work 34 in the cocoa growing areas of Côte d’Ivoire and Ghana. The worst forms ofchild labor other than hazardous work and hazardous unpaid household services are notincluded in this measure.
Children in Hazardous Child Labor: Common Definition
As a proxy for the WFCL, hazardous child labor is constructed, using the common definition,from six subcategories and a child is determined to have participated in hazardous work if they
have been exposed to at least one subcategory of the common definition as described below.
1. Land clearing
2. Carrying heavy loads
3. Using agro-chemicals
4. Using sharp tools
5. Long working hours
6. Night work
Exposure to land clearing: A child is exposed to a land clearing related hazard if the childengages in clearing of land, felling and chopping of trees, or burning within the reference period.
Exposure to carrying Heavy loads: A child is exposed a heavy load related hazard if the child

carries a heavy load of wood and other loads while working in agriculture within the reference
period. The definition of “heavy” is based on the child’s own perception on whether the load
carried was heavy or not. 35
Exposure to agro-chemical products: A child is exposed to agro-chemicals if the child isengaged in spraying, carrying water for spraying, or working with agro-chemicals during thereference period.
A child is considered to be engaged in spraying if the child:■

Was involved in spraying of pesticides or insecticides■

Was present or worked in the vicinity of a farm during pesticide spraying, or■Reentered a sprayed farm within less than 12 hours of spraying.

34

Based on ILO Convention 182, Article 3(d) and Recommendation 190.

35

It is the research teams’ view that this is the most valid way to measure “heavy loads” without necessitating theuse of scales and diary-based data collection methods.

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Working with agro-chemicals includes a child having been involved in handling agro-chemical
products such as purchase, transport, storage, mixing, loading, washing of containers andspraying machine, and/or disposal.
Exposure to sharp tools: A child is exposed to sharp tool use if the child uses machetes/longcutlasses for weeding, handling motorized equipment or machines, knapsack sprayer and/or
chainsaw, harvesting with a machete or sickle, harvesting overhead cocoa pods with a harvesting
hook, or breaking cocoa pods with a knife or a sharp object/tool during the reference period.
Exposure to long working hours: A child is exposed to long working hours if a child works 43

hours or more per week during the reference period.
Exposure to night work: A child is exposed to night work if a child goes to or returns from thefarm alone, or working on the farm between 6.00 p.m. and 6.00 a.m. 36
Figure 2: Child Labor Common Definition

Children in Hazardous Child Labor: National Definitions
We provide the national definitions below to understand the metrics the local governments findmost important. The Ghanaian definition of hazardous work is broken into ten subcategories andthe Ivoirian definition of hazardous work is broken into seven subcategories based on country
specific legislation. Children are considered exposed to hazardous work if they are exposed to at

36

According to the 18th ICLS, a child is considered to be working at night if the work schedule includes hours ofwork defined as night work prohibited for children under national definition, where it exists. In the case of children,
the period of time spent commuting unaccompanied between work and home should be considered as part of thework schedule.

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

least one subcategory of a country specific definition. There are also minor changes to thedefinition of child labor based on country specific definitions of child labor, but this section
focuses on hazardous child labor only. In this report statistics disaggregated by country use thecommon definition and information on country specific statistics can be found in Section 7. A
detailed description of country specific hazardous child labor definitions can be found in Annex
10.3.
3.3.4.1

Ghanaian National Legislation (Local Definition) and Sub-Categories ofHazardous Work

The Ghanaian definition of hazardous child labor consists of ten sub-categories:
1. Working full time and not attending school,
2. Withdrawing from school during cocoa season to do farm work,
3. Land clearing,
4. Carrying heavy loads,
5. Spraying and agrochemicals,
6. Use of sharp tools,
7. Climbing trees,
8. Night work,
9. Working in isolation, and
10. Working without protective clothing.
A child is considered exposed to hazardous work if they were exposed to at least one
subcategory during the reference period. A complete crosswalk for the definitions of child labor
can be found in Annex 10.3.
3.3.4.2

Ivorian National Legislation (Local Definition) and Sub-Categories ofHazardous Work

The Ivorian definition of hazardous child labor consists of seven sub-categories:
1. Inadequate rest,
2. Land clearing,
3. Charcoal production,
4. Carrying heavy loads,
5. Use of agrochemicals,

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6. Use of sharp tools, and7. Night work (between 7 p.m. to 7 a.m.) or work during school/business hours.
A child is considered exposed to hazardous work if they were exposed to at least one
subcategory during the reference period. The Côte D’Ivoire definition differs from international
norms because if a child is 16 or 17 years old and has received appropriate training relating touse of hazardous materials in agriculture, they are not considered to have been exposed tohazardous work. A complete description of the definitions of child labor can be found in Annex
10.3.

4 Methodology
In order to develop population estimates for the prevalence of working children, child labor andhazardous child labor in the cocoa growing areas of Côte d’Ivoire and Ghana, and use thoseestimates to measure changes in child labor prevalence between 2008/09 and 2018/19, NORC
undertook a sectorally representative survey during the 2018/19 cocoa harvest season in cocoa
growing areas of Côte d’Ivoire and Ghana. The following sections briefly describe the activities
and methodology undertaken during the 2018/19 round of data collection.
4.1

Sampling Methodology and Implementation of the 2018/19 Survey Round
Sampling Approach and Stratification 37

In brief, NORC used a multi-stage stratified cluster sampling technique. In this approach theprimary sampling unit (PSU) was the census Enumeration Areas (EAs) in the cocoa producing
areas of Côte d’Ivoire and Ghana and the secondary sampling unit (SSU) was agricultural
households. The first step was identifying a given number of Enumeration Areas (EAs) fromwhich the SSUs (households) were selected. NORC formally requested the assistance of the
Ghana Statistical Service (GSS) and the Insitut National de la Statistique de Côte d’Ivoire (INS)to select the EAs based on a defined sampling plan designed to keep the survey frames as similar
as possible across rounds. In the first stage, NORC stratified the cocoa-growing districts of
Ghana and departments of Côte d’Ivoire into high, medium, and low cocoa production strata
based on the most recent available cocoa production data. 38 Using the district/department levelproduction data, the rural EAs in each of the districts/departments were classified into high,medium and low production strata by GSS in Ghana and INS in Côte d’Ivoire.

37

Please note that our sampling approach did not include encampments (unless directly under an EA administrativeclassification) or protected forest lands.

38

The data on recent production provided by COCOBOD in Ghana and the Coffee-Cocoa Council in Côte d’Ivoire.

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

In the second sampling stage, drawing from the list of all EA stratified into high/medium/low
cocoa production, GSS and INS randomly drew a total of 150 EAs respectively, specificallydrawing:■

80 EA from high cocoa production stratum■

50 EA from medium cocoa production stratum■

20 EA from low cocoa production stratum

Next, half of each stratum of EAs was randomly assigned to target EAs and the other half kept as
the replacement EAs. The final target sample for each country consisted of:■

40 EAs from high cocoa production stratum■

25 EAs from medium cocoa production stratum■

10 EAs from low cocoa production stratum

A more detailed description of the sampling approach adopted by NORC is provided in Annex
10.1.
Types and number of interviews completed
The research team administered six survey instruments during the 2018/19 data collection round;
1. Household roster,
2. Household head,
3. Child,
4. Community,

Coverage of Survey
There were 2,821 roster surveys, 2,809
household head surveys, 5,543 child surveys,
158 community surveys, 372 cocoa shed
surveys, and 260 school surveys administeredacross Ghana and Côte d’Ivoire.

5. Cocoa Shed, and
6. School 39.
The number of total child and household head interviews completed by region for Côte d’Ivoire
and Ghana in the 2018/19 survey round can be found in Table 41 in Annex 10.4.1. There were2,809 child and 1,314 household head interviews completed in Ghana spread across six regions.
For Côte d’Ivoire there were 2,734 child and 1,495 household head interviews completed acrossfifteen regions. Overall, there were 2,821 roster surveys, 2,809 household head surveys, 5,543
child surveys, 158 community surveys, 372 cocoa shed surveys, and 260 school surveys
administered across Ghana and Côte d’Ivoire. Additional information on the number of surveys
completed by country and comparisons to the 2008/09 round can be found in Section 4.2.

39

Please see Annex 10.1 for a complete description of each survey instrument

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Implementation of 2018/19 Survey RoundOnce enumeration areas (the PSUs) were identified, NORC conducted a complete household
listing of each EA for 2018/19. For the purpose of sampling, only households identified as
agricultural households having at least one child 5-17 during the listing were determined to beeligible households (both cocoa growing households and other agricultural households). There
were 8,858 total households and 6,399 eligible households listed in Côte d’Ivoire across 75 EAs
and 9,200 total households and 3,969 eligible households in Ghana across 75 EAs. After thelisting exercise, agricultural households (the SSUs) with at least one eligible child aged 5-17
were randomly selected in each EA. In Côte d’Ivoire 25 eligible agricultural households (ofwhich 5 were replacements) and in Ghana 23 eligible agricultural households (of which 5 were
replacements) were randomly drawn for data collection.
Table 1 below shows the listing data for all eligible households in our sample and reflects theimportance of cocoa agriculture in Côte d'Ivoire and Ghana. Looking at the percentage of
agricultural households that grew cocoa, in Côte d'Ivoire approximately 7,500 out of 8,900
households (85%) grew cocoa, and similarly in Ghana approximately 8,000 out of 9,200
households (87%) grew cocoa during the 2018/19 survey round. Overall, cocoa is a key part ofagriculture in the cocoa growing areas of both countries as can be seen from the fact that a
predominant majority (more than 85 percent) of agricultural households grow cocoa.
Table 1: Distribution of Sample*: Agricultural Households, Cocoa Growing
Households and Non-Cocoa Growing Agricultural Households, in Côte d’Ivoire andGhana, 2018/19
Total

Côte
d’Ivoire

Ghana

All agricultural households

18,058

8,858

9,200

Number of cocoa growing households

15,528

7,547

7,981

Percentage of cocoa growing households

86%

85%

87%

Number of non-cocoa growing agricultural households

2,530

1,311

1,219

Percentage of non-cocoa growing agricultural households

14%

15%

13%

Source: NORC sample 2018/2019, strata 1-3*Only households with children were surveyed, which dropped the eligible households for Ghana to 6,125
and Côte d’Ivoire to 6,399.

Data collection took place in Ghana from November 23rd, 2018 to January 27th, 2019 and forCôte d’Ivoire from February 9th, 2019 to March 7th, 2019. Data collection timing was purposiveand conducted during the main harvest season in both countries. The household roster was firstadministered to each sampled household to determine which children would be eligible forFINAL REPORT | 38

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghanainterviews and to identify the household head. A household was complete once there was ahousehold roster survey, household head survey, and a child survey for each eligible child. Table
42 in Annex 10.4.1 details the household head and child survey response rates by householdroster survey. Overall, at least 99% of the sampled eligible households had a household headsurvey and over 90% of sampled eligible households have at least one child survey for Côte
d’Ivoire and Ghana.
The community, cocoa shed, and school interviews were all conducted at the EA level. The EA
level surveys were then linked to the households from the same EA to provide additional
information on the communities those households are a part of. Additional information on datacollection and survey implementation can be found in Annex 10.1.
4.2

Description of Sample
Comparison of Sample Sizes between 2008/09 and 2018/19 Round of
SurveyBelow we present a comparison of sample sizes between the baseline (2008/09) and endline(2018/19) survey rounds. The 2008/09 and 2018/19 survey round each had five of the same
surveys administered (roster, household head child, community, and cocoa shed) with the school
survey being added in the 2018/19 survey round. A target of 1,500 households in Côte d'Ivoire
and 1,300 households in Ghana was set to obtain at least 2,300 child surveys per country for the
2018/19 round (see Annex 10.1.1 for additional information on the sample size calculations).
Data collection in 2018/19 ended with 1,504 completed households in Côte d'Ivoire and 1,317
completed households in Ghana resulting in 2,734 and 2,809 completed child surveys
respectively. The comparisons of sample size by survey type can be found in Table 2 below
where the number of surveys increased between rounds for each type of survey.
Table 2: Comparison of Sample Sizes by Survey Type, All Agricultural Households
With At Least One Child Age 5-17, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19

Household Roster
Head of Household
Child
Community
Cocoa Shed
SchoolTotal
2008/092018/19
1,656
2,821
1,638
2,809
4,443
5,543
82
140
61
372
N/A
260

Côte d’Ivoire
2008/092018/19
806
1,504
803
1,495
2,165
2,734
40
79
32
168
N/A
158

Ghana
2008/092018/19
850
1,317
835
1,314
2,278
2,809
42
79
29
204
N/A
102

Source: Survey 2008/09 and 2018/2019, strata 1-3

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Respondent Characteristics
The median age of child respondents in Côte d'Ivoire and Ghana was 10 years. On average, bothcountries had an almost equal proportion of male and female child respondents.
Table 3 shows a breakdown of the household head characteristics in Côte d'Ivoire and Ghana andTable 4 shows the characteristics of the child respondents). 40 The median age of respondents wasapproximately 44 years in Côte d'Ivoire and 46 years in Ghana. There was a higher percent ofmale respondents in Côte d'Ivoire (approximately 90%) than in Ghana (approximately 75%).
The median age of child respondents in Côte d'Ivoire and Ghana was 10 years. On average, bothcountries had an almost equal proportion of male and female child respondents.
Table 3: Respondent Characteristics: Head of Household, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2018/19
Côte d'Ivoire

Ghana

Average age (years)

46.3

48.3

Median age (years)

44.0

46.0

Number

1,338

982

Percent

91%

75%

Number135

322

Percent

9%

25%

Male
Sex
FemaleSource: NORC Roster survey 2018/2019, strata 1-3

Table 4: Respondent Characteristics: Children 5-17 Years, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2018/19
Côte d'Ivoire

Ghana

Average age (years)

10.2

10.5

Median age (years)

10.0

10.0

Number

1,425

1,478

Percent

52%

53%

Number

1,309

1,331

Percent

48%

47%

Male
Sex
FemaleSource: NORC Child survey 2018/2019, strata 1-3

40Note that these are self-reported head-of-household and most likely suffers from gender bias in reporting.

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4.2.2.1

Nationality of the Child Respondents

Examining the nationality and birthplace of children sheds light on how much immigration ispresent in the cocoa growing areas of each country. Table 43 in Annex 10.4.1 shows birthplaceand current nationality of children in the 2018/19 survey round. The majority of child
respondents were natural-born citizens in their respective countries (Ghana and Côte d’Ivoire).
However, Côte d'Ivoire does have some children born outside of Côte d'Ivoire and of anothernationalities, mostly representing immigration from Burkina Faso and Mali.
Area of Land Under Cultivation
Given that the sample consists of agricultural households and most of these households werecocoa growing households, we present the data of land under cultivation and land undercultivation for cocoa farming in Table 44 in Annex 10.4.1. The average size of land undercultivation in Côte d'Ivoire was 20.5 acres and 8.4 acres in Ghana. The average area under cocoa
cultivation in Côte d'Ivoire was 8.6 acres and 6.4 acres in Ghana. The difference in area of land
under cultivation indicates that the average agricultural household in Ghana operated on muchlarger plot size for cocoa cultivation.
4.3

Limitations and Methodological Consideration: Coverage of Data andComparison of Data Across Rounds

Before moving on to the comparison of study rounds, there are a few important methodologicalconsiderations the reader should understand. The first issue deals with the comparison of
2013/14 data to 2018/19 data. The second consideration deals with the comparison of 2008/09
data with 2018/19 data.
To start, there was incomplete documentation on the exact methods used in previous survey
rounds and NORC was unable to recover data required for an exact replication of the samplingframe used in the earlier rounds. This lack of information made it challenging at times to design
the 2018/19 survey to allow for precise comparisons across rounds. NORC approached this issue
by striking a balance between precision and comparability, allowing for comparability on key
metrics while improving upon the sampling frame construction with an aim of increasing the
precision of the 2018/19 estimates. This balance allows stakeholders to use 2018/19 data as astrong base with more precise estimates moving forward while also allowing for comparisonsacross study rounds.

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2013/14 Data Quality Issues 41
Due to an error in survey implementation during the 2013/14 data collection round, the survey
collected data only from cocoa growing households in Côte d’Ivoire (omitting the non-cocoa
growing agricultural households from the sample).
This error was discovered after data collection was complete, and, in an attempt to correct theerror, a different sampling method was adopted for selecting non-cocoa households to be
surveyed as part of the 2013/14 data collection round. 15 clusters (from 60 clusters that were partof the 2013/14 data collection round) were sampled to supplement the sample of non-cocoa
agricultural households. The sampling method selected 11 purposively sampled clusters and 4
randomly selected clusters (rather than 15 randomly selected clusters). This raises a concern
regarding the reliability of such a sampling method and potential bias involved in generating
population estimates where a high proportion of sampled clusters were purposively selected.
In addition, the survey of non-cocoa households was conducted one year after the main survey
was done in 2013/14 cocoa harvest season. This affects the comparability of data from the cocoa
growing households and non-cocoa agricultural households.
Based on analyses of 2013/14 data, we concluded that the sampling of non-cocoa households
and weighting schema used for the clusters selected for the supplemental sample potentiallyintroduced bias and that the estimate of child labor was not of the most accurate population
estimate of child labor in cocoa in agricultural households in the cocoa growing areas.
As such, and to err on the side of caution, the data collected on non-cocoa growing households
from 2013/14 was not used and this report avoids making any direct comparisons between
2013/14 and 2018/19 survey rounds in terms of non-cocoa growing households. These issues
did not impact the estimates of cocoa growing households between rounds, which is presented
throughout the report.
A detailed explanation of the issues in the 2013/14 round can be found in Annex 10.2.
2008/09 and 2018/19 Comparability Issues with Population Estimate ofCountsThe 2008/09 (and 2013/14 rounds) used regions as the primary stratification level and the
2018/19 round used districts/departments (which are geographically smaller and could be
assigned to a stratification level more precisely than the larger area).

41

NORC was not involved in the 2008/09 or 2013/14 data collection rounds and these issues were discovered in an
attempt by NORC to recreate the statistics found in the earlier reports

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This implies that the sampling frames were not exactly identical across survey rounds (onestarted at the regional level and the other at the district/department level) and thus population
total estimates are not comparable. For the purposes of the report and analysis, this means wecannot make claims in terms of changes found in population counts across rounds and hence, wehave not presented any comparison of population counts across survey rounds. It is important tostress here the difference between counts and prevalence rates (and ratios). While the datacomparability issue affected the comparison of population counts across survey rounds, it was
still possible to make a statistically valid comparison of the prevalence rates (ratios) across the
2008/09 and 2018/19 rounds (described in more detail in Section 4.3.3 and in Annex 10.2).
A second issue with the comparability of data from the 2008/09 round is due to the lack of
documentation of the 2008/09 survey round. Although NORC received the data from 2008/09,
the “key” linking individual children to their respective cocoa growing households was notavailable in the data. As a result, it was not possible for NORC to generate the child labor
estimates for cocoa growing and non-cocoa growing households. What this means for the reportis that we are unable to compare cocoa growing households in terms of child labor from2008/09–2018/19.
Table 5:Comparisons for Population Count and Prevalence Rate
All agricultural households

Cocoa growing households

Population
count

Population
count

Survey RoundPrevalence rate

Prevalence rate

Comparison to be done

2008/09 vs 2018/19

No

Yes

No

No

2013/14 vs 2018/19

No

No

No

Yes

Table 5 above indicates that we can compare the estimates of prevalence rate for all agricultural
households (cocoa and non-cocoa growing households combined) between 2008/09 and 2018/19.
However, we are not able to differentiate between the cocoa and non-cocoa growing households
in the 2008/09 data and hence, not able to make a comparison of cocoa growing households
between 2008/09 and 2018/19.
The issue is inversed for the comparison of 2013/14 data to 2018/19 data. For the 2013/14 to
2018/19 comparison, we can compare the estimates of prevalence rate for only cocoa growing
households between 2013/14 and 2018/19 and not for all agricultural households. However, weare not able to compare the estimate of population counts between 2018/19 and 2008/09 andbetween 2018/19 and 2013/14 round.

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We can compare the estimates of prevalence rates between 2008/09 and 2018/19 for all
agricultural households, and between 2013/14 and 2018/19 for cocoa growing households. 42
However, our numbers can differ to those previously published by Tulane. The difference ismainly due to two factors: (i) the Tulane reports did not consider all children working in cocoa
farming as children working in agriculture, but this report does; and (ii) the Tulane reports did
not include knives as a subcategory of sharp tools, but this report includes knives in calculating
sharp tool use.
A detailed explanation of comparability issues between the 2008/09 and 2018/19 rounds can befound in Annex 10.2.
Expert Group Review of limitations on the statistical comparisons ofprevalence rates and ratios across survey rounds
As described in Sections 4.3.1 and 4.3.2, the sampling frame between the 2018/19 survey roundused by NORC and the previous survey rounds used by Tulane University were not identical.
Thus, the prevalence estimates of 2008/09 and 2013/14 rounds are not fully comparable to that of
the 2018/19 round. While the difference in sampling frames undermine the comparability of
population counts, empirical illustrations indicate that the variance is unlikely to significantly
affect the point estimates of prevalence rates and ratios. However, it is still possible thatcomparisons of prevalence rates/ratios may involve some degree of bias (even though,negligible).
Given the methodological complexities described in those sections and above, an Experts Groupof statisticians and survey professionals was formed in July 2020 to look carefully at the
sampling methodology and comparisons made in this report. The Expert Group’s complete
findings can be found in Annex 10.12. Their main findings and recommendations were:
1. Include household selection probabilities when estimating sampling weights for the 2018/19
surveys.
2. Include estimates of population counts and prevalence rates for 2018/19 using the weights
based on the household selection probabilities and construct variance estimates accordingly.
3. Attach to the major outcomes of interest (e.g. number of working children, number ofchildren in child labor, number of children in hazardous work, child labor rate, and hazardous
work rate) a precision measure (such as variance, standard error, confidence internal orcoefficient of variation) so readers can form their own conclusions on the accuracy of the
data.

42

Prevalence rate comparisons are done only using households with at least one child in the age group 5-17.

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4. Add clarification on the methodological limitations of comparison of prevalence rates andproportions of different variables across survey rounds due to the difference between thesampling frame used by NORC for the 2018/19 survey round used by NORC and that usedby Tulane University in previous survey rounds, which could lead to potential bias in thecomparison of point estimates across survey rounds.
Following the guidance provided by the Expert Group, we revised our sampling weight
construction and generated population estimates of counts, prevalence rates and proportions for
2018/19 survey round. The corresponding updated estimates are presented in this final report.
Additionally, following the recommendations of the EG, for undertaking the tests of significanceof difference between the prevalence rates/proportions of 2018/19 -2008/09 and 2018/192013/14 survey rounds appropriate test statistics have been used. Further, we only present
differences on key outcomes of interest found to be significant at a 1 percent level of significance(i.e. p-values less than or equal to 0.01).This ensures that the conclusions of the study are
unlikely to be affected by methodological limitations.
Acknowledging this methodological limitation, NORC outlined a procedure to make thecomparison analyses as robust as possible to ensure inference closely approximates that of thetrue differences between survey rounds. Specifically, for hypothesis testing between rounds,
approximations for the standard errors of point estimates for previous rounds were based on thecalculated standard errors of the 2018/19 point estimates that are in turn based on samplingweights that account for household selection probabilities. In addition, to reduce the probabilityof falsely identifying a difference in ratios between survey rounds (that is, a Type I statisticalerror), we used a level of significance of 1 percent (implying there is a 1% or smaller probabilityof incorrectly inferring that differences in ratios exist even though they are not truly different).
This ensured that the conclusions drawn from the study observations and analyses of changes areless likely to be affected by any methodological limitations.
While this approach reduces the likelihood of making incorrect inferences, it is always the factthat the point estimates of the differences between survey rounds are an approximation for thetrue differences. However, that possibility does not affect the high-level objectivity of this study– to assess whether and how the prevalence rates changed over time.

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

5 Part I: Main Findings
In the following section we present the main findings starting with population estimates for the
2018/19 survey round and then presenting the comparison of prevalence of child labor andhazardous child labor over the study period between survey rounds. The analysis begins from thehighest level looking at children in all agricultural households and moves down towards more
focused analysis on only those children involved in cocoa production.
Figure 3 below outlines each level of analysis found in this section and can help orient readers on
the analytic framework used for this study.
Figure 3: Measurement Framework on Child Labor in Cocoa Growing Areas

Population estimates for the 2018/19 survey round are included below in Table 6 for the sample
in aggregate and individually by country using the common definition. There are an estimated1.41 million agricultural households with at least one eligible (age 5-17) child in the cocoa
growing areas of Côte d’Ivoire (847,719 households) and Ghana (566,591 households) inaggregate. The majority of agricultural households in the cocoa growing areas of Côte d’Ivoire
and Ghana are cocoa households with 1.22 million cocoa growing households in aggregate.
The estimates for the number of children working in 2018/19 is also important to note. In
aggregate 2.33 million children are estimated to have worked in the past 7 days, including 1.32
million in Côte d’Ivoire and 1.01 million in Ghana. More children have reported working in theFINAL REPORT | 46

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

past 12 months in the cocoa growing areas of Côte d’Ivoire and Ghana with 2.78 million inaggregate estimated as working in the past year (1,619,127 children in Côte d’Ivoire and1,159,216 children in Ghana).
In 2018/19 in the cocoa growing areas of Côte d’Ivoire and Ghana there were approximately
1.56 million children working in child labor in cocoa production, including 790,647 children inchild labor in Côte d’Ivoire and 765,754 children in child labor in Ghana. Additionally, 1.48
million children were estimated to be in hazardous child labor with 765,233 in Côte d’Ivoire and713,419 in Ghana. In aggregate the most common hazardous work activity was working withsharp tools (1,244,040 children), followed by carrying heavy loads (991,000 children), working
with agro-chemicals (839,927 children), night work (80,027 children), and long working hours(22,800 children). 43

43

Note that children can be involved in multiple activities, so the number of children in hazardous child labor is not
the sum of the children involved in each activity.

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Table 6: Population Count and Prevalence Rate Estimates for Households, Working Children, and Children Exposed toChild Labor and Hazardous Child Labor, in Côte d’Ivoire and Ghana, 2018/19
Total
Estimate

Côte d’Ivoire

95%
Confidence Interval

Estimate

Ghana

95%
Confidence Interval

Estimate

95%
Confidence Interval

Population Counts
Number of agricultural
households with eligible child

1,414,310

1,273,244

1,555,376

847,719

731,215964,223566,591

488,448644,733

Number of cocoa householdswith eligible child

1,216,506

1,064,105

1,368,906

707,873

580,731

835,014508,633

425,927591,339

Children working in the past 7
days

2,333,811

2,081,748

2,585,874

1,324,497

1,129,514

1,519,480

1,009,314

847,279

1,171,348

Children working in the past 12
months2,778,343

2,480,096

3,076,590

1,619,127

1,376,699

1,861,555

1,159,216

979,135

1,339,297

Children in child labor in cocoa

1,556,401

1,330,386

1,782,415

790,647

619,932

961,362765,754

617,017914,491

Children in hazardous child laborin cocoa

1,478,651

1,264,529

1,692,774

765,233

598,297

932,168

713,419577,916

848,922

Children involved in land clearing
in cocoa (V1)

651,453

527,459

775,447

458,718

342,720574,716

192,735

150,530

234,940

Children involved in heavy loads
in cocoa (V2)991,000

845,480

1,136,519

541,487

431,964

651,009449,513353,787545,239

Children involved in agrochemicals in cocoa (V3)839,927

716,138

963,717394,503

308,157

480,848445,425355,643535,207

Children involved in sharp tools in
cocoa (V4)1,244,040

1,062,462

1,425,619

646,413499,330

793,496597,628

485,740

709,515

Children involved in long working
hours in cocoa (V5)22,80013,69631,905

17,375

8,588

26,161

5,426

1,897

8,955

Children involved in night work incocoa (V6)80,027

58,914

101,140

41,78428,52955,03938,243

21,582

54,904

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Total
Estimate

Côte d’Ivoire

95%
Confidence Interval

Estimate

Ghana

95%
Confidence Interval

Estimate

95%
Confidence Interval

Prevalence Rates
Children working in the past 7
days

67%

64%

70%

64%

60%

67%

72%

67%

78%

Children working in the past 12
months

80%

78%

82%

78%

75%

81%

83%

79%

87%

Children in child labor in cocoa

45%

41%

48%

38%

34%

42%

55%

50%

60%

Children in hazardous child laborin cocoa

43%

39%

46%

37%

32%

41%

51%

47%

56%

Children involved in land clearing
in cocoa (V1)

19%

16%

21%

22%

18%

26%

14%

12%

16%

Children involved in heavy loads
in cocoa (V2)

29%

26%

31%

26%

23%

29%

32%

27%

37%

Children involved in agrochemicals in cocoa (V3)

24%

22%

26%

19%

16%

22%

32%

28%

36%

Children involved in sharp tools in
cocoa (V4)

36%

33%

38%

31%

27%

35%

43%

39%

47%

Children involved in long working
hours in cocoa (V5)

1%

0%

1%

1%

0%

1%

0%

0%

1%

Children involved in night work incocoa (V6)

2%

2%

3%

2%

1%

3%

3%

2%

4%

Source: Head of Household and Child surveys, 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

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5.1

Understanding Contextual Factors

Before presenting the comparison of the main outcome variables of interest, the prevalence rateof child labor and the prevalence rate of children’s exposure to hazardous work, it is important toconsider some key contextual factors the research team believes are useful for a better
understanding of the observed changes in the prevalence of child labor and hazardous childlabor, keeping in mind that correlation does not indicate causation
Cocoa Production and Cocoa Price
According to the International Cocoa Organization (ICCO) production figures, this research took
place during a period of notable cocoa industry expansion, as overall cocoa production and cocoa
price increased significantly over the course of the research period from 2008/09 to 2018/19(Table 7). It should be noted that while ICCO provides cocoa production figures separately for
Ghana and Côte d’Ivoire, cocoa price figures are not available at the country-level and are thus
presented overall.
Cocoa production, as measured as estimated tons of cocoa produced, increased 62 percent during
the ten-year evaluation period in Ghana and Côte d’Ivoire, from 1.89 million tons in 2008/09 to
over 3 million tons in 2018/19. The increase was more pronounced in Côte d’Ivoire where cocoa
production increased 76 percent from 1.22 million to 2.15 million tons, versus a 36 percent
increase from 662,400 to 897,000 tons in Ghana. In Ghana, estimated production in terms of tonsof cocoa produced reached a peak in the 2013/14 round and then decreased slightly(approximately 2.5%) by 2018/19.
During this period of cocoa expansion, ICCO figures also show a more modest but still
significant 16 percent increase in cocoa price from $2,263 USD/ton to $2,626 USD/ton on the
global market. 44 According to ICCO, price per ton of cocoa
Quantitative Insightjumped 26 percent after 2008/09 to peak in 2013/14 before
Estimates of cocoa growing households as
a proportion of all agricultural households
deflating slightly by 2018/19. These changes in production
experienced a large statistically significant
over time and upward trend in cocoa price are important toincrease between 2008/09 and 2018/19,
from 55 percent to 84 percent.
understand the findings related to child labor as part of thisstudy.

44

The reported prices were adjusted for inflation (reported in real USD figures) and generated after dropping thehouseholds with cocoa production in the bottom 10 percentile of the distribution to avoid over-estimation of
expenditure per-ton of cocoa production.

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Significance of Cocoa Production among Agricultural Household
Given the increase in production mentioned above, it is useful to explore whether increased
production led to changes in the importance of cocoa farming in agriculture and whether cocoa
cultivation expanded among new agricultural households over time.
To explore changes in prevalence estimates in relation to the growth of cocoa and non-cocoa
agricultural households between survey rounds we present Table 7. In 2018/19, there wereapproximately 1.41 million agricultural households with at least one eligible child in cocoa
growing areas of Côte d’Ivoire and Ghana, and a majority of these were cocoa growing
households (86%). Estimates of cocoa growing households as a proportion of all agricultural
households experienced a large and statistically significant increase between 2008/09 and2018/19, from 55 percent to 86 percent, mirroring the increases in production. This increase in
the share of cocoa growing households among agricultural households, and possibly new cocoa
farms, most likely contributed to the increase in production over the same period.
At the country level, the change in estimates of cocoa and non-cocoa growing households as aproportion of agricultural households mirrored the same trend as the aggregate trend discussed
above. A significantly greater proportion of households in Côte d’Ivoire and Ghana were cocoa
growing households in 2018/19 than 2008/09.

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Table 7: Cocoa Production by Tons of Cocoa and Prices by USD/Ton, and the Percentage of Cocoa Growing
Households, in Côte d’Ivoire and Ghana, 2008/09, 2013/14, and 2018/19
Total
2008/09
Tons ofCocoa

2018/19

Côte d’Ivoire
Sig of2013/14diff^

2018/19

Sig ofdiff^

2013/14

267,000 1,223,200 2,150,000

N/A

1,746,000 662,400 900,000

Number 1,885,600 3,050,000

N/A

Percentchange^^

N/A

N/A

62%

Ghana

2008/09

76%

N/A

N/A

2008/09 2018/19

36%

Sig of2013/14
diff^
N/A

897,000

N/A

N/A

Price of Cocoa(USD/ton)*

2,263

2,626

N/A

2,819

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Percentage of
cocoa growing
households

55%

86%

***

N/A

50%

84%

***

N/A

63%

90%

***

N/A

Source: Head of Household survey, 2008/09 and 2018/2019, strata 1-3^Significance of Difference *** p<0.01^^Calculated by dividing the difference between the 2008/09 and 2018/19 figures by the base (2008/09) figure*ICCO estimates for January 2009 and 2019, not available at the country level

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Use of Agro-Chemical Products
Given that cocoa production increased by approximately
70% in aggregate across Côte d’Ivoire and Ghana, it is
The likelihood of agro-chemical use
expected to find increased usage of agro-chemical
increased among the cocoa growing
households. This lends further evidence products among cocoa growing households. Hence, weto the claim that agro-chemical use andincreased cocoa production are related. explore whether there was any increase in agro-chemical
use among cocoa growing households. Increased
production and increasing use of agro-chemical products influences the likelihood of children’s
exposure to agro-chemicals and, therefore, exposure to hazardous work and is useful forunderstanding the complex interrelated factors impacting children. For this purpose, we compare
the recent trends in use of agro-chemical products between the 2013/14 and 2018/19 survey
rounds.
Quantitative InsightIn the survey, household heads of cocoa-growing and non-cocoa-growing households were asked
to self-report on usage of agro-chemicals including fertilizers, pesticides, and herbicides. Data
reported in Table 8 shows that agro-chemical usage increased significantly between 2013/14 and2018/19 in both Ghana and Côte d’Ivoire among cocoa growing households. This lends further
evidence to the hypothesis that agro-chemical use and increased cocoa production are related.
Table 8: Estimates of Change in Household Use of Agro-Chemicals in the Last 12
Months in Cocoa Households, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19
Total

Côte d'Ivoire

Ghana

Percentage ofhouseholds
using

2013/
2014

2018/
2019

Sig of
diff^

2013/
2014

2018/
2019

Sig of
diff^

2013/
2014

2018/
2019

Fertilizer(s)

25%

35%

***

22%

35%

***

32%

34%

Pesticide(s)

54%

77%

***

49%

73%

***

66%

82%

***

Herbicide(s)

46%

71%

***

44%

67%

***

51%

75%

***

Sig ofdiff^

Source: Head of Household survey, 2013/14 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

Among cocoa growing households in both countries between 2013/14 and 2018/19, there was astatistically significant increase in proportion of all groups using each agro-chemical except for
fertilizer in Ghana (Table 8). Usage of pesticides and herbicides (percentage of household
reporting using the input) each saw increases of over 20 percentage points overall, with over 20
percentage points each in Côte d’Ivoire in just five years. Fertilizer usage increased less, by 13
percentage points in Côte d’Ivoire and 2 percentage points in Ghana, though as mentioned theincrease in Ghana is not statistically significant.

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The data reported in Table 8 clearly indicates thata greater proportion of cocoa producing
households are using agro-chemical products,
especially pesticides and herbicides. Given that
increased usage of agro-chemical products by thehousehold is likely to increase children’s
exposure to agro-chemicals, this may have
implications on the child labor and hazardous
child labor rates described in Section 5.2.
Trend in School Attendance

Qualitative Insight
Qualitative findings indicate that households
are using more agro-chemicals in their cocoa
production practices. Households in Ghana
reported that government-subsidized inputs
and mass spraying efforts resulted in moreconsistent agrochemical use on farms.
Households in Côte d’Ivoire and Ghana
reported that good agricultural practices
training significantly changed their cocoa
production practices, as they were encouraged
to use agrochemicals to maximize yield andmanage diseases and pests.

One notable survey finding was a significant improvement in school attendance estimates in bothcountries between 2008/09 to 2018/19 (Table 9). These increases were seen for both sexes andacross each age bracket but were especially notable in Côte d’Ivoire.
Table 9: School Attendance for All Children in the Last 12 Months, All Agricultural
Households, Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Children attending school

Côte d'Ivoire
2018/
Sig of
2019
diff^
80%
***

2008/
2009
89%

Ghana
2018/
2019
96%

Sig ofdiff^***Children

2008/
2009
58%

Sex
Boys 5-17 years
Girls 5-17 years

61%
53%

83%
78%

***
***

90%
89%

96%
96%

***
***

Age GroupChildren 5-11 years
Children 12-14 years
Children 15-17 years

60%
68%
39%

81%
88%
66%

***
***
***

89%
93%
85%

97%
98%
89%

***
***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

In Côte d’Ivoire, overall school attendance amongchildren 5-17 years increased 22 percentage
points as school attendance for children in eachsex and age category experienced highly
statistically significant increases of at least 20
percentage-points.

Qualitative Insight
Qualitative findings attribute increases inschool attendance to government-and NGOsponsored school reforms, including new
school construction, school materials
provision, and school rehabilitation. Teachers,
children, and their caregivers report that due tothese changes, children attend school moreconsistently.

In Ghana, school attendance increased a moremodest but still statistically significant 7 percentage-points, with highly significant increases ofFINAL REPORT | 54

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at least 5 percentage-points in every age and gender group except for children 15-17 years.
Groups with larger than average increases include younger children 5-11 years (8 percentage
points increase) and girls 5-17 years (7 percentage points increase).
The improvement in school attendance indicates improvements in access to schools and/or
improvements in school infrastructure, both of which can help fight child labor and exposure tohazardous work. Ghanaian education reforms around pre-k and kindergarten education are most
likely the direct cause of the increases seen in the 5 – 11 age group. 45 According to the Ghanaian
Constitution and the Education Act, primary education is free and compulsory from kindergarten
through junior high school. Additionally, the government extended free education through highschool in 2017. In recent years, Ivoirian education reforms include making school attendancecompulsory for all children aged 6-16 years and significant increases in education spending.
5.2

Main Findings: All Agricultural Households
Children’s Engagement in any Economic Activities

In order to understand how children’s engagement in economic activities changed between the
2008/09 and 2018/19 study rounds, we present the estimate of children who worked for at least
one hour during the reference period in any economic activity, 46 either paid or unpaid. We
construct the estimate of their engagement based on usual activity status (reference period oflast twelve months) and current activity status (reference period of last seven days). 47
Table 10 shows that approximately 80 percent of children in cocoa growing areas were
economically active in 2018/19 in Ghana and Côte d’Ivoire, performing any type of work in the
twelve-month reference period before the survey.
Overall, from 2008/09 to 2018/19, there is a 14 percentage point increase (from 66% to 80%) in
the proportion of children who were usually working in cocoa growing areas in the last twelve
months. During 2008/09, 58 percent and 78 percent of children were economically active in Côte
d’Ivoire and Ghana, respectively. The proportion of children working in the last twelve months
in Côte d’Ivoire increased by 19 percentage points, while it increased in Ghana by 5 percentage
points, between 2008/09 and 2018/19.

45

https://www.earlychildhoodworkforce.org/sites/default/files/resources/Brief-Ghana_0.pdf

46Note that this includes both cocoa and non-cocoa activities as well as non-agricultural activities.

47

Please see the 18th ICLS for use of these terminologies: https://www.ilo.org/wcmsp5/groups/public/---dgreports/--stat/documents/meetingdocument/wcms_099577.pdf

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Table 10: Estimates of Change in Children Working in the Last 12 Months and in theLast 7 Days, All Agricultural Households, 5-17 Years, in Côte d’Ivoire and Ghana,
2008/09 and 2018/19
Last 12 Months
2008/09

Last 7 Days

2018/19

Diff
(pp)*

Sig ofdiff^

2008/092018/19

Diff
(pp)*

Sig of
diff^

80%

14

***

52%

67%

15

***

78%

19

***

40%

64%

24

***

83%

5

71%

72%

1

Total Children Working
Percent

66%

Côte d’Ivoire Children Working
Percent

58%

Ghana Children Working
Percent

78%

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes
rounding^Significance of Difference *** p<0.01

In aggregate, the proportion of children who were working in cocoa growing areas in the last
seven days is lower than those working in cocoa growing areas in the last twelve months,
indicating the seasonal nature of working in cocoa
Analytic Insight
growing areas.
In aggregate, the proportion of children
who were working in cocoa growing areasin the last seven days is lower than thoseworking in cocoa growing areas in the lasttwelve months, indicating the seasonal
nature of working in cocoa growing areas.

Between 2008/09 and 2018/19, the proportion ofchildren working in cocoa growing areas in the last
seven days increased by 15 percentage points (from
52% to 67%) in aggregate. There was a significant
increase in the proportion of children currently active from 2008/09 to 2018/19 in Côte d’Ivoire,increasing 24 percentage points (from 40% to 64%). There was no statistically significant change
in the proportion of Ghanaian children currently active during the same period.
Estimate of Working Children in Agriculture
Next, we explore children’s engagement in agriculture in cocoa growing areas in Côte d’Ivoire
and Ghana including both cocoa and other non-cocoa agriculture. Table 11 presents their usualengagement (in the last 12 months) and current engagement (in the last 7 days) in agriculture.
Table 11 shows that the proportion of children working in agriculture (both cocoa and non-cocoa
agriculture) in the last twelve months increased by 6 percentage points, while the proportion ofchildren working in agriculture in the last seven days decreased by 5 percentage points inaggregate between 2008/09 and 2018/19. In Côte d’Ivoire there was a 10 percentage points
increase (from 54% to 64%) in children working in agriculture in the last twelve months. In

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Ghana, there was no significant change in children working in agriculture in the last twelve
months between the 2008/09 and 2018/19 rounds.
Consistent with the trends in children working in cocoa growing areas discussed earlier, theproportion of children working in agriculture in cocoa growing areas in the last twelve months
was much higher in Ghana (73% in 2008/09 and 2018/19) than in Côte d'Ivoire (54% in 2008/09
and 64% in 2018/19). The trend in children’s engagement indicates that, while children’s usualengagement in Ghana remained stable between 2008/09 and 2018/19, a higher proportion ofchildren were engaged in agriculture in Côte d'Ivoire during the 2018/19 round.
The estimate of current engagement shows that while in 2018/19 a higher proportion of children
worked in economic activities in general (Table 10), a smaller proportion of children were
engaged in agriculture in 2018/19 compared to 2008/09. This indicates an increased engagement
in non-agricultural activities contributes to an increase in economically active (or working)children. While there was no change in the proportion of Ivoirian children working in agriculture
in cocoa growing areas in the last seven days, there was a 16 percentage-point decrease in the
proportion of Ghanaian children working in agriculture in cocoa growing areas in the last sevendays.

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Table 11: Estimates of Children Working in Agriculture in the Last 12 Months and the Last 7 Days, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Last 12 Months
2008/092018/19

Last 7 Days

Diff (pp)*Sig of diff^2008/092018/19

Diff (pp)*Sig of diff^

6

***

48%

43%

-5

***

10

***

40%

41%

1

61%

46%

-16

Total Children Working in Agriculture
Percent

61%

68%

Côte d’Ivoire Children Working in Agriculture
Percent

54%

64%

Ghana Children Working in Agriculture
Percent

73%

73%

0

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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Estimate of Child Labor in Agriculture
To allow a deeper understanding of the nature of work undertaken by children in agriculture in
the cocoa growing areas of Côte d'Ivoire and Ghana, we compare the data on children’s
engagement in child labor in agriculture. Respondents were asked to report their working hours
and engagement in different types of activities in agriculture, including hazardous activities.
Using the estimates of working hours and responses of children relating to their exposure toactivities considered hazardous, we generated estimates of children’s engagement in child laborin agriculture.
Table 12: Estimates of Change in Children Working in Agriculture, and in Children
Engaged in Child Labor in the Last 12 Months, All Agricultural Households, 5-17 Years,
in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Children Working in Agriculture
Percent
Total
Côte
d'Ivoire
Ghana

2008/0961%

2018/1968%

2008/0954%

2018/1964%

2008/0973%

2018/1973%

Diff. (pp)*Sig of
diff^

7

***

10

***

0

Children Engaged in Child Labor in
Agriculture
Percent
58%
60%
52%
57%
68%
65%

Diff. (pp)*Sig of diff^

2
5

***

-3

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes
rounding^Significance of Difference *** p<0.01

Table 12 above presents the estimate of children engaged in child labor in agriculture (including
cocoa and other non-cocoa agriculture) in the past twelve months. In Ghana and Côte d’Ivoire,children working in agriculture in the last twelve months increased by 7 percentage points
between 2008/09 and 2018/19. Children engaged in child labor in agriculture in cocoa growing
areas increased by 5 percentage points in Côte d’Ivoire andQuantitative Insightremained stable in Ghana at around 68 percent.
In cocoa growing areas of Côte
d’Ivoire and Ghana, a higher
percentage of boys were engaged inchild labor in agriculture than girls.

More information on the breakdown of how children’s
engagement in child labor evolved over time and varies by sex
and across age group can be found in Table 45 of Annex 10.4.2. The data indicates that in cocoa
growing areas of Côte d’Ivoire and Ghana, a higher percentage of boys were engaged in child

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labor in agriculture than girls (57% versus 43% in 2018/19). There was no change in the
proportion of boys and girls who were engaged in child labor between 2008/09 and 2018/19.
To reduce child labor, various stakeholders including the governments of Côte d’Ivoire andGhana, the international chocolate industry and other multilateral organizations focused on
improving access to education as well improving quality of education. Annex 10.4.2 presents
school attendance of children engaged in child labor in agriculture by age group to understandwhether there were any improvements in school attendance among the children engaged in childlabor in agriculture.
Data presented in Table 46 in Annex 10.4.2 indicates that 92 percent of children in the 5-11 agegroup were attending school overall in 2018/19, including 88 percent in Côte d’Ivoire and 99
percent in Ghana. In the 12-14 age group in 2018/19, 93 percent of children were attending
school in aggregate, including 89 percent in Côte d’Ivoire and 98 in Ghana. Finally, in the 15-17
age group 77 percent of children were attending school in aggregate, including 66 percent inCôte d’Ivoire and 89 percent in Ghana.
To explore whether the prevalence of child labor is lower among those attending school, we
present a comparison of the prevalence of child labor by
Quantitative Insightschool attendance in Annex 10.4.2. The data presented in
There was a much higher
prevalence of child labor among
Table 47 in Annex 10.4.2 shows that there was a muchchildren in agricultural households
higher prevalence of child labor among children in
who were attending school (65%)
than among children in agricultural
agricultural households who were attending school (65%)
households who were not attending
than among children in agricultural households who wereschool (50%).
not attending school (50%) in cocoa producing areas ofCôte d’Ivoire and Ghana in 2018/19. The age-group disaggregation indicates that this trend wasmostly driven by the difference within the 5-11 years age group where the child labor prevalence
rate was 24 percentage points higher among those attending school compared to the children thatwere attending school. A similar trend was found among the older age groups (12-14 and 15-17
years), however the differences were not statistically significant. However, given the
counterintuitive nature of these findings, it is important to note that these naïve differences may
be driven by other factors not accounted for in our research.
Estimate of Working Children, and Average Hours of Work AmongChildren Working in Cocoa Production
One of the main objectives of this report is to measure the progress made in reducing child laborand hazardous child labor in cocoa production. We now present data on children’s engagement
in cocoa production related activities.

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5.2.4.1

Estimate of Working Children in Cocoa Production

Child respondents who worked in agriculture were asked whether they were engaged in cocoa
production related activities in the twelve-month period before the surveys were fielded during
the main cocoa harvest season in both countries. Using the responses of children relating toengagement in cocoa production, we generated estimates of children’s engagement in child laborand in hazardous child labor in cocoa production related activities. Table 13 presents how
children’s engagement with cocoa production changed between 2008/09 and 2018/19 reportingon both children’s usual (in the last twelve months) and current (in the last seven days)
engagement in cocoa production.
We find a substantial increase in the proportion of children engaged in cocoa production using a
twelve-month reference period. In aggregate, the proportion of children in agricultural
households usually active (in the last twelve months) in cocoa production increased significantly,
by 16 percentage points between 2008/09 and 2018/19.
Quantitative Insight

In Côte d’Ivoire, children’s engagement in cocoa
The proportion of children in
production in the last twelve months increased from 23
agricultural households usuallypercent to 40 percent while in Ghana it increased from 46
active (in the last twelve months) inpercent to 60 percent. It is important to note again that thecocoa production increased
significantly, by 16 percentage
current study focused on a 12-month recall period topoints between 2008/09 andremain consistent with previous rounds as well as to
2018/19.
capture the seasonal aspects of labor in cocoa production.
This naturally leads to higher rates of child labor when compared to using a 7-day reference
period because a 7-day reference period would not capture labor associated with land
preparation, land maintenance, and post-harvesting activities. For a description of such activities
related to cocoa production, see Table 15.
While a larger proportion of children in agricultural households were usually active in cocoa
production in 2018/19 (over 12 months), there was not a notable change in the proportion ofchildren who were currently active (over 7 days) in cocoa production.
These trends indicate that while more children were engaged in cocoa production related
activities throughout the year, there was not much change in children’s engagement during themain cocoa harvest season between 2008/09 and 2018/19. This finding indicates greater
involvement of children in land preparation, planting and maintenance activities over the entire
cocoa season. This could also be due to the possibility that as production expands and new farms
start, children participate more in the pre-harvest activities. In Section 6 we look closely at therole of new cocoa farms and the level of cocoa production which supports these claims.

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Table 13: Estimates of Children in Cocoa Production in the Last 12 Months and in the Last 7 Days, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Last 12 Months
2008/092018/19

Diff (pp)*Last 7 Days
Sig of diff^2008/092018/19

Diff (pp)*

***

21%

22%

2

17

***

14%

18%

4

14

***

32%

30%

-2

Sig of diff^

Total Children Working in Cocoa Production
Percent

32%

48%

16

Côte d’Ivoire Children Working in Cocoa Production
Percent

23%

40%

Ghana Children Working in Cocoa Production
Percent

46%

60%

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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5.2.4.2

Average Hours Worked by Children in Cocoa Production

In the earlier discussion we saw that in 2018/19, overall, a significantly higher proportion ofchildren were working in cocoa production compared to 2008/09 (Table 13). However, an
important consideration to determine whether the nature of their work constitutes child labor or
hazardous child labor is the number of hours worked per week. According to ILO guidelines 48,
children under the age of twelve should not be engaged in any work, and older children may only
work in non-hazardous activities and for a specified number of working hours per week
depending on their age. Therefore, it is useful to explore the data on hours worked in any
economic activity by children in different age groups.
Table 14 presents the data on average hours worked in any economic activity by children
working in cocoa production by age-group. While presenting this table, for each age group, we
also present the percentage of children working in cocoa production that exceeded the maximum
hours of work for any economic activity allowed by ILO guidelines – a violation that wouldclassify children in a given age group as child labor.
Overall, the percent of children in cocoa production age 5 to 17 working more than the allowable
number of hours increased from 14 to 20 percent between 2008/09 and 2018/19. These changes
were persistent in Côte d’Ivoire where the percent working more than the allowable number ofhours increased from 11 to 18 percent, while there was no statistically significant change in
Ghana. The average number of hours that children working
Quantitative Insightin cocoa production reported having worked in the past
Overall, the proportion of children
week declined overall from 11 to 8 hours. Additionally, thewho were working more than the ILO
average number of hours worked decreased in Côte
recommended maximum hours per
week increased from 14 percent tod’Ivoire from 14 to 10 hours and in Ghana from 8 to 6
20 percent between 2008/09 andhours. The overall increase in the percentage of children
2018/19. Average hours worked per
week significantly dropped byworking in cocoa production combined with the decrease inapproximately 2 hours in the week
hours worked in any economic activity suggests thatprior to the survey.
although new children started working, they may not havebeen working very many hours.
According to ILO standards, children under the age of 12 years, or the minimum age of lightwork 49, should not be engaged in any work activities. Consequently, any children in that age
48

International Labour Organization (ILO), Convention 138 Concerning Minimum Age for Admission to
Employment, (26 June 1973).

49

According to Article 7 of ILO Convention No. 138, national laws or regulations may permit the work of persons
as from 13 years of age (or 12 years in countries that have specified the general minimum working age of 14 years)
in light work which is: (a) not likely to be harmful to their health or development; and (b) not such as to prejudice

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghanagroup who worked for at least one hour in any economic activity during the reference period areconsidered as child labor. Between 2008/09 and 2018/19, the proportion of children, 5-11, in
agricultural households in cocoa production working one hour or more per week in any
economic activity increased by 8 percentage points in Côte d’Ivoire. Simultaneously, the average
number of hours worked in the week prior to the survey by children 5-11 (who worked at least
one hour) decreased significantly, from 11 hours to 7 hours. In Ghana, the proportion of children
in agricultural households, 5-11, working in cocoa production for one hour or more per week inremained stable. Similar to Côte d’Ivoire, the number of hours worked by children in Ghana incocoa production in the 5-11 age group decreased from 7 to 5 hours in the week prior to thesurvey.
Based on ILO standards, children 12-14 years can undertake up to (but not including) 14 hours
of non-hazardous activities weekly which is considered light work. In Côte d’Ivoire, there was anincrease in the proportion of children working in cocoa production in agricultural households inthe 12-14 age group who worked 14 hours or more per week in any economic activity, from 13
percent to 20 percent. There was no change in the average number of hours worked per week
which remained stable at around 12 hours. In Ghana, on the other hand, there was no change inthe proportion of children in cocoa production in agricultural households in the 12-14 age group
who exceeded ILO’s recommended weekly working hours in any economic activity, remaining
stable at around 11 percent, indicating that those who are working in cocoa production in this age
group are mostly undertaking light work. The average number of hours worked per weekremained stable for children working in cocoa production in the 12-14 age group at around 7
hours in the week prior to the survey.
Children in the 15-17 age group can undertake regular work and can engage in up to (but notincluding) 43 hours of non-hazardous work weekly, per ILO standards. In Côte d’Ivoire, theproportion of children, 15-17, in cocoa production who were working more than the ILO
recommended maximum hours per week in any economic activity remained constant at 3
percent. Average hours worked per week in any economic activity dropped from 20 hours to 14
hours in the week prior to the survey. Between 2008/09 and 2018/19, there was no change in the
proportion of children in this age group working 43 hours or more per week in Ghana, remaining
constant at around 1 percent of children in cocoa production in agricultural households in the 1517 age group. Consequently, there was no significant change in the average number of hours
worked in any economic activity per week among the 15-17 year olds in Ghana, which remained
constant around 9 hours per week.

their attendance at school, their participation in vocational orientation or training programs approved by thecompetent authority, or their capacity to benefit from the instruction received.

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Sex disaggregation of data on average hours worked can be found in Table 48 in Annex 10.4.2.
In Côte d’Ivoire, there was a statistically significant increase in the proportion of Ivoirian malechildren in cocoa production working more than the allowable number of hours in any economic
activity which increased from 12 to 22 percent. This increase was mostly driven by increases formales in the 5-11 and the 12-14 age groups. A similar trend was found for the female Ivorianchildren in cocoa production working more than the allowable number of hours which increased
from 11 to 14 percent. The average number of hours decreased for both male children from 14 to
11 hours overall and female children from 14 to 9 hours overall.
In Ghana, there was no change in the percent of male children in cocoa production working morethan the allowable number of hours in any economic activity in any age group, which stayed
constant at around 22 percent overall. Male children in Ghana in the 5-11 age group in cocoa
production worked fewer hours overall in 2018/19 than in 2008/09 (from 7.1 to 5.1 hours). The
proportion of female children in Ghana in the 5-11 age group working in cocoa production
increased in 2018/19 compared to 2008/19 (from 21% to 32%). Female children working incocoa production in Ghana also worked fewer hours in 2018/19 than in 2008/09 (decreased from
7 to 6 hours).
Data presented in Table 49 in Annex 10.4.2 shows the trends in the proportion of children aged
12-14 years in cocoa production engaged in less than 14 hours of non-hazardous light work andchildren of legal age group (15-17 years) engaged in less than 43 hours of non-hazardous regular
work in cocoa production between 2008/09 and 2018/19. Overall, there were statisticallysignificant increases in the proportion of children in cocoa production engaged in light work inaggregate. However, there was no statistically significant change in the proportion of children incocoa production engaged in regular work.

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Table 14: Working Hours in Any Economic Activity and Minimum Age, Children Working in Cocoa Production, All
Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Total

Côte d'Ivoire

Ghana

2008/092018/19

Sig ofdiff^

2008/092018/19

Sig ofdiff^

2008/092018/19

5-11
years

% Working 1+ hour per week

17%

26%

***

13%

21%

***

26%

33%

Average # of hours worked

8.7

6.3

***

10.7

7.4

***

6.9

5.1

12-14
years

% Working 14+ hour per week

13%

15%

13%

20%

***

13%

9%

Average # of hours worked

10.3

9.3

13.6

11.6

7.9

6.6

15-17
years

% Working 43+ hour per week

2%

3%

3%

3%

1%

2%

Average # of hours worked

14.4

11.5

***

19.9

14.0

***

9.7

9.2

% Working more than allowable
hours per week

14%

20%

***

11%

18%

***

18%

22%

Average # of hours worked

10.6

8.3

***

13.9

10.1

***

7.9

6.4

All
years

Sig of
diff^
***

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

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5.2.4.3

Activities Performed by Children in Cocoa Production

In this section we present estimates on child involvement in various work activities in cocoa
production for Côte d’Ivoire and Ghana to gain insights on whether there was more or lessinvolvement in certain types of work in different phases of cocoa agriculture. The activities
cover pre-harvest, harvest, and post-harvest activities that are broadly classified in six categories:
land preparation (land clearing, felling and chopping, burning, and stumping); planting(preparing seedlings, planting seedlings, and sowing at stake); farm maintenance (weeding,working with insecticides/herbicides/fungicides/other chemicals, and carrying water for
spraying), cocoa harvest activities (plucking, gathering, or breaking cocoa pods), and postharvest activities (carting fermented cocoa beans, drying cocoa beans, and carting dry cocoa
beans to shed).
Table 15: Child Work Involved in Cocoa Production, All Children 5-17 Years Working
in Cocoa Production, All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09
and 2018/19
Total
Percentage of children

2008/
2009

2018/
2019

Land preparation activities incocoa production

38%

40%

Planting activities in cocoa
production

17%

29%

Farm maintenance activities
in cocoa production

57%

56%

Harvest activities in cocoa
production

79%

92%

Post-harvest activities incocoa production

45%

60%

Côte d'Ivoire

Ghana

2008/
2009

2018/
2019

Sig of
diff^

2008/
2009

2018/
2019

Sig of
diff^

64%

56%

***

16%

25%

***

28%

27%

9%

31%

***

52%

53%

61%

59%

***

79%

91%

***

79%

93%

***

***

53%

64%

***

39%

56%

***

Sig ofdiff^***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

Table 15 shows there is a significant increase in the involvement of children in Côte d’Ivoire andGhana in pre-harvest, harvest, and post-harvest activities in cocoa production between 2008/09
and 2018/19. The largest change in children involvement is in harvest and post-harvest activities– such as, plucking, gathering, heaping, and breaking cocoa pods with involvement in harvest
activities increased 13 percentage points (from 79% to 92%) between 2008/09 and 2018/19 inaggregate and involvement in post-harvest activities increased 15 percentage points (from 45%
to 60%). Similar trends are found in both countries.

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Table 50 in Annex 10.4.2 presents the breakdown of different activities under each of the six
groups of activities reported in Table 15. Among various types of activities done by children in
2018/19, the five most common activities children were involved in were gathering and heaping
cocoa pods, breaking cocoa pods and fermentation, drying cocoa beans, weeding, and carting
fermented cocoa beans.
Although children involvement in farm maintenance activities was primarily in weeding, theproportion of children involved in weeding significantly increased in aggregate between 2008/09
and 2018/19. In 2018/19, carrying water for spraying became the second most prevalent farm
maintenance activity among children working in cocoa production. This, in part, can be
explained by the increase use of agro-chemicals discussed in
Quantitative Insight
Section 5.1.3 50.
In 2018/19, the five most common
cocoa production activities children
were involved in are; gathering andheaping cocoa pods, breaking
cocoa pods and fermentation,
drying cocoa beans, weeding, andcarting fermented cocoa beans.

Harvest activities consistently involved a large proportion ofchildren in Côte d’Ivoire and Ghana. Between 2008/09 and2018/19, there is a significant increase in the proportion ofchildren engaged in gathering and heaping cocoa pods (from
16% to 36% in Côte d’Ivoire and 33% to 53% in Ghana),
and children’s involvement in breaking cocoa pods also increased over the period. Among thepost-harvest activities, there is a significant increase in children’s engagement in carting
fermented beans (from 9% to 19% in Côte d’Ivoire and 14% to 25% in Ghana) and drying cocoa
beans (from 9% to 23% in Côte d’Ivoire and 13% to 24% in Ghana).
Estimate of Child Labor and Hazardous Child Labor in Cocoa Production
In this section we present the data on children engagementin child labor and in hazardous work in cocoa production the primary outcomes of interest of this report.

Quantitative Insight
Between 2008/09 and 2018/19, theproportion of children in agricultural
households engaged in child laborin cocoa production increased by
14 percentage points

Children who exceed the maximum allowable working
hours (specific to each age group) in any economic activity,
and/or who are exposed to any of the six different types of hazardous activities in cocoa
production are considered as child labor in cocoa production. The data presented in Table 16compares the prevalence rates of child labor and exposure to hazardous work in cocoa
production between the 2008/09 and 2018/19 rounds of survey for children in agricultural
households in cocoa growing areas of Côte d’Ivoire and Ghana.

50

The research team believes that it is possible that some of the variance associated with earlier findings of very
limited child involvement in agro-chemical use may be related to an issue in survey administration in previous
rounds.

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In 2018/19, 45 percent of all children in agricultural households in cocoa growing areas were
engaged in activities which placed them within child labor in cocoa production. Between
2008/09 and 2018/19, the proportion of children in agricultural households engaged in childlabor in cocoa production increased by 14 percentage points (from 31% in 2008/09 to 45% in
2018/19).
Between 2008/09 and 2018/19, there was a 17 percentage point increase in the proportion of
Ivoirian children in agricultural households who were working in cocoa production (from 23% to40%). The proportion of children engaged in child labor in cocoa production increased by 15
percentage points between 2008/09 and 2018/19 in Côte d’Ivoire (from 23% to 38%).
In Ghana, 60 percent of children in agricultural households were working in cocoa production in
2018/19 which increased by 14 percentage points between 2008/09 and 2018/19. Among thechildren in agricultural households who worked in cocoa production 55 percent were engaged inchild labor in 2018/19, an increase of 11 percentage points between 2008/09 and 2018/19.
Between 2008/09 and 2018/19, in the cocoa growing areas of Côte d’Ivoire and Ghana, theproportion of children engaged in hazardous work in cocoa production increased by 13
percentage points, with 43 percent of children in agricultural households in 2018/19 exposed toany of the six types of hazardous work in cocoa production in the last twelve months inaggregate between the two countries.
In Côte d’Ivoire, the proportion of children engaged in hazardous work in cocoa production
increased by 14 percentage points (up from 23% in 2008/09 to 37% in 2018/19). In Ghana, theproportion of children engaged in hazardous work in cocoa production increased by 8 percentage
points between 2008/09 and 2018/19 with 51 percent of children from agricultural households
engaged in hazardous work in cocoa production in 2018/19.
Thus, in both countries, children’s exposure to hazardous work in cocoa production increased
between the 2008/09 and 2018/19 survey rounds conducted during the main cocoa harvest
season.
Data reported in Table 51 in Annex 10.4.2 presents the changes in children engaged in childlabor and in hazardous work in cocoa production in Côte
Quantitative Insightd’Ivoire and Ghana, disaggregated by sex and age group. In
In 2018/19, 61 percent ofchildren engaged in hazardous
both countries, there was no significant change in the genderchild labor were male while 39
disaggregated proportion of children engaged in child labor and percent were female.
hazardous work. Overall, there were predominantly more malechildren engaged in child labor and hazardous child labor, in 2018/19 61 percent of children
engaged in hazardous child labor were male while 39 percent were female.

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Table 16: Estimates of Change in Children Working in Cocoa Production, Children Engaged in Child Labor in Cocoa
Production, and Children Engaged in Hazardous Work in the Cocoa Sector in the Last 12 Months, 5-17 Years, All
Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Children in all
agricultural
households
Total
Côte
d'Ivoire
Ghana

Children Working in Cocoa
ProductionPct2008/0932%

2018/1948%

2008/0923%

2018/1940%

2008/0946%

2018/1960%

Diff (pp)*Sig of diff^

16

***

17

***

14

***

Children Engaged in Child Labor in
Cocoa Production
Pct
31%
45%
23%
38%
44%
55%

Diff (pp)*Sig of diff^

14

***

15

***

11

***

Children Engaged in Hazardous
Work in Cocoa Production
Pct
30%
43%
23%
37%
43%
51%

Diff (pp)*Sig of diff^

13

***

14

***

8

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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Table 51 shows that there were some significant changes in
the age-disaggregated proportion of children engaged inComparison of data indicates that
there was an increase in children’schild labor or in hazardous work in Côte d’Ivoire for the 15exposure to hazardous work in
17 age group, and no statistically significant change incocoa production between 2008/09
Ghana. In Côte d’Ivoire, the proportion of children engaged
and 2018/19.
in child labor in the 15-17 age group decreased by 6
percentage points. Similarly, hazardous child labor prevalence decreased by 5 percentage points
in the oldest age group in Côte d’Ivoire.
Quantitative InsightComparison of data across rounds indicates there was an increase in children’s exposure to theworst forms of child labor 51 in cocoa production between 2008/09 and 2018/19 and thus, thetargets of the Declaration and Framework of reducing hazardous child labor by 70 percent
between 2008/09 and 2018/19 within agricultural households in cocoa growing areas of Côte
d’Ivoire and Ghana were not met.
5.2.5.1

Children’s Engagement in the Components of Hazardous Labor in Cocoa
Production

Comparison of 2008/09 and 2018/19 data shows the prevalence rate of children’s exposure tohazardous work in cocoa production increasing by 12 percentage points between 2008/09 and2018/19. This change in exposure to hazardous work can be better understood by investigating
the changes in the six different types of hazards related to cocoa agriculture. Table 17 presents
the data on exposure to each of the six different types ofhazards related to cocoa production among all children in
Quantitative Insightagricultural households in cocoa growing areas of Côte
Overall, use of sharp tools was themost commonly performedd’Ivoire and Ghana.
hazardous activities in cocoa
agriculture followed by carrying
heavy loads, exposure to agrochemical, and land clearing
activities. A very small proportion ofchildren were exposed to long
working hours or night work incocoa production in both periods.

In 2018/19, 43 percent of children in agricultural households
were exposed to at least one of the six hazardous activities
involved in cocoa production, up 13 percentage points fromthe corresponding estimate from 2008/09. Comparison ofdata indicates increased exposure to five of the six hazard
types that are prominent in cocoa agriculture. Overall, use of sharp tools was the most commonly
performed hazardous activities in cocoa agriculture (with exposure rate of 36% in 2018/19 and
28% in 2008/09), followed by carrying heavy loads, exposure to agro-chemicals, and land
clearing activities. A small proportion of children were exposed to long working hours or nightwork in cocoa production in both periods.

51

Note that we used for hazardous child labor as a proxy for WFCL per the Harkin-Engel Protocol.

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Comparison of data shows that among these components, exposure to agro-chemical products
has become pervasive between 2008/09 and 2018/19 as the proportion of children exposed toagro-chemicals increased by approximately five times between 2008/09 and 2018/19, from 5%
to 24%. As described in Section 5.1.3 there was a significant increase in agro-chemical use
among cocoa growing households during this period.
Quantitative Insight
In Ghana, use of sharp tools,
exposure to agro-chemicals andcarrying heavy loads were the mostprominent sources of exposure tohazardous work. The trends in
exposure indicate that similar to
Côte d’Ivoire, among various
categories of hazardous activities,
exposure to agro-chemical
increased by the greatest extent.

Exposure to land clearing, sharp tool use and carrying heavyloads increased during the same period as well, but by amuch smaller extent - approximately between 6 to 8
percentage points. This indicates among the six hazardous
activity categories, exposure to agro-chemicals increased by
the greatest extent.

Disaggregation of data by country using the common
definitions indicates that consistent with the overall trend,
the most commonly performed hazardous activities in Côte
d’Ivoire were using sharp tools (31% in 2018/19 and 21% in 2008/09), followed by carrying
heavy loads (26% in 2018/19 and 18% in 2008/09), land clearing (22% in 2018/19 and 15% in
2008/09), and exposure to agro-chemicals (19% in 2018/19 and 4% in 2008/09). Among the six
different categories of hazard, the increase in exposure to agro-chemicals was most prominent.
In Ghana, similar to the overall trend, use of sharp tools (remained stable at around 39%),
exposure to agro-chemicals (32% in 2018/19 and 7% in 2008/09) and carrying heavy loads (32%
in both 2018/19 and 2008/09) were the most prominent
Quantitative Insightsources of exposure to hazardous work. The trends in
exposure indicate that like Côte d’Ivoire, among various
Data indicates that consistent withthe overall trend, the mostcategories of hazardous activities, exposure to agrocommonly performed hazardous
chemical increased by the greatest extent (by more than six
activities in Côte d’Ivoire were using
sharp tools, followed by carrying
times) – an increase from 7 percent to 32 percent.
heavy loads, land clearing, andexposure to agro-chemicals.

Overall, children were exposed to more types of hazardous
work activities on average in 2018/19 than in 2008/09 (from 0.7 to 1.1 activities). This
statistically significant increase was found in both Ghana (0.9 to 1.2 activities) and Côte d’Ivoire(0.6 to 1.0 activities).

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Table 17: Estimates of Percentages of all Children Exposed to Hazardous Work Activities in the Cocoa Sector, 5-17
Years, All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19*
Percentage of children in
agricultural households:

Total

Côte d'Ivoire

Ghana

2008/092018/19

Sig ofdiff^

2008/092018/19

Sig ofdiff^

2008/092018/19

Sig ofdiff^

V1: Land clearing

12%

19%

***

15%

22%

***

7%

14%

***

V2: Heavy loads

23%

29%

***

18%

26%

***

32%

32%

V3: Agro-chemicals

5%

24%

***

4%

19%

***

7%

32%

V4: Sharp tools

28%

36%

***

21%

31%

***

39%

43%

V5: Long working hours

1%

1%

1%

1%

0%

0%

V6: Night work

0%

2%

***

1%

2%

***

0%

3%

***

Exposed to multiple hazards

30%

43%

***

23%

37%

***

43%

51%

***

Average number of hazards

0.7

1.1

***

0.6

1.0

***

0.9

1.2

***

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Measured based on Variables 1-6, as described in section 3.3 of this report.
**Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points^Significance of Difference *** p<0.01

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Comparison of the trends in exposure to various types of hazardous activities in cocoa
production indicates that in both countries, agro-chemical use has become a substantial source of
exposure to hazardous activities over the past 10 years, while use of sharp tools, exposure to land
clearing, and carrying heavy loads remain persistent source of hazardous work in cocoa growing
areas of Côte d’Ivoire and Ghana.
Data presented in Table 52 and Table 53 in Annex 10.4.2 show the changes in children’s
exposure to each of the six different types of hazards related to cocoa agriculture disaggregatedby age groups and gender in Côte d’Ivoire and Ghana. Earlier we saw that the most prominentchange in exposure to either of the hazardous activities was in exposure to agro-chemicals. There
were significantly large increases in the proportion of children exposed to agro-chemicals amongthe 12-14 years and 15-17 years age group and smaller increases in the 5-11 years age groupbetween 2008/09 and 2018/19. The change in the average number of activities that children were
exposed to was also the highest in the 15-17 year old age group ( increase from 1.1 to 2.0),
followed by the 12-14 year old age group (increase from 1.0 to 1.7), and the 5-11 year old age
group (increase from 0.5 to 0.7).
Sex disaggregation shows a much larger increase in agro-chemical exposure among boys than
girls between the survey periods. Additionally, in both periods, there was a consistently higher
proportion of boys exposed to any of the 6 hazardous activities compared to girls. For instance,
in 2018/19, close to half of the proportion of boys in agricultural households were using sharp
tools versus a quarter of the proportion of girls in these households. Additionally, 27 percent ofboys were engaged in land clearing activities versus 10 percent of girls, and 33 percent of boys
were carrying heavy loads versus 24 percent of girls. Consequently, in 2018/19, approximately
half of the boys in agricultural households (50%) were exposed to one or more of the six
hazardous activities compared to 34% of girls. Additionally, the change in the average number ofhazardous activities boys were exposed to was higher than for girls, 70 percent and 43 percent
respectively. This indicates that boys were more vulnerable to hazardous work when they wereinvolved in cocoa production.
5.2.5.2

Children’s Exposure to Multiple Hazardous Activities Related to Cocoa
Production

In addition to the estimate of children exposed to any of the six different hazardous activities, it
is also important to compare the incidence of facing multiple hazards, since children may be
involved in more than one hazardous activity. Focusing only on the rate of exposure to any one
hazardous activity provides an incomplete picture of the realities on the ground. For this purpose,
given that the hazards considered here are specific to cocoa production, we present the data on
how exposure to multiple hazards changed between 2008/09 and 2018/19 among children
working in cocoa production in Table 18.

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Between 2008/09 to 2018/19, there was a marginal decrease in the exposure to any hazardous
activities among children working in cocoa production. The proportion of children exposed toany hazardous activities decreased from 98 percent in 2008/09 to 92 percent in 2018/19 in Côte
d’Ivoire and from 93 percent to 85 percent in Ghana during
Quantitative Insightthe same period.
Although the proportion of children
working in cocoa production who
were engaged in multiple
hazardous activities decreased,
those who were engaged inhazardous activities were engaged
in a greater number of hazardous
activities in 2018/19.

The data in Table 18 indicates that children working incocoa production were vulnerable to being exposed tomultiple types of hazardous activities in Côte d’Ivoire andGhana, as approximately 75 percent of all children working
in cocoa production were exposed to more than one
hazardous activity in 2018/19 in Côte d’Ivoire while in Ghana, approximately 65 percent of allchildren working in cocoa production were exposed to more than one hazardous activities in
2018/19.
Although the 2018/19 figures demonstrate the importance of considering multiple hazards, there
was a significant decrease in this risk between survey rounds. The proportion of children
working in cocoa production exposed to multiple hazards decreased from 87 percent in 2008/09
to 75 percent in 2018/19 in Côte d’Ivoire while in Ghana it remained stable around 65 percent.
However, the proportion of children engaged in four or more hazardous activities more thandoubled in Côte d’Ivoire (from 13% in 2008/09 to 29% in 2018/19) and increased by more than
10 times in Ghana (from 1% in 2008/09 to 16% in 2018/19). Thus, the data indicate that whilelesser proportion of children working in cocoa production were engaged in multiple hazardous
activities, those who were engaged in hazardous activities were undertaking a greater number ofhazardous activities in 2018/19 as compared to 2008/09.

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Table 18: Estimates of Exposure of Children Working in Cocoa Production, 5-17 Years, to Multiple Types of Hazardous
Work, All Agricultural Households, in Côte d'Ivoire and Ghana, 2008/09 and 2018/19
Percent of children exposed
to:

Total

Côte d'Ivoire

Ghana

2008/092018/19

Sig ofdiff^

2008/092018/19

Sig ofdiff^

2008/092018/19

Sig of
diff^

Not exposed to any hazard

4%

11%

***

2%

8%

***

7%

15%

***

1 type of hazard

19%

18%

11%

17%

***

27%

20%

***

2 types of hazard

39%

25%

***

33%

23%

***

43%

28%

***

3 types of hazard

31%

23%

***

41%

24%

***

22%

22%

4 types of hazard

6%

19%

***

11%

25%

***

1%

14%

***

5 types of hazard

1%

3%

***

2%

4%

***

0%

2%

***

6 types of hazard

0%

0%

0%

0%

0%

0%

Average number of hazards

2.2

2.3

2.6

2.5

1.8

2.1

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

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5.2.5.3

Children’s Exposure to Various Components of Agro-Chemical Products

As reported earlier in this section, among the six hazardous activities related to cocoa agriculture,
exposure to agro-chemicals recorded a steep increase in both countries between 2008/09 and2018/19. However, related to the importance of understanding the component parts of hazardous
child labor generally, it is also important in this case to look at the sub-components that
constitute agro-chemical related hazards. Table 20 reports data on children’s exposure to fivesub-components of agro-chemical related hazards.
Comparison of trends in exposure to different sub-components of agro-chemical use amongchildren working in cocoa production indicate that between 2008/09 and 2018/19 there is asignificant increase in exposure to spraying pesticides/insecticides (by 6 percentage points),
working in the vicinity of a farm during pesticide spraying (by 20 percentage points), re-enteringa sprayed farm within less than twelve hours of spraying (by 9 percentage points), carrying water
for spraying (by 24 percentage points), and working with agrochemicals (by 16 percentage
points) in aggregate.
Disaggregation of data by country shows there was a similar trend in changes in exposure to thevarious sub-components of agro-chemicals exposure in both countries. The most prominent
increase was in the proportion of children who were present or working in the vicinity of farmduring pesticide spraying and carrying water for spraying.
Table 54 in Annex 10.4.2 shows breakdowns for exposure to agro-chemical hazards by age
group and sex. In Côte d'Ivoire, there was a decrease in exposure to agro-chemical hazards forchildren in the 5-11 age group, an increase for children in the 12-14 age group, and nostatistically significant difference for the 15-17 age group. There was also an increase in
exposure to agro-chemicals for male children compared to a decrease for female children in Côte
d'Ivoire. There are no statistically significant differences in agro-chemical exposure by sex in
Ghana or age group in Ghana. This shows that overall increases in exposure to agro-chemicalhazards was mainly driven by the male children and children in the 12-14 age group in Côte
d'Ivoire.

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Table 19: Disaggregation of Exposure to Agro-Chemicals, Children Working in Cocoa Production in the Last 12 Months,
All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Number and percentage
of children working incocoa exposed to V3

Total

Côte d'Ivoire

Ghana

2008/092018/19

Sig of diff^2008/092018/19

Sig of diff^2008/092018/19

Sig of diff^

Exposed to V3 (agrochemicals)

15%

50%

***

15%

47%

***

15%

53%

***

Spraying pesticides or
insecticides

2%

8%

***

1%

9%

***

2%

7%

***

Present in vicinity of farmduring pesticide spraying

4%

24%

***

1%

23%

***

6%

24%

***

Reentering sprayed farmwithin 12 hours of spraying

2%

11%

***

0%

11%

***

4%

10%

***

Carrying water for spraying

11%

35%

***

13%

31%

***

10%

40%

***

Involved in working with
agrochemicals*

1%

17%

***

1%

17%

***

2%

18%

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Such as purchasing, transport, storage, mixing, loading, spraying/applying, washing of containers and spraying machine, and/or disposal^Significance of Difference *** p<0.01

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Injuries Suffered while Working in Agriculture in the Past 12 Months and
Health Consequences for Children
As mentioned before in this report, child labor rates are only one factor to consider when trying
to understand the lives of children in cocoa production. The following section focuses on injuries
and health consequences among children working in agriculture. It is important to note that the
majority of these children were working in cocoa production during the 2018/19 survey round. 52
Children working in agriculture are susceptible to various injuries and it is important to not only
reduce child labor but also reduce injuries associated with child labor. As part of our childsurvey, children were asked to self-report whether they suffered injuries while working in
agriculture and whether they suffered specific types of injuries. Below we document the common
injuries children report while doing agricultural work and the resulting health consequences.
Table 20 presents a comparison of injuries suffered by children while working in agriculture
from cocoa growing households (as percentage of working children in agriculture) with thoseworking in agriculture from non-cocoa growing households.
In Côte d’Ivoire and Ghana, the most common injuries for children working in agriculture were
wounds/cuts, muscle/back/other pains, and skin itchiness or scratches. Among these injuries,
wounds and cuts were most common in agriculture in general as around 30 percent of thechildren working in cocoa and non-cocoa agriculture sustained wounds/cuts.
In Côte d’Ivoire, children working in agriculture, including
Qualitative Insightin cocoa production, were more likely to suffer broken
Qualitative data reflect thesebones, back pains, or burns, than those working in otherfindings, although children reported
working in other forms of
non-cocoa agriculture as a higher proportion of children
agriculture, they most often
working in cocoa production reported these injuries
reported injuries specific to cocoa
production. In some instances,compared to their counterparts working in non-cocoa
children stated a preference for
agriculture. Children’s exposure to hazardous work reported other agricultural activities because
in 5.2.5 indicates that a large proportion of children in cocoa they were not as physicallyintensive.
agriculture carry heavy loads, undertake land clearing, andare exposed to agro-chemical products. Although speculative, the injuries reported by children
seem to be reflecting the consequences of these hazards related to cocoa agriculture.

52

Due to methodological limitations that restrict comparability of data related to injuries, comparison of data withprevious rounds of survey are not possible.

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Table 20: Injuries Experienced by Children While Working in Agriculture, Children 5-17 Years, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2018/19
Total

Côte d’Ivoire

Ghana

Percentage ofchildren working
in agriculture

Cocoa
HHs

NonCocoa
HHs

Diff
(pp)*

Sig of
diff^

Cocoa
HHs

NonCocoa
HHs

Diff
(pp)*

Sig of
diff^

Cocoa
HHs

NonCocoa
HHs

Diff
(pp)*

Sig ofdiff^

Number of children

2,046,114

301,480

N/A

N/A

1,119,035

217,400

N/A

N/A

927,079

84,080

N/A

N/A

Wounds/cuts

30%

29%

1

31%

32%

-1

28%

22%

7

Back pains

0%

0%

0

0%

0%

0

1%

0%

1

Muscle pains

1%

1%

-1

1%

1%

-1

0%

1%

-1

Skin itchiness/scratches

8%

4%

3

6%

2%

4

10%

10%

0

Other pains

8%

5%

3

6%

4%

2

9%

6%

3

Other

4%

3%

1

3%

3%

0

6%

4%

1

Burns

1%

0%

1

1%

0%

1

2%

1%

1

Snake bites

6%

3%

3

6%

3%

3

6%

4%

2

Broken bones

4%

4%

0

4%

4%

0

4%

3%

1

Type of injury

***

***

***

***

***

***

***

Source: NORC Child survey 2018/19, weighted, strata 1-3*Calculated as the difference between children in cocoa and non-cocoa in percentage points, includes rounding^Significance of Difference *** p<0.01

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In Ghana, both children of cocoa growing households and non-cocoa agricultural households
report similar types of injuries and there is no statistically significant difference in incidence ofsustaining injuries between children from cocoa and non-cocoa households, except for higher
incidence of broken bones and burns among children from cocoa households.
Given the danger involved in hazardous work, we expect that children engaged in hazardous
work are more vulnerable to injuries than those who are not exposed to hazardous work. Table
21 presents the data on injuries by children’s exposure to hazardous work which supports thisexpectation.
Overall, in both countries, injuries such as wounds/cuts, back/muscle pains, burns, and skinitchiness or scratches are more common among children working in agriculture and engaging inhazardous work than their counterparts engaging in non-hazardous work in 2018/19.
In Côte d’Ivoire, there are significant differences in experiencing wounds/cuts, back pains,
muscle pains, burns, and skin itchiness or scratches between children engaging in hazardous
work and those engaging in non-hazardous work. Ivoirian children engaging in hazardous work
were approximately six times as likely to sustain back/muscle pains and skin itchiness/scratches
compared to those engaging in non-hazardous work. In Ghana, these differences were more
pronounced as the difference in proportions between the children working in cocoa and noncocoa agriculture were at least as high or greater than in Côte d’Ivoire. These significant
differences in experiencing back/muscle pains and skin itchiness/scratches among the children
exposed to hazardous work clearly indicates they are more vulnerable to work related injuries
and need assistance and proper care to mitigate these risks.
Finally, we examine the resulting health consequences of injuries suffered. Children were asked
to report what consequences they faced after experiencing an injury while working.
Data reported in Table 22 indicate that facing injuries while undertaking agricultural work
activities has several health consequences for the more than 1 million children working in
agriculture across the two countries. In 2018/19, the most commonly reported consequences forchildren working in cocoa production were experiencing tiredness and being in very bad pain. In
each country, 41 percent of children working in cocoa production felt very tired due to injuries
sustained while working in agriculture. Additionally, 26 percent of Ivoirian children and 42
percent of Ghanaian children working in cocoa production experienced bad pain as a result of
injuries sustained while working in agriculture. In Côte d’Ivoire, close to 1 in 5 children working
in cocoa production did not feel well for a long time and had to receive treatment at a healthcenter. Moreover, sustaining injuries while working led to 7 percent of children in cocoa
production not being able to go to school and 10 percent of children in cocoa production not
being able to continue working overall.

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Table 21: Injuries Experienced by Children While Working in Agriculture by Exposure to Hazardous Work, Children 5-17
Years, All Agricultural Households, in Côte d’Ivoire and Ghana, 2018/19
Total

Côte d’Ivoire

Ghana

Percentage ofchildren working
in agriculture

HazWork^^

Non-HazWork^^^

Diff
(pp)*

Sig ofdiff^

HazWork^^

Non-HazWork^^^

Diff
(pp)*

Sig ofdiff^

HazWork^^

Non-HazWork^^^

Diff
(pp)*

Sig ofdiff^

Number of children

1,936,326

412,240

N/A

N/A

1,106,329

230,105

N/A

N/A

829,997

182,135

N/A

N/A

Wounds/cuts

35%

8%

26

***

36%

9%

27

***

32%

7%

25

***

Back pains

0%

0%

0

***

0%

0%

0

1%

0%

1

***

Muscle pains

1%

0%

1

***

1%

0%

1

***

1%

0%

1

***

Skin itchiness/scratches

8%

1%

8

***

6%

1%

5

***

12%

1%

11

***

Other pains

9%

1%

8

***

7%

1%

7

***

10%

0%

10

***

Other

5%

1%

3

***

3%

1%

3

***

7%

2%

4

***

Burns

2%

0%

1

***

1%

0%

1

***

2%

0%

2

***

Snake bites

6%

0%

6

***

6%

0%

6

***

7%

0%

6

***

Broken bones

4%

2%

2

5%

1%

4

***

3%

4%

0

Type of injury

Source: NORC Child survey 2018/19, weighted, strata 1-3*Calculated as the difference between children engaged in hazardous work and non-hazardous work in percentage points, includes rounding^Significance of Difference *** p<0.01^^Engaged in hazardous work activities^^^Engaged in non-hazardous work activities

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Table 22: Health Consequences from Injuries Experienced While Working inAgriculture, Children 5-17 Years Working in Cocoa Production, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2018/19
Percentage of children working in agriculture

Total

Côte
d’Ivoire

Ghana

Population of children working in agriculture

1,667,575

831,937

835,638

Felt very tired or exhausted

41%

41%

41%

Was in very bad pain

34%

26%

42%

Felt very sick

25%

24%

26%

Other

19%

4%

34%

Had to receive traditional treatment

15%

16%

14%

Did not feel well for a long time

13%

17%

8%

Had to receive treatment at a hospital/health center

12%

13%

10%

Could not continue working

10%

11%

9%

Could not go to school

7%

12%

2%

Consequences

Source: NORC Child survey 2018/19, weighted, strata 1-3

Children’s Engagement in Non-Cocoa Agriculture and Non-Agricultural
SectorSince the proportion of children working in the cocoa sector increased between 2008/09 and2018/19, it is insightful to look at changes in children’s involvement in sectors apart from thecocoa sector in agriculture and non-agricultural sectors in Côte d’Ivoire and Ghana. Table 23
presents the data on children’s engagement in non-cocoa agriculture and in non-farm activities.
Overall, in the cocoa growing areas of Côte d’Ivoire and Ghana, there was a 9 percentage points
decrease (from 29% to 20%) in the proportion of children involved in agriculture work outside of
the cocoa sector between the survey rounds. This indicates that, given the increasing importanceof cocoa farming, children shifted away from other agricultural activities to cocoa production(keeping in mind that correlation does not mean causation). In Côte d’Ivoire, around 24% ofchildren engaged in agriculture work outside the cocoa sector in 2008/09 and 2018/19. In Ghana,
13 percent of children engaged in agriculture work outside the cocoa sector in 2018/19, down 14
percentage points from the 2008/09 estimate.

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Table 23: Estimates of Change in Children Working in Agriculture Other than the
Cocoa Sector, in Non-Agricultural Sector, 5-17 Years, All Agricultural Households, in theLast 12 Months, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Children working
in

Total
Côte
d'Ivoire
Ghana

Agriculture other than the cocoa
sector

Sectors other than agriculture

Pct

Pct

2008/0929%

2018/19

20%

2008/0931%

2018/1924%

2008/0927%

2018/19

13%

Diff. (pp)*Sig of diff.^

-9

***

5%
12%

Diff. (pp)*Sig of diff.^

8

***

9

***

5

***

4%
-7

-14

14%
***

5%
11%

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes
rounding^Significance of Difference *** p<0.01

Quantitative Insight

There was a 9 percentage point
While looking at the choice between farm and non-farmdecrease (from 29% to 20%) in theactivities, the data indicate that between the same periods,
proportion of children involved inagriculture work outside of thechildren involvement in non-agricultural sectors increased by 8
cocoa sector between the survey
percentage points. In Côte d’Ivoire, there was a 9 percentagerounds. This indicates that, given
the increasing importance of cocoa
point increase while in Ghana there was a 5 percentage-point
farming, children shifted away fromincrease in the proportion of children working in nonother agricultural activities to cocoa
production.
agricultural sectors. This is consistent with the general trend
towards the shrinking of the agricultural sector as a whole andexpansion of opportunities outside agriculture such as mining, fishing and service sectors. 53 54

5.3

Main Findings: Cocoa Growing Households (2013/2014 and 2018/2019)

In this section we explore recent trends in changes in children engagement in cocoa production
activities, child labor, and hazardous child labor for the subpopulation of children from cocoa
growing households working in cocoa production. For this purpose, we use data from the2013/14 survey round and compare it with data from the 2018/19 round. While NORC was able
to use 2008/09 data for a comparison of all agricultural households as seen in Section 5.2, due tothe methodological issues with the 2008/09 data, it was not feasible for NORC to use that datafor comparison of cocoa growing households as described in detail in Section 4.3 and Annex
53

http://www.fao.org/3/a-i4337e.pdf

54

https://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_624872.pdf

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10.2. However, based on available documentation about the 2013/14 survey round, NORC
concluded that there were no such methodological issues with the data collected from the subpopulation of cocoa growing households. Consequently, based on discussions with thestakeholders and given the importance of comparing cocoa growing households across rounds,
we use the data from the cocoa growing household of 2013/14 for the comparison of progress
made among this sub-population of agricultural households.
It is important to note that cocoa households constitute about 86 percent of the survey sample in
2018/19 and 76 percent of the sample in 2013/14. So while the comparison of 2013/14 data on
all agricultural households with the 2018/19 data is not possible, the comparison of data fromcocoa households between 2013/14 and 2018/19 still provides the status of progress of most of
the agricultural households in the cocoa growing areas of Ghana and Côte d’Ivoire.
However, it is useful to note that the comparison of cocoa and non-cocoa households of 2018/19
survey round reported in Table 55 in Annex 10.4.2 indicates that the proportion of children
engaged in child labor and children engaged in hazardous child labor is not different between the
non-cocoa households compared to the cocoa households, again indicating the similarity between
these two sub-populations.
Cocoa Growing Households: Estimate of Working Children
Table 24 presents estimates of children in cocoa households in cocoa production based on usual
and current activity status.
Overall, a higher proportion of children in cocoa growing households were usually active in
2018/19 in cocoa production compared to 2013/14 – a trend similar to the trend found among all
agricultural households between 2008/09 and 2018/19. On the other hand, involvement based on
current activity status (reference period past 7 days) for children in cocoa households remained
stable between 2013/14 and 2018/19.
In Côte d’Ivoire and Ghana individually, there was no change in the proportion of children incocoa households usually working in cocoa growing areas in the last twelve months. The
proportion of children in cocoa households reporting work in cocoa production in the last twelve
months was much higher than those working in the last seven days. In Côte d’Ivoire, there was a
14 percentage-point decrease in the proportion of children working in the last seven days, while
in Ghana there was a 13 percentage-point increase from 2013/14 to 2018/19.

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Table 24: Estimates of Children in Cocoa Households Working in Cocoa Production in
the Last 12 Months and in the Last 7 Days, in Côte d’Ivoire and Ghana, 2013/14 and2018/19
Last 12 Months
2013/142018/19

Diff
(pp)*

Last 7 Days
Sig ofdiff^

2013/142018/19

Diff
(pp)*

Sig of
diff^

***

29%

25%

-4

34%

20%

-14

***

19%

32%

13

***

Total Children Working in Cocoa Production
Percent

47%

53%

6

Côte d’Ivoire Children Working in Cocoa Production
Percent

41%

45%

5

Ghana Children Working in Cocoa Production
Percent

60%

64%

5

Source: Child survey 2013/14, and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2013/14 and 2018/19 rates in percentage points, includes
rounding^Significance of Difference *** p<0.01

Quantitative Insight
Table 56 in Annex 10.4.2 shows the working hours andminimum age for children in cocoa households disaggregated A higher proportion of children in
by age group and data in Table 57 in Annex 10.4.2 shows the cocoa growing households wereusually active in 2018/19 in cocoa
working hours and minimum age for children in cocoa
production compared to 2013/14 –households disaggregated by sex. These tables show that the a trend similar to the trend foundamong all agricultural households
proportion of children working more than the allowable
between 2008/09 and 2018/19. On
the other hand, involvement based
number of hours and the number of hours worked for eachon current activity status
gender and age group remained stable in Côte d’Ivoire. At(reference period past 7 days) for
children in cocoa households
the same time, average number of hours worked by maleremained stable between 2013/14
children overall did decreased (from 12.5 hours to 10.5
and 2018/19.
hours) during this period. In Ghana, there was a decrease in
the percent of children in the 12-14 age group working overall (18% to 9%) and within each sex.
The total number of hours worked decreased overall in Ghana from 8.8 to 6.4, which was driven
by both male and female children in the 12-14 and 15-17 age groups.

Cocoa Growing Households: Estimate of Child Labor and Hazardous
Child Labor
Next, we present the prevalence rates of child labor and exposure to hazardous work in cocoa
production between 2013/14 and 2018/19 for children in cocoa households in Côte d’Ivoire andGhana in Table 25.

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The data presented in Table 25 indicates that in cocoa growing areas of Côte d’Ivoire and Ghana,between 2013/14 and 2018/19, there was an increase (6 percentage-points in aggregate) in the
proportion of children working in cocoa production, children engaged in child labor, andchildren engaged in hazardous work. When the data was disaggregated by country, it shows
there was no statistically significant change in the
Quantitative Insightproportion of children engaged in child labor and in
The prevalence rates of hazardous
hazardous work in either country. These findings may
child labor in cocoa production
increased between 2013/14 andreflect stakeholders increased interest in reducing child
2018/19 among the cocoa growing
labor and hazardous child labor following the 2013/14 data
households in aggregate.
collection round.
Data presented in Table 58 in Annex 10.4.2 shows the changes in children in cocoa households
engaged in child labor and in hazardous child labor in Côte d’Ivoire and Ghana, disaggregated by
sex and age group. Comparison of data by sex indicates that in Côte d’Ivoire a larger proportionof male children were engaged in child labor, while a smaller proportion of children in the 15-17
age group and a higher proportion in the 5-11 age group were engaged in hazardous child labor.
There were no significant changes by sex or age group in Ghana.

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Table 25: Estimates of Change in Children in Cocoa Household, 5-17 Years, Working in Cocoa Production, Children
Engaged in Child Labor in Cocoa Production, and Children Engaged in Hazardous Work in the Cocoa Sector in the Last
12 Months, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19
Children in cocoa
households
Total
Côte
d’Ivoire
Ghana

Children Working in Cocoa
ProductionPct2013/1447%

2018/1953%

2013/1441%

2018/1945%

2013/1460%

2018/1964%

Diff (pp)*Sig of diff^

6

***

5
5

Children Engaged in Child Labor in
Cocoa Production
Pct
44%
50%
37%
43%
57%
59%

Diff (pp)*Sig of diff^

6

***

5
2

Children Engaged in Hazardous
Work in Cocoa Production
Pct
42%
47%
36%
41%
55%
55%

Diff (pp)*Sig of diff^

5

***

6
0

Source: Child survey 2013/14, and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2013/14 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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5.3.2.1

Children’s Engagement in the Components of Hazardous Work in Cocoa
Production

It is important to explore how exposure to different hazards related to cocoa agriculture havechanged among children engaged in cocoa production in the cocoa growing households. Similar
to our analysis of multiple hazards within agricultural households presented in section 5.2,
focusing solely on the rate of exposure to any one hazardous activity provides an incomplete
picture of the realities on the ground. Table 26 presents the data on exposure to each of the six
different types of hazards related to cocoa agriculture among children in cocoa households incocoa growing areas of Côte d’Ivoire and Ghana.
Data presented in Table 26 indicates that overall, the most prominent change is in exposure toagro-chemicals which increased by 17 percentage points (from 10% to 27% in 2018/19),
followed by an increases in land clearing (by 8 percentage points) and sharp tool use (by 7percentage points) in the cocoa growing areas of Côte d’Ivoire and Ghana. However, there aresome interesting differences at the country level. Following the overall trend, there weresignificant increases in exposure to agro-chemicals in both countries (by approximately 15
percentage points), while exposure to land clearing increased by larger extent in Ghana (by 14
percentage points against 6 percentage points increase in Côte d’Ivoire). Also, in Ghana, among
cocoa growing households, children’s exposure to carrying heavy loads fell by 9 percentage
points between 2013/14 and 2018/19, and children’s exposure to carry loads did not change inCôte d’Ivoire. Additionally, the average number of hazardous activities children were exposed toincreased from 0.9 to 1.2 overall, including similar increases at the country level.
Data presented in Table 59 in Annex 10.4.2 shows the
Quantitative Insightchanges in exposure to each of the six different types of
The most prominent change inhazards related to cocoa agriculture among children inhazardous child labor is in
exposure to agro-chemicals which
cocoa households disaggregated by age groups and genderincreased by 15 percentage points,
in both countries. Prominent changes across age groups
followed by increases in land
were observed in exposure to agro-chemicals, land clearing, clearing and sharp tool use.
sharp tool use, and night work. The largest increase in the
proportion of children exposed to agro-chemicals or agro-chemicals between 2013/14 and2018/19 was among the 12-14 and 15-17 age groups. Exposure to sharp tools in cocoa
significantly increased among children in the 5-11 and 12-14 age group. There were also
increases across each age group for the number of hazardous work activities children were
exposed to.

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Table 26: Estimates of Change in Children in Cocoa Households, 5-17 Years, Exposed to Various Types of Hazardous
Work Activities in the Cocoa Sector, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19*
Percentage of children in cocoa
growing households exposed to:

Total

Côte d’Ivoire

Ghana

2013/142018/19

Sig ofdiff^

2013/142018/19

Sig ofdiff^

2013/142018/19

Sig ofdiff^

Land clearing in cocoa (V1)

12%

20%

***

18%

24%

***

1%

15%

***

Heavy loads in cocoa (V2)

31%

31%

24%

29%

44%

35%

***

Agro-chemicals in cocoa (V3)

10%

27%

***

5%

21%

***

20%

34%

***

Sharp tools in cocoa (V4)

33%

40%

***

28%

35%

***

42%

46%

Long working hours in cocoa (V5)

1%

1%

1%

1%

1%

0%

Night work in cocoa (V6)

1%

3%

***

1%

2%

0%

3%

Exposed to one or more variables in
cocoa

42%

47%

***

36%

41%

55%

55%

Average number of variables exposed
to in cocoa

0.9

1.2

***

0.8

1.1

1.1

1.3

***

***

***

***

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

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Sex disaggregation reported in Table 60 in Annex 10.4.2 indicates only marginal differences inthe trend in change in exposure between male and female children with the proportion of girls
exposed to agro-chemicals, land clearing, and night work being marginally greater than theincreases in the proportion of boys exposed to either of these between the survey rounds.
Additionally, in both rounds, there was a consistently higher proportion of boys exposed to fiveof the six hazardous activities compared to girls. There was also an increase for each gender in
the number of hazardous work activities children are exposed to. It is important to note thatchildren who are exposed to one hazard are more likely to be exposed to a greater number ofhazards.
Table 61 in Annex 10.4.2 shows data on incidence of multiple hazards among children in cocoa
households. Between 2013/14 to 2018/19, there were increases in the exposure to four or morehazardous activities and decreases in exposure to either two or one hazardous activity in bothCôte d’Ivoire and Ghana. The proportion of children exposed to any hazardous activities
increased from 88 percent in 2013/14 to 92 percent in 2018/19 in Côte d’Ivoire and decreased
from 92 percent to 86 percent in Ghana during the same period. The average number ofhazardous work activities children were exposed to increased from 1.9 to 2.5 in Côte d’Ivoire
and from 1.8 to 2.1 in Ghana.
5.3.2.2

Exposure to Various Components of Agro-Chemical Product Use

Table 27 shows that among children in cocoa households working in cocoa production, exposure
to agro-chemicals increased significantly in both countries between 2013/14 and 2018/19.
Additionally, the table presents the data on exposure to agro-chemicals broken down into
different sub-components for children in cocoa households between 2013/14 and 2018/19.
Table 27 indicates that similar to the trend in agricultural households described in Section
5.2.5.3, exposure to each of the sub-components of hazards related to agro-chemical exposure
increased for children working in cocoa production in cocoa households. Similar to the case of
all agricultural household, among the sub-components, the proportion of children carrying water
for spraying increased by largest extent (by around 21 percentage points) in cocoa growing areasof Côte d’Ivoire and Ghana, followed by being present or working in the vicinity of a farmduring pesticide spraying and being involved in working with agrochemicals. At the country
level, there was a similar trend in changes in exposure to the various sub-components of agrochemicals exposure in both countries.

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Table 27: Disaggregation of Exposure to Agro-Chemicals, Children in Cocoa Households Working Cocoa Production in
the Last 12 Months, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19
Number and percentage of children
in cocoa households working incocoa exposed to V3

Total

Côte d’Ivoire

Ghana

2013/142018/19

Sig ofdiff^

2013/142018/19

Sig ofdiff^

2013/142018/19

Sig ofdiff^Children exposed to V3 (agrochemicals)

22%

50%

***

13%

47%

***

34%

54%

***

Spraying pesticides or insecticides

4%

8%

***

4%

9%

***

3%

7%

***

Present in vicinity of farm during
pesticide spraying

7%

23%

***

5%

23%

***

10%

24%

***

Reentering a sprayed farm within 12hours of spraying

3%

10%

***

3%

10%

***

2%

11%

***

Carrying water for spraying

15%

36%

***

7%

31%

***

26%

40%

***

Involved in working with agrochemicals*

2%

17%

***

3%

17%

***

1%

18%

***

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3*Such as purchasing, transport, storage, mixing, loading, spraying/applying, washing of containers and spraying machine, and/or disposal^Significance of Difference *** p<0.01

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While the comparison of trends in exposure to hazardous work indicate that overall exposure tohazardous work in cocoa production did not increase between 2013/14 and 2018/19,
disaggregation of exposure to the six sub-components of hazardous work indicate there isincreased exposure to some of the sub-components such as exposure to agro-chemical use, land
clearing, and sharp tool use. Additionally, further disaggregation of exposure to agro-chemical
by sub-components indicates that for children who were exposed to agro-chemicals, incidence of
exposure to each of the sub-components increased between 2013/14 and 2018/19. Thus, thefindings presented in this section suggest that while there was no increase in the proportion ofchildren engaged in hazardous work, those who were exposed, were more vulnerable toincreasing incidence rates of exposure to individual hazards.
5.4

School Attendance among Children in All Agricultural Households

In the following sections, we present findings from changes in school attendance, measures ofliteracy, and numeracy for children from agricultural households between the two survey
periods. Access to education has been a priority for the Governments of Côte d’Ivoire andGhana, and references to a child’s school attendance are included in both countries’ hazardous
activities frameworks. Therefore, it is important to get a sense of the progress that has been madein this area. Table 28 reports school attendance by sex and age group.
Between 2008/09 and 2018/19, school attendance among children between 5-17 years
significantly increased in Côte d’Ivoire and Ghana. Following a 22 percentage-point increase inCôte d’Ivoire, in 2018/19 80 percent of Ivoirian children were attending school in the last twelve
months. Additionally, school attendance among Ghanaian children increased from 89 percent to96 percent between the two survey periods.
Gains in attendance were seen across both boys and girls. School attendance also increased
across all age groups between 2008/09 and 2018/19. In Côte d’Ivoire, the greatest gains in school
attendance were seen in the 15-17 years age group within which the proportion of children
attending school increased from 39 percent to 66 percent. In Ghana, the greatest gains in school
attendance were seen in the 5-11 years age group within which the proportion of children
attending school increased from 89 percent to 97 percent. The school attendance data indicatesthat reform in both countries and a greater push for education has led to significant gains inlevels of school attendance among children in agricultural households.

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Table 28: School Attendance for Children in the Last 12 Months, All Agricultural Households, in Côte d’Ivoire and Ghana,
2008/09 and 2018/19
Children attending school in thelast 12 months

Côte d'Ivoire

Ghana

2008/092018/19

Sig of diff^2008/092018/19

Sig of diff^

58%

80%

***

89%

96%

***

Boys 5-17 years

61%

83%

***

90%

96%

***

Girls 5-17 years

53%

78%

***

89%

96%

***

Children 5-11 years

60%

81%

***

89%

97%

***

Children 12-14 years

68%

88%

***

93%

98%

***

Children 15-17 years

39%

66%

***

85%

89%

Children 5-17 years
Sex

Age Group

Source: Child survey 2008/09 and 2018/19, weighted data, strata 1-3^Significance of Difference *** p<0.01

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5.5

School Attendance among Children Working in Cocoa Production

Working in agriculture could be an obstacle to school attendance and children’s ability to learn.
Table 29 presents the trend in school attendance for children working in cocoa production in thecocoa producing areas of Côte d’Ivoire and Ghana.
Consistent with the overall trends in school attendance for children in agricultural households
discussed earlier, there were significant increases in school attendance across the board amongchildren working in cocoa production. School attendance among girls increased more than thatamong boys in Côte d’Ivoire (by 32 percentage points among girls against 21 percentage amongboys). In Côte d’Ivoire, the greatest gains in school attendance were seen in the 15-17 age group(21 percentage points increase), and in Ghana in the 5-11 age group (8 percentage points
increase).

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Table 29: School Attendance for Children Working in Cocoa Production in the Last 12 Months, All Agricultural
Households, by Age Group and Gender, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Children working in cocoa
production attending school in thelast 12 months

Côte d'Ivoire

Ghana

2008/092018/19

Sig of diff^2008/092018/19

Sig of diff^

59%

84%

***

91%

96%

***

Boys 5-17 years

65%

86%

***

91%

96%

***

Girls 5-17 years

49%

81%

***

91%

97%

***

Children 5-11 years

67%

88%

***

91%

99%

***

Children 12-14 years

67%

89%

***

93%

98%

***

Children 15-17 years

34%

65%

***

88%

89%

Children 5-17 years
Sex

Age Group

Source: Child survey 2008/09 and 2018/19, weighted data, strata 1-3^Significance of Difference *** p<0.01

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5.6

School Attendance among Children in Child Labor and Hazardous Child
Labor in Cocoa Production

Next, we explore whether there was any improvement in school attendance among the children
engaged in child labor and hazardous child labor in cocoa production. Table 30 presents data on
school attendance for children engaged in child labor and hazardous labor in cocoa production by
age group.
In aggregate, a higher proportion of children engaged in child labor in cocoa production acrossall age groups attended school in 2018/19 compared to 2008/09. Overall, among children
engaged in child labor in cocoa production in the 5-11 age group, there was a 14 percentage
points increase in the proportion attending school between 2008/09 to 2018/19. In Côte d’Ivoire,the proportion of children engaged in child labor and attending school in the 5-11 age groupincreased from 67 percent to 89 percent between 2008/09 and 2018/19, while in Ghana it
increased from 91 percent to 99 percent during the same period.
School attendance among children in the 12-14 age group also experienced an increase of 12
percentage points between 2008/09 and 2018/19 in aggregate. In Côte d’Ivoire, 89 percent ofchildren engaged in child labor attended school in 2018/19 and, 97 percent of Ghanaian children
in the 12-14 age group in child labor attended school.
Qualitative Insight

Between the survey periods, school attendance amongchildren engaged in child labor in the 15-17 age groupincreased 14 percentage points, entirely driven by gains inCôte d’Ivoire. School attendance among Ivoirian children
engaged in child labor in the oldest age group almost
doubled between 2008/09 and 2018/19. In Ghana, there wasno change between the same periods.

Qualitative findings indicate that inmany instances, children were
engaging in child labor due to theirinability to access a nearby school,or their caregivers’ inability to
manage the costs associated withschooling. Findings indicate thatchanges in access to schooling
have mitigated these barriers andimpacted how much time children
spend on farms.

The trends in school attendance across age groups (and themagnitude of change) for children engaged in hazardous work were consistent with the trends forchildren engaged in child labor. There were highly statistically significant increases in school
attendance among children engaged in hazardous work attending school across the 5-11 agegroup (14 percentage points), 12-14 age group (12 percentage points), and 15-17 age group (14percentage points) between 2008/09 to 2018/19. Changes in the 15-17 age group were primarily
driven by improvements in school attendance in Côte d’Ivoire.

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Table 30: School Attendance for Children Engaged in Child Labor in Cocoa Production and Children Engaged in
Hazardous Work in Cocoa Production, All Agricultural Households, By Age Group, 5-17 Years, in Côte d’Ivoire andGhana, 2008-09 and 2018-19
Children Engaged in Child Labor in Cocoa Production
Total

Côte d’Ivoire

Ghana

Age Group:
AttendingSchool

2008/
2009

2018
20/19

Diff
(pp)*

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

Sig ofdiff^

5-11 Years

80%

94%

14

***

67%

89%

21

***

91%

99%

8

***

12-14 Years

81%

93%

12

***

67%

89%

22

***

92%

97%

5

***

15-17 Years

63%

77%

14

***

34%

65%

30

***

88%

89%

1

Children Engaged in Hazardous Work in Cocoa Production
5-11 Years

80%

93%

14

***

67%

88%

21

***

91%

99%

8

***

12-14 Years

81%

93%

12

***

67%

89%

22

***

92%

97%

5

***

15-17 Years

63%

77%

14

***

34%

65%

30

***

88%

89%

1

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes rounding
Significance of Difference *** p<0.01

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Table 63 in Annex 10.4.2 presents data on school attendance for children in cocoa households
engaged in child labor and hazardous labor in cocoa production by age group which
demonstrated similar trends between 2013/14 and 2018/19 as found in the case of all agricultural
households described above.
Next, we look at how basic literacy and numeracy have changed amidst increasing levels ofschool attendance in both countries.
5.7

Basic Literacy and Numeracy among Children Working in Cocoa Production

In order to explore the benefits of school attendance, we present the data on basic literacy andbasic numeracy in Table 31. Measurement of basic literacy involves two dimensions – ability toread a short simple statement and ability to write a short simple statement. Numeracy ismeasured through the ability to perform simple calculations.
Table 31: Basic Literacy and Numeracy for Children Working in Cocoa Production 55,
All Agricultural Households, 5-17 Years, in Côte d’Ivoire and Ghana, 2018/19
Côte d’IvoireChildren working in cocoa
production, 5-17 years

Ghana

Cocoa
HHs

Noncocoa
HHs

Sig of
diff^

Cocoa
HHs

Noncocoa
HHs

Sig ofdiff^

N/A

726,543

71,763

N/A

57%

58%

667,40269,30853%56%

886,787

87,99670%

71%

Who can read ashort simple
statement

Number

652,438

141,566

Percent

38%

40%

Who can write ashort simple
statement

Number744,017

174,941

Percent43%50%

Who can perform
simple calculations

Number826,858

198,541

Percent

48%

56%

N/A

N/A

N/A

N/A

Source: Child survey 2008/09, 2013/14, and 2018/19, weighted data, strata 1-3^Significance of Difference *** p<0.01

Overall, we see that in Ghana and Côte d’Ivoire, there was no statistically significant differencein literacy or numeracy rates between the cocoa and non-cocoa households. There is no
statistically significant difference in the ability to read a short simple statement between children
working in cocoa production and belonging to cocoa households and those belonging to noncocoa households across Côte d’Ivoire and Ghana.

55

Children working in production include all children who work in cocoa production, regardless of whether or nottheir parents work in cocoa production. Children can live in a household where the household head does not produce
cocoa and the child works on someone else’s cocoa farm, and still be considered as working in cocoa production.

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5.8

Estimate of Children’s Work Interfering with Education among Children
Working in Cocoa Production

Working in cocoa production could likely interfere with the education of children enrolled inschool. For instance, children engaged in this work might be forced to drop out of school during
harvest season, sustain injuries that have negative health consequences that prevent them from
attending school, or might be too tired to engage in school-related activities due to fatigue fromcocoa production activities.
To assess whether involvement in work affected schooling, we report data on whether children’s
work interfered with their schooling for children working in cocoa production in the cocoa
growing areas of Côte d’Ivoire and Ghana. The schooling of a child, 6-14 years, is considerednegatively impacted by work performed in cocoa agriculture if he or she worked in cocoa
farming during the previous twelve months and reported either having been withdrawn fromschool during cocoa season to do farm work and/or reporting that schooling has been affected by
his/her work.
In aggregate, the proportion of children reporting that cocoa work was interfering with schoolingincreased significantly from 5 percent in 2008/09 to 13 percent in 2018/19, which may be related
to increases in overall school attendance. 56- In Côte d’Ivoire, the proportion of children reportingthat work interfered with schooling increased by 3 percentage points (from 3 percent in 2008/09
to 6 percent in 2018/19), while the proportion of Ghanaian children reporting the same increased
by 13 percentage points (from 7 percent to 20 percent between 2008/09 and 2018/19). The
percent of children not attending school decreased from 2008/09 to 2018/19, which is consistent
with the data in the previous section on school attendance.

56

This indicates that the interference of cocoa work on education became more prevalent.

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Table 32: Estimates of Children Working in Cocoa Production Not Attending School, and Work Interferes with Schooling,
6-14 Years, All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Children 6-14 years
working in cocoa
production

Total

Côte d’Ivoire

Ghana

2008/092018/19

Sig ofdiff^

2008/092018/19

Sig ofdiff^

2008/092018/19

Sig ofdiff^

V7: Percent not attending
school

19%

7%

***

33%

11%

***

8%

2%

***

V8: Percent work interferingwith schooling

5%

13%

***

3%

6%

***

7%

20%

***

Percent exposed to either V7
or V8

24%

19%

36%

17%

***

14%

21%

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points^Significance of Difference *** p<0.01

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6 Analytic Insights: Role of Production Stratum, Agrochemical Use & Household Demographics
Comparison of the 2008/09 and 2018/19 survey data presented in the previous sections indicatethat in the past ten years, there has been a statistically significant increase in the proportion ofchildren engaged in child labor and in hazardous work in cocoa production. There may be
several factors influencing those findings on child labor and hazardous work. In this section, we
provide additional insights from survey data to understand the potential interplay of such factors.
Here we consider the role of the following factors 57:
1. Increased production and geographic expansion of cocoa production into areas withrelatively less production and new areas: We explore how the prevalence rate of child laborand exposure to hazardous work changed within different production stratum.
2. Increased usage of agro-chemical products among cocoa growing households
3. Changes in household demographics
6.1

Child Labor and Hazardous Work in Cocoa Production by Production
Stratum

Cocoa production is an important part of the agricultural sector of both countries and has
increased substantially over the years (by 62% between 2008/09 and 2018/19). As overall
production increases, areas with already high cocoa production become saturated and thus cocoa
production activities expand to other areas with historically lower production levels. This
expansion of production into new areas and areas with low production can potentially lead toincreased engagement of children in cocoa production, especially in the early stages when themarket is underdeveloped, and related increases in child labor.
In order to explore whether increases in cocoa production and consequent shifts in production to
new areas plays a role in affecting child labor and children engaged in hazardous work in cocoa
production, we present in Table 33 the prevalence of child labor and children engaged inhazardous work disaggregated by whether the areas are high, medium, or low production strata.

57

Note that these are not an extant list of possible contributing factors but only the most prominent that came out of
the research team’s analysis of the data.

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Table 33: Estimates of Change in Children Engaged in Child Labor and Exposure to Hazardous Labor of Children
Working in Cocoa Production in Areas with High, Medium and Low Cocoa Production, All Agricultural Households, in Côte
d’Ivoire and Ghana, in 2008/09 and 2018/19

Country

OverallCôte
d’Ivoire

Ghana

StrataChildren Engaged in Child Labor in Cocoa
Production
2008/092018/19

Diff (pp)**

High

43%

47%

4

Medium

33%

50%

16

Low

6%

33%

Total

31%

High

Sig of diff^Children Engaged in Hazardous Labor in Cocoa
Production*
2008/092018/19

Diff (pp)**

42%

46%

3

Sig of diff^

***

33%

46%

13

***

27

***

6%

32%

27

***

45%

14

***

30%

43%

12

***

40%

45%

5

39%

44%

5

Medium

19%

38%

19

***

19%

36%

17

***

Low

5%

31%

26

***

5%

30%

26

***

Total

23%

38%

15

***

23%

37%

14

***

High

56%

52%

-5

55%

49%

-7

Medium

43%

59%

16

***

42%

55%

12

***

Low

16%

42%

26

***

16%

41%

25

***

Total

44%

55%

11

***

43%

51%

8

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Measured based on Variables 1-6, as described in section 3.3 of this report.
**Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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Comparison of data by stratum indicates that among the three strata, between 2008/09 and2018/19, the prevalence of child labor and exposure to
Quantitative Insighthazardous work in cocoa production did not change in the
The prevalence of child labor andhigh cocoa production stratum. On the other hand, there are
the exposure to hazardous work did
substantial increases in the prevalence rate within the low
not see significant increases in thehigh production strata, while weand medium production strata. Between the two survey
observe substantial increases inperiods, child labor prevalence increased 16 percentage
prevalence within the low andpoints (from 33% to 50%), and 27 percentage points (frommedium production strata. There
was a similar trend in the change in
6% to 33%) in medium and low cocoa production areas
prevalence of exposure torespectively. There was a similar trend in the change inhazardous work during the same
period.
prevalence of exposure to hazardous work during the same
period.
Changes in prevalence of child labor and exposure to hazardous work within different production
strata in Côte d’Ivoire were similar to the overall trends. While there was no increase inprevalence rates in high production stratum, the prevalence rate of child labor increased by 19
and 26 percentage points in the medium and low production areas respectively with similar
trends found in the prevalence rate of hazardous child labor.
In Ghana, similar to the trend in Côte d’Ivoire the prevalence of child labor increased by 13
percentage points and 27 percentage points in medium and low cocoa production areas
respectively between the two survey periods. Children’s exposure to hazardous work during thesame period also increased by the largest extents in low production areas.
The breakdown of child labor prevalence by production stratum and comparison of prevalence
rates clearly indicate that while child labor and exposure to hazardous work prevalence rates
were relatively stable in the high production stratum, most of the increase in the prevalence ofchild labor between the survey rounds took place in the areas that produce relatively less cocoa.
These findings suggest that as high production areas become increasingly saturated with cocoa
farms, cocoa production activities permeate other areas where the infrastructure is still weak andawareness related to child labor and hazardous work is limited. Additionally, interventions
targeting child labor over the past ten years have likely focused on the high production areas
where child labor is more prevalent and the perceived need for such interventions is greatest.
Thus, it seems that the expansion of production to less saturated and new areas may haveresulted in increased child labor and exposure to hazardous work in cocoa production.
6.2

Expenditure on Agro-Chemical per ton of Cocoa Produced

Comparison of the trends in exposure to various types of hazardous activities in cocoa
production reported in Section 5.2.5.1 reveals that exposure to agro-chemical products has

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become a prominent source of exposure to hazardous activities in cocoa production, which
increased from 5 percent of children in agricultural households in both countries in 2008/09 to 24
percent in 2018/19. Also, data reported in Section 5.3.2.1 shows that a substantially larger
proportion of cocoa growing households were using agro-chemical products in agriculture.
While a larger proportion of cocoa growing households were using agro-chemical products, theintensity of use can play an important role in influencing exposure to hazardous activities.
In order to explore this relationship we present the data on how average expenditures on agrochemical products (as a proxy for amount of usage) among cocoa growing household changed
during 2013/14 and 2018/19 period in Table 34. The data reported here is the expenditure per tonof cocoa produced for households that reported using respective agro-chemical component andadjusted for inflation (reported in constant dollar value).
Table 34: Estimates of Change in Cocoa Households’ 58 Use of Agro-Chemical
Products Expenditure Per Ton of Cocoa in USD in the Last 12 Months, in Côte d’Ivoire
and Ghana, 2013/14 and 2018/19
USD/Ton of
Cocoa

Total

Côte d'Ivoire

2013/
2014

2018/
2019

Sig of
diff^

2013/
2014

2018/
2019

Fertilizer(s)

556

1,254

***

530

663

Pesticide(s)

267

745

***

199

376

Herbicide(s)

230

481

***

169

345

Ghana
2013/
2014

2018/
2019

Sig of
diff^

598

2,192

***

***

383

1,207

***

***

361

646

***

Sig ofdiff^

Source: Head of household survey 2013/14 and 2018/19, weighted data, strata 1-3^Significance of Difference *** p<0.01

The per-ton expenditure on fertilizer, pesticide, and herbicide per ton of cocoa showed an overall
increasing trend between 2013/14 and 2018/19. In Côte d’Ivoire, there were statisticallysignificant increases in expenditure on pesticides (from USD 199 to USD 376 per ton of cocoa)
and herbicides (from USD 169 to USD 345 per ton of cocoa). In Ghana, there was a statisticallysignificant increase in expenditure on fertilizers (from 598 to 2,192 USD), pesticides (from 383
to 1,207 USD), and herbicides (from USD 361 to USD 646).
The analysis presented here indicates that increases in cocoa farming and production led to bothan increased usage and significantly greater intensity of use of agro-chemical products in Côte
d’Ivoire and Ghana. Given the large proportion of children engaged in cocoa growing activities,
it is likely that this increased usage of agro-chemical products in cocoa production led toincreased exposure to agro-chemicals related hazards among children in cocoa growing areas of58

Bottom 10 percentile of households in cocoa production have been removed to no longer have high outliers.

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Côte d’Ivoire and Ghana. Future interventions around reducing child labor may want to have asignificant focus on mitigating agro-chemical related hazards.
6.3

Household Composition: Distribution of Children in Household

Table 35 shows that between 2008/09 and 2018/19, average household composition by age
shifted in each country as the overall number of children age 5-17 decreased (both overall andacross most age categories). In most cases, these differences are highly statistically significant.
The average number of children age 5-17 per household decreased 10 percent from 2.7 to 2.4 perhousehold. Decreases were statistically significant in the aggregate as well as at the country
level, though the decrease was slightly larger in Côte d’Ivoire where the total number decreased
from 2.8 to 2.5 children per household (11% decrease) than in Ghana where the total decreased
from 2.6 to 2.4 children per household (9% decrease).
The group with the largest decrease across rounds were children age 15-17, which saw a 22
percent decrease from 0.5 per household in 2008/09 to just 0.4 in 2018/19, primarily driven by
the highly significant 30 percent decrease between rounds in Côte d’Ivoire. The average numberof children age 5-11 saw a more muted 10 percent decrease overall, with a highly significant 11
percent drop in Côte d’Ivoire and no change in Ghana. Difference in average number of children
12-14 per household is not significant overall.
Thus, comparison of average number of children in the three age groups clearly indicates there
were fewer children in agricultural households and likely indicates the average family size
decreased between the 2008/09 and 2018/19 period. This may imply there are fewer children in
the agricultural households in cocoa growing areas of Côte d’Ivoire and Ghana in 2018/19.
Given the increase in production, and simultaneous reduction in average number of children incocoa growing areas, it may indicate a greater demand for existing child labor, and thus a greater
likelihood of children’s engagement in child labor.

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Table 35: Estimates of Average Number of Children, 5-17 Years, All Agricultural Households, by Age Group, in Côte
d’Ivoire and Ghana, 2008/09 and 2018/19
Total

Côte d'Ivoire

Ghana

Age group

2008/
2009

2018/
2019

Diff
(pp)*

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

5-11 years

1.7

1.5

-10

***

1.8

1.6

-11

***

1.5

1.4

-6

12-14 years

0.6

0.5

-1

0.5

0.5

10

0.6

0.6

-14

15-17 years

0.5

0.4

-22

***

0.5

0.3

-30

***

0.5

0.5

-13

All years

2.7

2.4

-10

***

2.8

2.5

-11

***

2.6

2.4

-9

Sig ofdiff^***

Source: Household roster survey, 2008/09 and 2018/19, weighted, strata 1-3*Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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7 Findings Based on Country Specific Definitions ofHazardous Work (2018/19 Survey Round)In earlier sections of this report, we discuss child labor and hazardous child labor prevalence
based on the common definition used to aggregate data between Côte d’Ivoire and Ghana. The
Governments of both Côte d’Ivoire and Ghana have made concerted efforts to address childlabor and hazardous child labor prevalence, including passing legislation and establishingguidance frameworks that include country-specific definitions of child labor and hazardous childlabor. Although aggregate information is helpful for the international community to understandthe impact of cocoa agriculture on children, it is less helpful for each individual government
when they design country-specific programs to address child labor in cocoa. Below we present
findings based on country specific definitions of child labor and hazardous child labor to help
local stakeholders better address the issue of child labor.
We first present data on children’s exposure to hazardous work activities based on national
legislation in Côte d’Ivoire and Ghana. We then present the estimate of the prevalence rate ofchildren’s exposure to child labor and exposure to hazardous work based on the national
definitions.
7.1

Estimate of Children Engaged in Child Labor, and Hazardous Work in Cocoa
Production in Côte d’Ivoire based on Ivoirian Legislation

In Côte d’Ivoire 59, per the national legislation there are seven types of hazardous activities that
Ivoirian children could likely be exposed to while working in cocoa production. These include:
not getting a full rest day, land clearing, charcoal production, carrying heavy loads, exposure toagro-chemicals, using sharp tools, and night work. Table 36 presents the data on exposure toeach of these hazardous activities.

59

The Ivoirian country definition of what is considered hazardous work activities is derived from the list ofhazardous activities the published by Ministry of Civil Service and Labor in Côte d’Ivoire released a list ofdangerous child work in March 2005 and subsequent revisions to the list made in 2012 and the new hazardous work
list published on 2nd June 2017 (ARRETE N°2017-017 MEPS/CAB) and a Light Work List (ARRETE N°2017-016
MEPS/CAB du 02 Juin 2017)

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Table 36: Estimates of Children, 5-17 Years, Exposed to Various Types of Hazardous
Work Activities by the Côte d’Ivoire Country Definition, in Côte d’Ivoire, 2018/19*
Percentage of children exposed to:
Number of children 5-17 years

Côte d’Ivoire
2018/192,082,507

Hazardous Work Activities
No rest day (V1)*3%Land clearing (V2)26%

Charcoal production (V3)11%

Carrying heavy loads (V4)8%Agro-chemicals (V5)9%

Sharp tools (V6)26%Night work (V7)8%

Exposed to one or more variables

42%

Average number of variables exposed to

0.9

Source: NORC Child survey 2018/19, weighted, strata 1-3*All variables for the 12 month reference period, except no rest day is for the 7 day reference period.

In 2018/19, 42 percent of children in cocoa growing areas in Côte d’Ivoire were exposed to atleast one of the seven hazardous activities. Approximately one in four children were exposed tousing sharp tools (26%) such as, machetes, long cutlasses, axes, chainsaws etc., or land clearing
activities (26%). One in ten children were working in charcoal production and likely subject toan unsafe working environment (11%). On average children were exposed to 0.9 Côte d’Ivoire
definition hazardous work activities in 2018/19 in cocoa growing areas.
7.2

Estimate of Children Engaged in Child Labor, and Hazardous Work in Cocoa
Production in Ghana based on Ghanaian Legislation

Ghanaian legislation defines ten types of hazardous activities that Ghanaian children could likely
be exposed to while working in cocoa production. These include – not attending school,
withdrawing from school, land clearing, carrying heavy loads, exposure to agro-chemicals, using
sharp tools, climbing trees, night work, working in isolation, and lack of protective clothing.
Table 37 presents the data on exposure to hazardous activities for each of these categories ofhazard specified in the Ghanaian legislations.

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Table 37: Estimates in Children, 5-17 Years, Exposed to Various Types of Hazardous
Work Activities by the Ghana Country Definition, in Ghana, 2018/19*
Ghana
2018/19
1,394,016

Percentage of children exposed to:
Number of children 5-17 years
Hazardous Work Activities
Not attending school (V1)
Withdrawing from school (V2)Land clearing (V3)
Carrying heavy loads (V4)
Agro-chemicals (V5)
Sharp tools (V6)
Climbing trees (V7)Night work (V8)Work in isolation (V9)No protective clothing (V10)
Exposed to one or more variables
Average number of variables exposed to

0%
9%
14%
10%
14%
50%
6%
3%
5%
16%
56%
1.3

Source: NORC Child survey 2018/19, weighted, strata 1-3

In 2018/19, more than half of children in Ghana (56%) were exposed to at least one of the ten
hazardous activities. The most commonly reported hazardous activity was using sharp tools, 50
percent of Ghanaian children reported using sharp tools in 2018/19. Lack of protective clothing(16%), engaging in land clearing activities (14%), exposure to agro-chemicals (14%), andcarrying heavy loads (10%) were other commonly reported hazardous activities. On averagechildren were exposed to 1.3 Ghana definition hazardous work activities in 2018/19.
Using the definitions of hazardous activities as specified by each country described above, we
present the estimates of prevalence of child labor and hazardous work as per the national
definitions. Table 38 presents the estimate of prevalence of child labor and exposure tohazardous activities for in Côte d’Ivoire and Ghana.
Table 38: Prevalence of Children Engaged in Child Labor and Hazardous Child Labor
by Country Definitions, All Agricultural Households, 5-17 Years, in Côte d’Ivoire andGhana, 2018/19
Exposure to:Côte d’Ivoire country
definition
Ghana country definition

Number
Percent
Number
Percent

Child Labor
1,216,688
58%
900,407
65%

Hazardous Child Labor
865,56542%775,676
56%

Source: NORC Child survey 2018/19, weighted, strata 1-3

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Estimates of child labor prevalence based on the country-specific definitions align with theestimates based on the common definition reported in Section 5.2.5. In 2018/19, 58 percent and42 percent of Ivoirian children, and 65 percent and 56 percent of Ghanaian children were
engaged in child labor and hazardous child labor respectively. The rates of child labor andhazardous child labor as higher for the national definitions because the national definitions areusually more restrictive in defining child labor and hazardous child labor than the common
definition.

8 Part II: Assessment of the Effects of Interventions
on Child Labor
8.1

Objectives

The second main objective of this report is to undertake an assessment of effectiveness ofvarious interventions funded by the members of the CLCCG and other stakeholders. Under thisobjective, using a mixed-methods approach, we address a set of specific research questions inorder to understand how different types of interventions were effective in addressing child labor
issues and to examine the impact of multiple child labor interventions on the prevalence of childlabor and hazardous child labor. Our assessment looks at overall effectiveness of fundedinterventions and efforts related to the reduction of child labor, in general, and hazardous childlabor, in particular, in the two countries.
It is important to note that our analysis does not assess the effectiveness of individualinterventions implemented by a particular partner or organization. Given both the disparate types
and overall number of interventions conducted between 2008/09 and 2018/19, it was notmethodologically feasible to assess the effectiveness of each individual intervention due to data
limitations. Rather we assess the effectiveness of different categories of interventions such as
education related interventions, livelihoods programs, and occupational safety and healthinterventions, to name a few.
8.2

Methodological Approaches
General Quantitative Approach

To address the research questions identified in the previous section, NORC employed a suite ofquantitative analyses to generate robust conclusions. The quantitative analyses are based on
statistical modelling techniques. Whenever possible, we employ a set of statistical modelling
techniques in an effort to understand attribution by contrasting actual outcomes with those that

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It is worth noting that our proposed approach to assessment is not “design-based” (that uses
randomized control trials (RCTs)), but “model-based” 61. Given both the disparate types andoverall number of interventions conducted between 2008/09 and 2018/19, it is not practicallyfeasible to conduct RCTs on the entire set of interventions. 62 The goal of the modelling approach
used here is to generate findings that can be causally attributed to the interventions being
evaluated.
General Qualitative Approach
The qualitative component of the 2018/19 Child Labor Survey provides context for thequantitative results and a deeper understanding of how various key players understand childlabor within the cocoa sector in Ghana and Côte d'Ivoire. This component also provides nuanced
perspectives on the topics covered in the surveys, including complex concepts such as nightwork, heavy loads, and sharp tool use. Qualitative data helps identify factors contributing toobserved prevalence rates, changes in prevalence rates, and changes in hazardous work trends.
The qualitative component was based on focus group discussions (FGDs) with children andcaregivers in cocoa growing regions of Ghana and Côte d'Ivoire and key informant interviews(KIIs) with the community leaders, donors, International Chocolate and Cocoa Industry
members, government officials, implementers and civil society organizations. The research teaminterviewed both beneficiary and non-beneficiary children in both countries and information isdisaggregated by beneficiary status where possible 63. A summary of the respondent groups canbe found in Annex 10.1.2. The detailed methodological approach is available in Annex 10.1.7.1.
8.3

Research Questions, Analysis and Findings

In this section we present the key research topics (RTs) addressed in this assessment highlighting
the effectiveness of various thematic areas of intervention as well as overall effectiveness andsustainability of interventions implemented by various stakeholders aimed at reducing theprevalence of child labor and hazardous child labor in the cocoa sector. We then provide a brief

60

The attribution analysis explores if the interventions led to a statistically significant decrease in prevalence rates ofchild labor and hazardous child labor, over and above the influence of other confounding factors.

61

The model-based approach use theoretical model to specify the relationship between the outcome variables ofinterest and set of covariates that influence the outcome and estimate the impact of an intervention.
62

The most commonly used method required either a randomized rollout of interventions into treatment and control
location and/or a random assignment of beneficiaries into the treatment and control group.

63

Beneficiaries are those individuals who live in communities receiving interventions

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description of the analyses undertaken to address the RTs and finally presents the assessment
findings.
The following figure summarizes the key RTs and methods used to address them.
Figure 4: Research Questions and Analyses
Research Questions
Interventions/Theme

Sub-themes

Type of Analysis
Quantitative

Impact on children’s attitudes towards
education

Impact of education andvocational training

Impact of provision of education material
assistance

Impact of occupationalsafety and healthtraining

X
X

X

Impact of school construction/infrastructure

X

Impact of school feeding/school supply
programs

X

Impact of vocational training

X

Impact of livelihood services on childlabor/hazardous child labor
Impact of livelihood
services

Qualitative

X

X

Aspects of livelihood services were mostimportant in deterring child labor

X

Perception of gains of livelihood services

X

Use of appropriate safety gear

X

X

Exposure to hazardous work

X

X

Influence on awareness of children,
parents, community leaders

X

Influence on their attitude and behavior

X

Overall effectiveness ofinterventions

Themes emerged in terms of effectivenessof interventions

X

Overall sustainability ofinterventions

Strategies of promoting sustainability andintervention sustainability

X

Overall Impact ofinterventions on theprevalence of child laborand hazardous childlabor

Impact of multiple interventions on childlabor/hazardous child labor

Impact of awarenessraising campaigns

X

Impact of Education and Vocational Training
Provision of education and vocational training to children serves as one of the importantinterventions in reducing CL & HCL. Between 2008/09 and 2018/19, various stakeholders
implemented projects focusing on education and vocational training for children in the cocoa
sector in Côte d’Ivoire and Ghana. Thus, assessing effectiveness of education and training

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sector in Côte d’Ivoire and Ghana. Thus, assessing effectiveness of education and training
interventions on prevalence of CL and HCL in the cocoa sector in Côte d’Ivoire and Ghana
becomes an important research question.
8.3.1.1

Children’s Attitudes towards Education

To anchor findings on the impact of education programs, this section begins with an analysis ofchildren’s attitudes towards education, and their perceptions of the costs and benefits ofeducation.
Children value their education, noting that completing school was a necessary requirement forachieving future aspirations. Children said they enjoyed their lessons as well as the social aspects
of attending school, including seeing their friends and playing sports.
However, children reported significant barriers to school attendance. In both Ghana and Côte
d’Ivoire, children explain that school absences were due to illness and financial difficulties
where parents were unable to provide money for lunch, school supplies, or uniforms. Children
also cite lack of transportation as a barrier to school attendance, as some had to travel five or
more kilometers to the nearest school. In these instances, inclement weather and long distances
between home and school made the journey to class difficult.
Children also said they occasionally miss school to help their parents, particularly during thecocoa harvest season. In some instances, these absences were anticipated and, in other cases,
children explained that after going to the farm in the morning, they would return home too late to
be able to get to school on time. As a result, they stayed home to avoid punishment from theirteachers. Notably, children who were supporting older caregivers in farm activities (such as
grandparents) more often attributed their school absences to work.
During interviews, implementers reported similar trends, noting that the aging population of
cocoa farmers was a contributing factor to child labor. Implementers and government officials
indicated that in many cases older farmers are physically unable to complete certain tasks andtherefore rely on children more heavily.
Children reported engaging in a variety of on-farm and off-farm activities, including weeding,bean transportation, drying cocoa, and applying chemicals. Children broadly indicated that they
enjoyed knowing they were supporting their parents and working alongside their friends on
cocoa farms. Children in Côte d’Ivoire more often reported that working on cocoa farms fulfilled
a sense of duty to their families and enabled them to support their caregivers.
Although children enjoyed feeling like they were helping their parents, neither beneficiary nor
non-beneficiary children indicated a preference for working on cocoa farms over attending

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school 64. In fact, beneficiary and non-beneficiary children preferred to be at school and felt that
supporting their caregivers on cocoa farms would also support their schooling:
You can also take out the cocoa seeds and dry it and when they sell the cocoa they
can support your education with the proceeds. Beneficiary child, Côte d’Ivoire,
Male
When I think about the fact that my mother will give me money she gets fromharvesting the cocoa for school it makes me happy. Beneficiary child, Ghana,
Male
When I help my mother to pick and gather the cocoa she gives me money for myschool fees. Non-beneficiary child, Ghana, MaleOverall, children in both countries reported similar perspectives on the benefits of education.
Children indicated that going to school would enable them to achieve their future goals andallowed them to socialize with their peers.
8.3.1.2

Education Material Assistance and Child Labor in the Cocoa Sector

To help families that lack resources to afford children’s education-related materials and supply,
stakeholders implemented projects offering material assistance to families in the cocoa-growing
areas of Côte d’Ivoire and Ghana. While the interventions were implemented in both Côte
d’Ivoire and Ghana, the coverage of this intervention type was more extensive in Côte d’Ivoire.
The material assistance provided included uniforms, school bags, and other scholastic materials
such as textbooks, pens, and pencils.
The expectation is that material assistance supports families that typically cannot afford those
items and thus helps children avoid absenteeism and/or reduces dependence on child labor to pay
for supplies. To examine the effectiveness of such interventions, we used quantitative analysis to
explore whether the households receiving educational material support (such as school supplies,
text books, and uniforms) are less likely to engaged children in child labor and in hazardous
child labor in cocoa production than households that did not receive such support.
The quantitative analysis uses a model-based approach to assess how provision of material
support affects the likelihood of children’s engagement in child labor and hazardous child laboras well as rates of child labor and hazardous child labor in a family, after controlling for otherobservable influences in a regression framework. Based on a theoretical model of the household
decision-making process, we first identify factors that may influence households’ decision to64

Beneficiaries are those individuals who live in communities receiving interventions

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engage children in child labor in cocoa production. We then estimate whether, after controllingfor such factors, households receiving material support are less likely to engage children as childlabor and in hazardous child labor in cocoa production.
The self-reported data show that in Côte d’Ivoire 605 households (44%) had at least one child
who received benefits and in Ghana 164 households (14%) with at least one child that received
material assistance related to education.
Please refer to Annex 10.8.1 for a detailed description of the quantitative methodology and data
sources used for addressing this research question.
8.3.1.3

Findings from Quantitative Analysis

The detailed statistical model used for testing whether households receiving educational material
assistance are less likely to have child labor and children engaged in hazardous child labor incocoa production is reported in Annex 10.8.1.
The model was estimated for Côte d’Ivoire and Ghana separately and reported in Table 74 andTable 76 in Annex 10.8.1. Regression results presented in the second and third columns in Table
74 and Table 76 in Annex 10.8.1 indicate that the likelihood of children’s engagement in childlabor and in hazardous child labor in cocoa production among households receiving educational
material support were not statistically different from the households that did not receive such
benefits in each country.
Similarly, results presented in the last two columns in Table 74 and Table 76 in Annex 10.8.1
indicate there was no statistically significant difference in the rate of child labor and hazardous
child labor in cocoa production between the beneficiary and the non-beneficiary households ineach country.
In sum, our analysis was not able to detect any statistically significant difference in child laborand hazardous child labor among the households receiving educational material support, even ata modest level of significance (10%). This indicates that the educational material support
programs were probably not strong enough to generate a large enough difference in child laborand hazardous child labor rates detectable by the given design. A second possibility is that
educational support programs are not effective when implemented by themselves. Given thecomplex nature of child labor it may be the case that no one intervention will lead to significant
decreases in child labor and/or hazardous child labor and a system approach, addressing multiple
factors within a community, is needed to see significant impacts. Hence, it is important tounderstand the potential methodological reasons for these observed “null effects”. Please refer toAnnex 10.10 for a detailed discussion of the quantitative analysis.

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8.3.1.4

Impact of School-Based Interventions

Quantitative data indicate a significant increase in children’s school attendance in both countries.
Qualitative findings indicate improved access to schooling, improved infrastructure, and theprovision of school materials contributes to increases in children’s school attendance. Children
residing in school-based intervention communities reported significant changes, including
infrastructural and administrative improvements to their schools, new school building
construction, school building rehabilitation, the addition of new classrooms or grade levels, andelectricity.
8.3.1.4.1 School Construction and Infrastructure Improvement
In communities where school building construction took place, children said they noticed asignificant reduction in the time and effort previously required to get to school. For somechildren, the nearest school is in a neighboring community several kilometers away. As such,traveling to and from school is arduous, especially during inclement weather. Students also
indicate that travel distances to and from school sometimes prohibit their attendance, particularly
when they knew they would be late. In other communities that were sites for school construction,
children report that before their new schools were built, classes were conducted outdoors, withmakeshift blackboards. Children said that the new school buildings made them more motivated
to attend class, and less likely to miss school when it was raining.
Among teachers and caregivers in beneficiary communities, improved physical access to schools
for children additionally contributed to positive outcomes in children’s school attendance. Prior
to school construction initiatives, many children lacked access to nearby schools. Consequently,
caregivers could not leave their children at home, and would take them instead to the farm. With
the construction of schools more proximate to their homes, children report going to school
instead of staying home or going to the farm due to school distances.
Teachers indicate that children who have difficulty accessing nearby schools are at higher risk ofengaging in child labor. However, teachers also made specific distinctions between socializing
work and accompaniment, reporting that in some cases, children accompanied their parents
because they did not have anywhere else to go, but were not necessarily working.
Since the parent himself must go to the field. If there is class, the child stays inschool, he knows that his child is safe with the masters. But if he leaves him alone
in the village, he does not know what his child is doing behind him. So, to be able
to have an eye on their child, they prefer to go with them to the field. Like that,they are all together. They have time to watch over them. So, I do not think it's in
any other sense that parents do it. The parents would not necessarily want hischild to become a farmer tomorrow. Primary School Teacher, Côte d’Ivoire

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Aside from new school building construction, other reported infrastructural improvements
include the addition of fences, washrooms, electricity, water pumps, and boreholes. The
improvements brought a sense of safety to the children and enhanced their ability to focus duringclass. New construction also eliminated burdensome school chores. As one student explained:
We used to fetch water at a longer distance but now we have water in our school,
and we can access water easily. Beneficiary Child, Ghana, Female
There were gendered differences in perceived benefits from school infrastructure improvements.
Only girls reported that latrine construction or improvement made them feel safer. Girls werealso more likely to report that prior to the availability of water on school grounds, they, rather
than boys, were responsible for fetching water for their schools. These activities caused girls to
miss some lessons in school and made it difficult to catch up.
In communities without school rehabilitation programs, caregivers in Ghana and Côte d’Ivoire
expressed concern about their children’s safety in schools needing infrastructure improvements.
8.3.1.4.2 School Feeding Programs & School Supply Provision
Focus group results also indicate children see significant benefits of school support programs.
Among these, children report the most significant gains from school feeding programs andcanteens. Prior to the school feeding programs, children would sometimes go without meals
during the day as their caregivers could not afford to send them to school with money for food.
The lack of money for school lunch also discouraged children from going to school to avoid
going hungry and/or watching others eat.
Teachers also found school feeding programs helpful in improving school attendance andchanging caregiver and children’s attitudes towards schooling. Teachers indicate that school
feeding programs improve performance, and enabled children to be more engaged in school:
It has really helped the children because before the school feeding started, theclass is always bored after 12pm because the children are hungry. The
introduction of the school feeding has helped the children to concentrate for thelast lesson after they have eaten. It has also encouraged more children to attend
school because most of the children were not coming to school due to theirparents not giving them pocket money. Primary School Teacher, Ghana
The canteen has helped a lot. It is undeniable that when there is food, it stabilizes
the children. So even at noon, when they have eaten here, they don't even find it
important to go home. Before, they would go home and did not come back.
Primary School Teacher, Côte d’Ivoire

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Teachers who saw a link between school-based interventions and child labor report that children
from vulnerable families were most at risk of engaging in child labor, and most at risk of notattending school. These teachers believe that school feeding programs enable these students toaccess schooling, and therefore, lower their risk of engaging in child labor. These teachers also
explain that caregivers who engage their children in child labor do so because they can not afford
to send their children to school.
Yes, there have been changes in the parents’ attitude, because previously, when aparent could not afford to give the child money for school, the parents will rather
ask the child to follow the parent to the farm but things have changed. Now,
parents allow their children to come to school without money knowing there isschool feeding. Primary School Teacher, Ghana
In addition to school feeding programs, the provision of school supplies and uniforms was
helpful when parents could not afford to provide these. Specifically, caregivers noted thesignificant impact of uniform provision and school feeding on their children’s ability to attend
school.
Some time ago, the government brought free uniforms for the children here.
Some of the children were orphans, and they all had a share of the free school
uniforms. The children who were once staying home are back to school becauseof the free school uniform intervention when their friends shared the good news
with them. Beneficiary caregiver, Ghana, Female
For a small set of students, it was reported that an increase of teachers in schools (hence a
reduction in the teacher to student ratio) led to better relationships with their instructors. Children
noticed increased dedication in their teachers and reduced incidences of corporal punishment.
These improvements ultimately diminished students’ fear of teachers and catalyzed excitementin learning.
In communities where such interventions were not in place, caregivers reported that they
believed their children would greatly benefit from canteens. As one caregiver reports:
There is no canteen at school, where the children can stay there to eat. They haveto come back to the house, and their parents are not there to give them food. The
distance is very long to go back to school, so sometimes they just stay home. Nonbeneficiary caregiver, Côte d’Ivoire, Male

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8.3.1.5

Persisting Challenges

While school-based interventions are improving overall attendance and reducing work-related
school absences, teachers report persisting challenges in discouraging caregivers from engaging
their children in work. Consistent with children’s self-reported absences, teachers disclose thatmany students are late to school because they spent the morning on cocoa farms. Teachers also
indicate a spike in absences during the harvest season and on market days, where children
support their families in various tasks and consequently miss school.
Notably, teachers in Ghana and Côte d’Ivoire saw the potential of future collaboration withcaregivers as a means of reducing child labor. In communities where school-based interventions
took place, teachers noticed improved relationships with caregivers. In some communities,
teachers and caregivers were mobilized to raise funds and materials for school infrastructure
improvement. This collaboration provided teachers an opportunity to discuss their students
wellbeing with caregivers and raise concerns about involvement in child labor. Teachers also felt
more comfortable visiting children’s homes and discussing absences with caregivers. This wasmost common in communities where teachers reside in the community, and in locales where
teachers’ quarters were recently constructed within the community. This proximity to families
was instrumental in building relationships where teachers could encourage caregivers to send
their children to school and have one-on-one conversations about how going to farms before
school affects students’ performance.
These initiatives, because for parents, when we meet during PTA meetings; andthen they put forward all these things, sometimes we try to educate them and why
they should support their wards in education; to go through their education,
especially when we were building the KG 65 building. We told them thefundamentals are very important, so they should support it. So most of them camearound to help, so I think the attitude is changing. Primary School Teacher,GhanaHere, we specify the worst forms of child labor. We did several training on thissubject. With the support of [implementer] we mobilize parents, to make themaware of the fight against child labor and especially the worst forms of childlabor. Primary School Teacher, Côte d’Ivoire
Teachers see an opportunity to build on the strides made by school-based interventions and move
conversations on child labor forward within regular school-related meetings with parents.

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Kindergarten

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8.3.1.6

Impact of Vocational Training Programs

Provision of vocational training to children is an important intervention for reducing child laborby helping children gain the skills necessary to secure safer alternative employment. During theassessment period, various stakeholders implemented projects focusing on vocational training forchildren in the cocoa sector. Here we assess the impact of vocational training interventions onchildren’s engagement in child labor and hazardous child labor in the cocoa sector.
During the research design stage, the research team planned on addressing this question using
quantitative analysis. Accordingly, in the 2018/19 child-labor survey, children were asked toreport whether they attended vocational or skill training or apprenticeship program outside of
their school. The self-reported data show that in Côte d’Ivoire only 24 households (less than 2%)
had at least one child who received vocational training and in Ghana only 3 households (less than1%) had at least one child that received vocational training. 66
Given the small number of children reporting exposure to vocational training, this researchquestion could not be addressed using quantitative methods and was subsequently dropped
during analysis due to insufficient data. However, qualitative analysis was used to gauge theeffect of vocational training.
Qualitative findings on exposure to, and the impact of, vocational training programs are also
limited. Despite best efforts to ensure thorough representation of communities exposed to
vocational training interventions, most respondents did not participate in such programs.
However, for the communities that did have vocational training, children and caregivers reported
significant gains.
For example, children in Ghana and Côte D’Ivoire who participated in vocational training
programs received skills training in sewing, soap making, and agricultural practices. Children
over fifteen years old in both countries received training in best practices in cocoa pruning andmaintaining other agricultural commodities. Children often cited vocational training as mosthelpful for improving their understanding of the options available to them for their future
careers. This was especially pronounced for children who felt it was unlikely that they would go
on to tertiary education.
Among beneficiaries, girls reported more benefits from vocational training than boys.
Beneficiary girls state that they and other girls in their communities find vocational training a
66

Intervention data collected from the CLCCG partners corroborated with the self-reported data. The intervention
data demonstrated that vocational training was rarely offered: only 8 communities in Côte d’Ivoire (out of the 75
communities covered in the 2018/19 survey round) had any vocational training intervention implemented by the
CLCCG partners, while in Ghana, none of the 75 communities covered in the 2018/19 survey round had any
vocational training intervention implemented by the CLCCG partners.

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valuable resource for learning skills they can use to take care of themselves after they complete
school, or when their parents are no longer able to support their education. One girl shared theexperience of another in her community:
She said she wanted to go to school but her father told her he won’t get the money
to fund her education so she should stay home and when she was staying at home
her father didn’t mind her. Then the opportunity came, now she feels good and it
has changed her life. Beneficiary child, Ghana, Female
Both beneficiary and non-beneficiary children voiced desire for additional training. However,there are gendered differences between the types of trainings preferred. While girls sought
additional training around sewing, textiles, and hair dressing, boys opted for training aroundagricultural practices, automobile repair, and motorcycle repair. There were also notabledifferences by country – boys in Côte d’Ivoire more often express a desire for vocational training
in cocoa production and other agricultural endeavors than boys in Ghana.
I would like to learn cocoa culture or how to plant rice or rubber because there isa lot of money in it. Beneficiary child, Côte d’Ivoire, MaleI would like to be an apprentice to a carpenter and learn how to build things so I
can sell them. Beneficiary child, Ghana, Male
Caregivers also see benefits for vocational training programs for their children. Caregivers inboth Ghana and Côte d’Ivoire believe that vocational training programs helped their children
become more responsible and motivated about school. Caregivers also indicated that vocational
training programs enabled youth to explore opportunities within their own communities andreduced the frequency with which children were migrating to larger cities to find work uponfinishing their studies. In some communities, parents explained that participation in vocational
training programs requires enrollment in school. Therefore, children became more motivatedabout being in school to meet the qualifications for participation. Similar to children’s anecdotes,
caregivers report that vocational training programs help children understand the possibilities
available to them once they are finished with their schooling:
I think that this training can lead to other open-mindedness. That is, when they doafter a training, the child may be able to orient himself. To know what activity hewants to lead himself. Since he will see all that this training can teach him as aprofession. And help him to take care of himself. And to fit into the social fabric.
So he will have the choice now to decide. Given the training he did, if he decides
to continue school, ok. But if he decides to settle down, and do theentrepreneurship, ok. Beneficiary Caregiver, Côte d’Ivoire, Male

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In communities where vocational trainings were not present, caregivers felt their children could
benefit from participating in such programs. Caregivers’ perceived benefits of vocational training
participation closely align with reported benefits from communities where they do exist:
We want the government to establish vocational training centers because we seelots of them going on in other places. If they vocational training, after completing
school, they can have other means of employment. So we are asking thegovernment to provide us with a vocational center. Beneficiary Caregiver, Ghana,Male
The majority of teachers interviewed did not have firsthand experience with vocational training
programs in their communities, thus this evaluation cannot gauge teacher assessments of thisintervention.
Overall, children who participated in education and vocational training programs experienced
significant benefits. These benefits addressed key barriers to education, including school
distances, and material costs of school attendance. Administrative and infrastructural
improvements facilitated more consistent attendance, promoted feelings of safety, and improved
teacher-student interactions. Vocational training programs enabled children to explore
opportunities for skills acquisition and future income generation.
Impact of Livelihood Services
Provision of livelihood support to vulnerable families can help improve the economic conditionof such households and is expected to indirectly affect the prevalence of child labor andhazardous child labor in these families by releasing their resource constraints and, in turn,
reducing the need for the household to put children to work. Hence, the theory of change would
expect that children’s engagement in child labor and in hazardous child labor is lower among thefamilies receiving some livelihood services as compared to the families not receiving suchservices.
The livelihood assistance offered most commonly included training on good agricultural
practices (GAP), microfinance services, and market linkage services. In this section, we use
statistical analysis to test whether the households whose members received livelihood assistancewere less likely to have child labor and children engaged in hazardous child labor in cocoa
production than the households whose members did not receive such assistance. 67

67

For the evaluation design, we ignore selection bias at the community level assuming that the factors affecting
selection of a community will be uncorrelated to the outcomes once the community level infrastructure related
indicators are included in the attribution equation.

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The self-reported data show that in Côte d’Ivoire 128 households (9%) had at least one member
who received livelihood support and in Ghana 70 households (5.8%) with at least one member
who received livelihood support.
Please refer to Annex 10.1.7.1 for a detailed description of the quantitative methodology anddata sources used for addressing this research question.
8.3.2.1

Findings from Quantitative Analysis

We used a multivariate regression technique to test whether the households that received
livelihood services were less likely to have children engaged in child labor and in hazardous
child labor in cocoa production.
Given the relatively small sample size of households that received livelihood services in Ghana,the model was estimated only for Côte d’Ivoire and reported in Table 78 in Annex 10.8.2. Allproceeding regressions were pre-conditioned by performing entropy-balanced (matched) sample
of comparison households. Regression results are shown in Table 78 in Annex 10.8.2, withestimates of the effect of livelihood services on the likelihood of having at least one child
exposed to child labor in cocoa production in the second column and the effect on the likelihoodof children’s exposure to hazardous child labor in the third column.
The results in the second column indicate that the likelihood of having at least one child engaged
in child labor among households that received livelihood support was not statistically different
from the likelihood of having at least one child engaged in child labor among the households that
did not receive such benefits. Similarly, the results in the third column indicate that there was no
statistically significant effect of livelihood support on the likelihood of exposure to hazardous
child labor in cocoa production. In other words, the results of both analyses were unable to detect
any effect of livelihood service treatment on the likelihood of having at least one child engaged
in child labor and in hazardous child labor.
Next, we turn to the effect of intervention themes on rates of exposure to child labor and tohazardous child labor, namely, the proportion of children in agricultural households exposed tochild labor and hazardous child labor. The regression results in the fourth column in Table 78 in
Annex 10.8.2 indicate that in cocoa production the rate of child labor among the households thatreceived livelihood services was 10-percentage-points lower than the rate of child labor among
the set of comparable (matched) households that did not receive such services. Thus, the results
imply that provision of livelihood service led to a lower proportion of child labor among
agricultural households in the cocoa growing areas of Côte d’Ivoire. However, the results
reported in the last column of Table 78 of Annex 10.8.2 show that there was no statisticallysignificant effect of livelihood services on the rates of hazardous child labor.

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While our analysis was not able to detect any statistically significant effect (at 10% level ofsignificance) of livelihood services on the likelihood of having at least one child engaged in childlabor or in hazardous child labor, our results show that the rate of child labor was lower among
the households that received livelihood services compared to the households that did not receive
such services; no such impact of livelihood services was detected on children’s exposure tohazardous child labor in cocoa production. That said, it is important to note some caveats andmethodological limitations related to the above evaluation findings. Please refer to Annex 10.10
for a detailed discussion caveats and limitations of the quantitative analysis and some potential
explanations for the lack of effect of this intervention on child exposure to hazardous child labor.
8.3.2.2

Findings from Qualitative Analysis

Focus group findings among Ghana and Côte d’Ivoire caregiver households show varying levels
of involvement with livelihoods services. Despite attempts to ensure proper representation ofcommunities that received livelihood services, caregivers indicate limited exposure to suchservices. Caregivers who did receive livelihoods services, however, reported significant
outcomes for their household, ranging from improvements in knowledge, practice, and boosts inincome, each with implications for the frequency and length of time their children supported
them on farms.
Among caregivers receiving livelihood services, the majority of their households received
assistance in the form of good agricultural practices (GAP) training, material resource provision,
support for the creation of savings and loans groups, and training on other agricultural and nonagricultural income-generating activities. Among agricultural income-generating activities,
households most often reported receiving training on vegetable production. Within nonagricultural income-generating activities, households reported receiving training on batik-making
and soap-making.
Households receiving GAP training felt this training was critical to addressing the challenges ofincome generation from cocoa. Households most often described challenges related to pests anddiseases, yields, and crop quality. Despite persisting challenges in cocoa production, households
receiving GAP training report increased yield, and reduced expenditures for pest management.
Households also report that GAP training results in time and labor-saving practices that were
essential to changing their children’s engagement on farms. As one caregiver opined:
Yes, with what they taught us concerning how to plant inline, we were able to dothese things easier and faster on our own without engaging the children.
Beneficiary caregiver, Côte d’Ivoire, Male

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According to caregivers, GAP training also results in increased household income from cocoa
production, enabling payment for laborers instead of engaging their children. Community leaders
also notice that households now have more money to manage expenses for their children.
Yes, [my child’s] time in the farm reduced because I was able to hire laborers
instead of using the kids, so the children were not going to the farm always.
Beneficiary caregiver, Ghana, Male
Furthermore, caregiver households found the provision of material support for cocoa production
helpful in increasing their overall income and improving cocoa yield. These include support formass spraying, fertilizer provision, and farm equipment provision. In some cases, theimplementers or institutions providing material support also discouraged households fromengaging their children as a condition of receiving support.
There is this organization called [implementer name]. They helped the men
especially by providing them with cutlass, boot etc., so the parent could help
himself or herself and stop using the children for farming activities. Beneficiary
caregiver, Ghana, MaleGroup savings and loans also supported household income generation and access to credit.
Those who got involved in the savings, we started not long ago. Since we started,
we are now 3 months into it, but still, people go for loans for their needs. So, I
think we will progress as time goes on. We were only 15 when we started, but
because those who took the loan testified to others, we are 30 now. So, I knowthat we will increase as time goes on and many people too will get involved.
Beneficiary caregiver, Ghana, Male
Community leaders, likewise, note that savings and loan groups are instrumental for easing thefinancial burdens of households because they replace income that may be otherwise generated
from child labor. Leaders notice that many in their communities no longer want to engage theirchildren in work but do not feel they have a choice. Savings and loans groups, however, help
families address these financial challenges and, consequently, the extent to which their children
are engaged in child labor.
If you compare to the previous years, we were doing child labor, because ourfinances were not stable. The children were doing some works that they were notsupposed to do. And now, too, things have really changed we are going forward.
Community leader, Côte d’Ivoire

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Lastly, households receiving livelihood support find training in income-generating activities
impactful for improving financial outcomes. While the majority of focus group respondents did
not have income-generating activities outside of cocoa, those who received training notedchanges in overall income, especially during the off-season. Households reported similar benefits
from both agricultural and non-agricultural trainings. Community leaders concluded that suchsupport was key to tackling child labor within their communities:
So, because of that initiative, it helped everyone in the community to get garden
eggs and okra. It helped us to get money in our pockets, so when the people getmoney, they are able to hire people to come and help them with the farm work
which means that the children too will be free. Community leader, Ghana
While households in both Ghana and Côte d’Ivoire see similar benefits from livelihood services,
there were variations in gender, and between countries in the types of livelihood services
received. Beneficiaries in Ghana more often received material support for cocoa production,
including inputs, cocoa pods, and mass spraying. Additionally, women in both Ghana and Côte
d’Ivoire more often report receiving training in non-agricultural income-generating activities,
including fabric-making, food sale, and soap-making. Male caregivers more often report
receiving GAP training and participating in Village Savings groups.
Overall, beneficiaries report receiving livelihood services in the form of GAP training, material
support for cocoa production, skills training, and savings and loans groups. These activities
facilitated income generation and credit access and supported agricultural expense management.
The reduction in financial burden enabled some farmers to hire laborers and reduce children’s
engagement in farm work.
Impact of Occupational Safety and Health training
Provision of training to understand and address issues of occupational safety and health (OSH)
related to cocoa production can lead to a transition of youth of legal working age who are
engaged in hazardous labor into safe, acceptable work adhering to the national laws andinternational labor standards. Thus, OSH interventions play an important role to protect youth
workers in the cocoa sector from injuries and other occupational hazards with strong healthconsequences. Below assess whether OSH interventions influence youth workers to use proper
safety equipment or reduce their exposure to hazardous child labor.
We use statistical analysis to investigate whether the youth beneficiaries who received OSH fromformal sources 68 were more likely to work with appropriate safety equipment and or more likely
68

Formal OSH training was defined as training provided by an employer, NGO, or other organization and delivered
in a planned/structured manner.

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to engage with non-hazardous work as compared to their counterparts who did not receive suchtraining.
Children were asked to report whether they received any training on occupational safety andhealth and/or training on using appropriate safety equipment at work from formal and informal
sources. The self-reported data show that in Côte d’Ivoire, 16 youth of age 15-17 (4% of all 1517 year old children) received formal OSH training and in Ghana, 156 youth of age 15-17 (31%of all 15-17 year old children) received formal OSH training.
In the child survey, children were asked to report whether they used any protective gear whileworking in agriculture. The types of protective gear considered include protective boots(Wellington boots, Afro Moses), gloves, protective clothing (overalls, long sleeves, trousers),
nose mask or gas mask, helmet, goggles, and other protective wear. Based on the responses, anindicator variable was constructed to specify whether the children reported using any one of theabove categories of protective gear while working in agriculture in the past 12 months before thesurvey. The data indicates that in Ghana, 41 percent of youth of age 15-17 were using at least
one of the listed protective gear.
Please refer to Annex 10.8.3 for a detailed description of the quantitative methodology and data
sources used for addressing this research question.
8.3.3.1

Findings from Quantitative Analysis

As reported above, in Côte d’Ivoire a very small number of youth received OSH training, andthus it was not feasible to conduct statistical analysis with the sample from Côte d’Ivoire.
Therefore, the statistical analysis reported below used the data from Ghana only reported by 1517 year old children.
Comparison of usage of protective gear data between the treatment group (youth who received
formal OSH training) and the matched comparison group indicate that 49 percent of the youth in
the treatment group reported using at least one of the seven different types of protective gear,
while 35 percent of the youth from the comparison group reported using protective gear. The
difference between the two groups was statistically significant at 1% level of significance. Thus,
the results show that the youth in Ghana who received formal OSH training were more likely to
use at least some of the protective gear while working in agriculture.
Finally, we used a multivariate regression technique to test whether the youth who received OSH
training were less likely to engage in hazardous child labor in cocoa production. The results fromthe analysis (shown in Table 80 in Annex 10.8.3) indicate that after controlling for various
covariates that affect children’s engagement in hazardous child labor, there was no statisticallysignificant effect of OSH training on the youth’s likelihood of exposure to hazardous child labor

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in cocoa production in Ghana. In other words, the analysis was unable to detect any effect of
OSH training treatment on the likelihood of exposure to hazardous child labor among youth.
While our analysis indicates that youth’s engagement in hazardous child labor was not affectedby their participation in formal OSH training, it is important to note that the lack of effect of
OSH training on youth’s exposure to hazardous child labor could be due to the inability of theregression analysis to detect the effect through quasi-experimental design as explained in Annex
10.8.3.
8.3.3.2

Findings from Qualitative Assessment

In assessing changes in occupational safety habits among beneficiary youth, focus group resultsin Ghana and Côte d’Ivoire suggest youth acquired at least some level of awareness of measuresthat may protect them from the hazards of cocoa farm activities. Respondents said they learned
about using protective gear when working with chemicals, and how to cover themselvesadequately for on-farm activities, including wearing goggles and closed shoes. Importantly,
youth express that trainings on safety helped improve their understanding of the importance ofsafety precautions, and of the potential health consequences of non-adherence to the practices
promoted. When asked about changes in their farm activities or activities they were not allowed
to do, youth most often said that they were no longer allowed to handle chemicals and when theydid, they were only allowed to do so with the correct protective gear.
Changes in occupational safety practices are more often reported by boys than girls in bothcountries. This correlates with previously noted differences in girls and boys reported on-farmand off-farm activities, as boys reported more chemical use and sharp tool use as part of their onfarm activities. Additionally, changes in occupational safety practices are most often reported byyouth who indicate their caregivers received these training as well. In communities wherecaregivers received occupational safety training, there were more supporting anecdotes fromyouth regarding caregiver-led precautions and changes to hazardous labor practices. In cases
where caregivers did not receive similar training, youth reported challenges in talking to theircaregivers about their desire to implement new occupational safety and health practices.
They told us our parents should not to give us heavy loads to carry. When I went
home and told my father about it, he told me to go and stay with the man who said
that so that when I go to the farm with him he will not give me heavy loads tocarry. But I told my father it was an education they gave us on health and how tostay strong. Me for instance I get sick often so if you give me heavy loads to carryit make me sick [more often]. Beneficiary youth, Ghana Female
In both Ghana and Côte d’Ivoire, a portion of beneficiary youth believe that while theirknowledge around occupational health and safety practices has improved, their practices remain

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the same. In these cases, youth want to adopt changes, but are unable to do so due to theunavailability of protective materials such as boots, masks, and gloves. While there weresignificant reported changes in chemical use and wearing protective gear when conducting otherfarming activities, youth did not report changes in sharp tool use.
Both beneficiary and non-beneficiary youth reported wanting more training on how to be safer
during farming activities.
I want to know how to operate the spraying machine so that when mysurroundings grow weedy I could spray with weedicides without it coming into
contact with my body. Beneficiary youth, Ghana MaleOverall, occupational safety and health interventions resonated among youth, especially when
their caregivers received similar training. Youth report that these interventions have helped themunderstand the importance of keeping themselves safe, and that when possible, they havemodified their practices accordingly.
Impact of Awareness-raising campaigns
Although hazardous labor increased in Ghana and Côte d’Ivoire, qualitative findings among
beneficiary caregivers offer more nuanced outcomes and dynamics regarding knowledge,
attitudes, and practices. Beneficiaries in both countries show increases in knowledge aroundchild labor, particularly hazardous labor.
8.3.4.1

Exposure to Child Labor Campaigns

Adult caregiver beneficiaries in Ghana and Côte d’Ivoire were exposed to child labor awarenesscampaigns in the form of posters, visits from NGOs and government-affiliated groups, good
agricultural practices trainings, and radio ads. Beneficiaries also reported exposure to child labor
sensitization efforts during parent teacher association (PTA) meetings and community
gatherings. In both countries, adult beneficiaries indicated that awareness raising efforts werealso administered through child protection committees that worked closely with community
leaders to integrate child labor sensitization into community meetings, and through one-on-one
meetings with community members. Caregivers, as a result, learned about the dangers of childlabor, especially within hazardous work. These awareness campaigns emphasized the adverse
health effects of child labor, appropriate working ages for children, and the potential legal
consequences caregivers could face if they subjected their children to child labor.
They taught us everything. They said that a child who is not up to the age of 18,
someone from 5 years – 15 years, if you let that child carry a bag full of farm
produce the police can arrest you because it is a form of child labor so when weheard that, I for instance I have not been to school so if my child has been to

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school and she is telling me this and I have also seen the posters outside then itmeans I have to be serious with it because the police can arrest me. I then decided
that I will not do it for the police to arrest me so I adhere to the advice that thechild is giving me so for me I know it has helped me. Caregiver, Ghana, Female
8.3.4.2

Child Labor Attitudes and Practices

Caregiver beneficiaries in both Ghana and Côte d’Ivoire reported changes in their farming
practices, particularly around sharp tool use, carrying heavy loads and chemical use. Caregivers
indicated that as a result of these campaigns, they no longer allowed their children to use sharp
tools or chemicals or carry heavy loads. Some also indicated that as a result of these campaigns,
they no longer allow their younger children to do any type of work. In describing these changes,
caregivers used key language from awareness campaigns about hazardous activities and proper
working ages. Community leaders in Ghana and Côte d’Ivoire also noticed changes in practices
related to child labor, most often around carrying heavy loads, and sharp tool use. Community
leaders in some locales say that members of their community no longer engaged their young
children in any type of work.
Before we had the teachings when the child comes back from school, the parents
could tell the child to come to the farm after school, even the child’s lunch, thechild should come and have his or her lunch on the farm and after that you will
see the child carrying firewood and some food stuffs but after the teachings I can
see that parents who engage in such acts has all stopped doing that. Community
leader, Ghana
Among children (5 to 17 years of age) in both countries, many engaged in both on and off-farmactivities, including weeding, extraction, bean transportation, and drying cocoa, focus groups
reveal. Those engaged in these activities used machetes, sickles, and fertilizer. Among older
children (13 and up), there was more reported use of fertilizers and spraying machines.
Consistent with assessments among caregivers, hazardous practices have changed for children as
well, with beneficiary children reporting a difference in their use of sharp tools, chemicals, andcarrying heavy loads. When asked to reflect on how these tasks have changed, children of all age
groups report that the use of spraying machines, fertilizers, and other chemicals had changed themost. Caregivers, meanwhile, express favorable views of the campaigns, asserting theinformation educated them on the harmful effects of chemicals on their children’s health.
Similarly, children reported that they were no longer allowed to use chemicals, because it could
result in compromised health or injury.
To measure children’s attitudes about child labor, children were asked what they liked and did
not like about helping their parents on farms. Children said that many on-farm and off-farm

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tasks, such as weeding, or carrying cocoa pods are physically painful. Children also cite
incidences of animal bites as a reason they did not enjoy working. When asked about what they
do like about working, social benefits appear to shape children’s views about work, as
respondents point out the importance of parental validation, working alongside friends, inaddition to contributing to supporting their families. In this regard, there were differences bycountry. Children in Ghana more often quoted the social aspect and parental validation as
favorable, while children in Côte d’Ivoire tend to enjoy working because they were fulfilling
their familial responsibility.
We like helping because they suffer for us, and they have to pay for our school.
You have to work in the farm to have enough to eat. Beneficiary child, Côte
d’Ivoire, Male
Caregivers report notable differences in their perceptions of child labor campaigns based on themode of delivery. For instance, caregivers who report receiving awareness training within good
agricultural practices training, interactions with community groups, and trainings from NGOs
learned more about the health impacts of child labor and were more receptive to these teachings
than those who only received awareness training. These caregivers more often attributed changes
in their practices to the promotion of good health for their children.
Well, these awareness campaigns, first there was a community relay, which wasformed against the worst forms of child labor. So we were sensitized, and also in
training, when we do training, we are told not to use children, that's it! Wechange because there are diseases that attack children, because the weight thatthe child bears is more than his own strength. So the child can get sick. But today,these diseases are no longer attacking children, so we are still evolving, really it's
good. Caregiver, Côte d’Ivoire, Male
Caregivers who report more exposure to other information dissemination modes, such as radio
ads and child protection committee presentations, learned more about national anti-child laborlaws and punitive consequences for engaging their children in child labor. These caregivers more
often explained changing their practices to avoid arrest or facing other consequences from local
authorities. Notably, caregivers had less favorable perceptions of campaigns that focused on thepunitive aspects of child labor engagement, reporting that these laws did not take their economic
circumstances or familial dynamics into account.
Community leaders in Ghana and Côte d’Ivoire also saw changes in their own attitudes towards
child labor overall, and credit the shift to increased attention and encouragement fromgovernment-affiliated groups. Community leaders in both countries engaged in awarenesscampaigns through community meetings, collaborations with local government authorities, andFINAL REPORT | 132

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collaborations with NGOs. Leaders indicate that their involvement in designing awarenesscampaigns is critical to creating messaging that resonates with community members:
They placed the posters outside so that if there is a child who has carried a lot of
cocoa that has made the neck [bent] If they paste the posters and even someone
cannot read and the person sees the picture and sees even a child...saw in the
picture that the child falls inside a pit or falls down because of what he or she iscarrying. So if the parents or the elders see it, then they say if I make my childwork very hard like this, this is what will happen to the child. Community leader,
Ghana
Focus group results reveal dramatic differences in caregiver and youth perceptions of childengagement in farm activities. Importantly, caregivers most often report that their children
worked only on weekends and were not allowed to weed or collect cocoa. By contrast, children
report working weekends, before and after school, and engaging in the activities their caregivers
reported they did not allow them to do. Moreover, though caregivers said only their older
children (15 and over) were allowed to work, children of all ages revealed they engaged in onfarm and off-farm activities.
We go to the farm on Saturdays but sometimes my father can tell me not to go toschool but rather go and help him in the farm so I go to the farm on Saturdays
and any day I don’t go to school. We go in the morning and come back in theafternoon around 1:00 to 2:00 pm. Beneficiary child, Ghana, Female
Results suggest that caregivers may underreport child labor, possibly due to several factors.
While caregivers were open about the household economic imperatives that necessitatechildren’s work, they were less open about their own children’s engagement in child labor.
Despite attempts to ensure neutral wording in all questions, caregivers may have opted for moresocially favorable responses, or feared punitive consequences. Community leaders also
demonstrated similar trends, reporting that there was no child labor in their communities whilechildren in the same communities reported engaging in farm work. Given community leader’sroles in awareness campaigns it is possible that community leaders also opted for sociallyfavorable responses that demonstrated progress within their communities. Alternatively,
community leaders may have feared consequences for their community’s and eligibility for
future interventions.
Government officials, implementers, and donors report changes in knowledge and attitudes
towards child labor. These stakeholders convey that awareness raising efforts are extremely
successful in improving knowledge on child labor. When asked about the most significant recentchanges in child labor attitudes, one implementer reports:

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The biggest change is in knowledge and awareness. There are a lot of awarenessraising programs and programs that have increased knowledge around childlabor. There is a great improvement in farmers’ awareness of child labor, andwhat they should be doing to prevent it. Implementer, dual-country program
Despite improvements in attitudes and knowledge around child labor, cocoa sector stakeholders
conclude that awareness-raising efforts are insufficient for changing practices around child labor.
Government officials, donors, and implementers explain that creating broad change within childlabor practices requires the implementation of complementary activities. There is a newopportunity to build on the gains made through awareness raising efforts including continued
collaboration between implementers, government stakeholders, and donors to design programs
melding awareness raising with other types of interventions.
There was limited evidence that changes in child labor awareness campaigns led to changes in
attitudes about education. Both caregiver and child respondents indicate that most school
absences were not due to low prioritization of education, but rather, limited resources. However,a few teachers and community leaders in each community report that child protection
committees played an important role in monitoring absences in school and discouraging
caregivers from having their kids skip school to help on the farm during peak cocoa season.
Overall, awareness-raising campaigns have significantly improved knowledge on child labor forchildren, caregivers, community leaders, and teachers. Most notably, children and caregivers in
Ghana and Côte d’Ivoire report decreases in children’s interactions with chemicals. Althoughmost caregivers and community leaders insist that awareness raising campaigns reducechildren’s engagement in cocoa work, their children still report regularly engaging in on and offfarm activities.
Emerging Themes on Intervention EffectivenessThis section combines perspectives from beneficiaries, implementers, donors, and government
officials to illustrate findings related to intervention efficacy. Several themes emerge related tointervention efficacy, best practices, and challenges with implementation.
8.3.5.1

Theme 1: Beneficiary and Community Ownership

According to beneficiary respondents and implementers, interventions that promote beneficiary
participation in planning and implementation are most effective. This was most evident inschool-based interventions, which had the highest levels of success in Ghana and Côte d’Ivoire.
While many school-based interventions received some support from government or NGOs,
community leaders, teachers and caregivers were encouraged to mobilize their own funds tomake additional contributions to school construction and school rehabilitation. These
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respondents raised funds for school materials, school furniture, borehole construction, teachers’quarters’ construction, and new school construction. Motivated by NGO and governmentsponsored improvements to community schools’, caregivers and community leaders collaborated
with teachers to support their children’s schools.
That is one thing I like about this community. Anytime we want to embark on a
project for the school the parents go all out to help the school. Teacher, Ghana
Community-funded supplements to sponsored interventions facilitated increased impact andefficacy of school-based interventions. In both Ghana and Côte d’Ivoire, school-based
interventions were critical to addressing key barriers to school attendance, particularly thoserelated to lack of finances and long distances to school.
Implementers, donors, and government officials have similar perspectives on intervention
efficacy. Asked to share their views on the types of interventions that were most effective, they
most often cited interventions promoting beneficiary ownership and community mobilization.
Implementers and government officials mentioned engaging community leaders, especially inchild labor awareness efforts, as most effective. This was most pronounced in theimplementation of community-level child labor monitoring efforts through child protection
committees. As one respondent from an implementing organization reports:
The most impactful interventions is the establishment of Community Child
Protection Committees in the communities. We have mentioned that the outsider
is only a facilitator and you have no power to sanction the farmer. The farmers
understand the issues well when the issue is led by their own community members.
Implementer, dual-country program
Community leaders also report that child protection committees worked closely with previously
established leadership frameworks within the community, including faith leaders and womenleaders. Leaders noted that these committees were key to promoting awareness-raising efforts inlanguage that was accessible to community members.
8.3.5.2

Theme 2: Potential of indirect interventions

Community leaders, implementers, donors, and government officials report that in most cases,
factors related to poverty and truancy contribute to high child labor rates. Notably, some of themost effective interventions were those that address the root causes of child labor but were notnecessarily designed to support child labor prevention only. These include school construction,
good agricultural practices training, and road construction between cocoa communities andlarger communities.

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Respondents note that while awareness raising efforts are extremely effective in improving
knowledge around child labor, they are less effective in changing actual behaviors. Rather
initiatives that address barriers to schooling and income generation are felt to be more effective.
Government officials and implementers report a variety of strategies used to improve cocoa
production outcomes have indirect effects on child labor. For example, the provision of core
inputs, good agricultural practices training, and road construction from remote farms tocollection centers had significant effects on farmer incomes from cocoa. Input provision allowed
farmers to minimize agricultural expenses, while road construction allowed them to minimize
transportation expenses for their cocoa. Further, good agricultural practices training helped
farmers prevent pests and diseases, maximize their yield, and minimize expenses related to pest
and disease management. Respondents from implementing organizations and donor institutions
report that in most cases, farmers use their extra income to re-invest in their farms, hire labor, orinvest in children’s education or wellbeing. Beneficiary caregivers also reported using any extra
income to hire day laborers for arduous tasks, engaging their children less often in those tasks.
8.3.5.3

Theme 3: Awareness campaigns

As previously noted, findings from focus group discussions indicate that beneficiary caregivers
in Ghana and Côte d’Ivoire have improved their knowledge on child labor issues, especially as
they relate to hazardous labor. However, these findings also indicate that changes in practices arestill lagging. Although caregivers and community leaders report community-wide changes inchild labor practices, children in the same communities mostly report changes around hazardous
labor, but not around time worked, or ages at which work begins. Caregivers in both countries
report that awareness campaigns did not always account for the financial situation withinhouseholds. When asked about their perceptions of the campaigns, a small set of caregivers notethat while they understand the objectives of the campaign, they are not in a position to hirelaborers for their farms and do not have any choice in using their children as laborers.
Community leaders report similar challenges, indicating that caregivers have difficulty changing
their practices due to their financial state:
As I said it’s about money, if I have money I won’t let my child to go to the farm,
but if I don’t have money for weeding and can’t weed then I will tell the child tohelp me weed. Because you told me you need a new shoe then you will go if I tell
you I will buy you a new shoe when we come. Community leader, Ghana
In addition to situational barriers to changing practices, teachers and community leaders also
referenced a few cases where awareness campaigns have not changed caregiver attitudes towards
child labor.

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I do not think the education from [government program] had any impact on theparents. The parents claim they are the ones that feeds their children therefore
they could use their children for any work they want. Primary School Teacher,
Ghana
In some communities, caregivers maintain that since they provide for their children, they candetermine how often their children should work. Although
Qualitative Insightthese caregivers were more transparent about the work theirchildren did, beneficiary caregivers did report implementing Findings in Ghana and Côte
d’Ivoire suggest that awarenessnew practices around occupational safety. Findings incampaigns resonate more withcaregivers when messaging is
Ghana and Côte d’Ivoire also suggest that awarenessfocused on the health implications
campaigns resonate more with caregivers when messaging
of engaging children in child labor,
is focused on the health implications of engaging children in rather than punitive measures forparents who engage their children
child labor, rather than punitive measures for parents whoin child labor.
engage their children in child labor. Community leadersbelieve that community members are much more receptive to messaging that is tailored to themand does not demonize caregivers. Child protection committee members (both caregivers andcommunity leaders) used similar approaches. Committee members maintain that confronting
caregivers who were engaging their children in child labor or hazardous labor was challenging
when caregivers were afraid that committee members would report them to the police. In these
instances, caregivers became hostile, or would avoid conversation with committee members.
However, when child protection committee members focused their messaging on the collective
wellbeing of children in the community, caregivers were much more receptive.
Implementers report similar findings, noting that awareness-raising efforts must be tailored to
local audiences and engage community members as champions in order to encourage behavioral
change. One implementer explains:
The awareness has gone far and in almost all the interventions, but I must saythat one of the challenges is that, you know it is attitudinal and behavioral
change, one’s attitude and beliefs cannot be changed in a day. It requiresconsistent efforts in engaging the farmers. It should be more of a participatory
dialogue to help the farmers understand the issues and address it themselvesbecause if the farmers are not addressing the issues themselves, we cannot
address it for them.
Focus group and interview data suggest that future awareness campaigns messaging from childprotection committees should focus more on the health effects of child labor, and less on thelegal consequences of engaging children in farm work. This may enable caregivers to be moreforthcoming about the situations in which they engage their children in farm work, and theFINAL REPORT | 137

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challenges they experience in implementing practices promoted by awareness campaigns.
Further, future awareness campaigns should actively engage locals in message formation anddistribution in order to promote behavioral change.
8.3.5.4

Theme 4: Timeliness of material support

Material support, including input provision, and schooling kits had significant positive impacts
on beneficiaries. However, the timeliness of these materials is key to maximizing positive
impact. In Ghana, caregivers reported that while input provision and mass spraying significantly
supported their farms, there were instances in which these supports arrived too late to be helpful.
Implementers echoed this sentiment, noting that the timeliness of these inputs is key tofacilitating real change for farmers:
Yes, the government provides some of the inputs, some companies also have their
inputs in shops but the question is do they come at the right time for the farmers
and are the prices okay for the farmers? They have to start applying fertilizers in
May but that is the month most farmers do not have money so they rely on thegovernment subsidized or free fertilizers. There is [also] a mass spraying exercise
done by the government, where they spray the farms for free but as to whether thenumbers are enough to cover the farmers at the right time because if you arespraying certain chemicals, they should be sprayed at the right time.
In Côte d’Ivoire, caregivers report that the provision of school supply kits helped ease financial
burdens related to schooling, while teachers in Côte d’Ivoire reported that these kits resulted in
improved attendance. However, when schooling kits came late, or were incomplete, caregivers
noted that it affected their ability to enroll their children, as they did not have all the materials
required.
Qualitative Insight

8.3.5.5

Theme 5: Intervention coordination

Respondents indicate that NGOs
should work more closely with local
authorities and one another tomobilize resources, avoid
duplication and inefficient
deployment of resources, andincrease intervention activity reach.

Community leaders, implementers, and government officials
report that in some communities, there are interventions
conducting similar activities that can benefit from improved
coordination. These respondents indicate that NGOs should
work more closely with local authorities and one another to mobilize resources, avoid
duplication and inefficient deployment of resources, and increase intervention activity reach.
Adult beneficiaries in the same communities also reported having participated in similarinterventions, with varying levels of success. These respondents indicate that within somecommunities, there were multiple livelihood initiatives with similar components.

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Overall, interventions that engaged community members in their design and implementation
were most effective, while those that took more of a top-down approach were less effective. In
both countries, school-based interventions reported the most success, especially when coupledwith community mobilization, and deep engagement with caregivers, teachers, and community
leaders. Future interventions must engage community
Qualitative Insightmembers early in their design and roll-out. Qualitative
In both countries, school-based
interventions reported the mostanalysis found that future awareness campaign messaging
success, especially when coupledshould avoid focus more on the health consequences ofwith community mobilization, anddeep engagement with caregivers,
engaging children, rather than the legal consequences ofteachers, and community leaders.
engaging in child labor. Community leaders and childprotection committees should be deeply engaged in the formation of awareness messaging, andany subsequent monitoring activities.
Overall Sustainability of Interventions
The following section combines perspectives from implementers, donors, and government
officials to outline strategies for promoting intervention sustainability, challenges tosustainability, and perceptions of future sustainability.
8.3.6.1

Strategies for promoting sustainability

Implementers, donors, and government officials in Ghana and Côte d’Ivoire identified several
strategies for promoting the sustainability of their interventions.
First, respondents recommend that engaging community leaders, including representatives for
women and youth early in the design of the intervention helped promote sustainability.
Implementers explained that during the planning stages for new interventions, they worked
closely with key community members to ensure that intervention objectives were realistic, andrelevant to each community.
Next, respondents recommended promoting sustainability through integrating interventions
within existing community structures. Respondents reported that in many communities, theylooked to existing structures for leadership and child protection and conducted extensive capacity
building efforts within these structures to implement awareness-raising and child protection
committees. Implementers explained that creating open and early relationships with government
partners at national and regional levels was key to promoting sustainability and gaining earlybuy-in. Collaboration with government actors ensured that when the intervention was over,
government officials at regional and district levels, would be well-positioned to moveintervention activities forward. Implementers also assert this strategy was critical to identifying
practical ways to integrate intervention activities into community structures. As one implementer
reports:

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Critical partnerships were from government. Like I mentioned in the community
action plan, usually these stakeholders support it and when they support it, they
are able to integrate it into the district medium term development plan or theannual development plan. When they integrate this into the district plan, then, thedistricts are in the position to support the plans that they have integrated into
their own plans.
Lastly, the creation of national action plans and national committees on child labor are key topromoting intervention sustainability. Many implementers argued that such groups enable thoseworking both directly and indirectly on child labor issues to better coordinate efforts, exchange
lessons learned, and collaboratively explore improvements to future programming.
8.3.6.2

Intervention Sustainability

Respondents held a range of perspectives on the types of interventions they believed had themost promise for maintaining outcomes beyond external material assistance. Respondents sharethe view that promoting community and beneficiary ownership of intervention activities
promotes sustainability of all types of interventions. Donors and implementers report that someinterventions, particularly livelihood services, need more financial investment to realize the fullpotential of outcomes, thus addressing some of the root causes of child labor. Of allinterventions, community leaders, implementers, and government officials agreed that schoolbased interventions hold the most promise for sustainability, as school-based interventions haverendered consistent and effective community resource mobilization. Stakeholders also believethat schools provide an opportunity for collective conversations and trainings with caregivers onchild wellbeing.
Regarding the sustainability of other interventions, implementer assessments are more varied.
For example, although awareness campaigns earned praise from caregivers and community
leaders, campaigns in and of themselves are unlikely to yield sustainable outcomes, according to
implementers as well as government officials. As demonstrated in the previous section, increased
awareness on child labor does not readily translate into improved practice. Indeed, habits are
slower to change. To facilitate awareness that is more sustainable, awareness efforts should beincorporated into other initiatives, such as GAP training that support farmers.
In some communities, the current and most popular implementer models of livelihoodinterventions are not sustainable, in the view of local leaders. These community leaders noticethat while community members are gaining new skills, their ability to generate income fromthose skills is limited, as their potential buyers reside in the community.

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The farmers are still poor because as I was saying the people from [Implementer]taught us to make soap. If all of us are doing the soap who will buy fromsomeone? Community leader, Ghana
Community members further see challenges in securing the necessary capital to move somelivelihood activities forward. In this regard, community leaders suggest more material
investment in income-generating activity interventions, and careful consideration of the types ofactivities that can generate income within the community.
National action plans and steering committees are critical to maintaining and building on the
progress made thus far. Donors, implementers, and government officials, alike, assert thesecommittees promote dialogue on effective and ineffective methods of child labor prevention andremediation. Committees also allow implementers to report on innovative approaches to childlabor prevention and remediation, and gain buy-in from government stakeholders early.
Implementers report that due to increased investment in child labor remediation fromgovernment stakeholders, these committees have resulted in productive dialogue and planningaround child labor prevention and remediation, with tangible goals. In both Ghana and Côte
d’Ivoire, government stakeholders have drawn from implementer expertise, lessons learned, andmonitoring efforts to produce national plans of action for child labor monitoring, prevention, andremediation. In Ghana, Phase II of the National Plan of Action to Eliminate the Worst Forms ofChild Labor builds on the insight gained from Phase I, while Côte d’Ivoire the 2018–2020
National Action Plan of the Fight against Trafficking, Exploitation, and Child Labor builds on
previous efforts as well.
Implementers and donors indicate that these national plans of action have promising potential for
promoting intervention sustainability. Implementers andQualitative Insightdonors also cited the CLCCG meeting, specifically, as key
Implementers and donors bothto sustaining momentum, and international coordination.
mentioned the annual CLCCG
meetings are key sustaining
These platforms provide a unique opportunity for the
moments of internal coordination incontinued exchange of best practices, collaborative
the fight of child labor andprogramming, and exploration for new approaches.
hazardous child labor in cocoa
production.

Overall, implementer and stakeholder interviews reveal anarray of perspectives on sustainability of interventions. Respondents perceive school-based
interventions as most sustainable, while other interventions should aim to address the root causesof child labor to be sustainable. Promoting beneficiary and community ownership of intervention
activities is considered a useful method for facilitating sustainability. Working groups, national
action plans, and coordinated activities also hold significant potential for facilitating current andfuture sustainability. Moving forward, international donors believe that more rigorous research isFINAL REPORT | 141

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needed to determine which interventions are most effective and sustainable. Other stakeholders
believe additional community-level and supply-chain monitoring efforts are needed to truly
understand the breadth and complexity of child labor issues in each community, and to determine
which outcomes are most feasible to sustain.
Impact of Funded Interventions on the Prevalence of Child Labor andHazardous Child Labor in Cocoa Production
Over the past decade, various stakeholders including the CLCCG partners and other internationalorganizations implemented different types of interventions in Côte d’Ivoire and Ghana with theobjective of reducing child labor and children’s exposure to hazardous child labor in cocoa
production. 69 Significant resources have been spent on various interventions during this period
and, thus, it is important to assess whether these interventions were effective in fighting
children’s engagement in child labor and hazardous child labor in cocoa production.
For the CLCCG partnership, the three main sources for funding over the past decade have been
USDOL, the Industry partners, and the local governments in Côte d’Ivoire and Ghana. The
USDOL have committed $24 million through ILAB toward projects aimed at preventing andreducing child labor since 2010. The Governments of Côte d’Ivoire and Ghana have madesignificant progress in adopting legislation and implementing programs targeted at reducing
child labor in cocoa production. The “National Plan of Action for the Elimination of the Worst
Forms of Child Labor (NPA)” in Côte d’Ivoire includes a $52 million budget for interventions
targeted at reducing child labor in cocoa production. Additionally, the “Ghana Child Labor
Monitoring System (GCLMS)” in Ghana is working to improve women’s economic
empowerment in cocoa growing areas and increase community awareness. The Industry
committed $10 million to reducing child labor between 2010 and 2016 through individualcompanies implementing their own interventions. The companies have joined together for alarger scale CocoaAction with an estimated value of investment of $400 million by the Industry
between 2015 and 2020. 70
Comparison of data from 2008/09 and 2018/19 survey rounds presented in Section 5.2 indicatesthat both the rate of child labor and the rate of children’s exposure to hazardous child labor
increased between 2008/09 and 2018/19. It is also important to note that during this period there
was a substantial increase cocoa production. Data presented in Section 5.1 also indicate thatcocoa-growing households as a proportion of all agricultural households experienced a large
statistically significant change between 2008/09 and 2018/19, increasing from 55 percent to 86
percent. This also suggests that as more agricultural households engaged in cocoa production it is69

See Annex 10.8 for the combination of interventions.

70

Self-reported by the international cocoa industry.

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possible there was more (not less) child labor employed. Given the interplay of different forces,
it is important to assess whether the interventions funded by the stakeholders to reduce childlabor and the hazardous child labor in cocoa-growing areas had a statistically significant impact,even if the use of child labor increased over time.
In order to evaluate whether the interventions implemented by various stakeholders has any
impact on child labor and hazardous child labor, it is important to recognize that evaluating theimpact of any one type of intervention would most likely to be methodologically challenging as
the impact of the particular program being considered needs to be large enough to detect with thegiven sample size. So it would be better to rather explore whether there was any impact when
multiple types of interventions were implemented by the partners. Keeping that in mind, we
evaluate the impact of implementing combinations of interventions on the prevalence of childlabor and hazardous child labor by undertaking an attribution analysis. Due to methodologicallimitations, we addressed this research question using the data from 336 households from 18
matched communities from Côte d’Ivoire. Please refer to Annex 10.8.4 for a detailed descriptionof the quantitative methodology and data sources used for addressing this research question.
8.3.7.1

Findings from Quantitative Analysis

We used a multivariate regression technique to test whether implementation of combinations ofinterventions in communities had any impact on children’s engagement in child labor and inhazardous child labor in cocoa production. The results from the analysis are summarized below.
Effect on having at least one child in child labor and hazardous child labor: Regression
results in Table 84 in Annex 10.8.4 provide estimates of the effect of being in a treated community (i.e., receiving some combination of interventions) on the likelihood of a household
having at least one child exposed to child labor in cocoa production in column two and on thelikelihood of children’s exposure to hazardous child labor in column three.
The results in the second column indicate that the households from the treated communities wereless likely to have at least one child engaged in child labor from the households from comparison
communities where no treatment was offered by the stakeholders. Specifically, when we control
for the household, community, and school characteristics in regression, households in thetreatment communities were 25 percentage points less likely to have at least one child engaged inchild labor than the households from comparison communities.
Similarly, the results in the third column in Table 84 indicate that when we control for thecommunity and school characteristics in the regression, implementation of multiple treatment
had a statistically significant impact on the likelihood of having at least one child engaged inhazardous child labor in cocoa production. The results indicate that the households living in thecommunities where multiple treatments were implemented during 2010 and 2018, were, on

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average, 28 percentage points less likely to have a child exposed to hazardous child labor incocoa production than the households from communities that did not receive any intervention
from the stakeholders during the period.
Thus, these results indicate that the households in communities receiving multiple interventions
were less likely to have children exposed to child labor and hazardous child labor in cocoa production.
Effect on the rate of child labor and hazardous child labor: Next, we turn to the effect ofreceiving multiple interventions on prevalence of children’s exposure to child labor and tohazardous child labor, estimating the effect on the proportion of children in a cocoa-growing
household exposed. The regression results presented in Table 85 in Annex 10.8.4 in the second
column indicate that the rate of child labor in cocoa production among the households in thecommunities exposed to some combination of interventions was approximately 17 percentagepoints lower than the rate of child labor among the households in the communities that did notreceive any intervention. Similarly, results presented in the third column shows that the rate ofchildren’s exposure to hazardous child labor was 17 percentage-points lower among thehouseholds in treated communities than the rate of hazardous child labor among the households
in the comparison communities.
To sum up, the results of our quasi-experimental analysis indicate that after controlling for thecovariates that typically influence children’s exposure to child labor and hazardous child labor,households in communities that received multiple types of interventions had a lower likelihoodand lower rates of child labor and hazardous child labor in cocoa production.
While the analysis presented above demonstrates that the rates of child labor and hazardous childlabor were lower in communities that were exposed to multiple intervention, it is important tonote that this analysis was based on data from only 18 communities (including the treatment andcomparison communities) and the conclusions may not be generalizable for the entire cocoa
growing area as a whole. Please refer to Annex 10.10 for a discussion on the caveats andmethodological limitations related to the above evaluation findings.
8.4

Caveats and Limitations

The quantitative and qualitative analyses used to address assessment questions and detect andquantify the impact of different interventions on child labor and hazardous child labor, while
being rigorous in nature, have some limitations. It is useful to summarize those limitations to
help in interpreting the findings of the quantitative results and qualitative findings.

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Limitations of Quantitative Analyses
Model-based approach: One of the major factors weakening the ability of any assessment

methodology to detect impact is that the interventions being assessed were not implemented(geographically or via roll-out) in a way that facilitates assessment. Ideally, groups ofcommunities would have been randomly assigned to receive the interventions (or various
combinations of intervention categories). Such an approach was not feasible in this case due tothe number and disparate types of interventions under investigation.
Since the interventions did not permit randomizing which villages received the intervention, amodel-based approach was used to construct a comparison group from untreated villages. The
model based approach estimates the impact based on “observables” (that is, only on factors that
could be and were measurable). Thus, the credibility of the evaluation depends on the degree towhich the salient explanatory variables were accounted for by the models’ specifications and themodelling of how the implementers selected beneficiaries. In addition, since this assessment has
been undertaken retrospectively, there was no pre-intervention data (baseline data) on
beneficiaries to construct a strong counterfactual.
Sample-size issues: The observational nature of the assessment sample and the sample sizes oftreated and comparison units were entirely dependent on the implementer’s earlier choices ofwhich communities to serve and, often, which households within them to treat. As a result, thestudy sample size was pre-determined rather than being drawn as a dedicated sample with a prespecified target sample size of the treated and non-treated households leading to a highly
unfavorable distribution of sampling units. That led to an evaluation design with a small sample,
leading to low-precision and an inability to detect small impacts with satisfactory precision.
Limitations of Qualitative Analyses
Due to the purposive nature of qualitative sampling, qualitative data collection did not reflect thefull array of interventions taking place in beneficiary communities. Despite attempts to ensurethat beneficiaries of vocational training programs were well-represented, there were limited
findings within the communities selected for data collection. Further, the perspectives
represented by donors and government officials reflect only those who were available forinterview.

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9 Conclusions and Recommendations for Future
Research
9.1

Overview

This report is the conclusion of five years of research on child labor within the cocoa growing
regions of Ghana and Côte d’Ivoire. The study integrates quantitative survey data from three datacollection rounds (2008/09, 2014/15, and 2018/19), quantitative implementation data from key
stakeholders over a ten year period (2008/09–2018/19), and qualitative data from children,
caregivers, school teachers, farmers, and key international and national stakeholders. The
quantitative survey data are used to present estimates on the prevalence of child labor andhazardous child labor, while both quantitative and qualitative data are used to assess theeffectiveness of different types of interventions on child labor and hazardous child labor in thecocoa growing areas of Côte d’Ivoire and Ghana.
Survey findings indicate that in 2018/19, approximately 1.56 million children (45% of children
in agricultural households) were engaged in child labor in cocoa production in cocoa growing
areas of Côte d’Ivoire and Ghana. Among them, approximately 1.48 million children (43% ofchildren in agricultural households) were engaged in hazardous child labor in 2018/19.
Over the assessment period (2008/09- 2018/19), the proportion of children engaged in hazardous
child labor in agricultural households in the cocoa growing areas of Côte d’Ivoire and Ghana
increased 14 percentage points. Thus the target of the Declaration and Framework to reduce theworst forms of child labor in the cocoa sector by 70 percent between 2008/09 and 2018/19 was
not met. However, it is worthwhile to consider this finding in the context of the growth in cocoa
production, which increased 62 percent during the ten-year assessment period.
Interestingly, comparison of data on cocoa growing households (rather than all agricultural
households) between the 2013/14 and 2018/19 rounds indicates the prevalence rate of children’s
exposure to hazardous work in cocoa production remained stable for this group during this time
period although production during this period increased 14% in aggregate across Côte d’Ivoire
and Ghana.
In addition, comparisons of the prevalence of child labor and hazardous child labor by cocoa
production stratum indicates prevalence rates increased substantially in the medium and low
production strata (child labor increased by 16 and 27 percentage points respectively in themedium and low strata) with no change within the high production stratum. These findings
suggest that as cocoa production increased and cocoa farming expanded in historically lower
cocoa growing areas, the use of child labor in those areas also expanded. As one senior

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stakeholder mentioned to the research team on seeing these results, summarizing them well
“We’ve been chasing the cocoa rather than chasing the children.”At the same time, child labor in high production stratum remained stable even in the face ofincreased production and a corollary increase in the proportion of agricultural households
growing cocoa. Intervention data indicate that the penetration of interventions implemented
seemed to be fairly low 71, and that most of the interventions were heavily concentrated in thehigh production stratum (where the problem of child labor may have been more severe to startwith), with some interventions in the medium and almost no interventions in the low production
stratum. This may indicate child labor interventions focused on high cocoa producing areas ofCôte d’Ivoire and Ghana have been effective to offset the impact of increased cocoa production
on child labor.
These findings, in conjunction with the findings from the assessment of the effectiveness ofvarious interventions, paint a fuller picture of the situation around child labor and hazardous
child labor in Côte d’Ivoire and Ghana, indicating varying degrees of success. Although theresearch team found some impacts of livelihood interventions on child labor, it was only when
multiple interventions were implemented in one area that we find a significant impact on therates of child labor and hazardous child labor.
Qualitatively though, children, teachers, and farmers all felt there was a strong impact ofinterventions on behavior. These respondents consistently described the importance ofinterventions on reducing child labor and hazardous child labor pointing to school-based reforms
as most important.
While the quantitative analyses were only able to detect a limited impact of interventions, it isimportant to note that lack of detection of impact does not mean lack of an effect, only that thedesign of the intervention and the amount of data collected did not permit a level of statisticalprecision sufficient to detect an effect, given its size.
Overall, this report makes a strong case for understanding child labor and hazardous child laborin cocoa production as a complex problem requiring multiple complementary solutions. Such an
approach is often called a systems approach in which it is essential to understand the phenomena
as being interrelated with, and dependent on, different facets of the system itself. For example,
understanding the relationship between child labor prevalence and increased cocoa production

71

Implementation data provided by major stakeholders indicate that less than 50% of the randomly selected
communities (representative of the cocoa growing areas of Côte d’Ivoire and Ghana) received any type ofinterventions in the past 10 years.

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and/or the global demand for cocoa. Layering that understanding on top of the role of production
stratum and the power of interventions begins to bring the system as a whole into perspective.
Another important takeaway from this report are the achievements on the ground in confronting
child labor and hazardous child labor by both national and international stakeholders. Although
the goals of the Harkin-Engle Protocol were not met, we do see an impact on child labor in areas
with high production and multiple interventions. While assessment results suggest that sustained
efforts to fight child labor are successful, it is important to understand what types andcombinations of interventions are more effective in reducing child labor and hazardous childlabor and how effectiveness may vary under different local conditions. This points to thenecessity for further research on this topic.
In terms of hazardous child labor, data suggests that it is essential to understand each component
part of hazardous labor (such as land clearing, agro-chemical use, sharp tool use, and carrying
heavy loads) in order to focus efforts on the dimensions of hazardous labor most prevalent in aparticular area. There is significant debate around the differences between national andinternational definitions of child labor. However, the issue of hazardous child labor is lesscontentious, and the component parts of hazardous child labor can directly impact the physicaland psychological development of a child. Thus, looking directly at hazardous child labor andespecially at the frequency of exposure to different hazard, rather than a binary categorization ofa child as either “in” or “out” of child labor may be a way forward that stakeholders can agreeon.
In terms of agro-chemical product usage, agricultural households use of agrochemical products
increased steadily, and at the same time, children’s exposure to agro-chemical products increased
by approximately five times between 2008/09 and 2018/19. Further studies, both quantitative andqualitative, looking specifically at agro-chemical use and its drivers may be warranted.
This, again, points to the importance of a systems approach in which multiple interventions (orsingle multi-armed interventions) are co-developed to address specific aspects of the system suchas raising awareness in conjunction with livelihood services in conjunction with increased accessto schools in conjunction with changes in national legislation. These interventions can then be
focused on, for example, the component parts of hazardous child labor that are most prevalent
within particular areas.
9.2

Lesson Learned and Future Research

Beyond the main findings of this report, there are multiple important topics and takeaways that
can help guide future research on child labor and hazardous child labor. Below we present
several of these takeaways for future research.

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Stakeholder engagement
The research team believes stakeholder engagement can be the single most important factor in a
successful child labor data collection project. There is, at times, an atmosphere of distrust among
stakeholders when approaching issues such as this one that touch on economies, cultural beliefs,
and national legislation. As such, we feel stakeholder engagement, bringing together tripartite
partners in a mutual and equal dialogue, is essential to the process. 72 These engagements shouldtake place at the start of a project, during the design of questionnaires and field plans, and at theend of the project to help structure analysis and the final report. Stakeholders are more open to
accepting the results of such projects if they feel a sense of ownership over the process. In
addition, stakeholder engagement has the added benefit of improving the research design andimplementation, streamlining the data collection permission process, IRB process, andoftentimes provides access to national level administrative and census datasets.
In terms of the current project, the research team consulted with the governments of Côte
d’Ivoire and Ghana (including cocoa regulatory institutions, national statistics bureaus, andvarious ministries in each country), the international cocoa industry through the WCF,
international organizations such as the ILO and UNICEF, local and international civil society
organizations, and the U.S. Department of Labor. These engagements positively impacted thequality of the data used as part of this study.
Common and National Definitions
The research team agrees with the recommendations made in the 20th International Conference ofLabour Statisticians Guidelines in reference to using common concepts and definitions in childlabor/forced labor data collection projects. Common definitions allow for international
benchmarks, and discussions around labor issues can then be outlined within a global
framework. However, local definitions are at times equally important to ensure ownership andbuy-in from local stakeholders. The cost for including two definitions is oftentimes marginal andallows for comparisons based on both local and international definitions as shown in this report.
For example, there is significant debate around the international definition of child labor and therole of what stakeholders in Côte d’Ivoire and Ghana call “socializing work” done by children
with oversight of their parents to teach children the necessary skills needed for life on a farm.
Although the research team agrees that international definitions are necessary to allow for a
global response to child labor issues, future researchers should be aware of this debate and work
to address it by including local definitions where possible.

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Such engagement activities are also stressed in the current ICLS guidelines

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Child Labor, Hazardous Child Labor, and the component parts ofHazardous Child Labor
Based on a deeper understanding of data and issues related to the binary nature of the target
outcome of interest, we believe that measuring child labor by itself is insufficient to accurately
describe realities on the ground. Child labor and hazardous child labor are dichotomous variables
in which children are identified as either in or out of a child labor and/or hazardous child labor
situation. For example, an 11-year-old child who works one hour with their parent on the farm isconsidered identical to an 11-year-old child who spends 40 hours spraying hazardous chemicals.
While reducing the exposure to any hazardous work is an important target, it is equally important
to consider the implications of children’s exposure to specific types of hazard as well as
exposure to multiple hazards to identify strategies and programs that can help address them.
Nuances such as these are often points of contention between tripartite partners and being able toreport on not only child labor but also on the relative impact of the various component parts ofchild labor and hazardous child labor is essential. Such an analysis also provides policymakers
with concrete examples of where exactly interventions need to be targeted to be most effective.
Enumerator Training
Enumerator training is an often under considered aspect of high-quality data collection. The
current project had 21 days of training across activities and within each country. This included 5days of listing training, 5 days of supervisor training, and 10 days of enumerator training andpiloting. This extensive training, with a core group of supervisors and enumerators, should not be
underestimated and led directly to the high data quality used for our analysis.
Evaluation based on Randomized Control Trials
In conducting an assessment of the effectiveness of various interventions in the cocoa sector, it
became evident to the research team that a model based approach to identify the impacts ofinterventions (and combinations of interventions) is challenging given the multitude of factorsthat influence the prevalence of child labor and hazardous child labor. In order to produce
actionable inputs that stakeholders can use to fight child labor in cocoa production, it is useful tounderscore the importance of undertaking randomized control trials to detect impact with greater
degree of internal validity that can provide more concrete estimate of impact. Similarly, tounderstand complementarities of different types of interventions, it is important to design andimplement randomized control trial studies to unpack the relative effectiveness of eachintervention.
Final Remarks
The research team hopes that information in this report is useful for national and international
stakeholders to better understand how child labor and hazardous child labor rates changed

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between 2008/09 and 2018/19, areas where more attention is needed, and how futureinterventions can be designed and implemented to further improve the lives of children in West
Africa. In addition, the strength of the 2018/19 data collection round and integration of both local
and international definitions allows this study to be used in the future as a new baseline tomeasure the progress towards eliminating the worst forms of child labor in the cocoa growing
areas of Côte d’Ivoire and Ghana.
The evidence of effectiveness of interventions in reducing child labor highlighted in this study,
even if limited to only specific areas and to specific conditions, clearly advocates for thecontinuation and intensification of the efforts undertaken as part of the Harkin-Engel Protocol.
The Protocol brought national and international partners together towards a common goal toaddress the issue of child labor, and it is important to ensure that collaboration and dialogue
among stakeholders continues and intensifies to reduce and eliminate hazardous child labor in
the cocoa sector in Côte d’Ivoire and Ghana.

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10 Report Annexes
10.1 Annex I: Detailed Survey Methodology and Implementation in the 2018/19
Round
Sampling design
The sampling design for the 2018/19 Child Labor Survey in Ghana, and Côte d'Ivoire maximizes
the potential for comparability of estimates between the baseline 2008/09 and 2018/19 round toensure we can make valid inferences of change while also producing current and accurate crosssectional estimates. In our sampling methodology we try to strike a balance between accuracy inestimation and comparability between rounds.
In order to generate estimates representative of agricultural households in the cocoa producing
areas of Ghana and Côte d'Ivoire, NORC collected survey data from 75 clusters from eachcountry in the 2018/19 cocoa harvest season.
10.1.1.1 Sampling Method
Following the method used in the previous rounds of the Child Labor Survey administered byTulane University and ISSER/ENSEA, NORC employed a multi-stage stratified cluster sampling
method to select the Census Enumeration Areas (CEAs/clusters/communities ) which are thePrimary Sampling Units (PSUs) of the 2018/19 data collection.
The sampling frame consists of three steps where, in the first step, we identified a given numberof CEAs (communities) as described below. In the second step, once the CEAs were identified,
we undertook a complete enumeration of all households (listing) in those areas. Finally, from thelist of the households in each CEA, a given number of households were randomly sampled forthe 2018/19 survey.
10.1.1.2 Selection of Census Enumeration Areas
For selecting the CEAs based on a stratified cluster sampling two approaches were considered by
the NORC team:■Preferred approach: CEA stratification based on amount of cocoa produced per CEA.

■

Alternative approach: CEA stratification based on amount of cocoa produced at next
available higher level of geographic unit above the CEAs.

The NORC team first explored whether it was feasible to use the preferred approach which
required current cocoa production data at the CEA level. However, after several rounds ofdeliberation with the governments of Ghana and Côte d’Ivoire, we realized that the CEA level

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production data was not available due to lack of current agricultural census data. As a result,
NORC decided to adopt the alternative approach of CEA stratification noted above and followed
a stratification strategy described below:■

In the first stage:■►

Collected data on cocoa production at the district /department level 73 (rather than the
CEA level)►

Classified the districts/departments into high/medium/low cocoa production stratum
based on the most recent available cocoa production data.

►

Identified the cocoa growing communities from each of the districts/departments andassigned the identified communities into high/medium/low cocoa production stratum
based on the classification of the districts/departments they belong to.

In the second stage, from the list of all cocoa growing communities classified into
high/medium/low cocoa production stratum, used random sampling methods to sample CEAs
from each the above three stratum to select a total of 75 CEAs from high, medium and low
cocoa production stratum with oversampling of CEAs from the high cocoa production
stratum (in this case stratum are districts/departments)►

Since CEA level data on cocoa production was not available in this approach, GSS in
Ghana/INS in Côte d’Ivoire used a simple random sampling method to sample the CEAs
within the districts/departments.

►

40 CEAs from high, 25 CEAs from medium and 10 CEAs from low cocoa production
stratum were sampled in each country

Figure 5: Range Used for Stratification of Department/Districts: 2018/19 Survey Round
Côte d'Ivoire

Ghana

Department total cocoa
production in ton

District total cocoa
production in ton

High stratum

More than 40,000 ton

More than 20,000 ton

Medium Stratum

10,000-40,000 ton

8,000-20,000 ton

Low StratumLess than10,000 ton

Less than 8,000 ton

Stratification

10.1.1.3 Selection of Households from Identified CEAs for Survey
Once the clusters were identified, NORC undertook a fresh household listing exercise in each of
the selected CEAs to enumerate the households. After the listing exercise, NORC randomly
73

Most recent data on the quantity of cocoa produced at the district level in Ghana provided by Ghana COCOBOD
and at the department level Côte d’Ivoire by the Coffee-Cocoa Council of Côte d’Ivoire.

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sampled agricultural households with at least one eligible child aged 5-17 for conducting themain child labor prevalence survey of 2018/19 cocoa harvest season.
The 2018/19 survey was set to collect data from 1,500 households in Côte d'Ivoire and 1,300
households in Ghana resulting in approximately 2,300 children interviewed in each country. 74
10.1.1.4 Important Considerations
It is important to note that our proposed sampling methodology diverges from the methodology
used by Tulane in previous survey rounds. This is mainly due to the fact that the NORC’s
stratification of CEAs (the PSUs) was based on district/department cocoa production levels,
whereas, according to the available documentation, Tulane used the region/district level cocoa
production data respectively for Ghana and Côte d'Ivoire 75 to stratify the CEAs. Since our PSU is
the CEA, stratifying based on cocoa production at the district/department will lead to improved
controls on the sample and more precise estimates. However, our approach of stratification of the
PSUs based on district/department cocoa production levels, rather than on region/district levels,
would not affect the comparability of national estimates of prevalence rates between the rounds,
instead, it will improve precision of national estimates by reducing the potential sampling error
related to stratification of PSUs based on production data at a higher level. Please see Annex
10.2 for a complete explanation of this issue.
Survey methodology
10.1.2.1 Quantitative approach
The main source of quantitative data was the three rounds of sectorally representative surveys ofchild labor (2008/09, 2013/14 and 2018/19) from Côte d’Ivoire and Ghana. In order to addresspossible seasonality concerns, the surveys were fielded during the main coco harvesting season
in both countries and close to the fielding of earlier rounds of the survey as possible.
The 2018/19 survey obtained survey data from 1,507 households in Côte d’Ivoire and 1,317
households in Ghana, resulting in approximately 2,743 child interviews in Côte d’Ivoire and2,809 in Ghana. We collected survey data from the heads of each sampled household as well as

74

The calculation of total number of households to be surveyed is based on the target of surveying at least 2300
children and average number of children per household. The data on average number of children per household ineach country is available from the Tulane report for 2008/09 and 2013/14 survey rounds. Based on the experience ofprevious survey rounds, we expect 20% and 15% reduction in average number of children per household in Côte
d'Ivoire and Ghana respectively in the 2018 survey round and calculated total number of household required tosurvey 2300 children.

75

Note that Ghanaian ‘regions’ are equivalent to ‘districts’ in Côte d'Ivoire

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community-level data from local community leaders, schools, and cocoa shed operators toprovide additional insights.
10.1.2.2 Qualitative approach
Qualitative data collection took the form of focus group discussions (FGDs), key informant interviews (KIIs), and fieldwork observation. All focus groups were led by a trained moderator who
followed a pre-determined set of questions articulated in a discussion guide. The participants
were chosen or recruited based on criteria in a screening questionnaire or by being clustered in ageographic location.
The qualitative component of the 2018/19 Child Labor Survey provided context for quantitative
results, as well as a deeper understanding of how various key players understand child laborwithin the cocoa sector in Ghana and Côte d'Ivoire. This component provided nuanced
perspective into the topics covered by quantitative surveys, including contextual understandingof complex concepts such as night work, heavy loads, sharp tool use, and others.
10.1.2.2.1

Key Informant Interviews

KIIs informed how different key players understand child labor and interventions aimed at
reducing child labor. These interviews outlined country-specific complexities that may affectchild labor rates, and the efficacy of interventions aimed at reducing child labor and focused onelites, donors, and CSOs/implementers of interventions to reduce child labor. NORC conducted
KIIs with the following groups in each country:■

Agricultural extension agents■Community leaders■

Cocoa growers■

Donors and cocoa industry members■

Government officials■

Implementers (CSOs and NGOs)

For all interviews, NORC engaged cocoa industry members, and implementers in identifying
potential respondents. NORC closely collaborated with USDOL and Industry members to ensurethat all KII questions were relevant, logical, and coherent. NORC also engaged implementers
who receive funding outside of USDOL and the cocoa industry.
NORC took a snowball sampling approach to KIIs, in which respondents were asked to identify
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this way While KIIs did not have a fixed sample size, NORC ensured that similar numbers of
respondents from each respondent group and each country were interviewed. The following
figure details the institutions interviewed:
Figure 6: KII Interviews: 2018/19 Survey RoundInstitution

Interviewees

Education Information Branch

1

Ghana Education Service

1

Ministry of Employment and Labour Relations

2

Ghana COCOBOD

6

Commission on Human Rights and Administrative Justice

1

MOCA

4

Fairtrade Africa

1

ILO

1

Free the Slaves (Ghana)

1

UNICEF Ghana

1

UNICEF Côte d'Ivoire

1

USDOL Bureau of International Affairs

3

Fairtrade International

1

International Cocoa Initiative

2

World Cocoa Foundation

1

Action Against Child Exploitation

2

Prior to each interview, we outlined the interview procedure, including purpose and expected
duration of the interview. Interviewees were informed that their participation is voluntary, allresponses are confidential, and they may choose not to have their interview recorded. Any quotes
used in subsequent reports removed all personally identifying information, or any details thatwould make it clear that any quote likely came from a specific person or entity. All interviews
were administered by NORC staff and Kantar senior staff with extensive qualitative experience.
10.1.2.2.2

Focus Group Discussions

Focus groups contextualized the quantitative component of the assessment and outlined the onthe-ground realities for cocoa farmers, including common experiences, perceptions ofinterventions, and perceptions of education. Group discussions primarily focused on beneficiary
cocoa communities but included some non-beneficiary communities to allow for comparison
between beneficiary and non-beneficiary communities.
Focus groups were disaggregated by sex to allow gendered differences to emerge. Focus groups
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discussion alongside their peers. Prior to age and gender disaggregation, focus group discussions
were broadly separated into the following groups: beneficiary children, non-beneficiary children,
beneficiary caregivers, and non-beneficiary caregivers.
In general, FGDs were comprised of roughly 6-10 participants and focus groups with children
were on average 45 minutes to an hour in length, while focus groups with adults were one hourto 90 minutes in length.
10.1.2.2.3

Sampling

To select communities for focus groups and community-level KIIs, NORC used quantitative
survey responses to generate a comprehensive list detailing average responses for key
intervention and hazardous labor data. The team closely analyzed surveyed communities along
the following parameters:■

Training exposure: % of respondents exposed to occupational safety training, vocational
training, awareness training, and livelihood projects■

Reported activities: % of respondents reporting land clearing, agrochemical use, night work,
working hours■

Child labor rate: % of children in EA engaged in child labor.

To maximize variances in perspectives, the team members individually selected EAs withvarying rates in training exposure, reported activities, and child labor. The team then narrowed
the list of communities by focusing on communities with inverse relationships between training
exposure and reported activities/child labor rates, and high training exposure and low child labor
rates/reported activities, low training exposure and high reported activities/child labor rates. The
team also individually selected communities where training exposure rates were high, but childlabor and hazardous activity rates were also high, and communities where training exposure waslow, but child labor rates and hazardous activity rates were also low. This was an iterative
exercise in which after each pass, team members discussed their reasoning for selecting each
community. Finally, the team agreed on 15 communities in each country that represented varying
rates of child labor and hazardous child labor, and varying levels of intervention of exposure.
Following this exercise, NORC used GPS data and STATA to generate a map of each country
that displayed where each selected community was positioned. The purpose of this exercise wasto ensure that selected communities were spread across each country and represented an array ofinterventions. The team worked closely with our local subcontractor to ensure the accuracy ofthe maps generated and selected the final communities. The following figures outline thecommunities for focus groups and community-level KIIs in each country:

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Figure 7: Ghana FGD Communities
RegionDistrict

Community NameCentralAssin South

Dossi

Ashanti

Atwima Mponua

Mmawaninha

AshantiAhafo Ano South

Aponaponso

AshantiAhafo Ano South

Essienkyem

Western

Sefwi Wiawso

Kofikrom

Brong Ahafo

Asunafo North

Akorabuokrom (Duase)AshantiAmansie Central

Fenaso No. 3

CentralAsikuma Odoben Brakwa

Baffokrom (Adandan No. 1)

Figure 8: Cȏte d’Ivoire FGD Communities
Department

Sub-Prefecture

Community Name

Adzopé

Becedi-Brignan

Becedi-Anon

Gueyo

Gueyo

Lakota Carrefour

LakotaZego

GoboueOumé

Diégonéfla

Goudi

TiassaléMorokroKoyékroTiassalé

N'Douci

Badasso (Abeve)

Yamoussoukro

KossouZatta

Design of survey instruments
Quantitative questionnaires cover a wide array of subjects aimed at addressing the many factorsthat contribute to child labor and hazardous child labor in the cocoa sector. For each sampled
household, all consenting children aged 5-17 were interviewed. In addition, the household heador other knowledgeable household member was interviewed using a household headquestionnaire and a household labor roster. Within each sampled CEA, interviews wereconducted with all village chiefs, cocoa shed operators, and K-12 public/private school headteachers.
To ensure comparability between data collection rounds, the aforementioned quantitative
instruments were modeled upon those used in the 2008/09 and 2013/14 survey rounds. Prior todata collection, all survey instruments were vetted and reviewed through in-country stakeholder
workshops which included representatives of host governments as well as industry and NGO
partners. In addition, instruments were thoroughly reviewed by our local research teams followed
by a field-based pre-test within CEAs that are demographically similar to, but outside of, thesampled communities. Learnings from the workshops, reviews, and pre-test were included toFINAL REPORT | 158

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inform the final instrument design and carefully documented. ILAB was involved and provided
input at each step in this process.
Child Questionnaire
The child questionnaire captures data used to construct all child labor indicators and population
estimates and is therefore of central importance to the study. To minimize bias and in accordancewith ILO best practices, enumerators were trained to administer the child survey in private after
obtaining parental consent to do so. Given the complex subject matter of the survey, enumerators
conducted a pre-interview developmental assessment to determine the cognitive capacity of thechild to comprehend key concepts and definitions covered in the survey. In cases where thedevelopmental assessment suggests a child will not understand the majority of the survey
questions, parents were asked to support the child in the interview (in all cases, enumerators are
required to record information on the presence of other persons and the extent to which they
influenced the child’s responses). Where appropriate, cognitive interviewing techniques are
employed with younger children to reduce the risk of suggestibility, confabulation, and sourcemonitoring error. All child interviewing protocols, tools, and techniques were thoroughly
covered in the enumerator training and enumerator manuals. The child questionnaire covers thefollowing topics:
Migration and Movement. Respondents are asked questions about migration patterns, which areoften a common component of the agricultural sector. These questions address respondents’countries/towns/communities of origin, identify driving factors for migration, and identify
decision-makers about migration.
Work Activities. Respondents are asked general questions about the nature of the work they do.

This includes extensive probes on activities that may not be typically perceived as work among
respondents, including unpaid household farming or business activities. This module also asks
about the types of agricultural tasks performed, such as land clearing, burning, and carrying
water for spraying. These questions address the extent to which a given respondent regularly
performs these tasks, or only performs them from time to time.
Working Hours. Respondents are asked various questions about the hours they work, including

times of day, the length of time in a given day, and the amount of time in a given week. These
questions will address the extent to which working hours are typical for that respondent.
Injury and Illness. Respondents are asked to recall the extent to which they have experienced

injuries or pain as a result of agricultural work. These may include broken bones, wounds, backpain, muscle pain, and others.

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Heavy Loads. Respondents are asked to recall the types of loads they have carried, thecircumstances under which they were carried, and the distance they may have carried them. With
younger child respondents, NORC will ensure that enumerators ask children to estimate weights
or distances using familiar items and locations within the community (versus units ofmeasurement). However, youth aged 14-17 are asked to provide estimations based on distance,
weight, and transportation mode. All children are asked to recall the extent to which carrying
heavy loads resulted in immediate and/or ongoing physical pain.
Exposure to Environmental Hazards and Other Dangers. Respondents are asked to recall

their levels of exposure to environmental hazards and chemicals. This may include the use ofpesticides and herbicides, exposure to flames or fumes, and work at dangerous heights. These
questions also address whether respondents experienced any health consequences as a result of
this exposure, and the severity of any health consequences experienced.
Tools, Equipment, and Machinery. Respondents are asked to recall the types of equipment

they normally use when performing agricultural activities. This includes the use of machetes,
mist blowers, knives, or animal-drawn tools, and any injuries that may have resulted from the useof such tools. Conversely, respondents are also asked to recall the types of protective equipment
they may have used while carrying out these activities. This may include protective boots,masks, and other gear.
Education. This module will assess the extent to which children have received or are receiving

education or training. It will include brief a literacy and numeracy assessment as well as capture
any reasons for missing school, dropping out, or repeating classes.
Project Activities and Sensitization. As various interventions will be taking place, respondents

are asked to recall the extent to which they have benefitted from various project activities andsensitization efforts.
10.1.4.1 Household Roster
The household roster collects basic demographic information on all household members,
including sex, age, marital status, education, literacy status, as well as labor status over the past 7days and 12 months.
10.1.4.2 Household Head Questionnaire
The household head questionnaire is administered to the person(s) determined by the sampled
household to be most knowledgeable about household farming practices and income, spending,and borrowing. The household head questionnaire includes modules on household socioeconomic status, farming characteristics, migration patterns, use of and opinions on child labor,

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access to and use of credit (including input financing), participation in community projects, andfuture risk of agriculture-related injuries.
10.1.4.3 Community Leader Questionnaire
The community leader questionnaire is administered to all village chief(s) within the CEA. In
cases where a traditional leader cannot be interviewed, local assemblymen will be interviewed intheir place. The community leader questionnaire collects a broad range of community-levelindicators including on migration patterns, infrastructure, socio-economic status, governance,
trends in cocoa production, project activities, and the incidence of child and forced labor.
10.1.4.4 School Questionnaire
The school questionnaire is administered to head teachers (or their designated proxies) at all
public and private schools serving K-12 pupils within the CEA. The school questionnaire
collects general information on the school including trained teachers, enrollment figures, andschool fees. In addition, the survey collects information on working children as well as headteacher opinions on the extent to which agricultural work influences educational outcomes in thecommunity.
10.1.4.5 Cocoa Shed Questionnaire
The cocoa shed questionnaire is administered to all cocoa shed operators/managers within the
CEA. This brief survey collects information on cocoa shed capacity/volume and purchases aswell as the extent to which the shed uses child labor and rates of pay for child workers.
Training
10.1.5.1 Training of Trainers
To help facilitate the main enumerator training, a training of the trainers (TOT) was conducted
for supervisors who were tasked to lead breakout sessions in the main training. The training of
the supervisors was conducted from November 1st-3rd, 2018 for Ghana and January 10-17th, 2019
for Côte d’Ivoire. A total of 15 supervisors, 2 regional coordinators, 4 managerial team members
from Kantar and 3 facilitators from NORC attended each training.
The training lasted for two days and the topics covered were;■

Cognitive Interview Technique (CIT),■

Head of household questionnaire review,■

Child questionnaire review,■

Roster questionnaire review,

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Community leader questionnaire review,■

School questionnaire review,■

Cocoa shed questionnaire review,■

Entry protocols, and■Role of trainees during main enumerator training.

At the end of training, feedback from supervisors was incorporated into the review of the scripts.
Supervisors were better equipped to lead smaller groups during the enumerator training.
10.1.5.2 Enumerator Training
The main training for Ghana was conducted from November 5-14th and was conducted fromJanuary 18-28th, 2019 for Côte d’Ivoire. A total of 113 participants were present for the training
in Ghana and 98 participants were present for the training in Côte d’Ivoire.
Training was based on the following;■

Understanding the objectives of the research,■

Understanding the questions and its administration requirement,■

To be conversant with the field operations, survey methodology and protocols,■

To state roles, responsibilities and expectations of interviewers and supervisor’s involvementin the survey, and■To carry out effective interview, using CAPI (Nfield).

10.1.5.2.1

Outcomes of Training

By the end of the training, participants were;■

Familiar with all the instrument,■

Able to administer the assigned instruments for survey with confidence and accuracy,■

Able to follow data collection process and plan as expected, and■Effective handling of field materials.

Training for the household and community teams were run co-currently. The household (head ofhousehold, child and the roster tools) were facilitated by NORC while the community
questionnaires (school, community leader and cocoa shed) was handled by Kantar.
The topics that were addressed across all teams included;■

Techniques for interviewing young children,

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Guidelines for tablets practice,■Child questionnaire guide,■

Head of household questionnaire guide,■

Community (Cocoa shed, community leader, school) questionnaire guide,■Child protection protocol,■

Confidentiality and informed consent,■Interviewing techniques,■

Stimulus worksheets, and■Child safety referral.

10.1.5.2.2

Qualitative Training

NORC conducted back to back trainings in Ghana and Cȏte d’Ivoire. The qualitative training
was four days long, including one day of pilot, and one day of post pilot debrief.
Moderators and note-takers were trained in best practices in focus group moderation, including
topics around managing group dynamics, minimizing risk for adult and child respondents,
maintaining neutrality throughout the discussion, and maintaining intragroup confidentiality.
Moderators were also trained on how to manage child disclosures of abuse and forced labor,
including response and reporting to appropriate authorities. Moderators were provided with arisk and response protocol outlining the appropriate procedures of bringing attention todisclosure. Focus group moderation teams were also provided with operative definitions forintervention categories, and a comprehensive list of off-farm and off farm activities. Focus groupmoderation teams were required to be very familiar with both lists in order to probe effectively
and recognize local names for various tools.
During training, moderators held extensive practice rounds and discussions to ensure that theproper local words were being used. Moderators also made considerations for regional variations
for the names of key terms, and concepts. Following the pilot, data collection teams made thenecessary adjustments to the data collection instruments. These changes maintained themeanings of each question, but were reworded to be more direct, and ensure consistenttranslation to local languages in the field.
Focus groups and community-level KIIs were recorded, translated, and transcribed into English
and French. Transcripts were transmitted through NORC’s secure file transfer platform.
Pilot
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provide on-field learning experience for trainees and to ensure scripting instructions were
properly implemented as well as skip patterns working accurately. Piloting was conducted in
Ghana on November 10th, 2018 and in Côte d’Ivoire on January 26-27th, 2019. The household
team screened and scheduled appointments with eligible households and later interviewed
household heads and eligible children. The community teams conduct interviews with
community leaders, schools’ heads, and cocoa shed managers.
Data collection
Data collection for Ghana lasted from November 23rd, 2018 to January 27th, 2019 and for Côte
d’Ivoire from February 9th, 2019 to March 7th, 2019. Fifteen teams were deployed to field. Eachteam comprised a supervisor and four household interviewers, and one community interviewer.
Each team was accompanied by one quality control officer. The teams were provided vehicles tofacilitate their movement across the different communities as the roads leading to most of thelocalities were in bad shape. In each locality, our teams met the administrative and villageauthorities to explain the purpose of the study before starting the data collection.
The team (led by the supervisor) debriefed daily before the start of the field. The supervisor
contacted households and assigned them to enumerators to conduct interviews after household
heads had consented. For each enumeration area, 20 households were screened for eligibility andsurveyed. In the evening after the day’s work the supervisor synchronizations all tablets andsends a report for the day.
10.1.7.1 Methodology
A list of 23 agricultural households per EA (of which 5 were for replacement) were sampled andprovided for the ILAB survey. The Roster gets to the community, observe all the necessarycommunity entry protocols with or without the supervisor. The roster team visited the EA a day
earlier to screen the household for eligibility and recruit the head of household and the children(5 to 17) who were available during the stay of the team in the EA.
Generating the Sampling Weights
The enumeration areas (EAs) were stratified into three strata based on a prior measure of thequantity of cocoa produced as per the data provided by the COCOBOD in Ghana and the CoffeeCocoa Council in Côte d’Ivoire. A predetermined number of EAs from each stratum wereselected for the sample, the distribution of which is presented in Table 39.

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Table 39: Number of EAs per StratumCȏte d’Ivoire

Ghana

Stratum 1 (High Cocoa Production)

40

40Stratum 2 (Medium Cocoa Production)

25

23

Stratum 3 (Low Cocoa Production)

10

10

Total

75

73

Stratum

Household and child-level weights were generated for each EA and calculated for eachhousehold head and child respondent, as follows.
■

Household-level EA Weights

Household-level sample weights were based on two stages of calculations, both of whichcorrespond to a stage of the sampling design. Suppose household 𝑖𝑖 was situated within stratumℎ and enumeration area (EA) 𝑘𝑘. The first-level weight was based on the corresponding number(1)of enumeration areas (EAs) within the stratum, and was evaluated as 𝑤𝑤𝑖𝑖

=

𝑁𝑁ℎ
𝑛𝑛ℎ

where 𝑁𝑁ℎ was

the number of EAs in stratum ℎ and 𝑛𝑛ℎ was the number of EAs that were selected at the firststage of the sampling design.

The second-level weight was based on the corresponding number of eligible households (with at(2)least one child age 5-17) for interview within the EA. This was evaluated as 𝑤𝑤𝑖𝑖

=

𝑀𝑀𝑘𝑘

𝑚𝑚𝑘𝑘

where

𝑀𝑀𝑘𝑘 was the number of eligible households for interview in EA 𝑘𝑘 and 𝑚𝑚𝑘𝑘 was the number ofhouseholds that were selected within this EA at the second stage of the sampling design. The
final sampling household-level weight for each EA was the product of these values, namely 𝑤𝑤𝑖𝑖 =
(1)

𝑤𝑤𝑖𝑖

■

(2)

× 𝑤𝑤𝑖𝑖 . All households within a given EA received the same weight as constructed above.

Child-level EA Weights

In order to generate the child level weights, , a third-level weight was constructed based on thecorresponding number of children observed across all selected households within thecorresponding EA. Suppose child 𝑗𝑗 resides within household 𝑖𝑖, as from above. The corresponding(3)

third-level weight for this child was evaluated as 𝑤𝑤𝑗𝑗

=

𝐶𝐶𝑘𝑘
𝑐𝑐𝑘𝑘

where 𝐶𝐶𝑘𝑘 was the number of children

eligible for interview in EA 𝑘𝑘 and 𝑐𝑐𝑘𝑘 was the number of children that were interviewed within
EA 𝑘𝑘 and at the third stage of the sampling design. The purpose of basing the third-level weighton the number of children observed within each EA was to mitigate the influence ofheterogeneous weights, that may arise due to basing the weighting scheme on the number ofchildren eligible for observation within the corresponding household, on the sampling
distribution of the point estimates. The final child-level sampling weight was the product of these(1)

three values, namely 𝑤𝑤𝑗𝑗 = 𝑤𝑤𝑖𝑖

(2)

× 𝑤𝑤𝑖𝑖(3)

× 𝑤𝑤𝑗𝑗 .
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All child respondent within a given EA received the same weight as constructed above.
The following provides an example of how the household head- and child-level weights were
calculated. Suppose EA 𝑘𝑘 is situated within stratum ℎ. The hypothetical number of EAs in
stratum ℎ, households in EA 𝑘𝑘, and children observed within EA 𝑘𝑘 are as presented in Table 40.
Table 40: Illustration of Weight Construction
Weight construction
Total number of EAs in stratum h

1000

Number of EAs selected in stratum h

40

EA k
Total number of eligible HHs (with at least one child age 5-17) listed

100

Total number of eligible HH interviewed

20

Total number of Children in EA k

200

Total number of Children interviewed in EA k

50

For a sampled household 𝑖𝑖 within the enumeration area k, the calculated sampling weight is
(1)

(2)

𝑤𝑤𝑖𝑖 = 𝑤𝑤𝑖𝑖

× 𝑤𝑤𝑖𝑖(1)

× 𝑤𝑤𝑗𝑗=1000 100
×= 125.
40
20=

1000 100 200
××= 500.
40
20
50

For a sampled child respondent 𝑗𝑗 within the enumeration area k, the calculated sampling weightis
𝑤𝑤𝑗𝑗 = 𝑤𝑤𝑖𝑖

(2)

× 𝑤𝑤𝑖𝑖Hypothesis Testing(3)

For comparing the changes in data between 2018/19 -2008/09 rounds and between 2018/19 and2013/14 rounds, hypothesis testing was conducted which was based on the Wald test statistic.
The set up for the comparison analysis is as follows. Suppose 𝜇𝜇𝐴𝐴 is a population quantity
corresponding to the study population at the time of one of the 2008/09 or 2013/14 studies, suchas a population prevalence. Similarly, let 𝜇𝜇𝐵𝐵 be the population quantity at the time of the 2018/19
study. The null and alternative hypotheses state that 𝐻𝐻0 : 𝜇𝜇𝐴𝐴 = 𝜇𝜇𝐵𝐵 and 𝐻𝐻𝐴𝐴 : 𝜇𝜇𝐴𝐴 ≠ 𝜇𝜇𝐵𝐵 or 𝐻𝐻𝐴𝐴 : 𝜇𝜇𝐴𝐴 <𝜇𝜇𝐵𝐵 (depending on the response).
The test statistic is evaluated as 𝑇𝑇 = (𝑥𝑥̅𝐴𝐴 − 𝑥𝑥̅𝐵𝐵 )/𝑠𝑠𝑠𝑠(𝑥𝑥̅𝐴𝐴 − 𝑥𝑥̅𝐵𝐵 ). Due to 2008/09 and 2013/14 datacollection information unavailable to this study team, the standard error of 𝑥𝑥̅𝐴𝐴 is approximatedwith that from the 2018 calculations so that the actual test statistic used is 𝑇𝑇 = (𝑥𝑥̅𝐴𝐴 −𝑥𝑥̅𝐵𝐵 )/ (√2 × 𝑠𝑠𝑠𝑠(𝑥𝑥̅𝐵𝐵 )).

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10.2 Annex II: Notes on Comparability of Data and Population Estimates across
2008/09, 2013/2014 and 2018/19 Survey Rounds
As part of the statement of work assigned to NORC at the beginning of project was a data quality
review task on the data collected during previous rounds of the child labor survey. We began byexamining the documentation and data available on the previous survey rounds provided by theprevious contractor. During this exploration stage, we examined the documentation and data withthe following objectives:■

Whether appropriate and complete documentation was available for designing the 2018/19
sampling frame that is fully comparable with the sampling frame used in the previous survey
rounds in 2008/09 and 2013/14.

■

Whether the datasets contained all variables required to generate the child labor andhazardous work related indicators.

■

Whether all documentations were available to replicate the construction of child labor andhazardous work related indicators from the raw survey data of previous rounds.

■

Whether the formulas and algorithms used to construct the child labor and hazardous work
related indicators from the raw survey data were appropriately coded.

■

Whether all documentations were available to replicate the sampling weights of previous
survey rounds that were necessary to generate population estimates of child labor andhazardous work related indicators of 2018/19 round from the raw survey data.

The explorations undertaken by NORC identified three sets of issues that have bearing on thecomparability of data and population estimates across the previous survey rounds (2008/09,
2013/14) and the 2018/19 round undertaken by NORC as described below:■

Incomplete documentations on construction of sampling frame used by the previous
contractor for 2008/09, 2013/14 survey rounds■

Missing data to link the child respondents to their respective households for 2008/09 survey
round, and■Errors made in survey administration in Côte d’Ivoire during 2013/14 survey round and theprocess used for correction of the implementation errors.
Incomplete documentation and implication on comparison of population
estimates between the survey rounds

In designing the 2018/19 sampling methodology NORC’s mandate was to ensure comparability
between rounds as well as design a more robust baseline for future studies. After examining theavailable documentation on how the sampling frame was constructed in the previous rounds, it
became evident that there was not enough information available on the exact methods used inconstruction of sampling frames in the previous survey rounds. NORC brought this issue to theFINAL REPORT | 167

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notice of USDOL and after repeated discussions with the institutions that were involved indesigning the sampling frame of the past survey rounds 76, NORC reached the conclusion that itwas not feasible to recover the required information and that essential data needed for an exact
replication of the sampling frame used in the earlier rounds was missing.
This lack of information made it challenging at times to design the 2018/19 survey to allow for
precise comparisons across rounds. NORC approached this issue by striking a balance between
precision and comparability, allowing for comparability on key metrics while improving upon
the sampling frame construction with an aim of increasing the precision of the 2018/19
estimates.
The 2008/09 and 2013/14 survey rounds used regions as the stratification level and NORC used
districts/departments 77 in 2018/19 (which are geographically smaller and can be assigned to a
stratification level more precisely than the larger area). Although this means the population
estimates generated from the 2018/19 survey round are more precise than those used previously,
it also means the sampling frames were not exactly identical (one started at the regional level andthe other at the district/department level) and thus population total estimates are not fullycomparable as explained below:
Population estimates of the total number of children is computed by multiplying the average
population of children in each stratum by the total number of enumeration areas (EAs) in eachstratum (which would be the sum of the EAs in the regions for the 2008/09 survey round, while
it would be the sum of all EAs in the cocoa producing districts with a coco producing region). It
is important to note the not all of the districts in a region would be cocoa producing, and thus, theregional population totals derived from EAs selected to represent regions will naturally be higher
than those derived from EAs selected to represent districts/departments since the count ofregional total number of EAs will be greater than the district/department total number of EAs.
This indicates that the differences in total population of children estimated between survey
rounds are not comparable though both are valid estimates for the population frames the samples
represent. 78
However, it is important to note that the difference in sampling frames that led the population
estimate of total number of children to be non-comparable, does not affect comparison of themain outcomes of interest (the prevalence rates of children in child labor and children engaged in
76

Includes Tulane University, Ghana Statistical Service (GSS), the Insitut National de la Statistique de Côte d’Ivoire(INS), École Nationale de Statistique et d’Economie Appliquée (ENSEA) in Côte d’Ivoire and the Institute of
Statistical, Social And Economic Research (ISSER) in Ghana.
77
In Ghana the next stratification level are named “districts” and in Côte d’Ivoire they are labeled “departments”.
78

However based upon our review of both datasets, there is no evidence that the underlying distribution of childlabor characteristics are different in the 2018/19 round as compared to the 2008/09 round.

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hazardous work). As described in Annex 10.1 EAs in both the 2008/09 sample and the 2018/19
sample were stratified into high, medium and low cocoa production stratum. While there was no
documentation available on the exact cutoff range used for stratification of high, medium andlow EAs in 2008/09 round, examination of child labor survey data of 2008/09 and 2018/19
rounds and comparison of distributions among high, medium and low EAs substantiates theirsimilarity. Computing prevalence rates, under most circumstance, is not affected by the regional
or district stratum totals. This is because the comparison of rates involves, for example, thenumber of children in hazardous work for the entire population divided by the total number ofchildren in the population. Thus, for rate calculation, both the denominator and numerators
include the population projection based on the same weights for each stratum, the rates are notaffected by the difference in the stratum totals as involved in generating the population estimateof total number of children and children in child labor and children in hazardous work. As such,the rates of prevalence are comparable across the survey rounds.
A second important consideration is that, while we cannot compare population counts acrossrounds, we can provide very precise and accurate counts for the 2018/19 round as a single pointin-time estimate.
Although not ideal, this balance allows stakeholders to use 2018/19 data as a strong base withmore precise estimates moving forward while also allowing for comparisons across study
rounds.
Missing data to link the child respondents to their respective households
in 2008/09 survey round and its implication on comparison of prevalence
estimates
While exploring the database of 2008/09 survey round, NORC found that although the data fromthe child survey, the head of the household survey and household roster survey were available,
there was no way to map the children from the child labor survey to the data collected from theirrespective cocoa growing households. After deliberation with the previous contractor, it was
realized that the “key” linking individual children to their respective households was missing.
This implied that while the estimates of child labor prevalence rate and prevalence rate ofhazardous work can be compared between the 2008/09 and 2018/19 survey rounds for the entire
sample (including all agricultural households), it was not feasible to compare the estimatesbroken down by household type (cocoa growing and non-cocoa growing households). As aresult, for the report, we are unable to compare the prevalence rate of child labor and prevalence
rate of hazardous work for cocoa growing households between 2008/09 and 2018/19 survey
rounds.

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Errors made in survey administration in Côte d’Ivoire during 2013/14
survey round and implication on comparison of population estimates
between the 2013/14 and 2018/19 survey rounds.
The previous contractor selected 60 clusters in both Côte d’Ivoire and Ghana for the 2013/14
round of the child labor survey. The previous contractor conducted a household listing in the 60
selected clusters to create a sampling frame and identify both cocoa and non-cocoa growing
agricultural households to be surveyed in 2013/14. They then undertook data collection in bothcountries and completed data collection activities in Ghana as per the survey design. However,while administering the 2013/14 survey in Côte d’Ivoire, due to error in survey implementation,
field teams collected data only from the cocoa growing households and did not survey the
sample of non-cocoa growing households. So, the data collected from Côte d’Ivoire included
only cocoa growing households. This error was discovered after data collection was complete,and, in an attempt to correct the error, the study team went back to the field one year later (inearly 2015). However, for the supplemental survey, the study team adopted a different sampling
method for selecting the non-cocoa growing households who were supposed to be surveyed aspart of the main sample of the 2013/14 survey round. Ideally, it would have been appropriate tofollow the original survey design and to survey non-cocoa growing households from each of the
60 clusters sampled in the 2013/14 round. However, instead of sampling non-cocoa households
from each of the 60 clusters, the study team sampled non-cocoa households from only 15 clusters(out of 60) based on a combination of random and systematic sampling methods. 79
In this process, the team systematically selected 11 clusters and randomly selected 4 clusters.
Then approximately 14-16 non-cocoa growing households were surveyed in each cluster in early2015.
Given that only a small number of clusters were selected to survey the non-cocoa growing
households, most of which were “systematically sampled” instead of being randomly sampled, it
was important to check the reliability of such sampling method and potential bias involved ingenerating population estimates. Since there was no information about the process adopted to“systematically sample” these clusters, NORC decided to examine whether these systematicallyselected clusters were similar to the randomly selected clusters.
NORC compared the unweighted and weighted estimates (using sampling weights provided by
the previous contractor) between the systematically and randomly selected clusters which
generated separate estimates for the full sample (including both cocoa and non-cocoa growing
households) and for the non-cocoa growing households. The findings from NORC’s analysis

79

Tulane University, Final Report, 2013/14 Survey Research on Child Labor in West African Cocoa Growing
Areas.

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indicated that the prevalence rate of child labor working in cocoa production in thesystematically chosen clusters was higher than the prevalence rate of the randomly selectedcluster. This raised serious concerns that the method of sampling of non-cocoa growing
households and weighting schema used for the clusters selected for the supplemental sample
potentially introduced bias in the population estimates of child labor for all agricultural
households in the cocoa growing areas of Côte d’Ivoire. In addition, since the supplemental
sample surveyed only 14 -16 households from a small number of clusters (15), the estimates
generated from these clusters might be associated with larger sampling error, producing
unreliable estimates of the population. Finally, it is important to note that the difference in timingof the survey of non-cocoa households (conducted one year after the main survey was done in
2013/14 cocoa harvest season), also raised significant concerns regarding the comparability ofdata from the cocoa growing and non-cocoa growing households. These findings indicated thatthe population estimate of child labor generated by the previous contractor for the 2013/14
survey round was probably not a true representation of the population estimate of child labor incocoa production in agricultural households in the cocoa growing areas of Côte d’Ivoire, andhence produced a biased estimate for the aggregate population estimate of child labor in thecocoa growing areas of Côte d’Ivoire and Ghana. To err on the side of caution, NORC has not
utilized the data collected on non-cocoa growing households from 2013/14 and avoided making
any direct comparisons between 2013/14 and 2018/19 as well as between 2008/09 and 2013/14,
in terms of all agricultural households as well as the non-cocoa growing households. This
indicates that the prevalence rates of child labor for all agricultural households in 2013/14 and2018/19 rounds and 2008/09 and 2013/14 rounds are not statistically comparable.
However, it is important to note that since the survey was administered as per the survey design
among the cocoa growing households in Côte d’Ivoire in 2013/14 round, these issues did notaffect the population estimates of prevalence rates of child labor and rate of exposure tohazardous work in cocoa growing households in 2013/14 round. As a result, is still feasible to
make a statistically valid comparison of population estimate of prevalence rates of child labor
and rate of children’s exposure to hazardous work in cocoa growing households between
2013/14 and 2018/19 survey rounds.
10.3 Annex III: Common Definition and Local Definitions of Child Labor &Hazardous Child LaborThis section first provides a description of the different components used to form the common
definitions used to measure aggregate (between Côte d’Ivoire and Ghana) progress against thegoals of the Harkin-Engel Protocol and then describes the local definitions of hazardous childlabor. It is important to note that in many cases the local definitions are more proscriptive thanthe common definition and using local definitions leads to higher rates of child labor andhazardous child labor.

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Common definition
Unacceptable working hour conditions for the common definition is defined as a child under 12
years old engaging in at least one hour of work, a child between 12 and 14 engaging in 14 ormore hours of work, or a child between 15 and 17 engaging in 43 or more hours of work within a
12 month reference period.
The common definition of hazardous child labor consists of six sub-categories;
1. Land clearing,
2. Carrying heavy loads,
3. Spraying and agro-chemicals,
4. Sharp tools,
5. Long working hours, and6. Night work.
A child has been exposed to hazardous work during the reference period if they were exposed toat least one subcategory during the reference period.
Land clearing (1) is defined as a child engaging in land clearing, felling and chopping, or burning
within the reference period. Heavy loads (2) is defined as a child carrying a heavy load of woodand other things during land clearing, loads of water for spraying, loads of fermented cocoa
beans, loads of dry cocoa bean to the shed, or other loads within the reference period. The child’s
own definition of “heavy” is used.
Agro-chemicals (3) is defined as spraying, carrying water for spraying, or working with agrochemicals during the reference period. Spraying includes a child spraying of pesticides or
insecticides, being present or working in the vicinity of farm during pesticide spraying, orreentering a sprayed farm within less than 12 hours of spraying. Working with agro-chemicals
includes a child having been involved in working with agro-chemical products.
Use of sharp tools (4) includes using machetes/long cutlasses for weeding, handling motorized
equipment or machines, knapsack sprayer and/or chainsaw, harvesting with a machete or sickle,
harvesting overhead cocoa pods with harvesting hook, or breaking cocoa pods with knife or a
sharp object/tool during the reference period. Long working hours (5) is defined as a childworking 43 hours or more during the reference period. Night work (6) is defined as a child goingto or returning from the farm alone, or working on the farm between 6.00 p.m. and 6.00 a.m.

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For Cȏte d’Ivoire local legislative definitions, the definition of the unacceptable working hours
condition for children between 5 and 11 is defined as working at least one hour a day. For 12 and16 year olds unacceptable working hours is defined as engaging in more than two hours of worka day or 10 hours a week on school days or four of more hours a day or 14 or more hours ofwork during non-school days. For children 17 years old unacceptable working hours is defined
as working more than 40 hours a week. The hours are on school days cannot be evaluated for thetwelve month reference period.
The Cȏte d’Ivoire definition of hazardous child labor consists of seven sub-categories
1. Adequate rest,
2. Land clearing,
3. Charcoal production,
4. Carrying heavy loads,
5. Agrochemicals,
6. Sharp tools, and7. Night work.
A child has been exposed to hazardous work during the reference period if they were exposed toat least one subcategory during the reference period. The Cȏte d’Ivoire definition is different
than the other definitions because if a child is 16 or 17 and has received any training in the fieldthey are not considered to have been exposed to hazardous work.
Adequate rest (1) is defined as less than one full day of rest per week for children between 13
and 16 years old. This subcategory can only be evaluated for the seven day reference period.
Land clearing (2) includes land clearing, tree felling and chopping, bush burning, tree stump
removal, or working with animal-drawn cultivation for children 12 to 15. For children 16 and 17
only those who participated in any land clearing activities without receiving training on landcleaning are considered to be exposed to hazardous work.
Charcoal production (3) includes working in charcoal production, working as a lumberjack, or
hunting with a weapon. Additionally, any child that is between 12 and 15 years old that dug ahole or was involved in holing/planting of seedlings, and any child between 16 and 17 that wasinvolved in the same activities but did not receive a training on either is considered to be exposed
to hazardous labor.

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Carrying heavy loads (4) varies by age, gender, and type of transportation used. A complete
breakdown of the heavy load definition can be found in Annex 10.3, and there is no heavy load
training that would make this permissible.
Agrochemical (5) use includes children between 12 and 15 years old and who report having
participated in the sale, transportation, handling and application of agro-pharmaceutical products.
Additionally, children between 16 and 17 years old who have not received any training on
applying pesticides, insecticides and fertilizers, and reports having been involved in the sale,transport, or handling of agro- chemical products is considered to be in agrochemical hazardous
work. Finally, children between 16 and 17 years old who have not received any training on
occupational safety and health, and report having been involved in washing containers of agrochemical products and spraying machine, and/or disposal of agro-chemical products are also
considered to be in agrochemical hazardous work.
Sharp tool (6) use is defined as children between 12 and 15 years old who report having
harvesting with a machete or sickle, handled motorized equipment/machines, or broke cocoa
pods with knife or a sharp object/tool. Additionally, children between 16 and 17 years old whoreport harvesting with a machete or sickle without receiving training on harvesting, handlingmotorized equipment/machines without training on driving motorized vehicles, and breaking
cocoa pods with a knife or a sharp object/tool with training on cocoa pod breaking are considered
to be in sharp tool hazardous work.
Night work (7) is defined as going to or returning from the farm alone or working on the farm
between 7pm and 7am, and there is no night work training that would make this permissible.
Ghana
For the Ghanaian definition of unacceptable working hour conditions for children between 5 and
11 is defined as working at least one hour a day. For 12 and 14 years old is unacceptable working
hours is defined as engaging in more than two hours of work a day on a school day or three ofmore hours a day or 18 or more hours a week during non-school days. For children between 15
and 17 years old unacceptable working hours is defined as working more than three hours a day
or more than 18 hours a week. The hours are on school days cannot be evaluated for the twelve
month reference period.
The Ghanaian definition of hazardous child labor consists of ten sub-categories;
1. Working full time and not attending school,
2. Withdrawing from school during cocoa season to do farm work,
3. Land clearing,

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4. Carrying heavy loads,
5. Spraying and agrochemicals,
6. Sharp tools,
7. Climbing trees,
8. Night work,
9. Working in isolation, and
10. Working without protective clothing.
A child has been exposed to hazardous work during the reference period if they were exposed toat least one subcategory during the reference period.
Working full time and not attending school (1) is defined as a child working 43 hours or more ona farm and not attending formal or non-formal school during the reference period. Withdrawing
from school during cocoa season to do farm work (2) can only be evaluated on a 12 monthreference period.
Land clearing (3) includes a child engaging in land clearing, felling of trees, or bush burning
during the reference period. Carrying heavy loads (4) beyond permissible carrying weight isdefined as a child carrying a load over one third of the child’s body weight.
Agro-chemicals (5) is defined as spraying or working with agro-chemicals during the reference
period. Spraying includes a child spraying of pesticides or insecticides, being present or working
in the vicinity of farm during pesticide spraying, or reentering a sprayed farm within less than 12hours of spraying. Working with agro-chemicals includes a child having been involved inworking with agro-chemical products.
Use of sharp tools (6) is defined as; using machetes or long cutlasses for weeding, handlingmotorized equipment or machines, harvesting with a machete or sickle, harvesting overhead
cocoa pods with harvesting hook or sickle, or breaking cocoa pods with knife or sharp object ortool. Climbing trees (7) is defined as climbing a tree that is three meters or higher to cut
mistletoe with a cutlass.
Night work (8) is defined as going to or returning from the farm alone or working on the farm
between 6pm and 6am. Working in isolation (9) is defined as working alone on the farm beyond
the visible or audible range of the nearest adult. Working without protective clothing (10) is
defined as working without adequate basic foot wear (protective boots) and protective clothing(overalls, long sleeves, and trousers).

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

10.4 Annex IV: Supplementary Tables I
Survey respondents
Table 41: Types and Numbers of Interviews Completed, by Region, All Agricultural
Households, in Côte d’Ivoire and Ghana, 2018/19
Child

Household Head

Total

5,543

2,809

Ghana

2,809

1,314

Ashanti995

468

Brong Ahafo

324

144

Central459

181

Eastern

190

108

Volta

89

54

Western752

359

Côte d’Ivoire

2,734

1,495

Agnebytiassa

211

102

Bas Sassandra

174

120

Belier103

40

Cavaly64

41

Gbokle

203

100

Goh

270

157

Guemon317

192

Haut Sassandra

458

242

Indeniedjouabke

93

61

Lac

81

40

Lame

104

60

Loh Djiboua

306

160

Marahoue

173

80

Tonkpi

107

60

Worodougou

70

40

Source: NORC sample 2018/2019, strata 1-3

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 42: Household Head and Child Survey Response Rates, Côte d’Ivoire andGhana, All Agricultural Households, 2018/19
Percent of households with:

Total

Côte d’Ivoire

Ghana

Total household rosters

2,821

N/A

1,504

N/A

1,317

N/A

Household head survey

2,809

99%

1,495

99%

1,314

100%

At least one child survey

2,586

92%

1,384

92%

1,202

91%

Correct number of child surveys*

2,324

82%

1,140

76%

1,184

90%

No eligible children

166

6%

82

5%

84

6%

Data collection complete*

2,321

82%

1,139

76%

1,182

90%

Source: NORC household roster, household head, and child surveys 2018/2019, strata 1-3*The number of children listed in the household roster is equal to the number of children surveyed in thathousehold.
**Percentage of households with a roster survey, a HH Head survey, and child surveys for all eligible
children.

Table 43: Place of Birth and Nationality of Survey Respondents: Children, All
Agricultural Households, 5-17 Years, in Côte d'Ivoire and Ghana, 2018/19
Côte d'Ivoire

Ghana

Place of BirthCôte d'Ivoire

2,610

95%

3

0%

Ghana

4

0%

2,794

100%

Burkina Faso

98

4%

1

0%

Mali

15

1%

0

0%

Other

1

0%

1

0%

No response

1

0%

0

0%

Ivorian nationality

2,043

75%

1

0%

Ghanaian nationality

2

0%

2,796

100%

Burkinabe nationality

582

21%

3

0%

Malian nationality

65

2%

0

0%

Other

15

1%

1

0%

No response

6

0%

0

0%

Nationality

Source: NORC roster survey 2018/2019, strata 1-3

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 44: Household Land Under Cultivation and Under Cocoa Cultivation, All
Agricultural Households, in Côte d’Ivoire and Ghana, 2018/19
Côte d'Ivoire

GhanaLand under cultivation by households involved in agriculture (in acres)

20.5

8.4

Land under cocoa cultivation by cocoa-producing households (in acres)

8.6

6.4

Source: NORC head of household survey 2018/19, weighted, strata 1-3

Descriptive analysis
Table 45: Selected Characteristics (Age Group, Gender) of Children Engaged in Child
Labor in Agriculture, All Agricultural Households, 5-17 Years, in Côte d’Ivoire andGhana, 2008/09 and 2018/19
Children engaged inchild labor in agriculture

Total

Côte d’Ivoire

Ghana

2008/092018/19

2008/092018/19

2008/092018/19

Male

57%

57%

56%

58%

57%

57%

Female

43%

43%

44%

42%

43%

43%

5-11 Years

47%

50%

49%

52%

44%

48%

12-14 Years

28%

30%

26%

30%

31%

30%

15-17 Years

25%

20%

25%

17%

25%

22%

Sex

Age group

Source: Child survey 2008/09, 2013/14, and 2018/19, weighted, strata 1-3*Includes children who worked in both cocoa production and other agricultural and non-agricultural
economic sectors.

Table 46: Selected Characteristics (Age Group, School Attendance) of Children
Engaged in Child Labor in Agriculture, All Agricultural Households, 5-17 Years, in Côte
d’Ivoire and Ghana, 2008/09 and 2018/19
Children engaged in childlabor in agriculture

Total

Côte d’Ivoire

Ghana

2008/092018/19

2008/092018/19

2008/092018/19

5-11
Years

Attending School

77%

92%

66%

88%

92%

99%

Not attending school

23%

7%

34%

11%

8%

2%

12-14
Years

Attending School

79%

93%

66%

89%

92%

98%

Not attending school

21%

7%

34%

11%

8%

2%

15-17
Years

Attending School

55%

77%

31%

66%

85%

89%

Not attending school

45%

22%

69%

32%

15%

12%

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 47: Prevalence of Child Labor By School Attendance, All Agricultural
Households, 5-17 Years, in Côte d’Ivoire and Ghana, 2018/19
AttendingschoolNot attending
schoolDiff. (pp)*Sig of diff^

All

65%

50%

16

***

5-11 Years

56%

32%

24

***

12-14 Years

81%

73%

8

15-17 Years

84%

80%

4

Prevalence of Child Labor

Source: NORC child survey 2018/19, weighted, strata 1-3*Calculated as the difference between attending and not attending school rates in percentage points,
includes rounding^Significance of Difference *** p<0.01

Table 48: Working Hours and Minimum Age, Children Working in Cocoa Production,
By Sex, All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Number and percentage of children
working in cocoa in agricultural
households

Côte d'Ivoire

Ghana

2008/092018/19

Sig ofdiff^

2008/092018/19

***

31%

34%

7.1

5.1

14%

11%

Sig ofdiff^

Sex: Male
5-11
years

% Working 1+ hour per week

13%

24%

Average # of hours worked

9.0

7.3

12-14
years

% Working 14+ hours per week

15%

26%

Average # of hours worked

14.4

12.4

8.4

7.0

15-17
years

% Working 43+ hours per week

4%

4%

1%

2%

Average # of hours worked

21.5

15.2

***

10.2

10.4

% Working more thanallowable hours per week

12%

22%

***

20%

23%

Average # of hours worked

13.9

10.7

***

8.3

6.9

21%

32%

6.5

5.2

All
years

***

***

Sex: Female
5-11
years

% Working 1+ hour per week

12%

17%

Average # of hours worked

13.1

7.6

12-14
years

% Working 14+ hours per week

11%

13%

12%

7%

Average # of hours worked

12.3

10.2

7.1

6.0

15-17
years

% Working 43+ hours per week

2%

2%

1%

1%

Average # of hours worked

16.9

11.5

9.1

7.3

% Working more thanallowable hours per week

11%

14%

15%

21%

Average # of hours worked

13.8

9.0

7.3

5.9

All
years

***

***

***

***

Source: NORC child survey 2008/09 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 49: Estimates of Change Children, 12-17, Engaged in Light Work and RegularWork in the Cocoa Production, All Agricultural Households, in Côte d'Ivoire and Ghana,
2008/09 and 2018/19
Children 12-14 years engaged in
non-hazardous light work* in cocoa
production
Pct
Total
Côte
d’Ivoire
Ghana

2008/091%

2018/193%

2008/090%

2018/192%

2008/092%

2018/19

6%

Diff (pp)***Sig of diff^

2***

Children age 15-17 years engaged inregular work** in cocoa production
Pct

Diff (pp)***Sig of diff^

1%

1

3

1%
0%
1%
2%
1%

0

1

-1

Source: NORC child survey 2008/09 and 2018/19, weighted, strata 1-3*Children aged 12-14 who work less than 14 hours per week in non-hazardous work**Children aged 15-17 who work less than 43 hours per week in non-hazardous work***Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes
rounding^Significance of Difference *** p<0.01

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 50: Child Work Involved in Cocoa Production, All Children 5-17 Years, All Agricultural Households, in Côte d’Ivoire
and Ghana, 2008-09 and 2018-19
Total
Percentage of children

2008/
2009

2018/
2019

Land clearing

12%

16%

Felling and chopping

1%

9%

Burning

1%

Stumping

Côte d'Ivoire
2008/
2009

2018/
2019

15%

20%

***

1%

11%

5%

***

1%

0%

6%

***

Preparing seedlings

3%

9%

Planting seedlings

2%

Sowing at stake

Ghana
2008/
2009

2018/
2019

7%

9%

***

2%

6%

***

5%

***

2%

6%

***

1%

6%

***

0%

5%

***

***

3%

8%

***

2%

10%

***

9%

***

2%

7%

***

3%

11%

***

3%

7%

***

4%

5%

1%

11%

***

Weeding

17%

21%

***

10%

17%

***

29%

28%

Spraying insecticides

0%

4%

***

0%

4%

***

0%

3%

***

Applying fertilizer

0%

4%

***

0%

4%

***

0%

3%

***

Applying fungicides/herbicides/other chemicals

0%

3%

***

0%

3%

***

0%

3%

***

Carrying water for spraying

4%

17%

***

3%

12%

***

5%

24%

***

Doing sanitation and pruning

1%

4%

***

1%

4%

***

0%

5%

***

Doing mistletoe control

2%

4%

***

2%

4%

***

1%

3%

***

Plucking cocoa pods

11%

17%

***

9%

17%

***

14%

16%

Gathering and heaping cocoa pods

22%

43%

***

16%

36%

***

33%

53%

***

Breaking cocoa pods and fermentation

16%

26%

***

13%

26%

***

21%

27%

***

Sig ofdiff^

Sig ofdiff^

Sig ofdiff^

Land preparation activities in cocoa production

Planting activities in cocoa production

Farm maintenance activities in cocoa production

Harvest activities in cocoa production

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Total
Percentage of children

Côte d'Ivoire

Ghana

2008/
2009

2018/
2019

Sig of
diff^

2008/
2009

2018/
2019

Sig of
diff^

2008/
2009

2018/
2019

Sig ofdiff^

Carting fermented cocoa beans

11%

21%

***

9%

19%

***

14%

25%

***

Drying cocoa beans

10%

23%

***

9%

23%

***

13%

24%

***

Carting dry cocoa beans to shed

6%

13%

***

5%

13%

***

8%

12%

***

Post-harvest activities in cocoa production

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 51: Children Engaged in Child Labor and Hazardous Work in Cocoa Production, All Agricultural Households, 5-17
Years, by Sex and Age Group in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Children Engaged in Child Labor in Cocoa Production
Total
2008/092018/19

Male

61%

FemaleCôte d'Ivoire
Sig of diff^2008/092018/19

61%

61%

39%

39%

5-11 years

45%

12-14 years
15-17 years

Ghana

Sig of diff^2008/092018/19

64%

60%

58%

39%

36%

40%

42%

47%

47%

47%

43%

47%

30%

32%

28%

33%

32%

30%

25%

22%

26%

20%

25%

23%

Sig of diff^Sex

Age group

***

***

Children Engaged in Hazardous Work* in the Cocoa Production
Sex
Male

60%

62%

61%

65%

60%

59%

Female

40%

38%

39%

35%

40%

41%

5-11 years

44%

44%

46%

45%

42%

43%

12-14 years

30%

33%

28%

34%

32%

32%

15-17 years

26%

23%

26%

21%

25%

25%

Age group***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Measured based on Variables 1-6, as described in section 3.3 of this report**Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points^Significance of Difference *** p<0.01

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 52: Estimates of Percentages of Children Exposed to Hazardous Work* Activities in the Cocoa Sector, 5-17
Years, All Agricultural Households, by Age Group in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Percentage of children in agricultural
households:

Total

Côte d'Ivoire

2008/
2009

2018/
2019

Diff
(pp)*

V1: Land clearing

7%

10%

V2: Heavy loads

17%

V3: Agro-chemicals

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

2

9%

12%

19%

2

13%

4%

14%

11

***

V4: Sharp tools

19%

23%

4

V5: Long working hours

1%0%

V6: Night work0%

Exposed to multiple hazards
Average number of hazards

Ghana
Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

Sig of
diff^

3

4%

6%

2

17%

4

24%

22%

-2

3%

11%

8

***

5%

20%

15

***

14%

20%

6

***

27%

27%

0

0

***

1%

0%

-1

***

0%

0%

0

1%

1

***

0%

1%

1

0%

1%

1

22%

30%

8

***

16%

26%

10

***

33%

37%

4

0.5

0.7

41%

***

0.4

0.6

51%

***

0.6

0.8

26%

V1: Land clearing

17%

33%

16

***

23%

40%

17

***

9%

22%

13

V2: Heavy loads

34%

42%

8

***

28%

41%

12

***

42%

45%

3

V3: Agro-chemicals

6%

36%

30

***

4%

30%

26

***

9%

45%

36

V4: Sharp tools

42%

55%

13

***

32%

50%

18

***

55%

64%

9

V5: Long working hours

1%

1%

0

1%

2%

0

0%

0%

0

V6: Night work

1%

4%

3

***

1%

3%

2

0%

5%

5

***

Exposed to multiple hazards

45%

62%

17

***

35%

56%

21

***

57%

71%

14

***

Average number of hazards

1.0

1.7

70%

***

0.9

1.7

84%

***

1.1

1.8

57%

***

Age group: 5-11 years working in cocoa

***

***

Age group: 12-14 years working in cocoa
***

***

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Percentage of children in agricultural
households:

Total

Côte d'Ivoire

Ghana

2008/
2009

2018/
2019

Diff
(pp)*

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

Sig ofdiff^

V1: Land clearing

23%

36%

14

***

28%

41%

13

***

16%

31%

15

***

V2: Heavy loads

34%

48%

14

***

29%

46%

17

***

40%

50%

10

V3: Agro-chemicals

7%

47%

41

***

5%

39%

34

***

9%

57%

48

***

V4: Sharp tools

42%

61%

20

***

34%

52%

19

***

53%

72%

19

***

V5: Long working hours

2%

2%

0

3%

3%

0

1%

2%

1

V6: Night work

1%

7%

5

***

2%

7%

5

1%

6%

6

***

Exposed to multiple hazards

44%

67%

23

***

35%

59%

23

***

56%

77%

21

***

Average number of hazards

1.1

2.0

86%

***

1.0

1.9

87%

***

1.2

2.2

82%

***

Age group: 15-17 years working in cocoa

Source: Child survey 2008/09and 2018/19, weighted, strata 1-3* Measured based on Variables 1-6, as described in Chapter 3.3 of this report.
**Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 53: Estimates of Percentages of Children Exposed to Hazardous Work* Activities in the Cocoa Sector, 5-17
Years, By Sex, All Agricultural Households, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Percentage of children in
agricultural households

Total

Côte d'Ivoire

Ghana

2008/
2009

2018/2
019

Diff
(pp)*

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

Sig of
diff^

2008/
2009

2018/
2019

Diff
(pp)*

Sig ofdiff^

V1: Land clearing

15%

27%

11

***

20%

32%

11

***

8%

19%

11

***

V2: Heavy loads

27%

33%

6

***

21%

31%

10

***

36%

34%

-2

V3: Agro-chemicals

5%

29%

24

***

4%

24%

21

***

7%

36%

29

V4: Sharp tools

32%

45%

13

***

24%

42%

18

***

44%

50%

6

V5: Long working hours

1%

1%

0

1%

1%

0

0%

1%

0

V6: Night work

1%

3%

3

***

1%

3%

2

***

0%

4%

3

***

Exposed to multiple hazards

34%

50%

16

***

26%

46%

20

***

48%

57%

9

***

Average number of hazards

0.8

1.4

70%

***

0.7

1.3

87%

***

1.0

1.4

49%

***

V1: Land clearing

8%

10%

2

9%

11%

3

7%

8%

1

V2: Heavy loads

19%

24%

4

15%

20%

5

26%

30%

4

V3: Agro-chemicals

4%

19%

14

3%

13%

10

6%

27%

21

V4: Sharp tools

23%

25%

2

17%

19%

2

32%

35%

2

V5: Long working hours

1%

0%

0

1%

0%

-1

0%

0%

0

V6: Night work

0%

1%

1

0%

1%

1

0%

2%

2

Exposed to multiple hazards

26%

34%

8

***

19%

27%

8

***

37%

45%

8

Average number of hazards

0.6

0.8

43%

***

0.5

0.6

43%

***

0.7

1.0

41%

Sex: Boys work in cocoa***

Sex: Girls work in cocoa

***
***

***

***

***

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3*Measured based on Variables 1-6, as described in section 3.3 of this report**Calculated as the difference between the 2008/09 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

FINAL REPORT | 186

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 54: Exposure to Agro-Chemicals, Children Working in Cocoa Production in the Last 12 Months, All Agricultural
Households, by Age Group and by Sex, in Côte d’Ivoire and Ghana, 2008/09 and 2018/19
Exposed to AgroChemicals

Total

Côte d'Ivoire

Ghana

2008/092018/19

Sig of diff^2008/092018/19

Sig of diff^2008/092018/19

5-11 years

47%

37%

***

54%

37%

***

40%

37%

12-14 years

28%

34%

***

22%

36%

***

33%

33%

15-17 years

25%

28%

23%

27%

27%

30%

Male

58%

64%

56%

67%

***

60%

61%

Female

42%

36%

44%

33%

***

40%

39%

Sig of diff^Age group

Sex

Source: Child survey 2008/09 and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

Table 55: Prevalence of Children Engaged Child Labor and Hazardous Child Labor in Cocoa and Non-Cocoa
Households, 5-17 Years, in Côte d’Ivoire and Ghana, 2018/19
Côte d’IvoireChild labor
Hazardous
child labor

Ghana

Cocoa
Households

Non-cocoa
Households

Diff
(pp)*

Sig of
diff^

Cocoa
Households

Non-cocoa
Households

Diff
(pp)*

Sig ofdiff^

Number

1,059,218

204,853

N/A

N/A

859,355

76,257

N/A

N/A

Percent

61%

58%

-3

68%

62%

-6

Number970,493

167,828

N/A

764,278

68,798N/A

Percent

56%

48%

-9

60%

56%

-5

N/A

N/A

Source: Child survey 2018/19, weighted, strata 1-3*Calculated as the difference between the cocoa and non-cocoa households rates in percentage points, includes rounding^Significance of Difference *** p<0.01

FINAL REPORT | 187

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 56: Working Hours in Any Economic Activity and Minimum Age, Children in Cocoa Households Working in Cocoa
Production, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19
Côte d'Ivoire

Number and percentage of children in cocoa
production in cocoa households

2013/142018/19

5-11
years

% Working 1+ hour per week

20%

Average # of hours worked

12-14
years

Ghana
Sig of diff^2013/142018/19

Sig of diff^

24%

37%

35%

7.2

7.3

6.5

5.1

***

% Working 14+ hours per week

19%

20%

18%

9%

***

Average # of hours worked

12.5

11.3

9.1

6.5

***

15-17
years

% Working 43+ hours per week

5%

3%

2%

2%

Average # of hours worked

17.9

14.3

12.4

9.2

All
years

% Working more than allowable hours per week

18%

20%

26%

24%

Average # of hours worked

11.5

9.9

8.8

6.4

***
***

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3.
^Significance of Difference *** p<0.01

FINAL REPORT | 188

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 57: Working Hours in Any Economic Activity and Minimum Age, Children in Cocoa Households, Working in Cocoa
Production, By Sex, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19
Number and percentage of children in cocoa
production in cocoa households

Côte d'Ivoire
2013/142018/19

Ghana
Sig ofdiff^

2013/142018/19

Sig ofdiff^

Sex: Male
5-11
years

% Working 1+ hour per week

25%

28%

39%

36%

Average # of hours worked

7.8

7.2

6.9

5.0

***

12-14
years

% Working 14+ hours per week

21%

27%

20%

11%

***

Average # of hours worked

13.1

12.0

8.5

6.8

15-17
years

% Working 43+ hours per week

7%

4%

3%

3%

Average # of hours worked

19.6

15.6

12.4

10.5

% Working more than allowable hours per week

21%

24%

27%

24%

Average # of hours worked

12.5

10.5

8.8

6.8

All years

***

***

Sex: Female
5-11
years

% Working 1+ hour per week

14%

20%

34%

34%

Average # of hours worked

6.1

7.6

6.0

5.3

12-14
years

% Working 14+ hours per week

17%

13%

16%

7%

***

Average # of hours worked

11.3

10.0

10.2

6.1

***

15-17
years

% Working 43+ hours per week

4%

2%

2%

1%

Average # of hours worked

13.2

11.5

12.4

7.4

% Working more than allowable hours per week

13%

16%

24%

23%

Average # of hours worked

9.4

8.9

8.8

5.9

All years******

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3.
^Significance of Difference *** p<0.01

FINAL REPORT | 189

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 58: Children in Cocoa Households, Engaged in Child Labor and Hazardous Child Labor* in Cocoa Production, 517 Years, by Sex and Age group in Côte d’Ivoire and Ghana, 2013/14 and 2018/19
Children Engaged in Child Labor in Cocoa Production
Total

Côte d'Ivoire

2013/142018/19

Diff(pp)**

Male

64%

61%

Female36%

5-11 years

Sig of
diff^

Ghana

2013/142018/19

Diff(pp)**

Sig ofdiff^

2013/142018/19

Diff(pp)**

-3

70%

64%

-6

***

57%

58%

1

39%

3

30%

36%

6

***

43%

42%

-1

43%

47%

5

42%

48%

5

43%

47%

4

12-14 years

32%

31%

-1

33%

32%

-1

31%

30%

-1

15-17 years

26%

22%

-4

25%

21%

-4

26%

23%

-3

Sig of
diff^

Sex

Age group******Children Engaged in Hazardous Work* in the Cocoa Production
Sex
Male

65%

62%

-2

70%

65%

-5

57%

59%

2

Female

35%

38%

2

30%

35%

5

43%

41%

-2

5-11 years

40%

44%

4

40%

46%

6

40%

43%

3

12-14 years

33%

32%

-1

34%

33%

-1

32%

32%

0

15-17 years

27%

23%

-4

26%

21%

-5

28%

25%

-3

Age group******

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3*Measured based on Variables 1-6, as described in section 3.3 of this report**Calculated as the difference between the 2013/14 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

FINAL REPORT | 190

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 59: Estimates of Percentages of all Children in Cocoa Households, 5-17 Years, By Age Group, Exposed toHazardous Work* Activities in the Cocoa Sector, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19
Percentage of children
in cocoa households
exposed to:

Total
2013/
2014

2018/2019

Côte d'Ivoire

Diff(pp)**

Sig of
diff^

2013/
2014

2018/
2019

Diff
(pp)**

***

9%

13%

15%

Ghana
2013/
2014

2018/
2019

Diff
(pp)**

Sig of
diff^

4

0%

6%

6

***

19%

5

33%

24%

-9

***
***

Sig ofdiff^

Age group: 5-11 years in cocoa work
V1: Land clearing

6%

10%

4

V2: Heavy loads

20%

21%

1

V3: Agro-chemicals

4%

16%

12

***

2%

13%

11

***

10%

22%

12

V4: Sharp tools

20%

26%

6

***

18%

24%

6

***

25%

29%

4

V5: Long working hours

0%

0%

0

0%

0%

0

0%

0%

0

V6: Night work

0%

1%

1

0%

1%

1

0%

1%

1

Exposed to multiple
hazards

28%

34%

6

***

23%

30%

7

***

39%

39%

1

Average number ofhazards

0.5

0.7

46%

***

0.4

0.7

59%

***

0.7

0.8

21%

***

26%

43%

17

***

2%

23%

21

33%

42%

9

57%

47%

-10

***

Age group: 12-14 years in cocoa work
V1: Land clearing

18%

34%

17

V2: Heavy loads

41%

44%

3

V3: Agro-chemicals

15%

39%

24

***

8%

32%

24

***

28%

48%

20

***

V4: Sharp tools

45%

60%

15

***

39%

54%

15

***

57%

67%

10

***

V5: Long working hours

1%

1%

0

2%

2%

0

0%

0%

0

V6: Night work

1%

4%

3

***

2%

3%

2

1%

5%

4

Exposed to multiple
hazards

58%

66%

8

***

51%

60%

9

72%

75%

3

Average number ofhazards

1.2

1.8

51%

***

1.1

1.8

62%

1.4

1.9

31%

***

***

***

***

FINAL REPORT | 191

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Percentage of children
in cocoa households
exposed to:

Total
2013/
2014

2018/2019

Côte d'Ivoire

Diff(pp)**

Sig of
diff^

2013/
2014

2018/
2019

Diff
(pp)**

***

42%

45%

51%

Ghana
2013/
2014

2018/
2019

Diff
(pp)**

Sig of
diff^

3

4%

33%

29

***

51%

0

60%

54%

-6

16%

43%

27

43%

61%

19

Sig ofdiff^

Age group: 15-17 years in cocoa work
V1: Land clearing

27%

39%

13

V2: Heavy loads

55%

53%

-2

V3: Agro-chemicals

27%

52%

25

V4: Sharp tools

65%

67%

2

60%

58%

-2

73%

78%

5

V5: Long working hours

4%

3%

-1

5%

3%

-2

2%

2%

0

V6: Night work

3%

7%

5

4%

8%

4

1%

7%

6

Exposed to multiple
hazards

73%

73%

0

68%

65%

-4

80%

82%

3

Average number ofhazards

1.8

2.2

23%

1.8

2.1

17%

1.8

2.4

29%

***

***

***

***

***

***

***

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3*Measured based on Variables 1-6, as described in section 3.3 of this report**Calculated as the difference between the 2013/14 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

FINAL REPORT | 192

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 60: Estimates of Percentages of all Children in Cocoa Households, 5-17 Years, By Sex, Exposed to Hazardous
Work* Activities in the Cocoa Sector, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19*
Percentage of children incocoa households
exposed to:

Total

Côte d'Ivoire

2013/
2014

2018/
2019

Diff
(pp)**

Sig of
diff^

2013/
2014

2018/
2019

Diff
(pp)**

V1: Land clearing

19%

29%

10

***

27%

35%

V2: Heavy loads

34%

35%

1

29%

V3: Agro-chemicals

14%

32%

18

***

V4: Sharp tools

42%

50%

8

***

V5: Long working hours

1%

1%

0

V6: Night work

1%

4%

2

Exposed to multiple hazards

49%

55%

Average number of hazards

1.1

V1: Land clearing

Ghana
2013/
2014

2018/
2019

Diff
(pp)**

Sig of
diff^

8

2%

20%

18

***

35%

6

46%

36%

-9

8%

27%

19

***

26%

39%

13

38%

47%

9

***

49%

53%

4

2%

1%

0

1%

1%

0

***

1%

4%

2

1%

3%

3

6

***

45%

52%

7

58%

60%

2

1.5

35%

***

1.1

1.5

42%

***

1.2

1.5

23%

***

4%

11%

7

***

6%

13%

7

***

0%

8%

8

***

V2: Heavy loads

26%

27%

1

18%

23%

4

42%

33%

-10

V3: Agro-chemicals

6%

21%

15

***

2%

15%

13

***

14%

29%

15

V4: Sharp tools

22%

28%

6

***

16%

21%

5

***

33%

37%

4

V5: Long working hours

1%

0%

0

1%

1%

0

0%

0%

0

V6: Night work

0%

1%

1

0%

1%

1

0%

2%

2

Exposed to multiple hazards

33%

38%

5

24%

30%

6

50%

49%

-1

Average number of hazards

0.6

0.9

50%

0.4

0.7

69%

0.9

1.1

23%

Sig ofdiff^

Sex: Boys in cocoa work

***

***

***

Sex: Girls in cocoa work

***

***

***

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3*Measured based on Variables 1-6, as described in section 3.3 of this report**Calculated as the difference between the 2013/14 and 2018/19 rates in percentage points, includes rounding^Significance of Difference *** p<0.01

FINAL REPORT | 193

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 61: Estimates of Exposure of Children Working in Cocoa Production in Cocoa
Households, 5-17 Years, to Hazardous Work, by Count, in Côte d'Ivoire and Ghana,
2013/14 and 2018/19
Percentage of children exposed tohazardous work (V1-V6)

Côte d'Ivoire
2013/142018/19

6 Variables

0%

5 Variables

Ghana
Sig ofdiff^

Sig ofdiff^

2013/142018/19

0%

0%

0%

1%

4%

0%

2%

***

4 Variables

6%

24%

2%

13%

***

3 Variables

24%

24%

23%

22%

2 Variables

33%

23%

***

38%

28%

***

1 Variable

24%

17%

***

29%

20%

***

Not exposed to any hazard

12%

8%

***

8%

14%

***

Average number of hazards exposed

1.9

2.5

***

1.8

2.1

***

***

Source: Child survey 2013/14, and 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

FINAL REPORT | 194

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 62: Disaggregation of Exposure to Agro-Chemicals, Children in Cocoa Households Working in Cocoa Productionin the Last 12 Months, by Age Group and Sex, in Côte d’Ivoire and Ghana, 2013/14 and 2018/19
Total
2013/142018/19

Côte d'Ivoire
Sig ofdiff^

2013/142018/19

Ghana
Sig ofdiff^

2013/142018/19

Sig ofdiff^

Spraying pesticides or insecticides
Age group
5-11 years

3%

16%

0%

13%

7%

20%

12-14 years

24%

31%

26%

37%

20%

24%

15-17 years

73%

53%

***

74%

50%

***

72%

57%

Male

98%

82%

***

98%

83%

***

97%

80%

***

Female

2%

18%

***

2%

17%

***

3%

20%

***

18%

36%

***

17%

39%

***

31%

35%

31%

35%

Sex

Being present or working in the vicinity of farm during pesticide spraying
Age group
5-11 years

18%

37%

***

12-14 years

31%

35%

15-17 years

51%

27%

***

51%

29%

***

51%

26%

Male

75%

66%

***

85%

68%

***

69%

64%

Female

25%

34%

***

15%

32%

***

31%

36%

14%

34%

***

15%

20%

***

Sex

Reentering a sprayed farm within less than 12 hours of spraying
Age group***

5-11 years

14%

27%

12-14 years

47%

40%

46%

34%

49%

45%

15-17 years

39%

34%

40%

31%

36%

36%

FINAL REPORT | 195

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Total

Côte d'Ivoire

Ghana

2013/142018/19

Sig ofdiff^

2013/142018/19

Sig ofdiff^

2013/142018/19

Male

80%

66%

***

88%

65%

***

59%

68%

Female

20%

34%

***

12%

35%

***

41%

32%

Sig ofdiff^

Sex

Carrying water for spraying
Age group
5-11 years

29%

34%

32%

33%

27%

35%

29%

12-14 years

34%

33%

30%

34%

35%

33%

34%

15-17 years

38%

33%

37%

33%

38%

32%

38%

Male

72%

63%

***

84%

68%

***

67%

59%

Female

28%

37%

***

16%

32%

***

33%

41%

9%

26%

***

7%

19%

***

39%

38%

21%

41%

***

Sex

Having been involved in working with agrochemicals*
Age group
5-11 years

8%

22%

***

12-14 years

34%

40%

15-17 years

58%

38%

***

52%

36%

***

72%

40%

***

Male

91%

74%

***

90%

76%

***

93%

72%

***

Female

9%

26%

***

10%

24%

***

7%

28%

***

Sex

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3*Such as purchasing, transport, storage, mixing, loading, spraying/applying, washing of containers and spraying machine, and/or disposal^Significance of Difference *** p<0.01

FINAL REPORT | 196

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghana

Table 63: Children in Cocoa Households, Children Engaged in Child Labor andHazardous Work in Cocoa Production, by School Attendance, 5-17 Years, in Côte
d’Ivoire and Ghana, 2013/14 and 2018/19
Age group

Total
2013/14

Côte d’Ivoire

2018/19

Ghana

2013/142018/192013/142018/19

Children Engaged in Child Labor in Cocoa Production
5-11
Years

Attending school

35%

41%

29%

36%

44%

47%

Not attending school

16%

18%

16%

18%

26%

20%

12-14
Years

Attending school

59%

67%

51%

61%

72%

74%

Not attending school

55%

56%

54%

52%

79%

85%

15-17
Years

Attending school

73%

74%

65%

63%

79%

82%

Not attending school

74%

71%

72%

68%

89%

83%

Children Engaged in Hazardous Work in Cocoa Production
5-11
Years

Attending school

31%

36%

26%

33%

39%

40%

Not attending school

15%

17%

15%

17%

19%

18%

12-14
Years

Attending school

59%

67%

51%

61%

72%

74%

Not attending school

54%

56%

53%

52%

79%

85%

15-17
Years

Attending school

73%

74%

65%

63%

79%

82%

Not attending school

74%

71%

72%

68%

89%

83%

Source: Child survey 2013/14 and 2018/19, weighted, strata 1-3*Measured based on Variables 1-6, as described in section 3.3 of this report

10.5 Annex V: Supplementary tables II
Comparison of exposure to Hazardous Work indicators using 7 days vs.
12 months reference periods for 2018/19
The following section investigates the difference between using a 7-day (current activity
status) versus a 12-month (usual activity status) reference period for understanding child laborand grew from debates among various stakeholders on the pros and cons of each approach. Usinga 7-day reference period is thought to be cognitively easier for children to answer and situates achild directly within a particular growing season. However, a 7-day reference period ignores
seasonality concerns (you may grow cocoa in one season but prepare the fields in anotherseason) and temporal bias (if a study is conducted during the school year or when less work is
being conducted) which can impact the estimates of child labor through undercounting. The
current activity status would be expected to be lower than the usual activity status since allactivities that were performed in the past seven days are, by definition, also performed in the past
year. Hence the 12-moth reference period includes the 7-day reference period as well as
capturing children who may not have worked in just the last seven days.

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To better understand this issue we compare the estimates of child engagement based on usual
activity status (reference period of last twelve months) and current activity status (reference
period last seven days).
Table 64 shows significant increases in the percent
Quantitative Insightof children working in agriculture, engaged in child
There were 2.10 million children engaged inlabor, and engaged in hazardous child labor between child labor in the last year (60%) comparedto 1.32 million in the last week (38%) andthe seven day and twelve month reference periods
1.94 million engaged in hazardous workfor both Côte d’Ivoire and Ghana. The increases in(56%) compared to 1.10 million (32%).
Ghana were slightly higher for working (27percentage points compared to 24 percentage points), engaged in child labor (24 percentage
points compared to 21 percentage points), and engaged in hazardous work (25 percentage points
to 23 percentage points). In aggregate, there were 2.35 million children working in agriculture in
the past twelve months (68%) compared to 1.49 million in past seven days (43%). Additionally,
there were 2.10 million children engaged in child labor in the last year (60%) compared to 1.32
million in the last week (38%) and 1.94 million engaged in hazardous work (56%) compared to1.10 million (32%).
The large increases across the board between the usual activity status and current activity statusshow that many children do not work year round and are only involved in seasonal work. The
fact that there are similar percentage point increases across the different categories even though
the overall number of children engaged in activities declines from working to child labor tohazardous work may imply that the hazardous work activities are more driven by the seasonalityof the activities. Some activities (land clearing, heavy loads, agro-chemicals, and sharp tools) are
more seasonal than others (long working hours and night work) and seasonal activities that takeplace more often in the harvest season would be expected to see the largest differences. To
further explore the increase in children exposed to hazardous work activities, Table 65 compares
the changes in the different hazardous work activities to see which particular activities are
driving the changes seen above.

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Table 64: Estimates of Change in Children Engaged in Child Labor and Exposure to Hazardous Labor of Children
Working in Agriculture By the 7 Day and 12 Month Definitions, in Côte d’Ivoire and Ghana, in 2018/19
Children in all
agricultural
households

AllchildrenChildren Working in Agriculture

NumberNumberPct1,482,065

43%

7 Day
Total
Côte
d'Ivoire
Ghana

12 Month
7 Day
12 Month
7 Day
12 Month

3,476,523

2,082,507

1,394,016

2,348,567

68%

845,18241%

1,336,435

64%

636,88246%

1,012,132

73%

Diff
(pp)*

Sig of
diff^

25

***

24

***

27

***

Children Engaged in Child Laborin Agriculture
NumberPct1,316,185

38%

2,097,212

60%

752,57136%

1,194,033

57%

563,61440%

903,180

65%

Diff
(pp)*

Sig of
diff^

22

***

21

***

24

***

Children Engaged in Hazardous
Work in Agriculture
NumberPct1,102,854

32%

1,936,326

56%

626,33530%

1,106,329

53%

476,519

34%

829,997

60%

Diff
(pp)*

Sig of
diff^

24

***

23

***

25

***

Source: NORC Child survey 2018/19, weighted, strata 1-3*Calculated as the difference between the 7 day and 12 month rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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Table 65: Prevalence of Children, 5-17 Years, Exposed to Various Types of Hazardous Work Activities by the 7 Day and
12 Month Definitions, in Côte d’Ivoire and Ghana, 2018/19*
Percentage of children in
agricultural households
exposed to:
Number of children 5-17 years inagricultural households

Total
7
Day

12
Month

3,476,523

Côte d’Ivoire

Diff
(pp)*

Sig of
diff^

N/A

N/A

7
Day

12
Month

2,082,507

Ghana

Diff
(pp)*

Sig of
diff^

N/A

N/A

7
Day

12
Month

1,394,016

Diff
(pp)*

Sig of
diff^

N/A

N/A

Hazardous Work Activities
V1: Land clearing

8%

19%

12

***

10%

23%

13

***

5%

14%

9

***

V2: Heavy loads

17%

36%

19

***

17%

37%

20

***

18%

36%

18

***

V3: Agro-chemicals

11%

29%

18

***

11%

25%

14

***

11%

34%

24

***

V4: Sharp tools

25%

46%

21

***

24%

44%

20

***

27%

50%

22

***

V5: Long working hours

1%

1%

0

2%

2%

0

1%

1%

0

V6: Night work

1%

3%

1

***

1%

2%

1

***

1%

3%

2

Exposed to multiple hazards

33%

57%

24

***

32%

55%

23

***

35%

60%

24

***

Average number of hazards

0.6

1.3

111

***

0.6

1.3

106

***

0.6

1.4

120

***

Source: NORC Child survey 2018/19, weighted, strata 1-3*Calculated as the difference between the 7 day and 12 month rates in percentage points, includes rounding^Significance of Difference *** p<0.01

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Table 65 shows for children’s exposure to hazard in agriculture in general, there weresignificantly higher levels of engagement in each activity for both Côte d’Ivoire and Ghana for
all activities except long working hours (stayed constant at 1%) and night work (only significant
for Côte d’Ivoire). The largest overall increases were found in work that was seasonal; sharp tooluse (20 percentage points in Côte d’Ivoire and 22 percentage points in Ghana), followed by agrochemicals (14 percentage points in Côte d’Ivoire and 24 percentage points in Ghana), heavyloads (20 percentage points in Côte d’Ivoire and 18 percentage points in Ghana), and land
clearing (13 percentage points in Côte d’Ivoire and 9 percentage points in Ghana). There was amuch smaller increase in night work (1 percentage points in both Côte d’Ivoire and remained
stable in Ghana at 1%), which along with long working hours are activities that are less likely to
be seasonal in nature.
The large increases in the seasonal activities (V1-V4) show that much of the increase in children
involved in hazardous labor is driven by the increases in seasonal activities in agriculture insteadof just an overall increase in the number of children working. If the increase was only due toincrease in children working then there would be expected to be a large and consistent change
across all six categories. It is important to note that the overall incidence of hazardous labor islower in non-seasonal activities (1 percent for long working hours and night work in the sevenday reference period), so the large increase in absolute terms would not be expected. However,the fact that these numbers are low and stay low show that much of driving force behind the levelof children involved in hazardous work in agriculture is driven by the more seasonal activities.
Between the two countries, of the four seasonal activities, the trend in increase in exposure tohazardous work is generally similar.
Frequency of exposure to Hazardous Work in Cocoa Production in
2018/19 over the 12 months reference periods 80
The frequency that children engaged in hazardous work in cocoa production is also important indetermining the prevalence and impact/intensity of child labor and hazardous labor. Merely
looking at the binary of whether or not a child was exposed to any activities does not paint thefull picture, and knowing how often these activities occur is an important step in determining
how wide spread the issue is. Additionally, breaking the different activities of cocoa production
into components shows how different aspects of the activities play into the narrative. Tables 6669 show the frequency of which components of hazardous work children were exposed to.
Table 66 shows that of the children who were exposed to land clearing (V1) in cocoa production,
the most common overall subcomponent to be exposed to was land clearing (32%), followed by
80

Note that frequencies of components can only be calculated for V1-V4, as the survey data does not include
frequencies of V5 (long working hours) and V6 (night work).

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felling and chopping (18%), and burning (10%). The general trend for land clearing and fellingand chopping was that highest frequency level of six or more times saw the largest percent of
those engaged in that activity (16% for land clearing and 8% for felling and chopping for Côte
d’Ivoire and Ghana). The opposite trend was suggested for burning with only 2 percent reporting
six or more times in Côte d’Ivoire and Ghana. These frequencies suggest that the biggest
drivers of V1 were also the activities that happened the most frequently, and that the incidence ofland clearing was high.
The types and frequency of heavy loads carried in cocoa production is shown in Table 67 forthose children that carried a heavy load. The most common types of loads carried overall weregathering/heaping cocoa pods (35%) and loads during land clearing (26%). Each of these typesof loads saw the general trend that the higher frequency levels saw a higher percentage ofchildren engaged in that activity in both Côte d’Ivoire and Ghana (16% and 13% for gathering
and 14% and 13% for loads during land clearing for Côte d’Ivoire and Ghana in the highest
frequency level).
The next most common types of loads were fermented cocoa beans (24%), other loads (15%),
and water for spraying (18%), and the least common was dry cocoa beans to shed (11%). For all
the loads (except other) the level of engagement by children stayed constant across the different
frequency levels. This indicates that the most common types of loads carried were also the loads
that were the most frequently carried, and that the overall incidence of carrying heavy loads washigh.
The different types of exposure to agro-chemicals in cocoa production and the frequency of thisexposure for children who were exposed can be found in Table 68. The two most common typesof exposure were carrying water for spraying (35%) and being present during pesticide spraying(23%). Carrying water for spraying was more common for higher frequencies of exposure (12%for Côte d’Ivoire and 16% for Ghana for the highest frequency level), this is inconsistent withcarrying a heavy load of water for spraying, which stayed constant across frequency levels.
Conversely being present during spraying decreased in percentage of children exposed as thefrequencies increased.

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Table 66: Prevalence of Children, 5-17 Years, Exposed to Land Clearing Components in Cocoa by Frequency, in Côte
d’Ivoire and Ghana, 2018/19
Percentage of children in
agricultural households
exposed to:

Total
0
times

Number of children working in
cocoa

1-2
times

Côte d’Ivoire

3-5
times

6+
times

0
times

1,667,575

1-2
times

3-5
times

Ghana
6+
times

0
times

831,937

1-2
times

3-5
times

6+
times

835,638

Land Clearing (V1) in cocoa
Land clearing in cocoa

68%

8%

8%

16%

52%

11%

12%

25%

84%

4%

4%

7%

Felling and chopping in cocoa

82%

5%

5%

8%

74%

7%

7%

12%

90%

3%

2%

5%

Burning in cocoa

90%

5%

3%

2%

89%

6%

3%

2%

91%

5%

3%

2%

Source: NORC Child survey 2018/19, weighted, strata 1-3

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Table 67: Prevalence of Children, 5-17 Years, Exposed to Heavy Loads Components in Cocoa by Frequency, in Côte
d’Ivoire and Ghana, 2018/19
Percentage of children in
agricultural households
carrying different loads:

Total
0
times

Number of children working in
cocoa

1-2
times

Côte d’Ivoire

3-5
times

6+
times

0
times

1,667,575

1-2
times

3-5
times

Ghana
6+
times

0
times

831,937

1-2
times

3-5
times

6+
times

835,638

Heavy loads (V2) in cocoa
Loads of wood and other loads
during land clearing in cocoa

74%

5%

7%

14%

73%

5%

7%

14%

74%

5%

8%

13%

Loads of water for spraying in
cocoa

82%

5%

7%

7%

80%

5%

7%

8%

85%

4%

6%

5%

Loads while gathering andheaping cocoa pods in cocoa

65%

10%

11%

14%

64%

9%

11%

16%

66%

10%

11%

13%

Loads of fermented cocoa
beans in cocoa

76%

7%

8%

9%

77%

9%

6%

9%

75%

6%

10%

9%

Loads of dry cocoa beans toshed in cocoa

89%

4%

4%

4%

87%

5%

4%

5%

91%

3%

3%

3%

Other heavy loads in cocoa

85%

3%

4%

7%

79%

5%

6%

11%

91%

2%

3%

4%

Source: NORC Child survey 2018/19, weighted, strata 1-3

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Table 68: Prevalence of Children, 5-17 Years, Exposed to Agrochemicals Components in Cocoa by Frequency, in Côte
d’Ivoire and Ghana, 2018/19
Percentage of children in agricultural
households exposed to:

Total
0
times

1-2
times

Number of children working in cocoa

Côte d’Ivoire

3-5
times

6+
times

0
times

1,667,575

1-2
times

3-5
times

Ghana
6+
times

0
times

831,937

1-2
times

3-5
times

6+
times

835,638

Agro-chemicals (V3) in cocoa
Spraying of pesticides, insecticides in cocoa

92%

4%

3%

2%

91%

4%

3%

2%

93%

3%

3%

1%

Being present or working in the vicinity offarm during pesticide spraying in cocoa

77%

11%

7%

5%

77%

11%

6%

6%

76%

11%

8%

4%

(Re)entering a sprayed farm within lessthan 12 hours of spraying in cocoa

90%

5%

3%

2%

89%

6%

2%

2%

90%

5%

4%

2%

Carrying water for spraying in cocoa

65%

11%

10%

14%

70%

9%

9%

12%

61%

12%

12%

15%

The sale, transport, or handling of agrochemical products in cocoa

88%

5%

5%

3%

88%

4%

4%

3%

87%

5%

5%

3%

Washing containers of agro-chemical
products and spraying machine, and/or
disposal of agro-chemical products in cocoa

90%

4%

4%

2%

89%

4%

5%

2%

91%

4%

3%

2%

Source: NORC Child survey 2018/19, weighted, strata 1-3

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Table 69: Prevalence of Children, 5-17 Years, Exposed to Sharp Tools Components in Cocoa by Frequency, in Côte
d’Ivoire and Ghana, 2018/19
Percentage of children in
agricultural households exposed to:

Total
0
times

Number of children working in cocoa

1-2
times

Côte d’Ivoire

3-5
times

6+
times

0
times

1,667,575

1-2
times

3-5
times

Ghana
6+
times

0
times

831,937

1-2
times

3-5
times

6+
times

835,638

Sharp tools (V4) in cocoa
Using machetes or long cutlasses forweeding in cocoa

35%

13%

16%

37%

35%

15%

14%

36%

35%

10%

17%

38%

Handling motorized equipment or
machines in cocoa

96%

1%

1%

1%

97%

1%

1%

1%

96%

1%

1%

2%

Harvesting with a machete or sickle in
cocoa

69%

8%

9%

14%

65%

11%

9%

15%

72%

6%

8%

14%

Harvesting overhead cocoa pods withharvesting hook or sickle in cocoa

81%

5%

6%

8%

80%

5%

5%

9%

82%

4%

6%

8%

Breaking cocoa pods with knife or a
sharp object/tool in cocoa

57%

11%

13%

19%

52%

14%

13%

21%

62%

9%

12%

17%

Source: NORC Child survey 2018/19, weighted, strata 1-3

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The next most common types of exposure were the sale/transport/handling of agrochemicals(12%), re-entering a sprayed farm (10%), washing/disposal of agrochemicals (10%), andspraying of pesticides (8%). Each of these types of exposure also decreased in percentage ofchildren exposed as the frequencies increased like being present during spraying. The fact thatfor all types of exposure (except carrying water for spraying) has lower frequencies of exposureshows that the intensity of exposure to agro-chemicals is less prominent than for land clearing
and carrying heavy loads.
Table 69 shows the frequency levels for different types of sharp tool use in cocoa agriculture
for children who used sharp tools. The most common use of sharp tools by far was using
machetes/long cutlasses (65%), and this was more common at higher frequencies of exposure(36% for Côte d’Ivoire and 35% for Ghana in the highest frequency level). The next mostcommon uses of sharp tools were breaking cocoa pods (43%), harvesting with a machete/sickle(31%), and harvesting cocoa pod with a harvesting hook/sickle (19%). Each of these types of
sharp tool use were also more common at higher frequencies, which shows that the incidence of
sharp tool use is also very high overall.
Across the four hazardous work activities in cocoa production, there were generally highintensity of exposure for land clearing, heavy loads, and sharp tools as measured by frequency ofactivity levels. The only hazardous work activity with lower intensity was use of agro-chemicals.
In section 4.2 the largest increase in exposure to a hazardous work activity from 2008/09 to
2018/19 came from use of agro-chemicals, however since the intensity of agro-chemical use isoverall not very high this makes that result less troubling as the overall impact of agro-chemical
use is not as high relatively.
10.6 Annex VI: Additional Insights from 2018/19 Survey RoundIn this section we explore how different household, community, and school characteristics are
related to children’s exposure to child labor in agriculture and to hazardous work within ahousehold. Each of the variables analyzed below most likely play some role in determining childlabor rates and lend further evidence to the necessity of taking an ecosystem approach to thecomplex issue of child labor. To do this we present the proportion of households having children
in child labor in agriculture and children exposed to hazardous work within a household
disaggregated by the given demographic, economic characteristics of households, by community
characteristics and by school characteristics in the neighborhood of cocoa producing areas inCôte d’Ivoire and Ghana. It is important to note that these comparisons do not imply
attribution/causality, rather indicates potential correlation only. Tables 70-72 present the valuesof proportion of children in child labor in agriculture and children exposed to hazardous work byparent and household characteristics, by community and school characteristics.

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Data presented in Tables 70-72 indicate that mother education level and household head’s
awareness of child labor are negatively correlated with the prevalence of child labor.
Proximity to schools may influence child labor and hazardous child labor prevalence, where ahigher rate of child labor and hazardous child labor would be expected in areas without schools
because sending children to school would be more costly than sending them to work.
Among community infrastructure, in Côte d’Ivoire and Ghana no correlation between improved
roads or microfinance institutions was found.
In addition to proximity to schools, it is also likely that school quality might affect attendanceand hence child labor and hazardous child labor. As a proxy of school quality, we test whether
prevalence of child labor and hazardous child labor were lower in communities where greater
proportion of schools had toilets, piped drinking water and canteens.
In Côte d’Ivoire and Ghana no changes in child labor or hazardous child labor were found to becorrelated with the school characteristics.
Data presented in Table 70 explore maternal and paternal education, household income, area
under cocoa cultivation, head-of-household migration status and awareness of child labor. Theseissues likely have an influence on children’s exposure to child labor and hazardous work. In
Ghana a higher proportion of children whose mother have below secondary level education were
exposed to child labor and hazardous child labor compared to children whose parents have atleast secondary level education (65% versus 48% for child labor and 62% versus 44% forhazardous child labor). However, in Ghana there was no statistically significant difference inchild labor and hazardous child labor for children whose father had at least a secondary
education (around 58% child labor and 54% hazardous child labor). In Côte d’Ivoire there wasno statistically significant difference in child labor & hazardous child labor by either parents’
education level (around 38% child labor and 36% hazardous child labor for mother’s educationlevel and 40% and 39% for father’s education level).
Higher education levels may imply greater awareness on the issue of child labor, which might, inturn, lead to lower child labor and hazardous child labor rates. While the differences in thelikelihoods were not present in Côte d’Ivoire, they were more prominent Ghana, as exposure tochild labor and hazardous child labor was much larger when mothers had below secondary
education versus when mothers had at least secondary education.

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Table 70: Estimate of Proportion of Children Engaged in Child Labor and Exposure to
Hazardous Child Labor in Cocoa by Parent and Household Characteristics, All
Agricultural Households, in Côte d’Ivoire and Ghana, 2018/19
Côte d’Ivoire

Ghana

Exposure toChild Labor in
Cocoa

Exposure to
HazardousChild Labor in
Cocoa

Exposure toChild Labor in
Cocoa

Exposure to
HazardousChild Labor in
Cocoa

Below secondary

40%

38%

65%

62%

Secondary or above

36%

34%

48%

44%

***

***

Parent and Household
Characteristics

Education of Mother

Significance
Education of Father
Below secondary

42%

41%

62%

58%

Secondary or above

38%

36%

55%

50%

Low income*

37%

36%

56%

53%

High income**

38%

36%

55%

50%

Less than 5 acres

43%

42%

56%

53%

Greater than 5 acres

43%

42%

62%

57%

Significance
Income Group

SignificanceArea under Cocoa Cultivation

SignificanceHousehold Heads' Awareness of Child Labor
Head not aware

39%

38%

59%

55%

Head aware

37%

35%

53%

49%

Significance
Migration: Head ever migrated to and from another village
Not migrated

35%

34%

56%

52%

Migrated

42%

41%

55%

50%

Significance
Source: NORC Household Head survey 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01* Low income: with income within the lower 60% of income distribution*High income: with income within the upper 40% of income distribution

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Household income is often considered as one of the most important factors that influences theprevalence of child labor and hazardous child labor. In low-income households, it is likely that
more children are working in agriculture to support their parents and as a result, potentially havegreater proportion exposed to child labor or hazardous child labor. However, the data reported inTable 70 indicates that there was no statistically significant difference in child labor andhazardous child labor rates between low income families (with income in the lower 60% ofincome distribution) and high income families (incomes quintiles 4-5). An important caveat isthat the NORC research program did not include a detailed household income module but rather
asked a few, self-reported measures of household income. Future studies, with a household
economy focus, should continue this investigation into the relationship between householdincome and child labor. However, taking an ecosystem approach is essential, and householdincome is most likely only one variable among others to consider when attributing changes inchild labor.
The comparison of child labor and hazardous child labor rates by household’s area under cocoa
cultivation indicates that there was no statistically significant difference in child labor andhazardous child labor prevalence between households with small cocoa farms (less than 5 acres)and households with large are under cocoa (more than 5 acres) in both countries.
Household head’s awareness of child labor and the effects of it on children did not lead to fewer
children from these households being exposed to child labor in both countries and hazardous
child labor in Côte d’Ivoire. There was also however no statistically significant difference inhazardous child labor in Ghana by head’s awareness.
Household migration could also affect child labor with households that migrated being more inneed of money and therefore have higher levels of child labor. This was not found in the data
where there was no statistically significant difference in child labor or hazardous child labor in
Côte d’Ivoire and Ghana between the households with head migrated at least once versus thehousehold where head did not migrate.
The next section will discuss the potential effects of community characteristics on child laborand hazardous work.
Table 71 shows how community and infrastructure characteristics such as ease of access to JHS
and SHS schools (based on distance to the nearest school), access to improved roads, access togrid electricity, and presence of microfinance institutions could influence prevalence of childlabor and hazardous child labor in Côte d’Ivoire and Ghana.

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Table 71: Estimate of Proportion of Child Labor and Exposure to Hazardous Child
Labor in Cocoa by Community Characteristics, All Agricultural Households, in Côte
d’Ivoire and Ghana, 2018/19
Côte d’Ivoire

Ghana

Exposure toChild Laborin Cocoa

Exposure to
HazardousChild Laborin Cocoa

Exposure toChild Laborin Cocoa

Exposure to
HazardousChild Laborin Cocoa

Closet primary less than 1 KM away

42%

39%

68%

62%

Closet primary more than 1 KM away

38%

37%

52%

48%

Community Characteristics

Primary School Location

Significance***Junior High School (JHS) Location
Closet JHS less than 2 KM away

39%

37%

62%

57%

Closet JHS more than 2 KM away

36%

35%

52%

49%

Closet SHS less than 5 KM away

38%

36%

59%

55%

Closet SHS more than 5 KM away

40%

38%

49%

46%

Community has improved road

43%

43%

53%

50%

Community does not have improved road

37%

36%

56%

51%

Has access to grid electricity

38%

36%

52%

49%

Does not have access to grid electricity

38%

37%

67%

60%

Community has institution

55%

55%

56%

52%

Community does not have institution

37%

35%

54%

51%

SignificanceSenior High School (SHS) Location

SignificanceImproved Road

Significance
Grid Electricity Available

SignificanceMicrofinance Institution

Significance
Source: NORC Community survey 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

Quantitative Insight
In Ghana communities where theclosest primary school was within 1
km saw lower rates of child labor.

Proximity to schools may influence child labor andhazardous child labor prevalence, where a higher rate ofchild labor and hazardous child labor would be expected inareas without schools because sending children to school
would be more costly than sending them to work. In Côte

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d’Ivoire, exposure to child labor and hazardous child labor was not significantly different incommunities where the closest JHS was less than 2 km away and where the closest SHS was lessthan 5 km away, and there was no statistically significant difference with the closest primaryschool being within 1 km. In Ghana communities where the closest primary school was within 1
km saw lower rates of child labor (68% versus 52%), but no statistically significant difference
was found on the distance to the nearest JHS and SHS.
Community infrastructure in terms of improved roads and availability of grid electricity are also
factors potentially influencing the extent to which children are exposed to child labor or
hazardous child labor. Côte d’Ivoire In Côte d’Ivoire and Ghana no statistically significant
difference in child labor and hazardous child labor rates was found in communities withimproved roads. Similarly, having access to grid electricity had no statistically significant
difference on exposure to child labor or hazardous child labor.
Microfinance institutions provide financial services to individuals in communities in the form ofloans that can be used to expand agricultural activities. Access to credit is considered as one of
the factors that can influence households’ ability to hire labor from the market. Lack of smooth
access to finance can make a household vulnerable to liquidity constraints and thus influencethem to use children from own household to substitute hired labor. However, in Côte d’Ivoire
and Ghana, there was no statistically significant difference in child labor and hazardous childlabor rate by the access to a microfinance institute.
Next, we explore whether exposure to child labor and hazardous vary among communities withdifferent school infrastructure.
As school attendance has increased in Côte d’Ivoire and Ghana, it is likely that school
infrastructure related factors such as –toilets, piped drinking water and canteens – also influence
the prevalence of child labor and hazardous child labor in these countries. However, no such
effect was found in Côte d’Ivoire or Ghana.
Some of the household level factors such as parental education seemed to be correlated tochildren’s exposure to child labor and hazardous child labor. Overall, child labor and hazardous
child labor rates may be lower in areas with better access to schools. In Côte d’Ivoire and Ghana,child labor and hazardous child labor rates did not seem to differ by community level
infrastructure considered here.

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Table 72: Estimate of Proportion of Child Labor and Exposure to Hazardous Child
Labor in Cocoa by Child, School Characteristics, All Agricultural Households, in Côte
d’Ivoire and Ghana, 2018/19
Côte d’Ivoire

Ghana

Exposure toChild Laborin Cocoa

Exposure to
HazardousChild Laborin Cocoa

Exposure toChild Laborin Cocoa

Exposure to
HazardousChild Laborin Cocoa

School does not have toilet

38%

37%

56%

52%

School has toilet

38%

37%

52%

48%

School does not have piped drinking
water

39%

38%

55%

51%

School has piped drinking water

34%

32%

55%

52%

School does not have kitchen

37%

36%

55%

51%

School has kitchen

40%

38%

53%

50%

School Characteristics

Toilet in School

Significance
Piped Drinking Water in School

SignificanceSchool has Canteen/Kitchen

Significance
Source: NORC School survey 2018/19, weighted, strata 1-3^Significance of Difference *** p<0.01

10.7 Annex VII: Supplement I: Quantitative Analysis of Assessment of
Effectiveness of Interventions
Literature review
In order to assess the effectiveness of interventions, we first need to identify the various channels
through which interventions influence child labor. Through a literature review, we identified thefollowing factors that typically affect child labor and, correspondingly, the major channels
through which these factors affect and moderate child-labor outcomes: poverty, opportunity costsof child labor, household composition, access to capital markets, regulatory factors andproduction-related factors.
Poverty, especially in the form of adult household members’ wages, has been shown to have an
important influence on child labor. Blunch, in an evaluation of the 1997 Core Welfare Indicators
Survey in Ghana, finds that “Poverty affects the likelihood of engaging in harmful child labor
positively.” Edmonds’s 2001 study (as cited in Basu and Tzannatos 2003), found that increased
household income can explain 94 percent of the decline in child labor for households at thepoverty line, illustrating the key role that income plays with regards to the level of child labor.
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This is also documented by Levy (1985), Rosenzweig (1981), Sakellariou and Lall (1998), andCartwright (1998) who find that increases in women’s wages significantly decrease female childlabor (as cited in Canagarajah and Nielsen 2001). In fact, the first two of these studies find that“that a 10 percent increase in women’s wage rates would decrease the female child’s labor force
participation by as much as 10 percent”, while the last two “reach a similar conclusion.”
Together, these studies highlight the important influence of poverty, and especially of wages, onchild labor rates.
Opportunity costs, in the form of the expected returns to labor and its clearest substitute —education, also play an important role in affecting child labor. The daily wage earned throughchild labor, for example has a significant positive impact on the hours of work for children,
according to Bhalotra and Heady (1998), as cited in Canagarajah and Nielsen (2001). The returns
on child labor are also variable based on farm size, such that larger farms, which require morelabor but which do not have the ability to mechanize, see increased child labor.
Households also weigh the potential returns to education and its costs when determining the levelof child labor. In an analysis of child labor in Zambia, Nielsen (1998), using the school’s roofing
as a proxy for school quality, found that in some cases, a school roof’s poor condition increases
the probability of working by 15 percentage points (as cited in Canagarajah and Nielsen (2001)).
Accessibility to a primary school is likewise a determinant of child labor, as shown by Nielsen(1998) who found that presence of a primary school increases school attendance by 10
percentage points in some cases, whereas the availability of a passable road decreases child laborby more than 10 percentage points and also increases school attendance significantly. As
children lack the agency to make these decisions themselves, their guardians will weigh thesefactors, the demand for labor and its expected returns, against the supply, quality and returns to
education when determining levels of child labor.
Also at play within these decisions is the household’s composition, which includes household
size and education levels, and shows intra-household variance for child labor based on age. Thislatter condition is summarized in the idea of “sibling complementarity,” described by Basu andTzannatos (2003) as the condition “where one child’s labor makes it possible for another child to
go to school.” Citing DeGraff, Bilsborrow, and Herriman (1993), Canagarajah and Nielsen(2001) noted that the incidence of child labor is higher for the older children than for the younger
children. Complementary to this, Nielsen (1998) finds that the higher the number of older
siblings, the lower the probability of working and the higher the probability of attending school.
Younger children, therefore, should be less likely to engage in child labor than their older
siblings.
The relationship between age and child labor, however, is more complicated when considering
the ages of older household members and the age of the household head. Grootaert (1998) andFINAL REPORT | 214

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Nielsen (1998), for example, find that the higher the age of the household head the lower the
probability of working. This is presumably influenced by the household head’s own increased
wealth over time. Older household members, on the other hand, who may themselves affect thedependency ratio in the household, have a negative impact on school attendance. Canagarajah
and Coulombe (1998), for example, “find that the presence of household members older than 60
increases the probability of working and decreases the probability of attending school. In Ghana,
the effect varies from 1 to 4 percentage points (Canagarajah and Nielsen, (2001). These impacts
highlight the differential effects of household distributions within and across generations on childlabor.
Capital markets are likewise a determinant of child labor, especially as it relates to the ability ofa household to manage shocks. Households that lack access to credit and assets to shed see thegreatest increase in child labor from such shocks. Nielsen (1998) finds that an indicator forwhether or not a household owns an asset has a significant effect on both the probability ofworking and the probability of attending school by as much as 10-percentage-points.
However, regulatory factors can also include informal mechanisms such as culture, which dictate
the cultural norms around child labor. Coulombe evaluates the differences of child labor as they
related to religion and finds that Christians are more likely to attend school, and in rural areas
they are also less likely to work than Muslims and those who practice traditional religion(Coulombe, 1998). He further finds that traditions and attitudes have a significant impact onchild labor and can increase the probability of child labor by 30 percentage points. Similar tothis, Webbink (2013) finds that culture context have a significant effect and that these factors
affect child labor generally and may also have differential effects based on the gender of thechild.
Production-related factors also serve an important role in determining child labor and are influenced by the sector, mode of production, and pricing for products. Perhaps most fundamental toproduction-related factors are the differential labor demands for across products and the ability
for children to supply the required labor for these goods. In a rural setting, for example, the labordemands for different agricultural crops may vary significantly as will the demands for childlabor for irrigation or application of pesticides. Regulatory factors also overlap with productionrelated factors in such cases where production is gender-based, creating differential labordemands across children. For example, Cogneau (2012) finds that cocoa bean harvesting is morea male task, whereas plantains are more a female crop. Production-related factors can further
differential child labor based on age, as Cogneau argues that young kids are probably too young
to be put to work significantly in cocoa bean harvesting.
As illustrated above, the factors which influence child labor may interact with one another. There
are also variations in the relative importance of these factors depending on the context andFINAL REPORT | 215

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market under consideration. However, as the literature demonstrates, these factors are key
mechanisms in determining the supply and demand for child labor. As such, these same factorsof poverty, opportunity costs of child labor, household composition, access to capital markets,regulatory factors and production-related factors are the key avenues for affecting child-laboroutcomes.
10.7.1.1 References
Basu, K. and Tzannatos, Z., 2003. The Global Child Labor Problem: What do we know and what
can we do?. The World Bank Economic Review, 17(2):147-173.
Blunch, N.H. and Verner, D., 2001. Revisiting the link between poverty and child labor: the
Ghanaian experience.
Bhalotra, S. and Heady, C., 2003. Child farm labor: The wealth paradox. The World Bank
Economic Review, 17(2):197-227.
Canagarajah, S. and Coulombe, H., 1998. Child labor and schooling in Ghana.
Canagarajah, S. and Nielsen, H.S., 2001. Child labor in Africa: A comparative study. The
ANNALS of the American Academy of Political and Social Science, 575(1):71-91.
Cartwright, K. 1998. Child Labor in Colombia. In The Policy Analysis of Child Labor: A
Comparative Study, ed. C. Grootaert and H. A. Patrinos. Washington, DC: World Bank.
Cogneau, D. and Jedwab, R., 2012. Commodity price shocks and child outcomes: the 1990 cocoa
crisis in Cȏte d’Ivoire. Economic Development and Cultural Change, 60(3):507-534.
Coulombe, H. 1998. Child Labor and Education in Côte d'Ivoire. Background paper, World
Bank, Washington, DC.
DeGraff, D.S., Bilsborrow, R.E. and Herrin, A.N., 1993. The implications of high fertility forchildren’s time use in the Philippines.
Edmonds, E. 2001. "Will Child Labor Decline with Improvements in Living Standards?"
Working Paper 01-09. Dartmouth College, Department of Economics, Hanover, N.H.
Grootaert, C. 1998. Child Labor in Côte d'Ivoire. In The Policy Analysis of Child Labor: A
Comparative Study, ed. C. Grootaert and H. A. Patrinos. Washington, DC: World Bank.
Levy, V. 1985. Cropping Patterns, Mechanization, Child Labor and Fertility Behavior in a
Farming Economy: Rural Egypt. Economic Development and Cultural Change 33:777-91.

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Nielsen, H. S. 1998. Child Labor and School Attendance in Zambia: Two Joint Decisions.
Working Paper No. 98-15, Centre for Labour Market and Social Research, Aarhus, Denmark.
Rozenzweig, M.R. 1981. Household and Non-Household Activities of Youth: Issues ofModelling, Data and Estimation Strategies. In Child Work, Poverty and Underdevelopment, ed.
G. Rodgers and G. Standing. Geneva: International Labor Organization.
Sakellariou, C. and A. Lall. 1998. Child Labor in the Philippines. In The Policy Analysis ofChild Labor: A Comparative Study, ed. C. Grootaert and H. A. Patrinos. Washington, DC:
World Bank.
Webbink, E., Smits, J. and de Jong, E., 2013. Household and context determinants of child laborin 221 districts of 18 developing countries. Social Indicators Research, 110(2):819-836.
Modelling Child Labor and Hazardous Child Labor
One of the main objectives of this study is to assess how different interventions, either
independently or in conjunction with other interventions, affect the main outcome variables ofinterest - children’s engagement in child labor and hazardous work as well as their prevalence
rates within a household.
For addressing research questions relating to the effect of interventions, it is important to develop
a model-based approach which will be able to empirically test whether, after controlling for
observable influences of different factors, interventions affect the outcome variables of interest.
For this purpose, we apply a regression framework using a two-step approach. First we used atheoretical model of the household’s decision-making process to identify factors that might
influence children’s exposure to child labor and hazardous work in cocoa production. Then we
estimate, whether, after controlling for such factors, the interventions affect children’s exposure
to child labor and hazardous work in cocoa production.
In this section we develop a generic model that examines the relationship between children’s
exposure to child labor and hazardous work in cocoa production and their determinants using atheoretical model of the household’s decision-making process. This generic model will serve as
the base of the empirical analysis to be undertaken for addressing the research questions on theinfluence of different interventions on children’s exposure to child labor and hazardous work incocoa production.
There are several factors that might affect the trade-offs that a family faces between sending
children to school and engaging them in child labor in production activities. Using guidancefrom the literature review of previous research studies (see Annex 10.7.1), we developed atheoretical model that examines for community c the household’s decision to subject its children

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to child labor. The approach accounts for the head of household i’s concern for their children’s
welfare (𝑊𝑊𝑐𝑐,𝑖𝑖 ), opportunity cost of child (hazardous) labor (𝑂𝑂𝑐𝑐,𝑖𝑖 ), and the relevant household
characteristics. Toward that end consider the following model:

where 𝐿𝐿CL
𝑐𝑐,𝑖𝑖 is the observed child labor function, and 𝑍𝑍𝑐𝑐,𝑖𝑖 are household characteristics and 𝜀𝜀 𝑖𝑖 is

Normal deviate. Consider each of the explanatory variables in turn.

Child welfare might be proxied by the number of children in household, (Cc,i) (more children

implies less concern), level of household member’s education, (Ec,i) (the higher it is the more achild might be valued) and head’s perception about child work and benefit of education (𝑀𝑀𝑐𝑐,𝑖𝑖 ).
These result in the following sub-model:Child labor has two opportunity costs, one financial and immediate, the other investment-related
and delayed. The financial component comprises wages foregone (or the child works) and wages
paid out to a labor substitute. A potential proxy variable for these would be the average productof labor for a household (𝐴𝐴𝐴𝐴𝑐𝑐,𝑖𝑖 , which is a function of farm and household characteristics) andaverage wage in the community (𝑤𝑤𝐶𝐶 ). The investment component might be captured by school
quality and infrastructure (𝑆𝑆𝐶𝐶𝐶𝐶 ). These result in the following sub-model:
𝑂𝑂𝑐𝑐,𝑖𝑖 = 𝑓𝑓{(𝐴𝐴𝐴𝐴𝑐𝑐,𝑖𝑖 ) (𝑤𝑤𝐶𝐶 ); (𝑆𝑆𝐶𝐶𝐶𝐶 )}Household characteristics might comprise the demographic influence(𝐷𝐷𝑐𝑐,𝑖𝑖 ) includes religion, number of household members, head age, proportion of children indifferent age groups, proportion of female children in the household, whether household
migrated, presence of non-relative children in household, household wealth and liquidity (𝐻𝐻𝑊𝑊𝑐𝑐,𝑖𝑖 )and farming characteristics (𝐹𝐹𝑐𝑐,𝑖𝑖 ).

Finally, these sub-models are substituted into the model for 𝐿𝐿CL
𝑐𝑐,𝑖𝑖 and the combination is estimatedthis using a reduced form specification:
𝐿𝐿CL
𝑐𝑐,𝑖𝑖 = 𝛽𝛽𝐶𝐶 𝐶𝐶𝑐𝑐,𝑖𝑖 + 𝛽𝛽𝐸𝐸 𝐸𝐸𝑐𝑐,𝑖𝑖 + 𝛽𝛽𝑀𝑀 𝑀𝑀𝑐𝑐,𝑖𝑖 + 𝛽𝛽𝑆𝑆𝐶𝐶 𝑆𝑆𝐶𝐶𝑐𝑐 + 𝛽𝛽′𝐷𝐷 𝐷𝐷𝑐𝑐,𝑖𝑖 + 𝛽𝛽′𝐻𝐻 𝐻𝐻𝑊𝑊𝑐𝑐,𝑖𝑖 + 𝛽𝛽𝐻𝐻 𝐹𝐹𝑐𝑐,𝑖𝑖 + 𝛽𝛽𝑉𝑉 𝑉𝑉𝑐𝑐 + 𝑢𝑢𝑐𝑐,𝑖𝑖

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where 𝑉𝑉𝑐𝑐 is a set of village/community characteristics that influence opportunity cost and average
wage (through labor demand) in the community 81 and 𝑢𝑢𝑐𝑐,𝑖𝑖 is the normal error term assumed to be
independently and identically distributed.
This equation will be used as the fundamental model of child and hazardous labor while weestimate program impact of different interventions as specified in Section 8.3. Based on thisexpression of the child labor function above, the following sets of variables are included in the
regressions:■Children number: Number of children (total).

■

Household demographic characteristics: Head age, gender, total adult members, total
member with secondary/above education, proportion in different age groups (5-11, 12-14 &15-17), proportion of female children in the household, religion, whether family migrated
and whether non-relative children live in household.

■Household Head’s perception: Value for education of children, perception about whether
children should be working for pay below age 18.

■

Farming characteristics: Type of crop produced by the household.

■Household wealth and liquidity: Asset (indicator of home quality and household
asset/wealth) liquidity (whether can borrow to meet needs)■

Community characteristics: Importance of cocoa (most important source of income),
having improved road, having access to senior high school (less than 5 KM), remoteness(distance from district capital).

■

School quality/infrastructure: Indicator variable for concrete building, toilet inside school,
having access to improved water source.

10.8 Annex VIII: Supplement II: Quantitative Analysis of Assessment of
Effectiveness of Interventions
This annex presents the methodology used for conducting statistical analyses as a part ofquantitative assessment of effectiveness of different interventions.
Education Material Assistance and Child Labor in the Cocoa Sector
Here we present the quantitative analysis undertaken to address the following research question:81

We hypothesized that wages are highly correlated with the average product of labor (APL) and then introduced an
additional model in which the average product of labor is a function of farm and household characteristics which areincluded in the model.

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Are children in households that received materials assistance related to education of children(such as school supplies, text books, uniform, etc.) less likely to be involved in child labor in thecocoa sector than their peers who did not receive such assistance?
10.8.1.1 Methodology
Given that provision of material support would be limited by the amount of resources available
to the implementers, it is likely that beneficiary selection criteria were used to select thehouseholds who would most benefit from it. Thus, comparison of outcome differences between
the recipients and non-recipients necessitates first identifying potential differences in these twogroups that might have influenced the beneficiary selection process. Such differences on theirown could have led to the observed differences in outcomes rather than the effectiveness of theassistance. If not controlled for, these differences can lead to selection bias in estimation. In
order to address the potential selection bias which might influence the estimated effect oftreatment (educational material assistance) on outcome variables (child labor and hazardous
child labor), we used a quasi-experimental design. The quasi-experimental design was based on a
two-step approach:
1. Generate counterfactual: To address selection bias where a group of household wereselected by the implementers for disbursement of benefits, we identify a set of characteristics
that may influence selection of beneficiaries such as number of children in different age
groups, households’ demographic characteristics and economic profile, among others. We
then use entropy balancing, 82 a multivariate reweighting technique that generates a synthetic
comparison group in such a way that the treated (beneficiary) households and the non-treated(comparison) households become statistically very similar (balanced) in observablecharacteristics that are likely to influence beneficiary selection process.

82

Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce
Balanced Samples in Observational Studies. Political Analysis, 20(1): 25-46).

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2. Use regression model to estimate the impact: Next, we estimate an attribution model to test
whether the households that received educational material support were less likely to havechild labor and children engaged in hazardous child labor in cocoa production. We estimate
two models: one model defines the outcome variable as the likelihood (probability) of having
at least one child in child labor/hazardous child labor; the other model defines the outcome
variable as the percent of children in the household exposed to child labor/hazardous childlabor. The models specify the outcome variables (child labor/hazardous child labor) as afunction of household, community, and school characteristics. Finally, the model tests
whether provision of material support related to education had any statistically significant
effect on children’s engagement in child labor and in hazardous child labor in cocoa
production after controlling for other factors that influence a household’s decision to engage
children in child labor and in hazardous child labor. 83
10.8.1.2 Data Source
The data sources we used for examining the research question is the sectorally representative
child-labor and head-of -household surveys conducted by NORC during the 2018/19 main cocoa
harvesting season. The 2018/19 child-labor survey captured data on children’s exposure to childlabor and hazardous child labor, and the head-of-household surveys captured whether there wasany child in the household who benefited from material support related to education. In addition,
we used data collected from a survey of community leaders and schools to control forcommunity and school infrastructure related influences on the outcome variable of interest.
Examples of these include the presence of improved roads, distance to the schools from thecommunity, school building construction material, availability of toilets inside school, source ofdrinking water, etc.
The self-reported data show that in Côte d’Ivoire 605 households (44%) had at least one child
who received benefits and in Ghana 164 households (14%) with at least one child that received
material assistance related to education.
10.8.1.3 Analysis and Results
For generating the comparison group, the first step of the analysis was to use entropy balancing
on the sample of households leading to a synthetically designed group of comparison households
that were very similar to the treatment group (the counterfactual). Table 73 and Table 74 present
the result of entropy balancing showing the difference in variables with influence on selection
process before balancing and after balancing for Côte d’Ivoire and Ghana.

83

Other influencing factors not of specific interest to research are often referred to as “covariates”.

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Table 73: Ghana Entropy Balancing: Differences in Covariates Affecting Selection
Before and After Balancing
Comparison
BeforeTreatment
After

BeforeMean

Variance

Mean

Variance

Mean

Variance

Total number of children age 5-11

1.37

0.99

1.48

0.99

1.48

0.99

Total number of children age 12-14

0.55

0.47

0.65

0.46

0.65

0.46

Dummy: Household head withsecondary education

0.33

0.22

0.41

0.24

0.41

0.24

Dummy: Head believe that children
should receive at least seniorsecondary education

0.98

0.02

1

0

1

0

Dummy: Household paid for school fees

0.51

0.25

0.37

0.23

0.37

0.24

Dummy: Household performs food crop
farming

0.87

0.11

0.91

0.08

0.91

0.08

Dummy: Land cultivated is above
median

0.54

0.25

0.58

0.24

0.59

0.24

Dummy: Agricultural labor main income
source

0.15

0.13

0.21

0.16

0.21

0.17

Dummy: Petty trade main income
source

0.32

0.22

0.31

0.21

0.31

0.22

Asset Index: Home quality 84

0.24

1.56

0.06

1.12

0.06

1.12

Asset Index: Non-agricultural assets 85

0.00

2.01

-0.05

1.90

-0.05

1.90

Dummy: High income household

0.36

0.23

0.41

0.24

0.41

0.24

After the counterfactual was constructed, we used multivariate regression model to estimate
whether provision of material support related to education had any statistically significant effecton children’s engagement in child labor and in hazardous child labor in cocoa production after
controlling for other factors that influence a household’s decision to engage children in childlabor and in hazardous child labor. The following table presents the regression results 86 for
Ghana.

84

First Principal Component based index of home quality (having house made of stone/burnt brick/cement/concreteand having toilet inside house).

85

First Principal Component based index of ownership of household non-agricultural assets such as radio, TV,
refrigerator, bicycle, motor bike, cell phone, computer, sewing machine, and car.

86

Included controls for missing observations and whether anyone other than the child was present during interview(not reported).

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Table 74: Ghana, Probit Regression and Generalized Linear Regression: Education
Material Assistance and Child Labor and Hazardous Child Labor

Estimated treatment effectProbit Regression^
Child
Hazardous
Labor
Child Labor
0.026
0.025
(0.031)(0.032)
Regression output:
Probit model:

Treatment dummy: Members received
educational material assistance
Total number of children% of 12-14 children% of 15-17 children% of girl children 5-17
Head age
Head age-squared
Head Gender: Male
Total number of adults
Number of adults with secondary/above
education
Dummy: Christian
Dummy: Cocoa household
Dummy: Household produce commercial
crop
Dummy: Household produce food crop
Dummy: Household has livestock farm

0.124
(0.149)0.359***(0.0616)
1.281***(0.294)
1.253***(0.320)-0.382**(0.162)-0.00286(0.00773)
2.91e-06(7.36e-06)
0.229(0.187)-0.0320(0.0887)
0.0675(0.0890)0.00436(0.169)1.204***(0.273)-0.191(0.197)-0.117(0.189)0.390***(0.134)0.112(0.145)0.343***(0.0657)
1.556***(0.287)1.567***(0.328)-0.474***(0.163)-0.000627(0.00707)
7.85e-07(6.74e-06)
0.268(0.183)0.00235(0.0841)-0.00150(0.0847)-0.131(0.170)1.170***(0.276)-0.143(0.189)-0.0551(0.204)0.320**(0.129)Generalized Linear
Regression^^Child
Hazardous
Labor
Child Labor
0.032
0.025
(0.030)(0.027)
Regression output:
Generalized Linear
Model 87:
0.167
(0.157)-0.0116(0.0449)
1.613***(0.351)1.813***(0.358)-0.412**(0.179)0.00158(0.00725)-1.19e-06(6.89e-06)
0.0728(0.199)0.00848(0.0930)-0.0484(0.0908)0.0646(0.218)1.224***(0.376)-0.255(0.197)0.0482(0.152)0.378***(0.146)0.133(0.141)-0.00285(0.0486)
2.029***(0.357)2.250***(0.369)-0.507***(0.196)0.00409(0.00727)-3.48e-06(6.92e-06)
0.128(0.198)-0.00139(0.0880)-0.0757(0.0824)-0.0739(0.213)
1.198***(0.402)-0.128(0.193)0.117(0.171)0.289**(0.141)

87

Generalized non-linear least square regression with binomial family and logit link function, estimated using
maximum likelihood estimation.

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Amount of cocoa sold in 2017/18 harvest
Asset Index: Home quality 88
Asset Index: Non-agricultural assets 89
Dummy: Land holding above median
Dummy: Head can sufficiently borrow
Dummy: Children's family migrated
Dummy: Non-relative children staying
Dummy: Household head believe children
start work for pay below 18
Dummy: Cocoa is the most importantsource of income in community
Dummy: Community has improved road
Dummy: District capital more than 50 KM
Dummy: Secondary school within 5 KM
Dummy: Community has mobile
connectivity
Dummy: School has concrete building
Dummy: Toilet inside school
Dummy: School has improved water
Dummy: Strata1 (High cocoa production)
Dummy: Strata2 (Medium cocoa production)

Probit Regression^
Child
Hazardous
Labor
Child Labor
5.73e-05-4.54e-05(0.000105)(9.37e-05)-0.0368
0.0114(0.0743)(0.0730)-0.126***-0.147***(0.0425)(0.0462)0.06930.0506(0.131)(0.140)0.428***
0.310**(0.143)(0.146)0.01360.149(0.127)(0.128)-0.408-0.231(0.275)(0.286)0.459***
0.404**(0.177)(0.164)1.008**
1.148***(0.405)(0.388)-0.373-0.148(0.229)(0.248)-0.567-0.277(0.380)(0.332)-0.179-0.178(0.164)(0.153)-0.885-0.594(0.786)(0.753)0.246
0.128(0.291)(0.267)0.309
0.223(0.291)(0.298)1.478
0.962(1.357)(1.198)0.625
0.520(0.594)(0.570)-0.596-0.106(0.531)(0.519)Generalized Linear
Regression^^Child
Hazardous
Labor
Child Labor-7.55e-06-9.18e-05(0.000116)(0.000112)-0.0194-0.000292(0.0852)(0.0848)-0.100*-0.119**(0.0530)(0.0588)0.230*
0.214(0.132)(0.148)0.330*
0.317*(0.176)(0.180)0.03550.141(0.124)(0.130)-0.216-0.0166(0.406)(0.392)0.416**
0.248(0.199)(0.194)
1.193***
1.062**(0.460)(0.463)-0.413-0.335(0.305)(0.338)-0.600-0.533(0.426)(0.410)-0.121-0.118(0.209)(0.204)-1.448-1.406(1.090)(1.105)0.238
0.219(0.340)(0.332)0.318
0.106(0.402)(0.417)1.809
0.743(1.490)(1.408)1.130
0.850(0.821)(0.810)-0.709-0.576(0.673)(0.714)

88

First Principal Component based index of home quality (having house made of stone/burnt brick/cement/concreteand having toilet inside house).

89

First Principal Component based index of ownership of household non-agricultural assets such as radio, TV,
refrigerator, bicycle, motor bike, cell phone, computer, sewing machine, and car.

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Constant
Observations
Pseudo R2

Generalized Linear
Regression^^Child
Hazardous
Labor
Child Labor-3.316***-3.198**(1.263)(1.333)
1,179
1,179
N/A
N/A

Probit Regression^
Child
Hazardous
Labor
Child Labor-2.818***-3.170***(0.925)(0.923)
1,173
1,173
0.365
0.368

^ Dependent variables: Likelihood of having at least one child engaged in Child Labor and Hazardous
Child Labor^^ Dependent variables: Rate of Child Labor and Hazardous Child Labor in the Household
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Similar to Ghana, we used entropy balancing to generate counterfactual for Côte d’Ivoire. The
results of the entropy balancing is presented below for Côte d’Ivoire.
Table 75: Côte d’Ivoire Entropy Balancing: Differences in Covariates Affecting
Selection Before and After Balancing
Comparison
BeforeTreatment
After

BeforeMean

Variance

Mean

Variance

Mean

Variance

Total number of children age 5-11

1.20

0.81

1.35

0.97

1.35

0.97

Total number of children age 12-14

0.39

0.38

0.49

0.43

0.49

0.43

Dummy: Household head with secondary
education

0.13

0.12

0.21

0.16

0.21

0.16

Dummy: Head believe that children
should receive at least senior secondary
education

0.65

0.23

0.70

0.21

0.70

0.21

Dummy: Household paid for school fees

0.84

0.13

0.95

0.05

0.95

0.05

Dummy: Household performs food crop
farming

0.74

0.19

0.85

0.12

0.85

0.12

Dummy: Land cultivated is above median

0.52

0.25

0.60

0.24

0.60

0.24

Dummy: Agricultural labor main income
source

0.11

0.10

0.08

0.08

0.08

0.08

Dummy: Petty trade main income source

0.39

0.24

0.42

0.24

0.42

0.24

Asset Index: Home quality

-0.19

0.97

-0.40

1.26

-0.04

1.26

Asset Index: Non-agricultural assets

-0.12

1.73

0.01

1.82

0.01

1.82

Dummy: High income household

0.34

0.22

0.33

0.22

0.33

0.22

After the counterfactual was constructed, we used a multivariate regression model to estimate
whether provision of material support related to education had any statistically significant effecton children’s engagement in child labor and in hazardous child labor in cocoa production afterFINAL REPORT | 225

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controlling for other factors that influence a household’s decision to engage children in childlabor and in hazardous child labor. The following table presents the regression results for Côte
d’Ivoire. 90
Table 76: Côte d’Ivoire, Probit Regression and Generalized Linear Regression:
Education Material Assistance and Child Labor and Hazardous Child Labor

Estimated treatment effectTreatment Dummy: Members received
educational material assistance
Total number of children% of 12-14 children% of 15-17 children% of girl children 5-17
Head age
Head age-squared
Head Gender: Male
Total number of adults
Number of adults withsecondary/above education
Dummy: Christian
Dummy: Cocoa Household

Probit Regression^
HazardousChild Labor
Child Labor-0.0040.001(0.027)(0.027)
Regression output:
Probit model:-0.0138
0.00365(0.0891)(0.0898)
0.152***
0.155***(0.0331)(0.0337)0.904***
0.941***(0.150)(0.147)0.914***
0.932***(0.173)(0.171)-0.273***-0.318***(0.0868)(0.0908)0.0407*0.0483**(0.0217)(0.0216)-0.000403*-0.000462**(0.000208)(0.000208)-0.0333-0.0524(0.115)(0.114)-0.0521*-0.0538*(0.0286)(0.0300)
0.06930.0818(0.0572)(0.0566)-0.0146-0.0103(0.0875)(0.0866)
0.822***
0.871***(0.135)(0.131)

Generalized Linear
Regression^^Child
Hazardous
Labor
Child Labor-0.015-0.005(0.030)(0.025)Regression output:
Generalized Linear Model 91:-0.0766(0.127)0.0514(0.0403)1.370***(0.210)1.486***(0.220)-0.418***(0.127)0.00883(0.0297)-8.47e-05(0.000281)-0.0690(0.176)-0.0658(0.0414)0.112(0.0797)
0.0449(0.120)1.347***(0.210)-0.0270(0.123)0.0517(0.0407)1.463***(0.208)1.551***(0.215)-0.484***(0.136)0.0119(0.0294)-0.000102(0.000280)-0.116(0.176)-0.0588(0.0425)0.104(0.0754)0.0318(0.122)
1.411***(0.209)90

Included controls for missing observations and whether anyone other than the child was present during interview(not reported).

91

Generalized non-linear least square regression with binomial family and logit link function, estimated using
maximum likelihood estimation.

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Dummy: Household produce
commercial crop
Dummy: Household produce food crop
Dummy: Household has livestock farm
Dummy: Amount of cocoa sold in
2017/18 harvest
Asset index: Home quality
Asset index: Non-agricultural assets
Dummy: Land holding above median
Dummy: Head can sufficiently borrow
Dummy: Children's family migrated
Dummy: Non-relative children staying
Dummy: Household head believe
children start work for pay below 18
Dummy: Cocoa most important sourceof income in community
Dummy: Community has improved
road
Dummy: District capital more than 50
KM
Dummy: Secondary school within 5 KM
Dummy: Community has mobile
connectivity
Dummy: School has concrete building
Dummy: Toilet inside school
Dummy: School has improved water
Dummy: Strata1 (High cocoa
production)
Dummy: Strata2 (Medium cocoa
production)

Probit Regression^
HazardousChild Labor
Child Labor-0.111-0.0933(0.0996)(0.0926)
0.181
0.182
(0.115)(0.117)-0.0661-0.0673(0.0963)(0.102)3.87e-05
3.96e-05(2.95e-05)(3.15e-05)-0.00701
0.00370(0.0410)(0.0421)-0.110***-0.0986***(0.0364)(0.0365)
0.0165-0.0335(0.0939)(0.0941)-0.201**-0.238***(0.0874)(0.0835)
0.158*
0.161*(0.0847)(0.0849)
0.02830.0508(0.174)(0.179)0.193**
0.198**(0.0930)(0.0918)0.03580.0210(0.136)(0.138)-0.0458-0.0854(0.141)(0.139)-0.340**-0.451***(0.169)(0.165)-0.360***-0.361***(0.129)(0.134)0.373*
0.411**(0.191)(0.180)-0.496***-0.558***(0.187)(0.190)-0.01240.0424(0.148)(0.139)-0.255-0.505(0.508)(0.484)0.175
0.154(0.362)(0.366)-0.692*-0.983**(0.369)(0.402)

Generalized Linear
Regression^^Child
Hazardous
Labor
Child Labor-0.203-0.161(0.144)(0.139)0.166
0.163(0.159)(0.165)0.01020.0209(0.123)(0.127)5.34e-05
5.68e-05(4.39e-05)(4.51e-05)-0.0147-0.00303(0.0553)(0.0553)-0.137***-0.121***(0.0477)(0.0460)
0.00662-0.0613(0.129)(0.129)-0.320***-0.338***(0.116)(0.115)0.125
0.156(0.114)(0.113)0.04580.0224(0.214)(0.202)0.288**
0.270**(0.125)(0.127)
0.09310.00476(0.178)(0.172)0.01950.0278(0.183)(0.175)-0.429*-0.562***(0.220)(0.210)-0.449***-0.470***(0.161)(0.163)0.460*
0.483*(0.262)(0.260)-0.602**-0.699***(0.239)(0.247)0.168
0.272(0.211)(0.205)-0.568-0.876(0.666)(0.650)0.867**
0.754*(0.439)(0.428)-1.061**-1.398***(0.508)(0.487)

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Generalized Linear
Probit Regression^
Regression^^
HazardousChild
HazardousChild Labor
Child Labor
Labor
Child Labor-1.379**-1.433**-1.365-1.307
Constant(0.679)(0.667)(1.024)(1.035)Observations
1,357
1,357
1,374
1,374
Pseudo R2
0.211
0.222
N/A
N/A
^ Dependent variables: Likelihood of having at least one child engaged in Child Labor and Hazardous
Child Labor^^ Dependent variables: Rate of Child Labor and Hazardous Child Labor in the Household
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Livelihood Support and Child Labor in the Cocoa Sector
Here we present the quantitative analysis undertaken to address the following research question:
Are children in households where members received livelihood services (such as agriculturaltraining, microfinance, and market access) less likely to be engaged in child labor in the cocoa
sector than are children from households that did not receive such services?
10.8.2.1 Methodology
We use a model-based regression approach to assess whether households that received livelihood
services (GAP training, microfinance services, and market linkage) were less likely to engage
children in child labor and hazardous child labor or had lower rates of child labor and hazardous
child labor compared to households that did not receive livelihood services, after controlling forother observable influences. For that, we follow the approach described in Annex 10.8.1. We
first express the outcome variables (exposure to child labor and hazardous child labor) as afunction of household, community, and school characteristics. Then, we estimate a regression
equation to test whether, after controlling for such factors, the households where at least one
member received livelihood services were less likely to engage children in child labor and inhazardous child labor in cocoa production and had lower rates of child labor and hazardous childlabor in cocoa production than households that did not receive livelihood services.
It is important to note that when implementing partners offer livelihood services to a community
not all the households participate. Only a subgroup of households in the community would either
be eligible or self-select to participate in such programs. It is possible that the households thatwere eligible or self-selected to participate in such programs were fundamentally different fromthe households that did not participate in such program, leading to the problem of selection bias.
This could have occurred, for example, if some of the characteristics of the household that
influenced its program eligibility or uptake also influenced the household’s response(“performance”) as a result of the intervention. One such example would be if entrepreneurial

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farmers were not only more likely to participate in a microfinance program but also more likely
to put their children into school. Likewise, a farmer that accepts GAP training may be more
likely to reduce the number of children in child labor and hazardous child labor than those who
don’t participate in GAP training.
In order to address the potential selection bias which might influence the estimated effect oftreatment (livelihood services) on outcome variables (child labor and hazardous child labor), we
used a quasi-experimental design. The quasi-experimental design is based on a two-step
approach described below:
a. Generate counterfactual: To address selection bias where households self-select to participate in livelihood-support initiatives, we identify a set of variables that are expected toinfluence households’ decision to participate in livelihood services offered in a community.
We identify factors by examining the differences between the households that received/participated in such activities and the households that did not in the communities where the services were offered. These factors include household demographic characteristics (such as
head’s gender and age, and number of adults with secondary or above education), farming
characteristics (such as engage in cocoa, plant other commercial crops, size of landholding),
economic profile (such as having income from agricultural labor, petty trade, other self-employment etc., plus an indicator of wealth), variable on whether household recently migrated
to the village, etc. In order to select the comparison group that could serve as thecounterfactual, we decided to select households from the communities where the livelihood
services were not offered (based on self-reported data by the head of the households). Thiswas done to avoid the possibility of spillover effect influencing the results in the treatmentcommunities. We then use entropy balancing on the sample of households from the nontreated communities, to generate a synthetically designed group of comparison households insuch a way that the synthetic comparison group, on average, looks like the treatment group(that received livelihood services) in observable characteristics that might have influence on
selection of beneficiaries.
b. Use regression model to estimate the impact: Next, we estimate whether the households
that received livelihood services were less likely to engage children in child labor and inhazardous child labor in cocoa production. As described in Section 10.8.1.1, we estimatedtwo models: one where the outcome variable was the probability of having at least one childengaged in child labor & hazardous child labor; and the other with the outcome variable as
the rate of exposure to child labor & hazardous child labor among the children in thehouseholds. The models specified the outcome variables (exposure to child labor/hazardous
child labor) as a function of household, community, and school characteristics. Finally, themodel tested whether, receiving livelihood services had any statistically significant effect on

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children’s engagement in child labor and in hazardous child labor in cocoa production
controlling for other covariates.
10.8.2.2 Data Source
The data source to examine this research question is the child labor, head of the household,community and school surveys conducted by NORC during the 2018/19 main cocoa harvestingseason in Côte d’Ivoire and Ghana.
The self-reported data show that in Côte d’Ivoire 128 households (9%) had at least one member
who received livelihood support and in Ghana 70 households (5.8%) had at least one member
who received livelihood support.
Given the relatively small sample size of households that received livelihood services in Ghana,the model was estimated only for Côte d’Ivoire.
10.8.2.3 Analysis and Results
The first step of the analysis was to use entropy balancing on the sample of households from thenon-treated communities, to generate a synthetically designed group of comparison households.
Table 77 presents the result of entropy balancing for Côte d’Ivoire showing the difference invariables with influence on selection process before balancing and after balancing which
generated the synthetic comparison group.
Table 77: Côte d’Ivoire Entropy Balancing: Differences in Covariates Affecting
Selection Before and After Balancing
Comparison
BeforeTreatment
After

BeforeMean

Variance

Mean

Variance

Mean

Variance

Land cultivated is above median

0.854

0.25

0.64

0.23

0.64

0.23

Household performs cocoa farming

0.86

0.12

0.86

0.12

0.91

0.08

Household performs commercial farming

0.47

0.25

0.54

0.25

0.54

0.25

Agricultural labor main income source

0.12

0.11

0.12

0.11

0.12

0.11

Self-employment main income source

0.10

0.09

0. 09

0.09

0.09

0.09

Asset Index: home quality

-0.11

1.16

-0.23

0.78

-0.23

0.78

Asset Index: non-agricultural assets

-0.11

1.64

-0.29

2.24

0.29

2.24

Household head gender

0.88

0.10

0.97

0.02

0.98

0.02

Number of adults in household

3.03

2.56

3. 93

2.83

3.39

2.83

Head’s perception: Girls should startworking before 18

0.41

0.24

0.38

0.24

0.38

0.24

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After the counterfactual was constructed, we used a multivariate regression model to estimate
whether provision of livelihood services had any statistically significant effect on children’s
engagement in child labor and in hazardous child labor in cocoa production after controlling forother factors that influence a household’s decision to engage children in child labor and inhazardous child labor. The following table presents the regression results for Côte d’Ivoire.
Table 78: Côte d’Ivoire, Probit regression and Generalized Linear Regression:
Livelihood Service Support and Child Labor and Hazardous Child Labor

Treatment effectProbit Regression^
HazardousChild Labor
Labor-0.091-0.068(0.081)(0.080)Regression output:
Probit model:

Treatment Dummy: Members received
livelihood support
Total number of children% of 12-14 children% of 15-17 children% of girl children 5-17
Head age
Head age-squared
Head Gender: Male
Total number of adults
Number of adults withsecondary/above education
Dummy: Christian
Dummy: Cocoa Household-0.332(0.299)0.243***(0.0601)
0.771***(0.253)1.220***(0.284)-0.240(0.184)0.121***(0.0428)-0.00114***(0.000409)-1.050**(0.489)-0.0490(0.0545)-0.0687(0.104)0.188(0.174)
1.877***(0.284)-0.252(0.294)0.239***(0.0605)
0.797***(0.257)1.232***(0.281)-0.310(0.193)0.125***(0.0427)-0.00117***(0.000408)-1.110**(0.472)-0.0530(0.0560)-0.0569(0.107)0.140(0.169)1.841***(0.300)Generalized Linear
Regression^^
HazardousChild Labor
Labor-0.105*-0.090(0.062)(0.061)
Regression output:
Generalized Linear
Model 92:
-0.612*(0.366)0.0892(0.0678)
1.194***(0.387)1.996***(0.414)-0.639**(0.266)0.128**(0.0652)-0.00108*(0.000648)-1.482*(0.826)-0.113(0.0893)0.00154(0.144)0.0823(0.232)2.706***(0.406)-0.529(0.365)
0.0966(0.0663)1.240***(0.397)2.064***(0.417)-0.719**(0.283)0.129**(0.0647)-0.00108*(0.000640)-1.638**(0.793)-0.109(0.0907)-8.34e-05(0.147)-0.0189(0.238)2.634***(0.438)92

Generalized non-linear least square regression with binomial family and logit link function, estimated using
maximum likelihood estimation.

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Dummy: Household produce
commercial crop
Dummy: Household produce food crop
Dummy: Household has livestock farm
Dummy: Amount of cocoa sold in
2017/18 harvest
Asset Index: Home quality
Asset Index: Non-agricultural assets
Dummy: Land holding above median
Dummy: Children's family migrated
Dummy: Non-relative children staying
with the family
Dummy: Household head value
education 93
Dummy: Household head believe
children start work for pay below 18
Dummy: Cocoa most important sourceof income in community
Dummy: Community has improved
road
Dummy: District capital more than 50
KM
Dummy: Secondary school within 5
KM
Dummy: Community has mobile
connectivity
Dummy: School has concrete building
Dummy: Toilet inside school
Dummy: School has improved water
Dummy: Strata1 (High cocoa
production)
93

Probit Regression^
HazardousChild Labor
Labor-0.0515-0.0598(0.188)(0.181)-0.0433-0.00342(0.185)(0.192)0.233
0.237(0.165)(0.174)1.96e-05
4.69e-05(5.54e-05)(5.45e-05)
0.155*
0.170*(0.0912)(0.0919)-0.0748-0.0739(0.0636)(0.0631)-0.215-0.278(0.219)(0.215)0.176
0.179(0.188)(0.191)1.438***
1.446***(0.316)(0.314)-0.498**-0.501**(0.208)(0.211)0.201
0.166(0.151)(0.150)0.327
0.213(0.347)(0.351)0.103
0.125(0.272)(0.277)-0.752***-0.797***(0.236)(0.234)0.114
0.0896(0.256)(0.257)1.406***
1.383***(0.503)(0.489)-1.053***-1.128***(0.334)(0.335)-0.330-0.318(0.277)(0.279)-1.921**-2.027***(0.761)(0.771)
1.467**
1.434**(0.673)(0.655)Generalized Linear
Regression^^
HazardousChild Labor
Labor-0.175-0.146(0.249)(0.246)0.02120.0824(0.286)(0.283)0.265
0.337(0.226)(0.237)0.0001020.000120*(7.57e-05)(7.25e-05)
0.156
0.125
(0.129)(0.122)-0.0755-0.0868(0.0782)(0.0778)-0.480*-0.558**(0.283)(0.277)0.310
0.341(0.266)(0.263)1.233***
1.167***(0.387)(0.367)-0.625**-0.682**(0.275)(0.280)0.320
0.218(0.204)(0.197)0.979***
0.794**(0.370)(0.383)-0.164-0.0107(0.355)(0.375)-0.992***-0.959***(0.287)(0.311)0.150
0.108(0.274)(0.273)
1.164**
1.125**(0.485)(0.455)-1.035**-1.255***(0.435)(0.438)0.443
0.450(0.341)(0.347)-2.677***-2.715***(0.846)(0.888)15.58***
15.19***(1.502)(1.577)

Household Head value education: head believes children should complete at least secondary education.

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Dummy: Strata2 (Medium cocoa
production)
Constant
Observations
Pseudo R2

Probit Regression^
HazardousChild Labor
Labor-0.402-0.358(0.709)(0.699)-4.746***-4.582***(1.457)(1.456)
569
569
0.307
0.314

Generalized Linear
Regression^^
HazardousChild Labor
Labor
11.58***
11.21***(1.499)(1.679)-18.34***-17.36***(2.226)(2.234)
579
579
N/A
N/A

^ Dependent variables: Likelihood of having at least one child engaged in Child Labor and Hazardous
Child Labor^^ Dependent variables: Rate of Child Labor and Hazardous Child Labor in the Household
Robust standard errors in parentheses 94*** p<0.01, ** p<0.05, * p<0.1

Occupational Safety and Health Training and Hazardous Child Labor in
the Cocoa Sector
Here we present the quantitative analysis undertaken to address the following research questions:
Do youth beneficiaries of occupational safety and health (OSH) interventions report working
with appropriate safety equipment? Is the exposure to hazardous work lower among thebeneficiaries?
10.8.3.1 Methodology
In order to examine whether beneficiary youth report more frequent use of safety equipment andless involvement in hazardous child labor than the non-beneficiary youth, it is important to notethat among all the youth only a subgroup would either be eligible or self-select to participate in
OSH programs. It is possible that the youth that were eligible or self-selected to participate insuch programs were fundamentally different from the youth that did not participate in suchprogram leading to the problem of selection bias which can generate misleading conclusions. As
an example, youth from high income families might be more likely to participate in suchtraining, and might report more frequent use of safety equipment and less involvement inhazardous child labor than the non-beneficiary youth. This result could lead to a misleading
conclusion about true program impact since the youth from high income families might havebetter access to safety gear, and thus use them more frequently. As a result, just a simple
comparison of difference in usage between those who received training and those who did not
could lead to incorrect conclusions about the usefulness of OSH training.

94

Regression included controls for missing observations, whether anyone other than the child was present during
interview and department dummies (not reported).

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In order to address the potential selection bias, we used a quasi-experimental design. The quasiexperimental design is based on a multi-step approach described below:
a. Generate counterfactual: To address selection bias where youth self-select to participate
in OSH programs, we use statistical matching technique. We identify a set of variables
that are expected to influence youth’s participation in OSH program. These factors
include youth’s age and gender, household demographic characteristics (such as head’s
gender and education), type of farming done in household (such as cocoa, othercommercial crops, food crop), indicator of household assets (housing quality and nonfarm assets), and whether head values education of children . We then use entropy
balancing on the sample of youth who did not participate in OSH to generate asynthetically designed comparison youth group in such a way that the synthetic
comparison group, on average, looks like the group that participated in OSH program in
observable characteristics that might influence participation in the program.
b. Use statistical test of significance to detect difference in usage of safety equipment:
After identifying the comparison group of youth, we use a statistical test of significanceto test the difference in usage of safety gear between the youth who received OSH
training and the matched comparison group of youth.
c. Use regression model to estimate the correlation between participation in OSH andlikelihood of undertaking hazardous child labor: We use a model-based regression
approach to assess whether youth who participated in OSH training program were lesslikely to engage in hazardous child labor compared to youth who did not receive suchtraining, after controlling for other observable influences. For that, we follow a two-step
approach. We first express the likelihood of exposure to hazardous child labor as afunction of youth characteristics, household, community, and school characteristics.
Then, we estimate a regression equation to test whether, after controlling for such factors,
the youth who received OSH training were less likely to engage in hazardous child laborin cocoa production than those who did not receive OSH training.
10.8.3.2 Data Source
The data source to examine this research question is the child labor, head of the household,community and school surveys conducted by NORC during the 2018/19 main cocoa harvestingseason in Côte d’Ivoire and Ghana. However, the data indicates very few youth in Côte d’Ivoire
received OSH. So the analysis below uses data from Ghana only.
In the child survey, children were asked to report whether they used any protective gear whileworking in agriculture. The types of protective gears considered include protective boots(Wellington boots, Afro Moses), gloves, protective clothing (overalls, long sleeves, trousers),

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nose mask or gas mask, helmet, goggles, and other protective gear. Based on the responses, anindicator variable was constructed to specify whether the children reported using any one of theabove categories of protective wear while working in agriculture in the past 12 months before thesurvey. The data indicates that in Ghana, 48% of youth of age 15-17 were using at least one ofthe protective gears listed above.
10.8.3.3 Analysis and Results
In the first step of our analysis, we performed entropy balancing to generate a synthetic group ofcomparison youth using the data from Ghana to generate the counterfactual (the comparisongroup of youth which on average, looks like the group that participated in Occupational Safety
and Health training program in observable characteristics that might influence participation in
the program). The entropy balancing result reported in Table 79.
Table 79: Ghana Entropy Balancing: Differences in Covariates Affecting Selection
Before and After Balancing
Control
BeforeTreatment
After

BeforeMean

Variance

Mean

Variance

Mean

Variance

Child age

15.87

0.70

15.
81

0.60

15.81

0.60

Child sex (male=1)

0.56

0.25

0.49

0.25

0.49

0.25

Household head age

78

57

6184

57

6183

Number of adults with secondary
education

0.86

.94

0.94

1.25

0.94

0.94

Dummy: Household performs cocoa
farming

0.93

0.06

0.88

0.11

0.88

0.11

Dummy: Household produces
commercial farming

0.14

0.11

0.16

0.14

0.16

0.14

Dummy: Household produces foodcrop farming

0. 91

0.08

0.92

0.07

0.92

0.07

Household's cocoa output

596

959948510

571,489510

571,674

Asset Index: home quality

-0.01

1.02

0.49

1.74

0.49

1.74

Asset Index: agricultural assets

0.0

1.47

-0.03

1.51

-0.03

1.51

Dummy: Household head believes
children start work for pay below 18

0.26

0.19

0.27

0.20

0.27

0.20

24955

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After identification of the comparison youth group, we ran multivariate regression technique totest whether the youth who received OSH training were less likely to engage in hazardous childlabor in cocoa production. The following table presents the regression results for Ghana. 95
Table 80: Ghana Participation in Occupational Safety and Health Training and
Likelihood of Exposure to Hazardous Child Labor
Probit Regression^
Hazardous Labor
Estimated treatment effect-0.009(0.050)Probit Regression output
Treatment Dummy: Members received formal OSH training
Child age
Dummy: Male childTotal number of children% of 12-14 children% of 15-17 children
Head age
Head age-squared
Head Gender: Male
Total number of adults
Number of adults with secondary/above education
Dummy: Christian-0.0299(0.174)-0.0330(0.0801)0.290**(0.132)-0.321***(0.0772)-0.0345(0.141)0.343**(0.169)0.0126*(0.00700)-1.07e-05(6.72e-06)-0.0919(0.238)-0.0274(0.0967)-0.0959(0.0996)-0.302(0.214)95

Included controls for missing observations and whether anyone other than the child was present during interview(not reported).

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Probit Regression^
Hazardous Labor
Dummy: Cocoa Household
Dummy: Household produce commercial crop
Dummy: Household produce food crop
Dummy: Household has livestock farm
Dummy: Amount of cocoa sold in 2017/18 harvest
Asset Index: Home quality
Asset Index: Non-agricultural assets
Dummy: Land holding above median
Dummy: Head can sufficiently borrow
Dummy: Children's family migrated
Dummy: Non-relative children staying
Dummy: Household head value education

0.749**(0.323)0.00964(0.297)0.150(0.239)0.355**(0.168)0.000141(0.000122)-0.124(0.0816)-0.0750(0.0604)
0.252(0.170)0.302(0.186)0.293*(0.153)0.713**(0.356)-0.637(0.913)

Dummy: Household head believe children start work for pay
below 18

-0.0239

Dummy: Cocoa most important source of income incommunity

0.851***

Dummy: Community has improved road
Dummy: District capital more than 50 KM
Dummy: Secondary school within 5 KM
Dummy: Community has mobile connectivity
Dummy: School has concrete building(0.178)(0.286)0.158(0.163)0.953**(0.453)-0.146(0.173)-0.823**(0.334)0.480***(0.185)

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Probit Regression^
Hazardous Labor
Dummy: Toilet inside school
Dummy: School has improved water
Dummy: Strata1 (High cocoa production)
Dummy: Strata2 (Medium cocoa production)-0.824***(0.245)-2.545***(0.695)-0.192(0.245)-0.696**(0.279)0.821(1.696)

Constant
Observations

465^ Dependent variable: Likelihood of having at least one child engaged in Hazardous Child Labor
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Impact of Multiple Interventions and Child Labor in the Cocoa Sector
Here we present the quantitative analysis undertaken to address the following research question:
Did interventions funded by the stakeholders (including the CLCCG members and otherorganizations) to reduce the prevalence of child labor and children’s exposure to hazardous childlabor in cocoa production have any impact?
10.8.4.1 Methodology
In order to evaluate whether interventions funded by the stakeholders were effective, we evaluate
the impact of implementing combinations of interventions on the prevalence of child labor andhazardous child labor by undertaking an attribution analysis. The challenge in the present case of
conducting attribution analysis is that there was no control group or even explicit counterfactualgroup identified at the start of the interventions. Another significant concern is that communities
exposed to multiple types of interventions appear to have been selected purposively, notrandomly by the implementer. Lack of random assignment of a community to an intervention
means that it is hard to disentangle the effect of the intervention from the effect of the selection
criteria. Thus, the evaluation design is constrained to make the most of the pattern of intervention
assignments that resulted from the site selection decisions of the various Implementing Partners(IPs). A consequence of this is that in what follows there are analyses constrained by the limited
sample size and, therefore, whose power to detect the expected effect sizes (impacts) is low. This

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does not imply that there was no impact, only that the size of the impact was not large enough tohave been able to detect it, given the available sample size. 96
In order to address the methodological challenges associated with the unfavorable intervention
assignment of communities, we adopted a two-step approach. In the first step, we address thepotential site selection issue related to the IPs’ choice of communities to strategically implement
multiple interventions. In the second step, once the community selection issue has beenaddressed, we estimate an attribution equation to test whether, after controlling for otherinfluences of children’s engagement in child labor and in hazardous child labor, the rate of childlabor and in hazardous child labor were lower among households in communities wherecombinations of interventions were implemented.
10.8.4.2 Data Source
The stakeholders funded and implemented more than 15 different categories of interventions in
the cocoa-growing areas of Côte d’Ivoire and Ghana since 2010 (see Annex 10.11 for a
description of the different intervention categories). Ideally, an evaluation would aim to assess
which intervention categories (or combination of categories) have the greatest impact, so that
implementers can better target their resources and efforts. However, the ability of an evaluation
to scientifically detect the impact of interventions and their relative effectiveness criticallydepends on the availability of data and on feasibility of constructing a valid counterfactual.
In order to assess the effectiveness of interventions in fighting child labor, we collectedintervention data from the CLCCG partners and other institutions such as the ILO, UNICEF and
Fairtrade focusing on interventions they implemented in communities during 2010-2018 in thecocoa-growing areas of Côte d’Ivoire and Ghana. Combining the data shared by the CLCCG
partners and other institutions, we constructed an intervention database that contains information
on different types of projects funded by the partners, as well as on the projects’ coverage at thenational, regional, and district level. We then used information provided by the stakeholders on
whether they implemented interventions in each of the communities covered by the 2018/19
survey round and overlaid them with the implementation data (also from the 2018/19 survey).
The objective was to link the interventions implemented by the stakeholders with the prevalenceof child labor and hazardous child labor as captured by the 2018/19 survey rounds.
The majority of these interventions covered here were either implemented or supported by the
CLCCG partners. Table 81 and Table 82 present a summary of the data on the exposure of96

Recall that these tests are “opportunistic” in the sense that the sample could only be identified after datacollection; the evaluation had no control over where the IPs conducted their interventions nor were the combinationsof interventions at each site known in advance.

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communities surveyed in the 2018/19 round to different interventions based on the intervention
database.
Table 81: Distribution of communities in Côte d’Ivoire by treatment combination
Combination

Total
EAs

A

41

B

16

C

7

D

3

E

2

F

2

G

1

H
I

Categories of Interventions*
1

2

3

4

5

6

7

8

9

10

11

12

13

14

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

1

x

x

x

x

x

x

1

x

x

x

x

x

x
x

x

x

x

x

x

15

x
x

x

x

x

*1: Education, 2: Vocational training, 3: Sensitization/awareness raising program, 4: OSH program, 5:
Capacity building, 6: Child labor monitoring and remediation systems, 7: Community action/development
plans, 8: Promotion of sustainable livelihood, 9: labor saving practices, 10: Improving access to public
services, 11: Women’s empowerment, 12: Research, 13: Material assistance, 14: Enforcement of antichild labor regulations, 15: Compliance initiatives

Table 82: Distribution of communities in Ghana by treatment combination
Combination

Total
EAs

A

41

B

13

C

9

D

3

E

Categories of Interventions*
1

2

3

4

5

6

x

x

x

x

x

x

x

x

x

x

3

x

x

x

x

x

F

1

x

x

x

x

x

G

1

x

x

x

x

x

H

1

x

x

x

x

I

1

x

x

x

x

x

7

8

9

10

11

12

13

14

15

x
x
x

x

x

x

x

x

x

x

x

x

x

x

x

x

x
x

x

x

x

x

*1: Education, 2: Vocational training, 3: Sensitization/awareness raising program, 4: OSH program, 5:
Capacity building, 6: Child labor monitoring and remediation systems, 7: Community action/development
plans, 8: Promotion of sustainable livelihood, 9: labor saving practices, 10: Improving access to public
services, 11: Women’s empowerment, 12: Research, 13: Material assistance, 14: Enforcement of antichild labor regulations, 15: Compliance initiatives

Table 81 and Table 82 show the combinations of intervention categories implemented in thesecommunities/Enumeration Areas (EAs) and the total number of EAs that were exposed to a

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given combination. For example, the first row indicates a total of 41 EAs included in the 2018/19
survey round received Combination A (corresponding to value zero for each of the 15 treatment
categories, indicating no treatment being reported by the stakeholders for any those 41 EAs).
Similarly, the second row (Combination B) indicates total of 16 EAs included in the 2018/19
survey round received intervention Categories 3, 4, 5, and 6. In examining the tables the reader
should observe, in particular,■

which combinations have enough observations for analysis (number of EAs times 15
households per EA sampled); and■which combinations comprise intervention categories that are subsets of other combinations
so that in comparing them the non-overlapping categories can be evaluated.

Table 81 and Table 82 suggest that one analysis strategy would be to test whether exposure tosome – one or more – categories of intervention has an impact when compared no exposure atall. This would compare the 33 EAs in Table 81 that received some set of interventions against
the 41 EAs that were not exposed to any intervention. However, this approach is not likely to be
successful since many of the exposed combinations contain intent-to-treat interventions – that is,interventions at the community level who probable impact would be very diffuse and therefore
undetectable at any financially affordable sample size (not to mention the often highly
inadequate sizes to which the evaluation was constrained). A more successful approach – which
we describe, below – is to limit the evaluation of impact to just those EAs that received the mostsignificant, or highest “dosage” of treatment. For Côte d’Ivoire, we do this by comparing the 11
EAs belonging to Combinations C, D and I to the 41 EAs remaining unexposed. However, for
Ghana, we had only 3 villages with similar dosages of interventions (Combinations F, G and I),
we were not able to do any analysis of effectiveness due to small sample size issue. 97 Table 81
and Table 82 also makes clear the limits of the potential analyses that can be undertaken.
Although they permit examining the impact for aforementioned combinations, these tables
illustrate that our ability to evaluate the effectiveness of the vast majority of intervention
category combinations – not to mention individual categories of intervention – is severely
limited. First, they reveal the unfavorable distribution of intervention categories across the
sampled communities, so combinations generally have too few EAs or more than necessary inorder to compare combinations. Thus, even for the data we do have, with two or three exceptions
in each country, the small number of communities receiving a given combination of intervention
categories means there are too few observations for detecting all but the biggest impacts – andbigger than those anticipated for the interventions.

97

It was by design that both countries had 41 EAs untreated.

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With the objective of assessing effectiveness of funded interventions, this research question
explores whether the interventions, when implemented together, were successful in reducing theprevalence of child labor and hazardous child labor in the cocoa sector. We do this by testingwhether community exposure to a given combination of intervention categories (as reported by
the IPs) had any detectable impact on the exposed communities. Given that for Ghana there were
only 3 communities with multiple interventions, it was only feasible to assess the impact ofmultiple interventions in Côte d’Ivoire.
10.8.4.3 Analysis and Results
10.8.4.3.1

Construction of Counterfactual

It is likely that the IPs' choice of which enumeration areas (EAs) received a given intervention
combination was not random. This means that in order to isolate intervention impact thepotential effects of purposive selection must be disentangled from the effect of the intervention
itself. Otherwise, a “selection bias" may exist, leading the evaluator to risk misattributing impact
or lack thereof. This is especially likely in the present case where only an endline and no baseline
is available. For example, if IPs selected communities (possibly unwittingly) based on proximity
to major roads (ease of access to the community) and major roads facilitated business, thentesting the effectiveness of multiple interventions by comparing treated communities to untreatedcommunities might simply be picking up the effect of proximity to a major road, not theeffectiveness of multiple interventions.
To mitigate the potential for community selection bias we statistically match communities thatreceived intervention combinations to the communities that did not receive any intervention(comparison communities). The statistical matching method is used to identify a set ofcomparison communities that are very similar to the treatment communities with respect toobservable characteristics that are correlated to selection of sites by the implementers.
Based on our discussion with the stakeholder who implement multiple interventions and ourunderstanding of the selection process of communities by IPs, we identified the community
characteristics that likely played an important role in the selection process. In our matchingprocess, we first used the survey data (from the community leaders’ survey) to explore if there
were some particular characteristics that differentiate the multiply treated communities fromthose that received no intervention (comparison communities). The survey data showed that for
all communities selected for multiple interventions by IPs, cocoa production was the mostimportant source of income, and these communities were all close to market (distance to themarket less than 10 km). Thus, we started the matching exercise by narrowing down the set ofcomparison communities to only those in which cocoa production was the most important sourceof income and which were less than 10 km to the market. This left us with 11 treatedcommunities to be matched with 32 comparison communities in Côte d’Ivoire.
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We then used one to one matching based on Mahalanobis metric matching (distance matching).
The matching based on the Mahalanobis distance measures is a non-parametric method and isconsidered to have superior performance than other popular alternative matching methods suchas the Propensity Score Matching (PSM). We used matching with replacement (so one
comparison community was allowed to serve as a match for more than one treatment community
if it was more similar to a given treatment community than other potential comparison
communities based on its similarity of characteristics to the treatment community inconsideration). Here it is important to remember that the objective was to identify a set oftreatment and comparison communities that are very similar with respect to community
characteristics that influence community selection by the implementers and also outcomes. These
characteristics can be classified as community infrastructure indicators (having access to gridelectricity) remoteness indicator (community less than 20 km away from the district capital); andsocio-economic indicators (whether migrant live in the community).
The Mahalanobis metric matching was then applied to the 11 treatment and 32 comparison
communities in Côte d’Ivoire in order to create two groups of communities such that the averagevalue on an array of variables was the same for both groups (i.e., the groups of communities
were statistically “balanced”). The matching method generated a group of 9 treated communities(those that received multiple interventions) and 9 comparison communities that were mostsimilar to the treated communities with respect to the variables identified above. This led to adataset that we used to assess the impact of intervention combinations on the likelihood and rateof children’s engagement in child labor and in hazardous child labor.
10.8.4.3.2

Use of attribution model to estimate the impact of multiple interventions

Next, we used an attribution model to test whether the households in the communities wheremultiple interventions were implemented had a lower rate of child labor and children’s exposure
to hazardous child labor than the communities that were similar but did not receive suchinterventions.
We estimate two regression models: one where the outcome variable was the probability of ahousehold having at least one child in child labor and in hazardous child labor; and the otherwith the outcome variable as the rate of exposure to child labor and in hazardous child labor
among the children in the households. The models specified the outcome variables as a functionof household, community, and school characteristics. Finally, the model tested whether, controlling for other covariates, there was any statistically significant difference in children’s
engagement in child labor and in hazardous child labor in cocoa production among thehouseholds in the treatment communities compared to the households in the matched comparison
communities.

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For this research question, in the first step of analysis, we used Mahalanobis metric matching
technique to identify a set of treatment and comparison communities (from total 11 treatment and32 comparison communities in Côte d’Ivoire) that are most similar with respect to observed
characteristics that were expected to influence IP’s community selection process. The following
table presents the results of balance check before and after Mahalanobis metric matching wasapplied.
Table 83: Matching Balance Check: Covariates Balance Before and After Mahalanobis
Metric Matching in Côte d’Ivoire
Comp
Used in Matching

Treat

BeforeMatching

Comp

Treat

After
Matching

Diff

P-value

Dummy: Community has electricity grid

63%

82%

78%

78%

0%

1.00

Dummy: Migrants live in the community

95%

73%

89%

89%

0%

1.00

Dummy: District capital more than 20km

43%

73%

67%

67%

0%

1.00

Dummy: Community has primary school within
1km

98%

91%

100%

100%

0%

1.00

Dummy: Community has cell phone coverage

88%

91%

89%

100%

11%

0.33

Dummy: Community has improved roads

38%

55%

33%

67%

34%

0.18

Dummy: Low income EA*

33%

18%

22%

22%

0%

1.00

Other Community Characteristics*EA where at least 50% of households are low income.

Using Mahalanobis metric matching technique, we matched nine treatment communities withnine comparison communities that were very similar with respect to the community importantcharacteristics that are expected affect community selection. This led to a total of 18 matchedcommunities which serves as the sample for running the attribution analysis.
Next, we present the results of estimation of the attribution model which tested whether thehouseholds in the communities where multiple interventions were implemented had a lower rateof child labor and children’s exposure to hazardous child labor than the communities that weresimilar but did not receive such interventions. Table 84 presents the results where the outcome
variable was the likelihood of children’s exposure to child labor and hazardous child labor, while
Table 85 presents the results where the outcome variable was the rate of children’s exposure tochild labor and hazardous child labor. 98

98

Included controls for missing observations and whether anyone other than the child was present during interview(not reported).

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Table 84: Impact of Multiple Treatment on Likelihood of having Child Labor andHazardous Child Labor in Côte d’Ivoire: Probit Regression

Estimated treatment effect: Household living incommunity exposed to multiple interventions
Total number of children% of 12-14 children% of 15-17 children% of girl children 5-17
Head age
Head age-squared
Head Gender: Male
Total number of adults
Number of adults with secondary/above education
Dummy: Religion Christian
Dummy: Cocoa Household
Dummy: Household produce commercial crop
Dummy: Household produce food crop
Dummy: Household has livestock farm
Amount of cocoa sold in 2017/18 harvest season
Asset Index: Home quality
Asset Index: Non-agricultural assets
Dummy: Children's family migrated
Dummy: Non-relative children staying in thehousehold

Probit Regression^
Child Labor
Hazardous Labor-0.254*-0.276*(0.139)(0.151)0.112***
0.0968***(0.0279)(0.0272)
0.276**
0.306***(0.107)(0.110)0.531***
0.503***(0.116)(0.0933)-0.139-0.189**(0.0855)(0.0892)
0.01670.0176(0.0207)(0.0206)-0.000156-0.000154(0.000217)(0.000213)-0.140-0.133(0.137)(0.122)0.002180.00535(0.0265)(0.0299)-0.0101
0.00378(0.0625)(0.0641)-0.102**-0.0863**(0.0483)(0.0366)
0.423***
0.416***(0.0890)(0.0782)
0.0116-0.00908(0.116)(0.113)0.07180.0821(0.0559)(0.0559)-0.0542-0.0245(0.0790)(0.0928)-3.14e-07
1.03e-05(2.14e-05)(2.11e-05)-0.0310-0.00619(0.0569)(0.0556)-0.0225-0.0143(0.0274)(0.0265)0.00179
0.0490(0.0970)(0.0900)
0.201
0.212
(0.203)(0.203)

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Dummy: Household Head value education 99
Dummy: Household Head believe children start
work for pay below 18
Dummy: Cocoa is the most important source ofincome in community
Dummy: Community has improved road
Dummy: District capital more than 50 KM
Dummy: Secondary school within 5 KM
Dummy: Toilet inside school
Dummy: Low income Community
Amount of cocoa sold in 2017/18 harvest season(from cocoa shed)
Dummy: Cocoa farming relatively new in thecommunity
Observations
Pseudo R2

Probit Regression^
Child Labor
Hazardous Labor
0.05950.0324(0.0773)(0.0779)
0.0173-0.00274(0.0780)(0.0833)-0.1450.180(0.548)(0.468)0.296
0.113(0.236)(0.238)-0.487**-0.379*(0.203)(0.219)0.300-0.132(0.399)(0.429)-0.221**-0.215**(0.0924)(0.0889)-0.285-0.148(0.252)(0.274)5.96e-08
2.97e-08(6.37e-08)(6.11e-08)
0.120-0.0842(0.302)(0.278)
336
336
0.233
0.240

^ Dependent variables: Likelihood of Child Labor and Hazardous Child Labor
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

99

Household head value education: head believes children should complete at least secondary education.

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Table 85: Impact of Multiple Treatment on the Household-level rate of Child Labor andHazardous Child Labor in cocoa production: Average treatment effects estimates basedon GLM in Côte d’Ivoire
Generalized Linear Regression^^

Estimated treatment effectChild Labor

Hazardous Labor-0.172***-0.174***(0.062)(0.065)Regression output: Generalized Linear Model 100:Treatment Dummy: Community exposed tomultiple interventions
Total number of children% of 12-14 children% of 15-17 children% of girl children 5-17
Head age
Head age-squared
Head Gender: Male
Total number of adults
Number of adults with secondary/above education
Dummy: Religion Christian
Dummy: Cocoa Household
Dummy: Household produce commercial crop
Dummy: Household produce food crop

-0.928**-0.973**(0.382)(0.415)0.159**

0.119(0.0771)(0.0749)

1.064***

1.157***(0.391)(0.400)1.733***

1.748***(0.475)(0.440)-0.645**-0.766***(0.252)(0.282)-0.0300-0.0126(0.0730)(0.0748)0.000450

0.000302(0.000734)(0.000749)-0.149-0.0744(0.545)(0.489)-0.0331-0.0118(0.125)(0.135)0.0567

0.0564(0.253)(0.247)-0.187-0.192(0.193)(0.163)

1.508**

1.597**(0.751)(0.779)0.105

0.0827(0.387)(0.391)0.296*

0.342*(0.177)(0.189)100

Generalized non-linear least square regression with binomial family and logit link function, estimated using
maximum likelihood estimation.

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Generalized Linear Regression^^Child Labor

Hazardous Labor-0.00360

0.0965(0.323)(0.343)-2.27e-06

9.75e-06(6.69e-05)(6.44e-05)-0.118-0.0376(0.207)(0.207)-0.174*-0.155(0.0953)(0.0970)-0.1200.00383(0.320)(0.296)0.0354-0.0186(0.361)(0.311)0.131

0.0522(0.234)(0.261)

Dummy: Household head believe children start
work for pay below 18-0.00734-0.0447(0.288)(0.307)

Dummy: Cocoa is the most important source ofincome in community-1.421-0.446(2.478)(2.366)

1.636

1.077

(1.071)(1.027)-1.781-1.400(1.137)(1.139)

2.227

0.858

(2.205)(2.147)-0.469*-0.454*(0.282)(0.260)-1.618-1.135(1.161)(1.145)2.39e-07

1.42e-07(2.56e-07)(2.53e-07)

0.935

0.349

(1.346)(1.301)-2.111-2.925(2.132)(2.187)336

336

Dummy: Household has livestock farm
Amount of cocoa sold in 2017/18 harvest season
Asset Index: Home quality
Asser Index: Non-agricultural assets
Dummy: Children's family migrated
Dummy: Non-relative children staying in thehousehold
Dummy: Household head believe children shouldhave at least secondary education

Dummy: Community has improved road
Dummy: District capital more than 50 KM
Dummy: Secondary school within 5 KM
Dummy: Toilet inside school
Dummy: Low income Community
Amount of cocoa sold in 2017/18 harvest season(from cocoa shed)
Dummy: Cocoa farming relatively new in thecommunity
Constant
Observations^^Dependent variables: Rate of Child Labor and Hazardous Child Labor in the Household.
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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10.9 Annex IX: Qualitative Methodology and Supplemental Analysis
General Qualitative Approach
The qualitative component of the 2018/19 Child Labor Survey provides context for thequantitative results, and a deeper understanding of how various key players understand childlabor within the cocoa sector in Ghana and Côte d'Ivoire. This component also provides nuanced
perspectives on the topics covered in the surveys, including complex concepts such as nightwork, heavy loads, and sharp tool use. These helped to identify factors contributing to observed
prevalence rates, changes in prevalence rates, and changes in hazardous work trends. A summaryof each of the research questions and corresponding respondent groups are in Annex 10.1.
10.9.1.1 Key Informant Interviews
NORC conducted KIIs at the community, national, and international level with the following
groups: community leaders, donors, cocoa industry members at different points in the value
chain, government officials, and implementers. At the community level, NORC conducted 25
KIIs with teachers and community leaders in seven communities in Côte d’Ivoire with, and 28
KIIs with teachers and community leaders in eight communities in Ghana. These interviews
provided key stakeholder perspectives on child labor, and interventions aimed at reducing childlabor.
10.9.1.2 Focus Group Discussions
NORC conducted focus group discussions with children and caregivers in cocoa growing regionsof Ghana and Côte d'Ivoire. Focus groups were disaggregated by sex and beneficiary status(project beneficiary communities vs. non-beneficiary communities). For the purposes of thisanalysis, we use the term “caregivers” instead of parents, as we found that in many communities,
children lived with aunts, uncles, grandparents, or other family members. Respondents were notasked about their familiarity with specific interventions, but rather, types of interventions. For
focus groups with children, groups were separated into two age groups: children 5-11 years oldand children 12-17.
10.9.1.3 Sampling
For national and international level KIIs, we used a snowball sampling approach, in which
respondents were asked to identify other appropriate KII respondents for the study. NORC also
leveraged opportunities at workshops and meetings related to child labor in cocoa andapproached potential respondents in this way. We also conducted community-level KIIs with
community leaders and teachers. These respondents were identified after the team completed thesampling approach below.

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To select communities for focus groups and community-level KIIs, NORC used quantitative
survey responses 101 to generate a comprehensive list detailing average responses for key
intervention and hazardous labor data. The team closely analyzed surveyed communities along
the following parameters:■

Training exposure: percent of respondents exposed to occupational safety training, vocational
training, awareness training, and livelihood projects■

Reported activities: percent of respondents reporting land clearing, agrochemical use, nightwork, working hours■

Child labor rate: percent of children in EA engaged in child labor.

To maximize variance in perspectives, we purposively selected EAs with varying rates oftraining exposure, reported activities, and child labor. The team then narrowed the list ofcommunities by focusing on communities with inverse relationships between training exposureand reported activities/child labor rates, high training exposure and low child labor rates/reported
activities, and low training exposure and high reported activities/child labor rates. The team also
individually selected communities where training exposure rates were high, but child labor andhazardous activity rates were also high, and communities where training exposure was low, but
child labor rates and hazardous activity rates were also low. This was an iterative exercise inwhich after each pass, team members discussed their reasoning for selecting each community.
Finally, the team agreed on 15 communities in each country representing varying rates of childlabor and hazardous child labor and varying levels of intervention of exposure.
Following this exercise, NORC used GPS data and STATA to generate a map of each country
displaying where each selected community was located. The purpose of this exercise was toensure that selected communities were spread across each country and represented an array ofinterventions. The team worked closely with our local subcontractor to ensure the accuracy ofthe maps generated and selected the final communities. Final communities for FGDs andcommunity-level KIIs are listed in Annex 10.1.2.2.
It is important to note that this sampling approach was purposive and intended to obtain a widearray of perspectives on beneficiary experiences with interventions. Therefore, the sample
selected is not representative of all cocoa growing communities in Côte d’Ivoire and Ghana.

101

Survey responses (from Part 1) included community leader responses about the types of interventions in the area,
as well as calculated rates for sharp tool use, night work, and other forms of hazardous labor

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10.9.1.4 Training
NORC’s qualitative expert conducted trainings in Ghana and Côte d’Ivoire. Our local
subcontractor recruited experienced focus group moderators for this exercise. The qualitative
training was four days long, including one day of pilot, and one day of post pilot debrief. In
Ghana, the training took place April 23rd to 26th, 2019, and April 29th to May 3rd, 2019 in Côte
d’Ivoire. Moderators and note-takers were trained in best practices in focus group moderation,
including topics around managing group dynamics, minimizing risk for adult and child
respondents, maintaining neutrality throughout the discussion, and maintaining intragroupconfidentiality. Moderators were also trained on how to manage child disclosures of abuse andforced labor, including response and reporting to appropriate authorities. Moderators were
provided with a risk and response protocol outlining the appropriate procedures of bringing
attention to disclosure. Focus group moderation teams were also provided with operative
definitions for intervention categories, and a list of common off-farm and off farm activities incocoa. Focus group moderation teams were required to be very familiar with both lists in order toprobe effectively and recognize local names for various tools.
During training, moderators held extensive practice rounds and discussions to ensure that theproper local words were being used. Moderators also made considerations for regional variations
for the names of key terms, and concepts. Following the pilot, data collection teams made thenecessary adjustments to the data collection instruments. These changes maintained themeanings of each question, but were reworded to be more direct, and ensure consistenttranslation to local languages in the field.
Following the pilot, the data collection team held a debrief session in which moderators outlined
the questions and key concepts that were harder to convey in the field. Following this, the mademinor edits to focus group and community-level KII instruments. Data collection teams deployed
immediately after instrument modification. Fieldwork in Ghana began the week of April 29th,
2019, while fieldwork in Côte d’Ivoire began the week of May 6th, 2019. Focus groups andcommunity-level KIIs were recorded, translated, and transcribed into English and French.
Transcriptions were transmitted through NORC’s secure file transfer platform
Analysis
Qualitative analysis took a multistep approach. NORC received a total of 96 transcripts, 48 fromeach country. Following receipt, NORC’s qualitative expert conducted phenomenologicalanalysis by reviewing two transcripts in each respondent group in each country and developed alist of key emerging themes. These key themes served as preliminary codes and aligned withresearch questions. Next, the qualitative expert used preliminary findings from the quantitative
data to identify trends for further investigation. These themes were used to develop a preliminary
coding framework to be tested. Next, the qualitative expert coded one transcript from each

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respondent group to identify gaps in the exiting coding framework. This framework wasamended with additional codes to produce a final codebook. All transcripts were then coded in
NVivo. Following coding, the qualitative expert conducted in-depth analysis of each code,
identified patterns between codes, patterns and variances across respondent groups, and patterns
and variances across countries.
10.9.2.1 Education and Vocational Training
To gather perceptions and impact of education and vocational training programs, NORC asked
community leaders, teachers, caregivers and children about their exposure to and perspectives on
education and vocational training interventions
Older children who received vocational training were asked to reflect on the skills they learned,
how these skills affected their future aspirations, and any additional skills they would have liked
to learn. Community leaders and teachers were asked about their perspectives on vocational
training programs, particularly how vocational training programs affected children’s propensity
to engage in child labor, and children’s prioritization of education. Caregivers (parents,
guardians and those who are responsible for the care children) were also asked their opinions on
vocational training programs, but specifically focused on how their children benefitted fromthese initiatives.
Measurement of perceptions and the impact of education interventions followed the same
approach. Additionally, NORC asked caregivers, teachers, and community leaders to reflect on
how education interventions affected children’s school attendance. Caregivers and teachers wereasked about the instances where children missed school and the extent to which school-based
interventions had any impact on attendance. These responses were then triangulated with datafrom children’s focus groups. Children were asked to reflect on the ways their schools hadchanged in recent years, and how they benefitted from these changes. NORC then comparedresponses from children, caregivers, teachers, and communities where school-based interventions
were taking place, and where they were not. To assess differences in child labor engagement
between beneficiaries and non-beneficiaries of school-based interventions, NORC comparedchild-reported responses related to on-farm activities, off-farm activities, days and times worked,
and the ages of working children. NORC also compared beneficiary and non-beneficiarychildren’s responses regarding perceptions on education, and the situations in which they
typically missed school.
To assess children’s reported gains in education or training interventions, children were first
asked to discuss their schools, and any recent changes they’d observed in their schools. Children
were asked about how these changes affected them or changed what they liked about school.
Only focus groups with older children (aged 12 to 17) included discussion of vocational training

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programs. In these discussions, children were asked to discuss their involvement in vocational
training programs, how they benefitted from vocational training programs, and any additional
skills they would like to learn. Those who had no involvement in vocational training programs
were asked to consider the types of skills training they would like to receive if training were to
become available in their communities.
To assess caregiver’s perspectives on their children’s gains, caregivers were asked to reflect on
infrastructural and administrative issues at their children’s schools, and how various initiatives
have addressed these issues. Caregivers were also asked to report on any changes in theirchildren’s opinions of their schools, and how these changes affected their attitudes towards
school. For vocational training programs, caregivers were asked about their familiarity with suchprograms, whether their children were participating, and whether this participation affected theirchildren’s aspirations.
To measure teachers’ perceptions of education and training interventions, teachers were asked
about their familiarity with school-based interventions and vocational training programs in thearea. Teachers who were familiar with these programs were then asked to report on the impactsof these programs on student and caregiver attitudes towards learning. Teachers were also asked
to provide definitions of child labor in their own words, and report on the prevalence of childlabor in the communities in which they resided. Teachers were asked directly about whether
these interventions changed the likelihood that a child would be engaged in child labor.
Teachers’ responses to questions about beneficiary children’s likelihood to engage in child laborwere compared to their responses about familiarity with these interventions, and the detail withwhich they could describe the education and training interventions to which they were exposed.
Teacher anecdotes concerning their interactions with caregivers, particularly to discusschildren’s wellbeing were also included in this analysis.
10.9.2.2 Impact of Livelihood Services
To assess the impact of livelihood services, community leaders and caregivers were provided
with operative definitions of livelihood services, including examples. They were then asked toreport on their level of exposure to such services, and their impact, including whether theirinvolvement in livelihood services changed the frequency, or nature of their children’s assistancewith cocoa. To measure the impact of livelihood services, community leaders and caregivers
were provided with operative definitions of livelihood services, including examples. They werethen asked to report on their level of exposure to such services, and their impact, including
whether their involvement in livelihood services changed the frequency, or nature of theirchildren’s assistance with cocoa. Caregivers were also asked to describe other income-generating
activities in which they were involved outside of cocoa. These responses were triangulated with
reported increases in income, changes in yield, and capacity to hire laborers. In each community,

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caregiver responses to these questions were triangulated with child responses regarding changes
in on-farm and off-farm activities. This produced a broader picture of the impact of livelihood
services on child activities.
10.9.2.3 Occupational Safety & Health (OSH) Training
To assess exposure to occupational safety and health interventions, older children (aged 12-17,
hereafter referred to as youth) were asked whether they have received any training on messaging
on how to be safer on farms, and from whom this messaging came. To measure the extent towhich these youth were less likely to be exposed to hazardous work, NORC analyzed responses
related to on-farm and off-farm activities, particularly the activities youth reported had changed,
or they were not allowed to do. These responses were then compared to reported past andongoing injuries, and reported changes in methods of self-protection.
10.9.2.4 Awareness-Raising Campaigns
Community leaders, teachers, and caregivers were asked about their exposure to awarenesscampaigns, their perceptions of these campaigns, and the impact of campaigns of thesecampaigns on child labor practices in their communities. Community leaders were asked toreport on the types of awareness campaigns, their involvement in the promotion of awarenesscampaigns, and any changes in prevalence of child labor in their communities. Teachers wereasked about the types of awareness campaigns they were exposed to, their involvement in the
promotion of any campaigns, Caregivers were asked about their exposure to campaigns, theirperceptions of campaigns, and whether these campaigns affected their practices. Donors,
government officials, and implementers were also asked about their involvement with andperceptions of awareness campaigns, including their efficacy, and challenges in changing
knowledge and practices around child labor.
Children were not asked directly about child labor, or their exposure to child labor awarenesscampaigns. To measure children’s attitudes towards child labor, children were asked questions
about what they liked about helping their caregivers, and what they did not enjoy. Children inboth age groups were asked the same questions about their activities, including the day and times
they worked. To ensure age appropriateness of all questions related to child labor, children wereseparated into age groups, five to eleven years old, and then twelve to seventeen years old. Olderchildren were asked to report how long they had engaged in on and off-farm activities, and if
there were any changes in levels of responsibility in their tasks.
To measure the on-the-ground impact of awareness campaigns on reported practices, analysis
compared caregiver and child responses to questions about the types of activities children
engaged in, and the types of activities they were not allowed to do. These comparisons took
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responses from community leaders with these communities. To measure the broader impacts ofawareness-raising campaigns, government officials, donors, and implementers were asked toreflect on recent changes in attitudes and practices around child labor and provided anecdotes on
key successes and challenges in implementing awareness campaigns.
10.9.2.5 Relative Effectiveness of Interventions
To assess the efficacy of interventions, beneficiary respondents were asked to report on changes
they experienced as a result of intervention activities. Child respondents were only asked aboutschool-based interventions, vocational training programs, and occupational safety interventions,while adult beneficiaries and community leaders were asked about all interventions. Eachbeneficiary reported on the impact of interventions, and any challenges they had participating inintervention activities. These responses were compared with teacher and community leader
perceptions of the specific interventions in their communities to inspect emerging themes related
to efficacy, challenges with interventions, and recommendations.
Implementers, donors, and government officials were asked directly about which interventions
were most effective, and the strategies they put in place to promote the efficacy of theseinterventions. These respondents were also asked about which interventions were morechallenging to implement or did not meet expectations of expected impact. These responses were
triangulated with beneficiary, community leader, and teacher responses to reveal several lessons
learned, and considerations for future interventions.
10.9.2.6 Sustainability
To measure the overall sustainability of interventions, community leaders, implementers, donors,
and government officials were asked to reflect on strategies used to promote the sustainability ofinterventions, and to report on which intervention outcomes were most likely to be sustained
outside of external material assistance. Beneficiary anecdotes revealing common challenge tointervention participation or efficacy were also analyzed to generate findings on which types ofinterventions were most sustainable.
10.10 Annex X: Caveats and limitations of Quantitative Analysis
While an extensive effort was undertaken to address the research questions posed by this study
using appropriate statistical methods, it is important to point out some caveats and limitations of
the methods adopted. Understanding the caveats and context of some of the results indicating
there were few impacts from interventions as well as interpreting the findings where we foundstatistically significant impact.
Model-based approach and sample-size issues: One of the major factors weakening the abilityof any evaluation methodology to detect impact in the context of the present intervention is theFINAL REPORT | 255

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that the interventions being evaluated were not implemented (geographically or via roll-out) in a
way that facilitated evaluation or addressing the study’s research questions. Ideally, group ofcommunities would have been randomly assigned to receive the interventions (or various
combinations of intervention categories). Such randomization could have been spatial or overtime. Instead, the lack of random assignment was made even more challenging by theretrospective nature of the study, and a lack of baseline data on beneficiaries with the present
one. This limited the evaluation to a single cross-section, preventing the conditioning of
performance on baseline levels and reducing further the scope for identifying pre-treatment
variables with which to construct a strong counterfactual.
Facing such unfavorable constraints, we addressed the research questions using available data byretro-fitting a model-based approach onto a quasi-experimental design. There were two mainconsequences of this approach. First, a model-based approach is based on “observables”, that is,
only on factors that could be and were measurable. Thus, the credibility of the evaluation
depends on the degree to which the salient explanatory variables (e.g., household skills,
perceived opportunity costs of own-children) were not omitted from the attribution models’
specifications and the modelling of how the implementers selected beneficiaries. While ourapproach modeled sources of selection very carefully and checked for relationships that wererobust to specification perturbations, it is still not perfect and may be vulnerable to unknown
inaccuracies and biases.
Second, the observational nature of the evaluation sample and the sample sizes of treated andcomparison units were entirely dependent on the implementer’s earlier choices of which
communities to expose and, often, which households within them to treat (e.g., give material
support). NORC’s 2018/19 round of survey, rather than being drawn as a dedicated sample witha pre-specified target sample size of the treated and non-treated households, had to make do witha highly unfavorable distribution of sampling units. To wit, among the 1,495 household surveyed
in the 2018/19 round in CDI, 605 households (44% of the sample) had at least one childbenefiting from the material-support programs, while only 120 household (14% of the sample)
surveyed in Ghana had at least one child benefiting. Thus, this design has restricted and unbalanced treatment and comparison sample sizes, which led to an evaluation design with per se low
statistical power and, in spite of NORC’s extensive efforts to consider every possible causal
channel and influence, resulted in the model’s inability to detect an effect of anticipated size, had
there been one, with satisfactory precision.
“All-or-nothing” outcome indicator (“dependent” variable). It would be very unlikely for adichotomous (“dummy”) outcome variable to detect an effect since the only way to register apositive outcome would have been for the household to withdraw children in child labor andhazardous labor entirely, rather than simply to respond to treatment by reducing its occurrence.
Given how hard it is for a family to end the practice of child labor it would be extremely unlikely

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to see a household’s total withdrawal from CL or HCL. To do so would have required a muchlarger dataset (ideally with a baseline) or a design-based evaluation (i.e., a randomized control
trial). For the purposes of policy and of evaluation, therefore, where the former is not feasible
and the latter is not affordable, it would be more useful to take as the outcome variables thechild’s number of hours worked and number of hazards exposed to.
Lack of a baseline. While an evaluation can be rigorously conducted with just an endline (i.e.,
one cross-section), such an empirical strategy is not advisable when observational units havevery different initial (pre-treatment) levels on characteristics thought to influence performanceon the outcomes of interest. For example, with a single cross-section it is not possible to knowwhether a household that acknowledges engaging in two types of hazardous labor after treatment
previously had engaged in four types or no types. The former suggests effective treatment andthe latter ineffective treatment. The present evaluation was hobbled by the existence of just an
endline when much greater precision was called for given all the other empirical challenges ofthe study.
Lack of direct link between treatment and outcome. According to the theory of change, manyof the individual interventions were meant to have their effect operate indirectly. For example,
the education related material-support intervention does not affect CL directly, but only throughits effect on school attendance. In other words, this theory of change posits that (i) the household
decides not to send the child to school due to lack of appropriate material support and (ii) withthe child not in school would be more likely to engage in work and potentially exposed to CL
and HCL. Thus, in order to influence the rates of CL or HCL exposure, the intervention focusedon reducing absenteeism, such as by providing material support. 102 An analogous theory ofchange exists for livelihood support, which does not directly lower CL but seeks to increasehousehold income so as to reduce the need for an extra set of hands to generate income. The
indirect nature of these interventions again had the result of lowering the precision of thestatistical analysis. Finally, most of the interventions were indirect in another way. Rather thanoffering a good or service directly to the household, interventions targeted communities. Herethe theory of change posited that there would be follow-on exposure to the treated communities’
households. For example, awareness campaigns that are typically implemented at the community
level. From an evaluation perspective, these types of “intent-to-treat” effects are always harder todetect and, ceteris paribus require more data to achieve a desired level of precision.
Strategic targeting of communities for administering intervention and limited sample size.
Due to the inauspicious choice (from the evaluation point of view) of communities to be treated102

We note that one could test this theory of change by examining whether the exposure rate among children who do
not attend school is much larger than those who attend school, and whether provision of material support leads to asignificant fall in school absenteeism.

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and not treated, the evaluators were often faced with a paucity of primary statistical units withwhich to conducting match (in an effort to confront potential selection bias). For example, formeasuring the impact of multiple interventions as reported in 10.8.4, since we have a very small
number of treatment communities (only 10) available for analysis, the efficiency of matching
method used for generating the counterfactual 103 in producing acceptable range of bias reduction
through matching was not great. In other words, while matching resulted in there being no statistically significant difference between the treatment and matched comparison communities theprecision of such tests was poor (see above for a discussion of precision).
10.11 Annex XI: Definitions of the Child Labor Intervention Categories
Education
Any intervention that seeks to improve and expand education opportunities to children(excluding vocational training interventions). To be classified in this category, an intervention
does not need to specifically or explicitly target child labor reduction.
Please note: Any intervention that offers or facilitates access to formal and non-formal
education (e.g. kindergartens) should be included.
The interventions in this category can include:■

Construction of schools/classrooms, improvement of infrastructure (classroom/canteen/water/toilet/energy such as provision of benches, tables etc., the drilling of a bore hole,provision of a water pump on school premises, toilet facility, construction of teacher housing,the installation of solar panels for the school etc.)■

Mobile schools, provision of birth certificates■

School feeding (provision of meals/food/canteens/equipment)■

School nutrition programs (school gardens, nutrition awareness-raising, supplementary
feeding)■

School uniform/book, provision of scholastic materials (study material)■

Improvement of the school environment (playgrounds, etc.)■

Support services to school: capacity of school management committees and teachers, andreinforcing child protection in schools■

Provision of teachers training■

Actions related to violence in schools■

Conditional cash transfer to families (conditional on sending their children to school)

103

The “comparison communities” that are very similar to the treatment communities with respect to the factors that
influenced the selection of the treatment communities

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Non-formal education (to those who are not enrolled/dropped out such as bridging classes)■

Parent engagement programs (e.g., to convince parents to send their children to school and to
improve child protection etc.)■

Literacy and numeracy activities
Vocational Training

Any intervention that provides or facilitates access to vocational training to children. To beclassified in this category, an intervention does not need to specifically or explicitly target childlabor reduction.
The interventions in this category can include:■

Training related to participation in formal jobs, improvement of skills, life-skills training, andlinking with formal employment■

Apprenticeships
Sensitization/awareness raising program

Any intervention that seeks to raise awareness of child labor practices (such as what
differentiates permissible child work from child labor, what constitutes child labor and hazardous
work for children), and awareness of legislation against use of child labor. This includes
interventions raising awareness of importance of education (but not the actual provision of
education, which is covered in Category 1), awareness of the laws and legislation around childlabor, the risk of child labor, common child labor practices to avoid, and awareness of themeasures and resources communities can use to prevent child labor practices (such as where toreport violations and seek remediation).
The interventions can be implemented at the community, school, or household level and betargeted at children, adults, or both.
Occupational Safety and Health program for youth of legal working age
Interventions that provide occupational safety or health-related services or training to promote
safe working conditions for youth of legal working age. Such interventions do not need to
explicitly or directly target the reduction of exploitative child labor or hazardous work, butwould contribute to the reduction of hazardous child labor.
Capacity building including community, regional, and national level
initiatives
Interventions that build the capacity of community leaders and members of community
committees, cooperative staff, or members of other types of farmers organizations, law

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enforcement staff, or other public officials (e.g. teachers, school inspectors, social services staff,
extension agents) to reduce the risks and incidences of child labor in their communities. This
includes provision of training (but not actual implementation of monitoring/remediation which iscovered in Category 6) to improve stakeholders’ ability to identify child labor practices andemployers exploiting child labor, and sensitizing families to child labor prevention. Regionaland national-level initiatives may also be counted in this category.
This category does not include the implementation of the follow-up activities at which thegreater capacity was targeted to better deliver, and will be captured under other categories (suchas community action plan covered under Category 7 & enforcement covered under Category 14).
This intervention also excludes any capacity building that accompanies the setting-up ofcommunity-based monitoring systems (see Category 6).
National, Community-based and/or supply-chain based monitoring andremediation systems/Child Protection and Child Labor Monitoring andRemediation Systems
Interventions that set-up or strengthen national, community-level or supply-chain based systems
that seek to assess, identify and monitor instances of child labor. This type of intervention also
includes any local capacity building that accompanies the setting-up of the monitoring system(but excludes any capacity building that is covered in Category 5). This category can include
interventions effective at national level as well as both, community-based and supply-chain
based models (implemented through the cocoa supply chain, and the community). This can
include remediation activities that are not covered in other categories.
Community Action Plans and Community Development Plans
Interventions that develop participatory tools used to build the capacity of community members
in taking action in accordance with the problems, needs, and potential of the community, as wellas the implementation of those plans when this is not covered into one of the other categories.
This category should include community action plans that are explicitly driving childprotection/child-centered community development.
Promotion of sustainable livelihood for vulnerable households
Interventions that seek to strengthen the economic situation of households that are vulnerable toengaging in child labor practices. This includes, but is not limited to, interventions to improve
agricultural income or productivity (not directly related to labor savings such as use of fertilizer
and pesticides), and interventions that seek to strengthen other economic opportunities forhouseholds.

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Please note that for inclusion into this category, the interventions need to target promotion of
livelihood for households that are vulnerable to the use/engagement of child labor. Interventions
that are targeted to improve livelihood and income generation of low income HHs with children
should be captured here.
Other economic opportunities may include, for example:■

Linking households to mobile money providers or other financial services (e.g. micro-credit,
formal banking systems)■

Supporting income generation activities and employment services aim to increase
employment opportunities, and job retention: These may include:■►

Provision or linkage to employment assistance programs, micro-finance programs, job
placement, apprenticeships and public works programs.

►

Skill training services aim to provide participants with the basic skills and knowledge
necessary to benefit from social services, provision of business or leadership training,financial education, and literacy and numeracy programs.

Supporting the implementation of VSLAs or other savings and/or loan activities
Labor saving practices

Interventions that provide services, training, or materials that specifically target reduction in theneed for labor-intensive practices including child labor. This could include, for example:■

Provision of improved forms of transport for materials related to agriculture, including thebasic wheelbarrow (which helps prevent the carrying of heavy loads for long distances)■

Provision of materials that may, for example, reduce the need for manual pulling of weeds(such as herbicides), so children do not need to be used as a cheap labor source for weeding
Improving access to existing public services for families vulnerable tochild labor

Interventions that aim to link families to existing social and public services provided by the state,such as health services, social services, cash transfers, and other infrastructure (e.g. water andsanitation activities) and help them to avail the benefits offered by such infrastructure andservices. This will include only services focusing on child protection or prevention of child labor.
Gender and Women’s Empowerment
Interventions with a defined gender and empowerment component. These interventions are
expected to improve women’s decision making power relating to various households decisions
including education of children and engagement of children in work.

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Research
Interventions that support research into the prevalence and nature of child labor, the risk factors
for child labor, or any other child labor-related area.
Material AssistanceInterventions that provide material assistance to at-risk children or children engaged in childlabor including provision of food (excluding school supplies and School Feeding program
included in Category 1), other goods and services (medical/health services), family
stipend/monetary assistance (not related to school that are covered under Category 1),
housing/shelter etc.
Enforcement of anti-child labor regulations
Interventions that facilitate enforcement of laws and regulations in preventing child labor andhazardous child labor through local, state, and national enforcement agencies. This can include
preparing documents and harmonized tools to promote an adequate and holistic care of children
victims and prevent abuse, violence and exploitation; setting up of call center for assistingchildren victims; and strengthening the operational capacity of legislative units including
additional staff, and provisioning of resources (such as vehicle and fuel, and other supply) tolabor inspectors, police and other departments in charge of implementation of anti-child-labor
regulations.
Compliance initiatives (Code of conduct/certification)
Interventions that facilitate establishing/incorporating code of conduct for cocoa
cooperatives/cocoa farms (or other members of the cocoa supply chain) and certification of
cocoa farms that prohibits use of child labor/engaging children to hazardous labor.
10.12 Annex XII: Expert Group Findings
Background
The United States Department of Labor (USDOL) Bureau of International Labor Affairs (ILAB)
awarded a cooperative agreement to NORC at the University of Chicago to conduct a childlabour survey of cocoa-growing areas of Côte d’Ivoire and Ghana during the 2018/19 cocoa
harvesting season. The main objective of this study was to assess and measure changes in theprevalence of working children, children in child labour, and children in hazardous work in thecocoa growing areas of Côte d’Ivoire and Ghana between 2008/09 and 2018/19. In order todevelop prevalence estimates and use those estimates to measure changes in child labour
prevalence between 2008/09 and 2018/19, NORC undertook a survey during the 2018/19 cocoa
harvest season.

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The governments of Côte d’Ivoire and Ghana have raised methodological concerns at different
points in the process, most recently since NORC shared the preliminary survey results in July
2019. Nevertheless, for transparency, USDOL agreed to put together an external andindependent group of experts to review and assess specific methodological issues raised by thetwo governments. This Expert Group is charged with answering the question of whether the
methodologies utilised by NORC for this study were technically sound to meet mandates
stipulated in NORC’s cooperative agreement with USDOL.
Composition of the Expert GroupThe expert Group is composed of three persons:Pierre Lavallée, Ph.D. (Survey sampling theory) (Team Leader), Gatineau (Québec), CANADA.
Former Assistant Director in Methodologist Branch at Statistics Canada
Debi Prasad Mondal, M.Sc. (Statistics, with specialization in Applied Statistics and data
Analysis), New Delhi, INDIA. Former Director General of National Sample Survey Office ofIndia
James Byiringiro, M.Sc. (Applied labour economics for development), Kigali, RWANDA.
Survey Program Manager at the National Institute of Statistics of Rwanda
III. Mandate
The Expert Group has been asked to assess the following two areas:
Validity of the 2018/19 Prevalence Estimates
The 2018/19 survey results (ratios) are representative of cocoa-growing areas of Côte d’Ivoire
and Ghana. Is this statement technical sound?
Were the procedures and bases that NORC followed scientifically sound and able to generatereliable population counts for 2018/19 within cocoa growing areas of Côte d’Ivoire and Ghana?
Comparability of 2018/19 survey round with prior surveys.
Sampling Frames:
There were changes made to the sampling design from the surveys in 2008/09 and 2013/14 to theone in 2018/19 concerning how stratification by the level of cocoa production was done. Given
the adjustments, is it technically sound for this study to make the following comparisons?

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Given the changes in the sampling procedure (i.e., stratification and definition of cocoa
producing areas), the 2018/2019 population counts are not comparable with prior rounds ofsurvey results. Is this an accurate description?
The prevalence estimates (ratios) with agricultural households are comparable between
2008/2009 and 2018/2019. Is this an accurate description?
The prevalence estimates (ratios) with cocoa-growing households are comparable between
2013/14 and 2018/2019. Is this an accurate description?
Weighting Scheme:
Was the weighting method used by NORC to generate sampling weights (that replicated theweighting method used in previous rounds to address comparability of weighted estimates acrossthe rounds) technically sound and capable of generating reliable population estimates (withacceptable margin of error at a 5%)?
Preliminary Remarks
To review the methodology used for the 2018/19 surveys on child labour in Côte d’Ivoire andGhana in cocoa production, the Expert Group read carefully the 2018/19 Draft Cocoa Report
produced by NORC at the University of Chicago, concentrating on chapter 3 and 9. These
chapters are the ones directly related to the mandate of the Expert Group above. The Expert
Group also had access to the reports of the previous 2008/09 and 2013/14 surveys. NORC kindly
answered the questions raised by the Expert Group while reading the reports.
It should be noted that despite the yes/no type of questions asked in the mandate, none of theanswers of the Expert Group are given as simply yes or no. Although this type of definite answer
is asked for, it is felt that a straight yes or no would not provide the nuances that the answers
need. For example, for a simple question such as “Are the estimates comparable?”, answering
yes or no requires, in addition, to what extent these estimates are comparable or not. In thepresent report, the Expert Group hopes that the answers provided to the mandate’s questions will
be sufficiently precise to satisfy all parties.
Assessment Results on the Validity of the 2018/19 Prevalence Estimates
10.12.5.1 The 2018/19 survey results (ratios) are representative of cocoa-growing areasof Côte d’Ivoire and Ghana. Is this statement technical sound?
In survey sampling theory, there is no formal definition of representativeness. For instance,
Kruskal and Mosteller (1979) provide not less than nine different definitions of a representative
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representative if it is unbiased and is containing enough observations (or units). This definitiontranslates into two statistical conditions for representativeness. First, for the sample to beunbiased (or, in other words, to be able to produce unbiased estimates), each unit of interest musthave a non-zero chance of being selected in the sample. That is, the sampling frame must containall units of the target population and each of them must have a selection probability greater thanzero. Second, if the sample contains enough units, we should be able to produce estimates ofsufficient precision. Hence, the variances should be at a sufficiently low level for the estimates tobe usable by the stakeholders. Note that the sample size is still a condition for sample
representativeness: since the variances need to be estimated from the sample, a minimum sample
size is required for these estimates of precision to be themselves precise. Hence, in addition to
sample unbiasedness and sufficient precision, we must have for representativeness a minimal
sample size.
As we just mentioned, the 2018/19 survey results are representative of cocoa growing areas if theultimate unit in Cocoa growing area were selected following a probability sampling design. Thatis, each household in the target population has a known positive probability of being sampled.
Now, according to the Draft Cocoa Report, the surveyed population for the child questionnaire
was drawn from household with at least one child (5-17 years old) within rural areas of cocoa
growing districts. Thus, some areas negligibly involved in cocoa producing, non-agricultural
households and households without child were excluded from the sample while they are part ofcocoa growing areas. Consequently, estimated ratios do not represent, per se, the population incocoa growing areas. They represent instead the population living in agricultural households
having at least one 5-17 child within rural cocoa growing areas. Therefore, if we consider thedefinition of the population being the one of agricultural households having at least one childaged 5-17 within rural cocoa growing areas, we have that the sample used for the 2018/19
surveys is not biased.
Recalling that the second condition for the sample to be representative is to be able to produce
precise estimates, presently, this condition cannot be assessed based on the results presented in
Draft Cocoa Report. Unfortunately, the Draft Cocoa Report does not provide any measures ofprecision (variances, standard errors, coefficients of variation, confidence intervals, etc.). From arequest from the Expert Group, NORC computed some estimates together with their precision
expressed in terms of standard errors (SE) and confidence intervals (CI). The results are provided
in Table 86 below.
In looking at Table 86, we should for now concentrate only on results related to Method 1, whichhave been computed using the weighting scheme presently used in the Draft Cocoa Report.
Looking at the confidence intervals, we can see that they are quite wide. For instance, looking at
the rate for “Total Number of Children in Child Labour” in Ghana, the confidence interval
ranges from 49% to 58%. The estimated rate is then about 54% ± 5%, and the margin of error

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represents about 9% (5÷54). This largely exceeds the acceptable margin of 5% (see the end of
Section 10.12.6.2 below), the same comment applies to the rate for “Total Number of Children in
Hazardous Labour”. Similar results are also found for Côte d’Ivoire. Thus, the Expert Group
cannot consider the estimates produced by the 2018/19 surveys as being precise. Now, the resultspresented in Table 86 are for the whole population of interest (i.e., the whole population ofagricultural households having at least one child aged 5-17 within rural cocoa growing areas),
and any estimates produced for sub-populations (for example, the population of male children
leaving in agricultural households having at least one child aged 5-17 within rural cocoa growing
areas) are likely to be even less precise. Thus, because they exceed the acceptable margin of 5%,
the 2018/19 surveys results cannot really be considered as representative of the population ofagricultural households having at least one child aged 5-17 within rural cocoa growing areas.
It should be noted that the variances used to derive the standard errors and confidence intervalsof Table 86 have been computed using a linearisation method. To compute the variances in thepresent context of a multi-stage sampling design, other methods can also have been used by
NORC. For instance, in addition to the classical analytical formulas, NORC can use resampling
methods (bootstrap or jackknife). For more details on variance estimation, see Wolter (2007).
RECOMMENDATIONS:
Attach to each estimate, or at least to the major ones (e.g., number of working children, numberof children in child labour, number of children in hazardous work, child labour rate, hazardous
work rate) a precision measure such as variance, standard error, confidence interval orcoefficient of variation.
We again stress the fact that the results of the 2018/19 surveys (i.e., estimates of populations andof prevalence rates) presented in the Draft Cocoa Report are representing the population living in
agricultural households having at least one 5-17 child within rural Cocoa growing areas, rather
than the cocoa growing areas as a whole. Given that this definition of the population of interest
corresponds to sub-population of the 2008/09 population, we could consider this sub-population
as a domain of interest and re-compute estimates for this domain using 2008/09 data. That is,using the survey data of 2008/09 (or 2013/14), it would be of interest to compute 2008/09 (or2013/14) estimates for the sub-population of agricultural households having at least one childwithin rural cocoa growing areas (i.e., the one used to define the 2018/19 population). This
would create comparable estimates between years. However, NORC does not have access to the
2008/09 (or 2013/14) raw survey data collected by Tulane in the previous rounds. Unfortunately,
the reverse is also not possible using the 2018/19 survey data of NORC: because the 2008/09 and2013/14 populations are larger, 2018/19 survey data cannot be used to produce estimates for the
population of households having eligible children or not within cocoa growing areas.

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10.12.5.2 Were the procedures and bases that NORC followed scientifically sound andable to generate reliable population counts for 2018/19 within cocoa growing
areas of Côte d’Ivoire and Ghana?
As mentioned in (a), the sampling frame used by NORC allows to produce estimates for the
population living in agricultural households having at least one 5-17 child within rural cocoa
growing areas, not the cocoa growing areas as a whole. Now, the sample selection methodology
used by NORC for the 2018/19 surveys is relatively standard. If the sampling design described in
the Draft Cocoa Report has been correctly followed on the field, it can produce reliable estimatesof the population counts for 2018/19 for the population living in agricultural households having
at least one 5-17 child within rural cocoa growing areas of Côte d’Ivoire and Ghana. However,two problems have been identified that could potentially create problems: the use of replacement
units and the computation of the weights.
“Replacement EAs” (enumeration areas) and “replacement households” were used tocompensate for nonresponse. This type of treatment of nonresponse can be quite hazardous,
especially if the resulting dataset is treated as if no nonresponse occurred. However, NORC
certified the Expert Group that they provided the replacement list to the enumeration teams andno in-field household replacements were allowed outside of the households NORC provided andin the order NORC provided them. No random walks were used, and all primary households andreplacements were chosen by NORC before data collection began. EA replacements had stricter
guidelines and the data collection team had to notify NORC personnel before any EA
replacements were allowed. At which point NORC would provide the new EA from randomly
drawn replacements. Note that one sample EA in Ghana was dropped as there were only fivehouseholds. Statistically, it was not to be dropped as the replacement would contribute to someoverestimation of counts (which, of course, may not be significant). NORC added that household
sampling included 18 selected households and 5 replacement households per EA. The
replacement households were used for 246 households in Ghana and 86 for Côte d’Ivoire. The
most common reason for replacements was that no eligible respondent was at home at time of theinterview and that sampled household was not available. The pattern of household characteristicsof the households requiring replacement was likely to be different from the rest. However, theExpert Group feels that in the end, sample replacement probably had little impact.
The weighting method used by NORC is questionable. For instance, the estimates presented in
the Draft Cocoa Report might be biased. See section 10.12.6.2 below for a deeper discussion on
the weighting issue.
To have a more definite answer to this question, the samples of the 2018/19 surveys should firstbe reweighted according to the inverse of the selection probabilities (see section 10.12.6.2
below) and estimates should be recomputed together with their corresponding variances.

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Assessment of the Comparability of 2018/19 Survey Round with Prior
Surveys10.12.6.1

Sampling Frames:

There were changes made to the sampling design from the surveys in 2008/09 and 2013/14 to theone in 2018/19 concerning how stratification by the level of cocoa production was done. Given
the adjustments, is it technically sound for this study to make the following comparisons?
First, we need to clarify the issue of stratification. According to sampling theory, a change instratification does not create bias, but can affect, positively or negatively, the precision of theestimates. That is, for a given sampling frame (a list frame or an area frame), dividing (orstratifying) this frame using different criteria will only affect the precision of the estimates, andno bias will be introduced due to the stratification. As mentioned in the Draft Cocoa Report, “the2008/09 and 2013/14 rounds used regions as the primary stratification level and the 2018/19
round used districts/departments (which are geographically smaller and could be assigned to a
stratification level more precisely than the larger area).” Therefore, assuming only a change ofstratification, in theory, comparisons between 2008/09, 2013/14 and 2018/19 estimates can bemade without problems.
10.12.6.1.1 Given the changes in the sampling procedure (i.e., stratification anddefinition of cocoa producing areas), the 2018/2019 population counts are
not comparable with prior rounds of survey results. Is this an accurate
description?
As mentioned above, the change in stratification does not affect the comparability of estimates of
population counts.
As mentioned earlier, the 2018/19 surveys produced results for the population of agricultural
households having at least one child aged 5-17 within rural cocoa growing areas. It should be
noted that the last population definition does not correspond exactly to the definition of the
population of the 2008/09 surveys. In Tulane’s report for 2013/14 (page 24, paragraph 5.2), it ismentioned that in 2008/09 and 2013/14 agricultural households having no eligible child werealso considered. Therefore, the definition of the population for 2018/19 is not consistent with theprevious surveys. This clearly decreases the comparability of results between 2008/09, 2013/14
and 2018/19.
In the Draft Cocoa Report, it is mentioned that “although this means the 2018/19 population
estimates are more precise than those used previously, it also means the sampling frames were
not exactly identical (one started at the regional level and the other at the district/department
level) and thus population total estimates are not fully comparable.” Actually, for Ghana, this led
to a reduced number of districts in the sampling frame of 2018/19 compared to 2008/09. NORC
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explained that as per the documentation they received from Tulane, the 2008/09 sampling frame
included EAs from cocoa producing areas only. However, Tulane used the higher geographicalarea of “regions” as their initial frame whereas NORC used a lower geographical unit
(district/department). Given the lack of information, NORC operated under the assumption that
Tulane sampled correctly to include only cocoa producing EAs in 2008/09. Although restricting
sampling to EAs from cocoa producing areas only was the right thing to do by NORC, there
seems to be some problems since we should expect for Ghana (and Côte d’Ivoire) to have asimilar number of districts in the 2018/19 sample. Because cocoa producing is an expanding
sector, we could even find a larger number of districts, which has not been the case. One possible
explanation can be the source of data used to construct the sampling frames.
The sampling frames of 2018/19 were coming from different sources than the ones of 2008/09.
For 2018/19, cocoa production areas were provided by COCOBOD in Ghana104 and the Coffee
Cocoa Council (CCC) in Côte d'Ivoire105 based on the cocoa production data they collected
throughout their respective countries. Using these data, NORC defined cocoa producing areas
consisting of the districts/departments that produced cocoa as per the data shared by COCOBOD
and CCC. For 2008/09, Tulane had access to government census data for constructing thesampling frames. The last available census data in Côte d’Ivoire were from 1998 while in Ghanaa census was implemented in 2000 as well as 2010. In both countries and both years of datacollection, Tulane mentions that both rural and semi-urban areas were included in the samplingframe, while NORC mentions that only rural areas were included. This difference in the data
sources for constructing the sampling frames might be another reason why Ghana noticed a
reduced number of districts in the sampling frame of 2018/19 compared to 2008/09.
At the end, the reduced number of districts in Ghana is likely to have created a difference in the
coverage of the population over years, which can affect the capacity of comparing the population
estimates between 2008/09, 2013/14 and 2018/19. Despite NORC’s efforts to mimic what
Tulane did (i.e., include EAs from cocoa producing areas only), it is not clear that the samplingframes are comparable over the years. Hence, the capacity of comparing the population estimates
between 2008/09, 2013/14 and 2018/19 is questionable.
The Draft Cocoa Report mentions that “it is important to note the not all of the districts in aregion would be cocoa producing, and thus, the regional population totals derived from EAs
selected to represent regions will naturally be higher than those derived from EAs selected torepresent districts since the count of regional total number of EAs will be greater than the district
total number of EAs.” Given that the districts/departments are contained in the regions, we could104

https://www.cocobod.gh/

105

http://www.conseilcafecacao.ci/

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consider the population of districts/departments as a domain of interest (or a sub-population) andproduce estimates for this domain. That is, using the survey data of 2008/09 (or 2013/14), it
would be of interest to compute 2008/09 (or 2013/14) estimates for the sub-population of thesame districts/departments used to define the 2018/19 population. This would create comparable
estimates between years. However, this requires the availability of the 2008/09 (or 2013/14)
survey data, which are not available, according to NORC. Unfortunately, the reverse is notpossible using the 2018/19 survey data of NORC: because the regions are larger than the
districts/departments, 2018/19 survey data cannot be used to produce population estimates at theregion level.
10.12.6.1.2 The prevalence estimates (ratios) with agricultural households arecomparable between 2008/2009 and 2018/2019. Is this an accurate
description?
In the Draft Cocoa Report, NORC claims that “computing prevalence rates does not involve useof regional or district stratum totals. Comparison of rates involves, for example, the number ofchildren in hazardous work for the entire population divided by the total number of children in
the population. Thus, since the rates are not affected by the difference in the stratum totals as
involved in generating the population estimate of total number of children and children in childlabor and children in hazardous work, rates of prevalence are comparable across the survey
rounds.” This is not exactly true since both numerator and denominator of a prevalence rate (orratio) require estimates of population counts. We could imagine that a change in the population
coverage would influence both numerator and denominator in the same direction, but the amountof change between the two can be different. Returning to the above example, by defining the
2018/19 sampling frames based on districts/departments, rather than regions, NORC probably
removed some districts/departments not involved (or negligibly involved) in cocoa producing.
Considering the fact that estimates of Child Work, Child Labour and Children in Hazardous
Work may normally be lower in the omitted areas, those omitted areas in 2018/19 have an
incremental effect on produced estimates in 2018/19 while they have a decremental effect on
estimates of 2008/09 because they were included. Thus, the estimated prevalence rates (ratios)with agricultural households are not directly comparable between 2008/2009 and 2018/2019
because they are emanated from two different household populations.
10.12.6.1.3 The prevalence estimates (ratios) with cocoa-growing households arecomparable between 2013/14 and 2018/2019. Is this an accurate
description?
The same answer as (ii) applies here.

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10.12.6.2

Weighting Scheme:

Was the weighting method used by NORC to generate sampling weights (that replicated theweighting method used in previous rounds to address comparability of weighted estimates acrossthe rounds) technically sound and capable of generating reliable population estimates (withacceptable margin of error at a 5%)?
NORC computed stratum-level weights for both selected households and selected children,
considering that the EAs were stratified based on the amount of cocoa produced and that a
number of EAs were selected for the sample from each stratum. Now, according to sampling
theory, the weighting scheme should be based on the selection probabilities of sampled units.
That is, in the present context of a multi-stage sampling design, the weights should be computed
based together on the selection probabilities of the primary sampling units (the EAs) and theselection probabilities of the secondary sampling units (the households). See Appendix A fortechnical details.
While conducting their review in response to the questions of the Expert Group, NORC
discovered an error in the formulae Tulane used (and NORC copied). This error is beyond theconcern of using stratum level weights instead of EA level weights that duly accounts forsampled households’ inclusion probability from EAs of different size. In the Tulane weighting
method, NORC discovered:
The average number of children per agricultural household was estimated as the total number ofchildren in the roster of sampled households with at least one eligible child in the age group 5-17
years divided by the total number of households with at least one eligible child (so it is theaverage number of children in households with at least one eligible child).
However, the count of total number of children in the stratum was obtained by multiplying the
average number of children per agricultural household, as derived in step (i), by all agricultural
households ( instead of the total number of agricultural households with at least one eligible
child).
As a result, the count of total children, children in child labour and children in hazardous childlabour are all overestimated (since it inherently and incorrectly assumed that all agricultural
households, even the households with no eligible child, had children equal to the average numberof children per agricultural households with at least one eligible child).
To demonstrate how the use of all agricultural households inflated the population counts, NORC
constructed sampling weights with four alternative methods of generating sampling weights:

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Method 1: Stratum level weights, all agricultural households (following Tulane’s method aspresently available in the NORC Draft Report)
Method 2: Stratum level weights, eligible households (following Tulane’s method using only
households with at least one eligible child aged 5-17)
Method 3: EA level weights, all agricultural households (following Expert Group proposed
method with all agricultural households, instead of all eligible households)
Method 4: EA level weights, using only households with at least one eligible child aged 5-17
(following Expert Group proposed method).
By looking at Table 86, we can see that resulting population count estimates clearly
demonstrate that following the same Tulane’s method but replacing all agricultural householdswith at least one eligible child (Method 2), generates much lower estimates of population counts.
These are fairly close to the population counts that NORC generated following the Expert Group
proposed method that is based on the use of selection probabilities. NORC believe that thesefindings demonstrate that the main reason behind the large difference in population counts
between the Tulane’s method and the Expert Group proposed method is due to using the count of
all agricultural households in the weighting method rather than using the count of householdswith at least one eligible child.
Now, using the prevalence rate estimates derived from the four different methods (Method 1 to
Method 4) as presented in Table 86, we find out that the estimates of prevalence rates are barely
affected. Actually, when stratum level weights are used instead of the use of EA level weights(as proposed by the Expert Group), we can see that prevalence rates derived from Method 1 andMethod 3 or derived from Method 2 and Method 4 are very close (approximately 1 percentage
point difference). As well, when all agricultural households are used instead of using thehouseholds with at least one eligible child for generating EA level weights, the prevalence rates
derived from Method 3 and Method 4 are also very close (approximately 0.2 percentage point
difference).
Therefore, based on the findings from this empirical illustration presented Table 86, NORC
believe that even though they were able to replace the sampling weights of the 2008/09 round to
replicate the Expert Group method based on the use of selection probabilities, the estimates of
2008/09 prevalence rates would not vary so much compared to their current estimates (based on
Tulane’s method). The Expert Group agrees on the above conclusion.
Now, the results presented in Table 86 are for the whole population of interest (i.e., the whole
population of agricultural households having at least one child aged 5-17 within rural cocoa
growing areas), and many estimates are also produced for sub-populations (for example, theFINAL REPORT | 272

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population of male children leaving in agricultural households having at least one child aged 517 within rural cocoa growing areas). Therefore, it is not clear that similar results will beobtained from sub-population estimates. Because of this, the Expert Group recommends thatweights based on selection probabilities be used (Method 4) for producing the estimates of both
population counts and prevalence rates of the Draft Cocoa Report.
Indeed, the weighting scheme currently used by NORC (Method 1) or its corrected version(Method 2) can lead to reduced sampling variances, but it can also introduce serious biases in theestimates. NORC chose this weighting scheme because it was the one used for the 2008/09
surveys, expecting the survey results to be more comparable between 2008/09 and 2018/19.
Despite this reason, because of potential bias, the Expert Group does not feel that the present
weighting scheme (Method 1 or 2) should be one to be used for the 2018/19 surveys.
Table 86: Comparison of Population Counts, Prevalence Rates with Different
Sampling Weights

2008

Method 1: Stratum level weights, allagricultural households (following Tulane
method as presently available in the
NORC Report)
Method 1: Stratum level weights, allagricultural households (following Tulane
method as presently available in the
NORC Report)
Method 2: Stratum level weights, alleligible households (following Tulane
method with only HHs with at least one
eligible child)

Ghana
2018

Method 3: CEA level weights, allagricultural households (following EG
proposed method with all agricultural
households instead of all eligible
households).

Method 4: CEA level weights, all eligible
households (EG proposed method).

Total
NumberofChildren

Total
Number ofChildren inChild Labor

Total
Number ofChildren in
HazardousLabor

2,159,456

947,076

930,315

Rate

44%

43%

SE

0.013

0.013

CI

41%

46%

40%

46%

1,997,780

1,071,221

1,002,406

Rate

54%

50%

SE

0.02360.0225CI
1,331,556

49%

58%

713,97946%

55%

668,115

Rate

54%

50%

SE

0.02360.0224

CI

49%

58%

46%

55%

2,092,111

1,141,059

1,060,168

Rate

55%

51%

SE

0.02460.0237

CI

50%

59%

46%

55%

1,394,016

765,754

713,419

Rate

55%

51%

SE
CI

0.025750%

60%

0.0233
47%

56%

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2008

Method 1: Stratum level weights, allagricultural households (following Tulane
method as presently available in the
NORC Report)
Method 1: Stratum level weights, allagricultural households (following Tulane
method as presently available in the
NORC Report)
Method 2: Stratum level weights,
eligible households (following Tulane
method with only HHs with at least one
eligible child)

CDI
2018

Method 3: CEA level weights, allagricultural households (following EG
proposed method with all agricultural
households instead of all eligible
households).

Method 4: CEA level weights, all eligible
households (EG proposed method).

Total
NumberofChildren

Total
Number ofChildren inChild Labor

Total
Number ofChildren in
HazardousLabor

3,550,449

817,079

805,482

Rate

23%

23%

SE

0.00850.0084

CI

21%

25%

21%

24%

2,813,249

1,029,256

991,870

Rate

37%

35%

SE

0.02080.0204

CI

32%

41%

31%

39%

2,031,861

743,338

716,336

Rate

37%

35%

SE

0.02080.0204

CI

32%

41%

31%

39%

2,838,357

1,029,918

992,767

Rate

36%

35%

SE

0.02020.0199

CI

32%

40%

31%

39%

2,082,507

790,647

765,233

Rate

38%

37%

SE

0.02270.0219

CI

34%

42%

32%

41%

RECOMMENDATIONS:
Recompute the weights using selection probabilities for the 2018/19 surveys (Method 4).
Recompute the estimates of population counts and prevalence rates for 2018/19 using theweights based on the selection probabilities (Method 4).
Recompute the variance estimates of population counts and prevalence rates for 2018/19 using
the weights based on the selection probabilities (Method 4).
In the present question on the weighting scheme, we have the sub-question: Was the weighting
method used by NORC to generate sampling weights […] capable of generating reliable
population estimates (with acceptable margin of error at a 5%)?
Looking at Table 86 above, the acceptable margin of error of 5% has not been attained. For
instance, considering the “Total Number of Children in Child Labour” in Ghana, the estimatedFINAL REPORT | 274

NORC | Assessing Progress in Reducing Child Labor in Cocoa Production in Cocoa Growing Areas of Côte d’Ivoire and Ghanarate is about 54% ± 5%, and thus, the margin of error represents about 9%. As mentioned earlier,the results presented in Table 86 are for the whole population of agricultural households having
at least one child aged 5-17 within rural cocoa growing areas, and any estimates produced for
sub-populations (for example, the population of male children leaving in agricultural households
having at least one child aged 5-17 within rural cocoa growing areas) are likely to be even lessprecise. Thus, because the acceptable level of precision of 5% has not been attained at the global
level, we can say that the acceptable margin of error of 5% will also not be attained for otherestimates of subpopulations.
Expert Group Appendix
The sample design used is a multi-stage stratified cluster sampling method. Assuming that
simple random sample (SRS) has been used at each stage, at the first-stage, a stratified SRS of
EAs is selected, and at the second stage, within each selected EA, a SRS of agricultural
households containing at least one child aged 5-17 is selected. All children aged 5-17 areinterviewed within the selected households. According to sampling theory (see, for instance,
Särndal, Swensson and Wretman, 1992), the sampling weight to be attached to agricultural
household j of EA i of stratum h should be:
𝑤𝑤ℎ𝑖𝑖𝑗𝑗 =𝑁𝑁ℎ 𝑀𝑀ℎ𝑖𝑖
𝑛𝑛ℎ 𝑚𝑚ℎ𝑖𝑖

for j ∈ i ∈ hwhere 𝑁𝑁ℎ : number of EAs in stratum h, h=1, 2, 3;
𝑛𝑛ℎ : number of selected EAs in stratum h;𝑀𝑀ℎ𝑖𝑖 : number of agricultural households with at least one child aged 5-17 in EA i of stratum h;𝑚𝑚ℎ𝑖𝑖 : number of selected agricultural households with at least one child aged 5-17 in EA i ofstratum h.

Since all children of a selected agricultural household are interviewed, the sampling weight ofchild k of agricultural household j of EA i of stratum h should simply be given by
𝑤𝑤ℎ𝑖𝑖𝑗𝑗𝑘𝑘 = 𝑤𝑤ℎ𝑖𝑖𝑗𝑗 for k ∈ j ∈ i ∈ h

To account for child nonresponse, the above weight can be adjusted as

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where 𝐾𝐾ℎ𝑖𝑖𝑗𝑗 is the number of children aged 5-17 in agricultural household j of EA i of stratum h,

𝑅𝑅
and 𝐾𝐾ℎ𝑖𝑖𝑗𝑗
is the corresponding number of responding children aged 5-17. The two sets of weights{𝑤𝑤ℎ𝑖𝑖𝑗𝑗 } andprovide unbiased (or nearly unbiased estimates).

Rather than individual weights as above, NORC has computed stratum-level weights for both
selected households and selected children. The NORC weight to be attached to agricultural
household j of EA i of stratum h is computed using the following formula:
𝑁𝑁𝑁𝑁𝑅𝑅𝐶𝐶
𝑤𝑤ℎ𝑖𝑖𝑗𝑗
=

𝑛𝑛

𝑛𝑛 𝑀𝑀
∑ℎ ∑𝑖𝑖=1ℎ𝑖𝑖∑ℎ 𝑛𝑛ℎ

𝑥𝑥

𝑛𝑛

𝑁𝑁ℎ

ℎ 𝑚𝑚
∑𝑖𝑖=1
ℎ𝑖𝑖

for j ∈ i ∈ hNow, as mentioned by NORC, an error was discovered in the formulae Tulane used (and NORC
copied). In the Tulane weighting method, NORC discovered that the count of total number ofchildren in the stratum was obtained by multiplying the average number of children per
agricultural household by all agricultural households (instead of the total number of agricultural
households with at least one eligible child). Using the above notation, the total number of𝑛𝑛𝑛𝑛
agricultural households with at least one eligible child aged 5-17 is given by ∑𝑖𝑖=1
𝑀𝑀ℎ𝑖𝑖 . On the
𝑛𝑛

∗
∗
𝑛𝑛
other hand, the total number of agricultural households is given by ∑𝑖𝑖=1
𝑀𝑀ℎ𝑖𝑖
where 𝑀𝑀ℎ𝑖𝑖
is the number
𝑛𝑛

𝑛𝑛

∗
𝑛𝑛
𝑛𝑛
of agricultural households in EA i of stratum h. Of course, ∑𝑖𝑖=1
𝑀𝑀ℎ𝑖𝑖 ≤ ∑𝑖𝑖=1
𝑀𝑀ℎ𝑖𝑖
.
𝑛𝑛

∗
𝑛𝑛
Using the total number of agricultural households given by ∑𝑖𝑖=1
𝑀𝑀ℎ𝑖𝑖, Tulane’s weights (alsoreferred to as Method 1) are defined asUsing the total number of agricultural households with at least one eligible child aged 5-17 given𝑁𝑁ℎby ∑𝑖𝑖=1𝑀𝑀ℎ𝑖𝑖 , NORC’s constructed weights (also referred to as Method 2) are defined as

References
Kruskal, W., Mosteller, F. (1979). Representative Sample III: The Current Statistical Literature.
International Statistical Review, Vol. 47, pp. 245-265.
Särndal, C-E-, Swensson, B., Wretman, J. (1992). Model Assisted Survey Sampling. SpringerVerlag, New York, 694 pages.

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Wolter, K. (2007). Introduction to Variance Estimation, Second Edition. Springer, New York,447 pages.
10.13 Answers from the Chair of the Expert Group on NORCs Response
NORC would like to thank the Expert Group (EG) for their thorough review and feedback on thedraft cocoa report. Given the ambiguity on certain points in the EG report, we respectfully put
together a series of discussions and questions relating to these points. Given this is a highly
publicized project covering a 10 year timespan and over five years of NORC effort, it is essential
for the international community that the findings and reporting are based on defendable androbust inference procedures, and further offer insights that, in spite of certain methodologicallimitations, can be used by policymakers to assess trends related to child labor and hazardous
child labor in cocoa production.
To begin, NORC is currently finalizing the report using the Method 4 weighting scheme forgenerating unbiased estimates for the 2018/19 round. Moving forward, it can be assumed that the
updated estimates will be in the final report.
Topic 1: The EG has expressed concern over the ability to perform hypothesis testing andcomparison analyses between the 2008/09 and 2018/19 studies, primarily because of thedifference in the sampling frames used for the two studies. Given the ambiguity around thecomparability of the sampling frames, we feel it is important to first clarify the relationshipbetween the NORC and Tulane sampling methods. Essentially, our observation is that bothmethods were identical. Below we describe two parts related to the issue of the sampling frames
and then provide related inquiries for the EG.
Source of Data used for generating the sampling frame:
Based on our understanding, it seems the EG believes the data sources used for the samplingframes between 2008/09 and 2018/19 were different, which may have resulted in sampling fromtwo populations that are not fully comparable. The EG report indicates that they believe Tulane
based their sampling frame on census data (page 7, paragraph 2), whereas NORC based theirsampling frame on secondary data sources. To the best of our knowledge, both frames started
with identification of non-urban cocoa growing areas; these were defined as regions for the
2008/09 study and districts/departments for 2018/19 study. Next, based on available secondary
data sources on cocoa production, study areas were stratified into cocoa production strata; for2018/19, production data was provided by COCOBOD in Ghana and the Cocoa Council in CDI.
Non-urban and cocoa producing census enumeration areas (CEAs) were then allocated by GSS
in Ghana and INS in Côte d’Ivoire into production strata based on the most recent census data

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available to construct the sampling frame. Finally, a given number of CEAs were drawn fromeach stratum to construct the sample. 106
Types of areas included in the sampling frame:
In Ghana there was a change in the census classification system between 2008/09 and 2018/19,
in which “semi-urban” was removed as a census classification. However, in both the 2008/09
round and the 2018/19 round non-urban EAs were sampled in both countries. The EG seems tobelieve that both this and the resulting sampling design at the district level, as implemented by
NORC in the 2018/19 survey, had a significant impact on the study in Ghana since, as per the
EG’s observation, these differences led to decreased number of districts in the sampling frame in
2018/19 relative to 2008/09 (although, NORC does not have access to any documentation onwhich districts were included in the sampling frame in 2008/09).
However, the area of interest between the two studies remains the same -the non-urban cocoa
growing areas. In both rounds the best possible available secondary sources were used to aid inconstructing the sampling frame. As a result, NORC believes comparability of the prevalence
rates derived from the two independent samples (that are representative of the respective rounds)
is possible using the EG proposed method of conducting the hypothesis test of significance ofdifference between the estimates of two population proportions. 107
Given this additional information on the sample frames in each round, we would like to inquireas to:
Does the EG still feel that, while being clear with the limitations in the final report, the frames do
not sufficiently intersect to allow for comparability of the estimates?
Without numbers and a clear list of areas contained in each frame, this sentence is difficult toanswer. However, it is mentioned above that “To the best of our knowledge, both frames started
with identification of non-urban cocoa growing areas”. Now, was this identification exhaustivefor each frame? Was there undercoverage or overcoverage? What criteria were used to define a
cocoa growing area? Was the same definition used for both frames? In which category were put
the semi-urban areas? Urban or rural (non-urban)? If discrepancies occurred in the creation of106

One of Tulane’s shared documents, entitled “Sample Allocation by Region – Ghana”, on sampling notes “Inorder to arrive at the final sampling frame, using the listing of enumeration areas (EAs) from the most recent census,
urban areas and areas without cocoa production were excluded. ……..While some information on the EAs wasavailable from census records, exact production statistics on EA-level were not.”107

The EG suggested basing standard error calculations on the Rao-Wu Bootstrap method, and standard errors
corresponding to the 2018/19 study calculations should be used for the unknown standard errors for the estimatescorresponding to the 2008/09 study.

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both frames, they might not be totally comparable, i.e., they might not cover the same population
over time.
NORC intends to add a section clearly describing the limitations of such comparisons, alongwith the proper confidence intervals of the 2018/19 point estimates and results of hypothesis
testing based on the EG proposed method. Therefore,
Can we present the results from such comparison analyses for the purpose of assessing thechange in the prevalence rates over time, which will facilitate assessing the progress madetowards a reduction in child labor and hazardous child labor?
EG Response on Topic 1
Yes, if limitations are clearly mentioned. Yet, the national results that the EG saw areshowing clear statistically significant differences between 2008/09 and 2018/19. That is,even if some biases are present in the computed estimates of prevalence rates and theirstandard errors, it is likely that the biases might not affect the national conclusions. Notethat the estimates that were sent to the EG are national ones: for sub-national estimates (e.g.,
results by sex, age or region), the differences might not be as significant. This means that somecaution should be used before stating that a given difference of prevalence rates isstatistically significant or not. For instance, the p-value should be much lower than simply
0.05 before concluding that the difference of two estimates is statistically significant.
Topic 2: In terms of precision, NORC and Tulane targeted an absolute margin of error of 5%.
Correspondingly, NORC’s sample size calculations were based on a target of achieving an“absolute margin of error” of 5%. With the absolute margin of error, one can see that the 5%
criterion has been met in 2018/19 round. However, the EG’s criteria for commenting on the
precision is based on the “relative margin of error” rather than the absolute margin of error. We
have determined that, based on randomly removing subsets of the sample and basing inference
on the remaining subsets, even doubling the sample size of the study would not allow us to reacha 5% level of precision with respect to the relative margin of error; such a level of precisionappears to be a higher standard than that seen in most studies.
Since NORC’s sample size calculations were not designed to achieve a precision of 5%
according to the relative margin of error, if NORC also includes the confidence intervals basedon the EG proposed SE calculation in the report then would this alleviate the EGs concerns
regarding precision of the estimators?

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EG Response on Topic 2
The answer is yes. The EG does not really have the mandate of choosing the acceptable level oferror. Producing the confidence intervals will throw some light to the users on whether or not the
produced statistics are precise enough to be used for their purpose. The width of the confidence
intervals is a good way to display the precision of estimates, especially for differences,
proportions or rates.
Topic 3: The EG concluded that the 2018/19 round estimates are representative of AG-HHs withat least one eligible child, rather than being representative of all AG-HHs. Based on a statisticalreview of the methodology and inference procedure, we have determined that the current
estimates can be deemed to be representative of all AG-HHs. Our rationale is provided below.
Our study sought to determine the prevalence and size of the population of children aged 5-17
exposed to child labor and hazardous labor conditions. We therefore defined the population units
to be the children in agricultural households in the cocoa growing areas. Sample selection
consisted of selecting solely from the agricultural households with at least one eligible childsince the agricultural households without any eligible children do not contain any population
units; observations from such households would make an inexistent contribution to estimation(note that there are no individuals/responses of which to make record for inference purposes).
An alternative argument is based on the use of a household-level weighting approach where theresponses attached to each household are based on the count of children within the household
and which possess the response of interest. In the context of this study, it can be seen that thisapproach leads to estimates and standard error calculations that coincide with an individual-level
weighting approach. Since all responses among the strata comprising the ineligible households
are zero, they therefore do not affect the resulting point or standard error calculations.
Based on the aforementioned observation, our conclusion is that the current estimates based on
method 4 (of the EG suggestions) related to children level outcomes (such as population estimateof children, working children, children in child labor and hazardous child labor and prevalence
rates of child labor and hazardous child labor) generated from the revised weighting method
proposed by the EG (Method 4), are not just representative of the eligible agricultural
households, but in fact all agricultural households.
If we indicate in the report that while the actual sampling design is representative of AG-HHs
with at least one eligible child, by construction, our estimates are also valid population estimate
for all AG- HHs. Would the EG agree?

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EG Response on Topic 3
No. The targeted population is clearly the population of children aged 5-17 in rural cocoa
growing areas. The statement “all agricultural households” implies that agricultural households
without eligible children are also present in the target population, which is not the case. By
construction, these households have no chance of being selected in the sample (i.e., theirprobabilities of selection are equal to zero). We realize that households without eligible children
are not entering into the computed statistics, but this does not allow to claim that the target
population includes theses households.

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