Abstract
Background
The Convention on the Rights of the Child states that children need to be protected from ‘any work that is likely to be hazardous or to interfere with the child’s education, or to be harmful to the child’s health or physical, mental, spiritual, moral or social development’. We aimed to determine the prevalence and correlates of child labour in five low-income African countries using the sixth wave of UNICEF Multiple Indicator Cluster Surveys (MICS6).
Methods
Data on child labour, reported by the household respondent for a randomly selected child (5–17 years), were extracted from MICS6 reports from Chad, Guinea Bissau, Malawi, Sierra Leone and Togo. Prevalence rates were extracted from three categories of child labour (household chores, economic activity and hazardous work) stratified by gender, age, wealth and residence.
Results
140 598 children aged 5–17 years (61.2% 5–11; 22.0% 12–14; 16.8% 15–17) were included in the reports; 59 090 (42%) were engaged in child labour. The highest proportion of child labourers by age was 12–14 years old (61.1%) followed by 15–17 years old (51.1%) and 5–11 years old (36.1%). There were differences between countries, with Chad having the highest proportion of working children. Gender differences in working were limited (43.9% boys vs 40.3% girls); rural children were almost twice as likely to be working compared with urban children (47.5% rural vs 25.6% urban) as were children in the poorest quintile compared with those in the wealthiest quintile (46.9% vs 23.7%). Over a third (35.3%) of working children were exposed to hazardous conditions. Older, male, rural or poor children were over-represented among those in hazardous work.
Conclusions
Almost half of all children in these five sub-Saharan African countries are engaged in labour, of which one-third are in hazardous work. MICS6 surveys do not report on working children’s health; however, working puts their health and development at risk.
Keywords: Child Health, Low and Middle Income Countries, Adolescent Health, Epidemiology, Data Collection
What is already known on this topic
Child labour is a persistent problem in the world today.
Global progress against child labour has stalled since 2016.
Sub-Saharan Africa is the region with the highest prevalence and most significant number of children in child labour.
What this study adds
Data on child labour from five sub-Saharan African countries confirm that child labour is common.
Rural children are far more likely to work than urban children, and gender differences were insignificant.
There were between-country differences in the prevalence of child labour, with Chad having the highest prevalence and proportion of children engaged in labour.
How this study might affect research, practice, or policy
Eliminating child labour is not possible without tackling the root causes; poverty is the single biggest driver; thus, action on tackling poverty is essential.
Since children’s work is often invisible and enmeshed within family relations, working in partnership with families and communities is essential.
Education, health and social protection services for children, and more effective employment and social security for families are foundational strategies in reducing child labour.
Introduction
Child labour remains widespread worldwide, especially in the least developed countries. Work that children around the world are routinely engaged in is classified as child labour when they are either too young to work or are involved in hazardous activities that may compromise their physical, mental, social or educational development (see definition of hazardous work in the Methods section).1 Child labour legislation in most countries is based on three conventions. The International Labour Organization (ILO) has two conventions ratified by most of the world’s countries: ILO Convention No. 138 concerning the minimum age for admission to employment and Recommendation No. 146 (1973) and ILO Convention No. 182 concerning the prohibition and immediate action for the elimination of the worst forms of child labour and Recommendation No. 190 (1999).2 The UN Convention on the Rights of the Child (UNCRC)3 is the third convention related to child labour ratified by all but one country (the USA). State Parties, who are signatories to the UNCRC, ‘recognize the right of the child to be protected from economic exploitation and from performing any work that is likely to be hazardous or to interfere with the child’s education, or to be harmful to the child’s health or physical, mental, spiritual, moral or social development’ as specified in Article 32.1 of the UNCRC3; consequently, eliminating child labour has been claimed to be central to advance children’s rights.4
Ratification in 1973 of ILO Convention 138 has been followed in the majority of countries by legislation to protect children and youth under 18 years of age from hazardous work; however, a minority of countries do not have such legal protection, and in others, loopholes are used to evade the laws.5 As a result, legislation and enforcement are poor, and prevalence rates remain high across low-income and middle-income countries.1 Child labour still exists in high-income countries, accounting for 1% of the workforce,6 and the number of children employed illegally has increased threefold since 2015 in the USA.7
Despite the detrimental effect of child labour on children’s health and development, as highlighted by the UNCRC, powerful drivers are ensuring its continuation. According to the ILO,8 the principal driver is poverty and income from children’s work, sometimes up to 40% of the household income, which is often essential for the survival of the child and the family. Lack of educational opportunities, high levels of adult unemployment, a low-paying economy and longstanding cultural factors all contribute to the complex causes of child labour. The latest global child labour report (from 2020) suggests that at least 160 million children are engaged in labour, with nearly half of them engaged in hazardous work; progress against child labour has stalled.9
Low-income countries (LICs) in sub-Saharan Africa (SSA) have the highest prevalence and most significant number of working children. As part of a collaborative research undertaken by the Street and Working Children Working Party, we aimed to provide descriptive data on the prevalence and correlates of child labour in five SSA countries that are among the world’s poorest countries, contributing to a collection of papers on their health and well-being that is being brought together by the International Society for Social Pediatrics and Child Health in conjunction with the BMJ Paediatrics Open journal. Using data from Multiple Indicator Cluster Survey 6 (MICS6) findings final reports, our objective was to present up-to-date, descriptive data on the prevalence of child labour, including household chores and economic activity above age-specific thresholds and hazardous work, by age, gender, rural/urban residence and household wealth.
Method
Descriptive data on child labour above the age-specific threshold as defined by UNICEF concerning the Sustainable Development Goal 8, Target 8.7,10 reported by the household respondent for a randomly selected child aged 5–17 years were extracted from MICS6 findings reports from Chad (2019), Guinea Bissau (2018–19), Malawi (2019–20), Sierra Leone (2017) and Togo (2017).
Inclusion criteria for countries
Inclusion of the five countries was based on the following criteria:
Classification as an LIC, which is among the world’s poorest countries, by the World Bank.11
A SSA country.
Availability in July 2023 of MICS6 survey findings’ final reports, the most recent survey wave for which complete data are available, for these countries on the MICS data website.12
MICS6 sample selection
The MICS surveys employed a standardised sampling methodology with variation depending on the geography, governance and, in some cases, ethnic structures of the individual countries. The household surveys use a multistage sample design based on an existing sample frame to achieve a representative population sample.13 Sampling is undertaken in two stages. In the first stage, census enumeration areas (or equivalent) are selected using probability proportional to size. In the second stage, households are selected using random systematic sampling, from which the survey clusters with a moderate size of 20–25 households are formed. Certain individuals, for example, a random child aged 5–17 years, are selected from each household. Details of sample selection in each of the five included countries are given in online supplemental material in document S1.
Data collection
MICS data are collected in face-to-face interviews with respondents, usually the child’s mother, based on globally recommended questionnaires customised at the country level to meet their specific needs, including question deletion or addition and language customisation. The child labour modules are administered for children aged 5–17 and include questions on the type of work a child does and the number of working hours each day. The detailed questions completed by caregivers on the child/young person’s labour the week before the interview are shown in the Child Labour module of the MICS6 Questionnaire for 5–17 years old (see online supplemental table S1).
Outcomes of interest
1.Total aggregated rate of child labour.
2.Household chores and economic activity above age-specific thresholds by age group (see box 1).
Box 1. Age-specific hourly thresholds for children aged 5–17 years engaged in economic activities and household chores*.
Child labour for the 5–11 age range: children working 1 hour or more per week in economic activity and/or involved in unpaid household services for 21 hours or more per week.
Child labour for the 12–14 age range: children working 14 hours or more per week in economic activity and/or involved in unpaid household services for 21 hours or more per week.
Child labour for the 15–17 age range: children working 43 hours or more per week in economic activity (no hourly threshold is set for household chores for ages 15–17).
*Source: UNICEF10
3.Hazardous work defined by Recommendation No. 190, which accompanies ILO Convention No. 182, as work which exposes children to physical, psychological or sexual abuse; work underground, underwater, at dangerous heights or in confined spaces; work with dangerous machinery, equipment and tools or carrying heavy loads; exposure to hazardous substances, agents or processes, or to temperatures, noise levels or vibrations damaging to health; work for long hours, night work and unreasonable confinement to the premises of the employer.
Correlates
Age grouped as follows: 5–11 years; 12–14 years; 15–17 years.
Gender: girls versus boys.
Place of residence: rural versus urban.
Wealth quintiles: Q1 (poorest); Q2; Q3; Q4; Q5 (wealthiest). The Wealth Index is a composite measure of the cumulative living standard of a household based on a definition derived from MICS economic indicators. It is calculated using data on a household’s ownership of a selected set of assets, such as televisions, bicycles and cars; dwelling characteristics, such as flooring material; type of drinking water source; and toilet and sanitation facilities.14
Analysis
We performed a simple descriptive statistical analysis, including the number and frequencies of aggregated data from the five countries on total and subcategories of child labour extracted from the final reports from the five countries, which were entered into a spreadsheet (see online supplemental table 2a-d). χ2 tests were used to determine the difference between proportions. (https://www.socscistatistics.com/tests/).
Patient and public involvement
Patients or public were not involved in the design, or conduct, or reporting, or dissemination plans of our research as the study involved secondary analysis of previously published data from the included countries.
Results
The final MICS6 reports from the five countries included 140 598 children aged 5–17 years (61.2% 5–11; 22.0% 12–14; 16.8% 15–17) (table 1). The proportion of girls in the aggregated total sample of participants was marginally higher than boys (50.2% vs 49.8%). The surveys included three times as many rural children as those from urban areas (74.7% vs 25.3%). The numbers of children in each wealth quintile were broadly similar, although the wealthiest quintile (Q5) had slightly fewer than the other four quintiles. 59 090 (42.0%) were reported to be engaged in child labour.
Table 1. Characteristics of children included in MICS6 survey data by country and proportion of child labour by age, gender, residence and wealth (aggregated data from five low-income sub-Saharan countries)*.
| Chad(2019) | Guinea-Bissau(2018–2019) | Malawi(2019–2020) | Sierra Leone(2017) | Togo (2017) | Total | Child labour | ||
| No. (%) | No. (%) | No. (%) | No. (%) | No. (%) | No. (%) | No. (%) | P value | |
| Number of children | 45 973 (32.7) | 16 661 (11.9) | 40 744 (29.0) | 25 194 (17.9) | 12 026 (8.6) | 140 598 (100) | 59 090 (42.0%) | |
| Age groups (years) | <0.0000 | |||||||
| 5–11 | 29 068 (63.0) | 10 238 (61.4) | 23 862 (58.6) | 15 678 (62.2) | 7131 (59.3) | 85 977 (61.2) | 31 116 (36.1) | |
| 12–14 | 9806 (21.3) | 3645 (21.9) | 9757 (23.9) | 5042 (20.0) | 2669 (22.2) | 30 919 (22.0) | 18 890 (61.1) | |
| 15–17 | 7099 (15.7) | 2778 (16.7) | 7126 (17.5) | 4474 (17.8) | 2225 (18.5) | 23 702 (16.8) | 12 103 (51.1) | |
| Gender | <0.0001 | |||||||
| Girls | 22 577 (49.1) | 8409 (50.5) | 20 533 (50.4) | 12 717(50.5) | 5741(47.7) | 70 521 (50.2) | 28 397 (40.3) | |
| Boys | 23 395 (50.9) | 8252 (49.5) | 20 211 (49.6) | 12 477(49.5) | 6285 (52.3) | 70 077 (49.8) | 30 736 (43.9) | |
| Residence | 0.0000 | |||||||
| Rural | 37 590 (81.8) | 10 769 (64.6) | 35 014 (85.9) | 14 103 (56.0) | 7624 (63.4) | 105 101 (74.7) | 49 998 (47.5) | |
| Urban | 8383 (18.2) | 5892 (35.4) | 5730 (14.1) | 11 091 (44.0) | 4401 (36.6) | 35 497 (25.3) | 9088 (25.6) | |
| Household wealth quintile | <0.0001†<0.0000‡ | |||||||
| Q1 (poorest) | 9235 (32.7) | 3331 (11.8) | 8042 (28.4) | 4977 (17.6) | 2690 (9.5) | 28 275 (100) | 13 281 (46.9) | |
| Q2 | 9116 (32.2) | 3337 (11.9) | 8162 (29.8) | 5089 (18.3) | 2620 (8.4) | 28 324 (100) | 13 678 (48.3) | |
| Q3 | 9186 (31.7) | 3457 (11.9) | 8635(29.8) | 5304 (18.3) | 2423 (8.4) | 29 005 (100) | 13 458 (46.4) | |
| Q4 | 9488 (33.7) | 3319 (11.8) | 8263 (29.4) | 4837 (17.2) | 2235 (7.9) | 28 142 (100) | 11 707 (41.6) | |
| Q5 (richest) | 8948 (33.3) | 3216 (12.0) | 7642 (28.5) | 4986 (18.6) | 2058 (7.7) | 26 850 (100) | 6351 (23.7) | |
| Total engaged in child labour | ||||||||
| 25 791 (56.1) | 5865 (35.2) | 11 775 (28.9) | 9826 (39.0) | 5833 (48.5) | 59 090 (42.0) | |||
Data derived from Table PR.3.4 in the Chad, Guinea-Bissau and Malawi survey reports and Table PR.3.3 in the Sierra Leone and Togo survey results. Linear trend; Q1 Q5.
Linear trend.
Q1 versus Q5.
MICS6Multiple Indicator Cluster Survey 6
All forms of child labour
The highest proportion of child labourers by age was 12–14 years old (61.1%) followed by 15–17 years old (51.1%) and 5–11 years old (36.1%). The same pattern by age was held for all five individual countries (table 1). Boys were more likely than girls to be engaged in child labour (43.9% vs 40.3%) (table 1), although, in three individual countries (Guinea-Bissau, Malawi and Sierra Leone), girls were more likely to be in employment than boys (table 1).
Almost half the children in rural areas (47.5%) were employed compared with a quarter of children in urban areas (25.6%) (table 1), and this pattern was similar across all five individual countries (online supplemental table S2a). There is a social gradient in child labour by wealth index quintile; children in the poorest quintile (Q1) are twice as likely to be in employment as those in the wealthiest quintile (Q5) (table 1). Three of the five countries (Guinea-Bissau, Sierra Leone and Togo) show gradients and twofold differences between Q1 and Q5; however, Chad and Malawi show plateaued rates (no gradient) across Q1–Q4 but lower rates in Q5 (online supplemental table S2a).
Household work above the age threshold
Children aged 12–14 years (17.8%) were more likely than those aged 5–11 years (8.9%) to be engaged in household work (table 2). No data on household work among 15–17 years old were available for three countries (Chad, Guinea-Bissau and Malawi). Girls (12.6%) are almost twice as likely as boys (6.9%) to be engaged in household work (table 2), a consistent pattern across all five countries (online supplemental table S2b).
Table 2. Household work above age threshold by age, gender, residence and wealth—aggregated data from five low-income SSA countries.
| Number (%) | P value | |
| Age groups (years) | <0.0000 | |
| 5–11 | 7650 (8.9) | |
| 12–14 | 5498 (17.8) | |
| 15–17 | NA* | |
| Gender | <0.0000 | |
| Girls | 8909 (12.6) | |
| Boys | 4863 (6.9) | |
| Residence | <0.0000 | |
| Rural | 11 654 (11.1) | |
| Urban | 2147 (6.0) | |
| Household wealth quintile: | Linear trend p<0.0001Q1 versus Q5 p<0.0000 | |
| Q1 (poorest) | 3150 (11.1) | |
| Q2 | 3119 (11.0) | |
| Q3 | 3086 (10.6) | |
| Q4 | 2719 (9.7) | |
| Q5 (richest) | 1699 (6.3) |
SSAsub-Saharan Africa
Household work was almost twice as likely among rural compared with urban children (11.1% vs 6.0%) (table 2), a consistent pattern across all five countries (online supplemental table S2b). There was a clear social gradient across wealth quintiles (table 2), and 11.1% of children in household work in Q1 compared with 6.3% in Q5. The social gradient was very marked in Togo (Q1: 23.8% through Q3: 10.5% to Q5: 7.5%) but less clear in the four remaining countries; however, differences between children in the poorest compared with the wealthiest quintiles were evident in all five countries (online supplemental table S2b).
Economic activity above the age threshold
Compared with household work, age and gender economic activity patterns differed. A higher proportion of children aged 5–11 years (28.8%) compared with those aged 12–14 years (13.6%) and those aged 14–17 years (4.1%) were working in paid or unpaid economic activity (table 3). Boys (32.8%) were almost twice as likely as girls (18.8%) to be in this type of work (table 3). Similar patterns by age and gender were present in all five countries (online supplemental table S2c).
Table 3. Economic activity and hazardous work above age threshold by age, gender, residence and wealth.
| Economic activity above age threshold | Hazardous work | |||
| Number (%) | P value | Number (%) | P value | |
| Age groups (years) | ||||
| <0.0000* | <0.0000 | |||
| 5–11 | 24 804 (28.8) | 8408 (9.8) | ||
| 12–14 | 4208 (13.6) | 8379 (27.1) | ||
| 15–17 | 964 (4.1) | 6286 (26.5) | ||
| Gender | ||||
| <0.0000 | <0.0000 | |||
| Girls | 13 288 (18.8) | 9083 (12.9) | ||
| Boys | 16 683 (32.8) | 11 553 (16.5) | ||
| Residence | ||||
| <0.0000 | p<0.0000 | |||
| Rural | 26 056 (23.8) | 20 331 (19.3) | ||
| Urban | 3915 (11.0) | 1728 (4.9) | ||
| Household wealth quintile | ||||
| <0.0000*<0.0000† | <0.0000* | |||
| Q1 (poorest) | 7177 (25.4) | 5295 (18.7) | ||
| Q2 | 7107 (25.1) | 5542 (19.6) | ||
| Q3 | 6891 (23.8) | 5263 (18.1) | ||
| Q4 | 5568 (19.8) | 4511 (16.0) | ||
| Q5 (richest) | 3146 (11.7) | 1299 (4.8) | ||
Aggregated data from five low-income SSA countries.
Linear trend;. Q1 Q5.
Q1 versus Q5.
SSAsub-Saharan Africa
Rural children (23.8%) were more likely than urban children (11%) to be engaged in economic activity (table 3), a pattern reflected in all five countries (online supplemental table S2c). The social gradient in economic activity shows a gentle slope from Q1 (25.4%) to Q4 (19.8%) and a sharp fall in the proportion of children in Q5 (11.7%) (table 3). The wealth gradients in economic activity in Chad and Malawi were flat from Q1 to Q4, and although the proportion in Q5 was lower, the fall was not steep. The gradients in the other three countries followed the same pattern as the aggregate data (online supplemental table S2c).
Hazardous work
Hazardous work is universally considered dangerous for children, so age thresholds do not apply. Older children aged 12–14 years (27.1%) and aged 15–17 years (26.5%) were more likely to be exposed to hazardous work than those aged 5–11 years (9.8%) (table 3). Exposure to hazardous work was more frequent among boys than girls, although the proportions were slight (16.4% vs 12.9%). Hazardous work by age group and gender followed a similar pattern in the five individual countries except for Guinea-Bissau, where a higher proportion of girls than boys were in hazardous work (29.1% vs 27.3%) (online supplemental table S2d).
Rural children had a fourfold likelihood of exposure to hazardous work as urban children (19.3% vs 4.9%) (table 3). The wealth gradient in dangerous work showed a slight reduction in the proportion of children between Q2 and 4 (−3.6%) with a sharp fall to Q5 (−11.2%). Children in the poorest quintile were over three times more likely than those in the wealthiest quintile to work in dangerous conditions (18.7% vs 4.8%). The gradients by wealth in Togo were steep (Q1: 50.2% through Q3: 30.3% to Q1: 9.3%); however, gradients in the remaining four countries were similar to the aggregated data (online supplemental table S2d).
Survey reports of three of the five countries (Chad, Guinea-Bissau and Malawi) include the proportion of working children exposed to particular types of hazardous work (figure 1). Exposure to hot, cold or humid working conditions was the most common type of hazard experienced by working children in these countries (40.3%), followed by dust or gas exposure (31%) and carrying heavy loads (19.4%). Some children are exposed to more than one type of hazardous work.
Figure 1. Proportion (%) of working children by exposure to each type of hazardous working conditions.
Discussion
The data presented in this paper are taken from the MICS6 Survey Findings Reports to demonstrate the prevalence and correlates of child labour in five of the world’s poorest countries. The MICS6 surveys use a standard sampling methodology ensuring the representativeness of surveyed populations12 and globally recommended questionnaires enabling reliable cross-country comparison and data collation. Aggregated data for the five countries showed that over two-fifths (42%) of children aged 5–17 years worked for periods above the internationally agreed age thresholds. Boys, children from rural households, children in less wealthy households and aged 12–14 years were most likely to be working. The pattern by age group, gender, residence and household wealth for economic and hazardous work was the same as for all child labour except that girls were likelier than boys to be engaged in household work. Individual countries showed some differences in the prevalence and correlates of child labour, with Chad (56%) and Togo (49%) having the highest rates; however, the differences were minor, and patterns of child labour were broadly similar. We know that child labour has significant consequences for children and young people’s long-term health, development and well-being, so these estimates from SSA are concerning.4 15 Of particular concern was the prevalence of hazardous work (35.4% of all child labour) despite universal agreement that exposure of children to this type of work is dangerous and detrimental to their health.
Comparison with published data
The joint ILO/UNICEF report published in 2021 is the most comprehensive and up-to-date account of global trends in child labour among 5–17 years old children and is based on 66% of the world’s population in that age group.9 The rate of child labour recorded in the report has reduced from 16% (246 million) in 2000 to 9.6% (160 million) in 2020; however, the rate in SSA over the same period has only fallen from 25.3% to 23.9%. The higher rate of child labour (42%) in the five SSA countries reported here is likely to reflect the economic situation of families in these LICs and variations in the definition and measurement of child labour. Whereas MICS6 standardised questionnaires were used in the five countries, data for the ILO/UNICEF report were aggregated from surveys using different methodologies (p80),9 and the prevalence rates reported here might be underestimated.16,20 The prevalence rates might also be influenced by the season and year of the data collection.
The findings from the present study on the prevalence of child labour by gender and rural residence are consistent with those reported by the ILO/UNICEF report, with some differences likely related to measurement variation. The marginal difference in exposure of boys (43.9%) compared with girls (40.3%) (table 1) to child labour does not reflect the way in which gender roles affect ‘the type of work activities that boys and girls undertake’.21 Prevalence rates across the age groups were similar in the ILO/UNICEF report (about 9%).
Prevalence rates of hazardous work vary somewhat by study, possibly caused by the survey season and methodology. Significantly, in our study, about 15% of children aged 5–17 years were working in hazardous conditions compared with about 5% of the population in the ILO/UNICEF report.
Barriers and challenges to the reduction of child labour in LICs
Our study focuses on five low-income SSA countries, and the prevalence rates of child labour, including hazardous work, indicate the urgent need for social, economic and legal measures to protect children. However, child labour is embedded in the social and cultural context in which children live, grow and develop, and their work contributes to the subsistence economy of the family. Moreover, it has a complex role concerning poverty and education, as without work, there is no money to attend school.22 Child labour is a violation of child rights23 as it impedes educational achievement24 and puts health and development at risk.15 Underpinning this sociocultural context is a political economy characterised by poverty, debt, corruption, war, unfair trade and structural adjustment programmes imposed by the World Bank, IMF and conservative governments in some countries22 25 with particularly severe consequences for SSA.26 Studies in SSA27,30 confirm the key role of poverty in child labour in the region. Admassie27 in a study of aggregated data from the SSA region reported country-level poverty as one of the main correlates of child labour participation. Studies based on surveys in Malawi,28 West Africa (Ghana, Mali, Sierra Leone and Liberia),29 and Tanzania30 identified poverty as a major driver of child labour. A further barrier to child labour reduction is the cultural concept of what constitutes ‘childhood’ as reported by Gatsinzi and Hilson in their study in Ghana.31 Effective policies to reduce child labour in these LICs must consider these sociocultural and political contexts.
According to the US Department of Labor, Bureau of International Labor Affairs (ILAB) reports,16,20 while child labour rates remain high, four countries (Chad, Malawi, Sierra Leone and Togo) have made moderate advancements in efforts to eliminate the worst forms of child labour, but advancement in Guinea-Bissau has been minimal. All five countries have ratified almost all the articles of the International Conventions on Child Labour2; however, the ILAB reports for all five countries16,20 identify gaps within the operations of national agencies that prevent adequate enforcement of their child labour laws.
Strengths and limitations
MICS6 surveys are population based, using standardised, robust sample selection and data collection methodologies, which minimise selection bias and allow the collection of comparable results across different countries. Whereas the ILO/UNICEF global report9 estimated prevalence data for the whole subregion, our study focused on five low-income SSA countries providing child labour prevalence data among the world’s most vulnerable child populations. Data were collected within a narrow time frame (2017–2020).
The study was based on a secondary analysis of existing data, so it shares the limitations of the survey datasets, such as the absence of data on the health of children older than 5 years. Sampling is based on households, so some children who live and work on the streets are likely to be excluded. Respondents are adult carers, which increases the likelihood of reporting bias. MICS surveys are serial cross sectional; however, trends in child labour cannot be presented as the data analysed for this paper are from the latest survey for which final data were available. The study analysis was descriptive based on univariate analysis. As we analysed secondary rather than primary data, the study did not include the association of educational attendance with child labour participation.
Conclusions
The global prevalence of child labour has stalled since 2016 with an estimated 160 million children working in 2020. Although prevalence fell in some regions, notably Latin America and Caribbean, and Asia and the Pacific, the estimated rate increased in SSA from 22.4% in 2016 to 23.9% in 2020. Our study shows that the aggregated prevalence in five of the poorest SSA countries, collected between 2017 and 2020, was close to twice (42%) the region rate. In some of the world’s poorest countries, just over two-fifths of children and adolescents aged 5–17 years are engaged in work above accepted age thresholds, and 39% are in hazardous working conditions. Rural and poorer children in these countries are at the most significant risk of exposure to child labour and hazardous work is almost four times as common among these groups. Our findings demonstrate that, not only are prevalence rates high in the poorest SSA countries, but children in the poorest households are most likely to be exposed. Legislation, though necessary, is insufficient to counter the drivers of child labour. Households, particularly the poorest and those dependent on agriculture, are vulnerable to the complex influences of the sociocultural and political–economic factors which force them to depend on children’s contribution to the work and finances of the family. The cultural context, in particular what constitutes ‘childhood’ in SSA needs to be understood more fully. If such drivers are not effectively addressed with contextualised and targeted policy measures, child labour above agreed age thresholds will continue to pose risk to the health and well-being of children.
supplementary material
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Ethics approval: Not applicable.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information.
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