64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Child Labor Statistics using MICS: Work and Risky Work Estimates for 36 Low- and Middle-Income Countries


Deborah Levison



64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Abstract

Keywords: child, labor, teenage, work

Session: CPS 07 - Statistical estimation II

Monday 17 July 8:30 a.m. - 9:40 a.m. (Canada/Eastern)


In this paper, we will summarize recent global patterns of child labor and domestic work in low- and middle-income countries, including the prevalence of potentially hazardous work, using a newly available data resource, IPUMS-MICS.
Edmonds and Pavcnik's influential 2005 paper analyzed patterns in child labor for 5-14 year-old children using UNICEF’s Multiple Indicator Cluster Survey (MICS) samples from 36 low-income countries for 2000-2001. They weighted nationally representative participation rates and hours worked to derive aggregate measures which they examined by gender, urban/rural, and age group. Some of their most important MICS findings showed that data about children’s work does not reflect popular images of child labor in factories or for abusive strangers, instead showing substantial variation across and within countries but with most children’s work taking place in the context of their families.
We use samples from Round 6 of the MICS between 2017 and 2020, taking advantage of a soon-to-be-released data resource. The IPUMS-MICS data collection is a harmonization of the UNICEF MICS data that facilitates comparison across countries and time. Since the early 2000s, the MICS surveys have expanded, including more detailed information on adolescents and covering up to age 17. To build upon the work of Edmonds and Pavcnik, we will explore recent patterns, summarize more detailed characteristics of children’s work, and examine patterns by level of economic development and household relationship status. The Round 6 MICS include data from 36 low- and middle-income countries, with 332,681 children sampled in total.
Five MICS variables determine whether a child engaged in labor force work, allowing distinction between working for family/self versus working for others, and also measuring total labor force hours. We will calculate aggregate labor force participation rates for family, non-family, and any work, as well as average labor force hours, overall and by covariates. Eight additional MICs variables address whether children’s labor force work exposed them to potential hazards, such as dangerous tools or extreme temperatures. We will derive aggregate frequencies of overall exposure and by type of hazard, tabulated by family versus non-family work and by covariates. Another 12 variables report on children’s engagement in unpaid household chores (e.g., fetching water, cooking). We will calculate aggregate participation rates for nine categories of chores and average total chore time, each tabulated by covariates and by labor force status. In deriving these aggregate statistics, like Edmonds and Pavcnik, we plan to use within-country weights from the MICS data that reflect survey design to render nationally representative statistics for each sample, and age-specific population-based weights from UNICEF vital statistics for aggregating across countries.
In 2020, the ILO estimated that there were 160 million children in child labor worldwide. Reporting since then suggests substantial increases in child work as families who lost income in pandemic-related shutdowns scrambled to meet basic needs. MICS patterns will also indicate children’s pre-Covid-pandemic levels of family and non-family work, providing a baseline for understanding their involvement in family livelihoods – and with implications for schooling – at the cusp of the 2020s.