Child Labor in Sindh, Pakistan: Patterns and Areas in Need of Intervention

Child labor remains a predominant issue in Pakistan despite the country’s existing policies and frameworks aimed at abolishing it. Through this study, we investigated the child labor distribution across Sindh and examined the factors that shape the regional patterns. We analyzed the data available through the 2018–19 Sindh Multiple Indicator Cluster Surveys, MICS 6, from 20,030 households with 40,633 children in the 5–17 age bracket. By applying prevalence statistics, chi-square tests, and regression modeling to these data, we investigated the trends in child labor prevalence, identified the correlation between child labor and various socioeconomic and geodemographic variables, and finally mapped the geospatial patterns of child labor across districts in Sindh, enabling us to identify and prioritize the districts in need of immediate intervention. The findings revealed that about 20 percent of the children in Sindh are engaged in child labor, with a high prevalence among males and in the 15–17 age bracket. Moreover, poverty and rural dwellings raise this issue. Other socioeconomic and geographic factors reinforcing this issue are a lack of education among children, mothers, or caretakers and mothers’ or caretakers’ functional difficulties. However, children’s functional difficulties lower their prevalence in labor. Among the 29 districts across Sindh, Kambar Shahdadkot has the highest prevalence of child labor.

Authors

Institutions

Publication Details

Journal
Stats
Published
2024-11-28
DOI
https://doi.org/10.3390/stats7040084
Citations
2
Primary Topic
Poverty, Education, and Child Welfare
Type
article
Field-Weighted Citation Impact
1.05

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Child Labor in Sindh, Pakistan: Patterns and Areas in Need of Intervention

Paul Newton, Nadia Maqbool, Tayyab Shah
2 citations
Stats
Poverty, Education, and Child Welfare
1.05
article

Child Labor in Sindh, Pakistan: Patterns and Areas in Need of Intervention

Paul Newton, Nadia Maqbool, Tayyab Shah
article en
2 citations

Abstract

Child labor remains a predominant issue in Pakistan despite the country’s existing policies and frameworks aimed at abolishing it. Through this study, we investigated the child labor distribution across Sindh and examined the factors that shape the regional patterns. We analyzed the data available through the 2018–19 Sindh Multiple Indicator Cluster Surveys, MICS 6, from 20,030 households with 40,633 children in the 5–17 age bracket. By applying prevalence statistics, chi-square tests, and regression modeling to these data, we investigated the trends in child labor prevalence, identified the correlation between child labor and various socioeconomic and geodemographic variables, and finally mapped the geospatial patterns of child labor across districts in Sindh, enabling us to identify and prioritize the districts in need of immediate intervention. The findings revealed that about 20 percent of the children in Sindh are engaged in child labor, with a high prevalence among males and in the 15–17 age bracket. Moreover, poverty and rural dwellings raise this issue. Other socioeconomic and geographic factors reinforcing this issue are a lack of education among children, mothers, or caretakers and mothers’ or caretakers’ functional difficulties. However, children’s functional difficulties lower their prevalence in labor. Among the 29 districts across Sindh, Kambar Shahdadkot has the highest prevalence of child labor.

StatsVol. 7(4)
University of Saskatchewan (CA)
UNICEF
No poverty
Openalex Percentile: Top 17%
Poverty, Education, and Child Welfare
1.05
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.