The Impact of Public School Enrolment on Child Labor in Punjab ...

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6Hamna Ahmed(1999) conclude that school quality has had an insignificant impact on theincidence <strong>of</strong> child labor in Tanzania.In the context <strong>of</strong> this empirical ambiguity concerning child laborand schooling, it becomes important to address the following questions:Does schooling have the potential to displace child labor? What is thedegree <strong>of</strong> substitutability between hours worked and school attendance?Unlike Hazarika and Bedi (2003) who employ an indirect approach toexamine the link between child labor and schooling, this study attempts toanalyze the direct causal impact <strong>of</strong> school enrolment on child labor. Thisentails using the receipt <strong>of</strong> free textbooks and access to a public primary schoolfacility as instruments for school enrolment while controlling for unobservedtehsil heterogeneity and a corner solution dependent variable.<strong>The</strong> nature <strong>of</strong> the dataset we have used distinguishes this studyfrom other studies on child labor in Pakistan. Prior work on the subject inPakistan constitutes either case studies (Khan, 2001; Chaudhry & Khan,2002) or is based on household survey datasets that are rather dated. Ray(2000), Bhalotra and Heady (2003), and Hazarika and Bedi (2003) use datafrom the Pakistan Integrated Household Survey (PIHS) for 1991. Burki andFasih (1998) and Rosati and Rossi (2003) employ data provided by theChild Labor Survey conducted in 1996 by the Federal Bureau <strong>of</strong> Statistics incollaboration with the Ministry <strong>of</strong> Labour, Manpower and OverseasPakistanis, and the ILO.This study uses the latest round <strong>of</strong> the Multiple Indicator ClusterSurvey (MICS) for 2007/08, which helps extrapolate more recent estimates<strong>of</strong> the incidence <strong>of</strong> child labor and enrolment in Punjab, Pakistan. This isthe first dataset on Pakistan that is representative at the tehsil level, 2making it possible to control for tehsil-level effects. Compared to the PIHS(4,795 households and 36,109 individuals) and the Child Labor Survey(10,453 households and 77,684 individuals), the MICS dataset comprises amuch larger sample (91,075 households and 594,802 individuals). Usingthis dataset, therefore, has the dual advantage <strong>of</strong> providing acomprehensive, in-depth analysis as well as substantially increasing theexplanatory power <strong>of</strong> the results yielded by the larger sample.2 Pakistan comprises four provinces, <strong>of</strong> which Punjab is the largest in terms <strong>of</strong> population and thesecond largest in terms <strong>of</strong> area. Each province is divided into districts, which are subdivided intotehsils. Punjab has 36 districts and 143 tehsils in all.
<strong>The</strong> <strong>Impact</strong> <strong>of</strong> <strong>Public</strong> <strong>School</strong> <strong>Enrolment</strong> on Child Labor in Punjab, Pakistan 73. Data<strong>The</strong> MICS for 2007/08 spans 91,075 households across Punjab andis, as mentioned above, representative at the tehsil level. Out <strong>of</strong> a totalsurveyed population <strong>of</strong> 594,802, 25.2 percent fall in the 5–14 years agebracket. Compared to urban areas, almost twice the population resides inrural areas (Table A1, Appendix A). It is worth mentioning that, eventhough we have focused on paid child labor, we present basic statistics onthe incidence <strong>of</strong> domestic and family work (Figures B2 and B3, AppendixB) to give a snapshot <strong>of</strong> the incidence <strong>of</strong> child work across thesedimensions.<strong>The</strong> working sample constitutes 59,993 households with at leastone child in the 5–14 years age bracket. Of these, 3.3 percent report theincidence <strong>of</strong> paid child labor. A comparison <strong>of</strong> households with andwithout child labor reveals that school enrolment is lower in thosehouseholds that engage in paid child labor. <strong>The</strong>y also receive a smalleramount <strong>of</strong> books on average and are poorer than those households thatdo not engage in child labor (Table 1).Table 1: Descriptive statistics by work statusHousehold* with paidchild laborHousehold without paidchild laborVariableMean Std. dev. Mean Std. dev.<strong>Enrolment</strong> ratio 0.32 0.38 0.45 0.44Receives books 0.07 0.25 0.11 0.31Primary facility access 0.86 0.35 0.87 0.34Wealth score 3 ‐0.22 0.99 ‐0.04 0.98Land ownership 0.24 0.43 0.34 0.48Land area 6.28 12.70 7.61 28.05Head’s education 1.91 1.30 2.32 1.45Head gender 0.95 0.22 0.95 0.21Males < 0–4 0.43 0.68 0.44 0.69Females < 0–4 0.41 0.66 0.43 0.69Age ratio 1.10 0.90 0.78 0.86Gender ratio 0.80 0.90 0.80 0.91Residential location 0.43 0.50 0.34 0.47N 1,992 58,001Source: MICS (2007/08) and author’s calculations.3 <strong>The</strong> wealth index ranges between -2 and +2. Details on how it has been constructed are given inTable A2, Appendix A.
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