The Effect of Child Labor on Learning Achievement

The Effect of Child Labor on Learning Achievement

CHRISTOPHER HEADY *

University of Bath and Organisation for Economic Co-operation and Development,Paris, France

Summary. — This paper analyzes the effect of children�s work on learning achievement. Itsparticular significance is that it goes beyond the analysis of the effects on school enrollment orattendance by using measures of skills learned in reading and mathematics. The results show thatwork outside the household has a substantial effect on learning achievement. Although theyconfirm the accepted wisdom, they introduce a new view of how that arises. A large part of it isdirect, and not through school attendance. The direct link could exist because of exhaustion orbecause of a diversion of interest away from academic concerns.� 2003 Elsevier Science Ltd. All rights reserved.

Key words — child labor, education, Africa, Ghana

1. INTRODUCTION

The purpose of this paper is to analyze theeffect of children�s economic activity on theirlevel of learning achievement. Its particularsignificance is that it goes further than the fairlycommon analysis of the effect of child work onschool enrollment or attendance 1 by usingmeasures of the skills that children have learnedin reading and mathematics. This is madepossible by the administration of tests thatmeasure reading achievement and mathemati-cal achievement to about half of the individualssurveyed as part of the second wave of theGhana Living Standards Survey (GLSS2),conducted in 1988–89. 2 The new insights ob-tained from this approach demonstrate itsvalue, and suggest that similar analyses shouldbe carried out for other countries, once thenecessary data have been gathered.

This analysis also has implications for theliterature on the effects of schooling on learningachievement, much of it inspired by Knight andSabot (1990). This literature attempts to ex-plain test scores in terms of school attendance,natural ability and other variables. But it typi-cally does not include child work as a possibleexplanation. 3 This paper, therefore, providesan indication of whether child work variablesshould be included in such studies.

The possible importance of reduced learningachievement is well recognized as one of themajor harmful effects of child work, and thishas been reflected in a number of projects

around the world that are designed to mitigatethis effect. Although child work has a numberof other possible harmful effects, includingdamage to health and psychological develop-ment, particular attention has been paid to itseducational effects for two reasons. First, edu-cation is seen as fundamental to improving thequality of life in developing countries, by liftingthe people who are educated out of poverty andby improving the quality of human resourcesthat are available for national economic de-velopment. Second, the impact of child workon education is both easily believable (a childthat is working cannot be at school or doing

World Development Vol. 31, No. 2, pp. 385–398, 2003� 2003 Elsevier Science Ltd. All rights reserved

Printed in Great Britain0305-750X/03/$ - see front matter

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* Earlier work on this topic by the author was funded

by the UNICEF International Child Development

Centre and appeared as one of their Discussion Papers. I

would like to thank John Micklewright for his com-

ments on that earlier work, Fiona Coulter for research

assistance, the Living Standards Measurement Survey

Unit at the World Bank for supplying the data and Paul

Glewwe for help in interpreting the data. This work

forms part of a larger study of child labor, undertaken

with Tony Addison and Sonia Bhalotra, which was

funded by the UK�s Economic and Social Research

Council (grant number R000237121) and the UK�sDepartment for International Development. I would

also like to thank two anonymous referees for their

valuable comments on this paper. The views expressed

and the remaining errors are, of course, the responsi-

bility of the author alone. Final revision accepted: 13

October 2002.

385

homework at the same time) and has beenreadily quantifiable from household surveydata, at least as measured by school attendance.

School attendance as a measure of learningachievement is not however, ideal for estimat-ing the harm that child work causes. On the onehand, it might overestimate the harm of childwork, partly because many schools in devel-oping countries are of very poor quality andpartly because a child might learn informally(from work or just daily experiences). On theother hand, it might underestimate the harm ofchild work, because children that work as wellas go to school may find themselves less able tolearn, as a result of exhaustion or insufficienttime to complete homework. Therefore, there isa strong case for measuring the effects of childwork directly on what children are able to do,instead of simply on how long they spend inschool.

These problems have lead researchers to lookfor indicators of school achievement that gobeyond simple attendance. Thus, Patrinos andPsacharopoulos (1995) found that several fac-tors that increase child labor (age, gender,language and number of siblings) both reduceschool attendance and increase the chances ofgrade repetition in Paraguay. This was fol-lowed, in Patrinos and Psacharopoulos (1997)by the inclusion of a child work variable inequations that were used to estimate the chan-ces of age–grade distortion in Peru. The esti-mated coefficient on this variable was positive,indicating that it increased the chances of thechild being too old for his/her grade, but wasalso statistically insignificant.

Grade repetition and age–grade distortionare not, however, perfect indicators of learningachievement, as schools may not apply uniformstandards in enforcing grade repetition. What isneeded is some measure of actual competence.Akabayashi and Psacharopoulos (1999) usemeasures of reading ability (being able to reada newspaper) and mathematics (being able todo written calculations) in Tanzania. They findthat predicted 4 hours of work reduce ability,while predicted school attendance and hours ofstudy increase ability. The coefficients wereoften statistically insignificant, however perhapsbecause of the small sample size and the poor fitof the predicting equations. More seriously, theauthors recognized the possible unreliability ofthe ability measures, as they were based onparental judgement. The present paper avoidsthat difficulty by using ‘‘objective’’ tests ofreading and mathematical competence. It also

uses a measure of innate ability as a control inestimating the effects of work and school onlearning achievement. This is similar to theapproach taken by Dustmann, Micklewright,Rajah, and Smith (1996) for looking at the ef-fect of part-time work on examination perfor-mance in the United Kingdom.

Measurement of the effects of child work onlearning achievement can make several contri-butions. First, it will help in an understandingof the decisions that households make as towhether or not their children should work.Second, it will provide an idea of the level ofeducational effort (perhaps through schoolingat more convenient times or less formal edu-cation) that might be desirable to mitigate theeffects of work on education. Third, it willprovide a better idea of one of the benefits ofpolicies and projects to reduce child work, andso lead to the design of better policies andprojects.

As this is a study of just one country, theestimates reported in this paper cannot be seenas applicable everywhere. The paper doeshowever, provide a new view of how to mea-sure the effects of child work on education, andpresents a methodology that can both be re-fined in the future and applied to other coun-tries as and when data become available.

A further limitation of this study us that itdoes not seek to explain the incidence of childwork, but takes it as given. There has beenconsiderable progress in this area, as exempli-fied by Grootaert and Patrinos (1999), but theavailable data do not allow the simultaneousconsideration of the determinants of child workand the effects of such work on learningachievement.

Section 2 presents a picture of the pattern ofchild work and school attendance in Ghana.Section 3 presents a description of the educa-tional test results that were obtained by chil-dren between the ages of nine and 18. Section 4provides a model that can be used to analyzethe impact of work and schooling on testscores. Section 5 reports on the results of usingsimple statistical methods based on the modelof Section 4. Section 6 concludes with a sum-mary of the results.

2. CHILD WORK AND SCHOOLATTENDANCE IN GHANA

GLSS2 is a representative sample survey ofthe household population in Ghana for 1988–

WORLD DEVELOPMENT386

89. It records considerable detail about the in-come and expenditure of each household, andof the education, health and economic activitiesof each person in the household aged morethan seven years. It is a fairly typical exampleof the living standard measurement study(LSMS) household surveys that have been un-dertaken in a number of developing and tran-sitional economies with the encouragement andtechnical assistance of the World Bank. It isbetter than some LSMS surveys for studyingthe effects of child labor because it asks ques-tions about economic activity from children ofan early age.

The educational tests that are discussed belowwere only administered to people aged nineyears or more, and only to those who lived inhalf of the sampling clusters. As we are con-cerned with people who can be regarded as chil-dren, and who might reasonably be expected toattend school, the analysis is confined to thoseaged 18 years or less. Thus, the analysis in thispaper relates to people aged between nine and18. The analysis in this section covers childrenwho were not tested, as well as those who were.

The definition of work used here is thestandard ILO definition, including work pro-vided on the labor market and work forhousehold farms and enterprises, even if it isunpaid. It excludes housework in the familyhome (such as cleaning, cooking or washing).There are several reasons for this. First, thedebates surrounding the potentially damagingeffects of child labor do not include housework.Second, the information in the survey onhousework is less detailed than on ILO-definedwork. Third, almost all children in Ghanaclaim to do some housework, and so the ana-lysis of participation in housework would notbe revealing. Nonetheless, it is important to notethat girls in Ghana report almost twice as manyhours of housework as boys (16 hours perweek, compared to nine hours per week). Theneglect of housework would therefore ignorean important gender dimension to educationalachievement. For this reason, the reportedhours of housework are used as a possible ex-planatory variable in the empirical analysis.

As far as practicable, the children themselvesprovided the information on their schoolingand economic activities. For the purpose of thispaper, the important schooling question waswhether the child had attended school in thepast 12 months. This was used in preference tothe question on whether they had attendedschool in the last seven days, because school

holidays or illness could affect the answer tothis question. Similarly, the important questionon economic activities was whether the childhad worked in the past 12 months. Childrenwho worked were asked a range of questionsabout their work, including how many weeksthey had worked in the last 12 months and howmany hours per week they had usually workedin the last 12 months. The answers to thesequestions will be used as measures of work in-tensity in this paper. But the reliability of theanswers to these work intensity questions islikely to be lower than for the participationquestions, as much of the work is informal andmany children are poor judges of time.

The survey reveals the extent of informalworking, with 96% of working children under-taking work for the family farm or enterpriseand 89% reporting the same occupation as oneof their parents. It is therefore not surprisingthat the industrial composition of this work ishighly concentrated in agriculture (83%), re-tailing (7%) and food manufacture (3%).

Table 1 reports the proportion of childrenwho only work, who only go to school, who doboth, and who do neither. Children are re-corded as working if they report that they haveworked in the last 12 months. They are re-corded as being in school if they report thatthey have been in school in the last 12 months.The first point to note about the data is thatworking and school attendance are not straightalternatives. Until the age of 16, most work-ing children attend school. Moreover, in theyounger age range, increases in working mainlyconsist of children working while still atschool. It is only at age 17 that there is a clearmovement out of school and into work. Thesecond point to note is that there is a smallgroup of children who neither work nor go toschool. The third point to note is that the dif-ference in behavior between boys and girls isnot large compared to other developing coun-tries (Bhalotra & Heady, 2001a). Girls how-ever, are more likely to be neither at work norin school, less likely to combine work andschooling, and slightly more likely to onlywork. This could be because they also havegreater housework duties.

These points suggest that the effect of childwork on school attendance is fairly small,partly because most can combine work andschool and partly because some of those whoonly work may do so as an alternative to doingnothing, rather than as an alternative to goingto school. One reason why it appears to be easy

387CHILD LABOR AND LEARNING ACHIEVEMENT

to combine work with schooling is that most ofthe children do not work very much: the meanhours worked per week are 15.5 (with a stan-dard deviation of 16.3) and the mean weeks peryear are 14.6 (with a standard deviation of17.0). In both cases, the median is below themean. So, this gives a picture of most childrenonly working a little and a much smallernumber working an amount that could plausi-bly interfere with schooling.

It is worth noting however, that the effect ofworking on school hours is very small, al-though highly statistically significant. Themean school hours per week for working chil-dren who attended school was 21.1, onlyslightly less than the mean of 22.2 hours fornonworking children who attended school.Moreover, for children attending school, therewas no relationship between hours of schoolingand hours of work.

In summary, this analysis suggests that childwork in Ghana has relatively little effect onschool attendance. As argued however, in Sec-tion 1, the effect of child work on educationalachievement may not be accurately reflected byits impact on school attendance. The rest of thispaper, therefore, proceeds to an analysis ofeducational test results.

3. EDUCATIONAL TEST RESULTS

In half the sampling clusters of GLSS2, in-dividuals between the ages of nine and 55 were

asked to take educational tests. These includeda test of ‘‘innate ability’’ (Raven test), an easyreading test, an easy mathematics test, an ad-vanced reading test, and an advanced mathe-matics test. Children only took the advancedtests if they achieved above a minimum score(four out of eight) in the corresponding easytest. The Raven test is a colored progressivematrices test (Raven, 1956 and Raven, Court,& Raven, 1977), which was used by Knight andSabot (1990) and much of the subsequent lit-erature. The advanced reading and mathemat-ics tests are also the same as those used byKnight and Sabot (1990, Appendix C). Theeasy reading and mathematics tests were de-vised for the GLSS2 and are presented in Gle-wwe (1999, Appendix 4.1).

There were 1,848 children between the agesof nine and 18 in the sampling clusters wherethe tests were administered. Of these, 1,563took the Raven test, 1,024 took the easymathematics test and 585 took the easy readingtest. Children did not take the Raven test for avariety of reasons, including illness, travellingand outright refusal. They did not take any ofthe other tests. A large part of the reduction innumbers from the Raven test to the easymathematics test was due to the fact that thelatter test was only supposed to be administeredto those who had completed three years ofschooling. 5 The much-reduced participationin the easy reading test was due to the fact thatthe test was in English, and some schools donot introduce English language instructionuntil the fourth year. 6

Table 1. Work and school attendance

Number % Only work % Only school % Work and school Neither

Age9 211 6.2 67.3 16.6 10.010 217 7.4 57.1 24.4 11.011 161 10.6 48.4 31.7 9.112 234 15.0 42.3 36.8 6.013 169 21.9 37.9 33.1 7.114 189 20.6 38.1 37.6 3.715 197 25.3 32.5 36.0 6.116 169 28.4 26.6 37.9 7.117 120 36.7 23.3 30.8 9.218 131 42.0 17.6 29.8 10.7

SexMale 961 17.2 42.2 36.1 4.5Female 837 22.6 39.8 25.8 11.8

Total 1,798 19.7 41.1 31.3 7.9

Note: Row percentages do not sum to 100 because of rounding.


Less than half (269) of those taking the easyreading test did well enough to qualify for theadvanced reading test and 253 actually took it.A similar pattern applied with mathematics:500 scored more than four in the easy test and453 took the advanced test. In many cases, thelow scores in the easy tests were due to theyoung age of the children.

The results of these tests are given in Table 2.Each test has its own grading scheme and nosignificance can be attached to comparisons ofscores between tests. The significant compari-sons are those between entries in the samecolumn. The first two lines of Table 2 comparethe mean scores in each test of all workingchildren and all nonworking children. Thescores for Raven, easy math and advancedreading are higher for nonworkers. The scoresfor advanced math are the same for workers

and nonworkers, while the workers obtained ahigher mean score for easy reading. The resultsfor easy math and advanced reading supportthe view that child work harms educationalachievement, but the results for advanced mathand easy reading do not.

The comparison in the first two lines ishowever, distorted by the difference in agecomposition between the working and non-working children: working children are olderand older children do better in the tests, thusmaking working children appear to do better.This distortion can be removed by comparingthe scores of working and nonworking childrenat each age, as is done in the main body of thetable. Comparisons here show that, with fewexceptions (seven out of the 40 comparisons,none of which were statistically significant),working children did worse in the reading and

Table 2. Mean test scores by work status, sex and age

Group Work Raven Easy reading Easy math Advanced reading Advanced math

All Yes 17.2 3.9 4.3 11.9 7.5No 18.2 3.8 4.5 12.7 7.5

SexMale Both 18.6 4.1 4.6 12.8 8.1Female Both 16.7 3.5 4.1 11.5 6.6

Age9 Yes 14.6 0.0 2.6 None None

No 15.6 3.3 3.6 9.6 4.3

10 Yes 15.3 1.0 3.3 10.0 6.5No 15.8 1.9 3.4 11.0 4.7

11 Yes 15.2 0.7 3.2 4.0 5.2No 17.0 3.0 4.4 9.4 6.4

12 Yes 16.3 1.9 3.8 10.6 6.1No 18.0 3.3 4.6 12.5 6.8

13 Yes 16.1 3.3 4.1 9.5 5.7No 19.2 4.8 4.8 14.3 9.0

14 Yes 17.3 3.6 4.4 9.6 7.0No 17.7 2.7 4.4 10.2 6.7

15 Yes 17.7 4.6 4.3 12.1 6.4No 21.2 4.0 5.1 11.8 7.4

16 Yes 18.5 4.7 5.0 11.7 8.4No 21.7 4.4 5.2 14.6 9.4

17 Yes 19.3 4.6 5.2 12.3 8.7No 24.3 5.6 5.3 14.4 9.3

18 Yes 20.1 5.2 5.4 14.3 9.4No 23.5 5.6 5.6 14.4 10.9

Note: Numbers in bold are significantly different from each other at the 10% level.


mathematics tests than nonworking children.This provides strong support for the view thatchild work harms educational achievement.

It is interesting to note that nonworkingchildren also do better than working children inthe Raven test. This test is designed to measureinnate ability and should therefore not be in-fluenced by educational experience or whethera child is working. Instead, the simplest inter-pretation of this observation is that householdsprotect more able children from working inorder to allow them to develop their abilities tothe full. A more complex variant of this type ofexplanation is to hypothesize that more ablechildren have more able parents, and thereforecome from richer households that are less likelyto have to put their children to work.

The fact that working children have lowerRaven scores raises the possibility that theirlower scores for reading and mathematics aredue to their lower innate ability, rather than totheir work. This possibility is examined inSection 4, together with other factors thatmight affect test results.

Finally, Table 2 shows surprising results forthe difference in test scores between girls andboys: girls score substantially lower in all tests,including the Raven test. This is particularlysurprising for the Raven test, as it is designed tobe gender neutral and there is no reason toexpect girls to have less innate ability thanboys. But the difference is unlikely to be due todifferences in work and school experience 7

(quite apart from the fact that the Raven test issupposed to be unaffected by such experience)because, as shown in Table 1, there is littledifference between boys and girls in this respect.Even larger gender differences were found byAlderman, Behrman, Ross, and Sabot (1996)for Pakistan, but they were unable to provide acomplete explanation. There is clearly scope formore research on this issue.

4. MODELING THE DETERMINANTS OFTEST SCORES

Section 3 showed that working children ob-tained lower test scores for reading and math-ematics than nonworking children of the sameage, but raised the possibility that this was dueto differences in innate ability. At the sametime, it is likely that test scores are influencedby other factors apart from innate ability, age,sex and work status. The purpose of this sec-tion is to provide a simple model of how vari-

ous factors affect test scores, and use this todevelop an empirical methodology for appli-cation in the next section.

(a) The basic model

In general terms, one can think of the testscore of a child depending on a range of vari-ables, which can be represented as follows(details of how to measure these variables arediscussed below):

t ¼ F ða; y; e;w; s; zÞ; ð1Þwhere t is the test score, a is ability, y is age, e iseducation, w is work, s is the sex of the child, zrepresents household characteristics.

The effect of w on t in Eqn. (1), as representedby the partial derivative of F with respect to w,can be regarded as the ‘‘direct’’ effect of workon the test score. This direct effect is that whichapplies when the amount of schooling (andother variables) is kept constant, and resultsfrom such factors as tiredness and lack of timeto complete homework.

As discussed above, however, there is a pos-sibility that work (along with other factors) willinfluence the level of education. This idea canbe formalized as:

e ¼ Gða; y;w; s; zÞ; ð2ÞEqn. (2) shows that work can have an addi-tional ‘‘indirect’’ effect on the test score. Achange in w will, via the function G, produce achange in e. This change in e, will in turn affectthe value of t through the function F in Eqn.(1). Thus, the indirect effect can be representedas the product of the partial derivative of Gwith respect to w and the partial derivative of Fwith respect to e.

The overall (direct plus indirect) effect ofwork on the test score can be obtained by usingEqn. (2) to substitute out e from Eqn. (1):

t ¼ Hða; y;w; s; zÞ� F ða; y;Gða; y;w; s; zÞ;w; s; zÞ: ð3ÞEqn. (3) shows that to estimate the total ef-

fect, direct plus indirect, it is necessary to ex-clude the schooling variables themselves fromthe estimated equation. It is however, necessaryto include as many other variables as possiblethat might affect the level of schooling, in orderto prevent the work variables picking up theeffects of other influences. In this case, the co-efficients on the work variables will capture theeffect the work on schooling, and thus on ed-


ucational achievement (the indirect effect), inaddition to the direct effect. This is reported incolumn (1) of each of Tables 3–6.

In order to estimate the direct effect alone, asshown in Eq. (1), it is necessary to include theschooling variables, so that the coefficients onthe work variables are only picking up the effectof work, given the level of schooling. In Tables3–6, the addition of schooling variables is givenin two steps: in column (2), the addition of thechild�s years of schooling, and in column (3) theaddition of current school attendance and othermeasures of current school inputs (includingschool hours and school charges). This two-step approach is used as the years of schoolingvariable is less likely to be influenced by currentwork activity than current schooling variables.

Finally, column (4) of Tables 3–6 reports theresults of excluding the work variables. Thisindicates the results that would be obtainedfrom the standard educational achievementliterature, in which child work is ignored. Itallows us to judge whether the omission of childwork variables biases the estimates of the re-turns to education.

(b) Choice of variables

The discussion above shows that the estima-tion of the effects of work on test scores re-quires variables that represent t, a, y, e, w, s andz. The choice of variables for t, a, y and s wasstraightforward. The test score was used for t,the Raven score was used for a, y was repre-sented by age in years, 8 and s was representedby a dummy variable that took the value of onewhen the child was a girl.

The choice of variables to represent educa-tion was more complex. In order to captureboth quantity and quality dimensions, thevariables used were available: the child�s yearsat school (child�s schooling), a dummy forwhether the child was in school in the year ofthe test, the number of hours per week atschool, the amount paid in school fees and theamount paid for school books. As there was co-linearity between the last two variables and notheory to provide guidance, the amount paidfor school books was chosen because it fittedthe data better.

The variables available for measuring workwere a dummy for whether the child wasworking at home (not including housework) inthe year of the test, a dummy for whether thechild was working outside the home in the yearof the test, the number of weeks the child works

per year, the number of hours the child worksper week, the number of years for which thechild has worked and the number of hours perweek the child spends doing housework. Thethree measures of work intensity––weeks, hoursand years––are all expected to be important butthey were co-linear and so only one of themcould be chosen in any equation if reasonablywell-determined coefficients were to be ob-tained. As with the education cost variables, thechoice was made on the basis of which fitted thedata better, which depends both on their realimportance and the accuracy with which theyare measured. Weeks of work fitted better forthe easy tests and hours of work for the ad-vanced tests. This is reasonable, as the childrenwho take the easy tests are typically youngerand so have more difficulty in estimating theirhours of work. The inclusion of both a workdummy and a work intensity variable providesa first-order approximation to any (differentia-ble) nonlinear relationship between work effortand schooling achievement.

Finally, the variables available for measuringhousehold characteristics were the number ofyears of education of the father, the number ofyears of education of the mother, and a setof dummy variables (omitted from the tables,along with the constant, for lack of space) foreach sampling cluster to pick up differences inavailability and quality of schooling as well aslocal attitudes to education (including the im-portant rural–urban differences). The educationof the parents can be seen as having two pos-sible affects. First, it could represent thehousehold�s attitude to education, and thusschool age working. Second, it is a proxy forthe income of the household and so measureability to afford schooling and quality of thehome study environment. 9 Household incomeitself was not a suitable variable, as it dependson child work. Unsurprisingly, years of edu-cation of the father and mother were highlycorrelated. Father�s education was chosen as itfitted the data slightly better.

(c) Estimation issues

One problem in estimating equations of thissort is that variables such as work status andschool attendance cannot be regarded as pre-determined: they are the result of householdchoices. In principle, this problem can be dealtwith by using instrumental variables that arenot already included in the equation. But, allvariables in the dataset that might influence


household choice on work and education arealso variables that should be included in the testscore equation. Thus, there are no availableinstruments, and the only option was to useordinary least squares.

This would produce biased estimates if any ofthe work or education variables were correlatedwith the random error in the equation. Thiswould occur if those children with higher thanexpected test scores had systematically differentlevels of work or schooling than those withlower than expected test scores. This mightseem likely, as the results of Section 3 suggestthat more able children are less likely to work,and it is natural to suppose that they are morelikely to go to school. But since innate ability(measured by the Raven test) is included in allthe regressions, the problem only arises if chil-dren with higher easy math (say) scores thanexpected from their ability are less likely towork and more likely to go to school. Thisseems much less likely. But it is worth notingthat, if it were true, this would lead to anoverestimate of any negative relationship be-tween work and test scores, and an overesti-mate of any positive effect of schooling.

A second possible problem is selection bias,as many of the children do not take all the tests.This raises the possibility that the childrentaking the tests are more likely to be those whoexpect to do well, so that the error term is notdistributed symmetrically around zero. Thiswill not be a problem if, as is the case for manyof the children, the reason for not taking thetest is exogenous (such as language of instruc-tion or age). But the fact that the selection forthe advanced tests depends on the scores on theeasy tests raises cause for concern. Unfortu-nately, however, there are no additional vari-ables that are available to identify a selectionmodel, and so the ideal solution is not avail-able. Another alternative, treating the score ofall those who do not take a test as zero wouldbe unsatisfactory: those with the threshold easytest score often obtain advanced test scores thatare well above zero, suggesting that many ofthose below the threshold would obtain non-zero advanced test scores if they had taken thetest.

In these circumstances, all that can be done isto note the possible bias that could arise fromsample selection. As the main interest in thispaper is on the effect of work on learningachievement, we should be most concernedabout the bias on the coefficients of the workvariables. Those who work are less likely to

take the tests, as the opportunity cost of theirtime is higher and, as shown below, their per-formance in easy tests is lower and so they areless likely to qualify for the advanced tests. Thismeans that the probability of working childrentaking the test is positively related to their in-terest in schooling (which counteracts the op-portunity costs effect) and other unobservedinfluences on test performance (which affectstheir easy test scores). Thus, working childrenwho take the tests are more likely to havepositive error terms (positive unobserved in-fluences on their test scores), and so the coef-ficients on the work variables are likely to bepositively biased. This means that any negativeeffect of work on test scores will be under es-timated. In other words, any finding that workreduces test scores is unlikely to be the result ofsample selection.

Finally, there is a possibility that the Raventest does not accurately measure innate ability,and this could bias the results. In order to testthe extent to which the results depend on theassumed accuracy of the Raven test, the equa-tions were estimated with the Raven variableomitted. This had little effect on the estimatedcoefficients of the work and education vari-ables, although their standard errors increasedas a result of the worse fit of the equation. Thisfinding suggests that the results below are rea-sonably robust to measurement error of innateability.

Overall, therefore, while the estimation pro-cedure used in this study was not ideal, becauseof the lack of identifying variables, it is unlikelythat any estimated negative impact of childwork on educational achievement is the resultof these shortcomings.

5. EMPIRICAL RESULTS

This section reports the results of applyingthe model developed in the previous section toeach of the four test scores in turn.

(a) The easy reading test

Table 3 presents the results for the easyreading tests. Column (1) reports the resultswithout the school attendance variables. Col-umns (2) and (3) report the results with theschooling variables. Column (4) reports theresults without the work variables. The num-bers recorded in parentheses are the absolute t-ratios of the coefficients, based on robust


standard errors that have been adjusted forcluster effects.

Column (1) of Table 3 shows that age, work(whether at home or not), innate ability(Raven) and father�s schooling are the signifi-cant determinants of the easy reading score,and each has the expected sign. It is interestingthat girls do slightly worse than boys, even afterallowing for their lower Raven scores, althoughthe effect is statistically insignificant. It is alsointeresting that the best measure of work in-tensity (weeks of work) has no significant im-pact. This could be because of errors in people�sestimates of the time they spend working. Fi-nally, note that the effect of hours of house-work is statistically insignificant.

The introduction of the child�s years ofschooling in column (2) affects the coefficientson age, ability, father�s schooling and workoutside the home. The coefficient on age be-comes insignificant as years of schooling ishighly correlated with age among children. Thereduction in the coefficients on father�s school-ing, ability and work outside the home indi-cates the fact that some of their effect oneducational achievement operates via their ef-fect on years of schooling. The child�s years ofschooling are highly significant, but it is inter-esting that the magnitude of the coefficient isless than half that of the coefficient of work athome and less than a third of the coefficient onwork outside the home. Thus, participation inwork at home has so much effect on readingscores as to offset more than two years of

schooling, while work outside the home offsetsmore than three.

The introduction of the other schoolingvariables in column (3) shows that none ofthem are significant and that their addition haslittle effect on the existing coefficients, apartfrom a slight reduction in the coefficients onwork. This indicates that some, but very little,of the effect of work on schooling achievementis indirect, via its effect on schooling.

Finally, column (4) shows that dropping thework variables has little effect on the coeffi-cients on the schooling variables. This indicatesthat, despite the evident effect of work on ed-ucational achievement, its omission from theestimated equation does not bias the estimatedreturns to education.

The important conclusion from Table 3 isthat work reduces reading scores and that theeffect is almost twice as large when the worktakes place outside the home. Even when thechild works at home, the reduction in readingscores from working is equivalent to two yearsof schooling.

(b) The easy mathematics test

Table 4 presents the results for the easymathematics test, in the same format. Column(1) shows that, as with the easy reading test, thesignificant determinants include age, innateability and father�s schooling. But it is onlywork outside the home that has a significanteffect. Moreover, the hours of housework now

Table 3. Estimation results for easy reading test (dependent variable: score in easy reading test)

Independent variables (1) (2) (3) (4)

Age 0.258�� (4.51)a )0.036 (0.49) )0.008 (0.09) )0.028 (0.34)Female )0.106 (0.49) )0.207 (0.97) )0.118 (0.54) )0.154 (0.81)Raven 0.186�� (7.43) 0.152�� (6.47) 0.142�� (5.44) 0.140�� (5.41)Work at home )0.848� (1.79) )0.895� (1.92) )0.878� (1.76)Work outside home )1.596�� (2.16) )1.434� (1.89) )1.31� (1.72)Weeks of work 0.016 (1.28) 0.018 (1.44) 0.012 (0.98)Housework hours )0.009 (0.62) )0.010 (0.46) )0.006 (0.48)Father�s schooling 0.064�� (3.67) 0.050�� (2.88) 0.057�� (2.84) 0.057�� (2.86)Child�s schooling 0.395�� (5.34) 0.386�� (4.01) 0.384�� (3.97)Attend school )0.155 (0.28) )0.110 (0.21)School hours 0.019 (1.29) 0.020 (1.33)School books 0.022 (0.34) 0.030 (0.49)Number of observations 578 578 525 525R-squared 0.590�� 0.623�� 0.629�� 0.624��

aNumbers in parentheses are absolute t-ratios.* indicates significance at 10% or less.** indicates significance at 5% or less.*** indicates significance at 1% or less.


has a significant negative effect. Work intensity,as measured by weeks of work, continues to beinsignificant.

Column (2) shows that, as with the easyreading test, the introduction of the child�syears of schooling affects the coefficients onage, ability and father�s schooling. But the sizeof the coefficient on work outside the homeactually increases slightly. Again, the coefficienton child�s years of schooling is about a third ofthe value of the coefficient on work outside thehome.

However, the introduction of currentschooling variables in column (3) has a strik-ingly different effect from the case of the easyreading test. The coefficient on work outsidethe home is halved in magnitude 10 and thecoefficients on the school variables are gener-ally better determined. Thus, half of the effectof work on educational achievement is indirect,via schooling. This contrast with reading sug-gests that children lose their mathematical skillsmore quickly after leaving school. Finally, aswith the easy reading test, column (4) showsthat the omission of work variables does notbias estimates of returns to schooling.

The most important conclusion from Table 4is that work has a substantial negative impacton children�s mathematical skills, but only ifthey work outside the home. The magnitudeof this reduction can be seen as equivalentto about three years of schooling. Half ofthis effect is direct and half is indirect, viaschooling.

(c) The advanced reading test

Table 5 presents the results for the advancedreading test. Column (1) shows a differentpattern from that in Tables 3 and 4. Age andinnate ability are still significant factors, butnone of the other variables are individuallysignificant. In fact, the work at home and workoutside the home variables have an unexpect-edly positive, but insignificant effect. Furtheranalysis shows however, that the lack of indi-vidual significance for work outside the homeand hours of work is misleading: the two vari-ables are highly co-linear (with workers outsidethe home working an average of 43 hours whilethose who work at home average nine hours)and the joint significance of the two variables(shown in the last line of the table) is very high.The coefficients imply that the average childworking outside the home suffers a reduction of2.6 in their test score. This suggests either thathours of work become more important in theireffect at higher levels of academic achieve-ment 11 or that work outside the home is con-tinuing to be more harmful than work at home.

The fact that the father�s education is nolonger significant implies that success at higherlevels of study no longer depends on parentaleducation, provided that it was sufficient to getthe child to this stage.

As before, the introduction of child�sschooling in column (2) affects the coefficientson age and ability. It is interesting that it alsomakes the coefficient on female become signif-

Table 4. Estimation results for easy mathematics test (dependent variable: score in easy mathematics test)


Age 0.150�� (5.10)a )0.035 (0.88) 0.022 (0.49) )0.002 (0.04)Female )0.056 (0.56) )0.115 (1.16) )0.068 (0.67) )0.127 (1.31)Raven 0.143�� (7.51) 0.115�� (14.65) 0.110�� (12.75) 0.110�� (12.72)Work at home 0.041 (1.83) )0.046 (0.21) 0.039 (0.16)Work outside home )0.771�� (2.38) )0.799�� (2.31) )0.367 (1.00)Weeks of work )0.010 (1.47) )0.006 (0.95) )0.010 (1.42)Housework hours )0.016� (1.66) )0.016� (1.66) )0.015 (1.51)Father�s schooling 0.021� (1.68) 0.009 (0.70) 0.012 (0.83) 0.011 (0.79)Child�s schooling 0.255�� (7.43) 0.220�� (5.48) 0.224�� (5.60)Attend school 0.359 (1.30) 0.407 (1.51)School hours 0.006 (0.63) 0.006 (0.61)School books 0.079�� (2.33) 0.076�� (2.40)Number of observations 1,010 1,010 924 925R-squared 0.423�� 0.461�� 0.463�� 0.457��



icantly negative. The positive coefficient onworking outside the home increases, thus re-ducing the average overall impact of workingoutside the home to 1.8, reflecting the fact that(controlling for age) children who work outsidethe home have fewer years of schooling.Nonetheless, the reduction in the test scorefrom working outside the home is still equiva-lent to two years of schooling.

The addition of the current schooling vari-ables in column (3) increases the negative co-efficient on hours of work, restoring the averageoverall effect of working outside the home to2.5. This is because, controlling for other vari-ables, children who work longer hours spendmore on school books, and so increase the ef-fectiveness of their education. This suggeststhat most of the detrimental effect of work oneducational achievement is direct rather thanindirect. It is also worth noting that the coeffi-cient on current school attendance is negative,but that this is substantially offset by the coef-ficients on hours and expenditure on schoolbooks.

Finally, column (4) shows that dropping thework variables has little effect on the schoolingcoefficients, except for the contradictory coef-ficients on current school attendance. So, onceagain, estimates of return to years of schoolingare not substantially affected by omitting childwork variables.

The main conclusion from Table 5 is thatchild work only harms the advanced reading

score for children who work substantiallymore than average and therefore are morelikely to work outside the home. In that case,the effect appears to be much more direct thanindirect.

(d) The advanced mathematics test

Finally, Table 6 presents the results for theadvanced mathematics test. Column (1) showsthat age, ability, hours of work and father�sschooling are significant. As with reading, themove from the easy to advanced test of math-ematics has changed the significant aspect ofwork from simply whether the child works andwhether the work is in the home to how muchthey work. The average overall effect for chil-dren working at home is a reduction of 0.6, andfor those working outside the home it is 1.4.

The effect of adding the child�s years ofschooling in column (2) is to reduce the agecoefficient, reduce the Raven coefficient, in-crease the size of the female coefficient and re-duce the size of the coefficient on father�sschooling. It also reduces the average harmfuleffect of work, both at home (to 0.5) and out-side the home (to 0.9). The size of the coefficienton child�s schooling is a bit less than the typicaleffect of working outside the home but a bitmore than the effect of working at home. Thus,working can be expected to reduce the score ofthe typical child by the equivalent of about oneyear of schooling.

Table 5. Estimation results for advanced reading test (dependent variable: score in advanced reading test)


Age 0.524�� (3.25)a )0.268 (1.03) )0.274 (0.91) )0.317 (1.06)Female )1.138 (1.35) )1.431� (1.84) )1.668�� (2.20) )1.942�� (2.30)Raven 0.154�� (2.45) 0.090 (1.46) 0.056 (0.86) 0.055 (0.85)Work at home 1.015 (0.85) 1.107 (0.85) 1.639 (1.17)Work outside home 1.055 (0.27) 1.799 (0.47) 1.783 (0.46)Hours of work )0.084 (0.76) )0.083 (1.54) )0.099� (1.78)Housework hours )0.050 (0.76) )0.055 (0.97) )0.072 (1.46)Father�s schooling 0.057 (0.75) 0.030 (0.41) 0.041 (0.55) 0.074 (1.00)Child�s schooling 0.967�� (4.04) 0.849�� (2.68) 0.904�� (2.94)Attend school )2.944 (1.62) )1.450 (0.87)School hours 0.082 (1.29) 0.054 (0.84)School books 0.257� (1.83) 0.242� (1.94)Number of observations 248 248 231 231R-squared 0.508�� 0.570�� 0.611�� 0.587��

F -statistic for outworkand hours

4.88�� 2.84�� 4.23��



Column (3) shows that the introduction ofthe current school variables produces a furtherreduction in the negative effect of work to nearzero, both in and outside the home. This sug-gests that most of the influence is indirect. Thepattern of the coefficients on the currentschooling variables is similar to that for theadvanced reading test.

Finally, column (4) shows, once again, thatthe bias from leaving child work variables outof the equation has little affect on the estimatesof the returns to schooling.

The main conclusion from Table 6 is thatwork does reduce the advanced mathematicstest score, and that most of that effect is indi-rect. In addition, the harmful effect is stronglyrelated to the hours of work.

6. CONCLUSIONS

This paper has presented the results of ap-plying a new method of analyzing the effects ofchild work on learning achievement, based on adataset that is unusually rich in information onwork, schooling and test results. The findingsdemonstrate the value of this new analysis, andof collecting data of this sort.

The results show that work has a substantialeffect on learning achievement in the key areasof reading and mathematics if it is performedoutside the home. The effect is much less clearfor work at home, which has important impli-cations for judging the harm of child work. It isworth noting that the statistical significance of

work is substantially higher than that obtainedby either Patrinos and Psacharopoulos (1997)or Akabayashi and Psacharopoulos (1999).This may well be the result of using more ac-curate measures of achievement, differentiatingbetween work in and outside the home andcontrolling for innate ability, although therewere also differences in sample characteristicsand statistical methodology. 12 This suggeststhat our understanding of this important topiccould be furthered by the collection of data ofthis sort from other countries. Despite thedemonstrated importance of work, its omissionwas not found to substantially bias estimates ofreturns to schooling.

Although these results confirm the acceptedwisdom of the effects of work on learningachievement, they introduce a new view of howthat arises. First, these effects are substantialeven though Section 2 showed that work hadrelatively little impact on school attendance.Second, Section 5 showed that a substantialproportion of the effect is direct rather thanindirect, via schooling. This is important be-cause much of the work on the educationalharm of child work has focused on its effects onschooling.

The direct link between work and learningachievement, holding education constant, couldbe because of exhaustion or because of a di-version of interest away from academic con-cerns. It could also be caused by those childrenwho work being innately less interested in ac-ademic achievement. This latter possibilityneeds further investigation, as it would imply

Table 6. Estimation results for advanced mathematics test (dependent variable: score in advanced mathematics test)


Age 0.661�� (7.14)a 0.125 (0.89) 0.164 (1.05) 0.143 (1.04)Female )0.721 (1.69) )0.861�� (1.97) )0.848� (1.81) )0.907�� (2.10)Raven 0.126�� (3.05) 0.079�� (2.23) 0.103�� (2.98) 0.103�� (3.08)Work at home )0.181 (0.27) )0.037 (0.06) 0.332 (0.43)Work outside home 0.588 (0.32) 1.084 (0.60) 1.753 (0.96)Hours of work )0.047�� (2.15) )0.046�� (2.34) )0.048�� (1.98)Housework hours )0.018 (0.49) )0.017 (0.52) )0.028 (0.82)Father�s schooling 0.078�� (1.99) 0.044 (1.11) 0.036 (0.95) 0.041 (1.08)Child�s schooling 0.651�� (5.01) 0.560�� (3.80) 0.562�� (3.89)Attend school )0.675 (0.58) )0.413 (0.40)School hours 0.052 (1.57) 0.050 (1.52)School books 0.264�� (2.51) 0.253�� (2.25)Number of observations 444 444 407 407R-squared 0.430�� 0.474�� 0.524�� 0.518��



that it is not work that harms educationalachievement, but a lack of motivation that af-fects both work and learning. If it were true,efforts to improve the educational qualifica-tions of children should be aimed at designingschool curricula to stimulate children�s interest,rather than simply discouraging child work.

As well as these major conclusions, it is in-teresting to note some of the detail of the re-sults in Section 5. Schooling was moreimportant in mathematics than reading. In

addition, the advanced tests were less affectedby whether a child worked than by the amountof work that they undertook. The same mayalso be true for the easy tests, but be obscuredby the difficulties of younger children in accu-rately reporting time worked. As far as genderis concerned, girls were found to do worse in allthe tests, even allowing for their lower Ravenscores. Girls also suffer from more housework,which was shown to reduce the easy mathe-matics score.

NOTES

1. Examples of this approach are Kanbargi and

Kulkarni (1991), who show that working children in

Karnataka (India) are less likely to attend school; and

Akabayashi and Psacharopoulos (1999), who show that

factors which increase schooling are also factors that

reduce child work. As discussed below, the latter study

also looks at the effects of work and schooling on

educational achievement.

2. Other work based on the educational data used here

is presented in Glewwe (1999).

3. One exception to this is Appleton (1995) who finds

that some child work variables affect examination

performance in Kenyan Primary Schools.

4. The predictions are obtained from separately esti-

mated equations.

5. In fact, a few children with less than three years of

schooling did take the test.

6. This is supported by the much greater difference

between the numbers taking the easy reading and easy

mathematics tests among younger children and those

with few years of schooling.

7. In fact, a simple regression that included work and

schooling variables could not explain the differences

between boys and girls.

8. Age was also introduced in a quadratic term and

interacted with school attendance and work participa-

tion. None of these however, produced statistically

significant effects. Thus, the data confirm that a simple

linear term is the best way of representing the effect of

age on test scores.

9. Grootaert and Patrinos (1999) show that poverty

can affect both child work and school attendance in

some countries. But Bhalotra and Heady (2001b) show

that the effect of income on child work in Ghana is

more complex, and better represented by a continuous

variable. The same approach is therefore taken here,

using a continuous proxy for household income as an

influence on learning achievement, rather than a poverty

measure.

10. The coefficient also becomes statistically insignifi-

cant. But work outside the home and weeks of work are

jointly significant at the 10% level (F ¼ 2:57).

11. It may also reflect the greater ability of older

children to judge time.

12. The sample sizes in this paper are larger than those

in Akabayashi and Psacharopoulos (1999) but smaller

than those in Patrinos and Psacharopoulos (1997). The

use of predicted rather than actual values in Akabayashi

and Psacharopoulos may also have contributed to the

frequent insignificance of estimated effects. They used

predicted values to avoid the bias that could result

from using endogenous variables. As argued in Section

4, this problem of bias is likely to be much smaller in this

paper because of the use of innate ability as a control

variable.

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The Effect of Child Labor on Learning Achievement