Cambodian Child s Wage Rate, Human Capital and Hours Worked Trade-off: Simple Theoretical and Empirical Evidence for Policy Implications

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1 GSIS Workng Paper Seres ambodan hld s Wage Rate, Human aptal and Hours Worked Trade-off: Smple Theoretcal and Emprcal Evdence for Polcy Implcatons Han PHOUMIN Sech FUKUI No. 6 August 2006 Graduate School of Internatonal ooperaton Studes Kobe Unversty

2 ambodan hld s Wage Rate, Human aptal and Hours Worked Trade-off: Smple Theoretcal and Emprcal Evdence for Polcy Implcatons Han Phoumn* Dr. Sech Fuku * Han Phoumn: Ph.D canddate, Graduate School of Internatonal ooperaton Studes (GSIS), Department of Economc Development and Polcy, Kobe Unversty, Rokkoda, Nada-Ku, Kobe, , Japan. Emal: Sech Fuku: A professor at Graduate School of Internatonal ooperaton Studes (GSIS), Department of Economc Development and Polcy, Kobe Unversty, Rokkoda, Nada-Ku, Kobe, , Japan. We are grateful to anonymous referees for helpful comments and suggestons. We also wsh to thank the Mnstry of Plannng n amboda for allowng us to use amboda hld Labor Survey survey dataset.

3 Abstract As t s often sad that chld labor comes on the expense of schoolng. But the fact n amboda case s quet dfferent because most chldren are lkely to combne work and study together. Ths means that chld labor contrbutes to human captal of the chld as long as we stll fnd chldren combne both work and study. Ths study tres to nvestgate that perhaps hours worked of chldren rather than chld partcpaton rate n the labor force are really trade-off wth schoolng outcomes of chldren. If chldren s ncome play sgnfcant role n parents decson over human captal formaton of ther chldren, thus we need to estmate the rate of return to chld labor as well. By employng smple theoretcal and emprcal model, we found that educaton of the chld plays crucal role n ther wage rate and/or earnng capacty, whch s a consderable results to help encourage parents to nvest n ther chldren s educaton. Fnally the most strkng results out of ths study s that workng chldren contrbute to ther human captal because chldren s workng hours stay below the threshold level of 3 hours per day. Key words: chld labor, Rate of Return, human captal, hours worked, trade-off, amboda. JEL lassfcaton odes: J3, J2, D1, 31, 35

4 I. Introducton 1) Background hld labor has long been recognzed as the drect conflct to human captal formaton of chldren and ts common explanaton s that schoolng competes wth labor ntensve jobs for chldren such as wage labor, employment n famly enterprses, or collecton actvtes. By ths vew, the low current ncome of famles keeps ther chldren out of school and thus perpetuates poverty nto the next generaton (Ravallon and Wodon, 2002). However, ths has come to our questonable hypothess snce most chldren n amboda are found n the combned actvty of work and study. Most hldren n developng countres s needed to help smoothenng household s consumptons and at the same tme they are able to stay n school. Of course, evdences on the chld labor are mxed based on ther cultural and adjustable strateges to ther needs. Some studes found that poverty s the cause of chld labor (Basu and Van, 1998; Edmonds and Turk, 2002), and thus t mplcates that eradcatng poverty wll also eradcatng chld labor. Perhaps most, f not all, would agree wth ths strong assocaton, however, the queston s that how s the transtonal mechansm to get one out of poverty. Ths s the most dffcult task and has been challenged to all leaders n the world that have tred through ther collectve actons reflectng n the UN Mllennum Summt and subsequently the Mllennum Development Goals (UN, 2000). And at the same tme, human captal formaton through schoolng and tranngs are among the most mportant factors to fght aganst poverty. These bndng correlatons among poverty, human captal and chld labor are presentng trade-off and thus trap n the vcous cycle of poverty. However, the stuaton does not really be lke that because human are the most flexble and adjustable to ther maxmzaton needs. The results are mxed that some are purely chld labor, some are purely chld schoolng and many are combned both work and study at tme n the context of amboda. Based on the above observaton of outcomes of chld labor and schoolng, we have formulated our hypothess of nterests that chld labor ndeed lead to human captal formaton of chldren n developng countres, takng the example of amboda case study, and we wll try to prove that n general chld labor has negatve relatonshp wth chld schoolng, but t s ther hours worked rather than ther partcpaton rate that s trade-off. Lke any theory ranked from producton functon to rate of return to human captal (Mncer, 1974; Becker, 1975), chld labor supply hours wll be trade-off wth ther schoolng outcomes f chldren contnue to work more than a threshold hours. Therefore, ths study s mportant for polcy makers to set up a threshold hours for whch chldren wll be montored and allowed to work at the mnmum hours, less than ths level, and otherwse, ther workng wll have negatve mpact on schoolng. If the assumpton of chld labor benefts ther human captal, thus the estmaton of chldren s rate of return to ther educaton s also our nterests. Mncer s model provdes that schoolng s the forgone earnng, and adults wll start earnng after the completon of ther school. However our case s dfferent because chld laborers started earnng at the same tme of schoolng. Ths s nterestng to know that whether educated chldren wll have 1

5 better barganng power at ther wage rate or not, and f t s so, ths wll encourage parents to choose both chld labor and schoolng at the same tme for household whose consumptons has to be supplemented by chldren s ncome. A bunch of lteratures are at our dsposal n terms of chld labor and ts schoolng trade-off, for nstance., Ravallon and Wodon (2000), Patrnos and Psacharopoulos (1997), Basu (1999), Jensen and Nelsen (1997). However, ther analyses mostly are based on chldren s partcpaton rate rather than hour worked. Exceptons are only Rossat and Ross (2003), and Ray and Lancaster (2004) where hours worked of chldren had been accounted for. Ths, perhaps, ndcates that data on labor supply of hours worked were unavalable n many countres, thus lmted the analyss only n the doman of chld partcpaton rate. Wthout renventng the wheels, we model after exstng lteratures especally Ray and Lancaster (2004) who tred to estmate the mpact of chld labor on schoolng. However, the dfferences are that () there s yet to have theoretcal model of hour worked of chldren and schoolng trade-off. Based on our smple theoretcal model, we found several nterestng propostons whch leads to the emprcal estmatons; () n the emprcal model, we allow our model of estmaton to have more omtted varables to enter drectly nto the explanatory varables because non-ncluson of omtted varables nto the model can cause serous problem on parameter estmates (Wooldrdge, 2003), and the rank of sample covers chldren age 5-14 year old rather a specfc target group years because chld labor are beleved to be actvely engaged n domestc work snce they were young and ths age group s complance to the defnton of chld labor n amboda; () we employ Probt model to estmate the bnary school enrolment rather than usng OLS. Due to dfferent technques and flexblty, we found dfferent fndngs on the threshold level. Ths study contrbutes to the growng evdence on the supply of chld labor s hours and schoolng trade-off. Buldng from smple theoretcal and emprcal model, ths study estmates the rate of return to chld labor, and subsequently move to estmate the man objectve of hours worked and schoolng trade-off by usng Two-stages Least Square method as we presumed the endogenety of hours worked of chldren n the our structural equaton of schoolng outcomes. 2) Objectve of the study Wth the fact that majorty of chldren n amboda are found n the combned work and study, we presume that chld labor ncrease human captal of chldren f chldren worked wthn a threshold level hours. And of course, chldren are able to guarantee ther schoolng because of ther chld labor. In other words, chldren s ncomes are needed to smooth the household consumpton. Hence, the rate of return of chld labor s very mportant to be estmated before the queston of how much tme that each chld labor has to work. Wth ths vew, ths study ams to provde the followng nvestgatons: Estmate the rate of return to chld labor. Here the study of chld labor and ther earnng capacty become of nterest because ther earnng start at the same tme of studyng for those chldren who are n the category of both work and study. 2

6 Fnd out salent determnants of schoolng outcomes, and estmates the trade-off hours between hours supply of chld labor and schoolng outcomes. 3) Organzaton of ths paper Ths paper s organzed as follows. In Secton 2 we present lterature revew on rate of return of chld labor, and trade-off between hours worked of chldren wth ther human captal formaton. In Secton 3 we present a smple theoretcal and emprcal framework. The theoretcal model gves several nterestng propostons on the mportant of wage rate of chld labor, and subsequently the trade-off between chld labor and ther hours worked. Followng these propostons, we bult the emprcal specfcaton to estmates our hypotheses. In Secton 4 we dscuss the results of estmaton. Secton 5 s the concluson. We present summary and further studes. Fnally, Annex A contans the table of correlaton among covarates used n our specfcaton functon. II. Lterature Revew: 1) Rate of Return to Human aptal Investment Snce the begnnng of the ndustral revoluton, lteracy and knowledge have become ncreasngly valuable relatve to basc manual sklls. Ths ncreasng value has led to wage premums for educated workers. Not surprsngly, an educated workforce s the domnant factor n explanng dfferences n regonal growth and prosperty. As a result economsts have extensvely researched educaton s mportance n determnng ndvdual dfferences n wages and regonal dfferences n economc growth. The paper on nvestment n human captal by Becker (1962, 1975) emphaszes on educaton and tranng, the most mportant nvestments. Of course, formal educaton s not the only way to nvest n human captal. Workers also learn and are traned outsde of schools, especally on jobs. A number of studes use Mncer s human captal earnngs functon because ths model s the most commonly employed method n labor economcs. Mncer (1974) uses a smple regresson model wth a lnear schoolng term and a low-order polynomal n potental experence. Usng Mncer s model, we can easly adopt ths method for the estmaton of rate of return to human captal of chld labor. We smply bref the method as follow: Supposed that: n = length of workng plus length of schoolng of chldren = length of workng for chldren wthout schoolng Y s V s r t d = annual earnngs of ndvdual chld wth s years of schoolng = present value of an ndvdual s chld earnngs at start of schoolng = dscount rate = 0, 1, 2,, n tme, n years = dfference n the amount of schoolng, n years 3

7 e = base of natural logarthms Then, the rate of returns to human captal of the chld when dscountng process s dscrete s: V s n = Y (1 + r) s t= s+ 1 t And when dscountng process s contnuous s: V s = Ys s n e rt Ys ( e dt = rs e r rn ) Smlarly the present value of earnngs of an ndvdual chld who engages n s-d years of schoolng s: V s d = Ys n e rt ( s d ) Y dt = s d ( e r ( s d ) r e rn ) The rato, K s, s-d, of annual earnngs after s years to earnngs after s-d years of schoolng s found by lettng V s =V s-d : K s, s d Y e e r( s d ) rn r( n+ d s s = = = rs rn r( n s) Ys d e e e e ) 1 1 The raton, K s, s-d, s clearly larger than unty, and a postve functon of r, and a negatve functon of n. Ths take to mean that people wth more schoolng have hgher annual pay, the dfference between earnngs of ndvduals s due to dfference n nvestment of d years of schoolng, and dfference s larger the shorter the general span of workng lfe. Snce we are nterested n the n as a fxed span of earnng lfe, then we can wrte the raton, K s, s-d, as follow: V V s s d K n+ s rt Ys rs rn = Ys e dt = e (1 e ); s r n+ s d rt Ys d = Ys d e dt = (1 e s d r r( s d ) Ys e rd = = = e rs Y e s, s d s d rn ) e r( s d ) Now supposed that s d = 0, then we obtan: wrte the followng formula: ; Y rs, e. Takng the logarthms, we can s K s 0 = = Y0 lny s = lny0 + rs. Ths equaton shows that percentage ncrements n earnngs are strctly proportonal to the absolute dfferences n the tme spent at school, wth the rate of return as the coeffcent of proportonalty. Precsely the equaton shows the logarthm of earnngs as a strct lnear functon of tme spent at school. 4

8 2) Hours worked of chldren and schoolng trade-off In recent decade, the study on chld labor and ts trade-off wth human captal formaton has been wsely presumed (Basu 1999, Baland and Robnson, 2000; Fan, 2004; Rosat and Tzannatos, 2000; Basu and Tzannatos 2003; Ray and Lancaster, 2004; Patrnos and Psacharopoulos, 1995; Rosat and Ross, 2004; Heady, 2003), but there s very lttle data that analyze hours worked of chldren and ts trade-off wth schoolng outcome. In ths secton, we revews and hghlght a few exstng results that related to our study. Basu and Van (1998) gave mportant contrbuton to the polcy analyss on the trade-off between chld labor and ther schoolng status gven the role of parents wage rate the only most mportant determnant of chld actvtes. In the same settng, Baland and Robnson (2000) use bequests constrant of parents and captal market mperfectons to conclude the ratonale decson of parents about the tradeoff between chld labor and the accumulaton of human captal. Patrnos and Psacharopoulos (1995) found that factors predctng an ncrease n chld labor also predct reduced school attendance and ncreased chance of repetton. Smlarly, Heady (2003) use drect measure of readng and mathematcs ablty to conclude on a negatve relatonshp between chld labor and educatonal achevement n Ghana. However, small ncrease n chld labor may not be trade-off wth human captal nvestment (Fan, 2004) and ncreases n schoolng do not necessarly translate nto declnes n chld labor (Edmonds, 2005) snce the postve mpact of ncreased fnancal resources on educaton may outwegh the negatve mpact of reduced tme of study. The outcome of chld labor has been argued over decades and fndngs are vared from one to another based on hstorcal, poltcal, socal and economcal background (Han, 2005). Unlke the above studes on the trade-off between chld labor and ther schoolng that much of the evdence s on the mpact of chldren s labor partcpaton rates, rather than hours worked by chldren on chld schoolng, Akabayash and Psacharopoulos (1999) use tme-log data from a 1993 survey n the Unted Republc of Tanzana to nvestgate the relatonshp between chld work and human captal development. It found that factors that ncrease chldren s workng hours also decrease ther hours of study and those hours of work are negatvely correlated wth studyng ablty. Ray and Lancaster (2004) concludes n the case study usng evdence from Belze, amboda, Namba, Panama, Phlppnes, Portugal, and Sr Lanka that chldren s work, even n lmted amounts, adversely affects the chld s learnng as reflected n a reducton n the school attendance rate and n the length of schoolng receved by the chld. However, the paper suggests that f some lght work s permtted for chldren n the ages of 12 and 13 years, as suggested n ILO onventon 138, Art. 7, then t should be accompaned by a campagn to mprove adult educaton levels. Better educated adults wll, by ensurng that ther chldren make more effcent use of the non labor tme for study, help to reduce the damage done to the chld s learnng by her work hours. A smlar study on chld labor supply (Rossat and Ross, 2003) seems to reject the assumpton that a few hours of work only have neglgble effects on human captal accumulaton n the case of Pakstan and Ncaragua. 5

9 III. Theoretcal and Emprcal Framework 1) Theoretcal Model The conceptual framework of chld labor supply and human captal formaton trade-off s based on the standard economc assumpton that ndvduals are ratonal utlty-maxmzers (see, e.g., Becker, 1965). In ths smple theoretcal model, we assumed that ndvdual parents wll allocate ther chldren tme between workng and schoolng through ther maxmzaton of household utlty. The human captal functon of the chld s assumed to be a functon of tme spent at school and school s expendture for whch ndvdual household spent on school fee, textbooks, and other extra cost of chld-schoolng durng the a year. All decsons are made by altrustc parents, and chldren are treated as recpents. We assume that parents utlty functon s defned by equaton (1): Max l, x U ( p, h, a) Eq (1) Where p s the consumpton of parents, h c s human captal functon of the chld, and a, s the household s characterstcs. As mentoned, the human captal formaton of the chld s the functon of school s expendtures, x, and total avalable for study denotng (1-l c ), whch s expressed as: h > 0 h = h ( x, 1 l ); h x Eq (2) < 0 l And household budget constrant can be expressed as: + x = A + I + W l ; p p Where A s household assets, I p s parents ncome and W c l c s chld s ncome. We assume that parents ncome functon I p (.) s a functon of household assets, educaton, and other household characterstcs. For smplcty we can wrte the household budget constrant as: p = A + I (.) + W l x Eq (3) p Parents n each household choose to maxmze ther utlty (1) subject to (2) and (3) whch gve the followng propostons after the frst order condton (FO) s made wth respect to l c and x. l x = W p = p. h + ( 1) 0 h (1 l ) = h ( 1) +. = 0 h x Eq (4) Eq (5) The results of FO n equaton (4) and equaton (5) gve us the followng propostons: Proposton 1: If (1-l c )>0, and W c s exogenous 6

10 The equaton (4) tells us that f (1-l c )>0, the margnal utlty of household s consumpton s maxmzed when chldren are located at school, and at the same tme wage rate of chldren s postve, W c >0. l = W p. h = ( 1) h (1 l ) + Eq (6) Ths means that each ndvdual household would choose ther chldren to do combned work and study at the same tme. Therefore, chld wage rate s very mportant for household who chose to send ther chldren to school as well as workng. Ths leads to the estmaton on the rate of return to human captal formaton of chld labor. Most mportantly, the equaton (6) shows that ncreasng chld labor wll lead to ncreasng human captal of chldren as long as (1-l c )>0, and W c >0. Therefore we can summarze the propostons 1 as follow: h If W >. ; hld labor and schoolng at the same tme h (1 l ) p h If W =. ; The maxmzaton case where chld s workng hours s h (1 l ) p yet to be trade-off wth chld s schoolng hours. h If W <. ; hld s workng hours s negatvely correlated wth h (1 l ) p chld s schoolng hours. However, we shall note that snce W c s exogenous, there happened sometme that W c =0, then margnal utlty of chld labor s hours s totally negatve relatonshp wth chld s schoolng hours. l. h = ( 1) h (1 l ) Eq (7) Ths means that some households would prefer to send ther chldren to school only f they value the chld labor s bad for ther chldren human captal formaton. Proposton 2: If (1-l c )=0, and W c s exogenous In ths proposton, t s clearly that households would locate ther chldren n the labor market only. In ths case, t s very serous for the future human captal formaton snce socety are full of chld labor, thus leads to low human captal stock n the future. Therefore, approprate polcy must be ntroduced mmedately to avod ths knd of hazard stuaton. 7

11 2) Emprcal Model 2-A) Rate of Returns to Human aptal of hld Labor In the emprcal specfcaton of the human captal earnng functon, Mncer (1974) notced that there s less of an nteracton, f any, between experence and schoolng and than between age and schoolng. Experence profles of log earnng are much more nearly parallel than age profles. If so, n an earnng functon n whch earnngs are logarthmc, years of work experence shall be enter addtvely and n arthmetcal form. The experence term s not lnear but concave. Therefore, the earnng functon s: 2 ln E = ln Es + β 1t β 2t + β3h + β 4Z + U ; {1,2,3... N} Eq (1) where t s years of experence, E s s earnng capacty after completon of schoolng, and h and Z are characterstcs of the chld and communty respectvely, and U s a dsturbance term. ln E s ln E = ln E = ln E rs ; + rs + β t β t + β h 3 + β Z 4 + U Eq (2) If work experence s contnuous and starts mmedately after completon of schoolng, then work experence s equal to current age mnus age at completon of schoolng. However, we shall note that most chld labor are dong combned work and study at the same tme. t = ( A s b) Where A s current age and b s age at the begnnng of schoolng. Thus the use of age alone nstead of experence n the earnng functon results n the omsson of some varables, as can be seen f the expresson for t, above, s substtuted n the functon. 2-B) Human aptal Formaton of hldren and Ther Hours Worked Trade-off Ths emprcal model on human captal formaton and hours worked trade-off s derved from the conceptual framework that expresses the negatve relatonshp between human captal formaton and hours worked trade-off. We use learnng measures such as varable of Schoolng Attanment Relatve to Age (SAGE ndex), years of schoolng, and school enrolment of the chld as dependent varables. We model the learnng measure of the chld on set of explanatory varables such as age, age^2, educaton of parents, gender of the chld, number of chldren n the household (age 5-14), number of babes n the household (aged 0-4), poverty status of each household (1 f above poverty lne), school s expendture, and hours worked of the chld and ts square. The ncluson of chld labor hours, H, and ts square s desgned to capture the trade-off pont between labor hours and learnng measure of the chld. Accordng to Orazem and Gunnarsson (2003), the chld labor hours s lkely to be endogenous, thus OLS coeffcent estmates may yeld basedness. Therefore, we need to seek for approprate nstrumental varables n the dataset that have hgh correlaton wth hour worked of the chld, but they are not correlated wth the dsturbance term of the structural equaton. Wth these notons n mnd, we can establsh our regresson model as follow: 8

12 L = β + β H + β H + β h + β SchEx + β X + β Z + U Eq.(1) H = δ + δ h + δ SchEx + δ X + δ IV + δ Z + U * Eq.(2) From the structural equaton (1), we have varable hours worked of chldren and ts squares whch are endogenous wth learnng measures. Avodng ths endogenety, we have used nstrumental varable IV contanng varable such as access to clean water, access to electrcty, assets of TV, rados, cars, moble phone, boats, motor-boats, tractors, bcycle, and other household assets. Note that the varable SchEx school s expendture s not avalable n the dataset of LS-2000; however, snce ths varable s very mportant for our structural equaton, we have obtaned the coeffcent estmates of from SES-1999 by regress on sets of chld and household characterstcs. Because SchEx varable n the structural equaton (1) s predcted value from other regresson as above mentoned; therefore ths varable s consdered as ndependent and uncorrelated wth the dsturbance term. If SchEx s avalable from the dataset drectly, we beleve that ths varable would be endogenous wth learnng measure, and thus wll be nstrumented as well, n whch the estmaton of structural equaton s obtaned through the search for coeffcents estmate from set smultaneous equatons ether through Seemngly Unrelated Regresson (SUR) or Three Stages Least Square (3SLS). Addtonal mportant household characterstc was added to the model such as poverty status varable rather than household s ncome because we try to avod endogenety of ths varable n the structural model. The poverty status was constructed as a dummy varable takng value of 1 f ndvdual household stays above poverty lne, and value zero for otherwse. Fnally the estmaton of the structural equaton (1) s smply the reduced form: L = β + β H + β H + β h + β SchEx + β X + β Z + U Eq (3) * * Frst Order ondton (FO) on (3), wth respect to hours worked s to check the turnng pont of the hours worked of the chld that beyond ths turnng pont hours worked threshold of the chld wll * 3 trade-off wth human captal formaton. We can derve: H = s the trade-off hours worked wth * human captal formaton of the chld. Among the three types of dependent varable of learnng measure, there s only varable school enrolment s a dummy varable, n whch the structural model need to be estmated through Probt model. Now, let L* s the dchotomous varable, take on the value {1} for chld enrollment n school, and zero {0} for otherwse. Therefore the specfcaton model s expressed through the latent varable as follow: * β 2β 4 9

13 L U * 1 = x β + U x 0 1 x β = β + β H 1 ~ N(0,1) * + β H 2 *2 + β h 3 + β SchEx + β X β Z 6 Eq (4) where x β, the aggregated form of the explanatory varables. Therefore, the dependent varable (school enrollment of the chld) can be observed as follow: L 1( L Pr( Y * > 0) * *2 = 1x ) = G( β + β H + β H + β h + β SchEx + β X + β Z ) Eq (5) 0 = G( x β ) = Φ( z) Then the standard normal cumulatve dstrbuton functon and the standard normal densty are: Φ( z) = ( z) 2 1 ( z) φ( v) dv ; φ( z) = exp[ 2 2π ] And the lkelhood functon s: s ln L = w lnφ{1 ( xβ )} + w lnφ( xβ ) N = 1..., s,( s + 1)... n n s+ 1 ; 3) Dataset and Varables In ths study, we have used dataset of amboda hld Labor Survey, 2001 for our data analyss on chld labor hours and other salent characterstcs of the chld. The sample sze of ths dataset covered 12,000 households whch were ntervewed on the nature of chld labor n both economc and noneconomc actvty. Although the survey covers detaled nformaton of chld labor n the age group 5-17 years old for the comparatve purpose of chld labor n the regons as well as a complance to ILO conventon No.138, but we only consder workng chldren n the age group of 5-14 years to be our sample n ths study because the adopted new labor code n 1997 by amboda Natonal Assembly sets the mnmum age of employment at 15 only (Artcle 177). We have, therefore, chldren breakng down nto male and female contan n the sample. Table 1 presents descrptve statstcs used n the emprcal model. The varable SAGE meanng Index of Schoolng Attanment Relatve to Age s constructed to be one among the alternatve measure of schoolng outcomes. The ndex s smply derved from the equaton that: 10

14 Years of schoolng SAGE = 100. The hgher of the ndex s the better of schoolng Age Age of School Entry outcomes of chldren. We also notce that the varable of school s expendture s not avalable n the LS 2001 dataset. However, we have obtaned the predcted value of ths varable by usng the coeffcents estmates from another regresson of amboda Soco-economc Survey Ths technque s very smart and eases our burden on the search of proxy of ths varable. Table 1: Descrptve statstcs (sample conssts of chldren aged 5-14) Varable Defnton Mean Std. Dev. Years of Schoolng Number of Years of Schoolng of the hld School Enrolment =1 f hld Enrol n School SAGE Index of Schoolng Attanment Relatve to Age Hours worked ENE Number of Hours Worked of the hld for Economc and Non-economc Actvty Hour worked E Number of Hours Worked of the hld for Economc Actvty School s expendture Expendture on hld s Schoolng Such as School fee etc per hld per Year Age of chld Age of the hld Age of chld ^2 Age of the hld Squared Female hld =1 f Female hld; 0 for Male hld Father s educaton =1 f Father ompleted Secondary Educaton or Hgher Mother s educaton =1 f Mother ompleted Secondary Educaton of Hgher Poverty status =1 f Non-poor Household; 0 f Poor (Measure aganst Natonal Poverty Lne) Nb. Babes Number of Babes n Each Household (Age Years Old) Nb. hldren Number of hldren n Each Household (Age Years Old) HH sze Number of People n Each Household Gender of HH =1 f Male Is Household Head; 0 f Female Age of HH Age of the Household Head Rural =1 f Rural; 0 f Urban Source: Author s calculaton 11

15 Total observaton: IV. Results of Emprcal Estmates 1) Result Estmates on Rate of Return to Human aptal of hld Labor Table 2 presents the coeffcent estmates of rate of return to human captal of chld labor n percentage term, whle table 3 presents the same result but n real amounts of return n Rels. Because numbers of hours worked of chldren could be assocated wth decsons made by parents as whether ther chldren shall work more hours or not, and that all dependng on the wage rate of chldren. Therefore, we nstrumented hours worked of chldren n the past seven days wth approprate varables such as household and chld characterstcs whch are not n the predetermned varables. In both table 2 and 3, column (a) presents the coeffcents estmates performed by Ordnary Least Square (OLS), and column (b) presents the coeffcents estmates performed by Two-stages Least Square (2SLS) under assumpton of endogenety exts n ndependent varable. Usng Hausman-test of endogenety, we confrmed that we do not have enough evdence to reject our null hypothess of OLS. Therefore, we tend to use the results of OLS estmates rather than 2SLS estmates. The results are nterestng to our hypothess of chld labor and chld schoolng at the same tme. Because most ambodan chldren are lkely to combned actvty of schoolng and workng, therefore, ther wage rate or earnngs are most mportant to mantanng ther household consumpton and other securty. As the result of chld labor, then they are able to be located n school by ther parents. Let us fnd the sgnfcance of these results f the educaton and ther experence do have role n determnng ther earnng ncome. We found that educaton among the chld laborers gve hgher return around 14 percent to ther ncome equvalence of 0.69 USD per week. Ths rate of return s hgh and can be comparable to the rate of return among adult labor of non-farm men n the Unted Sate of Amerca n 1959 who earned n real amounts of 2,130 USD per average annual earnngs (Mncer, 1974). The coeffcent of experence of chldren workng ndcates that all workng chldren wth less than three years experence do not have negatve mpact on ther earnngs. At least chldren wth four years of experence wll gve rate of return around four percent to ther ncome. Ths fndng s somehow the frst fndng of rate of return to chld labor n terms of ther educaton and experence. As adult workers, once they completed ther schoolng and start workng at the frst tme, the frst year of experence does gve return to ther ncome n smaller amounts than another year of experence. However, our case s dfferent because chldren at the 1st year tll the 3 rd years of experence stll have negatve mpact on ther earnng power. Unlke adult labor, chld laborers need at least 4 years of experence n order to have postve mpact on ther earnngs. 12

16 Table 2: Estmates of logarthms earnng ncome of chldren n the past 7 days Varable Logarthms of earnng ncome of chldren n the past 7 days (OLS)-olumn (a) Logarthms of earnng ncome of chldren n the past 7 days (2SLS)-olumn (b) oeffcent P-value oeffcent P-value Hours worked n the past days Experence Experence^ hld s edu Female chld Rural onstant Source: Author s calculaton Test: Ho: dfference n coeffcents not systematc χ 2 = 5.87 P-value = (a)- Report of OLS statstc: Number of obs = 171 F( 6, 164) = 9.09 Prob > F = R-squared = Adj R-squared = Root MSE = (b)- Report of Instrumental Varable statstc: Number of obs = 171 F( 6, 164) = 5.55 Prob > F = R-squared = Adj R-squared = Root MSE = Table 3: Estmates of earnng ncome of chldren n the past 7 days Varable Earnng ncome of chldren n the past 7 days (OLS) (a) Earnng ncome of chldren n the past 7 days (2SLS) (b) oeffcent P-value oeffcent P-value Hours worked n the past days Experence

17 Experence^ hld s edu Female chld Rural onstant Source: Author s calculaton Test: Ho: dfference n coeffcents not systematc χ 2 = 3.68 P-value = (a)- Report of OLS statstc: Number of obs = 171 F( 6, 164) = Prob > F= R-squared = Adj R-squared = Root MSE = (b)- Report of Instrumental Varable statstc: Number of obs = 171 F( 6, 164) = 5.57 Prob > F= R-squared = Adj R-squared = Root MSE = ) Result Estmates on Hours Worked and Its Schoolng Trade-off Table 4, 5 and 6 present results of OLS, 2SLS and Maxmum Lkelhood estmate of chldren s learnng measure schoolng outcomes. Among the dependent varables, we have used chld enrolment whch s a nonlnear bnary response model. All tables n the analyss of hours worked of chldren and schoolng trade-off contaned of column (a) and (b) whch presents coeffcents and/or margnal effects, and P-value of the 2SLS and OLS respectvely. The man objectve of our estmates s to fnd the turnng pont of hours worked of chldren wth ther schoolng outcomes. In order to capture the turnng pont, we allow varable of hours worked of chldren enter nto our structural equaton n lnear and nonlnear form (square of hours worked of chldren). Before comng nto our man objectve of the trade-off between hours suppled of chld labor and ther human captal formaton, we are also nterested n nterpretng the rest of explanatory varables such as age, age square of the chld, a dummy varable takng value of 1 f female chld, 0 otherwse, number of babes n the household, number of chldren n the household, household sze, a dummes varable takng value of 1 f father s educaton or mother s educaton has hgher educaton than prmary school, poverty status takng value of 1 f each ndvdual household s above poverty lne, school s expendture on each chld per annum, and a dummy varable takng value of 1 f rural area, 0 otherwse. 14

18 Because the results n table 4, 5 and 6 are almost the same among the dfferent knds of estmatons due to the dfferent types of learnng measure schoolng outcomes that used. We, therefore, wll nterpret the dmensons of correlatons n general and detaled nterpretaton on hours worked and ts schoolng trade-off n specfc. Each table below conssts of column (a) and (b) reportng coeffcents estmate and ts P-value. By usng Hausman-test statstcs, we found that our hours worked of chldren are endogenous wth structural equaton of learnng measures. Hence, we tend to use the coeffcents estmates from the 2SLS rather than OLS. The results show that schoolng outcomes are a nonlnear functon of age. Beng a female chld reduce the chance of beng at school. hldren n rural area have dsadvantage for ther study f compared wth urban chldren, the present large number of household sze and wth addtonal number of babes or chldren tend to reduce the probablty of school enrolment. hldren of non-poor household tend to be n school rather than workng, parents wth completon of prmary or hgher educaton play sgnfcant role n human captal formaton of ther chldren. School s expendture shows strong postve correlaton wth schoolng outcomes of chldren. School expendture (school fee, extra-fee, text book, books, pencl, and other school s expense) s necessary for chld schoolng and t ndcates that the larger expense on chldren s schoolng wll ncrease ther human captal formaton further. Male household head tends to reduce the probablty of chld s schoolng. Ths ndcates that amboda s stuaton of ncreasng chldren s educaton has constraned by old paradgm of parents who are male domnant n the householddecson makng. Fnally we come to the most mportant ceters parbus of hours worked of chldren. We fnd that the turnng pont or another word trade-off pont between hours suppled of chld labor and ther human captal formaton s n general about 3.10 hours. Detaled results of each regresson of learnng measures wth regard to hours worked of chldren are () for the learnng measure Schoolng Attanment Relatve to Age: SAGE ndex, the result shows that chldren can possbly work up to hours per week (5 workng days) or hours per day wthout havng negatve mpact on human captal formaton. Beyond ths threshold hours, chld s hours worked wll have negatve effects on ther schoolng; () for the learnng measure Years of Schoolng, chldren can possbly work up to hours per week (5 workng days) or 3.06 hours per day wthout havng negatve mpact wth human captal formaton; and () for the learnng measure hld s School Enrolment, chldren can possbly work up to hours per week (5 workng days) or hours per day wthout havng negatve mpact wth human captal formaton. These results somehow provde us confdence that chld labor n amboda do help boostng ther educaton hgher as ther ncome are endogenous n the parents decson of ther schoolng. Because of these evolvng decsons of each ndvdual households wth regard to ther utlty, the hgher wage rate of chld labor wll promnently support chld educaton and thus have long term mpact on ther human captal formaton. Table 2 of descrptve statstcs shows that an average hour worked of chldren n amboda s around 2.30 hours per day whch stay yet below the trade-off hours of 3.10 hours. 15

19 Table 4: Regresson coeffcent estmates of Schoolng Attanment Relatve to Age: SAGE ndex Varable SAGE (2SLS) (a) SAGE (OLS) (b) oeffcent P-value oeffcent P-value Hours worked Hours worked ^ School s expendture Age of chld Age of chld ^ Female hld Father s educaton Mother s educaton Poverty status Nb. babes Nb. chldren HH sze Gender of HH Age of HH Rural onstance Test: Ho: dfference n coeffcents not systematc Source: Author s calculaton χ 2 = P-value = (a) Report of goodness of ft of IV (2SLS) Number of obs = F( 15, 21137) = Prob > F = R-squared = Adj R-squared = Root MSE =

20 (b) Report of goodness of ft of OLS Number of obs = F( 15, 21137) = Prob > F = R-squared = Adj R-squared = Root MSE = Table 5: Regresson coeffcent estmates of years of chld s schoolng Varable Years of hld s Schoolng (2SLS) Years of hld s Schoolng (OLS) oeffcent P-value oeffcent P-value Hours worked Hours worked ^ School s expendture Age of chld Age of chld ^ Female hld Father s educaton Mother s educaton Poverty status Nb. babes Nb. chldren HH sze Gender of HH Age of HH Rural onstance Test: Ho: dfference n coeffcents not systematc Source: Author s calculaton χ 2 = P-value = (a) Report of goodness of ft of IV (2SLS) 17

21 Number of obs = F( 15, 21137) = Prob > F = R-squared = Adj R-squared = Root MSE = (b) Report of goodness of ft of OLS Number of obs = F( 15, 21137) = Prob > F = R-squared = Adj R-squared = Root MSE = Table 6: Probt coeffcent estmates of school enrolment of chldren Varable School Enrolment (IV-Probt) (a) School Enrolment (Probt) (b) oeffcent P-value oeffcent P-value Hours worked Hours worked ^ School s expendture Age of chld Age of chld ^ Female hld Father s educaton Mother s educaton Poverty status Nb. babes Nb. chldren HH sze Gender of HH Age of HH Rural onstance Test: Ho: dfference n coeffcents not systematc 18

22 Source: Author s calculaton χ 2 = P-value = (a) Report of goodness of ft of probt-iv (2SLS) Number of obs = LR ch2(17) = Prob > ch2 = Log lkelhood = Pseudo R2 = (b) Report of goodness of ft of Probt Number of obs = LR ch2(15) = Prob > ch2 = Log lkelhood = Pseudo R2 = V. oncluson and Polcy Implcatons Only a few studes focuses on the trade-off between hours worked of chldren wth ther human captal formaton. By careful revews on these exstng lteratures, there s yet to develop a theoretcal model that can gve a proposton of hours worked of chldren and ts schoolng outcomes trade-off. Emprcal model had been developed to answer ths hypothess, but there s no consstent use of the technques and assumptons. Lancaster and Ray (2004) employed several technques by allowng endogenety of hours worked of chldren, but stll the results among the four dfferent types of schoolng outcomes yelds nconsstency n terms of drecton and correlaton of workng hours and schoolng outcomes,.e., the regresson of chld schoolng years of schoolng and ablty to read and wrte showed that the more hours chldren work, the better of schoolng outcomes, whle the regresson of SAGE and school attendance showed a contrary to the earler. Therefore, ths study amed to provde consstent estmaton wth further technques deployed to the model. Ths study, off course, s the further extenson from Lancaster and Ray (2004) n terms of emprcal estmaton by provdng dfferent flavor n terms of flexblty, for nstance., () by allowng salent ndependent varables to enter the schoolng outcomes equaton, especally the school s expendture and household poverty status, t helps make the results even more nterestng n terms of the calculatng U-shape of hours worked and schoolng trade-off and ts consstency of results among regresson results. The school s expendture s not avalable n the dataset tself, but we have estmated t from Soco-economc survey wth approprate technques as explaned n the model; () we employ probt model to estmate a bnary outcome of school enrolment rather than OLS; () the U-shape calculaton turn out dfferent result n terms of the trade-off pont. 19

23 Besdes these mentoned dfferences, we have estmated the rate of return to chld labor because t turn out to be mportant f chld wage rate wll endogenously help smoothenng chldren s households consumptons, and thus leads to the balance decson of chld schoolng and workng at the same tme. Now the results under our estmaton are summarzed as follow: () Even though they are chld labor, ther educaton do play sgnfcant role n ther wage rate determnant. We found that, n general, the rate of return to educaton of chld laborers s about 14 percent consderably hgh and comparable to the Mncer s model estmaton of Amercan adult labor of non-farm men n 1954 (Mncer, 1974). Ths result ndcates the mportance of logc behnd the household s decsonmakng of allowng chldren to carry both work and study at the same tme because chld s educaton even though among the chld laborers are proved to be sgnfcant n generatng ncomes, thus help parents to nvestng more on ther chldren s educaton. () We found parents educaton s very mportant n the determnant of schoolng outcomes of ther chldren. However, ths educaton has a postve mpact only for parents whose educaton had at least completed prmary school or hgher. Ths fndng provdes consstency n the earler works though the yardstck of measurement may have dfferent technques such as allowng a unt change n parents schoolng rather that a completon of prmary, or secondary school. The smlarty to Lancaster and Ray (2004), Rossat and Ross (2001), Ray (2000), Deb and Rosat (2004), Nels et.al (2002), Bhalotra and Heady (2003), and Khanam (2003), s that there exsts postve lnk between parent s educaton and the lkelhood of a chld attendng school, and smlarly a negatve lnk between parent s educaton and the lkelhood of a chld workng. () The probablty of chldren to enroll n school s hgher among the non-poor households. However, ths does not meant that the poor households dd not send ther chldren to school. However, the evdence by Han (2006) and chld labor survey report (2002) show that most chldren n amboda lke to combne both work and study, whch ndcated that majorty of amboda populaton lve around poverty lne. Ths fndng s also consstent wth the prevous theoretcal and emprcal works such as the work of Basu and Van (1998), Rosat and Ross (2003), Ray and Lancaster (2004), Basu and Tzannatos (2003), Lee and Westaby (1997), Saupe and Bentley (1994), Km and Zepeda (2004), hakraborty and Das (2004), hrstaan and Rav (1995), hao and Alper (1998), Duryea and Arends (2001), Basu, Arnab K., and Nancy H. hau (2003), and Blunch and Verner (2000). (v) Generally, hours worked of ambodan chldren stay below the threshold level hours of Ths ndcates that chld labor s rather ncrease human captal formaton of the chld n developng economy lke amboda and t supports our hypothess n ths study. Also, ths fndng tends to renforce the theory of Fan (2004), whch states that a small ncrease n chld labor may not be trade-off wth human captal nvestment, snce the postve mpact of ncreased fnancal resources on educaton may outwegh the negatve mpact of reduced tme of study. Ths s smply that chldren s labor market partcpaton rases the fnancal resources and spent on ther educaton. 20

24 (v) The presence number of babes and chldren n each household do have negatve mpact on schoolng outcomes of chldren. Of course, ths s rather smple to understand though we have not ncluded ths vew n the theoretcal model. Ths ndcates that the more babes n the famly wll reduce tme of schoolng. Reference Akabayash, H. and P. George, 1999, The Trade-off between hld Labour and Human aptal Formaton: A Tanzanan case study, Journal of Development Studes, 35, pp Angrst, J., 2001, Estmaton of Lmted Dependent Varable Models wth Dummy Endogenous Regressors: Smple Strateges for Emprcal Practce, Journal of Busness and Economc Statstcs, 19, pp Baland, J. M. and Robnson, J. A., 2000, Is hld Labor Ineffcent? Journal of Poltcal Economy, 108, pp Bardhan, P. and Udry,., 1999, Development Mcroeconomcs, chapter 3: Populaton, pp Basu, K. and Tzannatos, Z., 2003, The Global hld Labor Problem: What Do We Know and What an We Do? The World Bank Economc Revew, 17, pp Basu, K. and Van, P., 1998, The Economcs of hld Labor, The Amercan Economc Revew, 88, pp Basu, K., 1999, hld Labor: ause, onsequences, and ure, wth Remarks on Internatonal Labor Standard, Journal of Economc Lterature, 37, pp Becker, G. S, 1975, Human aptal: A Theoretcal and Emprcal Analyss, 2nd Ed., olumba Unversty Press (for Natonal Bureau of Economc Research), New York. Becker, G. S., 1962, Investment n Human aptal: A Theoretcal Analyss, Journal of Poltcal Economy, 70, pp 9-49 Becker, G. S., 1965, A Theory of the Allocaton of Tme, The Economc Journal, 75, pp Beegle et.al, 2003, hld Labor, rop Shocks, and redt onstrants, Natonal Bureau of Economc Research, World Bank Workng Paper No Bhallotra, S. and Heady,., 2003, hld Farm Labor: The Wealth Paradox, World Bank Economc Revew, 17, pp Blunch, Nels.H., Sudharshan,.,and Sangeeta, G. (2002): Short and Long-term Impacts of Economc Polces on hld Labor and Schoolng n Ghana, Socal Protecton Dscusson Paper, No

25 Blunch, Nels.H., Verner, Dort (2000): Revstng the Lnk between Poverty and hld Labor, the Ghanaan Experence, World Bank, Afrca Techncal Famles, Human Development 3 and Latn Amerca and the arbbean Regon. anagarajah, S. and H. oulombe (1997): hld Labor and Schoolng n Ghana, Polcy Research Workng Paper No.1844, World Bank, Washngton, D. hao, Shyan and Omer, Alper (1998): Accessng Basc Educaton n Ghana, Studes n Human Development No.1, World Bank, Washngton, D. Duryea, Suzanne and Arends-Kuennng, Mary (2001): School Attendance, hld Labor and Local Labor Markets n Urban Brazl, Inter-Amercan Development Bank Edmonds V. Erc (2005): hld labor and schoolng response to antcpated ncome n south Afrca, Forthcomng n Journal of Development Economcs. Fan. Smon (2004): Relatve Wage, hld Labor, and Human aptal, Oxford Economc Paper, 56, pp Grootaert, hrstaan and Kanbur, Rav (1995): hld Labor: An Economc Perspectve, Internatonal Labour Revew, 134, pp Heady, hrstopher. (2003): The Effect of hld Labor on Learnng Achevement, World Development, 31: Khanam, Rasheda (2003): hld Labor and School Attendance : Evdence from Bangladesh, JEL lassfcaton: I21, D12, J13, O53, Dscplne of Economcs, Unversty of Sydney, Australa Km, Jongsoog and Zepeda, Lyda (2004): Factor Affectng hldren s Partcpaton and Amount of Labor on Famly Farms, Journal of Safety Research, 35, pp Lee, B.., Jenkns, L. S., and Westaby, J. D (1997): Factors nfluencng exposure of chldren to major hazards on famly farms, Journal of Rural Health, 13, pp Mncer, Jacob (1974): Schoolng, Experence and Earnngs, olumba Unversty Press, for Natonal Bureau of Economc Research, New York. Mnstry of Plannng (2001): Report on amboda hld Labor Survey, amboda. Nelsen, Helens Skyt (1998): hld Labor and School Attendance : Two Jont Decsons, L-WP 98-15, entre for Labor Market and Socal Research, Aarhus, Denmark. Phoumn, Han. (2005): Underlyng Root auses of hld Labor: Evdence and Polcy Implcatons, the ase of amboda, The Workng Paper Publshed at Professor Sech Fuku s Semnar, GSIS, Kobe Unversty. Ray, Ranjan (2000): hld Labor, hld Schoolng, and Ther Interacton wth Adult Labor: Emprcal Evdence for Peru and Pakstan, The World Bank Economc Revew, 14, pp Ray, Ranjan and Lancaster, Geoffrey (2004): The Impact of hldren s Work on Schoolng, ILO/IPE Workng Paper. 22

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