Returns to Experence n Mozambque: A Nonparametrc Regresson Approach Joel Muzma Conference Paper nº 27 Conferênca Inaugural do IESE Desafos para a nvestgação socal e económca em Moçambque 19 de Setembro de 2007
Returns to Experence n Mozambque: A Nonparametrc Regresson Approach Joel Muzma jmuzma@gmal.com Abstract Ths paper studes the mpact of nvestments n labor market on earnngs for the low-sklled labor force n Mozambque. In partcular, t uses a nonparametrc regresson approach n order to estmate returns to for two groups of workers (those who have completed EP1 and EP2 school level). The data used n ths estmatons comes from the natonally representatve labor force survey (IFTRAB, 2004-05) conducted by the Natonal Insttute of Statstcs. The man fndngs ndcate that earnngs of full-tme workers wth EP2 tend to domnate those of workers wth EP1 level over the workng lfe. Furthermore, we fnd that there are postve returns to educaton between completng EP2 level of schoolng as compared to EP1. The paper also shows that gender dfferentaton on earnngs s clearly evdent from the data. For example, n overall men s returns to are always domnatng those of women regardless of the level of schoolng and regonal locaton. Ultmately, and unlke what human captal theory would predct, however, t s found that low-sklled workers n Mozambque tend to substtute schoolng for workng mostly due to hgh opportunty n terms of foregone earnngs, poverty, and other credt constrants.
Returns to Experence n Mozambque: A Nonparametrc Regresson 1. Introducton Approach Modern labor economcs theory dentfes the prospect of mproved lfetme earnngs as beng a major nducement for people to nvest n an educatonal or tranng program (Ehrenberg and Smth, 1999:335). However, one mportant queston s how often ths predcton can be vndcated on the emprcal grounds. Therefore, t seems worthy nvestgatng these ssues n order to fully understand the nteracton between human captal nvestment and earnngs. Ths paper ams to estmate the effect of labor market or on-the-job tranng on the determnaton of the dstrbuton of earnngs for two groups of workers and gven ther educatonal background EP1 and EP2 levels, respectvely. The paper focuses on the mpact of human captal accumulaton on earnngs for two reasons. Frst, t s recognzed that workers wth more years of educaton assocated wth labor market tend to have hgher earnngs whch n turn can translate nto drect mpact n terms of poverty reducton, snce ths group can be able to meet ts basc needs. Second, human captal accumulaton s an mportant means for poor countres to ncrease labor productvty, whch can consequently attract more foregn drect nvestment n order to expand economc growth. The analyss are carred out usng data from the natonally representatve Labor Force Survey IFTRAB2004-05 whch was conducted by the Natonal Insttute of Statstcs (INE). The man objectve of ths paper s to fll the exstent gap n the lterature related to the nteractons between schoolng and labor market n Mozambque, snce lttle s known about the mpact of human captal accumulaton on the dstrbuton of earnngs. For example, the exstng lterature has often focused on the mpact of lower or upper prmary educaton on rasng per capta consumpton (Handa and Smler, 2000; Handa, Smler and Harrower, 2004; Jones, 2004) but lttle attenton has been pad to the 2
mpact of the combnaton of educaton and on-the-job-tranng on earnngs probably due to the lack of data on the later. The paper s organzed as follows. After the ntroducton; secton two presents the lterature revew surroundng ssues of the assocaton between labor market and earnngs; secton three descrbes the data; secton four outlnes a general neoclasscal model of nvestment n human captal, whch s used here to gude the emprcal nvestgatons; secton fve evaluates the mpact of labor market on earnngs gven dfferent levels of worker s educaton EP1 and EP2. Moreover, dstrbutons of margnal returns to are computed for the two groups of workers. The analyss s also extended to nclude gender segmentaton and area of resdence. Fnally, secton sx concludes the paper. 2. Lterature revew Numerous studes have attempted to dentfy the mpact that work has on wages. Mncer (1957, 1958) poneered the explct study of the effect of labor market or on-the-job-tranng on the determnaton of and dstrbuton of earnngs. Hs model provded an analyss of the manner n whch on-the-job-tranng nfluences dfferences n earnngs across ndvduals and ths determnes the nequalty and skewness of earnngs. The model, whch was based on the assumpton of ratonal economc behavor by ndvduals n the labor market served as the base for several strands of research n labor economcs. Mncer (1962) estmated rates of returns from on-the-job-tranng for several dfferent occupatons (apprentces, journeymen n contrast to those of the operatves) and found that the rate of return from nvestment n on-the-job-tranng were about 9 to 13 percent. However, these rates were slghtly hgher than those of the returns dervng from schoolng. Mncer (1962:66) attrbuted these dfferences to numerous measurement ssues. 3
In a later work, Mncer (1962) also dscussed nvestment n on-the-job-tranng by women compared to men. He found that ncentves for women to make these nvestments were less because the average female expects to spend less than half her workng lfe n the labor force, and has a hgh probablty of droppng out of the labor force for chldrearng. Mncer also noted that for these reasons employers would be more reluctant to nvest n frm-specfc tranng for women than for men. Roshlom et al (2006) usng frm data from Sub-Saharan Afrca, evaluate the effect of onthe-job-tranng on log wages usng matchng estmators. Ther fndngs show that tranng tends to mprove wages and that the effect s larger and well-determned for long tranng duraton and n large frms. Smlarly, Booth and Bryan (2006), and usng data from Brtan also show that work-related tranng s potentally mportant from a dstrbutonal standpont, snce t sgnfcantly ncreases ndvdual s longer term earnngs prospects. Shultz (2003) combnng the mpact of human captal accumulaton n the form of both schoolng and labor market on earnngs fnds that prvate returns to nvestment n human captal n sx Afrcan countres (Kenya, Ghana, Cote d Ivore, South Afrca, Ngera, and Burkna Faso) are much hgher for secondary and postsecondary educaton nowadays. Ths contrasts wth earler fndngs from Psacharoupoulos (1985) where returns were hgher for prmary educaton. The lterature on the effect of labor market on earnngs has expanded to nclude ssues of gender dfferentals on wages. For example, Ollvet (2006) usng the PSID data studed the evoluton of gender dfferentals n rates of return to labor market between the 1970s and the 1990s. Over ths tme perod t was found that returns to ncreased wthn gender groups. Surprsngly, these were relatvely larger for women than for men. 1 1 Ths fndng may have been generated by sample selecton problems and smultanety bas as acknowledged by Ollvet (2006). Nonetheless, Psacharoupoulos (1985) usng cross-country evdence from 60 countres also confrms a smlar pattern n whch women s return to and schoolng are much larger than those for men. 4
3. The Data The database, collected from October, 2004 to September, 2005 by the Natonal Statstcs Insttute n coordnaton wth the Mnstry of Labor (MITRAB) the Inquérto Integrado à Força de Trabalho (IFTRAB, 2004-05), s the frst post ndependence labor force survey of the country. The IFTRAB, 2004-05 s a multpurpose household survey, and contans detaled nformaton on employment, unemployment, under-employment, sectors of economc actvty of the labor force, number of hours worked, total volume of earnngs, and also an addtonal questonnare on chld labor characterstcs. The sample was desgned to be representatve at natonal level, provncal, and by area of resdence (rural and urban). The selected sample corresponds to 17800 households, from whch 17151 households were ntervewed at natonal level, beng 8681 n urban areas whle 8470 were n rural areas. Ths represents a coverage rate of 96.4 percent of the selected sample. 4. Theoretcal Framework The lnks between schoolng, labor market or on-the-job-tranng and earnngs have been explored extensvely n the lterature regardng human captal accumulaton. Labor market has a potental of mprovng overall welfare of households through ncreased future wage earnngs as well as an ndrect role n the welfare of households through ts mpact on the human captal accumulaton as predcted by the human captal theory. In ths secton, we outlne the theoretcal framework used to examne the effect of labor market on earnngs. 4.1. Theory The estmaton of the mpact of labor market on earnngs s guded by a nonparametrc regresson approach as dscussed n Deaton (1997). The man advantage of ths approach s the fact that t assumes no functonal form for the relatonshp, allowng the data to choose, not only the parameter estmates, but the shape of the curve tself. However, the prce of the flexblty s the much greater data requrements and the 5
dffculty of handlng hgh-dmensonal problems, and to a lesser extent, computatonal costs. But ths seems not problematc n the context of ths paper snce we are usng household survey data whch contans enough nformaton. For estmaton purposes, we defne labor market by subtractng the age of completon of schoolng from reported age mnus sx, as suggested by Mncer (1974:47). Moreover, we wll nvestgate the mpact of on log wages of two dstnct groups of workers (those wth completed EP2 school level and EP1) and by gender and area of resdence (urban and rural). The model can be wrtten as follows: E ( w y exp) = g( y exp) (1) g ( y exp ) = m( y exp, S ) + e * (1 ) Where = 1 n ndexes of ndvduals and w =logarthm of monthly earnngs, years of potental (.e. age years of schoolng 6), and y exp = * S =1 f ndvdual has completed prmary EP2 level and zero f ndvdual has completed prmary EP1 level. For smplcty, we also assume that y exp and * S are strctly exogenous for e. Snce dffers from ts expectaton by a resdual ( e ) that s, by constructon, uncorrelated wth the latter, the statstcal defnton concdes wth the standard lnear regresson model when the regresson functon s lnear. Note that the functon w g (y exp), whch n general wll not be lnear, s the regresson functon of w on y exp. However, t should also be noted that, gven a jont dstrbuton of any set of varables, we can always calculate the regresson functon for any one varable condtonal on the others. Usng the statstcal propertes of condtonal expectatons t s possble to lnk equaton (1) to the underlyng dstrbuton. In partcular, we can wrte: E( w y exp) = wfc ( w y exp) dw = wf J ( w, y exp) dw/ f M ( y exp) (2) Where the C, M, and J subscrpts denote condtonal, margnal and jont dstrbutons, respectvely. Alternatvely, the regresson functon can be wrtten entrely n terms of the jont dstrbuton: g( y exp) = E( w y exp) = wf ( w, y exp) dw/ f ( w, y exp) dw (3) J J 6
Ths equaton suggests a nonparametrc method for estmatng the regresson functon; estmate the jont densty usng kernel (or other) methods, and use the results to calculate (3). However, an obvous way to calculate a regresson functon s to use the sample nformaton to calculate the average of all w -values correspondng to each y exp. Wth an nfnte sample, or dscrete explanatory varables, such an approach would be feasble. Gven the nature of data used n ths paper, ths approach seems to be approprate. Moreover, as wth densty estmaton, weghtng s desrable so as to avod dscontnutes n the regresson functon as ndvdual observatons move nto and out of the bands, and ths can be dealt wth by calculatng kernel regressons that are closely analogous to kernel estmates of denstes. Indeed, the concept s perhaps already more famlar wth regressons; the common practce of smoothng tme seres by calculatng a movng average over a number of adjacent ponts s effectvely a (rectangular) kernel regresson of the form: ~ n 1 h h f ( y exp) = 1 y exp y exp (4) nh = 1 2 2 where h s the bandwdth and n s the sample sze. Note that the choce of the bandwdth value s very mportant ssue when dong serous research. In general, the pont to note s that the bandwdth ought to be smaller the larger s the sample sze. 2 Whle the estmator n (4) captures the essental dea of nonparametrc densty estmaton usng kernel method, however, by gvng all ponts nsde the band equal weght, the estmator does not gve consstent estmates. Instead, one soluton should be that of usng a kernel functon that gves more weght to ponts that are near y exp and less to those far away, so that the ponts have a weght of zero both just outsde and just nsde the band. Ths can be done by replacng the ndcator functon n (4) by a kernel functon K(.), so that: ~ n 1 y exp y exp f ( y exp) = K (5) nh = 1 h 2 Slverman (1986) provdes an extensve treatment of ths topc. For further detals about alternatve nonparametrc regresson approaches see: Gallant (1981) and Cleveland (1979). 7
The kernel regresson estmator can be wrtten as follows: ~ n n y exp y exp y exp y exp g( y exp) = w K / K (6) = 1 h = 1 h Whch, usng the wrtten as: ~ ( f y exp) the kernel estmate of the densty at y exp from (5), can be ~ n 1 y exp y exp ~ g( y exp) = w K / f ( y exp) (7) nh = 1 h Ths equaton can be assumed as a drect mplementaton of (2) wth the kernels actng to smooth out the dscrete sample ponts. Usng (6), the estmate of the regresson functon can also be wrtten as a weghted average of the w -values. ~ n g( y exp) = k ( y exp) (8) = 1 w Where the weghts k are gven from equaton (6). Accordng to equaton (8), the estmated regresson s a weghted average of all the dependng on how far away each correspondng w n the sample wth the weghts y exp s from the pont at whch we are calculatng the functon. Therefore, combnng equaton (1 ) wth the result n (8), bascally we are estmatng the followng relatons: * g( y exp ) = m( y exp,0) m( y exp,1) * E[ S y exp] (9) + These are locally-weghted average of m y exp,1) and m y exp,0), respectvely. ( ( Thereafter, and usng the result n (8), several theoretcal hypothess can be tested. Most specfcally, we are nterested n evaluatng the followng: (a). m ( y exp,1) m( y exp,0) > 0 (there are postve returns to educaton EP2 vs. EP1 gven ) * m( y exp, S ) (b). = m y exp > 0 (there are postve returns to ) y exp (c). m y exp,1) m ( y exp,0) 0 (Are the two forms of human captal y exp ( y exp >< accumulaton schoolng and labor market are complements or substtutes). 8
Furthermore, we extend the neoclasscal model of nvestment n human captal n order to nvestgate the way through whch ndvdual s decson to nvest n labor market affects ther margnal returns. In ths framework, we assume that workers make on-the-job-tranng nvestment decsons to maxmze ther net wage earnngs or utlty (U). Utlty s defned by subtractng the costs of nvestment n labor market ( r I ) ) from gross wages w y exp ). Utlty s maxmzed subject to cost of ( j ( j nvestment constrant. 3 A smple verson of ths model can be wrtten as: Max U (yexp j ) = w( y exp ) r( I ) * y exp (Utlty) (10) j j j Subject to: r( I j ) η+ λi j (Cost of nvestment n on-the-job-tranng) (11) = _ Takng FOC n order of yexp j, we have that: ' w ( y exp ) = η w j ' ( y expj ) = f j η+ λ I =0 (Margnal returns to ) (12) f j I =1 (Margnal returns to ) (13) The theoretcal framework above dscussed s then used to test the followng hypothess: (a). Dfference n margnal returns between workers wth EP1 and EP2 school level Let I j =1 f worker have EP2 level and zero for EP1. If λ >0, then t mples that margnal returns to for workers wth EP2 level are hgher than those of workers wth EP1 school level. In other words, margnal earnngs dfference between the two groups s postve whch renforces nequalty. (b). Dfference n margnal returns by gender gven same level of educaton Let I j =1 f worker have EP2 level and s male, and zero f worker have EP2 and s female. If λ >0, then t mples that gven the same level of educaton; however; male workers enjoy hgher margnal returns to than ther counterpart. 3 The nvestment cost on acqurng labor market can assume dfferent forms: for example, some on-the-job-tranng s learnng by dong (as one hammers nals month after month, one skll s naturally mprove) and therefore requre both psychologcal and physcal efforts from the workers. Other costs nclude credt constrants, opportunty costs of foregone earnngs or low wages, as well as uncomfortable workng condtons. 9
Puttng t more smply, we can conclude that gender dscrmnaton perssts gven same level of schoolng. (c). Dfference n margnal earnngs by area of resdence and gven same level of educaton Let I j =1 f worker have EP2 level and lves n urban area, and zero f worker have EP2, and lvng n rural area. If λ >0, then t mples that gven the same level of educaton; however; workers n urban areas enjoy hgher margnal returns to than ther counterpart n rural areas. More specfcally, we can conclude that wage dfferentaton across areas of resdence perssts whch can contrbute to wden ncome nequalty between urban and rural areas. 5. Results Ths secton dscusses the man fndngs of the paper. Frst, the -earnngs profle of workers wth EP2 and EP1 school level s analyzed. Second, the hypothess of exstence of postve returns to educaton (between workers wth EP2 versus EP1 school level), and gven years of labor market s dscussed. Thrd, estmates of returns to are reported and analyzed. Fourth, the hypothess of whether labor market and schoolng are substtutes or complements s nvestgated by means of margnal earnngs dfference dstrbuton curves. It s mportant to note before dong the nonparametrc regressons one has to choose an approprate kernel functon. Snce we are nterested n estmatng margnal returns or dervatve that s contnuous, then we choose the quartc or bweght kernel functon: 4 15 16 2 ( 1 z ) 2, f -1 z 1 K (z) = (14) 0, f z 1 4 There are many possble choces of kernel functon. Because s a weghtng functon, t should be postve and ntegrate to unty over the band, t should be symmetrc around zero, so that ponts below x get the same weght as those an equal dstance above, and t should be decreasng n the absolute value of ts argument. Examples of kernel functons are: Epanechnkov kernel and Gaussan Kernel, Rectangular kernel among. 10
Another crtcal ssue when estmatng nonparametrc regresson s that of bandwdth choce, snce there s a trade-off between sample sze and the former. In ths partcular applcaton a bandwdth of 2.0 was chosen for a sample sze of about 10178 observatons of postve wage earners. 5.1. Labor market -earnngs profle Human captal theory predcts that average earnngs of full-tme workers rse wth the level of educaton. However, earnngs profles mply a declne n on-the-job-tranng nvestments wth age, whch s attrbuted to the declne wth age n the length of the remanng workng lfe. Fgure 1, 2 and 3 graph the 2004-05 earnngs of workng labor force wth two dstnct levels of completed educaton, EP1 and EP2 respectvely. An examnaton of these fgures reveals the followng notable characterstcs: () Average earnngs of full-tme workers wth EP2 level of schoolng are much greater than those of workers wth EP1 level, for the frst fve years after they start workng; () The most rapd ncrease n earnngs occurs early for full-tme workers wth EP1 school level n urban areas, whereas n rural areas, earnngs start pretty hgh and then they drop sharply before stablzng for the remanng workng lfe. The ntal drop n earnngs may n part reflect earnngs volatlty wthn ths group of workers, snce they are the least sklled of the whole labor force, thus vulnerable to shocks (especally n rural areas). () In overall, earnngs of full-tme workers wth EP2 tend to domnate those of ther counterpart over the workng lfe. Ths mples that schoolng also plays a sgnfcant role n the determnaton of earnngs for these groups of workers known as the least endowed. (v) One mportant fndng s the fact that earnngs gap between these two categores of workers tend to shrnk as they acqure more of labor market over ther workng lfe. Ths mples that nvestng n on-the-job tranng, especally for workers wth EP1 levels of schoolng can help to reduce nequalty n the dstrbuton of earnngs between these groups of workers and thus mpact postvely on poverty reducton. 11
Fgure 1: Relatonshp between labor market and wages - Natonal Estmated log(wages) gven 6.2 6.4 6.6 6.8 7 earnngs for EP2 level earnngs for EP1 level Fgure 2: Relatonshp between labor market and wages Rural areas Estmated log(wages) gven 6 6.2 6.4 6.6 6.8 7 earnngs for EP2 n rural earnngs for EP1 n rural Fgure 3: Relatonshp between labor market and wages Urban areas Estmated log(wages) gven 6.2 6.4 6.6 6.8 7 earnngs for EP2 n urban earnngs for EP1 n urban 12
5.2. Returns to educaton gven same labor market In the precedng secton we used human captal theory to analyze the mpact of labor market on earnngs for the two groups of workers (those wth completed EP1 level of schoolng, and those wth EP2). From the analyss descrbed above we can also nvestgate the mpact of another source of nvestment n human captal (e.g. schoolng) on earnngs. Ths can be done as follows: gven same amount of labor market, we then derve the dfference on earnngs than s accounted by worker s level of educaton. If the dfference turns to be postve then we can conclude that there are postve returns to educaton, all thngs beng equal. In fact the later assumpton carres mportant mplcatons for emprcal analyss. For example, people wth EP1 schoolng level and who have ablty to learn quckly are lkely to be presented by employers wth, tranng opportuntes as well as greater remuneraton than those wth EP2. In that case, returns wll tend to look pretty much hgher for EP1 workers than those wth completed EP2. Fgures 4, 5 and 6 depct earnngs dfferences between workers wth completed EP2 and those wth EP1. The analyss were done for three domans; natonal, rural and urban areas. The results ndcate that n Mozambque there are evdences of postve and dmnshng returns to educaton between the group of workers who have completed EP2 and those wth EP1. The reason why worker s returns for completng EP2 than EP1 are dmnshng over workng lfe may be due to the fact that as EP1 workers acqure more labor, then ther earnngs start to rse more sharply and therefore the earnngs gap begns to shrnk. Note that ths effect s much pronounced n rural areas where earnngs dfference converges more quckly towards zero before both groups of workers have completed 10 years of labor market. 13
Fgure 4: Earnngs dfference gven labor market - Natonal curve dfference -.2 0.2.4.6 Fgure 5: Earnngs dfference gven labor market Rural areas curve dfference -.5 0.5 1 Fgure 6: Earnngs dfference gven labor market Urban areas curve dfference -.2 0.2.4.6.8 14
5.3. Returns to A comparson of Fgures 7, 8 and 9 dscloses mmedately that there are postve returns to. Moreover, returns to on-the-job tranng of workers that have completed EP1 are greater than those of workers wth completed EP2 schoolng level. The gap s much greater n urban areas whle n the rural areas t tends to get narrow over workng lfe. For example, and durng the frst fve years of workng the returns to for workers wth EP1 n rural areas s about 15 percent aganst 11 percent for workers wth EP2. Ths mples a 4 percentage pont s gap n returns of nvestment n labor market. In contrast, n urban areas the returns to gap between these two categores of workers averages 6 percentage ponts for the correspondng workng. Therefore, and based on ratonal expectatons model we can deduce that urban workers wth completed EP1 have more ncentves to nvest n on-the-job tranng than ther counterparts n rural areas. Lke earnngs- profles, returns to labor market generally rse steeply early on, and then flatten, and may eventually fall. In fact, the early ncreases are so steep relatve to those on the latter levels of workng. Ths may probably reflects a potental deprecaton of sklls or a declne n on-the-job tranng nvestments wth age (Chswch, 2003). Furthermore, t s mportant to note that the data reflected n Fgures 7, 8, and 9 do not follow specfc ndvduals through tme; rather, they match earnngs wth potental labor market n a gven year. Thus the generally declnng profles for workers wth more than 20 years of potental could reflect reduced job opportuntes for unsklled labor force, changes n the composton of workng labor force that have completed EP1 and EP2 and workng full-tme, or some factor that may depressed earnngs. 15
Fgure 7: Returns to Natonal dervatve of log(wages).02.04.06.08.1.12 earnngs for EP2 level earnngs for EP1 level Fgure 8: Returns to Rural areas dervatve of log(wages) gven 0.05.1.15 earnngs for EP2 rural earnngs for EP1 rural Fgure 9: Returns to Urban areas dervatve of log(wages) gven.04.06.08.1.12 earnngs for EP2 urban earnngs for EP1 urban 16
5.4. Trade-off between nvestments n labor market and schoolng Human captal theory predcts a postve correlaton between acquston of labor market and schoolng. In general, better-educated workers tend to nvest more n onthe-job tranng than ther counterparts (Ehrenberg and Smth, 1999). Therefore, ths secton s amed at testng whether ths emprcal fndng s also holds for the case of the least sklled workers. An nvestgaton of the Fgures 10, 11, and 12 clearly reveals a strkng result as compared to what human captal theory would predct. It s shown that there s a trade-off n nvestng on both schoolng and labor market for the least sklled workers (EP1 and EP2 schoolng level). That s, workers wth completed EP1 level often tend to substtute nvestments n schoolng for acquston of more of workng lfe. Ths fndng may be attrbuted to lqudty constrants as well as to hgh opportunty costs n terms of foregone earnngs. Poverty may also be a cause of ths trade-off snce most of adult members of poor households n Mozambque have not even completed EP2 and are forced to drop out of school and enter labor market n order to earn some ncome to meet ther basc needs. Fgure 10: Margnal earnngs dfference - Natonal curve dfference -.06 -.04 -.02 0.02 17
Fgure 11: Margnal earnngs dfference Rural areas curve dfference -.15 -.1 -.05 0.05 Fgure 12: Margnal earnngs dfference Urban areas curve dfference -.06 -.04 -.02 0.02 5.5. Earnngs dfference by area of resdence Fgures 13 and 14 dsplay the dstrbuton of earnngs dfferences between urban and rural workers who have completed the EP1 and EP2 level, respectvely. The results show that whle earnngs gap s qute narrow among workers n both areas of resdence, however, t tends to wden over workng lfe for the case of workers wth EP2 n urban areas as compared to those n the rural areas. Ths means that wthn the same schoolng level (EP2) nequalty n earnngs tend to ncrease wth labor market and regonal locaton. Ths may n part reflect a depresson on earnngs of EP2 workers n rural areas as compared to ther counterparts n the urban areas. 18
Therefore, polces amed at creatng stable and more labor ntensve jobs n the rural areas would be worthy n order to narrow ths gap. Fgure 13: Earnngs dfference for workers wth EP1 by area of resdence urban vs. rural earnngs dfference urban-rural EP1-1 -.5 0.5 1 Fgure 14: Earnngs dfference for workers wth EP2 by area of resdence urban vs. rural earnngs dfference urban-rural EP2 -.4 -.2 0.2.4 Another nterestng relatonshp to analyze s whether nvestments n schoolng and labor market are substtutes or complements across regons and gven same level of educaton. Fgures 15 and 16 depct margnal earnngs for workers wth completed EP1 and EP2 n urban vs. rural areas. Conclusons about whether margnal earnngs dfference s postve or negatve among EP1 workers n urban areas as compared to those n rural areas s not pretty clear from the data snce the dstrbuton s centered around zero. Nevertheless, there are evdences that ndcate a postve assocaton between nvestments 19
n schoolng and workng, at least for the frst 5 years after workers wth completed EP2 level n urban areas start workng. However, eventually and beyond that level of workng they start substtutng schoolng for work as shown by the negatve values of margnal earnngs dfference whch le below zero (see Fgure 16). Fgure 15: Margnal earnngs dfference for workers wth EP1 urban vs. rural earnngs dervatve dfference urban-rural EP1 -.1 -.05 0.05 Fgure 16: Margnal earnngs dfference for workers wth EP2 urban vs. rural earnngs dervatve dfference urban-rural EP2 -.1 -.05 0.05.1 20
5.6. Returns to by gender and area of resdence A bref nspecton of Fgures 17, 18, 19 and 20 reveal that there are postve returns to regardless of gender and geographcal locaton. Nevertheless, one notable characterstc that arse clearly from the data s the fact that men s returns to are always greater than those of women gven same level of educaton and regonal locaton. In addton to that, the gap n returns s much larger among workers who have only completed the EP1 level, as llustrated by Fgures 17 and 19. For example, and for workers wth EP1 level n rural areas, men s returns to averages about 10 percent after fve years of work, and can rse to 14 percent after 25 years of workng. In contrast, women s returns start very low at about 7 percent and declne to less than 6 percent after 25 years of work. Smlar pattern s also observed for the case of urban areas. Gven what we have seen above, t seems relevant to queston about the man factors that make women s returns so low as compared to men. Accordng to human captal theory, dfferences n expected work lfe between men and women consttute a prmary cause of dfferences n returns to workng. For example, the ncentves for women to make nvestment n a longer workng lfe are less because the average female expects to spend less than half her workng lfe n the labor force, and has a hgh probablty of droppng out of the labor force for chld-rearng (Chswch, 2003; Ehrenberg and Smth). Whle ths fndng may be reasonable for the case of female workers who have only completed the EP1 level of schoolng, however recent changes n the labor force partcpaton of women, especally marred women of chldbearng age and who are relatvely well-educated (prmary EP2 and beyond), are causng dramatc changes n the acquston of schoolng and workng, and consequently on the dstrbuton of earnngs. In fact, ths can be confrmed by the data n the case of rural workers wth completed EP2. For example, whle men s returns to start at 6 percent aganst only 4 percent for women after 5 years of work, however women s returns eventually start catchng up as a result of more nvestment n both schoolng and labor market. Consequently, n rural areas the return s to gap 21
between men and women tends to fade away after about 25 years of workng as llustrated n Fgure 18. Fgure 17: Returns to for workers wth EP1 and by gender Rural areas dervatve of earnngs.04.06.08.1.12.14 5 10 15 20 25 male earnngs EP1 rural female earnngs EP1 rural Fgure 18: Returns to for workers wth EP2 and by gender Rural areas dervatve of earnngs 0.05.1.15 5 10 15 20 25 male earnngs EP2 rural female earnngs EP2 rural 22
Fgure 19: Returns to for workers wth EP1 and by gender Urban areas dervatve of earnngs.02.04.06.08.1.12 5 10 15 20 25 male earnngs EP1 urban female earnngs EP1 urban Fgure 20: Returns to for workers wth EP2 and by gender Urban areas dervatve of earnngs.04.06.08.1 5 10 15 20 25 male earnngs EP2 urban female earnngs EP2 urban 23
6. Conclusons In ths paper we hghlghted the mpact of work-related on earnngs dstrbuton. We demonstrated that earnngs of full-tme workers wth EP2 tend to domnate those of workers wth EP1 level over the workng lfe. Ths mples that schoolng also plays a sgnfcant role n the determnaton of earnngs for these groups of workers known as the least endowed. We then showed that earnngs gap between these two categores of workers tend to shrnk as they acqure more of labor market whch mples that nvestng n on-the-job tranng, especally for workers wth EP1 schoolng can help to reduce nequalty n the dstrbuton of earnngs thus mpact postvely on poverty reducton. Furthermore, we fnd that there are postve returns to educaton between completng EP2 level of schoolng as compared to EP1. Ths fndng ndrectly renforces prevous research on human captal, household welfare and schoolng n Mozambque whch ndcated a postve and sgnfcant correlaton between completon of EP2 level and mprovements n household consumpton (Handa, Smler and Harrower, 2004). In addton, the paper also provded evdence that workers who have completed the EP1 level of schoolng tend to have greater returns to labor market than ther counterpart. Ths fndng although surprsng, however, may fnd an explanaton on the ratonal decson of low-sklled workers n terms of nvestng more on work-related rather than schoolng n order to meet ther basc needs. It also mportant to note that snce opportunty costs of nvestng on schoolng nstead of work are hgh for ths group then gven that n equlbrum margnal costs have to be equal to margnal benefts, therefore the fndng above seems reasonable. Gender dfferentaton on earnngs s clearly evdent from the data. For example, n overall men s returns to are always domnatng those of women regardless of the level of schoolng and regonal locaton. Nevertheless, women s returns seem to catch up wth those of men, especally at the top of the dstrbuton and for workers who have 24
completed EP2. Ths fndng appeals to the need of more nvestment on both schoolng and work-related for women lvng n rural areas n order to reduce poverty and nequalty n ncome dstrbuton. The paper also showed that unlke what human captal would predct, however, there s a trade-off between nvestng on schoolng and labor market at least for ths group of low-sklled workers n Mozambque. Therefore, elmnaton of credt constrants and other opportunty costs that makes worker s nvestments n schoolng more expensve (especally n rural areas) would be worthy. Ths would ultmately mpact postvely n terms of rasng productvty of labor, and thus expand economc growth and welfare n the grassroots. 25
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