Overeducation, undereducation and earnings

Transcription

1 Overeducaton, undereducaton and earnngs Further evdence on the mportance of ably and measurement error bas 1 Deter Verhaest $ Hogeschool-Unverse Brussel (HUB) Faculty of Economcs and Management, Human Relatons Research Group Warmoesberg 26, 1000 Brussels, Belgum Ghent Unversy Faculty of Economcs and Busness Admnstraton, SHERPPA Eddy Omey Ghent Unversy Faculty of Economcs and Busness Admnstraton, SHERPPA Tweekerkenstraat 2, 9000 Ghent, Belgum October 2011 Forthcomng n the Journal of Labor Research 1 We thank Gerde Everaert, Walter Van Trer and an anonymous referee for ther valuable comments on a prevous verson of ths paper. $ Correspondence: Deter Verhaest, Hogeschool-Unverse Brussel, Warmoesberg 26, 1000 Brussel, Belgum; Deter.Verhaest@HUBrussel.be 1

2 Overeducaton, undereducaton and earnngs Further evdence on the mportance of ably and measurement error bas Abstract It s generally found that overeducated (undereducated) workers earn less (more) than adequately educated workers wh smlar years of educaton. We nvestgate the mportance of ably and measurement error bas for these outcomes by applyng a fxed-effects nstrumental varable approach on data for Flemsh young workers. Ths approach results n substantally hgher overeducaton penaltes and undereducaton bonusses than a standard random effects approach. Ths suggests that the upward bas resultng from unobserved worker heterogeney s more than compensated by the negatve bas resultng from measurement error. Further, we also fnd some evdence on heterogeneous effects of msmatches accross job levels and years of experence. Keywords Return to educaton, overqualfcaton, educatonal msmatch, underemployment, panel data 2

3 I. INTRODUCTION In a hghly nfluental artcle, Duncan and Hoffman (1981) were the frsts to estmate a so-called ORU-earnngs functon. Ther approach was to replace years of educaton (YEDUC) n the standard Mnceran earnngs equaton by years of overeducaton (YOVER), years of requred educaton (YREQ) and years of undereducaton (YUNDER). Snce then, the ORU approach has been followed by a large number of authors and delvered farly smlar results (see Hartog, 2000, Rubb, 2003). Partcularly strkng s the fndng that the return to years of overeducaton s posve, but smaller than the return to years of requred educaton. Put dfferently, overeducated workers earn more than adequately educated workers n jobs wh smlar requrements, but less than f they would have been employed n jobs wh requrements that match wh ther educaton. Undereducated workers, on the other hand, are generally found to earn less than ther adequately colleagues n jobs wh smlar requrements. Yet, they earn more than f they would have been employed n jobs that match wh ther level of educaton. The ORU lerature suffers, however, from a number of econometrc problems that mght result n based conclusons. A frst problem s that most studes are based on cross-sectonal data, whch neglect that ndvduals wh a smlar level of educaton mght be largely heterogeneous. Hence, the estmated overeducaton penalty mght be overestmated f overeducated workers have lower average ably levels than adequately educated workers wh a smlar educatonal background. If ths s ndeed the case, the lerature on overeducaton would be much ado about nothng as Sloane (2003) states. Only a lmed number of studes have already nvestgated ths problem. McGunness (2003), Chevaler (2003), Chevaler and Lndley (2009), and Sohn (2010) ncluded ably related ndcators n the earnngs equaton and stll found substantal wage penaltes for overeducaton. Also studes based on quantle regresson by McGunness and Bennett (2006) and Budría (2011) provded lmed support for the hypothess that overeducaton s only a sgn of lower ably. Panel-data analyses, however, delvered much more mxed results. Korp and Tåhln (2009), for nstance, concluded that ably bas s rather lmed on the bass of a fxed-effects panel-data analyss for Swedsh workers. Conversely, smlar panel-data analyses by Bauer (2002) and Frenette (2004) for German and Canadan workers revealed only small or even no negatve earnngs consequences of beng overeducated. Another ssue that mght result n based outcomes s measurement error. Over- and undereducaton have been measured on the bass of dfferent methods (see Groot and Maassen van den Brnk, 2000). These varous measures are typcally found to be only loosely correlated to each other (van der Velden and van 3

4 Smoorenburg, 1999; Battu et al., 2000; Verhaest and Omey, 2006a), whch suggests that the problem of measurement error mght be rather severe. Unlke worker heterogeney, measurement error s lkely to result n an underestmated overeducaton penalty. Also wh respect to ths ssue, ltle research has been executed. One way to correct for random measurement error s to apply an nstrumental varable (IV) approach, usng one measure of overeducaton as nstrument for another. By applyng ths approach on cross-sectonal data for the US, Robst (1994) ndeed found a substantally hgher overeducaton penalty. In partcular, the earnngs of overeducated workers were not found to be dfferent from those of adequately educated workers n jobs wh smlar requrements. Also Dolton and Slles (2008) appled such an IV approach, but on the bass of panel data. Ther estmates for UK graduates suggested that the upward bas n the estmated overeducaton penalty nduced by worker heterogeney s compensated by an equvalent downward bas nduced by measurement error. In ths paper, we contrbute to ths dscusson wh an nstrumental varable panel-data analyss for Flemsh young workers. Our analyss dffers n three man ways from the work of Dolton and Slles (2008). Frstly, our data do not only nclude unversy graduates, but also young workers from lower educatonal levels. Ths enables to dentfy effects of both over- and undereducaton. Among the other papers that addressed the ssues of ably or measurement error bas, also those of Robst (1994), Bauer (2002) and Korp and Tåhln (2009) nvestgated effects for undereducaton. Overall, ther conclusons were parallel to those wh respect to overeducaton. Robst (1994) concluded that measurement error downwardly bases the estmated wage bonus for undereducaton, whereas Bauer (2002) found that unobserved worker heterogeney could explan the full earnngs dfferental between undereducated and adequately educated workers wh a smlar educatonal background. Fnally, also Korp and Tåhln (2009) noted an upward bas from unobserved heterogeney, but the estmated wage bonus remaned noteworthy. Another contrbuton s that we nvestgate whether and how the mpact of over- and undereducaton on wages changes over the workers early career. Some cross-sectonal studes already nvestgated ths ssue. Cohn and Ng (2000) and Cohn et al. (2000) found evdence for a negatve nteracton effect between overeducaton and experence, whereas the undereducaton bonus ncreased wh years of experence. Ths suggests that overeducated workers experence less skll acquston or even a deprecaton of ther sklls surplus; undereducated workers seem to compensate ther skll defc wh more skll acquson on-the-job. That overeducated workers mght face a deprecaton of ther sklls s also more drectly llustrated by a study of de Grp et al. (2008), who noted that overeducaton results n cognve declne. Mendes de Olvera et al. (2000), however, noted a posve nteracton effect between overeducaton and tenure. They attrbuted ths result to occupatonal 4

5 upgradng resultng from technologcal change. Potental explanatons for these countradctng fndngs are cohort effects and measurement error. Hence, an IV panel-data analyss mght delver more clary. Fnally, whereas Dolton and Slles (2008) used a subjectve measure for educatonal mstmatches as nstrument for another subjectve measure, we use two subjectve measures as nstrument for a job analyss measure. Both job analyss and self-assessements mght be vald approaches to measure over- and undereducaton (Hartog, 2000; Verhaest and Omey, 2006b). Yet, standard cross-secton studes have demonstrated that the sze of the returns to years of over- and undereducaton mght depend on the appled measure (Rubb, 2003; Verhaest and Omey, 2006c). Moreover, measurement errors mght be correlated across alternatve subjectve measures. By usng subjectve measures as nstruments for an objectve one, or the other way around, ths problem of common-method bas s lkely to be avoded (Verhaest and Omey, 2006b). The remander of the paper s structured as follows. In secton II, we gve an overvew of the used data and our emprcal methodology. The results are overvewed and dscussed n secton III. We end the paper n secton IV wh some general conclusons. II. DATA AND EMPIRICAL MODEL The analyss s based on data for two cohorts of Flemsh youngsters, whch were respectvely born n the years 1976 and Each cohort conssts of representatve samples of about 3000 ndvduals, whch were ntervewed at age In addon, follow-up surveys were conducted at age 26 wh response rates of almost 70% 2. These data were collected whn the context of the SONAR-project, whose am s to nvestgate the transon from school to work n Flanders. The data contan nformaton on the overeducaton status and on net wages, both at the start of the workng career and at the age of Omtng the self-employed, observatons wh mssng values or extreme values of wages (cf. nfra), respondents for whch we have only one observaton, and those wh a change n ther level of educaton between the two observaton ponts 4, leaves a sample of 2838 ndvduals and 5676 observatons. Models whout and wh nteracton terms wh experence are estmated. For the specfcaton whout nteracton effects, the followng two earnngs equatons are estmated, dependng on whether random (1.1) or fxed (1.2) effects are assumed: (1.1) lny = α YOVER + β YUNDER + δ YEDUC + X ϕ + Iθ + µ + ε

6 (1.2) lny = α YOVER + β YUNDER + I θ + µ + ε wh net wages Y of ndvdual at tme t (wh t=1 for start frst job, t=2 for observaton at age 26); YOVER = years of overeducaton, YUNDER = years of undereducaton; YEDUC = years of educaton; X = a vector of fxed observed ndvdual characterstcs; I = a vector of observed characterstcs that vary whn ndvduals; µ or µ : an ndvdual random or fxed effect; and error terms ε and ε Equatons (1.1) s equvalent to the ORU model as YEDUC YREQ + YOVER YUNDER. By ncludng YEDUC n the random-effects models, we ensure that the reference base (.e. the ndvdual) s smlar to the fxed effects model snce YEDUC s constant whn ndvduals. By ncludng YREQ, coeffcents would not be comparable. Further, we estmate smlar nteracton-effects models wh YEDUC YEXP, YOVER YEXP, and YUNDER YEXP as extra ncluded explanatory varables and wh YEXP = years of work experence. The central measure of over- and undereducaton n ths study s based on a job analyss (JA) approach. All occupatons are coded followng the 2001 update of the 1992 Standard Occupaton Classfcaton of Statstcs Netherlands (CBS, 2001). The classfcaton conssts of fve functonal levels, whch correspond to the followng educatonal levels: less than lower secondary (<LS), lower secondary (LS), hgher secondary (HS), lower tertary (LT) and hgher tertary educaton (HT). The converson nto years of requred educaton s based on the mnmal years of educaton that are typcally needed to acheve each level: <LS = 6 years, LS = 10 years, HS = 12 years, LT = 15 years, and HT = 16 years 5. Ths measure performed que well n a number of relably and valdaton studes (van der Meer, 2006; Verhaest and Omey, 2006c, 2010). A major advantage s that ths classfcaton does not start from the job tle, but from the complexy and content of the tasks to be executed on the job. It allows to classfy jobs wh a smlar job tle at dfferent functonal levels. Nevertheless, a certan degree of measurement error remans unavodable. Hence, we apply an nstrumental varable approach by usng two subjectve measures of over- and undereducaton as nstruments. The frst one s a drect self-assessment measure (DSA) whch s based on the followng survey queston: Accordng to your own opnon, do you have a level of educaton that s too hgh, too low or approprate for your job?. As nstrumental varables, we use two dummes, one for overeducaton and another one for undereducaton. The second subjectve measure s an ndrect self-assessment measure (ISA) whch s based on survey questons regardng the requred level to get the job. Respondents were asked frstly: Was a qualfcaton requred to get your job?. If the answer was posve, 6

7 they got the queston: To get your job, what educatonal level were you requred to have?. We use the same educatonal classfcaton as for the JA measure. If no qualfcaton was requred to get the job, they are classfed n the category <LS. Both subjectve measures have some dsadvantages compared to the JA measure. Although the DSA measure seems a straghtforward subjectve ndcator of overeducaton, does not delver nformaton regardng years of over- and undereducaton. The ISA measure s conceptually less attractve as s based on the requred level to get the job and not on the requred level to do the job. Yet, both measures mght serve as useful nstruments to correct for the random measurement error n the JA measure 6. To conclude, the specfcatons for the frst stage estmatons are the followng for the models whout nteracton terms between the msmatch varables and years of experence: (1.3) JA ISA ISA DSA DSA YOVER = η YOVER + κ YUNDER + λ OVER + π UNDER + δ YEDUC + X ϕ + I θ + µ + ε (1.4) JA ISA ISA DSA DSA YUNDER = η YOVER + κ YUNDER + λ OVER + π UNDER + δ YEDUC + X ϕ + Iθ + µ + ε (1.5) JA ISA ISA DSA DSA YOVER = η YOVER + κ YUNDER + λ OVER + π UNDER + + µ + ε (1.6) JA ISA ISA DSA DSA YUNDER = η YOVER + κ YUNDER + λ OVER + π UNDER + + µ + ε Wh µ and µ beng ndvdual random effects, and µ and µ beng ndvdual fxed effects. In the case of the nteracton-effects models, also the nteracton terms are consdered to be endogenous, and the followng nstruments are addonally ncluded n the frst stage equatons: ISA ISA YOVER * YEXP, YEXP YUNDER *, DSA DSA OVER * YEXP, and YEXP UNDER *. Table 1 reports some summary statstcs on the three measures of over- and undereducaton n our sample. On the bass of JA, the ncdence of overeducaton amounts to 51.0% at the start of the frst job. At age 26, ths ncdence s clearly lower and amounts to 41.3%. The ncdence of undereducaton slghtly ncreases from 5.4% n the frst job to 8.2% at age 26. Smlar longudnal changes are noted on the bass of the two subjectve measures. As other studes, we note relatvely low correlatons between these ndcators; the correlaton between YOVER on the bass of JA and those on the bass of ISA, for nstance, s no more than 0.41 (see Appendx C). Also n lne wh other studes are the lower overeducaton ncdences f measured on the bass of subjectve approaches (see Verhaest and Omey, 2006a). Gven these substantal dfferences, mght be questoned whether the JA and DSA ndcators ndeed measure the same concept. As argued n Verhaest and Omey (2010), mght make sense to dfferentate also conceptually between objectve and subjectve overeducaton. 7

8 Varables such as job satsfacton or job search behavour are lkely to depend prmarly on subjectve overeducaton. For other ndcators such as wages, however, we mght expect that partcularly objectve overeducaton matters. Nevertheless, ths ssue remans open for dscusson and therefore, we end the paper wh some addonal evdence on the bass of the DSA measure, whle usng the JA and ISA measures as nstruments. Table 1 here For the dependent varable ( ln Y ), we make use of reports on net monthly wages. For the 1976 cohort ntervew at the age of 23, respondents had to report n ntervals of BEF for lower wages and n ntervals of BEF for hgher wages. In the other surveys, respondents were asked to report ther exact net monthly wage. If they refused to answer, they got the nterval queston. The mdpont of the nterval s used wh respect to the nterval observatons 7. Answers are converted nto real hourly wages. Observatons wh extreme values of wages,.e. two standard devatons above or below the average natural logarhm wage n the sample, are excluded from the analyss. Table 2 here In table 2, we report average wages by job level and type of observaton. Workng at a hgher job level seems to delver a wage bonus, wh one excepton; we fnd no wage dfference between the two lowest job levels at the start of the workng career. A logcal explanaton s the relatvely hgh Belgan mnmum wages, whch compress the wage structure at the lower end of the dstrbuton. Another explanaton s delvered by Büchel (1994), who noted smlar wage pattern for German school leavers. Snce employment n unsklled jobs requres only small nvestments n specfc tranng, new workers become fully productve almost mmedately after job entry. Moreover, ths lack of tranng costs also lms the need for senory based wage systems to prevent turnover. To account for these wage effects, we also estmate alternatve models wh YEDUC and YOVER beng spl up dependng on whether they apply to the lowest 10 years of educaton ( YEDUCL and YOVERL ) or to hgher years of educaton ( YEDUCH and YOVERH ) 8. Gven the small number of undereducated ndvduals wh the lowest educatonal degree n our sample (see Appendx A), we do not mplement a smlar decomposon for YUNDER 9. 8

9 The ncluded fxed observed control varables ( X ) are dummes for gender (1 dummy), non-european descent (1), and year of brth (1). As ndvdual varyng observed characterstcs ( I ), we nclude experence, experence squared, tenure, tenure squared, percentage of employment (full-tme = 100%), dummes for havng a chld (1 dummy), cohabng (1), observaton at age 26 (1), year of observaton (10) 10, frm-sze (4), ndustry (12), regon of employment (7), shft work (2) and nght work (3) 11. III. ESTIMATION RESULTS We start our overvew wh some standard random effects evdence (cf. Table 3, column (1)). Two types of models are estmated: one based on the assumpton that effects are equal across all job levels (Upper panel) and another one that dfferentates between effects that apply to the lowest job levels and those that apply to the other levels (Lower panel). The estmated effects on the bass of the frst model are n lne wh the stylsed facts of the lerature: overeducated (undereducated) workers earns less (more) than adequately educated workers wh smlar years of educaton, and more (less) than adequately educated workers wh smlar years of requred educaton. The second model reveals substantal dfferences n returns dependng on the job level to whch they apply (see table 3, lower panel). Whereas the earnngs penalty for one year of overeducaton at hgher job levels s estmated to be 2.7%, s found to be less than 1% at the lowest job level. Overall, these estmated effects are smaller than those typcally found n cross-sectonal studes. On the bass of Rubb s (2003) lerature overvew, average effects of 9.6% for years of (requred) educaton, -4.4% for years of overeducaton and 4.8% for years of educaton are noted 12. However, our estmatons are based on net earnngs, whle most other studes make use of gross salary data. Gven the strong progressvy of ncome taxaton n Belgum, net returns are substantally lower than gross returns. Both cross-sectonal and random-effects panel-data estmates wll be based f the unobserved heterogeney s correlated wh the observables such as overeducaton. Hence, we also execute fxed-effects analyses (cf. table 3, column (2)). As the other researchers that have nvestgated ths ssue, we fnd that a fxed-effects analyss delvers lower estmated overeducaton penaltes and undereducaton bonusses. The estmated penalty for overeducaton at the lowest job level s now even statstcally nsgnfcant. Yet, ths s not the case for the penalty for overeducaton at hgher job levels and more than four ffths of the orgnal coeffcent value cannot be explaned by worker heterogeney. Wh respect to undereducaton, results are smlar; the estmated bonus only drops from 2.4% to 1.9%. Overall, the outcomes for employment at hgher job levels are n lne wh those 9

10 of Korp and Tåhln (2009), who also found that ably bas n the estmated effects of educatonal msmatches s rather modest. Table 3 Further, we execute nstrumental varable panel-data estmates to account for measurement error (cf. Table 3, columns (3) and (4)). For ths, we use two subjectve measures of educatonal msmatches as nstruments for the job analyss measure. Except wh respect to employment at the lowest job level, the nstrumental varable random-effects model delvers a much hgher overeducaton penalty compared to the standard panel-data model. Besdes, ths penalty s now found to almost fully compensate the posve return to years of educaton. Put dfferently, we fnd hardly any earnngs dfference between overeducated workers and adequately but lower educated workers who are employed at the same job level. Moreover, also undereducated workers are now found to be pad more or less n accordance wh ther job level 13. Incluson of fxed effects nstead of random effects results n a slghtly lower estmated penalty of overeducaton at hgher job levels. Yet, the estmate of 5.2% s stll substantally hgher than the estmate of 2.7% that was found on the bass of the standard random-effects model. Hence, the upward bas n the estmated penalty resultng from unobserved heterogeney s more than compensated by the negatve bas resultng from measurement error. Ths outcome slghtly dffers from that of Dolton and Slles (2008), who found that these two effects more or less balance out. As n our study, they found that the estmated overeducaton penalty becomes about twce as large f based on IV models 14. Hence, seems that ably bas s relatvely less mportant for our sample. Also surprsng s the fndng that the undereducaton bonus s estmated to be hgher on the bass of the IV fxed-effects compared to the IV random-effects model. Ths s n opposon both wh our standard panel-data results and those of other studes (see, e.g., Bauer, 2002). Yet, our IV fxed-effects estmate for ths bonus s relatvely mprecse. To assess whether these returns and penaltes change over the career, we addonally estmate some models that nclude nteracton effects wh years of experence (cf. Table 4). On the bass of all models, we fnd a posve nteracton effect between years of experence and the frst ten years of educaton. For years of overeducaton, no such nteracton effect shows up (column (4), lower panel). Hence, also for more experenced overeducated workers wh a lower secondary educaton degree, we fnd no evdence on an overeducaton penalty. For overeducaton at hgher job levels, we fnd a negatve nteracton effect. Yet, ths estmate s very mprecse. Wh respect to undereducaton, we fnd no evdence of a wage bonus at the start of the career once 10

11 we specfy fxed nstead of random ndvdual effects. Yet, a posve nteracton effect shows up between years of experence and undereducaton. Put dfferently, undereducated workers need some experence to realze a wage bonus for ther employment at a job level above ther level of educaton. These outcomes only partally confrm those of Cohn and Ng (2000), who noted a posve nteracton effect wh respect to undereducaton and a negatve nteracton effect wh respect to overeducaton. Table 4 Fnally, as a robustness check, we also report some evdence on the bass of the DSA measure, whle usng the JA and ISA measures as nstruments (see Table 5). As there s no nformaton regardng job levels on the bass of the DSA measure, we are only able to estmate models that nclude dummes for over- and undereducaton ( OVER and UNDER ) and under assumpton that effects are dentcal across all job levels. The core results are largely smlar to those on the bass of JA. The upward bas n the estmated penalty for overeducaton resultng from unobserved worker heterogeney seems to be largely compensated by the downward bas resultng from measurement error. Smlarly, the estmated wage bonus for undereducaton s much hgher on the bass of the nstrumental varable fxed effects model than on the bass of the standard random effects model. Fnally, we note a posve nteracton effect between undereducaton and years of educaton on the bass of the fxed-effects IV model. Table 5 IV. DISCUSSION AND CONCLUSION It s generally found that overeducated workers earn less than adequately educated workers wh a smlar educatonal background. Smlarly, undereducated workers seem to earn more than adequately educated workers wh a smlar educatonal background. In ths paper, we focussed on the role of ably and measurement error bas n these fndngs. To corrrect for both types of bas, we conducted a fxed-effects IV analyss on the bass of data for Flemsh young workers. For the measurement of over- and undereducaton, we reled both on job analyss and worker assessements, and used two worker-assessment measures as nstruments for the job analyss measure. 11

12 A major concluson of our analyss s that unobserved heterogeney and measurement error can be mportant sources of bas n the estmated mpact of over- and undereducaton on earnngs. In general, our fxedeffects IV estmates delvered substantally hgher overeducaton penaltes and undereducaton bonusses than the standard random-effects estmates. Ths suggests that the upward bas resultng from unobserved worker heterogeney s more than compensated by the negatve bas resultng from measurement error. Also addonal analyses, whch used the job analyss measure as nstrument for a drect worker assessment measure of educatonal msmatches, delvered such a concluson. Hence, seems that ths outcome s not measurement-specfc. However, the concluson that downward measurement error bas more than compensates upward ably bas can not smply be generalsed to other samples as, n a smlar study by Dolton and Slles (2008) for UK graduates, was found that both effects more or less balance out. More specfcally, seems that ably bas s somehow less sgnfcant n our study, whereas measurement error bas (more than) halves the estmated effects n both studes. Ths strong measurement error bas should not be too surprsng as overeducaton measures are generally found to be only loosely correlated. Overall, these outcomes plead for a more general applcaton of IV estmators n overeducaton studes. Another concluson s that the effects of educatonal msmatches are n some cases not homogeneous across years of experence and job levels. Frst, the wage bonus for undereducaton was found to be posvely related to years of experence. Ths suggests that a shortage of formal educaton can be compensated by more post-school skll acquson. For overeducated workers n contrast, we found no clear evdence on changng effects over the career. Ths last fndng seems to be n dsagreement wh the results of de Grp et al. (2008), who noted that overeducated workers face a deprecaton of ther sklls. However, the statstcally nsgnfcant nteracton term between overeducaton and experence n our study mght be explaned by the applcaton of the less effcent IV fxed-effects method n combnaton wh a relatvely modal sample sze. Moreover, we only observed ndvduals up to the age of 26. Hence, our observaton perod mght be too short to detect any changes n overeducaton penaltes over the career 15. Second, we found no evdence on an overeducaton penalty for employment at the lowest job level. At the start of the career, ths seems to result from overall wage compresson at the lower end of the dstrbuton, whch s probably explaned by mnmum wages and job specfc tranng nvestments. However, as ths wage compresson decreases wh years of work experence, remans somehow puzzlng why such an overeducaton penalty does not show up later on. Nevertheless, also wh respect to ths result, some cauton s called for due to relatvely small sample szes n combnaton wh the relatvely short observaton perod. 12

13 Overall, our results clearly reject the statement that the lerature on the mpact of over- and undereducaton on earnngs s much ado about nothng : n many suatons, both types of msmatches seem to have a substantal mpact on earnngs. Nevertheless, several ssues reman open for dscusson and more research relyng on data for other countres, on larger samples and on longer observaton perods would be usefull. REFERENCES Battu, H., Belfeld, C., and Sloane, P. (2000), How well can we measure graduate overeducaton and s effects?, Natonal Instute Economc Revew, no. 171, Bauer, T. (2002), Educatonal msmatch and wages: a panel analyss, Economcs of Educaton Revew, 21 (3), Büchel, F. (1994), Overqualfcaton at the begnnng of a non academc workng career: the effcency of the German dual system under test, Konjuncturpolk, 40 (3-4), Budría, S. (2011), Are educatonal msmatches responsble for the nequaly ncreasng effect of educaton?, Socal Indcators Research, 102 (3), CBS (2001), Standaard Beroepenclassfcate 1992 ede 2001, Den Haag: SDU. Chevaler, A. (2003), Measurng Over-educaton, Economca, 70 (3), Chevaler, A., and Lndley, J. (2009), Overeducaton and sklls of UK graduates, Journal of the Royal Statstcal Socety A, 172 (2), Cohn, E., and Ng, Y. (2000), Incdence and wage effects of overschoolng and underschoolng n Hong Kong, Economcs of Educaton Revew, 19 (2), Cohn, E., Johnson, E., and Ng, Y. (2000), The ncdence of overschoolng and underschoolng and s effects on earnngs n the Uned States and Hong Kong, Research n Labor Economcs, 19, de Grp, A., Hosma, H., Wllems, D., and van Boxtel, M. (2008), Job-worker msmatch and cognve declne, Oxford Economc Papers, 60, Dolton, P., and Slles, M. (2008), The effects of over-educaton on earnngs n the graduate labour market, Economcs of Educaton Revew, 27, Duncan, G., and Hoffman, S. (1981), The ncdence and wage effects of overeducaton, Economcs of Educaton Revew, 1 (1), Frenette, M. (2004), The overqualfed Canadan graduate: the role of the academc program n the ncdence, 13

14 persstence, and economc returns to overqualfcaton, Economcs of Educaton Revew, 23 (1), Groot, W., and Maassen van den Brnk, H. (2000), Overeducaton n the labor market: a meta-analyss Economcs of Educaton Revew, 19 (2), Hartog, J. (2000), Overeducaton and earnngs: where are we, where should we go?, Economcs of Educaton Revew, 19 (2), p Korp, T., and Tåhln, M. (2009), Educaton msmatch, wages, and wage growth: Overeducaton n Sweden , Labour Economcs, 16 (2), McGunness, S. (2003), 'Graduate overeducaton as a sheepskn effect: evdence from Northern Ireland', Appled Economcs, 35 (5), McGunness, S., and Bennett, J. (2006), Overeducaton and the graduate labour market: a quantle regresson approach, Economcs of Educaton Revew, 26 (5), Mendes de Olvera, M., Santos, M.C., and Kker, B.F. (2000), The role of human capal and technologcal change n overeducaton, Economcs of Educaton Revew, 19 (2), Robst, J. (1994), Measurement error and the returns to excess schoolng, Appled Economcs Letters, 1, Rubb, S. (2003), Overeducaton n the labor market: a comment and re-analyss of a meta-analyss, Economcs of Educaton Revew, 22 (6), Sloane, P. (2003), Much ado about nothng?, n Overeducaton n Europe, Büchel F., de Grp A. and Mertens A. (eds), Cheltenham: Edwar Elgar, Sohn, K. (2010), The role of cognve and noncognve sklls n overeducaton, Journal of Labor Research, 31, SONAR (2003), Hoe maken Vlaamse jongeren de overgang van school naar werk?, bassrapporterng cohorte 1978 (eerste golf), 147p. SONAR (2004), Hoe maken Vlaamse jongeren de overgang van school naar werk?, bassrapporterng cohorte 1976 (tweede golf), 116p. Stewart, M. (1983), On least squares estmaton when the dependent varable s grouped, Revew of Economc Studes, 50 (163), van der Velden, R., and van Smoorenburg, M. (1999), Overscholng en belonng: Het effect van verschllende meetmethoden, Tjdschrft voor Arbedsvraagstukken, 15 (2),

15 Verhaest, D., and Omey, E. (2006a), Measurng the ncdence of over- and undereducaton, Qualy & Quanty, 40, Verhaest, D., and Omey, E. (2006b), Dscrmnatng between alternatve measures of overeducaton, Appled Economcs, 38 (10), Verhaest, D., and Omey, E. (2006c), The mpact of overeducaton and s measurement, Socal Indcators Research, 77 (3), Verhaest, D., and Omey, E. (2010), The determnants of overeducaton: dfferent measures, dfferent outcomes?, Internatonal Journal of Manpower, 31 (6), NOTES 1 For the samplng, a mult-stage procedure was appled based on geographcal areas. No stratfcaton schemes were used. 2 An overvew of the data collecton process and basc statstcs can be found n SONAR (2003, 2004). 3 For those who were whout jobs at the tme of the ntervew, nformaton was gathered wh respect to the end of ther last job. 4 Ths results n a drop of 32 ndvduals. Consequently, the effect of years of educaton cannot be dentfed n the fxed-effects analyss. Yet, as ths s a hghly selectve group of ndvduals, results on years of educaton would probably be unrelable. 5 A decomposon of our sample wh respect to job levels and educatonal levels can be found n Appendx A. 6 As dscussed n Verhaest and Omey (2006b), the deal procedure would be to use as nstrument an ISA measure that s based on the requred level to do the job. Yet, such a measure s not avalable for the 1976 cohort. Moreover, for the 1978 cohort, s only avalable for those wh a frst job that started before the age of 23. Fnally, as shown n Appendx B, addonal tests on the relably and valdy of the nstruments dd not reveal problems. F-test statstcs on the contrbuton of the IV s n the frst stage equatons are always strongly statstcally sgnfcant. Further, the valdy of the nstrumental varables s never rejected on the bass of Sargan tests on overdentfyng restrctons. 7 Usng the mdpont of each nterval mght lead to nconsstent results (see Stewart, 1983). We re-estmated the lnear effects models wh random effects on the bass of an nterval random-effects regresson. Yet, the estmaton results were largely smlar to the reported standard random effects estmates. 15

16 8 For nstance, someone wh a HT degree ( YEDUC = 15 ) and workng at the lowest job level (.e. YREQ = 6, and YOVER = 9 ), we defne the followng values: YEDUCL = 10, 5 YEDUCH =, YOVERL = 4, and 5. A YOVERH = smlar decomposon s executed wh respect to the ISA-measure, whch serves as nstrument. 9 Unreported IV fxed-effects estmates based on the decomposon of YUNDER dd not reveal statstcally sgnfcant dfferences between the effect of YUNDERL and that of YUNDERH. 10 The age cohort nature of the data results n a strong connecton between the year of observaton and years of experence. Yet, n- or excluson of these year dummes dd not lead to fundamentally dfferent conclusons. 11 Frm sze, ndustry, regon of employment, shft work and nght work each nclude a dummy for observatons wh unknown category. 12 Rubb reported average ORU coeffcents. The coeffcent values that are comparable to our model were computed on the bass of YEDUC YREQ + YOVER YUNDER. j j j 13 Addonal tests dd not reject the hypothess that over- and undereducated workers at hgher job levels are pad as ther adequately educated colleagues who were employed at the same job level. 14 In the study of Robst (1994), the sze of the bas was found to be even more substantal. The usage of an ISA measure as nstrument for a JA measure, for nstance, resulted n an ncrease of the estmated overeducaton penalty from 4% to 11%. 15 The average level of experence n our sample at age 26 s 4.4 years. 16

17 Table 1: Incdence and average years of over- and undereducaton JA DSA ISA Start frst job At age 26 Start frst job At age 26 Start frst job At age 26 OVER UNDER YOVER YUNDER OVERL OVERH YOVERL YOVERH Data source: SONAR 1976 (23), 1976 (26), 1978 (23), and 1978 (26), own calculatons; Number of ndvduals =

18 Table 2: Average net hourly wages (LNWAGE) by job level (JA) and type of observaton Start frst job At age 26 JA level JA level JA level JA level JA level All levels Data source: SONAR 1976 (23), 1976 (26), 1978 (23), and 1978 (26), own calculatons. 18

19 Table 3: The mpact of over- and undereducaton (job analyss) on earnngs: specfcatons whout experence nteracton effects Standard panel-data estmates IV panel-data estmates Random effects Fxed effects Random effects Fxed effects (1) (2) (3) (4) YEDUC 0.043*** (.002) *** (.003) - YOVER *** (.001) *** (.002) *** (.004) *** (.007) YUNDER 0.028*** (.003) 0.019*** (.005) 0.040** (.016) 0.085** (.035) Overall R² Whn R YEDUCL 0.020*** (.005) *** (.008) - YEDUCH 0.051*** (.002) *** (.004) - YOVERL *** (.002) (.003) (.010) (.021) YOVERH *** (.002) *** (.003) *** (.006) *** (.013) YUNDER 0.024*** (.003) 0.019*** (.006) 0.049*** (.014) 0.083** (.035) Overall R² Whn R YEDUC = years of educaton, YOVER = years of overeducaton, YUNDER = years of undereducaton. The followng control varables are ncluded n every model: experence, experence squared, tenure, tenure squared, percentage of employment (full-tme = 100%), dummes for cohabng (1), havng a chld (1), ndustry (12), frm sze (4), regon of employment (7), nght work (3), shft work (2), observaton at age 26 (1), and year of observaton (10); The followng tme-nvarant control varables are addonally ncluded n the random-effects models: dummes for gender (1), non- European descent (1), and year of brth (1). * p < 0.10; **: p < 0.05; ***: p < 0.01; Standard errors are n parentheses. Number of ndvduals = 2838; Number of observatons =

20 Table 4: The mpact of over- and undereducaton (job analyss) on earnngs: specfcatons wh experence nteracton effects Standard panel-data estmates IV panel-data estmates Random effects Fxed effects Random effects Fxed effects (1) (2) (3) (4) YEDUC 0.040*** (.002) 0.043*** (.004) YOVER *** (.002) *** (.002) *** (.003) *** (.008) YUNDER 0.024*** (.005) 0.013* (.007) (.021) (.045) YEDUC*YEXP/ ** (.006) 0.022*** (.006) 0.026* (.010) 0.056*** (.015) YOVER*YEXP/ (.005) (.005) (.020) (.023) YUNDER*YEXP/ (.010) 0.021* (.012) (.053) 0.147** (.059) Overall R² Whn R YEDUCL (.006) (.010) YEDUCH 0.051*** (.002) 0.064*** (.005) YOVERL ** (.003) (.004) (.013) (.021) YOVERH *** (.002) *** (.003) *** (.007) *** (.013) YUNDER 0.017*** (.005) (.007) 0.035* (.019) (.043) YEDUCL*YEXP/ ** (.014) 0.084*** (.015) 0.086*** (.029) 0.140*** (.031) YEDUCH*YEXP/ (.007) (.007) (.011) (.013) YOVERL*YEXP/ (.008) (.009) (.021) (.023) YOVERH*YEXP/ (.007) (.008) (.027) (.031) YUNDER*YEXP/ ** (.011) 0.040*** (.013) (.043) 0.114** (.047) Overall R² Whn R YEDUC = years of educaton, YOVER = years of overeducaton, YUNDER = years of undereducaton. The followng control varables are ncluded n every model: experence, experence squared, tenure, tenure squared, percentage of employment (full-tme = 100%), dummes for cohabng (1), havng a chld (1), ndustry (12), frm sze (4), regon of employment (7), nght work (3), shft work (2), observaton at age 26 (1), and year of observaton (10); The followng tme-nvarant control varables are addonally ncluded n the random-effects models: dummes for gender (1), non- European descent (1), and year of brth (1). * p < 0.10; **: p < 0.05; ***: p < 0.01; Standard errors are n parentheses. Number of ndvduals = 2838; Number of observatons =

21 Table 5: The mpact of over- and undereducaton (drect self-assessment) on earnngs Standard panel-data evdence IV panel-data evdence Random effects Fxed effects Random effects Fxed effects YEDUC 0.034*** (.002) *** (.002) - OVER *** (.006) *** (.009) *** (.019) *** (.026) UNDER (.010) 0.037*** (.013) 0.424*** (.068) 0.277*** (.077) Overall R² Whn R YEDUC 0.032*** (.002) *** (.003) - OVER *** (.007) *** (.010) *** (.021) *** (.027) UNDER (.015) (.019) 0.499*** (.138) (.136) YEDUC*YEXP 0.001* (.001) 0.002*** (.001) (.001) 0.002*** (.001) OVER*YEXP (.002) (.003) *** (.009) (.010) UNDER*YEXP 0.008** (.003) 0.010** (.004) (.023) 0.054** (.022) Overall R² Whn R The followng control varables are ncluded n every model: experence, experence squared, tenure, tenure squared, percentage of employment (full-tme = 100%), dummes for cohabng (1), havng a chld (1), ndustry (12), frm sze (4), regon of employment (7), nght work (3), shft work (2), observaton at age 26 (1), and year of observaton (10). The followng tme-nvarant control varables are addonally ncluded n the random-effects models: dummes for gender (1), non- European descent (1), and year of brth (1). * p < 0.10; **: p < 0.05; ***: p < 0.01; Standard errors are n parentheses. Number of ndvduals = 2838; Number of observatons =

22 Appendx A: Sample composon by job level (JA) and level of educaton Level of educaton <LS LS HS LT HT Overall Level Frst Job <LS 1.1% 1.9% 7.2% 0.5% 0.5% 11.2% LS 0.7% 3.0% 16.3% 3.1% 1.3% 24.4% HS 0.3% 0.7% 14.3% 9.5% 3.5% 28.3% LT 0.0% 0.1% 2.2% 17.5% 7.3% 27.1% HT 0.0% 0.0% 0.4% 0.9% 7.7% 9.0% Level Job age 26 <LS 0.8% 1.3% 4.1% 0.1% 0.1% 6.4% LS 1.0% 2.6% 13.9% 1.8% 0.6% 19.9% HS 0.2% 1.7% 18.5% 8.9% 2.4% 31.8% LT 0.1% 0.1% 3.3% 19.3% 8.1% 30.9% HT 0.0% 0.0% 0.5% 1.3% 9.2% 11.0% Overall 2.0% 5.8% 40.3% 31.5% 20.4% 100.0% <LS = less than lower secondary educaton; LS =lower secondary educaton; HS = Hgher secondary educaton; LT = lower tertary educaton; HT = hgher tertary educaton 22

23 Appendx B: Tests on the relably and valdy of the IV s n the IV fxed-effects model Table 4 Table 5 Upper panel Lower panel Upper panel Lower panel F test frst stage equaton of excluded IV s YOVER 95.6 (0.000) 53.9 (0.000) YOVERL 34.8 (0.000) 20.3 (0.000) YOVERH 70.9 (0.000) 39.2 (0.000) YUNDER 24.9 (0.000) 20.6 (0.000) 12.4 (0.000) 10.7 (0.000) YOVER*YEXP 54.5 (0.000) YOVERL*YEXP 23.0 (0.000) YOVERH*YEXP 42.7 (0.000) YUNDER*YEXP 34.0 (0.000) 30.6 (0.000) Sargan Ch² test of overdentfyng restrctons 0.2 (0.893) 0.2 (0.907) 2.6 (0.625) 0.5 (0.974) p-values n parentheses 23

24 Appendx C: Correlatons between pars of measures YOVER (JA) YUNDER (JA) OVER (DSA) UNDER (DSA) YOVER (ISA) YUNDER (JA) OVER (DSA) UNDER (DSA) YOVER (ISA) YUNDER (ISA)