Returns on education and overqualification – case of the EU and the Czech Republic

178 RETURNS ON EDUCATION AND OVERQUALIFICATION – CASE OF THE EU AND THE CZECH REPUBLIC Václa U bánek Uni e si y o Economics, P ague Facul y o Finance and Accoun ing Depa men o Public Finance Wins on Chu chill Squa e 4, 130 67 P aha 3, Czech Republic [email p o ec ed] Abs ac The aim o his pape is o analyse and e alua e he cu en s a e o o e quali ica ion and unde quali ica ion in he EU coun ies and in he Czech Republic. The e a e s eadily inc easing numbe s o educa ion sys em g adua es and pe sis en demand o educa ion in he popula ion. This leads o ea s ha he misma ch be ween jobs equi emen s and wo k o ce quali ica ions will inc ease. Public educa ional policy seems o suppo his con o e sy. While numbe s o en olled s uden s a seconda y schools and public uni e si ies and consequen ly numbe s o g adua es a e ising, he e could be sho age o skilled le el jobs in he u u e. The pape discusses a ious me hods used o measu e he le el o educa ion equi ed o he job and he ele an heo ies (sea ching and ma ching, human capi al heo y, assignmen heo y e c.) ha can be used o in e p e a ion o o e quali ica ion. The las chap e analyses and e alua es cu en s a e o o equali ica ion in he EU coun ies and in he Czech Republic and deals wi h he empi ical esul s o he impac o o e quali ica ion on ea nings. In oduc ion The pape is a ollow-up o he p e ious s udy on o e educa ion and labou misma ch in he ACC Jou nal 2012/3 (see U bánek, [27], p. 209 .). The esul s o his p e ious a icle we e subs an ially ex ended and only pa s o i we e used o be e unde s anding o he me hodology in his pape . New chap e s we e added – own esea ch based on da a om Eu opean Social Su ey 5, yea 2010, including s a is ics o educa ion misma ch, impac o educa ion misma ch on ea nings and eg ession analysis o e u ns o educa ion, o e - and unde quali ica ion. S eadily inc easing numbe s o educa ion g adua es and pe sis en demand o seconda y and uni e si y educa ion in he popula ion in he las decades lead o conce ns abou he misma ch be ween jobs equi emen s and wo k o ce quali ica ions. En olmen s o schools, oca ional ins i u ions and uni e si ies a e g owing, especially a e ia y educa ion ins i u ions. The cha (Figu e 1) shows he inc ease o he numbe s g adua es om e ia y educa ion ins i u ions (mainly uni e si ies) be ween yea s 1995 and 2011. In he Czech Republic, numbe o g adua es om e ia y educa ion ins i u ions ype A (Mas e ’s Deg ee ins i u ions) inc eased om 12.6% o age-speci ic g oup in he yea 1995 o 40.62% in he yea 2011. Simila ends can be seen in almos all de eloped coun ies; howe e , by con as o g owing numbe s o ype B g adua es (Bachelo ’s Deg ee) in many OECD coun ies be ween 1995 and 2011, in he Czech Republic numbe s o Bachelo ’s Deg ee g adua es dec eased. I seems ha e ia y- ype B p og ammes a e ecen ly being phased ou and g adua ion a es om hese p og ammes ha e allen in a ou o mo e academically o ien ed e ia y educa ion. The consequence o his end om e ia y educa ion Type B owa ds Type A is g owing job misma ch as will be analysed la e in his a icle. 179 Sou ce: [21], own calcula ions Fig. 1: Fi s - ime g adua ion a es in e ia y- ype A and B educa ion (1995 and 2011) Simila ly, numbe s o s uden s en olled in o uppe seconda y educa ion ha e inc eased subs an ially in almos all de eloped coun ies (see Figu e 2). 40 50 60 70 80 90 100 2011 2005 2000 1995 %% Sou ce: [21] Fig. 2: En olmen a es o 15-19 yea -olds (1995, 2000, 2005 and 2011) Ye many a ious s udies ha e indica ed ha hese seconda y and e ia y educa ion g adua es a e en e ing labou o ce wi h mo e educa ion han is ac ually equi ed o hei jobs – hey a e o e educa ed. Al hough he inc ease in all educa ional le els has been accompanied by g ow h o high skill jobs demand, he a e o his g ow h was a guably slowe han supply o quali ied, i.e. g adua ed wo ke s. The esul o his di e ence be ween highe supply o 180 g adua es and demand o hem a he labou ma ke leads o o e quali ica io and alloca ion o skills may be less han op imal. O e quali ica ion is a p oblem b oadly discussed in he economic and sociological li e a u e o las wo o h ee decades and i has se ious consequences o labou ma ke e ec i eness and educa ional in es men . 1 Human capi al heo y and p oblem o o e quali ica ion F om he poin o iew o human capi al heo y, o e quali ica ion is somewha puzzling. People should no in es in hei educa ion which hey canno and will no ully u ilize. When on he labou ma ke , acco ding o he neoclassical economic heo y, hey will ea n wage ully co esponding o hei educa ion and demand o hei skills will gi e hem same e u n o hei o e quali ica ion as o equi ed quali ica ion. The e has appea ed abundan li e a u e on o e quali ica ion in ecen decades, bo h in heo e ical and in empi ical ields (see o example me a-analysis o 25 s udies on o e quali ica ion in an a icle by G oo e al ([13], p.153). Pe e Sloane no es ha his ield o esea ch is coming o age ([25], p. 11) and his is e lec ed – among o he s – in a special issue o he Economics o Educa ion Re iew on O e schooling ([9]). Subs an ial li e a u e is also summa ized in Sloane’s a icle ([25]) and he e a e 33 a icles and pape s e iewed in his ex . Gene ally speaking, he economic analysis o o e quali ica ion was s a ed by Richa d B. F eeman in his The O e educa ed Ame ican om a mac oeconomic poin o iew in he yea 1976 (see [3]). F eeman ound ha he a e o e u n o highe educa ion had allen in he se en ies in he U.S.A. and a ibu ed i o an excess supply o g adua es. Howe e , ecen li e a u e (as men ioned abo e) mainly ocuses on he income e ec s o o e quali ica ion and on indi idual le el. All abo e men ioned s udies (and many o he s) show ha e u n o o e quali ica ion is highe han ha o people ha ing equi ed quali ica ion o his job bu is lowe compa ed o he e u n o people ha ing co ec job o his highe quali ica ion. Being o e quali ied c ea es a p emium ela i e o he job bu penal y ela i e o he quali ica ion ([3]). The e a e se e al possible explana ions o he exis ence o o e quali ica ion ([15]): Fi s , i can be a compensa ion o he lack o o he human capi al endowmen s (e.g. abili y, expe ience, on- he-job aining), o in o he wo ds o e educa ed wo ke s a e subs i u ing o mal o in o mal human capi al o a e less capable han adequa ely educa ed indi iduals ([17], p. 521). Also in his human capi al pe spec i e, o e quali ica ion can s em om he delibe a e choice o o e quali ied wo ke en e ing low-skill job as an oppo uni y o ini ial expe ience as an addi ional human capi al in es men . This pa o human capi al explana ion was es ed by Siche man ([24]) wi h good esul s. Second explana ion o o e quali ica ion is connec ed wi h ca ee mobili y and in his sense, o e quali ica ion is a empo a y si ua ion ([15]). “Sea ching and ma ching” p ocess is an e ec o impe ec in o ma ion in he labou ma ke en i onmen and as such, i can be empo a y si ua ion. I means ha his explana ion is no mu ually exclusi e wi h abo e men ioned addi ional human capi al in es men ([15]). In ex eme con as o human capi al heo y explana ion o o e quali ica ion is job compe i ion model c ea ed in 1975 by Les e Thu ow. In his model, i is assumed ha ma ginal p oduc i i y is de i ed om he job a he han om he wo ke and he employe s use pe sonal quali ies (incl. educa ion) only o hi ing. Wages a e paid acco ding o jobs and e u n o human capi al o e he le el equi ed o he job is ze o. Wo ke s ha a e mo e educa ed a e hi ed on supposi ion ha o hei aining will be necessa y ewe cos s. 181 Finally, job assignmen model is a s and o li e a u e based on he p oposi ion ha he e is an alloca ion p oblem in assigning wo ke s o a ious jobs. Labou supply and labou demand a e complex en i ies and measu ing ma ch quali y is in line wi h a en ion o he assignmen o he e ogeneous wo ke s o he e ogeneous jobs ([15]). Ea nings in his model a e a unc ion o bo h wo ke and job cha ac e is ics. Acco ding o human capi al heo y, e u ns o educa ion a e bes measu ed using da a on ac ual ea nings o g adua es du ing hei li e ime (ei he longi udinal da a, which is no easily ob ained, o c oss-sec ional da a). The e a e se e al sou ces o his da a howe e , especially o he Czech Republic, he da a is no comp ehensi e and co e ed ime in e al is no long enough o hese calcula ions. This da a hen is used o es ima e en i e age-ea ning p o ile as can be seen in Fig. 3 and also e u n o in es men can be calcula ed using he ollowing equa ion ( o ull discussion see [22]): 0 ) 1( )()( ) 1( )()( ** 001                    C E E E G E R G (1) whe e: E0( ) = p e-uni e si y educa ion ea nings unc ion, E1( ) = uni e si y educa ion ea nings unc ion, C( ) = di ec cos s unc ion, E = beginning o educa ion age, G = g adua ion educa ion age, R = e i emen age, * = a e o e u n o in es men o educa ion. Due o he sca ci y o good longi udinal da a c oss-sec ional da a ha e o be used in o de o make longi udinal s a emen s. The e a e some good es ima es ha con ol o di e ences be ween longi udinal and c oss-sec ional da a. The e is ano he me hod o calcula ion app oxima e e u ns o educa ion ha is no e y dependen o longi udinal da a and he e o e easy o apply. This is so called sho -cu me hod ([23]): AESk AEAE j ji   * (2) whe e: * = a e o e u n o in es men o educa ion, AEi = mean ea nings o an indi idual wi h uni e si y educa ion, AEj = mean ea nings o an indi idual wi h uni e si y seconda y educa ion, S = numbe o yea s o uni e si y educa ion, k = coe icien o di ec cos s. I is impo an o no e ha human capi al heo y has also possibili y o con ol i s empi ical esul s h ough eg ession analysis o demand o educa ion as i has been pionee ed by Ga y Becke , Theodo e Schul z and Jacob Mince (see [1]). The mos quo ed and simple model is Mince ’s ea nings equa ion, which is empi ical app oxima ion o he human capi al heo e ical amewo k: u ex exbs X wi i ii i i    2 ln (3) whe e wi is an ea ning measu e o an indi idual i such as ea ning pe hou o week, si ep esen s a measu e o hei schooling, exi is an expe ience measu e, Xi is a se o o he a iables assumed o a ec ea nings and ui is a dis u bance e m. In his con ex , b can be conside ed he p i a e e u n o schooling (see [11]). 182 Sou ce: [23], own adap a ion Fig. 3: Age-ea nings p o ile and ull me hod o e u ns o educa ion In es ima ing he a e o e u n om schooling, he coe icien o he schooling a iable is o en in e p e ed as he pe cen age inc ease in he hou ly wage associa ed wi h one addi ional yea o schooling and is, acco ding o Psacha apoulos and Pa inos [23] no accu a ely e e ed o as he a e o e u n o schooling, ega dless o wha educa ional le el his yea e e s o. Though con enien his me hod equi ing ewe da a migh be, i is in e io o he di ec me hod as i assumes la age-ea nings p o iles o di e en le els o educa ion. Howe e , nei he e e ing o wage e ec s as e u ns o schooling no la age-ea nings p o iles assump ions is “damaging o un ealis ic”. [23] 2 Me hodology Following human capi al heo y and Mince ’s ea nings equa ion ( o de ails see also U bánek, [27], p. 213) he e is a possibili y o c ea e an equa ion adding oge he Mince ’s (i means human capi al heo y model) and Thu ow’s job compe i ion models. Some imes his equa ion is e e ed o as he Duncan and Ho man o he ORU model (ORU s ands o O e quali ica ion – Requi ed quali ica ion – Unde quali ica ion): u ex exSbSbSbw i i iuuoo i     2 0 ln (4) whe e ln wi is loga i hm o ea nings, β0 is a cons an ; b ; bo; bu a e es ima ed coe icien s o quali ica ions (o schooling) and q a e quali ica ion a iables: S o yea s o quali ica ions equi ed o do he job; So o yea s o o e quali ica ions; Su o yea s o unde quali ica ions. The Mince ’s human capi al speci ica ions implies ha b = bo = -bu; Thu ow’s job compe i ion speci ica ion implies ha bo = bu ([25], p. 14). Since he basic e sion o he ‘Mince ian’ unc ion does no dis inguish be ween di e en le els o schooling, an ex ended ea nings unc ion was de eloped, which subs i u es a se ies o 0–1 dummy a iables o S, co esponding o disc e e educa ional le els. The ex ended ea nings unc ion may be exp essed as ollows: Ea nings di e en ial Cos s Ea nings and cos s Age Sh N E0( ) E1( ) R G E 0 C( ) 183 u ex ex D b D b D b X wi i i u u s s p p i i    2 ln (5) wi is an ea ning measu e o an indi idual i such as ea ning pe hou o week, b(p,s,u) ep esen coe icien s o schooling a p ima y, seconda y o uni e si y le els espec i ely and D(p,s,u) a e dummy a iables o p ima y, seconda y o uni e si y le els espec i ely, exi is an expe ience measu e, Xi is a se o o he a iables assumed o a ec ea nings and ui is a dis u bance e m. The p i a e a e o e u n be ween le els o educa ion can hen be calcula ed om he ex ended ea nings unc ion by he ollowing o mulae: SS bb SS bb S b su su s ps ps s p p p      ;; (6; 7; 8) whe e p is he a e o e u n o p ima y educa ion, s is he a e o e u n o seconda y educa ion and u is he a e o e u n o uni e si y educa ion and S is yea s o schooling. The a ionale o his p ocedu e is ha he a e o e u n is compu ed by means o he ollowing o mula ha is educa ional le el speci ic: Sww ij j  lnln (9) whe e j is index o highe le el o educa ion han i;  S is di e ence be ween yea s o schooling a indi idual school le els. Acco ding o Cohn ([7]), ano he model gi es good and compa able esul s:     u EXPUNDERSCH EXP OVERSCH EXP ADSCH UNDERSCHOVERSCHADSCH X w i i iii i i i i ii            * * * ln 6 45 321      (10) whe e ln wi is na u al loga i hm o g oss ea nings, δ and α a e eg ession coe icien s espec i ely, ADSCHi is numbe o yea s o adequa e schooling, OVERSCH and UNDERSCH a e numbe s o yea s o o e schooling and unde schooling (OVERSCH = SCHOOL – ADSCH, whe e SCHOOL is numbe o yea s o ac ual educa ion; simila ly UNDERSCH = ADCH - SCHOOL), EXP a e yea s o expe ience, Xi is a se o o he a iables assumed o a ec ea nings and ui is a dis u bance e m (index i is o indi idual i). As o measu ing o equi ed, o e - and unde quali ica ion, h ee al e na i e measu emen me hods can be used o ind he deg ee o o e quali ica ion o unde quali ica ion ( o mo e de ails, see U bánek [27], p. 211 . ): 1) Sys ema ic job e alua ion by p o essional job analys s who speci y he equi ed le el o educa ion (deg ee) o he job and occupa ional classi ica ion. O e educa ion o unde educa ion is di e ence be ween equi ed and ac ual educa ion. This ype o measu emen is e e ed o as an objec i e measu e. 2) Wo ke sel -assessmen – he wo ke s hemsel es speci y he quali ica ion equi ed o he job answe ing he ques ion as e.g. “Wha kind o educa ion does a pe son need in o de o pe o m you job?” Di e ence be ween ac ual and assessed educa ion is o e - o 184 unde educa ion. This ype o measu emen is e e ed o as a subjec i e measu e and i was used in his pape . 3) F om ealized ma ches, whe e equi ed educa ion is de i ed om ac ual le el o wo ke s' educa ion as a mean (o some imes mode) o hei educa ional a ainmen . O e educa ion hen occu s when he le el o educa ion is mo e han one s anda d de ia ion abo e he mean; simila ly, unde educa ion is one s anda d de ia ion below he mean. This me hod o measu emen is called empi ical me hod. Job analysis by expe s could b ing bes esul s ([13]). Howe e , his da a is a ely a ailable and we can ind subjec i e measu e in mos o e quali ica ion analyses ([13], [16]). F om he me a-analysis o 25 s udies o o o e quali ica ion ([13], p.153) we e ob ained 50 es ima es on he incidence o o e quali ica ion and 36 es ima es o he incidence o unde quali ica ion. The unweigh ed a e age o he incidence o o e quali ica ion is 23.3% (s anda d de ia ion 9.9%) and unweigh ed a e age o he incidence o unde quali ica ion is 14.4% (s anda d de ia ion 8.2%). In he s udy o U.K. g adua e labou ma ke ([9]), 38% o g adua es we e o e educa ed in hei i s job. This p opo ion ell o 30% a e six yea s. Resul s o he Czech Republic can be ound in he s udy o 25 Eu opean coun ies ([12]) and a e as ollows: 49.5% o e educa ed; 44.3 unde educa ed. Re u ns o educa ion a e usually calcula ed using equa ions simila o abo e p esen ed equa ions. Resul s o 25 s udies included in me a-analysis o o e quali ica ion in he labou - ma ke ([13], p.153) show ha e u n o a yea o educa ion equi ed was 7.9% in 1970s and 1980s; in 1990s a e o e u n o a yea o educa ion equi ed inc eased o abou 12%. Fo all hese yea s, a e o e u n o a yea o o e quali ica ion was 2.6%, while he a e o e u n o a yea o unde quali ica ion was –4.9%. De ailed esul s o e u ns o o e quali ica ion and unde quali ica ion and also alues o incidence o o e quali ica ion and unde quali ica ion a e in G oo ([13]). The s udy o Galasi ([12]) shows o 25 Eu opean coun ies esul s simila o Table 1, howe e o he Czech Republic he e u ns o educa ion o equi ed yea is equal o e u n o educa ion o a ained yea – bo h e u ns a e 7.1%. Pooled sample da a shows he e u ns o educa ion o equi ed yea equal 9.7% and e u n o educa ion o a ained yea equal 7.2%. 3 Da a C oss-sec ion da a come om Eu opean Social Su ey, ound 5, yea 2010. Su eys we e ca ied ou in 26 coun ies and o iginal numbe o esponden s in all su eys oge he was 52 458. All coun y samples a e ep esen a i e o all pe sons aged 15 and o e , ega dless o hei na ionali y, ci izenship o language and indi iduals a e selec ed by s ic andom p obabili y me hods. Fo his pape we e used da a o 21 EU coun ies (no da a we e a ailable o Aus ia, I aly, La ia, Luxembou g, Mal a, Romania). F om his sample o coun ies da a we e selec ed only o employees (omi ed we e sel -employees and esponden s wi hou economic ac i i y, such as pensione s e c.) and his educed se con ains 11 137 esponden s (EU incl. Czech Republic). Sepa a e se o da a was c ea ed o he Czech Republic wi h 544 cases ( ep esen a i e sample consis ed o 249 emales and 295 males). Responden s epo ed, among o he hings, hei g oss sala y, educa ion, equi ed educa ion o hei posi ion, yea s o expe ience and o he a iables. Requi ed quali ica ion o he posi ion was ob ained by he ques ion “Abou how many yea s o educa ion o oca ional schooling beyond compulso y educa ion would hey (possible candida es o you posi ion) need?” O he independen a iables used in he eg ession included gende , coun y and age. Basic desc ip i es a e in Tables 1 and 2. 185 Tab. 1: Basic s a is ics o he sample o he EU – means, s anda d de ia ions in pa en heses Male Female S (yea s o educa ion) 12.53 (2.430) 12.86 (2.431) Age (yea s) 41.69 (11.874) 42.18 (11.445) Expe ience (yea s) 20.77 (12.258) 19.52 (11.610) Mon hly sala y (EUR) 2300.26 (1997.98) 1635.77 (1426.96) Sou ce: ESS5 [10], own calcula ions Tab. 2: Basic s a is ics o he sample o he CZ – means, s anda d de ia ions in pa en heses Male Female S (yea s o educa ion) 11.99 (1.660) 12.25(1.723) Age (yea s) 40.86 (10.992) 41.73 (10.278) Expe ience (yea s) 19.76 (11.249) 18.95 (10.951) Mon hly sala y (EUR) 904.57 (445.07) 692.47(254.31) Sou ce: ESS5 [10], own calcula ions O e quali ica ion and unde quali ica ion a ios we e calcula ed using subjec i e measu e, i.e. answe o he ques ion “Abou how many yea s o educa ion o oca ional schooling beyond compulso y educa ion would hey (possible candida es o you posi ion) need?” These answe s c ea ed alues o adequa e educa ion o he job and we e compa ed wi h educa ion o he esponden s. Calcula ions based on hese answe s a e in he Table 3. Resul s in his able a e consis en wi h inding o G oo ([13]) and Galasi ([12]), howe e pe cen ages o o e quali ica ion o he Eu opean emales (o e 50%) a e qui e high and i seems ha i can be a esul o high a es o unemploymen in he EU (especially in he Eu ozone coun ies), when people a e – due o igh labou ma ke – willing o ake jobs bellow hei quali ica ions. Resul s o he Czech Republic a e highe in he pa o unde quali ica ion – in he same ein as abo e, i can be esul o be e si ua ion on he labou ma ke whe e also candida es wi h lowe han necessa y quali ica ion can ob ain a job. A he same ime, he e can be also bias owa ds highe le els o adequa e schooling, as people o e s a e equi emen s o hei job o gi e mo e impo ance o hei posi ion. The esul is highe pe cen age o unde quali ied wo ke s. Resul s in Table 4. show a e age yea s o o e schooling, unde schooling, yea s o educa ion necessa y o he job, a e age age o esponden s and yea s o expe ience, sepa a ely o EU coun ies and he Czech Republic. Values o he EU coun ies a e gene ally highe , wi h he excep ion o highe o e schooling o Czech emales. Tab. 3: Pe cen age dis ibu ion o adequa e quali ica ion, unde - and o e -quali ica ion EU Coun ies All Male Female Unde quali ied 29.34 32.40 26.43 Adequa ely quali ied 22.43 21.36 23.42 O e quali ied 48.24 46.24 50.14 Czech Republic Unde quali ied 41.85 41.03 42.86 Adequa ely quali ied 19.10 16.92 21.74 O e quali ied 39.04 42.05 35.40 Sou ce: ESS5 [10], own calcula ions 186 Tab. 4: Means o o e schooling, unde schooling, adequa e schooling, educa ion, age, yea s o expe ience. Yea s o o e schooling Yea s o unde schooling Yea s o educa ion necessa y o job Yea s o ull- ime educa ion Age o esponden Yea s o expe ience EU COUNTRIES All N=11137 Mean (S d. de ia ion) 2.86 (1.63) 1.95 (1.29) 12.51 (2.58) 12.70 (2.44) 41.94 (11.66) 20.12 (11.94) Males N=5393 Mean (S d. de ia ion) 2.79 (1.58) 1.98 (1.38) 12.44 (2.61) 12.53 (2.43) 41.69 (11.87) 20.77 (12.26) Females N=5744 Mean (S d. de ia ion) 2.93 (1.66) 1.92 (1.18) 12.58 (2.55) 12.86 (2.53) 42.18 (11.44) 19.52 (11.61) CZECH REPUBLIC All N=544 Mean (S d. de ia ion) 2.76 (1.55) 1.37 (.81) 11.89 (2.44) 12.11 (1.69) 41.26 (10.67) 19.39 (11.11) Males N=295 Mean (S d. de ia ion) 2.59 (1.46) 1.23 (.59) 11.59 (2.28) 11.99 (1.66) 40.86 (10.99) 19.76 (11.25) Females N=249 Mean (S d. de ia ion) 3.01 (1.66) 1.54 (.99) 12.25 (2.58) 12.25 (1.72) 41.73 (10.29) 18.95 (10.95) Sou ce: ESS5 [10], own calcula ions E ec s o o e quali ica ion and o unde quali ica ion on ea nings (i.e. on e u ns o adequa e schooling, o e - and unde schooling) we e es ima ed using augmen ed Mince ’s ea nings equa ion p oposed by Cohn ([7]) and p esen ed in his pape as an Equa ion (10). Resul s o eg ession analysis using his model a e in he Tables 5, 6, and 7. Tab. 5: Model summa y Model Eq. (10) R R Squa e Adjus ed R Squa e S d. E o o he Es ima e Du bin- Wa son 0.544a 0.296 0.294 0.88903 0.708 a. P edic o s: (Cons an ), Yea s necessa y o job calcula ed o ull ime educa ion (ADSCH), Yea s o o e schooling = SCHOOL-ADSCH, Yea s o unde schooling=ADSCHOOL-SCHOOL, Adschool*Expe ience, O e schooling*Expe ience, Unde schooling*Expe ience b. Dependen Va iable: LN w Sou ce: ESS5 [10], own calcula ions Tab. 6: ANOVA Model Eq. (10) Sum o Squa es d Mean Squa e F Sig. Reg ession Residual To al 1.842.232 6 307.039 781.480 0.000a 4372.909 11130 0.393 6215.141 11136 Sou ce: ESS5 [10], own calcula ions