Compensation for Earnings Risk under Worker Heterogeneity

Transcription

1 DISCUSSION PAPER SERIES IZA DP No Compenation for Earning Rik under Worker Heterogeneity Peter Berkhout Joop Hartog Dinand Webbink April 2006 Forchungintitut zur Zukunft der Arbeit Intitute for the Study of Labor

2 Compenation for Earning Rik under Worker Heterogeneity Peter Berkhout SEO Amterdam Joop Hartog Univerity of Amterdam, Tinbergen Intitute, AIAS, CESifo and IZA Bonn Dinand Webbink CPB, The Hague and SCHOLAR Dicuion Paper No April 2006 IZA P.O. Box Bonn Germany Phone: Fax: Any opinion expreed here are thoe of the author() and not thoe of the intitute. Reearch dieminated by IZA may include view on policy, but the intitute itelf take no intitutional policy poition. The Intitute for the Study of Labor (IZA) in Bonn i a local and virtual international reearch center and a place of communication between cience, politic and buine. IZA i an independent nonprofit company upported by Deutche Pot World Net. The center i aociated with the Univerity of Bonn and offer a timulating reearch environment through it reearch network, reearch upport, and viitor and doctoral program. IZA engage in (i) original and internationally competitive reearch in all field of labor economic, (ii) development of policy concept, and (iii) diemination of reearch reult and concept to the intereted public. IZA Dicuion Paper often repreent preliminary work and are circulated to encourage dicuion. Citation of uch a paper hould account for it proviional character. A revied verion may be available directly from the author.

3 IZA Dicuion Paper No April 2006 ABSTRACT Compenation for Earning Rik under Worker Heterogeneity * We ue two large Dutch dataet to etimate the Rik Augmented Mincer equation and tet for rik compenation in expected earning. We replicate earlier finding of a poitive premium for rik and a negative premium for kew and add confirmation of the key reult if we control for individual ability. We find that immigrant have graduated in more riky education but obtain identical rik compenation. Among recent graduate, women receive higher rik compenation than men, conitent with their higher rik averion, while for a labour force cro-ection, lower average compenation for women i conitent with their preence in le riky education. Lower average compenation for vocational graduate than for univerity graduate i conitent with preumed higher rik averion and lower oberved rik. JEL Claification: J31 Keyword: wage, rik, earning function Correponding author: Joop Hartog Department of Economic Univerity of Amterdam Roetertraat WB Amterdam The Netherland J.Hartog@uva.nl * An earlier draft wa preented at the EEA Meeting, Amterdam, Augut 25-27, 2005.

4 Introduction In the tandard Mincer earning equation, the rate of return to education i etimated a the regreion coefficient of log earning on year of chooling. Under trict condition, it i the compenation for potponing earning by going to chool. Much recent reearch eek to refine or even quetion thi model, by focuing on econometric iue (endogeneity biae) or on the theoretical bai of the imple model itelf (heterogeneity and elf-election). The baic model underlying the equation aume a world without rik: future earning for any length of chooling are known for ure. Yet, the prevalence of rik urrounding the choice of education and occupation barely need elaboration. An individual conidering an education doe not anticipate ome level of pot-chool earning, but an entire ditribution of earning. And generally, the individual will not know for ure where in that ditribution he will end up. She cannot fully anticipate her abilitie to benefit from the education, he doe not know her future proficiency in the occupation that follow after chool, he cannot predict with perfection the future market value of the kill learnt in chool: uncertaintie abound. The uncertaintie will not be identical for every potential education, and hence, they will affect the individual choice. And with individual generally hying away from rik, a properly functioning labour market will generate compenation for uch rik. In a erie of recent paper, compenation for earning rik ha been etablihed for everal countrie (Hartog, Plug, Diaz Serrano and Vieira, 2003; Hartog and Vijverberg, 2002; Diaz Serrano, Hartog and Nielen, 2003; Diaz Serrano and Hartog, 2004; Chritianen, Joenen and Nielen, 2004; for a ummary ee Hartog 2005). Expected earning are indeed higher for occupation and education with higher earning variance. Mot interetingly, they are lower for occupation and education that are more kewed. The relevance of kew wa firt pointed out (and etablihed) by McGoldrick (1995). Intuitively, poitive kew point to the mall probability of obtaining large gain, omething people appreciate and are willing to pay for. Appreciation for kew (or kew affection) can alo be hown to be required for declining abolute rik averion, a condition one can hardly dipute. Skew affection ha been confirmed empirically in betting behaviour and lottery participation (ee Hartog and Vijverberg, 2002, for reference). In everal analye, compenation for rik averion and kew affection ha been etablihed at the level of occupation, ometime in combination with a few education type. Rik and kew are meaured a variance and third moment of reidual from a Mincer earning equation, after grouping reidual by occupation (without electivity correction, a dicued below). But occupational attachment are not univerally fixed for life, and potentially, worker may move out after receiving a bad earning draw. The problem of elective mobility i abent at the level of education, a individual cannot undo their accomplihed education. In Diaz Serrano, Hartog and Nielen (2003) we ued obervation by education for Denmark (uing 75 type), and found the uual poitive compenation for rik and negative compenation for kew. We alo confirmed the baic reult with panel data and even more relevant, we found confirmation when we ued alternative rik meaure baed on individual movement through the earning ditribution over time. In Hartog and Vijverberg (2002) we have teted many different econometric pecification and etimated implied utility function. Thu, the baic relation, which we may call the Rik Augmented Mincer equation, ha already found ubtantial empirical upport. We may alo note that the relation i by no mean trivial or a tatitical artefact. Conider, for example, a model where higher education level are choen by individual with higher level of initial ability (uch a childhood IQ). Then, the oberved earning ditribution by chooling level emerge a the egment of the initial ability ditribution (preumably normal) tranformed by the production of human capital at each of thee chooling level. Individual choice guarantee that longer 2

5 education have higher mean earning, but the variance can not be predicted: it can jut a well increae or decreae with level of education (ee the model in Hartog and Diaz Serrano, 2004), a indeed i confirmed in cro-national comparion of earning diperion by education level (Hartog, Van Ophem and Bajdechi, 2004). In other word, a reaonable model of educational choice and election doe not predict an inevitably poitive relationhip between mean and variance of earning. The Mincer earning function, with log wage linear in chooling year and quadratic in potential experience, i a tandard pecification in empirical work. A noted above, reearch in the lat decade ha emphaied the endogeneity of chooling, heterogeneity and elf-election (ee Card, 1999). The model that have been developed and etimated have painted a richer picture of the return to education than the OLS Mincer equation ugget. Still, in thi paper we will tart out from the baic homogenou model underlying the Mincer equation. Thi i imply a matter of reearch trategy, a we conider the canonical earning equation a natural tarting point for empirical work. Reidual earning variance a oberved in the labour market i only a proper meaure of rik if individual are not better informed and if they cannot inure or otherwie evade the earning uncertainty. The iue of information and electivity bia will be dicued in Section 1. Like many author (Blanchard and Fiher, 1989: 283; Shaw, 1996), we do not believe that individual can inure themelve againt the financial rik of inveting in chooling. A Shaw (1996:626) tate: The method of reducing rikine that are available in financial market, namely, diverification, exchange, and inurance, are not option for reducing the rikine of return to human capital invetment. Recent work on the poibilitie to reduce the rik of education by financial invetment upport thi view. For example, Davi and Willen (2000) how that an optimal portfolio of financial and human capital require totally unrealitic tock holding. Palacio-Huerta (2003) find that at the aggregate level, the mean-variance frontier, tracing maximum compenation for given rik, doe not improve if return from financial aet are added to return from human capital, wherea in the convere cae (adding human capital to financial aet) the frontier doe improve (for eparate demographic group, the reult vary by level of education.) We will thu tick to uing earning variance a our meaure of rik. We will ue obervation by education for The Netherland, a in our Danih (and Spanih) data and replicate the tandard reult. Our main contribution i a tet for the effect of worker heterogeneity by ability and an invetigation of heterogeneou rik attitude. In Jacob, Hartog and Vijverberg (2005), we how that unoberved ability difference will give a downward bia in the etimated rik coefficient if rik and ability are independent, but that the bia cannot be determined if they are correlated. We will how that ability bia i not reponible for our core reult. We interpret different outcome for different group of worker by referring to difference in rik attitude and in the ditribution of rik. We can give conitent explanation for our finding, but are unable to perform the neceary tet for non-linearity. We will briefly outline the underlying model in ection 2, introduce the data in ection 3, preent the baic reult in ection 4 and then compare reult for ubgroup in ection 5. Section 6 conclude. 1. The Rik Augmented Mincer equation In Hartog and Vijverberg (2002) we formally derive the Rik Augmented Mincer equation. Here, we jut preent the main argument. Aume, individual face two alternative: go traight to work and earn an annual non-tochatic income Y 0 for the ret of their working life, or go to 3

6 chool for year, and after chool earn a tochatic income Y for the ret of their working life, with realiation of Y revealed after completing chooling. We aume individual have an uninhibited choice between alternative, jut a in the eminal Mincer framework. In equilibrium, lifetime utility hould be equal. We write the tochatic pot chool earning option a mark-up on the afe no-chooling alternative, one for rik, one for potponing earning: ( ) ( ) 1 1 Y = µ = 1 Π 1 M Y exp( ε ) (1) 0 With a tandard utility function U(Y) and uing Taylor erie expanion up to the third order we can write M δ ( 1 e ) = UY ( ) 1 U ( Y ) Y (2) 1 m 1 m Π= V V F (3) r 2 r r 3 µ µ V U ( µ ) = V µ = 0 U ( µ ) µ > r a (4) U ( µ ) F = F µ = 0 U ( µ ) µ > r a (5) ( ) 2 2 Y E m µ 2 Y E µ = = 2 2 µ µ µ ( ) 3 3 Y E m µ 3 Y E µ = = 3 3 µ µ µ (6) (7) M i the compenation for potponing earning (with dicounting at rate δ), reduced to the tandard Mincer compenation under earning maximiation (when U(Y) = Y). π i the compenation for earning uncertainty, eparately for variance and kew, with coefficient V r reflecting relative rik averion and coefficient F r reflecting relative kew affection. We ue the latter phrae a both caual obervation and analytical analyi ugget individual like poitive kew: they appreciate a mall probability of a large gain (ome fat in the upper tail) and the inevitable aumption of decreaing abolute rik averion implie a negative third derivative of the utility function and hence, poitive F r. Thu, individual are willing to pay for poitive kew. With a CRRA utility function (Contant Relative Rik Averion, at rate ρ ), the earning function reduce to 4

7 δ 1 m 1 m E(ln Y) = ln Yo + + ρ ρ(ρ + 1) 1 ρ 2 µ 6 µ (8) a imple equation in chooling year, variance (6) and kew (7). Hence with obervation on relative variance and relative kew we could etimate a Mincer earning equation augmented with rik compenation. If we don't aume CRRA, the parameter of (8) will not be contant but depend on income level. However, a a linearization, it would till be a good tarting point for empirical work. Admittedly, our baic model i very imple, with jut two period and all uncertainty eliminated at the tart of the econd period. But thi i not unuual, and in fact imilar to the eminal model preented in Levhari and Wei (1974) and more recently, the real option approach preented by Hogan and Walker (2002). We think it i a ueful approach to analye the choice facing a tudent about to embark on education and hence, at the beginning of the firt period. From that perpective, lifetime uncertainty may very well be compreed to uncertainty in the period beyond education. For etimation, we apply a traightforward two-tep procedure. Imagine an individual conidering whether to engage in extended education. How would thi individual ae the financial rik? We believe that the individual will imply look around and ae earning rik by oberving the variance of earning in the education under conideration, allowing for the effect of chooling length and experience. That i, he will conider the ditribution of reidual from a Mincer earning function. One might conjecture that the reidual hould be purged from election effect. In ome recent literature, chooling choice are modeled and conditional on thi modeling and etimation, oberved ex pot variance in earning i ditinguihed from ex ante uncertainty (Chen, 2005; Cunha, Heckman and Navarro, 2005). Selectivity correction i indeed imperative if one want to meaure true rik, but in our cae one can forcefully argue that it i not needed. It will only be neceary if individual themelve ue electivity corrected etimate to ae their rik. The rik compenation we claim hould be etablihed by upply reaction to perceived rik (upply i withheld at inufficient compenation). Thu we need to meaure the rik perceived by individual when they make their chooling deciion. Cunha et al. conclude that a large hare of ex pot variability in earning i ex ante forecatable by tudent, and hence preent no rik. But thi i an interpretation of ex pot oberved choice and realied earning baed on an elaborate tructural model, not on direct obervation of individual information et. Dominitz and Manki (1996) have et a tandard for meauring the uncertainty in tudent expectation, by intelligent interviewing. The diperion that tudent on average perceive i ubtantially larger than the actual diperion. Wolter (2000) applie the ame method to tudent in Switzerland; wherea American tudent overetimate the inter-quartile range, Swi tudent underetimate the range relative to their actual value. Webbink and Hartog (2004) compare an individual tated expected earning with realied earning and find that frehmen are unable to predict their poition within the actual ditribution of tarting alarie after graduation, only four year later: the correlation between prediction and realiation i The literature generally indicate that individual certainly have a fairly good perception of difference in mean earning between type of education (Botelho and Pinto, 2004; Webbink and Hartog, 2004), but clearly cannot accurately predict their poition within each ditribution. We firmly believe that thi line of reearch, on direct obervation of tudent perception hould be extended. We think that our hypothei that tudent perception of earning rik can be meaured by reidual diperion i part of a ound reearch trategy. 5

8 A our aumption may not convince everyone, we have invetigated the poible impact of elfelection on etimated coefficient. We conclude that if ability and rik are independent, ignoring electivity from individual uperior information will bia the etimated rik compenation coefficient downward: if the reidual will alo reflect ability heterogeneity, rik will be overetimated, and the coefficient underetimated (Jacob, Hartog and Vijverberg, 2005). With ability and rik correlated, we can no longer draw unequivocal concluion. However, empirically we know next to nothing on thi correlation; all we have i mere peculation 2. Baeline reult. In our preent empirical procedure, we firt etimate for each year eparately the following croection log-earning equation Y X β α d ε (9) ln ij = i + j j + ij j where the ubcript i and j denote individual and the education cell the individual belong to repectively. Y i hourly earning and the d j are dummy variable for education cell. The variable included in X are year of education, age and age quared and, depending on pecification, dummie for gender and ethnicity. We ue age intead of experience becaue it i exogenou. The education fixed-effect α j are included in order to control for the effect of omitted variable that may bia our meaure of rik and kew within an education cell. We ue the etimated reidual to compute meaure of R and K, a in McGoldrick (1995), and Hartog, Plug, Diaz-Serrano, and Vieira (2003) (1) 1 j N j ( ) 2 ij K ( ) 3 j = eij e j R = e e j i (1) 1 N j i (10) where e ij i the exponential of the etimated reidual ε ij in equation (9). In (10), R and K are imply etimated a the econd and third moment of the ditribution of exp(ε j ). 1 In the econd tep we include etimated value for R and K in the following wage equation lny ij X i β γ R R j γ K K j ij = + + +ε (11) where we expect that γ R >0 and γ K <0. Contrary to equation (9), in equation (11) we do not include dummie for education cell ince R and K are already fixed in a given education cell. In (10), we do not include any other explanatory variable in X, a the common variable that may be available (uch a indutry, firm and job characteritic) are all unknown to the individual at the time of deciding on education, and hence, hould not be controlled for. However, in the econd tage regreion we want an unbiaed etimate of the rik compenation and hence uch control hould be included. In Diaz Serrano and Hartog (forthcoming), we corrected for the fact that R and K are generated regreor and that conventionally etimated tandard error in (11) may be biaed; uing Spanih data, we found correction to be immaterial. Hence, we will not apply that correction here. 1 Of coure, we could alo ue the empirical counterpart of (6) and (7), but thi would make no difference. In our Danih analyi (Diaz-Serrano, Hartog and Nielen, 2003) the meaure correlate better than 0.99 in each of 17 year. 6

9 For our analye, we ue two Dutch dataet. The firt, called LSO 1997, i a large nationwide urvey on labour earning. The data are from the Wage Structure Survey (Loon Structuur Onderzoek (LSO)) held by Statitic Netherland (CBS). Data on gro hourly wage are taken from adminitrative ource (firm or adminitration on inured people). The dataet alo contain information on gender, age and job characteritic. We ue the urvey of 1997, covering approximately 120,000 employee. The advantage of the dataet are the large number of obervation, many education type (66) and very reliable earning obervation. The data are characteried in Appendix A. Table 1a give baic etimation reult. We find (but do not report) common reult for return to education at 4 to 5%, the uual concave age-earning profile, and earning diadvantage for women and immigrant. Acknowledging heterocedaticity a a central feature of our model, we etimate robut tandard error in all the regreion reported in thi paper. Computed tandard error are alo adjuted for cluter ampling by education (allowing for correlated error for individual with the ame education); allowing for clutering ubtantially increae etimated tandard error 2. The regreion for the entire ample olidly confirm the key theoretical prediction of poitive compenation for rik and negative compenation for kew 3. The coefficient for R in the total ample i Appendix A give typical value of R around 0.15; an increae by 0.15 point (which i well within the interval of oberved value) would increae earning by 18.6 %. At a typical value of K, the elaticity for K i 0.1. The wage elaticity for R, at about 0.2, i within the range of earlier etimate (Hartog, 2005), the elaticity for K i relatively high. We alo etimate rik compenation for ome ubgroup. The reult for immigrant and native are identical. The reult for men are markedly tronger than the reult for women, in the ene of larger coefficient and maller tandard error. In fact, the reult for women are not ignificant. We alo ditinguih between the public and the private ector, although thi i not quite proper: the choice between public and private ector i endogenou, and may be governed by difference in rik attitude. A etimated, the public ector pay much le compenation for rik, and ha higher rebate for kew; the rik compenation i not ignificantly different from zero. In ection 5 of thi paper we will dicu the difference between group of worker. Table 1a. Replication reult, LSO data R t K t N All Men Women Immigrant Native Public Private Regreion include year of chooling, age and age quared; t-value from tandard error clutered by education type. 2 The equation in Table 2 have alo been etimated with aggregate value by education. The main concluion are very imilar to thoe from etimate with individual obervation acknowledging clutering. 3 If we drop K from the regreion, the coefficient for R barely change, but tandard error increae a little. 7

10 The econd dataet i called the Elevier/SEO urvey, held among graduate from tertiary education. A new cohort of graduate ha been interviewed every year ince 1996, with focu on outcome in the firt 20 month in the labour market. Dutch tertiary education i baically divided into two level: higher vocational education (in Dutch abbreviated a HBO) and academic education (WO). HBO-education prepare tudent for pecific (categorie of) profeion. It i taught at about 60 pecial intitute evenly pread over the Netherland. On average, 50,000 tudent graduate each year from HBO. WO-education i conidered to be of a omewhat higher intellectual level and ha a more general academic character. It i taught at 14 univeritie. Approximately 23,000 tudent graduate every year. At HBO-level tudent can chooe between 250 different coure of tudy, while at WO-level they may chooe between 260 different pecialization. Mot of them, however, produce only mall number of graduate, making tatitical analyi unreliable. About 80 percent of the tudent population i concentrated in the 100 larget degree ubject. The urvey i retricted to thee 100 degree ubject (tudie) which divide evenly over HBO and WO. Thi mean the urvey i repreentative of 80 percent of the yearly outflow of graduate at HBO- and WO-level. Every year a ample of on average 7,500 obervation i drawn. The pecial feature of the urvey i the large number of tudie within tertiary education and the focu on tarting alarie; a alarie are elf-reported, they will contain more noie than the LSO meaurement. The data are decribed in Appendix B. We pool 7 cohort with a time dummy to ditinguih them. Earning are defined a net hourly wage at the time of the urvey, i.e. on average 20 month after graduation (reported earning are divided by reported hour). For our empirical purpoe, we excluded all repondent who are elfemployed, part time employed (le than 32 hour a week) and all thoe for whom data on control variable are unavailable. To eliminate outlier, we dicarded both the highet and the lowet 1% of the ample (the meaure of K i rather enitive to outlier). In the Elevier/SEO data individual were aked for their average exam grade in tertiary education. We ue thi information to control for compenation for employer rik. We take the diperion of exam grade, for all tudent with a given type of tertiary education, a an indication of individual heterogeneity within a given education, by auming that the ditribution reflect the ditribution of true kill that employer are intereted in. It indicate the employer rik when hiring a young graduate. The interpretation require the aumption that at the individual level the exam grade doe not ufficiently reveal the individual true kill. The aumption may very well hold in the Dutch context, a Dutch employer do not pay much attention to tudent grade. We indeed find a negative coefficient on the variance of exam grade when included in a wage regreion. By analogy to the cae of worker rik, we include the third moment of the grade ditribution within an education, and find a poitive ign: employer are willing to pay for poitive kew, the poibility of catching a worker on the high end of the ditribution. 4 The reult ugget that tarting alarie are affected imultaneouly by the rik for employee aociated with chooing an education and the rik for employer when hiring a worker. A we are here only intereted in the effect of uncertainty aociated with tudent chooling choice, we will leave further analyi of the latter for a eparate paper. Baic regreion reult for thi dataet are given in Table 1b. A before (and throughout thi paper), tandard error have been adjuted for clutering. In the firt tage regreion, we only include a dummy for education (and cohort dummie), a thi ample i homogenou by experience and year of education. In the econd tage, we ue all the relevant variable that are 4 The reult are ignificant for all tertiary educated, weakly ignificant for univerity educated and not ignificant for HBO graduate. We alo included the mean grade in a given education. 8

11 available in the dataet: highet education level of the parent, time elaped ince graduation, time pent unemployed, time worked while till in chool, region, job level, labour market tenion (unemployment/vacancy ratio), dummie to ditinguih even indutrie. Reult on thoe variable contain no urprie. For tarting alarie in tertiary education, our baic concluion on rik compenation i confirmed: a poitive premium for earning rik, earning reduction for kew. The magnitude of the etimated rik compenation coefficient in the Elevier/SEO data i omewhat maller than for the LSO data. If we double R from it mean value of for univerity graduate, their earning would increae by 9%. An increae in K by 10% would reduce their earning by 0.24%. In the ubgroup etimation, we find no ignificant effect of kew for men, neither in the total ample nor in the ub-ample. If we plit the ample by education, we find eentially the tandard reult for the univerity educated. For HBO graduate, we find the proper ign, but no coefficient i tatitically ignificant. The effect of decompoition by education i imilar for men and for women. Rik compenation for women i larger than for men. Below, we will return to difference between group. Table 1b. Replication reult, Elevier/SEO data R t K t N All tertiary Total Men Women Vocational Total Men Women Univerity Total Men Women Second tage regreion include parental education, time worked, unemployed/work/unemployment after graduation, region, job level, labour market tenion, indutry dummie; t value baed on tandard error clutered by education type. 4. Controlling for ability An important point of concern in interpreting the reult that have been obtained o far i the poible confuion of rik with heterogeneity. The reidual will reflect both the return to unoberved individual quality difference and true unpredictable earning fluctuation. To the extent that individual know the qualitie that we do not oberve (and their earning impact), we 9

12 overetimate rik. We may tet the argument by purging the reidual a much a poible from the effect of quality difference between individual. We hould then look deliberately for indicator that individual indeed will know when they have to make their deciion. In the Elevier/SEO urvey, individual were aked to report their average exam grade in econdary chool. They may condition their perception of earning rik with tertiary education on their econdary chool exam grade, a a meaure of ability. To mimic thi, we plit the ample in quartile of average grade in the final exam of econdary education and then apply the tandard analyi. Thu, we aume that the labour market i egmented by ability quartile, with individual ability indexed by the individual average exam grade. Every egment will then have it own rik and may generate compenation, baed on upply reaction by individual who meaure rik (and kew) from the reidual for their own ability quartile. We ue quartile (rather than, ay, decile) to retain a ufficient number of obervation. Reult are given in Table 2. We etimate a ingle equation baed on quartile pecific ditribution meaure, hence with identical rik compenation coefficient for each quartile. We now even find tronger reult than before, a all coefficient are ignificant at 10% or better (if we include control for employer rik). We have alo etimated regreion in which the coefficient on R and K are allowed to vary by chool grade quartile (while retricting coefficient on other variable to be identical). However, equality of the coefficient could not be rejected at conventional ignificance level. We conclude that the reult we have obtained o far are not due to confuing ability heterogeneity and rik. If we control for ability information from chool grade that we hare with individual themelve, we till get clear upport for our key finding of rik compenation. The magnitude of rik compenation for vocational education i marginally higher if we control for chool grade quartile, which i in line with underetimation predicted in Jacob, Hartog and Vijverberg (2005) under independence of ability and rik. For univerity graduate the chool grade control ubtantially reduce the etimated rik coefficient, which may be related to covariance between ability and rik. In the abence of olid information on correlation between ability and rik we cannot tet the conitency of thi interpretation, however. Table 2. Rik compenation: controlling for worker heterogeneity (Elevier/SEO) All tertiary Vocational Univerity R t K t N t value baed on tandard error clutered by education type. 5. Heterogeneity 5.1 Difference between group Both caual obervation and empirical reearch (Hartog, Ferrer-i-Carbonell and Jonker, 2002; Harrion, Lau and Ruttrom, 2004) indicate that attitude toward rik differ among individual and group of individual. A we have reult on ome ub-ample, we can check our reult for 10

13 conitency with uch known or aumed difference. Higher meaured rik averion for women a compared to men i well documented. One would therefore predict that etimated rik compenation for women i higher. Thi i not what we find in the LSO data: Table 1a report ubtantially lower rik compenation (we focu on compenation for the variance R). Alternatively, women might find refuge in le riky education, and therefore claim lower compenation. The ditribution function of rik for men and women in the LSO data i given in Figure 1 (the frequency of rik level i the number of repondent with education at that level of rik). Interetingly, both ditribution function eem cloer to a uniform rik ditribution than to a normal ditribution. Women indeed have a rik ditribution that ha hifted to the left, relative to men, although with le probability ma in the econd quartile of the ditribution. Thu, there i a conitent tory: women are more rik avere than men, and accommodate thi by eeking le riky education rather than by requiring higher rik compenation in wage (the explanation require that the reervation price for rik for a given group i not a contant, a we will dicu below). In the Elevier/SEO data (Table 1b), we found rik compenation clearly higher for women than for men, which i directly conitent with higher rik averion. In Figure 2 we preent the rik ditribution function; they are cloer to the normal ditribution than thoe from the LSO data. Now, the ditribution function only cro once, with women in le riky education in the firt quartile of the ditribution, but thereafter in more riky education. The difference between LSO reult and Elevier/SEO reult i not due to the latter retriction to tertiary education: if we retrict LSO obervation to tertiary education only, we find the ame reult. It may reflect different education choice by the mot recent entry cohort, in comparion to the cro-ection of cohort in the LSO ample. With recent cohort of women much more focuing on labour market career, their educational choice (and aociated rik propertie) may indeed be different from thoe of older cohort. Apparently, they no longer eek the le riky education, but even take up more riky chooling than men, and demand good compenation for it. In the Elevier/SEO data, without conditioning rik on chool performance (ability) quartile, rik compenation for univerity graduate i much larger than for vocational graduate. Conditioned on performance quartile, the difference are maller but in the ame direction 5. To a large extent thi i compatible with the difference in the rik ditribution function preented in Figure 3. Up until about the 70th percentile, percentile poition for vocational graduate are at le riky education than thoe for univerity graduate. Beyond that, vocational graduate have higher denitie over ome range of high-rik education. We have no information on difference in rik attitude between WO and HBO graduate. One might, perhap, preume vocational tudent to be more rik avere than univerity tudent: they may prefer the vocational education preciely becaue it i le riky, with it more tructured and guided programme and it lower reliance on tudent independence and initiative. Then, again, there i a conitent interpretation. Univerity graduate are le rik avere than graduate from higher vocational education, but they obtain higher rik compenation becaue they are in more riky education. Figure 4 how that for equal percentile poition, immigrant face higher earning rik than native, up to about two third of the ditribution. Table 1a how that their rik compenation i equal to that of native, which ugget identical rik attitude. Thee reult are jointly 5 In the LSO data, we only have 8 type of univerity education and 10 type of vocational education. In eparate etimation for thee two group, rik compenation for univerity graduate i larger than for vocational graduate (the effect of kew i not ignificant). 11

14 conitent. One might perhap conjecture that immigrant are le rik avere than people from their home country who do not migrate, but of coure thi give no information on their rik attitude relative to worker in the detination country. A recent tudy by Bonin et al. (2006b) indeed find that immigrant are more rik avere rather than le, while their decendant born in Germany have the ame rik attitude a native German. Figure 5 give the ditribution of rik for civil ervant and for private ector worker. A anticipated, the public ector ha ubtantially lower earning rik than the private ector. Lower rik for public ervant i compatible with the notion that the more rik avere worker opt for the public ector preciely for thi reaon. The rik in the public ector i o much lower than in the private ector that higher rik compenation for civil ervant i not neceary. Note however, that the public-private ector decompoition i different from the other decompoition, a it doe not reflect dijoint categorie in the labour market: at any moment every individual alway can witch between the public and the private ector. 5.2 A reflection on heterogeneou rik attitude We have etimated compenation for earning rik in a linear pecification and we have interpreted different compenation coefficient among population group. However, proper analyi reveal that thing are more complicated. Rik attitude may vary in two way. Firt, for a given individual, the required rik compenation may depend on the ituation (uch a wealth or income) or vary with the level of rik 6. If it varie with the level of rik, a linear pecification i inappropriate. Second, the appreciation of rik may differ between individual. Thi alo call for a non-linear pecification. A linear pecification i only warranted if all individual have identical rik attitude, and if all demand the ame contant compenation per unit of rik. In that cae, a regreion of wage on rik R hould yield the ame coefficient for any ample of individual and for all rik level. Now uppoe that all individual have identical rik attitude, but the reervation price of rik varie with the level of rik. We could tet for uch variable individual reervation price with a non-linear regreion on R. A declining diutility of rik i not very likely, we would expect the price of rik to increae with it level and predict a convex function in rik R. The ituation i more complicated if the appreciation of rik differ acro individual. Now, the allocation of individual to different poition i no longer immaterial. We expect individual with low level of rik averion to occupy the more riky poition, a thi will generate the cheaper allocation. Aigning the typically highly rik avere civil ervant to commiion-baed real etate ale work would be too cotly. A competitive market will etablih uch an efficient arrangement. Clearly, thi implie a declining reervation price of rik with increaing rik, a we find le rik avere individual at the more riky poition: a concave function in rik R. With difference in individual rik attitude, the etimated coefficient in a linear regreion are le informative and in fact, linearity i a mipecification. Alo, our reconciliation of higher rik averion with lower etimated compenation coefficient by conidering the ditribution of rik require heterogenou rik attitude, a otherwie lower rik averion can only lead to lower rik compenation. Thu, to maintain our conitent interpretation above, we hould tet for linearity of the compenation function. The cae i quite intereting, a the two hypothee yield exactly oppoing prediction on the nature of the rik compenation function: convex for increaing 6 Indeed, Sak and Shore (2003) find that tudent from wealthier background chooe more riky occupation. 12

15 individual rik averion, concave for heterogeneity. Indeed we have attempted to tet by etimating a general non-linear compenation function in the econd tage regreion: 2 2 (, ) CRK= rr+ rr+ kk+ kk+ qrk (20) Unfortunately, in all our ample the correlation between R and R 2 and between K and K 2 i above Thi preclude any meaningful teting for non-linearity and we hall have to look for other dataet to attempt thi Concluion Our empirical work tarted out from etimating Rik Augmented Mincer equation on two new dataet, one for a labour force cro-ection and one for recent graduate from tertiary education. Replication generally confirm the baic reult of poitive compenation for earning variance and a negative effect for kew. Preciion of the etimate varie between ubgroup. While for ubgroup ome coefficient are not ignificantly different from zero, we never find an oppoite ign that i tatitically ignificant. We found (but did not report comparion) that allowing for correlation of error within education (clutering) had a ubtantial effect on etimated tandard error and hence, on ignificance level. The paper add two contribution to the exiting evidence. Firt, our baic reult are upheld if we allow for ability difference a reflected by exam grade in econdary chool. Thi i important, a the reidual that we ue to ae rik will alo include unoberved heterogeneity. If ability and rik are uncorrelated, thi will lead to an underetimate of the rik compenation coefficient (a rik i overetimated), but empirically we are very poorly informed on thi correlation. For vocational graduate we find that controlling for exam grade marginally increae the etimated rik compenation coefficient, while for univerity graduate the coefficient are ubtantially reduced. Second, we analye difference between ub-ample. We find that native and immigrant have identical rik compenation coefficient. Immigrant face higher earning rik by education than native. For decompoition by gender, in the Elevier/SEO ample of tarting alarie, we find ubtantially higher rik compenation coefficient for women than for men, in line with their higher rik averion. In the LSO cro-ection, compenation for women i ubtantially lower, but they find refuge in le riky education (in the recent cohort urveyed by Elevier/SEO, women have moved into rikier education). In the Elevier/SEO ample, we find omething imilar for vocational graduate compared to univerity graduate. One might conjecture that tudent who opted for vocational education are more rik avere. Thi i not reflected in a higher rik compenation coefficient, but in concentration in le riky earning ditribution. Our interpretation of the reult require that the price of rik i not a contant. However, we could not tet for non-linearity of rik compenation, becaue of trong multicolinearity. Thi call for new dataet that exhibit more variation in thi repect. Obviouly, there i much more other work to be done a well. Probably we hould dig deeper in the proce of aigning worker with heterogeneou rik attitude to poition with different degree of earning rik. Recent work by Bonin et al (2006a) clearly indicate a negative 7 Correlation between R and K are low in all ample. 13

16 correlation between earning rik and degree of rik averion. Reference Blanchard, O. J. and S. Fiher (1989), Lecture on macroeconomic, Cambridge, Ma: MIT Pre Bonin, H., T. Dohmen, A. Falk, D. Huffman and U. Sunde (2006a), Earning variability and occupational orting: the role of rik attitude, Paper prepared for the Workhop Schooling and Rik, Bonn: IZA. April Bonin, H., A. Contant, K. Tatiramo and K. Zimmermann (2006b), Native-migrant difference in rik attitude, Bonn: IZA Dicuion Paper 1999 Botelho, A. and L. Cota Pinto (2004), Student expectation of economic return to college education: reult of a controlled experiment, Economic of Education Review 23 (6), Card, D. (1999), The caual effect of education on earning, in O. Ahenfelter and D. Card (editor), Handbook of Labor Economic, volume 3B, chapter 30 Chen, S.H. (2005),? Etimating the variance of wage in the preence of elction and unoberved heterogeneity, Albany: Working paper Department of Economic, SUNY Chritianen, C., J. Joenen and H.S. Nielen (2004), The rik-return trade-off in human capital invetment, Working paper, Univerity of Aarhu. Cunha, F. J,. Heckman and S. Navarro (2005), Separating uncertainty from heterogeneity in life cycle earning, Oxford Economic Paper, 57 (2), Davi, S. and P. Willen (2000), Occupation-level income hock and aet return: their covariance and implication for portfolio choice, NBER Working Paper Diaz Serrano, L. and J. Hartog (2004), I there a rik-return trade-off acro education? Evidence from Spain, IZA Bonn, Dicuion Paper 1355, forthcoming (revied) in Invetigacione Economica Diaz Serrano, L,. J. Hartog and H. Skyt Nielen (2003), Compenating wage differential for chooling rik in Denmark, Bonn: IZA DP 963 Dominitz, J. and C. Manki (1996), Eliciting tudent expectation of the return to chooling, The Journal of Human Reource, XXXX, 1-26 Harrion, G., M. Lau and E. Ruttrom (2004), Etimating rik attitude in Denmark: a field experiment, Copenhagen: Center for Economic and Buine Reearch Dicuion Paper Hartog, J. (2005), Schooling a a riky invetment, Koninklijke Nederlande Akademie van Wetenchappen: Mededelingen van de Afdeling Letterkunde, Nieuwe Reek Deel 68 no 2, 14

17 (Proceeding Royal Dutch Academy of Science) Hartog, J. and L. Diaz Serrano (2004), On the ditribution of earning by education, working paper Amterdam Hartog, J., A. Ferrer-i-Carbonell and N. Jonker (2002), Linking meaured rik averion to individual characteritic, Kyklo, 55(1), 3-26 Hartog, J., H. van Ophem and S. Bajdechi (2004), How riky i invetment in human capital?, Muenchen: CESifo DP 1261 Hartog, J. E. Plug, L. Diaz Serrano and J. Vieira (2003), Rik compenation in wage- a replication, Empirical Economic, 28, Hartog, J. and W. Vijverberg (2002), Do wage really compenate for rik averion and kewne affection? Bonn: IZA DP 426 Hogan, V. and I. Walker (2002), Education choice under uncertainty, mimeo Univerity College Dublin Jacob, B., J. Hartog and W. Vijverberg (2005), Self-election bia in etimated wage premium for earning rik, Working paper, Univerity of Amterdam Levhari, D., and Y. Wei (1974), The effect of rik on the invetment in human capital, American Economic Review, 64(6), McGoldrick, K. (1995), Do women receive compenating wage for earning rik? Southern Economic Journal, 62, Palacio-Huerta, I.(2003), An empirical analyi of the rik propertie of human capital return, American Economic Review, 93 (3), Sak, R. and S. Shore (2003), Rik and career choice, paper preented at the AEA Meeting, San Diego, January 2004 Shaw, K.L. (1996), An empirical analyi of rik averion and income growth, Journal of Labor Economic 14 (4), Webbink, D. and J. Hartog (2004), Can tudent predict their tarting alarie? Ye!, Economic of Education Review, 23 (2), Wolter, S. (2000), Wage expectation: a comparion of Swi and US tudent, Kyklo, 53 (1),

18 Appendix A. LSO data The data are taken from the o-called Wage Structure Survey (Loon Structuur Onderzoek (LSO)) held by Statitic Netherland (CBS). Data on wage are obtained through the annual urvey on employment and wage among firm (Enquête naar Werkgelegenheid en Lonen) and partly through adminitration on inured people (Verzekerden Adminitratie (VZA)). Thi mean that all information, on gro hourly wage, come from adminitrative ource (firm or adminitration on inured people). The dataet alo contain information on gender, age and job characteritic. Data on education are obtained from the annual labour force urvey (Enquête Beroepbevolking (EBB)) and matched with the wage data. The matched dataet i called the Wage Structure Survey. We ue data from the urvey of Thi urvey conit of approximately 120,000 employee. Statitic Column 1: education in SOI code: 1000 le than baic 2000 baic 3000 lower econdary (lbo/mavo) 4000 upper econdary (havo/vwo/mbo) 5000 tertiary: higher vocational 6000 tertiary: univerity 7000 pot-graduate Column 2: mean log wage Column 3: mean exp(e), e = Mincer reidual Column 4: variance exp(e) Column 5: kew exp (e) Column 6: mean length of education (decimal becaue of intitutional change in length) Column 7: number of obervation 16

19 Table A1 Statitic by type of education, LSO 1997 (1) (2) (3) (4) (5) (6) (7) Log wage Rik Education Mean mean variance kew duration N 1000 Le than primary education 3,07 1,05 0,13 2, Primary education 2000 Primary education 3,16 1,05 0,13 2, Lower vocational/ general econdary education 3000 General 3,19 1,06 0,16 3,35 9, year of general econdary education 3,31 1,07 0,18 2, General econdary education 3,23 1,05 0,12 2, Language/ cultural 3,11 1,07 0,18 2, Agricultural 3,25 1,04 0,12 3, Technical general 3,26 1,04 0,11 3,50 9, Technical contruction 3,31 1,04 0,08 2,06 9, Technical Metal 3,30 1,04 0,09 2,17 9, Technical electro technic 3,30 1,04 0,11 4, Tranport 3,34 1,04 0,10 1, Medical 3,21 1,02 0,05 0, Economic/ adminitrative 3,14 1,05 0,11 1,45 9, Social cultural 3,16 1,10 0,49 5, Peronal/ ocial care 3,10 1,07 0,17 1,27 9, Peronal/ ocial care 3,00 1,06 0,12 0,19 9, Public order/ afety 3,23 1,04 0,10 2, Higher general econdary, pre-univerity, intermediate vocational 4000 General intermediate vocational 3,28 1,05 0,11 1, Higher general econdary 3,27 1,05 0,14 2, Pre-univerity (4-6 year) 3,71 1,10 0,26 1,78 11, Gymnaium 3,35 1,07 0,17 1, Agricultural intermediate vocational 3,26 1,04 0,10 2, Technical 3,37 1,05 0,13 2, Contruction 3,37 1,04 0,10 3, Contruction, road and water 3,47 1,04 0,10 3, Metal 3,35 1,03 0,08 1, Machinery 3,38 1,04 0,11 3, Electro technique 3,38 1,04 0,09 2, Graphical technique 3,38 1,05 0,11 1, Proce technique 3,44 1,04 0,10 0, Other technique 3,15 1,06 0,13 1, Other 3,35 1,05 0,11 1, Tranport, communication, traffic 3,44 1,05 0,13 2, Medical 3,30 1,03 0,07 1, Economic, adminitrative general 3,51 1,04 0,08 1, Economic 3,45 1,06 0,16 2,

20 4613 Adminitrative 3,33 1,05 0,14 2, Commercial 3,39 1,06 0,15 2, Trade 3,24 1,05 0,14 2, Social cultural 3,29 1,04 0,09 1, Peronal/ ocial care 3,15 1,05 0,11 1, Public order/ afety,48 1,04 0,08 1, Higher vocational education 5000 Teacher/ education 3,53 1,03 0,06 1, Language/ cultural 3,39 1,06 0,13 0, Agricultural 3,49 1,06 0,13 1, Technical/ nature 3,67 1,05 0,14 2, Tranport 3,80 1,12 0,37 2, Medical 3,44 1,03 0,06 1, Economic/ adminitrative 3,59 1,06 0,16 2, Social cultural 3,48 1,05 0,11 1, Peronal/ ocial care 3,45 1,08 0,20 2, Public order / afety 3,87 1,05 0,13 1, Univerity education 6000 Education 3,71 1,03 0,06 0, Language/ cultural 3,58 1,06 0,12 1, Agricultural 3,68 1,06 0,15 1, Technical/ nature 3,81 1,08 0,20 1, Medical 3,66 1,07 0,15 1, Economic/ adminitrative/ juridical 3,81 1,08 0,20 1, Social cultural 3,67 1,05 0,12 1, Peronal/ ocial care 3,48 1,07 0,14 0, Pot graduate education 7000 Education 3,63 1,04 0,11 2, Technical/ nature 3,98 1,06 0,13 1, Medical 4,04 1,06 0,14 1, Economic/ adminitrative/ juridical 4,05 1,07 0,18 1, Social cultural 3,84 1,08 0,18 0,

21 Appendix B: Elevier / SEO data Thi urvey of graduate with a tertiary education ha been conducted on a yearly bai ince Every year a new cohort of graduate i examined. The urvey focue on outcome in the firt 20 month in the labour market. Dutch tertiary education i baically divided into two level: higher vocational education (in Dutch abbreviated a HBO) and academic education (WO). HBO-education prepare tudent for pecific (categorie of) profeion. It i taught at about 60 pecial intitute evenly pread over the Netherland. On average, 50,000 tudent graduate each year from HBO. WO-education i conidered to be of a omewhat higher intellectual level and ha a more general academic character. It i taught at 14 univeritie. The yearly output amount to approximately 23,000 graduate per year. At HBO-level tudent can chooe between 250 different coure of tudy, while at WO-level they may chooe between 260 different pecialization. Mot of them, however, produce only mall number of graduate, making tatitical analyi cumberome. About 80 percent of the tudent population i concentrated in the 100 larget degree ubject. The urvey i retricted to thee 100 degree ubject (tudie) which divide evenly over HBO and WO. Thi mean the urvey i repreentative of 80 percent of the yearly outflow of graduate at HBO- and WO-level. Every year a ample of on average 7,500 i drawn. Baic data are given in Appendix Table B1. We pool the 6 cohort with a time dummy to ditinguih them. Earning are defined a net hourly wage at the moment the urvey wa held, i.e. on average 20 month after graduation. For our empirical purpoe, we excluded all repondent who are elf- employed, part time employed (le than 32 hour a week) and all thoe for whom data on control variable are unavailable. Statitic Column 1: Education Column 2: mean log wage Column 3: mean exp(e), e = Mincer reidual Column 4: R = variance exp(e) Column 5: K = kew exp (e) Column 6: N = number of obervation 19

22 Table B 1 a. HBO (Higher vocational education) education Sample mean Lnwage Exp(e) R K N VOCATIONAL Buine Economic/Buine Science 2,159 1,013 0,041 1, Commerce 2,179 1,018 0,057 1, Buine Informatic 2,208 1,015 0,051 1, Communication 2,154 1,021 0,047 1, Accountancy 2,174 1,023 0,057 1, International Buine and Language 2,135 1,024 0,040 1, Tourim & Leiure 2,067 1,032 0,040 1, Hotel Management 2,154 1,025 0,067 2, Small Buine en Retail Management 2,179 1,044 0,066 2, Management, Economic & Law 2,163 1,021 0,057 2, Logitic & Economic 2,171 1,012 0,048 2, Facility Service 2,150 1,023 0,068 3, Journalim 2,197 1,028 0,054 1, Buine Management 2,128 1,007 0,044 1, Fical Economic 2,221 1,025 0,053 1, European profeion 2,150 1,032 0,079 2, Leiure Management 2,069 1,035 0,042 1, Peronnel & Labour 2,181 1,016 0,040 0, Socio-Cultural Studie 2,144 1,024 0,051 1, Social Work and Service 2,218 1,015 0,042 1, Social Pedagogy 2,160 1,012 0,034 1, Socio-Legal Service 2,189 1,015 0,035 1, Information Management 2,169 1,030 0,065 2, Medical Laboratory Technician 2,101 1,009 0,042 2, Nuring 2,191 1,016 0,037 2, Phyiotherapy 2,365 1,035 0,108 1, Speech Therapy 2,208 1,034 0,098 2, Nutrition & Dietetic 2,176 1,026 0,071 2, Ergotherapy 2,240 1,018 0,054 2, Medical Imaging & Radiotherapy 2,126 1,010 0,022 1, Oral Hygiene 2,337 1,041 0,105 2, Environmental Management?Science/Technology 2,177 1,036 0,056 2, Agri-Buine 2,188 1,019 0,045 1, Animal Hubandry 2,133 1,054 0,071 1, Food Technology 2,175 1,018 0,031 0, Primary School Teacher 2,237 1,015 0,037 3, Phyical Education Teacher, Grade 1 2,331 1,042 0,093 1, Dutch Teacher 2,253 1,030 0,078 1, Economic Teacher (general & buine) 2,220 1,036 0,069 2, Special Need Teacher 2,264 0,996 0,037 3, Social Studie Teacher 2,144 1,020 0,043 1, Education 2,211 1,025 0,076 3, Science Teacher 2,310 1,032 0,081 1, Geography/Hitory Teacher 2,276 1,042 0,089 1,

23 Art & Craft Teacher 2,137 1,043 0,106 1, Englih/French/German Teacher 2,332 1,049 0,100 0, Viual Art & Deign 2,124 1,047 0,093 1, Muic 2,299 1,044 0,113 1, Chemical Technician 2,124 1,027 0,040 1, Structural Engineering 2,141 1,023 0,039 1, Electrical Engineering 2,196 1,009 0,034 1, Civiel Engineering 2,148 1,022 0,044 1, Chemical Engineering 2,186 1,020 0,032 0, Applied Informatic 2,221 1,025 0,052 1, Mechanical Engineering 2,171 1,023 0,054 2, Maritime Officer 2,064 1,047 0,091 1, Fahion Management and Technology 2,081 1,019 0,029 0, TOTAL 2,191 1,021 0,051 2,000 18,854 Table B 1 b Univerity UNIVERSITY Dutch 2,259 1,026 0,068 2, Englih 2,229 1,029 0,084 1, Other language 2,245 1,036 0,101 2, Philoophy/Teology 2,250 1,035 0,075 1, Hitory 2,251 1,028 0,067 1, Language & Culture (general) 2,230 1,031 0,084 2, Art Hitory & Archeology 2,156 1,024 0,068 3, Corporate Communication 2,217 1,018 0,057 2, European Studie 2,235 1,032 0,094 2, Film, Televiion & Theatre Studie 2,162 1,026 0,056 0, Chemitry 2,132 1,030 0,083 3, Computer Science 2,223 1,023 0,065 2, Biology 2,135 1,036 0,097 3, Pharmacy 2,440 1,026 0,058 0, Pure Mathematic/Phyic 2,168 1,024 0,073 1, Agricultural Science 2,233 1,028 0,071 3, Chemical/Technological Agri-cience 2,220 1,024 0,058 1, Architecture 2,247 1,024 0,040 1, Mechanical Engineering 2,313 1,023 0,051 1, Electrical Engineering 2,311 1,025 0,063 2, Chemical Engineering 2,276 1,026 0,067 2, Civil Engineering 2,277 1,019 0,051 3, Techology & Management 2,352 1,015 0,051 1, Indutrial Deign 2,259 1,018 0,044 1, Aeropace Engineering 2,290 1,008 0,032 0, Applied Computer Science 2,288 1,012 0,043 2, Applied Mathematic/Phyic 2,239 1,021 0,068 2, Economic 2,310 1,034 0,056 2,185 1,100 Buine Science 2,316 1,016 0,057 1, Econometric 2,334 1,023 0,060 1, Fical Economy 2,361 1,012 0,030 1, Dutch Law 2,293 1,015 0,045 1, Notarial Law 2,278 1,019 0,040 1,

24 Fical Law 2,384 1,019 0,055 2, Healthcare 2,270 1,025 0,064 1, Medicine 2,380 1,024 0,061 1, Dentrity 2,808 1,065 0,148 0, Biomedical Science 2,162 1,020 0,059 1, Veterinary Science 2,302 1,029 0,040 0, Sociology 2,271 1,024 0,054 1, Pychology 2,270 1,031 0,072 1, Politi 2,312 1,030 0,078 2, Education Science 2,298 1,023 0,059 1, (Applied) Education 2,300 1,023 0,060 2, Cultural Anthropology 2,219 1,033 0,104 2, Communication 2,258 1,026 0,065 2, Socio-Cultural Science 2,281 1,024 0,054 1, Punlic Adminitration 2,318 1,015 0,050 1, Human Geography & Planning 2,251 1,019 0,050 2, TOTAL 2,289 1,025 0,060 1,935 19,560 22

25 Figure 1. Ditribution function of rik, men and women, LSO Figure 2. Ditribution function of rik, men and women, Elevier/SEO 23