Does Higher Education Enhance Migration?

DISCUSSION PAPER SERIES IZA DP No. 7754 Does Hgher Educaton Enhance Mgraton? Mka Haapanen Petr Böckerman November 2013 Forschungsnsttut zur Zukunft der Arbet Insttute for the Study of Labor

Does Hgher Educaton Enhance Mgraton? Mka Haapanen Jyväskylä Unversty School of Busness and Economcs Petr Böckerman Labour Insttute for Economc Research and IZA Dscusson Paper No. 7754 November 2013 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mal: za@za.org Any opnons expressed here are those of the author(s) and not those of IZA. Research publshed n ths seres may nclude vews on polcy, but the nsttute tself takes no nsttutonal polcy postons. The IZA research network s commtted to the IZA Gudng Prncples of Research Integrty. The Insttute for the Study of Labor (IZA) n Bonn s a local and vrtual nternatonal research center and a place of communcaton between scence, poltcs and busness. IZA s an ndependent nonproft organzaton supported by Deutsche Post Foundaton. The center s assocated wth the Unversty of Bonn and offers a stmulatng research envronment through ts nternatonal network, workshops and conferences, data servce, project support, research vsts and doctoral program. IZA engages n () orgnal and nternatonally compettve research n all felds of labor economcs, () development of polcy concepts, and () dssemnaton of research results and concepts to the nterested publc. IZA Dscusson Papers often represent prelmnary work and are crculated to encourage dscusson. Ctaton of such a paper should account for ts provsonal character. A revsed verson may be avalable drectly from the author.

IZA Dscusson Paper No. 7754 November 2013 ABSTRACT Does Hgher Educaton Enhance Mgraton? * Ths paper examnes the causal mpact of educaton on wthn-country mgraton. A major hgher educaton reform took place n Fnland n the 1990s. It gradually transformed former vocatonal colleges nto polytechncs and expanded hgher educaton to all regons. The reform created exogenous varaton n the regonal supply of hgher educaton. Usng the reform as an nstrument, our estmaton results show that polytechnc graduates have a 7.5 (13.7) percentage ponts hgher mgraton probablty durng a three-year (sx-year) follow-up perod than vocatonal college graduates. JEL Classfcaton: J10, J61, I20, R23 Keywords: mgraton, hgher educaton, vocatonal educaton, polytechnc educaton, school reform Correspondng author: Petr Böckerman Kapteennkatu 3D35 00140 Helsnk Fnland E-mal: petr.bockerman@labour.f * We would lke to thank Paul J. Devereux, Kar Hämälänen, Chrstopher Jepsen, Vktor Venhorst and semnar partcpants at the Regonal Studes Assocaton European Conference (Tampere, 2013), the Faculty of Economcs, Unversty College Dubln (2013), the XXX Summer Meetng of Fnnsh Economsts (Jyväskylä, 2013) and the Labour Insttute for Economc Research (Helsnk, 2013) for helpful comments. Ths study s part of a project supported by the Academy of Fnland (project No. 251071). Paul A. Dllngham has kndly checked the Englsh language.

1. Introducton Unemployment s notorously hgh and persstent n Europe. Wthn-country mgraton s one of the few mechansms that stll postvely contrbute to the adjustment towards equlbrum n the labour market. Ths s mportant, because the European economes are facng paramount structural problems that requre ncessant turnover of workers both across ndustres and regons n order to mprove the allocaton of labour. Even ths mechansm wll be compromsed n future, because the populaton s ageng rapdly n Europe. As the populaton become older, the average mgraton ntensty decreases, 1 whch tends to further worsen the msmatch between unemployed job seekers and avalable vacances. But at the same tme, the populaton n Europe s also becomng more educated than ever. The young age cohorts that enter the labour market are much more educated than the old age cohorts that ext t. Assumng that the mgraton ntensty s ncreasng wth the level of educaton, the ongong expanson of the share of hghly educated persons can counterbalance the negatve effects of populaton ageng on the effcency of matchng n the labour market, at least to some degree. 2 Although the relatonshp between educaton and mgraton has been studed n the extensve earler lterature, only the emprcal studes by Machn et al. (2012), Malamud and Woznak (2012), and McHenry (2013) have taken advantage of polcy reforms to examne the causal effect of educaton on wthn-country mgraton. The results are nconclusve. Usng a Norwegan prmary school reform, Machn et al. (2012) fnd that the length of compulsory educaton has a postve causal mpact on mgraton. They show that one addtonal year of educaton ncreases the annual mgraton rates by 15 per cent from a low base rate of one per cent per year. In contrast to the results n 1 Molloy et al. (2011) argue that ageng of the populaton could result n declnng mgraton rates n the U.S. 2 In addton, evdence suggests that hgh home ownershp produces negatve externaltes upon the labour market, reducng labour moblty (Blanchflower & Oswald, 2013). 1

Machn et al. (2012), McHenry (2013) reports that addtonal schoolng at low educaton levels has a sgnfcant negatve effect on mgraton n the U.S. context, explotng varaton n schoolng due to compulsory schoolng laws. Furthermore, Malamud and Woznak (2012) use varaton n college attanment n the U.S. caused by draft-avodance behavour durng the Vetnam War. Ther results mply that the addtonal years of hgher educaton sgnfcantly ncreased the lkelhood that the affected men, later n lfe, resded outsde the states where they had been born. Taken at face value, prevous research suggests that the effect of educatonal attanment on mgraton s lkely to vary accordng to the level of educaton. Ths paper explots the polytechnc educaton reform that took place n Fnland n the 1990s. The am s to estmate the causal effect of educatonal attanment on wthncountry mgraton. The reform gradually transformed former vocatonal colleges nto polytechncs that offered a Bachelor s degree and expanded the supply of hgher educaton to all regons. In a nutshell, the reform brought hgher educaton to regons that dd not have a unversty n the pre-reform system. The reform s partcularly polcy-relevant, because t ncreased the share of persons wth hgher educaton. The school reforms often am exactly at ths n the advanced countres. Wth ths paper, therefore, we provde evdence of whether educaton has an effect on subsequent mgraton at the upper part of the educaton dstrbuton. We use the number of new polytechnc study places n the regon of resdence as an nstrument for graduatng from a polytechnc. The analyses are based on rch longtudnal data on graduated hgh school students. Usng ths settng, we fnd that obtanng a polytechnc degree nstead of a vocatonal degree causally ncreases the probablty of mgraton. Our estmates also reveal that ths effect s notably larger n the long run. The quanttatve magntude of ths effect s substantal at 13.7 percentage ponts over a sx-year follow-up perod (7.5 n the frst three years). 2

Our paper s also related to the lterature that has studed non-pecunary outcomes of addtonal schoolng. The evdence s, for example, mountng n favour of the exstence of a causal relatonshp leadng from hgher educaton to better health (Cutler & Lleras- Muney, 2008) and varous domans of satsfacton (Oreopoulos & Salvanes, 2011). For example, Oreopoulos and Salvanes (2011) estmate that as much as three-quarters of the postve effect of schoolng on lfe satsfacton can be attrbuted to non-pecunary factors. From ths broader perspectve, the potental effects on mgraton behavour consttute another unntended non-pecunary outcome of educaton. The paper s organsed as follows. Secton 2 dscusses the theoretcal arguments that lnk educaton to mgraton. Secton 3 descrbes the polytechnc educaton reform. Secton 4 ntroduces the data. Secton 5 descrbes the emprcal approach that allows us to control for unobserved heterogenety by the jont estmaton of educaton and mgraton decson. The results are reported n Secton 6, and the last secton concludes. 2. Theoretcal lnks between educaton and mgraton Followng the semnal work by Sjaastad (1962), mgraton s regarded as a means of nvestng n human captal (see also Becker, 1964, 1993; Bodenhöfer, 1967). Heterogeneous ndvduals have dfferent utlty functons and, consequently, encounter dfferences n the net (monetary and non-monetary) benefts of lvng n a specfc locaton. In ths framework, ndvduals move to other locatons f ther expected future benefts of mgraton exceed ts costs. Consequently, nterregonal moblty s necessary to brng hgher expected returns to ndvdual human captal nvestments. The postve correlaton between educaton and mgraton consttutes a well-known stylsed fact n the emprcal lterature. For example, Borjas s (2013, p. 321) labour economcs textbook documents a hgher mgraton rate across the U.S. states for college graduates than for hgh school graduates. Ehrenberg and Smth (2009, p. 327) even 3

regard educaton as the sngle best ndcator of who wll move wthn an age group ; see also revews by Greenwood (1975, 1997). 3 Several theoretcal explanatons have been proposed for the postve relatonshp. 4 Many of these relate to job search. The frst one s the exstence of a greater earnngs dfferental between regons thus greater potental benefts from movng for the hghly educated (Armstrong & Taylor, 2000, p. 155). Educaton s a form of general human captal, whch s easly transferable to dfferent geographcal locatons. For example, Levy and Wadyck (1974) found that the hghly educated are more responsve to wages n alternatve locatons. 5 In related research, Woznak (2010) has shown that the hghly educated are also more responsve to local labour demand. Second, educaton ncreases a person s capablty of obtanng and analysng employment nformaton, and of usng more sophstcated modes of nformaton and search methods (Greenwood, 1997, p. 406). Hence, hghly educated workers may have better access to nformaton about job prospects and lvng condtons n other regons. Thrd, a hgher level of educatonal attanment may open up new opportuntes n the labour market (e.g. Greenwood, 1997, p. 406; see also McCormck, 1997). As educaton mproves, sklls become more portable and the market for ndvdual occupatons at each level of educaton tends to become geographcally wder but quanttatvely smaller n a gven locaton (Schwartz, 1973, p. 1160). For example, the market for cashers s local, and many are needed; on the other hand, relatvely fewer nuclear scentsts are needed but ther market s nternatonal. 3 In Fnland, the relatonshp between educaton and mgraton has been studed by Rtslä and Ovaskanen (2001); see also Pekkala and Tervo (2002), Hämälänen and Böckerman (2004) and Haapanen and Rtslä (2007) for more general studes of Fnnsh mgraton patterns. 4 There are several ndvdual-level characterstcs such as rsk-takng preferences that arguably correlate wth educaton but are not controlled for n the tradtonal mgraton equatons. For ths reason, the exstng lterature offers lttle gudance about whether educaton has an ndependent effect on mgraton or not. 5 They argue that the hghly educated are more moble, prmarly because they have better access to nformaton and greater ncentves to make addtonal nvestments n a search of better opportuntes. 4

Fourth, psychc costs resultng from the agony of departure from famly and frends are lkely to decrease wth educaton (Schwartz, 1973). Hgher educatonal groups are more homogeneous over space n terms of ther culture and manners. Therefore, they are more receptve to new envronments. Educaton may also reduce the mportance of tradton and famly tes and ncrease the ndvdual s awareness of other localtes and cultures. Greenwood (1975, p. 406) argues that the rsk and uncertanty of mgratng may be lesser for the better educated because they are more lkely to have a job pror to movng. Therefore, a hgher level of educaton may also moderate the ncome rsks assocated wth mgraton. That beng sad, hgher educaton may also expand an ndvdual s local networks and provde ncreased labour market stablty (e.g. less rsk of unemployment, shorter unemployment spells and hgher earnngs). Ths ncreases the opportunty costs of movng and thus reduces the necessty to move to another regon (see Farber, 2004; McHenry, 2013, p. 38). However, smultanety of the relatonshp between educaton and the psychc costs of mgraton should not be overlooked (Schwartz, 1973). The atttude toward the psychc costs of mgraton may also, n part, contrbute to the amount of formal educaton that ndvduals wsh to complete. Ceters parbus, those wth lower psychc costs of mgraton may nvest more n ther educaton, because obtanng educaton requres, n many cases, movng to a new regon. That beng sad, unwllngness to move for workrelated reasons may also result n extensve nvestment n educaton, f a person lves n a regon wth good educatonal opportuntes. For the reasons dscussed above, educatonal attanment s almost always ncluded n the set of varables explanng an ndvdual s mgraton decson (see e.g. Faggan, McCann, & Sheppard, 2007; Jaeger et al., 2010; Tunal, 2000). Stll, some authors mantan that educaton affects mgraton only through ts mpact on earnngs (see Elasson, Nakosteen, Westerlund, & Zmmer, 2013; Falars, 1988, p. 527; Nakosteen, 5

Westerlund, & Zmmer, 2008, p. 777). 6 Regardless of whether the assumpton s correct or not, ths ndrect lnk provdes another possble reason for the postve correlaton between educaton and mgraton: the hgher ncomes of professonal workers enable them to cover the costs of mgraton more easly. 7 In contrast to Machn et al. (2012), Malamud and Woznak (2012), and McHenry (2013), other analyses use smple statstcal models that treat educaton as exogenously determned. However, educaton and mgraton decsons are evdently co-determned by unobserved factors such as personalty trats (e.g. wllngness to take rsks, motvaton, patence and conscentousness) and parental values. Indeed, the endogenety of the educaton decson s taken as granted n other felds of research (see Card, 1999). Therefore, most of the precedng estmates can be serously based. The sze and drecton of ths bas s not known. Even though educaton s postvely correlated wth mgraton, we do not know whether the sgnfcant correlaton can be nterpreted as a causal effect whch s relevant for polcy makng. Also, the general correlatons n the total populaton do not provde evdence about the effect of educaton on mgraton at the upper part of the educaton dstrbuton. In ths paper, we apply an nstrumental varables strategy to provde polcy-relevant evdence on the causal effect of educaton on wthn-country mgraton. To accomplsh ths goal, we take advantage of the polytechnc reform that exogenously altered the avalablty of hgher educaton over tme and across regons. 6 That s, these studes nclude earnngs but not educaton n ther mgraton equaton, whch mposes a partcularly strong assumpton about the mechansm between educaton and mgraton. 7 We do not control for an ndvdual s ncome n our models below, because we are nterested n the overall effect of educaton on the propensty to move. 6

3. The Fnnsh polytechnc reform Snce the polytechnc educaton reform the hgher educaton system has comprsed two parallel sectors: unverstes 8 and polytechncs. In essence, the reform brought hgher educaton to regons that dd not have a unversty before the reform. 9 The polytechnc degrees are bachelor-level hgher educaton degrees wth a vocatonal emphass. These degrees take from three and a half to four years to complete. A major dfference between the sectors s that polytechnc schools are not engaged n academc research lke unverstes. The frst 22 polytechncs were establshed under a temporary lcence n 1991 (e.g. Lampnen, 2001). The polytechncs were created by gradually mergng 215 vocatonal colleges and vocatonal schools. The gradual mplementaton of the reform s reflected n the fact that students who had started ther studes before a partcular vocatonal college transformed tself nto a polytechnc contnued ther studes along the old college lnes and they eventually graduated wth vocatonal college degrees. Hence, the tmng of the reform vared consderably across schools and regons and provded quasexogenous staged varaton, as descrbed n Böckerman et al. (2009, p. 674 675). Seven new temporary lcences were granted durng the 1990s. The frst graduates from the new polytechncs entered the labour market n 1995. The expermental phase was judged to be successful and snce 1996 the temporary polytechncs have gradually become permanent. Currently there are 27 multdscplnary polytechncs. Unlke the unversty sector, the network of polytechncs covers the whole country. The supply of educaton s controlled by the Mnstry of Educaton through ts decsons on the number of study places and the fundng of other schools. The number of applcatons to unverstes and to the most popular polytechncs exceeds the number of 8 The Fnnsh unversty sector conssts of 20 unverstes and art academes, all of whch carry out research and provde educaton-awardng degrees up to doctorates. For further detals on the unversty sector, see e.g. Mnstry of Educaton (2005). 9 More detaled descrpton of the polytechnc educaton reform s avalable n Böckerman et al. (2009, p. 674 675). 7

avalable places by a factor of four. Untl the end of the 1990s polytechnc study places ncreased very rapdly and vocatonal college study places decreased accordngly (Fgure 1). By 1996 the number of new polytechnc students exceeded the number of new unversty students, and by the end of the 1990s hardly any new vocatonal college places were made avalable. 10 Fgure 1 around here The most mportant am of the polytechnc reform was to respond to new demands for vocatonal sklls that were seen to arse n the regonal labour markets. The geographcally broad network of hgher educaton was also regarded as a means to equalse regonal development, for example, by reducng the bran dran from the less developed regons to the metropoltan areas. The polytechncs are located further away from hgh school graduates than lower-level educatonal nsttutons are. Therefore, the polytechnc reform s lkely to ncrease the mgraton propenstes to specalsed educaton after hgh school. For ths reason, t s lkely to have an ndrect effect on school-to-work mgraton, because those who have moved n the past are more lkely to move agan (see e.g. DaVanzo, 1983). 4. Data The ndvdual-level data are based on the Longtudnal Census Fle and the Longtudnal Employment Statstcs Fle constructed by Statstcs Fnland. These two basc regster fles were updated annually from 1987 to 2006 (and every fve years n 1970 1985). By matchng ndvduals unque personal dentfers across the censuses, these panel data sets provde a varety of relable, regster-based nformaton on the resdents of Fnland. Ths means that, contrary to surveys, for example, the comprehensve, regster-based data contan a mnmal amount of measurement error; cf. Malamud and Woznak (2012). Furthermore, regster data on spouses and the regon of 10 The remanng vocatonal college lnes provde very specalsed tranng that s typcally avalable n only one locaton. 8

resdence are lnked to the ndvdual records. Wth longtudnal lnkages of the data we can also obtan nformaton on the educaton that the graduates parents receved. The sample that s used n the estmatons comprses a seven per cent random sample of the ndvduals who resded permanently n Fnland n 2001. 11 The sample was further restrcted to the ndvduals who had completed hgh school (general upper secondary educaton, luko n Fnnsh) whch ends n matrculaton. Wth a few exceptons, hgh school s requred for tertary-level (ncludng polytechnc) educaton. From the populaton of the matrculated we collected all those (19,537) ndvduals who had ther frst graduaton from specalsed educaton (upper secondary school, vocatonal college, polytechnc or unversty) n 1988 2001. 12 In the analyses, we consder long-dstance mgraton between the 18 Fnnsh NUTS3 regons, followng e.g. Nvalanen (2004). 13 These mgraton flows allow us to examne the changes n the geographcal dstrbuton of human captal. Focusng on mgraton between the NUTS3 regons s also practcal, because the locaton of the educatonal nsttuton where an ndvdual graduates s known at ths regonal level n the data. Furthermore, mgraton of shorter dstances between muncpaltes or sub-regons most lkely reflects housng market condtons rather than labour market prospects. Intally we consder the short-run and long-run mgraton rates that descrbe the proporton of ndvduals who move durng the graduaton year or the followng two and fve years, respectvely. The effects of educaton on wthn-country mgraton are typcally estmated n the lterature for the total populaton. The key advantage of focusng on recent graduates s that we avod the potental complcatons caused by the 11 The ndvduals graduatng from the Åland Islands are not ncluded n the sample. Åland s a small solated regon wth approxmately 26,000 nhabtants. It dffers from the other Fnnsh regons n numerous ways (e.g. most of the nhabtants speak Swedsh as ther natve language). 12 We treat the bachelor s degree as an ntermedate phase of the master s degree, because t was very uncommon to fnsh one s studes wth a bachelor s degree from a Fnnsh unversty n the 1990s (.e. before the Bologna process was adopted n 2005). In addton, only ndvduals graduatng at an age less than 35 years are kept n the data. 13 Appendx, Fgure A4 shows a map of the NUTS3 regons n Fnland. 9

accumulaton of frm-specfc human captal on the turnover of workers (cf. Jovanovc, 1979). Before turnng to emprcal modellng, t s useful to document that there are consderable dfferences n the mgraton rates accordng to the level of educaton (Fgure 2 and 3). 14 It s a general pattern that the more educated have greater propensty to move. For our analyss, the most mportant observaton s that new polytechnc graduates are more lkely to move than vocatonal graduates before and after the reform. But the mgraton rates between polytechnc and unversty graduates do not dffer much. The mgraton patterns are smlar n the short run and the long run. Fgure 2 and 3 around here Towards the end of the reform the mgraton gap between vocatonal and polytechnc graduates narrows. However, the vsual mpresson can be hghly msleadng n ths respect because there were only a few graduates from specalsed vocatonal schools towards the end of the nvestgaton perod (cf. Fgure 1 and Fgure A1). Interestngly, the cyclcal varaton n the mgraton rates s also notably dfferent accordng to the level of educaton; there was a sudden drop and a subsequent rapd recovery of the mgraton rates for unversty graduates durng the depresson of the early 1990s (Fgure 2). Addtonally, the hghly educated experenced the decreasng mgraton rates after 1993 1994. 15 In the estmaton we restrct the analyses to graduates from the reform years 1995 2001, because t s not possble to construct a comparson group wthn the same year before 1995, snce the frst polytechnc graduates entered the labour market n 1995. Ths 14 Appendx, Fgure A3 shows the mgraton rates after graduaton from vocatonal or polytechnc educaton by the feld of educaton. 15 Kaplan and Schulhofer-Wohl (2012, p. 11) document the secular declne n nterstate mgraton n the Unted States between 1991 and 2011. The mgraton rates have dropped for ndvduals wth and wthout a bachelor s degree. They argue that the fall n mgraton s due to a declne n the geographc specfcty of returns to occupatons, together wth an ncrease n workers ablty to learn about other locatons before movng there, through nformaton technology and nexpensve travel. 10

sample selecton also provdes substantal varaton n the nstrument that s needed for the dentfcaton of the endogenous treatment model. 5. Emprcal specfcatons 5.1. Treatng educaton as exogenous Our purpose s to estmate the (causal) effect of polytechnc educaton on mgraton. For comparson, we frst assume that the ndvdual s level of educaton s exogenously determned. Namely, we model the mgraton probablty of ndvdual usng the standard bnary logt model; that s, we assume that t s determned accordng to the logstc densty functon f. exp( d γ + x α) Pr( m = 1 x, d ) = f ( d γ + x α) (1) 1+ exp( d γ + x α) where m s a dummy varable ndcatng whether or not (s)he mgrates durng the follow-up perod across NUTS3 regons. The vector = [ d d, d d ] d represents an 0, 1 2, 3 ndvdual s choce between four levels of educaton d j after matrculaton: upper secondary (j = 0), vocatonal (j = 1; reference group), polytechnc (j = 2) or master s degree (j = 3). 16 Frst, we estmate the mgraton probablty for the three-year follow-up perod consstng of the graduaton year and the followng two years, and then we extend the observaton perod by three years. Later we also consder alternatve defntons of the dependent varable. All the control varables, x, are measured n the year before an ndvdual graduates from specalsed educaton after matrculaton, so that the consequences of mgraton are not confused wth the causes of mgraton. Ths tmng dfference consderably lessens the lkelhood that acqured specalsed educaton could affect the (future) values of the control varables and hence bas the estmates. Followng, for example, Greenwood 16 One of the educaton dummes,.e. the dummy for the vocatonal educaton, has been dropped from the model. Thus we defne, for dentfcaton. γ 1 = 0 11

(1997), Nvalanen (2004) and Haapanen and Tervo (2012), we use the standard set of covarates; see the Appendx (Table A1) for the detaled defntons of the control varables and ther mean values. Concernng personal characterstcs, we control for age, gender and mother tongue. To account for past mgraton experence, we use a dummy varable ndcatng whether a person s graduaton regon dffers from the matrculaton regon. There s extensve pror lterature confrmng that ndvduals who have moved n the past are more lkely to move n the future (see e.g. DaVanzo, 1983). Another potental determnant of mgraton (and the choce of educaton level) s pror scholastc achevement. Matrculaton exam scores 17 from hgh school are used as the measure of ths achevement. It s expected that an ndvdual s ablty s postvely correlated wth mgraton because of hs or her attendance at nsttutes of hgher educaton, and also for the reasons dscussed n Secton 2 (e.g. ablty s lkely to correlate postvely wth the potental monetary benefts from movng). The data also allow us to dstngush the effect of the educaton level from the feld of educaton. Household characterstcs comprse martal status, havng chldren, a spouse s employment status, labour ncome and the level of educaton, and the locaton of the sample ndvdual s parents at the NUTS3 level. For example, n the absence of a spouse s ncome level as a control, 18 the dfferences n the ablty to fnance the mgraton costs can partly create the observed postve correlaton between educaton and mgraton. Fnally, we control for the effects that are specfc to the regon of matrculaton, and the year and the regon of the graduaton from specalsed educaton. The year fxed effects are used to capture the cyclcal fluctuatons of wthn-country mgraton that are 17 The matrculaton examnaton s a natonal compulsory fnal exam taken by all students who graduate from hgh school. The answers n each test are frst graded by teachers and then revewed by assocate members of the Matrculaton Examnaton Board outsde the schools. The exam scores are standardsed so that ther dstrbuton s the same every year. The range of the matrculaton exam scores s 1 6. 18 We do not nclude a person s own earnngs among the controls because the level of educaton may already affect the earnngs durng the study perod and, hence, bas the estmates of educaton on mgraton. 12

common to all regons (Mlne, 1993; Saks & Woznak, 2011; Venhorst, Van Djk, & Van Wssen, 2011). The regonal fxed effects pck up all the regonal dfferences n the mgraton ntensty that are stable over tme. 19 5.2. Accountng for endogenety of educaton An obvous lmtaton of the tradtonal mgraton model (1) s the assumpton about the exogenety of the choce of educaton. Generally, three approaches have often been adopted n observatonal data to deal wth the complcaton of endogenety and selfselecton: () nstrumental varables, () control functons, and () full parametrc specfcaton of the outcome and treatment equatons. In the model defned below, we wll follow Deb and Trved (2009) and use a combnaton of the thrd and frst approaches (Deb, L, Trved, & Zmmer, 2006; Deb & Trved, 2006). Ths s partcularly useful n our context, because t allows us to generalse a logt model by assumng the jont determnaton of the choce of educaton and the mgraton decson. Treatment of the educaton choce as endogenous allows us to control for unobserved latent characterstcs (e.g. personalty trats of ndvduals such as the atttudes toward rsk that are not avalable even n the rch regster-based data), l = [ l l, l l ] 0, 1 2, 3, that affect both educaton choce and mgraton decson. Condtonal on the latent factors, an ndvdual s choce between the four levels of educaton, d j (j= 0, 1, 2, 3), s modelled usng the multnomal logt (MNL) model: 20 exp( x β j + ϕ j z + δ jlj ) Pr( dj = 1 x, z, l ) = 3 exp( x β + ϕ z + δ l g k = 0 ({ x β + ϕ z + δ l : k = 0,1, 2,3} ) k k k k k k k k ) (2) 19 We have expermented wth the unemployment rate and the share of servce sector workers at the subregonal (NUTS4) level as addtonal controls, but they are not ncluded n the fnal specfcatons. The worry s that the students mght move durng ther studes to muncpaltes that are more attractve n terms of job opportuntes and amentes, and commute to educaton from there. These controls also have lmted varaton wthn regons over tme. Our robustness checks suggest that ther ncluson has only a mnor nfluence on the estmates. 20 Note that the choce probablty for each educaton alternatve j depends on all the parameters of all alternatves. 13

where x denotes the vector of observed control varables (dscussed above). Although the model descrbed above can be techncally dentfed by ts functonal form, for more robust dentfcaton an nstrumental varable z ncluded n the educaton choce equaton but excluded from the mgraton equaton s needed (see Deb & Trved, 2006). δ s are parameters assocated wth the latent factors j l j. The bnary mgraton decson s agan modelled though a logstc densty functon f: exp( d γ + x α + l λ) Pr( m = 1 x, d, l ) = f ( d γ + x α + l λ) (3) 1+ exp( d γ + x α + l λ) where the vector representng the educaton choce d s treated as endogenous. The ncluson of the latent varables l n (3) (and n 2) should elmnate the endogenety bas. Our nstrument for the level of educaton, z, s the supply of polytechnc educaton for an ndvdual when matrculatng from hgh school. The supply s measured as the number of new polytechnc study places n the NUTS3 regon of resdence n the year of matrculaton. The dentfcaton strategy s based on the assumpton about the exogenety of the polytechnc reform. 21 Consequently, we assume that the supply of polytechnc startng places s exogenously determned after controllng for other factors potentally nfluencng mgraton decsons. For the correct dentfcaton of the effect of the reform t s, however, not necessary for the supply to be ndependent of the fxed regonal effects, snce we control for such factors wth a set of fxed dummes. Snce the educaton and mgraton choce equatons are ndependent after condtonng on explanatory varables ncludng the common latent factors, the jont probablty of ndvdual choosng educaton level d and mgraton decson j m s the product of the two respectve condtonally ndependent probabltes: 21 Regressons predctng the expanson of polytechncs across regons and over tme support the exogenety of the reform (Böckerman & Haapanen, 2013, p. 603). 14

({ x β + ϕ z + δ l k }) Pr( m, d x, z, l ) = f ( d γ + x α + l λ) g : (4) j k k k k The problem n estmaton arses because l j s are not observed. However, f we assume a densty functon h j for l j s then ts effect can be ntegrated out of the jont probablty functon. In partcular, the resultng lkelhood functon for the jont model s: L( m, d j x, z ) = N [ f ( d + + ({ γ xα lλ) g xβk + ϕk z + δ klk: k} )] = 1 N = 1 1 S S s= 1 ~ f ( d γ + x α + l λ) g ({ x β + ϕ z + δ ~ l : k} ) k k k k h ( l )dl j j j (5) ~ ~ where l, l j contan draws of l j from densty h j. Here, the denstes of the latent factors, h, are assumed to follow ndependent standard normal dstrbutons. Ths maxmum j smulated lkelhood (MSL) estmator s consstent and asymptotcally normal (Gouréroux & Monfort, 1996). In the estmaton, we use S = 3,000 quas-random draws based on the Halton sequences to ncrease the speed of convergence (Tran, 2009). 22 Fnally, normalsatons are requred for the dentfcaton of the model. Frst, a normalsaton s requred on ether λ or j δ s because otherwse the varances of the j MNL choce equatons are not dentfed (see Deb & Trved, 2006). We set δ j = 1 for each j and estmate the values of λ. Second, snce β 1 = 0, ϕ 0 and δ 0 are requred j 1 = for the normalsaton n the MNL model (when vocatonal educaton, j = 1, s the reference category), we assume l 0 wthout the loss of generalty. Fnally, the 1 j = educaton dummy for the vocatonal educaton γ 0 s undentfed as before. 1 = 1 = It s well-known that the MNL model has the ndependence of rrelevant alternatve (IIA) property that places restrctons on the underlyng structure of preferences. 23 As dscussed n Deb and Trved (2009), ths would be an mportant lmtaton f our am 22 Deb and Trved (2009) recommend usng as large draws S as s computatonally feasble. They set S = 1,000 n ther applcaton. We expermented wth smaller (S = 2,000) and larger (S = 4,000) values to confrm the stablty of the convergence. 23 Note, however, that MNL s less restrctve than the ordered logt model that s often used n modellng educatonal choces. 15

were to analyse the structure of preferences over dscrete alternatves. However, n our settng the man role of the MNL model s to allow us to control for endogenety of the choce of schoolng. The estmated parameters of the models descrbed above do not drectly provde the estmates of the margnal effects of educaton on the mgraton decson. To uncover these effects, we smulate the dscrete changes n the predcted mgraton probabltes, µ, by changng the educatonal attanment but keepng the same background characterstcs, ~ x, fxed n the comparson. For example, the treatment effect of polytechnc educaton vs. vocatonal educaton s calculated as 24 µ m = 1 d = 1; ~ x ) µ ( m = 1 d = 0; ~ ). Because the margnal effects are not constant ( 2 2 x across ndvduals, we present them for varous hypothetcal values of the covarates. Frst, we defne ~ x wth the mean characterstcs for all graduates over the perod 1995 2001. Later we wll also use only the mean and medan characterstcs of polytechnc and vocatonal graduates to allow for better comparson of the mgraton rates between these two levels of educaton. 6. Results 6.1. Educaton as exogenous To begn the analyss of the effect of educaton on wthn-country mgraton, we frst assume the exogenety of the educaton choce and estmate the mgraton decson wth a logt model usng maxmum lkelhood, followng the earler emprcal lterature. Because we explot the polytechnc reform, vocatonal educaton s used as the reference group n all models and the sample conssts of graduates over the perod 1995 2001. 24 Ths effect can only be nterpreted as the causal effect of educaton f polytechnc graduates have acqured more educaton than vocatonal graduates. Fgure A2 n the Appendx shows that polytechnc graduates are, on average, around one year older than vocatonal graduates, whch mples that the vocatonal colleges were not smply relabelled as polytechncs. We would also expect to fnd no effect on mgraton n ths case. 16

Table 1 reports the margnal effects of educaton on short-run mgraton. 25 The most parsmonous specfcaton n Column 1 that does not nclude any controls shows that havng a polytechnc educaton ncreases the probablty of mgratng to another NUTS3 regon n the short run by 5.2 percentage ponts. The effect of polytechnc educaton remans postve but the statstcal sgnfcance levels are low throughout as we load n controls from Column 2 onwards. These controls have been dentfed n the lterature on mgraton as standard confounders. The specfcaton n Column 3 that adds an ndcator for prevous mgraton for studes gves a notably low pont estmate. However, the quanttatve magntude of the effect of polytechnc educaton on mgraton remans relatvely stable after the addton of further controls (Columns 4 6). LR-rato tests clearly reveal that the addton of controls sgnfcantly mproves the ft of the model. For ths reason, the estmates n Column 6 consttute the preferred model specfcaton for short-run mgraton. Our preferred model shows that the margnal effect of polytechnc educaton on short-run mgraton s 2.6 percentage ponts (13.3%; p-value = 0.068) from the base rate of 19.6 per cent (for those wth vocatonal educaton). For comparson, Table A2 n Appendx shows the correspondng estmates for the full sample usng data on graduates over the perod 1988 2001. Our conclusons reman ntact but the quanttatve magntude of the margnal effects s hgher across the board n Table A2. In the preferred model, polytechnc educaton ncreases the mgraton rates by 3.2 percentage ponts (16.6%; p-value = 0.012) from the 19.3 per cent. 26 Table 1 around here Next, we proceed to examne the effects of polytechnc educaton on long-run mgraton. The structure of Table 2 s dentcal to Table 1. In the preferred model 25 Heteroskedastcty-robust standard errors that are clustered by graduaton-regon-by-year cells are reported for all models. 26 We have also computed the margnal effects as averages over all observatons (see Cameron & Trved, 2005, p. 467). These AMEs are very smlar (0.025 and 0.032) to those obtaned usng the mean characterstcs of graduates. 17

(Column 6) the margnal effect of polytechnc educaton on long-run mgraton s 3.5 percentage ponts (11.9%; p-value = 0.028) from the base rate of 26.9 per cent. 27 Thus, the long-run effect s hgher n absolute sze than the short-run effect (0.035 vs. 0.026) but lower n percentages (11.9% vs. 16.6%). We also observe that the margnal effect of havng polytechnc educaton vares more as we add the controls from Column 2 onwards (Table 2). Therefore, t s more mportant to use the complete set of controls when we estmate the long-run effect of educaton on mgraton. Agan the addton of controls s supported by LR-rato tests and the preferred specfcaton s reported n Column 6. Table A3 n the Appendx documents the correspondng estmates for the full sample, usng graduates over the perod 1988 2001. As n Table 1 vs. Table A2, the effect of polytechnc educaton on mgraton s estmated to be hgher (at 0.042; 15.8%; p-value = 0.004) n the full sample (Table A3) compared to the restrcted sample n Table 2. 28 Table 2 around here 6.2. Educaton as endogenous The estmates n Tables 1 2 treat the educaton choce as an exogenous varable. Ths assumpton s not realstc, because there are both theoretcal and emprcal reasons to treat the educaton choce as an endogenous varable n the estmated model. In partcular, there are lkely to be unobservable factors such as personalty trats that are correlated both wth the educaton choce and the subsequent mgraton behavour of graduates that were not controlled for n Tables 1 2 wth the standard vector of covarates. Ths mples that our baselne results n Tables 1 2 do not consttute unbased (causal) estmates for the effect of polytechnc educaton on mgraton. For 27 Average unemployment rate was 3.5 per cent lower for polytechnc graduates (23.9%) than for vocatonal graduates (24.8%) at the end of the graduaton year n 1996-1999. But the range of unemployment rates across NUTS3 regons was much hgher (+29.8%) for polytechnc graduates than for vocatonal graduates. In contrast, n the UK the unemployment rates of hghly educated workers are relatvely smlar across regons. Larger regonal dfferences n the unemployment rates n Fnland provde one mechansm through whch educaton ncreases mgraton. 28 In the Appendx, Table A4 and A5, we report the robustness of the short-run and long-run margnal effects to alternatve specfcatons of the control varables. 18

ths reason, there s an apparent need to estmate specfcatons that account for the endogenous educaton choce and to emprcally evaluate the valdty of the conclusons based on the exogenety assumpton. To accomplsh ths goal, we estmate the equatons for the educaton choce and mgraton jontly, as descrbed n Secton 5.2. Table 3 reports the estmaton results for the educaton choce equatons. These results are based on the jont estmaton of the choce between four levels of educaton and short-run mgraton. 29 (The results for longrun mgraton are almost smlar.) As prevously, the table reports the estmates for three levels of educaton whle treatng vocatonal educaton as the reference group. In the models that account for the endogenous educaton choce we use the supply of polytechnc educaton n a person s matrculaton regon (NUTS3) as the dentfyng nstrument. Ths mples that the varable s ncluded n the educaton choce equaton but s excluded from the mgraton equaton. The nstrument s a hghly statstcally sgnfcant (p-value < 0.001) explanatory varable for the graduaton from a polytechnc, as documented n Column 2 of Table 3. Ths postve correlaton confrms the relevance of the nstrument. We also fnd some support that the supply of polytechnc educaton n the matrculaton regon decreases the probablty of graduatng from unversty (the estmate s negatve but not sgnfcant). Ths result s reasonable, because both the unversty sector and polytechncs provde hgher educaton. Thus, they are substtutes for each other. Table 3 also reveals some other nterestng patterns of the educaton choce that we summarse only brefly. We fnd that a person s completed level of educaton ncreases monotoncally wth the matrculaton exam score. Thus, those who have better (measured) ablty tend to obtan a hgher level of formal educaton, other thngs beng 29 We also estmated the educaton choce equatons separately to nvestgate the IIA assumpton. Two Hausman tests usng a subset of alternatves resulted n negatve test statstcs, and the thrd resulted n a postve, nsgnfcant one. Hence, the tests do not support the rejecton of the IIA assumpton (see Hausman & McFadden, 1984, p. 1226). 19

equal. The parameter estmate of the matrculaton exam score s partcularly hgh for completng Master s degree. The other mportant fndng that s also drectly lnked to mgraton behavour s that unversty students are partcularly prone to mgrate pror to enterng educaton. Agan the observed pattern s plausble, because unversty educaton s avalable n fewer regons than upper secondary, vocatonal or polytechnc educaton s. For ths reason, there s a much greater to need to mgrate n order to obtan unversty educaton. Table 3 around here Table 4 documents the determnants of both short-run and long-run mgraton n the models where the educaton choce s treated as endogenous. We report both the estmated parameters and the correspondng margnal effects. These results reveal that there s more evdence for selecton nto educaton on the bass of unobserved heterogenety n the long-run model for mgraton than n the short-run model. Ths pattern s reasonable, because graduates observable characterstcs such as martal status and havng chldren are measured mmedately before ther graduaton. Therefore, they are able to account for better behavour shortly after graduaton. LR-rato tests show that the coeffcents (λ s) of the latent factors (.e. unobserved characterstcs) are jontly statstcally sgnfcant n the long-run model (p = 0.087). 30 In contrast, n the short-run model the coeffcents of the latent factors are not jontly statstcally sgnfcant but the latent factor for upper secondary and polytechnc educaton ponts to the exstence of consderable unobserved heterogenety. Table 4 around here The selecton effects have a consderable mpact on the quanttatve magntudes of the estmated coeffcents on the educaton varables of the mgraton equatons when the 30 The estmated negatve coeffcents (λ s) suggest that there are some latent factors (such as conscentousness) that correlate postvely wth level of educaton and negatvely wth mgraton ntensty. These latent factors are not accounted for n the logt models that assume exogenous educaton choce. 20

jont estmator s appled (Table 4). The most mportant fndng s that the varable of nterest (.e. polytechnc educaton) remans postve and s statstcally sgnfcant at the 10 per cent level n all models. But t s also useful to compare the quanttatve magntude of the estmates that are based on the exogenety and endogenety assumpton. After accountng for the endogenety of the educaton choce both short-run and long-run effects are sgnfcantly larger than n the logt models that assume strct exogenety (cf. Tables 1 2). Ths s a natural mplcaton from the estmated negatve λ s. Note, however, that the effect of polytechnc educaton on mgraton s stll estmated to be smaller than the effect of unversty educaton both n the short run and long run (Table 4). Prevous studes have also found IV estmates to be larger than those assumng exogenous schoolng choce (see Machn et al., 2012; Malamud & Woznak, 2012). Importantly, also when the effects are jontly estmated, the margnal effect of polytechnc educaton on subsequent mgraton s larger n the long run (0.131; p-value = 0.037) than n the short run (0.071; p-value = 0.058). Exactly the same pattern prevals for the other levels of educaton as well. Reassurngly, the estmated mpacts of the exogenous covarates n Table 4 are n accordance wth the pror lterature on mgraton. Note also that the matrculaton examnaton score s strongly postvely related to mgraton both n the short and long run. Ths result mples that graduates wth better (measured) ablty are more lkely to mgrate, even condtonal on completed educaton. Table 5 dsplays the heterogenety of the treatment effect of polytechnc vs. vocatonal educaton on mgraton. These margnal effects are now calculated usng only the characterstcs of the vocatonal and polytechnc graduates. The most mportant fndng s that the earler reported patterns reman ntact,.e. polytechnc educaton has a sgnfcantly larger postve effect on mgraton n the long run (0.137) than n the short run (0.075). We also observe that condtonal on the medan characterstcs of the 21

graduates the effect of polytechnc educaton on mgraton s consderably smaller when the mean characterstcs are used. 31 The medan regon n the data s Uusmaa, where the populaton share s the hghest of all NUTS3 regons n Fnland (28%) and where the Helsnk metropoltan area s located. Because the local labour markets are much thcker n Uusmaa compared to other regons, t s relatvely easy for graduates n Uusmaa to fnd a job wthout mgratng to other regons because of more effectve matchng between job seekers and avalable vacances. To gve a more detaled pcture of the geographcal varaton n the estmated effects, the margnal effects for all NUTS3 regons are depcted n Fgure A4. The lower estmates for Uusmaa are strkng. Table 5 around here 6.3. Extensons Table 6 reports the effect of polytechnc educaton on mgraton usng alternatve nstrumental varables (Panels A C) and alternatve specfcatons of the dependent varable (Panels D F). 32 Overall, the long-run effects are agan consstently larger (n absolute sze) n all extensons of the model than the short-run effects. Accountng for the endogenety of the educaton decson s also more mportant n the long run,.e. unobserved latent characterstcs are jontly sgnfcant n most of the long-run models. The polytechnc reform can be used n several alternatve ways to construct an nstrument for an ndvdual s educaton. One convenent way to measure the avalablty of polytechnc educaton s to use the number of polytechnc nsttutons n the matrculaton regon. We found that ths nstrument s statstcally stronger when the number of permanent polytechncs s used nstead of temporary or all polytechncs. Reassurngly, the results remaned ntact (Panel A). A frequently used nstrument for a person s educatonal attanment s her/hs parent s educaton (see e.g. Lemke & 31 The dstrbutons of the ndvdual-level treatment effects are also consderably skewed (cf. Deb & Trvedy 2009). Sample medans are larger than the means n both cases (short-run and long-run mgraton). 32 See the Appendx, Table A6, for a descrpton of the varables used only n the robustness checks. 22

Rschall, 2003). The effect of polytechnc educaton on wthn-country mgraton changes only slghtly when we use the father s educaton dummes as addtonal nstruments (Panel B). Ths stablty of the estmated effects s encouragng, 33 although the use of parental educaton as an nstrument has been crtcsed by Card (1999, p. 1822-1826) on the ground that parental educaton often drectly affects labour market outcomes such as earnngs or s at least correlated wth the error term. Fnally, we altered the defnton of mgraton. In Panel C we report the results for mgraton between 79 NUTS4 sub-regons, nstead of NUTS3 regons, and n Panel D for longer-dstance mgraton between the four NUTS2 regons. 34 The estmated longrun margnal effects are now consderably smaller than n the baselne. Thus, ncreased mgraton was mostly between the NUTS3 regons. The short-run effect of polytechnc educaton s nsgnfcant when usng mgraton between NUTS4 or NUTS2 regons and lvng n the matrculaton regon as the outcome varable. Only the effect on lvng n the graduaton regon after the follow-up perod s statstcally sgnfcant n the short run (Panel E). The long-run effects are sgnfcant at the 5 10 per cent level n Panels C F. Table 6 around here 7. Conclusons The postve relatonshp between educaton and mgraton s taken as granted n much of the lterature. But the actual emprcal evdence that there s a causal effect of educaton on wthn-country mgraton s very lmted. Only recently has economc research addressed ths ssue (Machn et al., 2012; Malamud & Woznak, 2012; McHenry, 2013). But the exstng causal estmates are nconclusve and the evdence 33 Unfortunately the tests of over-dentfyng restrctons have been not developed for ths emprcal framework. 34 In terms of land area, the Fnnsh NUTS2 regons are larger compared to the EU average and smaller compared to the US states: the Fnnsh average s 60,895 km 2, the EU average s 15,869 km 2, and the US state average s 183,637 km 2. In 2010, the populaton densty was 18 nhabtants per km 2 n Fnland, 117 n the EU and 35 n the US. Sources: Eurostat (2007, 2011), US Census Bureau (2012). 23

about the effects at the upper part of the educaton dstrbuton on mgraton s even thnner. In ths paper, we examned the effects of the avalablty of educaton and the level of educaton on wthn-country mgraton usng comprehensve longtudnal data. A major hgher educaton reform took place n Fnland n the 1990s. Ths quas-exogenous staged reform gradually transformed former vocatonal colleges nto polytechncs and expanded hgher educaton to all regons. We exploted the polytechnc educaton reform to dentfy the causal effect of educaton on the mgraton of the young adults who had graduated from specalsed educaton after matrculaton. Consstent wth Malamud and Woznak (2012), our estmaton results show that polytechnc graduates have a 7.5 (13.7) percentage ponts hgher mgraton probablty durng a 3-year (6-year) follow-up perod than vocatonal college graduates. Ths mples that the expanson of educaton mproves the allocaton of labour across regons. Therefore, the sgnfcant postve effects of the reform on labour market outcomes such as employment and earnngs, reported n Böckerman et al. (2009), may have resulted partly from an ncrease n wthn-country mgraton. Interpreted from a broader perspectve, our results provde evdence that the expanson of hgher educaton mtgates the adverse effects of populaton ageng on the effcency of matchng n the labour market. References Armstrong, M., & Taylor, J. (2000). Regonal economcs and polcy (3rd ed.). Oxford: Wley-Blackwell. Becker, G. S. (1964). Human captal. New York: Columba Unversty Press (for NBER). Becker, G. S. (1993). Human captal: A theoretcal and emprcal analyss, wth specal reference to educaton (3rd ed.). Chcago: Unversty of Chcago Press. 24

Blanchflower, D. G., & Oswald, A. J. (2013). Does hgh home-ownershp mpar the labor market? (Workng Paper No. 19079). Natonal Bureau of Economc Research. Retreved from http://www.nber.org/papers/w19079 Bodenhöfer, H.-J. (1967). The moblty of labor and the theory of human captal. The Journal of Human Resources, 2(4), 431 448. do:10.2307/144764 Borjas, G. J. (2013). Labor economcs (6th ed.). New York: McGraw-Hll. Böckerman, P., & Haapanen, M. (2013). The effect of polytechnc reform on mgraton. Journal of Populaton Economcs, 26(2), 593 617. do:10.1007/s00148-012- 0454-4 Böckerman, P., Hämälänen, U., & Uustalo, R. (2009). Labour market effects of the polytechnc educaton reform: The Fnnsh experence. Economcs of Educaton Revew, 28(6), 672 681. do:10.1016/j.econedurev.2009.02.004 Cameron, A. C., & Trved, P. K. (2005). Mcroeconometrcs: Methods and applcatons. New York: Cambrdge Unversty Press. Card, D. (1999). The causal effect of educaton on earnngs (Chapter 30). In O. C. Ashenfelter & D. Card (Eds.), Handbook of Labor Economcs (Vol. 3, Part A, pp. 1801 1863). Amsterdam: Elsever. Retreved from http://www.scencedrect.com/scence/artcle/p/s1573446399030114 Cutler, D., & Lleras-Muney, A. (2008). Educaton and health: Evaluatng theores and evdence. In J. House, R. Schoen, G. Kaplan, & H. Pollack (Eds.), Makng Amercans Healther: Socal and Economc Polcy as Health Polcy. New York: Russell Sage Foundaton. Retreved from https://www.russellsage.org/publcatons/books/080117.779084 DaVanzo, J. (1983). Repeat mgraton n the Unted States: Who moves back and who moves on? The Revew of Economcs and Statstcs, 65(4), 552 559. do:10.2307/1935923 25

Deb, P., L, C., Trved, P. K., & Zmmer, D. M. (2006). The effect of managed care on use of health care servces: Results from two contemporaneous household surveys. Health Economcs, 15(7), 743 760. do:10.1002/hec.1096 Deb, P., & Trved, P. K. (2006). Specfcaton and smulated lkelhood estmaton of a non-normal treatment-outcome model wth selecton: Applcaton to health care utlzaton. Econometrcs Journal, 9(2), 307 331. do:10.1111/j.1368-423x.2006.00187.x Deb, P., & Trved, P. K. (2009). Provder networks and prmary-care sgnups: Do they restrct the use of medcal servces? Health Economcs, 18(12), 1361 1380. do:10.1002/hec.1432 Ehrenberg, R. G., & Smth, R. S. (2009). Modern labor economcs: Theory and publc polcy. Boston: Pearson/Addson Wesley. Elasson, K., Nakosteen, R., Westerlund, O., & Zmmer, M. (2013). All n the famly: Self-selecton and mgraton by couples. Papers n Regonal Scence, forthcomng. do:10.1111/j.1435-5957.2012.00473.x Eurostat. (2007). Regons n the European Unon. Nomenclature of terrtoral unts for statstcs, NUTS 2006/EU-27. Luxembourg: European Unon. Eurostat. (2011). Europe n fgures - Eurostat yearbook 2011. Luxembourg: European Unon. Faggan, A., McCann, P., & Sheppard, S. (2007). Human captal, hgher educaton and graduate mgraton: An analyss of Scottsh and Welsh students. Urban Studes, 44(13), 2511 2528. do:10.1080/00420980701667177 Falars, E. M. (1988). Mgraton and wages of young men. The Journal of Human Resources, 23(4), 514 534. do:10.2307/145811 Farber, H. S. (2004). Job loss n the Unted States, 1981 2001. Research n Labor Economcs, 23, 69 117. do:10.1016/s0147-9121(04)23003-5 26

Gouréroux, C., & Monfort, A. (1996). Smulaton-based econometrc methods. Oxford: Oxford Unversty Press. Greenwood, M. J. (1975). Research on nternal mgraton n the Unted States: A survey. Journal of Economc Lterature, 13(2), 397 433. do:10.2307/2722115 Greenwood, M. J. (1997). Internal mgraton n developed countres (Chapter 12). In M. R. Rosenzweg & O. Stark (Eds.), Handbook of populaton and famly economcs (Vol. 1, Part B, pp. 647 720). Elsever. Retreved from http://www.scencedrect.com/scence/artcle/p/s1574003x97800049 Haapanen, M., & Rtslä, J. (2007). Can mgraton decsons be affected by ncome polcy nterventons? Evdence from Fnland. Regonal Studes, 41(3), 339 348. do:10.1080/00343400701282087 Haapanen, M., & Tervo, H. (2012). Mgraton of the hghly educated: Evdence from resdence spells of unversty graduates. Journal of Regonal Scence, 52(4), 587 605. do:10.1111/j.1467-9787.2011.00745.x Hausman, J., & McFadden, D. (1984). Specfcaton tests for the multnomal logt model. Econometrca, 52(5), 1219 1240. do:10.2307/1910997 Hämälänen, K., & Böckerman, P. (2004). Regonal labor market dynamcs, housng, and mgraton. Journal of Regonal Scence, 44(3), 543 568. do:10.1111/j.0022-4146.2004.00348.x Jaeger, D. A., Dohmen, T., Falk, A., Huffman, D., Sunde, U., & Bonn, H. (2010). Drect evdence on rsk atttudes and mgraton. Revew of Economcs and Statstcs, 92(3), 684 689. do:10.1162/rest_a_00020 Jovanovc, B. (1979). Job matchng and the theory of turnover. Journal of Poltcal Economy, 87(5), 972 990. do:10.2307/1833078 Kaplan, G., & Schulhofer-Wohl, S. (2012). Understandng the long-run declne n nterstate mgraton (Workng Paper No. 18507). Natonal Bureau of Economc Research. Retreved from http://www.nber.org/papers/w18507 27

Lampnen, O. (2001). The use of expermentaton n educatonal reform: The case of the Fnnsh polytechnc experment 1992 1999. Tertary Educaton and Management, 7(4), 311 321. do:10.1023/a:1012710220796 Lemke, R. J., & Rschall, I. C. (2003). Skll, parental ncome, and IV estmaton of the returns to schoolng. Appled Economcs Letters, 10(5), 281 286. do:10.1080/13504850320000078653 Levy, M. B., & Wadyck, W. J. (1974). Educaton and the decson to mgrate: An econometrc analyss of mgraton n Venezuela. Econometrca, 42(2), 377 388. do:10.2307/1911985 Machn, S., Salvanes, K. G., & Pelkonen, P. (2012). Educaton and moblty. Journal of the European Economc Assocaton, 10(2), 417 450. do:10.1111/j.1542-4774.2011.01048.x Malamud, O., & Woznak, A. (2012). The mpact of college on mgraton: Evdence from the Vetnam generaton. Journal of Human Resources, 47(4), 913 950. McCormck, B. (1997). Regonal unemployment and labour moblty n the UK. European Economc Revew, 41(3 5), 581 589. do:10.1016/s0014-2921(97)00024-x McHenry, P. (2013). The relatonshp between schoolng and mgraton: Evdence from compulsory schoolng laws. Economcs of Educaton Revew, 35, 24 40. do:10.1016/j.econedurev.2013.03.003 Mlne, W. J. (1993). Macroeconomc nfluences on mgraton. Regonal Studes, 27(4), 365 373. do:10.1080/00343409312331347625 Mnstry of Educaton. (2005). OECD thematc revew of tertary educaton: Country background report for Fnland. Helsnk. Retreved from http://www.mnedu.f/opm/julkasut/2005/oecd_thematc_revew_of_tertary_e ducaton_country_background_r?lang=en 28

Molloy, R., Smth, C. L., & Woznak, A. (2011). Internal mgraton n the Unted States. Journal of Economc Perspectves, 25(3), 173 196. do:10.1257/jep.25.3.173 Nakosteen, R. A., Westerlund, O., & Zmmer, M. (2008). Mgraton and self-selecton: Measured earnngs and latent characterstcs. Journal of Regonal Scence, 48(4), 769 788. do:10.1111/j.1467-9787.2008.00576.x Nvalanen, S. (2004). Determnants of famly mgraton: Short moves vs. long moves. Journal of Populaton Economcs, 17(1), 157 175. do:10.1007/s00148-003- 0131-8 Oreopoulos, P., & Salvanes, K. G. (2011). Prceless: The nonpecunary benefts of schoolng. The Journal of Economc Perspectves, 25(1), 159 184. do:10.2307/23049443 Pekkala, S., & Tervo, H. (2002). Unemployment and mgraton: Does movng help? Scandnavan Journal of Economcs, 104(4), 621 639. do:10.1111/1467-9442.00305 Rtslä, J., & Ovaskanen, M. (2001). Mgraton and regonal centralzaton of human captal. Appled Economcs, 33(3), 317 325. do:10.1080/00036840122485 Saks, R. E., & Woznak, A. (2011). Labor reallocaton over the busness cycle: New evdence from nternal mgraton. Journal of Labor Economcs, 29(4), 697 739. Schwartz, A. (1973). Interpretng the effect of dstance on mgraton. Journal of Poltcal Economy, 81(5), 1153 1169. do:10.2307/1830643 Sjaastad, L. A. (1962). The costs and returns of human mgraton. Journal of Poltcal Economy, 70(5), 80 93. do:10.2307/1829105 Tran, K. E. (2009). Dscrete choce methods wth smulaton (2nd ed.). New York: Cambrdge Unversty Press. Tunal, I. (2000). Ratonalty of mgraton. Internatonal Economc Revew, 41(4), 893 920. do:10.1111/1468-2354.00089 29

US Census Bureau. (2012). Unted States 2010 census. Retreved August 30, 2012, from http://2010.census.gov/2010census Venhorst, V., Van Djk, J., & Van Wssen, L. (2011). An analyss of trends n spatal moblty of Dutch graduates. Spatal Economc Analyss, 6(1), 57 82. do:10.1080/17421772.2010.540033 Woznak, A. (2010). Are college graduates more responsve to dstant labor market opportuntes? Journal of Human Resources, 45(4), 944 970. 30

TABLES Table 1. Short-run margnal effects of educaton on mgraton (exogenous educaton choce) (1) (2) (3) (4) (5) (6) Upper secondary degree -0.0422** -0.0435*** -0.0393*** -0.0495*** -0.0451*** -0.0415*** (0.0213) (0.0128) (0.0134) (0.0139) (0.0146) (0.0147) Polytechnc degree 0.0510 0.0259* 0.0222 0.0287* 0.0263* 0.0262* (0.0341) (0.0141) (0.0145) (0.0149) (0.0146) (0.0144) Master s degree 0.0748*** 0.1318*** 0.1138*** 0.0989*** 0.1136*** 0.1041*** (0.0234) (0.0145) (0.0147) (0.0157) (0.0160) (0.0162) Regonal and year no yes yes yes yes yes dummes a Mgraton for studes no no yes yes yes yes Feld of educaton no no no yes yes yes Other ndvdual-level no no no no yes yes controls Famly-level controls no no no no no yes Log-lkelhood -5,856.81-5,145.80-5,031.47-5,020.33-4,973.71-4,785.35 LR-test over restrcted specfcaton p < 0.001 (df = 40) p < 0.001 (df = 1) p < 0.001 (df = 3) p < 0.001 (df = 6) p < 0.001 (df = 6) Notes: Number of observatons s 9,906 n all models (.e. graduates from 1995 2001). Dependent varable: NUTS3 mgraton durng the graduaton year or the followng two years. Reference level of educaton s vocatonal degree. Margnal effects are calculated at mean values of other explanatory varables usng logt model. Controls are defned n Appendx, Table A1. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. df = degrees of freedom. a Include dummes for the matrculaton regon, graduaton regon and graduaton year. *** p<0.01, ** p<0.05, * p<0.1. 31

Table 2. Long-run margnal effects of educaton on mgraton (exogenous educaton choce) (1) (2) (3) (4) (5) (6) Upper secondary degree -0.0284-0.0298** -0.0239* -0.0349** -0.0386** -0.0341** (0.0217) (0.0134) (0.0141) (0.0147) (0.0158) (0.0163) Polytechnc degree 0.0465 0.0297** 0.0257 0.0332** 0.0351** 0.0351** (0.0369) (0.0151) (0.0158) (0.0160) (0.0156) (0.0160) Master s degree 0.0657*** 0.1326*** 0.1137*** 0.0988*** 0.1332*** 0.1240*** (0.0252) (0.0176) (0.0179) (0.0186) (0.0189) (0.0193) Regonal and year no yes yes yes yes yes dummes a Mgraton for studes no no yes yes yes yes Feld of educaton no no no yes yes yes Other ndvdual-level no no no no yes yes controls Famly-level controls no no no no no yes Log-lkelhood -6,404.85-5,632.05-5,518.76-5,507.12-5,446.75-5,257.89 LR-test over restrcted specfcaton p < 0.001 (df = 40) p < 0.001 (df = 1) p < 0.001 (df = 3) p < 0.001 (df = 6) p < 0.001 (df = 6) Notes: Number of observatons s 9,906 n all models (.e. graduates from 1995 2001). Dependent varable: NUTS3 mgraton durng the graduaton year or the followng two years. Reference level of educaton s vocatonal degree. Margnal effects are calculated at mean values of other explanatory varables usng logt model. Controls are defned n Appendx, Table A1. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. df = degrees of freedom. a Include dummes for the matrculaton regon, graduaton regon and graduaton year. *** p<0.01, ** p<0.05, * p<0.1. 32

Table 3. Parameter estmates from educaton choce equatons (endogenous educaton choce; vocatonal degree as reference) Upper secondary degree Polytechnc degree Master's degree Mgrated for studes -0.0513 0.2081 0.5158** (0.2194) (0.2281) (0.2444) Technology 1.8765*** 0.1092 0.2215 (0.3259) (0.2013) (0.1541) Health care 1.3822*** 0.0555-1.3513*** (0.2847) (0.1940) (0.1526) Other felds of educaton 2.7054*** -1.3430*** 0.9396*** (0.2838) (0.1786) (0.1197) Age -4.4340*** 3.5117*** 7.1410*** (0.2368) (0.3374) (0.3882) Age squared 8.0745*** -6.5435*** -12.1970*** (0.4486) (0.6766) (0.7062) Female -0.0979 0.3239*** 0.0263 (0.0977) (0.1005) (0.1154) Swedsh -0.2737-0.3159 0.3275 (0.1853) (0.3165) (0.2787) Matrcul. result -0.3413*** 0.3826*** 1.7930*** (0.0353) (0.0440) (0.0712) Marred 0.6363*** -0.5825*** -1.3326*** (0.1977) (0.1773) (0.2043) Sp. empl. -0.1260-0.1248-0.4022*** (0.1424) (0.1730) (0.1463) Sp. educ. -0.2019** 0.3265*** 0.6617*** (0.0816) (0.0760) (0.0778) Sp. ncome 0.0049-0.0589-0.1283* (0.0876) (0.0734) (0.0740) Chldren 0.3327* -0.4569*** -0.8046*** (0.2006) (0.1745) (0.1694) Parents locaton 0.2347 0.1857 0.2616 (0.1891) (0.1955) (0.2021) Supply of polytechnc educaton 0.0738 0.1265*** -0.1640 (0.0467) (0.0358) (0.1187) Regonal and year dummes yes yes yes Notes: Number of observatons s 9,906 (.e. graduates from 1995 2001). Results are based on jont estmaton of choce between the four levels of educaton and short-run mgraton. Lkelhood s smulated wth 3,000 quas-random draws based on Halton sequences. Reference educaton s vocatonal degree. Choce-specfc constants and dummy for matrculaton exam score not mssng are not reported for brevty. See Appendx, Table A1 for defntons of varables. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. *** p<0.01, ** p<0.05, * p<0.1. 33

Table 4. Determnants of short-run and long-run mgraton (endogenous educaton choce) Short-run mgraton Long-run mgraton Parameter estmate Margnal effect Parameter estmate Margnal effect Upper secondary degree 0.0016 0.0002 0.2315 0.0394 (0.2135) (0.0291) (0.2901) (0.0474) Polytechnc degree 0.4531* 0.0713* 0.6843* 0.1308** (0.2378) (0.0377) (0.3598) (0.0629) Master s degree 0.6962*** 0.1176*** 0.8583*** 0.1703*** (0.1631) (0.0274) (0.2388) (0.0390) Mgrated for studes 0.7931*** 0.1233*** 0.8159*** 0.1459*** (0.1576) (0.0292) (0.1686) (0.0316) Technology -0.0676-0.0091-0.1489-0.0231 (0.0950) (0.0124) (0.1114) (0.0153) Health care 0.0027 0.0004-0.0323-0.0051 (0.0861) (0.0117) (0.1000) (0.0157) Other felds of educaton 0.2390** 0.0335** 0.2098* 0.0341* (0.1128) (0.0167) (0.1202) (0.0204) Age 0.5843** 0.0795** 0.3900 0.0621 (0.2479) (0.0326) (0.2755) (0.0405) Age squared -1.1934*** -0.1623*** -0.8621* -0.1372* (0.4578) (0.0600) (0.5191) (0.0749) Female -0.0453-0.0062-0.0615-0.0098 (0.0678) (0.0093) (0.0678) (0.0109) Swedsh -0.1995-0.0255-0.2164-0.0324 (0.1517) (0.0183) (0.1513) (0.0205) Matrcul. result 0.0833** 0.0113** 0.0753** 0.0120** (0.0360) (0.0050) (0.0361) (0.0059) Marred -0.7657*** -0.0980*** -0.7827*** -0.1174*** (0.1550) (0.0202) (0.1904) (0.0205) Sp. empl. -0.1902-0.0250-0.2447* -0.0374* (0.1339) (0.0174) (0.1367) (0.0202) Sp. educ. 0.0390 0.0053 0.0518 0.0082 (0.0575) (0.0078) (0.0581) (0.0087) Sp. ncome -0.3202*** -0.0436*** -0.2683*** -0.0427*** (0.0846) (0.0113) (0.0732) (0.0107) Chldren 0.1287 0.0182-0.0283-0.0045 (0.1361) (0.0201) (0.1287) (0.0202) Parents locaton -0.5767*** -0.0861*** -0.6412*** -0.1115*** (0.1752) (0.0273) (0.1979) (0.0287) λ (Upper secondary degree) -0.3856-0.5509 (0.2486) (0.3739) λ (Polytechnc degree) -0.3623-0.6280 (0.2696) (0.4213) λ (Master s degree) -0.1174-0.2713 (0.1735) (0.2305) Regonal and year dummes Yes Yes Log-lkelhood -12,543.74-13,014.44 LR-test for jont sgnfcance of latent factors p = 0.406 (df = 3) p = 0.087 (df = 3) Notes: Number of observatons s 9,906 n all models (.e. graduates from 1995 2001). Dependent varable: NUTS3 mgraton durng the graduaton year or the followng two years. Results are based on jont estmaton of choce between the four levels of educaton and short-run mgraton. Lkelhood s smulated wth 3,000 quas-random draws based on Halton sequences. Reference level of educaton s vocatonal degree. Margnal effects are calculated at mean values of other explanatory varables usng logt model. Controls are defned n Appendx, Table A1. Estmates for dummy of matrculaton exam score not mssng are not reported for brevty. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. df = degrees of freedom. *** p<0.01, ** p<0.05, * p<0.1. 34

Table 5. Margnal effects of polytechnc educaton on short-run and long-run mgraton: Heterogenety Short-run mgraton Long-run mgraton Exogenous Endogenous Exogenous Endogenous educ. choce educ. choce educ. choce educ. choce Mean 0.0276* 0.0753* 0.0365** 0.1373** (0.0151) (0.0394) (0.0166) (0.0665) [0.2109] [0.1752] [0.2888] [0.2161] Medan 0.0096* 0.0284* 0.0153** 0.0602** (0.0053) (0.0155) (0.0069) (0.0244) [0.0599] [0.0540] [0.9111] [0.0705] Graduaton regon Uusmaa 0.0090* 0.0227** 0.0150** 0.0473*** (0.0050) (0.0115) (0.0069) (0.0167) Other regons 0.0354* 0.0987* 0.0420** 0.1642* (0.0193) (0.0518) (0.0191) (0.0840) Notes: Number of observatons s 9,906 n all models (.e. graduates from 1995 2001). Margnal effects have been calculated usng only characterstcs of the vocatonal and polytechnc graduates (mean or medan). Coeffcent shows the treatment effect of polytechnc vs. vocatonal educaton on mgraton. Results are based on models reported n Tables 3 4. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. *** p<0.01, ** p<0.05, * p<0.1. Predcted mgraton probabltes for the mean and medan ndvduals condtonal on vocatonal degree are reported n square brackets. 35

Table 6. Margnal effects of polytechnc educaton on short-run and long-run mgraton: Extensons Short-run mgraton Long-run mgraton Exogenous Endogenous Exogenous Endogenous educ. choce educ. choce educ. choce educ. choce Baselne For model reported n Tables 3 4 0.0276* 0.0753* 0.0365** 0.1373** (0.0151) (0.0394) (0.0166) (0.0665) Alternatve nstrumental varables Panel A: Number of permanent polytechncs n the matrculaton NUTS3 regon Panel B: Father s level of educaton and supply of polytechnc educaton Changng dependent varable Panel C: NUTS4 (shorterdstance) mgraton Panel D: NUTS2 (longer-dstance) mgraton Panel E: Lvng n the graduaton regon after follow-up perod Panel F: Lvng n the matrculaton regon after follow-up perod 0.0276* 0.0782* 0.0365** 0.1449** (0.0151) (0.0407) (0.0166) (0.0731) 0.0276* 0.0741* 0.0365** 0.1419** (0.0151) (0.0381) (0.0166) (0.0661) 0.0173 0.0378 0.0284 0.0849* (0.0166) (0.0368) (0.0190) (0.0441) 0.0068 0.0408 0.0111 0.0779* (0.0107) (0.0301) (0.0129) (0.0402) -0.0299** -0.0695* -0.0340** -0.0870** (0.0133) (0.0363) (0.0156) (0.0428) -0.0274-0.0722-0.0450** -0.1050** (0.0179) (0.0363) (0.0185) (0.0501) Notes: Number of observatons s 9,906 n all models (.e. graduates from 1995 2001). Margnal effects have been calculated usng mean characterstcs of the vocatonal and polytechnc graduates n the graduaton regon. Coeffcent shows the treatment effect of polytechnc vs. vocatonal educaton on mgraton. Varables are defned n Appendx, Table A1 and A6. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. *** p<0.01, ** p<0.05, * p<0.1. ( ) jontly sgnfcant latent factors at 0.05 (0.1) rsk level. 36

FIGURES Number of 1st-year students 0 10000 20000 30000 40000 Vocatonal colleges Polytechncs Unverstes 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Fgure 1. New vocatonal, polytechnc and unversty students n Fnland 1990 2008. 37

3-year mgraton rate 0.05.1.15.2.25.3.35.4 1988 1990 1992 1994 1996 1998 2000 Upper secondary degree Polytechnc degree Vocatonal degree Master's degree Fgure 2. Three-year mgraton rates after graduaton from the frst specalsed educaton. Note: See Fgure A1 for the number of graduates at dfferent levels of educaton. 6-year mgraton rate 0.05.1.15.2.25.3.35.4.45.5 1988 1990 1992 1994 1996 1998 2000 Upper secondary degree Polytechnc degree Vocatonal degree Master's degree Fgure 3. Sx-year mgraton rates after graduaton from the frst specalsed educaton. Note: See Fgure A1 for the number of graduates at dfferent levels of educaton. 38

APPENDIX Table A1. Descrpton of covarates and ther mean values for the two samples Descrpton (1) (2) Dependent varables Short-run mgraton 1 f person mgrates between NUTS3 regons durng the 0.2752 0.2840 Long-run mgraton Level of educaton Upper secondary degree graduaton year or the followng two years, 0 otherwse 1 f person mgrates between NUTS3 regons durng the graduaton year or the followng fve years, 0 otherwse 1 f the 1st degree after hgh school s upper secondary, 0 otherwse Vocatonal degree 1 f the 1st degree after hgh school s vocatonal, 0 otherwse (reference category) Polytechnc degree 1 f the 1st degree after hgh school s polytechnc, 0 otherwse Master s degree 1 f the 1st degree after hgh school s master s, 0 otherwse Instrument Supply of polytechnc Number of 1 st year polytechnc students n the NUTS3 educaton regon durng the year of matrculaton (1,000 students) Control varables Matrculaton regon Regonal dummes (18) ndcate the NUTS3 regon where person matrculates Graduaton regon Regonal dummes (18) ndcate the NUTS3 regon where person graduates from specalsed educaton after hgh school Graduaton year Year dummes (1988 2001) ndcate when person graduates from specalsed educaton after hgh school Mgrated for studes 1 f person s graduaton NUTS3 regon (1 st degree) dffers from the matrculaton NUTS3 regon; 0 otherwse 39 0.3527 0.3538 0.2410 0.2267 0.3330 0.2354 0.0935 0.1844 0.3325 0.3534 0.3995 0.7740 0.1904 0.2434 Busness 1 f the feld of educaton busness, admnstraton and 0.2859 0.2590 socal scences; 0 otherwse (reference category) Technology 1 f the feld of educaton technology or transport; 0 0.2113 0.2168 otherwse Health care 1 f the feld of educaton health care or welfare; 0 0.1886 0.2002 otherwse Other felds 1 f the feld of educaton s somethng else; 0 otherwse 0.3142 0.3239 Age Age n years 24.668 24.862 Age squared Age/10 squared 6.1736 6.2667 Female 1 f female, 0 f male 24.657 24.820 Swedsh 1 f person belongs to the Swedsh mnorty, 0 otherwse 6.1631 6.2409 Matrcul. result General grade from matrculaton exam. Range from 1 0.6063 0.6055 (lowest grade) to 6 (hghest grade). 0 f mssng Matr. result not mss. 1 f matrculaton result s not mssng, 0 otherwse 0.0474 0.0472 Marred 1 f marred or cohabtng, 0 otherwse 0.3319 0.3731 Sp. empl. 1 f spouse s employed, 0 otherwse 0.1588 0.2309 Sp. educ. Spouse s level of educaton (0 f no spouse, 1 f 0.8283 0.9378 comprehensve educ.,, 5 f hgher tertary educ.) Sp. ncome Annual ncome of spouse, 10,000 0.3999 0.4493 Chldren 1 f chldren under 18 years n the famly, 0 otherwse 0.0779 0.0777 Parents locaton 1 f graduated from a NUTS3 regon where ether of the 0.7939 0.7410 parents was lvng, 0 otherwse Number of observatons 19,537 9,906 Notes: Control varables are measured on a year before an ndvdual graduates from the frst specalsed educaton after hgh school. Sample ncludes: (1) Full sample of graduates from 1988 2001; (2) Restrcted sample of graduates from 1995 2001.

Table A2. Short-run margnal effects of educaton on mgraton (exogenous educaton choce, full sample) (1) (2) (3) (4) (5) (6) Upper secondary degree -0.0400*** -0.0460*** -0.0445*** -0.0579*** -0.0481*** -0.0443*** (0.0121) (0.0073) (0.0076) (0.0079) (0.0085) (0.0083) Polytechnc degree 0.0623* 0.0370*** 0.0330** 0.0333** 0.0320** 0.0320** (0.0321) (0.0127) (0.0129) (0.0130) (0.0126) (0.0127) Master's degree 0.0981*** 0.1530*** 0.1373*** 0.1254*** 0.1430*** 0.1318*** (0.0164) (0.0096) (0.0098) (0.0108) (0.0114) (0.0111) Regonal and year no yes yes yes yes yes dummes a Mgraton for studes no no yes yes yes yes Feld of educaton no no no yes yes yes Other ndvdual-level no no no no yes yes controls Famly-level controls no no no no no yes Log-lkelhood -11,342.42-10,152.37-10,032.42-10,009.18-9,920.47-9,590.10 LR-test over restrcted specfcaton p < 0.001 (df = 47) p < 0.001 (df = 1) p < 0.001 (df = 3) p < 0.001 (df = 6) p < 0.001 (df = 6) Notes: Number of observatons s 19,537 n all models. Dependent varable: NUTS3 mgraton durng the graduaton year or the followng two years. Reference level of educaton s vocatonal degree. Margnal effects are calculated at mean values of other explanatory varables usng logt model. Controls are defned n Appendx, Table A1. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. df = degrees of freedom. a Include dummes for the matrculaton regon, graduaton regon and graduaton year. *** p<0.01, ** p<0.05, * p<0.1. Table A3. Long-run margnal effects of educaton on mgraton (exogenous educaton choce, full sample) (1) (2) (3) (4) (5) (6) Upper secondary degree -0.0213-0.0288*** -0.0264*** -0.0417*** -0.0416*** -0.0369*** (0.0135) (0.0090) (0.0093) (0.0095) (0.0100) (0.0101) Polytechnc degree 0.0504 0.0418*** 0.0373*** 0.0385*** 0.0419*** 0.0424*** (0.0349) (0.0141) (0.0144) (0.0144) (0.0143) (0.0148) Master's degree 0.0859*** 0.1555*** 0.1394*** 0.1253*** 0.1639*** 0.1543*** (0.0175) (0.0116) (0.0117) (0.0125) (0.0132) (0.0132) Regonal and year no yes yes yes yes yes dummes a Mgraton for studes no no yes yes yes yes Feld of educaton no no no yes yes yes Other ndvdual-level no no no no yes yes controls Famly-level controls no no no no no yes Log-lkelhood -12,595.49-11,200.93-11,086.15-11,068.81-10,961.05-10,616.68 LR-test over restrcted specfcaton p < 0.001 (df = 47) p < 0.001 (df = 1) p < 0.001 (df = 3) p < 0.001 (df = 6) p < 0.001 (df = 6) Notes: Number of observatons s 19,537 n all models. Dependent varable: NUTS3 mgraton durng the graduaton year or the followng two years. Reference level of educaton s vocatonal degree. Margnal effects are calculated at mean values of other explanatory varables usng logt model. Controls are defned n Appendx, Table A1. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. df = degrees of freedom. a Include dummes for the matrculaton regon, graduaton regon and graduaton year. *** p<0.01, ** p<0.05, * p<0.1. 40

Table A4. Robustness checks of the short-run margnal effects of educaton on mgraton (exogenous educaton choce) (1) (2) (3) (4) (5) (6) Upper secondary degree -0.0415*** -0.0451*** -0.0405*** -0.0402*** -0.0396*** -0.0384*** (0.0147) (0.0142) (0.0144) (0.0147) (0.0143) (0.0143) Polytechnc degree 0.0262* 0.0253* 0.0278* 0.0271* 0.0298** 0.0305** (0.0144) (0.0142) (0.0142) (0.0143) (0.0143) (0.0142) Master's degree 0.1041*** 0.1149*** 0.1062*** 0.1058*** 0.1186*** 0.1201*** (0.0162) (0.0172) (0.0159) (0.0160) (0.0167) (0.0165) Log-lkelhood -4,785.35-4,777.49-4,774.02-4,780.59-4,760.15-4,755.94 Notes: Number of observatons s 9,906 n all models (.e. graduates from 1995 2001). Dependent varable: NUTS3 mgraton durng the graduaton year or the followng two years. Reference level of educaton s vocatonal degree. Column (1) shows baselne reported n Table 1 (column 6); In column (2) feld of educaton s defned wth eght categores nstead of four; In column (3) mgrated for studes dummy s replaced wth lvng n the provnce of brth dummy; In column (4) other regonal-level controls are added; In column (5) earnngs pror graduaton s added; In column (6) both other regonallevel controls and earnngs pror graduaton are added to the baselne. Margnal effects are calculated at the mean values of other explanatory varables usng logt model. Controls are defned n Appendx, Table A1. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. *** p<0.01, ** p<0.05, * p<0.1. Table A5. Robustness checks of the long-run margnal effects of educaton on mgraton (exogenous educaton choce) (1) (2) (3) (4) (5) (6) Upper secondary degree -0.0341** -0.0391** -0.0329** -0.0323** -0.0326** -0.0309* (0.0163) (0.0159) (0.0159) (0.0163) (0.0160) (0.0160) Polytechnc degree 0.0351** 0.0340** 0.0375** 0.0361** 0.0395** 0.0404** (0.0160) (0.0158) (0.0157) (0.0159) (0.0160) (0.0158) Master's degree 0.1240*** 0.1356*** 0.1271*** 0.1260*** 0.1396*** 0.1414*** (0.0193) (0.0204) (0.0190) (0.0191) (0.0201) (0.0200) Log-lkelhood -5,257.89-5,250.06-5,239.38-5,252.96-5,233.92-5,229.38 Notes: Number of observatons s 9,906 n all models (.e. graduates from 1995 2001). Dependent varable: NUTS3 mgraton durng the graduaton year or the followng two years. Reference level of educaton s vocatonal degree. Column (1) shows baselne reported n Table 1 (column 6); In column (2) feld of educaton s defned wth eght categores nstead of four; In column (3) mgrated for studes dummy s replaced wth graduated from the regon of brth dummy; In column (4) other regonal-level controls are added; In column (5) earnngs pror graduaton s added; In column (6) both other regonallevel controls and earnngs pror graduaton are added to the baselne. Margnal effects are calculated at the mean values of other explanatory varables usng logt model. Controls are defned n Appendx, Table A1. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. df = degrees of freedom. *** p<0.01, ** p<0.05, * p<0.1. 41

Table A6. Descrpton of varables used only n the extensons and robustness checks Dependent varables Short-run NUTS4 mgraton Long-run NUTS4 mgraton Short-run NUTS2 mgraton Long-run NUTS2 mgraton In graduaton regon two years after In graduaton regon fve years after In matrculaton regon two years after In matrculaton regon fve years after Instruments No. of permanent polytechncs Father s level of educaton Control varables Feld of educaton detaled Graduate from the regon of brth Unemployment rate Descrpton (1) (2) 1 f person mgrates between NUTS4 regons durng the graduaton year or the followng two years, 0 otherwse 1 f person mgrates between NUTS4 regons durng the graduaton year or the followng fve years, 0 otherwse 1 f person mgrates between NUTS2 regons durng the graduaton year or the followng two years, 0 otherwse 1 f person mgrates between NUTS2 regons durng the graduaton year or the followng fve years, 0 otherwse 1 f lvng n the graduaton regon two years after fnshng specalsed educaton; 0 otherwse 1 f lvng n the graduaton regon fve years after fnshng specalsed educaton; 0 otherwse 1 f lvng n the matrculaton regon two years after fnshng specalsed educaton; 0 otherwse 1 f person s lvng n the graduaton regon fve years after fnshng specalsed educaton; 0 otherwse Number of permanent polytechncs n the NUTS3 regon durng the year of matrculaton Father s level of educaton wth fve dummes; basc educaton as the reference category 0.3372 0.3465 0.4289 0.4315 0.1856 0.1945 0.2411 0.2456 0.7459 0.7377 0.6944 0.6963 0.6462 0.6190 0.6099 0.5967 0.1084 0.2137 Feld of educaton s defned wth eght categores nstead of the four categores. 1 f person graduates from the NUTS3 regon of brth; 0.6811 0.6462 0 otherwse Unemployment rate n the NUTS4 regon (.e. travelto-work 11.984 16.198 area), % Amentes Servce sector workers n the NUTS4 regon, % 5.8530 6.1428 Earnngs Annual earnngs subject to state taxaton, 10,000 0.6524 0.7662 Number of observatons 19,537 9,906 Notes: Control varables are measured on a year before an ndvdual graduates from the frst specalsed educaton after hgh school. Sample ncludes: (1) Full sample of graduates from 1988 2001; (2) Restrcted sample of graduates from 1995 2001. 42

Number of graduates 0 100 200 300 400 500 600 700 1988 1990 1992 1994 1996 1998 2000 Upper secondary degree Polytechnc degree Vocatonal degree Master's degree Fgure A1. Number of graduates from the frst specalsed educaton n 1988 2001 (sample data). Average age 20 21 22 23 24 25 26 27 28 1988 1990 1992 1994 1996 1998 2000 Upper secondary degree Polytechnc degree Vocatonal degree Master's degree Fgure A2. Average age at the frst graduaton after hgh school (sample data). 43

Busness Busness 3-year mgraton rate 0.2.4.6.8 1988 1990 1992 1994 1996 1998 2000 Technology 3-year mgraton rate 0.2.4.6.8 1988 1990 1992 1994 1996 1998 2000 Health care 3-year mgraton rate 0.2.4.6.8 1988 1990 1992 1994 1996 1998 2000 3-year mgraton rate 0.2.4.6.8 6-year mgraton rate 0.2.4.6.8 1988 1990 1992 1994 1996 1998 2000 Technology 6-year mgraton rate 0.2.4.6.8 1988 1990 1992 1994 1996 1998 2000 Health care 6-year mgraton rate 0.2.4.6.8 1988 1990 1992 1994 1996 1998 2000 Other felds Other felds 6-year mgraton rate 0.2.4.6.8 1988 1990 1992 1994 1996 1998 2000 1988 1990 1992 1994 1996 1998 2000 Vocatonal degree Polytechnc degree Fgure A3. Short-run and long-run mgraton rates after graduaton from vocatonal or polytechnc degree n 1998 2001: Descrptve statstcs by the feld of educaton (sample data). 44

Short-run effect 0.1103 to 0.1124 0.0968 to 0.1074 0.0715 to 0.0758 0.0227 to 0.0227 Lapland Long-run effect 0.1635 to 0.1694 0.1430 to 0.1459 0.1283 to 0.1385 0.0473 to 0.0473 Lapland Central Ostrobothna Prkanmaa Kanta- Häme Varsnas-Suom South Ostrobothna Central Fnland Ostrobothna Uusmaa North Savo South Savo Kanuu Päjät-Häme North Karela South Karela Kymenlaakso Central Ostrobothna Satakunta Prkanmaa Kanta- Häme Varsnas-Suom South Ostrobothna Central Fnland Satakunta Ostrobothna Uusmaa North Savo South Savo Kanuu Päjät-Häme North Karela South Karela Kymenlaakso Fgure A4. Margnal effects of polytechnc educaton on short-run and long-run mgraton: Regonal dfferences. Notes: Margnal effects have been calculated usng the jont model of educaton and mgraton choce reported n Tables 3 4 and the averages of the characterstcs of the vocatonal and polytechnc graduates n the NUTS3 regon. 45

WEB APPENDIX Table W1. Margnal effects of polytechnc educaton on short-run and long-run mgraton: Heterogenety by graduaton regon Short-run mgraton Long-run mgraton Exogenous Endogenous Exogenous Endogenous Graduaton regon educ. choce educ. choce educ. choce educ. choce Uusmaa 0.0090* 0.0227** 0.0150** 0.0473*** (0.0050) (0.0115) (0.0069) (0.0167) Varsnas-Suom 0.0264* 0.0720* 0.0344** 0.1283** (0.0148) (0.0384) (0.0160) (0.0626) Satakunta 0.0370* 0.1031* 0.0426** 0.1664* (0.0201) (0.0538) (0.0193) (0.0852) Kantahäme 0.0380* 0.1074* 0.0429** 0.1694* (0.0207) (0.0568) (0.0195) (0.0874) Prkanmaa 0.0277* 0.0758* 0.0355** 0.1334** (0.0150) (0.0399) (0.0161) (0.0645) Päjät-Häme 0.0376* 0.1049* 0.0428** 0.1675* (0.0203) (0.0546) (0.0193) (0.0859) Kymenlaakso 0.0373* 0.1051* 0.0428** 0.1686* (0.0203) (0.0552) (0.0194) (0.0870) Central Fnland 0.0345* 0.0968* 0.0421** 0.1657* (0.0188) (0.0517) (0.0191) (0.0857) South Karela 0.0393* 0.1113* 0.0426** 0.1684* (0.0214) (0.0586) (0.0193) (0.0861) North Karela 0.0375* 0.1053* 0.0429** 0.1692* (0.0203) (0.0556) (0.0195) (0.0873) South Savo 0.0391* 0.1111* 0.0365** 0.1385** (0.0212) (0.0572) (0.0163) (0.0642) North Savo 0.0392* 0.1108* 0.0428** 0.1691* (0.0213) (0.0581) (0.0194) (0.0871) Ostrobothna 0.0279* 0.0758* 0.0377** 0.1430** (0.0153) (0.0398) (0.0172) (0.0704) South Ostrobothna 0.0394* 0.1116* 0.0429** 0.1694* (0.0214) (0.0583) (0.0194) (0.0873) Central Ostrobothna 0.0395* 0.1118* 0.0428** 0.1693* (0.0215) (0.0584) (0.0194) (0.0873) North Ostrobothna 0.0266* 0.0715* 0.0356** 0.1310** (0.0150) (0.0375) (0.0164) (0.0623) Kanuu 0.0385* 0.1103* 0.0370** 0.1459** (0.0211) (0.0573) (0.0169) (0.0721) Lapland 0.0396* 0.1124* 0.0416** 0.1635** (0.0215) (0.0588) (0.0189) (0.0825) Notes: Number of observatons s 9,906 n all models (.e. graduates from 1995 2001). Margnal effects have been calculated usng mean characterstcs of the vocatonal and polytechnc graduates n the graduaton regon. Coeffcent shows the treatment effect of polytechnc vs. vocatonal educaton on mgraton. Results are based on models reported n Tables 3 4. Heteroskedastcty-robust standard errors reported n parentheses allow for clusterng on the matrculaton-year-by-regon cells. *** p<0.01, ** p<0.05, * p<0.1. 46

Upper secondary degree Vocatonal degree Densty 0.1.2.3 0.1.2.3 Polytechnc degree Master's degree 20 25 30 35 20 25 30 35 Age at graduaton, years Fgure W1. Hstograms by the level of educaton: Age at the frst graduaton after hgh school (sample data). 47