How Universities Determine Economic Development in 27 EU Member States

106 ACC JOURNAL 2022, Volume 28, Issue 2 DOI: 10.15240/ ul/004/2022-2-009 HOW UNIVERSITIES DETERMINE ECONOMIC DEVELOPMENT IN 27 EU MEMBER STATES Olesya Pe enko Uni e si y o Wes Bohemia, Facul y o Economics, Depa men o Economics and Quan i a i e Me hods; Uni e zi ní 22, 301 00 Pilsen, Czech Republic e-mail: [email p o ec ed] Abs ac Many Eu opean coun ies nowadays a e collabo a i ely ocused on b inging oge he he mos up- o-da e echnologies and he b igh es minds o deal wi h social, economic, and ecological ma e s and ind a sus ainable equipoise. Uni e si ies may help o p oduce app op ia e knowledge o such challenges and os e economic and social inno a ion. This pape e iews e idence o exis ing bonds be ween e ia y educa ion and economic g ow h measu ed in GDP pe capi a p o iding a quan i a i e e alua ion o such dependencies using he now widely c i icized Cobb-Douglas p oduc ion unc ion o building O dina y Leas Squa es (OLS) and Bayesian A e aging o Classical Es ima es (BACE) econome ic models. The esul s ob ained in his esea ch showed ha e ia y educa ion expendi u es and he numbe s o mainly male BA and MA g adua es (in echnologies, sciences and medicine obus ly and pa ially co ela e wi h economic g ow h. Well-dis ibu ed in es men in he de elopmen o e ia y educa ion STEM majo s can po en ially s eng hen uni e si ies’ posi i e impac on sus ainable economic g ow h. Keywo ds Economic g ow h; Te ia y educa ion; Sus ainable de elopmen ; STEM majo s. In oduc ion Rising in e es in he ole o e ia y ins i u ions in economic g ow h is c ea ing space o scien i ic discussions and esea ch in educa ion, he economics o educa ion and human esou ces managemen [17], [2], [26]. I migh he e o e be impo an o de e mine wha impac e ia y educa ion o ganiza ions ha e on economic g ow h. One majo heo e ical issue ha has domina ed he ield o many yea s is whe he GDP and i s de i a i es a e an adequa e measu e o economic g ow h and de elopmen . This concep has been challenged by a numbe o empi ical s udies [7], [16], [36], [37]. Mo eo e , schola s a e now wo king ha d o es ablish he de ini ion o ‘e ec i e’ uni e si ies and hei impac on inno a ion implemen a ion and he g ow h o egional economies. Huggins and Johns on desc ibed uni e si ies as d i e s o he egional inno a ion sys em in 2009. Acco ding o some esea che s [1], [25], [15], uni e si ies a e iewed by socie y as a whole as social and educa ional enues ha help indi iduals acqui e speci ic skills o be able o mee economic needs, be capable o making an e ec i e con ibu ion o economic de elopmen and mee cu en ma ke demands. Acco ding o con empo a y esea ch and ela ed policies, economic g ow h is closely connec ed o he human capi al endowmen . Fo ins ance, Ba o and Lee [5] poin a causal ela ion be ween yea s o educa ion and economic g ow h. Ne e heless, he ela ion seems less s aigh o wa d han de ined in [16]. A simple causa ion ela ionship be ween educa ion and g ow h may no always sui all he economies and is being ques ioned 107 especially du ing economic c ises like he Global Financial C isis (GFC) o he cu en Co id- 19 pandemic. Wha migh be o g ea e impo ance han he numbe o school yea s o he a io o a well-educa ed popula ion ela ed o he whole popula ion in gene al, is s a egic planning o e ia y educa ion. I was demons a ed in [26] ha imposing s a egical hinking (aligning p io i ies, alues, and incen i es o he uni e si y o hose de ined by local, egional, and o na ional au ho i ies) o uni e si y managemen is one o he key elemen s needed o enhance a uni e si y’s pe o mance and e iciency. Se e al s udies dealing wi h his issue ha e been published and se e as he ou look on pe o mance measu emen in highe educa ion [8], [23], al hough e y ew models o pe o mance measu emen a e o his day ans e ed om he o -p o i sec o and la e adjus ed o sui such public o ganiza ions. 1 Resea ch Objec i es The p ocess o building a anspa en sys em ha could help o de e mine he e iciency o a highe educa ion o ganiza ion would g ea ly need o be explained clea ly o manage s and policy make s so ha syne gy among policy make s, managemen , human esou ces, p o essionals, and s uden s can be eached and he o e all sys em can wo k mo e e ec i ely, p o iding he wo ld wi h be e oppo uni ies o sus ainable g ow h. S a emen o he p oblem: The cause-e ec ela ionship be ween e ia y educa ion and economic g ow h and de elopmen has been a ocus o mul iple esea ch p ojec s. Howe e , so a he e has been li le discussion abou he economic e ec s o e ia y educa ion in Eu opean coun ies. This s udy a emp s o de e mine he impac o highe educa ion sys ems on economic g ow h wi hin 27 EU Membe s a es o e he pas ew decades, as well as he e ec s o Mas e ’s and Ph.D. le el g adua es o e all and di ided by gende in he ields o science, medicine, and echnology. The aim o he esea ch: his s udy a emp s o ca y ou eliable empi ical es ima es o he ela ionship be ween e ia y educa ion and economic de elopmen in he selec ed Eu opean Union coun ies du ing he pe iod o 2013-2019 in o de o de e mine he impac o e ia y educa ion on he economic de elopmen o he EU membe s a es. Limi a ion o he esea ch: In he amewo k o his s udy, se e al indica o s o economic g ow h and de elopmen we e used – he g ow h a e o eal GDP pe capi a, GDP pe capi a in PPP p ice a es, and e ia y educa ion ep esen ed by a sha e o he popula ion holding a e ia y deg ee. Rela ionships be ween speci ic e ia y deg ees in social sciences, echnical sciences, chemis y, biology, e c., and economic g ow h we e no conside ed in his pape , which is ano he limi a ion o he cu en esea ch. Al e na i e measu emen s o economic g ow h we e no used o c ea e a da a se . Despi e hese limi a ions, i is expec ed ha he esul s o his esea ch will be able o be used o conside u he s eps owa ds building sus ainable educa ion in he EU Membe S a es. Resea ch Ques ions 1. Wha is he impac o he cha ac e is ics o highe educa ion sys ems on economic g ow h in Eu opean coun ies? 2. Wha is he impac o human capi al educa ed in science, echnology, and medicine (Bachelo ’s and Mas e ’ le el) on he economic g ow h? 3. Wha is he impac o male and emale g adua es (Bachelo ’s and Mas e ’ le el) o all majo s on he economic g ow h? 4. Wha is he impac o male and emale g adua es (Bachelo ’s and Mas e ’ le el) majo ing in science, echnologies and medicine on g ow h? 2 Theo e ical Backg ound In oday’s economy, e ia y educa ional se ices ha e become an impo an commodi y as HR specialis s ecognize he employee’s quali ica ions, knowledge, and skills o ensu e 108 p oduc i i y and inno a ion wi hin a numbe o businesses ( ans-na ional co po a ions, medium-sized companies ope a ing globally). Th ee key di ec ions o educa ion-g ow h ela ionship a e de ined in [14]: i is c ucial o enhance he le els o knowledge, expe ise, and skills o he popula ion, ans e new knowledge and ideas esponsibly and p o ide incen i es o inno a ion wi hin o ganiza ions and he economy as a whole. In gene al, educa ion can be conside ed one o he main ac o s o economic g ow h and echnological p og ess [5], [21]. I has been claimed ha economic g ow h leads o highe pa icipa ion in e ia y educa ion as well-educa ed human capi al p omo es g ea e economic pe o mance. A lo o esea ch has been ocused on he cause-e ec ela ionship be ween educa ion in gene al (and e ia y educa ion in pa icula ) and economic g ow h [17], [5], [2], [9], [13], [14] wi h da a ga he ed om de eloping and de eloped coun ies om 1960 o 2019. Bo h a iables o educa ion ( e ia y educa ion) and economic g ow h we e measu ed in nume ous ways. The e o e, he causal ela ionship be ween e ia y educa ion and economic g ow h can be o mally desc ibed by h ee main app oaches: 1) educa ion causes economic g ow h; 2) economic g ow h causes mo e people o seek e ia y educa ion 3) he cause-e ec ela ionship may unc ion bo h ways. Acco ding o Hanushek and Woessman [19] who s udied he ela ionship o cogni i e skills in educa ion and g ow h in mul iple coun ies, adding mo e yea s o educa ion on a e age does no ensu e economic g ow h. The esea che s a gued ha he di e ences in cogni i e skills – in o he wo ds, wha is known in ce ain coun ies, can be conside ed an explana o y a iable o some o he di e ences in he economic de elopmen in a ious coun ies. In acco dance wi h UNESCO’s Sus ainable De elopmen Goal 4 (SDG 4) o educa ion [27] sus ainabili y in e ia y educa ion will be c ucial o p epa ing cu en s uden s o sol e he mos challenging asks ha he wo ld’s economy encoun e s in he nea u u e. Eu opean Commission ep esen a i es s ess in hei epo s ha mo e STEM educa ion in e ia y disciplines is needed by 2050 [38, p.13-14]. Ano he epo clea ly s a es ha “we mus spend mo e on esea ch and educa ion”, he e o e, i migh be implied ha mo e s uden s will be suppo ed o g adua e om e ia y educa ion p og ams [39, p. 56]. Sus ainable e ia y educa ion goals lea e no choice o uni e si ies bu o supply hei po en ial g adua es wi h global educa ion and aining so ha ewe nega i e ou comes a ise o be se led by u u e gene a ions. The e o e, in o de o comple e SDG 4 i may be impo an o ensu e ha e ia y educa ion gi es s uden s p ope sou ces o knowledge and mo i a ion o assess p oblems om a ious pe spec i es so ha hey a e able o make in o med decisions elying on mul iple epu able sou ces o da a a ailable. Te ia y educa ion o ganiza ions need o de ine wha e iciency in e ia y educa ion is because esea che s and educa o s wo ldwide o en eel conce ned abou echniques how o pu sue so-called “e iciency”. A ew es ima ion me hods we e de eloped in he la e 20 h cen u y, such as non-pa ame ic: e.g. da a en elopmen analysis (DEA) desc ibed in [12] and [4] and pa ame ic: e.g. s ochas ic on ie analysis (SFA) ope a ed by [3] and [6]. These me hods g ea ly helped o de ine e iciency in he con ex o e ia y educa ion. These conce ns mainly s em om he idea ha i educa o s and uni e si y s a in gene al s a ocusing mo e on being e icien , he e y concep o highe educa ion migh su e as mo e a en ion is gi en o mee ing ce ain equi emen s, i.e. [20] and [24]. The e m “e iciency” is ypically explained as he oppo uni y o p o ide he bes educa ional p oduc o a gi en budge . I can gene ally wo k in wo main di ec ions: uni e si ies may use he same amoun o esou ces o po en ially acqui e be e esul s o lowe he amoun o esou ces o ecei e he same esul s han in p e ious pe iods. O e he las ew decades i is becoming inc easingly impo an o calcula e and ecognize he economic impac o uni e si ies and o he o ganiza ions p o iding e ia y educa ion, see Figu e 1. 109 Sou ce: Au ho ’s own calcula ions based on da a om Cla i a e Da abase 2022 Fig. 1: F equency change in he numbe o scien i ic esea ch pape s in economics pe yea , in coun s, published ia Web o Science 2001-2021 ha ea u e he wo ds “economic impac o uni e si ies” in hei headlines and abs ac s Huggins and Johns on [20] desc ibed uni e si ies as d i e s o egional inno a ion sys em in 2009. I is c ucial, howe e , o look a he s uc u e o he human capi al and build up a common sys em o manage i ( ec ui , ain and sus ain) in o de o de e mine possibili ies o sus ainable g ow h in esul s in highe educa ion. 3 Me hods The da a was ga he ed om he Eu os a (2021), OECD (2021) and Wo ld Bank (2021) o icial da abases. A lis o a iables and co esponding da a sou ces can be ound in Table 7. The c i e ia o choosing he a iables we e as ollows: 1) A ailabili y – he da a a e a ailable o he majo i y o he cu en EU Membe s S a es om 1990 o p esen ; 2) Consis ency – cong uen e ia y educa ion da a o he 27 EU Membe S a es ha was calcula ed and ecei ed by ca ying ou exac ly he same p ocedu e o yea s 2013-2019. Despi e he ac ha he pe iod o six yea s could be conside ed o be an imi a ion o he cu en esea ch, i is expec ed ha a common ela ionship be ween e ia y educa ion and economic g ow h will be ound as he lis o coun ies chosen o his s udy seems o be homogeneous as i consis s o coun ies wi h de eloped economies acco ding o he Wo ld Bank (2019). The ad an age o analyzing such a da a se is da a cohe ence – an impo an da a quali y componen ha ensu es uni o mi y as well as exis ing logical connec ions and comple eness o he da ase . Cohe ence could also enable he making o a logical dis inc ion be ween concep s and a ge popula ions, which means ha mos majo p oblems could be easily de ec ed du ing he da a p epa a ion s age. These es ic ions esul ed in he selec ion o a ew a iables, hei means and s anda d de ia ions a e shown in Table 7. To analyze he da a, i was decided o use he Cobb-Douglas p oduc ion unc ion (CDPF) (1) using capi al s ock, capi al and labo se ices, despi e he ac ha CDPF o en e u ned a nega i e sign o capi al [30], [31] and has been conside ed as “ ans o ma ion o income iden i y” [29] and [10]. The main ad an age o his unc ion used in i s gene al o m is i s abili y o explain he agg ega e ou pu c ea ion and economic g ow h isually. The unc ion illus a es cons an e u ns o scale (α + β = 1) when elas ici ies o p oduc ion on p oduc ion ac o s equal ac o sha es, wi h bo h coe icien s being posi i e numbe s anging [0, 1]. -60 -40 -20 0 20 40 60 80 100 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 110 𝑌 𝑡= 𝐴𝑡∙(𝐾𝑡)𝛼∙(𝐿𝑡)𝛽, (1) whe e A is o al ac o p oduc i i y, K is capi al, and L is labo . Due o he ac ha mos o he unce ain y o he economic g ow h models’ hinde s ag eemen on speci ic ac o s ha cause economic g ow h, his pape uses panel da a models wi h coun y-speci ic ixed e ec s. Woold igde [28] poin ed ou ha i migh be mo e e icien o use Fixed E ec s – Random E ec s models ins ead o he egula OLS eg essions while wo king wi h panel da ase s in which he e is he e ogenei y. This app oach o assess long- e m and sho - e m economic g ow h has been commonly used in he economic li e a u e due o i s gene al simplici y. The main d awback migh be a limi ed numbe o a iables ha could be explained empi ically. Thei quan i y o en depends on “wha e e lis he i s esea che happened o selec ” [33]. To deal wi h he issue o biases, qui e a ew esea che s also conside using Bayesian model a e aging echniques [32] which may help o de e mine model unce ain y so ha he ela ionship be ween model-speci ic es ima es is assessed, e ealed and explained. Following his idea, economis s Sala-I-Ma in, Doppelho e and Mille [35] c ea ed a Bayesian Model A e aging o Classical Es ima es (a so-called SDM’s BACE app oach) in o de o unde s and which eg esso s should be included in o c oss-coun y linea eg essions. Such models build es ima es o e e y possible combina ion o a iables by applying he weigh ed a e aging OLS me hod in o de o ind ou which a iables do ela e o g ow h obus ly and how s ong hese ela ionships migh be. The e o e, when designing he cu en esea ch, i was decided o build an al e na i e model o compa e and e alua e he esul s ecei ed om he Fixed E ec s model. Mo e ecen ly, li e a u e on applied econome ics o e s “ex eme bounds analysis” which was designed o e eal obus empi ical ela ionships o he de e minan s o economic g ow h. This es consis s o wo s eps: 1. i is necessa y o iden i y (by p io analysis) which a iables could be ela ed o economic g ow h; 2. o check i a a iable z is obus , equa ion (2) o eg essions needs o be sol ed: 𝛾 = 𝛼𝑗+ 𝛽𝑦𝑗 ∙ 𝑦 + 𝛽𝑧𝑗 ∙ 𝑧 + 𝛽𝑥𝑗 ∙ 𝑥𝑗+ 𝜀, (2) whe e y is a ec o ha ep esen s ixed posi ions o he eg esso s (ce ain a iables which a e always included in o eg essions – e.g. income, in es men a e, seconda y school en ollmen a e, a e o popula ion g ow h [34]), z is he a iable o be examined, and x is a “ ool” ec o which ypically consis s o a combina ion o h ee a iables selec ed o he analysis. The i s es s pe o med a he beginning o he 1990s we e widely c i icized in he economic li e a u e as hey disca ded mos o he a iables as no obus due o he ac ha hese eg esso s did no sys ema ically co ela e wi h economic g ow h. Consequen ly, Sala-i- Ma in [35] sugges ed making a ansi ion om “ex eme bounds” o a iables ha would ha e a ce ain deg ee o con idence. Bo h heo y and s a is ical calcula ions based on he BACE app oach a e explici ly explained in [35] and o ha eason u he heo e ical desc ip ion is omi ed in his pape . The e has been much deba e be ween economis s on he subjec o whe he o no he e is a ixed se o a iables which can be obus ly co ela ed wi h economic g ow h. In o de o 111 answe he esea ch ques ions lis ed in he in oduc ion o his a icle, i is conside ed use ul o ind ou es ima es o g ow h om a much la ge se o models wi h he help o he BACE app oach. 4 Resul s The null hypo hesis o answe he i s esea ch ques ion (“Wha is he impac o he cha ac e is ics o highe educa ion sys ems on economic g ow h in Eu opean coun ies”) was s a ed as: The e is no impac o e ia y educa ion cha ac e is ics on economic g ow h. We cons uc ed an OLS model wi h ixed e ec s o he panel da ase ha ing lagged a iables o 4 yea s o bachelo s uden s and 2 yea s o mas e s uden s. The dependen a iable was cu en GDP pe capi a in pu chasing powe pa i y (Table 1) wi h he R-squa ed o 0.79. The eg esso s used o his model included he ones ha a e gene ally used by a ious esea che s: g oss capi al o ma ion, Thousand hou s wo ked(as K and L a iables o he Cobb-Douglas unc ion), gene al economic a iables (unemploymen a e, popula ion and popula ion g ow h sha e, gene al go e nmen consump ion expendi u es, openness o he economy) and he a iables ha e e ed o e ia y educa ion ( o al numbe o g adua ed bachelo and mas e s uden s, go e nmen expendi u es on e ia y educa ion and sha e o he popula ion aged 30-35 wi h a e ia y diploma). I could be assumed ha g adua ed bachelo and mas e s uden s ha e a posi i e impac on economic g ow h. The same da ase was used o o m mul iple BACE models, he esul s ollow in Table 2. Tab. 1: Model 1: Fixed-e ec s, Robus (HAC) s anda d e o s, dependen a iable: LOG GDP in cu en PPP pe capi a Coe icien S d. e o - a io p- alue Cons an 4.891050000 0.301363000 16.2300 <0.0001 *** Thousand hou s wo ked −0.000162264 0.000206555 −0.7856 0.4392 G oss capi al o ma ion 0.001290160 0.000750301 1.7200 0.0974 * Sha e o age g oup 30-35 wi h a e ia y diploma −0.000644451 0.001286540 −0.5009 0.6206 Unemploymen a e −0.006935170 0.002318630 −2.9910 0.0060 *** G ow h o popula ion sha e 0.000426793 0.000172614 2.4730 0.0203 ** G adua ed bachelo s uden s_4 3.11526e-06 1.53880e-06 −2.0240 0.0533 * G adua ed mas e s uden s_2 2.02081e-06 8.50523e-07 2.3760 0.0252 ** G adua ed Ph.D. s uden s −2.13229e-06 2.18339e-06 −0.9766 0.3378 Go e nmen expendi u es on e ia y educa ion 0.009721160 0.038023200 0.2557 0.8002 Go e nmen consump ion expendi u es 0.006115850 0.001735630 3.5240 0.0016 *** Openness o he economy −0.001259470 0.001395340 −0.9026 0.3750 Sou ce: Own The BACE modelling analysis me hod applying pos e io momen s wi h uncondi ional and condi ional inclusion e u ned he ollowing esul s: g oss capi al o ma ion, unemploymen a e obus ly and ma ginally co ela es wi h economic g ow h. The numbe s o g adua ed bachelo s uden s obus ly mode a ely co ela e wi h g ow h. The hypo hesis o no impac o e ia y educa ion on economic g ow h is ejec ed because o al numbe o bachelo and mas e s uden s obus ly and posi i ely co ela e wi h GDP g ow h pe capi a bo h in he ixed e ec s model and BACE models. 112 Tab. 2: BACE Models (61 models accep ed ou o 1024). Dependen a iable: LOG GDP in cu en PPP pe capi a PIP Mean S d. de . Cond. mean Cond. s d. de Cons an 1.000000 4.296766 0.171460 4.296766 0.171460 Go e nmen expendi u es on e ia y 0.999996 −0.120231 0.022694 −0.120232 0.022693 Thousand hou s wo ked 0.914534 −0.000166 0.000075 −0.000182 0.000058 G oss capi al o ma ion 0.596062 0.002394 0.002323 0.004017 0.001592 Unemploymen a e 0.588710 −0.002529 0.002504 −0.004295 0.001750 G adua ed bachelo s uden s_4 0.307443 0.000000 0.000000 0.000000 0.000000 G adua ed mas e s uden s_2 0.203128 0.000000 0.000000 0.000000 0.000000 G ow h o popula ion sha e 0.067852 −0.000004 0.000392 −0.000058 0.001503 Sou ce: Own The hypo hesis o answe he second esea ch ques ion was s a ed as: The e is no signi ican impac human capi al educa ed in science and echnology (Bachelo ’s, Mas e ’s and Ph.D. le el) on economic g ow h. We cons uc ed an OLS model wi h ixed e ec s o he panel da ase ha ing lagged a iables o 4 yea s o bachelo s uden s and 2 yea s o mas e s uden s. The dependen a iable was cu en GDP pe capi a in pu chasing powe pa i y (Table 3) wi h he R-squa ed o 0.788. The esul s o g adua ed Ph.D. s uden s did no e u n any s a is ically signi ican esul s which migh mean ha i is a he cos ly o educa e p ospec i e scien is s and i akes ime o he economy o posi i ely eac o high quali y human capi al. I could be assumed ha g adua ed bachelo and mas e s uden s ha e a posi i e impac on economic g ow h. The same da ase was used o o m mul iple BACE models, he esul s ollow in Table 4. Tab. 3: Model 2: Fixed-e ec s, Robus (HAC) s anda d e o s, dependen a iable: LOG GDP in cu en PPP pe capi a Coe icien S d. e o - a io p- alue Cons an 4.674090000 0.341751000 13.6800 <0.0001 *** G oss capi al o ma ion 0.001252570 0.000755304 1.6580 0.1093 Sha e o age g oup 30-35 wi h a e ia y diploma −0.000698896 0.001363940 −0.5124 0.6127 Unemploymen a e −0.009768940 0.003306760 −2.9540 0.0066 *** G ow h o popula ion sha e 0.000437482 0.000160227 2.7300 0.0112 ** Go e nmen expendi u es on e ia y educa ion −0.033137300 0.049195400 −0.6736 0.5065 Go e nmen o al educa ion expendi u es 0.038256000 0.025660700 1.4910 0.1480 Go e nmen consump ion expendi u es 0.004575030 0.002762040 1.6560 0.1097 Openness a io −0.000925810 0.001187970 −0.7793 0.4428 G adua ed bachelo s uden s in sciences, echnology and medicine_4 4.35001e-06 1.88375e-06 2.3090 0.0291 ** G adua ed mas e s uden s in sciences, echnology and medicine_2 2.11347e-06 1.32463e-06 1.5960 0.0227 ** Sou ce: Own 113 The BACE me hod e u ned he ollowing esul s: Go e nmen consump ion expendi u es, he sha e o he popula ion wi h a e ia y educa ion diploma and go e nmen spending on e ia y educa ion obus ly and ma ginally co ela e wi h economic g ow h. The g oss capi al o ma ion, unemploymen a e and numbe s o g adua ed bachelo s uden s obus ly mode a ely co ela e wi h g ow h. Wha changed om he i s se o BACE modelling esul s is ha he numbe o bachelo s uden s g adua ing om majo s linked o science, echnology and medicine migh co ela e obus ly and ma ginally wi h economic g ow h. The hypo hesis o no impac o Mas e ’s and Bachelo ’s science, echnologies and medicine s uden s on economic g ow h is ejec ed as he numbe o s uden s in such majo s obus ly and posi i ely co ela e wi h GDP g ow h pe capi a bo h in he ixed e ec s model and BACE models. Tab. 4: BACE Models (70 models accep ed ou o 1024), dependen a iable: LOG GDP in cu en PPP pe capi a PIP Mean S d. de . Cond. mean Cond. s d. de Cons an 1.000000 4.262602 0.179509 4.262602 0.179509 Go e nmen expendi u es on HE 0.999910 −0.120103 0.022934 −0.120113 0.022907 Employmen by indus y b eakdowns 0.883652 −0.000154 0.000078 −0.000175 0.000058 G oss capi al o ma ion 0.625332 0.002577 0.002358 0.004121 0.001591 Unemploymen a e 0.570417 −0.002449 0.002511 −0.004294 0.001770 To al g adua ed bachelo s uden s in sciences and echnology 0.532771 0.000000 0.000000 0.000001 0.000000 To al g adua ed mas e s uden s in sciences and echnology 0.180553 0.000000 0.000000 0.000000 0.000000 G ow h o popula ion sha e 0.066751 −0.000000 0.000387 −0.000007 0.001498 Sou ce: Own The hypo hesis o answe he hi d esea ch ques ion was s a ed as: The e is no signi ican impac he impac o men and women g adua es (Bachelo ’s and Mas e ’s le el) on he economic g ow h. We cons uc ed an OLS model and unde wen he same p ocedu e (Table 5) wi h he R-squa ed o 0.834 and Du bin-Wa son s a is ic o 1.72. I is isible ha bo h emale and male mas e and bachelo s uden s end o posi i ely in luence economic g ow h, howe e , he coe icien s o men a e la ge in his model. These indings may poin ou ha he e migh be a gende pay gap. I could be de i ed ha male and emale g adua ed bachelo and mas e s uden s ha e a posi i e impac on economic g ow h. The esul s ecei ed wi h he applica ion o BACE modelling o check he same hypo hesis a e demons a ed in Table 6 and show ha he numbe o g adua ed bachelo male s uden s migh ha e obus ma ginal co ela ion wi h economic g ow h. Howe e , he numbe o emale g adua e s uden s, bo h educa ed a Bachelo ’s and Mas e ’s le els, demons a e insigni ican co ela ion wi h economic g ow h. 114 Tab. 5: Model 3: Fixed-e ec s, Robus (HAC) s anda d e o s, dependen a iable: LOG GDP in cu en PPP pe capi a Coe icien S d. e o - a io p- alue Cons an 4.746010000 0.301638000 15.730 <0.0001 *** G oss capi al o ma ion 0.001393160 0.000674290 2.066 0.0489 ** Unemploymen a e −0.006738990 0.002574910 −2.617 0.0146 ** G ow h o popula ion 0.000551393 0.000192559 2.864 0.0082 *** Go e nmen expendi u es on HE −0.062667600 0.051709000 −1.212 0.2364 Openness a io −0.001786610 0.001423370 −1.255 0.2206 Male G adua ed bachelo s uden s 1.19535e-06 5.54429e-07 −2.156 0.0405 ** Female G adua ed bachelo s uden s 1.21790e-06 5.84251e-07 −2.085 0.0471 ** Male G adua ed mas e s uden s 3.09333e-06 8.97448e-07 3.447 0.0019 *** Female G adua ed mas e s uden s 2.51806e-06 7.73175e-07 3.257 0.0031 *** Sou ce: Own Tab. 6: BACE Models (237 models accep ed ou o 1094), dependen a iable: LOG GDP in cu en PPP pe capi a PIP Mean S d. de . Cond. mean Cond. s d. de Cons an 1.000000 4.247664 0.185112 4.247664 0.185112 Go e nmen expendi u es on HE 0.999934 −0.118010 0.023213 −0.118020 0.023194 Employmen by indus y 0.848613 −0.000150 0.000082 −0.000170 0.000059 G oss capi al o ma ion 0.602108 0.002442 0.002344 0.004055 0.001607 Unemploymen a e 0.600080 −0.002640 0.002556 −0.004400 0.001771 Male g adua ed bachelo s uden s 0.412162 0 0 0 0 Male g adua ed mas e s uden s 0.238469 0 0 0.000001 0 Female g adua ed mas e s uden s 0.158910 0 0 0 0.000001 Female g adua ed bachelo s uden s 0.133351 0 0 0 0 G ow h o popula ion sha e 0.064747 −1e-06 0.000382 −1.3e-05 0.001499 Sou ce: Own 121 JAK UNIVERZITY URČUJÍ EKONOMICKÝ ROZVOJ VE 27 ČLENSKÝCH STÁTECH EU Mnoho e opských zemí se současnos i společně zaměřuje na spojení nejmode nějších echnologií a nejchy řejších myslí, aby se ypořádaly se sociálními, ekonomickými a ekologickými záleži os mi a nalezly ud ži elnou o no áhu. Uni e zi y mohou pomoci y áře hodné znalos i p o ako é ýz y a podpo o a hospodářské a sociální ino ace. Ten o článek sh nuje důkazy exis ujících azeb mezi e ciá ním zdělá áním a ekonomickým ůs em měřeným HDP na hla u a posky uje k an i a i ní hodnocení ěch o zá islos í pomocí nyní ši oce k i izo ané Cobb-Douglaso y p odukční unkce p o y áření obyčejných nejmenších č e ců (OLS) a Bayesiánského p ůmě o ání klasických odhadů (BACE) ekonome ických modelů. Výsledky získané om o ýzkumu ukázaly, že ýdaje na e ciá ní zdělá ání a poč y pře ážně absol en ů - mužů BA a MA ( echnologiích, ědách a medicíně) obus ně a čás ečně ko elují s ekonomickým ůs em. Dobře ozložené in es ice do oz oje e ciá ního zdělá ání STEM mohou po enciálně posíli pozi i ní dopad uni e zi na ud ži elný hospodářský ůs . WIE DIE UNIVERSITÄTEN DIE ÖKONOMISCHE ENTWICKLUNG IN DEN 27 MITGLIEDSSTAATEN DER EU BESTIMMEN Viele eu opäische S aa en konzen ie en sich de zei au die Ve bindung de mode ns en Technologien und de klügs en Geis e , dami diese sich mi sozialen, ökonomischen und ökologischen Angelegenhei en auseinande se zen und ein nachhal iges Gleichgewich inden. Die Uni e si ä en können bei de Scha ung geeigne e Kenn nisse ü solche He aus o de ungen hel en und wi scha liche und soziale Inno a ionen un e s ü zen. Diese A ikel ass die Beweise exis ie ende Bindungen zwischen de e iä en Bildung und dem im HDP p o Kop gemessenen ökonomischen Wachs um zusammen und lie e eine quan i a i e Bewe ung diese Abhängigkei en mi Hil e de zu zei k i isie en Cobb-Douglas’schen P oduk ions unk ion ü die Bildung gewöhnliche kleins e Quad a e (OLS) und de Bayes’schen Be echnung des Du chschni s klassische Schä zungen (BACE) ökonome ische Modelle. Die in diese Un e suchung gewonnenen E gebnisse haben gezeig , dass die Ausgaben ü die e iä e Bildung und die Anzahl de Absol en en (Männe mi BA- und MA- Abschluss in Technologien, Wissenscha en Medizin) obus und eilweise mi dem ökonomischen Wachs um ko elie en. Gu e eil e In es i ionen in die En wicklung de e iä en Bildung STEM können die posi i e Auswi kung de Uni e si ä en au das nachhal ige ökonomische Wachs um s ä ken. W JAKI SPOSÓB UNIWERSYTETY DETERMINUJĄ ROZWÓJ GOSPODARCZY W 27 PAŃSTWACH CZŁONKOWSKICH UE Wiele k ajów eu opejskich skupia się obecnie wspólnie na łączeniu najnowocześniejszych echnologii i najzdolniejszych umysłów w celu ozwiązania p oblemów społecznych, gospoda czych i ekologicznych o az znalezienia wałej ównowagi. Uniwe sy e y mogą pomóc w gene owaniu odpowiedniej wiedzy dla akich wyzwań i wspie ać innowacje gospoda cze i społeczne. Niniejszy a ykuł podsumowuje dowody na is niejące powiązania między szkolnic wem wyższym a wz os em gospoda czym mie zonym w PKB pe capi a i p zeds awia ilościową ocenę ych zależności, wyko zys ując obecnie sze oko k y ykowaną unkcję Cobba-Douglasa do wo zenia zwykłych najmniejszych kwad a ów (OLS) i Bayesowskiego uś edniania klasycznych oszacowań (BACE) modeli ekonome ycznych. Wyniki uzyskane w amach p zep owadzonych badań pokazały, że wyda ki na szkolnic wo wyższe o az liczba absolwen ów, p zeważnie płci męskiej, s udiów licencjackich i magis e skich (w zak esie echnologii, nauk i medycyny) są mocno i częściowo sko elowane ze wz os em gospoda czym. Dob ze ozłożone inwes ycje w ozwój ksz ałcenia wyższego na kie unkach STEM mogą po encjalnie zwiększyć pozy ywny wpływ uniwe sy e ów na z ównoważony wz os gospoda czy.