Do jobs-follow-people or people-follow-jobs? A Meta-analysis for Europe and the US

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1 RUG1 Do jobs-follow-people or people-follow-jobs? A Mea-analysis for Europe and he US Jouke van Dijk, based on join work wih Gerke Hoogsra and Raymond Florax Professor of Regional Labour Marke Analysis, Universiy of Groningen, Faculy of Spaial Sciences, Deparmen Economic Geography, The Neherlands Plenary Lecure 20 h APDR Congress Renaissance of he Regions of Souhern Europe, Universiy of Évora, Évora, Porugal, July 10-11, Jouke.van.dijk@rug.nl Websie: Overview Moivaion Theoreical debae Resuls Mea-analysis 64 empirical sudies of he Carlino-Mills model for jobs-follow-people versus people-follow-jobs Conclusion and discussion 2 Classis quesion abou regional growh sill in debae Lieraure: do jobs-follow-people or people-follow-jobs? (Bors and Sein 1964; Seinnes and Fisher 1974) or relaed chicken-or-egg (Muh 1971). Laer The Deerminans of Couny Growh by Carlino and Mills (1987) wih lagged adjusmen framework. The quesion relaes o quesions like: Do people move for economic facors (jobs) or ameniies and qualiy-of-life facors? (e.g. Lowry,1966; Parridge 2010). Is he residenial locaion decision made before or afer he job locaion decision? (e.g., Deding e al. 2009). Are employmen locaions of firms really exogenous o residenial locaions? Or vice-versa (as assumed in he monocenric ciy model)? Duelling heoreical models New Economic Geography (Krugman, 1991): falling ranspor cos lead o concenraion Ameniy migraion (Graves, mid1970s): people or moving o nice places, warm climaes Agglomeraion effecs, araciveness of (big) ciies, high level faciliies, culural ameniies (Gleaser e al, 2001 ec., Florida, 2003) Sorper & Sco (2009): people only move o nice places wih suiable employmen Parridge (2010): for he US, Graves is he winner! 5 Policy relevance The quesion wha deermines growh plays a cenral role in policy discussions: is caering o he wishes of firms by improving he business climae of a place a beer sraegy han caering o wishes of people and improving he people climae of a place? China: changing locaion paerns of firms (inland move), changing migraion paerns, especially of higher educaed and richer people wih changing preferences Changing policy focus from only economic goals like GDP, income and (un-)employmen o broader goals like well-being and qualiy of life: e.g. OECD-projec How is life in your region? Well-being Qualiy of life - Happiness The problem of definiion Many erms for more or less he same hing (how well one s life is going) - Qualiy of life - Welfare / Well-being - Healh - Happiness shor erm: emoional feelings of happiness long erm: life saisfacion 1

2 People s Well-being: changing preferences Resilience of ciies/regions Objecive measures Life expecancy Moraliy raes Povery Crime Income Un-/employmen Educaion Gender balance Working hours Subjecive measures Healh percepion Access o services Maerial deprivaion Safey and rus Life saisfacion Happiness Capabiliies Equal opporuniies Work life balance Environmenal resilience Social resilience Bearable Equiable Susainable Viable Economic resilience Regional developmen: European Economic space Employmen rae 2010: dark is beer (jobs per inhabians years) 10 The world is spiky: concenraion of people and economic aciviies. BUT big ciies have higher iniial GDP, bu NOT higher growh raes! (Broersma & Van Dijk, 2008 and OECD, Regional Oulook, 2011) Populaion densiy Rural urban ypology Agglomeraion and growh Growh Lineair unfinie growh? Finie growh? Source: EU-Commision (November 2010), Invesing in Europe s fuure, 5-h Repor on Economic, 11Social and Terriorial Cohesion Trade off beween agglomeraion benefis vs congesions cos? Size 2

3 Source: OECD, Regional Oulook, 2011 Big ciies have higher iniial GDP, bu NOT higher growh raes! Opporuniies for growh are observed in all ype of regions! Source: OECD, Regional Oulook, 2011 Do jobs follow people or people follow jobs? A mea-analysis of Carlino-Mills sudies Gerke Hoogsra, Raymond Florax en Jouke van Dijk (2014) Modelling do jobs follow people or people follow jobs? Lae 1960s variey of echniques were pu forward, bu in a small and fragmened group of sudies. Lae 1980s, he number of research sudies has rapidly grown and here has been relaively lile disagreemen abou he choice of mehodology due o he publicaion of The Deerminans of Couny Growh by Carlino and Mills (1987), which marked a radical deparure from previous causaliy sudies in wo respecs. To illusrae he imporance of he publicaion: i was he mos cied regional science aricle of Isserman (2004) Innovaive feaures of he Carlino-Mills models: Firs, US naionwide analysis of populaion employmen ineracions a a very deailed spaial scale (couny level). Second, and even more imporanly, i was he firs sudy o invesigae hese ineracions by using a simulaneous equaions model similar o he one inroduced by Seinnes and Fisher (1974), bu wih a lagged adjusmen framework buil in. Criicism: he idenificaion of he simulaneous equaions sysem is ofen problemaic because of he lack of good insrumens and ha he resuls may herefore no be reliable (see, e.g., Rickman 2010). 18 3

4 Carlino-Mills model srucures Taxanomy of Carlino-Mills model specificaions levels vs changes wih/wihou cross/spaial auoregressive lags Table 1. Taxonomy of Carlino Mills model specificaions / (LHS) / RHS) 1 2 δ1/δ2* δ1/δ2* δ3** δ4*** Inroduced by: a Carlino & Mills (1987) b Mills & Carlino (1989) c Boarne (1992) d Luce (1994) e Vias (1998) f Henry e al. (2001) g Carruhers & Mulligan (2008) h Kim (2008) Noe: LHS (RHS) refers o variables on he lef-hand-side (righ-hand side) of he equaions. * 0 = populaion/employmen levels and 1 = populaion/employmen changes. ** 0 = wihou spaial cross-regressive lags and 1 = wih spaial cross-regressive lags. *** 0 = wihou spaial auoregressive lags and 1 = wih spaial auoregressive lags. See also Equaions (1) (6). Mea-analysis of 64 sudies for US and Europe The applicaion of saisical echniques o collecions of empirical findings from previous sudies for he purpose of inegraing, synhesising, and making sense of hem (Wolf, 1986) We will use a mulinominal logi model and base he inerpreaion on he marginal effecs obained from his model 21 Mea-analysis based on 64 sudies wih 321 resuls journal aricle oher (e.g., book chaper, working paper, hesis, disseraion) Journal aricles 23 Carlino-Mills model wih simulaneous equaions: possible oucomes ~ P ~ a W ~ E E ~ 0 a 1P 1 a 2 ( I ) u b b E b ( I W ~ ) P ~ v a2 > 0 (people follow jobs) b2 > 0 (jobs follow people) 2 a 2 0 a 2 > 0 b 2 0 b 2 > 0 No ineracion people follow jobs only jobs follow people only dual causaliy 4

5 Classificaion of he resuls Significance Unweighed sample ,3 23,2 34,3 29,1 45,2 39,4 23,3 23,4 22,4 23,1 19,8 18,4 12,7 13,1 13,4 13,1 12,7 12,1 Weighed sample 40,8 34,3 29,6 35,2 24,3 28,1 0% 20% 40% 60% 80% 100% Resuls are weighed based on he daase used NI JP PJ DC Mea analysis wih conrol variables Model specificaion: changes/levels, spaial weighs Area scaling: densiies VS shares Linear VS Non-linear (mosly logarihm) specificaion Two or more equaions in he simulaneous sysem Weighmarix: flows vs disance/no Geographical area: (pars of) US, Europe Area size: small medium large Period: 1970s s VS 1990s s Wih Land use, Income, Economic variables included Toal populaion/employmen vs subgroups Journal vs non-journal aricles Noe: only sudies wih resuls a 5% significance are used for he mulivariae mea analysis Esimaion resuls mulinomial logi model (marginal effecs a he means) NI JP PJ DC Subsanive sudy facors US Wes.586 (.103).149(.099).100(.049) (.097) US Eas.329(.094).137 (.137).369 (.139) (.109) Non-US.226 (.091).476 (.189).098 (.116) (.134) Enire US* Small sized area obs..614 (.137) (.143).025(.070) (.124) Large sized area obs (.109) (.281).692(.260) (.135) Medium sized* 1970s s daa.092 (.076) (.112).026 (.107) -.007(.085) 1990s daa* Subgroups.729(.085) -.329(.098) -.102(.064) (.079) In parenheses he sandard errors. Significan a he 5% level Mehodological sudy facors NI JP PJ DC LHS & RHS levels (.100).700 (.144) (.081) (.115) RHS changes & LHS levels.127(.396).238 (.295) (.086) (.183) LHS & RHS changes* Densiies -.256(.095) (.117).104 (.135).313 (.158) Shares* Non-linear funcion form (.091) (.106) (.086).576 (.155) Linear Flow marix -.381(.052) (.142) (.108).530 (.210) Oher, like disances* Wih SAR.086 (.131).033 (.164) (.090) (.087) 2+ Equaions (.121) (.183).120 (.122).248 (.238) Land use variables incl..119(.086).000 (.090) (.078).025 (.073) Income variables incl..384 (.112) (.172) (.126) (.143) Economic variables incl (.091).212 (.108).042 (.099).000 (.126) Exernal sudy facors Non-journal aricle.083(.095) (.119) (.077).198 (.120) In parenheses he sandard errors. Significan a he 5% level Conclusions and discussion Empirical evidence from 64 sudies for he US and Europe on jfp-pfj: sill mixed and inconclusive resuls One hird each for no-ineracion, jfp+pfj, dual causaliy Jobs-follow-people > people-follow-jobs (abou 2x more) Daa maer: resuls vary by geographic locaion of he regions, spaial resoluion and populaion and employmen characerisics, bu no by ime period Mehodology: resuls vary by levels vs changes, funcional form, specificaion weighmarix, sandardizaion by densiy or shares, number of equaions, inclusion of oher variables; bu no by SAR No difference by publicaion ype 29 Suggesions for fuure research on jfp-pfj Evidence from ouside he US and Europe e.g. China! Use models ha permi causiliy running in differen direcions and es robussness wih alernaive models Include variables for land use, spaial policies, income and economic condiions. Naural and culural ameniies, locaion and demographics are less imporan W-marix wih flows is preferred, bu less exogenous Mea-analysis on size of he parameers insead of sign Or: Microlevel analysis of underlying processes based on firm-employee micro-daa 30 5

6 Policy relevance The quesion: improve he business climae for firms or he living condiions for he people? depends on he characerisics of he region place based policies needed. Mos likely improving boh is needed Wha goals o reach: from purely economic or broader well-being perspecive? Wha are he peoples preferences? Wha are effecive and efficien policy measures? Thank you for your aenion 6