The Role of Economic Integration for European Cities and Border Regions. Abdella Mohammed Oumer

Transcription

1 The Role of Economic Integrtion for Europen Cities nd Border Regions Abdell Mohmmed Oumer

2 Publisher: University of Groningen, Groningen, The Netherlnds Printed by: Ipskmp Drukkers P.O. Box AH Enschede The Netherlnds ISBN: (book) (e-book) 2014 Abdell Mohmmed Oumer All rights reserved. No prt of this publiction my be reproduced, stored in retrievl system of ny nture, or trnsmitted in ny form or by ny mens, electronic, mechnicl, now known or herefter invented, including photo-copying or recording, without prior written permission of the uthor.

3 The Role of Economic Integrtion for Europen Cities nd Border Regions PhD Thesis to obtin the degree of PhD t the University of Groningen on the uthority of the Rector Mgnificus Prof. E. Sterken nd in ccordnce with the decision by the College of Dens. This thesis will be defended in public on Mondy 2 June 2014 t 16:15 hours by Abdell Mohmmed Oumer born on 10 June 1980 in Melkbello, Ethiopi

4 Supervisors Prof. dr. S. Brkmn Prof. dr. J.H. Grretsen Co -supervisor Dr. G. Mrlet Assessment committee Prof. dr. H. L. F. de Groot Prof. dr. C. J. G. M. vn Mrrewijk Prof. dr. R. J. M. Alessie

5 Acknowledgements I extend my grtitude to severl people nd institutions beyond my PhD study period. I would like to thnk the dministrtors of the Eric Bleumink Fund (EBF) which funded my mster study where my connection with the university ws first creted. I lso wish to thnk my mster thesis supervisor nd the current rector mgnificus, prof. dr. Elmer Sterken. Prof. dr. Pul Elhorst nd other members of the Fculty of Business nd Economics who offered vrious forms of dvice nd ssisted me throughout the dys of new socil nd cdemic environments lso deserve my grtitude. I m very grteful nd most pprecitive of my supervisors, prof. dr. Steven Brkmn nd prof. dr. Hrry Grretsen, for their vluble guidnce nd ssistnce s well s the instructive nd vluble contributions in the co-uthored nd other chpters of my thesis. I pprecite your understnding when I took extr times when I ws experiencing difficulty with the dt serch nd other issues nd, bove ll, for the fun nd refreshing jokes in nd out of our meetings. I would like to extend my grtitude for the pprecition of my work which lwys gve me extr energy nd mde me feel t ese t ll times. I lso wnt to thnk prof. dr. Chrles vn Mrrewijk for the initil lesson regrding the importnce of being precise, which I lerned during the work on the chpter co-uthored with him, nd for the contribution to the chpter. My specil thnks go to the members of the reding committee: prof. dr. Henri de Groot, prof. dr. Chrles vn Mrrewijk nd prof. dr. Rob Alessie for their time reding my thesis nd for their vluble comments. I m very gld tht my work is evluted by such incredible nd high level cdemicins nd reserchers. I would like to thnk SOM nd beloved GEM secretries for fcilitting my work spce supports nd for their help in mking me feel t home. I would lso like extend my grtitude to those t the university of Groningen nd other institutions in the Netherlnds s well s those in other countries who hve provided me with support during the dt collection. I m lso grteful Dr. Dirk Stelder with whom I, sometimes, consulted when I hd technicl questions relted to dt nd the simultion nlysis. My grtitude lso goes to the Pltform31(former NICIS) for (prtilly) funding my PhD reserch project nd to the representtives of the Pltform31 projects for their vluble comments during the Pltform31(NICIS) seminr meetings. Specil thnks goes to Dr. Gerrd Mrlet for his dvice nd ssistnce rnging from being the contct person with my reserch project sponsors, Pltform31; rrnging the Pltform31(NICIS) seminrs; t times, being my Dutch trnsltor in meetings; helping me with the collection of certin specil dt; constructive correction nd comments to detiled discussions of my results; nd mny more. I pprecite his ttending our meetings so mny times t my office s well s sometimes t the trin sttion in Groningen coming ll over from Utrecht to sve me time. Lst, but not lest, I lso cknowledge his time nd contribution to the co-uthored chpter.

6 To my collegues nd friends tht I hve met in Groningen, in nd outside the university, thnks for your time discussing reserch s well s hving fun with sports nd other socil ctivities. To my prents, thnks for the unique support tht is difficult to put into words. To my reltives from Hrmy, thnk you s well. I could not continue without mentioning my elementry school techer, Smuel (Smi) Aber, long with his wife, Alem. You were the first person to tell me so mny yers go tht I should continue my studies t lest until obtining PhD. Smi, you kept in contct with me nd continued to give me morl support throughout the yers. Alem, you re the person I miss the most in the entire world, nd I m deeply sddened s I type these words s you re no longer round to see my chievements nd your children s ccomplishments. Even though some of them re not even ble to red nd understnd this text, I still wish to thnk ll of my friends bck in our villges who helped me bck in those dys with so mny things including the vction times frm works so tht I could go bck to school in time. Finlly, thnks to ll of the people tht I hve met on my journey from elementry school up to tody who tught me lesson of kindness. Thnks Dine, Abs, Asi, Ahmedhdi, Sfo, Ashun, Um, Sufin, Eftu, Mme Mustef, Keml, Jmes, Mwerdi, two Abdi s, nd Firdows, to mention few. To ll of you who touched my life one wy or nother Abdell (Abdi) Groningen, Mrch 2014

7 Contents Chpter One: Introduction... 1 Chpter Two: Ntionl Borders nd Mrket Access Introduction Ntionl Borders, Trde, Mrket Access nd Wge The Empiricl Model The Dt Estimtion Results Conclusions Appendices Chpter Three: The Border Popultion Effects of EU Integrtion Introduction EU Enlrgement nd the Introduction of the Euro Theoreticl Frmework The Dt Empiricl Strtegy Estimtion Results EU Enlrgement The Introduction of the Euro Conclusions Appendices Dt Description Rndom Border Chpter Four: Asymmetric Border Effects of EU Integrtion: Evidences from Dutch, Belgin nd Germn Municiplities Introduction EU Enlrgement, Border Regions nd Reserch Motivtion Theoreticl Bckground The Dt Estimtion Strtegy Estimtion Results Bseline Estimtes Densely Populted nd Less Populted Municiplities Extensions nd Evlutions Conclusions Appendices Chpter Five: Town Twinning nd Growth in Germn Cities Introduction Town Twinning: History, Motives nd Theory The Dtset Estimtion Strtegy Estimtion Results Bseline Results Additionl estimtions nd robustness checks Conclusions Appendices

8 Chpter Six: Long-run Effects of Improved Trnsporttion Links on Size of Dutch Cities Introduction The Models Agglomertion Effects of Trnsporttion Cost nd Congestion The Policy Scenrios: Abolition of Trffic Congestion The Long-Run Effects Conclusions Chpter Seven: Conclusions Smenvtting (Summry in Dutch) References

9 List of Tbles 2.1: Summry of smple border nd non-border cities : The wge rte nd the totl mrket ccess : The wge rte, nd the ntionl nd foreign mrket ccess. 18 2A.1: The Smples Cities A.2: The wge, distnce to the borders nd the lnguge.21 2A.3: The wge rte nd different ntionl borders 22 2A.4: The wge rte, nd the totl mrket ccess (vrious scenrios) 22 2A.5: The wge rte nd the foreign mrket ccess (vrious scenrios) A.6: Vrious mrket ccess scenrios A.7: Exmple of selection of the epsilon (ε) : Overview of Europen Union enlrgement process : Included countries with # of regions nd # of cities : Overview of ffected continentl lnd borders in smple period : Urbn nd regionl popultion shre growth rtes; bseline estimtes : Urbn popultion shre growth rtes; vritions in distnce : Urbn popultion shre growth rtes; extent of distnce effect : Urbn nd regionl popultion shre growth rtes; timing effect estimtes : Urbn nd regionl popultion shre growth rtes; smll nd lrge res : Urbn nd regionl popultion shre growth rtes; symmetry: old nd new members : Urbn nd regionl popultion shre growth rtes; introduction of the euro A.1: Bsic urbn nd regionl informtion (EU integrtion) A.2: Urbn nd regionl popultion shre growth rtes; rtificil border : EU Enlrgement Process nd Integrtion Shocks : Smple dt : EU Integrtion Shocks Sttus nd Expected Effects on the Smple Countries : Direct Integrtion Effects on Popultion Growth; Bseline estimtes : Indirect Integrtion Effects on Popultion Growth; Bseline estimtes : Direct Integrtion Effects on Popultion Growth; Low Density Municiplities : Direct Integrtion Effects on Popultion Growth; High Density Municiplities : Structurl brek test in popultion growth : EU Integrtion Shocks nd breks in border popultion growth A.1: Direct Integrtion Effects; Bseline Estimtes with Common Integrtion A.2: Indirect Integrtion Effects; Bseline Estimtes with Common Integrtion A.3: Direct Integrtion Effects; less dense municiplities with Common Integrtion A.4: Direct Integrtion Effects; highly dense municiplities with Common Integrtion A.5: Indirect Integrtion Effects; less dense municiplities with Common Integrtion A.6: Indirect Integrtion Effects; highly dense municiplities with Common Integrtion A.7: Direct Integrtion Effects; Netherlnds nd Germny common smple periods...81

10 4A.8: Indirect Integrtion Effects; Netherlnds nd Germny common smple periods A.9: Direct Integrtion Effects; ll smple countries common smple periods A.10: Indirect Integrtion Effects; ll smple countries common smple periods A.11: Optiml brek points (M +1 segments) A.12: Summry optiml brek points (test sttistics) : Germn twinning with the interntionl cities nd towns : Top 40 Germn twinning prtners (2012) : Twinning by Germn cities nd popultion growth (full smple) : Twinning with Frnce : All twinning, IV estimtes : Twinning with Frnce, IV estimtes : Twinning with the EC nd EU countries, IV estimtes (whole Smple) : Twinning with Neighboring countries, IV estimtes (whole smple) : Twinning with Neighboring countries, IV estimtes (smll vs lrge) : Twinning with Frnce, dditionl IV estimtes (prtnerships + friendships) : Twinning with Neighboring countries, dditionl IV estimtes(prtnership+friendship).106 5A.1: Twinning with Frnce, IV estimtes (With IV set b vribles) A.2: Twinning with Frnce (smll vs lrge) A.3: Twinning with Frnce, IV estimtes (smll vs lrge) A.4: Twinning with Frnce (erly vs lte twinners) A.5: Twinning with Frnce, IV estimtes (erly vs lte twinners) A.6: Twinning with the EC nd EU countries, (whole Smple) A.7: Twinning with the EC nd EU countries, IV estimtes (whole Smple, IV c) A.8: Twinning with the EC nd EU countries, IV estimtes (erly vs lte) A.9: Twinning with Neighboring countries (smll vs lrge) A.10: Correltions: twinning nd the instruments A.11: Merging Twinning nd popultion dt : Summry of the projects trvel time (T rs ) effects : The prmeters configurtion : Summry: Men gins within ech model option nd cross the models : Number of net gining municiplities within ech model option : Correltion between chnges in the popultion shre nd trvel time : Detiled version of tble 6.5 for AUTOP : Correltion of % effects of projects with the chnge sum in the trvel time : More detiled version of tble : More detiled version of tble : Correltion between chnges in the cities size nd sum of % chnges in trvel time...138

11 List of Figures 2A.1: The nnul results: border versus non-border cities 75 kilometers A.2: The nnul results: border versus non-border cities 85 kilometers A.3: The nnul results: non-border smples 75 kilometers nd 85 kilometers A.4: Chnge in mrket ccess under different scenrios : Historicl expnsion of the Europen Union, : The Europen Union in : Averge nnul compounded popultion growth rtes : Dutch (border) Municiplities : Germn (border) Municiplities : Belgin (border) municiplities A.1: The Robust Error terms; Exmple from the Netherlnds municiplities A.2: Structurl breks in Popultion (shre) growth: ll Netherlnds bordering Belgium : The geogrphy of town twinning in Europe : Men number of twinning b: Number of municiplities/counties with t lest one twinning connection c: Municiplities/counties with t lest one (or men) twinning : The geogrphicl distribution of Germn twinning nd time trend : Mjor twinning prtner countries for Germny : Rndstd, the Netherlnds : Chnging Trnsporttion T ij = T i, fixed Congestion fctor ( ) b: Chnging Congestion fctor ( ), fixed Trnsporttion T ij = T i : Chnges in the Mrket Potentil : Approximte initil distribution : Chnges in the cities size (Core-Periphery model) b: Chnges in the cities size (Congestion model) : Long-run effects of the projects (T = trvel time in minutes) : Long-run effects of the projects on lrge nd polder municiplities : Long-run effects of the projects (top gins)

12 Chpter One Introduction There re significnt disprities in economic ctivity between countries nd regions. To some extent these disprities between loctions re due to differences in institutions nd nturl endowments. Both the inter- nd intr-ntionl disprities re typiclly more significnt when the distnce between loctions is lrger nd when loctions re less integrted. This suggests tht geogrphicl brriers nd trnsporttion costs, or economic geogrphy in generl, lso ply role in explining sptil disprities. It lso suggests tht the reduction in distnce, or trnsction costs in generl, chnges the reltive ttrctiveness of loctions/cities. This cn be explined by the new economic geogrphy (NEG) literture which emphsizes the importnce of mrket ccess s well s by the urbn economics literture which would point towrds chnges in the locl chrcteristics (e.g., following incresed trde opportunities) in determining such sptil vritions. The present thesis nlyses the impct of economic integrtion on the sptil lloction of people nd economic ctivity nd does so in prticulr for border loctions in the Europen Union (EU). Differentiting between the contributions of ech of the fctors to sptil economic disprities is difficult. The rel chllenge here is discovering chnges nd ttributes to the prticulr fctors. The effects of some shocks to urbn systems hve been studied before. For instnce, Dvis nd Weinstein (2008) s well s Bosker et l., (2007), studied the exogenous shocks of the destruction of cities in Jpn nd Germny during World Wr II (WWII), respectively, nd exmined the consequence of the reduced popultion in cities nd destroyed economic ctivities. They scertined tht the cities only returned to their originl equilibrium decdes fter the wr ws over. Mrket ccess, the concept tht is centrl to the new economic geogrphy literture, cn be ffected by exogenous shocks tht lter the ccessibility to other mrkets, especilly in geogrphicl proximity. It is well known tht ntionl borders significntly dd to trnsporttion costs nd reduce (interntionl) trde. Few studies, however, isolte the effect of borders on border gglomertions. This is remrkble s it cn be expected tht, for exmple, border cities re generlly ffected by the nerby borders nd tht sudden chnges tht re relted to these borders re felt especilly by the border cities. The work tht is most similr in this respect is Redding nd Sturm (2008) which nlyzes the effects of division nd reunifiction of West nd Est Germny on popultion growth of cities ner the newly creted border. They discovered significnt decline following the division nd subsequent recovery fter reunifiction. Their study is limited to nd exmines the West Germn side of the border nd provides n interesting insight. The division of West nd Est Germny ws creted within the sme territory tht used to be sme country for few decdes. This thesis expnds this by ddressing whether the sme results would hold if the 1

13 integrtion shocks re occurred on ctul multiple ntionl borders seprting countries for much longer periods of times s well s with different types of integrtion shocks. The chllenge in ddressing these issues is the lck of proper dt regrding the economic ctivities with detils of loctions. This thesis employs vrious EU integrtion shocks s qusinturl experiment nd ddresses this question in vrious wys. In Europe, there re no dequte dt systemticlly vilble t city level dt on income, jobs, trde nd other economic ctivities. Therefore, in this thesis, we minly exploit the popultion dt which re generlly more ccessible s the best proxy mesuring the extent of economic ctivities in cities/towns. In this spect, the min contributions of this thesis to the literture is tht it expnds on wider rnge of integrtion shocks minly in border integrtion, in trnsporttion infrstructure, nd socil integrtion cross geogrphiclly wider rnge of interntionl cities nd towns. We employ estimtion nd simultion pproches nd provide rnge of new results to the literture nd for utiliztion in policy mking. This thesis consists of five relted chpters. Chpter Two explores mrket ccess cross ntionl borders. It uses dt from the oldest EU members nd reflects on the importnce of mrket ccess in geogrphicl proximity s well s the differences between loctions ner ntionl borders nd centrl loctions. Chpter Three investigtes the effect of the entire EU integrtion process on the popultion of, especilly, ner border loctions. Chpter Four undertkes similr nlysis s in Chpter Three but in more detiled mnner by employing more detiled, but geogrphiclly limited, loctions. This section seprtes different sides of the sme borders between the two countries under considertion. Chpter Five tkes the nlysis of the integrtion process beyond the geogrphicl scope. It investigtes individul integrtion processes between individul cities nd towns with cities nd towns round the world nd not just in the EU re. Chpter Six exmines the potentil effects of the reduction of trnsporttion brriers within ntionl boundries. The objective of Chpter Two is to explicitly compre the mrket ccess of border cities to non-border cities with nd without border brriers; nd the reltionship of the wge rtes with the mrket ccess to the other side of ntionl borders. The nlysis in this chpter consists of two min sections. We clculte the mrket ccess nd estimtes the wge model while simulting the opening up of the border restrictions. Redding nd Venbles (2003), Boulhol et. l. (2008) s well s Boulhol nd De Serres (2010) explin how distnce (trnsporttion cost) nd other restrictions on mrket ccess led to vrition in wge structure. In the first section, we exploit dt from 1995 to 2006 from 107 cities in three countries (Belgium, Germny nd the Netherlnds) s these countries were the erliest to bolish their common border brriers in the EU integrtion process. Furthermore, this smple includes two types of ntionl borders. One is the Netherlnds-Belgium border with n historiclly longer period (since 1948) of free trde nd where the sme lnguge (Dutch) is spoken on both sides of the border. The other type is the Netherlnds-Germny border with rther more recent history of free trde nd where different lnguges (Dutch in the Netherlnds nd Germn in Germny) re spoken cross the borders. We demonstrte tht the bolition of borders increses the mrket ccess for ll cities; however, the increse is higher in cities tht re locted closer to the ntionl borders. The estimtion results confirm tht the mrket 2

14 ccess to the neighboring countries mrket is more significnt for the bordering cities thn it is for the non-border cities. The results lso show tht, fter more thn 50 yers from the implementtion of the first free trde greements, the cities tht re closer to the ntionl borders still hve proportionlly lower wge rtes throughout the entire smple period. Moreover, we discovered evidence tht the two borders re different. Chpter Three expnds on Redding nd Sturm (2008) to encompss the entire EU re. We tke into considertion severl discretionry policy-induced chnges or (qusi-) nturl experiments of the EU integrtion process to elucidte the consequences of chnges in mrket ccess to the popultion of towns nd regions close to the ntionl borders with nd without the border brriers. We lso investigted other integrtion shocks such s monetry union or doption of the EURO single currency. Certin significnt destructive shocks such s the llied bombing of Jpnese cities (see Dvis nd Weinstein, 2002) or similr exercises of bombing Germn cities by llied forces (see Brkmn et l., 2004) during World Wr II (WWII) demonstrte tht the development of cities indeed follows reltively stble pth in the sense tht cities tend to return to their pre-shock stte following the shock. At the sme time, it is possible tht the development of cities progresses to nother development pth, see Bosker et l. (2007). Some less drmtic experiments or shocks such s chnges in the degree of economic integrtion, s in the EU cse or Germny unifiction, illustrte tht the effects, for border cities in prticulr, cn still be substntil. The novelty of this chpter is tht the border effects of the multiple stge EU integrtion process on cities long ntionl borders hs not been previously nlyzed which is in contrst to studies tht emphsize the importnce of the border effect on trde in generl. We employ the difference-in-difference estimtion pproch nd scertin tht the EU enlrgement process leds to dditionl growth s mesured by the growth in popultion shre long the integrtion borders. This integrtion effect decreses with distnce nd, over time, is pproximtely the sme for new nd old members nd is more significnt for lrge cities nd regions. Despite this EU integrtion effect ssocited with EU enlrgements, being locted long border remins burden in view of the (lrger) generl negtive border integrtion effect. We do not find similr border-integrtion effects s result of the introduction of the euro. Chpter Four exmines the erlier nlysis in more detil employing similr pproch s in the erlier chpter but utilizing sptilly more detiled nd longer time dimension dtset of limited number of the oldest EU member countries, specificlly, Belgium, Germny nd the Netherlnds. The new dt llow us to investigte whether borders hve two sides; re cities on ech side of the borders ffected differently? Moreover, we test for structurl breks in the popultion growth in the border loctions following vrious EU integrtion shocks. We lso ssess the possible vrition in the (in)direct integrtion effects on border loctions over time s the EU expnds to countries tht do not involve the smple borders. Using popultion growth dt from municiplities of Belgium, Germny nd the Netherlnds, our results confirm the existence of symmetric border effects. We lso scertin different indirect effects from the EU expnsion on the non-neighboring countries. Generlly, the positive integrtion effects on the border loctions continue for limited periods of time but persist for longer periods for some borders more thn others. 3

15 In Chpter Five, the nlysis focuses on town-twinning (TT). Town-twinnings re specil reltionships between cities in different countries. These reltions vry between letters between school children to more specific economic reltions. It cn be expected tht town-twinning reduces trnsction costs between cities tht re not relted to border loction s such, but still might ffect MA. In this chpter, we exmine the consequences of town-twinning of cities tht re involved in these inititives. Due to the vilbility of dt nd becuse of the specil position of Germny in Town-twinning we focus on Germn twinning with the rest of the world nd scertin tht twinning cities grow fster thn non-twinning cities. Using the instrumentl vrible (IV) estimtion pproch, we find tht there is cusl reltionship between popultion growth nd twinning. Town-twinning might fcilitte city growth through incresed trde or migrtion, but this is difficult to prove due to lck of dt on trde nd migrtion between the town-twinning prtners. Chpter Six focuses on the nlysis of the effects of reduced trnsporttion costs within ntionl boundries through improved trnsporttion links between cities which would subsequently reduce trde cost. Very high or very low trde costs fvor the dispersion of economic ctivities while gglomertion would emerge for intermedite vlues of these costs once the sptil mobility of workers is low (Fujit nd Thisse, 1996). Although dispersion is usully unfvorble when compred to gglomertion (Tbuchi, 1998) or not necessrily beneficil (Bldwin et l., 2003) from welfre perspective, dispersion necessrily tkes plce when such policy intervention mkes trnsporttion cost sufficiently less (Tbuchi, 1998). We employ the soclled Core-Periphery (CP) model nd its extension, the Core-Periphery Congestion (CPC) model of the New Economic Geogrphy, with the interregionl fctor mobility by Krugmn (1991). Prior to the simultion nlysis of improved trnsporttion policy scenrios, we nlyze the spreding nd gglomertion effects of trnsporttion cost nd congestion in multiple-regions cse. We nlyze the long-run implictions of four different rod nd rilwy projects tht re intended to improve trnsporttion between the lrge cities in the west of the Netherlnds clled Rndstd nd nerby smller municiplities. With the simultion nlysis, we ttempt to nswer the following questions. Does this intervention led to the reloction of firms nd workers into the municiplities ner the projects t the expense of the other municiplities? Do ll municiplities benefit from this intervention, or do only lrge municiplities gin over smll ones in the vicinities of the projects? How do the effects differ cross municiplities of different sizes nd cross municiplities tht re t different distnces from the project loctions? The results re, in generl, in ccordnce with the literture. The spreding or gglomertion effects of the trnsporttion cost nd congestion cost in multiple-regions setting supports the results bsed on the two-regions setting. The results from the CP model suggest tht the reduction in trde cost through reduced trvel time, in generl, leds to incresed gglomertion in the lrger municiplities. However, the CPC model, s one would expect, demonstrtes the opposite. In the ltter, there re dispersions principlly from the lrge municiplities ner the project loctions to municiplities modertely frther wy s well s from bigger to reltively smller municiplities. In generl the thesis finds tht shocks, nd by this we minly refer to (policy induced) chnges in the degree of economic integrtion reduces brriers between the core nd the periphery, nd in generl reduce trnsction costs between cities nd regions nd this leds to fster 4

16 popultion growth of the loctions directly ffected. Following bolition border brriers, more firms nd workers my prefer to locte ner the borders due to ccess to the mrkets nd jobs on the other side of the border. However, not ll economic integrtion shocks hve this effect, notbly the introduction of the Euro. Furthermore, economic integrtion ffects cities on two sides of the border differently, nd town-twinning fcilittes popultion growth of the prticipting cities. Improved trnsporttion links potentilly led to dispersion of economic ctivities from high gglomertion to less populted res, but this effect reduces with distnce. A lrge scle integrtion involving severl countries in EU brings bout severl nd complex chnges such s opening up severl pre-integrtion peripheries to severl pre-integrtion cores. This my crete new cores nd new peripheries. In future reserch, the exploittion of dditionl dt regrding employment, migrtion nd trde on detiled sptil level in the estimtions s well s the utiliztion of more comprehensive model in the simultion pproch would be very beneficil. 5

17 6

18 Chpter Two Ntionl Borders nd Mrket Access 2.1. Introduction The size nd distribution of cities nd the resulting mrket ccess re determined by the reltive strength of centripetl forces nd centrifugl forces, see f.i. Krugmn (1991). Centripetl forces comprise the physicl proximity to mrkets, humn cpitl, infrstructure, vriety of consumer goods, nd thick lbor mrkets. Centrifugl forces re forces such s excessive trffic congestion, incresed living costs s well s considerble trnsporttion costs. The former led to the concentrtion of economic ctivities, wheres the ltter stimulte spreding of economic ctivity (Krugmn, 1980, 1991 nd 1995; nd Fujit et l., 1999). Although city s individul chrcteristics such s soring levels of humn cpitl strongly correlte with cities growth in both popultion nd income per cpit (Gleser et l., 1992), there is lso widespred evidence tht incresing mrket ccess contributes to growth nd rising income levels (Boulhol et l., 2008). An improved ccess to mrkets is determined, mong other things, by the geogrphicl proximity to other cities, ccess to inexpensive trnsporttion routes nd the bsence of rtificil obstcles such s border restrictions. Thus, geogrphicl distnce nd ntionl borders led to vrition in commodity prices, nd this subsequently leds to vrition in wge rtes between cities. The vrition of the price is much higher for two cities locted in different countries thn for two equidistnt cities within the sme country. Wges tend to be higher in metropolitn res with greter mrket ccess thn in res with miniml mrket ccess (Fllh et l., 2010). The (bolitions of) ntionl border brriers ffect trde. Chnges in the trde due to chnges in the border brriers cn ffect the wges of differently skilled workers differently (see, for exmple: Feenstr, 2000). Border brriers negtively ffect interntionl trde. McCllum (1995) finds tht the presence of the Cnd US border results in, on verge, 20 times lower trde cross the border thn comprble within-country trde. Although subsequent reserch suggests tht this number is too high nd should be reduced to fctor 10, border effects remin lrge (Feenstr, 2004, Anderson nd vn Wincoop, 2003). Despite these findings of lrge negtive border effects, borders chnge nd so do the border effects. Hnson (2001), for exmple, finds positive nd strong correltion between NAFTA nd Mexicn export growth, nd Redding nd Sturm (2008) find tht the construction of the Iron curtin nd the subsequent Fll of the Wll within Germny effected border cities significntly. They employ multi-region version of the geogrphicl economics model by Helpmn (1998) to demonstrte how firms mrket ccess (FMA) s well s consumers mrket ccess (CMA) determine the equilibrium on the lbor mrket. Geogrphicl border brriers limit the mrket ccess of both firms nd consumers nd, therefore, gglomertion. We will employ this pproch in our nlysis in the subsequent chpters. 7

19 The min objective of this chpter is to tke the initil step in the thesis nd ly the groundwork for the ssessments of the effects of vrious forms of integrtion shocks in the successive chpters. In this spect, we begin with the ssessment of the differences between cities tht re locted ner ntionl borders nd those cities tht re centrlly locted in terms of wges nd mrket ccess. In doing so, we simulte the effects of opening the borders between the Netherlnds, Belgium, nd Germny on the cities nd towns locted ner the common borders versus those tht re centrlly locted. In contrst to most existing studies on border effects which focus on trde of commodities, we concentrte on wge nd mrket ccess. We lso exploit cities s the units of nlysis insted of regions nd countries, which re the most often used units of nlysis. In this inugurl chpter, we lso tke the first step in directly modeling the border brriers s component of mrket ccess. Furthermore, we elborte on the difference in the importnce of mrket ccess cross ntionl borders for two groups of cities, border cities nd non-border cities. This chpter is rrnged s follows. In Section 2.2, we discuss relted literture. Although the literture we discuss is not exclusively NEG, in this chpter we dd to the importnce of borders from different points of view. The reserch methodology nd empiricl model is introduced in Section 2.3. We employ the New Economic Geogrphy (NEG) equilibrium wge model following Krugmn (1991) ccounting for loction (being locted close to borders) nd foreignness. Section 2.4 is devoted to dt description. We exploit nnul dt from three countries (Belgium, Germny, nd the Netherlnds) encpsulting the periods from 1995 to The dt include verge nnul per cpit income, verge nnul wge rte, geogrphicl (rod) distnce between the smple cities, nd cities loction reltive to ntionl borders. These countries hve been selected becuse they were the first to bolish their economic border brriers in the EU integrtion process, nd possible effects should be evident in the dt. In section 2.5, we present the estimtion results. Our estimtion results confirm tht mrket ccess to neighboring countries mrkets is significntly more importnt for border cities thn for non-border cities. The results lso indicte tht, fter more thn 50 yers following the implementtion of the first free trde greements, the cities tht re nerer to ntionl borders continue to hve, in generl, proportionlly lower wge rtes thn in centrlly locted cities throughout the entire smple period. Moreover, we find evidence of symmetric effects on the two borders, i.e., the negtive border effect is more significnt nd stronger cross the Netherlnds-Germny border thn it is cross the Netherlnds-Belgium border where the erlier free trde implementtion nd/or common lnguge cross the border my hve been of ssistnce. Finlly, section 2.6 summrizes nd concludes Ntionl borders, trde, mrket ccess nd wge Border brriers hve direct reltionship with trnsporttion nd trde costs. According to the NEG literture, trnsporttion costs re mong the most importnt fctors tht ffect loction of economic ctivities (see, for instnce, Krugmn, 1980, 1991b, 1998; Fujit, et l., 1999; Fujit nd Krugmn, 2004; nd Fujit nd Mori, 2005). These reltionships hve been extensively 8

20 documented (see for survey Brkmn et l., 2009). Redding nd Venbles (2003), Boulhol et l., (2008) s well s Boulhol nd De Serres (2010). In this chpter we focus on border brriers tht, s we emphsize in this chpter, led to vrition in wges cross cities nd regions. The impct of distnce or trnsporttion cost on the size of cities hve been extensively discussed (for exmple; Henderson, 1974; Krugmn, 1980, 1991; Tbuchi et l., 2005; Prtridge et l., 2008; Redding nd Venbles, 2003; Boulhol nd De Serres, 2010; nd Fllh et l., 2010). Border brriers, however, re specil, s the interntionl trde literture shows. Using different types of distnces 1 such s stright line distnce or ctul rod distnces, vrious studies indicte tht intrntionl s well s interntionl distnces ffect trde (see McCllum, 1995; Anderson nd vn Wincoop, 2003; nd Mnchin nd Pinn, 2009), trded commodity prices (see, for exmple, Nitsch, 2000; Wolf, 2000; nd Hillberry nd Hummels, 2003; Redding nd Venbles, 2004; Asplund et l., 2007; Clemente et l., 2009; Boulhol nd De Serres, 2010) nd even rurl income (exmple: Prtridge nd Rickmn, 2008). As sid, we focus on border brriers which cn be expected to be importnt to explin the growth of border cities. Although border loctions limit competition by shielding loction from outside competition (see Behrens et l., 2006) they in generl hve severl disdvntges. Border brriers cut off the mrkets within geogrphicl proximity nd thus reduce trde nd mrket ccess which subsequently leds lower (city) growth (see, for exmple, Redding nd Sturm, 2008) reltive to non-border loctions (see Redding nd Venbles, 2003 nd Fllh et l., 2010). Border brriers dd to distnce nd ffect wges negtively. Since distnt regions/cities suffer mrket ccess penlty on their sles nd lso fce dditionl costs on imported inputs, firms in these cities/regions cn only fford to py lower wges reltive to centrl loctions (Redding nd Venbles, 2003; nd Boulhol nd De Serres, 2010). Tht cretes wge disprity between the border res nd the centrl loctions. Engel nd Rogers (1996) demonstrte tht the distnce between cities explins the significnt mount of vrition in prices of similr goods in different cities. In ddition, border brriers dd to lbor immobility by isolting the border regions nd cities from nerby foreign mrkets cross the borders. This leds wge vritions between the two sides of the ntionl borders. According to Gleser nd Kohlhse (2004), lbor immobility is more importnt fctor thn trnsporttion costs in explining the wge differentils between cities. Emphsizing the drmtic decline in trnsporttion costs over recent decdes, they imply tht wge differentils continue to exist in world where it is essentilly free to move goods becuse it is more expensive to move people, i.e., lbor immobility. Ntionl border brriers ply significnt role in this spect by isolting mrkets cross ntionl borders. Thus, ntionl borders nd restrictions on the flow of goods nd lbor forces cross ntionl borders hve direct impliction for mrket ccess (therefore, on trde) nd wge rtes. As consequence, economic integrtion nd lbour mobility re closely relted through wge chnnel (see Schöbnd 1 In this chpter, we use ctul rod distnces (see sections 2.3 nd 2.4 for more discussion). Rod network cn be endogenous since bigger cities hve better rods thn smller towns. However, this doesn t mke much difference in rod distnces in country with flt topogrphy such s the Netherlnds nd thus we don t expect this to ffect our results. 9

21 Wildsin 2007). Opening up borders obviously ffects wge rtes through different chnnels including incresed mrket ccess, lbour mobility nd trde. The economic performnce of border regions/cities lso correltes with the performnce of bordering regions/cities of the neighboring countries even with the border brriers still in plce. Exploiting dt for the 20 Mexicn nd U.S. border cities over the period 1975 to 1997, Hnson (2001) finds positive nd strong correltion between growth in the mnufcturing of export goods in Mexico cities long the US border nd employment growth in U.S. cities long the Mexico border over the smple period. Opening or reducing the border brriers increses trde, mrket ccess, fctor mobility, nd incomes cross the borders. Thus, to the extent it increses trde between countries, bolition of ntionl borders enhnces production nd the demnd for goods nd services, employment, nd wge rtes cross the border regions/cities. It seems tht lower border restrictions re more importnt for growth thn lower trnsporttion costs (for instnce, see Bier nd Bergstrnd, 1997). Using the dt of the OECD countries during 1958 to 1988, they show tht decresing triffs were twice s importnt s decresing trnsporttion costs for growth in bilterl trde. By opening borders, the resulting incresed trde implies higher wges nd more employment in production of trded goods nd services. However, the effects cn be different for vrious types of workers (Feenstr, 2000). Importntly negtive border effects seem to be persistent, even fter forml bolition of border brriers. For instnce, when they joined the EU, Centrl Estern Europen Countries (CEECs) trded mong themselves more thn with other (older) member countries (Mnchin nd Pinn, 2009). There re fctors tht reduce the estimted negtive border effects but never mke them completely dispper (Wolf, 2004). Wolf shows tht border effects continued to ffect trde flows even fter 15 yers of the complete removl of the border brriers. Some of these persisting brriers re lnguge nd culturl brriers2. Ntionl borders re more thn just physicl brrier. Even if the ntionl border brriers re removed, following economic integrtion, some types of trde brriers continue to exist: ntionl borders mtter (McCllum, 1995). Regions nd ntions continue to trde in fmilir nd old mrkets (history mtters) even fter bolition of trde brriers nd monetry union. This chpter deprts from (dds to) the existing literture in two wys. First, it focusses on cities rther thn regions or countries. Cities re the economic centers of economic ctivity. Therefore, it is interesting to ssess whether or not similr effects exist t city level s on the region or country level. Second, we focus on city loctions t the border itself. 2 Exchnge rte vribility my no longer be significnt fctor in countries such s the common currency euro res, wheres ll other fctors cn still collectively explin the persistence of border effects mong the member ntions. We will test for the effects of the doption of the common currency, euro, in Chpter Three. 10

22 2.3. The empiricl model Following Hrris (1954), reserchers use the following simple distnce weighted model to clculte mrket potentil of region (MP ) in empiricl studies: MP N i1; M i D i. N is number of cities; M i is mesure of the size of economic ctivity of region i; D is mesure of the distnce i or nother proxy of trnsport costs between region nd region i; nd n is the number of regions/cities. Another commonly used geogrphicl distnce weighted mrket potentil or Mrket Access (MA), bsed on Krugmn (1991) is: MA is the mrket ccess of region or city ; Y i MA 1 where is per cpit income; I i N i i1; i Y I T ; 1 Di(1 ) i is the price index for mnufctured goods; 1 is the elsticity of substitution; T is the trnsport cost prmeter; nd D i is the distnce between loctions nd i. By equting the wge rte with the MA, we ttin the widely used equilibrium wge model of core new economic geogrphy (NEG) model by Krugmn (1991) s presented in eqution (2.1) W 1 ; nd N Y I i i1; i T 1 Di(1 ) i (2.1) Our nlysis in this chpter is bsed minly on this NEG equilibrium wge eqution; however, we lso use the Hrris (1954) pproch in the descriptive nlysis of chnges in the mrket potentil of the border versus non-border cities (see section 2.5). The mrket ccess summrizes city s (or region s) proximity to demnd in ll mrkets nd determines the highest nominl wge tht firms in city cn fford to py (Redding nd Venbles, 2004; Hering nd Poncet, 2010). Geogrphicl distnce or trnsporttion cost T ffects the cities (economic) size through mrket ccess. The i demnd in mrket i for city s vrieties depends on the totl (lbor) income in city i. As the geogrphicl distnce or brrier (cost of remoteness) increses, the mrket ccess decreses s does the wge rte nd the other city s (economic) size nd ctivities. Wge rtes re higher when income nd demnd in surrounding mrkets re higher nd when there is improved ccess to those mrkets. For instnce, mrket ccess is incresed with lower trnsport costs T or shorter geogrphicl distnce D i. Thus the bolition of border brriers is expected to increse the mrket ccess (MA), nd, s consequence, the wge rtes. We use ctul rod distnce, for our distnce dt. 1 The elsticity of substitution,, mesures the elsticity of substitution between two different vrieties, tht is, it mesures the difficulty in substituting one vriety of mnufctures for nother vriety (see Krugmn 1979, 1980, 1991). 1 1 ; where is love-of-vriety effect of consumers. The price index i I mesures the extent of competition from neighboring regions. A low price index reflects tht mny vrieties re produced in nerby regions nd re, therefore, not subject to high trnsporttion costs which reduces the level of demnd for locl mnufcturing 11

23 vrieties. Therefore, low (high) price index reduces (stimultes) regionl wges (Brkmn et l., 2004b). The core NEG model ssumes set of trde nd migrtion equtions between just two regions. Over time, the theory nd its pplictions hve moved towrds relity with more thn two regions of different sizes. In their multiple regions nlysis, studies on generl equilibrium s well s the wge model nlysis of multiple regions or cities del with one individul country t time ssuming tht ntionl economy is independent of other countries including the neighboring countries. However, n economy is not independent of neighboring countries especilly in plces such s the EU where workers nd commodities cn freely move cross ntionl borders. Thus, it is importnt to ccount for the mrket ccess to the neighboring country s mrket in the supply of jobs nd demnd for consumer goods nd services. After ccounting for the border loction nd foreignness, (2.1) tkes the following form: W 1 ; where N Y I i i1; i 1 i T D i (1 ) 1 nd mesures vrious physicl nd culturl brriers between two countries. In the event of single country 0 ; nd the model remins the sme s in (2.1). We employ two mjor components of the brrier term: b for border city indictor nd B rs for being foreign city; i.e.,. Therefore, the model cn be rewritten s in (2.2): b B i W 1 ; nd N Y I i i1; i 1 i T D (1 ) 1b i B i (2.2) b is border dummy vrible which tkes the vlue of one if city is within given rnge of distnce from the ntionl border nd zero in other instnces. B i is dummy vrible tht tkes the vlue of one if city nd city i belong to different countries nd zero in other instnces. Y i,, 1, T, nd D i re s defined in (2.1) bove. We cn rewrite the totl mrket ccess ( the sum of ntionl nd foreign mrket ccess corrected for the border relted brriers. I i MA ) s MA N 1 Di[(1 )(1b Bi)] Yi Ii T i1; i Totl MA 1 1 n Di[(1 )(1 b)] N 1 Yi Ii T i Di[(1 )(1 b Bi)] 1 Yi Ii T 1; i i n 1; i Ntionl MA Foreign MA 1 (2.3) N is totl number of smple regions/cities; n is the number of ntionl regions/cities; nd (N n) is the number of regions in foreign countries tht re in the smple. The first expression on the right hnd side is the ntionl mrket ccess wheres the second one is the foreign mrket ccess. Note tht the foreignness brrier B i does not exist in ntionl mrket ccess. There re more nd less expensive wys of trveling to nd shopping in city in the sme country thn trveling to nd shopping in city t the sme distnce but in other country. Similrly, the B i is foreignness brriers term tht ccounts for other brriers such s differences in lnguges, culture, nd governmentl policies. It is esier to procure employment or shop in city in the sme country 12

24 thn in foreign city due to lnguge differences, employers preferences, vrious governmentl policies, nd so on. Given D i ( 1)(1 b Bi) 1, nd b nd B i is lwys negtive. Thus, the use of b nd on the mrket ccess for the border nd foreignness. re non-negtive, the expression B i produces downwrd effect Redding nd Sturm (2008) introduce border or division dummy to ccount for both distnce nd border brriers in the nlysis of division nd reunifiction of Est nd West Germny. The dummy vrible tkes vlue of one if city belongs to the sme country s city i, nd zero in other instnces. In their nlysis of freeness of trde between regions, Bosker et l., (2007b) lso employ n indictor dummy tht tkes the vlue of zero if two regions belong to the sme country nd one if not. The dummy vrible in the former tkes vlue of one s totlly opposite of the ltter becuse of the form of the model in which the dummy vribles re used. The dummy ws in the numertor of the mrket potentil of the former wheres it is in denomintor of the freeness expression in the ltter. Therefore, both re used to llow interntionl trde nd other cross-border economic ctivities to differ from intr-ntionl trde nd domestic economic ctivities due to fctors other thn geogrphicl brriers nd trnsporttion cost or due to fctors tht re beyond geogrphicl brriers nd trnsporttion cost including triffs, differences in lnguge, nd culture. The foreignness dummy (B i ) lso serves the sme purpose in our model. By tking logs nd dding constnt nd error term, we hve: log N 1 Di(1 ) 1 bbi W log Y I T 1 i i1; i i c t (2.4) where c is constnt nd t is the error term. We employ model (2.4) for the nlysis of the wge structure cross border versus non-border cities. We lso dopted different scenrios of the distnce nd different vlues of the border (b) nd the foreignness (B i ) dummies. Moreover, we defined two different distnce rnges for border cities. In one of them, cities tht re within 75 kilometer rnge from the ntionl border re considered to be border cities; wheres, in the other smple, cities tht re within n 85 kilometer rnge from the ntionl borders re considered to be border cities. In both cses, the rest of the cities re ctegorized s non-border cities. This pproch would enble us to chieve the mjor objective of this chpter, which is to nlyze the wge structure of the border versus non-border cities using n empiricl model where the geogrphicl distnce, the ntionl border, nd foreignness re modeled together in such wy tht they ffect the mrket ccess. The results for ll of the different smples nd mesurement options nd scenrios re presented nd discussed in Section The dt We use nnul dt on verge nnul per cpit income nd wge rte from three countries (Belgium, Germny nd the Netherlnds) encompssing the period 1995 to The use of these three countries smple hs specil dvntges. These countries re the erliest countries to bolish 13

25 their common border brriers in the EU integrtion process. This provides the longest time reference in investigting whether there re remining border effects or not. For instnce, if there re negtive border effects cross the borders of these countries, it is logicl to expect the negtive border effect to exist long the borders between reltively newer EU member countries. Moreover, our smple provides two types of ntionl borders for comprtive nlysis. One of them is the Netherlnds-Belgium border with longer historicl period (since 1948) of free trde nd where the sme lnguge (Dutch) is spoken on both sides of the border. The other is the Netherlnds- Germny border with rther more recent history of free trde nd where different lnguges (Dutch in the Netherlnds nd Germn in Germny) re spoken cross the border. Belgium nd the Netherlnds lso hve much longer common history. In this chpter, we use ctul geogrphicl (rod) distnce (D i ) connecting the smple cities. The min tsk in this chpter is to investigte whether there re differences in the reltionship between wge rte nd mrket ccess mong cities tht re centrlly locted nd those cities in the border res. To chieve this objective, we must involve two groups of cities, i.e. the border cities nd the non-border cities. In their nlysis of popultion growth in Germn cities, Redding nd Sturm (2008) defined border cities s those cities in rnge of 75 kilometers from the border between the former Est Germny nd West Germny. We lso used similr distnce rnge to divide the cities into border cities nd non-border cities for our bseline estimtion. Moreover, we hve defined nother distnce rnge of 85 kilometers which is used to check for stbility or robustness of our results. The number of cities in the smple is limited becuse of dt limittions. We use 107 cities from three countries (20 Belgin cities nd regions, 40 Germn cities, nd 47 Dutch cities). See Tble 2.1 for the summry of these smple cities nd Tble 2A.1 for the whole list of the cities. Although we don t nticipte the smple limittion to hve effect on the comprtive nlysis of border versus non-border cities, more comprehensive dt covering cities of ll size re welcome in future nlysis. Tble 2.1: Summry of smple border nd non-border cities Smple Cities Number of cities The whole smple cities 107 Within 85 kilometers rnge (border cities) 47 Outside 85 kilometers rnge (non-border cities) 60 Within 75 kilometers rnge (border cities) 38 Outside 75 kilometers rnge (non-border cities) 69 The list of the entire smple of cities is presented in the ppendices. We use ctul rod distnce between cities, implying tht the distnces of 75 nd 85 kilometer rnges identifying cities close to borders ( s the crow flies distnces to the border re in generl smller). We limit the border smples to these two rnges since using border rnge tht comprises rnge of less thn 75 kilometers results in too few cities s border cities, wheres border rnge tht is greter 14

26 thn 85 kilometers comprises cities tht re close to the center of country in the border smple. The vlue of the elsticity of substitution; i.e., epsilon ( ) is estimted through itertion method (see Tble 2A.7 in the ppendix nd the prgrph preceding the tble). The price index is normlized to unity due to lck of dt t locl level. Iceberg trnsporttion cost of 10 percent (T = 1.10) is used in the bseline estimtions. Using these prmeters nd the ctul dt, we clculte the right hnd side of eqution (2.4) s mesure of mrket ccess (MA) nd then estimte the regression eqution. We check for the robustness nd stbility of our results by chnging the prmeter vlues, prticulrly, the epsilon ( ) nd trnsporttion cost (T). Further discussion regrding the dt nd the estimtion results continues in the next section nd the ppendices Estimtion results In this section, we will present the estimtion results of the bsic wge model. We compre the results for the non-border cities smple nd the border cities smple. In our bsic result, we estimte one without border nd foreign mrket brriers nd one with border nd foreign mrket brriers (scenrio_4). First, we present the results for the totl mrket ccess nd then the results where we divide it into ntionl mrket ccess nd foreign mrket ccess. The results for the totl mrket ccess re presented in Tble 2.2. Columns (1) nd (4) exhibit the positive nd significnt reltionship between the wge rte nd the mrket ccess (MA) for the combined smple under both scenrios. This implies tht the wge rtes re proportionlly significntly higher in the cities tht hve greter mrket ccess. One of the mjor objectives in this chpter is to go step further nd investigte whether this reltionship vries cross smple cities, i.e. border cities versus nonborder cities. Columns (2) nd (5) depict the results for the non-border cities; wheres columns (3) nd (6) show the results for the border cities. The results illustrte tht the coefficients re smller for the border cities under both cses. This mens tht the percentge chnge in the wge rte ssocited with percentge chnge in the mrket ccess is positive nd significnt but lower for border cities. In columns (1) through (3) of Tble 2.2 we use no border or foreignness penlty (b = B i = 0); wheres we use (b = B i = 1) in columns (4) through (6). Our estimtion results do not directly show the border nd/or integrtion effects since the border effects re ccounted for in the mrket ccess term. Thus, to understnd the results nd clculte the border nd/or integrtion effects, we compre the results for the border cities before (column 3) nd fter (column 6) ccounting for the border loctions nd foreignness. The difference between the results in the columns (1) to (3) nd (4) to (6) is derived from the border loction nd foreignness ccounted only in the mrket ccess, the right hd side of eqution (2.2) nd (2.4). Column (3) demonstrtes tht wge rte is percent higher for every extr percent of mrket ccess without ccounting for the border effects. However, fter correcting for the mrket ccess with the border effects, the wge rte is higher by 0.2 percent (see column 6) for every extr percent in the mrket ccess. Without ccounting for border effects, the 0.2 percent extr in the wge rte requires 1.6 percent increse in the mrket 15

27 ccess, 1.6 = (0.200/0.125). Otherwise stted, the mrket ccess is lower by [(0.200/0.125) 1 = 0.6] percent due to the border penlties. Tble 2.2: The wge rte nd the totl mrket ccess b = B i = 0 for ll cities border cities b = 1; foreign cities B i = 1 VARIABLES (1) (2) (3) (4) (5) (6) log(ma) 0.258*** (0.0145) 0.291*** (0.0170) 0.125*** (0.0182) 0.269*** (0.0102) 0.355*** (0.0143) 0.200*** ( ) Constnt 6.938*** (0.144) 6.569*** (0.169) 8.537*** (0.188) 6.506*** (0.113) 5.492*** (0.160) 7.626*** (0.108) Yer Effects yes yes yes yes yes yes Country Effects yes yes yes yes yes yes Smple All smple cities Non-border cities Border cities All smple cities Non-borders cities Border cities Observtions 1, , R-squred Robust stndrd errors in prentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; the MA is clculted hed of the regression using prmeter vlues nd ctul dt s described in section 2.4; nd sme vlue of epsilon ( ) is used throughout ll columns (see tble 2A.7). We repet the sme for the non-border cities nd compre columns (2) nd (5). Border penlty reduces the mrket ccess of the non-border cities s well, but by smller percentge. Compred to the percent increse in wge rte (see column 2) for every extr percent in the mrket ccess, the percent increse (see column 5) for ech percent increse in the mrket ccess (with border penlty) would require (0.355/0.291) = 1.22 percent increse in the mrket under the ssumption of zero border brriers. Tht is, the mrket ccess is lower by [(0.355/0.291) 1 = 0.22] due to the border brriers. The difference of ( = 0.38) is the effect of the border brriers on the border cities reltive to the non-border cities. Otherwise stted, the bolition of the border brriers increses the mrket ccess of the border cities by 0.38 higher thn tht of non-border cities. We lso estimte the model for the vrious scenrios including chnging the prmeter vlues of the border nd foreignness. This proportion of the chnges in the mrket ccess versus without border brriers generlly remins consistent with these results (see Tble 2A.4 in the Appendix).The bsolute gin in the mrket ccess hs lso been higher for the border cities during our smple period. See the Appendix for the descriptive nlysis. Next we ccount for lnguge similrities/differences nd the distnce to the borders. In this spect, we nswer the question of whether or not the bove results imply tht the border cities hve lower wge rte. In doing so, we included the distnce of the cities from the borders. The result for the border cities indictes tht the longer the distnce is from the ntionl border, the higher the distnce coefficient (see Appendices, Tble 2A.2, column 2). Thus, the wge rte is proportionlly lower for the cities tht re closer to the ntionl borders. In this estimtion, we lso 16

28 included the lnguge dummy tht tkes the vlue of one if the lnguge spoken cross the border is the sme (for exmple, in Netherlnds-Belgium border where the sme lnguge, Dutch, is spoken) nd zero in other instnces. However, the results show no significnt reltionship between the lnguge similrity nd the wge rte cross the borders. However, by defining two border dummy vribles, one for the Netherlnds-Belgium border nd nother for the Netherlnds- Germny border, we discover tht the two borders re different, consistent with the symmetric border effects discussed by Feenstr (2002) nd lso Anderson nd vn Wincoop (2001). The negtive border effect is stronger nd more consistent long the Netherlnds-Germny border thn it is cross the Netherlnds-Belgium border (see Tble 2A.3 in the Appendices). This my imply tht there is improved lbor mobility cross the Netherlnds-Belgium border due to the similrity in lnguge, longer common history nd the erlier implementtion of the reltively free trde greement cross this border. We further differentite between ntionl mrket ccess nd the foreign mrket ccess. Becuse of geogrphicl proximity (lower trnsporttion cost) nd improved connection to ntionl cities, the greter proportion of mrket ccess comes from the ntionl mrket. Thus, it cn be expected tht the ntionl mrket ccess is more importnt for the centrlly locted (nonborder) cities. Similrly, compred to non-border cities, border cities re reltively close to nd better connected to foreign cities. Thus, the implied gin in the mrket ccess following the bolition of ntionl border restrictions is likely to be higher for the border cities compred to the non-border cities. Thus, we expect the foreign mrket ccess to be more significnt for the border cities. Therefore, to investigte the importnce of the foreign mrket ccess, we divide the totl mrket ccess, right hnd side of (2.2 nd/or 2.4) bove, into ntionl mrket ccess nd foreign mrket ccess: N. i1 n. N i1 in1. ; where 1 Di(1 )(1b Bi). Y I T i i ; n is number of ntionl smple cities nd N n is number of foreign smple cities. 3 The results (Tble 2.3) exhibit tht the percentge chnge in wge rte hs positive nd significnt reltionship with the percentge chnge in the foreign mrket ccess for only the border cities (see columns 3 nd 6). This demonstrtes the importnce of geogrphicl proximity of the border cities to the foreign mrkets under economic integrtion 4. Our result is in greement with the rgument tht foreignness itself does not ffect purchses of imported goods but loction does (Evns, 2001). The wge rte in the border cities hs positive nd significnt reltionship with mrket ccess to the neighboring countries becuse of their loction proximity to the foreign mrkets. This is consistent with Hnson (2001) who discerns positive nd strong correltion between growth in the mnufcturing of export goods in Mexico cities long the US border nd employment growth in 3 In the expression right hnd side, n 1 Di (1 )(1b Bi). Yi Ii T i1.., B i = 0 for ll the ntionl mrket ccess, i.e., the first term on the 4 In Chpter Six, we simulte the opening of Dutch borders to Belgium nd Germny using much more detiled dt; nd the results show tht, in the long run, the municiplities in the border loctions gin reltively more thn the non-border municiplities following the opening up (better connection) to the neighboring mrkets. 17

29 U.S. cities long the Mexico border, signifying the importnce of the foreign mrket for the bordering res. Tble 2.3: The wge rte, nd the ntionl nd foreign mrket ccess b = B i = 0 for ll cities border cities b = 1; foreign cities B i = 1 VARIABLES (1) (2) (3) (4) (5) (6) log(ntionl MA) 0.275*** 0.373*** 0.168*** 0.244*** 0.348*** 0.185*** (0.0107) (0.0138) ( ) ( ) (0.0155) ( ) log(foreign MA) *** *** *** *** ** ( ) ( ) ( ) ( ) ( ) ( ) * Constnt 6.729*** 5.862*** 7.485*** 7.062*** 5.813*** 7.713*** (0.122) (0.155) (0.0977) (0.111) (0.171) (0.0947) Yer Effects yes yes yes yes yes yes Country Effects yes yes yes yes yes yes Smple All smple cities Non-border cities Border cities All smple cities Non-borders cities Border cities Observtions 1, , R-squred Robust stndrd errors in prentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; the MA is clculted hed of the regression using prmeter vlues nd ctul dt s described in section 2.4; nd sme vlue of epsilon ( ) is used throughout ll columns (see tble 2A.7). We lso checked for vrious scenrios nd robustness. We hve seen tht the reltionship between the wge rte nd the totl, s well s the ntionl, mrket ccess is stronger for nonborder cities thn it is for the border cities (n dvntge of being non-border city). However, the reltionship with the foreign mrket ccess is stronger for the cities bordering n economic integrtion member country (the dvntge of being border city). We estimte the wge model using different options in order to determine whether or not these results re consistent. First, we estimte the model for the vrious scenrios. This includes chnging the prmeter vlues of the border nd foreignness. We lso ccount for the country nd time fixed effects. The proportion of the chnges in the mrket ccess of with versus without border brriers under the different scenrios generlly remins consistent with our bsic results in Tble 2.2 (see Tble 2A.4 in the ppendix). Second, we estimte for different border rnge of 85 kilometers ctul rod distnce insted of 75 kilometers. The coefficients over the new border rnge still remin smller for the border cities. This result lso holds t n nnul level throughout the smple period for both the 75 nd 85 kilometer border rnges (see Figure 2A.1 nd 2A.2 in the Appendix for nnul coefficients plotted on grph). Since we scertin lower coefficients for the border cities, we lso expect the coefficients to become smller for the non-borders s we include more nd more cities tht re closer to the borders. Consistent with this, Figure 2A.3 illustrted tht, the closer to the border we 18

30 move (from 85 to 75 kilometers), the smller the coefficients become which indictes tht border effects remin throughout the smple period consistent with the literture showing persisting border effects (exmples re Nitsch, 2000; Anderson nd vn Wincoop, 2003; nd Chen, 2004). The results of the lterntive scenrios re lso consistent with these results (see Figure 2A.2). Moreover, the results from the lterntive scenrios (different border nd foreignness prmeters nd different types of distnces) show tht the foreign mrket ccess is more importnt for the border cities thn it is for the non-border cities (lso see Tble 2A.5 in the Appendix). From the results, we cn lso scertin tht the lower the distnce penlty, the more importnt both the ntionl mrket ccess nd the foreign mrket ccess re. For instnce, when we use logrithm of distnce, the foreign mrket ccess is positive nd significnt for both border cities nd nonborder cities, but still greter for the border cities. Theoreticlly, free lbor mobility leds to wge rte equliztion. Additionlly, economic integrtion fcilittes free lbor mobility. With free lbor mobility following the integrtion, we expect strong tendency for workers to migrte to the cities with higher wge rtes or greter mrket ccess. If this is the cse, it is very likely tht the migrtion of the workers leds to incresed lbor supply in those cities which drives the wge rte down. This eventully results in wge equliztion cross the cities nd cross the borders. However, we do not find evidence for this. Thus, wht we understnd from this is tht the effects of border brriers exist even fter severl yers of economic integrtion cross our smple borders Conclusions In this chpter, we demonstrte the significnce of mrket ccess to neighboring countries cross ntionl borders. We clculte the mrket ccess employing the ctul rod distnce, the ntionl borders nd foreignness s brriers to trde nd/or lbor mobility nd identify it in such wy tht they simultneously ffect the mrket ccess. We exploit nnul dt of 107 cities in Belgium, Germny nd the Netherlnds encompssing the periods from 1995 to We use ctul geogrphicl (rod) distnce between the smple cities. We identify the cities tht re within 75 kilometer rnge of the ntionl borders to be border cities nd the reminder s non-border cities. We subsequently estimte the wge eqution. Our results indicte tht, following the simulted opening of the border brriers, the border cities gin mrket ccess more thn centrlly locted cities. This implies the importnce of borders. Our estimtion results lso demonstrte tht foreign mrket ccess is more importnt for wges of the border cities (geogrphicl proximity mtters) thn it is for the non-border cities. The results lso indicte tht there hs been negtive border effect throughout the smple period, persisting border effects or irreversibility of sptil economic development s specified by Fujit nd Mori (1996). These results remin stble nd consistent cross different smples of different distnce rnges from the borders nd throughout the smple period. Our results lso show tht the negtive border effect is stronger cross the Netherlnds- Germny border thn it is cross the Netherlnds-Belgium border. This my imply tht there is better lbor mobility cross the ltter due to the similrity in lnguge nd the reltively erlier 19

31 implementtion of free trde greement. This is consistent with the concept tht border brriers hve symmetric effects on countries of different size (Feenstr, 2004). This chpter bsiclly serves s n introduction to the subsequent chpters. The border effect is cptured in the present chpter by border (brrier) dummy in the mrket ccess term in the wge eqution (recll eqution (2.2)). This is beneficil but lso rther simple wy of ttempting to mesure border effect. For the entire smple period, the border dummy bsiclly shows the wge depressing effect of being border city due to the reltively incresed limited mrket ccess compred to non-border cities. The effect of economic integrtion is pproximted by ssuming tht borders do not exist t ll. On the positive side, the eqution hs well-defined micro-foundtion. However, if the key interest is to truly mesure the effect of n economic integrtion shock nd to seprte this effect from the border loction effect s such, tht is, from the effect tht border cities remin border cities even when economic integrtion is estblished, it would be beneficil to mesure the wge or income effects of economic integrtion over time, specificlly before nd fter the integrtion shock. In ccordnce with Redding nd Sturm (2008) nd Brkmn et l. (2004), this is precisely wht the subsequent chpters intend to do bsed on Difference-in-Differences estimtion strtegy. This strtegy seprtes the border effect from integrtion effects; however, the price is tht the empiricl specifiction is more loosely connected to the micro-foundtions in other chpters thn wht is done is this chpter. The estimtion results in section 2.5 serving s n interesting benchmrk nd, foreshdowing the upcoming nlysis, we will scertin tht the more refined estimtion strtegy in Chpters Three to Five will confirm some of the bsic findings of this Chpter, notbly, see Tble 2.2, tht economic integrtion is boost for border cities but, even then, they re still t disdvntge compred to non-border cities. 20

32 2.7. Appendices (I) Tbles Tble 2A.1: The cities in the smples Belgium Germny The Netherlnds Alst, Antwerp, Arlon, Brugge, Brusselse, Chrleroi, Gent, Hsselt, Ieper, Kortrijk, Liege, Mons, Mouscron, Nmur, Oostende, Roeselre, Turnhout, Tourni, Mechelen, Luxemburg, Berlin, Bielefeld, Bremen, Dusseldorf, Erfurt, Essen, Frnkfurt, Freiburg, Hmburg, Hnnover, Ingolstdt, Kssel, Kiel, Koln, Leipzig, Mgdeburg, Mnnheim, Munich, Nuremberg, Oldenburg, Rostock, Stuttgrt, Trier, Ulm, Wurzburg, Achen, Augsburg, Bonn, Dortmund, Dresden, Göttingen, Heilbronn, Kiserslutern, Koblenz, Lübeck, Münster, Osnbrück, Pderborn, Wuppertl, Wolfsburg. Alkmr, Almelo, Almere, Amsterdm, Apeldoorn, Arnhem, Assen, Bergen op Zoom, Bred, Delft, Delfzijl, Den Hg, Den Helder, Doetinchem, Eindhoven, Emmen, Enschede, Groningen, Hrlem, Hrlemmermeer, Heerenveen, Heerlen, Hilversum, Hoorn, Kerkrde, Leeuwrden, Leiden, Lelystd, Mstricht, Middelburg, Nijmegen, Oss, Roermond, Rotterdm, s Hertogenbosch, Sittrd-Geleen, Smllingerlnd, Sneek, Steenwijkerlnd, Terneuzen, Tiel, Tilburg, Utrecht, Velsen, Venlo, Weert, Zwolle, Tble 2A.2: The reltionship between wge, the MA, distnce to the borders nd the lnguge similrity vribles (1) (2) log( MA ) 0.290*** (0.0175) 0.128*** (0.0195) Sme lnguge ( ) Distnce from common border (kilometers) *** ( ) Constnt 7.007*** (0.182) ** (0.0216) 8.539*** (0.190) Yer Effects yes yes Country Effects yes yes Smple non-border cities border cities Observtions R-squred The dependent vrible is wge. MA = the mrket Access; Robust stndrd errors in prentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. The results show tht the further wy from the borders the significntly higher the wge rte is. 21

33 Tble 2A.3: The wge rte nd different ntionl borders VARIABLES (1) (2) (3) (4) log(totl MA) *** ( ) (0.114) *** ( ) *** ( ) Netherlnds-Belgium border ** ( ) Netherlnds-Germny border *** ( ) Constnt 9.461*** (0.111) ( ) *** ( ) 8.330*** (1.584) * ( ) *** ( ) 9.185*** (0.114) ( ) *** ( ) 8.738*** (0.105) Epslon ( ) b for border cities B rs for foreign cities Distnce (D i /10) log(d i ) (D i /10) (D i /10) Yer Effects yes yes yes yes Observtions 1,272 1,272 1,272 1,272 R-squred Robust stndrd errors in prentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; Although the mgnitude is different, the ge rte consistently significntly nd negtively correlted with the ntionl borders. Tble 2A.4: The wge rte, nd the totl mrket ccess (vrious scenrios) b = B i = 0 for ll cities; ε = 3; 10kms =1 unit D i border cities b = 0.5; foreign cities B rs = 0.5; ε = 3; 10kms =1 unit D i VARIABLES (1) (2) (3) (4) (5) (6) log(ma) 0.202*** 0.262*** 0.153*** 0.283*** 0.348*** 0.201*** ( ) (0.0136) ( ) (0.0105) (0.0143) ( ) Constnt 7.425*** 6.708*** 8.052*** 6.563*** 6.030*** 7.574*** (0.114) (0.151) (0.0949) (0.123) (0.162) (0.108) Yer Effects yes yes yes yes yes yes Country Effects yes yes yes yes yes yes Smple All smple Non-border Border All Non-border Borders cities cities cities cities cities Observtions 1, , R-squred Robust stndrd errors in prentheses; *** p < 0.01, ** p < 0.05, * p <

34 Tble 2A.5: The wge rte nd the foreign mrket ccess (vrious scenrios) b = B i = 0 for ll cities; ε = 3; distnce = log( D i ) border cities b = 0.5; foreign cities B i = 0.5; ε = 3; 10kms = 1 unit D i VARIABLES (1) (2) (3) (4) (5) (6) log(ntionl MA) 1.076*** (0.0628) 1.204*** (0.0820) 1.070*** (0.0518) 0.263*** (0.0102) 0.362*** (0.0144) 0.180*** ( ) log(foreign MA) 0.255*** (0.0668) 0.319*** (0.0912) 0.495*** (0.0735) ( ) *** ( ) *** ( ) Constnt 6.973*** (1.381) 9.469*** (1.818) 10.20*** (1.208) 6.573*** (0.114) 5.662*** (0.159) 7.710*** (0.103) Yer Effects yes yes yes yes yes yes Country Effects yes yes yes yes yes yes Smple All smple Non-border Border cities All smple Non-border Border cities cities cities cities cities Observtions 1, , R-squred Robust stndrd errors in prentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; foreign mrket ccess re consistently positive nd significnt for the border cities. Tble 2A.5: Ctd. VARIABLES border cities b = 1; foreign cities B i = 1; ε = 3; distnce = log( D i) border cities b = 1; foreign cities B i = 1; ε = 5; distnce = log( D i) (7) (8) (9) (10) (11) (12) log(ntionl MA) 1.007*** (0.0525) log(foreign MA) *** (0.0336) 1.136*** (0.0719) 0.116** (0.0472) 0.914*** (0.0447) 0.219*** (0.0401) 0.648*** (0.0285) *** (0.0175) 0.882*** (0.0445) ** (0.0241) 0.610*** (0.0248) 0.128*** (0.0181) Constnt 3.477*** (0.809) 5.335*** (1.092) 3.921*** (0.665) 1.734*** (0.416) (0.551) 1.684*** (0.316) Yer Effects yes yes yes yes yes yes Country Effects yes yes yes yes yes yes Smple All smple Non-border Border All smple Non-border Border cities cities cities cities cities cities Observtions 1, , R-squred Robust stndrd errors in prentheses; *** p < 0.01, ** p < 0.05, * p <

35 (II) Figures Figure 2A.1: The nnul results: border versus non-border cities 75 kilometers % chnge in wge per % chnge in MA yer non-border cities border cities (within 75 kms) Figure 2A.2: The nnul results: border versus non-border cities 85 kilometers 0.40 % chnge in wge per % chnge in MA non-border cities yer Border cities (within 85 kms) Note: Figure 2A.1 nd 2A.2 show tht percentge chnge in wge rte per percentge in the mrket ccess is lower for the border cities throughout the smple period. 24

36 Figure 2A.3: The nnul results: non-border smples 75 kilometers nd 85 kilometers 0.40 % chnge in wge per % chnge in MA non-border cities (outside 85 kms) yer non-border cities (outside 75 kms) Note: Figure 2A.3 lso shows tht percentge chnge in wge rte per percentge in the mrket ccess is lower s we go towrd the borders even within non-border cities throughout the smple period. (III) Descriptive nlysis: Gins in the mrket ccess Here, we first demonstrte the reltively lrger gin in mrket ccess of border cities following the loosening of ntionl border restrictions. At lest in bsolute terms, ll regions of the economiclly integrted countries cities gin higher mrket ccess fter the bolition of the border restrictions. However, given tht other things re constnt, the gins by the old (bolished) common border cities re higher thn tht of non-border cities. See equtions (2A.1 through 2A.3) below for the clcultion. All the prmeters nd vribles re s defined in section 2.3. D i is the rod distnce between city nd city i in kilometers. N demonstrtes the summtion over the entire smple, indicting ccess to the ntionl s well s the foreign mrkets fter bolition of the border; n indictes summtion over ntionl smple indicting the ccess to the ntionl mrket lone given the existence of border brriers; nd T is the length of the smple period 1995 to The gin is higher for the border cities, implying the reltively more importnce of foreign mrket ccess of locting on borders with other ntions under economic union: MA N T i1, i t 1 (1 )(1 ) n T 1 Di b Bi 1 D (1 )(1b B ) i i Y I T Y I T i i n T i1, i t 1 Y I 1/ T 1 i i i1, i t 1 Di (1 )(1b Bi) i i 1/ 1/ 100 (2A.1) These mrket potentil gins mesure the gin in excess of the entire ntionl mrket potentil including the home mrket of the city itself. Although the bsolute level of gins in the 25

37 mrket ccess re different, the reltive gins by cities of different loctions remin the sme whether we use b 0 nd B 0 or b 0 nd B 0. In both cses, the cities with the gretest i gin re cities such s Kerkrde, Sittrd-Geleen, Heerlen, Mstricht nd Eindhoven of the Netherlnds, Achen of Germny nd other cities tht re close to the bolished borders; wheres, those cities further wy from these borders gin less. However, this result is bsed on the ssumption of no ccess to the foreign mrket before economic integrtion nd full ccess to the foreign mrket fter economic integrtion. Due to the nture of the ssumption, this clcultion derives the sme result whether the economic integrtion ctully occurs or not. Becuse of this reson, next, we drop this ssumption nd compre the ctul gins in the mrket ccess of the two groups of cities during the smple period. Therefore, we ssume tht there ws ccess to the foreign mrket even before the bolition of the ntionl borders or the smple period. It would be more dvntgeous if we could ggregte dt from before nd fter the bolition of the borders, but we don t hve the dt from before. However, it is still very useful nd informtive to compre the gins during the smple period tht we currently hve for the two groups of cities, border nd non-border cities. Thus, the gin is now clculted s follows. Non-border cities: MA nb: i 1 N nb N nb 1 N i1 Y I T 1 i i D i (1 )(1b B N i1 i Y I ) 1 i i 1/ T 2006 D i N i1 Y I (1 )(1b B 1 i i i ) T D i 1/ 1995 (1 )(1b B i ) 1/ (2A.2) Border cities: MA b : 1 N b N b 1 N i1 N 1 D (1 )(1 ) i b Bi 1 Di(1 )(1b Bi) Y I T Y I T i i N i1 Y I 1 i i 1/ T 2006 D i i1 (1 )(1b B i i i ) 1/ / (2A.3) where N nb = number of non-border cities; N b = number of border cities; nd N = N nb + N b. Note tht both of the summtions t the beginning of the smple (1995) nd t the end of the smple (2006) in (2A.2) nd (2A.3) encpsulte the entire smple, N, not only over the ntionl mrket, n, s in the second prt of the numertor nd the denomintor in equtions (2A.1) bove. This indictes tht cities hve ccess to foreign mrkets during the entire smple period. We used seven lterntive scenrios of this mrket gin using different mesures of the distnce nd the border (b) s well s foreignness (B i ) prmeters. These scenrios re depicted in Tble 2A.6. 26

38 Tble 2A.6: Vrious mrket ccess scenrios b for B rs if s is Scenrios border city foreign city distnce Remrk Scenrio_ D i no borders Scenrio_ (D i /10) no borders Scenrio_ log(d i ) no borders Scenrio_ (D i /10) with borders Scenrio_ (D i /10) with borders Scenrio_ log(d i ) with borders Scenrio_ log(d i ) with borders Scenrio_1 ssumes tht being border city or being foreign city is of no significnce. The sme is true for Scenrio_2 nd Scenrio_3 except the use of smoother distnce penlty by tking 10 kilometers s unit of distnce nd logrithm of distnce, respectively. Under Scenrio_4 nd scenrio_5, being in border nd/or foreignness mtters, however, the difference is smller in the ltter. Scenrio_6 nd scenrio_7 lso llow for border nd foreignness penlty except tht the ltter employs different vlue of. The chnges in the mrket ccess under the vrious scenrios re depicted in the figure below. Under Scenrio_3, much smoother distnce; i.e., log(distnce), combined with bsence of border penlty nd foreignness leds to smller differences between gins in the border nd non-border cities. In ll the other different scenrios, the border cities gined more Mrket Access thn the non-border cities over the smple period (see Figure 2A.4). This implies tht, lthough the border cities re disdvntged due to remote loction in the ntionl mrket, they benefit reltively more thn the non-border cities under economic integrtion. Figure 2A.4: Chnge in mrket ccess under different scenrios 50 % chnge in Mrket Access( ) Border cities Non-border cities 0 Scen.1 Scen.2 Scen.3 Scen.4 Scenrios Scen.5 Scen.6 Scen.7 27

39 3 5 The elsticity of substitution ( ) or ( ) is used in the nlysis. To identify proper vlue of the epsilon ( ), we use itertion method. We use different vlues of the epsilon ( ) for the clcultion of the mrket ccess in the prenthesis on the right hnd side of eqution (2.4); nd then we estimte the regression eqution itself to estimte the coefficient 1/. We repet this process until the vlue of the plugged in epsilon ( ) nd the estimted slope coefficient (1/ ) in the eqution converges. Thus, in the itertion process, we use the whole pnel to come up with single epsilon ( ) vlue. Some of the plugged in vlue of the epsilon ( estimted 1/ 1/ ), suggested nd the bsolute vlue of the difference between the ltter two re reported in tble 2A.7. From the tble we see tht the suggested nd the estimted vlues of epsilon converge 3 t suggested t 5. For epsilon vlues less thn or greter thn 3, the differences between the estimted nd 1/ re lrger. When we impose the constnts to be zero, then the convergence occurs. Thus, we used this vlue too to check whether the results chnge or not., Plugged in Tble 2A.7: Exmple of selection of the epsilon (ε) Suggested 1 Estimted 1 suggested 1 estimted 1 with constnt *** yes *** yes *** yes *** yes *** no *** no *** no *** no ***, **, * significnt t 0.01, 0.05, 0.1 level 28

40 Chpter Three The Border Popultion Effects of EU Integrtion Introduction Systems of cities chnge slowly over time nd pper to be stble over long time periods. This stbility hs often been observed by urbn historins. 6 However, subsets of cities do evolve over time, following chnges in the economy, institutionl chnges or technologicl developments (Desmet nd Rossi-Hnsberg, 2009, 2010). These evolutions cn tke decdes or even centuries to complete (Biroch, 1988). The time dimension cretes prcticl difficulties in nlyzing the ultimte cuses of chnges in city systems s consistent dt for mny countries nd sufficiently lrge number of cities over long time period re not redily vilble, see Bosker et l. (2008) for n exception. Only reltively recently hve discretionry policy chnges or (qusi-) nturl experiments been used to shed light on wht drives chnges in the development of (systems of) cities nd to investigte stbility of the system fter shock. Dvis nd Weinstein (2002), for instnce, nlyze the consequences of the llied bombing of Jpnese cities during World Wr II (WWII). A similr exercise ws performed by Brkmn et l. (2004) for the bombing of Germn cities by llied forces during the sme period. These studies show tht the development of cities follows indeed reltively stble pth in the sense tht cities tend to return to their pre-shock pth following the shock. At the sme time, it is possible tht the development of cities lep-frogs to nother development pth, see Bosker et l. (2007). Some less drmtic experiments or shocks, like chnges in the degree of economic integrtion, illustrte tht the effects for border cities in prticulr, cn be substntil. According to Hnson (2001, 2005) the integrtion process between Mexico nd the USA ccounts for sizeble portion of employment growth in U.S. border cities over the smple period. The opposite of integrtion is segregtion. Redding nd Sturm (2008) nlyze the effects on border cities long the new border following the post WWII division of Germny into Est nd West Germny in They, like Hnson (2001), find tht the effects on (west) Germn cities long the newly creted intr-germn border re substntil; trditionlly centrlly locted cities suddenly found themselves in the periphery of Germny, resulting in shrp decline of the popultion (more so for smll thn for lrge border cities). At n even more disggregted scle, Ahfeldt et l. (2010) show for the cse of the Berlin Wll nd the city of 5 This chpter is bsed on published pper in the Journl of Regionl Science s Brkmn et l., (2012), co-uthored with Steven Brkmn, Hrry Grretsen nd Chrles vn Mrrewijk. 6 Hohenberg (2004, p. 3051) notes tht, tking both the resistnce nd the resilience of cities together, it is perhps not surprising tht the Europen system should rest so hevily on plces mny centuries old, despite the enormous increse in the urbn popultion nd the trnsformtion in urbn economies. 29

41 Berlin, tht lso within city division (nd subsequent reunifiction) cn led to remrkble chnges with respect to the economic structure of city, especilly long its borders. Border cities re of specil interest in the wke of these integrtion shocks, becuse they experience more drstic chnges in their so-clled mrket ccess (see below) thn more centrl cities (Hnson, 2005). 7 The novelty of this chpter is tht, s fr s we re wre of, the sptil effects of multiple stge EU integrtion process on cities long ntionl borders hs not been nlyzed before, in contrst to studies tht highlight the importnce of the border effect on trde in generl (see Feenstr, 2004, for survey). The enlrgement of the Europen Union (EU) nd the introduction of the euro cn be looked upon s two policy-induced shocks, tht cn shed light on the consequences of chnges in mrket ccess. Centrl to our nlysis in this chpter is the notion tht cities or regions tht re close to the border re most ffected by these chnges in EU integrtion, s they re especilly confronted with chnges in mrket ccess, wheres the effects for cities or regions further wy from the border re more subdued. Note tht chnges in mrket ccess re not necessrily positive or negtive (see section 3.3 for discussion). Also note tht with brriers to mobility in Europe, integrtion shocks likely ffect nominl wges more thn popultion due to reloction of firms mong other resons. Since we do not hve sufficiently detiled nominl wge dt our nlysis concentrtes on the impct of the distribution of popultion nd s such understtes the totl integrtion response. Similrly, problem tht hunts qusi-nturl experiments is the nticiption effect. In our cse, integrtion is process spred over long time period while we focus on the impct of entry into the EU itself. Since gents will begin to respond to chnges before they re implemented, these long-term nticiption effects imply tht we understte the integrtion effects. For these two resons our findings will understte the true integrtion response. The rest of this chpter is structured s follows. In section 3.2 we summrize the two EU integrtion experiments (EU enlrgement nd the introduction of the euro) tht we nlyze in the reminder of the chpter, where our emphsis will be on EU enlrgement. Bsed on Redding nd Sturm (2008), section 3.3 provides the theoreticl bckground. Section 3.4 describes the dt nd section 3.5 introduces the centrl empiricl specifiction. The chrcteristics of our pproch re tht (i) we focus on the consequences of economic integrtion for cities nd regions, (ii) our results re most likely not ffected by other spects such s chnges in nturl resources or climtic chnges, nd (iii) we hve sufficiently lrge number of observtions to nlyze vrious effects (timing, distnce decy, border symmetry nd size symmetry). Section 3.6 discusses the estimtion results. As fr s we re wre, we provide the first nlysis to find, both t the urbn nd t the regionl level, positive impct of the EU enlrgement process s mesured by the growth in popultion shre long the integrtion borders, leding to n extr growth rte of bout 0.15 percentge points per nnum. This integrtion effect declines with distnce, is bout the sme for new nd old members, nd is more importnt for lrge cities nd regions. Despite this EU integrtion effect ssocited with EU enlrgements, being locted long border remins burden 7 In generl, in studies like these demnd linkges between cities or regions re strong, but the geogrphicl rech is limited, which motivtes why especilly border cities might experience fundmentl chnges in mrket ccess, rther thn n economy wide smple of cities (Bosker nd Grretsen, 2010). 30

42 in view of the (lrger) generl negtive border integrtion effect. We do not find similr borderintegrtion effects s result of the introduction of the euro. Section 3.7 concludes EU enlrgement nd the introduction of the Euro Europen integrtion hs mny fces, but two developments in recent yers stnd out: EU enlrgement with new member sttes nd the introduction of the Euro (see Bldwin nd Wyplosz, 2009, or vn Mrrewijk, 2007, for detils). The Europen economic integrtion process strted fter WWII with the Europen Col nd Steel Community (ECSC), estblished in 1951 by the Trety of Pris. As the nme indictes, the ECSC ws n greement relted to specific sectors nd estblished free trde mong the member countries for the (t tht time very importnt) col nd steel sectors only. Although the strengthening of the economic integrtion process ws initilly imed to reduce the probbility of future wrs, one of the most importnt consequences of the development of the EU is to increse economic integrtion. Mny importnt enlrgement steps were tken to this end s summrized in Tble 3.1. Tble 3.1: Overview of Europen Union enlrgement process 1951 ECSC Europen Col nd Steel Community Membership Belgium, Frnce, Luxembourg, the Netherlnds, Itly, nd W. Germny 1957 EURATOM Europen Atomic Energy Community 1957 EEC Europen Economic Community 1967 EC Europen Communities; combining ECSC, EEC, nd EURATOM 1973 Membership + United Kingdom, Irelnd, nd Denmrk 1981 Membership + Greece 1986 Membership + Spin nd Portugl 1990 Membership + Est Germny (reunifiction of West nd Est Germny) 1993 EU Europen Union 1995 Membership + Finlnd, Austri, nd Sweden 1999 EMU Economic nd Monetry Union 2002 Euro Introduction of the euro 2004 Membership + Cyprus, Czech Rep., Estoni, Hungry, Ltvi, Lithuni, Mlt, Polnd, Sloveni, nd Slovki Membership + Bulgri nd Romni. Source: own compiltion from vrious sources, minly, 31

43 Figure 3.1 describes the chnges in the size of the EU in terms of the popultion involved. The verticl xis mesures the totl size of the popultion of the member sttes. The jumps in the line indicte tht ech EU enlrgement increses the totl ffected popultion bruptly. Associted with this process is the simultneous bolishment of border in n economic sense, resulting in sudden drop of trnsction costs cross borders. In this respect, especilly the first enlrgement in 1973 (with Denmrk, Irelnd nd the UK), the third enlrgement in 1986 (with Spin nd Portugl), nd the Estern enlrgement in 2004 (with ten new members long the estern border of the EU) stnd out. The totl popultion of the EU is now close to 500 million people, mking it one of the lrgest integrted mrkets in the world. For our nlysis it is importnt to note tht enlrgements substntilly increse the (potentil) mrket ccess for the EU members. Figure 3.1: Historicl expnsion of the Europen Union, Historicl expnsion of the Europen Union popultion (million) Denmrk, Irelnd, UK Frnce, W Germny, Itly, Belgium, Netherlnds, Luxembourg Cyprus, Czech Rep, Estoni, Hungry, Ltvi, Lithuni, Mlt, Polnd, Sloveni, Slovki Austri, Finlnd, Sweden Portugl, Spin Greece strt city informtion E Germny Bulgri, Romni strt region informtion euro yer The second experiment tht we will, more briefly, look t is the introduction of the Euro. This ws the culmintion of process fter the collpse of the Bretton-Woods system in 1972 vi fixed exchnge rtes to single currency in Europe. The history ws succession of successes nd filures within the Europen Monetry System, but finlly governments greed on the introduction of the Euro, nd s of Jnury 1 st, 2002 Euro coins nd notes were introduced. 8 The Mstricht trety stipultes tht certin mcro-economic criteri hve to be met, relted to government debt, infltion, etc., before countries cn introduce the Euro. In prctice this implies 8 Formlly the Monetry Union strted in

44 tht sub-set of countries tht re member of the EU lso belong the Euro-re. 9 Furthermore, the introduction of the Euro cn be viewed upon s n integrtion experiment reducing brriers to trde such tht the potentil mrket ccess of those involved increses. A priori, the effects of this experiment re expected to be smller thn for economic integrtion becuse ever since the fll of the Bretton-Woods system, Europen policy mkers imed (with mixed success) t more or less fixed exchnge rtes, nd in prctice border cities were often ccustomed to dul exchnge rtes for dy-to-dy pyments (tht is, foreign currencies often circulted in border cities). In ddition, the introduction of the Euro took plce in 2002 (or, techniclly, in 1999) s the Euro-members lredy experienced high degree of economic integrtion. This might hve ffect commuting nd shopping ptterns cross ntionl borders. Note, tht neither of these policy experiments ws imed t border cities in prticulr; possible border integrtion effects re, from policy perspective, unintended side effects. As in Hnson (2001) nd Redding nd Sturm (2008) we expect tht especilly cities nd regions long the border benefit disproportionlly from the incresed (export) mrket ccess. However, s lso stressed by Overmn nd Winters (2006), incresed (export) mrket ccess is not the only force experienced by border cities or regions. Incresed (import) competition could work in the opposite direction. In the New Economic Geogrphy (NEG) models this effect is the so clled price-competition effect. The net effect hs to be determined empiriclly. The integrtion experiments we nlyze re less spectculr thn the Germn division studied by Redding nd Sturm (2008) nd the vrition in the dt following n integrtion shock is likely to be smller thn for the Germn division in Redding nd Sturm (2008) rgue tht economic integrtion might be endogenous. However, it is not cler how especilly border cities or regions could induce these interntionl policy chnges. Border cities s such re not the min trget of economic integrtion. Since we use much lrger smple of cities nd regions in substntilly more countries thn Redding nd Sturm (2008) the smller size of the shocks we study is compensted by lrger number of observtions. Finlly, the question rises how long the border integrtion effects lst. Bsed on the estimtes of Redding nd Sturm (2008) for border cities in Germny, we initilly tke this durtion to lst bout 40 yers. 10 With respect to the EU enlrgements it took more thn 20 yers, fter the cretion of the ECSC in 1951, before the first EU enlrgement occurred in 1973 (see Tble 3.1). This implies tht the first enlrgement in 1973 nd ll subsequent enlrgements fll within the 40 yers durtion period. Since our city smple strts in 1979 nd the first chnge (needed for the empiricl specifiction, see below) is only observed in 1989, the durtion period of 40 yers hs effectively only elpsed for the founders of the EU. Consequently, no border integrtion effects re ctive between Frnce, West Germny, Itly, Belgium, Luxembourg nd the Netherlnds for the period of observtion (these countries were the initil members in 1951). All 9 In 2011 the Euro-re consists of Belgium, Germny, Irelnd, Greece, Spin, Frnce, Itly, Cyprus, Luxembourg, Mlt, The Netherlnds, Austri, Portugl, Sloveni, Slovki, Finlnd, nd Estoni. 10 We lso include some sensitivity nlyses with respect to the durtion of the integrtion effect. 33

45 other border integrtion chnges, including tht of the introduction of the Euro, re ctive for the entire smple period of observtion once they occurred Theoreticl frmework The theoreticl frmework is bsed on multi-region version of Helpmn s (1998) geogrphicl economics model, s used in Redding nd Sturm (2008). As usul in these models (see Brkmn et l Ch. 3-4), the combintion of incresing returns to scle nd trnsport costs leds to gglomertion forces s firms wnt to locte production ner lrge mrkets (home mrket effect) nd consumers wnt to live in lrge mrkets (consumer love of vriety nd trnsport costs result in low cost of living effect). At the sme time, the model exhibits spreding forces s plethor of competitors in lrge mrket mke less-crowded loctions more ttrctive (competition effect) nd (in this specific model) lrge mrket rises the costs of (non-trded) locl services, thus leding to higher costs of living ner lrge mrkets (congestion effect). The tug of wr between the gglomertion nd spreding forces in the model determines the distribution of popultion mong the vilble loctions. The economy consists of number of loctions or res be either cities or regions. Ech re hs n exogenous stock H { 1,.., A}, where the res cn of non-trdble services, referred to s housing in Helpmn (1998). The number of consumers or lborers is mobile cross loctions nd ech supplies one unit of lbor inelsticlly, spends shre (0,1) of income on horizontlly differentited vrieties nd the remining shre 1 L on the non-trdble services. The production of vrieties tkes plce under incresing returns to scle (with fixed cost nd constnt mrginl cost in terms of lbor) nd is bsed on monopolistic competition with constnt elsticity of substitution between vrieties of 1 (Dixit nd Stiglitz, 1977). 11 There re iceberg trnsport costs for vrieties, such tht units must be shipped from loction to mke sure one unit rrives in loction i. T i 1 The popultion of res is endogenously determined by migrtion decisions of workers between loctions to ensure tht the sme rel wge holds in ll populted res in the long-run equilibrium. If we let w, L, M P, n, nd p be the (nominl) wge rte, the number of lborers, the Dixit-Stiglitz price index for vrieties, the number of vrieties produced, nd the locl (free on bord) price of such vriety (ll t loction ), respectively, then it cn be shown (see Redding nd Sturm, 2008) tht the equilibrium rel wge (which holds for ll res) cn be reformulted s n equilibrium popultion L of re : 12 L M 1 / (1 ) 1 /[(1 )( 1)] ( wi Li )( Pi / T i ) n j ( p jtj ) H i F MA j C MA, (3.1) 11 In principle, it is strightforwrd to include more incresing returns industries, ech with different elsticity of substitution. Thus, lrge cities or regions cn host more industries thn smller cities. 12 Niebuhr nd Stiller (2004) provide survey of the relevnt literture. 34

46 where is function of prmeters nd the common rel wge. The terms FMA nd denote firm mrket ccess nd consumer mrket ccess, respectively. Firm mrket ccess CMA FMA mesures the proximity of firms locted in to the demnd from ll mrkets, including the mrket of its own loction (depending on lbor income in loction, the ssocited price index, nd the trnsport costs of getting goods from to ll mrkets). It determines the wge rte tht firms cn fford to py in zero profit equilibrium nd combines both the home mrket effect nd the competition effect mentioned bove (if surrounding res re chrcterized by reltive low price indices, the current loction fces more competition nd is less ttrctive especilly for high elsticities of substitution nd low trnsporttion costs). Consumer mrket ccess mesures CMA consumer s ese of ccess to trdble vrieties (depending on the number of vrieties produced in loction, the loclly chrged price, nd the costs of getting goods from there to ). It cptures the cost of living effect mentioned bove. Finlly, the term H (stock of non-trdble services) is ssocited with the congestion effect. Note tht the model ssumes lbor mobility (resulting in rel wge equliztion for ll res, ). 13 It is well-known tht lbor mobility in the EU is reltively limited. This implies tht if integrtion, or for tht mtter ny shock, hs some impct on L this is dditionl evidence of the strength of the integrtion effects. Eqution (3.1) shows tht loctions in the vicinity of country borders, which pose significnt obstcles to trde flows (leding to high trde costs T i) nd thus tend to hve lower firm nd consumer mrket ccess, hve lower popultion levels in long-run equilibrium. Redding nd Sturm (2008) tke the division between Est nd West Germny fter WWII until the reunifiction in 1990 s n exmple of shock tht cretes n integrtion effect. They clibrte the bove model nd show tht (i) cities close to the border decline in popultion through chnges in T i nd T j in eqution (3.1) (n effect tht diminishes s the border distnce increses), nd (ii) the border integrtion effect is weker for lrge cities s these initilly home to lrger set of sectors re ble to specilize nd ccess export mrkets more esily thn smll cities. Their empiricl estimtes find strong support for (i) nd (ii). Note tht, theoreticlly, creting obstcles or removing them hve similr but opposite effects on the long-run equilibrium of the city size distribution, which is bsed on the mrket ccess of ech city. This does not men tht creting nd removing obstcles is spce-neutrl becuse the mrket-ccess mesures re loction-dependent. Suppose tht we first crete big (rtificil) border s n obstcle to trde- nd interction flows (s hppened in Germny fter WW II), subsequently remove tht obstcle 45 yers lter, nd then wit nother 45 yers for the finl equilibrium to settle down. Will this finl equilibrium be the sme s the equilibrium tht would hve settled fter 90 yers in the bsence of creting nd removing the obstcle? The nswer is: no. Pth-dependence or hysteresis plys prominent role in geogrphicl economics models. The initil cretion of the obstcle ffects in prticulr the mrket ccess of cities in the vicinity of the obstcle, resulting in reduction of their (economic) size. The concomitnt 13 See Redding nd Sturm (2008, p. 1772), eqution (3.1). See Brkmn, Grretsen, Vn Mrrewijk (2009, ch. 3) for n in depth discussion of the forces tht drive eqution (3.1). 35

47 redistribution of mrket ccess for ech city is t lest prtilly locked-in, both for firm mrket ccess nd for consumer mrket ccess s given in eqution (3.1). The subsequent removl of the obstcle only prtilly restores the initil sitution (quite prt from the myrid of other chnges tht occur in the mentime). In ll likelihood, therefore, the cities in the vicinity of the obstcle will suffer permnent consequences from its cretion even fter its subsequent removl. Our emphsis in this chpter is on reverse policy shock, insted of division we will thus look t integrtion. The Europen integrtion process strives to reduce interntionl obstcles between countries (leding to lower trde costs T i). On the one hnd, the process of Europen integrtion is rgubly more grdul nd its impct on border loctions not s strong, brupt nd severe s the Germn division fter WWII. One would thus expect the impct on border popultion size to be smller nd hrder to find for the EU integrtion process. On the other hnd, the number of countries, regions, nd cities involved in the EU integrtion process is considerbly lrger thn for the cse of the Germn division (see the next section), such tht if there is n economiclly meningful impct we should be ble to find it. Following Redding nd Sturm (2008) our min hypothesis is s follows: I. Cities or regions tht re close to n bolished border s result of EU integrtion shock experience reltive popultion increse. Bsed on the discussion bove, we cn formulte sub-hypotheses II-c: II. ) The border integrtion effect is different for lrge nd smll border res. b) The border integrtion effect is stronger for EU enlrgement compred to the introduction of the euro. c) The border integrtion effect declines s the distnce to the border rises. As discussed bove whether the border integrtion effect is indeed positive is n empiricl question (see the discussion of eqution (3.1) bove). Redding nd Sturm (2008) stipulte tht the mrket ccess effect will be dominnt, but the competition effect counter-cts the home mrket effect. Brkmn et l. (2009, Chpter 11) provide n illustrtion of the forces t work in relted simultion experiment. They show tht building bridge between two loctions in multiloction NEG setting ffects ll loctions, but those ner the bridge (or in the present cse, ner disppering border) re ffected the most. The simultions indicte tht the competition effect for stndrd prmeter vlues does not dominte the other forces nd tht integrtion benefits the border res, which is the min reson why we priori expect the border integrtion effect to be positive. 36

48 3.4. The dt We collected two bsic, non-blnced pnel dt sets: one for Europen cities, using dt from Brinkhoff ( nd nother for Europen regions, using dt from Eurostt. 14 For the nlysis in this chpter we included informtion from 34 Europen countries, leding to totl number of 1,457 regions nd 2,410 cities, see Tble 3.2 for list of countries nd the number of regions nd cities for ech country. Tble 3.2: Included countries with # of regions nd # of cities Country # regions # cities Country # regions # cities Austri Luxembourg 1 28 Belgium Mcedoni 8 34 Bosni & Herzegovin n.. 24 Mlt 2 30 Bulgri Montenegro n.. 25 Croti Netherlnds Czech Republic Norwy Denmrk Polnd Estoni 5 30 Portugl Finlnd Romni Frnce Serbi n.. 62 Germny Slovki 8 42 Greece Sloveni Hungry Spin Irelnd 8 54 Sweden Itly Switzerlnd Ltvi 6 32 Turkey Lithuni UK Totl 1,457 2,410 Note tht the numbers in Tble 3.2 re neither proportionl to country s totl popultion nor to its size. Frnce, for exmple, hs only limited number of cities included in the dt set, while Germny hs lrge number of regions compred to other countries. Consequently, in our smple Germny nd Frnce hve more regions thn cities, which is in contrst to the other countries under considertion tht hve more cities thn regions in the smple. Seven countries in Tble 3.2 re not current EU member countries (lthough some re cndidte countries, see Figure 3.2 below), these re Bosni nd Herzegovin, Croti 15, Mcedoni, Montenegro, Norwy, Serbi, nd Switzerlnd (in the estimtions we differentite between EU countries only, nd ll countries). Note tht Bosni nd Herzegovin, Montenegro, nd Serbi re only included in the city nlysis, while Mcedoni is only included in the region nlysis. The other 30 countries re included both in the city s well s in the region nlysis. 14 See the dt ppendix for detiled description of the dt. 15 Croti becme EU member in 2013, bout yer fter publiction of the this chpter rticle. 37

49 Figure 3.2: () The Europen Union in 2010 (b) The Euro re by by 2013 Source: Figure 3.2 depicts the vrious EU countries nd cndidte EU countries in The nlysis focuses on clssic border integrtion effects, mening tht we focus on lnd connections. Furthermore, borders res (cities or regions) re only defined s border res if t some point in the history of our smple they re ffected by n integrtion shock. An exmple is Germny. Border res long the Dutch-Germn border re excluded s they experience no integrtion shock with respect to integrtion since the entry of The Netherlnds nd Germny into (the forerunner of) the EU lredy took plce in However, border res long the Germn-Polish border re included in the definition of border res s they re ffected by integrtion (in 2004). For the cse of the Euro shock we follow the sme procedure (implying tht for the Euro shock border res long the Dutch Germn borders re included in the border definition). 38

50 Tble 3.3: Overview of ffected continentl lnd borders in smple period Enlrgement Affected border of enlrgement between yer Country 1 Country Denmrk West Germny 1981 n.. n Spin Frnce Spin Portugl 1990 West Germny Est Germny 1995 Sweden Finlnd Austri Germny (west) Austri Itly 2004 Estoni Ltvi Ltvi Lithuni Lithuni Polnd Polnd Germny (est) Polnd Czech Republic Polnd Slovki Czech Republic Germny Czech Republic Austri Czech Republic Slovki Hungry Slovki Hungry Austri Hungry Sloveni Sloveni Austri Sloveni Itly 2007 Romni Hungry Romni Bulgri Bulgri Greece As is cler from Figure 3.2 nd Tble 3.3, most EU enlrgements were relted to lnd borders. However, there re enlrgements relted to crossing se borders, such s UK Frnce or Denmrk Sweden. 16 Focusing on lnd borders, we still hve to determine when region or city clssifies s border region or city tht is ffected by EU integrtion. For regions this is simple: if two regions in different countries re contiguous t lnd border tht is ffected in the EU integrtion process, they clssify s border region. For cities we hve to specify some cut-off distnce nd wy of mesuring it in order to clssify s border city. In the bseline setting, we include ll cities with mximum rod distnce of 70 kms (firly close to the 75 kms cutting point 16 A sensitivity test with respect to non-lnd borders is vilble upon request; this does not ffect the results mentioned in the min text. 39

51 in Chpter 2 nd still different from n s the crow flies distnce) to the ffected border s border cities. 17 Other rod distnces (50 km nd 85 km) re prt of our sensitivity nlysis. Combining the informtion in Figure 3.2 with the timing nd EU enlrgement schedule in Tble 3.1, nd the smple period shown in Figure 3.1, we hve complete overview of ll ffected EU enlrgement borders nd their strting yer over the entire smple period. 18 As noted bove, the effect remins opertive until the end of the observtion period once it strts. The tble shows tht there ws/were: 1 ffected border in the 1973 enlrgement, no ffected borders in 1981, 2 ffected borders in 1986, 1 ffected border in 1990, 3 ffected borders in 1995, 14 ffected borders in 2004, nd 3 ffected borders in The mjority of EU integrtion ctivity thus concentrtes towrds the end of the period, lthough some cities nd regions re ffected throughout the entire period. Figure 3.3: Averge nnul compounded popultion growth rtes* 1.4 Averge nnul compounded growth rtes (%) cities regions EU non-eu ll border -0.2 * border refers to the EU integrtion cities nd regions, not the euro cities nd regions Tble 3A.1 in the ppendix provides some bsic informtion on the different types of cities nd regions identified in the EU integrtion process. The verge city size in the EU (110,000) is both lrger thn the non-eu cities (82,000) nd lrger thn the size of the cities long the EU integrtion border (93,000). The sme holds for the medin city size, which is 51,000 for EU 17 The rod distnce ws mesured mnully for ll cities using Google Mps; dt vilble on request. Furthermore, s robustness check we lso defined border cities s the group of cities tht is prt of border region. This definition is problemtic tht the size of dministrtive regions of some countries re much lrger thn for other countries, implying tht country bis might be introduced. In generl, however, the results re grosso modo comprble. 18 Note tht we exclude the only non-continentl lnd border between Irelnd nd the UK ffected by EU enlrgement. Including it does not ffect our results. 40

52 cities, 26,000 for non-eu cities, nd 33,000 long the integrtion border. When clculting the verge nnul compounded growth rtes (in percent), we observe (see Figure 3.3) tht the smller non-eu cities grow fster thn the lrger EU cities, nmely 1.35 percent compred to 0.35 percent. More interesting for this study, however, is the fct tht the cities long the EU integrtion border grow even slower (0.12 percent), which mkes it priori unlikely to find positive EU integrtion effects. The nlysis below, however, distinguishes between the generl border integrtion effect (which is expected to be negtive) nd the EU integrtion border integrtion effect (which is thus expected to be positive). Since the negtive generl border integrtion effect typiclly turns out to be stronger thn the (temporry) positive EU integrtion border integrtion effect, the net border integrtion effect is negtive (s illustrted in Figure 3.3). Similr observtions hold for the regionl dt, since (i) the verge popultion size of EU regions (374,000) is lrger thn long the integrtion borders (296,000), (ii) the medin size of EU regions (251,000) is lrger thn long the integrtion borders (181,000), nd (iii) the verge growth rte of EU regions (0.17 percent) is lrger thn long the integrtion borders (-0.09 percent). The non-eu regions gin grow more rpidly (0.35 percent) thn the EU regions (see Figure 3.3). 19 In ll cses, the growth rte of regions is smller thn the growth rte of the concomitnt cities, indictive of generl process of urbniztion Empiricl strtegy To investigte the hypotheses discussed in section 3.3, we use difference-in-differences methodology by compring the growth performnce of Europen res close to border bolished during the EU integrtion process (tretment group) to the growth performnce of other Europen res (control group). Consequently, we focus on the distribution of popultion over the regionl or urbn system within ech country. Let be the popultion of re in time t, shre pop / t t C pop t (where C pop t is the country index) be the shre of the popultion in the regionl or urbn system nd the popultion shre growth is given s: shregrowth follows: 20 shre shre, t1, t, t, t1, t1 shre. Our bseline empiricl specifiction is s 19 The size of non-eu regions is lrger thn the size of EU regions (in contrst to the size of cities), nmely n verge of 624k nd medin of 314k. 20 The link between equtions (3.1) nd (3.2) cn be seen by log-differentiting (3.1). The borderintegrtion dummy cptures the combined effect of chnges in FMA nd CMA cused by chnges in trnsport costs. The implicit ssumption is tht the integrtion dummy cptures the effects on popultion growth through: the price index, mrket size (wges*initil popultion), nd the number of vrieties (firms). The min concern when considering econometric bises in estimtes like these re omitted vribles. To some extent the dummy vribles (fixed effects) del with this. Below we del seprtely with the FMA term in the sense tht smller cities might experience n integrtion shock differently thn lrge cities (tht might lredy be home to importnt export industries). 41

53 shregrowt h border ( border integrtion, t s, t t) d D t C t, (3.2) where shregrowt, from time period h, ts t t s is the nnulized rte of growth (percent) in the popultion shre of re to t ; border border group s whole nd zero otherwise 21 ; let B is dummy equl to one when n re is member of the B A border 1, then integrtio n t dummy equl to one t time t if nd n EU integrtion border within its rech ws bolished t most 40 yers go. A similr resoning pplies to the cse of the introduction of the euro. In this wy we cn distinguish within the border group s whole, whether the selected group of border regions or cities tht experience Europen integrtion (or the introduction of the Euro) perform differently from those not ffected by Europen integrtion (or the introduction of the Euro). Furthermore, is full set of time dummies; is full set of country dummies; nd is the d t error term. Note tht the term integrtio n t D C t is does not only depend on time but lso on loction s opposed to Redding nd Sturm (2008). This is cused by the fct tht during the EU history severl borders were bolished t different loctions nd different time periods, see Tble 3.3 for n overview. This dummy is therefore, for exmple, equl to zero for cities long the Austri-Itly border (either in Austri or in Itly) until 1994 nd equl to one from 1995 onwrds. 22 Eqution (3.2) llows for unobserved fixed effects in re popultion levels which re differenced out by computing growth rtes. The time dummies control for common mcroeconomic shocks ffecting the popultion growth throughout Europe nd trends in popultion growth rtes. The country fixed effects tke cre of unobserved heterogeneity between countries, s our res re prt of different ntionl (urbn) systems with different policies (for exmple regrding the extent to which they stimulte ctivity in border res). The coefficient cptures ny systemtic difference in popultion growth rtes of border res versus other res. The key coefficient is, on the interction between border res nd EU integrtion nd the reltive performnce of popultion growth for tretment nd control res. The prediction is tht this coefficient is positive. 21 See section 3.4 on the definition of ffected cities or regions. 22 Similrly, for n Austrin city such s Linz (close to both the Germn nd Czech Republic border), this dummy is equl to zero up to 1994 nd equl to one from 1995 onwrd (s prt of Austri-Germny border region) nd equl to one from 2004 onwrd (s prt of the Czech Republic-Austri border region), tht is the dummy is one until 2043 (for period longer thn 40 yers). For our period of observtion, this time extension beyond 40 yers is never n issue. 42

54 3.6. Estimtion results EU enlrgement The bseline estimtion results for both urbn- nd regionl popultion shre growth rtes re given in Tble 3.4. Columns (1) nd (3) provide the results when informtion from ll countries with vilble dt re included, while columns (2) nd (4) restrict ttention to dt from EU countries only (thus slightly nrrowing the size of the control group). The results re virtully the sme in ll cses. The first line indictes tht border res re indeed poor performers reltive to more centrl loctions. The popultion shre growth rte is 0.21 percentge points per yer for border cities nd 0.31 percentge points for border regions. Our key coefficient of interest on the interction between border res nd EU integrtion ( ), is given in the border integrti on t second row of the tble. The effect is positive nd highly significnt. As result of the integrtion process, the popultion shre growth rte for border res rises by bout 0.15 percentge points, both for cities nd regions. On the one hnd, this is n indiction of the success of the EU integrtion process. On the other hnd, we observe tht it is not sufficient to reverse the reltive decline of border res, neither for cities nor for regions. Tble 3.4: Urbn nd regionl popultion shre growth rtes; bseline estimtes border border integrtio n t Urbn popultion Regionl popultion (1) (2) (3) (4) 0.210*** (0.0549) 0.147*** (0.0499) 0.227*** (0.0568) 0.180*** (0.0516) 0.312*** (0.0415) 0.145*** (0.0542) 0.314*** (0.0418) 0.148*** (0.0561) Yer effects yes yes yes yes Country effects yes yes yes yes Smple cities / regions ll cities ll cities ll regions ll regions Smple countries ll countries EU countries ll countries EU countries Observtions 6,286 5,239 23,096 20, Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1 R 2 Our definition of ffected border cities is, s discussed, bsed on n cross-the-rod trvel distnce to the border of 70 km. This is, of course, to some extent n rbitrry mesure nd specific to the context of our dtset, lthough it is in line with the extent of distnce effect found by Redding nd Sturm (2008) for the Germn division process. Tble 3.5 provides the bseline estimtes for urbn popultion shre growth for two lterntive distnce mesures, nmely 50 km 43

55 nd 85 km cross-the-rod trvel distnce to the border. 23 The results re in line with our previous findings, with effects positive nd highly sttisticlly significnt, in the border integrti on t rnge of 0.11 to 0.17 percentge points rise per yer. Agin, this is not sufficient to offset the reltive decline of border cities. Recll, from Chpter Two, tht we show neighboring countries mrket to be more importnt for the bordering cities thn it is for the non-border cities. And fter more thn 50 yers from the implementtion of the first free trde greements, the cities tht re closer to the Belgium Netherlnds nd Germny the Netherlnds borders still hve proportionlly lower wge rtes compred to non-borders. Tble 3.5: Urbn popultion shre growth rtes; vritions in distnce border border integrtio n t 50 km border 85 km border (1) (2) (3) (4) 0.176*** (0.0550) 0.111* (0.0613) 0.191*** (0.0561) 0.142** (0.0623) 0.145*** (0.0548) 0.131* (0.0706) 0.168*** (0.0537) 0.174** (0.0689) Yer effects yes yes yes yes Country effects yes yes yes yes Smple cities ll cities ll cities ll cities ll cities Smple countries ll EU ll EU Observtions 6,286 5,239 6,286 5,239 2 R Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1 Nturlly, this rises the question on the sptil rech of the border integrti on t interction effect, recll hypothesis IIc. The results in Tble 3.6 re presented to nswer this, where we subdivide the border cities into cities (i) within the rnge of 50 km from the border, (ii) within the rnge 50 to 70 km from the border, nd (iii) within the rnge of 70 to 85 km from the border. For the first two types of cities, the border integrti on effect is positive nd significnt. For the third type of cities (within the rnge 70 to 85 km from the border), the border integrti on t is positive, but not sttisticlly significnt. This leds us to conclude tht we cn sfely restrict ttention to cities within the 70 km rnge, which is in line with the findings of Redding nd Sturm (2008). Note tht this implies tht our regionl estimtes include collection of border cities (within the 70 km rnge) s well s non-border cities (outside the 70 km t 23 The tble reports the results for urbn popultion shre of columns (1) nd (2) in Tble 3.4 for the lterntive specifiction of 50 km nd 85 km border distnce. We lso looked t ll borders in the smple, i.e. not only the border res tht re ffected by shock. Also those border cities re dversely effected by the border loction, but less so (by fctor two) thn the border cities t the ffected borders. Border regions long the borders of these core EU members show smll positive effect.. 44

56 rnge). The positive effect is the lrgest for the EU countries in the second group of distnce rnge (see 3 rd row, column (2)). This implies tht the most ffected cities re found within moderte distnce of the borders, neither too close nor too fr. In ddition, we constructed n rtificil border to see if the estimtes re sttisticl rtifcts. To this end we selected, t rndom, 416 cities nd 306 regions nd defined these s border res (the sme numbers s in the smple). Next, we repeted the estimtes for this rndom border smple for integrtion shocks. The tretment group nd timing ws lso constructed t rndom. The results (see ppendix II) indicte tht this exercise resulted in non-significnt outcomes, both for border res in generl s well s for the tretment group. Tble 3.6: Urbn popultion shre growth rtes; extent of distnce effect 24 border border integrtio n t 50km border integrtio n t 5070km border integrtio n t 7085km (1) (2) 0.200*** (0.0584) 0.124** (0.0552) 0.194*** (0.0702) (0.125) 0.219*** (0.0605) 0.163*** (0.0575) 0.242*** (0.0719) (0.125) Yer effects yes yes Country effects yes yes Smple cities ll cities ll cities Smple countries ll countries EU countries Observtions Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1 R 2 The next effect we nlyze is the durtion of the border integrti on effect, which is tken to be 40 yers in the bseline scenrio. To do tht, we creted four seprte dummy vribles, ech covering period of 10 yers fter the bolishment of n EU border. The dummy vrible border integrti on t 1020yers, for exmple, equls one if n EU border ws bolished for the respective border re between 10 nd 20 yers go (nd zero for the other time dummies). As Tble 3.3 shows, the border between Spin nd Frnce ws bolished in This implies tht for the cities nd regions long the Spin Frnce border the vrible border integrti on t 1020yers border cities the is equl to one in the period Tble 3.7 shows tht for border integrti on effect is opertive (positive nd significnt) for period of t bout 20 yers. This is significntly shorter thn the (opposite) effect on the durtion of the t 24 The tble reports the results for individully exclusive distnces for the bseline 70 km specifiction. 45

57 Germn division found by Redding nd Sturm (2008), which lsts for 40 yers. We think tht the impct of the much more drmtic shock experienced in Germny is responsible for this longer durtion, but the limited number of observtions we hve for the EU integrtion effect for time periods of more thn 20 yers lso plys role. 25 The results in Tble 3.7 on the durtion of the EU integrtion effect re bit less strightforwrd for the regionl dt, which indictes tht this effect is positive nd significnt for the 0 10 yers nd yers periods nd not significnt for the other periods. The inclusion of both border nd non-border cities in the border region dt my prtilly explin this finding. This ctegoriztion lso sometimes leds to specific group of countries to be in group. For instnce, the countries in their yers since the integrtion cn only be the oldest EU members, nmely, Belgium, Denmrk, Frnce, Germny, Greece, Irelnd, Itly, Luxembourg nd UK. Tble 3.7: Urbn nd regionl popultion shre growth rtes; timing effect estimtes border Urbn popultion Regionl popultion (1) (2) (3) (4) 0.200*** (0.0561) 0.219*** (0.0583) 0.288*** (0.0411) 0.290*** (0.0414) border integrtio n t 010 yers 0.128** (0.0528) 0.161*** (0.0544) 0.206*** (0.0542) 0.213*** (0.0558) border integrtio n t 1020 yers 0.154** (0.0699) 0.204*** (0.0721) (0.0613) (0.0623) border integrtio n t 2030 yers (0.154) (0.154) 0.604*** (0.185) 0.604*** (0.189) border integrtio n t 3040 yers (0.261) (0.261) (0.172) (0.170) Yer effects yes yes yes yes Country effects yes yes yes yes Smple cities / regions ll cities ll cities ll regions ll regions Smple countries ll EU ll EU Observtions 6,286 5,239 23,096 20, Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1 R 2 Tble 3.8 nlyzes the difference in economic impct of EU integrtion for cities nd regions of different size, see hypothesis II. We divide the cities, for instnce, into two groups: lrge nd smll. We define city to be lrge if its erliest observtion exceeds the medin of ll erliest observtions nd to be smll otherwise. A similr procedure for regions would lump 25 As Tble 3.3 shows, only the Germn-Dnish border genertes observtions within the yers of durtion, leding for both cities nd regions to limited number of observtions in this rnge. 46

58 together lrge geogrphicl res or regions with mny smll cities or with one big city s lrge regions. Insted, we opted for more coherent definition, in which region is lrge if it includes city whose popultion size exceeds the medin of cities. Tble 3.8 shows tht the overll positive EU integrtion effect for border res is driven by the results for lrge cities/regions. For smll cities/regions the integrtion effect is usully not even sttisticlly significnt, nd the sme hold for the border dummy s such. This differs from the findings of Redding nd Sturm (2008, tble 3.7, p.1794) for the reunifiction of Germny, which is rgubly smller shock thn the Germn division. They find some evidence tht the reunifiction hd positive effects, but differentiting between lrge nd smll cities results in [p.1793]: coefficients substntilly smller in mgnitude thn for the division nd re not sttisticlly significnt t conventionl levels. Be s it my, for our smple we find tht lrger cities nd regions re the ones tht receive positive integrtion thereby confirming hypothesis II. 26 Tble 3.8: Urbn nd regionl popultion shre growth rtes; smll nd lrge res. Urbn popultion shre growth rtes border All countries EU countries (1) (2) (3) (4) *** (0.0641) (0.0898) *** (0.0646) (0.0953) border integrtio n t *** (0.0720) (0.0715) *** (0.0728) * (0.0779) Yer effects yes yes yes yes Country effects yes yes yes yes Smple cities lrge cities smll cities lrge cities smll cities Smple countries ll countries ll countries EU countries EU countries Observtions 3,248 3,036 2,908 2, R 2 b. Regionl popultion shre growth rtes border border integrtio n t All countries EU countries (1) (2) (3) (4) *** (0.0479) (0.0842) *** (0.0486) (0.0821) *** (0.0629) (0.0968) *** (0.0655) (0.0934) Yer effects yes yes yes yes Country effects yes yes yes yes Smple regions b lrge regions smll regions lrge regions smll regions Smple countries ll countries ll countries EU countries EU countries Observtions 16,314 6,782 15,060 5,610 2 R Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1 Lrge is bigger (nd smll is less) thn medin of erliest observtions, where erliest observtion is the erliest yer popultion dt re vilble for the city b A region is lrge if it includes city whose popultion size exceeds the medin of cities. 26 Estimtes for the complete smple but introducing dummy for lrge cities or regions gives similr results. 47

59 Tble 3.9: Urbn nd regionl popultion shre growth rtes; symmetry: old nd new members since 2004 Urbn popultion Regionl popultion (1) (2) (3) (4) border border integrtio, n t old border integrtio, n t new *** (0.0536) *** (0.0611) ** (0.0622) *** (0.0557) *** (0.0611) *** (0.0645) *** (0.0406) * (0.0512) *** (0.103) *** (0.0409) * (0.0521) *** (0.109) Yer effects yes yes yes yes Country effects yes yes yes yes Smple cities / regions ll cities ll cities ll regions ll regions Smple countries ll countries EU countries ll countries EU countries Observtions 6,286 5,239 23,096 20, R 2 Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; border, see the min text for detils. border rt refers to n rtificilly creted Finlly, Tble 3.9 nlyzes symmetric border integrtion effects, where we disentngle the border integrtion effects for the existing EU members nd the new entrnts, specificlly for the substntil enlrgements in 2004 nd Note gin tht for instnce Germn cities long the Polish border re included s border cities of the existing EU member Germny nd Germn cities long the Dutch or French border re included s non-border cities. As the tble indictes, our min results re not ffected. More specificlly: (i) there is significnt nd negtive generl border integrtion effect nd (ii) there is significnt nd border integrtion effect, both for the border cities of the old nd new EU members, like for instnce Germn cities long the Polish border nd vice vers respectively. The tble lso shows tht the border integrtion effect is bout the sme t the city level for old nd new members, while it is higher for the new members thn for the old members t the regionl level. 27 We ttribute this difference gin to the more coherent unit of observtion t the urbn level thn t the regionl level. 27 At the city level n F-test for equlity of the border-integrtion coefficients for old nd new members cnnot be rejected t ny stndrd significnce level. In contrst, this equlity hypothesis is rejected t the 5 percent level for the regionl estimtes. We lso estimted old nd new border integrtion effects for the whole period nd found similr results. 48

60 The introduction of the Euro The second integrtion experiment described in section 3.2 is tht of Europen monetry integrtion, ultimtely resulting in the introduction of the euro for 12 countries in 2002 (enlrged in the period to 17 countries). 28 As lredy discussed bove, the dditionl effects of the introduction of the euro on the mrket ccess vribles of border cities or regions compred to non-border cities or regions (which ultimtely determines loction decisions) re expected to be smller thn the dditionl effects of the EU integrtion process s mesured by ccession, see hypothesis IIb. Not only is the euro relted to smller prt of the economic forces, but lso (nd more importntly) monetry unifiction ws much more grdul process with mny decdes of experimenttion with fixed or mnged exchnge rtes nd long period of dhering to strict rules before the ctul introduction of Euro coins nd bills in 2002 took plce. Our results re summrized in Tble 3.10, which shows tht (i) the popultion shre growth rtes re significntly smller long the borders of the euro re (bout 0.13 percent for cities nd 0.20 percent for region) nd (ii) there is no discernible positive effect on these growth rtes tht cn be ttributed to the introduction of the euro. 29 Border cities nd regions hve no benefits in terms of their popultion growth shre growth from introducing the euro. Tble 3.10: Urbn nd regionl popultion shre growth rtes; introduction of the Euro border euro border euro euro t Urbn popultion Regionl popultion (1) (2) (3) (4) *** (0.0450) (0.0577) *** (0.0459) (0.0580) *** (0.0286) (0.0451) *** (0.0283) (0.0456) Yer effects yes yes yes yes Country effects yes yes yes yes Smple cities / regions ll cities ll cities ll regions ll regions Smple countries ll countries EU countries ll countries EU countries Observtions 6,286 5,239 23,096 20,670 2 R Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < Or 20 countries if one includes Sn Mrino, Monco, nd the Vticn. 29 Note tht the selection of border cities nd regions for the introduction of the euro is quite different from tht of the EU integrtion (ccession) process, nd in prticulr includes cities nd regions long the borders of the countries tht strted the process: Frnce, Germny, Itly, Netherlnds, Belgium, nd Luxembourg. Tking 1999 insted of 2002 s the strting yer for the erly 11 countries involved does not chnge our results. 49

61 3.7. Conclusions Urbn historins hve shown tht the evolution of cities follows reltively stble pth (Biroch, 1988). At the sme time, long time series on city popultion lso revel tht (sub-sets of) cities cn switch to new development pths. Reltively recently, discretionry policy chnges or nturl experiments hve been used to shed light on wht drives these chnges in the development of (sub-sets of) cities nd to investigte whether they re, indeed, stble fter shock or policy chnge. Redding nd Sturm (2008) nlyze the effects of the post WWII division of Germny into Est nd West Germny in 1949 on border cities long the new border within Germny. They find tht the effects of the Germn division on the cities long the intr-germn border were substntil, resulting in shrp decline of the popultion long the new border (more so for smll thn for lrge cities). We pply the methodology developed by Redding nd Sturm (2008) to the cse of the EU enlrgements tht took plce from 1973 onwrds, which we expect to ffect especilly border cities s these cities experience lrger chnges in mrket ccess thn cities further wy from the border. We lso nlyze regionl dt nd look t the effects of the introduction of the Euro on border loctions. Both t the urbn nd regionl level, we find positive effect of the EU integrtion process s mesured by the growth in popultion shre long the integrtion borders, leding to n dditionl growth of bout 0.15 percentge points per nnum. The positive integrtion effect ssocited with EU enlrgements holds on both sides of the integrtion border, is ctive for limited distnce (up to 70km) nd time period (up to 30 yers), nd is driven by the lrger cities nd regions. Despite this positive EU integrtion effect, being locted long border remins burden in view of the (lrger) generl negtive border integrtion effect. We do not find similr border integrtion effects s result of the introduction of the euro. In short, we find support for our hypotheses tht, following the economic integrtion, border cities/regions grow reltively fster. The border integrtion effect is stronger thn just monetry union. Smll nd lrge cities/regions re ffected differently nd the integrtion effects decy over distnce from the borders. 50

62 3.8. Appendices Dt description The dt consist of two non-blnced pnel dt sets on loction nd popultion, one for Europen cities nd one for Europen regions. The dt for Europen cities were collected from Brinkhoff ( wheres the dt on the Europen regions were obtined from Eurostt ( The urbn popultion dt covers the period from 1979 to 2010, with irregulr intervls. The regionl dt cover the period from 1990 to 2008, with only few missing observtions. Border regions re defined s regions tht hve common border with neighboring EU country. The loction of cities ws collected from Google mps ( Border cities re cities within rod distnce of 70 kilometers from the nerest ntionl border(s). We lso experimented with border cities within 50 kilometers nd 85 kilometers rod distnce from ntionl border. The totl number of cities is 2410, nmely 1950 EU cities nd 460 non-eu cities (Tble 3A.1, ). Out of the 1950 EU cities 416 (21 percent) re border cities (using the 70 kilometers border distnce). The regionl dt set consists of 1457 regions, nmely 1302 EU regions nd 155 non-eu regions. Out of the 1302 EU regions 306 (24 percent) re border regions (see Tble 3A.1, b). Tble 3A.1: Bsic urbn nd regionl informtion (EU integrtion) Popultion growth *. Urbn dt # cities men medin rte (%) EU Cities 1, ,484 50, Non-EU cities ,483 26, All Smple Cities 2, ,631 44, EU integrtion border cities (70 km) ,054 32, b. Region dt # regions men medin rte (%) EU Regions 1, , , Non-EU regions , , All Smple regions (totl) 1, , , EU integrtion borders , , * Since we don t hve nnul dt, the verge nnul compounded growth rte (%) is clculted bsed on beginning nd end vlue 51

63 Rndom border Tble 3A.2 reports the effects of n rtificilly creted border from rndom selection of 416 nonborder cities nd 306 non-border regions (equl to the number of border cities or regions). The strt of the integrtion period for ech city or region ws chosen rndomly from one of the periods relevnt for this country30 nd ctive henceforth. As the tble shows, creting this rtificil border integrtion effect within the EU does not led to ny significnt border integrtion effects. Tble 3A.2: Urbn nd regionl popultion shre growth rtes; rtificil border Urbn popultion Regionl popultion (1) (2) (3) (4) border rt border integrtio, rt n rt t (0.0781) (0.0928) (0.0347) (0.0391) (0.0920) (0.106) (0.0509) (0.0543) Yer effects yes yes yes yes Country effects yes yes yes yes Smple cities / regions ll cities ll cities ll regions ll regions Smple countries ll countries EU countries ll countries EU countries Observtions 6,286 5,239 23,096 20, R 2 Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; rtificilly creted border, see the min text for detils. border rt refers to n 30 For countries not ctively ffected by integrtion in the whole period, such s Belgium, the nerest border effect ws chosen, in this cse The list is vilble on 52

64 Chpter Four Asymmetric Border Effects of EU Integrtion: Evidences from Dutch, Belgin nd Germn Municiplities Introduction The nlysis of the consequences of the llied bombing during World Wr II on Jpnese cities by Dvis nd Weinstein (2002) nd similr study of the consequences of the llied bombing during World Wr II on Germn cities by Brkmn et l. (2004) suggests tht cities (prtilly) return bck to their long-run growth pth following systemic shock. A more common but less drstic shock is cretion or bolition of border brriers. In their seminl pper Redding nd Sturm (2008) nlyze, in New Economic Geogrphy frmework, the effects on border cities long the new border following the post-wwii division of Germny into Est nd West Germny in Building on the nlysis by Redding nd Sturm (2008), in Chpter Three, we investigte the EU integrtion effects on cities nd regions bordering other EU member countries. Both Redding nd Sturm (2008) nd the results in Chpter Three show tht the cities nd regions ner the borders experience stronger negtive effects of borders s well s more positive effects of integrtion thn centrlly locted cities or regions following the bolition of border brriers. Chpter Three, in prticulr, nlyzes the effects of EU integrtion on popultion distribution of the cities nd regions of the EU member countries cross the ntionl border nd centrl loctions. The results show tht negtive border effects re compensted for by higher popultion growth following EU integrtion shocks. In the nlysis it is inter li ssumed tht country is ffected only once t the time when the country joined EU nd/or when neighbor country joins EU. It is lso ssumed tht the integrtion effects re symmetric cross ll the borders nd ll member countries. Furthermore, the integrtion remins ctive for limited period (mx. 40 yers). In this chpter we relx these ssumptions. We thereby extend the nlysis of the previous chpter in two wys: llow for symmetric effects, nd llow for indirect integrtion effects. We use the difference-in-difference estimtion pproch s employed by Redding nd Sturm (2008). Our results show tht borders re ffected differently, hence conforming the ide of symmetric border integrtion effects. The results lso show tht the border integrtion effects re limited in time for Belgium nd the Netherlnds. However, the results lso show tht the integrtion effects on the Germn border municiplities lst longer. This is so likely due to Germny s geogrphicl proximity to most of the Est Europe new EU members who joined EU t lter stges in time. Also indirect integrtion effects re present. The reminder of this chpter is rrnged s follows. Section 4.2 contins brief discussion of the EU enlrgement process nd possible (in-)direct links to our current smple 31 This chpter is bsed on joint work with Steven Brkmn nd Hrry Gretsen 53

65 borders. Section 4.3 discusses the model introduced by Redding nd Sturm (2008). Section 4.4 is bout the dt set. The dt cover three of the oldest EU member countries (Belgium, Germny nd the Netherlnds), s in Chpter Two. We expect tht the effects in popultion here re consistent with the effects in wge in Chpter Two. The vilbility of long time series nd detiled cross-sections for these countries mke them suitble for our reserch questions. We ssess the possible vrition in the (in-)direct integrtion effects on border loctions over time s EU expnds to countries tht do not involve the smple borders. In ddition to using popultion shre growth, this llows us to use popultion growth since there re integrtion shocks which cn be the sources of the growth s opposed to the cse in Chpter Three where ll the shocks re within the smple loctions. Section 4.5 describes the estimtion strtegy, nd section 4.6 the estimtion results. Finlly, section 4.7 discusses the mjor findings compred to relted reserch. Finlly, section 4.8 summrizes nd concludes EU Enlrgement, border regions nd reserch motivtion Most forms of economic integrtion involve reduction or elimintion of brriers on cross-border mobility of commodities nd fctors of production. The ongoing process of EU integrtion is good exmple. It strted with the 1951 Europen Col nd Steel Community (ECSC) formed by five members nmely Belgium, Frnce, Luxembourg, the Netherlnds, Itly, nd (West-) Germny nd over time vrious enlrgements took plce (see tble 4.1). The tble demonstrtes where nd when the popultion relted integrtion shocks took plce which in ddition chnge the totl EU popultion. In ddition, 17 of the 27 current member countries use the euro. Free trde nd mobility of workers increse the mrket ccess of member ntions. Tble 4.1: EU enlrgement process nd integrtion shocks Time line Integrtion New members 1951 Europen Col nd Steel Community Belgium, Frnce, Luxembourg, the (ECSC) Netherlnds, Itly, nd the then West Germny 1957 Europen Atomic Energy Community (EURATOM) No EU popultion by then in millions Europen Economic Community (EEC) No EC Europen Communities (combining ---- ECSC, EEC, nd EURATOM) No 1973 New Membership United Kingdom, Irelnd, nd Denmrk New Membership Greece New Membership Spin nd Portugl New Membership Est Germny (reunifiction of West nd 345 Est Germny) 1993 EU Europen Union No New Membership nd Schengen vis Finlnd, Austri, nd Sweden Economic nd Monetry Union(EMU) No Euro Introduction of the EURO No New Membership Cyprus, Czech Rep., Estoni, Hungry, Ltvi, Lithuni, Mlt, Polnd, Sloveni, nd Slovki 2007 New Membership Bulgri nd Romni 497 Source: dpted nd modified from Chpter Three tble

66 Within NEG frmework the loction decisions of firms nd workers depend on the combintion of gglomertion forces, nd spreding forces (see Redding nd Sturm, 2008). With the reduction of border brriers between two countries, mrket ccess of border res increse more thn tht of centrl loctions. As result firms nd workers my relocte to border res s these become more ttrctive. We focus on the municiplities of the oldest members of the EU, nmely Belgium, Germny nd the Netherlnds, tht hve been prt of the EU from the strt Theoreticl bckground The theoreticl frmework is the sme s in Chpter Three nd is bsed on Redding nd Sturm (2008) which we recp in this section with slight chnge to ccount for indirect mrket ccess. It is bsed on multi-region version of the geogrphicl economics model by Helpmn (1998). In generl, in models of geogrphicl economics, the combintion of incresing returns to scle, size of the mrket, nd trnsport costs mkes tht firms wnt to locte in or ner lrge mrkets nd lrge mrkets offer consumers vriety of consumer goods nd low trnsport costs (for detils see Brkmn et l. 2009). Moreover, nd this is specific to the Helpmn (1998) model, lrge mrkets rise the costs of (non-trded) locl services, thus leding to higher costs of living (congestion effect). The optiml loction of workers nd firms is determined by the blnce of these opposing forces. The model demonstrtes how both firms mrket ccess (FMA) nd consumers mrket ccess (CMA) determines equilibrium on the lbor mrket. This cn be summrized by the following equilibrium eqution: L FMA CMA H (4.1) FMA i w ( w ) L i k i P M i ( P M k ) T i 1 mrkets of ll loctions including their own loction; w i L i M P i is mrket ccess of firms locted in loction to ll the CMA (Helpmn, 1998). FMA shows how esily firms operte in loction. It depends on lbor income in loction i, the ssocited price index, nd the trnsport costs of getting goods from loction to ll mrkets. It includes the home mrket effect in which lrge mrkets or those ner lrge mrkets ttrct lbour inflows. It determines the equilibrium wge rte tht firms cn fford to py in equilibrium, depending on the home mrket dvntge nd the competition from other (surrounding loction) mrkets. Here we ssume tht loction hs ccess to loction i s in Chpter Three. The difference with Chpter Three is tht there re mrkets k djcent to loction i but not to loction. The mrket ccess of loction k by both loctions nd i is limited by 55 j n ( n ) j k p ( p ) T j k 1 j consumers mrket ccess to consumer goods in loction. is function of prmeters nd the common rel wge; nd,,, n nd i workers, the CES price index for vrieties, the number of vrieties produced, nd the locl (free on bord) price of vriety, ll t loction i. The economy consists of number of loctions {1,..., M}, where the loctions cn be either cities, municiplities (s it is in this chpter) or regions. Ech loction hs n exogenous stock p i re respectively, the nominl wge rte, the number of H of non-trdble services, referred to s housing is

67 ntionl borders. Once k nd i re integrted, k ffects MA t loction i directly nd MA t loction through loction i, indirectly. Thus, is indirectly ffected by wge,, lbour supply,, nd price index, M P k t loction k. FMA CMA reflects the cost of living in loction. It depends on the number of vrieties produced in the loction, the loclly chrged price, p t loction j, nd j trnsporttion costs, T. j w k L k The difference here with the model in Chpter Three is tht we ssume the consumer mrket ccess of loction is ffected not only by the sme or neighboring country mrket j, but lso indirectly by the mrket k of country neighboring j lthough not neighbor of itself. Here n j ffects CMA directly nd n j itself is function of number of vrieties n k produced t loction k s the number of vrieties produced t ech loction chnges following the opening of trde between loction j nd k. This is importnt since we nlyze integrtion where countries join the EU t different points in time nd some hve no common borders. For instnce, when the Netherlnds nd Germny opens for trde the totl number of consumer vrieties n j produced in both countries together increse, but vrieties produced in ech country decreses due to speciliztion. Lter, when Polnd joins EU the totl number of vrieties vilble in the mrket increses due to from the new mrket, but number of vrieties produced in ech country n k decreses once gin due to speciliztion. Thus, n j is negtive function of n k. One cn see this in different wy. One wy is tht if we ssume tht the sum of ll vrieties produced nd trded with the union members is 100% = 1, i.e., ; nd when third country joins the union the sum of ll vrieties still 100%, i.e., jk j n j 1 1 n j n k, implying the proportion of vrieties produced in ech member country is smller 32. This holds even in cse integrtion enhnces technologicl dvncement or diffusion which expnds vriety of products vilble s Grossmn nd Helpmn (1991, Ch.3). Another wy is tht we ssume the totl number of vrieties produced fter integrtion remins the sme s before. This is lso consistent with second type of model (see Grossmn nd Helpmn, 1991, Ch.4) where ny technologicl dvncement, tht my rise from integrtion in our context, leds to incresing speciliztion. Then, before nd jk n n N1 j k n 1 j j N fter the integrtion implying ech member produce fewer vrieties when new member joins the union, no mtter whether they hve common borders or not. Similrly price p j t loction j is ffected by price pk t loction k. When country with low (high) production cost nd low (high) commodity prices joins the union, the wges nd prices in the old members mrkets lso gets lower (higher); i.e., given other things constnt, p ( p ) p j k k ; where 0; 0 ; 0 w ( w ) w nd ; 0 ; nd nd re positive constnts 33. The i k k 32 Note tht, in bsolute terms, the sum of the vrieties re smller fter integrtion: j k t j k t 1 n n nd nk nk t t1 other words j t j t 1 33 For 1nd 1 the reltionships re liner, nd else non-liner. n n n n where t + 1 is the time fter the third country joins the union. ; or in 56

68 number of workers L is mobile cross loctions: ech supplies one unit of lbor in-elsticlly, spends shre of income on horizontlly differentited consumers vrieties nd the remining shre on the non-trdble services. Supply of cheper lbour from the new member lso derives the wge rte down in the old member mrkets. Production of vrieties tkes plce under incresing returns to scle (with fixed cost nd constnt mrginl cost in terms of lbor) with constnt elsticity of substitution between vrieties. Trnsport costs for 1 vrieties re of the ice-berg type, such tht sure one unit rrives in loction i. Thus, the proportion costs, T i 1 T i 1 units must be shipped from loction to mke T i 1 mesures the trde cost. Trde, is function of the distnce between the two loctions. This implies tht the strength of competition from other regions decreses s distnce increses. Anything tht chnges the trnsporttion cost nd the mrket ccess leds to new equilibrium through lbor mobility. The reltive wge rte my increse or decrese in the short run due to two opposing effects. (i) Home mrket effect: other things equl, the wge rte will tend to be higher in the lrger mrket. (ii) The extent of competition: workers in the region with the smller mnufcturing lbor force will fce less competition for the locl workers mrket thn those in the more populous loctions. Furthermore, rel wges re higher in the lrger mrket due to lower price for mnufctured goods (see Krugmn, 1991). The popultion size in the vrious loctions is endogenously determined by migrtion decisions of workers such tht rel wges re equlized between loctions in the long-run equilibrium. The long-run equilibrium popultion of loction r in this model is bsed on the ssumption of free mobility of the workers. In the context of EU integrtion process lbor mobility, especilly between the member countries, is lrgely limited to ntionl mrkets before joining the EU. After country joins the EU, the integrtion process triggers new dynmics by reducing the trnsporttion cost T i especilly of bordering loctions cross the ntionl borders into the newly ccessed neighboring mrkets. Redding nd Sturm (2008) tke division between Est nd West Germny fter WWII until the reunifiction in 1990 s n exmple of negtive (obstcle) shock to the lbor mobility nd so higher T. They provide evidence for lower popultion growth in i West Germny ner the rtificilly creted borders between the Est nd West Germny. Similrly, in Chpter Three we tke the opposite (removl border brriers by EU integrtion process) nd show evidence of n increse in popultion (shre) of the border cities nd regions. The focus of this chpter is on the extension of the nlysis of the EU integrtion process (the removl of the obstcles) for 3 EU member countries, Belgium, Germny nd the Netherlnds. As we stted in the introduction of this chpter, nd in contrst to Redding nd Sturm (2008) or Chpter Three of this thesis, nd by using popultion growth 34 s our centrl vrible we wnt to test whether: 34 Note tht growth is pproprite vrible in this chpter s opposed to the erlier since the smple here res re subset of the res ffected by the integrtion, hving externl sources for growth or the opposite. 57

69 i) the EU integrtion process goes long with symmetric effects on border nd non-border regions in the 3 countries under considertion ii) the EU integrtion process possibly not only hs direct effects on common border res but lso indirect effects, tht is to sy n impct on non-bordering res cross our 3 countries The dt We use municiplity level dt with reltively long time dimension nd detiled sptil units for three of the oldest EU member sttes: Belgium, Germny nd the Netherlnds. The sources of our bsic dt re for the Belgium municiplities, for the Germny municiplities nd for the Netherlnds municiplities 35. The Belgin dt cover reltive short period, However, the sptil units re very detiled with 589 municiplities. The dt for the Netherlnds cover 418 municiplities for longer period, 1960 to The Germn dt lso cover reltive long period of time, for 440 Germn municiplities. Initilly we tke 10 kilometers border rnge to define border. The dt summry is given in tble 4.2. Tble 4.2: Smple dt number of Countries municiplities yers covered border municiplities (10 kms) non-border municiplities totl observtions Belgium Germny Netherlnds We define six different groups of border municiplities, two for ech smple country. These borders re: (i) The Dutch municiplities bordering Germny, (ii) The Netherlnds municiplities bordering Belgium, (ii) Germn regions bordering the Netherlnds, (iv) Germn regions bordering Frnce, (v) Belgin regions bordering the Netherlnds, nd (vi) Belgin regions bordering Frnce. Belgin municiplities bordering Germny s well s Germn municiplities bordering Belgium re too smll in number so we could not include them in our nlysis. The dt of the Netherlnds nd Germny provide us with ctive 36 integrtion for 30 to 40 yers. The dt for the two countries strt in 1960 nd 1976, respectively. The integrtion of 1951 between the smple countries long the borders of these countries is expected to be possible ctive for yers (see Redding nd Sturm, 2008; nd Chpter Three). Different integrtion shocks occurred throughout our smple period (see tble 4.3 below). We ssume tht the lter 35 The Germny nd the Netherlnds dt were secured free of chrge wheres Belgium municiplities dministrtive unit dt were purchsed from the corresponding sttistics offices. 36 Integrtion sttus is ssumed to be ctive for 30 to 40 yers fter the doption of prticulr integrtion policy (see Redding nd Sturm, 2008; nd the previous chpter). 58

70 integrtion shocks s new members join EU might hve indirect effects on popultion of the municiplities of the smple countries (older members). Thus, we explicitly include indirect s well s direct integrtion effects. We expect the EU entry of the new members (United Kingdom, Irelnd, nd Denmrk in 1973; Greece in 1981; Spin nd Portugl in 1986; Est Germny: reunifiction of West nd Est Germny in 1990; Finlnd, Austri, nd Sweden in 1995; Cyprus, Czech Republic, Estoni, Hungry, Ltvi, Lithuni, Mlt, Polnd, Sloveni, nd Slovki in 2004 nd Bulgri nd Romni in 2007) to hve indirect effects on the municiplities in our smple. Tble 4.3: EU integrtion shocks sttus nd expected effects on the smple countries Integrtion shock yer Integrtion New members Integrtion ctive till 37 Belgium, Frnce, 1951 Europen Col nd Steel Luxembourg, the erly 1990s (ECSC1951) Community (ECSC) Netherlnds, Itly, nd West Germny 1957 Europen Atomic Energy Expected effects 38 direct direct (EURATOM1957) Community (EURATOM) No lte 1990s 1957 Europen Economic Community No direct (EEC1957) (EEC) lte 1990s EC Europen Communities direct 1967 (combining ECSC, EEC, nd No lte 2000s (EC1967) EURATOM) 1973 New Membership United Kingdom, Irelnd, indirect (EC1973) nd Denmrk through end 1981(EC1981) New Membership Greece through end indirect 1986(EC1986) New Membership Spin nd Portugl through end indirect Est Germny through end direct & 1990 New Membership (reunifiction of West indirect 39 (EC1990) nd Est Germny) 1993(EU1993) EU Europen Union No through end direct 1995 New Membership + Schengen Finlnd, Austri, nd through end direct & (EU1995) Vis Sweden indirect 1999(EMU1999) Economic & Monetry No through end direct Union(EMU) 2002(EURO2002) Euro Introduction of the EURO No through end direct Cyprus, Czech Rep., 2004 New Membership Estoni, Hungry, Ltvi, through end indirect (EU2004) Lithuni, Mlt, Polnd, Sloveni, nd Slovki 2007(EU2007) New Membership Bulgri nd Romni through end indirect Note: ctive through end = ctive through the end of the smple period 37 See footnote The integrtion effect is expected to be direct if the prticulr integrtion shock involves the smple countries or bordering country; else the effect is expected to be indirect. 39 Since Germny is prt of this prticulr integrtion shock, it hs direct effects on the Germn borders wheres the effects on the Germn municiplities bordering Belgium, Frnce, Luxembourg, Frnce, nd the Netherlnds s well s on the other smple countries (Belgium nd the Netherlnds) re indirect. 59

71 In our estimtions, we distinguish between the direct nd the indirect integrtion shocks (see tble 4.3). The direct shocks used in the estimtion re the EC1967, EU1993, EMU1999 (or EURO2002 s n lterntive) since they ffect the smple countries simultneously nd the borders between them. The indirect shocks do not ffect the smple borders of the smple countries; nd we used the EC1973, EC1986, EU1995 nd EU2004 for the estimtion. The ECSC1951, EURATOM1957 nd EEC1957 re excluded since our dt do not cover the periods when these shocks hppened; wheres the EC1981, EC1990 nd EU2007 re excluded due to either limited sptil coverge or limited time dimension. Another integrtion shock during 1995 ws the Schengen Agreement which led to the cretion of Europe's borderless Schengen Are in The trety ws initilly signed on 14 June 1985 between five of the then ten member sttes of the Europen Economic Community ner the town of Schengen in Luxembourg. It proposed the grdul bolition of border checks t the signtories' common borders which becme effective in It llows people crossing borders t ny convenient points nd bolishes stops t border controls within the member countries Estimtion strtegy In this chpter we use the sme empiricl strtegy of difference-in-differences (DID) methodology s lso employed by Redding nd Sturm (2008) or the previous chpter. Here we recp the discussion of the estimtion strtegy with respect to the new dt nd the new vribles. The DID method llows for time-invrint unobserved differences between the control nd tretment groups. In prticulr it removes differences in unobserved chrcteristics tht re constnt over time nd tht ffect individul vrible (for instnce municiplity popultion in this chpter) in constnt wy. Time dummies control for other common shocks which ffect popultion of the whole smple countries. There re two mjor differences worth mentioning bout the dt used in Chpter Three nd the ones used in this chpter, since they somehow ffects our clcultions nd the vribles we use in eqution (4.2). First, the smple dt in this chpter re more detiled sptil level s well s nnul dt s opposed to the whole EU dt in the previous chpter. These llow us to use nnul growth s well s seprte between different borders. Second, in Chpter Three the smple covers the whole integrtion re, nd thus the popultion effects come from redistribution of popultion only from within the smple loctions, EU. Thus, it is more pproprite to look t popultion shre growth thn bsolute growth. However, in this chpter the smple re is smller thn integrtion re. Thus, integrtion shocks outside the smple re cn ffect bsolute popultion growth. Thus, use of both the bsolute popultion growth nd popultion shre growth re pproprite. Thus, we compre the popultion (shre) growth performnces of border municiplities of the smple countries with the popultion (shre) growth performnce of their centrlly locted non-border municiplities following n EU integrtion policy implementtion. We define popultion growth s, popgrowth popultion popultion popultion., t1, t, t, t1, t1 The bseline empiricl model is specified s follows: 60

72 popgrowth, t 1, t border ( border integrtio n, ts) Dt t (4.2) The border dummy, border tkes vlue of one if municiplity r is within given rnge of n integrtion border nd zero otherwise; n integrtion dummy, integrtio n, t s tkes vlue of 1 from time t when n integrtion effect strts nd on wrd. D t is full set of time dummies; nd is stochstic error term. The time dummies control for common shocks ffecting the popultion growth throughout the smple countries nd trends in popultion growth rtes. The coefficient cptures ny systemtic difference in popultion growth rtes of border municiplities versus non-border municiplities. The focus of our nlysis here is on the interction coefficient, which cptures the reltive performnce of popultion growth for tretment groups, the bordering municiplities, compred to the controls, the non-border municiplities. Border municiplities re expected to gin reltively more in popultion (shre) growth following the integrtion. The size of the dministrtive units in Germny is much lrger thn tht of the municiplities size in Belgium nd the Netherlnds. However, the DID pproch is best used for comprble control groups (for instnce see Bertrnd et l., 2004; nd Cmeron nd Trivedi, 2005). We tke cre of this issue s follows. First, we pply our bseline estimtions t ntionl level where our control group of the border municiplities re very much comprble. Second, we extend our nlysis by differentiting between lrge nd smll municiplities to mke the control s well s the tretment group even more comprble. Another concern with this pproch is the ssumption of homoscedstic error term nd the correctness of defult error term estimted using OLS techniques. Here we estimte robust stndrd errors to void this problem. Moreover, this problem is less likely since our dt hve reltively long time dimension. The error term will be homoscedstic under t lest two circumstnces (Donld nd Lng, 2007): (i) if the number of observtions per group is lrge, or (ii) if there re no within-group vrying chrcteristics, nd the number of observtions is the sme for ll groups. As further checks of the correctness of the stndrd errors we clustered the errors by municiplity s this would void possible inflted t- vlues. We ssume tht there might be n indirect integrtion effect even when country joining EU t time t hs no common border with ny of the 3 smple countries. We use the sme specifiction to estimte for the direct nd the indirect integrtion effects seprtely (see gin Tble 4.3 for the list of the direct nd indirect integrtion shocks). Moreover, we define C shre popultion popultion = shre of municiplity popultion in the ntionl t t t popultion t time t; where C is number of municiplities (or cities or regions in generl sense) in country. Using the shre we clculte ; nd we estimte the model by substituting popgrowth, shregrowth, t1 t by shre shre, t1, t, t, t1, t1 shregrowt, h, t1 t shre t in the bove specifictions to check if there re differences in the shre growth nd the ctul popultion growth. As opposed to Redding nd Sturm (2008) nd Chpter Three, our two min hypotheses re thus tht: (i) There re direct integrtion effects from the country itself or from bordering country s well s indirect 61

73 integrtion effects from third non-bordering EU member country; (ii) The integrtion effects cn be symmetric. Tht mens, for instnce, the Netherlnds side nd the Germny side of the Netherlnds-Germny border cn be ffected differently by the sme integrtion shock due to differences in reltive strength of home mrket effect nd competition. From Chpter Two, we cn remember tht neighboring countries mrket is more importnt for the bordering cities thn it is for the non-border cities; nd this is different cross different ntionl borders Estimtion results Bseline estimtes The bseline estimtions refer to the direct nd the indirect integrtion effects on six smple border groupings: Belgium bordering the Netherlnds, Belgium bordering Frnce, the Netherlnds bordering Belgium, the Netherlnds bordering Germny, Germny bordering the Netherlnds nd Germny bordering Frnce. As the smple covers different time spns for different countries comprisons over time re not pproprite. Thus comprisons re t ntionl level; i.e., the Netherlnds border municiplities re compred with centrlly locted Netherlnds municiplities. An dvntge is tht it improves the relibility of the results from differences-in-differences (DID) pproch. For common integrtion effects we refer to Chpter Three. The bseline estimtion results for the direct integrtion effects re given in Tble 4.4. Columns (1) nd (2) show the estimtion results for two Belgin borders (1) bordering Frnce nd (2) bordering the Netherlnds. The results show tht the municiplities bordering Frnce hve significntly lower growth compred to the centrlly locted municiplities s opposed to the municiplities bordering the Netherlnds. However, the reverse hppened fter the Europen monitory Union (EMU) of 1999 [s n lterntive we lso look t 2002 when the ctul euro notes nd coins were introduced]; i. e., the municiplities bordering the Netherlnds grew significntly lower thn centrlly locted municiplities fter the doption of the monitory union or the EURO currency. Since the doption of the monetry union (EMU1999) nd the introduction of the common currency (EURO2002) were so close we present these s lterntives. The results re lso very similr for these two integrtion shocks. Columns (2) nd (3) compre either sides of the border between Belgium nd the Netherlnds. The border coefficient during the EMU1990 nd EURO2002, the period for which the dt were vilble on both sides of the border, the municiplities on both sides of the border hve non-negtive growth. However, fter the EMU [or the EURO] the Belgin side municiplities hve hd significntly lower growth compred to the centrl loctions wheres the Netherlnds side municiplities hve lso grown slower but insignificntly. Columns (3) nd (4) compre the two borders of the Netherlnds, one bordering Belgium nd nother bordering Germny. The municiplities bordering Germny hd significntly higher growth rte following the erly integrtion shock EC1967 wheres the municiplities bordering Belgium lso hd higher but insignificnt growth. Moreover, the Netherlnds municiplities 62

74 bordering Belgium hve non-negtive growth erlier nd for reltively longer period thn the municiplities bordering Germny. This result is very much in line with the finding in Chpter Two, where the wge rte hs stronger connection with the mrket ccess t round this border thn round the Netherlnds Germny border. Both groups of the Netherlnds border municiplities hve hd lower growth rte following the ltest direct integrtion shocks of EMU1999 (or EURO2002). Column (5) nd (6) provide the estimtion results for the two sides of the border between Germny nd the Netherlnds. The results show tht the Germny side municiplities hve non-negtive growth reltive to the centrlly locted municiplities during the smple periods. The results lso show tht, these municiplities hve hd significntly higher growth rte following the EU1993, EMU1999 nd the EURO2002 integrtion shocks. Columns (5) nd (6) present the results for the two Germny borders, one bordering the Netherlnds nd the other bordering Frnce. The negtive border coefficients in column (6) show tht the Germny municiplities bordering Frnce hve lower growth rtes thn the municiplities bordering the Netherlnds which hve significntly higher growth rte even without the lte integrtion shocks. However, both groups of the municiplities hve significntly higher growth following the EU1993, EMU1999 nd the EURO2002 integrtion shocks. Tble 4.4: Direct integrtion effects on popultion growth; bseline estimtes Integrtion shock EC1967 EU1993 EMU1999 EURO2002 (1) bordering Frnce border n n (0.344) border integrtion t n n (0.518) border n n (0.441) border integrtion t n n (0.614) border 0.389*** (0.0700) border integrtion t (0.0760) border 0.396*** (0.0467) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering Bordering Netherlnds Belgium Germny Netherlnds (0.0771) 0.167** (0.0807) (0.0484) (0.403) (0.468) (0.378) (0.316) 0.660* (0.372) (0.177) (0.431) (0.171) (0.569) (0.179) n n (0.0715) 0.370*** (0.0808) 0.160*** (0.0561) 0.267*** (0.0686) 0.191*** (0.0507) (6) Borderin g Frnce n n (0.0915) 0.216** (0.103) (0.0695) 0.222** (0.0896) (0.0621) border integrtion t (0.0570) 0.143*** (0.0547) (0.472) (0.618) 0.244*** (0.0618) 0.272*** (0.0921) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny Observtions 4,023 3,995 14,132 16,493 8,336 7,930 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = the dt re not vilble (or not sufficient) to estimte for this shock. 40 Observtions, nd R-squred re comprble over the different regression results using different integrtion shocks. 63

75 Tble 4.5 gives the estimtes of the indirect EU integrtion effects. The indirect integrtion effects re positive nd more significnt for the smple border municiplities tht re geogrphiclly closer to the countries joining EU following specific integrtion shock. We defined the EC1973, EC1986, EU1995 nd EU2004 s indirect integrtion shocks since they did not directly involve the smple borders. Columns (1) nd (2) give the results for the two Belgin borders. The municiplities bordering Frnce hve significntly lower growth rte for longer durtion thn the municiplities bordering the Netherlnds. Following the indirect integrtion shock of EU2004 the municiplities bordering Frnce hve hd non-negtive growth reltive to centrlly locted municiplities wheres the municiplities bordering the Netherlnds hve significntly lower growth rtes thn more centrl loctions. Columns (2) nd (3) provide the estimtes for the two sides of the Netherlnds-Belgium border. The results show tht, compred to municiplities on the Belgium side, the municiplities on the Netherlnds side hve mostly non-negtive growth rtes. Columns (3) nd (4) compre the two border groups of the Netherlnds municiplities. The municiplities bordering Germny hve in generl lower growth rtes for longer durtion of the smple period nd benefit more from the ll the indirect integrtion shocks EC1973, EC1986, EU1995, EU2004 compred to the municiplities bordering Belgium. This my be becuse the Netherlnds-Belgium border municiplities hve been more integrted due to lnguge similrities. Columns (4) nd (5) show the estimtes for the two sides of the Netherlnds-Germny border. The Netherlnds side municiplities gin more following the EC1986, wheres the Germn municiplities gin significntly higher growth rte following the lter integrtion shocks of EU1995 nd EU2004. Germny is geogrphiclly closer to these countries thn The Netherlnds. Columns (5) nd (6) give the results for the two Germny smple borders. The municiplities bordering Frnce hve reltively lower growth rtes throughout the smple period nd gin significntly following ll the integrtion shocks nd gin more thn the municiplities bordering the Netherlnds following EC

76 Tble 4.5: Indirect integrtion effects on popultion growth; bseline estimtes Integrtion shock EC1973 EC1986 EU1995 EU2004 (1) bordering Frnce Belgium The Netherlnds Germny (2) bordering Netherlnds (3) bordering Belgium border r n n (1.105) border integrtion t n n (1.131) border n n (0.533) border integrtion t n n (0.646) border 0.341*** (0.0275) *** (0.0227) (0.415) border integrtion t (0.646) border 0.396*** (0.0467) (0.0484) (0.365) (4) bordering Germny 0.414* (0.233) 0.661** (0.319) 0.322* (0.169) 0.857** (0.359) (0.168) (0.480) (0.172) (5) bordering Netherlnds n n (0.109) (0.116) (0.0646) 0.357*** (0.0759) 0.215*** (0.0472) (6) bordering Frnce n n 0.254* (0.139) 0.388*** (0.147) (0.0829) 0.167* (0.0964) (0.0581) border integrtion t (0.0570) 0.143*** (0.0547) (0.426) (0.846) 0.191*** (0.0633) 0.233** (0.104) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny Observtions 4,023 3,995 14,132 16,493 8,336 7,930 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = the dt re not vilble (or not sufficient) to estimte for this shock. --- the interction term between the border nd the integrtions shock of EU1995 drops due to multicollinerity Densely populted nd less populted municiplities Next we differentite between smll nd lrge municiplities bsed on popultion density to mke the comprison mong more similr nd comprble groups of municiplities for two mjor resons. First, different size municiplities my be ffected by the integrtion shocks differently (see Chpter Three). Second, the difference-in-difference pproch is more efficient when pplied to more comprble control nd tretment groups of units. In Chpter Three, we found tht lrger border cities benefit from the positive integrtion effects more thn smll cities. Note, tht we use municiplities insted of cities. Municiplity includes rurl res nd my consist of more thn one cities or towns. We distinguish smll from lrge municiplities in two wys. First using totl popultion size s in Chpter Three. Smll municiplities re the ones with totl popultion of less thn medin ntionl popultion nd lrge municiplities re the municiplities with popultion lrger thn medin popultion. In ddition, we distinguish lrge from smll municiplities bsed on 65

77 popultion density. Low density municiplities hve popultion density less thn ntionl medin nd high density municiplities hve density tht is higher thn ntionl medin density. Tble 4.6: Direct integrtion effects on popultion growth; low density municiplities Integrtion shock EC1967 EU1993 EMU1999 EURO2002 (1) bordering Frnce border n n (0.987) border integrtion t n n (1.128) border n n (0.656) border integrtion t n n (0.886) border 0.505*** (0.0906) border integrtion t (0.0989) border 0.488*** (0.0599) border integrtion t (0.0743) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering Bordering Netherlnds Belgium Germny Netherlnds (0.126) (0.132) (0.0785) (0.0890) (0.590) (0.724) (0.556) (0.692) (0.888) (0.936) (0.322) (0.637) (0.310) (0.724) (0.318) (0.449) n n (0.107) 0.510*** (0.121) (0.0831) 0.532*** (0.0980) 0.146** (0.0742) 0.590*** (0.0891) (6) Bordering Frnce n n (0.149) (0.167) (0.112) (0.149) (0.100) 0.355** (0.152) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny smple municiplities low low low low low low density density density density density density Observtions 2,070 1,710 6,117 7,488 3,558 3,390 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = dt not vilble (or not sufficient) to estimte for this shock. A problem with the popultion size criterion is tht municiplity cn consist of two or more smll cities. Also, smll municiplity cn consist of only one lrge city. Therefore, we prefer the second criterion. Tbles 4.6 nd 4.7 give the results of the direct integrtion effects for the low density nd high density municiplities, respectively. These results re in generl consistent with the findings in Chpter Three. The high density Belgin municiplities bordering Frnce gin more thn low density municiplities following the EMU1999 [or EURO2002] (see column (1) in both tbles (4.6) nd (4.7)). This loss in growth is higher nd more significnt for high density municiplities thn less dense municiplities. The highly dense Netherlnds municiplities bordering Belgium gin more following the EC1967 nd EU1993 nd experience significntly lower growth following the EMU1999 [or EURO2002] compred to less dense municiplities (see column (3)). Compring column (1) of tbles (4.6) nd (4.7), the high density municiplities of the Netherlnds bordering Germny gin higher growth thn less dense 66

78 municiplities following ll the direct integrtion shocks. The results for the Germny municiplities re the opposite. Less dense municiplities of both smple borders generlly gin significntly higher following the ltest three direct integrtion s opposed to the high density municiplities who gined growth significntly only from the EU1993 integrtion shock (see columns (5) nd (6)). This shows tht both criteri of dividing between the municiplities do not chnge the results. The effects of the indirect integrtion shocks re bsiclly the sme with the direct integrtion shocks for ll the smple borders except the result of the EC1986 integrtion shock for Germny municiplities bordering Frnce (see tbles (4.8) nd (4.9)). Column (6) shows tht on the contrry to the direct integrtion shocks, the high dense municiplities gin significntly higher compred to the less dense municiplities in this border loction following this indirect integrtion shock. EC1967 EU1993 EMU1999 EURO2002 Tble 4.7: Direct integrtion effects on popultion growth; high density municiplities Integrtion shock (1) bordering Frnce border n n 0.865*** (0.207) border integrtion t n n 1.205*** (0.418) border n n 0.304*** (0.117) border integrtion t n n (0.934) border 0.444*** (0.0953) border integrtion t (0.105) border 0.491*** (0.0629) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering Bordering Netherlnds Belgium Germny Netherlnds (0.0918) 0.178* (0.0964) 0.102* (0.0581) (0.404) 0.827* (0.451) (0.377) 0.594*** (0.195) 0.609* (0.320) 0.275* (0.153) (0.622) 0.236* (0.135) (0.960) (0.135) n n (0.115) 0.294** (0.125) 0.222** (0.0876) (0.103) 0.251*** (0.0786) (6) Bordering Frnce n n (0.121) 0.271** (0.133) (0.0909) (0.112) (0.0809) border integrtion t 0.169** (0.0797) 0.173*** (0.0661) 0.826* (0.458) (1.324) (0.0904) (0.114) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny Smple municiplities high density high high high high high density density density density density Observtions 1,953 2,285 8,015 9,005 4,778 4,540 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = dt not vilble (or not sufficient) to estimte for this shock. The bolition of border brriers triggers reloction of people nd firms to the loctions tht hve higher mrket ccess following the integrtion shocks, in this cse the border municiplities. In the cse of Redding nd Sturm (2008) nd in Chpter Three, the smples cover ll the res 67

79 tht re ffected by the integrtion shock (nd not only the smple countries in this chpter). To check for this we estimted the bseline model using popultion shre growth; nd the results generlly remin the sme with the results from the ctul popultion growth. We lso crried out number of dditionl robustness checks: (i) we checked whether the positive border effects, exist in ll peripherl municiplities or only those who hve lnd borders with nother country benefits from the bolition of the borders. We test for the existence of positive effects in ny peripherl municiplities by looking t costl municiplities. The costl municiplities border with the se nd hve no immedite mrket ccess on the other side of the border. We re-estimted the bseline model by substituting border by cost. The estimted coefficient for the interction between the cost nd integrtion is lwys negtive for both Belgium nd the Netherlnds. It is negtive nd significnt for Belgium throughout the smple period, nd negtive nd sttisticlly significnt for the Netherlnds during 1990s nd generlly negtive, but insignificnt, throughout the rest of the smple period. These municiplities hve been growing slower thn the rest of the municiplities nd compred to their own erlier growth. This implies the norml lnd borders with other countries re more ttrctive to people thn the costl municiplities following the integrtion. The border coefficient shows tht the Germny costl municiplities hve significntly lower growth throughout the smple period compred to the rest of Germny. However, lthough they re growing slower thn the rest of the country, the Germny costl municiplities hve been growing significntly fster since 1990 to the end of the smple period compred to their own erlier growth. This could be due to Germny s geogrphicl proximity to mny Est Europen countries who joined EU in See section 4.7 below for more discussion bout the possible reltion of this with immigrtion from the Est Europen countries. Moreover, these municiplities re lrge nd they potentilly hve good economic opportunities for people since they hve borders with some mjor se ports. (ii) We re-estimte the regression model by using stndrd errors clustered by the municiplities to check whether the results chnge. The results generlly remin the sme. We lso estimted fixed effect model nd tested for the vlidity of the pooled OLS model. Agin, the results from the fixed effect model re generlly the sme; nd the tests do not support the hypothesis tht the fixed effect model is better thn the pooled OLS model. We lso looked t the stndrd errors to check for existence of ptterns over time nd between groups. The error terms of vlid estimtion model should not hve pttern over time nd between the control nd the tretment groups. The distribution of the errors terms from both heteroscedsticity robust error terms nd the error terms clustered by municiplity re firly rndomly distributed between the groups nd over time. The distribution is not ffected due to chnging the integrtion points s well s for chnging the dependent vrible between bsolute popultion growth nd the popultion shre growth. An exmple of the result is given in Figure 4A.1 (see in the ppendices) for the Dutch municiplities with respect to the two border tretment groups, bordering Belgium nd bordering Germny. (iii) Finlly, we checked whether the length of the smple period hs significnt influence on the results. The dt for the Netherlnds cover from 1960 to 2009; wheres the 68

80 Germny nd Belgium dt cover from 1976 to 2007 nd from 1990 to 2010, respectively. We now tke common periods only. The results for the common periods re given in tbles 4A.7, 4A.8, 4A.9 nd 4A.10 (see the ppendices). The results of symmetric border effects bsiclly remin consistent Extensions nd evlutions This chpter, due to dt limittions does not cover ll of the EU. Thus, the integrtion effects should not necessrily come from the smple re itself; nd some loctions within the smple should not necessrily grow t slower rte. To control for this we estimte for the common integrtion effects of both the control (non-border regions) nd tretment (border regions) groups seprtely. This gives us informtion on whether non-border municiplities hve experienced positive direct nd/or indirect integrtion effects too nd if so whether this is the cse t erly stges or lter during the smple periods. In this cse eqution (4.2) becomes: popgrowth border integrtio n t border integrtio n, t 1, t, ts, ts D t. (4.3) One of the benefits of this specifiction is tht lthough the time dummies control for the common effects of the integrtion shock itself (see Redding nd Sturm, 2008), it helps to estimte for common direct nd indirect effects especilly when the integrtion effects on the border municiplities lone is not visible. This lso helps us to know whether the positive significnt gins by borders re reltive to their erlier growth lone or reltive to centrl loctions or both. As opposed to the full set of time dummies which cptures unspecific but generl common shocks, helps us to cpture common effects to both the control nd tretment groups, but of specific integrtion policy shocks. Since we do not hve dt on the origin nd destintion of people, the comprison between the common integrtion nd the interction term helps to evlute s to whether people move to the border loctions or the vice vers during specific periods following n integrtion shock. Adding this common integrtion term does not ffect our results presented in the previous section. The results re given in the ppendix (see tbles 4A.1 nd 4A.2 for direct nd indirect integrtion shocks, respectively). Also the timing of integrtion effects cn be n issue; some shocks might be nticipted, nd for some the results might tke time. We ccount for this by using time t = t + s; where s k, k nd k is positive integer nd t s t, 0 t f where t 0 is the strt nd t f is the end of observtions in the smple. Here we ssume tht specific integrtion shock my hve nticipted s well s lgged integrtion effects. We estimte eqution (3) severl times by chnging the time of the integrtion effect s if it hppens every yer. Since the dt re missing for the beginning of the erliest ctive integrtion shock during 1950s for the Netherlnds dt, we introduce the first possible integrtion shock in 1965, few yers fter the strt of the dt nd the sme pplies to Belgium nd Germny. This is bsed on the ssumption of nticipted or lgging integrtion effect nd keeping the integrtion dummy from the strt of the dt (i.e., integrtion t =1 from the strt) is, techniclly, not possible since (border ) nd (borderr integrtion t ) re 69

81 exctly the sme. The estimtion results for the six groups of the border municiplities re summrized nd given by figures 4.1, 4.2 nd 4.3. The grphs 41 plot the estimted coefficients, nd. The dotted line in ech figure represents the estimted vlues of the border, coefficient ; the dshed line plots the estimted coefficient of the common integrtion term ; nd the coefficient of the interction between the border nd the integrtion is represented by the solid line. The circulr dots overlpping ny of these curves represent sttisticl significnce of the estimtes. The left pnel of figure 4.1 shows the results for the Netherlnds municiplities bordering Germny. The border coefficient remins negtive for most of the smple period until the erly 2000s. The difference between the growth of the border nd non-border municiplities hs been continuously declining. The popultion growth with respect to the interction between border nd integrtion remins higher until erly 2000s compred to the verge growth including the nonborder municiplities. The integrtion effect on this border municiplities remin positive significnt from the erlier periods till round the end of 1980s. This result is well in line with limited durtion of integrtion effects found by Redding nd Sturm (2008) nd Chpter Three of this thesis. The lter integrtion shocks hve insignificnt effects nd thus the popultion growth of the border municiplities re declining ltely compred to the non-border municiplities nd their own erlier growth. The positive integrtion effects were much quicker for the Netherlnds municiplities bordering Belgium (the right pnel of figure 4.1). However, the interction coefficient shows tht the decline for this group of municiplities is bigger over recent periods compred to non-border municiplities nd to their own erlier growth. The common integrtion effect remins consistently positive nd significnt especilly round mjor integrtion shocks of the EC1986, EU1993, nd EU2004 EU expnsion to the Est Europen countries. Figure 4.1: Dutch (border) municiplities Note: #,## (Dutch style decimls) re the sme s #.## (interntionl style). The Germn municiplities dt only strt in 1976 nd we could not look t pre-1980 integrtion effect. Compred to the Netherlnds side of the Germn-Netherlnds border, the Germn side border municiplities hve non-negtive growth but insignificnt for most of the smple period compred to non-border municiplities (see figure 4.2). The interction between the 41 The grphs show severl estimte coefficients by moving the integrtion point over time s the estimte tbles re together too big to report here. The estimtes tbles re either given in the ppendices or cn be provided up on request. 70

82 border nd the integrtion is positive nd significnt specilly strting from mid-1980s through the end of the smple period. Since lte 1990s these border municiplities hve positive significnt nd higher growth thn the non-border municiplities. In contrst to this, the Germn municiplities bordering Frnce hve negtive growth throughout the smple period compred the non-border municiplities. However, the interction between the border nd integrtion shows tht they hve been growing significntly fster following number of direct nd indirect integrtion shocks compred the non-borders nd their own erlier growth. The estimtes of the common integrtion coefficient show tht centrlly locted Germn municiplities hve been growing significntly slower thn the municiplities bordering both the Netherlnds nd Frnce. This shows tht the Germn border municiplities seem to cese to be disdvntged peripheries in terms of popultion growth, t lest during the smple period (i.e., no more negtive border effects). Figure 4.2: Germn (border) municiplities Note: #,## (Dutch style decimls) re the sme s #.## (interntionl style). The fct tht the positive significnt integrtion effects lst throughout the smple period for Germn border municiplities shows tht these effects re lsting for more thn 50 yers contrry to the erlier findings of 30 to 40 yers by Redding nd Sturm (2008) nd in Chpter Three. This cn be explined by indirect effects of lter integrtion shocks which opened the Estern Germny borders to the Est Europen countries. One of the resons why the municiplities bordering with the Netherlnds nd Frnce re more ttrctive thn the non-border municiplities could be the fct tht the municiplities in the western borders hve big cities tht re within shorter distnces of one nother compred to centrl Germny. Moreover, they re more ttrctive s they re geogrphiclly closer to the high income neighboring countries with high mrket potentil on its west borders. Belgin municiplities bordering Frnce hve significntly lower growth thn non-border municiplities wheres the municiplities bordering the Netherlnds hve positive growth for most of the smple period, though insignificnt, compred to non-border municiplities (see figure 4.3). The interction between the border nd the integrtion is insignificnt for the municiplities bordering Frnce wheres it is negtive significnt for the municiplities bordering the Netherlnds showing recent yers declines in popultion growth compred to non-border municiplities nd their own erlier growth. The common integrtion effects re positive nd 71

83 significnt throughout the smple period showing tht centrlly locted municiplities hve grown fster since lte 1990s. Figure 4.3: Belgin (border) municiplities Note: #,## (Dutch style decimls) re the sme s #.## (interntionl style). In summry, ll the six different tretment groups of border municiplities hve different types of growth over the time periods covered by the smple. The Netherlnds border municiplities hve grown significntly fster thn the non-border municiplities erlier nd declining recently. Centrlly locted municiplities of the Netherlnds nd Belgium hve been growing fster thn before nd fster thn border municiplities during the most recent times of the smple periods. However, it is the reverse in Germny. Germny border municiplities hve been growing fster thn centrlly locted municiplities throughout the smple periods. From this nlysis we hve lerned tht Germn border municiplities re ffected by the integrtion shocks more thn Belgium nd the Netherlnds municiplities. The use of the concept of nticipted nd lgged integrtion effects nd using integrtion shocks every yer shows us the limittions of the difference-in-differences (DID) pproch in testing for policy impcts. This is becuse the significnt effects of the integrtion re not limited to the yers of the ctul integrtion shocks. Moreover, it is difficult to distinguish between the effects of different integrtion shocks especilly when they re within only few yers of ech other. The third nd finl extension is to test for structurl breks. We use the method suggested by Bum et l. (1999) s well s the method by Bi nd Perron (1998, 2003) to locte breks (if ny). The former identifies two breks t time wheres the lter identifies ll possible breks nd suggests the optiml number of breks. The Bi nd Perron (1998, 2003) method identifies ll possible breks (M=1, M=2,, M=n); nd the optiml number of breks is the one with the smllest Byesin informtion criterion (BIC) given tht there is t lest brek (M 1). The results re summrized in tble 4.8. The detils nd choices of the optiml breks re given in the ppendix (tble 4A.11). In both pproches the erliest breks of the lte 1960s in the popultion growth of the Netherlnds bordering municiplities Germny s well s Belgium re the only significnt breks. These breks cn be ssocited with the formtion of bigger Europen Communities (EC) combining ECSC, EEC, nd EURATOM in The slight devition of the 72

84 detected brek points re likely due to the lgs in response of the business nd consumers to the policy shock. The dt for Belgium cover only 1990 to 2010 nd we do not find ny brek during the smple period covered by the dt in either of the popultion growth or popultion shre growth. It could be due to short period of time of the smple coverge tht we could not detect the breks. With the first pproch we identify four mjor breks in the Germn municiplities popultion growth nd ll of them re sttisticlly significnt. The brek points re six for both Germny borders when we use the Bi nd Perron (1998, 2003). This is likely due to the fct tht ll dt re used in identifying the brek (i.e. there is not exclusion s in the first pproch). There re three optiml breks for the smple bordering the Netherlnds nd two optiml breks for those bordering Frnce. All of them re sttisticlly significnt. Tble 4.8: Structurl brek test in popultion growth () Bum, Brkouls nd Cglyn (1999) pproch Country bordering with Mximum identified breks optiml breks significnt breks Belgium the Netherlnds none n.. -- Frnce none n.. -- Germny the Netherlnds 1986, 1995, 1998, 2002 n.. ll The Netherlnds Frnce 1991, 1995, 1998, 2002 n.. ll Germny 1970, 1983, 1999, 2002, 2005 n Belgium 1969, 1988, 1995, 1998, 2001, 2003 n (b) Bi nd Perron (1998, 2003) pproch 42 bordering with Mx. identified breks optiml breks significnt breks Belgium the Netherlnds none Frnce none Germny the Netherlnds 1981, 1985, 1989, 1993, 1997, , 1993, 2003 ll Frnce 1980, 1985, 1989, 1993, 1997, , 1994 ll The Germny 1967, 1974, 1984, 1991, Netherlnds Belgium 1968, 1975, 1982, 1989, n.. = does not pply The serch for the brek points show tht the smple countries hve experienced different types of effects. The breks following the policy doptions re significnt for the Netherlnds only during erly stges wheres it is significnt throughout for Germny municiplities bordering the Netherlnds s well s those bordering Frnce. It lso revels tht the breks either coincide with the policy shocks or within the vicinity of the yer of the ctul policy shocks. For instnce, the formtion of the Europen single free mrket in 1993 ws the likely source of the 1993 brek in Germny bordering with the Netherlnds nd 1994 (one yer lter) brek in those bordering Frnce. Due to nticiption effects or lgging effects people nd business my relocte yer or two prior to or yer or two lter thn given ctul policy implementtion leding to different brek points thn the ctul yer of the policy implementtion (see tble 4.9 for summry of the closest breks with the specific integrtion shocks). For instnce, the EU expnsion s well s the doption of the Schengen vis re in 1995 re the most likely cuses of the breks in the sme 42 See tble 4A.11 for the whole list of breks nd choice of the optiml breks. Moreover tble 4A.12 in the ppendix gives the summry nd test sttistics of the optiml brek points. 73

85 yer or in 1997 (two yers lter) in both Germny borders (see tble 4.8). The closest brek points from the lterntive pproches re used in the summry, tble 4.9. Identifying the exct nd specific sources of ech chnge requires more detiled dt; nd we would like to ddress this in our future works. Tble 4.9: EU integrtion shocks nd breks in border popultion growth Closest/ssocited brek points Integrtion the Netherlnds Germny bordering shocks Belgium Germny the Netherlnds Frnce EC n.d. n.d. EC n.d. n.d. EC EU EU/Schengen EMU EURO EU Note: n.d. = no sufficient dt for the period. There re insufficient dt during the 1950s nd fter 2007 to find breks round these integrtion shocks; nd no breks identified for Belgium due to limited time dimension of the dt 4.8. Conclusions Vrious nturl or policy induced shocks my chnge regionl development pths either temporrily or permnently. In this chpter we nlyze the popultion effects of EU integrtion using the sme methodology s Redding nd Sturm (2008) nd in Chpter Three while using more detiled nd extensive dt set for Belgin, Germn nd Dutch municiplities to nlyze the impct of EU integrtion on border nd non-border municiplities. Compred to previous studies s well s Chpter Three, in this chpter we include symmetric effects of the EU integrtion nd llow for the existence of indirect integrtion effects. Consistent with our finding of border symmetry in chpter two, we find tht the popultion of the border municiplities of Belgium nd the Netherlnds hve grown reltively fster thn non-border municiplities, but only so for limited period of time following the erliest integrtion shocks. However, the Germn border municiplities hve hd significntly higher growth thn centrlly locted municiplities throughout the smple period. This mens tht the disproportionte growth effects on the Germn border municiplities hve lsted for longer periods thn the Belgium nd the Netherlnds counterprts. This might prtly be due to Germny s geogrphicl proximity to immigrnts from the new EU members. By llowing for indirect integrtion effects we find tht the integrtion effects lst longer thn predicted in previous studies on the border effects of (EU) integrtion. Our results lso show tht different countries borders nd even different borders of the sme country bordering different countries re ffected differently, tht is to sy we find evidence to support the notion of symmetric border effects. Seprting the integrtion effects from other fctors such s diminishing effects of wr in the border re is potentil re for future reserch. Another re of extension in future reserch on this topic cn be determining whether the cuse of symmetric effect re geogrphicl fctors such s hinterlnd effects or historicl pth differences in socioeconomic fctors. 74

86 4.9. Appendices The results in the ppendices provide extr informtion (lso discussed in the min body of the chpter). For instnce, we seprte between integrtion (integrtion t ) in generl nd integrtion in the border (border integrtion t ). The results from tble 4A.1, for instnce, show tht the integrtion effect ws stronger in the centrl Germny in 1990s, but stronger in the borders lter in time(see column (5) nd (6). Tble 4A.1: Direct integrtion effects on popultion growth; bseline estimtes with common integrtion Integrtion shock EC1967 EU1993 EMU1999 (1) bordering Frnce border n n (0.344) integrtion t n n (1.117) border integrtion t n n (0.518) border n n (0.441) integrtion t n n 0.502*** (0.147) border integrtion t n n (0.614) border 0.389*** (0.0700) integrtion t 0.786*** (0.0391) border integrtion t (0.0760) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering Bordering Netherlnds Belgium Germny Netherlnds (0.0771) 0.504*** (0.0255) 0.167** (0.0807) (0.403) 0.293*** (0.0565) (0.468) (0.316) (0.320) 0.660* (0.372) (0.177) 0.365** (0.144) (0.431) (0.171) (0.101) (0.569) n n n (0.0715) 0.653*** (0.0608) 0.370*** (0.0808) 0.160*** (0.0561) 0.361*** (0.0341) 0.267*** (0.0686) (6) Bordering Frnce n n n (0.0915) 0.682*** (0.0632) 0.216** (0.103) (0.0695) 0.373*** (0.0367) 0.222** (0.0896) EURO2002 border 0.396*** (0.0467) integrtion t 0.784*** (0.0392) (0.0484) 0.768*** (0.0384) (0.378) 0.545*** (0.145) (0.179) 0.187* (0.107) 0.191*** (0.0507) 0.361*** (0.0342) (0.0621) 0.267*** (0.0429) border integrtion t (0.0570) 0.143*** (0.0547) (0.472) (0.618) 0.244*** (0.0618) 0.272*** (0.0921) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny Observtions 4,023 3,995 14,132 16,493 8,336 7,930 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = the dt re not vilble (or not sufficient) to estimte for this shock. 43 Observtions, nd R-squred re the sme over the different regression results using different integrtion shocks 75

87 Tble 4A.2: Indirect integrtion effects on popultion growth; bseline estimtes with common integrtion Integrtion shock Belgium The Netherlnds Germny EC1973 EC1986 EU1995 EU2004 (1) bordering Frnce (2) bordering Netherlnds (3) bordering Belgium border n n (1.105) integrtion t n n 0.269** (0.0563) border integrtion t n n (1.131) border n n (0.533) integrtion t n n 0.231** (0.0612) border integrtion t n n (0.646) border 0.341*** (0.0275) integrtion t 0.787*** (0.0389) *** (0.0227) 0.762*** (0.0382) (0.415) 0.228** (0.0672) border integrtion t (0.646) border 0.396*** (0.0467) integrtion t 0.784*** (0.0392) (0.0484) 0.768*** (0.0384) (0.365) 0.298** (0.0561) (4) bordering Germny 0.414* (0.233) 0.149** (0.0598) 0.661** (0.319) 0.322* (0.169) (0.0717) 0.857** (0.359) (0.168) (0.0880) (0.480) (0.172) 0.387** (0.188) (5) bordering Netherlnds n n n (0.109) 1.441*** (0.0950) (0.116) (0.0646) 0.258*** (0.0407) 0.357*** (0.0759) 0.215*** (0.0472) 0.360*** (0.0343) (6) bordering Frnce n n n 0.254* (0.139) 1.474*** (0.0994) 0.388*** (0.147) (0.0829) 0.607*** (0.137) 0.167* (0.0964) (0.0581) 0.375*** (0.0365) border integrtion t (0.0570) 0.143*** (0.0547) (0.426) (0.846) 0.191*** (0.0633) 0.233** (0.104) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny Observtions 4,023 3,995 14,132 16,493 8,336 7,930 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = dt not vilble (or not sufficient) to estimte for this shock. The effects of integrtion re more common to ll municiplities in smller countries (see Belgium nd the Netherlnds) thn specificlly to the border loctions which is the cse in bigger country (see Germny), especilly in lter integrtions. This generlly holds both in the previous nd below tbles. 76

88 Tble 4A.3: Direct integrtion effects on popultion growth; low density municiplities Integrtion shock EC1967 EU1993 EMU1999 EURO2002 (1) bordering Frnce border n n (0.987) integrtion t n n (0.104) border integrtion t n n (1.128) border n n (0.656) integrtion t n n 0.208** (0.105) border integrtion t n n (0.886) border 0.505*** (0.0906) integrtion t 0.825*** (0.0609) border integrtion t (0.0989) border 0.488*** (0.0599) integrtion t 0.822*** (0.0612) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering Bordering Netherlnds Belgium Germny Netherlnds (0.126) 0.788*** (0.0664) (0.132) (0.0785) 0.789*** (0.0666) (0.590) 0.191** (0.0946) (0.724) (0.556) (0.0946) (0.888) (0.101) (0.936) (0.322) (0.145) (0.637) (0.310) (0.158) (0.724) (0.318) 0.211** (0.102) n n n (0.107) 0.987*** (0.102) 0.510*** (0.121) (0.0831) 0.583*** (0.0487) 0.532*** (0.0980) 0.146** (0.0742) 0.586*** (0.0488) (6) Bordering Frnce n n n (0.149) 0.972*** (0.105) (0.167) (0.112) 0.272*** (0.0782) (0.149) (0.100) 0.619*** (0.0492) border integrtion t (0.0743) (0.0890) (0.692) (0.449) 0.590*** (0.0891) 0.355** (0.152) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny smple municiplities low low low low low low density density density density density density Observtions 2,070 1,710 6,117 7,488 3,558 3,390 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = dt not vilble (or not sufficient) to estimte for this shock. 77

89 Tble 4A.4: Direct integrtion effects on popultion growth; high density municiplities Integrtion shock EC1967 EU1993 EMU1999 EURO2002 (1) bordering Frnce border n n 0.865*** (0.207) integrtion t n n 0.776*** (0.232) border integrtion t n n 1.205*** (0.418) border n n 0.304*** (0.117) integrtion t n n 0.272*** (0.0908) border integrtion t n n (0.934) border 0.444*** (0.0953) integrtion t 0.769*** (0.0488) border integrtion t (0.105) border 0.491*** (0.0629) integrtion t 0.766*** (0.0489) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering Bordering Netherlnds Belgium Germny Netherlnds (0.0918) 0.747*** (0.0449) 0.178* (0.0964) 0.102* (0.0581) 0.751*** (0.0450) (0.404) 0.365*** (0.0735) 0.827* (0.451) (0.377) 0.367*** (0.0742) 0.594*** (0.195) 0.246*** (0.0735) 0.609* (0.320) 0.275* (0.153) 0.622*** (0.224) (0.622) 0.236* (0.135) 0.593** (0.243) (0.960) (0.135) (0.173) n n n (0.115) 0.431*** (0.0700) 0.294** (0.125) 0.222** (0.0876) 0.166*** (0.0424) (0.103) 0.251*** (0.0786) 0.163*** (0.0425) (6) Borderin g Frnce n n n (0.121) 0.490*** (0.0753) 0.271** (0.133) (0.0909) (0.0509) (0.112) (0.0809) (0.0511) border integrtion t 0.169** (0.0797) 0.173*** (0.0661) 0.826* (0.458) (1.324) (0.0904) (0.114) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny Observtions high high high high high high density density density density density density R-Squred 1,953 2,285 8,015 9,005 4,778 4, Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = dt not vilble (or not sufficient) to estimte for this shock. 78

90 Tble 4A.5: Indirect integrtion effects on popultion growth; low density municiplities Integrtion shock EC1973 EC1986 EU1995 EU2004 (1) bordering Frnce border n n (1.604) integrtion t n n 0.247*** (0.0904) border integrtion t n n (1.645) border n n (0.798) integrtion t n n 0.166* (0.0981) border integrtion t n n (0.949) border 0.425*** 0.147*** (0.0367) (0.0371) (0.618) integrtion t 0.827*** (0.0606) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering bordering Netherlnds Belgium Germny Netherlnds 0.784*** (0.0662) 0.204* (0.117) border integrtion t (0.930) border 0.488*** (0.0599) (0.0785) (0.537) integrtion t 0.822*** (0.0612) 0.789*** (0.0666) 0.173** (0.0865) (0.502) (0.106) (0.603) (0.339) (0.122) (0.569) (0.306) (0.159) (0.696) (0.305) (0.109) n n n (0.148) 1.622*** (0.0783) (0.163) (0.0970) 1.210*** (0.286) 0.515*** (0.112) 0.193*** (0.0703) 0.586*** (0.0489) (6) bordering Frnce n n n (0.181) 1.703*** (0.0771) (0.204) (0.132) 1.181*** (0.300) (0.159) (0.0946) 0.618*** (0.0492) border integrtion t (0.0743) (0.0890) (0.604) (0.504) 0.547*** (0.0924) (0.170) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny smple low low low low low low density density density density density density Observtions 2,070 1,710 6,117 7,488 3,558 3,390 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = dt not vilble (or not sufficient) to estimte for this shock. 79

91 EC1973 EC1986 EU1995 EU2004 Tble 4A.6: Indirect integrtion effects on popultion growth; highly dense municiplities Integrtion shock (1) bordering Frnce border n n 1.158*** (0.178) integrtion t n n 0.305*** (0.0761) border integrtion t n n 1.783*** (0.454) border n n 0.417*** (0.147) integrtion t n n 0.292*** (0.0815) border integrtion t n n 1.274* (0.660) border 0.359*** ** (0.0408) (0.0279) (0.110) integrtion t 0.770*** (0.0487) Belgium The Netherlnds Germny (2) (3) (4) (5) (6) bordering bordering bordering bordering Netherlnds Belgium Germny Netherlnds 0.743*** (0.0448) 0.264*** (0.0959) border integrtion t (1.066) border 0.491*** (0.0629) integrtion t 0.766*** (0.0489) 0.102* (0.0581) 0.751*** (0.0450) (0.362) 0.823*** (0.234) 0.764*** (0.177) 0.227*** (0.0760) 0.940*** (0.342) 0.402*** (0.149) 0.210** (0.0863) (0.466) 0.279* (0.145) 0.609*** (0.228) (0.703) (0.130) 0.520* (0.311) n n n (0.188) 1.324*** (0.147) (0.195) (0.103) 0.172** (0.0847) 0.269** (0.116) 0.257*** (0.0729) (0.0480) bordering Frnce n n n (0.195) 1.324*** (0.154) 0.421** (0.202) (0.109) 0.206** (0.0888) (0.123) (0.0753) 0.150*** (0.0474) border integrtion t 0.169** (0.0797) 0.173*** (0.0661) (0.488) (1.830) (0.0888) (0.127) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny smple high high high high high high density density density density density density Observtions 1,953 2,285 8,015 9,005 4,778 4,540 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; n = dt not vilble (or not sufficient) to estimte for this shock. 80

92 Tble 4A.7: Direct integrtion effects; Netherlnds nd Germny common smple periods (1976 on wrd) Integrtion shock EU1993 EMU1999 The Netherlnds (1) bordering Belgium border (0.313) integrtion t 0.236*** (0.0638) border integrtion t (0.529) border (0.342) integrtion t 0.560*** (0.144) border integrtion t (0.417) (2) bordering Germny (0.262) 0.365** (0.144) (0.473) (0.239) 0.367** (0.157) (0.593) (3) Bordering Netherlnds (0.0715) 0.653*** (0.0608) 0.370*** (0.0808) 0.160*** (0.0561) 0.361*** (0.0341) 0.267*** (0.0686) Germny (4) Bordering Frnce (0.0915) 0.682*** (0.0632) 0.216** (0.103) (0.0695) 0.373*** (0.0367) 0.222** (0.0896) EURO2002 border (0.311) integrtion t 0.545*** (0.145) (0.248) 0.434*** (0.161) 0.191*** (0.0507) 0.361*** (0.0342) (0.0621) 0.267*** (0.0429) border integrtion t (0.421) (0.641) 0.244*** (0.0618) 0.272*** (0.0921) Yer effects yes yes yes yes Country Netherlnds Netherlnds Germny Germny Observtions 9,790 11,387 8,336 7,930 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p <

93 Tble 4A.8: Indirect integrtion effects; Netherlnds nd Germny common smple periods Integrtion shock EC1986 EU1995 (3) bordering Belgium border 0.759*** (0.222) integrtion t 0.231*** (0.0612) border integrtion t 1.232*** (0.427) border (0.281) integrtion t 0.228*** (0.0672) border integrtion t (0.569) The Netherlnds (4) bordering Germny (0.244) 0.348** (0.139) 0.797** (0.400) (0.238) (0.0880) (0.508) (5) bordering Netherlnds (0.109) 1.441*** (0.0950) (0.116) (0.0646) 0.258*** (0.0407) 0.357*** (0.0759) Germny (6) bordering Frnce 0.254* (0.139) 1.474*** (0.0994) 0.388*** (0.147) (0.0829) 0.607*** (0.137) 0.167* (0.0964) EU2004 border (0.297) integrtion t 0.565*** (0.145) (0.232) 0.387** (0.188) 0.215*** (0.0472) 0.360*** (0.0343) (0.0581) 0.375*** (0.0365) border integrtion t (0.370) (0.860) 0.191*** (0.0633) 0.233** (0.104) Yer effects yes yes yes yes Country Netherlnds Netherlnds Germny Germny Observtions 9,790 11,387 8,336 7,930 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p <

94 Tble 4A.9: Direct integrtion effects; ll smple countries common smple periods (1990 on wrd) Integrtion shock EMU1999 (1) bordering Frnce border 0.389*** (0.0700) integrtion t 0.786*** (0.0391) border integrtion t (0.0760) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering Bordering Netherlnds Belgium Germny Netherlnds (0.0771) 0.504*** (0.0255) 0.167** (0.0807) 1.482* (0.846) 0.560*** (0.144) 1.761** (0.879) (0.532) 0.367** (0.157) (0.760) 0.252*** (0.0654) 0.257*** (0.0407) 0.174** (0.0764) (6) Bordering Frnce (0.0671) 0.264*** (0.0428) (0.0876) EURO2002 border 0.396*** (0.0467) integrtion t 0.784*** (0.0392) (0.0484) 0.768*** (0.0384) (0.663) 0.545*** (0.145) 0.863* (0.491) 0.187* (0.107) 0.291*** (0.0547) 0.361*** (0.0342) (0.0560) 0.376*** (0.0366) border integrtion t (0.0570) 0.143*** (0.0547) (0.721) (0.768) 0.145** (0.0652) 0.176** (0.0881) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny Observtions 4,023 3,995 5,577 6,452 5,475 5,225 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1 Tble 4A.10: Indirect integrtion effects; ll smple countries common smple periods (1990 on wrd) Integrtion shock EU1995 EU2004 (1) bordering Frnce border 0.341*** (0.0275) integrtion t 0.787*** (0.0389) Belgium The Netherlnds Germny (2) (3) (4) (5) bordering bordering bordering bordering Netherlnds Belgium Germny Netherlnds *** (0.0227) 0.762*** (0.0382) (0.964) 0.494*** (0.149) border integrtion t (1.084) border 0.396*** (0.0467) integrtion t 0.784*** (0.0392) (0.0484) 0.768*** (0.0384) (0.590) 0.565*** (0.145) (0.714) 0.353** (0.149) (0.843) (0.426) 0.387** (0.188) (0.0835) 0.258*** (0.0407) 0.369*** (0.0925) 0.320*** (0.0481) 0.360*** (0.0343) (6) bordering Frnce (0.0933) 0.607*** (0.137) (0.105) ** (0.0507) 0.375*** (0.0365) border integrtion t (0.0570) 0.143*** (0.0547) 1.265** (0.629) (0.931) (0.0639) (0.0999) Yer effects yes yes yes yes yes yes Country Belgium Belgium Netherlnds Netherlnds Germny Germny Observtions 4,023 3,995 5,577 6,452 5,475 5,225 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p <

95 Popultion growth Popultion shre growth Tble 4A.11: Optiml brek points (M +1 segments) Brek Brek points (yer) BIC Brek points (yer) BIC Points () Netherlnds bordering Germny (b) Netherlnds bordering Belgium M (M) = M = M = , , M = , 1991, 2001, , 1975, M = , 1984, 1991, 2001, , 1975, 1989, M = , 1974, 1984, 1991, 2001, , 1975, 1982, 1989, (c) Germny bordering the Netherlnds (d) Germny bordering Frnce M = M = M = , , M = , 1993, , 1994, M = , 1993, 1997, , 1989, 1993, M = , 1989, 1993, 1997, , 1989, 1993, 1997, M = , 1985, 1989, 1993, 1997, , 1985, 1989, 1993, 1997, (e) Belgium bordering the Netherlnds 2003 (f) Belgium bordering Frnce No brek(too short dt time periods) No brek (too short dt time periods) () Netherlnds bordering Germny (b) Netherlnds bordering Belgium M = M = M = , , M = , 1984, 1991, , 1977, M = , 1984, 1991, 2001, , 1977, 1989, M = , 1977, 1984, 1991, , 1975, 1982, 1989, (c) Germny bordering the Netherlnds 1997 (d) Germny bordering Frnce M = M = M = , , M = , 1990, , 1987, M = , 1990, 1996, , 1987, 1994, M = , 1986, 1990, 1996, , 1987, 1994, 1998, M = , 1986, 1990, 1995, 1999, , 1987, 1991, 1995, 1999, (e) Belgium bordering the Netherlnds 2003 (f) Belgium bordering Frnce No brek (too short dt time periods) No brek (too short dt time periods) Note: The highlighted rows re the optiml brek points (smllest BIC for M 1) Tble 4A.12: Summry optiml brek points (test sttistics) Brek Brek points (yer) Points () Netherlnds bordering Germny (M) Popultion growth Pop. Shre growth Tests Ch2(Prob > Ch2) Brek points (yer) (b) Netherlnds bordering Belgium Tests Ch2(Prob > Ch2) M = (0.3649) (0.2963) M = (0.0701) (0.0198) (c) Germny bordering the Netherlnds (d) Germny bordering Frnce M = (0.0000) (0.0504) (0.0000) M = (0.0000) (0.0004) () Netherlnds bordering Germny (b) Netherlnds bordering Belgium M = (0.3574) (0.2940) M = (0.0281) (0.0206) (c) Germny bordering the Netherlnds (d) Germny bordering Frnce M = (0.0016) (0.0003) (0.0000) (0.0000) (0.0000) (0.0196) Note: significnt breks when p-vlue in the prenthesis (p < 0.01; ** p < 0.05; * p < 0.1) 84

96 Figure 4A.1: The robust error terms; exmple from the Netherlnds municiplities Figure 4A.2: Structurl breks in popultion (shre) growth: ll Netherlnds bordering Belgium 85

97 86

98 Chpter Five Town Twinning nd Germn City Growth Introduction Shocks like the cretion or bolition of ntionl borders re ssocited with chnge in mrket ccess. The fll of the Berlin wll in Germny in 1989 is n exmple of such shock. This creted sudden economic opportunities for cities long the former border between western nd estern Germny. After the reunifiction, these former border cities experienced higher popultion growth rtes thn more centrlly locted cities within Germny (Redding nd Sturm, 2008, see lso Ahlfeldt, et l, 2012). Other exmples of shocks re the expnsion of the Europen Community (EC), lter the Europen Union (EU). The incresed economic integrtion between member countries nd between new members incresed mrket ccess for cities long the borders of the EU. Brkmn et l. (2012) show, for instnce, tht the involved cities nd regions long borders tht experienced EC/EU economic integrtion were positively ffected by this chnge in mrket ccess, which compenstes, to some extent, the negtive effect of being (peripherl) border loction. In this chpter we nlyze so-clled town twinning (herefter, TT), which is nother form of integrtion tht might ffect the interntionl economic or mrket ccess of city. TT involves co-opertion, in the brodest sense, between towns or cities cross ntionl borders. Although TT hs long history, dting bck to the 19 th century, the heydys of TT begn fter WWII (Zelinsky, 1991, Furmnkiewitcz, 2005, nd Clrke, 2009). The need between countries to recquint themselves with their former enemies ws prticulrly felt in the post-wr period, nd in prticulr so in Germny. As side effect of this lrgely politiclly motivted twinning episode, trnsction costs would be reduced nd interction or flow of people between cities tht becme prt of TT, nd we hypothesize tht s result popultion growth could be more pronounced compred to other cities tht hd no or fewer interntionl TT prtners. Although our dt don t llow us to prove this mechnism, TT might help people nd firms find their optiml cities where they re more productive. 45 The centrl topic of this chpter is to nlyze whether TT indeed hs positive effect on popultion growth in Germn cities. To our knowledge the only empiricl ttempts to mesure effects of TT re de Villiers et l. (2007) nd Bycn-Levent et l. (2010), both bsed on the survey of municipl officils tht were sked whether they considered TT successful. However, full-fledged econometric nlysis is missing. In this chpter we try to fill this gp. Our rgument is thus tht twinning cities hve dvntges over other cities s they, by co-operting with ech other, reduce trnsction costs nd increse economic proximity. At the sme time, the 44 This chpter is bsed on joint work with Steven Brkmn nd Hrry Gretsen. 45 More productive cities cn be more ttrctive to people nd firms from twinning prtners s well s non-prtner cities. 87

99 orgniztion nd mintennce of TT involves (coordintion) costs; so it is not priori cler whether TT will be beneficil for the cities concerned. The difference between this chpter nd Redding nd Sturm (2008) or Brkmn et l. (2012) is tht we do not put specil emphsis on ntionl borders, nd do not nlyze shocks, but focus on the evolutionry influence tht TT hs on city popultion growth. To this end we construct complete dtset on TT for Germny. We focus on Germny becuse Germny, s we rgue in section 5.2, is the min ctor in TT in post WWII Europe. The chpter is rrnged s follows. In section 5.2 we briefly discuss the history of TT, nd wht it implies in prctice. Section 5.3 describes the dtset. Our vribles of interest re popultion growth nd the TT in Germny with cities outside Germny. The estimtion strtegy is developed in section 5.4. The min estimtion results re described in section 5.5. In generl, nd fter lso conducting rnge of robustness checks, we do find evidence of significnt positive reltionship between TT nd Germn city growth, in prticulr when we tke the number of TT reltionships into ccount nd focus on TT with French cities or cities in neighboring countries more generlly. Finlly, Section 5.6 concludes Town twinning: history, motives nd theory TT is reltive old phenomenon. 46 The term ws used s erly s the 1850s to describe the coopertive ctivities of building trnsporttion nd other public infrstructure between, for exmple, the neighboring cities of Minnepolis nd St. Pul, Minnesot, USA, (see Borchert 1961). The world firs tht were initited in the 19th century lso stimulted contcts between cities (Fighier 1984, cited in Zelinsky, 1991). Following these erly ttempts mny others followed in order to enhnce coopertion between cities. For exmple, the foundtion of the Interntionl Union of Locl Authorities (IULA) t Ghent in Belgium in 1913 ws specificlly imed t stimulting interntionl coopertion between cities (Zelinsky, 1991). Ties between cities were lso stimulted by d hoc inititives by city councils or privte enthusists for more coopertions between cities (Clrke, 2009). The concept of TT is s such rther opque. It involves ll sorts of interctions tht re imed to foster mutul understnding between the inhbitnts of cities tht tke prt in the inititives, such s: bilterl visits of officils, musicl events, lnguge courses, or exchnges of letters between schoolchildren. However, it lso encompsses the shring of technicl expertise, the shring of knowledge nd dvice tht hve more direct economic consequences (Zelinsky, 1991). All these ctivities cn result in form of TT. The term town twinning is dopted from the reltionship tht existed between the twin cities of Minnepolis nd St. Pul, Minnesot, USA, but incresingly ws used to describe the reltionship between interntionl prtner cities, which is 46 We do not discuss co-opertion between cities tht were motivted by religious motives (missionry efforts), inititives by freemsons, Rotrins nd the like, s systemtic dt for these inititives re lcking nd becuse the inititives re imed t specil interest groups. 88

100 how we will lso use the term. As is cler from the historicl overview in Zelinsky 1991, nd, inter li, Clrke (2009, 2010), TT is very much Europen phenomenon. From Zelinsly (1991, Tble 3, p.12), it cn be deduced tht the top-20 of countries in 1988 tht re involved in interntionl twinning is dominted by EU countries (15 out of the 20), nd tht the leding TT countries re Frnce, the UK nd Germny tht together hve lmost 8500 twinning reltions, which is comprble to the other 17 countries combined. Proximity is lso importnt; most TTs tke plce with neighboring countries (Zelinsky, 1991). Dt on TT show tht it becme very populr fter WWII, especilly during the 1950s (Flkenhin et l., 2012; Furmnkiewicz, 2005; Jyne, 2011, 2013; Joenniemi nd Sergunin, 2009; Ppgroufli, 2006; Vion 2002, Cmpbell, 1987; nd Zelinsky, 1991). The promotion of the TT ws one of the priorities of the Council of Europen Municiplities which explins the huge increse in the number of TTs in the 1950s. The WWII experience ws gret stimulus for TT inititives. 47 As consequence, most of the TTs were between towns from countries tht were enemies during WWII. Germny becme the center of the twinning ctivities. By 2012, Germn municiplities together hve over 5000 interntionl twinning prtners, mostly with Europen prtners, especilly Frnce. The TT orienttion towrds Frnce is not surprising if one relizes tht Frnce nd Germny were rch-enemies in three min wrs between 1870 nd 1945 so post-wwii pece policy in Western Europe focused on these two countries. During the cold wr n ideologicl dimension ws dded to the motives to form prtnerships; TT could help to promote understnding for different ideologicl systems. The ltter inititives were often met by distrust of more centrl governments (Clrke, 2010), nd it is questionble whether these ideologicl forms of TT reduced trnsction costs in wy tht could stimulte popultion growth. Figure 5.1 shows the recent dt on town twinning in the Europen countries. The mp shows tht TT is most populr in Germny nd Frnce. 47 See for history of TT in some individul countries: for the UK -Clrke, 2009; Clrke, 2010; Clrke, 2011 nd Jyne, 2011, for Frnce - Vion, 2002; nd Cmpbell, 1987, for Greece - Ppgroufli, 2006, for northern Europe - Joenniemi nd Sergunin, 2009, nd for Polnd - Furmnkiewicz, 2005 nd

101 Figure 5.1: The geogrphy of town twinning in Europe Source: own construction, bsed on Zelinsky (1988) nd CEMR (2010); (Dutch number style) is sme s 3,000 or 3000 (interntionl number style). Our brief overview of TT suggests tht, in generl, two motives for TT seem to stnd out: - A politicl motive, following WWII, TT ws used s tool in the process of reconcilition between former enemies (f.i. Flkenhin et l., 2012), Clrke, 2010, Vion, 2002). - An economic motive, TT is imed t economic co-opertion nd by doing so genertes interntionl flows of goods nd people, becuse economic distnce is reduced vi the reduction in inter-city trnsction costs (Grosspietsch, 2009, Jyne et l., 2011, Jyne et l., 2013). In the literture on TT few exmples exist to mesure the effects of TT empiriclly. De Villiers et l. (2007) nd Bycn-Levent et l. (2010) use opinion polls mong municipl officils. The results suggest tht the success of TT depends on the existence of lredy existing reltions with prtner cities nd similrities in the urbn problems they fce. Flkenhin et l. (2012), show tht geogrphicl proximity is n importnt fctor for twinning density. Clrke (2009, 2010, 2011) uses nrrtives to nlyze TT. Jyne et l. (2011) emphsizes reltionl geogrphy versus territoril geogrphy where towns extend their boundries through spce nd time. This chpter dds to this literture by explicitly mesuring nd estimting the effects of TT on city popultion growth for Germn cities. We hypothesize tht TT increses interntionl mrket ccess of cities by specificlly reducing trnsction costs between cities tht hve interntionl prtners nd lso reduces direct trnsporttion costs between prtner cities (see for the micro economic foundtions, Redding nd Sturm, 2008, Brkmn et l. 2012). We lso hypothesize tht these positive effects of TT cn outweigh the coordintion costs of being engged in TT such tht TT cn indeed hve n overll positive effect on cities. Germn cities involved in 90

102 TT re locted throughout Germny, implying tht we do not focus on border effects per se, but concentrte on those cities or loctions tht hve TT reltions with foreign cities. The reduction in economic distnce between these loctions nd foreign cities, ceteris pribus, is thought to stimulte locl economies nd boost popultion growth. A theoreticl nlysis of the effects cn, for instnce, be found in Brkmn et l. (2009, ch. 11, tble 11.4). In twelve city simultion, bsed on Krugmn-type new economic geogrphy model (Krugmn, 1991), it cn be shown tht building bridge between pirs of cities, stimultes growth in cities on the two sides of the bridge. TT is expected to hve similr effect. Town twinning is not something which is enforced upon cities but it is deliberte choice by 2 cities whether or not to engge in mutul town twinning. They do so when the perceived economic nd non-economic benefits re thought to outweigh the set up nd mintennce coordintion costs. The former cn be looked upon s qusi fixed in the sense tht these costs re lower when Germn city hs lredy more TT reltionships, prticulrly so when the existing TT reltionships re with cities in the sme foreign country nd if ceteris pribus these countries (nd thus twinning cities) re more nerby. This leds us to expect tht the lleged positive growth effects of TT re lrger for cities tht hve lrger number of TT reltionships The dtset We focus our nlysis on TT relted to Germn cities. As discussed in section 5.2, Germny is the center of twinning ctivities nd dt for Germny re systemticlly vilble (in contrst to most other countries). The dt re obtined from Rt der Gemeinden und Regionen Europs, nd the Germn section of the Council of Europen Municiplities nd Regions (CEMR). The smple includes over 5000 twinning reltionships of over 600 Germn towns, cities nd municiplities with loctions round the world. The popultion dt re obtined from the Sttistisches Bundesmt Our dt cover the period 1976 to The popultion dt relte to the municiplities level or the county level. If possible we use dt for the lowest level of ggregtion. The sptil units of the popultion dt nd the TT dt differ nd we refer to the Appendix (Tble 5A.11) s to how the popultion nd TT dt were mtched so s to pply to the sme sptil unit. We use Kreise s the smllest sptil unit of observtion. Cities within Kreise tht re involved in TT re ggregted. The dt on sptil units re obtined from GFK GeoMrketing, Tbles 5.1 shows some summry sttistics. The dt for Germny cover two forms of TT reltionships: prtnerships nd friendship. Prtnership is form of twinning in which the prtners engge in ctivities bsed on contrcts, wheres friendships re less fr-reching nd re bsed on greements with limited forml ctivities or projects. We therefore expect the effects of prtnership TT on popultion growth to be reltively stronger. Tble 5.1 shows tht number of twinning connections is lrger thn the number of twinning towns nd cities; cities cn nd often do hve more thn one twinning reltionships. 366 Germny towns nd municiplities with 91

103 complete coverge for ll yers did hve 1502 twinning connections by This incresed to 419 Germn towns hving 3071 twinning connections in 1990 nd 610 towns hving 5067 twinning connections in Tble 5.1: Germn town twinning , prtnerships nd friendships ll twinnings (Prtnership + Friendship) Prtnership Friendship yer number % number % number % () Cumultive twinning towns nd cities 48 (b) Cumultive twinning connections % % 65 18% % % % % % % % % 76 5% % % 181 6% % % % Note: The percentges under prtnership nd friendship don t dd up to 100% becuse town cn hve one or more prtnership with town(s) s well s one or more friendship connections with other towns nd/or cities. A city or town cn hve more thn one twinning prtnership nd/or friendship. Figure 2 shows the verge numbers for Germn TT where ll municiplities/counties includes non-twinners s well, wheres, the group twinning municiplities/counties include only those with t lest one town twinning reltionship. In 1976 twinning municiplities/counties hd on verge bout 4.5 twinning prtners. Including non-twinners reduces this number to bout 3. By the yer 2012, the figures grew to bout 13 nd 10 twinning connections, respectively. So for both groups grdul increse in the verge number of TT reltionships is visible. Figure 2b shows the bsolute number of municiplities/counties or Kreise with t lest one twinning connection in the ctegories of prtnership, friendship, or both, over time. In figure 2b, the prtners nd prtners + friends re very similr becuse the sme city which hs prtnership TT lso typiclly hs some friendship TT connections. This implies tht prtnership nd friendship connections re not mutully exclusive. 48 The percentges under prtnership nd friendship don t dd up to 100 percent becuse town cn hve one or more prtnership with town(s) s well s one or more friendship connections with other towns nd/or cities. 49 A city or town cn hve more thn one twinning prtnership nd/or friendship. 92

104 Figure 5.2: Men number of twinning Note: #,## (Dutch style decimls) re the sme s #.## (interntionl style) Figure 5.2b: Number of municiplities/counties with t lest one twinning connection Out of over 2000 Germn cities nd towns, 366 of them hd t lest one twinning connection in 1976, nd 610 cities nd towns hd twinning reltionship in 2007 (see tble 1). Even fter ggregting into the municiplities/counties or Kreise lrge number of Germn Kreise still do not hve ny town twinning connection t ll. In our estimtions we lso look t the intensity of twinning. Figure 2c gives sense of the difference between town twinning s such nd the intensity. The striped brs show whether 93

105 Germn towns re engged in town twinning t ll by hving t lest one twinning connection, nd the solid brs show the intensity by displying the number of Germn Kreise with more thn the men number twinning connections. Figure 2c illustrtes tht the growth of Germn town twinning in our smple period occurred until 2000 nd then leveled off. The number of towns with more thn the verge number of TT is pproximtely 120. Figure 5.2c: Municiplities/counties with t lest one (or men) twinning When it comes to the geogrphy of the Germn TT counterprts, Tble 5.2 shows tht 36 % of ll Germn TTs re with French cities; over 90 % of TTs re with Europen countries, including Russi. Within Germny, the twinning ctivities re historiclly concentrted in the western prt of Germny, see Figure

106 Tble 5.2: Top 40 Germn twinning prtners (98 %), 2012 s.n. Prtner # of % Cum. s.n. Prtner # of % Cum. 1 Frnce Greece country Britin twins % country Ukrine twins % Polnd Nicrgu Itly Romni Austri Lithuni Hungry Croti Czech Rep Ltvi USA Luxemburg Netherlnds Portugl Russi Sloveni Belgium Slovki Denmrk Republik Estoni Isrel Belrus Turkey Norwy Switzerlnd Irelnd Chin Burkin Fso Finlnd Bosni &Herz Sweden Bulgri Jpn Rund Spin Serbi Source: own clcultion from the dt Figure 5.3: The geogrphicl distribution of Germn twinning nd time trend Source: own clcultion from the dt 95

107 As visuliztion of Tble 5.2, Figure 5.3 shows the geogrphicl distribution of the mjor twinning prtners countries nd gin illustrtes the fct tht the neighbors of Germny re most importnt for Germn TT. Figure 5.4: Mjor twinning prtner countries for Germny Source: own clcultion from the dt bsed on bsolute number of twinning prtners 5.4. Estimtion strtegy We pply difference-in-differences (DID) method. Furthermore, we use instrumentl vribles to del with reverse cuslity. Our min rgument is tht Germn twinning cities hve dvntges over non-twinning cities s they enter into greements (locl policy shock) tht increse economic proximity by reducing trnsction costs with the non-germn prtner cities, nd indirectly the countries involved, nd s result these Germn TT cities grow reltively fster. The DID pproch cn be used to nlyze the effects of (non-)policy mesures pplied to sub-smples of the complete smple. The DID method llows for time-invrint unobserved differences between the control nd tretment groups. In prticulr it removes differences in unobserved chrcteristics tht re constnt over time nd tht cn ffect the dependent vrible, here popultion growth. The bsic specifiction is (see lso Brkmn et l., 2012 for discussion): popgrowth, m, t twinning m, t Dt Dl m t (5.1) twinning m, t prtners m, t Dt Dl m t popgrowth m, t twinning m, t, (5.2) where t; twinning mt popgrowth m, t is nnul popultion growth of Germn municiplity (or county) m t time indictes whether twinning reltionship between Germn municiplity with 96

108 interntionl prtner city exists. It equls 1 if the municiplity hs one or more interntionl twinning prtner(s) nd 0 otherwise. We lso include the number of prtners explicitly ssuming tht the lrger the number of prtners, the lrger the reduction in trnsction costs; the vlue of twinning mt thn equls to number of interntionl prtners. The vrible prtners m, t refers to prticulr country or group of countries with which TT exists, like for instnce only the subsmple of French TT prtner cities. Treting s dummy vrible refers to wht twinning mt might be clled the extensive mrgin of TT (is there ny TT t ll?), wheres treting s the ctul number of TT prtners thn refers to the intensive mrgin (how much TT twinning mt is going on, the volume of TT reltionships so to sy). Given tht TT lso invokes (coordintion) costs on the prt of the Germn TT city, we expect tht for Germn cities which re more hevily involved in TT, nd thus hve more experience in setting up nd mintining TT reltionships, the effect of TT on popultion growth to be stronger. In other words we expect the effect of the intensive mrgin of TT to be stronger s compred to wht we dubbed the extensive mrgin of TT. We thus lso expect tht the nture of the TT rrngement might mtter; prtnership TT would then be more relevnt thn friendship TT. For the vrible prtners m, t we look t the following subsmples: French TT counterprts, TT with only neighboring countries, TT with Europen countries, TT with the founding fthers of the EU (EU 6), or the 1980s members (EC12) nd the 1990s members (EU15)., nd indicte time dummies, loction dummies nd stochstic error term. The time dummies re nnul, wheres loction dummies re relted to the 15 sttes of Germny (Bundesländer) nd s such cpture unobserved chrcteristics of vrious sttes. The time dummies control for common shocks ffecting the popultion growth throughout the smple. The DID pproch is best used for comprble control groups (see Bertrnd et l., 2004; nd Cmeron nd Trivedi, 2005). By differentiting between lrge nd smll Germn counties nd municiplities we lso control for city size ffects in our results. Furthermore, we estimte by using robust stndrd errors (Donld nd Lng, 2007). In our robustness checks, we lso used clustered stndrd errors to control for the fct the popultion growth of Germn cities need not to be (sptilly) independent. We lso ddress the issue of reverse cuslity, tht is, whether TT stimultes popultion growth or whether stronger economic performnce nd hence popultion growth re formlized in TT ctivities. We use dt on the WWII destruction of Germn cities s instruments. Specificlly, the level of destruction of residentil houses, number of people killed, tx revenue loss nd tons of rubbles resulting from bombing of the Germny towns nd cities during WWII re used s instruments. The motivtion for these instruments is tht especilly cities tht experienced WWII destruction directly or more intensively, re more motivted to strengthen ties between former enemies in order to increse mutul understnding nd prevent future wrs. The dt for the instruments re obtined from Brkmn et. l (2004). We employ the Srgn (1958) s well s Bsmnn (1960) tests to check the power of the instruments. Other qulities of the instruments re lso checked. For instnce, the instruments should correlte with the right-hnd side only. D t D l mt 97

109 5.5. Estimtion results Bseline results Tble 5.3 presents the bseline results when estimting eqution (5.1) only. We thus use loction fixed effects tht re relted to the 15 sttes in Germny. Ech of the Kreise in our smple is prt of one of the sttes, nd becuse Kreise re lower level of ggregtion, sttes consist of more thn one Kreis. The inclusion of stte fixed effects cptures the ide tht sttes might hve specil tretments for TT (which re unobserved) 50. The columns indicted by dum=1 or dummy=1 correspond to eqution (5.1), nd cpture whether TT exists t ll, columns with inten= n or intensity=1, cpture the intensity of TT nd uses n the number of TT reltionships explicitly. Furthermore, time dummies re used. We lso differentite between prtnerships nd friendships, s the ties between cities in prtnership re thought to be stronger. Tble 5.3: Twinning by Germn cities nd popultion growth (full smple) Vribles Twinning mt prtnerships + friendships prtnerships only friendships only (dum=1) (1) (0.0566) (inten=n) (2) *** ( ) (dum = 1) (3) * (0.0559) (inten = n) (4) *** ( ) (dum = 1) (5) 0.108*** (0.0218) (inten = n) (6) *** ( ) Yer effects yes yes yes yes yes yes Loction effects yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; inten = intensity = n= number of twinning prtner cities; dum = dummy =1 if municiplity hs one or more twinning prtner(s). The results for popultion growth for twinning s such re mixed (columns 1, 3, nd 5). Only in the cse of TT friendships, significnt nd positive reltion exists. When we mesure TT by the number of TT contcts the popultion growth effect (the intensive mrgin of TT) is positive throughout (columns 2, 4, nd 6). As Frnce is by fr the most importnt twinning prtner of Germny, we focus on Frnce seprtely in Tble 5.4; prtners m, t stnds for the TT prtners between Germny nd Frnce. Seprting Frnce from TT in generl shows tht Frnce domintes the positive popultion growth effects of TT. The twinning vrible becomes mbiguous nd is only significntly positive in columns (5) nd (6). Hving prtner in Frnce is importnt for Germn cities; both from the 50 Since we use stte fixed effects this lso dels with the difference in TT between the former sttes of West nd Est Germny prior to Germn re-unifiction in

110 extensive (column 4) nd in prticulr from the intensive (column 5) mrgin perspective. 51 include loction fixed effects to seprte estern from western Germn cities. We Tble 5.4: Twinning with Frnce Vribles prtnerships + friendships prtnerships only friendships only (dum=1) (1) (inten=n) (2) (dum=1) (3) (inten=n) (4) (dum=1) (5) (inten=n) (6) Twinning mt 0.218*** (0.0661) (0.0020) 0.244*** (0.0660) (0.0024) 0.123*** (0.0228) *** (0.0063) 52 Twinningmt Frnce mt 0.441*** (0.0815) *** (0.0044) 0.443*** (0.0785) *** (0.0048) 0.101** (0.0403) (0.0246) Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; inten = intensity = n= number of twinning prtner cities; dum = dummy =1 if municiplity hs one or more twinning prtner(s). The conclusion is tht TT hs smll but detectble effect on popultion growth when the TT with French cities is concerned. This effect is due to the more fr reching form of TT, prtnerships. Twinning cn stimulte popultion growth, but it seems relevnt to focus on subgroups of TT reltionships, here French cities. We lso do so for other subsmples in section 5.2 The question we will, however, ddress first is tht of reverse cuslity; it could be the cse tht (trde) reltions re good between groups of countries nd their respective cities (which s such boosts popultion growth), nd tht these ties re formlized in TT. To ddress this, we use n instrumentl vrible estimtion. As instruments we use the level of destruction of residentil houses, the number of people killed, tx revenue loss, nd tons of rubble resulting from bombing of the Germn towns nd cities during the WWII by llied forces. The motivtion to include wr relted instruments is tht loctions tht were hit prticulrly hrd by WWII could hve been more motivted to get involved in TT thn other cities. The perceived importnce of mutul understnding in these cities is stronger thn in others; see tble A10 in the ppendix for n nlysis of the strength of the instruments. We used the instruments in three ctegories: = ll the four instruments used together; b = residentil buildings loss, rubble per cpit, nd tx revenue loss, nd c = residentil buildings loss, nd tx revenue loss. Tble 5.5 shows the results of the IV estimtes when we estimte eqution (5.1) with IV. It includes full set of fixed effects. The results for the extensive mrgin re gin mbiguous, 51 Other neighboring countries give, in qulittive sense, similr results. Results re vilble upon request. 52 Frncemt = Shre of Frnce towns nd cities in the totl interntionl twinning prtners of Germny municiplity or county 99

111 but the intensive mrgin stnds out. In ll vrints tht del with the number of twinning reltions the effect of twinning is positive. Tble 5.5: All twinnings, IV estimtes prtnerships + friendships prtnerships + friendships prtnerships + friendships Vribles (dum=1) (1) (inten=n) (2) (dum=1) (3) (inten=n) (4) (dum=1) (5) (inten=n) (6) Twinning mt 3.762*** *** 4.521*** *** 6.666*** *** (0.875) ( ) (1.052) (0.0118) (1.515) (0.0122) Instruments b b c c Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Srgn score (p-vlue) 21.60(0.000) 20.55(0.000) 16.69(0.000) 3.48(0.176) 4.89(0.027) 1.77(0.183) Bsmnn score(p-vlue) 21.55(0.000) 20.50(0.000) 16.64(0.000) 3.47(0.177) 4.87(0.027) 1.76(0.184) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; dum = dummy; inten = intensity The Srgn (1958) s well s Bsmnn (1960) test sttistics show tht the instrument doesn t meet the requirement of the over-identifying restriction. However, the instruments b nd c fulfill the test of over-identifiction restriction when we consider the intensity of TT (columns (4) nd (6). In these cses the number of TTs hs positive nd sttisticlly significnt effect on popultion growth. Tble 5.6 shows the IV estimtes of tble (5.4) with singling out Frnce s the twinning prtner. As in the other cses it includes full set of fixed effects. In line with the estimtion results in Tble (5.4), the results indicte tht the extensive mrgin s well s the intensive mrgin of TT with Frnce is positive nd significnt. The tests for over-identifying restrictions show tht the instruments meet the requirement of the over-identifying restrictions. 53 Cusl reltionship is confirmed s the instruments combintions re generlly vlid; the vlidities of columns (1), (3), (4) nd (5) not being rejected t ll, wheres columns (2) nd (6) re rejected t 1 nd 5 percent levels, respectively. 53 Seprting prtnership nd friendship for ech group of instruments does not ffect the results, or the vlidity of the instruments lso in generl remin vlid. For instnce, see tble 5A.1 in the ppendix for the seprte estimtes using the instruments group b. Cusl reltionship is confirmed s ll the instruments combintions re vlid; the vlidities of columns (1), (2), (3), (5) nd (6) not being rejected t ll wheres column (4) is rejected only t 10 percent level. 100

112 Tble 5.6: Twinning with Frnce, IV estimtes Vribles Twinning mt prtnerships + friendships prtnerships + friendships prtnerships + friendships (dum=1) (1) 0.720*** (0.106) (inten=n) (2) *** (0.0163) (dum=1) (3) 0.737*** (0.108) (inten=n) (4) 0.153*** (0.0261) (dum=1) (5) 0.745*** (0.109) (inten=n) (6) 0.154*** (0.0262) Twinning mt Frnce mt 1.997*** (0.280) 0.163*** (0.0327) 2.049*** (0.287) 0.324*** (0.0526) 2.076*** (0.290) 0.326*** (0.0529) Instruments b b c c Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Srgn score (p-vlue) 3.05(0.383) 23.88(0.000) 2.26(0.322) 4.23(0.121) 1.81(0.178) 4.05(0.044) Bsmnn score(p- 3.04(0.385) 23.82(0.000) 2.26(0.324) 4.21(0.122) 1.80(0.180) 4.03(0.045) vlue) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; dum = dummy; inten = intensity; we estimte the regression equtions using different sptil level fixed effects; nd the results remin firly the sme. For instnce, when estimting using municiplity level fixed effects insted of sttes the coefficient of Twinningmt Frnce mt is insted of in column (1) nd remin strongly significnt. The literture suggests tht lrge urbn loctions re not only more efficient thn smller ones, but they hve lso n dvntge in innovtion, nd their economies cn grow fster thn smller loctions, see lso Ludem nd Wooton (1999) who show tht trde liberliztion initilly benefits lrger gglomertions. We therefore define Germn municiplities tht re smller thn the medin popultion size s smll, nd those tht re lrger thn the medin popultion size s lrge (see Tble 5A.2 nd 5A.3 in the ppendix). Without using instruments introduced bove, TT hs positive effects for lrge nd smll municiplities, prticulrly when we ccount for the intensity of twinning (for exmple see Tble 5A.2 s well s Tble 5A.9). After instrumenting, however, the significnt nd positive TT effects only remin vlid for lrge municiplities (tble 5A.3), we return to this difference between lrge nd smll cities in the next sub-section. Timing could lso be fctor. We looked t erly versus lte twinning (see tble 5A.5). We choose 1960 nd 1970 s dividing line to discriminte between erly nd lte TT. These dtes distinguish between the originl but limited EU integrtion nd the time when EU expnsion strted (with UK, Irelnd nd Denmrk becoming the members in 1973 which ws followed by other countries joining the EU in the 1980s, 1990s nd 2000s). Tble 5A.5 in the ppendix presents the results using instruments nd c. The results for instrument b re not reported for spce resons nd becuse they re very similr with the results for instrument. Tbles 5A.4 (no instruments) nd 5A.5 (instruments) in generl show tht erly TT hs stronger effect thn lter TT, lthough the effects remin positive over the whole period. 101

113 Additionl estimtions nd robustness checks As Germn TT with Frnce turns out to be importnt for the effects of TT on Germn popultion growth, we now investigte whether EU connections more generlly re importnt for the impct of twinning. Countries tht re more involved in Germn TT thn other countries re, for instnce, the countries tht re (founding) members of the EC/EU. The originl six members of the pre Europen Communities (EC6) re: Belgium, Frnce, Luxembourg, the Netherlnds, Itly, nd (West) Germny; the EC9 includes the EC6 s well s United Kingdom, Irelnd, nd Denmrk who joined in 1973; EC12 includes the EC9 s well s Greece, Spin nd Portugl who joined in the 1980s; the EU15 of includes EC12 members s well s Finlnd, Austri, nd Sweden who joined in 1995; nd EU25 includes the EU15 s well s Cyprus, Czech Rep., Estoni, Hungry, Ltvi, Lithuni, Mlt, Polnd, Sloveni, nd Slovki who joined EU since 2004 (for more detils see Brkmn et l., 2012). Estimting seprtely for the prtnership + friendship, prtnership only nd friendship only gives similr pttern of results s bove; i.e., the results re stronger for prtnerships thn friendships. The results in Tble 5.7 combine the IV estimtion results of both TT prtnerships nd friendships; i.e., prtnership + friendship. Controlling for the EC (or EU) membership shows tht now only the extensive mrgin of TT, so the number of TT reltionships, with the EC6 member countries hs significnt seprte effect throughout ll estimtions but the sign is now negtive. However, in the EC6 cse, the instruments re wek implying tht there is no strong evidence of TT with the EC nd EU members leding to higher popultion growth (see lso Tbles 5A.6 nd 5A.7). This is perhps so becuse of Germn cities limited level of twinning with these groups of countries nd the twinning effect doesn t stnd out in the phse other fctors especilly the EC/EU integrtion effects. The sme holds when we seprte between erly nd lte twinning for the EC nd EU (see tble 5A.8). Tble 5.7: Twinning with the EC nd EU countries, IV estimtes Vribles Twinning mt (dum=1) (1) 1.38*** (0.437) EC6 EC12 EU15 EU25 (inten=n) (2) 0.289*** (0.0793) (dum=1) (3) (0.544) (inten=n) (4) 0.432*** (0.099) (dum=1) (5) (0.550) (inten=n) (6) 0.411*** (0.0924) (dum=1) (7) 2.159*** (0.626) (inten=n) (8) 0.20*** (0.047) Twinning mt EC(U) j 1.871*** (0.622) 0.01*** (0.0028) (0.763) 0.01*** (0.003) (0.769) 0.011*** (0.003) 3.14*** (0.874) 0.01*** (0.0011) Instruments Yer effects yes yes yes yes yes yes yes yes Loction effects yes yes yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Srgn score (p-vlue) (0.000) (0.001) (0.000) 5.72 (0.126) (0.000) 7.08 (0.070) (0.000) (0.000) Bsmnn score (p-vlue) (0.000) (0.001) (0.000) (0.127) (0.000) (0.070) (0.000) (0.000) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; EC(U) j Є (EC6, EC12, EU15, EU25) ; dum = dummy; inten = intensity 102

114 After observing the differences between the effects from twinning with Frnce nd TT with the vrious historicl compositions of the EC nd EU countries, we relize tht geogrphicl proximity or contiguity lso could be fctor. Countries tht re nerby in geogrphicl sense re lso, ceteris pribus, ner ech other in other respects, like common culture. It my be then reltively more esy (or less costly) to set up TT reltionships with these countries (recll lso tht these countries, like Frnce, were typiclly invded by Germny during WWII). From Tble 5.2 we cn see tht in ddition to Frnce, 7 neighboring countries (with dditionlly 1200 TT reltionships) re in the top-15 of Germn TT prtners. Tble 5.8: Twinning with neighboring countries, IV estimtes prtnerships + friendships prtnerships + friendships prtnerships + friendships Vribles (dum=1) (1) (inten=n) (2) (dum=1) (3) (inten=n) (4) (dum=1) (5) (inten=n) (6) Twinning mt 0.710*** (0.102) *** (0.0160) 0.724*** (0.104) 0.123*** (0.0195) 0.737*** (0.105) 0.128*** (0.0200) Twinning mt Neighbor mt 1.289*** (0.176) 0.147*** (0.0241) 1.319*** (0.180) 0.198*** (0.0294) 1.345*** (0.182) 0.206*** (0.0302) Instruments b b c c Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Srgn score (p-vlue) 1.64(0.651) 12.50(0.006) 0.92(0.632) 2.62(0.269) 0.12(0.730) 1.06(0.303) Bsmnn score(p-vlue) 1.63(0.653) 12.45(0.006) 0.91(0.633) 2.63(0.269) 0.12(0.731) 1.06(0.304) Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; dum = dummy; inten = intensity As cn be seen in Tble 5.8, we find positive nd significnt of both extensive (columns 1, 3 nd 5) nd intensive mrgin tht is the number of twin towns (columns 2 4 nd 6), of TT on popultion growth which is not very surprising given the dominnce of (neighbor) Frnce for Germn TT, recll Tble (5.6). Dividing the smple into lrge nd smll loctions shows tht the results re gin only significnt for the lrger municiplities, see Tble 5.9. This supports the rgument tht lrge urbn loctions re not only more efficient thn smller ones, but lso hve n dvntge in innovtion, nd grow fster thn smller loctions. Recll tht this is true for the border integrtion lso s we hve seen in Chpter Three nd Chpter Four tht lrger cities nd benefit more from the EU integrtion. These results clerly show tht both types of integrtion shocks tht reduce brriers to trde nd lbor mobility, nmely the border integrtion nd TT benefit lrger urbn res thn smller ones. 103

115 Tble 5.9: Twinning with neighboring countries, IV estimtes (smll vs lrge Germn cities) Vribles prtnerships + friendships prtnerships + friendships prtnerships + friendships (dum=1) (1) (inten=n) (2) (dum=1) (3) (inten=n) (4) Smll Municiplities (dum=1) (5) (inten=n) (6) Twinning mt (0.351) (0.0752) (0.351) (0.0770) (0.359) (0.0789) Twinning mt Neighbor mt (0.482) (0.0935) (0.482) (0.0957) (0.495) (0.0980) Instruments b b c c Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 4,588 4,588 4,588 4,588 4,588 4,588 R-Squred Srgn score (p-vlue) 0.73(0.867) 2.03(0.566) 0.73(0.694) 1.96(0.375) 0.66(0.417) 1.54(0.214) Bsmnn score(p-vlue) 0.72(0.868) 2.02(0.569) 0.72(0.670) 1.95(0.378) 0.65(0.419) 1.53(0.216) Lrge municiplities Twinning mt 0.856*** (0.0632) *** ( ) 0.908*** (0.0655) 0.145*** (0.0112) 0.911*** (0.0655) 0.148*** (0.0114) Twinning mt Neighbor mt 1.465*** (0.0804) 0.167*** (0.0122) 1.549*** (0.0849) 0.235*** (0.0166) 1.554*** (0.0851) 0.240*** (0.0168) Instruments b b c c Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 4,526 4,526 4,526 4,526 4,526 4,526 R-Squred Srgn score (p-vlue) 18.99(0.000) 59.45(0.000) 8.46(0.015) 5.55(0.062) 7.36(0.007) 0.82(0.365) Bsmnn score(p-vlue) 18.87(0.000) 59.62(0.000) 8.39(0.015) 5.50(0.064) 7.30(0.007) 0.81(0.367) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; dum = dummy; inten = intensity We lso provide couple of other sensitivity checks. One of them is tht we estimte the regression equtions using different sptil level fixed effects; nd the results remin firly the sme. For instnce, when estimting using municiplity level fixed effects insted of sttes the coefficient of Twinningmt Frnce mt is insted of in column (1) of Tble 5.6 nd remin strongly significnt. In Tbles 5.10 nd 5.11, we provide lterntive results for twinning with Frnce nd with neighboring countries in generl. Insted of dividing the smple into smll nd lrge municiplities, we include city size dummy. The dummy Lrge_1970s is bsed on the initil popultion size (in the 1970s) nd includes city if the city size ws lrger thn the medin size. In columns (3) nd (6) we use the shre of the initil popultion size s Shre_1970s. Our dt set strts in For both the size dummy nd the initil popultion shre vrible, we used the first vilble yer of dt. For instnce, if yer 1976 dt is missing for municiplity, we use 1977 popultion s initil popultion, nd so on until the end of 1970s. In both Tbles 10 nd 11, we use the IV estimtion using instruments b. Columns (1) through (3) 104

116 use robust stndrd errors; wheres, Columns (4) through (6) use clustered robust stndrd errors to ccount for the possibility of sptil interdependence. Columns (1) nd (4) show the results with the two types of stndrd errors. Columns (2) nd (5) ccount for the initil size in the form of the city size dummy. In columns (3) nd (6) we use the shre of the initil yer popultion. Tble 5.10: Twinning with Frnce, dditionl IV estimtes (prtnerships + friendships) Vribles (1) (2) (3) (4) (5) (6) Twinning dummy =1 Twinning mt 0.737*** (0.0766) Twinning mt Frnce mt 2.049*** (0.1832) 0.727*** (0.0951) 2.002*** (0.1743) 0.738*** (0.0990) 2.025*** (0.1814) 0.737*** (0.2450) 2.049*** (0.6613) 0.727** (0.3296) 2.002*** ( ) 0.738** (0.3640) 2.025*** (0.7295) Instruments b b b b b b Yer effects yes yes yes yes yes yes Loction effects yes yes yes yes yes yes Lrge_1970s (0.0319) (0.0494) Shre_1970s (0.0898) (0.1418) St. Errors robust robust robust cluster-robust cluster-robust cluster-robust Observtions 11, , R-Squred IV OI test score (pvlue) 5.601(0.061) 1.466(0.480) 1.379(0.502) N N n Twinning intensity = n Twinning mt 0.153*** (0.0237) 0.228*** (0.0349) 0.536*** (0.1394) 0.153* (0.0834) 0.228* (0.1351) (0.8137) Twinning mt Frnce mt 0.324*** (0.0413) 0.428*** (0.0581) 0.956*** (0.2337) 0.324** (0.1619) 0.428* (0.2404) (1.4157) Instruments b b b b b b Yer effects yes yes yes yes yes yes Loction effects yes yes yes yes yes yes Lrge_1970s (0.0536) (0.2384) Shre_1970s *** (1.0351) (6.4731) St. Errors robust robust robust cluster-robust cluster-robust cluster-robust Observtions 11, , R-Squred IV OI test score(pvlue) (0.003) 8.023(0.018) 0.640(0.726) N n n Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; IV OI test: Instrumentl Vribles overidentifiction test. n = not vilble since IV OI test is not vilble with cluster robust errors. 105

117 Tble 5.11: Twinning with Neighboring countries, dditionl IV estimtes (prtnerships + friendships) Vribles (1) (2) (3) (4) (5) (6) Twinning dummy =1 Twinning mt 0.724*** (0.0730) 0.664*** (0.0880) 0.676*** (0.0906) 0.724*** (0.2159) 0.664** (0.2833) 0.676** (0.2999) Twinning mt Neighbor mt 1.319*** (0.1102) 1.294*** (0.1066) 1.311*** (0.1097) 1.319*** (0.3069) 1.294*** (0.3156) 1.311*** (0.3444) Instruments b b b b b b Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Lrge_1970s * (0.0294) * (0.0298) Shre_1970s (0.0888) (0.1141) St. Errors robust robust robust clusterrobusrobusrobust cluster- cluster- Observtions R-Squred IV OI score (p-vlue) 2.610(0.271) 0.689(0.709) 0.753(0.686) n n N Twinning intensity = n Twinning mt 0.123*** (0.0146) 0.147*** (0.0171) 0.187*** (0.0249) 0.123*** (0.0442) 0.147*** (0.0541) 0.187* (0.1038) Twinning mt Neighbor mt 0.198*** (0.0201) 0.221*** (0.0229) 0.274*** (0.0327) 0.198*** (0.0663) 0.221*** (0.0766) 0.274* (0.1412) Instruments b b b b b b Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Lrge_1970s ** (0.0342) (0.1012) Shre_1970s *** (0.2001) (0.9599) St. Errors robust robust robust clusterrobusrobusrobust cluster- cluster- Observtions R-Squred IV OI test score(pvlue) 6.879(0.032) 1.586(0.453) 3.161(0.206) n n N Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; IV OI test: Instrumentl Vribles Overidentifiction test. n = not vilble since IV OI test is not vilble with cluster robust errors. The min messge from Tbles 5.10 nd 5.11 is tht the positive effects of TT (with neighboring countries) re still present. The results suggest tht Germn municiplities or countries twinning with Frnce hve on verge bout 2 percent higher popultion growth thn non-twinning municiplities over the smple periods (see the top hlf of Tble 5.10). The effect is round 1.3 percent when we look t twinning with ll neighboring countries (see the top hlf of both Tble 5.11); nd when we look t intensity of twinning, the effects re smller in both cses 106

118 (see the bottom hlf of both the tbles). This is becuse of the fct tht the dummy compres twinning cities with non-twinning cities; wheres, intensity mesures the effect per unit of twinning Conclusions Although Town Twinning (TT) hs been round for long time it relly took off fter WWII. In the post-wwii period, TT ws imed t politicl reconcilition nd enhncing mutul understnding between former enemies, in prticulr so for Germny. If successful, TT could be looked upon s reducing the economic distnce between the cities tht re involved in these inititives, which cn be seen s to stimulte the growth of the cities involved in TT. Existing reserch on TT is to lrge extent descriptive nd we dd to this literture by explicitly focusing on the quntittive consequences of TT, tht is, for the cse of Germny we estimte whether TT stimultes popultion growth in the cities tht re involved in TT. We focus on Germny becuse Germny becme the min ctor in TT fter WWII. Applying difference-in-differences pproch, nd distinguishing between the extensive mrgin of TT (whether TT exist t ll for given city) nd the intensive mrgin (the number of TT reltionships), our results show tht Germn counties nd municiplities tht engge in town twinning often hve hd significntly higher popultion growth compred to Germn cities tht do not hve twinning prtners. Especilly the number or intensity of twinning reltions s well s town twinning with French cities, nd with neighboring countries more generlly, turn out to hve hd positive effect on city growth. We lso find tht the positive popultion growth effects of town twinning re confined to the lrger Germn cities. Town twinning could fcilitte reloction or migrtion of workers nd firms to more optiml loctions for their skills nd business, respectively. Thus, s the cities get more productive, they re likely to grow fster. As future reserch use of dt on popultion flow could be very useful in estblishing the exct mechnism s to how TT leds to city growth. 107

119 5.7. Appendices Tble 5A.1: Twinning with Frnce, IV estimtes (With IV set b vribles) Vribles Twinning mt prtnerships + friendships prtnerships only Friendships only (dum=1) (1) 0.737*** (0.108) (inten=n) (2) 0.153*** (0.0261) (dum=1) (3) 0.774*** (0.111) (inten=n) (4) 0.180*** (0.0311) (dum=1) (5) 1.523*** (0.345) (inten=n) (6) 0.694*** (0.133) Twinning mt Frnce mt 2.049*** (0.287) 0.324*** (0.0526) 2.027*** (0.286) 0.357*** (0.0590) 11.26*** (2.347) 6.719*** (1.240) Instruments b b b b b b Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Srgn score (p-vlue) 2.26(0.322) 4.23(0.121) 2.99(0.224) 5.23(0.073) 1.36(0.51) 0.34(0.844) Bsmnn score(p-vlue) 2.26(0.324) 4.21(0.122) 2.98(0.225) 5.21(0.074) 1.35(0.51) 0.34(0.844) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; dum = dummy; inten = intensity. Consistent with the results in the min sections, twinning with Frnce remins to hve strong nd significnt effects. Tble 5A.2: Twinning with Frnce (smll vs lrge) Vribles prtnerships + friendships prtnerships only friendships only (dum=1) (1) (inten=n) (2) (dum=1) (3) (inten=n) (4) Smll Municiplities (dum=1) (5) (inten=n) (6) Twinning mt (0.168) ( ) (0.166) * ( ) 0.111*** (0.0347) ** (0.0112) Twinning mt Frnce mt 0.235* (0.137) ** ( ) 0.236* (0.130) *** ( ) (0.0596) (0.0396) Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 4,588 4,588 4,588 4,588 4,588 4,588 R-Squred Lrge municiplities Twinning mt 0.242*** (0.0520) ** ( ) 0.264*** (0.0526) ** ( ) 0.123*** (0.0251) ** ( ) Twinning mt Frnce mt 0.643*** (0.0621) *** ( ) 0.661*** (0.0607) *** ( ) (0.0513) (0.0300) Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 4,526 4,526 4,526 4,526 4,526 4,526 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; dum = dummy; inten = intensity 108

120 Tble 5A.3: Twinning with Frnce, IV estimtes (smll vs lrge) Vribles prtnerships + friendships prtnerships + friendships prtnerships + friendships (dum=1) (1) (inten=n) (2) (dum=1) (3) (inten=n) (4) Smll Municiplities (dum=1) (5) (inten=n) (6) Twinning mt (0.343) (0.0934) (0.343) (0.0991) (0.344) (0.151) Twinning mt Frnce mt (0.675) (0.141) (0.675) (0.150) (0.676) (0.100) Instruments b b c c Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 4,588 4,588 4,588 4,588 4,588 4,588 R-Squred Srgn score (p-vlue) 1.20(0.753) 2.72(0.436) 1.20(0.549) 2.67(0.263) 0.86(0.355) 1.82(0.178) Bsmnn score(p-vlue) 1.19(0.756) 2.70(0.440) 1.19(0.552) 2.65(0.266) 0.85(0.357) 1.80(0.180) Lrge municiplities Twinning mt 0.961*** (0.0736) 0.102*** (0.0114) 1.011*** (0.0766) 0.202*** (0.0234) 1.012*** (0.0767) 0.203*** (0.0235) Twinning m t Frnce mt 2.308*** (0.137) 0.229*** (0.0224) 2.423*** (0.145) 0.430*** (0.0465) 2.426*** (0.145) 0.431*** (0.0467) Instruments b b c c Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 4,526 4,526 4,526 4,526 4,526 4,526 R-Squred Srgn score (p-vlue) 8.76(0.033) 75.72(0.000) 1.25(0.535) 5.32(0.070) 0.85(0.355) 4.97(0.026) Bsmnn score(p-vlue) 8.70(0.034) 76.23(0.000) 1.24(0.539) 5.28(0.071) 0.85(0.357) 4.93(0.026) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; dum = dummy; inten = intensity. Lrge municiplities benefit more, confirming erlier results. Tble 5A.4: Twinning with Frnce (erly vs lte twinners) (erly) Vribles (1) Twinning mt 0.398*** (0.0385) reference yer 1960 reference yer 1970 (lte) (2) (0.0535) (erly) (3) 0.31*** (0.0430) (lte) (4) (0.0916) Twinning mt Frnce mt 0.878*** (0.0869) 0.240** (0.104) 0.601*** (0.0586) (0.193) Yer effects yes yes yes yes Loction fixed effects yes yes yes yes Observtions 11,191 11,191 11,191 11,191 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; the effects re lrger mong the erlier twinners. 109

121 Tble 5A.5: Twinning with Frnce, IV estimtes (erly vs lte twinners) Vribles reference yer 1960 (erly) (1) (lte) (2) reference yer 1970 (erly) (3) (lte) (4) (erly) (5) reference yer 1960 (lte) (6) reference yer 1970 (erly) (7) (lte) (8) Twinning mt 1.240*** (0.177) 1.83*** (0.292) 0.855*** (0.114) 4.67*** (1.372) 1.319*** (0.186) 1.973*** (0.310) 0.870*** (0.116) 11.41*** (4.213) Twinning mt Frnce mt 3.319*** (0.499) 4.609*** (0.711) 2.105*** (0.295) 14.15*** (4.094) 3.547*** (0.524) 4.953*** (0.756) 2.148*** (0.301) 34.30*** (12.58) Instruments c c c c Yer effects yes yes yes yes yes yes yes yes Loction effects yes yes yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Srgn score (p-vlue) Bsmnn score (p-vlue) 7.34 (0.062) 2.69 (0.441) 2.70 (0.441) (0.002) 5.13 (0.024) 0.02 (0.890) 7.32 (0.063) 2.68 (0.443) 2.69 (0.443) (0.002) 5.11 (0.024) 0.02 (0.890) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < (0.154) (0.156) 0.27 (0.600) 0.28 (0.600) Tble 5A.6: Twinning with the EC nd EU countries, (whole smple) Vribles (dum=1) (1) Twinning mt 0.44*** (0.0724) EC6 EC12 EU15 EU25 (inten=n) (2) 0.01*** ( ) (dum=1) (3) 0.47*** (0.0835) (inten=n) (4) 0.012*** ( ) (dum=1) (5) 0.48*** (0.0870) (inten=n) (6) 0.013*** ( ) (dum=1) (7) 0.53*** (0.0898) (inten=n) (8) 0.013*** ( ) Twinning mt EC(U) j 0.529*** (0.0653) *** (5.25e-05) 0.55*** (0.0858) *** (5.63e-05) 0.57*** (0.0920) *** (5.38e-05) 0.64*** (0.0969) ** (5.46e-05) Yer effects yes yes yes yes yes yes yes yes Loc. fixed effects yes yes yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; EC(U) j Є (EC6, EC12, EU15, EU25); dum = dummy; inten = intensity. Twinning with whole EC/EU s seen before seems to hve been winning over Germny (negtive effects). The sme holds in the next tbles. 110

122 Tble 5A.7: Twinning with the EC nd EU countries, IV estimtes (whole smple, IV c) Vribles (dum=1) (1) Twinning mt 5.14*** (1.023) EC6 EC12 EU15 EU25 (inten=n) (2) 0.529*** (0.146) (dum=1) (3) 17.92** (6.995) (inten=n) (4) 0.589*** (0.146) (dum=1) (5) 19.72** (8.076) (inten=n ) (6) 0.575*** (0.139) (dum=1) (7) 5.106*** (1.154) (inten=n) (8) 0.569*** (0.129) Twinning mt EC(U) j 7.268*** (1.463) 0.019*** ( ) 25.35** (9.852) 0.017*** ( ) 27.80** (11.34) 0.02*** ( ) 7.27*** (1.616) 0.013*** ( ) Instruments c c c c c c c c Yer effects yes yes yes yes yes yes yes yes Loction effects yes yes yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Srgn score (p-vlue) 7.86 (0.005) 1.80 (0.179) (0.952) 0.33 (0.566) (0.944) 0.77 (0.379) (0.000) 0.66 (0.418) Bsmnn score (p-vlue) 7.84 (0.005) 1.80 (0.180) (0.952) 0.33 (0.567) (0.944) 0.77 (0.380) (0.000) 0.65 (0.419) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; EC(U) j Є (EC6, EC12, EU15, EU25) Tble 5A.8: Twinning with the EC nd EU countries, IV estimtes (erly vs lte) Vribles (erly) (1) Twinning mt 2.364*** (0.715) EC6 EC12 EU15 (lte) (2) 0.402*** (0.0717) (erly) (3) 16.44*** (6.149) (lte) (4) 0.403*** (0.0730) (erly) (5) 19.72** (8.076) (lte) (6) 0.343*** (0.0718) Twinning mt EC(U) j 4.352*** (1.268) *** ( ) 23.34*** (8.687) *** ( ) 27.80** (11.34) 0.112*** (0.0171) Instruments c c c c c c Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 11,191 11,191 11,191 11,191 11,191 11,191 R-Squred Srgn score (p-vlue) Bsmnn score (p-vlue) (0.000) (0.092) (0.958) (0.071) (0.944) (0.000) (0.093) (0.958) (0.072) (0.944) Note: Stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; EC(U) j Є (EC6, EC12, EU15); erly(lte) = before(fter) joining EC6/EC12/EU15 (0.045) 4.00 (0.046) 111

123 Tble 5A.9: Twinning with neighboring countries (smll vs lrge) Vribles prtnerships + friendships prtnerships only friendships only (dum=1) (1) Twinning mt (0.216) Twinning mt Neighbor mt 0.322* (0.187) (inten=n) (2) *** ( ) *** ( ) (dum=1) (3) (inten=n) (4) Smll Municiplities (0.213) (0.179) *** ( ) *** ( ) (dum=1) (5) (0.0402) 0.294*** (0.0443) (inten=n) (6) 0.038*** (0.0141) 0.164*** (0.0255) Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 4,588 4,588 4,588 4,588 4,588 4,588 R-Squred Lrge municiplities Twinning mt 0.494*** (0.0568) *** ( ) 0.523*** (0.0585) *** ( ) (0.0322) 0.024*** ( ) Twinning mt Neighbor mt 0.869*** (0.0567) *** ( ) 0.895*** (0.0589) *** ( ) 0.201*** (0.0365) 0.118*** (0.0197) Yer effects yes yes yes yes yes yes Loction fixed effects yes yes yes yes yes yes Observtions 4,526 4,526 4,526 4,526 4,526 4,526 R-Squred Note: Robust stndrd errors in prentheses; *** p < 0.01; ** p < 0.05; * p < 0.1; twinning with neighbors hve positive nd significnt effects gin s opposed to whole EC/EU members in the bove tbles. Tble 5A.10: Correltions: twinning nd the instruments twinning twinning residentil buildings loss % rubble per cpit tons tx revenue loss % # of csulties by wr residentil buildings loss % *** rubble per cpit *** *** tx revenue loss % *** *** *** # of csulties by wr *** *** *** *** *** = significnce t 1% level 112

124 Tble 5A.11: Merging twinning nd popultion dt (1) Twinning dt: 2614 cities nd towns nd 610 of them involved in twinning ltest by 2007 City,town /yer Abtsgmünd Achberg Achern Adelberg Adelmnnsfelden Adelsdorf Adendorf Adenstedt Adlkofen Afflterbch Ahlen Ahorn Aich vorm Wld Aichch Aidenbch Aken (Elbe) Albbruck Albersdorf Albersweiler Zeulenrod Triebes Zeven Zierenberg Zirndorf Zittu Zornheim Zschopu Zülpich Zuzenhusen Zweibrücken Zwicku Zwieflten Zwingenberg Zwönitz (2) Popultion dt: 440 municiplities/ counties Munciplty,county(kreis)/yer Aken (Elbe) Achen, Stdt Ahrweiler Aichch-Friedberg Alb-Donu-Kreis Altenburger Lnd Altenkirchen (Westerwld) Altmrkkreis Slzwedel Altötting Alzey-Worms Amberg Amberg-Sulzbch Ammerlnd Wuppertl Würzburg (Lnd) Würzburg (Stdt) Zollernlbkreis Zweibrücken Zwicku

125 Tble 5A.11: continued. (3) # 1 nd #2 merged: one or more rows of twinning dt from #1 re dded nd mtched with dt in #2, resulting in: munciplty/county(kreis) popultion twinning Aken (Elbe) Achen, Stdt Ahrweiler Aichch-Friedberg Alb-Donu-Kreis Altenburger Lnd n Altenkirchen (Westerwld) Altmrkkreis Slzwedel Altötting Alzey-Worms Amberg Amberg-Sulzbch Ammerlnd n Wuppertl Würzburg (Lnd) Würzburg (Stdt) Zollernlbkreis Zweibrücken Zwicku n Zwickuer Lnd n

126 Chpter Six Long-run Effects of Improved Trnsporttion Links on Size of Dutch Cities Introduction Cities re prime loctions of economic ctivities nd hve become incresingly importnt to policy mkers nd reserchers. Different types of economic integrtion ffect the growth of cities by chnging their mrket ccess. In previous chpters, we nlyzed the effects of ntionl border integrtion nd integrtion through interntionl town twinning (TT) on cities popultion growth. There re lso other forms of integrtion. One of them is (improving) integrtion of cities nd regions through improved trnsporttion links. In this chpter, we look t this third type of integrtion nd its effects on cities growth. The economic wellbeing of popultion of city depends, mong other things, upon its own chrcteristics such s sector-structure, the popultion size, nd its skilled popultion (see Gleser et l., 1995). Moreover, it depends on the city s loction reltive to other cities nd trnsporttion routes. Economic ctivities tend to cluster in lrge urbn res due to positive gglomertion effects, which do not exist in smll towns. There re, however, other fctors or repulsion forces tht mke lrge cities less ttrctive nd my led to the spreding of economic ctivities. These include higher wges nd other production costs, higher living costs such s housing, nd congestion. The size of these economic ctivities cn be reflected in the size of cities. The size nd distribution of cities re determined by the reltive strength of such positive forces of ttrction to gglomerted loctions nd the repulsion forces (Krugmn, 1991, 1995; Fujit nd Mori, 2005; nd Fujit et l., 1999). Very high or very low trde costs fvor the dispersion of economic ctivities while gglomertion would emerge for intermedite vlues of these costs once the sptil mobility of workers is low (Fujit nd Thisse, 1996). Vrious nturl s well s policy induced interventions cn chnge the center of blnce between the two forces. Depending on the degree of the shift in the blnce, this my trigger reloction of economic ctivities with minly firms nd workers which, in turn, ffects the size of the cities. The outcomes re either further gglomertion or dispersion of economic ctivities. An exmple of such intervention is the construction of new or improving existing trnsporttion routes connecting cities. Such n investment reduces trnsporttion or trde costs between the cities or regions. 54 This chpter is bsed on joint work together with Gerrd Mrlet bsed on reserch in coopertion with the cities of Almere nd Lelystd. We thnk Gerhrd Dekker, Mrinne Huismn nd Hinne Pul Krolis of the Municiplity of Almere nd Robert Jn Moormn, Dick Everwijn, Peter Reinsch nd Jeroen Kruk of the municiplity of Lelystd for their contribution to this reserch project. 115

127 In this chpter, we use simultions pproch to nlyze the long-run effects of four trnsporttion projects in the Netherlnds using the New Economic Geogrphy (NEG) model bsed on Krugmn (1991), Helpmn (1998) nd Hnson (1998). We specificlly use the Core- Periphery (CP) model nd minly focus on its extension clled the Core-Periphery Congestion (CPC) model of the New Economic Geogrphy with interregionl fctor mobility by Krugmn (1991). Figure 6.1: Rndstd, the Netherlnds Source: dopted from We nlyze the long-run implictions of four rod nd rilwy projects tht re imed t improving trnsporttion between the lrge cities in the west of the Netherlnds clled Rndstd nd nerby smller municiplities in Flevolnd (e.g. the new towns Almere nd Lelystd). With the simultion nlysis, we try to nswer the following questions. Does this intervention led to reloction of firms nd workers into the municiplities ner the projects t the expense of the other municiplities? Do ll municiplities benefit from this intervention or do only lrge municiplities gin over smll ones in the vicinities of the projects? Does the intervention led to divergence or convergence between the lrge nd smll cities s well s between the municiplities in the Rndstd nd the cities outside? How do the effects differ cross municiplities of different sizes nd cross municiplities tht re t different distnces from the project loctions? The rest of this chpter is rrnged s follows. In Sections 6.2 nd 6.3, we discuss the NEG models tht we use in our nlysis. In Section 6.4, we discuss lterntive policy scenrios. We use four potentil policy interventions tht re imed t the reduction of trvel time nd the subsequent trnsporttion cost within the Rndstd re of the Netherlnds nd the trnsporttion routes connecting them with smller neighboring municiplities. The simultion results of the policy intervention re given in Section 6.5. Section 6.6 gives the summry nd conclusions. 116