The complex regional effects of macro-institutional change: evidence from EU enlargement over three decades

Mitze, Timo; Breidenbach, Philipp Article — Published Version The complex regional effects of macro-institutional change: evidence from EU enlargement over three decades Review of World Economics Provided in Cooperation with: Springer Nature Suggested Citation: Mitze, Timo; Breidenbach, Philipp (2024) : The complex regional effects of macro-institutional change: evidence from EU enlargement over three decades, Review of World Economics, ISSN 1610-2886, Springer, Berlin, Heidelberg, Vol. 160, Iss. 4, pp. 1443-1475, https://doi.org/10.1007/s10290-024-00528-6 This Version is available at: https://hdl.handle.net/10419/315080 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Review of World Economics (2024) 160:1443–1475 https://doi.org/10.1007/s10290-024-00528-6 ORIGINAL PAPER The complex regional effects ofmacro‑institutional change: evidence fromEU enlargement overthree decades TimoMitze1,2 · PhilippBreidenbach2 Accepted: 13 February 2024 / Published online: 3 May 2024 © The Author(s) 2024 Abstract The EU enlargement process has thrust EU internal border regions into the spotlight of the European single market. This study explores how this specific macroinstitutional change has impacted their socio-economic development. Tracking outcomes across four EU enlargement waves from 1986 to 2007, we identify integration effects across EU NUTS3 regions. Pooled over all waves and border regions, positive integration effects emerge for per capita GDP, labor productivity, patents per capita, and night light emissions in border regions compared to nonborder areas. These effects diminish with increasing spatial distance from the enlargement border. At a detailed level, structural heterogeneities become evident across enlargement waves and region types. Internal border regionsin established EU member countries benefit relatively in terms of GDP per capita and labor productivity but experience relative declines in employment rates and population. In contrast, border regions in new member countries, particularly during the 2004 and 2007 eastern enlargements, gain from deepening economic integration in terms of population and employment growth. Sector-specific estimations indicate postenlargement specialization of economic activities in border regions in line with standard trade theories. Keywords Economic integration· EU enlargement· Internal border regions· Regional development· Treatment effect estimation JEL Classification C23· F15· O47· R11 * Timo Mitze [email protected] 1 Department ofEconomics, University ofSouthern Denmark, Campusvej 55, DK-5230Odense, Denmark 2 RWI – Leibniz Institute forEconomic Research, Essen, Germany 1444 T.Mitze, P.Breidenbach 1 Introduction Regions located along the border of economically integrating countries are highly exposed to this macro-institutional change and we address the question if this exposure translates into specific integration effects for the regions’ socio-economic development path. As borders are a natural barrier to economic interaction (Capello etal., 2018a), the dismantling of border impediments through economic integration may, on the one hand, improve their market access by shifting border regions from the country’s periphery to the heart of the newly formed economic block (e.g., Percoco, 2015). While this argument speaks in terms of positive EU integration effects in border vis-à-vis non-border regions, there are also arguments for a relative weaker performance of border regions: The ‘path dependency’ or ‘lock in’ argument, for instance, states that when borders have pertained for a long time, border regions may have suffered from a gradual process of marginalization that deprives them of the absorptive capacities and scale effects needed to benefit from economic integration more than their better endowed, agglomerated non-border counterparts (Floerkemeier etal., 2021; Petrakos & Topaloglou, 2008). A significant growth premium in border regions would also be absent if trade costs were sufficiently low so that closer geographical proximity to new markets itself is not a decisive factor for reaping the benefits of open borders and economic integration as it is, for instance, predicted in Krugman (1991)-type core-periphery models. And finally, socio-economic development levels in countries on both sides of the integration border may differ in such a way that gains from economic integration in established (old) and new member states, i.e., effects stemming from widening and deepening EU integration, are unevenly distributed across border regions so that overall effects in border regions are difficult to measure. Thus, what seemed straightforward on a first glance, namely, to identify the treatment effects of economic integration for border regions along the integration border may, in fact, be quite complex and subject to structural, temporal, and spatial heterogeneities. In this paper, we take this ‘complexity’ perspective as starting point for an in-depth study of the integration effects associated with four consecutive enlargement waves of the European Union between 1986 and 2007. Essential research questions are: 1. Do we find evidence for common integration effects of EU enlargement for internal border regions in terms of key socio-economic outcomes such as GDP, employment, and population growth? 2. What types of structural, temporal, and spatial heterogeneity determine the direction, magnitude, and duration of integration effects across enlargement waves and sub-groups of border regions considered? Providing answers to both research questions shall help policy makers to better assess the benefits and costs associated with EU economic integration in the context of regional commonalities but also heterogeneities. When it comes to the selection of EU internal border regions as treatment group in our empirical investigation, we argue that this focus is well deserved out of 1445 The complex regional effects ofmacro‑institutional change:… significance and relevance considerations. First, as stated by the EU Commission (2017), border regions account for approximately one third of EU population and a similar aggregate production share in the EU.1 At the same time, border regions, on average, have a weaker economic performance, lower levels of labor market integration and public service provision compared to non-border regions in the EU Commission (2017), which is why EU regional policy supports the socio-economic development in border regions through different funding programmes (most notably through Interreg project funding and the b-solutions initiative, see EU Commission (2021). Second, beyond their status as being a specific (disadvantaged) regional group within the wider internal economic geography of the EU, the EU Commission sees border regions as important “living labs of European integration” (EU Commission, 2021). The idea of living labs is that they allow to study integration effects in border regions under the magnifying glass and that findings obtained here provide important general insights on the overall progress of EU integration and cooperation at large. Extending this logic, we argue that EU internal border regions are particularly well-suited to investigate the economic returns to EU economic integration at the regional level as they have been particularly exposed to associated shifts in the EU’s internal economic geography, while the enlargement process itself can be seen as an exogenous source of variation to their development path. The exogeneity of the enlargement ‘shock’ at the small-scale regional level can be motivated by the fact that political decisions for EU enlargement were made at the national and supra-national level with goals not specifically tailored to the needs and economic conditions of border regions. The same logic applies to the allocation of the bulk of EU regional funding volumes, which focus on the regions’ development status irrespective of their geographical location within a country (Breidenbach et al., 2019), so that EU regional funding alone cannot explain a potential growth premium associated with economic integration in border regions. We accordingly argue that the focus on border regions enables us to study the effects of EU integration in a quasi-experimental manner. Prior empirical evidence on a potential growth and development premium associated with EU accession and the economic freedoms associated with the European single market has remained inconclusive. While, e.g., Campos etal. (2019) report significant positive income growth effects of EU membership at the country level (with few exceptions), Andersen etal. (2019) generally do not find evidence for an EU membership growth premium. With respect to the focus of this study, there is also a knowledge gap on how the potential gains from economic integration are distributed across the different regions within integrating countries (Niebuhr & Stiller, 2004, Braakmann and Vogel, 2010, and Heider, 2019). It is generally supposed that regional ability to reap welfare gains from EU integration chiefly depend on a region’s relative competitiveness driven by industry composition and settlement structure, its institutional setup, trade intensity as well as size and geographical 1 Based on a definition of internal border regions of the EU-28 (including the UK) and also including EU border regions to EEA countries (see EU Commission, 2017). 1446 T.Mitze, P.Breidenbach proximity to the enlargement border (e.g., Brakman et al., 2012; Brülhart et al., 2012, 2018; McCallum, 1995). While earlier studies have mainly focused on GDP growth as sole outcome variable, a novelty of our analysis is that we conduct a comprehensive empirical analysis of the complex border regional effects associated with the EU enlargement process during the 1980s, 1990s and 2000s. Specifically, we employ a broad set of outcome variables covering region-specific time patterns of per capita GDP, (sectoral) labor productivity, research and development (R&D) and innovation activity, employment, population development, night light emissions. As there is no information on the stock of public (and also private) infrastructure at the level of NUTS3 regions (the observational unit used in this paper), night light data fill an important gap. An increase in night light emissions reflects changes of the public infrastructure (such as streets or public buildings) but also changes of private activities such as housing stocks or firm density/activity.2 It can thus be regarded as a general measure for agglomeration trends and has previously been used to map the development of population and firm density across regions (e.g., Mellander etal., 2015). Also, prior empirical analyses have used night light data to measure processes of economic integration, growth, and convergence, especially when other economic data are missing (see, e.g., Henderson etal., 2012; Galimberti, 2020). But even for geographical areas with fairly good data provision, Mellander etal. (2015) as well as Lessmann and Seidel (2017) have shown that night light data still delivers important insights on economic development trends and differences. Four EU enlargement periods are covered in our analysis: First, the EU accession of Spain and Portugal in 1986 (third enlargement wave); second, the EU membership of Austria, Sweden, and Finland in 1995 (fourth enlargement wave); third, the so-far largest EU enlargement of mostly central and eastern European countries in 2004 (fifth enlargement wave); and fourth, the accession of Romania and Bulgaria to the EU in 2007 (sixth enlargement wave). To identify treatment effects of EU integration in border regions vis-à-vis non-border regions during these enlargement periods, we conduct an analysis for the 1289 NUTS3 regions of the EU-27 (including the UK but not Croatia) over the time period 1981–2014. We apply static and dynamic difference-in-difference (DiD) estimation to identify pooled and group-specific effects. The DiD approach has shown a high degree of flexibility and robustness when previously been applied to spatio-temporal analyses of border regional growth effects such as for the division and reunification of Germany (Redding & Sturm, 2008) and economic transformations after the fall of the iron curtain (Brülhart etal., 2012, 2018) among other applications. In the estimations we particularly account for the fact that the distribution of integration effects may be fuzzy with regard to spatial and temporal aspects. As such, we explicitly control for the circumstance that EU enlargement cannot be treated 2 The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU. The NUTS regulation mirrors the territorial administrative division of the EU member states and defines minimum and maximum population thresholds for the size of regions. At the NUTS Level 3 regions have a population size of between 150,000 and 800,000 inhabitants (for details see https:// ec. europa. eu/ euros tat/ web/ nuts/ princ iples). 1447 The complex regional effects ofmacro‑institutional change:… as precisely timed event. For example, in 2004, EU accession followed a process covering early agreements between old and new EU member states initiated in the aftermath of the collapse of the Soviet system (Dangerfield, 2006). This potentially results in ‘early anticipation’ effects that weaken the power of static DiD estimations, which rely on a precise classification of a single pre- and post-treatment period.3 To account for these methodical challenges, we apply a flexible DiD approach that estimates time-heterogeneous coefficients for the different stages around the timing of EU enlargement. In addition, we account other confounding factors, which may either affect all EU countries equally, such as the deepening of economic integration through the EU single market in 1992, or is confined to individual countries and country groups, such as the introduction of the Euro currency in 1999, by adding a multidimensional ‘fixed effects’ structure to our DiD specifications. We also run several robustness tests to see if the obtained results hold to variations in the data and regression specification. The remainder of this paper is organized as follows: Sect. 2 outlines the underlying theory related to border regional growth effects of economic integration. This section also summarizes prior empirical findings for the economic effects of EU enlargement and identifies research gaps in the literature. Section3 describes our empirical study design, which is followed by a description of the data and variables used in Sect.4. Section5 reports our empirical results for pooled and heterogeneous treatment effects of EU economic integration together with a series of robustness tests. Finally, Sect.6 discusses policy implications and concludes the paper. 2 Border regional effects ofeconomic integration: theory, evidence andgaps 2.1 A complexity perspective ofeconomic integration Models of regional growth, international trade, and economic geography stress the role of trade related to market size, market access and transport cost for regional development (e.g., Krugman & Venables, 1990; Percoco, 2015). It can be conjectured that border regions gain from EU enlargement due to their unique geographic location and the associated improvement of market access. These effects may, however, be partly or fully offset by sustaining border impediments, lacking absorptive capacities and this insufficient scale economies in border regions, which may lock regions in a peripheral position (Capello etal., 2018a; Petrakos & Topaloglou, 2008). Our conceptual approach, which takes these opposing factors into account starts with a fairly general specification of a regional production function defined as Y=A(K𝛼L𝛽 𝐍 φ) , where Y is a measure of regional output (typically GDP or GVA), 3 A similar problem would arise if effects only gradually ‘phase in’ over time because of institutional arrangements such as the 2 + 3 + 2 regulation, which allowed established member countries to temporarily protect their labor markets from free labor mobility associated with EU accession of new members. According to the 2 + 3 + 2 regulation the maximum protection period amounted to seven years (for details see https:// www. eurof ound. europa. eu/ en/ europ eanindus trialrelat ionsdicti onary/ mobil ityworke rs). 1448 T.Mitze, P.Breidenbach A is technology, K is capital, L denotes labor input and 𝐍 is a vector of further inputs; α, 𝛽 and φ are the respective output elasticities. If we write this regional production function as growth specification in intensive form, we get where Δyit is as measure for per worker (or per capita) output growth for region i at time t, y=Y∕L , k=K∕L and similar for the remaining inputs ( nr=Nr∕L ). In an earlier analysis with national data for the EU-15, Badinger (2005) has focused on two potential channels how economic integration affects Δyit as: i) a technology channel ( ΔA it =𝛾 A0 +𝛾 A1 ΔINT it) and ii) a physical investment channel ( Δk it =𝛾 k0 +𝛾 k1 ΔINT it) with Δ INT being an indicator for changes in the level of integration at time t; 𝛾A0 and 𝛾k0 are exogenous components of technological progress and capital formation, respectively. This logic can be straightforwardly extended to the integration effects of other inputs such as for input r as ( Δn r,it =γ r,n0 +γ r,n1 ΔINT it) and we can measure the relative performance of border regions for these inputs separately. Alternatively, the input channels can be aggregated to an overall effect of economic integration on per capita income growth as with 𝛿0 = � 𝛾 A0 +𝛼𝛾 k0 + ∑r r=1 𝜑𝛾 r,n0� and 𝛿 1= � 𝛾A1+𝛼𝛾k1+ ∑ R r=1𝜑𝛾r,n1 � . Given our focus on border regions, Eq.(2) can be extended by incorporating a spatial component into the analysis of growth effects from economic integration as where ( 1 DIST𝜃 i) measures proximity for each region i to the newly integrated unit (with DISTi being some distance measure to the integration border or a specific point of interest across the border). Equation(3) thus splits the growth effects of integration 𝛿1 into a general non-spatial component 𝜌1 and a growth premium for regions with closer proximity to the border ( 𝜌2 ) with 𝛿1=𝜌1+𝜌2 . While distance/ proximity can be measured in different dimensions (Boschma, 2005), we refine to geographical distance as a catch-all term for other forms such as cultural, social, and historical proximity. This extension reflects that benefits from economic integration do not affect each region equally but predicts that regions closer to the integrated market receive larger benefits as typically found in gravity-type models of interregional trade such as in McCallum (1995).4 The parameter θ shown in Eq. (3) expresses the power of distance decay. For instance, for sufficiently high values of θ, (1) Δ yit =ΔAit +𝛼Δkit + R ∑ r=1 𝜑rΔnl, it (2) Δyit =𝛿 0 +𝛿 1 ΔINTit (3) Δ yit =𝛿0+ ( 𝜌1+𝜌2 ( 1 DIST𝜃 i)) ΔINT it 4 Referring to the argument of intensified inter-regional trade after EU enlargement, FigureA1 in the Supplementary Online Materials provides an overview of trade flows between German NUTS1 regions and their two neighboring Eastern countries (Poland and Czechia). What can be seen is that those Ger- 1449 The complex regional effects ofmacro‑institutional change:… we expect to only observe a spatial growth premium for regions directly adjacent to the enlargement border. Ways to empirically proxy the spatial proximity to the enlargement border will be presented below. The role of distance decay as a factor determining trade cost and eventually output effects from economic integration is also stressed in models of the New Economic Geography (NEG). Krugman and Venables (1990), for instance, show for an NEG model application to the EU single market in 1992 that with reduced transport costs more firms may find it attractive to relocate to the periphery as a way take advantage of factor price differentials between countries. Other NEG models similarly predict that regions with a lower distance and thus transport cost to international markets reap the largest benefits from economic integration (Brülhart etal., 2004; Crozet & Koenig, 2004). Behrens et al. (2007) and Monfort and Nicolini (2000) show in NEG model settings that a country’s internal economic geography constitutes a significant conditioning factor for the regional economic effects of international economic integration. For instance, Rauch (1991) presents a model in which costal border regions are the main trade hub of a country. In this case, border regions can particularly benefit from trade integration. Overman and Winters (2006), study the impact of UK accession to the larger European market and find evidence for this setup indicating that coastal (border) regions hosting a port with better market access for exports and intermediate inputs experience higher employment compared to other similar regions. If border regions suffer from locational disadvantages, model predictions may differ, though. Without scale effects emanating from locational advantages, consumers typically have to pay higher prices and firms can only supply goods to the market at higher cost when being located in a border region (Niebuhr & Stiller, 2004). Increased proximity to foreign markets of integrating countries then only allows border regions to grow faster than non-border regions if they possess specific territorial assets (Capello et al., 2018a). If such assets are missing, there is the risk of a ‘tunnel effect’, i.e., a bypassing of border regions after integration, which could further marginalize border regions if trade patterns after EU enlargement are dominated by central core regions (Petrakos & Topaloglou, 2008). In this case, 𝜌2 can be expected to be zero or even negative. 2.2 Prior empirical evidence andremaining research gaps Several empirical contributions have been concerned with the identification of growth effects of economic integration – predominately at the national level (e.g., man NUTS1 regions located in geographical vicinity to the enlargement border experienced a much stronger export growth to Poland and Czech Republic after 2004 than other German regions. In line with this stylized finding and with regard to the expected economic effects of EU integration for border regions, it can be hypothesized that improved cross-border exchange increases the regions’ potential for economic development (see also EU commission, 2001; Brülhart etal., 2004; Hanson, 2005; Brülhart, 2011). Footnote 4 (continued) 1450 T.Mitze, P.Breidenbach Andersen etal., 2019; Badinger, 2005; Campos etal., 2019; Henrekson etal., 1997). Bridging the gap between the available national and scarce regional-level evidence, Monastiriotis etal. (2017) analyze the spatial effects of EU integration for Central and Eastern European (CEE) regions. Using an event-study approach, the authors find that the process of EU accession has particularly strengthened agglomeration forces in CEE countries favoring regions with a high market potential, industry concentration and regional specialization in increasing returns sectors.5 Brülhart etal. (2012) and Brülhart etal. (2018) analyze the wage and employment effects of trade liberalization caused by the fall of the iron curtain for Austrian border towns. Their empirical results indicate that improved access to Eastern markets has a positive impact on employment and nominal wages in these regions vis-à-vis the rest of the country. The results in Brülhart et al. (2018) additionally suggest that larger cities benefit more strongly from the border shock in terms of wages, whereas smaller cities experience larger employment effects with a peak for towns with a population of around 150,000. Taken together, their evidence suggests that residents of medium-sized towns gain the most from a given opening of cross-border trade. Brakman etal. (2012) focus on the population effects of EU integration in EU border regions. Analyzing data for 1457 regions and 2410 cities since 1973, the authors find evidence for positive population growth effects in border regions visà-vis non-border regions. This effect is significant at the regional and urban level within a 70km radius from national borders. It holds for both sides of the integration border. Relatedly, Heider (2019) focusses on the population growth effects of German and Polish border town in the course of the EU enlargement in 2004. The author finds evidence for positive population growth effects for German but not for Polish border towns. While the majority of studies thus reports positive population and economic effects of trade liberalization and economic integration in border regions of the EU (particularly in the EU15), there is also empirical evidence for insignificant or negative effects as, for instance, reported in Braakmann and Vogel (2011) or Marin (2011). Using data for firms located in East Germany close to Germany’s eastern border, Braakmann and Vogel (2011) find no short-run employment effects of the EU enlargement in 2004 except for firms active in wholesale and retail trade, hotels, and restaurants. Negative wage effects are found for skilled workers in consulting, research, and related activities. This points to sector-specific effects in border regions subject to EU enlargement. Studying employment growth from the perspective of firms in Central and Eastern European Countries (CEECs), Serwicka etal. (2022) find a significant increase in foreign investment and employment growth after the 2004 EU enlargement. While the prior literature has started to shed light on regional effects of EU integration for selected outcomes, mainly income levels and individual enlargement 5 Niebuhr (2008) adds to this finding by studying the income effects of EU enlargement in 2004 using a three-region economic geography model calibrated with pre-accession data for 1995–2000. The simulation results indicate that border regions realize higher integration benefits than non-border regions with the strongest effects found for Central and Eastern European (CEE) regions along the former external EU15 border. 1457 The complex regional effects ofmacro‑institutional change:… Table 1 Definitions and summary statistics for variables used in the empirical analysis for 1289 NUTS3 periods during 1981–2014 Variable Description Mean S.D Min Max GDPpc Per capita GDP (in 1000 Euro, in 2005 prices) 20.114 11.070 1.083 188.679 Yprod Labor productivity defined as gross value added (GVA) per employee (in 1000 Euro) 40.974 17.292 1.238 310.492 Yprod (agriculture) > Agriculture (NACE Rev. 2, Sector code A) 22.777 35.174 0.002 2048.154 Yprod (construction) > Construction sector (F) 37.553 18.350 0.209 420.587 Yprod (industry excl. construction) > Industry excl. construction (B-E) 50.004 37.242 0.538 1157.735 Yprod (WR services, I&C) > Wholesale, retail, transport, accommodation & food services, information, and communication (G-J) 35.210 15.872 1.044 299.409 Yprod (financial & business services) > Financial & business services (K-N) 94.973 62.386 1.094 2028.800 Yprod (non-market services) > Non-market services (O-U) 33.515 14.325 0.485 287.712 Patent Patent applications per 1000 inhabitants in region 0.081 0.225 0 5.729 Emprate Employment per population in region (1 = 100%) 0.433 0.108 0.121 1.224 Emprate (agriculture) > Agriculture (NACE rev. 2, sector code A) 0.037 0.054 0 0.816 Emprate (construction) > Construction sector (F) 0.033 0.013 0 0.188 Emprate (industry w/o. Construction) > Industry excl. construction (B-E) 0.092 0.052 0.001 0.539 Emprate (WR services, I&C) > Wholesale, retail, transport, accommodation & food services, information, and communication (G-J) 0.108 0.042 0.007 0.362 Emprate (financial & business services) > Financial & business services (K-N) 0.046 0.033 0.000 0.397 Emprate (non-market services) > Non-market services (O-U) 0.118 0.048 0.006 0.417 Empshare (agriculture) Sectoral employment share for agriculture in total regional employment (1 = 100%) 0.090 0.116 0 0.920 Empshare (construction) Sectoral employment share for construction in total regional employment (1 = 100%) 0.077 0.032 0 0.442 Empshare (industry excl. Construction) Sectoral employment share for industry excl. construction (manufacturing sectors) in total regional employment (1 = 100%) 0.212 0.100 0.002 0.799 Empshare (WR services, I&C) Sectoral employment share for wholesale and retail services, information, and communication in total regional employment (1 = 100%) 0.249 0.063 0.017 0.610 Empshare (financial & business services) Sectoral employment share for financial and business services in total regional employment (1 = 100%) 0.101 0.054 0.000 0.726 1458 T.Mitze, P.Breidenbach Table 1 (continued) Variable Description Mean S.D Min Max Empshare (non-market services) Sectoral employment share for non-market services in total regional employment (1 = 100%) 0.272 0.081 0.022 0.644 Pop Population level of NUTS3 region (in 1000 persons) 370.476 428.355 6.748 6418.41 Nlight Night light emission level per NUTS3 region 17.251 15.536 0 63 Border regions (EU enlargement 1986) Binary dummy for direct border regions of EU enlargement in 1986 (see Fig.1) 0.008 0.088 0 1 Border regions (EU enlargement 1995) Binary dummy for direct border regions of EU enlargement in 1995 (see Fig.1) 0.026 0.160 0 1 Border regions (EU enlargement 2004) Binary dummy for direct border regions of EU enlargement in 2004 (see Fig.1) 0.046 0.209 0 1 Border regions (EU enlargement 2007) Binary dummy for direct border regions of EU enlargement in 2007 (see Fig.1) 0.007 0.083 0 1 Data at the level of NUTS3 regions; all data (except for night light emissions and patent applications) have been gathered from the European regional database (ERD) of Cambridge Econometrics (version 2017). Night light emissions have been extracted from: https:// light trends. light pollu tionm ap. info; patents extracted from the OECD RegPat database, see Maraut etal. (2008) 1459 The complex regional effects ofmacro‑institutional change:… Non-border regions Indirect [300km] Indirect [250km] Indirect [200km] Indirect [150km] Indirect [100km] Direct border regions Fig. 3 Direct and indirect border regions for EU enlargement 1986, 1995, 2004 and 2007. Information on the territorial borders of EU-27 (including UK, without Croatia) NUTS3 regions has been obtained from the GISCO statistical unit dataset available at: https:// ec. europa. eu/ euros tat/ web/ gisco/ geoda ta/ refer encedata/ admin istra tiveunits- stati sticalunits/ nuts. Maps for border regions by EU enlargement wave and region type (established vs new) are provided in FigureA2 in the appendix Given the temporal distribution of EU enlargement events throughout our sample period 1981–2014, we can estimate dynamic treatment effects for a maximum of five years prior to and seven years after the institutional changes for all four EU accession waves (except for night light emissions, which is only available from 1992 1460 T.Mitze, P.Breidenbach onwards).10 While it would be preferable to extend the data to periods beyond 2014 and also include Croatia’s EU accession, there are also reasons to restrict the sample to 2014. Particularly, the EU migration crisis of 2015 and 2016 with substantial migration flows to border regions of several EU countries may bias at least all outcomes on “per capita” levels. To measure the degree of spatial heterogeneity and neighborhood effects, we define indirect border regions based on their geographical distance from the border. To do so, we calculate for all regions not classified as direct border regions the geographical distance from the region’s centroid to the closest location at the border. Using 50km threshold distances g with k = {100km, 150km, …, 300km}, we then build additional treatment group dummies for regions within these 50km distance belts from the border and test for spatially distributed integration effects (with k = 0km being direct border regions along the integration border).11 A graphical overview of direct and indirect border regions for our sample of 1289 NUTS3 regions for all four EU enlargement waves is given in Fig.3. 5 Empirical results 5.1 Baseline estimates Table2 reports the estimation output for our pooled static DiD specification according to Eq.(4). Accounting for the multidimensional ‘fixed effects’ structure including country-specific time trends as most general specification to account for latent timevarying confounding factors, three significant findings emerge: First, border regions relatively increase their labor productivity relative to non-border regions after treatment (Panel A) and have higher levels of night light emissions (Panel E) as a general measure for agglomeration effects (Mellander etal., 2015). While the specifications shown in columns (I) and (II) thereby use the average development of non-border regions in the sample as benchmark, the inclusion of country-specific time trends tightens the benchmark to non-border regions in the respective country of border regions considered. Effect size points to a roughly 4–5% relative increase in the levels of labor productivity and night light emissions. In terms of labor productivity, this corresponds to an approx. increase of 1500–2000 Euro per worker increase evaluated at the sample average for labor productivity of 40,800 Euro per worker in non-treated regions. Since night light emissions levels are measured on a relative scale between 0 and 63, the above reported percentage increase is difficult to interpret. However, we can illustrate its magnitude with the help of an example. Evaluated against the sample average of night light emissions in non-treated regions of about 20, a 4–5% increase means an additional night 10 For this reason, we also exclude the 1981 EU accession of Greece as additional treatment. Besides, by the time of EU accession, Greece did not share any territorial border with an established EU country so that no treatment group can be identified here. 11 All distances are calculated based on the regions’ centroids. We merge the first two slices of 50km and 100km distances as there are few indirect border regions with a maximum distance of 50km to the enlargement border. 1461 The complex regional effects ofmacro‑institutional change:… light emission level of 0.8–1. The latter corresponds to an accumulation of night light intensities for growing metropolitan regions such as Madrid and Hamburg of about Table 2 Baseline treatment effects of EU enlargement for direct border regions ***,**,* = denote significance at the 1%, 5% and 10% critical level; robust standard errors clustered at the regional level are given in brackets. Sample period 1981–2014; 1289 NUTS3 regions. See Table 1 for details on outcome variable definitions. Variables are log transformed; in the case of patents per capita a box-cox transformation has been applied as log transformation did not meet the normality assumption. The difference in observations between columns (I), (II) and column (III) is due to missing values for regional controls (sectoral employment shares) Specification (I) (II) (III) Panel A: GDP per capita EU enlargement 0.0475*** (0.01215) 0.0313** (0.01274) 0.0258** (0.01280) R20.65 0.78 0.79 Obs 42,012 42,012 41,974 Panel B: labor productivity EU enlargement 0.0719*** (0.01330) 0.0530*** (0.01350) 0.0492*** (0.01256) R20.52 0.72 0.74 Obs 42,012 42,012 41,974 Panel C: patents per capita EU enlargement 0.0281*** (0.00552) 0.0115** (0.00537) 0.0108** (0.00539) R20.32 0.37 0.37 Obs 42,012 42,012 41,974 Panel D: employment rate EU enlargement − 0.0252** (0.00998) − 0.0268*** (0.00979) –0.0277*** (0.00948) R20.15 0.38 0.46 Obs 42,012 42,012 41,974 Panel E: population EU enlargement − 0.0086 (0.00822) − 0.0012 (0.00845) − 0.0064 (0.00784) R20.18 0.41 0.43 Obs 42,012 42,012 41,974 Panel F: night light emissions EU enlargement 0.0804*** (0.01620) 0.0474*** (0.01247) 0.0427*** (0.01203) R20.51 0.80 0.81 Obs 27,065 27,065 27,041 Region FE Yes Yes Yes Time FE Yes Yes Yes Time × Ctry FE No Yes Yes Regional controls No No Yes 1462 T.Mitze, P.Breidenbach 5–7years. Panel C also reports an increase in regional patent applications per capita following EU enlargement, which points to the working of the technology channel ΔAit of economic integration as outlined in Eq.(1). However, the development of the employment rate falls behind the overall EU-trend during the sample period by around 3%-points (for an average employment rate of approx. 43% in our data sample). No significant effects are observed for regional population development. 5.2 Structural heterogeneity Differences in treatment effects may be driven by structural heterogeneity across the group of border regions and underlying compositional effects associated with region-sector combinations, which may not be fully captured by our set of regional controls (sectoral employment shares). To gain a deeper understanding of the underlying mechanisms at play, we disaggregate effects by enlargement waves (Panel A of Table3) and country groups, that is, we distinguish between effects for border regions in old (established) and new member states for each EU enlargement wave (Panel B of Table3) as outlined in Fig.1. Especially the enlargement waves in 2004 and 2007 saw larger structural differences between established EU member countries and CEECs in their transition from planned to market economies after the fall of the iron curtain. This meant that per capita income levels, labor productivity and labor market parameters were significantly different in established EU member countries and newly joining CEECs in 2004 and 2007. As Panel A shows, the estimated positive treatment effects for labor productivity and night light emissions are mainly driven by the 1995 and 2004 enlargement waves. Negative effects on the employment rate are similarly found for the 1995 and 2004 enlargement waves in particular. The latter also show a decline in population development and regional innovativeness in border regions. A positive development in terms of regional innovativeness measured through patents per capita is found for the 1986 enlargement wave. Panel B of Table3 further indicates that estimated treatment effects not only differ across enlargement waves but are also determined by the regions’ development level and, hence, the region’s absorptive capacity at the timing of integration. Positive productivity effects of the 1995 and 2004 enlargement wave are captured by border regions in established EU countries. For these regions the 1995 enlargement also induced general agglomeration effects measured in terms of a positive population development and increases in night light emissions relative to non-border regions. This effect is less significant for border regions in established EU member countries during the fifth enlargement wave in 2004. Relative population levels are observed to decline in the process of EU integration. On the other hand, border regions in new EU member states grow in terms of innovativeness (1986) and general agglomeration effects (night light emissions) in 2004 together with a strong increase in the employment rate by approximately 17% in 2007. The latter effect is likely driven by persistent wage differences between established EU countries (Greece) and the new member states Bulgaria and Romania who joint in 2007. Only during the 1986 EU enlargement do border regions in new member countries see a relative productivity increase and a improvements in innovativeness. 1463 The complex regional effects ofmacro‑institutional change:… Table 3 Treatment effects for different EU enlargement waves and country groups Specification (I) (II) (III) (IV) (V) (VI) Outcome GDP per capita Labor productivity Patents per capita Employment rate Population Night light emissions Panel A: symmetric effects across border regions in established and new EU member states Border region × 0.0298 0.0326 0.0515*** 0.0030 0.0091 n.a Enlargement 1986 (0.01856) (0.02164) (0.01585) (0.01378) (0.03198) Border region × 0.0324 0.0532** 0.0079 –0.0309** 0.0371*** 0.0867*** Enlargement 1995 (0.02370) (0.02201) (0.0092) (0.01235) (0.00950) (0.02896) Border region × 0.0207 0.0469*** 0.0112 − 0.0274*** − 0.0409*** 0.0326** Enlargement 2004 (0.01593) (0.01300) (0.00825) (0.01046) (0.00956) (0.01344) Border region × − 0.0319 − 0.0049 − 0.0215** − 0.0331 − 0.0084 0.0250 Enlargement 2007 (0.03533) (0.01310) (0.01033) (0.07558) (0.02406) (0.03904) R20.79 0.74 0.37 0.46 0.44 0.81 Obs 41,974 41,974 41,974 41,974 41,974 27,041 Panel B: asymmetric effects across border regions in established and new EU member states Border region (old) × 0.0379 0.0031 0.0140 0.0134 0.0344 n.a Enlargement 1986 (0.03066) (0.02905) (0.01384) (0.01176) (0.04181) Border region (old) × 0.0764** 0.0978*** 0.0109 − 0.0268* 0.0308** 0.0969*** Enlargement 1995 (0.03176) (0.02774) (0.01045) (0.01538) (0.01248) (0.03437) Border region (old) × 0.0370* 0.0543*** 0.0166* − 0.0175 − 0.0582*** 0.0216 Enlargement 2004 (0.01944) (0.01612) (0.01002) (0.01142) (0.01218) (0.01592) Border region (old) × − 0.0451 0.1085 − 0.0229 − 0.1778** − 0.0049 − 0.0216 Enlargement 2007 (0.05206) (0.10202) (0.01648) (0.08244) (0.02981) (0.04854) Border region (new) × 0.0214 0.0633** 0.0904*** − 0.0078 − 0.0171 n.a Enlargement 1986 (0.01993) (0.02683) (0.01737) (0.02473) (0.04609) Border region (new) × − 0.0526*** − 0.0345 0.0023 − 0.0371* 0.0487*** 0.0666 Enlargement 1995 (0.02037) (0.02488) (0.01754) (0.01998) (0.01268) (0.05150) 1464 T.Mitze, P.Breidenbach ***,**,* = denote significance at the 1%, 5% and 10% critical level; robust standard errors clustered at the regional level are given in brackets. Sample period 1981–2014; 1289 NUTS3 regions. See Tables1, 2 for further details on the definition of outcome variables. The terms (old) and (new) refer to border regions in established and new EU states, respectively Table 3 (continued) Specification (I) (II) (III) (IV) (V) (VI) Outcome GDP per capita Labor productivity Patents per capita Employment rate Population Night light emissions Border region (new) × − 0.0341 0.0141 − 0.0033 − 0.0533** 0.0038 0.0558** Enlargement 2004 (0.02425) (0.02179) (0.01275) (0.02082) (0.00735) (0.02381) Border region (new) × − 0.013 − 0.1738*** − 0.0193** 0.1816*** − 0.014 0.0865 Enlargement 2007 (0.04175) (0.06490) (0.00792) (0.05834) (0.03987) (0.05343) R20.79 0.74 0.37 0.46 0.44 0.81 Obs 41,974 41,974 41,974 41,974 41,974 27,041 Region FE Yes Yes Yes Yes Yes Yes Time FE Yes Yes Yes Yes Yes Yes Time FE × Ctry FE Yes Yes Yes Yes Yes Yes Regional controls Yes Yes Yes Yes Yes Yes 1465 The complex regional effects ofmacro‑institutional change:… 5.3 Temporal heterogeneity Static treatment regressions may be biased if estimated effects show significant patterns of early anticipation or gradual phasing-in. Figure4 therefore plots the results of a flexible DiD approach, which estimates yearly treatment effects relative to the timing of EU enlargement. The pre-enlargement year (t − 1) is used a reference year against which pre- and post-enlargement effects are evaluated. The results largely confirm the static treatment effect estimates in terms of positive and significant effects for labor productivity and night light emissions. In addition, Panel A of Fig.4 also reports a positive and significant relative GDP per capita development in EU internal border vis-à-vis non-border regions. Maximum effect size for the 7-year lag period considered is an GDP per capita increase of about 2% (compared to 2.6% in the baseline static estimation approach). Annual treatment effects for labor productivity levels are found to range between 2 and 4% during the first seven years after EU enlargement. The temporal distribution of GDP and productivity effects point at a levelling out of additional growth effects after approximately 5–7years, which supports the view of a medium-term growth bonus associated with EU integration (Baldwin & Wyplosz, 2015; in’t Veld, 2019). Annual treatment effects prior to EU enlargement are statistically insignificant and do not point to early anticipation effects associated with potential confounding factors around treatment start. Effects turn significant with a time lag of 4–5years after the enlargement event. This indicates that positive economic effects from economic integration need to unfold until they are fully visible in the regional economy. Likely reasons for this gradual phasing-in process are that associated private and public investment effects typically only show up over time (Breidenbach etal., 2019; Eberle etal., 2019). The difference between the statistically significant static estimation results for the development of patents per capita and the insignificant annual dynamic effects in the first seven years after enlargement underline the role of gradual phasing in effects and the time needed to transform regional innovation systems in treated regions (Isaksen & Trippl, 2016). But not only did technology transformation and the adaption of production systems takes time, also labor market opening after EU enlargement followed a gradual pattern, particularly for the EU eastern enlargement waves in 2004 and 2007, determined, for instance, by the 2 + 3 + 2 regulation that restricted employment access in (some) incumbent EU member states by citizens of new EU member after an up to seven years transition period.12 Also, the Schengen entry of new member countries followed EU accession with a temporal lag of about three years. Compared to the static baseline case, the flexible DiD estimates show negative, albeit marginally statistically insignificant effects of EU enlargement on the employment rate in internal border regions (Panel C, evaluated at 95% confidence intervals). Finally, the dynamic estimates confirm positive and statistically significant increases in night light activity as general agglomeration effect in border regions. 12 For the case of the largest enlargement wave in 2004, only two states (Germany and Austria) have utilized the full 7-year duration of the 2 + 3 + 2 rule. 1466 T.Mitze, P.Breidenbach Panel A: GDP per capita Panel B: Labor productivity Panel C: Patents per capita Panel D: Employment rate Panel E: Population level Panel F: Night light emissions Fig. 4 Dynamic treatment effects of EU enlargement on border regions. Diamonds show point estimates for annual treatment effects of EU enlargement in border regions together with 95% confidence intervals (vertical lines; based on robust standard errors clustered at the regional level). The vertical dashed line indicates the pre-enlargement year (t − 1) used a reference year against which pre- and post-enlargement effects are evaluated. Underlying flexible DiD estimates include region FE, year FE, country-year FE and regional controls. For further details see main text. Sample period 1981–2014; 1289 NUTS3 regions 1473 The complex regional effects ofmacro‑institutional change:… Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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