Economic Growth in European City Regions A New Turn for Peripheral Regions in CEE Member States After the EU Enlargements of 2004/2007?

Transcription

1 Economic Growth in European City Regions A New Turn for Peripheral Regions in CEE Member States After the EU Enlargements of /2007? SCORUS Conference A new urban agenda? Uwe Neumann, Rüdiger Budde, Christoph Ehlert Brussels, 16 June 2014

2 Contents 1. Issues/Literature Review 2. Data and empirical framework 3. Analysis 4. Robustness checks 5. Findings/Conclusions Uwe Neumann et al. 16 June

3 1. Issues/Literature Review Issues i. Has urban/regional economic growth accelerated in the countries joining the EU in and 2007? ii. iii. In particular, did the poorest regions begin to catch up and and did disparities within CEE countries begin to diminish? Should regional policy in Europe concentrate more on fostering the very poorest regions? 3

4 1. Issues/Literature Review Agglomeration or dispersion of economic activity? Convergence analysis: disparities tend to equalise: dispersion New Economic Geography: home market effect` depending on market size, transport costs: agglomeration/inequality Polarisation hypotheses (Perroux 1950); regional economic clusters (Porter 1990): agglomeration/inequality Fujita et al. (1999): a hierarchical urban system reflecting Christaller s (1933) central place theory is likely to emerge Diverse urban system in Europe (many relatively small cities): Cities suited as spatial entities for analysis of regional disparities 4

5 2. Data and empirical framework Empirical framework: base model Growth equation derived from neoclassical growth model *(1/T) log(y it+t /y it ) = a + b log(y it ) + c log(y Nit /y it ) + d X it + e C i + f R i + u it y it is per capita output in city-region i at time t (2001, ) y Nit is per capita output in regions adjacent to city-region i X is a set of additional characteristics of city i at time t C is a city type, R a macro-regional fixed effect T is the observation interval (2001-, -2008) u it is a disturbance term *OLS estimation of (1/T) log(y it,0+t /y i,t0 ) = a [ (1-e -ßT )/T] log(yi, t0 ) + other variables + u it 2 observation periods: (just) before and (just) after the EU enlargements of /2007 5

6 2. Data and empirical framework Empirical strategy spatial autocorrelation and spatial heterogeneity avoided to a relatively large extent - by focusing on cities spatial interdependence: controls for city type and macro-region y i,t0 unknown; comparison of short-term dynamics before and after EU enlargements of /2007 relation between log(y it ) and log(y it+t ) conditioned on regionspecific steady state-levels of economic development robustness check I: SAR model incorporating spatial weights robustness check II: eliminating influence of unobserved timeinvariant regional characteristics by panel methods 6

7 3. Analysis City types A Principal Metropolises B Regional Centres C Smaller Centres D Towns & Cities of the Lagging Regions Urban Audit (EU) Urban Audit (non-eu) Size of circle is relative to population in core city* in *Paris: Kernel 10,000,000 1,000, , ,000 Own calculation based on the Urban Audit (); typology derived on basis of 21 indicators by regional factor and optimised cluster analysis (cf. European Commission (ed.) (2010), Second State of European Cities Report. Brussels) using data about 329 cities from EU27, Norway and Switzerland representing more than half of all cities >100,000 inhabitants in EU27 7

8 2. Data and empirical framework North Northern Macro-Regions Central West Western Central South Southern Year of accession to EU indicated if or later

9 3. Analysis Descriptive statistics by city type () ann. growth in real GDP/head (-2008) (in %, NUTS 3) real GDP/head (in, 2005 prices, NUTS 3) Nr. of cities mean all cities Type A Type B Type C Type D ,320 32,163 28,449 20,459 6,389 total population ,603 1,048, , , ,918 ann. population growth (-2008, in %) unemployment rate (in %) multi-modal accessibility (EU27 = 100) patent intensity (applications per 1 Mill. inhabitants, NUTS 3) tourist overnight stays per resident population Own calculation. Data source: Urban Audit (core cities), unless indicated as NUTS 3 9

10 Analysis GDP per head in 2001 and subsequent growth in real GDP per head* NUTS 3 regions GDP/head (in ) in 2001 EU15 Accession to EU in Accession to EU in 2007 *2005 prices; own calculation based on regional statistics provided by Eurostat 10

11 Analysis GDP per head in and subsequent growth in real GDP per head* NUTS 3 regions GDP/head (in ) in EU15 Accession to EU in Accession to EU in 2007 *2005 prices; own calculation based on regional statistics provided by Eurostat 11

12 3. Analysis OLS, dependent variable: (1/T) log(y it+t /y it ), t = 2001,, t+t =, 2008 independent variables all cities (core city level) (1) (2) (3) base year real GDP per head (log) (std. error) ( ) *** ( ) ( ) *** ( ) (0.0116) *** ( ) GDP/head (hinterland) (log) *** ** * total population (log) population (outer zone) unemployment rate * multi-modal accessibility patent intensity * *** * * tourist overnight stays City Types (Base category: Type C, Smaller Centres)) A Principal Metropolises ** B Regional Centres D Lagging Regions Macro-Regions (Base Category: Southern Europe) North ** ** CEE *** West year 0.459*** 0.554*** 0.384** R² observations

13 4. Robustness checks I. Spatial Autoregressive (SAR) Model allowing for spatial effects among dependent variable and error term n k (1/T) log(y it+t /y it ) = λ j=1 w ij (1/t) log(y jt+t /y jt ) + p=1 x ipt ß pt + u it k u it = ρ p=1 m ijt u jt + ɛ it w ij and m ij : spatial weights, inversely related to distance between cities i and j x: set of k independent variables (including y it, city type and macro-region) II. Fixed Effects Regressions eliminating unobserved, time-invariant region-specific characteristics a. measuring variation between the first and second period (1 T 2 ) log(y it2 +T 2 /y it2 ) (1 T 1 ) log(y it1 +T 1 /y it1 ) = b (log y it2 (log y it1 ) + c(x it2 X it1 ) + u it2 u it1, with t 1 = 2001, t 2 =, T 1 = 3, T 2 = 4 b. drawing on a yearly time-series from 2001 to 2008 (NUTS 3/UA cities) log(y it+1 /y it ) log(y it+1 /y it ) = b (log y it log(y i )) + c(x it X i ) + u it u i 13

14 4. Robustness checks Average Annual Growth in real GDP per Head 2001-/-2008 Spatial Autoregressive Regression (GS2SLS), All Cities Base year (1) (2) (3) (4) (5) (6) real GDP/head (log) * ** *** ** ( ) ( ) ( ) ( ) ( ) ( ) conditioning variables No No Yes Yes Yes Yes A: Principle Metrop * B: Regional Centres D: Lagging Regions North * Central West *** constant *** 0.149* 0.160* Λ *** *** ** Ρ *** 2.504*** 1.469*** *** real GDP/head (log): ATDI ( ) ( ) ( ) ( ) ( ) ( ) observations Authors calculation based on the Urban Audit and regional statistics from Eurostat. Standard errors in parentheses; */**/*** = significant at 10/5/1%-level; ATDI: average total direct impact, standard deviation in parentheses; real GDP per head (hinterland), CEE: cf. Tables 5-6; λ = spatial-autoregressive (SAR) parameter of dependent variable; ρ = SAR parameter of disturbance 14

15 5. Findings/Conclusions Findings Data support ß-convergence in post-enlargement phase (only), i.e. the lower y i,t(0) the higher the growth rate conditional ß-convergence in post-enlargement phase around 1.8% if spatial interdependence is accounted for, i.e. half of distance to steady state income diminishes after around 40 years; this finding complies with empirical regularity found in literature std. dev. of log(y it ): 0.84 (2001), 0.84 (), 0.71 (2008) estimations using differences over time corroborate ß-convergence 2001-: below-average growth in cities of lagging regions (Type D): divergence no considerable catching-up process in Southern Europe even within city types, growth interrelates strongly with initial per capita output 15

16 5. Findings/Conclusions Conclusions i. Poor cities and regions have started to catch up after, but no common steady state of income: Large and prosperous cities also grow at above-average rates ii. iii. iv. Before-and-after comparison: no causal evidence for effect of EU enlargement, but strong descriptive evidence for regional convergence In Central Europe high concentration of wealth in capital and large cities, where adjustment of living conditions to (Western) European standards will proceed much more rapidly before it can disperse Policy implication for Central Europe: supporting regional innovation networks, which connect research, education, and business infrastructure of capitals with remote regions; no short-term equalisation v. Prosperity in Europe depends on strength of urban growth poles and economic core zone 16

17 Acknowledgements This paper elaborates on the results of the Second State of European Cities Report, which was prepared by RWI in cooperation with the German Institute of Urban Affairs (Difu, Berlin), NEA Transport Research and Training (Zoetermeer) and Planning Research and Consultancy (PRAC, Bad Soden) under Contract No. 2008CE160AT024 for the European Commission, DG Regional Policy. Journal Publication: Eastern European Economics (forthcoming) 17