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ECONOMIC INTEGRATION, GROWTH CYCLES AND THE BEHAVIOUR OF REGIONAL DISPARITIES ACROSS EUROPE S. Brettell*, B. Gardiner*, R. Martin** and P. Tyler*** *Cambridge Econometrics **Department of Geography, University of Cambridge *** Department of Land Economy, University of Cambridge Presentation, RSA Conference, Lisbon April 2007 1 Introduction • Europe is at historical cross roads • Challenges of globalisation, intensifying • • • international competition and new knowledge economy Enlargement to include several low income, old economy states The reconfiguration of the Structural Funds in favour of new enlargement states Key imperative of improving the competitive position of the EU 2 Introduction • Three key spatial aims: • Spatial-economic integration - major • • advances over past 25 years (single market, monetary union, etc) Regional cohesion - promoting regional convergence in per capita GDP Improving regional competitiveness throughout the Union 3 Structural Funds: Convergence and Competitiveness Objectives in EU25 4 European Commission, 2006 Introduction • Within this context, focus here is on the • • • • implications of economic integration for regional cohesion (regional disparities) Will economic integration in EU promote regional convergence or divergence? Some 15 years ago Krugman (1993) broached this question by drawing inferences from USA experience USA has long been the sort of economic and monetary union that EU aspires to So it might hold clues as to what expect in EU as 5 it becomes increasingly integrated Krugman’s Thesis • Krugman uses experience of Massachusetts, and other US regions (and cities) to theorise about combined impact of 1992 and EMU on EU regions: Integration leads to increased trade which leads to increased regional economic specialisation Specialisation means instability of regional exports and idiosyncratic regional shocks Regional instability reinforced by procyclical capital movements (export booms reinforced by investment booms, and vice versa in slumps) Factor mobility leads to divergent long-run 6 regional growth Krugman’s Thesis • Argument is that EU economic integration will • make American-style regional fluctuations more pronounced Evidence adduced to support this contention: Broad US regions more specialised than European countries Industries far more localised in US than in Europe Employment growth much less stable (more cyclical) in US regions and cities than in EU countries Disparities in long-run growth rate of GDP per capita far greater amongst US regions than amongst EU countries 7 Krugman’s Thesis • Problems with Krugman’s argument: • Comparison of US regions and cities with • • • European countries misplaced (different sizes, different types of economic unit) Level of spatial disaggregation in general too coarse to pick up localisation effects of increased economic integration in EU Analysis only up to late-1980s, and hence not in period of main EU economic integration Fails to compare regional convergence/divergence over time (trends and cycles) 8 Questions • Given Krugman’s argument, how do regional • • • disparities in the USA (a long-established economic and monetary union) behave over growth cycles (convergence or divergence)? Has behaviour of regional disparities (convergence or divergence) in Europe changed as integration has progressed? Has Europe’s pattern become more like that of the US? How do different types of European region behave over the economic cycle? Has this pattern changed with increasing integration? 9 The Evidence • Look at NUTS 3 data for EU15 ‘established’ • • union areas ..and compare with the CSA (metro/micropolitan) ‘FUR’ data for US (covering 93% of US population) 1980-2005, in five year growth zones to capture cyclical content 10 Real GDP growth US and EU 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 -1.0 1981 -2.0 -3.0 US 1986 1991 1996 2001 EU15 11 High income volatility of the US states 11 Massachusetts New York Illinois Minnesota Florida Texas Colorado California 9 7 5 3 1 -1 -3 1970 1975 1980 1985 1990 1995 2000 2005 12 Income per capita US Metropolitan and Micropolitan FUR Areas, 2005* Personal income, 2000 $ 30,000 to 26,000 to 22,000 to 0 to No data Metropolitan: at least one urbanized area (county) has a population of at least 50,000. Micropolitan: 10,000-50,000, NB nonCSA=6.6% population, 4.8% personal income in 2005. *CE projection from 2004 base 62,000 29,999 25,999 21,999 13 US regional income distribution for 938 CSAs* is even.. 1 cugdprel 0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 cupoprel *Estimated population = 277m in 2005 Average Personal income per capita = $27,100 2000 prices 14 .. In spite of sustained differences in US real GDP growth rates Source: BEA Oct 2006: http://bea.gov/newsreleases/regional/gdp_state/2006/xls/gsp1006.xls 15 accentuated in accelerating GDP growth rates by US state.. 10 y = 0.3567x3 - 2.9913x2 + 8.7825x - 5.6575 2 R = 0.5587 2004-2005 %change GDP 8 6 4 2 0 0 1 2 3 4 5 6 -2 1997-2004% pa change GDP 16 ..and income per capita for CBSAs shows just random shocks over time 8 6 4 2 0 -2 0 200 400 600 800 -4 -6 1985-90 1995-2000 Linear (1995-2000) 17 Migration is the reason: Population versus income change US states 6 6 5 5 4 4 3 3 2 2 1 1 0 -1 0 1 2 3 4 5 6 7 8 -2 -1 0 -1 0 1 2 3 4 -2 6 5 5 4 4 3 3 2 2 1 1 0 -1 0 1 2 3 4 5 6 7 8 0 -1 0 -2 -2 1990-95 6 7 8 1 2 3 4 5 6 7 1995-2000 6 5 4 3 2 1 0 -1 0 -2 0.5 1 1.5 2 2.5 2000-2005 3 3.5 4 9 1985-90 1980-85 6 5 4.5 18 8 But the pecking order has not changed very much US MSA 1980-2005 levels of income and income divergence o mean rel (SD/mean rank MSA )rel US income 2005 US rank 2005* diff rank Bridgeport-Stamford-Norwalk, CT (MSA) 58,989 1.79 1.53 1 1 0 San Francisco-Oakland-Fremont, CA (MSA) 45,754 1.44 1.24 2 2 0 San Jose-Sunnyvale-Santa Clara, CA (MSA) 45,201 1.42 1.42 3 3 0 Washington-Arlington-Alexandria, DC-VA-MD-WV43,812 (MSA) 1.36 1.13 4 4 0 Naples-Marco Island, FL (MSA) 38,491 1.35 1.12 5 11 -6 New York-Northern New Jersey-Long Island, NY-NJ-PA 40,473 (MSA) 1.30 1.14 6 7 -1 Trenton-Ewing, NJ (MSA) 41,584 1.30 1.20 7 6 1 Boston-Cambridge-Quincy, MA-NH (MSA) 43,026 1.29 1.40 8 5 3 Anchorage, AK (MSA) 33,588 1.27 0.36 9 38 -29 Sebastian-Vero Beach, FL (MSA) 36,217 1.24 1.22 10 18 -8 Hartford-West Hartford-East Hartford, CT (MSA) 38,268 1.24 1.03 11 12 -1 Boulder, CO (MSA) 40,325 1.22 1.40 12 8 4 Reno-Sparks, NV (MSA) 36,330 1.21 0.96 13 17 -4 Barnstable Town, MA (MSA) 38,683 1.21 1.17 14 10 4 Napa, CA (MSA) 37,905 1.20 1.14 15 13 2 Minneapolis-St. Paul-Bloomington, MN-WI (MSA)37,893 1.20 1.14 16 14 2 Seattle-Tacoma-Bellevue, WA (MSA) 39,633 1.20 1.24 17 9 8 Ann Arbor, MI (MSA) 35,486 1.19 1.02 18 25 -7 Sarasota-Bradenton-Venice, FL (MSA) 34,940 1.19 1.03 19 28 -9 Denver-Aurora, CO (MSA) 37,573 1.19 1.19 20 15 5 19 So GDP/capita convergence/divergence oscillates as ‘catch up’ in US states 1980-85 1985-90 y = -0.1257x + 1.3179 R2 = 0.1015 0.3 0.3 0.2 0.2 0.1 0 8.5 9 9.5 10 10.5 y = -0.0246x + 0.3384 R2 = 0.0052 0.4 1985-90 1980-85 0.4 Jackso n, W Y -ID M icro p o lit an SA 0.1 0 11 8.5 -0.1 -0.1 -0.2 -0.2 9 9.5 10 10.5 11 Raymo nd ville, TX M icro p o lit an SA -0.3 -0.3 1985 1980 1990-95 1995-2000 y = -0.0913x + 0.9668 R2 = 0.1137 0.4 y = 0.0747x - 0.6042 R2 = 0.0678 0.5 0.3 0.4 0.2 0.3 1995-2000 1990-95 San Jo se-SunnyvaleSant a Clara, CA (M SA ) 0.1 0 8.5 9 9.5 10 10.5 B rid g ep o rt -St amf o rd No rwalk, CT (M SA ) 0.2 0.1 11 -0.1 0 -0.2 -0.1 8.5 -0.3 9 9.5 10 Tallulah, LA M icro p o lit an SA 10.5 -0.2 1990 1995 2000-2005* y = -0.0989x + 1.0361 R2 = 0.0929 0.4 0.3 2000-2005* 0.2 0.1 0 8.5 9 9.5 10 10.5 11 -0.1 -0.2 20 -0.3 2000 11 US CSA Growth and convergence by growth phases 1980-2005 4 3 λ% pa 2 g% pa 1 λ% pa spatial 0 1980-85 1985-90 1990-95 -1 19952000 20002005* v e rs io n -2 gy = c – (1-e-βt)ln(y0) + Xδ + γWgy + ε λ = -ln(1+β)/t 21 ..but a rising trend sigma plot for US CSAs as incomes grow over time SD(log income per capita) US % pa 6 5 4 3 2 1 0 -1 0.14 -2 19 80 19 81 19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 0.22 0.21 0.2 0.19 0.18 0.17 0.16 0.15 22 ..but disparate contributions from the richest and poorest regions SD(log income per capita) 0.25 0.2 0.15 0.1 0.05 sigma US CSAs Q1 Q2 Q3 Q4 04 20 02 20 00 20 98 19 96 19 94 19 92 19 90 19 88 19 86 19 84 19 82 19 19 80 0 Q5 23 Phase residual correlations from US CSA Beta convergence plots 0.3 0.2 0.1 2000-2005 0 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.1 -0.2 -0.3 1995-2000 24 Enlargement phases of the EU – EU15 a test bed for Krugman? (GDP/cap relative EU=100 in 2004) EU6 (1951) =149.8 Pop GDP EU9 (1973) New Members=132.1 EU12 (1986) (GR81) New Members=84.7 EU15 (1995) New Members=183.9 EU25 (2004) New Members=28.8 EU27 (2007) New Members=10.9 EU27+HR+MK+TR Proposed Members=17.4 1973, Denmark, Ireland and the United Kingdom, 1981, Greece 1986, Spain and Portugal 1995, Austria, Finland and Sweden 2004, 10 countries of Central and Eastern Europe and the Mediterranean: Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia and Slovenia. 2007 Romania and Bulgaria 25 EU NUTS3 regional distribution of GDP per capita 2005 GDP Per Capita, 2005, EU25=100 116.4 to 606.4 98.3 to 116.3 85.1 to 98.2 71.0 to 85.0 0.0 to 70.9 No data 26 EU NUTS 3 Employment Growth, 1980-1995 Employment Growth, 1980-1995, % pa 0.7 to 34.7 0.2 to 0.6 0.0 to 0.1 -0.8 to -0.1 -7.0 to -0.9 No data 27 EU NUTS 3 Employment Growth, 1995-2005 Employment Growth, 1995-2005, % pa 1.4 to 26.3 1.0 to 1.3 0.5 to 0.9 0.1 to 0.4 -16.0 to 0.0 No data 28 EU NUTS 3 GDP Per Capita Growth 1980-1995 GDP Per Capita, 1980-1995, % pa 2.0 to 11.7 1.5 to 1.9 1.0 to 1.4 0.1 to 0.9 -4.0 to 0.0 No data 29 EU NUTS 3 GDP Per Capita Growth, 1995-2005 GDP Per Capita, 1995-2005, % pa 2.5 to 11.0 2.0 to 2.4 1.5 to 1.9 1.1 to 1.4 -4.0 to 1.0 No data 30 EU15 regional cumulative GVA distribution 967 EU15 NUTS3* regions 1 0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 *Excluding Eastern Laender DE, extra continental Portugal, France, Spain, Flevoland NL - estimated population = 380m in 2005 Average GVA per capita = €24,808 at 2000 prices 31 EU15 regional GDP/cap growth ranking NUTS3 regions 0.6 0.4 0.2 0 0 200 400 600 800 -0.2 -0.4 1985-1990 1995-2000 Linear (1995-2000) 32 EU15 regional employment growth ranking NUTS3 regions 10 5 0 0 200 400 600 -5 -10 1985-90 *excluding 1995-2000 Linear (1995-2000) regions with less than 50,000 employment in 2005 33 1985-1990 1980-1985 y = -0.0366x + 0.1601 R2 = 0.0299 0.8 y = -0.0523x + 0.2887 R2 = 0.0427 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 1.0 -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 1.5 2.0 2.5 3.0 1995-2000 1990-1995 3.5 4.0 4.5 y = -0.0318x + 0.2088 R2 = 0.0223 y = -0.0543x + 0.1888 R2 = 0.0569 0.6 0.6 0.5 0.4 0.4 0.3 0.2 0.2 0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 -0.2 0.1 0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -0.1 -0.4 -0.2 -0.6 -0.3 2000-2005 y = -0.0433x + 0.1872 R2 = 0.0464 0.5 0.4 0.3 0.2 0.1 0.0 -0.1 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 -0.2 -0.3 -0.4 -0.5 34 4.5 EU15 NUTS 3 convergence by growth phases 1980-2005 4 EU15 λ % pa 3 g %pa 2 λ %pa spatial 1 v e r s io n 0 1980-1985 1985-1990 1990-1995 1995-2000 2000-2005 -1 with border effect gravity weights -2 35 Phase residual correlations from EU15 NUTS3 convergence plots 0.4 0.3 0.2 0.1 2000-2005 0 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 -0.1 -0.2 -0.3 1995-2000 36 The level of spatial detail in measurement is important 4 3 EU15 NUTS2 λ %pa 2 EU15 g %pa 1 EU15 NUTS2 λ %pa 0 s p a t ia l v e r s io n 1980-1985 1985-1990 1990-1995 1995-2000 2000-2005 -1 -2 37 US/EU15 comparative convergence by growth phases 1980-2005 4 3.5 3 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 US g% pa US λ% pa spatial v e r s io n EU g %pa EU λ %pa spatial v e r s io n 1980-85 1985-90 1990-95 1995-2000 20002005* 38 EU27? 1272 NUTS 3 regions convergence 1990-2005 5 EU27 λ % pa 4 3 EU27 g % pa 2 EU27 λ % pa 1 s p a t ia l v e r s io n 0 EU27 λ %pa 1980-1985 1985-1990 1990-1995 1995-2000 2000-2005 -1 s p a t ia l P T / E S dum m y -2 39 Sigma convergence– EU15 NUTS3 comparison with US 0.55 0.5 0.45 0.4 0.35 0.3 sdlnGVA/Pop US 0.25 0.2 19 80 19 83 19 85 19 88 19 91 19 94 19 97 20 00 20 03 0.15 40 Sigma convergence – EU15 NUTS3 productivity decomposition 0.6 0.5 0.4 0.3 0.2 logGVA/Pop sdlogGVA/EMP sdlogEMP/Pop scdProdDep 0.1 19 80 19 83 19 86 19 89 19 92 19 95 19 98 20 01 20 04 0 41 Sigma plots decomposed EU15 NUTS3 0.7 0.6 0.5 logGVA/Pop 0.4 logGVAmfg/Empmfg 0.3 0.2 logEmpser/Emptot 0.1 19 80 19 83 19 86 19 89 19 92 19 95 19 98 20 01 20 04 logEmptot/Pop 42 Sigma plots contributions EU15 NUTS3 extremes 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 GVA/Pop top quintile quintile 2 quintile 3 quintile 4 quintile 5 19 80 19 83 19 86 19 89 19 92 19 95 19 98 20 01 20 04 0 43 What does the Evidence show? • Convergence in GVA per caput in US and EU • • is mainly down to the contribution of productivity Adjustment processes in the US are complex but strongly mediated by migration, with ‘escape’ of the high income regions generating ‘catch up’ by the poorest EU regions remain very unresponsive by US standards but some small evidence of a transition for the ‘established’ union regions in the last decade 44 Questions raised by the Evidence? • What explains the apparent cessation of • • regional convergence in the EU from the mid1990’s onwards? How accurate is Krugman’s depiction of the US regional growth pattern and how relevant is the Thesis in EU case? Is sectoral competition in the EU15 becoming more important than spatial competition? 45