Urban-rural interactions More important than ever! Prof. Eveline van Leeuwen, Urban Economics group, Wageningen University
Overview Theoretical background urban-rural interactions Study I: Economic performance EU level Study II: Employment growth NL level Places or people? 2
Main message Several descriptive studies show that intermediate and accessible rural areas performed better than large cities and remote rural areas. (Dijkstra etal., 2015; van Leeuwen 2017) Urban and rural areas need each other: They benefit from each other They depend on each other Even more so in the post-2020 Cohesion policy period! 3
Urban-rural interactions
Theoretical background What distinguishes urban from rural? DENSITY Agglomeration advantages: economies of scale/network effects Transport costs Consumer market Labour market Knowledge spill-overs 5
And rural areas? Capacity advantages Economies of Space Economies of Scale of Ecosystem services 6
Urban-Rural interactions? Labour market Consumer market Industry linkages (e.g. Food, energy) Recreation variety Cultural linkages (shared history, cultural heritage) Ecosystem linkages (flood prevention, air purification) 7
How to measure the extent of interaction? Labour market flows Exchange of workers and job opportunities Migration flows Exchange of ideas Consumption flows Transport flows Nutrient flows 8
EU-level Urban-rural proximity and economic performance 9
The impact of interregional patterns Relationship between urban-rural interactions and economic performance. Effect of proximity to regions that differ in level of urbanity Control for spatial configuration within the region EU-Nuts3: 1075 regions, 2000-2007 Joint work with Daniel Arribas-Bel, University of Liverpool
Interregional interactions Share of different neighbours Contiguity spatial weights matrix
Intra-regional patterns The number of urban clusters within the region Normalised by population Larger value, more scattered pattern Brussels Shape (or compactness) of the urban clusters Compares the shape with a square Higher values, less compact (more complex) Berlin
Empirical Strategy Spatial lag model y i =employment; GDP; Population UR i = intra regional characteristics (cluster and shape); distance to large city; share of different neighbors X i = 2000 levels; LQ; period entering EU
Descriptives spatial variables Intra-regional Inter-regional 100 90 Clusters 80 70 60 50 Shape 40 30-1.00-0.50 0.00 0.50 1.00 20 10 Urban Intermediate Rural 0 Rural Intermediate Urban
Descriptives all variables GDP growth Pop. Growth Emp. Growth GDP_2000 Emp_2000 Pop_2000 Agri_lq Serv_lq Nmser_lq Clusters Shape -1.00-0.50 0.00 0.50 1.00 Urban Intermediate Rural
Results Employment GDP Population 1 2 3 4 1 2 3 4 1 2 3 4 CONSTANT -9.224 * -6.144-3.056-5.408 38.407 *** 16.435 ** 30.627 *** 17.895 *** -7.584 *** -4.434 *** -4.782 *** -4.363 *** Emp_00-0.056 *** -0.051 *** -0.043 *** -0.049 *** 0.042 * 0.015 0.000 0.015-0.012 *** -0.004-0.003-0.004 GDP_00 0.000 0.000 0.000 0.000 0.000 *** 0.000 ** 0.000 ** 0.000 ** 0.000 *** 0.000 *** 0.000 *** 0.000 *** Pop_00 0.027 *** 0.024 *** 0.019 *** 0.023 *** -0.006-0.001 0.002-0.001 0.008 *** 0.002 0.001 0.002 W_dep 0.283 *** 0.139 0.367 *** 0.766 *** 0.000 0.764 *** 0.942 *** 0.796 *** 0.941 *** EU_b 10.111 *** 7.665 *** 6.756 *** 3.640 ** 0.704 1.177 2.787 *** 0.610 ** 0.600 ** EU_c 5.097 *** 4.502 ** 4.388 ** 55.534 *** 12.239 ** 11.519 ** -2.999 *** 0.900 0.858 * lq_agr00-2.110 *** -2.124 *** -2.874 *** -2.263 *** 3.269 * -0.240-2.997 *** -0.037 0.396 ** 0.270 ** 0.159 0.255 ** lq_nms00 1.138-0.742-1.846-1.604-20.700 *** -10.472 *** 2.652-10.660 *** -1.459-0.508-0.750-0.538 lq_serv00 11.212 *** 9.394 *** 6.312 *** 8.972 *** -2.432-0.580-6.570 * -0.806 5.596 *** 2.368 *** 2.952 *** 2.315 *** dist500k 7.090 ** 5.853 ** 1.471 5.838 ** 40.337 *** 9.181 * -6.813 7.719 1.462-0.872-1.296-0.781 clusters 0.000 0.001 0.002 0.001 0.012 ** 0.003-0.006 ** 0.004 0.002 * 0.001 *** 0.001 * 0.001 *** shape -0.266-0.167-0.203-0.113 1.105 ** 0.068-0.747 ** -0.057 0.063 0.049-0.016 0.051 Diff_NB 0.042 *** 0.038 *** 0.038 *** 0.056 ** 0.051 *** 0.021 0.031 *** 0.021 *** 0.020 *** wrxi 0.032 * 0.080 *** 0.021 *** wrxr 0.012-0.009-0.001 wrxu 0.070 ** 0.105 * 0.021 * wuxi 0.042-0.029 0.018 ** wuxr 0.116 ** -0.031 0.039 ** wuxu -0.008 0.005 0.000 F.E. no no yes no no no yes no no no yes no N 1075 1075 1075 1075 1074 1074 1074 1074 1075 1075 1075 1075 R^2 0.25 0.27 0.33 0.28 0.67 0.88 0.92 0.88 0.29 0.63 0.65 0.63
So: positive effects of urban-rural interactions Effects go both directions! Empl: Intermediate and urban regions benefit from rural neighbours Rural regions benefit from urban neighbours GDP: Rural regions benefit from urban and intermediate neighbours >>Not the other way around Population: Intermediate and urban regions benefit from rural regions Intermediate and rural regions benefit from urban regions
But this says nothing about the mechanisms What causes these effects?
Dutch labour markets 19
Employment effects in the Netherlands Sector diversity and its effect on employment dynamics in urban and rural municipalities in the Netherlands. Sierdjan Koster, Aleid Brouwer, Eveline van Leeuwen
Diversity Rural development policies often focus on diversifying the rural economy: Portfolio effect Resilient Economy Spill-over effects But, is diversity a key to success? Negative effect of LQ in agriculture on employment Positive effect of LQ in market services on employment
Spatial Scale Rural: density < 150 km2; > 50% lowest urban level; Urban: density >1000 km2; > 50% two highest urban Intermediate: rest
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Shannon Index Shannon Index Diversity trends 5.35 5.25 5.15 5.05 4.95 4.85 Diversity across NACE_2 Sectors (employment) 5.35 5.25 5.15 5.05 4.95 4.85 Diversity across NACE_2 Sectors (Establishments) URBAN (91) INTERMEDIATE (213) RURAL (76) Source: URBAN (91) INTERMEDIATE (213) RURAL (76) Source: LISA
Model Dependent: - Employment level (Log jobs per municipality) - Period 1996-2012 - Fixed effects models >> growth Independent: - Diversity of the local economy - Shannon index of diversity - Jobs (P). - European Classification of Economic Activities (NACE) - Specialization - Crowley index - Squared location quotients >2
Spatial interaction Taking into account relationships with other municipalities - Spatial lagged independent variables Weight matrix: - WO: job location of residents > consumption effects of wage earned elsewhere - WI: residential location of employees > additional access to labour markets - WQ: queen continuity assumption WO WI WQ
Results general VARIABLES Model 1 Model 2 Model 3 Model 4 Population density 0.022** 0.019** 0.017** 0.021** Population 25-45 -1.712*** -1.180*** -1.132*** -1.370*** Share manu_jobs 0.008 0.088 0.154 0.106 Share agr_jobs -1.848*** -1.743*** -1.832*** -1.723*** Specialisation 0.003 0.002 0.002 0.003 Diversity -0.068-0.102** -0.095* -0.087* WO_population density 0.049*** WO_specialisation -0.007 WO_diversity 0.909*** WI_population density 0.079*** WI_specialisation -0.011 WI_ diversity 1.203*** WQ_population density 0.017** WQ_Specialisation 0.003 WQ_Diversity 0.477*** Constant 9.876*** 6.734*** 6.619*** 7.398*** Observations 6,935 6,935 6,935 6,935 R-squared 0.58 0.62 0.63 0.61 1. Own diversity has a negative effect, specialisation has no effect. 2. Nearby diversity has a significant positive effect. 3. The three weigh matrices show quite similar results. Biggest effect of nearby diversity when using residential location of employees (additional access to labour markets)
Results urbanity VARIABLES Rural Intermediate Urban Population density 0.531*** 0.018 0.015* Population 25-45 -0.716*** -1.294*** -0.587 Share manu_jobs 0.491*** 0.164-0.394 Share agr_jobs -1.603*** -1.801*** -3.244* Specialisation 0.001 0.004 0.006 Diversity -0.010-0.114* 0.001 WI_pop. density 0.125*** 0.083*** 0.047** WI_specialisation -0.071** -0.006 0.072 WI_ diversity 0.850*** 1.170*** 1.529*** Constant 6.227*** 6.696*** 5.464*** Observations 1,291 4,658 986 R-squared 0.78 0.61 0.55 1. Own diversity only has a negative effect on intermediate areas. 2. Population density of neighbours has a positive effect on all areas, with the largest effects for rural areas 3. Diversity of neighbours has a positive effect on all areas, with the largest effects for cities
Conclusions It is all about the region Functional labour market areas Importance of cooperation between municipalities Good connections are important
Places or people? Cohesion Policy post-2020 29
Future challenges Circular Economy Waste management Bio-based products Low carbon economy Renewable energy sources Bio-based energy Climate change adaptation 30
Bio-based production The urban-rural fringe locates the most productive farms. In line with Von Thünen: lower transport costs, higher land rents near cities Concentration of high yielding products (van Leeuwen et al., 2010)
Gross Value Added in agriculture and forestry as part of total GVA 35% GVA of agricultural sector (2015) 30% 25% 20% 15% 10% 5% 0% Urban Intermediate Rural
But Distribution agricultural GVA (2015) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Urban Intermediate Rural
Perception of interaction? Less knowledge about origin of food Also due to globalisation No knowledge about destination of waste Political divide, not feeling heard Difference in attitudes and behaviour (e.g. towards climate change) 34
Cooperation between urban and rural areas? Trust Urban rural divide in protest-votes Activities and Initiatives Urban residents more pro-environment attitude Rural residents more pro-environment behaviour Common language? 35
European Social Survey Satisfaction, Trust, Concerns of EU residents 20,000 observations Type of (perceived) place of residence: Large Cities Intermediate areas: suburbs and towns or small cities Rural areas: country village or home/farm in countryside Country fixed effects and Urbanity of NUST2 region Controlling for personal characteristics: Income, years of education, paid job, gender, age, health, in a relationship, migrant
Urban-Rural differences? 0.5 Satisfaction 0.5 Participation 0 Life Health Economical Government 0 Voting Right-side Active in politics -0.5-0.5 Cities Intermediate areas Rural areas Cities Intermediate areas Rural areas 0.5 Trust 0 People Political system Goverment EU -0.5 Cities Intermediate areas Rural areas 37
Climate Change Concern Climate Change Climate change actions 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0-0.1-0.2-0.3 Do you think world's climate is changing Climate change How worried about good or bad impact climate change across world To what extent feel personal responsibility to reduce climate change 0-0.1-0.2-0.3 How likely, limiting own energy use Favour increase taxes on fossil fuels Favour subsidise renewable energy -0.4-0.4-0.5-0.5 Cities Intermediate areas Rural areas Cities Intermediate areas Rural areas 38
Or member-state differences? Satisfaction Satisfaction 3 3 2 2 1 1 0-1 Life Health Economical Government 0-1 FR CZ SI EE IE PL BE AT NO IS FI CH NL -2-2 -3-3 -4-4 Cities Intermediate areas Rural areas Life Health Economical Government 39
Trust Trust 2 2 1.5 1.5 1 1 0.5 0.5 0-0.5 People Political system Goverment EU 0-0.5 PL SI FR CZ AT EE NL CH IS FI NO BE IE -1-1 -1.5-1.5-2 Cities Intermediate areas Rural areas -2 People Political system Goverment EU 40
Climate change actions 1 0.5 0-0.5 How likely, limiting own energy use Favour increase taxes on fossil fuels Favour subsidise renewable energy -1-1.5-2 Cities Intermediate areas Rural areas 41
Policy reccomendations I Lower trust in national and EU policymakers in rural areas More social cooperation and initiatives last longer (Haartsen et al More bottum-up approach Higher trust in policymakers Stronger pro-environmental attitudes More top-down interventions in daily living space 42
Policy reccomendations II Invest in regional strategies Urban and rural areas can benefit from each other Invest in good connections For transport For eco-system services (?) Acknowledge the importance of bio-based economy Stimulate high value added bio-based activites in rural regions; Allow rural regions to benefit from the competition for space resulting from biobased production. 43
Future research Look at broader welfare measures Health Life-satisfaction Environmental quality Take objective & subjective interaction variables into acount Urban-rural linkages in an input-output framework 44
Thank you! Prof. Eveline S. van Leeuwen Chair Urban Economic group Wageningen University & Research Eveline.vanleeuwen@wur.nl Website UEC group 45
References Dijkstra, L., Garcilazo, E., & McCann, P. (2015). The effects of the global financial crisis on European regions and cities. Journal of Economic Geography, 15(5), 935-949. Haartsen, T., & de Haan, E. (2015). Succespercepties van burgerinitiatieven in Randland. [Perceptions of succes of citizen s inititatives in the Randstad] Rooilijn, 48(4), 296-302. Van Leeuwen, E., (2010). Urban-rural Interactions. Towns as Focus Points in Rural Development. In: Contributions to Economics. Springer, Heidelberg. van Leeuwen, E., Strijker, D., & Terluin, I. (2010). Regional concentration and specialisation in agricultural activities in EU-9 regions (1950-2000). European Spatial Research and Policy, 17(1), 23-39. Van Leeuwen, E. (2015). Urban-Rural Synergies: An Explorative Study at the NUTS3 Level. Applied Spatial Analysis and Policy, 8(3), 273-289. 46