1. Ian Smith (University of the West of England, Bristol) RTPI: Planning for the Future of Small and Medium Sized Towns, Colwyn Bay, September 2014 The state of small towns in Europe 2001-11

2. Introduction 2 European small towns are important (as a group) but problematic to quantify at level of individual settlement Small towns across Europe constitute a diverse group of places but on average they appear to be different from large cities (although this can vary country by country) What factors are associated with stronger growth 2001-11?

3. What is a town? Llandrindod Wells 3 Administrative “town” Morphological “town” Functional “town”

4. Key facts for towns? 4

5. Classify towns: migration vs natural change

6. Classify towns: employment profiles 6

7. On average, small towns (in database) are different from large cities on a range of measures: Social (older working population, more pensioners, fewer lifetime migrants Economic (greater proportion employment in manufacturing, more self-employment (in the UK), more likely to be net importer of labour, less diverse) Housing issues (more second homes) Are small towns (SMSTs) different? 7

8. How well is a town doing? Economically (as place of production)? In terms of wealth (and consumption)? Well-being? Externally defined? Policy based definition - Smart, green and inclusive? Often a diversity of views within towns Can any of these be measured? How to understand town ‘performance’? 8

9. NUTS2 region – morphological town Base year (1999-2002) to end year (2007-11) Territorial (aggregate) growth model

10. Population growth: what makes a difference? 10 Dependent variable: population growth population change model without housing variable population change model with housing variable Fixed Part Cons : 0.300.14**0.300.14 ** case study region dummy region -0.220.16-0.220.16 proportion of NUTS2 area covered by city (HDUC) region -0.010.01-0.010.01 capital city region dummy region 0.550.33*0.510.32 regional population change region 0.130.01**0.130.01 ** inter-seasonal TCI region -0.040.02**-0.020.02 coastal town dummy town 0.660.07**0.630.07 ** distance to city town -0.010.00**-0.010.00 ** proportion of children under 15 years town -0.030.02*-0.030.02 proportion of older adults 65 years and older town -0.120.01**-0.120.01 ** economic activity rate for 15-64 year olds town 0.010.00*0.010.00 ** proportion of working age adults who are unemployed town -0.020.01**-0.030.01 ** population size of town (standardised) town -1.460.51**-1.360.51 ** proportion of dwelling stock registered as vacant in base year town ::0.010.00 ** Random Part Level: 2 (regional) cons/cons : 0.230.05**0.210.04 ** Level: 1 (town) cons/cons : 1.760.05**1.750.05 ** -2*loglikelihood: : 10282.1810269.60 Units: NUTS2 region : 86 Units: towns : 2985 coefficient of partition : 11.5%:10.7%:

11. Model vs Observation (for Wales) 11 Predicted membership of Webb category (based on obseved independent variables) Total % within predicted migration enhanced aging growing labour exporting dying shortened Webb category (four types) - 'observed'/meas ured migration enhanced aging Count1712121 % within measured 81.0%4.8%9.5%4.8%100.0%38.2% growing Count1181020 % within measured 5.0%90.0%5.0%0.0%100.0%36.4% labour exporting Count16209 % within measured 11.1%66.7%22.2%0.0%100.0%16.4% dying Count22105 % within measured 40.0% 20.0%0.0%100.0%9.1% Total Count21276155 % within measured 38.2%49.1%10.9%1.8%100.0%

12. 12 Dependent variables: annual change in (workplace-based) employment Annual employment model with regional and town variables Annual employment model with businesses per capita Fixed Part cons-0.950.43**-0.280.43 case study region dummy0.080.40 0.290.37 proportion of NUTS2 area covered by city (HDUC)-0.040.02**-0.030.02* capital city region dummy-1.290.88 -1.540.88* regional change in workplace jobs0.100.04**0.120.03** inter-seasonal TCI-0.010.05 0.040.06 log transformed gross fixed capital formation per capita3.270.94**1.961.02* coastal town dummy0.100.14 0.150.16 distance to city-0.010.00**-0.010.00** population size of town (standardised)-2.491.03**-2.121.12 proportion of working age adults who are employees0.00 0.040.01** proportion of working age adults who are unemployed-0.040.02**-0.070.02** proportion of working age population with ISCED 5-6 level qualifications 0.020.01**-0.010.01 proportion of working age population with ISCED 3-4 qualifications 0.080.02**0.050.02** proportion of workplace employment in 'industry'-0.030.00**-0.030.01** number of business units per 10000 residents0.180.09** Random Part Level: 2 (regional) cons/cons1.500.30**0.980.22** Level: 1 (settlement) cons/cons4.090.14**4.470.16** -2*loglikelihood:7618.3536947.802 Units: NUTS26557 Units: towns17601579 coefficient of partition26.8%17.9%

13. Demographic change associated with: Being near a large city (market access), population change in wider region, employment rate/labour market conditions and housing occupancy Job growth associated with: Employment change in wider region, skilled resident working age population, small business economy, not having an over- representation of industry Some issues not influenced by policy – climate and coast Need to profile towns individually What underpins ‘better’ performance? 13

14. So what? 14 Town have experienced a range of outcomes over the period (within study area) – Net migration is the most important demographic change Employment may follow high human capital – it does not follow ‘spare labour’/it is not attracted by existing industry In practice the trajectories of small towns are framed by their national/regional context – some of which (climate/location) towns can do little about What are the policy implications?