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

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 ?

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

Key facts for towns? 4

Classify towns: migration vs natural change

Classify towns: employment profiles 6

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

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

NUTS2 region – morphological town Base year ( ) to end year ( ) Territorial (aggregate) growth model

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 : ** ** case study region dummy region proportion of NUTS2 area covered by city (HDUC) region capital city region dummy region * regional population change region ** ** inter-seasonal TCI region ** coastal town dummy town ** ** distance to city town ** ** proportion of children under 15 years town * proportion of older adults 65 years and older town ** ** economic activity rate for year olds town * ** proportion of working age adults who are unemployed town ** ** population size of town (standardised) town ** ** proportion of dwelling stock registered as vacant in base year town :: ** Random Part Level: 2 (regional) cons/cons : ** ** Level: 1 (town) cons/cons : ** ** -2*loglikelihood: : Units: NUTS2 region : 86 Units: towns : 2985 coefficient of partition : 11.5%:10.7%:

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 Count % within measured 81.0%4.8%9.5%4.8%100.0%38.2% growing Count % 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 Count % within measured 38.2%49.1%10.9%1.8%100.0%

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 ** case study region dummy proportion of NUTS2 area covered by city (HDUC) ** * capital city region dummy * regional change in workplace jobs ** ** inter-seasonal TCI log transformed gross fixed capital formation per capita ** * coastal town dummy distance to city ** ** population size of town (standardised) ** proportion of working age adults who are employees ** proportion of working age adults who are unemployed ** ** proportion of working age population with ISCED 5-6 level qualifications ** proportion of working age population with ISCED 3-4 qualifications ** ** proportion of workplace employment in 'industry' ** ** number of business units per residents ** Random Part Level: 2 (regional) cons/cons ** ** Level: 1 (settlement) cons/cons ** ** -2*loglikelihood: Units: NUTS26557 Units: towns coefficient of partition26.8%17.9%

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

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?