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

Introduction

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• 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?

What is a town? Llandrindod Wells

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Administrative Administrative “town”“town”

Morphological Morphological “town”“town”

Functional “town”Functional “town”

Key facts for towns?

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Classify towns: migration vs natural change

Classify towns: employment profiles

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• 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?

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• 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’?

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• NUTS2 region – morphological town

• Base year (1999-2002) to end year (2007-11)

Territorial (aggregate) growth model

Population growth: what makes a difference?

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Dependent variable: population growth

population change model without housing variable

population change model with housing variable

Fixed PartCons : 0.30 0.14 ** 0.30 0.14 **

case study region dummy region -0.22 0.16 -0.22 0.16proportion of NUTS2 area covered by city (HDUC) region -0.01 0.01 -0.01 0.01capital city region dummy region 0.55 0.33 * 0.51 0.32regional population change region 0.13 0.01 ** 0.13 0.01 **

inter-seasonal TCI region -0.04 0.02 ** -0.02 0.02coastal town dummy town 0.66 0.07 ** 0.63 0.07 **

distance to city town -0.01 0.00 ** -0.01 0.00 **

proportion of children under 15 years town -0.03 0.02 * -0.03 0.02proportion of older adults 65 years and older town -0.12 0.01 ** -0.12 0.01 **

economic activity rate for 15-64 year olds town 0.01 0.00 * 0.01 0.00 **

proportion of working age adults who are unemployed town -0.02 0.01 ** -0.03 0.01 **

population size of town (standardised) town -1.46 0.51 ** -1.36 0.51 **

proportion of dwelling stock registered as vacant in base year

town : : 0.01 0.00 **

Random PartLevel: 2 (regional) cons/cons : 0.23 0.05 ** 0.21 0.04 **

Level: 1 (town) cons/cons : 1.76 0.05 ** 1.75 0.05 **

-2*loglikelihood: : 10282.18 10269.60Units: NUTS2 region : 86 86Units: towns : 2985 2985coefficient of partition : 11.5%: 10.7%:

Model vs Observation (for Wales)

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Predicted membership of Webb category (based on obseved independent variables)

Total % within predictedmigration

enhanced aging

growing labour exporting dying

shortened Webb category (four

types) - 'observed'/meas

ured

migration enhanced aging

Count 17 1 2 1 21 % within

measured 81.0% 4.8% 9.5% 4.8% 100.0% 38.2%

growingCount 1 18 1 0 20

% within measured 5.0% 90.0% 5.0% 0.0% 100.0% 36.4%

labour exportingCount 1 6 2 0 9

% within measured 11.1% 66.7% 22.2% 0.0% 100.0% 16.4%

dyingCount 2 2 1 0 5

% within measured 40.0% 40.0% 20.0% 0.0% 100.0% 9.1%

Total Count 21 27 6 1 55 % within

measured 38.2% 49.1% 10.9% 1.8% 100.0% 100.0%

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Dependent variables: annual change in (workplace-based) employment

Annual employment model with regional and town variables

Annual employment model with businesses per capita

Fixed Partcons -0.95 0.43 ** -0.28 0.43 case study region dummy 0.08 0.40 0.29 0.37 proportion of NUTS2 area covered by city (HDUC) -0.04 0.02 ** -0.03 0.02 *capital city region dummy -1.29 0.88 -1.54 0.88 *regional change in workplace jobs 0.10 0.04 ** 0.12 0.03 **inter-seasonal TCI -0.01 0.05 0.04 0.06 log transformed gross fixed capital formation per capita 3.27 0.94 ** 1.96 1.02 *coastal town dummy 0.10 0.14 0.15 0.16 distance to city -0.01 0.00 ** -0.01 0.00 **population size of town (standardised) -2.49 1.03 ** -2.12 1.12 proportion of working age adults who are employees 0.00 0.00 0.04 0.01 **proportion of working age adults who are unemployed -0.04 0.02 ** -0.07 0.02 **proportion of working age population with ISCED 5-6 level qualifications

0.02 0.01 ** -0.01 0.01

proportion of working age population with ISCED 3-4 qualifications

0.08 0.02 ** 0.05 0.02 **

proportion of workplace employment in 'industry' -0.03 0.00 ** -0.03 0.01 **number of business units per 10000 residents 0.18 0.09 **

Random PartLevel: 2 (regional) cons/cons 1.50 0.30 ** 0.98 0.22 **Level: 1 (settlement) cons/cons 4.09 0.14 ** 4.47 0.16 **

-2*loglikelihood: 7618.353 6947.802Units: NUTS2 65 57Units: towns 1760 1579coefficient of partition 26.8% 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?

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So what?

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• 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?