Transcript UB in practice

Urban Benchmarking
Urban Benchmarking in practice – a few
examples
6 XI 2013 | Warsaw| Jakub Rok
Aim of the presentation
To present the process of results’ benchmarking, basing on
examples applying Polish databases and ESPON tools.
Introductory remarks
Examples presented here should be treated as excersises only - they are not
a full-fledged benchmarking process. What doest it mean?
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We focused on results assessment; tapping the full learning potential of
UB requires analysing the processes as well.
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We employed only quantitative data; qualitative data would allow us to
deepen the analysis
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Each example is based on one chosen data source; using various
databases allows to select more appropriate indicators
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We didn’t include the civic participation in the examples. However, this
process is crucial for shaping the research agenda and collective
interpretation of results. Feedbacks obtained in a consultation process
allow for an on-going refinement of the whole benchmarking activity.
Our framework
Starting point: what is our need?
Assessed unit
Thematic field
Defining the aim
Strategic context
Priorities and key challenges
Choosing the reference group
Spatial scope of the comparison
Selection criteria
Choosing indicators
Selecting the database/tool
Defining indicators
Calculations
Calculating statistics, overcoming the methodological challenges
Analysis of results
Visualization of results
Basic interpretation
UB: the central administration’s perspective I
AIM: Comparing socio-economic performance in coal-based
industrial regions which undergo restructuration  with Slaskie
voivodeship
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Strategic context: Europe 2020
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Thematic field: labour market, demography, strength of the
economy
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Reference group: similar economic background + comparable role
in the national economy + Central and Eastern Europe
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Ruhr area and Saar area (Germany), Ostrava area (Czech Republic),
Jiu Valley (Romania)
Selecting indicators – ESPON HyperAtlas
UB: the central administration’s perspective II
INDICATORS
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Labour market
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Demography
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Economically active population (15-64 y.o.)
Unemployment rate
Share of young people (15-29 y.o.) in the economically active population
Strength of the economy
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GDP per capita PPP
Labour productivity PPP
Reference level:
adjacent regions
Source: own
elaboration,
based on
ESPON
HyperAtlas
UB: the central administration’s perspective II
GDP per
capita:
typology
Source: own
elaboration
based on
ESPON
HyperAtlas
Indicator’s
value
Relative to
contry
average
Region
Unemployment
Economic
activity
Share of youth in
economically active pop.
GDP per capita PPP
Labour
productivity PPP
Silesia (PL)
19% | 107%
72% | 103%
33% | 96%
12400 | 108%
17200 | 105%
Moravskoslezský kraj (CZ)
14% | 175%
72% | 101%
31% | 100%
14400 | 85%
20000 | 83%
Vest (RU)
7% | 93%
71% | 101%
33% | 95%
8870 | 113%
12500 | 111%
Saarland (DE)
11% | 97%
66% | 99%
25% | 95%
25400 | 97%
38600 | 98%
Arnsberg (DE)
12% | 109%
66% | 98%
26% | 99%
24300 | 93%
37100 | 95%
UB: the regional administration’s perspective I
AIM: Evaluation of the environment protection performance in major
cities of the Kujawsko-Pomorskie Voivodeship
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Drawing on the challneges identified in the National Strategy for
Energy Security and Environment and Regional Development
Strategy
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Thematic fields: land management, energetics, air quality, water
quality, waste management, ecological awareness
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Reference: average performance of 4 major cities
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Bydgoszcz, Toruń, Grudziądz, Włocławek
Selection of indicators – BDL database
UB: the local administration’s perspective I
And now, the real UB example – Łódź city 2011
AIM: Provide evidence-based arguments for the municipal, long-term
strategy of development
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Thematic fields:
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Reference group: competetive cities
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Attracting investors, Public transportation system, Civic participation,
Communal services, Metropolitan area cooperation, Labour market,
Municipally-owned companies
Białystok, Gdynia, Kraków, Poznań, Rzeszów, Warszawa, Wrocław
Data sources: quantitative data from various sources + qualitative
data from own research
Conclusion
3 BASIC MODES OF BENCHMARKING
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Universal comparisons (e.g. major cities of a given region)
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Comparisons based on a specific feature (e.g. coal-based industry)
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Distance to top performer
WHAT TO THINK OF WHEN PLANNING URBAN BENCHMARKING?
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Thematic field: does it match the aim? Does it include the strategic
context?
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Reference group: does it match the aim? Does it allow for effective
comparison?
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Data: do variables have a discrimatory power? Are they reliable?
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Calculations: how to improve the indicators’ appropriateness? How to
increase their explanatory power?
Thank you for your attention
Jakub Rok
[email protected]
Center for European Regional and Local Studies (EUROREG)
University of Warsaw
www.euroreg.uw.edu.pl