1. Urban Benchmarking Urban Benchmarking in practice – a few examples 6 XI 2013 | Warsaw| Jakub Rok

2. Aim of the presentation To present the process of results’ benchmarking, basing on examples applying Polish databases and ESPON tools.

3. Introductory remarks Examples presented here should be treated as excersises only - they are not a full-fledged benchmarking process. What doest it mean? We focused on results assessment; tapping the full learning potential of UB requires analysing the processes as well. We employed only quantitative data; qualitative data would allow us to deepen the analysis Each example is based on one chosen data source; using various databases allows to select more appropriate indicators 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.

4. Our framework Analysis of results Visualization of resultsBasic interpretation Calculations Calculating statistics, overcoming the methodological challenges Choosing indicators Selecting the database/toolDefining indicators Choosing the reference group Spatial scope of the comparisonSelection criteria Strategic context Priorities and key challenges Starting point: what is our need? Assessed unitThematic fieldDefining the aim

5. UB: the central administration’s perspective I AIM: Comparing socio-economic performance in coal-based industrial regions which undergo restructuration  with Slaskie voivodeship Strategic context: Europe 2020 Thematic field: labour market, demography, strength of the economy Reference group: similar economic background + comparable role in the national economy + Central and Eastern Europe Ruhr area and Saar area (Germany), Ostrava area (Czech Republic), Jiu Valley (Romania) Selecting indicators – ESPON HyperAtlas

6. INDICATORS Labour market Economically active population (15-64 y.o.) Unemployment rate Demography Share of young people (15-29 y.o.) in the economically active population Strength of the economy 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

7. RegionUnemployment 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% GDP per capita: typology Source: own elaboration based on ESPON HyperAtlas Relative to contry average Indicator’s value Three spatial levels of deviation UB: the central administration’s perspective II

8. UB: the regional administration’s perspective I AIM: Evaluation of the environment protection performance in major cities of the Kujawsko-Pomorskie Voivodeship Drawing on the challneges identified in the National Strategy for Energy Security and Environment and Regional Development Strategy Thematic fields: land management, energetics, air quality, water quality, waste management, ecological awareness Reference: average performance of 4 major cities Bydgoszcz, Toruń, Grudziądz, Włocławek Selection of indicators – BDL database

9. And now, the real UB example – Łódź city 2011 AIM: Provide evidence-based arguments for the municipal, long-term strategy of development Thematic fields: Attracting investors, Public transportation system, Civic participation, Communal services, Metropolitan area cooperation, Labour market, Municipally-owned companies Reference group: competetive cities Białystok, Gdynia, Kraków, Poznań, Rzeszów, Warszawa, Wrocław Data sources: quantitative data from various sources + qualitative data from own research UB: the local administration’s perspective I

10. Conclusion 3 BASIC MODES OF BENCHMARKING Universal comparisons (e.g. major cities of a given region) Comparisons based on a specific feature (e.g. coal-based industry) Distance to top performer WHAT TO THINK OF WHEN PLANNING URBAN BENCHMARKING? Thematic field: does it match the aim? Does it include the strategic context? Reference group: does it match the aim? Does it allow for effective comparison? Data: do variables have a discrimatory power? Are they reliable? Calculations: how to improve the indicators’ appropriateness? How to increase their explanatory power?

11. Thank you for your attention Jakub Rok j.rok@uw.edu.pl Center for European Regional and Local Studies (EUROREG) University of Warsaw www.euroreg.uw.edu.pl