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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.

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Urban benchmarking

Urban Benchmarking

Urban Benchmarking inpractice – a fewexamples

6 XI 2013 | Warsaw| Jakub Rok

Urban benchmarking

Aim of thepresentation

  • To presenttheprocess of results’ benchmarking, basing on examplesapplyingPolishdatabases and ESPON tools.

Urban benchmarking


  • Examplespresentedhereshould be treated as excersisesonly - theyare not a full-fledged benchmarking process. Whatdoestitmean?

    • We focused on resultsassessment; tappingthefull learning potential of UB requiresanalysingtheprocesses as well.

    • We employedonlyquantitative data; qualitative data wouldallowus to deepentheanalysis

    • Eachexampleisbased on one chosen data source; usingvariousdatabasesallows to selectmoreappropriateindicators

    • We didn’tincludethecivicparticipationintheexamples. However, thisprocessiscrucial for shapingtheresearch agenda and collectiveinterpretation of results. Feedbacksobtainedin a consultationprocessallow for an on-goingrefinement of thewhole benchmarking activity.

Urban benchmarking


Urban benchmarking

UB: the central administration’sperspective I

  • AIM: Comparingsocio-economic performance incoal-based industrial regions whichundergorestructuration withSlaskievoivodeship

  • Strategiccontext: Europe 2020

  • Thematic field: labour market, demography, strength of theeconomy

    • Reference group: similareconomicbackground + comparable role inthe national economy + Central and Eastern Europe

      • Ruhr area and Saararea (Germany), Ostravaarea (Czech Republic), Jiu Valley (Romania)

    • Selectingindicators – ESPON HyperAtlas

Urban benchmarking

UB: the central administration’sperspective II


  • Labour market

    • Economicallyactivepopulation (15-64 y.o.)

    • Unemploymentrate

  • Demography

    • Share of youngpeople (15-29 y.o.) intheeconomicallyactivepopulation

  • Strength of theeconomy

    • GDP per capita PPP

    • Labourproductivity PPP

  • Referencelevel: adjacent regions

    Source: ownelaboration, based on ESPON HyperAtlas

    Urban benchmarking

    UB: the central administration’sperspective II

    GDP per capita:


    Source: ownelaborationbased on ESPON HyperAtlas

    Threespatiallevels of deviation

    Relative to contry average


    Urban benchmarking

    UB: theregionaladministration’sperspective I

    • AIM: Evaluation of theenvironment protection performanceinmajor cities of the Kujawsko-Pomorskie Voivodeship

      • Drawing on thechallnegesidentifiedintheNational Strategy for Energy Security and Environment andRegional Development Strategy

      • Thematicfields: land management, energetics, air quality, waterquality, waste management, ecologicalawareness

      • Reference: average performance of 4 major cities

        • Bydgoszcz, Toruń, Grudziądz, Włocławek

      • Selection of indicators – BDL database

    Urban benchmarking

    UB: thelocaladministration’sperspective I

    • And now, thereal UB example – Łódź city 2011

    • AIM: Provideevidence-basedarguments for themunicipal, long-term strategy of development

      • Thematicfields:

        • Attractinginvestors, Public transportation system, Civicparticipation, Communal services, Metropolitanareacooperation, Labour market, Municipally-ownedcompanies

      • Reference group: competetivecities

        • Białystok, Gdynia, Kraków, Poznań, Rzeszów, Warszawa, Wrocław

      • Data sources: quantitative data fromvarioussources + qualitative data fromownresearch

    Urban benchmarking



    • Universal comparisons (e.g. major cities of a given region)

      • Comparisonsbased on a specificfeature (e.g. coal-based industry)

      • Distance to top performer


      • Thematic field: doesitmatchtheaim? Doesitincludethestrategiccontext?

      • Reference group: doesitmatchtheaim? Doesitallow for effectivecomparison?

    • Data: do variableshave a discrimatorypower? Aretheyreliable?

      • Calculations: how to improvetheindicators’ appropriateness? How to increasetheirexplanatorypower?

    Urban benchmarking

    Thankyou for yourattention

    Jakub Rok


    Center for EuropeanRegional and LocalStudies (EUROREG)

    Universityof Warsaw


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