Patterns of public eService development across European cities

1. 2ndInternational EIBURS-TAIPS conference on:“Innovation in the public sector and the development of e-services” Davide Arduini, Annaflavia Bianchi, Alessandra Cepparulo, Luigi Reggi and Antonello Zanfei Eiburs-TAIPS Team, University of Urbino, Italy antonello.zanfei@uniurb.it Patterns of public eService development across European cities University of Urbino April 18-19th, 2013

2. Outline • Focus and motivation • Novelty of this line of research • Research questions • Background literature • Data and indicators • Cross-country comparisons of eService development • Analysis of eService development at the city level • City characteristics and the development of eServices • Conclusions and implications

3. Focus This presentation evaluates public eService diffusion as part of a smart growth strategy in Europe … and motivation • eServicedevelopmentacrossEuropeis a key aspectofinnovation in the public sector and contributesto EU long termcompetitiveness. • extantbenchmarking and studies can hardly account for the actualpatternsof public eServicedevelopmentforseveralreasons: • theymostoften focus on eGovernment, largelydisregardingother web based public activities • evenwhenattentionisgiventoothereServicecategories, these are notexaminedwithcomparablemethods • comparative studies are mostlyfocused on nationalpatterns, withlimitedattentionto the regional or locallevelofanalysis

4. Novelty of this research line • A novel dataset (EIBURS-TAIPS Database) providing comparable data at the following levels: • across four service categories (eGovernment, eProcurement, eHealth and Intelligent Transport Systems) • across countries: EU15 member states • across 229 large and medium sized cities in EU15 countries • An in-depth analysis of heterogeneity of public eService diffusion at all three levels (across service categories, nations and cities) • An exploratory analysis of the characteristics of European cities associated with public eService development • A focus on the links between public eServices and the “smartness” of cities

5. Research questions • How heterogeneous is public sector innovation across EU15 nations in terms of four public eServices ? • Is heterogeneity higher across countries or cities? • Does heterogeneity persist when considering clusters of cities with comparable levels of socio-economic development across Europe? • Are Smart cities also best performers in terms of public eService development? • What characteristics of European cities are associated with public eService development?

6. Background literature (1/4) • Fast growing literature on the diffusion of public eServices (Arduini&Zanfei 2011) • Frequent use of composite indicators (CI)in this field (European Commission 2001-2010, UN 2001-2010) • Most studies focus on eGovernment, very few deal with other public e-services and none assesses national or regional performances across different public e-services. • While there is a relatively long tradition of CIs designed at the national level, their use at the regional and local level is still largely under-developed. • Recent exceptions: • EC (2009) for eProcurement, and CapGemini (2010) for eGovernment have introduced for the first time a regional focus of analysis. • Academic papers have developed CIs using data at the local level, but based on individual services and with low impact at the policy making level (Baldersheim et al. 2008, Flak et al. 2005, Arduini et al. 2010, Codagnone &Villanueva 2011). • T • The focus on individual services at the country level, combined with data constraints encountered at a more disaggregated level, has long led to: • Erroneously conclude that evidence on e-service diffusion in one area of public sector activity can be used to make inference on e-service diffusion in other areas. • disregard the extreme heterogeneity of regions in terms of e-service development

7. Background literature (2/4) • Need to better capture heterogeneity in public sector innovation • Two academic traditions have been feeding the discussion concerning urban innovation and the role of public sector in it: 1) Systemic theory of innovation; and 2) the literature of Smart cities • Systemic theory of innovation • The systemic theory of innovation was initially formulated at the national level • Lundvall (1992), Nelson (1993) and Edquist (2005, 2008) • Gradual shift towards the regional and local levels • (Braczyk et al., 1997; Cooke and Morgan, 1998; Asheimand Coenen, 2005) • Innovation as an evolutionary process, as a result of complex interactions among different actors, including public institutions • Public technology procurement literature emphasises the roles of public sector in systemic innovation (Edquist et al. 2000;Hommen and Rolfstam, 2009) • Purchaser and end users of technology • Catalysers of innovation •  Differences in the nature, behaviour and organisation of players involved, including the public sector, combined with the characteristics of technologies determine a high heterogeneity of innovation processes across countries and regions (Fagerberg, 2005; Tether and Metcalfe, 2004)

8. Background literature (3/4) • 2) The literature of Smart cities • One distinctive feature of smart cities is their performance in the field of innovation (Arribas et al. 2012; Deakin, 2012; Capello et al., 2012) • Smart cities defined as territories combining: the creativity of talented individuals, the development of institutions that enhance learning and innovation, and of digital spacesfacilitating knowledge transfer(Eger, 1997; Graham and Marvin, 2001, Komninos, 2006; Sotarauta, 2010, Boulton et al., 2012) • The support of local government innovation policies is fundamental to the design and implementation of smart city initiatives (Lindskog, 2004; Lepouras et al., 2007; Ingram et al., 2009; Giffinger and Gudrun, 2010) • City smartness will likely feed-back on local government capacity to innovate their organisation and activities, including their services • Smarter cities are likely to have more innovative public sector and more advanced public services • Heterogeneity of innovation within regions and across cities as a result of different mix of city level characteristics and evolution

9. Background literature (4/4) • Based on these converging streams of literature we should thus observe: • A high heterogeneity in public sector innovation across nations • Heterogeneity in terms of overall eService diffusion • Heterogeneity in terms of eService portfolio and specialisation • Heterogeneity is even higher at the local level, reflecting the variety of actors and local government roles • Cities will differ in terms of eServices reflecting their smartness

10. Data and indicators • Ourstudycombinestwodatasets: • 1) EIBURS-TAIPS Dataset(source: University of Urbino) • Data characteristics: information collectedby the TAIPS team throughwebsite-surfing to monitor public e-services provided by local public transport companies, municipalities and hospitals at the city level (15-EU). Data refer to availability of eServices in 2012, corrected to account for standard quality measures. • Sample design: 229 cities representing the EU15 subset of the 322 cities monitored in Eurostat’s Urban Audit dataset • Variables: info on the provision of 23 eServices classified into four categories • ITS/Infomobility (based on ITIC-Between methodology, 2010) • eHealth (Based on Empirica methodology, 2008; and Deloitte methodology, 2011) • eProcurement (based on IDC methodology, 2010) • eGovernment (based on Capgemini methodology, 2010)

11. Data and indicators • 2) Urban Audit Dataset (source: Eurostat) : • Data characteristics: comparable information on 322 cities, out of which the EU15 sample of 229 cities is derived • Sample design: citiesincludedcorrespondto • 20% of the national population • the geographic distribution of population within the country (peripheral, central) • the size distribution within countries (medium-sized cities having a population of 50,000 – 250,000 inhabitants, large cities with >250 000) • Time coverage: six waves • 1989 - 1993; 1994 - 1998; 1999 - 2002; 2003 - 2006; 2007 - 2009 • Variables: • demography, social aspects, economic aspects, civic involvement, training and education, environment, ICT, travel and transport, information society, culture and recreation

12. Data and indicators: E-HEALTH

13. Data and indicators : ITS/INFOMOBILITY

14. Data and indicators :E-PROCUREMENT

15. Data and indicators: EGOVERNMENT

16. Measuring service availability and quality

17. Construction of a Composite Indicator (CI) -1 • An indicator of public eServices development is calculated as the average of a city’s performance in the four domains considered (eHealth, Infomobility, eGovernment, eProcurement) • For each domain, we used existing analytical frameworks in order to: (a) define the different dimensions of phenomena studied, including standard measures of quality of eServices available (e.g. interactivity); (b) define the nested structure of the various sub‐groups that will guide the aggregation process; (c) select the underlying basic indicators • The indicators obtained are then normalized (MIN-MAX method) in order to make the scores of each city in the four domains fully comparable.

18. Construction of a Composite Indicator (CI) -2 Final aggregation = Arithmetic mean

19. Cross-country comparisons of eService development

20. Country index vs EU average index

21. Heterogeneity across eServices and across countries Frontrunners Group ofcountrieswithall or moste-servicessuppliedabove the EU average

22. Good Performers Group of countries with one or two e-services supplied above the EU average

23. HeterogeneityacrosseServices and acrosscountries Group of countries with one e-service supplied above the EU average

24. Lagging behind Group of countries with average or below average performance

25. AnalysisofeServicedevelopment at the city level

26. Comparing heterogeneity across countries and across municipalities EU average

27. Comparing eServices across cities • To compare the municipalities in termsoftheir e-service diffusion and sophistication, weneedtorefertoclustersofhomogenousmunicipalities • To do so wefollow a threestep procedure: • Drawing data fromUrbanAudit, weuse PCA toidentify a few “summaryvariables” (components) that can beheldtoberepresentativeofdifferentaspectsofmunicipalities • Weidentify the clustersofmunicipalitiesbased on the abovementionedcomponents • UsingEiburs-TAIPS data on eGov, Infomobility, eProcurement and eHealth at the city levelwe illustrate howclusters can becharacterised in termsofeServicedevelopment Thesecomparisons are possiblefor 148 citiesonly, due to data constraints

28. First step: PrincipalComponentAnalysis Demographic characteristics: Percentage of residents over 65 Population density: total resident pop. per square km Infrastructural characteristics Length of public transport network / land area Percentage of households with Internet access at home Civil society Participation rate at city elections Number of female elected city representatives Human capital Prop. of working age population qualified at level 5 or 6 ISCED Economic Characteristics: Gross Domestic Product per inhabitant in PPS of NUTS43 Unemployment rate Sectoral specialization: No. Manufacturing (and service?)Companies (all sectors?) Number of persons employed in provision of ICT services Prop. of employment in financial and business services (NACE Rev.1.1 J-K) Environmental sensibility: Annual amount of solid waste (domestic and commercial) that is recycled Attractiveness: Total annual tourist overnight stays in registered accommodation

29. Validated PCA -2

33. Comparisonof the municipalitiesacross and withinclusters in termsofeServicessupplied

34. Clustersindex vs Cluster averageindex

35. Cluster 3 – “medium low industrial/infrastructuraldevelopment and medium low share of business services”Ranking ofcities in termsofeServices Northern Europe Southern Europe

36. Cluster 6 – “medium high industrial/infrastructuraldevelopment and medium high share of business services”Ranking ofcities in termsofeServices Southern Europe

37. Correlation among E-services index- Smart index and its components Source: European Smart Cities (Vienna University of Technology ,  Delft University of Technology and the University of Ljubljana). *significance level 5%

38. In search of determinants of eService index Given the resultsofcorrelationswesearchfordriversof public eServicesbased on the empiricalliterature on determinantsof Smart city developmentcf. European Smart Cities (2007), Caragliu, Del Bo, Nijkamp(2011), Caragliu & Del Bo (2012)

39. Determinants of the development of smart cities - Gross Domestic Product of city/region/country (Euro) - New business that have registered in the reference year* - Self-employment rate - Proportion in part time - Proportion of population aged 15-64 qualified at tertiary level (ISCED 5-6) living in Urban Audit cities - % - Total book loans and other media per resident* - Number of tourist overnight stays in registered accommodation per year per resident population - Total number of recorded crimes per 1000 population - Number of hospital beds - Cinema attendance (per year)* - Theatre attendance (per year)* - Number of museum visitors (per year) - Length of public transport network per inhabitant - Number of stops of public transport - Number of deaths in road accidents Smart economy component Smart mobility component Smart people component * Large number of missing values Smart living component

40. Correlationscheckamongourindex and the potentialdeterminants Our research question thus translates in an empirical model of the form: where ESindex is the composite indicator of eService development subscript i refers to cities and supra-script j refers to the a specific component of city smartness Variables Gdppp: Gross Domestic Product purchasing power parity Selfemploy:Self-employment rate Propparttime:Proportion in part time on total workforce bedhosital:Number of hospital beds Museum:Number of museum visitors (per year) Isced56: saher of population qualified at tertiary level (ISCED 5-6) Tourist:Number of tourist overnight stays in registered per year Public transport stops(Stopbsn):Number of stops of public transport Length transport: km of public transportation network per resident population Crime: Total number of recorded crimes per 1000 population Road accident: Deaths in road accidents per year

41. Testingdeterminantsofe-servicesindex -1

42. Testingdeterminantsofe-servicesindex -2

43. CONCLUSIONS • Thispresentationfillsthreegaps: • Coverageof public eServicesbeyondeGovernmentwithcomparable data • ComparingeServicedevelopmentacrosscountries and cities • LinkingeServiceswithsmartnessofcities • Heterogeneity in Public eServicedevelopmentis high acrosscountries and across service categories • Heterogeneityisevengreaterwhenexamined at the city level and acrossclustersofrelativelyhomogeneouscities • Citiesfromnordic and centralEuropeancountries are largely ranking high, butthereisheterogeneityalsoacrossthesecities a regional and sub-regional approachneeded • “Smart cities” alsoexhibit high levelsofeServicedevelopment • Smart city characteristicsthat are mostassociatedwith public eServicediffusion are: human capital and transportationinfrastructuredevelopment

44. Thanks

45. Data and indicators City sample

46. Descriptive statistics

47. PCA -3

48. Step 2: cluster analysis

49. Cluster 7 – “medium low industrial/infrastructuraldevelopment and medium high share of business services”Ranking ofcities in termsofeServices Central Europe Northern Europe Southern Europe

50. Cluster 5 – “medium industrial/infrastructuraldevelopment and high share of business services”Ranking ofcities in termsofeServices