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Exploring government contracting data to hold governments accountable in the Big Data Era

Exploring government contracting data to hold governments accountable in the Big Data Era. Mihály Fazekas University of Cambridge , mf436@ cam.ac.uk. Data for Policy conference , Cambridge, UK, 17/6/2015. The challenge of public procurement. 9.3 trillion USD per year *

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Exploring government contracting data to hold governments accountable in the Big Data Era

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  1. Exploring government contractingdata to hold governmentsaccountable in the Big Data Era MihályFazekas University of Cambridge, mf436@cam.ac.uk Data for Policy conference, Cambridge, UK, 17/6/2015

  2. The challenge of publicprocurement 9.3 trillion USD per year* 20-25% losttocorruption** Shouldwe be worried? * http://www.open-contracting.org/data-standard-announcement **OECD (2013), Implementing the OECD Principles for Integrity in Public Procurement. Progress since 2008. OECD, Paris

  3. Overview • Big Data ingovernmentcontracting? • State of data and datacollectioninnovations • Indicators • 3 examples • Howyoucangetinvolved?

  4. Whyweneedbigdatain PP? 2012, EU, TED

  5. State of data I DISASTER

  6. Oneexampledisaster

  7. Averageadminerror TED:2009-13 13 mandatoryitems: • Organisationname, address • Contractvalues • Subcontract • Dates

  8. State of data II • Patchwork of datasources • Formats: xml,html,pdf, etc • Contents • Quality • Linking • Administrativedataprovision is notreadyfor Big Data • Public initiatives: OCDS • Civil society/reserachcommunity: DIGIWHIST

  9. DIGIWHISTThe Digital Whistleblower. Fiscal Transparency, Risk Assessment and Impact of Good Governance Policies Assessed • Goals • Advancinganticorruption, transparency, and spendingefficiencyinpublicprocurement • Open data and indicatorsfor 35 European countries: EU, EEA, Caucasus • Enablinglosers of corruptiontomobilize • Helping audit bodiesfightingcorruption, fraud, and collusion • Scope • March 2015 – February 2018 • 3 millioneur (Horizon2020 funding) • Consortium:Cambridge, Hertie, Open KnowledgeFoundation DE, CRCB, Datlab, Transcrime

  10. DIGIWHIST: Data • Transparencyand procurementlegislation • Procurementdata • Companyinformation • Public organisationinformation • Assetdeclarations

  11. DIGIWHIST: Indicators+utilization • Indicators: • Corruption/favouritism • Transparency • Administrativequality • Utilization • Web portals, mobile apps • Whistleblowerreporting: Big Data+userinfo • Riskassessment software forpublicservants

  12. Indicator building • New approachtocorruptionin PP • harnessing BIG DATA, • builton thorough understanding of context, and • ‚open-ended’ • Indicatorcharacteristics: • Specific • Real-time • ‘Objective’/hard • Micro-level • Aggregatable + comparative

  13. 3 hands-onexamples For • The activist • The policy maker • The economist

  14. Big Data foraccountability • www.tendertracking.eu • Goal: Holding governmentsaccounatble • Allregulatedcontractsince 2005 in Hungary • Structuredmicrodata+aggregates • Indicatorsmakingsense of Big Data • Targetgroups: • Citizens, journalists • Policy makers • Researchers

  15. Discussionpoints Whichfeatureswouldyouliketoseeonsuch a portal?

  16. Big Data forausterity • http://www.crcb.eu/?p=867 • Goal: identifyingcompanieswithpreferentialtreatment • Data: publicprocurement+companydata: UK • Targetgroup • Policy makers (thosenotbenefitting of course) • Losingbidders • Journalists

  17. Political Influence Indicator (PII) • politicalinfluence oncompanies’ market success • Change of governmentas a naturalexperiment • Company performance before-aftergov’tchange • Evidence of favouritism: tendering redflags

  18. Paradigmaticcase: Hungary Governmentchange + CRI 2009-2012

  19. Howdoesthe UK score? • Centralgovernmentonly • 2009-2010-2013 • Company panel data • TED data • largecontracts • Imperfectcompanyidentification • Download link: http://www.crcb.eu/?p=867 • Let’sexplorethedatatogether!

  20. SOME suspicion◄ 2009h1 largest 5▼2013h2 largest 5 xtlinesum_ls_org if best==1, ylabel(10(2)24) xlabel(#10, angle(vertical)) ytitle("log contract value won") overlay xtlinesum_ls_org if best==2, ylabel(10(2)24) xlabel(#10, angle(vertical)) ytitle("log contract value won") overlay

  21. Suspiciouserrortermpatterns Systematicdeviationsfromtheregression line twoway (scatter alresid2 alresid1)(lfit alresid2 alresid1), xlabel(-5(2.5)5) ylabel(-5(2.5)5) legend(off) ytitle("alresid2")

  22. UK centraladmin: PII

  23. Discussionpoints • Anycommentsonthemethodology? • Howtousetheresultsifnothing is apparentlyillegal? • Someonewantstowrite an articleaboutthesecompanies?

  24. Big Data forcompetition • Bidderdata, Hungary, 2007&2009 • Goal: identifyingsuspiciousbiddingpatterns • Targetgroup: • Competitionauthorities • Losingbidders

  25. Network dataset-up • Co-biddingnetwork • Construction work for pipelines, communication and power lines, for highways, roads, airfields and railways • 3 regional and 1 nationalmarketsin 2007&2009 • Nods: biddingfirms (winners and losers) • size=numberof tenderswon • colour=networkposition(green=cut-point) • Edges: biddingonthesame tender • width=number of timescompaniesco-bid

  26. Co-biddingpatterns: benchmark • 2007 • Densenetworks • Fewcutpoints • Cutpointsdon’t benefitfromposition

  27. Co-biddingpatterns: suspicion • 2009 • Densenetworks • Manycutpoints • Cutpointsseemtobenefitfromposition

  28. Discussionpoints • Anycommentsonthemethodology? • Howtomainstreamsuch Big Data analysisingovernmentwork? • Howtobringsuspiciouscasestocourt?

  29. Gettinginvolved • Email me: mf436@cam.ac.uk • Visit: digiwhist.eu • As a User • Hunt forsuspicioustransactions • Downloadthedataforanalysis • As a Contributor • Letusknowwhatyouneed • Tell usaboutyour story • Link yourdata

  30. Backgroundmaterial SOON COMING UP: digiwhist.eu Fazekas, M. andTóth, I. J. (2014). From corruption to state capture: A new analytical framework with empirical applications from Hungary. CRC-WP/2014:01, Budapest: Corruption Research Centre. Czibik, Ágnes; Fazekas, Mihály; Tóth, Bence; and Tóth, István János (2014), Toolkitfordetectingcollusivebiddinginpublicprocurement. Withexamplesfrom Hungary. Corruption Research Center Budapest, CRCB-WP/2014:02. Fazekas, M., Chvalkovská, J., Skuhrovec, J., Tóth, I. J., & King, L. P. (2014). Are EU funds a corruptionrisk? The impact of EU fundson grand corruptioninCentral and Eastern Europe. In A. Mungiu-Pippidi (Ed.), The AnticorruptionFrontline. The ANTICORRP Project, vol. 2. (pp. 68–89). Berlin: Barbara BudrichPublishers. Fazekas, M., Tóth, I. J. (2014), Three indicators of institutionalised grand corruption using administrative data. U4 Anti-CorruptionResourceCentre, Bergen, U4 Brief 2014:9. Fazekas, M., Tóth, I. J., & King, L. P. (2013). Anatomy of grand corruption: A composite corruption risk index based on objective data. CRC-WP/2013:02, Budapest: Corruption Research Centre. Fazekas, M., Tóth, I. J., & King, L. P. (2013). Corruption manual for beginners: Inventory of elementary “corruption techniques” in public procurement using the case of Hungary. CRC-WP/2013:01,Corruption Research Centre, Budapest. Fazekas, M., Tóth, I. J., & King, L. P. (2013). Hidden Depths.The Case of Hungary. In A. Mungiu-Pippidi (Ed.), Controlling Corruption in Europe vol. 1 (pp. 74–82). Berlin: Barbara Budrich Publishers.

  31. Thanks a lot!

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