Mihály Fazekas University of Cambridge,

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Presentation transcript:

Mihály Fazekas University of Cambridge, mf436@cam.ac.uk Exploring government contracting data to hold governments accountable in the Big Data Era Mihály Fazekas University of Cambridge, mf436@cam.ac.uk This is a workshop so stay tuned-in, discussion is open any time Data for Policy conference, Cambridge, UK, 17/6/2015 2018.11.07.

The challenge of public procurement 9.3 trillion USD per year* 20-25% lost to corruption** Should we 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 Surveys, rough estimates 2018.11.07.

Overview Big Data in government contracting? State of data and data collection innovations Indicators 3 examples How you can get involved? 2018.11.07.

Why we need big data in PP? 2012, EU, TED 2018.11.07.

State of data I DISASTER 2018.11.07.

One example disaster  Structured text 2018.11.07.

Average admin error TED:2009-13 13 mandatory items: Organisation name, address Contract values Subcontract Dates procuring body address winner name winner address winner town winner post code winner country contract signature date nuts-3 code of contract performance including EU funds or not number of bids received type of assessment criteria used contract value using subcontracts or not 2018.11.07.

State of data II Patchwork of data sources Formats: xml,html,pdf, etc Contents Quality Linking Administrative data provision is not ready for Big Data Public initiatives: OCDS Civil society/reserach community: DIGIWHIST 2018.11.07.

DIGIWHIST The Digital Whistleblower DIGIWHIST The Digital Whistleblower. Fiscal Transparency, Risk Assessment and Impact of Good Governance Policies Assessed Goals Advancing anticorruption, transparency, and spending efficiency in public procurement Open data and indicators for 35 European countries: EU, EEA, Caucasus Enabling losers of corruption to mobilize Helping audit bodies fighting corruption, fraud, and collusion Scope March 2015 – February 2018 3 million eur (Horizon2020 funding) Consortium: Cambridge, Hertie, Open Knowledge Foundation DE, CRCB, Datlab, Transcrime Partnerships with authorities 2018.11.07.

DIGIWHIST: Data Transparency and procurement legislation Procurement data Company information Public organisation information Asset declarations 2018.11.07.

DIGIWHIST: Indicators+utilization Corruption/favouritism Transparency Administrative quality Utilization Web portals, mobile apps Whistleblower reporting: Big Data+user info Risk assessment software for public servants 2018.11.07.

Indicator building New approach to corruption in PP harnessing BIG DATA, built on thorough understanding of context, and ‚open-ended’ Indicator characteristics: Specific Real-time ‘Objective’/hard Micro-level Aggregatable + comparative Objective in a sense that it is ‚hard’ data and lot of people checked them, they are manupilated, but that can be measured 2018.11.07.

Public procurement simplified example 2018.11.07.

3 hands-on examples For The activist The policy maker The economist 2018.11.07.

Big Data for accountability www.tendertracking.eu Goal: Holding governments accounatble All regulated contract since 2005 in Hungary Structured micro data+aggregates Indicators making sense of Big Data Target groups: Citizens, journalists Policy makers Researchers 2018.11.07.

Standard search with some add-ons: CRI 2018.11.07.

Descriptive stats I 2018.11.07.

Descriptive stats II 2018.11.07.

Localize procurement! 2018.11.07.

Track corruption risks 2018.11.07.

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Which features would you like to see on such a portal? Discussion points Which features would you like to see on such a portal? 2018.11.07.

Big Data for austerity http://www.crcb.eu/?p=867 Goal: identifying companies with preferential treatment Data: public procurement+company data: UK Target group Policy makers (those not benefitting of course) Losing bidders Journalists 2018.11.07.

Political Influence Indicator (PII) political influence on companies’ market success Change of government as a natural experiment Company performance before-after gov’t change Evidence of favouritism: tendering red flags 2018.11.07.

Paradigmatic case: Hungary Government change + CRI 2009-2012 2018.11.07.

How does the UK score? Central government only 2009-2010-2013 Company panel data TED data large contracts Imperfect company identification Download link: http://www.crcb.eu/?p=867 Let’s explore the data together! 2018.11.07.

SOME suspicion ◄ 2009h1 largest 5 ▼2013h2 largest 5 xtline sum_ls_org if best==1, ylabel(10(2)24) xlabel(#10, angle(vertical)) ytitle("log contract value won") overlay xtline sum_ls_org if best==2, ylabel(10(2)24) xlabel(#10, angle(vertical)) ytitle("log contract value won") overlay 2018.11.07.

Suspicious error term patterns Systematic deviations from the regression line twoway (scatter alresid2 alresid1)(lfit alresid2 alresid1), xlabel(-5(2.5)5) ylabel(-5(2.5)5) legend(off) ytitle("alresid2") 2018.11.07.

UK central admin: PII 2018.11.07.

Discussion points Any comments on the methodology? How to use the results if nothing is apparently illegal? Someone wants to write an article about these companies? 2018.11.07.

Big Data for competition Bidder data, Hungary, 2007&2009 Goal: identifying suspicious bidding patterns Target group: Competition authorities Losing bidders 2018.11.07.

Network data set-up Co-bidding network Construction work for pipelines, communication and power lines, for highways, roads, airfields and railways 3 regional and 1 national markets in 2007&2009 Nods: bidding firms (winners and losers) size=number of tenders won colour=network position (green=cut-point) Edges: bidding on the same tender width=number of times companies co-bid 2018.11.07.

Co-bidding patterns: benchmark 2007 Dense networks Few cutpoints Cutpoints don’t benefit from position Vertex=bidding company (size=number of tenders won) Edge=cobidding (width=number of tenders) Colour=position (green=cut-point) Hihglight how a healthy market looks like: bottom right dense network 2018.11.07.

Co-bidding patterns: suspicion 2009 Dense networks Many cutpoints Cutpoints seem to benefit from position Higher prices More subcontracting 2018.11.07.

Discussion points Any comments on the methodology? How to mainstream such Big Data analysis in government work? How to bring suspicious cases to court? 2018.11.07.

Getting involved Email me: mf436@cam.ac.uk Visit: digiwhist.eu As a User Hunt for suspicious transactions Download the data for analysis As a Contributor Let us know what you need Tell us about your story Link your data 2018.11.07.

SOON COMING UP: digiwhist.eu Background material SOON COMING UP: digiwhist.eu Fazekas, M. and Tóth, 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), Toolkit for detecting collusive bidding in public procurement. With examples from 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 corruption risk? The impact of EU funds on grand corruption in Central and Eastern Europe. In A. Mungiu-Pippidi (Ed.), The Anticorruption Frontline. The ANTICORRP Project, vol. 2. (pp. 68– 89). Berlin: Barbara Budrich Publishers. Fazekas, M., Tóth, I. J. (2014), Three indicators of institutionalised grand corruption using administrative data. U4 Anti-Corruption Resource Centre, 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. 2018.11.07.

Thanks a lot! 2018.11.07.