Corruption in public procurement: an EU perspective with a focus on Romania Mihály Fazekas University of Cambridge and Government Transparency Institute

Controlling Government: Measuring Corruption Risks in Public Procurement Data, Bucharest, 23/03/2016

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[email protected]

Key messages • Not all news are bad news: Romania as a frontrunner

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• A lot to be done and how Big Data can help

Starting point Available indicators of corruption are either biased or not detailed enough

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 Using Big Data to define corruption proxies on the contract level

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A lot of money involved

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Crucial role in development (e.g. capital accumulation)

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Indicates the broader quality of institutions

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Why public procurement?

Why public procurement? Very corrupt

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Corruption definition In public procurement, the aim of corruption is to steer the contract to the favored bidder without detection. This is done in a number of ways, including: • Avoiding competition through, e.g., unjustified sole sourcing or direct contracting awards. • Favoring a certain bidder by tailoring specifications, sharing inside information, etc.

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See: World Bank Integrity Presidency (2009) Fraud and Corruption. Awareness Handbook, World Bank, Washington DC. pp. 7.

FULL data template Public procurement data

Company financial and registry data Company ownership and management data Political officeholder data Treasury accounts of public organisations

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Arbitration court judgements

Romanian PP data 1.

Tenders Electronic Daily (TED): EU PP Directive • Above 130K/5M EUR

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National PP database: national PP law

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• Below 130K/5M EUR • Above 30K EUR

Conceptualizing public procurement corruption indicators Politically Driven Performance Indicator (PDPI)

Corruption Risk Index (CRI)

Contracting body

Contract

Supplier

Particularistic tie

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Supplier Risk Index (SRI) Political connections Indicator (PCI)

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Contracting Body Risk Index (CBRI)

Blueprint for measuring institutionalised grand corruption in PP 1) Corruption Risk Index (CRI): restricted access to contracts – e.g. tailored tender conditions 2) Political Connections Indicator (PCI): direct/indirect political connections of contractors – e.g. same person owning the supplier and evaluating the tenders 3) Supplier Risk Index (SRI): award to risky businesses – e.g. supplier tax haven registration 4) Contracting Body Risk Index (CBRI): political control of the bureaucracy – e.g. political appointments 5) Politically Driven Performance Indicator (PDPI): outcomes driven by politics rather than open competition

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– e.g. political influence on companies’ market success

Corruption Risk Index (CRI) Risk of institutionalised grand corruption 0 ≤ CRIt ≤ 1

where 0=minimal corruption risk; 1=maximal observed corruption risk Composite indicator of elementary risk (CI) indicators CRIt = Σj wj * CIj t

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Tailored to country context

CRI construction 1.

Wide set of potential components • • • •

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30 red flags from Fazekas et al, 2013 (HU+) 19 red flags from JBF (PL) 10 red flags from zIndex (CZ) Challenge: capturing needs assessmentimplementation

Narrowing down the list to the relevant components • •

CRI calculation: determining weights • •

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Equal weights Norming to 0-1 band

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3.

Checking whether CI fits corruption logic Set of regressions on single bidder (and winner contract share:work in progress)

Indicators tested so far Single bidder contract Call for tenders not published in official journal Procedure type Length advertisement period Weight of non-price evaluation criteria Length of decision period

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1. 2. 3. 4. 5. 6.

Validity: Number of bidders predicts prices Price savings by the number of bidders

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543,705 contracts, EU27, 2009-2014

Validity: political connections&CRI Companies with political connections have higher CRI Hungary, 2009-2012

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Mean CRI Std. Err.

Std. Dev.

95% Conf.Interval

0=no political connection

2900

0.254

0.002

0.111

0.250

0.258

1=politically connected

1449

0.265

0.003

0.110

0.260

0.271

combined

4349

0.258

0.002

0.111

0.254

0.261

0.011***

0.004

-0.018

-0.004

difference (CRI1-CRI0)

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Group

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Some interesting comparative results

Average admin error TED:2009-13 13 mandatory items:

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• Organisation name, address • Contract values • Subcontract • Dates

Single bidding in the EU context (TED) Medium performance...

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... ahead of IT, GR

When it goes wrong it gets ugly

CRI predicts prices (relative contract value)

Effect size per country

EU+EEA, 20092013

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*=significant at 5% level

Patterns of state capture

Captured organisations’ network 2015. 9. 5. Hungary, 2011-2012Q2

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Regional differences are considerable across Europe Corruption Risk Index averages across the EU/EEA

2009-2014

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TED data

Further readings Government Transparency Institute: http://govtransparency.eu/ Fazekas, M. and Tóth, I. J. (2016). From corruption to state capture: A new analytical framework with empirical applications from Hungary. Political Research Quarterly, forthcoming. Fazekas, M., Tóth, I. J., & King, L. P. (2016). Anatomy of grand corruption: A composite corruption risk index based on objective data. European Journal of Criminal Policy and Research, forthcoming Charron, N., Dahlström, C., Fazekas, M., & Lapuente, V. (2015). Carriers, connections, and corruption risks in Europe. Working Paper: 2015:6, Quality of Government Institute, Gothenburg. Fazekas, M., & Kocsis, G. (2015). Uncovering High-Level Corruption: Cross-National Corruption Proxies Using Government Contracting Data. GTI-WP/2015:02, Government Transparency Institute, Budapest. Fazekas, M., Lukács, P. A., & Tóth, I. J. (2015). The Political Economy of Grand Corruption in Public Procurement in the Construction Sector of Hungary. In A. Mungiu-Pippidi (Ed.), Government Favouritism in Europe The Anticorruption Report 3 (pp. 53–68). Berlin: Barbara Budrich Publishers. Czibik, Ágnes; Fazekas, Mihály; Tóth, Bence; and Tóth, István János (2014), Toolkit for detecting collusive bidding in public procurement. With examples from Hungary. GTI-WP/2014:02, Government Transparency Institute, Budapest. 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., & King, L. P. (2013). Corruption manual for beginners: Inventory of elementary “corruption techniques” in public procurement using the case of Hungary. GTI-WP/2013:01, Government Transparency Institute, Budapest. 2016. 03. 31.

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Fazekas, M., Tóth, I. J. (2014), Three indicators of institutionalised grand corruption using administrative data. Budapest: U4-Policy Brief, Bergen, Norway