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Developing and Implementing
Data Proxies for Detecting Corruption
Mihály Fazekas*- István János Tóth+
*: University of Cambridge and Corruption Research Center Budapest, [email protected]
+: Hungarian Academy of Sciences and Corruption Research Center Budapest, [email protected]
2014.12.04. 1
Salzburg Data Science Symposium,
Salzburg, 20 November 2014
This project is co-funded by the
Seventh Framework Programme for
Research and Technological
Development of the European Union
"When you can measure what you are speaking
about, and express it in numbers, you know
something about it; but when you cannot express
it in numbers, your knowledge is of a meagre and
unsatisfactory kind; it may be the beginning of
knowledge, but you have scarcely, in your
thoughts, advanced to the stage of science,
whatever the matter may be."
Lord Kelvin
Idea vera debet cum suo ideato convenire
[Spinoza: Ethica; Axiomata VI.]
Staff & Support
Staff:
Ágnes Czibik economist
Mihály Fazekas Ph.D., University of Cambridge; head of research CRCB
Gyula Fóra ELTE, student
Júlia Orbán economist
Bálint Szalai journalist
Bence Tóth economist
István János Tóth Ph.D, senior research fellow, IE HAS; director CRCB
Experts:
Zoltán Kelemen lawyer
Jenő Gyenese computer programmer
Zoltán Nagy ecomomist (former head of Hungarian Competition Authority)
Sándor Rácz computer programmer
Zoltán Siposs journalist
Tamás Uhrin computer programmer
Data cleaning: Balázs Agárdy, Mónika Bak, Mária Balla, Eszter Bíró, Linda Borbély, Eszter Csizmás, Kinga Csizmás, Olivér Csukás, Eszter Farkas, Annamária Ferenczi, Katalin Gajdos, János Gáspár, Diána Groszmann, Tamás Heizer, Erik Herbák, Erzsébet Hoffman, Eszter Kádár, Emese Koplányi, Gabriella Korom, Balázs Kovács, Dávid Levendel, Máté Magyar, Anna Markó, Eszter Milibák, Ákos Nagy, Laura Pacsa, Ilona Pallagi, Tibor Pallagi, Ágnes Parkot, Erika Révész, Flóra Samu, Levente Séd, Eszter Simon, Dóra Somogyi, Nóra Staub, Adrienn Süli, Tímea Szabó, Adrienn Szigili, Győző Teplán, Petra Vancsura, Attila Varga, Kinga Varga
Support:
voluntary work, EU FP7, TAMOP, Hungarian Competitive Authority
2014.12.04. 4
Overview
• Motivation
• Corruption at a Glance
• Measurement: old & new method
• Database building
• Proposed corruption risk indicators
• Illustrative results
• Further research
2014.12.04. 5
motivation corrupion measurement data buiding indicators illustrations plans
Motivation
motivation corrupion measurement data indicators illustrations plans
Policy problems
• How much public money is lost due to
corruption in public procurement?
• Which tender should be investigated by
the authorities?
• What works for reducing corruption risks in
public procurement?
2014.12.04. 7
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Why public procurement?
1. A lot of money involved
2. Crucial role in development (e.g. capital
accumulation)
3. Indicates the broader quality of state
institutions
2014.12.04. 8
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Why public procurement?
4. Very corrupt
2014.12.04. 9
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Corruption at a Glance
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Corruption at a Glance
• „The misuse of public/private [entrusted]
power power for private gain”
• Actors: principal - agent - client PA model from the micro-economics
(e.g.: government – gov. officials – comp. manager)
• Petty corruption vs. Grand corruption
2014.12.04. 11
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Corruption at a Glance
• Where could occur?
– Issuing licences
– Inspection (police, tax authorities, e.t.c.)
– Regulation, Legistlation
– Public Procurement
2014.12.04. 12
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Corruption at a Glance
• Main types:
– bribery
– extortion
– emblezzement
– fraud
2014.12.04. 13
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Definition of instutionalised grand corruption
• Specific definition (just like measurement)
• Institutionalised grand corruption in public procurement
institutionalised grand corruption in public procurement refers to the regular particularistic allocation and performance of public procurement contracts by bending universalistic rules and principles of good public procurement in order to benefit a group of individuals while denying access to all others.
2014.12.04. 14
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Measurement of Corruption
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Old methods
• Available indicators are either biased or too idiosyncratic
– Perception-based survey instruments measure PERCEPTIONS (e.g. TI CPI)
– Experience-based survey instruments suffer from conformity bias and lack of access
– Audits and case studies measure narrow phenomena
Need for new indicators
2014.12.04. 16
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New methods for measurement of Corruption
• Perception indicators are not good enough
• New paradigm of measurement
– harnessing BIG DATA,
– built on thorough understanding of context,
– understanding mechanisms
– and
– ‚open-ended’
2014.12.04. 17
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Our philosophy for measurement
• Corruption is a hidden (exremly latent)
phenomenon, it is impossibe to observe at
actors’ level
• Instead to observe corruption we observe
the risk of corruption
• Informations from micro (actors’ ) level
2014.12.04. 18
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Corruption like a Black Hole
2014.12.04. 19
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New approach for measurement
• Our Indicators’ characteristics:
– Specific
– Real-time
– ‘Objective’/hard
– Micro-level
– Aggregatable + comparative
2014.12.04. 20
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Building a linked database
• Public procurement data
• Company financial and registry data
• Company ownership and management data
• Political officeholder data
• Treasury accounts of public organisations
• Arbitration court judgements
2014.12.04. 21
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PP data - State of the art
• Global standardization effort: www.open-contracting.org/
• TED data: DG Markt, http://ted.openspending.org/, etc.
• National datasets:
– Developed economis: CA, IT, US
– Transition economies: CZ, HR, HU, RO, SK, SL
– Developing countries: GEO: http://tendermonitor.ge/en
• Development agencies
– World Bank
– UNDP
2014.12.04. 22
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Initial data and database building
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The Worst and the Ugly • The Hungarian authorities do not have a
comprehensive database about PP (from 1998- )
• Majority of information is string, not numeric
• Only individual cases => case studies
• Lack of measurement and => lack of reliable analysis
• Weak knowledge about causes and mechanisms of corruption in PP
2014.12.04. 24
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Hungary: aggregate data [pdf]
http://www.kozbeszerzes.hu/static/uploaded/document/Sajt%C3%B3anyag_2011%20%C3%
A9v%202012%20%20els%C5%91%20negyed%C3%A9v_v%C3%A9gleges.pdf
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Hungary: individual data [txt, html, php]
http://www.kozbeszerzes.hu/ertesito/megtekint/portal_15125_2012/
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First Period - 1998-2004: txt
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Second Period - 2005-2012: html
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Third Period - 2013 - : html, xml
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An ideal case: Chile
http://www.chilecompra.cl/
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MaKAB: 1998-2014
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Our tasks • Database architecture (MySQL)
• Dowloading text, html, xml data form HPP webpage
190.000 observations and cca. 300 variables
• data cleaning (Python, Java, PHP)
(e.g. „Budapist”, „Budipest”, „Budpest” to „Budapest”)
• Data corrections: e.g. values in EUR, HUF, USD => HUF
with and without VAT => net values
• And so on…
2014.12.04. 33
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Our tasks • Tests and data improvement (imput data from external
sources: e.g. company data)
• Indicator building
• Indicator tests, validation
• Analysis (STATA, SPSS, R)
2014.12.04. 34
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Building a linked database
• Public procurement data
• Company financial and registry data
• Company ownership and management data
• Political officeholder data
• Treasury accounts of public organisations
• Arbitration court judgements
2014.12.04. 35
motivation corrupion measurement data indicators illustrations plans
PP data - State of the art
• Global standardization effort: www.open-contracting.org/
• TED data: DG Markt, http://ted.openspending.org/, etc.
• National datasets:
– Developed economies: CA, IT, US
– Transition economies: CZ, HR, HU, RO, SK, SL
– Developing countries: GEO: http://tendermonitor.ge/en
• Development agencies
– World Bank
– UNDP
2014.12.04. 36
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Proposed indicators
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Blueprint for measuring institutionalised
grand corruption in PP
1. Corruption Risk Index (CRI): generation and allocation of rents [intervall]
2. Political Influence Indicator (PII): political influence on companies’ market success [intervall]
3. Political Control Indicator (PCI): direct political control of contractors [0,1]
2014.12.04. 38
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CRI
• A composit indicator (information from elementary indicators)
• Information from the PP documents (tender docs, assessment,
delivery )
• Daily data
• Micro (from micro to macro level)
• Initiative step: CRI at contract level
• And:
CRI at issuer level and
CRI at winner, competitor level
CRI at country level
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CRI
• Number of bids (if 1 => high corruption risk)
• Call for tender not published in official journal (if yes => high CR)
• Procedure type (if not open => high CR)
• Length of eligibility criteria (if long => high CR)
• Exceptionally short submission period (if yes => high CR)
• Relative price of tender documentation (if high => high CR)
• Call for tenders modification (if yes => high CR)
• Weight of non-price evaluation criteria (if high => high CR)
• Annulled procedure re-launched subsequently (if yes, high CR)
• Length of decision period (if short => high CR)
• Contract modification (if yes => high CR)
• Contract value increase (if yes => high CR)
• Winner's market share (if high => high CR)
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Share of PP with one bidder by month,
in Hungary, 2009.01-2014.10, N = 108.287
An elementary indicator: number of bids motivation corrupion measurement data indicators illustrations plans
Corruption Risk Index (CRI) • Probability of institutionalised grand corruption to occur
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
• Elementary risk indicators are combined to reflect a corrupt rent extraction logic
2014.12.04. 42
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Components of CRI
2014.12.04. 43
X = available
* = potentially available
CZ HU SK TED
Single bidder contract (valid/received) 1=1 bid received x x x x
Call for tenders not published in official journal 1=NO call for tender published in official journal x x x x
Procedure type open, invitation, etc. x x x x
Relative length of eligibility criteria Number of characters relative to market average x x * *
Length of submission period Number of days between the publication of call for tenders and the submission deadline x x x x
Relative price of tender documentation price of tender documentation DIVIDED BY contract value x
Call for tenders modification 1=call for tender modified during submission period x x * *
Exclusion of all but one bid 1=all but one bid excluded x
Weight of non-price evaluation criteria Sum of weights for evaluation criteria which are NOT related to prices x x x x
Annulled procedure re-launched subsequently 1=contract awarded in procedure annulled, but re-launched x
Length of decision period number of days between submission deadline and announcing contract award x x x x
Contract modification 1=contract modified during delivery * x
Contract lengthening/value increase relative contract extension (days) or relative contract price increase * x
overall winner contract share 12-month total contract value of winner / 12-month total awarded contract value (by issuer) x x x *
9 14 7 6
11 14 9 9
Number of availab le components
Number of potentially availab le components
sub-
mission
assess-
ment
delivery
Proc.
phaseIndicator name Indicator values
availability
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Validation
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What kind of CRI distributions arise?
2014.12.04. 45
average
CRI
Per
winning
bidder
2009-
2012
Hungary
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Political favoritism in Hungary
2014.12.04. 46
Hungary, total public procurement market, HU, 2009-2012
0%
10%
20%
30%
40%
50%
60%
70%
2009 2010 2011 2012
tota
l mar
ket
shar
es
"surprise" losers "surprise" winners
Election year
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Political Control Indicator (PCI)
• Whether a company has direct political
connections
PCIi =
2014.12.04. 47
1, if company i has pol. connections
0, if company i does NOT have pol. conn.
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Indicator validity
• The indicators co-vary
• CRI + PCI, HU, 2009-2012
2014.12.04. 48
Group N 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
difference (CRI1-CRI0)
-0.011*** 0.004
-0.018 -0.004
0.011**
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Indicator validity at EU level • Our indicators relate to external variables as expected: money
laundering, diversion of funds
• Financial Secrecy Index + CRI in EU, 2009-2013, N=26.309
2014.12.04. 49
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Some illustrative findings
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Applications - overview
Evaluating political change: Hungary before and after the general elections (2010), from socialist to Orban’s gov.
Evaluating countries: against each other or the same country over time
Evaluating large funding programmes: e.g. EU structural funds in CEE
2014.12.04. 51
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Identifying focal points for policy intervention: e.g. networks of corruption
Evaluating the impact of single regulatory or organisational changes on corruption: e.g. loosening transparency regulations
Risk-based audit of actors/transactions
2014.12.04. 52
Applications - overview
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CRI in Hungary before and after Orban’s government,
N= 43.642
0.1
0.2
0.3
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0.5
0.6
20
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upper-bound CRI (per contract)
gov't change
Application – Gov. Change
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Application - EU Funding in CEE
• EU Funds increase corruption risks in Cz and Hu
(Ncz= 39.320, Nhu = 38.862)
• EU Funds decrease corruption risks in Sk (N=15.760)
2014.12.04. 54
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Application - EU Funding in CEE
• Divergent temporal patterns
• Effect of new government in Hungary (2010)
2014.12.04. 55
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
20091 20092 20093 20094 20101 20102 20103 20104 20111 20112 20113 20114 20121 20122 20123 20124
me
an
CR
I
CZ:EU CZ:non-EU HU:EU HU:non-EU
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
20091 20092 20093 20094 20101 20102 20103 20104 20111 20112 20113 20114 20121 20122 20123 20124
me
an
CR
I
SK:EU SK:non-EU
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Type of Issuers (hospitals): Share of PP with One Bidder by
Issuer Type, [Hungary, 1998-2004, N = 24.251]
Application – assessment at insititutional level motivation corrupion measurement data indicators illustrations plans
Applications - State capture
2014.12.04. 57
Captured
org.s’
network,
HU,
2009-
2010Q2
Socialist
gov.
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Applications - State capture
2014.12.04. 58
Captured
org.s’
network,
HU,
2011-
2012Q2,
Orban’s
gov.
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Further applications
motivation corrupion measurement data indicators illustrations plans
From research to impact
• DIGIWHIST (H2020 project):
– The Digital Whistleblower. Fiscal Transparency, Risk Assessment and Impact of Good Governance Policies Assessed
– Consortium of Cambridge, Hertie, CRCB, Datlab, Open Knowledge Foundation, Transcrime
• Goals
– Help to anti-corruption efforts
– Open data and indicators for 35 European countries: EU, EEA, Caucasus
– Enabling losers of corruption to mobilize
2014.12.04. 60
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• Data:
– Transparency and procurement legislation
– Micro-level procurement data
– Company information
– Public organisation information
– Asset declarations
• Indicators:
– Corruption
– Transparency
– Administrative quality
2014.12.04. 61
DIGIWHIST: key outputs
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Further information Corruption Research Center Budapest: www.crcb.eu
Fazekas, M., Tóth, I. J. (2014), New ways to measure institutionalised grand corruption in public procurement. U4 Brief October 2014:9, U4 Anti-Corruption Resource Centre, Bergen, Norway.
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 Alina Mungiu-Pippidi (ed.) The Anticorruption Frontline. The ANTICORRP Project, vol. 2., (pp. 68–89). Berlin: Barbara Budrich Publishers.
Fazekas, M., Tóth, I. J. (2014), In respectable society: on how elite configuration influences patterns of state capture in Hungary. Conference paper, MPSA Annual Conference, Chicago, USA, 3 April 2014.
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.
2014.12.04. 62
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Thank you for your attention!
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