Mobilizing Big Data to support investigations into fraud ...€¦ · Mobilizing Big Data to support...
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Mobilizing Big Data to support
investigations into fraud & corruptionThe case of public procurement
Mihály FazekasCentral European University and
Government Transparency Institute
2018. 10. 31. 1
19th Conference of International
Investigators, Incheon, South Korea,
10-11/10/2018
Two main topics
1. Recent innovations in corruption
measurementGlobal availability of data and risk indicators
2. Data can be mobilized for investigations
Currently available or easily acceible data can be used for
risk mapping as well as investigation support
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Why would you care?
2018. 10. 31. 3
‚Fat’ Leonard & the corruption probe of 60 admirals
I. Corruption proxies & underlying
data
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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 contract awards.
– Favoring a certain bidder by tailoring specifications, sharing inside information, etc.
See: World Bank Integrity Presidency (2009) Fraud and Corruption. AwarenessHandbook, World Bank, Washington DC. pp. 7.
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Conceptualizing public procurement
corruption indicators
October 31, 2018 6
Contracting
bodySupplierContract
Particularistic tie
Tendering Risk Indicators
(TRI)
Supplier Risk
Indicators (SRI)
Contracting Body
Risk Indicators
(CBRI)Political
Connections
Indicators (PCI)
Source: Fazekas, M., Cingolani, L., & Tóth, B. (2016). A comprehensive review of objectivecorruption proxies in public procurement: risky actors, transactions, and vehicles of rent extraction: GTI-WP/2016:03. Government Transparency Institute. Budapest.
What kinds of data can help anticorruption?
A complex data template
• Public procurement data: contract-level
• Company data: registry, financials, ownership/management
• Political officeholder data
• Treasury accounts of public organisations
• Legal challenge, prosecutions, debarrments
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Ok, but where is this data?
Sometimes
in text files
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Ok, but where is this data?
Sometimes in
structured html
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Ok, but where is this data?
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RARELY in
downloadable
dataset (e.g.
json)
Open data available: DIGIWHIST,
BA/DFID & beyond
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Unprecedented open data available!
1. Full European data&indicators on DIGIWHIST watchdogportals: https://opentender.eu
17.5 million contracts, 32 countries+EC
2. Development aid funded procurement+selected developingcountries: World Bank, IDB, Europeaid + Tanzanian nationaldata (www.govtransparency.eu/index.php/category/databases
3. Approach scaleable and standardized: ongoing work in▪ Selected developing countries’ national data: Brazil, Chile,
Colombia, India, Indonesia, Jamaica, Mexico, Paraguay, South Africa , Uganda
▪ Selected developed countries: US(federal contracting)
If you are interested, get in touch, happy to share data and collaborate!
2018. 10. 31.
Modelling corrupt
contracting: single bidding
12
Distribution of contracts according to
the advertisement period
Probability of single bid submitted for contracts
compared with the market norm of 48+ days
Source: EU’s Tenders
Electronic Daily (TED),
Portugal , 2009-2014
Single bidding
Tight deadline
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Lack of competition ~ single bidding(on national competitive markets)
Single bidding correlates with subjective indicators of corruption
SRI: company age at the time of
winning a contract
Ms. Winston Wolkoff, 6 weeks old company & the 26 million USD
SRI: Expected success of companies by
age
1. Gradual
build-up of
contracts
2. Natural
fluctuation
over time
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SRI: Observed success of companies at
‚specific’ ages
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1. ‚Just’ founded
companies
2. Companies
founded
under party
last in power
Hungary, 2010
Source: 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.
Clusters of risk: young firms
2018. 10. 31. 17
Source: Fazekas, Mihály , & Tóth, Bence, (2017), Proxy indicators for the corrupt misuse of corporations. U4 Brief. October 2017:6. U4 -Chr. Michelsen Institute, Bergen, Norway.
2018. 10. 31.
18
Surprise success
goes together with
procurement red
flags (CRI)
SRI: Politically driven company success: Hungary a paradigmatic case
Companies lose/win
surprisingly when
government changes
Hungary, 2009-2012
Source: Dávid-Barrett, Elizabeth and Fazekas, Mihály, (2016). Corrupt Contracting: Partisan
Favouritism in Public Procurement. GTI-WP/2016:02, Budapest: Government
Transparency Institute.
2018. 10. 31.
19
Few companies
lose/win surprisingly
when government
changes
UK, 2009-2012
Surprise success
sometimes goes
together with
procurement red flags
(CRI)
SRI: Politically driven company success: UK exception to the rule
Source: Dávid-Barrett, Elizabeth and Fazekas, Mihály, (2016). Corrupt Contracting: Partisan
Favouritism in Public Procurement. GTI-WP/2016:02, Budapest: Government
Transparency Institute.
Minimum data
scope for a
corruption&
collusion risk
assessment
See:
https://opentender.eu/
blog/2017-03-towards-
more-transparency/
Variable group Variable
BuyerBuyer’s nameBuyer’s unique IDBuyer’s address
Bidder / bids
Bidder’s nameBidder’s unique ID/tax IDBidder’s address Number of bids submittedNumber of bids excludedBid price (details on total and unit prices)Exact time of bid submissionBid type (winner/loser bid)Beneficial owners
Tender / contract
Procedure typeFramework agreement (1st/2nd stage)Estimated price (details on total or unit prices)Procurement type (service, supply, work)CPV codes (by product type weight)NUTS code(s) of contract implementationStatus (cancelled, pending, etc.)
Dates
Call for tender publication dateBid submission deadlineContract start and end datesPublication date of contract awardDate of contract completion
Subcontracting Subcontractor’s name and unique ID (tax ID)Subcontractor’s share
Consortium Consortium members’ name and unique ID (tax ID)Consortium members’ share
Contract performance
Contract performance end date
Was performance according to the contract
Explanation in case of deferring from contract
Information on contract modification
Information on performance qualityOctober 31, 2018 20
II. Risk management tools
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Using data for corruption prevention
1. Risk assessment:– Mezo-level (e.g. sectoral, regional)– Counterpart-level– Project/tender-level
2. Policy advice: e.g. identifying vulnerabilities&trends
3. Automatic compliance checks: e.g. applyingprocurement rules
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Sectoral risk scoring:
infrastructure subsectors
2018. 10. 31. 23Source: Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks ininfrastructure provision across Europe. In Hammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The Governance Report 2016. Oxford University Press, ch 11.
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Corruption risks in infrastructure spending
by region
Some regions
in otherwise
low corruption
risk countries
carry high
risks
Source: Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks in infrastructureprovision across Europe. In Hammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The GovernanceReport 2016. Oxford University Press, ch 11.
Organisational risk scoring: EIB exampleEIB counterpart avg. organisational risk scores
General PP behavior ~ EIB funded procurement behavior
250,000+ tenders, 10 tailored red flags
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02
04
06
08
0
Fre
que
ncy
0 .2 .4 .6 .8(mean) cri_eib
Corruption & overpriced projects
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Source: Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks ininfrastructure provisionacross Europe. InHammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The Governance Report 2016. Oxford University Press, ch11.
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Corruption and road prices
Source: Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks ininfrastructure provision across Europe. In Hammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The Governance Report 2016. Oxford University Press, ch 11.
Considerable clustering of risks
buyer-supplier bimodal network
N org. contract >=5 or <=50
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Complex risk scoring: Corruption
risks cluster in contracting networks
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i) ii)
iii) iv)
80
85
90
95
100
avera
ge
ann
ou
nce
men
t com
ple
ten
ess
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015year
Continental Western Europe Anglo-Saxon
Scandinavia Central and Eastern Europe
Mediterranean Europe
46
81
01
2
avera
ge
bid
de
r n
um
ber
(tri
mm
ed
)
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015year
Continental Western Europe Anglo-Saxon
Scandinavia Central and Eastern Europe
Mediterranean Europe
05
10
15
20
avera
ge
sa
vin
gs r
ate
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015year
Continental Western Europe Anglo-Saxon
Scandinavia Central and Eastern Europe
Mediterranean Europe
01
02
03
0
sin
gle
bid
din
g
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015year
Continental Western Europe Anglo-Saxon
Scandinavia Central and Eastern Europe
Mediterranean Europe
Quality of governance change over time
Source: Fazekas, Mihály, (2017): Assessing the Quality of Government at the Regional Level Using Public Procurement Data. WP 12/2017, Brussels: European Commission, Directorate-General for Regional Policy.
Automatic compliance checks:
Misplaced tenders: avoiding TED
30
0
150
300
450
600
Num
ber
of te
nde
rs
-100000 -50000 0 50000 100000Estimated tender value
from national database from TED
Source: Tóth, B., Fazekas, M. (2017): Compliance and strategic contract manipulation around single market regulatory thresholds – the
case of Poland. GTI-WP/2017:01, Budapest: Government Transparency Institute. See:
http://www.govtransparency.eu/index.php/2017/08/28/compliance-and-strategic-contract-manipulation-around-single-market-regulatory-
thresholds-the-case-of-poland/
Number of contracts around the EU publication threshold – Services, central government, Poland
Potential contract slicing
Number of tenders around the EU publication threshold in 2010-2011 (left) and 2012-2013 (right) – Services, local government, Poland
31
0
200
400
600
800
Nu
mb
er
of te
nd
ers
100000 150000 200000 250000 300000
Tender value
0
200
400
600
800
100
0
Nu
mb
er
of te
nd
ers
100000 150000 200000 250000 300000
Tender value
Source: Tóth, B., Fazekas, M. (2017): Compliance and strategic contract manipulation around single market regulatory thresholds – the
case of Poland. GTI-WP/2017:01, Budapest: Government Transparency Institute. See:
http://www.govtransparency.eu/index.php/2017/08/28/compliance-and-strategic-contract-manipulation-around-single-market-regulatory-
thresholds-the-case-of-poland/
Does gaming matter?
Ratio of single bidder contracts around the EU threshold (2010-2015) – local authorities, services, Poland
32
0.1
0.3
0.5
0.7
0.9
Me
an
of sin
gle
b
-40000 -20000 0 20000 40000estorfin_value_distance_ext_alt
Source: Tóth, B., Fazekas, M. (2017): Compliance and strategic contract manipulation around single market regulatory thresholds – the
case of Poland. GTI-WP/2017:01, Budapest: Government Transparency Institute. See:
http://www.govtransparency.eu/index.php/2017/08/28/compliance-and-strategic-contract-manipulation-around-single-market-regulatory-
thresholds-the-case-of-poland/
3 options for tapping into Big Data
analytics
1. Use existing national systems: many countries
have sufficient national data (LINKING!)
– Can get started tomorrow☺
2. Invest into national systems: e.g. WB’s work in
Bangladesh
– Mid-term solution: 3-5 years until quality data is ready
3. Build on donor PP system
– Many donors have such a system or building one, BUT
it is hard to get right
2018. 10. 31. 33
Further readings: digiwhist.eu/resources
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Fazekas, M., & Kocsis, G. (2017). Uncovering High-Level Corruption: Cross-NationalCorruption Proxies Using Government Contracting Data. British Journal of PoliticalScience, available online.
Fazekas, Mihály, (2017): Assessing the Quality of Government at the Regional Level Using Public Procurement Data. WP 12/2017, Brussels: European Commission, Directorate-General for Regional Policy.
Dávid-Barrett, Elizabeth, Fazekas, Mihály, Hellmann, Olli, Márk, Lili, & McCorley, Ciara, (2017): Controlling Corruption in Development Aid: New Evidence from Contract-Level Data. SCSC Working Paper No. 1. Sussex Centre for the Study of Corruption, University of Sussex.
Tóth, B., Fazekas, M. (2017): Compliance and strategic contract manipulation around single market regulatory thresholds – the case of Poland. GTI-WP/2017:01, Budapest: Government Transparency Institute.
Fazekas, Mihály , & Tóth, Bence, (2017), Proxy indicators for the corrupt misuse of corporations. U4 Brief. October 2017:6. U4 - Chr. Michelsen Institute, Bergen, Norway
Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks in infrastructureprovision across Europe. In Hammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The Governance Report 2016. Oxford University Press, ch 11.
Fazekas, M., Cingolani, L., & Tóth, B. (2016). A comprehensive review of objectivecorruption proxies in public procurement: risky actors, transactions, and vehicles of rent extraction: GTI-WP/2016:03. Government Transparency Institute. Budapest.
Fazekas, M. and Tóth, I. J. (2016). From corruption to state capture: A new analytical framework with empirical applications from Hungary. Political Research Quarterly, 69(2).
Dávid-Barrett, Elizabeth and Fazekas, Mihály, (2016). Corrupt Contracting: Partisan Favouritism in Public Procurement. GTI-WP/2016:02, Budapest: Government Transparency Institute.