Scoring and Litigation in Public
Procurement
Ari Hyytinen, University of Jyväskylä and YJS
Otto Toivanen, K U Leuven and C...
Motivation
•

Litigation: Some bidder who lost sues the government body who
organized the procurement

•

The purpose of t...
Why to litigate?
•

Procurer may may mistakes on purpose (favoritism) or mistakes by
accident (lack of know-how, bad luck ...
Institutional context
• Invitations to tender are published on a government
website (Hilma). This announcement became obli...
Data
•

We have the Hilma data base from 1.6.2007 until 15.5.2009. About
40k observations

•

We have collected data from ...
Panel C
Total: 29729 tenders
Scoring

Price Only

65.41 %

34.59 %

Litigation

No litigation

Litigation

No litigation

...
Reduced form: Court
Bidder wins in court
Variable
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
scoring
0.62** 0.30 0.79...
Reduced form: Litigation
Litigation
Variable
Coef. Std Err. Coef
Coef
Std. Err
Coef. Std Err. Coef
Std. Err
Coef. Std Err....
Reduced form: Scoring
Variable
Coef.
Std. Err.
contract divisible
0.06
0.25
many winners
-0.08
0.30
continuous contract
0....
Simulating a theoretical model
• E.g. assume a level playing field between scoring and
price-only in court. This could ari...
Results (preliminary)
• Finding #1: Scoring auctions end up in the court more
often
• Finding #2: The plaintiff wins the c...
Policy implications (preliminary)
• Do not use scoring if quality is not important
• Reduce benefits of strategic litigati...
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Millaiset hankinnat päätyvät Markkinaoikeuteen ja miten siltä vältytään? Janne Tukiainen

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Kuinka onnistun tietojärjestelmien ostamisessa - Codenton aamiaisseminaari Helsingissä 16.1.2014

Janne Tukiainen, VATT: Millaiset hankinnat päätyvät Markkinaoikeuteen ja miten siltä vältytään?

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Millaiset hankinnat päätyvät Markkinaoikeuteen ja miten siltä vältytään? Janne Tukiainen

  1. 1. Scoring and Litigation in Public Procurement Ari Hyytinen, University of Jyväskylä and YJS Otto Toivanen, K U Leuven and CEPR Toivanen K.U. Janne Tukiainen, VATT and HECER Breakfast seminar, Codento 16 January 2014
  2. 2. Motivation • Litigation: Some bidder who lost sues the government body who organized the procurement • The purpose of the system is to reduce corruption and favoritism and enchance competition but this is not free lunch competition, • Typically public procurement authorities consider litigation to be their yp yp p g second most important concern (first is attracting enough bidders) • Probability of litigation may not be that high but costs may be if end up in court, e.g. postponed production 2
  3. 3. Why to litigate? • Procurer may may mistakes on purpose (favoritism) or mistakes by accident (lack of know-how, bad luck etc.) • Litigation may take place due to genuine perceived mistakes or due to strategic reasons (keep old contract pressure on competition ) contract, competition…). Strategic incentives increase in length of the process (average 387 d) • We study empirically which procurements end up in court and what happens in court to asses these questions • Lessons: – Can contract design lead to less litigation? E.g. How to deal with quality – Can policy maker do something to improve the system? 3
  4. 4. Institutional context • Invitations to tender are published on a government website (Hilma). This announcement became obligatory and the Hilma data base is available from 1.6.2007 • In Market court, 2-3 (or 4 in difficult cases) judges decide a case. No court hearing, only letters from plaintiff and defendant. No real settlement stage. Market court publishes its official decisions on its webpage 4
  5. 5. Data • We have the Hilma data base from 1.6.2007 until 15.5.2009. About 40k observations • We have collected data from Market Court site. We link these data to Hilma. Court data includes 448 decided cases that were announced in our Hilma time frame (not all in Hilma) • Court data is selected based on the tender annoucement date not date, the court decision date. Thus we avoid the possible selection of similar/easy/fastly decided cases to the sample • Court data has richer information than Hilma. We conduct a survey to collect more information on the control group group. 5
  6. 6. Panel C Total: 29729 tenders Scoring Price Only 65.41 % 34.59 % Litigation No litigation Litigation No litigation 0.77 0 77 % 64.64 64 64 % 0.15 0 15 % 34.44 34 44 % Bidder w ins Bidder loses Bidder w ins Bidder loses 0.29 % 0.48 % 0.03 % 0.12 % Panel D P l Total: 29729 tenders Scoring Price Only 65.41 65 41 % 34.59 34 59 % Litigation No litigation Litigation No litigation 1.18 % 98.92 % 0.43 % 99.57 % Bidder ins Bidd w i Bidder loses Bidd l Bidder ins Bidd w i Bidder loses Bidd l 37.39 % 62.61 % 18.18 % 81.82 % Notes: Panel C reports data frequencies and Panel D reports frequencies conditional on the previous row . These statistics y y p only include the data available in Hilma and only those market court cases from our sample that w e can link to Hilma. 6
  7. 7. Reduced form: Court Bidder wins in court Variable Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. scoring 0.62** 0.30 0.79** 0.35 0.85** 0.37 contract divisible -0.76** 0.37 -0.68* 0.38 many winners 0.57 0.42 0.54 0.44 continuous contract i 0.86*** 0.27 1.00*** 0.32 0 86*** 0 27 1 00*** 0 32 expected value -0.013 0.032 -0.018 0.033 expected value sq. 0.000 0.000 0.000 0.000 unexperienced 0.55 0 55 0.31 0 31 0.46 0 46 0.32 0 32 less than 4 bidders 0.37 0.27 0.38 0.28 more than 8 bidders 0.13 0.41 0.13 0.43 close to reform 0.31 0.49 0.38 0.50 industry FE No No Yes procurer type FE No No No WESML No No No 2 Pseudo R N 0.01 392 0.07 392 0.08 392 Coef. Std. Err. 0.81** 0.37 -0.73* 0.39 0.66 0.45 1.02*** 0.33 1 02*** 0 33 -0.016 0.033 0.000 0.000 0.29 0 29 0.39 0 39 0.36 0.28 0.28 0.43 0.18 0.53 Yes Yes No 0.10 392 All specifications are estimated using logit. Industry FE has 8 classes and a reference group. Procurer type FE h 6 classes and a reference group. *** d has l d f denotes 1% statistical % i i l significance, ** 5% significance and * 10% significance. 7
  8. 8. Reduced form: Litigation Litigation Variable Coef. Std Err. Coef Coef Std. Err Coef. Std Err. Coef Std. Err Coef. Std Err. Coef Std. Err Coef. Std Err. Std. Err scoring 0.76*** 0.18 0.61*** 0.20 0.85*** 0.22 0.92*** 0.22 contract divisible -0.21 0.22 -0.15 0.23 -0.06 0.24 many winners -0.82*** 0.27 -0.99*** 0.29 -1.05*** 0.29 continuous contract 0.65*** 0.18 0.91*** 0.21 0.91*** 0.22 expected value 0.087*** 0.032 0.083*** 0.032 0.082** 0.033 expected value sq. -0.00041** 0.00017 -0.00038** 0.00017 -0.00037** 0.00018 p 0.31 0.23 0.22 0.24 0.19 0.28 unexperienced less than 4 bidders -0.42** 0.18 -0.35* 0.19 -0.32 0.19 more than 8 bidders 0.12 0.29 0.16 0.30 0.24 0.30 close to reform 0.31 0.39 0.34 0.40 0.24 0.41 industry FE No No Yes Yes procurer type FE No No No Yes No No No No WESML 2 Pseudo R N 0.02 685 0.06 615 0.08 615 0.10 615 Coef. Std Err. Coef Std. Err 1.35*** 0.35 -0.16 0.27 -1.08*** 0.35 0.91*** 0.31 0.069 0.065 -0.00033 0.00035 0.01 0.35 -0.28 0.26 0.33 0.40 0.64 0.52 Yes Yes Yes 0.12 511 All specifications are estimated using logit. Industry FE has 8 classes and a reference group. Procurer type FE has 6 classes and a reference group. *** denotes 1% statistical significance, ** 5% significance and * 10% significance. 8
  9. 9. Reduced form: Scoring Variable Coef. Std. Err. contract divisible 0.06 0.25 many winners -0.08 0.30 continuous contract 0.32 0.20 expected value 0.080* 0 080* 0.047 0 047 expected value sq. -0.0003 0.0006 unexperienced 0.56* 0.29 less than 4 bidders -0.22 0.21 more than 8 bidders -0.19 0.32 close to reform 0.83 0.55 industry FE No procurer type FE No WESML No Scoring Coef. Std. Err. Coef. Std. Err. 0.14 0.28 0.12 0.28 0.17 0.34 0.21 0.34 -0.35 0.25 -0.33 0.26 0.12** 0 12** 0.053 0 053 0.12** 0 12** 0.053 0 053 -0.0005 0.0004 -0.0005 0.0005 0.81*** 0.31 0.60* 0.36 -0.39* 0.23 -0.42* 0.23 -0.30 0.35 -0.29 0.36 0.82 0.58 0.70 0.58 Yes Yes No Yes No No Coef. Std. Err. 0.24 0.35 -0.06 0.40 -0.03 0.34 0.066 0 066 0.058 0 058 -0.0002 0.0003 0.91** 0.41 -0.23 0.29 0.45 0.42 1.46 0.96 Yes Yes Yes 2 Pseudo R 0.03 0.15 0.15 0.17 N 615 615 615 511 All specifications are estimated using logit. Industry FE has 8 classes and a reference group. Procurer type FE has 6 classes and a reference group. *** denotes 1% statistical significance, ** 5% significance and * 10% significance. 9
  10. 10. Simulating a theoretical model • E.g. assume a level playing field between scoring and price-only in court. This could arise e.g. if auctioneers invest more in know-how and thus make no more mistakes with scoring than no scoring • We find that even then, firms litigate more with scoring than ith th with no scoring i • O explanation (b not the only) i strategic li i i One l i (but h l ) is i litigation 10
  11. 11. Results (preliminary) • Finding #1: Scoring auctions end up in the court more often • Finding #2: The plaintiff wins the court case more often if scoring has been used • Finding #3: Scoring auctions litigated more often even when they do equally well in court • Finding #4:Scoring used too little because of litigation g g g risks 11
  12. 12. Policy implications (preliminary) • Do not use scoring if quality is not important • Reduce benefits of strategic litigation by reducing decision times in Market Court. e.g. by hiring more judges. Would lead to a positive loop of less cases and shorter times • Increase costs of strategic litigation by increasing bidders payments for lost court cases. But no idea what is optimal level 12

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