bergman lundberg lundberg stake ippc2014
DESCRIPTION
IPPC6TRANSCRIPT
Using spatial econometric techniques to detect collusive behavior in procurement auction data
Mats Bergman, Johan Lundberg, Sofia Lundberg, Johan Stake
Summary
• Test to see if bidding behavior can be captured by spatial econometric techniques due to non-independent bidding between cartel members
• Use data from known Swedish asphalt cartel during the 1990s
• Test if bids between lowest bid in cartel and the rest of the cartel bids can be observed econometrically
• Find significant results of non-independence between cartel members bids using spatial econometrics, which dissapears during the time after the cartel
• Problems with one specification which returns significant results in the case after the cartel was dissipated
Background
• Procurement auctions used frequently for public contracts in the EU (1994 directive)
• First-price sealed bid auctions theoretically assigns to bidder with lowest marginal cost – assuming there is no collusion!
• Swedish Competition Authority conducted dawn raids in October 2001 at several asphalt paving companies
• Trials lasted for over 40 days and in 2007 nine companies were convicted to pay over 1.2 billion dollars in fines
Previous work
• Jakobsson and Eklöf (2003) analyzed the same asphalt cartel using a reduced form model describing non-independent bidding
• Collusion in public contracts has been analyzed in fields such as: • frozen seafood (Koyak & Werden, 1993) • school milk (Pesendorfer, 1995; Porter & Zona, 1999) • highway constructions (Porter & Zona, 1993) • highway repair (Bajari & Ye, 2003)
• Detecting collusion difficult – most papers econometrically confirm the cartel
• Following Bajari & Ye, non-collusive bidding should fulfill; 1. Conditional independency – independent bids when controlling for production cost effects 2. Exchangability - bids independent of other bidders
• We contribute to this literature by using spatial econometric techniques to test for collusive behavior
Econometric setup
• A specific number of bidders create a cartel with intention to collude in procurement auctions
• Consider a set of contracts C, for which two types of bidders bid, A and B;
A
B
C
Cartel – bids are non-independent
No cartel – bids are independent
Bids between types A and B are independent
Econometric setup
• So, define bid b for contract c by bidder i belonging to group A; 𝑏𝑖,𝑐𝐴
• One firm, i, in the cartel (type A) bids a low bid; 𝑏𝑖,𝑐𝐴
• While the rest of the cartel members, j, bid high; 𝑏𝑗,𝑐𝐴 𝑓𝑜𝑟 𝑖 ≠ 𝑗
• With C contracts and on average 𝐴 + 𝐵 bidders, we define a weight matrix W;
𝐶 × (𝐴 + 𝐵) × 𝐶 × 𝐴 + 𝐵
with elements such that 𝑤𝑖𝑐𝐴,𝑗𝑐
𝐴 > 0 and; 𝑤𝑖𝑐
𝐵,𝑗𝑐𝐵 = 𝑤𝑖𝑐
𝐵,𝑗𝑐𝐴 = 𝑤𝑖𝑐
𝐴,𝑗𝑐𝐵 = 𝑤𝑖𝑐
𝐴,𝑖𝑐𝐴 = 𝑤𝑖𝑐
𝐵,𝑖𝑐𝐵 = 0
Econometric setup
• A simple test for collusion among bidders of type A could then be performed;
𝑏 = 𝜌𝑾𝑏 + 𝑿𝜷 + 𝜀
𝑏 = 𝑣𝑒𝑐𝑡𝑜𝑟 𝑜𝑓 𝑎𝑙𝑙 𝑏𝑖𝑑𝑠 𝑿 = 𝑚𝑎𝑡𝑟𝑖𝑥 𝑜𝑓 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 𝑐𝑜𝑣𝑎𝑟𝑖𝑎𝑡𝑒𝑠 𝜀 = 𝑒𝑟𝑟𝑜𝑟 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡
• 𝜌 and 𝛽 are the coeffients to be estimated
• If the bids are non-independent: 𝜌 ≠ 0
• Note also that 𝜌 < 1 is consistent with a Nash equilibrium
Econometric setup
• It is not obvious what value we should assign 𝑤𝑖𝑐𝐴,𝑗𝑐
𝐴. Theory gives no guidance in this matter – how should we express the degree of dependence between different cartel members?
• Two approaches of defining the weight matrix are used; • 𝑏𝑖,𝑐
𝐴 is regressed on the sum of cartel members bids (Row standardized)
• 𝑏𝑖,𝑐𝐴 is regressed on the average of cartel members bids (Non-row standardized)
• We also test to exclude the lowest cartel bid from the regression, which, using both weight matrixes above should produce even stronger effects.
• Since our regression equation is a spatial lag model which becomes biased and inconsistent with OLS, we apply an IV estimator using 𝑾𝑿 as instruments for 𝑾𝒃
• 𝑾 should also preferably be exogenous, which is the case here.
Data
• Data consists of observations from the Swedish Road Administration, all procurements from 1992 up to and including 2009
• We gathered data on region, year, procurement procedure, bids, number of bidders, quantity (where applicable)
• Exclude combinatorial bids, since this might influence bidding behavior
• Vast majority of procurements use a simplified procurement procedure, since many contracts below the threshold value (5.1 million euros in 2014)
• Bids are measured as bid per square meter of asphalt
Table 1: Descriptive statistics
Mean Std. dev. Min Max
Whole sample (1992-2009)
Bid per square kilometer 𝑏 4.889 23.226 0.013 308.222
Volume 𝑉𝑜𝑙𝑢𝑚𝑒𝑐 59.546 101.418 0.133 1,397.753
Competition 𝐶𝑜𝑚𝑝𝑐 5.433 1.522 1 10
Population density 𝐷𝑒𝑛𝑠𝑐 55.871 56.945 3.289 200.471
Number of procurements 568
Observations 2,801
1992 – 2000
Bid per square kilometer 𝑏 5.222 24.918 0.026 308.222
Volume 𝑉𝑜𝑙𝑢𝑚𝑒𝑐 45.644 57.734 0.133 607.613
Competition 𝐶𝑜𝑚𝑝𝑐 5.691 1.489 1 10
Population density 𝐷𝑒𝑛𝑠𝑐 67.217 57.690 3.317 195.275
Number of procurements 422
Observations 2,207
2004 – 2009
Bid per square kilometer 𝑏 3.651 15.340 0.013 144.582
Volume 𝑉𝑜𝑙𝑢𝑚𝑒𝑐 11.120 181.038 0.170 1,397.753
Competition 𝐶𝑜𝑚𝑝𝑐 4.475 1.235 1 7
Population density 𝐷𝑒𝑛𝑠𝑐 13.716 25.911 3.289 200.471
Number of procurements 146
Observations 594
Empirical model
• The empirical model for this study is defined as;
𝑏 = 𝛼𝑡 + 𝜌𝑾𝑏 + 𝑓 𝐶𝑜𝑚𝑝, 𝑉𝑜𝑙𝑢𝑚𝑒, 𝑞𝑟 , 𝑡 + 𝜀
Where,
𝛼𝑡 capture time effects,
𝐶𝑜𝑚𝑝 measures competition (number of bidders per contract),
𝑉𝑜𝑙𝑢𝑚𝑒 is the quantity of the contract, and
𝑞𝑟 is a control for regional disparaties (SRAs 7 regions)
Row standardized weights matrix, 𝐖 2. Period 1992-2000. Row standardized weights matrix, 𝐖 2. Period 2004 – 2009.
(1) (2) (3) (4) (1) (2) (3) (4)
𝜌 - - 0,434
(3,67)
0,400
(3,31)
- - 0,630
(0,43)
0,379
(0,50)
𝜌 (ln) 0,084
(2,65)
0,102
(3,29)
- - -0,253
(-0,83)
-0,135
(-1,52)
- -
𝛽𝑐𝑜𝑚𝑝 - - -4,794
(-0,55)
- - - 5,382
(0,31)
-
𝛽𝑐𝑜𝑚𝑝2 - - 0,570
(0,71)
- - - -0,231
(-0,11)
-
𝛽ln (𝑐𝑜𝑚𝑝) 1,521
(3,90)
- - - -2,204
(-0,47)
- - -
𝛽𝑑𝑒𝑛𝑠 - - - 2,904
(1,07)
- - - 47,126
(0,55)
𝛽𝑑𝑒𝑛𝑠2 - - - -0,008
(-1,13)
- - - -0,565
(0,57)
𝛽ln (𝑑𝑒𝑛𝑠) - -8,979
(-4,94)
- - - -21,497
(-1,92)
- -
𝛽𝑠𝑞𝑟𝑡 - - -0,176
(-6,18)
-0,177
(-6,14)
- - -0,034
(-1,21)
-0,044
(-2,39)
𝛽𝑠𝑞𝑟𝑡2 - - 0,000
(5,73)
0,000
(5,69)
- - 0,000
(1,27)
0,000
(2,37)
𝛽ln (𝑠𝑞𝑟𝑡) -0,861
(-33,86)
-0,817
(-34,95)
- - -0,904
(-6,45)
-0,849
(-18,53)
- -
Results
Non-row standardized weights matrix, 𝐖𝟐. Period 1992-2000. Non-row standardized weights matrix,𝐖𝟐. Period 2004 – 2009.
(1) (2) (3) (4) (1) (2) (3) (4)
𝜌 - - 0,154
(5,64)
0,160
(7,13)
- - 0,204
(0,32)
0,523
(1,48)
𝜌 (ln) 0,050
(4,92)
0,054
(6,19)
- - -0,101
(-1,19)
-0,070
(-2,42)
- -
𝛽𝑐𝑜𝑚𝑝 - - -8,023
(-0,86)
- - - 0,355
(0,02)
-
𝛽𝑐𝑜𝑚𝑝2 - - 0,606
(0,64)
- - - 0,623
(0,29)
-
𝛽ln (𝑐𝑜𝑚𝑝) 2,567
(5,83)
- - - -0,550
(-0,39)
- - -
𝛽𝑑𝑒𝑛𝑠 - - - 3,279
(1,36)
- - - 47,405
(0,53)
𝛽𝑑𝑒𝑛𝑠2 - - - -0,009
(-1,37)
- - - -0,571
(-0,55)
𝛽ln (𝑑𝑒𝑛𝑠) - -9,160
(-5,18)
- - - -20,118
(-1,80)
- -
𝛽𝑠𝑞𝑟𝑡 - - -0,147
(-5,79)
-0,151
(-7,76)
- - -0,042
(-2,25)
-0,036
(-2,54)
𝛽𝑠𝑞𝑟𝑡2 - - 0,000
(5,04)
0,000
(6,96)
- - 0,000
(2,37)
0,000
(2,62)
𝛽ln (𝑠𝑞𝑟𝑡) -0,838
(-33,77)
-0,782
(-38,88)
- - -0,883
(-10,82)
-0,854
(-23,85)
- -
Results
Row standardized weights matrix, 𝐖 𝟏. Period 1992-2000. Row standardized weights matrix, 𝐖 𝟏. Period 2004 – 2009.
(1) (2) (3) (4) (1) (2) (3) (4)
𝜌 - - 0,326
(2,54)
0,341
(2,96)
- - 0,154
(0,12)
0,823
(0,82)
𝜌 (ln) 0,120
(3,75)
0,088
(3,01)
- - -2,828
(-0,37)
-0,220
(-2,68)
- -
𝛽𝑐𝑜𝑚𝑝 - - -13,111
(-0,89)
- - - 14,804
(0,77)
-
𝛽𝑐𝑜𝑚𝑝2 - - 1,214
(0,88)
- - - -1,165
(-0,50)
-
𝛽ln (𝑐𝑜𝑚𝑝) 2,134
(4,46)
- - - -21,727
(-0,34)
- - -
𝛽𝑑𝑒𝑛𝑠 - - - 3,442
(1,10)
- - - 49,184
(0,54)
𝛽𝑑𝑒𝑛𝑠2 - - - -0,010
(-1,17)
- - - -0,598
(-0,57)
𝛽ln (𝑑𝑒𝑛𝑠) - -9,132
(-4,89)
- - - -21,090
(-1,90)
- -
𝛽𝑠𝑞𝑟𝑡 - - -0,209
(-7,89)
-0,206
(-8,49)
- - -0,043
(-3,55)
-0,047
(-4,18)
𝛽𝑠𝑞𝑟𝑡2 - - 0,000
(6,54)
0,000
(7,20)
- - 0,000
(3,59)
0,000
(4,00)
𝛽ln (𝑠𝑞𝑟𝑡) -0,871
(-44,04)
-0,842
(-45,97)
- - -1,373
(-0,86)
-0,834
(-28,51)
- -
Results – excluding lowest cartel bid
Non-row standardized weights matrix, 𝐖 𝟏. Period 1992-2000. Non-row standardized weights matrix, 𝐖 𝟏. Period 2004 – 2009.
(1) (2) (3) (4) (1) (2) (3) (4)
𝜌 - - 0,165
(3,62)
0,162
(4,86)
- - 0,110
(0,16)
0,381
(0,80)
𝜌 (ln) 0,062
(4,55)
0,062
(5,22)
- - -0,013
(-0,08)
-0,129
(-3,47)
- -
𝛽𝑐𝑜𝑚𝑝 - - -12,590
(-0,70)
- - - 1,558
(0,06)
-
𝛽𝑐𝑜𝑚𝑝2 - - 0,871
(0,49)
- - - 0,480
(0,16)
-
𝛽ln (𝑐𝑜𝑚𝑝) 2,649
(6,29)
- - - 1,655
(0,79)
- - -
𝛽𝑑𝑒𝑛𝑠 - - - 4,028
(1,32)
- - - 48,540
(0,56)
𝛽𝑑𝑒𝑛𝑠2 - - - -0,011
(-1,36)
- - - -0,589
(-0,59)
𝛽ln (𝑑𝑒𝑛𝑠) - -9,084
(-4,92)
- - - -18,081
(-1,65)
- -
𝛽𝑠𝑞𝑟𝑡 - - -0,185
(-6,78)
-0,199
(-10,07)
- - -0,047
(-3,87)
-0,049
(-4,71)
𝛽𝑠𝑞𝑟𝑡2 - - 0,000
(5,25)
0,000
(8,13)
- - 0,000
(3,80)
0,000
(4,43)
𝛽ln (𝑠𝑞𝑟𝑡) -0,875
(-43,06)
-0,825
(-49,52)
- - -0,795
(-12,68)
-0,836
(-30,55)
- -
Results – excluding lowest cartel bid
Results
• Relatively clear and unambigious results – spatial econometrics show sign of collusion
• 𝜌 is significant and therefore implies non-independence in the cartel period, and produces no significant effect in the latter period (using a row standardized weight matrix and all cartel bids included)
• Other estimation also follow this, but the estimation using log of population density and log of volume consequently implies non-independent bids • Possible explanations?
• Opens up for possibilities to use spatial econometrics to scan procurement data by testing different cartel specifications (hopefully!)