cartel behavior - university of...
TRANSCRIPT
Cartel Behavior
Why does collusion occur?
In the competitive model, firms enter until the last In the competitive model, firms enter until the last firm earns zero economic profits
Competition is clearly tough on firms
In economic theory, cartels form in order to increase profits above the competitive level.
Quote:
“Organized crime in America takes in over forty billion dollars a year and spend very little on office supplies”dollars a year and spend very little on office supplies , Woody Allen.
Cartel Behavior
A very simple (but useful!) first model of a cartel is that they behave as a monopolisty p
An assumption in our competitive model was that firms were price takersp
A firm’s revenue is pq
That is, firms take prices as given to them by the market
Cartel Behavior
A cartel, however, coordinates the decisions of firms
By doing so, it can choose any price quantity pair on the demand curvep
Cartel Price/Quantity Choices
Price
P1
P2
P
Quantity
D
P3
QuantityQ1 Q2 Q3
Cartel Behavior
The total revenue to the cartel is:
TR(Q)=P(Q)QTR(Q) P(Q)Q
Where P(Q) is the inverse demand curve
Let C(Q) be the total cost to the cartel for producing Let C(Q) be the total cost to the cartel for producing output of Q
For simplicity, let’s assume that all N firms in the p y,cartel are identical
Let’s also assume that they allocate the market shares evenly i e q=Q/Nevenly, i.e. q=Q/N
Then C(Q)=Nc(q) where q=Q/N
Cartel Behavior
The cartel’s total profit is:
TR(Q)-C(Q)
The profit maximizing rule for the cartel is to set marginal revenue equal to marginal cost
Ma ginal e en e MR(Q) is the additional e en e f om Marginal revenue, MR(Q) is the additional revenue from producing one additional unit
Claim: If the demand curve is linear, it can be shown that marginal revenue has twice the slope of the demand curvemarginal revenue has twice the slope of the demand curve
If MR(Q) >MC(Q) then profits can be increased by producing one more unit
If MR(Q)<MC(Q) then profits can be increased by producing one less unit
Cartel Versus Competitive Outcomes
MC
P*
P
M k t D dMR
Market Demand
Q*Q=nq Q*Q=nq
Cartel Behavior
Price is greater than the competitive level
Q tit i l th th titi l l Quantity is less than the competitive level
-The cartel uses its market power
-Restricts the supply below the competitive level in order to increase profits
Cartel Behavior
The marginal costs for the cartel is less than the pricep
This generates a deadweight loss
Thi d d i h l f h i This deadweight loss comes from not having competitively functioning markets (which are efficient and have no deadweight loss)efficient and have no deadweight loss)
Inefficiency of Collusion
MC
P*
P Deadweight Loss
M k t D dMR
Market Demand
Q*Q=nq Q*Q=nq
Cartel Behavior
Cartel Members have an incentive to “cheat”
mc(q)<P for all members of the cartel mc(q)<P for all members of the cartel
The marginal cost to a member is less than the price
Cartel members are therefore tempted to expand output above q=Q/N
A t l i t t bl f f l An extremely important problem for a successful cartel is how to monitor and prevent such cheating
Empirical Regularities
Hay and Kelly, Journal of Law and Economics, 1974.
Examined Department of Justice memoranda on Examined Department of Justice memoranda on collusion investigations from Jan 1963-Dec 1972.
Cases that generated guility verdicts or nolo t d lcontendere pleas.
Horizontal cases (vertical cases excluded).
Remark: These were the cartels that were caught.
Hay and Kelly
Outline:
1 F t th t f ilit t ll i1. Factors that facilitate collusion.
2. The Data
3. Results.
Factors Which Facilitate Collusion
1. Fewness of numbers.
Th ll th b f l i th The smaller the number of people in the conspiracy, the less likely that disagreements will occur.g
Easier to detect cheating.
Hay and Kelly
2. Product Homogeneity.
If th d t i t “ li t d” it i If the product is not “complicated”, it is simpler to negotiate collusive agreements.
If d bl i If products are stable over time, conspiracies more likely to persist.
Hay and Kelly
3. Demand Inelasticity.
The rewards to collusion are greater with inelastic The rewards to collusion are greater with inelastic demand.
Need to reduce output by less.
4. Sealed Bidding.
If all prices are publicly announced, it is easier to p p y ,monitor cheating on the collusive arrangement.
Highly transparent markets most susceptible to collusioncollusion
Hay and Kelly
5. Industry Social Structure.
A d i t fi i t t l d th t l A dominant figure exists to lead the cartel.
Hay and Kelly
The authors use fact memoranda and other supporting documents created by the DOJ.
Most collusion was found by a complaint from a competitor, customer or a grand jury investigation.
T bl 2 h Table 2 shows:
1) In some cases, larger groups (more than 10) do conspire, but they usually have a trade assocation.conspire, but they usually have a trade assocation.
2) In 7/8 cases with 16 or more firms, there was a trade assocation.
Hay and Kelly
Number of Conspirators and Trade Assn InvolvementNumber of Conspirators and Trade Assn. InvolvementNumber of
Conspirators 2 3 4 5 6 7 8 9 10 11-
15 16-20
21-25
>25 Total
N b f 1 7 8 4 10 4 3 5 7 5 2 0 6 62Number of Cases
1 7 8 4 10 4 3 5 7 5 2 0 6 62
Trade Assn. I l
0 0 1 0 4 1 0 1 3 1 1 Na 6 18 Involvement
Hay and Kelly
3) A comparison of the number of conspirators with the number of firms shows that not all firms typically collude.
Hay and Kelly
Most of the cases are in markets with homogenous products.g p
The fact memoranda showed there were examples of dominant individuals who helped p pto lead the cartel.
Hay and Kelly
Bid-rigging was a factor in 15 cases.
Biddi i ft f l j t Bidding is often for lumpy projects.
More concentrated markets tended to have l l i ilonger last conspiracies.
Factors that Facilitate Cartel Formation
Three factors are needed to make collusion successful:
1) Deter entry/increased competition by non-cartel firms.
2) Expected punishment low compared to expected gains from collusion.expected gains from collusion.
3) Cost of enforcing agreement must be low relative to expected gainsrelative to expected gains.
Limiting Competition by Non-Cartel Firms.
If demand is very inelastic, industry profits can be substantially increased by reducing y y gquantity from the competitive level.
However, this might increase entry by new , g y yfirms.
New to have effective barriers to entry such New to have effective barriers to entry such as high entry costs, control over strategic input, or intimidation of entrants.
Cost of Enforcing Agreements
Rises with number of firms in the market.
Ri d t b Rises as products become more differentiated.
Ri d d i l il Rises as demand is more volatile.
Falls as transaction data becomes more available.
Falls if an industry association exists.
Where Collusion is Most Prevalent
The number of firms is small.
Th d t h The products are homogenous.
Demand does not fluctuate a lot.
The actions of other colluders can easily be observed.
Methods of Preventing Cheating
Divide market by buyers or geographic areas.
A “most favored nation clause” Promise all A most favored nation clause . Promise all customers that they will get the best price. If firms lower the price, they have to compensate past customerscustomers.
Meeting the competition clauses. Agree to match the price of all competitors.p p
Trigger price: If price falls below a certain level, a price war is anticipated.
Some Facts
Hay and Kelly (1974) examine 62 cases of collusion
Fact memoranda prepared by Department of Justice Antitrust Staff
Section 1 (Sherman act) cases which were won in trial or nolo contendre pleas from Jan 1963 to Dec 1972
Data is a bit sketchy Data is a bit sketchy
-only included cartels that are caught
-set of explanatory variables a bit limitedp y
-cross industry study
However, findings are representative of other later studies and conventional wisdomconventional wisdom
Number of Conspirators
Intuition and economic theory both suggest that collusive arrangements will be easier to
h th b f i tagree upon when the number of conspirators is small
Monitoring collusive arrangements is also Monitoring collusive arrangements is also easier when the number of conspirators is smaller
Easier to detect price cuts or expansion of output
Number of Conspirators
Number of Conspirators and Trade Assn InvolvementNumber of Conspirators and Trade Assn. InvolvementNumber of
Conspirators 2 3 4 5 6 7 8 9 10 11-
15 16-20
21-25
>25 Total
N b f 1 7 8 4 10 4 3 5 7 5 2 0 6 62Number of Cases
1 7 8 4 10 4 3 5 7 5 2 0 6 62
Trade Assn. I l
0 0 1 0 4 1 0 1 3 1 1 Na 6 18 Involvement
Number of Conspirators
The average number of conspirators is 7.25
79 percent of the conspiracies involve ten or fewer firms
In 7/8 cases with more than 15 firms, a trade industry association was involved
Trade associations may facilitate collusion by improved market monitoring and cartel disciplineand cartel discipline
Even without trade associations “Many conspiracies were in fact highly organized with chairmen, rules of order, agendas and regularly scheduled meetings”
This is all consistent with our model which predicts that members have incentives to cheat
Monitoring and discipline is central to a functioning cartelg p g
Concentration
Table 2 also displays 4 firm concentration ratio
Higher concentration may help in monitoring and planning cartel agreements Low p g gconcentration, easy to enter industries may not be able to sustain collusive prices
In 38/50 cases, concentration was above 50 percent
Homogeneity
Hay and Kelly argue that product homogeneity is “high” for the markets in their g y gdata set
With a homogenous product, the cartel has g p ,to negotiate fewer price/quantity schedules for the collusive arrangement
Simplifies bargaining and monitoring
Market Transparency
Many of the cartels involved sealed bidding for government contracts
Sealed bidding simplifies the cartels monitoring problem
C titi bid t i ll d h tl Competitive bids are typically announced very shortly after the tender
Deviations can be detected immediately Deviations can be detected immediately
More generally, highly transparent markets are more subject to collusion
Market Transparency
Spectrum Auctions- government allocates rights to run mobile telephone licenses and other wireless services for a particular geographic marketp g g p
Simultaneous ascending auction
-many licenses for sale at oncea y o a a o
-bids increase over the course of the auction
Small number of firms win most of the licenses Small number of firms win most of the licenses
Bidding occurs over 3 or more months
Bids and identity of bidders are observed in real time Bids and identity of bidders are observed in real time
Market Transparency
Crampton and Schwartz (2000) find brazen instances of collusion
For example, firms use the license number in the trailing digits of bids to signal rivalsg g g
Firms submit retaliatory bids in order to limit competition for most preferred licensecompetition for most preferred license
Market Transparency
The Federal Communications Commission disguised the identity of bidders in most recent auctions
Bajari and Yeo (2009) find that retaliatory bids fall
Also, a higher percentage of the bids are “straightforward”a g o a d
That is, bidders appear to submit slowly increasing bids on most preferred items as the rounds of the auction progressauction progress
As markets become less transparent, the ability of firms to collude is lessened
Price Wars
A final prediction of our theory is that collusive markets may be subject to price wars
This is particularly true if the market is subject to independent demand/cost shocks or the market is not perfectly transparent
Porter (1983) studies Joint Executive Committee
Cartel of US railroads that operated in the 1880’s prior to the Sherman actto the Sherman act
JEC controlled eastbound freight from Chicago in the 1880s.
Price Wars
JEC office took weekly accounts so that the shipments could be monitored.
D d it i bl d h d t di t Demand was quite variable and hard to predict
Cheating on collusion, as reported by Railway Review, occurs 40 percent of the time.
Price Wars had an average duration of about ten weeks
Price was 66% higher in cooperative periods and q antit 33% lo equantity 33% lower.
As a whole, the cartel could expect to earn 11% higher revenues during cooperative periods
Price Wars
Factors that Facilitate Cartel Formation
Three factors are needed to make collusion successful:
1) Deter entry/increased competition by non-cartel firms.
2) Expected punishment low compared to expected gains from collusion.expected gains from collusion.
3) Cost of enforcing agreement must be low relative to expected gainsrelative to expected gains.
Limiting Competition by Non-Cartel Firms.
If demand is very inelastic, industry profits can be substantially increased by reducing y y gquantity from the competitive level.
However, this might increase entry by new , g y yfirms.
Need to have effective barriers to entry such Need to have effective barriers to entry such as high entry costs, control over strategic input, or intimidation of entrants.
Cost of Enforcing Agreements
Rises with number of firms in the market.
Ri d t b Rises as products become more differentiated.
Ri d d i l il Rises as demand is more volatile.
Falls as transaction data becomes more available.
Falls if an industry association exists.
Gains Versus Losses of Collusion
Expected cost of collusion rises with probability of enforcementp y
Gains are higher if demand is inelastic
If l b i il bl i If many close substitutes are available, gains from collusion are lower
Methods of Preventing Cheating
Divide market by buyers or geographic areas.
A “most favored nation clause” Promise all A most favored nation clause . Promise all customers that they will get the best price. If firms lower the price, they have to compensate past customerscustomers.
Meeting the competition clauses. Agree to match the price of all competitors.p p
Trigger price: If price falls below a certain level, a price war is anticipated.
Bid Rigging
Bid-rigging is one of the most commonly b d f f ll iobserved forms of collusion
Economic models of competitive bidding have two robust predictionsPrediction 1. Non-collusive bids should be
i d d tindependent
-Sealed bidding in non-collusive markets implies that a firm is ignorant of competitors bidsg p-This implies that the bid should be uncorrelated (after controlling for observed cost information)C ll i h i ll i d l i-Collusive schemes typically induce correlation
Independent Bids
9Plot of Noncollusive Bids
7
8
6
7
irm 2
Bid
4
5Firm
2
3
1 2 3 4 5 6 7 8 92
Firm 1 Bid
Collusive Bids
9
10Plot of Collusive Bids
7
8
4
5
6
Firm
2 Bid
2
3
2 3 4 5 6 7 8 90
1
Firm 1 Bid
Competitive Versus Collusive Bidding
Three firms A,B and C
Submit sealed bids for infrastructure project Submit sealed bids for infrastructure project
Low bidder wins
Two cases:
1. A,B and C submit bids competitively
2. A and B collude, C submits a non-collusive bid
How to distinguish collusive from competitive bids How to distinguish collusive from competitive bids
Competitive Versus Collusive Bidding
Firm Identity Cost for 1st Competitive d f t
Collusive Bid f tProject Bid for 1st
Projectfor 1st
ProjectA $1.0 Million $1.19 Million $1.29 Million
B $1.2 Million $1.2 Million Phony $ $ yBid>$1.29 Million
C $1 3 Million $1 3 Million $1 3 MillionC $1.3 Million $1.3 Million $1.3 Million
Competitive Versus Collusive Bidding
Firm Identity Cost for 2nd Competitive d f 2 d
Collusive Bid f 2 dProject Bid for 2nd
Projectfor 2nd Project
A $1.0 Million $1.19 Million $1.19 Million
B $1.3 Million $1.3 Million Phony Bid > $ $ y$1.19 Million
C $1.2 Million $1.2 Million $1.2 Million$ $ $
Competitive Versus Collusive Bidding
Prediction 2. Bids reflect costs and the normal use of market powerp
The competitive bids permute with the costs
Th ll i bid d i h h The collusive bids do not permute with the costs
Bid Rigging Porter and Zona examine bids of convicted
colluders in New York city paving and Ohiocolluders in New York city paving and Ohio school milk. They find:1 Colluders’ bids have a high positive correlation1. Colluders bids have a high, positive correlation2. Non-winning bids are unrelated to costs
Closest firm typically wins-Closest firm typically wins-Second lowest bidder is not typically the second closest firmclosest firm
3. Collusion raises bid levels compared to a price index of other marketsof other markets
Bid Rigging
B j i d Y i i bid i h U Mid Bajari and Ye examine construction bids in the Upper Midwest.
Bids are well explained by cost controls and measures of market power
-97 percent of the variation in bids can be explained by cost controls
-closest firm is more likely to win
-firm with the lowest backlog is more likely to win
Bids are independent after controlling for costs Bids are independent after controlling for costs
Collusion in Wisconsin DOT
Vinton Construction and Streu Construction were fined for rigging bids on Wisconsin DOT gg gprojects
They also hired an employee at James J. y p yCape Co. to provide them with inside information about upcoming bids
The bids are much more correlated with each other than competing bids
1
. reg bidtotalv bidtotals, robust Linear regression Number of obs = 36 F( 1, 34) = 5.83 Prob > F = 0.0213 R-squared = 0.1664 Root MSE = .03784 ------------------------------------------------------------------------------ | Robust bidtotalv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotals | -.3929823 .1627247 -2.42 0.021 -.7236786 -.062286 _cons | 1.365371 .1647844 8.29 0.000 1.030489 1.700253 ------------------------------------------------------------------------------ . reg bidtotalv bidtotals Source | SS df MS Number of obs = 36 -------------+------------------------------ F( 1, 34) = 6.79 Model | .00971523 1 .00971523 Prob > F = 0.0135 Residual | .04867777 34 .001431699 R-squared = 0.1664 -------------+------------------------------ Adj R-squared = 0.1419 Total | .058393 35 .001668371 Root MSE = .03784 ------------------------------------------------------------------------------ bidtotalv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotals | -.3929823 .1508594 -2.60 0.014 -.6995654 -.0863991 _cons | 1.365371 .1518481 8.99 0.000 1.056779 1.673963 ------------------------------------------------------------------------------
y = ‐0.393x + 1.3654(p=0.030, 0.000)
R² = 0.1664
0.85
0.9
0.95
1
1.05
0.9 0.95 1 1.05 1.1 1.15
Vinton's total bid
Streu's total bid
WI‐DOT Road Construction Bids, Vinton Construction vs. Streu Construction
2
. reg bidtotalv bidtotalj, robust Linear regression Number of obs = 35 F( 1, 33) = 3.47 Prob > F = 0.0715 R-squared = 0.1264 Root MSE = .04343 ------------------------------------------------------------------------------ | Robust bidtotalv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalj | -.2719361 .1460433 -1.86 0.072 -.5690635 .0251913 _cons | 1.240871 .146294 8.48 0.000 .9432336 1.538508 ------------------------------------------------------------------------------ . reg bidtotalv bidtotalj Source | SS df MS Number of obs = 35 -------------+------------------------------ F( 1, 33) = 4.78 Model | .009007058 1 .009007058 Prob > F = 0.0361 Residual | .062231579 33 .001885805 R-squared = 0.1264 -------------+------------------------------ Adj R-squared = 0.1000 Total | .071238638 34 .002095254 Root MSE = .04343 ------------------------------------------------------------------------------ bidtotalv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalj | -.2719361 .1244296 -2.19 0.036 -.5250901 -.0187821 _cons | 1.240871 .1262553 9.83 0.000 .9840028 1.497739 ------------------------------------------------------------------------------
y = ‐0.2719x + 1.2409(p= 0.022; 0.000)
R² = 0.1264
0.8
0.85
0.9
0.95
1
1.05
0.85 0.9 0.95 1 1.05 1.1 1.15
Vinton's total bid
James Cape's total bid
WI‐DOT Road Construction Bids,Vinton Construction vs. James Cape & Sons
3
. reg bidtotalv bidtotalz, robust Linear regression Number of obs = 27 F( 1, 25) = 0.30 Prob > F = 0.5897 R-squared = 0.0220 Root MSE = .04458 ------------------------------------------------------------------------------ | Robust bidtotalv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalz | -.1670466 .3057385 -0.55 0.590 -.7967269 .4626336 _cons | 1.120466 .3016344 3.71 0.001 .4992382 1.741693 ------------------------------------------------------------------------------ . reg bidtotalv bidtotalz Source | SS df MS Number of obs = 27 -------------+------------------------------ F( 1, 25) = 0.56 Model | .001117376 1 .001117376 Prob > F = 0.4604 Residual | .049690876 25 .001987635 R-squared = 0.0220 -------------+------------------------------ Adj R-squared = -0.0171 Total | .050808252 26 .001954164 Root MSE = .04458 ------------------------------------------------------------------------------ bidtotalv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalz | -.1670466 .2227955 -0.75 0.460 -.6259025 .2918093 _cons | 1.120466 .2215549 5.06 0.000 .664165 1.576767 ------------------------------------------------------------------------------
y = ‐0.167x + 1.1205(p=0.653; 0.000)
R² = 0.022
0.8
0.85
0.9
0.95
1
1.05
0.92 0.94 0.96 0.98 1 1.02 1.04
Vinton's total bid
Zignego's total bid
WI‐DOT Raod Construction Bids,Vinton Construction vs. Zignego Companies
4
. reg bidtotals bidtotalj, robust Linear regression Number of obs = 35 F( 1, 33) = 4.53 Prob > F = 0.0409 R-squared = 0.1014 Root MSE = .04289 ------------------------------------------------------------------------------ | Robust bidtotals | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalj | -.2371479 .1114567 -2.13 0.041 -.4639083 -.0103875 _cons | 1.243675 .1147948 10.83 0.000 1.010123 1.477226 ------------------------------------------------------------------------------ . reg bidtotals bidtotalj Source | SS df MS Number of obs = 35 -------------+------------------------------ F( 1, 33) = 3.72 Model | .006849956 1 .006849956 Prob > F = 0.0623 Residual | .060697091 33 .001839306 R-squared = 0.1014 -------------+------------------------------ Adj R-squared = 0.0742 Total | .067547047 34 .001986678 Root MSE = .04289 ------------------------------------------------------------------------------ bidtotals | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalj | -.2371479 .122886 -1.93 0.062 -.4871613 .0128655 _cons | 1.243675 .124689 9.97 0.000 .9899931 1.497356 ------------------------------------------------------------------------------
y = ‐0.2371x + 1.2437(p=0.004; 0.000)
R² = 0.1014
0.9
0.95
1
1.05
1.1
1.15
0.85 0.9 0.95 1 1.05 1.1 1.15
Streu's total bid
James Cape's total bid
WI‐DOT Road Construction Bids,Streu Construction vs. James Cape & Sons
5
. reg bidtotals bidtotalz, robust Linear regression Number of obs = 26 F( 1, 24) = 1.41 Prob > F = 0.2462 R-squared = 0.0349 Root MSE = .05026 ------------------------------------------------------------------------------ | Robust bidtotals | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalz | -.2457865 .2067805 -1.19 0.246 -.6725604 .1809874 _cons | 1.250986 .2111306 5.93 0.000 .8152339 1.686738 ------------------------------------------------------------------------------ . reg bidtotals bidtotalz Source | SS df MS Number of obs = 26 -------------+------------------------------ F( 1, 24) = 0.87 Model | .002190368 1 .002190368 Prob > F = 0.3610 Residual | .060622018 24 .002525917 R-squared = 0.0349 -------------+------------------------------ Adj R-squared = -0.0053 Total | .062812387 25 .002512495 Root MSE = .05026 ------------------------------------------------------------------------------ bidtotals | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalz | -.2457865 .2639423 -0.93 0.361 -.7905366 .2989636 _cons | 1.250986 .2630732 4.76 0.000 .7080296 1.793943 ------------------------------------------------------------------------------
y = ‐0.1385x + 1.133p=(0.533; 0.000))
R² = 0.0301
0.92
0.94
0.96
0.98
1
1.02
1.04
0.9 0.95 1 1.05 1.1 1.15
Streu's total bid
Zignego's total bid
WI‐DOT Road Construction Bids,Streu Construction vs. Zignego Companies
6
. reg bidtotalz bidtotalj, robust Linear regression Number of obs = 27 F( 1, 25) = 0.40 Prob > F = 0.5322 R-squared = 0.0419 Root MSE = .03917 ------------------------------------------------------------------------------ | Robust bidtotalz | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalj | .1215172 .1918283 0.63 0.532 -.2735606 .516595 _cons | .8701703 .1921898 4.53 0.000 .4743481 1.265992 ------------------------------------------------------------------------------ . reg bidtotalz bidtotalj Source | SS df MS Number of obs = 27 -------------+------------------------------ F( 1, 25) = 1.09 Model | .001677916 1 .001677916 Prob > F = 0.3057 Residual | .038364831 25 .001534593 R-squared = 0.0419 -------------+------------------------------ Adj R-squared = 0.0036 Total | .040042747 26 .001540106 Root MSE = .03917 ------------------------------------------------------------------------------ bidtotalz | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalj | .1215172 .1162116 1.05 0.306 -.117825 .3608594 _cons | .8701703 .1183628 7.35 0.000 .6263975 1.113943 ------------------------------------------------------------------------------
y = 0.1215x + 0.8702(p=0.768; 0.049)
R² = 0.0419
0.9
0.92
0.94
0.96
0.98
1
1.02
1.04
0.85 0.9 0.95 1 1.05 1.1 1.15
Zignego's total bid
James Cape's total bid
WI‐DOT Road Construction Bids,Zignego Companies vs. James Cape & Sons
7
. reg bidtotall bidtotale, robust Linear regression Number of obs = 619 F( 1, 617) = 0.20 Prob > F = 0.6569 R-squared = 0.0066 Root MSE = .09249 ------------------------------------------------------------------------------ | Robust bidtotall | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotale | .0861011 .1937677 0.44 0.657 -.2944231 .4666253 _cons | .9013715 .192434 4.68 0.000 .5234664 1.279277 ------------------------------------------------------------------------------ . reg bidtotall bidtotale Source | SS df MS Number of obs = 619 -------------+------------------------------ F( 1, 617) = 4.10 Model | .035079655 1 .035079655 Prob > F = 0.0433 Residual | 5.27862756 617 .008555312 R-squared = 0.0066 -------------+------------------------------ Adj R-squared = 0.0050 Total | 5.31370722 618 .008598232 Root MSE = .09249 ------------------------------------------------------------------------------ bidtotall | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotale | .0861011 .0425206 2.02 0.043 .0025985 .1696037 _cons | .9013715 .0419777 21.47 0.000 .818935 .9838081 ------------------------------------------------------------------------------
y = 0.0861x + 0.9014(p=0.383; 0.002))
R² = 0.0066
0.8
0.9
1
1.1
1.2
1.3
1.4
0.7 0.8 0.9 1 1.1 1.2 1.3
Lunda's total bid
Edward's total bid
WI‐DOT Road Construction Bids,Lunda Construction vs. Edward Kraemer & Sons Inc.
8
. reg bidtotalz bidtotale, robust Linear regression Number of obs = 257 F( 1, 255) = 2.10 Prob > F = 0.1481 R-squared = 0.0466 Root MSE = .10705 ------------------------------------------------------------------------------ | Robust bidtotalz | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotale | -.2867073 .1976243 -1.45 0.148 -.6758909 .1024763 _cons | 1.330584 .1999074 6.66 0.000 .9369046 1.724264 ------------------------------------------------------------------------------ . reg bidtotalz bidtotale Source | SS df MS Number of obs = 257 -------------+------------------------------ F( 1, 255) = 12.46 Model | .142755941 1 .142755941 Prob > F = 0.0005 Residual | 2.92224634 255 .01145979 R-squared = 0.0466 -------------+------------------------------ Adj R-squared = 0.0428 Total | 3.06500228 256 .011972665 Root MSE = .10705 ------------------------------------------------------------------------------ bidtotalz | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotale | -.2867073 .0812325 -3.53 0.000 -.4466793 -.1267352 _cons | 1.330584 .0809811 16.43 0.000 1.171107 1.490061 ------------------------------------------------------------------------------
y = ‐0.2867x + 1.3306(p=0.228;0.008)R² = 0.0466
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
0.7 0.8 0.9 1 1.1 1.2 1.3
Zenith's total bid
Edward's total bid
WI‐DOT Road Construction Bids,Zenith Tech. Inc. vs. Edward Kraemer & Sons Inc.
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. reg bidtotalz bidtotall, robust Linear regression Number of obs = 482 F( 1, 480) = 2.48 Prob > F = 0.1159 R-squared = 0.0269 Root MSE = .10058 ------------------------------------------------------------------------------ | Robust bidtotalz | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotall | -.1947029 .1236045 -1.58 0.116 -.4375757 .0481698 _cons | 1.222867 .1218656 10.03 0.000 .9834114 1.462323 ------------------------------------------------------------------------------ . reg bidtotalz bidtotall Source | SS df MS Number of obs = 482 -------------+------------------------------ F( 1, 480) = 13.28 Model | .134320087 1 .134320087 Prob > F = 0.0003 Residual | 4.85551145 480 .010115649 R-squared = 0.0269 -------------+------------------------------ Adj R-squared = 0.0249 Total | 4.98983154 481 .01037387 Root MSE = .10058 ------------------------------------------------------------------------------ bidtotalz | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotall | -.1947029 .0534317 -3.64 0.000 -.2996918 -.089714 _cons | 1.222867 .0525081 23.29 0.000 1.119693 1.326042 ------------------------------------------------------------------------------
y = ‐0.1947x + 1.2229(p=0.900;0.008)R² = 0.0269
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
0.7 0.8 0.9 1 1.1 1.2 1.3 1.4
Zenith's total bid
Lunda's total bid
WI‐DOT Road Construction Bids, Zenith Tech. Inc. vs. Lunda Construction
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Documentation of Preliminary Results from Wisconsin, Minnesota, and Illinois Departments of Transportation (DOT) Datasets of Highway Procurement Bids
This document contains regression results and histograms of Wisconsin, Minnesota, and Illinois bids. These states are chosen to contrast the markedly different bid patterns between a known case of price collusion in Wisconsin and non-collusive firms in the other two states. Non-collusive firms from Wisconsin are also included to contrast with the collusive ones in the same state for robustness. Our result has never been documented to the best of knowledge. Total bids, as opposed to unit bids, are used in this preliminary analysis. A total bid is defined as the sum of unit bids of all items involved multiplied by quantity. Since projects are of wide range of sizes, all total bids are normalized, by dividing by engineer’s estimates (or average, if estimate is not available). These three datasets are constructed from bid abstracts publicly available on the respectively DOT websites.
The results are divided into four sections: (I) Collusion Case in Wisconsin; (II) Non-Collusion Cases in Wisconsin; (III) Non-Collusion Cases in Minnesota; and (IV) Cases in Illinois. (I) Collusion Cases in Wisconsin In November 2005, the owners of Vinton Construction Co. (Two Rivers, WI) and Streu Construction Co. (Manitowoc, WI), together with a former employee of James Cape & Sons Company (Racine, WI) were convicted of bid-rigging in highway procurement auctions, involving illegal exchange of price information and private allocation of projects among the three firms.1 More than 30 projects were involved from 1997 to 2003. Our first approach is to regress James Cape & Sons’ bids on Streu and Vinton’s bids, among the contracts where all three firms submitted bids. This is the regression result with robust standard errors: . reg bidtotalj bidtotalstreu bidtotalvinton, robust Linear regression Number of obs = 35 F( 2, 32) = 7.37 Prob > F = 0.0023 R-squared = 0.2944 Root MSE = .05182 ------------------------------------------------------------------------------ | Robust bidtotalj | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalst~u | -.5650517 .3256907 -1.73 0.092 -1.228462 .0983587 bidtotalvi~n | -.5897853 .1589249 -3.71 0.001 -.9135047 -.2660659 _cons | 2.149345 .4498661 4.78 0.000 1.232998 3.065692 ------------------------------------------------------------------------------
1 News release from the Office of Inspector General can be found at http://www.oig.dot.gov/item.jsp?id=1718
2
Both Streu and Vinton’s coefficients are negative and statistically significant. This reflects that the three firms indeed allocated projects among themselves: once the “winner” was internally decided, it would submit a low bid while the other two firms would submit significantly higher bids, as pretense of competition.
Our second approach looks at the distribution of difference in bids for pairs of firms among the three colluders:
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Two observations are that (i) these distributions are not normal or “bell-shaped,” and that (ii) these distributions are not centered at zero.
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(II) Non-Collusion Cases in Wisconsin Edward Kraemer & Sons Inc., Lunda Construction Co., and Zenith Tech. Inc. were chosen as the three firms among non-colluders that bid in the most number of contracts. We first regress Edward’s bids on Lunda and Zenith’s bids: . reg bidtotale bidtotall bidtotalz, robust Linear regression Number of obs = 253 F( 2, 250) = 0.99 Prob > F = 0.3737 R-squared = 0.0844 Root MSE = .07892 ------------------------------------------------------------------------------ | Robust bidtotale | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotall | -.1875022 .2084944 -0.90 0.369 -.5981315 .2231271 bidtotalz | -.1617131 .1187339 -1.36 0.174 -.3955593 .0721332 _cons | 1.345117 .3144649 4.28 0.000 .7257792 1.964455 ------------------------------------------------------------------------------ The coefficients, although still negative, are much smaller in magnitude and are, in fact, not statistically different from zero. Also note a much smaller R2 value than the regression in the previous section: a fellow non-collusive competitor’s bids have much less explanatory power than a fellow colluder’s. Now we plot the bid difference between pairs formed among these three non-collusive firms:
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These distributions stand in stark contrast with those in the previous section that (i) they are roughly normal or “bell-shaped,” and that (ii) they are centered at zero. This indicates that (i) difference between bids among non-colluders are not premeditated, and furthermore, that (ii) profit margins are tight and roughly the same among bidders under competition.
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(III) Non-Collusion Cases in Minnesota We repeat the same exercise for three pairs of Minnesota firms, chosen because they have the largest number of contracts where both firms bid in. Note the statistically significant positive regression coefficients as well as the roughly normal or “bell-shaped” distribution of bid differences, a feature shared by non-collusive firms in Wisconsin. (1) Between Bauerly Bros Inc. and Duininck Brothers Inc.: . reg bidtotalnormb bidtotalnormd, robust Linear regression Number of obs = 86 F( 1, 84) = 94.57 Prob > F = 0.0000 R-squared = 0.4688 Root MSE = .10264 ------------------------------------------------------------------------------ | Robust bidtotalno~b | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalno~d | .5945739 .061141 9.72 0.000 .4729883 .7161594 _cons | .3400107 .0577993 5.88 0.000 .2250705 .4549509 ------------------------------------------------------------------------------
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(2) Between Bauerly Bros Inc. and Central Specialties Inc.: . reg bidtotalnormb bidtotalnormc, robust Linear regression Number of obs = 83 F( 1, 81) = 24.64 Prob > F = 0.0000 R-squared = 0.4517 Root MSE = .08449 ------------------------------------------------------------------------------ | Robust bidtotalno~b | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalno~c | .3830712 .0771743 4.96 0.000 .2295186 .5366238 _cons | .5473665 .0716899 7.64 0.000 .404726 .690007 ------------------------------------------------------------------------------
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(3) Between Hardrives Inc. and Valley Paving Inc.: . reg bidtotalnormh bidtotalnormv, robust Linear regression Number of obs = 63 F( 1, 61) = 29.83 Prob > F = 0.0000 R-squared = 0.4149 Root MSE = .09703 ------------------------------------------------------------------------------ | Robust bidtotalno~h | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalno~v | .5769023 .1056243 5.46 0.000 .3656935 .7881112 _cons | .4841873 .1135161 4.27 0.000 .2571978 .7111768 ------------------------------------------------------------------------------
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(IV) Cases in Illinois We again repeat the same exercise for four pairs of Illinois firms, chosen because they have the largest number of contracts where both firms bid in. Note that regression coefficients are rarely statistically different from zero and that distribution in bid difference is not normal or “bell-shaped.” (1) Between K-Five Construction Co. and Gallagher Asphalt Co.: . reg bidtotalnormf bidtotalnorml, robust Linear regression Number of obs = 42 F( 1, 40) = 2.00 Prob > F = 0.1648 R-squared = 0.0680 Root MSE = .07903 ------------------------------------------------------------------------------ | Robust bidtotalno~f | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalno~l | .3453994 .2440819 1.42 0.165 -.1479085 .8387074 _cons | .6516845 .2435466 2.68 0.011 .1594584 1.143911 ------------------------------------------------------------------------------
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(2) Between Civil Construction Inc. and Brandt Construction Co.: . reg bidtotalnormc bidtotalnormr, robust Linear regression Number of obs = 33 F( 1, 31) = 3.68 Prob > F = 0.0643 R-squared = 0.1564 Root MSE = .12492 ------------------------------------------------------------------------------ | Robust bidtotalno~c | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalno~r | .538977 .2809642 1.92 0.064 -.0340532 1.112007 _cons | .5157967 .2561533 2.01 0.053 -.0066313 1.038225 ------------------------------------------------------------------------------
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(3) Between E. T. Simonds Construction Co. and Perry County Construction Co.: . reg bidtotalnorme bidtotalnormp, robust Linear regression Number of obs = 31 F( 1, 29) = 0.02 Prob > F = 0.8861 R-squared = 0.0005 Root MSE = .08387 ------------------------------------------------------------------------------ | Robust bidtotalno~e | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalno~p | .0184402 .1275808 0.14 0.886 -.2424918 .2793722 _cons | .9164423 .129205 7.09 0.000 .6521884 1.180696 ------------------------------------------------------------------------------
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(4) Between D. Construction Inc. and Gallagher Asphalt Co.: . reg bidtotalnormd bidtotalnorml, robust Linear regression Number of obs = 31 F( 1, 29) = 0.00 Prob > F = 0.9497 R-squared = 0.0001 Root MSE = .1022 ------------------------------------------------------------------------------ | Robust bidtotalno~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- bidtotalno~l | -.006976 .1096097 -0.06 0.950 -.231153 .217201 _cons | .9635098 .1137121 8.47 0.000 .7309424 1.196077 ------------------------------------------------------------------------------