A Field Experiment on Antitrust Compliance
Kei Kawai

Jun Nakabayashi ∗University of California, Berkeley

Kyoto University& University of Tokyo & NBER
September 7, 2023

Abstract
We study the effectiveness of firms’ compliance programs by conducting a field
experiment in which we disclose to a random subset of Japanese firms evidence of illegal
bid-rigging. We find that the information that we disclose affects the bidding behaviorof the treated firms: our test of bid-rigging fails to reject the null of competition whenapplied to the bidding data of the treated firms after the intervention. We find evidence
that these changes are not the result of firms ceasing to collude, however. Our findings
instead suggest that firms continue to collude even after our information disclosureintervention and that the changes in the bidding behavior we document are the resultof active concealment of evidence by cartelizing firms.
Keywords: Regulatory Compliance, Scoring Auctions, Collusion.

∗

Contact information, Kawai: kawaixkei@gmail.com, Nakabayashi: nakabayashi.jun.8x@kyoto-u.ac.jp1

1

Introduction

In many policy areas, there is an increasing trend towards delegating the day-to-day monitoring and enforcement of regulations to the regulated firms themselves (Sigler and Murphy,
1988, Ayres and Braithwaite, 1992).1 This trend reflects the rapid growth in the scope,scale and complexity of government regulations without commensurate growth in regulatory
resources.2 While regulatory agencies retain the authority to investigate and intervene, inmany policy areas, this authority is exercised only when firms exhibit repeated noncompliance or when firms’ compliance functions are deemed ineffective. Thus, regulatory regimes
often take the form of a hierarchical structure in which the firm’s compliance function handles much of the routine regulatory violations, while the regulatory agencies, on the other
hand, are primarily charged with overseeing the compliance function of the firms, taking
direct action only under a limited set of circumstances.3
While the trend towards delegating monitoring and enforcement to firms have been especially salient for policy areas such as investor protection,4 environmental protection,5 andconsumer protection,6 this trend is also starting to affect policy areas such as antitrust whichhave traditionally had a strong emphasis on external enforcement.7 For example, in some re1

According to one estimate, regulatory compliance accounts for about 1.34% of the total wage bill of
U.S. firms (Trebbi and Zhang, 2022).
2
See e.g., Davis (2017) for a discussion of the rapid growth in government regulations in the U.S.
3
The regulatory hierarchy is described in Ayres and Braithwaite (1995) as follows: “achievement ofregulatory objectives is more likely when agencies display both a hierarchy of sanctions and a hierarchy of
regulatory strategies of varying degrees of interventionism. The regulatory design requirement we describe isfor agencies to display two enforcement pyramids with a range of interventions of ever-increasing intrusiveness(matched by ever-decreasing frequency of use). Regulators will do best by indicating a willingness to escalate
intervention up those pyramids or to deregulate down the pyramids in response to the industry’s performancein securing regulatory objectives.”
4
For example, the Bank Secrecy Act and the Dodd-Frank Act require financial institutions to designate acompliance officer who is responsible for implementing compliance programs within the firm. The complianceofficer is required to certify to the regulators that the firm is in compliance with all regulations.
5See, e.g., the EPA’s audit policy, formally titled “Incentives for Self-Policing: Discovery, Disclosure,
Correction and Prevention of Violations”. According to the EPA, the audit policy provides “several major
incentives for regulated entities to voluntarily discover, self-report and correct violations of federal environmental laws and regulations”, “making formal EPA investigations and enforcement actions unnecessary.”6
For example, FTC and FDA send warning letters to firms to warn them of possible law violations, oftenwith regards to advertising or marketing practices. The letter typically contains an explanation of why therecipient is being sent a warning letter.
7
See, e.g., a series of papers written by Sokol, e.g., Sokol (2017, 2012)2

cent antitrust violation cases, the U.S. DOJ has started “seeking court-supervised probation
as a means of assuring that the company devises and implements an effective complianceprogram”(Assistant Attorney General Baer, 2014).8 Implementation of antitrust compliance programs and designation of antitrust compliance officers have become key components
of many consent decrees.9 The DOJ’s emphasis on effective compliance, treating it as agoal in and of itself, departs from the traditional regulatory model that focuses on ex-post
punishment of violations.
Although compliance functions within firms have become an important part of antitrust
enforcement, and of regulation of firms more generally, there is still relatively limited evidence on the effectiveness of within-firm compliance functions. In this paper, we provide
empirical evidence on one aspect of regulatory compliance, specifically, the extent to whichfirms can take remedial action when confronted with evidence of illegal activity. How firms
respond to evidence of regulatory violations – whether firms take steps to end wrongdoing,
ignore evidence and continue to engage in wrongdoing, or actively seek to conceal incriminating evidence – can shed light on how best to incorporate within-firm compliance into theregulatory environment. It can also shed light on the effectiveness of various policy toolsthat seek to guide firm behavior without being legally binding, such as regulatory guidelines
or warning letters sent by agencies such as the FTC and FDA. Many of these policy tools
require at least some level of voluntary compliance from firms to be effective.
In order to study how firms respond to evidence of illegal activity, we conduct a field
8
See, for example, the following remarks by Bill Baer, the Assistant Attorney General of the Antitrust
Division on Sept 10, 2014.

We also expect companies to take compliance seriously once they have pleaded guilty or havebeen convicted. Taking compliance seriously includes making an institutional commitment tochange the culture of the company. Companies should be fostering a corporate culture thatencourages ethical conduct and a commitment to compliance with the law.
In such cases, the division will consider seeking court-supervised probation as a means ofassuring that the company devises and implements an effective compliance program. We reserve
the right to insist on probation, including the use of monitors, if doing so is necessary to ensure
an effective compliance program and to prevent recidivism.
9

See, e.g., US v. DirecTV Group Holdings and AT&T (2017), US v. Charleston Area Medical Centerand St. Mary’s Medical Center (2016), etc.

3

experiment in which we disclose to a set of construction firms in Japan evidence implicatingthem of illegal bid-rigging.10 To do so, we first develop a statistical test of bid-rigging
for scoring auctions in which allocation is determined by both price and quality of the
proposal. We then apply the test to bidding data from auctions let by the Ministry of Land
Infrastructure and Transportation in Japan. We identify 242 firms whose bidding behavior
is inconsistent with competitive bidding. We then subject a random subset of these firms
to a treatment in which we send out a letter that explains the statistical test we ran andthe outcome of the test (i.e., competition is rejected) for the firm in question. We compare
the subsequent bidding behavior of the treated and the control firms to identify how firms
respond to evidence implicating them of bid-rigging.
Our first finding is that firms do respond to the information provided to them in theletter that we send out. In particular, we find that our statistical test of bid-rigging fails toreject the null of competition when applied to the bidding data of the treated firms after theintervention. For the control firms, our test continues to be effective at identifying bid-rigging
behavior. Using Fisher’s randomization test, we can reject (at the 1% significance level) thestrong null hypothesis that the treatment induces no change in firm bidding behavior withrespect to the ability of our statistical test to detect collusion.
Our second finding is that the change in firms’ bidding behavior is likely to be the resultof an adaptive response by the firms to evade detection without stopping collusion. First, we
do not find significant changes in the level of bids or the quality of the proposals, which areoften associated with a shift from collusion to competition. Moreover, we find a statisticallysignificant decrease in the number of bidders submitting valid bids, or an increase in the
proportion of bidders who submit bids above the reserve price conditional on participating.
Because we study procurement auctions, bids above the reserve price have no chance of
winning. These responses are consistent with continued collusive behavior. Finally, we
document additional direct evidence of continued collusion using a test developed in Kawai
et al. (2023). Our findings are consistent with concealment of incriminating evidence without
10

Bid-rigging in procurement auctions is illegal in Japan and firms face fines of up to 10 % of all relevant
sales in the affected market, in addition to the direct monetary gains that results from collusion.

4

stopping collusive behavior.
The results of our experiment suggest that, at least for the subset of construction companies participating in procurement auctions, existing levels of compliance capacity within
colluding firms are unlikely to complement formal regulatory actions in achieving regulatorycompliance.11 These findings are somewhat different from previous studies that document
firms stopping illegal behavior after allegations of wrongdoing are made (See, e.g., Christie
et al., 1994 for the NASDAQ collusion case, and Monticini and Thornton, 2013, for the LIBOR manipulation case). Our findings suggest that, in the absence of widespread publicity,internal compliance functions may be ineffective in changing firm behavior. Our findings
also suggest that regulators should be cautious in sharing evidence with firms when using
informal channels to put pressure on them to change their behavior. If evidence of regulatory
violations can be concealed with minimum cost and without having to stop illegal behavior,
sharing detailed information with the firms may make detection and formal prosecution ofillegal behavior more difficult in the future.
Related literature. There is a large literature in law and organizational behavior thatanalyzes how within-firm compliance functions complement the work of regulatory agencies,
for example, Braithwaite (1985), Ayres and Braithwaite (1995), Parker (2002), Sokol (2012).
There is also a small theoretical literature on regulatory compliance in economics that studies
the trade-off between regulatory capture and efficient use of private information, e.g., Gehrig
and Jost (1995) and Grajzl and Murrell (2007).12 In these models, firms are better informed
about the business environment than the regulators making firms better positioned to identify
the types of regulations that are efficient. This informational asymmetry makes it potentiallymore efficient to delegate rule-setting to firms. However, given the obvious conflict of interest,

11

It should be noted that in Japan, there is no equivalent of qui tam law suits in which private partiesare compensated for assisting governments recover damages from illegal activities. For example, in the U.S.,
the False Claims Act allows private parties to bring suit on behalf of the government and receive a portionof the damages recovered. See, e.g., Kovacic (2001), and Engstrom (2013) for analysis on qui tam law suits.
On the topic of private enforcement of laws more generally, see, e.g., Landes and Posner (1975) and Polinsky(1980).
12
Relatedly, there is also a literature on self regulation, e.g., baron, Egorov, Harstad.

5

the regulator may not want to delegate rule-setting and enforcement entirely to the firms.13
Our paper is also related to papers that study firm adaptation in environments where
regulatory agencies use screens to select the set of firms that go under scrutiny. For example, Wollmann (2019) and Cunningham et al. (2021) document evidence that firms adapt
to the merger notification threshold set by the Hart-Scott-Rodino Act. The possibility offirm adaptation implies that the design of screens should account for the firms’ equilibrium
response. The importance of taking an equilibrium view of regulatory screening has been
made previously by Cyrenne (1999), LaCasse (1995), Harrington (2004), Ortner et al. (2022),
etc.
Previous work examining how firms react to evidence of incriminating evidence include
Christie et al. (1994) and Monticini and Thornton (2013). Christie et al. (1994) document
immediate changes in the quotes offered by market makers in the NASDAQ market after
newspapers reported potential collusion by dealers. Monticini and Thornton (2013) findevidence of banks stopping underreporting of LIBOR rates after the Wall Street Journal
reported potential manipulation of the rates. For both the NASDAQ collusion case andthe LIBOR manipulation case, there was a lot of publicity created by the news reports. In
contrast, the firms in our study were not subject to media exposure.
Lastly, our paper is related to the literature on detecting cartels in auctions. Early seminal
work includes Hendricks and Porter (1988), Baldwin et al. (1997) and Porter and Zona (1993,1999). More recent work includes Bajari and Ye (2003), Abrantes-Metz et al. (2006), Athey
et al. (2011), Conley and Decarolis (2016), Schurter (2017), Kawai and Nakabayashi (2022),
Chassang et al. (2022), Martin et al. (2022), Kawai et al. (2023) and Baránek et al. (2023).14
The strategy for cartel detection we adopt in this paper extends ideas proposed in Kawai
13

Other related work include Innes (1999), who studies remediation activities that are offered voluntarily
by violators and Kaplow and Shavell (1994), who study self-reports of violations by perpetrating firms. Thereis also a literature that studies the efficacy of leniency programs and the incentives they create for firms toreport their involvement in collusion, e.g., Motta and Polo (2003), Aubert et al. (2006), Spagnolo (2005),
Chen and Harrington (2007), Harrington (2008a) and Miller (2009).
14
Other related work includes Pesendorfer (2000), who studies bidding rings with and without sidepayments, and Asker (2010), who studies knockout auctions among cartel members. Ohashi (2009) and
Chassang and Ortner (2019) document how changes in auction design can affect the ability of bidders tosustain collusion. Clark et al. (2018) analyze the breakdown of a cartel and its implications on prices. Fora survey of the literature, see Porter (2005) and Harrington (2008b).

6

et al. (2023) to scoring auctions.15

2

Institutional Background and Auction Format

Our paper analyzes the bidding behavior of firms that participate in auctions let by the
Ministry of Land Infrastructure and Transportation (MLIT). This section provides a brief
description of the institutional background and the auction format used by the MLIT.
MLIT is the largest procurement buyer in Japan, letting each year about 9,000 auctions
for construction projects, worth a combined total of about 1.7 trillion yen (about $17 billion
USD). The range of projects let by the MLIT include road paving, building and repairing
bridges, installation of electrical equipment and other machinery as well as civil engineering
work.
Since around 2006-7, almost all MLIT auctions are let using scoring auctions. In ascoring auction, each bidder, in addition to a price, submits a proposal which is converted
into a scalar quality measure. Allocation is determined by each bidder’s score, an index thatcombines both price and quality of the bid. The MLIT scores each bid according to the rules = q/p,where q is the quality measure and p is the price submitted by the bidder. Bidders submit
sealed bids simultaneously and the project is allocated to the bidder with the highest score,
subject to the secret reserve price.
Depending on the type of project, the quality component of the bid can be more or lesspredictable. For simple projects, all bidders satisfying certain requirements receive the same
quality measure. For these auctions, q is essentially fixed and allocation is determined byprice competition. For more complicated projects, however, the quality of the proposals playa more important role in determining q. The range of quality measure a firm can obtain15

See e.g., Che (1993), Asker and Cantillon (2008, 2010), etc. for analysis of competitive equilibrium inscoring auctions.

7

is typically between [100, 150]. The lower bound of the quality measure is always fixed at
q = 100, while the upper bound can be higher or lower depending on the complexity of the
project.
One institutional detail that is worth mentioning is that when the price of bidder i exceeds
the secret reserve price, quality of the bid is either not recorded entirely, or is assigned thelowest possible measure of 100. This fact explains certain features of the data pattern that
we document in our analysis below.

3

Model and Test Statistic

This section specifies a model of scoring auctions and derives results that we use to test forcollusion. Our model is similar to Che (1993), and Asker and Cantillon (2008, 2010). The
results that we derive are similar to Kawai et al. (2023).
To preview the results, Corollary 1 states that, under the null of competition, if two
bidders i and j almost tie for first place so that si ≈ sj ≥ sk (∀k), the price componentsof the scores, pi and pj , must also be similar, pi ≈ pj . This result should be intuitive:
conditional on winning or losing by a very small margin, the winner and the loser should
be similar, on average. Similarly, Corollary 10 states that, if i and j are almost tied forsubmitting the lowest price so that pi ≈ pj ≤ pk (∀k), the scores must also be similar,
si ≈ sj . We use these results to test for competition: we test (1) whether or not the pricecomponent of marginal losers are the same as that of the marginal winners, on average; and(2) whether or not the scores of bidders who marginally bid the lowest are the same, on
average as those of bidders who marginally bid above the lowest. If there are systematicdifferences, it will be evidence against competition.
Game form. A buyer procures a single item from a finite set N of potential suppliers. The
procurement contract is allocated through a sealed-bid auction with a secret reserve price r,which is drawn from a distribution Fr . Each potential bidder i ∈ N decides whether or not
to participate in each auction. Conditional on participation, a bidder incurs participation8

cost k > 0, and submits a bid b consisting of a price-quality pair b = (p, q). Profit from
non-participation is normalized to 0. A bid is valid if the price is below the reserve price, i.e.,p ≤ r. The bidder who submits a valid bid with the highest score is allocated the project,
where the score is computed according to the formula s = q/p. Ties are broken with uniform
probability. We denote by ∨st the highest score among participating bidders in auction t, by
s−i,t ≡ (sj,t )j6=i the scores of firms other than i, and by ∨s−i,t ≡ maxj6=i sj,t the highest scoreamong i’s participating competitors. Let ∨s−i ≺ si denote the event that bidder i wins thecontract, i.e. si is the highest score and possible ties are broken in favor of bidder i. Bids
are publicly revealed at the end of each period.
The bidder’s profit conditional on winning is given by p−C(q, θ), where θ is the privatelyknown cost type of the bidder. The function C(·, θ) represents the cost of providing higher
quality for each type θ. In practice, the cost function C depends on observable auction
characteristics as well, but we suppress this dependence. We assume that C(·, θ) is increasing,convex and continuously differentiable for each θ.
Information. Each bidder i privately observes a signal zi ∈ Zi . We do not impose any
Qassumptions on the distribution of the signal profile zt = (zi,t )i∈N ∈ Z = i∈N Zi . Signals
may be arbitrarily correlated. The distribution of zi can be asymmetric. We denote by FZ (·)
the joint distribution of the signals.
Cost types θ = (θi )i∈N ∈ RN are drawn independently conditional on each private signal
zi , i.e.,
θi |zi ∼ θi |zt , θ−i .
Bidder i’s cost type does not provide information about the cost type of other bidders
beyond the information already provided in the private signal zi .16 This class of information
structures nests asymmetric independent private values, correlated values, and complete
information. We denote by Fθ (·|zt ) the conditional distribution of the profile of cost types θ
given signals z.
16

Because the signals are allowed to be correlated, zi,t helps bidder i predict the cost of other bidders.

9Indirect Profit Function Because any bid (p, q) that satisfies p/q = s guarantees thebidder the same winning probability, optimal bidding behavior requires a bid (p, q) with
p/q = s to be the solution to the following maximization problem:
max p − E[C(q, θ)|zi ]p,qs.t. q/p = s,where the expectation E is taken with respect to θi conditional on zi .17 The objectivefunction, p − E[C(q, θ)|zi ], is the profit the firm obtains if it wins. We denote the solutionof this problem as πi (s, zi ). The function πi (s, zi ) corresponds to the indirect profit functionof the firm when it bids a score of s and its signal is zi . Note that πi (s, zi ) is continuously
differentiable in s, by the Envelope theorem.18
Competitive Bidding We now derive implications of Bayes Nash equilibrium that we
use to construct our test of competitive bidding. Our first result establishes that conditionalon being a close winner or loser, any bidder believes that they win with probability greaterthan 50%.
Proposition 1. For any bidder i and for any positive number η > 0, there exists ε such thatprob(i wins | zi , |si − ∨s−i | < ε) ≥ 1/2 − η.
Proof. Let Di (si |zi ) denote prob(si  ∨s−i |zi ), the probability of winning the auction conditional on signal zi . Incentive compatibility of the bidder implies
Di (si |zi )πi (si , zi ) ≥ D(si + ε|zi )πi (si + ε, zi )andDi (si |zi )πi (si , zi ) ≥ D(si − ε|zi )πi (si − ε, zi ).
17
18

If bidder i’s cost, θi , is part of the private signal, zi , the expectation would be unnecessary.
Note that πi0 (s, zi ) < 0 for any s.

10Noting that Di (si |zi ) must be strictly positive and continuous in equilibrium,19
prob(i wins | zi , |si − ∨s−i | < ε) =Di (si |zi ) − Di (si − ε|zi )Di (si + ε|zi ) − Di (si − ε|zi )i)
1 − DDi (si (si −ε|zi |zi )= Di (si +ε|zi )Di (si |zi )i)− DDi (si (si −ε|zi |zi )i ,z)1 − πiπ(si (si −ε,z)≥ Di (si +ε|zi )Di (si |zi )

≥

=

=

i ,z)
− πiπ(si (si −ε,zi )i ,zi )
1 − πiπ(si (si −ε,zi)

πi (si ,zi )i ,zi )− πiπ(si (si −ε,zπi (si +ε,zi )i)
1
1− πi (si −ε,zi )πi (si ,zi )11− πi (si −ε,zπi (si +ε,zi )
i)
d
ds



1
πi (si ,zi )

d
ds





1
πi (si ,zi )

× ε + o(ε)× 2ε + o(ε)

As πi (si , zi ) is continuously differentiable and π 0 (si , zi ) < 0, the last term converges to
0.5 as ε → 0.
Proposition 1 shows that the winning probability must not be much lower than 0.5
conditional on close bids.20 As the next proposition shows, because at most one bidder canwin, and because there are at least two close bidders conditional on the existence of close
bids, it cannot be that firms’ winning probability is strictly larger than 1/2. For any  > 0,let -close denote the event that the winning bid of the auction is within  of the next highest
score. For any ε > 0, let E[·| -close] denote the expectation over z conditional on the event
-close.

19

Recall that participation costs k are strictly positive, which implies that Di (si ) needs to be strictly
positive. If Di (si ) is not continuous at si = s0 , then there exists some bidder j who bids exactly s0 withpositive probability. In this case, all bidders other than j will have no incentive to bid in a small interval
right below s0 , which in turn, makes bidding s0 with positive probability suboptimal for bidder j. In otherwords, bidder j can gain strictly by bidding slightly below s0 . Hence, we have a contradiction.
20
This proof is essentially the same as that of Proposition 1 of Kawai et al. (2023).

11

Proposition 2 (as-if random bids). For all η > 0 there exists  > 0 small enough such thatE

1
prob(i wins | zi and |si − ∨s−i | < ) −2-close ≤ η.

(1)

Proof. See Appendix.
In words, Proposition 2 states that winning is as-if random conditional on close bids
under competition. This result motivates our regression discontinuity test of competition.
The following Corollary formalizes the link between equilibrium bidding and our regression
discontinuity test.
Corollary 1. For any bounded function f : (p, q) 7→ f (p, q) ∈ R,
lim |E [f (pi , qi ) | si − ∨s−i ∈ (0, )] − E [f (pi , qi ) | si − ∨s−i ∈ (−, 0)]| = 0.

&0+

In particular, if f (pi , qi ) = pi ,lim |E [pi | si − ∨s−i ∈ (0, )] − E [pi | si − ∨s−i ∈ (−, 0)]| = 0.

&0+

(2)

.
Proof. See Appendix.
The first part of Corollary 1 guarantees that, under the null of competition, the conditional expectation of f (pi , qi ), when bidder i is a marginal winner (i.e., si − ∨s−i ∈ (0, )),is the same as the conditional expectation of f (pi , qi ) when bidder i is a marginal loser (i.e.,si − ∨s−i ∈ (−, 0)). In particular, setting f (pi , qi ) = pi , the second part of the Corollary
1 claims that the price of the marginal winner and the price of the marginal loser must be
the same, in expectation. If, to the contrary, the conditional expectation of the marginal
winner and the marginal loser is different, we can reject the null that bidder i is bidding
competitively. We use expression (2) to test for competitive bidding.

12Remarks on Corollary 1 We now make several remarks. First, there is a subset of
auctions in which the quality component of the bidders are essentially fixed at some level q.
In other words, all qualified bidders are given the same quality level q. For these auctions,si − ∨s−i → 0 ⇔ pi − ∧p−i → 0,
where ∧p−i is the lowest price among bidder i’s rivals. This implies that we can replace theconditioning set in expression (2) to bepi − ∧p−i ,when q is fixed at q. Another set of auctions for which we can replace the running variable
to be pi − ∧p−i in Corollary 1 is when the equilibrium distribution of {pi }i∈N is smooth. If
the distribution of {pi }i∈N is smooth, a modified version of Proposition 1 holds, i.e.,prob(pi is lowest | zi , |pi − ∧p−i | < ε) ≥ 1/2 − η.

(3)Hence, a version of Corollary 1 with pi − ∧p−i as the conditioning set must hold as well.
The formal statement of the foregoing discussion is as follows:
Corollary 10 . If the quality component can take only one value, qi = {q} for all i, or if
the distribution of p admits positive density on its support, then, for any bounded functionf : (p, q) 7→ f (p, q) ∈ R,
lim |E [f (pi , qi ) | pi − ∧ ∨ p−i ∈ (0, )] − E [f (pi , qi ) | pi − ∧p−i ∈ (−, 0)]| = 0.

&0+

In particular, if f (pi , qi ) = pi /qi ,lim |E [si | pi − ∧p−i ∈ (0, )] − E [si | pi − ∧p−i ∈ (−, 0)]| = 0.

&0+

.
13

(20 )

Proof. See Appendix.
In our empirical exercise, we use both (2) and (20 ) to test for competition.
In Corollary 1 and 10 , the conditional expectation is taken with respect to f (pi , qi ). It is
straightforward to show that an analogous expression holds for any predetermined covariate
xi of bidder i (e.g., bidder’s backlog). In Section XYZ, we exploit this fact by testing for
discontinuities in the backlog between marginal winners and marginal losers. We find thatmarginal winners tend to have lower backlog than marginal losers, suggesting that firms in
the treated groups continue to collude even after the intervention.

4

Identifying Firms That Do Not Bid Competitively

Illustration: Case of Nippo Corporation We now demonstrate how we test eachfirm for competitive bidding using the results obtained in the previous section. We startby focusing on bidding data from one firm, Nippo Corporation, between 2012 to 2015.
Although the period 2012-2015 is not within the time frame we focus on, Nippo Corporation
was involved in a bid-rigging scheme that was discovered and prosecuted in 2013 by theJapanese competition authority. By applying our test separately to the sample of auctions
before and after the investigation, we illustrate the usefulness of the test in identifying
collusion. We also discuss why collusive bidding is prone to fail the test.
Let Tk (k ∈ {Before, After}) denote the partition of the set of auctions in which Nippo
Corporation participated to those taking place before and after the investigation. For eachauction t ∈ Tk , let i∗ (t) be the winner of the auction, i.e., the bidder who had the highest
score.21 For each non-winner i(6= i∗ (t)) who participated in auction t, we construct ∆si,t =
si,t −si∗ (t),tsi∗ (t),tand ∆pi,t =

pi,t −pi∗ (t),t.
pi∗ (t),t

The variable ∆si,t is the margin of defeat for bidder i,normalized by winner’s score si∗ (t),t . The variable ∆pi,t is the price difference between bidder
i and the winner, again normalized by the winner’s price, pi∗ (t),t . We then test for expression(2) by testing whether or not ∆pi,t converges to 0, as ∆si,t converges to 0.
21

There were no ties for the highest score in the sample.

14

Figure 1: Binned scatter plot of ∆si,t and ∆pi,t . Left panel corresponds to the auctions in which
NIPPO participated before before being investigated for collusion. Right panel corresponds
to auctions after the investigation.

Figure 1 is a binned scatter plot of (∆si,t , ∆pi,t ) for TBefore (left panel) and TAfter (rightpanel). Figure 1 also plots the (nonparametric) mean regression of ∆pi,t on ∆si,t . Notice that,in the left panel, the mean regression does not pass through the origin. In other words,
lim&0+ E ∆pi,t |∆si,t ∈ (−, 0) > 0, so that ∆pi,t does not converge to 0 as ∆si,t converges to
0. On the other hand, in the right panel of Figure 1, the mean regression goes through theorigin.
Panel (A) of Table 1 reports our local linear estimates of E ∆pi,t |∆si,t at ∆si,t = 0 for
TBefore (first column) and TAfter (second column).22 We find that, for the sample of auctions
22

Our estimates are obtained using a local linear regression with a coverage error rate optimal bandwidthand a triangular kernel with a bias correction procedure as proposed in Calonico et al. (2014). We obtainstandard errors by clustering at the auction level.

15



during which the firm was involved in bid rigging, the intercept of E ∆pi,t |∆si,t at ∆si,t = 0 is
estimated to be 0.034, and statistically different from 0 at the 95% confidence level. Hence, we
can reject the null hypothesis that lim&0+ E ∆pi,t |∆si,t ∈ (−, 0) = 0 with 95% confidence.
On the other hand, our estimate of the intercept is 0.002, and statistically indistinguishable
from 0 for the sample of auctions after the investigation.

16

(1)
(2)
BeforeAfterpsPanel (A) : E [∆i |∆i = 0]β̂0.037∗∗∗(0.006)0.002(0.006)

Bandwidth ĥ
Obs.

0.019
73

0.024
259



Panel (B) : E si − si(1) |pi − pi(1) = 0β̂−0.042∗∗∗(0.007)−0.050(0.017)

Bandwidth ĥ
Obs.

0.027
73

0.035
211

Panel (A) corresponds to the sample of auctions in.
*, **, and *** respectively denote significance atthe 10%, 5%, and 1% levels.

Table 1: Intercept of Partial Linear Regression, NIPPO Corporation.

17

As we discussed at the end of Section 3, we also consider a second test of competition bycomparing the score of the bidder who bid the lowest price with that of the bidder who bid
the second lowest price. Under the null of competition, the average scores should be equal
to each other when we condition only on those who bid almost the same.
In order to implement the test, let i† (t) denote the bidder who submits the lowest pricein auction t. For each i 6= i† (t), define the running variable to be the price difference,
˜ p = pi,t −pi† (t),t and the outcome variable to be ∆˜ si,t = si,t −si† (t),t . By construction, ∆˜ p > 0.
∆
i,t
i,t
p†
s†
i (t),ti (t),t˜p ,∆˜ si,t ) for all bidders i 6= i† (t) for Tbefore (left panel)
Figure 2 is a binned scatter plot of (∆i,tand Tbefore (right panel). The left panel shows that bidders who bid marginally higher thanthe lowest price have much lower scores than i† (t), the bidder who bid the lowest. This alsomeans that these bidders have much lower quality than i† (t). The right panel corresponds tothe set of auctions after the investigation. We find that bidders who bid marginally higher
than i† (t) have, on average, the same score as i† (t) in the right panel. The curves in the
figure correspond to the nonparametric regression.
hi˜ p = 0. We find˜ si,t |∆˜ p at ∆The bottom panel of Table 1 reports the estimate of E ∆i,ti,tthat the estimate is negative and statistically significant for TBefore while it is statisticallyindistinguishable from 0 for TAfter .
Why collusive bidding may fail the test Corollary 1 and 10 show that the regressionshi˜ si,t |∆˜ p should both converge to 0 as ∆si,t and ∆˜ p converge to 0. The
E ∆pi,t |∆si,t and E ∆i,ti,tCorollaries do not, however, show that these conditions must fail under collusion. Here, we
discuss why our test has power against certain types of collusive behavior.
Under most bid rigging arrangements, the ring preallocates projects so that one of its
members is a designated winner and other members are designated losers. Often there
is heterogeneity among the ring members in terms of their ability to submit high quality
proposals. The quality of the proposals depends on the technology and machinery available
to each firm. When the designated winner is a low quality firm, the designated losers will
have to submit a price that is substantially higher than the designated winner since their

18

Figure 2: Nippo

19

proposals will likely receive a higher quality measure than that of the designated winner.
For these auctions, the designated losers often come out as narrow losers, but with a muchhigher price and a much higher quality measure. The designated winner, on the other hand,comes out as the narrow winner, but with a much lower price and a much lower quality
measure than the marginal losers. This bidding pattern is consistent with the data pattern
illustrated in the left panel of Figure 1.
When the cartel designates a high quality bidder to be the winner of the auction, thelow quality bidders can bid lower in terms of prices than the designated winner and still
ensure that the auction is allocated to the intended bidder. In practice, however, many
low quality bidders bid slightly higher than the designated winner, perhaps out of caution.
When this is the case, bidders who bid marginally above the lowest bidder are often low
quality designated losers whose overall scores are much lower than the designated winner.
These types of bidding patterns will result in bidding patterns illustrated in the right panelof Figure 2.
Screening for non-competitive bidders For each firm that participated in at least 5
MLIT auctions between April 2015 and March 2017, we test the following two hypothesesH0 : E ∆pi,t |∆si,t = 0− = 0 v.s. H1 : E ∆pi,t |∆si,t = 0− > 0
and
h

i

h
i
˜ si,t |∆˜ p = 0+ = 0 v.s. H1 : E ∆˜ s |∆
˜ p = 0+ < 0
H0 : E ∆
i,t
i,t
i,t
during that time period. Based on the two tests, we initially select 1,143 firms that fail eitherof the tests at 5% significance.23 We then further screen these firms by visually inspecting
the binned scatter plot and determine whether or not the intercept seems clearly different
from zero. The reason for adopting this two-step procedure instead of mechanically selecting
the firms with the highest t-statistics is because there is occasionally a disconnect between
23

For the first test, we select firms with t-statistics of above 1.65. For the second test, we select thosewith t-statistics of below -1.65. Of the 1,143 firms that we initially select, 635 firms failed the first test, 601
firms failed the second test, and 93 failed both.

20

the magnitude of the t-statistic and how strong the evidence appears based on the binned
scatter plot. For this reason, we choose a threshold of 5% confidence in terms of the t-statistic
to initially preselect the set of potentially non-competitive firms. We then follow up witha visual inspection of the binned scatter plot of each firm to avoid making type I errors.24
Ultimately, we end up with 240 firms which we feel confident classifying as noncompetitive.25
Because we conduct tests for all firms in the data that bid at least 5 times, a simple t-test
with a significance level of 5% is going to reject about 5% of the firms even in the absenceof collusion. In order to get a sense of the false discovery rate (FDR), or the proportion ofcompetitive firms that we wrongly identify as uncompetitive, we plot the empirical CDF of
the estimated t-statistic from our first test (i.e., test of E ∆pi,t |∆si,t = 0− = 0) along with
the CDF of the standard normal in Figure 3. Note that under the null of competition, ourtest statistic follows the standard normal distribution. Figure 3 shows clearly, however, thatthe empirical CDF exhibits large excess mass at the right tail of the distribution relative
to the standard normal: for example, 15% of the estimated t-statistics are higher than
1.65 as opposed to 5%. This implies that the (positive) FDR at critical value of 1.65 is at
most 0.05/0.15≈ 33%.26 Using empirical Bayes shrinkage to shrink the distribution of thet-statistic only changes the fraction of t-statistic exceeding 1.65 to 14%.
As we discussed above, we use a cutoff of 1.65 in terms of the t-statistics to determine theinitial set of noncompetitive firms. We then visually inspect the binned scatter plot for eachof these firms to further narrow down the set to about 240. If, instead of visual inspection,
we were to mechanically pick the same number of firms with the largest t-statistics, thecorresponding FDR, computed using Benjamini et al. (1995), would be about 2%. To theextent that our visual inspection is no worse at screening out type I errors than mechanicallychoosing the firms with the highest t-value, the FDR among the chosen 240 firms is lower
24

See Korting et al. (2023) for a discussion of the benefits of combining a formal econometric test with avisual examination to achieve lower type I errors.
25
We initially select 242 firms, but we end up dropping 2 firms after performing the clustering procedure
we discuss next section. See Online Appendix Section ?? for details.
26
The original definition of the FDR proposed by Benjamini and Hochberg (1995) is E[V /R|R > 0] Pr(R >0), where R is the number of total rejections and V is the number of Type 1 errors. The positive FDR is
defined to be E[V /R|R > 0] (See, e.g., Storey, 2002).

21

Figure 3: Histogram of t-statistic for E[∆pi,t |∆si,t = 0− ].

than 2%.27
Finally, we note that while the selection of the 240 firms are based on somewhat subjective
criteria, all of the subsequent statistical analysis are based on randomization inference which
gives us exact p-values. Regardless of whether the 240 firms were selected subjectively orbased on objective measures, our inference is valid because the treatment is randomized
given this sample.

5

Experimental Design

Assignment of Treatment The 240 firms that we identify as noncompetitive in theprevious section often bid on the same auctions and hence we expect some communication
to take place among these bidders. In order to contain potential treatment spillovers acrossunits, we partition the 240 firms by grouping those that frequently bid together using a
27

Korting et al. (2023) conclude that visual inference achieves a type I error rate that is slightly lower
than the Imbens and Kalyanaraman (2012) and Calonico et al. (2014) procedures. Moreover, they find thatvisual and econometric inferences are complementary.

22

clustering algorithm.28 The resulting partition has the property that firms within each groupbid on the same auction frequently while firms in different groups rarely do. We end up with
26 groups of firms. We then construct 13 matched pairs based on the group’s geographicallocation, type of work (e.g., landscaping, paving, etc.), and the number of firms in the group.
Finally, we assigned treatment status with rerandomization (see, e.g., Morgan and Rubin,2012) to achieve balance between the treatment and the control with regards to the mean
winning bid, mean of the t-statistics of the two tests, and the average number of auctions
firms participate.29 We take the effect of rerandomization into account when conducting our
statistical tests below.
Treatment We send (physical) letters to 13 groups, or a total of 106 firms on Feb 12,
2019. We send the letters to the addresses of the firms as recorded in the MLIT’s registry.
In the letter, we first explain that we have developed a screen for bid rigging and that we are
exploring its usefulness and applicability. We explain the mechanics of our test by walking
through Figures 1 and 2 of Nippo Corporation in the letter. We then include corresponding
figures for the firm in question and discuss the similarities between the firm’s bidding patterns
with those of Nippo Corporation before the investigation, i.e., the regression lines do notpass through the origin. We also include a list of auctions used to analyze the firm’s bidding
pattern so as to emphasize that our analysis is specific to the firm. Note that there is alag between the period of analysis contained in the letter (April 2015 to March 2017) andthe date the letters are sent out (Feb 12, 2019). Finally, we ask the firm whether the firm
is aware of various screening methods to detect noncompetitive bidding, and whether such
screens can help the firm improve antitrust compliance. We include a return envelope forthe firm to send back its reply, asking them to do so by March 15, 2019. A copy of the letter
sent to the firms in the treatment groups is in the Online Appendix.

28

In particular, we use a hierarchical agglomerative linkage procedure. We provide the details in Online
Appendix Section ??
29
We explain in more detail the rerandomization procedure in the online appendix.

23

Baseline Summary Statistics Table 2 and 3 report the summary statistics of the auctions and the bidders for the sample of 240 firms that we select as noncompetitive. The
summary statistics correspond to the sample of auctions we used to detect noncompetitive
bidding, i.e., auctions let between April 2015 to March 2017. The average reserve price of
the auctions is about 128 million yen, or about $1.3 million. The winning bid is about 94%
of the reserve price and the average number of bidders is 4.81. The fact that there are only
very minor differences between the treatment and the control is by design. We assigned
treatment by a matched pairs design and, moreover, we rerandomized the assignment toachieve balance for the winning bid.
Table 3 reports firm characteristics. We report the mean annual sales, profits, the numberof engineers employed by the firm, and the t-statistic corresponding to our intercept estimate.
The first three variables are obtained from the registry maintained by the MLIT. Although
the total number of employees is not recorded in the registry, the ratio of engineers to thetotal number of employees is usually about 1:2 to 1:3, based on the subset of firms that report
the number of employees on their web pages. Given that the average number of engineers isabout 27, this implies that the total number of employees is likely to be about 55 to 80, on
average. The average t-statistics associated with the intercept estimates of each firm before
the intervention is about 3.5 for the first test (i.e., test of E ∆pi,t |∆si,t = 0− = 0), and it isih˜ si,t |∆˜ p = 0+ = 0). By construction,
about -3.0 and -3.5 for the second test (i.e., test of E ∆i,tfor each firm, the t-statistics are higher than 1.65 for the first test or lower than -1.65 forthe second test.

6

Results

Changes in the bidding behavior We first document changes in the firms’ bidding
behavior in relation to the ability of our test to detect collusion. Figures 4 and 5 plot24

Reserve
WinBid / Reserve
Bid / Reserve
Quality
Number of Bidders
N

Treatment

Control

128.358(72.350)0.939(0.034)
0.942(0.039)155.722(6.836)4.794(2.792)
1,300

128.787(72.432)
0.939(0.034)
0.942(0.039)155.746(6.839)4.794(2.799)1,289

Note: The table shows the summary statistics of auctions let during fiscal years
2015 through 2017. Reserve is reported in millions of yen. Standard errors arein parenthesis.

Table 2: Summary Statistics (Auctions)

Annual Sales
Annual Profits# Engineerst-stat (E ∆pi,t ) )
t-stat (E ∆si,t ) )N

Treatment

Control

2,087.12(2,278.15)
146.52(192.54)26.75(18.97)
3.45(4.09)-3.00(4.39)
107

2,122.89(3,623.24)
137.95(297.86)
27.63(34.29)3.75(8.69)-3.54(4.97)
133

Note: Sales and profits are reported in million of yen. There are 240 firms inour sample. We could not get the data on the annual sales and profits, and thenumber of engineers for one firm in the control group. The reported numbers
for these variables for the control group are based on the averages of 132 firms.

Table 3: Summary Statistics (Firms)(∆p , ∆s ) for non-winners before and after the intervention.30 Figure 4 corresponds to the30

The sample used for the left panel (before period) consist of those let between April 1, 2015 and February
15, 2019, the date we sent out the letters. The sample for the right panel (after period) consist of those let25treatment group and Figure 5 corresponds to the control group. Comparing the left andright panels of Figure 4, we find that the intercept of the regression line (depicted in gray)at ∆s = 0 is positive in the left panel while it is close to 0 in the right panel. This implies
that marginal losers stop bidding substantially higher than the marginal winner in terms ofprices after the intervention, suggesting that bidder behavior changed after the intervention
for the treated firms. For the control group, the regression lines illustrated in Figure 5 seemto intersect the y-axis above the origin in both panels.

Figure 4: E[∆p | ∆s ] for Treatment Firms
˜ p, ∆˜ s ). For the treatment sample (Figure
Figures 6 and 7 plot the binned scatter plot of (∆6), we find that the estimated intercept of the the regression line changes from −0.017 before
the intervention to −0.006 after the intervention, while for the control sample (Figure 7),
between March 15, 2019, the date by which we asked the firms to respond to our survey and March 31, 2021.

26

Figure 5: E[∆p | ∆s ] for Control Firms

the estimate does not change much: The estimate is −0.016 before the intervention, and it
is −0.014 after.
While the figures are suggestive that the intervention induced changes in the bidding
behavior of at least some firms, we need to ascertain that these changes are unlikely tobe the result of chance. In order to do so, we now run a Fisher’s test of the strong null
hypothesis (See, e.g., Imbens and Rubin (2015) for a textbook treatment).
Specifically, for each group g ∈ {1, · · · , 26}, let Yg denote the change in the t-statistic
before and after the intervention,
Yg = tAfter− tBefore
,
g
g

27

˜s | ∆
˜ p ] for Treatment Firms
Figure 6: E[∆
where tBefore
and tAfterare the t-statistic of the estimated intercept, computed using thegg
sample before the intervention and after the intervention. Now, for any partition of thegroups G and G0 , consider the difference in the average Yg between groups in G and groups
in G0 :Y G−G0 =

1 X
1 X
Yg −
Yg .
| G | g∈G
| G0 | g∈G0

The statistic Y G−G0 corresponds to a measure of the extra decline in the t-statistics exhibitedby groups in G relative to G0 . If we set G = GT and G0 = GC , where GT and GC are theset of treated groups and control groups, Y GT −GC corresponds to the extra decrease in thet-statistic for the treatment groups relative to the control groups. Note, however, that we
can compute Y G−G0 for arbitrary partition G and G0 , not just for GT and GC .
Under the strong null that our intervention had no effect on the bidding behavior of any

28

˜s | ∆
˜ p ] for Control Firms
Figure 7: E[∆of the treated groups whatsoever, the partition of the groups into the set of treated groups,
GT , and the control groups, GC , is of no particular significance. In other words, under thestrong null of no effect, the random variable Y GT −GC , should have the same distribution as
Y G−G0 for arbitrary partition G and G0 . Fisher’s randomization test compares the realized
value of Y GT −GC to the distribution of Y G−G0 for all possible partitions G and G0 . If thevalue of Y GT −GC is extreme relative the distribution of Y G−G0 , we can reject the null thatthe treatment has no effect.
Figure 8 reports the distribution of Y G−G0 for all possible partitions of G and G0 as well
as the value of Y GT −GC . The left panel corresponds to the t-statistic of E ∆pi,t |∆si,t = 0−
h
i
s ˜p
+
˜
and the right panel corresponds to E ∆ |∆ = 0 . The number of possible partitions are asubset of 213 (= 8, 192) that satisfy the rerandomization criteria.31 The left panel corresponds
31

Recall that we have a matched pair design in which one of the pair is treated and the other is not. The

29

to the distribution of Y G−G . The realization of Y GT −GC is marked as a vertical line in the
figure. Since the realization of Y GT −GC is far off from realizations of τG−G0 for other possible
partitions (p-value of 0.031), we can reject the null that the intervention had no effect on
the treated groups. We plot the corresponding figure for the case in which the underlying
˜ p as the running variable and ∆˜ s as the outcome variable. For
t-statistic is computed using ∆reasons noted in the previous section, we do not find that the value of Y GT −GC is different
from realizations of Y G−G0 for other partitions. Hence, we cannot reject the null that firms
changed the bidding behavior relative to the power of the second test to detect collusion.

Figure 8: β of RD

Changes to other outcome variables In this section, we explore the effect of the intervention on other auction outcomes to understand whether the changes in the bidding patternthat we document in the previous section are the result of cartel firms stopping to collude
or it is the result of concealment of incriminating evidence with continued collusion. Figure
9 plots the time series average of the winning bid (Left panel) and the average losing bids
number of all possible treatment assignments is 213 . Because we rerandomize to maintain balance between
the treated and the control groups, we have a smaller number () of possible assignments.

30

(Right panel) separately by treatment and control groups. We normalize the bids by thereserve price to make them comparable across auctions. The light colored line corresponds
to the control group, the dark line corresponds to the treatment group, and the solid verticalline corresponds to the date of the intervention. We find that both for the treatment and thecontrol groups, the bids are quite stable across time, at around 95% (of the reserve price)for the winning bid and at around 97% for the losing bids. In particular, we do not see any
breaks in the bids for the treatment group around the time of the intervention.

Figure 9: Time Series Plot of Bids, Normalized by the Reserve Price. Left panel corresponds
to the winning bid (divided by the reserve price) and the right panel corresponds to the losing
bids (divided by the reserve price). Light colored lines correspond to the control group andthe dark lines correspond to the treatment group.
Since breakdowns in cartels typically result in significant drops in the price, Figure 9
suggests that the firms in the treatment groups are unlikely to have stopped colluding after
the intervention.
Figure 10 plots the time series average of the quality measure of the winning bidders (leftpanel) and the losing bidders (right panel). The dark lines correspond to the treatment andthe light colored lines correspond to the control. Although there do not seem to be significant
31

changes in the quality measure for the winning bidders, there is a substantial drop in thequality for the treatment group around the time of the intervention for the losing bidders.
As we mentioned in Section 2, firms that submit bids exceeding the secret reserve priceare assigned a quality of 100 points, which is the lowest possible points attainable. In orderto study the extent to which the changes in the right panel of Table 10 are driven by bids thatexceed the reserve price, the left panel of 11 plots the time series average of the probability
that a losing bidder submits a bid that exceeds the reserve price.32 The left panel of the
figure suggests that there is a large increase in the probability of submitting a bid above thereserve price for the treatment group after the intervention.
The right panel of 11 plots the time series average of the number of bidders that submit a
bid below the reserve price. The right panel of the figure suggests that the number of bidders
that submit bids below the reserve price falls for the treatment group after the intervention,
consistent with the results of the left panel.

Figure 10: Winner’s Raw Quality (Left) and Losers Raw Quality (Right)
The dark line on the left panel represents the raw quality of the winning bid in the auction in which one ormore treatment firms participate. The light colored line represents the raw quality of the winning bid forthe auction in which one more more control firms participate. The dark line in the right panel represents
the raw quality of the losing bid in the auction in which one or more treatment firms participate. The lightcolored line represents the raw quality of the losing bid in the auction in which one more more control firms
participate.

32

The probability that the winner’s bid exceeds the secret reserve price is very close to zero, so we do not32Figure 11: Losing Bids Above Reserve (Left) and Number of Valid Bids (Right)
The dark line on the left panel represents the probability that the losing bid is above the reserve price in theauction in which one or more treatment firms participate. The light colored line represents the probability
for the auction in which one more more control firms participate. The dark line in the right panel represents
the probability that the losing bid is above the reserve price in the auction in which one or more treatment
firms participate. The light colored line represents the probability for the auction in which one more more
control firms participate.

In order to assess the statistical significance of these findings, we again conduct Fisher’s
randomization test for each outcome. In particular, for each outcome variable Yg , constructτG−G0 as before for all possible partitions G and G0 , and compare the value of τGT −GC
against the distribution. The left panel of Figure 12 corresponds to the winning bid and theright panel corresponds to the losing bids. We find that we cannot reject the null that theintervention had an impact of the winning bid.
Figure 13 plots the histogram for quality. The left panel corresponds to the winner andthe right panel corresponds to the losers. While we cannot reject the null that the winner’squality is not affected by the treatment, we can reject the null that the treatment has noeffect on the losers’ quality at 10% significance.
We can conduct analogous exercises for the probability that one of the losers submits a bid
above the reserve price, and the number of bidders that submit bids below the reserve price.

33

Figure 12: Percentage Bids

Figure 13: Quality
Figures 14 and ?? correspond to the Fisher’s test. We reject the null at 10% significance.
It is difficult to rule out completely the possibility that the changes in the bidding behavior
report the corresponding figure for the winner.

34

Figure 14: Bids Above Reserveof the treatment firms captured by Figures 4 and 8 result from breakdown of collusion.
However, the fact that the bids do not decrease and the quality of the losers decrease fortreated firms suggest that this is unlikely. Typically, increased competition is associatedwith decreases in bids and increases in quality and number of bids. Our results are mostconsistent with the hypothesis that bidders continued to collude, but adapted to our screen
by changing how they bid.
Additional evidence of collusion Lastly, we provide additional direct evidence thatbidders in the treated group continued to collude even after the intervention. In order todo so, we apply the test developed in Kawai et al. (2023) directly to the sample of treatedfirms. In Kawai et al. (2023), we construct a test that compares the backlog (i.e., amountof recently awarded projects) of marginal winners and marginal losers. Under the null ofcompetition, any bidder is just as likely to be the marginal winner as the marginal loser.
This implies that marginal winners and losers should, on average, have the same amount ofbacklog under the null.
Figure 15 corresponds to the bin scatter plot of backlog measures. We take ∆s on the35horizontal axis. Backlog is constructed by summing up the value of auctions won by eachfirm in a T -day window before the auction. In the figure, we use T equal to 30, 45, 60, 90,
120 and 150. For each T , there are two bin scatter plots, one corresponding to the period
before the intervention and the period after. We find that there is a visible discontinuity
for T = 45, 60 and 90 both for the period before and after the intervention. These results
suggest that the treated firms continued to collude even after the intervention.

Figure 15: Binned scatter plot of backlog

36

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