Effect on Institutions: Analysis of Japanese Municipalities’ Public
Procurement*
Koki Arai+
7/3/2012
Abstract
Using data from Japanese municipalities, this article analyzes the impact of institutions
on the average winning bid for the municipality’s public procurement. The results are (i)the general competitive bidding method led to a decrease in the average winning bid,and (ii) bidding reform led to a decrease in the average winning bid. The former is acompetition factor, and the latter is an attention factor that offers an appropriate
atmosphere for the reformed bidding system through a procurement authority; it is likethe “Hawthorne effect.”
Keywords: average winning bid, Hawthorne effect, institution, public procurement* I would like to thank Hiroyuki Odagiri and Yasushi Kudo for their useful comments,

and participants in CPRC seminars.
+ Japan Fair Trade Commission and Competition Policy Research Centere-mail koki.arai@nifty.ne.jp
The views expressed in this article are my own, and not of my organization.
1

1. Introduction
Using data from Japanese municipalities, this article analyzes the impact ofcompetition-enhanced institutions on the average winning bid for the municipality’s
public procurement.1
In Japan, against the background of several problems in public procurement,
the Act for Promoting Proper Tendering and Contracting for Public Works wasintroduced and enacted on February 16, 2001. The purposes of this Act are to secure
citizens’ trust in public works, and to promote sound development of the constructionindustry that contracts public works commissioned by the Central Government,
quasi-governmental agencies, and local governments; this is put into practice byannouncing relevant information, taking measures against various improper actions andmeasures to promote proper implementation of public works, while simultaneously
developing a legislative system. According to the Act, there is a Guiding Principle on
measures for promoting proper tendering and contracting (last amended on August 9,2011). Article 19 of this Act requires the Central Government to collect, organize, andprovide information, the promulgation of which would be useful to promote proper
tendering and contracting for public works. The data used in this study were provided
by the Central Government in December 2008 and February 2010.
We take a simple ordinary least squares regression (OLS) based on themunicipality and prefecture data. The results show the following: (i) the generalcompetitive bidding method led to a decrease in the average winning bid, and (ii) the
presence of bidding system reformation, such as a lower limit price to disqualify
bidding, led to a decrease in the average winning bid. The former is a competition factor,
and the latter created the appropriate atmosphere for the reformed bidding system. The
importance of competition is widely common sense; but even if the effect seems toprevent competition, the posture of a reformed bidding system by the procurement
authority is also important for bidding efficiency; a related phenomenon is the“Hawthorne effect,” which will be discussed in this article. Furthermore, this paper is ofimportance not only for its modest policy implication, but also as a case study exampleof the effect of not aggregating each plant worker but rather of aggregating each groupof people as a firm that participates in the municipality’s procurement bidding.
This article consists of six sections. Section two examines related literature that
The term municipality in this article includes cities, towns, villages, and special
wards, not including ordinance-designated cities such as Tokyo, Osaka, Yokohama, andso on.

12

describes the Japanese bidding situation and recent policy developments. Section three
explains the data used in the analysis and its descriptive statistics. Section fourdemonstrates the estimation result and provides some discussion, while section fiveprovides some caveats, and section concludes the paper.

2. Related literature
Regarding the Japanese bidding system, two articles explain recent generalcircumstances and several others outline specific issues. First, Kanda et al. (2010)
describe the Japanese bidding system from a historical and policy-oriented perspective.
They observe that government procurements had been undertaken, in principle, by anopen competitive tendering procedure since Japan moved to a modern constitutional
state in the 1860s. However, this procedure was often of limited use in practice, on the
grounds that it allows anyone to participate in the tendering process and thus resulted in
the procurement of “cheap” and “bad quality” goods. In practice, a selective tendering
procedure has been adopted and has played a major role. The selection of contractors
was left to the discretion of the government agencies that placed the orders. Decisions
regarding ordering units were also left to the discretion of the ordering agencies, andthey were thus empowered to split an order into smaller units. The number of bidders
was restricted through the selection process, and this enabled less efficient, smallerfirms to accept orders. This practice gave rise to criticisms of higher costs, corruption,and illegal cartel agreements among bidders. Since the 1990s, the discretion of ordering
government agencies has been curtailed through the “expansion of open competitive
tendering” as part of a deregulation and regulatory reform program.
Second, Fujitani (2010) deals with government procurements from a publicfinance law perspective. The principle of economic efficiency, one of the fundamental
principles of public procurement, states that government procurement must ensure thatpublic funds are disbursed efficiently. In practice, however, government procurements
often incorporate policy objectives that are inconsistent with this principle. Indeed, acloser examination of current statutory rules also confirms that the principle is not somuch a binding substantive legal rule as a guiding theory for the government
procurement system, which centers the competitive bidding for government contracts.
Based on these analytical descriptions, we estimate the empirical evidence on
the recent economic efficiency generated by competition and concern by authorities
regarding bidding. On the one hand, the recent financial retrenchment had a major effect3

on the economic efficiency of public procurement2. On the other hand, an importantfactor in enhancing competition is the competition authority advocating against thebackground of strict enforcement of bid-rigging violations of the Antimonopoly Act.
Regarding the relationship between public procurement and the Antimonopoly
Act, Shiraishi (2010) summarizes the system and issues under the Anti-monopoly Act ofJapan (including related laws governing the acts of contractors in government
procurement) from three viewpoints: collusion, exclusion, and exploitation. After thereform following a bidding case, Arai (2012) examines oil procurement antitrust cases
in Japan and Korea.
These articles explain the Japanese bidding system and its problems, but thefactors affecting the actual winning bid have been analyzed in very few studies. One
useful exception is Ohashi (2005), who examines the effects of improved transparency
in the bidder qualification process, using experience gained from a case study ofmunicipal public works auctions with difference-in-differences analysis.
This article contributes two points to the public procurement literature. One isan actual policy evaluation; reforming the public procurement bidding process isimportant in adopting an actual reform process. The second is to develop the simple
principles that competition and positive engagement are useful for economic efficiency.
The policy suggestion is then simple: enhance competition and maintain engagement.

3. Data
The data used in this analysis are taken from the “Survey on implementation situation
based on the Act for Promoting Proper Tendering and Contracting for Public Works”
conducted in December 2008 and February 2010, and especially include data frommunicipalities’ bidding systems. These surveys examined the bidding systems of 1793
(in 2008) and 1779 (in 2009) municipalities on September 1, 2008, and September 1,
2009. We used the following institutional variable data and other various data; thedescriptive statistics are shown in Table 1.

See “Summary of Revenues and Expenditures in General Account” as of June 2010:
available at <http://www.mof.go.jp/english/budget/statistics/201006/index.html>

2

4

Variable name
RATE_08(09)GENERAL_08(09)LOLIM_08(09)
ANNCLL_08(09)CP_08(09)CPNO_08(09)EXAM_08(09)ANNCEXAM_08(09)Content
Average winning bid rate
Whether general competitive bidding is implemented
or notWhether a lower limit price system to disqualify
bidding is implemented or not
Whether an ex post announced lower limit price todisqualify bidding is implemented or not
Whether a comprehensive evaluation bidding system
is implemented or not
The number of comprehensive evaluation bids
Whether a system examining an extreme lower priceis implemented or not
Whether an ex post announced level of examination ofan extreme lower price is implemented or not

Note
(%)
Yes=1, no=0
Yes=1, no=0
yes=1, no=0
yes=1, no=0
yes=1, no=0
yes=1, no=0*: 08(09) is the year index.
(Table 1: Descriptive statistics of institutional variables)
Note that this variable has two special characteristics that must be handled cautiously.
First, the value is the simple average of each winning bid divided by the predetermined
price. Second, the definition of the rate means that the value is smaller than one andlarger than about 0.6, as is predetermined by the above institution of LOLIM. The
kernel density graphic figure is shown in Figure 1.
(Figure 1)
Other various variables’ descriptive statistics are compiled in Table 2.
Variable name
POPULATIONSQUARE
AMA
EXP08
REV08
PRIVATE_08(09)LICENSE_08(09)PRATE_08(09)OVER65LANDPRICEUNEMPLOYMENT
ROAD
PARK
CRIMICALCASEContent
Note
Municipality population
Square kilometers of the municipality
The number of Antimonopoly Act violations in the prefecture
Municipality expenditures
Municipality revenues
Value of private sector sales in the construction industry
Number of construction business enterprise licenses
Average winning bid rate of the municipality in the prefecture
Population over 65 years old
Land price (residence area)
Number of unemployed
Length of roadways (kilometers)
Number of urban parks
Number of criminal cases*: 08(09) is the year index.
5

(Table 2: Descriptive statistics of other variables)
The main dependent variables, RATE_08 and 09, are nearly equal values, but the mean
and median of RATE_09 are slightly higher than 08. Compared to PRATE_08 and 09,
the mean and median of RATE_08 and 09 are statistically significantly higher than theformer in both indices of the same year. Regarding the independent variables, almost allvalues for financial year 09 are higher than those for financial year 08.

4. Estimation
First, we analyze RATE_08 and 09 by using a regression of their basic fundamentals,
that is, the population, size, expenditure, expenditure/revenue ratio of the municipality,and prefecture dummy. The equation is as follows (1):
RATE i ,t   1   11 vi   12 prefecture _ dummy k   1i ,t(1)where RATEi is a variable of each municipality’s average winning bid (i=1,..., 1583;
t=_08 and _09), 1 is constant, vi is a vector of the population, square, expenditure, andexpenditure/revenue ratio of the municipality, prefecture_dummyk is a dummy variableof the prefecture (k = 1,..., 46), and 1i,t is an error term.
Table 3 shows the result, in which 33% of the variation is explained by these variables3.
(Table 3)
According to the Hausman test, the estimation has no fixed or random effect in thepanel analysis. As shown by the results, larger population, smaller square kilometer area,and sounder municipality finances lead to decreased average winning bids.
Second, we deal with institution variables such as GENERAL_08(09), LOLIM_08(09),and so on. The equation is as follows (2):3

The calculation was done by EViews 5.0 produced by IHS Inc.
6

RATE i ,t   2   21 v i   22 prefecture _ dummy k  23 GENERALi ,t   24 LOLIM i ,t   25 ANNCLL i ,t   26 CPi ,t   27 CPNO i ,t(2)

  28 EXAM i ,t   29 ANNCEXAM i ,t   210 AMAk   2i ,twhere 2 is a constant, the same variables of (1) have the same meaning, additionalvariables for each municipality’s institutions are GENERALi,t, LOLIMi,t, ANNCLLi,t,
CPi,t, CPNOi,t, EXAMi,t, and ANNCEXAMi,t (i=1,..., 1583; t=_08 and _09), AMAk is avariable of the number of cases of the Antimonopoly Act violation in a prefecture (k =1,..., 46), and 2i,t is an error term.
Table 4 shows the result, in which 36% of the variation is explained. There is also no
fixed or random effect.
(Table 4)
As shown in the result, when there is general competitive bidding, the average winningbid decreased by 1.1%, a lower limit price to disqualify bidding led the rate to decreaseby 0.7%, an ex post announced lower limit price to disqualify bidding also caused adecrease of 0.9%, and a system examining an extreme lower price also led to a decreaseof 0.8%. On the other hand, a comprehensive evaluation bid system, an ex post
announced level of examination of an extreme lower price, and an ex post announced
predetermined planned price led the average winning bid to increase, while the numberof comprehensive evaluation bids, an ex ante announced predetermined planned price,and the number of cases of Antimonopoly Act violations led the rate to decreasebroadly.
Third, a regression of the differences in the variables was introduced in order to analyze
the effect on the institution. The equation is as follows (3), and does not have any scalarvariables because of the difference in the year trend between two years:
RATEi ,t   3   31GENERALi ,t   32 LOLIM i ,t   53ANNCLLi ,t  34 CPi ,t   35 CPNOi ,t   36 EXAM i ,t   37 ANNCEXAM i ,t   3i ,t(3)where 3 is a constant, variables with delta are deferred between the value of financial
7year 2008 and that of 2009, and 3i,t is an error term.
The result is shown in Table 5.
(Table 5)
Pursuant to the result, introducing the system of announcing of a lower limit price todisqualify bidding led the average winning bid to decrease.
In this regards, the existence of a general competitive bidding (GENERAL), a lower
limit price to disqualify bidding (LOLIM), an ex post announced lower limit price todisqualify bidding (ANNCLL), and a system examining an extreme lower price(EXAM) also led to a decrease. These systems seem to have an effect on preventing(extremely) price competition. In reality however, a municipality’s positive action toprevent dumping delivers a fair atmosphere for enterprises to compete in, thus bringing
about a lower winning bid.

5. Discussion
1) Endogeneity
In the case in which the winning bid has an effect on the establishing institutions’decisions, the result of the regression may not be the correct estimation (inconsistency).
However, several policy measures have recently been taken to enhance proper tenderingand contracting for public works, and several requests have been issued by the Ministerof Land, Infrastructure and Transport, the Minister of Internal Affairs andCommunications, and the Minister of Finance to review the system of examining an
extreme lower price, and to ask to stop an ex ante announced predetermined planned
price. The institutions for enforcing policy are usually created by these directions andimplemented exogenously.
In addition, the department that creates the bidding process and contracting is often
derived from the department that calculates the predetermined price and averagewinning bid. It is therefore difficult to link a small increase in the average winning bid
to large and sustained pressure to review an institution. We have thus created theestimation equation, since the related institutions have an effect on the average winningbid.
82) Economic situation in prefectures
The economic situation of a prefecture may affect the average winning bid, as obtaining
alternative revenues from private construction sales in the prefecture may allow greater
flexibility. Furthermore, the average winning construction bid provided by prefectures
may be influenced by the bidding competition in the prefecture in general. Therefore,
we add these variables as a vector of the economic situation in the prefecture inequation (2), as follows:

RATEi ,t   4   41vi   42 prefecture _ dummyk  43GENERALi ,t   44 LOLIM i ,t   45 ANNCLLi ,t   46 CPi ,t   27 CPNOi ,t  48 EXAM i ,t   49 ANNCEXAM i ,t   410 AMAk(4)

  411 vp t   2i ,twhere vpt is a vector of the value of the sales of the private construction sector in theprefecture during the year, the value of the number of construction firm licenses for theyear, and the average winning bid in the prefecture (t = _08, and _09).
The result is shown in Table 6.
(Table 6)
In line with the results, the number of licensed firms is negative and statisticallysignificant. The number of competing firms is an important factor to be fixed in the
average winning bid. The average winning bid in the prefecture affects that of themunicipality positively and is statistically significant. In addition, adding these variables
does not notably change the institutional parameters, and the effect on the parameters
does not change from invalid to valid in terms of statistical significance.
3) Social economic circumstances in the municipality
In addition to these factors, a municipality’s social economic circumstances may havean impact on the average winning bid, such as through the input price affected by the
number of unemployed, or the municipality’s land price. We thus add these variables asa vector of the social economic circumstances of the municipality in equation (4) andobtain the following equation:
9

RATEi ,t   5   51vi   52 prefecture _ dummyk  53GENERALi ,t   54 LOLIM i ,t   55 ANNCLLi ,t   56 CPi ,t   57 CPNOi ,t(5)  58 EXAM i ,t   59 ANNCEXAM i ,t   510 AMAk   511 vp t   512 vm   5i ,twhere vm is a vector of the value of the population over age 65, average land price(residence area), number of unemployed, length of roadways, number of parks, andnumber of criminal cases. The same variables have the same meanings in equation (4).
The result is shown in Table 7.
(Table 7)
According to the results, the number of parks is only positive and statisticallysignificant. These added variables do not notably change the institutional parameters,
and the effect on the parameters does not change from invalid to valid in terms ofstatistical significance.
4) Real motive of institutional effect
The existence and introduction of a lower limit price to disqualify bidding (LOLIM),
and a system examining an extreme lower price (EXAM) also led to a decrease of theaverage winning bid. We have already pointed out that these systems seem to have aneffect on preventing (extremely) price competition by, for example, increasing the
average winning bid; in contrast, the results of our estimation do not provide these
institutes increasing the average winning bid. In this regard, two inferences are
considered as follows:
Inference one) All of these institutions have a function of delineation of a particularfield of bid competition, from a field below a predetermined planned price, to a field
consisting of figures between below a predetermined lower limit price and above alower limit price (or being examined as an extreme lower price). When a bid auction
opens, some conditional auctions may be better than normal auctions; this phenomena
resembles the presence of reserve prices in an auction4. Thus, the type of condition that
There is a role of reservation prices under some conditions. See Engelbrecht-Wiggans(1987), Levin and Smith (1994, 1996), and De Silva, et al. (2009).
410

defines the range of a particular field of bid competition work functioned well in themunicipality bid system, such that the average winning bids among these institutions arelower when these conditions are met.
Inference two) All of these institutions seem to have the effect of preventing pricecompetition at first sight. In reality, a municipality’s positive action itself delivers a fairatmosphere for enterprises to compete in, thus bringing about a lower winning bid; thisis similar to the “Hawthorne effect5.” Although the effect is based on individuals, wheneach firm in a municipal bidding process perceives that atmosphere, the efficiency ofbidding is expected to be enhanced, and the average winning bid among these
institutions is expected to decrease.
To distinguish between these two inferences, we focus on the transition of averagewinning bid from 2008 to 2009 in terms of these variables. The municipality that had
the systems of LOLIM and/or EXAM in both years took a lower average winning bid
than that of the municipality that did not have the system from 2008 to 2009. The
municipality that introduced the systems took lower values than that of the municipalitythat did not. Table 7 (7-3s, 7-3t, 7-4s and 7-4t) shows these results such as LOLIM09=1< LOLIM09=0, in second and third rows of 7-3s (existence of the system LOLIM leads
to lower average winning bid) or LOLIM09=1 & LOLIM08=0 > 0, in the second row
and third column of 7-2s (introduction of the system LOLIM leads to a decrease in the
average winning bid).
If inference one is true, then the difference in the average winning bid (08 - 09) as aresult of removing the system of LOLIM and/or EXAM should have taken a negativesign. If inference two is true, then the sign is positive because the participants havegathered the attention of the procurement authority. Both of the signs are positive interms of statistical significance. This means that removing the systems leads to adecrease in the average winning bid. Therefore, our empirical evidence shows thatinference two is more reasonable than inference one.

The “Hawthorne effect” is defined very broadly by the Oxford English Dictionary(OED) as meaning “an improvement in the performance of workers resulting from achange in their working conditions, and caused either by their response to innovation orby the feeling that they are being accorded some attention.” See meta-analysis in Adair,et al. (1989). However, Levitt and List (2011) review the effect and show little evidence.
They extract suggestive meanings that the effect of artificial manipulation is greaterthan that of natural situation change.

511

We can found the decrease of the average winning bid rate due not to a specificinstitutional change but to a change in any institutions. That seems to be an
improvement in the performance of firms engaged in the procurement results from achange in their business conditions. This result is reminiscent of the Hawthorne effect.
In contrast, this analysis provides a case study example of the effect not of aggregatingeach worker, but of aggregating each group of people as a firm that participates in themunicipality’s procurement bidding.
There is a discussion that an external effort/force often deteriorates a person’s internalmotivation. For example, Falk and Kosfeld (2006) states that how restricting an agent’s
strategy can backfire on a principal by experimental data. However, changes ininstitutions are not for the control for a specific person based on principal-agent contract
but for the instruction for a specific type of firms. Because of this structure, thedisincentive from that control effect is not come to realization in this situation.
There seems to be something ambiguous with the fair atmosphere. The good effect inconsidering steady elaboration of the institution as the atmosphere is exemplified by thefollowing two evidences weekly: First, there is very little but negative relationshipbetween a governor change and an average winning bid. We explore the pooled OLS
estimation between the average winning bid of a prefecture from 2000 to 2008 and adummy variable of the governor change of the prefecture, and found a negative
relationship but not significantly. The change in governor is not the institutional change
but a highly influential policy change for various factors that may include procurement
policy. The result implies that some policy change may lead to decrease the averagewinning bid (but it is very weak statistically; see appendix Table 8.).
Second, a research which is in the study field of the construction management andeconomics shows motivation ranking of bidding factors by a construction firm in the
procurement process. The first ranking of the motivation at bidding is to consider
difference between planed estimated price by the authority and in-house estimated price,
the second is to save a general management cost, and the third is to win rivals in price6.
The result of this research implies that a firm may have a guess for any change in theinstitution as the authority’s contemplation for the procurement efficiency rather thanfor no change in the institution as that one.
Special Subcommittee on Assessment of Public Procurement Institution, Committee
on Construction Management, Japan Society of Civil Engineers (2010)

6

12

These are the reinforcing evidence of the Hawthorne effect, and ones of the backgroundmechanisms.

6. Concluding remark
This article uses data from Japanese municipalities to analyze the impact of institutions
on the average winning bid. The results can be summarized as follows: the generalcompetitive bidding method led to decrease in the average winning bid, and thepresence of bidding reform led to a decrease in the average winning bid.
The general competitive bidding method is a competition factor. This concept is basedon another factor, namely the number of licensed firms in the prefecture, which has anegative and statistically significant effect. When there were a large number of licensed
firms, the competition caused the average winning bid to decrease; as such, this is
another rationale for competition-enhancing efficiency.
Strategies of bidding reform, such as setting a lower limit price to disqualify bidding,created an appropriate atmosphere for the reformed bidding system. The existence ofbidding reform institutions such as LOLIM, CP, and EXAM led to a statisticallysignificant decrease in the average winning bid. In addition, there was no exception for
the introduction of new institutions such as LOLIM, CP, and EXAM (D(LOLIM),
D(CP), and D(EXAM)) causing the average winning bid to decrease. The reason wetested is the procurement authority’s positive commitment to the reform of the bidding
system. This resembles the Hawthorne effect in the sense that some postures caused an
improvement in the performance of the procurement system (above inference two).
Therefore, an authority’s effort to tackle the bidding problem is likely to bring about apositive atmosphere for the implementation of efficient bidding.
These results in prefectures or municipalities were not changed in terms of their signand significance by several indexes aimed at controlling for circumstantial factors. They
have been selected with concrete evidence among the alternative inference, and theresults are therefore robust. The above conservative interpretations are valid andeffective.
In general, we have a background of general pressure to enhance efficiency in public
13

procurement in view of budget constraints. The stimulation of competition is one
efficient measure that is familiar in a general sense, while the commitment ofprocurement authority is a useful measure to avoid strong objections. This is the modest
policy implication of this paper’s overall contribution.
In this study, we focus on the average winning bid, and analyze the trends and factors
relating to the bidding situation. However, we should extend this research to accumulate
individual bidding research under the effect of new or existing institutions. Anotherfrontier of research is a comparative study of countries in terms of bidding systems. For
example, in Italy, it is interesting that an average bidding system is used (Conley and
Decarolis, 2010). In the United States (US), there are three bidding systems: sealed
competitive bidding, competitive negotiation, and designated negotiation. In theEuropean Union (EU), there are four bidding systems: open procedures, restricted
procedures, competitive dialogue, and negotiated procedures. Our future work will
involve comparing various systems in various countries, and analyzing the systems
empirically.

14

References:
Adair, J. G., Sharpe, D. and Huynh, C-L. 1989. Hawthorne Control Procedures in
Educational Experiments: A Reconsideration of Their Use and Effectiveness,
Review of Educational Research, 59(2), 215-228.
Arai, K. 2012. Case Studies of Bid-rigging and Procurement Reform Assessment inFuel Oil Supply in Japan and Korea, Journal of Public Procurement,
forthcoming.
Conley, T. G. and Decarolis, F. 2010. Detecting Bidders’Groups in Collusive Auctions:
Evidence from Average Bid Auctions, available tohttp://www.wiwi.uni-bonn.de/gall/gaming/conleydecarolis.pdf
De Silva, D. G., Jeitschko, T. D., and Kosmopoulou, G. 2009. Entry and Bidding in
Common and Private Value Auctions with an Unknown Number of Rivals,
Review of Industrial Organization, 35(1-2), 73-93.
Engelbrecht-Wiggans, R. 1987. On Optimal Reservation Prices in Auctions,Management Science, 33(6), 763-770.
Falk, A. and Kosfeld, M. 2006. The Hidden Cost of Control, American Economic
Review, 96(5), 1611-1630.
Fujitani, T. 2011 Making Sense of “Principle of Economy” in the Government
Procurements – A Public Finance Law Perspective, Financial Review, 104
Kanda, H., Omae, K., and Takano, T. 2011. Discretion, Responsibility and the
Allocation of Duties in Government Contracting - Taking "Negotiations" and"Split Orders" as Examples, Financial Review, 104
Levin, D. and Smith, J. L. 1996. Optimal Reservation Prices in Auctions, Economic
Journal, 106(438), 1271-1283.
Levin, D. and Smith, J. L. 1994. Equilibrium in Auctions with Entry, American
Economic Review, 84(3), 585-599.
Levitt, S. D. and List, J. A. 2011. Was There Really a Hawthorne Effect at the
Hawthorne Plant? An Analysis of the Original Illumination Experiments,
American Economic Journal: Applied Economics, 3, 224-238.
Ohashi, H. 2009. Effects of Transparency in Procurement Practices on Government
Expenditure: A Case Study of Municipal Public Works, Review of Industrial
Organization, 34(3), 267-285.
Shiraishi, T. 2011. Government Procurement and Competition Law, Financial Review,
104.
Special Subcommittee on Assessment of Public Procurement Institution, Committee onConstruction Management, Japan Society of Civil Engineers. 2010. Research15

on Public Works the Bidding Strategy of Targeting Japan Construction
Companies. (in Japanese)
Available at http://www.jsce.or.jp/committee/jyuten/files/H21j_06.pdf (04/04/12)

16

(Figure 1)
108

Density

6

4

2

0
0.55

0.60

0.65

0.70

0.75

0.80

RATE_20 Kernel

17

0.85

0.90

0.95

RATE_21 Kernel

1.00

1.05

（Table 1： Descriptive statistics）
RATE_08

RATE_09 POPULATIONSQUARE

REV08

EXP08

AMA

Mean

0.908

0.910

62928

214.37724245934

23287550

3.727

Median

0.920

0.923

29120

123.180

11573053

11080870

2.000

Maximum

0.995

1.000

8411652177.670

545000000

539000000

18.000

Minimum

0.618

0.606

214

3.470

9727468989770.000

Std. Dev.

0.054

0.052

96799

246.66537504747

36003672

4.719

Observations

1583

1583

1583

1583

1583

1583

1583

GENERAL_08 GENERAL_09 LOLIM_08 LOLIM_09 ANNCLL_08
Mean

ANNCLL_09

0.585

0.604

0.721

0.747

0.321

0.355

Median

1

1

1

1

0

0

Maximum

1

1

1

1

1

1

Minimum

0

0

0

0

0

0

Std. Dev.

0.493

0.489

0.448

0.435

0.467

0.479

Observations

1583

1583

1583

1583

1583

1583

CP_08
Mean

CP_09

CPNO_08

CPNO_09

EXAM_08

EXAM_09

0.419

0.627

1.333

2.182

0.358

0.365

Median

0

1

0

0

0

0

Maximum

1

1

95

120

1

1

Minimum

0

0

0

0

0

0

Std. Dev.

0.494

0.484

4.485

7.213

0.480

0.482

Observations

1583

1583

1583

1583

1583

1583

ANNCEXAM_08

ANNCEXAM_09

0.163

0.184

Median00Maximum

1

1

Minimum

00Std. Dev.

0.369

0.388

Observations

1583

1583

Mean

18

(Table 2: Descriptive statistics of other variables)
POPULATION SQUARE

AMA

EXP08

REV08

PRIVATE_08 PRIVATE_09

Mean

62927.79

214.377

3.72712328755024245934

722945.7618084.1

Median29120

123.18211080870

11573053

180143.6

176964.7

Maximum

8411652177.6718

5.39E+08

5.45E+08

10514531

8671518

Minimum

214

3.47

0

89897797274611532.21

19628.34

Std. Dev.

96798.58

246.6654.719

36003672

37504747

2089552

1745239

1583

1583

1583

1583

1583

1583

1583

Observations

LICENSE_08 LICENSE_09 PRATE_08 PRATE_09 OVER65
Mean

14061.95

14120.44

0.883

0.895 79312.91

Median

9403

9400

0.895

0.908

Maximum

46245

46860

0.945

Minimum

2521

2464

Std. Dev.

10679.13
1583

Observations

ROAD
Mean

36330.08

11911.67

8715

25050

1108.50.943

2295527

311700372500

0.792

0.769

26

1600

0

10815.13

0.041

0.040 234152.1

36236.31

39334.03

1583

1583

1583

1456

1576

PARK

CRIMINALCASE

3681.593 283.8204

5293.579

Median

529.3

16

333.5

Maximum

55934.6

7270

205708Minimum

90

08954.003 910.0131

20724.95

Std. Dev.
Observations

1541

LANDPRICE UNEMPLOYMENT

1559

1576

19

1576

（Table 2: Average winning rate – basic data)
Dependent Variable: RATE
Method: Pooled Least Squares
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C
POPULATIONSQUARE
EXP08
EXP08/REV08
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
P21
P22
P23
P24
P25
P26
P27
P28
P29
P30
P31
P32
P33
P34
P35
P36
P37
P38
P39
P40
P41
P42
P43
P44
P45
P46

0.943
0.000
0.000
0.000
-0.0140.010-0.006-0.015-0.048-0.0090.003-0.0180.006-0.0100.016-0.018-0.008-0.003-0.040-0.0060.026-0.028-0.002-0.014-0.015-0.004-0.006-0.070-0.005-0.073-0.069-0.072-0.098-0.061-0.055-0.0180.008-0.040-0.048-0.050-0.069-0.013-0.028-0.004-0.027-0.020-0.026
0.019
0.001
0.002
0.018

0.009
0.000
0.000
0.000
0.008
0.006
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.008
0.008
0.007
0.007
0.007
0.008
0.008
0.008
0.007
0.010
0.009
0.008
0.008
0.007
0.008
0.012
0.009
0.009
0.007
0.007
0.008
0.008
0.009
0.009
0.009
0.009
0.009
0.009
0.010
0.009
0.008
0.007
0.009
0.009
0.007
0.009
0.008
0.008

99.452-5.0533.293-0.735-1.7371.603-0.773-1.827-6.138-1.0590.387-2.6230.778-1.2071.914-2.559-1.196-0.385-5.016-0.7003.028-4.047-0.191-1.475-1.963-0.459-0.853-8.673-0.445-7.948-7.970-9.680-13.076-7.985-6.614-1.9360.881-4.676-5.357-5.441-7.812-1.303-3.172-0.502-3.896-2.239-2.907
2.655
0.093
0.290
2.376

0.000
0.000
0.001
0.462
0.083
0.109
0.440
0.068
0.000
0.290
0.699
0.009
0.437
0.227
0.056
0.011
0.232
0.700
0.000
0.484
0.003
0.000
0.849
0.140
0.050
0.646
0.394
0.000
0.656
0.000
0.000
0.000
0.000
0.000
0.000
0.053
0.379
0.000
0.000
0.000
0.000
0.193
0.002
0.616
0.000
0.025
0.004
0.008
0.926
0.772
0.018

R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.342
0.331
0.043
5.889
5460.10432.3310.000

Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat

0.909
0.053
-3.417-3.319-3.382
1.976

20

（Table3: Average winning rate – institutional factor)
Dependent Variable: RATE
Method: Pooled Least Squares
VariableC
GENERALLOLIM
ANNCLLCP
CPNO
EXAM
ANNCEXAM
AMA
POPULATIONSQUARE
EXP08
EXP08/REV08
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
P21
P22
P23
P24
P25
P26
P27
P28
P29
P30
P31
P32
P33
P34
P35
P36
P37
P38
P39
P40
P41
P42
P43
P44
P45
P46
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

Coefficient Std. Error
0.950-0.011-0.007-0.0090.001
0.000-0.0080.003-0.001
0.000
0.000
0.000
-0.0090.014-0.006-0.017-0.037-0.0090.002-0.017
0.013
0.001
0.019
-0.0070.005
0.011-0.0270.002
0.033-0.0270.003-0.005-0.011
0.001
0.002-0.061
0.002-0.070-0.061-0.063-0.089-0.058-0.055-0.021
0.012-0.035-0.043-0.047-0.068-0.003-0.0180.000-0.023-0.019-0.018
0.018
0.006
0.000
0.018

t-Statistic

0.010
0.002
0.002
0.002
0.002
0.000
0.002
0.003
0.001
0.000
0.000
0.000
0.008
0.009
0.008
0.008
0.008
0.008
0.008
0.007
0.008
0.008
0.008
0.007
0.009
0.013
0.008
0.008
0.009
0.007
0.010
0.009
0.008
0.008
0.007
0.008
0.012
0.009
0.009
0.008
0.008
0.008
0.008
0.009
0.009
0.009
0.009
0.009
0.009
0.010
0.009
0.008
0.007
0.009
0.009
0.007
0.009
0.008
0.008

0.367Mean dependent var
0.355S.D. dependent var
0.043Akaike info criterion
5.660Schwarz criterion
5522.863Hannan-Quinn criter.
31.091Durbin-Watson stat
0.000

Prob.

99.830-5.816-3.368-4.3120.566-1.321-3.761
1.183-0.973-3.7085.122-0.079-1.1331.465-0.732-2.107-4.686-1.0890.226-2.441
1.695
0.071
2.311
-1.0070.594
0.850-3.3050.188
3.839-3.9250.296-0.489-1.4640.135
0.243-7.7000.182-7.663-7.015-7.469-11.667-7.481-6.625-2.2771.330-4.055-4.803-5.075-7.798-0.274-2.015-0.005-3.290-2.096-2.054
2.521
0.615
0.007
2.229

0.000
0.000
0.001
0.000
0.572
0.187
0.000
0.237
0.331
0.000
0.000
0.937
0.257
0.143
0.464
0.035
0.000
0.276
0.821
0.015
0.090
0.944
0.021
0.314
0.553
0.396
0.001
0.851
0.000
0.000
0.767
0.625
0.143
0.892
0.808
0.000
0.856
0.000
0.000
0.000
0.000
0.000
0.000
0.023
0.184
0.000
0.000
0.000
0.000
0.784
0.044
0.996
0.001
0.036
0.040
0.012
0.539
0.994
0.026
0.909
0.053
-3.452-3.339-3.411
1.985

21

（Table 4: Difference）
Dependent Variable: D(RATE)
Method: Pooled Least Squares
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

0.003

0.001

2.649

0.008

D(GENERAL)

0.002

0.005

0.464

0.643

D(LOLIM)-0.0040.005-0.784

0.433

D(ANNCLL)-0.0060.004-1.667

0.096

D(CP)

0.000

0.001

-0.194

0.846

D(CPNO)

0.000

0.000

-1.6230.105

D(EXAM)

0.004

0.006

0.572

0.567

D(ANNCEXAM)

0.000

0.005

0.031

0.975

R-squared

0.006

Mean dependent var

0.002

Adjusted R-squared

0.001

S.D. dependent var

0.039

S.E. of regression

0.039

Akaike info criterion-3.645Sum squared resid

2.398

Schwarz criterion-3.6172892.654

Hannan-Quinn criter.

-3.634F-statistic

1.251

Durbin-Watson stat

1.948

Prob(F-statistic)

0.271

Log likelihood

D( ) is the differences between 08 and 09.

22

（Table 5：Prefectures factors）
Dependent Variable: RATE
Method: Pooled Least Squares
Variable

Coefficient

Std. Error

t-Statistic

Prob.

0.884
C
-0.011
GENERAL-0.007LOLIM-0.009ANNCLL
0.001
CP
0.000
CPNO
-0.008EXAM
0.003
ANNCEXAM-0.001
AMA
0.000
POPULATION
0.000
SQUARE
0.000
EXP08-0.009EXP08/REV08(P1 ~ P46 Omitted)
0.000
PRIVATE
0.000
LICENSE
0.203
PRATE

0.098

9.000

0.000

0.002

-5.862

0.000

0.002

-3.406

0.001

0.002

-4.330

0.000

0.002

0.863

0.388

0.000

-1.228

0.219

0.002

-3.787

0.000

0.003

1.184

0.237

0.001

-0.971

0.332

0.000

-3.732

0.000

0.000

5.139

0.000

0.000

-0.077

0.938

0.008

-1.133

0.257

0.000

0.074

0.941

0.000

-2.047

0.041

0.074

2.723

0.007

R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.371Mean dependent var

0.909

0.359S.D. dependent var

0.053

0.043Akaike info criterion-3.4565.623Schwarz criterion-3.3385533.419Hannan-Quinn criter.

-3.41430.071Durbin-Watson stat

1.998

0.000

23

（Table 6：Municipality factors)
Dependent Variable: RATE
Method: Pooled Least Squares
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C
0.902
GENERAL-0.010
LOLIM-0.007
ANNCLL-0.010
CP
0.002
CPNO
0.000
EXAM
-0.007
ANNCEXAM
0.004
AMA-0.001
POPULATION
0.000
SQUARE
0.000
EXP08
0.000
EXP08/REV08-0.010(P1 ~ P46 Omitted)
PRIVATE
0.000
LICENSE
0.000
PRATE
0.199
OVER650.000
LANDPRICE
0.000
UNEMPLOYMENT
0.000
ROAD
0.000
PARK
0.000
CRIMINALCASE
0.000

0.102
0.002
0.002
0.002
0.002
0.000
0.002
0.003
0.001
0.000
0.000
0.000
0.008

8.824
-4.915-2.905-4.3971.363-0.335-3.0701.276-0.889-4.2624.893
0.484-1.215

0.000
0.000
0.004
0.000
0.173
0.738
0.002
0.202
0.374
0.000
0.000
0.629
0.225

0.000
0.000
0.078
0.000
0.000
0.000
0.000
0.000
0.000

-0.445-2.228
2.546
0.372
0.137
-1.3810.405
1.990-0.435

0.656
0.026
0.011
0.710
0.891
0.168
0.685
0.047
0.663

R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.361Mean dependent var
0.346S.D. dependent var
0.043Akaike info criterion
5.015Schwarz criterion
4949.363Hannan-Quinn criter.
23.310Durbin-Watson stat
0.000

24

0.910
0.053
-3.450-3.307-3.3982.022

(Table 7)(LOLIM)(EXAM)

NumberNumber(7-1s)LOLIM08=1

LOLIM080(7-2t)

EXAM08=1

EXAM08=0

LOLIM09=1

1125

58

EXAM09=1

552

26

LOLIM09=0

17

383

EXAM09=015990

Difference of average winning bid (08-09)

Difference of average winning bid (08-09)(7-2)(7-2t)LOLIM08=1

LOLIM08=0

LOLIM09=1-0.00370.0102

EXAM09=1-0.0009

0.0012LOLIM09=00.0109-0.0002

EXAM09=00.0157-0.0033

Average winning bid 08(7-3)

LOLIM08=1

EXAM08=1

EXAM08=0

Average winning bid 08
LOLIM08=0(7-3t)

EXAM08=1

EXAM08=0

LOLIM09=1

0.90040.9114

EXAM09=1

0.90420.9094LOLIM09=00.92650.9285

EXAM09=00.90010.9099Average winning bid 09(7-4)

LOLIM08=1

Average winning bid 09
LOLIM08=0(7-4t)

EXAM08=1

EXAM08=0

LOLIM09=1

0.90410.9012

EXAM09=1

0.90510.9082LOLIM09=00.9156

0.9285

EXAM09=00.88450.913225

Appendix(Table 8: Eection and average winning bid)

Dependent Variable: Average Winning Bid
Method: Pooled Least Squares
Sample: 2000 2008
Included observations: 9
Cross-sections included: 47
Total pool (balanced) observations: 423
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

88.4510.318

277.947

0.000

election-0.0951.022-0.0930.926

R-squared

0.00002

Mean dependent var

88.442Adjusted R-squared-0.002

S.D. dependent var

6.212

S.E. of regression

6.220

Akaike info criterion

6.498

Sum squared resid

16286.36

Schwarz criterion

6.517

Log likelihood-1372.336

F-statistic

0.009

Prob(F-statistic)

0.926

Durbin-Watson stat

0.576

26