Silviase/jgov_v1.4_full_ann huggingface dataset:
In comparison with these theoretical and empirical studies conducted previously, the authors recognize that this study would suggest new aspects about actual bid-rigging practices, encourage further advancement of theoretical and empirical studies, and at the same time present useful background information for competition-related policymakers to develop new survey methods based on objective data. As mentioned earlier, theoretical and empirical researchers have different problem consciousness each other when working on their studies. Theoretical researchers put various assumptions or offer various bid-rigging mechanisms in order to analyze asymmetric information or efficiency of bid-rigging practices. It is a very important task to examine the similarity of these assumptions or mechanisms to those employed in actual bid-rigging practices. Empirical researchers would provide more accurate research outcomes if they could identify empirical research methods for frequently-used mechanisms or important points for processing data on actual bid-rigging practices. In addition, if competition officials embark on investigation efforts after identifying actual bid-rigging mechanisms to a certain extent, they are expected to yield satisfactory outcomes more quickly by using smaller amount of resources. In this respect, the authors believe that the impacts of this study would never be marginal.

### 3. Survey Outline and Analysis

### 3.1 Survey outline

Based on JFTC decisions, the authors sort out bid-rigging mechanisms in the following manner. First of all, as characteristics of bid-rigging mechanisms, we picked up six factors (i) that pose important impacts from the viewpoint of economics and (ii) that are identifiable from descriptions of JFTC decisions. It should be noted that these factors are not conflicting each other. A bid-rigging mechanism that would simultaneously satisfy several factors might possibly exist.

(1) Does the mechanism confirm the expected bidder’s willingness or unwillingness for order acceptance?

(2) Does the mechanism pay attention to observable costs?

(3) Does the mechanism pay attention to the signals of private information?
(4) Does the mechanism pay attention to the fairness of simply allocating successful bidding?

(5) Does the mechanism pay attention to the fairness of allocation in line with opportunity cost?

(6) Is it a collusive bidding initiated by government agencies?

(7) Others

First of all, the first factor is “Does the mechanism confirm the expected bidder’s willingness or unwillingness for order acceptance?” This is for determining whether or not the mechanism confirms the basic element, which is the expected bidder’s willingness for accepting an order. As already mentioned in the theoretical research outcomes section, bid rigging essentially involves asymmetry of information. In order to determine whether or not an actual bid-rigging mechanism pays attention to this aspect, we have picked up this element, which is frequently described in JFTC’s decisions. It should be noted, however, that some mechanisms, in very rare cases, automatically designate a successful tenderer without confirming the expected bidder’s willingness for order acceptance. In these cases, there are three possible scenarios: JFTC does not simply describe this factor (although it has actually confirmed the contractor’s willingness); JFTC intentionally omits this factor because all expected bidders are obviously willing to accept a new order; or JFTC intentionally avoids (or solves) the problems of asymmetric information by employing this approach. Unfortunately, we are unable to draw concrete conclusions from the data available this time.

Then, the second factor is “Does the mechanism pay attention to observable costs?” This is to ascertain whether or not the bid-rigging mechanism pays attention to efficiency. The observable costs depend on the distance between construction site and the contractor’s headquarter (i.e., if the headquarters are closer from the construction site, the contractor can easily transport construction equipment; further, since the contractor would also have a thorough knowledge of the neighboring locations or actual conditions in this case, he would be able to perform the tasks smoothly), as well as on whether or not the contractor has undertaken past construction works in the case of continuous works (i.e., if the new work is a repair or refurbishing work of the past construction work by the same contractor, the contractor would enjoy more advantages by employing his past experiences than winning an order for a totally new work; the retail sector would also have similar characteristics). In this context, “efficiency”
means the efficiency from the viewpoint of the entire community consisting of bid-rigging participants. If revenues from winning a new order are constant, production cost should be minimized in order to maximize the earnings for the entire community.

The third factor is “Does the mechanism pay attention to the signals of private information?” This is for examining whether or not the mechanism has any framework that would address the contractor’s uncertain willingness for order acceptance, which results from asymmetric information. A contractor with stronger willingness will have stronger incentives for breaking up the bid-rigging scheme. $ ^{12} $ In this sense, if there is a contractor more willing to win a new order, it is desirable to avoid, from the viewpoints of stability and efficiency of bid-rigging scheme, a situation in which a less willing contractor makes a successful bid (by disguising his order acceptance willingness through leveraging asymmetric information). As one of schemes for eliminating such asymmetric information, this factor determines whether or not the bid-rigging mechanism actually employs the so-called “signaling” in the context of game theories. For example, signals in this context refer to the sales efforts directed toward the outsourcers or the activities of the bid-rigging community.

The fourth factor is “Does the mechanism pay attention to the fairness of simply allocating successful bidding?” This is for determining whether or not a bid-rigging mechanism employs automatic allocation rules. As discussed by Pesendorfer (2000) and Ishii (2008, 2009), empirical research efforts have revealed that some bid-rigging mechanisms designate a successful tenderer on the basis of the order amount or equalization of successful bidding opportunities. This factor would be helpful to examine how much actual bid-rigging mechanisms are employing automatic equalization approach in this manner.

The fifth factor is “Does the mechanism pay attention to the fairness of allocation in line with opportunity cost?” This is for analyzing whether or not a bid-rigging mechanism pays attention to the allocation of public works on the basis of the corporate power of the participating contractors. For example, suppose that (a small number of) contractors are nominated almost every time in the process of designated competitive bidding, while (a lot of remaining) other contractors are nominated only sometimes. In this case, if the percentage of successful biddings is the same for these
two contractor categories, profits are allocated among bid-rigging participants without respect to possible corporate profits under the competitive conditions. This factor would be helpful to a certain extent to examine the manner in which the actual bid-rigging mechanism operates.

The sixth factor is “Is it a collusive bidding at the initiative of government agencies?” This helps in understanding if a collusive bidding at the initiative of government agencies might be regarded as a different type of bid-rigging community. Fundamentally, this study does not cover a collusive bidding at the initiative of government agencies because the ordering party is in collusion with order-accepting parties. In this sense, we have added this factor in order to clearly distinguish collusive biddings at the initiative of government agencies.

The seventh factor is “Others,” which represent other factors that do not fall under the aforementioned six factors.

### 3.2 Data

Cartels and bid riggings are mainly regulated by the latter half of Article 3 of Antimonopoly Act (Paragraph 5, Article 2 of Antimonopoly Act sets forth applicable definitions). However, bid riggings in Japan are also regulated by other articles, such as Subparagraph 1, Paragraph 1, Article 8 (four cases are falling under this category) and Article 6. This study basically covers the violation cases under this article.

In very rare cases, some research projects in the past have attempted to identify horizontal collusive relationships in Antimonopoly Act violation cases other than business combinations, and aimed to grasp the actual practices of cartels and bid riggings. On the basis of these research outcomes, this study presents deeper and more detailed analysis. For more information on data and important points in relation with the data please refer to our database. However, from this research project, we have eliminated all of the “order acceptance adjustment” cases as seen in the “pattern” section of the classification table. $ ^{13} $

Based on this database, we have developed a table that includes data from all the
bid-rigging cases $ ^{14} $ on JFTC decisions and selected and sorted out JFTC decision statements that fall under the aforementioned six factors. If a statement of JFTC decision includes more than one factor, it is listed in multiple cells.

### 3.3 Category of bid riggings

Let us now return to the point of how are bid-rigging cases sorted out. After having determined bid-rigging mechanism's factors, we have checked out these factors in each of the total 173 bid-rigging cases as seen in the JFTC decisions. In this process, figure "1" means that the corresponding factor is included in a bid-rigging mechanism, while figure "0" suggests that it is not included in the mechanism. In this way, we express the results of all the seven factors in the form of 7-digit binary numbers. For example, if a bid-rigging mechanism includes the first factor "Does the mechanism confirm contractor's willingness or unwillingness for order acceptance?" and the third factor "Does the mechanism pay attentions to signals of private information?" but does not include all other factors, the bid-rigging mechanism is expressed in "1010000." The results are presented in Table 1 below.

## (Table 1: Distribution table)

Table 1 clearly shows that a lot of bid-rigging mechanisms actually employed during our research period in Japan are relatively simple mechanisms. The most common case is “1100000,” which confirms contractor’s order acceptance willingness and simply designates the successful bidder in line with observable costs (such as sphere of influence). The second common case is “0001000,” which allocates public works so that successful bidding opportunities or order amount would be equalized. The third common case is “0100000,” which is basically similar to the most common case. These top three bid-rigging practices account for almost half of the overall bid-rigging cases.

Contrary to our preliminary expectation, the actual bid-rigging mechanisms do not pay attention to the fifth factor, or the contractor’s past records (such as the number of times being nominated for the competitive tender in the past) in many cases. This is clearly indicated in Table 2. This table illustrates the overlapping of several factors and is suitable for examining the depth of relationships among multiple factors. For
example, the number of mechanisms employing the first factor and the third factor at the same time is 13, from the column of $ (1, 3) $ (or $ (3, 1) $ ). The $ (5, 5) $ column of this table shows that only 26 cases employ the fifth factor, which only account for 15% of the overall bid-rigging cases.

In addition, the column (2, 2) of Table 2 clearly shows that the second factor is employed in more than a half of the overall bid-rigging mechanisms. This suggests that bid-rigging participants appear to give a top priority to decision-making based on objective data, while taking efficiency into consideration to a certain extent. This phenomenon is compatible with the fact that the first factor is employed in approximately 45% of bid-rigging mechanisms.

(Table 2: Factors overlapping table)

Table 3 describes the correlation coefficients that suggest the depth of relationships between two factors. $ ^{15} $ Unfortunately, not many clear relationships are observable from this table. There seems a negative correlation between the second and fourth factors as shown in the (4, 2) element, and there is a slight positive correlation among the first, second, and third factors.

Nonetheless, this fact suggests an interesting corollary. First of all, since the combination of the second and fourth factors is less likely, bid rigging participants do not often employ a compound rule of these factors. In other words, (if these factors are employed) each of these factors independently serves as a pivotal rule in bid-rigging mechanisms. Since the second factor is frequently used as mentioned earlier, bid-rigging mechanisms possibly have a structure consisting of two layers with some kind of the influence sphere factor and without such factor. For example, there might be a mechanism that divides public work involving the sphere of influence factor and those without such factor, and then awards the public work to the holders of the sphere of influence, while designating successful bidders in accordance with some other sub-mechanisms in the case of public work that does not involve the sphere of influence factor.

In addition, if the first or third factor is mainly related with the second factor, it would be beneficial for researchers and practitioners to take into consideration the high
compatibility of these factors. Taking into consideration this aspect, Table 4 attempts to grasp the possible causal relationships between these two factors. With $ n_{ij} $ representing the $ (i,j) $ element of Table 2, $ a_{ij} $ (the $ (i,j) $ element of this table) is expressed in the following formula.

$$ a_{ij}=\frac{n_{ij}}{n_{ii}} $$

$ a_{ij} $ shows a causal relationship immeasurable with the correlation coefficients. For example, $ (2, 3) = 0.23 $ , while $ (3, 2) = 0.92 $ . This suggests that the second factor does not always accompany the third factor, while the third factor involves the second factor in almost all cases. This fact might not necessarily suggest a causal relationship, but we can assume that the third factor (i.e., paying attention to signals of private information) is employed to supplement the second factor (i.e., paying attention to observable costs). This kind of causal relationship would be possibly identified through more detailed analysis in the future.

(Table 3: Table showing correlation coefficients)

(Table 4: Factors overlapping percentage table)

### 4. Conclusion

According to the abovementioned viewpoints, this article has calculated and sorted actual bid-rigging cases on JFTC decisions in line with the factors suitable to economic analysis. If researchers will make efforts to theoretically identify why simple bid-rigging mechanisms are preferred (at the cost of efficiency) or to improve accuracy in efficiently detecting simple mechanisms, we will be able to understand the actual bid-rigging practices in a better way.

The most significant problem of this study is the trustworthiness of the available data. Roughly speaking, trustworthiness in this context has two different meanings: the first refers to trustworthiness resulting from fact-finding surveys, while the other refers to trustworthiness related with JFTC decisions.

The matter of trustworthiness emerges from fact-finding surveys because all of bid-rigging cases on our database are “successfully detected” by JFTC. In other words, they are detected because of some specific characteristics. Sophisticated and well-planned bid-rigging schemes are highly likely to stay invisible. If a lot of such
sophisticated and undetected bid-rigging schemes exist, we will be unable to grasp the actual bid-rigging practices no matter how many serious efforts are undertaken to analyze the “successfully detected cases.” This kind of problem inevitably emerges because a bid-rigging scheme is an illegal act. There is no drastic solution to this problem. $ ^{16} $

The matter of trustworthiness also emerges in relation with JFTC decisions because JFTC might omit necessary statements on its decision formats. In other words, since JFTC does not deliver its decisions in a suitable manner to economic analysis, JFTC decisions might not describe the entire picture of bid-rigging factors that would pose no impacts on JFTC's decision. For example, even if they actually employ bid-rigging mechanism factors in a certain case, JFTC does not describe such a fact as long as it is not associated with providing evidence to prove bid-rigging scandals. In this case, even if the factor value “1” should have been allocated to some bid-rigging cases, they might be expressed as the factor value of “0.” $ ^{17} $ To mitigate such problems, it is an idea to conduct the same type of analysis by putting exclusive focus on JFTC's examination decisions. $ ^{18} $ However, since a limited number of examination decisions are available from JFTC in Japan, it is more likely that sufficient samples are not available, preventing effective classification of bid-rigging cases. If bid-rigging cases subject to JFTC's examination decision process have some kind of common tendency, such tendency will be naturally reflected, posing difficulty in correctly identifying the actual bid-rigging schemes.

As mentioned at the outset of this paper, cartels and bid riggings have been around for a long time, and continue to be one of the most important issues from the viewpoint of competition policies. It is almost impossible to solve this problem immediately due to its characteristics, but we obviously need to improve the situation gradually in a step-by-step manner. As the first step for solving the problem, we sorted out JFTC decision's database. In this regard, various stakeholders are expected to have open
discussions from their perspectives in the future.

## References:

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Bajari, P., Ye, L., 2003. Deciding Between Competition and Collusion. Review of Economics and Statistics 85, 971-989.

Ishibashi, I., Shimizu, D., 2008. Collusive Behaviors under a Leniency Program, mimeo.

Ishii, R., 2008. Collusion in repeated procurement auction: a study of a paving market in Japan, mimeo.

Ishii, R., 2009. Favor exchange in collusion: empirical study of repeated procurement auctions in Japan. International Journal of Industrial Organization 27, 137-144.

McAfee, R. P., McMillan, J., 1992. Bidding Rings. American Economic Review 82, 579-599.

Motta, M., 2004. Competition Policy: Theory and Practice, Cambridge Univ Press.

Motta, M., Polo, M., 2003. Leniency programs and cartel prosecution. International Journal of Industrial Organization 21, 347-379.

Pesendorfer, M., 2000. A Study of Collusion in First-Price Auctions. Review of Economic Studies 67, 381-411.

Porter, R. H., Zona, J. D., 1993. Detecting of Bid Rigging in Procurement Auctions. Journal of Political Economy 101, 518-538.

Porter, R. H., Zona, J. D., 1999. Ohio School Milk Markets: An Analysis of Bidding. Rand Journal of Economics 30, 263-288.

Tanno, T., Yokota, T., Uno, T., Kato, M., 2008. Fact-finding Survey and Economic Analysis on Cartels. CR 03-07 CPRC Research Report (in Japanese).

Yanagawa, N., Kimura, Y., Suzuki, T., 2005. Economic Analysis of Bid-rigging. CR 03-05 CPRC Research Report (in Japanese).
Table 1: Distribution table

type	number	1	2	3	4	5	6	7
1100000	36	○	○
0001000	32	○
0100000	21	○
0000010	10	○
1001000	9	○	○
0110000	8	○	○
0101000	7	○	○
1000000	6	○
1100100	6	○	○	○
1110000	6	○	○	○
0000100	5	○
1110100	5	○	○	○	○
1000100	4	○	○
0000001	3	○
1101000	3	○	○	○
0001100	2	○	○
0011000	2	○	○
1000001	2	○	○
1111100	2	○	○	○	○	○
0001001	1	○	○
0100100	1	○	○
0111000	1	○	○	○
1001100	1	○	○	○
Table 2: Factors overlapping table

1	2	3	4	5	6	7
1	willingness	80	58	13	15	18	0	2
2	observable cost	58	96	22	13	14	0	0
3	private information signal	13	22	24	5	7	0	0
4	simple allocation	15	13	5	60	5	0	1
5	opportunity cost	18	14	7	5	26	0	0
6	government initiation	0	0	0	0	0	10	0
7	others	2	0	0	1	0	0	6
Table 3: Table showing correlation coefficients

1	2	3	4	5	6	7
1	willingness	1
2	observable cost	0.3174	1
3	private information signal	0.0638	0.2921	1
4	simple allocation	-0.3105	-0.4959	-0.1168	1
5	opportunity cost	0.1939	-0.0139	0.1588	-0.1365	1
6	government initiation	-0.2297	-0.2766	-0.0994	-0.1805	-0.1042	1
7	others	-0.0491	-0.2116	-0.0761	-0.0717	-0.0797	-0.0469	1
Table 4: Factors overlapping percentage table

1	2	3	4	5	6	7
1	willingness	1	0.725	0.1625	0.1875	0.225	0	0.025
2	observable cost	0.6042	1	0.2292	0.1354	0.1458	0	0
3	private information signal	0.5417	0.9167	1	0.2083	0.2917	0	0
4	simple allocation	0.25	0.2167	0.0833	1	0.0833	0	0.0167
5	opportunity cost	0.6923	0.5385	0.2692	0.1923	1	0	0
6	government initiation	0	0	0	0	0	1	0
7	others	0.3333	0	0	0.1667	0	0	1
## Ⅱ 調査結果の概要

## [学校調查，学校通信教育調查（高等学校）]

## 1 幼稚園

## (1) 園数 (表 1)

幼稚園数は11,252園（国立49園，公立4,127園，私立7,076園）で，前年度より422園減少している。

表 1 幼稚園の設置者別園数

(单位：園)

区分	計	国立	公立	私立
平成 18 年度	13,835	49	5,469	8,317
23	13,299	49	5,024	8,226
24	13,170	49	4,924	8,197
25	13,043	49	4,817	8,177
26	12,905	49	4,714	8,142
27	11,674	49	4,321	7,304
28	11,252	49	4,127	7,076

## (2) 学級数 (表 2)

学級数は 60,055 学級で，前年度より 2,312 学級減少している。

## (3) 園児数（表2，表3，図1）

園児数は1,339,761人（男子678,658人，女子661,103人）で,前年度より62,687人減少している。①園児数を年齢別にみると，3歳児384,109人（全園児数の28.7%），4歳児460,583人(同34.4%)，5歳児495,069人（同37.0%）である。

②一学級当たりの園児数は22.3人で，前年度より0.2人減少している。

③本務教員一人当たり園児数は13.4人で，前年度より0.4人減少している。

④小学校及び義務教育学校第1学年児童数に対する幼稚園修了者の比率は48.5%で，前年度より5.0ポイント低下している。

表 2 幼稚園の学級数及び年齢別園児数

区 分	学級数	園	児	数
計	男	女	3 歲	比率(%)
平成 18 年度	73,386	1,726,520	875,108	851,412	423,770	24.5
23	70,538	1,596,170	809,581	786,589	443,750	27.8
24	70,560	1,604,225	813,694	790,531	442,508	27.6
25	70,083	1,583,610	803,539	780,071	440,512	27.8
26	69,470	1,557,461	789,801	767,660	441,834	28.4
27	62,367	1,402,448	710,913	691,535	398,054	28.4
28	60,055	1,339,761	678,658	661,103	384,109	28.7

## つづき

(单位：人)

区 分	園 児 数	一学級当たり園児数	本務教員一人当たり園児数
4歳	比率(%)	5歳	比率(%)
平成 18 年度	629,348	36.5	673,402	39.0	23.5	15.6
23	570,750	35.8	581,670	36.4	22.6	14.5
24	566,985	35.3	594,732	37.1	22.7	14.5
25	554,321	35.0	588,777	37.2	22.6	14.3
26	540,560	34.7	575,067	36.9	22.4	14.0
27	488,412	34.8	515,982	36.8	22.5	13.8
28	460,583	34.4	495,069	37.0	22.3	13.4
(万人)

図1 幼稚園の園児数の推移

表 3 都道府県別にみた小学校及び義務教育学校第 1 学年児童数に対する幼稚園修了者数の比率

区分	比率(%)	区分	比率(%)	区分	比率(%)
全国	48.5	富山	21.9	岛根	23.9
北海道	52.1	石川	25.8	岡山	43.3
青森	24.7	福井	18.9	広島	41.9
岩手	32.5	山梨	26.0	山口	49.0
宫城	60.6	長野	20.8	徳島	58.1
秋田	18.1	岐阜	45.3	香川	56.0
山形	37.2	静岡	56.9	愛媛	46.0
福島	56.9	愛知	45.4	高知	21.6
茨城	44.3	三重	43.4	福岡	49.3
栃木	46.8	滋賀	45.1	佐賀	25.0
群馬	39.2	京都	44.8	長崎	33.9
埼玉	62.1	大阪	52.8	熊本	28.6
千葉	61.4	兵庫	53.4	大分	55.3
東京	56.5	奈良	51.0	宮崎	28.7
神奈川	61.1	和歌山	32.4	鹿児島	33.6
新潟	18.4	鳥取	17.1	沖縄	72.8