Silviase/jgov_v1.4_full_ann huggingface dataset:
(2) Results of estimation of the model for the multiple cartel formation

Next, in addition to whether or not cartels exist, we apply ordered logit model (OLM) to analyze whether or not there were multiple cartel cases. Table 5 summarizes the results of estimating the model. When comparing the OLM results to the BLM results, almost all of the results on signs of coefficient, estimated magnitudes of coefficient and those significance were not different each other, except for the machinery/equipment acquisition value per establishment. Therefore, the interpretations provided in the results of BLM are applicable to interpreting the results of OLM.

However, it should be paid attention to interpretation of results concerning signs of coefficients, positive or negative. In the BLM, the dependent variable is whether or not a cartel exists and it takes either 0 or 1. If the sign of a certain independent variable is positive, then it can be interpreted to have a positive impact on a cartel formation because it increases the probability that a cartel will be formed. Conversely, because a dependent variable takes 0, 1, or 2 in the OLM, it is not always the case that an independent variable with the positive estimated coefficients increases probability that a cartel will be formed once or more. Therefore, judging the effects of factors related to the formation of cartels in the OLM requires computation of partial derivatives to evaluate the marginal effect of factors on cartel cases. That is, for each of values of the dependent variable 0, 1, or 2, we need to evaluate the partial derivatives of independent variables.

Table 6 summarizes marginal effects of independent variables (industrial factors) on the probability of cartel formation. As the marginal effects vary depending on values which each independent variable takes, in this study the marginal effects were evaluated at mean values of industrial factors. According to calculations of the effects, we see that industrial factors with a positive sign in the OLM (value of shipments, earnings per employee, inventory per establishment and machinery/equipment acquisition value per establishment) contribute to decrease the probability of non cartel, and to increase the probability that cartel occurs one time only or on multiple occasions. Similarly, factors with a negative sign in the OLM (the growth rate in value of shipments and its fluctuations) leads to increase in probability of non cartel, and to decrease in the probability that cartel activities occur one time only or on multiple occasions. $ ^{7} $

## < Tables 5-6 around here >

## (3) Results of estimation of the model for the frequency of cartel

The above involves analyses of the effect of each factor on the probability of cartel formation. Next, we apply a Negative Binomial Model (NBM) to analyze the relationship between industrial factors and frequencies of cartel activities. The dependent variable in the NBM is the number of cartel formations,
which takes 0 or a positive integer. In the study by OFT, they use the ordinary least squares estimator. However, in this study we use the NBM because the dependent variable takes positive integers, not a continuous dependent variable, and therefore the NBM is better than the OLS. $ ^{8} $

The NMB estimate results are summarized in Table 7. $ ^{9} $ Regarding the effect of each factor on the number of times a cartel is formed, the results for signs of coefficients were the same as ones in the BLM, but statistical significances are different. For example, in regards to the influence of demand factors (the growth rate of value of shipments and its fluctuations) on the formation of cartels, as with the results obtained in the BLM, fluctuations in the growth rate of value of shipments had significant negative effects on cartel occurrences, but the growth rate of value of shipments is negative but not significant. In other words, while the growth rate of value of shipments can be seen as a significant factor in explaining whether or not cartels exist, it does not stand as a significant factor in explaining the number of cartel occurrences.

As proxy variables for barriers to entry, we use the inventory per establishment and machinery/equipment per establishment. The former has a significant positive effect on the number of cartel cases, while the latter also has a positive effect but it is not significant. Furthermore, the machinery/equipment acquisition value per employee has a negative effect as in the BLM, but this is not significant in the NBM. Conversely, market concentration (CR3) is not significant in either the BLM or the OLM but it is a significant negative effect in the NBM. Similar to the BLM and OLM, the earnings per employee has a significantly positive effect on the frequency of cartel occurrences, but cash earnings per establishment has a significantly negative effect.

##

## (4) Predictions on the probability of cartel and the number of cartel occurrences

Using the estimates obtained in the series of three estimation explains so far, we predict the probability of cartel formation, the probability whether or not multiple cartels exist, and the number of cartel occurrences.

Column 3 in Table 8 through Table 10 summarizes the number of discovered cartel cases which the JFTC took action during the sample period (1990 – 2004) while column 4 and beyond respectively summarize the results of BLM-based predictions for the probability of cartels (hereafter “predicted cartel formation probability”), the results of OLM-based predictions for the probability of 0, 1, or 2 cartels (hereafter “predicted probability of multiple cartels”), and NBM-based predictions for the number of cartel
occurrences (hereafter “predicted number of cartel cases”). We estimated three different equations (the basic, the CUR and the ratio models) in each of the BLM, the OLM, and the NBM, where equations have different independent variables within each estimation technique. Therefore, for each model we calculated the predicted values for the cartel formation probability, the probability of multiple cartels, and the number of cartel cases (Table 8 summarizes the basic model, Table 9 summarizes the CUR model and Table 10 summarizes the ratio model).

We calculated correlation coefficients of the predicted values for cartel formation probability, probability of multiple cartels, and the number of incidents among the three equations, all three types of predictions returned a high correlation coefficient of 0.9 or higher, which Table 16 summarizes. Thus it is judged that similar prediction values would be gained regardless of the model used. Overall, the predicted values are not significantly influenced by the selection of independent variables.

##

However, because predicted values for individual industries are thought to be influenced by selection of independent variables, we first looked at the predicted values of the cartel formation probability and for each model we selected the 40 industries with the highest probability and then picked the top 40 industries in all models. Table 11 summarizes such selected 32 industries which ranked within 40 industries in all models. Cartels were captured in 22 industries out of total 131 industries, and of these 13 industries ranked in the top 40. The industry with the highest predicted cartel formation probability is MOTOR VEHICLES, PARTS AND ACCESSORIES (JPSIC 301), for which one cartel case is found during the sample period. The second highest predicted probability is for FABRICATED CONSTRUCTIONAL AND ARCHITECTURAL METAL PRODUCTS (JPSIC 254), which also had one case during the sample period, and the $ 3^{rd} $ highest is ROLLING OF NON-FERROUS METALS AND ALLOYS, INCLUDING DRAWING AND EXTRUDING (JPSIC 243). Conversely, there were industries against which action is taken but still had a low predicted probability. For example, in LIVESTOCK PRODUCTS (JPSIC 91) and CANNED AND PRESERVED FRUIT AND VEGETABLE PRODUCTS (JPSIC 93) industries there is one cartel discovered by the JFTC during the sample period but these predicted probability is low. The reason for this low probability is that there could be other industry factors not augmented in the regression equations, and further research for the factor is required.

While Table 11 provides the predicted cartel formation probability ranked in top 40 in all the models regardless of whether or not a cartel is confirmed in industry, in Table 12 we focused specifically on 109 industries for which no cartels were confirmed during the sample period and used the same methods to select 33 industries from among the 40 industries with the highest predicted probability. Of these, both HOUSEHOLD ELECTRIC APPLIANCES (JPSIC 272) and ELECTRICAL GENERATING, TRANSMISSION, DISTRIBUTION AND INDUSTRIAL APPARATUS (JPSIC 271) are ranked in the top
##

Next, we evaluate the predicted number of cartel cases. Table 13 provides it for all industries and Table 14 summarizes the industries with the predicted numbers in order of the highest predicted cartel probability based on the BLM basic model. Based on the predictions with NBM basic model, the predicted number of cartel cases for each of the top three industries, MOTOR VEHICLES, PARTS AND ACCESSORIES, FABRICATED CONSTRUCTIONAL AND ARCHITECTURAL METAL PRODUCTS, and ROLLING OF NON-FERROUS METALS AND ALLOYS, is 1.7, 1.2 and 1.4 incidents, respectively. As each industry has one cartel, there is no significant divergence between the prediction and the actual number of cartel. However, for industries in which cartel were confirmed but had a low predicted cartel formation probability, the predicted number of cartels is significantly low compared to the actual number of cartels. For example, FERROUS METAL MACHINE PARTS AND TOOLING PRODUCTS (JPSIC 235) and OIL AND FAT PRODUCTS, SOAPS, SYNTHETIC DETERGENTS, SURFACE-ACTIVE AGENTS AND PAINTS (JPSIC 175) have two and five cartels, respectively, these predicted numbers is approximately 0.4.

Table 15 provides the predicted number of cartels in industries which no cartels are captured by the JFTC during the sample period zero ordered from highest to lowest predicted cartel formation probability by the basic BLM. Regardless of which NBM model is applied, there are many industries with a value of less than 1.0 for the predicted number but looking at the mean value for all the three models, the industries with the highest number are CHEMICAL FERTILIZERS, SUGAR PROCESSING, and HOUSEHOLD ELECTRIC APPLIANCES.

However, as typical in the cross section analysis $ ^{10} $ , the pseud- $ R^{2} $ value for these regression results is low and thus it is implied that factors besides the structural factors, such as advertising and/or research and development activities and behavioral factors, of industries used in this analysis could have significant influences.

### 5. Conclusion

In this paper, we use data on Japanese manufacturing industries to conduct an empirical analysis for the relationship between structural factors of industries and cartel formation. Because we are unable to collect the total number of cartels (both overt and hidden) in manufacturing industries, our data
Having noted the above, we would like to state three limitations in this analysis. The first is that, as noted several times previously, this analysis focuses only on industries against which legal action is taken by the competition authority. The second is that the relationships between cartels and structural factors of industries used in this analysis are related to the manufacturing industries, and we do not focus on the characteristics of non-manufacturing industries due to data availability. We should hope for future analyses that incorporate data for non-manufacturing industries. The third is the other industrial factors such as advertising, research and development and behavioral factors are not included in this analysis observed by the estimated pseudo $ R^{2} $ value. Of the factors influencing cartels, the structural factors used in this analysis explained approximately 22%. Considering this analysis is a cross-sectional analysis, this is not necessarily a low value. $ ^{12} $ As indicated by the theoretical literature for collusion and confirmed empirically by this research, the structural factors are vital to cartel formation. Conversely, there is thought to be a significant dependence on other factors and, in particular, as noted in Section 2, the theoretical literature tells us that the quality of goods (substitutes or complements), the type of competition within an industry (price or quantity competition), product differentiation (horizontal or vertical product distinction) can affect cartel formation. By conducting an analysis that incorporates these other factors, it will be possible to empirically examine the relationship between cartels and those factors in greater detail.

Finally, the data used in this analysis is from cartels between 1990 and 2004, but in 2005 the leniency program is enacted in the Japan that reduces surcharge. Until today, because many firms use this system, the number of cartels captured has been increasing. Using the updated data on cartels since the enactment of this system to examine the influence of the system and reconfirming the relationship to structural factors of industries, we look forward to future research that clarifies the relationship between cartel formation and factors not explored in this research.
## References

Ishibashi, Ikuo, Koki Arai and Rieko Ishii (2010) “Survey and Analysis of the Mechanisms of Bid-Rigging,” CR01-09, Competition Policy Research Center (In Japanese).

Feuerstein, Switgard (2005) “Collusion in Industrial Economics,” Journal of Industry, Competition, and Trade, Vol. 5, No. 3-4, December, pp. 163-198.

Greene, William H. (2003) Econometric Analysis, Fifth Edition, Upper Saddle River: Prentice Hall.

Harrington, Joseph E., Jr. (2008) “Detecting Cartels,” in Paolo Buccirossi ed., Handbook of Antitrust Economics, Cambridge: MIT Press.

Levenstein, Margaret C., and Valerie Y. Suslow (2006) "What Determines Cartel Success?" Journal of Economic Literature, Vol. 44, No. 1, March, pp. 43-95.

Jacquemin, Alexis, Tsuruhiko Nambu, and Isabelle Dewez (1981) "A Dynamic Analysis of Export Cartels: The Japanese Cases," Economic Journal, Vol. 91, No. 363, September, pp. 685-696.

Motta, Massimo (2003) Competition Policy: Theory and Practice, Cambridge University Press.

Office of Fair Trading (2005) “Predicting Cartels,” OFT773.

Porter, Robert H. (2005) “Detecting Collusion,” Review of Industrial Organization, Vol. 26, No. 2, March, pp. 147-167.

Suslow, Valerie Y. (2005) “Cartel Contract Duration: Empirical Evidence from Inter-War International Cartels,” Industrial and Corporate Change, Vol. 14, No. 5, October, pp. 705-744.

Symeonidis, George (2003) “In Which Industries is Collusion More Likely? Evidence from the UK,” Journal of Industrial Economics, Vol. 51, No. 1, March, pp. 45-74.

Tanno, Tadanobu, Takeshi Yokota, Takashi Uno and Masatoshi Kato (2008) “Empirical Survey and Economic Theoretical Analysis of Cartels,” CR03-07, Competition Policy Research Center (In Japanese).

Yanagawa, Noriyuki, Yuji Kimura and Toshiko Suzuki (2005) “Economic Analysis of Bid-Rigging,” CR03-05, Competition Policy Research Center (In Japanese).
Table 1: Japan Standard Industrial Classification and Names of Industries

3-digit CodeIndustry Name	4-digit Code	Industry Name	Cases	Yes/No
09MANUFACTURE OF FOOD	091	LIVESTOCK PRODUCTS	1
092	SEAFOOD PRODUCTS	0
093	CANNED AND PRESERVED FRUIT AND VEGETABLE PRODUCTS	1
094	SEASONINGS	0
095	SUGAR PROCESSING	0
096	FLOUR AND GRAIN MILL PRODUCTS	0
097	BAKERY AND CONFECTIONERY PRODUCTS	0
098	ANIMAL AND VEGETABLE OILS AND FATS	0
099	MISCELLANEOUS FOODS AND RELATED PRODUCTS	0
10MANUFACTURE OF BEVERAGES, TOBACCO AND FEED	101	SOFT DRINKS AND CARBONATED WATER	0
102	ALCOHOLIC BEVERAGES	0
103	TEA AND COFFEE	0
104	MANUFACTURED ICE	0
105	TOBACCO MANUFACTURES	0	×
106	PREPARED ANIMAL FOODS AND ORGANIC FERTILIZERS	0
11MANUFACTURE OF TEXTILE MILL PRODUCTS, EXCEPT APPAREL AND OTHER FINISHED PRODUCTS MADE FROM FABRICS AND SIMILAR MATERIALS	111	SILK REELING PLANTS	0
112	SPINNING MILLS	0
113	TWISTING AND BULKY YARNS	0
114	WOVEN FABRIC MILLS	0
115	KNIT FABRICS MILLS	0
116	DYED AND FINISHED TEXTILES	0
117	ROPE AND NETTING	1
118	LACE AND OTHER TEXTILE GOODS	0
119	MISCELLANEOUS TEXTILE MILL PRODUCTS	1
12MANUFACTURE OF APPAREL AND OTHER FINISHED PRODUCTS MADE FROM FABRICS AND SIMILAR MATERIALS	121	TEXTILE OUTER GARMENTS AND SHIRTS, INCLUDING BONDED FABRICS AND LACE, EXCEPT JAPANESE STYLE	0
122	KNITTED GARMENTS AND SHIRTS	0
123	UNDERWEAR	0
124	JAPANESE STYLE APPAREL AND "TABI"-SOCK	0
125	OTHER TEXTILE APPAREL AND ACCESSORIES	0
126	MISCELLANEOUS FABRICATED TEXTILE PRODUCTS	0
13MANUFACTURE OF LUMBER AND WOOD PRODUCTS, EXCEPT FOURNITURE	131	SAWING, PLANNING MILLS AND WOOD PRODUCTS	0
132	MILLWORK, PLYWOOD AND PREFABRICATED STRUCTURAL WOOD PRODUCTS	0
133	WOODEN, BAMBOO AND RATTAN CONTAINERS	0
134	MISCELLANEOUS MANUFACTURE OF WOOD PRODUCTS, INCLUDING BAMBOO AND RATTAN	0
14MANUFACTURE OF FURNITURE AND FIXTURES	141	FURNITURE	2
142	FURNITURE FOR RELIGIOUS PURPOSES	0
143	SLIDING DOORS AND SCREENS	0
144	MISCELLANEOUS FURNITURE AND FIXTURES	0
15MANUFACTURE OF PULP, PAPER AND PAPER PRODUCTS	151	PULP	0
152	PAPER	0
153	COATED AND GLAZED PAPER	0
154	PAPER PRODUCTS	0
155	PAPER CONTAINERS	0	×
156	MISCELLANEOUS PULP, PAPER AND PAPER WORKED PRODUCTS	1	×
16PRINTING AND ALLIED INDUSTRIES	161	PRINTING	1
162	PLATE MAKING FOR PRINTING	0
163	BOOKBINDING AND PRINTED MATTER	0	×
164	SERVICE INDUSTRIES RELATED TO PRINTING TRADE	0	×
17MANUFACTURE OF CHEMICAL AND ALLIED PRODUCTS	171	CHEMICAL FERTILIZERS	0
172	INDUSTRIAL INORGANIC CHEMICALS	0
173	INDUSTRIAL ORGANIC CHEMICALS	3
174	CHEMICAL FIBERS	0
175	OIL AND FAT PRODUCTS, SOAPS, SYNTHETIC DETERGENTS, SURFACE-ACTIVE AGENTS AND PAINTS	5
176	DRUGS AND MEDICINES	0
177	COSMETICS, TOOTHPASTE AND TOILET PREPARATIONS	0
178	MISCELLANEOUS CHEMICAL AND ALLIED PRODUCTS	0
18MANUFACTURE OF PETROLEUM AND COAL PRODUCTS	181	PETROLEUM REFINING	0
182	LUBRICATING OILS AND GREASES (NOT MADE IN PETROLEUM REFINERIES)	0
183	COKE	0
184	PAVING MATERIALS	0
185	MISCELLANEOUS PETROLEUM AND COAL PRODUCTS	0
19MANUFACTURE OF PLASTIC PRODUCTS, EXCEPT OTHERWISE	191	PLASTIC PLATES, BARS AND RODS, PIPES AND TUBES, PIPE FITTINGS AND PROFILE EXTRUSIONS	1
192	PLASTIC FILMS, SHEETS, FLOOR COVERINGS AND SYNTHETIC LEATHER INDUSTRIAL PLASTIC PRODUCTS	0
193	INDUSTRIAL PLASTIC PRODUCTS	0
CLASSIFIED	194	FOAMED AND REINFORCED PLASTIC PRODUCTS	0
195	COMPOUNDING PLASTIC MATERIALS, INCLUDING RECLAIMED	0
199	MISCELLANEOUS PLASTIC PRODUCTS	0
20	201	TIRES AND INNER TUBES	0
MANUFACTURE OF RUBBER PRODUCTS	202	RUBBER AND PLASTIC FOOTWEAR AND ITS FINDINGS	1
203	RUBBER BELTS AND HOSES AND MECHANICAL RUBBER GOODS PRODUCTS	1
209	MISCELLANEOUS RUBBER PRODUCTS	0
21	211	LEATHER TANNING AND FINISHING	0
212	MECHANICAL LEATHER PRODUCTS, EXCEPT GLOVES AND MITTENS	0
213	CUT STOCK AND FINDINGS FOR BOOTS AND SHOES	0
MANUFACTURE OF LEATHER TANNING, LEATHER PRODUCTS AND FUR SKINS	214	LEATHER FOOTWEAR	0
215	LEATHER GLOVES AND MITTENS	0
216	BAGGAGE	0
217	HANDBAGS AND SMALL LEATHER CASES	0
218	FUR SKINS	0	✗
219	MISCELLANEOUS LEATHER PRODUCTS	0	✗
22	221	GLASS AND ITS PRODUCTS	3
222	CEMENT AND ITS PRODUCTS	2
223	STRUCTURAL CLAY PRODUCTS, EXCEPT THOSE OF POTTERY	0
224	POTTERY AND RELATED PRODUCTS	1
225	CLAY REFRACTORIES	0
226	CARBON AND GRAPHITE PRODUCTS	0
227	ABRASIVE PRODUCTS	0
228	AGGREGATE AND STONE PRODUCTS	2
229	MISCELLANEOUS CERAMIC, STONE AND CLAY PRODUCTS	0
23	231	IRON INDUSTRIES	0	✗
232	STEEL, WITH ROLLING FACILITIES	0	✗
233	STEEL MATERIALS, EXCEPT MADE BY SMELTING FURNACES AND STEEL	1
234	COATED STEEL	0
235	FERROUS METAL MACHINE PARTS AND TOOLING PRODUCTS	2
239	MISCELLANEOUS IRON AND STEEL	2
24	241	PRIMARY SMELTING AND REFINING OF NON-FERROUS METALS	0
242	SECONDARY SMELTING AND REFINING OF NON-FERROUS METALS, INCLUDING NON-FERROUS ALLOYS	0
243	ROLLING OF NON-FERROUS METALS AND ALLOYS, INCLUDING DRAWING AND EXTRUDING	1
244	ELECTRIC WIRE AND CABLE	0
245	NON-FERROUS METAL MACHINE PARTS AND TOOLING PRODUCTS	0
249	MISCELLANEOUS NON-FERROUS METAL PRODUCTS	0
25	251	TIN CANS AND OTHER PLATED SHEET PRODUCTS	0
252	TABLEWARE (OCCIDENTAL TYPE), CUTLERY, HAND TOOLS AND HARDWARE	0
253	HEATING APPARATUS AND PLUMBING SUPPLIES	0
254	FABRICATED CONSTRUCTIONAL AND ARCHITECTURAL METAL PRODUCTS, INCLUDING FABRICATED PLATE WORK AND SHEET METAL WORK	1
255	METAL MACHINE PARTS AND TOOLING PRODUCTS	0
256	METAL COATING, ENGRAVING AND HEAT TREATING, EXCEPT ENAMELED IRONWARE	0
257	FABRICATED WIRE PRODUCTS	0
258	BOLTS, NUTS, RIVETS, MACHINE SCREWS AND WOOD SCREWS	0
259	MISCELLANEOUS FABRICATED METAL PRODUCTS	0
26	261	BOILERS, ENGINES AND TURBINES	0	✗
262	AGRICULTURAL MACHINERY AND EQUIPMENT	0	✗
263	MACHINERY AND EQUIPMENT FOR CONSTRUCTION AND MINING	0	✗
264	METAL WORKING MACHINERY	0	✗
265	TEXTILE MACHINERY	0	✗
266	SPECIAL INDUSTRY MACHINERY	0	✗
267	GENERAL INDUSTRY MACHINERY AND EQUIPMENT	1	✗
268	OFFICE, SERVICE INDUSTRY AND HOUSEHOLD MACHINES	0	✗
269	MISCELLANEOUS MACHINERY AND MACHINE PARTS	0
27	271	ELECTRICAL GENERATING, TRANSMISSION, DISTRIBUTION AND INDUSTRIAL APPARATUS	0
272	HOUSEHOLD ELECTRIC APPLIANCES	0
273	ELECTRIC BULBS AND LIGHTING FIXTURES	0
274	ELECTRONIC EQUIPMENT	0
275	ELECTRIC MEASURING INSTRUMENTS	0
279	MISCELLANEOUS ELECTRICAL MACHINERY EQUIPMENT AND SUPPLIES	0
28	281	COMMUNICATION EQUIPMENT AND RELATED PRODUCTS	0
MANUFACTURE OF INFORMATION AND COMMUNICATION ELECTRONICS EQUIPMENT	282	ELECTRONIC DATA PROCESSING MACHINES, DIGITAL AND ANALOG COMPUTER, EQUIPMENT AND ACCESSORIES	0
29	291	ELECTRONIC PARTS AND DEVICES	0
ELECTRONIC PARTS AND DEVICES
30	301	MOTOR VEHICLES, PARTS AND ACCESSORIES	1
302	RAILROAD EQUIPMENT AND PARTS	0
MANUFACTURE OF TRANSPORTATION EQUIPMENT	303	SHIPBUILDING AND REPAIRING, AND MARINE ENGINES	0
304	AIRCRAFT AND PARTS	0
305	INDUSTRIAL TRUCKS AND PARTS AND ACCESSORIES	0	✗
309	MISCELLANEOUS TRANSPORTATION EQUIPMENT	0	✗
31	311	MEASURING INSTRUMENTS, ANALYTICAL INSTRUMENTS AND TESTING MACHINES	1
MANUFACTURE OF PRECISION INSTRUMENTS AND MACHINERY	312	SURVEYING INSTRUMENTS	0
313	MEDICAL INSTRUMENTS AND APPARATUS	0
314	PHYSICAL AND CHEMICAL INSTRUMENTS	1
315	OPTICAL INSTRUMENTS AND LENSES	0
316	OPHTHALMIC GOODS, INCLUDING FRAMES	0
317	WATCHES, CLOCKS, CLOCKWORK-OPERATED DEVICES AND PARTS	0
32	321	PRECIOUS METAL PRODUCTS, INCLUDING JEWEL	0	✗
322	MUSICAL INSTRUMENTS	0
323	TOYS AND SPORTING GOODS	0
324	PENS, LEAD PENCILS, PAINTING MATERIALS AND STATIONERY	0
325	COSTUME JEWELRY, COSTUME ACCESSORIES, BUTTONS AND RELATED PRODUCTS, EXCEPT PRECIOUS METALS AND JEWELRY	0	✗
MISCELLANEOUS MANUFACTURING INDUSTRIES	326	LACQUER WARE	0
327	SUNDRY GOODS OF STRAW, "TATAMI" MATS, UMBRELLAS AND OTHER DAILY COMMODITIES	0
328	MANUFACTURE OF ORDNANCE AND ACCESSORIES	0
329	Miscellaneous manufacturing industries, n.e.c.	0

Note) Classification based on 11 $ ^{th} $ revision of Japan Standard Industrial Classification (March 2002). Number of cartels cases is the sum from 1990 to 2004. There are 150 industries in 4-digit classification but the 18 industries with an "x" in the "Yes/No" column are not used in this analysis because continuity was lost with the revisions to the classification method and because for the tobacco industry, the exclusive manufacture of tobacco by JT is authorized.