Abstract
Antitrust fines are usually set for restitution and to dissuade potential offenders. The seriousness and duration of the offense usually determines the size of the fine imposed. Aggravating circumstances can also influence the size of the fine, while attenuating factors result in leniency. Economists have developed the “optimal fine” theory as a guiding principle in setting antitrust fines. Using a sample of fines that were imposed on 76 companies in China, we find that actual fines are strongly correlated with what the optimal fine theory predicts, while the price overcharge ratio appears quite moderate: at only 4.9% on average.
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Notes
“Seriousness” is defined as “the ability of that type of conduct to affect competition and ultimately consumers, as well as its significance in the economic context where it occurred.” See “The Guidelines on the Method of Setting Fines Imposed pursuant to Articles 15(2) of Regulation 17 and Articles 65(5) of the ECSC treaty.”
This novel methodology was suggested to us by one of the two anonymous referees of our previous version, to whom we are immensely grateful.
Leegin Creative Leather Products, Inc. v. PSKS, Inc., 551 U.S. 877 (2007), a US Supreme Court ruling that marks the beginning of the rule of reason for vertical price restraints. Previously, they were illegal per se under section 1 of the Sherman Act.
Note that \(\rho (OF_{j} s_{j} ) + \rho \left( {1 - \rho } \right)b\delta (OF_{j} s_{j} ) + \rho \left( {1 - \rho } \right)^{2} b^{2} \delta^{2} (OF_{j} s_{j} ) + \cdots + \rho \left( {1 - \rho } \right)^{n} b^{n} \delta^{n} (OF_{j} s_{j} ) \ge x_{j}\)
The reason why we include PCM in Eq. (3) is because by doing so Eq. (3) can be rewritten in a way that incorporates elasticities of demand, for which estimates can be easily obtained from several prior empirical studies. The other reason is that understanding the impact of price overcharge on the optimal fine rate is one of our goals in this study. The optimal fine rate that is predicted from the theory with the price overcharge parameter is one variable of interest in our empirical studies.
In the rest of the paper, we continue to use the letter i to refer to a firm and letter j to denote an industry. The notation at the case level is suppressed for brevity.
Firms are classified as foreign, domestic, or joint ventures. Thus joint venture companies are captured when both variables Domestici and Foreigni are equal to 0.
According to the property of the standard constant elasticity of substitution utility function, price elasticity of demand is equivalent to elasticity of substitution (in absolute terms).
Another measure of industry-level PCM is the average PCM of all firms weighted by the sales of each firm that belongs to the industry. The estimation results are qualitatively the same as the ones with the median PCM that are shown in Table 3. Detailed results are available upon request.
These cases include: five price-fixing collusion cases in ready-mixed concrete, gold jewelry, auto parts and auto insurance; and five vertical restraint RPM cases in of liquor, baby formula, eyeglasses, and auto dealerships.
These companies are Shenshi Dalian Insurance, Dinghong Insurance, Huakai Insurance, Tongchang Insurance and Huacheng Insurance.
“China automotive industry risk study—enhance auto dealership’s capability to survive in L-shaped economy,” Deloitte, 2016.
We have also conducted similar analysis in logarithmic functional form. The results are shown in the first two columns in Table 4.
Because horizontal agreements are normally more surreptitious and harder to detect, we naturally expect the detection rate of horizontal agreements to be lower than in vertical cases. However, typically the NDRC investigates a relatively large portion (3 out of 10 in our sample) of cases based on whistleblower, which tend to increase the detection rate of horizontal agreements. Thus we use a detection rate at 15% for horizontal cases.
One may expect that horizontal cases are expected to be more harshly fined because they are usually more surreptitious and harder to detect. Also, even though vertical restraint lawsuits have been tried in Chinese courts under the rule-of-reason principle, the competition authority in China appears to treat both horizontal and vertical offenses as per se illegal, and its decisions on fines appear to reflect its legal position.
We sincerely appreciate one referee’s suggestion on random coefficient models. However, we encountered numerical problems in estimation. Instead, we use a model that allowed for random intercepts and slopes on OFj.
We sincerely appreciate that the editor pointed out this endogeneity concern.
The website www.tianyancha.com is a Chinese website that focuses on the potential risks in business transactions. It allows users to check a firm’s registration information, development history, legal risks, operation status, IPR performance, etc. It is widely used by various media outlets.
For comparison, the replaced US elasticities for these five industries are as follows: ready-mixed concrete (0.05); eye glasses (1.3); auto parts (1. 3); automobiles (1.4); and gold jewelry (0.72). We use the US elasticities of demand for insurance, Chinese liquor, and baby formula. Insurance is service that is not covered in the trade data for merchandise goods. It is hard to find an estimated elasticity in Kee et al. (2008) that refers to Chinese liquor. As for baby formula, we believe that the US elasticity is more appropriate than the ones that are estimated from international trade data. Compared with other industries, baby formula is a special market in which the whole demand in the industry changed considerably through 2015 when the case data were collected. In 2008, a China baby formula scandal occurred, and Chinese customers lost their confidence on domestic baby formulas. See, for example, https://en.wikipedia.org/wiki/2008_Chinese_milk_scandal#Quest_for_milk_substitutes. Thus, we think that the baby formula elasticity from US data is more appropriate than the one that is estimated from international trade data. The baby formula elasticity from US data is 0.15; China’s elasticity in Kee et al. (2008) is 1.61.
Due to the limitation of space, we did not show the results in tables. They are available upon request.
Due to the limitation of space, we did not show the results in tables. They are available upon request.
Due to the limitation of space, we did not show the results in tables. They are available upon request.
This overcharge ratio appears to be quite low compared to other cartel or vertical anticompetitive behaviors. This may reflect more intense competition in general in China. The average net profit margin among the list of top 500 companies in China is only at around 5% for many years, and in 2018 it was only 4.5% (according to a Xinhua News report on October 8, 2018). So it might be difficult to overcharge substantially even with the help of anticompetitive behaviors.
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Acknowledgements
Ran Jing would like to acknowledge the support of the National Science Foundation in China (No. 71673045) and a grant from UIBE for Distinguished Young Scholars (No. 18JQ01). Jiong Gong would like to acknowledge the support of the National Science Foundation in China (No. 71433002). We would like to thank Yongmin Chen, John Kowka, R. Preston McAfee, Jianpei Li, Jin Zhang, and other participants of the antitrust and competition policy conference at the University of International Business and Economics in June, 2015. We are also grateful to Lawrence J. White, the editor of this journal, and two anonymous referees for helpful comments. Fang Yi would like to acknowledge the support of the Project of Beijing Philosophy and Social Science (No. 17JDYJB020).
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