metadata: authors: 0: author: Reed,Tristan
1: author: Pereira Lopez,Mariana De La Paz
2: author: Urrutia Arrieta,Ana Francisca
3: author: Iacovone,Leonardo
count: Mexico
docna: 0: docna: Cartels, Antitrust Enforcement, and Industry
            Performance : Evidence from Mexico
docty: Policy Research Working Paper
abstracts: cdata!: Forty percent of economic activities in
            Mexico weighed by sales have been investigated for illegal
            monopolistic practices since the Federal Competition
            Commission was established in 1993. By exploiting some
            unique features of the Mexican investigative system, and
            using a synthetic control approach, this paper examines the
            causal impact of antitrust sanctions on industry performance
            and aggregate outcomes. Sanctions cause sales and wages to
            increase and profit margins to fall in the sanctioned
            sectors, thus benefiting consumers and workers. Overall,
            antitrust enforcement contributes roughly half a percent of
            per capita gross domestic product growth. Outcomes of
            investigations that are closed without sanction fail to
            reject the hypothesis that some harmful conduct is not
            sanctioned because investigators lack resources to prove it
            conclusively. An implication is that the Commission could
            generate greater benefits with additional investigative resources.
display_title: Cartels, Antitrust Enforcement, and
            Industry Performance : Evidence from Mexico
listing_relative_url: /research/2022/12/33963730/cartels-antitrust-enforcement-industry-performance-evidence-mexico
topicv3: Competition Policy and Growth m1326469 1080,Competition Policy m1326982 406,Competition Agency Effectiveness m1326987 2116,Competition Law m1327469 1031
publishtoextweb_dt: 2022-12-22T00:00:00Z
dois: 10.1596/1813-9450-10269
author_keys: 0: author_key: 948891
1: author_key: 1349146
2: author_key: 1855491
3: author_key: 900307
model_extractions: Policy Research Working Paper 10269 Cartels, Antitrust Enforcement, and Industry Performance

# Evidence from Mexico

_Tristan Reed Mariana Pereira López Ana Urrutia Arrieta Leonardo Iacovone_ Development Research Group & Macroeconomics, Trade and Investment Global Practice December 2022
Policy Research Working Paper 10269

# **Abstract**

Forty percent of economic activities in Mexico weighed by sales have been investigated for illegal monopolistic practices since the Federal Competition Commission was established in 1993. By exploiting some unique features of the Mexican investigative system, and using a synthetic control approach, this paper examines the causal impact of antitrust sanctions on industry performance and aggregate outcomes. Sanctions cause sales and wages to increase and profit margins to fall in the sanctioned sectors, thus benefiting consumers and workers. Overall, antitrust enforcement contributes roughly half a percent of per capita gross domestic product growth. Outcomes of investigations that are closed without sanction fail to reject the hypothesis that some harmful conduct is not sanctioned because investigators lack resources to prove it conclusively. An implication is that the Commission could generate greater benefits with additional investigative resources.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at treed@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Cartels, Antitrust Enforcement, and Industry Performance: Evidence from Mexico**<sup>∗</sup>

Tristan Reed<sup>a</sup> , Mariana Pereira L ́opez<sup>b</sup> , Ana Urrutia Arrieta<sup>c</sup> , Leonardo Iacovone<sup>d</sup> Originally published in the Policy Research Working Paper Series on _December 2022._ This version is updated on _March 2023._ To obtain the originally published version, please email prwp@worldbank.org.

<u>JEL Classifications:</u> K21, L40, 047 <u>Keywords:</u> abuse of dominance, antitrust, antitrust enforcement, cartels, collusion, competition law, Federal Competition Commission, Federal Economic Competition Commission, market power, Mexico, monopolistic practices, per se illegal, rule of reason

> ∗The authors thank Alejandro Casta ̃neda Sabido, Seidu Dauda, Santiago Levy Algazi, Penny Goldberg, Charl Jooste, Pete Klenow, Eduardo Mart ́ınez Chombo, Graciela Miralles Murciego, James A. Robinson, James Sampi, Chad Syverson, Ekaterina Vostroknutova, Shang-Jin Wei and seminar participants at Banco de M ́exico, CIDE, COFECE, Colegio de M ́exico, Universidad Iberoamericana, and the World Bank for extremely valuable comments and suggestions. Francisco Lucero Bravo and Joaquin Ramirez Cabaracas provided excellent research assistance. Reed thanks the Development Research Group Research Support Budget and the Knowledge for Change Program III for funding. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank or its affiliated organizations, or those of the Executive Directors of the World Bank, their Managements, or the governments they represent. The authors thank Natalia Volkow and INEGI for access to the Economic Census microdata. All results have been reviewed to ensure that confidential information is not released. Any errors remain our own. a: World Bank Development Research Group. Corresponding author, Phone: +1 (202) 505-0925, 1818 H St NW, Washington, DC, 20433, treed@worldbank.org.; b: International Finance Corporation, c: World Bank; d: World Bank
# **1 Introduction**

Recent evidence of increasing market power and growing profits (De Loecker, Eeckhout and Unger, 2020; De Loecker and Eeckhout, 2021) has led to the recommendation of vigorous antitrust enforcement (Khan, 2017; Shapiro, 2018; Wu, 2018; Berry, Gaynor and Scott Morton, 2019; Marinescu and Posner, 2019; Rose, 2019; Salop, 2021). Implementation of this recommendation raises practical questions about which there is limited empirical evidence. How much can an antitrust authority expect to shape microeconomic outcomes using tools available under the law, like investigations and judicial discretion? If an antitrust authority can shape microeconomic outcomes, what could be its contribution to aggregate outcomes like productivity, inequality, and employment, and what are the underlying mechanisms?

This paper answers these questions using outcomes of investigations by Mexico’s Federal Competition Commission (in 2013, renamed the Federal Economic Competition Commission, abbreviated as COFECE in Spanish) into alleged illegal monopolistic practices. A distinctive practice in Mexican competition law allows us to identify the causal effects on economic outcomes of antitrust sanctions, which involve a financial penalty for agents involved and a monitored order to cease illegal conduct. In jurisdictions like the United States, a description of the relevant market in an antitrust case is not always known to researchers except after prosecutors bring it to a public trial. In Mexico, to officially start an investigation, the Commission has the obligation of publishing in the Federal Official Gazette (Diario Oficial de la Federaci ́on in Spanish) a document ( _acuerdo de inicio_ ) that, among other information, has to describe the relevant market. This applies to all ex-officio antitrust investigations and credible public complaints, including closed investigations that do not produce sufficient evidence to find agents responsible for illegal conduct and so do not go to trial. These closed investigations allow us to observe and measure changes in markets where illegal conduct was suspected but not punished and to use these changes as a counterfactual for assessing the impact of sanctions.

Our strategy corrects an issue with the difference-in-differences approach used in prior ex-post evaluations of antitrust (for examples of this approach, see Sproul (1993) and other papers reviewed by Ilzkovitz and Dierx (2015)). In the prior approach, the counterfactual is taken to be another industry in the same sector with similar cost and demand drivers rather than a market in which illegal activity is suspected but not punished. If illegal conduct emerges in industries with unique trends that make them different from other industries in the same sector, the prior approach is not valid. Our new approach also ensures that the counterfactual includes any potential deterrent or encouragement effect of a “shot across the bow” investigation that is made public to market participants but does not go to trial.

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Using this new counterfactual in a triple difference, we estimate a positive effect of antitrust sanctions on industry sales and a negative effect on the operating profit margin. In industries with closed investigations, we find that sales are declining and the margin is rising relative to other industries in the same sector, so our estimates using the new counterfactual are larger than estimates using the prior approach, which assumes counterfactual trends are the same as industries in the same sector.<sup>1</sup> In addition to this novel identification approach, our paper measures industry performance using an economic census, which in Mexico enumerates every firm in the economy with a fixed location, including those that are small, informal, and privately held. These data enable us to quantify the aggregate effects of antitrust sanctions, and also illustrate the mechanisms behind these effects, informing current debates about how antitrust may or may not address an aggregate productivity slowdown in the United States and Europe that has coincided with a rise in markups. We estimate the effects of sanctions on industry total factor productivity (TFP), employing recent insights from Ackerberg, Caves and Frazer (2015) and De Loecker and Syverson (2021) to flexibly allow for different factor shares across industries, potential decreasing or increasing returns to scale, and imperfect competition. After antitrust sanctions, productivity increases for all firms in an industry. Aggregating this effect and accounting for the small share of output the Commission has resources to investigate each year, we attribute roughly half a percent (or 1/200) of the steady state per capita gross domestic product growth to antitrust sanctions.

The mechanisms behind this effect are very interesting. De Loecker et al. (2020) document that two-thirds of the growth in markups in the United States is due to the reallocation of sales towards high markup firms. If lax antitrust enforcement is responsible for rising markups in the United States, one might expect that antitrust enforcement would cause a reallocation of sales across firms. In Mexico, this is not what we find. Increases in productivity after sanction are fairly homogeneous across cases, and measures of factor misallocation like the correlation of market share and productivity (Olley and Pakes, 1996) or the variance of productivity (Hsieh and Klenow, 2009), which have been suggested as an explanation for low productivity in Mexico and other middle-income economies, do not change on average. Though merger control<sup>2</sup> could conceivably have different effects, these results suggest pros-

> 1The estimation involves two steps. First, we use the synthetic difference-in-differences (SDID) approach proposed by Arkhangelsky, Athey, Hirshberg, Imbens and Wager (2021) to quantify the change in outcomes in relevant markets after the Commission’s decision to either close a case or sanction agents for illegal conduct within a three-digit sector. Second, we regress the SDID effects of all cases on a constant and a dummy for whether the case ended in a sanction, allowing in some specifications for potential heterogeneity in treatment effects, as recommended in the context of antitrust by Vita and Osinski (2016). The coefficient on the dummy can be interpreted as a triple difference, illustrating the effects of sanctions relative to the counterfactual of a closed case.

> 2Merger control is the other major activity of antitrust enforcement agencies in addition to investigating

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ecution of illegal conduct need not affect productivity through reallocation. Rather than a model in which sanctions induce reallocation of factors towards more productive firms, these results suggest a model in which sanctions lift the productivity of all firms in a market. The results are consistent with a cartel breakup model in which the industry equilibrium changes from joint profit maximization to a more competitive equilibrium. That new equilibrium incentivizes firms to innovate and move away from a ‘quiet life’ (Hicks, 1935, pg. 8).

We also document the distributional effects of antitrust sanctions, particularly how gains are shared with workers in addition to consumers. The benefit to consumers is clear from the increase in sales, which is consistent either with falling prices and increasing quantity as firms move down the demand curve, or increased quality, which need not coincide with lower prices. Perhaps more surprisingly, the wage bill also rises after sanctions, driven by both an increase in the average wage and an increase in employment. This result is consistent with illegal conduct having previously restricted quantities produced and, therefore, labor demand. Higher wages could result both from moving up the labor supply curve and from workers themselves becoming more productive, as the total factor productivity results suggest is the case. The result rejects a model in which workers are better off under illegal conduct because supracompetitive profits are shared with workers.<sup>3</sup> Marinescu and Posner (2019) argue that the labor market effects of monopoly have been overlooked by researchers and antitrust enforcement, and highlight that due to workers being differently substitutable across industries, the relevant market for labor market power in principle could be quite different from a relevant product market. Our results indicate that enforcement focused on product markets can nonetheless generate significant benefits for workers, consistent with limited substitutability of workers across product markets in our context.<sup>4</sup> To provide reassurance that our results are causal, in quadruple differences, we explore heterogeneity across cases with different origins and subject to different legal standards. Implicit in our research design is the idea that conditional on an investigation taking place, whether agents are ultimately sanctioned is exogenous. What we have in mind is that investigators have a professional responsibility to exert equal effort in all cases and whether they ultimately find enough evidence for the Plenary of Commissioners to convict in trial is left to chance. To investigate this assumption, we first test for differences in effects between investigations that originate ex-officio and those originating from public complaints. and sanctioning illegal conduct. An evaluation of the effects of merger control in Mexico is left for future research.

> 3This model may nonetheless apply in other contexts. B ̈ohm, Metzger, Str ̈omberg et al. (2022) show that workers in the financial sectors of Sweden and the United States have captured increasing rents over time. See also Katz, Summers, Hall, Schultze and Topel (1989).

> 4In Brazil, Felix (2021) estimates that the elasticity of substitution for workers across labor markets is similar in magnitude to the elasticity of substitution across firms within a market.

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Ex-officio cases are those that investigators initiate themselves based on a desire to target economically significant industries, and it is plausible that investigators are motivated to work harder in these cases. Yet, we find that estimated effects are similar in both types of cases, indicating that differential investigative effort in certain cases is unlikely to drive our results. A second test investigates whether effects are different in cases where the Plenary itself has more or less discretion. One might argue that conditional on evidence provided by investigators, sanctions are not random if the Commission uses discretion to sanction only industries that are growing for exogenous reasons, and which can afford it. This type of behavior would be consistent with the results presented so far. However, under Mexican law, the Commission’s discretion is constrained in certain cases, specifically those concerning agreements to fix prices or divide a market, which are called “absolute monopolistic practices” or collusive practices<sup>5</sup> or cartels. Such conduct is per se illegal, such that the Commission is required to impose a sanction whenever there is conclusive evidence of the conduct, for instance, a written agreement to fix prices, and the agents are not given the opportunity to bring evidence that their conduct did not harm consumers given limited market power. We find that the positive effects of sanctions are in fact larger in these cartel cases where sanctions are deterministic based on evidence of conduct alone. We infer from these results that the effects of sanctions we estimate cannot be the result of the Commission’s discretion.

Beyond identifying the positive causal effects of antitrust and the mechanisms behind them, our paper also informs the debate about the best way to scale up antitrust enforcement in an economy. Could the Commission create more benefits simply by operating under its current legal mandate with more resources, or is a change in the law or judicial presumptions required? A specific debate concerns cases of what is called in Mexico “relative monopolistic practices,” or in other jurisdictions abuse of dominance or abuse of market power<sup>6</sup> , such as price discrimination or vertical restraint, where under a rule of reason agents have the opportunity to argue that their practices actually improve consumer welfare. In such cases, the authority can agree with agents and choose not to impose a sanction, even if evidence of the conduct is conclusive. The debate about whether certain conduct should be per se illegal or subject to debate about intent or negative effects has a long history (Bork, 1966; Beckner and Salop, 1999). Recently, Khan (2017) argues using a decision theory framework that in cases where judges have discretion, they are biased towards making Type II errors (false negatives, or not sanctioning agents when their conduct harmed competition) due to an incorrect presumption that Type I errors (false positives, or sanctioning agents when

> 5See Casta ̃neda Sabido and Ruiz (2021) for further explanation of these practices in the context of telecommunications. In the United States, the Supreme Court has established a precedent that price fixing and market division are per se illegal. Still, this idea is not codified in the statute as it is in Mexico.

> 6See Casta ̃neda Sabido and Ruiz (2021).

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their conduct did not harm competition) are more costly. Salop (2017) provides several examples of when Type II errors can be more costly, for instance, if the alleged conduct creates a reputation for prediction that raises entry barriers. If Type II errors are more costly, one policy response would be for judges to reject defense arguments more readily in cases of relative monopolistic practice. Another would be to change the law so that relative monopolistic practices are per se illegal, avoiding judicial discretion.

In Mexico, the outcomes of closed cases fail to reject the hypothesis that the Commission is making Type II errors. Declining sales with rising profit margins are still observed when the Commission investigates but does not impose a sanction, suggesting harmful conduct could emerge and persist in those cases.<sup>7</sup> If there were no false negatives, one would expect a uniformly negative or null change in sales, wages, and profit margins after the Commission closes a case without sanction. We also find that the negative changes in sales and wages after closed cases are more negative in cases of absolute monopolistic practices compared to cases of relative monopolistic practices. This implies that, in this context, Type II errors should be most costly in cases of absolute monopolistic practices, where judges already do not have discretion. Type II errors would emerge in such cases because investigators did not have the resources to develop conclusive evidence, leaving judges with no choice but to acquit. Under these circumstances, reducing the costliest Type II errors requires greater investigative resources to develop more conclusive evidence rather than different legal standards or administrative discretion.

Overall, our results suggest the Commission is already pursuing its mandate effectively but could expand its impact with more investigative resources. Currently, its investigations target industries where trends suggest possible anti-competitive conduct, indicating an efficient allocation of investigative resources, and that the marginal sanction in these investigations could be beneficial. Changes in judicial presumptions in relative monopolistic practice cases could also have an impact, but not as much as additional investigators if they are able to win more convictions in absolute monopolistic cases.

Though Mexico is a specific country, the evidence it provides has global relevance. In cross-country analysis, Besley, Fontana and Limodio (2020) show that increased scope for antitrust law reduces profits in the non-tradable sector, but beyond this evidence, most evidence on the outcomes of antitrust comes from case studies in the European Union and the United States (see Baker, 2003; Crandall and Winston, 2003; Ilzkovitz and Dierx, 2015). This is in part because antitrust authorities are long-established in high-income economies

> 7One explanation for these outcomes could be that anti-competitive conduct emerges as a response to exogenous declines in an industry. This was confirmed anecdotally by a firm involved in collusion in the pharmaceutical market, which claimed “we cooperate or we exit.”

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(the United States Federal Trade Commission was founded in 1914; the first European Commissioner for Competition was designated in 1958) and their history in middle-income economies is more recent. Mexico established the Federal Competition Commission in 1993 as part of a broad reform agenda focused on economic modernization led by President Carlos Salinas de Gortari. The Commission today is regarded by peers as a member of the antitrust vanguard (OECD, 2020). Narrowly, the Mexican experience is informative for other middleincome countries about what an antitrust authority can achieve in a relatively short period when starting from scratch and following international good practices. Broadly, the Mexican experience highlights the potential contribution of antitrust to aggregate outcomes, especially in an economy characterized by higher inequality, slower productivity growth, and greater misallocation compared to other OECD countries (Levy Algazi and Walton, 2009; Hsieh and Klenow, 2014; Levy Algazi et al., 2018).<sup>8</sup> Our effort to quantify the aggregate effects of antitrust enforcement stands on the shoulders of a large literature that has studied the effects of antitrust sanctions in individual cases. One strand of this literature uses event studies to estimate changes in the equity prices of publicly traded firms subject to investigation immediately after news of an indictment is released (Bittlingmayer, 1993; Duso, Neven and R ̈oller, 2007; Aguzzoni, Langus and Motta, 2013). In contrast, we look at effects on entire industries over horizons of about 10 years, more similarly to Sproul (1993), which studies the effects of US Department of Justice indictments on prices over 10 years but not other outcomes and does not use closed cases as a counterfactual as we do. While equity prices are expected to respond immediately to news about enforcement, effects on industry-wide productivity and concentration could take longer to emerge and so it is advantageous to evaluate them over a longer time horizon.

Another strand of literature combines reduced form and structural analysis to estimate the effects of antitrust sanctions on welfare in a single industry (Porter and Zona, 1993; Asker, 2010; Igami and Sugaya, 2021; Starc and Wollmann, 2022; Barkley, 2023). This literature has focused primarily on cartels engaging in bid rigging and price fixing, practices which ex-ante are expected to be harmful and so are per se illegal in most jurisdictions. Like this literature, we evaluate the effects of sanctioning these so-called absolute monopolistic practices, but in contrast to this literature, we are also able to compare these effects to sanctions of relative monopolistic practices like price discrimination, and vertical control, which theoretically could have beneficial effects, and so are subject to different legal standards. We find that sanctions of such practices do create benefits for consumers and workers, but that these benefits are smaller than sanctions of absolute monopolistic practices. This result validates

> 8See Iacovone, Munoz Moreno, Olaberria and Pereira Lopez (2022) for a recent analysis of Mexico’s productivity trends and dynamics.

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the focus of the Mexican antitrust agency on absolute monopolistic practice cases, though does not indicate it should give up on relative monopolistic practices entirely. Though some have advocated for greater enforcement against relative monopolistic practices (Khan, 2017) actions in the United States have been rare since the 1970s (Areeda, Kaplow, Edlin and Hemphill, 2021). As a result, economics research on price discrimination and vertical control (e.g., Mortimer, 2007, 2008) does not observe them in the context of an enforcement action, as could become more common. To the best of our knowledge, this is the first paper to systematically compare the quantitative effects of antitrust enforcement actions across these different types of conduct.

Finally, our paper contributes to the literature focused on competition authorities themselves and their decision-making. This literature has focused primarily on merger control, and so we complement it by providing evidence on enforcement related to illegal conduct. Kwoka (2014) conducts a meta-analysis of all available merger retrospectives in the United States, concluding that enforcement has been too lenient and that more mergers should be blocked on the margin. Our result that the Commission has made Type II errors is similar in spirit, suggesting that there are gains from more sanctions on the margin. Affeldt, Duso and Sz ̈ucs (2021) use a comprehensive database of European merger reviews to document how the approach to merger control in Europe has changed over time. Similarly, our analysis of closed cases describes the characteristics of industries that are targeted for investigation by the Commission.

The paper unfolds as follows. Section 2 provides background on the Commission and the data. Section 3 describes our approach to estimating difference-in-differences outcome changes after each case, the triple difference approach to identifying the effects of sanctions, and three case examples. Section 4 reports the results, including the triple difference analysis of the effects of sanctions on sales, wages, employment, and profit margins, as well as the heterogeneity by cases of absolute and relative monopolistic practices. Section 5 reports the effects on total factor productivity and measured misallocation and discusses implications for aggregate outcomes. Section 6 concludes with policy implications.

# **2 Institutional Background and Data**

This section provides background on Mexican competition law and the Federal Competition Commission, before describing the data used in our study.

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# **2.1 Institutional Background**

**The Federal Competition Commission of Mexico** The 1917 Political Constitution of the United Mexican States prohibits all but a few state monopolies (e.g., petroleum extraction, the telegraph) in the republic, with Article 28 requiring that the law “severely punish” the concentration of necessary consumption goods in the hands of one or a few agents. Yet it was not until 1993 that the Federal Competition Commission was established to enforce this law. A motivation for the legislation establishing an independent commission was that it would abrogate previous rules that concentrated enforcement of Article 28 in the Presidency, and had been used to justify measures like price controls.<sup>9</sup> The Commission was intended to bring antitrust enforcement up to date with internationally-agreed good practices, following for instance the United States Federal Trade Commission. Aydin (2016) provides further detail on the history of Mexico’s competition laws, including reforms that provided indemnity for whistle-blowers, strengthened the role and independence of this authority, and also in 2013 renamed the Commission as the Federal Economic Competition Commission, abbreviated as COFECE in Spanish.

The Commission comprises a Board of seven Commissioners, nominated by the President and confirmed by the Senate, who are responsible for deciding on the Commission’s cases; and an investigative authority empowered to investigate suspected illegal conduct and barriers to competition. As specified by law, strict operational separation is maintained between the two functions, with the investigative authority operating autonomously and interacting with the Board only when submitting recommendations to the Board’s secretariat. The Commission is analogous to the Federal Trade Commission (FTC) in the United States in that it is responsible for both the investigation and adjudication of monopolistic practices, with operational separation between these two functions, though the agencies have different mandates. The FTC has an additional mandate on consumer protection, and unlike Mexico’s Commission does not investigate economic cartels since in the United States those are prosecuted as criminal cases by the Department of Justice.

The Commission is highly regarded internationally. A peer review by competition authorities in other OECD economies found that Mexico’s competition regime is “equipped with strong powers, solid institutions, and enforcement tools,” and has staff that are highly qualified and viewed as professional and committed by Mexico’s antitrust community (OECD, 2020). While antitrust enforcement actions in the United States have declined over the past 40 years (Babina, Barkai, Jeffers, Karger and Volkova, 2022), in Mexico they have expanded

> 9The Law Granting Powers to the Executive Branch on Economic Matters (Ley de Atribuciones del Ejecutivo en Materia Econ ́omica in Spanish) gave discretionary power to the Executive branch in terms of controlling prices and even intervening in firms.

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and touched all sectors of the economy. Figure 1 shows that today about 40 percent of economic activities weighed by sales have been subject to at least one investigation into potential illegal monopolistic practices. The value of sales in each sector in 2018 is reported by a blue horizontal bar and a portion of the bar is shaded red to indicate the 6-digit industrial classifications within the sector that have been subject to investigation since 1993.

**The investigative process** Figure 2 describes the process through which the Commission investigates and decides on cases. As in other jurisdictions, investigations are initiated either by a formal complaint from the public ( _denuncia_ ) or the Commission itself ( _ex-officio_ ). After a complaint, a preliminary screen determines whether a full investigation is warranted. Given scarce investigative resources, if the alleged conduct does not constitute a violation of competition law, or if the complainant is not able to provide additional information, the case will be dismissed.

If the alleged illegal conduct is deemed possible, an investigation proceeds, and the Commission discloses a short note ( _acuerdo de inicio_ ) with a description of the relevant market and the conduct under investigation. A formal definition of the legal market is not made in every case, but as a practical matter investigators must define the relevant market in order to assemble evidence, for instance through interviews with agents and analysis of data from a specific product market.

Once evidence is assembled, the investigative authority then acts as a prosecutor, gathering and organizing evidence to demonstrate agents’ responsibility for illegal conduct. Once the investigation is complete, the investigative authority advises the Board as to whether it should proceed with a trial procedure ( _procedimiento en forma de juicio_ ), or close the case in the absence of sufficient evidence of a violation. In practice, trials are only recommended when the investigative authority is fairly sure the defendant is guilty, as with trial indictments by antitrust agencies in the United States Cases that are closed and which lead to a trial procedure are those that we consider.

**The trial-like procedure** In the case the investigators recommend a trial, the Board secretariat notifies the defendants of the charges, to which they have the right to respond and submit contrary evidence. The scope for administrative discretion depends on whether the alleged conduct is an absolute monopolistic practice or a relative monopolistic practice according to Mexican law. Absolute monopolistic practices, which include price fixing and agreements to segment a market, are _per se_ illegal. In such cases, the Commission’s Board must impose a sanction when there is conclusive evidence of illegal conduct, for instance, a written agreement to fix prices. In contrast, in cases of relative monopolistic practices,

10
such as price discrimination or exclusive dealing, defendants have the opportunity to argue that their practices actually enhance efficiency, mitigate possible anti-competitive effects, and improve consumer welfare. In such cases, the authority can choose not to impose a sanction, even if evidence of the conduct is conclusive.

Lancieri, Posner and Zingales (2022) attribute a historical decline in United States antitrust sanctions to discretion by professional judges who retained a permissive “Chicago” school of thought that asserted many conspiracies by firms could promote efficiencies, and so should not be punished if they can be shown to benefit consumers. For instance Bork (1966), who was educated at the University of Chicago and taught at Yale University, argued that firms may fix prices if they use shared marketing services, to avoid a situation where a firm with a low-priced product benefits disproportionately from others’ marketing expenditure. If firms did not fix prices in this example, firms might reduce spending on marketing, reducing overall sales. Mexican law adheres to a more skeptical “post-Chicago” school since it identifies price fixing and market division agreements as punishable without evidence of market power or consumer harm. Only in certain “relative monopolistic practices” can defendants argue their conduct was beneficial.

Once the arguments and evidence are presented, the secretariat proposes a resolution to the Board that includes the amount of a financial sanction ( _sanci ́on_ ) to punish the illegal conduct. The Board adopts a final decision based on the proposal. In rare cases, one of which is discussed in Section 3, the Board’s decision can be appealed to a higher court, which may later ask the Board to reverse its decision and issue a new resolution.

# **2.2 Data**

**Relevant markets in antitrust investigations** Our analysis begins with the universe of 261 investigations into suspected monopolistic practices as of the end of 2018, since the founding of the Commission in 1993. The Commission’s website provides a list of all cases and access to the disclosures of each investigation and final resolutions. Unlike in other jurisdictions including the United States, the Commission discloses the relevant market for each investigation, not just those that go to trial.

A task in antitrust investigation is the definition of the relevant market, or the set of products and geography in which the agent or agents under investigation compete (see Davis and Garc ́es, 2009). To analyze outcomes in investigated markets, we map the relevant market for each investigation to economic activities classified in the Economic Census. In doing so, we address two challenges highlighted by Shapiro (2018) and Benkard, Yurikoglu and Zhang (2021) that arise when mapping relevant markets in antitrust investigations to industrial 11
classifications in economic census data.

First, industrial classifications in censuses are based on points of production rather than consumption, and so may include or exclude goods and services that are not in the relevant product market. Even so, for purposes of data management, the Commission indicates a high-level industrial classification of the relevant market in each case and then provides further detail in a written description because the relevant markets in question are more specific than those covered by the high-level classification. We use this information to assign each relevant market a 6-digit industry classification from the North American Industry Classification System (NAICS), the lowest level of aggregation possible.<sup>10</sup> We define this as the _treated industry_ . We then work forward and backward from the treatment date to define a consistent industry classification that captures the same set of activities from the first to last census in our dataset, including new activities that emerge during that time period. These classifications are generally more inclusive than a 6-digit classification, but there are many multiples within 3-digit classifications.

Second, relevant markets sometimes have specific geographic boundaries. In the majority of investigations, the relevant market is national (e.g., the national market for soft drinks). In the remaining investigations, we use relevant market definitions in the disclosures to define geographic boundaries for each market as a 6-digit industry within a set of municipalities or states. Within these boundaries, we aggregate establishment-level micro-data to construct series for the treated industry (e.g., all real estate brokers in the municipalities surrounding Lake Chapala in Jalisco state).

We define a pool of _control industries_ to be all other 6-digit industries in the same 3- digit sub-sector as the treated industry. The motivation for this is that other industries in the treated industry should share common (unobserved) trends in demand and costs, while still being outside the relevant market defined by investigators. This approach to identifying controls in similar industries has been used in other studies of antitrust (Sproul, 1993; Kwoka, 2014). We successfully identify treatment industries for 130 of 261 investigations. Some cases are excluded because the decision of the Commission occurs after 2018, which means we do not observe treated outcomes, or because the decision occurs prior to 1998, which means we would not observe trends in prior outcomes, as required by the difference-in-differences estimator described in Section 3. Of these investigations, we are able to find control industries in the same sub-sector for 90 investigations. For some cases, control industries could not be identified in the same 2-digit or 3-digit sub-sector. These are industries with unique economic

> 10NAICS is a joint classification developed by the Statistical Offices of the United States, Mexico, and Canada. It offers high-level comparability across the three countries in terms of the conceptual framework but still leaves room for country-level specific codes.

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characteristics and no clear comparison group, such as natural gas pipelines and commercial banks, or industries with rapid technological change over the study period, such as wired and wireless telecommunications. There are case studies of interventions in some of these industries (Casta ̃neda Sabido and Elbittar, 2013; Castellanos, Del Angel<sup> ́</sup> and Garza-Garc ́ıa, 2016).

We cover a greater subset of investigated industries than several previous studies of antitrust enforcement, which faced the similar challenge of matching relevant markets to plausibly similar controls. In his analysis of the effects of United States antitrust indictments, Sproul (1993) identifies relevant market control prices for 25 of “roughly 400” price-fixing indictments under the Sherman Act. In his analysis of United States antitrust decisions regarding horizontal mergers, Kwoka (2014) identifies 49 transactions for which rigorous retrospective studies of their effects are available. These transactions span the period 19762009, and during the period 1996-2011 alone, the FTC investigated 264 horizontal mergers.

**The Economic Census** Having defined treatment and control industries in each case, we construct a series of economic variables for these industries using the establishmentlevel micro-data of the quinquennial Economic Censuses covering the calendar years 1993 to 2018. These censuses include every establishment in Mexico in every sector except agriculture, forestry, and hunting, and survey all physical establishments, including sole proprietorships and firms with so-called informal workers that do not contribute to the national social security scheme. The censuses are carried out by Mexico’s national statistics body INEGI ( _Instituto Nacional de Estad ́ıstica, Geograf ́ıa e Inform ́atica_ ), which provided us with access to the establishment-level microdata that we use to construct key variables: sales, employment, wages, operating profit margin, and total factor productivity (TFP). Sales and employment are measured as totals for each industry and year. Wages are measured as total remuneration to employees divided by total employment for each industry and year. The operating profit margin is measured as value-added minus total remuneration, divided by sales for each industry and year. TFP is estimated for each establishment using an estimate of the production function, as described in Section 5.

# **3 Empirical Design and Case Examples**

In this section, we describe the difference-in-differences estimator used to measure outcome changes associated with each antitrust case, and then report the difference-in-differences estimator graphically for two example cases to illustrate the hypotheses that arise.

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**Informational setting** Unanticipated news can lend a causal interpretation to a differencein-differences estimator (Athey and Imbens, 2022). In our setting, the Commission initiates most investigations and decides whether to sanction the defendants based on the evidence within two years. The news of a final decision is plausibly unanticipated by market participants. Since the Commission’s jurisdiction is national and there are many relevant markets, it would not be easy to predict which markets the Commission will investigate in a given year, and, given that many cases are closed without trial, it would not be easy to predict how the Commission will decide in any one case.

We are interested primarily in the outcomes of the Commission’s final decision to either sanction defendants or clear them of wrongdoing, so we identify the date of treatment with the date of the final decision by the Commission to either sanction the defendants or close the case. In most cases, investigations are initiated and cases closed between census years. In several cases, the investigation is initiated long enough prior to the final decision such that the existence of the investigation, but not its outcome, has been disclosed during some pre-treatment outcomes observed in the series. In these cases, the difference-in-differences estimator that we use to measure the outcome change after each decision matches trends in sales in the control industries during the pre-treatment period in which the investigation is disclosed, making it unlikely that the investigation disclosure causes a difference in treatment and control trends in the pre-treatment period that could contaminate the estimated effect of treatment. Even so, as a robustness analysis, in such cases, we will regress the differencein-differences estimator on a measure of how long the case takes to close, and test whether it alters our conclusions.

**Synthetic Difference-in-Differences** The term _yit_ is the outcome of interest in industry _i_ and time _t_ . For each investigation _j_ , let the term _θ_<sup>ˆ</sup> _j_ indicate the observed outcome change after the final decision whether to sanction agents or not, and let _σ_ ˆ _j_ indicate the standard error of that outcome change. We estimate these using the synthetic difference-in-differences (SDID)<sup>11</sup> proposed by Arkhangelsky et al. (2021), which can be represented as a weighted two way fixed effect regression of the form

> 11Like the difference-in-differences (DID) method, the SDID method is invariant to additive market-level shifts in the outcome, allowing more flexibility than the synthetic control (SC) method (Abadie, Diamond and Hainmueller, 2010) that requires that levels and not only trends in the outcome match exactly when comparing treatment and synthetic control in the pre-treatment period.

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where _i ∈I_<sup>_j_</sup> indexes the set of treatment and control industries in case _j_ ordered so that i =1 is the treated industry. Control industries include those in the same 3-digit sector that are not in the relevant market and have never been investigated by the Commission. Standard errors _σ_ ˆ _j_ are calculated using the placebo method described by Arkhangelsky et al. (2021), in which the treatment effect is estimated in 400 industries drawn with replacement from the donor pool of control industries.

Recent work (see the helpful summary in Borusyak, Jaravel and Spiess, 2021) has identified several issues that arise when estimating a two-way fixed effect regression with multiple units treated at different times. Our approach avoids such issues by identifying a never treated control group separately for each case, and estimating Equation (1) separately for each case.<sup>12</sup> The case-specific time fixed effects _βjt_ are estimable because we have a control group for each case.

# **3.1 Examples of Outcomes of Antitrust Enforcement Decisions**

Three case studies demonstrate how the SDID estimator works in our setting and the issues and hypotheses that arise. In this discussion, we focus on (the log of) sales as an outcome because it is a basic indicator of the value of an industry, including the value provided to customers. The two case studies here are selected non-randomly, specifically because they are outliers in certain ways. Later in Section 4 we will compare and contrast these cases with averages from across many cases in order to highlight the value of our meta regressions in disciplining inference from individual case studies.

First, we describe the largest positive effect of a sanction on (the log of) sales, which is a sanction of a real estate brokerage cartel around Lake Chapala. Second, we describe the largest negative effect of a decision on market revenue, which is a closed case regarding refusal to deal requirements in the wedding supply industry of Guadalajara.

## **Example A) Sanction for price fixing by real estate brokers at Lake Chapala**

This investigation began in 2007, after a complaint was filed alleging the real estate brokers’ associations and their members were fixing brokerage rates on the shore of Lake Chapala, Mexico’s largest freshwater lake. The accused parties were alleged to have arranged agreements to fix prices, which is per se illegal. One piece of evidence collected was written regulation of Del Lago Real Estate Group association that states explicitly that agents should

> 12Callaway and Sant’Anna (2021) have an approach to compare a treatment group to units that are treated in the future, rather than never treated. In our case, there were only 4 industries that were treated after another industry in the same subsector. Including these in the control group did not materially alter the results.

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gather to share information and fix brokerage rates (i.e., at a minimum 7 percent rate for sales above a certain value). Internal fines were imposed against agents that do not comply with these rates. In fact, the individual filing the complaint had been associated until he was forced to resign after offering clients discounts relative to the agreed rates.

We identified the treated activity as #531210 “realtors and real estate brokers” in the municipalities around Lake Chapala. This treated activity is compared to other 6-digit activities in the same 3-digit classification called #531 “real estate services,” which include, among other activities, housing and office units that are rented directly by the owners without the use of a broker. Panel A of Table A1 reports the control industries in real estate services and the synthetic control weights that give their weighted average the same pretreatment trend as the treated industry. Weights are roughly equal across control industries. For consistency with the cases that have national relevant markets, national sales rather than local sales are used as the data for the control group.

After investigation and several appeals by the defendants, in 2014 the Commission issued a resolution finding the defendants responsible for price fixing, sanctioning them a fine totaling 30,000,000 Mex$, and ordering the suppression of such practices. Figure 3 shows the SDID effect of this sanction on (the log of) sales. The thick blue line connects the average of (the log of) sales in the treatment industry after and before the sanction. The thick red line connects these same averages in the synthetic control industry. The counterfactual outcome in the treated industry is constructed by shifting the thick red line up along the dotted lines so that the pre-treatment average control outcome matches the pre-treatment average treatment outcome. This counterfactual outcome is indicated with a red dot. The SDID is the difference between this counterfactual and the observed outcome in the treated industry, indicated by the arrow. This corresponds to an annualized increase in sales of 5.05 (standard error = 1.39) percent. Sanctioning the cartel apparently caused the industry to grow.

This case took seven years to be resolved because of countersuits for extraordinary constitutional protection of the defendants ( _amparo_ ) and other legal maneuvers, raising the possibility of a potential anticipatory effect before the sanction, which could contaminate the SDID estimate. It would be concerning if there was a substantial jump in the difference between treatment and control groups during the period that the investigation was being contested, but there does not seem to be cause for concern. The thin blue line in Figure 3 connects the observations of (the log of) sales for realtors and real estate brokers around Lake Chapala, and appears to be flat or potentially declining during the period of the investigation. This slowdown in growth could potentially be due to the cartel’s operation. The thin red line plots the same outcome in the synthetic control industry, which exhibits a very 16
similar pre-trend in (the log of) market revenue prior to the decision. Since the synthetic control industry is weighed to track the treated market even during the brief period after 2007 when the investigation was disclosed but a final decision had not been made, there is no apparent difference in pre-trends prior to the final decision.

**Example B) Closing a case of exclusive contracting in the wedding planning and supply industry of Guadalajara** In 2003, Revistas Novias y Eventos S.A. (Brides and Events Magazine) filed a complaint against Producciones y Convenciones S.A. (Productions and Conventions) for the alleged commission of relative monopolistic practices in wedding planning and supply services in Guadalajara city. Revistas Novias y Eventos is a company dedicated to the publication of magazines on topics related to weddings and other social events, as well as the organization of exhibitions promoting the sale of goods and services used in those kinds of celebrations. The complaint alleged that Producciones y Convenciones conditioned the sale of stands at its separate exhibition _Expo Tu Boda_ (Your Wedding Expo) to various exhibitors on the requirement that they did not participate in future events organized by Revistas Novias y Eventos. According to the complaint, the prestige of _Expo Tu Boda_ would have led the exhibitors to agree to refuse to deal with Revistas Novias y Eventos.

Under the law, relative monopolistic practices like exclusive contracts should be punished unless they are shown to not harm consumer welfare. Here, the Commission did not find convincing evidence of harm, and so closed the case without sanctioning the practice in 2004. In interviews, various exhibitors supposedly withdrew from _Expo Tu Boda_ after refusing to stop requesting the services of Revistas Novias y Eventos. This case highlights the ambiguity judges face in evaluating exclusive dealing arrangements (see, e.g., Gavil and Salop, 2019). Even if agents refuse an exclusive contract, should offering it still be punished if it was intended to restrict competition?

In this case, the treated industry is identified as #463214 “retail of wedding and regional dress” in the municipalities of Guadalajara. Firms in that industry would have participated in the exhibitions. Panel B of Table A1 shows the control industries and their synthetic control weights. Of six industries, one has 55 percent of the weight, retail trade of disposable diapers and sanitary products, and another has 20 percent, retail trade of hosiery and items used in sewing. It is not difficult to imagine young newly-weds driving a similar trend in wedding dresses and diapers, so this matching provides some proof of concept that the weighting approach selects industries with plausibly similar drivers of demand. Inspection of Figure 4 shows that the treatment and control lines do have a similar trend prior to treatment though the history is shorter than in the previous example. In this figure, the 17
SDID effect of closing the case is an annualized decrease in sales of 12.3 (1.41) percent. Sales in the treatment industry also fall in absolute terms. Whatever happened at the time of the case caused retail sales of wedding dresses in Guadalajara to decline. While the investigation did not find conclusive evidence of harm, the result here suggests it is possible, though not conclusive, that some went undetected. Yet, this case also illustrates that exogenous factors could drive differences between treatment and control industries, raising questions about the difference-in-difference alone as a means to identify the effects of antitrust. The divergence of diapers and wedding dresses could relate to population aging, as second children are born without additional marriages.

**Summary and implications for estimation of causal effects.** These three cases studies illustrate heterogeneous outcomes across cases. They also raise questions about the validity of using the difference-in-difference approach alone to identify the effect of antitrust sanctions on industry performance. The average difference-in-differences effect of sanctions in Figures 3 and 4 assumes that the synthetic control industry is similarly affected by all causal influences other than those leading to the Commission’s final decision. However, industries with similar pre-trends in the same sub-sector may not be the appropriate control.

Indeed, the Commission explicitly targets investigations towards industries where illegal conduct is suspected, as in Example A, with about half of the investigations beginning because agents denounce conduct by others in their industry or firm. Treatment industries could be undergoing unique changes caused by or that cause the illegal conduct that is being investigated. If true, this means that difference-in-differences changes in sanctioned industries provide an incomplete picture of the effect of antitrust sanctions.

One hypothesis is that illegal conduct emerges as a response to specific exogenous trends, such as declining demand. In Example B in wedding dress retail, for instance, the impetus for exclusive contracts may have been that the industry was declining, as the population aged and traditional weddings became less popular. In a very recent case, an agent in one sanctioned cartel suggested collusion was the only way to overcome a decline in the industry and was recorded saying to cartel members either “we cooperate or we exit.”<sup>13</sup>

> 13In a recent resolution, the pharmaceutical firms Casa Marzam (Marzam), Casa Saba, F ́armacos Nacionales (Fanasa), and Nadro y Almac ́en de Drogas were fined 903 Mex$ for restriction of supply of medicaments to increase the price during 2008 to 2016.

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# **4 Results**

We first briefly the average of the difference-in-differences estimator in cases where the Commission imposed sanctions, as in prior work such as Sproul (1993), as a benchmark. Figure A1 reports the SDID changes in the log of sales after 35 cases, all of those for which we could identify: (i) the treated industry, and (ii) never-treated control industries in the same sub-sector. The figure also reports the average change in the log of sales after a sanction, weighting cases by the inverse variance of the SDID estimator for that case. This is a socalled “fixed effect” meta-analysis framework, which assumes the effect of sanctions on the log of sales is constant across industries. The overall effect of sanctions measured in this way is an annualized 1 _._ 3 percent with a tight 95 percent confidence interval [0.7,1.9], indicating sanctions are followed by an increase in sales growth of 1.3 percentage points annually. Although the inverse variance weighting does give as much as 20 percent weight to single observations, the point estimate of the average effect is within the confidence intervals of most cases. Figure A2 shows the same table where the outcome is the change in the log of the wage bill. Here we find a positive overall annualized average change in wages of 1 percent [0.2, 1.8] indicating sanctions are followed by a statistically significant increase in wage bill growth of 1 percentage point annually. Consumers and workers appear to benefit.

Unlike the United States context in Sproul (1993), in Mexico, we are able to observe outcomes in industries where illegal conduct is suspected yet unpunished as in Example B. These outcomes provide an alternative counterfactual for the treated industries that allows us to disentangle the effect of the Commission’s sanctions from the selection effect that arises in the difference-in-differences estimator due to the targeting of investigations. The only example of an analysis like this within investigated markets that we know of is Kwoka (2014). He exhaustively compiles from the literature difference-in-differences price changes after mergers, where control industries are chosen similarly as in our study from the same sector. In a triple difference analysis, he then compares the outcome changes after mergers where the regulator imposed a conduct remedy, which allows firms to merge but restricts their conduct, with outcome changes in mergers where the regulator did not. This approach controls for the fact that merging industries are undergoing unique changes in the competitive environment and could be different due to exogenous factors that lead to such change. He finds that most mergers raise prices, and that conduct remedies are not associated with lesser price increases. Unlike in Mexico, the authority in the United States does not disclose which mergers are investigated, rather, given their size, Kwoka (2014) is able to credibly assert with “virtually certainty” that an investigation is conducted.

In addition to providing an alternative counterfactual for industries treated with a sanc19
tion, the closed cases allow us to observe the effect of “a shot across the bow” by the competition agency. That is, the effect of an investigation of potential legal conduct and a public announcement that accused economic agents were not guilty of illegal monopolistic practices. In principle, this effect could go either way. On one hand, firms might be deterred from illegal conduct once they realize the threat of investigation. On the other hand, illegal conduct could be emboldened if the decision to close the case is, on average, an error as in the example provided by Khan (2017).

# **4.1 Meta-Analysis of Investigations by the Federal Competition Commission of Mexico**

We use the SDID method to estimate the pair ( _θ_<sup>ˆ</sup> _j_<sup>_y,_ˆ</sup><sup>_σ_</sup> _j_<sup>_y_),where</sup><sup>_y_indicatestheoutcomeof</sup> interest, for instance (the log of) sales, (the log of) the wage bill, or (the percentage point) operating profit margin. To allow for a comparison in outcome changes across the same set of cases, we estimate the SDID for all variables using the same set of weights, which match treatment and control on (the log of) sales.<sup>14</sup> For robustness, we also report results using the DID and SC estimators, which weight control industries differently.

The results are reported in the meta regression where _Si ∈{_ 0 _,_ 1 _}_ indicates a sanction is imposed by the Commission. The term _τ_ 0<sup>_y_isthe</sup> average outcome change after an investigation that does not lead to a sanction, and the term _τ_ 1<sup>_y_istheadditionaloutcomechangewhenthereisasanction.Undertheassumptionthat</sup> news of each decision is unanticipated, we interpret _τ_ 1<sup>_y_as the causal effect of sanctions within</sup> the set of industries where illegal conduct is suspected. This effect is measured controlling for any unique characteristics of industries where sanctions may occur.

Vita and Osinski (2016) suggest antitrust analysis should account for sampling variance in analysis of difference-in-difference price changes after mergers. We incorporate such variance into our analysis. Measurement error _σ_ ˆ _j_<sup>_y_isestimatedinthefirststageandincludedinthe</sup> meta regression in the term _ε_ ˆ<sup>_y_</sup> _j_<sup>_∼N_(0</sup><sup>_,_ˆ</sup><sup>_σ_</sup> _j_<sup>_y_).Insomespecifications,theregressionallowsfor</sup> heterogeneity in the difference-in-differences due to differences across industries through the term _u_<sup>_y_</sup> _j_<sup>_∼N_(0</sup><sup>_, γ_</sup> _j_<sup>_y_).Thisiscalledarandomeffects(RE)meta-analysisframework,usedin</sup> the medical sciences literature (Borenstein, Hedges, Higgins and Rothstein, 2021). The fixed effect (FE) meta-analysis framework would set _γj_<sup>_y_= 0 and</sup><sup>_uy_</sup> _j_<sup>= 0</sup><sup>_∀j_, assuming that the effect</sup>

> 14 Matching on other outcome variables produced qualitatively similar results.

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is constant across the population. Random effects meta estimators that allow for _γj_<sup>_y>_0 are</sup> discussed by Vita and Osinski (2016) and are recommended by Borenstein et al. (2021) in their review of the meta-analysis literature. In the context of antitrust, random effects seem consistent with the approach in empirical industrial organization, in which industry-specific case studies are preferred given that differences in demand and technology imply potentially substantial differences across industries in the economic effects of antitrust policy (Berry et al., 2019). Though the RE model makes use of more information, it also may have worse finite sample properties compared to the FE model, as noted by Angrist and Pischke (2009, footnote, p. 223). For this reason, we begin by reporting the FE specification that restricts _u_<sup>_y_</sup> _j_<sup>=0</sup><sup>_∀j_,andthenmovetotheREspecification,whichwepreferontheaprioriground</sup> that the effects of antitrust should be different across industries. In practice, the random effect framework proves to be conservative, delivering larger standard errors and statistically similar point estimates, and placing more similar weight on each case as compared to the FE approach.

**Antitrust and sales** Sales summarize the value an industry provides to customers. Table 1 reports estimates of Equation (2) where the left hand side variable is the change in (the log of) sales after the Commission’s decision to sanction or close a case. The different columns report alternative measures of the outcome change. Across all columns, the constant _τ_ 0<sup>_sales_</sup> is negative and the dummy for sanctions _τ_ 1<sup>_sales_</sup> is positive, while both are statistically significant. This indicates that investigated industries on average have declining sales, but that financial sanctions can reverse this trend.

As a starting point, Column (1) reports the fixed effects specification where the outcome change is measured not using the SDID but the first difference (FD): the average value of (the log of) sales after treatment minus the average value (the log of) sales before treatment. In this specification, the constant, or the outcome change in a closed case, is not identified because there is no control group that controls for sector-specific trends, like the price deflator. Nonetheless, the double difference, or the comparison of average first difference in cases ending in sanction, and the average first differences in cases closed without trial. The value of this estimate is _τ_ 1<sup>_sales_</sup> = 0 _._ 037 (standard error = 0.007), suggesting that, after sanctions sales growth is 3.7 percent higher annually, relative to closed cases. This suggests that the estimated effect of sanctions, relative to the counterfactual of a closed case, is positive independently of the choice of the control group in the difference-in-differences estimator.

Columns (2) - (4) report the fixed effect specification where the alternative difference in difference estimators are used on the left-hand side. Across all columns, the values of the constant _τ_ 0<sup>_sales_</sup> are negative, the opposite of Column (1) where it was understandably 21
positive due to nominal sales growth. The first specification in Column (2) is the SDID specification where _τ_ 0<sup>_sales_</sup> = _−_ 0 _._ 025 (0.002), indicating sales are on average declining by 2.5 percent annually in investigated industries relative to the control.<sup>15</sup> This coefficient estimate has a smaller standard error compared to the DID specification in Column (3) and the SC specification in Column (4). This is consistent with evidence in Arkhangelsky et al. (2021) who show the use of the SDID can be more precise.

The contrast, when cases end in sanction Column (2) shows _τ_ 1<sup>_sales_</sup> = 0 _._ 038 (0.004) and implying an increase in sales of 3.8 percent annually relative to cases that are closed without trial. Moreover, this coefficient is larger than the coefficient _τ_ 0<sup>_sales_</sup> indicating that a financial sanction on average completely reverses the decline in sales observed in investigated cases, increasing sales overall.

In Column (5), we return to the SDID outcome change, but include in the meta regression the random effects parameter _uj ∼ N_ (0 _, γj_ ) and weigh the regression instead by the term <u>1</u><sup>This yields coefficients that are larger in</sup> _σj_<sup>2+</sup><sup>_γ_2, distributing weight more equally across cases.</sup> absolute value, though they are estimated less precisely. The random effects model produces _τ_ 0<sup>_sales_</sup> = _−_ 0 _._ 071 (0.011) and _τ_ 1<sup>_sales_</sup> = 0 _._ 058 (0.018), reproducing the qualitative pattern that sales are declining after closed cases, but that this trend is reversed when there is ˆ a sanction. The variance of the random effect term estimated at _γ_<sup>2</sup> = 0 _._ 00625, or 0.625 percent, indicating modest heterogeneity across industries in the effect of antitrust on sales.

**Antitrust and workers** The effects of antitrust on workers’ outcomes are reported in Table 2. Our discussion focuses on the random effects specification using the SDID estimator in Column (5), though results are qualitatively similar in Columns (1) - (4), as in Table 1. Panel A reports results where the outcome is the industry wage bill. In Column (5) _τ_ 1<sup>_wagebill_</sup> = 0 _._ 055 (0 _._ 016) and _τ_ 0<sup>_wagebill_</sup> = _−_ 0 _._ 054 (0 _._ 010). This implies a statistically significant 5.4 percent annual decline in wages relative to the synthetic control when an investigation is closed, but, relative to this counterfactual, a 5.5 percent annual increase in wages when there are sanctions. Panel B reports results when the outcome change is the log of employment, or number of workers, yielding _τ_ 1<sup>_employment_</sup> = 0 _._ 033 (0 _._ 014) and _τ_ 0<sup>_wage_</sup> = _−_ 0 _._ 047 (0 _._ 009). Panel C reports the results when the outcome change is the log of the total wage bill divided by total employment in the industry, yielding _τ_ 1<sup>_wagerate_</sup> = 0 _._ 028 (0 _._ 008) and _τ_ 0<sup>_wage_</sup> = _−_ 0 _._ 014 (0 _._ 005). The results in Panels B and C are quantitatively similar to Panel A. The values in Panel C (wage rate) are not exactly the values in Panel A (wage bill) divided by the values in Panel B

> 15 SDID effects are annualized by dividing the SDID effect and its standard error by the number of years that elapse between the pre-treatment and post-treatment averages. This number varies slightly across cases because the SDID estimator weights pre-treatment periods differently in each case to improve precision (Arkhangelsky et al., 2021).

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(employment) because the inverse variance weights differ across panels. Investigations target markets where wages and employment are falling, and that sanctions can reverse these trends. Evaluated relative to the counterfactual of an industry where collusive conduct is suspected but not sanctioned, antitrust sanctions create additional growth in jobs of 3.3 percent per annum, and annual wage growth of 2.8 percent per annum. In Section 5, we will explore the extent to which this increase in wages reflected increased improvements in productivity, rather than movement up the labor supply curve.

**Antitrust and the operating profit margin** In Table 3 we turn to the effects on the operating profit margin, measured in percentage points. If the mechanism for increased sales after antitrust sanctions is lower prices, and for increased wage bill, a higher wage rate, we would expect antitrust to put downward pressure on the operating profit margin. We exclude any (net) financial costs when calculating profit to control for differences in capital structure and facilitate comparisons across industries that have different fixed and sunk costs. Column (5), again reporting the SDID and random effect specifications, shows that without sanctions, the average profit margin of an establishment increases by investigated industries by _τ_ 0<sup>_profit_</sup> = 1 _._ 630 (1 _._ 313) percentage points. We do not annualize this effect, so it reflects a change over about 15 years. This suggests that investigators are targeting investigations towards industries where profits are rising by 1.63 percentage points. Sanctions appear to reverse this with _τ_ 1<sup>_profit_</sup> = _−_ 0 _._ 677 (2 _._ 028). Unlike the effects on sales and wages, these results are not statistically significant at standard levels. This is because the random effects model estimates much greater heterogeneity in the effect of antitrust on profit margins, relative to effects on sales. In Column (5) we estimate _γ_<sup>2</sup> = 56 _._ 16, equivalent to 56 percentage points of profit margin. This result indicates that while the effects of antitrust on sales and wages can be fairly homogeneous, effects on profits may be more variable. In the FE specifications in Columns (1) - (4) the coefficients are estimated much more precisely. The SDID estimator with fixed effect is quite similar in magnitude to Column (2) with _τ_ 0<sup>_profit_</sup> = 1 _._ 604 (0 _._ 333), and is statistically significant. This gives us confidence that, on average, closed cases do have rising profit margins, in addition to declining sales and wages.

An interpretation of the sales and wages results so far is that they are consistent with the classic anti-competitive conduct of firms restricting quantities to raise prices. One might argue for an alternative interpretation that, despite its mandate to investigate anti-competitive conduct, the Commission is investigating industries that are declining for exogenous reasons. This explanation accounts only for declining sales and wages, but not the rising profit margins in treated industries documented in Table 3. Considering the results on profits, prima facie it appears that industries where investigations are closed are characterized by emergent 23
anti-competitive conduct.

# **4.2 False Negatives or False Positives?**

Antitrust enforcement can be viewed as a decision theory problem, where the competent authority tries to identify and sanction all harmful conduct under incomplete information using available investigative resources and administrative discretion (Beckner and Salop, 1999; Salop, 2017). One area of debate regards what presumptions judges should make about guilt in the presence of incomplete information.

Khan (2017) argues that judges are biased towards making Type II errors (false negatives, or not finding violations when the conduct harmed competition) due to a presumption that Type I errors (false positives, or finding violations when the conduct did not harm competition) are especially costly relative to Type II errors. Salop (2017) provides examples of when Type II errors are especially costly, suggesting such a presumption, if it exists, could be inappropriate in at least some cases. One policy response would be for judges to instead presume that Type II errors are more costly than Type I errors, and reject defense arguments more readily.

In Mexico, the fact that decreases in sales and wages and increases in profit margins are observed when the Commission investigates but does not impose a sanction fails to reject the hypothesis of Type II errors. If there were no false negatives, one would expect a uniformly negative or null change in sales, wages, and profit margin after the Commission decides not to sanction, rather than the pattern we observe. The results are also not consistent with harmful false positives. In the presence of a harmful false positive, one would expect a greater decline in sales in wages after sanctions relative to after closed cases, which is the opposite of what we find.

# **4.3 Assessing Threats to Identification through Analysis of Heterogeneity by Type of Investigation**

Relative to the sample of only cases ending in sanctions, the sample including closed cases helps to correct for a sampling bias due to the fact that only cases likely to end in sanction are brought to a trial (or administrative “trial-like procedure” in the case of the Commission). This analysis makes the assumption that sanctions are quasi-random within investigations, effectively assuming it is by chance whether investigators collect enough evidence of illegal conduct for the Plenary to impose a financial sanction on the agents. In support of this assumption, we present evidence that the effects on sales, the wage bill, and profit margins hold in two subsets of the data: (i) cases of ex-officio investigations, which were initiated 24
by the Commission, and in which investigators plausibly (though not necessarily) have a greater motivation to search for all available evidence, (ii) cases of “absolute monopolistic practices” where administrative discretion to sanction is limited by law, (iii) case duration, and (iv) whether the case includes a conduct remedy.

In this subsection, we report results from the meta regression We are particularly interested in _τ_ 3<sup>_y_whichgivesthedifferenteffectofsanctionsinsubsets</sup> of investigations defined by the covariates in _Xj_ . In the table we report results for three outcomes, sales, the wage bill, and operating profit margin, and different covariates _Xj_ .

**Public complaints vs. ex officio investigations** Sanctions could fail to be quasirandom within investigated cases if the investigative authority brings different effort in certain cases. Interviews with Commission staff and past Commissioners suggest this is unlikely, as investigators have a professional responsibility to work equally hard to find evidence in all cases.

To assuage any remaining skepticism, Table 4 reports an estimate of Equation (3) where in place of _Xj_ a variable is used indicating whether the origin of the case was an ex-offcio investigation, rather than a complaint from the public. Over the years, the Commission has targeted its own investigation based on various criteria, including whether the industry has linkages to other important industries, and whether there are large gaps between wholesale and consumer prices, or high prices relative to international markets. In such cases, (though not necessarily) investigators plausibly have a greater incentive to search for all available evidence, since they initiated the investigation in the first place. In the table, we find that sanctions do not have significantly different effects in cases where the investigation comes from a public complaint. However, the effects of a closed ex-officio investigation on sales and wages, _τ_ 2<sup>_sales_</sup> and _τ_ 2<sup>_wage_</sup> , are negative and statistically significant. This suggests that Type II errors are potentially less costly in such cases and that it is in ex officio investigations where the stakes are the highest. One explanation for this is that the ex officio cases focus more on absolute monopolistic practice cases, which as we have shown above appear to have higher stakes for sales and wages.

**Absolute vs. relative monopolistic practices** Table 5 reports an estimate of Equation (3) where in place of _Xj_ is used a variable indicating whether the conduct in question is an absolute monopolistic practice under Mexican law. As described in Section 2, Mexican law makes clear that certain conduct is subject to a different legal standard. Cases

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of absolute monopolistic practices are per se illegal. In such cases, the Commission must impose a sanction when there is conclusive evidence of the conduct. In contrast, in cases of relative monopolistic practice, defendants have the opportunity to argue that their practices actually improve consumer welfare and the authority can choose not to impose a sanction even if evidence of the conduct is conclusive. Table 5 reports results where the effect of sanctions is interacted with a dummy variable indicating an absolute monopolistic practice case. The results suggest the effects of sanctions are much more economically significant in absolute monopolistic practice cases. For instance we have _τ_ 1<sup>_sales_</sup> + _τ_ 3<sup>_sales_</sup> = 0 _._ 071 (0.027). Further, in closed cases of absolute monopolistic practices, wages appear to decline by more than in relative monopolistic practice cases, with _τ_ 2<sup>_wages_</sup> = _−_ 0 _._ 038 (0.022). The pattern is similar for wages and the profit margin.

There are two inferences from these results. First, this mitigates concerns that the previous estimates of _τ_ 1<sup>_sales_</sup> , _τ_ 1<sup>_wage_</sup> , _τ_ 1<sup>_profit_</sup> are driven by administrative discretion, wherein the Plenary sanctions industries that are growing for exogenous reasons. Since the positive effects of sanctions are concentrated in absolute monopolistic practice cases where there is no administrative discretion, the effects cannot be due to administrative discretion. Rather sanctions in these cases occurred because there was conclusive evidence that per se illegal conduct had taken place.

Second, these results are informative about how one might reduce Type II errors. The coefficients on the dummy for absolute monopolistic practice ( _τ_ 2<sup>_sales_</sup> _, τ_ 2<sup>_wage_</sup> _, τ_ 2<sup>_profit_</sup> ) give the outcome change in absolute monopolistic practice cases that are closed without trial, compared relative to monopolistic practice cases that are closed without trial. These indicate the potential impact of Type II errors, or false negatives. Here we see that _τ_ 2<sup>_sales_</sup> = _−_ 0 _._ 009 (0.025), _τ_ 2<sup>_wage_</sup> = _−_ 0 _._ 038 (0.022), and _τ_ 2<sup>_profit_</sup> = _−_ 0 _._ 307 (3.161), implying that declines in the wage bill in closed absolute monopolistic practice cases are significantly more negative than in closed relative monopolistic practice cases. Evidence on the changes in sales and profit margin is inconclusive due to the larger standard errors.

Outcome changes in closed cases can be understood to reflect the cost of Type II errors. Looking at wages, the cost of a Type II error seems to be larger in cases of absolute monopolistic practice. These results suggest the best margin for improving welfare by reducing Type II errors may be an expansion of investigative resources to develop more conclusive evidence in cases of absolute monopolistic practice, rather than different administrative discretion in cases of relative monopolistic practice.

**Time between investigation and final decision** Crandall and Winston (2003) observe that antitrust decisions may have a lesser impact on market outcomes if they occur a

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long time after the alleged anti-competitive conduct occurred. This could be due to the fact that enforcement takes so long after the alleged illegal conduct that market conditions change naturally and make intervention no longer effective. The authors provide the example that a final decision to break up Standard Oil in the United States took over a decade from the first indictment, during which time United States oil production became more competitive with new entrants from Texas, reducing the impact of the remedy on Standard Oil’s monopoly position.

We test the hypothesis that case duration affects outcomes in the Mexican context in Table A2 where the sanction indicator is interacted with a variable called duration equal to the number of years between the beginning of the investigation and the final decision. The duration is normalized as a Z-score by subtracting the average duration and dividing by the standard deviation. In these regressions we find that the interaction effects are not statistically significant, suggesting that the effects of sanctions are not statistically different in longer cases. The interaction effects on sales and wages are positive however, suggesting that, if anything, the effect on production is positive rather than negative as hypothesized.

The coefficients on the duration variable _τ_ 2<sup>_sales_</sup> , and _τ_ 2<sup>_wage_</sup> however are statistically significant and negative. This suggests that, for closed cases, the ultimate decline in sales and wages is greater in industries where the cases have a long duration. An interpretation of this result is that cases that are most contested by defendants, and thus have longer appeals processes, are also the ones where conduct was most harmful.

**Conduct remedies** In addition to sanctions, a small number of cases require conduct remedies from market participants. Rather than additional requirements placed on firms as in the conduct remedies studied by Kwoka (2014) these are typically requirements that public agencies remove barriers to entry. For example, in one case, state authorities were required not to give preferential treatment to unions or business associations in the granting of concessions. In another, a state was required to eliminate an authorization requirement for the entry of pasteurized and ultra-pasteurized milk into the state, a barrier to interstate commerce. In Table A3 we also include a dummy for such remedies. We find that, if anything, they reduce sales and wages, the opposite effect of sanctions. An interpretation of this result is that reducing such state-imposed barriers to entry in this context does not have as great an effect as sanctioning illegal conduct by private firms.

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# **5 Aggregate Effects of Antitrust**

The Mexican economy is characterized by significantly higher inequality and slower productivity growth than high-income countries (Levy Algazi and Walton, 2009; Hsieh and Klenow, 2014; Levy Algazi et al., 2018). What can we learn from the case of Mexico about the potential effect of antitrust on aggregate outcomes and the mechanism behind it in this context?

The evidence presented so far suggests that antitrust contributes to reducing aggregate inequality. The Commission has investigated about 40 percent of industries, and in the investigations that do not end in a sanction, sales and wages are declining relative to a control while profit margins are rising. This indicates that a good share of the economy has characteristics consistent with possible monopolization. Sanctions appear to reduce the harmful effects of this conduct on consumers and workers. Increases in sales and wages after sanctions suggest a reallocation of surplus from shareholders to consumers and workers, which will reduce inequality if shareholders are those at the top of the welfare distribution.

Another measure of inequality is the dispersion of total factor productivity across establishments, which has a direct link to aggregate productivity. In this section, we first describe first the effects of antitrust on concentration and informality, which can shape productivity dispersion. Then we measure total factor productivity at the establishment level, and report how the productivity distribution changes after investigations. We close with a discussion and quantification of how this might aggregate into national productivity growth.

**Antitrust, Concentration, and Informality** Table 6 reports the RE meta-regression specification where outcomes are alternative measures of concentration. In Column (1) the left-hand side variable is the SDID change in the log of the number of firms. There _τ_ 0<sup>_firms_</sup> = 0 _._ 140 (0 _._ 083) is negative and statistically significant, indicating industries have 14 percent fewer firms on average relative to those in the industry after a closed case. Investigated industries are those where firms are exiting, consistent with the declining sales and wage bill observed previously. The term _τ_ 1<sup>_firms_</sup> is positive, indicating after sanctions there is relatively less exit, though this effect is not statistically significant. The heterogeneity term _γ_<sup>2</sup> = 0 _._ 197 indicating limited heterogeneity. In Column (2) the left-hand side variable is the SDID change in the Herfindahl-Hirschman Index (HHI) or the sum of squared percentage market shares, which ranges from 0-10,000. Interestingly, the coefficient _τ_ 0<sup>_hhi_</sup> = _−_ 510 _._ 553 (198.522) is negative and statistically significant, indicating sales after closed cases are becoming less concentrated, despite the industry having fewer firms as shown in Column (1). Similarly to the number of firms, _τ_ 1<sup>_hhi_</sup> is positive suggesting antitrust increases concentration, though

28
this effect is not significant. A decrease in concentration after closed cases are economically meaningful. Nocke and Whinston (2022) for example find that harms to consumers may outweigh potential efficiency gains from mergers when the HHI increases by 200, among industries that already have a low HHI of 1500-2500.

Changes in informality can explain this unexpected result. Columns (3) and (4) show specifications where the left-hand side variable is the SDID change in the concentration ratios, respectively CR4, the sales share of the four largest firms, and CR1, the sales share of the largest firm, each expressed as a percentage ranging from 0-100. The terms _τ_ 0<sup>_CR_4</sup> and _τ_ 0<sup>_CR_1</sup> are quantitatively small and statistically insignificant, suggesting the decrease in the HHI in closed cases is not coming from changes in the market shares of the largest four firms or the largest firm declining by a large amount and must be due to decreased concentration of sales among smaller (informal) firms. Columns (5) and (6) report specifications where the outcome change is in measures of legal and illegal informal employment proposed by Levy Algazi (2010) that can be measured in the Economic Census. Legal informal employment is unremunerated labor,<sup>16</sup> illegal informal employment is remunerated labor without employer contributions to social security. In Column (5) the left-hand side variable is the change in the percentage of workers in establishments with workers but no recorded remuneration. This indicates that workers are compensated out of profits, as in the case of a family business. Here we see _τ_ 0 _legalinformal_ = 1.445 (0.643), suggesting closed cases see a 1.445 percentage point increase in the share of workers operating in firms that record no remuneration. In Column (6) the left-hand side variable is employer contributions to social security as a percentage of the wage bill. The term _τ_ 0 _illegalinformal_ = -1.504 (0.551), suggesting closed cases see a statistically significant 1.504 percentage point decrease in employer contributions to social security, relative to the wage bill. These results suggest that investigators target industries with decreasing concentration of sales due to increased market shares of informal companies. These results paint a nuanced picture of the relationship between antitrust and concentration. Overall, antitrust investigations tend to target industries where firms are exiting and the share of informal employment is rising, however, these industries are not undergoing significant changes in the market share of the largest firms. There is significant treatment effect heterogeneity across Columns (2) - (6) indicated by the _γ_<sup>2</sup> term, indicating there is no hard and fast rule about how market structure may be affected by antitrust. Moreover, on average, antitrust sanctions do not seem to alter market structure, except in the case of the share of employment in legal informal firms. We now turn to how these trends relate to the distribution of productivity.

> 16Typically unpaid family members working in the business.

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**Establishment-Level Productivity Measurement** Total factor productivity is measured as the log of sales minus the logs of labor, capital, and materials, each multiplied by their output elasticities in the production function. We estimate production functions in each case using the control function approach with refinements by Ackerberg et al. (2015) in the longitudinal establishment panel compiled from the Economic Census by Busso, Fentanes T ́ellez and Levy Algazi (2019).<sup>17</sup> Specifically, for each treatment and control industry we estimate a distinct Cobb-Douglas production function for sales as a function of labor, capital, and intermediate inputs. The factor shares are not restricted to sum to one, and indeed are consistent with decreasing, constant, and increasing returns to scale across different specifications. To account for potential omitted variable bias when estimating the production function, materials are included in a control function to stand for the component of productivity that is unobserved to the econometrician, but was forecast by the firm when it made its decision to fix capital assets in the previous period, as in Levinsohn and Petrin (2003). In the control function we also include the log of the firm’s specific wage rate, which is set equal to zero if the firm has no remuneration; a dummy for whether the firm has no remuneration; and a dummy indicating whether the antitrust decision has occurred. These choices follow the advice of De Loecker and Syverson (2021) to control for factors that could affect firms’ input decisions or the competitive environment. The estimation also includes a selection function to account for potential attrition bias as in Olley and Pakes (1996), though in practice this made little difference to the results, nor did including investment instead of materials in the control function.

Distinct production functions are estimated for each treatment industry and all control industries in each case. When estimating the production function for control industries, fixed effects for each six-digit control industry are included in the control function. Total factor revenue productivity (TFPR) for each establishment _k_ in case _j_ in each year is measured by subtracting from (the log of) sales, (the log of) employment, (the log of) fixed capital assets, and (the log of) materials consumption where the three inputs are multiplied by their output elasticities in a Cobb-Douglas production function (with unrestricted returns to scale).

**Effects of Antitrust on Revenue Productivity and Measured Misallocation** Table 7 reports the results of the meta regression in Equation (2) where outcomes are changes in alternative measures of productivity and misallocation. In Column (1) the outcome is the annualized change in the average of the log of TFPR _jk_ within an industry, where the average is weighted by the market (sales) share of firm _j_ . The effect of sanctions here is

> 17A match is not available between firms in the Economic Censuses of calendar years 1994 and 1999. Therefore the production function estimation only makes use of the censuses between 2003 and 2018.

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_τ_ 1<sup>_TFPR_</sup> = 0 _._ 009 (0 _._ 005), a statistically significant increase in productivity of about 1 percent per year. In Column (2) the outcome is the annualized change in the unweighted average of the log of TFPR _jk_ . Here the result is similar with _τ_ 1<sup>_TFPR_</sup> = 0 _._ 011 (0 _._ 006), suggesting that the increase in productivity was broad-based, across all establishments in the industry, rather than just a few. In both columns the coefficient _τ_ 0<sup>_TFPR_</sup> is a precisely estimated zero, indicating investigations do not target industries with specific productivity trends.

The next columns present estimates where the outcomes are changes in alternative moments of the productivity distribution. In Column (3) the outcome is the change in the correlation of market share and the log of TFPR _jk_ (Olley and Pakes, 1996) and in Column (4) the outcome is the change in the variance of the log of TFPR _jk_ (Hsieh and Klenow, 2009). These measures proxy for changes in the extent of factor misallocation relative to the output maximizing allocation. In both columns, we see no evidence that sanctions increase or decrease misallocation. Sanctions do not increase the correlation between market share and revenue productivity or reduce the variance of revenue factor productivity. Rather than a model in which antitrust changes industry concentration leads to a significant reallocation of factors, these results are consistent with a model in which antitrust sanctions change in the industry equilibrium from a joint profit-maximizing cartel to a competitive equilibrium, and that new equilibrium incentivizing a broad-based increase in productivity across the productivity distribution, as firms benefiting from monopolistic practices move away from a ‘quiet life’ of limited innovation (Hicks, 1935, pg. 8).

Interestingly we do find that industries targeted by closed cases appear to be having an increase in the variance of revenue productivity. This is consistent with the evidence that closed cases have increasing informality. Since informal firms are numerous and less productive, greater informality increases productivity dispersion (Levy Algazi et al., 2018). At the same time, these industries also experience an increase in the correlation of market share and revenue productivity, which can reflect a greater mass of informal low-productivity firms with little scale or market share.

One concern with these results is that measures of TFPR may conflate profitability and productivity. Foster, Haltiwanger and Syverson (2008) distinguish TFPR, which is measured using revenues, from total factor quantity productivity (TFPQ), which is measured using physical output. If high markup firms are also the most productive, consumers will substitute away from high markup firms, and aggregate productivity will be less than if all firms had the same markup. Hsieh and Klenow (2009) show that it is possible to infer price vs. quantity, and therefore TFPR vs. TFPQ using an assumed demand function. Though only sales (revenues) are observed in the Economic Census, establishments with high physical output must have a lower price to explain why buyers would demand more 31
physical output. It is possible to invert a constant elasticity of substitution (CES) demand function and recover from revenues, up to a constant, a counterfactual measure of TFPQ _j_ equal to the value of industry productivity if marginal products for all factors were equalized across establishments. This approach requires only an assumption about demand and profit maximization and does not assume anything about the structure of factor markets.<sup>18</sup> Columns (5) and (6) show this measure of TFPQ _j_ for alternative values of the elasticity of substitution parameter. The effects of financial sanctions are positive and statistically significant in both columns, indicating that in addition to raising average TFPR, sanctions increase industry-wide TFPQ, with _τ_ 1<sup>_TFPR_</sup> = 0 _._ 076(0 _._ 030) in Column (5) which assumes a relatively low substitution parameter _σ_ = 3, and _τ_ 1<sup>_TFPR_</sup> = 0 _._ 108(0 _._ 061) in Column (6) which assumes _σ_ = 5. These results confirm that the mechanism behind the effects of antitrust on aggregate productivity is largely improvements in TFPQ at all firms rather than a reduction in the dispersion of TFPR. Further they suggest that in the absence of misallocation, effects of antitrust on productivity could be higher.

**Aggregation of Effects on Productivity** How much could the Commission’s contribution to aggregate productivity growth in the status quo be, without additional reforms to reduce misallocation? GDP is simply the sum of value added in the Economic Census, plus some adjustments, and GDP grows at the growth rate of total factor productivity in the steady state. Roughly, as shown in Figure 1 the Commission has investigated 40 percent of output over 25 years, implying it can investigate about 1.6 percent of output per year. In our sample, 40 percent of investigations lead to trial. Taking the estimate from Column (1) of Table 10 of the effect of sanctions on market share weighted average TFPR as the effect of these sanctions, this implies the contribution of the Commission to GDP growth is 0 _._ 016 _×_ 0 _._ 4 _×_ 0 _._ 009 = 0 _._ 0000576, which divided by 0 _._ 01, the long run average GDP per capita growth rate, is about 0.576 percent of aggregate productivity growth in the steady state. This number is small in relative terms, but economically significant. We can attribute about half a percent of GDP per capita growth per year to sanctions by the commission. The evidence on false negatives suggests this effect could be larger if the Commission had more resources with which to investigate more of the economy more frequently, and was also able to collect sufficient evidence to secure sanctions at trial.

> 18Another method to assess whether TFPR overstates TFPQ due to a markup would be to estimate the markup itself using alternative assumptions. We experimented with the markup estimator of De Loecker and Warzynski (2012), but the results were noisy and so inconclusive.

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# **6 Conclusion and Implications**

This paper provides evidence on the outcomes and effects of a relatively new antitrust law enforcement authority in a middle-income economy over 25 years. Outcomes of investigations are estimated using the synthetic difference-in-differences estimator proposed by Arkhangelsky et al. (2021). Previous work has conducted ex-post evaluation of antitrust sanctions considering only difference-in-differences effects of investigations that go to trial (e.g. Sproul, 1993). Such results may be subject to selection bias, since antitrust investigations can target industries where sales and wages are falling, and firms are exiting, while profit margins are rising.

An empirically relevant practice is that the Federal Competition Commission of Mexico discloses the relevant market for all investigations. This provides an alternative counterfactual for an industry subjected to antitrust sanctions—the evolution of industry in which anticompetitive behavior is suspected but not punished. Using this counterfactual, the positive effects of antitrust on sales and wages are larger than when measured using difference-indifferences. Disclosure of all investigations by other jurisdictions will allow further research into the causal effects of antitrust.

These results provide insight into Mexico’s slow growth story. Hsieh and Klenow (2014) and Levy Algazi et al. (2018) document that industries in Mexico are characterized by significant misallocation, which can account for slow GDP growth at less than 1 percent per capita per year since 1982 when liberalization began under the administration of President Miguel de la Madrid. Our results suggest that antitrust has contributed significantly to productivity growth, but challenge the idea that Mexico’s measured misallocation or dispersion of productivity is due to cartel conduct by firms. Growth in productivity after sanctions occurs without changes in standard measures of misallocation. Even so, our estimates of positive effects on aggregate TFPQ following Hsieh and Klenow (2009) suggest that the productivity benefit of antitrust would be larger in the absence of misallocation. Levy Algazi (2010) argues for another explanation for misallocation, which is that the tax and social security regime creates incentives for firms to remain small. It is likely that these incentives bind regardless of the competitive equilibrium.

The results suggest practically how the Commission could increase its impact in the future. Falling sales and wages after closed cases leave open the possibility that some harmful conduct goes undetected, perhaps because investigators have insufficient resources to collect evidence and confirm all per se violations. Rose (2019) provides concrete recommendations from the United States context about how to increase enforcement resources, for instance by hiring more investigative staff. Such reforms can be implemented without a change in law, 33
and simply require providing more budget to the Commission.

Our evidence is supportive of increased enforcement focus on conduct that is not per se illegal. Yet, declines in wages in closed relative monopolistic practice cases are smaller than in absolute monopolistic practice cases, suggesting the stakes are possibly less high. The effects of sanctions are also quantitatively smaller in relative monopolistic practice cases. Nonetheless, decreases in sales and wages and increases in profits are economically and statistically significant in closed relative monopolistic practice cases and so we fail to reject the judicial false negatives in relative monopolistic practice cases hypothesized by Khan (2017).

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<!-- Start of picture text --> No antitrust investigation ever<br>At least one antitrust investigation<br>Commerce and transport<br>Manufacturing<br>Construction, mining, and utilities<br>Professional services<br>Recreation<br>Social services<br>Agriculture<br>0 5 10 15<br>Total sales (Trillions Mex$, 2018)<br><!-- End of picture text -->

**Figure 1: About 40 percent of economic activities have been subject to at least one antitrust investigation** . Sales are coded as being derived from an activity that has had at least one antitrust investigation if the six digit activity corresponding to those sales has been investigated by the Federal Competition Commission. The source is the Economic Census (INEGI, 2020).
<!-- Start of picture text --> If case does Case Dismissal<br>not meet the if facts do not<br>requirement violate<br>of an objective Competition<br>cause Law<br>Case not<br>Prevention. presented if Case closure if<br>Case Actors are lack of there are not<br>initiated Prior to required to |——+|_ response or enough<br>externally investigation, provide insufficient elements to<br>from verification if further elements prove<br>complaints the case meets elements provided responsibility<br>or requirement of<br>internally an objective<br>initiated as cause Investigation by COFECE<br>Ex officio ; Ieectination design Beginning of trial Alleged Resoluti<br>«preliminary sector procedure if perpetrators ne aha<br>analysis enough elements manifest wi "Jo on<br>* Data collection exist to prove themselves . j i<br>* responsibility of and provide commitment<br>Analysis and .<br>. . agents evidence<br>interpretation<br><!-- End of picture text -->
<!-- Start of picture text --> Financial sanction of real estate brokerage cartel near Lake Chapala<br>17<br>Real estate brokers near Lake Chapala<br>Synthetic control<br>Financial sanction of price fixing (30M Mex$)<br>16<br>15<br>14<br>13<br>12<br>11<br>1990 1995 2000 2005 2010 2015 2020<br>Log of sales<br><!-- End of picture text -->

**Figure 3: Change in industry sales after the sanction of a real estate brokers’ cartel near lake Chapala.** The vertical black line indicates the date when the real estate brokers’ cartel near Lake Chapala was sanctioned. The thin blue line indicates the log of sales of real estate brokers near Lake Chapala, the treated industry. The thin red line indicates the synthetic control, or the weighted average of the log of national sales in control industries, with weights selected so the weighted average has a similar pretreatment trend to the treated industry. The thick blue and red lines connect average values of treatment and control before and after the sanction. The counterfactual for the treated industry, indicated by the arrow, is derived by shifting the thick red line upwards. The black arrow shows the implied synthetic difference-in-differences, which corresponds to an annual increase in sales between the pre- and post-treatment periods of 5.05 (standard error = 1.39) percent. The case id ( _expediente_ ) is DE-019-2007.
<!-- Start of picture text --> SO<br>ya<br>a<br><!-- End of picture text -->
**Table 1: Antitrust and Sales.** The regression left-hand side variable is the estimated change in the log of industry sales after a final decision by the Commission. The right-hand side variables are a constant and a dummy for whether the final decision is to sanction the agents. The coefficient on the constant therefore equals the average outcome change after a decision to close the case (and to not impose a sanction). In the fixed effect (FE) meta regression estimator, the regression is weighted by the inverse of the asymptotic variance of the outcome change. In the random effects (RE) meta regression estimator the regression is weighted by the inverse of the sum of the asymptotic variance of the outcome change and the estimated heterogeneity parameter _γ_<sup>2</sup> . The FD, SDID, DID, and SC estimators are different ways to measure the outcome change on the left hand side of the meta regression. The first difference (FD) estimator is the difference between the averages of the log of sales before and after the decision. The FD estimator does not use a control group. The difference-in-differences (DID) estimator is the difference between the average of the log of sales before and after the decision, controlling for the average difference in industries in the same sub-sector. The synthetic control (SC) estimator is similar to the DID except that it weighs control industries so they have the same level and trend as the treatment group prior to treatment. The synthetic difference-in-differences (SDID) estimator is similar to the DID except that it weighs control activities so they have a parallel trend in (the log of) sales with the treatment group prior to treatment, though not necessarily the same level. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

|Change in (the log of)|(1)<br>sales|(2)<br>sales|(3)<br>sales|(4)<br>sales|(5)<br>sales|
|---|---|---|---|---|---|
|Financial sanction (=1)|0.037***|0.038***|0.034***|0.032**|0.058***|
||(0.007)|(0.004)|(0.005)|(0.013)|(0.018)|
|Constant|0.053***|-0.025***|-0.037***|-0.058***|-0.071***|
||(0.004)|(0.002)|(0.003)|(0.009)|(0.011)|
|_τ_ <sup>2</sup>|||||0.00625|
|Number of cases|130|90|90|90|90|
|Meta analysis framework|FE|FE|FE|FE|RE|
|Outcome change estimator|FD|SDID|DID|SC|SDID|
**Table 2: Antitrust and Workers.** Panels B and C do not sum to Panel A because the meta regression weights observations differently across panels. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A) Change in (the log of the)|wage bill|wage bill|wage bill|wage bill|wage bill|
|Financial sanction (=1)|0.021***|0.048***|0.095***|0.048***|0.055***|
||(0.005)|(0.005)|(0.004)|(0.016)|(0.016)|
|Constant|0.040***|-0.038***|-0.091***|-0.060***|-0.054***|
||(0.004)|(0.002)|(0.002)|(0.011)|(0.010)|
|_γ_<sup>2</sup>|||||0.00452|
|Panel B) Change in (the log of)|employment|employment|employment|employment|employment|
|Financial sanction (=1)|0.007***|0.065***|0.068***|-0.015|0.033**|
||(0.002)|(0.004)|(0.003)|(0.014)|(0.014)|
|Constant|0.021***|-0.057***|-0.067***|-0.030***|-0.047***|
||(0.001)|(0.002)|(0.002)|(0.007)|(0.009)|
|_γ_<sup>2</sup>|||||0.00368|
|Panel C) Change in (the log of the)|wage rate|wage rate|wage rate|wage rate|wage rate|
|Financial sanction (=1)|0.013***|0.013***|0.035***|0.025***|0.028***|
||(0.005)|(0.001)|(0.003)|(0.004)|(0.008)|
|Constant|0.039***|-0.009***|-0.021***|-0.006*|-0.014***|
||(0.003)|(0.001)|(0.002)|(0.003)|(0.005)|
|_γ_<sup>2</sup>|||||0.000721|
|Observations|130|90|90|90|90|
|Meta analysis framework|FE|FE|FE|FE|RE|
|Outcome change estimator|FD|SDID|DID|SC|SDID|
**Table 3: Antitrust and Profits.** The regression left-hand side variable is the estimated change in the operating profit margin after a decision by the Commission to either impose a sanction or close the case. Operating profit margin is value added (sales minus the value of intermediate inputs) minus the wage bill, divided by sales. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

|Change in (the percentage point)|(1)<br>operating<br>proft<br>margin|(2)<br>operating<br>proft<br>margin|(3)<br>operating<br>proft<br>margin|(4)<br>operating<br>proft<br>margin|(5)<br>operating<br>proft<br>margin|
|---|---|---|---|---|---|
|Financial sanction (=1)|-0.030|-0.539|-3.749***|-5.069***|-0.677|
||(0.618)|(0.628)|(0.652)|(0.531)|(2.028)|
|Constant|1.429***|1.604***|8.127***|1.700***|1.630|
||(0.474)|(0.333)|(0.394)|(0.326)|(1.313)|
|_γ_<sup>2</sup>|||||56.16|
|Number of cases|130|90|90|90|90|
|Meta analysis framework|FE|FE|FE|FE|RE|
|Outcome change estimator|FD|SDID|DID|SC|SDID|
**Table 4: Antitrust and Origin of Investigation.** Ex-officio investigations are those initiated by the Federal Competition Commission itself, rather than requested by a member of the public or other another federal agency, as described in Article 66 of the Federal Economic Competition Law. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)|(2)|(3)|
|---|---|---|---|
|Change in|the log<br>of<br>sales|the log<br>of the<br>wage bill|operating<br>proft<br>margin|
|Ex-ofcio investigation X sanction (=1)|0.020|0.028|-3.953|
||(0.038)|(0.033)|(4.130)|
|Financial sanction (=1)|0.047|0.042*|1.134|
||(0.029)|(0.025)|(3.128)|
|Ex-ofcio investigation (=1)|-0.011|-0.039*|4.639*|
||(0.023)|(0.020)|(2.616)|
|Constant|-0.066***|-0.034**|-0.618|
||(0.016)|(0.015)|(1.818)|
|Number of cases|90|91|90|
|Meta analysis framework|RE|RE|RE|
|Outcome change estimator|SDID|SDID|SDID|
|Sanction after ex-ofcio investigation|0.067***|0.069***|-2.819|
||(0.024)|(0.021)|(2.696)|
**Table 5: Antitrust and Legal Standard for Type of Monopolistic Practice.** Absolute monopolistic practices are as defined in Article 53 of the Federal Economic Competition Law. Such practices “shall be null and void...and the Economic Agents that engage in such practices shall be subject to the sanctions provided in this Law, regardless of any criminal or civil liability that may arise.” Practices that are not absolute monopolistic practices are relative monopolistic practices as defined in Articles 55 of the Law, which shall be punished “unless the Economic Agent proves these practices produce gains in efficiency and favorably impact upon the process of economic competition and free market access, thus overcoming their possible anticompetitive effects, and consequently result in an improvement of consumer welfare.” Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

|Change in|(1)<br>the log<br>of<br>sales|(2)<br>the log<br>of the<br>wage bill|(3)<br>operating<br>proft<br>margin|
|---|---|---|---|
|Absolute monopolistic practices X sanction(=1)|0.029|0.029|-0.570|
||(0.039)|(0.035)|(4.597)|
|Financial sanction (=1)|0.042|0.049*|-0.174|
||(0.028)|(0.025)|(3.130)|
|Absolute monopolistic practices (=1)|-0.009|-0.038*|-0.307|
||(0.025)|(0.022)|(3.161)|
|Constant|-0.068***|-0.042***|1.721|
||(0.014)|(0.012)|(1.515)|
|Observations|90|90|90|
|Meta analysis framework|RE|RE|RE|
|Outcome change estimator|SDID|SDID|SDID|
|Sanction in absolute case|0.071***|0.078***|-0.744|
||(0.027)|(0.024)|3.368|
**Table 6: Antitrust, Concentration, and Informality.** The regression left hand side variable is the estimated change in an outcome measured by the SDID estimator, where synthetic control weights match pretreatment trends in the log of sales for comparability with previous results. All specifications use a random effects meta regression. Firms with multiple establishments are treated as a single firm. The Herfindahl-Hirschman Index is the sum of firms’ squared percentage market shares, and ranges from 0 and 10,000. The CR4 ratio is the market share of the four largest firms in a market, expressed as a percentage from 0 to 100. The CR1 ratio is the market share of the largest firm in a market, also expressed as a percentage. The legal informal share of employment is the percentage of workers in establishments with workers but no recorded remuneration, indicating workers are compensated out of profits, as in the case of a family business. The social security share of wage bill is employer contributions to social security as a percentage of the wage bill. Levy Algazi (2010) proposed definitions of legal and illegal informal employment that can be measured in the Economic Census. Legal informal employment is unremunerated labor, illegal informal employment is remunerated labor without employer contributions to social security. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Change in|The log of<br>the number of<br>frms|Herfndahl-<br>Hirschman<br>Index|CR4: Sales share<br>of four<br>largest frms|CR1: Sales share<br>of largest<br>frm|Legal informal<br>share of<br>employment|Social security<br>share of<br>wage bill|
|Financial sanction (=1)|0.056|253.237|-0.121|0.816|-2.405**|1.072|
||(0.130)|(316.398)|(3.351)|(3.356)|(1.045)|(0.908)|
|Constant|-0.140*|-510.553**|-0.038|-1.535|1.445**|-1.504***|
||(0.083)|(198.522)|(2.205)|(2.092)|(0.643)|(0.551)|
|_γ_<sup>2</sup>|0.197|1.686e+06|135.9|147.3|18.76|12.98|
|Observations|90|90|90|90|90|90|
|Meta analysis framework|RE|RE|RE|RE|RE|RE|
|Outcome change estimator|SDID|SDID|SDID|SDID|SDID|SDID|
_αj βj γj_ **Table 7: Antitrust, Misallocation, and Productivity.** The term _TFPRjk ≡ PjkYjk/Ljk_<sup>_K_</sup> _jk_<sup>_M_</sup> _jk_<sup>isthetotalfactorrevenue</sup> productivity of establishment _k_ in the treated industry of case _j_ , where _Pjk_ is the price of sales, _Yjk_ is the quantity of sales, _Ljk_ is the number of employees, _Kjk_ is the value of fixed assets, and _Mjk_ is the value of intermediate inputs. The term _TFPQj ≡_ � _k_ �( _PjkYjk_ ) _σ−σ_ 1 _/Lαjkj_<sup>_K_</sup> _jkβj_<sup>_M_</sup> _jkγj_ � _σ−_ 1 is industry total factor quantity productivity in the absence of distortions as in Hsieh and Klenow (2009), which is derived assuming a CES demand function and within industry substitution parameter _σ_ . This corresponds to the productivity in a benchmark economy where there are no differences in markups or factor market constraints across firms. Production function parameters _αj_ , _βj_ , and _γj_ are estimated separately for treatment and control industries using a control function approach in *** ** * the Busso, Fentanes T ́ellez and Levy Algazi (2019) panel for each case _j_ . Standard errors in parentheses. p _<_ 0.01, p _<_ 0.05, p _<_ 0.1.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Change in|market share|unweighted|correlation of|variance of|_σ_ = 3|_σ_ = 5|
||weighted average|average|market share and||||
||ln(TFPR_jk_)|ln(TFPR_jk_)|ln(TFPR_jk_)|ln(TFPR_jk_)|ln(TFPQ_j_)|ln(TFPQ_j_)|
|Financial sanction (=1)|0.009*|0.011*|-0.013|0.034|0.076***|0.108*|
||(0.005)|(0.006)|(0.056)|(0.115)|(0.030)|(0.061)|
|Constant|0.000|-0.002|0.133***|0.187***|-0.052***|-0.040|
||(0.003)|(0.004)|(0.036)|(0.072)|(0.019)|(0.038)|
|_γ_<sup>2</sup>|0.000153|0.000605|0.0510|0.265|0.0112|0.0425|
|Number of cases|90|90|90|90|90|90|
|Meta analysis framework|RE|RE|RE|RE|RE|RE|
|Outcome change estimator|SDID|SDID|SDID|SDID|SDID|SDID|