Abstract
We study the factors that influence the decisions that are made by the Brazilian competition authority on mergers and compare these factors with the Brazilian merger policy guidelines. Estimates of discrete choice models show that high post-merger market share and adverse entry conditions—both of which are included in the guidelines—significantly increase the likelihood of mergers’ being challenged. Market regulation—a factor that is not included in the guidelines—also influences these decisions. Our analysis indicates that the guidelines do not cover all relevant aspects of the decision-making process and shows evidence of the interaction between merger and regulatory policies.
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Notes
We use the term “merger” in a broader sense, which also includes acquisitions, asset permutations, and joint ventures.
In the sectors of energy and telecommunications, the legislation allows regulatory agencies such as the National Agency for Electrical Energy (ANEEL) and National Agency for Telecommunications (ANATEL) to fulfill this role (CADE, 2007).
Law n.12.529 of November 30, 2011 superseded Law n.8.884 of June 11, 1994. These legal documents are available at http://en.cade.gov.br/topics/topics/legislation/laws.
Though we examine records starting from 2002, the earliest decision satisfying these criteria in our dataset was made in 2004. Furthermore, most blocking decisions apply to all relevant markets in a merger. The only exception in our data refers to an acquisition of minority shares of plants in geographically isolated markets, which made it possible to block some markets while conditioning the remaining ones.
Though cases that are approved after extensive analysis contain potentially useful data, there are no public records that distinguish these few cases from the thousands of cases that are approved without extensive analysis. For this reason, we could not include fully approved cases in our study.
The Guidelines have market share and C4 as threshold rules only, while our models use these variables both as continuous and thresholds. In the extended appendix (Cardoso et al., 2020), we estimate models that omit continuous variables to show that the threshold rules alone yield an incomplete representation of the decision process.
The simple majority rule is a naïve model that is used as a benchmark. This rule predicts the class or outcome of every observation as the class that is most frequently observed in the data—thus ignoring all other variables.
Probit models yield similar results, which we report in the extended appendix.
Another approach would be to interact market share (or C4) and dummy variables. However, as we show in the extended appendix, interacted terms are not statistically significant, and their inclusion does not qualitatively change the results.
Figures 1 and 2 display point estimates of predicted probabilities (or adjusted margins). Standard errors of predicted probabilities vary along the horizontal and are larger at low values of market shares. Point estimates and standard errors for predicted probabilities are presented in the extended appendix.
Even though the vector of coefficients is the same for all outcomes, the APEs for each outcome are different because they are calculated at different cut points of the latent variable. See Greene (2012) for details.
The very low number of blocked mergers in our dataset precludes the estimation of a multinomial logit model with sector controls. Nevertheless, the estimated APEs of the consistently significant factors are generally similar to the ordered model with sector controls.
We present the details of the disagreement cases in the extended appendix.
Redefined and added relevant markets do not map directly into the markets defined in the technical reports, thus generating different units of observation. For this reason, we cannot use the observations in these reports as instruments.
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Acknowledgements
We are grateful for comments and feedback from the editor, two anonymous referees, and Ivan Rudik. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil (Finance Code 001).
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D.S.C. received funding in the form of a Master’s Level Scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil (Finance Code 001). The authors have no conflicts of interest to declare that are relevant to the content of this article.
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Appendices
Appendix A: Relative Importance of Entry Conditions
As defined in the Guidelines, entry is deemed adverse if any of the three entry conditions fail: likelihood, timeliness, and sufficiency. Since one failed condition is sufficient for adverse entry, in most relevant markets we do not observe all three conditions. For this reason, we did not include the separate conditions in our models. Nevertheless, we can explore the relative importance of each condition by observing their conditional frequencies, as is shown in Table 9. Panels A through C show the number of observations in which each condition is satisfied, not satisfied, or missing relative to the decision outcome and other conditions.
Of the three conditions, entry likelihood seems to be the most relevant for commissioners: It is the most evaluated condition, whereas timeliness and sufficiency are observed in less than half of the relevant markets. Furthermore, likelihood and sufficiency appear to be individually important: Whenever either of these conditions is the only one failing, mergers were not allowed. Mergers were conditioned in all 79 relevant markets in which likelihood was the only failed condition; for sufficiency as the only failed condition, 44 relevant markets were conditioned, and 15 were blocked. Last, we also note that mergers were more frequently allowed when entry was likely and at least another condition failed.
Appendix B: Structural Breaks
The models reported in the paper estimate the average effects for a set of factors. Though we explore the interplay of factors based on the non-linearity of partial effects, the conditional APEs are based on average coefficients. Nevertheless, coefficients may vary across subsets of the sample defined by observable characteristics; this is a feature that is often referred to as a structural break. In this appendix, we perform Chow-like tests of structural breaks (Greene, 2012) over four characteristics: entry, nationality, time, and sector.
Let I be a binary variable of interest that determines two non-overlapping partitions of the sample. We test whether the sample presents a structural break over I by estimating a model that includes interactions of I with all other covariates; the structural break test is then obtained from a joint significance test of the interacted terms. This approach also informs whether specific coefficients have significantly different values in each partition: structural breaks for each covariate.
Table 10 displays the results for Chow-like tests of structural breaks. The bottom two rows report the joint test statistic (Wald) and its corresponding exact probability for each of the four characteristics tested. Columns 2 and 3 show tests that are based on covariates for adverse entry and national firm; column 4 tests for differences on early years (2004–2008) versus late years (2009–2012); and column 5 tests for consumer products and services sectors versus heavy industry and agriculture. Note that this table shows the estimated coefficients and not the APEs.
These tests suggest the presence of structural breaks over nationality, time, and sector. We interpret these results as an indication that our main models may not fully capture the relevant heterogeneity of factor effects. However, we refrain from assessing regression coefficients individually for two reasons: First, our sample is somewhat limited for the task and does not grant us sufficient statistical power for testing the inclusion of several interaction terms. Second and most important, testing was only possible by dropping covariates and controls due to estimation constraints on small subsamples. Since some of the dropped variables were previously shown to be statistically (and economically) relevant, we likely induced some estimation bias.
Hence, we conclude that while our main models focus on average effects, they may present an incomplete picture of how effects vary with interactions of factors; moreover, data limitations hinder our ability to estimate appropriately the suggested heterogeneity components.
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Cardoso, D.S., Pitelli, M.M. & Figueiredo, A.M. An Econometric Analysis of the Brazilian Merger Policy. Rev Ind Organ 59, 103–132 (2021). https://doi.org/10.1007/s11151-021-09812-3
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DOI: https://doi.org/10.1007/s11151-021-09812-3