WPS4527
Policy ReseaRch WoRking PaPeR 4527
An Empirical Analysis of Mexican
Merger Policy
Marcos Avalos
Rafael E. De Hoyos
The World Bank
Development Prospects Group
February 2008
Policy ReseaRch WoRking PaPeR 4527
Abstract
A newly created dataset including 239 decisions made probability of a case being issued. The findings also show
by the Mexican Federal Competition Commission on that factors different from the ones explicitly mentioned
horizontal mergers between 1997 and 2001 is used to by the Commission have a significant effect on the
estimate the different factors affecting the Commission's Commission's final decision. In particular, the presence
resolution. The paper approximates the decision making of a foreign company among the would-be merger firms
process using two different discrete choice models. significantly increases the likelihood of observing an
The results indicate that, contrary to the Commission's allowed merger.
objective, the presence of efficiency gains increases the
This paper--a product of the Development Prospects Group--is part of a larger effort in the department to evaluate
competition policy in middle income countries. Policy Research Working Papers are also posted on the Web at http://
econ.worldbank.org. The author may be contacted at rdehoyos@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
AN EMPIRICAL ANALYSIS OF MEXICAN MERGER POLICY
Marcos Avalos and Rafael E. De Hoyos
Faculty of Economics and Business, Anáhuac University
Development Prospects Group, The World Bank
JEL Classification: L51, D43, C35
Keywords: Competition Commission, Mexican Merger Policy.
Av. Lomas Anáhuac s/n, Fracc. Lomas Anáhuac Huixquilucan, México, C.P. 52786.. Tel. 52(55) 56 27 02
10 Ext. 8627 Fax 8120 Correspondance Author: mbracho@anahuac.mx
Development Prospects Group, WB, 1818 H Street, NW, Washington, DC 20433Tel: 202-458 4823 Fax:
202-522 2578 rdehoyos@worldbank.org
I. Introduction
The year 2004 marked the 10th anniversary of the Competition Commission (FCC),
Mexico's institution responsible for designing, implementing and enforcing competition
law. Between April 1997 and December 2001, more than 300 merger cases were evaluated
by the Commission with more than 50 of them being issued or sanctioned. Despite of the
important role played by the FCC, to the best of our knowledge, no study had formally
analysed how the decisions of the FCC are affected by different economic factors. This
study represents an effort to close this significant gap in the literature contributing to our
understanding of the accuracy of competition policy implementation. In order to estimate
the relationship between the FCC's decisions and observable market structure variables, a
new database was constructed by the authors. Each of the 239 reports available
representing the same number of merger decisions occurring between 1997 and 2001 were
read and their information mapped into qualitative variables in the dataset. Using this new
dataset, the paper presents the results from two different discrete choice models of
horizontal merger cases decided by the FCC under Mexico's Federal Economic
Competition Law (FECL). The main purpose of this study is to identify the relative
importance of different economic factors relevant to the FCC's decision. Our approach uses
a simple statistical analysis to assess the relationship between the Commission's outcomes
and the factors which, according to the FECL, should have determined those resolutions.
The merger provisions, discussed in some detail in Section II, are governed by the FECL
and its Code of Regulations. The FECL states explicitly that structural factors, such as the
market share, cannot be the sole determinants of outcomes. In addition, it provides a list of
factors that must also be considered. Thus, the mergers provisions allow the FCC to
consider several factors while deciding upon the outcome of a merger case.
What makes the FCC prohibit a merger? How important is the information contained in the
mergers provision for the final decision taken by the FCC? Has the FCC been more likely
to judge against a merger based on a decision that assigns more weight to a particular
economic factor? These are all relevant questions. In the present paper, we address these
2
issues with the help of econometric analysis based on 239 Mexican merger cases occurring
between 1997 and 2001.
Several studies, based on country-specific information, have endeavored to identify the
determinants of the antitrust enforcement authorities' decisions. In the United States for
instance a number of papers, including those by Posner (1979), Katsmann (1980) and Coate
et al. (1990, 1992), are concerned with the case selection process. With the use of a probit
model, Coate et al. (1990, 1992) found evidence that political variables, such as pressure
from the Congress, have an influence on merger decisions. The same study shows that the
authority did not consider efficiency gains while evaluating a would-be merger. The
authors also found that lawyers at the Federal Trade Commission (FTC) had more
influence than the authority's economists. More recently, Coate and Kleit (2005) modeled
the merger review process in which the FTC interacts with the acquiring firm to determine
the outcome of antitrust regulation. They analyzed what sort of factors influence in the
firm's decision to litigate, fold or settle. Their main finding was that the efficiency variable
played a significant role in the firm's decision-making process. Potential efficiencies are
positively linked with the probability of a firm engaging into a litigation process if the FTC
challenges the merger. Moreover, the authors found that firms deterred from fighting the
FTC by the potential of negative impacts on their reputations. In the United Kingdom, Weir
(1992, 1993) uses a probit model to evaluate the relationship between the resolutions
reached by the Monopolies and Mergers Commission (MMC) and the criterion set out in
the statutory "public interest" test, such as prices and quality, cost reduction, new entry and
foreign trade among the others. Weir shows that very few of the issues which are part of
the "public interest" appear to influence the Commission's decision. For example, the
author found that mergers are more likely to be allowed if they do not affect either
competition or prices. However, potential benefits, such as greater employment or
increased exports, do not consistently help the would-be merged firm. Davies et al. (1999)
applied the same binary approach to 73 monopoly cases handled by the MMC finding that
the Commission's decision is greatly explained by market shares of the participating firms.
Khemani and Shapiro (1993) found that the Canadian antitrust authority has applied the
merger guidelines in a consistent manner. Bergman et. al. (2005) used a logit model to
3
analyze merger decisions in the European Commission finding results similar to those
reported by Khemani and Shapiro (1993). Kouliavtsev (2005) studies the effectiveness of
anti-merger relief in the United States as an outcome of bargaining game between the
antitrust agency and parties to the merger. Kouliavtsev finds that the structural relief
(divestiture) depends on the extent of merger-specific efficiencies, the anticompetitive
potential of the merger, and the hostage effect facing the merging firms, as well as the
degree of media coverage of the case, and partisan composition of the Congress. There is a
recent literature lead by Coate (2005a, 2005b) that founds that in addition to the standard
structural variables, the FTC appears to make extensive use of factual information on "hot
documents", "event studies", and "validate customer complaints" while taking decisions.
Finally, to the best of our knowledge, no formal quantitative study had focused on the
decision process undertaken by the Mexican FCC.
The rest of the paper is structured as follows. Section II provides a brief definition of the
merger guidelines determining the FCC decision process; this section also explains how the
dataset was constructed and shows the mean values of the variables used in the
econometric analysis. Section III discusses the research design and explains the advantages
of the two discrete choice models used. Section IV presents the results of the ordered and
multinomial logit models. Finally section V offers some general conclusions.
II. Mexican Competition Policy
2.1 Merger Guidelines1
The FCC enforces the FECL, including those laws applicable to mergers. The FCC
decision process concerning the resolutions on possible mergers consists of a two stage
procedure. In the first stage, an economic and juridical report is prepared by the
Commission's staff members. At the end of each report, the staff members produce an
informal recommendation stating whether the merger should be rejected, conditioned or
passed without any commitments. The report is based in turn on data submitted to the
1For further details, see the FECL and the Code of Regulations of the same law.
4
Commission by would-be merging firms, as required by the FECL, and on information
developed independently by FCC staff lawyers and economists. In the second stage, the
Commission takes formal action through a majority vote of the sitting commissioners
(ordinarily five). The commissioners carefully review the report and vote whether or not to
challenge a merger. Article 39 of the FECL allows the potential merger entity to appeal the
Commission's decision through an institutional device called "reconsideration resource."
However, this device is an institutional one and only the FCC can review and ratify, modify
or revoke the original resolution.2 Moreover, the merger firms can make use of a legal
process called "juicio de amparo" (judicial review).3
The primary objective of FECL is to protect and/or enhance economic efficiency.4 This
topic is evident in the merger provisions of the FECL. The criterion specified in the statute
states that "The Commission shall challenge and sanction those concentrations which
objective or effect is to diminish, damage or deter competition and free access to equal,
similar or substantially related goods and services." The approach adopted is similar to that
employed in the United States under the Merger Guidelines published by the Antitrust
Division, Department of Justice [1984]. Essentially, to diminish damage or deter
competition is considered to occur when a merger enables the firm(s) to, unilaterally or
interdependently with others, implement market power. In other words, competition is
hampered when a firm or group of firms can unilaterally set prices or substantially restrict
supply in the relevant market. Focus is normally placed on the ability of the firms to
influence price or intends to unjustifiably displace competitors; however, other aspects of
competition policy, such as efficiency gains, variety, service, and advertising, are also
considered where applicable.
2In practice, most of the "reconsideration resources" have been ratified.
3The FCC reports that nearly one in eight of its non-approving decisions ends up being blocked by an
amparo. It is important to mention that unlike in the US, the District Local Court can challenge the legality or
constitutionality of the Commission's decision, but not the essence of the resolution.
4For a detailed discussion of the FECL, see Levy (2000).
5
Concentration analysis, based on the Herfindahl-Hirschman (HHI) and Dominance (DI)
indexes are probably the guideline's best known aspects. The guideline's concentration
criterion establishes the following index classifications:5
1. Where the post-merger HHI index increases less than 75 points or is less than 2,000
points, a merger has a low probability of being blocked. Thus, a HHI index less than 2,000
is a safe harbor; mergers falling below that level will rarely be blocked.
2. If the post-merger DI reduces or is less than 2,500 points, a merger has a low
probability of being blocked. Thus, a DI index less than 2,500 is a safe harbor.
The Law states that any factor that the Commission deems appropriate, given the nature of
the relevant market, e.g. sales indicators, number of customers or productive capacity and
so on, will be used as the input while constructing the concentration indexes. Furthermore,
the FECL sets out a list of factors that, in addition to concentration indices, should be used
to determine whether competition has been lessened substantially. Factors listed are: 6
1. Actual or possible effective import competition;
2. Availability of substitutes;
3. Any barriers to entry including financial costs, amount of the required investment,
and regulatory control over entry, and any effect of the merger on such barriers to entry;
4. Share equity of the firm(s) or agents involved in the merger on other firms
participating directly or indirectly in the relevant market or in related markets;
5See Official Journal of the Federation (OJF), July 24th 1998. The HHI index, which is equal to the sum of
the squares of the market shares, is well known in the literature and extensively used in many antitrust
authorities around the world as the first screen for merger approval. For example, the Horizontal Merger
Guidelines of the Department of Justice (DOJ) and the Federal Trade Commission (FTC) establish a
presumption of illegality when a post-merger HHI exceeds 1,800 and the merger-induced change in the index
exceeds 100 points. Expressing the shares in percentage terms, the maximum value the HHI can attain is
10,000 in a situation where all supply is concentrated in a single firm, and the minimum value is 10,000/N
when all firms have equal market shares. In Mexican merger control the FCC also relies on the so-called
Dominant Index (DI) as a first screening device. For a description of DI index, see Appendix 1.
6These factors are not mutually exclusive nor arranged in order of importance.
6
5. Priori participation of the merged entity in the relevant market or related markets;
and
6. Evaluation of possible efficiency gains by the merged firm. Such efficiency gains
including economies of scale and scope, significant reduction of administrative costs,
transfer of production technology and lowering of production or costs derived from the
expansion of an infrastructure or distribution network.
Thus, the merger provisions not only specify that concentration cannot be the sole criteria
for determining outcomes, but also provide a list of additional factors to be considered.
However, the FECL and Merger Guidelines provide no guidance as to the relative
importance of each factor, nor any instructions as to how they ought to be weighted relative
to market concentration or market share.
2.2 Data Construction
We constructed a dataset from different sources including: the official public resolutions
(merger decisions) produced by the FCC and published in the Gaceta de Competencia
Económica, Annual Reports and indirect sources, such as specialized magazines.7 The FCC
considered a total of 350 cases covering the period from April 1997 to December 2001.
Nevertheless, our dataset includes only those cases having complete information; therefore
the final sample contains a total of 239 cases which represent the great majority of
horizontal mergers examined by the FCC during this period. Each case file was carefully
read by the authors in order to extract and classify the necessary information.8
7An alternative to get accurate information is through the internal files that include the economic report made
by the Commission's staff members. Although we made a formal approach with the Commission's officials,
we were not able to obtain access to such information, as it is classified by law as confidential. Actually, this
becomes a restriction to build information on mergers before 1997; the first official publication of merger
resolutions was not before April 1997. Coate et. al. (1990) were the only scholars able to build data from the
internal files of the FTC by agreeing not to publish the data.
8The complete dataset used in this study is available at:
http://www.anahuac.mx/gof/index.php?IDPagina=Avalos%20Brachocv
7
A large number of qualitative variables were recorded in an effort to capture the factors that
determined the Commission's decision. The variables were recorded in such a way to
solely reflect the judgments of the Commission's officers. After the evaluation a merger
could be allowed, conditioned or blocked.9 The distribution of cases according to the FCC
decisions is shown in Table I. The great majority of the cases, almost 80 per cent, were
allowed while quite a few of them (less than 5 per cent) were blocked.
Table I: Variable Definition and Mean Value by Resolution
Variable Allowed Conditioned Blocked
Number of Cases 188 40 10
Percentage 79 % 16.7 % 4.1 %
Concentration Variables (H)
HHI: Herfindahl index 1,534 2,190 2,811
DI: Dominance index 1,948 2,978 4,209
Variables explicitly stated in the
merger provisions (Z)
HIST: prior participation in the relevant
market 0.048 0.075 0.200
EQ: equity share 0.005 0.075 0.400
EF: efficiencies present 0.016 0.100 0.100
IMP: import competition present 0.238 0.200 0.100
EB: entry barriers 0.174 0.400 1.000
Variables not present in the merger
provisions (X)
MKT: combined market share < 25% 0.460 0.225 0.100
FRG: foreign firm present 0.873 0.700 0.500
* All set of variables (H,Z,X) are defined over the relevant market which may be local, regional, or
national.
9The FCC can impose three kinds of conditions on a merger: preventive, contractual and structural. A
preventive condition could be the continuous monitoring of the dominant firm in the relevant market for a
specific period of time. The contractual condition practically leaves market structure unchanged; it only
affects ancillary agreements associated with the merger. Initially, the structural condition implied the banning
(partially or totally) of the proposed merger. With the reform of the Competition Law in 2006, the structural
condition takes the form of requiring the dismantling of some of the joint actives of the would-be mergers as
a pre-condition for approving the merger.
8
Table I shows mean values by resolution for three groups of variables:10 concentration
variables containing the Herfindahl (HHI) and Dominance (DI) indices; variables explicitly
stated by the FECL as being relevant to the ultimate decision (as explained in the previous
section); variables that are not present in the merger provision but were nevertheless
mentioned in the FCC documents and hence might be relevant in the decision process. It is
important to notice that the mean value of all three groups of variables differ across the
three outcomes, suggesting that a dichotomous decision process--allow versus issue
(condition or challenge)--might be misleading.
As it is shown in the top part of Table I, not surprisingly, conditioned and blocked mergers
took place within markets that exhibited higher degree of concentration as measured with
the HHI and DI. The average HHI was around 600 points higher between allowed and
conditioned cases and between the latter and blocked. The second and third group of
variables shown in Table I are binary ones. These variables were created case by case,
where a value of one was recorded when the FCC's report indicated that the factor was
relevant in the decision process. HIST, EQ, EF, IMP, and EB are all factors explicitly listed
in the Law and Merger Guidelines. HIST indicates if one of the firms involved in the
merger has been operating previously in the relevant market. EQ is a dummy variable
taking the value of 1 when one of the merging firms has a patrimonial relationship (or
equity share) with a third firm within the relevant or related market(s), and zero
otherwise.11 EF indicates if efficiency gains are present as stated by the would-be merging
firms. The FECL and the Merger Guidelines established the evaluation of efficiency gains
10Some economic variables either mentioned by the economic theory or that are used by antitrust authorities
were excluded from the analysis. The reason for doing this is because they are not present in the FECL and
the Code of Regulations or because some factors were simply impossible to code. For instance, the number of
significant rivals is a relevant factor in the mergers evaluation done by the Federal Trade Commission in USA
(see Coate, 2005b). Nevertheless, in the Mexican case, this factor doesn't seem to be a relevant one,
appearing in only few file cases. This lack of information impedes us to form a variable with the number of
significant rivals.
11The rationale behind the inclusion of EQ as a determinant of the FCC's resolution is rooted in the Mexican
antitrust law. The law is concerned with possible dominance practices by one agent that has a significant
patrimonial presence in two (or more) markets that are vertically integrated.
9
as a factor that may save a merger from being blocked.12 Merger-specific efficiency gains
are inferred from the information made available to the Commission.13 IMP reflects the
Commission's perception on the relevance of import competition. EB denotes if the
Commission finds high entry barriers in the relevant market. No quantitative measures of
these entry determinants are provided, leaving one with the Commission's view if entry
barriers are present or not. As we would have expected, the value of HIST, EQ and EB
increases as we move from left to right in Table I, indicating that there is a higher
probability of being issued (either conditioned or blocked) as the value of these market
concentration indicators increase.
The last group of variables MKT and FRG are the "other factors" that were regularly
mentioned in the documents, but are not explicitly established in the merger provisions.
FRG indicates if a foreign firm is involved in the merger. MKT is equal to one when the
FCC mentioned that the post-merger combined market share of the participating firms is
less than or equal to 25 per cent. We use 25 per cent as the critical level, since this is the
level below which dominance is presumed not to exist, while 30% is the level below which
dominance is presumed not to exist (Bergman et al., 2005, p 726-727).14 While MKT is an
indicator variable capturing market structure, FRG has no a-priori effect upon the FCC's
decision. However, simple descriptive statistics show that when a foreign firm was
involved, most of the mergers were allowed.
12According to the law, the efficiency gains must be accredited by the firms involved in the would-be merger.
13While it would be preferable to have a direct estimate of the value of the cost savings, this information is
not available. Instead, we define this dummy variable that takes a value of one when the efficiency argument
was mentioned on the FCC's resolutions.
14Notice that MKT and HHI, although related, are conceptually different. While HHI captures the
concentration of the relevant market as a whole, MKT picks up the relative importance of the firms involved
in the merger. For some authors, the 25 percent share cut-off is to low. For instance, the US Merger
Guidelines establish that the dominant firm cannot be presumed when the combined share of the merging
parties is less than 35 percent (see US Merger Guidelines Section 2.22).
10
The variables mentioned above are evaluated in quantitative terms using discrete choice
econometric models. After considering the available evidence, the decision making process
goes as follows:
i. The merger could be approved as initially planned by the involving firms,
we called these cases allowed;
ii. In some specific circumstances the merger could be monitored for a period
of time or restructured by the FCC in order to alleviate concerns regarding substantial
lessening of competition, we called these cases conditioned;15 and
iii. The merger, in whole or in part, could be blocked.
III. Empirical Strategy
Before outlining our empirical strategy, it is important to mention that the paper does not
aim to develop a structural model describing the FCC's decision process. Our focus lies on
finding the statistical relationship between those factors that the FCC claims to be taking
into account while evaluating a would-be merger and to Commission's final decision.
Let us define an unobservable continuous latent variable Vj , which determines the
probability of falling into each of three possible outcomes: allowed, conditioned or
blocked; define H as an index capturing market concentration; let Z be a matrix whose
columns contain the variables explicitly stated in the merger provisions; and X a matrix
formed of variables not present in the merger provision, but that could potentially influence
the Commission's final decision. We model the probability of observing outcome "s",
15Although the Code of Regulations and the Internal Manual Criteria process of the FCC establishes that it is
possible that in some specific circumstances the merger could be monitored for a period of time (second point
above), so far the FCC has implemented only once this criterion. We therefore merged together the possible
monitored resolutions with the restructured ones, into a single outcome which we called conditioned.
11
Pr(j=s), as a function of market concentration (H), the variables explicitly stated in the
merger provisions (Z), and those ones not stated, but could influence the decision process
(X). Assuming that the latent variable is a linear function of elements H, Z, and X, Vj can
be modeled in the following way:
Vj = + H + + j + j (1)
j j j
Where j is a random component and j, j, j, and j are constant parameters. The
probability of observing outcome "s" is a function of the deterministic part of (1),
Pr( j = s) = F(Vs ) where function F(V*) is defined by the cumulative distribution function
*
of j. Therefore, the model used will depend on the assumption made about the distribution
of the random component, j. For simplicity, in our two models, we assume that j are i.i.d.
with extreme value distribution, i.e. they follow a logistic distribution function.
To allow for more flexibility while analyzing the data, we test two different model
specifications. In the first one, we assume that a set of constant slopes across outcomes
determines the probability of falling into any of the three categories; controlling for
observables, the differences across outcomes are captured by two cut-off points or
intercepts. Therefore, we estimate an ordered logit. In the second specification, we allow
for full parameter heterogeneity across outcomes, where intercepts and slopes in
specification (1) change between categories; therefore, we estimate a multinomial logit.
3.1 Constrained Model: Ordered Logit
Suppose that the parameters defining (1) do not differ across outcomes and that, controlling
for observables, the differences in probabilities for each outcome are captured by a shift in
an arbitrary constant, c. Therefore, the probabilities of observing each of the three
outcomes will be given by the following equations:
12
Pr(allowed) = F(V*)
Pr(conditioned) = F(V* + c) - F(V*) (2)
Pr(challenged) =1- F(V* + c)
Given the assumption that the distribution of j follows a logistic form, model (2) is the
ordered logit. Constant c is a parameter to be estimated within the model and its
significance can be interpreted as indicative of a correct model specification (Maddala,
1983).
3.2 Unconstrained Model: Multinomial Logit
A less restrictive specification will allow the parameters in (1) to differ across outcomes. In
this case the selection criteria will not be constraint to follow an ordered structure.
Therefore, the FCC's decision will be characterized by the following expression:
Y = s Vs > max(Vj) j=1,2,3 (3)
js
Equation (3) implies that those parameters defining Vj are the outcome of a probability-
maximizing process. The probability, Pr(Y=s), will depend on the assumption we make
about the error term (j). Assuming the residuals, j are independently and identically
,
distributed with type I extreme-value distribution give rise to the well-known multinomial
logit model:
Pr(Y = s) = exp(s + sH + s + s)
exp( (4)
j+ H + +j)
j j
j
13
Notice that we are not assuming any structure on the underlying process governing the
FCC's decision; we are simply modeling the probability of falling into any of the three
outcomes.16
A sensible criticism of our empirical strategy could question the advantage of adopting two
different models instead of only one or the superiority of our two preferred specifications
over other discrete choice models. For instance, if the Commission's final decisions are
indeed characterized by a preferred, ranked or ordered criterion, then the use of a
multinomial logit (which ignores ordering of the outcome variable) could be misleading.
Nevertheless, the use of both models (ordered and multinomial logit) to analyze the same
problem is justified on the basis of the uncertainty about the ordered nature of the
outcomes.17 Furthermore, it might also be the case that a simple binary model (allowed
versus not allowed) could capture, in a better way, the relationship between explanatory
variables and the Commission's decision. To explore this possibility, we estimated a logit
where outcomes "conditioned" and "blocked" were merged together. However, the ordered
logit showed a better fit than the logit model with significant cut-off points. Moreover, the
Wald tests for parameter homogeneity across outcomes "conditioned" and "blocked" was
rejected for two of the RHS variables included in our model supporting a multinomial
model as opposed to a binary one. Finally, we explored the possibility of a nested structure
where the FCC first decide on whether to allow or issue a merger and then, given that a
case was issued, decided to condition or challenge it. This model showed poor results with
inclusive values being not significant. Therefore, we believe that the ordered and
multinomial logit models were the best ones to describe the data, both in statistical and
intuitive sense.
16The ordered and multinomial logit models can also be interpreted as the outcome of a utility-maximizing
process, where the selected outcome maximizes the indirect utility of a rational agent. See McFadden (1974,
1984).
17As stated by Long (1998): "If there is any question about the ordinality of the dependent variable, the
potential loss of efficiency in using models for nominal outcomes is outweighed by avoiding potential bias."
14
IV. Results
Two different specifications were estimated for each of the models. In the first one, a
version of equation (1) containing only H and Z determined the FCC's decision and in a
second one, the full set of independent variables (H,Z,X) was used. The purpose of having
two specifications is to uncover the possible influence of X upon the relationship between
Pr(j=s) and (H,Z). In other words, if the impact of a variable explicitly stated by the
Commission (j in equation 1) changes once we control for factors not explicitly stated by
the Commission (X), then we can conclude that not taking into account X will lead to an
incorrect (biased) estimation of the influence of (H,Z) upon the Commission's decision. In
all regressions, the log of HHI is used as the concentration indicator variable however none
of our results change if DDI had been used.18 All our results are based on heteroskedastic-
robust standard errors.
4.1 Ordered Logit Results
Estimations of model (2) are presented in Table II. The first column shows the results of
the model excluding matrix X. The cut-off points for both thresholds are positive and
significant, suggesting the presence of a dependent variable with an ordinal structure. As
we expected a-priori, HHI, EQ, and EB are positively related with a higher probability of
being issued. When the merger occurs in a highly concentrated market, or if it shows
market entry barriers, or if one of the participants holds a large proportion of the equity
shares, the case has a higher probability of being issued (either conditioned or blocked).
The presence of import competition, IMP, is only marginally significant and with the
expected negative sign, i.e. mergers occurring in import-competing markets face a lower
probability of being issued. Contrary to what the Commission explicitly states, the presence
18The reason for entering HHI in log form is to account for potential non-linearities between the FCC's
decision on a merger and the level of market concentration. Including HHI in levels rather than in logs does
not change the results presented in Tables II and III, although the models showed a better fit when HHI was
entered in logs.
15
of efficiency gains (EF) is not significant at conventional levels, having no apparent
influence on the Commission's decision.19
The value of the coefficients derived from the model cannot be interpreted directly; they
are simply telling us the effect of the independent variables on the index determining the
latent variable, V. Since we are interested in the marginal impact upon the probability of
observing each outcome, we have to carry out the following transformations:20
Pr(allowed | x)
xj = -(x'^)^j
Pr(conditioned | x)
(5)
xj =[(-x'^)-(c- x'^)]^j
Pr(challenged | x)
xj =(c- x'^)^j
where x is a matrix of independent variables in (2) and is the logistic p.d.f. The marginal
effects for each outcome are shown in the bottom part of Table II. For a continuous
independent variable, say x1, its marginal effect can be interpreted as the change in the
probability of observing an outcome given an infinitesimal change around the mean of x1
while setting all other independent variables at their mean value. For a discrete independent
variable, say x2, the "marginal" impact is the change in the probability given a change in x2
19Similar results were obtained by Coate and McChesney (1992), and Weir (1992, 1993).
20As a matter of fact, contrary to the sign of the estimated coefficient, the marginal effect of a particular
coefficient will differ across the ordered logit outcomes. From expression (5) we see that the sign of a
particular coefficient will oppose the probability of it being allowed. By the same token, the sign of the
estimated coefficient and the probability of being blocked will be the same. However, nothing can be said a
priori about the marginal effect of xj upon the probability of being conditioned. Therefore, to interpret the
effects of the coefficients of an ordered logit on the three potential outcomes, it is necessary to compute the
marginal effects (Greene (2003), pg. 738.)
16
from 0 to 1 keeping all other RHS variables at their mean.21 In our case, all marginal
effects but the HHI are the outcome of discrete changes in the independent variables.22
For the three significant variables HHI, EQ and EB, the marginal effect showed high values
for outcomes "allowed" and "conditioned." Controlling for everything else, a percentage
increase in the HHI reduces in more than 0.14 percentage points the probability of a merger
being allowed and at the same time, increases the probability of it being conditioned in
more than 0.12 percentage points, ceteris paribus. On the other hand, for a case to be
blocked, the commission will need to deem more important the presence of equity share
holders--of other firms within the relevant market (EQ)--where the would-be merger is
taking place. Everything else being equal, the presence of this effect in a merger case
reduces its probability of being allowed by more than 54 percentage points.
Table II: Constrained Model: Order Logit
V = V (H,Z) V = V (H,Z, X )
ln(HHI) 1.03*** 1.20***
[3.52] [2.95]
HIST 0.54 0.85
[0.79] [1.23]
EQ 2.54*** 2.24**
[2.73] [2.08]
EF 0.52 0.76
[0.86] [1.27]
IMP -1.07* -1.11**
[1.80] [2.08]
EB 1.31*** 1.20***
[3.41] [3.22]
MKT -0.11
[0.17]
FRG -1.48***
21In the presence of several binary RHS variables, it is possible to estimate the marginal effects setting all
other RHS dummy variables at their most likely binary value. However, it is not clear that this method is
preferred over the conventional one; the expected value of a binary variable is still its simple mean and this
can also be interpreted as a probability.
22Notice that the significance of the marginal effects though associated with is not necessarily equal to the
significance of the estimated coefficient (). In our case, all those variables that showed a significant
coefficient had also a significant marginal effect; therefore t-statistics for marginal effects are excluded from
Table II.
17
[3.16]
Cut-Off
C1 9.25*** 9.28***
[4.29] [3.02]
C2 11.68*** 11.85***
[5.41] [3.85]
Marginal Effects
Allowed Conditioned Blocked Allowed Conditioned Blocked
ln(HHI) -0.142 0.125 0.017 -0.155 0.139 0.016
HIST -0.087 0.075 0.012 -0.141 0.124 0.017
EQ -0.545 0.390 0.154 -0.470 0.375 0.095
EF -0.085 0.073 0.011 -0.125 0.110 0.015
IMP 0.122 -0.108 -0.014 0.117 -0.106 -0.012
EB -0.223 0.190 0.033 -0.192 0.169 0.023
MKT 0.014 -0.013 -0.001
FRG 0.256 -0.222 -0.034
Observations 239 239
Pseudo R2 0.20 0.24
Notes: (1) Robust z-statistics in brackets; *, ** and *** represent statistical significance at 90, 95 and 99
percent level of significance, respectively. (2) Marginal effects of binary variables measure the effect of a
change from 0 to 1.
In the upper right part of Table II we present the results of the model with all three
elements of equation (1), i.e. H, Z and X. The two variables included in X (MKT and FRG)
affect the FCC's decision in a significant way; moreover their high explanatory power is
shown by the increase in 4 percentage points in the pseudo-R2. Once we control for
elements in X, variable EQ loses significance, indicating a possible correlation between this
variable and elements included in matrix X.23 The results show that when the firms
involved in the merger concentrate a low market share (less than 25 per cent), their case is
more likely to be allowed than if they concentrate a high share of the market. More
importantly, the marginal effects show that, contrary to what is explicitly stated in the
mergers provisions, one of the strongest determinant affecting the Commission's decision
is the presence of a foreign firm in the merger. Everything else being equal, foreign firms
participating in a Mexican merger have a probability 25.6 percentage points lower than
mergers involving only Mexican firms of being issued (either conditioned or blocked).
23Although some regressors show some degree of correlation, the Variance Inflation Factors (VIF) test for
multicollinearity didn't show a serious problem.
18
4.2 Multinomial Logit Results
As it was mentioned before, model (2) is restricting the parameters to be the same across
outcomes and it is assuming that the dependent variable follows an ordinal structure. This
section shows the results from model (4) which relaxes these two constraints. The results of
the multinomial logit model are presented in Table III.24
Outcome "allowed" is taken as the base category; therefore, the coefficients in the upper
part of Table III are interpreted as the effect on the likelihood of observing a particular
outcome compared to observing an "allowed" case. Let us first concentrate in the results of
a model specification with only variables explicitly mentioned in the mergers provisions
(H,Z), included as regressors (left part of Table III).
The qualitative effect of HHI does not change with the new specification; mergers
occurring within more concentrated markets are more likely to be issued. However, the
multinomial logit specification shows that the effect of HHI varies substantially across
outcomes.25 Notice that the marginal effects of HHI across outcomes in this unconstrained
model are practically corroborating our previous findings, i.e. the probability of being
conditioned rises substantially when a merger is taking place in a concentrated market.
Table III indicates that the significance of variables EQ, IMP and EB shown by the ordered
model was indeed coming from the differences between the probabilities of a case being
allowed versus being blocked.26 By allowing the parameters to differ across the two
outcomes, we can see that these three variables do not have a significant effect on the
probability of observing a "conditioned" outcome. Quite the contrary can be said about the
24The two tests undertaken to evaluate the IIA assumption showed opposing results with the Hausman test
supporting the IIA and the Small-Hsiao strongly rejecting it.
25This type of insights can only be obtained when we allow for full parameter heterogeneity; having at least
one coefficient that differs across outcomes justifies our multinomial specification.
26We dropped variable EB from the "blocked" equation due to a lack of variation of this variable within the
blocked cases. As it is seen from Table I, all the blocked outcomes occurred in the presence of EB. One
should interpret EB as being a highly significant predictor of a blocked outcome.
19
presence of efficiencies (EF). Unexpectedly, the presence of market efficiencies has a
positive and significant impact on the probability of being conditioned relative to being
allowed.
Let's turn now to the marginal effects of the multinomial logit. We applied the following
transformation to the estimated coefficients:
Pr(Y = s | x) 3
(6)
x = Pr(Y = s) - Pr(Y = k)k
j
k=1
where s,k = (allowed, conditioned, blocked) and Pr(.) is given by equation (4). The
marginal effect results are presented in the bottom part of Table III. Concerning the
estimation of model V = V(H,Z), the most important result is the one on EF. As we
mentioned above, in the ordered logit estimations efficiency gains, although stated as one
of the most important determinants of the FCC's decision, was not significant (see the right
column of Table II). However, by running a separate equation for each outcome, we are
able to identify a significant and counter-intuitive positive coefficient of EF on the
probability of observing a "conditioned" merger relative to observing an allowed one.
Moreover, the marginal effect of EF is the largest among RHS for the conditioned
outcomes,27 the presence of efficiency gains in a would-be merger increases its probability
of being conditioned (relative to being allowed) by more than 26 percentage points.
The right part of Table III shows the results of the multinomial logit including the elements
in matrix X as explanatory variables [model V = V (H, Z, X ) ]. As it was the case with the
ordered model, the variables in X entered significantly in the regression and increased the
explanatory power of the model (the pseudo R2 passed from 0.19 to 0.23), confirming the
importance of variables in X in the FCC's ultimate decision. The other important result is
the effect of the presence of a foreign firm which remains significant having a negative
effect on the probability of observing either "conditioned" or "blocked" outcomes. When a
27The marginal effect of EQ is larger, though its estimated coefficient is not significantly different from zero.
20
foreign firm is taking part in a merger, the probability of that case being issued is reduced
by around 22.7 percentage points.
Surprisingly, the coefficient on EF hardly changed. Once the variables in X were included,
EF turned out to be the single most important variable determining a conditioned outcome.
This result shows that, contrary to the FCC's objective, when economic efficiencies are
present--as it is understood from the staff memorandum, with the information given by the
firms participating in the would-be merger--the commissioners are more likely to
condition a merger than to allow it ceteris paribus. There are two ways in which the
apparent anomaly behind the positive coefficient on EF can be explained.28 First, a would-
be merging entity that is aware of potential increases in market concentration as a result of
the merger, may be inclined to make larger efficiency claims to try to counter-act its effect
on concentration. Second, the authorities may account for the counteracting effect just
explained, and hence be particularly skeptical about ambitious claims of efficiency gains. If
either of these hypotheses (or both) is correct, the final outcome would be a positive
correlation between efficiency gains and the probability of being conditioned. This result
might reflect that the Mexican merger policy involves the delicate balancing of
anticompetitive effects against possible efficiency gains. In assessing this trade-off, the
antitrust authority often relies on very limited and imperfect information. Not only is the
evaluation of market power inherently imprecise, but the merging parties typically have
better information about potential efficiency gains than the regulator.29 Of course, the
merger review process is designed to extract as much information as possible from the
parties, but it is reasonable to assume that some asymmetries remain.30
28We thank an anonymous referee for valuable comments on the possible intuition behind this result.
29In practice, most mergers claim to achieve some kind of efficiency gains or "synergies," i.e. some form of
cost reduction or quality improvement. White (1987) and Fisher (1987) argue that efficiencies gains are
typically easy to claim, but hard to prove. Fisher (1987) argues in favour of very high standards for proving
actual efficiencies, based on several examples where efficiencies gains were claimed but they were not
materialised.
30Efficiency gains from horizontal mergers are a relevant policy issue to date. For example, Roller and
Verboven (1999) discuss this issue extensively and point out that there is a debate within U.S. and EU
antitrust agencies whether to include a more precise treatment of efficiency defence in their merger
21
Our results suggest that the FCC decision is not entirely based on the mergers provisions.
Moreover, when ever the merger provision was stating the presence of "other economic
factors" (matrix X) the Commission based its decision heavily on this factors. Among the
most important factors not explicitly stated in the merger guidelines, was the presence of a
foreign firm (or firms) in the merger. We found robust results showing that the presence of
a foreign firm in the merger increases the probability of a case being allowed. These results
are robust to model specification and to the assumption made about the ordinality of the
outcome variable.
Table III: Unconstrained Model: Multinomial Logit Results
V = V (H,Z) V = V (H,Z, X )
Allowed Conditioned Blocked Allowed Conditioned Blocked
Constant -8.47*** -19.87*** -7.85** -17.97***
[3.43] [4.92] [2.44] [3.53]
Ln(HHI) 0.93*** 2.20*** 0.99** 2.20***
[2.73] [4.03] [2.31] [3.23]
HIST 0.34 1.35 0.63 2.24*
[0.37] [1.41] [0.73] [1.93]
EQ 2.10 5.80*** 1.90 5.05***
[1.56] [3.52] [1.19] [2.60]
EF 1.36** 1.42 1.42** 1.78
[2.12] [1.00] [2.15] [1.19]
IMP -0.64 -4.16** -0.76 -4.20**
[1.15] [2.36] [1.48] [2.28]
EB 0.53 - 0.43 -
[1.25] [1.00]
MKT -0.20 -1.16
[0.32] [1.18]
FRG -1.30** -2.43***
[2.68] [2.61]
regulations or not. Among their conclusions the authors mentioned that the European Commission's Merger
Regulation has to be reinterpreted or amended in order to improve merger policy regarding the account of
efficiency gains.
22
Marginal Effects
Allowed Conditioned Blocked Allowed Conditioned Blocked
ln(HHI) -0.134 0.118 0.016 -0.132 0.122 0.010
HIST -0.064 0.045 0.019 -0.117 0.087 0.030
EQ -0.647 0.137 0.510 -0.502 0.245 0.257
EF -0.263 0.249 0.014 -0.269 0.255 0.014
IMP 0.089 -0.071 -0.019 0.092 -0.080 -0.011
EB -0.075 0.076 -0.001 -0.057 0.057 0.000
MKT 0.029 -0.024 -0.005
FRG 0.227 -0.204 -0.023
Observations 239 239
Pseudo R2 0.19 0.23
Notes: (1) Robust z-statistics in brackets; *, ** and *** represent statistical significance at 90, 95 and 99
percent level of significance, respectively. (2) All marginal effects of binary variables measure the effect of a
change from 0 to 1. (3) "Allowed" is the base category.
V. Conclusions
Based on a newly created dataset with information on Mexican mergers, we estimate the
probability that a would-be merger falls into any of three possible resolutions reached by
the Mexican FCC: allowed, condition or blocked. Given the discrete nature of the problem
and the unknown ordinality of the dependent variable, an ordered and a multinomial logit
models were estimated. The results indicate that, overall, the FCC's decisions are in fact
reflecting a consistent application of the mergers provisions outlined in the Federal
Economic Competition Law and expanded in the Merger Guidelines. Three variables
included in the merger's provisions have a significant and robust impact in the
Commissions' decision: market concentration, entry barriers and the equity share. A model
containing just the variables explicitly mentioned in the mergers provisions would correctly
predict 8 of every 10 Commission's decision.
23
An alleged positive aspect of the Merger Guidelines has been their role in enhancing
economic efficiency through the FCC's decision. Thus arguing that a merger should
improve efficiency, the presence of such gains does not appear to benefit the bidding firm,
on the contrary, its probability of being condition increases. Perhaps more surprising is the
influence of factors such as the presence of a foreign firm among the would-be mergers,
which do not appear explicitly in the merger provisions, but have, nevertheless, a
significant effect on the Commission's decision.
A comparison of the results presented in this study and those from the large literature
analyzing U.S. merger policy uncovers substantial differences in merger policy
implementation between Mexico and the U.S. These differences emphasize the potential
difficulties in achieving uniformity on criteria and coordination on this area. This is
particularly worrisome given the increasing degree of economic integration between the
two countries.
The paper represents a first attempt to understand the important process of implementing
regulation policies in developing countries. Although much more economic structure is
needed to perform an accurate assessment of Mexican regulation policies, we believe that
future research should increasingly rely on empirical analysis while pursuing this aim.
Appendix 1
The Herfindahl HHI index, equal to the sum of the squares of the market shares, is a well
known measure of market concentration that has been extensively used by many antitrust
authorities around the world. One of HHI's main properties is that its value unambiguously
increases when, ceteris paribus, a merger takes place. However, as shown by Farrell and
Shapiro (1990), some mergers can increase the level of competition rather than reducing it.
Under these particular circumstances, the HHI would misinform the policy maker. The DI,
on the other hand, may decrease following a merger between relatively small firms or a
24
small firm merging a relatively bigger one.31 Therefore, DI is better suited to measure
changes in the level of competition as a result of mergers occurring within relatively small
firms.
The DI can be expressed as:
s 4
i
DI = i
HHI 2
where si represents the market shares of firm "i", and HHI indicates the Herfindahl-
Hirschman index. As in the case of the HHI, the DI is bounded between zero and one (or
between zero and 10,000 if the shares are expressed in percentage terms). The main
proprieties of the DI are:
Property 1. The value of the dominance index is larger than or equal to the value of the
HHI with the equality holding only when all firms are of equal size.
Property 2. An output transfer from any one firm to the biggest firm will increase the value
of the DI index. In the opposite sense, also an output transfer from the biggest firm to any
other firm will reduce the DI.
Property 3. Any merger leading to a firm with more than half of the market increases the
value of the DI.
Property 4. If there is one firm with more than half of the market, any merger not involving
that firm reduces the value of the DI. This result is related with the threshold of the 50
percent market share established in the theory of IO. According to this criteria, any merger
that entails a market share lower than 50 percent, as first screening, is not anticompetitive.
31For a formal derivation and a discussion on the Dominance Index see García (1990).
25
Acknowledgement
The draft has benefited from thoughts and comments of a number of colleagues. However,
the authors would like to specially thank the useful comments made by Malcolm Coate,
Peter Holmes, Hans Timmer and seminar participants at the 33rd Conference of the
European Association for Research in Industrial Economics, Amsterdam. Data and
editorial assistance by Arturo Lamadrid and Amanda Bailey, respectively, are also
appreciated. The usual disclaimer applies.
References
Bergman, M., Jakobsson, M. and Razo, C. (2005) An Econometric Analysis of the European
Commission's Merger Decisions. International Journal of Industrial Organization, 23, 717-
737
Coate, M.B., Higgins, R.S. and McChesney, F.S. (1990) Bureaucracy and Politics in FTC
Merger Challenges. Journal of Law and Economics XXXIII, 463-483.
Coate, M.B. and McChesney, F.S. (1992) Empirical Evidence on FTC Enforcement of the
Merger Guidelines. Economic Inquiry XXX, 277-293.
Coate, M.B. (2005a) Empirical Analysis of Merger Enforcement Under the 1992 Merger
Guidelines. Review of Industrial Organization 27, 279-301.
Coate, M.B. (2005b) Economic Models in Merger Analysis: A Case Study of Merger
Guidelines. Potomac Working Paper in Law and Economics 05-04.
Davies, S.W., Driffield, N.L. and Clarke, R. (1999) Monopoly in the UK: What Determines
Whether the MMC Finds Against the Investigated Firms? The Journal of Industrial
Economics XLVII, 263-83.
Fisher, F.M. (1987) Horizontal Mergers: Triage and Treatment. Journal of Economic
Perspectives, 1, 23-40.
26
García, P.A. (1990) Un enfoque para medir la Concentración Industrial y su Aplicación para el
Caso de México. El Trimestre Económico 2, 317-341.
Greene, W.H. (2003) Econometric Analysis (Fifth Edition), Prentice Hall, New Jersey.
Katzmann, R.A. (1980) Federal Trade Commission. In: Wilson, J.K., (Ed.) The Politics of
Regulation, New York: Basic Books.
Khemani, R.S. and Shapiro, D.M. (1993) An Empirical Analysis of Canadian Merger Policy.
The Journal of Industrial Economics 41, 161-177.
Kouliavtsev, M. S. (2005) Some Empirical Evidence on the Effectiveness of Antimerger Relief
in the United States. Economic Inquiry XLIII, 370-384.
Levy, S. (2000) Observaciones sobre la Nueva Legislación de Competencia Económica en
México. In: Tovar, L.R., (Ed.) Lecturas en Regulación Económica y Polítca de
Competencia, pp. 167-179. México, D.F.: ITAM, Grupo Editorial Porrúa.
Long, J.S. (1998) Regression Models for Categorical and Limited Dependent Variables.
Advanced Quantitative Techniques in the Social Sciences Series, Sage Publications.
McFadden, D.L. (1974) The Measurement of Urban Travel Demand. Journal of Public
Economics 3, 303-28.
McFadden, D.L. (1984) Econometric Analysis of Qualitative Response Models. In: Griliches,
Z. and Intriligator M.D., (Eds.) Handbook of Econometrics, pp. 1396-1446. Elsevier
Science Publishers.
Maddala, G. (1983) Limited Dependent and Qualitative Variables in Econometrics,
Cambridge: Cambridge University Press.
Roller, L.H., Stennek J. and Verboven, F. (1999) Efficiency Gains from Mergers. CEPR,
Report for European Commission, DGII.
Posner, R.A. (1979) A Statistical Study of Antitrust Enforcement. Journal of Law and
Economics 13, 365-426.
27
Weir, C. (1992) Monopolies and Mergers Commission, Merger Reports and the Public Interest:
a Probit Analysis. Applied Economics 24, 27-34.
Weir, C. (1993) Merger Policy and Competition: Analysis of the Monopilies and Mergers
Commission's Decisions. Applied Economics 24, 57-66.
White, L.J. (1987) Antitrust and Merger Policy: A review and Critique. Journal of Economic
Perspectives 1, 13-22 .
28