# Local Labor Market Effects of Mergers and Acquisitions in Developing
Countries: Evidence from Brazil

Vítor Costa Economics PhD Candidate at Cornell University.

I use matched employer-employee records merged with corporate tax information
from 2003 to 2017 to estimate labor market-wide effects of mergers and
acquisitions in Brazil. Labor markets are defined by pairs of commuting zone
and industry sector. In the following year of a merger, market size falls by
10.8%. The employment adjustment is concentrated in merging firms. For the
firms not involved in M&As, I estimate a 1.07% decline in workers’ earnings
and a positive, although not significant, increase in their size. Most mergers
have a predicted impact of zero points in concentration, measured by the
Herfindahl–Hirschman Index (HHI). I spillover firms, earnings decline
similarly for mergers with high and low predicted changes in HHI. Contrary to
the recent literature on market concentration in developed economies, I find
no evidence of oligopsonistic behavior in Brazilian labor markets.

Keywords: Mergers and Acquisitions, Labor Market Concentration, Monopsony,
Oligopsony, Local Labor Markets, Developing Economies

JEL Codes: G34, J42, K21, L40, M50

##  1 Introduction

A growing body of research in recent years has pointed in the direction of
non-competitive behavior in labor markets (Card, 2022). The formalization of a
model where profit-maximizing firms internalize an upward-sloping labor supply
curve and, as a result, choose to hire fewer workers at a lower wage rate,
dates back to the 1930s (Robinson, 1933), but a renewed interest in the
subject was sparked by the debate around the reasons for the labor share
decline observed in developed economies (Autor et al., 2020). The rise of
firms that concentrate ever larger shares of industry sales in a globalized
market, the so-called _superstar_ firms, has launched many in the profession
into the empirical investigation of labor market imperfection related to the
increase in the size of employers. While the debate about the underlying
causes of the fall in the labor share of the GDP is far from settled (Grossman
and Oberfield, 2021), one cannot neglect the burgeoning literature pointing to
a negative relationship between labor market outcomes and employment
concentration, and proposals for policy intervention to protect workers
abound, especially within the reach of antitrust regulation (Naidu et al.,
2018; Marinescu and Hovenkamp, 2019; Marinescu and Posner, 2020).

Naturally, mergers and acquisitions (M&As) raise immediate concern about the
competitiveness in labor markets. By means of the consolidation of different
employers under the same ownership and management, mergers mechanically alter
the number of firms competing for labor services and, therefore, might in
principle, tilt the balance of bargaining power in an unfavorabe manner to
workers. The relationship between a lower number of employers and worse
workers’ outcomes relies on more than just intuition, and it has both
theoretical and empirical grounds. Building upon the monopsony model from
Robinson (1933), Boal and Ransom (1997) show that, similar to the case of
oligopoly, a model of _oligopsony_ à la Cournot generates wage markdowns that
decrease as the number of employers falls, i.e., wages represent a lower
fraction of the marginal revenue product of labor as employment gets
concentrated among fewer firms. Negative wage elasticities with respect to
employment concentration have been confirmed in various contexts 111See
Martins (2018); Lipsius (2018); Azar et al. (2019); Marinescu et al. (2021);
Azar et al. (2020); Devereux and Studnicka (2023); Dodini et al. (2023);
Benmelech et al. (2022); Rinz (2020); Bassanini et al. (2023).. Typically,
employment concentration is measured by the Herfindahl–Hirschman Index (HHI)
over firms’ workforce shares in local markets, sometimes defined by a
combination of geographical region and industry sector, or region and
occupational codes. This measurement is made possible by the use of linked
employer-employee administrative datasets.

In this paper, I investigate the local labor market effects of mergers and
acquisitions, and the role played by labor market concentration as a channel
of these effects. I combine two different administrative datasets from Brazil
that allow me to identify merged and acquired establishments, delineate local
labor markets, and causally estimate changes in employment, workers’ earnings,
and local concentration measured by the HHI. The findings consist of three
sequential steps. First, I show that null effects of the M&As on workers
earnings and market HHI cannot be rejected, while employment significantly
declines in markets that witness a firm consolidation event. Next, I split the
estimation procedure between two separate groups of firms: the ones that
participate in M&As, and the bystander employers in the same market, which I
will call spillover firms. The separation of the two types of firms shows that
they respond differently to merger activity. The negative employment effects
found at the market level are primarily carried out by merging firms, while
spillover firms show a small, albeit not significant, increase in their size.
By looking at the trajectory of hires and separations, I find that the
negative adjustment in merging firms’ employment is given by an abrupt decline
in new hiring while separations are kept at pre-merger levels for at least one
year after the event.

The third part of the analysis contains the main result of this paper. In
order to evaluate if larger increments in local labor market concentration
deepen the earnings effects of mergers, as predicted by the oligopsony theory,
I compare the estimates in spillover firms between mergers with no change in
concentration and mergers at the top of the distribution of concentration
shocks. Contrary to the previous literature, I find that the earnings in
spillover firms are similar in both cases, which indicates that concentration
plays little to no role in explaining the market-wide effects of M&As in
Brazil. In mergers that induce no change in concentration, workers’ earnings
in spillover firms fall by 1.1%; the same estimate is obtained from within-
market mergers with significant increases in local HHI. Moreover, the events
with no change in concentration seem to induce a growth in the size of
spillover firms, consistent with the logic that the negative employment
adjustment promoted within merging firms prompts an increase in the supply of
labor to other firms in the same labor market. This increase in the labor
supply available to spillover firms is accommodated by an increase in their
employment but at lower wage rate. This is confirmed when I explicitly compare
the _pre_ and _post_ -merger earnings of new hires in spillover firms.

The empirical strategy of this paper relies on a study event design based on
the comparison of different local labor markets regarding the year that they
witness their first merger event. Due to the concern of treatment rollout and
heterogeneity giving rise to problematic estimates (Sun and Abraham, 2021;
Roth et al., 2022; de Chaisemartin and D’Haultfœuille, 2022), I depart from
the more traditional two-way fixed effects model and implement the estimation
proposed in Callaway and Sant’Anna (2021), where the control group to treated
markets consists of other markets that will eventually be treated in
subsequent years. Also, given that I’m interested in market-wide competition
for labor services, I attempt to mitigate the effects of alternative
mechanisms connecting merger activity to labor market outcomes. Employment
level and wages may be altered by M&A activity through mechanisms other than
the competition for labor services. When two firms in the same industry sector
merge, higher price setting in their output market has ramifications for the
market for inputs not necessarily related to higher bargaining power with
upstream service providers – for instance, lower wages and employment could be
driven by the monopolist’s decision to produce below the competitive
benchmark, absent of changes to their ability to set wages. Another
possibility is that merged or acquired firms are better equipped to change the
composition of their workforce – in case bigger firms can hire younger, less
experienced workers, or workers with lower educational attainment, without
hindering productivity, lower observed wages could be merely a consequence of
lower compensation corresponding to these attributes. In order to best control
for the product market concentration and labor compositional mechanisms, I
restrict the analysis to tradable sectors only and, leveraging on the
demographic details available in the data, I estimate effects on an earnings
measure that takes observable attributes of the workers into account.

This paper contributes to two strands of the literature on the effects of
employer consolidation on labor outcomes. The first one is related to the
direct effects of mergers and acquisitions on employment and earnings in
acquired and merged establishments (Shleifer and Summers, 1987; Brown and
Medoff, 1987; Conyon et al., 2001, 2002; Lehto and Böckerman, 2008; Siegel and
Simons, 2010; He and le Maire, 2019; Lagaras, 2023; Guanziroli, 2022).
Expanding on this literature, I focus not only on target establishments or
firms, but I also keep track of earnings and employment at the other employers
operating in the same labor market. This observation increases the
understanding of market-wide effects of merger activity. The second related
literature is the one that directly studies the relationship between labor
market concentration and wages, and employment (Martins, 2018; Lipsius, 2018;
Azar et al., 2019; Marinescu et al., 2021; Azar et al., 2020; Devereux and
Studnicka, 2023; Dodini et al., 2023; Benmelech et al., 2022; Rinz, 2020;
Bassanini et al., 2023). Here, my contribution is to expand the evidence
beyond the context of developed economies, and look at the role of
concentration in earnings and employment effects in a developing economy. To
the best of my knowledge, this is the first study exploring market-wide
effects of merger activity from multiple M&A events, in a wide range of
industry sectors, in a developing economy. The most closely related works to
this paper are Arnold (2022) and Prager and Schmitt (2021); they both estimate
M&A effects in U.S. labor markets, and study the role of employment
concentration in mediating these effects, with the difference that Arnold uses
administrative data for a wide range of industry sectors, while Prager and
Schmitt focus on the hospital sector. They both confirm that, as predicted by
oligopsony theory, mergers that induce little to no effect in concentration
also have negligible earnings effects, and significant wage declines are only
found among merger events in the top of the distribution of concentration
changes. My work departs from theirs in the finding that M&As with larger
concentration increases do not generate sharper wage declines in the Brazilian
case, and mergers with predicted null change in concentration are followed by
an increase in the size of spillover firms222While Arnold (2022) does not
report market wide employment effects, employment pre-trends in Prager and
Schmitt (2021)’s event studies preclude them from making assertive claims on
the size of labor markets after hospital consolidation events..

The remainder of the paper is organized as follows. In Section 2, I describe
the data used in the paper. The main results are reported in Section 4, where
I subdivide the analysis starting from merger activity effects in general,
then across different firms in the labor market with respect to their
participation in M&As, and, lastly, across events with different predicted
impacts in local concentration. I present the robustness of the findings with
respect to the possibility of treatment anticipation in Section 5. Section 6
offers a discussion of the results in view of the literature and auxiliary
evidence of management practices in middle to low-income countries. Section 7
concludes the paper.

##  2 Data

The data used in this paper is composed of two different administrative
releases by the Brazilian federal government. The first one is the _Relação
Anual de Informações Sociais_ \- RAIS, the main source for worker and job
characteristics information. The second is _Dados Públicos CNPJ_ \- DPC, a
business registry from which I extract the list of establishments that went
through a merger or acquisition. More detail about each dataset is provided
below. For a complete step-by-step o the data handling and construction, see
Appendix A.

###  2.1 Worker data - RAIS

RAIS is a matched employer-employee administrative record provided by the
Brazilian Ministry of Labor on a yearly basis, and it covers the entirety of
the country’s formal labor market, which employs around 70% of the workers
(Hallak Neto et al., 2012). In terms of the U.S. Census database, RAIS is
similar to the Longitudinal Employer-Household Dynamics - LEHD.

Each observation of RAIS contains separate worker and establishment
identifiers. The establishment identifier is hierarchical, and from its first
8 digits, or its _root_ , I can also retrieve the firm identifier. As for the
workers, the available characteristics are color/race, sex, age, and
educational achievement. In the case of establishments, it is possible to see
the city where they are located, and the establishment’s 5-digit industry
sector. The data also contains variables related to the job itself, such as
the average monthly earnings, contractual weekly hours, date of admission, and
date of separation, in case the job was terminated within that year.
Differently from the U.S. Census LEHD, RAIS also reports the occupation of the
worker in that particular job.

I use the variables of location and industry sector to delineate local labor
markets. Local labor market details are discussed in Section 3.1. For this
paper, I use RAIS in years ranging from 2002 up to 2017, totaling more than
1.1 billion observations. Access to the data at the individual level is
restricted by a confidentiality agreement.

###  2.2 Business Data - DPC

The _Dados Públicos CNPJ_ \- DPC, is a business registry made available by
_Receita Federal_ , the Brazilian tax collection agency. A similar counterpart
to the DPC in the U.S. Census system is the Longitudinal Business
Database—LBD. The DPC contains information on the universe of establishments
ever registered with the agency, and it is updated on a monthly basis. There
is a total of more than 42 million observations. Access to the DPC is public,
and the files can be downloaded from the revenue agency’s website333See
https://www.gov.br/receitafederal/pt-br/assuntos/orientacao-
tributaria/cadastros/consultas/dados-publicos-cnpj.

From the DPC, it is possible to see an establishment’s identifier, postal
code, industry sector, and, to some extent, its capital table. Primarily
important for this paper, DPC also discloses the variable describing the
reason for the termination of a business. By law, anytime a firm or
establishment is acquired by or merged with another, its identifier is
retired, and a new one is issued for the newly created enterprise. Therefore,
merged and acquired establishments can be flagged by the reported reason its
identifier was retired. Other grounds for business termination include
bankruptcy and various forms of tax penalties.

Despite the richness of details in the business registry, one cannot use it to
identify all the parties involved in an M&A event. Only the _acquired_ or
_pre-merger_ entities are flagged as having been targeted by an M&A. However,
the identifiers of the firms and establishments after the consolidation are
not reported, i.e., the _acquirer_ and newly-_merged_ firm identifiers are not
available. The identification of all parties is important to distinguish
mergers that happen within any given labor market from those between employers
previously operating in separate markets – such distinction will be used to
derive an important result regarding the role of employment concentration and
the outcome effects of M&As. In order to identify all firms involved in a
concentration event, I use the dynamic feature of the worker data, RAIS. More
specifically, I observe the flows of workers departing from acquired
establishments to help me identify the acquirer firm. The most common
destination of these workers in the last year I observe acquired
establishments is considered to be the acquirer business444Figure 11, in
Appendix A.2, shows the distribution of coalition sizes leaving acquired
establishments in years preceding the M&A..

###  2.3 Commuting Zones

The commuting zones are imported from the Brazilian Decennial Census of 2010.
Each commuting zone represents contiguous cities between which people commute
either for study or work. The worker data is merged with the commuting zone
information using the city codes in both datasets.

##  3 Empirical Strategy

The parameter of interest in this paper is the average treatment effect of an
M&A on local labor markets. A local labor market is defined as pairs of
commuting zone and industry sector code. The main outcome variables are the
workers’ earnings, employment, and the local level of employment
concentration. This section presents more detail about the estimation
procedure and the identification assumptions.

###  3.1  Local Labor Markets

In order to measure the employment concentration of labor markets, one has to
choose how to define the labor markets in the first place. Recent studies have
used either a combination of industry sector codes and commuting zone (Rinz,
2020; Benmelech et al., 2022; Prager and Schmitt, 2021; Arnold, 2022),
occupation codes and commuting zone (Azar et al., 2019, 2020; Marinescu et
al., 2021), or a data-driven approach that leverages on worker-flow
observations (Nimczik, 2018). In this paper, I will use the definition of
local labor markets based on industry codes and commuting zones, i.e., each
market will represent an industry activity geographically located in
economically integrated cities. One drawback of this definition is that not
all workers in a labor market are equally bound to the same industry sector or
commuting zone. Accountants, for instance, have skills easily transferrable
across different industries within the same region, while nurses are bound by
job opportunities in the health sector only, and thus will likely be more
willing to switch cities if necessary. On the other hand, I see two advantages
to this approach – (i) this is a definition readily available to researchers
and policymakers alike, as it does not require further modeling assumptions
based on other characteristics of jobs or workers, and (ii) it makes the
present investigation comparable to most other studies in the related
literature using a similar definition of labor markets.

As I am interested in the labor market effects of mergers, I will also
restrict the analysis to tradable sectors only. The idea is that unobserved
changes in _product_ markets that possibly follow M&As might explain some, if
not all, of the post-merger wage and employment decline in treated labor
markets. By virtue of increased pricing power on the product market after an
M&A, bigger consolidated firms may decrease their output level and, therefore,
reduce their demand for labor. Any declines in wages and employment could then
be, to some extent, the result of lower overall demand for labor followed by
increased monopoly power, and not necessarily a consequence of any changes in
the competition for labor services. A strategy to deal with changes in
downstream competition is to narrow the analysis to tradable sectors only
(Arnold, 2022). The rationale behind this strategy is that the competition of
foreign products and services precludes the rise in the market power of the
merged firms, thus shutting down the monopoly channel connecting M&A events to
labor market outcomes555The selection of tradable industries follows the
classification in Dix-Carneiro and Kovak (2017)..

The employment concentration measure I will use in the remainder of the paper
is the Herfindahl–Hirschman Index (HHI) computed at the local labor market
level in each year. More specifically, for a market $m$ and its set of firms
$\mathcal{F}_{m}$, the HHI in year $t$ is given by

$HHI_{m,t}=\sum_{f\in\mathcal{F}_{m}}s_{f,t}^{2}$

where $s_{f,t}$ is the percentage share of employment of firm
$f\in\mathcal{F}_{m}$ as of December 31st in year $t$. When the local labor
market has one single employer, the HHI registers 10,000 points. For an HHI
level of $h$ points, the equivalent number $n$ of equally sized employers is
given by $n=10,000/h$. Also, if two employers with shares $s_{1}$ and $s_{2}$
merge, the equivalent increase in HHI, absent of employment effects, is given
by the product $2s_{1}s_{2}$. Lastly, an increase of $\delta$ points in HHI is
equivalent to the merger of two equally sized firms with employment shares
equal to $\sqrt{\delta/2}$ each.

###  3.2 Event Study Setup

It is possible to assume that consolidation of employers might have lingering
effects on local labor markets, and therefore the measure of effects across
different lengths of time after mergers take place should be part of the
analysis. More so, for the years before the merger, it is also important to
observe the difference between the outcomes of treated markets and their pool
of control units. Thus, I will base the empirical analysis on the estimation
of an event study around the time that local labor markets experience a
merger.

The predominant approach to specifying the event study with multiple cross-
sectional units is to use a two-way fixed effects equation with leads and lags
of an indicator variable for the treated units (Callaway and Sant’Anna, 2021).
However, some concerns raised in the recent literature investigating the two-
way fixed effect approach seem applicable in the context of this paper (Roth
et al., 2022). First, there is variation in the treatment timing of local
labor markets, i.e., not all markets witness a merger in the same year.
Second, the treatment effects may not be the same for different cohorts of
treated markets, e.g., the treatment effect on markets hosting a merger in
2008 may differ from that of markets treated in 2010, and so on. With multiple
treatment periods and heterogeneous effects across cohorts, the two-way fixed
effects specification generates questionable event study estimates that have
been only recently brought to light666See de Chaisemartin and D’Haultfœuille
(2022) for a survey of the literature on problems associated with TWFE models
and proposed solutions. \- namely the so called “negative weights” issue and
the possibility of “cross-lag” contamination laid out in Sun and Abraham
(2021).

In any particular year, the treatment rollout creates three different types of
markets, namely the (i) treated-markets, (ii) the not-yet-treated markets, and
(iii) the never-treated markets, meaning the markets that never witness an M&A
in the years under observation. If never hosting an M&A event is correlated to
unobserved characteristics of the never-treated markets, then their ability to
serve as counterfactuals to treated markets can be compromised. At the same
time, if participation in the treated pool is also endogenous to the treated
market unobserved characteristics, then the endogeneity related to ever
hosting M&A activity is likely ameliorated by using not-yet-treated markets as
controls; after all, the parameter of interest is the average treatment effect
on _treated_ units, the ATT. For these reasons, I apply the estimation
proposed by Callaway and Sant’Anna (2021) in my investigation. With their
method, I can estimate the ATT of merger activity on local labor markets’
outcomes using the not-yet-treated units as the counterfactual group and avoid
the TWFE specification’s issues reported in the literature. In addition, for
the context of mergers, treatment anticipation is a possibility that can be
explicitly addressed by their ATT estimation. Next, I expose the
identification assumptions from Callaway and Sant’Anna (2021) in the context
of merger activity in local labor markets.

Let $Y_{m,t}$ denote the outcome variable of local labor market $m$ in year
$t$. If an M&A takes place at market $m$ in a year $g$, the outcome variable
is denoted by $Y_{m,t}(g)$, for all years $t$. Begin by considering the
parameter

$ATT(g,t)=\mathbb{E}\left[Y_{m,t}(g)-Y_{m,t}(0)\mid G_{g}(m)=1\right]$ (1)

where $Y_{m,t}(0)$ is the potential outcome of market $m$ in year $t$ had it
not been subject to an M&A, and $G_{g}(m)$ is an indicator function that is
equal to $1$ if $m$ is treated in year $g$ and $0$ otherwise. $ATT(g,t)$ is
what Callaway and Sant’Anna (2021) call a _group-time average treatment
effect_ , and in the present case, it represents the expected change in
outcome $Y$ in year $t$ for all markets that had an M&A event in year $g$,
e.g., $ATT(2008,2012)$ is the $ATT$ value in 2012 for markets treated in 2008.
The group-time average treatment effects can be combined into coefficients
that recover event-study type estimates (Callaway and Sant’Anna, 2021). More
precisely, the expected change in outcome $Y$ at market $m$ after $l$ periods
of exposure to treatment is given by

$\beta^{Y}_{ES}(l)=\sum_{g\in\mathcal{G}}\mathbbm{1}\left\\{g+l\leq\mathcal{T}\right\\}P(G=g\mid
g+l\leq\mathcal{T})ATT(g,g+l)$ (2)

where $\mathcal{G}$ is the set of all treatment years and $\mathcal{T}$ is the
last year of observation. The term $P(G=g\mid g+l\leq\mathcal{T})$ weighs all
the group-time $ATT(g,t)$ in which $t$ is observed, and $t=g+l$. The more
units in a treatment cohort, the more weight that cohort gets in the event
study coefficient $\beta^{Y}_{ES}(l)$.

####  3.2.1 Identification

The causal identification of $ATT(g,t)$ in Equation (1) relies on a modified
version of the parallel trends’ assumption used in canonical $2\times 2$
difference-in-differences settings. This and all the other assumptions needed
for identification are presented below.

Assumption 1

(Irreversible Treatment) For $t=1,\dots,\mathcal{T}$, $G_{1}(m)=0$ almost
surely (a.s.), and, if $G_{t}(m)=1$, then $G_{t^{\prime}}(m)=1$ for all
$t^{\prime}>t$ almost surely.

Assumption 2

(Random Sampling) Treatment status and outcome variables of individual markets
are independent and identically distributed, i.e.
$\left\\{Y_{m,t},G_{t}(m)\right\\}_{(m,t)=(1,1)}^{(M,\mathcal{T})}$ is $iid$.

Assumption 3

(Treatment Anticipation of $\zeta$ Years) Let $\mathcal{G}$ be the set of all
years of treatment. For all $t<g-\zeta$ and $g\in\mathcal{G}$,

$\mathbb{E}\left[Y_{m,t}(g)\mid
G_{g}(m)=1\right]=\mathbb{E}\left[Y_{m,t}(0)\mid G_{g}(m)=1\right]\text{
a.s.}$ (3)

Assumption 4

(Parallel Trends Based on “Not-Yet-Treated” Units) For all $g\in\mathcal{G}$,
$t\geq g-\zeta$, and $s>t+\zeta$, the following equality holds a.s.

$\mathbb{E}\left[Y_{m,t}(0)-Y_{m,t-1}(0)\mid
G_{g}(m)=1\right]=\mathbb{E}\left[Y_{m,t}(0)-Y_{m,t-1}(0)\mid
G_{g}(m)=0\text{, }G_{s}(m)=0\right]$ (4)

Assumption 5

(Overlap) For each $t\in\\{2,\dots,\mathcal{T}\\}$, and group $g$ in
$\mathcal{G}$, at least one market is treated in year $g$, and for years
$g+t$, at least one market remains untreated.

Assumption 1 imposes that once a local labor market is treated with an M&A, it
remains treated throughout the analysis and does not contribute again to the
control group of the estimation. For markets with multiple M&A events in
different years, I will use the first event as the treatment date. Most
markets never have an M&A in the years of observation. Following that, out of
any particular number of years with merger activity, the second most common
case is that of markets with only one year of M&A events (Figure 1(a))
777Markets with one or multiple M&As in the same year are equally considered
to be treated in that year.. Assumption 2 is met by the use of a balanced
panel. Assumption 3 is a relaxation of the canonical _no treatment
anticipation_ condition in $2\times 2$ DiD setups. Here, one can allow the
treatment to begin as far back as the context requires, effectively resulting
in two changes: (i) moving the more commonly used normalization point in event
studies from $t=-1$ to $t=-\zeta-1$; and (ii), removing the units already
under anticipated treatment dynamics from the control units pool. Mergers and
Acquisitions can be lengthy processes, and the news of an M&A might induce
changes in the behavior of firms and workers before the reported year of the
event. I present results with no anticipation of treatment, i.e. $\zeta=0$,
but I also find robustness to $\zeta=1$.

Assumption 4 states the main requirement for identification, namely that the
year-over-year change in the outcome of treated markets, had they not been
treated with an M&A, is the same as that of markets that will eventually get
treated in the following years—the _not-yeat-treated_ units. In conjunction
with Assumption 3, the parallel trends assumption imposes that, in year $t$,
the counterfactual of previously treated markets are the ones that get treated
in years later than $t+1+\zeta$ – e.g., for a given treated market $m$, and
anticipation $\zeta=0$, its counterfactual in year, let’s say, 2010,
corresponds to all markets that will be treated in 2011 or later; notice that,
in this example, markets treated in 2010 are already subject to treatment
dynamics, and therefore cannot be used as controls to treated markets in 2010.

Figure 1(b) shows the percentage of markets by treatment status every year
according to the occurrence of the first M&A event. The majority of labor
markets have no M&A activity throughout the years of observation. For pairs of
commuting zone and 3-digit tradable industry codes, the share of never-treated
markets is 86.72% - or 7,526 out of 8,678 markets. The remaining markets are
either treated or not-yet-treated, depending on the year. In addition, Figure
1(b) shows that in all years, there is a positive fraction of markets that
remains untreated, on top of never there being a year with 100% of markets
treated, which contemplates Assumption 5. At the same time, the trajectory of
the fraction of Treated and Not-Yet-Treated markets is smooth across the
years, not indicating any noticeable discontinuities that could demand
inference by different time windows. As is common in event studies, fewer
markets remain untreated towards the end of the timeline under observation.
For this reason, I’ll restrict the analysis to a window of 5 years around the
merger event. Standard assumptions guarantee the estimation of cluster-robust
standard errors by means of a multiplier bootstrap procedure 888An exposition
of the assumptions necessary for inference is beyond the scope of this paper,
and I refer the reader to Section 4, page 211, in Callaway and Sant’Anna
(2021)..

Figure 1: Years With Merger Activity per Market and Treatment Status

(a) Years With Merger Activity per Market

(b) Treatment Status

Note: Local labor markets are defined by pairs of commuting zone and 3-digit
industry code. The Not-Yet-Treated status refers to markets that eventually
witness an M&A event in the following observed years. Most markets never
experience an M&A event. The second most common case is that of markets with
only one year of merger activity.

####  3.2.2 The Estimand and Control Groups

Under the assumptions outlined above, one can rewrite the treatment effect in
1 as

$ATT(g,t)=\mathbb{E}\left[Y_{m,t}-Y_{m,g-1}\mid
G_{g}(m)=1\right]-\mathbb{E}\left[Y_{m,t}-Y_{m,g-1}\mid G_{t+1}(m)=0\right]$
(5)

effectively obtaining the main estimand used in this paper. Let me take a
moment to describe the terms of the expression in Eq. 5. Suppose that a group
of local labor markets had M&As in year $g$. The average treatment effect on
this group, at any year $t$, the value $ATT(g,t)$, is equivalent to the
difference in the average increment of their outcome variable since one year
before treatment, i.e., year $g-1$, and the average increment of all markets
not yet subject to an M&A by year $t+1$, relative to that same base year
$g-1$. In other words, the 2012 wage effect on a labor market treated in 2008,
noted by $ATT(2008,2012)$, is identified by the difference between the wage
growth since 2007 of all markets treated in 2008, and the wage growth among
eventually treated markets that remain not treated by 2013. Notice that, in
the case of allowing for a positive time length of treatment anticipation,
$\zeta=1$, for instance, the markets used in the control group would have to
be those that remain untreated up to 2014, i.e., two years later than the year
at which the effect is being measured, also moving the reference year from
2007 to 2006.

###  3.3 The Earnings Variable

One of the possible consequences of mergers is the change in workforce
composition within merged firms, and ultimately in the whole labor market.
This is specially relevant if, for instance, bigger firms are able to hire
younger less skilled workers to replace more experienced, more educated, and
thus more costly ones. Any estimated decline in wages would then be the result
of turnover towards employees with less attractive observable attributes, and
not necessarily due to market-wide changes in the competition for labor
services. Given the details about workers’ observed attributes in the data, I
will estimate the effects of mergers on a measure of earnings that takes such
attributes into account, thus obtaining an effect that is not driven by
changes in the composition of attributes of workers. Similar to Arnold (2022),
I estimate local labor market-level wages that control for workers
characteristics available in the data. The parameter I look for is
$\theta_{m,t}$ in

$w_{i,m,t}=\theta_{m,t}+\beta_{t}X_{i,t}+u_{i,m,t}$ (6)

where $w_{i,m,t}$ is worker $i$’s log annual earnings in market $m$ and year
$t$, and $X_{i,t}$ is their vector of observable attributes 999$X_{i,t}$
contains dummies for race, college and high school diplomas, sex, and a
quadratic binomial on age.. This model is estimated via OLS for every year in
the data. This way, $\theta_{m,t}$ captures the annual market-level log wage
net of trends in the workforce composition $X_{i,t}$101010Later on, in Section
4, when I split the analysis between spillover and merged firms, their
respective earnings measures are obtained from a re-estimation of Equation 6
for each group separately..

###  3.4 Summary Statistics

Table 1 presents summary statistics from the pool of eventually treated local
labor markets, where all means are computed based on the year prior to the
first M&A event in the market. Despite the apparent large number of firms per
local labor market (almost 63 on average), the HHI score shows that employment
is unevenly distributed among employers – 2,847.50 is above the threshold of
2,500 used in DOF-FTC Horizontal Merger Guidelines to consider a product
market highly concentrated. This score is equivalent to a market with 3.51
equally sized employers, and is above the overall measured concentration in
the U.S. (around 1,500 (Rinz, 2020)), but below the one found looking only at
manufacturing (3,380 (Benmelech et al., 2022)).

Table 1: Summary Statistics on Local Labor Markets

Mean Std. Dev. Annual Earnings (in 2010 BRL) 3,905.40 1,717.94 Workers
1,727.10 2,618.95 Workers in Merging Firms (%) 39.66 32.89 Firms 62.91 142.59
HHI 2,847.50 2,549.25 Market-Year Observations 10,348 • Notes: These are
summary statistics of local labor market characteristics. Local labor markets
are defined by pairs of commuting zone and 3-digit industry sector codes.
Annual earnings were adjusted by inflation, with base year in 2010. All means
are computed one year before the M&A event.

##  4 Results

In this section, I present the study events as well as overall estimates of
the treatment effects of M&As on workers earnings, log employment, and
concentration measured by HHI. I also report estimates from among only the
firms that constitute the merger deal, and all other firms in the same labor
market. Finally, I explore the role of employment concentration on the
treatment effects by comparing the estimates from events with predicted zero
impact on HHI - the out-of-market mergers, and those on the top of the
distribution of concentration increases – the high-impact mergers.

###  4.1 Market-level Effects

Figure 2(c) shows the event study estimates on earnings, employment, and
concentration measured by HHI. In years before $t=0$, the date of the M&A
event, the outcome variables trend similarly among treated and not-yet-treated
labor markets, which supports the plausibility of the parallel trends
assumption. In the years post-M&A, I cannot rule out that market-level
earnings of workers in treated local labor markets have changed on par with
the earnings of workers in not-yet-treated units, despite the negative point
estimates. In the next section, I estimate separate effects for the treated
firms, i.e., those firms that took part in the M&A, and all other firms in the
same market. This distinction will shed light on the source of negative
earnings estimates and possible market interactions responsible for generating
these results.

Notwithstanding the stability in earnings differences, the effect of M&As is
negative on employment, measured in log, corresponding to $-0.1143$
($SE=0.0249$) one year after the event. The remaining lag estimates indicate
that the level of employment does not recoup to its counterfactual trajectory.
Overall, in the five years post-treatment, there is a 6.78 percent relative
decline in employment on treated markets111111This percentage effect is
obtained from exponentiating the overall measured effect, $1-\exp(-0.0702)$..
The fall in employment takes place even when the estimates show a close to
null change in employment concentration, as can be seen in Panel 2(c). M&As
are expected to increase local concentration mechanically, but the dynamic
estimates show that the workers in treated markets are not subject to a
significantly more concentrated labor market after the M&A.

Figure 2: ATT of Earnings, Employment, and HHI

(a) Workers Earnings

(b) Log Employment

(c) Employment HHI

Note: 95% confidence intervals with standard errors clustered at the market
level. The year $t=0$ marks the first M&A in a local labor market, defined by
pairs of commuting zone and 3-digit level industry code. Markets are weighted
by their relative employment size in each year.

###  4.2 Effects from Within Merged Firms and Spillover Dynamics

_A priori_ , there is no reason to expect that all employers in a labor market
will be equally impacted by a merger event, or that employment and wage
adjustments will be similar across all workers. The merging firms might
experience changes intrinsical to the merger that might not propagate to the
rest of the labor market as a whole. I will now, on one hand, compare the
earnings and employment effects only among firms that participate in the
merger. On the other, I look at the earnings and employment from all other
firms within the same labor market, which I will call spillover firms from now
on. The observation of the two separate outcomes, one for within-merged and
another for spillover firms, sheds light on what can be the market-wide effect
of the M&As and what is related to unobserved changes mergers enact in their
entities. It is possible to expect that the new ownership leads to changes in
managerial practices, or even in worker productivity, both of which can affect
earnings and employment within the newly merged firm, but not necessarily
those of competitors in the same labor market.

Figure 3(f) shows the event study estimates of earnings and employment effects
of the merged and spillover firms separately. Panel 3(a) shows that earnings
in merged firms do not diverge from the earnings in not-yet-merged firms in
other labor markets, with lag point estimates close to zero, especially after
the second year of treatment exposure. Differently, earnings in spillover
firms decline after the merger. The 95 percent confidence intervals contain
zero for the separate lag estimates, but the overall effect five years after
treatment represents a 1.07 percent decline in wages (Figure 4). The
employment estimates show a different dynamic. Compared to their baseline
difference from other merger participants in not-yet-treated markets,
employment in merged firms is significantly lower. The estimate of one year of
exposure to the M&A is -0.2923 (SE=0.0052), and, for all five years after the
event, I estimate a 23.07 percent decrease in employment in merged firms
(Figure 4). At the same time, I fail to reject zero employment effects in
spillover firms – although the overall effect is positive but imprecisely
estimated (Figure 4). Taken together, these findings show that the negative
employment effects shown in Figure 2(c) were carried out primarily by the
firms participating in the merger, while the negative wage point estimates
originated from a decline in earnings in spillover firms, with a positive but,
not significant, increase in employment of spillover firms. The bottom two
panels in Figure 3(f) show the estimates for hiring and separations of each
type of firm. Panel 3(e) shows that the negative employment in merged firms is
adjusted via an abrupt decline in hires, while separations only start
declining after the first year of exposure to treatment. Panel 3(f) reinforces
the findings for employment in spillover firms, showing that both hires and
separations remain similar to their _pre_ -merger levels.

Figure 3: Effects from Within and Spillover Firms

(a) Workers Earnings

_Within M &A Firms_

(b) Workers Earnings

_Spillover Firms_

(c) Log Employment

_Within M &A Firms_

(d) Employment

_Spillover Firms_

(e) Log Hires and Log Separations

_Within M &A Firms_

(f) Log Hires and Log Separations

_Spillover_

Note: 95% confidence intervals with standard errors clustered at the market
level. The year $t=0$ marks the first tradable M&A in a local labor market,
defined by pairs of commuting zone and 3-digit level industry code. Tradable
M&As are mergers and acquisitions of tradable industry sectors. Spillover
effects are obtained at the local labor market level by excluding the workers
employed in the merged or acquired firms. Incumbents are employees with at
least 30 months of tenure in their current job by the end of each year. New
hires are workers with less than 12 months of tenure by Dec. 31 of each year.
Markets are weighted by their relative size in each year.

Figure 4: Overall Effects

(a) Workers Earnings

(b) Log Employment

Note: The graph shows the ATT for all five years after treatment. 95%
confidence intervals with standard errors clustered at the market level.
Markets are defined by pairs of commuting zone and 3-digit level industry
code. Markets are weighted by their relative size each year.

###  4.3  Effects from M&As with Different Concentration Changes

There are many channels through which mergers can induce changes in wages and
employment levels in local labor markets. On the side of merged firms
themselves, one can think that new management or changes to worker
productivity might be the cause of the observed outcomes. At a market level,
on the other hand, changes in the competition for labor services might be the
driver of the decline in wages and employment, especially if mergers foster an
anticompetitive behavior from employers. Recent studies that look at wage and
employment effects of mergers have found that labor market concentration is an
important mediator of the relationship between employer consolidation and
earnings declines. Using U.S. hospital mergers, Prager and Schmitt (2021) find
that only consolidations in the top quartile of local concentration shocks
induce negative wage effects on health sector workers. In a more general
setting, using a similar definition of local labor market as the one in this
paper, Arnold (2022) finds that mergers below the 80th percentile of predicted
concentration change do not have significant impact on workers’ earnings. The
fact that larger negative wage effects are found only when mergers enact
larger shifts in concentration is consistent with the oligopsony theory à la
Cournot of competition in labor markets (Azar et al., 2019; Arnold, 2022). In
such models, the first-order condition of each firm’s profit maximization
problem can be combined to obtain a negative relationship between market wages
and the employment concentration measured by the HHI. That is the main reason
why wage declines associated with higher levels of concentration are viewed as
supportive evidence of anticompetitive behavior in labor markets (Card, 2022).

My analysis so far has not distinguished the M&A events by their predicted
impact on market HHI scores, and their effect on employment concentration was
measured to be positive, although imprecise, point estimates (Panel 2(c)). In
this section, I report the effects from two different types of mergers, namely
_out-of-market_ and _high-impact_ M&As. Out-of market mergers are
consolidation events where the merging firms were not simultaneously active in
the same labor market before the event date. In terms of employment
concentration, these are the events with no predicted change in HHI, where the
predicted change is the difference between a simulated measure of HHI where
the two or more merging firms are considered as one single employer, and the
employment HHI actually observed one year before the merger. In case the
merging parties operate in the same labor market, their merger has a positive
predicted change in HHI, as the sum of their employment shares is greater than
any of their individual shares in the year before their merger, making the
local simulated HHI greater than the one observed in the data. I label as
_high-mpact_ the mergers at the 85th percentile, or above that, of the
distribution of predicted change in HHI 121212One year post-merger, events at
the 80th percentile and above cause a change in HHI of 253.48 points
(SE=65.61), equivalent to the combination of two equally sized employers with
an 11.25% employment share each. At the 85th percentile, this effect rises to
430.70 points (SE=112.35), analogous to the merger of two 14.67%-share
partners, making it thus more suitable to test the effects of a change in HHI
induced by the merger against out-of-market M&As. Moving the threshold higher
up in the predicted change in HHI distribution, however, reduces the number of
contributing markets from which to infer the effects, which compromises the
statistical power and feasibility of the analysis.. The relevance of out-of-
market M&As lies in the fact that they do not mechanically induce any changes
in concentration131313As it soon will be shown, out-of-market M&As do not
induce changes in distribution post-treatment either., and thus, their effects
are expected to be unrelated to market-wide anticompetitive behavior resulting
from any increases in concentration. Diversely, effects from high-impact M&As
can be indicative of anticompetitive behavior related to the increase in
concentration that they elicit. Table 2 shows that, out of all mergers and
acquisitions in the sample of tradable industries, the median predicted change
in HHI is 0, while the 85th percentile has a predicted change of 5.53 points –
an event analogous to the merger of two equally sized employers with a 1.67%
share of employment each. In Table 3 I report summary statistics of the
markets used in the estimation of the effects of the two types of events. The
markets in the out-of-market pool have similar earnings to those in the high-
impact’s, a little over 4,000 BRL, and they also depart from a close average
number of firms, 81.16 and 79.83, respectively.

Table 2: Distribution of the Predicted change in HHI

Percentile 50th 75th 80th 85th 90th 95th Predicted Change in HHI 0.00 0.10
0.87 5.53 42.55 333.11 • Notes: The predicted change in HHI is the difference
between the simulated HHI and the observed HHI one year before the M&A takes
place. The simulated HHI is obtained by considering the merging firms as one
single employer in the year before the merger, effectively summing their
employment shares in that year.

Table 3: Summary Statistics of Markets in Out-of-Market and High-Impact Events

[m]  Out-of-Market High-Impact Mean Std. Dev. Mean Std. Dev. Market Earnings
(2010 BRL) 4,066.80 1,917.33 4,024.70 1,477.92 Workers 2,060.02 3,055.59
2,342.88 3,058.78 Workers in Merging Firms (%) 38.11 40.50 46.13 32.33 Firms
81.16 213.94 79.83 169.01 HHI 2,836.08 2,604.09 2,242.50 2,084.95 Market-Year
Observations 9,581 2,886 • Notes: Summary statistics of local labor market
characteristics. Local labor markets are defined by pairs of commuting zone
and 3-digit industry sector codes. Annual earnings were adjusted by inflation,
with the base year in 2010. Out-of-Market M&As are the ones with no predicted
increase in HHI before the consolidation date. High-Impact M&As are the ones
in the top 15% of predicted change in HHI. All means are computed one year
before the M&A event.

In Figure 5(f), the first two panels confirm that out-of-market M&As and high-
impact M&As have distinctive post-treatment concentration dynamics. As
expected, high-impact M&As increase local employment concentration – the two-
year treatment exposure point estimate shows an increase in HHI of 522.48
points (SE=118.64), an increase analogous to one generated by a merger of two
equally sized employers with a 16.16% share of local employment each. Post-
treatment, I cannot reject the null effects of out-of-market M&As on local
market concentration. In Panels 5(c) and 5(d), I observe similar point
estimates from both types of mergers, and in both contexts, that of merged
firms and spillovers. Within merged firms, earnings seem to decline especially
after the second year of exposure to treatment, while a downward trajectory is
noticeable from the start in spillover firms. As for the employment outcome,
the event studies in panels 5(e) and 5(f) indicate a different pattern of
effects between the two types of mergers, with negative estimates in the case
of high-impact M&As both within merged firms and spillovers141414The lead
estimates of out-of-market M&A employment effects in Panel 5(e) show a
potential positive linear trend in the employment of merged firms relative to
their not-yet-treated counterparts in other markets. In such cases, the
negative post-treatment estimates can be interpreted as a reverse in their
employment growth trajectory. Either way, the qualitative conclusion remains
the same and is consistent with the case of all mergers in Section 4.1,
employment in merged firms would have been higher if not for the merger.. For
out-of-market M&As, while treatment effects are negative within the merged
firms, they are positively estimated among spillover employers.

The earnings and employment effects of the two types of consolidation events
are summarized in Figure 6(b). The overall estimates for the five-year post-
treatment show a similarity in the magnitude of earnings decline within M&A
firms for both out-of-market and high-impact mergers, -0.0083 (SE=0.0084) and
-0.0095 (SE=0.0108) respectively. In the case of spillover firms, the
estimates are -0.0108 (SE=0.0051) for out-of-market mergers and -0.0117
(SE=0.011) for high-impact. This finding is at odds with studies of mergers in
the context of the U.S. labor market in two aspects. First, both in the
specific case of hospital consolidation (Prager and Schmitt, 2021), and in the
more general context of multiple industries (Arnold, 2022), mergers with
little to no change in concentration do not have a significant impact on
earnings. Here, I find a significant earnings decline after out-of-market
mergers in spillover firms and, although less precisely estimated, within
merged firms too. Even in the absence of changes to local concentration,
mergers impart a significant decrease in earnings. Second, looking at the case
of spillover firms, the effects from the two types of events are similar in
magnitude, indicating a decline of 1.1% in earnings. This similarity is
surprising in view of the result that larger increases in concentration are
followed by larger wage declines, both theoretically and empirically. Assuming
no changes to labor supply elasticity and productivity in the first five years
after the merger, a likely scenario for the case of firms not involved in the
merger, the theory of oligopsony à la Cournot would have predicted a more
negative wage effect for the case of high-impact events. This finding also
shows that, at least in the context of Brazilian labor markets, it should not
be assumed that mergers only affect wages and employment, via the
concentration channel only, an assumption that has been used before in
instrumental variable estimations of the relationship between wages and
employment concentration (Benmelech et al., 2022; Arnold, 2022). Without
distinguishing between spillover and merged firms, the _All Firms_ column in
Panel 6(a) confirms that out-of-market mergers have a significant negative
effect on earnings, estimated at -0.0143 (SE=0.0045).

Panel 6(b) shows the overall employment effects. Here, the out-of-market
mergers have a similar result to the one found in the case of all mergers
presented in Section 4.1 \- while a negative employment adjustment is observed
in merged firms, spillover firms grow after the merger of their competitors,
although at a rate that does not compensate for the separations in merged
firms, as the _All Firms_ column in Panel 6(b) indicates. For the case of
high-impact mergers, the employment effect is negative across all firms,
notwithstanding the estimates are less precisely estimated in spillover firms.
In principle, this is an expected result. Contrary to out-of-market events,
the high-impact mergers necessarily reduce the number of employers in the
market, canceling to some extent the possibility of workers reallocating
within the same labor market. Figure 7 shows that the higher concentration
observed after high-impact mergers may not only stem from a change in the
distribution of workers among big and small firms, but also from the reduction
in the number of employers altogether. The number of employers is close to
9.05% lower in markets that witness a high-impact merger two years after the
event, while a null effect cannot be rejected in case of out-of-market
mergers. Another reason why employment may decline more sharply in high-impact
mergers is that the merging entities might have more redundancy among their
workers once under the same ownership and management, while the same level of
overlap is likely not achievable in out-of-market consolidations.

Figure 5: Out-of-Market and High-Impact M&As

(a) Employment HHI

_Out-of-Market M &A_

(b) Employment HHI

_High-Impact M &A_

(c) Workers Earnings

_Within M &A Firms_

(d) Workers Earnings

_Spillover_

(e) Log Employment

_Within M &A Firms_

(f) Log Employment

_Spillover_

Note: 95% confidence intervals with standard errors clustered at the market
level. The year $t=0$ marks the first tradable M&A in a local labor market,
defined by pairs of commuting zone and 3-digit level industry code. Markets
are weighted by their relative size in each year.

Figure 6: Overall Effects by Type of M&A

(a) Workers Earnings

(b) Log Employment

Note: The graph shows the ATT for all five years after treatment. 95%
confidence intervals with standard errors clustered at the market level.
Markets are defined by pairs of commuting zone and 3-digit level industry
code. Markets are weighted by their relative size each year. Out-of-market
M&As are mergers with no predicited change in market HHI, while high-impact
are the ones in the top 15% of the predicted change in HHI distribution.

Figure 7:  Employers

Note: ATT of the M&As on the log number of employers in a local labor market.
95% confidence intervals with standard errors clustered at the market level.
Markets are defined by pairs of commuting zone and 3-digit level industry
code. Markets are weighted by their relative size each year.

##  5 Robustness to Anticipation

Mergers and acquisitions may take a long time to conclude, and both workers
and firms can respond to the news of the event before its official date
reported in administrative records. Thus, it is important to check the
robustness of the results to the possibility of treatment anticipation.
Effectively, this means that some not-yet-treated markets that contribute to
the control pool under the assumption of no treatment anticipation are not
appropriate counterfactuals anymore, specially if firms engage in pre-merger
adjustments ahead of the deal taking place. To take this possibility into
account, I modify the Limited Treatment Anticipation Assumption in Section
Assumption 3  by changing the value of the parameter $\zeta$ from 0 to 1. This
implies that for markets treated in year $g$, their average treatment effect
in any year $t$ will be based upon the not-yet-treated markets by year $t+2$,
and not $t+1$ as before151515It is worth noticing that this adjustment is
different from simply moving the normalization period in two-way fixed effects
specifications from the more commonly reported lead $t-1$ to $t-2$, in view of
the fact that it effectively changes the pool of units in the control group
(Callaway and Sant’Anna, 2021)..

I present the overall 5-year ATT under the one-year treatment anticipation
assumption in Figures 8(b) and 9(b). When pooling all mergers together, the
lesson from the treatment anticipation case is similar to the findings from
the no treatment anticipation (Figure 4) - (i) workers earnings are lower in
spillover firms, although not as precisely estimated as before, at the same
time that these firms grow in size, and (ii) merging firms primarily drive the
negative employment adjustment observed in the whole market, while the null
effect on their workers earnings cannot be rejected. The comparison between
out-of-market and high-impact events with treatment anticipation is presented
in Figure 9(b). Here, I do not find a conclusive evidence that refutes the
previous finding of similar earnings effects between the two types of events
among spillover firms, which shows that even under the treatment anticipation
assumption, the increase in concentration does not generate clearly
distinguishable earnings effects outside the merging firms. Within merged
firms, the seemingly more negative wage and employment effects of high impact
events may originate from a higher degree of job titles overlap between the
merging firms when they already belong to the same labor market.

Figure 8: Overall ATT with Treatment Anticipation of One Year

(a) Workers Earnings

(b) Log Employment

Note: The graph shows the ATT for all five years after treatment, under the
assumption of treatment anticipation of one year. 95% confidence intervals
with standard errors clustered at the market level. Markets are defined by
pairs of commuting zone and 3-digit level industry code. Markets are weighted
by their relative size each year.

Figure 9: ATT with Treatment Anticipation of One Year by Type of Merger

(a) Workers Earnings

(b) Log Employment

Note: The graph shows the ATT for all five years after treatment, under the
assumption of treatment anticipation of one year. 95% confidence intervals
with standard errors clustered at the market level. Markets are defined by
pairs of commuting zone and 3-digit level industry code. Markets are weighted
by their relative size each year. Out-of-market M&As are mergers with no
predicited change in market HHI, while high-impact are the ones in the top 15%
of the predicted change in HHI distribution.

##  6 Discussion of Results

To summarize, mergers and acquisitions in the context of the Brazilian labor
markets are shown to have significant negative employment effects at local
labor market level. The post-treatment dynamics also shows increases in
concentration and declines in worker earnings, although not significant. By
splitting the sample between the firms that participate in the merger and all
other firms in the same market, I show that the two types of firms have
diverse responses on their employment and earnings margins. The negative
employment effects are found primarily within the merged firms, while
spillover firms show a tendency to increase in the years after the event, but
their increase is not large enough to offset the reduction in size of merging
firms. When it comes to earnings, I cannot reject the null effect hypothesis
from the merged firms’ sample, but earnings are significantly lower in
spillover firms. Most mergers and acquisitions have little to no impact on
concentration. It is only in the top 15% of the distribution of _a priori_
increases to HHI that I find noticeable employment concentration changes. The
comparison of out-of-market and high-impact mergers reveals seemingly
indistinguishable earnings effects in spillover and merging firms. The out-of-
market employment effects follow the overall pattern found before, merging
firms get smaller and spillover firms grow in size. These findings are robust
to the possibility of treatment anticipation, and are likely not related to
changes in the composition of the labor force (given the construction of the
earnings variable) and changes in product market power (given the restriction
to tradable industry sectors only).

The concentration channel connecting mergers and negative wage effects found
in Prager and Schmitt (2021) and Arnold (2022) seems to be thus absent in the
context of Brazilian labor markets. In addition, I find negative wage effects
that affect other firms in the labor market even in the case of merger
activity not followed by increases in concentration. I do not empirically find
a confirmation of the connection between increases in concetration and sharper
wage declines from oligopsony models of the labor market (Boal and Ransom,
1997; Azar et al., 2019). But if not changes in concentration, what could be
driving the observed decline in earnings of firms not related to the M&As and
the market-wide decline in employment?

###  6.1 M&A’s Synergies, Managerial Practices, and Within-Market Dynamics

A way to rationalize the decreases in workers’ earnings and employment, even
in the case of mergers that do not affect local concentration, is to admit the
possibility of efficiencies created by employer consolidation. Merger
proponents argue that cost-saving measures can be taken once the merging
parties operate under the same ownership. While the economics profession has
been skeptical of efficiency claims made by merger proponents161616For a
discussion on the credibility of such claims in the U.S. context, see Fisher
(1987) and Farrell and Shapiro (2000). The possibility of merger-related
efficiency gains is also explicitly acknowledged by regulators. See, for
example, the DOJ-FTC Horizontal Merger Guidelines, Section 4., the present
case shows that merging firms do engage in a significant reduction of
personnel, while other employers in the labor market show a tendency to grow
after the merger. On the earnings margin, the results showed that workers that
remain in the merging firms do not face a significant decline in earnings,
while the opposite happens with spillover firms’ workers. What this contrast
suggests is that the adjustment towards lower employment generated by
potential efficiency gains in merging firms increases the supply of labor to
all the other firms in the same market. Assuming a stable curve of demand for
labor in spillover firms, the increased supply of workers is accommodated at a
higher equilibrium level of employment and lower earnings in these firms.
Indeed, I estimate negative earnings effects among new hires in spillover
firms after an out-of-market merger (Figure 10), where the fiver year post-
treatment average effect is -0.0156 (SE=0.0051).

Figure 10: New Hires’ Earnings in Spillover Firms After an Out-of-Market
Merger

Note: Local labor markets are defined by pairs of commuting zone and 3-digit
industry code. New hires’ earnings refer to the log wage adjusted from
equation 6 estimated among the pool of new hires in spillover firms. New hires
are workers with less than 12 months of tenure in the current job by December
31st of each year. Spillover firms are the employers in the labor market that
do not participate in any M&A neither as acquirers, targets, nor merging
partner. An out-of-market merger is a merger with zero predicted change in
local concentration measured by the HHI.

The question of why cost-saving measures in the form of overhead reduction are
more pervasive in Brazilian merger activity if compared to the context of the
U.S. labor market remains171717Due to a pre-trend in their employment event
study, Prager and Schmitt (2021) refrain from making an assertive conclusion
about the employment effects of hospital mergers. At the same time, Arnold
(2022) finds negative employment effects in M&A establishments that range from
5% to 10% on average depending on the predicted change in concentration, I
find a sharper decrease of 23.07% in merging firms’ size in the case of all
mergers.. One possibility is that changes in ownership and management are able
to collect higher gains from efficiency in emerging economies due to
inadequate management practices in target firms. Bloom et al. (2010) presents
a comparison between the productivity of firms in developing economies vis-à-
vis their counterparts in richer countries. In 2005, the sales per employee in
American firms was more than 3.2 times as large as that of Brazilian firms.
While previous studies have elicited structural, economy-wide reasons for the
productivity gap, such as developing countries’ lack of infrastructure, lower
human capital, and regulation, Bloom conjectures that managerial practices can
also be playing an important role. Compared to higher income economies, middle
and low-income countries, including Brazil, have a lower prevalence of
management practices related to clear target setting, production monitoring,
and proper pay incentives (Scur et al., 2021). To the extent that changes in
ownership in Brazil can allow merging firms to reap managerial gains and
dismiss excess workers in the process, this could in part explain the
difference in employment effects between merging firms and other employers in
the same labor market. Simultaneously, M&As in developed countries such as the
U.S. might not be able to collect the same cost related efficiency gains given
their superior management practices beforehand.

##  7 Conclusion

What are the effects of merger and acquisition activity in the labor markets
of a middle-income country? I attempt to answer this question by exploring
linked employer-employee administrative records from Brazil to identify merger
events, locate them in labor markets defined by pairs of industry and
commuting zone, and, by means of an event study design, I estimate their
impact on workers earnings, employment, and local concentration measured by
the HHI. The worker-flow identification of merger events allows me to
distinguish the changes in size and worker compensation, both in merging firms
and all the other employers doing business in the same labor market. Overall,
mergers have clear negative impacts on labor market size, while null effects
on earnings and local concentration cannot be rejected. I find that the
market’s negative employment adjustment is exclusively concentrated in merging
firms, while other employers in the same market experience a positive,
although not significant, size effect, at the same time that their
workers’earnings show a subtle modest trend.

The apparent null effect of mergers in local concentration is explained by the
fact that most M&As are of the out-of-market type, i.e., either the acquirer
or the merging partner were not active in that same market before, and thus
the predicted change in local HHI is zero. It is only at the top 15% of the
predicted change in HHI distribution that I find a noticeable local
concentration impact of M&A events. Contrary to the previous literature
findings, workers’ earnings in spillover firms decline similarly irrespective
of the impact on concentration from the merger event. The earnings decline in
spillover firms can be rationalized by a positive shift of the labor supply
curve in these firms, originated by a halt in hiring from the merging
competitors. At the margin, I confirm that new hires in spillover firms earn
relatively lower wages after an out-of-market event. By comparing the effects
of M&As inducing different changes in local employment concentration, this
paper also adds to the empirical investigation of oligopsony models that
predict lower wages in concentrated labor markets.

The body of evidence showing the negative relationship between employment
concentration and labor outcomes in developed economies has prompted the
suggestion that antitrust authorities should use HHI benchmarks to flag
mergers’ potential anticompetitive impacts on labor markets (Naidu et al.,
2018). In contrast, by explicitly comparing out-of-market and high-impact
mergers earnings’ effects, I find that wage declines in spillover firms are
similar in both cases. However, the finding that mergers with substantial
increases to concentration are not followed by stronger wage reductions should
not be taken as proof that labor markets in Brazil are hence perfectly
competitive, and that the local antitrust authority should not be cognizant of
mergers impacts on workers. The oligopsony theory is one way to rationalize
wage markdowns, but models of job search friction, such as employer
differentiation, and job ladder, can also generate firm-specific upward
sloping labor supply curves independent of employment concentration181818See
Card (2022) for a comparison and historic perspective on models with
employer’s wage setting power.. What my result shows is that _ad-hoc_
thresholds of local concentration may not be as informative about the
competitiveness of developing economies’ labor markets as they are in
developed countries. Additionally, the existence of non-compete clauses common
in high-skilled occupations, and anti-poaching agreements documented among
various U.S. franchisees are worth the attention of antitrust policy
regardless of their connection to local market concentration (Balasubramanian
et al., 2020; Krueger and Ashenfelter, 2022). The pervasiveness of such
practices in the Brazilian context is still unexplored, and undoubtedly
deserve the attention of future research.

## References

  * (1)
  * Arnold (2022) Arnold, David, “Mergers and Acquisitions, Local Labor Market Concentration, and Worker Outcomes,” 2022.
  * Autor et al. (2020) Autor, David, David Dorn, Lawrence F Katz, Christina Patterson, and John Van Reenen, “The Fall of the Labor Share and the Rise of Superstar Firms*,” The Quarterly Journal of Economics, May 2020, 135 (2), 645–709.
  * Azar et al. (2019) Azar, José, Ioana Marinescu, and Marshall Steinbaum, “Measuring Labor Market Power Two Ways,” AEA Papers and Proceedings, May 2019, 109, 317–321.
  * Azar et al. (2020) ,  ,  , and Bledi Taska, “Concentration in US labor markets: Evidence from online vacancy data,” Labour Economics, October 2020, 66, 101886.
  * Balasubramanian et al. (2020) Balasubramanian, Natarajan, Jin Woo Chang, Mariko Sakakibara, Jagadeesh Sivadasan, and Evan Starr, “Locked In? The Enforceability of Covenants Not to Compete and the Careers of High-Tech Workers,” Journal of Human Resources, May 2020.  Publisher: University of Wisconsin Press Section: Article.
  * Bassanini et al. (2023) Bassanini, Andrea, Cyprien Batut, and Eve Caroli, “Labor Market Concentration and Wages: Incumbents versus New Hires,” Labour Economics, April 2023, 81, 102338.
  * Benmelech et al. (2022) Benmelech, Efraim, Nittai K. Bergman, and Hyunseob Kim, “Strong Employers and Weak Employees: How Does Employer Concentration Affect Wages?,” JHR, 2022, 57 (7), S200–S250.  Publisher: University of Wisconsin Press.
  * Bloom et al. (2010) Bloom, Nicholas, Aprajit Mahajan, David McKenzie, and John Roberts, “Why Do Firms in Developing Countries Have Low Productivity?,” American Economic Review, May 2010, 100 (2), 619–623.
  * Boal and Ransom (1997) Boal, William M. and Michael R Ransom, “Monopsony in the Labor Market,” Journal of Economic Literature, 1997, 35 (1), 86–112.  Publisher: American Economic Association.
  * Brown and Medoff (1987) Brown, Charles and James L. Medoff, “The Impact of Firm Acquisitions on Labor,” June 1987.
  * Callaway and Sant’Anna (2021) Callaway, Brantly and Pedro H. C. Sant’Anna, “Difference-in-Differences with multiple time periods,” Journal of Econometrics, December 2021, 225 (2), 200–230.
  * Card (2022) Card, David, “Who Set Your Wage?,” American Economic Review, April 2022, 112 (4), 1075–1090.
  * Conyon et al. (2001) Conyon, Martin J, Sourafel Girma, Steve Thompson, and Peter W Wright, “Do hostile mergers destroy jobs?,” Journal of Economic Behavior & Organization, August 2001, 45 (4), 427–440.
  * Conyon et al. (2002) Conyon, Martin J., Sourafel Girma, Steve Thompson, and Peter W. Wright, “The impact of mergers and acquisitions on company employment in the United Kingdom,” European Economic Review, January 2002, 46 (1), 31–49.
  * Cornwell et al. (2016) Cornwell, Christopher, Jason Rivera, and Ian M. Schmutte, “Wage Discrimination When Identity Is Subjective: Evidence from Changes in Employer-Reported Race,” Journal of Human Resources, August 2016.  Publisher: University of Wisconsin Press Section: Article.
  * de Chaisemartin and D’Haultfœuille (2022) de Chaisemartin, Clément and Xavier D’Haultfœuille, “Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey,” June 2022.  arXiv:2112.04565 [econ].
  * Devereux and Studnicka (2023) Devereux, Kevin and Zuzanna Studnicka, “Local Labour Market Concentration and Wages in Ireland,” March 2023.
  * Dix-Carneiro and Kovak (2017) Dix-Carneiro, Rafael and Brian K. Kovak, “Trade Liberalization and Regional Dynamics,” American Economic Review, October 2017, 107 (10), 2908–2946.
  * Dodini et al. (2023) Dodini, Samuel, Michael Lovenheim, Kjell Salvanes, and Alexander Willén, “Monopsony, Job Tasks, and Labor Market Concentration,” January 2023\.
  * Farrell and Shapiro (2000) Farrell, Joseph and Carl Shapiro, “Scale Economies and Synergies in Horizontal Merger Analysis Symposium: Antitrust at the Millennium: Part 2,” Antitrust L.J., 2000, 68 (3), 685–710.
  * Fisher (1987) Fisher, Franklin M, “Horizontal Mergers: Triage and Treatment,” Journal of Economic Perspectives, November 1987, 1 (2), 23–40.
  * Grossman and Oberfield (2021) Grossman, Gene M. and Ezra Oberfield, “The Elusive Explanation for the Declining Labor Share,” August 2021.
  * Guanziroli (2022) Guanziroli, Tomas, “Does Labor Market Concentration Decrease Wages? Evidence from a Retail Pharmacy Merger,” 2022.
  * He and le Maire (2019) He, Alex Xi and Daniel le Maire, “Mergers and Managers: Manager-Specific Wage Premiums and Rent Extraction in M&As,” SSRN Electronic Journal, 2019.
  * Krueger and Ashenfelter (2022) Krueger, Alan B. and Orley Ashenfelter, “Theory and Evidence on Employer Collusion in the Franchise Sector,” Journal of Human Resources, April 2022, 57 (S), S324–S348.  Publisher: University of Wisconsin Press Section: Articles.
  * Lagaras (2023) Lagaras, Spyridon, “M&As, Employee Costs and Labor Reallocation,” 2023.  (Forthcoming) Journal of Finance.
  * Lehto and Böckerman (2008) Lehto, Eero and Petri Böckerman, “Analysing the employment effects of mergers and acquisitions,” Journal of Economic Behavior & Organization, October 2008, 68 (1), 112–124.
  * Lipsius (2018) Lipsius, Ben, “Labor Market Concentration Does Not Explain the Falling Labor Share,” November 2018.
  * Marinescu and Posner (2020) Marinescu, Ioana Elena and Eric A. Posner, “Why Has Antitrust Law Failed Workers?,” Cornell L. Rev., 2020, 105 (5), 1343–1394.
  * Marinescu and Hovenkamp (2019) and Herbert Hovenkamp, “Anticompetitive Mergers in Labor Markets,” ILJ, 2019, 94 (3).
  * Marinescu et al. (2021) Marinescu, Ioana, Ivan Ouss, and Louis-Daniel Pape, “Wages, hires, and labor market concentration,” Journal of Economic Behavior & Organization, April 2021, 184, 506–605.
  * Martins (2018) Martins, Pedro S., “Making their own weather Estimating employer labour-market power and its wage effects,” October 2018.
  * Naidu et al. (2018) Naidu, Suresh, Eric A. Posner, and Glen Weyl, “Antitrust Remedies for Labor Market Power,” Harvard Law Review, December 2018, 132 (2).
  * Hallak Neto et al. (2012) Neto, João Hallak, Katia Namir, and Luciene Kozovits, “Setor e emprego informal no Brasil: análise dos resultados da nova série do sistema de contas nacionais - 2000/07,” Economia e Sociedade, April 2012, 21 (1), 93–113.
  * Nimczik (2018) Nimczik, Jan Sebastian, “Job Mobility Networks and Endogenous Labor Markets,” 2018, p. 86.
  * Prager and Schmitt (2021) Prager, Elena and Matt Schmitt, “Employer Consolidation and Wages: Evidence from Hospitals,” American Economic Review, February 2021, 111 (2), 397–427.
  * Rinz (2020) Rinz, Kevin, “Labor Market Concentration, Earnings, and Inequality,” Journal of Human Resources, October 2020.  Publisher: University of Wisconsin Press Section: Article.
  * Robinson (1933) Robinson, Joan, The Economics of Imperfect Competition, Palgrave Macmillan London, 1933.
  * Roth et al. (2022) Roth, Jonathan, Pedro H. C. Sant’Anna, Alyssa Bilinski, and John Poe, “What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature,” January 2022.  arXiv: 2201.01194.
  * Scur et al. (2021) Scur, Daniela, Raffaella Sadun, John Van Reenen, Renata Lemos, and Nicholas Bloom, “The World Management Survey at 18: lessons and the way forward,” Oxford Review of Economic Policy, June 2021, 37 (2), 231–258.
  * Shleifer and Summers (1987) Shleifer, Andrei and Lawrence H. Summers, “Breach of Trust in Hostile Takeovers,” August 1987.
  * Siegel and Simons (2010) Siegel, Donald S. and Kenneth L. Simons, “Assessing the Effects of Mergers and Acquisitions on Firm Performance, Plant Productivity, and Workers: New Evidence from Matched Employer-Employee Data,” Strategic Management Journal, 2010, 31 (8), 903–916.  Publisher: Wiley.
  * Sun and Abraham (2021) Sun, Liyang and Sarah Abraham, “Estimating dynamic treatment effects in event studies with heterogeneous treatment effects,” Journal of Econometrics, December 2021, 225 (2), 175–199.

##  Appendix A  Data Handling

###  A.1 Preparing RAIS

The _Relação Anual de Informações Sociais_ \- RAIS version used in this paper
starts in 2002 and ends in 2017. The files are made available by the Ministry
of Labor in Brazil, and their transfer is conditional on a confidentiality
agreement celebrated between Cornell’s Labor Dynamic Institute and the
Ministry. The files are hosted in a secured cluster within Cornell’s BioHPC
server ecosystem. RAIS’ raw files are year-by-region (in some years, year-by-
state) .txt tables. I fist stack all files within the same year, then I merge
the yearly files with the commuting zone list using the city codes as the key.
Job records outside the reach of commuting zones are dropped.

For each job record, RAIS reports the status of the job in December 31st – if
this variable has entry 0 it means that the work contract was terminated at
some point in that year. I keep only the job records with active employment
contracts on December 31st. As standard, in case the same worker has more than
one employer, I keep the highest paying job. For the demographics used in the
estimation of 6, I use the worker’s _age_ , _age squared_ , and dummy
variables for _female_ , _white_ , _college or higher_ (codes greater than or
equal to ‘9’), and _high school_ (codes ‘7’ or ‘8’). RAIS is an employer-
reported database, and in some occasions, a worker’s history will show
different colors/races, depending upon either the perception of the current
employer or the worker’s informed race when the job started (Cornwell et al.,
2016). If a worker is ever reported as _non-white_ (race/color codes different
than ‘2’), I set the _white_ dummy to 0.

In Section 4.2 I present event studies looking at the dynamic of new hires and
separations within merged and spillover firms. In order to exclude spurious
work contract terminations, such as transfers across establishments of the
same firm or to the newly merged firm, I exclude the separations with reported
reason coded with labels ‘30’ and ‘31’. New hires are flagged using the tenure
variable, measured in months as of December 31st of each year. Active jobs on
December 31st with tenure less than or equal to 12 months are flagged as new
hires. In order to avoid counting spurious admissions, similar to the case of
separations, I exclude the admissions coded with types ‘3’ and ‘4’.

###  A.2 Identification of Establishments in M&A events

The identification of merger activity starts with the _Dados Públicos de CNPJ_
\- DPC, released monthly by the revenue agency in Brazil. The release I used
in the paper is from September 5th, 2020. The file contains approximately 42.5
million observations at establishment level. Every time an establishment is
acquired or merged with others, its identifier is retired, and a new one is
issued by the revenue agency. The establishment identifier, also called the
establishment’s CNPJ, is a hierarchical 14-digit code, where the first eight
digits identify the firm, the following four digits identify the
establishment, and the last two digits are used for checksums. I rely on the
column describing the reason for retirement of a CNPJ to flag acquired (code
‘2’) or merged (code ‘3’) establishments.

After flagging the acquired or merged establishments, I resort to the matched
character of RAIS to keep a record of the next destination of workers who are
re-employed in other firms. For each destination firm, I compute the relative
size of the leaving coalition, and choose the largest coalition’s next firm as
the most common destination. Figure 11 shows the distribution of worker
coalition sizes departing from acquired or merged establishments towards the
most common destination firm in the last few years of such establishments. In
the last year of an acquired establishment, in at least 70% of cases, more
than 50% of workers are reported to be working at the same top destination
firm in the following year. In the second to last year, and before that, a
coalition of less than 10% of the acquired establishment workers can be found
in the following year’s most common employer. Therefore, I declare the firm
that admits the most number of workers from an acquired or soon-to-be merged
establishment, after the last year of observation of this establishment, as
the buyer, or newly-merged firm, side of the M&A. The identification of both
acquirer and acquired allows me to compute the predicted impact on HHI of each
local labor market event, or events, in a given year. The predicted HHI is
then used to separate out-of-market events from those that induce higher
concentration changes.

Figure 11: Distribution of the Percentage of Workers Departing From Acquired
or Merged Establishments to Most Common Destination Firm

Note: Consider that year $T$ is the last year of an acquired or merged
establishment in RAIS, the worker data. There is a positive fraction of
workers leaving acquired or merged establishments to the most common
destination in years $T-1$, $T-2$, and $T-3$. In year $T$, however, the
fraction of workers decamping towards the same firm is significantly larger
than in previous years. The most common destination firm of workers leaving in
year $T$ is flagged as the acquirer or newly merged firm.