Mergers and Employee Wagespublic.kenan-flagler.unc.edu/faculty/ouimetp/mergerwages.pdfacquisition, on average, by 9.4%. Wages increase more following those mergers with the greatest

Mergers and Employee Wages

Paige Ouimet

University of North Carolina, Chapel Hill

Rebecca Zarutskie

Duke University

November 2009

Shleifer and Summers (1988) argue takeovers are largely motivated by the opportunity to renege

on implicit labor contracts. Alternatively, if employees share in any rents generated by the

merger, wages may increase. In this paper, we use confidential micro-data from the US Census

to document the effects of mergers on wages and find that wages at target firms increase post-

acquisition, on average, by 9.4%. Wages increase more following those mergers with the greatest

expected productivity gains, indicating employees are indeed sharing in the merger surplus and

that measures of merger gains which consider only shareholder returns are underestimating the

total merger surplus. We also find that wages are negatively correlated with merger-related job

losses, indicating employees may receive extra compensation for changes to job security.

We thank Bert Grider for his diligent assistance with the data and clearance requests. The research in this paper

was conducted while the authors were Special Sworn researchers of the U.S. Census Bureau at the Triangle Census

Research Data Center. Research results and conclusions expressed are those of the authors and do not necessarily

reflect the views of the Census Bureau. This paper has been screened to insure that no confidential data are revealed.

Ouimet can be reached at [email protected]; Zarutskie can be reached at [email protected].

mailto:[email protected]

mailto:[email protected]

I. Introduction

On average, mergers are associated with value gains and a rich literature has investigated

how these gains are divided between the target and the acquirer.1 However, not all merger gains

will necessarily accrue to either target or acquirer shareholders. Employees at the target or

acquiring firms may capture a fraction of the merger gains in the form of higher post-merger

wages or subsidize shareholder returns in the form of lower post-merger wages. To evaluate the

total surplus generated by merging two firms, employee wages must also be considered.

Shleifer and Summers (1988) argue takeovers are largely motivated by the opportunity to

renege on implicit labor contracts. Given that labor accounts for such a large fraction of the costs

at a firm, any decrease in these costs could, at least partially, explain merger synergies. In which

case, we should observe that wages decline post-merger. Alternatively, in a competitive labor

market, employee wages will reflect marginal productivity. Maksimovic and Phillips (2001)

show that acquiring firms enhance productivity at their targets post-acquisition by bringing new

capital, technology, know-how and synergies. With a greater surplus to be shared between labor

and capital ex-post, wages may increase following the merger.

To explore these issues, we use a sample of 1,372 successful mergers and acquisitions of

public U.S. targets by either public or private U.S. acquirers between the years 1982 and 2001.

We match this data to the Longitudinal Business Database (LBD), a U.S. Bureau of Census data

set with information on the number of employees and total payroll for all U.S. business

establishments. This Census data is an improvement over the wage and employment data

reported in Compustat. Census data is available at the establishment level which allows us to

identify changes at one specific facility as opposed to having to rely on firm-level data.

1 See Betton, Eckbo, and Thorburn 2008 for a review of the relevant literature.

In our sample, we find that wages at target firms increase post-acquisition. On average,

raw wages increase by 9.4%.2 To observe whether these wage increases reflect changes in the

industry or local economy, we create a measure of excess wages. Excess wages are defined as

the residual after regressing raw wages on 1) the mean wages for all establishments located in the

same state and matched by year and 2) the mean wages for all establishments operating in the

same industry and matched by year. Similar to the pattern in raw wages, we find that excess

wages at the target increase post-acquisition.

This increase in wages is not observed at the establishments which were previously

owned by the acquirer. In fact, we find that log raw wages decline by 0.6% at acquirer

establishments. Nor do we find the same wage increases in a set of control firms which share

similar characteristics to our target firms but were not themselves targets.

We argue that labor appears to share in the merger surplus, capturing higher wages when

the merger leads to productivity gains. In a neo-classical model of firm organization, firms buy

assets when anticipating productivity gains from doing so. Under a competitive labor market, as

productivity increases, wages will increase. If this labor productivity increase is specific to the

merged acquirer and target assets, the firm may not have to pay workers for their full marginal

productivity. However, search theory suggests that more profitable firms will pay higher wages

due to the relatively higher costs associated with an unfilled vacancy (Lang 1991). Alternatively,

Akerlof and Yellen (1990) argue that wages will increase with firm profitability to reflect “fair”

wages. As profitability increases, workers will demand higher wages (a “fair” share of the

surplus) and shirk if wages fall short.

We proxy for expected productivity increases with the takeover premium. An acquirer

should be willing to pay a larger premium when productivity is expected to increase more. We

2 Log raw wages increase by 3.01%.

find a 1% increase in the premium paid leads to a 1.1% increase in excess wages at the target.

This result holds even after controlling for firm characteristics associated with the takeover

premium, including acquirer size and acquisition consideration.

We also find evidence that as job security decreases post-acquisition, as proxied by the

change in employment at the target’s other establishments, wages increase. If employment at the

target on average declines post-acquisition, then the greater the decline, the higher the post-

acquisition wages. However, if employment on average increased, then there is a positive but

statistically insignificant relation between wages and change in employment. This result is

consistent with the model in Berk, Stanton, and Zechner (2009) and results shown in

Chemmanur, Cheng, and Zhang (2009) where employee wages increase as job security decreases

due to an increased bankruptcy risk associated with higher leverage.

We rule out several mechanical explanations of this finding. Of the 176,616

establishments we observe pre-acquisition, we observe only 120,364 establishments post-

acquisition. We are unable to confirm that all of these establishments were closed following the

merger and some of these unobserved establishments may reflect tracking errors in the LBD

dataset. If missing, these observations could potentially introduce a bias. However, even when

we limit the sample to acquisitions where 100% of the target establishments observed pre-

acquisition are also observed post-acquisition, we still find an increase in wages. Furthermore,

our results are robust to controls for acquirer firm size, acquirer mean wages, and

macroeconomic conditions.

This paper is most similar to Lichtenberg and Siegel (1990) and McGuckin and Nguyen

(2001). Considering a sample of leveraged buyouts (LBOs), Lichtenberg and Siegel (1990) find

that hourly pay for production workers increases modestly, on average, following an LBO. Using

a sample of mergers in the manufacturing industry from the 1980s, McGuckin and Nguyen

(2001) find that wages do not decline post-acquisition.

II. Data

The sample covers mergers and acquisitions involving US public targets and US public

or private acquirers announced between 1982 and 2001 as identified in Thompson’s Security

Database Clearinghouse (SDC). The M&A sample is limited to deals where the acquirer

successfully purchases 50% or more of the target. The public targets and acquirers in the M&A

sample are then matched to Compustat.

The M&A sample of public targets is then matched to the Longitudinal Business

Database (LBD), a U.S. Bureau of Census dataset that tracks all U.S. business establishments

with at least one employee or positive payroll. The database is formed by linking years of the

standard statistical establishment list (SSEL) maintained by the Internal Revenue Service of the

U.S. Treasury Department.3 The SSEL contains information on the number of employees

working for an establishment and total annual establishment payroll. The LBD identifies the

owner of each establishment. The LBD also links the establishments over time using a unique

identifies, the LBDNUM. The LBDNUM is establishment-specific and remains constant even in

the event of a change in control.

To match the M&A data to the LBD, we identify all establishments owned by a public

target. For each establishment, we capture two specific points in time. First, using the cusip to

match, we identify the most recent observation which strictly pre-dates the merger

announcement. This is our pre-merger observation. We then tack track these establishments

over time using the LBDNUM and, as our second point in time, record the observation which

3 See Jarmin and Miranda (2002) for more information.

strictly follows the one year anniversary of the merger completion. This is our post-merger

sample. For 1,372 completed mergers and acquisitions from the SDC sample, we are able to

observe both pre- and post- merger observations in the LBD data for the target firm.

III. Results

In this section, we begin by looking at mean and median wages at target and acquirer

establishments for the pre- and post- merger periods. Next, we attempt to explore these changes

by looking at how changes in wages are correlated with deal, target, and acquirer characteristics.

A. Wages at target establishments increase post-merger

Our compensation data provides establishment-level annual payroll, which includes all

taxable forms of compensation, such as salaries, wages, commissions, and bonuses. Furthermore,

wages are normalized to 2008$ using the Consumer Price Index (CPI). Our measure of wages

per employee is the ratio of annual payroll (in thousand dollars) to the number of employees. All

measures of wages are winsorized at the 1% and log-transformed.

In Table 1, Panel A, we use the full set of target establishments observed either pre- or

post-merger. We observe that wages increase following the merger. The mean (median) log

transformed raw wage per employee at an establishment owned a target firm and measured

before the merger announcement is 2.97 (2.99).4 After the merger is completed, the mean

(median) log transformed raw wage for these establishments is 3.04 (3.07).5 This increase is

statistically significant at the 1% level.

4 This corresponds to a mean (median) raw wage per employee (not log-transformed) of $19,492 ($19,886).

5 This corresponds to a mean (median) raw wage per employee (not log-transformed) of $20,905 ($21,542).

To observe whether these wage increases reflect changes in the industry or local

economy, we create a measure of excess wages. Excess wages is the residual from the following

regression: log wages per employee = a0 + a1 state-year mean wage + a2 industry-year mean wage

+ ε. State-year mean wage is the log mean wage per employee in the state of location of the

establishment and matched by year. Industry-year mean wage is the mean log wage per

employee matched to the establishment’s industry and by year. Both state-year and industry-

year mean wages are calculated using establishments owned by either public or private firms.

Similar to the pattern in raw wages, we find that excess wages at the target increase post-

acquisition. Before the acquisition, the mean (median) excess wage is -1.04 (-0.11). After the

acquisition, the mean (median) excess wage is -0.63 (-0.03). We also note that mean excess

wages are negative both before and after the acquisition. This is likely reflecting the fact that we

are only considering public target firms and public firms have been shown to pay lower wages

relative to private firms.

Of the 176,616 pre-merger establishments, we observe only 120,364 establishments

during the post-merger period. Establishments identified in the pre-merger period may not be

observed in the post-merger period if the establishment was closed due to business conditions

unrelated to the merger or due to changes brought on by the merger itself. However, we cannot

exclude the possibility that errors in the data linking establishments over time led to some

observations being dropped from the data in the second period.

To ensure that differences in the two samples are not driving our results, in Table 1, Panel

B, we consider a subset of the observations in Panel A. Panel B has a balanced panel where the

same establishments are observed in both the pre- and post-periods. We also further restrict the

sample in Panel B to include only those observations with data on 1) the premium paid for the

target; 2) merger consideration; and 3) target and acquirer industries. These variables are used in

the subsequent regression tests. By dropping these variables, we are able to maintain a constant

sample for a number of our future tests.6

Using the balanced panel subset, we find that both raw and excess wages increase post-

acquisition at the target, as reported in Table 1, Panel B. These results indicate that the wage

gains observed in earlier tests were not being driven by differences in the pre- and post-

acquisition samples. These wage increases are not only statistically significant but also

economically significant. We find that log raw wages increase, on average, by 3% and raw

wages (not log-transformed) increase by over 9%.

B. Wages at acquirer establishments do not increase post-merger

We next explore wages at establishments owned by the acquirer. We start with the same

sample of M&A deals from SDC as was used to create the target sample. However, as we are

matching our acquirer data to the establishments in the LBD using cusips, we can only include

establishments owned by public acquirers in our acquirer sample. Thus, unlike our target

sample, our acquirer sample does not include mergers by private acquirers. For the pre-

acquisition period, the acquirer sample includes all establishments owned by the acquirer. For

the post-acquisition period, the sample captures only a fraction of the acquirer – the portion of

the firm which was not previously part of the target. As in Table 1, Panel B, we create a

balanced panel by dropping any acquirer establishment from the acquirer sample with missing

data for either the pre- or post- acquisition sample.

6 One benefit of a maintaining a constant sample is that it allows us to directly compare the results from different

tests. Furthermore, utilizing fewer samples is advantageous when using confidential data as this minimizes disclosure risk.

We report the results from the acquirer sample in Table 2. Unlike our target results, we

find that wages do not increase at the acquirer. We observe that both raw and excess wages

decline, on average, following the acquisition at establishments owned by the target. While the

average decline is statistically significant it is of modest economic magnitude. Log raw wages

on average decline by 0.6%. Median raw and excess wages are unchanged. It appears the wages

gains reported earlier are unique to establishments affiliated with targets. In the next set of tests,

we attempt to explain these wage gains.

C. Regression Results

In the following section we explore the target, acquirer, and deal characteristics

associated with the greatest post-merger wage gains. We investigate three explanations of our

findings 1) surplus associated with merger productivity gains being shared with labor; 2)

compensation for decreased job security; and 3) agency conflicts at the acquirer.

For our regression tests, we define a new variable, Excess Wage Change, calculated as

the difference in the log excess wage per employee at the target post-merger minus the pre-

merger value. All regressions use excess wage change as the dependant variable. We also

cluster our standard errors at the deal-level to control for any correlation in error terms between

target establishments bought in the same merger.

C.1. Wages increase as expected merger gains increase

In a neo-classical model of firm organization, firms buy assets when anticipating

productivity gains from doing so. Consistent with this neo-classical motivation, Maksimovic and

Phillips (2001) find most asset sales are followed by productivity gains at the target

establishments.

In a competitive labor market, as productivity increases, wages will increase. If this labor

productivity increase is specific to the merged acquirer and target assets, the firm may not have

to pay workers for their full marginal productivity. However, labor may still be able to capture

some of the gains. Search theory suggests that more profitable firms will pay higher wages due

to the relatively higher costs associated with an unfilled vacancy (Lang QJE 1991).

Alternatively, Akerlof and Yellen (1990 QJE) argue that wages will increase with firm

profitability to reflect “fair” wages. As profitability increases, workers will demand higher

wages (a “fair” share of the surplus) and shirk if wages fall short. These theories are supported

by Hildreth and Oswald (1997) which finds that increases in productivity are followed by

increases in pay.

In table 3, we use the merger premium as a proxy for the expected change in post-merger

productivity. All else equal, the greater the expected increase in post-merger productivity the

more the acquirer should be willing to pay for the target. The premium paid by the acquirer is

calculated as the percent difference between the offer price and the market price recorded four

weeks prior to the announcement. Following Officer (2003) and Moeller, Schlingemann, and

Stulz (2004), we exclude observations where the estimated premium is either negative or greater

than 200% from the summary statistics. In column 1, we estimate the relation between excess

wage change and the merger premium and find target wages increase as the target premium

increases. This finding is consistent with a hypothesis that post-merger wage gains reflect a

sharing of the merger benefits with labor.

To confirm that the positive relation between excess wage changes and merger premium

is not being driven by an omitted variable, we next consider several deal characteristics which

have been shown to be correlated with the merger premium. Moeller, Schlingemann, and Stulz

(2004) find that large acquirers and stock acquisitions are associated with higher merger

premiums. Bargeron et al (2007) finds that private acquirers pay less for targets compared to

public acquirers. In column 2, we add controls for acquirer size, as measured by the log of total

assets, and the merger consideration. Cash, is a dummy variable if the acquisition is 100% cash-

financed and Stock is dummy variable if the acquisitions is 100% stock-financed. We find no

relation between acquirer firm size or merger consideration and post-merger excess wage

changes. When controlling for acquirer size, we must limit the sample to public targets thus we

consider whether or not the acquirer is private in a separate regression. In column 3, we find no

relation between private acquirers and post-merger wage changes. Moreover, as reported in

columns 2 and 3, even after including additional controls variables which predict the merger

premium, the premium continues to be positively correlated with excess wage changes.

Lang, Stulz and Walking (1991) find that joint target and acquirer merger announcement

returns are highest when the target has a low Tobin’s Q and the acquirer has a high Tobin’s Q.

In this case, the more productive acquirer is expected to have the greatest productivity impact on

the less- productive target. If employees are sharing in these post-merger productivity gains, we

would expect acquisitions of low-Q targets by high-Q acquirers to be associated with the greatest

wage increases. In column 4, we proxy for Q as industry-adjusted market to book ratios. Qit is

measured as fiscal year-end market value of equity plus market value of preferred stock plus

total liabilities divided by total assets. We follow Bebchuk and Cohen (2005) and industry adjust

Q by subtracting the median Q matched by industry (3-digit SIC code) and year. We find no

relation between acquirer Q and excess wage changes. However, consistent with our prediction,

the lower the Q at the target firm, the greater the excess wage change.

Maksimovic and Phillips (2001) finds that establishments with low productivity are

relatively less likely to be sold during recessions, as compared to establishments with high

productivity. Given that low-productivity establishments are associated with the greatest post-

merger productivity increase, we predict mergers announced during recessions will be associated

with lower productivity increases and lower wage increases. Indeed, in column 5 we find that

excess wage changes are lower for mergers announced during an NBER recession.

C.2. Wages increase as uncertainty increases

In Table 4 we consider the possibility that wages increase to compensate employees for

additional uncertainty post-merger. The psychology literature has argued that a job loss has a

strong negative effect on measures of happiness (Helliwell 2003) and mergers are often

associated with significant job losses. If firms must compensate workers for higher post-merger

job security risk, then wages will rise and rise most at those firms where job security has been

most adversely affected. A similar argument has been made by Berk, Stanton and Zechner

(2009) and shown in Chemmanur, Cheng and Zhange (2009) where wages increase as job

security declines.

For an establishment x, we proxy for the change in job security by measuring the percent

change in employment at all other establishments involved in the same merger but excluding

establishment x. We define this variable as Employee Change. By excluding establishment x we

avoid the potential data bias that wage changes at an establishment may be correlated with

change in employment at that specific establishment if lower-wage workers are easier to dismiss.

We assume that job losses or gains at other establishments owned by the same target proxy for

expected changes to job security.

In column 1, we observe that employee change is uncorrelated with excess wage change.

Employee change can take on positive values if, on average, employment increases post-merger

or negative values if employment is declining. We expect employee change to have different

effects on excess wage change depending on whether employment is increasing or decreasing. If

employment is decreasing, then the more employment decreases, the more wages will increase to

compensate workers for the additional job security risk. On the other hand, if employment is

increasing, then there may be a positive relation between employment change and wages if

increasing demand for new workers puts a positive pressure on wages.

In column 2, we separate between those mergers where employment is increasing or

decreasing by using a dummy variable Employee Change < 0. We find no relation between

employee change < 0 and excess wage change. In column 3, we interact employee change with

this dummy variable. We find that if employment is decreasing, then the more negative the

employment change, the greater the increase in excess wages post-merger. If employment is

increasing, we find a positive but statistically insignificant relation between employee change

and excess wage change.

C.3. Wages increases are uncorrelated with agency conflicts at the acquirer

In Table 5, we consider whether wage increases are correlated with agency conflicts at

the acquirer. Bertrand and Mullainathan (2003) find that worker compensation is higher at firms

with lower external governance. We proxy for weak external governance by identifying those

acquisitions which appear most likely to have been motivated by private benefits accruing to the

manager of the acquirer and by measuring industry competition. We consider two proxies for

agency-motivated acquisitions: 1) acquirer free cash flow; and, 2) whether or not the acquisition

is diversifying.

Jensen (1986) suggested that private benefits associated with empire building may

motivate some mergers. Following the approach of Lang, Stulz, and Walkling (1991), we proxy

for agency motivated acquisitions by identifying acquiring firms with both low growth options

and high free cash flow. Acquirer growth options are proxied by the book to market ratio.

Acquirer free cash flow is estimated following Lehn and Poulsen (1989) as the firm’s operating

income before depreciation minus interest expense, taxes, preferred dividends, and common

dividends. This value is normalized by total assets. We find no evidence that free-cash flow

motivated acquisitions are associated with greater excess wage increases, as reported in column

one.

Diversifying acquisitions are often assumed to be more likely to be motivated by private

benefits accruing to the acquirer’s manager. Amihud and Lev (1981) suggest that managers may

pursue diversifying acquisitions to diversify the risk to their human capital. Shleifer and Vishny

(1990) argue that managers may seek acquisitions diversifying into industries where their skills

are better matched, thereby making themselves more valuable to the firm. In support, Morck,

Shleifer and Vishny (1990) find evidence that acquiring firms realize lower returns when

announcing diversifying acquisitions and Schoar (2002) finds that overall firm productivity

declines following a diversifying acquisition. We define an acquisition as diversifying if the

acquirer and target do not share the same 3-digit SIC code. In column 2, we find no relation

between excess wage change and diversifying acquisitions.

Strong competition limits managerial slack and complacency, thereby reducing agency

problems between managers and shareholders (Alchian, 1950; Friedman, 1953; Hart, 1983).

More recently, Guadalupe and Wulf (2007) provide evidence that competition improves

governance, and Giroud and Mueller (2009) demonstrate that product market competition serves

as an effective external governance mechanism. Thus, we use also product market competition as

a proxy for the strength of external corporate governance.

To measure the competitiveness within a firm’s industry, we estimate the industry

concentration by developing an employee-based Herfindahl-Hirschman Index (eHHI). This

index is created in a similar manner as a traditional sales-based HHI, except that the measure is

based on the fraction of the industry’s labor force employed at a firm rather than the fraction of

industry sales attributable to a firm. The benefit of this employee based index over sales-based

index using Compustat is that our index includes all firms, avoiding the error due to the

exclusion of private firms (Maksimovic and Phillips, 2001; Ali, Klasa and Yeung 2008).

Our eHHI is calculated as follows. In the first step, we identify the primary industry

associated with a firm. The Census databases we use in our analysis only report establishment-

level data. As such, we have information on the SIC codes for all of the establishments linked to

a firm but do not have a single firm-level SIC code. To identify the primary industry associated

with a firm, we sum the total workers at all of the establishments linked to a firm, per 3-digit SIC

code. We then define the firm’s industry as the 3-digit SIC code which captures the largest

fraction of the firm’s total workforce and assign all of the firm’s employees to this firm-level 3-

digit SIC code.

We identify the total employee count for each industry as the sum of the employees at all

firms assigned to that industry. The employee market share of an individual firm is defined as the

firm’s employees divided by the total employees in that industry. The eHHI is then estimated as

the sum of the squares of the employee market share of all firms in that 3-digit SIC code.7 All

establishments affiliated with a firm are assigned to the same eHHI value regardless of the

establishment-level industry.

In column 3, we regress excess wage change on the eHHI for the acquirer’s industry and

find no correlation. It is possible that industry competition will have the greatest effect on excess

wage change when both the target and the acquirer are in the same industry. As such, we interact

eHHI with the diversify dummy variable in column 4. We find that eHHI has no statistically

effect on excess wage change regardless of whether the acquisition is diversifying or horizontal.

IV. Robustness Tests

In the following section we consider several robustness checks on our results. First, we

note that a set of firms which are similar to the target firms except that they were not the subject

of a successful merger do not realize the same wage increases over the following years. Second,

we further investigate the potential for bias associated with target firm establishments which are

not observed in the post-merger period. Finally, we consider and rule out the possibility that

wage changes reflect either 1) acquirer firm size; 2) acquirer practices of over-paying workers;

and 3) increased hours worked at the target establishments.

A. Matched Sample Results

In Table 6, we explore the possibility that the post-merger wage gains we observe at the

target firm do not reflect the acquisition per se and instead reflect unique characteristics of the

7 As measured over our sample, the eHHI index has a correlation of 22.6% with a traditional sales-based Herfindahl

index calculated using Compustat data.

target firms and/or the fact that the establishment survived through the observation window. We

create a sample of firms which share similar observable firm characteristics to our target firms,

however, these firms were not themselves targets of a successful takeover. We then track these

firms for two years and see if they realize a similar increase in wages.

We create the matched sample by starting with the universe of public firms in Compustat.

We exclude any firm-years in which a firm was an acquisition target. We match each firm in our

target sample to the nearest match from this pool of non-target firms. The matched firm must

operate in the same industry and in the same year where the match year is defined as the year of

the acquisition announcement (Year 0). We then pick the matched firm from this pool of

possible matches as the firm with total assets which are closest to the target firm. For each

matched firm, we start with all establishments owned by this firm at year 0. We then track these

establishments over two years and estimate their wages at the end of year 2. If the establishment

is not observed in the post-period, then we drop this establishment from the matched sample.

We are left with a balanced panel of 43,852 establishments owned by the matched firms.

In Table 6, we report the mean and median raw and excess wages of these matched firms.

We find that the matched firms are associated with higher raw and excess wages relative to our

sample of target firms with one exception. Median excess wages post-acquisition for our

balanced sample of public targets is higher as compared to the median excess wage for the

sample of matched firms in year 2. Furthermore, we observe no wage gains at these matched

firms over the 2 year observation window. In fact, both mean raw and excess wages decline over

this period. As such, it appears that our results are not being driven by observable firm

characteristics.

B. Missing Observations

We next consider the potential for a bias associated with target establishments which are

not observed in the post-merger period. We are unable to confirm that all of these

establishments were closed following the merger and some of these establishments which are

unobserved in the post-merger period may reflect tracking errors in the LBD dataset. As noted

in Table 7, of the 176,616 establishments we observe pre-acquisition, we only observe 120,364

post-acquisition. Establishments not observed in the post-period are associated with higher raw

wages but lower excess wages. They also tend to be smaller with an average (median) of 40 (9)

employees as compared to an average (median) of 64 (14) at establishments which are observed

in the post-period.

To ensure that our results are not being driven by these unobserved establishments, in

Table 8, we consider a subset of our merger deals. We only include those merger deals where

100% of the target establishments, involved in a given deal, which are observed pre-acquisition

are also observed post-acquisition. We then report the pre- and post-merger wages for the targets

involved in these deals. As with the full sample, we continue to observe an increase in the

average and median raw wages and an increase in average excess wages. However, for this

subset of deals, we do observe that the median excess wages decline.

C. Regression Robustness Tests

A number of papers have shown that larger firms pay higher wages.8 Thus, it is possible

that the observed increase in wages post-merger does not reflect the merger per se and instead

reflects the fact that the merged firm is larger than the original target firm. In Table 8, column

1, we regress excess wage change on acquirer firm size (as measured by total assets) and find a

8 For example, see Brown and Medoff (1989) and Fox (2009).

negative relation. In column 2 we regress excess wage change on acquirer and target firm size.

In neither test do we find evidence indicating that the wage gains are being driven by firm size.

We next explore the possibility that firms have a wage “style” and tend to pay their

workers above or below-mean wages. If acquirers, on average, tend to pay higher wages and

they continue this pattern at the target establishments, we would expect to observe a post-merger

wage increase. We test this story by regressing excess wage change on the mean excess wage

per employee at the acquirer, as measured prior to the acquisition. We find no relation between

the wage change at the target firm and the mean excess wage at the acquirer.

Finally, we consider the possibility that the increase in wages at the target firms post-

merger reflects an increase in hours worked per employee at these firms. To test this story, we

create a dummy variable which captures whether or not the target establishment operates in the

manufacturing sector, as defined by SIC codes between 3000 and 3999. Of all industries,

manufacturing establishments are most likely to see dramatic shifts in hours worked. Thus, if

our results are being driven by change in hours, we would expect to see a particularly strong

effect at targets in the manufacturing sector. However, as reported in column 4, we observe that

establishments operating in the manufacturing sector are not associated with any greater wage

increases.

V. Conclusion

We find that wages, on average, increase at target establishments following a merger

event. This is not consistent with arguments that renegotiating labor contracts is a primary

motivation in most mergers. Instead, it appears that workers are sharing in the merger surplus.

This result implies that measures of merger value gains estimated by considering only

shareholder returns are underestimating the total gains. Labor and shareholders are sharing the

merger synergies.

We also find that wages are negatively correlated with merger-related job losses,

indicating employees may receive extra compensation for changes to job security. We find no

evidence that agency conflicts at the acquirer are driving these wage increases.

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Table 1, Panel A. Raw and excess wages around the acquisition at target establishments

using the full sample. The sample consists of all establishments linked to public US targets of

acquisitions by public or private US acquirers and where control was successfully transferred

(defined as an acquisition for at least 50% of the target). The "Pre-acquisition” period is defined

as the fiscal year before the deal was announced. The "Post-acquisition” period is defined as the

fiscal year following the one year anniversary of the deal completion. Means are winsorized at

1%. Raw wages are estimated as total annual payroll/total employees and then log transformed.

Excess wages is the residual from a regression: raw wages = industry-year mean wages + state-

year mean wags + error. State-year mean wage is the log mean wage per employee in the state

of location of the establishment and matched by year. Industry-year mean wage is the mean log

wage per employee matched to the establishment’s industry and by year. Significance is noted as

***, **, * for 1%, 5% and 10% respectively.

Pre-acquisition Post-acquisition Difference

Mean raw wages 2.97 3.04 0.07 ***

(t-value 23.28)

Median raw wages 2.99 3.07 0.08 ***

(z = 26.09)

Mean excess wages -1.04 -0.63 0.42 ***

(t-value 58.82)

Median excess wages -0.11 -0.03 0.08 ***

(z = 43.50)

N 176,616 120,364

Table 1, Panel B. Raw and excess wages around the acquisition at target establishments

using the balanced panel. The sample consists of establishments linked to public US targets of

acquisitions by public or private US acquirers and where control was successfully transferred

(defined as an acquisition for at least 50% of the target). For an establishment to be included in

the sample, wages during both the pre- and post-acquisition period must be observed. The "Pre-

acquisition” period is defined as the fiscal year before the deal was announced. The "Post-

acquisition” period is defined as the fiscal year following the one year anniversary of the deal

completion. Means are winsorized at 1%. Raw wages are estimated as total annual payroll/total

employees and then log transformed. Excess wages is the residual from a regression: raw wages

= industry-year mean wages + state-year mean wags + error. State-year mean wage is the log

mean wage per employee in the state of location of the establishment and matched by year.

Industry-year mean wage is the mean log wage per employee matched to the establishment’s

industry and by year. Significance is noted as ***, **, * for 1%, 5% and 10% respectively.


Mean raw wages 2.99 3.08 0.09 ***

(t-value 24.22)


(z=27.92)

Mean excess wages -1.01 -0.30 0.71 ***

(t-value 85.56)

Median excess wages -0.10 0.03 0.13 ***

(z=60.94)

N 90,491 90,491

Table 2. Raw and excess wages around the acquisition at acquirer establishments using a

balanced panel. The sample consists of establishments linked to public US acquirers involved in

acquisitions of public US targets and where control was successfully transferred (defined as an

acquisition for at least 50% of the target). The sample is limited to establishments owned by the

target before the acquisition. For an establishment to be included in the sample, wages during

both the pre- and post-acquisition period must be observed. The "Pre-acquisition” period is

defined as the fiscal year before the deal was announced. The "Post-acquisition” period is

defined as the fiscal year following the one year anniversary of the deal completion. Means are

winsorized at 1%. Raw wages are estimated as total annual payroll/total employees and then log

transformed. Excess wages is the residual from a regression: raw wages = industry-year mean

wages + state-year mean wags + error. State-year mean wage is the log mean wage per

employee in the state of location of the establishment and matched by year. Industry-year mean

wage is the mean log wage per employee matched to the establishment’s industry and by year.

Significance is noted as ***, **, * for 1%, 5% and 10% respectively.


Mean raw wages 3.49 3.47 -0.02 ***

(t-value 9.69)


(z=3.75)

Mean excess wages 0.01 -0.00 -0.01***

(t-value 9.83)

Median excess wages -0.01 -0.01 0.00

(z=7.23)

N 237,432 237,432

Table 3. Wage changes around mergers by proxies for ex-post productivity gains. The

dependent variable is excess wage change at the target firm. Excess wages is the residual from a

regression: raw wages = industry-year mean wages + state-year mean wags + error. State-year

mean wage is the log mean wage per employee in the state of location of the establishment and

matched by year. Industry-year mean wage is the mean log wage per employee matched to the

establishment’s industry and by year. Excess wage change is calculated as excess wages in the

post-period minus excess wages in the pre-period. The "Pre-acquisition” period is defined as the

fiscal year before the deal was announced. The "Post-acquisition” period is defined as the fiscal

year following the one year anniversary of the deal completion. Premium is the premium paid for

the target by the acquirer. Cash (stock) is a dummy variable which takes a value of 1 if the deal

was 100 cash (stock) financed. Acquirer firm size is measured as total assets in 2008$. This

variable is log-transformed. Private acquirer is a dummy variable if the acquirer is private.

Standard errors are robust and clustered at the deal level. Significance is noted as ***, **, * for

1%, 5% and 10% respectively.

1 2 3 4 5

Intercept 0.131

(0.173)

-0.235

(0.399)

0.348

(0.249)

0.363

(0.205)

*

0.527

(0.121)

***

Premium 0.011

(0.005)

**

0.016

(0.008)

**

0.010

(0.004)

**

Cash 0.086

(0.457)

0.135

(0.340)

Stock -0.457

(0.333)

-0.575

(0.219)

***

Acquirer firm size 0.00

(0.041)

Private acquirer 0.253

(0.495)

Acquirer industry-adjusted MB ratio 0.003

(0.005)

Target industry-adjusted MB ratio -0.015

(0.009)

*

NBER recession -0.682

(0.156)

***

Number of clusters 1,372 421 1,372 421 1,372

Number of observations 90,491 27,445 90,491 27,445 90,491

R-squared 0.032 0.118 0.079 0.006 0.001

Table 4. Wage changes around mergers by proxies for changes to job security. The








year following the one year anniversary of the deal completion. Employee change for

establishment x is the percent change in employment at all other establishments involved in the

same merger but excluding establishment x itself. Employee change < 0 is a dummy variable

which takes a value of 1 if the employee change is less than 0. Standard errors are robust and

clustered at the deal level. Significance is noted as ***, **, * for 1%, 5% and 10% respectively.

1 2 3

Intercept 0.525

(0.121)

***

0.477

(0.189)

***

0.435

(0.191)

**

Employee change -0.061

(0.176)

-0.034

(0.177)

0.122

(0.168)

Employee change <0 0.083

(0.227)

-0.211

(0.235)

Employee change * employee change < 0 -1.938

(0.798)

**

Number of clusters 1,372 1,372 1,372

Number of observations 90,491 90,491 90,491

R-squared 0.000 0.001 0.031

Table 5. Wage changes around mergers by proxies for agency conflicts at the acquirer. The








year following the one year anniversary of the deal completion. Acquirer free cash flow is

estimated as the firm’s operating income before depreciation minus interest expense, taxes,

preferred dividends, and common dividends. This value is normalized by total assets. Diversify

is a dummy variable which takes a value of 1 if the acquirer and target operate in different

industries as defined by 3-digit SIC codes. eHHI is an employee-based Herfindahl index. This

variable is divided by 1000. Standard errors are robust and clustered at the deal level.


1 2 3 4

Intercept 0.274

(0.194)

0.420

(0.151)

***

0.538

(0.133)

***

0.471

(0.176)

***

Acquirer FCF 0.141

(1.194)

Acquirer BM 0.044

(0.182)

Acquirer FCF * BM 2.670

(3.248)

Diversify 0.210

(0.244)

0.147

(0.271)

Acquirer industry eHHI -0.093

(0.219)

-0.440

(0.272)

Acquirer industry eHHI * diversify 0.508

(0.414)

Number of clusters 421 1,372 1,372 1,372

Number of observations 27,445 90,491 90,491 90,491

R-squared 0.014 0.001 0.069 0.006

Table 6. Matched firm results. The sample consists of establishments owned by firms matched

to our sample of targets but which were not targets themselves. We create the matched sample

by starting with the universe of public firms in Compustat. We exclude any firm-years in which

a firm was an acquisition target. We match each firm in our target sample to the nearest match

from this pool of non-target firms. The matched firm must operate in the same industry and in

the same year where the match year is defined as the year of the acquisition announcement (Year

0). We then pick the matched firm from this pool of possible matches as the firm with total

assets which are closest to the target firm. For each matched firm, we start with all

establishments owned by this firm at year 0. We then track these establishments over two years

and estimate their wages at the end of year 2. If the establishment is not observed in the post-

period, then we drop this establishment from the matched sample. Means are winsorized at 1%.

Raw wages are estimated as total annual payroll/total employees and then log transformed.

Excess wages is the residual from a regression: raw wages = industry-year mean wages + state-

year mean wags + error. State-year mean wage is the log mean wage per employee in the state

of location of the establishment and matched by year. Industry-year mean wage is the mean log

wage per employee matched to the establishment’s industry and by year. Significance is noted as

***, **, * for 1%, 5% and 10% respectively.

Year 0 Year 2 Difference

Mean raw wages 3.42 3.39 -0.03 ***

(t-value 6.07)


(z=3.53)

Mean excess wages -0.01 -0.01 -0.01 *

(t-value 1.76)

Median excess wages -0.00 -0.02 0.02 ***

(z=-4.13)

N 43,852 43,852

Table 7. Panel A. wage changes by establishments which remain in the sample during the

post period versus those establishments which are not found in the post period. The sample

consists of all establishments linked to public US targets of acquisitions by public or private US

acquirers and where control was successfully transferred (defined as an acquisition for at least

50% of the target). The "Pre-acquisition” period is defined as the fiscal year before the deal was

announced. The "Post-acquisition” period is defined as the fiscal year following the one year

anniversary of the deal completion. Means are winsorized at 1%. Raw wages are estimated as

total annual payroll/total employees and then log transformed. Excess wages is the residual from

a regression: raw wages = industry-year mean wages + state-year mean wags + error. State-year



establishment’s industry and by year. Significance is noted as ***, **, * for 1%, 5% and 10%

respectively.

Observed in the post-

acquisition period

Not observed in the

post-acquisition period

Difference

Mean raw wages 2.96 3.00 0.04 ***

(t-value = 10.13)


(z = 11.69)

Mean excess wages -1.00 -1.13 -0.13 ***

(t-value = 11.83)

Median excess wages -0.08 -0.17 -0.09 ***

(z = 12.63)

Mean number of

employees

63.63 39.99 -23.65 ***

(t-value = 21.54)

Median number of

employees

14.00 9.00 -5.00 ***

(z = 56.43)

N 120,364 56,252

Table 7. Panel B. Wage changes using set of deals where 100% of target establishments are

observed both before and after. The sample consists of establishments linked to public US

targets of acquisitions by public or private US acquirers and where control was successfully

transferred (defined as an acquisition for at least 50% of the target). For an establishment to be

included in the sample, wages for all target establishments involved in the merger must be

observed during both the pre- and post-acquisition period. The "Pre-acquisition” period is

defined as the fiscal year before the deal was announced. The "Post-acquisition” period is

defined as the fiscal year following the one year anniversary of the deal completion. Means are

winsorized at 1%. Raw wages are estimated as total annual payroll/total employees and then log

transformed. Excess wages is the residual from a regression: raw wages = industry-year mean

wages + state-year mean wags + error. State-year mean wage is the log mean wage per

employee in the state of location of the establishment and matched by year. Industry-year mean

wage is the mean log wage per employee matched to the establishment’s industry and by year.



Mean raw wages 3.25 3.34 0.09 ***

(t-value 7.13)


(z = 5.82)

Mean excess wages -0.41 -0.27 0.15***

(t-value 5.48)

Median excess wages 0.11 0.04 -0.07 ***

(z = 5.88)

N 7,284 7,284

Table 8. Wage changes around mergers with robustness controls. The dependent variable is

excess wage change at the target firm. Excess wages is the residual from a regression: raw wages

= industry-year mean wages + state-year mean wags + error. State-year mean wage is the log

mean wage per employee in the state of location of the establishment and matched by year.

Industry-year mean wage is the mean log wage per employee matched to the establishment’s

industry and by year. Excess wage change is calculated as excess wages in the post-period minus

excess wages in the pre-period. The "Pre-acquisition” period is defined as the fiscal year before

the deal was announced. The "Post-acquisition” period is defined as the fiscal year following the

one year anniversary of the deal completion. Acquirer and target firm size is measured as total

assets in 2008$. These variables are log-transformed. Manufacturing dummy variable takes a

value of 1 if the target operates in the manufacturing industry as defined by SIC codes 3000-

3999. Standard errors are robust and clustered at the deal level. Significance is noted as ***, **,

* for 1%, 5% and 10% respectively.

1 2 3 4

Intercept 0.896

(0.421)

**

0.667

(0.352)

*

0.375

(0.214)

*

0.523

(0.132)

***

Acquirer Firm size -0.064

(0.033)

*

-0.226

(0.124)

*

Target firm size 0.214

(0.139)

Acquirer firm-level mean excess wages

-0.450

(0.410)

Manufacturing dummy variable 0.016

(0.249)

Number of clusters 421 421 421 1,372

Number of observations 27,445 27,445 27,445 90,491

R-squared 0.00 0.041 0.006 0.000

Documents

Mergers and Employee Wagespublic.kenan-flagler.unc.edu/faculty/ouimetp/mergerwages.pdfacquisition, on average, by 9.4%. Wages increase more following those mergers with the greatest