The Effect of Mergers on Human Capital:Evidence from Sell-Side Analysts

PARTH VENKAT∗

NOVEMBER 2016

Abstract

I find that when brokerage houses merge, the analysts of acquiring houses tem-

porarily produce less accurate estimates. This temporary impairment suggests that

the merging process can distract high-skill employees. Further, among redundant an-

alysts, high-quality target-house analysts are the most likely to leave upon merger

announcement. This suggests that employees exercise outside options when redeploy-

ment requires abandoning human capital. As a consequence of these effects, forecast

error in merging houses remains elevated by 10% in relation to non-merging houses

for two years. I conclude that mergers can temporarily, but significantly impair firms’

ability to acquire, develop, and retain human capital.

∗Department of Finance, McCombs School of Business, University of Texas at Austin, parth@utexas.edu.I would like to thank Laura Starks, Jonathan Cohn, Cesare Fracassi, Andres Almazan, and Michael Clementfor their invaluable advice and support; Jacelly Cespedes, William Grieser, Shuo Liu, Zack Liu, GonzaloMaturana, Carlos Parra, Nathan Swem, Adam Winegar, and Ben Zhang for their comments as colleagues;the McCombs Finance Department for their seminar comments; Jarrad Harford and the other participantsat the FMA Student Symposium; and Paul Irvine for his discussion at the FMA. All errors are my own.

While existing research characterizes the overall value implications of mergers, delving

into how mergers affect the value of specific assets, through which mergers create or destroy

value, remains challenging.1 Perhaps least understood is how mergers impact the value of

human capital, arguably the most important asset class in the modern economy.2 While

merger synergies may increase the value of human capital, the process of integrating two

workforces may impose costs that limit those synergies. In a recent survey, companies that

reported their own mergers as “failing” assigned some blame to “people and integration

issues.”3 Certain issues can be short-term, such as employee or management distraction,

while others can be longer term, such as unresolved cultural mismatch or lost and unreplaced

key talent. The goal of this paper is to understand how mergers impact merging firms’ ability

to acquire, develop, and retain human capital.

To understand how mergers impact the value of human capital assets, I explore the

performance and retention of sell-side analysts employed by merging brokerage houses. This

setting conveys several advantages. First, analyst groups consist almost solely of human

capital (i.e., their expertise and connections), allowing me to isolate the effect of mergers on

human capital from effects on other classes of assets.4 Second, analysts publish a stream of

corporate earnings forecasts that can be compared to actual earnings to construct frequently

time-varying measures of employee performance. Third, because analysts can be tracked

across brokerages, I can study the retention and separation of analysts. Finally, industry

consolidation provides a diverse set of mergers, creating a statistically powerful setting.

The primary contribution of my paper is to document and interpret two facts. First,

analysts who work for acquiring houses before a merger become less accurate after the merger.

This effect dissipates within four months, suggesting that distractions due to the integration

1See Betton, Eckbo, and Thorburn (2008) on combined announcement returns and Healy, Palepu, andRuback (1992) and Andrade, Mitchell, and Stafford (2001) a on operating performance.

2I follow Goldin (2016) and explicitly define human capital as “the stock of skills that the labor forcepossess.” I cite to Acemoglu (2002), Autor, Levy, and Murnane (2003), and Abowd, Haltiwanger, Jarmin,Lane, Lengermann, McCue, McKinney, and Sandusky (2005) with respect to the increasing importance ofhuman capital.

3The survey of almost 90 M&A professionals from McKinsey & Company lists issues such as “culturalmismatch, loss of key talent, lack of management commitment [and] lack of employee motivation” (Deutschand West (2010) and referenced in Shermon (2011)).

4See Brown, Call, Clement, and Sharp (2015) for evidence about the value of connections to managementand Swem (2016) about the value of connections to institutional investors

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of operations or cultures temporarily impair employee performance. Second, target analyst

monthly attrition increases substantially after a merger is announced, especially for high

quality target analysts. This loss of key talent may have longer-lasting implications for

the value of a merged houses’ collection of human capital.5 As a consequence of these two

effects, the average forecast error of all forecasts produced by merging houses increases by

10% relative to forecasts of non-merging houses for the same set of covered firms. To put

this effect in context, it is slightly larger than the difference between a perfect estimate and

the estimate at the 25th percentile.6 Consistent with a drop in output quality, the combined

houses reduce overall equity coverage by 5%.

I document the first fact that acquiring house analysts suffer temporary output im-

pairment using a difference-in-differences framework that includes individual analyst fixed

effects.7 This drop is larger than the difference between the average star analyst’s accuracy

and non-star analyst’s accuracy, where star is defined by Institutional Investor All-America

Research Team designations. This impairment is consistent with Schoar (2002), which finds

that mergers can result in the impairment of assets already in place. The effect is short

lived, dissipating within a year, which implies merger impairments to individual analysts’

human capital are temporary rather than permanent. Anecdotal evidence from discussions

with sell-side analysts suggests that employees can be distracted by junior-staff shuffling,

training, client-base expansion, or moving offices.

In order to study how mergers impact brokerages collection of human capital, I construct

a new measure of analyst quality. Previous literature typically uses star designations to proxy

for quality. However, this approach omits public information that would be indicative of an

analyst’s historical performance and cannot differentiate between non-star analysts. I create

a more comprehensive measure by fitting a logit regression for non-merger analysts to predict

negative career outcomes.8 By conditioning on actual decisions brokerage houses make, such

5There is no significant change in the accuracy of target house analysts who are retained or any changein acquiring house analyst attrition.

6Forecast error increases in 27 of 34 mergers. This effect is not driven by outliers and is robust tononparametric specifications

7Target analysts who keep their jobs improve their accuracy at times, but this effect is not significantacross all mergers.

8I consider instances when an analyst leaves the data entirely or moves to a less prestigious house as a

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as analyst terminations, this regression captures brokerage houses’ revealed preference for

analyst observables. I define quality as 1 minus the predicted values from this regression.

Using quality, I study attrition to document fact two. I find that during the time between

the merger announcement and its completion, which I term the interim period, target analyst

monthly attrition increases to 13% (compared to 1% unconditionally) relative to similar

analysts not involved in a merger, while the acquiring house analyst attrition does not

change. This is true even though acquiring house analysts are not systematically higher

in quality. Focusing on target analysts’ attrition, I show that the highest quality target

analysts’ (top quintile) monthly attrition increases to 20%, while the lowest quality target

analysts’ (bottom quintile) monthly attrition only increases to 8%.9 While this might be

partially driven by cost savings (because wages are likely correlated with quality), for the set

of target analysts who separate during a merger, higher quality analysts are more likely to

find another analyst job, suggesting that the availability of outside options impacts target

analyst separation decisions.

To understand why high-skill analysts leave, I examine redundancy. Analysts may antic-

ipate being asked to cover new firms (i.e., be redeployed) if they cover firms already covered

by the acquiring house analysts.10 However, redeployment may be costly for analysts, be-

cause it may require abandoning their firm and industry expertise and connections.11 I define

Redundancy as the fraction of firms an analyst covers before the merger that are also cov-

ered by the other merging house. Redundant target analyst attrition increases significantly

more (22%) than attrition for unique analysts (Redundancy equal to 1 and 0) from the same

house (6%). This effect is over 50% larger for the highest quality target analysts, which is

consistent with redeployment being costly and the existence of outside options increasing

with their quality. Further, when target analysts get new jobs, either at new firms or the

merged entity, they almost always continue to cover the firms that they previously covered.

negative career outcome and use observables, such as star rating, accuracy, productivity, optimism, firmscovered, tenure, and experience.

9This effect is monotonically increasing across quintiles.10Brokerage houses require a singular view on firms they cover.11Brown et al. (2015) cites the ability to communicate with senior management as an analysts primary

driver of value. These relationships take time to build.

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In contrast to Tate and Yang (2015), who show that diversifying mergers can improve out-

put by facilitating human capital redeployment, my evidence suggests that redeployment

synergies may be difficult to unlock in industries that involve employees who have valuable

but specific human capital.

To explore whether high-quality analysts are choosing to leave during mergers, I exam-

ine how analysts react to an increasing probability of termination. Because redundancy is

generally only known after the merger announcement, it is a plausibly exogenous shock to

the probability that an analyst separates from the target house in the interim period. I

find that the increased probability of separation reduces productivity, which is measured as

the number of reports an analyst produces. This result suggest that when target analysts

know that job retention likely involves switching roles, they shift their efforts elsewhere (e.g.,

towards leisure or finding a new job) rather than increase their efforts to compete within the

merged firm.

A related industry-wide increase in forecast error has been documented by Hong and

Kacperczyk (2010), who attribute the merger-related performance decline to industry con-

solidation and the accompanying decrease in analyst competition. Hong and Kacperczyk

(2010) argue that forecast error increases because analysts intentionally decide to bias es-

timates upwards in order to cater to corporate clients. The differential impact cannot be

explained by competition declines because competition has the same impact on estimates

for the same firms. Similarly, because competition does not recover, it cannot explain the

temporary forecast error increase. Using whether an underlying firm is covered by both the

target and acquirer versus just one or the other as a shock to the intensity of the compe-

tition decline, I divide estimates into large competition shock and small competition shock

estimates. I find no differential impact on forecast error or positive bias further supporting

the idea that competition declines are not the cause of the increased forecast error.

To further differentiate unintended errors from workforce integration issues from intended

bias from changing priorities, I exploit a structural shift in analyst incentives, specifically

the 2003 Global Analyst Research Settlements (GARS), which created “brick walls” between

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the research and investment banking divisions of large investment firms.12 Even after this

plausibly exogenous shock to analysts’ incentives, the results persist, implying that results

are more likely due to workforce integration issues as opposed to changing priorities.

Finally, I run two cross-sectional tests in order to confirm that workforce integration

issues are related to human capital issues and overall forecast error increases. I analyze the

merger announcements’ text and mark 14 mergers in which human capital or expanding

services is not mentioned as a merger motivation. Independently, I mark the tercile of

mergers that have the greatest ex post increases in forecast error. I restrict my tests to the

two subsets above, and in both subsets I find that the retained analyst accuracy declines

by 70% more than it does in the full sample and that high-quality target analyst attrition

increases almost 100% more than in the full sample. Thus, workforce integration issues (e.g.,

employee distraction, loss of key talent) are largest for mergers for which it is publicly stated

that human capital is not the primary motivation for the merger and for mergers that suffer

the largest overall forecast error declines.

Sheen (2014) and Hoberg and Phillips (2010) use micro-data to show that mergers create

value by consolidating production to reduce costs and by facilitating product differentiation.

Alternatively, I use micro-data to argue that while mergers create value via synergies, those

synergies may have implementation costs. These implementation costs, such as employee

distraction or a loss of key talent, are consistent with the theory of the firm literature that

finds limits to integration due to human capital ownership rights (Grossman and Hart (1986),

Fulghieri and Hodrick (2006), and Fulghieri and Sevilir (2011)). Ouimet and Zarutskie (2016)

find that some mergers aim to acquire human capital. My results suggest that managers

should consider the integration consequences of such acquisitions.

My results further suggest that brokerage house mergers are unlikely to provide an exoge-

nous shock to industry competition, because workforce integration effects are not excluded.

Alternatively, these mergers do negatively impact stock market information, as in Kelly and

Ljungqvist (2012). One concern with their method is that several brokerage house mergers

happen during the tech-bubble burst, and it is possible that firms impacted by the merger

12See https://www.sec.gov/news/speech/factsheet.htm.

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shock were disproportionately impacted by the recession. My expanded list of mergers can

help mitigate that concern if researchers run their tests using my non-recession subset of

mergers, which still impair accuracy due to merger related issues. Finally, all results should

be temporary because the information shock is not permanent.

My attrition results are consistent with Wu and Zang (2009), but their analysis of how

attrition impacts forecast error differs. First, Wu and Zang (2009) find that star or top

performer attrition does not impact forecast error. This may be due to their use of a less

comprehensive measure of quality, which results in a lack of power. Second, I document

that forecast error increases are not permanent, and although workforce integration does

reduce human capital stock, houses are able to recover. Finally, their result is insufficiently

identified. Because analysts are optimistic, crashes can create a spurious correlation between

attrition and forecast error.13 By explicitly controlling for market downturns, I verify that

workforce integration issues drive forecast error increases.

I discuss my data and the mergers in the next section. In Section II, I present results and

the accompanying empirical strategies. In Section III, I discuss some identification issues.

In Section IV, I conclude.

13Many of their mergers occurred in 1999 to 2001 before the DotCom Crash.

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1 Data

Information on analysts and earnings estimates comes from the Thomson Reuters Insti-

tutional Brokers Estimate System (IBES) database spanning the period 1980 through 2013.

IBES provides individual analyst earnings forecasts, buy-sell-hold recommendations, and re-

ported earnings. Analyst earnings estimate level information comes from the detail history

U.S. earnings estimate file. Unique analyst identifiers allow the tracking of analyst careers

across brokerage houses.14 In addition to the detail file, the recommendation file within

IBES is used to help map brokerage house names to news stories.

I identify star analysts using Institutional Investor magazine rankings (e.g., Gleason and

Lee (2003), Clement and Tse (2005), Cohn and Juergens (2014)). Because the magazine

does not contain IBES identifiers, I hand-match stars to I/B/E/S using name, brokerage

house and time of employment. I label analysts as stars only if all three match.

I also use news releases regarding merger announcements from Factiva and corporate

websites for company history.

1.1 Mergers

My sample includes 34 brokerage house mergers, listed in Table 1.15,16 These mergers

impact 2,327 distinct analysts: 884 from target houses and 1,718 from acquiring houses.

Matching the target and acquirer to the IBES data is difficult because the brokerage house

14I drop all observations with analyst ID number (ANALYS) equal to 1 or 0, as these are placeholders.I drop observations covering several indices (DOWI, MID1, RUS2, S4, S5, SAP1, SAP6), as these analystsupdate their estimates at a very high rate, which makes their activity measures outliers. Previous studiesfocus on annual estimates. While the majority of estimates are annual, 40% are also quarterly, so I focus onall estimates, but also run analyses on the annual estimates alone for robustness. I control for fiscal periodwhere appropriate.

15Thirteen are taken directly from Hong and Kacperczyk (2010), which those authors isolate by mappingSDC mergers that belong to SIC code 6211 (Investment Commodity Firms, Dealers, and Exchanges) tothe IBES database. I supplement with four mergers from Kelly and Ljungqvist (2012), and I also collectan additional 17 mergers by finding brokerage house closures in the data and by using news articles andcompany histories to determine whether the cause of closure was in fact a merger.

16Two financial crisis mergers, Bear Stearns being acquired by JP Morgan and Merrill Lynch by Bankof America, were omitted because of the federal government’s involvement in encouraging and subsidizingthe mergers. Also, because of the financial crisis, attrition and forecast error are uniquely high. I am lesscomfortable with the external validity from these mergers. All results are robust to their inclusion andusually have larger partial effects.

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names in the recommendation file are shortened nicknames that are often based on historical

names as opposed to current brokerage house names.17 Thus, matching requires a careful

reading of each brokerage house’s corporate history to determine whether the IBES nickname

corresponds to any preious historical names of the brokerage house. I require that at least

some target analysts who leave the target join the acquiring firm at the merger dates, and I

require that the target house no longer appears in the data after merger completion.

For merger announcement dates, I use the earliest date that a merger is mentioned

in the Factiva, news aggregation service. I use details from these press releases to classify

mergers into two categories: those that appear to highly value the target’s human capital and

those that do not. Mergers in which research expansion, increased services or the analysts

themselves are mentioned as a primary motivation for merging are labeled as Labor Valued

while mergers for which increasing assets under management or access to new clients is the

primary driver are labeled as Labor Not Valued.

My list of mergers are provided Table 1. They cover a relatively long period, with

the earliest merger occurring in 1988 and the most recent in 2012. There is considerable

clustering in the late 90s and early 2000s. Eight of the merger targets have fewer than seven

analysts, while four have over 50 analysts. Justifications for the mergers vary, including (but

not limited to) acquiring an underperforming house, deregulation, industry-wide conditions,

and strategic or geographic expansion. Within four months after the merger announcement,

most mergers are completed and no analysts remaining under the target house name.

For most tests, I treat each merger as an independent event, even if they overlap in

calendar time. I create periods in event time, extending 30-day periods in each direction

from the merger announcement.

17For instance, Wachovia is represented by the name WHEAT from one of its predecessors, J.C. Wheat& Co.

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2 Results and Empirical Design

2.1 Overall Estimate Level Changes - Difference in Differences

To document the overall impact of mergers on human capital output, I run difference-

in-difference specifications on forecast error, the absolute deviation of the estimate and the

actual earnings scaled by the firms previous stock price. The first difference is between before

merger announcements and after merger completions, and the second for estimates produced

by merging houses versus non-merging houses. I present the evidence both graphically and

as regressions. The regression specification is below.

ForecastErrore,t,f,p = β1Postp,Subsumed+

β2Inmergerh + β3Postp ∗ InMergerh + β4Timelinesse,t,f,p + αe,t,f (1)

Where timelinesses defined as the days between each estimate’s publication date and the

actual earnings announcement. Timeliness of each estimate lies on a spectrum from timely

(published early in the fiscal period) and untimely (published very close to the announce-

ment). Controlling for timeliness is important because of previous work showing that as

timeliness decreases analysts become more accurate (better information) but less optimistic

(incentive to allow firms to beat their estimates). Regressions include fixed effect transfor-

mations for Event, Period and Fiscal Period (quarterly or annual estimate) and are clustered

at the event level.

Figure 1 panel A shows that even though all estimates experience some increase in forecast

error, the increase is significantly larger, around 10 basis points, in estimates produced by

brokerage houses involved in mergers. This result is confirmed in panel C, where I plot the

coefficient estimates of a difference-in-differences regression with 90% confidence intervals

In Table 2, I present the coefficient estimates for the same regression. Column 1 shows the

overall average impairment due to mergers which is around 14 and 10 basis points (90 day

and 1year sample respectively). The differential impact on merging houses is significant but

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also temporary, recovering in the third year. To put this effect in context it is around 10%

increase with respect to the mean forecast error of 1% and is slightly larger than the difference

between a perfect estimate (no forecast error) and the estimate at the 25th percentile (9.6

basis points).

This overall effect can be decomposed into four different pieces. First, either the analysts

from the target or the analysts from the acquirer who keep their job in the merged firm

can become more or less accurate. Second if high or low quality analysts from the target or

acquirer are not retained after the merger, the collection of analysts in the merged firms can

impact overall quality.

2.2 Impact of Mergers on Individual Analyst Accuracy

First, I test how individual analyst’s forecast error changes from their pre-merger base-

line to their post merger estimates. Note that previous tests were run at the estimate

level while these tests are run at the analyst-month level. Variables such as forecast er-

ror are averaged for each analyst-month. Controlgroups are limited to analysts who cover

at least 50% of overlapping firms. I run Difference-in-Differences (DID) regressions with

event#analyst#employer fixed effects on analyst accuracy.18 The tight fixed effect specifi-

cation guarantees that variation comes from within individual analysts who do not change

employers around the merger (with the exception of when target analysts join the merged

firm). The DID specification controls for market downturns and other underlying stock

related shocks. Standard errors are clustered at the Event level.

Table 3 shows the within analyst changes in forecast error from the four months before

the merger announcement to the 4 months after merger completion. Column 1 compares all

merger analysts to non-merger analysts while columns 2 and 3 compare target and acquiring

house analysts to similar non-merger analysts respectively. I find average monthly forecast

accuracy for analysts in the four months after the merger completion drops 6 basis points

more for analysts involved in a merger than analysts who are not. Restricting the sample

18Employer is defined post-merger to properly account for target analysts who are retained by the mergedentity.

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to just Acquiring house analysts, the result increase to 11 basis points. In columns 4 and

5 I show this effect mostly dissipates within the year. 4 shows there is no pre-trend and

that there is only significance for the first four month period, not the subsequent four month

periods. Column 5 compares the first four month post-period to the next eight month post-

period and shows the 11 basis point effect is reduced by 8 basis points. To put these changes

in perspective, a 10 basis point drop is the difference between a perfect analyst-month and

the 25th percentile.

This suggests acquiring house analysts suffer an productivity shock that temporarily but

not permanently harms their human capital output.19 This is consistent with Schoar (2002)

who finds a positive effect on acquired physical assets, but an offsetting and larger impair-

ment of the physical assets already in place. While there is no direct evidence to explain

why this temporary impairment occurs, anecdotal evidence from discussions with sell-side

analysts suggest that mergers are often accompanied by management shakeups, shuffling of

junior staff, training new staff, catering to new clients, moving offices or excessive meetings.

The impairment I document is both economically and statistically significant even with-

out considering physical capital magnifying integration issues, which suggests operational

disruptions from merging firms may be large.

2.3 Impact of Mergers on Collection of Human Capital

2.3.1 Analyst Quality

I develop a measure of individual analyst quality. I fit a logit regression for analysts

outside of mergers with the dependent variable being an indicator variable for whether an

analyst experiences a negative career outcome during a month against past analyst observ-

ables. The negative career outcomes I capture in the data are non-promotion separations.

Separation is a binary variable equal to 1 for any analyst-period-house observation that is

the last period in which an analyst releases estimates for a particular brokerage house. Using

brokerage house size (both by number of firms covered and analysts employed) as a proxy

19The target analysts who keep their jobs improve their accuracy at times, but this effect is not significanton average across all mergers.

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for brokerage house prestige, I exclude separations in which an analyst promptly switches to

a more prestigious job because these are likely positive career events and would be affected

by quality in the opposite direction. Characteristics I use include whether an analyst is a

star, an analyst’s ranking amongst other stars, the analysts’ estimate accuracy, productivity,

optimism, the types of firms the analyst covers, job tenure and overall analyst experience.

The regression specifications used to predict quality take the form:

separationt+1,i = α + β ∗ analystcharacteristicst + αi + θt + ε. (2)

Regressions are run both as a Linear Probability Model (LPM) as shown in equation 2

and as conditional logits to account for the binary dependent variable. The conditional logit

with no fixed effects is used for the quality measure.

The results from these regressions are presented in appendix A1. While the R2 for these

predictive regressions (without fixed effects) is very low (1.4%), the coefficients are very stable

across fixed effect and LPM v Logit specifications. They almost all load directionally in their

expected decision. For instance, Star analysts and more accurate analysts are less likely to

lose their job while analysts who update their estimates less frequently are more likely to

lose their job. I define quality as 1 minus the predicted values from this regression for ease

of interpretation (higher value equals higher quality). It captures the revealed preferences

of brokerage houses for analyst traits.

2.3.2 Redundancy

Next I create a measure, redundancy, that captures how duplicative an analyst’s ex-

pertise is within each merger. I define analyst redundancy at the analyst-event level as the

fraction of firms an analyst covers that are also covered by the alternate house of the merger.

Specifically, the measure is calculated as (Distinct # of companies an analyst covers 150 days

prior to the merger which are also covered by the alternate house of the merger) / (Distinct

# number companies an analyst covers). I create this measure for target analysts as well as

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analysts unaffected by the merger around the merger announcement.20,21,22

Because tests should be agnostic to which companies an analyst covers, I create an

additional measure, target non-merger redundancy, which is the fraction of firms a non-

merger analyst covers that are also covered by the InMerger house and restrict non-merger

analysts to analysts that have a target coverage overlap of at least 0.5. With this filter

the control group analysts should be affected similarly by idiosyncratic shocks in firms they

cover to merger analysts.

I also use a related measure, popularity, which refers to the competitiveness of the analysts

environment. It is defined as the average number of other analysts that cover the stocks

an analyst covers in a given month. An analyst with high popularity is operating in a

very competitive environment, covering stocks that lots of analysts cover, while an analyst

with low popularity operates in a low competition environment, covering stocks that few

analysts cover. While redundancy is specific to the merger (requires the alternate house as

a reference), popularity is independent of the merger and when used as a control to mitigate

concerns that redundant analysts are different than non-redundant analysts by controlling

for the level of competition an analyst faces.

2.3.3 Post-Merger Attrition

In order to test the second channel, how mergers can alter a firm’s collection of human

capital, I study analyst attrition using DID regressions with analyst fixed effects. To do so,

I run regressions of the form

Separatione,h,a,t = β1postt ∗ InMergere,h + αe,a,h + ωt (3)

where e denotes event, h house, a analyst, t, event-time. β1 is the main variable of interest

20The analysts in the acquiring house have redundancy 1̄ by construction21When events overlap in calendar time, the same control group analysts will appear multiple times in

the data with different cov per and different period definitions all based on the specific acquiring firm andspecific announcement date.

22For robustness I run all the tests with the total number of companies covered as sum(COVERED), anddummies SOME COV = 1 if cov per > 0 and HALF COV = if cov per > 0.5 or 0 otherwise.

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and ω and α denotes unobserved heterogeneity. β1 measures the within analyst changes

in pr(separation) comparing 90 days prior to the merger announcement to 90 days after

the announcement prior to merger completion. I define the dependent variable, Separation

Month as equal to 1 for all analyst-months prior to the month an analyst separates from

their current house and 0 otherwise.23 The variables Past and InMerger are subsumed by

the fixed effects.

Table 4 shows these results. Column 1 compares the change in attrition of analysts

who are subject to a merger announcement (either as a target or acquirer) versus analysts

who cover similar underlying firms but are not subject to the merger announcement. Ana-

lysts subject to a merger announcement experience increased attrition of almost 4% (4x the

unconditional average of 1%).

In Table 4 column 2, I split the analysts impacted by the merger into acquiring and target

house analysts using the dummy Post*InMerger*TargetMerger. Attrition increases 12% for

target analysts in relation to similar control analysts while there is no significant increase

in attrition for acquiring house analysts. I confirm this result in column 3 by running the

regression on only target house analysts and their comparable control group analysts.

In Table 4 column 4, I subdivide target house analysts to help determine where attrition

is the largest using the triple difference specification of:

Separatione,h,a,t = β1postt ∗ InMergere,h+

β2postt ∗Redundancye,a,h + β3postt ∗ InMergere,h ∗Redundancye,a,h + αe,a,h + ωt (4)

where redundancy is defined in the previous subsection. Attrition is much higher for re-

dundant analysts in the target house v unique analysts in the target house. Attrition for

unique (0 firms covered by this analyst were covered by the acquiring house prior to the

merger announcement) analysts increases by over 6% as measured by β1. β2 captures the

difference for control group analysts’ attrition differences between unique versus redundant

23unlike before, I do not remove promotion-like separations. Previously my goals was to measure the stockof human capital whereas now I am interested in all forms of separation.

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analysts and tells us redundant analysts unaffected by the merger are slightly more likely to

keep their job. This is likely due to the fact that redundant analysts are often high quality

analysts who cover popular stocks (stocks covered by more analysts). β3 is the variable of

interest which tells us attrition for a fully redundant analyst (all firms covered by this analyst

were covered by the acquiring house) increases by over 21% when compared to unique target

house analysts.24 The takeaway is that merging firms downsize by reducing head count and

that reduction comes predominately from analysts within the target house, not the acquiring

house, who are redundant rather than unique.

Next I incorporate the proxy for analyst quality with target house separations within

mergers. In Table 5 column 1, I show that when I look at only redundant analysts from

the target and acquiring firm, quality has no differential impact on attrition within mergers

ruling out the possibility that acquiring house analysts are systematically higher in quality

than target house analysts. In Column 2, I look only at target house analysts with no fixed

effects, which allows interpretation of each coefficient of the triple difference. Post captures

the non-merger trend in attrition which is positive by construction. InMerger captures

the pre-merger announcement differential in attrition between target house and non-merger

analysts. This coefficient is indistinguishable from 0 meaning mergers are not being initiated

based on underlying analyst quality. z(Quality) and Post*z(Quality) load negatively which

provides an out of sample test of the quality proxy, high quality analysts outside of mergers

are less likely to separate than low quality analysts. Post*InMerger captures the increase

in attrition after the merger announcement for low quality target analysts and is small but

significantly greater than zero. The triple difference coefficient, Post*InMerger*z(Quality),

is the variable of interest and captures the differential impact in attrition for high versus

low quality analysts within merger targets. This coefficient is economically and statistically

significant and is interpreted as a standard deviation increase in analyst quality makes a

target house analyst 4% more likely to separate from the firm in a given month post merger

announcement.

24Most analysts are not either fully redundant or completely unique. I show the results using a standardizedredundancy measure and find for a standard deviation change in redundancy attrition increases by almost7%.

15

In Table 5 Column 3, fixed effects subsume Post, InMerger, z(Quality), and InMerger*z(Quality).

The triple difference coefficient is additional evidence that after merger announcements high

quality analysts are 4% more likely to leave than low quality analysts. In columns 4 through

8 the data is split into quality quintiles with 1 being analysts of the lowest quality and 5 be-

ing the highest in order to confirm the result from column 2 and 3. Attrition differential for

high quality analysts is 19% while the differential is only 6% for the lowest quality quintile.

The result monotonically increases across quintiles. Finally in column 9, I show the result

from column 3 is 50% larger when the sample is restricted to only analysts with redundancy

of over 1/2.

Next, I show evidence that the separation is at least in part due to higher quality target

analysts choosing to leave their firms upon the merger announcement. First, in Table 6 I

show that contingent on separation, higher quality analysts are more likely to find another

analyst job than lower quality analysts implying that at least some of the target analysts

choose to leave because they have stronger outside options. In Table 7, I study how often

analysts drop coverage. For the set of target analysts that find a new analyst job, either

in an alternate brokerage, New Job, or in the merged entity, Kept Job what percentage of

firms an analyst covered before the merger do they continue to cover after it. This fraction

is very high. It is 91% for the median analyst who switches to a new house and 97% for

the median analyst who keeps his job. This suggests that the human capital of an analyst,

their expertise and connections, are not easily transferable and that dropping coverage is

consistent with abandoning human capital. So, not only are higher quality analysts more

likely to find a new job contingent on separation, they are likely to perform the same job as

before just at a new house.

Second, if redundant target analysts expect they are unlikely to retain their job in their

current role because of redundancy, rather than work harder to keep their job, upon the

merger announcement they may shift their effort elsewhere, for example, towards finding

a new job. I use InMerger redundancy, shown above to be strongly related attrition, as a

plausibly exogenous shock to the probability an analyst separates from the firm and test how

16

the probability of separation impacts productivity. I define productivity as the number of

reports an analyst releases in a month by counting unique ticker-date pairs for each analyst

month.25

Table 8 shows within analyst changes in productivity around merger announcements.

Column 1 contains a triple difference comparing unique to redundant analysts who are

targets of the same merger. I find that when comparing production changes within target

house analysts, redundant analysts reduce their productivity by 0.8 reports in comparison

to unique analysts. This is a drop of about 16% of the mean productivity for target house

analysts prior to the merger announcement (Mean productivity is 5.1 reports). Because

redundant analysts are the ones facing the highest pr(separation) this is consistent with

high quality target analysts who are likely to leave shifting their effort in anticipation of

finding a new job.

In column 2, instead of using the triple difference specification I use redundancy to

instrument for the probability an analyst separates. As shown in table 4, for target analysts,

redundancy is associated with a 20% in attrition (relevance) and is arguably not associated

with an analyst’s within merger change in productivity for any other reason other than the

increase in attrition (exclusion). This makes in-merger redundancy a plausible instrument

for identifying the impact on a change in the pr(separation) on an analyst’s productivity.

Confirming the finding in column 1, we observe a large drop off in productivity for analysts

facing a 100% increase in pr(separation).

In columns 3-5 we run the specification from column 1 but on smaller subsets. In column

3 I exclude analysts for which we cannot estimate quality due to missing data and in columns

4 and 5 I split that group by above median and below median quality. Consistent with the

flight of human capital results, high quality analysts drop productivity over 50% more than

low quality ones do. This result is consistent with analysts analysts looking for a new job

when they expect to be redeployed.

25Results are robust to alternate productivity specifications such as total firms covered or days with areport. All analyst-periods containing less than 30 days due to analyst separation are removed to not biasthe results with partial months.

17

2.4 Competition, Crashes or Merger Integration Issues

The findings in the previous sections can be driven by two alternate channels other than

the merger related channel that is the subject of this paper. First, Hong and Kacperczyk

(2010) argue that mergers reduce competition between analysts at different firms because

at least some analysts leave the industry. They argue that analysts face a trade-off between

winning an external tournament by being accurate and pleasing corporate clients by being

optimistic. Because analysts in the tournament are judged by relative accuracy and not

absolute accuracy they argue, when competition is reduced, all analysts covering the same

firm can afford to be more optimistic. This is an important alternate channel because I

am arguing that forecast error increases are due to unintended errors whereas this story is

arguing that the increases are doing to intended decisions analysts make. A second alternate

theory is because analysts are on average optimistic, unexpected market crashes can cause

temporary increases in earnings forecast error across all analysts.26

Going back to the original graphs, Figure 1 Panel A shows that even though all estimates

experience some increase in forecast error, the increase is significantly larger in estimates pro-

duced by brokerage houses involved in mergers and this difference is temporary, lasting only

two years. Because competition should impact analysts who cover the same firms equally, it

is hard to reconcile the differential impact seen by either explanation. The temporary nature

of the effect is not consistent the competition story because there is not off-setting new entry

of analysts. While there are possibilities that analysts are choosing short term-catering,

Clarke, Khorana, Pate, and Rau (2007) cast doubt on that channel by studying star analyst

transitions and finding that optimism has no impact on investment banking deal flow.

To be even more specific, I divide the estimates into redundant estimates and not-

redundant estimates, where redundant estimates are of firms which are covered by the target

and acquirer prior to the merger announcement and non-redundant are covered by just one

or the other are much more likely to suffer a competition shock.27 As shown in Figure 1

26See Brav and Lehavy (2003) and Bradshaw, Brown, and Huang (2013) for evidence of analyst optimism.27Recall that estimates are only included for firms that are covered by at least one of the two and that

attrition is highest amongst redundant target analysts.

18

Panel B and Panel D, estimates divided by the intensity of the competition shock have no

significant difference from each other in forecast error increase. This is true for forecast error

and positive bias.

In order to treat the recession channel, first I argue that the difference-in-differences

framework with period fixed effects should mitigate most concerns. But I also run my

results for mergers that happen during or right before a recession versus those that do not.

While the overall increase in forecast error does not increase for the non-recession mergers,

there is still a significant and differential impact of mergers even for mergers that are not

related to recessions.

Because this differential impact is not caused by a drop in competition, and not fully

explained by external market downturns, that leaves the mergers themselves as the primary

driver of the impairment.

2.5 Cross-Sectional Results based on Merger Subsets

I examine the two channels for impairment of human capital output during the merger

process in more depth by creating three independently created subsets outlined in Table 9.

First, in order to gain an ex-ante measure of human capital importance, I conduct a textual

analysis on the merger announcements, marking 14 mergers in which human capital, labor

or expanding services is not mentioned as a motivation for the merger and 20 mergers in

which these motivations are mentioned.

Second, I note the considerable variation in the cross section of forecast error increases of

mergers. In three mergers forecast error increases by over 1% (doubling), and in ten mergers

forecast error increases by 0.5% (increasing by 50%). On the other end of the distribution,

there is one merger in which forecast error is reduced by over 1% and seven which have at

least some improvement. In order to ex-post test that my channels actually correspond to

the same mergers that have an overall increase in forecast error, my second subset includes

only the top tercile of mergers with respect to overall forecast error increase.

Third, in 2003 the industry underwent a structural shift when the U.S. regulatory bodies

19

reached the Global Analyst Research Settlements (GARS), forcing walls to be constructed

between the research and investment banking divisions of the largest investment firms.28

This event creates a source of exogenous variation to a brokerage houses ability to cater to

corporate clients and I test my results for mergers before and after the structural shift.

In table A4, I show that the overall forecast error increases are double for the subset of

mergers in which labor is not valued. Meanwhile there is no significant difference between

pre and post global settlement forecast error increases.

In table 11, the temporary deterioration of quality, channel 1, is almost 70% larger for

mergers which ex-ante labor is not valued and ex-post suffer the largest drops in forecast

error. The partial effects before and after the global settlement remain for the most part

unchanged.

In table 12, high quality target analyst attrition, channel 2, increases from 6 basis points

to 10 basis points for mergers which ex-ante labor is not valued and doubles for mergers

which ex-post suffer the largest drops in forecast error. The one surprising result is that

the affect disappears post global settlement but this may warrant extra attention due to the

small sample size.

2.6 Mergers Uncontrolled Impact on Human Capital Output

To confirm the overall difference-in-differences results are driven by changes in merging

houses and not changes in the control group, I run uncontrolled regressions for robustness.

The sample for this analysis includes the set of earnings estimates published by the acquirer

and the target prior to the merger announcement (the merging houses) as compared to the

set of earnings estimates produced by the merged entity after the merger completion. I

restrict the sample to estimates for firms that are covered both before and after the merger

to mitigate any coverage decision selection concerns. Table A2 presents the merger level

results. Overall, across the merged firms, I find that average forecast error increases from

1.03% to 1.41%. This increase can be seen visually in Figure 1, Panel A, represented by the

28See https://www.sec.gov/news/speech/factsheet.htm

20

InMerger line. Although there were only 34 merger observations, this difference in brokerage

house aggregate forecast error is both economically and statistically significant (a change

in magnitude of over 35%). Nonparametric tests (not shown), such as a Wilcoxin sign-

rank test, confirm that these differences are different than zero, which stems from 27 of

34 mergers having at least some negative impact. Further, consistent with output quality

impairment, the combined houses reduce equity coverage by over 1% of the entire universe

of covered stocks (5% in relation to the mean), produce 273 fewer overall estimates, and

exhibit stronger optimism bias, which I define as the difference between the estimated and

the actual earnings scaled by stock price.

The increase in forecast error is not long-lived. Figure 1, Panel A, shows that forecast

error continues to increase in the second year (quarters 5-8), peaks in quarter 8, and then

drops sharply over the next 4 quarters. In Table A3, I confirm the results above using an

estimate-level, single-difference regression with the following specification:

ForecastErrore,t,f,p = β1Postp + β2Timelinesse,t,f,p + αe,t,f , (5)

where e denotes event, t ticker, f fiscal period, p is pre (target or acquirer) or post

(merged entity), and β1 is the variable of interest.

In Table A3, Post captures the average change in forecast error in moving from two

separate houses to one combined house. Column 1 shows that the forecast error for estimates

produced 90 days before the merger announcement are 24 basis points lower than estimates

produced 90 days after the merger completion. Column 2 extends the windows to a year on

both sides and the forecast error increase becomes 36 basis points. In Column 3, I compare

estimates for the base quarter, the one before the merger announcement, to estimates for

the three quarters before that quarter (pre-trend) and to the estimates a year after, two

years after, and three years after the merger completion. I see no significant difference

before the merger announcement (i.e., YN1 is not different from zero), while Y1 and Y2

are both significantly greater than 0 (.30 and .45, respectively). Confirming the temporary

nature of the result, the Y3 coefficient is not significantly different from zero. Some might

21

argue that the Y3 coefficient is .20, so not technically zero (even though it is statistically

indistinguishable from zero), so in Column 4, I run the same regression broken down into

quarters, which show that the effect is in fact temporary, as the large partial effect is driven

only by the first quarter of the third year. I add Event*Fiscal Period (FPI) fixed effect

transformations to control for unobserved permanent heterogeneity in the events, whether

the estimates are annual or quarterly, and I cluster standard errors at the event level.

3 Further Discussion of Identification Issues

The main identifying assumption is that the factor that drives the mergers (and their

announcements) are not correlated with the changes to analyst attrition, forecast error or

productivity. Reverse causality is unlikely to be an issue because analysts’ career concerns

or future output changes are unlikely to drive the mergers. Additionally, by using triple

difference specifications I compare before and after changes of analysts within the same

brokerage house who are affected by the merger.

In the appendix, I also run pre-event falsification tests using a false merger date 2 months

prior to the merger announcement and show that there are no differential trends in analyst

behaviors.

Omitted variables may influence both the outcome and the explanatory variables. Results

in this paper are run including analyst#event#house and event-time (or sometimes period,

defined as event#event-time) fixed effects. Using within analyst variation (especially over

the short time window around the merger announcement) controls for variables such as

analyst ability. It also mitigates concerns over selection bias with regard to who gets fired,

resigns or stays at the firm. 29

The period fixed effects mitigate time trend concerns, the largest being quarterly cycli-

cality in earnings season and reports as well as major market crashes. Note there are several

overlapping events, thus these are NOT month fixed effects but significantly more conserva-

29All the results hold for analyst#event fixed effects, but I also interact brokerage house to only capturevariation from analysts within the target house who have not switched houses yet.

22

tive 30 day period fixed effects that are independently defined for each merger event.

The results are clustered at the event level. Because explanatory variables are constant

across periods within an event for a given analyst, clustering time periods is essential. I

cluster my main results at the event level to be conservative. I cluster my falsification tests

at the event-analyst levels to work against falsification.

4 Conclusion: Beyond Analysts

This paper provides evidence on how mergers impact the acquisition, performance and

retention of human capital by analyzing sell-side analyst output quality and career outcomes

around brokerage house mergers. I find evidence suggesting that analyst output quality is

impaired. This impairment is driven by a failure to retain high-quality analysts from the

target house and by the output quality deterioration of retained analysts from the acquir-

ing house. These effects are especially large in merger subsets for which human capital

acquisition does not appear to be of first-order importance.

These effects are unlikely unique to brokerage houses. Because analyst output is observ-

able to the labor markets and managers, one might expect it would be easier to measure

quality resulting in more complete contracts and thus this is a lower bound for individual

employees of acquiring firms. I observe the opposite because of the mobility and the lack of

contract completeness common to high human capital employees.

Finally, note that I can say little about overall merger efficiency. Sufficient value may

be transferred from labor to shareholders through cost savings, or the brokerage division

may be a small portion of a larger firm and merger gains may be earned elsewhere. How-

ever, given that sell-side research is considered a public good due to its positive impact on

informational efficiency (Kelly and Ljungqvist (2012)), impairment of research quality can

negatively impact investors and firms. The FTC and DOJ should more carefully review the

consumer impact of mergers that occur between firms that operate in industries in which

human capital is crucial, but the merging firms do not appear to value human capital.

23

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26

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4/20/2007

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9/26/2007

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23

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fel

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28

Table 2: Estimate Level Operation Changes - Difference-in-Differences

DepVar: Estimate Forecast Error 90d 1y 3Y 12Q

Post * InMerger 0.14*** 0.10***(0.00) (0.00)

InMerger 0.11*** 0.10*** 0.11*** 0.11***(0.00) (0.00) (0.00) (0.00)

YN1 / QN3 * InMerger -0.01 -0.02(0.74) (0.45)

QN2 * InMerger -0.01(0.82)

QN1 * InMerger 0.00(0.86)

Y1 / Q1 * InMerger 0.10** 0.17***(0.01) (0.00)

Q2 * InMerger 0.11**(0.03)

Q3 * InMerger 0.08**(0.05)

Q4 * InMerger 0.07(0.12)

Y2 / Q5 * InMerger 0.10*** 0.10**(0.01) (0.02)

Q6 * InMerger 0.12***(0.01)

Q7 * InMerger 0.14**(0.01)

Q8 * InMerger 0.06(0.25)

Y3 / Q9 * InMerger 0.04 0.04(0.35) (0.35)

Q10 * InMerger 0.01(0.89)

Q11 * InMerger 0.03(0.61)

Q12 * InMerger 0.08(0.18)

z(Timeliness) 0.47*** 0.46*** 0.46*** 0.46***(0.00) (0.00) (0.00) (0.00)

Observations 563,363 3,212,344 6,570,582 6,570,582Adjusted R2 0.069 0.059 0.050 0.050Event Period FPI FE YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Difference-in-differences are reported using merger announcement and completions as treatment events from1988 to 2012. I compare the difference in annual and quarterly estimate forecast error before the mergerannouncement from the target and acquiring house and the merged entity after merger completion, todifferences in non-merger estimates of the same firms over the same periods. Results are presented for 90days, 1 year, 3 years, and 12 quarters. Forecast Error is defined as the absolute deviation from actualearnings scaled by current stock price. The binary independent variable InMerger is equal to 1 for estimatesof the merging houses and 0 otherwise. Post*InMerger is the interaction of InMerger and an indicator equalto 1 for all estimates after merger completion and 0 otherwise. z(Timeliness) is defined as the number ofdays before the earnings announcement the estimate is released. All specifications include event, Periodand FPI fixed effects to control for unobserved heterogeneity. Parentheses contain p-values computed fromstandard errors clustered at the event level. 29

Table 3: Analyst Changes in Forecast Error

DepVar: (1) (2) (3) (4) (5)Analyst Forecast Error Merger Acq Target Acq By Period Acq Post

Post * InMerger 0.06* 0.11** -0.04(0.08) (0.01) (0.42)

MN12-N9 * InMerger 0.01(0.80)

MN8-N5 * InMerger 0.00(0.94)

M1-4 * InMerger 0.09**(0.03)

M5-8 * InMerger 0.04(0.37)

M9-12 * InMerger 0.04(0.37)

M5-12 * InMerger -0.08*(0.08)

Observations 109,598 105,971 103,109 316,893 147,941Adjusted R2 0.171 0.173 0.174 0.156 0.215Analyst*Event EventTime FE YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Difference-in-difference estimates are reported using merger announcements as treatment events from 1988to 2012. The sample compares analyst quarterly and annual earnings estimates from four months before themerger announcement to up to 3 years after merger completion for analysts who retain their job post merger.The dependent variable Forecast Error, is measured as the monthly average absolute difference between ananalysts estimates and the actual earnings per share scaled by the current stock price. The control group isrestricted to include analysts with at least a 50% overlap with the target or the acquiring house before themerger. Column 1 compares all merger analysts to non-merger analysts. Columns (2), (4) and (5) excludetarget house analysts while Column (3) excludes acquiring house analysts. Column 4 compares the originalpost period, four months after merger completion, to the next four months. Column 5 compares every fourmonth period to the original pre-merger period, four months before merger announcement. All specificationsinclude Event-Time and Event×Analyst×House fixed effects transformations to control for unobserved het-erogeneity and to mitigate selection bias concerns. Parentheses contain p-values computed from standarderrors clustered at the event level. Specification 1: ForecastErrore,h,a,t = β1postt∗InMergere,h+ωt+αe,a,h

30

Table 4: Attrition around merger announcements driven by Target Redundancy

DepVar: (1) (2) (3) (4)Separation Month Targ & Acq Targ v Acq Just Targ Redundancy

Post * InMerger 0.04*** 0.01 0.12*** 0.06*(0.00) (0.40) (0.00) (0.05)

Post * InMerger * TargetMerger 0.12***(0.00)

Post * Redundancy -0.02*(0.10)

Post * InMerger * Redundancy 0.21**(0.01)

Observations 148,429 148,429 43,395 43,395Adjusted R2 0.153 0.155 0.157 0.158EventTime Job FE YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Linear probability model estimates are reported for difference-in-difference and triple-difference specifica-tions using merger announcements as treatment events from 1980 to 2012. The binary dependent variableSeparation is equal to 1 in months in which analysts separate from their brokerage house and 0 otherwise.The variable Redundancy is the fraction of coverage overlap that an analyst has with the acquiring housebefore the announcement. The control group is restricted to analysts with at least a 50% coverage overlapwith the target house. Specifications in Columns 1 and 2 include both acquirer and target house analystsas treated observations, while specifications in Columns 3 and 4 include only target house analysts. Allspecifications include Event-Time and Event×Analyst×House fixed effects transformations to control forunobserved heterogeneity and to mitigate selection bias concerns. Parentheses contain p-values computedfrom standard errors clustered at the event level.Specification 4: Separatione,h,a,t = β1postt ∗ InMergere,h + β2postt ∗ Redundancye,a,h + β3postt ∗InMergere,h ∗ Redundancye,a,h + αe,a,h + ωt where e denotes the event, h the house, a the analyst, trepresents the event time. β3 is the main variable of interest, and ω and α denotes unobserved heterogeneity.

31

Tab

le5:

Att

riti

onar

ound

mer

ger

annou

nce

men

ts-

Qual

ity

Dep

Var

:C

omb

Tar

gT

arg

Qual

ity

Quin

tile

Tar

g

Sep

arat

ion

Mon

thR

edN

oF

EF

E1

(Low

)2

34

5(H

igh)

Red

Pos

t*

InM

erge

r0.

10**

*0.

09**

*0.

12**

*0.

06*

0.09

***

0.13

***

0.16

***

0.19

***

0.20

***

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

7)

(0.0

1)

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

0)

Pos

t*

z(Q

ual

)-0

.03*

**-0

.01*

-0.0

3***

-0.0

3***

(0.0

0)

(0.0

8)

(0.0

0)

(0.0

0)

Pos

t*

InM

erge

r*

z(Q

ual

)-0

.00

0.04

**0.

04**

*0.

06**

(0.8

3)

(0.0

2)

(0.0

0)

(0.0

4)

Pos

t0.

04**

*(0

.00)

InM

erge

r0.

00(0

.96)

z(Q

ual

)-0

.02*

**(0

.00)

InM

erge

r*

z(Q

ual

)-0

.00

(0.8

3)

Con

stan

t0.

04**

*(0

.00)

Obse

rvat

ions

36,6

7943

,894

43,3

959,

003

8,74

18,

405

8,37

98,

867

17,9

27A

dju

sted

R2

0.30

40.

022

0.16

10.

166

0.15

50.

152

0.14

70.

160

0.15

9pva

lin

par

enth

eses

,StE

rrC

lust

ered

atE

vent

Lev

el***

p<

0.0

1,

**

p<

0.0

5,

*p<

0.1

Lin

ear

pro

bab

ilit

ym

od

eles

tim

ates

are

rep

orte

dfo

rtr

iple

-diff

eren

cesp

ecifi

cati

on

su

sin

gm

erger

an

nou

nce

men

tsas

trea

tmen

tev

ents

from

1988

to2012.

Th

ista

ble

splits

the

resu

ltfr

omT

able

4byz(Quality),

aco

nti

nu

ou

sst

an

dard

ized

qu

ali

tym

easu

regen

erate

din

Ap

pen

dix

A1.

Th

eb

inary

dep

end

ent

vari

ab

leSeparation

iseq

ual

to1

inm

onth

sin

wh

ich

anal

yst

sse

para

tefr

om

thei

rb

roke

rage

hou

sean

d0

oth

erw

ise.

Th

eco

ntr

ol

gro

up

isre

stri

cted

toin

clu

de

anal

yst

sw

ith

atle

ast

a50

%ov

erla

pw

ith

the

targ

eth

ou

se.

Colu

mn

1co

mb

ines

Acq

uir

ers

an

dT

arg

ets

as

asi

ngle

hou

sean

din

clu

des

on

lyan

aly

sts

wit

hre

du

nd

ancy

mea

sure

sov

er1/

2.C

olu

mn

2co

nta

ins

no

fixed

effec

ttr

an

sform

ati

on

wh

ile

inC

olu

mn

s3-9

,Post

,InMerger,

z(Quality),

an

dInMerger*z(Quality)

are

sub

sum

edby

the

even

t-ti

me

and

anal

yst

fixed

effec

ttr

an

sform

ati

on

s.S

pec

ifica

tion

s4

thro

ugh

8are

quality

qu

inti

les

wit

h1

bei

ng

an

aly

sts

of

the

low

est

qu

alit

yan

d5

bei

ng

anal

yst

sof

the

hig

hes

tqu

alit

y.T

he

fin

al

colu

mn

issi

mil

ar

toC

olu

mn

3b

ut

itin

clu

des

on

lyan

aly

sts

that

hav

ea

red

un

dan

cyof

at

least

1/2.

Par

enth

eses

conta

inp

-val

ues

com

pu

ted

from

stan

dard

erro

rscl

ust

ered

at

the

even

tle

vel.

32

Table 6: Pr(Find Analyst Job)|Separation for Target Analysts

DepVar: (1) (2) (3)Find New Job LPM LPM CL OR

z(Quality) 0.0762*** 0.0780*** 1.706***(0.00464) (0.000614) (0.00283)

Constant 0.379***(1.07e-08)

Observations 468 468 437Adjusted R-squared 0.020 0.160Event FE No YES YESNumber of event 23

Robust pval in parentheses*** p<0.01, ** p<0.05, * p<0.1

Linear probability model estimates and conditional logit odds ratios are reported using merger announce-ments from 1980 to 2012 as treatment events. Table 6 studies only target house analysts who separatearound the merger announcement. The binary dependent variable Find New Job is equal to 1 if the analystfinds another analyst job after separation and 0 otherwise. Specifications in Columns 2 and 3 include Event-Time and Event×Analyst×House fixed effects transformations to control for unobserved heterogeneity andto mitigate selection bias concerns. Parentheses contain p-values computed from standard errors clusteredat the event level.

33

Table 7: Redeployment Post Job Transfer

New Job Kept Job

N 266 N 304

Mean 0.83 Mean 0.88

Level Quantile Level Quantile

100% Max 1 100% Max 1

99% 1 99% 1

95% 1 95% 1

90% 1 90% 1

75% Q3 1 75% Q3 1

50% Median 0.91 50% Median 0.97

25% Q1 0.73 25% Q1 0.82

10% 0.50 10% 0.64

5% 0.33 5% 0.50

1% 0.20 1% 0.17

0% Min 0.07 0% Min 0.07

Table 7 shows summary statistics for human capital abandonment. For target analysts that remain in thedatabase, either at a New Job or within the new merged entity, kept job, I calculate the fraction of firms theanalyst still covers that they covered previously.

34

Table 8: Target Analyst Productivity around Merger Announcements

DepVar: (1) (2) (3) (4) (5)# Reports Triple Diff Inst Qual 6=. Low Qual High Qual

Post * InMerger 0.193 1.026 0.269 0.178 0.305(0.396) (0.177) (0.248) (0.523) (0.337)

Post * Redundancy -0.003 -0.270 -0.075 -0.308 0.145(0.987) (0.198) (0.708) (0.175) (0.586)

Post * InMerger * Redundancy -0.827* -1.293*** -0.952* -1.557***(0.059) (0.006) (0.084) (0.007)

Separation Month (Instrumented) -6.874**(0.020)

Popularity -0.008*(0.086)

Observations 37,790 40,292 31,108 15,201 15,904Adjusted R2 0.508 0.443 0.507 0.478 0.514Job Period FE YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Triple difference and instrumental-variable estimates are reported using merger announcements as treatmentevents from 1980 to 2012. The dependent variable, Productivity, is measured as the number of reportsan analyst produces in a given 30-0day period. The sample compares three months before the mergerannouncement to the three months after the merger announcement but before the merger closure excludingall periods less than 30 days due to analyst separation. Redundancy is the fraction of coverage overlap thatan analyst has with the acquiring house before announcement. The control group is restricted to includeanalysts with at least a 50% coverage overlap with the target house. Column 1 reports triple-differenceestimates comparing unique to redundant analysts within the target house. In Column 2, redundancy isused as an instrument for pr(separation). The IV specification allows inclusion of time-varying controls,so I add a control for Popularity. Popularity is the average number of other analysts who also coverthe stocks the analyst covers. Column 3 includes only analysts for which I can estimate quality. InColumns 4 and 5, I divide this group into high and low-quality. All specifications include Event-Time andEvent×Analyst×House fixed effects transformations to control for unobserved heterogeneity and mitigateselection bias concerns. Parentheses contain p-values computed from standard errors clustered at the eventlevel.

Specification 1: Productivitye,h,a,t = β1postt ∗ InMergere,h + β2postt ∗ Redundancye,a,h + β3postt ∗InMergere,h ∗Redundancye,a,h + ωt + αe,a,h where e denotes event, h house, a analyst, t period. β3 is themain variable of interest, and ω and α denotes unobserved heterogeneity.

35

Tab

le9:

Mer

gers

Subse

ts

Mer

ger

An

nC

omp

Targ

etA

cqu

irer

Fore

cast

Err

or

Lab

or

Post

Glo

bal

#D

ate

Dat

eD

iffV

alu

edS

ettl

emen

t

275/

079/

07C

och

ran

,C

aron

iaS

ecu

riti

esF

ox-P

itt

Kel

ton

2.87%

Yes

Yes

302/

086/

08F

erri

sB

aker

Watt

sR

BC

Wea

lth

Man

agem

ent

1.86%

No

Yes

2911

/07

1/08

Op

pen

hei

mer

CIB

C1.30%

No

Yes

285/

079/

07A

.G.

Ed

war

ds

an

dS

on

sW

ach

ovia

0.94%

No

Yes

261/

074/

07R

yan

Bec

k&

Co

Sti

fel

Fin

an

cial

0.90%

Yes

Yes

18/

887/

89B

utc

her

&C

o.,

Inc

Wh

eat

Fir

stS

ecu

riti

es0.74%

Yes

No

137/

0011

/00

Pai

ne

Web

ber

UB

S0.66%

Yes

No

159/

001/

01C

has

eM

anh

atta

n/

Ham

bre

cht

JP

Morg

an

0.61%

No

No

2510

/06

12/0

6M

ille

rJoh

nso

nS

teic

hen

Kin

nard

Sti

fel

Fin

an

cial

Corp

0.58%

Yes

Yes

111/

006/

00S

chro

der

sS

olo

mon

Sm

ith

Barn

ey0.52%

Yes

No

148/

0010

/00

Don

ald

son

,L

ufk

in&

Jen

rett

eC

redit

Su

isse

0.50%

No

No

82/

984/

98W

esse

lsA

rnol

d&

Hen

der

son

Dain

Rau

sch

er0.48%

Yes

No

124/

006/

00JC

Bra

dfo

rd&

Co.

Pain

eWeb

ber

Gro

up

0.45%

Yes

No

226/

0511

/05

Leg

gM

ason

Cit

igro

up

0.45%

No

Yes

169/

0011

/01

Dai

nR

ausc

her

Rb

cC

ap

ital

Mark

ets

0.34%

No

No

49/

9711

/97

Sal

omon

Bro

ther

sS

mit

hB

arn

ey0.34%

No

No

59/

973/

98Jen

sen

Sec

uri

ties

Co.

DA

Dav

idso

n0.34%

Yes

No

174/

0110

/01

Wac

hov

iaS

ecu

riti

esF

irst

Un

ion

0.32%

Yes

No

103/

9910

/99

EV

ER

EN

Cap

ital

Corp

Fir

stU

nio

nC

orp

0.29%

No

No

2410

/06

12/0

6P

etri

eP

arkm

an

&C

o.

Mer

rill

Lyn

ch&

Co

0.27%

Yes

Yes

210

/94

12/9

4K

idder

Pea

bod

y&

Co

Pain

eWeb

ber

0.23%

Yes

No

712

/97

2/98

Pri

nci

pal

Fin

an

cial

Sec

uri

ties

EV

ER

EN

Cap

ital

0.23%

Yes

No

3312

/11

3/12

Mor

gan

Kee

gan

&C

om

pany

Ray

mon

d0.19%

Yes

Yes

910

/98

6/99

Ale

xB

row

n-

Ban

kers

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stD

euts

che

Ban

k0.18%

Yes

No

612

/97

6/98

Un

ion

Ban

kO

fS

wit

zerl

an

dS

wis

sB

an

kC

orp

ora

tion

0.15%

No

No

208/

0410

/04

Sch

wab

Sou

nd

vie

wC

ap

ital

Ub

s0.04%

No

Yes

239/

051/

06A

dam

sH

arkn

ess

Can

acc

ord

Cap

ital

Corp

ora

tion

0.02%

Yes

Yes

32/

974/

97D

ean

Wit

ter

Morg

an

Sta

nel

y-0.04%

No

No

212/

056/

05P

arke

r/

Hu

nte

rIn

cJan

ney

Montg

om

ery

Sco

tt-0.07%

Yes

Yes

3411

/12

2/13

Kee

feB

run

net

teW

oods

Sti

fel

Fin

an

cial

Corp

-0.10%

Yes

Yes

188/

0110

/01

Tu

cker

Anth

ony

Su

tro

Cap

ital

Rb

cC

ap

ital

Mark

ets

-0.32%

No

No

199/

012/

02Jos

ephth

alL

yon

&R

oss

Fah

nes

tock

-0.41%

Yes

No

324/

107/

10T

hom

asW

eise

lP

art

ner

sS

tife

lF

inan

cial

Corp

-0.48%

Yes

Yes

318/

0911

/09

Fox

-Pit

tK

elto

nm

acq

uari

e-1.70%

Yes

Yes

Th

ista

ble

ran

ks

all

34m

erge

rsby

dec

reas

ein

fore

cast

erro

r.M

erger

sfo

rw

hic

hm

erger

an

nou

nce

men

tsd

on

ot

men

tion

hu

man

cap

ital

or

exp

an

din

gse

rvic

esar

em

arke

das

No

forLaborValued

and

Yes

oth

erw

ise.

Th

eto

pte

rcil

eof

mer

ger

sd

ivid

edby

fore

cast

erro

rin

crea

se,

are

mark

edw

ith

ab

lack

lin

e.T

he

fin

al

colu

mn

mar

ks

mer

gers

that

occ

urr

edaf

ter

the

Glo

bal

An

aly

stR

esea

rch

Set

tlem

ents

.

36

Table 10: Estimate Level Operation Changes - Difference-in-Differences - by Merger Type

(1) (2) (3) (4) (5) (6)DepVar: Forecast Error LNV PGS PGS no 2009 LNV PGS PGS no 2009

Post * InMerger 0.26*** 0.09 0.14**(0.00) (0.20) (0.02)

InMerger 0.07*** 0.12*** 0.09*** 0.05** 0.12** 0.09***(0.00) (0.01) (0.00) (0.05) (0.01) (0.00)

YN1 * InMerger 0.00 -0.04* -0.03(1.00) (0.09) (0.29)

Y1 * InMerger 0.18** 0.06 0.08**(0.01) (0.33) (0.05)

Y2 * InMerger 0.15*** 0.12** 0.12**(0.00) (0.04) (0.03)

Y3* InMerger 0.10* -0.00 0.00(0.08) (0.97) (0.94)

z(Timeliness) 0.48*** 0.43*** 0.43*** 0.46*** 0.42*** 0.36***(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Observations 233,980 334,224 325,629 3,025,456 3,787,328 3,207,414Adjusted R2 0.065 0.062 0.060 0.054 0.046 0.043event period FPI FE YES YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level

*** p<0.01, ** p<0.05, * p<0.1

Difference-in-difference estimates are reported using merger announcement and completions as treatmentevents from 1988 to 2012. I compare the difference in annual and quarterly estimate forecast error beforemerger announcement from the target and acquiring house to estimates of the merged entity after mergercompletion, to differences in non-merger estimates of the same firms over the same periods. Results arepresented for 90 days and three years. Columns (1) and (4) include only mergers in which labor is not highlyvalued while the remaining columns include only mergers after the GARS. In Columns (3) and (6), I removeall observations from 2009. Forecast Error is defined as absolute deviation from actual earnings scaled bycurrent stock price. The binary independent variable InMerger is equal to 1 for all estimates of the mergedentity, target, or acquirer, and 0 otherwise. Post*InMerger is the interaction of InMerger and an indicatorequal to 1 for all estimates after merger completion and 0 otherwise. z(Timeliness) is defined as the numberof days before the earnings announcement the estimate is released. All specifications include event, Periodand FPI fixed effects to control for unobserved heterogeneity. Parentheses contain p-values computed fromstandard errors clustered at the event level.

37

Table 11: Analyst Changes in Forecast Error - By Merger Type

(1) (2) (3) (4) (5) (6) (7) (8)DepVar: All LNV FE3T PGS

Analyst Forecast Error 120d By4M 120d By4M 120d By4M 120d By4M

Post * InMerger 0.11** 0.17** 0.17*** 0.10(0.01) (0.03) (0.01) (0.11)

MN12-N9 * InMerger 0.01 0.04 0.03 0.00(0.80) (0.55) (0.71) (1.00)

MN8-N5 * InMerger 0.00 -0.03 -0.00 -0.03(0.94) (0.46) (0.94) (0.43)

M1-4 * InMerger 0.09** 0.13* 0.14** 0.11*(0.03) (0.06) (0.03) (0.05)

M5-8 * InMerger 0.04 -0.00 0.06 0.07(0.37) (0.97) (0.27) (0.35)

M9-12 * InMerger 0.04 0.01 -0.01 -0.02(0.37) (0.89) (0.93) (0.82)

Observations 105,971 316,893 49,048 145,712 38,408 116,048 61,490 182,686Adjusted R2 0.173 0.156 0.181 0.143 0.179 0.149 0.189 0.177Analyst*Event EventTime FE YES YES YES YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Difference-in-differences estimates are reported using merger announcements as treatment events from 1988to 2012. The sample compares analyst quarterly earnings estimates from 4 months and 12 months prior tothe merger announcement to 4 months and 12 months after merger completion for analysts who retain theirjob post merger. The dependent variable Forecast Error, is measured as the monthly absolute differencebetween an analysts estimates and the actual earnings per share scaled by the current stock price. Thecontrol group is restricted to include analyst’s with at least a 50% overlap with the target or the acquiringhouse beore the merger. Columns 1 and 2 are repeated from Table 3. Columns 3-8 restrict the sampleto merger subsets defined in Table A2, between mergers in which the press release commented on labornot being valued, the tercile of mergers with the largest increase in forecast error, and mergers after theglobal settlement. All specifications include Event-Time and Event×Analyst fixed effects transformations tocontrol for unobserved heterogeneity and to mitigate selection bias concerns. Parentheses contain p-valuescomputed from standard errors clustered at the event level.

38

Table 12: Attrition around Merger Announcements - Split by Merger Type

(1) (2) (3) (4) (5) (6) (7)DepVar: No Labor Valued? Forecast Error Global Settlement

Separation Month Split No Yes Inc Dec Pre Post

Post * InMerger 0.20*** 0.23*** 0.18** 0.15* 0.25** 0.22*** 0.19**(0.00) (0.01) (0.04) (0.10) (0.04) (0.00) (0.04)

Post * z(Qual) -0.03*** -0.02*** -0.04*** -0.04*** -0.02** -0.03*** -0.03***(0.00) (0.00) (0.00) (0.00) (0.02) (0.00) (0.00)

Post * InMerger * z(Qual) 0.06** 0.10** -0.01 0.12** -0.04 0.08** 0.02(0.04) (0.04) (0.82) (0.03) (0.70) (0.02) (0.78)

Observations 17,927 11,238 6,689 4,047 5,464 10,678 7,249Adjusted R2 0.159 0.156 0.172 0.184 0.151 0.169 0.149EventTime Job FE YES YES YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Linear probability model estimates are reported for triple-difference specifications using merger announce-ments as treatment events from 1988 to 2012. Table 12 divides the result from Table 5 by merger type. Thebinary dependent variable Separation is equal to 1 in months in which analysts separate from their brokeragehouse and 0 otherwise. The control group is restricted to include analysts with at least a 50% overlap withthe target house. Columns 2 and 3 split the sample by mergers in which the press release commented on laborbeing valued versus mergers focused on acquiring only physical assets. Columns 4 and 5 compare the tercileof mergers with the largest increase versus the largest decrease in forecast error. Columns 5 and 6 dividemergers between before and after the global analyst settlement. All specifications include Event-Time andEvent×Analyst×House fixed effects transformations to control for unobserved heterogeneity and to mitigateselection bias concerns. Parentheses contain p-values computed from standard errors clustered at the eventlevel.

39

APPENDIX

40

Table A1: Baseline Pr(Separation) outside of merger announcements

(1) (2) (3) (4) (5) (6)Separation Month LPM LPM LPM Logit Logit Logit(no promotions) (OR) (OR) (OR)

Annual Star Analyst -0.00861*** -0.0103*** -0.00794*** 0.479*** 0.464*** 0.419***

(2.23e-08) (1.11e-10) (0.00283) (2.18e-05) (9.14e-06) (1.10e-06)

Annual Star Ranking -0.000625** -0.000569 -0.00245*** 0.845** 0.844** 0.857**

(0.0464) (0.201) (0.000405) (0.0284) (0.0276) (0.0478)

z(Report #) -0.00226*** -0.00254*** -0.00181*** 0.892*** 0.840*** 0.810***

(0) (0) (2.79e-05) (2.23e-09) (0) (0)

z(Estimates / Report) 0.00119*** 0.00110*** 0.000365 1.044*** 1.064*** 1.057***

(2.11e-05) (7.30e-05) (0.333) (0.00543) (0.000234) (0.00517)

Annual Optimism Dummy -0.00309*** -0.00297*** -0.00350*** 0.850*** 0.843*** 0.862**

(0.00108) (0.00157) (0.00350) (0.00616) (0.00434) (0.0292)

Annual Relative Boldness -0.00520*** -0.00556*** -0.00775*** 0.756*** 0.780*** 0.791**

(0.00265) (0.00176) (0.00520) (0.00162) (0.00561) (0.0243)

z(popularity) 0.000621** 0.000753*** 0.000569 1.069*** 1.067*** 1.081***

(0.0223) (0.00376) (0.323) (5.52e-06) (2.22e-05) (3.16e-05)

Annual Relative Accuracy -0.0211*** -0.0203*** -0.0203*** 0.360*** 0.363*** 0.351***

(0) (0) (1.34e-05) (0) (0) (0)

mean accuracy p 0.0510*** 0.0510*** 0.0524*** 19.62*** 12.17*** 20.04***

(0.00548) (0.00293) (0.00721) (0.000122) (0.00177) (0.00130)

absforacc 0.0375** 0.0313* 0.0264 2.085 1.602 2.070

(0.0264) (0.0566) (0.358) (0.292) (0.511) (0.392)

Annual Relative Timeliness 0.0560*** 0.0607*** 0.0783*** 29.52*** 31.51*** 39.14***

(0) (0) (7.68e-08) (0) (0) (0)

z(Avg Estimate Change %) -8.28e-05 -0.000237 -0.000188 0.990 0.994 0.981

(0.734) (0.390) (0.509) (0.475) (0.682) (0.248)

% of Estimates Confirmed 0.00767 0.00953* 0.0112 1.518* 1.449 1.436

(0.176) (0.0933) (0.241) (0.0900) (0.143) (0.201)

z(Monthly dayselapsed 0.000640** 0.000511** 0.000916*** 1.053*** 1.051*** 1.039**

(0.0243) (0.0473) (0.000185) (0.000629) (0.00137) (0.0395)

Years in Data -0.000743*** -0.000744*** -0.965*** 0.951*** 0.967*** 0.961***

(2.72e-06) (5.42e-07) (0.000484) (2.03e-09) (8.02e-05) (4.83e-05)

Years in Data Squared 2.49e-05*** 2.46e-05*** -8.11e-05*** 1.002*** 1.001*** 1.001***

(8.22e-05) (4.57e-05) (0.000129) (5.48e-06) (0.00371) (0.00105)

Years on Job -0.000224** 8.31e-06 0.00137*** 0.975*** 0.982*** 1.000

(0.0111) (0.919) (0.00611) (7.63e-06) (0.00107) (0.953)

daysbeforeclose fix -8.04e-07*** -0.00186 1.000*** 1.000***

(0.00490) (0.999) (1.55e-06) (3.73e-07)

cnt anal 7.04e-05* 0.000155 0.000187 1.005*** 1.005*** 1.011***

(0.0997) (0.125) (0.276) (6.44e-09) (3.47e-08) (1.23e-08)

cnt tick -1.14e-05 -1.75e-05 -2.26e-05 0.999*** 0.999*** 0.999***

(0.174) (0.136) (0.199) (2.42e-06) (2.98e-05) (5.16e-09)

Constant 0.00766***

(0)

Observations 306,627 305,122 304,732 316,341 312,447 75,889R2 0.014 0.131 0.201FE Period House#Period Analys House#Period None Period House#PeriodClustering House period House period House year None Period House#Period

Linear probability model estimates and logit odds ratios are reported for analysts not impacted by mergers.The binary dependent variable Separation - No Promotion is equal to 1 in months in which analysts separatefrom their brokerage house and do not join a more prestigious house and 0 otherwise. House prestige is definedby the houses total number of analysts. Columns 1-3 are LPMs while Columns 4-6 are logits with odd ratiospresented. Spec 4 (logit no FE) is used to to generate a quality proxy.

41

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42

Table A3: Estimate Level Operation Changes - Regressions

DepVar: (1) (2) (3) (4)Estimate Forecast Error 90d 1yr 3yr 12Q

Post 0.24** 0.36**(0.02) (0.01)

YN1 -0.07(0.21)

QN3 -0.04(0.68)

QN2 -0.14**(0.02)

QN1 -0.04(0.23)

Y1 / Q1 0.30** 0.27**(0.01) (0.02)

Q2 0.27*(0.06)

Q3 0.30**(0.01)

Q4 0.35***(0.01)

Y2 / Q5 0.45** 0.42**(0.01) (0.01)

Q6 0.40**(0.02)

Q7 0.48**(0.03)

Q8 0.52*(0.05)

Y3 / Q9 0.20 0.39(0.21) (0.11)

Q10 0.21(0.22)

Q11 0.09(0.54)

Q12 0.11(0.41)

z(Timeliness) 0.52*** 0.49*** 0.49*** 0.50***(0.00) (0.00) (0.00) (0.00)

Observations 42,406 228,589 415,344 415,344Adjusted R2 0.066 0.059 0.056 0.056Event#FPI FE YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

OLS estimates of single difference regressions use merger completions as treatment events from 1988-2012.The sample compares annual and quarterly estimates before merger announcement from the target andacquiring house to those of the merged entity after merger completion. Forecast Error is the absolutedeviation scaled by current stock price. Post is equal to 1 for all estimates of the merged entity and 0 forestimates of the target and acquiring house before the merger announcement. z(Timeliness), the number ofdays before the actual announcement the estimate is made, helps control for patterns in earnings estimates.Specifications differ in the length of the sample size, with (1) being 90 days before and after, (2) being oneyear before and after, and (3) and (4) being one year before and three years after.Specification 2: ForecastErrore,t,f,p = β1Postp + β2Timelinesse,t,f,p + αe,t,f

43

Table A4: Forecast Error Changes - by Merger Type

(1) (2) (3) (4) (5) (6)DepVar: 90d 1yr

Estimate Forecast Error All LNV PGS All LNV PGS

Post 0.24** 0.50*** 0.34*** 0.36** 0.62*** 0.40***(0.02) (0.00) (0.00) (0.01) (0.00) (0.00)

Post * Labor Valued -0.45** -0.46*(0.01) (0.08)

Post * Post Settlement -0.18 -0.06(0.31) (0.80)

z(Timeliness) 0.52*** 0.53*** 0.52*** 0.49*** 0.49*** 0.49***(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Observations 42,406 42,406 42,406 228,589 228,589 228,589Adjusted R2 0.066 0.068 0.066 0.059 0.061 0.059Event#fpi FE YES YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Difference estimates are reported using merger announcements as treatment events from 1988 to 2012. Eachcolumn compares the full sample (columns presented in earlier tables) to estimates from two restrictedsamples: 1) mergers in which labor does not appear to be highly valued in the merger announcement pressrelease and 2) mergers after the Global Analyst Settlement. Regressions (1)-(3) include only 90 days beforeand after while columns (4)-(6) include one year before and after All specifications include Event#FPIfixed effects transformations to control for unobserved heterogeneity and to mitigate selection bias concerns.Parentheses contain p-values computed from standard errors clustered at the event level..

44

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Coefficient Estimate (Forecast Error)

QN

3Q

N2

QN

1Q

1Q

2Q

3Q

4Q

5Q

6Q

7Q

8Q

9Q

10Q

11Q

12

Qua

rter

Not

in R

eces

sion

(d)

−.10.1.2.3

Coefficient Estimate (Forecast Error)

QN

3Q

N2

QN

1Q

1Q

2Q

3Q

4Q

5Q

6Q

7Q

8Q

9Q

10Q

11Q

12

Qua

rter

In R

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sion

45

Table A5: Estimate Level Operation Changes - Difference-in-Differences - By in Recession

(1) (2) (3) (4)DepVar: Forecast Error In Down turn Not In Down turn

InMerger 0.07*** 0.07*** 0.15*** 0.15***(0.01) (0.01) (0.00) (0.00)

QN3 * InMerger -0.02 -0.02(0.39) (0.48)

QN2 * InMerger 0.01 -0.03(0.61) (0.25)

QN1 / YN1 * InMerger 0.01 0.03* -0.03 -0.03(0.58) (0.09) (0.30) (0.43)

Q1 * InMerger 0.20*** 0.14*(0.00) (0.06)

Q2 * InMerger 0.05 0.15**(0.13) (0.03)

Q3 * InMerger 0.05* 0.08(0.07) (0.15)

Q4 / Y1* InMerger 0.08** 0.05 0.10* 0.06(0.02) (0.43) (0.06) (0.28)

Q5 * InMerger 0.10 0.07(0.11) (0.14)

Q6 * InMerger 0.12** 0.11*(0.04) (0.07)

Q7 * InMerger 0.09* 0.16**(0.05) (0.03)

Q8 / Y2 * InMerger 0.09** 0.05 0.10* 0.05(0.03) (0.15) (0.08) (0.63)

Q9 * InMerger -0.01 0.06(0.88) (0.39)

Q10 * InMerger -0.00 -0.01(0.98) (0.92)

Q11 * InMerger 0.03 0.00(0.74) (0.95)

Q12 / Y3 * InMerger 0.05 0.17* 0.01 -0.03(0.43) (0.06) (0.87) (0.53)

z(Timeliness) 0.44*** 0.44*** 0.48*** 0.48***Observations 3,008,449 3,008,449 3,562,133 3,562,133Adjusted R2 0.053 0.053 0.047 0.047event period FPI FE YES YES YES YES

Robust pval in parentheses clustered at the Event Level*** p<0.01, ** p<0.05, * p<0.1

Difference-in-Difference estimates are reported using merger announcements as treatment events from 1988-2012. The sample compares analyst quarterly earnings estimates from before the merger announcementto after merger completion split by whether the merger occurred just prior or within a recession. Allspecifications include Event, Period and Fiscal Period fixed effects transformations to control for unobservedheterogeneity and mitigate selection bias concerns. Parentheses contain p-values computed from standarderrors clustered at the event level.

46

Table A6: Redundant v Non-Redundant - Difference-in-Differences

(1) (2) (3) (4)DepVar: Forecast Error 1y 3y 3y Non-Merger 3y In-Merger

Post * DupCov -0.01(0.91)

DupCov -0.18** -0.15 -0.14 -0.26**(0.02) (0.12) (0.15) (0.02)

YN1 * DupCov -0.01 -0.01 -0.03(0.90) (0.92) (0.65)

Y1 * DupCov -0.03 -0.03 0.05(0.77) (0.74) (0.71)

Y2 * DupCov -0.05 -0.06 0.03(0.70) (0.67) (0.88)

Y3 * DupCov 0.01 0.00 0.10(0.94) (0.98) (0.52)

z(Timeliness) 0.46*** 0.46*** 0.45*** 0.51***(0.00) (0.00) (0.00) (0.00)

Observations 3,212,344 6,570,582 6,163,057 407,525Adjusted R2 0.059 0.050 0.050 0.054event period FPI FE YES YES YES YES

pval in parentheses, StErr Clustered at Event Level

*** p<0.01, ** p<0.05, * p<0.1

Difference-in-Differences are reported using merger announcement and completions as treatment events from1988-2012. I compare the difference in annual & quarterly estimate forecast error prior to merger announce-ment from estimates for firms covered by both the target and acquirer prior to the merger and estimatescovered by one or the other. Results are presented for one year, three years. Forecast Error is definedas absolute deviation from actual earnings scaled by current stock price. The binary independent variableDupCov is equal to 1 for all estimates of firms covered by both the target and acquirer prior to the mergerannouncement and 0 otherwise. Post*Dupcov is the interaction of InMerger and an indicator equal to 1for all estimates after merger completion and 0 otherwise. z(Timeliness) is defined as # of days prior tothe earnings announcement the estimate is released. All specifications include Event, Period & FPI fixedeffects to control for unobserved heterogeneity. Parentheses contain p-values computed from standard errorsclustered at the event level.

47

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48