Advertising, Attention, and Acquisition Returns∗

 

Eliezer M. Fich LeBow College of Business

Drexel University Philadelphia, PA 19104

(215) 895-2304 emf35@drexel.edu

Laura T. Starks McCombs School of Business

University of Texas Austin, TX 78712-1179

(512) 471-5899 lstarks@mail.utexas.edu

Anh L. Tran Cass Business School

City University London London, EC1Y 8TZ, UK

+44-207-040-5109 anh.tran@city.ac.uk

October 4, 2015

Abstract

We examine the hypothesis that advertising allows a takeover target’s management to increase the firm’s profile and their own negotiating power, leading to higher subsequent takeover premiums. Our evidence from 7,095 merger bids supports this hypothesis. Moreover, we find an economically significant decrease in the acquirer’s market capitalization during the announcement period. To consider the possibility of codetermination of target advertising and takeover premiums, we employ instrumental variable tests as well as propensity matching methods and our results hold. Further, we find targets that advertise are more likely to be pursued by multiple bidders and receive revised increased bids.

                                                                                                                         ∗ We appreciate the helpful comments from seminar participants at the University of Texas, Mississippi State University and Kansas State University.

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Firms, particularly those that are smaller and less well known, often struggle with gaining

recognition from investors. As shown theoretically by Merton (1987), this lack of recognition

can have stock-valuation consequences. Indeed, public companies with limited visibility, (i.e.,

less investor awareness), often have higher costs of capital and lower values. Empirical evidence

supporting this theory suggests that a firm that successfully increases its investor recognition

should achieve a related increase in firm value.1

This lack of recognition would be particularly problematic for firm management if they are

interested in selling the firm or concerned that they will be subject to a hostile takeover with

what they consider to be insufficient valuation. These managers have limited strategies for

gaining investor recognition. Typically they could advertise or try to use the media to draw

attention to their firms’ products, accomplishments and expected performance. In fact, recent

studies suggest that managers opportunistically use advertising to not only attract customers, but

to also attract investor recognition and influence their firms’ stock prices.2 However, researchers

have argued that attention-grabbing activities by firms, such as advertising or press releases,

result in short-term increases in stock prices, but the activity by itself may not generate a

sustained increase in equity valuations.

However, in the event of opportunistic advertising just before a desired corporate action,

such as an IPO, SEO or takeover bid, the effect of the advertising does not need to be long-lived.

                                                                                                                         1 See, for example, Kadlec and McConnell (1994), Foerster and Karolyi (1999), Gervais, Kaniel, and Mingelgrin (2003), Lehavy and Sloan (2011), and Kaniel, Ozoguz, and Starks (2012) among others. Other theoretical and empirical literature has also focused on the effects of investor attention on financial markets and firm value. See, for example, Hirshleifer and Teoh (2003), Barber and Odean (2008). 2 For example, studies have found that firms with a greater level of advertising exhibit significantly lower bid-ask spreads (Grullon, Kanatas, and Weston, 2004); firms that signal their higher valuations by increasing product-market advertising prior to the IPO have lower underpricing (Chemmanur and Yan, 2009), and firms’ short-term stock returns are susceptible to adjustments in advertising expenditures (Lou, 2014). These results are also consistent with Stein’s (1996) argument that in an inefficient market, short-horizon managers interested in maximizing their firm’s short-term stock price can exploit investors’ misperceptions by catering to time-varying investor sentiment.

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It needs simply to be sufficient to affect the firm’s valuation by investors at a specific point in

time. We examine this hypothesis in the current paper. Specifically, we examine whether

advertising by eventual takeover targets during the year prior to receiving an acquisition bid

affects the gains to the shareholders in those firms (including managers) as well as the gains to

the bidders. We hypothesize that, everything else equal, managers in firms with an interest in (or

concern about) becoming a takeover target will increase their advertising in order to not only

increase customer awareness of their products, making them a more attractive takeover target,

but to also increase investor awareness, allowing the firm’s management and shareholders to

capture a larger share of the rents from such a takeover. Supportive of this hypothesis, evidence

exists that those on the other side of these transactions (the acquirers) have used advertising or

the media to affect acquisition gains. For example, Lou (2014) documents a sharp increase in

advertising spending before stock-financed merger deals that essentially “pumps up” the

acquirer’s stock price. In addition, Ahern and Sosyura (2013) find that, by originating more news

during private merger negotiations, acquirers generate a short-lived run-up in their stock prices

during the period when the stock exchange ratio is determined

To test our hypothesis we analyze a sample of 7,095 (completed and withdrawn) M&A bids

submitted for U.S. publicly traded targets and announced during the 1986-2011 period. Our

empirical analyses indicate that a management strategy of advertising prior to a takeover attempt

benefits their shareholders. We find that increasing advertising by a single standard deviation

(about $1.72 million) is associated with a one percentage point increase in the premium paid to

target shareholders. This higher premium represents an increase of $10.65 million in terms of

deal value for the average target in our sample.

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Given our central hypothesis, there are further implications as well. If increased advertising

by the target is indeed important to investor awareness and the future merger negotiations, then it

follows that the target firm’s advertising should affect not only the target’s stock price and

returns, but also the returns accruing to their acquirers. That is, while the target advertising

increases the merger gains for the target, it should reduce the gains to the bidder. Consistent with

this argument, we find that a single standard deviation increase in target firm advertising is

related to a decline of 45 basis points in their acquirer’s merger announcement return. Such a

drop has significant economic consequences as it implies a decline of $45.36 million in terms of

market capitalization for the average bidder in our sample.

Moreover, if our contention is true that managers of less known firms can increase their

firm’s recognition in financial markets through their advertising, then it follows that the

advertising itself helps make the target more attractive. As a result, it should increase the

probability of interest by more than one bidder, which will also cause the initial merger bid to be

revised upwards. These conjectures are supported by our data. We find that the targets with

increased advertising are sought by more than one bidder and are more likely to have initial

takeover bids revised upwards.3

Using the method in Comment and Schwert (1995) we find that target advertising adds value

unconditionally by increasing the combination of the premium, conditional on a takeover, and

the probability with which such a deal occurs. Given this result, we also evaluate the net impact

of advertising on target shareholder wealth by considering its joint effect on premiums and the

probability of deal completion. We find that a one standard deviation increase in advertising

                                                                                                                         3 Louis and Sun (2010) find that investors sometimes exhibit inattention during merger announcements. Importantly, we are not arguing that investors are inattentive during merger transactions. We argue that advertising increases investor attention during acquisitions and that such increase has a material effect on the premiums offered to takeover targets.

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spending is associated with a higher probability of deal completion: from 81% to 84%. This

result, combined with the increase in deal value documented in our premium tests, indicates that

on average, target shareholder net gains increase by 5% (or $43 million) with a one standard

deviation increase in advertising. Overall, our empirical evidence suggests that increased

advertising heightens the target firm’s stock market value as well as management’s position in

the merger negotiations, which translates to higher premiums paid for firms that become

takeover targets.

A natural concern with these results and our interpretation is the problem of endogeneity in

the relation between target firm advertising and subsequent takeover premiums. To consider this

possibility, we employ two alternative methodologies, an instrumental variables approach and a

propensity score matching procedure. With respect to the first approach, given that this method

requires an instrument that is correlated with a firm’s advertising expenditure but uncorrelated

with the residuals in the premium regression, a possible instrument would be the Average

Competitor Advertising Spending during the year prior to the acquisition bid.4 The suitability of

this instrument is based on the notion (which our first stage tests confirm) that the target firm,

independent of its own characteristics, will likely spend more for advertising in a given period

whenever its competitors advertise more intensely during the same period.

Consistent with our earlier analyses, the instrumental variable tests also document a positive

association between advertising spending and merger premiums. However, we cannot test the

exclusion restriction and it could be the case that advertising by competitors may correlate with

the residuals in the second stage premium regressions. For example, takeover premiums in some

industries with higher advertising could be higher. Consequently, we also perform a propensity

                                                                                                                         4 Gurun and Butler (2012) use a similar instrument.

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score matching analysis in which we match target firms that advertise with control firms that do

not. This approach circumvents the problem that firms’ advertising choices are a function of their

characteristics. Therefore, except for the choice to advertise, both groups exhibit similar

attributes on a variety of dimensions. Results from our propensity score matching analysis

suggest that increased advertising by target firms causes an increase in the takeover premiums

these companies receive. We also conduct a number of robustness tests and find consistent

results.

Our paper contributes to several different strands of the literature. First, our findings

documenting that advertising by takeover target companies affects the wealth of both target and

acquirer shareholders contribute new evidence to the vast literature on mergers and acquisitions,

particularly papers examining the role of investor attention in the process [e.g.: Ahern and

Sosyura (2013)].5 Second, our results add to a growing body of work linking product market

advertising and firm value [Grullon, Kanatas, and Weston (2004), Fehle, Tsyplakov and

Zdorovtsov (2005), Chemmanur and Yan (2009), Fee, Hadlock, and Pierce (2009), Gurun and

Butler (2012), and Lou (2014)] and the results add to the extensive literature on the economics of

advertising [Telser (1964), Nelson (1974), Bagwell and Ramey (1994), Grossman and Shapiro

(1984), Kihlstrom and Riordan (1984), Milgrom and Roberts (1986), and Becker and Murphy

(1993)]. Finally, our study advances the literature on investor attention [Gervais, Kaniel, and

Mingelgrin (2001), Seasholes and Wu (2007), Hou, Peng, and Xiong (2009), Barber and Odean

(2008), Yuan (2008), and Da, Engelberg, and Gao (2011)] and on investor recognition [Kadlec

and McConnell (1994), Forester and Karolyi (1999), Gervais, Kaniel, and Mingelgrin (2003),                                                                                                                          5 Ahern and Sosyura (2013) conclude that the division of gains during completed mergers that are financed with the bidder’s stock is positively related to news origination. We have a different sample (both completed and withdrawn deals and both cash and stock-financed transactions). More importantly, we have a different focus in terms of which party to the merger takes actions in order to draw attention and also on the type of actions taken. That is, whereas Ahern and Sosyura examine press releases by the acquirers we study advertising expenditures by the targets.

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Chen, Noronha and Singal (2004), Hou and Moskowitz (2005), Bodnaruk and Ostberg (2009),

Lehavy and Sloan (2011), Kim and Meschke (2011), and Kaniel, Ozoguz, and Starks (2012)].

The rest of our paper is organized as follows. Section I describes our data. Section II contains

our main empirical tests and Section III describes a number of additional analyses. Section IV

provides our conclusions.

 

I. Data and Variable Definitions

This section details the sample of M&A bids we analyze as well as the proxies we use to

track the product market advertising expenditure by the target firms we study.

A. Sample Overview

We begin with all M&A offers of at least $1 million in value submitted for publicly traded

U.S. companies from 1986-2011 reported in the Securities Data Company (SDC) database. We

retain transactions involving target companies for which stock market and accounting data are

available from the Center for Research in Security Prices (CRSP) and Compustat, respectively.

Because we want major bids without tertiary issues, we implement a sample selection procedure

similar to that used by Bargeron, Schlingemann, Stulz, and Zutter (2008). Specifically, we

exclude observations involving spinoffs, recapitalizations, exchange offers, repurchases, self-

tenders, privatizations, acquisitions of remaining interest, and partial interests or assets. This

process yields 8,616 transactions announced during our sample period. From this set, we drop

1,521 bids because we cannot obtain acquisition premium data from SDC, SEC filings or trade

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publications (such as Mergers & Acquisitions or Investment Dealers’ Digest). These criteria

yield our final sample of 7,095 deals.

Panel A of Table 1 reports the temporal distribution of the targets in our sample. We note that

the annual frequency of the transactions we study is consistent with the conjecture in Shleifer

and Vishny (2003) that stock market health drives merger activity. For example, we find that in

periods of economic expansion and higher stock market valuations such as 1998-2001, the

number of transactions is greater. In contrast, during periods of economic contraction, such as

the beginning of our sample or the 2008-2009 period, the number of bids declines.

Panel A of Table 1 also reports the industrial distribution of our sample targets based on the

Fama and French (1997) classification. The distribution across industries is wide spread with

some concentration in the business services sector (which includes software) at 13.5% and the

banking sector at 14.3%.

In Panel B of Table 1 we provide summary statistics for particular key characteristics related

to the sample deals. (We provide more detailed definitions of these characteristics and other

variables in the Appendix.) In comparing the key characteristics provided in the table to other

studies on mergers and acquisitions we find similar magnitudes. For example, transactions in our

sample are completed 81% of the time and tender offers account for 24% of the sample. Both the

target and the bidder operate in the same industry in 53% of the transactions. These statistics are

comparable to those in Officer (2003). He reports a completion rate of 83%, a tender offer

proportion of 20%, and a same industry incidence of 52% in his merger sample during 1988-

2000. Similarly, 50% of our bids being all cash transactions is close to the 46% in the Masulis,

Wang and Xie (2007) study of mergers during 1990-2003. At 44.67%, the average relative size

ratio (target/acquirer) in our sample is comparable to that of 44.2% reported by Hartzell, Ofek

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and Yermack (2004) in their sample of deals occurring between 1995 and 1997. We find that

over 89% of transactions in our sample consist of friendly acquisitions, which is close to the 93%

in Moeller’s (2005) study of mergers during 1990-1999. Finally, our sample targets exhibit an

average market value of equity of $601 million, Tobin’s Q of 1.64, and leverage of almost 23%.

For the same characteristics, Bates and Lemmon (2003) report a mean market value of equity of

$592 million, Q of 1.63 and leverage of 23% for the targets they study. Overall, in a number of

important dimensions, our sample resembles those used in previous studies in the M&A

literature.

B. Product Market Advertising

In Panel C of Table 1 we report the summary statistics of our sample target firm’s advertising

spending. We report statistics for four different advertising proxies, each of which is based on

the raw dollars of advertising spending (in million US$). Annual data on advertising

expenditures (Compustat data item 45) are measured at the fiscal year end before the merger

announcement date. The advertising proxies are: (1) ln(Advertising spending), defined as the

natural logarithm of (1 + advertising spending), (2) Scaled advertising spending, calculated as

advertising spending scaled by total assets, (3) Advertising intensity, computed as advertising

spending divided by the firm’s total sales and (4) Advertising growth, estimated as the

percentage change in advertising during the two fiscal years immediately preceding the initial

bid.6

According to the information reported in Panel C of Table 1, the mean advertising intensity

of targets in our sample is 0.94, which is close to the ratio for the Gurun and Butler (2012)                                                                                                                          6 We note that (3) and (4) are set to zero for firms that do not spend on advertising.

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sample of Compustat firms during 2002-2006. These measurements are calculated across firms

regardless of whether they advertise. We also report the results when the sample is restricted to

the 2,377 target firms (about 34% of our sample) that have positive advertising spending. The

average intensity for the subset of firms that advertise is close to 3%.7

II. Empirical Analyses

Provided that firms can call attention to themselves by heightening the advertising of their

products, it is possible that the increased attention could translate into higher premiums and more

bids if these companies become takeover targets. In this section, we perform several empirical

tests in order to shed light on these issues.

A. Probability of Becoming a Takeover Target

Before examining whether advertising by takeover targets affects the merger offers these

firms receive, we note that firms are unlikely to receive an acquisition bid randomly. Indeed,

existing studies [e.g., Comment and Schwert (1995) and Palepu (1986)] show that the likelihood

of receiving a merger offer has its own determinants. Therefore, in a sample of 140,839 firm-

year observations (with complete data in CRSP and Compustat) over the 1985-2011 period, we

estimate four probit regressions of the probability of becoming a target. Our explanatory

variables are similar to the ones used in those papers. Specifically, in the four regressions

reported in Panel A of Table 2, the dependent variable is equal to one if the firm becomes a

takeover target and equal to zero otherwise. Unlike previous work, however, our determinants of

                                                                                                                         7 Comparably, about 35% of the firms analyzed by Lou (2014) spend on advertising during 1974-2010 and have a mean advertising intensity of 4%.

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the probability of becoming a target also include the firm’s advertising expenditures, which have

the potential of affecting investor attention. Specifically, the main independent variable in these

tests are the four proxies for advertising: the natural logarithm of advertising spending (in model

(1)), the scaled advertising (in model (2)), the advertising intensity (in model (3)) and the

advertising growth (in model (4)).

Parameter estimates in Panel A of Table 2 indicate that all of our advertising proxies attain

positive and significant coefficients.8 The marginal effect we estimate in model (1) indicates that

a single standard deviation increase in advertising spending augments the likelihood of becoming

a target by 0.8 percentage points.9 To put this result into context, the unconditional probability of

becoming a target in the sample analyzed in Panel A of Table 2 is 4.4%.

B. Deal Initiation

The estimates in Panel A of Table 2 indicate that firms that advertise are more likely to

become acquisition targets. One question about this result is whether targets who are engaging in

increased advertising to garner attention for a potential takeover bid would also capitalize on this

attention by initiating their own sale through a takeover. Thus, from our original sample of 7,095

merger and acquisition bids announced during 1986-2011, we determine those bid contests in

which one or several bidders bid for a single target and where we can find the deal background

from the merger proxies filed by either the target or the acquirer with the SEC (S-4, DEFM 14,

                                                                                                                         8 With respect to the other control variables, we note that firm size is the only variable that attains a statistically significant coefficient. This finding conforms to the arguments in Schwert (2000, p.2620). He reviews several papers that estimate takeover probability regressions and concludes that the only consistent predictor in the literature is size. 9 The marginal effects are computed by first calculating the probability of becoming a target using the sample means for all continuous independent variables and zeroes for all (0,1) indicator explanatory variables (the base predicted probability). The probability of becoming a target is then re-calculated by changing each independent variable (in turn) by adding one standard deviation to the mean of continuous variables (or using a one for each indicator variable). We use the same procedure to compute marginal effects for all binary response models in this paper.

11

SC 14D9, SC TO, DEF 14, 8-K). We use information from the first bid in the contest to identify

the party (target or acquirer) who initiates the M&A transaction. In Panel B of Table 2, we run

logit regressions of deal initiation probability similar to those in Aktas, de Bodt and Roll (2010).

The dependent variable equals one if the deal is first initiated by the target. The main

independent variables are again the four advertising proxies. We note that 39.38% of transactions

in our sample are initiated by the target which is comparable to the 42% reported in Aktas, de

Bodt, and Roll (2010). The results in Panel B indicate that targets who increase their advertising

expenditures are more likely to also initiate their own takeover. A one standard deviation

increase in advertising spending (in Model (1)) increases the likelihood of a target initiating a

deal by 3.78%.

C. Takeover Premiums

The results in Panels A and B of Table 2 are consistent in showing that increasing advertising

spending raises the probability of becoming a takeover target. In our setting, an implication of

the Merton’s (1987) theory is that increased advertising by target firms should increase investor

recognition, which should benefit their shareholders. However, Aktas et al. (2008) find that

targets that initiate their own sale get lower premiums. They argue that this occurs because, by

initiating the transaction, these firms give up considerable bargaining power. Consequently, we

next examine the merger premiums offered for our sample targets to consider these issues.

In Panel C of Table 2, we report four premium regressions in which the four-week final

premium reported by SDC is the dependent variable.10 Our target premium tests closely follow

                                                                                                                         10 In order to mitigate problems with outliers, we limit the premium to values between 0 and 2 (or 200%) as does Officer (2003).

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those in Bargeron, Schlingemann, Stulz, and Zutter (2008). We expand their basic specification

by using our four advertising proxies as the respective key independent variables in each of the

four premium regressions. These tests also include year- and industry-fixed effects. In addition,

because firms do not randomly become acquisition targets, we use the Heckman (1979)

methodology to address issues related to self-selection. Therefore, we estimate an inverse Mill’s

ratio from each of the four models in Panel A of Table 2 and respectively use them as additional

controls in the regressions reported in Panel C.

Other studies estimate premium regressions similar to ours and we note that several control

variables in Panel C generate estimates that are in agreement with those in prior work. As in

Gaspar, Massa and Matos (2005), we find premiums to be higher when there are competing bids

or when the transaction is classified as a tender offer. We also document acquisition premiums to

be increasing in the targets’ leverage (Cai and Sevilir, 2012). Premiums are also higher when the

deal includes a target termination fee (Officer, 2003), when the bid is hostile (Bargeron,

Schlingemann, Stulz, and Zutter, 2008) and when the transaction is structured as a cash-only deal

(Aktas, de Bodt and Roll, 2010). Conversely, takeover premiums are inversely related to the size

of the target firm (Bargeron, Schlingemann, Stulz, and Zutter, 2008) and also drop in deals

characterized as a merger of equals (Wulf, 2004, and Wang and Xie, 2009).

More importantly, the coefficients for our advertising variables are statistically significant in

all of the premium regressions reported in Panel C of Table 2. These tests document an

economically important positive association between each of our advertising proxies and the

takeover premiums. According to the estimates in model (1), increasing advertising spending by

one standard deviation translates into a premium increase of 1 percentage point. For the average

transaction in our sample, this increase implies an additional $10.65 million in terms of deal

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value for the target shareholders. Put differently, for the average deal in our sample, an extra

dollar of advertising is related to an increase in deal value of $6.19.

C.1. Endogeneity of Target Premiums and Advertising: Instrumental Variables Approach

One potential concern about our results is the possibility of simultaneity bias, that is, that our

variables of interest (takeover premiums and advertising) are jointly determined. Additionally,

the possibility exists that our premium tests are susceptible to an omitted variables bias. This

could happen if the documented association between the premiums and the advertising

explanatory variables partly reflect omitted factors related to both variables. To address these

issues, we estimate separate two-stage least squares (2SLS) systems in each of which we

instrument for one of the target advertising proxies. To properly specify the systems, we need

instruments correlated with a target’s advertising spending in the first stage regressions (the

relevance condition) but not with the residuals in the second stage premium regressions (the

exclusion restriction).

Gurun and Butler (2012) conjecture that if a firm’s industry peers advertise more

aggressively during a given period, then the firm, regardless of its own characteristics, will be

inclined to do the same during that period. Following a similar logic, our instruments are based

on the Average Competitor Advertising Spending during the year prior to the takeover offer. That

is, we estimate four separate first stage regressions to respectively instrument for the Average

Competitor Advertising Dollar Spending, for the Average Competitor Scaled Advertising, for the

Average Competitor Advertising Intensity and for the Average Competitor Advertising Growth.

For each sample target, we compute these variables for all of its competitors during the year

before the target receives a public takeover bid. We define competitors as companies (with data

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available in CRSP and Compustat) operating in the same Fama and French (1997) industry as a

target firm.

Table 3 reports our 2SLS tests. We note that our first stage regressions, reported as models

(1), (3), (5), and (7) indicate that the relevance condition appears to be satisfied.11 In each of

these tests, the average advertising by competitors variables are positively associated with the

dependent variables measuring advertising spending by the target firms. The second stage

regressions, reported as models (2), (4), (6) and (8) show that the fitted values from their

respective first stage tests exhibit positive coefficients. The results are economically meaningful.

For instance, according to the fitted parameter estimate for advertising spending in model (2),

increasing advertising by one standard deviation leads to a premium increase of 1.09%. Overall,

the results from our instrumental variables tests suggest that increased advertising by targets

during the year before a takeover causes an increase in the takeover premiums these firms obtain.

C.2. Endogeneity of Target Premiums and Advertising: Propensity Score Matching Approach

Our 2SLS analyses document a positive association between advertising spending and

premiums. However, because the exclusion restriction cannot be tested we cannot rule out that

advertising by competitors may correlate with the residuals in the second stage premium

regressions. To alleviate this issue, in Table 4 we use a propensity score matching procedure to

estimate an average treatment effect (ATE) of target advertising on acquisition premiums. An

attractive feature of the propensity score matching technique is that it enables us to make causal

                                                                                                                         11 Our estimations are efficient since the first-stage R2 values in Table 3 are large [22.71% in model (1), 17.75% in model (3), and 11.97% in model (5)]. Furthermore, in these regressions the F-statistics for Total Advertising Spending, for Scaled Advertising Spending, and for Advertising Intensity are 25.76, 18.92, and 11.93, respectively. Therefore, the F-statistics on the instruments are above critical values according to a Stock and Yogo (2005) weak identification test.

15

inferences from the analysis because it sidesteps the fact that firms’ advertising preferences are a

function of their own characteristics.12

The first column of Panel A in Table 4 reports a logit model of the probability of being in the

treatment group (i.e., of advertising) as a function of observable characteristics. From this model,

we use the estimated ex ante probability of advertising to form matched samples of treatment and

control target firms where both groups display a similar estimated ex ante probability of being in

the treatment group but different ex post realizations of the treatment. In other words, our

method estimates the counterfactual outcomes of target firms by using the outcomes from a

subsample of matched target firms from the other group (treatment or control). Following Abadie

and Imbens (2008), we obtain confidence intervals using a matching estimator that uses a

Gaussian kernel with 500 bootstrap repetitions. Since we are matching jointly on multiple

variables, treatment and control samples may not have the same size or similar characteristics in

all matched dimensions. Nevertheless, our results do not change if (a) we employ different

subsets of these matching characteristics, or (b) we use neighborhood matching instead of

Gaussian kernel.

The last three columns in Panel A compare the treatment and the control group and document

no significant differences in the mean values related to several characteristics that determine

advertising spending. The ATE reported in Panel B of Table 4 shows that in deals in which the

target firm spends on advertising target shareholders are offered a takeover premium that is about

3.3 percentage points higher. As with the findings in Tables 2 and 3, those from our propensity

                                                                                                                         12 Rosenbaum and Rubin (1983) define treatment assignment to be strongly ignorable if two conditions are met. The first (also known as unconfoundness) states that treatment assignment is independent of the potential outcomes conditional on the observed baseline covariates. The second condition (also known as overlap) requires every subject to have a nonzero probability to receive either treatment. Rosenbaum and Rubin (1983) show that if treatment assignment is strongly ignorable, then conditioning on the propensity score leads to unbiased estimates of the ATE.

16

matching procedure suggest that advertising by target firms causes an increase in the takeover

premiums these companies obtain.

D. Unconditional Premiums

In Table 5 we report four unconditional premium regressions, which we estimate in a sample

of 140,839 firm-years with data available from CRSP and Compustat during 1985-2011. Using

the method in Comment and Schwert (1995), in all tests the dependent variable is equal to zero

in nontakeover firm-years. Otherwise, this variable is equal to the actual takeover premium as

recorded in SDC if there is a takeover associated with the firm-year. The key explanatory

variables in the four regressions in Table 5 are our four advertising spending proxies,

respectively.

The estimates related to all of our key independent variables in Table 5 indicate that

unconditional premiums increase in advertising. According to the coefficient in model (1), a one

standard deviation increase in advertising is related to an unconditional premium increase of 6

basis points. Since the unconditional takeover premium combines the effects of a conditional

takeover premium and the likelihood with which a takeover bid occurs, this result suggests that

advertising by the target firm adds value unconditionally by increasing some combination of the

premium conditional on a takeover (as in Panel B of Table 2) and the probability with which

such a deal occurs (as in Panel A of Table 2). Moreover, the beneficial effect of advertising

during takeovers (documented in Tables 2, 3, and 4) is probably understated since, as the tests in

Table 5 suggest, advertising increases the unconditional value of the firm.

17

E. Acquirer Returns

So far, our results show that during M&A deals premiums increase in the target firm’s pre-

takeover advertising spending. This finding appears consistent with the idea that increased

advertising raises the attention levied upon the target firm. Thus, while target shareholders

benefit from their firm’s advertising it is unclear whether (and how) advertising by the target will

affect the shareholders of the acquirer. To illuminate this issue, in Table 6, we estimate

regressions explaining the three-day merger announcement cumulative abnormal return (CAR)

for the 3,036 publicly traded acquirers in our sample. This CAR is centered on the acquisition

announcement day, and is calculated as the cumulated residuals from a market model estimated

during the one-year window ending four weeks prior to the merger announcement.

The four acquirer return regressions reported in Table 6 control for variables similar to those

in the acquirer return tests performed by Moeller et al. (2004) and by Masulis et al. (2007),

except that we augment the specification in those studies by including our four target advertising

spending proxies as the respective key independent variables. The results indicate that acquirer

returns decrease in the targets’ advertising spending. According to the parameter estimates in

model (1), a one standard deviation increase in total advertising spending by the target is

associated with a 45 basis points decrease in the return to the acquirer. This drop implies a value

decline of over $45.36 million for shareholders in the average bidder in our sample. In other

words, a single dollar increase in total advertising by the target is related to a drop in market

capitalization of nearly $26.37 for the average acquirer in our sample.

We observe that the control variables in Table 6 yield results similar to those by other

authors. For instance, as in Moeller (2004) the coefficient for the bidder’s leverage is positive

and the estimate for the targets’s industry liquidity index is negative. Similar to Wang and Xie

18

(2009) our tests also indicate that all-cash transactions are associated with higher bidder CARs.

Like Officer (2003) and Bates and Lemmon (2003) we find that tender offers are greeted with

more enthusiastic market reactions.

F. Deal Completion

It is possible that managers invest more in advertising to increase the odds of being acquired

and receiving a larger premium. Yet, even if managers are not interested in selling their

companies, receiving a takeover bid could help increase investors’ attention toward their firms

and, in turn, increase the value of their firms. Indeed, Malmendier, Opp, and Saidi (2012) find

that firms experience a permanent revaluation of up to 15% (based on their pre-bid market value)

when they receive a cash merger offer that is subsequently withdrawn.

Moreover, given that the unconditional takeover premium combines the effects of a

conditional takeover premium and the probability of selling the firm, we need to study the effect

of advertising on the probability of deal completion to estimate the net effect of advertising on

the wealth of target shareholders.

In Table 7 we report the estimation of four logit models in which the dependent variable

equals one if the target is sold and zero if it is not. The results for the control variables in Table 7

are consistent with those in the existing M&A literature. For example, transactions are more

likely to materialize if there is a target termination fee (Officer, 2003). As in Bates and Lemmon

(2003), deals are less likely to be completed if there is prior bidding. In addition, deals classified

as hostile are less likely to be completed (Schwert, 2000).

19

As for our key advertising explanatory variables in Table 7, we note that all of their

parameter estimates are positive and statistically significant. The marginal effect related to the

coefficients in model (1) imply that increasing advertising spending by a one standard deviation

raises the probability of merger completion by 3.25 percentage points. This effect is

economically important since the unconditional probability of deal completion in our sample is

81.09%.

This result of increased probability of merger completion, combined with the deal value

increase of $10.65 million associated with a one standard deviation increase in advertising

spending (Table 2) indicates that the wealth of shareholders increases from $860 million

(81.09% X $1.06 Bn) to $903 million (84.34% X $1.07 Bn). Therefore, the average effect of

raising advertising by one standard deviation is a net gain to target shareholders of $43 million or

about 5%.

III. Additional Analyses

In this section we perform further tests in order (i) to explore whether target advertising

affects other facets of the acquisition process and (ii) to probe the robustness of the preceding

findings.

A. Bid Competition

A further implication of increased advertising resulting in increased investor attention

towards target firms is that the increased attention could attract additional bidders. Thus, we

examine the hypothesis that target companies with increased advertising are more likely to be

20

pursued by multiple bidders. In Table 8 we estimate logit regressions in which the dependent

variable is set to one for targets that receive a public takeover offer from more than one bidder,

and set to zero otherwise. In these tests we examine a subsample of 6,502 bid contests.13 Our

four target advertising proxies are the respective key explanatory variables in the four models

reported in Table 8. Aside from these, all other controls are similar to those in Officer (2003).

The parameter estimates in Table 8 suggest that advertising by the target is associated with

competing bids in takeovers: the coefficient estimates for our advertising proxies are

significantly positive in all models. According to model (1), the marginal economic impact

related to a one standard deviation increase in advertising spending implies a 7.13 percentage

point increase in the probability that more than one bidder submits a public offer for the target.

This is quite a considerable effect when benchmarked against the 7.4% incidence of bid

competition for the transactions in our sample.

The results in Table 8 suggest that investor attention (generated by product market

advertising) triggers additional interest in acquiring the targets, promoting competition to buy

these firms. The increased competition prompted by the increased attention could explain the

higher takeover premiums paid to firms with more advertising and the lower merger

announcement returns earned by their acquirers.

B. Offer Revisions

Given that advertising by target firms generates interest by multiple bidders to buy these

firms, we now study whether the bidders are more likely revise their bids upwards in order to                                                                                                                          13 As in Eckbo (2010), the contest may be single-bid (first offer is accepted or rejected with no further observed bids) or multiple-bid (several bids and/or bid revisions are observed). The initial bidder may win, a rival bidder may win, or all bids may be rejected (no bidder wins).

21

acquire the targets. We define a bid revision as the percent difference between the initial and

final bid premium offered for the target firm as recorded by SDC.14 We note that 829 (or

11.68%) of the bids in our sample experience a bid revision. This frequency compares favorably

to that of 10.32% in Bates, Lemmon and Linck (2006).

In Table 9 we estimate four bid revision logit regressions. In these tests, the dependent

variable is set to one if the bid is revised upward and set to zero otherwise. The variables used to

control for target advertising are similar to those we use in the deal completion tests.

Our bid revision regressions indicate that all of our advertising proxies are associated with

increases in the bid premium offered to the target firms. According to the marginal effect we

estimate in model (1), a one standard deviation increase in advertising spending raises the

probability of an upward bid premium revision by 3.62 percentage points. Together, with the

findings from our bid competition tests, those in Table 9 suggest that increased investor attention

resulting from increased advertising by target companies, all but prompts a bidding war to

acquire these firms.

C. Alternative Metrics of Premiums and Acquirer Returns

The regressions presented in Panel C of Table 2 use the four-week premium reported by SDC

as the dependent variable. We re-estimate the same regressions using two different premium

measures as dependent variables. The first is the target’s CAR during the window (-20, +1)

relative to the announcement date as in Jarrell and Poulsen (1989). Our second measure follows

Schwert (1996) and uses the target’s CAR during the window (-42, +126). To conserve space, in

                                                                                                                         14 We cannot observe any bid revisions that are privately negotiated before the initial bid is publicly announced.

22

Panel A of Table 10, we report the regression results related to our four advertising proxies when

these premium alternatives are used. As with our earlier tests, the estimates for all four target

advertising spending variables are positive and significant.

We also estimate alternative bidder return regressions similar to those reported in Table 6. In

these tests we follow the procedure in Masulis Wang, and Xie (2007) and replace the acquirer’s

return (-1, +1) with the CAR accruing to the bidder on deal announcement during the (-2, +2)

and (-5, +5) windows. In Panel B of Table 10, we note that the coefficients for our target

advertising spending variables are still negatively related to the acquirer’s return as measured

during these alternative windows.

We retain the residuals from the four advertising tests in the first-stage regressions (models

(1), (3), (5) and (7)) of Table 3. These residuals (which measure the abnormal level of our

advertising spending proxies) serve as the respective key independent variables in four premium

regressions, which are specified similar to those in Panel C of Table 2. We report the estimates

for the abnormal level of advertising in Panel C of Table 10. These coefficients capture the effect

of advertising that is purged from the effect of the performance or size of the target firm. We find

that the abnormal advertising spending estimates are positive and significantly associated with

the bid premium. These findings (together with those from the endogeneity tests in Tables 3 and

4) lessen the concern that our results are due to the fact that better performing or larger target

firms are better able to advertise.

We also conduct three falsification tests. In the first test we build our advertising proxies with

advertising expense data from three years before the deal (rather than from the fiscal year before

the M&A deal as in our earlier tests). This test allows us to determine whether current

advertising matters more than past advertising. In the second test we use R&D expense rather

23

than the advertising proxies, which could result if we are picking up growth with the advertising

variable. In the third test we use excess cash instead of advertising spending proxies under the

premise that the bidder is trying to buy the target’s excess cash balance. The results of these tests

are shown in Panels D, E and F of Table 10. In all three of the falsification tests we find no

significant relationships between these alternative variables and either the takeover premium or

the abnormal return to the bidder at the announcement of the acquisition.

D. Target Firms with Consumer Products

It is possible that target firms with products or services sold to consumers (instead of to other

businesses) may extract more benefits from advertising. If this pattern is pervasive in our data, it

is possible that target’s with business to consumer (B2C) products could be driving our results.

To explore this possibility, in Panel G of Table 10 we estimate acquisition premium regressions

for subsamples of targets that belong to B2C industries and of those in other industries. Targets

are classified as B2C if they operate in consumer-oriented industries which we identify following

the taxonomy in Sharpe (1982). The key independent variables in the premium regressions are

our four proxies to measure target advertising.

For both B2C and non-B2C targets, the results in Panel G indicate a positive and significant

association between our advertising proxies and premiums. This evidence mitigates the concern

that B2C targets drive our results. Still, we note that for three of our proxies, differences in

parameter estimates show that advertising is related to higher premiums for B2C targets. Based

on the advertising spending tests, a one standard deviation increase in advertising is related to a

premium increase of 1.91% for B2C targets and only 65 basis points for non-B2C targets.

24

E. Managerial Incentives

We argue that managers can draw attention to their firms’ products, accomplishments and

expected performance through advertising. Moreover, academic work by Grullon, Kanatas, and

Weston, 2004, Chemmanur and Yan, 2009, and Lou, 2014 (among others) suggest that managers

deliberately use advertising to attract investor recognition and influence their firms’ stock prices.

Two questions that follow from the above evidence are (1) whether managers with stronger

incentives are more likely to advertise and (2) whether advertising is associated with larger

valuation effects when stronger incentives are present. To address these issues, we examine a

subsample of 2,777 M&A transactions with available target CEO ownership data from either the

Execucomp database or the Thomson Financial Insider database.

First, in an untabulated logit regression similar to that in the first column of Panel A of

Table 4, we find that raising ownership by a single standard deviation (16.80%) is associated

with a 13.84 percentage point increase in the probability of advertising. This result is

economically important given that the unconditional probability of advertising in the subsample

is about 33% (close to that of 33.50% in the full sample of 7,095 deals). We also re-estimate our

four premium regressions interacting target CEO ownership with each of the four respective

advertising proxies. The results, reported in Panel H of Table 10, show that the positive

association between target advertising and the premiums paid to these firms increases in target

CEO ownership. According to the first regression in Panel H, increasing advertising spending by

one standard deviation is associated with a premium increase of 1%. However, a similar increase

in advertising spending produces a premium increase of 2.11% when accompanied by a standard

deviation increase in target CEO ownership.

25

IV. Conclusions

We hypothesize that managers interested in (or concerned about) being taken over will

employ advertising in order to not only raise customer and investor awareness, but to also

increase their negotiating position in the case of a merger. We test this hypothesis through

examining a sample of 7,095 (completed and withdrawn) M&A bids for U.S. publicly traded

targets announced during 1986-2011.

Consistent with our hypotheses, we find that the relation between a firm’s increased

advertising and its takeover premium is strongly and significantly positive. Specifically, we find

that a $1 increase in a target firm’s advertising expenditure is associated with a $6.19 deal value

increase, on average. Moreover, this premium increase tends to be paid for out of the acquirer’s

share of takeover gains as the $1 increase in the target firm’s advertising is also associated with a

$26.40 decrease in the acquirer’s market capitalization during the announcement period. Our

other empirical results are also consistent with our hypothesis in that targets that advertise are

more likely to be pursued by multiple bidders and these bidders are more likely to revise their

bids upwards. Thus, our evidence suggests that increased advertising heightens not only

customer attention, but also investor attention and manager negotiation positions, which

translates to higher premiums paid for firms that become takeover targets.

Our empirical evidence suggests that managers have considerable ability to materially affect

their firm’s profile in the eyes of investors through their advertising. Moreover, our evidence is

consistent with managers having ownership incentives to engage in such attention-gathering

activity – we find that the relation between advertising and firm value is heightened in firms with

greater managerial ownership.

26

References

Abadie, A., and Imbens, G. W., 2008. On the failure of the bootstrap for matching estimators.

Econometrica 76, 1537-1557.

Ahern, K. R., and Harford, J., 2013. The importance of industry links in merger waves. Journal of Finance 69, forthcoming.

Ahern, K. R., and Sosyura, D., 2013. Who writes the news? Corporate press releases during merger negotiations. Journal of Finance 69, 241-291.

Aktas, N., de Bodt, E., and Roll, R., 2010. Negotiations under the threat of an auction. Journal of Financial Economics 98, 241-255.

Bagwell, K., and Ramey, G., 1994. Coordination economies, advertising and search behavior in retail markets. American Economic Review 84, 498-517.

Barber, B. M., and Odean, T., 2008. All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies 21, 785-818.

Bargeron, L., Schlingemann, F., Stulz, R., and Zutter, C., 2008. Why do private acquirers pay so little compared to public acquirers? Journal of Financial Economics 89, 375-390.

Bates, T., and Lemmon, M., 2003. Breaking up is hard to do? An analysis of termination fee provisions and merger outcomes. Journal of Financial Economics 69, 469-504.

Bates, T., Lemmon, M., and Linck, J., 2006. Shareholder wealth effects and bid negotiation in freeze-out deals: Are minority shareholders left out in the cold? Journal of Financial Economics 81, 681-708.

Becker, G. S., and Murphy, K. M., 1993. A simple theory of advertising as a good or bad. Quarterly Journal of Economics 108, 941-964.

Bodnaruk, A., and Ostberg, P., 2009. Does investor recognition predict returns? Journal of Financial Economics 91, 208–226.

Cai, Y., and Sevilir, M., 2012. Board connections and M&A transactions. Journal of Financial Economics 103, 327-349.

Chen, H., Noronha, G., and Singal, V., 2004. The price response to S&P 500 index additions and deletions: Evidence of asymmetry and a new explanation. Journal of Finance 59, 1901–1930.

Chemmanur, T., and Yan, A., 2009. Product market advertising and new equity issues. Journal of Financial Economics 92, 40-65.

Comment, R., and Schwert G.W., 1995. Poison or placebo? Evidence on the deterrence and wealth effects of modern antitakeover measures. Journal of Financial Economics 39, 3-43.

27

Da, Z., Engelberg, J., and Gao, P., 2011. In search of attention. Journal of Finance 66, 1461-1499.

Eckbo, B. E., (Ed.). 2010. Corporate Takeovers: Modern Empirical Developments. Academic Press.

Fama, E., and French, K., 1997. Industry costs of equity. Journal of Financial Economics 43, 153-193.

Fee, C., Hadlock, C., and Pierce, J., 2009. Investment, financing constraints, and internal capital markets: Evidence from the advertising expenditures of multinational firms. Review of Financial Studies 22, 2361-2392.

Fehle, F., Tsyplakov, S., Zdorovtsov, V., 2005. Can companies influence investor behavior through advertising? Super bowl commercials and stock returns. European Financial Management 11, 625–647.

Foerster, S., Karolyi, G. A., 1999. The effect of market segmentation and investor recognition on asset prices: Evidence from foreign stock listing in the U.S. Journal of Finance 54, 981–1013.

Gaspar, J., Massa, M., and Matos, P., 2005. Shareholder investment horizons and the market for corporate control. Journal of Financial Economics 76, 135-165.

Gervais, S., Kaniel, R., and Mingelgrin, D. H., 2001. The high-­‐volume return premium. Journal of Finance 56, 877-919.

Grossman, G. M., and Shapiro, C., 1984. Informative advertising with differentiated products. Review of Economic Studies 51, 63-81.

Grullon, G., Kanatas, G., and Weston, J., 2004. Advertising, breadth of ownership, and liquidity. Review of Financial Studies 17, 439-461.

Gurun, U. G., and Butler, A. W., 2012. Don't believe the hype: local media slant, local advertising, and firm value. Journal of Finance 67, 561-598.

Hartzell, J., Ofek, E., and Yermack, D., 2004. What’s in it for me? CEOs whose firms are acquired. Review of Financial Studies 17, 37-61.

Heckman, J., 1979. Sample selection bias as a specification error. Econometrica 47, 153-161.

Hirshleifer, D. and S. Teoh, 2003. Limited attention, information disclosure, and financial reporting, Journal of Accounting and Economics 26, 337-386.

Hou, K., and Moskowitz, T., 2005. Market frictions, price delay, and the cross-section of expected returns. Review of Financial Studies 18, 981-1020.

Hou, K., Peng, L., and Xiong W., 2009. A tale of two anomalies: The implications of investor attention for price and earnings momentum. Working paper, Ohio State University.

28

Jarrell, G., and Poulsen, A., 1989. Stock trading before the announcement of tender offers: insider trading or market anticipation? Journal of Law, Economics and Organizations 5, 223-248.

Kadlec, G., McConnell, J., 1994. The effect of market segmentation and illiquidity on asset prices: Evidence from exchange listings. Journal of Finance 49, 611–636.

Kaniel, R., Ozoguz, A. and L. Starks, 2012. The high volume return premium: Cross-country evidence, Journal of Financial Economics 103, 255-279. .

Kihlstrom, R. E., and Riordan, M. H., 1984. Advertising as a signal. Journal of Political Economy 92, 427.

Kim, Y., and Meschke, F., 2011. CEO interviews on CNBC. Working paper, University of Kansas.

Lehavy R. and R. Sloan, 2008. Investor recognition and stock returns, Review of Accounting Studies 13, 327-361.

Lou, D., 2014. Attracting investor attention through advertising. Review of Financial Studies, forthcoming.

Loughran, T., and Ritter, J., 2004. Why has IPO underpricing changed over time? Financial Management 33, 5-37.

Louis, H., and Sun, A., 2010. Investor inattention and the market reaction to merger announcements. Management Science 56, 1781-1793.

Malmendier, U., Opp, M., and Saidi, F., 2012. Cash is king: Revaluation of targets after merger bids. Working paper, University of California at Berkeley.

Masulis, R., Wang, C., and Xie, F., 2007. Corporate governance and acquirer returns. Journal of Finance 62, 1851-1889.

Merton, R. C., 1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510.

Milgrom, P., and Roberts, J., 1995. Complementarities and fit strategy, structure, and organizational change in manufacturing. Journal of Accounting and Economics 19, 179-208.

Moeller, S., Schlingemann, F., and Stulz, R., 2004. Firm size and the gains from acquisitions. Journal of Financial Economics 73, 201-228.

Moeller, T., 2005. Let's make a deal! How shareholder control impacts merger payoffs. Journal of Financial Economics 76, 167-190.

Nelson, P., 1974. Information and consumer behavior. Journal of Political Economy 81, 729-754.

Officer, M., 2003. Termination fees in mergers and acquisitions. Journal of Financial Economics 69, 431-467.

29

Palepu, K., 1986. Predicting takeover targets: A methodological and empirical analysis. Journal of Accounting and Economics 8, 3-35.

Rosenbaum, P. R., and Rubin, D. B., 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.

Schlingemann, F., Stulz, R., and Walkling, R., 2002. Divestitures and the liquidity of the market for corporate assets. Journal of Financial Economics 64, 117–144.

Schwert, G.W., 1996. Markup pricing in mergers and acquisitions. Journal of Financial Economics 41, 153-192.

Schwert, G.W., 2000. Hostility in takeovers: In the eyes of the beholder? Journal of Finance 55, 2599-2640.

Seasholes, M. S., and Wu, G., 2007. Predictable behavior, profits, and attention. Journal of Empirical Finance 14, 590-610.

Sharpe, W. F., 1982. Factors in New York Stock Exchange security returns, 1931-1979, Journal of Portfolio Management 8, 5-19.

Shleifer, A., and Vishny, R., 1986. Large shareholders and corporate control. Journal of Political Economy 94, 461-488.

Stein, J. C., 1996. Rational capital budgeting in an irrational world. Journal of Business 69, 429-455.

Stock J., and Yogo M., 2005. Testing for weak instruments in linear IV regression. Andrews DWK Identification and Inference for Econometric Models. Cambridge University Press, 80-108.

Telser, L. G., 1964. Advertising and competition. Journal of Political Economy 72, 457-466.

Wang, C., and Xie, F., 2009. Corporate governance transfer and synergistic gains from mergers and acquisitions. Review of Financial Studies 22, 829-858.

Wulf, J., 2004. Do CEOs in mergers trade power for premium? Evidence from mergers of equals. Journal of Law, Economics, and Organization 20, 60-101.

Yuan, Y., 2008. Attention and trading. Working Paper, University of Pennsylvania.

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Table 1: Sample characteristics This table describes our sample which consists of 7,095 U.S. merger and acquisition bids for public targets announced during 1986-2011 and tracked in the Securities Data Company’s (SDC) merger and acquisition database. We screen deals from SDC following the criteria in Bargeron, Schlingemann, Stulz, and Zutter (2008). In addition, we require that target firms have stock market and accounting data available from the Center for Research in Security Prices and Compustat, respectively. In Panel A we report the temporal and Fama and French 48 industrial distribution of the sample targets. In Panel B we report deal status, mode of acquisition, method of payment, deal attitude, deal value, and target financial characteristics. In Panel C, we report summary statistics for four advertising spending measures for the entire sample of 7,095 targets and for the sub-sample of 2,377 targets with positive advertising spending. For the ln(1+Advertising spending) and ln(Adv spending) – ln(Adv spending prior year) variables, we report the actual value of spending (in $US million) and advertising spending growth rate, respectively, which we estimate with the standard eX – 1 transformation. All financial variables are measured at the end of the fiscal year before the merger public announcement date and inflation-adjusted to the end of 2011. Panel A: Temporal and industrial distribution

Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 Deal count 228 261 349 259 123 103 83 137 197 Percent 3.21 3.68 4.92 3.65 1.73 1.45 1.17 1.93 2.78

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 Deal count 367 353 488 514 563 476 352 201 251 Percent 5.17 4.98 6.88 7.24 7.94 6.71 4.96 2.83 3.54

Year 2004 2005 2006 2007 2008 2009 2010 2011 Total Deal count 220 247 297 318 204 129 219 156 7,095 Percent 3.1 3.48 4.19 4.48 2.88 1.82 3.09 2.2 100

Industry Count % Industry Count % Agriculture 20 0.28 Shipbuilding, Railroad Equipment 15 0.21 Food Products 89 1.25 Defense 6 0.08 Candy & Soda 7 0.10 Precious Metals 17 0.24 Beer & Liquor 20 0.28 Industrial Metal Mining 12 0.17 Tobacco Products 2 0.03 Coal 7 0.10 Recreation 45 0.63 Petroleum and Natural Gas 206 2.90 Entertainment 125 1.76 Utilities 202 2.85 Printing and Publishing 50 0.70 Communication 206 2.90 Consumer Goods 99 1.40 Personal Services 58 0.82 Apparel 65 0.92 Business Services 958 13.50 Healthcare 181 2.55 Computer Hardware 319 4.50 Medical Equipment 237 3.34 Computer Software 328 4.62 Pharmaceutical Products 247 3.48 Measuring and Control Equipment 155 2.18 Chemicals 78 1.10 Business Supplies 83 1.17 Rubber and Plastic Products 87 1.23 Shipping Containers 18 0.25 Textiles 44 0.62 Transportation 171 2.41 Construction Materials 130 1.83 Wholesale 213 3.00 Construction 51 0.72 Retail 317 4.47 Steel work 88 1.24 Restaurants, Hotels, Motels 149 2.10 Fabricated Products 24 0.34 Banking 1017 14.33 Machinery 188 2.65 Insurance 224 3.16 Electrical Equipment 57 0.80 Real Estate 51 0.72 Automobiles and Trucks 59 0.83 Trading 316 4.45 Aircraft 25 0.35 Others 29 0.41 Total 7,095 100.00

31

Panel B: Deal and firm characteristics Proportion of sample Mean Median Deal characteristics Completed 0.8109 Tender offer 0.2389 All cash payment 0.5080 Friendly attitude 0.8937 Same industry 0.5333 Deal value (US$ billion) 1.0652 0.1812 Relative size (Target/Acquirer) 0.4267 0.1506 Target characteristics Market value of equity (US$ billion) 0.6012 0.0897 Q 1.6356 1.2042 Leverage 0.2298 0.1800

Panel C: Target’s advertising spending

Mean Q1 Median Q3 Σ (1) Advertising spending: ln(1+Advertising spending) 0.6135 0.0000 0.0000 0.4780 1.7281 (2) Scaled advertising spending 0.0095 0.0000 0.0000 0.0015 0.0283 (3) Advertising intensity 0.0094 0.0000 0.0000 0.0083 0.0256 (4) Advertising growth: ln(Adv spending) – ln(Adv spending prior year)

-0.0659 0.0000 0.0000 0.0000 2.2339

Conditional on positive advertising spending (5) Advertising spending: ln(1+Advertising spending) 3.1666 0.4690 1.7999 8.3512 2.6161 (6) Scaled advertising spending 0.0283 0.0015 0.0112 0.0365 0.0430 (7) Advertising intensity 0.0279 0.0082 0.0143 0.0309 0.0379 (8) Advertising growth: ln(Adv spending) – ln(Adv spending prior year)

0.1010 -0.1051 0.1005 0.4289 1.2803

32

Table 2: Target’s advertising spending and acquisition premiums The sample consists of 7,095 mergers and acquisitions announced during 1986-2011 described in Table 1. Panel A presents first stage regressions of the probability of becoming a takeover target using 140,839 firm-years with data from CRSP and Compustat during fiscal years 1985-2011. These tests are similar to those in Palepu (1986) and Comment and Schwert (1995). Standard errors are clustered at the firm level. In Panel B, from the original sample, we examine 2,326 bid contests in which one or several bidders bid for a single target and where we can find the deal background from the merger proxies filed by either the target or the acquirer with the SEC (S-4, DEFM 14, SC 14D9, SC TO, DEF 14, 8-K). We use information from the first bid in the contest. We run logit regressions of deal initiation probability similar to those in Aktas, de Bodt and Roll (2010). The dependent variable equals one if the deal is first initiated by the target. In Panel C we estimate OLS regressions of merger premiums similar to those in Bargeron, Schlingemann, Stulz, and Zutter (2008). The dependent variable is the final offer premium reported by SDC. The main independent variables in all three panels are advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). We include the inverse Mill’s ratio obtained from the corresponding first stage tests in Panel A to control for target self-selection (Heckman, 1979). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Panel A: Probability of becoming a target Dependent variable = Target (0,1) Model (1) Model (2) Model (3) Model (4) Advertising spending 0.0494** (0.0169) Scaled advertising spending 1.8292** (0.0136) Advertising intensity 1.7021** (0.0283) Advertising growth 0.0149** (0.0119) Size -0.4361*** -0.4031*** -0.4110*** -0.4204*** (0.0001) (0.0001) (0.0001) (0.0001) Q 0.0006 0.0005 0.0005 0.0004 (0.6084) (0.6485) (0.6420) (0.6788) Leverage 0.0067 0.0162 0.0149 0.0156 (0.5881) (0.1672) (0.2055) (0.1850) OCF -0.0741 -0.0794 -0.0765 -0.0755 (0.4177) (0.3856) (0.4025) (0.4090) Prior year market adjusted return -0.0043 -0.0047 -0.0044 -0.0047 (0.6327) (0.6012) (0.6273) (0.5983) Industry Herfindahl-Hirschman Index 0.8251 0.8223 0.8294 0.8534 (0.1857) (0.1867) (0.1829) (0.1715) Industry liquidity index 0.3497 0.3551 0.3450 0.3497 (0.1825) (0.1752) (0.1884) (0.1824) One year macroeconomic change 0.0099 0.0098 0.0099 0.0109 (0.5772) (0.5782) (0.5742) (0.5397) Constant -27.4937 -23.8407 -24.2939 -21.1273 (0.5746) (0.6109) (0.6048) (0.6392) Year and industry fixed effects Yes Yes Yes Yes N 140,839 140,839 140,839 140,839 Regression’s p-value 0.0001 0.0001 0.0001 0.0001

33

Panel B Target’s advertising spending and deal initiation

Dependent variable = Target initiated (0,1) Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.1601*** (0.0005) Scaled advertising spending 5.6441** (0.0185) Advertising intensity 7.8485*** (0.0008) Advertising growth 0.1213** (0.0415) Target characteristics Size -0.1741*** -0.1410*** -0.1441*** -0.1451*** (0.0001) (0.0001) (0.0001) (0.0001) Q -0.0132 -0.0212 -0.0239 -0.0196 (0.6500) (0.4711) (0.4214) (0.5004) Leverage 0.3011 0.3101 0.3108 0.3105 (0.1774) (0.1654) (0.1642) (0.1655) OCF -0.3256 -0.3141 -0.2355 -0.3429 (0.1682) (0.1830) (0.3177) (0.1480) Prior year market adjusted returns -0.0234 -0.0301 -0.0254 -0.0296 (0.7193) (0.6444) (0.6976) (0.6490) Deal and market characteristics Private acquirer (0,1) 0.3920* 0.3998* 0.3974* 0.3974* (0.0575) (0.0523) (0.0539) (0.0545) Cash only payment (0,1) 0.2328* 0.2196* 0.2244* 0.2269* (0.0519) (0.0663) (0.0609) (0.0578) Tender offer (0,1) -0.3481** -0.3454** -0.3593** -0.3302** (0.0149) (0.0158) (0.0123) (0.0205) Hostile deal (0,1) -1.6835*** -1.6582*** -1.6534*** -1.6493*** (0.0006) (0.0007) (0.0007) (0.0007) Competed deal (0,1) -0.2301 -0.2258 -0.2073 -0.2316 (0.2765) (0.2829) (0.3244) (0.2716) Toehold -0.0099 -0.0099 -0.0094 -0.0089 (0.3225) (0.3203) (0.3449) (0.3725) Target termination fee (0,1) 0.0381 0.0344 0.0364 0.0383 (0.7275) (0.7530) (0.7396) (0.7261) Lockup (0,1) -0.2161 -0.2478 -0.2393 -0.2440 (0.4600) (0.3950) (0.4125) (0.4018) Same industry (0,1) 0.0968 0.0917 0.0923 0.0817 (0.3586) (0.3838) (0.3811) (0.4373) Merger of equals (0,1) 0.3911 0.3589 0.3643 0.3697 (0.2508) (0.2864) (0.2804) (0.2718) Target industry liquidity index 0.4450* 0.4406* 0.4326* 0.4219 (0.0887) (0.0915) (0.0974) (0.1058) One year macroeconomic change 0.0568 0.0530 0.0547 0.0558 (0.1413) (0.1694) (0.1574) (0.1485) Constant 0.4427 0.2840 0.3037 0.3794 (0.5699) (0.7159) (0.6970) (0.6279) Year and industry fixed effects Yes Yes Yes Yes N 2,326 2,326 2,326 2,326 Regression’s p-value 0.0001 0.0001 0.0001 0.0001

34

Panel C: Target’s advertising spending and acquisition premiums Dependent variable = Acquisition premium Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.0100** (0.0386) Scaled advertising spending 0.4390*** (0.0079) Advertising intensity 0.4899*** (0.0054) Advertising growth 0.0083** (0.0237) Target characteristics Size -0.0343*** -0.0322*** -0.0325*** -0.0329*** (0.0001) (0.0001) (0.0001) (0.0001) Q -0.0034 -0.0040 -0.0041 -0.0050* (0.2418) (0.1760) (0.1669) (0.0862) Leverage 0.2016*** 0.2042*** 0.2031*** 0.2015*** (0.0001) (0.0001) (0.0001) (0.0001) OCF -0.0471** -0.0483** -0.0475** -0.0136 (0.0398) (0.0349) (0.0380) (0.1526) Prior year market adjusted returns -0.0306*** -0.0306*** -0.0304*** -0.0308*** (0.0001) (0.0001) (0.0001) (0.0001) Deal and market characteristics Private acquirer (0,1) -0.0676*** -0.0669*** -0.0668*** -0.0649*** (0.0001) (0.0001) (0.0001) (0.0001) Cash only payment (0,1) 0.0225** 0.0223** 0.0224** 0.0221** (0.0327) (0.0345) (0.0337) (0.0365) Tender offer (0,1) 0.0666*** 0.0663*** 0.0663*** 0.0637*** (0.0001) (0.0001) (0.0001) (0.0001) Hostile deal (0,1) 0.0500** 0.0506** 0.0505** 0.0589*** (0.0177) (0.0165) (0.0167) (0.0059) Competed deal (0,1) 0.0935*** 0.0931*** 0.0934*** 0.0927*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold -0.0013** -0.0013** -0.0013** -0.0013** (0.0245) (0.0228) (0.0234) (0.0231) Target termination fee (0,1) 0.0208** 0.0204** 0.0203** 0.0189* (0.0435) (0.0473) (0.0487) (0.0699) Lockup (0,1) -0.0704** -0.0722** -0.0717** -0.0747** (0.0398) (0.0349) (0.0362) (0.0290) Same industry (0,1) 0.0104 0.0104 0.0102 0.0088 (0.2799) (0.2821) (0.2872) (0.3623) Merger of equals (0,1) -0.1770*** -0.1772*** -0.1770*** -0.1757*** (0.0001) (0.0001) (0.0001) (0.0001) Target Herfindahl-Hirschman Index -0.3430** -0.3435** -0.3385** -0.3456** (0.0211) (0.0209) (0.0229) (0.0202) Target industry liquidity index -0.0147 -0.0137 -0.0141 -0.0221 (0.5144) (0.5434) (0.5306) (0.3229) One year macroeconomic change -0.0056* -0.0056* -0.0057* -0.0060* (0.0932) (0.0952) (0.0902) (0.0740) Constant 0.5424*** 0.5317*** 0.5338*** 0.5496*** (0.0001) (0.0001) (0.0001) (0.0001) Heckman self-selectivity correction Yes Yes Yes Yes Year and industry fixed effects Yes Yes Yes Yes N 7,095 7,095 7,095 7,095 Regression’s p-value 0.0001 0.0001 0.0001 0.0001

35

Table 3: Endogeneity of advertising spending and acquisition premiums This table addresses the endogeneity of advertising spending and acquisition premiums with a two stage approach using 7,095 mergers and acquisitions announced during 1986-2011 described in Table 1. Models (1), (3), (5), and (7) present the first stage regression of the determinant of advertising spending. Models (2), (4), (6), and (8) present the second stage regression of the acquisition premium on the advertising spending instruments obtained from the first stage. All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Dependent variable = Advertising spending

Acquisition premium

Scaled adv spending

Acquisition premium

Advertising intensity

Acquisition premium

Advertising growth

Acquisition premium

1st stage 2nd stage IV 1st stage 2nd stage IV 1st stage 2nd stage IV 1st stage 2nd stage IV Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8) Target’s advertising spending measures Fitted advertising spending 0.0109*** (0.0014) Fitted scaled advertising spending 0.4410*** (0.0003) Fitted advertising intensity 0.6843*** (0.0001) Fitted advertising growth 0.0420** (0.0363) Instruments Average competitor advertising spending 0.2101*** (0.0001) Average competitor scaled adv. spending 0.8045*** (0.0001) Average competitor advertising intensity 0.8521*** (0.0001) Average competitor advertising growth 0.1300*** (0.0001) Target characteristics Size 0.1628*** -0.0491*** 0.0005* -0.0432*** 0.0001 -0.0440*** 0.0422*** -0.0451*** (0.0001) (0.0001) (0.0916) (0.0001) (0.6878) (0.0001) (0.0001) (0.0001) Q -0.0004 0.0001 0.0000 0.0001 0.0000 0.0001 0.0001 0.0001 (0.3155) (0.4904) (0.5234) (0.5728) (0.7592) (0.5803) (0.8150) (0.5374) Leverage -0.2735*** 0.0505** -0.0097*** 0.0696*** -0.0059*** 0.0714*** 0.0623 0.0606*** (0.0001) (0.0325) (0.0001) (0.0030) (0.0006) (0.0023) (0.4531) (0.0099) OCF 0.0924* 0.0319** 0.0071*** 0.0195 0.0050*** 0.0222 0.0199 0.0144 (0.0992) (0.0483) (0.0001) (0.2377) (0.0010) (0.1728) (0.5203) (0.1339) Prior year market adjusted return -0.0377* -0.0715*** -0.0007 -0.0718*** -0.0006 -0.0718*** -0.0021 -0.0734*** (0.0696) (0.0001) (0.2456) (0.0001) (0.3256) (0.0001) (0.9379) (0.0001)

36

Target Herfindahl-Hirschman Index 0.0120 -0.1903** 0.0005 -0.2067** -0.0007 -0.2093** 0.7794 -0.1451* (0.9741) (0.0215) (0.9650) (0.0130) (0.9477) (0.0117) (0.1089) (0.0775) Target industry liquidity index -0.2098*** 0.0356** -0.0061*** 0.0376** -0.0045*** 0.0370** 0.0203 0.0517*** (0.0002) (0.0294) (0.0001) (0.0212) (0.0032) (0.0236) (0.7802) (0.0008) One year macroeconomic change -0.0205** -0.0079*** -0.0005** -0.0082*** -0.0003 -0.0078*** -0.0149 -0.0084*** (0.0135) (0.0001) (0.0469) (0.0001) (0.2579) (0.0001) (0.1699) (0.0001) Deal characteristics Private acquirer (0,1) -0.0817*** -0.0821*** -0.0819*** -0.0773*** (0.0001) (0.0001) (0.0001) (0.0001) Cash only payment (0,1) 0.0088 0.0086 0.0095 0.0104 (0.3882) (0.3988) (0.3487) (0.3087) Tender offer (0,1) 0.0951*** 0.0943*** 0.0942*** 0.0985*** (0.0001) (0.0001) (0.0001) (0.0001) Hostile deal (0,1) 0.0584*** 0.0585*** 0.0593*** 0.0612*** (0.0056) (0.0055) (0.0049) (0.0037) Competed deal (0,1) 0.0960*** 0.0961*** 0.0962*** 0.0995*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold -0.0014** -0.0014** -0.0014** -0.0013** (0.0183) (0.0176) (0.0152) (0.0246) Target termination fee (0,1) 0.0338*** 0.0338*** 0.0340*** 0.0318*** (0.0004) (0.0004) (0.0004) (0.0010) Lockup (0,1) -0.0448 -0.0452 -0.0446 -0.0501 (0.1909) (0.1863) (0.1927) (0.1438) Same industry (0,1) 0.0061 0.0064 0.0055 0.0037 (0.5168) (0.4954) (0.5613) (0.6914) Merger of equals (0,1) -0.1754*** -0.1765*** -0.1749*** -0.1811*** (0.0001) (0.0001) (0.0001) (0.0001) Constant -0.7037*** 0.6250*** 0.0125*** 0.5969*** 0.0052 0.5931*** -0.2182 0.6128*** (0.0001) (0.0001) (0.0005) (0.0001) (0.1235) (0.0001) (0.1806) (0.0001) Year and industry fixed effects Yes Yes Yes Yes Yes Yes Yes Yes N 7,095 7,095 7,095 7,095 7,095 7,095 7,095 7,095 Regression’s p-value 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

37

Table 4: Propensity score matching estimates for advertising spending on premiums In Panel A, we report the results of the propensity score matching estimates, the sample means of the treatment and control samples, and the p-values of the difference in means. In Panel B, we report the average treatment effects on premiums where the treatment is defined as “Advertising spending > 0”. Matching estimates use the Gaussian kernel with a fixed bandwidth of 0.10. We report the p-value of the treatment effects using 500 bootstrap repetitions in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Propensity score model estimates for advertising spending

Dependent variable = 1 if advertising spending > 0

Treatment sample mean

Control sample mean

p-value for difference

Size 0.0416** 5.4888 5.4530 0.4712 Q 0.0208 1.6050 1.6874 0.1074 Leverage -0.2197 0.1748 0.1618 0.0126 Operating cash flow 0.0399 0.0639 0.0679 0.5568 Prior year market adjusted return 0.0001 -0.0034 0.0044 0.7156 Private acquirer (0,1) -0.1107 0.2049 0.1926 0.2880 Cash payment (0,1) 0.0270 0.5301 0.5359 0.6882 Tender offer (0,1) 0.1597** 0.2507 0.2449 0.6435 Hostile deal (0,1) -0.0481 0.0501 0.0477 0.7083 Competed deal (0,1) 0.1142 0.1342 0.1254 0.3684 Toehold 0.0009 2.0801 1.9861 0.6654 Target termination fee (0,1) -0.0533 0.5002 0.4852 0.3018 Lockup (0,1) 0.1776 0.0172 0.0203 0.4431 Same industry (0,1) 0.0643 0.5313 0.5300 0.9253 Merger of equals (0,1) 0.2064 0.0126 0.0122 0.9074 Target Herfindahl-Hirschman Index -1.2862 0.0590 0.0612 0.2131 Target industry liquidity index -0.5527*** 0.3457 0.3654 0.0282 One year macroeconomic change -0.0447** 1.8638 1.8987 0.7182 Intercept -1.7973*** Year and industry fixed effects Yes N (treated observations) 2,377 N (untreated observations) 2,368

Panel B: Average treatment effect on premiums for advertising spending

Average treatment effect (p-value)

Premiums (Advertising spending > 0 vs. = 0) 0.0330*** (0.0022)

38

Table 5: Advertising spending and unconditional premiums This table presents unconditional premium regressions similar to those in Comment and Schwert (1995). The dependent variable is the final offer premium reported by SDC. The premium is set to zero in non-takeover firm-years. All models use 140,839 firm-years with data available from CRSP and Compustat during fiscal year 1985-2011. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. Standard errors are clustered at the firm level. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Acquisition premium Model (1) Model (2) Model (3) Model (4) Advertising spending 0.0007** (0.0334) Scaled advertising spending 0.0422*** (0.0016) Advertising intensity 0.0366*** (0.0018) Advertising growth 0.0002* (0.0537) Size -0.0006*** -0.0005*** -0.0005*** -0.0005*** (0.0001) (0.0001) (0.0001) (0.0001) Q 0.0000 0.0000 0.0000 0.0000 (0.9730) (0.9777) (0.9768) (0.9796) Leverage -0.0005 -0.0001 -0.0001 -0.0004 (0.7262) (0.9428) (0.9261) (0.7900) OCF -0.0012* -0.0012* -0.0012* -0.0012* (0.0726) (0.0693) (0.0705) (0.0712) Prior year market adjusted return 0.0000 0.0000 0.0000 0.0000 (0.9744) (0.9676) (0.9711) (0.9968) Herfindahl-Hirschman Index -0.0137* -0.0142* -0.0139* -0.0133* (0.0742) (0.0631) (0.0696) (0.0816) Industry liquidity index 0.0010 0.0010 0.0009 0.0008 (0.7661) (0.7721) (0.8020) (0.8256) One year macroeconomic change 0.0000 0.0000 0.0000 0.0000 (0.6512) (0.6222) (0.6313) (0.5940) Constant 0.0004 0.0000 0.0000 0.0002 (0.7504) (0.9743) (0.9908) (0.8911) Year and industry fixed effects Yes Yes Yes Yes N 140,839 140,839 140,839 140,839 Regression’s p-value 0.0001 0.0001 0.0001 0.0001

39

Table 6: Target’s advertising spending and acquirer returns From the original sample of 7,095 mergers and acquisitions announced during 1986-2011 described in Table 1, we examine 3036 deals in which the acquirer is publicly traded. We run OLS regressions of acquirer announcement returns similar to those in Moeller, Schlingemann, and Stulz (2004) and Masulis, Wang and Xie (2007). The dependent variable is the acquirer’s cumulative abnormal return (CAR) over three days around the merger announcement date. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Acquirer CAR [-1,+1] Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending -0.0045*** (0.0001) Scaled advertising spending -0.1432*** (0.0022) Advertising intensity -0.1531*** (0.0014) Advertising growth -0.0016*** (0.0013) Acquirer characteristics Size -0.0002 -0.0007 -0.0006 -0.0008 (0.7156) (0.2885) (0.3428) (0.1678) Q 0.0000 0.0000 0.0000 0.0000 (0.8475) (0.7734) (0.7928) (0.7419) Leverage 0.0169** 0.0166** 0.0167** 0.0164** (0.0222) (0.0247) (0.0236) (0.0268) OCF -0.0111 -0.0108 -0.0107 0.0054 (0.1502) (0.1649) (0.1669) (0.3605) Prior year market adjusted return 0.0095*** 0.0093*** 0.0093*** 0.0094*** (0.0001) (0.0001) (0.0001) (0.0001) Target characteristics Q 0.0000 0.0000 0.0000 0.0000 (0.8229) (0.8981) (0.8883) (0.9360) Leverage -0.0020 -0.0033 -0.0034 -0.0029 (0.7311) (0.5737) (0.5590) (0.6248) OCF 0.0062 0.0063 0.0060 0.0014 (0.3684) (0.3619) (0.3876) (0.4171) Prior year market adjusted return 0.0037** 0.0038** 0.0037** 0.0039** (0.0184) (0.0163) (0.0193) (0.0138) Deal and market characteristics Relative size (Target / Acquirer) -0.0001 -0.0001 -0.0001 -0.0001 (0.8405) (0.7671) (0.8348) (0.8133) Cash only payment (0,1) 0.0246*** 0.0255*** 0.0253*** 0.0251*** (0.0001) (0.0001) (0.0001) (0.0001) Tender offer (0,1) 0.0059* 0.0058* 0.0061* 0.0056 (0.0862) (0.0918) (0.0800) (0.1065) Hostile deal (0,1) -0.0075 -0.0092 -0.0091 -0.0086 (0.2086) (0.1199) (0.1242) (0.1454) Competed deal (0,1) -0.0027 -0.0032 -0.0033 -0.0031 (0.5231) (0.4507) (0.4344) (0.4722) Toehold 0.0004 0.0004 0.0004 0.0004 (0.1025) (0.1146) (0.1149) (0.1134) Merger of equals (0,1) 0.0269** 0.0258** 0.0259** 0.0272**

40

(0.0204) (0.0263) (0.0256) (0.0191) Same industry (0,1) 0.0022 0.0018 0.0019 0.0015 (0.4841) (0.5629) (0.5340) (0.6410) Competitive industry (0,1) 0.0045 0.0042 0.0043 0.0043 (0.1426) (0.1630) (0.1609) (0.1592) Unique industry (0,1) 0.0018 0.0020 0.0022 0.0011 (0.5942) (0.5694) (0.5334) (0.7505) High tech industry (0,1) -0.0053 -0.0057 -0.0057 -0.0047 (0.2560) (0.2257) (0.2211) (0.3177) Target Herfindahl-Hirschman Index 0.0619 0.0616 0.0605 0.0603 (0.2068) (0.2096) (0.2180) (0.2198) Target industry liquidity index -0.0107* -0.0109* -0.0109* -0.0110* (0.0980) (0.0925) (0.0948) (0.0886) One year macroeconomic change -0.0005 -0.0005 -0.0004 -0.0004 (0.5517) (0.5971) (0.6203) (0.6041) Constant -0.0103 -0.0067 -0.0069 -0.0065 (0.4971) (0.6576) (0.6456) (0.6642) Heckman self-selectivity correction Yes Yes Yes Yes Year and industry fixed effects Yes Yes Yes Yes N 3,036 3,036 3,036 3,036 Regression’s p-value 0.0001 0.0001 0.0001 0.0001

41

Table 7: Target’s advertising spending and deal completion

The sample consists of 7,095 merger and acquisition bids announced during 1986-2011 described in Table 1. We run logit regressions of merger completion probability similar to those in Bates and Lemmon (2003) and Officer (2003). The dependent variable equals one if the proposed bid is completed. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Deal completion (0,1) Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.1537*** (0.0004) Scaled advertising spending 3.8094** (0.0181) Advertising intensity 4.6809** (0.0107) Advertising growth 0.1150*** (0.0001) Target characteristics Size -0.0622** -0.0374 -0.0389 -0.0429* (0.0153) (0.1326) (0.1170) (0.0853) Q 0.0202 0.0156 0.0145 0.0136 (0.5137) (0.6115) (0.6392) (0.6592) Leverage -0.3433 -0.3331 -0.3383 -0.3331 (0.1218) (0.1330) (0.1270) (0.1338) OCF -0.1117 -0.1138 -0.1109 -0.2231 (0.5722) (0.5643) (0.5741) (0.2639) Prior year market adjusted returns 0.2662*** 0.2668*** 0.2664*** 0.2659*** (0.0009) (0.0009) (0.0009) (0.0009) Deal and market characteristics Private acquirer (0,1) -0.8083*** -0.8034*** -0.8020*** -0.8077*** (0.0001) (0.0001) (0.0001) (0.0001) Cash only payment (0,1) 0.1402 0.1403 0.1406 0.1420 (0.1178) (0.1174) (0.1164) (0.1138) Tender offer (0,1) 1.7871*** 1.8016*** 1.8024*** 1.8047*** (0.0001) (0.0001) (0.0001) (0.0001) Hostile deal (0,1) -2.4226*** -2.4169*** -2.4192*** -2.4124*** (0.0001) (0.0001) (0.0001) (0.0001) Competed deal (0,1) -2.0470*** -2.0464*** -2.0454*** -2.0662*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold 0.0089* 0.0086* 0.0086* 0.0086* (0.0549) (0.0618) (0.0618) (0.0628) Target termination fee (0,1) 1.8601*** 1.8556*** 1.8563*** 1.8543*** (0.0001) (0.0001) (0.0001) (0.0001) Lockup (0,1) 0.5790 0.5543 0.5584 0.5386 (0.1220) (0.1375) (0.1346) (0.1495) Same industry (0,1) 0.4323*** 0.4300*** 0.4317*** 0.4260*** (0.0001) (0.0001) (0.0001) (0.0001) Merger of equals (0,1) -0.3182 -0.3225 -0.3207 -0.3290 (0.3529) (0.3461) (0.3487) (0.3364) Target industry liquidity index 0.0390 0.0278 0.0261 -0.0056 (0.8355) (0.8822) (0.8892) (0.9761) One year macroeconomic change 0.0503* 0.0486* 0.0475* 0.0484* (0.0759) (0.0862) (0.0931) (0.0875) Constant 0.2589 0.1759 0.1911 0.2793 (0.5698) (0.6997) (0.6751) (0.5415) Year and industry fixed effects Yes Yes Yes Yes N 7,095 7,095 7,095 7,095 Regression’s p-value 0.0001 0.0001 0.0001 0.0001

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Table 8: Target’s advertising spending and bid competition From the original sample of 7,095 merger and acquisition bids announced during 1986-2011 described in Table 1, we examine 6,502 bid contests in which one or several bidders bid for a single target. We run logit regressions of bid competition probability similar to those in Officer (2003). The dependent variable equals one if the contest involves multiple bidders. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Multiple bidders (0,1) Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.1267** (0.0110) Scaled advertising spending 3.7071** (0.0213) Advertising intensity 5.0197** (0.0122) Advertising growth 0.2544*** (0.0037) Target characteristics Size 0.1997*** 0.2286*** 0.2252*** 0.2226*** (0.0001) (0.0001) (0.0001) (0.0001) Q -0.2838*** -0.2993*** -0.2960*** -0.2736*** (0.0003) (0.0002) (0.0002) (0.0004) Leverage -0.5489* -0.5336* -0.5537* -0.5684* (0.0860) (0.0952) (0.0830) (0.0758) OCF 0.2937 0.2822 0.2774 0.1761 (0.3226) (0.3428) (0.3519) (0.5486) Prior year market adjusted returns 0.3168*** 0.3178*** 0.3155*** 0.3105*** (0.0015) (0.0014) (0.0015) (0.0019) Deal and market characteristics Private acquirer (0,1) 0.4454*** 0.4536*** 0.4577*** 0.4554*** (0.0008) (0.0006) (0.0006) (0.0006) Cash only payment (0,1) 0.2016 0.1957 0.1956 0.1888 (0.1102) (0.1206) (0.1210) (0.1353) Tender offer (0,1) 0.5053*** 0.5108*** 0.5114*** 0.5189*** (0.0001) (0.0001) (0.0001) (0.0001) Hostile deal (0,1) 0.8337*** 0.8355*** 0.8375*** 0.8460*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold -0.0122* -0.0124* -0.0124* -0.0124* (0.0706) (0.0652) (0.0666) (0.0670) Target termination fee (0,1) -0.0010 -0.0009 -0.0011 0.0019 (0.9935) (0.9942) (0.9932) (0.9880) Lockup (0,1) -0.5627 -0.6142 -0.6018 -0.6051 (0.3013) (0.2603) (0.2695) (0.2676) Same industry (0,1) 0.1589 0.1605 0.1607 0.1691 (0.1624) (0.1581) (0.1577) (0.1369) Merger of equals (0,1) 0.2294 0.2212 0.2207 0.2306 (0.6450) (0.6562) (0.6570) (0.6430) Target industry liquidity index -0.0076 -0.0032 -0.0085 -0.0034 (0.9767) (0.9902) (0.9740) (0.9896) One year macroeconomic change -0.0396 -0.0406 -0.0419 -0.0411 (0.3646) (0.3527) (0.3372) (0.3483) Constant -4.4930*** -4.6285*** -4.6004*** -4.5905*** (0.0001) (0.0001) (0.0001) (0.0001) Year and industry fixed effects Yes Yes Yes Yes N 6,502 6,502 6,502 6,502 Regression’s p-value 0.0001 0.0001 0.0001 0.0001

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Table 9: Target’s advertising spending and bid revision The sample consists of 7,095 mergers and acquisitions announced during 1986-2011 described in Table 1. We run logit regressions of bid revision probability similar to those in Bates, Lemmon, and Linck (2006). The dependent variable equals one if there is an upward bid revision. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Upward bid revision (0,1) Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.1878*** (0.0001) Scaled advertising spending 6.3718*** (0.0001) Advertising intensity 4.6054*** (0.0068) Advertising growth 0.2595*** (0.0032) Target characteristics Size 0.1169*** 0.1613*** 0.1552*** 0.1506*** (0.0005) (0.0001) (0.0001) (0.0001) Q -0.1170** -0.1159** -0.1109* -0.0680 (0.0452) (0.0465) (0.0525) (0.1894) Leverage -0.2511 -0.2169 -0.2184 -0.1648 (0.3824) (0.4495) (0.4452) (0.5645) OCF 0.5178** 0.4890** 0.5141** 0.0091 (0.0254) (0.0353) (0.0268) (0.9579) Prior year market adjusted returns -0.0827 -0.0751 -0.0789 -0.0915 (0.4382) (0.4808) (0.4581) (0.3919) Deal and market characteristics Private acquirer (0,1) 0.3322*** 0.3380*** 0.3419*** 0.3627*** (0.0096) (0.0084) (0.0075) (0.0047) Cash only payment (0,1) 0.0244 0.0233 0.0272 0.0388 (0.8383) (0.8450) (0.8195) (0.7453) Tender offer (0,1) 0.4615*** 0.4654*** 0.4746*** 0.4973*** (0.0002) (0.0002) (0.0001) (0.0001) Hostile deal (0,1) 2.9311*** 2.9271*** 2.9172*** 2.9265*** (0.0001) (0.0001) (0.0001) (0.0001) Competed deal (0,1) 1.6621*** 1.6536*** 1.6530*** 1.6248*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold 1.1028*** 1.0945*** 1.0885*** 1.1016*** (0.0001) (0.0001) (0.0001) (0.0001) Target termination fee (0,1) -0.3771*** -0.3770*** -0.3778*** -0.3637*** (0.0010) (0.0010) (0.0010) (0.0015) Lockup (0,1) -1.1868 -1.2163* -1.1686 -1.1916 (0.1072) (0.0995) (0.1116) (0.1041) Same industry (0,1) 0.1843 0.1797 0.1769 0.1852* (0.1012) (0.1098) (0.1151) (0.0996) Merger of equals (0,1) -1.4681* -1.4497* -1.4430* -1.4226* (0.0642) (0.0686) (0.0696) (0.0734) Target industry liquidity index -0.0397 -0.0343 -0.0522 0.0364 (0.8682) (0.8859) (0.8271) (0.8770) One year macroeconomic change -0.0105 -0.0095 -0.0133 -0.0113 (0.7572) (0.7809) (0.6953) (0.7398) ln (Initial offer premium) -0.2470*** -0.2469*** -0.2437*** -0.2482*** (0.0001) (0.0001) (0.0001) (0.0001) Constant -4.7748*** -4.9344*** -4.8981*** -4.9855*** (0.0001) (0.0001) (0.0001) (0.0001) Year and industry fixed effects Yes Yes Yes Yes N 7,095 7,095 7,095 7,095 Regression’s p-value 0.0001 0.0001 0.0001 0.0001

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Table 10: Additional analyses In Panel A we estimate target return regressions similar to those in Panel B of Table 2. In Panel B we estimate OLS regressions of acquirer announcement returns similar to those in Table 6. In Panel C, we estimate acquisition premium regressions similar to those in Panel B of Table 2. In Panel D, we estimate acquisition premium and acquirer return regressions using advertising data three fiscal years before the merger announcement date. In Panel D and Panel E, we estimate acquisition premium and acquirer return regressions with each advertising spending proxy interacted with above median R&D spending (0,1) and above median excess cash (0,1), respectively. The main independent variable in Panel C is the residuals from advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). The residuals are estimated from the corresponding first stage regressions in Table 3 Models (1), (3), (5), and (7). In Panel G we estimate acquisition premium regressions with each advertising spending proxy for subsamples of targets that belong to B2C industries and of those in other industries. B2C industries are consumer-oriented ones following the classification by Sharpe (1982). In Panel H, we estimate acquisition premium regressions with each advertising spending proxy interacted with the target’s managerial (CEO) ownership using a subsample of 2,777 deals with ownership data available from Execucomp and Thomson Financial Insider databases. To save space, we do not report the control variables in the regressions. All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Panel A: Target’s advertising spending and target return alternatives Dependent variable = Target return alternatives CAR [-20,+1] CAR [-42,+126] Advertising spending 0.0061** 0.0194*** (0.0324) (0.0004) Scaled advertising spending 0.3032*** 0.6332*** (0.0098) (0.0008) Advertising intensity 0.4210*** 0.7852*** (0.0008) (0.0001) Advertising growth 0.0048* 0.0075** (0.0540) (0.0349)

Panel B: Target’s advertising spending and acquirer return alternatives Dependent variable = Acquirer return alternatives CAR [-2,+2] CAR [-5,+5] Advertising spending -0.0057*** -0.0077*** (0.0001) (0.0001) Scaled advertising spending -0.1308** -0.0846** (0.0272) (0.0474) Advertising intensity -0.1757*** -0.1560** (0.0036) (0.0363) Advertising growth -0.0012* -0.0023*** (0.0653) (0.0032)

Panel C: Abnormal advertising spending and acquisition premiums Dependent variable = Acquisition premium Model (1) Model (2) Model (3) Model (4) Advertising spending residual 0.0131*** (0.0073) Scaled advertising spending residual 0.3574** (0.0316) Advertising intensity residual 0.4413** (0.0129) Advertising growth residual 0.0084** (0.0208)

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Panel D: Falsification tests using advertising spending 3 years before merger announcement Dependent variable = Acquisition premium Acquirer CAR [-1,+1]

Advertising spending 0.0056 -0.0015 (0.2066) (0.1949) Scaled advertising spending 0.2193 -0.0566 (0.2804) (0.2301) Advertising intensity 0.2630 -0.0232 (0.2428) (0.6095) Advertising growth 0.0025 0.0000 (0.3411) (0.7567)

Panel E: Alternative explanations: R&D Dependent variable = Acquisition premium Acquirer CAR [-1,+1]

Advertising spending × High R&D (0,1) 0.0058 -0.0010 (0.5185) (0.5981) Scaled advertising spending × High R&D (0,1) 0.0990 -0.0248 (0.7703) (0.8210) Advertising intensity × High R&D (0,1) 0.0407 -0.0478 (0.9159) (0.6915) Advertising growth × High R&D (0,1) 0.0049 -0.0029 (0.5043) (0.1656)

Panel F: Alternative explanations: Excess cash Dependent variable = Acquirer return alternatives Acquisition premium Acquirer CAR [-1,+1] Advertising spending × High excess cash (0,1) 0.0012 -0.0000 (0.8871) (0.9863) Scaled advertising spending × High excess cash (0,1) -0.3521 0.0518 (0.2578) (0.5928) Advertising intensity × High excess cash (0,1) -0.1317 0.0707 (0.7157) (0.5158) Advertising growth × High excess cash (0,1) -0.0038 -0.0004 (0.6126) (0.7650)

Panel G: Advertising spending, B2C industries and acquisition premiums Dependent variable Advertising = Acquisition premium proxy =

Advertising spending

Scaled adv. spending

Advertising intensity

Advertising growth

Coefficient of advertising proxy for 0.0191*** 0.6461*** 0.7163*** 0.0092** targets in B2C industries (1) (0.0078) (0.0036) (0.0056) (0.0171) Coefficient of advertising proxy for 0.0065** 0.3278* 0.3835** 0.0080* targets in other industries (2) (0.0455) (0.0697) (0.0468) (0.0653) Difference (1) – (2) 0.0126* 0.3183* 0.3328* 0.0012 (0.0696) (0.0756) (0.0604) (0.6080)

Panel H: Advertising spending, managerial ownership and acquisition premiums Dependent variable Advertising = Acquisition premium proxy =

Advertising spending

Scaled adv. spending

Advertising intensity

Advertising growth

Advertising proxy 0.0220*** 0.5993** 0.8286*** 0.0097** (0.0068) (0.0167) (0.0045) (0.0100) Managerial ownership -0.0750 -0.0629 -0.0504 -0.0034 (0.1337) (0.1875) (0.2821) (0.9345) Advertising proxy × Managerial ownership 0.0659** 1.2315** 0.9798* 0.0011* (0.0216) (0.0210) (0.0665) (0.0979)

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Appendix: Variable definitions Advertising spending proxies Advertising spending the natural logarithm of (1+advertising spending in million $US) Scaled advertising spending advertising spending scaled by total assets Advertising intensity advertising spending scaled by sales Advertising growth the log difference of advertising spending Deal characteristics Acquisition premium the offer price divided by the target’s stock price four weeks before the

merger announcement date, as reported by SDC and limited between 0% and 200%

Target CAR the target’s cumulative abnormal return over the window around the merger announcement date, calculated as the residual from the market model estimated during the one year window ending four weeks prior to the merger announcement

Acquirer CAR the acquirer’s cumulative abnormal return over the window around the merger announcement date, calculated as the residual from the market model estimated during the one year window ending four weeks prior to the merger announcement

Completion (0,1) one if the announced deal is completed Upward revision (0,1) one if the offer price is revised upward Multiple bidders (0,1) one if the deal involves multiple bidders Private acquirer (0,1) one if the bidder is not publicly traded Cash payment (0,1) one if the deal is paid entirely in cash Tender offer (0,1) one if the form of the deal is tender offer Hostile deal (0,1) one if the deal is classified hostile by SDC Competed deal (0,1) one if the deal has a competed offer identified by SDC Toehold fraction of the target’s shares owned by the bidder Target termination fee (0,1) one if the target has a termination fee provision in the merger contract Lockup (0,1) one if the deal includes a lockup of target or acquirer shares Merger of equals (0,1) one if the deal is classified by SDC as a merger of equals Same industry (0,1) one if both the target and the acquirer belong to the same Fama and

French (1997) 48 industrial classification group Market characteristics Target Herfindahl-Hirschman index the competitiveness of the target industry. An industry’s Herfindahl

index is computed as the sum of squared market shares of all firms in the industry using data on sales, as in Masulis, Wang and Xie (2007).

Target industry liquidity the liquidity of the market for corporate control for the target firm’s industry. This variable is defined as the value of all corporate control transactions for US$1 million or more reported by SDC for each year and industry divided by the total book value of assets of all Compustat firms in the same industry and year, as in Schlingemann, Stulz and Walkling (2002)

One year macroeconomic change the difference in the industrial production index over one year period before the merger

Competitive industry (0,1) one if the bidder’s industry is in the bottom quartile of all industries sorted annually by the Herfindahl index. An industry’s Herfindahl index is computed as the sum of squared market shares of all firms

47

in the industry using data on sales (as in Masulis, Wang and Xie, 2007)

Unique industry (0,1) one if the bidder’s industry is in the top quartile of all industries sorted annually by industry-median product uniqueness. Product uniqueness is defined as selling spending scaled by sales (as in Masulis, Wang and Xie, 2007)

High tech industry (0,1) one if bidder and target are both from high tech industries defined by Loughran and Ritter (2004)

Financial characteristics Size the natural logarithm of the market value of assets Q the market value of assets divided by the book value of assets Leverage the book value of debt divided by the sum of book value of debt and

market value of equity. OCF the cash flow from operations scaled by the value of assets Prior year market adjusted return the cumulative abnormal return during the one year window ending

four weeks prior to the merger announcement, calculated as the residual from the market model estimated during the year before

R&D the research and development expenditure scaled by the value of assets Excess cash the residual from Fresard and Salva (2010) model: ln(Cash) =

β1ln(Assets) + β2 Cash Flow + β3 Net Working Capital + β4 Q + β5Capex + β6 Leverage + β7 R&D + β8 Dividend + firm, industry, and time fixed effects

Managerial ownership the equity ownership by the CEO as a proportion of the number of shares outstanding

Instruments Average competitor advertising

spending the natural logarithm of the industry average advertising spending in

million $US, excluding the firm’s contribution to the industry Average competitor scaled

advertising spending industry average advertising spending scaled by total assets, excluding

the firm’s contribution to the industry Average competitor advertising

intensity industry average advertising spending scaled by sales, excluding the

firm’s contribution to the industry Average competitor advertising

growth industry average advertising growth in log difference of advertising

spending, excluding the firm’s contribution to the industry Other variables Heckman self-selectivity the Heckman (1979) lambda in a two stage process. In the first-stage,

we estimate the probability of becoming a target. In the second stage, the inverse Mill's ratio from the first stage model is included in the estimation as a variable to control for self-selection.