Should Ad Spending Increase or Decrease Prior to a Product

“Should Ad Spending Increase or Decrease Prior to a Product Recall Announcement?” © 2014 Haibing Gao, Jinhong Xie, Qi Wang, and Kenneth C. Wilbur; Report Summary © 2014 Marketing Science Institute MSI working papers are distributed for the benefit of MSI corporate and academic members and the general public. Reports are not to be reproduced or published in any form or by any means, electronic or mechanical, without written permission.

Marketing Science Institute Working Paper Series 2014 Report No. 14-112 Should Ad Spending Increase or Decrease Prior to a Product Recall Announcement? Haibing Gao, Jinhong Xie, Qi Wang, and Kenneth C. Wilbur

Report Summary Suppose a product harm crisis is going to be announced in two weeks. What should you do with your advertising before the recall announcement: spend more, spend less, or maintain your current plan? Recent research argues that advertising should fall before a crisis is announced because the harm of the recall will reduce the short-term benefits of advertising, reducing profitability. However, a product harm crisis often not only reduces product profit but also damages firm value. While a retreat of pre-recall advertising avoids inefficient marketing spending on the recalled product, will investors interpret it as an admission of deeper systemic problems? More generally, when and how does pre-recall advertising affect post-recall stock price? To answer this question, Haibing Gao, Jinhong Xie, Qi Wang, and Kenneth Wilbur investigate how automakers’ stock prices reacted to 110 product recall announcements between 2005 and 2012. Their analysis finds that stock price is significantly affected by pre-announcement ad expenditures, and that the direction of this effect depends on the seriousness of the product defect and whether the recalled product is a newly introduced model.

Their analysis uncovers two primary effects. First, in the case of a minor recall affecting a newer model, a steep increase in pre-recall advertising raises the automaker’s cumulative abnormal returns. The authors call this a signaling effect, as investors have less prior information about newer models and increasing pre-recall advertising can underscore the fact that the hazard is not very serious. Second, when the recall is a major hazard affecting an older model, the opposite effect occurs. That is, increasing advertising expenditures prior to announcing the recall worsens the automaker’s cumulative abnormal stock returns. The authors call this an expectations effect: the increase in advertising sets up investors’ expectations of good future performance, which are then disappointed upon learning the details of the recall. These findings show that pre-recall advertising is one tool a firm can use to strategically soften the negative impact of a product recall on stock market value. Firms are typically aware of a pending recall (whether firm-initiated or government-initiated) before it is announced and can therefore act prior to the announcement. However, the optimal reaction requires an understanding of the type of hazard and the type of product. The authors’ data imply that automakers may not fully understand the existence of these effects, as there have been many cases in which firms have increased pre-recall ad spending for older models with major hazards. In these cases, the firm could have benefited by foregoing an advertising increase for the model in question, but did not do so. Overall, firms anticipating a recall announcement should consider the seriousness of the defect and the novelty of the product. To minimize damage to firm value, in the case of minor harm to new products, pre-recall advertising should be increased; in the case of major harm to older products, pre-recall advertising should be held constant or reduced. Finally, when recalling a new product, marketers should consider the possible trade-offs between product profitability and stock price. Haibing Gao is a doctoral student in marketing at the University of Florida. Jinhong Xie is JC Penny Eminent Scholar Chair and Professor of Marketing at the University of Florida. Qi Wang is Associate Professor of Marketing at State University of New York at Binghamton. Kenneth C. Wilbur is Assistant Professor of Marketing at the University of California, San Diego.

Marketing Science Institute Working Paper Series 1

Product recalls have been on the rise over the past decade. As shown in a recent report

by the ACE Group, one of the world’s largest property and casualty insurers, 2,363 recalls of

consumer products, pharmaceuticals, medical devices and food took place in the United States

in 2011, representing a 14 percent increase from the prior year, and a 62 percent increase from

2007.1 Based on auto recall announcements published by the National Highway Traffic

Safety Administration (NHTSA), the average number of recalls per year for all manufacturers

selling vehicles in the U.S. was 339 from 1994-2003, but 599 from 2004-2013, which

represents an increase of 76 percent between the two 10-year periods.2 This uptrend in

product recall is driven by several emerging forces, such as increased globalization of

production, growing complexity of products, and more stringent product-safety legislation

(Chen, Ganesan, and Liu 2009; Dawar and Pillutla 2000). As these emerging forces further

develop, firms are expected to face an even higher risk of a product-harm crisis (Chen and

Nguyen 2013).

Product recalls can impose severe financial damage on the firms involved. Over the

last several decades, Wall Street has witnessed many instances where a firm’s share price was

slashed after a recall announcement. Consider several recent cases: Boston Scientific’s stock

price fell 13 percent after announcing a recall of its implantable defibrillators on March 15

2010;3 Cochlear’s share price promptly dropped 20 percent after a voluntary recall of its

Nucleus 5 implant products on September 12 2011;4 Toyota’s shares lost about 22 percent in

less than two weeks after its recall of 2.3 million vehicles in the U.S. due to problems related

to accelerator pedals in 2010;5 and, most recently, as reported by USA Today on April 11,

2014, “GM stock below IPO price as recall talk swirls.” 6 The harmful financial

consequences of product recalls are not merely “bad luck” that only occurs occasionally. The

finance literature has extensively investigated the impact of product recalls on firm value. A

negative relationship between a product recall announcement and the recalling firm’s stock

price has been found in a wide range of industries, including (but not limited to) automobiles,

pharmaceuticals, food, toys, electronics, cosmetics, and outdoor products (see Barber and

Darrough 1996; Chen and Nguyen 2013; Chu, Lin, and Prather 2005; Davidson and Worrell

1 http://www.acegroup.com/us-en/assets/advisenpaper_reducing-the-impact-of-a-product-recall.pdf 2 http://www-odi.nhtsa.dot.gov/recalls/recallmonthlyreports.cfm 3 http://online.wsj.com/news/articles/SB20001424052748703909804575123441387311792 4 http://www.macrobusiness.com.au/2011/09/did-you-hear-about-cochlear/ 5 http://news.bbc.co.uk/2/hi/business/8497448.stm 6 http://www.usatoday.com/story/money/cars/2014/04/11/gm-stock-price-ipo-recall-barra/7611009/


1992; Hoffer, Pruitt and, Reilly 1987; Jarrell and Peltzman 1985; Pruitt and Peterson 1986;

Thomsen and McKenzie 2001).

Motivated by the dramatic increase in product recalls in recent years and their severe

financial consequences, this paper examines whether or not a marketing variable such as

pre-recall advertising can be used to reduce the damage incurred by a recall on a firm’s

market value. Specifically, we ask: (1) Is the severity of the negative abnormal return to a

product recall associated with the brand’s pre-recall advertising spending?; (2) If such a

connection does exist, to protect its post-recall share value, should the firm increase or

decrease advertising spending when anticipating a product recall?; and (3) What specific

characteristics of product recalls may determine the direction/effectiveness of the moderating

effect of pre-recall advertising?

Answers to these questions are important from both theoretical and practical points of

view. First, the marketing literature has largely focused on crisis management issues from a

marketing perspective.7 For example, recent studies have offered valuable insights into the

impact of product-harm crises on brand equity (Dawar and Pillutla 2000), firms’ image and

consumer loyalty (Souiden and Pons 2009), consumer purchase decisions (Cleeren, Dekimpe,

and Helsen 2008; Zhao, Zhao, and Helsen 2011), sales and profits (Rubel, Naik, and

Srinivasan 2011), and the effectiveness of marketing strategies (Cleeren, Van Heerde, and

Dekimpe 2013; Van Heerde, Helsen, and Dekimpe 2007). However, product recalls not only

damage firms’ marketing performance but also harm their financial value, which calls for

research that integrates the two perspectives. For example, a recent marketing study (Rubel,

Naik, and Srinivasan 2011) discovered that, when envisioning a product harm crisis, it is

optimal for the firm to reduce pre-crisis advertising to maximize the brand profit as the crisis

likelihood (or the damage rate) increases. While a downward adjustment in pre-recall

advertising spending can be beneficial from a marketing perspective, it is unknown whether

or not such a change would intensify the ex-post harm to the firm’s financial value.

Investigating the impact of pre-recall advertising on post-recall abnormal stock returns can

help to identify conditions under which the marketing and finance objectives clash or agree.

Such an understanding facilitates the design of an overarching crisis management strategy,

which maximizes the firm’s overall interests.

Second, in practice, firms often anticipate a product recall long (months to years)

7 One exception is Chen, Genesan and Liu (2009), which examines how a firm’s product-recall strategy (Proactive vs. Passive) affects its financial value based on an event study of recalls announcements by the Consumer Product Safety Commission (CPSC).


http://ajae.oxfordjournals.org/search?author1=Andrew+M.+McKenzie&sortspec=date&submit=Submit

before the actual recall announcement. Consider the automobile industry, where product

safety recalls can be classified into two categories: firm-initiated and government (i.e.,

NHTSA)-initiated. According to the NHTSA’s records, the majority of product recalls are

initiated by the firms.8 For such recalls, the manufacturer determines whether a safety defect

exists through its own inspection procedures and information-gathering systems and decides

if and/or when to issue a recall. Recalls initiated by the NHTSA often involve a lengthy

procedure, consisting of a preliminary investigation (taking 120 days on average) and an

engineering analysis (taking 365 days on average).9 During this process, manufacturers are

required to provide necessary information (e.g., data on complaints, crashes, injuries,

warranty claims, modifications, and part sales) to the NHTSA, and have the opportunity to

present their own views regarding the alleged defect or present their new analysis. The

standard recall procedure10 suggests that, for both types of recalls, manufacturers often have

time to make ex-ante strategic decisions before the recall is formally announced. Thus,

pre-recall advertising as a strategic variable for managing a product harm-crisis is not only

theoretically desirable but also practically implementable. We offer the first prescriptive

advice about the conditions under which an automobile manufacturer might want to increase

advertising in order to build investor confidence in anticipation of a product recall.

Drawing from both marketing and finance literature, we propose two possible effects of

pre-recall advertising spending on post-recall investors’ behavior. (1) A positive (negative)

signaling effect – a high (low) level of pre-recall advertising spending, due to information

asymmetry between the firm and investors, can signal the firm’s high (low) confidence in

future demand based on its private information about the recalled products (e.g., the severity

of the defect, possible consumer responses, and its potential impact on future profitability);

and (2) a negative expectation effect – a high level of pre-recall advertising raises

expectations, which in turn leads to a high level of post-recall disappointment (due to

unfulfilled expectations). The overall impact of pre-recall advertising on post-recall firm

value depends on the accumulative strength of these two forces, which may, in turn, depend

on the specific characteristics of the recall. Specifically, we expect a stronger signaling effect

when the recall involves newly introduced models (information asymmetry is likely stronger

for new models than for older models) and a stronger expectation effect when the recall is

8 For example, our data set consists of recalls for the six largest automakers (Toyota, Honda, Nissan, General Motors, Ford, and Chrysler) from 2005 to 2012. Among them, 61.8% are firm-initiated recalls. 9 www.sesptc.com/2010Presentations/NHTSA_ODI_SESPTC2010.ppt 10 See http://www.nhtsa.gov/Vehicle+Safety/Recalls+&+Defects/Motor+Vehicle+Safety+Defects+and+ Recalls+Campaigns for more detailed discussion about the recall procedure.


http://www.sesptc.com/2010Presentations/NHTSA_ODI_SESPTC2010.ppt

http://www.nhtsa.gov/Vehicle+Safety/Recalls+&+Defects/Motor+Vehicle+Safety+Defects+and

http://www.nhtsa.gov/Vehicle+Safety/Recalls+&+Defects/Motor+Vehicle+Safety+Defects+and+Recalls+Campaigns

due to a major hazard (post-recall disappointment is likely stronger for products with a major

hazard than for those with a minor hazard). Based on our conceptual development, we

propose specific hypotheses and test our theory using automobile safety recalls administrated

by the NHTSA and concurrent daily advertising expenditures during the period 2005–2012.

Our empirical analysis demonstrates that a firm’s pre-recall advertising can moderate

the fall in its stock price precipitated by the recall. However, this moderating effect differs

depending on the direction of advertising adjustment as well as on the specific recall

characteristics. Specifically, increasing pre-recall advertising spending can lessen firms’

ex-post loss in stock price when the recall involves newly introduced models with a minor

hazard. However, if the recalled products are older models with a major hazard, the opposite

holds: Increasing pre-recall advertising spending worsens the negative impact of the recall on

the firm’s value. Our results also reveal that decreasing the pre-recall advertising worsens the

negative impacts of the recall on stock price as long as the recall involves new models,

regardless of the degree of hazard. However, such a downward adjustment does not affect

post-recall firm value if the recalled products are older models. We offer empirical evidence

to support our proposed theory.

This paper makes several contributions. First, it adds to the marketing literature of

product-harm crisis management by identifying a connection between a pre-crisis marketing

decision and the post-crisis financial consequences. A striking insight of our research is that,

in product-harm crisis management, it is possible for profit maximization and shareholder

value maximization to be in direct conflict with each other. Specifically, the marketing

literature finds that, when envisioning a severe recall, the firm can maximize its marketing

performance by reducing pre-recall advertising spending (Rubel, Naik, and Srinivasan 2011).

However, our results reveal that doing so will intensify the firm’s loss in share value if the

recall involves newly introduced models. In such a case, the firm should make trade-offs

between marketing and financial objectives (i.e., profit vs. share value). The good news is

that such trade-offs may not be required if the recalled products are older models, suggesting

that the firm can protect its marketing interests without sacrificing its financial interest. Our

findings also bring attention to the opposite (and as yet unexplored) strategic move in crisis

management, increasing pre-recall advertising spending. We demonstrate that, for a recall of

new models with a minor hazard, the firm can protect its financial value by strategically

boosting pre-recall advertising spending. These findings both advance the theoretical

understanding of effective crisis management and help firms to develop integrated

product-harm crisis management strategies.


Second, this paper extends prior studies of the marketing-finance interface to the crisis

environment. In recent years, an increasing number of studies have investigated how the

stock market is affected by firms’ marketing actions/metrics, such as advertising (Joshi and

Hanssens 2009; 2010), new product introductions (Pauwels et al. 2004), R&D expenditures

(Chan, Lakonishok and Sougiannis 2001), customer satisfaction (Fornell et al. 2006; Gruca

and Rego 2005), word of mouth (Luo 2007; 2009), and corporate social responsibility (Luo

and Bhattacharya 2006). Several studies have examined the impact of advertising on firm

value in some specific events, such as new movie release (Chen, Liu, and Zhang 2012; Joshi

and Hanssens 2009) and IPO (Luo 2008). To the best of our knowledge, this is the first paper

that investigates the impact of advertising on firm value in the context of product-harm crises.

Finally, this paper contributes to the finance literature by suggesting a new strategic

action to protect firms’ value during a product recall. The finance literature has extensively

studied investors’ responses to product recalls. Most of the studies have focused on testing

the existence of a negative abnormal stock return after recall announcements, both across

industries (e.g., Davidson and Worrell 1992; Jarrell and Peltzman 1985; Thomsen and

McKenzie 2001) and across time (e.g., Barber and Darrough 1996; Hoffer, Pruitt, and Reilly

1987; Jarrell and Peltzman 1985). While our study also confirms a negative impact of product

recalls on the stock market, we take one step further to test whether or not such financial

harm can be reduced by strategically adjusting a marketing variable, advertising spending,

before the recall announcement. Our findings are encouraging.

We organize the remainder of this paper as follows: First, we present our conceptual

development and formally state our hypotheses. Next, we introduce the modeling

methodology (an event study and a cross-sectional regression model). We then discuss our

data, define our variables, and present the empirical results. Finally, we conclude the paper

with theoretical contributions, managerial implications and several suggested directions for

future research.

Theoretical Framework and Hypotheses

Pre-recall advertising

Pre-recall advertising can be a strategic variable for firms to manage a potential

product-harm crisis after a recall is announced. The strategic use of pre-recall advertising is

not only practically feasible, according to the standard recall procedure illustrated earlier, but

also theoretically viable, because adjusting expenditure of advertising before a recall can


http://ajae.oxfordjournals.org/search?author1=Andrew+M.+McKenzie&sortspec=date&submit=Submit

affect investors’ responses in the stock market after the recall is announced. Specifically, we

propose that advertising adjustments before a recall announcement is made may impose two

possible effects on the stock market after the actual announcement: the signaling effect and

the expectation effect.

The impact of pre-recall advertising: signaling effect and expectation effect

Signaling Effect. Many studies in the economics and marketing literature have

documented the evidence of a signaling effect of the marketing mix on consumers’ perception

of product quality (e.g., Nelson 1974; Milgrom and Roberts 1986; Wernerfelt 1988; Erdem

and Keane 1996; Byzalov and Shachar 2004; Zhao, Zhao, and Helsen 2011). The

fundamental mechanism under which marketing actions can signal product quality to

consumers is the information asymmetry between sellers and consumers. Sellers know more

about the quality of their products than consumers do and, thus, consumers may infer product

quality based on some observable firm-initiated actions. Among some identified marketing

signaling devices (e.g., branding (Erdem and Swait 1998; Wernerfelt 1988), price (Milgrom

and Roberts 1986), warranty (Boulding and Kirmani 1993; Price and Dawar 2002)),

advertising expenditure can credibly signal quality because it is only economically optimal

for high-quality firms to spend large amounts on advertising, because low-quality firms

would not see a return on their advertising investment (Kihlstrom and Riordan 1984;

Milgrom and Roberts 1986). Specifically, if a firm spends large sums of money on

advertising, its claim about high quality must be true because the real quality would be

revealed through consumer trial and would thus determine future purchases; conversely, firms

producing low-quality goods would not be able to recover the cost of advertising (Kirmani

and Rao 2000). For this reason, advertising expenditure can serve as a credible quality signal

even if the advertising contains no direct information about product quality (Kihlstrom and

Riordan 1984; Milgrom and Roberts 1986).

In the stock market, information asymmetry exists between firms and investors, i.e.,

firms have private information about their financial value that investors do not know (Myers

and Majluf 1984). Because of this information asymmetry, investors may actively use

firm-initiated actions as disclosed signals to interpret the firm’s expected cash flows

(Bhattacharya 1979; Ross 1977). In recent years, marketing scholars have proposed and

examined the signaling effect of advertising on the stock market. For example, Joshi and

Hanssens (2010) suggested that advertising can signal a firm’s financial well-being or


competitive viability to investors. Kim and McAlister (2011) argued that, because advertising

expenditure affects consumers, it is reasonable to assume that the stock market is also aware

of such effects and interprets advertising expenditure as a signal of the firm’s future earnings.

In the context of product-harm crisis, we argue that pre-recall advertising can create a

signaling effect on the stock market after the recall is announced, because a recall

announcement creates new uncertainty in the implicated firm’s financial market and

intensifies the information asymmetry between firms and investors. As the literature suggests,

firms typically possess more private information about the nature of the product hazard and

its potential consequences than do investors (Chen, Genesan, and Liu 2009). In the face of

intensified information asymmetry, investors have a stronger incentive to use firm-initiated

activities immediately prior to a recall as internal information to interpret the potential

financial consequences after the recall is announced. If the firm increases its advertising

expenditure before the recall, investors may interpret such a proactive adjustment as a

positive signal that the recalled products do not have a severe quality defect. The anticipated

recall thus may not seriously affect the implicated firm’s future sales and earnings, because

the firm could not recoup its advertising expenditure if the recall would hurt its baseline sales.

Therefore, increasing pre-recall advertising can enhance investors’ confidence in the

implicated firm’s prospects and hence create a positive signaling effect, thereby lessening the

damage of the product recall on the stock market. In contrast, a decrease in pre-recall

advertising can deliver a negative signal with regard to the severity of product defects and

their potential harmful impact on future earnings, which, as a result, creates a negative

signaling effect, thereby worsening the damage of product recalls on the stock market.

Expectation Effect. In addition to the signaling effect, adjusting advertising near a

recall can also affect investors’ expectations of product quality before the recall. A recall may

result in a discrepancy between investors’ prior expectations of product quality and the actual

quality defect that triggered the recall, which in turn may affect investors’ confidence in the

firms’ future earnings. The expectation effects on consumer and investor behavior have long

been recognized and studied (Bonomo et al. 2011; Cardozo 1965; Gul 1991; Hirshleifer 2001;

Joshi and Hanssens 2009, 2010; Kopalle and Lehmann 2006; Routledge and Zin 2010;

Solomon 2012). For example, the marketing literature has suggested that advertising, among

other marketing metrics (e.g., innovation (Dutta, Narasimhan, and Rajiv 1999), price and

brand name (Rao and Monroe 1989), and distribution channel (Wallace, Giese, and Johnson

2004)), plays an important role in influencing customer expectations. A high expenditure on

advertising can develop a high expectation of product quality (Kopalle and Lehmann 2006).


Consequently, when the product does not come up to customers’ expectations (i.e., of its

perceived quality), customer satisfaction is lower than when the product meets expectations

(Cardozo 1965), which further lowers customers’ willingness to pay and decreases the

likelihood of future purchases (Fornell, Rust and Dekimpe 2010; Homburg, Koschate, and

Hoyer 2005). Concerning the stock market, the finance literature has suggested that

unfulfilled expectation can lead to investor disappointment (Hirshleifer 2001). According to

the theory of disappointment aversion (Gul 1991; Routledge and Zin 2010; Bonomo et al.

2011), investors would over-discount the utility of a stock with lower-than-expected

outcomes and exhibit risk-aversion to such a disappointing stock.

In the context of a product recall, we expect that an increase in pre-recall advertising

can create a negative expectation effect on the recalling firm’s stock market valuation,

because the increase of pre-recall advertising can generate the expectation that the advertised

products will be of high quality, especially for most investors who are not aware of a

forthcoming product recall due to information asymmetry. When the recall is actually

announced, the discrepancy between investors’ prior expectations of high quality and the

actual quality problem indicated by the recall can lead to high dissatisfaction and

disappointment (Anderson 1973; Routledge and Zin 2010). This, in turn, hurts investors’

confidence in the firm’s future sales and worsens the negative impact of product recalls on

firms’ stock market value. In the case of a decrease of pre-recall advertising, we do not

assume a significant expectation effect since a downward adjustment in advertising would not

develop investors’ expectations of high product quality.

Overall, an adjustment in pre-recall advertising may create both a signaling and an

expectation effect. The net impact of an advertising adjustment near a recall is dependent

upon the relative strength of these two effects, the direction of the advertising adjustment (i.e.,

increase vs. decrease) as well as the specific recall characteristics. In the following section,

we propose hypotheses as to how increasing or decreasing pre-recall advertising affects

firms’ financial valuations under a recall crisis with specific characteristics.

Increasing pre-recall advertising

As discussed earlier, an increase in pre-recall advertising for recalled products may

create a positive signaling effect and a negative expectation effect, both of which moderate the

detrimental impact of product recalls on firms’ stock market valuations. With these two

opposite effects in place, we propose that the overall impact of increasing pre-recall


advertising depends on the characteristics of the recalled products. We identify two specific

recall characteristics as conditions under which we are able to propose hypotheses concerning

the overall impact of increasing pre-recall advertising. These recall characteristics are 1) the

degree of product newness; and 2) the degree of product hazard.

The Degree of Product Newness and Signaling Effect. We expect that the strength of

the positive signaling effect varies with the degree of product newness. Specifically, we

expect the positive signaling effect to be stronger when the recall involves a newly introduced

model. Earlier, we discussed that the signaling effect of pre-recall advertising on the stock

market occurs because of the information asymmetry between firms and investors. When a

newly introduced model is recalled, such information asymmetry is greater. Compared to

firms that possess unique inside information about the severity of a quality defect and the

potential damage to their future earnings, investors have less external information because the

new model products have neither fully penetrated the market nor have they been completely

tested by experts or consumers. For example, at edmunds.com, a popular website of consumer

reviews for cars, the newly launched Toyota Sequoia 2014 received only two reviews up to

March 2014, compared to 115 for Sequoia 2008. Thus, it is difficult for investors to develop

any sound estimate of the potential impact of the new model’s recall based on such limited

information. With such a high degree of information asymmetry, we expect the signaling

effect to become stronger by increasing advertising prior to new model recalls because

investors would make greater use of such information to form their evaluation of the recall in

the absence of extensive information from other sources.

When a product model has been on the market for a longer period of time, and as

more public information is available to investors (e.g., from news releases, expert analyses,

consumer reviews, etc), the information asymmetry between firms and investors becomes

smaller. Thus, when older models are recalled, investors can use multiple sources of available

information to form their estimation regarding the severity of the product defect and the

consequence of the recall on the implicated firms. As a result, we expect the signaling effect

of increasing pre-recall to become weaker in older model recalls.

The Degree of Product Hazard and Expectation Effect. We expect the strength of the

negative expectation effect of increasing pre-recall advertising to be dependent upon the

degree of product hazard. Specifically, we expect the negative expectation effect to be

stronger (weaker) when the recalls are due to a major (minor) hazard. The level of safety

hazard is a critical measure of product recall by government agencies that administer product

safety, e.g., the Food and Drug Administration (FDA) for product safety relevant to public


health, the Consumer Product Safety Commission (CPSC) for non-auto product safety, and

NHTSA for automobile safety. In particular, we separate product recalls into either major or

minor hazard categories according to the severity of quality defects. According to the product

defects released by the NHTSA, the major hazards of fire and crash can occur if vehicles’

defects are related to fuel leakage, steering problems, acceleration problems, braking failure,

repeated stalling, and visibility. For example, Toyota’s recall announcement on Oct 5, 2009

clearly indicated that “a stuck open accelerator pedal may result in very high vehicle speeds

and make it difficult to stop the vehicle, which could cause a crash, serious injury or death.”

We consider recalls not falling within the category of major hazard to be caused by minor

hazard.

Sensational hazards, such as fire, crash, and death strengthen the message of product

failure, and the discrepancy between the higher expectations of quality generated by

increased pre-recall advertising and the actual poor quality indicated by the major hazard

becomes larger. When such recalls are announced, investors may experience a stronger sense

of unfulfilled expectations, which amplifies their dissatisfaction and disappointment, thus

leading to a stronger negative expectation effect as a result of increasing pre-recall

advertising. In contrast, however, when recalls are due to a minor hazard, the sense of

unfulfilled expectations is relatively low. Hence, investors may have relatively weaker

feelings of disappointment after such recalls are announced, which corresponds to a relatively

weaker negative effect.

Overall, when considering these two recall characteristics, product newness and

product hazard, we can clearly predict the overall impact of increasing pre-recall advertising

under two scenarios: (1) product recalls of newly introduced models because of a minor

hazard; and (2) product recalls of older models due to a major hazard. Under the first scenario,

as an increase in pre-recall advertising generates a stronger positive signaling effect (because

the recall involves newly introduced models) and a weaker negative expectation effect (due to

minor hazard), we expect the net impact of increasing pre-recall advertising to be positive

because the stronger positive signaling effect tends to dominate the weaker negative

expectation effect. Under the second scenario, however, we expect the net impact of

increasing pre-recall advertising to be reversed. Specifically, as an increase in pre-recall

advertising generates a weaker positive signaling effect (because the products to be recalled

are older models) and a stronger negative expectation effect (due to a major hazard), we

expect the net impact of increasing pre-recall advertising to be negative because the stronger

negative expectation effect tends to dominate the weaker positive signaling effect. In sum, we


propose the following two hypotheses concerning the impact of increasing pre-recall

advertising under these two scenarios:

Hypothesis 1: For product recalls of newly introduced models with a minor hazard,

increasing the recalled products’ pre-recall advertising lessens the negative

impact of product recalls on firms’ stock returns.

Hypothesis 2: For product recalls of older models with a major hazard, increasing the

recalled products’ pre-recall advertising worsens the negative impact of

product recalls on firms’ stock returns.

There are two additional scenarios when describing a recall with the characteristics of

product newness and product hazard: (1) product recalls of newly introduced models with a

major hazard; and (2) product recalls of older models with a minor hazard. In the former case,

as increasing pre-recall advertising generates both a stronger positive signaling effect and a

stronger negative expectation effect, the net impact of increasing pre-recall advertising may

not be significant since these two effects may cancel each other out. Similarly, in the latter

case, because increasing pre-recall advertising generates both a weaker positive signaling

effect and a weaker negative expectation effect, the net impact of increasing advertising may

also be insignificant because these two effects may also cancel each other out. Thus, we do

not propose specific hypotheses for these two scenarios.

Decreasing pre-recall advertising

Corresponding to the positive signaling effect created by increasing pre-recall

advertising, we expect that an adjustment of advertising downward near a recall can create a

negative signaling effect. Such a negative signaling effect becomes stronger for recalls of

newly introduced models, but weaker when the recalled products are older models. Similar to

our argument on the positive signaling effect, the negative signaling effect created by

decreasing pre-recall advertising varies with the degree of product newness for the same

reason, i.e., the magnitude of information asymmetry.

Specifically, because information asymmetry is larger when the recalled products

contain newly introduced models (because investors have limited external information

regarding the quality of newly introduced products, responses of a limited number of product

users, and the recall’s potential impact on future sales), decreasing advertising before a recall


can send a stronger negative signal to investors that the product defects are severe and the

recall may cause a great loss to the firm. This strategy hurts investors’ confidence in the

recalling firm’s future profitability, which in turn creates a stronger negative signaling effect

on the firm’s stock market valuation. On the other hand, the information asymmetry

concerning older models is weaker because investors can use multiple information sources to

estimate the potential impact of product recalls. Thus, decreasing pre-recall advertising

generates a weaker negative signal effect on the recalling firm’s stock market returns when

the recalled products are older models.

We suggest, however, that a decrease in pre-recall advertising may not generate a

significant expectation effect, regardless of the degree of product hazard. While investors

may experience stronger disappointment in the case of a major hazard recall, such a negative

feeling may not change significantly in the face of a decrease or no adjustment in pre-recall

advertising because such may not affect investors’ quality expectations (neither strategy can

generate high expectations). Similarly, while investors may experience a weaker sense of

disappointment concerning a minor hazard recall, such a feeling would not change

significantly in the face of a decrease or no adjustment in pre-recall advertising.

Taking into consideration both the negative signaling effect and the insignificant

expectation effect by decreasing pre-recall advertising, it is clear that a decrease in pre-recall

advertising can generate a stronger negative signaling effect for product recalls of newly

introduced models, regardless of the degree of recall hazard. In the case when the recalled

products are older models, because decreasing pre-recall advertising generates a weaker

negative signaling effect, it is unclear whether or not the overall impact of decreasing

pre-recall advertising is negative. Therefore, we propose the following hypothesis

considering only the cases of new model recalls.

Hypothesis 3: For product recalls of newly introduced models, decreasing pre-recall

advertising worsens the negative impacts of product recalls on firms’ stock

returns.

Model

Event study

We adopt an event study analysis to examine the impact of pre-recall advertising on

the stock market values of the recalling firms. In recent years, the event study methodology

has been widely used in the marketing literature to investigate the stock market impacts of


marketing initiatives, such as new product development alliances (Kalaignanam, Shanker and

Varadarajan 2007), innovation (Sood and Tellis 2009), product placement (Wiles and

Danielova 2009), new distribution channels (Geyskens, Gielens and Dekimpe 2002), product

quality (Tellis and Johnson 2007), movie advertising (Joshi and Hanssens 2009), and product

recall strategies (Chen, Genesan and Liu 2009). This method relies on the efficient market

hypothesis (EMH) (Fama 1970), which suggests that the price of a stock should immediately

reflect all publicly available information and any abnormal stock returns reflect the impact of

newly available public information. In this study, a publicly reported auto recall is defined as

an event that delivers new information to the stock market.

In our empirical study, we first derive the abnormal returns resulting from the events

of auto recall announcements and then examine the impact of pre-recall advertising

adjustments on the recalling firms’ abnormal returns. Specifically, the abnormal return of the

recalling firm i is calculated as the difference between the actual return during the event

window and the expected normal return. Following the literature (MacKinlay 1997), the

expected normal return is estimated using the market model given by:

Rit = αi + βiRmt + εit

where Rit is the stock return of firm i on day t and Rmt is the base return of a value-weighted

market index on day t. Following prior studies (e.g., Chen, Genesan and Liu 2009;

MacKinlay 1997), we choose an estimation period of 250 prior trading days (i.e., day -271 to

day -22) to estimate the normal component of stock returns. We then apply the estimated iα

and iβ

to calculate firm i’s expected returns within the event window. The abnormal return

of an event is hence the difference between the actual return and the expected return during

the event window: ARi(τ) = Ri(τ) - E[Ri(τ)] = Ri(τ) - [ iα + iβ

Rmτ], where τ∈[τ1,τ2]. Finally, the

cumulative abnormal return (CAR) is aggregated over the event window [τ1,τ2], that is,

2

1 2

1

( , ) ( )i iCAR ARτ

τ τ ττ τ=

= ∑ .

Cross-sectional analysis

To examine the impact of pre-recall advertising adjustments on firm i’s abnormal

returns resulting from product recalls, we conduct a cross-sectional analysis by regressing the

cumulative abnormal returns on pre-recall advertising adjustments, the interaction terms

between pre-recall advertising adjustments and two recall factors, as well as control variables.

The cross-sectional model is presented in Eq. (1):


CARij = b0 + bincincij + bdecdecij + bnew newij + bhazard hazardij

+ binc*new incij * newij + bdec*new decij * newij

+ binc*hazard incij * hazardij + bdec*hazard decij * hazardij

+ bcontrol controlij +ηij, (1)

where the dummy variables incij and decij refer to an increase and a decrease in pre-recall

advertising for recalled products, respectively. Specifically, if firm i increases its advertising

for the anticipated recalled products before the recall j, incij is equal to 1 and decij is equal to

0. In contrast, if firm i decreases its advertising for the recalled products before the recall j,

incij is 0 and decij is 1. When there is no adjustment in firm i's pre-recall advertising, both incij

and decij are equal to 0. We also incorporate two dummy variables of newij, hazardij, and their

interactions with pre-recall advertising adjustments, to capture the moderating effects of these

two recall characteristics on the impact of the pre-recall advertising. The dummy variable

newij is equal to 1 if the recall involves new models, and the dummy variable hazardij is 1 if

the recall is due to major hazards. A vector of control variables, such as other recall factors

and firm characteristics, is also incorporated into the model. The definitions and

measurements of these control variables are introduced in the next section. To derive the

overall impact of increasing and decreasing pre-recall advertising under certain recall

characteristics, we sum all coefficients related to the advertising adjustment and its

interaction with the specific recall characteristic. To test Hypothesis 1, we must sum the

coefficients of inc and inc*new (i.e., binc + binc*new) and test its significance. Similarly, to test

Hypothesis 2, we sum the coefficients of inc and inc*hazard (i.e., binc + binc*hazard). To test

Hypothesis 3, we consider the cases of both major and minor hazards. We sum the two

coefficients of dec and dec*new (i.e., bdec + bdec*new) for recalls of new models with minor

hazards. We also sum the three coefficients of dec, dec*new, and dec*hazard (i.e., bdec +

bdec*new + bdec*hazard) for recalls of new models with major hazards.

Empirical Analysis

Data

This study examines the impact of pre-recall advertising adjustments on firms’

abnormal returns to safety recalls in the automotive industry. We collected recall data from

the National Highway Traffic Safety Administration (NHTSA). Our sample consists of

vehicle safety recalls by the six largest automakers (Toyota, Honda, Nissan, General Motors,


Ford, and Chrysler) from 2005 to 2012, as these six automakers account for about 90 percent

of the U.S. motor-vehicle market for cars and light trucks. Following prior studies (Barber

and Darrough 1996; Jarrell and Peltzman 1985; Hoffer, Pruitt, and Reilly 1988), we included

a vehicle recall in the sample if the recall size is adequately large or if it is reported by the

Wall Street Journal (WSJ). Specifically, referring to the practice of Jarrell and Peltzman

(1985), the thresholds of recall size were set proportional to firm size: Toyota 50,000;

General Motors, Ford, and Chrysler 40,000; Honda 30,000; and Nissan 20,000.

We identified the recall announcement date based on the information provided by the

NHTSA and media reports such as the WSJ. If a recall was reported on multiple dates by

multiple sources, we used the earliest one as the recall announcement date. To prevent

contamination of our data by information leakage before the event date, we followed the

literature (e.g., Chen, Genesan and Liu 2009; Davidson and Worrell 1992) and excluded the

recalls for which there were news reports about related accidents and safety issues in the WSJ

before the recall announcement. To rule out potential confounding effects, we also excluded

the recalls for which confounding events were reported in the WSJ, such as earnings surprises,

earnings warnings, new plants, new products, mergers and acquisitions, joint ventures,

bankruptcy, layoffs, and changes in top management. This screening procedure ensures that

the abnormal returns over the event window are strictly due to the announcements of

unexpected vehicle recalls. Our final sample consists of 110 automobile safety recalls.

We collected the stock price and market index data from the Center for Research in

Security Prices (CRSP) at the University of Chicago. Firm characteristics such as firm size

and firm liability were obtained from COMPUSTAT. Firm reputation scores were collected

from annual surveys conducted by Fortune magazine. Recall information and characteristics

were obtained from the NHTSA database. Finally, advertising data were collected from

Kantar Media.

Variables

Pre-recall advertising adjustment. To identify if a firm adjusts its advertising

spending before an anticipated auto recall, we first need to specify a benchmark period and

an adjustment period. Conceptually, the benchmark period is the time period before a recall

when the stock market perceives relatively consistent advertising spending, whereas the

adjustment period is the near-recall period when the market perceives an adjustment in

advertising spending. Figure 1 illustrates these two periods in relation to the event window of

a product recall, where τ0=0 denotes the recall announcement date, [τ0, τ1] denotes the event


window, and ν1, ν2 denote two cutoff dates for the adjustment and benchmark periods,

respectively. Accordingly, la in Figure 1 denotes the length of the adjustment period between

τ0 and ν1, while lb denotes the length of the benchmark period between ν1 and ν2. Given these

two periods, pre-recall advertising adjustment can be defined by the difference in advertising

spending between these two periods. Specifically, if the firm’s weekly average advertising

spending in the adjustment period is two standard deviations above (below) that in the

benchmark period, we define the advertising adjustment to be increasing (decreasing);

otherwise we define it as no adjustment.11 (Figures and tables follow References.)

To determine the benchmark and adjustment periods empirically, we considered the

lengths of the adjustment period from 1 to 3 weeks before the recall announcement date τ0;

and the lengths of the benchmark period from 3 to 6 weeks, respectively. Then we

experimented with eight different sets of adjustment and benchmark periods. For example,

one choice of adjustment and benchmark periods [la, lb] can be [1, 4], indicating the

adjustment period of one week before the recall announcement date τ0 and the benchmark

period of four weeks. We applied our model to multiple alternative sets of benchmark and

adjustment periods and found consistent results (see the section of Robustness and Validity of

Results). Among them, the model with the adjustment and benchmark periods of [1, 4]

provides the most significant results and the best model fit. Hence, our discussion of the

empirical analyses focuses on and, unless otherwise stated, the estimation results in all tables

are based on the adjustment and benchmark periods of [1, 4]. As shown in Table 1, for this

specific classification, among the 110 auto recalls in our data, the number of cases of

increasing, decreasing, and not adjusting pre-recall advertising is 30, 30, and 50,

respectively.

Two recall characteristics. The first recall characteristic involves the newness of the

recalled model. We classify a recall as a New-Model recall when the recall involves vehicles

introduced in the current or previous year. The second recall characteristic regards the

severity of the defect hazard. Following the literature (Rupp and Taylor 2002; Rupp 2004),

we classify a recall due to a Major Hazard if severe quality defects (such as fuel leakage,

steering problems, acceleration problems, break failure, or repeated stalling, which may cause

fire or car crash) were involved. Both variables were collected from the NHTSA website. As

11 We also tested the categorization of advertising adjustments using a one-standard-deviation cutoff, but this resulted in less significant results and an inferior model fit in the respective regressions, which suggests that only a significant adjustment in advertising spending (i.e., higher or lower than two standard deviations from the average advertising spending in benchmark period) can be detected by the stock market and processed by investors.


shown in Table 1, out of the 110 automobile safety recalls, 62 recalls involved new models

and 56 were due to major hazards.

Control variables. Our empirical analysis incorporates two types of controls: (1)

product recall factors and (2) characteristics of the recalling firm. In line with the extant

literature on auto recalls (Rupp and Taylor 2002; Rupp 2004, 2005), we included recall size,

airbag recall, recall initiator, and publicity as control variables. Recall size, rcsize, is

measured as the logarithm of the total number of vehicles affected by the recall. The dummy

variable airbag denotes whether a recall is due to a defect in the airbag. The dummy variable

nhtsa denotes if the recall is initiated by the NHTSA rather than by the firm. The information

on these recall factors was also collected from the NHTSA. In addition, we also incorporated

a dummy variable, y2009, indicating whether the recall occurred during or after Toyota’s

2009 recall crisis.

Both digital and print media contribute to the publicity surrounding a safety recall. To

facilitate managerial use, publicity is operated as a categorical variable with four levels:

negligible, local, national, and supranational, where negligible is measured as 0, local as 1,

national as 2, and supranational 3. Specifically, the publicity level is categorized as national

if a product recall was reported by the print media with a total circulation above any of the

five major national newspapers: Wall Street Journal (2,117,796), USA Today (1,829,099),

New York Times (916,911), Washington Post (550,821), and New York Post (522,874).

Publicity is categorized as supranational if a recall was reported with a total circulation

above the sum of two of the five national newspapers with one being WSJ. Publicity is

categorized as local if the recall was reported with a total circulation level ranging from the

Chicago Daily Herald (104,053) to the Houston Chronicle (364,724). A publicity level below

that is then defined as negligible. The circulation data of the print media were collected from

the news database Factiva.

The control variables representing characteristics of the implicated firms include firm

size, firm liability, and firm reputation (Chen, Ganesan, and Liu 2009). Firm size, fsize, is

measured as the logarithm of the firm’s sales revenue in the year of the recall, whereas firm

liability, fdebt, is calculated as the logarithm of the firm’s long-term liability. We collected

firms’ sales revenues and long-term liability from COMPUSTAT. Firm reputation, frep, is an

overall score from Fortune magazine’s annual survey of “America’s Most Admired

Companies.” Finally, two dummy variables inc_u and dec_u are also incorporated to control

the impact of the recalling firm’s adjustments in pre-recall advertising (i.e., increase or


decrease) for products unaffected by the recall. The classification of advertising adjustments

for unaffected products and the variable definitions are similar to those for recalled products.

Table 2 summarizes all variable definitions, their data sources, and descriptive statistics.

Results

Abnormal returns resulting from product recalls. To examine the cumulative abnormal

returns resulting from the events of product recalls and to minimize potential confounding

events (McWilliams and Siegel 1997), we focus on three relatively short event windows: (1)

the event date (i.e., the day 0); (2) the day after the event date (i.e., the day +1); and (3) both

the event day and its following day (i.e., [0, 1]). Table 3 reports the cumulative abnormal

returns over these three event windows.

As shown in Table 3, the abnormal returns are significantly negative on both days, the

event date (i.e., the day 0) and the day following the event date (i.e., the day +1). For example,

Table 3 shows that on the event announcement date, the average abnormal return of the

implicated firms is CAR[0,0] = -.535% (t = -4.91, p<.01); whereas on the day after the event

date, the average abnormal return is CAR[1,1] = -.357% (t = -3.59, p<.01). Together, the

cumulative abnormal return over these two days is CAR [0,1] = -.891% (t = -7.05, p<.01).

These results are consistent with prior findings on the detrimental impacts of product recalls

on firms’ stock returns (Jarrell and Peltzman 1985; Barber and Darrough 1996; Davidson and

Worrell 1992; Thomsen and McKenzie 2001). Since the abnormal returns are significantly

negative on both days, we chose [0, 1] as the event window for the following analyses.

The impact of pre-recall advertising adjustments. Table 4 presents the results of a

simple univariate analysis, which directly tests whether or not post-recall abnormal returns of

the recalling firms differ across different pre-recall advertising adjustments. To underscore

the significance of the two specific recall characteristics identified in this paper (i.e., product

newness and product hazard), we present the results ignoring these recall characteristics in

Panel A and provide the results considering them in Panel B.

Without considering specific recall characteristics, Panel A offers comparisons for two

cases (increasing pre-recall advertising vs. no adjustment, and decreasing pre-recall

advertising vs. no adjustment). As shown in Panel A, the average abnormal return of recalls

with increasing pre-recall advertising does not significantly differ from that of recalls with no

adjustment (∆CAR =-.0004, p>0.1), and the average abnormal return of recalls with

decreasing pre-recall advertising is significantly lower than that of recalls with no adjustment


(∆CAR =-.0061, p<0.05). Panel B, incorporating the specific recall characteristics, offers

comparisons for six cases (four for increasing pre-recall advertising vs. no adjustment, and

two for decreasing pre-recall advertising vs. no adjustment). Panel B reveals three significant

results: (1) When the recalled products contain new models with a minor hazard, the average

abnormal return is significantly higher for firms who increased their pre-recall advertising

than for those who did not make a pre-recall advertising adjustment (∆CAR=.0064, p < .1),

which is consist with Hypothesis 1. (2) When the recalled products are older models with a

major hazard, the average abnormal return is significantly lower for firms who increased their

pre-recall advertising than for those who did not make a pre-recall advertising adjustment

(∆CAR=-.0120, p < .05), which is consistent with Hypothesis 2. (3) When the recalled

products contain new models, the average abnormal return is significantly lower for firms

who decreased their pre-recall advertising than for those who did not make a pre-recall

advertising adjustment (∆CAR=-.0083, p < .05), which is consistent with Hypothesis 3.

These results suggest that, when ignoring specific recall factors (Panel A), one may

mistakenly conclude that increasing pre-recall advertising does not affect firm value, but

decreasing it always harms firm value. However, when considering the two recall specific

factors (Panel B), we show that increasing pre-recall advertising can significantly affect firm

value, and we identify specific conditions under which an upward adjustment in pre-recall

advertising weakens (i.e., when new models are recalled due to a minor hazard) or intensifies

(i.e., when older model are recalled due to a major hazard) the negative financial

consequence of a product recall. We also show that decreasing pre-recall advertising does not

always worsen the damage of recalls to firm value, and we show that a downward adjustment

harms firm value only when new model products are affected by recalls.

To further examine the impact of pre-recall advertising on the abnormal returns

resulting from product recalls, we also estimated the cross-sectional model in Eq. (1). We first

conducted several tests concerning potential issues of heterogeneity and multicollinearity.

The Lagrange multiplier (LM) test (Breusch and Pagan 1980) rejects the existence of

unobserved heterogeneity. The largest variance-inflation factor (VIF) of all variables is less

than 5, rejecting the possibility of multicollinearity. Hence, we estimated Eq. (1) using pooled

OLS with heteroskedasticity-consistent standard errors.

We presented two regression results in Table 5. The cross-sectional regression (1) in

Table 5 reports the regression results of a partial cross-sectional model without incorporating

the interaction terms between pre-recall advertising adjustments and the two recall factors,


while the cross-sectional regression (2) in Table 5 reports the regression results of the full

cross-sectional model in Eq. (1). Consistent with the univariate analysis results in Panel A of

Table 4, without considering the interactions between pre-recall advertising adjustments and

the recall factors, the coefficient of increasing pre-recall advertising is insignificant (binc

= .001, p > .1), whereas the coefficient of decreasing pre-recall advertising is significantly

negative (bdec = -.0074, p < .05). The former result concerning the impact of increasing

pre-recall advertising further suggests the existence of possible contingent effects of the recall

factors.

When incorporating the interactions between pre-recall advertising adjustments and

the recall factors (i.e., the cross-sectional regression (2) using a full cross-section model in Eq.

(1)), we found conditions under which increasing/decreasing pre-recall advertising can lessen

or worsen the harmful impact of product recalls on firms’ stock returns. As shown in the

results of cross-sectional regression (2) in Table 5, the coefficient of inc*new is significantly

positive (binc*new = .0095, p < .05), whereas the coefficients of inc*hazard and dec*new are

significantly negative (binc*hazard = -.0132, p < .05; bdec*new = -.0120, p < .05). Thus, when the

recalls involve new models with a minor hazard (i.e., new = 1 and hazard = 0), the overall

impact of increasing pre-recall advertising, which is the sum of the coefficients of inc and

inc*new, is found to be significantly positive (binc + binc*new = .0109, p < .05). This result,

consistent with the univariate analysis shown in Panel B of Table 4, provides further

empirical evidence to support our Hypothesis 1.

When the recalled products are older models with a major hazard (i.e., hazard = 1 and

new = 0), the overall impact of increasing advertising, which is the sum of coefficients of inc

and inc*hazard, is found to be significantly negative (binc + binc*hazard = -.0118, p < .05).

Consistent with the univariate results in Panel B of Table 4, the overall negative impact of

increasing pre-recall advertising under the product recalls of older models with major hazards

further supports our Hypothesis 2. With regard to a decrease in pre-recall advertising, we test

the sum of the coefficients of dec and dec*new as well as the sum of dec, dec*new, and

dec*hazard and find significantly negative impacts of decreasing pre-recall advertising for

newly introduced models regardless of hazard (bdec + bdec*new = -.0116, p < .05 for new

models with minor hazards; bdec + bdec*new + bdec*hazard = -.0120, p < .05 for new models with

major hazards), which is also consistent with the univariate results in Panel B of Table 4, and

further supports our Hypothesis 3.


Robustness and validity of results

We conducted several additional analyses to examine the robustness and validity of

our estimation results. First, as stated earlier, we experimented with eight different sets of

benchmark and adjustment periods when classifying the advertising adjustment to be

increasing, decreasing or no adjustment before a recall announcement. Table 6 presents the

cross-sectional regression results when the adjustment and benchmark periods of [1, 3], [1, 4],

[1, 5] and [1, 6] are used, respectively, whereas Table 7 presents the cross-sectional

regression results when the adjustment and benchmark periods of [2, 4], [3, 4], [2, 5] and [3,

5] are used, respectively. For ease of discussion, we refer to these estimation results using

different lengths the adjustment and benchmark periods as Estimation [la, lb]. For example,

Estimation [1, 4] refers to the estimation results using the adjustment period of one week

before the recall announcement date and the benchmark period of four weeks. As shown in

Tables 6 and 7, the regression results concerning the interactions between pre-recall

advertising adjustments and the two recall factors are generally consistent throughout all

models using different adjustment and benchmark periods.

Furthermore, our results using different combinations of benchmark and adjustment

periods also provide important findings with regard to when and how the financial market

responds to firms’ pre-recall advertising adjustments. Among different lengths of benchmark

periods (i.e., 3 to 6 weeks) and adjustment periods (i.e., 1 to 3 weeks), the models using

relatively moderate lengths of benchmark periods (i.e., 4 or 5 weeks) and a short adjustment

period (i.e., 1 week) generate the most significant results and the best model fit (R2 = .43 in

Estimation [1, 4]). For example, as shown in Table 6, when varying the benchmark period

from a shorter window (i.e., 3 weeks) to longer ones (i.e., 5 and 6 weeks) while keeping the

adjustment period at 1 week, Estimation [1, 4] and Estimation [1, 5] provide a better model

fit, R2, in comparison with Estimation [1, 3] and Estimation [1, 6]. Also as shown in Table 7,

when varying the adjustment periods from a shorter period (i.e., 1 week) to longer ones (i.e.,

2 and 3 weeks) while keeping the benchmark period as medium (i.e., 4 or 5 weeks), the

models with the shorter adjustment period (i.e., Estimation [1, 4] and Estimation [1, 5])

provide more significant results and a better model fit. Thus, our results suggest that adjusting

advertising in the time period closer to a product recall (i.e., the last week before a recall) can

create significant market responses, which is consistent with the efficient market hypothesis

(that the capital market responds to the most updated information).

Second, we also estimated the cross-sectional equation (1) by correcting possible

endogeneity. In response to an anticipated product recall, forward-looking firms may adjust


their marketing strategy (e.g., advertising expenditure) based on recall factors or firm

characteristics in order to minimize the potential damage of a recall (e.g., Shaver 1998). Thus,

the issues of self-selection and endogeneity (Heckman 1979) may exist in the estimation of

the impacts of pre-recall advertising adjustments. Following the literature (e.g., Chen,

Genesan and Liu 2009; Heckman 1979), we used a Heckman two-step approach to test and

correct for potential self-selection bias and endogeneity in advertising adjustments.

Specifically, in the first step of the Heckman approach, the choice probabilities of advertising

adjustments (i.e., increase and decrease) were estimated as a function of observed recall and

firm characteristics, while, in the second step, we estimated the cross-sectional analysis in

Equation (1) with the correction terms incorporated in order to obtain a consistent estimate

(Lee 1983; Wooldridge 2001). The correction term was constructed from the first step based

on the estimated choice probabilities of advertising adjustments. The estimation results after

correcting the potential endogeneity are presented in Table 8, and the technical details of our

Heckman two-step procedure is reported in the Appendix. As shown in Table 8, the

coefficient of the correction term is not significant (p > .4), suggesting that the sample

selection and endogeneity are not severe issues in the estimation of the cross-sectional model

in Eq. (1). More important, the results concerning the impacts of pre-recall advertising

adjustments under different recall conditions still hold when endogeneity is corrected.

Third, to further validate our empirical results, we constructed a control sample in

which the abnormal returns of recalling firms in a recall-free window are estimated to

examine whether or not the moderating effects of pre-recall advertising presented earlier are

strictly due to product recalls. Specifically, we used the same sample of firms/products in our

data, but randomly selected a two-day, event-free window for each firm (i.e., no auto recalls

and no news reported by the WSJ). We constructed the same measures of advertising

adjustments in such a two-day, event-free window and conducted the cross-sectional

regression in Eq. (1) to test whether or not the advertising adjustment demonstrates similar

moderating impacts in the event-free scenario. The results in Table 9 indicate no significant

impact of advertising adjustments on the firms’ abnormal returns when there is no major

recall, which verifies that the moderating effects of pre-recall advertising identified in this

study are specific to the recall events.


Conclusion

Despite the rising number of product recalls and the catastrophic consequences of

severe product-harm crises in recent years, our knowledge of product-harm crisis

management is still limited in both theory and practice (Smith, Thomas and Quelch 1996).

This paper develops a theoretical framework to investigate whether and how adjusting

pre-recall advertising can affect a firm’s stock market value under a product recall. Our

theoretical framework and empirical findings contribute to the marketing-finance literature as

well as to the crisis management literature and provide important guidance for marketers to

effectively implement advertising strategies in managing a product-harm crisis.

Theoretical contributions

This article makes several theoretical contributions. First, as a fast-growing number of

firms continue to globalize their business and outsource some parts of their operations to

global partners, the risk of product-harm crisis is increasing. While crisis management has

become essential knowledge to marketers in the global market place, it has drawn limited

attention from marketing scholars. With the exception of a recent study investigating how a

firm’s product-recall strategy (proactive vs. passive) affects its stock values (Chen, Genesan

and Liu 2009), most extant marketing research focuses on examining the harmful impact of a

product recall on consumers and on firms’ marketing effectiveness. This paper contributes to

the marketing literature in crisis management by demonstrating the importance of marketing

strategies in pre-crisis management. Specifically, this paper theoretically proposes and

empirically investigates conditions under which adjusting advertising ex ante can lessen or

worsen the detrimental impacts of product recall on the recalling firms’ financial performance.

More important, we bring attention to an unexplored strategic move in crisis management,

increasing pre-recall advertising spending, and show that the firm can protect its financial

value by strategically boosting pre-recall advertising spending when anticipating a recall of

new products with a minor hazard. These findings provide insights into how to implement

marketing strategies ex ante in managing a recall crisis ex post.

Second, our findings also reveal some potential conflicting impacts of marketing

strategies in crisis management when considering different strategic objectives. Specifically,

a striking insight of our research is that, in product-harm crisis management, profit

maximization and shareholder value maximization can be in direct conflict with each other.

For example, while a recent marketing study suggests that, when envisioning a potential


recall due to a major hazard, a firm can maximize its marketing performance (i.e., profit) by

reducing pre-crisis advertising (Rubel, Naik, and Srinivasan 2011), our results show that

simply cutting advertising near recalls can exacerbate the harmful impacts of product recalls

on firms’ financial valuation if the recalled products involve new models. Moreover, this

paper identifies scenarios under which firms can achieve one objective with or without

sacrificing another. We show that, if the recalled products are older models, firms can protect

their marketing objective by reducing pre-recall advertising without sacrificing their financial

objective. These findings advance our understanding of effective crisis management and

highlight the importance of investigating and integrating the impacts of marketing strategies

on different strategic goals in crisis management.

Third, to the best of our knowledge this is the first paper to investigate how marketing

affects firms’ financial market valuations in the crisis environment. Although a growing

number of studies in the marketing-finance interface have examined how marketing

initiatives affect firms’ financial valuations in the regular market environment, no study to

date has investigated the marketing-finance relationship in a crisis environment. With regard

to the relationship between advertising and its impact on financial performance, prior studies

have shown that, in the regular market environment, advertising generally plays a positive

role in moderating the impact of marketing actions (e.g., product placement, new product

introduction), because advertising spending raises awareness of firms’ actions and creates

positive signals regarding marketing’s contributions to future earnings, both of which help

build brand equity and enhance firms’ financial values (Wiles and Danielova 2009;

Srinivasan et al. 2009). In a crisis environment, however, our study shows that such a positive

moderating impact of advertising may not exist, because a high level of advertising can also

create a negative expectation effect when confronted with product recall crises and, as a result,

the overall impact of advertising is contingent upon recall characteristics. Such a difference in

the impact of advertising underscores the importance of extending prior studies of the

marketing-finance interface to the crisis environment.

Managerial implications

When a firm envisions a product recall, what should managers do to minimize its

potential damage? Specifically, should forward-looking managers increase or decrease

pre-crisis advertising? As discussed earlier, the answers to these questions have thus far been

ambiguous: While Cleeren, Dekimpe, and Helsen (2008) argued that an increase in pre-crisis


advertising may create a buffer against the negative publicity of the crisis, Rubel, Naik, and

Srinivasan (2011) suggested the recalling firms decrease pre-crisis advertising to maximize

post-crisis profits. Our empirical findings provide some managerial guidance on how firms

should adjust pre-recall advertising strategies when considering both profit and financial

value maximization.

When to increase pre-crisis advertising? Our findings suggest that increasing

advertising ex ante can lessen the negative impact of product recalls on stock returns when

the recall involves new models with a minor hazard, because doing so can send a strong

positive signal to the stock market concerning the recalling firms’ confidence in managing the

crisis effectively. Hence, if protecting stock market returns is the most important objective (as

opposed, for example, to the marketing objective of profit maximization), the firm can signal

its confidence to the stock market by increasing its advertising spending in order to minimize

the potential harmful impact on its stock market returns when the recalled products involve

newly introduced models with a minor hazard.

When to decrease pre-crisis advertising? As stated earlier, the literature has suggested

that firms can maximize their marketing performance (i.e., profits) by reducing pre-crisis

advertising when they expect that a forthcoming recall would lead to reduced marketing

effectiveness (Rubel, Naik, and Srinivasan 2011). Our findings suggest that doing so will not

further hurt financial performance (i.e., stock market returns) only if the recalled products are

older models. Thus, when a firm envisions a potential recall of older models, reducing

pre-crisis advertising can be a strategic choice to achieve better marketing performance

without further damaging its financial market performance.

When not to adjust pre-crisis advertising? Our results also provide insights into the

conditions under which it is better for firms not to adjust advertising near a product recall.

Specifically, our results show that when firms have to recall newly introduced models with

major hazards, reducing pre-recall advertising can further deepen the loss in stock returns

because of the negative signaling effect. In this case, while increasing pre-recall advertising

would not harm stock returns further, as suggested by our results, the marketing performance

of advertising spending would be weakened, according to the literature (Rubel, Naik, and

Srinivasan 2011). In such a case, therefore, it is better for firms not to adjust their advertising

expenditure when considering both marketing and financial performance.

If adjusting pre-recall advertising, the timing? The timing of a pre-recall advertising

adjustment is also of strategic importance to managers as adjusting it too early may not

generate any significant impact on the stock market. According to our results in Tables 6 and


7, we show that the stock market responds most actively to advertising adjustments one week

prior to the announcement of a recall. Hence, to maximize the strategic impact of adjusting

advertising spending in crisis management, managers should not make the adjustment too

early, because the stock market may not capture such an adjustment as recall-relevant

information.

When is integrated crisis management needed? Managers should also be aware of

when they need to develop an integrated product-harm crisis management strategy.

Specifically, when the recalled products are new models, crisis management needs to

integrate the impacts of its strategic move on both marketing performance and financial

performance. This integration is necessary because, while it is the best strategic choice to

increase pre-recall advertising from the perspective of stock returns when a recall involves

new models and is due to a minor hazard, it is not the best strategy from the perspective of

marketing performance, because the effectiveness of marketing spending will be weakened

due to product-harm crisis. On the contrary, while it is optimal for firms to decrease pre-recall

advertising (because of weakened marketing effectiveness), this strategy is not optimal when

considering stock market performance, because doing so can further intensify the financial

loss in the stock market. Therefore, when the recalled products involve new models with a

minor hazard, it is crucial for managers to investigate the impacts of their marketing

movement on both consumer market performance (i.e., profits) and financial market

performance and develop an integrated crisis management strategy.

Future research

This study provides several directions for future research. First, this study uses

spending as the metric of advertising. Future research can consider other potential dimensions

of advertising such as advertising content (Resnik and Stern 1977; Smith 1991; Xu et al. 2014)

and advertising creativity (Smith, Thomas and Quelch 2007; Yang and Smith 2009). The

former helps convey specific information about quality or safety issues, while the latter

makes the communication during a recall crisis more effective. Investors (or consumers) may

react to the adjustment of these advertising metrics differently in a crisis environment than in

a regular market environment.

Second, this paper investigates how advertising affects abnormal returns as a result of

a recall crisis. It would also be interesting to study how a recall crisis affects the effectiveness

of advertising on firms’ stock market valuation, and compare the impact of advertising on


firm value before and after a recall. While Van Heerde, Helsen, and Dekimpe (2007)

compared effectiveness of advertising in the consumer market before and after a recall, how

its effectiveness in the financial market differs before and after a recall remains unexplored.

Third, future studies might also investigate how competitors’ marketing strategies

affect a focal firm’s financial market returns under a recall crisis. Empirical evidence has

shown that a product harm crisis inspires competitive responses, which may intensify the

recall’s damage to the focal firm. For instance, competitors may increase their advertising

spending or run an incentive program targeting the focal firm’s consumers. In the Toyota

recall crisis, General Motors offered a $1,000 rebate to attract Toyota car owners.12 During

the 1996 recall of Kraft peanut butter, the competitor’s brand Sanitarium launched an

advertising campaign to emphasize that its peanut butter was not contaminated (Van Heerde,

Helsen, and Dekimpe 2007). Van Heerde, Helsen, and Dekimpe (2007) found that a serious

recall can cause both “an increased cross sensitivity to rival firms’ marketing-mix activities”

and “a decreased cross impact of its marketing-mix instruments on the sales of competing,

unaffected brands.” It would be interesting to examine whether or not such effects also exist

in the financial market.

Finally, future research can extend our study to other marketing mixes with respect to

their strategic impacts on financial markets under recall crises. For example, how does the

stock market interpret a sales promotion near a recall? In particular, when a large sales drop is

inevitable after the recall, would a sales promotion before the recall improve or hurt a firm’s

financial value? As suggested earlier, it is important for both practitioners and researchers to

understand how the stock market interprets the adjustments of firms’ marketing strategies

near a recall. Such studies can further contribute to the marketing literature in crisis

management.

12 http://money.cnn.com/2010/01/27/autos/gm_toyota_incentives/?postversion=2010012716


Appendix: The Adapted Heckman Two-Step Estimation Approach

The original Heckman two-step procedure involves a probit regression of a binary

choice model. Our study presents three alternatives of advertising adjustments, so we use a

generalized Heckman approach to deal with multiple choices. We use a conditional logit

model proposed by McFadden (1980) as the first step. Specifically, in this step, we assume

that the utility 'k k ku z r z= + of choosing alternative ( 1,2,3)k k = is a function of z that

represents relevant recall and firm factors. Based on the principle of maximum utility,

alternative k is chosen if and only if '( ),k s s kz r r s kz z− > − ∀ ≠ . Therefore, under an extreme

value-distribution ( )kF z , the probability of choosing k has a closed form solution:

3' '

1{ '( ), } /k sz r z r

k k s s ks

p pr z r r s k e ez z=

= − > − ∀ ≠ = ∑ (A1)

The first step of the Heckman approach runs the conditional logit to obtain choice

probabilities, which are used to construct the correction term of sample selection. However,

the errors in the conditional logit do not follow a normal distribution as does the probit model,

while normality is a fundamental assumption of the Heckman procedure. To address this

issue, Lee (1983) generalized the original Heckman approach by making it robust to the

departure from normality, and this generalized version also accommodates multiple choices.

For each alternative k, the correction term, which transforms non-normality into normality,

has the generalized form:

1[ ( ' )]/ ( ' )k k kJ z r F z rλ φ=

where φ is the density function of the standard normal (0,1)N ; Φ is the distribution

function of (0,1)N ; ( )F is the distribution function of error kz in the choice model; 1

1( ) [ ( )]J F−= Φ is the transformation function; kr is the estimate of the conditional logit;

We denote kp as the estimate of the choice probability for alternative k. Under this

specification, ( ' )k kF z r p= and the correction term above is reduced to:

1[ ( )]/k k kp pλ φ −= Φ

Therefore, the cross-sectional regression accounting for sample selection becomes:

0 ( )ij inc ij dec ij r ij control ij ijCAR b b inc b dec r b controlπ λ η= + + + + + (1)’

This model adds the correction term ( )rλ for the choice of adjustments of pre-recall

advertising for recalled products. This correction term corresponds to one of three alternatives


in advertising adjustment: increase, decrease, or no adjustment. Shaver (1998) used a similar

practice for a binary choice. Using this corrected cross-sectional regression equation (1)’, we

can test the potential bias in the selection of pre-recall advertising adjustments.


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Table 1: The Distribution of Pre-recall Advertising Adjustments

Advertising Adjustment

Increase Decrease No Adjustment Total

Overall 30 30 50 110 New Model Recall 20 20 22 62 Old Model Recall 10 10 28 48

Major Hazard 16 17 23 56 Minor Hazard 14 13 27 54

Note: The three categories of advertising adjustments are classified based on the adjustment period of 1 week before the recall date and the benchmark period of 4 weeks.


Table 2: Variable Definitions and Data Statistics Variable Definition/Operationalization Source MEAN SD

Advertising adjustments

inc Whether pre-recall advertising for recalled products increases (1) or not (0) TNS Media Intelligence

0.273 0.447

dec Whether pre-recall advertising for recalled products decreases (1) or not (0) 0.273 0.447

Recall characteristics

new Whether the recall involves new models (1) or not (0) NHTSA 0.563 0.498

rhazard Whether the recall is due to major safety hazard (1) or not (0) NHTSA 0.509 0.502

NHTSA Whether the recall is initiated by the NHTSA (1) or not (0) NHTSA 0.382 0.488

rcsize The logarithm of the total number of vehicles affected by the recall NHTSA 5.305 0.530

airbag Whether the recall is due to the airbag problem (1) or not (0) NHTSA 0.064 0.245

y2009 Whether the recall is after Toyota’s recall crisis since 2009 (1) or not (0) NHTSA 0.427 0.497

publicity The level of publicity of a product recall with four possible categories:

Factiva news database 1.509 0.983 0=negligible, 1=local, 2=national, 3=supranational

Firm characteristics

fsize Firm size as measured by the logarithm of the firm’s sales revenue COMPUSTAT 5.173 0.148

fdeb Firm debt as measured by the logarithm of the firm’s long-term liability COMPUSTAT 4.634 0.321

frep The level of firm reputation, a overall score surveyed by the Fortune magazine Fortune magazine 5.606 0.993

inc_u Whether pre-recall advertising for unaffected products increases (1) or not (0) TNS Media Intelligence

0.273 0.447

dec_u Whether pre-recall advertising for unaffected products decreases (1) or not (0) 0.309 0.464


Table 3: Abnormal Returns of Auto Recalls over Different Event Windows

Event Window Abnormal Return Standard Error T-statistics P-Value

[0,0] -.0054 .0011 -4.91 <0.01

[1,1] -.0036 .0010 -3.59 <0.01

[0,1] -.0089 .0013 -7.05 <0.01


Table 4: Abnormal Returns under Different Pre-Recall Advertising Adjustments and Different Product Recalls

Panel A: Pre-Recall Advertising Adjustments and Abnormal Returns Accruing to Product Recallsa

“Increasing Pre-Recall Advertising” vs. “No Adjustment” “Decreasing Pre-Recall Advertising” vs. “No Adjustment” All -.0004b -.0061**

Panel B: Pre-recall Advertising Adjustments and Abnormal Returns Accruing to Different Product Recalls

“Increasing Pre-Recall Advertising” vs. “No Adjustment” “Decreasing Pre-Recall Advertising” vs. “No Adjustment” Major Hazard Minor Hazard

New .0011 .0064* -.0083** Old -.0120** .0027 .0009

Note: ** p< .05; * p< .10 a: The advertising adjustments are classified based on the adjustment period of 1 week and the benchmark period of 4 weeks. b: The abnormal return reported here is ∆CARinc-no,[0,1], that is, it is the abnormal return accruing to the product recalls of those firms who increased their pre-recall

advertising subtracting the abnormal return of those firms who made no pre-recall advertising adjustment. Similarly, all the abnormal returns reported in this table are relative to those under no pre-recall advertising adjustment.


Table 5: Estimation Results of Cross-Sectional Regressions

Cross-Sectional Regression (1) Cross-Sectional Regression (2)

Estimate SE Estimate SE

Intercept -.0084 .0482

-.0042 .0451

inc*new

.0095** .0048

dec*new

-.0120** .0047

inc*rhazard

-.0132** .0052

dec*rhazard

-.0004 .0051

inc .0010 .0028

.0014 .0039

dec -.0074** .0026

.0004 .0045

new -.0071** .0026

-.0063** .0031

rhazard -.0057** .0020

-.0025 .0029

nhtsa -.0062** .0027

-.0046* .0024

rcsize -.0007 .0022

-.0010 .0020

airbag -.0075** .0037

-.0060** .0030

y2009 .0017 .0023

.0027 .0022

publicity -.0030** .0013

-.0042** .0012

fsize .0046 .0108

.0030 .0101

fdeb .0004 .0046

.0009 .0044

frep -.0009 .0014

-.0005 .0012

inc_u .0020 .0031

.0026 .0026

dec_u -.0079** .0026

-.0086** .0023

Observations 110

110

R square 0.30 0.43

Note: ** p<.05; * p<.10. The advertising adjustments are classified based on the adjustment period of 1 week and the benchmark period of 4 weeks.


Table 6: Estimation Results of different Benchmark Periods

Estimation [1, 3]a Estimation [1, 4] Estimation [1, 5] Estimation [1, 6]

Variable Estimate SE Estimate SE Estimate SE Estimate SE

Intercept .0184 .0469 -.0042 .0451 -.0097 .0441 -.0006 .0450 inc*new .0089* .0059 .0095** .0048 .0072* .0050 .0075* .0053 dec*new -.0119* .0053 -.0120** .0047 -.0134** .0056 -.0062 .0050 inc*rhazard -.0103* .0064 -.0132** .0052 -.0150** .0055 -.0177** .0065 dec*rhazard .0005 .0058 -.0004 .0051 -.0056 .0055 -.0039 .0056 inc .0014 .0047 .0014 .0039 .0012 .0042 .0036 .0052 dec .0022 .0057 .0004 .0045 .0033 .0053 -.0012 .0049 new -.0066** .0034 -.0063** .0031 -.0045 .0033 -.0074** .0037 rhazard -.0045 .0033 -.0025 .0029 -.0007 .0031 -.0008 .0039 nhtsa -.0041 .0030 -.0046* .0024 -.0047* .0025 -.0052** .0026 rcsize -.0027 .0023 -.0010 .0020 -.0004 .0021 .0006 .0023 airbag -.0070* .0041 -.0060** .0030 -.0078** .0034 -.0062* .0036 y2009 .0028 .0025 .0027 .0022 .0025 .0022 .0007 .0025 publicity -.0036** .0013 -.0042** .0012 -.0042** .0013 -.0041** .0014 fsize -.0025 .0106 .0030 .0101 .0030 .0099 .0015 .0106 fdeb .0039 .0046 .0009 .0044 .0011 .0043 .00004 .0048 frep -.0009 .0014 -.0005 .0012 -.0005 .0013 -.0005 .0014 inc_u .0053 .0033 .0026 .0026 .0029 .0027 .0026 .0030 dec_u -.0023 .0023 -.0086** .0023 -.0066** .0025 -.0059** .0025

Observation 110 110 110 110

R square 0.33 0.43 0.39 0.35 Note: ** p<.05; * p<.10. a: Estimation [1, 3] refers to the estimation results using the adjustment period of 1 week before the recall date and the benchmark period of 3 weeks.


Table 7: Estimation Results of different Adjustment Periods

Estimation [2, 4]a Estimation [3, 4] Estimation [2, 5] Estimation [3, 5]

Variable Estimate SE Estimate SE Estimate SE Estimate SE

Intercept -.0175 .0423 -.0259 .0464 -.0037 .0433 .0033 .0447 inc*new -.0016 .0051 .0020 .0059 -.0010 .0057 -.0016 .0057 dec*new -.0193** .0050 -.0119** .0048 -.0141** .0046 -.0175** .0046 inc*rhazard -.0177** .0051 -.0158** .0051 -.0161** .0054 -.0143** .0052 dec*rhazard -.0035 .0051 .0011 .0050 -.0018 .0055 .0015 .0053 inc .0123** .0048 .0103** .0050 .0092* .0054 .0107** .0051 dec .0073 .0045 .0026 .0043 .0019 .0044 .0039 .0044 new -.0025 .0031 -.0048 .0031 -.0036 .0033 -.0024 .0031 rhazard -.0027 .0031 -.0041 .0032 -.0038 .0038 -.0047 .0034 nhtsa -.0025 .0026 -.0058** .0025 -.0042* .0025 -.0041* .0024 rcsize -.0015 .0022 -.0007 .0022 -.0008 .0023 -.0008 .0023 airbag -.0047 .0032 -.0048 .0047 -.0059* .0036 -.0053 .0045 y2009 -.0004 .0025 -.0005 .0027 -.0023 .0026 .0005 .0028 publicity -.0042** .0013 -.0029** .0012 -.0031** .0012 -.0032** .0012 fsize .0073 .0095 .0102 .0106 .0066 .0097 .0023 .0106 fdeb -.0006 .0042 -.0024 .0047 -.0022 .0048 -.0005 .0048 frep -.0009 .0013 -.0012 .0014 -.0016 .0013 -.0008 .0014 inc_u .0017 .0023 -.0015 .0028 -.0006 .0026 -.0011 .0025 dec_u -.0065** .0027 -.0080** .0034 -.0080** .0029 -.0055* .0033 Observation 110 110 110 110 R square 0.41 0.34 0.39 0.35 ** p<.05; * p<.10. a: Estimation [2, 4] refers to the estimation results using the adjustment period of 2 week before the recall date and the benchmark period of 4 weeks.


Table 8: Results of Heckman Two-Step Estimation

Variable Choice of Advertising

Adjustments Cross-sectional regression with

correction

Cross-sectional regression with

correction Increase Decrease

inc*new .0102** (.0057)

dec*new -.0116** (.0049)

inc* rhazard -.0133** (.0052)

dec* rhazard -.0001 (.0052)

inc .0017 (.0030)

.0009 (.0047)

dec -.0066** (.0029)

-.0002 (.0054)

new 1.057* (.593)

.613 (.562)

-.0068** (.0026)

-.0067* (.0035)

rhazard .118 (.515)

.248 (.498)

-.0056** (.0020)

-.0025 (.0029)

nhtsa -.196 (.586)

-.865 (.593)

-.0064** (.0027)

-.0046* (.0024)

rcsize 1.096* (.598)

.115 (.548)

-.0006 (.0022)

-.0010 (.0020)

airbag -1.120 (1.168)

-1.148 (1.172)

-.0075** (.0036)

-.0059* (.0030)

y2009 -.214 (.633)

.00242 (.628)

.0016 (.0023)

.0028 (.0022)

fsize -3.506 (2.437)

-1.272 (2.360)

.0047 (.0110)

.0029 (.0101)

fdeb -.728 (1.101)

-.0490 (1.078)

.0001 (.0046)

.0010 (.0044)

frep -.00101 (.327)

-.0748 (.313)

-.0010 (.0014)

-.0005 (.0012)

publicity -.0029** (.0013)

-.0042** (.0012)

inc_u .0020 (.0031)

.0026 (.0026)

dec_u -.0078** (.0026)

-.0086** (.0023)

correction -.0028 (.0037)

.0010 (.0040)

Chi square 214.26

R square .31 .43

Observation 110 110 110

Note: ** p<.05; * p<.10. The advertising adjustments are classified based on the adjustment period of 1 week and the benchmark period of 4 weeks.


Table 9: Estimation Results Using the Event-Free Sample

Estimate SE

Intercept -.0315 .0456

inc -.0002 .0030

dec -.0011 .0026

fsize .0042 .0119

fdeb .0004 .0050

frep .0013 .0012

inc_u .0009 .0027

dec_u -.0013 .0026 Observations 110 R square 0.03


Figure 1: The Time Frame of Advertising Adjustments near a Product Recall

Recall Date

ν2 ν1 τ0 τ1 Time

Benchmark Period lb Adjustment Period la Event Window


Documents

Should Ad Spending Increase or Decrease Prior to a Product