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FDA SAFETY ALERTS AND FIRM LOBBYING: THE FRIDAY EFFECT AND ITS CONSEQUENCES

Luis Diestre IE Business School Alvarez de Baena, 4

Madrid, 28006, Spain Tel: +34 (91) 5689600 Fax: +34 (91) 5689747

e-mail: luis.diestre@ie.edu

Benjamin Barber IV IE Business School Alvarez de Baena, 4

Madrid, 28006, Spain Tel: +34 (91) 5689600 Fax: +34 (91) 5689747

e-mail: benjamin.barber@ie.edu

Juan Santaló IE Business School Alvarez de Baena, 4

Madrid, 28006, Spain Tel: +34 (91) 5689600 Fax: +34 (91) 5689747

e-mail: juan.santalo@ie.edu

Work in progress, please do not cite or circulate without permission

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We integrate the corporate political activity literature with impression management

research to explore whether lobbying allows firms to influence the timing of negative news by the

FDA. First, we show that FDA safety alerts announced on Fridays experience a lower diffusion by

healthcare experts and the media. Furthermore, we find that firms who lobby the FDA are more

likely to have safety alerts for their drugs announced on Fridays. We find this effect to be stronger

for severe safety alerts. Finally, we explore the public health implications of the lower diffusion

of Friday safety alerts and find that, although safety alerts are in general effective in reducing

patients’ adverse effects, this effectiveness is substantially lower for alerts announced on Fridays.

Specifically, compared to non-Friday alerts, Friday safety alerts are associated with 30% more

deaths, 28% more serious adverse events (death, hospitalization, disability, life-threatening, and/or

congenital anomaly) and 26% more adverse events in general.

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Firms are dependent on governments and public institutions for their success (Bonardi,

Hillman, and Keim, 2005; De Figueiredo and Richter, 2014; Hillman, Keim, and Schuler, 2004).

Public officials determine firms’ fates by restricting market entry (e.g., issuing licenses),

determining the competitive environment (e.g., regulating prices and issuing patents), or

administering sanctions (e.g., issuing fines for regulatory non-compliance). Given this strong

dependence on the public sphere, it is not surprising firms undertake political activities to cope

with the inherent policy uncertainty. Firm’s political activities have been shown to influence

decisions about taxes (Richter, Samphantharak, and Timmons, 2009), federal contracts (Ridge,

Ingram, and Hill, 2017), and regulated prices (Bonardi, Holburn, and Bergh, 2006). Overall, the

corporate political activities (CPA) literature has provided rich evidence that political efforts can

shape public officials’ decisions in the firm’s favor.

Yet, government officials not only make policy decisions but, in the majority of the cases,

they also communicate these decisions to the public. This communication is critically important

since the way officials communicate news can affect the firm as much as the content of the

decisions themselves. Prior impression management studies show how the manner in which

corporate news are communicated to external audiences—e.g., when is the information made

public, or through which channel—strongly determines external audiences’ interpretation and

reaction to that new information (Elsback, Sutton, and Principe 1998; Graffin, Haleblian, and

Kiley, 2016). When it comes to policy decisions this is especially true. Because there is a lot of

uncertainty about how a new policy will affect a specific company, the way in which a firm’s

stakeholders will interpret and react to a policy decision depends on how such decision is

communicated. Ideally, then, firms would want policy decisions be communicated to the public

in the way that triggers the most positive (or least negative) reactions. This is exactly what firms

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do when it comes to communicating internal corporate news: the impression management

literature has provided broad evidence that firms are very strategic when designing their

communication activities in an attempt to manage audiences’ perceptions (Bolino, Kacmar,

Turnley, and Gilstrap, 2008; Elsbach, 2006, 2012; Graffin et al., 2016). Yet, when it comes to

policy decisions, it is public officials, not firms, who communicate news to the public. The

question is then: can firms “persuade” public officials to implement impression management

tactics similar to the ones firms implement when they communicate internal corporate news? Are

political activities helpful not only at shaping policy-making, but also at shaping policy-

communication? To our knowledge, this is an unexplored question in the CPA literature.

We aim to fill this gap by looking at a specific type of policy communication: the

reporting of drug safety news by the U.S. Food and Drug Administration (FDA). The FDA is

responsible for identifying and reporting potential safety issues on marketed pharmaceutical

drugs. When the agency discovers that a marketed drug has a previously unknown side-effect

that represents a risk for patients’ health, it releases a safety alert communication where it

explains the severity and scope of the drug’s safety issues, and the suggested changes in doctors’

prescription behavior. Obviously, these alerts have negative consequences for the firm selling the

drug (Chen, Ganesan, and Liu, 2009). First, the announcement that the firm missed an important

side-effect during the development of the drug is likely to trigger a negative reputation, which

may lead to greater scrutiny in the future (Ahmed, Gardella, and Nanda, 2002; Dowdell,

Govindaraj, and Jain, 1992; Cheah, Chang, and Chieng, 2007). In addition, drug sales are likely

to drop due to changes in doctors’ prescription behavior and patients’ reactions to safety scandals

(Dusetzina et al., 2012; Hurren, Taylor and Jaber, 2011).

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In this study, we claim that the magnitude of these negative consequences will depend

upon the way the FDA releases the news. Prior research in impression management has

identified several factors that are likely to affect how strongly external audiences react to

negative corporate news (Bolino et al., 2008; Elsbach, 2006, 2012). In this study we focus on one

particular factor: the timing of the communication. How stakeholders react to safety news

depends upon how quickly, and broadly, such news diffuses. Key information intermediaries, i.e.

the media and industry experts, typically are the ones to provide this type of technical news to

the public, however these intermediaries’ attention is not constant over time (Deephouse and

Heugens, 2009; Hoffman and Ocasio, 2001). A large literature on organizational behavior and

labor economics has shown how cognitive attention varies significantly over the workweek.

Specifically, on Fridays productivity and motivation are at the lowest (Accountemps, 2013;

Sotak et al., 2015), absenteeism is at the highest (Herrman and Rockoff, 2012; Johns and Hajj,

2016; Miller Murnane, and Willet, 2008), and professionals work the least amount of hours

(Beckers et al., 2008; Harrison and Hulin, 1989; Nader et al., 2012). This means professionals

are less likely to pay attention, assess, and react to events happening on Fridays. We build on this

logic to propose that healthcare professionals and media will be less attentive to FDA safety

alerts that take place on Fridays. Accordingly, we expect Friday alerts to experience a slower and

narrower diffusion. This means that the negative consequences associated with safety alerts—

negative reputation and drop in sales—should be less negative for Friday alerts.

Based on this, we expect firms will prefer their safety alerts reported on Fridays. We

build on the CPA literature to examine whether firms’ corporate political activities, specifically

lobbying activities, allow them to influence when the FDA communicates safety alerts. We argue

that lobbying establishes a communication channel with the FDA, increasing firms’ ability to

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influence public officials’ decisions about when to release safety news. Given that firms are

likely to prefer low diffusion of safety news, we predict that corporate lobbying should increase

the probability that a firm’s alert is announced on a Friday.

We then build on the assumption that firms have limited political capital. With limited

political capital firms cannot exploit their relationship with public officials without some cost.

Under this assumption, we expect firms to be selective and use their political influence when it is

most valuable. In our context, we expect that firms will be more likely to use their influence on

the FDA for severe safety alerts—i.e., those that have a dramatic impact on patients’ health.

These alerts are more likely to trigger a stronger reputational loss and a larger drop in drug sales

(Cheah et al., 2007). Therefore, we expect that the positive effect of lobbying on the probability

that an alert is issued on a Friday will be greater for severe safety alerts.

We test our predictions in a sample of 441 safety alerts reported by FDA between 1999

and 2016. First, we find that alerts reported on Fridays receive weaker diffusion by healthcare

experts and mass media. To capture diffusion by healthcare experts we look at whether such

experts shared safety alert news within their professional network (retweets of safety alert news

in their twitter accounts), whereas to capture diffusion by mass media we look into the number of

articles in U.S. newspapers covering a specific safety alert. We find that Friday alerts have far

less retweets and news articles than alerts announced any other weekday. Furthermore, we find

that firm lobbying increases the chances of having an alert released on a Friday by 63%. The

effect is even greater for drugs whose consequences for patients’ health were severe. In these

cases, the chance of a Friday alert goes from about 12% for non-lobbying firms to roughly 40%

for lobbying firms. This suggests that firms strategically use their political connections to

(indirectly) implement impression management tactics in the release of negative policy news.

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Finally, we examine the public health implications of the implementation of this Friday

effect. The goal of safety alerts is to inform patients and doctors of new side-effects so they can

adjust their prescription and consumption behavior accordingly and stop experiencing those

negative effects (Dusetzina et al., 2012; Hurren et al., 2011). Yet, if Friday safety alerts

experience a narrower and slower diffusion, it may be the case that those alerts announced on

Fridays are less effective in reducing patients’ adverse reactions. We explore this potential public

health implication relying on the FDA’s Adverse Event Reporting System (FAERS), a database

providing information about adverse events suffered by patients on specific drugs, and we find

support for our suspicion. Specifically, we find that the number of reported medical

complications decreases in the days after a safety alert communication, but that this decrease is

significantly weaker for Friday alerts. Specifically, the consequences on health are significant:

Friday safety alerts are associated with 30% more deaths, 28% more serious complications

(death, hospitalization, disability, life-threatening, and/or congenital anomaly), and 26% more

complaints in general.

CONTEXT: DRUG SAFETY ALERTS

One of the roles of the FDA—the regulatory agency for pharmaceutical products in the

U.S.—is to develop and disseminate information to the public regarding safety issues on

marketed drugs (CDER, 2007). After a drug is approved, the FDA may learn of new adverse

experiences (i.e., new side effects in a subpopulation of patients) from post-approval clinical

studies or patients’ reports to the FDA. When such information becomes available, the agency

actively engages in scientific efforts to evaluate whether there is indeed a potential drug safety

concern that should be communicated to the public and healthcare professionals. All this

evidence is evaluated by the Drug Safety Oversight Board (a branch of the FDA), which is

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responsible of deciding whether the emerging drug safety information should be made public or

not. With each new piece of evidence, the board faces the tension between the goal of having

people informed about potentially important safety information as early as possible and the goal

of having that information thoroughly substantiated (CDER 2007). Thus, only when the Drug

Safety Oversight Board has concluded that the evidence of a causal relationship between the

drug and the adverse events is reliable enough, such safety information is communicated.

Safety information is made public in the form of safety alert communications. Safety

alerts provide the following information: a description of the newly found adverse effects (i.e.,

summary of the scientific findings) and a set of recommendations for healthcare professionals

regarding how/when the drug should be prescribed based on the new evidence (changes in the

drug’s label). Since 1993, these safety alerts are communicated through the FDA’s MedWatch

web site. In addition, patients and healthcare professionals can obtain safety alert updates from

other channels such as email subscription or, since 2011, the FDA’s MedWatch twitter account.

We believe this is an ideal context to examine whether firms’ political efforts can

influence public officials’ communication activities for the following reasons. First, these alerts

have negative consequences for the firm. They usually harm the firm through a reputational

crisis and a drop in sales (Chen et al., 2009; Dusetzina et al., 2012; Hurren et al., 2011). Second,

although safety alerts are communications that clearly affect firm outcomes, the firm has little, if

any, influence on the process under which safety alerts emerge. Attending to the FDA’s statutes

regarding the communication of safety information (CDER, 2007), the FDA has no obligation to

keep the firm informed of its decisions on how and when to communicate safety information.

The FDA specifically states that it will “intend to inform the sponsor [the firm marketing the

drug] at least 24 hours before the alert is communicated” but it is not bound to do so. This

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suggests that firms, not only have little influence on how and when safety information regarding

their drugs will be communicated to the public, but also have little information about how the

FDA is managing the whole process or that such a process is taking place at all. This is a context,

then, where political activities can make a difference in that they may create a relationship

between the firm and the agency that is more permeable to the transfer of information giving the

firm a way to influence the process. We explore such a possibility in the following sections.

THEORY AND HYPOTHESES

We build on attention-based theories (Barnett, 2014; Hoffman and Ocasio, 2001; Ocasio,

1997, 2011) to analyze how safety alerts communicated on Fridays are diffused less broadly than

alerts communicated any other weekday.1 Next, we draw from the CPA literature to examine

how firms may strategically influence the timing of safety alert communications to their

advantage: we explore if firm lobbying increases the probability that alerts are issued on Fridays.

The diffusion of safety alerts: Information intermediaries’ attention

The process through which external audiences are informed about corporate events is

mediated by information intermediaries—e.g., media or industry experts (Dalton et al., 1998;

Deephouse, 2000; Lounsbury and Rao, 2004; Pollock and Rindova, 2003; Rao, Greve, and

Davis, 2001). These information intermediaries play the role of information brokers that

determine what information regarding organizations reaches external audiences and when/how is

the information communicated (Deephouse and Heugens, 2009; Madsen and Rodgers, 2015).

Yet, because the attention of information intermediaries is selective, and some events are more

likely to capture their attention than others, not all events are equally diffused to the general

public (Deephouse and Heugens, 2009; Hoffman and Ocasio, 2001; Ocasio, 1997, 2011).

1 Safety alerts are not issued on the weekends.

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Information intermediaries’ selective attention has both, cognitive and motivational roots

(Kaplan and Henderson, 2005; Ocasio, 2011). The motivational view proposes that people’s

goals, intentions and prior beliefs determine what events they pay attention to (Barnett, 2014;

Ocasio, 2011). Consistent with this, prior work finds that events that resonate more tightly with

the intermediary’s identity and agenda, in the case of mass media outlets for example, have a

greater likelihood of capturing their attention (Deephouse and Heugens, 2009).

The cognitive view of selective attention recognizes that there are multiple stimuli

competing for people’s limited attention (Ocasio, 1997), meaning that individual and situational

factors affecting people’s cognitive capabilities are likely to determine why some events are paid

attention instead of others (Barnett, 2014). Information intermediaries are professional

individuals (e.g., mass media journalists and industry experts) that in order to cover and diffuse

an event they need to (a) notice the event, (b) assess the event, and (c) react to the event (Barnett,

2014; Deephouse and Heugens, 2009; Hoffman and Ocasio, 2001). Noticing, assessing, and

reacting are activities that clearly demand information intermediaries’ cognitive resources, yet

the amount of cognitive resources available are not constant. This means that those events that

take place when information intermediaries’ cognitive capabilities are at their lowest level are

the ones with a greater probability fall under the radar of intermediaries’ attention and thus

experience a lower diffusion to external audiences.

In our study, we argue that one of the reasons why information intermediaries’ cognitive

capabilities are not constant is the presence of a weekly pattern: cognitive capabilities are

systematically lower in certain days of the week. Specifically, we propose that information

intermediaries’ cognitive resources are at their lowest level on Fridays. Extant evidence in the

organizational behavior and labor economics literatures is consistent with this claim. First,

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research on employee motivation, a key determinant of cognitive resources, shows that

motivation peaks on Mondays and Tuesdays, and is lowest on Fridays (Sotak et al., 2015). In a

similar vein, surveys on employee productivity reveal that Tuesdays is the weekday in which

employees report being most productive, while Fridays is the weekday in which productivity is

the lowest (Accountemps, 2013). Research looking at absenteeism—when cognitive capabilities

are simply null—reported higher levels of absenteeism on Fridays (Herrman and Rockoff, 2012;

Johns and Hajj, 2016; Miller et al., 2008), as well as a greater probability that employees take

vacation days (paid absenteeism) on Fridays (Harrison and Hulin, 1989). In addition, studies

looking at the allocation of working hours throughout the week by professionals with time

flexibility (e.g., academics) found that such professionals worked the least amount of hours on

Fridays (Beckers et al., 2008; Nader et al., 2012), which implies that such weekday is the one in

which employees have less cognitive resources available for their work-related activities. Recent

studies in finance and accounting provide further evidence of this effect by showing how stock

analysts and investors are less likely to react to events taking place on Fridays (quarterly

earnings [DellaVigna and Pollet, 2009; Hirshleifer, Lim, and Teoh, 2009] and mergers and

acquisitions [Louis and Sun, 2010]). This is consistent with the claim that such professionals’

cognitive resources are lower those days of the week. All this evidence that professionals exhibit

a lower cognitive capacity on Fridays implies that information intermediaries will be less likely

to attend to Friday events and, thus, such events will be diffused less broadly.

We apply this rationale into our context, where we explore the diffusion of information

concerning the safety of pharmaceutical drugs. Safety-related information may arise from many

different sources (e.g., federal agencies, patient advocacy groups, or scientific journals). Thus,

keeping up to date with all those sources requires an amount of time and effort that many

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external audiences lack (Advera, 2013). Therefore, this context is clearly one where information

intermediaries play a fundamental role as brokers that disseminate safety news. In this context

there are at least two main information intermediaries: healthcare experts and mass media.

Healthcare experts represent one of the key intermediaries that diffuse safety-related information

within the healthcare community (Advera, 2013). Those healthcare professionals that cover drug

safety events play the role of opinion leaders, and thus represent an important source of safety-

related information. Mass media, in addition, is an active diffusor of safety-related information

about pharmaceutical products. Major safety scandals are broadly covered in media news and

represent an effective channel through which such information reaches the general public

(Ahmed et al., 2002; Cheah et al., 2007). Then, applying the logic proposed above whereby

information intermediaries’ attention is lower on Fridays, we expect that healthcare professionals

and media—i.e., the key information intermediaries in our context—will be less likely to diffuse

safety alerts released on Fridays. This leads to our first two hypotheses:

Hypothesis 1a: The diffusion of safety alerts through healthcare experts will be lower for

safety alerts announced on Fridays.

Hypothesis 1b: The diffusion of safety alerts through mass media will be lower for safety

alerts announced on Fridays.

Consequences of FDA safety alerts

The publication of a drug safety alert by the FDA has several negative consequences for

the firm marketing the drug: a reputational loss and a drop in sales (Chen et al., 2009; Dusetzina

et al., 2012; Hurren et al., 2011). First, these communications are likely to affect the firm’s

reputation (Ahmed et al., 2002; Dowdell et al., 1992; Cheah et al., 2007). External audiences

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may interpret this event as a signal of the presence of key weaknesses in the firm’s drug

development activities. Maybe the reason why such safety alert took place is that the firm lacks

the ability to identify safety liabilities during clinical tests, which means that more safety alerts

may take place for other drugs in the future. This reputational shock may have strong

consequences for the firm in terms of a greater scrutiny in future drug development projects.

Moreover, a lower reputation may translate into a lower ability to attract consumers, alliance

partners, and even employees. Also, the stigmatization that follows one of such safety crisis may

hamper the firm’s ability to secure support from key stakeholders in the industry, such as

advocacy groups or consumer associations.

Second, beyond a reputational loss, firms are likely to experience a drop in sales after

safety alerts. There is evidence in the medical literature that safety alerts are followed by a

reduction in drug consumption (Dusetzina et al., 2012; Hurren et al., 2011). Safety alert

communications dictate new prescription recommendations for healthcare professionals.

Therefore, when doctors become aware of these new prescription recommendations they are

likely to reduce the medication of those patients that are subject to the safety risks reported in the

safety alert. In addition, patients may decide to stop taking that medication—or do not start

taking it in the case of new users—without the advice of a doctor, or irrespective of the doctor’s

recommendation (Dusetzina et al., 2012). Given the sense of urgency and alarm that many of

these safety crises generate, it is not rare that patients stop taking a medication after an alert even

before seeking medical advice (Szefler, Whelan, and Leung, 2006).

We now claim that all these costs will vary across safety alerts. Specifically, we claim the

drop in sales and the reputational loss that follows a safety alert communication will be lower for

alerts released on Fridays. Because Friday alerts are less likely to be diffused by healthcare

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experts (H1a) and mass media (H1b), we expect Friday alerts to generate the least negative

consequences for the firm. First, we expect that doctors will be less likely to adjust their

prescription behavior after Friday alerts. Safety-related information may arise from many

different sources (e.g., federal agencies, patient advocacy groups, or scientific journals) and

doctors complain that they lack the time to keep up to date with all those sources (Advera, 2013).

They acknowledge that, frequently, the way in which they firstly become informed about safety

issues is through their close professional network: conference meetings, conversations with

pharmacy specialists, sharing best practices and information with other doctors (Advera, 2013).2

This means that the probability that doctors get to know about safety news is partly determined

on how broad and fast such information diffuses throughout the network of healthcare

professionals. That is, it depends on the extent to which safety experts in the healthcare

community—the opinion leaders on safety-related information—diffuse safety news. Given that

such experts are less likely to diffuse safety alerts taking place on Fridays (H1a), we expect that

in these cases it will take longer for doctors to adjust their prescription behavior.

Second, when it comes to patients, these are unlikely to follow FDA alerts directly from

the MedWatch alert system. Instead, patients usually obtain safety-related information from mass

media. Then, since Friday alerts receive lower media coverage (H1b), and thus are less likely to

trigger a strong sense of alarm, we expect a weaker reaction by patients to such alerts—i.e., a

lower probability that they stop taking the medication.

2 One would think that doctors become immediately informed about safety issues, yet this does not seem to be the case (Advera, 2013). These professionals frequently complain that they do not get updated quickly enough on safety-related issues, which means that in some cases there might be a significant delay until they incorporate new safety information in their prescription decisions (Advera, 2013). Although doctors will ultimately get informed about new safety information thanks to changes in the drug’s label and in the software doctors use to prescribe medications, these changes are not immediate.

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Finally, if other key stakeholders such as advocacy groups, investors, competitors, or

scientists, are less likely to be informed about Friday alerts due to the weaker diffusion of such

alerts through the healthcare network and mass media (H1a and H1b), we expect that, not only

the potential drop in sales, but also the reputational loss that follows safety alerts will be weaker

for Friday alerts. The weaker coverage by media and industry experts of Friday alerts implies a

lower probability that the alert leads to a scandal.

Lobbying and the timing of FDA safety alerts

All this means that, if firms could choose when to release safety alerts, they would rather

have them issued on Fridays in that this would reduce the negative consequences associated to

such alerts. Yet, firms do not announce safety alerts, the FDA does. The question is then: can a

firm persuade the FDA into releasing a safety alert on a Friday? To answer this question, we first

need to understand how the FDA itself decides when to communicate safety alerts.

Once the FDA learns about a potential safety concern, its role is to gather as much

evidence and information as possible so that it can determine if the safety concern is indeed

associated with the consumption of the drug in question and what subpopulation of patients is

affected by such safety issues (CDER, 2007). This task is done by the Drug Safety Oversight

Board, a branch of the FDA that is responsible of deciding whether and when emerging drug

safety information should be made public. Only when the Drug Safety Oversight Board believes

that there is enough evidence linking the consumption of the drug with the specific safety

outcome (e.g., a side-effect), and it has enough information about who is affected by those safety

issues, the FDA makes a safety alert communication (CDER, 2007).

In theory then, a firm could influence this process by strategically providing key

information to the FDA relative to the causal link between the drug’s consumption and the safety

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concern, as well as information about which patients are affected by such safety concern. Firms

are likely to have information about this issue—obtained through its pre- and post-marketing

clinical trials—and this information could affect the Drug Safety Oversight Board’s assessment

about when to issue the safety alert. Thus, providing such information to the Drug Safety

Oversight Board is one way in which a firm could influence the timing of safety alert

communications. The problem, however, is that the Drug Safety Oversight Board is a branch of

the FDA to which firms have little access, meaning that firms are likely to be unaware that a

safety evaluation of one of their drugs is taking place. Attending to the FDA’s statutes regarding

the communication of safety information (CDER, 2007), the FDA has no obligation to inform

the firm about the fact that it is evaluating the safety of one of its drugs. The FDA will “intend to

inform the sponsor [the firm marketing the drug] at least 24 hours before the alert is

communicated” but it is not bound to do so. This means that an information provision strategy is

hard to implement, not only because the firm lacks a direct channel with the Drug Safety

Oversight Board, but also because once the firm is aware that a safety evaluation is taking place

it might be too late.

We build on the CPA literature to propose that lobbying activities may provide such a

communication channel between the FDA’s Drug Safety Oversight Board and the firm (Hillman

and Hitt, 1999; Hillman et al., 2004). Corporate lobbying has in fact been defined as an

“information provision strategy”, a definition that is consistent with the Lobbying Disclosure Act

(2 U.S.C. § 1601) that defines lobbying as the sharing of information with policy makers and

agencies by individuals representing the firm interests (Hillman and Hitt, 1999).3 Therefore,

lobbying activities towards the FDA may allow to open a communication channel with the

3 Irrespective of whether they were implemented by the firm itself (e.g., through its public affairs department) or through lobbying consulting agencies.

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agency. Then, this communication channel should allow the firm to provide key information to

the agency with respect to the safety concern being evaluated. This should increase the firm’s

ability to influence the timing of the whole process, and thus when the safety communication

will be made. Moreover, it is important to highlight that such a communication channel may

work in both directions, meaning that information may leak from the agency towards the firm as

well. This should increase the probability that the firm is aware that a safety evaluation on one of

its drugs is taking place, which may give the firm more time to design and implement its

information provision strategy in a more effective manner.

In sum, we propose that firms will rather have safety alerts issued on Fridays by the FDA

to the extent that the negative consequences of such alerts will be weaker. Firm lobbying, we

claim, provides the firm with a potential communication channel with the agency so that it can

implement a more effective information provision strategy. This, we argue, increases the firm’s

ability to influence the timing of safety alert announcements to its advantage. Consequently, we

propose that firm lobbying will increase the probability that a safety alert is issued on a Friday:

Hypothesis 2: Firm lobbying will increase the probability that a safety alert is announced

on a Friday.

Political capital, however, is a finite resource. Lobbying provides a communication

channel with the agency that allows the firm to gain influence on the agency’s decisions. Yet, the

firm cannot use this influence indiscriminately. There is an opportunity cost associated with

using political leverage on the FDA. Then, if firms can only influence a few of the governmental

decision-making processes, they will pick the ones that maximize their benefit. Consequently,

we expect firms to exploit their political influence—i.e., try to control the timing of the FDA’s

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safety communications—for those alerts that will trigger the greatest negative consequences for

the firm: i.e., alerts that refer to severe safety issues. Severe safety problems are those referring

to potential side-effects that may cause dramatic consequences for patients’ health. These

concerns are more likely to be diffused by mass media and healthcare experts, and trigger the

greatest sense of alarm among patients and the healthcare community (Cheah et al., 2007). Thus,

these alerts are the ones that most dramatically affect the reputation of the company (Cheah et

al., 2007). Similarly, these are the alerts to which both patients and doctors will react more

aggressively, leading to the largest drop in sales (Chen et al., 2009). Accordingly, these are the

alerts in which the firm has more to lose if they are not announced on a Friday. Therefore, we

expect that firms will be more likely to take advantage of the political influence provided by

lobbying activities for severe safety alerts. This leads to our final hypothesis:

Hypothesis 3: The positive effect of firm lobbying on the probability that a safety alert is

announced on a Friday will be greater for severe safety alerts.

METHODS

Data

To test our hypotheses, we compiled data from various sources. First, to identify drug

safety alerts we looked into the FDA’s MedWatch website (Carpenter et al., 2012; Cheah et al.,

2007). This web provides information for all safety alerts reported since 1996. Specifically, it

provides information about the date the alert was issued, the drug(s) involved in the alert, the

nature of the safety problem(s), and the FDA’s new prescription recommendations. The

description provided with respect to the nature of the safety concern allowed us to assess the

severity of each safety alert, a measure we used to test our last hypothesis.

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Second, in order to capture coverage and diffusion by healthcare professionals and mass

media we relied on two different datasets. To capture dissemination by healthcare experts we

look at how many healthcare professionals interested in safety-related issues decided to share

safety alerts information. The FDA opened a twitter account in 2011 where it started announcing

safety alerts (@medwatch). Since this twitter account only reports safety-related information, it

is mainly followed by healthcare professionals with a special interest on drug safety. Therefore,

we believe this is a place where we can find healthcare professionals with expertise on safety

issues and capture the extent to which these experts disseminate safety-related information.

Accordingly, we look at the number of retweets of the safety alert communications done by the

FDA through its @medwatch twitter account to capture dissemination of safety-related

information throughout the healthcare community.

To capture the dissemination of safety alerts by mass media we look into the Factiva

database for articles covering safety alerts in U.S. newspapers. Specifically, we searched for all

the articles published in between the day of the alert and six days after the alert, where the name

of the drug appeared in the article. We then read these drug-related articles and kept those where

the article referred to the drug safety alert in question. We build on the assumption that diffusion

of safety alerts by these media outlets captures how quickly the public becomes informed about

safety news.

To identify how much firms are lobbying the FDA we use data from the Center for

Responsive Politics’ OpenSecrets database.4 This database tracks all lobbying activities

disclosed by government mandated reports from registered lobbyists in regard to their lobbying

activities. In accordance with the Lobbying Disclosure Act all lobbyists, both internal and

4 https://www.opensecrets.org/

20

external to the firm, are required to file quarterly reports about their lobbying activities. This

database is available for the 1998 to 2016 period, and these reports include the name of the

client/employer, lobbying expenditures, and importantly for this study, which agency/agencies

were lobbied.

Finally, to create our control measures we draw from the FDA’s orange book database to

find information about the drugs that are in the market and the firms that own each of these

drugs; and the FDA’s @drugs data to obtain information on the regulatory approval of those

drugs (i.e., post-marketing requirements, priority reviews, etc.).

Sample

To create our final list of drug alerts we take the following steps. First, because lobby

data (OpenSecrets) is available since 1998, we look at safety alerts for the 1999 to 2016 period.

Second, we only look at alerts on drugs and do not consider alerts on other products such as

medical devices. Third, we restrict our sample to safety alerts on branded drugs (i.e., not

generics), since for these we can identify the company that owns the drug. Alerts on branded

drugs (not generics) represented around 74% of all drug safety alerts in our studied period.

Fourth, we remove those safety alerts that refer to drugs that are owned by more than one

company. In these cases, since there is more than one firm linked to the drug, we would not

know which firm’s characteristics are affecting our main outcomes, and whose firm’s lobbying

activities will influence the timing of the safety alert announcement. Finally, in those few cases

where we have more than one alert on the same drug on the same day, we “collapse” both alerts

into one single alert. After all these steps, we end up with a sample of 441 drug safety alerts.

For the test in which we look at the diffusion of safety alerts by healthcare experts our

sample is smaller. Because we look at the number of experts that retweet the FDA’s MedWacth

21

tweet informing about a safety alert, and this twitter account was opened in 2011, we can only

look at alerts between 2011 and 2016. The sample for this test includes 139 drug safety alerts.

Measures

Healthcare experts diffusion. As explained above, we look at the number of retweets

done by healthcare experts to safety alert tweets (through the FDA’s MedWatch twitter account).

The FDA opened up the MedWatch twitter account in 2011 to provide a means to disseminate

safety alerts information throughout the healthcare community. Those healthcare professionals

that play the role of opinion leaders when it comes to safety-related information are likely to

follow such twitter account. Thus, we expect that the intensity with which these healthcare

experts share and comment the safety alert information through their social median accounts will

capture the extent to which such safety alert is diffused.

Mass media diffusion. To capture media diffusion we look at the number of articles that

mention each safety alert the day after the FDA makes the announcement. To obtain such

information we looked at newspaper articles in the U.S. using the Factiva dataset. Our final

measure consistent in the total number of articles in the three days after the safety alert

announcement.5

Friday. We create a dummy variable that takes a value of 1 if the safety alert was

published on a Friday and 0 otherwise. It may be the case that an alert is released on a Thursday

and that Friday is a holiday. We found three of such cases and decided to treat those days as a

Friday in that attention should also we weaker before a holiday.6

5 We tried alternative windows in the robustness tests section. We also tried an alternative measure consisting on a dummy variable taking the value of 1 if there were no news at all on the alert and 0 otherwise (see robustness tests section). 6 Removing these three observations provides almost identical results (available upon request).

22

Lobbying the FDA. We first gather all lobbying activities for those public and private

pharmaceutical firms that suffered a drug safety alert. Second, we only account for lobbying

efforts that target the FDA, since this is the kind of lobbying that may allow the firm influence

the decision on when to announce drug safety alerts. We do not expect, for example, that

lobbying the Department of Defense or the Department of Transportation will help firms

influence FDA decisions on safety alerts. Then, we create a dummy variable that takes the value

of 1 if the firm lobbied the FDA and 0 otherwise.7 We look whether the firm lobbied the FDA in

the two years before the safety alert, assuming that such time window captures the presence of

political ties with the agency.8

Severity. To capture each safety alert’s severity we constructed a dummy variable that

took the value of 1 when the safety risks reported in the safety alert communication refer to

major (life-threatening) health problems and 0 otherwise.

Controls. We include several controls in our tests. At the firm level, we add the following

measures. First, we control for the number of branded drugs the firm got approved in the last ten

years as a proxy for firm size (prior drugs firm). We obtain this information from the FDA’s

orange book, which lists all drug approvals for each firm. Second, we control for the number of

safety alerts the firm has suffered in all of its drugs in the previous five years, which is obtained

from the Medwatch website described above (prior alerts firm). We expect that the presence of

prior safety alerts on the same firm may affect how broadly a new safety alert is covered and

disseminated. Third, we control for whether the firm is publicly traded or not, as a way to capture

the firm’s visibility (firm public). Finally, for our tests looking at how lobbying the FDA affects

7 We tried an alternative measure consisting on the actual amount of lobbying expenditures and we found similar support for our theory (see robustness tests section). 8 We tried two alternative windows, 1-year and 3-year, and found similar support for our theory (see robustness tests section).

23

the probability that a safety alert is announced on a Friday, we also include a control for how

much the firm has lobbied agencies other than the FDA (other lobbying). This way we rule out

the possibility that our measure of lobby is in reality capturing some firm unobserved factor.

At the drug-alert level, we control for the following factors. First, we include a measure

of the number of safety alerts the drug had in the previous five years, which we obtain from the

MedWatch website (prior alerts drug). The presence of previous alerts on the same drug may

affect how doctors and patients react to new alerts. Second, we control for whether the drug

required post-marketing tests after approval (post-marketing). In some cases, the manufacturer is

required by the FDA to undertake post-marketing clinical trials to assess some safety aspects

about the drug that could not be assessed during drug development, and this may affect how the

healthcare community reacts to safety news. Third, we also include the average number of

adverse events on the drug in the year before the alert, to control for the safety characteristics of

the drug before the communication (prior adverse events). Fourth, we add a dummy variable that

takes the value of 1 if there were other alerts communicated that same day and 0 otherwise (other

alerts) and another dummy that takes the value of 1 if the alert in question refers to more than

one single drug in its communication and 0 otherwise (other drugs). Finally, we include a control

for whether the drug enjoyed a priority review (priority review) and the logged number of days

since the FDA approved the drug (time since approval). If the FDA is seen as needlessly fast-

tracking the drug, this could be seen as the FDA acting too quickly. Likewise, if the drug has

problems soon after the FDA approved the drug as safe and effective, the FDA might be seen in

a negative light. In both cases the FDA may have an interest to communicate the alert in a day

that the reaction will be weaker, i.e., a Friday.

Analysis

24

Identification strategy

For our tests on the effect of Friday on healthcare experts diffusion and mass media

diffusion, due to the count nature of these outcomes, we relied on a negative binomial estimation

(H1a and H1b).9 When we test the effect of lobbying the FDA and alert severity on Friday, given

the binary nature of this dependent variable, we use a logistic regression estimation (H2 and H3).

We include year fixed-effects to control for temporal dynamics in the reaction to drug safety

alerts in all of our estimations.

Note that the regressions on the effect of Friday on healthcare experts diffusion and mass

media diffusion may report biased coefficients if the day in which alerts are announced is not

exogenous. According to our theory, firms may be influencing the announcement day. Hence,

there may be a positive correlation on the likelihood that an alert is announced on a Friday and

the importance of the alert for the firm, i.e., how much coverage the alert will receive. This

means that the coefficient of the Friday variable may be upwards biased. In the results section

below we show how the effect of Friday on the number of retweets and media articles is negative

and significant. Therefore, if this coefficient is upwards biased, the real impact of announcing

alerts on Fridays should be even more negative than what our estimations display.

RESULTS

In Table 1 we report descriptive statistics and correlations. Before undertaking our

regression analysis, we examine the validity of our story by performing some simple descriptive

comparisons with our final sample. Specifically, we look into the distribution of safety alerts

along the days of the week. In Figure 1 we show such distribution and how there are more alerts

9 We also tried an OLS estimation and the results provide similar support for our theory (available upon request).

25

announced as the days of the week go on, with Friday having more announcements than any

other day with having about 27% of all announcements.

[Insert Table 1 and Figure 1 about here]

However, we argue that the announcement of Fridays will not be random: we expect

politically active firms to be more likely than politically inactive firms to get announcements on

Fridays. Figures 2a and 2b show the distribution of safety alerts for both types of firm. We can

see a drastic difference between the firms that are politically active and those that are not. In

Figure 2b, which includes safety alerts on drugs owned by firms that lobby the FDA, there is a

greater frequency of Fridays. A Kolmogorov–Smirnov test shows that this distribution is

statistically different from a distribution of available weekdays in that same period at the 0.1%

level. Conversely, for firms that do not lobby the FDA (Figure 2a), the distribution of alert

announcements is relatively uniform. Although Fridays are still the most often day, a

Kolmogorov–Smirnov test shows that this distribution is not statistically different from a

distribution of available weekdays. The fact that the distribution of alerts throughout the

weekdays for non-lobbying firms is not different from a distribution of available weekdays

suggests that the FDA does not seem to have a “natural” tendency to release safety alerts on a

particular day.

[Insert Figures 2a and 2b]

Finally, because we predict this difference to be greater for severe safety alerts, we look

at this in Figures 3a and 3b, where we show the distribution of severe safety alerts only along

weekdays for lobbying and non-lobbying firms. When find that Friday alerts occur in

approximately 35% of the cases for firms that Lobby but in only 22% of the cases for firms that

do not lobby.

26

[Insert Figures 3a and 3b]

In the first four columns of Table 2 we report the effect of Friday on healthcare experts

diffusion and mass media diffusion. If Friday alerts receive less attention, then we should see

fewer people retweeting MedWatch safety alerts when the announcement is made on Fridays.

Likewise, we would expect fewer news articles being written about the alert for Friday alerts

than for the alerts announced any other weekday. The results in Table 2 provide support to our

predictions in Hypotheses 1a and 1b. Models 1 and 3 just include the control variables. Models 2

and 4 show that Friday alerts have fewer retweets and media articles (β = -0.501, p < 0.01 and β

= -0.261, p < 0.01 respectively).

[Insert Table 2 about here]

In Models 5, 6 and 7 of Table 2 we estimate the effect of lobbying the FDA on the

probability that the alert is released on a Friday. Model 5 just includes control variables. In

Model 6 we include lobbying the FDA, and find that this variable has a positive and significant

effect on the probability that an alert is communicated on a Friday (β = 0.834, p < 0.01). This

evidence supports hypothesis 2. Finally, in Model 7 we add the interaction between lobbying the

FDA and safety alert severity. We find that this interaction has a positive and marginally

significant effect on the probability of Friday (β = 1.221, p < 0.10). This finding provides partial

support for hypothesis 3. These results mimic the descriptive analysis we provided above:

lobbying is positively and significantly associated with an increased likelihood of the FDA

releasing an alert on Friday, and this effect is even stronger for severe safety alerts.

Graphical analysis

While Table 2 shows the statistical relationship between lobbying the FDA and Friday,

the interpretation of logistic models is not straightforward. For nonlinear estimations, a graphical

27

interpretation of the size and significance of the effects is necessary.10 For this we use a

simulation-based approach developed by King, Tomz, and Wittenberg (2000), which was

introduced into the management literature by Zelner (2009). We analyze the main and interaction

effects by taking 100,000 post-estimated draws from a random multivariate normal distribution

using the coefficients and variance-covariance matrices from our estimations in Models 6 and 7.

We then multiply the coefficients obtained in each draw with the real values of the underlying

data, but altering our main explanatory variables lobbying the FDA and safety alert severity. This

creates a statistical counterfactual that allows us to estimate the predicted probability of an alert

being on a Friday depending on whether the firm lobbied or whether the drug alert was severe,

while everything else for each observation stayed the same. Figure 4a shows the results for the

main effect of firm lobbying (Model 6) while Figure 4b shows the results for the interaction

effect (Model 7).

[Insert Figures 4a and 4b about here]

These graphical analyses provide further evidence in support for H2 and H3. First, Figure

4a shows how lobbying the FDA strongly increases the predicted probability of an alert being on

Friday. The baseline percentage of an alert being on Friday with no lobbying is about 22%.

When firms lobby this increases up to 36%, which corresponds to over a 63% increase in the

likelihood of a Friday alert. This suggests that having political connections with the FDA

increases the probability of receiving a favorable alert date. Looking at Figure 4b we can see that

this relationship is much stronger for severe safety alerts. Figure 4b shows that, for severe alerts,

lobbying increases the probability of Friday from about 12% to 40% (a 233% increase).

10 The interpretation of the size and statistical significance of the main and interaction coefficients is not straightforward in nonlinear estimations in that the relationship between an independent variable and a dependent variable depends on the values of the other variables included in the model (Ai and Norton, 2003; Hoetker, 2007).

28

Robustness tests

We try several alternative measures for media coverage. First, we look into different time

windows beyond the three-day window used in our main tests. We look at the number of articles

covering the safety alert in the one, two, four, five, and six days right after the announcement

also. All these measures provide similar support to H1b. Second, we look at a dummy variable

taking the value of 1 if there were no articles at all covering the alert and 0 otherwise. We run a

logistic regression on this alternative measure and again find support for H1b.

In addition, to ensure that our results are not simply a byproduct of a specific

specification of lobbying, we set out to test several alternative specifications of our lobbying the

FDA variable in Table 3. Here we look at three alternative ways to calculate lobbying: 1) the

amount of money the firm spent lobbying the FDA in the previous two years, 2) a dummy

variable that takes a value of 1 if the firm lobbied the FDA within the previous year, and 3) a

dummy variable that takes a value of 1 the firm lobbied the FDA in the previous three years.

Each of these measures is designed to assure that we are capturing something stable about the

relationship between the firm and the FDA. Our results are essentially identical across all of

these specifications: both the main effect of lobbying the FDA and its interaction with safety alert

severity are positive and significant (available upon request). This helps assure that our results

are not being driven by an arbitrary specification of lobbying but rather from a stable relationship

between the firm and the FDA.

Moreover, as with all studies looking at the impact of lobby on policy outcomes, our

study needs to be mindful about endogenity (De Figueiredo and Richter, 2014; Richter et al.,

2009). However, it is difficult to imagine a story about reverse-causality. A firm would need to

realize years in advance that it might receive an alert (on a Friday) and start lobbying more. This

29

seems implausible. Nonetheless, to safeguard against this potential problem, we run a Heckman

selection model in order to account for a potential endogenous selection into lobbying. In the

first stage, we regress firm lobbying on our control variables and the amount of campaign

donations to politicians in the same time period as an instrument. Because firms that engage in

one type of political strategy are likely to engage in other political strategies as well, we assume

that firms that engage more in political donations are more likely to lobby the FDA. Moreover, it

is unlikely that political donations will influence the FDA’s decision on when to announce the

FDA given that donations to candidates allow firm to enjoy political leverage once these

candidates are elected, something that will take place after the alert is announced. Hence,

lobbying in other activities satisfies the two conditions needed to be a good instrumental

variable: relevance and exogeneity. Next, in the second step, we re-estimate Friday as a function

of lobbying the FDA including the inverse Mills ratio calculated from the first step (Hamilton

and Nickerson, 2003; Shaver, 1998).11 We find similar results: firms that are politically active

with the FDA are still more likely to receive alerts on Fridays (available upon request).

PUBLIC HEALTH IMPLICATIONS: PATIENT ADVERSE EVENTS

So far, we have shown that politically connected firms are much more likely to get FDA

drug alerts on Fridays, the day in which attention is at its lowest. Yet, there might be a potential

unattended consequence: this increases the prevalence of the kind of alerts (Fridays) that may be

the least effective in achieving their function. The goal of safety alerts is to inform patients and

doctors of new side-effects so they can adjust their prescription and consumption behavior

accordingly and stop experiencing those negative effects (Dusetzina et al., 2012; Hurren et al.,

11 The inverse Mills ratio, λ, was calculated as λ1=(ϕ(βX))/(Φ(βX)) when lobbying the FDA is equal to 1 and λ0=−ϕ(βX)/([1−Φ(βX)]) when lobbying the FDA is equal to 0, where ϕ(ꞏ) is the standard normal pdf and Φ(ꞏ) is the standard normal cdf.

30

2011). Yet, if Friday safety alerts experience a narrower and slower diffusion then it could be the

case that those alerts announced on Fridays are less effective in reducing patients’ adverse

reactions. By increasing the prevalence of Friday alerts, the FDA would be making this problem

worse.

Data. We explore this potential public health implication using the FDA’s Adverse Event

Reporting System (FAERS), a database providing information about adverse events suffered by

patients on specific drugs. This data is available since 1998 and provides information on the day

in which a given adverse event was experienced, the severity of such adverse event (e.g.,

whether it led to death, hospitalization, etc.), and the drug that caused the adverse event. These

adverse events are reported by doctors, pharmacists, nurses, manufacturers, and patients, and

provide a viable proxy for the prevalence of side-effects with marketed drugs. We explore if

alerts indeed lead to a reduction in adverse events (i.e., if safety alerts are effective), and if this

reduction is weaker for Friday alerts.

Sample. For this, we match the 441 drug-alerts in our sample with the FAERS database.

This matching, however, is not straightforward. Often times, the name of the drug would be

abbreviated in the FAERS database. Take for example Adderall: there is Adderall or Adderall

XR. Yet, the FAERS database will just report Adderall as the drug causing the reported adverse

event. Therefore, we match adverse events to drug safety alerts by looking at the first name of

the drug only (e.g., Adderall). This means that we may be incorrectly assigning adverse events to

certain drug safety alerts. We believe this is mainly adding noise, biasing our estimates

downwards and making it harder to find statistically significant effects.

Next, to explore whether the number of reported adverse events is reduced after a safety

alert, and if this reduction depends on the weekday in which the safety alert is announced (Friday

31

versus non-Friday), we need to transform our sample of 441 alerts. Since we want to compare the

number of adverse events reported in the days before and after the drug alert, we need to look

into several days, some before and some after the announcement, for each safety alert. It is

unclear, however, how many days before and after the alert we need to look at to identify

differences in the responses to Friday and non-Friday alerts. This depends on how long it takes

for doctors and patients to react and incorporate safety news into their drug prescription and

consumption behavior respectively. We adopt a conservative approach and look into the three

months before and after the announcement.12 Moreover, because some drugs received more than

one alert in our period of study, it is important to account for the possibility that reactions to a

given drug alert are “contaminated” by the temporal proximity to another alert on the same drug.

Accordingly, in those cases where we have two alerts on the same drug whose windows overlap,

we remove those two alerts from the sample.

Measures. To identify the day in which a patient suffered an adverse event with a specific

drug, we look at the specific information included in the FAERS dataset where it provides the

date in which the patient reported experiencing the adverse event. This date is different from the

date in which this adverse event was reported to the FDA, a date that is also provided in this

database. Although in the vast majority of the cases, the date in which the adverse event was

reported to the FDA is very close to the date when the patient experienced the event, in some

other cases these two dates are very far apart. Since we are interested on how drug alerts impact

whether patients keep experiencing the same complications associated with the drug, we use the

date the patients experienced the adverse event to create our main outcome in this first test.

12 We look also into 1-month and 6-months windows and the results are substantially the same, although with smaller differences between Friday and non-Friday alerts as we increase the window size (available upon request).

32

Often times, the reports of these adverse events spike in time, with no events being

reported and then suddenly dozens being reported on a random day. To account for the peaks and

valleys in reporting, we add one to all of these variables and take the natural logarithm in order

to control for its skewed distribution. Moreover, we look into three different types of adverse

events: total adverse events, serious adverse events, and death adverse events. Total adverse

events includes all adverse events reported in the FAERS database. This is the broadest measure:

it does not differentiate between a headache and a death. We also look at serious adverse events,

which we measure by looking at adverse events that were recorded as death, hospitalization,

disability, life-threatening, and/or congenital anomaly. Lastly, we look at death adverse events,

which only captures those adverse events that led to the death of the patient. The last two

measures can only be used for safety alerts announced after 2004, the year in which information

about the seriousness of concerns become available.

Analysis. We use a difference-in-differences approach where we compare the number of

adverse events before and after the alert announcement. We rely on an ordinary least squares

(OLS) regression to estimate the amount of adverse events on a given day as a function of (1) a

dummy variable (after) that takes a value of 1 for those days after the alert was announced, (2) a

dummy variable (Friday) that takes a value of 1 if the alert was announced on a Friday, and (3)

all the control variables used in our tests. Thus, we expect after to have a negative coefficient: if

safety alerts are effective, we should find less adverse events reported after the alert. Yet, we

expect this reduction to vary depending on the weekday in which the alert is announced.

Specifically, if less people are paying attention to Friday alerts, we expect these alerts to generate

33

a lower reduction in adverse events, meaning that the coefficient of the interaction between after

and Friday should be positive. We use fixed-effects at the year and day levels.13

Results. The results of these estimations are reported in Table 3. Overall, for all three

types of adverse events, we find strong evidence that safety alerts are in average effective: the

coefficient of the variable after is always negative and significant at the 1% level. In addition, we

find that the interaction between the variables Friday and after is positive and significant at the

1% level in all three models, suggesting that Friday alerts do not reduce adverse events in the

same amount as non-Friday alerts do. Note that the interaction coefficient saps a large portion of

the benefits of the alert (coefficient of the main effect of after), and in the case of serious and

death adverse events it essentially negates all of the benefits gained by the alert. This suggests

that alerts may not reduce serious adverse events and deaths when they are released on Fridays.

[Insert Table 3 about here]

DISCUSSION

This study shows that Friday safety alerts are paid less attention than alerts taking place

other days of the week. Friday alerts are shared less intensively by healthcare experts and are

associated with fewer articles in mass media, suggesting that healthcare professionals and media

are paying less attention to Friday safety news. We argue this decreased attention is why firms

are pushing for Friday announcements by the FDA. Indeed, the alerts announced on Fridays are

disproportionately associated with firms that have been actively lobbying the FDA in the recent

years. This suggests that firms who are politically connected are able to affect the timing of

safety alerts. Furthermore, we show that releasing drug safety alerts on Fridays is associated with

increased patient problems when compared to alerts released any other weekday.

13 We tried an alternative specification including firm, drug, and alert fixed-effects and the results of these estimations provide similar support (available upon request).

34

Theoretical contributions

We believe our paper contributes to several literatures. First, we show how corporate

political activities allow firms to influence public officials’ communication strategies. While the

CPA literature has mostly focused on how firms’ political efforts can shape the content of public

policy, our paper shows that there is an additional dimension of public activities—

communication—that firms can influence through their non-market strategies. Through

lobbying, firms are able to persuade public officials to implement similar impression

management tactics to the ones they implement in their communication of internal corporate

news. In addition, our paper builds on the assumption that political capital is limited, and as any

scarce resource, used strategically. Our evidence suggests that firms are more likely to leverage

on their political influence when they have more to gain from it.

Second, our paper bridges the CPA and the impression management literatures. It shows

how firms can implement impression management tactics even when a third party, outside the

firm’s control, decides upon the communication strategy. Moreover, this paper shows how

timing can be a rather effective impression management tactic. Until now, the analysis of how

the timing of news affects stakeholder reactions has been largely relegated to the finance and

accounting literatures in the context of earning reports and acquisition announcements. Our study

shows how the day of the week in which events take place strongly influences the extent to

which media and industry experts cover and diffuse such events.

Practical implications

We believe our paper has important policy implications in the context of public health.

Extant evidence suggests that prescription drugs cause about 2 million hospitalizations and

100,000 deaths every year in the United States due to known side-effects (Lazarou, Pomeranz,

35

and Corey, 1998; Light, Lexchin, and Darrow, 2013). This means that FDA safety

communications are not always effective. Our study suggests one plausible reason why some of

these alerts are not effective in reducing patients’ adverse reactions: those alerts announced on

Fridays are not diffused as quickly and broadly as alerts announced any other weekday. This

finding leads to clear policy recommendations: (1) alerts should not be released on Fridays and

(2) the method through which the healthcare community gets informed about safety issues

should be improved.

Limitations and future research

Our paper has several limitations. First, we look into a very unique context: drug safety

alerts. It is unclear whether our conclusion that firms’ lobbying activities help firms influence the

timing of policy decisions will apply into other policy decisions. Second, we proxy diffusion of

safety news throughout the healthcare community by looking at retweets and media articles.

These are just two of all the many channels through which this type of information is diffused.

Therefore, it is unclear if our proxies provide a valid approach to capture the presence of a Friday

effect in the dissemination of safety news. Future research with richer and more sophisticated

data could shed light on this issue. Finally, we argue that lobbying the FDA explains the timing

of safety alerts, but the mechanism through which this happens is unclear. How does lobby work

is still a black box, and thus a limitation of almost every study in the CPA literature.

Overall, we believe our study provides a novel approach towards understanding the

extent to which political efforts allow to influence the timing of policy news, and the potential

implications of such strategies. We hope this will spur further research on this relevant topic.

36

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41

Table 1 Descriptive Statistics and Correlations a

N = 441 Mean s.d. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 Mass media diffusion 7.67 6.57 1.00

2 Healthcare experts diffusion 3.99 4.70 0.27 1.00

3 Friday 0.27 0.44 -0.25 -0.12 1.00

4 Lobbying the FDA 0.33 0.47 0.00 0.09 0.17 1.00

5 Other lobbying 4.22 5.49 -0.04 -0.01 -0.02 -0.11 1.00

6 Severity 0.42 0.36 0.09 0.13 -0.08 0.21 0.25 1.00

7 Prior drugs firm 1.95 0.89 0.08 0.08 -0.18 0.13 0.07 -0.06 1.00

8 Prior alerts firm 6.18 7.60 -0.17 0.09 0.09 0.28 -0.03 0.06 0.22 1.00

9 Firm public 0.46 0.50 0.03 0.22 -0.03 0.38 0.24 0.25 0.31 0.18 1.00

10 Prior alerts drug 0.94 1.41 0.00 0.04 0.00 0.13 0.02 -0.02 0.19 0.45 0.05 1.00

11 Post-marketing 0.39 0.49 0.13 0.19 -0.10 0.06 -0.15 0.06 0.34 -0.05 -0.04 0.19 1.00

12 Prior adverse effects 6.17 2.25 0.04 0.16 -0.03 0.12 0.07 0.02 0.17 0.33 0.19 0.22 -0.01 1.00

13 Other alerts 0.27 0.44 -0.02 0.00 0.02 -0.01 0.22 0.25 0.09 0.12 0.10 0.05 0.02 0.23 1.00

13 Other drugs 0.24 0.43 0.23 0.09 -0.22 -0.16 -0.32 -0.29 0.07 -0.29 -0.16 -0.12 0.12 -0.23 -0.22 1.00

13 Priority review 0.25 0.43 -0.14 0.13 0.05 0.20 0.19 0.27 0.02 0.12 0.18 0.16 0.24 0.04 0.25 -0.31 1.00

14 Time since approval 7.37 0.96 0.08 -0.03 0.20 0.08 -0.01 -0.11 -0.21 0.10 0.03 -0.17 -0.36 0.46 -0.13 -0.15 -0.24 1.00

a Descriptive statistics and correlations with Healthcare experts diffusion are calculated on a sample of 139 observations .

42

Table 2 Main Results a, b

a Significance levels: ** p < 0.01, * p < 0.05, + p < 0.10. b All models include alert year fixed-effects. Robust standard errors in parentheses.

Dependent Variable Healthcare experts diffusion Mass media diffusion Friday Model 1 2 3 4 5 6 7

Intercept 0.809

(0.742) 0.615

(0.715) 1.323** (0.455)

1.387** (0.445)

-2.480+ (1.290)

-2.388* (1.327)

-2.301 (1.461)

Friday - -0.501** (0.181)

- -0.261** (0.101)

- - -

Lobbying the FDA - - - - - 0.834** (0.299)

0.274 (0.459)

Lobbying the FDA x Severity - - - - - - 1.221+ (0.764)

Other lobbying - - - - -0.020 (0.024)

-0.002 (0.025)

0.001 (0.027)

Severity 0.433* (0.174)

0.358* (0.177)

0.136 (0.126)

0.130 (0.125)

-0.302 (0.377)

-0.332 (0.381)

-0.856+ (0.487)

Prior drugs firm 0.055

(0.079) 0.058

(0.076) -0.001 (0.061)

-0.016 (0.062)

-0.306+ (0.175)

-0.363* (0.181)

-0.349+ (0.193)

Prior alerts firm 0.017

(0.073) 0.008

(0.070) 0.002

(0.009) 0.002

(0.009) -0.024 (0.026)

-0.037 (0.026)

-0.041 (0.029)

Firm public 0.042

(0.151) 0.039

(0.145) 0.201* (0.085)

0.229* (0.083)

0.512+ (0.265)

0.162 (0.283)

0.188 (0.308)

Prior alerts drug 0.123

(0.120) 0.154

(0.117) 0.108** (0.034)

0.111* (0.034)

-0.028 (0.106)

-0.030 (0.108)

-0.026 (0.105)

Post-marketing 0.111

(0.189) 0.129

(0.183) -0.105 (0.101)

-0.101 (0.099)

0.321 (0.277)

0.351 (0.278)

0.318 (0.280)

Prior adverse effects -0.011 (0.040)

-0.019 (0.036)

0.098** (0.020)

0.095** (0.020)

-0.026 (0.067)

-0.042 (0.066)

-0.032 (0.066)

Other alerts 0.224

(0.283) 0.361

(0.303) 0.204+ (0.111)

0.240* (0.110)

1.042** (0.326)

1.047** (0.327)

1.067** (0.318)

Other drugs 0.189

(0.169) 0.084

(0.162) 0.134

(0.130) 0.100

(0.126) -0.670 (0.353)

-0.574 (0.354)

-0.605 (0.366)

Priority review -0.295 (0.191)

-0.230 (0.188)

-0.044 (0.098)

-0.034 (0.101)

0.046 (0.322)

0.050 (0.324)

0.095 (0.315)

Time since approval 0.153+ (0.091)

0.199* (0.089)

-0.122* (0.051)

-0.118* (0.051)

0.152 (0.153)

0.132 (0.152)

0.141 (0.157)

Observations Log Likelihood

139 -400.8

139 -397.3

441 -989.8

441 -986.2

416 -214.6

416 -210.9

416 -209.5

43

Table 3 Effectiveness of safety alerts a, b

a Significance levels: ** p < 0.01, * p < 0.05, + p < 0.10. b All models include alert year and day fixed-effects. Robust standard errors in parentheses.

Dependent Variable Total adverse events Serious adverse events Death adverse events Model 1 2 3 4 5 6 8 9 10

Intercept 0.331** (0.025)

0.331** (0.025)

0.336** (0.025)

0.024 (0.023)

0.019 (0.023)

0.024 (0.023)

-0.072** (0.013)

-0.073** (0.013)

-0.071** (0.012)

After -0.035** (0.005)

-0.035** (0.005)

-0.046** (0.006)

-0.020** (0.005)

-0.020** (0.005)

-0.028** (0.005)

-0.007** (0.003)

-0.007** (0.003)

-0.012** (0.003)

Friday - 0.052** (0.006)

0.031** (0.008)

- 0.014* (0.006)

0.002 (0.008)

- -0.010** (0.003)

-0.020** (0.004)

After x Friday - - 0.042** (0.011)

- - 0.031** (0.011)

- - 0.019** (0.006)

Severity -0.054** (0.007)

-0.052** (0.008)

-0.052** (0.008)

-0.074** (0.007)

-0.073** (0.007)

-0.073** (0.007)

-0.038** (0.004)

-0.038** (0.004)

-0.038** (0.004)

Prior drugs firm 0.019** (0.003)

0.023** (0.003)

0.023** (0.003)

-0.003 (0.003)

-0.002 (0.003)

-0.002 (0.003)

-0.017** (0.002)

-0.018** (0.002)

-0.018** (0.002)

Prior alerts firm 0.002** (0.001)

0.001** (0.001)

0.001** (0.001)

0.004** (0.001)

0.004** (0.001)

0.004** (0.001)

0.003** (0.001)

0.003** (0.001)

0.003** (0.001)

Firm public 0.019** (0.005)

0.016** (0.005)

0.016** (0.005)

-0.012** (0.005)

-0.014** (0.005)

-0.014** (0.005)

-0.001 (0.003)

-0.001 (0.003)

-0.001 (0.002)

Prior alerts drug 0.030** (0.003)

0.030** (0.003)

0.030** (0.003)

0.032** (0.003)

0.032** (0.003)

0.032** (0.003)

0.004+ (0.002)

0.004+ (0.002)

0.004+ (0.002)

Post-marketing 0.002

(0.006) -0.001 (0.006)

-0.001 (0.006)

0.011+ (0.005)

0.010+ (0.005)

0.010+ (0.005)

0.018** (0.003)

0.019** (0.003)

0.019** (0.003)

Prior adverse effects 0.917** (0.004)

0.915** (0.004)

0.915** (0.004)

0.602** (0.004)

0.602** (0.004)

0.602** (0.004)

0.181** (0.003)

0.181** (0.003)

0.181** (0.003)

Other alerts -0.054** (0.007)

-0.062** (0.007)

-0.062** (0.007)

-0.048** (0.007)

-0.051** (0.007)

-0.051** (0.007)

0.021** (0.004)

0.023** (0.004)

0.023** (0.004)

Other drugs -0.006 (0.006)

-0.006 (0.006)

-0.006 (0.006)

-0.014* (0.006)

-0.014* (0.006)

-0.014* (0.006)

-0.007* (0.003)

-0.007* (0.003)

-0.007* (0.003)

Priority review -0.009 (0.006)

-0.009 (0.006)

-0.009 (0.006)

0.033** (0.006)

0.034** (0.006)

0.034** (0.006)

0.051** (0.003)

0.051** (0.003)

0.051** (0.003)

Time since approval -0.032** (0.002)

-0.033** (0.002)

-0.033** (0.002)

0.007** (0.002)

0.007** (0.002)

0.007** (0.002)

0.009** (0.001)

0.009** (0.001)

0.009** (0.001)

Observations R2

78,554 0.530

78,554 0.530

78,554 0.531

62,264 0.407

62,264 0.407

62,264 0.407

62,264 0.178

62,264 0.178

62,264 0.178

44

Figure 1. Distribution of safety alerts.

Figure 2a and 2b. Distribution of safety alerts as a function of FDA lobbying.

Figure 3a and 3b. Distribution of severe safety alerts as a function of FDA lobbying.

Monday Tuesday Wednesday Thursday Friday

FDA Drug Alert Announcement Day

% o

f FD

A A

nno

unc

emen

ts

0.0

00

.05

0.1

00.

15

0.2

00

.25

Monday Tuesday Wednesday Thursday Friday

FDA Announcement Day without Lobbying

% o

f FD

A A

nno

unc

emen

ts

0.0

00.

05

0.1

00.

15

0.2

00

.25

0.3

00.

35

Monday Tuesday Wednesday Thursday Friday

FDA Announcement Day when Firms Lobby

% o

f FD

A A

nno

unc

emen

ts

0.0

00.

05

0.1

00.

15

0.2

00

.25

0.3

00.

35

Monday Tuesday Wednesday Thursday Friday

Severe Alerts Announcement Day without Lobbying

% o

f F

DA

Ann

oun

cem

ent

s

0.0

00

.05

0.1

00.

15

0.20

0.2

50.

30

0.3

5

Monday Tuesday Wednesday Thursday Friday

Severe Alerts Announcement Day when Firms Lobby

% o

f F

DA

Ann

oun

cem

ent

s

0.0

00

.05

0.1

00.

15

0.20

0.2

50.

30

0.3

5

45

Figure 4a. Effect of Lobbying the FDA on the probability of Friday.

Figure 4b. Effect of lobbying the FDA on the probability of Friday for severe safety alerts.

Friday Alerts & Lobbying

Predicted Probability of Fr iday Alert

Den

sity

0.0 0.1 0.2 0.3 0.4 0.5 0.6

05

1015

●

●

LobbyingNo Lobbying

Friday Alerts, Lobbying, and Alert Severity

Predicted Probability of Fr iday Alert

Den

sity

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

02

46

810

●

●

Severe Alert − LobbyingSevere Alert − No Lobbying