Asymmetric Social Interactions in Physician Prescription

Behavior: The Role of Opinion Leaders

Harikesh Nair

Assistant Professor of Marketing, Graduate School of Business

Stanford University, 518 Memorial Way, Stanford, CA 94305

Phone: 650-723-9675; Fax: 650-725-7979

Puneet Manchanda

Associate Professor of Marketing, Ross School of Business

University of Michigan, 701 Tappan St., Ann Arbor, MI 48109

Phone: 734-5591; Fax: 734-936-8716

Tulikaa Bhatia

Assistant Professor of Marketing, Rutgers Business School

94 Rockefeller Road, Piscataway, NJ 08854, Phone: 732-445-5274

Past versions: June 2006, May 2008

This version: December 3, 2008

Abstract

We quantify the impact of social interactions and peer effects in the context of prescription choices by physicians. Using detailed individual-level prescription data, along with self-reported social network information, we document that physician prescription behavior is significantly influenced by the behavior of research-active specialists, or “opinion leaders” in the physician’s reference group. We leverage a natural experiment in the category, whereby new guidelines released about the therapeutic nature of the focal drug generated conditions where physicians were more likely to be influenced by the behavior of specialist physicians in their network. We find important, statistically significant peer effects that are robust across model specifications. We use the estimates to measure the incremental value to firms of directing targeted sales-force activity to these opinion leaders, and present estimates of the social multiplier of detailing in this category. KeyKeyKeyKey----wordswordswordswords: Social Interactions, Peer effects, Social Multiplier, Contagion, Physician Prescription Behavior, Pharmaceutical Industry.

* The authors are listed in reverse alphabetical order. The authors would like to thank Ron Burt,

Pradeep Chintagunta, Tim Conley, Wes Hartmann, Peter Reiss, Christophe Van den Bulte, Raphael

Thomadsen and seminar participants at Berkeley (Haas), Chicago GSB (O&M), Christian Albrechts

University, Erasmus, Michigan (Information Systems; College of Pharmacy) and participants at Frank

M. Bass (Dallas, 2007), INFORMS Practice of Marketing Science (Wharton, 2007), IOFest (Stanford,

2006), Marketing Science (Pittsburgh, 2006) and SICS (Berkeley, 2006) conferences, and at the 7th

Choice Symposium (Wharton, 2007) for feedback; and an anonymous pharmaceutical company for

providing the data. Manchanda would like to thank the Kilts Center for Marketing and the True North

Communications faculty research fund at the University of Chicago for research assistance. The

authors can be contacted via e-mail at harikesh.nair@stanford.edu (Nair),

pmanchan@bus.umich.edu (Manchanda) and tulikaa@rci.rutgers.edu (Bhatia). The usual

disclaimer applies.

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1. Introduction

Marketers, sociologists and economists have traditionally been interested in the role of

interpersonal communication (i.e., communication outside the firm’s control) on consumer

choice and consumption behavior. These interactions have been variously labeled as “peer-

effects,” “contagion” and “word-of-mouth effects.” In this paper, we test and provide empirical

support for asymmetric peer-effects. These effects arise when some consumers exert a

stronger influence on the attitudes and behavior of other consumers than vice versa. Such

consumers have typically been labeled “opinion leaders” in the literature (Rogers 2003,

Chapter 8). There is little research in marketing that has tested for the existence of these

asymmetric peer-effects.

The context of our analysis is prescription drug choice by physicians. An asymmetric

social interaction or “peer effect” arises in this setting because non-specialist physicians may

rely on prominent physicians, the “opinion leaders,” to help reduce the uncertainty around

their prescription choices. The role of opinion leaders becomes most salient when changes

occur in the therapeutic environment, as these typically lead to increased uncertainty about

drug efficacy among the non-specialist physicians. The pharmaceutical industry believes in

the existence of such opinion leaders, and has invested in targeting marketing activities at

opinion leaders (CIE 2004). However, to date, there is little empirical evidence that opinion

leaders “matter” i.e., significantly influence the opinions and behavior of other physicians.

Coleman, Katz and Menzel 1966 (the classic study in this field) found no asymmetries in peer

effects between nominators and their opinion leader’s adoption pattern for a new drug.

Recent work using the same data as that study found no peer effects at all (Van den Bulte

and Lillien 2001). Finally, using simulations based on computational models of network

tipping, Watts and Dodds (2007) also find little or no role for opinion leaders. Our main

empirical question therefore is to test for, and to measure the extent of asymmetric peer

effects in this category. We then use our analysis to explore the implications of these peer

effects for targeted allocation of marketing effort in the form of personal sales-calls or

“detailing” to these opinion leaders. More generally, the issues we address in econometrically

identifying and measuring peer effects are relevant across a broad range of social networking

situations in which firms are interested in understanding the return on investment of

marketing activity to opinion leaders (e.g. Godes and Mayzlin 2004).

Asymmetric social interactions have important implications for the allocation of

marketing effort by firms. If present, they increase the return-on-investment to marketing

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activity targeted at agents having stronger influence. In the pharmaceutical context, if

actions of opinion leaders have a true causal effect on the prescription behavior of other

physicians, then marketing effort directed at the opinion leader will generate a multiplier

effect. The multiplier arises because an incremental sales-call to an opinion leader increases

the opinion leader’s prescriptions, and on the margin, induces the physicians he influences to

prescribe more. The extent to which net prescriptions are higher due to these cross-physician

spillovers is the social multiplier (c.f. Becker and Murphy 2000). Given that the

pharmaceutical industry in the US currently sets physician-level detailing based on past

prescription volume (c.f. Manchanda, Rossi and Chintagunta 2004), the presence of

significant social multipliers would imply that the return-on-investment of detailing to

opinion leaders may be much higher than is suggested by just the opinion leader’s

prescription volume. We use our estimates to measure the social multiplier of detailing in

our data.

To test for these effects, we leverage a novel dataset that is based on a combination of

primary (survey) and secondary (behavioral) data. Broadly speaking, there are five major

challenges that arise in measuring these effects. First, some effort needs to be made to

identify the opinion leaders that constitute the reference group for a given physician. Second,

once these opinion leaders have been identified, some change in the environment needs to

take place in a manner that this change affects the attitudes and/or behavior of the opinion

leader. Third, these changes then needs to be transmitted to the agents whose opinions

and/or behavior is affected by the opinion leader’s behavior (or opinions). Fourth, there

should be a resultant change in the behavior of these consumers. Finally, we need to be able

to distinguish between correlation and causation in the observed behavior of physicians and

their opinion leaders. As we discuss below, correlation in behavior can arise from three

possible sources – endogenous group formation, correlated unobservables and/or simultaneity

– and we need to be able to control for these explanations. As the past literature on social

interactions has pointed out (c.f. Manski 1993, Moffitt 2001), solving this identification

problem is a formidable challenge. Our dataset, which comes from the pharmaceutical

industry, enable us to formulate empirical strategies that address most, if not all, of these

issues. Our data contain survey information on the social networks of physicians, as well as

individual-level panel data on the prescription behavior of these physicians and the doctors

they nominate. We believe our identification strategy is novel to the literature, and is

relevant across a broad range of situations involving the analysis of data arising from social

interactions.

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Our analysis of the data reveals significant evidence for peer effects. These effects

persist after allowing for endogenous group formation, targeted marketing activity,

correlated unobservables and simultaneity, and are also robust to functional form. We find

that opinion leaders behavior significantly affects physician behavior after an exogenous

change in the market that resulted in a change in the therapeutic environment.

Interestingly, in the changed environment, we find that peer effects have more marginal

impact on prescriptions than targeted detailing. Our empirical results also find that peer

effects in this category are “asymmetric” in the sense that opinion leaders’ prescriptions are

not statistically significantly affected by the prescription pattern of the physicians they

influence.

We then use our results to explore the implications of targeting detailing at the

opinion leaders. We quantify the direct effect of detailing on opinion leader prescriptions, as

well as the indirect effect on prescriptions by the corresponding nominating physician

generated via peer influence. We find that for the average opinion leader, who influences

1.56 physicians, social interactions alone provide an additional 5% increase in prescription

revenue. This implies a social multiplier of detailing in this category of about 1.05. For the

top opinion leader, who influences 17 physicians, we find a social multiplier of 1.35. The

large differences underscore the importance of both the correct identification of opinion

leaders, as well the identification of top influencers among these opinion leaders in order to

make optimal resource allocation decisions.

In summary, our key contributions are as follows. First, we document the existence of

asymmetric peer effects amongst a specific social network. Specifically, we provide evidence

for these effects in the domain of physician prescription decisions. Further, we document the

finding that peer influence can significantly impact on behavior even in stable, mature

categories. These are novel findings that add to the literature on peer effects in the presence

of marketing, especially in the pharmaceutical industry. Second, we discuss and clarify how

the identification issues that arise in measuring and testing for causal peer effects may be

overcome for data-rich settings such as ours. Third, we measure empirically the extent to

which peer effects matter in driving prescriptions of both physicians and opinion leaders, and

show that these are robust to functional form and alternative specifications of peer effects.

Further, we use our estimates to derive implications for marketing resource allocation for

firms in the industry, and present estimates of the social multiplier effects of detailing.

Finally, the increasing salience of social networks in the economy has generated renewed

interest among theorists in many fields to incorporate aspects of social networks into their

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frameworks. Robust evidence of social effects increases the practical, real-world relevance of

these models.

The rest of the paper is organized as follows. The next section discusses the industry

background and reviews the relevant literature. Sections 3 and 4 present the model and

describe the data. Section 5 presents results from estimation. The last section concludes.

2. Industry Background and Related Literature

2.1 Industry Background

As mentioned above, the pharmaceutical industry strongly believes that opinion leaders (also

referred to as thought leaders) play an important role in the adoption and usage of new

products by practicing physicians. These opinion leaders are typically believed to be

physicians who have an academic title with the department of a medical school and have

contributed peer-reviewed publications (Tan 2003). Both these characteristics are believed to

lend credibility and authority to their opinions and beliefs about various products.

As would be expected, the industry believes that the role of the opinion leaders is the

strongest when a new product is launched (or is about to be launched). For example, the

industry spends an estimated 24% of their new product commercialization budget on opinion

leader activities (CIE 2004). The same study also showed that the 15 largest pharmaceutical

manufacturers spend 32% of their total marketing expenditures on opinion leaders. Opinion

leader activity is also stepped up when environmental changes occur. In the pharmaceutical

industry, these are typically the launch of a new competitive drug, the withdrawal of a drug,

issue of new guidelines by the Department of Health and Human Service and/or the National

Institutes of Health (NIH) or the emergence of new scientific evidence on the efficacy of a

drug or class of drugs. Physicians also socialize at meetings and symposia, and exchange

knowledge through scientific and medical journal articles. These interpersonal

communications between physicians can provide information to a physician about the

efficacy of new drugs in trials and in practice, new trends in the treatment of particular

diseases of interest, availability of generic substitutes, etc. As noted before, these information

flows can potentially affect the prescribing behavior of the influenced physicians.

Marketing to opinion leaders is typically managed by direct contact with these

physicians through detailing. In some pharmaceutical companies, special teams consisting of

higher caliber detailers carry out most of this detailing activity. Members of such teams are

typically designated “Medical Scientific Liaisons” (MSLs). A typical team in a large

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pharmaceutical company consists about of about 45 MSLs. Industry estimates suggest that

about half of the large pharmaceutical companies have MSL teams (CIE 2004).

In conclusion, the existence of opinion leaders is taken for granted in the

pharmaceutical industry. While firms try and manage their relationships with these opinion

leaders via marketing, anecdotal evidence suggests that the identification of opinion leaders

and the extent to which they impact other physicians are issues that the industry grapples

with. Specifically, first, firms may not usually have a clear idea about who these opinion

leaders are.1 Second, there is little systematic understanding of the mechanisms through

which opinion leaders and nominating physicians interact. Finally, there is little

quantification of the return on investment from targeting these opinion leaders.

2.2. Related Literature

Our work is related to the sociology, economics and marketing literature on social networks

(e.g. Burt 1987; Coleman, Katz and Menzel 1966, Van den Bulte and Lilien 2001, Duflo and

Saez 2001). The main focus of this related literature has been to test for social interactions

and peer effects using micro-level data.2 There is some work that has postulated the

existence of asymmetric peer effects. For example, Reingen and Kernan (1986) focus on

identifying the links of a given social network via surveys for a piano tuner service. They

note that individuals with stronger ties are more likely to activate the flow of referral.

However, they do not explicitly try to document the existence and effectiveness of opinion

leaders. Other researchers such as Summers (1970) have tried to examine the characteristics

of opinion leaders. They found that opinion leaders are typically more knowledgeable about a

product category, but do not explore the quantification of the effect of opinion leaders for

outcomes. A few other papers from the medical literature have used surveys and/or field

1 For information on firms providing tools for identifying and targeting physician opinion-leaders see

for e.g., www.estcomedical.com/thoughtleader/ and mattsonjack.com/keymd.asp, and also the “Medical

Science Liaison Quarterly,” http://www.mslquarterly.com/. These reports indicate that most

pharmaceutical companies rely on sociometric approaches (described in the next section) to identify

opinion leaders. There are some attempts to identify opinion leaders using behavioral (i.e., secondary)

data. However, these are subject to the Manski (1993) critique that estimates of social interactions

derived via post-hoc identification of peers from outcome data are likely biased upward. 2 A related stream of work in the diffusion of innovations literature has modeled asymmetric contagion

effects using a macro-level modeling approach. The general approach is to build a mathematical model

with an ex-ante assumption on how two (usually) groups of consumers respond to peer behavior -

specifically, ‘imitators’ are affected by the actions of all other previous adopters while ‘innovators’ are

not affected by peer actions. The resulting model is then applied to aggregate sales data to infer the

size of the two groups. Some representative publications that follow this approach are Tanny and

Derzko (1988), Steffens and Murthy (1992) and Van den Bulte and Joshi (2007)). With only aggregate

data, this literature cannot test for (asymmetric) peer effects at the individual level. Thus, our work

must be seen as complementary to this stream of work.

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experiments to test for opinion leader effects. (e.g., Valente et al. 2003, Lomas et al. 1991 and

Celentano et al. 2000). In economics, researchers have investigated social interaction effects

more generally in the context of crop-technology adoption (Bandiera and Rasul 2006; Conley

and Udry 2000), welfare participation (Bertrand, Luttmer, and Mullainathan 2000), health-

plan choices (Sorensen 2005); and retirement plan choices (Duflo and Saez 2002), to name a

few. A small, but growing number of recent papers in the marketing literature has also

investigated the potential role of peer-effects in new product adoption (e.g. Van den Bulte

and Lilien 2001, Manchanda, Xie and Youn 2004 and Iyengar et al. 2008 on new drug

adoption; Bell and Song 2007 on Internet grocer adoption; Nam, Manchanda and

Chintagunta 2006 on video-on-demand adoption). We refer the interested reader to

Hartmann et al. 2007 for a recent and broad overview of the social interactions literature,

which also discusses approaches from several related fields.

Broadly speaking, relative to the previous literature cited above, our approach has

several distinguishing characteristics. These include documenting the asymmetric nature of

peer interactions, distinguishing causal peer effects as opposed to correlated outcomes that

do not rely on peer effects, and the determination of peer effects in mature product categories

i.e., using post-adoption behavior. In terms of the causal effect determination, we believe this

paper is one of the first to comprehensively outline and address the identification issues

related to endogenous network formation, correlated unobservables and simultaneity, and to

include specific controls for targeted marketing activity in the analysis of social interactions

in the presence of marketing.

3. Model

We now discuss our model framework and empirical strategy. Our empirical framework is a

descriptive linear model of prescription behavior, which we interpret as the reduced form of

the behavioral process generating prescriptions for physicians and their opinion leaders (for

structural approaches see Brock and Durlaf 2001; and more recently, Hartmann 2008). In

the “robustness” section later, we discuss some extensions of this linear model that

accommodate alternative specifications of the effect of peers as well as relax the linearity

assumption (via the use of a count model). In the context of this model, we clarify and discuss

how we address the main identification issues inherent in measuring peer effects using

micro-level data. We index physicians by i, i’s opinion leader by j(i), and time by t. Let D

denote detailing, and y and x denote new prescriptions for physicians and opinion leaders

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respectively. The starting point of our empirical specification for physician prescriptions is a

linear regression:

( ),it it itj i ty D xβ δ υ= + + (1)

Here vit denotes unobserved factors that shift prescriptions of physician i over time. While

ideally we would like to include the actual opinions of the opinion leader as a covariate to

capture the social interaction, these are unavailable in our data. Here, we think of the

prescriptions of the opinion leader as a proxy for these opinions (later, we present extensive

sensitivity checks to different proxies for leader opinions). Formally, our test for the

asymmetric peer effect in prescription behavior is whether δ is statistically significantly

different from zero. An alternative model that uses the share, rather than levels of

prescriptions is equivalent to (1), since the overall volume of prescriptions written for the

disease condition remained roughly constant across the months in our data. Identification of

peer effects in this model requires us to resolve five issues described earlier - reference-group

determination, change in the external environment, existence of a communication

mechanism between the physician and the opinion leader, an outcome variable that can be

measured and the ability to rule out correlation in observed behavior between the physician

and the opinion leader arising from endogenous group formation, targeted marketing

activity, correlated unobservables and simultaneity. We discuss these in sequence below.

Reference group/peer determination

First, we need to identify the proper reference group or reference peer for each agent, such

that the effect of the group/peer’s behavior on the agent’s actions can be measured. Manski

(1993 & 2000) discusses in detail the need for exogenously defined social network

information to identify peer effects from behavioral data. Intuitively, one cannot use behavior

itself to define reference groups, if the goal is to obtain the effect of a reference group’s

behavior on an agent’s actions. By grouping agents with ex-post similar actions together, a

researcher attempting this approach essentially produces an upward bias in any peer effects

unearthed through subsequent analysis. Similarly, geographic or location specific proxies for

reference groups cannot sort between peer effects and common unobservables that affect the

actions of all agents in the location similarly. We overcome these challenges in our

application by using a new dataset that contains detailed social network information

obtained via a “sociometric” approach (e.g., Coleman et al. 1996; Valente et al. 2003; Valente

and Pumpuang 2007). In the sociometric approach, individuals units are directly surveyed to

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obtain information about other individuals who exert a peer effect on their behavior.3 Each

physician in the survey self-reports the doctor whose opinions he incorporates in his

prescription decisions, thus identifying his social network. This provides us an exogenous

measure of the physician’s reference group or peer, circumventing the need to rely on

behavior, location or geography-based proxies. Thus, in the setup of equation (1), j(i) is

known exogenously. Our use of the term “opinion leader” is to be interpreted in this sense as

referring to doctors nominated by physicians in this survey (described in detail in a later

section).

Change in the External Environment

In stable product categories with well-established brands, agents tend to have little

uncertainty about product quality, and may need rely on other’s actions to make decisions. In

stable drug categories, general practitioners may have little uncertainty about the usage and

efficacy of the drugs they prescribe. Peer effects may be hard to uncover in such settings.

Changes in the environment add exogenous variation that assist in unearthing the peer

effect. An advantage of our data is that it covers a time-period where there was a significant

change in the recommended usage of drugs in the therapeutic category. For the therapeutic

category that we study, this environmental change relates to new treatment guidelines

issued by the National Institutes of Health (NIH) regarding appropriate treatment for

specific disease indications (we describe the new guidelines later in the paper.) The

guidelines were issued by the NIH following fresh evidence available from post-release drug

trials. This environmental change occurs around the mid-point of the data, and is exogenous

to behavior as it arises from the behavior of a third party that is not affected by the actions of

physicians and their opinion leaders.

Interaction between the Physician and the Opinion Leader

In the survey, physicians also report their mode of interactions with their opinion leaders.

Hence, our data also allow us to provide some insights into the mechanism through which

the opinion leader effect manifests itself.

3 In contrast, some studies follow the “key informant” approach, where a few individuals are polled to

determine the identity of individuals with social influence (e.g., Celentano et al. 2000). Interestingly,

Iyengar et al. (2008) find that the set of self-reported opinion leaders are different from those identified

via a sociometric approach. Others in development economics who have adopted sociometric approaches

include, Conley and Udry (2000) and Kremer and Miguel (2004). In the absence of such data,

researchers have often defined networks based on geographical location (Bell and Song 2007;

Manchanda et. at. 2004); dorm/work location (Sorensen 2005; Dufflo & Saez 2003; Sacerdote 2001); and

ethnic/cultural proximity (Munshi and Myaux, 2002; Bertrand et al., 2000).

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Outcome Variable

The external change in the treatment environment generates higher uncertainty regarding

drug efficacy among family and general practitioners, making them more likely to value the

opinion of specialists in the category (we discuss this in detail later). The exogenous variation

aids in identification. Given the nature of the exogenous change, all physicians are likely to

adapt their prescription behavior to reflect the new guidelines. Hence, our identification

strategy focuses on how changes in prescription behavior of nominating physicians (y in

equation 1) before and after the issuance of the guidelines, is related to changes in

prescription behavior of their corresponding opinion leaders.

Distinguishing Causality from Correlation

As mentioned above, peer effects imply that the behavior of agents in the same reference

group would tend to be correlated. However, correlation in the behavior of agents per se does

not imply that any one agent’s action has a causal effect on the actions of others in the group.

In addition to peer effects, such correlation in behavior could arise due to three other factors,

viz. endogenous group formation, correlated unobservables and simultaneity (see Moffitt

2001 for a discussion.) Only a causal peer effect implies a social multiplier; hence it is

important to sort out causal effects from each of these sources of correlation. In our

application, another factor that could lead to correlation is targeted marketing.

Endogenous group formation

Endogenous group formation arises in our context if physicians choose doctors with similar

“tastes” for prescriptions as their opinion leaders. For instance, physicians who face patient

bases requiring treatments using a specific class of therapeutic drugs may meet experts in

that therapeutic category at conferences organized by drug companies. If physicians choose

these experts as opinion leaders, it is likely that such physician-opinion leader pairs tend to

prescribe more in the therapeutic category than average. In this case, the observed

correlation in the behavior of the physician and his opinion leader could arise from omitted

individual characteristics that are correlated within the group. In equation (1), such

endogenous group formation implies that physician i’s unobserved tastes (vit) and opinion

leader j’s tastes for prescriptions could be correlated – if opinion leader j’s tastes also drive

his prescriptions, xjt, this generates a correlation between xjt and vit leading to a upward bias

in the estimates of δ.

The solution to the group formation problem is facilitated by the availability of panel

data (as noted by Manski 1993, the prospect for identification of peer effects in cross-

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sectional data are poor). Panel data enables us to include physician-specific fixed effects in

the regression (1). In terms of our model, we write,

it i itυ α η= + (2)

where αi is a fixed effect specific to physician i, which controls for unobserved (to the

econometrician) time invariant tastes for prescriptions. By controlling for physician i’s tastes,

we control for the portion of vit that is correlated with xjt via correlation with opinion leader

j’s tastes, thus accommodating the endogenous group formation problem. The identifying

assumption is that, we assume that group selection is fixed over time, and that physician

group formation is not influenced by changes in the external environment.

Correlated unobservables

A second concern is whether there exist correlated unobservables that drive prescriptions of

both the physician and the opinion leader similarly. If uncorrected, these manifest

themselves as peer effects. An obvious source of correlation is sales-force activity (i.e.

detailing) directed at physicians and opinion leaders by drug companies. One can partly

control for this source of correlation by including time-period fixed effects that pick up

common trends in marketing activity to physicians (e.g. Van den Bulte and Lilien 2001). In

our setting, we fully control for such marketing activity by obtaining direct data on detailing

to physicians, which we include as explanatory variables in the regression. A potential

concern with using this variation arises because detailing may be targeted to physicians. As

documented in the recent literature (e.g. Manchanda, Rossi and Chintagunta 2004) many

pharmaceutical companies in the US, including our firm, decide detailing allocations based

on a volume-based rule, whereby physicians are allocated detailing levels corresponding to

their position in deciles of past prescription volume in the focal category (we find evidence for

this detailing pattern in our data.) Then, the volume-based detailing rule implies that D may

be correlated with vit.

Our control for this potential endogeneity derives from the nature of the targeting

rule. In effect, on account of stable patient bases, physicians rarely move across deciles (we

find this in our data as well). Thus, the inclusion of physician fixed effects pick out the

across-physician variation in detailing, and controls for the endogeneity concern. Thus, only

within-physician, across-time variation in detailing is used for identification. Fixed effects do

not fully absorb all detailing variation however, as in practice, actual detailing levels are

centered around, but not exactly equal to top-down allocated levels due to several

unanticipated factors that affect visits. These include physicians’ time-constraints (e.g. the

physician is not in his office during a detailing visit), or unanticipated detailer time-

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constraints (a patient is taking too long, requiring postponement of the visit). This deviation

from pre-allocated levels is orthogonal to physician unobservables, and is used for

identification. Thus, the underlying identifying assumption is that after controlling for αi,

within-physician detailing is independent of other physician and time-period specific

unobservables, ηit.

We also consider the possibility that there may be additional correlated

unobservables that generate co-movement in prescriptions. Candidates for such

unobservables include trends in overall prescriptions across all physicians in the category, as

well as any spatially correlated region/location specific shocks to prescription behavior that

are captured by ηjt. We address these as follows. First, we include a full set of time-period

fixed effects. These control for any time-trends common across all physicians and opinion

leaders. Second, recall we include physician fixed effects. As none of the surveyed physicians

in our data share a zip code, physician fixed effects are equivalent to including a full set of

zip code fixed effects. Hence, time-invariant spatially correlated unobservables are also fully

controlled for. A final issue is whether there are unobservables that are correlated at the

level of the zip code and time.

To consider this issue, we discuss a potential difference-in-difference approach. We

have access to the prescription behavior of all physicians in the country. We use this data to

compute the mean prescription of all other physicians in physician i’s zip-code, denoted by z-

i,t, which we include as a covariate in the regression. Essentially z-i,t proxies for all

unobserved time-period and location-specific shocks to prescriptions that are common to all

physicians in i’s location.4 By including these in the regression, we essentially use the

prescription behavior of other physicians in i’s location as a control. Thus, we further

decompose (2) as,

−= +it it itzη γ ε (3)

where εit is a mean zero error term. Note this strategy is subject to the implicit caveat that a

given physician’s opinion leader does not influence other physicians in his zip code.

Unfortunately, data on the social networks of the universe of physicians is not available to

test this. Hence, our approach will be to present extensive sensitivity checks in which z-i,t is

included or excluded from the regression. Tests for correlated unobservables (presented in

§5.1) suggest that most of the spurious correlation is along the temporal dimension, which is

fully picked up by time-period fixed effects. Hence, the effect of z-i,t on our results is small.

4 Less than 10% of opinion leaders and nominators are in the same zip code in our data. Our results do

not change if we exclude these physicians from our sample.

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Simultaneity

The final factor that must be considered here is simultaneity. Simultaneity implies that

physician i’s actions and opinion leader j(i)’s actions may be contemporaneously

interdependent. If peer effects exist, the fact that opinion leaders affect physicians while

physicians simultaneously affect them leads to an upward bias in the estimation of the

interactions. In the context of our model, if physician i and opinion leader j’s prescriptions

are simultaneously determined, high values of εit would tend to induce high values of xj(i),t,

thus leading to an upward bias.

We control for the simultaneity problem via exclusion restrictions. In our context,

detailing to the opinion leader, Dj(i),t, as well as the mean prescriptions of all other physicians

in the opinion leader’s zip-code, z-j(i),t, form excluded variables that affect the prescriptions of

the opinion leader (the endogenous variable), and can be excluded from the prescription

equation for physician i. Both Dj(i),t and z-j(i),t impact the opinion leader’s prescriptions, and

are thus correlated with the endogenous variable xj(i),t, but uncorrelated with εit. Thus they

serve as instruments for xj(i),t thus addressing the simultaneity concern. An alternative

approach would be to assume that only past opinion leader prescriptions affect the

physician’s current prescriptions (i.e. there is no contemporaneous linkage in behavior.) We

explore model sensitivity to such specifications in the “Robustness” section.

Note that if we assumed that, given their “expert” status (details in the “Data”

section below), opinion leaders were not affected by physicians, there would be no

simultaneity problem by construction. Rather than assume away simultaneity concerns a

priori in this manner, we use the data to check whether peer effects are truly asymmetric.

We run the analogous regressions for opinion leader j(i) (i.e. xj(i),t regressed on αj(i), Dj(i),t, yit

and z-j(i),t) to check whether physician-prescriptions have a significant effect on the

prescription behavior of their opinion leaders. Analogously, Dit and z-it are excluded variables

for the opinion leader’s prescription equation, and serve as instruments for yit in the opinion

leader’s prescription equation.

Final Specification

Based on the above discussion, our final specification for physician i’s prescriptions is,

( ) ,,, 1,.., ; 1,..,α γ β δ γ ε−+= + + + + = =

tit i it i t itj i ty D x z i N t T (3)

The corresponding specification for i’s opinion leader is:

( ) ( ) ( ) ( ) ( ), ,, ,, 1,.., ;α τ ϖ ς ζ ε

−+= + + + + =

t itj i t j i j i t j i t j i tx D y z t T (4)

13

We estimate both specifications via fixed-effects panel data linear instrumental variables

regression. Note that the issues we identify and try to control for above are relevant for any

analysis of behavioral data involving social interactions, whether using structural or

reduced-form models. We now describe our data.

4. Data Description

Our data pertain to physician prescription behavior in a large therapeutic class (we cannot

reveal the name of this class due to confidentiality concerns). The drugs in this class address

a serious chronic disease condition that affects about a quarter of all adults in the United

States. We consider a combination drug category in this therapeutic class of drugs that treats

the chronic disease.

The dataset we use in our analysis is a combination of primary and secondary data.

The primary data come from a market survey data of 1500 physicians chosen randomly from

a set of 56,000 regularly prescribing physicians across the United States in this therapeutic

category. The survey was commissioned by a large pharmaceutical company and carried out

by a market research firm with the pharmaceutical company bearing all costs

(confidentiality reasons preclude us from naming the companies). The survey was carried out

in Jan-Feb 2004. The main objective of the survey was to obtain names of those doctors

whose actions influence the nominating physician’s approach to the treatment of the chronic

disease treated by combination drugs. Nominating physicians were encouraged to name

doctors who were known to them (by reputation or otherwise) and then queried about the

mechanisms by which they were able to obtain information about the opinion leader’s beliefs

and actions.5 From this survey, we have access to information on 290 physician-opinion

leader pairs. Note that our use of the term “opinion leader” is to be interpreted in this sense

as referring to physicians nominated in this survey. The opinion leaders so identified reflect

each nominating physician’s subjective opinion regarding who in the field he considers an

expert, and whose opinion he incorporates while making prescription decisions in this

therapeutic category. We believe this individual-specific measure is the appropriate one for

identifying peer effects in such settings.

5 The specific questions asked during the survey were (a) “Whose opinions do you value most regarding

the treatment and/or management of [disease condition] among [disease condition] patients?”, and, (b)

“How do you obtain information from that Influencer about the treatment/management of [disease

condition]?” It is possible that the wording of this question could “encourage” respondents to name an

opinion leader even when there isn’t one. However, if a respondent “made up” the name of an opinion

leader, the peer effect we calibrate using (independently) obtained behavioral data should be

attenuated. In other words, the survey bias, if present, works against our finding of peer effects.

14

These data were then supplemented by secondary data – also collected with the help

of the company – on the prescription behavior and the marketing activity directed at both the

physicians and the opinion leaders. These data span a period of 24 months (from April 2002

to March 2004 inclusive) and contain the prescription counts for the combination-drug

category and the count of details received for each month for the universe of physicians for

the company’s drug. Interestingly, for this drug, the pharmaceutical firm had decided to rely

almost entirely on regular detailing to manage relationships with the opinion leaders. Our

interviews with the managers of the firm indicated that while the firm would have liked to

consider other forms of marketing, such as the setting up of an MSL team, they were not

doing that during the period of our data. We also learnt from the firm that only about 50% of

the doctors identified as opinion leaders in the survey were on the firm’s own list of opinion

leaders.

Descriptives: Primary data

We have demographic and location information for 290 opinion leader-physician pairs

(including primary affiliation, zip-code and specialty). There are 267 unique nominating

physicians. There are 182 unique nominated physicians (“opinion leaders”). The distribution

of nominations in the survey is presented in Tables 1 and 2. Interestingly, over 91% of

physicians reported being influenced by only one opinion leader. About 38% of the nominated

doctors were named as opinion leaders by more than one physician. We do not see any

overlap between opinion leaders and nominating physicians.6

The typical opinion leader is a research-active specialist physician in the therapeutic

category and is associated with a university-based hospital. 97.4% of the opinion leaders are

specialists. Over 90% of the opinion leaders in the sample are associated with hospitals, and

about 30% are affiliated with University hospitals. The average opinion leader has published

about 7.2 refereed papers (st. dev. 9.68, min 0, max 40) in this therapeutic class, confirming

his status as an “expert.” The survey also queried the nominating physicians about their

mode of interaction with the opinion leader. The dominant mechanism of information

transfer as reported by the physicians was direct contact, with about 94.5% of nominating

physician mentions. This provides some support for our model formulation (equation 1) in

6 The pharmaceutical firm that commissioned the survey asked the market research firm for a list of

only those physicians who nominated an opinion leader (and the opinion leader identity). Thus, we

have only information on physicians who have an opinion leader. The consequence is that in essence,

we measure the “treatment on the treated”, where the “treatment” is the effect of an opinion leader.

Note that conditional on having an opinion leader, we do control for the selectivity of the chosen opinion

leader (i.e. the endogenous group formation problem) via inclusion of physician fixed-effects that pick

up common unobserved traits that could lead to group formation (details below).

15

which the nominating physician is assumed to respond to the prescriptions of the

corresponding OPL. Other mechanisms of interaction included symposia/conferences (78%),

meeting in clinical and/or hospital settings (67%) and via scientific articles published by the

opinion leader (32%).

Prescription/detailing data

The secondary dataset contains information on 24 months of new prescriptions for the

combination-drug category for the entire universe of physicians in the therapeutic class of

the disease. The data also contains information on monthly physician-level detailing activity

by the focal firm in the category. Unfortunately, detailing activity for the other competing

drugs in the category is not available at the individual physician-level. The combination drug

category we considered has 4 prescription drugs. Table 3 reports the market shares of the 4

drugs during the time-period of our data. Based on this, we focus on the two largest drugs

viz. drug 2, the focal drug, and drug 1, the main competitor. We supplemented our data with

monthly national aggregate detailing for drug 1 (detailing for drugs 3 and 4 was negligible).

The distribution of aggregate detailing for the two drugs is presented in Figure 1.

Descriptive statistics for the sample are provided in Table 4. The table shows that

nominating physicians typically write a larger number of prescriptions (almost twice that of

the opinion leaders) and also receive a higher level of detailing (about 50% higher). This is

consistent with anecdotal evidence that opinion leaders tend to be focussed more on medical

research and academic publication rather than practice. As can be seen from the table,

opinion leaders are detailed less than the nominating physicians. This is likely to be a

function of the category volume rule followed in the industry (whereby physicians who

prescribe more at the category level get correspondingly higher details.)7

Change in drug-usage guidelines

7 Looking at z-it in Table 4, we see that the average physician in a zip code prescribes 0.75 new

prescriptions per month in this disease category, which is much higher than that prescribed by

physicians included in our survey. This is driven by the fact that we are averaging over many

physicians in each zip code who do not prescribe often in this disease category (for instance, a

nephrologist may not prescribe drugs for heart disease – a “disease condition” – but is included among

the set of physicians in a focal doctor’s zip code). An alternative approach is to include only physicians

who are “active prescribers” in this disease category (the population of interest from the firm’s point-of-

view) in computing z-it. Our motivation for adopting the current approach is a) it is possible that the

guidelines affected all doctors in the zip-code, not just active prescribers, and hence including the

prescriptions of all doctors in computing z-it would better pick up the overall effect of the guidelines; b)

since we did not have access to the database of physicians from which the company picked the random

sample, we would otherwise have to make arbitrary guesses about who to include when computing z-it.

Given this, we also present several robustness checks in which we drop z-it from the regressions,

instead controlling for its effects via a full set of month-fixed effects. We thank an anonymous referee

for suggesting this approach.

16

An important aspect of the data is that it covers a time-period where there was a significant

change in the guidelines for the usage of drugs in this therapeutic category. This change in

the treatment environment is important for us since it is then that family and general

practitioners are most likely to seek and value the opinion of specialists in the category.

Appendix A provides excerpts from published sources and the summary findings from survey

of physicians attesting that, in general, a change in guidelines usually increases the

uncertainty in terms of physician prescription decisions. The increased uncertainty of

nominating physicians regarding drug efficacy in the changed environment thus aids in

identifying the effect of opinion leaders on prescription behavior. In our context, an

exogenous change in the market occurred in May 2003, in the form of an announcement by

the National Institutes of Health (NIH) affiliate releasing new treatment guidelines for the

disease. Thus, we have 13 months of behavioral data before the guidelines were released and

11 months after. The guidelines suggested that, as against the prevailing norm, the

initiation of treatment for severe cases of this condition should comprise of at least two

agents (or molecules). The guidelines also stated that more than one drug would be required

to treat most cases of the disease. These guidelines tended to favor the so called combination

drugs in this category. A combination drug typically had the two agents in the same pill and

results in “polytherapy”. This had the obvious advantage of increasing compliance amongst

patients i.e., taking two pills is much easier than taking one. However, there were additional

therapeutic benefits for patients beginning therapy using these combination drugs. These

(combination) drugs also had been shown to have therapeutic advantages such higher

efficacy (than for two drugs taken individually), lower side effects and higher potency with

lower dosages. Thus, we expect that all combination drugs, including drug 2, should show an

increase in prescriptions after these guidelines were issued. Prior to the issuance of new

guidelines, combination drugs were generally considered “aggressive” therapy. 8

We now document the changes in prescription and detailing behavior before and

after the release of the guidelines. Figure 2 presents the distribution of mean monthly

prescriptions in the combination-drug category for nominating physicians and opinion

leaders before and after May 2003 (when the new treatment guidelines were introduced). For

each physician, we compute the mean monthly new prescriptions before and after and

present them in a box plot. As can be seen from the figure, both sets of physicians prescribe

more of the combination drugs category. The mean increase in new prescriptions across both

8 The firm also showed us survey data collected from 319 physicians after the guidelines were released.

These physicians noted that, based on the guidelines, they would expect polytherapy to become more

prevalent, leading to an increase in the prescription levels of combination drugs.

17

groups is about 10%. Again, as before, the nominating physicians prescribe more than the

opinion leaders before and after.

We now turn our attention to the distribution of monthly detailing for drug 2 across

all physicians and months. Figure 3 presents the distribution of mean monthly detailing for

drug 2 for physicians and opinion leaders before and after. As before, we compute the mean

monthly detailing before and after for each physician, and present them in a box plot.

Interestingly, the firm seems to deviate from the detailing allocation rule cited above just

after May 2003. This can be seen from the figure as the firm details more to opinion leaders

after the change even though they write fewer prescriptions in the combination-drug

category (see Figure 2). At the same time, detailing to the “regular” physicians remains

relatively unchanged before and after the issuance of the guidelines (the difference is not

statistically significant.) This suggests that the firm has some knowledge of the opinion

leader-status of these specialists. As noted earlier, about half of the nominated opinion

leaders had been characterized as opinion leaders by the firm prior to the survey.

To summarize, we have a unique dataset that combines information from primary

and secondary sources. Our data suggest that opinion leaders are less heavy prescribers in

the therapeutic category relative to the average physician. We find that the NIH guidelines

impact the prescription behavior of both nominating physicians and opinion leaders – on

average the prescription quantity goes up by 10%. This is consistent with the content of the

guideline (as described above). We also find that the firm that markets drug 2 changes the

allocation of its detailing resources after the release of the guidelines. Specifically, before the

release of the new guidelines, the firm devotes a lesser amount of detailing to the opinion

leaders. However, this pattern is reversed after the guidelines are released. This is

consistent with firm feedback that detailing is the main instrument that is used by this firm

in this therapeutic category in terms of managing its relationship with opinion leaders.

5. Results

We now present the results from the analysis of the data. We first present some model-free

evidence for the presence of correlated unobservables in prescriptions between the physicians

and the opinion leaders. We then present results from various OLS and fixed effects

instrumental variables specifications that control for the identification issues discussed in

section 2. We then discuss robustness of our main results to alternative model specifications.

We conclude by using our estimates to measure the incremental value of targeted sales force

18

activity to the opinion leaders in the data, which we use to estimate the social multiplier of

detailing.

5.1. Testing for correlated unobservables

Recall that unobservables correlated across physicians and the opinion leader which shift

prescriptions similarly can result in spurious correlation, and bias the estimate of the peer

effect parameters. One approach to this problem was to use the mean prescriptions of other

physicians in the focal physician’s zip code to control for such potentially correlated

unobservables. Correlated unobservables could arise along both the temporal and spatial

dimensions. For example, the new NIH guidelines could generate an overall incentive to

prescribe the combination drugs for both physicians and opinion leaders. This manifests

itself as an overall trend in prescriptions, which when ignored, could generate a spurious

correlation in the physician and opinion leader’s prescriptions over time. Alternatively,

spurious correlation could arise from unobserved shocks to prescriptions of both physicians

and opinion leaders that are contemporaneously spatially correlated.

To check whether mean prescriptions of other physicians in the focal physician’s zip

code reveal a trend over time (i.e. a common temporal shock to prescriptions) we regress z-it,

the mean prescriptions of all other physicians in i’s zip-code, on physician and time period

specific fixed effects. The time period specific fixed effects from this regression are presented

in Figure 4 (all the time-period dummies are significant). The plot reveals an upward trend

in mean prescriptions over time. By controlling for z-it we do not use this potentially common

component of variation in prescriptions over time to identify peer effects. For comparison, we

also plot the corresponding coefficients from regressing the mean prescriptions of other

physicians in the opinion leader’s zip code (z-OPL,t) on month dummies. Recall that z-OPL,t

contains unobserved shocks that shift the opinion leader’s prescriptions over time. This plot

also reveals an upward trend. The picture suggests that there exist correlated temporal

shocks in the prescriptions of both physicians and Opinion leaders, which if not controlled

for, could lead to spurious findings of peer effects.

We also check the extent to which spurious correlation could arise from spatial

dependence in unobservables that drive prescriptions of the physician and the opinion

leader. Recall that we use the mean prescriptions of other physicians in the focal physician’s

zip code to control for the unobservables. We can get a sense of the extent of spatial

correlation holding the temporal variation fixed, by looking at the dependence in the mean

prescriptions of other physicians in the physician and the opinion leader’s zip-codes time-

period by time-period. Table 5 shows the correlations between z-it and z-OPL,t in the data each

19

month (Physician-opinion leader combinations that shared a common zip-code were dropped

- about 9.6% of the observations.) In general, we do not find much evidence in the data of

statistically significant spatially correlated shocks, suggesting that the main component of

correlated unobservables is along the temporal dimension. Analogously, we check for

correlation in z-it and z-OPL,t along the temporal dimension after controlling for across

physician variation (i.e. we first regress z-it and z-OPL,t on physician and opinion leader fixed

effects respectively, and report the correlation in the residuals from this regression). This

correlation in the data was 0.0502, which is statistically significant (p < 0.0001).

5.2. Parameter estimates

In the first subsection, we first present results from our primary specification (equation 3), in

which we test whether physician prescriptions is significantly affected by the opinion leader’s

prescription behavior. We then present results for the reverse regressions (equation 4) in

which we test whether physician prescriptions is significantly affected by the nominating

physician’s prescription behavior.

Effect of Opinion Leader behavior on Physician Prescriptions

OLS Estimation Results

We start with discussing the estimates from OLS and fixed effects specifications of the

model. The dependant variable in all regressions is the total new prescriptions by the

nominating physician in the category. Results from OLS linear regression are presented in

Table 6. From the table, we see that the effect of the OPL’s prescriptions is positive and

significant. Further, the magnitude of the effect is higher after month 13 (May 03), when the

NIH guidelines were introduced. This is consistent with a basic pattern of correlation

between the physician and opinion leader’s prescriptions. The OLS estimate of the detailing

coefficient is also large and strongly statistically significant. However, this specification does

not take into account the fact that heavier detailing is targeted by firms towards higher

volume physicians. Hence, the OLS estimate is likely biased upward.

Fixed-Effects Estimation Results

We present the results from the fixed effects linear regression in Table 7. Recall that fixed

effects control for both potential endogenous group formation as well as the targeting of

detailing to high-volume prescribers. However, these estimates do not control for potential

simultaneity biases. Not surprisingly, the magnitude of the detailing coefficient drops under

fixed effects. The F-statistics from the regression strongly reject the null that all the fixed

20

effects are zero. Although not reported, the hypothesis that the fixed effects are uncorrelated

with the included variables is also strongly rejected in all specifications. The variable z-it

controls for both temporal and spatially correlated shocks, and is statistically significant. The

regressions in columns 3 & 4 in Table 7 indicate that after controlling for physician fixed

effects and z-it, the effect of opinion prescriptions after May 2003 is positive and strongly

significant (t = 3.09). The results indicate that the effect of the opinion leader’s prescription is

significant only after the release of the guidelines. In particular, the OPL has little effect on

prescriptions prior to May 2003, when the category was relatively stable. Given the timing of

the survey (Jan-Feb 2004), these results cannot be explained by a mere measurement effect

i.e., opinion leaders and their behavior becoming more salient because the survey evoked the

relationship between the nominating physician and the opinion leader. These results are

consistent with the observed nature of change in category and suggest peer effects following

the guideline release. Interestingly, after controlling for fixed effects, detailing is not

significant in explaining physicians’ prescriptions over time. The marginal effect of an

opinion leader’s prescription is more than 100 times larger than the detailing effect in the

latter time periods. This suggests that targeting opinion leaders with detailing with the aim

of increasing their prescriptions is a better strategy for firms seeking to increase category

volume among physicians in this market. We explore this strategy toward the end of this

section.

Fixed-effects Instrumental Variable Results

We now discuss the results from the fixed-effects instrumental variable (IV) regressions.

Recall that we use instrumental variables to accommodate potential biases arising due to

concerns related to simultaneity. The endogenous variable we instrument for is xj(i),t. We use

the detailing to the opinion leader, Dj(i),t, as well as the mean prescriptions of all other

physicians in the opinion leader’s zip-code, z-j(i),t, along with squared terms of both, as

instruments. Results from the first-stage regressions of xj(i),t on the instruments are

presented in Table 8. Columns 1 & 2 present results from the regressions of the endogenous

variable on only the excluded instruments, while columns 3 and 4 present the results from

the regressions of the endogenous variable on the entire first-stage matrix. The F-statistics

from both regressions strongly reject the null that the exogenous instruments have no

explanatory power. The first-stage is able to explain about 59% of the variation in the

opinion leader’s prescriptions. Thus, it is clear that we do not have a weak instruments

problem. The signs of the parameters also make sense intuitively. In particular, detailing to

the opinion leader has a positive and significant effect on the opinion leader’s prescriptions,

21

and the mean prescriptions of other doctors in the opinion leader’s zip-code is positively

significantly correlated with the opinion leader’s prescriptions. These suggest that the

instruments are working correctly.

Results for the fixed-effects IV regressions are presented in Table 9. The IV estimates

parallel the results from Table 5. Looking at columns 3 - 4 in Table 8, we find that opinion

leader’s prescriptions have a statistically significant effect on the nominating physicians

behavior after May 2003 (t =2.06), when the NIH guidelines were released. In Table 10, we

repeat the IV regressions including a full set of month fixed effects, and continue to find a

significant effect for the opinion leader’s prescriptions post-guidelines. Table 10 also presents

sensitivity to the inclusion of z-it in the regression. Recall that in computing z-it, we included

the prescriptions of all other doctors in a physician’s zip-code, which may include physicians

who are not in the target population of interest to the firm (the target population for the firm

includes only “active prescribers” in this disease category). In that case, z-it may not be

correctly capturing unobservables relevant to the focal physician’s prescriptions. We present

robustness to z-it in the following way. From Table 5, we see that the z-it are capturing

unobservables that are correlated mostly along the temporal, rather than spatial, dimension.

These effects may thus be captured via time-period fixed effects. On a referee’s suggestion,

we also explored month-fixed effects that are specific to each zip-code, but find that the data

are too thin to support this specification.

We present specifications with a full set of month-fixed effects, with and without

including z-it, and find our results continue to be robust. We interpret these results as

evidence that there exist peer effects in prescription behavior in these data. In subsequent

sections below, we will examine whether the peer effects we find have economically

significant consequences for firm’s marketing efforts in this industry.

Effect of Physician behavior on Opinion Leader Prescriptions

We now check whether peer effects in this category are asymmetric, by testing whether the

prescription behavior of nominating physicians has statistically significant effects on the

actions of their opinion leaders. Rather than assume each nominated doctor has a

disproportionate impact on the physician-doctor dyad, we wish to verify whether the data

supports the notion that the nominated doctors are experts, and are less affected by the

behavior of the nominators. The specifications we estimate correspond to equation 4. For

brevity, we present the fixed effect IV regressions that incorporate a full set of fixed effects

for the opinion leaders, and uses the detailing to the nominating physician and the mean

prescriptions of all other doctors in the nominating physician’s zip-code (along with squared

22

terms) as instruments for the nominating physician’s prescriptions. We present

specifications with and without the mean prescription behavior of all other doctors in the

opinion leader’s zip-code as controls for unobservables; and with and without a full set of

month-fixed effects. Table 11 presents the results. We find that the nominating physician’s

behavior does not have a statistically significant effect on the opinion leader’s prescriptions.

These results hold after allowing for a post-guideline interaction effect (columns 3 & 4).

Detailing to the opinion leader is found to have a strong significant effect on prescriptions.9

We take the results in Table 11 as evidence that peer effects tend to be asymmetric, at least

in this particular context.

Robustness

We now present several specification checks to ascertain the robustness of our findings.

First, we present regressions in which peer effects are interacted with month-fixed effects.

Second, recall that in our empirical model, we use the number of prescriptions by the opinion

leader as a proxy for the opinion leader’s opinions regarding drug use in this therapeutic

category, which are transmitted to the nominating physician via interactions, and which

subsequently affect the nominating physicians behavior. As noted before, we believe that this

is a reasonable measure since 94.5% of physicians in the survey report direct contact with

the opinion leader, thus justifying the assumption maintained in (1) that the opinion leader’s

prescriptions, xj(i),t, could be known to the physician. Rather than take a stand on a specific

parametric model of interactions, our approach tests for these measures using a reduced-

form approach. In this section, we verify that the qualitative nature of our findings are

robust to alternative specifications of the manner in which the opinion leader’s behavior

affects the nominating physician.

Robustness to varying specifications of peer effects

We now examine robustness to alternative specifications of the peer effect. We explore five

different specifications that vary in the timing as well as the nature of the effect of the

opinion leader’s behavior on the nominating physicians. Let fOPL(t) denote the variable

summarizing the opinion leader’s behavior on the physician’s prescriptions in month t. The

various specifications of fOPL(t) we consider are:

9 One potential reason why detailing effects may be stronger for opinion leaders may be that

pharmaceutical firms assign their “better” sales-people to make calls to these physicians. Separating

out such “detailer” effects require data on the identity of the detailer in addition to the number of

details made. Unfortunately, these data are not available.

23

1. fOPL(t) = ( ), 1j i tx

−≡ Lagged Opinion Leader prescriptions.

2. fOPL(t) = ( )( ),j i tI x ≡ Indicator for whether Opinion Leader prescribed this month.

3. fOPL(t) = ( )( ), 1j i tI x

−≡ Lagged indicator for whether Opinion Leader prescribed.

4. fOPL(t) = ( )1 ,

t

j ixτ τ=∑ ≡ Cumulative prescriptions by Opinion Leader as of this month.

5. fOPL(t) = ( )11 ,

t

j ixτ τ

−=∑ ≡ Lagged cumulative prescriptions.

Table 12 presents results from linear fixed effects IV regressions in which each of the five

fOPL(t) functions are included as regressors. As before, we use Dj(i),t, as well as the mean

prescriptions of all other physicians in the opinion leader’s zip-code, z-j(i),t, along with squared

terms of both, transformed analogously to fOPL(t) as instruments (see last 2 rows of Table 10

for precise definitions.) Looking at Table 12, we see that the main message from our early

results remains robust to these alternative definitions: Opinion leaders have a statistically

significant effect on prescriptions after the guidelines were issued. We also checked

robustness to our assumption of linearity by estimating a fixed effects negative binomial

(NBD) regression model (Hausman, Hall, Griliches 1984). This model accounts for the count

nature of prescription data, allows for over dispersion, and also accommodates potential non-

linearities in the prescription response function. The results from the NBD regression

(available from the authors on request) support the basic finding that opinion leader

prescriptions have a positive and significant effect on physician prescriptions post-guidelines

remains unchanged. We interpret this as providing some robustness checks on the results

from our linear model.

Finally, to check whether the peer effects varied by physician and opinion leader

characteristics, we also estimated versions of the linear fixed effects model (results not

reported here) in which the opinion leader’s prescriptions were interacted with opinion

leader’s and physician characteristics (specialty & number of published papers) as well as

the channel of social interaction. We found that these interactions were not statistically

significant. A goal for future research is to further identify the mechanism through which

social effects occur – this provides a motivation for collecting more data of the form we have.

An alternate approach may also be to design interventions with randomized treatments

(Sacerdote, 2001; Duflo and Saez, 2003; Kremer and Miguel, 2004), or through field studies

(Godes and Mayzlin 2004).

24

In sum, our results provide significant evidence for asymmetric peer effects. These

effects persist after controlling for problems of endogenous group formation, targeted

marketing activity, correlated unobservables and simultaneity, and are also robust to

functional form. We find that opinion leader behavior significantly affects physician behavior

after an exogenous change in the market that resulted in a change in the therapeutic

environment.

Targeting sales force activity to the Opinion Leaders

We now use our results to explore the implications of targeting detailing at the opinion

leaders. In particular, we wish to estimate the social multiplier associated with detailing in

this industry. As was discussed previously, the focal firm seemed to be aware of the

relevance of opinion leaders and did indeed shift detailing toward opinion leaders after the

guideline release. An incremental detail to an opinion leader has two effects: first, it

increases the prescriptions by the opinion leader; and second, the increase in the

prescriptions by the opinion leader increases prescriptions by the corresponding physician.

We use our model estimates to measure both effects.

We first estimate a prescription response function for the opinion leaders (given our

results from the previous section, we do not include the nominating physician’s prescriptions

as a covariate). We regress opinion leader prescriptions on opinion leader detailing while

controlling for the common shocks using the mean prescriptions of all other physicians in the

opinion leaders zip-code. Table 13 presents results from both OLS and fixed effects

specifications. As noted before, the effect of detailing is strongly significant for opinion

leaders. The effect persists even after using fixed effects to control for potential targeting by

firms of opinion leaders. As expected, the coefficient of detailing drops in absolute magnitude

when moving from the OLS to the fixed effects model. However, the marginal effect of

detailing of drug 2 is 0.136 – this is significantly larger than the corresponding effect for

nominating physicians.

We now use these estimates to compute the incremental revenue of an additional

detail by both drugs 1 and 2 to an opinion leader. Note that total marginal effect of drug 1

and 2 detailing is 0.127 (0.136-0.009). In the aggregate data for the category, we find that one

new prescription generates on average two renewals (obtained by subtracting one - the new

prescription - from the number of total prescriptions generated per prescriptions for our

therapeutic category). Further, the revenue from one additional dose of a combination drug is

around $ 50.00. Thus, the incremental revenue from an additional prescriptions is about $

150.00. Given this, the incremental revenue from one additional detail in the category is

25

$19.05 ($150.00*0.127). We then compute the incremental revenue arising from the increase

in opinion leader prescriptions for a nominating physician for each month after the guideline

change. We use the results from the main regression in the last column in Table 10. Post

guidelines, the additional revenue from a nominating physician as a result of the increase in

opinion leader prescriptions is 0.127*0.032*$ 150 = $ 0.61. From Table 2, the average opinion

leader in our sample influences 1.56 physicians. Hence, the total effect arising out of a social

interaction is 1.56*$0.61 = $0.95. For the average opinion-leader, this accounts for about

4.76% (=0.95/(19.05+0.95)) of the total revenue effect of detailing; implying a social multiplier

of detailing in the category of 1.05. For the top opinion leader, who influences 17 physicians,

we obtain a social-multiplier of about 1.35% (Figure 5 provides the distribution of social

multipliers for the physicians in our data). Note these are likely the lower bound on the peer

effect since z-it could contain some prescriptions generated via the opinion leader effect. Even

so, we find that peer effects alone provide a 5%- 35% lift in the return-on-investment from

targeting marketing at opinion leaders. A take-away from the analysis is that opinion leader

identification is of key importance to the company.

6. Conclusions

Our paper adds to the small but growing literature that documents peer effects using

individual consumer level data. The unique and novel features of our application include the

asymmetric nature of the interaction and the presence of marketing activity whose effects

are moderated by the interactions among agents. The existence of the peer effect generates

spillovers that multiply the returns to targeting influential agents within a group.

Our key contributions can therefore be seen as providing evidence for asymmetric

peer effects in physician prescription decisions via the use of a novel dataset from the

pharmaceutical industry. Previous academic and industry literature has provided little

support for these effects in this context, in spite of strong industry beliefs in the existence of

these effects. We do this while addressing the identification challenge – separating out

causality from multiple sources that give rise to correlations – inherent in this problem. The

detected effects are robust to model specification and functional form. Finally, we use the

estimated causal effects to derive implications for marketing resource allocation for firms in

the industry and present estimates of the social multipliers effects of marketing activity

(detailing). Our estimates indicate important peer effects in prescription choice, and

economically significant social multiplier effects in detailing.

26

Our analysis has some limitations. First, the network in our approach is “thin”

relative to networks used in previous research. Richer networks could allow researchers to

investigate effect of network structure on the peer effect. Second, given that we only have the

treatment effect “on the treated,” we cannot compute the social multiplier across all types of

physicians. Our conversations with the firm suggest that most general practitioner doctors in

this category may be subject to some form of peer influence; hence treatment on the treated

is likely the right policy-relevant treatment effect. Third, we do not have direct

measurements on the information or opinions shared by the opinion leader with the

nominating physician. While this is a limitation, our results are robust to many functions of

the opinion leader’s prescription activity. An interesting extension of our research would be

to investigate how peer effects are moderated by the nature of nominator and opinion leader

interactions (e.g., in person, via e-mail etc.) over time. Fourth, our data also come from one

specific therapeutic category and from one survey. We also do not have any data on the

identities of the sales force and patients seen by the physicians in our sample. Fifth, we also

do not have access to marketing activity for all drugs in this category at the individual

physician level e.g., we only have aggregate detailing for drug 1 (the market leader). Finally,

we have limited demographics on opinion leaders and nominating physicians. We are also

unable to accommodate unobserved heterogeneity in peer effects; on the other hand, given we

found limited evidence for observed heterogeneity in peer effects, this may be not be

unreasonable for these data. Our hope is that with access to richer data, these limitations

will be addressed in future work.

References

Bandiera, O. and Imran Rasul (2006), “Social Networks and Technology Adoption in

Northern Mozambique,” Economic Journal, 116, 869-902.

Becker, G. and K. Murphy (2000), Social Economics. Cambridge: Harvard University Press.

Becker, Marshall H. (1970), “Sociometric location and innovativeness: Reformulation and

extension of the diffusion model,” American Sociological Review, 35, 267-283.

Bell, David R. and Sangyoung Song. (2007), “Social Contagion and Trial on the Internet:

Evidence from Online Grocery Retailing,” Quantitative Marketing and Economics, 5(4),

361-400.

Bertrand, M., E. Luttmer, and S. Mullainathan (2000). “Network Effects and Welfare

Cultures,” Quarterly Journal of Economics, 115(3), 1019–1055.

Brock, William A. and Steven N. Durlauf. (2001). “Discrete Choice With Social Interactions,”

Review of Economic Studies, 68(235,Apr), 235-260.

Burt, Ronald S. (1987), “Social Contagion and Innovation: Cohesion versus Structural

equivalence,” American Journal of Sociology, 92, 1287-1335.

27

Celentano, David D., Katherine C. Bond, Cynthia M. Lyles, Sakol Eium Trakul, Vivian F.-L

Go, Chris Beyrer, Chainarong na Chiangmai, Kenrad E. Nelson, Chirasak Khamboonruang

and Chayan Vaddhanaphuti. (2000), “Preventive Intevention to Reduce Sexually

Transmitted Infections,” Archives of Internal Medicine, 160, Feb 28, 535-540.

Coleman, James S., Elihu Katz, and Herbert Menzel. (1966), “Medical Innovation: A

Diffusion Study,” The Bobbs-Merrill Company, Indianapolis, IN.

Conley, T. G. and C. R. Udry. (2000), “Learning About a New Technology: Pineapple in

Ghana,” Working Paper, University of Chicago.

Cutting Edge Information (CIE). (2004), “Pharmaceutical Thought Leaders: Brand Strategies

and Product Positioning,” Report PH64.

Dufflo, E. and Emmanuel Saez. (2003), “The Role of Information and Social Interactions in

Retirement Plan Decisions: Evidence from a Randomized Experiment,” Quarterly Journal

of Economics 118(3), pp. 815-842.

Godes, David, and Dina Mayzlin. (2004). “Firm-Created Word-of-Mouth Communication: A

Field-Based Quasi-Experiment,” Marketing Science, forthcoming.

Granovetter, M.S. (1985). “Economic action and social structure: the problem of

embeddedness”, American Journal of Sociology, vol. 91, pp. 481–510.

Hartmann, Wesley (2008), “Demand Estimation with Social Interactions and the

Implications for Targeted Marketing,” Working paper, Stanford University.

Hartmann, Wesley R., Puneet Manchanda, Harikesh Nair, Matthew Bothner, Peter Dodds,

David Godes, Kartik Hosanagar and Catherine Tucker (2007), “Modeling Social

Interactions: Identification, Empirical Methods and Policy Implications,” Marketing

Letters, forthcoming.

Hausman, Jerry, Bronwyn H. Hall and Zvi Griliches. (1984), “Econometric Models for Count

Data with an Application to the Patents-R&D Relationship,” Econometrica 52: 909-938.

Iyengar, R., Valente, T. and Van den Bulte, C. (2008), “Opinion Leadership and Social

Contagion in New Product Diffusion,” working paper, University of Pennsylvaia.

Kremer, M. and Miguel, E. (2004). “Worms: identifying impacts on education and health in

the presence of treatment externalities,” Econometrica, vol. 72, pp. 159–218.

Lomas, Jonathan, Murray Enkin, Geoffrey M. Anderson, Walter J. Hannah, Eugene Vadya

and Joel Singer. (1991), “Opinion Leaders vs Audit and Feedback to Implement Practice

Guidelines,” Journal of the American Medical Association, 265 (17), 2202-2207.

Manchanda, Puneet, Peter E. Rossi and Pradeep K. Chintagunta (2003), “Response modeling

with non-random marketing mix variables,” Journal of Marketing Research, 41, 467-478.

Manchanda, Puneet, Ying Xie and Nara Youn (2004), “The Role of Targeted Communication

and Contagion in New Product Adoption,” Marketing Science, forthcoming.

Manski, C. F., (1993), “Identification of Endogenous Social Effects: The Reflection Problem,”

Review of Economic Studies 60, pp. 531-542.

Manski, C. F., (2000), “Economic Analysis of Social Interactions,” Journal of Economic

Perspectives 14(3), 115–136.

Moffitt, R., (2001). “Policy Interventions, Low-Level Equilibria, and Social Interactions,” in

Durlauf, S. and Young, P. (Ed.) Social Dynamics, Brookings Institution Press and MIT

Press 45-82

28

Munshi, K. and Myaux, J. (2002). “Development as a process of social change: an application

to the fertility Transition”, mimeo, Brown University.

Nam, Sungjoon, Puneet Manchanda and Pradeep K. Chintagunta (2006), “The Effects of

Service Quality and Word-of-Mouth on Customer Acquisition, Retention and Usage,”

working paper, University of Michigan.

Reingen, Peter H. and Jerome B. Kernan. (1986), “Analysis of Referral Networks in

Marketing: Methods and Illustration,” Journal of Marketing Research, 23 (November), 370-

378.

Rogers, Everett M. (2003), “Diffusion of Innovations,” 5th Ed., The Free Press, New York.

Sacerdote, B. (2001). “Peer effects with random assignment: results for Dartmouth

roommates”, Quarterly Journal of Economics, vol. 116, pp. 681–704.

Sorensen, A. (2005), “Social Learning and Health Plan Choice,” forthcoming, RAND Journal

of Economics.

Steffens, P.R., and D.N.P. Murthy (1992), “A mathematical model for new product diffusion:

The influence of innovators and imitators,” Mathematical and Computer Modeling, 16 (4),

11-26.

Summers, John O. (1971), “The Identity of Women’s Clothing Fashion Opinion Leaders,”

Journal of Marketing Research, 7 (May), 178-185.

Tan, Robert S. (2003), “Physician executives as opinion leaders in biotechnology and

pharmaceuticals,” Physician Executive, 29 (3), 26.

Tanny, S.M., and N.A. Derzko (1988), “Innovators and imitators in innovation diffusion

modeling,” Journal of Forecasting, 7, 225-234.

Valente, Thomas and Pumpuang, Patchareeya (2007), “Identifying Opinion Leaders to

Promote Behavior Change,” Health Education and Behavior, 34, 881-896.

Valente, Thomas H., Beth R. Hoffman, Annamara Ritt-Olson, Kara Lichtman and C.

Anderson Johnson. (2003), “Effects of a Social-Network Method for Group Assignment

Strategies on Peer-Led Tobacco Prevention Programs in Schools,” American Journal of

Public Health, 93 (11), 1837-1843.

Van den Bulte, Christophe and Yogesh V.Joshi (2007), “New Product Diffusion with

Influentials and Imitators,” Marketing Science, forthcoming.

Van den Bulte, Christopher and Gary L. Lilien. (2001), “Medical Innovation Revisited: Social

Contagion versus Marketing Effort,” American Journal of Sociology, 106(5), 1409-35.

Watts, D.J. and P.S. Dodds, (2007) “Influentials, Networks, and Public Opinion Formation,”

Journal of Consumer Research., Vol. 34.

Appendix A: The Effect of New Guidelines

In general, guidelines are released to advocate new courses of therapy, to report the efficacy

of the drug to alleviate symptoms not considered in the past, to report interactions with

existing drugs etc. Thus, the primary outcome of these new guidelines are changes in

prescription behavior, especially prescriptions. Note, however, that the release of new

guidelines usually results in a period of confusion about the implications and the intended

use of those guidelines. This confusion arises as guidelines are phrased in general terms and

29

not for each individual patient. In addition, many times, the guidelines can give rise to

questions that need to be answered once the guidelines have been followed.

We illustrate the increased uncertainty post guideline issuance using three cases of new

guidelines (all text in italics in the excerpts below is ours). First, in the case of the American

Heart Association’s guidelines for women’s use of aspirin, the issued guidelines were not

precise, leading to confusion – as is evident from this excerpt below (from www.health.yahoo.com):

“Women and Heart Disease, Part 1: Aspirin Confusion” Posted by Simeon Margolis, M.D.,

Ph.D. on Thu, Mar 08, 2007, 4:27 am PST

“The 2007 update of the American Heart Association's guidelines for women has led to some confusion regarding the use of aspirin. The guidelines do indeed state that women 65 years or older should consider taking low-dose aspirin (81 mg daily or 100 mg every other day), and there is no mention that the presence of risk factors should affect this decision.”

The second case is that of screening guidelines for cervical cancer using Pap tests. We

include excerpts from www.aafp.org and Market Wire below:

“Although the American Cancer Society, American College of Obstetricians and Gynecologists, and U.S. Preventive Services Task Force have released new guidelines for screening, differing language, requirements

and timelines in these guidelines can confuse family physicians and their female patients.” www.aafp.org,

Oct 11, 2005.

“Confusion Over Pap Test Guidelines”

“There's a wealth of misleading information about Pap tests and cervical cancer. Headlines are filled with reports of new guidelines and the possibility of a vaccine that could prevent cervical cancer. But this all creates confusion for women and may discourage them from getting a Pap test. Pathologists, physicians who care for patients through laboratory medicine, say that even with the new guidelines, every woman needs to have a regular Pap test. For some women, that means every year. For other women, that may mean every other year.”

Market Wire, December, 2002.

The final illustration is that of the FDA guidelines for pharmacist guidelines on drug

compounding.

“Despite FDA's new compounding guidelines, confusion reigns

“Reaction to the new guidelines was mixed. Susan Bishop, manager of regulatory affairs and political action for the American Pharmaceutical Association, said pharmacists who were hoping for guidance from the FDA would find more confusion than clarification. Rather than a strict distinction between compounding and manufacturing, the agency is laying out broad directions for enforcement and reserving the right to change course without warning. "I don't know if I would call the document useful," Bishop said. It doesn't give pharmacists any comfort to hear that they may be breaking the law, but that FDA might also decide to

change its mind on whether or not they're breaking the law.” www.drugtopics.com, July 1,

2002

We also polled a convenience sample of physicians (n=4, details on physician demographics

below) and asked them the following question – “In your opinion, when new guidelines are

30

released by bodies such as the FDA and NIH, do they in general, lead to lower or higher

uncertainty in treatment especially in the first few months following the guideline release?

In addition to your answer, please provide the context in which we should interpret your

answer.”

We summarize the findings below: 1. All four physicians felt that guidelines lead to increased uncertainty. 2. This is because the “clinical presentation” of each patient is different and the

physician needs to “individualize” the treatment for the patient. Guidelines do not provide a clear indication of when they would be applicable and when not leading to confusion about whether the treatment is relevant for a given patient. For example, an issue with moving to a combination drug is that the use of combination drug dramatically increases the possibility of negative interactions with other courses of therapy that the patient may be undergoing. The guidelines do not explicitly detail this and therefore the physician needs to verify, for each patient, the possibility of such negative interactions.

3. In general, the role of guidelines is to increase awareness of treatment options. It is not to provide “rules of thumb” that would lead to lower treatment uncertainty.

4. One of the physicians offered up a specific example of her dilemma. A recent release of clinical data suggests that Vytorin/Zetia does not help patients trying to lower cholesterol. If guidelines based on this result are released, then this physician said that the guidelines would have to be interpreted on a patient-by-patient basis – causing her more uncertainty. Specifically, her options would be to take all her patients off the drug, leave patients who are responding positively to the drug on the drug or follow a phased withdrawal.

Physicians Consulted (full names not revealed due to confidentiality reasons):

� Dr. P.K. – Specialist, Houston Northwest medical center, Houston, TX � Dr. R.K. – Former Medical director, Robert Wood Johnson hospital rehabilitation

Center, Cranbury, NJ � Dr. P.S. – Internist, LaPeer Regional Hospital, Detroit, MI � Dr. R.T. – Family Practice, UMDNJ, New Jersey

Taken together, these data illustrate why and how new guidelines lead to increased

uncertainty in most cases.

Table 1: Distribution of Nominations

Number of Nominations Number of Nominators

1 245

2 21

3 1

Total 267

Notes: To be read as: there were 245 physicians who nominated 1 opinion leader, 21 who

nominated 2 opinion leaders, and 1 who nominated 3 opinion leaders.

31

Table 2: Distribution of Opinion Leader Nominations

Number of

nominations

Number of

Opinion Leaders

1 112

2 56

3 7

4 5

8 1

17 1

Total 182

Notes: to be read as, there are 112 doctors who were nominated as opinion leaders by exactly

1 physician, 56 doctors who were nominated as opinion leaders by exactly 2 physicians, etc,

and 1 doctor who was nominated as an opinion leader by 17 physicians.

Table 3: In-sample market-shares of combination drugs

Physicians Opinion

Leaders

Drug 1 0.924 0.861

Drug 2 0.073 0.138

Drug 3 0.003 0.002

Drug 4 0.000 0.000

Table 4: Sample descriptives

Variable Mean Std. Dev. Min Max

Physician prescriptions 4.16 4.40 0 39

Opinion Leader prescriptions 2.23 4.85 0 54

Physician details (drug 2) 0.75 1.35 0 11

Opinion Leader details (drug 2) 0.52 1.15 0 10

Z-it 0.75 0.94 0 13.7

Z-OPL,t 0.40 0.38 0 3.3

Notes: Number of observations in sample = 6960. Z-it refers to the mean prescriptions of all other

physicians in nominator i’s zip-code; Z-OPL,t refers to the mean prescriptions of all other physicians in

nominator OPL’s zip-code;

32

Table 5: Correlation between mean prescriptions of other physicians in the physician’s and the opinion leader’s zip-codes

Month Apr-02 May-02 Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03

Correlation 0.103 0.083 0.012 0.043 0.029 0.037 0.062 0.027 0.020 0.044 0.031 -0.004

p-value 0.097 0.180 0.851 0.493 0.637 0.548 0.318 0.662 0.745 0.483 0.614 0.949

Month Apr-03 May-03 Jun-03 Jul-03 Aug-03 Sep-03 Oct-03 Nov-03 Dec-03 Jan-04 Feb-04 Mar-04

Correlation -0.020 0.054 -0.025 0.009 0.021 0.038 -0.017 0.021 0.052 0.005 0.012 0.074

p-value 0.751 0.381 0.687 0.888 0.730 0.545 0.783 0.734 0.402 0.937 0.846 0.235

Table 6: OLS Regressions of Physician prescriptions on Opinion Leader’s prescriptions1

Param t-stat Param t-stat Param t-stat

Constant 4.079 8.03 4.039 7.93 4.173 8.19

Drug 2 Detailing 0.825 21.80 0.826 21.82 0.821 21.68

Drug 1 Detailing (aggregate) -0.012 -1.38 -0.012 -1.39 -0.017 -1.91

OPL_Nrx 0.071 6.77 0.070 6.59 0.048 0.88

Z-it 0.059 1.09 0.050 3.36

I(t>=May 03) 0.298 2.63

OPL_Nrx* I(t>=May 03) 0.039 1.86

F 174.91 131.48 90.76

R2 0.0701 0.0703 0.0726

Table 7: Fixed-Effect Regressions of Physician prescriptions on Opinion Leader’s prescriptions1

Param t-stat Param t-stat Param t-stat

Constant 4.243 12.61 4.104 12.07 4.297 12.65

Drug 2 Detailing 0.014 0.40 0.012 0.35 0.001 0.02

Drug 1 Detailing (aggregate) -0.002 -0.29 -0.002 -0.30 -0.007 -1.27

OPL_Nrx 0.002 0.21 0.002 0.20 -0.022 -1.79

Z-it 0.190 2.73 0.138 1.99

I(t>=May 03) 0.369 4.92

OPL_Nrx* I(t>=May 03) 0.043 3.09

F 46.9500 47.020 47.4700

R2 0.6050 0.6054 0.6087

1Fixed effects for nominating physicians included, but not reported. Nobs =6960.

33

Table 8: First stage regressions of OPL prescriptions on instruments

On only excluded

instruments

On first-stage

instrument matrix1

Param t-stat Param t-stat

Drug 2 detailing to nominating

physician

-0.041 -1.02

Aggregate Drug 1 detailing (1000-s) -0.014 -2.11

I(t>May 03) 0.002 0.03

Z-it -0.057 -0.72

Drug 2 detailing to OPL 2.311 20.89 1.296 14.37

(Drug 2 detailing to OPL)2 -0.167 -7.29 -0.110 -6.30

Z-OPL,t 2.173 6.72 1.765 4.04

(Z-OPL,t)2 -0.993 -5.40 -0.243 -1.06

Constant 0.724 7.42 -0.816 -1.00

F 343.88 47.97

R2 0.1651 0.5904

N 6960

1Fixed effects for nominating physicians included, but not reported. Nobs =6960

Table 9: Fixed-Effect Instrumental Variable Regressions of Physician prescriptions on

Opinion Leader’s prescriptions1

Param t-stat Param t-stat Param t-stat

Drug 2 Detailing 0.078 1.95 0.073 1.83 0.000 0.01

Drug 1 Detailing (aggregate) 0.011 0.32 0.010 0.27 -0.007 -1.21

OPL_Nrx -0.001 -0.18 -0.001 -0.19 0.011 0.28

Z-it 0.188 2.70 0.139 1.99

I(t>=May 03) 0.386 3.80

OPL_Nrx* I(t>=May 03) 0.032 2.06

J-stat (Sargen) 10.572 10.354 9.476

Chi2 pvalue (degrees of freedom) 0.0143 (3) 0.0158 (3) 0.1485 (6)

1Fixed effects for nominating physicians included, but not reported. Nobs =6960.

Table 10: Regressions of Physician prescriptions on Opinion Leader’s prescriptions with

full set of physician and time-period fixed effects1

Fixed effects Fixed-Effects IV

Param t-stat Param t-stat Param t-stat Param t-stat

Constant 1.678 2.17 1.627 2.10

Drug 2 Detailing -0.009 -0.25 -0.010 -0.27 -0.009 -0.25 -0.010 -0.26

Drug 1 Detailing (aggregate) 0.042 3.26 0.042 3.22 0.139 3.95 0.137 3.88

OPL_Nrx -0.023 -1.83 -0.023 -1.82 0.001 0.02 0.000 -0.01

Z-it 0.119 1.71 0.120 1.72

OPL_Nrx* I(t>=May 03) 0.042 3.02 0.042 3.01 0.031 2.06 0.031 2.05

Time-period fixed effects Y Y Y Y

F 47.710 47.730

R2 0.6125 0.6127

J-stat (Sargen) 7.299 7.302

Chi2 pvalue (degrees of freedom) 0.2941 (6) 0.2938 (6)

1Nobs = 6960.

34

Table 11: Fixed effects instrumental variable regressions of OPL prescriptions

Param t-stat Param t-stat Param t-stat Param t-stat

Nominator_NRx 0.001 0.02 -0.037 -0.48 -0.058 -0.76 -0.041 -0.46

Nominator_NRx*I(t >=May

03) 0.044 0.90 0.063 1.31 0.058 1.13

Drug 2 detailing 0.136 4.35 0.115 3.61 0.110 3.41 0.115 3.59

Drug 1 detailing

(aggregate: 1000-s) -0.009 -2.06 -0.012 -2.70 0.000 0.01 0.008 0.21

Z-it 0.752 3.22 0.538 2.28 0.480 2.03

I(t >=May 03) 0.066 0.34

All month fixed effects? N N Y Y

J-stat (Sargen) 6.81 5.23 4.85 2.14

Chi2 pvalue (degrees of

freedom) 0.0782 (3) 0.2645 (4) 0.3027 (4) 0.3437 (2)

Notes: 1New treatment guidelines issued in May 03. Fixed effects for each opinion leader estimated,

but not reported. Robust t-stats reported.

Table 12: Robustness checks: Fixed effects IV regressions of Physician prescriptions on

functions of OPL prescriptions

[1] [2] [3] [4] [5]

f(OPL_NRx):

( ), 1j i tx

−

OPL

prescriptions

last month

( )( ),j i tI x

Did OPL

prescribe?

( )( ), 1j i tI x

−

Did OPL

prescribe last

month?

( )1 ,

t

j ixτ τ=∑

How much has

OPL prescribed

as of this month?

( )11 ,

t

j ixτ τ

−=∑

How much has

OPL prescribed

as of last month?

Param t-stat Param t-stat Param t-stat Param t-stat Param t-stat

Drug 2 detailing -0.021 -0.56 -0.018 -0.47 -0.023 -0.61 -0.019 -0.50 -0.019 -0.51

Drug 1 detailing

(aggregate: 1000-s) 0.412 3.37 0.347 2.20 0.315 1.80 0.526 3.95 0.525 3.95

f(OPL_NRx) -0.018 -0.43 0.582 0.72 1.339 1.33 -0.007 -1.91 -0.007 -1.19

f(OPL_NRx)*I(t >=May 03)1 0.036 3.04 0.303 3.64 0.802 3.30 0.005 1.94 0.005 1.93

Z-it 0.112 1.57 0.104 1.45 0.133 1.77 0.126 1.75 0.126 1.75

Month fixed effects included? Y Y Y Y Y

Instruments2 ( ), 1j i t−w ( ),j i t

w ( ), 1j i t−w

( )1 ,

t

j iτ τ=∑ w ( )1 , 1

t

j iτ τ= −∑ w

Chi2 pvalue (degrees of freedom) 0.262 (4) 0.316 (4) 0.1197 (4) 0.7642 (4) 0.7523 (4)

Notes: Each column presents results from fixed-effects IV regressions of a physician’s prescriptions

on a function of his opinion leader’s prescriptions. For example, column [1] presents results from

fixed-effects IV regressions of a physician’s prescriptions on a lagged values of of his opinion leader’s

prescriptions; the effect of lagged opinion-leader prescriptions pre-guidelines is -0.018, and post-

guidelines is -0.018+0.036 = 0.018. Dependant variable in all IV regressions is Physician NRx = yit.

Physician and month fixed-effects estimated in all specifications, but not reported. 1Guidelines

released in May 2003. 2w denotes the vector of available instrumental

variables: ( ) ( ) ( ) ( )( )2 2

, , , ,, , ,

it j i t j i t j i t j i tx x z z

− −=w .

35

Table 13: Responsiveness of OPL prescriptions to detailing

OLS Fixed Effects1 Variable

Param t-stat Param t-stat

Constant 2.167 4.05 2.358 9.02

Drug 2 Detailing 1.614 34.09 0.136 4.35

Drug 1 detailing (aggregate) -0.018 -1.98 -0.009 -2.06

Z-OPL,t 0.679 4.75 0.753 3.44

F 426.33 12.79

R2 0.155 0.826

N 6670

Notes: Fixed effects for each OPL estimated, but not reported. Robust t-stats reported.

Figure 1: Aggregate detailing: Combination drugs

50

55 57

65

57

50

61

54 53

60 58 59

65

54

68 65

60 58

64

51

43

53

62

57

13

17 15 16

15 15 16

12 10

13

20

15 12

19

24 26

20 21 19

17 19

24 24 25

0

10

20

30

40

50

60

70

80

Apr- 02

May- 02

Jun- 02

Jul- 02

Aug- 02

Sep- 02

Oct- 02

Nov- 02

Dec- 02

Jan- 03

Feb- 03

Mar- 03

Apr- 03

May- 03

Jun- 03

Jul- 03

Aug- 03

Sep- 03

Oct- 03

Nov- 03

Dec- 03

Jan- 04

Feb- 04

Mar- 04

Month

1000-s of details Drug 1 details

Drug 2 details

36

Figure 2: Distribution of Category prescriptions before and after issuance of new NIH

guidelines

1 2

3.5

4

4.5

5M

ea

n N

Rx p

er

mo

nth

Physicians

1 2

1.9

2

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

Me

an

NR

x p

er

mo

nth

OPL-s

Before BeforeAfter After

Figure 3: Distribution of detailing before and after issuance of new NIH guidelines

1 2

0.2

0.4

0.6

0.8

1

1.2

1.4

Me

an

de

tails

pe

r m

on

th

Before

Physicians

BeforeBefore1 2

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Me

an

de

tails

pe

r m

on

th

After

OPL-s

After Before

37

Figure 4: Variation in mean prescriptions of other physicians in the

physician’s zip-code over time, controlling for across physician variation

0 5 10 15 20 25-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

Month (1 = May 2002)

Me

an

NR

x o

f o

the

r p

hysic

ian

s in

zip

-co

de

Z-i

Z-OPL

Figure 5: Distribution of social-multipliers in data

1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Social Multipliers

Pro

portio

n