Market Size and Pharmaceutical Innovation
Pierre Dubois, Olivier de Mouzony, Fiona Scott-Mortonz, Paul Seabrightx

This Version: March 2011{

Abstract
This paper quanti…es the relationship between market size and innovation in the pharmaceuticalindustry. We estimate the elasticity of innovation, as measured by the number of new chemical
entities appearing on the market for a given disease class, to the potential market size represented
by the willingness of su¤erers of diseases in that class (and others acting on their behalf such as
insurers and governments) to spend on their treatment during the patent lifetime. We …nd positive
signi…cant elasticities with a point estimate under our preferred speci…cation of 25.2%. This suggeststhat at the mean market size an additional $1.8 billion is required in additional patent life revenue
to induce the invention of one additional new chemical entity. An elasticity substantially andsigni…cantly below one-half is also a plausible implication of the hypothesis that innovation inpharmaceuticals is becoming more di¢ cult and expensive over time, as costs of regulatory approvalrise and as the industry runs out of "low hanging fruit".
Key words: Innovation, Market Size, Elasticity, Pharmaceuticals.
JEL codes: O31, L65, O34.

Toulouse School of Economics (GREMAQ, IDEI), pierre.dubois@tse-fr.eu
Toulouse School of Economics (GREMAQ, INRA), olivier.demouzon@tse-fr.euz
Yale University, …ona.scottmorton@yale.edu
xToulouse School of Economics (GREMAQ, IDEI), paul.seabright@tse-fr.eu{
We thank Tamer Abdelgawad, Amber Batata, Bruno Jullien, Bernard Salanié and seminar participants at Toulouse,
PEPC Paris, Imperial College for useful comments. The statements, …ndings, conclusions, views and opinions containedand expressed herein are based in part on data obtained under license from IMS Health Inc.’s MIDAS c database. All
rights reserved. We thank P…zer Inc for its center research support to IDEI. The statements, …ndings, conclusions, views
and opinions contained and expressed herein are those of the authors only.
y

1

1

Introduction

This paper quanti…es the relationship between …nancial returns and innovation in the pharmaceuticalindustry. More precisely we shall estimate the elasticity of innovation (as measured by the numberof new chemical entities appearing on the market for a given disease class) to the expected potential
market size represented by the willingness of su¤erers of diseases in that class (and others acting on
their behalf such as insurers and governments) to spend on their treatment. This potential market
size is in‡uenced by broadly three types of factor, and in order to understand how to interpret our
results it is necessary to distinguish these factors carefully.
First there are factors such as demographic and socio-economic change, which a¤ect the numbersof people who are likely to su¤er from a particular medical condition and the resources they are likely
to have available to spend on alleviating their condition. A motivating example concerning research on
gout from the New York Times illustrates the incentives for R&D. "Often called the “disease of kings”
because of its association with the rich foods and copious alcohol once available only to aristocrats,
gout is staging a middle-class comeback as American society grows older and heavier. ...Companies
are now racing to improve upon decades-old generic drugs that do not work well for everyone. Alreadythis year the Food and Drug Administration has approved the …rst new gout drug in more than 40
years...".1 Many other examples evidently spring to mind of research motivated primarily by changing
demographic and socio-economic factors, such as research into cardiovascular disease and Alzheimer’s
disease.
Secondly, there are factors particular to the pharmaceutical and health-care industries, such as
the degree of competition among …rms and the strategies that …rms use to innovate, cut costs, and1"Disease of Rich Extends Its Pain to Middle class", New York Times, June 12, 2009 . The story continued:"...a product called Uloric from Takeda Pharmaceutical. Another new drug, Krystexxa, made by Savient Pharmaceuticals of East Brunswick, N.J., will be reviewed for possible approval by an F.D.A. advisory committee on Tuesday. And
several other companies are testing drugs in clinical trials. “It’s kind of like the forgotten disease,” said Barry D. Quart,
chief executive of one of those companies, Ardea Biosciences of San Diego. Ardea discovered accidentally that an AIDS
drug it was developing might work against gout. Now the company has shifted its focus to gout, envisioning annual salesof $1 billion if its drug is successful. That would mean a huge increase in spending on gout medicines, which had sales of
only $53.4 million last year, according to IMS Health, a health care information company. Uloric, the drug from Takeda,
sells a daily pill for at least $4.50 compared with 10 to 50 cents for the most commonly used generic, allopurinol. It isestimated that two million to six million Americans have gout... Various studies suggest that the number of cases in thiscountry has as much as doubled in the last three decades".

2

win customers, that a¤ect the pro…tability of innovation. For example, intensi…ed competition fromgenerics for branded products occurs not only as a response to patent expiry but also in response to
the purchasing environments. Cost-control pressures from managed care create incentives for genericuse and reduce the expected market size of an innovation. The response of …rms to a given potential
market size may also depend on such considerations as their degree of symmetry in competence: thecompetition between two …rms of similar size and employing similar talent pools may be quite di¤erent
from competition between a leader and a follower …rm.
Thirdly, there are public policies, including policies towards intellectual property protection, drug
safety and testing, pricing and reimbursement, and public funding of research. As a matter of comparison, in 2004, research spending by NIH reached $28.5 billion while the members of the Pharmaceutical
Research and Manufacturers of America report R&D spending of about $40 billion - see CBO 2006.
Policy innovations such as the introduction of Medicare in the 1960s have had a large impact on potential market size, and various researchers have noted the consequences for research and innovationin drugs for the elderly that were the probable consequence (Acemoglu et al., 2006).
In principle it might be thought that, from a policy point of view, the only really interesting factorsa¤ecting potential market size are those in the third category, and that therefore any study such as
ours should focus on the elasticity of innovation with respect to these factors only. Such estimates
could be used, for instance, to calculate the social cost of a particular drug pricing regime or of a
proposed change in the length or breadth of patent protection, or the social returns to additional
spending by the NIH. However, this argument is ‡awed, even if one ignores the possibility that thein‡uence of the other types of factor may be of independent interest (such as for predicting future
trends in research and innovation). Estimating the elasticity of innovation with respect to past changes
in policy would be possible only if we could observe genuine policy experiments, conducted without
reference to the many factors, unobserved to the econometrician, that might in‡uence the potential
rate of innovation. But most policy changes are not like that, and arguably most of them should not
be. They are typically endogenous to the rate of innovation itself, either because public policy may
feel it does not need to intervene to favor areas where innovation is already coming along nicely, or3because public policy likes to bask in the glow of supporting visibly successful areas, or because once
key drugs have been developed it is tempting to lower their prices to users, or for any number ofother reasons. This makes the observed elasticity of innovation with respect to past policy changes
unreliable as a guide to the elasticity of innovation with respect to future policy changes.
Factors of the …rst type, by contrast, are rarely under the in‡uence of either government or industry
participants, at least not in the short- to medium-term. Far from making them less interesting froma public policy point of view, this makes them worthwhile studying, because they represent a wayof estimating the impact of genuinely exogenous changes of potential market size, and therefore of
representing the potential impact of a change in public policy that otherwise holds all factors constant.
In essence, using the exogenous demographic and health changes to estimate the elasticities, freed of
the confusing interference on market size of the two other types of factor, allows us to simulate thee¤ect of future policy changes.
While existing studies of these issues have focused on US data, we use both US and international
data to re‡ect the fact that all markets, including those outside the US, constitute an important
incentive for innovation. There is an older literature relating market size in a country to the R&D
undertaken in that country. Because there are unobserved factors such as the level of education in acountry that might a¤ect both R&D and health spending, we do not use this source of variation. The
variation we exploit in estimating the responsiveness of innovation to market size is across therapeuticclasses and over time, rather than across geography. Additionally, biopharmaceutical research iscarried out all across the globe by both local and international …rms. The products invented bythese …rms might treat local healthcare needs or global ones. Therefore, a product generated byresearch from one country is likely to be useful, and therefore commercialized, in many countries. The
appropriate level of analysis for a study of the determinants of innovation – both in terms of which
products to include and which markets to count - is global.
Our identi…cation strategy is as follows. If the cardiovascular market is expected to grow due toan aging population, there will be an increase in cardiovascular R&D and we will see the release of
new cardiovascular products on the market at the time of the demand increase. Di¤erent therapeutic4markets that grow di¤erently over time due to demographic and income di¤erences drive changes ininnovative activity. We can measure the elasticity of new products to market size using this variation.
The relatively recent literature on the topic of the elasticity of pharmaceutical innovation to market
size contains two broad approaches. The older of the two uses accounting data to estimate thedeterminants of R&D (see Grabowski and Vernon (2000)). In theory, under perfect capital markets
R&D would be chosen only in response to expected future pro…tability of the project. However, if
capital markets are imperfect and external funds are more expensive than internal cash ‡ow, current
revenue (market size) will have a positive impact on the amount of research funded by the …rm.
Of course, if current market size is a proxy for future market size, then current research may be
responding to future sales opportunities also, but these two e¤ects cannot be disentangled. Giacotto
et al. (2005) regress R&D-to-sales ratio on the pharmaceutical price index from the previous year andother variables. Again, this is a model of innovation responding not to expected future market size,
but to recent prices. They …nd a 1% increase in price leads to a 0.58% increase in R&D spending. A
second stream of the literature uses alternate measures both for outcomes and market size. Innovationis measured as clinical trials (Blume-Kohout and Sood, 2009), journal articles or disease regimens(Lichtenberg, 2006), while potential market size is (negative) disability-adjusted life years (DALY)
or mortality (Civan and Maloney, 2006,2009, Lichtenberg, 2005). Lichtenberg (2005) …nds resultsof similar magnitude: a 1% increase in the number of people with cancer leads to a .58% increasein chemotherapy regimens. Civan and Maloney (2006, 2009) …nd that a 1% increase in expected US
entry price leads to .5% increase in the number of drugs in the drug development pipeline. Lichtenberg(2005) …nds that a 1% increase in DALY leads to a 1.3% increase in global drug launches.
The paper most closely related to ours is by Acemoglu and Linn (2004), hereafter AL. Using
variation from 1970-2000 in what US cohorts purchase and in FDA approvals, AL …nd that a 1%
increase in contemporaneous expenditure shares leads to a 4% increase in the number of new drugs
released on the market. This is a markedly higher elasticity than found in the previous literature. We
will discuss the di¤erences between our methods and data and theirs in detail below.
We examine the response of global new drug launches to global market size measured in revenues.

5

Of course, a simple regression of new drugs on revenues will pick up both causal directions mentioned
above: revenues attract innovation and also drug innovations generate revenue. Isolating the …rst
causal relationship requires an instrument. We use measures of population and disease prevalence inour sample of countries as our instruments for revenue. Our strategy makes sense because, as one
might expect, demographics are strongly correlated with revenue. However, the invention of a new
drug does not change the contemporaneous propensity for populations of a particular country to su¤erfrom cardiovascular disease, for example. Thus we expect demographics to be uncorrelated with theerror term in our regression of new drug counts. For our assumption not to hold, it must be the casethat a novel therapy generates additional measurable diagnoses in the speci…c area. While this makes
sense for narrowly targeted treatments and diagnoses, it seems much less likely to be operating at thelevel of fairly coarse disease areas tracked by WHO, such as “cardiovascular disease”. By utilizing this
instrumental variables approach we can isolate the impact of revenue changes driven by demographics
and determine their impact on innovation.
While we will estimate the impact of …nancial incentives on the launch of new products, we are
cautious about drawing conclusions about the welfare bene…ts of those new products. There are various
reasons why this is not straightforward. One is that there may be diminishing marginal bene…ts tobe had from innovation within a disease class: the …rst radical innovation in a therapeutic class may
produce much greater overall bene…ts than a drug that is just su¢ ciently di¤erent from it to be
granted a patent (though the opposite could be true if the second drug avoids debilitating side-e¤ects
associated with the …rst). A second reason is that it is particularly di¢ cult in this line of research
to calculate the welfare of a new innovation in the absence of measures of consumers’ valuation or
willingness to pay. Most patients are insured and therefore do not face a marginal price when buying
biopharmaceuticals. The buyer in most cases is either the nation in the case of national health systems,
or the large PBMs, in the case of private healthcare (USA), or some national systems like Germany.
This buyer, while not the patient, is the one that controls the formulary and pays at the margin, andso, from the point of view of the researcher, has revealed a valuation for the treatment. However, someof these buyers may have monopsony power and face political constraints, so using negotiated prices

6may not closely re‡ect consumer welfare. An alternative approach to calculating welfare is to simply
measure life years saved by the new innovations and multiply by QALY, though comparable data formany of these products is sparse. Our elasticities should therefore be interpreted only in terms of new
product numbers and great care should be exercised before any welfare conclusions are explicitly or
implicitly drawn from them.
We begin in section 2 by describing a simple model with testable implications we are investigating.
Section 3 describes our data. Section 4 gives our detailed results. Section 5 concludes.

2

Theoretical Framework and Testable Implications

We are interested in the e¤ect of expected market size on innovation. The model will consider that amarket represents a set of products in a given therapeutic class (though in the empirics we shall test
the robustness of our estimates to variations in the breadth of de…nition of such classes).
Consider the question of how …rms expect the share of the potential revenues in a given market tovary according to their R&D investments. This problem has two parts: how much their investments
will a¤ect either the probability of discovering new drugs or the date at which they are discovered (orboth), and how the discovery of new drugs will a¤ect their revenues given the expected presence ofother drugs on the market. Several simple models can be considered to study this relationship, andpredictions di¤er according to the assumed exogeneity of the structure of the industry, to the numberof …rms, to the cost structure of R&D, and to the existence of R&D spillovers.
The model in AL describes a market in which drugs within a given therapeutic category are only
vertically di¤erentiated; at any one moment the best drug, and only the best, will capture the entire
market. However, the top drug may not remain the best for long, and ongoing research may at any
moment produce a better drug which will in turn capture the entire market, reducing the revenuesof the previous best drug immediately to zero. The e¤ort of pharmaceutical research will therefore
consist in trying to produce the drugs that are better than existing drugs and hold that position forlong enough to sustain their development costs before the next improved drug in the class arrives.
The key point to note is that in their model …rms adapt the ‡ow rate of innovation precisely to the7market size at that period (this does not imply perfect ‡exibility of innovation outcomes, merely that…rms have enough warning of future changes in market size - and enough foresight of the behavior ofother …rms - to be able to bring their innovations on stream at precisely the opportune moment). Also
it is only market size at that point that matters, because a product’s e¤ective economic lifetime isessentially contained between the date of its launch and the date of the launch of a superior alternative,
which in equilibrium may follow quite soon afterwards (though this short lifetime is compensated - toa degree that remains to be determined empirically - by the fact that in its short lifetime the productcaptures the entire market).
We do not follow AL’s modeling assumptions for several reasons. First, as a practical matter,
it is clear that many di¤erent treatments within the same therapeutic class have positive revenue inthe same time period. This, along with the …ndings in the clinical literature, indicates that there issubstantial horizontal di¤erentiation in pharmaceutical markets. We want our model to re‡ect thisfeature of the industry. Secondly, we are interested in developing a model that will demonstrate alink between market size and the number of products. A model that allows the …rm to choose its
level of R&D will yield results that depend on the trade-o¤ between horizontal and vertical productdi¤erentiation, issues that are not well explored in a model in which the highest quality productcaptures the entire market. For instance, Sutton (1991) shows that increasing market size may notalter concentration in the industry if increased quality successfully attracts consumers away fromhorizontally di¤erentiated alternative products. In these circumstances a larger market size will leadthe …rm to invest in vertical di¤erentiation which naturally results in higher quality products, but not in
the entry of new …rms. The reason is that margins must support the …xed costs desired by consumers;
if they want high quality, then both market share and margins must contribute to covering those …xed
costs, which means concentration must also be high. Increasing the degree of horizontal di¤erentiation
weakens the Sutton result because of the built-in incentive for a …rm to serve a di¤erentiated nicheeven with lower quality. Berry and Waldfogel (2010) illustrate this trade-o¤ in their work comparing
restaurants to newspapers. Because restaurants are more horizontally di¤erentiated than newspapers
and costs are mostly variable (unlike in newspapers), increasing market size does call forth new8

…rms and reduce concentration. By contrast, newspaper quality is improved using …xed costs andnewspapers compete on vertical quality measures, so increased market size does not create new …rms
but rather raises the quality of incumbents. In their paper, the type of di¤erentiation and the cost
structure have the same response to increased market size and work together to create maximaldi¤erence in outcomes between the two industries. Our setting, pharmaceuticals, has signi…canthorizontal di¤erentiation but also signi…cant …xed costs, thus not …tting neatly into the Berry andWaldfogel (2010) framework. We present below a model that allows for both horizontal and verticalproduct di¤erentiation and where quality depends on …xed cost. We show that the number of …rms
increases in market size when …xed costs are exogenous. To address the Sutton point, we repeat theanalysis for endogenous …xed costs and …nd the conditions under which we achieve the same result.
Lastly, our …nal concern with the AL vertical di¤erentiation setup is that it is di¢ cult to operationalize empirically, a point we will return to below.

2.1

A Simple Model of R&D with Horizontal Di¤erentiation

The model is a version of the well-known circle model due to Salop (1979), extended to incorporate
vertical di¤erentiation in the manner of Armstrong and Weeds (2005). The Salop circle model is anice choice for our problem because a natural number of …rms …t round the circle in response to
consumers’ willingness to pay and …xed costs. In this framework, when a …rm considers whether itshould invest or not, it forms its expectations about revenues of the product over its lifetime, taking
into account the productivity of its own research programs and those of its rivals. We assume that…rms have rational expectations of the number of rivals, shares and prices.
Consider a circle of unit size, with a mass m of consumers uniformly distributed around the circle.
Around this circle N …rms are located at equal intervals. Firm i is located at point i=N , and incurs a…xed cost of investment to produce a drug of quality vi which it sells at price pi ; the number of …rms
N will be determined by the requirement that the marginal …rm makes zero pro…ts (we shall ignore
integer problems).
A consumer purchases at most one unit of the drug, and has an outside option yielding utility
normalized to zero, so will purchase uniquely if doing so yields weakly positive utility. To simplify the9algebra we assume marginal costs of production are zero, though nothing in the qualitative results
turns on this assumption.
Consider what happens if there is market coverage –every consumer buys from at least one …rm.
A consumer located at point c between …rm i and …rm i + 1 and who is indi¤erent between the two
will derive utility Uc whereUc = v i

pi

i
N

t c

= vi+1

pi+1

t

i+1
N

c

from which we can de…ne c, the location of the indi¤erent consumer under market coverage, as
c=

1
vi
i
+
+
N
2N

vi+1

(pi
2t

pi+1 )

where t is the (linear) “transport cost”, an indicator of the extent of horizontal product di¤erentiation.
In a symmetric equilibrium in which all …rms’prices and qualities are identical, this means that theutility of the marginal consumer becomes
Uc = v i

pi

t
2N

(1)

Now, consider what happens if market coverage does not obtain. Then the indi¤erent consumer isde…ned by
vi

pi

t c0

i
N

=0

where c0 is the marginal consumer in the absence of market coverage. This implies thatc0 =i(vi pi )+Nt

In practice we shall see that it is always more interesting to look at the case of market coverage,
since for a given market size m there will either be no entry or else …rms will enter until the available
space on the circle is entirely occupied.
Now consider the objective function

i of …rm i. We look at two cases. First, there is the case ofexogenous quality, then of endogenous quality with quadratic costs of investment in quality.

102.1.1

Exogenous quality

Let’s assume that the quality of products is …xed exogenously and consider …rst that the market
coverage is complete. Then, given the expressions for the indi¤erent consumers, we can derive thedemand for each …rm i and obtain the following objective function for the pro…ts of …rm i:!
2v i vi 1 vi+1
2pi pi 1 pi+1
1
K+
i = mpiN
2t
Taking …rst order conditions with respect to prices while qualities are …xed yields:!
2v i (vi 1 + vi+1 ) + pi 1 +pi+1
@ i
1
2pi
=m
+
=0
@pi
N
2t
t
pi =

2t
+ pi 1 +pi+1 + 2v iN

(vi 1 + vi+1 )In a symmetric equilibrium where qualities and prices are identical among …rms, this implies that
pi =

t
2N

(2)

We can now use the zero-pro…t condition to solve for the equilibrium value of N (ignoring integer
problems). Substituting for equilibrium prices in the pro…t function yields
i =

mt
2N 2

which when pro…ts are zero implies
N=

r

K

mt
2K

(3)

Then, industry pro…ts are zero and industry revenue is K.
This yields a monotonic, strictly concave, relationship between market size m and the number of…rms N (hence of pharmaceutical products in the market), with N proportional to the square root of
m. From this we can derive

@N
1
=
@m
2

r

t
N
=
2mK
2m

which implies that the elasticity of N with respect to m is 0:5.

11

(4)

Note that we would obtain a similar qualitative …nding with a quadratic transportation cost
mt
2K

function t(x) = tx2 , as we would obtain N =13with a corresponding elasticity of 31 .

To verify the conditions under which this is consistent with market coverage, note that substituting(2) into (1) implies that the indi¤erent consumer c has utility leveltN

Uc = vi

which for market coverage with exogenous quality implies, by substitution of (3), that
r

vi

2tK
m

So we can conclude that, if quality is exogenous and above some threshold (q2tKm ) that is increasingin the extent of product di¤erentiation (t) and in …xed entry costs (K), and decreasing in market size(m), then the number of drugs in the market increases as the square root of market size.
In the case where the market would not be fully covered, it is straightforward to show that
pi =

vi
2

Then, the pro…t function becomes
i =

mvi 2
=K
4

which implies that …xed costs of entry are either too high so that no entry occurs, or low enough sothat entry occurs until it exhausts all available opportunities. This suggests that market coverage isthe only interesting case to study.
2.1.2

Endogenous quality

In studying endogenous quality, we therefore look only at the case of full market coverage. Then, thepro…t function can be written as follows:
i = mpi

where K +

2v i
1
+
N

vi 1

vi+1

2pi
2t

pi 1 pi+1!

K+(vi v)2
is the cost of innovating with an innovation of quality level vi .
2

12

v)2

(vi
2

!

This di¤ers from the pro…t function when quality is exogenous only in that the …xed cost contains
an element that is quadratic in the cost of increasing quality above a certain base level v.
Assume that …rms …rst choose product quality as part of their R&D decisions, then choose prices
once qualities are …xed. When choosing their R&D decisions they take the R&D decisions of other…rms as …xed and therefore also their entry decisions, so N is taken as given by …rms choosing quality.
In solving for prices we can no longer presume equilibrium is symmetric. We assume neverthelessthat equilibrium is symmetric among …rms other than …rm i. Writing pj = pi+1 = pi 1 and analogouslyfor qualities, taking …rst order conditions with respect to pi and substituting in the analogous …rst
order conditions for pj yields
pi =

2 (vi vj )t+N
3

Substituting for prices in the objective function yields
i =m(vi vj )
t
+
N
3

vi vj
1
+
N
3t

K+

v)2

(vi
2

!

and taking …rst order conditions with respect to vi yields
m 4m (vi vj )
+
+ v
N
9t

vi = 0which, since equilibrium is symmetric, implies thatvi = v +m
N

This incidentally implies that the condition for market coverage can be written as
v+

m
N

t
N

t
2N

0
3t 2m
2 N .

which implies a threshold condition for v, namely that v

We can now use the zero-pro…t condition to solve for the equilibrium value of N . Substituting forequilibrium qualities and prices in the pro…t function yields
i =

mt
N2

K+

m2
2 N2

1

m
2t

which when pro…ts are zero implies
N=

s

mt
K
13

m
2t , which will hold so long as the cost of improving the

A real solution for N requires that
product is su¢ ciently great.

Then, we also have the equilibrium quality of products which isvi = v +

1

s

mK
t

m
2

which is increasing in K, decreasing in , t.
We can then di¤erentiate N with respect to m to yield:
t m
@N
= r
@m
2 mtK 1
which is strictly positive so long asm2t> m=2t.

The general conclusion about the relationship between market size and innovation derived in themodel with exogenous quality therefore carries over to the model with endogenous quality, so long as
the cost of improving quality is su¢ ciently high.

2.2

Empirical Implications

The choice made in modeling competition has an impact on the empirical work due to the need tomeasure potential market size. Recall that in AL, competition among pharmaceutical products isvertical; that is, consumers rank products identically and all purchase the best one. A …rm’s sales arethus zero in all periods in which its drug is not the highest quality. In the period when it has thebest quality it has 100% market share in the category. The measure of market size that most closely…ts the model is revenues in the entire class in the time period the new drug is launched. In practice,there are a number of drugs in that therapeutic class on the market earning positive revenues. All
these are included in the measure of the size of the market, as a vertically superior drug could capture
those revenues over a window of time.
Our model, by contrast, allows for horizontal competition. In our data, it is not typically the casethat drugs take 100% market share serially upon entry. Rather, drugs are somewhat di¤erentiatedin their side e¤ects and e¢ cacy for di¤erent groups, and each can make positive sales over timewith others in the same class. When a …rm is considering investing in a horizontally-di¤erentiated14innovation, we assume it is aware of these empirical patterns and has rational expectations about
the market size of the therapeutic class and revenues a typical drug in that class might earn over its
lifetime. We operationalize this as the present discounted value of sales of the average drug in thatcategory, invented in that time period, over its lifetime (20 years).
To summarize, AL measure market size as the revenues that accrue to all drugs in a categorythat are available on the market in a given time window (regardless of the year of their launch),while we measure market size as the lifetime revenue accruing to the average product that is launchedduring a particular time window. Our approach has the advantage of taking into account horizontal
competitors in the innovation process that are innovating around the same time.
Observed pharmaceutical revenues are an outcome of the equilibrium relationship resulting fromcompetition on the basis of innovation among …rms in the biopharma industry. We consider that…rms take investment in research and development decisions based on rational expectations of the
pro…tability of such innovations. Given the usual time lag between R&D and innovations, we expect…rms to anticipate market size and rationally invest years in advance. For example, a …rm releasing
a drug at time t is supposed to have begun the research process at t

15 if it expects that it willtake 15 years to get approval for the new drug on a given market, taking into account the di¤erent
phases of the innovation process from fundamental research through the …nal phase clinical trials.
Thus assuming rational expectations by …rms, the realization of an innovation should happen at thetime it has been anticipated and be driven by the revenue …nally realized by such innovation.
A virtue of our approach is that it does not require us to examine the laws and regulations of thenations in our dataset to determine the lifecycle and pro…tability of a new innovation in each country.
Rather, we use the empirical measurement of brand revenue over time to tell us the e¤ective patent
length and market size (marginal costs are very low in the pharmaceutical industry). This allows us toabstract from the exact nature of competition among brands and how many entrants enter a market
on average. Likewise, the state of generic competition when the patent does end is important indetermining the e¤ective period of brand exclusivity because di¤ering regulations result in the brandkeeping a substantial share of the market after generic entry in some countries and not in others.

15

Additionally, in some countries (such as the USA) the entry of the generic does not drive down brandprices, whereas in others it may. Any other aspect of the IP regime that a¤ects the brand’s ability
to earn pro…ts, such as litigation by generics to end the patent term early, compulsory licensing, etc.
will appear in the data as part of the revenue lost by the brand. Alternative IP provisions such asdata exclusivity will likewise lengthen the e¤ective period of brand exclusivity by keeping the cost ofgeneric entry high (the entrant has to perform all the clinical trials again). In summary, the revenuesof the brand over time will tell us what the e¤ective market size is. We use revenue therefore as asummary statistic for di¤erences in IP protections, entry costs, regulations, population, and wealthacross countries.
Lastly, our measure of innovation is a count of new chemical entities (NCE) by therapeutic category
launched anywhere in the 14 countries from which we have revenue data. While lumpy because of
their small numbers, we consider these products to be uncontroversially innovative. Drug approval
agencies such as the FDA in the US will approval new varieties and forms of existing medications(e.g. extended release, injectable versus oral) and generic drugs; these do not fall into our de…nitionof innovative.
A …nal important empirical issue is that of reverse causality. We are interested in the extent towhich a larger potential market stimulates innovation. However, a successful product, the result of that
innovation, generates revenue, and thus the innovation can also create the market. AL use expenditurein the category in one of their …ve year time windows (times contemporaneous population) as a measureof potential market size for a new drug. They do not directly address the issue of whether unusually
successful new drugs cause expenditures in that time period and category, and therefore generatecausality moving in the reverse direction. Our method, as described in detail below, instruments formarket size. Population of di¤erent ages and disease incidence around the world are our instruments
for current market size. These measures are likely to be correlated with market size, yet una¤ectedby particular pharmaceutical innovations.

16

3

Data description

Our data set from IMS (Intercontinental Marketing Services) Health includes all product sales in 14
countries (Australia, Brazil, Canada, China, France, Germany, Italy, Japan, Mexico, Korea, Spain,
Turkey, United Kingdom, USA) between 1997 and 2007 (except for China, where 1997 and 1998
are missing). The data report sales by value and unit volume for all pharmaceutical and biologic
products. All values are de‡ated into 2007 US$ using the US consumer price index. The databasere‡ects sales for all compounds in all 14 countries through retail pharmacies, hospitals and HMOs, andincludes characteristics of drugs like their four-digit Anatomical Therapeutic Classi…cation (ATC4)(607 di¤erent classes are reported), the main active ingredient of the drug (6216 di¤erent active
ingredients are reported), the name of the …rm producing the drug, whether it has been licensed, thepatent start date, and the format of the drug (471 di¤erent formats are reported). Products in thesame ATC4 by de…nition have the same indication and mechanism of action. Some other available
and interesting drug characteristics are its brand type - licensed brand, original brand, other brand, or
unbranded - and (when applicable) its patent expiry date - ranging from 1966 to 2030 in the available
data, and which may di¤er from one country to another. We consider only ethical drugs and not
OTC drugs. Quantities are given in standard units, one standard unit corresponding to the smallest
common dose of a product form, as de…ned by IMS Health.
Overall, the initial dataset has 6,163,465 observations (one per drug, country, year) but only
2,972,419 have strictly positive revenues and quantities (many observations correspond to years where
the drug was not yet launched, or had disappeared in a given year and country, or to missing dataon revenues or quantities). As for China, all observations for 1997 and 1998 have been dropped,
revenues and quantities are zero at the same time for all drugs. It is clear that China was notobserved these years, for unexplained reasons. After removing observations with zero revenue orquantity, some active ingredients and ATC4 classes disappear but we still have 6091 di¤erent active
ingredients and 606 ATC 4 categories. Then, only the 251,558 branded drugs were kept (i.e. thosewhere the brand type is either a licensed brand or the original brand) in order to focus on revenues
from patented pharmaceutical innovations. This leads to dropping data from one country, India,

17

where brand type is always unavailable in the data (‘patent N/A’). In addition, this restriction leads
to dropping many other active ingredients (4503), formats (217) and ATC4 (217) that appear ingeneric form only. Finally, since patents last 20 years, only the 68,219 observations that have their…rst patent date (throughout the di¤erent countries) after 1977 are kept, in order to have at least one
observation (revenue & quantity) in all countries where the drug was sold. This leads to dropping
158,904 observations of old products with older …rst patent date, 5,568 with unknown …rst patent dateand 18,867 with uncoded …rst patent date. In total, our restrictions to current branded products withcomplete data leads us to drop 1083 active ingredients, 151 ATC4, 109 formats and 30 patent years.
We end up with a data set where 630 active ingredients are present in 238 ATC4 classes. We also
categorize drugs into coarser ATC categories.
In the IMS data set, there are around 900 distinct on-patent products (that is, products which areon patent at some time during the 11 years of the data). Some of these are basically the same productin a di¤erent package.
All sales and prices are at the manufacturer levels, as reported by IMS, and thus approximate
actual prices, except to the extent of o¤-invoice discounts. We believe these o¤-invoice discounts are
not a very large factor in our data. For the US, Danzon and Furukawa (2006) compared the IMS
prices with US average sales price (ASP), which includes all discounts, as reported by the Centers for
Medicare and Medicaid Services (CMS) for the corresponding quarter, and found that on average, the
IMS prices are similar to the ASPs. However, to the extent that unmeasured discounts exist, prices
and revenues may be overestimated here.
We also use demographic data (population and mortality data) from the World Health Organization(WHO)2 . Population has been extracted for the studied countries from available years since 1970. For
all available country-year, population is known by sex (M/F) and by age distribution (0, 1-4, 5-9,
. . . , 70-74, 75+), except for Mexico in 1977, where only total population by sex is available. Missing
population information is reconstructed through a regression in logs on a country speci…c e¤ect and atime trend which amounts to assuming that population evolves at a country speci…c annual growth rate
between available years of data that is allowed to change over time at the same rate across countries.
2

See http://www.who.int/whosis/mort/download/en/index.html.

18

Mortality data across years and countries are available per disease category as classi…ed using the
ICD-10 classi…cation. The International Classi…cation of Diseases (ICD) has evolved over time andthe most recent one is the ICD10 version. Of courses, disease categories and drug categories (ATCclasses) are di¤erent things but one can attribute to each ATC class a "most likely" disease category
in the ICD 10 classi…cation. These are summarized in Table 13 in the Appendix.
It is important to realize that the time series dimension of the demographic data contains little"news", at least from year to year. Population projections change only slowly over time, as do mortality
risks in any given demographic category.
The dependent variable is the total number of new chemical entities marketed anywhere in our
14 countries in a given disease category throughout a given time period. Only about one-third of
the drugs approved annually in the United States are new compounds, or chemical entities; the rest
represent modi…ed forms or new uses of existing drugs.
These data in combination allow us to measure how the global size of the market for a drug in aparticular category, for example, cardiovascular, shifts over years according to population and disease
pro…le, and also allows us to see how many new NCEs (New Chemical Entities) pharmaceutical …rms
launch as the market size changes.

4

Estimation Method

4.1

Heuristic description

The …rst step in estimating the elasticity of innovation is the construction of an expected revenue
measure. We do this by measuring market size over time for various disease categories in our set ofcountries. Then we measure how many new products are introduced over time, again by category. We
describe our empirical procedure below.
We exploit di¤erences across brand revenue in di¤erent therapeutic categories and di¤erent countries to estimate the potential market for an innovator in those di¤erent countries. For example, atlaunch the brand will have a share of revenues in the therapeutic category in the country. Theserevenues may change over time as the brand goes through its lifecycle, and will begin to decline when
the patent expires and the brand faces signi…cant new competition, or when the entry of substitute19

drugs causes the brand’s price and/or share to drop. These patterns will vary across jurisdictions.
Price levels are determined by willingness to pay and the level of competition among products. A
nation’s ability and willingness to pay for healthcare will depend on whether it is nationally or privately…nanced and on the preferences of consumers and voters. In general, prices for biopharmaceuticals
are higher in richer countries compared to poorer ones. Competitive conditions also vary by country
according to the conduct of buyers, and these a¤ect the realized market size available to the innovator.
The di¤erent income levels and competitive landscapes across di¤erent nations will a¤ect the pricelevels observed in the data. In general, prices, market structures, entry, market exclusivity and otherfeatures vary across countries. We will not model these because we can measure the average outcome,
namely revenue per drug, and we assume …rms anticipate these market conditions when making R&D
decisions.
Then, our data let us compute the true global revenue obtained per innovation as observed in sales
revenue in the data. The advantage of such an approach is that the global revenue from an innovation
depends on the population using the new drug during its life cycle (not only on a given shorter periodof time) and on the willingness to pay of drug users, which is allowed to vary over time (in contrast
to Acemoglu and Linn, 2004).
This approach thus takes into account the fact that each innovation has a life-cycle in the market
for pharmaceutical products which is clearly anticipated and which implies that sales are not uniformly
distributed during the duration of the patent life. It is expected that the di¤usion of an innovation
may take some time, and that consumer-prescribers may have switching costs or learning costs thatdelay the full adoption of an innovation. Moreover, the lifecycle of a drug is typically a¤ected by thecompeting and future innovations in the same drug category.
As mentioned above, there are two possible causal relationships that can appear in the data,
working in opposite directions. Market size and innovation might be positively related because aparticular market is expected to grow and therefore …rms invest in new products for that market.
This is the relationship of interest in the paper. A second possibility is that a successful innovation
generates large sales due to its quality and novelty, and this causes the market (measured in dollars)20

to be large.
To deal with this second possible relationship, we employ an instrumental variables approach. We
use as an instrument for each drug category a potential market size. We operationalize this idea byusing population by age or gender by country and disease prevalence in di¤erent countries. We do
not use actual expenditures in any way in our instrument. We note that AL de…nes ”expected market
size”and employs it as an explanatory variable, not an instrument. Expected market size is either thesum of expenditures on the class for all ages, or the sum of the number of drugs in the class used bydi¤erent ages weighted by population. A feature of our method we want to highlight is our e¤ort toavoid using the outcome of interest, innovation, in potential market size – either as number of drugs
or dollars.
Our instruments do not include expenditure, number of patients, or number of drugs available. Our
instruments are the population in di¤erent age categories by country and disease prevalence in di¤erent
countries. For these to be valid instruments we must assume innovation does not a¤ect potential
market size, which requires that there are no changes in WHO-measured morbidity or mortality thatare driven by the innovation in the short run. Indeed, innovation in class c at period t can a¤ect
future longevity and potential market size of later periods but not that of the current and subsequent
periods corresponding to the patent life of the innovation. It seems reasonable to assume that classspeci…c innovations cannot a¤ect population size in the short run because of competing risks in otherdisease categories are likely to limit the immediate e¤ect on mortality of an innovation.

4.2

Formal De…nition of Market Size

Let d denote an active ingredient (or chemical entity) in a particular use class, hereafter known asa drug (all revenues correspond to revenues summed at the level of the chemical entity for di¤erent
forms or brands with that chemical entity). We denote by Rdt the revenue obtained for drug d atyear t. Given that the IMS data are converted into current dollars taking already account of eachyear’s exchange rate, we measure these revenues by summing all countries’sales and transform theminto constant US dollars of 2007 using the Consumer Price Index for in‡ation. Our average drug has
positive revenues for about twenty years (while the patent only last for 20 years, the brand is not21

typically launched until years after …ling, and brands typically earn pro…ts after patent expiration in
many markets.) This leads us to write the total revenue obtained thanks to the patent for drug d as

where

ed =
R

Xt0 +20

t0 t0

t0 =t00

Rdt

is the discount factor. We assume that these discounted revenues per drug determine invest-

ment decisions of …rms.
We denote by C = f1; ::; Cg a partition of the set of therapeutic classes (the 1 digit ATC classes).
For a sub-set of drugs in set c 2 C that are launched in year t, we denote by Wct the sum of global
revenues obtained in this segment c by all innovations of period t :
Wct =

X

fd2c;t0 =tg

edR

We will call this the “period t innovation - class c” market size.
Then, we denote by Nct the number of new chemical entities (active ingredients) patented at t inclass c. There is no revenue Wct (Wct = 0) when no drug was patented that year in that category.
As our model shows, we expect a positive relationship between market size and innovation. In theempirics this shows up as the relationship between the number of new chemical entities per class inperiod t (Nct ) and the period t innovation size of class c (Wct ).
The arrival of new products on the market is stochastic due to uncertainties in the research process
and in regulatory approval. Our measure of innovation in a single year will be lumpy from year toyear and contain zeros. However, as discussed above, because there is little new news in demographics
every year, we use periods of 4 years for t, a procedure which smooths out our innovation measure Nct .

4.3

Imputation of Revenues over Drugs Life-cycle

Before going further on the empirical estimates, it is important to observe that we cannot forecast
expected revenues entirely by taking means of actual revenues by therapeutic class, entry date, andyear, because we do not see enough drugs nor the entire lifecycle of most drugs in the data since
we observe only 11 years of data. Thus, we implement an imputation method to obtain a series ofparameters for each ATC class that allows us to predict revenue for a drug over its entire lifecycle

22using the average lifecycle within the class. We then use this estimate as the forecast of manufacturers
making innovation decisions.
Thus, for a given drug d, the data do not allow us to observe all the Rdt for years t0 , t0 + 1, :::,
t0 + 19. As we want to take into account the lifecycle of drugs, we compute the average evolution ofrevenues of new chemical entities within a class c between patent age
patent age

and

and

+ 1 as the ratio between+ 1 of average revenue (across active ingredients) for all drugs of a given drug

category c. De…ning (c) as the set of drugs in class c, we can also use the following de…nition 3 :X Rt0 + +1
1
d
c =
t0 +
# (c)
R
d
d2 (c)
Assuming that the expected revenue of a drug of class c will follow the life-cycle pattern estimated using
thesec we can estimate the revenue at a given future patent year. Then, when Rd0t0 +c(d) Rd

we estimate it with Rdt + +1 =+1

is not observed,

which allows us to reconstruct the lifecycle revenues of any

drug on the market during the period of our data.

5

Empirical Results

We will consider two levels of ATC classes C and C 0 . The …ner level of classi…cation (2 digit ATC classes)
considered will be C 0 . A class denoted c0 will thus correspond an element of this …ner classi…cation.
tcWith the previous notations, W
N t is the revenue per new chemical entity in class c and patent periodct. All revenues are expressed in thousands of 2007 US dollars and the discount factor used is

5.1= 0:95.

Descriptive Statistics

Table 1 presents descriptive statistics by ATC class on the number of innovations and lifecycle revenues
per ATC class at level 1. Table 2 shows the same statistics as Table 1 but using the 2 digit ATCclassi…cation. Tables 3 and 4 present the same data as Tables 1 and 2, but averaged not over ATC classbut according to the number of new chemical entities. This shows a broad descriptive relationshipbetween market size and the number of new chemical entities.
3

Using a de…nition ofc =P

t0 + +1d2 (c) RdPt0 +d2 (c) Rdcthat gives weights to active ingredients proportional to their revenue in the class, we have

, which leads to similar results.

23

4 5 6 7 8 9 1011121314151617181920.50

1 2 3 4 5 6 7 8 9 1011121314151617181920

DERMATOLOGICALS
0 .5 1 1.5 2 2.5

1 1.5 2

1.5
1
.5
0

0

.5

1

1.5

ALIMENTARY_TRACT_AND_METABOLISMBLOOD_AND_BLOOD_FORMING_ORGANSCARDIOVASCULAR_SYSTEM

1 2 3 4 5 6 7 8 9 1011121314151617181920

3 4 5 6 7 8 9 1011121314151617181920

NERVOUS_SYSTEM

1.5
.5
0

1 2 3 4 5 6 7 8 9 1011121314151617181920

ANTIPARASITICSRESPIRATORY_SYSTEM

1 2 3 4 5 6 7 8 9 1011121314151617181920

3 4 5 6 7 8 9 1011121314151617181920

21

8 9 1011121314151617181920

0

0

0

2

1

4

2

6

0 .2 .4 .6 .8 1

3

MUSCULO_SKELETAL_SYSTEM

1 2 3 4 5 6 7 8 9 1011121314151617181920

3

6 7 8 9 1011121314151617181920

8

2 3 4 5 6 7 8 9 1011121314151617181920

1

0 .2 .4 .6 .8 1

4
0

0

2

.5

1

6

1.5

GENITO_URINARY_SEX_HORMONES SYSTEMIC_HORM.
ANTIINFECTIVES_FOR_SYSTEMIC_USE
ANTINEOPLASTIC_IMMUNOMODULATING

1 2 3 4 5 6 7 8 9 1011 21314151617181920

0.5 1 1.5 2

SENSORY_ORGANS

5 6 7 8 9 1011121314151617181920

Figure 1: Means of Revenue by Patent Age for Each ATC-1
On average, the total revenue per ATC class seems to be increasing with the number of innovations
except when the number of innovations is the largest. This result holds on aggregate and does notaccount for the heterogeneity of revenues and innovations across categories of ATC classes.

5.2

Lifecycle of drugs

In order to impute some unobserved revenues, we described above a method of imputation which
consists in estimating average lifecycle of drug revenues per ATC class. The estimation of these average
lifecycle of drugs is interesting and shown in next Figure 1 for all categories (digit 1 ATC classes) andpatent ages. Figure 1 shows the mean revenues of drugs by class for each patent age and ATC-1.
Remark that for these shapes to be seen on a same picture, we have normalized the total sales forpatent year 10 for all drug classes, which implies that the height of each vertical bar should not becompared across drug categories.
We can see that the lifecycle of sales of drugs is not uniform across classes. Some classes have longer24

4 5 6 7 8 9 1011121314151617181920

1 2 3 4 5 6 7 8 9 1011121314151617181920

DERMATOLOGICALS
1 1.5 2

1.5
1

0

.5

.5
0

0

.5

1

0 .2 .4 .6 .8 1

1.5

ALIMENTARY_TRACT_AND_METABOLISMBLOOD_AND_BLOOD_FORMING_ORGANSCARDIOVASCULAR_SYSTEM

1 2 3 4 5 6 7 8 9 1011121314151617181920

3 4 5 6 7 8 9 1011121314151617181920

0 .2 .4 .6 .8 1

1.5

4

3 4 5 6 7 8 9 1011121314151617181920

42

0 .2 .4 .6 .8 1

1 2 3 4 5 6 7 8 9 1011121314151617181920

1 2 3 4 5 6 7 8 9 1011121314151617181920

ANTIPARASITICS
6NERVOUS_SYSTEM

0 .5 1 1.5 2 2.5

MUSCULO_SKELETAL_SYSTEM

1 2 3 4 5 6 7 8 9 1011121314151617181920

8 9 1011121314151617181920

RESPIRATORY_SYSTEM
0 .5 1 1.5 2 2.5

6 7 8 9 1011121314151617181920

0

2 3 4 5 6 7 8 9 1011121314151617181920

0

0

1

.5

2

1

3

0 .2 .4 .6 .8 1

GENITO_URINARY_SEX_HORMONES SYSTEMIC_HORM.
ANTIINFECTIVES_FOR_SYSTEMIC_USE
ANTINEOPLASTIC_IMMUNOMODULATING

1 2 3 4 5 6 7 8 9 1011 21314151617181920

0.5

1 1.5 2SENSORY_ORGANS

5 6 7 8 9 1011121314151617181920

Figure 2: Estimatedc by ATC-1 and patent age

delays of penetration, some classes have lower duration. These lifecycle shapes also show that sales
are not at all uniform during the patent life and thus that it seems important to take into account the
expected lifecycle of the patent revenues of drugs in the incentives to innovate for the pharmaceuticalindustry.
This variability shows why the coe¢ cientsc will have some variation across patent age and acrossclasses. Figure 2 shows the obtained coe¢ cients for all classes and patent ages. These coe¢ cients
will be used for imputation of life-cycle revenues for future years on still young drugs. Though theselife-cycle of revenue per drug are averages, our estimated revenues using expected life-cycle patterns
are valid provided …rms do not have better information on expected revenues during the lifecycle of
new drugs.

25

5.3

Elasticity of Innovation to Market Size

We now look at the relationship between the number of innovations or New Chemical Entities Nct atperiod t in category c and our "market size" Wct . In each year we have data from 238 therapeuticclasses. As noted above, we sum revenue and innovation in four year periods (which reduces the
sample size but creates much more content to each observation). Our time periods t are thus 1977-80,
1981-84, 1985-88, 1989-92, 1993-96, 1997-2000, 2001-04.
We use several speci…cations to estimate the elasticity of innovation with respect to market size.
First, we estimate the following modelln Nct =ln Wct +t
c + t + "cwhich relates the number of innovations (number of NCEs) patented in period t in each class to thetotal revenue provided by sales of all drugs (on patent and licensed) in the class during the durationof the patents issued at t.
In this reduced form model,

can be interpreted as the elasticity of innovation to market size,

is a …xed unobserved e¤ect speci…c to the ATC class c,ct is a common unobserved period e¤ect and"tc an unobserved random shock on the innovation outcome.
Assuming all right hand side variables are exogenous means thatE "tc j ln Wct ;

c; t

=0

We …rst estimate such a model using OLS. Then we employ 2SLS because of the strong likelihood,previously discussed, that innovation drives market size. Our instrumental variables are demographic:
population and disease prevalence in di¤erent countries. To be valid instruments, demographics anddisease prevalence must be correlated with market size. It is intuitive that the population and theshare of the population likely to use drugs in each ATC class will be correlated with the revenue in thatclass. Secondly, innovation, or launch of new products, must not cause changes in demographics or
disease prevalence. We can imagine that at a very …ne level of categorization, this could be a problem.
For example, a pill for Ausberger’s (mild autism) might well increase diagnoses of Auberger’s andtherefore autism. But the WHO data we use are much coarser: for example, how many people died of26

cardiovascular diseases in period t. We do not think the therapies available for di¤erent cardiovasculardiseases a¤ect this measurement.
For this reason, we use male, female, and ”more than 50 years old” population variables, as wellas the number of deaths of males and females for the disease categories that each drug class (ATCclass) can be considered to target. This instrument is thus varying across periods and drug classes.
These set of instruments are interacted with dummy variables for 1-digit ATC classes or 2 digit ATCclasses depending on the cases.
For the population measures we compute the size of the population in countries where drugs ofeach ATC class are sold and sum this over countries and years for the duration of the patent. This
population is denoted Ptc . This instrument is varying not only over time but also across ATC classes.
We use a similar de…nition for male or female population, and for the ”over 50 years old”population.
Table 6 details the di¤erent sets of instruments used in the regressions below. As will be shown later,these sets of instruments satisfy the di¤erent usual tests of exclusion (Sargan test of overidentifying
restrictions) and of signi…cance in the …rst stage (F test of joint signi…cance of excluded IVs in the…rst stage regression). In our empirical work, set A will be used for regressions at the ATC-1 level
and B, C or D for regressions at the ATC-2 level.
Recall that the number of innovations Nct that can be observed on each market is censored atzero, and that Wct is unobserved when there are zero innovations. We have a fundamental problem ofunobserved potential market size of any innovation that did not happen and therefore need a truncatedregression model. In particular, it could be that "tc is not mean independent of all right-hand-side
variables because of the truncation of the model when Nct = 0.
With some parametric assumptions on "tc , one can estimate the model taking into account thetruncation4 . For example, as we are dealing with count data, we can assume a Poisson distribution
for the number of innovations, such that
P Nct = n =
4

exp (

)

n

n!

Nonparametric estimation of such a truncated regression model is di¢ cult and is a subject of ongoing research (Lewbel
and Linton, 2002, Chen 2009). Chen (2009) and Lewbel and Linton (2002) show that if the exogeneity assumption of righthand side variables is satis…ed, then with some additional technical assumptions, one can identify the non parametricconditional expectation of the truncated dependent variable conditionally on the right hand side variables.

27

where we specify the intensity parameter

as

= exp

Wct +

c + t . Such a model implies that

E Nct jWct ;

c; t

= exp

Wct +

c+ t

As the data are truncated at zero since Wct is unobserved when Nct = 0, we correct for the truncation
using the zero-truncated Poisson maximum-likelihood regression implying that
P Nct = njNct > 0 =exp ( ) nwithn! (1 exp ( ))

= exp

Wct +

c+ t

In this case, we take into account the endogeneity of Wct using the control function approach. As
suggested by Wooldridge (2002) and Blundell and Powell (2003), this technique is useful for non linear
models. It amounts to perform a …rst stage regression of the endogenous variables on all exogenous
variables and excluded instruments and then use residuals and polynomials of these residuals asadditional "control" variables in the main regression (here the zero-truncated Poisson). The resultsof this …rst stage regressions are shown in Table 14 in appendix and show that excluded instruments
are statistically signi…cant (as con…rmed also by the joint F test shown at the bottom of the Tables).
Results show that excluded IVs from sets A, B, C or D are signi…cant and the F test of joint signi…cancealways rejects strongly that they have no explanatory power. In the case of the control function
approaches, we used sets of instruments A for analysis of Nct and D for Nct0 but results are similar
with other sets of instruments. In the case of ATC-1 level regressions, instrumental variables A
proved satisfactory and consist in male and female population of corresponding countries, male andfemale deaths of corresponding ATC-1 class and countries. In the case of ATC-2 level regressions,
instrumental variables B, C or D proved satisfactory and consist di¤erent male or female demographic
variables interacted with ATC-1 or ATC-2 dummies.
In all following tables, standard errors are clustered at the class level and show in parentheses. Also,
dummy variables

t for time periods are not shown to conserve space.

They are always signi…cant.

Tables 7 and 8 show the results of estimating the linear model for the 1-digit and 2-digit ATC categories
respectively. Tables 9 and 10 show the corresponding results of the estimation of the count models.
In each case we show results with and without instrumenting for potential market size. All standard
errors are clustered at the ATC-1 level.
28

The speci…cations yield a range of elasticities between 6% and 40%, meaning that increasing
market size by 1% yields an increase in the number of new products of .06 to .4%. There is no clear
relationship between the elasticity and the type of speci…cation (linear versus count, one-stage versus
two-stage) or the instrument set. Overall, our preferred speci…cation among these is Equation (13).
It takes into account the truncated nature of the data and the need to use instrumental variables,
and it uses the …ner set of instruments in which the demographic variables are interacted with 2-digit
therapeutic categories, thus taking account of the fact that di¤erent demographic pro…les generatedi¤erent market sizes in the various treatment categories. It uses the …ner 2-digit disease classi…cation
for the dependent variable, which we prefer because we understand it is relatively rare for drugs in
one ATC-2 category to be discovered while searching for therapies in a di¤erent ATC-2 category.
Equation (13) yields an elasticity of 12.3%, with a t-ratio of around 5, which gives the elasticity acon…dence interval of around 8% to 17%5 Given the assumptions of our model, this implies that entry
costs must be increasing in market size (since the marginal innovation requires substantially higher
revenue than the average innovation). Since we have no independent measure of quality we cannot of
course rule out the possibility that this is because the quality of pharmaceutical products is increasing
as market size increases. However, another interpretation is possible, which is that innovation is subject
to signi…cant decreasing returns. This may be true both with respect to number of innovations in asegment (the industry may running out of "low-hanging fruit" - see Cowen 2011), and also true overtime - the costs of regulatory approval appear to have been rising in recent years. Although our results
do not directly test the "low hanging fruit" hypothesis they are certainly compatible with it.
However, for this very reason the assumption that the elasticity is the same across disease categories
may not be realistic, since the extent to which the industry has had low-hanging fruit may well vary
for scienti…c reasons from one disease category to another. To investigate this possibility we estimate
the count model with ATC speci…c market size coe¢ cientsc using

exp ( ) nwithn! (1 exp ( ))= exp

P Nct = njNct > 0 =tc Wc +c+ ttcwhich also implies that the elacticity to market size of the expected number of innovations is @@lnlnEN
Wt =
c

5

This point estimate enables us easily to reject the hypothesis of an elasticity equal to 21 or even to 13 (the latter being

the predicted elasticity under the hypothesis of quadratic transport costs of product di¤erentiation).)29

Wct .
Given the size of the elasticity, we can also compute how much additional revenue in a given drug
category is needed to obtain one additional innovation as the inverse of the elasticity times the averagerevenue per innovation observed on the market (because dWc =@ ln Nc@ ln Wc

1

Wc
Nc )

Table 11 reports such results and Table 12 reports then the obtained elasticities by ATC class.
We …nd higher elasticities on average than in the model with the elasticity constrained to be constantacross disease categories, though they remain within the range of elasticities found under previous
speci…cations.
We see that the elasticities of innovation vary by ATC class, and that the average lifecycle discounted market size increase needed on average to obtain one additional NCE also varies acrossclasses. For comparison, we estimate the log-linear model with ATC-1 speci…c elasticities (results notreported), and …nd larger absolute values than in a speci…cation without heterogeneity. The values
are a little smaller than those of this count model, varying between 8% and 30% depending on the
disease class.
Across all ATC classes, we …nd that the average elasticity of innovation to market size under thisspeci…cation is 25.2%, which implies that the average lifecycle discounted market size increase needed
to obtain one additional NCE is around $1.8 billion. Remember that we used a discount factor of
0.95 which implies that the $1.8 billion over the lifecycle of a drug is equivalent to a constant annual
revenue of $148 million per year over 20 years.
Next we consider whether this estimated $1.8 billion is reasonable. The most recent DiMasi et al.
study of drug development estimates that a new drug incurs approximately $800 million in development
costs (Adams, 2006, Di Masi et al. 2005 suggest 1$billion on average for one new chemical entity).
Included in this calculation is the cost of capital, the cost of failed drugs, and the cost of clinical trials,
so it is close to the total …xed economic cost of innovation. On top of this there will be variable costsof production, distribution and marketing. Industry sources have suggested to us that 50% of revenue
is a reasonable guess at the size of these costs (though they may be higher in the case of biologics,
where manufacturing costs are typically higher). This suggests that a new drug would need to cover30

costs of around $1.6 billion in order to yield a return to the innovator. This is a little lower than ourestimated market size increase of $1.8 billion needed to induce an additional innovation. Our elasticity
estimate therefore seems broadly plausible in the light of what is known from accounting data.
Comparing our elasticities to others in the literature is di¢ cult, if only because the dependent
variable changes across research designs from new drugs, to new cancer regimens, to new clinical trials,
to journal articles. However, both AL and we use new product launch as a measure of innovation.
Recall that AL estimate an elasticity of 4: for each 1% increase in revenue, the number of new products
increases by 4%, which is over an order of magnitude larger than our estimate (and of most othersin the literature). There are signi…cant empirical di¤erences that underlie these two di¤erent results.
For example, all new products including new forms, strengths, and generic versions are counted in
the AL methodology. The authors include only US revenue in their measure of market size. Lastly,
as noted above, the method in AL does not include an instrument for market size. Though it is hard
to know the impact of these methodological di¤erences, an elasticity as large as 4 appears to imply
that marginal costs of innovation are falling as more innovation takes place, which does not seem aplausible description of the pharmaceutical industry in the early 21st century.

6

Conclusion

This paper has attempted to quantify the relationship between market size and innovation in thepharmaceutical industry. We have estimated the elasticity of innovation (as measured by the numberof new chemical entities appearing on the market for a given disease class) to the potential market size
represented by the willingness of su¤erers of diseases in that class (and others acting on their behalf
such as insurers and governments) to spend on their treatment. We have found positive signi…cantelasticities with a point estimate under our preferred speci…cation of 25.2%. This suggests that at themean market size an additional $1.8 billion is required in additional revenue to induce the inventionof one additional new chemical entity, which appears a reasonable order of magnitude since estimatesof the true economic cost of developing a new chemical entity are around $800 million to $1 billion,
and marketing and related costs represent some 50% of revenue. An elasticity substantially below

31

one-half is also plausible in the light of other evidence that innovation in pharmaceuticals is becoming
more di¢ cult and expensive over time, and is compatible both with the hypothesis that the costs ofregulatory approval are rising and the hypothesis that the industry is running out of ”low hanging
fruit.”
Our results are robust to a number of speci…cation choices. However, the availability of data for
more years would undoubtedly help to re…ne our estimates and we leave this as a subject for future
research.

32

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35

7

Appendix

7.1

Analysis of Elasticity to Contemporaneous Revenue

Suppose we assume instead that ln Nct =Wct . In this case we would want to know the bias thatcomes from using the contemporaneous revenue R rather than W .in our empirical work This amounts
to study the derivative of E ln Nct jRct with respect to Rct . Then,00
1
td +20X
X 0 0 0X
t td t Aed =@R
ln Nct =
Rd
0 =t0
0 =t
fd2c;t0a =tg
t
d2c;t
f
g
d
d
0
1
0
1
t0d +20
X
X
X
B
C
B
t0 t0d t0 C=
Rdt A + @Rd A@
0 =t+100tfd2c;td =tg
fd2c;td =tg1
100
t0d +20
X 0 0 0C
X
C
B X
BX
t td t
Rd A=
Rct
Rdt A + @@00
t <t fd2c;t0 =t0 g
fd2c;t0d =tg t =t+1
d
P
P
P
P
because Rct = fd2cg Rdt = t0 <t fd2c;t0 =t0 g Rdt + fd2c;t0 =tg Rdt .
d
d
Then

E ln Nct jRct = Rctand both E

P

t0 <t

0

BX
E@

X

t0 <t fd2c;t0 =t0 gPtt
fd2c;t0d =t0 g Rd Rc

d

1

0

C
B
Rdt Rct A + E @

and E

P

fd2c;t0d =tg

X

t0d +20X

t0 t0dfd2c;t0d =tg t0 =t+1

Pt0d +20

t0 =t+1

t0 t0d0

Rdt Rct

10
C
Rdt Rct A

have no reason

to be zero if there is some correlation between revenues for the same drug across periods (which is
obviously the case). Remark that if revenues per drug are all independent and that we only have time
autocorrelation of revenues then both terms should be positive and thus E ln Nct jRct could be either
higher or lower than Rct .
If for example the revenues of a drug d in year t and t0d are proportional to a scale factor depending
t t0

t0

only on the class to which it belongs to and the patent age t t0d , then Rdt = c d Rcd and
1
2
0
13
0
t0d +20
X 0 0 t0 t0 C7
X
6
B X
C
BX
t td
d
E ln Nct jRct = Rct 41 + @Rdt Rct AE@A5
ct0 <t fd2c;t0 =t0 g
fd2c;t0d =tg t0 =t+1d
P
Pt0d +20 t0 t0 t0 t0ddwhere fd2c;t0 =tg t0 =t+1
would be equal to 20 if revenues are constant per drug during
c
d
P
P
t
t
the patent life and there is no discounting. Thus, unless E
t0 <t
fd2c;t0d =t0 g Rd Rc is also positive@E (ln Nct jRct )and very large,
would be much larger than .
@Rt
c

36

7.2

Main Tables
Mean
Wct

Wct
Nct

Nct
A: ALIMENTARY TRACT AND METABOLISM
1,997,804 163,861 11.14
B: BLOOD AND BLOOD FORMING ORGANS
3,702,647 500,500 6.67
C: CARDIOVASCULAR SYSTEM
3,308,496 244,188 15.40
D: DERMATOLOGICALS
154,468
26,1075.00
G: GENITO URINARY SYSTEM AND SEX HORMONES
766,613 186,571 5.33
H: SYSTEMIC HORM.PREP., EXCL. SEX&INSULINS
12,3705,821
2.20
J: ANTIINFECTIVES FOR SYSTEMIC USE
5,143,922 287,501 14.86
L: ANTINEOPLASTIC&IMMUNOMODULATING AGENTS 6,186,711 356,246 12.29
M: MUSCULO-SKELETAL SYSTEM
2,535,125 435,702 6.17
N: NERVOUS SYSTEM
922,066 110,055 8.86
P: ANTIPARASITICS INSECTICIDES REPELLENTS
9,098
4,550
1.50
R: RESPIRATORY SYSTEM
817,246 106,632 7.40
S: SENSORY ORGANS
176,557
51,686
4.40
V: VARIOUS
19,844
18,984
2.00
Table 1: Total lifetime revenues per 1-digit ATC class of NCEs patented by period,
ATC class (C)number of entities per period, and revenue per entity Unweighted averages by 1-digit
ATC class

37

Mean
Wct0

ATC class (C)

Wct0
Nct0

Nct0

388,462 102,304 2.19
2,019,626 829,969 3.64
612,684 195,831 3.04
51,48927,669 1.72
328,549 110,245 2.29
7,731
6,934
1.38
1,895,129 426,227 5.47
1,968,499 368,424 3.91
845,042 265,548 2.11
208,208
91,401 2.06
9,098
4,550
1.50
240,366
63,373 2.53
109,696
32,718 3.13
15,875
15,249 1.60
Table 2: Total lifetime revenues per 2-digit ATC class of NCEs patented by period,
A: ALIMENTARY TRACT AND METABOLISM
B: BLOOD AND BLOOD FORMING ORGANS
C: CARDIOVASCULAR SYSTEM
D: DERMATOLOGICALSG: GENITO URINARY SYSTEM AND SEX HORMONES
H: SYSTEMIC HORM.PREP., EXCL. SEX&INSULINS
J: ANTIINFECTIVES FOR SYSTEMIC USE
L: ANTINEOPLASTIC&IMMUNOMODULATING AGENTS
M: MUSCULO-SKELETAL SYSTEM
N: NERVOUS SYSTEM
P: ANTIPARASITICS INSECTICIDES REPELLENTS
R: RESPIRATORY SYSTEM
S: SENSORY ORGANS
V: VARIOUS

number of entities per year, and revenue per entity Unweighted averages by 1-digit ATC
class

38

t

c
Revenue Wct
Revenue per NCE W
Nct
Nct
Mean
Min
Max
Mean
Min
Max
1
44,201
3
372,40944,201
3
372,4092
173,872
13,027
727,520
86,936
6,513
363,760
3
195,064235
779,383
65,021
78
259,7944
443,2816,070
880,492
110,820
1,518
220,1235
3,381,172
409,923
9,158,344 676,234 81,985 1,831,669
6
746,740
38,124
1,808,956 124,457
6,354
301,493
74,157,717
8,753
17,409,894 593,960
1,250
2,487,128
8
1,157,414
54,902
2,212,882 144,677
6,863
276,6109
1,266,605
208,775
3,891,499 140,734 23,197
432,38910
971,439
672,811
1,344,233
97,14467,281
134,423
11
2,089,586
118,4875,354,649 189,962 10,772
486,78612
901,498
901,498
901,498
75,12575,12575,12513
4,552,097
1,309,560
7,794,634 350,161 100,735 599,587
14
2,670,899
1,908,117
3,433,681 190,778 136,294 245,263
15
2,510,007
1,005,747
3,344,799 167,334 67,050
222,987
16
2,124,496
2,124,496
2,124,496 132,781 132,781 132,781
18
6,913,570
2,370,214 12,676,093 384,087 131,679 704,227
19 12,188,488 12,188,488 12,188,488 641,499 641,499 641,499
20
1,038,019
126,984
1,949,054
51,901
6,349
97,453
21 13,795,792 13,795,792 13,795,792 656,943 656,943 656,943
22 18,427,938 18,427,938 18,427,938 837,634 837,634 837,634
23
5,039,901
3,835,333
6,244,468 219,126 166,754 271,499
24
3,958,308
3,958,308
3,958,308 164,929 164,929 164,929
Table 3: Total lifetime revenues per 1-digit ATC class of NCEs patented by period,

and revenue per entity Unweighted averages by 1-digit ATC class. Global revenue Wct
and the number of innovations Nct

39

Wt

Revenue Wct0

Revenue per NCE N ct0
c0

Nct0
Mean
Min
Max
Mean
Min
Max
1
81,85003,659,126
81,85003,659,1262
636,227
119
14,847,439 318,113
60
7,423,720
3
947,625
52
9,146,061 315,875
17
3,048,687
4
665,355111
2,880,434 166,339
28
720,1085
657,552
11
3,170,524 131,510
2
634,105
6
1,285,697
29,206
4,605,718 214,283
4,868
767,6207
1,733,441
7,723
3,284,163 247,634
1,103
469,1668
6,358,466
1,096,551 16,636,591 794,808 137,069 2,079,574
9998,505
998,505
998,505
110,945 110,945 110,945
10
5,381,413
117,730
10,645,097 538,141 11,773 1,064,510
11
2,939,500
2,403,055
3,475,944 267,227 218,460 315,995
12
10,976,667 10,976,667 10,976,667 914,722 914,722 914,722
13
9,739,879
9,739,879
9,739,879 749,221 749,221 749,221
16
1,242,968
1,242,968
1,242,968
77,68577,68577,68519
1,909,830
1,909,830
1,909,830 100,517 100,517 100,517
Table 4: Total lifetime revenues per 2-digit ATC class of NCEs patented by period,
and revenue per entity Unweighted averages by 1-digit ATC class40

ATC Class (C)Mean Price
A: ALIMENTARY TRACT AND METABOLISM
111
B: BLOOD AND BLOOD FORMING ORGANS
544
C: CARDIOVASCULAR SYSTEM
10
D: DERMATOLOGICALS22
G: GENITO URINARY SYSTEM AND SEX HORMONES
23
H: SYSTEMIC HORM.PREP., EXCL. SEX&INSULINS
266
J: ANTIINFECTIVES FOR SYSTEMIC USE
67
L: ANTINEOPLASTIC&IMMUNOMODULATING AGENTS
382
M: MUSCULO-SKELETAL SYSTEM
95
N: NERVOUS SYSTEM
6
P: ANTIPARASITICS INSECTICIDES REPELLENTS
8
R: RESPIRATORY SYSTEM
13
S: SENSORY ORGANS
187
V: VARIOUS
636
Table 5: Descriptive statistics on Average Price of Drugs (across countries)

41

Set
A

Instruments
Male and Female Population of corresponding countries
Male and Female Deaths of corresponding ATC-1 class and countries
B
Population aged 50 and over of corresponding countries, interacted with ATC-1
C
Male Population aged 50 and over of corresponding countries, interacted with ATC-1
D
Male Population aged 50 and over of corresponding countries, interacted with ATC-2
Female Population aged 50 and over of corresponding countries, interacted with ATC-2
Table 6: De…nition of Instrument Sets

42

Linear Model
VARIABLESln Wct
ALIMENTARY_TRACT_AND_METABOLISMANTIINFECTIVES_FOR_SYSTEMIC_USE
ANTINEOPLASTIC_IMMUNOMODULATINGANTIPARASITICSBLOOD_AND_BLOOD_FORMING_ORGANSCARDIOVASCULAR_SYSTEM
DERMATOLOGICALSGENITO_URINARY_SEX_HORMONESMUSCULO_SKELETAL_SYSTEMNERVOUS_SYSTEMRESPIRATORY_SYSTEMSYSTEMIC_HORM
Observations
R-squared
Instruments
Sargan Test
Degree freedom
P-value(1)
OLS
ln Nct0.216***(0.0289)
0.291***(0.0572)
0.549***(0.0903)
0.527***(0.0888)-0.133(0.170)0.325***(0.0314)0.681***(0.112)0.245***(0.0411)-0.128*(0.0659)
0.166**(0.0674)
0.192**(0.0788)0.407***(0.0525)0.248**(0.107)
74
0.832

(2)
2SLS
ln Nct0.291***(0.0465)
0.147(0.0907)0.318**(0.144)0.366***(0.139)0.282(0.280)0.348***(0.0401)
0.465***(0.165)0.330***(0.0443)-0.223**(0.0964)0.0564(0.0949)
0.0738(0.121)0.367***(0.0686)
0.567***(0.158)
74
A
2.776
3
0.428

N o te : A ll sta n d a rd e rro rs a re c lu ste re d a t th e AT C -1 le ve l.
* * * m e a n s sig n i…c a n c e a t 1 % le ve l, * * a t 5 % le ve l a n d * a t 1 0 % le ve l. Ye a r d u m m ie s in c lu d e d a re n o t sh ow n .

Table 7: Determinants of the number of NCEs per therapeutic class Linear model,
1-digit ATC categories

43Linear Model
VARIABLESln Wct0
ALIMENTARY_TRACT_AND_METABOLISMANTIINFECTIVES_FOR_SYSTEMIC_USE
ANTINEOPLASTIC_IMMUNOMODULATINGANTIPARASITICSBLOOD_AND_BLOOD_FORMING_ORGANSCARDIOVASCULAR_SYSTEM
DERMATOLOGICALSGENITO_URINARY_SEX_HORMONESMUSCULO_SKELETAL_SYSTEMNERVOUS_SYSTEMRESPIRATORY_SYSTEMSYSTEMIC_HORM
Observations
R-squared
Instruments
Sargan Test
Degree freedom
P-value(3)
OLS
ln Nct00.0962***(0.0106)-0.577***(0.124)-0.162(0.147)-0.245(0.140)-0.685***(0.145)-0.352**(0.141)-0.347**(0.147)-0.698***(0.119)-0.493***(0.132)-0.567***(0.121)-0.654***(0.122)-0.356**(0.128)-0.559***(0.0809)
231
0.715

(4)
2SLS
ln Nct00.0870***(0.0207)-0.208***(0.0244)0.251***(0.0582)
0.149***(0.0372)-0.350***(0.0545)0.0381(0.0390)0.0306(0.0267)-0.340***(0.0450)-0.129***(0.0346)-0.197***(0.0380)-0.284***(0.0330)
0.00362(0.0318)-0.205**(0.1000)
231
0.457
B
8.923
12
0.709

(5)
2SLS
ln Nct00.0873***(0.0206)-0.208***(0.0243)0.250***(0.0583)
0.149***(0.0373)-0.349***(0.0544)0.0377(0.0392)
0.0305(0.0267)-0.340***(0.0448)-0.129***(0.0345)-0.196***(0.0379)-0.284***(0.0329)0.00395(0.0316)-0.205**(0.0995)
231
0.458
C
8.846
12
0.716

(6)
2SLS
ln Nct00.111***(0.0174)-0.193***(0.0230)
0.184***(0.0456)
0.109***(0.0306)-0.285***(0.0542)0.00788(0.0379)
0.0235(0.0309)-0.309***(0.0419)-0.111***(0.0351)-0.174***(0.0367)-0.272***(0.0324)0.0349(0.0299)-0.130(0.0956)
231
0.466
D
31.23
24
0.147

N o te : A ll sta n d a rd e rro rs a re c lu ste re d a t th e AT C -1 le ve l.
* * * m e a n s sig n i…c a n c e a t 1 % le ve l, * * a t 5 % le ve l a n d * a t 1 0 % le ve l. Ye a r d u m m ie s in c lu d e d a re n o t sh ow n .

Table 8: Determinants of the number of NCEs per therapeutic class Linear model,
2-digit ATC categories

44

Count Models
VARIABLES
Wct

(7)
Nct
4.63e-08*(2.39e-08)
0.103
1.798***(0.101)(8)Nct1.36e-07***(5.05e-08)
0.303
0.748***(0.290)(9)Nct1.13e-07***(3.05e-08)
0.252
1.727***(0.157)

ANTIINFECTIVES_FOR_SYSTEMIC_USE

2.000***(0.128)0.548*(0.298)1.784***(0.232)ANTINEOPLASTIC_IMMUNOMODULATING

1.725***(0.221)

0.487(0.313)1.284***(0.410)ANTIPARASITICS-0.353***(0.136)-1.101***(0.391)-0.905***(0.130)

BLOOD_AND_BLOOD_FORMING_ORGANS

1.195***(0.153)0.401(0.313)

1.465***(0.211)CARDIOVASCULAR_SYSTEM

2.051***(0.115)1.059***(0.265)1.972***(0.193)

DERMATOLOGICALS

1.165***(0.107)1.086***(0.111)0.230(0.251)
0.0937(0.183)

1.098***(0.132)1.010***(0.142)MUSCULO_SKELETAL_SYSTEM

1.274***(0.0871)

0.234(0.268)1.170***(0.161)NERVOUS_SYSTEM

1.723***(0.114)
1.459***(0.0978)
0.282**(0.121)
74
Poisson0.704**(0.294)0.475**(0.210)-0.501**(0.207)
74
IV-Poisson
A

1.573***(0.162)
1.465***(0.115)0.0607(0.122)
74
Trunc-PoissonA
Yes

Corresponding Elasticity
ALIMENTARY_TRACT_AND_METABOLISM

GENITO_URINARY_SEX_HORMONESRESPIRATORY SYSTEMSYSTEMIC HORMONES
Observations
MethodInstruments
Control Function (IVs)
N o te : A ll sta n d a rd e rro rs a re c lu ste re d a t th e AT C -1 le ve l.

* * * m e a n s sig n i…c a n c e a t 1 % le ve l, * * a t 5 % le ve l a n d * a t 1 0 % le ve l. Ye a r d u m m ie s in c lu d e d a re n o t sh ow n .

Table 9: Determinants of the number of NCEs per therapeutic class Count model,
1-digit ATC categories

45

Count Models
VARIABLES
Wct0

(10)
Nct0
8.85e-08***(1.87e-08)
0.06300.382***(0.125)(11)Nct05.67e-07***(1.42e-07)
0.404-0.515**(0.233)(12)Nct02.86e-07***(1.06e-07)
0.203-0.0908(0.151)(13)Nct01.73e-07***(3.13e-08)
0.123-0.00909(0.171)ANTIINFECTIVES_FOR_SYSTEMIC_USE

1.314***(0.0998)-0.128(0.285)0.890***(0.223)0.978***(0.141)ANTINEOPLASTIC_IMMUNOMODULATING

0.861***(0.151)-0.313(0.260)0.633***(0.209)0.616***(0.167)ANTIPARASITICS-0.0607(0.184)--0.967***(0.158)-0.899***(0.227)BLOOD_AND_BLOOD_FORMING_ORGANS

0.774***(0.156)-0.165(0.356)0.652***(0.184)0.579***(0.136)CARDIOVASCULAR_SYSTEM

0.683***(0.139)0.191(0.121)-0.240(0.234)-0.531**(0.260)0.256(0.170)-0.382***(0.125)0.357*(0.189)-0.356**(0.180)

GENITO_URINARY_SEX_HORMONES

0.411***(0.138)-0.338(0.259)-0.00946(0.167)0.0476(0.201)MUSCULO_SKELETAL_SYSTEM

0.390***(0.109)-0.618**(0.268)-0.0599(0.140)-0.0713(0.152)NERVOUS_SYSTEM

0.399***(0.113)0.510***(0.140)0.188***(0.0730)
231
Poisson-0.461*(0.250)-0.289(0.251)-0.565**(0.266)
231
IV-Poisson
D-0.0534(0.138)0.136(0.157)-0.650***(0.0904)231
Trunc-PoissonA
Yes-0.00339(0.174)0.180(0.194)-0.628***(0.122)
231
Trunc-Poisson
D
Yes

Corresponding Elasticity
ALIMENTARY_TRACT_AND_METABOLISM

DERMATOLOGICALSRESPIRATORY SYSTEMSYSTEMIC HORMONES

Observations
MethodInstruments
Control Function (IVs)
N o te : A ll sta n d a rd e rro rs a re c lu ste re d a t th e AT C -1 le ve l.

* * * m e a n s sig n i…c a n c e a t 1 % le ve l, * * a t 5 % le ve l a n d * a t 1 0 % le ve l. Ye a r d u m m ie s in c lu d e d a re n o t sh ow n .

Table 10: Determinants of the number of NCEs per therapeutic class Count model,
2-digit ATC categories

46

Count Models
VARIABLES

(14)
Nct0

(15)
Nct0

(16)
Nct0

(17)
Nct0

ALIMENTARY_TRACT_AND_METABOLISM

0.248*(0.146)-0.206(0.282)-0.184(0.197)-0.180(0.195)ANTIINFECTIVES_FOR_SYSTEMIC_USE

1.191***(0.139)0.778**(0.341)0.970***(0.196)1.055***(0.156)ANTINEOPLASTIC_IMMUNOMODULATING

0.883***(0.143)0.342(0.331)0.565***(0.207)0.537***(0.186)ANTIPARASITICS-0.737(1.010)--12.68(485.9)-BLOOD_AND_BLOOD_FORMING_ORGANS

1.049***(0.186)0.345(0.426)0.842***(0.256)

0.788***(0.230)CARDIOVASCULAR_SYSTEM

0.653***(0.143)-0.000612(0.229)0.265(0.274)-0.343(0.452)0.373**(0.171)-0.661*(0.343)0.407**(0.168)-0.616*(0.342)

GENITO_URINARY_SEX_HORMONES

0.218(0.233)-0.403(0.322)-0.179(0.297)-0.158(0.296)MUSCULO_SKELETAL_SYSTEM

0.326*(0.193)-0.0223(0.491)-0.131(0.259)-0.0421(0.251)NERVOUS_SYSTEM

0.0758(0.187)0.423**(0.185)0.169(0.365)-0.255(0.401)0.169(0.291)-0.357(0.287)-0.566**(0.277)0.104(0.224)-0.579(0.642)-0.548**(0.277)0.144(0.224)-c0

DERMATOLOGICALSRESPIRATORY SYSTEMSYSTEMIC HORM
N o te : A ll sta n d a rd e rro rs a re c lu ste re d a t th e AT C -1 le ve l.

* * * m e a n s sig n i…c a n c e a t 1 % le ve l, * * a t 5 % le ve l a n d * a t 1 0 % le ve l. Ye a r d u m m ie s in c lu d e d a re n o t sh ow n .

Table 11: Determinants of the number of NCEs per therapeutic class Count model,
2-digit ATC categories

47

Count Models
VARIABLEStc0 W c0

(14)
Nct0

(15)
Nct0

(16)
Nct0

(17)
Nct0

ALIMENTARY_TRACT_AND_METABOLISM

2.78e-07***(7.42e-08)

1.02e-06***(2.86e-07)

4.12e-07***(1.08e-07)

4.36e-07***(9.18e-08)

ANTIINFECTIVES_FOR_SYSTEMIC_USE

1.28e-07***(2.53e-08)

1.45e-07***(4.53e-08)

2.66e-07***(6.88e-08)

1.64e-07***(3.03e-08)

ANTINEOPLASTIC_IMMUNOMODULATING

7.86e-08***(1.87e-08)

2.57e-07(1.80e-07)

3.04e-07***(7.45e-08)

2.14e-07***(4.00e-08)

ANTIPARASITICS

6.10e-05(6.79e-05)-

0.000689(0.0267)-

BLOOD_AND_BLOOD_FORMING_ORGANS-1.58e-08(4.13e-08)

4.75e-07(2.93e-07)

1.96e-07**(9.31e-08)

1.04e-07*(6.20e-08)

CARDIOVASCULAR_SYSTEM

1.02e-07(7.06e-08)

2.70e-07**(1.27e-07)

2.09e-07**(9.94e-08)

1.81e-07**(7.99e-08)

DERMATOLOGICALS

3.22e-06**(1.43e-06)

4.75e-06(9.35e-06)

4.83e-06***(1.62e-06)

4.84e-06***(1.62e-06)

GENITO_URINARY_SEX_HORMONES

5.96e-07**(2.71e-07)

2.09e-06***(6.17e-07)

7.88e-07**(3.11e-07)

8.68e-07***(3.08e-07)

MUSCULO_SKELETAL_SYSTEM

1.28e-07**(5.65e-08)

2.93e-07(6.42e-07)

3.46e-07***(9.57e-08)

1.87e-07***(6.10e-08)

NERVOUS_SYSTEM

1.22e-06***(3.60e-07)
3.58e-07**(1.81e-07)
3.79e-06***(8.58e-07)
5.37e-07(2.66e-05)
0.150
231
PoissonNo

1.50e-06(1.26e-06)
3.21e-07***(1.16e-07)
3.16e-06***(8.60e-07)
3.63e-05(4.43e-05)
0.350
231
IV-Poisson
D

1.86e-06***(4.54e-07)
4.85e-07**(1.98e-07)
3.57e-06***(9.05e-07)-7.41e-06(5.65e-05)
0.342
231
Trunc-PoissonA
Yes

1.95e-06***(4.51e-07)
4.56e-07**(1.93e-07)
3.77e-06***(9.08e-07)
0.252
221
Trunc-PoissonD
Yes

RESPIRATORY SYSTEMSENSORY ORGANSSYSTEMIC HORM

Corresponding Elasticity
Observations
MethodInstruments
Control Function (IVs)
N o te : A ll sta n d a rd e rro rs a re c lu ste re d a t th e AT C -1 le ve l.

* * * m e a n s sig n i…c a n c e a t 1 % le ve l, * * a t 5 % le ve l a n d * a t 1 0 % le ve l. Ye a r d u m m ie s in c lu d e d a re n o t sh ow n .

Table 11 - continued

48

Mean
Wct0

ATC class (C)Mean
1
E (Nct0 )(Elasticity)
0.169
1,581,130
0.211
5,105,871
0.111
2,770,592
0.249
157,8230.285
685,9400.311
2,739,536
0.421
2,068,826
0.158
3,768,493
0.407
377,4810.110
1,254,843
0.413
145,829All
0.252
1,809,658
Table 12: Elasticties and Market Size Generating One Innovation per class
A: ALIMENTARY TRACT AND METABOLISM
B: BLOOD AND BLOOD FORMING ORGANS
C: CARDIOVASCULAR SYSTEM
D: DERMATOLOGICALSG: GENITO URINARY SYSTEM AND SEX HORMONES
J: ANTIINFECTIVES FOR SYSTEMIC USE
L: ANTINEOPLASTIC&IMMUNOMODULATING AGENTS
M: MUSCULO-SKELETAL SYSTEM
N: NERVOUS SYSTEM
R: RESPIRATORY SYSTEM
S: SENSORY ORGANS

49

7.3

Additional Tables

ATC Class
A14, A16
Other A
B

ICD10 Chapter
IV
XI
III

ICD10 Blocks
E00-E90
K00-K93
D50-D89

C
D
G
H
J
L
M
N1,N2,N3
N4,N5,N6,N7
P
R
S1
S2
V

IX
XII
XIV
IV
I
II
XIII
VI
V
I
X
VII
VIII
XIX

I00-I99
L00-L99
N00-N99
E00-E90
A00-B99
C00-D48
M00-M99
G00-G99
F00-F99
A00-B99
J00-J99
H00-H59
H60-H95
S00-T98

Disease Title
Endocrine, nutritional and metabolic diseases
Diseases of the digestive system
Diseases of the blood and blood-forming organs
and certain disorders involving the immune mechanism
Diseases of the circulatory system
Diseases of the skin and subcutaneous tissue
Diseases of the genitourinary system
Endocrine, nutritional and metabolic diseases
Certain infectious and parasitic diseases
Neoplasms
Diseases of musculoskeletal system and connective tissue
Diseases of the nervous system
Mental and behavioral disorders
Certain infectious and parasitic diseases
Diseases of the respiratory system
Diseases of the eye and adnexa
Diseases of the ear and mastoid process
Injury, poisoning and certain other consequencesof external causes

Table 13: International Classi…cation of Diseases and ATC drug categories

50

Part I(2)
Nct-520,902(412,709)
1.382e+06(796,758)-74,779(791,142)
1.844e+06**(785,959)
4.969e+06***(959,893)-1.180e+06*(659,621)
116,226(520,704)
507,542(802,016)-471,161(712,665)-2.489e+06***(503,609)-396,866(474,993)

1.674e+06***(313,368)-1.285e+06***(218,251)
389,321(526,964)-1.175e+06***(53,656)
59,068(347,890)-3,339(170,307)
209,676(367,647)-264,436(284,108)
941,988(1.221e+06)-440,332(451,827)
20,557(498,769)-274,687(745,978)-1.478e+06*(678,640)-1.246e+06(1.708e+06)
2.283e+06(1.956e+06)-1.131e+06(1.191e+06)-127,264(614,196)-768,552(2.434e+06)
125,328(692,843)
932,277(1.308e+06)
760,836(804,415)

Exogenous Variables
ATC 1 Dummies
ATC=1
ATC=2
ATC=3
ATC=4
ATC=5
ATC=7
ATC=9
ATC=10
ATC=11ATC=13ATC=14First Stage OLS
(3)
Nct0

(1)
Nct

(4)
Nct0

(5)
Nct0

-71,384(64,261)-1.042e+06***(113,633)-88,429(103,646)
270,314***(72,067)-509,201***(114,421)
113,142*(58,539)
608,828***(87,030)
208,649*(115,297)
191,954**(81,466)-234,258**(94,549)-214,776(216,301)-73,370(64,382)-1.042e+06***(113,868)-80,242(104,823)
263,554***(72,024)-513,894***(113,525)
109,560*(58,111)
600,530***(86,475)
181,564(114,767)
187,484**(82,168)-250,259**(93,712)-215,580(217,314)

126,559(75,776)-958,351***(57,769)
54,147(51,337)-289,982**(114,341)-569,995***(56,469)-142,390*(68,503)
360,580(212,734)-224,673***(57,375)-131,826(104,594)-188,754*(88,608)-209,891(150,582)-360,962**(147,105)-392,985(224,137)-259,201(252,345)
525,628(572,411)-236,149(291,953)-349,357**(145,820)-383,928(220,573)-251,423(249,592)
532,433(571,705)-231,774(292,213)

16,678(184,656)
159,166(275,349)
317,205(260,051)
968,701*(539,184)-73,396(266,379)

Time Dummies
Time_1
Time_2
Time_3
Time_4
Time_5

Table 14: First stage Regressions

51

Part II
(1)
Nct
Excluded IVs

First Stage OLS
(2)
(3)
(4)
Nct
Nct0
Nct0

Male population
0 .0 5 5 4 *Female population-0 .0 5 2 6 *Nb of Deaths-4 .2 5 6 * * *Nb of Male Deaths
8 .5 7 9 * * *Male Pop Above 50 in countries
served by drugs of own ATC-2 level times ATC-1 dummyMale Pop Above 50*ATC=1-0 .4 8 8 * * *Male Pop Above 50*ATC=2
0 .1 7 9 * * *Male Pop Above 50*ATC=3-0 .8 9 4 * * *Male Pop Above 50*ATC=4-0 .2 4 1
Male Pop Above 50*ATC=5
0 .7 0 1 * * *Male Pop Above 50*ATC=6
1 .4 5 2 * * *Male Pop Above 50*ATC=7
0 .1 0 2
Male Pop Above 50*ATC=80 .1 1 1
Male Pop Above 50*ATC=90 .1 7 3 * * *Male Pop Above 50*ATC=10
0 .0 3 0 0
Male Pop Above 50*ATC=11
0 .0 1 5 5Male Pop Above 50*ATC=12-0 .0 0 5 1 2
Male Pop Above 50*ATC=13-0 .0 0 5 8 8Female Pop Above 50 in countries
served by drugs of own ATC-2 level times ATC-1 dummy
Female Pop Above 50*ATC=1
0 .4 2 5 * * *Female Pop Above 50*ATC=2-0 .1 5 0 * * *Female Pop Above 50*ATC=3
0 .7 8 6 * * *Female Pop Above 50*ATC=4
0 .2 0 6
Female Pop Above 50*ATC=5-0 .6 0 4 * * *Female Pop Above 50*ATC=6-1 .2 5 4 * * *Female Pop Above 50*ATC=7-0 .0 8 8 6
Female Pop Above 50*ATC=8-0 .0 9 4 5
Female Pop Above 50*ATC=9-0 .1 4 7 * * *Female Pop Above 50*ATC=10-0 .0 2 6 0
Female Pop Above 50*ATC=11-0 .0 1 1 6
Female Pop Above 50*ATC=120 .0 0 4 2 9
Female Pop Above 50*ATC=13
0 .0 0 4 5 7
Observations
74
74
231
R-squared
0.791
0.937
0.309
IVs
A
D
B
Test Excluded. IVs
F test
53.64
201.4
640.6
P value
1.48e-07
00
N o te : A ll sta n d a rd e rro rs (n o t sh ow n ) a re c lu ste re d a t th e AT C -1 le ve l.
* * * , * * , * m e a n s sig n i…c a n c e a t 1 % , 5 % a n d 1 0 % le ve ls

Table 14 - Continued 1

52

(5)
Nct0

0 .0 3 8 8 * * *0 .1 1 3 * * *0 .1 2 0 * * *-0 .1 5 5-0 .0 2 3 9
0 .0 8 3 2 * * *0 .0 2 3 5
0 .0 8 0 5 * * *0 .2 1 4 * * *0 .0 2 1 1 * *0 .0 7 2 2 * * *0 .0 2 8 30 .0 7 7 7 *-0 .0 3 3 6 * * *-0 .0 9 4 2 * * *-0 .1 0 2 * * *0 .1 3 20 .0 2 3 7-0 .0 7 0 7 * * *-0 .0 2 0 3-0 .0 7 0 0 * * *-0 .1 8 5 * * *-0 .0 1 8 0 * *-0 .0 6 2 5 * * *-0 .0 2 4 6-0 .0 6 7 1 *

231
0.310
C

231
0.509
D

655.5
0

53.06
9.10e-08

Part III
(1)
Nct
Excluded IVs

(2)
Nct

First Stage OLS
(3)
(4)
Nct0
Nct0

Pop Above 50 in countries
served by drugs of own ATC-1 level times ATC-1 dummy
Pop Above 50*ATC=1
0 .0 0 0 3 3 0 * * *Pop Above 50*ATC=20 .0 0 1 3 5 * * *Pop Above 50*ATC=30 .0 0 1 2 7 * * *Pop Above 50*ATC=4-0 .0 0 0 7 6 3
Pop Above 50*ATC=50 .0 0 1 1 0 * * *Pop Above 50*ATC=60 .0 0 0 6 4 8 * * *Pop Above 50*ATC=7
2 .0 0 e -0 5
Pop Above 50*ATC=8-0 .0 0 0 1 8 2 * *Pop Above 50*ATC=90 .0 0 0 4 5 5 * * *Pop Above 50*ATC=10
6 .2 4 e -0 5
Pop Above 50*ATC=11
0 .0 0 0 4 5 4 * * *Pop Above 50*ATC=120 .0 0 0 2 2 0 *Pop Above 50*ATC=13
0 .0 0 0 3 5 4Male pop Above 50 in countries
served by drugs of own ATC-1 level times ATC-1 dummyMale Pop Above 50*ATC=1
Male Pop Above 50*ATC=2
Male Pop Above 50*ATC=3
Male Pop Above 50*ATC=4
Male Pop Above 50*ATC=5
Male Pop Above 50*ATC=6
Male Pop Above 50*ATC=7
Male Pop Above 50*ATC=8
Male Pop Above 50*ATC=9
Male Pop Above 50*ATC=10
Male Pop Above 50*ATC=11
Male Pop Above 50*ATC=12
Male Pop Above 50*ATC=13
Observations
74
74
231
R-squared
0.791
0.937
0.309
IVs
A
D
B
Test Excluded. IVs
F test
53.64
201.4
640.6
P value
1.48e-07
00
N o te : A ll sta n d a rd e rro rs (n o t sh ow n ) a re c lu ste re d a t th e AT C -1 le ve l.
* * * , * * , * m e a n s sig n i…c a n c e a t 1 % , 5 % a n d 1 0 % le ve ls

Table 14 - Continued 2

53

(5)
Nct0

0 .0 0 0 7 0 5 * * *0 .0 0 2 8 8 * * *0 .0 0 2 7 0 * * *-0 .0 0 1 6 4
0 .0 0 2 3 7 * * *0 .0 0 1 3 9 * * *4 .0 6 e -0 5
-0 .0 0 0 3 8 4 * *0 .0 0 1 0 1 * * *0 .0 0 0 1 3 30 .0 0 0 9 9 3 * * *0 .0 0 0 4 6 3 *0 .0 0 0 7 5 6

231
0.310
C

231
0.509
D

655.5
0

53.06
9.10e-08