Katie Wood
Econ 499
Senior Honors Thesis
3-31-2008
Biotechnology Patents
The decision of the Supreme Court in the case Diamond vs. Chakrabarty to
broaden the concept of patent protection to include inventions derived from biological
advances broadened the scope of research that can be done regarding patents and the way
in which we must approach them (Ko). This decision held that genetically altered living
microorganisms are patentable, a decision most never thought would occur. For,
historically, patents have been in place to authenticate mechanical and chemical science
applications. This decision raises many challenges for the courts in determining manmade (which can be patented) from natural occurring genetic differences (which cannot
be patented). In Europe, the European Directive states that “although neither the human
body, nor any of its elements, can be patented, an element ‘isolated from the human body
or otherwise produced by a technical process, including sequence of a gene might be
patentable (Farnley 3).” These and other cases bring into the equation of patents morality
issues, though I will not touch on these in too much depth due to time constraints. As I
will discuss in later detail, I show in my research to what extent, holding all else constant,
patents are responsible for gaining a firm profit.
BACKGROUND INFORMATION
In terms of patents, the scope is the range of products or processes which the
holder of the patent has right to exclude others from making, using, or selling the
invention. Hence, scope is the most important aspect of the patent for the patent holder,
for it determines where they can maintain “monopoly” power, and ultimately impacts the
progress of biotechnology. Biotechnology is defined as the science of creating new
organisms using recombinant DNA techniques, cell fusion, and bioprocessing for
commercial applications. Biotechnology is a newly emerging field in the last 30 years.
With this, an increase need for inventors to stake claim in the discoveries has
materialized.
It is first, important, to understand what the biotechnology field’s research focus
is before seeing how patents affect the industry. In order to gain insight into the many
details of cell processes, firms and institutions in biotechnological fields employ specific
tools for research and development. Before looking at the complications with patenting
biotechnology innovation, it is important to look at the goal of research and how
researchers hope to reach this goal. The objective of most research involves charting the
path of a cell completely, from a single egg to a whole thriving organism. Starting from
the single cell, all organisms progress in basically the same manner as they increase in
size, replicate the genetic material and eventually divide. Cell differentiation occurs in
the path of each cell. That is, as they grow, each cell becomes specialized to perform
specific tasks. The goal of research is to understand these special tasks and study the
effect of turning certain genes off and others on. It is not enough, however, to know the
impact of each gene. Each process is broken down into the smallest possible bit of useful
information. Once each individual part is understood, the pieces can be reassembled “in
a way that provides insight into the inner workings of cells and, ultimately, of whole
organisms (BIO 1).” Researchers must also identify realistic external and internal factors
that regulate the process and make the turning on or off viable in real organisms. Any
disruption in the balance of external and internal factors is that which leads to
uncontrolled cell proliferation, disease, and death.
After cells differentiate, some tissues maintain a subset of undifferentiated cells
that can later replace damaged cells or replenish supply of cells used in other processes.
One type of these cells are known as Adult Stem Cells (ASCs). When an organism needs
to employ the use of ASCs, they divide in two, one differentiated and one
undifferentiated. However, these cells only differentiate into one specific cell type.
ESCs (Embryonic Stem Cells) can, instead, change into any cell type. Research
involving ESCs hope one day to be able to use a patient’s own genetic material to
develop replacement cells. A study done at Harvard University made hybrid cells of
ESCs and pure cells to reach the result of replenishing cells as would using a patient’s
genetic material.
Another focus of biotechnology research involves genes which contain the
information for making proteins. Then, when looking at the actual proteins, scientists
hope to be able to understand these genes that encode them. Biotechnology allows
researchers to probe the genetic basis of cell functions using several different techniques.
First, molecular cloning, which is known as the most essential biotechnological tool, is
responsible for research findings from characterizing genes and creating genetic maps to
associating genes with physical attributes. Molecular cloning involves inserting a new
piece of DNA into an existing plasmid, or circular DNA, into a cell so that it can be
maintained and replicated by the cell and later studied by scientists. The plasmid is used
to protect the DNA from being expelled from the cell as a foreign object. As a type of
recombinant DNA technology, the new DNA replicates every time the cell divides.
Cloning allows researchers to study and fully understand each gene as an
individual identity. However, this is not how genes behave. Rather, they work
simultaneously with one another. A way to study this is using microarray technology,
which can monitor the expression of thousands of genes at one time. This is useful in
understanding complete, complex systems. Pharmaceutical companies use microarrays
in conjunction with cell culture to test the safety and possible side effects of new drugs
they produce. As mentioned earlier, internal and external factors that affect gene
expression must also be studied. In order to do this, a few other techniques can be
employed. Antisense are small pieces of DNA. These small strands can be used in RNA
interference by blocking genes specifically. Once a gene is blocked, research can
determine the type of effect it has on the phenotype. Likewise, gene knockouts are used
for a similar reason. By deleting or disrupting specific genes, one can see how the
absence affects the whole organism. Gene knockouts, instead of seeing what a gene
does, determine what a gene does not affect.
Whatever process a company uses, or whatever type of research and end result in
which they are involved, must be protected in order for the company or individual to gain
from their innovation. Protection can come in many forms. If planning to sell the
product or process, a firm can gain on other firms by using a head-start, they can attempt
to keep the details secret or sell the invention to the highest bidder. They can also license
their product. To license is to give permission to another to use the invention. This,
however, comes along with a term, territory and renewal restrictions. The focus of my
research, however, will begin with an even stricter form of protection, patenting. Since
patents deter anyone other than the holder of the patent to utilize the new invention, more
patents may lead to inventions being used too little. Licensing can mitigate this cost
during the life of the patent. This is too much for now, but may be of interesting research
later.
A patent is “the exclusive right of the owner to exclude others from making,
using, or selling the invention as defined in the claims of the patent for twenty years
(Colitz).” Twenty years from the date the patent is filed is the designated length a patent
is valid in the United States. Different countries have different time spans and
restrictions on their patent control, but only US biotechnology companies will be
investigated in this paper, so this definition will prove sufficient. A patent, in the
traditional senses is used for mechanical and chemical applications. Recently this
definition has expanded to included fields such as biotechnology. The problems facing
this new definition due to the inherent difference of biotechnology as a science compared
to other disciplines will be discussed later. A patent, it is important for our purposes to
understand, can be for a product, a process or a part of a process.
As background for understanding the patent, we will look quickly at the patenting
process. It begins with the inventor’s disclosure which spells out exactly what the
invention is, how it works, how it can be used and how it is an improvement over other
similar inventions. The more detailed the beginning disclosure the more quickly a patent
can pass through the final stages. The process must begin within the first year of the first
public use, disclosure or sale. If not, the inventor automatically forfeits his/her right to
exclusivity. Though it is not necessary, an inventor can then engage in a patentability
search in order to determine the scope of patent protection that may be available to them.
This search involves researching like patents and can be done through the United States
Patent and Trademark Office (USPTO). Next comes preparing the patent application. A
patent becomes “patent pending” once it is filed in the court system. It is at this point and
after that licensing can take place. Following this, the courts examine the patent
application. It is in their jurisdiction to determine patentability.
For an invention to be patentable it must, by definition, be novel (never have
existed before), inventive (not obvious to a skilled worker), and applicable (able to be put
to practical use). This is the point that causes problems for courts in regard to
biotechnology patents versus the traditional sciences as mentioned previously. If at first
an invention does not pass this point in the courts it may be sent back for amendment,
more detail or a different scope. Finally, once all is approved, a patent is granted.
(Colitz)
There are about 150,000 patent applications submitted per year in the United States.
With so many applications it is simple to see how new research may be difficult if patent
protection is extremely strong. Protection can be strengthened in several ways. Patents
can be extended to new subjects, as was done when biotechnology was allowed to be
patented. The term of the right of the patent holder to exclude others from using their
patented product or process strengthens patent protection in an obvious manner. This
means that it is longer until any other individual can utilize or copy the patented subject
matter, and the patent holder also gains from the profits for a longer period of time.
Finally, to strengthen a patent, the patent holder can be given more power in infringement
lawsuits (which is the only way in which they can stake their claim if they see a product
too similar to theirs on the market when their patent is still in effect). It is interesting
enough to know how protection can be strengthened, but it is also good to note why this
is important other that the larger share of profit for the patent holder. With stronger
patent protection, investment in research and development increases. This is the case
because it is a safer investment for a firm to engage in research when they know how
likely it is they will be able to gain profits from successful research and for how long.
Monopoly power granted to the patent holder, due to scope as previously
discussed, lasts only the length of the patent. Once the patent expires, the patent holder is
required to grant full disclosure of the patent which quickly diffuses the benefits of the
patent to other companies. There are, however, inherent problems with patenting.
According to Richard Nelson’s On the Complex Economics of Patent Scope broad scope
tends to slow progress in any field. This is because of the added obstacles a patent
imposes on subsequent researchers to innovate around the patented product or process.
Bio research suggests a tragedy of the “anticommons.” This is a theory in which scarce
resources are underused because too many owners can block one another from further
research. Patent protection must take this into consideration when privatizing research in
order to sustain both upstream and downstream research and product development
(Heller 698). If consideration is not taken to patents blocking out one another, the
property rights may lead to fewer useful products and fewer developments all around.
This is especially true in the biotechnology industry. Biotechnology discoveries are
many times part of steps in a process, rather than one step to an end product. Any large
goals in the industry are thereby achieved step by step (Farnley 1). Also blocking in the
biotech industry can occur because much of the research performed involves living,
evolving organisms which can present overlapping claims that can induce obstacles.
Whatever realm a patent is allowed to cover inhibits researchers from inventing if it aids
the belief that efforts will not be compensated. An inventor demands compensation for
the amount of research and development investment they input. Compensation may not
occur because it is too difficult to fight in the courts the fact the invention truly is
inventive, so the “incentive-to-invent” motive decreases. If an invention is fully
disclosed immediately, free riders can copy the innovation and reap the benefits without
contributing to inputs. The free rider problem then drives the Marginal Cost to the
competitive equilibrium negating any profit for any company involved. Monopoly power
for the length of the patent insures the patent holder he/she will not have to worry about
this problem, and following, the price will reflect the value of the inventor’s inputs.
On the other side of this coin, the vast monopoly power can scare inventions off
the table in fear of wasted time and effort. If investment is made but then it is ruled that a
new innovation is within the scope of another patent, the invention is not realized and
effort is wasted. This is one of the main controversies surrounding biotech patents. What
is the extent to which the patent holder should be entitled to derive benefits from
downstream developments resulting from the patented tool (Farnley 2)? This type of
patent claim to a downstream product is called a reach-through claim. If many reachthrough claims are granted, the fear is that downstream research will halt altogether if no
gain is coming from it.
The incentive-to-disclose theory again shows an inherent problem with or without
patent protection. Patent protection conceals the invention from the public so they can
not reproduce it without permission. If patent protection was not in place, inventors have
an incentive to take this course of action anyway. At least patent protection requires full
disclosure after the allotted amount of time. Concealing an invention poses social costs
in depriving the public of a full range of benefits from the new innovation. Secrecy can
also lead to waste in terms of duplication of research between rival firms if the patent
system were not in place. Logically, if patents are in place to allow the inventor to
benefit from their invention fully, the scope should cover both the disclosed invention
and any and all variations a skilled worker could make given the concealed information.
Due to the fact biotechnical variations yield more unpredictable results than do
mechanical or chemical fields, this is a more difficult call to make. The trouble with
allowing patents to nearly eliminate rivalry for a time is while the patent is in effect, a
complacency can be induced to the patent holder since the costs of inaction during the
time are diminished. Allowing variations of the patent to also be in the control of the
patent holder has worked to offset this in other fields. Because of the unpredictable
quality of biotechnology mentioned earlier, it is more difficult to make this quick fix.
As an alternative to patent protection, race for scientific discovery is used in
mainly academic realms. Industrial companies shy away from this incentive as they rely
on profitability. Out of this paradox come strategic alliances between firms and
academia. The shear excitement of discovery and enhancing knowledge can be the goal
of the university, while a company can still profit from the market. In the biotechnology
field, scale-up operations are small and require very little capital investment. This means
start-up costs are not a significant barrier to entry, yet small and large firms form
alliances or partnerships readily in the industry.
Next, I will discuss some general trends in the Biotech industry to gain
topical knowledge on why the raw data for each company involved in the patenting
process looks as it does. The biotechnology industry relies heavily on outside funding to
perform research. In 1993 alone, $5.2 billion was raised, $2.3 through private and public
funding and $2.9 from strategic alliances (Financing). Before a company is profitable,
between $250 million and $500 million are used to fund development alone. The
position of a firm in the product cycle directly reflects how much they invest at the
current time and the level of inventor risk they are accruing.
ResearchÆ Product DevelopmentÆ Clinical TrialsÆ Field TrialsÆ Manufacturing
70% of firms are in the pre-clinical trials stage. This is where a great deal of risk
is involved. If a product or process does not work as expected, no inward cash flow is
even a possibility. Rather, funding is being spent only on Research and Development.
About 20% of the time firms are awaiting FDA approval and near the release of their
product. Finally, the remaining 10% of the time or so is spent when the firm has a
product on the market and has finally established cash flow. Only here are firms making
back and hopefully gaining on their initial investment. From beginning research to the
product entering the market is generally presumed to be a full cycle which takes about 12
years. Once the product is on the market and a company is gaining positive revenues,
81% of these sales revenues in the biotechnology industry are ciphered back in to the
development cycle to use for more research and development. This extreme length of
time and risk at the beginning of the cycle leads larger firms to form strategic alliances
with smaller start-up companies. Utilizing this, the large company does not have to
devote as much time to the development process, and the smaller firms do not have to
take the initial high cost risk of starting up a new project.
The top firms, in terms of patenting and research and development expenditures
are summarized in the table and graph below.
1. Amgen
2. Genetech
3. Genzyme
4. UCB-Celltech
5. Gilead
6. Serono
7. Biogen Idec
8. CSL
9. Cephalon
10. MedImmune
Millenium
Vertex Pharm
Revenue (millions $) R&D (millions $)
14268
3366
9284
1773
3187
650
3169.6
772
3026.1
383.9
2804.9
560.5
2683
718.4
2184.3
119.4
1764.1
403.4
1276.8
448.9
486.8
318.2
216.4
371.7
(Lahteenmaki 500)
Revenue and R&D in Biotech
900
800
700
600
Revenue (millions $) 500
400
300
200
100
0
3169.6
3187
2804.9
2683
1276.8
216.4
486.8
1764.1
3026.1
2184.3
0
500
1000
1500
2000
R&D (millions $)
(Top 2 Firms left out due to outliers)
2500
3000
3500
This supposes a rough general trend that more Research and Development expenditures
lead to greater revenue (and thus profits) for the firm. And, Research and Development
expenditures are used to innovate and invent which is the main concern of patents.
Much previous research within the biotechnology field on patents has already
been done. In his paper An Event Study Approach to Measuring Innovative Output: The
Case of Biotechnology, David Austin attempts to estimate the values of patents and the
effects these patents have on rival firms by analyzing individual patents. The relationship
measured is between value and patent scope, association with end product and
membership in a specific scientific class. Austin finds, in conclusion of his research, that
larger firms have smaller excess returns on average (.48% versus .92%), but larger dollar
excess returns ($1.94 million versus $1.50 million) than do smaller firms. Neither of
these findings was statistically significant. Product-linked patent events (identifiable
with an end product) being more valued than non-linked patent events was also
concluded.
Due to this fact shown lightly by Austin, it may follow that the larger excess
return for smaller firms versus larger firms leads to strategic alliances between the two.
Interfirm cooperation is investigated in a paper published by Weijan Shan and Gordon
Walker. They began with the fact that small firms hold a greater percentage of patents
than their share of sales. They decided that this must mean one of two things. Either
larger firms are less productive in Research and Development than small firms, or that
larger firms affiliate with smaller firms. With the high cost of bringing a new product
onto the market after going through each step, the study concluded small startup company
innovation output depends on their relationships with large firms but the output does not
attract them. This means the greater level of output for the small firms would not be
present if it was not for the intervention of large, developed companies. These ties occur
frequently and easily because small firms attract initial public funding which is an
attractive trait to the established firms.
In a related study, the difference between stocks and flows of knowledge applied
to patents was studied. The focus of the paper was to establish that which mainly leads to
a competitive advantage in the industry. Performance in the industry cannot be
determined based solely on the number of patents a company can claim. The number of
patents does not necessarily reflect the quality of those patents. Citations must also be
considered as well as the fact that patenting can be cost prohibitive due to the expensive
extensive filing process. All considered, the only significant variables in the study were
geographic location (flow variable) and citations and number of products in the pipeline
(stock variables). Variables such as the number of alliances and the research and
development intensity did not affect the competitive edge of one firm over another. In
conclusion, knowledge stock for a firm has a greater impact than does knowledge flow.
The focus of my research is to show how the number of patents an individual firm
accumulates affects the profit of the firm. I evaluate if patents directly are responsible
for a firm being more profitable, and if that fact alone is worth overcoming the obstacles
stated earlier in the background information.
PROCEDURE AND DATA
I use data sets derived from the National Bureau of Economic Research (NBER)
database. The data set “Pat63_99” which includes all granted patents from 1963-1999
along with assigned technological categories, assignee identifiers and alike descriptive
variables will be transformed. I use only patent data with the category distinction 3. This
refers to Drugs and Medicine patents. Within this category, I delete any observation in
category 3 that does not also have subcategory distinction 31, 32, or 33. These three
categories refer to biotechnology discoveries. I take only patents granted after 1980, the
year Congress began encouraging universities and institutions to patent biotechnology
discoveries and the year federal support of research in the biotechnology industry
dramatically increased. According to an article is Science from May 1998, in 1980
federal support of the biotechnology industry was responsible for 47% of the total
Research and Development (R&D). Before this date, technological advances, and
following patentable discoveries, are sensibly less than after this date. To enhance this
data set, I combine with it two other NBER files, “Cite75_99” and “Coname.” The same
general transformations appear in regards to year. The “Cite75_99” file lists all citations
made with each patent. This is a useful data set in that citations allow one to see how
much a given patent is used by other future patents and also to what degree the patent
utilized previous patents. This gives rise to the generality index, a way to quantify how
broad or narrow of a scope a patent can claim. The generality index for the Drugs and
Medicine category according to the NBER is close to the lowest for all categories
considered. Biotechnology is included in this category but seems to be a little higher in
the index in relation. This means most patented discoveries in Biotechnology have a
narrow scope, they are for very specific purposed which may prove also to be a reason
for their being so many patents in this area. Finally, “Coname” specifies each patent’s
assignee name and identifier.
This identifier is then used to look up specific company information for each
patent of interest. The company information is found on Moody’s databases. Here
information such as company size, R&D expenditure, years in operation, revenue etc. is
found. The file “match” from the NBER, is then used to merge the Moody’s data to the
patent data by way of assignee.
With the data sets integrated, I first ran OLS regression to determine simple
relationships between the variables of interest. These variables, why they were chosen,
and their significance to the question of the biotechnology industry and patents is
discussed later in the paper. After running OLS and looking for any endogeneity
problem, I then ran 2-staged-least squares to interpret the data more fully. This required
finding an instrument for the endogenous variable and re running the regression. Finally,
some selection problems seem to be of interest and deserve some merit which I discuss
next.
First, when merging the data sets from Moody’s and the Match dataset there were
several of the Moody’s companies listed as biotechnology companies that did not match
to any of the identifiers in the Match database. So, most likely, these companies are ones
which produce a product in the biotechnology industry, but do not innovate in the
industry. This does not consider some companies that may engage in strategic alliances
as discussed earlier, so they don’t innovate, but rather look as though they are just
producing. In reality these companies may be innovating by way of smaller start-up
companies. This is true in large part for Eli Lily. They have large amounts of Research
and Development expenditures, but look as though they are not innovating. When I
looked this up, they outsource much of their research to companies including Elan, ICOS
and LLY (Lahteenmaki). However, this is probably not something to be too worried
about in the scope of my research. Another reason some of the companies may not have
matched up could be due to the simple fact that their name (and therefore assignee) has
changed. They could either have undergone a name change or been involved in a merger
or acquisition thus changing the name they innovate under. I know this could be a factor
because, in 1997 alone, 10 companies underwent name changes, 25 were involved in
acquisitions (including Genetech by Hoffman-La Roche) and 7 were involved in mergers
(Lahteenmaki 499). If this is the facts in just one year, throughout the time series, these
could be a big factor, considering the match set is information on the NBER website that
is updated each year.
Second, there were firms in the patent dataset but not in the Moody’s dataset.
This implies these firms engaged in innovation, receiving and or applying for patents, but
did not produce in the given time frame. This could mean these firms received funding
from other companies (such as ones mentioned above) and those companies were ones
which did the production. It could also mean that the firms are start-up companies in one
of the last periods and have not entered the production stage as of yet. They could also
merely be under the radar, using a different name for innovation versus production or
outsourcing production to other companies as to stay in the forefront of invention. This is
a way, within the patent system the firm could be patenting and utilizing the secrecy
tactic. Any one of these problems could affect the data regressions.
-Descriptives for selection problems
2-STAGED LEAST SQUARES EQUATIONS
Stage 1:
amountit = β(0) + β(1)generalit + β(1)crecieveit + β(2)R&Dit + β(3)TRit+
β(4)cmadeit +εit
Stage 2:
profitit = θ(0) + θ(1)amountit+ θ(1)generalit + θ(2)crecieveit + θ(3)R&Dit +
θ(4)TRit +νit
I use 2-staged least squares due to an endogeneity problem I will discuss further in the
Results section. 2-staged least squares solves this problem using an instrument for the
problem variable. In this case, ‘cmade’ is the instrument used to predict values of
‘amount.’
A little discussion about the variables chosen for this regression:
‘Amount’: Amount is the total number of patents granted to each individual company in
the time-span of the data-set. I decided to check for the need of an instrument for
‘Amount’ because number of patents is correlated with items in the error term of the
profit regression. For example, number of patents depends on alliances the firm is
involved in, the number of years the company has been in business etc. These factors
also affect the profit of a firm.
‘Cmade’: ‘Cmade’ is the number of citations the given patent makes. Patents, as a
measure of innovation can be deceitful, as can be relying on the impact of a single patent
on society. Though one specific patent can lead to a life changing drug or invention, it
may be the work of many smaller patents upstream that made the discovery possible. So,
looking at a company’s number of patents in seclusion may not tell the whole story in
why one firm is more successful than another. Several small patented products or
processes versus one very significant, newsworthy patent will not have the same effect on
profits, which is why citations are in some aspects a count that deserves the most focus.
Citations are used in patents to note what other research the patentee utilized in
innovating their process or product. Patents give credit, especially in biotechnology, to
the research they are continuing.
I choose citations-made as a the instrument to generate fitted values of number of
patents because it is highly correlated to the number of patents a firm is credited with yet
uncorrelated to the profit level of the firm. The intuitive reasoning for this outcome is
that citations-made should be correlated with amount of patents because the more patents
a given patent application must cite means there were more hurdles that firm had to jump
through to draft the patent. As discussed in the background information, in order for a
patent to be granted, all previous research and sources must be cited. Therefore if more
work must go into this, the firm is unable to “put out” as many applications in a given
year. Citations-made is likewise understandably uncorrelated to profit because the
amount of previous patents similar to the given patent does not affect the quality and
breadth of the firm’s innovation. Quality is more likely that which is responsible for
changes in profit for a firm. Quality measures come from other variables in the
regression, ‘creceived’ and ‘general’ discussed below. Also, ‘cmade’ is not likely
correlated with those variables in the error term (alliances and years in business) that
were a problem for the amount variable. These exact correlations and more in depth
reasoning is discussed below.
‘Crecieved’: ‘Creceived’ is the number of times the given patent is cited by other
patents. The number of citations a patent receives can be difficult to understand
quantitatively. This is due to the idea that the longer a patent is in circulation, the more
companies are able to research it and thus the more chances for the patent to be cited.
Due to the years I have chosen to work with, this should not be a problem since most of
the patents studied have already, or are close to not being under patent protection any
longer. I use citations received in the regression as a way to measure quality of both the
patent itself, and of the firm’s Research and Development expenditures. The more times
a patent is cited by other patent applications, the more useful it is to society, and thus the
better the quality of patent. Also if a firm is constantly releasing quality patents, with
many citations received, then it would seem that the quality of the research and
development for the company is high as well.
‘R&D’: Research and Development expenditures are those which go directly to
innovation. As discussed earlier, this can prove to be a problem in the cases of strategic
alliances between small and large firms. Many firms have high levels of R&D
expenditures, yet surprisingly low levels of granted patents. This signals they are most
likely involved in outsourcing the actual innovation to other firms, and only receive
revenues from the patents and not their name on the actual patent. Likewise, firms on the
receiving end of this deal most likely have low R&D expenditures but a high level of
patents in their name.
‘TR’: Total Revenue is the amount of revenue a firm receives from the sale of its output.
This variable is an important indicator for profit level of a firm and also how much a firm
is able to re-invest for more research and thus more patenting. The variable total
revenue, is also used as a control for the size of the firm. In addition to size, TR can be
seen as a way to control for the cycling between periods of profits back into Research and
Development. This must be controlled for because, most likely, R&D does not only
affect a firms profit level, but a firm’s profit level also affects the amount dispersed into
Research and development.
‘General’: This variable stands for generality. The generality index was generated by
the NBER. It is a measure of the scope of the patent, how many citations it receives, and
how broad of fields these citations come from. In general, the generality index for
Biotechnology is much higher than the rest of the “Drugs and Medicine” category. A
high generality means the patent has a widespread impact (Hall 21). I use this variable in
the regression because I suppose a higher generality, thus higher reach to other industries
is related to (most likely positively) a firm’s profit.
The following is a table of averages for the variables in my regression. These are useful
for comparison purposes in determining whether a coefficient is substantial or in any way
meaningful.
Variable
Cmade
Creceive
General
RESULTS
TABLE A:
Mean
17.41
10.97
0.326
. corr amount general creceive r_d tr cmade Profit
(obs=2122)
amount
1.0000
-0.1078
-0.1430
-0.2360
-0.2824
-0.1144
-0.1979
1.0000
0.2818
0.0199
0.0115
-0.0744
-0.0048
1.0000
-0.0231
-0.0472
-0.1059
-0.0556
r_d
tr
1.0000
0.8397
0.0241
0.9572
1.0000
0.0744
0.7885
cmade
Profit
1.0000
0.0273
1.0000
0
200
R_D93
400
600
amount
general
creceive
r_d
tr
cmade
Profit
general creceive
0
200
400
600
amount
(All firms)
800
1000
200
150
R_D93
100
50
0
0
10
20
amount
(Firms with less than 30 patents)
TABLE B:
. regress amount general creceive r_d tr cmade
Source
df
MS
Model
Residual
31535016.8
230156220
5
2117
6307003.36
108718.101
Total
261691237
2122
123322.92
amount
general
creceive
r_d
tr
cmade
_cons
SS
Coef.
-96.73518
-2.434649
-.0007266
-.5201458
-2.523621
586.3806
Std. Err.
t
29.22776
.3514527
.1773131
.0702467
.4563961
14.40595
-3.31
-6.93
-0.00
-7.40
-5.53
40.70
Number of obs =
F( 5, 2117)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
=
=
=
=
2123
58.01
0.0000
0.1205
0.1184
329.72
[95% Conf. Interval]
0.001
0.000
0.997
0.000
0.000
0.000
-154.0533
-3.123878
-.3484527
-.6579055
-3.418652
558.1293
-39.41706
-1.74542
.3469995
-.3823861
-1.628589
614.6319
. regress Profit amount general creceive r_d tr
Source
SS
df
MS
Model
Residual
9477842.72
837183.882
5
2116
1895568.54
395.644557
Total
10315026.6
2121
4863.28458
Profit
amount
general
creceive
r_d
tr
_cons
Coef.
.0038392
-3.899894
-.0912215
.9377712
-.0180918
-16.89111
Std. Err.
.0013026
1.765598
.0213352
.0106724
.0042804
1.027213
t
2.95
-2.21
-4.28
87.87
-4.23
-16.44
Number of obs =
F( 5, 2116)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.003
0.027
0.000
0.000
0.000
0.000
=
=
=
=
=
2122
4791.09
0.0000
0.9188
0.9186
19.891
[95% Conf. Interval]
.0012847
-7.362383
-.1330616
.9168417
-.026486
-18.90556
.0063936
-.437406
-.0493813
.9587008
-.0096976
-14.87666
30
TABLE C:
. ivregress 2sls Profit general creceive r_d tr (amount = cmade), vce(robust) first
First-stage regressions
Number of obs
=
F(
5,
2116) =
Prob > F
=
R-squared
=
Adj R-squared
=
Root MSE
=
amount
general
creceive
r_d
tr
cmade
_cons
Coef.
-97.97803
-2.439596
-.0019617
-.5202176
-2.535916
587.3533
Robust
Std. Err.
29.6139
.3780927
.0709158
.0315495
.4868758
15.54826
t
-3.31
-6.45
-0.03
-16.49
-5.21
37.78
P>|t|
Profit
amount
general
creceive
r_d
tr
_cons
Instrumented:
Instruments:
TABLE D:
Coef.
-.0049498
-4.692397
-.1112287
.9383399
-.0229911
-12.09643
Robust
Std. Err.
.0104213
1.983016
.0324121
.0199088
.0113239
5.6313
[95% Conf. Interval]
0.001
0.000
0.978
0.000
0.000
0.000
Instrumental variables (2SLS) regression
-156.0534
-3.181069
-.1410337
-.5820888
-3.490721
556.8619
Number of obs
Wald chi2(
Prob > chi2
R-squared
Root MSE
z
-0.47
-2.37
-3.43
47.13
-2.03
-2.15
amount
general creceive r_d tr cmade
2122
197.14
0.0000
0.1211
0.1190
329.5736
P>|z|
0.635
0.018
0.001
0.000
0.042
0.032
=
5)
=
=
=
-39.90263
-1.698124
.1371102
-.4583464
-1.581111
617.8448
2122
=42899.47
0.0000
0.9171
20.075
[95% Conf. Interval]
-.0253751
-8.579036
-.1747552
.8993193
-.0451855
-23.13358
.0154755
-.8057578
-.0477022
.9773606
-.0007968
-1.059287
. ivregress 2sls Profit general creceive r_d tr (amount = cmade) if amount>30, vce(robust) first
First-stage regressions
Number of obs
=
F(
5,
1811) =
Prob > F
=
R-squared
=
Adj R-squared
=
Root MSE
=
amount
Coef.
-95.57887
-1.027118
-.2455392
-.5018056
-3.374758
655.5694
general
creceive
r_d
tr
cmade
_cons
Robust
Std. Err.
29.34079
.3688357
.0470458
.0237651
.7048535
16.86999
t
-3.26
-2.78
-5.22
-21.12
-4.79
38.86
P>|t|
Profit
Coef.
-.0072325
.4361913
-.0085207
.9825266
-.0506977
-9.072523
amount
general
creceive
r_d
tr
_cons
Instrumented:
Instruments:
Robust
Std. Err.
.0053084
1.409417
.0154133
.0043194
.003435
3.28135
[95% Conf. Interval]
0.001
0.005
0.000
0.000
0.000
0.000
Instrumental variables (2SLS) regression
-153.1242
-1.750506
-.337809
-.5484154
-4.75717
622.4827
Number of obs
Wald chi2(
Prob > chi2
R-squared
Root MSE
z
-1.36
0.31
-0.55
227.47
-14.76
-2.76
1817
353.23
0.0000
0.1648
0.1625
301.0623
P>|z|
0.173
0.757
0.580
0.000
0.000
0.006
=
5)
=
=
=
-38.03353
-.30373
-.1532694
-.4551957
-1.992347
688.6561
1817
= 2.6e+05
0.0000
0.9660
13.202
[95% Conf. Interval]
-.0176369
-2.326215
-.0387302
.9740607
-.0574301
-15.50385
.0031718
3.198598
.0216887
.9909924
-.0439653
-2.641196
amount
general creceive r_d tr cmade
ANALYSIS
In order to justify the use of the 2SLS model I first ran both equations in OLS to
see if there existed any endogeneity problem. These outputs are shown under Table B.
The first regression run is Stage 2. This shows that ‘amount’ is significant in explaining
a firm’s profits since the p value is 0.003. The second output shows that ‘cmade’ is
significant in explaining ‘amount’ and thus can be used as an instrument for the amount
of granted patents in a 2SLS model and thus will give, in the end, an unbiased estimate.
That is, I do not have to worry about the fitted values of ‘amount’ being correlated with
any of the variables in the error term of ‘profit.’
From Table A, the correlation between ‘amount’ and ‘profit’ is 0.49. This
justifies the use of the number of granted patents to explain the profit level of the firm.
The correlation between ‘amount’ and ‘cmade’ of -0.384 allows me to use citations made
as an instrument for number of granted patents in the profit equation. To finally conclude
this use, the correlation between ‘profit’ and ‘cmade’ is .0029. This means the number of
citations made by a firm hardly, if at all, affects the company’s profits. Thus, I may
continue with the 2SLS model. I also show the relationship between R&D and ‘amount’
graphically to show why I choose to run regressions with only firms with more than 30
granted patents as well.
I chose to run the regressions in two ways. First, I ran them with all companies
regardless of number of granted patents. Then, I chose to run the regressions with only
companies that had at least 30 granted patents in the time period. This choice was made
because of the “outsourcing” problem I discussed previously in depth. If companies
show less than 30 granted patents, it signals that they do not put their R&D expenditures,
most likely towards the goal of patenting. Rather, they probably are paying for smaller
firms to do the innovation on their behalf. Pharmaceutical Product Development (PPD)
consistently showed R&D expenditures of over $150 million, yet had few to no patents
each year. Taking a look further into their company, PPD engages exclusively in doing
research and then sell that research to other companies that then go through the patent
application process. Taking out firms that do not seem to focus on patenting, allowed me
to look only at the firms that try to use patents as an indicator and large input into their
profit. Removing outliers such as PPD dramatically increased the significance of my
regressions.
Before describing further, I note that all equations were run using robust standard
errors in order to correct for any heteroskedastic problems.
The first regression’s output is shown in Table C. In the equation used to
instrument the ‘amount’ variable, all variables are significant at the 5% confidence level
except for R&D. Generality, total revenue, and citations made and received are all show
p-values of 0.00. Most of the variables make sense in context as well. As generality
increases by 1 point, it is projected that the number of granted patents for the company
will decrease substantially, by 98. As citations made increase by 1 citation per patent, the
total number of granted patents decreases by 2.5, which makes sense because the more
citations a patent must make means more time is spent on writing the patent and more
time was most likely spent researching in order to build on so much previous research.
Citations received increasing by one other patent citing the given patent decreases patents
by 2.4. Higher quality in these firms’ patents probably mean more time is taken to do
research in all areas. The coefficient on total revenue does not make quite as much sense.
If total revenue increases by $1 million, granted patents is suspected to decrease. This
does not seem logical because one would assume firms re-invest revenue into more
research and thus more patents. This could actually be the problem, if R&D is taking
over some of this affect, money really is being re-invested into R&D. The R-squared of
this equation is 0.1 which is not as good as one would hope, but it is good enough to
continue on to the next step.
The second part of this regression is the final equation, using the instrumented
variable. Now, the instrumented variable, “amount” is only significant at the 50%
confidence interval. This shows that, in this regression, with these variables, number of
granted patents for a firm is not indicative of the profit level of that firm. Other values
including the generality count and R&D are, however, significant in the regression. R&D
for one positively affects a firm’s profit, which is what one would expect. Also, the Rsquared is 0.913 which means the equation explains ‘profit’ well. The constant term in
both this equation and the one I discuss next is negative. This indicates that holding all
variables constant, biotech companies on average operate for a loss rather than a profit,
which seems to be true glancing through the general numbers in the dataset.
The second set of 2SLS, Table D, used only firms that were granted more than 30
patents in the time span. In the instrumented variable regression (Stage 1), the adjusted
R-squared increased to 0.36 to show this second model fits better than taking into account
all companies. Most notably, the effect of Research and Development on the number of
granted patents becomes significant at the 5% confidence level where, before, it had a pvalue of 0.67. All other variables are also still significant at the 5% confidence level.
This makes more sense since Research and development should be very highly correlated
to patenting, as it is in most industries. This effect, however, highlights the argument
about biotechnology that many of the firms with high expenditures for research do not
actually do their own patenting; rather they may form alliances with smaller companies
who do the innovating for them. This regression seems to fit best with the question I
pose for my research since I am more interested in how patenting affects profit and not in
whether or not, and to what extent, firms choose to patent as their primary mode of
revenue.
When including this instrumented variable into equation, only considering firms
involved in patenting, ‘amount’ becomes more significant, from 0.5 to 0.17. This is good
in my research to actually draw some conclusions for firms that would inevitably care
and benefit from this research. The R-square of the regression is .92 which means the
model explains profits very well. In this regression, an increase in one more cited patent
suggests that profit decreases by $70,000. This would suggest that patenting actually
hurts a firm’s profits. On the other hand, surprisingly, the significance of the generality
index dramatically decreased from significant at the 5% level to not even significant at
the 70% level. The sign on the coefficient does change from negative to positive which
is what would be expected. Citations received coefficient signifies that, surprisingly, one
more patent citation the given patent receives actually decreases profit by $85,000. This
does not make much sense since higher quality patents should make a firm more money.
However, this variable is not significant, so the discussion is moot.
CONCLUSION
The biotechnology industry is difficult to assess the value of patenting to a firm’s
profits. This is the case for many reasons including the trend of outsourcing research
from a large company (with respect to R&D) to a small company. There is no clear
correlation between a larger amount of research and development expenditures to the
amount of patenting the given firm engages in. This also means that, then there is little
that can be said about patenting and profit for a firm. If firms ally with one another, their
profits come from different sectors and not just physical acts they engage in. For
example in an industry such as retail, the decisions one store makes as to spending and
sales directly reflect, almost exactly, that firm’s profits. This is not true for
biotechnology, because other factors come into play for firm’s profit margin.
My research, however, does show that, though it is not the relationship one would
expect, the number of patents a firm receives is significant in predicting that firm’s profit
level. When considering firms that seem to use patenting as a primary mode of profit-
making. The reason this relationship is negative could be due to several different factors.
It could be that firms that spend much effort in applying for patents do not produce and
sell as much and thus do not gain the profit that firms that do sell some product receive.
In most cases, the firms in the biotech industry that are innovators are paid for their
innovating as a blanket cost. Thus, they do not receive any added benefit from the scope
of the patent or any licensing the patent engages in. These benefits are received by the
“parent” company instead, the company that is initially paying for the research. That
means, on the other side of the issue, that firms which do not seem to engage in a great
deal of patenting are profiting more. With this relationship in mind, it is interesting to see
that the significant relationship between R&D and profit is highly positive.
Higher profits correlated to higher research and development expenditures can be
misleading. It is not necessarily that a firm with higher research and development costs
are gaining a higher profit because of it. The two variables may work in the other
direction. Many firms, if realizing a large profit margin will then cycle these profits back
into research and development costs. This means, that it may be a higher level of profits
that leads to higher levels of research and development instead of the other way around.
Thus it may be beneficial to observe the general trend of all profits, research and
development, and patents over time. Continued research could be done by focusing on
specific strategic alliances to see to what extent the relationship I predict is true. It would
also be interesting to look at more company data by taking the R&D of a parent company
and splitting it up to the small companies which innovate for it. In my research, these
companies were left out since the names in the two data sets did not match.
Though this is just a beginning in biotechnology patenting research, it is safe to
say that the biotechnology industry has many inconsistencies when it comes to profit
margins, research and development expenditures, and the focus on patenting. We know
that profit is explained to some degree by patenting, but to what extent exactly still
proves to be hazy.
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http://www.library.ca.gov/crb/96/BIOT_CH5.html.
Gallini, Nancy. The Economics of Patents: Lessons from Recent US Patent Reform.
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Heller, Michael. Can Patents Deter Innovation? Science Vol 280 No 5364 (May 1998).
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