Bargaining in technology markets: An empirical study of biotechnology alliances

Shinya Kinukawa
Komazawa University

Kazuyuki Motohashi
University of Tokyo

September 7, 2009

Abstract
The division of innovative labor between biotechnology and pharmaceutical companies has
become essential in pharmaceutical research since the first successful biotechnology products
reached the market in the early 1990s. A well-functioning market of knowledge assets is now
indispensable for health care innovation. However, if either sellers or buyers of biotechnologies
have too large bargaining power, the efficiency of the technology market could decrease. When
sellers have too large bargaining power, it could cause the “anti-commons.” If buyers have toolarge bargaining power and thus strong control rights in the research project, it could cause
inefficiency in research performance.
We empirically analyze the distribution of bargaining power at biotechnology markets
between sellers (mainly biotechnology companies) and buyers (mainly pharmaceuticalcompanies) by estimating the price increase and decrease due to bargaining by sellers andbuyers. The data is taken from RDNA database of Deloitte Recap, a company specializing in thebiotechnology industry since 1988. Using the data of contract price and technologicalcharacteristics (fields and stages of traded technologies) of 1516 alliance agreements fromJanuary 1990 to September 2008, we first estimate the two-tier stochastic frontier model
proposed by Polachek and Yoon (1987). Then, using estimated parameters and the equations

derived by Kumbhakar and Permeter (2008), we calculate the conditional expectations of theprice increase and decrease due to bargaining by the seller and buyer for individual allianceagreements.
We also examine the factors that could be related to sellers and buyers’ bargaining power: thecharacteristics of each individual alliance agreement and the parties. For the characteristics ofthe alliance agreement, we use RDNA’s twenty-six types of alliances. For characteristics of eachparty, we include sellers and buyers’ (i) regions (US, Europe, Japan, others), (ii) types(pharmaceutical, biotechnology, others), and (iii) whether public or not. We also include year
dummies. The relationship between these factors and the estimated conditional expectations of
the price increase and decrease due to bargaining are analyzed using OLS.
The results show that on average, the decrease of price due to buyer’s bargaining power islarger than the increase of price due to seller’s bargaining power, although the latter has been
increasing since 1990, after controlling types of alliances and each party’s characteristics. It isconjectured that pharmaceutical companies’ bargaining power has been larger thanbiotechnology companies but that biotechnology companies’ bargaining power overpharmaceutical companies has been improving over the past decades.
For the relationship between the effects of bargaining on contract price and each party’s
characteristics, we found that US sellers, which are mostly biotechnology companies, tend toincrease the contract price larger than other regions’ sellers do. It is conjectured that US
biotechnology companies may have less financial constraints possibly because US stock
markets are better suited for high-tech industries compared to other country’s markets.

1. Introduction
The division of innovative labor between biotechnology and pharmaceutical companies has
become essential in pharmaceutical research since the first successful biotechnology products
reached the market in the early 1990s.1 A well-functioning market for knowledge assets, where
biotechnology and pharmaceutical companies trade technologies in various types of allianceagreements, is now indispensable for health care innovation.
This paper examines how the knowledge market in the biopharmaceutical industry has beenworking by empirically analyzing the distribution of bargaining power of sellers (mainly
biotechnology companies) and buyers (mainly large pharmaceutical companies) of theknowledge assets. Both sellers and buyers of biotechnologies can have large bargaining powerin an alliance contract, and unevenly distributed bargaining power to either side can negatively
affect research performance and thus health care innovation.
For the sellers, strong patent rights can give large bargaining power to biotechnology
companies and cause the “anti-commons” (Heller and Eisenberg (1998)). If the price of research
input is too high, many pharmaceutical companies could be discouraged to use them, resulting
in the delay of new drugs.
For the buyers, financial constraints to biotechnology companies can give large bargaining
power to pharmaceutical companies (Lerner et al. (2003)). Biotechnology products usually takemany years of R&D and large amount of money for it. Moreover, because of considerable
information asymmetries in biotechnology research, it is often difficult for investors to assess
the progress of the research. Many research-intensive biotechnology companies have financed
themselves through both public equity issues and alliances with pharmaceutical companies.
However, because of the characteristics of biotechnology research, the amount of capital raised
from the public market has been highly variable.2 Thus, when it is difficult for a biotechnology
company to finance their R&D project, pharmaceutical companies can have large bargaining
power as the investor. This could cause inefficiency in the research project if the financing

1
2

For a history of the development of the biotechnology industry, see Audretsch (2001).
See Lerner et al. (2003), Figure 2 (page 418).
1company has a larger part of the control right in the research project.3
We analyze bargaining on biotechnology alliances applying an empirical framework proposedby Kumbhakar and Permeter (2008). Although their framework does not identify each party’s
bargaining power itself, it does estimate the surplus extracted by each party in the individual
alliances, which depends on the bargaining power. By using their framework, it can be analyzed
what kind of factors are related to the results of bargaining on each alliance.
The organization of the paper is as follows. Section 2 explains the empirical framework
proposed by Kumbhakar and Permeter (2008). Section 3 describes our dataset. Section 4
explains results. The final section discusses the results and concludes the paper.

2. Empirical framework
Both a seller and buyer extract surplus from an alliance agreement, but how the surplus isdivided between the seller and buyer depends on their bargaining power. In this paper, we apply
the empirical framework of Kumbhakar and Parmeter (2008), which is explained below, toexamine the effect of bargaining on alliance agreements.4
Let contract price of an alliance agreement, a seller’s reservation price, and a buyer’s
maximum offer be p , p , and p , respectively. Then, the contract price can be decomposed
as follows:()

p = p +η p − p ,(1)where 0 ≤ η ≤ 1 is the bargaining power of seller. Moreover, let μ ( x ) = E (v | x ) denote theexpected value of technologies traded in an alliance agreement conditional on characteristics ofthe technologies, where v is the unobservable expected value of traded technologies and x is avector of characteristics of the technologies. By construction, p ≤ μ ( x ) ≤ p . Then, theequation (1) is re-written as

(

)

(

)

p = μ ( x ) + η p − μ ( x ) − (1 − η ) μ ( x ) − p .
3(2)

See Lerner et al. (2003) for a survey of the theoretical literature on the relationship between
external finance and R&D firm’s performance.
4 Kumbhakar and Parmeter (2008) applied the framework to examine the relationship between wage
dispersion in labor markets and bargaining power in each job match.
2

The terms(p − μ (x )) and (μ (x ) − p ) in the equation (2) are a buyer’s and a seller’s

expected surplus from the transaction, respectively. In the equation (2), the difference between
the actual contract price and μ ( x ) is determined by each party’s bargaining power and surplus.
The seller can increase the price by extracting the buyer’s surplus(p − μ (x )) depending on thebargaining power η . Similarly, the buyer can decrease the price by extracting the seller’s(μ (x ) − p ) depending on the bargaining power (1 − η ) . Although η cannot be
identified, the surplus extracted by the seller and buyer, η ( p − μ (x )) and (1 − η )(μ ( x ) − p ),surpluscan be interpreted as the result of each party’s bargaining on the contract price. Then, based on
the equation (2), the following regression equation is formulated:p = x ′δ + w − u + v ,

(3)

(

)

where x ′δ = μ (x ) , δ is a vector of parameters for the covariates x , w = η p − μ (x ) ≥ 0 ,()

u = (1 − η ) μ (x ) − p ≥ 0 , and v is the usual error term.
To identify w, u, and v, the following assumptions are imposed: w follows an exponential
distribution with mean σ w , u follows an exponential distribution with mean σ u , and v follows
a normal distribution with zero mean and variance σ v2 . This speciation of the two-tierstochastic frontier model is proposed by Polachek and Yoon (1987). Then, the parameters δ ,σ w , σ u , and σ v can be estimated by the maximum likelihood method.
Using the estimated parameters, we further calculate the observation-specific expectation ofw and u conditional on the total residual ε ≡ w − u + v , which are driven by Kumbhakar andPermeter (2008). Having the estimate for E[w | ε ] and E [u | ε ] , we examine the relationshipbetween each of them and the characteristics of alliances and each party by using OLS.

3. Data
All the data for the empirical analysis is taken from RDNA database of Deloitte Recap, acompany specializing in the biotechnology industry since 1988. For the contract price ofalliance agreements (the dependent variable of the equation (3)), we use log of SIZE in RDNA,
where SIZE is deflated by using US GDP deflator. The SIZE variable is the deal size of analliance agreement including twenty-six types of alliances such as acquisition, joint venture, and3license.
For the technological characteristics of an alliance agreements (the control variables of theequation (3)), we use the types and the development stages of traded technologies in the allianceagreement. RDNA classifies technologies into more than fifty categories. Then, we integrated
them into twelve categories. For the development stages, we use nine stages in RDNA. One
alliance agreement can have several technologies, and thus also can have several development
stages. We include all technology dummies in such cases. For development stages, we use thelatest stage among them and thus one stage dummy for an alliance agreement.
Having estimated coefficients of equation (3), we calculate E[w | ε ] and E [u | ε ] ofindividual alliance agreements, that is, the increase and decrease in contract price due tobargaining by the seller and buyer in each alliance agreement. Since the dependent variable is inlog form, w and u approximate the rate of increase and decrease in the contract price,
respectively. Then, we examine the relationship between those bargaining effects and allianceagreement’s and each party’s characteristics. For alliance agreement’s characteristics, we use thetwenty-six alliance types of RDNA.5 Moreover, since changes in macro economy (e.g., stock
market) and government policy (e.g., patent policy) can affect bargaining power, we capturethem by year dummies. For seller/buyer’s characteristics, we use (i) dummy variables forcompany’s type (biotech, drug, others), (ii) dummy variables for company’s location (US,Europe, Japan, others), and (iii) listed public company dummy.
All the data downloaded from RDNA database includes 21,452 alliance agreements from
September 1973 to September 2008. The estimation of the two-tier stochastic frontier model andthe regression analysis of the effects of bargaining, E[w | ε ] and E [u | ε ] , are both based on
the same sample that consists of alliance agreements containing the data of all the above
variables. The sample, however, excludes the alliances including universities since they may
have been in a different competitive position from commercial organizations. Moreover, only
the sample of the alliance agreements from January 1990 is used because it is after the 1990s

5

In the database, there are several alliance types that are not listed in the database help file.
Alliances of those unlisted types are excluded from our dataset.
4that the relationship between pharmaceutical and biotechnology companies had developed
involving the commercialization of biotechnology products (Audretsch (2001)). Then, thesample is confined to 1516 alliance agreements.

4. Results
4.1 The two-tier stochastic frontier model

We first show the estimation results of the two-tier stochastic frontier model (equation (3)) intable 1. Since there are alliance agreements including several technologies, we use all thetechnology and development dummy variables instead of including the constant term. We also
include the product of technology dummies and 2000s dummy (1 for alliances since 2000 and 0
for alliances before 2000) to capture changes in the value of technologies. For the maximum
likelihood estimation of the equation (3), the estimated coefficients of OLS using the same
control variables are used as the initial values (the initial values of the variance parameters are
all set to one).

Table 1 Estimation results of the two-tiered stochastic frontier model

The results show that development stages are dominant factors to determine the contract price.
The coefficients of development dummies are all larger than technology dummies. Moreover,later stages tend to be priced higher than earlier stages, which is quite intuitive because laterstage technologies are close to the market than early stages.
Although the effects of technology dummies are relatively small, there are differences among
them. The estimated coefficients of technology dummies are the technological differences in
1990s, and the estimated coefficients of the products of technologies and 2000s dummies are thedifferences between 2000s and 1990s technology dummies’ coefficients. The results of Wald
test rejects the null hypothesis that changes of technology dummies in 2000s are all zero.
Among the changes in technology differences, the increase in Monoclonals, Synthetics, andDiagnotics are remarkable.
5The variance parameters of error terms are all statistically significant, and the parameter σ u
is more than twice larger than σ w . Since σ u and σ w are equal to means of u and w, which
follow exponential distributions, it can be implied that on average, buyers tend to have larger
bargaining power on biotechnology alliances.

4.2 OLS analysis for the effects of bargaining

Having the estimated coefficients of equation (3), the conditional expectation of w and u arecalculated using the equations of Kumbhakar and Parmeter (2008). Then, they are regressed on
characteristics of alliance agreement and each party. The results are shown in Table 2 and 3.
Since there is no alliance agreement classified into “merger” in the sample, alliance types in the
regressions are twenty-five.

Table 2 OLS analysis for the rate of increase in contract price ( E[w | ε ] )Table 3 OLS analysis for the rate of decrease in contract price ( E [u | ε ] )The results show that the effects of bargaining can be different by alliance types. For sellers,
the rate of increase is remarkably large in acquisition compared to other alliance types. For
buyers, the rate of decrease is larger in cross-license and sub-license than in other alliance types.
Although the estimated coefficients of those alliance types seem economically significant, they
are all statistically insignificant.
After controlling alliance types and sellers/buyers characteristics, estimated coefficients ofyear dummies show that w is increasing and u is decreasing. It can be conjectured that sellers’bargaining position against buyers may have been improving.

For the characteristics of parties, whether a party is a listed or unlisted company can affect its
bargaining power since unlisted company can face financial restrictions. However, the estimatedparameters are economically and statistically insignificant in both regressions. This can bebecause the sample includes only small number of unlisted companies. Among 1516 alliances,6

there are only 351 unlisted sellers and 314 unlisted buyers. If those unlisted companies aremostly consist of large companies such as a subsidiary of pharmaceutical company, they could
have been less financially restricted.
On the other hand, Wald tests reject hypotheses that there are no differences among sellers’
regions and among both sellers’ and buyers’ types (table 3), which implies that thesecharacteristics of each party may be related to their bargaining power.

Table 3 Wald tests for buyers/sellers characteristics

For the differences in sellers’ regions, the effects of price increase tend to be lower for
European, Japanese and other regions’ companies compared to US companies, although
European companies’ coefficient is both economically and statistically insignificant. It can beconjectured that regions affect bargaining position for sellers located in other than US.
Finally, for the differences in company’s types, drug companies as sellers tend to increase thecontract price larger than biotechnology and other companies do, and biotechnology companies
as buyers tend to decrease the contract price larger than drug and other companies do. Since
drug companies include many established pharmaceutical companies, they can have large
bargaining power as sellers too, though drug companies are mostly buyers as shown in table 4.
Table 4 also shows that biotechnology companies are mostly sellers. It can be conjectured that
small number of successful biotechnology companies may have exerted larger bargaining poweras buyers rather than as sellers of biotechnologies.

Table 4 Number of alliances by pairs of company’s types

5. Discussion
Because of the success of biotechnology, many new drugs for diseases that were previously

7intractable such as cancer and HIV are coming to markets.6 It is not only patients but also
investors to feel delighted to have such drugs because the markets of those drugs are quite large.
For example, it is reported that the market for hepatitis C medications could reach 4 billion to 5
billion dollars by 2015.7 For the health of both human body and the economy, division of labor
between biotechnology and drug companies should be efficient.
In this paper, we investigated distribution of bargaining power between sellers and buyers of
biotechnologies, though indirectly, by estimating the increase and decrease in contract price due
to bargaining by the seller and buyer. It is not sellers but buyers extracting large surplus fromthe transactions, which implies that pharmaceutical companies’ bargaining power has been
larger than biotechnology companies. Then, government policies to decrease financial
constraints of new biotechnology companies could improve the efficiency of biotechnology
market.
However, the results show that the seller’s effects of contract price increase has been
increasing after controlling types of alliance agreements and sellers’ characteristics, which
imply that biotechnology companies bargaining position against pharmaceutical companies may
have been improving since 1990. There could be several reasons for sellers’ increasing
bargaining power. One reason would be that success of developing biotech-based new drugs is
attracting more investors and thus improving financial conditions of biotechnology companies.
Another reason would be the trend of pro-patent. Pro-patent policy is often criticized for the
possibility to deter innovation by causing the anti-commons problem. Based on the result of thispaper, however, it can be conjectured that pro-patent policy may have been rather contributing
health care innovation by reducing too large bargaining power of pharmaceutical companies
against biotechnology companies.
The results also imply that sellers located in other than US might have been in weakerbargaining position compared to sellers in US. A possible reason of such differences in regions
6

See for example an article of Businessweek, August 25, 2005 (“Cancer Drugs: Therapy for
Stocks?”).
7 Businessweek, April 6, 2009, the article of Gilead Science in “The Best Performers” (pp. 46-47).
Businessweek rank Gilead Science, one of the world’s top developers of infectious disease drugs, atthe top of 13th annual ranking of the top-performing companies.
8

is that US investors may have superior technological knowledge compared to other regions’investors. For example, Pagano et al. (2002) argue that US exchanges tend to be suited to the needsof high-tech companies based on statistical analysis of cross-listing behavior of European and US
companies in 1986-1997. Thus, US biotechnology companies may have had easier access topublic market than other regions’ companies not listed in US.

9References
Audretsch, David B. (2001) “The role of small firms in U.S. biotechnology clusters,” Small
Business Economics, Vol. 17, 3-15.
Heller, Michael A. and Rebecca S. Eisenberg (1998) “Can patents deter innovation? The
anticommons in biomedical Research,” Science, Vol. 280, No. 5364, 698 - 701.
Kumbhakar, Subal C. and Christopher F. Parmeter (2009) “The effects of match uncertainty andbargaining on labor market outcomes: evidence from firm and worker specific estimates,”
Journal of Productivity Analysis, Vol. 31, 1-14.
Lerner, Josh, Hilary Shane, and Alexander Tsai (2003), “Do equity financing cycles matter?
Evidence from biotechnology alliances,” Journal of Financial Economics, Vol. 67,
411-446.
Pagano, Marco, Ailsa A. Roell, and Josef Zechner (2002), “The geography of equity listing:why do companies list abroad?” Journal of Finance, Vol. 57, 2651-2694.
Polachek, Solomon W. and Bong Joon Yoon (1987) “A Two-tiered Earnings Frontier Estimationof Employer and Employee Information in the Labor Market,” Review of Economics andStatistics, Vol. 69, 296-302.

10

Table 1 Estimation results of the two-tiered stochastic frontier model
Tech dummy

Tech dummy*
2000s dummy

Stage dummy

Error component
parameter
Tech-changes in 2000s

Genetics
Bioinformatics
Recombinant DNA
Other biotechnology
Monoclonals
Drug related components
In-licensed products
Synthetics
Screening
Diagnotics
DDS
OthersGenetics
Bioinformatics
Recombinant DNA
Other biotechnology
Monoclonals
Drug related components
In-licensed products
Synthetics
Screening
Diagnotics
DDS
OthersDiscovery
Lead Molecule
Preclinical
Phase I
Phase II
Phase III
Approved
BLA/NDA filed
Formulation
σv
σu
σw
Wald test statistics
p-value11

Coefficient
0.209-0.043-0.038-0.186-0.325-0.274-1.115-0.5240.134-1.108-0.727-0.325-0.569
0.325
0.558
0.661
1.231
0.750
-0.1241.596-0.158
1.386
0.656
0.272
3.333
3.610
3.810
4.109
4.290
4.420
4.550
3.832
3.606
0.833
1.250
0.548
219.917
0.000

S.E.
0.184
0.238
0.169
0.156
0.176
0.180
0.200
0.165
0.152
0.276
0.229
0.197
0.268
0.366
0.276
0.223
0.216
0.231
0.240
0.164
0.210
0.771
0.204
0.295
0.173
0.181
0.177
0.193
0.176
0.186
0.197
0.253
0.212
0.125
0.077
0.120

p-value
0.258
0.855
0.822
0.233
0.065
0.129
0.000
0.002
0.378
0.000
0.002
0.098
0.034
0.374
0.043
0.003
0.000
0.001
0.607
0.000
0.454
0.072
0.001
0.356
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

Table 2 OLS analysis for the rate of increase in contract price ( E[w | ε ] )Constant
Type

Year

RegionPublicParty
R-squareCoefficient
0.345
0.480
Acquisition
0.111
Asset Purchase-0.032
Assignment
0.040
Co-Development
0.058
Collaboration
0.002
Co-Market
0.082
Co-Promotion
0.055
Cross-license
0.053
Development
0.020
Distribution
0.076
Equity0.149
Joint Venture
0.145
Letter of Intent
0.008
License
0.076
Loan
Manufacturing-0.042Marketing
0.060
Option0.049
Research
0.037
Security
0.199
Settlement-0.046
Sublicense-0.005
Supply0.009
Termination
0.020
Warrant-0.0481991-0.035
1992
0.007
1993
0.029
1994
-0.019
1995
0.036
1996
0.070
1997
0.045
1998
0.156
1999
0.099
2000
-0.005
2001
0.095
2002
0.037
2003
0.017
2004
0.049
2005
0.107
2006
0.165
2007
0.267
2008
0.206
Europe-0.009Japan-0.071Others
-0.062-0.010Drug
0.124
Others-0.005
0.851

12

S. E.
p-value
0.033
0.000
0.394
0.224
0.032
0.001
0.043
0.453
0.021
0.052
0.016
0.000
0.046
0.965
0.021
0.000
0.099
0.576
0.012
0.000
0.029
0.481
0.016
0.000
0.065
0.021
0.085
0.089
0.022
0.725
0.034
0.027
0.039
0.282
0.071
0.397
0.019
0.010
0.015
0.011
0.155
0.199
0.031
0.131
0.046
0.906
0.015
0.555
0.015
0.183
0.021
0.023
0.024
0.154
0.030
0.808
0.037
0.439
0.025
0.459
0.030
0.233
0.026
0.008
0.026
0.086
0.037
0.000
0.031
0.001
0.023
0.831
0.041
0.020
0.028
0.185
0.027
0.544
0.029
0.091
0.035
0.003
0.038
0.000
0.044
0.000
0.094
0.029
0.020
0.655
0.028
0.013
0.020
0.002
0.026
0.682
0.039
0.002
0.024
0.823

Table 3 OLS analysis for the rate of decrease in contract price ( E [u | ε ] )Constant
Type

Year

RegionPublicParty
R-squareCoefficient
2.887-1.161
Acquisition-0.733
Asset Purchase
0.075
Assignment-0.301
Co-Development-0.190Collaboration-0.342Co-Market-0.358
Co-Promotion
0.436
Cross-license-0.373Development-0.227
Distribution-0.248Equity-0.700
Joint Venture-0.200
Letter of Intent-0.345
License-0.289Loan
Manufacturing-0.134Marketing-0.228
Option-0.072
Research-0.187
Security-0.811
Settlement-0.223Sublicense
0.542
Supply0.031
Termination-0.253Warrant-0.0331991-0.0781992-0.0221993-0.4481994-0.3701995-0.3651996-0.542
1997-0.6551998-0.727
1999-0.5812000-0.4612001-0.402
2002-0.3942003-0.4302004-0.552
2005-0.7172006-0.6452007-0.8772008-0.901
Europe-0.029Japan
0.072
Others0.000-0.081Drug-0.346
Others-0.103
0.693

13

S. E.
p-value
0.247
0.000
0.273
0.000
0.120
0.000
0.192
0.695
0.065
0.000
0.054
0.000
0.159
0.032
0.052
0.000
0.401
0.276
0.054
0.000
0.122
0.062
0.049
0.000
0.121
0.000
0.171
0.240
0.107
0.001
0.062
0.000
0.124
0.278
0.172
0.187
0.058
0.211
0.060
0.002
0.154
0.000
0.241
0.355
0.336
0.107
0.064
0.634
0.051
0.000
0.102
0.746
0.243
0.747
0.242
0.928
0.221
0.043
0.217
0.089
0.220
0.097
0.215
0.012
0.205
0.001
0.215
0.001
0.215
0.007
0.216
0.033
0.223
0.073
0.221
0.074
0.224
0.056
0.216
0.011
0.222
0.001
0.222
0.004
0.216
0.000
0.231
0.000
0.063
0.648
0.082
0.382
0.151
0.998
0.065
0.209
0.094
0.000
0.102
0.315

Table 3 Wald tests for buyers/sellers characteristics
SellerRegions
Parties

Buyer

Test Stat.

P-value

Test Stat.

P-value

15.16414.290

0.002
0.001

1.597
22.511

0.660
0.000

Table 4 Number of alliances by pairs of company’s types
BuyersBiotechSellers

BiotechDrug
Others
Total

Drug
41
26
77
144

14

Others
163
18
419
600

Total
188
92
492
772

392
136
988
1516