Sin t

1
Mergers and Innovation in Big Pharma Carmine
Ornaghi University of Southampton Toulouse,
January 2008
2
Outline
1 - MAs and Innovation Limitations of the
Literature 2 - Objectives of this Work 3 -
Theoretical Insights 4 - Empirical Models 5 -
Data and Variables 6 - Main Findings 7 - Mergers
and Innovation A Competition Policy perspective
3
1. Mergers and Innovation Limitations of the
Literature

• Empirical studies on MAs have found
  contradictory results about their effects on
  firms performance economists are still divided
  on whether mergers enhance economic efficiency or
  increase market power or neither of the two (e.g.
  managers interests).
• Main features of most of these studies
• Based on data of different industries.
• Focused on assessing the short-run effects on
  sales and profits (Guegler et all., 2003) and
  market value abnormal returns around the
  announcement day (Fuller et all., 2002).

4
1. Mergers and Innovation Limitations of the
Literature

• Limitations of this literature
• Recent empirical findings show the existence of
  industry clustering in merger activity (Andrade
  et al., 2001) mergers as a response to exogenous
  changes in industry structure ? Cross-industry
  studies can give inconclusive results.
• The post-merger performance of the merged
  entities is likely to depend on the relatedness
  of the merging parties ? Hardly considered in the
  literature.
• In RD intensive industry, relevant dimension of
  competition is innovation rather than price ?
  Short-run analysis on sales and profits is not
  suitable.

5
2 - Objectives of the Work

• This work tries to overcome these limitations by
  studying the effects of MAs on innovation in a
  single industry.
• Analysis conducted for the case of large mergers
  in the Pharmaceutical Industry
• Research questions
• Do mergers have a positive effect on the
  innovative ability of the firms involved, as
  their proponents often claim?
• (2) Is there any relationship between the ex-ante
  technological and product relatedness of merging
  parties and the ex-post effects?

6
3 Theoretical Insights Effects of MAs on
Research

• MAs affect optimal RD through different
  channels
• Avoidance of duplication of fixed costs (eg.
  library, labs, ) ? decrease in RD inputs
• Economies of scope and knowledge synergies ?
  increase in RD inputs and outputs
• Internalization of spillovers, reduction in the
  number of competitors and higher barriers to
  entry ? increase of RD inputs and outputs
• But knowledge is embodied in scientists and
  mergers usually imply a reduction of the
  employees. Moreover, cultural dissonances might
  disrupt innovation outcomes ? decrease in RD
  output
• It is not possible to define clear predictions on
  the net effects of these forces Empirical
  evidence is needed

7
3 Theoretical Insights Technology and Product
Relatedness

• Most of the effects above are driven by forces
  whose magnitude depends on the ex-ante technology
  relatedness (TR) of the merged parties (e.g.
  synergies due to cross fertilization of ideas or
  elimination of useless duplication).
• Product relatedness (PR) might also have an
  indirect effect on innovation through changes in
  the market equilibria for approved drugs
• An empirical questions arise
• Can TR and PR explain differences in post-merger
  results of consolidated companies and competitors?

8
4 Empirical Model

• To access the effects of mergers (up to 3 years
  after the deal), I use a dummy variable model

• where the dependent variable measures the
  percentage change in RD inputs/outputs, M0, M1,
  M2 and M3 are dummy variables that take on value
  of 1 if the firm goes through a merger in period
  t, in period t-1 (i.e. one-year ago), in t-2 or
  in t-3, respectively. T is a complete set of time
  dummies for the period 1988-2004.
• M0 represent a difference-in-difference estimate
  of the changes in Y due to the merger, and the
  other dummies assess whether there are lagged
  effects of consolidation in the following years.

9
4 Empirical Model Problem of Endogeneity

• Endogeneity of the merger decision can lead to a
  (spurious) correlation between the merger dummies
  and the outcome for reasons unrelated to the
  causal effect we are interested.
• Example It has been found that firms with
  important drugs coming off patents are more
  likely to pursue a merger. As patent expirations
  affect future revenues, we would find a negative
  correlation between mergers and growth of
  revenues even in the absence of a causal effect
  of the first on the second.
• I account for the selection problems in two
  ways
• Propensity score each acquirer and target is
  matched with firms with the closest probability
  of merging
• Technological relatedness exogenous
  technological shocks are likely to hit firms with
  similar research activities

10
4 Empirical Model Relatedness

• To assess the role of TR and PR in post-merger
  effects, I estimate the model

where ?(Xß) is the inverse Mills ratio which
controls for selection problems (Heckman
two-step procedure).
11
5 Data and Variables

• New dataset containing publicly traded
  pharmaceutical firms constructed using three main
  data sources
• - Financial Data (sales, stock market values, RD
  expenditures) from Compustat and Osiris
• - Patents Data from the US Patent Office (patent
  class and citation)
• Merger transactions data for 1988-2004 from
  Mergers Year Book.
• All observations double checked and completed
  with sources in the internet (mainly, web pages
  of firms and www.sec.gov)
• Our sample represents the universe of big pharma
  companies (excluding large generic producers such
  as Teva or Mylan) and the consolidations that
  they have been involved

12
5 Data and Variables

• Technological and Product Relatedness
• Correlation of Patent Classes (PatCr) Jaffe
  (1986)
• A similar measure has been constructed for
  Product Classes
• Overlapping between Cited Patents

BA (BT) is the set of Patents cited by the
patent portfolio of acquirer (target)
13
6 Main Empirical Findings

• EFFECTS OF MERGERS (DUMMY VARIABLE MODEL)
• Negative signs for RD inputs, output and
  productivity.
• Market value growth below the other non-merging
  firms.
• Results similar when accounting for endogeneity
  and selectivity issues (only the negative sign
  for Market Value growth is no longer
  statistically significant)

14
6 Main Empirical Findings

• THE ROLE OF TECHNOLOGICAL RELATEDNESS
• Results suggest that product relatedness has a
  positive effect on post-merger outcomes while
  technological relatedness seems to have
  detrimental impact
• Most interesting finding concerns the change in
  stock market value positive and statistically
  significant coefficient for PR and negative and
  statistically significant coefficient for TR.

15
7 - Competition Policy Implications

• Efficiencies are easy to promise, yet may be
  difficult to deliver''. Lawrence White
• Our results cast some doubts on the actual
  materialisation of the efficiency gains in RD
  commonly claimed by merging firms to defend
  consolidations.
• Mergers between firms with large technological
  relatedness are found to deliver worse outcomes.
• The importance of the questions here analysed and
  the difficulty involved in the empirical analysis
  impose extreme cautions in drawing any radical
  conclusions for competition policy.
• Relate ex-post effects to ex-ante characteristics
  is an important task for future research agenda.