Mergers and Innovation in Big Pharma Carmine Ornaghi University of Southampton Toulouse, January 2008

Outline 1 - M&As 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

1. Mergers and Innovation: Limitations of the Literature Empirical studies on M&As 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).

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 R&D intensive industry, relevant dimension of competition is innovation rather than price → Short-run analysis on sales and profits is not suitable. 1. Mergers and Innovation: Limitations of the Literature

This work tries to overcome these limitations by studying the effects of M&As on innovation in a single industry. Analysis conducted for the case of large mergers in the Pharmaceutical Industry Research questions: (1) 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? 2 - Objectives of the Work

M&As affect optimal R&D through different channels: 1.Avoidance of duplication of fixed costs (eg. library, labs, …) → decrease in R&D inputs 2.Economies of scope and knowledge synergies → increase in R&D inputs and outputs 3.Internalization of spillovers, reduction in the number of competitors and higher barriers to entry → increase of R&D inputs and outputs 4.… But knowledge is embodied in scientists and mergers usually imply a reduction of the employees. Moreover, cultural dissonances might disrupt innovation outcomes → decrease in R&D output It is not possible to define clear predictions on the net effects of these forces: Empirical evidence is needed 3 – Theoretical Insights: Effects of M&As on Research

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? 3 – Theoretical Insights : Technology and Product Relatedness

4 – Empirical Model where the dependent variable measures the percentage change in R&D 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 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. To access the effects of mergers (up to 3 years after the deal), I use a dummy variable model:

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

4 – Empirical Model: Relatedness To assess the role of TR and PR in post-merger effects, I estimate the model: where λ(Xβ) is the inverse Mills ratio which controls for selection problems (Heckman “two-step” procedure).

New dataset containing publicly traded pharmaceutical firms constructed using three main data sources: - Financial Data (sales, stock market values, R&D expenditures) from Compustat and Osiris - Patents Data from the US Patent Office (patent class and citation) -Merger transactions data for from Mergers Year Book. All observations double checked and completed with sources in the internet (mainly, web pages of firms and 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 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 5 – Data and Variables B A (B T ) is the set of Patents cited by the patent portfolio of acquirer (target)

6 – Main Empirical Findings EFFECTS OF MERGERS (DUMMY VARIABLE MODEL):  Negative signs for R&D 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)

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.

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 R&D 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.