Mergers and Innovation in Big Pharma Carmine Ornaghi University of Southampton

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

2. 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

3. 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).

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 R&D 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 M&As 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 M&As on Research • M&As affect optimal R&D through different channels: • Avoidance of duplication of fixed costs (eg. library, labs, …) → decrease in R&D inputs • Economies of scope and knowledge synergies → increase in R&D inputs and outputs • Internalization of spillovers, reduction in the number of competitors and higher barriers to entry → increase of R&D 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 R&D 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 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 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 λ(Xβ) 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, R&D 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 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)

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 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.