Motivation of the study

1. The Italian pharmaceutical market :a micro-analysisGitto L., Ratti M., Mennini F.S. CHEM (Centre for Health Economics and Management), CEIS, Faculty of Economics, University of Rome “Tor Vergata” Gitto L., Ratti M., Mennini F.S.

2. Motivation of the study • Variations in the economic results of pharmaceutical firms may be due to factors such as the development of new products, higher levels of R&D spending and productivity, regulatory interventions, changes in the epidemiological scenario (due to demographic increase and ageing population). • Such factors concurred to increase expectations by consumers and patients from pharmaceutical companies and determined the need for the latter to exploit new strategies. Gitto L., Ratti M., Mennini F.S.

3. Motivation of the study • Analyse the Italian pharmaceutical market, taking into consideration the leading firms in terms of pharmaceutical sales up to the reaching of 75% of the total turnover across the period 1994-2004. • Variations in their economic results might depend on: - an increase in the number of new products marketed and/or number of therapeutic classes within which each company operates; - the circumstance that the firm belongs to an established pharmaceutical group; - the circumstance that the firm has experienced a merger or - a combination of all these factors. • The novelty of the study is that it carries out an empirical analysis of pharmaceutical market in Italy whereas, at our knowledge, no other analysis has been performed so far. The conclusions reached can be useful for international comparisons. Gitto L., Ratti M., Mennini F.S.

4. Italian pharmaceutical market • highly regulated; • public sector – which is often the actual “monopolist client” of the industry - committed to detect measures to keep the balance of the national budget; • the measures necessary to check the increase in the public pharmaceutical spending, failed to provide - from an industrial view point - incentives designed to make the required adjustments to the new rules more gradual and, in general, to back up the most innovative industry. Gitto L., Ratti M., Mennini F.S.

5. Methodology Econometric analysis • We consider data related to the leading firms in terms of pharmaceutical sales up to the reaching of 75% of the total turnover across the period 1994-2004. • The objective is to model the micro-level behaviour of pharmaceutical firms, taking into account any variation that might occur through time and space simultaneously. • A common feature related to cross sectional time series data, like those we are considering, is unobserved heterogeneity, i.e. the possibility of unobserved time invariant effects due to each unit observed. • Fixed effects models and random effects models address the problem of unobserved heterogeneity by inserting an error term that is either assumed to be constant over time for each unit (fixed effects models) or to vary randomly over time for each unit (random effects models). Gitto L., Ratti M., Mennini F.S.

6. Methodology Econometric analysis • The specification of the fixed effect model is the following: Y it = (a + di) + Xitb + εit where the deterministic part of the equation, is composed by a constant term and an individual effect. • The specification of the “error component model” is the following: Y it = a + Xitb + ui + vt + wit Here, the stochastic part of the equation is split into ui, the individual or firm effect, vt, the time effect and wit, the interaction between the two effects. • Stochastic assumptions are: E(u) = 0; E(v) = 0 and E (w) = 0. • The variances are V(u) = su2 IN; V(v) = sv2 IT; V(w) = sw2 INT; there is independence two by two between u, v and w and there is no correlation between the X and the error term u. Gitto L., Ratti M., Mennini F.S.

7. Methodology • Estimations are carried out for both models (fixed and random effects) and the question related to the appropriateness of the model is solved through a Hausman test, carried out to compare fixed and random effects regressions. The following two hypothesis are verified: • Hypothesis A: Higher revenues are associated with firms’ choices in favour of an innovating strategy, mainly consisting in the increase in the number of products and therapeutic classes. • Hypothesis B: Firms undergoing a policy of mergers & acquisitions, or belonging to established groups have greater opportunities to obtain higher revenues. Gitto L., Ratti M., Mennini F.S.

8. Methodology • Hypothesis A is coherent with the conclusions reached by previous studies carried out both for Italy and foreign countries. • Hypothesis B has been formulated in the industrial economic literature and has been empirically verified in many analysis. Gitto L., Ratti M., Mennini F.S.

9. Methodology • A panel of 76 firms has been observed along 11 years (from 1994 up to 2004). • Log of total revenues is the dependent variable. • In order to verify hypothesis A, the level of revenues should be correlated with some “indicators of innovativeness”. Hence, we consider: - the number of products for each firm and their squared and cubed terms; - the number of therapeutic classes and their squared terms. Gitto L., Ratti M., Mennini F.S.

10. Methodology • In order to support hypothesis B, we should expect a positive value for the estimated coefficients associated to these variables: • mergers and acquisitions; • pharmaceutical groups. • Some control variables have been included in the analysis, together with some interaction terms: • rank of firms (i.e. the inclusion the first 10, 20, 30, etc. firms according to the level of revenue); • a time variable related to the last 3 years (that should signal the “popularity” of firms in the market); - an interaction term combining rank of firms together with the circumstance that a policy of mergers has been experienced. Gitto L., Ratti M., Mennini F.S.

11. Gitto L., Ratti M., Mennini F.S.

12. Number of companies Gitto L., Ratti M., Mennini F.S.

13. Revenues Gitto L., Ratti M., Mennini F.S.

14. Number of products Gitto L., Ratti M., Mennini F.S.

15. Number of ATC Gitto L., Ratti M., Mennini F.S.

16. The estimated models (with fixed and random effects) can be defined as follows: • Log revenuesit = a + number of products β11 + number of products2β12+ number of products3β13+ number of therapeutic classes β14 + number of therapeutic classes2β15 + high number of therapeutic classes β16 + ATC dummies β17 + rank*m&a γ11+ groups γ12+ ranking δ11+ time δ12+ firm dummies δ12 + ε. Gitto L., Ratti M., Mennini F.S.

17. Fixed effects model Gitto L., Ratti M., Mennini F.S.

18. Random effects model Gitto L., Ratti M., Mennini F.S.

19. Conclusion (1) • This study was aimed at verifying the following hypothesis: A) Higher revenues are associated with firms’ choices in favour of an innovating strategy, mainly consisting in the increase in the number of products and therapeutic classes and B) Firms undergoing a policy of m & a, or belonging to established groups have greater opportunities to obtain higher revenues. Gitto L., Ratti M., Mennini F.S.

20. Conclusion (2) Overall, these hypothesis have been verified: widening the number of products and increasing the number of therapeutic classes (even neglecting those classes that present the highest average revenue per product, i.e. the most profitable ones) has a positive impact on the level of revenues. The impact of a policy of m&a is less clear and should be better clarified. The choice of an innovating strategy, based on the increasing of R&D expenditure that has, as a consequence, the increase in the number of new products and ATCs, has been the winning option in the last decade, even if its potentialities appear to decrease, due to institutional factors that might determine arising difficulties for the introduction of new products. Gitto L., Ratti M., Mennini F.S.

21. www.ceistorvergata.it/sanitaGitto L., Ratti M., Mennini F.S. f.mennini@uniroma2.itlara.gi@tiscali.it Gitto L., Ratti M., Mennini F.S.