Skip to main content

Advertisement

SpringerLink
Log in

Measuring the efficiency of large pharmaceutical companies: an industry analysis

  • Original Paper
  • Published:
The European Journal of Health Economics Aims and scope Submit manuscript

Abstract

This paper evaluates the relative efficiency of a sample of 37 large pharmaceutical laboratories in the period 2008–2013 using a data envelopment analysis (DEA) approach. We describe in detail the procedure followed to select and construct relevant inputs and outputs that characterize the production and innovation activity of these pharmaceutical firms. Models are estimated with financial information from Datastream, including R&D investment, and the number of new drugs authorized by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) considering the time effect. The relative performances of these firms—taking into consideration the strategic importance of R&D—suggest that the pharmaceutical industry is a highly competitive sector given that there are many laboratories at the efficient frontier and many inefficient laboratories close to this border. Additionally, we use data from S&P Capital IQ to analyze 2071 financial transactions announced by our sample of laboratories as an alternative way to gain access to new drugs, and we link these transactions with R&D investment and DEA efficiency. We find that efficient laboratories make on average more financial transactions, and the relative size of each transaction is larger. However, pharmaceutical companies that simultaneously are more efficient and invest more internally in R&D announce smaller transactions relative to total assets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Large pharmaceutical companies operate globally, so they market their innovative drugs in more markets than Europe and the USA. Thus, our measure for innovative drugs that have been authorized by the EMA and the FDA for a given laboratory is a proxy for the expected authorization of the same innovative drug for other markets. Europe and USA are two key markets, and an innovative drug can be expected to be innovative in all the relevant markets. Given that laboratories operate globally, we consider our proxy to be a good one.

  2. Market capitalization is the result of a consensus in the market about current cash flows and expected future cash flows. In the case of pharmaceutical labs, it takes into account innovative drugs that are in the pipeline but have not yet been authorized because they are in the different phases of clinical research (Phase I, Phase II, Phase III), as well as some innovative drugs that have been authorized but are still in postmarketing surveillance (Phase IV).

  3. We thank one reviewer for this insight and suggestion.

References

  1. Achilladelis, B., Antonakis, N.: The dynamics of technological innovation: the case of the pharmaceutical industry. Res. Policy 30, 535–588 (2001)

    Article  Google Scholar 

  2. Ahn, J.H., Chang, S.G.: Assessing the contribution of knowledge to business performance: the KP3 methodology. Decis. Support Syst. 36, 403–416 (2004)

    Article  Google Scholar 

  3. Arora, A., Fosfuri, A., Gambardella, A.: Markets for technology and their implications for corporate strategy. Ind. Corp. Change 10, 419–451 (2001)

    Article  Google Scholar 

  4. Banker, R.D., Thrall, R.M.: Estimation of returns to scale using Data envelopment analysis. Eur. J. Oper. Res. 62, 74–84 (1992)

    Article  Google Scholar 

  5. Barr, R.S.: DEA software tools and technology: a state-of-the-art survey. Handb. Data Envel. Anal. Int. Ser. Oper Res. Manag. Sci. 71, 539–566 (2004)

    Google Scholar 

  6. Baršauskas, P., Šarapovas, T., Cvilikas, A.: The evaluation of e-commerce impact on business efficiency. Balt. J. Manag. 3, 71–91 (2008)

    Article  Google Scholar 

  7. Bena, J., Li, K.: Corporate innovations and mergers and acquisitions. J. Finance 69, 1923–1960 (2014)

    Article  Google Scholar 

  8. Boussofiane, A., Dyson, R.G., Thanassoulis, E.: Applied Data envelopment analysis. Eur. J. Oper. Res. 52, 1–15 (1991)

    Article  Google Scholar 

  9. Bowns, S., Bradley, I., Kneec, P., Williams, F., Williams, G.: Measuring the economic benefits from R&D: improvements in the MMI model of the United Kingdom National Measurement System. Res. Policy 32, 991–1002 (2003)

    Article  Google Scholar 

  10. Cabiedes, L.: Authorisation and withdrawal of pharmaceutical innovations in the European. Working paper, University of Oviedo, Spain (2005)

  11. Castelli, A., Street, A., Verzulli, R., Ward, P.: Examining variations in hospital productivity in the English NHS. Eur. J. Health Econ. 16(3), 243–254 (2015)

    Article  PubMed  Google Scholar 

  12. Caves, D., Christensen, L., Diewert, E.: The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 50(6), 1393–1414 (1982)

    Article  Google Scholar 

  13. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444 (1978)

    Article  Google Scholar 

  14. Charnes, A., Cooper, W.W., Golany, B., Seiford, L.M., Stutz, J.: Foundations of data envelopment analysis and Pareto–Koopmans empirical production functions. J. Econom. 30, 91–107 (1985)

    Article  Google Scholar 

  15. Coelli, T.J.: A guide to DEAP version 2.1: a data envelopment analysis (Computer) Program. Department of Econometrics, University of New England, Armidale, Australia (1996)

  16. Comanor, W.S., Scherer, F.M.: Mergers and innovation in the pharmaceutical industry. J. Health Econ. 32, 106–113 (2013)

    Article  PubMed  Google Scholar 

  17. Cooper, W.W., Li, S., Seiford, L.M., Thrall, R.M., Zhu, J.: Sensitivity and stability analysis in DEA: some recent developments. J. Prod. Anal. 15, 217–246 (2001)

    Article  Google Scholar 

  18. Cooper, W.W., Seiford, L.M., Zhu, J.: Data envelopment analysis: history, models, and interpretations. Handb. Data Envel. Anal. Int. Ser. Oper Res. Manag. Sci. 164, 1–39 (2011)

    Google Scholar 

  19. Cruz-Cáceres, C., Bayona-Sáez, C., García-Marco, T.: You can’t manage right what you can’t measure well: technological innovation efficiency. Res. Policy 42, 1239–1250 (2013)

    Article  Google Scholar 

  20. Danzon, P.M., Wang, Y.R., Wang, L.: The impact of price regulation on the launch delay of new drugs—evidence from twenty-five major markets in the 1990s. Health Econ. 14(3), 269–292 (2005)

    Article  PubMed  Google Scholar 

  21. Dukes, G.: The effects of drug regulations: a survey based on the European studies of drug regulation. MTP Press Limited, Lancaster (1985)

    Book  Google Scholar 

  22. European Medicines Agency: European Public Assessment Reports. http://www.ema.europa.eu/ema/index.jsp?curl=pages/medicines/landing/epar_search.jsp&mid=WC0b01ac058001d125 (2015)

  23. Food and Drug Administration: Drug Approval Reports. http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm?fuseaction=Reports.ReportsMenu (2015)

  24. García-Sánchez, I.M.: Efficiency and effectiveness of Spanish football teams: a three-stage-DEA approach. Cent. Eur. J. Oper. Res. 15, 21–45 (2007)

    Article  Google Scholar 

  25. Gascón, F., De la Fuente, D., Puente, J., Lozano, J.: On macroeconomic characteristics of pharmaceutical generics and the potential for manufacturing and consumption under fuzzy conditions. Artif. Intell. Med. 41, 223–235 (2007)

    Article  PubMed  Google Scholar 

  26. Girotra, K., Terwiesch, C., Ulrich, K.T.: Valuing R&D projects in a portfolio: evidence from the pharmaceutical industry. Manage. Sci. 53(9), 1452–1466 (2007)

    Article  Google Scholar 

  27. González, E., Gascón, F.: Sources of productivity growth in the Spanish pharmaceutical industry (1994–2000). Res. Policy 33, 735–745 (2004)

    Article  Google Scholar 

  28. Grabowski, H.G., Wang, Y.R.: The quantity and quality of worldwide new drug introductions, 1982–2003. Health Affair. 25(2), 452–460 (2006)

    Article  Google Scholar 

  29. Guan, J., Chen, K.: Modeling the relative efficiency of national innovation systems. Res. Policy 41, 102–115 (2012)

    Article  Google Scholar 

  30. Hashimoto, A., Haneda, S.: Measuring the change in R&D efficiency of the Japanese pharmaceutical industry. Res. Policy 37, 1829–1836 (2008)

    Article  Google Scholar 

  31. Hagedoorn, J., Wang, N.: Is there complementarity or substitutability between internal and external R&D strategies? Res. Policy 41(6), 1072–1083 (2012)

    Article  Google Scholar 

  32. Higgins, M.J., Rodriguez, D.: The outsourcing of R&D through acquisitions in the pharmaceutical industry. J. Financ. Econ. 80, 351–383 (2006)

    Article  Google Scholar 

  33. Honjo, Y., Haneda, S.: R&D evaluation of Japanese pharmaceutical firms using DEA. J. Sci. Policy Res. Manag. 13, 96–105 (1998)

    Google Scholar 

  34. IMS Institute for Healthcare Informatics: The Global Use of Medicines: Outlook Through 2015. http://www.imshealth.com (2011)

  35. IMS Institute for Healthcare Informatics: Global Outlook for Medicines Through 2018. http://www.imshealth.com (2014)

  36. Jalili, M., Rangriz, H., Shabani, S.: Using data envelopment analysis to rate pharmaceutical companies: a case study of Iran. Life Sci. J. 10, 336–341 (2013)

    Google Scholar 

  37. Jiménez-Sáez, F., Zabala-Iturriagagoitia, J.M., Zofio, J.L., Castro-Martínez, E.: Evaluating research efficiency within National R&D Programmes. Res. Policy 40, 230–241 (2012)

    Article  Google Scholar 

  38. Kang, K.H., Jo, G.S., Kang, J.: External technology acquisition: a double-edged sword. Asian J. Technol. Innov. 23(1), 35–52 (2015)

    Article  Google Scholar 

  39. Kesselheim, A.S., Wang, B., Avorn, J.: Defining “innovativeness” in drug development: a systematic review. Clin. Pharmacol. Ther. 94(3), 336–348 (2013)

    Article  CAS  PubMed  Google Scholar 

  40. Kinch, M.S., Raffo, J.: Sources of innovation: an assessment of intellectual property. Drug Discov. Today 20(5), 500–504 (2015)

    Article  PubMed  Google Scholar 

  41. Kounetas, K., Papathanassopoulos, F.: How efficient are Greek hospitals? A case study using a double bootstrap DEA approach. Eur. J. Health Econ. 14(6), 979–994 (2013)

    Article  PubMed  Google Scholar 

  42. Light, D.W.: Global drug discovery: Europe is ahead. Health Affair. 28(5), w969–w977 (2009)

    Article  Google Scholar 

  43. Mao, Y., Li, J., Liu, Y.: Evaluating business performance of China’s pharmaceutical companies based on data envelopment analysis. Stud. Ethno Med. 8, 51–60 (2014)

    Google Scholar 

  44. Mazumdar, M., Rajeev M.: A comparative analysis of efficiency and productivity of the Indian pharmaceutical firms: a malmquist-meta-frontier approach. Working Paper, The Institute for Social and Economic Change, Bangalore (2009)

  45. Motohashi, K.: The changing autarky pharmaceutical R&D process: causes and consequences of growing R&D collaboration in Japanese firms. Int. J. Technol. Manage. 39, 33–48 (2007)

    Article  Google Scholar 

  46. Naci, H., Carter, A.W., Mossialos, E.: Why the drug development pipeline is not delivering better medicines. BMJ 351, h5542 (2015). doi:10.1136/bmj.h5542

  47. Nishimura, J., Okada, Y.: R&D portfolios and pharmaceutical licensing. Res. Policy 43(7), 1250–1263 (2014)

    Article  Google Scholar 

  48. Parker, D.: The performance of BAA before and after privatisation: a DEA study. J. Transport Econ. Policy 33, 133–146 (1998)

    Google Scholar 

  49. Pindado, J., de Queiroz, V., de la Torre, C.: How do firm characteristics influence the relationship between R&D and firm value? Financ. Manage. 39, 757–782 (2010)

    Article  Google Scholar 

  50. Ramanathan, R.: Evaluating the comparative performance of countries of the Middle East and North Africa: a DEA application. Socio-Econ. Plan. Sci. 40, 156–167 (2006)

    Article  Google Scholar 

  51. Sarrico, C.S., Dyson, R.G.: Using DEA for planning in UK universities—an institutional perspective. J. Oper. Res. Soc. 51, 789–800 (2000)

    Google Scholar 

  52. Shimura, H., Masuda, S., Kimura, H.: Research and development productivity map: visualization of industry status. J. Clin. Pharm. Ther. 39, 175–180 (2014)

    Article  CAS  PubMed  Google Scholar 

  53. Simar, L., Wilson, P.W.: Two-stage DEA: caveat emptor. J. Prod. Anal. 36, 205–218 (2011)

    Article  Google Scholar 

  54. The Economist. Clinical trials. Spilling the beans. Failure to publish the results of all clinical trials is skewing medical science. Published in the print edition on July 25, 2015. http://www.economist.com/news/science-and-technology/21659703-failure-publish-results-all-clinical-trials-skewing-medical (2015)

  55. Thomas, R.R., Bar, R.S., Cron, W.L., Slocum Jr., J.W.: A process for evaluating retail store efficiency: a restricted DEA approach. Int. J. Res. Mark. 15, 487–503 (1998)

    Article  Google Scholar 

  56. Wang, E.C., Huang, W.: Relative efficiency of R&D activities: a cross-country study accounting for environmental factors in the DEA approach. Res. Policy 36, 260–273 (2007)

    Article  Google Scholar 

  57. Wang, W.K., Lu, W.M., Liu, P.Y.: A fuzzy multi-objective two-stage DEA model for evaluating the performance of US bank holding companies. Expert Syst. Appl. 41, 4290–4297 (2014)

    Article  Google Scholar 

  58. Wanke, P., Barros, C.: Two-stage DEA: an application to major Brazilian banks. Expert Syst. Appl. 41, 2337–2344 (2014)

    Article  Google Scholar 

  59. You, T., Chen, X., Holder, M.E.: Efficiency and its determinants in pharmaceutical industries: ownership, R&D and scale economy. Appl. Econ. 42, 2217–2241 (2010)

    Article  Google Scholar 

  60. Zhu, J.: Imprecise data envelopment analysis (IDEA): a review and improvement with an application. Eur. J. Oper. Res. 144, 513–529 (2003)

    Article  Google Scholar 

Download references

Acknowledgments

Fernando Gascón would like to acknowledge financing from the Government of Spain through national research project ECO2012-31772. Borja Ponte would like to thank the Government of the Principality of Asturias for financially supporting his work through the Severo Ochoa program (reference BP13011). We also would like to thank professor Laura Cabiedes for her helpful comments and suggestions.

Author information

Authors and Affiliations

  1. Faculty of Economics and Business, Department of Business Administration, University of Oviedo, Campus del Cristo s/n, 33006, Oviedo, Spain

    Fernando Gascón

  2. Polytechnic School of Engineering, Department of Business Administration, University of Oviedo, Campus de Viesques s/n, 33204, Gijón, Spain

    Jesús Lozano, Borja Ponte & David de la Fuente

Authors
  1. Fernando Gascón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jesús Lozano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Borja Ponte
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David de la Fuente
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Borja Ponte.

Appendices

Appendix 1

See Tables 8, 9 and 10.

Table 8 Datastream output measures; 37 laboratories versus 241 laboratories (data in million USD)
Full size table
Table 9 New drugs authorized by EMA and FDA in the period 2008 to 2013 without the effect of M&A
Full size table
Table 10 New drugs authorized by EMA and FDA in the period 2008 to 2013 belonging to M&A
Full size table

Appendix 2

See Tables 11 and 12.

Table 11 Outputs for the DEA model
Full size table
Table 12 Inputs for the DEA model
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gascón, F., Lozano, J., Ponte, B. et al. Measuring the efficiency of large pharmaceutical companies: an industry analysis. Eur J Health Econ 18, 587–608 (2017). https://doi.org/10.1007/s10198-016-0812-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10198-016-0812-3

Keywords

JEL Classification

Search

Navigation