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Quali Regole per la Spesa Farmaceutica? - Criticità, Impatti, Proposte

Author

Listed:
  • Fabio Pammolli

    () (Politecnico di Milano and CERM Foundation - Competitività, Regole, Mercati)

  • Paolo Bonaretti

    (ASTER Innovazione attiva)

  • Massimo Riccaboni

    () (IMT Lucca Institute for Advanced Studies and Department of Managerial Economics, Strategy and Innovation, K.U. Leuven)

  • Valentina Tortolini

    () (IMT Lucca Institute for Advanced Studies)

Abstract

Con questo paper Fondazione CERM raccoglie le prospettive e le analisi di un gruppo di ricerca in cui sono confluite sensibilità e competenze diverse e complementari, con l’obiettivo di contribuire al dibattito sul governo della spesa farmaceutica e sulla regolazione del settore. Le analisi e le proposte che qui presentiamo saranno offerte anche alla discussione in una serie di seminari dedicati, per raccogliere contributi specifici e prospettive capaci di stimolare integrazioni e revisioni rispetto al quadro che, sin qui, abbiamo tracciato. In Appendice al documento sono disponibili alcuni approfondimenti empirici su aspetti chiave trattati nella sezione principale. Il testo analizza alcune criticità dell’impianto attuale di regolazione della spesa farmaceutica e, alla luce di un principio guida di carattere generale, formula alcune proposte, realistiche ma ambiziose, che possono orientare la revisione delle regole del gioco.

Suggested Citation

  • Fabio Pammolli & Paolo Bonaretti & Massimo Riccaboni & Valentina Tortolini, 2019. "Quali Regole per la Spesa Farmaceutica? - Criticità, Impatti, Proposte," Working Papers CERM 01-2019, Competitività, Regole, Mercati (CERM).
    • Handle: RePEc:ern:wpaper:01-2019
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    References listed on IDEAS

    as
    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    2. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    3. Joshua Angrist & Pierre Azoulay & Glenn Ellison & Ryan Hill & Susan Feng Lu, 2017. "Economic Research Evolves: Fields and Styles," American Economic Review, American Economic Association, vol. 107(5), pages 293-297, May.
    4. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    5. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    6. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    7. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    8. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    9. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    spesa farmaceutica; regolazione; spesa sanitaria;
    All these keywords.

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • H50 - Public Economics - - National Government Expenditures and Related Policies - - - General
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • H70 - Public Economics - - State and Local Government; Intergovernmental Relations - - - General
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • H77 - Public Economics - - State and Local Government; Intergovernmental Relations - - - Intergovernmental Relations; Federalism
    • I00 - Health, Education, and Welfare - - General - - - General
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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