IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v42y2012i2p471-486.html
   My bibliography  Save this article

An empirical analysis of habit and addiction to antibiotics

Author

Listed:
  • M. Filippini
  • G. Masiero

Abstract

Because of bacterial resistance, current antibiotic consumption is reinforced by past use, and future utility is lower. The purpose of this article is to provide evidence on habit and addictive behavior toward antibiotics by exploring variations in the average consumption of antibiotics across 20 Italian regions. Using a balanced panel dataset (2000-2009), we estimate myopic and rational addiction models in which antibiotic consumption depends upon demographic and socioeconomic characteristics of the population, the supply of health care in the community, antibiotic price, and the "capital stock"of endogenous bacterial resistance measured by past and future consumption. Our empirical evidence shows that past antibiotic consumption stimulates current consumption and is also consistent with the rational addiction hypothesis. The low price elasticity of antibiotic demand suggests that policy measures targeted at antibiotic co-payments may not be effective in controlling antibiotic consumption. There is scope for other policy interventions, such as incentives and information campaigns targeted at doctors.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • M. Filippini & G. Masiero, 2012. "An empirical analysis of habit and addiction to antibiotics," Empirical Economics, Springer, vol. 42(2), pages 471-486, April.
  • Handle: RePEc:spr:empeco:v:42:y:2012:i:2:p:471-486
    DOI: 10.1007/s00181-011-0529-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00181-011-0529-1
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00181-011-0529-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Michael Beenstock & Daniel Felsenstein, 2007. "Spatial Vector Autoregressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(2), pages 167-196.
    2. Filippini, Massimo & Masiero, Giuliano & Moschetti, Karine, 2006. "Socioeconomic determinants of regional differences in outpatient antibiotic consumption: Evidence from Switzerland," Health Policy, Elsevier, vol. 78(1), pages 77-92, August.
    3. Herrmann, Markus & Gaudet, Gérard, 2009. "The economic dynamics of antibiotic efficacy under open access," Journal of Environmental Economics and Management, Elsevier, vol. 57(3), pages 334-350, May.
    4. Baltagi, Badi H & Griffin, James M, 2001. "The Econometrics of Rational Addiction: The Case of Cigarettes," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 449-454, October.
    5. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    6. Rudholm, Niklas, 2002. "Economic implications of antibiotic resistance in a global economy," Journal of Health Economics, Elsevier, vol. 21(6), pages 1071-1083, November.
    7. Jones, Andrew M., 1999. "Adjustment costs, withdrawal effects, and cigarette addiction," Journal of Health Economics, Elsevier, vol. 18(1), pages 125-137, January.
    8. Brown, Gardner & Layton, David F., 1996. "Resistance economics: social cost and the evolution of antibiotic resistance," Environment and Development Economics, Cambridge University Press, vol. 1(3), pages 349-355, July.
    9. Mark Harris & Laszlo Matyas & Patrick Sevestre, 2008. "Dynamic Models for Short Panels," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00279980, HAL.
    10. Becker, Gary S & Grossman, Michael & Murphy, Kevin M, 1994. "An Empirical Analysis of Cigarette Addiction," American Economic Review, American Economic Association, vol. 84(3), pages 396-418, June.
    11. Laxminarayan, Ramanan & Weitzman, Martin L., 2002. "On the implications of endogenous resistance to medications," Journal of Health Economics, Elsevier, vol. 21(4), pages 709-718, July.
    12. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    13. Laxminarayan, Ramanan & Brown, Gardner M., 2001. "Economics of Antibiotic Resistance: A Theory of Optimal Use," Journal of Environmental Economics and Management, Elsevier, vol. 42(2), pages 183-206, September.
    14. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    15. Giuliano Masiero & Massimo Filippini & Matus Ferech & Herman Goossens, 2010. "Socioeconomic determinants of outpatient antibiotic use in Europe," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 55(5), pages 469-478, October.
    16. Di Matteo, Livio, 2005. "The macro determinants of health expenditure in the United States and Canada: assessing the impact of income, age distribution and time," Health Policy, Elsevier, vol. 71(1), pages 23-42, January.
    17. Hidayat, Budi & Thabrany, Hasbullah, 2010. "Cigarette smoking in Indonesia: examination of a myopic model of addictive behaviour," MPRA Paper 30194, University Library of Munich, Germany, revised 17 May 2010.
    18. Herrmann, Markus, 2010. "Monopoly pricing of an antibiotic subject to bacterial resistance," Journal of Health Economics, Elsevier, vol. 29(1), pages 137-150, January.
    19. Filippini, Massimo & Masiero, Giuliano & Moschetti, Karine, 2009. "Small area variations and welfare loss in the use of outpatient antibiotics," Health Economics, Policy and Law, Cambridge University Press, vol. 4(1), pages 55-77, January.
    20. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    21. Fiorio, Carlo V. & Siciliani, Luigi, 2010. "Co-payments and the demand for pharmaceuticals: Evidence from Italy," Economic Modelling, Elsevier, vol. 27(4), pages 835-841, July.
    22. Silvia Tiezzi, 2005. "An empirical analysis of tobacco addiction in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 6(3), pages 233-243, September.
    23. Jones, Andrew M., 1994. "Health, addiction, social interaction and the decision to quit smoking," Journal of Health Economics, Elsevier, vol. 13(1), pages 93-110, March.
    24. Badi H. Baltagi & James M. Griffin, 2002. "Rational addiction to alcohol: panel data analysis of liquor consumption," Health Economics, John Wiley & Sons, Ltd., vol. 11(6), pages 485-491, September.
    25. Becker, Gary S & Murphy, Kevin M, 1988. "A Theory of Rational Addiction," Journal of Political Economy, University of Chicago Press, vol. 96(4), pages 675-700, August.
    26. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    27. Elamin H. Elbasha, 2003. "Deadweight loss of bacterial resistance due to overtreatment," Health Economics, John Wiley & Sons, Ltd., vol. 12(2), pages 125-138, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thomas Heister & Christian Hagist & Klaus Kaier, 2015. "Resistance Elasticity of Antibiotic Demand in Intensive Care," WHU Working Paper Series - Economics Group 15-01, WHU - Otto Beisheim School of Management.
    2. Filippini, M. & Heimsch, F. & Masiero, G., 2014. "Antibiotic consumption and the role of dispensing physicians," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 242-251.
    3. Massimo Filippini & Bettina Hirl & Giuliano Masiero, 2015. "Rational habits in residential electricity demand," IdEP Economic Papers 1506, USI Università della Svizzera italiana.
    4. Anikó Bíró, 2019. "Reduced user fees for antibiotics under age 5 in Hungary: Effect on antibiotic use and imbalances in the implementation," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-13, June.
    5. Akihiro Otsuka, 2019. "Natural disasters and electricity consumption behavior: a case study of the 2011 Great East Japan Earthquake," Asia-Pacific Journal of Regional Science, Springer, vol. 3(3), pages 887-910, October.
    6. Thomas Heister & Christian Hagist & Klaus Kaier, 2017. "Resistance Elasticity of Antibiotic Demand in Intensive Care," Health Economics, John Wiley & Sons, Ltd., vol. 26(7), pages 892-909, July.
    7. Filippini, Massimo & Hirl, Bettina & Masiero, Giuliano, 2018. "Habits and rational behaviour in residential electricity demand," Resource and Energy Economics, Elsevier, vol. 52(C), pages 137-152.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Massimo Filippini & Laura González & Giuliano Masiero, 2010. "Estimating dynamic consumption of antibiotics using panel data: the shadow effect of bacterial resistance," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 1011, USI Università della Svizzera italiana.
    2. Massimo Filippini & Bettina Hirl & Giuliano Masiero, 2015. "Rational habits in residential electricity demand," IdEP Economic Papers 1506, USI Università della Svizzera italiana.
    3. Filippini, Massimo & Hirl, Bettina & Masiero, Giuliano, 2018. "Habits and rational behaviour in residential electricity demand," Resource and Energy Economics, Elsevier, vol. 52(C), pages 137-152.
    4. Badi H. Baltagi, 2021. "Dynamic Panel Data Models," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 187-228, Springer.
    5. Biørn, Erik, 2012. "The Measurement Error Problem in Dynamic Panel Data Analysis: Modeling and GMM Estimation," Memorandum 02/2012, Oslo University, Department of Economics.
    6. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    7. Alberini, Anna & Filippini, Massimo, 2011. "Response of residential electricity demand to price: The effect of measurement error," Energy Economics, Elsevier, vol. 33(5), pages 889-895, September.
    8. Clark, Andrew & Etile, Fabrice, 2002. "Do health changes affect smoking? Evidence from British panel data," Journal of Health Economics, Elsevier, vol. 21(4), pages 533-562, July.
    9. Biørn, Erik & Han, Xuehui, 2012. "Panel Data Dynamics and Measurement Errors: GMM Bias, IV Validity and Model Fit – A Monte Carlo Study," Memorandum 27/2012, Oslo University, Department of Economics.
    10. Blázquez, Leticia & Boogen, Nina & Filippini, Massimo, 2013. "Residential electricity demand in Spain: New empirical evidence using aggregate data," Energy Economics, Elsevier, vol. 36(C), pages 648-657.
    11. Albert, Jason, 2021. "Strategic dynamics of antibiotic use and the evolution of antibiotic-resistant infections," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    12. Erik Biørn, 2015. "Panel data dynamics with mis-measured variables: modeling and GMM estimation," Empirical Economics, Springer, vol. 48(2), pages 517-535, March.
    13. Andrew M. Jones & Audrey Laporte & Nigel Rice & Eugenio Zucchelli, 2019. "Dynamic panel data estimation of an integrated Grossman and Becker–Murphy model of health and addiction," Empirical Economics, Springer, vol. 56(2), pages 703-733, February.
    14. Durand, Robert B. & Greene, William H. & Harris, Mark N. & Khoo, Joye, 2022. "Heterogeneity in speed of adjustment using finite mixture models," Economic Modelling, Elsevier, vol. 107(C).
    15. Andrew M. Jones & José M. Labeaga, 2003. "Individual heterogeneity and censoring in panel data estimates of tobacco expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(2), pages 157-177.
    16. Andrew Clark & Fabrice Etile, 1999. "The Effect of Health Information on Cigarette Consumption: Evidence from British Panel Data," Cahiers de la Maison des Sciences Economiques bla99090, Université Panthéon-Sorbonne (Paris 1).
    17. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    18. Collet, Roger & de Lapparent, Matthieu & Hivert, Laurent, 2015. "Are French households car-use addicts? A microeconomic perspective," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 54(C), pages 86-94.
    19. Jones, A. M. & Laporte, A. & Rice, N. & Zucchelli, E., 2014. "A synthesis of the Grossman and Becker-Murphy models of health and addiction: theoretical and empirical implications," Health, Econometrics and Data Group (HEDG) Working Papers 14/07, HEDG, c/o Department of Economics, University of York.
    20. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.

    More about this item

    Keywords

    Antibiotic consumption; Bacterial resistance; Dynamic model; Rational addiction; C21; C23; I1;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I1 - Health, Education, and Welfare - - Health

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:empeco:v:42:y:2012:i:2:p:471-486. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.