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Estimating dynamic consumption of antibiotics using panel data: the shadow effect of bacterial resistance

Author

Listed:
  • Massimo Filippini

    (Department of Economics, ETH Zurich, Switzerland)

  • Laura González

    (Department of Economics, University of Lugano, Switzerland)

  • Giuliano Masiero

    () (Department of Economics and Technology Management, University of Bergamo, Italy)

Abstract

To some extent, antibiotics are similar to addictive goods since current consumption is reinforced by past use because of bacterial resistance, which represents a growing concern in many countries. The purpose of this paper is to explore how consumers adjust their current level of antibiotic consumption towards desired levels over time. We construct a balanced panel dataset (2000-2007) for 20 Italian regions and estimate a dynamic model where 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 consumption. We apply alternative dynamic estimators for short panels: the bias-corrected least squares dummy variable (LSDVC) and the system Blundell-Bond GMM estimator (GMM-BB). The estimation results are stable across different model specifications and show that antibiotic use in previous periods has a positive impact on current antimicrobial consumption (between 0.14 and 0.39). This indicates that the process of adjustment to desired levels of consumption is relatively fast (approximately 1.2-1.6 years). Weak persistence in consumption may suggest that individuals are responsive to changes in antibiotic effectiveness.

Suggested Citation

  • 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.
  • Handle: RePEc:lug:wpaper:1011
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    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. 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.
    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. Rudholm, Niklas, 2002. "Economic implications of antibiotic resistance in a global economy," Journal of Health Economics, Elsevier, vol. 21(6), pages 1071-1083, November.
    5. Salima Bouayad-Agha & Lionel Védrine, 2010. "Estimation Strategies for a Spatial Dynamic Panel using GMM. A New Approach to the Convergence Issue of European Regions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(2), pages 205-227.
    6. 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.
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    More about this item

    Keywords

    Antibiotic consumption; bacterial resistance; dynamic model;

    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

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