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Econometric Modelling of Social Bads

Author

Listed:
  • William H Greene

    (Economics Department, Stern School of Business, New York University (NYU))

  • Mark N Harris

    (School of Economics and Finance, Curtin University)

  • Preety Srivastava

    (Department of Econometrics and Business Statistics, Monash University)

  • Xueyan Zhao

    (Department of Econometrics and Business Statistics, Monash University)

Abstract
When modelling 'social bads', such as illegal drug consumption, researchers are often faced with a dependent variable characterised by an excessive?amount of zero bservations. Building on the recent literature on hurdle and double-hurdle models, we propose a double-inflated modelling framework, where the zero observations are allowed to come from: non-participants; participant misreporters (who have larger loss functions associated with a truthful response); and infrequent consumers. Due to our empirical application, the model is derived for the case of an ordered discrete dependent variable. However, it is similarly possible to augment other such zero-inflated models (zero-inflated count models, and double-hurdle models for continuous variables, for example). The model is then applied to a consumer choice problem of cannabis consumption. As expected we find that misreporting has a significant (estimated) effect on the recorded incidence of marijuana. Specifically, we find that 14% of the zeros reported in the survey is estimated to come from individuals who have misreported their participation.

Suggested Citation

  • William H Greene & Mark N Harris & Preety Srivastava & Xueyan Zhao, 2013. "Econometric Modelling of Social Bads," Bankwest Curtin Economics Centre Working Paper series WP1305, Bankwest Curtin Economics Centre (BCEC), Curtin Business School.
    • Handle: RePEc:ozl:bcecwp:wp1305
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Ordered outcomes; discrete data; cannabis consumption; zero-inflated responses;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • D1 - Microeconomics - - Household Behavior
    • I1 - Health, Education, and Welfare - - Health

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