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Tobacco spending in Georgia: Machine learning approach

Author

Listed:
  • Maksym Obrizan

    (Kyiv School of Economics)

  • Karine Torosyan

    (International School of Economics at TSU)

  • Norberto Pignatti

    (International School of Economics at TSU)

Abstract

The purpose of this study is to analyze tobacco spending in Georgia using various machine learning methods applied to a sample of 10,757 households from Integrated Household Survey collected by GeoStat in 2016. Previous research has shown that smoking is the leading cause of death for 35-69 year olds. In addition, tobacco expenditures may constitute as much as 17% of the household budget. Five different algorithms (ordinary least squares, random forest, two gradient boosting methods and deep learning) were applied to 8,173 households (or 76.0%) in the train set. Out-of-sample predictions were then obtained for 2,584 remaining households in the test set. Under the default settings random forest algorithm showed the best performance with more than 10% improvement in terms of root-mean-square error (RMSE). Improved accuracy and availability of machine learning tools in R calls for active use of these methods by policy makers and scientists in health economics, public health and related fields.

Suggested Citation

  • Maksym Obrizan & Karine Torosyan & Norberto Pignatti, 2018. "Tobacco spending in Georgia: Machine learning approach," Working Papers 3184, Research Consulting and Development.
    • Handle: RePEc:rcd:wpaper:3184
    as

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    File URL: http://rcd.org.ua/RePEc/files/WP3184.pdf
    File Function: First version, 2018
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    References listed on IDEAS

    as
    1. Susan H. Busch & Mireia Jofre-Bonet & Tracy A. Falba & Jody L. Sindelar, 2004. "Tobacco Spending and its Crowd-Out of Other Goods," NBER Working Papers 10974, National Bureau of Economic Research, Inc.
    2. Torosyan, Karine & Pignatti, Norberto & Obrizan, Maksym, 2018. "Job market outcomes for IDPs: The case of Georgia," Journal of Comparative Economics, Elsevier, vol. 46(3), pages 800-820.
    3. Wang, Hong & Sindelar, Jody L. & Busch, Susan H., 2006. "The impact of tobacco expenditure on household consumption patterns in rural China," Social Science & Medicine, Elsevier, vol. 62(6), pages 1414-1426, March.
    4. Carla J. Berg & Marina Topuridze & Nino Maglakelidze & Lela Starua & Maia Shishniashvili & Michelle C. Kegler, 2016. "Reactions to smoke-free public policies and smoke-free home policies in the Republic of Georgia: results from a 2014 national survey," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 61(4), pages 409-416, May.
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    Cited by:

    1. Kadriye Hilal Topal & Ebru Çağlayan Akay, 2020. "Hanehalkı Tüketim Harcamalarının Mikroekonometrik Analizi: LAD-LASSO Yöntemi," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(33), pages 13-31, December.

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    More about this item

    Keywords

    Tobacco Spending; Household Survey; Georgia; Machine Learning;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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