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Public Policies and the Demand for Carbonated Soft Drinks: A Censored Quantile Regression Approach

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  • Gustavsen, Geir Waehler
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    Abstract

    Heavy consumption of soda may contribute to obesity, strokes, and cardiac problems. From a health perspective, the distribution of the consumption is at least as important as the mean. Censored as well as ordinary quantile regression techniques were used to estimate the demand for sugary soda based on household data from 1989 to 1999. It was found that heavy drinkers are more price- and expenditure-responsive than are light drinkers. The study shows that increasing the taxes on carbonated soft drinks will lead to a small reduction in consumption for small and moderate consumers and a huge reduction for heavy consumers.

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    File URL: http://purl.umn.edu/24737
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    Bibliographic Info

    Paper provided by European Association of Agricultural Economists in its series 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark with number 24737.

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    Date of creation: 2005
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    Handle: RePEc:ags:eaae05:24737

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    Keywords: soda demand; quantile regression; taxes; Agricultural and Food Policy; Food Consumption/Nutrition/Food Safety; D12; I10;

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    1. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    2. Manning, Willard G. & Blumberg, Linda & Moulton, Lawrence H., 1995. "The demand for alcohol: The differential response to price," Journal of Health Economics, Elsevier, vol. 14(2), pages 123-148, June.
    3. Hurd, Michael, 1979. "Estimation in truncated samples when there is heteroscedasticity," Journal of Econometrics, Elsevier, vol. 11(2-3), pages 247-258.
    4. Stewart, Hayden & Blisard, Noel & Jolliffe, Dean, 2003. "Do Income Constraints Inhibit Spending on Fruits and Vegetables Among Low-Income Households?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(03), December.
    5. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    6. Powell, James L, 1986. "Symmetrically Trimmed Least Squares Estimation for Tobit Models," Econometrica, Econometric Society, vol. 54(6), pages 1435-60, November.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    9. Arabmazar, Abbas & Schmidt, Peter, 1981. "Further evidence on the robustness of the Tobit estimator to heteroskedasticity," Journal of Econometrics, Elsevier, vol. 17(2), pages 253-258, November.
    10. Nelson, Forrest D, 1981. "A Test for Misspecification in the Censored Normal Model," Econometrica, Econometric Society, vol. 49(5), pages 1317-29, September.
    11. Jayachandran N. Variyam & James Blaylock & David Smallwood, 2002. "Characterizing the Distribution of Macronutrient Intake among U.S. Adults: A Quantile Regression Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(2), pages 454-466.
    12. Moschini, GianCarlo, 1995. "Units of Measurement and the 'Stone Index' In Demand System Estimation," Staff General Research Papers 5058, Iowa State University, Department of Economics.
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