IDEAS home Printed from https://ideas.repec.org/p/ags/eaae05/24737.html
   My bibliography  Save this paper

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

Author

Listed:
  • Gustavsen, Geir Waehler

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.

Suggested Citation

  • Gustavsen, Geir Waehler, 2005. "Public Policies and the Demand for Carbonated Soft Drinks: A Censored Quantile Regression Approach," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24737, European Association of Agricultural Economists.
  • Handle: RePEc:ags:eaae05:24737
    DOI: 10.22004/ag.econ.24737
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/24737/files/cp05gu01.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.24737?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
    ---><---

    References listed on IDEAS

    as
    1. 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(3), pages 1-16, December.
    2. Powell, James L, 1986. "Symmetrically Trimmed Least Squares Estimation for Tobit Models," Econometrica, Econometric Society, vol. 54(6), pages 1435-1460, November.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Arabmazar, Abbas & Schmidt, Peter, 1982. "An Investigation of the Robustness of the Tobit Estimator to Non-Normality," Econometrica, Econometric Society, vol. 50(4), pages 1055-1063, July.
    5. Giancarlo Moschini, 1995. "Units of Measurement and the Stone Index in Demand System Estimation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(1), pages 63-68.
    6. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    7. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    8. 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.
    9. 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.
    10. Nelson, Forrest D, 1981. "A Test for Misspecification in the Censored Normal Model," Econometrica, Econometric Society, vol. 49(5), pages 1317-1329, September.
    11. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    12. 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.
    13. 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.
    14. Hurd, Michael, 1979. "Estimation in truncated samples when there is heteroscedasticity," Journal of Econometrics, Elsevier, vol. 11(2-3), pages 247-258.
    15. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762.
    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. Zhen Miao & John C. Beghin & Helen H. Jensen, 2012. "Taxing Sweets: Sweetener Input Tax Or Final Consumption Tax?," Contemporary Economic Policy, Western Economic Association International, vol. 30(3), pages 344-361, July.
    2. Cash, Sean B. & Lacanilao, Ryan D., 2007. "Taxing Food to Improve Health: Economic Evidence and Arguments," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 36(2), pages 1-9, October.
    3. Zhen Miao & John C. Beghin & Helen H. Jensen, 2013. "Accounting For Product Substitution In The Analysis Of Food Taxes Targeting Obesity," Health Economics, John Wiley & Sons, Ltd., vol. 22(11), pages 1318-1343, November.
    4. Å tulec, Ivana & Petljak, Kristina & Naletina, Dora, 2019. "Weather impact on retail sales: How can weather derivatives help with adverse weather deviations?," Journal of Retailing and Consumer Services, Elsevier, vol. 49(C), pages 1-10.
    5. Lionel Cosnard, 2019. "Taxing Sugar and Sugary Products to Reduce Obesity: A CGE Assessment of Several Tax Policies," Post-Print hal-03148821, HAL.
    6. Cosnard, Lionel & Laborde, David, 2019. "Taxing Sugar and Sugary Products to Reduce Obesity: A CGE Assessment of Several Tax Policies," Conference papers 333067, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    7. Cash, Sean B. & Lacanilao, Ryan D. & Adamowicz, Wiktor L. & Raine, Kim, 2008. "An Experimental Investigation of the Impact of Fat Taxes: Prices Effects, Food Stigma, and Information Effects on Economics Instruments to Improve Dietary Health," Consumer and Market Demand Network Papers 45499, University of Alberta, Department of Resource Economics and Environmental Sociology.

    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. Daniel Pollmann & Thomas Dohmen & Franz Palm, 2020. "Robust Estimation of Wage Dispersion with Censored Data: An Application to Occupational Earnings Risk and Risk Attitudes," De Economist, Springer, vol. 168(4), pages 519-540, December.
    2. Daniel Pollmann & Thomas Dohmen & Franz Palm, 2020. "Dispersion estimation; Earnings risk; Censoring; Quantile regression; Occupational choice; Sorting; Risk preferences; SOEP; IABS," ECONtribute Discussion Papers Series 028, University of Bonn and University of Cologne, Germany.
    3. Gustavsen, Geir Waehler & Rickertsen, Kyrre, 2004. "For Whom Reduced Prices Count: A Censored Quantile Regression Analysis Of Vegetable Demand," 2004 Annual meeting, August 1-4, Denver, CO 20172, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Jolliffe, Dean, 2004. "The impact of education in rural Ghana: examining household labor allocation and returns on and off the farm," Journal of Development Economics, Elsevier, vol. 73(1), pages 287-314, February.
    5. Geir Wæhler Gustavsen & Kyrre Rickertsen, 2009. "The effects of taxes on purchases of sugar-sweetened carbonated soft drinks: a quantile regression approach," Applied Economics, Taylor & Francis Journals, vol. 43(6), pages 707-716.
    6. Franco Peracchi, 1988. "Bounded Influence Estimators for the Censored Regression Model," UCLA Economics Working Papers 487, UCLA Department of Economics.
    7. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
    8. Honore, Bo & Khan, Shakeeb & Powell, James L., 2002. "Quantile regression under random censoring," Journal of Econometrics, Elsevier, vol. 109(1), pages 67-105, July.
    9. Jolliffe, Dean, 2002. "The Gender Wage Gap in Bulgaria: A Semiparametric Estimation of Discrimination," Journal of Comparative Economics, Elsevier, vol. 30(2), pages 276-295, June.
    10. Gustavsen, Geir Waehler & Jolliffe, Dean & Rickertsen, Kyrre, 2008. "Censored Quantile Regression and Purchases of Ice Cream," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6534, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Wocken, Meike & Kneib, Thomas, 2012. "Tobit regression to estimate impact of EU market intervention in dairy sector," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122528, European Association of Agricultural Economists.
    12. William M. Rodgers, 2006. "Male White‐Black Wage Gaps, 1979‐1994: A Distributional Analysis," Southern Economic Journal, John Wiley & Sons, vol. 72(4), pages 773-793, April.
    13. P. Čížek & S. Sadikoglu, 2018. "Bias-corrected quantile regression estimation of censored regression models," Statistical Papers, Springer, vol. 59(1), pages 215-247, March.
    14. Kenneth Y. Chay & James L. Powell, 2001. "Semiparametric Censored Regression Models," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 29-42, Fall.
    15. Zhangong Zhou & Rong Jiang & Weimin Qian, 2013. "LAD variable selection for linear models with randomly censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(2), pages 287-300, February.
    16. Khan, Shakeeb & Powell, James L., 2001. "Two-step estimation of semiparametric censored regression models," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 73-110, July.
    17. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    18. Dariusz Wójcik & Eric Knight & Vladimír Pažitka, 2018. "What turns cities into international financial centres? Analysis of cross-border investment banking 2000–2014," Journal of Economic Geography, Oxford University Press, vol. 18(1), pages 1-33.
    19. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    20. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.

    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:ags:eaae05:24737. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/eaaeeea.html .

    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.