IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/48727.html
   My bibliography  Save this paper

Exploiting Zero-Inflated Consumption Data using Propensity Score Matching and the Infrequency of Purchase Model, with Application to Climate Change Policy

Author

Listed:
  • Bardsley, Nicholas
  • Buechs, Milena

Abstract

We apply propensity score matching (PSM) to the estimation of household motor fuel purchase quantities, to tackle problems caused by infrequency of purchase. The results are compared to an alternative, regression-based, imputation strategy using the infrequency of purchase model (IPM). Using data from the UK’s National Travel Survey (NTS) we observe that estimated mean obtained from the PSM imputation is closer to the estimated mean from the consumption diary, than that obtained from fitted values from the IPM. The NTS also contains an interview question on household mileage which can be used to assess the results of imputation. We find that the order statistics of the imputed distribution are more plausible for the PSM estimates than those obtained using the IPM, judging by the sample distribution of household mileage. We argue that there are some applications for which the PSM method is likely to be superior, including estimates of distributional effects of policies. On the other hand, the IPM is more suitable for analysing conditional effects and associations of consumption with covariates. We illustrate our arguments using a simple microsimulation exercise on CO2 emissions reduction policies, an area where methods for coping with zero-inflated data seem currently to be under-used.

Suggested Citation

  • Bardsley, Nicholas & Buechs, Milena, 2013. "Exploiting Zero-Inflated Consumption Data using Propensity Score Matching and the Infrequency of Purchase Model, with Application to Climate Change Policy," MPRA Paper 48727, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:48727
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/48727/1/MPRA_paper_48727.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kimhi, Ayal, 1999. "Double-Hurdle and Purchase-Infrequency Demand Analysis: A Feasible Integrated Approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 26(4), pages 425-442, December.
    2. Blundell, Richard & Meghir, Costas, 1987. "Bivariate alternatives to the Tobit model," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 179-200.
    3. Deaton, Angus & Irish, Margaret, 1984. "Statistical models for zero expenditures in household budgets," Journal of Public Economics, Elsevier, vol. 23(1-2), pages 59-80.
    4. John Gibson & Bonggeun Kim, 2012. "Testing the Infrequent Purchases Model Using Direct Measurement of Hidden Consumption from Food Stocks," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(1), pages 257-270.
    Full references (including those not matched with items on IDEAS)

    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. Richard J. Vyn & Getu Hailu, 2015. "Discount Usage and Price Discrimination for Pork Products in Canada," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 63(4), pages 449-474, December.
    2. Nicholas Bardsley & Milena Büchs & Sylke V Schnepf, 2017. "Something from nothing: Estimating consumption rates using propensity scores, with application to emissions reduction policies," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-23, October.
    3. Stewart, Hayden & Dong, Diansheng, 2018. "The Relationship Between Patronizing Direct-to-Consumer Outlets and a Household’s Demand for Fruits and Vegetables," Economic Research Report 276254, United States Department of Agriculture, Economic Research Service.
    4. Fiedler, John L. & Mwangi, Dena M., 2016. "Improving household consumption and expenditure surveys’ food consumption metrics: Developing a strategic approach to the unfinished agenda:," IFPRI discussion papers 1570, International Food Policy Research Institute (IFPRI).
    5. Hikaru Hasegawa & Kazuhiro Ueda & Kunie Mori, 2008. "Estimation of Engel Curves from Survey Data with Zero Expenditures," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 535-558, August.
    6. Brannlund, Runar & Nordstrom, Jonas, 2004. "Carbon tax simulations using a household demand model," European Economic Review, Elsevier, vol. 48(1), pages 211-233, February.
    7. Jaume García-Villar & Ángel López-Nicolás, 2015. "Who is afraid of smoking bans? An evaluation of the effects of the Spanish clean air law on expenditure at hospitality venues," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(8), pages 813-834, November.
    8. Stewart, Hayden & Dong, Diansheng, 2018. "How strong is the demand for food through direct-to-consumer outlets?," Food Policy, Elsevier, vol. 79(C), pages 35-43.
    9. Leffler, Kristyn K. & Carpio, Carlos E. & Boonsaeng, Tullaya, 2012. "Temporal Aggregation and Treatment of Zero Dependent Variables in the Estimation of Food Demand using Cross-Sectional Data," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124913, Agricultural and Applied Economics Association.
    10. Steven Yen, 1999. "Nonparticipation and corner solution: extramarital affairs reconsidered," Applied Economics Letters, Taylor & Francis Journals, vol. 6(7), pages 443-445.
    11. Nordström, Jonas & Thunström, Linda, 2009. "The impact of tax reforms designed to encourage healthier grain consumption," Journal of Health Economics, Elsevier, vol. 28(3), pages 622-634, May.
    12. David Aristei & Luca Pieroni, 2008. "A double-hurdle approach to modelling tobacco consumption in Italy," Applied Economics, Taylor & Francis Journals, vol. 40(19), pages 2463-2476.
    13. Carlin, Paul S. & Flood, Lennart, 1997. "Do children affect the labor supply of Swedish men? Time diary vs. survey data," Labour Economics, Elsevier, vol. 4(2), pages 167-183, June.
    14. C. Federico Perali & THOMAS L. COX, 1995. "Issues in Data Management of Expenditure Surveys: An Example from the Colombian 1984-85 Urban Survey," Wisconsin-Madison Agricultural and Applied Economics Staff Papers 389, Wisconsin-Madison Agricultural and Applied Economics Department.
    15. David Madden, 2000. "Relative Or Absolute Poverty Lines: A New Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 46(2), pages 181-199, June.
    16. C. Federico PERALI & Thomas L. COX, 1995. "Abstract Of Issues In Data Management Of Expenditure Surveys: An Example From The Colombian 1984-85 Urban Survey," Staff Papers 389, University of Wisconsin Madison, AAE.
    17. Moon, Wanki & Ward, Ronald W., 1999. "Effects Of Health Concerns And Consumer Characteristics On U.S. Meat Consumption," 1999 Annual meeting, August 8-11, Nashville, TN 21682, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    18. Marco Angrisani & Arie Kapteyn & Scott Schuh, 2014. "Measuring Household Spending and Payment Habits: The Role of "Typical" and "Specific" Time Frames in Survey Questions," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 414-440, National Bureau of Economic Research, Inc.
    19. Shimshack, Jay P. & Ward, Michael B. & Beatty, Timothy K.M., 2007. "Mercury advisories: Information, education, and fish consumption," Journal of Environmental Economics and Management, Elsevier, vol. 53(2), pages 158-179, March.
    20. David Aristei & Federico Perali & Luca Pieroni, 2008. "Cohort, age and time effects in alcohol consumption by Italian households: a double-hurdle approach," Empirical Economics, Springer, vol. 35(1), pages 29-61, August.

    More about this item

    Keywords

    propensity score matching; purchase infrequency; climate policy;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:pra:mprapa:48727. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.