IDEAS home Printed from https://ideas.repec.org/p/ifs/ifsewp/06-21.html
   My bibliography  Save this paper

Measurement errors in recall food consumption data

Author

Listed:
  • Naeem Ahmed

    (Institute for Fiscal Studies)

  • Matthew Brzozowski

    (Institute for Fiscal Studies)

  • Thomas Crossley

    () (Institute for Fiscal Studies and Institute for Fiscal Studies, University of Essex)

Abstract

Recall food consumption data, which is the basis of a great deal of empirical work, is believed to suffer from considerable measurement error. Diary records are believed to be very accurate. We study a unique data set that collects recall and diary data from the same households. Measurement errors in recall food consumption data appear to be substantial, and they do not have the properties of classical measurement error. We also find evidence that the diary measures are themselves imperfect. We consider the implications of our findings for modelling demand, measuring inequality, and estimating inter-temporal preference parameters. Keywords: expenditure, consumption, measurement error, survey data

Suggested Citation

  • Naeem Ahmed & Matthew Brzozowski & Thomas Crossley, 2006. "Measurement errors in recall food consumption data," IFS Working Papers W06/21, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:ifsewp:06/21
    as

    Download full text from publisher

    File URL: http://www.ifs.org.uk/wps/wp0621.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Martin Browning & Thomas F. Crossley & Guglielmo Weber, 2003. "Asking consumption questions in general purpose surveys," Economic Journal, Royal Economic Society, vol. 113(491), pages 540-567, November.
    2. Amemiya, Yasuo, 1985. "Instrumental variable estimator for the nonlinear errors-in-variables model," Journal of Econometrics, Elsevier, vol. 28(3), pages 273-289, June.
    3. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
    4. Sule Alan & Martin Browning, 2003. "Estimating Intertemporal Allocation Parameters using Simulated Residual Estimation," CAM Working Papers 2003-03, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    5. Angus Deaton & Christina Paxson, 1998. "Economies of Scale, Household Size, and the Demand for Food," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 897-930, October.
    6. Sule Alan & Orazio Attanasio & Martin Browning, 2009. "Estimating Euler equations with noisy data: two exact GMM estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 309-324, March.
    7. Orazio P. Attanasio & Hamish Low, 2004. "Estimating Euler Equations," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 7(2), pages 405-435, April.
    8. Keen, Michael, 1986. "Zero Expenditures and the Estimation of Engel Curves," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(3), pages 277-286, July.
    9. Orazio Attanasio & Erich Battistin & Hidehiko Ichimura, 2004. "What Really Happened to Consumption Inequality in the US?," NBER Working Papers 10338, National Bureau of Economic Research, Inc.
    10. Dirk Krueger & Fabrizio Perri, 2006. "Does Income Inequality Lead to Consumption Inequality? Evidence and Theory -super-1," Review of Economic Studies, Oxford University Press, vol. 73(1), pages 163-193.
    11. Gibson, John, 2002. " Why Does the Engel Method Work? Food Demand, Economies of Size and Household Survey Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 341-359, September.
    12. Joseph G. Altonji & Aloysius Siow, 1987. "Testing the Response of Consumption to Income Changes with (Noisy) Panel Data," The Quarterly Journal of Economics, Oxford University Press, vol. 102(2), pages 293-328.
    13. Isabel McWhinney & Harold Champion, 1974. "The Canadian Experience With Recall And Diary Methods In Consumer Expenditure Surveys," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 3, number 2, pages 411-437 National Bureau of Economic Research, Inc.
    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. Felipe Kast & Dina Pomeranz, 2013. "Saving More to Borrow Less: Experimental Evidence from Access to Formal Savings Accounts in Chile," Harvard Business School Working Papers 14-001, Harvard Business School, revised Jun 2014.
    2. Andrew Leicester, 2012. "How might in-home scanner technology be used in budget surveys?," IFS Working Papers W12/01, Institute for Fiscal Studies.
    3. Vassilopoulos, Achilleas & Klonaris, Stathis & Drichoutis, Andreas C. & Lazaridis, Panagiotis, 2012. "Modeling quality demand with data from Household Budget Surveys: An application to meat and fish products in Greece," Economic Modelling, Elsevier, vol. 29(6), pages 2744-2750.
    4. John Bagnall & David Bounie & Kim P. Huynh & Anneke Kosse & Tobias Schmidt & Scott Schuh, 2016. "Consumer Cash Usage: A Cross-Country Comparison with Payment Diary Survey Data," International Journal of Central Banking, International Journal of Central Banking, vol. 12(4), pages 1-61, December.
    5. Hitczenko, Marcin, 2013. "Optimal recall period length in consumer payment surveys," Working Papers 13-16, Federal Reserve Bank of Boston.
    6. Adam Bee & Bruce D. Meyer & James X. Sullivan, 2013. "The Validity of Consumption Data: Are the Consumer Expenditure Interview and Diary Surveys Informative?," NBER Chapters,in: Improving the Measurement of Consumer Expenditures, pages 204-240 National Bureau of Economic Research, Inc.
    7. Alice sanwald & Engelbert Theurl, 2014. "What drives out-of pocket health expenditures of private households? - Empirical evidence from the Austrian household budget survey," Working Papers 2014-04, Faculty of Economics and Statistics, University of Innsbruck.
    8. Martin Browning & Thomas Crossley, 2009. "Are Two Cheap, Noisy Measures Better Than One Expensive, Accurate One?," American Economic Review, American Economic Association, vol. 99(2), pages 99-103, May.
    9. Tobias Broer, 2013. "The Wrong Shape of Insurance? What Cross-Sectional Distributions Tell Us about Models of Consumption Smoothing," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(4), pages 107-140, October.
    10. Calogero Carletto & Dean Jolliffe & Raka Banerjee, 2015. "From Tragedy to Renaissance: Improving Agricultural Data for Better Policies," Journal of Development Studies, Taylor & Francis Journals, vol. 51(2), pages 133-148, February.
    11. Richard Dunn, 2015. "Labor supply and household meal production among working adults in the Health and Retirement Survey," Review of Economics of the Household, Springer, vol. 13(2), pages 437-457, June.
    12. Thomas F. Crossley & Joachim K. Winter, 2014. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters,in: Improving the Measurement of Consumer Expenditures, pages 23-50 National Bureau of Economic Research, Inc.
    13. Campos, Rodolfo G. & Reggio, Iliana, 2014. "Measurement error in imputation procedures," Economics Letters, Elsevier, vol. 122(2), pages 197-202.
    14. repec:eee:jfpoli:v:73:y:2017:i:c:p:62-74 is not listed on IDEAS
    15. Campos, Rodolfo G. & Reggio, Iliana, 2013. "Measurement error and imputation of consumption in survey data," UC3M Working papers. Economics we1219, Universidad Carlos III de Madrid. Departamento de Economía.
    16. John Gibson & Kathleen Beegle & Joachim De Weerdt & Jed Friedman, 2015. "What does Variation in Survey Design Reveal about the Nature of Measurement Errors in Household Consumption?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 466-474, June.
    17. Derek Yu, 2013. "Some factors influencing the comparability and reliability of poverty estimates across household surveys," Working Papers 03/2013, Stellenbosch University, Department of Economics.
    18. Deininger, Klaus & Carletto, Calogero & Savastano, Sara & Muwonge, James, 2012. "Can diaries help in improving agricultural production statistics? Evidence from Uganda," Journal of Development Economics, Elsevier, vol. 98(1), pages 42-50.
    19. Timothy K. M. Beatty, 2008. "Expenditure dispersion and dietary quality: evidence from Canada," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1001-1014.
    20. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2012. "Methods of household consumption measurement through surveys: Experimental results from Tanzania," Journal of Development Economics, Elsevier, vol. 98(1), pages 3-18.
    21. Justine S. Hastings & Jesse M. Shapiro, 2017. "How Are SNAP Benefits Spent? Evidence from a Retail Panel," NBER Working Papers 23112, National Bureau of Economic Research, Inc.
    22. Deininger, Klaus & Carletto, Calogero & Savastano, Sara & Muwonge, James, 2011. "Can diaries help improve agricultural production statistics ? Evidence from Uganda," Policy Research Working Paper Series 5717, The World Bank.
    23. Erling Røed Larsen, 2014. "Is the Engel curve approach viable in the estimation of alternative PPPs?," Empirical Economics, Springer, vol. 47(3), pages 881-904, November.
    24. Nicole Jonker & Anneke Kosse, 2013. "Estimating Cash Usage: The Impact of Survey Design on Research Outcomes," De Economist, Springer, vol. 161(1), pages 19-44, March.
    25. Derek Yu, 2008. "The comparability of Income and Expenditure Surveys 1995, 2000 and 2005/2006," Working Papers 11/2008, Stellenbosch University, Department of Economics.

    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    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:ifs:ifsewp:06/21. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Emma Hyman). General contact details of provider: http://edirc.repec.org/data/ifsssuk.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.