IDEAS home Printed from https://ideas.repec.org/p/wai/econwp/13-01.html
   My bibliography  Save this paper

What Does Variation in Survey Design Reveal About the Nature of Measurement Errors in Household Consumption?

Author

Listed:
  • John Gibson

    (University of Waikato)

  • Kathleen Beegle

    (World Bank)

  • Joachim De Weerdt

    (EDI Tanzania)

  • Jed Friedman

    (World Bank)

Abstract

We use data from eight different consumption questionnaires randomly assigned to 4,000 households in Tanzania to obtain evidence on the nature of measurement errors in estimates of household consumption. While there are no validation data, the design of one questionnaire and the resources put into its implementation make it likely to be substantially more accurate than the others. Comparing regressions using data from this benchmark design with results from the other questionnaires shows that errors have a negative correlation with the true value of consumption, creating a non-classical measurement error problem for which conventional statistical corrections may be ineffective.

Suggested Citation

  • John Gibson & Kathleen Beegle & Joachim De Weerdt & Jed Friedman, 2013. "What Does Variation in Survey Design Reveal About the Nature of Measurement Errors in Household Consumption?," Working Papers in Economics 13/01, University of Waikato.
  • Handle: RePEc:wai:econwp:13/01
    as

    Download full text from publisher

    File URL: ftp://sys-dmzhost.its.waikato.ac.nz/wai/econwp/1301.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. John Gibson & Bonggeun Kim, 2010. "Non‐Classical Measurement Error in Long‐Term Retrospective Recall Surveys," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 687-695, October.
    3. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    4. 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.
    5. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    6. Shahidur R. Khandker, 2005. "Microfinance and Poverty: Evidence Using Panel Data from Bangladesh," World Bank Economic Review, World Bank Group, vol. 19(2), pages 263-286.
    7. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Oxford University Press, vol. 69(2), pages 357-378.
    8. John Gibson, 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.
    9. Alderman, Harold & Hoogeveen, Hans & Rossi, Mariacristina, 2006. "Reducing child malnutrition in Tanzania: Combined effects of income growth and program interventions," Economics & Human Biology, Elsevier, vol. 4(1), pages 1-23, January.
    10. 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.
    11. John Gibson & Bonggeun Kim, 2007. "Measurement Error in Recall Surveys and the Relationship between Household Size and Food Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 473-489.
    12. Naeem Ahmed & Matthew Brzozowski & Thomas Crossley, 2006. "Measurement errors in recall food consumption data," IFS Working Papers W06/21, Institute for Fiscal Studies.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Decomposing response error to improve consumption survey design
      by Jed Friedman in Development Impact on 2016-04-26 04:43:00

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francisco G. Ferreira & Nora Lustig & Daniel Teles, 2015. "Appraising cross-national income inequality databases: An introduction," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(4), pages 497-526, December.
    2. Abay, Kibrom A. & Abate, Gashaw T. & Barrett, Christopher B. & Bernard, Tanguy, 2019. "Correlated non-classical measurement errors, ‘Second best’ policy inference, and the inverse size-productivity relationship in agriculture," Journal of Development Economics, Elsevier, vol. 139(C), pages 171-184.
    3. De Magalhães, Leandro & Santaeulàlia-Llopis, Raül, 2018. "The consumption, income, and wealth of the poorest: An empirical analysis of economic inequality in rural and urban Sub-Saharan Africa for macroeconomists," Journal of Development Economics, Elsevier, vol. 134(C), pages 350-371.
    4. Thomas F. Crossley & Yuqian Lu, 2018. "Returns to scale in food preparation and the Deaton–Paxson puzzle," Review of Economics of the Household, Springer, vol. 16(1), pages 5-19, March.
    5. Leandro De Magalhães & Raül Santaeulàlia-Llopis, 2015. "The Consumption, Income, and Wealth of the Poorest: Cross-Sectional Facts of Rural and Urban Sub-Saharan Africa for Macroeconomists," Bristol Economics Discussion Papers 15/655, School of Economics, University of Bristol, UK.
    6. Peter A.G. van Bergeijk, 2017. "Making Data Measurement Errors Transparent: The Case of the IMF," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 18(3), pages 133-154, July.
    7. Jed Friedman & Kathleen Beegle & Joachim De Weerdt & John Gibson, 2016. "Decomposing Response Errors in Food Consumption Measurement: Implications for Survey Design from a Survey Experiment in Tanzania," LICOS Discussion Papers 37516, LICOS - Centre for Institutions and Economic Performance, KU Leuven.
    8. McGovern, Mark E. & Canning, David & Bärnighausen, Till, 2018. "Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers," Economics Letters, Elsevier, vol. 171(C), pages 239-244.
    9. John Gibson & Susan Olivia & Geua Boe‐Gibson, 2020. "Night Lights In Economics: Sources And Uses," Journal of Economic Surveys, Wiley Blackwell, vol. 34(5), pages 955-980, December.
    10. Boulaga,Amadou Adamou Kalilou & Federighi,Giovanni & Hiernaux, Pierre & Zezza,Alberto & Boulaga,Amadou Adamou Kalilou & Federighi,Giovanni & Hiernaux, Pierre & Zezza,Alberto, 2014. "Milking the data : measuring income from milk production in extensive livestock systems -- experimental evidence from Niger," Policy Research Working Paper Series 7114, The World Bank.
    11. Hari Sharma & John Gibson, 2019. "Civil War and International Migration from Nepal: Evidence from a Spatial Durbin Model," Working Papers in Economics 19/06, University of Waikato.
    12. Friedman, Jed & Beegle, Kathleen & De Weerdt, Joachim & Gibson, John, 2017. "Decomposing response error in food consumption measurement: Implications for survey design from a randomized survey experiment in Tanzania," Food Policy, Elsevier, vol. 72(C), pages 94-111.
    13. John Gibson & Bonggeun Kim, 2018. "Economies of scale, bulk discounts, and liquidity constraints: comparing unit value and transaction level evidence in a poor country," Review of Economics of the Household, Springer, vol. 16(1), pages 21-39, March.
    14. Zezza, Alberto & Carletto, Gero & Fiedler, John L & Gennari, Pietro & Jolliffe, Dean M, 2017. "Food Counts. Measuring Food Consumption And Expenditures In Household Consumption And Expenditure Surveys (HCES)," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 260886, European Association of Agricultural Economists.
    15. van Bergeijk, P.A.G., 2017. "Measurement error of global production," ISS Working Papers - General Series 632, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    16. John Gibson, 2021. "Better Night Lights Data, For Longer," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 770-791, June.
    17. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).

    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. Jayasinghe, Maneka & Chai, Andreas & Ratnasiri, Shyama & Smith, Christine, 2017. "The power of the vegetable patch: How home-grown food helps large rural households achieve economies of scale & escape poverty," Food Policy, Elsevier, vol. 73(C), pages 62-74.
    2. Brzozowski, Matthew & Crossley, Thomas F. & Winter, Joachim K., 2017. "Does survey recall error explain the Deaton–Paxson puzzle?," Economics Letters, Elsevier, vol. 158(C), pages 18-20.
    3. Trevon D. Logan, 2011. "Economies Of Scale In The Household: Puzzles And Patterns From The American Past," Economic Inquiry, Western Economic Association International, vol. 49(4), pages 1008-1028, October.
    4. Gibson, John, 2003. "Does Measurement Error Explain a Paradox About Household Size and Food Demand? Evidence from Variation in Household Survey Methods," 2003 Annual meeting, July 27-30, Montreal, Canada 22198, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Blessing M. Chiripanhura & Miguel Niño-Zarazúa, 2016. "The impacts of the food, fuel and financial crises on poor and vulnerable households in Nigeria: A retrospective approach to research inquiry," Development Policy Review, Overseas Development Institute, vol. 34(6), pages 763-788, November.
    6. Perali, Federico, 2008. "The second Engel law: Is it a paradox?," European Economic Review, Elsevier, vol. 52(8), pages 1353-1377, November.
    7. Brzozowski, Matthew & Crossley, Thomas F. & Winter, Joachim K., 2017. "A comparison of recall and diary food expenditure data," Food Policy, Elsevier, vol. 72(C), pages 53-61.
    8. Carletto,Calogero & Deininger,Klaus W. & Muwonge, James & Savastano,Sara & Carletto,Calogero & Deininger,Klaus W. & Muwonge, James & Savastano,Sara, 2011. "Can diaries help improve agricultural production statistics ? Evidence from Uganda," Policy Research Working Paper Series 5717, The World Bank.
    9. Timothy J. Halliday, 2010. "Mismeasured Household Size and its Implications for the Identification of Economies of Scale," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(2), pages 246-262, April.
    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. 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.
    12. 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.
    13. Sanae Tashiro, 2009. "Differences in Food Preparation by Race and Ethnicity: Evidence from the American Time Use Survey," The Review of Black Political Economy, Springer;National Economic Association, vol. 36(3), pages 161-180, December.
    14. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    15. Karbasi, A. & Mohammadzadeh, S.H., 2018. "Estimating Household Expenditure Economies of Scale in Iran," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277152, International Association of Agricultural Economists.
    16. Abay, Kibrom A. & Abate, Gashaw T. & Barrett, Christopher B. & Bernard, Tanguy, 2019. "Correlated non-classical measurement errors, ‘Second best’ policy inference, and the inverse size-productivity relationship in agriculture," Journal of Development Economics, Elsevier, vol. 139(C), pages 171-184.
    17. Conforti, Piero & Grünberger, Klaus & Troubat, Nathalie, 2017. "The impact of survey characteristics on the measurement of food consumption," Food Policy, Elsevier, vol. 72(C), pages 43-52.
    18. Niño-Zarazúa, Miguel & Chiripanhura, Blessing, 2013. "The impacts of the food, fuel and financial crises on households in Nigeria. A retrospective approach for research enquiry," MPRA Paper 47348, University Library of Munich, Germany.
    19. Beegle, Kathleen & Carletto, Calogero & Himelein, Kristen, 2012. "Reliability of recall in agricultural data," Journal of Development Economics, Elsevier, vol. 98(1), pages 34-41.
    20. Naeem Ahmed & Matthew Brzozowski & Thomas F. Crossley, 2005. "Measurement Errors in Recall Food Expenditure Data," Quantitative Studies in Economics and Population Research Reports 396, McMaster University.

    More about this item

    Keywords

    consumption; Engel curves; household surveys; measurement error; Tanzania;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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:wai:econwp:13/01. 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: . General contact details of provider: https://edirc.repec.org/data/dewaknz.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 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: Geua Boe-Gibson (email available below). General contact details of provider: https://edirc.repec.org/data/dewaknz.html .

    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.