IDEAS home Printed from https://ideas.repec.org/p/fip/fedawp/90080.html
   My bibliography  Save this paper

Improved Estimation of Poisson Rate Distributions through a Multi-Mode Survey Design

Author

Listed:
  • Marcin Hitczenko

Abstract

Researchers interested in studying the frequency of events or behaviors among a population must rely on count data provided by sampled individuals. Often, this involves a decision between live event counting, such as a behavioral diary, and recalled aggregate counts. Diaries are generally more accurate, but their greater cost and respondent burden generally yield less data. The choice of survey mode, therefore, involves a potential tradeoff between bias and variance of estimators. I use a case study comparing inferences about payment instrument use based on different survey designs to illustrate this dilemma. I then use a simulation study to show how and under what conditions a hybrid survey design can improve efficiency of estimation, in terms of mean-squared error. Overall, this work suggests that such a hybrid design can have considerable benefits as long as there is nontrivial overlap in the diary and recall samples.

Suggested Citation

  • Marcin Hitczenko, 2021. "Improved Estimation of Poisson Rate Distributions through a Multi-Mode Survey Design," FRB Atlanta Working Paper 2021-10, Federal Reserve Bank of Atlanta.
  • Handle: RePEc:fip:fedawp:90080
    DOI: 10.29338/wp2021-10
    as

    Download full text from publisher

    File URL: https://www.frbatlanta.org/-/media/documents/research/publications/wp/2021/02/03/10-improved-estimation-of-poisson-rate-distributions-through-multi-mode-survey-design.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.29338/wp2021-10?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. Stella Chatzitheochari & Kimberly Fisher & Emily Gilbert & Lisa Calderwood & Tom Huskinson & Andrew Cleary & Jonathan Gershuny, 2018. "Using New Technologies for Time Diary Data Collection: Instrument Design and Data Quality Findings from a Mixed-Mode Pilot Survey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(1), pages 379-390, May.
    2. 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.
    3. 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.
    4. Michael Hurd & Susann Rohwedder, 2009. "Methodological Innovations in Collecting Spending Data: The HRS Consumption and Activities Mail Survey," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 435-459, December.
    5. Nicole Jonker & Anneke Kosse, 2009. "The impact of survey design on research outcomes: A case study of seven pilots measuring cash usage in the Netherlands," DNB Working Papers 221, Netherlands Central Bank, Research Department.
    6. 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)

    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. Marcin Hitczenko, 2022. "Improved Estimation of Poisson Rate Distributions Through a Multimode Survey Design," Sociological Methods & Research, , vol. 51(2), pages 699-727, May.
    2. Marcin Hitczenko, 2013. "Optimal recall period length in consumer payment surveys," Working Papers 13-16, Federal Reserve Bank of Boston.
    3. 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).
    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. Martin Browning & Thomas F. Crossley & Joachim Winter, 2014. "The Measurement of Household Consumption Expenditures," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 475-501, August.
    6. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2017. "The 2012 diary of consumer payment choice: technical appendix," Research Data Report 17-5, Federal Reserve Bank of Boston.
    7. 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.
    8. 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.
    9. 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, Universität Innsbruck.
    10. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2018. "The 2015 and 2016 diaries of consumer payment choice: technical appendix," Research Data Report 18-2, Federal Reserve Bank of Boston.
    11. Zeni Mattia & Bison Ivano & Giunchiglia Fausto & Reis Fernando & Gauckler Britta, 2021. "Improving Time Use Measurement with Personal Big Data Collection – The Experience of the European Big Data Hackathon 2019," Journal of Official Statistics, Sciendo, vol. 37(2), pages 341-365, June.
    12. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2015. "The 2013 Survey of Consumer Payment Choice: technical appendix," Research Data Report 15-5, Federal Reserve Bank of Boston.
    13. 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.
    14. Arne Bigsten & Abebe Shimeles, 2011. "The persistence of urban poverty in Ethiopia: a tale of two measurements," Applied Economics Letters, Taylor & Francis Journals, vol. 18(9), pages 835-839.
    15. Hoderlein, Stefan & Winter, Joachim, 2010. "Structural measurement errors in nonseparable models," Journal of Econometrics, Elsevier, vol. 157(2), pages 432-440, August.
    16. Susan Olivia & John Gibson, 2013. "Using Engel curves to measure CPI bias for Indonesia," Bulletin of Indonesian Economic Studies, Taylor & Francis Journals, vol. 49(1), pages 85-101, April.
    17. Andronis, Lazaros & Maredza, Mandy & Petrou, Stavros, 2019. "Measuring, valuing and including forgone childhood education and leisure time costs in economic evaluation: Methods, challenges and the way forward," Social Science & Medicine, Elsevier, vol. 237(C), pages 1-1.
    18. Bruce D. Meyer & James X. Sullivan, 2011. "Viewpoint: Further results on measuring the well‐being of the poor using income and consumption," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(1), pages 52-87, February.
    19. Jonker Nicole, 2011. "Card Acceptance and Surcharging: the Role of Costs and Competition," Review of Network Economics, De Gruyter, vol. 10(2), pages 1-35, June.
    20. 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.

    More about this item

    Keywords

    recall surveys; diaries; bias; mean-squared error; multi-level models;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

    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:fip:fedawp:90080. 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: Rob Sarwark (email available below). General contact details of provider: https://edirc.repec.org/data/frbatus.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.