IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2017-06.html
   My bibliography  Save this paper

Heaping at Round Numbers on Financial Questions : The Role of Satisficing

Author

Abstract

Survey responses to quantitative financial questions frequently display strong patterns of heaping at round numbers. This paper uses two studies to examine variation in rounding across questions and by individual characteristics. Rounding was more common for respondents low in ability, for respondents low in motivation, and for more difficult questions, all consistent with theories of satisficing. Questions that require more difficult information retrieval and integration of information exhibit more heaping. The use of records, which lowers task difficulty, reduces rounding as well. Higher episodic memory is associated with less rounding, and standard measures of motivation are negatively associated with rounding. These relationships, along with the fact that longer response latencies are associated with less rounding, all support the idea that rounding is a manifestation of satisficing on open-ended financial questions. Rounding patterns also appear remarkably similar across the two studies, despite being fielded in different modes and employing different question order and wording.

Suggested Citation

  • Michael Gideon & Brooke Helppie-McFall & Joanne W. Hsu, 2017. "Heaping at Round Numbers on Financial Questions : The Role of Satisficing," Finance and Economics Discussion Series 2017-006, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2017-06
    DOI: 10.17016/FEDS.2017.006
    as

    Download full text from publisher

    File URL: https://www.federalreserve.gov/econresdata/feds/2017/files/2017006pap.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.17016/FEDS.2017.006?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. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What Do We Learn from Recall Consumption Data?," Journal of Human Resources, University of Wisconsin Press, vol. 38(2).
    2. John J. McArdle & James P. Smith & Robert Willis, 2011. "Cognition and Economic Outcomes in the Health and Retirement Survey," NBER Chapters, in: Explorations in the Economics of Aging, pages 209-233, National Bureau of Economic Research, Inc.
    3. Paul Ruud & Daniel Schunk & Joachim Winter, 2014. "Uncertainty causes rounding: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 17(3), pages 391-413, September.
    4. Joanne W. Hsu, 2016. "Aging and Strategic Learning: The Impact of Spousal Incentives on Financial Literacy," Journal of Human Resources, University of Wisconsin Press, vol. 51(4), pages 1036-1067.
    5. Baumol, William J, 1979. " On the Contributions of Herbert A. Simon to Economics," Scandinavian Journal of Economics, Wiley Blackwell, vol. 81(11), pages 74-82.
    6. Manski, Charles F. & Molinari, Francesca, 2010. "Rounding Probabilistic Expectations in Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 219-231.
    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. Melissa Boyle & Justin Svec, 2022. "The Roundness of Antiquity Valuations from Auction Houses and Sales," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 48(4), pages 602-630, October.
    2. Tullio Jappelli & Luigi Pistaferri, 2020. "Reported MPC and Unobserved Heterogeneity," American Economic Journal: Economic Policy, American Economic Association, vol. 12(4), pages 275-297, November.
    3. Bachmann, Ronald & Bonin, Holger & Boockmann, Bernhard & Demir, Gökay & Felder, Rahel & Isphording, Ingo & Kalweit, René & Laub, Natalie & Vonnahme, Christina & Zimpelmann, Christian, 2020. "Auswirkungen des gesetzlichen Mindestlohns auf Löhne und Arbeitszeiten: Studie im Auftrag der Mindestlohnkommission," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 222998.
    4. Stanisławska, Ewa & Paloviita, Maritta, 2021. "Medium- vs. short-term consumer inflation expectations: Evidence from a new euro area survey," Bank of Finland Research Discussion Papers 10/2021, Bank of Finland.
    5. Dean Scrimgeour, 2022. "Reevaluating the evidence on seasonality in housing market match quality: Replication of Ngai and Tenreyro (2014)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1403-1409, November.
    6. Madeira, Carlos & Margaretic, Paula, 2022. "The impact of financial literacy on the quality of self-reported financial information," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    7. Sinha Roy,Sutirtha & Van Der Weide,Roy, 2022. "Poverty in India Has Declined over the Last Decade But Not As Much As Previously Thought," Policy Research Working Paper Series 9994, The World Bank.
    8. Clemens, Jeffrey & Strain, Michael R., 2022. "Understanding “Wage Theft”: Evasion and avoidance responses to minimum wage increases," Labour Economics, Elsevier, vol. 79(C).
    9. Bachmann, Ronald & Boockmann, Bernhard & Gonschor, Myrielle & Kalweit, René & Klauser, Roman & Laub, Natalie & Rulff, Christian & Vonnahme, Christina, 2022. "Auswirkungen des gesetzlichen Mindestlohns auf Löhne und Arbeitszeiten," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 264288.

    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. Breitmoser, Yves, 2016. "Stochastic choice, systematic mistakes and preference estimation," MPRA Paper 72779, University Library of Munich, Germany.
    2. Germ'an Reyes, 2022. "Coarse Wage-Setting and Behavioral Firms," Papers 2206.01114, arXiv.org, revised Mar 2024.
    3. Joanne W. Hsu, 2016. "Aging and Strategic Learning: The Impact of Spousal Incentives on Financial Literacy," Journal of Human Resources, University of Wisconsin Press, vol. 51(4), pages 1036-1067.
    4. Francesco D’Acunto & Daniel Hoang & Maritta Paloviita & Michael Weber, 2023. "IQ, Expectations, and Choice," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2292-2325.
    5. Nils Grevenbrock & Max Groneck & Alexander Ludwig & Alexander Zimper, 2021. "Cognition, Optimism, And The Formation Of Age‐Dependent Survival Beliefs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 887-918, May.
    6. Yves Breitmoser, 2021. "Controlling for presentation effects in choice," Quantitative Economics, Econometric Society, vol. 12(1), pages 251-281, January.
    7. Charles F. Manski, 2018. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Macroeconomics Annual, University of Chicago Press, vol. 32(1), pages 411-471.
    8. Junichi Kikuchi & Yoshiyuki Nakazono, 2023. "The Formation of Inflation Expectations: Microdata Evidence from Japan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(6), pages 1609-1632, September.
    9. Melissa Boyle & Justin Svec, 2022. "The Roundness of Antiquity Valuations from Auction Houses and Sales," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 48(4), pages 602-630, October.
    10. Delis, Manthos & Galariotis, Emilios & Monne, Jerome, 2021. "Economic condition and financial cognition," Journal of Banking & Finance, Elsevier, vol. 123(C).
    11. Annamaria Lusardi & Olivia S. Mitchell, 2014. "The Economic Importance of Financial Literacy: Theory and Evidence," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 5-44, March.
    12. Alexander Glas & Matthias Hartmann, 2022. "Uncertainty measures from partially rounded probabilistic forecast surveys," Quantitative Economics, Econometric Society, vol. 13(3), pages 979-1022, July.
    13. Paul Ruud & Daniel Schunk & Joachim Winter, 2014. "Uncertainty causes rounding: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 17(3), pages 391-413, September.
    14. Breitmoser, Yves, 2017. "Discrete Choice with Presentation Effects," Rationality and Competition Discussion Paper Series 35, CRC TRR 190 Rationality and Competition.
    15. Stefan Hoderlein & Bettina Siflinger & Joachim Winter, 2015. "Identification of structural models in the presence of measurement error due to rounding in survey responses," Boston College Working Papers in Economics 869, Boston College Department of Economics.
    16. Christelis, Dimitris & Dobrescu, Loretti I. & Motta, Alberto, 2020. "Early life conditions and financial risk-taking in older age," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).
    17. Tullio Jappelli, 2010. "Economic Literacy: An International Comparison," Economic Journal, Royal Economic Society, vol. 120(548), pages 429-451, November.
    18. Lin, Chien-An & Bates, Timothy C., 2022. "Smart people know how the economy works: Cognitive ability, economic knowledge and financial literacy," Intelligence, Elsevier, vol. 93(C).
    19. Sergio Longobardi & Margherita Maria Pagliuca & Andrea Regoli, 2018. "Can problem-solving attitudes explain the gender gap in financial literacy? Evidence from Italian students’ data," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1677-1705, July.
    20. Pindyck, Robert S., 2019. "The social cost of carbon revisited," Journal of Environmental Economics and Management, Elsevier, vol. 94(C), pages 140-160.

    More about this item

    Keywords

    Consumer surveys; Data collection and estimation; Satisficing;
    All these keywords.

    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
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

    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:fedgfe:2017-06. 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: Ryan Wolfslayer ; Keisha Fournillier (email available below). General contact details of provider: https://edirc.repec.org/data/frbgvus.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.