IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/16929.html
   My bibliography  Save this paper

Eliciting People's First-Order Concerns: Text Analysis of Open-Ended Survey Questions

Author

Listed:
  • Stantcheva, Stefanie
  • Ferrario, Beatrice

Abstract

This paper illustrates the design and use of open-ended survey questions as a way of eliciting people's first-order concerns on policies. Multiple choice questions are the backbone of most surveys, but they may prime respondents to select answer options that they would not naturally have thought about, and they may omit relevant options. Open-ended questions that do not constrain respondents with specific answer choices are a valuable tool for eliciting first-order thinking. We discuss three text analysis methods to analyze open-ended questions' answers. To illustrate how to apply these methods, we provide evidence from large-scale surveys on income and estate taxation. We show the that key concerns relate mostly to distribution issues, fairness, and government, rather than to efficiency concerns. There are large partisan gaps in the first-order concerns on policies.

Suggested Citation

  • Stantcheva, Stefanie & Ferrario, Beatrice, 2022. "Eliciting People's First-Order Concerns: Text Analysis of Open-Ended Survey Questions," CEPR Discussion Papers 16929, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:16929
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP16929
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Margaret Roberts & Brandon Stewart & Tingley, Dustin & Edoardo Airoldi, 2013. "The structural topic model and applied social science," Working Paper 132666, Harvard University OpenScholar.
    2. Stefanie Stantcheva, 2021. "Understanding Tax Policy: How do People Reason?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(4), pages 2309-2369.
    3. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    4. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    5. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    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. Demgensky, Lisa & Fritsche, Ulrich, 2023. "Narratives on the causes of inflation in Germany: First results of a pilot study," WiSo-HH Working Paper Series 77, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
    2. Jiang, Lingqing & Zhu, Zhen, 2022. "Information exchange and multiple peer groups: A natural experiment in an online community," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 543-562.
    3. Sebastian Link & Andreas Peichl & Christopher Roth & Johannes Wohlfart, 2023. "Attention to the Macroeconomy," ECONtribute Discussion Papers Series 256, University of Bonn and University of Cologne, Germany.
    4. An, Zidong & Binder, Carola & Sheng, Xuguang Simon, 2023. "Gas price expectations of Chinese households," Energy Economics, Elsevier, vol. 120(C).
    5. Conti, Gabriella & Giannola, Michele & Toppeta, Alessandro, 2022. "Parental Beliefs, Perceived Health Risks, and Time Investment in Children: Evidence from COVID-19," IZA Discussion Papers 15765, Institute of Labor Economics (IZA).
    6. Tobias Wekhof & Sébastien Houde, 2023. "Using narratives to infer preferences in understanding the energy efficiency gap," Nature Energy, Nature, vol. 8(9), pages 965-977, September.
    7. Fabienne Cantner & Geske Rolvering, 2022. "Does information help to overcome public resistance to carbon prices? Evidence from an information provision experiment," Working Papers 219, Bavarian Graduate Program in Economics (BGPE).
    8. Quentin Lippmann & Khushboo Surana, 2022. "The Hierarchy of Partner Preferences," Discussion Papers 22/08, Department of Economics, University of York.
    9. Tobias König & Renke Schmacker, 2022. "Preferences for Sin Taxes," CESifo Working Paper Series 10046, CESifo.

    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. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    2. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    3. Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers 2023-19, CEPII research center.
    4. Ulrich Fritsche & Johannes Puckelwald, 2018. "Deciphering Professional Forecasters’ Stories - Analyzing a Corpus of Textual Predictions for the German Economy," Macroeconomics and Finance Series 201804, University of Hamburg, Department of Socioeconomics.
    5. Nuccio Ludovico & Federica Dessi & Marino Bonaiuto, 2020. "Stakeholders Mapping for Sustainable Biofuels: An Innovative Procedure Based on Computational Text Analysis and Social Network Analysis," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    6. David Ardia & Keven Bluteau & Mohammad Abbas Meghani, 2021. "Thirty Years of Academic Finance," Papers 2112.14902, arXiv.org, revised Aug 2022.
    7. van Loon, Austin, 2022. "Three Families of Automated Text Analysis," SocArXiv htnej, Center for Open Science.
    8. Kohei Kawaguchi & Toshifumi Kuroda & Susumu Sato, 2021. "Merger Analysis in the App Economy: An Empirical Model of Ad-Sponsored Media," HKUST CEP Working Papers Series 202103, HKUST Center for Economic Policy.
    9. Fabrizio Gilardi & Charles R. Shipan & Bruno Wüest, 2021. "Policy Diffusion: The Issue‐Definition Stage," American Journal of Political Science, John Wiley & Sons, vol. 65(1), pages 21-35, January.
    10. Keith Carlson & Michael A. Livermore & Daniel N. Rockmore, 2020. "The Problem of Data Bias in the Pool of Published U.S. Appellate Court Opinions," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(2), pages 224-261, June.
    11. Sebastian Blesse & Friedrich Heinemann & Tommy Krieger, 2021. "Ökonomische Desinformation — Ursachen und Handlungsempfehlungen [Economic Disinformation — Causes and Recommendations for Action]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 101(12), pages 943-948, December.
    12. Sergio Davalos & Ehsan H. Feroz, 2022. "A textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases relating to the Sarbanes–Oxley Act," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 19-40, January.
    13. Rose, Rodrigo L. & Puranik, Tejas G. & Mavris, Dimitri N. & Rao, Arjun H., 2022. "Application of structural topic modeling to aviation safety data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    14. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    15. Blesse, Sebastian & Heinemann, Friedrich & Krieger, Tommy, 2021. "Informationsdefizite als Hindernis rationaler Wirtschaftspolitik: Ausmass, Ursachen und Gegenstrategien. Eine Studie mit Unterstützung der Brigitte Strube Stiftung," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 241989, September.
    16. Leonardo Cei & Edi Defrancesco & Gianluca Stefani, 2022. "What topic modelling can show about the development of agricultural economics: evidence from the Journal Citation Report category top journals," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(2), pages 289-330.
    17. Mortenson, Michael J. & Vidgen, Richard, 2016. "A computational literature review of the technology acceptance model," International Journal of Information Management, Elsevier, vol. 36(6), pages 1248-1259.
    18. Berk Wheelock, Lauren & Pachamanova, Dessislava A., 2022. "Acceptable set topic modeling," European Journal of Operational Research, Elsevier, vol. 299(2), pages 653-673.
    19. Nuccio Ludovico & Marc Esteve Del Valle & Franco Ruzzenenti, 2020. "Mapping the Dutch Energy Transition Hyperlink Network," Sustainability, MDPI, vol. 12(18), pages 1-24, September.
    20. Stefano DellaVigna & Ruben Durante & Brian Knight & Eliana La Ferrara, 2016. "Market-Based Lobbying: Evidence from Advertising Spending in Italy," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 224-256, January.

    More about this item

    Keywords

    Surveys; Open-ended questions; Preferences; Political economy; Taxation;
    All these keywords.

    JEL classification:

    • H20 - Public Economics - - Taxation, Subsidies, and Revenue - - - General
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

    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:cpr:ceprdp:16929. 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: the person in charge (email available below). General contact details of provider: https://www.cepr.org .

    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.