IDEAS home Printed from https://ideas.repec.org/a/aea/apandp/v112y2022p163-69.html
   My bibliography  Save this article

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

Author

Listed:
  • Beatrice Ferrario
  • Stefanie Stantcheva

Abstract

We illustrate the design and use of open-ended survey questions to elicit people's first-order concerns on policies. Closed-ended questions are the backbone of surveys but may prime respondents to select some answers and 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 and apply them to surveys on income and estate taxation. People's key concerns relate mostly to distribution issues, fairness, and trust in government rather than to efficiency, and they exhibit large partisan gaps.

Suggested Citation

  • Beatrice Ferrario & Stefanie Stantcheva, 2022. "Eliciting People's First-Order Concerns: Text Analysis of Open-Ended Survey Questions," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 163-169, May.
    • Handle: RePEc:aea:apandp:v:112:y:2022:p:163-69
    DOI: 10.1257/pandp.20221071
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20221071
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20221071.appx
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20221071.ds
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    File URL: https://libkey.io/10.1257/pandp.20221071?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Margaret Roberts & Brandon Stewart & Tingley, Dustin & Edoardo Airoldi, 2013. "The structural topic model and applied social science," Working Paper 132666, Harvard University OpenScholar.
    3. 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.
    4. 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.
    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. 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.
    2. Quentin Lippmann & Khushboo Surana, 2022. "The Hierarchy of Partner Preferences," Discussion Papers 22/08, Department of Economics, University of York.
    3. An, Zidong & Binder, Carola & Sheng, Xuguang Simon, 2023. "Gas price expectations of Chinese households," Energy Economics, Elsevier, vol. 120(C).
    4. Gabriella Conti & Michele Giannola & Alessandro Toppeta, 2022. "Parental Beliefs, Perceived Health Risks, and Time Investment in Children: Evidence from COVID-19," Working Papers 2022-045, Human Capital and Economic Opportunity Working Group.
    5. 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.
    6. Tobias König & Renke Schmacker, 2022. "Preferences for Sin Taxes," CESifo Working Paper Series 10046, CESifo.
    7. 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.
    8. 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).
    9. 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.

    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. 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.
    3. 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.
    4. Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers 2023-19, CEPII research center.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. David Ardia & Keven Bluteau & Mohammad Abbas Meghani, 2021. "Thirty Years of Academic Finance," Papers 2112.14902, arXiv.org, revised Aug 2022.
    14. van Loon, Austin, 2022. "Three Families of Automated Text Analysis," SocArXiv htnej, Center for Open Science.
    15. Berk Wheelock, Lauren & Pachamanova, Dessislava A., 2022. "Acceptable set topic modeling," European Journal of Operational Research, Elsevier, vol. 299(2), pages 653-673.
    16. 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.
    17. 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.
    18. 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.
    19. Charles Angelucci & Julia Cage & Michael Sinkinson, 2020. "Media Competition and News Diets," Sciences Po publications 2020-03, Sciences Po.
    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

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

    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:aea:apandp:v:112:y:2022:p:163-69. 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: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.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.