IDEAS home Printed from https://ideas.repec.org/p/fip/fedrwp/15-09.html
   My bibliography  Save this paper

Learning About Consumer Uncertainty from Qualitative Surveys: As Uncertain As Ever

Author

Listed:
  • Santiago Pinto
  • Pierre-Daniel G. Sarte
  • Robert Sharp

Abstract

We study diffusion indices constructed from qualitative surveys to provide real-time assessments of various aspects of economic activity. In particular, we highlight the role of diffusion indices as estimates of change in a quasi extensive margin, and characterize their distribution, focusing on the uncertainty implied by both sampling and the polarization of participants' responses. Because qualitative tendency surveys generally cover multiple questions around a topic, a key aspect of this uncertainty concerns the coincidence of responses, or the degree to which polarization comoves, across individual questions. We illustrate these results using micro data on individual responses underlying different composite indices published by the Michigan Survey of Consumers. We find a secular rise in consumer uncertainty starting around 2000, following a decade-long decline, and higher agreement among respondents in prior periods. Six years after the Great Recession, uncertainty arising from the polarization of responses in the Michigan Survey stands today at its highest level since 1978, coinciding with the weakest recovery in U.S. post-war history. The formulas we derive allow for simple computations of approximate confidence intervals, thus affording a more complete real-time assessment of economic conditions using qualitative surveys.

Suggested Citation

  • Santiago Pinto & Pierre-Daniel G. Sarte & Robert Sharp, 2015. "Learning About Consumer Uncertainty from Qualitative Surveys: As Uncertain As Ever," Working Paper 15-9, Federal Reserve Bank of Richmond.
  • Handle: RePEc:fip:fedrwp:15-09
    as

    Download full text from publisher

    File URL: https://www.richmondfed.org/-/media/richmondfedorg/publications/research/working_papers/2015/pdf/wp15-09.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew T. Foerster & Pierre-Daniel G. Sarte & Mark W. Watson, 2011. "Sectoral versus Aggregate Shocks: A Structural Factor Analysis of Industrial Production," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 1-38.
    2. Olivier Coibion & Yuriy Gorodnichenko & Saten Kumar, 2018. "How Do Firms Form Their Expectations? New Survey Evidence," American Economic Review, American Economic Association, vol. 108(9), pages 2671-2713, September.
    3. Smith, Jeremy & McAleer, Michael, 1995. "Alternative Procedures for Converting Qualitative Response Data to Quantitative Expectations: An Application to Australian Manufacturing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 165-185, April-Jun.
    4. Gourio, Francois & Kashyap, Anil K, 2007. "Investment spikes: New facts and a general equilibrium exploration," Journal of Monetary Economics, Elsevier, vol. 54(Supplemen), pages 1-22, September.
    5. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    6. Bachmann, Rüdiger & Elstner, Steffen, 2015. "Firm optimism and pessimism," European Economic Review, Elsevier, vol. 79(C), pages 297-325.
    7. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    8. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    9. David E. Runkle, 1998. "Revisionist history: how data revisions distort economic policy research," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 22(Fall), pages 3-12.
    10. Gianna Boero & Jeremy Smith & KennethF. Wallis, 2008. "Uncertainty and Disagreement in Economic Prediction: The Bank of England Survey of External Forecasters," Economic Journal, Royal Economic Society, vol. 118(530), pages 1107-1127, July.
    11. Gianna Boero & Jeremy Smith & Kenneth F. Wallis, 2008. "Uncertainty and Disagreement in Economic Prediction: The Bank of England Survey of External Forecasters," Economic Journal, Royal Economic Society, vol. 118(530), pages 1107-1127, July.
    12. Jason Bram & Sydney C. Ludvigson, 1998. "Does consumer confidence forecast household expenditure? a sentiment index horse race," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Jun), pages 59-78.
    13. Geoffrey H. Moore, 1983. "Business Cycles, Inflation, and Forecasting, 2nd edition," NBER Books, National Bureau of Economic Research, Inc, number moor83-1, March.
    14. Geoffrey H. Moore, 1983. "Introductory pages to "Business Cycles, Inflation, and Forecasting, 2nd edition"," NBER Chapters, in: Business Cycles, Inflation, and Forecasting, 2nd edition, pages -25, National Bureau of Economic Research, Inc.
    15. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    16. Jeong, Jinook & Maddala, G S, 1996. "Testing the Rationality of Survey Data Using the Weighted Double-Bootstrapped Method of Moments," The Review of Economics and Statistics, MIT Press, vol. 78(2), pages 296-302, May.
    17. Robert Rich & Joseph Tracy, 2010. "The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 200-207, February.
    18. O Claveria & E Pons & J Surinach, 2006. "Quantification of Expectations. Are They Useful for Forecasting Inflation?," Economic Issues Journal Articles, Economic Issues, vol. 11(2), pages 19-38, September.
    19. Geoffrey H. Moore, 1983. "Appendices to "Business Cycles, Inflation, and Forecasting, 2nd edition"," NBER Chapters, in: Business Cycles, Inflation, and Forecasting, 2nd edition, pages 453-473, National Bureau of Economic Research, Inc.
    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. Kladivko, Kamil & Österholm, Pär, 2020. "Can Households Predict where the Macroeconomy is Headed?," Working Papers 2020:11, Örebro University, School of Business.
    2. Nika Lazaryan & Santiago Pinto, 2017. "Using the Richmond Fed Manufacturing Survey to Gauge National and Regional Economic Conditions," Economic Quarterly, Federal Reserve Bank of Richmond, issue Q1-Q4, pages 81-137.
    3. Santiago Pinto & Pierre-Daniel G. Sarte & Sonya Ravindranath Waddell, 2015. "Monitoring Economic Activity in Real Time Using Diffusion Indices: Evidence from the Fifth District," Economic Quarterly, Federal Reserve Bank of Richmond, issue 4Q, pages 275-301.
    4. Santiago Pinto & Pierre-Daniel Sarte & Robert Sharp, 2020. "The Information Content and Statistical Properties of Diffusion Indexes," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 47-99, September.

    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. Santiago Pinto & Pierre-Daniel Sarte & Robert Sharp, 2020. "The Information Content and Statistical Properties of Diffusion Indexes," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 47-99, September.
    2. repec:zbw:bofrdp:037 is not listed on IDEAS
    3. Ambrocio, Gene, 2017. "The real effects of overconfidence and fundamental uncertainty shocks," Research Discussion Papers 37/2017, Bank of Finland.
    4. Pierre-Daniel G. Sarte, 2010. "Learning about informational rigidities from sectoral data and diffusion indices," Working Paper 10-09, Federal Reserve Bank of Richmond.
    5. repec:zbw:bofrdp:2017_037 is not listed on IDEAS
    6. Alexandros Botsis & Christoph Görtz & Plutarchos Sakellaris, 2020. "Quantifying Qualitative Survey Data: New Insights on the (Ir)Rationality of Firms' Forecasts," CESifo Working Paper Series 8148, CESifo.
    7. Zeno Enders & Franziska Hünnekes & Gernot Müller, 2022. "Firm Expectations and Economic Activity," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2396-2439.
    8. CHEN Cheng & SENGA Tatsuro & SUN Chang & ZHANG Hongyong, 2018. "Uncertainty, Imperfect Information, and Expectation Formation over the Firm's Life Cycle," Discussion papers 18010, Research Institute of Economy, Trade and Industry (RIETI).
    9. Robert Rich & Joseph Tracy, 2021. "A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 233-253, February.
    10. Joshua Abel & Robert Rich & Joseph Song & Joseph Tracy, 2016. "The Measurement and Behavior of Uncertainty: Evidence from the ECB Survey of Professional Forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 533-550, April.
    11. repec:zbw:bofrdp:2022_005 is not listed on IDEAS
    12. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
    13. Pierre‐Daniel Sarte, 2014. "When Is Sticky Information More Information?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1345-1379, October.
    14. Ambrocio, Gene, 2020. "Inflationary household uncertainty shocks," Bank of Finland Research Discussion Papers 5/2020, Bank of Finland.
    15. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.
    16. Sheen, Jeffrey & Wang, Ben Zhe, 2021. "Measuring macroeconomic disagreement – A mixed frequency approach," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 547-566.
    17. Anja Kukuvec & Harald Oberhofer, 2018. "The Propagation of Business Sentiment within the European Union?," WIFO Working Papers 549, WIFO.
    18. Glas, Alexander & Hartmann, Matthias, 2016. "Inflation uncertainty, disagreement and monetary policy: Evidence from the ECB Survey of Professional Forecasters," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 215-228.
    19. Istrefi, Klodiana & Mouabbi, Sarah, 2018. "Subjective interest rate uncertainty and the macroeconomy: A cross-country analysis," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 296-313.
    20. Lukas Buchheim & Sebastian Link, 2017. "The Effect of Disaggregate Information on the Expectation Formation of Firms," CESifo Working Paper Series 6768, CESifo.
    21. Andrade, Philippe & Crump, Richard K. & Eusepi, Stefano & Moench, Emanuel, 2016. "Fundamental disagreement," Journal of Monetary Economics, Elsevier, vol. 83(C), pages 106-128.
    22. Andrew J. Filardo, 1999. "How reliable are recession prediction models?," Economic Review, Federal Reserve Bank of Kansas City, vol. 84(Q II), pages 35-55.
    23. Anmol Bhandari & Jaroslav Borovicka & Paul Ho, 2019. "Survey Data and Subjective Beliefs in Business Cycle Models," Working Paper 19-14, Federal Reserve Bank of Richmond.

    More about this item

    Keywords

    Economic Uncertainty; Qualitative Data; Diffusion Index;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions

    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:fedrwp:15-09. 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: Christian Pascasio (email available below). General contact details of provider: https://edirc.repec.org/data/frbrius.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.