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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
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    References listed on IDEAS

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    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.

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    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

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