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Consensus in Business Tendency Surveys: Comparison of Alternative Measures

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  • Emilia Tomczyk
  • Barbara Kowalczyk

Abstract

In this article, we aim to compare various methods of evaluating consensus in qualitative business surveys in which respondents express expectations on the ordered scale. A reliable method of measuring degree of consensus would provide researchers with valuable information, offering a leading indicator of respondent sentiment. However, there is no single generally accepted mathematical measure applicable to evaluating agreement among respondents. Several approaches are mentioned in previous studies, including indicators based on statistical dispersion, Shannon entropy, and multi-dimensional simplex. We present measures of consensus defined in literature and discuss their advantages and limitations. We then employ these indicators to expectations expressed in Polish business tendency survey in manufacturing, and compare results across various economic variables. In several cases, we find patterns in the behavior of measures of consensus: expected prices are characterized by the highest degree of consensus among respondents, and expected production and orders – by the lowest degree of consensus. We also find linkages between the degree of consensus and degree of optimism among respondents as measured by the balance statistic for prices, employment, and general business conditions.

Suggested Citation

  • Emilia Tomczyk & Barbara Kowalczyk, 2023. "Consensus in Business Tendency Surveys: Comparison of Alternative Measures," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 17-29.
    • Handle: RePEc:sgh:gosnar:y:2023:i:4:p:17-29
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    References listed on IDEAS

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    1. 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.
    2. Krüger, Fabian & Nolte, Ingmar, 2016. "Disagreement versus uncertainty: Evidence from distribution forecasts," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 172-186.
    3. Aisling J. Daly & Jan M. Baetens & Bernard De Baets, 2018. "Ecological Diversity: Measuring the Unmeasurable," Mathematics, MDPI, vol. 6(7), pages 1-28, July.
    4. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    5. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    6. repec:iab:iabjlr:v:53:i:1:p:art.3 is not listed on IDEAS
    7. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Economic Uncertainty: A Geometric Indicator of Discrepancy Among Experts’ Expectations," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(1), pages 95-114, May.
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    More about this item

    Keywords

    manufacturing; consensus; expectations; qualitative data; business tendency surveys;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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