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A New Look at Uncertainty Shocks: Imperfect Information and Misallocation

Author

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  • Tatsuro Senga

    (Queen Mary University of London)

Abstract
Uncertainty faced by individual firms appears to be heterogeneous. In this paper, I construct new empirical measures of firm-level uncertainty using data from the I/B/E/S and Compustat. These new measures reveal persistent differences in the degree of uncertainty facing individual firms not reflected by existing measures. Consistent with existing measures, I find that the average level of uncertainty across firms is countercyclical, and that it rose sharply at the start of the Great Recession. I next develop a heterogeneous firm model with Bayesian learning and uncertainty shocks to study the aggregate implications of my new empirical findings. My model establishes a close link between the rise in firms' uncertainty at the start of a recession and the slow pace of subsequent recovery. These results are obtained in an environment that embeds Jovanovic's (1982) model of learning in a setting where each firm gradually learns about its own productivity, and each occasionally experiences a shock forcing it to start learning afresh. Firms differ in their information; more informed firms have lower posterior variances in beliefs. An uncertainty shock is a rise in the probability that any given firm will lose its information. When calibrated to reproduce the level and cyclicality of my leading measure of firm-level uncertainty, the model generates a prolonged recession followed by anemic recovery in response to an uncertainty shock. When confronted with a rise in firm-level uncertainty consistent with advent of the Great Recession, it explains 79 percent of the observed decline in GDP and 89 percent of the fall in investment.

Suggested Citation

  • Tatsuro Senga, 2015. "A New Look at Uncertainty Shocks: Imperfect Information and Misallocation," Working Papers 763, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:763
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    References listed on IDEAS

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    Cited by:

    1. Saijo, Hikaru, 2017. "The uncertainty multiplier and business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 78(C), pages 1-25.
    2. Chen, Cheng & Senga, Tatsuro & Sun, Chang & Zhang, Hongyong, 2023. "Uncertainty, imperfect information, and expectation formation over the firm’s life cycle," Journal of Monetary Economics, Elsevier, vol. 140(C), pages 60-77.
    3. Gaganan Awano & Nicholas Bloom & Ted Dolby & Paul Mizen & Rebecca Riley & Tatsuro Senga & John Van Reenen & Jenny Vyas & Philip Wales, 2018. "A firm-level perspective on micro- and macro-level uncertainty; An analysis of business expectations and uncertainty from the UK Management and Expectations Survey," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-10, Economic Statistics Centre of Excellence (ESCoE).
    4. Eric J. Bartelsman & Zoltan Wolf, 2017. "Measuring Productivity Dispersion," Tinbergen Institute Discussion Papers 17-033/VI, Tinbergen Institute.
    5. Dudley Cooke & Tatiana Damjanovic, 2020. "Optimal Fiscal Policy in a Model of Firm Entry with Financial Frictions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 35, pages 74-96, January.
    6. Straub, Ludwig & Ulbricht, Robert, 2015. "Endogenous Uncertainty and Credit Crunches," TSE Working Papers 15-604, Toulouse School of Economics (TSE), revised Dec 2017.
    7. Isaac Baley & Ana Figueiredo & Robert Ulbricht, 2022. "Mismatch Cycles," Journal of Political Economy, University of Chicago Press, vol. 130(11), pages 2943-2984.
    8. Stephen J. Terry, 2017. "Alternative Methods for Solving Heterogeneous Firm Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(6), pages 1081-1111, September.
    9. Straub, Ludwig & Ulbricht, Robert, 2019. "Endogenous second moments: A unified approach to fluctuations in risk, dispersion, and uncertainty," Journal of Economic Theory, Elsevier, vol. 183(C), pages 625-660.
    10. Isaac Baley & J. Andrés Blanco, 2016. "Menu costs, uncertainty cycles, and the propagation of nominal shocks," Economics Working Papers 1532, Department of Economics and Business, Universitat Pompeu Fabra.
    11. Cosmin Ilut & Matthias Kehrig & Martin Schneider, 2018. "Slow to Hire, Quick to Fire: Employment Dynamics with Asymmetric Responses to News," Journal of Political Economy, University of Chicago Press, vol. 126(5), pages 2011-2071.
    12. Hikaru Saijo & Cosmin Ilut, 2015. "Learning, Confidence, and Business Cycles," 2015 Meeting Papers 917, Society for Economic Dynamics.
    13. CHEN Cheng & SENGA Tatsuro & SUN Chang & ZHANG Hongyong, 2016. "Firm Expectations and Investment: Evidence from the China-Japan Island Dispute," Discussion papers 16090, Research Institute of Economy, Trade and Industry (RIETI).
    14. Fiori, Giuseppe & Scoccianti, Filippo, 2023. "The economic effects of firm-level uncertainty: Evidence using subjective expectations," Journal of Monetary Economics, Elsevier, vol. 140(C), pages 92-105.
    15. CHEN Cheng & SENGA Tatsuro & SUN Chang & ZHANG Hongyong, 2020. "Information Acquisition and Price Setting under Uncertainty: New Survey Evidence," Discussion papers 20078, Research Institute of Economy, Trade and Industry (RIETI).

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    More about this item

    Keywords

    Uncertainty; Learning; Misallocation and business cycles;
    All these keywords.

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing

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