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Explaining Bureaucratic Optimism: Theory and Evidence from U.S. Executive Agency Macroeconomic Forecasts

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  • KRAUSE, GEORGE A.
  • CORDER, J. KEVIN

Abstract

We offer a theory of intertemporal bureaucratic decision making which proposes that an agency's forecast optimism is related to the extent to which it discounts future reputation costs associated with bureaucratic incompetence. Agency forecasts of the distant future are more likely to be optimistic than short-term forecasts. We claim that unstable organizations will discount reputation costs at a steeper rate than stable organizations, and therefore will produce more optimistic forecasts. We test our theory using macroeconomic forecasts produced by the Office of Management and Budget (OMB) and the Social Security Administration (SSA) across six forecast horizons from 1979 to 2003. The statistical results are generally consistent with our theory: OMB generates more optimistic long-term forecasts than SSA. Further, differences in forecast optimism between these executive branch agencies widen as the forecast horizon increases. Our evidence suggests that more stable agencies place a premium on minimizing reputation costs. Conversely, less stable agencies are more likely to accommodate political pressures for forecast optimism. These findings underscore the importance of institutional design for understanding how executive agencies balance the conflicting goals of political responsiveness and bureaucratic competence within the administrative state.

Suggested Citation

  • Krause, George A. & Corder, J. Kevin, 2007. "Explaining Bureaucratic Optimism: Theory and Evidence from U.S. Executive Agency Macroeconomic Forecasts," American Political Science Review, Cambridge University Press, vol. 101(1), pages 129-142, February.
  • Handle: RePEc:cup:apsrev:v:101:y:2007:i:01:p:129-142_07
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    Cited by:

    1. Burgess, Matthew G. & Langendorf, Ryan E. & Ippolito, Tara & Pielke, Roger Jr, 2020. "Optimistically biased economic growth forecasts and negatively skewed annual variation," SocArXiv vndqr, Center for Open Science.
    2. Saed Alizamir & Francis de Véricourt & Shouqiang Wang, 2020. "Warning Against Recurring Risks: An Information Design Approach," Management Science, INFORMS, vol. 66(10), pages 4612-4629, October.
    3. George A. Krause & David E. Lewis & James W. Douglas, 2013. "Politics Can Limit Policy Opportunism in Fiscal Institutions: Evidence from Official General Fund Revenue Forecasts in the American States," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 32(2), pages 271-295, March.
    4. Tereza Capelos & Colin Provost & Maria Parouti & Julie Barnett & Jonathan Chenoweth & Chris Fife‐Schaw & Tanika Kelay, 2016. "Ingredients of institutional reputations and citizen engagement with regulators," Regulation & Governance, John Wiley & Sons, vol. 10(4), pages 350-367, December.

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