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Policy Analysis with Incredible Certitude

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  • Charles F. Manski

Abstract

Analyses of public policy regularly express certitude about the consequences of alternative policy choices. Yet policy predictions often are fragile, with conclusions resting on critical unsupported assumptions. Then the certitude of policy analysis is not credible. This paper develops a typology of incredible analytical practices and gives illustrative cases. I call these practices conventional certitudes, dueling certitudes, conflating science and advocacy, and wishful extrapolation. I contrast these practices with my vision for credible policy analysis.
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Suggested Citation

  • Charles F. Manski, 2011. "Policy Analysis with Incredible Certitude," Economic Journal, Royal Economic Society, vol. 121(554), pages 261-289, August.
  • Handle: RePEc:ecj:econjl:v:121:y:2011:i:554:p:f261-f289
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    Citations

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    Cited by:

    1. Hunt Allcott, 2012. "Site Selection Bias in Program Evaluation," NBER Working Papers 18373, National Bureau of Economic Research, Inc.
    2. Michael Funke & Michael Paetz & Qianying Chen,, 2012. "Market and Non-Market Monetary Policy Tools in a Calibrated DSGE Model for Mainland China," Quantitative Macroeconomics Working Papers 21207, Hamburg University, Department of Economics.
    3. Hashem M. Pesaran & Ron P. Smith, 2011. "Beyond the DSGE Straitjacket," CESifo Working Paper Series 3447, CESifo Group Munich.
    4. Whittington, Dale & Jeuland, Marc & Barker, Kate & Yuen, Yvonne, 2012. "Setting Priorities, Targeting Subsidies among Water, Sanitation, and Preventive Health Interventions in Developing Countries," World Development, Elsevier, vol. 40(8), pages 1546-1568.
    5. Muller, Sean, 2014. "Randomised trials for policy: a review of the external validity of treatment effects," SALDRU Working Papers 127, Southern Africa Labour and Development Research Unit, University of Cape Town.
    6. repec:spr:minecn:v:31:y:2018:i:1:d:10.1007_s13563-018-0153-z is not listed on IDEAS
    7. Charles F. Manski, 2015. "Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern," Journal of Economic Literature, American Economic Association, vol. 53(3), pages 631-653, September.
    8. Lawrence Mead, 2015. "Only connect: Why government often ignores research," Policy Sciences, Springer;Society of Policy Sciences, vol. 48(2), pages 257-272, June.
    9. Argentiero, Amedeo & Bovi, Maurizio & Cerqueti, Roy, 2015. "Over consumption. A horse race of Bayesian DSGE models," MPRA Paper 66445, University Library of Munich, Germany.
    10. Mester, Loretta J., 2016. "Acknowledging Uncertainty, 10-07-2016; Shadow Open Market Committee Fall Meeting, New York, NY," Speech 77, Federal Reserve Bank of Cleveland.
    11. Charles F. Manski, 2014. "Communicating Uncertainty in Official Economic Statistics," NBER Working Papers 20098, National Bureau of Economic Research, Inc.
    12. Douglas W. Elmendorf, 2015. "“Dynamic Scoring”: Why and How to Include Macroeconomic Effects in Budget Estimates for Legislative Proposals," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(2 (Fall)), pages 91-149.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H43 - Public Economics - - Publicly Provided Goods - - - Project Evaluation; Social Discount Rate
    • H68 - Public Economics - - National Budget, Deficit, and Debt - - - Forecasts of Budgets, Deficits, and Debt

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