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Policy-oriented inference and the analyst-client cooperation. An example from small-area statistics

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  • Nicholas T. Longford

Abstract

We show on an application to small-area statistics that efficient estimation is not always conducive to good policy decisions because the established inferential procedures have no capacity to incorporate the priorities and preferences of the policy makers and the related consequences of incorrect decisions. A method that addresses these deficiencies is described. We argue that elicitation of the perspectives of the client (sponsor) and their quantification are essential elements of the analysis because different estimators (decisions) are appropriate for different perspectives. An example of planning an intervention in a developing country’s districts with high rate of illiteracy is described. The example exposes the deficiencies of the general concept of efficiency and shows that the criterion for the quality of an estimator has to be formulated specifically for the problem at hand. In the problem, the established small-area estimators perform poorly because the minimum mean squared error is an inappropriate criterion.

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  • Nicholas T. Longford, 2015. "Policy-oriented inference and the analyst-client cooperation. An example from small-area statistics," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(1), pages 65-82, May.
    • Handle: RePEc:csb:stintr:v:16:y:2015:i:1:p:65-82
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    References listed on IDEAS

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    1. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    2. N. T. Longford, 1999. "Multivariate shrinkage estimation of small area means and proportions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 227-245.
    3. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
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