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Can specific policy indicators identify reform priorities?

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  • Aart Kraay
  • Norikazu Tawara

Abstract

Several detailed cross-country datasets measuring specific policy indicators relevant to business regulation and government integrity have been developed in recent years. The promise of these indicators is that they can be used to identify specific reforms that policymakers and aid donors can target in their efforts to improve the regulatory and institutional environment. Doing so, however, requires evidence on the partial effects of the many specific policy choices reflected in such datasets. In this paper we use Bayesian model averaging (BMA) to document the cross-country partial correlations between detailed policy indicators and several measures of regulatory and institutional outcomes. We find major instability in the set of policy indicators identified by BMA as important partial correlates of similar outcomes: specific policy indicators that matter for one outcome are, on average, not important correlates of other closely-related outcomes. This finding illustrates the difficulties in using highly-specific policy indicators to identify reform priorities using cross-country data. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Aart Kraay & Norikazu Tawara, 2013. "Can specific policy indicators identify reform priorities?," Journal of Economic Growth, Springer, vol. 18(3), pages 253-283, September.
    • Handle: RePEc:kap:jecgro:v:18:y:2013:i:3:p:253-283
    DOI: 10.1007/s10887-013-9092-2
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    Cited by:

    1. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    2. Maur,Jean-Christophe & Nedeljkovic,Milan & Von Uexkull,Jan Erik, 2022. "FDI and Trade Outcomes at the Industry Level—A Data-Driven Approach," Policy Research Working Paper Series 9901, The World Bank.
    3. Paul Holden & Alma Pekmezovic, 2020. "How accurate are the Doing Business indicators? A Pacific Island case study," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 7(3), pages 247-261, September.
    4. Ela Diaz & Rodrigo Valdes, 2020. "All That Glitters Is Not Gold: A Ranking of Global Rankings," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 223-254, December.
    5. Marcin Błażejowski & Jacek Kwiatkowski & Jakub Gazda, 2019. "Sources of Economic Growth: A Global Perspective," Sustainability, MDPI, vol. 11(1), pages 1-14, January.

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    More about this item

    Keywords

    Policy reforms; Growth; Bayesian model averaging; C11; C52; O43;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O43 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Institutions and Growth

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