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The analysis of the impact of regulatory environment on the pace of economic growth of the world countries according to the Bayesian Model Averaging

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Abstract

The study examines the relationship between the regulatory variables and economic growth on the basis of Bayesian model pooling applied to Blundell and Bond’s GMM system estimator. The areas of regulations (institutions) are measured by the following indicators: index of economic freedom, worldwide governance indicators, democracy index, doing business indicators, transition indicators. The models are estimated based on overlapping panel data and they include nonlinearities. In general, regulatory environment is an important determinant of economic growth. To achieve rapid growth, it is necessary to increase economic freedom, quality of governance, and market reforms. The association between regulatory variables and GDP dynamics is mostly nonlinear.The countries with greater scope of economic freedom record more rapid GDP growth but a given increase in economic freedom has a higher impact on growth in those countries that are economically not (or partly) free. However, the results are not robust in a lot of areas – with regard to the sample of countries, the exact measure of the regulatory variable, and the type of nonlinear impact (concave vs. convex functions). There are many factors affecting both regulations and GDP dynamics as well as many transmission channels between these areas and the results sometimes are mixed.

Suggested Citation

  • Mariusz Próchniak & Bartosz Witkowski, 2013. "The analysis of the impact of regulatory environment on the pace of economic growth of the world countries according to the Bayesian Model Averaging," NBP Working Papers 165, Narodowy Bank Polski.
    • Handle: RePEc:nbp:nbpmis:165
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    References listed on IDEAS

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    1. Pasquale Tridico, 2011. "Institutions, Human Development and Economic Growth in Transition Economies," Studies in Economic Transition, Palgrave Macmillan, number 978-0-230-31388-0, February.
    2. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    3. Próchniak, Mariusz & Witkowski, Bartosz, 2013. "Time stability of the beta convergence among EU countries: Bayesian model averaging perspective," Economic Modelling, Elsevier, vol. 30(C), pages 322-333.
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    More about this item

    Keywords

    economic growth; regulations; institutions; growth factors; Bayesian averaging;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • O43 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Institutions and Growth
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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