Exploring the Fiscal policy—income inequality relationship with Bayesian model averaging analysis

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作者
Hammed Oluwaseyi Musibau
Abdulrasheed Zakari
Farhad Taghizadeh-Hesary
机构
[1] University of Tasmania,Tasmanian School of Business and Economics
[2] University of Wollongong,Faculty of Business and Law
[3] Alma Mater Europaea ECM,Tokai Research Institute for Environment and Sustainability (TRIES)
[4] Tokai University,Adnan Kassar School of Business
[5] Lebanese American University,School of Global Studies
[6] Tokai University,undefined
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Gini coefficient; Fiscal policy; Bayesian model averaging (BMA); H53; C01; I38;
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摘要
The authors have employed several techniques to account for model uncertainty in the inequality-growth model. However, the BMA technique is the most prominent approach that solves model uncertainty in the inequality-growth literature. This study applied a recent BMA analysis using panel data to examine the role of fiscal policy on income inequality in 37 OECD countries from 2000 to 2015. Fiscal policy (in terms of tax revenue increase) serves as a redistributive tool or instrument to transfer income from higher income earners to lower earners and is considered a mechanism for income equality. To the best of the author’s knowledge, only a few empirical growth studies have considered fiscal policy impact in their income inequality model setup. Our work contributes to very little research on the fiscal policy–income nexus using a novel BMA and MCMC regression as a robust methodology. Our empirical evidence on the role of fiscal policy on income inequality has found three variables, namely, economic growth, fiscal policy, and urban population, to impact income inequality significantly. We also found that the countries are conditionally neither converging nor diverging because of the probability of their coefficient being high at 100%. As expected, the coefficient of fiscal policy has a significant negative relationship with income inequality, indicating that fiscal policy reduces income inequality significantly by an average of 22% (with 100% certainty) for both BMA and Bayes models in OECD countries.
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