Which Financial Inclusion Indicators and Dimensions Matter for Income Inequality? A Bayesian Model Averaging Approach

Rogelio Mercado and Victor Pontines

Working Papers from South East Asian Central Banks (SEACEN) Research and Training Centre

Abstract: This paper employs Bayesian model averaging (BMA) and uses posterior inclusion probability (PIP) values to evaluate which financial inclusion indicators, dimensions, and other determinants of income inequality should be considered in an empirical specification assessing the relationship between financial inclusion and income inequality, given model uncertainty. The results show that for the low-income country group, financial access and usage indicators and dimensions are the most relevant indicators. Unfortunately, nowhere in our baseline results and in almost all our sensitivity tests do we find PIP values higher than our set threshold value for any of our financial depth indicators and dimension. These results suggest that theoretical models linking financial inclusion nd income inequality could well focus on the role of financial access and usage by providing theoretical foundations on the mechanics as to how these two dimensions of financial inclusion impact income inequality.

Keywords: Bayesian model averaging; financial inclusion; income inequality; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 C52 O15 O16 (search for similar items in EconPapers)
Date: 2022-10
New Economics Papers: this item is included in nep-ban, nep-fdg, nep-fle, nep-mfd and nep-sea
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https://www.seacen.org/publication-working.php?pid=702001-100481 (application/pdf)

Related works:
Journal Article: Which financial inclusion indicators and dimensions matter for income inequality? A Bayesian model averaging approach (2024)
Working Paper: Which Financial Inclusion Indicators and Dimensions Matter for Income Inequality? A Bayesian Model Averaging Approach (2022)
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