Abstract
This paper fills the momentous gap by explicitly investigating the interplay of financial inclusion, financial stability, and shadow banking in 11 emerging market economies (EMEs) from 2010 to 2021. Employing panel quantile regression approaches, including simultaneous bootstrapped quantile regression and generalized quantile regression, the results indicate that while financial inclusion has a negative impact on financial stability, its impact is less pronounced in countries with high stability. Moreover, shadow banking, whether broadly or narrowly defined, tends to weaken the negative effects of financial inclusion, particularly in EMEs with medium and high levels of financial stability. Finally, Dumitrescu–Hurlin's panel causality test confirms bidirectional causality between financial stability and financial inclusion, financial stability and shadow banking, as well as financial inclusion and shadow banking. These findings highlight the need for policymakers in EMEs to prudently adjust shadow banking regulations to maximize their positive impact on financial inclusion and stability while concurrently minimizing potential risks.
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Data availability
Data used in this study is available from the author(s) upon request.
Notes
Author calculations based on FSB (2022) database.
FSB provides annual shadow banking data only for the following EMEs: Argentina, Brazil, Chile, China, India, Indonesia, Mexico, Russia, Saudi Arabia, South Africa, and Turkey.
The availability of data conditions the size of the panel.
Depositors with commercial banks (per 1,000 adults) and borrowers from commercial banks (per 1,000 adults) in WDI has been excluded from PCA due to large number of missing observations.
Before running the PCA for FI index, Bartlett's test of sphericity and the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy are performed (see Table 3). The significance of Bartlett's test of sphericity (p < 0.05) and the value of KMO index (> 0.5) indicate that the factor analysis is suitable (Hair et al. 2006; Tabachnick et al. 2007).
Stata (version 15) commands “sqreg”, and “genqreg” are used for the estimation of SQR, and GQR respectively.
The Stata code “genqreg” does not permit the statistical determination of the instruments’ robustness while implementing the GQR methodology. Nevertheless, this paper follows Reed (2015), Bellemare et al. (2017) and Isayev et al. (2023) who show that lagged variables serve as good instruments to deal with endogeneity problems.
It is worth reminding that, in models that involve multiplicative interactions, researchers should avoid interpreting the individual components of the interaction terms as unconditional or average effects (Brambor et al. 2006). The only inference the traditional results table provides is whether FI significantly affects FS for the unique case where shadow banking assets equal zero. Nonetheless, this is not the case in real-world situations. Hence, in Table 5, and 6 the coefficients of FI, SBB, and SBN cannot be interpreted as unconditional or average effects. That’s why before investigating interactions, first baseline model is established as shown in Table 4, to see the separate impact of FI on FS.
Results are available upon request.
Results are available upon request.
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Isayev, M. Unraveling the interplay of financial inclusion, stability, and shadow banking in emerging markets. Econ Change Restruct 57, 62 (2024). https://doi.org/10.1007/s10644-024-09657-2
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DOI: https://doi.org/10.1007/s10644-024-09657-2