Exploring the Relationship Between Banking Development Facets and Economic Volatility: Evidence from Developing Nations

This study investigates the impact of banking development on economic growth volatility in developing countries over the period 2004–2019, using a panel quantile approach. By examining different facets of banking development, our findings indicate a negative effect of banking sector depth, access, and quality on economic growth volatility. Banking development significantly reduces economic growth instability across different quantiles of economic growth volatility. Nevertheless, banking efficiency increases economic volatility. This study provides valuable insights to financial regulatory authorities in making decisions related to banking development-economic instability nexus.

Introduction

The development of financial systems, particularly following the financial liberalization of the 1980s and 1990s, has profoundly transformed the global economy, delivering significant benefits to developing economies. This transformation highlights three critical dimensions: the depth of the financial sector, the quality of financial services, and financial inclusion. Emphasizing these dimensions enables financial development to robustly support economic stability and ensure an equitable distribution of the benefits associated with a mature financial system.

The relationship between various dimensions of financial development and economic growth has been the subject of extensive literature. For instance, increased access to finance yields both social and economic benefits (Siddiki & Bala-Keffi, 2024; Tabashet al., 2024), while financial deepening fosters economic growth (Joshi & Kansil, 2024). Moreover, the performance of the banking sector enhances economic stability (Barraet al., 2024), supporting the notion of ‘more finance, more growth.’ However, financial development also poses challenges, particularly regarding economic growth volatility. As economies integrate further into the global financial system, their vulnerability to external shocks and crises intensifies (Ibrahim & Alagidede, 2017).

The theoretical framework posits divergent hypotheses regarding the effect of financial development on the volatility of economic growth. The first paradigm suggests that robust financial sector development can mitigate economic volatility by diversifying investment portfolios, lowering agency costs, and providing crucial information on the risks and returns of various investments. This leads to more efficient capital allocation and enhances borrowers’ ability to access credit (Bernanke & Gertler, 1989; Kiyotaki & Moore, 1997; Morganet al., 2004). By acquiring more information, financial intermediaries can achieve economies of scale in evaluating and managing non-financial borrowers, thus reducing information asymmetry (Greenwood & Jovanovic, 1990; Mishkin, 1996). The second paradigm examines the impact of financial development on exacerbating or alleviating shocks to growth volatility, suggesting that its nuanced effects depend on risk behavior, real or monetary shocks, and credit availability (Bacchetta & Caminal, 2000; Aghionet al., 2004). The third paradigm elucidates the causal link between financial development and economic volatility (Lopèz-Monti, 2020).

Extensive empirical research investigates the impact of financial sector depth on economic volatility. For example, Da-Silva (2002), Manganelli and Popov (2015), Subkhan and Hutajulu (2023), Singhet al. (2023), and Yilmaz (2024) suggest that financial development attenuates economic volatility. In contrast, other studies propose a nonlinear relationship between financial deepening and economic instability (Ibrahim & Alagidede, 2017; Ma & Song, 2017; Ghosh & Adhikary, 2023) using ARDL and dynamic panel regressions. This implies that while the initial stages of financial deepening tend to reduce economic volatility, this effect may reverse after surpassing a certain point. Recently, Sebaiet al. (2024) found a dynamic interrelationship between banking deepening and economic volatility using a GMM panel-VAR approach in 38 emerging countries from 1985 to 2020.

In addition to financial deepening, the quality and inclusivity of banks are identified as critical components of financial development (Beck, 2016; Chenet al., 2023). Fernándezet al. (2016) argue that financial stability is crucial for mitigating economic volatility, particularly in less competitive banking sectors during the period from 1989 to 2008. Creelet al. (2015) and Barra and Zotti (2022) emphasize the central role of a stable financial sector in bolstering economic stability in the European Union and Italy, respectively, using a GMM approach. Moreover, Klein and Weill (2022) demonstrate a positive impact of banking performance on economic growth, based on a GMM analysis of a sample of 132 countries over the period 1999–2013. Xue (2020) reports a non-monotonic relationship between banking profitability and economic volatility across both developing and developed nations, utilizing a panel threshold approach.

Additionally, Boachieet al. (2021) established a positive effect of financial inclusion on economic stability from 2008 to 2018, focusing on 18 countries in sub-Saharan Africa. Cavoliet al. (2019) reveal a nonlinear relationship between financial inclusion and economic volatility across 100 EMDEs over the period 1995–2013. Conversely, Gopalan and Rajan (2021) identify a negative association between digital financial inclusion and output volatility in 40 emerging markets. All these studies employ the GMM method. Furthermore, Ullahet al. (2024) underscore the transmission channels through which financial development influences economic instability. By employing PCSE¹1Panel corrected standard error. and GMM methods, their research demonstrates that institutional quality mitigates the adverse effects of various dimensions of financial development on economic volatility. However, their analysis does not find a significant interaction effect of regulatory quality on the relationship between market-based financial development and economic volatility.

Drawing from the discussion above, the existing empirical literature posits uncertainty regarding the nexus between financial sector development and economic volatility. The disparities in the reported outcomes may arise from variations in measures of financial development and the regression methods applied in the cited studies. This paper examines the impact of overall dimensions of banking development (namely, banking sector size, bank sector quality, and bank inclusive) on economic growth volatility within developing countries from 2004 to 2019. In doing so, this study makes two significant contributions to the scholarly field.

First, this paper employs principal component analysis (PCA) to assess the three major facets of banking development. Previous research has predominantly focused on indicators of banking depth, often overlooking crucial aspects such as banking sector quality and inclusivity. This gap in research has limited regulatory authorities’ understanding of the complete effect of banking development on economic volatility.

Second, this study utilizes panel quantile regression with fixed effects, introduced by Powell (2022), to examine the relationship between banking development and economic growth volatility. This approach enables the examination of the entire distribution of economic volatility by estimating the impact of banking development across different quantiles. Moreover, the panel data framework incorporates unobserved individual heterogeneity, thereby mitigating potential biases from its omission. This method facilitates robust estimation even in the presence of non-normally distributed error terms and skewed dependent variable distributions.

Data, Variables and Descriptive Statistics

Data

This paper investigates the relationship between banking development and economic growth volatility using a panel of 25 developing countries from 2004 to 2019. The data are sourced from the World Bank’s World Development Indicators (WDI), the International Monetary Fund (IMF), and Bankscope Data. A detailed list of the developing countries is provided in Table I.

Table I. List of Countries

Developing countries	Argentina, Brazil, Chile, China, Croatia, Egypt, Hong Kong, Hungary, India, Indonesia, Kenya, Kuwait, Malaysia, Mexico, Pakistan, Peru, the Philippines, Saudi Arabia, South Africa, Tunisia, Thailand, the Federation of Russia, Turkey, United Arab Emirates, Ukraine.

Independent Variable: Measurement of Financial Development

Banking development is analyzed through three key aspects: banking deepening, banking sector quality, and banking inclusion (Beck, 2016; Svirydzenka, 2016). Banking deepening involves the expansion of bank credit and the enhancement of financial system liquidity. The quality of the banking sector relies on the effectiveness, performance, and stability of banking institutions. Banking inclusion aims to provide individuals from diverse socio-economic backgrounds with rapid access to financial services at affordable costs. Despite extensive scholarly contributions, there remains a lack of standardized consensus on a banking development index (Tang & Tan, 2014). To address this issue and account for the high correlation among sub-indices of banking development, we employ principal component analysis (PCA) to establish indicators for each dimension of banking development.

In Terms of Size: Financial Deepening

Based on Tang and Tan (2014), our paper considers the following key indicators of banking deepening: domestic credit to the private sector as a percentage of GDP (Credit_GDP), financial system deposits to GDP (Deposit_GDP), ratio of liquid liabilities to GDP (Liquid_Liab), bank assets to GDP (ASSETS_GDP), and broad money to GDP (Broad_GDP).

In Terms of Access: Financial Inclusion

The construction of the banking inclusion indicator, which encompasses various dimensions, including the accessibility and availability of banking services, requires the adoption of the analytical framework proposed by Ahamed and Mallick (2019). This research undertakes an examination of two primary facets of penetration: demographic and geographic. To assess demographic penetration, the number of bank branches ($\text{BB}\mathrm{\_}{\text{POP}}_{\text{i}}$) per 100,000 adults is utilized. For the evaluation of geographic penetration, the number of bank branches ($\text{BB}\mathrm{\_}{\text{km}}_{\text{i}}^2)$ and ATMs ($\text{ATM}\mathrm{\_}{\text{km}}_{\text{i}}^2$) per 1000 km² is considered.

In Terms of Quality: Financial Performance

In assessing the quality of the banking sector, this study employs two essential measures: Bank Return on Equity (ROE) and Bank Return on Assets (ROA). These measures are fundamental for evaluating a bank’s profitability and overall financial health (Xue, 2020).

In Terms of Quality: Financial Efficiency

Expanding on Adnan’s (2011) research, this study employs net interest margin (NIM) and overhead costs (COST) as crucial measures of a bank’s management effectiveness.

In Terms of Quality: Financial Stability

In accordance with the research conducted by Xue (2020), the present study employs banking credit risk indicators, specifically non-performing loans (NPL), to assess banking stability.

Table II delineates the results of the principal component analysis applied to each financial development indicator, presenting the respective factor scores for each variable.

Table II. Banking Development Indicators in Developing Countries (2004–2019): Principal Component Analysis (PCA)

Banking development indicators	PC_1	PC_2	PC_3	PC_4	PC_5
Panel A. Banking Deepening Index (B_Deepening)
Eigen values	4.443	0.333	0.139	0.079	0.005
Percentage of variance	0.888	0.067	0.028	0.016	0.001
Cumulative percentage	0.888	0.955	0.983	0.999	1.000
Variables
Credit_GDP	0.424	0.686	0.542	−0.173	0.160
Broad_GDP	0.468	−0.176	−0.059	−0.431	−0.749
ASSETS_GDP	0.445	0.368	−0.633	0.513	−0.054
Deposit_GDP	0.439	−0.508	0.473	0.570	0.028
Liquid_Liab	0.459	−0.324	−0.279	−0.442	0.640
Panel B. Banking Inclusion Index (B_Inclusion)
Eigen values	2.109	0.882	0.009
Percentage of variance	0.703	0.294	0.003
Cumulative percentage	0.703	0.997	1.000
Variables
BB_km2	0.672	−0.217	−0.708
ATM_km2	0.672	−0.222	0.707
BB_POP	0.311	0.950	0.004
Panel C. Banking Performance Index (B_Performance)
Eigen Values	1.708	0.292
Percentage of variance	0.854	0.146
Cumulative percentage	0.854	1.000
Variables
ROA	0.707	0.707
ROE	0.707	−0.707
Panel D. Banking Efficiency Index (B_Efficiency)
Eigen values	1.261	0.739
Percentage of variance	0.631	0.369
Cumulative percentage	0.631	1.000
Variables
NIM	0.707	0.707
COST	0.707	−0.707

Table III. Variable Descriptions

Variables	Definition	Sources
Eco_Vol	Economic growth volatility, calculated as five-year rolling window standard deviations of GDP growth rate.	Authors’ calculation, WDI
Credit_GDP	Domestic credit to the private sector as a percentage of GDP.	WDI
Broad_GDP	Broad money to GDP.	WDI
ASSETS_GDP	Banks assets to GDP.	IMF
Deposit_GDP	Financial system deposits as a percentage of GDP.	WDI
Liquid_Liab	Liquid liabilities as a percentage of GDP.	WDI
ROE	Bank return on equity. It is a measure of banking performance, by country.	Bankscope
ROA	Bank return on assets. This measure reflects the profitability of the banking sector by country.	Bankscope
NIM	Assess the banking sector’s efficiency by determining the ratio of the bank’s net interest revenue to its total assets.	Bankscope
COST	The efficiency of the banking sector is evaluated by measuring overhead costs as a percentage of total assets.	Bankscope
NPLs	Nonperforming loans to gross loan. A higher value indicates a more risky portfolio.	WDI
BB_km2	Number of commercial bank branches per 1000 km2.	IMF
ATM_km2	Number of ATMs per 1000 km2.	IMF
BB_POP	Number of commercial bank branches per 100.000 adults.	IMF
FO	Ratio of the sum of foreign direct investment and portfolio inflows to GDP.	WDI
REER	Real effective exchange rate.	WDI
INTR	Real interest rate.	WDI
Gov_Consump	Government consumption divided by GDP.	WDI
Log_POP	Logarithm of total population.	WDI

Following Ahamed and Mallick (2019), the banking development index for each country has been normalized using a scale from 0 to 1. A value of zero denotes minimal banking development, while a value of one signifies full banking development.

Dependent Variable and Control Variables

The standard deviation of GDP growth rates over a five-year period, as utilized by Ullahet al. (2024), serves as a measure of economic growth volatility.

A set of control variables is employed, including financial openness (FO), real effective exchange rate (REER), Government consumption (GOV), real interest rate (INTR), and the logarithm of the total population (Log_POP). Detailed definitions and data sources for our variables are presented in Table III.

Table IV presents the descriptive statistics for the variables employed in this study, based on five aspects of banking development, with values ranging from 0 to 1. The standard deviations of the banking development variables are notably high, with banking inclusion reaching a maximum value of 0.180. The dependent variable, economic volatility (Eco_Vol), has a mean of 0.023 and a relatively low standard deviation of 0.018. The control variables, including financial openness (FO), real interest rate (INTR), real effective exchange rate (REER), government consumption (Gov_consump), and total population (Log_POP), display mean values of −0.468, 0.049, 0.971, 0.148, and 7.690, respectively. Both kurtosis and skewness coefficients deviate significantly from the expected values of 3 and 0, indicating non-normal distribution characteristics. The Doornik and Hansen (2008) normality test confirms statistical significance across all variables, rejecting the normality assumption. Consequently, classical regression techniques, such as ordinary least squares (OLS) estimation, may not be optimal for our data analysis. Instead, the panel quantile approach emerges as a suitable and robust method for addressing non-normal errors and outliers.

Table IV. Descriptive Statistics

Variables	Observation	Mean	Standard deviation	SKEW	KURT	Normality-test	P-value
Eco_Vol	375	0.023	0.018	1.420	5.090	74.98***	0.000
Credit_GDP	366	0.660	0.453	1.304	4.501	62.49***	0.000
Deposit_GDP	395	0.496	0.580	3.682	17.411	239.78***	0.000
ASSETS_GDP	395	0.670	0.428	1.839	7.599	119.33***	0.000
Broad_GDP	396	0.796	0.624	3.067	13.503	199.35***	0.000
Liquid_Liab	395	0.745	0.645	3.018	12.851	199.35***	0.000
BB_km2	390	7.192	266.76	4.628	22.465	281.88***	0.000
ATM_km2	394	20.964	554.626	4.759	24.099	291.87***	0.000
BB_POP	390	12.233	7.876	1.151	4.262	41.27***	0.000
ROA	391	0.016	0.019	−5.933	91.382	371.54***	0.000
ROE	370	0.173	0.110	−0.737	7.640	56.93***	0.000
NIM	391	0.035	0.019	1.436	5.956	85.77***	0.000
COST	393	0.025	0.052	11.813	174.41	529.77***	0.000
NPLs	300	0.034	0.082	4.369	25.555	217.63***	0.000
Gov_consump	400	0.148	0.044	0.416	2.437	17.54***	0.000
FO	400	0.025	0.026	3.280	18.715	228.97***	0.000
REER	224	0.972	0.120	−0.113	3.350	5.65	0.378
INTR	324	0.036	0.091	1.996	8.707	111.19***	0.000
Log_POP	400	7.670	0.665	0.279	2.814	5.65***	0.000

Panel Quantile Regression Analysis

Conventional regression techniques provide a description of the average relationship between banking development and economic growth volatility, neglecting distributional heterogeneity. To address the distributional heterogeneity and gain deeper insight into the association between banking development and economic growth volatility, a panel quantile regression with fixed effects (QRPD), developed by Powell (2022), is employed. This approach yields more robust estimations, particularly when the distribution of the dependent variable exhibits skewness or when the error term deviates from normality. To examine whether banking sector development has a negative or positive effect on economic volatility, the model is presented as follows:

where $Eco\mathrm{\_}Vo{l}_{it}$ represents the economic growth volatility of the country i in year t. $Banking\mathrm{\_}Developmen{t}_{it}$ is the variable encompasses multiple aspects of banking development, including banking deepening, banking performance, banking efficiency, banking stability, and banking inclusion. Details about other variables can be found in Table III. For a comprehensive insight into the model, please consult the research conducted by Powell (2022).

$$\begin{array}{rcl}Eco\mathrm{\_}Vo{l}_{it}& =& {\delta }_i+{\mu }_i+{\alpha }_{1\theta }Banking\mathrm{\_}Developmen{t}_{0it}\\ & +& {\alpha }_{2\theta }F{O}_{it}+{\alpha }_{3\theta }REE{R}_{it}+{\alpha }_{4\theta }INT{R}_{it}\\ & +& {\alpha }_{5\theta }Gov\mathrm{\_}Consum{p}_{it}+{\alpha }_{6\theta }Log\mathrm{\_}PO{P}_{it}+{\epsilon }_{it}\end{array}$$

Results of the Panel Quantile Regression

Panel Unit Root Test Results

To assess the suitability of variables for panel quantile estimation, the first step involves testing stationarity using a panel unit root test, specifically the augmented Dickey-Fuller (ADF) test. This test confirms the stationarity of the variables at a 1% significance level. Detailed results of the panel unit root test are provided in Table V.

Table V. Panel Unit Root

Variables	ADF
	At level	First difference
Eco_Vol	3.197	−15.363***
Credit_GDP	1.521	−7.097***
Deposit_GDP	−0.726	−12.854***
ASSETS_GDP	0.992	−9.573***
Broad_GDP	−0.414	−12.431***
Liquid_Liab	−0.340	−14.604***
BB_km2	2.122	−2.230***
ATM_km2	1.825	−4.748***
BB_POP	2.477	−3.639***
ROA	−7.050***
ROE	−4.739***
NIM	−5.431***
COST	−5.754***
NPLs	−4.102***
Gov_consump	1.601	−13.123***
FO	−5.832***
REER	0.746	−6.506***
INTR	−7.028***
Log_POP	−13.476***

Panel Quantile Regression Results

To address the distributional heterogeneity in the relationship between banking development and economic growth volatility in developing countries, a panel quantile regression is employed. The results, detailed in Tables VI–X, include measurements of the aforementioned banking development indicators: B_Deepening, B_Inclusion, B_Performance, B_Efficiency, and NPLs. The findings are presented for the ${10}^{\text{th}}$, ${20}^{\text{th}},{30}^{\text{th}},{40}^{\text{th}},{50}^{\text{th}},{60}^{\text{th}},{70}^{\text{th}},{80}^{\text{th}},{90}^{\text{th}}$ percentiles of the conditional economic volatility distribution. Overall, the results demonstrate a negative and significant impact of various banking development indices on economic growth volatility across different quantiles of the economic volatility distribution. In particular, an increase in the overall banking development indices corresponds to a reduction in economic volatility. The higher quantiles, such as the ${60}^{\text{th}},{70}^{\text{th}},{80}^{\text{th}},{90}^{\text{th}}$ percentiles, represent countries characterized by higher levels of economic growth volatility. Conversely, the lower quantiles, including the ${10}^{\text{th}},{20}^{\text{th}},{30}^{\text{th}},{40}^{\text{th}}\text{ and }{50}^{\text{th}}$ percentiles, are associated with countries exhibiting lower economic growth volatility. These findings are consistent with the empirical estimations of Cavoliet al. (2019), Klein and Weill (2022), and Singhet al. (2023).

Table VI. Banking Deepening and Economic Growth Volatility: Panel Quantile Regression

Dependent variable: Eco_Vol
Independent variables	Q10	Q20	Q30	Q40	Q50	Q60	Q70	Q80	Q90
B_Deepening	−0.008***	−0.010***	−0.021***	−0.023***	−0.023***	−0.025***	−0.031***	−0.047***	−0.055***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
FO	0.010***	0.011***	0.032***	0.039***	0.025***	0.039***	0.053***	−0.029***	0.110***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Gov_Consum	0.025***	−0.002***	−0.006***	−0.003***	−0.002	−0.0041***	0.096***	−0.184***	0.240***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.746)	(0.000)	(0.000)	(0.000)	(0.000)
REER	0.008***	0.010***	0.017***	0.022***	0.014***	0.026***	0.023***	0.057***	0.006***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
INTR	0.002***	0.026***	0.044***	0.051***	0.048***	0.042***	0.018***	0.193***	−0.073***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Log_POP	−0.002***	−0.003***	−0.003***	−0.004***	−0.004***	−0.005***	−0.004***	−0.019***	−0.006***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)

Table VII. Banking Inclusion and Economic Growth Volatility: Panel Quantile Regression

Dependent variable: Eco_Vol
Independent variables	Q10	Q20	Q30	Q40	Q50	Q60	Q70	Q80	Q90
B_Inclusion	−0.005***	−0.007***	−0.010***	−0.015***	−0.023***	−0.028***	−0.046***	−0.0228***	−0.067***
	(0.000)	(0.004)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
FO	0.010***	−0.023***	−0.013***	0.016***	0.008***	0.040***	0.040***	0.088***	0.134***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.000)
Gov_Consum	0.027***	−0.163***	−0.110***	−0.035***	−0.162***	0.059***	−0.089***	0.289***	0.421***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
REER	0.003***	0.029***	0.032***	0.026***	0.005**	0.029***	0.059***	0.041***	0.029***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.037)	(0.000)	(0.000)	(0.000)	(0.000)
INTR	−0.009***	0.053***	0.066***	0.071***	0.116***	0.083***	0.024***	−0.123***	−0.066***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Log_POP	−0.003***	−0.007***	−0.009***	−0.011***	−0.014***	−0.016***	−0.023***	−0.007***	0.008***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)

Table VIII. Banking Performance and Economic Growth Volatility: Panel Quantile Regression

Dependent variable: Eco_Vol
Independent variables	Q10	Q20	Q30	Q40	Q50	Q60	Q70	Q80	Q90
B_Performance	−0.001***	−0.003***	−0.005***	−0.007***	−0.008***	0.011***	−0.024***	−0.062***	−0.074***
	(0.000)	(0.000)	(0.000)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
FO	0.005***	0.004***	0.010***	−0.008**	0.010***	0.017***	0.001***	0.019***	0.060***
	(0.000)	(0.000)	(0.000)	(0.011)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Gov_Consum	0.022***	−0.015***	−0.033***	−0.033***	0.006***	0.073***	0.001	0.152***	0.396***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.004)	(0.000)	(0.197)	(0.000)	(0.000)
REER	0.006***	0.013***	0.014***	0.023***	0.006***	0.026***	0.010***	0.013***	0.031*
	(0.000)	(0.000)	(0.000)	(0.000)	(0.004)	(0.000)	(0.000)	(0.000)	(0.052)
INTR	−0.001***	0.044***	0.052***	0.025***	0.052***	0.019***	0.012***	−0.001	−0.085***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.817)	(0.000)
Log_POP	−0.002***	−0.004***	−0.006***	−0.003***	−0.004***	−0.003***	−0.006***	−0.006***	−0.00002
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.976)

Table IX. Banking Efficiency and Economic Growth Volatility: Panel Quantile Regression

Dependent variable: Eco_Vol
Independent variables	Q10	Q20	Q30	Q40	Q50	Q60	Q70	Q80	Q90
B_Efficiency	0.008***	0.013	0.051***	0.068***	0.070***	0.074***	0.090***	0.097***	0.127***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
FO	0.007***	0.007***	0.022***	0.012***	0.015***	0.014***	−0.040***	0.019***	0.034***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Gov_Consum	0.019***	0.018***	−0.021***	−0.006***	0.009***	−0.041***	0.037***	0.166***	0.301***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
REER	0.015***	0.011***	0.021***	0.024***	0.039***	0.016***	0.038***	0.037***	0.060***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
INTR	0.010***	0.023***	0.038***	0.028***	0.025***	0.029***	−0.086***	−0.044***	−0.011
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.488)
Log_POP	−0.004***	−0.004***	−0.004***	−0.005***	−0.006***	−0.007***	−0.006***	−0.004***	−0.010***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)

Table X. Banking Stability (NPLs) and Economic Growth Volatility: Panel Quantile Regression

Dependent variable: Eco_Vol
Independent variables	Q10	Q20	Q30	Q40	Q50	Q60	Q70	Q80	Q90
B_Stability	0.003***	0.008***	0.008***	0.017***	0.040***	0.141***	0.152***	0.180***	0.114***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
FO	0.004***	0.006***	0.003***	−0.003***	−0.002**	0.025***	0.009***	−0.002***	0.040***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.053)	(0.000)	(0.000)	(0.007)	(0.000)
Gov_Consump	0.022***	0.027***	−0.017***	−0.028***	−0.070***	0.011***	−0.080***	0.020***	0.194***
	(0.000)	(0.000)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
REER	0.007***	0.010***	0.015***	0.016***	0.010***	0.011***	0.014***	0.038***	0.049***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
INTR	0.002***	0.016***	0.054***	0.055***	0.077***	0.047***	0.056***	0.020***	−0.015**
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.017)
Log_POP	−0.001***	−0.004***	−0.006***	−0.005***	−0.006***	−0.006***	−0.013***	−0.011***	−0.002***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.904)

However, our analysis indicates that certain indicators of banking development, specifically banking efficiency (B_efficiency) and non-performing loans (NPLs), exert a statistically significant positive impact on economic volatility at the 1% significance level across all quantiles of the economic volatility distribution (Tables IX and X). The observed positive coefficient for banking efficiency across different quantiles may be explained by instances of borrower defaults and inadequate oversight within the banking sector. A higher net interest margin suggests potentially greater returns on interest-earning assets, which could incentivize riskier lending and investment behaviors. Such tendencies may contribute to heightened economic volatility as banking institutions pursue profit maximization through increased risk-taking, thereby affecting the broader economy. These findings align with the results of Fernándezet al. (2016) and Cavoliet al. (2019). Overall, the relationship between banking development and economic growth volatility demonstrates uniformity across all quantiles of the economic volatility distribution.

Conclusion

This research examines the impact of banking development on economic growth volatility across a sample of 25 developing countries from 2004 to 2019. Our empirical findings indicate a negative relationship between different facets of banking development and economic growth volatility. Nonetheless, banking efficiency and bank credit risk show a positive effect on economic volatility. This effect exhibits heterogeneity and uniformity across all quantiles of the economic volatility distribution.

The identified negative link between banking development and economic growth volatility underscores the necessity for policymakers in developing countries to enhance the stability, performance, and inclusivity of the banking sector. To effectively promote sustainable development, regulatory authorities should broaden their focus beyond traditional measures of banking depth. By acknowledging the relationship between banking development and economic instability, policymakers can initiate transformative measures that mitigate economic volatility and improve national resilience.