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few years, various startups as well as traditional financial institutions have been
actively developing fintech.
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pp. 168-171.
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DOES INTEREST RATE AFFECT NON-PERFORMING LOANS? EMPIRICAL
EVIDENCE FROM COMMERCIAL BANKS IN UZBEKISTAN
Isakov Olmas Kuchkarovich
Lecturer and PhD candidate in Economics
Westminster International University in Tashkent
Abstract.
This study investigates non-performing loans (NPL) and credit risk
in Uzbekistan's commercial banking sector, focusing on the period post-2016. Using
dynamic panel approach, bank-specific and macroeconomic factors have been
included in the econometric model to estimate their effects on NPL. According to
STATA results, loan-deposit ratio, GDP growth and loan interest rates have positive
impact on NPL. On the other hand, banks with foreign ownership experience lower
rate of NPL compared to local banks.
Since the beginning of 2017, the volume of loans have been increasing
significantly. As of July 1st, 2024, the total outstanding loans in commercial
banks of Uzbekistan amounted to 494 trillion Uzbek soums [1] which comprises
approximately 44% of the GDP of Uzbekistan. Significant expansion of loans to
greater population and improvements in financial inclusion are associated with
higher NPL ratio in credit portfolios. While this ratio was fluctuating between 2%
and 3% until the end of 2020, it started increasing during the pandemic period
reaching as high as 6.2% during mid 2021 (See Picture 2.5). This can be partially
explained by the fact that many households faced financial troubles during
“MILLIY IQTISODIYOTNI ISLOH QILISH VA BARQAROR RIVOJLANTIRISH ISTIQBOLLARI”
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pandemic lockdowns and they had to postpone their loan payment obligations.
Picture 1 illustrates the dynamic change of NPL for the beginning periods of 2020
–
2024 along with three financial indicators of commercial banks: total assets,
total deposits and total credit. According to the graph, all three indicators have
experienced significant growth while NPL has shown sharp increase during the
2021-2022 period.
Picture 1. Comparative illustration of NPL and main financial
indicators of commercial banks in Uzbekistan.
This study examines the main factors affecting the non-performing loans of
commercial banks, both bank-specific factors, such as leverage, size, loan-to-
deposit ratio, state ownership, as well as macroeconomic factors, such as annual
GDP growth rate, weighted average interest rate on loans and exchange rate.
Using panel data of 35 commercial banks of Uzbekistan on quarterly basis over
the period 2019-2023, we estimate the impact of aforementioned variables on
NPL. The brief description of each variable and its expected impact on NPL are
provided in Table 1.
Considering a dynamic panel data analysis in this study helps us to examine
the relationship between variables over time while taking into account
individual-specific effects and potential endogeneity issues. It also allows for the
inclusion of multiple time periods and considers the dynamic nature of the data,
capturing both the cross-sectional and time-series dimensions. This technique
became more prominent with the introduction of the Generalized Method of
Moments (GMM) estimators by Arellano and Bond in 1991 [2]. They introduced
a GMM estimator for dynamic panel data models, addressing issues like
autocorrelation, heteroskedasticity, and the presence of lagged dependent
variables. Later, Blundell and Bond [3] proposed a system GMM estimator, which
combines equations in levels and first differences to improve efficiency,
especially in cases with persistent data.
“MILLIY IQTISODIYOTNI ISLOH QILISH VA BARQAROR RIVOJLANTIRISH ISTIQBOLLARI”
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Table 1.
The description of variables being used for the analysis.
Variable
Description
Expected Sign
Dependent variable
Non-performing loans (NPL)
Troubled loans/Total loans
Independent variables
Bank-specific
Loan-deposit ratio (LDR)
Total loans/ Total deposits
Negative/Positive
Size
Natural logarithm of total assets
Negative/Positive
Leverage
Total liabilities/Total assets
Negative/Positive
State-ownership
= 1 if state ownership, 0 otherwise
Positive
Foreign-ownership
= 1 if foreign ownership, 0 otherwise
Negative
Macroeconomic
Weighted average interest rate on loans, %
Interest rate
Positive
Exchange rate
Change in UZS/USD exchange rate, %
Negative
GDP growth rate
Annual GDP growth rate, %
Negative
The specified regression model used in the study is as follows:
NPL
i,t
=
0
NPL
i,t-1
+
’
i
X
i,t
+
i,t
(1)
with
i,t
=
i
+
i,t
here the subscript i denotes the cross-sectional (banks) and t denotes time
dimension of the panel sample. NPL
i,t
is non-performing loan ratio, NPL
i,t-1
is its
lagged value,
’
i
is a 1xk vector of parameters, X
i,t
is a vector of independent
variables including their lagged values and
i,t
is the error term.
i,t
has two
orthogonal components:
i
are the unobserved individual effects and
i,t
are the
observed specific errors.
Table 2.
Estimation results using system GMM. Source: Author’s calculations
System GMM
Variable
Coefficient
Std. Error
NPL
(i-1)
0.7528***
0.0300
LDR
0.0035*
0.0019
Size
-0.0115
0.0118
Leverage
0.0716
0.0534
Exchange
0.0077
0.1420
GDP
0.8799***
0.1704
ROE
0.0021
0.0018
State-own
0.0440
0.0709
Interest
0.0075***
0.0019
Foreign-own
-0.1419***
0.0396
Constant
-0.1454*
0.0749
Observations
454
Wald Chi Square
1149.87***
*, **, *** indicate significance at 10%, 5% and 1%, respectively.
“MILLIY IQTISODIYOTNI ISLOH QILISH VA BARQAROR RIVOJLANTIRISH ISTIQBOLLARI”
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The positive and significant coefficients in both models indicate that past
values of NPL significantly influence current values of NPL. Specifically, a 1
percentage point increase in the previous period’s NPL is associated with a
0.7528 percentage point i
ncrease in the current period’s NPL. Loan
-deposit-
ratio, GDP growth and interest rates are also found to have positive impact on
NPL. The dummy variable for foreign ownership produced a coefficient of -
0.1419 with a standard error of 0.0396. This negative and statistically significant
(at 1%) coefficient indicates that foreign-owned banks tend to have lower NPL
compared to commercial banks with no foreign ownership. Previous studies
have also found similar relationship between foreign ownership and NPL and
they indicate that these institutions capitalize on their international expertise to
implement effective risk management practices in host countries, which helps to
reduce the likelihood of loan defaults and NPL [4], [5].
Based on the empirical results, the following policy recommendations are
provided to address the challenge of rising non-performing loans:
•
Since loan-deposit ratio (LDR) have positive impact the regulators should
set prudential limits on LDR to ensure banks maintain adequate liquidity and
avoid excessive risk-taking.
•
The positive impact of loan interest rates on NPL suggests that higher rates
may be making loan repayments more burdensome for borrowers. Banks could
be encouraged to offer more flexible and competitive loan terms or introduce
risk-based pricing strategies that adjust interest rates based on borrower
creditworthiness and risk profile.
•
Since banks with foreign ownership have lower NPL rates, encouraging
more foreign investment and partnerships in the banking sector could help
improve risk management practices and bring in international expertise.
Reference
1.
Central Bank of Uzbekistan. Periodic statistical datasets.
https://cbu.uz/oz/statistics/
2.
Arellano, M., Bond, S. Some tests of specification for panel data: Monte Carlo
evidence and an application to employment equations // Review of Economic Studies. 1991.
Vol. 58, No. 2. P. 277-297.
3.
Blundell, R., Bond, S. Initial conditions and moment restrictions in dynamic panel
data models // Journal of Econometrics. 1998. Vol. 87, No. 1. P. 115-143.
4.
De Haas, R. T. A., Van Lelyveld, I. Foreign banks and credit stability in Central and
Eastern Europe: A panel data analysis // Journal of Banking & Finance. 2006. Vol. 30, No. 7.
P. 1927-1952.
5.
Cull, R., Martínez Pería, M. S. 2013. Bank ownership and lending patterns during the
2008
–
2009 financial crisis: Evidence from Latin America and Eastern Europe // Journal of
Banking & Finance, 37(12), Pp. 4861
–
4878.
