Are private banks more sensitive
to changes in reserve requirements?
Evidence from an emerging market

Vighneswara Swamy
Department of Economics, ICFAI School of Social Sciences,

ICFAI Foundation for Higher Education, Hyderabad, India, and
Vijayakumar Narayanamurthy

Department of Commerce, School of Management, Pondicherry University,
Pondicherry, India

Abstract
Purpose – This article explores the effects of monetary policy rates and interest rate structures on bank
profitability.
Design/methodology/approach – We studied 65 Indian commercial banks over time, including economic
cycles, consolidation and the Great Financial Crisis. We categorized commercial banks by ownership (public,
private or foreign) and predicted how they will react to monetary policy changes. We employed the instrumental
variable estimate approach and panel Granger causality tests to give evidence of the direction of causation in the
monetary policy and bank performance nexus.
Findings – Private and international banks, we believe, are more sensitive to changes in reserve requirements
because they are more effective at maintaining statutory reserves. Private and international banks are more
susceptible to repo rate fluctuations than state banks. In contrast, public banks are more sensitive to bank rates
because they are more likely than private and international banks to use the bank rate window of accommodation.
Originality/value –We studied the impact of monetary policy rates on bank performance within the banking-
dominated financial system of an emerging economy – a focus that has not been previously explored. There has
been little research into the connection between monetary policy rates and bank performance in emerging
markets, notably in India.
KeywordsMonetary policy, Interest rate, Bank profitability, Financial crisis
Paper type Research paper

1. Introduction
Bank performance and monetary policy rates have become more closely linked since the global
financial crisis. Although policy rates are near zero, long-term interest rates have historically been
low. The central banks’ aggressive response in the early stages of the crisis prevented the financial
and economic disaster from worsening. However, continued expansionary monetary policy by
the central banks is observed with concern, as it might cause adverse side effects, particularly on
bank profitability (Dale, 2012; Plosser, 2012; Praet, 2012; Rajan, 2013).

Understanding the connectionbetweenpolicy rates andbankperformance is critical considering
the rising importanceof themonetarypolicy stance, as it influences the financial sector’s soundness.
Though policy rates are not the sole influence on the interest rate structure, they significantly
influence it because central banks set the short-term and long-term rates (Borio et al., 2015).

Several researchers have investigated the relationship between bank profitability and
business conditions. However, their examination of the relationship between the interest rate

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JEL Classification — C53, E43, E52, G21
© Vighneswara Swamy and Vijayakumar Narayanamurthy. Published in Journal of Economics,

Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published
under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute,
translate and create derivative works of this article (for both commercial and non-commercial purposes),
subject to full attribution to the original publication and authors. The full terms of this licence maybe seen
at http://creativecommons.org/licences/by/4.0/legalcode

The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2077-1886.htm

Received 5 September 2023
Revised 31 July 2024

6 November 2024
Accepted 8 November 2024

Journal of Economics, Finance and
Administrative Science

Vol. 30 No. 59, 2025
pp. 79-115

Emerald Publishing Limited
2077-1886

DOI 10.1108/JEFAS-11-2022-0261

http://creativecommons.org/licences/by/4.0/legalcode
https://doi.org/10.1108/JEFAS-11-2022-0261


structure and the profitability of banks is a byproduct. Demirguc-Kunt and Huizinga (1999)
investigated the relationship between bank profitability indicators and macroeconomic
variables. In a similar strand of literature, using the banking sector data for tenOrganization for
Economic Cooperation and Development (OECD) banking markets, Albertazzi and
Gambacorta (2009) notice a nexus between bank loss provisions and the short-term interest
rate. Similarly, Bolt et al. (2012) observed comparable results for their analysis of 18 OECD
bankingmarkets from1979 to 2007, allowing for asymmetrical effects over the business cycle.

In this paper, we set out three goals. First, we investigate the connection between Indian
monetary policy rates and bank performance. We take macroeconomic parameters, such as
gross domestic product (GDP) growth and inflation, as control variables. Second, we group
commercial banks into groups according to their ownership patterns as public, private and
foreign, and estimate how these bank groups respond to monetary policy changes. Third, we
conducted panel Granger causality tests to offer evidence of the direction of causality.

This research contributes to the literature in two ways. Firstly, we explore the connection
between policy rates and bank performance for 65 Indian banks. Secondly, this research is the
first of its kind to explore how policy rates influence bank performance indicators within a
banking-dominated financial system in a developing economy.

The analysis brings forth the following main results: First, higher interest rates increase bank
profits, indicating the nexus between the interest rate structure and bank performance indicators.
Furthermore, greater nonperforming assets (NPAs) relate to increased short-term interest rates
and, as a result, higher loan loss provisions. Higher reserve requirements have a detrimental
influence on bankperformance.During the post-crisis period, the short-termpolicy rate has risen
considerably – witnessing a similar rise in the NPAs – and bank profitability has experienced a
steady decline. Second,we find that private and foreign banks aremore susceptible to changes in
reserve requirementswhenwe scrutinize the influence ofmonetary policy on the bankgroupings
of ownership patterns – public, private and foreign. Private and international bank profitability is
more vulnerable to fluctuations in the repo rate than public bank profitability. On the other hand,
public banks aremore sensitive to fluctuations in the bank rate since they frequently use the bank
ratewindow of accommodation. Third, we demonstrate causation in the panel Granger causality
test findings, running from the repo rate to the bank performance factors.

The rest of the paper is organized as follows: The second section looks at the relevant
literature, such as the theoretical framework and empirical evidence. Section 3 describes the
approaches. Section 4 presents the findings of the analyses. Section 5 discusses the crucial
empirical findings. Finally, section 6 discusses policy consequences.

2. Literature review
In this section, we provide a comprehensive review of the existing literature related to the
influence of reserve requirements on bank performance and develop study hypotheses.
Monetary policy generally refers to central banks’ attempts to manage the money supply using
policy instruments such as reserve requirements, open market operations, repo rates, discount
rates, direct interest rate regulation, direct control of banking system lending and moral
suasion. Monetary policy seeks to control credit and money to accomplish economic
development and financial stability goals. Traditional theories of monetary policy mainly
focus on money supply and its changes, which change the interest rate and the level of
spending in the economy (Friedman and Schwartz, 1963;King andPlosser, 1984; Sims, 1992).
Long, fluctuating and unknown time delays characterize the transmission mechanism. We
believe that monetary policy operates through four primary channels: (1) credit, (2) asset
prices, (3) bank lending and (4) exchange rates. Monetary policy transmission has attracted
much attention, particularly considering recent events, with a particular emphasis on the
efficacy of the bank credit channel.

A monetary strategy focused on growth or amount of money implies interest rate
fluctuations, which have an influence on bank performance. The question is whether banks

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profit from the high interest rates mandated by monetary policy. There are three approaches to
this problem. First, bank earnings are determined by all asset and liability interest rates instead
of a single market rate [1]. Second, bank earnings are also determined by user expenses for all
financial goods and services. Third, an examination of the relative price movements of
financial and non-financial assets is necessary.

The “credit channel” underlines the significance of banking in transmitting monetary
policy since financial markets are deficient. Because banks are distinct in their competence in
financial intermediation, they manage and minimize financial frictions by adapting their loan
conditions to monetary policy changes. However, in accordance with the “interest rate
channel,” the influence of monetary policy on saving and investment endures even when
financial markets are closed. Monetary policy changes cause capital cost fluctuations and the
rate of return on savings, which impact spending choices. Though interest rates and credit
channels differ in emphasizing the importance of financial issues, we consider them
complementary. As a result, both these transmissions work together to shape the effects of
monetary policy on the economy, influencing borrowing costs, spending decisions and
economic growth.

The recent debate focuses on the “credit channel” that assigns banks an essential role in the
transmission of monetary shocks to the economy (Bernanke and Blinder, 1992; Friedman
et al., 1993; Gertler and Gilchrist, 1993; Kashyap and Stein, 1994; Bernanke and Gertler,
1995). However, Bernanke and Blinder (1992), among others, provide evidence that bank
lending contracts do when monetary policy becomes tighter.

Rakshit and Bardhan (2023) investigated how growing competition in the Indian banking
sector impacted monetary policy transmission via the bank lending channel from 1997 to
2017. The study, using a two-step system-generalized methods of moments (GMM)
methodology, shows that heightened market power reduces monetary policy efficacy in
different industries and ownership groups. Furthermore, increasing market dominance in the
loan and depositmarkets lessens the effect ofmonetary policy on the availability of bank loans.
The results point to the need for policy actions to improve monetary policy transmission by
mitigating the negative impacts of changes in bank rivalry.

In the “credit channel” represented by Gertler and Gilchrist (1993), Bernanke and Gertler
(1995), Calomiris and Hubbard (1990), Bernanke et al. (1996) and Oliner and Rudebusch
(1995), the financial position of borrowers has a crucial role in determining the ability to obtain
external finance.

In a world of perfect financial markets in which the Miller and Modigliani (1958)
proposition holds good, all forms of financing are perfect substitutes and yield the same
interest rates. However, the “credit channel” and the “interest rate channel” are not mutually
exclusive but complementary, implying that monetary policy can be effective through these
transmission channels simultaneously (Cecchetti, 1995). Rate reduction in the positive zone
tends to have a distinct effect. According toNucera et al. (2017), the impact of negative interest
rates on banks’ systemic risk is dependent on the bank’s business strategy. Large universal
banks and fee-based banks have seen positive effects from negative interest rates, whereas
other banks have not.

Samuelson’s (1945) hypothesis states that changes in interest rates affect bank performance
and, more specifically, profitability, via their effect on bank interest margins. Similarly,
Hancock (1985) illustrates that an increase in interest rates charged by the bank boosts bank
profitability, as lending rate elasticity is larger than deposit rate elasticity. Several studies have
established that an expansionary monetary policy of the central banks decreases long-term
interest rates (Krishnamurthy and Vissing-Jorgensen, 2011; Swanson and Williams, 2014;
Wright, 2012). Falling interest rates may encourage investment in high-risk activities. This is
because when interest rates are low, banks may lend money for less money, which encourages
them to lend to riskier borrowers. According toBuch et al. (2014),monetary policymay have a
major influence on bank risk taking, and this impact varies depending on the kind of bank and
the time period.

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Negative interest rates hurt bank stocks, particularly those that depend on deposits, due to
unexpected yield curve movements and flattening. Bats et al. (2023) studied bank stock
performance throughout positive and negative interest rate eras. They discover that changes in the
long-end yield curve slope have no effect on bank stocks under negative interest rates. Accounting
data demonstrate that the yield curve decreases harm to banks, mostly by reducing deposit profits.

The extant literature suggests that monetary policy could impact long-term interest rates
through the portfolio balance channel (Gagnon et al., 2011; Joyce et al., 2012). Another is the
signaling channel through which the central bank’s expansionary actions could signal to
market participants that central banks have changed their policy preferences (Bauer and
Rudebusch, 2013).

Monetary policy shocks have a direct impact on bank health. Jung (2023) investigated the
influence of the European Central Bank (ECB) on bank health following the 2008 financial
crisis. ECB’s policies and communication had varying effects on bank stocks and financing
costs in the long term. Policy easing shocks hurt banks after the crisis, but forward-looking
statements improved their health. The study found that ECB policy shocks affect banks in the
long run. When the yield curve goes up, bank health improves; when it goes down, bank health
decreases. These findings provide a newviewpoint on evaluating theECB’s effect on eurozone
banks in the face of economic crises.

The ECB carried out three nontraditional monetary policies between 2011 and 2018:
LTROs, asset purchase programs and interest rate adjustments. Dwyer et al. (2023) studied
how policies affected eurozone banks’ loans, government securities and cash at central banks.
According to the authors, the unorthodoxmonetary policies of theECBhad a beneficial impact
on bank lending, especially in crisis countries. The regulations also resulted in a decline in
government securities owned by banks in non-crisis nations.

Studies based on accounting measures of profitability report mixed evidence on the
response of bank profitability to the changes in the short-term rates or the slope of the yield
curve (Hanweck and Ryu, 2005; Memmel, 2011; Begenau et al., 2015).

Some recent studies, like Alper et al. (2018), observed that reserve requirements (RR) in
emerging markets smooth credit cycles, but their transmission remains unclear. Barroso et al.
(2017) investigated the impact of reserve requirements (RR) on credit supply in Brazil using a
large loan-level dataset. By employing a difference-in-difference methodology, the study
analyzes the effects of RR changes during and after the 2008 financial crisis. The findings
indicate that more liquid banks are more capable of lessening the effects of RR increases on
lending. Additionally, the study reveals that the easing of RR during the crisis had a larger
impact on credit supply than subsequent tightening measures. Foreign and smaller banks were
found to be less affected by RR changes. The paper also suggests that banks tend to reduce
lending to riskier firms in response to tighter monetary policy.

Fang et al. (2022) explored the impact of raising bank capital requirements on lending in
Peru, finding that higher requirements lead to lower credit growth, which becomes
insignificant after six months. Some studies suggest that higher capital requirements
temporarily reduce credit growth and increase lending rates, while others indicate that this
effect is short-lived and becomes insignificant within six months.

In brief, while allowing for macroeconomic variables, the preceding theoretical
underpinnings lead us to the following testable hypotheses:

(1) A positive and concave link exists between bank profitability indicators and policy
rates.

This hypothesis aligns with traditional views of monetary policy transmission, where interest
rates influence bank margins and profitability. Studies by Flannery (1981), Samuelson (1945)
and Hancock (1985) provide foundational support for this relationship. However, the concave
link implies a diminishing marginal impact of interest rates on profitability, which might
require further empirical investigation.

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(2) A negative and perhaps convex link exists between bank profits on investments and
advances and monetary policy rates.

This hypothesis is consistent with studies such as those by Nucera et al. (2017) and Bats et al.
(2023) that provide evidence that negative interest rates and yield curve flattening can
negatively impact bank profitability, particularly for banks dependent on deposit-based
funding. This suggests a negative relationship between monetary policy rates and bank profits
from investments and advances, with potential convexity due to the differential impact of
positive and negative rates.

(3) Performance indicators such as nonperforming assets and business per employee have
a positive and concave connection with monetary policy rates.

The hypothesis suggests that higher interest rates may lead to increased NPAs due to financial
distress among borrowers, indicating a positive relationship. The concave nature of this
relationship implies a diminishing marginal impact of interest rates on NPAs. While empirical
evidence supports the connection between monetary policy and asset quality, further
investigation is needed to clarify the specific relationship with NPAs. Studies by Rakshit and
Bardhan (2023) and Jung (2023) emphasize the link between monetary policy and bank
performance indicators, showing that tighter monetary policy can result in higher NPAs and
affect other performance metrics. This indicates a positive relationship that may weaken at
higher policy rates, reflecting the hypothesized concavity.

(4) A negative and perhaps convex link exists between private and international bank
profitability, on the one hand, and repo rates, on the other.

The hypothesis of a negative and potentially convex relationship between private and
international bank profitability and repo rates aligns with the findings of Bats et al. (2023)
regarding the impact of negative interest rates on bank profitability. The convexity implies that
the effect of interest rate changes might vary across different types of banks. Research by Buch
et al. (2014) andDwyeret al. (2023) supports the notion that the impact of monetary policy can
vary by bank type and ownership structure. For private and international banks, which may
engage more in risk-taking activities, higher repo rates can reduce profitability, suggesting a
negative and potentially convex relationship as these banks adjust differently to policy
changes compared to public banks.

(5) The treasury bill rate has a positive relationship with public bank returns on investment
and a negative relationship with international banks.

Studies by Gagnon et al. (2011) and Joyce et al. (2012) suggest that treasury bill rates, through
the portfolio balance channel, affect public and international banks differently. Public banks
may benefit from higher returns on safe investments like treasury bills, whereas international
banks – which might rely more on global financial markets – could experience reduced
profitability, reflecting the observed relationships.

By grounding each hypothesis in the existing literature, we aim to provide a comprehensive
framework that supports our empirical analysis and enhances the robustness and relevance of our
findings.

3. Method
This section outlines the research design, data and variables, and analytical procedures followed in
this study. The Indian banking sector has experienced a paradigm shift, transforming from
exclusivist into inclusivist. The broad brush of financial sector reforms has resulted in a change in
banking sector oversight away frommicro-level credit decision interference and toward prudential
regulation and monitoring. In addition, the sector has experienced interest rate deregulation, the
implementation of prudential standards and strengthened regulation and supervision.

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Bank branches have grown from a meager 8,262 during the first phase of bank
nationalization in 1969 to 149,775 in 2020. Bank branch expansion has increased by 149%
since the start of financial sector reforms in 1991. The total commercial bank credit has grown
from INR 116,301 crores in 1991 to INR 1,037,0861 crores in 2020, indicating an increase of
8,817%.However, “overhang” issues in the financial industry, such as nonperforming assets of
banks and financial institutions, continue to plague us, even though the gross nonperforming
assets ratio has fallen from a peak of 15.7% in 1996–1997 to a low of 9.6% in 2015–2016.

3.1 Research design/model
The study employs a quantitative approach with a focus on panel data to capture both temporal
and cross-sectional dimensions of bank performance. The study period spans from 2005 to
2016, utilizing data from 65 scheduled commercial banks in India. This period covers post-
liberalization phases, business cycles, consolidation periods and the global financial crisis.
Annual data are analyzed to derive insights into the banking sector’s performance.

We incorporate several key features to ensure an in-depth and robust analysis of the impact
of monetary policy on bank performance by leveraging panel data to examine changes in bank
performance over time, allowing for a dynamic understanding of trends and shifts.
Additionally, cross-sectional comparisons are generated to evaluate the performance of
different banks within the same time period, facilitating insights into relative performance
across the sector. To control for unobserved heterogeneity, we use fixed effects, which
effectively account for time-invariant bank-specific factors that could influence the results.

Themethodological framework employs a fixed effectmodel, as proposed by Islam (1995),
which is suitable for panel data to control for unobserved heterogeneity. The model
specification is as follows:

Bank Performancejt ¼ f
�
αþMonetary policy variablesjt þ Bank specific control variables

j
t

þMacroeconomic variablesjt
�

To address potential endogeneity concerns and capture the dynamic nature of the relationship,
we employ both panel least squares (PLS) with fixed effects [cross-section weights (PCSE)
standard errors and covariance] and GMM.

The PLS method allows us to control for unobserved heterogeneity by accounting for time-
invariant bank-specific factors, ensuring that our estimates are not biased by these fixed
characteristics. In contrast, the GMM approach addresses potential endogeneity issues,
providing more reliable estimates of the causal effects of monetary policy on bank
performance. Specifically, GMM panel regression enhances our analysis by incorporating
instrumental variables (IVs), which are crucial for obtaining consistent estimates in the
presence of endogeneity. This method is particularly beneficial when unobserved factors or
measurement errors could bias the results. In the context of panel data, GMM can utilize both
levels and differences of the variables, allowing for a more rigorous examination of the
relationships at play. By employingGMM,we ensure that our estimates are not only consistent
but also efficient, even in the presence of heteroskedasticity or autocorrelation in the error
terms. This dual approach strengthens the overall robustness of our findings.

3.2 Data and variables
Table 1 presents the variables’ description along with the notation used in the analysis, the
measure and the data source. The bank performance variables of the study include ROA (Al-
Harbi, 2019), ROE (Majumder and Li, 2018), return on advances (ROADV), return on
investments (ROI), net nonperforming assets (NNPA) (Canh et al., 2021) and business per
employee (BPE).

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Table 1. Description of variables

Notation Measure

Dependent variables:
1. Return on assets (ROA) Return on assets (ROA) is a profitability ratio which indicates the

net profit (net income) generated by total assets. It is computed by
dividing net income by average total assets. Formula – (Profit after
tax/Average total assets)*100

2. Return on equity (ROE) Return on equity (ROE) is a ratio relating net profit (net income) to
shareholders’ equity. Here equity refers to share capital reserves
and surplus of the bank. Formula – Profit after tax/(Total
equity þ Total equity at the end of the previous year)/2}*100

3. Return on advances (ROADV) ROADVis the ratio of interest earned on advances and bills to total
advances

4. Return on investments (ROI) ROI is the ratio of interest earned on investments to the total
investments

5. Net non-performing assets (NNPA) A non-performing asset, including a leased asset, becomes
nonperforming when it ceases to generate income for the bank.
NNPA is the Gross NPA – (Balance in interest suspense
account þ DICGC/ECGC claims received and held pending
adjustment þ Part-payment received and kept in suspense
account þ Total provisions held)

6. Business per employee (BPE) BPE is the ratio of total business to the number of employees

Monetary policy variables:
7. Cash reserve ratio (CRR) CRR is a specified minimum fraction of the total deposits of

customers, which commercial banks must hold as reserves either in
cash or as deposits with the central bank. For the second preceding
fortnight, a scheduled commercial bank must maintain a prescribed
CRR as a percentage of its net demand and time liabilities (NDTL).
Banks must maintain a minimum of 95% of the required CRR daily
and 100% on average during the fortnight

8. Statutory liquidity ratio (SLR) SLR is the ratio of banks’ liquid assets in gold, cash or other
approved securities to their net demand and time liabilities
(NDTL). A scheduled commercial bank must invest in
unencumbered government and approved securities, a certain
minimum amount as SLR daily

9. Policy repo rate (RR) RR is the rate at which banks borrow funds from the central bank
against eligible collaterals. The repo rate has emerged as the critical
policy for signaling the monetary policy stance

10. Reverse repo rate (RRR) RRR is the rate at which banks place their surplus funds with the
central bank under the liquidity adjustment facility (LAF)

11. Bank rate (BR) BR is the standard rate the central bank (Reserve Bank of India) is
prepared to buy or re-discount bills of exchange or other
commercial paper eligible for purchase under the Act

12. Treasury bill rate (TBR) TBR is the rate of the 91-day Treasury Bills (money market
instruments) that are issued weekly by the Government of India

Bank-specific control variables:
13. Credit to deposit ratio (CDR) CDR is the ratio of the loans created by the bank from the deposits it

receives
14. The ratio of deposits to total liabilities
(RD)

RD is the ratio of the total deposits to the total liabilities

15. The ratio of priority sector advances
to total advances (PSR)

PSR is the ratio of the priority sector loans to the total advances

16. Capital adequacy ratio (CAR) CAR is the ratio that divides the bank’s capital with aggregated
risk-weighted assets for credit, market and operational risks. The
higher the CAR of a bank, the better capitalized it is
NITA is the ratio of other income to the total assets

(continued )

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The CRR, SLR, RR, RRR,BR andTBR are all monetary policy indicators of importance to
us as described in Table 1. The bank-specific control variables include CDR, RD, PSR, CAR
(Majumder and Li, 2018), NITA, OICTA and OPPTA (Al-Harbi, 2019).

We employ GDP growth (GDPGR) as one of the control variables to account for
macroeconomic influences. GDPGR has a positive association with bank performance as the
prosperous economic conditions provide increased business opportunities and improved
recoveries, leading to an increase in profitability and credit growth (Lozano-Vivas and
Pasiouras, 2010;Chortareas et al., 2011;Kalyvas andMamatzakis, 2014).We differwithDelis
and Kouretas (2011) and Dietsch and Lozano-Vivas (2000), who find that GDPGR negatively
affects bank performance variables. One possible explanation is that higher operational
expenditures to provide a certain service level correlate with flourishing economic conditions.

We also consider INFL to be another control variable. We believe it to be negatively
associated with bank performance, as banks often find it difficult to adapt their rates to the
expected inflation levels immediately. This has a dampening impact on the revenues.
Furthermore, as high inflation levels hurt the general public, bank repayments and potential
bank loan prospects are negatively affected (Petersen, 1986; Lozano-Vivas and Pasiouras,
2010). Table 2 shows the descriptive data, while Table 3 shows the variables’ correlations.

Figure 1 shows the bivariate analysis of the monetary policy and bank performance
variables, while Figure 2 shows their relationships.

3.3 Analytical procedures
We start with a standard linear regression model, notwithstanding the conventional approach
of accounting for both apparent and unobservable variability. We also incorporate other major
macroeconomic factors to take into account their concurrent influence. We used the Islam
(1995) model with the fixed effect estimator since it is appropriate for a panel dataset.

Bank Performancejt ¼ f
�
αþMonetary policy variablesjt þ Bank specific control variables

j
t

þMacroeconomic variablesjt
�

(1)

Table 1. Continued

Notation Measure

17. The ratio of non-interest income to
total assets (NITA)
18. Ratio of intermediation cost to total
assets (OICTA)

OICTA is the ratio of operating expenses to the total assets

19. The ratio of operating profits to total
assets (OPPTA)

OPPTA is the ratio of operating profits to the total assets

Macroeconomic control variables:
20. GDP growth (GDPGR) GDPGR is India’s annual percentage growth rate at market prices

based on constant local currency. Aggregates are based on constant
2005 US dollars

21. Inflation (INFL) INFL, as measured by the consumer price index, reflects the annual
percentage change in the cost to the average consumer of acquiring
a basket of goods and services that may be fixed or changed at
specified intervals, such as yearly

Source(s): Reserve Bank of India database. We source the macroeconomic variables from the World
Development Indicators of the World Bank Database

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Table 2. Descriptive statistics

BPE BR CAR CDR CRR GDPGR INFL NITA NNPA OICTA OPPTA PPE PSR RD RIR ROA ROADV ROE ROI RR RRR SLR TBR

Mean 112.56 7.15 16.83 78.23 5.03 7.57 7.86 1.66 2.04 2.17 2.44 2.11 33.45 72.03 5.19 1.28 9.81 12.90 7.41 6.40 6.04 23.67 7.07
Median 96.40 6.00 13.23 72.92 4.94 7.46 7.40 1.17 1.21 1.89 2.10 0.61 33.27 81.70 5.20 1.09 9.87 13.04 7.35 6.50 5.75 24.00 7.29
Maximum 398.00 9.25 85.00 318.30 6.80 10.30 12.00 16.63 15.97 11.83 15.62 94.30 78.73 92.25 8.90 10.23 34.66 34.01 15.90 8.50 7.50 25.00 9.02
Minimum 1.40 6.00 7.51 0.60 4.00 3.90 4.20 0.05 0.01 0.56 0.00 0.01 1.18 6.92 0.60 0.01 0.03 0.06 1.58 3.25 5.00 21.50 4.38
Std. Dev 75.50 1.39 10.76 32.97 0.93 1.70 2.46 1.74 2.43 1.20 1.48 5.87 8.68 18.98 2.24 1.02 2.30 6.57 1.24 1.42 0.92 1.23 1.48
Obs 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780 780
Source(s): Authors’ own calculations

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Table 3. Correlation analysis

BPE BR CAR CDR CRR GDPGR INFL NITA NNPA OICTA OPPTA PPE

BPE 1
BR 0.39** 1
CAR 0.15** 0.011 1
CDR 0.47** 0.12** 0.057 1
CRR �0.35** �0.76** �0.013 �0.09* 1
GDPGR �0.16** �0.38** �0.01 �0.08* �0.08* 1
INFL 0.04 �0.05 0.06 0.042 0.13** �0.08* 1
NITA �0.08* �0.12** 0.22** �0.13** 0.10** 0.037 0.010 1
NNPA 0.09* 0.10** 0.31** �0.13** �0.17** 0.064 �0.17** �0.003 1
OICTA �0.32** �0.19** 0.17** �0.13** 0.09** 0.034 �0.053 0.70** �0.09** 1
OPPTA 0.16** �0.05 0.29** 0.072* 0.05 �0.006 0.07* 0.69** 0.002 0.212** 1
PPE 0.29** 0.12** 0.101** 0.15** �0.12** �0.044 �0.026 0.218** 0.06 �0.08* 0.39** 1
PSR �0.16** �0.06 0.05 �0.12** 0.04 0.045 �0.008 0.164** 0.21** 0.19** 0.041 �0.076*

RD �0.53** �0.05 �0.44** �0.51** 0.02 0.034 �0.021 �0.350** �0.21** �0.11** �0.51** �0.41**

RIR 0.14** 0.22** �0.06 0.007 �0.39** �0.043 �0.73** �0.040 0.16** �0.015 �0.057 0.088*

ROA 0.11** �0.04 0.34** 0.048 0.05 �0.031 0.09** 0.526** 0.11** 0.15** 0.73** 0.39**

ROADV �0.25** 0.17** �0.10** �0.19** �0.06 �0.18** 0.21** 0.096** �0.11** 0.17** 0.078* �0.126**

ROE �0.26** �0.18** �0.28** �0.15** 0.21** �0.01 0.14** 0.058 �0.36** �0.036 0.153** �0.016
ROI �0.05 0.17** �0.04 �0.11** �0.11** �0.12** �0.20** 0.12** 0.004 0.17** 0.154** �0.028
RR 0.220** 0.45** 0.03 0.10** �0.21** �0.24** �0.02 �0.105** 0.002 �0.044 �0.066 0.032
RRR 0.14** 0.32** 0.02 0.08* 0.04 �0.36** 0.06 �0.068 �0.073* �0.033 �0.016 0.011
SLR �0.43** �0.75** 0.01 �0.11** 0.67** 0.33** 0.19** 0.13** �0.138** 0.11** 0.091* �0.14**

TBR 0.25** 0.73** �0.002 0.118** �0.25** �0.65** �0.22** �0.09** 0.005 �0.055 �0.048 0.073*

(continued )

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PSR RD RIR ROA ROADV ROE ROI RR RRR SLR TBR

BPE
BR
CAR
CDR
CRR
GDPGR
INFL
NITA
NNPA
OICTA
OPPTA
PPE
PSR 1
RD 0.084* 1
RIR 0.001 0.012 1
ROA 0.018 �0.49** �0.09* 1
ROADV �0.006 0.219** �0.02 0.056 1
ROE 0.067 0.323** �0.17** 0.290** 0.11** 1
ROI 0.131** 0.071* 0.19** 0.096** 0.06 0.07* 1
RR �0.038 �0.044 �0.16** �0.047 �0.04 �0.11** �0.004 1
RRR �0.038 �0.046 �0.025 �0.001 0.10** �0.020 �0.003 0.71** 1
SLR 0.021 0.014 �0.46** 0.105** �0.10** 0.24** �0.13** �0.34** �0.11** 1
TBR �0.064 �0.050 0.28** �0.037 0.14** �0.12** 0.192** 0.61** 0.71** �0.521** 1

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Figure 2. Correlations between monetary policy and bank performance variables

Figure 1. Bivariate analysis of the relationship between monetary policy and bank performance variables

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Bank Performancejt ¼ α0 þ
Xn

j¼1
β1monetary policy variables

j
t þ βjX

j
t þ μj þ νt þ εjt (2)

where Xjt is the kth bank j variable at time t. μj denotes the idiosyncratic error term, νt is the
bank-specific unobserved effect and εjt represents the error term.

First, we run the model with ROA as the dependent variable. We rewrite Eq (2) as
shown below:

ROAjt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt þ β4CARjt þ β5NITAjt þ β6OICTAjt
þ β7OPPTAjt þ β8GDPGRjt þ β9INFLjt þ β10CRRjt þ β11SLRjt þ β12RRjt
þ β13BRjt þ β14TBRjt þ μj þ νt þ εjt

(3)

We repeat the estimation using ROE as the dependent variable as follows:

ROEjt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt þ β4CARjt þ β5NITAjt þ β6OICTAjt
þ β7OPPTAjt þ β8GDPGRjt þ β9INFLjt þ β10CRRjt þ β11SLRjt þ β12RRjt
þ β13BRjt þ β14TBRjt þ μj þ νt þ εjt

(4)

We repeat our experiment with two performance variables related to advances (ROADV) and
investments (ROI). We write our model as follows:

ROADVjt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt þ β4CARjt þ β5NITAjt þ β6OICTAjt
þ β7OPPTA

j
t þ β8GDPGRjt þ β9INFLjt þ β10CRRjt þ β11SLRjt þ β12RRjt

þ β13BRjt þ β14TBRjt þ μj þ νt þ εjt

(5)

ROIjt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt þ β4CAR
j
t þ β5NITA

j
t þ β6OICTA

j
t

þ β7OPPTAjt þ β8GDPGRjt þ β9INFLjt þ β10CRRjt þ β11SLRjt þ β12RRjt
þ β13BRjt þ β14TBRjt þ μj þ νt þ εjt

(6)

Finally, we analyze the remaining two bank performance variables, NNPA and BPE, with the
following specifications:

NNPAjt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt þ β4CARjt þ β5NITAjt þ β6OICTAjt
þ β7OPPTA

j
t þ β8GDPGRjt þ β9INFLjt þ β10CRRjt þ β11SLR

j
t þ β12RRjt

þ β13BRjt þ β14TBRjt þ μj þ νt þ εjt

(7)

BPEjt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt þ β4CARjt þ β5NITAjt þ β6OICTAjt
þ β7OPPTAjt þ β8GDPGRjt þ β9INFLjt þ β10CRRjt þ β11SLRjt þ β12RRjt
þ β13BRjt þ β14TBRjt þ μj þ νt þ εjt

(8)

3.3.1 Monetary policy affects the performance of bank groups. We divide our dataset into
bank groupings based on ownership patterns as public, private or foreign. As a result, our
specification has been changed as follows:

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Public banks0 performance variablejt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt
þ β4CARjt þ β5NITAjt þ β6OICTAjt
þ β7OPPTA

j
t þ β8GDPGRjt þ β9INFLjt

þ β10CRRjt þ β11SLRjt þ β12RRjt þ β13BRjt
þ β14TBRjt þ μj þ νt þ εjt

(9)

Prvate banks0 performance variablejt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt
þ β4CARjt þ β5NITAjt þ β6OICTAjt
þ β7OPPTAjt þ β8GDPGRjt þ β9INFLjt
þ β10CRRjt þ β11SLRjt þ β12RRjt þ β13BRjt
þ β14TBRjt þ μj þ νt þ εjt

(10)

Foriegn banks0 performance variablejt ¼ α0 þ β1CDRjt þ β2RDjt þ β3PSRjt
þ β4CARjt þ β5NITAjt þ β6OICTAjt
þ β7OPPTAjt þ β8GDPGRjt þ β9INFLjt
þ β10CRRjt þ β11SLRjt þ β12RRjt þ β13BRjt
þ β14TBRjt þ μj þ νt þ εjt

(11)

3.4 Testing for causality
We use pairwise Granger causality tests to assess whether the past values of one variable can
help forecast the present values of another. The study is carried out in a panel data framework,
which allows us to account for both the temporal and cross-sectional dimensions of the data.

Panel data bivariate regressions are as follows:

yi;t ¼ α0;i þ α1;iyi;t−1 þ . . .þ α1;iyi;t−1 þ β1;ixi;t−1 þ . . .þ β1;ixi;t−1 þ ei;t (12)

xi;t ¼ α0;i þ α1;ixi;t−1 þ . . .þ α1;ixi;t−1 þ β1;iyi;t−1 þ . . .þ β1;iyi;t−1 þ ei;t (13)

where t represents the panel’s temporal dimension and i is the cross-section dimension.
The Granger causality tests were performed using a model that included bivariate

regressions for each pair of variables in the panel. We analyze two equations (Eq 12 and 13) for
each pair of variables, y and x, in this “vanilla model.”

Using the pairwise Granger causality tests, we may ascertain if the lagged variables’ values
have a statistically significant impact on the “resent other variables” values. If such
causation is identified, it shows a temporal link between the two variables, suggesting that
changes in one variable might anticipate or “cause” changes in the other variable. These tests
help identify the strength and direction of any causal connections among the variables in the
panel data.

4. Results
The following section describes the empirical findings, detailing the relationship between
monetary policy and bank performance, with a particular focus on the differential impact
across bank ownership categories.

We start by running a series of tests to ascertain stationarity. The Levin et al. (2002) and
Breitung (2000) t-tests test the absence of a unit root in the panel under the assumption of a

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standard unit root process across the panel units. The PP-Fisher chi-square (Maddala and Wu,
1999) test assumes individual unit root processes, confirming that all data series are unit root
processes. Moreover, Hadri’s (2000) test strongly rejects the null of stationarity’ in all cases.
We also carried out panel unit root tests following Pesaran (2007) and verified the cross-
section correlation properties of the raw data. Table 4–6 displays the stationarity test results.

4.1 Effects on bank profitability
Figures 3 and 4 depict monetary policy variables’ interactions with ROA and ROE. Over a
year, the monetary policy contraction that raises the RR from 0 to 1% boosts the ROA by

Table 5. Panel unit root tests for performance variables

At level At first difference
Variable LLC test IPS test ADF test PP test LLC test IPS test ADF test PP test

BPE �0.45 �6.94 70.27 98.85 �19.77 �13.14 403.13 451.30
(0.33) (1.00) (1.00) (0.00) (0.00) (0.00) (0.00) (0.00)

NNPA 3.88 4.74 153.47 209.22
(0.99) (1.00) (0.07) (0.00)

ROA �13.25 �8.27 279.45 268.53
(0.00) (0.00) (0.00) (0.00)

ROADV �16.58 �8.86 289.59 280.12
(0.00) (0.00) (0.00) (0.00)

ROE �9.24 �5.87 239.85 271.85
(0.00) (0.00) (0.00) (0.00)

ROI �13.81 �7.13 256.49 280.74
(0.00) (0.00) (0.00) (0.00)

Note(s): This table reports the test statistic, followed by the probability values in parentheses, for the four tests
performed in ascertaining the stationarity of the variables. The first panel reports the test results in level form,
and the second panel reports the results in the first difference
LLC test: The Levin, Lin and Chu test; IPS test: The Im, Pesaran and Shin (IPS) test; ADF test: The Augmented
Dickey–Fuller (ADF) test; PPS test: The Augmented Dickey–Fuller (ADF) test
Source(s): Authors’ own calculations

Table 4. Panel unit root tests for policy rates

At level At first difference
Variable LLC test IPS test ADF test PP test LLC test IPS test ADF test PP test

BR �0.81 �3.03 52.95 52.95 �21.55 �13.32 389.46 389.59
(0.21) (0.99) (1.00) (1.00) (0.00) (0.00) (0.00) (0.00)

CRR �1.75 3.67 46.83 49.91
(0.04) (0.99) (1.00) (1.00)

RR �10.67 �1.94 125.19 117.22
(0.00) (0.02) (0.60) (0.78)

RRR �19.91 �11.07 334.87 345.03
(0.00) (0.00) (0.00) (0.00)

SLR 18.33 18.74 0.82 4.79 �45.96 �35.48 903.78 1024.30
(1.00) (1.00) (1.00) (1.00) (0.00) (0.00) (0.00) (0.00)

Note(s): This table reports the test statistic, followed by the probability values in parentheses, for the four tests
performed in ascertaining the stationarity of the variables. The first panel reports the test results in level form,
and the second panel reports the results in the first difference
LLC test: The Levin, Lin and Chu test; IPS test: The Im, Pesaran and Shin (IPS) test; ADF test: The Augmented
Dickey–Fuller (ADF) test; PPS test: The Augmented Dickey–Fuller (ADF) test
Source(s): Authors’ own calculations

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0.02%. Table 7 displays the panel regression results using PLS and the GMM approach. ROA
and ROE are influenced favorably and considerably by the CRR and the SLR. The central
bank’s RR issued to banks is positive and highly connected with ROA. On the other hand, RR
is significantly and negatively associated with ROE. Ayear of contractionary monetary policy
that rises the RR from 0 to 1% decreases the ROE by 0.15%.

4.2 Effects on return on advances and investments
Following our hypothesis, the results indicate a negative association of policy variables (RR,
CRR and SLR) with ROADVand ROI (Table 8). Particularly, CRR and RR exert a statistically
significant impact, indicating a substantial drain on the ROADV and ROI as the policy
variables increase. Figures 5 and 6 provide a graphical presentation of the interplay of
monetary policy factors with ROADVand ROI, respectively. Higher interest rates dampen the
return on advances and investments, negatively affecting net interest income (NII). Policy
rates have a concave connection with ROADVand ROI, evidencing our hypothesis that rising
monetary policy rates inhibit bank returns on their advances and investments.

4.3 Effects on nonperforming assets and employee business
The policy variable RR positively correlates with the performance variables NNPA and BPE
(Table 9). CRR and SLR, however, have a detrimental association with NNPA and BPE,
indicating that the RR has a large positive link while the long-term ratios have a negative

Table 6. Panel unit root tests for control variables

At level At first difference
Variable LLC test IPS test ADF test PP test LLC test IPS test ADF test PP test

CAR �9.42 �5.23 217.62 223.61
(0.00) (0.00) (0.00) (0.00)

CDR �30.95 �10.88 309.36 345.33
(0.00) (0.00) (0.00) (0.00)

GDPGR �31.30 18.20 545.18 510.12
(0.00) (0.00) (0.00) (0.00)

INFL �1.92 �1.30 113.63 113.63
(0.03) (0.09) (0.85) (0.85)

NITA �13.87 �7.94 285.91 308.06
(0.00) (0.00) (0.00) (0.00)

OICTA �13.98 �7.60 275.15 352.41
(0.00) (0.00) (0.00) (0.00)

OPPTA �14.40 �8.05 268.40 291.04
(0.00) (0.00) (0.00) (0.00)

PPE �6.01 �1.89 169.88 171.62
(0.00) (0.02) (0.01) (0.01)

PSR �8.14 �4.44 209.28 194.12
(0.00) (0.00) (0.00) (0.00)

RD �7.92 �6.99 266.56 283.02
(0.00) (0.00) (0.00) (0.00)

TBR �20.38 �8.43 282.39 145.19
(0.00) (0.00) (0.00) (0.17)

Note(s): This table reports the test statistic, followed by the probability values in parentheses, for the four tests
performed in ascertaining the stationarity of the variables. The first panel reports the test results in level form,
and the second panel reports the results in the first difference
LLC test: The Levin, Lin and Chu test; IPS test: The Im, Pesaran and Shin (IPS) test; ADF test: The Augmented
Dickey–Fuller (ADF) test; PPS test: The Augmented Dickey–Fuller (ADF) test
Source(s): Authors’ own calculations

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Figure 3. Impact of monetary policy on return on assets

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Figure 4. Impact of monetary policy on return on equity

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influence. Figures 7 and 8 provide a graphical presentation of the interface between monetary
policy variables with NNPA and BPE, respectively.

4.4 Effects on the performance of bank groups
The results of an examination into the influence of monetary policy determinants on bank
groups based on ownership patterns are provided below. The repo rate (RR) positively
correlates with the ROA of all three bank groups – public, private and foreign (Table 10). CRR

Table 7. Impact of monetary policy on bank profitability

Variable ROA ROE
PLS GMM PLS GMM

Credit-deposit ratio (CDR) �0.001 �0.001 �0.01 �0.01
(0.00) (0.00) (0.01) (0.01)

Ratio of deposits to total liabilities (RD) �0.01 �0.01* �0.03 �0.03
(0.00) (0.00) (0.04) (0.05)

Ratio of priority sector advances to total advances (PSR) 0.001 0.001 0.02 0.02
(0.00) (0.00) (0.04) (0.04)

Capital adequacy ratio (CAR) 0.01*** 0.01** �0.06** �0.06*

(0.00) (0.00) (0.02) (0.03)
Ratio of non-interest income to total assets (NITA) 0.12** 0.12*** �1.01** �1.01*

(0.05) (0.03) (0.47) (0.62)
Ratio of intermediation cost to total assets (OICTA) �0.13*** �0.13*** �0.06 �0.06

(0.04) (0.03) (0.62) (0.62)
Ratio of operating profits to total assets (OPPTA) 0.44*** 0.44*** 2.29*** 2.29***

(0.03) (0.02) (0.39) (0.47)
Gross domestic product growth (GDPGR) �0.01* �0.01 �0.11*** �0.11

(0.00) (0.01) (0.02) (0.13)
Inflation (INFL) 0.02*** 0.02*** 0.39*** 0.39***

(0.01) (0.001) (0.04) (0.12)
Cash reserve ratio (CRR) 0.04*** 0.04*** 1.40*** 1.40***

(0.01) (0.01) (0.06) (0.31)
Statutory reserve ratio (SLR) 0.02** 0.02 0.53*** 0.53***

(0.01) (0.01) (0.05) (0.17)
Repo rate (RR) 0.02** 0.02** �0.15*** �0.15

(0.01) (0.01) (0.02) (0.15)
Bank rate (BR) 0.001 0.001 0.24*** 0.24*

(0.01) (0.01) (0.02) (0.13)
Treasury bill rate (TBR) 0.01 0.01 0.07 0.07

(0.01) (0.01) (0.08) (0.23)
Intercept 0.07 0.07 2.97 2.97

(0.41) (0.35) (4.80) (5.83)
R-squared 0.86 0.86 0.56 0.56
Adj R-squared 0.84 0.84 0.51 0.51
D-W stat 1.31 1.31 1.10 1.10
VIF range 1.10–

4.92
1.10–
4.92

1.47–
5.30

1.47–
5.30

Fixed effects Yes Yes Yes Yes
Cross sections 65 65 65 65
Observations 715 715 715 715
Note(s): We report the results of the Panel Regression (linear) models of the baseline specification using the
profitability measures (ROA and ROE) as the dependent variables. We present the results of the Panel Least
Squares (PLS) with fixed effects (cross-section weights (PCSE) standard errors and covariance) and
Generalized Methods of Moments (GMM) with the coefficient values marked with significance levels in the
first row, followed by the standard errors (in parenthesis) in the second row. Asterisks ***, ** and * indicate
significance levels at 1, 5 and 10%, respectively
Source(s): Authors’ own calculations

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has been discovered to have a substantial positive link with the ROA of public banks and a
large negative association with the ROA of private and international banks. In the case of the
ROE, however, the CRR is noted to have a large positive association with public banks and an
adversarial relationship with private and international banks. These findings imply that
increased CRR has a favorable influence on public banks.

Table 8. Impact of monetary policy on bank returns

Variable ROADV ROI
OLS GMM OLS GMM

Credit-deposit ratio (CDR) �0.01** �0.01** �0.001 �0.002***

(0.00) (0.00) (0.00) (0.00)
Ratio of deposits to total liabilities (RD) 0.01 0.01 0.01 0.01

(0.01) (0.01) (0.01) (0.01)
Ratio of priority sector advances to total advances (PSR) �0.001 �0.001 0.01 0.01

(0.01) (0.01) (0.01) (0.01)
Capital adequacy ratio (CAR) �0.03*** �0.03*** �0.03*** �0.03***

(0.01) (0.01) (0.01) (0.01)
Ratio of non-interest income to total assets (NITA) 0.11 0.11 �0.34*** �0.34***

(0.09) (0.09) (0.07) (0.07)
Ratio of intermediation cost to total assets (OICTA) 0.25*** 0.25*** 0.43*** 0.43***

(0.09) (0.09) (0.08) (0.08)
Ratio of operating profits to total assets (OPPTA) 0.31*** 0.31*** 0.38*** 0.38***

(0.06) (0.06) (0.05) (0.05)
Gross domestic product growth (GDPGR) �0.13*** �0.13*** �0.05*** �0.05***

(0.02) (0.02) (0.02) (0.02)
Inflation (INFL) 0.23*** 0.23*** �0.07*** �0.07***

(0.01) (0.01) (0.01) (0.01)
Cash reserve ratio (CRR) �0.52*** �0.52*** �0.22*** �0.22***

(0.03) (0.03) (0.03) (0.03)
Statutory liquidity ratio (SLR) �0.05 �0.05 �0.01 �0.01

(0.04) (0.04) (0.03) (0.03)
Repo rate (RR) �0.31*** �0.31*** �0.12*** �0.12***

(0.02) (0.02) (0.02) (0.02)
Bank rate (BR) 0.01 0.01 0.02 0.02

(0.03) (0.03) (0.02) (0.02)
Treasury bill rate (TBR) 0.32*** 0.32*** 0.08*** 0.08***

(0.03) (0.03) (0.03) (0.03)
Intercept 10.35*** 10.35*** 8.35*** 8.35***

(0.97) (0.97) (0.94) (0.94)
R-squared 0.83 0.83 0.59 0.59
Adj R-squared 0.8 0.8 0.54 0.54
R-squared (between) 0.0409 0.0409 0.03
D-W stat 1.25 1.25 1.43 1.43
VIF range 1.22–

4.72
1.22–
4.72

1.19–
4.80

Fixed effects Yes Yes Yes Yes
Cross sections 65 65 65 65
Observations 715 715 715 715
Note(s): We report the results of the Panel Regression (linear) models of the baseline specification using the
bank return measures (ROADVand ROI) as the dependent variables. We present the results of the Panel Least
Squares (PLS) with fixed effects (cross-section weights (PCSE) standard errors and covariance) and
Generalized Methods of Moments (GMM) with the coefficient values marked with significance levels in the
first row, followed by the standard errors (in parenthesis) in the second row. Asterisks *** and ** indicate
significance levels at 1 and 5%, respectively
Source(s): Authors’ own calculations

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Figure 5. Impact of monetary policy on return on advances

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Figure 6. Relationship between monetary policy and return on investments

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Private and international banks, on the other hand, are more vulnerable to CRR increases,
which harm them. SLR shows a substantial positive association with public bank ROA but a
large negative link with private bank ROA, implying that private banks are more vulnerable to
SLR increases than public banks. We find that RR has a considerable favorable influence on
public bankROEwhile having a negative impact on private and international banks (Table 10).
TheBR has a strong favorable influence on public banks’ ROA andROE.However, this link is
unimportant in the case of private and international banks.

Table 9. Impact of monetary policy on bank performance – NNPA and BPE

Variable NNPA BPE
OLS GMM OLS GMM

Credit-deposit ratio (CDR) 0.001 0.001 0.10** 0.10**

(0.00) (0.00) (0.04) (0.04)
Ratio of deposits to total liabilities (RD) 0.01 0.01 – –

(0.01) (0.01) – –
Ratio of priority sector advances to total advances
(PSR)

0.03*** 0.03*** �0.25*** �0.25***

(0.01) (0.01) (0.06) (0.06)
Capital adequacy ratio (CAR) �0.02** �0.02** 0.001 0.001

(0.01) (0.01) (0.07) (0.07)
Ratio of non-interest income to total assets (NITA) 0.38*** 0.38*** �1.01 �1.01

(0.10) (0.10) (0.91) (0.91)
Ratio of intermediation cost to total assets (OICTA) �0.29*** �0.29*** �1.69* �1.69*

(0.11) (0.11) (0.98) (0.98)
Ratio of operating profits to total assets (OPPTA) �0.47*** �0.47*** 1.72** 1.72**

(0.07) (0.07) (0.68) (0.67)
Gross domestic product growth (GDPGR) �0.05* �0.05* �0.45** �0.45**

(0.03) (0.03) (0.20) (0.22)
Inflation (INFL) �0.19*** �0.19*** 0.34** 0.34**

(0.02) (0.02) (0.17) (0.16)
Cash reserve ratio (CRR) �0.09 �0.09 0.91*** 0.91***

(0.08) (0.08) (0.31) (0.33)
Statutory reserve ratio (SLR) �0.05 �0.05 �0.17 �0.17

(0.04) (0.04) (0.43) (0.41)
Repo rate (RR) 0.06** 0.06** 0.97*** 0.97***

(0.03) (0.03) (0.26) (0.26)
Bank rate (BR) 0.28*** 0.28*** �0.07 �0.07

(0.08) (0.08) (0.30) (0.30)
Treasury bill rate (TBR) �0.33*** �0.33*** �1.11*** �1.11***

(0.06) (0.06) (0.37) (0.38)
Intercept 4.66*** 4.66*** 8.78 8.78

(1.68) 1.68) (6.69) (6.80)
R-squared 0.67 0.67 0.3 0.30
Adj R-squared 0.63 0.63 0.21 0.21
D-W stat 1.31 1.31 2.04 2.04
VIF range 1.12–

15.04
1.12–
15.04

1.30–
4.30

1.30–
4.30

Fixed effects Yes Yes Yes Yes
Cross sections 65 65 65 65
Observations 715 715 715 715
Note(s): We report the results of the Panel Regression (linear) models of the baseline specification using the
performance measures (NNPA and BPE) as the dependent variables. We present the results of the Panel Least
Squares (PLS) with fixed effects (cross-section weights (PCSE) standard errors and covariance) and
Generalized Methods of Moments (GMM) with the coefficient values marked with significance levels in the
first row, followed by the standard errors (in parenthesis) in the second row. Asterisks ***, ** and * indicate
significance levels at 1, 5 and 10%, respectively
Source(s): Authors’ own calculations

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Figure 7. Interaction between monetary policy and net nonperforming assets

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Figure 8. Impact of monetary policy on business performance per employee

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We provide the findings of evaluating the influence of repo rate on bank groups’ ROA and
ROI (Table 11). The short-term policy rate RR has a substantial negative association with all
three bank groups’ ROADV and ROI. Similarly, CRR and SLR show a substantial negative
relationship with all three bank groups’ ROADV and ROI. TBR has a considerable positive
association with all three bank groups’ ROADV. TBR, however, shows a large positive link
with public bank ROI and a negative relationship with foreign bank ROI. The bank rate (BR)
has little effect on the ROADV and ROI of bank groups. However, in this situation, the
connection is significantly unfavorable.

Table 10. Impact of monetary policy on ROA and ROE across bank groups

Variable ROA ROE
Public Private Foreign Public Private Foreign

Credit-deposit ratio (CDR) �0.001 �0.001 �0.001 �0.001 �0.001* �0.001
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Ratio of deposits to total liabilities
(RD)

�0.001 �0.001* �0.001 �0.003 0.001 �0.001
(0.00) (0.00) (0.00) (0.01) (0.00) (0.01)

Ratio of priority sector advances to
total advances (PSR)

0.001 �0.001** 0.001 0.001 �0.001 �0.001
(0.00) (0.00) (0.00) (0.01) (0.00) (0.01)

Capital adequacy ratio (CAR) 0.001 0.001* �0.002 0.003* 0.001 �0.03**

(0.00) (0.00) (0.00) (0.00) (0.00) (0.01)
Ratio of non-interest income to
total assets (NITA)

�0.01*** �0.001*** 0.13*** �0.24** �0.00 0.35***

(0.01) (0.00) (0.02) 0.10 (0.00) (0.12)
Ratio of intermediation cost to total
assets (OICTA)

0.01 �0.001 �0.07*** 0.05 �0.01** �0.19***

(0.00) (0.00) (0.01) (0.07) (0.00) (0.05)
Ratio of operating profits to total
assets (OPPTA)

0.01*** 0.001*** 0.09*** 0.22*** �0.00 0.30***

(0.00) (0.00) (0.02) (0.08) (0.00) (0.08)
Gross domestic product growth
(GDPGR)

0.001 �0.001*** �0.001 �0.02 0.001** �0.01**

(0.00) (0.00) (0.00) (0.02) (0.00) (0.00)
Inflation (INFL) 0.001*** �0.001 �0.001 0.05*** 0.001*** �0.001

(0.00) (0.00) (0.00) (0.01) (0.00) (0.01)
Cash reserve ratio (CRR) 0.02*** �0.001*** �0.01*** 0.21*** �0.00 �0.02*

(0.00) (0.00) (0.00) (0.03) (0.00) (0.01)
Statutory reserve ratio (SLR) 0.01*** �0.001*** 0.001 0.12*** 0.001** 0.02**

(0.00) (0.00) (0.00) (0.02) (0.00) (0.01)
Repo rate (RR) 0.001*** 0.001* 0.001** 0.04** �0.001 �0.001

(0.00) (0.00) (0.00) (0.02) (0.00) (0.01)
Bank rate (BR) 0.001*** �0.001 �0.001** 0.05*** 0.001 0.001

(0.00) (0.00) (0.00) (0.01) (0.00) (0.01)
Treasury bill rate (TBR) �0.00** �0.001** 0.01*** �0.07*** 0.001*** 0.01*

(0.00) (0.00) (0.00) (0.03) (0.00) (0.01)
Intercept 0.22*** 0.35*** 0.42* 1.76*** 4.06*** 2.15***

(0.03) (0.01) (0.24) (0.67) (0.03) (0.56)
R-squared 0.91 0.97 0.89 0.91 0.97 0.91
Adj R-squared 0.89 0.97 0.87 0.89 0.97 0.90
D-W stat 0.82 1.01 1.52 0.82 1.04 1.75
Fixed effects Yes Yes Yes Yes Yes Yes
Cross sections 65 65 65 65 65 65
Observations 715 715 715 715 715 715
Note(s):We report the results of the linear models using the bank groups’ ROA and ROE, based on ownership
structures, as the dependent variables. We present the results of the Panel Least Squares (PLS) with fixed effects
(cross-section weights (PCSE) standard errors and covariance) with the coefficient values marked with
significance levels in the first row, followed by the standard errors (in parenthesis) in the second row. Asterisks
***, ** and * indicate significance levels at 1, 5 and 10%, respectively
Source(s): Authors’ own calculations

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Table 12 shows the findings of evaluating the influence of RR on the bank groups’ NNPA
and BPE. According to our findings, CRR has a considerable negative impact on the NNPA
and BPE of all bank groups. SLR has a considerable negative relationship with public bank
NNPA and private and foreign bank BPE. The RR has a considerable beneficial influence on
the NNPE and BPE of all three bank groups, implying that if the short-term policy rate rises, so
will theNNPA levels of all banks. All three bank groups’NNPAandBPE have a favorable link
with RR. However, there is a substantial negative association between TBR and the NNPA in
all three bank categories.

Table 11. Impact of monetary policy on ROADVand ROI across bank groups

Variable ROADV ROI
Public Private Foreign Public Private Foreign

Credit-deposit ratio (CDR) 0.001 �0.001 �0.01*** �0.001** 0.001 0.002*

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Ratio of deposits to total
liabilities (RD)

0.01*** �0.001*** �0.01** �0.001 0.001 0.001
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Ratio of priority sector
advances to total advances
(PSR)

�0.001 0.001 �0.01** 0.001*** 0.001* �0.002
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Capital adequacy ratio (CAR) 0.001 �0.001* �0.01** �0.001 0.001 �0.01***

(0.00) (0.00) 0.01 (0.00) (0.00) (0.00)
Ratio of non-interest income to
total assets (NITA)

0.06* 0.001 �0.02 �0.01* �0.01*** �0.01
(0.04) (0.01) (0.07) (0.00) (0.00) (0.03)

Ratio of intermediation cost to
total assets (OICTA)

�0.07 0.02*** 0.13*** 0.02*** 0.02*** 0.01
(0.05) (0.01) (0.03) (0.01) (0.01) (0.02)

Ratio of operating profits to
total assets (OPPTA)

�0.05* �0.001 0.18*** 0.01 0.01*** 0.07***

(0.03) (0.00) (0.04) (0.00) (0.00) (0.02)
Gross domestic product growth
(GDPGR)

�0.02*** �0.01*** 0.01* 0.001 �0.001*** �0.003***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Inflation (INFL) 0.04*** 0.01*** 0.03*** �0.01*** �0.001*** �0.001

(0.01) (0.00) (0.01) (0.00) (0.00) (0.00)
Cash reserve ratio (CRR) �0.07*** �0.04*** 0.00 �0.01** �0.01*** �0.003*

(0.01) (0.00) (0.02) (0.00) (0.00) (0.00)
Statutory reserve ratio (SLR) �0.001 �0.01 �0.07*** 0.002 �0.001 �0.001

(0.01) (0.00) (0.03) (0.00) (0.00) (0.00)
Repo rate (RR) �0.03*** �0.02*** �0.04*** �0.004*** �0.001** �0.004***

(0.01) (0.00) (0.01) (0.00) (0.00) (0.00)
Bank rate (BR) 0.01 �0.002 �0.03 0.001 0.001 0.001

(0.01) (0.00) (0.02) (0.00) (0.00) (0.00)
Treasury bill rate (TBR) 0.05*** 0.01*** 0.05** 0.001* 0.001 �0.002

(0.01) (0.01) (0.02) (0.00) (0.00) (0.00)
Intercept 3.72*** 3.52*** 5.63*** 2.98*** 2.98*** 2.24***

(0.16) (0.08) (1.04) (0.05) (0.05) (0.29)
R-squared 0.99 0.99 0.98 0.99 0.99 0.98
Adj R-squared 0.99 0.99 0.98 0.99 0.99 0.98
D-W stat 0.96 0.92 1.31 0.99 1.20 1.35
Fixed effects Yes Yes Yes Yes Yes
Cross-sections 65 65 65 65 65
Observations 715 715 715 715 715
Note(s): We report the results of the linear models using the ROADV and ROI of the bank groups, based on
ownership structures, as the dependent variables. We present the results of the Panel Least Squares (PLS) with
fixed effects (cross-section weights (PCSE) standard errors and covariance) with the coefficient values marked
with significance levels in the first row, followed by the standard errors (in parenthesis) in the second row.
Asterisks ***, ** and * indicate significance levels at 1, 5 and 10%, respectively
Source(s): Authors’ own calculations

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4.5 Estimating causality
The paired panel Granger causality test determines the causation and direction between
monetary policy and performance indicators. Table 13 shows the panel Granger causality
testing findings for the policy variables and the ROA, ROA, and ROE. The direction of
causation runs from CRR 0 ROA, BR 0 ROA and TBR0 ROA. However, in the ROE-
related analysis case, a bidirectional causality runs betweenRR5ROEandTBR 5ROE.We
notice unidirectional causationwith CRR0ROE and BR0ROE. Nevertheless, we overlook
causation from SLR to ROA or ROE. Table 14 displays the panel Granger causality test
findings for the policy variables ROADVand ROI. We notice bi-directional causation running

Table 12. Impact of monetary policy on NNPA and BPE across bank groups

Variable NNPA BPE
Public Private Foreign Public Private Foreign

Credit-deposit ratio (CDR) �0.001 0.001 �0.001 �0.001** 0.001 �0.002*

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Ratio of deposits to total
liabilities (RD)

0.001 �0.001 �0.002 – – –
(0.00) (0.00) (0.00) – – –

Ratio of priority sector advances
to total advances (PSR)

0.003 0.001*** 0.004*** �0.001*** 0.001 0.01
(0.00) (0.00) (0.00) (0.00) (0.00) (0.01)

Capital adequacy ratio (CAR) �0.001 �0.001 �0.003 �0.001 �0.001 0.01*

(0.00) (0.00) (0.00) (0.00) (0.00) (0.01)
Ratio of non-interest income to
total assets (NITA)

0.08** �0.00 �0.01 0.001 �0.004 0.05
(0.03) (0.00) (0.00) (0.00) (0.01) (0.09)

Ratio of intermediation cost to
total assets (OICTA)

0.03 0.01 �0.04* �0.01*** �0.03* �0.45***

(0.04) (0.00) (0.02) (0.00) (0.02) (0.08)
Ratio of operating profits to total
assets (OPPTA)

�0.07** 0.00 �0.02 �0.001 0.04*** 0.33***

(0.03) (0.00) (0.02) (0.00) (0.01) (0.06)
Gross domestic product growth
(GDPGR)

�0.01 0.001*** �0.002** �0.002*** �0.01*** �0.01***

(0.01) (0.00) (0.00) (0.00) (0.00) (0.00)
Inflation (INFL) �0.05*** �0.003*** 0.001 0.002*** �0.003 �0.02***

(0.01) (0.00) (0.00) (0.00) (0.00) (0.00)
Cash reserve ratio (CRR) �0.03** �0.003* �0.01 �0.003** �0.06*** �0.07***

(0.02) (0.00) (0.01) (0.00) (0.01) (0.01)
Statutory reserve ratio (SLR) �0.11*** 0.001 0.01*** 0.001 �0.01*** �0.02*

(0.03) (0.00) (0.01) (0.00) (0.00) (0.01)
Repo rate (RR) 0.01* 0.002*** 0.004* 0.004*** 0.01** 0.00

(0.01) (0.00) (0.00) (0.00) (0.00) (0.01)
Bank rate (BR) 0.08*** 0.004*** 0.001 0.00 0.00 �0.02***

(0.03) (0.00) (0.00) (0.00) (0.00) (0.01)
Treasury bill rate (TBR) �0.07*** �0.004*** �0.01** �0.002** �0.01 0.01

(0.03) (0.00) (0.00) (0.00) (0.01) (0.01)
Intercept 3.81*** 0.31*** 0.85*** 0.25*** 0.73*** 1.89***

(0.87) (0.02) (0.31) (0.02) (0.10) (0.41)
R-squared 0.72 0.78 0.84 0.87 0.82 0.83
Adj R-squared 0.69 0.75 0.82 0.86 0.79 0.81
D-W stat 1.08 0.90 1.22 0.91 0.68 1.17
Fixed effects Yes Yes Yes Yes Yes Yes
Cross sections 65 65 65 65 65 65
Observations 715 715 715 715 715 715
Note(s): We report the results of the linear models using the NNPA and BPE of the bank groups, based on
ownership structures, as the dependent variables. We present the results of the Panel Least Squares (PLS) with
fixed effects (cross-section weights (PCSE) standard errors and covariance) with the coefficient values marked
with significance levels in the first row, followed by the standard errors (in parenthesis) in the second row.
Asterisks ***, ** and * indicate significance levels at 1, 5 and 10%, respectively
Source(s): Authors’ own calculations

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from CRR 5 ROADV, SLR5 ROADV, RR5 ROADV, BR5 ROADV and
TBR5 ROADV.

Similarly, we notice bi-directional causation running from CRR 5, SLR and RR 5 ROI.
In contrast, no causality exists between BR to ROI and TBR to ROI. Finally, in Table 15, we
present the panel Granger causality test results using the policy variables and the NNPA and
BPE. Bi-directional causality runs from CRR 5 NNPA, SLR5 NNPA, RR5 NPA, BR5
NNPA and TBR5NNPA. Similarly, we notice bi-directional causality running from CRR5
BPE, SLR5 BPE, RR5 BPE and TBR5 NNPA.

5. Discussions
This section delves into the interpretation and implications of the study’s findings, comparing
them with existing literature, evaluating the robustness of the results and exploring the
practical and theoretical contributions to the literature on monetary policy’s impact on bank
performance.

This study examined the influence of monetary policy rates on the performance measures of
banks, such as profitability metrics (ROA and ROE), return on advances and investments
(ROADVand ROI), nonperforming assets (NNPA) and employee business performance (BPE).

Table 13. Panel Granger causality analysis for ROA and ROE

ROA ROE

Null hypothesis: Obs Lags
F-
statistic Prob. Null hypothesis: Obs Lags

F-
statistic Prob.

CDR L ROA 650 2 1.62 0.20 CDR L ROE 650 2 1.54 0.22
ROA 0 CDR 10.94 0.00 ROE L CDR 0.64 0.53
RD L ROA 650 2 23.28 0.00 RD L ROE 650 2 7.14 0.00
ROA L RD 1.35 0.26 ROE L RD 0.64 0.53
PSR L ROA 650 2 4.05 0.02 PSR L ROE 650 2 1.62 0.20
ROA L PSR 3.90 0.02 ROE L PSR 0.84 0.43
CAR L ROA 650 2 11.58 0.00 CAR L ROE 650 2 4.52 0.01
ROA L CAR 4.73 0.01 ROE L CAR 3.93 0.02
NITA L ROA 650 2 1.56 0.21 NITA L ROE 650 2 1.65 0.19
ROA L NITA 11.68 0.00 ROE L NITA 3.08 0.05
OICTA L ROA 650 2 0.98 0.37 OICTA L ROE 650 2 0.10 0.90
ROA L OICTA 3.94 0.02 ROE L OICTA 3.09 0.05
OPPTA LROA 650 2 13.53 0.00 OPPTA LROE 650 2 5.97 0.00
ROA LOPPTA 1.54 0.21 ROE LOPPTA 4.84 0.01
GDPGR L
ROA

650 2 6.97 0.00 GDPGR L
ROE

650 2 15.79 0.00

ROA L
GDPGR

0.09 0.92 ROE L
GDPGR

2.59 0.08

INFL L ROA 650 2 0.46 0.63 INFL L ROE 650 2 7.38 0.00
ROA L INFL 2.82 0.06 ROE L INFL 15.11 0.00
CRR L ROA 650 2 8.65 0.00 CRR L ROE 650 2 22.71 0.00
ROA L CRR 0.53 0.59 ROE L CRR 1.41 0.25
SLR L ROA 585 2 1.24 0.29 SLR L ROE 585 2 2.15 0.12
ROA L SLR 4.23 0.01 ROE L SLR 13.02 0.00
RR L ROA 650 2 0.83 0.43 RR L ROE 650 2 7.37 0.00
ROA L RR 0.75 0.47 ROE L RR 4.23 0.02
BR L ROA 585 2 2.78 0.06 BR L ROE 585 2 4.78 0.01
ROA L DBR 0.90 0.41 ROE L DBR 1.45 0.24
TBR L ROA 650 2 4.45 0.01 TBR L ROE 650 2 11.15 0.00
ROA LTBR 2.17 0.12 ROE L TBR 13.04 0.00
Note(s): L means does not Granger cause
Source(s): Authors’ own calculations

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To address potential endogeneity concerns, we use IVs that enhance the reliability of our
causal estimates. Specifically, we use policy instruments and institutional factors. IVs are
crucial in econometric models, particularly within the GMM framework, as they help to get
unbiased and consistent estimates of the parameters of interest.

In our analysis, policy instruments are hypothesized to influence bank performance
indirectly by impacting the economic environment in which banks operate. These instruments
are exogenous to banks’ decision-making processes, allowing us to isolate the impacts of
monetary policy from other confounding factors. Institutional factors, on the other hand,
capture underlying characteristics that may affect bank performance independently of
monetary policy actions.

By incorporating these IVs, we strengthen the robustness of our GMM analysis, enabling
more reliable estimates of the causal effects of monetary policy on bank profitability. This
approach effectively mitigates biases arising from endogeneity, ensuring that the estimated
impacts of monetary policy are not confounded by other variables. In this context, variables

Table 14. Panel Granger causality analysis for ROADVand ROI

ROADV ROI

Null hypothesis: Obs Lags
F-
statistic Prob.

Null
hypothesis: Obs Lags

F-
statistic Prob.

CDR L ROADV 650 2 7.44 0.00 CDR L ROI 650 2 1.56 0.21
ROADV L CDR 0.92 0.40 ROI L CDR 3.91 0.02
RD L ROADV 650 2 12.15 0.00 RD L ROI 650 2 0.60 0.55
ROADV L RD 4.01 0.02 ROI L RD 1.18 0.31
PSR L ROADV 650 2 3.25 0.04 PSR L ROI 650 2 7.58 0.00
ROADV L PSR 4.29 0.01 ROI L PSR 1.89 0.15
CAR L ROADV 650 2 4.50 0.01 CAR L ROI 650 2 8.62 0.00
ROADV L CAR 1.71 0.18 ROI L CAR 1.91 0.15
NITA L ROADV 650 2 12.48 0.00 NITA L ROI 650 2 2.67 0.07
ROADV L NITA 18.11 0.00 ROI L NITA 1.80 0.17
OICTA L
ROADV

650 2 9.44 0.00 OICTA L
ROI

650 2 6.13 0.00

ROADV L
OICTA

3.80 0.02 ROI L
OICTA

0.91 0.40

OPPTA L
ROADV

650 2 13.63 0.00 OPPTA L
ROI

650 2 1.84 0.16

ROADV L
OPPTA

21.65 0.00 ROI L
OPPTA

0.23 0.79

GDPGR L
ROADV

650 2 44.54 0.00 GDPGR L
ROI

650 2 19.86 0.00

ROADV L
GDPGR

6.15 0.00 ROI L
GDPGR

0.13 0.87

INFL L ROADV 650 2 6.64 0.00 INFL L ROI 650 2 37.64 0.00
ROADV L INFL 22.45 0.00 ROI L INFL 4.28 0.01
CRR L ROADV 650 2 19.20 0.00 CRR L ROI 650 2 37.42 0.00
ROADV L CRR 6.35 0.00 ROI L CRR 9.36 0.00
SLR L ROADV 585 2 12.30 0.00 SLR L ROI 585 2 10.51 0.00
ROADV L
DSLR

2.85 0.06 ROI L DSLR 3.70 0.03

RR L ROADV 650 2 6.22 0.00 RR L ROI 650 2 54.33 0.00
ROADV L RR 12.43 0.00 ROI L RR 4.09 0.02
BR L ROADV 585 2 2.37 0.09 BR L ROI 585 2 0.23 0.79
ROADV L DBR 5.21 0.01 ROI L DBR 29.16 0.00
TBR L ROADV 650 2 22.43 0.00 TBR L ROI 650 2 42.94 0.00
ROADV L TBR 24.10 0.00 ROI L TBR 0.66 0.52
Source(s): Authors’ own calculations

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such as the repo rate (RR), bank rate (BR) and treasury bill rate (TBR) are considered policy
instruments, while capital adequacy ratio (CAR), statutory liquidity ratio (SLR) and cash
reserve ratio (CRR) are institutional factors.

The findings are discussed further below.

5.1 Effects on bank profitability
According to the findings, monetary policy variables significantly influence ROA and ROE of
banks. The graphs in Figures 3 and 4 depict the link between monetary policy variables and
ROA and ROE, respectively. Table 7 shows panel regressions using PLS and generalized
GMM that validate these findings.

The repo rate has a positive and significant association with ROA, indicating that rising
interest rates boost bank profits. On the other hand, the repo rate shows a substantial negative
relationship with ROE, showing that higher interest rates reduce bank returns on equity. This
conclusion implies that, while higher interest ratesmight enhance short-term profitability, they
can also reduce long-term shareholder returns.

Table 15. Panel Granger causality analysis for NNPA and BPE

NNPA BPE

Null hypothesis Obs Lags
F-
statistic Prob. Null hypothesis Obs Lags

F-
statistic Prob.

CDR L NNPA 650 2 0.42 0.66 CDR L DBPE 585 2 6.74 0.00
NNPA L CDR 0.33 0.72 DBPE L CDR 2.64 0.07
RD L NNPA 650 2 0.67 0.51
NNPA L RD 3.35 0.04
PSR L NNPA 650 2 0.07 0.93 PSR L DBPE 585 2 0.42 0.66
NNPA L PSR 3.09 0.05 DBPE L PSR 0.70 0.50
CAR L NNPA 650 2 0.98 0.38 CAR L DBPE 585 2 0.07 0.93
NNPA L CAR 13.89 0.00 DBPE L CAR 3.42 0.03
NITA L NNPA 650 2 0.26 0.77 NITA L DBPE 585 2 1.99 0.14
NNPA L NITA 9.13 0.00 DBPE L NITA 4.92 0.01
OICTA L
NNPA

650 2 3.40 0.03 OICTA L
DBPE

585 2 0.93 0.39

NNPA L
OICTA

4.99 0.01 DBPE L
OICTA

6.65 0.00

OPPTA L
NNPA

650 2 0.88 0.42 OPPTA L
DBPE

585 2 4.16 0.02

NNPA L
OPPTA

11.56 0.00 DBPE L
OPPTA

0.19 0.83

GDPGR L
NNPA

650 2 8.85 0.00 GDPGR L
DBPE

585 2 2.08 0.13

NNPA L
GDPGR

1.94 0.14 DBPE L
GDPGR

1.79 0.17

INFL L NNPA 650 2 0.57 0.56 INFL L DBPE 585 2 1.22 0.30
NNPA L INFL 4.64 0.01 DBPE L INFL 1.44 0.24
CRR L NNPA 650 2 12.21 0.00 CRR L DBPE 585 2 2.95 0.05
NNPA L CRR 2.54 0.08 DBPE L CRR 1.50 0.22
SLR L NNPA 650 2 27.29 0.00 DSLR L DBPE 585 2 2.84 0.06
NNPA L SLR 0.32 0.73 DBPE L DSLR 3.45 0.03
RR L NNPA 650 2 15.90 0.00 RR L DBPE 585 2 3.79 0.02
NNPA L RR 6.05 0.00 DBPE L RR 3.37 0.04
BR L NNPA 650 2 15.00 0.00 DBR L DBPE 585 2 2.57 0.08
NNPA L BR 0.50 0.61 DBPE L DBR 3.83 0.02
TBR L NNPA 650 2 17.40 0.00 TBR L DBPE 585 2 3.94 0.02
NNPA LTBR 3.83 0.02 DBPE L TBR 0.89 0.41
Source(s): Authors’ own calculations

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ROA and ROE are positively and strongly related to CRR and SLR, implying that
maintaining more significant reserve requirements benefits bank profitability. These reserve
requirements buffer during economic downturns and improve financial stability, promoting
bank profitability.

5.2 Effects on return on advances and investments
The findings confirm that rising monetary policy rates reduce bank earnings on lending and
investments (Table 8, Figures 5 and 6). The negative relationship between policy variables
(RR, CRR and SLR) and ROADVand ROI suggests that higher interest rates reduce the return
on loans and investments, reducing NII.

The concave connection between policy rates, ROADVand ROI supports the hypothesis
that the effect on bank returns grows as policy rates rise. This conclusion emphasizes the
significance of adequately regulating interest rate policy to balance short-term benefits and
long-term profitability for banks.

5.3 Effects on nonperforming assets and employee business performance
The study found substantial correlations between policy factors and nonperforming assets
(NNPA) and employee business performance (BPE) (Table 9, Figures 7 and 8). The repo rate
(RR) shows a significant positive link with NNPA and BPE, meaning that higher interest rates
may increase nonperforming assets and have a negative impact on employee business
performance.

CRR and SLR, in contrast, have a negative association with NNPA and BPE, showing that
enhanced reserve requirements stabilize bank asset quality and employee business
performance. This conclusion emphasizes the importance of reserve requirements in risk
management and overall bank performance.

5.4 Effects on the performance of bank groups
The study also examined how monetary policy factors affected bank groupings depending on
ownership patterns (public, private and foreign banks). The repo rate (RR), according to the
data (Table 10), has a substantial positive link with the profitability (ROA) of all three bank
groups. However, the impacts of CRR and SLR differ depending on the bank’s ownership.

CRR has a sizeable positive link with ROA for public banks but a significant negative
relationship with private and international banks. This demonstrates that, while rising reserve
requirements benefit public banks more, private and international banks are more responsive
to the impact on their profitability.

Similarly, for public banks, SLR shows a strong positive connection with ROA but a
sizeable negative association for private banks. This suggests that private banks are more
affected by changes in SLR than public banks, underlining the importance of tailoring policy
measures to diverse bank groupings.

The findings also demonstrate that the short-term policy rate (RR) considerably influences
public bank ROE but negatively impacts private and international banks. This shows that
public banks may be better positioned to capitalize on short-term interest rate adjustments to
boost equity returns, but private and foreign banks may struggle to maintain comparable ROE
levels under the same conditions.

5.5 Estimating causality
Panel Granger causality tests were used to investigate causal links among monetary policy
variables and bank performance indicators. The findings (Tables 13–15) show causal solid
linkages in several directions.

The causal links in the case of bank profitability (ROA and ROE) flow from policy
variables (CRR, BR and TBR) to profitability indicators, implying that changes in these policy

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rates precede changes in bank profitability. However, evidence of bidirectional causation
between some policy variables (RR, TBR and ROE) shows that these variables are in a
feedback loop.

The causation between policy variables (CRR, SLR, RR, BR and TBR) and performance
indicators is bidirectional for return on advances and investments (ROADV and ROI),
implying that changes in both policy rates and bank performance impact each other over time.

The findings for nonperforming assets (NNPA) and employee business performance (BPE)
reveal bidirectional causation between policy variables (CRR, SLR, RR, BR and TBR) and
performance indicators for all three bank groups. This suggests that monetary policy actions
are dynamic with the quality of bank assets and employee business performance.

The study findings give insights into the intricate interplay among policy rates and bank
performance indicators. The findings indicate that monetary policy decisions can have
variable effects on many dimensions of bank performance, emphasizing the significance of
adequately calibrating policy measures to achieve desired economic outcomes. The study
findings add to current research on monetary policy transmission mechanisms and have
practical consequences for policymakers and financial institutions making decisions. More
research is needed, however, to evaluate the monetary policy’s long-term impacts on bank
performance and its implications for global financial stability.

We acknowledge the limitations that may affect the robustness of the findings. First, the
analysis is constrained by the availability and quality of data, which may introduce biases in
the results. Additionally, while we have included various control variables, there may be other
relevant factors, such as regulatory changes and technological advancements that were not
captured. Furthermore, the use of IVs while aimed at addressing endogeneity concerns relies
on the validity of the selected instruments, which can be challenging to establish. Lastly, the
focus on specific emerging markets may limit the generalizability of the findings to other
contexts. Future research should aim to address these limitations for a more comprehensive
understanding of this topic.

6. Conclusions
In this concluding section, we summarize the key findings of the study, highlight their
significance for the banking sector and offer practical recommendations for policymakers and
financial institutions based on the research results.

This study used a panel dataset of 65 scheduled commercial banks in India from 2005 to
2016 to assess the influence of monetary policy on bank performance. We accounted for both
traditional bank variables as well as macroeconomic factors. We examine the influence of
ownership patterns on the completely balanced panel of the entire sample first and
subsequently on the bank categories (public, private and foreign).

Three goals guided this investigation. The initial goal was to examine the connection
between monetary policy rates and bank performance indicators. The second goal was to
determine how ownership patterns across various bank groups responded to changes in
monetary policy. The third goal was to show evidence of monetary policy and bank
performance causality. We show that a bank lending channel may affect the efficacy and
distributional consequences of monetary policy and the information quality of various
measures used by policymakers. We use the model’s most relevant variables in the theoretical
foundations of the monetary transmission channels. The correlation between interest rate
levels and ROA is positive, and the panel Granger causality test results corroborate the same.

This study examines the macroeconomic consequences and concentrates on the effect of
monetary policy on bank profitability indicators before and after the crisis. The short-term
policy rate has risen significantly post-crisis, paralleling the growth in NPAs. As a result,
banks’ ROA and ROE have gradually declined since the crisis.

This research makes an essential addition by probing the effect of monetary policy
variables on bank groups depending on ownership patterns – public, private and foreign.

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Because private and international banks manage statutory reserves more efficiently, they are
more vulnerable to changes in CRR and SLR. Private and international bank profitability is
more susceptible to changes in repo rates than public bank profitability. However, public banks
are more vulnerable to fluctuations in the bank rate since they use this bank rate window of
accommodation more frequently than private and international banks.

6.1 Policy implications
The transmission of monetary policy is influenced by the bank lending channel, resulting in
differing effects across bank ownership groups. Policymakers need to take into account these
varying impacts when crafting policies.

Private and foreign banks, which are more responsive to changes in reserve requirements
and repo rates, may need closer monitoring and specific policy interventions to ensure
financial stability.

Public sector banks, reliant on the bank rate window, are more susceptible to fluctuations in
this policy rate. Policymakers should ensure adequate liquidity support for these banks during
periods of tight monetary policy.

The positive but concave relationship between policy rates and bank profitability suggests
that excessively strict monetary policy may diminish bank earnings. Policymakers should
strive for an optimal policy rate that balances inflation control and bank stability.

The adverse effects of increasing nonperforming assets on bank performance emphasize
the importance of maintaining asset quality. Policymakers should promote prudent lending
practices and create a supportive environment for banks to tackle distressed assets.

In summary, this study underscores the intricate and nuanced relationship between
monetary policy and bank performance in India. Policymakers must carefully consider the
ownership structure and business models of banks when evaluating the transmission and
impact of monetary policy actions. Targeted policy measures may be necessary to uphold
financial stability and foster sustainable credit growth.

Note
1. Flannery (1981) estimates the effect of interest rates on bank profits using the treasury bill rate as the

market rate.

References
Albertazzi, U. and Gambacorta, L. (2009), “Bank profitability and the business cycle”, Journal of

Financial Stability, Vol. 5 No. 4, pp. 393-409.
Al-Harbi, A. (2019), “The determinants of conventional banks’ profitability in developing and

underdeveloped OIC countries”, Journal of Economics, Finance and Administrative Science,
Vol. 24 No. 47, pp. 4-28, doi: 10.1108/jefas-05-2018-0043.

Alper, K., Binici, M., Demiralp, S., Kara, H. and €Ozl€u, P. (2018), “Reserve requirements, liquidity risk,
and bank lending behavior”, Journal of Money, Credit and Banking, Vol. 50 No. 4, pp. 817-827.

Barroso, J., Gonzalez, B. and Van Doornik, B.F.N. (2017), “Credit supply responses to reserve
requirement: loan-level evidence from macroprudential policy”, Working Papers Series, Central
Bank of Brazil, Research Department, No. 467.

Bats, J.V., Giuliodori, M. and Houben, A.C.F.J. (2023), “Monetary policy effects in times of negative
interest rates: what do bank stock prices tell us?”, Journal of Financial Intermediation, Vol. 53,
101003, doi: 10.1016/j.jfi.2022.101003.

Bauer, M. and Rudebusch, G. (2013), “Monetary policy expectations at the zero lower bound”, Federal
Reserve Bank of San Francisco Working Paper, 18.

Begenau, J., Piazzesi, M. and Schneider, M. (2015), “Banks’ risk exposures”, NBER Working Paper
Series No. 21334, July.

JEFAS
30,59

112

https://doi.org/10.1108/jefas-05-2018-0043
https://doi.org/10.1016/j.jfi.2022.101003


Bernanke, B. and Blinder, A. (1992), “The federal funds rate and the channels of monetary
transmission”, The American Economic Review, Vol. 82, pp. 901-921.

Bernanke, B. and Gertler, M. (1995), “Inside the black box: the credit channel of monetary policy
transmission”, The Journal of Economic Perspectives, Vol. 9 No. 4, pp. 27-48, doi: 10.1257/
jep.9.4.27.

Bernanke, B.S., Gertler, M. and Gilchrist, S. (1996), “The financial accelerator and the flight to
quality”, The Review of Economics and Statistics, Vol. 78, pp. 1-15, doi: 10.2307/2109844.

Bolt, W., de Haan, L., Hoeberichts, M., van Oordt, M.R.C. and Swank, J. (2012), “Bank profitability
during recessions”, Journal of Banking and Finance, Vol. 36 No. 9, pp. 2552-2264, doi: 10.1016/
j.jbankfin.2012.05.011.

Borio, C., Gambacorta, L. and Hofmann, B. (2015), “The influence of monetary policy on bank
profitability”, BIS Working Papers No. 514, Monetary and Economic Department, Bank for
International Settlements.

Breitung, J. (2000), “The local power of some unit root tests for panel data”, in Baltagi, B.H. (Ed.),
Nonstationary Panels, Panel Cointegration, and Dynamic Panels, Advances in Econometrics,
Vol. 15, pp. 161-178.

Buch, C.M., Eickmeier, S. and Prieto, E. (2014), “In search for yield? Survey-based evidence on bank
risk taking”, Journal of Economic Dynamics and Control, Vol. 43 No. 1, pp. 12-30, doi: 10.1016/
j.jedc.2014.01.017.

Calomiris, C. and Hubbard, R.G. (1990), “Firm heterogeneity, internal finance, and credit rationing”,
Economic Journal, Vol. 100 No. 399, pp. 90-104, doi: 10.2307/2233596.

Canh, P.N., Schinckus, C., Su, D.S. and Chong, F.H.L. (2021), “Institutional quality and risk in the
banking system”, Journal of Economics, Finance and Administrative Science, Vol. 26 No. 51,
pp. 22-40.

Cecchetti, S.G. (1995), “Distinguishing theories of the monetary transmission mechanism”, Federal
Reserve Bank of St. Louis Review, Vol. 77 No. 3, pp. 83-97, doi: 10.20955/r.77.83-97.

Chortareas, G., Garza-Garcia, J. and Girardone, C. (2011), “Banking sector performance in Latin
America: market power versus efficiency”, Review of Development Economics, Vol. 15 No. 2,
pp. 307-325, doi: 10.1111/j.1467-9361.2011.00610.x.

Dale, S. (2012), “Limits of monetary policy”, Speech at the 44th annual Money, Macro and Finance
Conference at Trinity College, Dublin, September 8, 2012.

Delis, M. and Kouretas, G. (2011), “Interest rates and bank risk-taking”, Journal of Banking and
Finance, Vol. 35 No. 4, pp. 840-855, doi: 10.1016/j.jbankfin.2010.09.032.

Demirguc-Kunt, A. and Huizinga, H. (1999), “Determinants of commercial bank interest margins and
profitability: some international evidence”, The World Bank Economic Review, Vol. 13 No. 2,
pp. 379-408, doi: 10.1093/wber/13.2.379.

Dietsch, M. and Lozano-Vivas, A. (2000), “How the environment determines banking efficiency: a
comparison between French and Spanish industries”, Journal of Banking and Finance, Vol. 24
No. 6, pp. 985-1004, doi: 10.1016/s0378-4266(99)00115-6.

Dwyer, G.P., Gilevska, B., Nieto, M.J. and Samart�ın, M. (2023), “The effects of the ECB’s
unconventional monetary policies from 2011 to 2018 on banking assets”, Journal of
International Financial Markets, Institutions and Money, Vol. 87, 101800, doi: 10.1016/j.
intfin.2023.101800.

Fang, X., Jutrsa, D., Martinez Peria, S., Presbitero, A.F. and Ratnovski, L. (2022), “Bank capital
requirements and lending in emerging markets: the role of bank characteristics and economic
conditions”, Journal of Banking & Finance, Vol. 135, p. 105806.

Friedman, M. and Schwartz, A.J. (1963), A Monetary History of the United States, 1867-1960,
Princeton University Press, Princeton.

Friedman, B.M., Kuttner, K.N., Bernanke, B.S. and Gertler, M. (1993), “Economic activity and the
short-term credit markets: an analysis of prices and quantities”, Brookings Papers on Economic
Activity, Vol. 1993 No. 2, pp. 193-266, doi: 10.2307/2534567.

Journal of
Economics,
Finance and

Administrative
Science

113

https://doi.org/10.1257/jep.9.4.27
https://doi.org/10.1257/jep.9.4.27
https://doi.org/10.2307/2109844
https://doi.org/10.1016/j.jbankfin.2012.05.011
https://doi.org/10.1016/j.jbankfin.2012.05.011
https://doi.org/10.1016/j.jedc.2014.01.017
https://doi.org/10.1016/j.jedc.2014.01.017
https://doi.org/10.2307/2233596
https://doi.org/10.20955/r.77.83-97
https://doi.org/10.1111/j.1467-9361.2011.00610.x
https://doi.org/10.1016/j.jbankfin.2010.09.032
https://doi.org/10.1093/wber/13.2.379
https://doi.org/10.1016/s0378-4266(99)00115-6
https://doi.org/10.1016/j.intfin.2023.101800
https://doi.org/10.1016/j.intfin.2023.101800
https://doi.org/10.2307/2534567


Flannery, M.J. (1981), “Market interest rates and commercial bank profitability: an empirical
investigation”, Journal of Finance, Vol. 36, pp. 1085-1101.

Gagnon, J., Raskin, M., Remache, J. and Sack, B. (2011), “The financial market effects of the federal
reserve’s large-scale asset purchases”, International Journal of Central Banking,
Vol. 7, pp. 3-43.

Gertler, M. and Gilchrist, S. (1993), “The role of credit market imperfections in the monetary
transmission mechanism: arguments and evidence”, The Scandinavian Journal of Economics,
Vol. 95 No. 1, pp. 43-64, doi: 10.2307/3440134.

Hadri, K. (2000), “Testing for stationarity in heterogeneous panel data”, Economic Journal, Vol. 3
No. 2, pp. 148-161, doi: 10.1111/1368-423x.00043.

Hancock, D. (1985), “Bank profitability, interest rates, and monetary policy”, Journal of Money, Credit,
and Banking, Vol. 17 No. 2, pp. 189-202, doi: 10.2307/1992333.

Hanweck, G. and Ryu, L. (2005), “The sensitivity of bank net interest margins and profitability to
credit, interest rate, and term-structure shocks across bank product specializations”, Working
Papers, School of Management, George Mason University.

Islam, N. (1995), “Growth empirics: a panel data approach”, Quarterly Journal of Economics, Vol. 110
No. 4, pp. 1127-1170, doi: 10.2307/2946651.

Joyce, M., Miles, D., Scott, A. and Vayanos, D. (2012), “Quantitative easing and unconventional
monetary policy–an introduction”, Economic Journal, Vol. 122 No. 564, pp. 271-288, doi:
10.1111/j.1468-0297.2012.02551.x.

Jung, A. (2023), “Are monetary policy shocks causal to bank health? Evidence from the euro area”,
Journal of Macroeconomics, Vol. 75, 103494, doi: 10.1016/j.jmacro.2022.103494.

Kalyvas, A. and Mamatzakis, E. (2014), “Does business regulation matter for banks in the European
Union?”, Journal of International Financial Markets, Institutions and Money, Vol. 32,
pp. 278-324, doi: 10.1016/j.intfin.2014.06.007.

Kashyap, A.K. and Stein, J.C. (1994), “Monetary policy and bank lending”, in Mankiw, G.N. (Ed.),
Monetary Policy, The University of Chicago Press, pp. 221-261.

King, R.G. and Plosser, C.I. (1984), “Money, credit, and prices in a real business cycle”, The American
Economic Review, Vol. 74 No. 3, pp. 363-380.

Krishnamurthy, A. and Vissing-Jorgensen, A. (2011), “The effects of quantitative easing on interest
rates: channels and implications for policy”, National Bureau of Economic Research, No.
w17555.

Levin, A., Lin, C.F., Chu, J. and Chia-Shang (2002), “Unit root tests in panel data: asymptotic and
finite-sample properties”, Journal of Econometrics, Vol. 108 No. 1, pp. 1-24, doi: 10.1016/
s0304-4076(01)00098-7.

Lozano-Vivas, A. and Pasiouras, F. (2010), “The impact of non-traditional activities on the estimation
of bank efficiency: international evidence”, Journal of Banking and Finance, Vol. 34 No. 7,
pp. 1436-1449, doi: 10.1016/j.jbankfin.2010.01.006.

Maddala, G. and Wu, S. (1999), “A comparative study of unit root tests with panel data and a new
simple test”, Oxford Bulletin of Economics and Statistics, Vol. 61 No. S1, pp. 631-652, doi:
10.1111/1468-0084.61.s1.13.

Majumder, M.T.H. and Li, X. (2018), “Bank risk and performance in an emerging market setting: the
case of Bangladesh”, Journal of Economics, Finance and Administrative Science, Vol. 23
No. 46, pp. 199-229, doi: 10.1108/jefas-07-2017-0084.

Memmel, C. (2011), “Banks’ exposure to interest rate risk, their earnings from term transformation,
and the dynamics of the term structure”, Journal of Banking and Finance, Vol. 35 No. 2,
pp. 282-289, doi: 10.1016/j.jbankfin.2010.08.003.

Miller, M. and Modigliani, F. (1958), “The cost of capital, corporation finance, and the theory of
investment”, The American Economic Review, Vol. 48 No. 3, pp. 261-297.

Nucera, F., Lucas, A., Schaumburg, J. and Schwaab, B. (2017), “Do negative interest rates make banks
less safe?”, Economics Letters, Vol. 159, pp. 112-115, doi: 10.1016/j.econlet.2017.07.014.

JEFAS
30,59

114

https://doi.org/10.2307/3440134
https://doi.org/10.1111/1368-423x.00043
https://doi.org/10.2307/1992333
https://doi.org/10.2307/2946651
https://doi.org/10.1111/j.1468-0297.2012.02551.x
https://doi.org/10.1016/j.jmacro.2022.103494
https://doi.org/10.1016/j.intfin.2014.06.007
https://doi.org/10.1016/s0304-4076(01)00098-7
https://doi.org/10.1016/s0304-4076(01)00098-7
https://doi.org/10.1016/j.jbankfin.2010.01.006
https://doi.org/10.1111/1468-0084.61.s1.13
https://doi.org/10.1108/jefas-07-2017-0084
https://doi.org/10.1016/j.jbankfin.2010.08.003
https://doi.org/10.1016/j.econlet.2017.07.014


Oliner, S.D. and Rudebusch, G.D. (1995), “Is there a bank lending channel for monetary policy?”,
Federal Reserve Bank of San Francisco Economic Review, Vol. 2, pp. 3-20.

Pesaran, M.H. (2007), “Simple panel unit root test in the presence of cross-section dependence”,
Journal of Applied Econometrics, Vol. 22 No. 2, pp. 265-312, doi: 10.1002/jae.951.

Petersen, W.M. (1986), “The effects of inflation on bank profitability”, in Recent Trends in Commercial
Bank Profitability – A Staff Study, Federal Reserve Bank of New York, pp. 89-114.

Plosser, C. (2012), “Economic outlook and the limits of monetary policy”, Speech at the Southern
Chester County Chamber of Commerce, October 11.

Praet, P. (2012), “Deleveraging and monetary policy”, Speech at the Hyman P Minsky Conference on
Debt, Deficits, and Unstable Markets, November 26.

Rajan, R. (2013), “A step in the dark: unconventional monetary policy after the crisis”, in Andrew
Crockett Memorial Lecture, BIS, Basel, June 23.

Rakshit, B. and Bardhan, S. (2023), “Does bank competition affect the transmission mechanism of
monetary policy through the bank lending channel? Evidence from India”, Journal of Asian
Economics, Vol. 86, 101595, doi: 10.1016/j.asieco.2023.101595.

Samuelson, P. (1945), “The effect of interest rate increases on the banking system”, The American
Economic Review, Vol. 35, pp. 16-27.

Sims, C.A. (1992), “Interpreting the macroeconomic time series facts: the effects of monetary policy”,
European Economic Review, Vol. 36 No. 5, pp. 975-1000.

Swanson, E. and Williams, J. (2014), “Measuring the effect of the zero lower bound on medium and
longer-term interest rates”, National Bureau of Economic Research, No. w20486.

Wright, J. (2012), “What does monetary policy do to long-term interest rates at the zero lower bound?”,
Economic Journal, Vol. 122 No. 564, pp. 447-466, doi: 10.1111/j.1468-0297.2012.02556.x.

Further reading
Baltagi, B. (2008), Econometric Analysis of Panel Data, John Wiley and Sons, Chichester.
Nguyen Phuc, C., Schinckus, C., Dinh Su, T. and Chong, F.H.L. (2021), “Institutional quality and risk

in the banking system”, Journal of Economics, Finance and Administrative Science, Vol. 26
No. 51, pp. 22-40, doi: 10.1108/jefas-01-2020-0012.

Wallich, H. (1977), “Inflation is destroying bank earnings and capital adequacy”, Bankers Magazine,
pp. 12-16, Autumn.

Corresponding author
Vighneswara Swamy can be contacted at: vswamypm@gmail.com

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mailto:vswamypm@gmail.com

	Are private banks more sensitive to changes in reserve requirements? Evidence from an emerging market
	Introduction
	Literature review
	Method
	Data and variables
	Analytical procedures
	Monetary policy affects the performance of bank groups

	Testing for causality

	Results
	Effects on bank profitability
	Effects on return on advances and investments
	Effects on nonperforming assets and employee business
	Effects on the performance of bank groups
	Estimating causality

	Discussions
	Effects on bank profitability
	Effects on return on advances and investments
	Effects on nonperforming assets and employee business performance
	Effects on the performance of bank groups
	Estimating causality

	Conclusions
	Policy implications

	Note
	References
	Further reading