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Article

Monetary Policy via Bank Lending Channel: Evidence from Lending Decomposition

by
Putra Pamungkas
1,*,
Fadli Septianto
1,
Irwan Trinugroho
1,
Rossazana Ab-Rahim
2,
Masagus M. Ridhwan
3,4 and
Bruno S. Sergi
5,6
1
Center for Fintech and Banking, Universitas Sebelas Maret, Surakarta 57126, Indonesia
2
Faculty of Economics and Business, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia
3
Bank Indonesia Institute, Jakarta 10410, Indonesia
4
Perbanas Institute, Jakarta 12940, Indonesia
5
Department of Economics, University of Messina, 98122 Messina, Italy
6
Harvard Center for International Development, Harvard University, Cambridge, MA 02138, USA
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(5), 249; https://doi.org/10.3390/jrfm18050249
Submission received: 11 March 2025 / Revised: 21 April 2025 / Accepted: 29 April 2025 / Published: 5 May 2025
(This article belongs to the Special Issue Banking Practices, Climate Risk and Financial Stability)
Versions Notes

Abstract

:
This paper examines the regional dimension of monetary policy transmission through the component of the bank lending channel in Indonesia. Understanding the effectiveness of this transmission channel at a regional level is crucial, given the diverse economic characteristics across Indonesian provinces. We employ panel regression to analyze the panel data consisting of provincial quarterly data from 2010–2023 for 33 provinces in Indonesia. The robustness of the results is further assessed through GMM estimation techniques. We find evidence of the bank lending channel through the use of the policy rate. Our findings are meaningful in the SME and consumer lending channel and are also more profound in Java than in the non-Java region. Further, using GMM estimation, we show that our results are robust. Our study highlights the significant role of regional differences in Indonesia when examining monetary policy effectiveness. Policymakers should therefore consider regional disparities and lending categories to enhance the efficacy of monetary policy interventions.

1. Introduction

Research in macroeconomics and financial studies has extensively explored how monetary policy is transmitted through the banking system, with the bank lending channel recognized as one of the most influential mechanisms. This channel highlights the central role banks play in adjusting their lending behavior in response to changes in monetary policy. Under restrictive monetary conditions, banks typically reduce credit supply, affecting real economic activity through consumption and investment (Bernanke & Gertler, 1995).
While transmission can occur through several pathways, such as the interest rate, exchange rate, asset prices, and liquidity channels (Can et al., 2020), the bank lending channel remains especially relevant in economies where banks dominate financial intermediation. The strength of this channel depends not only on macroeconomic conditions but also on bank-specific characteristics and the types of credit being extended. Recent empirical studies confirm that monetary policy influences different loan types in varied ways and that capital structure, funding stability, and asset quality further moderate this relationship (Albertazzi et al., 2021; Albrizio et al., 2019; Imbierowicz et al., 2021).
In particular, recent studies have emphasized that the bank lending channel functions not only as an aggregate value in lending but also as a specific type of lending (Caglio et al., 2021; Jiménez et al., 2012, 2020). Ivashina et al. (2022) found that the dynamics of credit growth and bank lending channels vary across bank loan types. However, while there is some understanding of how monetary policy affects lending in developing economies, particularly regarding regional disparities (Abuka et al., 2019; Liu, 2024), the specific impacts on different lending types like SME, consumer, and business loans remain underexplored.
Indonesia presents an ideal context to address these gaps. As a large archipelago state, Indonesia experiences significant regional economic and financial disparities (Nugraha & Prayitno, 2020). Java Island, which includes provinces such as Jakarta, West Java, Central Java, Yogyakarta, East Java, and Banten, functions as the economic, political, and financial center of the country. Although it covers only around 7% of Indonesia’s total land area, Java is home to more than 56% of the national population and contributes approximately 58% to the country’s total GDP. In comparison, regions outside Java, referred to as non-Java regions, consist of Sumatra, Kalimantan, Sulawesi, Bali, Nusa Tenggara, Maluku, and Papua. These areas tend to have lower levels of industrial development, financial inclusion, and access to formal credit. Data from the 2020 Village Potential Statistics (PODES) indicates that approximately 89% of districts in Java have at least one bank branch, whereas only about 53% of districts outside Java have similar access (Fikri, 2023). Such disparities may lead to variations in the effectiveness of monetary policy transmission across regions. Moreover, lending composition, including SME, consumer, and business lending, may respond differently to changes in policy rates, depending on regional economic structures and financial development.
The primary objective of this paper is to examine the effectiveness of monetary policy transmission through the bank lending channel by decomposing lending into three specific categories, namely SME lending, consumer lending, and business lending. The motivation for this study stems from existing evidence suggesting that monetary policy may influence these loan categories differently, highlighting the importance of identifying which types of lending respond more significantly to policy rate changes. Additionally, considering Indonesia’s notable regional economic disparities, this study explores whether monetary policy transmission operates differently between regions with varying levels of economic and financial development, specifically comparing the Java and non-Java regions.
This study contributes to the literature in two ways: first, by providing evidence in the debate on the bank lending channel through the decomposition and comparison of various types of credit to identify which type of credit is the most responsive to monetary policy transmission, and second, by identifying the lending channel in the Java and non-Java regions, which exhibit different economic growth characteristics.
Our unique data enables us to distinguish lending types into SME, business, and consumer lending. We also take into account the regional effects of the bank lending channel by separating the sample between the Java and non-Java regions. Using monthly data from 2010–2023 for 33 provinces in Indonesia, we found that increases in the policy rate significantly reduce SME and consumer lending, while business lending remains less affected. Regional disparities reveal a stronger monetary policy impact in the non-Java regions.
This paper is organized as follows. Section 2 reviews the relevant literature on monetary policy transmission through the bank lending channel. Section 3 depicts the data, variables, and methodology employed in the analysis. Section 4 presents the descriptive statistics. Section 5 discusses the empirical results, including robustness checks and regional disparities. Section 6 presents the conclusions.

2. Literature Review

2.1. Theoretical Foundation of Monetary Policy

Fisher’s (1911, 1912) quantity theory of money suggests a direct link between the money supply and price levels, assuming that both the velocity of money and output remain unchanged. This perspective underscores the significance of managing the money supply to maintain stable prices. Later, Friedman (1968) advanced the monetarist perspective by asserting that inflation is fundamentally a monetary phenomenon. He advocated for predictable growth in the money supply, supporting a rule-based approach to monetary policy rather than relying on discretionary interventions. Building on these theoretical foundations, Bernanke and Gertler (1995) developed the concept of the bank lending channel, which focuses on the loan supply provided by depository institutions such as banks. This mechanism is understood as a monetary contraction, in which a reduction in bank deposits and reserves limits the ability of banks to issue loans. Consequently, this decreases the funding available to borrowers who rely on banks, leading to a decline in overall spending and output. In the field of monetary policy transmission research, prior studies often investigate the bank lending channel as an aggregate (Breitenlechner et al., 2016; Fungáčová et al., 2016; Halvorsen & Jacobsen, 2016). Some researchers pay attention to specific lending instead of the growth of aggregate lending (Ciccarelli et al., 2015; Ivashina et al., 2022). Focusing on specific lending might provide deeper insights into understanding how the channel works.

2.2. The Informational Foundations of the Bank Lending Channel

Banks play a crucial role in the implementation of monetary policy, particularly due to persistent information asymmetries in loan markets. They mitigate these asymmetries by acquiring private borrower information that is not easily accessible to other lenders, resulting in borrower capture. Dell’Ariccia and Marquez (2004) demonstrate that this informational advantage enables banks to allocate credit more efficiently, especially in markets with high information asymmetry, and to retain less creditworthy but more dependent borrowers. In competitive or liberalized environments, banks strategically reallocate lending toward market segments where their informational advantage is strongest. Additionally, banks leverage transaction data from supply chains to assess the creditworthiness of SMEs. According to Yang et al. (2019), transaction signals derived from supplier relationships enable banks to infer firm quality under incomplete information, allowing them to design risk-sensitive interest rate policies and reduce financing constraints for firms embedded in supply chains.
Banks further enhance credit allocation through relationship lending and the adoption of digital technology. Zhao (2021) shows that ongoing lending relationships, such as credit lines, generate valuable borrower information that improves risk assessment and enables banks to offer favorable terms, particularly to opaque borrowers. Digital innovation strengthens this function by expanding the ability of banks to collect and integrate multidimensional borrower data. Zhang et al. (2025) highlight that technologies such as big data and machine learning allow banks to evaluate green loan applicants more effectively, reduce credit risks, and support sustainable lending. Moro et al. (2015) also emphasize the importance of information quality in credit provision. Their findings show that a one-unit improvement in information quality leads to a 12% increase in short-term credit, underlining the value of an information-rich lending environment.
In addition to improving credit allocation, banks manage informational frictions through mechanisms that address adverse selection and moral hazard. These frictions arise when banks differ in their unobservable characteristics, such as their monitoring efficiency, or when borrowers’ efforts are unobservable. To address these issues, banks employ incentive-compatible contracts that align incentives and promote truthful behavior. The model developed by Santibáñez et al. (2020) demonstrates that investors can use optimal contract menus to separate bank types and ensure that each bank exerts the desired monitoring effort. These dynamic contracts incorporate real-time monitoring, liquidation triggers, and contingent compensation based on continuation utilities and temptation values. As a result, loan performance improves, and default risks are mitigated.
The bank lending channel remains a central mechanism for monetary policy transmission, particularly in bank-based financial systems where information asymmetries between lenders and borrowers are persistent. Banks manage these asymmetries by acquiring private borrower information, building long-term lending relationships, and utilizing technological innovations to improve credit assessments. Although the literature has extensively examined the role of the bank lending channel, the specific relationship between monetary policy and bank-specific lending behavior remains inconclusive. Empirical evidence shows that these frictions significantly shape monetary transmission. For instance, Kandrac (2012) finds that during periods of monetary tightening, banks reduce lending to small firms and borrowers with high agency costs. Kishan and Opiela (2006) further show that banks with weaker capital experience sharper declines in loan growth following contractionary policy changes, particularly when regulatory pressures are high. More recently, Huang et al. (2024) provide evidence from international banking flows, revealing that domestic uncertainty can lead to a puzzling retrenchment of cross-border bank outflows. Their analysis suggests that financial development plays a mitigating role by reducing information asymmetry. In countries with more advanced financial systems, the decline in international lending during uncertain times is less severe, indicating that greater transparency and information-sharing capacity can soften the impact of domestic uncertainty on global banking activity. These findings collectively confirm that both institutional characteristics and informational frictions critically influence the strength and pattern of monetary policy transmission through the bank lending channel.

2.3. Regional Heterogeneity in Monetary Policy Transmission

Although monetary policy actions are typically implemented at the national level, research consistently shows that their effects vary across regions. Carlino and DeFina (1998) examined whether monetary policy exerts a similar effect across regions in the United States. A study by Furceri et al. (2019) showed that the impact of monetary policy shocks varies across U.S. states, with Wyoming, Arizona, and Michigan experiencing the largest economic contractions, while New York and Alaska are more resilient, and spatial spillover effects amplify the impact, indicating that economic interconnections between regions influence responses to interest rate changes implemented by the Fed. Ridhwan et al. (2014) demonstrate that in Indonesia, monetary policy shocks also result in heterogeneous regional outcomes, with the manufacturing-based provinces such as West Java and East Java experiencing significantly greater output losses than regions that are more service- or resource-based, like Bali and Riau. Their findings support the relevance of both the interest rate and credit channels at the subnational level. Regions with a higher share of small firms and greater dependence on small banks exhibit stronger responses to monetary tightening, indicating the importance of bank-based credit constraints and firm-level financial vulnerability. Guo and Masron (2014) investigated the provincial effects of monetary policy in China and found that provinces respond differently to monetary actions. Mishra et al. (2014) found that in low-income countries, weak financial development and banking concentration reduce the effectiveness of policy rate changes. Abuka et al. (2019) showed that In Uganda, banks with lower capital and higher liquidity, particularly those exposed to sovereign debt, transmit monetary tightening more strongly, resulting in uneven credit allocation across districts. Grandi (2019) demonstrated that in the euro area, banks in sovereign-stressed countries transmit monetary easing less effectively due to higher sovereign risk exposure. These findings suggest that the effectiveness of monetary policy depends on local financial conditions, supporting the relevance of studying Indonesia, where regional disparities in banking and institutional capacity are substantial. Indonesia presents a unique and policy-relevant setting for examining the lending channel of monetary policy due to several institutional and structural features of its financial system. As a bank-based economy, banks account for approximately 70% of total financial sector assets, making them the dominant source of external finance for firms and households (Park, 2011). Furthermore, Naiborhu (2020) finds that while both large and small banks reduce lending in response to monetary tightening, the extent of their responsiveness varies with bank-specific characteristics. In particular, the credit growth of large banks is more sensitive to changes in the BI rate when they possess lower capital buffers and liquidity positions. These characteristics tend to fluctuate across regions, as large banks may be concentrated in more developed provinces with greater exposure to economic cycles and regulatory scrutiny. Conversely, small banks, which are often locally based and operate in more peripheral regions, show less variation in transmission strength due to limited sensitivity to capital and liquidity. These findings suggest that monetary policy may have uneven effects across Indonesia’s regions, depending on bank size distribution, financial resilience, and local macroeconomic conditions.

3. Data and Methodology

Our empirical analysis is based on provincial level quarterly data from 33 provinces across Indonesia from 2010 to 2023. The choice of this sample is primarily determined by data availability and consistency across all key variables. The time frame from 2010 to 2023 reflects the period for which quarterly data are consistently available, while also capturing multiple economic phases, including the post-global financial crisis recovery, significant policy shifts by Bank Indonesia, and the COVID-19 pandemic shock. Furthermore, our complete dataset enables us to classify loans as SME, consumer, or business loans. This detailed information lets us investigate how different loan types react to shifts in monetary policy. This is informative, since each type of lending has different purposes and might lead to different outcomes. SME lending reflects financing to smaller, often credit-constrained firms that are more sensitive to interest rate shifts. Consumer lending captures household borrowing behavior, which is closely tied to monetary conditions and personal income cycles. Business lending, covering investment and working capital loans, is linked to productive economic activity. Disaggregating these lending types allows for a more precise analysis of how the bank lending channel operates across different borrower categories and regional economic environments.
We use the Bank Indonesia interest rate for monetary policy (MP) from the central bank as our main independent variable. An increase in the Bank Indonesia interest rate signifies a tightening of monetary policy, whereas a decrease in the Bank Indonesia interest rate reflects a monetary policy easing, according to Naiborhu (2020). We also use several control variables in the model. Deposit/GDP (DP) is the regional deposit normalized by the regional GDP. We use this variable as a proxy for the regional banks’ funding capacity, as a higher deposit base relative to GDP reflects stronger internal liquidity that enables banks to maintain loan supply during monetary tightening, thereby capturing the heterogeneity in the effectiveness of the bank lending channel (Kapoor, 2019). The number of bank branches in each province (BR) is used as a proxy for regional banking development and accessibility. In Indonesia, where digital banking is still concentrated in urban areas, physical branches remain essential for credit distribution, especially in rural regions. Branch presence enhances competition and outreach, making banks more responsive to monetary policy shocks (Segev & Schaffer, 2020). The NPL/GDP (NPL) represents the regional non-performing loan normalized by regional GDP. We use this variable to capture the effect of credit risk on banks’ lending behavior, as higher non-performing loans weaken banks’ willingness to lend and amplify the contractionary effects of monetary tightening by reducing credit supply responsiveness (Abuka et al., 2019). Finally, we use GDP growth (GDP) as a control variable to capture the cyclical variation in the strength of the bank lending channel, as the transmission of monetary policy through bank lending is significantly more potent during periods of low economic growth, when financial frictions are more binding and banks’ external finance premiums are higher (Sapriza & Temesvary, 2020, 2024). The sources of all variables are provided in Table 1.
We begin our estimation using a linear panel regression to capture the impact of monetary policy on bank lending. The linear specification allows us to efficiently estimate the average effect of changes in the central bank policy rate on bank lending behavior, while controlling for regional macroeconomic and financial conditions. This approach aligns with established empirical literature on the bank lending channel, including the work of Kishan and Opiela (2012) and Fungáčová et al. (2016), who use panel regression for the estimation. We apply a fixed effects model to account for time-invariant unobserved heterogeneity across provinces, and the Hausman test confirms that fixed effects are preferred over random effects. We construct a three-panel model using three dependent variables consisting of SME, consumer, and business lending. The following is the equation to be estimated:
Lit = αit + β1 MPt + β2 DPit + β3 BRit + β4 NPLit + β5 GDPit + εit
where Lit denotes the bank lending (SME, consumer, and business) normalized by regional GDP for province i at time t. Because of the slow response of the monetary stance, the usage of a lag of the monetary stance variable is considered. To capture those lags, we also estimate the model using two-step system GMM for the robustness check, following the methods of Matousek and Sarantis (2009). System GMM ensures consistent estimation by employing internal instruments derived from lagged explanatory variables. As noted by Shokr et al. (2014) and Nguyen and Dinh (2022), this estimator effectively captures the dynamic nature of bank lending while addressing unobserved heterogeneity and simultaneity bias. The two-step process involves an initial estimation using a homoskedasticity-assuming weighting matrix to produce consistent coefficient estimates, followed by a second estimation using a robust weighting matrix based on the first-step residuals. This second step accounts for heteroskedasticity and serial correlation, enhancing the efficiency and robustness of the estimates (Arellano & Bover, 1995; Blundell & Bond, 1998). The specifications of the system GMM model are as follows:
Lit = αit + β1 Lit–1 + β2 MPt + β3 DPit + β4 BRit + β5 NPLit + β6 GDPit + εit
To account for the dynamic nature of bank lending behavior, we incorporate the lagged dependent variable Lit–1, reflecting the notion that current lending decisions are partly shaped by past lending activity. Given the potential endogeneity of Lit–1, the GMM approach addresses this issue by employing internal instruments, specifically the lagged values of the dependent and explanatory variables, to produce consistent and unbiased estimates. Furthermore, we apply Hansen’s J-test and the Arellano–Bond first- and second-order correlation to indicate the over-identifying restriction.

4. Descriptive Statistics

Table 2 reports a summary statistic for all samples. As shown, the mean of our dependent variables, which consist of SME lending, consumer lending, and business lending, are 0.286, 0.391, and 0.511, respectively; the standard deviation of the dependent variables is 0.144, 0.169, and 0.472, respectively. Meanwhile, our key independent variable’s mean and standard deviation (policy rate) are 0.056 and 0.012, respectively.
Table 3 reports the summary statistics for the Java and non-Java region samples. As shown, the mean of SME lending, consumer lending, and business lending in the Java region are 0.274, 0.383, and 0.924, respectively. Meanwhile, the mean of SME lending, consumer lending, and business lending in the non-Java region are 0.289, 0.393, and 0.420, respectively. This shows that SME lending and consumer lending in the non-Java region are slightly greater than in the Java region. However, business lending in the Java region is greater than in the non-Java region, indicating that the large enterprises are concentrated in the Java region. However, the percentage of SMEs and consumption in both regions is nearly the same.

5. Results and Discussion

We investigate the impact of policy rates on monetary policy transmission through bank lending. We employ provincial-level data that is estimated using panel regression. Table 4 estimates the impact of policy rates on bank lending. The regression results for SME, consumer, and business lending are presented in column (1), column (2), and column (3), respectively. Based on the regression result, we acquired several findings.

5.1. SME Lending

First, as presented in Table 4, the estimation results show that the policy rate is negatively associated with SME lending. Our findings align with those of Gregor and Melecký (2018), who found that changes in monetary policy rates significantly affect SME lending rates. SMEs are particularly sensitive to interest rate fluctuations, which can impact their loan acquisition decisions and repayment behaviors (Alter & Elekdag, 2020; Musonda & C. Hapompwe, 2024). The greater sensitivity of SME to interest rate fluctuations can be explained by several structural factors. The study by Belas et al. (2018) highlights that low levels of financial education, insufficient family and social support, and poor understanding of capital cost lead SMEs to rely more heavily on external financing, without fully assessing the associated risks. These firm-level vulnerabilities are further intensified by macroeconomic transmission channels. Changes in macroeconomic variables, particularly interest rates, are transmitted through the financial system and influence lending conditions. Shareef and Shijin (2017) emphasize that fluctuations in short-term interest rates, often caused by fiscal imbalances or shifts in monetary policy, have a significant impact on the term structure of interest rates in the financial market. In the context of Indonesia, changes in Bank Indonesia’s policy rates are quickly transmitted to the financial sector, influencing the lending rates. Indonesian SMEs often depend on short-term loans and do not have many other funding options. When interest rates increase, SME borrowing costs will rise, making it harder for them to operate efficiently, stay competitive, and avoid defaulting on their loans. In addition, according to Favero et al. (2016), demographic trends, particularly the age composition of the population, play a crucial role in determining the long-term behavior of interest rates. The study finds that shifts in demographics, such as changes in the ratio between middle-aged individuals and younger cohorts, influence the equilibrium real interest rate. When a large portion of the population is in their high-saving years, typically middle-aged, aggregate savings tend to increase, which can exert downward pressure on interest rates. Conversely, when the population is younger or aging, aggregate savings may decline, potentially leading to higher equilibrium rates. These demographic influences evolve gradually over time, making them highly persistent. As a result, central banks must consider these trends when formulating monetary policy, which can contribute to more sustained periods of elevated interest rates following policy adjustments. In Indonesia, the demographic structure is relatively young, with a median age of around 30 years, suggesting strong economic potential (Worldometer, 2025). However, economic growth remains steady but modest, around 5% annually, which may not fully absorb the growing workforce (LPEM FEB UI, 2024). For SMEs, this means that they operate in an environment with a large consumer base and a young labor force, but they also face challenges such as rising financing costs and limited demand growth. As a result, Indonesia’s current demographic and economic trends may contribute to prolonged monetary tightening effects, making SMEs more vulnerable to high interest rates over time.
As for the control variable, the deposit-to-GDP ratio is positively associated with SME lending, indicating that greater deposit mobilization supports credit supply to smaller firms. A strong deposit base enhances bank liquidity and improves their lending capacity. The number of bank branches is also significantly positive, reflecting the importance of physical infrastructure in facilitating SME access to finance. In Indonesia, where many SMEs operate in rural or semi-urban areas with limited digital access, branches play a critical role in relationship-based lending (Janković et al., 2023). The NPL to GDP variable is not statistically significant for SME lending, suggesting that banks do not immediately adjust credit allocation to SME in response to rising credit risk at the aggregate level. GDP growth shows a negative and significant association. SMEs are more sensitive to credit availability. During economic booms, banks often shift their lending focus to larger firms or safer assets that provide higher returns and lower risk, which reduces the credit supply for SMEs. Additionally, SMEs that experience revenue growth during expansions may decrease their loan demand, preferring to use retained earnings for operations or investments. This results in a countercyclical pattern in SME lending.

5.2. Consumer Lending

Our results show a negative association of policy rate with consumer lending, meaning that when the policy rate increases, consumer lending tends to decrease, and vice versa. The weakening of monetary transmission to consumer lending can be caused by several factors. First, during our period of study (2010–2023), Indonesia experienced some economic downturns and financial crises due to the COVID-19 pandemic (Cakranegara, 2020; Sugandi, 2022). High uncertainty and economic downturn can dampen the effect of monetary policy on lending. During such times, banks may perceive higher risks and thus tighten lending standards, reducing the pass-through of policy rate changes. Financial crises often lead to weakened bank balance sheets due to declines in asset prices and increased debt burdens. This results in banks being more cautious in lending, thereby reducing the effectiveness of monetary policy transmission to consumer loan rates (Mora, 2014; Zentefis, 2020). Banks with significant exposure to interest rate risk may adjust their lending practices less in response to changes in monetary policy. This can lead to a weaker pass-through of policy rates to consumer lending rates (Gomez et al., 2021). Furthermore, banks with higher leverage or liquidity constraints may be less responsive to monetary policy changes, affecting their lending behavior and the transmission of policy rates to consumer loans (Abuka et al., 2019).
In addition, consumer-side behavior may also contribute to the weakening of transmission. When interest rates rise or the economy is uncertain, households often save more and spend less on credit. Lower-income or financially constrained households, which make up a large part of the Indonesian population, are less affected by interest rate changes because they have fixed spending needs and limited savings. This uneven response among borrowers can reduce the overall effect of policy rate changes on consumer credit demand. Moreover, certain aspects of Indonesia’s consumer credit market can reduce the impact of monetary policy changes. The heavy dependence on unsecured personal loans and the limited availability of formal credit in rural or less developed areas mean that changes in policy rates do not affect everyone equally. Further, the rapid growth of alternative lending platforms, such as peer-to-peer lending and fintech-based credit providers, has expanded access to consumer financing beyond traditional banking channels. In 2023, Indonesia’s alternative lending market was valued at approximately USD 5.78 billion and is projected to reach USD 12.63 billion by 2028 (Research & Markets, 2024), reflecting strong digital adoption and rising demand for accessible credit. While this development has improved financial inclusion, particularly for underserved populations, it also introduces new dynamics to the transmission of monetary policy. Unlike conventional banks, many fintech lenders operate independently of central bank policy rates, as confirmed by OJK Regulation POJK No. 40/POJK.05/2024, which emphasizes their role as intermediaries and requires them to use credit scoring systems for risk-based pricing, relying on investor-provided capital rather than on monetary policy-linked funding sources. As a result, changes in Bank Indonesia’s policy rate may have limited influence on the interest rates charged by these platforms. Consequently, a growing share of consumer lending is becoming less responsive to monetary tightening, thereby weakening the overall effectiveness of monetary policy in influencing household credit behavior. In periods of rising interest rates, these platforms may continue to provide credit at competitive terms, partially offsetting the contractionary effects intended by conventional monetary tools. Thus, this finding indicates that the impact of policy rates is more profound in SME and consumer lending than business lending.
As for the control variables, the deposit-to-GDP ratio is positive and significant, indicating that increased deposits enhance the ability of banks to lend to households. Similarly, the number of bank branches exerts a strong positive influence on consumer lending. Despite the rise in digital banking, physical branches remain crucial for processing consumer loans, offering trust, convenience, and tailored financial products. The NPL to GDP ratio is statistically insignificant, suggesting that household lending is not directly or immediately constrained by loan quality at the macro level. However, GDP growth is negatively and significantly related to consumer lending. During periods of strong GDP growth, rising household incomes reduce the need for consumer borrowing, as individuals are more able to finance consumption from their current income. Moreover, macroprudential tightening, such as stricter loan-to-value and debt-service ratio requirements, often accompanies economic expansion, which can limit access to personal loans and credit cards. Banks may also become more selective in their consumer lending to manage credit risks and reduce exposure to non-performing loans.

5.3. Business Lending

In contrast to SME and consumer lending, the relationship between the policy rate and business lending is statistically insignificant (see Table 4 and Table 5). This can be explained by some factors that differentiate corporate borrowers from SMEs and individual consumers. First, large businesses in Indonesia typically have access to diverse financing sources beyond bank loans, including retained earnings, bonds, and foreign credit lines. This reduces their reliance on domestic bank lending and makes them less sensitive to Bank Indonesia’s policy rate changes. Second, business lending decisions are often tied to long-term strategic investments, rather than short-term borrowing costs, as financial institutions increasingly evaluate firms’ preparedness for future societal transitions and allocate credit based on long-term value creation potential rather than immediate rate sensitivity (Kurznack et al., 2021). This is also supported by the work of Hasanah (2018), who found that the long-term interest rate pass-through (IRPT) for investment loans in Indonesia is incomplete, with lending rates only partially adjusting to changes in policy rates. Although the adjustment process is relatively fast, the degree of responsiveness remains limited, reflecting the structural nature of business lending, which is less reactive to short-term monetary shifts. Furthermore, recent data from OJK (2025) show that corporate loans grew by 15.81% year-on-year, far surpassing the 2.88% growth in loans to MSMEs. This evidence highlights the resilience of business lending amid tightening monetary conditions, reinforcing the view that policy rate hikes exert a limited dampening effect on credit allocated to large corporate borrowers.
As for the control variable, among all loan categories, the deposit-to-GDP ratio exerts the strongest effect on business lending. This result suggests that lending to businesses is highly dependent on the bank’s funding capacity, especially for large loan disbursements. The number of bank branches also has a positive and significant effect, although the effect size is slightly smaller than that for SME and consumer lending. While large firms may not rely on branches for routine transactions, branch presence can still indicate stronger institutional reach and credibility in facilitating business finance. As with other lending types, the NPL to GDP ratio remains statistically insignificant for business lending. GDP growth is negatively and significantly associated. In strong economic periods, corporations may utilize internal funds, such as retained earnings, rather than seek external bank financing. Additionally, many large firms have access to alternative funding sources like capital markets, bonds, or foreign credit lines, reducing their dependence on domestic bank loans. Consequently, despite favorable economic conditions, banks may experience a decline in demand for business loans.

5.4. Regional Analysis

We also divide our sample into Java and non-Java regions to capture the relationship between monetary policy and bank lending disparity. Table 5 provides the regression results for the effect of monetary policy on bank lending in the Java and non-Java region samples. The regression results for SME, consumer, and business lending in the Java region are presented in column (1), column (2), and column (3), respectively. Additionally, the regression results for SME, consumer, and business lending in the non-Java region are presented in column (4), column (5), and column (6), respectively.
As shown in Table 5, the policy rate has a negative and significant relationship with SME lending in the Java region. Meanwhile, in the non-Java region, the effect of the policy rate is significantly and negatively related to SME and consumer lending. It is shown that the effectiveness of the policy rate as a monetary policy instrument is more profound in the non-Java region than in the Java region. However, the coefficient of policy rate to SME lending in Java is greater than in the non-Java region. This divergence may reflect structural differences in financial system development and economic conditions across regions. Java, as the economic and financial hub of Indonesia, benefits from a more developed banking infrastructure, greater credit availability, and diversified sources of finance. These characteristics may make both SMEs and consumers in Java relatively less sensitive to policy rate fluctuations, as they can access alternative financial products or negotiate more favorable credit terms. In contrast, financial markets in the non-Java regions tend to be less mature, with fewer alternatives to bank credit and more limited financial literacy. As a result, borrowers in these areas are more exposed to interest rate changes, making monetary policy transmission more direct and effective.
Deposit-to-GDP and the number of bank branches consistently support lending across all categories, with stronger effects observed for business loans in Java and consumer loans in the non-Java region, reflecting regional differences in financial infrastructure and deposit mobilization. Interestingly, NPL is significantly and positively associated with all lending types in the non-Java region, suggesting a possible lag in risk recognition or credit continuation, despite rising defaults. Finally, GDP growth is negatively associated with lending across both regions.

5.5. Robustness Check Analysis

Table 6 provides a robustness check using the GMM model for the estimation. The estimation results for SME, consumer, and business lending are presented in column (1), column (2), and column (3), respectively. Our regression result shows the negatively significant effect of policy rate on SME, consumer, and business lending, confirming the main result of this study.
Specifically, the policy rate remains significantly negative for SME and consumer lending, reinforcing our conclusion that monetary tightening reduces credit to these segments. More notably, the coefficient for business lending becomes negative and statistically significant under the GMM estimation, suggesting that, after accounting for dynamic relationships and endogeneity, policy rate increases also constrain business lending. This finding implies that the earlier insignificance observed in the baseline estimation may be due to the inability of static models to capture delayed adjustment behavior in business credit decisions. These results suggest that the transmission of monetary policy to business lending does exist, but it is more nuanced and possibly slower to materialize compared to the effects on SMEs and consumers.
Furthermore, the robustness of the GMM estimates is supported by diagnostic tests. The Arellano–Bond test for autocorrelation shows no second-order serial correlation, while the Hansen J-test for overidentifying restrictions yields high p-values across all models, indicating that the instruments are valid and not overfitted. These results provide confidence in the consistency and efficiency of the GMM estimators. The robustness check confirms the overall validity of our main results and strengthens the argument that policy rate changes negatively influence credit growth across all loan categories, with the effect more pronounced in SME and consumer lending.

6. Conclusions

This paper examines the regional dimension of monetary policy transmission through the component of the bank lending channel in Indonesia. This study specifies that the bank lending channel functions not only as an aggregate value but also as a specific type of lending. We employ panel regression to analyze the panel data consisting of provincial quarterly data from 2010–2023 for 33 provinces in Indonesia. Our empirical results show that the bank lending channel operates in Indonesia, but its effectiveness varies across loan types and regions. We find that SME and consumer lending are more negatively affected by increases in the policy rate, while the effect on business lending appears weaker in the baseline estimation. The regional analysis reveals that the transmission of policy rates is stronger in the non-Java regions, where borrowers are more dependent on bank credit, and financial systems are less developed. Our robustness check using the system GMM estimator confirms these findings, which reveal that after controlling for endogeneity and dynamic effects, business lending is also significantly influenced by policy rate changes.
Our findings have important policy implications. Policymakers need to consider the potential unintended consequences of monetary tightening for financially vulnerable groups. To address this, Bank Indonesia and related financial authorities may consider implementing complementary policy instruments. These may include targeted credit guarantee schemes for SMEs; interest rate subsidies for essential consumer credit, such as for education and housing; and countercyclical lending programs administered through state-owned or regional development banks during periods of policy rate hikes. Furthermore, policies that promote financial inclusion and expand access to alternative forms of financing, such as digital lending platforms and financial technology-based services, can help strengthen borrower resilience and reduce dependence on conventional bank credit. Improving monetary policy communication and aligning it more closely with regional credit conditions can also enhance the effectiveness of policy transmission and reduce uncertainty for financial institutions and borrowers at the local level.
While our analysis is based on strong empirical evidence, we recognize certain limitations. First, we rely on provincial-level aggregated data and are unable to incorporate bank-specific characteristics such as capital adequacy, liquidity, or credit risk. Second, our analysis does not examine the downstream effects of credit changes on regional inflation, money supply, or economic growth, which would require a different modeling framework and macroeconomic data. For future research, we suggest extending this analysis to other emerging economies or countries with multi-regional structures to see if similar patterns of asymmetric transmission exist. Future studies could use micro-level banking data to explore how monetary policy interacts with bank balance sheet conditions, or assess the impact of financial inclusion, digital finance adoption, or regional institutional capacity on the credit channel’s effectiveness. Additionally, it would be valuable to investigate how changes in lending due to monetary policy affect broader real-sector outcomes like regional growth, investment, and inflation to better understand policy effectiveness across regions.

Author Contributions

Conceptualization, P.P.; Methodology, P.P.; Software, F.S.; Validation, I.T., R.A.-R., M.M.R. and B.S.S.; Formal analysis, P.P., F.S.; Data curation, F.S.; Writing—original draft, P.P.; Writing—review & editing, I.T., R.A.-R., M.M.R. and B.S.S.; Supervision, I.T., M.M.R. and B.S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Universitas Sebelas Maret Research Group Grant: 194.2/UN27.22/PT.01.03/2024.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data derived from public domain resources.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Variable definitions.
Table 1. Variable definitions.
VariableDefinitionData Source
SME
Lending/GDP (L)		Regional bank lending for SMEs normalized by regional GDPIndonesian Financial Services Authority (OJK)
Consumer
Lending/GDP (L)		Regional bank lending for personal and household
normalized by regional GDP		Indonesian Financial Services
Authority (OJK)
Business Lending/GDP (L)Regional bank lending for businesses (consisting of investment and working capital lending) normalized by
regional GDP		Indonesian Financial Services Authority (OJK)
Policy Rate (MP)Central bank interest rateBank Indonesia
Deposit/GDP (DP)Regional deposit normalized by regional GDPIndonesian Financial Services
Authority (OJK)
Branch (BR)Total regional bank branchesIndonesian Financial Services
Authority (OJK)
NPL/GDP (NPL)Regional non-performing loans normalized by regional
GDP		Indonesian Financial Services
Authority (OJK)
GDP Growth (GDP)Regional GDP percentage changeIndonesia Statistics Bureau
Table 2. Summary statistics.
Table 2. Summary statistics.
NMeanStd. Dev.MinMax
SME Lending/GDP18150.2860.1440.0391.439
Consumer Lending/GDP18150.3910.1690.0821.065
Business Lending/GDP18150.5110.4720.0793.626
Policy Rate18150.0560.0120.0350.077
Deposit/GDP18150.9810.70010.2965.272
Branch1815106.40121.7512576
NPL/GDP18150.0250.0460.0021.023
GDP Growth17820.0230.044−0.2170.282
Table 3. Summary statistics for Java and non-Java regions.
Table 3. Summary statistics for Java and non-Java regions.
JavaNon-Java
NMeanStd. Dev.MinMaxNMeanStd. Dev.MinMax
SME Lending/GDP3300.2740.0810.1460.52814850.2890.1550.0391.439
Cons. Lending/GDP3300.3830.1490.1480.75414850.3930.1730.0821.065
Buss. Lending/GDP3300.9240.9420.1823.62614850.4200.1730.0791.218
Policy Rate3300.0560.0120.0350.07714850.0560.0120.0350.077
Deposit/GDP3301.6671.3040.6175.27214850.8280.3060.2962.125
Branch330299.78169.9753576148563.4339.3812212
NPL/GDP3300.0450.1050.0111.02314850.0200.0100.0020.069
GDP Growth3240.0210.027−0.1170.10314580.0240.047−0.2170.282
Table 4. Full sample results.
Table 4. Full sample results.
SpecificationFull Sample
SME LendingCons. LendingBuss. Lending
(1)(2)(3)
Policy Rate−0.731 **−1.096 ***−0.524
(−2.28)(−4.05)(−1.29)
Deposit/GDP0.114 **0.107 **0.331 ***
(2.33)(2.65)(3.77)
Branch0.00131 ***0.00186 ***0.00149 **
(5.30)(5.04)(2.11)
NPL/GDP0.0434−0.0009840.0830
(0.71)(−0.03)(1.05)
GDP Growth−0.164 ***−0.198 ***−0.168 ***
(−3.11)(−5.95)(−2.95)
_cons0.08010.156 **0.0621
(1.31)(2.30)(0.57)
N178217821782
R20.0680.2730.263
adj. R20.0660.2700.261
Note; t statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.
Table 5. Java and non-Java region results.
Table 5. Java and non-Java region results.
JavaNon-Java
SME LendingCons. LendingBuss. LendingSMECons. LendingBuss. Lending
(1)(2)(3)(4)(5)(6)
Policy Rate−0.665 *−0.3920.259−0.671 **−1.072 ***−0.143
(−2.48)(−1.14)(0.33)(−2.36)(−3.71)(−0.31)
Deposit/GDP0.09600.04120.459 ***0.1050.134 **0.226 *
(1.38)(1.65)(7.84)(1.58)(2.41)(2.06)
Branch0.000936 ***0.00112 ***0.000922 **0.00149 *0.00321 **0.00366 ***
(5.14)(5.19)(3.77)(1.88)(2.63)(4.86)
NPL/GDP0.002680.000184−0.001512.639 ***1.551 **2.188 ***
(0.21)(0.02)(−0.08)(3.59)(2.58)(4.18)
GDP Growth−0.0893−0.206 ***−0.470−0.140 ***−0.165 ***−0.128 ***
(−1.70)(−4.36)(−0.95)(−2.92)(−5.08)(−3.26)
_cons−0.1260.00553−0.1180.09490.112−0.0327
(−0.87)(0.07)(−0.56)(1.68)(1.34)(−0.37)
N324324324145814581458
R20.4410.4880.3880.0840.3400.288
adj. R20.4330.4800.3780.0810.3380.286
Note; t statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.
Table 6. Robustness check.
Table 6. Robustness check.
Full Sample
SME LendingCons. LendingBuss. Lending
(1)(2)(3)
Lending-10.869 ***0.186 ***1.036 ***
(218.47)(23.58)(18.40)
Policy Rate−0.147 ***−0.614 ***−0.253 ***
(−8.05)(−23.16)(−3.42)
Deposit/GDP0.0684 ***0.133 ***0.335 ***
(11.77)(13.04)(31.00)
Branch0.000153 *0.000717 ***0.00279 ***
(1.89)(6.13)(21.58)
NPL/GDP−0.530 ***0.814 ***1.588 ***
(−5.81)(6.18)(8.83)
GDP Growth−0.199 ***−0.0109 *0.0939 ***
(−27.85)(−1.70)(5.06)
_cons−0.0184 *0.129 ***−0.426 ***
(−1.87)(8.87)(−21.07)
N178217821782
AR (1)0.0940.0920.020
AR (2)0.3180.5330.466
Hansen0.9780.9840.986
F96772.952644.568525.60
Note; t statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.
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MDPI and ACS Style

Pamungkas, P.; Septianto, F.; Trinugroho, I.; Ab-Rahim, R.; Ridhwan, M.M.; Sergi, B.S. Monetary Policy via Bank Lending Channel: Evidence from Lending Decomposition. J. Risk Financial Manag. 2025, 18, 249. https://doi.org/10.3390/jrfm18050249

AMA Style

Pamungkas P, Septianto F, Trinugroho I, Ab-Rahim R, Ridhwan MM, Sergi BS. Monetary Policy via Bank Lending Channel: Evidence from Lending Decomposition. Journal of Risk and Financial Management. 2025; 18(5):249. https://doi.org/10.3390/jrfm18050249

Chicago/Turabian Style

Pamungkas, Putra, Fadli Septianto, Irwan Trinugroho, Rossazana Ab-Rahim, Masagus M. Ridhwan, and Bruno S. Sergi. 2025. "Monetary Policy via Bank Lending Channel: Evidence from Lending Decomposition" Journal of Risk and Financial Management 18, no. 5: 249. https://doi.org/10.3390/jrfm18050249

APA Style

Pamungkas, P., Septianto, F., Trinugroho, I., Ab-Rahim, R., Ridhwan, M. M., & Sergi, B. S. (2025). Monetary Policy via Bank Lending Channel: Evidence from Lending Decomposition. Journal of Risk and Financial Management, 18(5), 249. https://doi.org/10.3390/jrfm18050249

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