The study investigates the effectiveness of monetary policy transmission in India, focusing on how changes in the policy rate affect bank lending rates. The Indian banking system plays a dominant role in financial intermediation, making the transmission of monetary policy signals through the banking system crucial for achieving price stability and growth. Since the deregulation of lending rates in 1994, the Reserve Bank of India (RBI) has mandated various benchmarks for loan pricing: the prime lending rate (PLR), benchmark prime lending rate (BPLR), base rate, and finally the marginal cost of funds-based lending rate (MCLR) in 2016. Each benchmark aimed to improve transparency and efficiency in loan pricing, but each had shortcomings. This study compares the pass-through of monetary policy to lending rates under the base rate and MCLR regimes using quarterly bank-level data from 2012 to 2019. It aims to be the first to comparatively analyze transmission under these two distinct internal benchmark regimes, contributing novel insights to the existing literature on monetary policy transmission.

The literature on monetary policy transmission is extensive, dating back to Keynes (1936) and encompassing contributions from Friedman and Schwartz (1963), Ando and Modigliani (1963), Tobin (1969), Taylor (1995), Obstfeld and Rogoff (1995), and Meltzer (1995), among others. These studies explore traditional interest rate channels and credit channels. More recent research by Mohanty and Turner (2008), Mukherjee and Bhattacharya (2011), Mishra et al. (2010), and Trichet (2011) examines the efficacy of various transmission channels in developed and emerging markets. While there's agreement on money's influence on aggregate demand and prices, there's disagreement on the relative importance of different transmission channels. Numerous studies have examined monetary transmission in India, employing different methodologies and examining various channels. Some studies, such as Singh and Kaliranjan (2007), emphasize interest rate channels, while others (Mallick and Sousa, 2012; Bhattacharya et al., 2010; Aleem, 2010; Pandit and Vashisht, 2011; Sengupta, 2014; Das, 2015) assess different aspects of transmission. Others utilize New Keynesian models (RBI, 2002; Patra and Kapur, 2012; Goyal, 2008; Anand et al., 2010), focusing on the Phillips curve (Patra and Kapur, 2000; Dua and Gaur, 2009; Paul, 2009; Patra and Ray, 2010; Mazumder, 2011; Singh, 2011). Studies using bank-level data to explore the interest rate channel in India are relatively fewer and include Bhaumik et al. (2010), Das (2013), John et al. (2018), and Mishra and Kelly (2017). However, no previous study has specifically examined the role of different benchmark regimes in influencing monetary policy transmission. This gap is addressed by the current research.

The study uses a two-pronged approach to assess monetary policy pass-through. First, it conducts an aggregate-level analysis using a vector autoregressive (VAR) framework to examine the relationship between the weighted average lending rate on fresh rupee loans (WALRF), the weighted average call rate (WACR), inflation, and real GDP. The ADF test confirms that all variables are I(1), and the Johansen cointegration test reveals a long-run equilibrium relationship, leading to the estimation of a vector error correction model (VECM) to analyze both short-run and long-run dynamics. Second, it performs a disaggregated analysis using a generalized method of moments (GMM) dynamic panel data regression model at the individual bank level. This addresses heterogeneity in pass-through across banks by incorporating bank-specific characteristics. The GMM model includes the change in the weighted average lending rate on fresh rupee loans as the dependent variable and the weighted average call rate (WACR) as the monetary policy indicator. Nine models are estimated, each incorporating a different bank-specific variable: term deposit rate, total asset size, liquidity, capital to risk-weighted assets ratio (CRAR), return on assets, non-performing assets (NPA), non-interest income, operating expenses, and investments in securities approved for statutory liquidity ratio (SLR). Inflation and real GDP growth are included as control variables to capture credit demand and lending risk. Dummy variables are introduced to account for the period when the marginal standing facility (MSF) rate became the de facto policy rate and the demonetization period. Bank-specific characteristics are normalized to facilitate interpretation of the average monetary policy effect. The Arellano-Bover (1995) system GMM is employed to address endogeneity issues and improve estimation efficiency. Diagnostic tests (Sargan test, AR(1), AR(2)) are used to validate the model.

The aggregate-level VECM analysis shows a positive and statistically significant relationship between WALRF and WACR, indicating that a 1 percentage point increase in WACR leads to a 0.36 percentage point increase in WALRF in the long run. The disaggregated GMM results reveal that the long-run effect of WACR on lending rates is positive and statistically significant across all nine models for the entire sample period. The estimated long-run multipliers of WACR range from 0.13 to 0.24 percentage points. Analysis of the base rate regime (2012-2016) shows a long-run effect of WACR on WALRF ranging from 0.11 to 0.19 percentage points. The MCLR regime (2016-2019) exhibits a significantly larger long-run effect, with a range of 0.26 to 0.47 percentage points. The study found that higher deposit rates, bank size, and operating expenses hinder monetary transmission during easing cycles, while higher capital adequacy ratios (CRAR) facilitate it. Interestingly, in the MCLR regime, higher non-performing assets (NPA) were associated with lower lending rates, possibly due to risk-averse lending strategies adopted by banks after the asset quality review (AQR).

The findings suggest that monetary policy transmission is more effective under the MCLR regime compared to the base rate regime. This difference is likely due to several factors, including the increased transparency of the MCLR system, better alignment of liquidity management with monetary policy, the introduction of the flexible inflation targeting (FIT) framework, and the reduced credit demand associated with decelerating economic activity. While the MCLR improved transmission compared to previous regimes, it wasn't fully satisfactory, prompting the RBI to mandate external benchmark-based pricing for certain loan categories. The increased responsiveness of lending rates to changes in the policy rate under the MCLR regime highlights the importance of improving transparency and reducing discretion in loan pricing mechanisms. The findings have implications for central bank policy design and highlight the role of bank-specific factors in determining the effectiveness of monetary policy transmission.

This study provides a comparative analysis of monetary policy transmission in India under the base rate and MCLR regimes. The results show significantly higher pass-through under the MCLR regime, which can be attributed to improved transparency and the macroeconomic context. The findings underscore the importance of well-designed benchmark systems for effective monetary policy implementation. Future research could explore the impact of external benchmark-based pricing, examine the role of specific bank characteristics in greater detail, or analyze regional variations in monetary transmission.

The study focuses solely on internal benchmark regimes. It doesn't incorporate external benchmark data, limiting its scope to understand the full evolution of monetary policy transmission in India. The study could be expanded to include data covering the transition to external benchmarks for a more complete picture. The impact of non-linearity on the lending rate may not have been fully explored due to data constraints. Future research could explore the role of non-linearity in the monetary transmission process.