How does monetary policy transmission happen in India?

Central banks leverage monetary policy instruments to control the supply of money in the economy, which in turn influences economic outcomes through various channels. Analysing Indian data from 2001-2022 – a period when the country underwent significant monetary reforms and structural changes – this article assesses the relative strength of monetary transmission through the channels of interest, credit, and exchange rates.

Any discussion on the effectiveness of macroeconomic policy entails a thorough examination of the transmission of policy, that is, how a change in the money supply impacts the economy through various channels. The monetary transmission in emerging market economies (EME) is of special interest as it differs from that of advanced economies (AE) due to challenges like asymmetric information, illiquidity, imperfect institutions, and lack of central bank independence. Against this backdrop, our research (Joseph and Dash 2025) highlights the relative strength of monetary transmission through the channels of interest, credit, and exchange rate in EMEs, with a focus on India.

India warrants special attention for two major reasons. First, India has undergone significant monetary reforms and structural changes over the study period from 2001-02 Q1 to 2021-22 Q2, which could substantially impact the monetary transmission mechanism¹. During this period, the Indian monetary regime underwent a shift to Flexible Inflation Targeting (FIT) in April 2016, accompanied by the introduction of the Liquidity Adjustment Facility (LAF)². The period since the introduction of LAF has seen a decline in the share of agriculture in total employment, whereas manufacturing, construction, and services have risen. Additionally, the share of agricultural output relative to the total decreased, while the shares of manufacturing and construction remained constant, with a notable increase in the contribution of services.

Second, major studies on Indian monetary transmission inconclusively highlight the higher effectiveness of either the interest (for example, Mohan and Patra 2009, Goyal and Agarwal 2020) or the credit channel (for example, Aleem 2010, Bhatia 2023)³. However, these studies do not restrict the impact of alternative channels while gauging the relative strength of a particular channel. The passthrough of monetary policy happens in parallel through different channels (Mishkin 1995), and hence, it is important to isolate the alternative channels while examining the relative effectiveness of a specific transmission channel (Ramey 1993).

Our research is motivated to address these concerns by analysing the relative strength of each monetary transmission channel while entirely restricting the impact of other channels, after accounting for structural changes.

Methodology and data

A key existing approach to understanding the monetary transmission mechanism is to employ a Vector Autoregression (VAR) method, which involves a system of equations that depicts the economy and assesses how a ‘shock’ in a policy instrument transmits across the system and impacts outcomes. The study adopts Weighted Average Call Money Rate (WACMR) as the policy instrument, as it strictly follows the monetary stance of the Central Bank determined by the repo and reverse repo rates. Following the literature, we define the interest, credit, and exchange rate channels within this system using the Government Bond Yield Rate (GBYR) (Bhoi et al. 2017, Bhatia 2023), Prime Lending Rate (PLR) (Aleem 2010, Mishra et al. 2016), and Nominal Exchange Rate (NER) (Aleem 2010, Patra et al. 2024), respectively, and restrict the alternatives while examining a specific channel. To this system, we add significant structural shifts in growth and inflation due to the lingering effects of demonetisation, implementation of GST (Goods and Services Tax), investment and credit slowdown, and the revision of CPI-IW (Consumer Price Index-Industrial Workers) base year.⁴

Main findings

Our results highlight the presence of a strong credit channel followed by the interest channel. Figure 1 shows that, following a contractionary monetary shock (intended to reduce the supply of money in the economy), the inflation rate falls by a maximum of 0.15% and 0.2% through the interest and credit channels, respectively, suggesting a relatively stronger credit channel. Similarly, the growth rate falls by a maximum of 0.77% and 0.78% via the interest and credit channels, respectively, as shown in Figure 2.

Figure 1. Response of inflation rate to policy shock

Source: Authors’ calculations.

The theoretically inconsistent results observed through the exchange rate channel may be explained by the ‘exchange rate puzzle’, widely witnessed and documented in EMEs. The exchange rate puzzle refers to the weak or counterintuitive response of exchange rates to monetary policy changes, where currencies appreciate less than expected or even depreciate following an interest rate increase, challenging conventional economic theory. The growing literature on the exchange rate puzzle attributes its presence to the increased role of expectations, credit market frictions, and fiscal dominance. Dornbusch (1976) theoretically linked more of a change in the current exchange rate to expectations with regard to economic fundamentals rather than a change in the policy rate. Alternatively, Kohlscheen (2014) attributed the puzzle to expectations of investors shaped by credit market frictions, whereas Ferrara et al. (2021) suggested the exchange rate dynamics to also be determined by fiscal spending shocks that in turn lead to inflationary pressures, rise in interest rate, fall in investor sentiments, and currency depreciation. Similar to our findings, Mishra et al. (2016) estimated a fall in the real effective exchange rate, following a positive policy shock, even though theory would imply an appreciation of the rupee in such as case. In addition, Patra et al. (2024) empirically found weak monetary transmission through the exchange rate channel in India, arguing that the anticipation of policy changes limits the impact that these have on exchange rates.

Figure 2. Response of growth rate to policy shock

Source: Authors’ calculations.

We check the robustness of the result by replacing the CPI-based inflation rate with the WPI (Wholesale Price Index)-based inflation rate. Monetary policy in India has been administered in consideration of the WPI-based inflation rate during its Multiple Indicator Approach (MIA)⁵. Since a major share of the study period falls under the MIA, we also examined the validity of our results by replacing CPI inflation with the WPI inflation rate. This exercise results reaffirmed that the findings are robust to alternative inflation measures.

Concluding remarks

Our study revisits the Indian monetary policy transmission to examine the most effective channel that transmits the policy, considering structural changes. The empirical findings establish a relatively stronger transmission through the credit channel, which has received less attention in the literature compared to the interest channel. Additionally, the behaviour of the exchange rate channel, contrary to expectations, reveals the existence of an exchange rate puzzle similar to what is observed in other EMEs.

The findings have three major implications for the conduct of monetary policy in India. First, it supports the plan of the Reserve Bank of India (RBI) to bring Non-Banking Financial Companies (NBFCs) under a common interest rate framework to enhance monetary policy transmission. The initiative is worth pursuing as the credit-to-GDP (Gross Domestic Product) ratio of NBFCs surpasses that of Schedule Commercial Banks (SCBs) and stands at 13.6% for 2023-24. Second, our findings call on the RBI to tighten its supervision of the lending rate of SCBs to ensure closer alignment with the policy rate. A time-bound adjustment of lending and deposit rates of SCBs also enables the effective transmission of policy rate to outcomes. Third, the analysis underscores the need for active liquidity management – balancing the reserve requirements and open market operations – to further improve transmission. Implicitly, the findings also suggest that the Central Bank should formulate its policies, keeping in mind the regional economic conditions, particularly in areas where entities rely heavily on bank credit for their operations.

Notes:

1. This study period has been selected due to the following reasons. The Liquidity Adjustment Facility was introduced in 1998, but continuous data on repo and reverse repo are only available from 2001-02 Q1. The availability of this data is necessary to assess whether the selected policy instrument – Weighted Average Call Money Rate – follows the monetary stance of the Central Bank.

2. Under FIT, the Central Bank strictly focuses on the rate of inflation while formulating its policies and the target is fixed at 4%, with a tolerance band of ±2%. The LAF enables the RBI to manage short-term liquidity in the banking system via repo and reverse repo operations.

3. The interest channel operates when a rise in interest rate increases the financing cost and thus slows down investments in the economy. On the other hand, the credit channel works through intermediaries such as banks, where entities that heavily depend on bank credit are affected the most.

4. GST was introduced in India on 1 July 2017, consolidating various indirect taxes into a single unified system. Additionally, in January 2006, the CPI-IW was revised to a new base year of 2001, with increased weighting for the services sector.

5. During the MIA regime (1998-2016), the RBI monitored a range of economic indicators such as inflation, credit, exchange rate, capital flows, and output to guide monetary policy decisions – rather than relying on a single target.