Thursday, September 01, 2016

Measuring the transmission of monetary policy in India

by Rajeswari Sengupta.

The Finance Bill, 2016 amended the RBI Act, 1934 to establish the objective for RBI (where previously there was none): an inflation target. With the enactment of this law, the RBI is committed to meet pre-announced inflation targets within a specific period of time. For long, India has faced the adverse consequences of a discretionary monetary policy (link, link). Inflation targeting (IT), if implemented successfully, will improve accountability, certainty and transparency in India's monetary policy, and help stabilise the Indian macroeconomic and financial environment.

The weak link today is the monetary policy transmission (MPT). In the absence of strong and reliable links between the policy instruments controlled by the RBI and aggregate demand in the economy, it becomes difficult to do IT. In a recent paper (Mishra, Montiel, and Sengupta, 2016), we present evidence of a weak monetary policy transmission in India.

We explore two main issues in the paper:

  1. How does India fare in the factors that affect MPT?
  2. How effective is the bank lending channel of MPT in India?

Factors affecting MPT

Changes in monetary policy instruments translate into changes in aggregate demand through three main channels: bank lending or the interest rate channel, the exchange rate channel, and the asset price channel. The effectiveness of these channels is shaped by the extent of capital controls, policy constraints on exchange rate flexibility, and the structure of the financial system.

Financial markets integration and Exchange rate regime: According to Robert Mundell's "impossible trinity", in an economy with fixed exchange rate, monetary policy loses autonomy of choice when there is high integration between domestic and international financial markets. On the other hand, under a floating exchange rate, as the degree of financial integration increases, the power of monetary policy to affect aggregate demand increases.

We show in the paper that India has a relatively closed capital account in de facto terms, compared to major emerging economies such as Argentina, Brazil, Chile, Colombia, Israel, Malaysia, Mexico, Thailand, Turkey, Russia and South Africa. The exchange rate of the Rupee is determined in the interbank market. The RBI periodically intervenes in that market, buying and selling both spot and forward dollars at the market exchange rate. The limited degree of financial markets integration and RBI's interventions in the foreign exchange market are likely to mute the exchange rate response to monetary policy.

Structure of the domestic financial system: According to Mishra, Montiel and Spilimbergo (2012), MPT works better as the size and reach of the financial system increase, the degree of competition in the formal financial sector goes up and the domestic institutional environment lowers the costs arising from financial frictions.

We present evidence in our paper that the size of the formal financial system in India, measured by conventional indicators (such as the number of bank branches scaled by population or the percentage of adults with accounts at a formal financial institution) is relatively small compared to other advanced and emerging economies. The formal banking sector does not intermediate for a large share of the economy and is highly concentrated. India lags behind advanced and emerging economies in developing its bond market. Indicators of domestic institutional environment such as rule of law, regulatory quality, control of corruption, and political stability, show that India is roughly at the global median.

This suggests that the kind of public goods on which the financial system depends (such as enforcement of property rights, accounting and disclosure standards) may not be as readily available in India as in other countries. This would make financial intermediation a costly activity, weakening the effect of monetary policy actions.

Bank lending channel of MPT

There are two stages of the transmission process in the bank-lending channel, (i) the transmission from policy instruments to bank lending rates and (ii) the transmission from bank lending rates to final outcomes such as inflation and output. We use a structural vector autoregression (VAR) model in the paper to estimate the effects of a shock to monetary policy instruments on outcome variables through the impact on bank lending rates. The VAR model captures the full dynamic interactions among all the variables of interest. Given a shock to say the policy rate, it is possible to trace out the responses of all other variables to that shock, period by period.

In India, two broad groups of instruments have historically been used by the RBI to conduct monetary policy: (i) price based instruments such as the repo rate and the reverse repo rate: these affect the cost of funds for banks, and (ii) quantity-based instruments such as the Cash Reserve Ratio (CRR) and Statutory Liquidity Ratio (SLR): these affect the supply of banks' loanable funds.

We consider the effects of four instruments in our analysis: (i) the repo rate, (ii) the average of repo and reverse repo rates (price indicator), (iii) the sum of CRR and SLR (quantity indicator), and (iv) a composite score-based indicator of monetary policy stance. The price and quantity indicators have generally moved in the same direction during our sample period of 2001 to 2014. The exception is between 2011 and 2012, when increases in the policy rates suggested a tightening of monetary policy while the quantity indicator continued to move in a loosening direction.

To address this complication, we construct a score-based indicator of monetary policy stance following Das, Mishra and Prabhala (2015). We assign scores of 0, +1, -1, respectively if there is no change, an increase, or a decrease in the values of the four monetary policy instruments in any given month during our sample period. We calculate the overall stance of monetary policy by taking an unweighted sum of the scores for the individual instruments.

We use the "benchmark prime lending rate (BPLR)" of the banking sector till June 2010 and the "base rate" thereafter. Till 2010, the BPLR determined the interest rates charged by Indian banks on different categories of loans. From July 2010, it was replaced by the average base rate charged by the five largest commercial banks. We use the seasonally adjusted headline CPI inflation as an outcome variable. Another outcome variable is the output gap measured using the Index of Industrial Production (IIP). Since IIP covers only the manufacturing sector, we interpret the results on transmission to output with adequate caution.

We motivate our choice of endogenous variables in the VAR model using a modified version of the simple, open-economy New Keynesian model developed by Adam et. al. (2016). The model consists of an IS equation, a New Keynesian Phillips curve, an uncovered interest parity condition, an interest rate pass-through equation, and a Taylor-type monetary policy rule. Consistent with this model, we estimate a VAR for India with five endogenous variables: output gap, inflation rate, exchange rate, bank lending rate and the monetary policy instrument.

Shocks to the world food and energy prices may exert important effects on inflation in India. Since India is less likely to affect world food and energy prices, these prices measured in US dollars can be considered exogenous to developments in India. So we include these as exogenous variables in some versions of our estimated VARs. This is important because to the extent that shocks to either of these variables may help predict future headline CPI inflation in India, excluding them would undermine the identification of monetary policy shocks in India.

We follow two alternative identification schemes in the paper. One in which the monetary policy variable is ordered first, reflecting the assumption that the RBI does not observe (or does not react to) macroeconomic variables within the month, but the macro variables are potentially affected by monetary policy shocks contemporaneously. In this scheme the monetary policy variable is ordered first, followed by the bank lending rate, output gap, CPI inflation and exchange rate.

In the second scheme, the RBI responds to macro variables within the month, but those variables in turn respond to monetary policy only with a lag. Monetary policy variable is ordered last in this scheme and the ordering of the other variables remains the same.


Across both identification schemes and for all four monetary policy measures, a tightening of monetary policy is associated with an increase in bank lending rates. However the effect is statistically different from zero only at the 90 percent confidence level. This suggests that there is weak evidence for the first stage of transmission in the bank lending channel.

The effect of monetary policy changes on bank lending rates is hump-shaped, with the peak effects appearing between 5-10 months in all the cases considered.

The pass-through from the policy rate to bank lending rates is incomplete. For example, an increase of 25 basis points in the repo rate, is associated with an increase in the bank lending rate of only about 10 basis points.

The effect of monetary policy changes on the exchange rate is not statistically significant for any of the four monetary policy measures used. This suggests a non-existent exchange rate channel of MPT in India.

Our results provide no support for the second stage of transmission in the bank lending channel. We do not find evidence of effect of monetary policy changes on either the CPI inflation rate or the output gap.


A low degree of de facto capital mobility, RBI's interventions in the foreign exchange market, and the structure of the financial system suggest that the exchange rate and the asset price channels of MPT have low effectiveness in India. The burden of monetary transmission is likely to fall on the bank lending or interest rate channel. We present new evidence in our paper that the bank lending channel of MPT does not work well either.

With the adoption of IT, RBI has taken a step in the right direction. The enactment of the law by itself will not achieve price stability. A strong transmission mechanism from the policy rate to aggregate demand is crucial for the successful implementation of the new monetary policy framework. The legal mandate of IT must now be used to improve the effectiveness of MPT.


Das, Abhiman, Prachi Mishra, and Nagpurnanand Prabhala (2015), The Transmission of Monetary Policy Within Banks: Evidence from India, mimeo.

Li, Bin Grace, Stephen O'Connell, Christopher Adam, Andrew Berg, and Peter Montiel (2016), VAR meets DSGE: Uncovering the Monetary Transmission Mechanism in Low-Income Countries, IMF Working Paper, No. 16/90.

Mishra, Prachi, Peter J. Montiel, and Antonio Spilimbergo (2012), Monetary Transmission in Low-Income Countries, IMF Economic Review, 60, 270-302.

Mishra, Prachi, Peter J. Montiel and Rajeswari Sengupta (2016), Monetary Transmission in Developing Countries: Evidence from India, IMF Working Paper, No. 16/167.

Rajeswari Sengupta is a researcher at the Indira Gandhi Institute for Development Research, Bombay.

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