This Selected Issues paper examines how surges in global financial market volatility spill over to emerging market economies (EMs) including India. The results suggest that a surge in global financial market volatility is transmitted very strongly to key macroeconomic and financial variables of EMs, and the extent of its pass-through increases with the depth of external balance-sheet linkages between advanced countries and EMs. The paper also looks at food inflation, which has often been singled out as a key driver of India’s high and persistent inflation.

Abstract

This Selected Issues paper examines how surges in global financial market volatility spill over to emerging market economies (EMs) including India. The results suggest that a surge in global financial market volatility is transmitted very strongly to key macroeconomic and financial variables of EMs, and the extent of its pass-through increases with the depth of external balance-sheet linkages between advanced countries and EMs. The paper also looks at food inflation, which has often been singled out as a key driver of India’s high and persistent inflation.

Monetary Policy in India: Transmission to Bank Interest Rates1

This chapter provides new evidence on the effectiveness of monetary policy transmission in India, focusing on the interest rate and credit channels of transmission. The analysis finds evidence of significant, albeit slow, pass-through of policy rate changes to bank interest rates in India. There is evidence of asymmetric adjustment to monetary policy: deposit rates do not adjust upwards in response to monetary tightening but do adjust downwards to loosening, while the lending rate adjusts more quickly to monetary tightening than to loosening.

1. Monetary policy transmission in India is often thought to be characterized by long and uncertain time lags (Mohanty 2014). This hinders policy making by making it difficult to predict the effects of policy actions on the economy. The path for a strengthened monetary policy framework in India has been laid out recently in the Patel Committee report to the Reserve Bank of India (RBI).2 Concerns about transmission are not unique to India, as the effectiveness of monetary policy transmission in developing countries as a whole has recently come into question. Mishra et al. (2013) survey the empirical literature and find that transmission is weak and unreliable in developing countries and Mishra et al. (2014) find large variation in the response of bank lending rates to monetary policy shocks across countries, with weaker transmission in developing countries.

2. The analysis in this chapter focuses on the interest rate and credit channels of monetary transmission, as these are the dominant ones in India. The following three questions are studied using a two-step Vector Error Correction (VEC) model:

  • (i) What is the extent of pass-through from changes in the monetary policy rate to deposit and lending rates of Indian banks?

  • (ii) What is the speed of adjustment to policy rate changes?

  • (iii) Is the response to tightening and loosening symmetric?

To answer these questions, the pass-through from monetary policy to bank interest rates is estimated in two steps: (1) from the monetary policy rate (repo rate) to the interbank market rate that is targeted by the monetary policy framework (weighted average call money rate (WACMR)), and then (2) from the target rate (WACMR) to bank interest rates (deposit and lending rates). There are several advantages to this stepwise estimation. First, the results from the first step indicate how well the operating framework is set up to control its target market rate. Second, the interpretation of relationships is clearer than it would be in a VEC model with multiple (three) cointegrating relationships.

3. The data used in the analysis consists of monetary policy rates (repo rate and the reverse repo rate), data on injections under the Liquidity Adjustment Facility (LAFnetinj), market rates (WACMR, three-month deposit rate, and the prime lending rate) and bank balance-sheet information. Each observation is a two-week period and the sample runs from end-March 2002 to end-October 2014. All variables used in the analysis are nonstationary (I(1)).

Table 1.

Descriptive Statistics

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Source: RBI, CEIC; IMF staff calculations.Note: 328 observations. NTDL refers to banks’ net time and demand liabilities.
A04ufig01

Monetary Policy Rate and Bank Interest Rates

Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A004

Source: CEIC.

4. The VEC estimation method is used due to the presence of cointegrating vectors in the variables. In the first step, trace statistics suggest the presence of a cointegrating vector between the repo rate and the WACMR. In the second step, no cointegrating vector between the deposit rate and the lending rate is found, but test results indicate two cointegrating vectors between the WACMR, the deposit rate, and the lending rate.

Summary of Cointegration Test Results

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Source: IMF staff estimates.Note: Results from Johansen tests for cointegration, at 95% confidence level.

5. Each step of the estimation has two stages, estimating the long-run relationship (the cointegrating vector, equation LR) and then the short-run relationship from which the speed of adjustment is ascertained (equation SR).

Step 1 – Pass-through to WACMR (target rate) from monetary policy

(LR)WACMRt=β0+β1 RepoRatet+εt(SR)ΔWACMRt=αECTt+Σk=1Kδ2kΔWACMRtk+δ3kΔ(LAFnetinj/NTDL)tk+vt

where the error correction term:

ECTt=WACMRt1β^0β^1RepoRatet1

is the residual from the LR equation, which measures period t-1 deviations from the long-run stationary relationship.

  • The average elasticity of WACMR with respect to the repo rate is η=β1mean(RepoRate)mean(WACMR).

  • α gives the share of the deviation from the long-run equilibrium that is closed each time period, thus representing the speed of adjustment.

Step 2 – Pass-through to bank interest rates from WACMR

(LR1)LendingRatet=θ10+θ11WACMRt+ε1t(LR2)DepositRatet=θ20+θ21WACMRt+ε2t(SR1)ΔLendingRateit=α1ECT1t+α2ECT2t+Σk=1K(δ3kΔLendingRateitk)+δ4kΔWACMRtk+δ5kΔLoans/Assetstk)+vitl(SR2)ΔDepositRateit=α1ECT1t+α2ECT2t+Σk=1K(δ3kΔDepositRateitk)+δ4kΔWACMRtk+δ5kΔLoans/Assetstk)+vitdwhere   ECT1t=ε^1t  and  ECT2t=ε^2t

6. The analysis find significant pass-through from policy rate changes to bank interest rates. The average elasticity of the WACMR with respect to the repo rate is 1.43. Over the two-steps of the analysis, the cumulative long-run elasticity of the deposit rate with respect to the repo rate is 1.58. This indicates that a 1 percentage point decrease in the repo rate leads to a 1.58 percentage point decrease in the deposit rate over time. Pass-through to the lending rate is partial—the cumulative long-run elasticity of the lending rate with respect to the repo rate is 0.43.

7. Pass-through to deposit and lending rates is relatively slow and the deposit rate adjusts more quickly to monetary policy changes than does the lending rate. In the first step of transmission, it takes 13 months for 80 percent of the pass-through from a change in the repo rate to the WACMR. Eighty percent of a change in the WACMR passes-through to the deposit rate in 9.5 months, and to the lending rate in 18.8 months (Table 2).

Table 2.

Speed of Adjustment: Number of Months Required to Complete 80% Pass-through of Repo Rate

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Source: IMF staff estimates.

Upper bound on time to achieve 80% of pass-through.

8. There is evidence of asymmetry in the pass-through to bank interest rates. The estimates of the speed of adjustment coefficients indicate that the lending rate adjusts more quickly to an increase in WACMR than to a decrease. In Table 3, the coefficient on ECT1 pos (lending) corresponds to a decrease in WACMR and the coefficient on ECT1 neg (lending) corresponds to an increase in WACMR. Similarly, the estimated speed of adjustment coefficients indicate that the deposit rate adjusts downwards when WACMR falls, but not upwards to a monetary tightening. The coefficient on ECT2 pos (deposit) corresponds to a decrease in WACMR, while the coefficient on ECT1 neg (deposit) corresponds to an increase in WACMR.

Table 3.

Bank Interest Rates and WACMR: Asymmetric SR VECM Results

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Source: IMF staff estimates.Notes: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Lags of differenced WACMR, lending rate, deposit rate, loans/assets, and constant not shown.

References

  • Mishra, P. and P. Montiel, 2012, How Effective Is Monetary Transmission in Developing Countries? A Survey of the Empirical Evidence, November, 2012, (IMF Working Paper No.12/143), Economic Systems, 2013, Elsevier, vol. 37(2), pages 187216.

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  • Mishra, P., P. Montiel, P. Pedroni, A. Spilimbergo, 2014, “Monetary Policy and Bank Lending Rates in Low-Income Countries: Heterogeneous Panel Estimates,” July 2014, forthcoming, Journal of Development Economics.

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  • Mohanty, D., 2014, “Addressing Impediments to Transmission of Monetary Policy,” Chapter 4 of “Report of the Expert Committee to Revise and Strengthen the Monetary Policy Framework,” Reserve Bank of India, January 21, 2014.

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1

Prepared by Sonali Das.

2

Report to the Expert Committee to Revise and Strengthen the Monetary Policy Framework, January 21, 2014, www.rbi.in/scripts/PublicationReportDetails.aspx?UrlPage=&ID=743.