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.
Summary
The background papers for the 2015 Article IV explore key issues affecting the Indian economy, and implications for fiscal, monetary, financial sector and other structural policies.
The first chapter examines how surges in global financial market volatility (including those triggered by uncertainties about monetary policy normalization in advanced economies and/or geopolitical tensions) 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 second chapter looks at food inflation, which has often been singled out as a key driver of India’s high and persistent inflation. India’s food inflation developments over the past decade appear to have reflected demand pressures driven by strong private consumption growth, which have often outpaced supply of key food commodities. Accordingly, supply side measures that will contain food inflation pressures on a durable basis remain critical to provide a robust foundation for the adoption of a low-inflation objective.
The third chapter employs a dynamic multi-country framework to analyze the macroeconomic transmission of El Niño weather shocks to India. The results show that India faces a short-lived fall in economic activity, as well as moderate inflationary pressures, as a consequence of an El Niño-based weather shock.
The fourth chapter examines monetary policy transmission in India, focusing on the interest rate and credit channels of transmission. The results indicate there is significant, albeit slow, pass-through of policy rate changes to bank interest rates. Furthermore, adjustment to monetary policy appears to be asymmetric: deposit rates adjust downwards in response to loosening but not upwards in response to tightening, while the lending rate adjusts more slowly to loosening than to tightening.
The fifth chapter examines India’s experience with fiscal rules. It outlines the main features of the Fiscal Responsibility and Budget Management Act (FRBMA), which was placed in abeyance following the 2008 global financial crisis. A discussion of possible modifications for a successor arrangement is also provided, drawing from international experience with fiscal rules.
The sixth chapter estimates the short- and long-run price and income elasticities of Indian exports, and investigates the role of structural rigidities in shaping export demand for Indian goods. While Indian exports respond positively to exchange rate depreciation in the short term, binding supply-side constraints dampen this responsiveness. This underscores the importance of exchange rate flexibility as a shock absorber, including in responding to external demand shocks. Policies to improve labor market flexibility can also help enhance India’s exports in the long run.
The seventh chapter assesses the effectiveness of major EMs’ policy actions since the taper episode of May 2013, focusing on India and how its experience compares with others. The policy actions are evaluated both for their immediate impact—the announcement effect on asset prices—and for their more medium-term effect in strengthening fundamentals. From a medium-term perspective, EMs with decisive and comprehensive policy actions (such as India) saw the largest improvements in fundamentals, and were relatively less affected during later bouts of market volatility.
The eighth chapter assesses the susceptibility of India’s non-financial corporate sector to a set of four commonly-used shocks—despite the positive turn that some financial variables have taken recently, corporate vulnerabilities remain at their highest level since about 2003. Furthermore, the profit margin is found to be highly correlated with future investments.
The ninth chapter uses firm-level data to investigate the role of corporate leverage in India’s recent investment slowdown. About one-third of the decline in India’s corporate investment-to-GDP ratio since 2011/12 can be attributed to the build-up of leverage, and the present strains in corporate and financial balance sheets will likely hold back investment activity in the near term.
The tenth chapter focuses on gains from enhancing financial inclusion and boosting access to credit in India. Using a micro-founded general equilibrium model, this chapter analyzes the effect of greater financial access on macroeconomic indicators, such as GDP growth, as well as on inequality and financial stability. It finds that removing certain constraints to finance, such as high collateral constraints, have large favorable effects on output and productivity while reducing income inequality.
The eleventh chapter catalogues India’s performance thus far on the eight Millennium Development Goals, which have a target date for achievement of end-2015. Progress has been mixed—India appears to be on track to meet one-and-a-half of these goals, will likely miss three-and-a-half of the goals, and will likely partially meet three of the eight goals by the target date.
The twelfth chapter examines the determinants of female labor force participation in India, against the backdrop of India having one of the lowest participation rates for women among peer countries. This chapter finds that a number of policy initiatives can help boost female economic participation, including increased labor market flexibility, higher investment in infrastructure, and enhanced social spending.
Spillovers from Surges in Global Financial Market Volatility1
How do surges in global financial market volatility (including those triggered by uncertainties about monetary policy normalization in advanced economies and/or geopolitical tensions) spillover to emerging market economies (EMs) including India? Does the magnitude of spillovers depend on the depth of financial linkages between advanced countries and EMs (i.e. the size of their external balance-sheets)? We study these questions empirically within a GVAR framework containing an index of global financial market volatility (capturing pressures in banking, securities, and exchange markets). Our 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.
1. In the summer of 2013, an indication by the U.S. Federal Reserve on plans to taper its security-purchase program created a surge in global financial market volatility, and resulted in adverse spillovers to EMs including India. Between May 22 and August 30, 2013, the rupee depreciated by about 20 percent, Indian 10-year domestic bond yields rose by 120 basis points, equity prices fell by 7 percent, and international reserves declined by 5 percent (reaching a low of US$275 billion or 5½ months of imports). In addition, India experienced significant portfolio outflows (around US$13 billion, primarily from debt markets).
2. Eichengreen and Gupta (2014) argue that a key determinant of the severity of the impact of tapering talks was the volume of prior capital inflows, and the external balance sheet exposure of EMs to advanced market economies (AMs). Countries that experienced rapid capital inflows and strong currency appreciation pressures during 2010-12 saw a sharp reversal in the 2013 episode of market volatility. Rey (2013) argues that there is a global financial cycle in capital flows, asset prices, and credit growth, and that cycle (proxied by VIX) is mainly driven by monetary policy settings of the United States—affecting leverage of global banks, and cross-border capital/credit flows. Moreover, Rajan (2014) has raised concerns about financial sector risks that may build up with prolonged use of unconventional monetary policies in advanced economies (due to increased leverage by banks and corporates; large cross-country capital flows; and excessive risk-taking by investors in a globally low-interest-rate environment). He has argued for more consideration by advanced countries of the effect that their policies will have on EMs and their eventual spillback to AMs.
3. This chapter examines the international spillover effects of surges in global financial market volatility (including those triggered by monetary policy normalization in advanced economies) and their dependence on the depth of financial linkages between advanced countries and EMs (i.e. the size of their external balance-sheets). Risk of excessive market volatility remains if advanced countries’ monetary policy tightening takes an uncertain turn, or occurs at an accelerated pace, especially in an environment where there has been significant capital flows to EMs between 2010–2012 (one major concern is the risk of an abrupt reversal of capital inflows to EMs). Our results show that in the event of sudden shifts in markets’ expectations about unconventional monetary policy (UMP) unwinding, asset prices can overshoot on the downside and normalization can be costly and may involve significant spillovers to other countries (including to EMs). In other words, with prolonged ultra-accommodative monetary policy in advanced economies, capital flows into recipient countries have often led to appreciating exchange rates, rising asset prices (beyond those justified by fundamentals), and procyclical policies. This makes the spillovers to EMs from an accelerated normalization larger, especially in EM countries with highly-leveraged corporates and large external/internal imbalances, which are at the same time more vulnerable to abrupt capital outflows.
4. We use a dynamic multi-country approach to analyze the international transmission of global financial market volatility shocks—based on an extended version of the global VAR (GVAR) models of Cashin et al. (2012, 2014a and 2014b).2 The framework comprises 26 region-specific models (including a single Euro Area region comprising 8 of the 11 countries that adopted the euro in 1999). These individual models are solved in a global setting where core macroeconomic variables of each economy are related to corresponding foreign variables (constructed exclusively to capture each country’s bilateral exposures to the other countries due to trade and financial linkages). The model has both real and financial variables: real GDP, inflation, the real equity price, the real exchange rate, short and long-term interest rates, and the price of oil. Furthermore, we add an index of financial stress (FSI) in advanced economies as an observable common factor to the GVAR framework, and investigate the effects of FSI shocks on macroeconomic variables of different countries.3 All data are quarterly in frequency, for the period 1979Q2 to 2013Q1.
5. Two key channels of transmission of financial market volatility shocks (which could be triggered by disorderly monetary policy normalization in AMs) are trade and financial linkages—that is, the external balance-sheet exposures of countries to each other. Financial linkages between AMs and EMs have grown rapidly in recent years, through cross-border credit exposures and cross-border holdings of debt and equity (see chart). This may be partly due (among other reasons) to prolonged use of ultra-loose monetary policies in AMs. Accordingly, we construct a series of financial weights at different points in time, 2009 and 2012, and study the size of financial market volatility spillovers originating in advanced economies over time. We construct the financial weights based on bilateral stocks of portfolio investment liability positions of countries, covering both equity and debt, derived from the IMF’s Coordinated Portfolio Investment Survey.
Figure. Portfolio Investment Liability Exposures of EMs to AMs
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Source: IMF, Coordinated Portfolio Investment Survey.Figure. Portfolio Investment Liability Exposures of EMs to AMs
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Source: IMF, Coordinated Portfolio Investment Survey.Figure. Portfolio Investment Liability Exposures of EMs to AMs
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Source: IMF, Coordinated Portfolio Investment Survey.6. Figure 1 visualizes the network of bilateral financial weights for the twenty-six countries in our sample. For each country in the network, the width of the line linking any given two economies is determined by the share of the bilateral portfolio investment liability (debt and equity) exposures in percent of the total as at end 2012. Not surprisingly, network-based indicators of the relative importance of any given country in global debt and equity markets point to the United States as the focal point. The U.S. is the world’s largest holder of debt and equity assets of other countries, and if financial conditions in the U.S. and consequently the rest of the world were to deteriorate, they would have non-negligible macroeconomic/financial effects on countries which are financially exposed to the United States. Figure 1 shows that India’s portfolio investment liability debt exposures to the rest of the world are relatively narrow (apart from exposures to Singapore), while equity exposures are stronger.
Network of Global Portfolio Investment Liability (Debt and Equity) Exposures 2012
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Source: IMF staff calculations based on Coordinated Portfolio Investment Survey (CPIS) data.Network of Global Portfolio Investment Liability (Debt and Equity) Exposures 2012
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Source: IMF staff calculations based on Coordinated Portfolio Investment Survey (CPIS) data.Network of Global Portfolio Investment Liability (Debt and Equity) Exposures 2012
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Source: IMF staff calculations based on Coordinated Portfolio Investment Survey (CPIS) data.7. Figure 2 focuses on the impact of a surge in global financial market volatility on a number of emerging market economies in Asia and in Latin America, and compares it to the impact on advanced countries using time-varying financial weights. The results indicate that a one-unit shock to the financial stress index (FSI)4—implying greater financial market volatility—translates into lower overall economic growth globally, and creates disinflation pressures in most countries (apart from India and Latin America). For the case of India, such a shock generates an output loss of around 1.1 percentage points during the first year after the shock, operating through trade and financial linkages. In Latin American countries, the commodity-price channel conveys an even larger adverse impact on economic activity (as oil prices fall by about 20 percent in our framework), where growth falls by more than two percentage points after one year. A widening of the output gap and lower commodity prices is likely to moderate inflation slightly by 25 basis points in advanced countries (not in India though). Nevertheless, there are significant heterogeneities across countries in their inflation responses. Equity prices are likely to fall by 10–20 percent, reflecting increased risk aversion; while the real exchange rate would depreciate to different degrees across countries (the impact on India is relatively small due to its historically-high inflation). Moreover, in most countries the term-premium (long-term interest rate minus the short-term interest rate) increases in response to a surge in global financial market volatility. Since the GVAR framework takes into account interlinkages between countries, we can also see the spillback effects on AMs from financial shocks (see the top left chart of Figure 2). Finally, the magnitude of impulse responses is generally higher when one uses the 2012 financial weights as opposed to the 2009 exposures, reflecting the impact of increased cross-country financial flows during periods of UMP program implementation in advanced economies.
Responses of Key Variables to Global Financial Market Volatility Shocks
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Note: Depicts change in macroeconomic/financial variables of a given country/region after one year associated with a one unit positive shock to an index of financial stress (FSI), implying an increase in global financial market volatility. Financial weights in 2009 and 2012 are used. U.S. dollar is the numeraire.Responses of Key Variables to Global Financial Market Volatility Shocks
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Note: Depicts change in macroeconomic/financial variables of a given country/region after one year associated with a one unit positive shock to an index of financial stress (FSI), implying an increase in global financial market volatility. Financial weights in 2009 and 2012 are used. U.S. dollar is the numeraire.Responses of Key Variables to Global Financial Market Volatility Shocks
Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A001
Note: Depicts change in macroeconomic/financial variables of a given country/region after one year associated with a one unit positive shock to an index of financial stress (FSI), implying an increase in global financial market volatility. Financial weights in 2009 and 2012 are used. U.S. dollar is the numeraire.8. The key findings of this chapter are as follows:
We confirm Rey’s (2013) view that there is a global financial cycle in capital flows and asset prices, as derived from our GVAR modeling framework.
We show that global financial market volatility (e.g. induced by monetary policy normalization uncertainty in advanced economies) has significant spillovers to emerging market economies (operating through trade and financial linkages, global liquidity and portfolio rebalancing channels).
We observe that there are heterogeneities across countries in their responses to a surge in global financial market volatility. This would reflect the scale of EMs’ trade and financial exposures to AMs, their individual cyclical positions, and their internal/external imbalances.
Consistent with Rajan (2014), we conclude that a prolonged term-premium compression raises financial stability concerns as the magnitude of financial spillovers has become larger over time, while asset prices and interest rates have become more correlated globally during the period of unprecedented monetary easing by advanced economies (see Figure 2).
We argue that strong fundamentals and sound policy frameworks per se are not enough to isolate countries from an increase in global financial market volatility. This is particularly the case where there is a sudden adjustment of expectations triggered by monetary policy normalization uncertainly in advanced economies. This argument is supported by the impulse responses in Figure 3, where no country (neither AMs nor EMs) appears immune from the impact of a surge in global financial market volatility.
There are also significant ‘spillbacks’ to advanced countries from financial shocks affecting EMs. We confirm that slowing growth in the rest of the world would weigh on advanced countries’ recovery. It is therefore very much in the source countries own interest to ascertain that financial stress is contained when tightening their monetary policy stances.
References
A. Chudik and M.H. Pesaran, 2014, “Theory and Practice of GVAR Modeling”, forthcoming in the Journal of Economic Surveys.
P. Cashin, K. Mohaddes, M. Raissi, and M. Raissi, 2014a, “The Differential Effects of Oil Demand and Supply Shocks on the Global Economy,” Energy Economics, Vol. 44, pp. 113–134.
P. Cashin, K. Mohaddes, and M. Raissi, 2014b, “Fair Weather or Foul? The Macroeconomic Effects of El Niño,” Cambridge Working Papers in Economics No. 1418.
P. Cashin, K. Mohaddes, and M. Raissi, 2012, “The Global Impact of the Systemic Economies and MENA Business Cycles,” IMF Working Paper WP/12/255 (Washington: International Monetary Fund).
B. Eichengreen and P. Gupta, 2014, “Tapering Talk: The Impact of Expectations of Reduced Federal Reserve Security Purchases on Emerging Markets,” Policy Research Working Paper Series 6754, The World Bank.
R. Cardarelli, S. Elekdag, and S. Lall, 2009, “Financial Stress, Downturns, and Recoveries”, IMF Working Paper WP/09/100 (Washington: International Monetary Fund).
R. Rajan, 2014, “Competitive Monetary Easing: Is It Yesterday Once More?”, Reserve Bank of India Monthly Bulletin, 1-12, available at: http://www.rbi.org.in/scripts/BS_SpeechesView.aspx?Id=886
H. Rey, 2013, “Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence”, Paper presented at the 25th Jackson Hole Symposium, Wyoming, August.
Prepared by Mehdi Raissi and Paul Cashin.
See also Chudik and Pesaran (2014) for a survey on theory and practice of GVAR modeling.
The FSI for advanced countries is constructed by Cardarelli et al (2009) as an average of the following indicators: the “beta” of banking sector stocks; TED spread; the slope of the yield curve; corporate bond spreads; stock market returns; time-varying stock return volatility; and time-varying effective exchange rate volatility. Such an index facilitates the identification of large shifts in asset prices (stock and bond market returns); an abrupt increase in risk/uncertainty (stock and foreign exchange volatility); liquidity tightening (TED spreads); and the health of the banking system (the beta of banking sector stocks and the yield curve).
One unit of FSI is equivalent to one standard deviation. This index measures price movements relative to trend, with a historical average value of zero (implying neutral financial market conditions). The magnitude of the shock is comparable to the 2002 episode of market volatility in AMs and is much smaller than the GFC shock.
