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India: trade wars and capital flight



How will US policies affect the Indian economy?

  • We argue that US trade and monetary policy will have a substantial effect on the Indian economy
  • In a global trade war, India could fall victim to adverse trade policies of the US and/or China. To analyse the effects on the Indian economy, we look into three possible trade war scenario’s where (i) China targets Indian exports, (ii) the US targets Indian exports and (iii) India retaliates against the US
  • Our calculations show that India will lose most in scenario iii, namely 2.3% of missed GDP growth by 2022. Mainly, this is because the tariffs will reduce exports and cause (imported) inflation, which will hurt Indian purchasing power and investments
  • Besides a possible trade war, a faster than expected tightening of US monetary policy will hurt India, namely via capital outflows. We estimate a model of Indian capital flows to quantify this effect and find that India’s capital flows fall substantially and result in India losing USD 22bn in missed capital inflows up to 2022. Our model also indicates that in case political risk rises in India and the INR depreciates sharply, missed capital flows will amount to USD 32bn by 2022
  • In both cases, India will face a shortage of capital since capital flows will not cover its finance requirements. The consequence will be further downward pressure on INR and possibly higher interest rates if India uses its foreign reserves to plug the difference between its finance requirements and capital flows

As US trade policy is becoming more protectionist while US monetary policy is tightening, it is likely that emerging markets will bear the brunt of the change, but by how much? In this Special, we look at the case of India and try to quantify the effects of a trade war and an unexpected acceleration in the tightening of US monetary policy on India’s economy and capital flows respectively.

The impact of a potential trade war on India

In the current trade spat between the US and China, many countries could end up being caught in the middle. To get an idea of the implications of a possible trade war on the Indian economy, we make an assessment of three possible scenarios: 1) China targets Indian exports, 2) the US targets Indian exports and 3) India retaliates US protectionism.[1]  

One thing we have learned since the US radically changed its foreign trade policy in February is that the Trump administration wants support from its allies in tightening the trade screws on China. So what would happen if India decides to line up with the US against China (for example by threatening with tariffs), and China decides to target Indian exports with 20% import duties? Currently, China (incl. Hong Kong) is India’s third most important export market after the US and United Arab Emirates, with more than USD 22bn in goods being shipped to Chinese shores each year. Half of this export basket consists of gold and jewelry (figure 1), which are not exactly necessity imports and renders them pretty easy targets of protectionism. A 20% import duty would result in higher import export prices of Indian goods, a drop of the Indian export market size and, consequently, a USD 11bn decline in Indian export volumes.[2] Ultimately, such a scenario would shave off 0.4ppt of Indian GDP in 2018 (calendar years). 

If India chooses not to side with the US in targeting China, and the US penalizes India with a 20% levy on Indian exports, our calculations show that the damage on GDP could total 0.9% up to 2022 (figure 2).[3] The higher impact is due to the fact that the US is a more important trading partner for India than China. In a third scenario, India retaliates the protectionist measures of the US by targeting US exports by an equal tariff of 20%. In this case, the Indian economy could face total losses as large as 2.3% up to 2022. The higher tariffs on US products would result in higher import prices, which prop up prices for both Indian consumers and producers (US intermediates will become more expensive as well). Higher domestic inflation leads to lower private consumption, lower investment, and an additional negative impact on export and import volumes[4].

Figure 1: Indian export to China and Hong Kong: 22bn
Figure 1: Indian export to China and Hong Kong: 22bnSource: UN COMTRADE, Rabobank
Figure 2: Impact of a trade war on India’s economic growth
Figure 2: Impact of a trade war on India’s economic growthSource: OECD, CSO, Rabobank, NiGEM

The results from our three trade war scenarios show that the direct impact on India’s GDP is limited. However, we have to stress that we have merely looked at the impact on trade flows, whereas we have abstracted from possible adverse effects stemming from financial market turbulence, a shake-up of integrated international value chains or a sudden stop of capital inflows. Moreover, India’s involvement in a trade war could also raise political tensions between China and India, which have become increasingly strenuous due to the dispute over the Doklam plateau. This might also lead capital to flows out of India as investors become wary of increased risk. However, such capital flight could also occur independently of a possible trade war, namely due to an unexpectedly rapid tightening of US monetary policy, which is what we analyze next.

Capital flight

Capital flight is the rapid outflow of capital from a country. Capital flows are mainly composed of Foreign Direct Investments (FDI), portfolio flows and remittances. In this study, we focus on portfolio flows, which encompasses the purchase or sale of Indian stocks and bonds by non-Indian residents. We abstract from FDI and remittances because portfolio flows react more quickly to changes in interest rates and political risk, and as such, are a more timely indicator of investor sentiment towards India. Portfolio flows are also important because India needs capital to plug its current account and budget deficits. In case portfolio flows reverse sizably and rapidly (i.e. capital flight) next to downward pressure on Indian assets, the Indian rupee will also face downward pressure as less of the country’s capital needs will be met by foreign capital.

A portfolio investment model

In order to quantify the effect of an unexpectedly rapid tightening of US monetary policy, we have developed a model to explain portfolio investment in India, based on the most important determinants in the literature: economic growth, stock market performance, the exchange rate, the fiscal deficit, political risk, the interest rate differential with the US and the change in the expected US Fed funds rate 1 year from now. The annex contains the background information on our model and its assumptions. We have used this model to run two scenarios: one where the Fed faces faster than expected inflation and consequently accelerates its tightening cycle. And a second one where (on top of scenario 1) political risk in India increases and the Indian Rupee (INR) depreciates faster than expected. Higher political risk in scenario 2 might be triggered by Trump’s trade war, Modi losing the general elections next year or the dispute with China over the Doklam plateau flaring up.


Our calculations show that in case the Fed would be speeding up monetary tightening (i.e. raise rates faster or more than expected by the markets right now) we could be looking at a total of missed portfolio investments in the range of USD 22bn up to 2022 (figure 3 – the region between the orange and blue line). In this risk scenario, which assumes that US inflation accelerates more rapidly, the Fed is forced to hike rates by more than 325bp above what is currently priced in by forward markets by mid-2020. In case political risk increases and the INR devaluates faster than expected, losses would add up to 32bn US (figure 3 – the region between the grey and blue line). These lower capital flows will also have consequences for India’s finance requirements. In our first scenario, the shortfall in financing sources will be as large as USD 16.8bn in 2018 and USD 12.3bn in 2022 (see blue dots in figure 4). And if FDI follows a less attractive trajectory than the one shown in Figure 4 (for example due to investor sentiment souring), problems will be even more substantial.

Figure 3: Acceleration of Fed tightening cycle would result in 22bn loss of portfolio flows
Figure 3: Acceleration of Fed tightening cycle would result in 22bn loss of portfolio flowsSource: EIU, Rabobank
Figure 4: Shortage in investment sources in 2018 and 2022 in Fed tightening scenario
Figure 4: Shortage in investment sources in 2018 and 2022 in Fed tightening scenarioSource: EIU, Rabobank

One way India could deal with the shortfall is to use its USD 425bn of foreign exchange reserves. However, as argued on Bloomberg (see here), using these reserves could push up the entire yield curve since lower reserves will reduce domestic monetary liquidity. The consequence of lower liquidity and (possibly much) higher interest rates will be damaging to India’s economic recovery and government stimulus plans.


India might me more vulnerable to US policies than financial markets are currently pricing in. We estimate that a trade war with the US could cost India more than 2% in GDP growth up to 2022. This does not include possible knock on effects on, for example, productivity and foreign investments capital. In addition, a faster than expected tightening of US monetary policy could cause capital outflow from India, which will also lead to a shortfall in India’s finance requirements. We estimate the effect on capital flows to India in such a scenario (in which we also add increased political risk and a substantial depreciation of INR) and find that India could face a shortfall in finance requirements of USD 17bn in 2018 and USD 12bn in 2022. If India uses its foreign reserves in such a case, interest rates could rise sharply as liquidity decreases. Even though India’s reserves are substantial, markets might still become jittery about the prospects of further declines. All in all, these developments will create major speed bumps on India’s road to economic prosperity. Although this scenario is not our base case, investors and companies dealing with India would be wise to keep this risk in mind.


Ahmed, S. & Zlate, A. (2014), Capital Flows to Emerging Market Economies: a Brave New World?, Journal of International Money and Finance, 48, pp. 221-248.

Baek, I. M. (2006). Portfolio investment flows to Asia and Latin America: Pull, push or market sentiment?, Journal of Asian Economics, 17(2), pp. 363-373.

Dahlhaus, T, & Vasishtha, G. (2014), The Impact of US Monetary Policy Normalization on Capital Flows to Emerging-Market Economies, Bank of Canada Working Paper no. 14-53.

De Vita, G. and Kyaw, K.S. (2008), Determinants of capital flows to developing countries: a structural VAR analysis, Journal of Economic Studies, 35(4), pp. 304-322.

Froot, K.A., P.G. O’connell and M.S. Seasholes (2001). The portfolio flows of international investors, Journal of financial Economics, 59(2), pp. 151-193.

Grinold, R., & Kroner, K. (2002), The equity risk premium: Analyzing the long-run prospects for the stock market, Investment Insights, 5(3), pp. 7-33.

Grinold, R., Kroner, K, & Siegel, L. (2011), A Supply Model of the Equity Premium, in Hammond, B., Leibowitz M. & Siegel L., eds., Rethinking the Equity Risk Premium, Charlottesville, VA: Research Foundation of CFA Institute.

Imbs, J. and Mejean I. (2010), Trade elasticities: a final report for the European Commission. Directorate General Economic and Financial Affairs (DG ECFIN), European Commission, no.432.

Koepke, R. (2015), What drives capital flows to emerging markets? A survey of the empirical literature. International Institute of Finance (IIF) Working paper.

Koepke, R. (2018), Fed policy expectations and portfolio flows to emerging markets, Journal of International Financial Markets and Money, forthcoming.

Le, Q.V & Zak, P.J. (2006), Political risk and capital flight, Journal of International Money and Finance, 25, pp. 308-329.

Montiel, P. & Reinhart, C.M. (1999), Do Capital Controls and Macroeconomic Policies

Influence the Volume and Composition of Capital Flows? Evidence from the 1990s, Journal of International Money and Finance, 18 (4), pp. 619-635.

Annex: A portfolio model for the Indian economy

Previous literature on the determinants of capital flows

Economic literature identifies several variables that influence capital flight from countries: economic growth, stock market performance, the exchange rate, the fiscal deficit, political risk, the expected US Fed policy rate the interest rate differential with the US. Broadly, these factors can be divided into domestics factors and external factors. Koepke (2015) calls these pull factors and push factors. Pull factors are domestic factors such as economic growth, stock market performance and country/political risk, while push factors are external factors such as global risk aversion, global economic growth and global interest rates.[5]

For our model, we use the seven variables previously mentioned to explain Indian capital flows and also add a dummy variable to capture the effect of demonetization in India in 2016. Some intuition behind these variables is warranted here. Starting with the latter two variables. There is wide consensus in the literature that US interest rates impact capital flows to emerging markets (e.g. Montiel and Reinhart, 1999 and Ahmed and Zlate, 2014). This relates to interest rate differentials between emerging markets and the US as well as a tightening of US monetary policy. The difference between the two is important because interest rate differentials between the US and emerging markets can go down without a change in US rates. Nonetheless, emerging markets tend to be sensitive to US interest rates. For one because a substantial part of emerging economies’ public and private debt is in USD, but also because many commodities are denominated in USD (making commodities and debt more expensive in local currency if USD strengthens). In addition, if US rates go up and reduce difference with emerging market interest rates, this makes emerging markets less attractive for investors.

To proxy expected changes in the US Fed policy rate, we follow Koepke (2018) and Dahlhaus and Vasishtha (2014). Koepke, for example, uses Eurodollar futures as a proxy for the expected Fed policy rates. Koepke finds that a change in the expected future Fed policy rate has a large and negative effect on portfolio flows in emerging markets. Similar to Koepke, we use the 1 year Eurodollar futures price to gauge the market’s expectation of the Fed policy rate 12 months from now. To proxy the interest rate differential between India and the US, we use the difference in (3 month) interbank rates between India (Mibor) and the US (Libor).

Regarding the other variables. Economic growth is likely to have a positive effect on capital flows (thus lead to inflow) as higher economic growth implies higher earnings for firms and more revenue for governments. Economic growth and has previously been linked to capital inflows by for example De Vita and Kyaw (2008). Higher stock prices are also likely to have a positive influence on capital flows because the stock market tends to be a leading indicator of economic growth and partially also higher stock prices lead to positive investor sentiment. Evidence of stronger capital flows due to higher asset prices has for example been found by Froot et al. (2001) and Koepke (2018). Exchange rate weakness is likely to be a negative for capital flows, one reason is that the weakening currency of a country eats away at returns of foreign investors (in this case, a weakening INR/USD rate would reduce the return for a US based investor). The effect of a country’s fiscal deficit is not clear cut. On the one hand, a higher deficit indicates a need for external capital, which implies that a higher deficit leads to higher capital flows. On the other hand, a higher fiscal deficit implies higher public debt in the future, which implies higher sovereign risk. The literature seems indicates that the former effect dominates (e.g. Baek, 2006). Political risk is likely to be negative for capital flows since it implies more uncertainty and increases the country specific discount rate on an asset and thus reduces its value. Le and Zak (2006) for example find string evidence that an increase in political risk leads to capital flight in the 45 developing countries they study.

Model and data

Our model has the following form:

where Pt are portfolio investments in equities and securities in India,  is the expected Fed policy rate based on euro-dollar one year future contracts,  is the US 3 month interbank rate (LIBOR),  is the Indian 3 month interbank rate (MIBOR), yt is the volume mutation of India’s gross domestic product (GDP), s is the value of India’s stock market index (measured by the BSE Sensex), ETt is the exchange rate (INR per USD), FDt indicates India’s fiscal deficit as a % of GDP and Dtd is a demonetisation dummy (picking up the impact of capital flight due to Modi’s announcement in November 2016 to replace 83% of all bank notes in circulation), PRt is an indicator from IHS Markit that gauges political risk in India and finally, c is the constant term and finally, et is a Gaussian error term.[6] We have tested each variable on stationarity using augmented Dickey-Fuller tests. In case of non-stationarity, we express the variable in first differences (indicated in the formula by Δ). Table A.1 shows all the data sources per variable. We estimate equation (1) using simple OLS and quarterly data covering the period 1997Q1-2017Q3 (indicated by t). 

Table A.1: Description of variables and data sources
Table A.1: Description of variables and data sourcesSource: Rabobank


Table A.2 shows our estimation results. Column (1) shows the results of our base model, which contains both interest spread variables and two ‘pull’ factors: the change in economic growth and the performance of the stock market. All variables show a statistically significant effect. The US interest differential (α1) has a coefficient of -1.5, which means that if financial market expectations about a Fed rate hike in one year changes by 1ppt, portfolio investments in India decrease by USD 1.5bn. Economic growth (α2) has a coefficient of 0.45, which means that an increase of growth by 1ppt has a positive impact on portfolio investment of 450 million. However, this effect is not extended beyond the quarter. In other words: continuous higher growth rates are necessary to attract higher portfolio flows. The coefficient of 0.22 for the stock market variable (α3) means that if returns on the stock market increase by 1ppt, portfolio investments to India increase by 220 million USD. The variable capturing the impact of the India-US interest rate spread on portfolio investment (α4) is 0.56. This means that if the spread decreases by 1ppt (for example due to a US rate hike), portfolio flows to India decrease by 560 million USD. 

Table A.2: Estimation results
Table A.2: Estimation resultsNotes: t-statistics are in parentheses. HAC standard errors and covariance. Significance at 10% and 5% is indicated by * and ** respectively.
a We lag political risk by three quarters to capture the forward looking effect of this measure.
Source: Rabobank
Figure A.1: Rabobank portfolio model for India has a solid fit
Figure A.1: Rabobank portfolio model for India has a solid fitSource: EIU, Rabobank

In column (2) we add the impact of the exchange rate (α5). Admittedly, there is probably a two-way causality of the variable and portfolio investment. If investors shift their portfolios away from India, they will sell Rupees and buy other currencies for their alternative investment, which puts downward pressure on the INR. Consequently, this signal will induce more investors to change preference and sell Indian assets. Despite the two-way causality, it is important to include this self-reinforcing mechanism in our model. The effect of the exchange rate is statistically significant and shows the correct sign: a 1% depreciation of the INR against the USD will result in decline in portfolio investment of 690 million. Column (3) shows the impact of the one quarter lagged change in the fiscal deficit on portfolio flows (α6). The effect is statistically weak. The demonetisation dummy (α7) captures the very substantial drop on portfolio flows after PM Modi’s announcement to ban 83% of all currency notes (figure A.1). In column (4), we add political risk as a final determinant(α8). The coefficient is -2.95, which means that if political risk (on a scale of 0-10) deteriorates by 1 point, portfolio flows decrease by almost USD 3bn.

Column (4) is the model we use in our scenario analyses. The fit of this model is shown in figure A.1. With the current set of determinants, we are able to explain more than 50% of the variance in India’s portfolio investment. This is a substantial amount, given the volatile nature of this variable.

Two scenarios

We use our estimated model to assess the impact on portfolio investment in three scenarios:

  1. Baseline
  2. Acceleration of the Fed tightening cycle
  3. Political turmoil and a depreciation of the INR


In our baseline scenario, we use our baseline forecasts on India’s GDP growth, the exchange rate and US and RBI rates. More information on these forecasts can be found in the Monthly Outlook of March (see here). With respect to Fed policy, we expect two more rate hikes in 2018: one in June and one in September (figure A.2). Forecasts of India’s stock market are based on the Grinold and Kroner model (Grinold and Kroner, 2002 and Grinold, Kroner and Siegel, 2011). In this model, future returns for the stock market of a country are determined by the sum of that country’s expected (i) real economic growth, (ii) inflation rate, (iii) dividend yield and share buybacks, and (iv) the re-pricing of valuation levels (measured by price earnings ratios). The idea behind this model is that in the long run, corporate profits cannot grow faster or slower than the economy and that valuation levels return to their long term values. For India, we expect (for the foreseeable future) a real economic growth of 7.9%, an inflation rate of 5% and an average dividend yield of 1.3% (same as the 10 year average for the BSE Sensex) and a repricing of approximately 6% (based on a price earnings ratio for the BSE Sensex of 23.3 at the time of writing, which we expect will revert to its long-term average of 18 over the forecast period).[7] Taken together, we expect the Indian stock market to return approximately 8% (7.9% + 5% + 1.3% -6% = 8%). With respect to the Indian fiscal metrics, we expect the deficit to be -3.5% in FY2018/2019, -3.3% in FY2019/2020 and -3.1% in FY2020/2021 (figure A.3). We assume political risk will stay constant in our base case. 

Figure A.2: Fed policy rate assumptions
Figure A.2: Fed policy rate assumptionsSource: Rabobank, Federal Reserve Bank
Figure A.3: Expected fiscal deficit
Figure A.3: Expected fiscal deficitSource: Ministry of Finance, Macrobond, Rabobank, EIU

Scenario I: Acceleration of the Fed tightening cycle

Our base case (and even the Fed’s own projections) assumes a gradual pickup in inflation over the next few years. There is an alternative scenario, which is not unthinkable. Amidst already low labour market slack and additional stimulus from Trump’s tax cuts, inflation may take off more sharply on the back of accelerating wage pressures. Another risk in this scenario stems from import tariffs imposed on Chinese goods. As inflation overshoots, the Fed is forced to tighten policy much more quickly and significantly in order to prevent a wage-price spiral (light-blue line in figure A.2). This ultimately leads to an inverted yield curve and a recession.

Scenario II: political turmoil and INR depreciation

In our second scenario, we assume political turmoil will rise. We incorporate this, by increasing the value of our IHS Markit political risk measure by one standard deviation (which for India has historically been 0.6) for each political risk related event. First, we assume India will be caught in the trade war. One of the effects of the trade war will be that policy uncertainty will increase, which we incorporate by increasing the value of the IHS Markit political risk index to 3 (figure A.4). Moreover, we assume that the opposition succeeds with its altered strategy to forge a grand coalition and beats Modi’s BJP in the general the elections schedule for 2019. This will result in higher government instability, which will lead to an increase in the political risk index to 3.6. Ultimately, political risk remains elevated on the back of returning tensions with China over the Doklam Plateau.

In this scenario, the increased political risk will also have a implications for India’s currency, which will depreciate against the USD at a faster pace than in our base case (figure A.5). Ultimately, the INR will lose more 18.2% up to 2022 in this scenario, compared to the 7.4% depreciation penciled in for our case base. 

Figure A.4: Political risk assumptions: two shocks
Figure A.4: Political risk assumptions: two shocksSource: Rabobank, IHS
Figure A.5: INR goes to 76.5 in political turmoil scenario
Figure A.5: INR goes to 76.5 in political turmoil scenarioSource: Macrobond, Rabobank


[1] Of course, one would like to see a fourth option: India retaliating Chinese protectionism, but unfortunately the tariff version of NiGEM, which we use to estimate the effects, lacks the possibility to assess the impact in this scenario.

[2] Here, we use the median export elasticity for China from Imbs and Mejean (2010) and abstract from any mitigating exchange rate effects.

[3] In contrast to the calculation on the impact of China tariffs on India export, we have used the macro-econometric model NiGEM to assess the effect. This model (based on New Keynesian principles) enables us to take into account mitigating impact of the central bank policy. In NiGEM we use nominal GDP targeting as the policy stance, which means that the Fed and the RBI will calibrate their policy rate on nominal GDP developments instead of the usual Taylor rule.

[4] Note that this will likely also have a negative impact on productivity, which we abstain from in this case to keep the analysis simple.

[5] Koepke explains that the push and pull framework is broader than the simple internal external dichotomy. However, to keep things simple, we broadly refer to push and pull factors as internal and external factors.

[6] More specifically, PR indicates IHS Markit’s political risk measure (ranging from 0.1 to 10), which is the average of three equally weighted sub components of political risk. IHS Market defines these subcomponents as: Government instability (the risk that the government will change), Policy instability (the risk that the government’s broad policy framework will shift) and State failure (the risk that the state is unable to function effectively). A higher number here indicates more political risk.

[7] We disregard the effect of share buybacks because data on this variable more difficult to attain.

Hugo Erken
RaboResearch Global Economics & Markets Rabobank KEO
+31 30 21 52308
Raphie Hayat
RaboResearch Global Economics & Markets Rabobank KEO
+31 30 21 51295
Marijn Heijmerikx
RaboResearch Global Economics & Markets Rabobank KEO
+31 6 1222 8523

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