what drives india’s exports and what explains the recent ......sajid z chinoy and toshi jain 3 3....

What Drives India’s Exports and What Explains the Recent

Slowdown? New Evidence and Policy Implications

Sajjid Z Chinoy Toshi Jain

J P Morgan Chase

India Policy Forum July 10–11, 2018

NCAER | National Council of Applied Economic Research

11 IP Estate, New Delhi 110002 Tel: +91-11-23379861–63, www.ncaer.org

NCAER | Quality . Relevance . Impact

http://www.ncaer.org/

The findings, interpretations, and conclusions expressed are those of the authors and do not necessarily reflect the views of the Governing Body or Management of NCAER.

What Drives India’s Exports and What Explains the Recent

Slowdown? New Evidence and Policy Implications*

Sajjid Z Chinoy Toshi Jain

J P Morgan Chase

India Policy Forum July 10–11, 2018

* Preliminary draft. Please do not circulate beyond the NCAER India Policy Forum 2018, for which this paper has been prepared. [email protected], [email protected]

Abstract The role of exports in India’s growth dynamics over the last two decades has been consistently under-

appreciated. India’s export surge in the mid-2000s was chiefly responsible for the 9% GDP growth during that time. Conversely, the sharp fall-off in exports over the last 6 years can explain the bulk of India’s GDP growth slowdown. So why have Indian exports slumped in recent years and failed to materially respond to accelerating global growth over the last year? To ascertain this, we analyze the determinants of India’s exports and, in particular, estimate their “income” and “price” elasticities from 2004-17. We find both are important determinants of export dynamics, but these elasticities have reduced in recent years, consistent with de-globalization. We also find a significant heterogeneity of these elasticities across sectors, which explains the changing composition of India’s export basket. Importantly, our model can explain a significant slowing of exports in recent years, thereby confirming both global demand and exchange rate dynamics have posed meaningful headwinds to exports in recent years, rather than the presumed temporary factors such as demonetization and GST. In particular, we find the sharp 20% real appreciation of the real exchange rate between 2014-17 likely impinged on export competitiveness in recent years. In turn, we posit the real appreciation itself was the inevitable upshot of the large, positive terms-of-trade shock that India experienced from lower oil prices – suggesting India was likely afflicted by the “Dutch Disease.” All this helps resolve the ostensible “macroeconomic puzzle” of export underperformance in recent years.

The policy implications flow naturally. As oil prices rise, and the positive terms-of-trade shock reverses, the equilibrium real exchange rate will likely depreciate and help improve competitiveness. Policymakers must not fight this depreciation. In the medium term, however, India must endeavor to boost underlying competitiveness, to correct the underlying deterioration of external imbalances. Finally, with global growth potential reducing, protectionism on the rise, and income elasticities falling, India must simultaneously seek other growth drivers in the coming decades.

JEL Classification: F10, F14, F32 Keywords: India, Exports, Exchange Rate, Income elasticity, Price elasticity, Dutch Disease

mailto:[email protected]

mailto:[email protected]

Sajid Z Chinoy and Toshi Jain 1

1. Introduction and Motivation

Over the last decade, there has been a growing appreciation of India’s financial integration with the rest of the world. Foreign direct investment (FDI) levels have increased in recent years and are now subject to constant discussion and analysis. Similarly, foreign portfolio flows into the equity and debt market have progressively increased and have provided much-need liquidity and a more-diversified investor base. More generally, asset prices in India are increasingly correlated with global asset prices – the ultimate manifestation of India’s growing financial integration.

Paradoxically, however, this integration is often most-recognized during adverse shocks. It is the “sudden stops” or “sudden outflows” of capital that India has episodically witnessed – the global financial crisis in 2008, the Europe sovereign debt crisis in 2010-11, the taper tantrum in 2013 – during which the exchange rate came under some pressure and domestic financial conditions tightened, that the full extent of India’s financial integration was appreciated.

In contrast, however, there is much less appreciation of India’s global integration on the real economy side. There is still a perception that India’s economic prospects are governed by its large domestic market, and that trade – both exports and imports – matter only at the margin. To harbor this perception, however, is to live in the old reality.

The share of exports and imports have risen materially as a share of GDP. Total Exports/GDP doubled from 11.3% of GDP in 1999 to 25.4% of GDP by 2013. Since then, exports have slowed, against the backdrop of de-globalization witnessed around the world. Despite that slowing, however, Exports/GDP are currently still at 19% of GDP – almost twice the level that existed at the start of the millennium, and at a level similar to that of Indonesia.

This real integration has also extended to the import side. Total imports to GDP increased from 13.1% of GDP in 1999 to 28.4% of GDP in 2012 before slowing in recent years as GDP growth slowed. Consequently, total trade (exports plus imports) as a

10

13

16

19

22

25

28

00 02 04 06 08 10 12 14 16Source: MOSPI.

% of GDP

India exports share in GDP

2 India Policy Forum 2018

percent of GDP rose from 24.4% in 1999 to 54% in 2012 before ebbing in recent years. All this has brought welcome exposure to global markets and the supply-chain efficiencies, technological transfer and productivity growth that comes with it.

2. The quiet revolution: from textiles to auto parts…

Quite apart from the growing quantitative role of exports in economic activity, it’s equally important to appreciate the structural change that has characterized this sector over the last two decades. Two revolutions characterize the last two decades. The first – more visible one– was the surge in service exports. Back in 2003, service exports constituted 30% of the total export basket. But in just in a matter of 4 years, service exports jumped to 40% of the total basket, reflecting the software and BPO revolution around the world, of which India was a major beneficiary. Interestingly, however, after that step jump service exports have plateaued at 40% of the total basket over the last decade.

Instead, over the last decade, there has been a quiet revolution occurring on the manufacturing side. Back in 2003, textiles, leather and gems/jewelry – India’s traditional exports – constituted nearly 60% of the merchandise export basket (ex petroleum). But their share has secularly fallen, and currently they account for just 40% of the basket. In contrast, engineering goods exports – auto parts, capital goods -- have grown at an average annual pace of almost 20% for 13 years, such that its share of the manufacturing export basket has leapt from 20% to 35% in just 12 years (see chart below). In a sense, therefore, India’s exports has become much more “high-tech” over the last two decades, and also improved in technological content, quality, sophistication and complexity (see, Anand et. al, 2015). By 2015, engineering goods, electronics and pharmaceuticals/chemical products constituted almost 60% of the non-oil merchandise basket. Equally, however, one could lament that all of this growth has occurred in the capital intensive sectors, when the need of the hour is job creation in labor-intensive sectors, whose share has reduced in the export basket.

20

30

40

50

60

00 02 04 06 08 10 12 14 16Source:

% of GDP

India total exports and imports


3. Exports drove the growth boom pre-crisis

Perhaps, the best appreciation of India’s increased openness is gleaned by the role that exports have played in India’s growth dynamics over the last two decades – both the sharp growth acceleration in the pre-Lehman period and the slowdown post the crisis. India’s growth surged in the mid-2000s with GDP growth averaging 8.8% between 2003-2008, the highest five-year-average in India’s history. What is less known is this was achieved largely at the altar of surging export growth, during this period of “hyper-globalization” around the world. India’s real export growth averaged 17.8% for five successive years, whereas domestic consumption (public and private) averaged less than half of that, at 7.2%. Consequently, the surge in private investment witnessed at the time (with gross fixed capital formation growing at 16.2% a year for 5 years) was largely responding to the buoyancy of external demand rather than domestic demand. Exports were driving investment, and this is also seen in the close correlation between exports and IP during those years. India had briefly turned Asian.

-10

-5

0

5

10

15

Engg Services Gems Leather Textile

% change of export basket, 2003 to 2015

India sectoral change in exports

Source: Ministry of Commerce


4. Slowing exports and the macroeconomic puzzle

Since 2012, however, India has witnessed a sharp export slowdown. Real exports growth (from GDP data) have slowed from an average of almost 15% between 2004 and 2012 to just 3.6% over the last 5 years, even accounting for some pick-up in 2017. This is a 75 percent fall-off in growth rates. Moreover, the slowdown has been broad-based across the basket.

It is tempting at attribute all of this to the global slowdown. But as the chart below reveals, both global GDP growth and global exports growth have slowed much less dramatically than India’s exports have. In particular, global exports growth averaged 3.8% (in real terms) between 2004 and 2011, and 2.6% over the last six years

8.87.2

17.8

6.9 6.7

3.6

0

5

10

15

20

GDP Consumption Exports

2003-08 2012-17

Source: MOSPI

% of GDP

GDP and Exports

16.217.8

5.33.6

0

4

9

13

18

22

Investments Exports

2003-08 2012-17

Source: MOSPI

% of GDP

Investments and Exports


– a drop off of 30% in growth rates. It is clear, therefore, that India has underperformed global exports over the last 6 years.

The flip side of the puzzle is the lack of pick-up over the last year as global growth re-accelerated meaningfully.. Indeed, 2017 witnessed the strongest global growth in 7 years, with global GDP accelerating from 2.7% in 2016 to 3.4% in 2017 – a 25 percent increase. Yet, India’s real manufacturing growth was stagnant at 5.7% in 2017 versus 5.4% in 2016. All this begs the question, why have exports underperformed in recent years, and why have they not reacted more enthusiastically to the global recovery over the 6 quarters?

-4

-2

0

2

4

6

-8

0

8

16

24

06 07 08 09 10 11 12 13 14 15 16 17Source: MOSPI, J.P. Morgan

% oya, 4QMA % oya, 4QMA

Global growth and India real exports

Global growthReal exports

-15

-9

-3

3

9

15

-10

-2

6

14

22

30

06 07 08 09 10 11 12 13 14 15 16 17Source:

Global exports and India real exports

% oya, 4QMA % oya, 4QMA

Global exports Real exports


5. Slowing exports explain all of the slowdown

Revealingly, the slump in exports over the last 6 years – both the slowdown and the failure to meaningfully re-accelerate – explains the bulk of India’s growth slowdown in recent years. Over the last six years, for example, GDP growth has averaged 6.9% versus 8.8% in the mid-2000s. Similarly, gross exports have averaged just shy of 4% over the last 6 years versus almost 18% in the mid-2000s. The import-content of exports is about 25%, so the domestic value-added component is about 75% (see, OECD, 2015). Furthermore, India’s share of exports in GDP has averaged about 22% over the last 6 years. Therefore, the contribution of the export slowdown to GDP -- the 14 percentage point slowdown in exports times the 22% share in GDP times the 75% value add – account for 2.3 percentage points. In other words, slowing exports alone account for the entire slowdown in growth over the last 6 years!

Once can argue that the old and new GDP series are not comparable. However, export growth is very similar across both the new and old series in the years where both series are overlap (2011-13) which is unsurprising because it is derived from monthly customs data. To be sure, headline GDP growth in the new series is, on average, higher by 1.1 percentage points compared to the old series in the overlap years. Therefore, if one were to undertake a level downshift of the new series by the quantum to make the series comparable, the growth slowdown increases from 1.9 to 3 percentage points. Still, however, slowing exports account for 75% of India’s headline GDP slowdown – even allowing for this adjustment.

Therefore, one does not need to look for creative or out-of-the-box explanations to understand India’s slowdown over the last few years. To be sure, there have been multiple domestic pushes and pulls (the stance of fiscal and monetary policy, implementation bottlenecks, stress asset resolution in the banking sector, and terms of trade shocks from the sharp movements in oil prices) but they have largely offset each other. Instead, the bulk of the slowing is account of exports – which has escaped much discussion.

6. Scope of the study: Three questions

The natural question to ask, therefore, is what has contributed the sharp slowdown in Indian exports and the lack of a more meaningful pick-up over the last year? Disentangling different impulses is important because there are different proximate factors at play. First, global growth has slowed and trade-linkages have become more tenuous. Second, India’s broad-trade weighted exchange rate (36 country REER) appreciated almost 20% between 2014 and 2017. Third, India witnessed successive (but presumably transient) supply shocks in the form of GST and demonetization in 2016 and 2017. All these factors have potentially contributed to the export slowdown.

We, therefore, attempt to answer three questions in this paper. First, what are the determinants of India’s exports and, in particular, what are the “income” and “price” elasticities of exports and how have they changed over time? This first question is important in light of the varied and inconclusive findings of income and price elasticities in the extant literature and the role of other factors (e.g. supply bottlenecks) that some have identified.


Our second question of interest is to ask how heterogeneous are these elasticities across different sectors, given the dynamism and changing composition of India’s export basket? The latter is important because previous work (see, Chinoy and Aziz 2010) has found the elasticities vary sharply across sectors.

Our third question is how much of the recent slowdown can we explain? In particular can the recent disappointment be explained just by external demand and price factors? Or are they explained by factors outside the model, which would suggest transient shocks from demonetization and GST. Disentangling the slowdown is important because it will throw light on the durability of the export slump, and also inform the policy response.

7. Price and Income Elasticities: Review of the literature

The literature analyzing the determinants of India’s exports is relatively sparse, which is not unsurprising given the unappreciated role of exports in India’s growth dynamics. For the literature that does exist, studies try and estimate the “price” and “income” elasticities of India’s exports – consistent with the approach found in cross-country studies. “Income elasticities” are measured by estimating the sensitivity of real exports to external demand (proxied differently in different studies through global growth, partner-country growth, global exports) while “price” elasticities are measured by estimating the impact on real exports from movements in the real exchange rate.

Raisse and Tulin (2015) is the most recent study that estimates both income and price elasticities at an industry-level from 1990 to 2013 and find a statistically and economically significant role for both income (1.3%) and price (-0.99) elasticities. They also find that supply-constraints are a determinant of exports and have constituted a drag in the case of India. However, their analysis ends in 2013 and therefore has missed the bulk of the export slowing over the last 6 years. It also does not focus on whether these elasticities have changed over time, and the impact of de-globalization on India.

In contrast, IMF (2012), finds a much lower price elasticity. The long-run elasticity is estimated at -0.1 for the full sample period (1982-2011) and somewhat higher at -0.2 for the post-1990s period, but much lower than what Raisse and Tulin find for about the same time period. The corresponding long-run elasticities on external demand is found to be 2.9 and 2.2 for the respective periods.

Kapur and Mohan (2014) get very different results. Using annual data they find a long-run income elasticity of 1.1-1.4, and a price elasticity of -0.2-0.6 – in between both the earlier studies. However, when they use quarterly data to estimate the same elasticities post the reform period (presuming a structural break after the reforms), they fund much higher elasticities, with that of external demand rising to 1.6-1.9 (when proxied through world exports) and 2.6-3.6 (when proxied through world GDP). Similarly, REER elasticities rise significantly to 1.1-1.5 in the post reform period. Importantly, however, the analysis stops a decade ago in 2008, so we don’t know if these elasticities still hold and how they have changed over time.

Chinoy and Aziz (2010), using quarterly data for 1996-2008, find a positive and statistically significant impact of external demand (real GDP growth in partner countries) on exports, with the estimated coefficient even larger at 4.6. However, the coefficient on the REER, while correct signed (-0.6), is statistically insignificant at the


aggregate level, even though it is economically and statistically significant for some sectors.

All this suggests there is a pressing need for updated work in this area. First, even the most recent of these studies uses data up to 2013. So there are no estimated export elasticities over the last 5 years, precisely when forces of de-globalization were in the ascendancy. It is important, therefore, to ascertain whether and how these elasticities have held up and how they have evolved over time.

Second, the extant literature is very inconclusive on the role of the exchange rate and price elasticities. Some studies find a strong impact and other studies find a minimal impact. Ascertaining the role of the exchange rate is particularly important in recent years given the sharp appreciation of India’s trade-weighted exchange rate over the last 4 years.

All this makes a compelling case for new work in this area to analyze what has contributed to India’s export performance in recent years and how much of the recent export slowdown can be explained by traditional determinants (global demand, exchange rate) and what fraction cannot be explained, and is therefore potentially attributable to idiosyncratic – and presumably transient – forces related to GST and demonetization.

8. The Approach

We use quarterly, time-series data from 4Q2004 to 4Q2017 to estimate the sensitivity of India’s non-oil exports to both global demand and the trade-weighted real exchange rate, controlling for potential bottlenecks, since supply constraints are also found to be a determinant in some earlier work (Raissi and Tulin, 2015). Our sample period starts from 2004 because the RBI’s 36-country-CPI-based-REER that we use in our baseline model, as well as the export unit value indices from the IMF, date back only to 2004.

We, therefore, estimate the standard equation linking real exports to global demand and the real effective exchange rate, and augment it to control for commodity prices (just in case the unit value deflators do not fully deflate out commodity price effects in the dependent variable), the global financial crisis, and supply constraints at home – proxied through quarterly data on “stalled projects”. This log-linear model has been the dominant and empirically-successful specification in the literature, given its relatively-undemanding data requirements and straightforward interpretation.

In particular, we estimate the following equation:

𝑙𝑛(𝑋) = 𝛼 + 𝛽𝑙𝑛(𝐺𝐺) + 𝜋 ln(𝑅𝐸𝐸𝑅) + µ ln(𝐶𝑅𝐵) + 𝑆𝑇𝐴𝐿𝐿𝐸𝐷 + 𝐺𝐹𝐶

In our baseline equation, the dependent variable X is real non-oil exports (the sum of quarterly real merchandise and service exports), GG = real global GDP, REER is the 36-country, broad trade-weighted real effective exchange rate produced by the RBI, GFC is a dummy for the global financial crisis, and STALLED is a measure of stalled-projects – our proxy for binding supply constraints.

As part of our robustness checks, we replace global growth by (i) global exports (GE), and (ii) India’s trade-weighted partner-country growth. We also replace RBI’s 36-country REER with the REER created by the Bank of International Settlements (BIS).


9. Estimation Methodology

We find that our key variables X, GG and REER are non-stationary I(1), we run both the Engel-Granger and Johannsen-Juselius Maximum Eigenvalue Co-Integration tests and find that the variables are cointegrated at order 1. Consequently, we estimate the above equation using both a Dynamic Ordinary Least Squares (DOLS) and a Vector Error Correction Model (VECM) to estimate the long-run price and income elasticities of exports.

The DOLS approach is a robust, single equation approach which corrects for regressor endogeneity by the inclusion of leads and lags of first differences of the regressors, and for serially-correlated errors by a Generalized Least Squares (GLS) procedure. This is important because exports and the real exchange rate are both simultaneously determined, and DOLS controls for this endogeneity. DOLS is the preferred estimation technique for small sample sizes, given its efficiency and robustness properties. Furthermore, Monte Carlo experiments show that with a finite sample, DOLS performs well relative to six other asymptotically-efficient estimators, including Johansen’s (1988) vector error correction maximum likelihood estimator (Stock and Watson, 1993). Lag-length for leads and lag has been chosen using the Akaike Information Criteria (AIC), subject to a maximum of 4 leads and lags given the limited length of the data-set.

As a robustness check, we also estimate the exports equation using the VECM approach

∆𝐿(𝑋𝑡) = 𝑎1 + 𝑎2(𝐸𝐶𝑀𝑡−1) + ∑𝑎3∆𝐿(𝑋𝑡−𝑖) + ∑𝑎4∆𝐿(𝐺𝐺𝑡−𝑖) + ∑𝑎4∆𝐿(𝑅𝐸𝐸𝑅𝑡−𝑖)+ 𝐺𝐹𝐶 + 𝑢𝑡

Table 1: Cointegration Test - Engle-Granger

Variables: X, REER, GG

Value Prob.*

Engle-Granger tau-statistic -3.5 0.04

Engle-Granger z-statistic -17.0 0.09

Table 2: Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Variables: X, REER, GG

No. of CE(s) Eigenv alue Prob.**

None * 0.4 0.0

At most 1 0.2 0.1

At most 2 0.0 0.2

* Max -eigenv alue test indicates 1 cointegrating eqn(s) at the 0.05 lev el


where ECM term capture the deviation of exports from the long-run equilibrium (L(X)-c1L(GG)-c2.L(REER))

10. Empirical Results

10.1 Income Elasticity

Our baseline results find a strong and statistically-significant impact of global growth on India’s real exports. It is important to caveat, however, that because the variables are co-integrated, the estimated coefficients should be interpreted as the “long-run elasticities” between these variables. As our baseline estimation reveals [Column 1 in Table 3], a 1 percent increase in global growth increases India’s real exports by 2.6 percent. The result is robust to how external demand is proxied. If instead of global GDP growth, we use global real export growth from the World Bank’s World Integrated Trade Solutions (WITS) database, the elasticity increases from 2.6 to 3.4 (Column 3 in in Table 3). Therefore, no matter what the definition of external demand, “income elasticities” are economically and statistically significant.

The result is also robust to different measures of exports (i.e. dependent variable). If we use real export growth from the GDP accounts, the elasticity of global growth increases from 2.6 to 3.2 in the baseline model and remains very statistically significant (Table 4). Similarly, all the different definitions of global growth continue to remain economically and statistically significant under the alternative dependent variable choice. So the result is very robust across different definitions of the dependent and independent variable.

Table 3: Elasticity estimates

Dependent variable: Log Real exports ex oil (customs)

Method: Dynamic Least Squares (DOLS)

Sample Period: Dec, 2004 to Dec, 2017

Results using 4 leads and 4 lev els

Baseline

Coeff Coeff Coeff Coeff Coeff

Cointegration regressors

LOG(REER)- RBI -1.44*** -2.0*** -2.3*** -1.4***

LOG(GG) 2.6*** 2.9*** 3.3*** 2.6***

Log(WE) 3.4***

LOG(REER)- BIS -2.2***

Deterministic regressors

GFC 0.3** 0.3*** 0.4*** 0.2* 0.3**

Log(CRB) 0.2** 0.2 0.1

STALLED -0.004

Adjusted R-squared 0.81 0.84 0.81 0.73 0.80

Robustness check


How does this magnitude compare to earlier studies? Our estimated coefficient is at the lower-range than found by Kapur and Mohan (2.6-3.6) and much lower than found by Aziz and Chinoy (4.6). Recall, however, that both those studies ended in 2008. In comparison, Raisse and Tulin (2015) find an income elasticity of 1.6, but their analysis extends till 2013. Prima facie, therefore, this suggests that income elasticities have potentially declined over the last decade, consistent with the de-globalization hypothesis.

To test this, we divide our sample up into two sub-samples, from 2004-11 and 2011-17 and estimate the coefficients separately in each sample period to ascertain if the former have changed over time. We, indeed, find that income elasticities have reduced over the last decade. The elasticity for the 2004-11 period is estimated at 3, while that for 2011-17 falls to 2.4. This pattern is robust to how external demand is proxied (world exports, partner country growth) and confirms the hypothesis that India has not escaped the de-globalization bug, in that any level of global growth translate into correspondingly lower export growth over the last 6 years. That said, while the elasticity has reduced from 3 to 2.4, the decline is not as large as commonly presumed. Global growth still has a significant bearing on India’s exports.

10.2 Price Elasticity

Apart from ascertaining whether income elasticities have fallen over the last decade, a key question of interest is whether, and to what extent, price elasticities matter for Indian exports, particularly given the heterogeneous and dated findings of this phenomenon in the extant literature.

Table 3: Elasticity estimates

Dependent variable: Log Real exports (GDP)


Results using 4 leads and 4 lev els

Coeff Coeff Coeff Coeff


LOG(REER)- RBI -1.6*** -2.2*** -2.2***

LOG(GG) 3.2*** 3.5*** 4.2***

Log(WE) 3.6***

LOG(REER)- BIS -2.8***

Deterministic regressors

GFC 0.2** 0.2*** 0.3*** 0.2**

0.2*** 0.2** 0.2

Adjusted R-squared 0.89 0.92 0.91 0.86

Log(CRB)

Robustness check

Note: *,**and*** indicates statistical significance at 15%,10% and 5%


As our baseline result reveals [Column 1 of Table 3], we find a strong and statistically-significant impact of the exchange rate on non-oil, exports, between 2004 and 2017. Every 1 percent appreciation of India’s 36-country REER reduces real exports by 1.4 percent. Furthermore, the result is very robust to the choice of REER, how the dependent variable is proxied and the time period under consideration.

In the baseline model, we use the RBI’s 36-country CPI’s based REER. If, however, the RBI REER model is replaced with the BIS REER, the elasticity actually increases to 2. Both versions of the REER (RBI and BIS) also remain economically and statistically significant if the dependent variable is proxied by GDP-exports instead. Finally, the statistical significance is not an artifact of this particular time period. If one were to start the sample a year or two later and/or end a year or two later, the result is significant.

This is a salient finding because the literature, thus far, has been very mixed on the role that exchange rates have played in impacting exports. Some studies (IMF, 2012) find a very muted impact of the REER on exports. Others (e.g. Kapur and Mohan (2014) find a much larger elasticity (1.1-1.5) but their study ends in 2008. IMF’s (2015) analysis extends to 2013, but they find a smaller elasticity at 0.9.

All this has led to the perennial questions of: how much do exchange rates matter for Indian exports? And how has it changed over time? Our results hope to end that debate by finding a large and significant relationship between the broad trade-weighted exchange rate and non-oil exports using data all the way till 2017.

The next question, however, is whether these elasticities have changed over time. Like in the case of external demand, we estimate the coefficient across two-sub-samples: 2004-11, 2011-17. Just like in the case of “income elasticities”, we find that “price elasticities” have also come down in recent years. The estimated price elasticity between 2005-11 is 1.9 but falls meaningfully to 1.1 between 2011 and 2016.

3.0

-1.9

2.4

-1.1

-2.0

-1.0

0.0

1.0

2.0

3.0

Income elasticity Price Elasticity

2005-2011 2011-2017

Export elasticities

Source: J.P. Morgan


10.3 Other Controls

In our baseline model, we also include a dummy for the global financial crisis. Interestingly, the sign of the dummy is consistently positive, which suggests that there was a temporary overshooting of exchange rates and global growth which did not actually impact exports as much as the proximate movement of those variables would suggest.

As a robustness check, we also control for other factors such as global commodity prices, to account for the possibility that real exports have not been fully deflated. In particular, we run the DOLS regression controlling for the commodity prices – CRB index -- as a deterministic regressor. We find that results do not material alter with the inclusion of the commodity prices. However as commodity prices are statistically significant only in certain cases, they are not part of the baseline model.

Finally, since some studies find that supply constraints matter for exports, we proxy supply constraints by the number of stalled projects (STALLED), as a fraction of projects under implementation. However, we do not find STALLED to be a statistically-significant determinant of exports, even though it is correctly signed.

10.4 Fitted Value

The baseline DOLS model estimates the co-integration equation well with an adjusted R2 of 81%. The chart captures both actual real non-oil exports and the model’s fitted value.

10.5 VECTOR ERROR CORRECTION MODEL (VECM)

As a robustness check, we also estimate the long-run trade elasticities based on a VECM. The results are presented below. Lags of the model are based on the AIC criteria with maximum 4 lags given the short sample size. The estimates based on VECM

300

400

500

600

700

800

06 07 08 09 10 11 12 13 14 15 16

USD mn

India real exports

Actual

Fitted


approach are broadly in line with the estimates based on DOLS. In particular the full-sample income elasticity is estimated at 3.1 and the price-elasticity is estimated at 2.2

11. Sectoral results

Having estimated the income and price elasticities, and examined how they have evolved over time, we move to our second question: how different are these elasticities across sectors.

We find that India’s new-age exports like services (which are main, software services), engineering goods, and pharmaceuticals are found to have the highest “income elasticities” over the full-sample period. In particular, the elasticity of pharmaceuticals and engineering goods is estimated at 4 and 3.8 respectively. Services follows suit with an elasticity of 3.1.

In contrast, India’s traditional exports (textiles, leather, gems and jewelry) are found to be much less sensitive to global growth. Textiles’ elasticity is estimated at 2.6, that of leather 2.2 but gems and jewelry is just 0.5 and not found to be statistically significant, suggesting that it is not “cyclical” in nature. Instead, we find that gold prices are a bigger determinant of gems and jewelry exports.

Similarly, price elasticities of India’s new-age exports are also correspondingly higher than the traditional exports. In particular, the price elasticity is found to be the highest for pharmaceutical (3.7), followed by engineering goods (3.1) and services (2.2). On the other hand price-elasticities for textiles (2.1), leather (2.0) and gems and jewelry (0.8), but all are statistically significant. Interestingly, while gems and jewelry are not elastic to global growth, they are sensitive to price changes.

High income and price elasticities for India's new-age exports suggest they are both discretionary (reflected in their cyclicality) as well as in highly competitive sectors (reflected in their high price elasticities).

Method: Vector Error Correction Estimates (VECM)


Coeff t-stat

Cointegration Equation with Log(real exports ex oil)

LOG(GG) 3.1 2.5***

LOG(REER) -2.2 3.9***


Like in the case of the overall basket, we break down each sector into two-subsamples to assess how these sectoral elasticities have changed over time. The tables below reveal our main findings.

Income and price elasticities for all sectors have declined in the 2011-2017 period versus the 2004-2011 period – consistent with the findings of the overall basket.

The new-age sectors (pharma, engineering goods, services) have seen a much sharper decline than India’s traditional exports. This has meant a greater convergence for both price and income elasticities across sectors in the latest period

However, some coefficients are statistically insignificant and perversely signed. We believe this is due to a very short sample size, which is unsuitable for the long-run relationship implicit in the DOLS and VECM models. We therefore have slightly lesser conviction when breaking the data-set into sub-samples.

Table 4: Sectoral elasticity estimates



Pharma Engg Serv ices Tex tile Leather Gems


LOG(GG) 4.0*** 3.7*** 3.1*** 2.6*** 2.2*** 0.5

LOG(REER)- RBI -3.7*** -3.1*** -2.2*** -2.1*** -2.0*** -0.8**

Adjusted R-squared 0.89 0.75 0.82 0.90 0.90 0.84

Sectoral

-4.0

-2.0

0.0

2.0

4.0

Ph

arm

a

En

gg

Ser

vice

s

Tex

tile

Lea

ther

Gem

s

Income elasticity

Price elasticity

Export elasticities (Full sample)

Source: J.P. Morgan


12. Can the model explain the sharp recent slowdown in exports?

We, therefore, now turn to our third and final question: whether, and to what extent, these factors can explain the recent slowdown of exports? As discussed earlier, an important recent macro-economic puzzle is why India’s exports have slowed so sharply in recent years and, in particular, why they have not accelerated commensurate to the global pick-up since 2016. It is important to analyze the recent slowdown because there are several proximate factors at play.

First, notwithstanding the recent recovery, average global growth in recent years is still meaningfully lower than the pre-crisis period, and should have had a depressing effect on export volume growth. Second, India’s broad 36-country REER appreciated almost 20% between 2014 and 2017 (discussed in more detail below) and should have posed a headwind to exports. Third, India was buffeted by back-to-back adverse supply-shocks in the form of demonetization and GST – that are hypothesized to have adversely impacted exports by disrupting domestic supply chains.

Our objective there is to try and disentangle these effects. First how much of the slowdown can our model explain? The more we can explain, the less the recent slowdown is ostensibly attributable to supply-shocks associated with demonetization and GST. Second, within the model itself, how much is attributable to weaker global growth versus a more appreciated real exchange rate?




Engg Pharma Serv ices Tex tile


LOG(GG) 5.4*** 4.7*** 3.1*** 1.6***

LOG(REER)- RBI -4.9*** -4.5*** -2.3*** -1.0***


Sectoral




Engg Tex tile Serv ices Pharma


LOG(GG) 2.4*** 1.1*** 0.8** 0.3

LOG(REER)- RBI -1.3*** -0.4** 0.2 0.7


Sectoral


So what do we find? Our approach involves using the conventional “out of sample” testing. In particular, we run the model from 2004-2014 and then compared the model’s “out-of-sample” forecasts with the actual outturn from 2015 to 2017.

What we find is the model comes close to explaining the actual outturn. In particular, over the last three years (2015-2017) real exports have averaged just 1.1%, versus an average export growth of 8.6% within sample. The model’s “out-of-sample” forecasts show a substantial deceleration of growth to between 3.6-4.4% (based on the choice of the DOLS versus VECM model). So global growth and REER dynamics are, itself, able to explain a significant deceleration of exports growth, with exports forecasted to less than half their in-sample growth.

That said, the model does not capture the full-slowdown over the last three years. This suggests that factors outside the model (e.g. demonetization/GST) are temporarily responsible for depressing export growth below what global growth and exchange rate dynamics would have suggested. Early evidence of this is reflected in the fact that exports growth has begun to re-accelerate in recent quarter as the aforementioned shocks fade, but is still not anywhere close to levels witnessed some years ago.

All told, therefore, the model is able to explain a substantial deceleration in export growth over the last three years. Part of this has to do with the sustained appreciation of the real-effective-exchange rate over the last three years. From 2015-2017, the REER appreciated, on average, by 4.4% a year. Using the estimated elasticities, the real appreciation pulled down export growth by 7.7 percent per year – which is an appreciable drag on export growth the last few years.

8.6

1.1

3.64.4

0

2

4

6

8

10

Actual realexportgrowth

Actual DOLSmodel

VECMmodel

% oya

Export growth: In and out-of-sample

2005-2014Out of sample (2015-17)

Source: J.P. Morgan


13. Role of REER: Dutch Disease

What all this suggests is that (1) slowing global growth is not the only reason why India’s export growth has slumped in recent years, and (2) the exchange rate has been an important determinant of India’s exports with the cumulative 20% real appreciation between 2014 and 2017 posing a significant headwind to exports.

From a policy perspective, it’s important to ascertain what factors were underpinning the 20% real appreciation between the start of 2014 (when the exchange rate had stabilized after the taper-tantrum) and the end of 2017.

We would argue that real appreciation was inevitable. How so? The collapse in oil prices in 2014 served as a large, positive terms of trade shock for India. Economic theory would argue that a positive terms-of-term shock should manifest in a more appreciated real exchange rate. The intuition is straightforward. To the extent that windfall gains from a positive terms of trade shock (either higher export prices or lower import prices) are spent, the price of non-tradables should rise vis-à-vis the price of tradables and drive real appreciation. We have previously estimated that India witnessed large windfall gains from the collapse in oil prices (3.1% of GDP across two years, of which two thirds was estimated to have been spent – see table below) So the collapse in oil prices should have put upward pressure on actual and equilibrium real exchange rates in India. The only choice Indian policymakers had was whether to accommodate this real appreciation through nominal appreciation or relatively-higher inflation. Operationally, this manifested itself in a collapse of the current account deficit (because of oil) and therefore a larger balance of payments surplus that was putting upward pressure on the Rupee. This was compounded by FDI flows almost doubling after the new government came to the power. All this exacerbated the terms of trade shock such that the CAD collapsed from 2014 to 2017 exerting sharp and sustained appreciation pressures.

98

103

108

113

118

123

10 11 12 13 14 15 16 17 18

Source: RBI

Index, 2004 =100

Real effective exchange rate (36 country)

REER appreciation

6.5% depreciation in 2018


In a sense, this is akin to the “Dutch Disease” problem -- a term developed to describe the situation in the Netherlands in the 1960s where a discovery of gas deposits in the North Sea, and the income boom that followed, lead to a real appreciation of the exchange rate that crowded out manufacturing exports. The “Dutch Disease” phenomenon has since been broadened to include the effects of positive terms of trade shocks, something that appears to have afflicted India over the last 4 years: the collapse in oil prices resulted in a large, positive terms of trade shock that drove up the actual and equilibrium real exchange rate which, in turn, has likely reduced the competitiveness of India’s exporting sector.

13.1 Still waters run deep

While we have analyzed the impact of the exchange rate on exports, it is plausible that the real appreciation has also induced imports, and therefore contributed to strong import growth in recent years. The combination of export under-performance and import-over-performance has meant that India’s underlying imbalance have deteriorated far more than the headline numbers suggest.

On the surface, India’s CAD -- which printed at 1.9% of GDP in 2016-17 and which is pegged to widen towards 3% of GDP if oil prices average $75/barrel – is still significantly lower than the CAD of nearly 5% of GDP in the run-up to the taper-tantrum of 2013.

But the real story lies below the radar: that the fall in oil and gold imports is masking a sharp and sustained deterioration in India’s underlying external imbalances. If one excludes net-oil and gold imports, India runs a sizeable current account surplus. But that current account surplus has seen a sharp and sustained deterioration over the last three years, declining by a more than 2 percentage points of GDP from 4.6% of GDP in the second half of 2014 (when the economy had stabilized after the taper tantrum shock) to 2.2 % of GDP in the second half of 2017 – see the chart below.

Oil prices

($/barrel)

ToT Shock (% of

GDP)

Boost to grow th (% of

GDP)

FY14 105

FY15 85 1.0 0.7

FY16 46 2.1 1.3

FY17 47 0.0 0.0

FY18 58 -0.6 -0.4

Source: J.P. Morgan

Estimated Impact of Oil on Indian Growth


All this is despite India’s growth differentials with the rest of the world narrowing over the last two years. If India’s growth-differential had increased vis-à-vis the world, one could argue that stronger growth in India – vis-à-vis the world – was inducing disproportionately stronger imports versus exports. But exactly, the opposite has happened. Despite India’s growth differential narrowing, which should have helped underlying imbalances, those imbalances have markedly widened and are at the weakest level in at least 14 years.

All this suggests that India’s underling competitiveness has reduced in recent years, and the sharp real appreciation from 2014 to 2017 likely played a part. That

1

2

3

4

5

6

05 07 09 11 13 15 17

Source: RBI

% of GDP,4QMA

Current account balance ex oil, gold

95

102

109

116

123

1301

2

3

4

5

6

05 07 09 11 13 15 17

Source: RBI

% of GDP,4QMA Index, reverse scale

Current account balance ex oil, gold

REER

REER appreciation

Current account ex oil, gold balance and REER


appreciation, however, was an inevitable consequence of the large, positive terms-of-trade shock that India was the beneficiary off – suggesting that India was, indeed, afflicted by the Dutch Disease phenomenon.

14. Policy Implications

At least five crucial policy implications follow from the aforementioned discussion – three in the short-run and two in the long run.

First, the 50% increase in crude prices over the last 9 months will have the symmetrically opposite impact on the real exchange rate. It partially reverses the earlier positive terms-of-trade shock and will thereby induce some actual and equilibrium real exchange rate depreciation. This should help mitigate some of the pressures on India’s tradable sector – thereby reversing the Dutch Disease. It is important, therefore, that policymakers should not fight this real depreciation or attempt to change the endpoint (since this is an equilibrium phenomenon), but simply use reserves to ensure the new equilibrium is reached in a calibrated manner, so as to avoid self-fulfilling panic and overshooting. Policymakers seem to be doing exactly that. Already between January and May, the 36-country REER has depreciated 6.5%. So the first implication is to let the real exchange rate gradually depreciate as the terms of trade shock reverses.

Second, there are bound to be inflationary consequences of the nominal Rupee depreciation. We estimate every 1% depreciation pushes up headline CPI inflation by 6-8 bps ( Chinoy et al., 2016). Under the new inflation targeting framework, therefore, higher inflation on account of the FX pass-through, will need to be met by monetary tightening. Policymakers will therefore have to reconcile that the new equilibrium will likely entail a weaker currency and higher rates.

Third, it is crucial that fiscal policy (at the central and state level) does not slip again. The more expansive fiscal policy is in India, the more real-appreciation it will induce and therefore offset the real depreciation that will naturally occur from the positive terms of trade shock reversing. Fiscal expansiveness therefore will indirectly contribute to impinging on tradable sector competitiveness.

These are, however, short-term policy implications. The broader policy implications are more important and medium-term in nature.

Fourth, India’s need to dramatically improve underlying trade competitiveness – quite apart from exchange rate dynamics – by boosting infrastructure, total factor productivity and assimilating into global value chains. Given the scale of what needs to be done on this front, a separate piece on this is warranted.

Fifth, India will need to look for new “growth drivers”. Exports boosted growth in the mid-2000s during the period of hyper-globalization. But with global growth softening (compared to that era), growing fears of protectionism around the world, and “income elasticities” reducing in recent years, the global economy is unlikely to provide the tailwinds it did in the mid-2000s. As such, either India will need to improve its competitiveness to the point that it increases its market-share in the exports arena (therefore capturing a bigger slice of a stagnant/shrinking pie) or look for new growth engines domestically. Either which way, Indian policymakers have their work cut out.


References

Anand, Rahul, Kalpana Kochhar, and Saurabh Mishra (2015),” Make in India: Which Exports Can Drive the Next Wave of Growth?,” Working Paper WP/15/119, International Monetary Fund

Aziz, Jahangir and Xiangming Li (2007), “China’s Changing Trade Elasticities,” Working Paper WP/07/266, International Monetary Fund

Chinoy, Sajjid and Jahangir Aziz (2010), “India: More Open Than You Think,” Economic Research, J.P. Morgan

Chinoy, Sajjid, Pankaj Kumar and Prachi Mishra (2016), “What is Responsible for India’s Sharp Disinflation?,” Working Paper WP/16/166, International Monetary Fund

Hooper, Peter, Karen Johnson and Marquez (2000), “Trade Elasticities for G-7 Countries,” Princeton Studies in International Economics No. 87, Princeton University, Princeton, New Jersey

Kapur, Muneesh and Rakesh Mohan (2014), “India’s Recent Macroeconomic Performance: An Assessment and Way Forward,” Working Paper WP/14/68, International Monetary Fund

Raissi, Mehdi and Volodymyr Tulin (2015), “Price and Income Elasticity of Indian Exports – The Role of Supply-Side Bottlemecks,” Working Paper WP/15/161, International Monetary Fund

Saikkonen, P. (1991), “Asymptotically Efficient Estimation of Cointegration Regrssions,” Econometric Theory, Vol. 7(1), pp. 1-21

Stock, J.H., and M.W. Watson, 1993, “A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems,” Econometrica, Vol.61, pp. 783-820

what drives india’s exports and what explains the recent ......sajid z chinoy and toshi jain 3 3....

Documents