# India’s Growth Rate

The discussions surrounding the Indian economy’s performance during the past three-and-a-half year rule of the National Democratic Alliance government have been entirely concerned with the ups and downs of particular year-on-year quarterly growth rates alone, and this amounts to missing the wood for the trees. This article attempts to contrast the notion of a growth rate that is normally employed in the growth theory with y-o-y quarterly rates for the Indian economy. It argues, in particular, that the policy failure (if any) associated with demonetisation cannot be judged with reference to growth rates.

The author acknowledges Pradip Maiti, Mihir Rakshit, and C Rammanohar Reddy for helpful discussions.

There is an ongoing debate about the performance of the Indian economy during the current National Democratic Alliance (NDA) government’s tenure. The general sentiment is adequately summed up by Rao and Rangarajan (2017) in a newspaper column which says: “The sharp deceleration in the growth of the economy as revealed by the first quarter estimate of GDP released a month ago has been widely commented upon.” The “sharp deceleration” referred to is captured by **Figure 1**, which is based on the data published by the Ministry of Statistics and Programme Implementation (MOSPI). It shows the year-on-year (y-o-y) quarterly growth rates of the Indian economy from the first quarter (Q1) of the fiscal year (FY) 2014–15 to Q1 of 2017–18.

The Rao and Rangarajan article is primarily concerned with the wisdom underlying attempts to raise the fiscal deficit to address the ailing growth rate. However, the objective of this article is somewhat different. It attempts to contrast the notion of a growth rate that is normally employed in the growth theory with the y-o-y quarterly rates for the Indian economy that policymakers and most commentators have recently been concerned with. It argues, in particular, that the policy failure (if any) associated with demonetisation cannot be judged with reference to growth rates. Properly defined, India’s growth rate continues to be robust, implying thereby that evidence of policy failure needs to be sought elsewhere.

**Different Approaches**

The growth theory is primarily concerned with the average annual growth rate that an economy maintains for successive years and how this rate can be linked to achievable levels of aggregate and per capita gross domestic product (GDP). In this context, one may try to work out a rough estimate of the average annual growth rate based on the y-o-y quarterly growth rates themselves. The y-o-y quarterly growth rate of the GDP of an economy for any given quarter of a fiscal year measures, as is well known, the rate at which GDP for that quarter grew compared to the same quarter of the preceding fiscal year. Given this definition, a simple average of four y-o-y quarterly growth rates for any fiscal year should produce an approximate estimate of the GDP growth rate over consecutive fiscal years.

The MOSPI data allows this to be easily checked out. The average of the four y-o-y quarterly growth rates for FY 2015–16 was 7.99%. This figure should represent the approximate value of the GDP growth rate of 2015–16 relative to 2014–15. Similarly, the average of the four quarterly growth rates of 2016–17 was 7.14%, which ought to be a rough estimate of the GDP growth rate of 2016–17 over 2015–16.

Based on this calculation, the average annual growth rate over the three fiscal years 2014–15, 2015–16, and 2016–17 was approximately 7.6%. The same annual growth rate may be calculated more directly. The GDP for each fiscal year is the sum of the quarterly GDPs for that year. So calculated, the annual GDP growth rate of 2015–16 over 2014–15 was 8.01% and that of 2016–17 over 2015–16 was 7.11%. This calculation too leads to an average growth rate of 7.6% for the three financial years. The two ways of calculating the average annual growth rates for the three fiscal years lead to identical results. The annual growth rate for 2017–18 cannot be found out yet, since the quarterly GDPs for the entire year will be known only a year from now. What the Q1 fall in the y-o-y quarterly growth rate will imply for the yearly growth rate ought not be predicted off hand. Of course, the government in power may well be concerned about the matter, since it will naturally be inclined to prevent similar drops in the remaining quarters.

Will the average annual growth rate of 7.56% be sustained? This is a vital question, for as far as growth theory is concerned, the importance of a sustainable annual growth rate lies in the fact that it is closely linked to attainable values of GDP and per capita GDP over time. That is, growth rates are important solely on account of their impact on GDP and per capita GDP and the latter are direct indicators of an economy’s welfare level.

A rule of thumb proposed by Robert Lucas (1988) throws light on the issue. Lucas showed that the GDP of an economy which has been growing at an average annual rate of *g* percent will double in approximately 70/*g* years, provided that the rate can be sustained.^{1} For example, the per capita GDP of Singapore, which grew at an average annual rate of 5.4% during the period of 1960–97, was expected to double in size every 13 years. And indeed, by the end of 1997 it had achieved the remarkably high value of $17,558. Similar results were found to hold true for the Far Eastern miracle economies of Hong Kong, Taiwan, and South Korea. The crux of the issue then is that non-performance of the economy during a quarter in isolation has no obvious implications for the future of the economy. In particular, it does not predict an economic cataclysm.

An alternative and probably more transparent approach to calculating the average annual growth rate, one that factors in data available for part years too, consists of analysing the behaviour of an economy across adjacent quarters, as opposed to annually separated ones. For simplicity, this procedure may be referred to as the trend growth rate method, and it attempts to find out the best statistical fit for the existing quarterly data. The trend growth rate is a single compound growth rate of the economy which best explains its behaviour over time, based on the raw quarterly data. What was the trend growth rate of the Indian economy for the time period covered so far?

**Trend Growth Rates for India**

The MOSPI data set can be used for the purpose. The data gives us quarterly GDP data calculated at 2011–12 prices for the period stretching from Q1 of 2011–12 to Q1 of 2017–18. **Figure 2** presents this data in natural logarithmic scale, including Q1 of 2017–18.

Unlike the calculation of the yearly growth rate based on y-o-y quarterly growth rates, fitting a trend line is not constrained by the non-availability of data for the last three quarters of 2017–18. The trend rate, in other words, is simply an average quarterly rate, built up from adjacent quarters of the entire period under review. As can be seen from the equation of the curve, the quarterly trend growth rate for the period was 1.63%. A per quarter growth rate of 1.63% implies an annual average growth rate of (1.63_{}×_{}4 =) 6.52%. The MOSPI data set shows the annual GDP for FY 2016–17 to be ₹121.90 trillion. Consequently, the Lucas formula tells us that India’s GDP is likely to double in 70/6.52 or 10.7 years or so. In other words, India’s GDP ought to be ₹243.8 trillion by 2027–28, provided of course that it continues to grow at the rate of 6.52%.

Further, during 2016 India’s population stood at an estimated value of 1.299 billion (or, 0.001299 trillion). Thus, its per capita GDP was roughly ₹121.9**/**0.001299 or ₹93,841.^{2} Assuming a population growth rate of 1.2% (and this could be an overestimate), the growth rate of per capita GDP turns out to be (6.52–1.2 =) 5.32%. With per capita GDP growing at the rate of 5.32%, the Lucas formula predicts that it should double in (70**/**5.32 =) 13 years approximately. Thus, if the annual per capita GDP growth remains stable, then around 2029–30, the per capita GDP will be of the order of ₹1,87,682.

Similar conclusions could have been arrived at using the average annual growth rate implied by the y-o-y quarterly rates. However, the recent discussions/criticisms surrounding the economy’s performance are almost entirely concerned with the ups and downs of particular y-o-y quarterly growth rates alone, and this amounts to missing the wood for the trees. Ultimately, growth rates—quarterly, annual or whatever—are important only insofar as their implications for the potential annual GDP are concerned.

Two caveats are in order here. First, a high per capita real GDP indicates little about the distribution of the GDP across the population. Inequality of income distribution is a major problem for India. Second, to repeat what has already been emphasised, the calculations are based on the assumption that the average annual rate of growth will remain stable. What assurance do we have that it will in fact be stable? Indeed, the recent furore over decelerating growth suggests that the average growth rate is falling. Is that indeed the case?

**Growth Rates 2014–15 Onwards **

It is worthwhile to ask what impact the quarterly growth rates for fiscal years 2014–15, 2015–16, and 2016–17 had on the growth rate calculated in the preceding section. An easy way of finding this out is to recompute the trend growth rate leaving out the three financial years preceding 2014–15, that is, the fiscal years 2011–12, 2012–13, and 2013–14.

For the time being, we ignore the first quarter of 2017–18 too, but this will be brought back as we proceed further. The result is presented in** Figure 3 **(p 45). The average quarterly growth rate was 1.78%, so that the annual trend growth rate amounted to 7.12%^{3} and this was higher* *than the trend growth rate calculated in Figure 2. If anything, the first three years of the present government helped raise the growth rate above the rate that would have prevailed had the growth rates for the fiscal years 2011–12, 2012–13, and 2013–14 persisted.

If the economy grows at the rate of 7.12%, the GDP at 2011–12 prices will double in (70/7.12 =) 9.8 years, so that the GDP figure of ₹243.8 trillion will be arrived at before the FY 2026–27. A similar calculation can be made for per capita GDP movements over time as well to show that the per capita GDP too will double sooner than estimated in the previous section.

To repeat the question already asked, what guarantees that the higher growth rate will be maintained? **Figure 4** shows that the growth rate changes with the addition of Q1 of 2017–18.^{4}

The quarterly trend growth rate falls to 1.74% and the yearly growth rate to 6.96%, which, incidentally, is still higher than the growth rate of 6.52% worked out in Figure 2. Thus, the favourable impact of the growth rate (of fiscal years 2014–15, 2015–16, and 2016–17) on potential GDP values for the future does not disappear. Even so, it is not irrelevant to ask why the trend growth rate falls with the incorporation of the first quarter of 2017–18.

**Demonetisation and the Fall**

There is a noticeable uniformity in the behaviour of the quarterly GDPs for all the figures. The quarterly GDP peaks in the fourth quarter (Q4) of each fiscal year. This is followed by a dip in Q1 GDP of the following fiscal year. For all the fiscal years under consideration, Q1 records the lowest and Q4 the highest value of GDP. A likely explanation of the peaking behaviour in Q4 lies in the arrival of the kharif crop towards the end of the third quarter (Q3) every year. By the end of Q1 of the following fiscal year, this effect vanishes completely and takes the economy back to its lowest GDP value for that fiscal year. This explains why the trend growth rate of any series of adjacent quarters ending with Q4 of fiscal year must necessarily fall with the extension of the data series till Q1 of the next fiscal year. This happens on account of the rigid pattern of quarterly data, quite independent of particular policies followed.

A nagging question remains to be addressed though. Is it possible that on account of the demonetisation exercise, the fall in Q1 of 2017–18 was more pronounced than Q1 dips of earlier years, or that the rise in Q4 for 2016–17 was less pronounced than the Q4 spike of 2015–16?** Table 1**, constructed once again with the aid of the MOSPI data series answers these questions. The rate of rise of Q4 GDP over Q3 GDP for 2015–16 was 7.02%, while the rise in Q4 GDP over Q3 GDP in 2016–17 turned out to be 6.17%. Similarly, the rate of fall in Q1 GDP of 2017–18 from Q4 GDP of 2016–17 was (–)3.66% and the corresponding fall in the previous year was (–)3.3%. What could explain these figures? The demonetisation exercise was carried out in Q3 of the FY 2016–17 and it has been generally maintained that it affected the cash dependent informal sector’s functioning the most.

Agriculture constitutes a significant part of the informal sector. It cannot be ruled out, therefore, that the smaller rate of rise in Q4 of 2016–17 compared to the rate of rise in Q4 of 2015–16 was caused by the demonetisation drive. The conclusion though may not be entirely valid, since Table 1 shows a similar phenomenon to have occurred in the consecutive fiscal years 2011–12 and 2012–13. The matter needs to be analysed further, though this author has used a Keynesian model to argue that demonetisation could be contractionary in the short run.^{5} The peak rise in 2012–13 was a clear 1.09 percentage points lower than the peak rise in 2011–12, whereas the peak rise in 2016–17 was only 0.85 percentage points lower than the peak rise in 2015–16. If the Q4 quarterly behaviour of GDP has to be understood in terms of the adverse impact of demonetisation on the informal sector, the larger squeeze in Q4 of 2012–13 needs to be figured out as well. Agriculture can malfunction on account of other well-known reasons too, and a clear analytical separation of the events of Q4 of 2012–13 and Q4 of 2016–17 is called for.

Going over now to the fall in Q1 GDP compared to Q4 GDP of the immediately preceding year, we may note that the fall in Q1 of 2017–18 (–3.66%) was higher than the similar fall for 2016–17 (–3.3%). This former fall may well reflect a carryover effect of the demonetisation exercise. However, an even larger fall occurred in Q1 of 2016–17 (–3.3%) compared to the fall in Q1 of 2015–16 (–2.22%). It is possible that agriculture performed better in Q4 of 2015–16 and the larger fall (–3.3% as opposed to –2.22%) merely reflected a higher cyclical fluctuation. In any case, these details need to be addressed clearly to understand the damage inflicted on the economy by demonetisation alone, though it is likely to have been one of the factors that caused the fall in Q1 of 2017–18.

As far as the noted fall in the trend growth rate goes (Figure 4 vis-à-vis Figure 3), it is too early to figure out if it reflects a major shock or a temporary phenomenon. Data for subsequent quarters need to be available before one can take a stand on this issue. It cannot be ruled out that the upcoming quarterly behaviour of the economy will pull up the trend growth rate^{6} and, as already noted, it is this trend alone that determines GDP values in the medium and long run.

In this context, it is worth recalling that the proximate goal of the demonetisation exercise was the elimination of black transactions in cash, accumulation of black wealth, counterfeit money, terrorism and so on.^{7} Whether these objectives will be adequately fulfilled is not yet clear. Media reports on the return of 99.9% of banned notes to the banking system need not imply a policy failure. These deposits are being studied by the authorities.^{8} On the other hand, the *Economic Survey 2016–17*, Volume II states that

Demonetization was expected to reduce black market transactions in real estate which would be manifested in reduced real estate prices … Even prior to demonetization, there was a deceleration in house price inflation, and there was a further reduction in prices post-demonetization. The decline has since been reversed, and prices appear to be rising again.

These conflicting reports make it difficult to judge if demonetisation has successfully cleansed the system of corruption or not. Apart from these observations on black transactions and wealth, Reddy (2017: ch 6) raises a serious question regarding the design of the demonetisation policy itself.

Demonetisation 2016 appears to have been aimed at dealing simultaneously with (i) black money and corruption, and (ii) terrorism finance by counterfeit currency which was the work of “anti-national” and “anti-social” elements. …

One should note that legally and technically demonetisation can only address the issue of counterfeit currency. Demonetisation by itself cannot directly deal with cash hoards of black money. … Those who are unable to launder their illegal hoards or do not want to risk having to explain these stocks during an exchange/deposit at the banks, will, of course, find the value of their hoards extinguished.

In any case, it is not clear how the policy is linked to other standard parameters, such as employment, infrastructural growth, spread of irrigation and so on, that capture the state of the economy. In fact, some believe that the serpentine queues in the banks that demonetisation led to indicated substantial loss of man hours of work.^{9} Of course, demonetisation will succeed in eliminating both corruption as well as counterfeit money to a large extent if it is followed by successful digitalisation of the financial system. One suspects though that, infrastructure-wise, digitalisation on a major scale is still a long way off in India.

**Base Year 2004–05 vs 2011–12**

The base year for the real GDP used so far was 2011–12. Prior to the adoption of 2011–12, the base year used to be 2004–05 and the MOSPI website contains data for quarterly real GDP at 2004–05 prices too, from Q1 of 2004–05 through Q3 of 2014–15. It is interesting to compare the trend rate of growth of GDP at 2004–05 prices with the calculations presented so far.** Figure 5** shows this trend for the United Progressive Alliance (UPA) 1 and UPA II periods put together. The quarterly rate of growth was 2.02% and the annual rate was consequently 8.08%. These figures were higher than the corresponding figures for Q1 of 2014–15 till Q4 of 2016–17 (1.78% and 7.12% respectively—[Figure 3]). Quite apart from the fact that the trend rate maintained by the NDA government will be better understood only after a longer time series data is available, one must not jump to conclusions from the finding in any case.

It is well known that the growth rate sustained during any given period of time changes with a change in the base year, and the base years for the UPA and NDA series presented here were indeed different.

**Figure 6** shows how a change in the base year may affect the trend growth rate. There is an overlap between the two data sets consisting of fiscal years 2011–12, 2012–13, and 2013–14. The quarterly trend as per the 2004–05 data series was 1.67%, so that the yearly trend was 6.68%. According to the 2011–12 data series, the quarterly and annual trends were 1.49% and 5.96%. The R^{2} values for both trend calculations are less than satisfactory. Nonetheless, the conclusion that base year choices affect the real rate of growth remains valid and it is this problem that has led to the computation of real GDP in chained currencies (such as chained dollars in US).^{10}^{}Be that as it may, Figure 5 shows that the two consecutive spells of the UPA government did exhibit an exemplary trend growth rate. However, even at 2004–05 prices, the last three fiscal years of the UPA II government exhibited a substantial fall in the growth rate to 6.68% from the trend growth rate of 8.08% for UPA I and UPA II put together. MOSPI does not provide a sufficiently long data series at 2004–05 prices to cover the NDA regime till date. If it did, a more meaningful comparison of the growth-wise performance of the two governments should have been possible.

**Concluding Remarks**

This short article argues that a fall or rise of a particular y-on-y quarterly growth rate cannot reveal too much about an economy’s evolution over time. Ultimately, economists and policymakers are interested in attainable levels of GDPs and per capita GDPs and these can be computed only with reference to annual growth rates. The y-on-y growth rates may be manipulated to produce annual growth rate figures. However, a more useful concept in this context is the trend growth rate implied by MOSPI’s data series for adjacent quarterly GDPs.

The debate concerning demonetisation too is addressed by the article. The general conclusion reached is that it is too early to pronounce judgment on the success or failure of the policy. It is not clear that the policy targeted standard macroeconomic goals, though critics feel that there were indirect negative effects of demonetisation on the growth performance of the economy. The article questions this view and demonstrates, with the help of MOSPI data, that economic growth, properly defined, has not been compromised by demonetisation.

Of course, one needs to avoid over-reliance on a trend growth rate, or the y-o-y quarterly growth rates, or any other growth rate for that matter to understand the behaviour of an economy vis-à-vis another. Recently, for example, there has been abundant discussion surrounding India’s economy catching up with that of China. India has also been hailed as the fastest-growing economy in the world. A growth rate as such does not reveal much about the economic might of a country, though a high growth rate is obviously preferable to one that is low. But a high growth rate per se does not make India’s economy a close competitor of China’s economy.

According to World Bank figures, China’s per capita GDP was $6,894.50 in 2016. As we have already observed, the combined MOSPI and demographic data suggests that India’s per capita GDP for 2016–17 was roughly ₹92,348.48. Assuming an exchange rate of ₹60 per dollar, the per capita Indian GDP for 2016–17 should have been around $1,539, which is significantly smaller than China’s. Where China will stand in 2025 is anybody’s guess, but it is safe to assume that India’s per capita GDP will not catch up with China for several years yet, even if India continues to maintain a stable and high annual growth rate, unless China’s annual trend growth rate falls dramatically.

**Postscript**

The conclusions reached in the main body of the article depended on data available till Q1 of 2017–18. It was noted there that a clearer picture will be visible with the availability of more data. The Central Statistical Office has published GDP data estimates for the second quarter of 2017–18. As far as y-on-y calculations are concerned, there is euphoria over the jump in the Q1 growth rate from 5.7% to a happier Q2 rate of 6.3%. Different sources have expressed satisfaction that the growth rate is showing signs of picking up. Following our trend growth rate calculation, however, one reaches exactly the opposite conclusion. This is shown in **Figure 7**.

Comparing Figures 3 and 4, we had already concluded that with the addition of the Q1 2017–18 data, the quarterly trend growth rate had fallen from 1.78% to 1.74% (with an implied fall in the annual growth rate from 7.12% to 6.96%). A comparison of Figures 4 and 7 now demonstrates a further fall of the quarterly trend growth rate from 1.74% to 1.71% (or, an approximate fall in the annual rate from 6.96% to 6.84%). These developments could well be reversed by developments over the year and the trend growth rate might be finally pulled up. The latest published data too will surely clean up over time and this in turn will have implications for our results. However, as matters stand right now, it will be a mistake to assume that the rise in the y-on-y growth rate from 5.7% to 6.3% is telling a sustainable growth rate story.

**Notes**

1 See Jones (2006: ch 1, p 10) for an elementary derivation of this formula.

2 At the risk of being casually empirical, however, one may note that the monthly earnings of low skilled home service providers in metropolitan cities range around ₹7,000 to ₹8,000 and this amounts to annual earnings of ₹84,000 to ₹96,000.

3 This is lower than, but probably more accurate than the average annual growth rate of 7.6% that the y-o-y quarterly growth rate figures led us to.

4 This calculation could not have been carried out if we had restricted attention to y-on-y growth rates alone.

5 See Dasgupta (2016).

6 Govinda Rao and Rangarajan (2017) discuss policies which might help to lift the growth rate.

7 See Reddy (2017, Chapter 6).

8 See, for example, *Times of India*, 6 November 2017.

9 The author owes this observation to Mihir Rakshit, who suggested it during a personal discussion.

10 See Blanchard (2017, ch 2, Appendix) for a lucid discussion of this issue.

**References**

Blanchard, Olivier (2017): *Macroeconomics*, Global 7th ed, New York: Pearson Education.

Central Statistical Office (2017): “Estimates of Gross Domestic Product for the Second Quarter (July–September) of 2017–18,” http://www.mospi.gov.in/sites/default/files/press_release/PRESS_NOTE-Q2_....

Dasgupta, Dipankar (2016): “Theoretical Analysis of “Demonetisation,” *Economic & Political Week**ly*, Vol 51, No 51.

*Economic Survey* (2016-17): Volume II, Government of India.

Jones, Charles I (2006): *Introduction to Economic Growth*, 2nd ed New York: WW Norton & Company.

Lucas, Robert E Jr (1988): “On the Mechanics of Economic Development,” *Journal of Monetary Economics*, Vol 1, pp 3–42.

Ministry of Statistics and Programme Implementation Data site, https://www.mospi.gov.in/data.

Rao, M Govinda and C Rangarajan (2017): “Avoid the Adventurous Path,” *Hindu*, 17 October, http://www.thehindu.com/opinion/lead/avoid-the-adventurous-path/article19872525.ece.

Reddy, C Rammanohar (2017): *Demonetisation and Black Money*, Hyderabad: Orient Blackswan.

*Time**s of India *(2017): “35,000 Shell Firms Deposited, Withdrew Rs 17,000 Crore Post-demonetisation**,” **6 November, https://timesofindia.indiatimes.com/india/35000-shell-firms-deposited-withdrew-rs-17000-crore-post-demonetisation/articleshow/61523109.cms.

**Updated On : 13th Dec, 2017**

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