Economic Survey of India: a big mess

Economic Survey of India is providing incomplete arguments. There are serious issues in presenting the data. Since data used in Economics Survey (NFHS-5) is not yet publicly available, we will try to understand where the economic survey’s argument is incomplete and why I am skeptical about the data and methodology used. (I am not going to discuss the chapters which were highlighted in the previous survey and ignored by given survey like the discussion of five trillion economy, doubling farmers income, etc. I will discuss it in my next post.)

First, predicted growth rate for 2021. IMF has predicted 11.5% where Survey also predicted that the growth rate will be around 11%. Why? On what basis, this double-digit figure is predicted. 

Let’s discuss the IMF’s prediction. 

IMF has predicted Y-Y growth rate is 11.5% where Q4 over Q4 is 1.69% in January 2021 World Outlook. This prediction was at 8.8% (for Y-Y growth rate) in October 2020. IMF has not given any strong reason for increasing the prediction from 8.8% to 11.5%. 

Now let’s see why this 11.5% growth rate is problematic. 

The following image shows my calculation based on the growth rate.

(Note: relationship between annual data and quarterly data is based on the methodology by FRED and that's why average is taken. In few cases, summation is also applied).

2021_Q1=2021_Q2=2021_Q3=114.77

If I don’t assume that GDP for three quarters are the same then there will be a GDP of more than 114.77 for one quarter. 

So if I plot these quarterly figures, then we can see that there is a spike in 2021’s first Quarter where at the end i.e. in 2021’s 4th Quarter there is a drastic decline. And that’s why I doubt these figures. Why there is a spike after the 4th Quarter of 2020 and why there is a decline in 2021’s 4th Quarter. 

Now I have done the above thing by averaging the Quarterly data (FRED gives annual GDP data which is the average of quarterly data). NSS gives quarterly data and for annual data, we have to make a summation of these quarterly data.  So let’s try this approach as well.

Year	GDP	Q	GDP (Q)
2020	100	2020Q1	25
2021	111.5	2020Q2	25
		2020Q3	25
		2020Q4	25
		2021Q1	28.6925
		2021Q2	28.6925
		2021Q3	28.6925
		2021Q4	25.4225

Now in 2020 GDP was 100 and with a projected 11.5%, it becomes 111.5. Now assume that GDP is equally divided. 25, 25, 25, and 25 in 2020. Now Q4 to Q4 is 1.69. So 2021Q4 will be 25.4225

Now final year GDP is 111.5 so for the remaining quarters it will be 111.5-25.4225= 86.0775. Again if I divide this 86.0775 into three quarters then it will 28.69. Now if I plot the graph then again there will a spike in 2021Q1 and a drop in 2021Q4.

Having negative growth of around 9-10% in 2020 due to shock itself suggests that next years will have good growth (if the shock is neutralized) due to the low base effect. The point is in which year? 2021 or 2022. IMF in October 2020 was predicting 8.8 in 2021 and 8 in 2022 but in January 2021, IMF is projecting 11.5 in 2021 and 6.8 in 2022. In the previous outlook, the projected growth rate for 2021 and 2022 was around 8-9 but in January 2021 outlook, it becomes 11.5 for 2021 and 6.8 for 2020. What happened in these last four months so that IMF changed the mind and suggest a complete recovery in 2021 (Not partial recovery in 2021 and 2022). Anyway having a -10 growth rate in 2020 and +11 in 2021 suggests that we are back at the previous level that's it. 

Now coming to the Economic Survey of India, my first concern comes from chapter two. The basic argument of this chapter is that growth leads to Debt sustainability but debt sustainability does ensure growth. But the previous economic survey by the same chief economic advisor suggests that the growth, boom in the previous decade leads to a credit boom which was a reason for NPA (Non- Performing Assets). Does growth really guarantee debt sustainability? The reason behind growth-lead-debt sustainability is that debt sustainability depends on the interest rate paid and growth witnessed i.e. interest rate growth rate differential (IRGD). So one can say that with high growth it becomes easy to maintain debt and if the interest rate paid is low then obviously debt is sustainable. But the basis of this argument is: high growth leads to high revenue. Is it happening in India? Is economic growth for India generating the taxes and revenue? The nature of Indian growth in the last few years is not revenue-generating. The tax buoyancy indicates that whether the nature of growth is more revenue-generating (or the tax-net is more effective) but for India tax buoyancy is low. Our growth is not helping us to generate revenue. So, if growth is revenue-less then will that growth lead to debt-sustainability? 

Economic Survey also ignores the Indian Fiscal Federalism. If I look at Indian states then are they witnessing debt sustainability? And will GSDP growth (i.e. growth rate of State Production) help them to maintain debt sustainability?   

The IRGD will be negative for advanced economies because of the nature of their fiscal policy (counter-cyclical fiscal policies i.e. spend more and cut taxes in a recession and spend less and increase taxes in boom). The survey does mention that it is also negative for India (again there can be a wide discussion over it like is interest rate understated? is growth overstated?). And a survey suggests the counter-cyclical fiscal policy. But at the same time, the survey mentions that because of the nature of Indian growth, there won't be a crowding-out due to government spending. So, in the boom period should we spend more or should we spend less? The economic survey is creating confusion. 

In the third chapter: Does India’s Sovereign Credit Rating reflect its fundamentals No!, CEA points out that India’s fundamentals are not getting reflected into Sovereign Credit Rating. A similar kind of discussion is also made in the old economic survey (2016th Economic Survey). Yes, it's true that considering the efforts (reforms) like GST, efforts related to controlling the fiscal deficit, inflation, etc with economic growth, we do deserve a good rating. Survey highlights that “never in the history of sovereign credit ratings has the fifth largest economy in the world been rated as the lowest rung of the investment-grade (BBB-/Baa3)”. Yes, we are the fifth-largest economy based on GDP. But we are still a "lower-middle-income nation". Our per capita GDP is low. One can say that the fifth-largest economy rarely has such a lower per capita income. And per capita GDP plays important role in determining the ratings. It is said that high per capita GDP countries can absorb the volatility. And more specifically, low per capita GDP can lead to socio-political instability. Now here inequality plays an important role (For the economic survey, inequality is irrelevant!). Low per capita GDP plus high inequality can actually create problems. So instead of targeting the GDP, one should aim at becoming the upper-middle-income country with a low level of inequality. And that will be reflected in the rating. 

There are very serious issues with the Fourth chapter: Inequality and Growth: Conflict or Convergence?

The survey suggests that inequality is a problem for advanced countries as it affects socio-economic indicators adversely. But the survey doesn't find such an adverse impact of inequality on socio-economic indicators. Where recent reports by agencies like Oxfam points out the exact opposite thing i.e. inequality affects socio-economic indicators negatively. Second thing, survey argues that we should focus more on economic growth and eradicating poverty. But literature does suggest that inequality can harm the process of poverty alleviation. In a more equal economy, economic growth can eradicate poverty more efficiently than the more unequal economy. So even if one is interested in poverty alleviation then also inequality can’t be ignored. 

The third important point is that survey uses NSS consumption expenditure data to calculate the state-wise Gini coefficient. Now can we really use NSS consumption data to calculate the state-wise Gini coefficient? That’s a very important point. And why state-wise? Why not for India as a whole? Calculating the inequality index for subnational can reduce the variation. But if I calculate the Gini coefficient for India then it will consider the consumption of poor in Bihar and consumption of rich in Gujarat together. But for a while let’s accept the state-wise Gini coefficient. But the survey has committed a serious mistake by plotting the scatter plot and correlation between the Gini coefficient and Socioeconomic indicators like birth rate, death rate. How? First, let’s discuss what exactly survey is suggesting. 

 

The first thing for comparison between Indian states and advances economies, Survey has used Gini coefficient for Indian states and 20:20 (ratio of top 20 to bottom 20) is used. One common indicator should have been used.)

Now let me discuss the above figure. The above figure suggests that the Death rate and birth rate are declining even if inequality (in terms of Gini coefficient). 

Now the point is this will happen in a case where one group (i.e. poor income category) is witnessing the increase in death rate and another group (i.e. rich income category) is witnessing the decline (drastic decline) in death rate. Let us discuss the following hypothetical case. 

In the above example, the poor income category is witnessing the increase in death rate with inequality where the rich income category is witnessing a drastic decline in death rate and therefore overall death rate is showing a decline with the increase in the Gini coefficient. So Survey should have discussed the trends income category-wise. 

Importantly there is a regression analysis (panel data regression) to understand the impact of growth (Real NSDP per capita) on poverty (Head Count Ratio). 

 

Now if we observe the result carefully, we can see that the Rich to the poor ratio of MPCE also has a statistically significant negative impact on the log of Headcount ratio for rural+urban and rural. And if we observe the coefficients of Ln(Real NSDP per capita) and Rich to the poor ratio of MPCE then there is no much difference in value but have a different sign. There is a chance of multi correlation (For panel data regression it is said that multi-correlation is less problematic).

So if the Real NSDP per capita is increasing only because of the rich population and another hand poor population is witnessing stagnated growth (or decline) then there won’t be any change in poverty (assuming that increase in Income will be reflected in MPCE in a similar manner) ( Rather it will increase). And the same thing is also discussed in the literature as inequality can create problems in poverty alleviation. Let us take an example. 

Now I am keeping all other variables constant and I am changing Ln(Per capita Real NSDP) and Rich to Poor MPCE. Now if per capita NSDP is doubled from 147450 to 294900 then Ln(per capita NSDP) is changed from 11.90 to 12.59. Now if Rich to Poor MPCE is increased from 1.2 to 2.2 then the change in Ln(Head Count Ratio) is actually positive. 

Now in the second scenario, rich to poor MPCE is decreased from 1.2 to 1 then the change in Ln(Head Count Ratio) is negative (more negative). This table itself suggests that inequality does play role in poverty alleviation. 

Again, in chapter JAY Ho: Ayushman Bharat's Jan Arogya Yojana (JAY) and Health Outcomes survey again making the doubtful comparison. 

To highlights the differences between states which have implemented the central government scheme called Pradhan Mantri Jan Arogya Yojna (PM-JAY) and states which haven't implemented the scheme, the survey has selected West Bengal as the state that did not implement PM-JAY and neighboring states that implemented PM-JAY – Bihar, Sikkim, and Assam. The difference in difference method is used. The same thing is used for all states that did not implement PM-JAY vis-à-vis all states that did. There are some serious issues in using the difference in difference method to understand the impact of PM-JAY by using NFHS surveys. We will discuss it in detail.

So survey from the above figure trying to convince that Bihar, Sikkim, and Assam have witnessed more reduction in Child Mortality compare to West Bengal (which has not implemented the scheme). Now observe this figure carefully. For Bihar, Sikkim, and Assam, the Infant mortality rate in NFHS 4 was 42 and it became 30 in NFHS 5 where for West Bengal it was 28 in NFHS 4 and became 22. Now, the survey is arguing that Bihar, Assam, and Sikkim have witnessed -28% (which have implemented the scheme) but West Bengal is witnessing the -20% i.e. less decline. But the point is reaching 22 from 28 is different than reaching 30 from 42. Both can’t be compared. If Bihar, Assam, and Sikkim had 28 infant mortality rates in NFHS-4 and if it became 18 in NFHS-5 then one could argue that Bihar, Sikkim, and Assam are improving because of Scheme implementation. But this is not the case. 

The economic survey should be written carefully before making final arguments. There are so many doubts in the present economic survey.