India’s K-Shaped Recovery – An analysis
Introduction
An article on the Indian economy critical of the Government
has something like this to say, “At roughly 15.9 million units, two-wheeler
sales in 2022-23 are where they were in 2014-15, when the Modi government came
to power. These data sets have been brought out by Vivek Kaul in an article in
The India Forum which makes the K-shaped recovery look increasingly
grotesque. There is something seriously wrong, indeed inexplicable, about the
structure of private consumption demand, going by the pattern of the sales of
two-wheelers. A similar pattern is seen for entry-level passenger cars.” (Source:
S1)
K-Shaped recovery is something which one would have heard
many times since we started recovering from the pandemic. As per Investopedia
(Source: S2), “A K-shaped recovery is one in which the performance of
different parts of the economy diverges like the arms of the letter
"K." In a K-shaped recovery, some parts of the economy may experience
strong growth while others continue to decline.”
In the context of the Indian economy, it simply means that the
top 10-20% of the population has shown growth in incomes compared to the
pre-pandemic levels while the rest has not yet recovered to the pre-pandemic
levels even in FY23. Or to put it in other words, the rich become richer, and
the poor becomes poorer.
Some other data points used to show the K-shaped recovery are:
- As per a Pew Survey, “the number of people who are poor in India (with incomes of $2 or less a day) is estimated to have increased by 75 million because of the COVID-19 recession.” (Source: S4).
- The above was largely substantiated by a World Bank study in 2022 (Source: S5) which said that over 56 million Indians were pushed into poverty in 2020.
The Indian economy has grown by almost 10% cumulatively from
FY20 to FY23 and this would be substantially driven by the top 10% of the
population (with or without a K-shaped recovery). As per studies by Thomas
Piketty et al. (Source: S3) the income shares of the top 10% of the population is
~55% in 2013.
Considering the above, leaving aside sales of two-wheelers,
entry levels cars etc., the most critical aspect is to see how the bottom 50%
of our population have fared in India. Are their conditions in 2022-23 worse
than what it was in 2019-20? Let’s look at some data.
Section 1: Poverty Rates
There was an IMF working paper published in April 2022 by Surjit
Bhalla et al. which concluded that extreme poverty was as low as 0.8 percent in
the pre-pandemic year 2019, and food transfers were instrumental in ensuring
that it remained at that low level in pandemic year 2020 (Source: S6).
There was another paper by Arvind Panagariya et al. (Source:
S7) which concluded that it is only during the strict lockdown period of
April-June 2020 that rural poverty saw a “modest rise”. But it fell for the
full year 2019-20, even if at a significantly lower rate. It witnessed a sharp
decline in 2020-21 as in the pre-Covid year of 2018-19.
Both these papers show that even during the peak of COVID the
poverty rates either remained stagnant or declined. These papers have come in
for many criticisms from academics.
The main points for and against a fall in poverty during the
pandemic year itself is:
Reasons for poverty reduction during the pandemic year
- While the rural population in India is almost 2/3rd of the overall population, the number of poor people in rural is almost 80% of the overall poor (Source: S8). In the pre-pandemic period, almost 60-65% (Source: S9 and author’s calculations) of the rural population were engaged in agriculture. A back of the envelope calculation would show that almost 50% of the poor people are engaged in agriculture. This would imply that agricultural growth would have a positive effect on reducing poverty. Our Periodic Labour Force Surveys (PLFS) are conducted using agricultural year as basis (June year ending). So, quarter ended June 2020 was during peak COVID, the calculations mentioned are from year ended June 2019 to year ended June 2021. From here onwards, fiscal years (year ending March) will be represented as FY (e.g., FY19 is year ended 31 March 2019) while agricultural years will be represented using the starting and ending years (e.g., 2018-19 represents year ended 30 June 2019). Agriculture gross-value added (GVA) grew by almost 10% (Source: S10) from 2018-19 to 2020-21 (based on agricultural year) which would imply a reduction in poverty rates.
- The Central Government’s food subsidy bill increased by almost 2.5% of GDP from FY20 to FY21 (Source: S11) on account of the free food grains and higher MGNREGS expenses. As per the study by Thomas Piketty et al. (Source: S3), the share of incomes of bottom 50% was around 15% in FY15. So, this additional expenditure was almost 17% of the income of bottom 50%. It should be noted that PMGKAY would benefit the bottom 50% the most and even assuming leakages and payment to FCI for FY21 of 50%, it would almost give an additional boost of 8-10% to the incomes of the bottom 50%. This would be act as a bulwark against the fall in incomes during the pandemic year.
Reasons against poverty reduction during the pandemic year
- The GDP fell by almost 6% in FY21. The brunt of the fall would have been taken by the unorganized sector which means the unorganized sector would have fallen by much a higher amount due to the pandemic shock. This would mean the incomes of the poor (since around 90% of our population works in the unorganized sector) would have fallen by a higher amount.
- With reference to the agricultural growth, the calculations using PLFS surveys (Source: S9) show that while agricultural GVA increased by 10% from 2018-19 to 2020-21, agricultural employment increased from 20.5 cr to 25.8 cr (almost 27%) driven by the migrant labourers’ movement from urban to rural areas. So, on a per capita basis even agricultural incomes fell.
- Considering these factors and the studies by Pew and World Bank, it would suggest that poverty rates would have increased during the pandemic year. Well, even our GDP growth rates were ~-6%, so, it is to be expected. Our question is more on whether the poverty rates remained higher than pre-pandemic years even in FY22, FY23 etc.
So, we go to a World Bank working paper by Sutirtha Sinha Roy et al. published in April 2022 (Source: S8) which show that India’s poverty rate reduced from around 22% in FY11-12 to 10% in FY19-20 (on the eve of the pandemic). Even this paper has come across criticism from academics generally critical of the Government, however, its acceptance seems to be much more among them than the other 2 papers. The main data used by them is the Centre for Monitoring Indian Economy’s (CMIE) Consumer Pyramids Household Survey (CPHS). However, since CPHS sampling methodology is not properly representative, this study made some adjustments to make it representative of the Indian population using various statistical methods. The World Bank used CPHS data (Source: S13) and similar adjustments made by the authors of the aforesaid paper and have arrived at poverty rate of 12.7% in FY20 which increased to 14.7% in FY21 an increase by almost 29 mn (much lesser than the 56 mn in the 2022 World Bank study and the Pew Study). In FY22, it came down to 11.9% which is even lower than the FY20 figures and in FY23 it came down further to 11.3% (Source: S51). It should be noted that FY22 is based on actual data and FY23 is a nowcast and may get revised once later. But this is the best estimate we have now.
Considering the larger impact of the pandemic on unorganized sector, there would have been some cushioning effect of PMGKAY which would have given a boost in consumption of the poor by 8-10%. This effect would wear off in FY24 (till December 2022, the scheme was carried forward) but would be compensated by rising incomes. In FY22 itself poverty has come below the pre-pandemic levels in line with the per capita incomes as per GDP calculations. This shows that the K-shaped recovery when it comes to the poorest of the population does not stand.
For comparison purposes, from 1993-94 to 2004-05, poverty reduced at 0.74% per year from 45.3% to 37.2%. From 2004-05 to 2011-12, the reduction substantially increased to 2.18% per year from 37.2% to 21.9% due to our high GDP growth rates as well as high growth in real wages (refer Section 2 below). From 2011-12 to 2019-20 the per year reduction slowed down to 1.15% per year from 21.9% to 12.7%. From 2019-20 to 2022-23, the per year reduction was around 0.5% from 12.7% to 11.3% which broadly tracks the lower GDP growth rates during this period (CAGR of ~3.3%).
The aforesaid poverty rate is at consumption levels of USD 2.15 in 2017 PPP which is very close to India's Tendulkar poverty line (i.e., INR 816 per month per person for rural areas and INR 1,000 per month per person for urban areas in 2011-12 prices). The World Bank has also given Lower Middle Income poverty rate (USD 3.65 in 2017 PPP) which is much higher than our Tendulkar Poverty line. This was 45.9% in FY20 which increased to 49.7% in FY21 but fell below the pre-pandemic number to 45.1% in FY23.
Thus, broadly speaking the bottom 50% of the population has recovered from the pandemic at least by FY23. Within the bottom 50% also, the poorest section recovered faster. This will get clearer once we analyze the PLFS data on wages (Section 3 below).
The Government of India has conducted the 2022-23 consumption
expenditure survey (results are awaited). This would give the poverty rates for
2022-23, but with that we would not be able to know how it trended just before
the pandemic.
Before we move forward, one would see headlines like “India’s
Poverty Soared Pre-Pandemic, Eased in 2021 but Remained Above 2018 Levels”
on the aforesaid World Bank study (Source: S14). These headlines seem to
suggest that poverty rates did not come back to pre-pandemic levels in FY22. A
careful reading of the World Bank study suggests that new poverty estimates are
only from FY20 to FY22. So, the FY19 poverty estimates mentioned in the article
quoted above are using a different methodology and is not strictly
comparable with the new FY20 poverty estimates. The FY20 poverty rates as per
the earlier methodology was 10% while FY19 was 11.1%. However, the FY20 poverty
rates as per revised methodology was 12.7% but we don’t have FY19 estimates
using the revised methodology. So, the news headlines do not show the correct
picture. Hence, our understanding that extreme poverty rates in FY22 moved lower than pre-pandemic
levels still stand.
Section 2: Wages
The data on wages is taken from two sources, one is the
Agricultural wages annual report for 2021-22 (the ones mentioned in AY in the
Pic below or Agricultural year) (Source: S15) published by Ministry of Agriculture & Farmers Welfare and the other is from RBI’s
annual publication titled Handbook of Statistics (Source: S16) on Indian States
which are on financial year basis (the ones mentioned in FY in the Pic below) which in turn is based on data from Labour Bureau, Government of India.
The Agricultural wages are mainly for agricultural labourers and the RBI data
is used for non-agricultural labourers like horticulture, construction etc.
The wage growth has been adjusted for CPI (rural) to arrive at real wage
growth.
The wage growth seems to show that for agricultural and
horticultural labourers, there has been real growth from the pre-pandemic years
while for other rural labourers, the real growth is negative. So, for most
rural labourers (may be two-thirds of them), there is positive real growth
while for the others its negative as per this. K-Shaped recovery will find
partial traction basis this data. It should be noted that both growth and
degrowth mentioned above is not substantial, one can say it has largely
remained stagnant.
Pic 1
Sources: S15, S16 and Author’s calculations
If we look at the long-term trends also, as per an RBI Working Paper dated April 2018 (Source: S52), the rural wages for men followed 3 distinct stages:
- Phase I (from January 2002 to September 2007): Average real rural wages were slightly negative
- Phase II (October 2007 to October 2013): Average real rural wages turned positive and were growing at 5-7% per year in real terms
- Phase III (November 2014 onwards): Average real rural wages were slightly positive which basically became stagnant since the onset of the pandemic
Even during Phase I when real rural wage growth was negative, our poverty rate had reduced from 42.3% in 1999-00 to 37.2% 2004-05 which means incomes/ consumption of the poorest still increased (Source: S53). While in Phase 2 our poverty rate had reduced much from 37.2% in 2004-05 to 21.9% which is a much faster fall compared to Phase I which is an obvious conclusion.
The reasons for fall in poverty during Phase I, could be:
- Same person moving from unskilled labour to skilled labour leading to higher wages for the same person over time. E.g., In the IT industry, the salary of an entry level employee would be say INR 4 lakhs per annum and it would have remained at a similar level for the past 10 years or so. So, the real wage growth for entry level IT employee would be negative. However, for a specific employee, over a 10 year span, that person would have received promotions and all and his/ her salary growth would have been higher than inflation rate.
- If there is an increase in the workforce participation rates (as a percentage of population) over the period, for a specific household, there would be an increase in household incomes even if there is a fall in real incomes per person. E.g. If only one person was employed in a household who earned INR 200 per day in 1999-00 but in 2004-05 in the same household if there are 2 people earning INR 150 per day, while at a per person level there is a fall in income, as a household, the incomes have risen by 50%.
- From 1999-00 to 2004-05, the wages of casual labourers in rural areas increased by 1.9% CAGR in real terms
- From 2018-19 to 2012-23, the wages of casual labourers in rural areas increased by 4.0% CAGR in real terms (can also be observed in Section 3 below)
With regards to the second point, we can confirm the following (based on author's calculations from EUS, PLFS and Source: S54):
- In 1999-00, the workforce (as a % of overall population excluding unpaid household helpers) was 31.3% which increased to 31.9% in 2004-05
- In 2018-19, the workforce (as a % of overall population excluding unpaid household helpers) was 30.7% which increased to 33.6% in 2022-23
Section 3: Wages data from PLFS
As per the PLFS, there are different classes of workers (Source: S9), which can be summarized as below:
- Self-employed: Persons who operated their own farm or non-farm enterprises or were engaged independently in a profession or trade on own-account or with one or a few partners were deemed to be self-employed in household enterprises.
- Regular wage/salaried employee: These were persons who worked in others’ farm or nonfarm enterprises (both household and non-household) and, in return, received salary or wages on a regular basis (i.e., not based on daily or periodic renewal of work contract).
- Casual labour: A person who was casually engaged in others’ farm or non-farm enterprises (both household and non-household) and, in return, received wages according to the terms of the daily or periodic work contract, was considered as a casual labour.
As per India Labour and Employment Report 2014 (Source: S17), in 2011-12, the poverty rates among regular wage earners were at 9%, self-employed at 25% and casual workers at 36% while the overall poverty rate was at 22% (Source: S8). Thus, if we track the movement in earnings of casual workers and self-employed, we should get a good idea on the post-pandemic recovery process among the bottom half of the population. In Pic 2 below, I have shown the Real indexed earnings (earnings of rural casual workers for 2018-19 indexed at 100) of these categories in rural and urban areas. As one can see, the earnings of casual workers (rural and urban) is around 15% higher in 2022-23 compared to 2018-19. The earnings for rural self-employed is around 6% higher while the earnings for urban self-employed and regular wage workers is largely stagnant. This is in line with the slower poverty reduction for the USD 3.65 PPP poverty line vis-à-vis the USD 2.15 PPP poverty line which we saw above.
The bottom three lines have shown growth compared to 2018-19 while the top 3
lines have remained flat. So, it is among the top 50% earning category that have not
yet recovered. On a lighter note, this is also K-shaped though the opposite of
what one initially thought.
In an earlier article titled, “Missing the Rich - Analysis
of NSSO Data” I had shown that PLFS data is not reliable to measure income
growth in the top decile category. Hence, one can broadly assume that basis the
below chart that the top 3 lines pertains towards the middle-income category
(from 51st to 90th percentile) and seems like they have
not fully recovered from the impact of the pandemic. There are some indications
to this effect from some indirect indicators as we shall see below.
Pic 2
Sources: S9 and Author’s calculations
Section 4: MGNREGS demand
As per a Business Standard article (Source: S19), the number
of person days of MGNREGS for the current fiscal year is 207 cr which is almost
10% higher than the previous fiscal (FY23). Further, the number of person days for full
year FY23 was 293 cr (Source: S20) which itself was almost 10% higher than the
person days generated in FY19 and FY20.
As per a study conducted by Prasanna Tantri et al. (Source: S18),
a rise in demand for MGNREGS in a district can serve as an early warning sign
to red flag a region in distress. This shows that all is not well with the poorer
sections of the country.
However, the following caveats should also be considered:
- MGNREGS was rolled out across India from FY09 onwards and it generated 216 cr person days of labour which increased substantially to 284 cr in FY10 (Source: S21) (like FY23 numbers but with a 15% lower population base) due to drought. Even in FY11 which was a high growth year overall and where agriculture grew by 9% (Source: S10), it remained higher at 258 cr person days (almost 20% higher than FY09 levels) (Source: S22). I am sure no one will say that poverty rates remained stagnant or increased from FY09 to FY11 when our economy clocked 9% growth on average.
- It came down to 215 cr i.e., FY09 levels only in FY12 (Source: S22). But these were the years where we saw a drastic reduction in poverty and high economic growth.
- Impact of unseasonal rains this year with an overall deficit of 6% vs normal while we had excess rains in FY23.
- In FY17 and FY18, the number of person days generated was ~235 cr which increased to around 267 cr in FY19 and remained at a similar level in FY20 as well (even with the slowdown in growth that year). Is the increase in FY24 on account of election year like the trend observed in FY18 to FY19?
- As per a recent article on the Business Standard (Source: S55), acceleration in rural housing construction (Pradhan Mantri Away Yojana - Rural (PMAY)) in FY23 and FY24 may be the reason behind higher MGNREGS costs. It mentioned that in FY24, out of the INR 60k cr budgeted for MGNREGS, almost INR 10k cr (~17%) may have gone towards PMAY. Even in the earlier periods since FY17, this may have contributed partly to higher MGNREGS spends (like in FY19), but the increase in FY23 and FY24 seems to be much more substantial.
Section 5: Free Food Grains
Another oft repeated argument by the critics of the
Government is that why do we need to provide free food grains to 80 crore
people unless they can’t survive without it. The only counter argument to it is
that the NFSA was passed in 2013. It was the period during which we got the
poverty data which showed the fastest reduction in India’s poverty rates from 37%
in 2004-05 to 22% in 2011-12 (Source: S8). The NDA Government just made the
food grains free instead of heavily subsidizing the price. One can conclude its
more of a political decision more than anything else.
Summarizing Sections 1 to 5
After weighing all the data mentioned in Sections 1 to 5 above, one can conclude that the argument for K-shaped recovery (at least when it comes to the bottom 50% of the population) is not quite strong considering:
- Poverty rate data and earnings as per PLFS go against the argument of K-Shaped recover.
- Rural wage data is a mixed bag since it largely shows that wages have remained stagnant
- MGNREGS data supports the K-shaped recovery to an extent though the argument is indirect and has some flaws as highlighted above
- The Food security argument would have political elements within it and is not relevant
Section 6: Other indicators
Vehicle sales
These are used most frequently (as quoted in the beginning) to prove K-shaped recovery. However, as per National Family Health Survey V data, only around 50% of households have two wheelers and only 7% have cars. So, two-wheeler data is not representative of a K-shaped recovery for the poorest sections. Anyway, let’s look at the data now.
The data is shown for FY19 and FY23 since in FY20 there was a substantial degrowth except for the used car sales (which showed 10% growth). As mentioned in the beginning, two-wheeler sales are substantially lower and so is entry level cars.
Pic 3
With reference to entry level cars,
one can see that used cars (which would be purchased at a similar rate as entry
level car) has shown substantial growth compared to FY19. There is an article
in FE (Source: S27) which says that first time buyers moving from hatchbacks to
SUVs. So, the entry level vehicle argument may be more of a choice than
anything else and is not indicative of K-shaped recovery.
With reference to two-wheelers, it
may show K-shaped recovery within the top 50% of the population and not for the
bottom 50% since only 50% of households own two-wheelers as mentioned above. What that means is may be the middle 51st to 90th percentile of
the population may not have recovered their incomes to their pre-pandemic
levels. Here also there is a caveat. As per a FADA press-release (Source: S28),
“Over the past few years, the prices of various two-wheelers have risen
significantly, impacting their affordability for consumers across India. This
surge in prices can be attributed to multiple factors, including the rising
cost of raw materials, stricter emission norms and higher taxes and levies. For
instance, the price of the popular Honda Activa has escalated from Rs. 52,000
in 2016 to Rs. 88,000 in 2023. Similarly, the Bajaj Pulsar has witnessed a
substantial increase from Rs. 72,000 in 2016 to Rs. 1,50,000 in 2023. The Hero
Splendor and TVS Jupiter have also experienced considerable price hikes over
the same period, with their prices soaring from Rs. 46,000 to Rs. 74,801 and
Rs. 49,000 to Rs. 88,498, respectively. The continuous rise in two-wheeler
prices has consequently led to a decline in sales, emphasizing the pressing
need for intervention and GST rate reduction to restore the industry's growth
trajectory.” It should be noted that out of the total 1.6 crore sales in
FY23, around 45% of the overall 2-wheeler category comes from these 4 brands
alone (Source: S45).
This means Honda Activa prices
increased at 7.8% CAGR from 2016 to 2023 while CPI and WPI was 5% during this
period. Similarly, Bajaj Pulsar prices increased by 11% CAGR and Splendor and
Jupiter prices increased at a CAGR of 7.2% and 8.8% respectively. Further, the
price increases from 2019 to 2022 would have been much higher than in the
period from 2016 to 2019. Hence, from 2019 onwards, the CAGR of price hikes would
be higher than the ones shown above.
However, if the earnings of the middle-income category has grown only around the CPI rates during 2019 to 2023 (i.e., around 6%) as shown in Pic 2 above, it is obvious that it would have a significant impact on demand. In the earlier period pre-pandemic incomes would have shown faster growth than two-wheeler prices. So, one should just not look at the sales numbers and conclude K-shaped recovery etc. though it could be one of the reasons.
Residential real estate (Source: S46)
Here, we shall look at the trend of residential sales in the top 8 cities (as per Knight Frank). The overall sales were 245,861 units in 2019 which increased to 310,592 for twelve months ended 30 June 2023. Out of this, the mid-segment (between INR 50 lacs to INR 1 cr per unit) sales increased from 81,409 to 117,183 while the premium segment (more than INR 1 cr per unit) almost doubled from 45,461 units to 89,846 units. This would imply the affordable segment sales fell from 118,991 units to 103,563 units. Another indication of a K-shaped recovery may be. Here also, it would be the middle-income segment who would be purchasing affordable housing and not the bottom 50%. Further, we should also factor the impact of inflation on the segmentation (i.e., an affordable segment home with INR 48 lacs in 2019 would be worth INR 60 lacs or so in 2023 and would fall in the mid-segment) and increase in interest rates before we conclude.
Smart phone sales (Sources: S35 to S38)
As per Annual Survey of Education Report 2022, in 2018 the number of rural households with smart phones were 36% which increased to 75% in 2022. With reference to number of smart phone sales, it declined by 1.7% in 2020 while it increased 11% in 2021 and a dip again in 2022 of 6% and a dip of 7-8% in 2023 up to September 2023. Does this imply K-shaped recovery or has market saturation got to do with the dip. Here also, the K-shaped recovery, if at all would be implied to the middle section of the population (not the richest or the poorest).
Nielsen FMCG sales (Sources: S28 to S34)
Here, we can look at sales growth from CY20 to CY23. So, FMCG sales clocked volume growth of -2% in CY20, 6.3% in CY21, -1.5% in CY22 and for the 3 quarters of CY23 it clocked 6.4% growth. Add all this together it will show that FMCG volumes may be 8% higher in CY23 compared to CY19. Though it is not much compared to historical FMCG growth rates, still being mass consumption products, it goes against a K-shaped recovery.
Another point is that the growth rates in rural areas is substantially lower compared to urban areas in 2022 and 2023. However, it should be noted that in the first year of the pandemic considering the migrant labour- movements and agricultural activity, rural areas did not slow down as much as urban. On average, overall FMCG value contracted 2% in CY20 while rural areas would have grown by 4%. Due to movement of the labourers back to urban areas and higher base effects (so to speak) FMCG volumes grew slower in rural areas in subsequent periods.
The Savings Rate Conundrum
As per Price ICE 360 income survey (Source: S47), out of the total savings, around 80% of our savings is coming from 1/3rd of the households and around 52% of total savings is coming from the middle class (who will basically form a large part of the middle 40% of the population). So, any income stagnation for this class will result in reduction in savings rate for this segment and a reduction in overall savings rate of the economy. Is this happening?
Pic 4
Source: S47
Basis a recent RBI publication, one would have seen headlines like “Household Savings Fall to Five-Decade Low in FY23, Debt Remains Sharply Elevated: Report” (Source: S48). If we read the article more carefully and see the RBI data, household net financial savings reduced from around 8% (pre-pandemic) of GDP to 5% in FY23. This may indicate a huge distress at the first glance. However, household savings include financial savings and physical savings. RBI has just published the financial savings, but data including physical savings (Gold, Real estate etc.) for FY23 will be published in February 2024. However, we can broadly estimate the physical savings as well. An article by Dhananjay Sinha and Purvi Mundhra in The Hindu BusinessLine tries to do this (Source: S49). As per their calculation, household overall savings reduced from 19% of GDP in FY20 to around 16% GDP in FY23. As incomes of top 10% would have increased as mentioned above, then the brunt of this decline would have been taken by the middle 40% of population. However, they made a significant error in assuming the growth rate of household physical savings at only around 6% from FY22 to FY23 based on growth rate from FY19 to FY22. This is a gross underestimate considering:
- The Household physical savings and household gross-fixed capital formation (GFCF) are almost equal. So, if we can guess the GFCF growth in FY23, we will have the answer.
- Now, the overall GFCF (households plus government plus private corporate sector) grew by 17% in FY23. Household sector contributes almost 40% of overall GFCF. If we apply just this number instead of the 6% used in the article quoted above, household savings would be 17.2% instead of 16%. However, growth in the private corporate sector GFCF may be slightly lower since we are not yet seeing full capex recovery from them. An analysis has been carried out by Motilal Oswal (Source: S50) which pegs the household GFCF growth at 24.4%. This would take the overall household savings to 17.9%. This is close to the levels seen in FY16, FY17 and is not unprecedented.
- So, one can safely assume that overall household savings for FY23 would be somewhere around 17.5-18.0% of the GDP while in FY20 it was ~19%. The fall is only 1-1.5% instead of 3%. There is a need for caution but alarm bells need not start ringing.
Railway passenger traffic in FY23 was lower than FY20 by around 24%. Airline passenger movement in FY23 was lower than FY20 by around 4%. Does this indicate K-shaped recovery when both modes of passenger traffic have not even come to pre-pandemic levels? On the other hand, petrol (not petroleum) consumption, which is a mass consumption product used by individuals, increased from 30 mn MT in FY20 to 35 mn MT in FY23.
Page Industries (Innerwear sales) (Source: 42)
Innerwear is another mass consumption item. If we look at the volume growth from FY20 to FY23 as per the Investor Call transcripts, volume growth was almost NIL in FY21, 29% in FY22 and 13% in FY23. It is in FY24 that we are seeing some degrowth, but still, it is quite above pre-pandemic levels. Considering the data on earnings from Pic 2 above and data on two-wheeler sales, residential real estate, smart phones, savings and even to an extent FMCG sales, we seem to get a clearer idea that it is rather the middle 40% of the population (51st to 90th percentiles of income) whose incomes have remained stagnant/ slightly declined. So, it’s rather like a badly written E with the middle line remaining straight while the other 2 lines slanting upwards.
Summary
In the manner shown above, one can pick and choose dozens of other parameters to “prove” or “disprove” a K-shaped recovery. However, indirect indicators (like vehicle sales, MGNREGS demand) need not show the true picture and one must give priority to direct indicators (consumption/ earnings). If we look at it that way:
- Poverty has reduced in FY22 itself compared to FY20 showing the bottom 20% odd of the population has recovered in FY22 thanks in part to schemes like PMGKAY. This is in-line with our GDP recovery from FY20 to FY22.
- Increase in earnings of casual wage earners and rural self-employed shows that a large portion of the bottom 50% of the population would also have recovered. As a proxy, one can note that there was faster growth in the construction sector (CAGR of 6% from FY20 to FY23) vis-à-vis overall GDP growth. This was led mainly by revival in real estate sector and Government capex on infrastructure. It should be noted that almost 45-50% of casual workers (Source: S9 and author’s calculations) work in construction and a large part of the remaining work in agriculture.
- MGNREGS demand and stagnant rural wage growth (as Labour Bureau and Ministry of Agriculture) seems to cast some doubts on the points above, but considering all, points (1) and (2) carries more prominence in my view.
- So, on an overall basis, the incomes of the bottom 50% would have recovered at rates somewhere closer to the GDP growth (may be slightly lower considering point (3) above) with the poorest sections recovering even faster.
- Recovery in GDP itself shows that top 10% has recovered and doing well. It is also shown in salary/ wage expenses of listed companies which increased from INR 9.5 tn in FY20 to INR 13.4 tn in FY23 (Source: S43). It seems that their incomes have grown even faster than the GDP growth rates.
- Now, coming to the middle 40% of the population (51st to 90th percentile), there are indications that they may not have fully recovered yet, at least not seen growth in real incomes, especially considering stagnant earnings of regular wage earners and urban self-employed. This can also be corroborated by indirect indicators like lower two-wheeler sales/ stagnant phone sales etc. These would be the people who (i) would not have received benefits from the Government via food grains etc. (ii) would be mostly not paying income tax pay and working in the informal sector (a good chunk of it would be in the urban areas which bore the brunt of the lockdown impact) that would have suffered more because of the pandemic (compared to the organized sector). A lot of these people would be working in the Trade, hotels and transportation sector which employs 9-10 crore people. As per GDP calculations as well, this sector has recovered the least and the CAGR from FY20 to FY23 for this sector is just ~1%.
- These conclusions are broadly covered by Dr. Raghuram Rajan in an interview dated December 2022 where he said (Source: 44): “Its not just about four or five industrialists, even the income of upper-middle class rose in the country…Extremely poor people get a lot of benefit from the government and the upper class was not affected by the pandemic, it was the lower-middle class who faced trouble to a larger extent.”
Outlook
- While there are views which suggest that we are in a full-blown crisis due to K-shaped recovery, it does not seem to be the case.
- For the bottom 50%, one must closely monitor the MGNREGS demand and the rural wage data. However, continued recovery in real estate sector and capex spending by Government (private capex recovery may still take time) will be able to keep this segment’s income growing. There would be some impact on real estate sector especially considering higher interest rates, the global growth slowdown, and the hiring freezes in IT sector for the next year or two. One also must look out for “Revdis” which may slowdown capex spending. However, they may be offset at least in the short-term due to higher revenue spending. So, caution is advised as always.
- For the top 10%, CY24 may be tougher considering the hiring freeze in IT, global growth slowdown etc. Once we are out of that phase things would get better.
- The middle 40% seems to have been the most affected though here also it’s not a full-blown crisis but rather a stagnation in incomes. However, with the continued recovery in the Trade, Hotels and Transportation sector and if the Government programs like National Logistics Policy have the intended effects, this segment will also start growing.
Sources
S1: https://thewire.in/economy/top-20-driving-gdp-growth-the-rest-are-travelling-steerage
S2: https://www.investopedia.com/k-shaped-recovery-5080086
S3: Indian income inequality, 1922-2015: From British Raj to
Billionaire Raj? By Thomas Piketty and Lucas Chancel, https://wid.world/document/chancelpiketty2017widworld/
S7: https://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-9994
S8: https://pib.gov.in/newsite/erelcontent.aspx?relid=97365
S9: https://dge.gov.in/dge/reference-publication-reports-annual
S10: https://www.mospi.gov.in/data
S11: https://www.indiabudget.gov.in/
S12: https://www.mospi.gov.in/publication/national-accounts-statistics-2023
S15: https://desagri.gov.in/wp-content/uploads/2023/10/Final-Copy-2021-22.pdf
S20: https://dashboard.rural.nic.in/dashboardnew/mgnrega.aspx
S21: https://pib.gov.in/newsite/erelcontent.aspx?relid=69534
S22: https://prsindia.org/theprsblog/mahatma-gandhi-national-rural-employment-guarantee-act-review-of-implementation
S24: https://vahan.parivahan.gov.in/vahan4dashboard/
S26: https://www.siam.in/statistics.aspx?mpgid=8&pgidtrail=14
S28: https://fada.in/images/press-release/1646604a0754ccPR%20on%20GST%20reduction%20in%202W%20from%2028%20per%20cent%20%20to%2018%C2%A0per%C2%A0cent.pdf
S38: https://www.canalys.com/newsroom/india-smartphone-shipments-Q3-2023
S39: https://ppac.gov.in/consumption/products-wise
S40: https://www.aai.aero/en/business-opportunities/aai-traffic-news#aai_traffic_news-page-1
S42: https://www.pageind.com/quarterly-release
S47: https://www.ice360.in/app/uploads/2022/11/middle-class-brochure-2022.pdf
S49: https://twitter.com/Jairam_Ramesh/status/1709832169194144060/photo/1
S50: https://www.motilaloswal.com/site/rreports/638218107607932110.pdf
S51: https://thedocs.worldbank.org/en/doc/5d1783db09a0e09d15bbcea8ef0cec0b-0500052021/related/mpo-ind.pdf
S52: https://rbidocs.rbi.org.in/rdocs/Publications/PDFs/03WPSRW2504201812B8150F2C58475A8EDB6E73D5AD7579.PDF
S53: https://www.sciencedirect.com/science/article/abs/pii/S1090944318301753#:~:text=Our%20trend%20analysis%20shows%20that,30.5%25%20and%2026.4%25%20respectively
S54: https://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/---sro-new_delhi/documents/publication/wcms_775940.pdf
S55: https://www.business-standard.com/economy/news/rural-housing-construction-may-be-driving-up-mgnregs-costs-says-ministry-123111400278_1.html
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