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Three Notes of Caution as India Celebrates Its GDP Growth

The official GDP growth data hides the reality of tepid growth and decline in good employment.
'Many women have left the workforce and stopped looking for work in the market.' Photo: Flickr/Leo Sauermann (CC BY-NC-ND 2.0)

Gross domestic product or GDP growth at 7.2% in the financial year 2022-23 on the back of an unexpectedly high fourth quarter growth rate of 6.1% has come as a shot in the arm for the government.

It has been claiming for some time that the Indian economy is the fastest growing large economy in the world and it has been patting itself on the back for the better management of the economy than the other countries. Certainly, compared to the neighbours and European Union nations which are facing the prospect of recession, India appears to be doing better. 


Three notes of caution are in order.

First, these figures are provisional.

The quarterly data are based on very limited data which are revised later on. This requires GDP data to also be revised. Even the two years earlier data for FY 2020-21 are now the second revised estimates. 

Second, the figures are aggregate, hiding the many variations in specific sectoral and functional data.

Look at the current price data. The largest component is Private Final Consumption Expenditure (PFCE). Its share in the GDP has declined from 61.1% last year to the present 60.6%. Government Final Consumption Expenditure (GFCE) has declined from 11.2% to 10.3%. Gross Fixed Capital Formation (GFCF) has risen from 28.9% to 29.2%. Net Exports have declined from -2.7% to -3.6%. Thus, most of the growth engines are not firing. The biggest change is in discrepancies from -0.9% to 1.7%. This captures flaws in data and methodology.

Third, there are flaws in data and methodology. The quarterly data does not independently measure the non-agriculture unorganised sector. Instead, the organised sector data is used as a proxy for it. This is patently incorrect given that the organised sector is growing while the unorganised sector is declining. Thus, not only the quarterly data has errors these do not get corrected in the annual data leaving large errors in the GDP data.

Further, quarterly data uses “financial performance of Listed Companies in the Private Corporate sector based on available quarterly financial results for these companies”. That means even for the organised sector complete data are not available. So, the quarterly data is doubly erroneous – assumption about the data from the unorganised non-agriculture is incorrect and the data for the organised sector is also partial.

Finally, benchmark and projections from the past are used to get current data.

However, when there are shocks to the economy, the parameters of the economy change. Then the use of earlier estimated benchmarks and projections lead to errors in the data. These need to be corrected but such corrections have not been carried out as yet. Since 2016-17, the economy has experienced several shocks – demonetisation, GST, forced digitisation, Non-Banking Financial Companies crisis and the lockdown.

The methodology used to estimate GDP was developed prior to 2015 when the new series of GDP was announced. Hence it is not applicable to current data.

Also read: Top 20% Driving GDP Growth, the Rest Are Travelling Steerage

Disaggregating GDP

Production requires use of labour and capital. Technology determines how much labour is required to produce one unit of output. That is referred to as the productivity of labour. More use of capital and/or better technology incorporated in capital results in higher productivity of labour.

So, growth of the economy is the sum of increase in the workforce and rise in the productivity of labour. So, if the GDP is growing at 7.2% we need to figure out how much of it is contributed by the increase in the workforce and how much by technological up gradation. 

For example, if a worker was producing one car a day and now with use of more robots she produces 1.1 cars a day, then we can say that productivity has gone up by 10%. Further if the factory increases the number of workers by 2% then output will rise by 12%.

If the increase in the number of workers is known then subtracting this from the growth rate of the economy, the contribution of change in technology can be obtained. Unfortunately, a large number of workers are either without work (unemployed) or under employed or facing disguised unemployment or not even looking for work (out of the labour force). The contribution of these people to recorded GDP is either zero or small.

Re-estimating GDP

There are two further challenges in estimating the number of workers. First, different agencies give varying estimates of the employed. The data from the government agency differs from that of CMIE, a private agency, or from the World Bank figures. However, what is common among them is that the workforce has largely stagnated since 2013. The implication crudely is that if the GDP growth figure of about 7% over the last 10 years is correct, the growth is being accounted for by mostly by technical change.

Technical change has been quite rapid with automation, computerization and use of artificial intelligence. For instance, e-commerce is displacing local trade and neighbourhood retail stores and wholesale trade. This brings us to the second challenge for analysis, namely, the existence of the vast unorganised sector, which as pointed out above is declining and this is not captured in GDP figures.

The unorganised sector employs 94% of the workforce and produces 45% of the output. Of this, 48% work in the non-agriculture unorganised sector and they produce 30% of the GDP. 6% work in the organised sector and produce 55% of the GDP. Thus, productivity in non-agriculture unorganised sector is 0.63 of average productivity while in the organised sector it is 9.17. That is, 14.56 times more output per worker. So, a shift in demand leads to increase in productivity but higher unemployment/under employment. 

Further, the data for the non-agriculture unorganised sector used in GDP calculation is not independently obtained. But, under certain assumptions, the above data can be used to calculate the shift in demand from the unorganised to the organised sectors. 

Assume that agriculture, etc. growth (4%) is correct and the GDP growth rate (7.3%) pertains to agriculture and the organised non-agriculture sector. Working backward, this yields the growth rate of the organised sector at 8.073%. At best, 3% of this can be taken to come from technical change. That is roughly the rate in the advanced economies and the organised sector resembles them. Further, this sector hardly employs more people due to increasing mechanization. So, the rest of its growth (5.073%) has to come from demand shifting from the unorganised to the organised sector. The implication is that 55% of 5.073% = 2.79% of GDP worth of demand has shifted from the unorganised to the organised sector. If 2.79% of GDP reduction has taken place in non-agriculture unorganised sector, which is 30% of GDP, it means a decline of 9.3%.

So, alternatively measured, non-agriculture unorganised sector has declined 9.3%, organised component grew at 8.073% and agriculture at 4%. Aggregating these three, GDP growth comes to 2.5% and not 7.2%. 

With more conservative assumptions about organised sector, namely, employment rise of 1% and technical change contributing a whopping 4% to its growth, unorganised sector decline would be 5.63% and GDP growth would be 3.45%. The Indian economy would not be the fastest growing large world economy.

This conclusion is not nullified by the strong growth in GST and direct taxes. They are collected from the organised sector. Purchases of private automobiles or air travel also largely reflect the growth of the organised sector.

Decline of between 9.3% and 5.63% in the unorganised non-agriculture implies massive loss of work. But in India given the poverty, workers have to do some residual work and face under-employment and disguised unemployment (in agriculture). Many women have left the workforce and stopped looking for work in the market. Since organised sector hardly generates jobs and employs contract labour at low wages, those losing work in the unorganised sector have few options. Finally, productivity of those employed rises but that is countered by the close to zero productivity of those joining the ranks of under employed and disguised unemployed.

In brief, the official GDP growth data hides the reality of tepid growth and decline in good employment. A corollary is, the higher the growth rate of the organised sector, worse the brewing crisis for the majority of workers. This emerges from disaggregated and reinterpreted GDP and employment data. 

Arun Kumar is retired professor of economics at JNU and author of Indian Economy’s Greatest Crisis: Impact of the Coronavirus and the Road Ahead, 2020.

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