India's GDP Growth: Economists Must Not Go by Face Value & Assess Data Robustly

A deeper, intersectional historical context is vital as short-term economical trends may confuse more than clarify.

As per the most recent quarterly growth figures for October-December 2022 period, India’s GDP growth is 4.4 percent in real terms.

(Photo: Aroop Mishra/The Quint)

As per the most recent quarterly growth figures for October-December 2022 period, India’s GDP growth is 4.4 percent in real terms (when adjusted for inflation) and 11 percent in nominal terms (down from 6.3% real growth seen in the July-September period) which appears to be back to pre-COVID levels.

While some economists see the quarterly growth slowdown to be shaped by “past revisions” done in growth data and a base effect causing the growth numbers to appear on the lower side, overall, a 4.4% headline growth number is hardly a good sign while evaluating the state of the Indian economy.

While in terms of growth composition, overall output numbers for the same period (11.6% at the end of December) show signs of marginal improvement (as compared to 9.4% seen in September), and services continued to outpace manufacturing activity. However, private consumption and macro-private investment growth still appear to be weak.

What Explains the Low Consumption Demand

As argued before, while reviewing the macro-elements and components of growth in any statistical interpretation exercise, it’s difficult to get the full picture one needs. For example, keeping aside the base effect, the fact that private consumption demand is still woefully low (say, rising only 2.1% in the last quarter as compared to 8.8% in the second quarter), indicates how much of the low-middle income and middle-income class in India is ‘consuming less’ and their demand remains compressed.

What’s vital while analysing the current numbers, is to not just be swayed by the interpretative meaning of recent trends, or take a short-term view of the numbers, but rather view India’s growth composition and its pattern in context to a broad-based historical fact.

For one, the weakness of the manufacturing sector (discussed at length here) isn’t part of some new trend but is reflected in the historical pattern of Indian growth’s sectoral composition and in its employment pattern too.

In terms of sector-wise value addition to growth (as % of GDP), India’s Manufacturing sector has almost remained at the same level since the 1960s (I explained some of the reasons behind this here).

Industry (including construction) added more to the Indian GDP since the post-mid-1970s but at an incremental rate as workers from rural areas left agriculture and migrated to cities for work. Agriculture, forestry and fisheries sectors waned in terms of their value addition to GDP (despite having most of India’s rural population employed by this one sector).

Service, on the other hand, with its rising value addition to GDP growth, particularly post-mid-1990s, became a part of India’s urban growth narrative. But, there’s a twist to this.

Why India's Growth Has Historically Remained Exclusionary

According to Development Economist Ajit Ghose’s seminal work, and recent 2023 paper, a closer look at India’s growth-employment data indicates how since the early 1990s, India’s economy experienced a pattern of exclusive growth, ie, a kind of growth that benefited the urban rich. The richest 10% of the population has been the recipient of a large and growing share of the incremental income generated by growth.

Based on the distribution of benefits of growth (in terms of income) amongst the % of the population as seen in the table below, sourced from Ghose’s paper, India’s national income quite clearly became increasingly concentrated in a thin layer of an already rich population in the post-1992 period.

Even the middle class failed to develop; persons or households commonly regarded as belonging to the middle class in India belong to the richest 10%.

Another trend of historical data that intersectionally helps validate the exclusionary growth tale is by seeing what’s happened in India’s overall employment pattern.

According to Ghose, “Employment of the skilled—the rich—has been (still) growing while the low skilled—the poor—have suffered progressive exclusion from employment.” What emerges is that the source of exclusive growth lies in the nature and characteristics of the lead sectors, namely “skill-intensive services".

The employment of the unskilled declined at an increasing rate from 1993–2017 (as seen in table 2 above). The growth of employment of the low-skilled was also decelerating rapidly and turned negative during 2011–17.

Remarkably, even the growth of employment of the skilled showed a sharp deceleration after 2011 (which is when the last census was conducted); while growth created jobs basically for the skilled, it did not create enough jobs even for them in this period.

But there are metric-focused issues with the GDP itself that may warrant more attention, especially in the context of the diagnosis of growth in the Indian scenario.

What GDP Fails To Measure and Account For

As argued back in an essay written in 2017, I discussed some of the fundamental limitations of the GDP as an indicator of growth measurement in the context of India and much of the unorganised-informal workforce-driven developing world. It might be worth (re)plugging some of those arguments here.

GDP measures the total monetary value of final goods and services produced within the domestic territory of the country over a period of time. While significant methodological advancements have been made in GDP accounting at both the national and global levels, one critical limitation of both GDP and GNP (Gross National Product) is that they only value outputs at market prices.

In a country like India, and elsewhere too, a majority of economic activities occur outside the market and the values of their outputs need to be somehow calculated. This is called the “imputed” value.

The above limitation can be explained with case illustrations like farmers involved in subsistence farming consuming the food they produce, where economists often fail to estimate the quantity of produce and impute market values to what such farmers produce but did not or could not sell in the market and consumed themselves; or for people who live in houses they own, where economists usually fail to impute the value of the “dwelling services” involved (if the house owners are paying the rents at market rates to themselves).

Moreover, all non-marketed transactions or output produced is missed out from the official GDP accounting process, whose value isn’t even imputed. A case in point can be made here to India’s massive informal sector, which was worst hit by the demonetisation drive, the hurried implementation of the GST, and the curfew-style lockdown.

Second, income measures like the GNP accounting for the incomes of all residents of a country living within or outside the country, fail to realistically represent the living standards of people. An optimal standard of living is vital for measuring developmental progress. However, with GDP and GNP, you can get a perspective on living standards only by accounting for total and average monetary income (as done by per capita income).

Even if we are rational as consumers, the existence of positional goods (a concept coined first by Economist Fred Hirsch and explained as a good which is only valued by the possessor because it’s not possessed by others) in any country, makes income an unreliable gauge of true living standard.

Positional goods are goods whose value derives from the fact that only a small proportion of potential consumers can have them.

This means, even if our income increases (with an 8% GDP growth rate), we may still be unable to acquire things like houses in prime locations, a good education, or access to top jobs. This point is connected to explaining India’s overall slowing productivity rate, exacerbating the skill gap, and worsening employment scenario.

Third, the distribution of income amongst and within households or different economic classes in given societies is not captured by GDP data. For most studies measuring and accounting for trends in income inequality, survey methods based on consumer spending and consumption behaviour are used and relied upon. Economists like Angus Deaton, Jean Dreze, Kaushik Basu, and Ravi Kanbur have worked extensively on developing such metrics.

Fourth, one of the most important limitations of GDP is that much of the ‘work’ done by people today remain under-measured or largely unaccounted for.

Despite an overwhelming emphasis on it across social sciences, the value of work – a definitive condition of humanity in most of our history – is sporadically covered by modern economics as a subject beyond the income it generates from the hours recorded at work.

Methods in mainstream Growth Economics too fail to incorporate the effect of some of the techno-capitalistic advancements made like the role of the internet, telecommunications, and so on in shaping the productive capabilities of people in modern economies and the nature of work itself.

In conclusion, GDP calculations much like most economic indicator-based analysis work primarily on ‘estimates’ made with ‘extrapolations’ from growth-driving sectoral trends based on real-time trends. The real question is whether these ‘extrapolations’ end up reflecting India’s actual economic situation during a period of time.

For this, a deeper, intersectional historical context is vital and fields of economic history, economic sociology, and development economics have provided enough methods to stop obsessions with short-term trends that may do little to clarify but may rather confuse.

(Deepanshu Mohan is Associate Professor of Economics and Director, Centre for New Economics Studies (CNES), Jindal School of Liberal Arts and Humanities, OP Jindal Global University. Other contributors to this article are Yuvaraj Mandal, Bilquis Calcuttawala and Vedika Singhvi who are Research Analyst with CNES InfoSphere.)