In a recent column, ex-CEA Arvind Subramanian and Josh Felman provided an assessment on the recent GDP growth numbers arguing for a need to look at the “production side” of growth evaluation measurement, while studying for discrepancies observed between the nominal and real growth figures.
Their analysis showed a divergence when the real growth numbers were positioned against the nominal numbers. The nominal figures track the real numbers until the first half of FY23 but then decline by a whopping 14% over the past three quarters.
This, according to them, this is “a narrative of an economy which has decelerated sharply to very modest levels”.
The Finance Ministry was quick to respond to this. Justifying its methodology for calculating GDP, the Ministry argued how India’s real GDP growth was 7.8% year-on-year in the first quarter of FY24 based on income or production approach.
As per the expenditure approach of GDP measurement, it would have been lower, it said. "So, a balancing figure–statistical discrepancy–is added to the expenditure approach estimate. These discrepancies are both positive and negative. Over time, they wash out."
What (Indian) GDP Fails To Measure and Account For
As argued back in an essay written in 2017, this author 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.
GNP, Distribution of Income, and 'Work'
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.
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.
Fifth, on inflation, as explained more recently, India’s notoriously high retail price inflation has a number of additional causal factors (read the evidence on seller’s inflation in the fuel-price-anchored inflation level) that find little focus or explanation. This counter-intuitively impacts the possibility of correctly diagnosing ‘real’ growth trends (which are inflation-adjusted). If, inflation or deflation measurement itself lacks a robust causal mechanism for measurement and validation, growth trends will showcase divergences between their real and nominal values.
Conclusions
Methods in mainstream growth economics also 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 summation, most GDP calculations much like most economic indicator-based analyses 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.
Intersectional mechanisms to validate growth figures require focus and empirical scrutiny that is independent from political and partisan bias. Such assessments with historical context and time-series validations may help the government to stop it’s obsessions with popularising/justifying short-term trends that may do little to clarify but may rather confuse more within its own ranks-and the critics too.
(Deepanshu Mohan is Professor of Economics and Director, Centre for New Economics Studies (CNES), Jindal School of Liberal Arts and Humanities, O P Jindal Global University. This is an opinion piece and the views expressed are the authors' own. The Quint neither endorses nor is responsible for them.)
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