A few months ago, a news made headlines, doing rounds in the social media about how it was a matter of much shame that India had fallen behind Bangladesh on the metric of Per Capita Income. In the fiscal year 2020-21, India’s per capita income stood at $1,947 while Bangladesh’s per capita income was reported $280 higher at $2,227. For the uninitiated, Per Capita Income measures the average income earned per person in a given area in a specified year. For calculating a country’s per capita income, its national income is divided by the population size. While on paper, this sounds like a good metric to compare as how any two countries are doing vis-à-vis each other, in reality this figure may often deviate from reality or much worse, it can hide something far more disconcerting, like widening inequalities within a country.

A figure like Per Capita Income can be a beneficial metric for comparison when it is applied to countries with a high degree of equality in terms of wealth or income distribution. However, countries like India which witness stark inequalities (as measured by metrics such as Gini Coefficient or the new metric Palma ratio), these terms prove to be fancy metrics for the economists which tend to obfuscate the reality much more than revealing something useful. We have been made to accept the humongous inequality as something acceptable, with more than a hundred billionaires ostentatiously displaying their wealth and millions of Indians struggling to make ends meet at the same time. This effectively means, that if a million Indians earn a dollar each or if a rich industrialist adds a million dollar to his/her wealth then the outcome in terms of additional to national income would remain the same, but the per capita income would increase by a dollar in both of the cases, thus obscuring the fact that whether rise in such a metric is benefitting a single person or reflecting a change for a wider set of people. Billionaires adding steady wealth deceivingly inflate such figures.

In India, the share of the top 1% in total income was 13% in 1961, and, in fact, declined gradually to 6.9% in 1981. Then, it started climbing up from the 1990s onwards (i.e., post-liberalisation), going up from 10.4% in 1991 to 21.7% in 2019. The share of the bottom 50% in total income stayed somewhat constant between 21% and 23% between 1961 and 1981. But thereafter it started declining, going down from 22.2% in 1991 to 14.7% in 2019.
Source: Wealth Inequality Database

In the context of India, the comparison of various economic indicators with our neighbours like Bangladesh, Pakistan, Afghanistan, Nepal, etc. are often portrayed by the media outlets either to portray a sense of superiority or it is projected as a matter of shame when one of the neighbouring countries outperforms us on some parameter. This is rarely done to prod the policymakers to improve the state of well-being of people but comes more from the paradigm of reveling on intellectual analysis based on economic indicators which unfortunately often tend to have a disconnect with the ground realities faced by the populace. It will be much more useful when we talk of real world indicators such as levels of malnutrition, per capita calorie intake (after all, millionaires cannot eat a thousand times more), hospital beds availability per hundred thousand population, access to housing, etc. Moreover, when it has come as a hard realization that we cannot talk of infinite growth in a finite planet limited by resources and the possible effects of Climate Change, the blatant push for adding more wealth to the GDP cannot be a collective goal to be pursued.  

We also need to be conscious of what purpose such comparisons with neighbouring countries or any other countries really achieve. Such surface-level (and often jingoist) comparisons that are not done in the correct spirit only feeds the rhetoric. They do not really benefit the people of these countries and are futile in a context when we actually ought to strive for South Asian solidarity to lift ourselves from our collective challenges. We need to keep questioning the utility of the data which often gets much more attention from the policymakers (and the media) than those which deserves more attention. 

Picture courtesy: A MH/Pixabay

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