Intervals are how we study the Kovid effect on inequality

Early last year, at the beginning of the Kovid pandemic, I argued in an article that the global spread and socio-economic impact of the virus would make it a ‘great level’, flattening or reducing the high wealth / income inequalities globally. Redefining pre-existing political and economic arrangements from one country to another. My argument comes broadly from the study of the economic history of such claims and their impact on global income inequality. These claims seem to be confirmed by the empirical evidence provided by Nobel laureate Angus Deaton in his recent paper Kovid-19 and Global Income Inequalities.

While closely studying data on cross country covid mortality rates and per capita incomes, Deaton presented three key analytical points in his paper on what will happen to global income inequality in 2020. He said: a) rich countries have better but higher mortality rates and technologically advanced medical systems than less developed countries; B) economic growth rates in countries with per capita gross domestic product (GDP) fell sharply; And c) more generally, for a shock like Kovid, economic growth falls to high-income countries per head, but the relationship of this one-to-one impact is less significant if data on countries is included. For their population sizes. “The last point is that when studying the relationship between shock and its economic impact, a small country like Macau cannot be equated with a country with a large population such as China. Income inequality has increased.

In explaining these contradictory results, Deaton is careful and humble in recognizing how ‘global inequality’ works under key exceptions and that economists must work within strict methodological limits. More research is needed on what happens to the ‘inequalities of income distribution’ in countries, which are exacerbated by the epidemic.

It also raises questions about the conceptual design and framework for analyzing economic inequality, which is still measured by a gross-income-centric approach, using resistance indicators such as ‘GDP per capita income’ and ‘GDP’ to reflect or describe the nature of the inequalities experienced by individuals or groups, and Substance, countries and between. Before criticizing the Deaton study, let us examine the opinion of the dominant macroeconomists on inequality, which appears in terms of income distribution (what the Gini multiplier wants to do).

Three concepts are commonly used to measure inequality. The first considers the scattering of per capita income in countries, each of which is under consideration. Here, each country is treated as an ‘individual’, and the calculations are made with levels of inequality relative to each other. The second considers the dispersal of per capita income between countries, but according to each population. This concept pretends that each person in the world has his or her country’s per capita income, and then calculates the inequality among all those people. The two concepts examine the inequality between nations and ignore each one.

Income distribution among all the peoples of the world, as Branco Milanovic (2011) calls Concept 3 inequality, extends the second concept by adding income distribution across countries. There were a lot of changes on this during the epidemic. The Deaton study, like many other economists’ recent studies on inequality, has little to do with this.

Whether income inequalities between countries increase or not depends on how one chooses data (according to country population or not), we do not know how severe inequalities in countries are, especially for less developed economies (such as China, India, Brazil, Nigeria, etc.). Most importantly, such a discourse on inequality and its measurement only from the perspective of per capita income, whether observed under the global or national lens, says very little about the actual inequalities that people experience and live on.

Thus inequality studies (as argued by Amartya Sen and many other scholars) need to include a more ‘interrelated’ perspective in developing a granular, meso-level approach that is holistic as a whole; Findings in terms of measurement and analytical observation. The epidemic has devastated the lives and livelihoods of those living in poor countries, which are characterized by large unorganized and informal socio-economic landscapes. Informality and how it shapes the relative life outcomes of different individuals is rarely studied for economic diagnoses of inequality.

Relational, inter-subjective lens helps to understand how inequalities persist and intensify almost continuously. For analysis, we can look at the differential access of individuals / groups to financial resources, social opportunities, welfare-security nets, etc. Differential access surrounds the human agency, so it allows inequality to extend ‘relatively’ from one to another over time. It is time to also consider the social gravels of gender, class, caste, race and ethnicity in broadening the conceptual basis of how we view inequality. Revenue data-sets are important, but many economists, including Dayton, fail to include or discuss other issues.

Dipanshu Mohan OP Jindal Associate Professor of Economics at Global University

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