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Are China and India highly unequal countries?

‘The Number of Countries with High Inequality’ is the World Bank’s newest inequality indicator, one which it says will guide work on its agenda to ‘end poverty on a livable planet’. The indicator uses a country’s most recent Gini score, classifying it as a high inequality country if Gini is 40 or higher (the Gini coefficient being a number between 0 and 100). The World Bank makes the determination based on Poverty and Inequality Platform (PIP) data, for consumption or income, and estimates that 52 countries fit the bill, down from 77 in 2000.

Importantly, China and India are not classified as high inequality countries by the World Bank. But should they be? As scholars with a strong focus on inequality, we consider this to be a critical question, particularly to assess if the international community is meeting the inequality reduction goals it has committed to in SDG 10: Reduced inequalities within and among countries. 

These two giant Asian countries account for more than a third of the world’s population. Which means they play an outsized role in estimates of worldwide inequality. 

The case of China

With a Gini threshold of 40, China should be considered a highly unequal country based on various existing survey-based estimates for income inequality (unlike other countries in the region, the predominant welfare measure used in the literature for this country is income).  For example, those from the National Bureau of Statistics of China and the Luxembourg Income Study (LIS), both retrieved from the World Income Inequality Database (WIID), put China’s Gini above 40. With the more complex estimates used to correct for plausible underestimation of top incomes, such as those from the World Inequality Lab, China’s inequality is even higher.

Even using consumption, as the World Bank does, the latest LIS-based figures in the WIID classify China as a highly unequal country—albeit by a smaller margin.  It is well-known that in a majority of surveys, reported income tends to be more highly concentrated than consumption. This does not seem to be the case in China where the distribution of income and consumption tend to be more similar, at least in the surveys used by LIS. Therefore, which welfare measure is used (income or consumption) is less relevant to China’s categorization than it is for other countries. 

Table: Gini index in China and India
 

Notes:  WIID-LIS is estimated by UNU-WIDER’s World Income Inequality Database (WIID) using microdata from LIS (using the Chinese Household Income Survey and the India Human Development Survey). ‘WIID-standardized’ refers to the standardized series in the WIID global dataset and is based on the latest WIID-LIS income distributions updated using recent NBSC (China) and PIP (India) changes. ‘WIL’ is the World Inequality Lab’s estimates using the World Inequality Database. ‘PIP’ is the World Bank’s Poverty and Inequality Platform. All cases refer to income or consumption per capita (except for WIL, which is per adult (equal-split method). The earnings inequality for India for 2022 is based on calculations using Periodic Labor Force Survey (PLFS) by Rahul Menon. The consumption inequality for India for 2022 is based on calculations using Household Consumption and Expenses Survey (HCES) 2022–23.  

The case of India

The case of India is more complex because consumption is used more often to measure its inequality. India unquestionably qualifies as a highly unequal country by the World Bank’s threshold if we use income or earnings as the welfare measure instead. For example, using LIS data in the WIID (income), the World Inequality Lab’s estimates (income), or using the Periodic Labor Force Survey (earnings). Using consumption—the latest estimates from LIS data in the WIID, or in the PIP, or from the Household Consumption and Expenditure Survey (HCES 2022-23) —India falls below the Gini 40 threshold.

The main issue is that income is much more unequally distributed than consumption in India, with a 13 Gini points difference in the latest 2012 income and consumption survey used in the WIID (compared with less than 3 in China in 2019). The gap may be due to both underestimation of the consumption of the most affluent households and the difference between incomes and consumption among rural households. 

The situation in India suggests something similar may occur in other South Asian countries and sub-Saharan Africa, where the limited existing evidence also suggests that income inequality is much higher than consumption inequality (e.g., almost 15 Gini points in Côte d’Ivoire in 2015).

New target is cause for celebration, but can be improved

The World Bank’s inclusion of this explicit target is something to celebrate. Many voices have strongly demanded it since the SDGs were first introduced.  However, it might not be enough. The threshold is much higher than others have previously proposed. For example, Doyle and Stiglitz (2014) suggested an ambitious goal to ‘eliminate extreme inequality at the national level in every country’ by achieving a Palma ratio of 1 or less, noting that only a few countries, mainly Scandinavian, would fulfill the target. Our estimates using the WIID companion (wiidcountry dataset) indicate that a Palma ratio of 1 roughly corresponds to a Gini index of 28, which aligns with a recent call for action pointing at a threshold Gini of 25 alongside a Palma ratio of 1.

This discussion may remind us of a similar one-size-fits-all issue regarding setting an international poverty line driven mainly by low African living standards. In our case, countries with extreme inequalities set the international threshold too high. Additionally, as previously discussed, using one threshold with two different welfare measures is problematic given the degree of variability, like the China and India cases indicate. Either we adjust the threshold or standardize the welfare measures to be the same in all countries (like the WIID companion does). 

The lack of adequate data with enough regularity may be the most significant challenge to monitoring high inequality in many countries. Among the various elements needed to monitor high inequality, as the World Bank points out, the choice of indicator (the Gini index as opposed to other measures such as MLD or Palma ratio), is probably less problematic given the high correlation among all possible candidates (and we can easily check the robustness of the results to the choice of other measures). What matters is using measures, like the Gini index, that account for inequality along the entire distribution. 

The initial target in SDG 10 was based on the progress made by the income share of the bottom 40%. Apart from lacking a quantitative goal in this case, this indicator could be seen more as a measure of relative poverty than of inequality, as it ignores distributional changes that shift income between the top and middle of the distribution. At the very least, one needs to see what is going on at the top tail of the distribution as well.

In our view, both China and India should be identified as countries that need to substantially reduce their high levels of inequality, based on both their levels of income concentration and our preference for a more ambitious threshold. Also, based on WIID Companion data, income inequality in several southern African countries is greater than 40 and they should likewise be classified as high inequality countries. Yet, regardless of its limitations, this new indicator certainly helps to put explicit goals on the international agenda for reducing inequality. 

The views expressed in this piece are those of the authors, and do not necessarily reflect the views of the Institute or the United Nations University, nor the programme/project donors.
 

Endnotes

¹For both China and India, see the discussion Gradín and Wu (2020), Income and consumption inequality in China: A comparative approach with India. China Economic Review, 62.

²The World Bank uses a different threshold to compare the list of high-inequality countries using the World Inequality Lab’s data (59.2) with the PIP, so they produce the same number of high-inequality countries.

³LIS income and consumption estimates are based on the China Household Income Project Survey, while the World Bank’s consumption estimates are based on the Households’ Income and Consumption Expenditure Survey. This indicates that the choice of dataset (or other apparently minor methodological choices) also matters when more than one is available for the same welfare measure.

⁴The surveys are Consumer Pyramids Household Survey (PIP) and Indian Human Development Survey (LIS).