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Declining trends in national inequality

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This is the thirteenth blog in a series of blogs about how countries can make progress on the interlinked objectives of poverty, shared prosperity and the livable planet. For more information on the topic, read the 2024 Poverty, Prosperity, Planet Report

The World Bank has started reporting the number of economies with high inequality as one of its indicators to measure shared prosperity. This indicator, and the indicator on the Global Prosperity Gap (see here), are introduced in the 2024 Poverty, Prosperity, Planet Report. Using a large database of household surveys, the report finds that the number of countries with high national inequality has decreased over the past few decades. This declining trend continued during the COVID-19 pandemic. While there is other evidence of declining trends in national inequality, limitations of survey data mean that there are important concerns regarding the levels of national inequality. This blog elaborates on these findings.

 

Declining national inequality trends based on household survey data

For 166 countries with data post-2000 in the Poverty and Inequality Platform (PIP), Figure 1 reports the number of economies with high inequality (i.e., those with a Gini index larger than 40), moderate inequality (Gini index between 30 and 40), and low inequality (Gini index less than 30) since 2000. The Gini indices are calculated using household per capita disposable income or consumption based on household survey data in PIP. The number of countries with high inequality has declined from 74 in 2000 to 51 in 2020. The number of countries with low inequality has increased from 20 to 32 in the same time frame. In all five-year intervals shown in Figure 1, more economies exited than entered high inequality. For every two economies that moved out of the high-inequality group (23 between 2000 and 2020), one was added to the moderate-inequality group (11) and one was added to the low-inequality group (12).
 

Figure 1: More economies moved to a lower-inequality group than to a higher-inequality group



The COVID-19 pandemic did not change this trend. To see this, Figure 2 plots the pre-covid (2019 or earlier) to post-covid (2021 or later) change in the Gini index against the pre-covid Gini. Within-country inequality fell in many countries (many datapoints fall below the horizontal axis), with greater reductions in countries that were more unequal before the pandemic (the line of best fit is downward sloping). This latter finding is in line with historical trends (for example, see Figure 2.12 of the 2016 Poverty, Prosperity, and Planet Report). Put differently, of the 72 countries with data either side of the pandemic, 20 experienced an increase and 52 experienced a decrease. Of the 52 countries that experienced a reduction in inequality, 11 each are in Sub-Saharan Africa and in Latin America and the Caribbean, 14 are in Europe and Central Asia, 3 are in the East Asia and Pacific, 1 each are in the Middle East and North Africa and South Asia, and 11 in Rest of the world group consisting of high-income countries.[1]
 

Figure 2: Most economies experienced a decline in inequality after the pandemic relative to before the pandemic


Limitations of household survey data to capture national inequality

The above findings must be qualified by at least two limitations in household survey data that could significantly underestimate national inequality. First, most developing countries report household surveys that capture consumption expenditure. Consumption surveys typically have a lower level of inequality than income surveys. Figure 3a plots the income Gini index (right axis) against the consumption Gini index (left axis) for all countries where the two sources of data are available after 2015. Consumption-based Gini indexes are almost always considerably lower than income-based Gini indexes. In cases where only consumption data is available, it is crucial to consider that inequality estimates calculated using income data could potentially be higher. In the same vein, comparing inequalities between countries using income data with countries using consumption data is not ideal. Furthermore, inequality of wealth, a concept of stock rather than flow of resource like income and consumption, are typically even higher than inequality captured by income or consumption.


Second, inequality estimates from household surveys often underrepresent the richest individuals because of issues such as underreporting and nonresponse. The small sample size of the very rich combined with their large income, which can affect measured aggregates, exacerbates this problem. Additionally, surveys typically fail to adequately capture entrepreneurial and capital income, which are concentrated among richer households. As a result, inequality measured using raw survey data is generally lower than those calculated using data adjusted for income of high earning individuals, by for example using administrative tax records that capture high earners better. This is evident from Figure 3b, which reports the Gini index calculated from raw survey data (Gini index, PIP) and those that are systematically adjusted for the missing top income using tax data (Gini index, WID). In all cases, the top-corrected level of inequality is greater than inequality estimated using data that are not top corrected.
 

Figure 3: Limitations of survey data in capturing inequality



National inequality captured using household survey data has been trending downward for many countries. The COVID-19 pandemic did not affect this trend. However, additional work is necessary to better understand the levels of national inequality, which are underestimated using household survey data. Using a dataset with systematically higher Gini indexes would also imply a higher threshold to track high inequality countries compared to the currently used Gini index of 40.


The authors gratefully acknowledge financial support from the UK Government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program.

[1] Alvaredo and Gasparini (2015) and World Bank (2016) have used a change of 1 Gini point as a rough check on statistical significance due to a lack of confidence intervals. When we limit the changes to greater than 1 Gini point, 6 countries experienced an increase while 28 experienced a decrease. Of these 28 countries, 9 were in Sub-Saharan Africa, 7 in Latin America and the Caribbean, 6 in Europe and Central Asia, 2 in East Asia and Pacific, 1 each in the Middle East and North Africa and South Asia, and 2 in the Rest of the world group of countries. 


Jing Xie

Master's in Public Affairs student at Princeton University, specializing in Development Economics.

Nishant Yonzan

Economist, Development Data Group, World Bank

Maria Eugenia Genoni

Senior Economist, Global Lead on Data Systems and Statistics Operations, Poverty and Equity Global Practice, World Bank

Christoph Lakner

Program Manager, Development Data Group, World Bank

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