metadata: authors: 0: author: Haddad,Cameron Nadim
1: author: Mahler,Daniel Gerszon
2: author: Diaz-Bonilla,Carolina
3: author: Hill,Ruth
4: author: Lakner,Christoph
5: author: Lara Ibarra,Gabriel
count: World
docna: 0: docna: The World Bank’s New Inequality Indicator :
            The Number of Countries with High Inequality
docty: Policy Research Working Paper
abstracts: cdata!: The World Bank recently introduced a new
            key indicator to guide its work: the number of countries
            with high inequality, defined as a Gini index above 40. The
            new indicator was introduced as part of the new World Bank
            vision of ending poverty on a livable planet. This paper
            reviews why reducing inequality matters for ending poverty
            on a livable planet, summarizes the advantages and
            disadvantages of using the Gini index to track inequality,
            outlines challenges in measuring inequality, and discusses
            what a Gini threshold of 40 implies. Using the most recent
            data for every country, 52 countries of a total of 169
            countries are classified as high inequality countries, which
            represents a decline from 77 countries at the beginning of
            the millennium.
display_title: The World Bank’s New Inequality
            Indicator : The Number of Countries with High Inequality
listing_relative_url: /research/2024/06/34338170/world-bank’s-new-inequality-indicator-number-countries-high-inequality
topicv3: Equality of Opportunities m1327180 1167,Inequality and Shared Prosperity m1327655 700,Income Inequality m1503891 2668
publishtoextweb_dt: 2024-06-10T00:00:00Z
dois: Policy Research working paper ; no. WPS 10796
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 26
author_keys: 0: author_key: 2278641
1: author_key: 1539953
2: author_key: 735205
3: author_key: 663673
4: author_key: 1206398
5: author_key: 1050590
model_extractions: Pol icy Resear ch Wor king Paper 10796 The World Bank’s New Inequality Indicator The Number of Countries with High Inequality _Cameron Nadim Haddad Daniel Gerszon Mahler Carolina Diaz-Bonilla Ruth Hill Christoph Lakner Gabriel Lara Ibarra_ Poverty and Equity Global Practice & Development Data Group June 2024
Pol icy Research Wor king Paper 10796

# **Abstract**

The World Bank recently introduced a new key indicator to guide its work: the number of countries with high inequality, defined as a Gini index above 40. The new indicator was introduced as part of the new World Bank vision of ending poverty on a livable planet. This paper reviews why reducing inequality matters for ending poverty on a livable planet, summarizes the advantages and disadvantages of using the Gini index to track inequality, outlines challenges in measuring inequality, and discusses what a Gini threshold of 40 implies. Using the most recent data for every country, 52 countries of a total of 169 countries are classified as high inequality countries, which represents a decline from 77 countries at the beginning of the millennium.

This paper is a product of the Poverty and Equity Global Practice and the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at dmahler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The World Bank’s New Inequality Indicator: The Number of Countries with High Inequality**

Cameron Nadim Haddad, Daniel Gerszon Mahler, Carolina Diaz-Bonilla, Ruth Hill, Christoph Lakner, and Gabriel Lara Ibarra<sup>1</sup> JEL classification: D63, I32.

Keywords: Inequality, Gini coefficient.

Originally published in the Policy Research Working Paper Series on _June 2024._ This version is updated on _May 2025._ To obtain the originally published version, please email prwp@worldbank.org.

## **1.** **<mark>Introduction</mark>**

For the first time, the World Bank is using an indicator of within-country inequality to track progress toward its vision (World Bank 2024a). The new indicator tracks the number of countries with a Gini index above 40, and is used alongside the Prosperity Gap (Kraay r al. 2023) to measure shared prosperity. These two indicators replace the growth of the bottom 40% in each country as the core measures of shared prosperity.

The objective to reduce within-country inequality builds on prior work by the World Bank and others suggesting that reducing inequality is key to sustainable development (World Bank 2005, World Bank 2016). In this paper, we shed light on this new objective in five different ways. First, we briefly review the literature on why inequality matters. Second, we summarize advantages and disadvantages of using the Gini index as the measure of inequality. Third, we outline challenges in measuring inequality with the available data. Fourth, we discuss how the threshold of a Gini index of 40 relates to previous definitions of high inequality, the distribution of Gini coefficients in the World Bank’s Poverty and Inequality Platform (PIP), and a poll of World Bank experts. Fifth, we analyze the countries that are classified as high inequality according to the threshold.

> 1 This note benefitted from feedback from Benu Bidani, Deon Filmer, Haishan Fu, Dean Jolliffe, Aart Kraay, Luis-Felipe Lopez Calva, Minh Nguyen, Nishant Yonzan, Regina Pleninger, Sutirtha Sinha Roy, Umar Serajuddin, and members of the Global Poverty Working Group. We gratefully acknowledge financial support from the UK Government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program.
# **2.** **<mark>Reducing inequality is key to ending poverty on a livable planet</mark>**

In this section we briefly review the literature on why reducing inequality matters for ending poverty on a livable planet, focusing on the impact of inequality on opportunities, elite capture, poverty reduction, economic growth, climate change, and social cohesion.

_Opportunities._ High levels of inequality often characterize unjust societies, where opportunities are driven by inherited characteristics, such as ethnicity, gender, parents’ socioeconomic status, or other factors beyond an individual’s control (Corak 2013, Brunori et al. 2013). Those without inherited opportunities may not be able to access education and employment, leading to wasted human potential (World Bank 2005). The inequality of outcomes of the current generation is the inequality of opportunities for the next generation (Atkinson 2015), exacerbating these consequences for future generations.

_Elite capture._ Economic inequality often spills over to political and social inequality, whereby politically powerful groups capture resources intended for the benefit of the broader population. These powerful groups may influence the political structures to prevent policies from leveling the playing field and ensuring broad-based economic growth. This form of corruption, known as elite capture, has been demonstrated to further exacerbate existing levels of poverty and inequality among a country’s most marginalized communities (Persha & Anderssen 2014, Dutta 2009, Platteau & Gaspart 2003). Elite capture has also been linked with reductions in the effectiveness of foreign aid (Andersen et al 2020).

_Poverty reduction._ Lowering inequality can be an effective strategy to reduce poverty. Everything else equal, reductions in inequality often increase the income or consumption of the worst-off, thus lowering their poverty severity or bringing them out of poverty altogether (Ravallion 2001). Furthermore, lowering inequality implies that the growth-poverty elasticity improves (Bourguignon 2003). Hence, with lower inequality, a given growth in GDP per capita will generate more poverty reduction. Put differently, for a given level of GDP per capita, countries with lower inequality require less growth to end poverty. Inequality reductions have the potential to have a sizeable impact on poverty reduction. If all countries reduced their Gini coefficient by 1%, global poverty would fall more than if all countries grew their economies 1 additional percentage point (Lakner et al. 2022).<sup>2</sup> _Economic growth._ Many studies have investigated the relationship between inequality and economic growth. There is evidence that inequality slows growth in both low-income and high-income countries (Barro 2000, Barro 2008, Banerjee & Duflo 2003), but also evidence that inequality may be beneficial for transitional growth in low-income countries (Brueckner & Lederman 2018). Due to the highly complex relationship between inequality and growth, it is difficult to arrive at any certain relationship between the two, yet it seems safe to conclude that the literature has not found unequivocal evidence in favor of inequality reductions harming economic growth. One of the likely reasons why inequality reductions need not come at the expense of economic growth is that inequalities reflect both differences in opportunities and differences in effort. Although taxes and transfers may reduce effort and hence limit economic growth, they may also expand opportunities. There is evidence suggesting that inequality of opportunity is bad for growth (Marrero & Rodriguez 2013, Ferreira et al. 2018) and particularly for the growth of the poor (Van der Weide & Milanovic 2018). Thus, reducing inequality, insofar as it reduces inequality of opportunity, will likely spur economic growth.

_Climate change._ Climate change is impacting the poorest within countries the most as they tend to live in areas with more exposure to extreme climatic events, and because their livelihoods are more impacted by temperature changes and natural disasters (Hallegatte & Rozenberg 2017). At the same time, the poorest are less able to adapt because they have limited resources (Dercon 2014). As such,

> 2 Over longer periods of time, growth rates are generally much higher than inequality changes, so despite this potential of inequality declines for poverty reduction, historically most poverty reduction has come from economic growth (Bergstrom 2022).

2
reductions in inequality that make the poorer better off will likely increase the ability of vulnerable populations to adapt and thus limit the welfare implications of climate change. Reducing inequality can also matter for mitigating climate change. If countries reduce inequality, they require less growth to end poverty, and as economic growth is a major driver of greenhouse gas emissions, inequality reductions imply that fewer emissions would be needed to end poverty (Wollburg r al. 2023).

_Social cohesion._ Inequality influences social cohesion. Higher inequality has been associated with greater political instability, including a greater risk of violence (World Bank 2011), and lower ability for countries to respond to economic shocks (Berg & Ostry 2011). Notions of economic fairness and perceptions of inequality also impact individual behavior (Brunori et al. 2013, Niehues 2014).

# **3.** **<mark>Choice of inequality measure</mark>**

Classifying countries as high inequality requires choosing a specific measure of inequality on which to group countries. The new World Bank indicator uses the Gini index as the measure of inequality. Several other inequality measures exist, each with their own benefits and shortcomings. Here we will look at a subset of measures and summarize the advantages and disadvantages of the Gini vis-à-vis these measures.

Concretely we consider five measures of inequality: the Gini index, the mean log deviation, the Theil index, Kuznets ratios, and quantile ratios, all of which are defined in Box 1. We evaluate these inequality measures along four criteria: whether they are commonly used, easy to explain, Lorenz consistent, and subgroup decomposable. Table 1 summarizes the conditions satisfied by the five inequality measures considered.

## **Box 1: Definitions of a subset of inequality measures**

_<mark>The Gini index</mark>_ <mark>is a measure of inequality ranging from 0 to 100 (or 0 to 1), where 0 indicates perfect equality and 100 perfect inequality (Gini 1912, Ceriani & Verme 2012). The Gini index is commonly defined through a Lorenz curve, which plots the cumulative income or consumption share for a country on the vertical axis against the cumulative population share on the horizontal axis. The Gini index is twice the area between the 45-degree line (which reflects perfect equality) and the observed Lorenz curve. Mathematically, the Gini coefficient is the average transfer needed to make two people in a country equal, expressed relative to the country’s mean income or consumption ( x ): G G =</mark> 1 ∑ ∑ �, where is the number of people in a country. ii=1 jj=1 ii jj 2xx̅NN<sup>2</sup> �xx −xx NN _<mark>The mean log deviation and the Theil index</mark>_ <mark>belong to the class of Generalized Entropy inequality measures (Shorrocks 1980), which were derived from their fulfillment of a range of desirable theoretical properties, these being subgroup decomposability, anonymity, the population principle, the transfer principle, and scale invariance. The various general entropy measures differ by how large weight they place on inequality at the bottom of distributions vis-à-vis inequality at the top of</mark> 1 distributions. The mean log deviation is given by ∑ ~~,~~ while the Theil index is given 1 NN xx̅ ∑ ~~.~~ NN ii=1 llGG xxii NN xxii xxii _<mark>Kuznets ratios</mark>_ NN ii=1 xx̅ llGG xx̅ <mark>measure the share of income going to the top quantiles divided by the income going to the bottom quantiles. The greater the ratio, the higher inequality. One example of a Kuznets ratio is the share of income going to the top 10% divided by the income share of the bottom 40%, also known</mark> as the Palma ratio (Palma 2011).

_<mark>Quantile ratios</mark>_ <mark>measure income ratios at particular quantiles, such as the income of the 90</mark><sup>th</sup> <mark>percentile divided by the income of the 10</mark><sup>th</sup> <u><mark>percentile.</mark></u> 3
_Table 1: Comparison of various inequality measures_

|Inequality measure|Commonly<br>used|Easy to<br>explain|Lorenz<br>consistent|Subgroup<br>decomposable|
|---|---|---|---|---|
|Gini coefficient|Yes|No|Yes|No|
|Mean log deviation|No|No|Yes|Yes|
|Theil index|No|No|Yes|Yes|
|Kuznets ratios|Yes|Yes|No|No|
|Quantile ratios|Yes|Yes|No|No|

Commonly used is difficult to track formally given that the Kuznets ratios and quantile ratios come in various forms. Due to their frequent use in inequality research, such as in Piketty (2014), we classify them as commonly used. Among the other three measures, Google returns about 5,000,000 hits for the Gini coefficient (or Gini index) while the Theil Index and mean log deviation receive around 100,000 and 15,000 hits, respectively.<sup>3</sup> This leads us to classify the Gini as commonly used and the other two measures as not.

Kuznets ratios and quantile ratios benefit from being expressed in units that are easy to explain. Most people can easily understand what it means if the top 10% has twice the income of the bottom 40%. By contrast, the other three measures do not have very intuitive scales or interpretations. One of the simplest (though still not straightforward) explanations of the Gini coefficient is that it represents the average transfer needed to make two people in a country equal, expressed relative to the country’s mean income (see Mahler & Baur (2023) for a visualization of this). For example, a Gini coefficient of 50 means that to equalize the incomes of two randomly chosen individuals, on average 50% of the country’s mean income should be transferred from the richer to the poorer individual. Many other interpretations of the Gini coefficient exist (Pyatt 1969, Sen 1973, Yitzhaki 1979, Lerman & Yitzhaki 1984).

Lorenz consistency indicates whether a measure satisfies certain theoretically desirable properties (Foster 1985), such as the Pigou-Dalton transfer principle, which (in its strong form) states that inequality must fall if income is transferred from a wealthier individual to a poorer individual. This does not hold for Kuznets ratios as transfers between individuals not considered in the groups chosen would not influence the inequality measure. For example, the Palma ratio ignores the middle class and is unaffected by what happens in the distribution between the 40<sup>th</sup> and 90<sup>th</sup> percentiles. Among the inequality indices that are Lorenz sensitive, they differ by which parts of the distribution they place larger weights on. The Gini coefficient is rank-informed, which means that the index changes in response to transfers depending on the rank of the involved. As a result of being rank-informed, the Gini coefficient is generally more sensitive to transfers in the middle of distributions (where there is more population mass, and hence more possibilities of re-rankings) than in the ends (Atkinson 1970, Sen 1973). In addition, compared to the mean log deviation, the Gini coefficient tends to be relatively more responsive to the top than the bottom.

Subgroup decomposability means that it is possible to decompose total inequality into inequality between groups of a country (say, geographical regions) and within groups of a country (Bourguignon 1979). The Theil index and mean log deviation were developed with this attribute in mind. The Gini coefficient, Kuznets ratios, and quantile ratios, by contrast, are not subgroup decomposable.

As can be seen, no measure satisfies all criteria, and in general there is a trade-off between selecting a measure with desirable theoretical properties and a measure that is easy to understand and familiar to many. The Gini index somewhat falls in the middle. It is likely familiar to the widest audience with a long history of use, while also satisfying basic theoretical properties, yet it is difficult to interpret.

> 3 The mean log deviation is also called a Theil index, which complicates the analysis, but should reinforce the results. The mean log deviation and the Theil index correspond to GE0 and GE1 in the General Entropy (GE) class.

4
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many attempts to correct for this bias, often combining surveys with tax records that represent the top more accurately (Flachaire et al. 2023, Jenkins 2017, Piketty et al. 2019). Outside high-income countries, this source of data remains limited and where available provides only an incomplete picture of the top due to the absence of comprehensive personal income taxes (Van der Weide et al. 2017). In addition, it is still not obvious how best to combine survey data with administrative records, and depending on how the two are bridged, inequality estimates may change notably (Auten and Splinter forthcoming, Alvaredo et al. 2023, Flachaire et al. 2023). Therefore, it is not possible at this time to adjust all countries in PIP for the underreporting of incomes at the top. When it becomes possible to do so, the inequality estimates reported here will be revised upward and more countries will have a Gini index above 40. However, it will then also become necessary to revisit the threshold since the underlying statistic has changed.

In Annex A.1, we test if using data from the World Inequality Database (WID), which adjusts for underreporting for a subset of countries where possible, would lead to different conclusions in terms of the countries classified as in high inequality. We find that 125 of the 151 countries present in both databases would be classified identically, and that most differences do not lead to large country rerankings albeit with a dozen or so notable exceptions. Using WID, the number of countries that have moved out of the high-inequality country category follows a similar but slightly more muted pattern.

There are other factors contributing to incomparable inequality estimates across countries. For example, given that rural areas tend to have lower price levels than urban areas, it matters whether countries account for such price differences when calculating the real income or real consumption expenditure of households. Currently, it differs whether countries use such spatial deflation (Mancini & Vecchi 2022). At times, there are also comparability issues within-countries over time, as countries frequently change the survey design and consumption aggregation methodology.

# **5.** **<mark>Choice of threshold</mark>**

Regardless of the inequality measure and data source used, there are no widely accepted standards for what constitutes a high level of inequality. The World Bank has decided to classify countries with Gini coefficients greater than 40 as high inequality countries. Here we show how this threshold relates to previous definitions of high inequality, the distribution of Gini coefficients in the World Bank’s Poverty and Inequality Platform (PIP), and a poll of World Bank experts.

## _5.1 Existing definitions of high inequality_

We reviewed existing reports that have proposed cut-offs designating high inequality. The United Nations Statistics Division occasionally conducts a ‘Progress Chart’ of the Sustainable Development Goals (SDGs) in which they classify the current level of selected SDG indicators into various groups. In the 2022 edition (United Nations 2022), they classified countries as having _low inequality_ if their Gini coefficient was less than 25, _moderately low_ if between 25-30, _moderately high_ if between 30-40, _highly unequal_ if between 40 and 45, and _very high_ if above 45. They rely on PIP data, and do not distinguish between consumption and income surveys when classifying. Hence, their threshold is identical to the one proposed by the World Bank.

A UNICEF report stated, “ _it’s commonly recognized that Gini index<0.2 corresponds with perfect income equality, 0.2–0.3 corresponds with relative equality, 0.3–0.4 corresponds with a relatively reasonable income gap, 0.4–0.5 corresponds with high income disparity, above 0.5 corresponds with severe income disparity_ ” (UNICEF 2018). A World Bank report on inequality in educational outcomes cited, “ _as a rule of thumb, a Gini coefficient [of earnings] above 40 is considered unequal and one above 50 as highly unequal_ ” (Porta 2011).<sup>5</sup> UNRISD and WHO have also produced reports where Gini

> 5 Earnings inequality tend to be more unequal than consumption inequality, so a corresponding consumption inequality threshold would likely be lower.

7
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