Inequality measures can be used to illustrate inequality between groups and within groups (Haughton & Khandker, 2009). The choice of measurement can have different policy implications.
A variety of databases provide data on inequality from a wide range of developed and developing countries. However, the data is hard to compare, as survey coverage is still relatively limited and data collection across countries is not harmonised (UNDESA, 2013).
Decile Dispersion Ratios are the simplest measurement of inequality. They sort the population from poorest to richest and shows the percentage of expenditure (or income) attributable to each fifth (quintile) or tenth (decile) of the population (Haughton & Khandker, 2009). They are defined as the expenditure (or income) of the richest decile divided by that of the poorest decile. They are popular but considered a crude measure of inequality, albeit easy to understand (Haughton & Khandker, 2009).
The UNU-WIDER’s World Income Inequality Database (WIID) provides a very comprehensive set of income inequality statistics but its data is not easy to compare (Solt, 2009). The latest version of the World Income Inequality Database (WIID3.3) was published in 2015.
The Luxembourg Income Study (LIS) calculates income inequality statistics for a small number of richer countries (Solt, 2009).
The most widely used measure of inequality is the Gini coefficient, which ranges from 0 (perfect equality) to 1 (perfect inequality, one individual has everything), but is typically in the range of 0.3 to 0.5 for per capita expenditures (Haughton & Khandker, 2009). It is derived from the Lorenz curve, which sorts the population from poorest to richest, and shows the cumulative proportion of the population on the horizontal axis and the cumulative proportion of expenditure (or income) on the vertical axis. The benefits of the Gini coefficient are described as: mean independence (if all incomes were doubled, the measure would not change), population size independence (if the population were to change, the measure of inequality should not change, all else equal), symmetry (if any two people swap incomes, there should be no change in the measure of inequality), and Pigou-Dalton Transfer sensitivity (the transfer of income from rich to poor reduces measured inequality; Haughton & Khandker, 2009); it is also the most commonly used measure. A problem, however, is that it cannot easily be broken down to show the sources of inequality, and it is very sensitive to changes in the middle distribution where there is often less change than at the extremes (Haughton & Khandker, 2009; Cobham & Sumner, 2013). Nor is it clear about its underlying normative assumptions about inequality (Cobham & Sumner, 2013).
The Standardized World Income Inequality Database (SWIID) provides Gini indices of gross and net income inequality for 176 countries for as many years as possible from 1960 to the present (Solt, 2016). It involves a huge amount of data, and ‒ while still not strictly comparable (UNDESA, 2013) ‒ it is the most comprehensive effort to date to improve data comparability while maintaining broad coverage. SWIID Version 5.1 was published in July 2016.
A newer measure of inequality is the ‘Palma’ ratio: the top ten per cent of the population’s share of gross national income, divided by that of the poorest forty per cent. It is suggested that it is intuitively easy to understand and is more sensitive to changes at the extremes, which may make it more useful for policy makers (Cobham & Sumner, 2013).
Theil’s T and Theil’s L indexes, break down inequality into the part that is due to inequality within areas (for example, urban and rural) and the part that is due to differences between areas (for example, the rural-urban income gap), as well as the sources of changes in inequality over time (Haughton & Khandker, 2009). Typically, at least three-quarters of inequality in a country is due to within-group inequality, and the remaining quarter to between-group differences (Haughton & Khandker, 2009). However, the Theil index does not have a straightforward representation and lacks the appealing interpretation of the Gini coefficient (Coudouel et al., 2002).
A Pen’s Parade graph can be useful in showing how incomes, and income distribution, change over time. It is helpful when comparing two different areas or periods (Haughton & Khandker, 2009).
Other important ways of measuring inequality in or across countries include looking at education and health distribution although these are less developed than income based measure of inequality (Peterson, 2014). Concentration indices can also be used to indicate health inequality. They use a similar methodology to Gini coefficient and control for the socioeconomic dimension of health (Peterson, 2014). Education inequality is harder to measure given the difficulty in finding the right indicator. Commonly used indicators include ‘distribution of primary school enrolments’; ‘number of average years of schooling’; ‘test scores’; ‘adult literacy’ and attempts to devise an indicator for quality of schooling (Peterson, 2014).
The World Bank’s Visualize Inequality dashboard provides information on inequality of opportunity for different countries across the world. It allows countries to monitor their progress in expanding access and reducing inequality in different dimensions over time but does not provide a global ranking of countries.
The UNDP’s Inequality-adjusted Human Development Index (IHDI) provides data on health, education and income distribution that can be used for comparisons, although it does not account for overlapping inequalities.
The World Inequality Database on Education (WIDE) provides data on education inequality.
Cross cutting comparison measures for gender inequality include:
- reproductive health measured by maternal mortality ratio and adolescent birth rates;
- empowerment, measured by proportion of parliamentary seats occupied by females and proportion of adult females and males aged 25 years and older with at least some secondary education; and
- economic status expressed as labour market participation and measured by labour force participation rate of female and male populations aged 15 years and older (UNDP: Gender Inequality Index).
The Gender, Institutions and Development Data Base (GID-DB) provides data on different aspects of gender inequality collected by the OECD’s Development Centre (Jütting et al., 2008). The GID-DB provides data covering key dimensions of gender equality, including information on social norms, traditions and family law, women’s access to resources (e.g. health and education), political empowerment, women’s economic status, and composite indicators measuring gender equality (Jütting et al., 2008).
The UNDP’s Gender Inequality Index (GII) provides data on gender inequality in over 150 countries in relation to reproductive health, empowerment, and economic status.
The World Bank’s Global Database of Shared Prosperity is a collection of comparable shared prosperity data from 83 countries circa 2008-2013. The World Bank’s shared prosperity goal captures two key elements, economic growth and equity, and it will seek to foster income growth among the bottom 40 percent of a country’s population. Without sustained economic growth, poor people are unlikely to increase their living standards. But growth is not enough by itself. Improvement in the Shared Prosperity Indicator requires growth to be inclusive of the less well-off. The Global Database of Shared Prosperity tracks annualised average growth rate in per capita real consumption or income among a nation’s bottom 40 per cent. It’s primary source is the World Bank’s PovcalNet database.
- Cobham, A., & Sumner, A. (2013). Putting the Gini back in the bottle? ‘The Palma’ as a policy-relevant measure of inequality. Kings College London.
- Coudouel, A., Hentschel, J. S., & Wodon, Q. T. (2002). Poverty measurement and analysis. In The PRSP Sourcebook. Washington, DC: World Bank.
- Haughton, J., & Khandker, S. R. (2009). Handbook on poverty and inequality. Washington, DC: World Bank.
- Jütting, J.P., Morrisson, C., Dayton‐Johnson, J., & Drechsler, D. (2008). Measuring gender (in)equality: The OECD gender, institutions and development data base. Journal of Human Development, 9(1), 65-86.
- Peterson, L. (2014). The measurement of non-economic inequality in well-being indices. Social Indicators Research, 119(2), 581-598.
- Solt, F. (2009). Standardizing the world income inequality database. Social Science Quarterly, 90(2), 231-242.