Frequently Asked Questions - Inequality-adjusted Human Development Index (IHDI)

The HDI represents a national average of human development achievements in the three basic dimensions making up the HDI: health, education and income. Like all averages, it conceals disparities in human development across the population within the same country. Two countries with different distributions of achievements can have the same average HDI value. The IHDI takes into account not only the average achievements of a country on health, education and income, but also how those achievements are distributed among its citizens by “discounting” each dimension’s average value according to its level of inequality.

The average world loss in HDI due to inequality is about 23.3 %—ranging from 5.4% (Czech Republic) to
43.9% (Angola). People in sub-Saharan Africa suffer the largest losses due to inequality in all three
dimensions, followed by South Asia and the Latin America and the Caribbean. Sub-Saharan Africa
suffers the highest inequality in health (39%), while South Asia is impacted by unequal distribution in
education (42%). Latin America and the Caribbean suffers the largest loss of any region due to inequality
in income (38.5%).

Generally, countries in the low human development group also tend to have more multidimensional
inequality and thus larger losses in human development due to inequality, while countries in the very high
group experience the least inequality in human development. The East Asia and the Pacific Region
performs well on the IHDI, particularly in access to healthcare and education, and former socialist
countries in Europe and Central Asia have relatively egalitarian distributions across all three dimensions.

Although this is the third year that the IHDI has been calculated, the IHDI was not recalculated for
previous years primarily due to the lack of time series data for inequality in education and income for a
majority of countries.

The approach is based on a distribution-sensitive class of composite indices proposed by Foster, Lopez-
Calva, and Szekely (2005), which draws on the Atkinson (1970) family of inequality measures. It is
computed as the geometric mean of dimension indices adjusted for inequality. The inequality in each
dimension is estimated by the Atkinson inequality measure, which is based on the assumption that a
society has a certain level of aversion to inequality.

The IHDI relies on data on income/consumption and years of schooling from major publicly available
databases, which contain national household surveys harmonized to common international standards:
Eurostat’s EU Survey on Income and Living Conditions, Luxembourg Income Study, World Bank’s
International Income Distribution Database, United Nations Children’s Fund’s Multiple Indicators Cluster
Survey, US Agency for International Development’s Demographic and Health Survey, World Health
Organization’s World Health Survey, and United Nations University’s World Income Inequality Database.
For inequality in the health dimension, we used the abridged life tables from the United Nations
Population Division.

The IHDI uses the HDI indicators that refer to 2012 and measures of inequality that are based on
household surveys from 2002 to 2011 and life tables that refer to the 2010-2015 period. We therefore use
the year to which the HDI indicators refer to, especially because we report the inequality-adjusted
indicators/indices in tables.

While the HDI can be viewed as an index of “potential” human development that could be obtained if
achievements were distributed equally, the IHDI is the actual level of human development (accounting for
inequality in the distribution of achievements across people in a society). The IHDI will be equal to the
HDI when there is no inequality in the distribution of achievement across people in society, but falls below
the HDI as inequality rises. The loss in potential human development due to inequality is the difference
between the HDI and IHDI, expressed as a percentage.

The IHDI captures the inequality in distribution of the HDI dimensions. However, it is not association
sensitive, meaning it does not account for overlapping inequalities—whether the same people experience
the multiple deprivations. Also, the individual values of indicators such as income can be zero or even
negative, so they have been adjusted to non-negative non-zero values uniformly across countries.

The IHDI allows a direct link to inequalities in dimensions of the HDI to the resulting loss in human
development, and thus it can help inform policies towards inequality reduction and to evaluate the impact
of various policy options aimed at inequality reduction.

The IHDI and its components can be useful as a guide to helping governments better understand the
inequalities across populations and their contribution to the overall loss of inequality.

The IHDI in its current form was inspired by a similar index produced by Mexico’s National Human
Development Report. The IHDI can be adapted to compare the inequalities in different subpopulations
within a country, providing that the appropriate data is available. National teams can use proxy
distributions for indicators, which may make more sense in their particular case.

The IHDI is one of three experimental indices introduced in 2010, alongside the Gender Inequality Index
and the Multidimensional Poverty Index. It will evolve over time like all the other human development
indices.

This is the most difficult aspect as life expectancy data are aggregate indicators. However, the inequality
is estimated from the abridged life table (usually five-year age cohort) data and reflects the current
inequality in mortality patterns—some people die under the age of one and others die at 75 or later.
Undoubtedly, the quality of these estimates is no better than the data in the life table itself.

One of the key properties of the approach is that it is “subgroup consistent.” This means that if inequality
declines in one subgroup and remains unchanged in the rest of population, then the overall inequality
declines. The second important property is that the IHDI can be obtained by first computing inequality for
each dimension and then across dimensions, which further implies that it can be computed by combining
data from different sources.

The Gini index is commonly used as a measure of inequality of income, consumption or wealth. There
was an attempt to apply the Gini index to measure multidimensional inequality (Hicks, 1998).
The choice of the Atkinson index was guided by three factors: (i) subgroup consistency, (ii) emphasis on
the inequality in the lower end of distribution, and (iii) simplicity of computation and mathematical
elegance of the resulting composite Inequality-adjusted Human Development Index.
(i) Subgroup consistency means that if inequality declines in one subgroup (region, ethnic group,
etc.) and remains unchanged in the rest of population, then the overall inequality declines. The Gini
coefficient does not have this property.
(ii) By its construction, the Gini coefficient puts equal weights to the entire distribution, while the
Atkinson index puts more weight to the lower end, thus accounting better for child mortality, illiteracy, and
income poverty.
(iii) Finally, the geometric form of the HDI in combination with the Atkinson index provides a
simple and elegant path-independent composite IHDI, obtained by first computing inequality for each
dimension and then across dimensions, which further implies that it can be computed by combining data
from different sources (life tables and different surveys for education and income).

No. Due to data limitations, the IHDI does not capture all overlapping inequalities—whether the same person experiences one or multiple deprivations.

By their very nature, income and consumption yield different levels of inequalities, with income inequality being higher than inequality in consumption. Income seems to correspond more naturally to the notion of “command over resources.” Consumption data are arguably more accurate in developing countries, less skewed by high values, and directly reflect the conversion of resources. Income data also pose technical challenges because of the greater presence of zero and negative values. In an ideal world, one would be consistent in the use of either income or consumption data to estimate inequality. However, to obtain sufficient country coverage, it was necessary to use both. The final estimates are lightly influenced by whether the data are income or consumption.

Inequality in the education dimension is approximated only by inequality in years of schooling of the adult population. For simplicity, the estimate of inequality in education is based only on the distribution of years of schooling across the population, drawn from nationally representative household surveys.

Expected years of schooling is an aggregate measure and inequality in its distribution would be reflected in current school enrolment ratios. Certainly, there is a difference in inequalities in the two distributions, with the distribution of expected years of schooling across the school-age population being lower. Thus, one can speculate that overall inequality in the HDI distribution would be reduced if expected years of schooling were used.

Years of schooling of adults is mostly derived from the highest level of schooling achieved. Using UNESCO’s country information on the duration of schooling needed for each level, the highest level of schooling is converted into years. While the duration of primary, secondary and most of post-secondary education is more or less standardized, the very high levels—masters and doctoral studies—vary across countries. However, the Atkinson measure of inequality which is used to assess inequality in HDI education components is less sensitive to differences at the upper end of a distribution.