Human Development Reports - United Nations Development Programme

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with such different human development outcomes. For example, the Bahamas and New Zealand have similar levels of income per person, but life expectancy and expected years of schooling differ greatly between the two countries, resulting in New Zealand having a much higher HDI value than the Bahamas. These striking contrasts can directly stimulate debate about government policy priorities.

As in past Human Development Reports, the HDI remains a composite index that measures progress in the three basic dimensions—health, knowledge and income. Under the previous HDI formula, health was measured by life expectancy at birth; education or “knowledge” by a combination of the adult literacy rate and school enrolment rates (for primary through university years); and income or standard of living by GDP per capita adjusted for purchasing-power parity (PPP US$).

Health is still measured by life expectancy at birth. But the 2010 HDI measures achievement in knowledge by combining the expected years of schooling for a school-age child in a country today with the mean years of prior schooling for adults aged 25 and older. The income measurement, meanwhile, has changed from purchasing-power-adjusted per capita Gross Domestic Product (GDP) to purchasing-power-adjusted per capita Gross National Income (GNI); GNI includes remittances and foreign assistance income, for example, providing a more accurate economic picture of many developing countries.

The indicators were changed for several reasons. For example, adult literacy used in the old HDI (which is simply a binary variable – literate or illiterate, with no gradations) is an insufficient measure for getting a complete picture of knowledge achievements. By including average years of schooling and expected years of schooling, one can better capture the level of education and recent changes.

Gross Domestic Product (GDP) is the monetary value of goods and services produced in a country irrespective of how much is retained in the country. Gross National Income (GNI) expresses the income accrued to residents of a country, including international flows such as remittances and aid, and excluding income generated in the country but repatriated abroad. Thus, GNI is a more accurate measure of a country’s economic welfare. As shown in the Report, large differences could exist between the income of a country’s residents, measured by GNI or GDP.

Previously, the HDI had a form of the arithmetic mean of dimension indices obtained from the corresponding indicators by normalization using the fixed minima and maxima. The normalisation refers to the transformation of indicators expressed in different units to the unit-less quantities taking values between 0 and 1. This year’s HDI has a form of geometric mean of dimension indices obtained from the indicators by normalization based on minima and maxima observed over the period for which the HDI has been computed and reported. Thus, the previous ‘cap’ on the income component has been replaced in the 2010 HDI by an ‘observed maximum’ per capita income level. Adopting the geometric mean produces lower index values, with the largest changes occurring in countries with uneven development across dimensions. The geometric mean has only a moderate impact on HDI rankings.

Unlike the old HDI, the new HDI based on the geometric mean takes into account differences in achievement across dimensions. Poor performance in any dimension is now directly reflected in the new HDI, which captures how well a country’s performance is across the three dimensions. There is no longer perfect substitutability across the dimensions. That is to say, a low achievement in one dimension is not anymore linearly compensated for by high achievement in another dimension. The geometric mean reduces the level of substitutability between dimensions and at the same time ensures that a 1 percent decline in say life expectancy at birth has the same impact on the HDI as a 1 percent decline in education or income. Thus, as a basis for comparisons of achievements, this method is also more respectful of the intrinsic differences across the dimensions than a simple average.

Income is instrumental to human development, but the contribution diminishes as incomes rise. GDP in the previous HDI was capped at $40,000 and was logarithmically transformed. The original HDI placed this cap on income to reflect the view that beyond some upper set amount, additional income does not expand human development opportunities. A further consideration was that while literacy rates and school enrolment and life expectancy have ‘natural’ caps (100 percent, mortality limits, and so on forth), the highest incomes would continue rising, skewing the upper ranks of the HDI to increasingly income-driven values and rankings over time.

There are other reasons why the cap on income is lifted. First, countries were increasingly bunched at the cap. This meant that we could not distinguish among an increasing number of countries at the top of the distribution. In 2007, the GDP of 13 countries exceeded the cap. Thus, the discriminatory power of capped income has been weakened, especially for discrimination between the very high developed countries. Second, it was not originally intended to be binding in the sense of totally disregarding additional income beyond a particular level. For example, the income cap of PPP$ 40,000 was not binding on countries when it was introduced in the mid-1990s but rather was an upper bound used to normalize the income dimension index Third, the use of geometric mean intensifies the diminishing returns of the logarithmic transformation of GNI compared to the arithmetic mean. Fourth, and very importantly, the use of real maximum values instead of caps allows the resulting indices to vary in similar ranges so that their implicit weights are more similar than had been the case under the previous method.

The new HDI uses the natural logarithm instead of the previously used logarithm with the base of 10. This minor change has no effect on the value of the income index and is motivated by the fact that most of the economic literature uses the natural logarithm of income. The caps in each dimension are lifted so one can say that they are equal to the observed maxima over the period (1980-2010) for which HDI trends are presented.

Yes. This year, the dimension indicators are transformed using the maximum levels for all sub-components observed over the period for which HDI trends are presented (from 1980). The minimum levels for the dimension indicators are set as follows: life expectancy at 20 years; both education variables at 0; and GNI per capita at PPP $163, which is the observed minimum. The choice of minimum values is motivated by the principle of natural zeros below which there is no possibility for human development. As noted already, this way of normalizing has the effect of making the component sub-indices of these dimensions vary along the similar range.

This is based on historical evidence (Maddison, 2010, and Riley, 2005)¹, which indicates 20 years as the minimum. If a society or a subgroup of society has a life expectancy below the typical age of reproduction, that society would die out. Lower values have occurred during some crises, such as the Rwandan genocide, but these were exceptional cases that were not sustainable.

1 Maddison, A. 2010. Historical Statistics of World Economy:1-2008 AD. Paris: Organization for Economic Cooperation and Development.

Riley, J.C. 2005. Poverty and Life Expectancy. Cambridge, UK: Cambridge University Press.

Noorkbakhsh (1998). The Human Development Index: Some Technical Issues and Alternative Indices. Journal of International Development 10, 589-605

Generally, the minimum values are set to the values that a society needs to survive over time. For both education indicators, the minimum is set to 0 since societies can subsist without formal education. For income, it is set at $163 per capita GNI, which is the lowest value attained by any country in recent history (Zimbabwe in 2008) and corresponds to less than 45 cents a day (just over a third of the World Bank’s $1.25 a day poverty line). The minimum values are essentially fixed. Should any country’s per capita GNI fall below $163, the minimum will be changed accordingly.

The maximum values are observed over the period for which HDI trends are presented (from 1980), so while there might be year to year variation of the maximum values, the changes are not going to have any impact on ranks. This is because of the multiplicative form of the new HDI, which preserves the relative position of countries when maximum values change, although, the HDI values are affected by the choice of the normalizing parameters.

No, each year HDI trends are recalculated from 1980 based on consistent time series data and the new maximum values. In any case, the HDI is not meant to monitor progress in the short term—it takes time before policy interventions reflect on indicators such as mean years of schooling and life expectancy at birth. This is why HDI trends are provided in five-year intervals.

There are arguments for and against transforming the health and education variables to account for diminishing returns. It is true that health and education are not only of intrinsic value; they, like income, are instrumental to other dimensions of human development not included in the HDI (Sen 1999). Thus, their ability to be converted into other ends may likewise incur diminishing returns. The approach is to value each year of age or education equally, and therefore the principle has been applied only to the income indicator.

The new HDI assigns equal weight to all three dimension indices; the two education sub-indices are also weighted equally. This is different from the previous HDI, which weighted them differentially. The choice of weights is based on the normative judgement that all three dimensions are equally important. Research papers that provide a statistical justification for this approach include Noorkbakhsh (1998) and Decanq and Lugo (2009)¹. The new HDI has more equal ranges of variation of dimension indices than the previous one, implying that the effective weighting is more equal than it was before.

1 Decanq, K. and Lugo, M.A. 2009. Weights in Multidimensional Indices of Well-Being. OPHI working paper No. 18. (to appear in Economic Reviews)

As a simple summary index, the HDI is designed to reflect average achievements in three basic aspects of human development – leading a long and healthy life, being knowledgeable and enjoying a decent standard of living. The policy of the Human Development Report Office has always been to construct additional complementary composite indices for covering some of the “missing” dimensions in the HDI. Gender disparity, inequality and human deprivation are measured by other indices (see Gender Inequality Index, Multidimensional Poverty Index and Inequality-adjusted HDI). Participation and other aspects of well-being are measured using a range of objective and subjective indicators and are discussed in the Report. Measurement issues related to these aspects of human development demonstrate the conceptual and methodological challenges that need to be further addressed.

The hybrid HDI is a different version of the HDI that applies the same aggregation formula as the new HDI, but to a set of indicators used in the previous HDI – life expectancy, adult literacy, gross enrolment ratio and GDP per capita. It is used in the 2010 Report for analysis of historical HDI trends since 1970 because there is much more data available from past decades for those indicators. The hybrid HDI uses annual data from 1970 to 2010 for 135 countries.

The Human Development Report Office strives to include as many UN member countries as possible in the HDI. To include a country in the HDI we need recent, reliable, and comparable data for all three dimensions of the Index. For a country to be included, data ideally should be available from the relevant international data agencies.

This year’s HDI has been calculated for 169 countries and territories. Countries not included because of missing data for one or more components are: Antigua and Barbuda, Bhutan, Cuba, Dominica, Eritrea, Grenada, Lebanon, the Occupied Palestinian Territories, Oman, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, Seychelles and Vanuatu. Micronesia has entered the HDI table for the first time this year, while Zimbabwe has re-entered after not being included in 2009 due to missing income values.

Lifting the cap on income for the United States plays only a minor role in the change. There are seven countries with a higher income that are ranked lower than the US (Liechtenstein, Luxembourg, Singapore, United Arab Emirates, Brunei Darussalam, Qatar, and Kuwait). Even if the income was capped at $51,300 (equivalent to PPP$40,000 in 1998, expressed in $PPP2008 dollars), the USA would stay at the 4 position. Use of the mean years of schooling instead of literacy made a huge difference, however. The mean years of schooling in the United States is 0.2 years behind the top ranking Norway, whereas literacy was set to 99 per cent, but 25 high developed countries had the literacy of 99 per cent too, so the literacy couldn’t discriminate between them. In general, the geometric mean favours a well-rounded performance on all three dimensions, which worked against some of the US competitors (Sweden, Germany, and Ireland).

Data availability determines HDI country coverage. Where reliable data are unavailable and there is significant uncertainty about the validity of existing data estimates, countries are excluded to ensure the credibility of the Human Development Report and the family of human development indices. There are four countries that have information on the other three HDI components but not on GNI: Cuba, Iraq, the Marshall Islands and Palau. In the past, GDP per capita (PPP US$) was estimated by the Center for International Comparisons of Production, Income and Prices (CICPIP) at the University of Pennsylvania to calculate the HDI for Cuba. These estimates rely on data from the salaries of international civil servants converted using the official exchange rate. However, because the markets in which foreigners purchase goods and services tend to be separated from the rest of the economy, such data can be an insufficient guide to prices faced by people in practice. The CICPIP recognizes this limitation and has graded the estimate of Cuba’s GDP as a “D”—the lowest grade. This is why Cuba is not in this year’s HDI.

Yes, the HDI indicators can be adapted for country specific relevant ones provided they meet other aspects of statistical quality. It can also be disaggregated at sub-national level to compare levels and disparities among different subpopulations within a country, provided that appropriate data at the level of disaggregation are available; or can be estimated using sound statistical methodology. The highlighting of internal disparities using HDI methodology has prompted constructive policy debates in many countries.

Life expectancy at birth is provided by the UN Department of Economic and Social Affairs; mean years of schooling by Barro and Lee (2010); expected years of schooling by the UNESCO Institute for Statistics; and GNI per capita by the World Bank and the International Monetary Fund. For few countries, mean years of schooling are estimated from nationally representative household surveys. Many data gaps still exist in even some very basic areas of human development indicators. While actively advocating for the improvement of human development data, as a principle and for practical reasons, the Human Development Report Office does not collect data directly from countries or make estimates to fill these data gaps in the Report.

The HDI attempts to make an assessment of 169 diverse countries and areas, with very different price levels. To compare economic statistics across countries, the data must first be converted into a common currency. Unlike market exchange rates, PPP (Purchasing Power Parity) rates of exchange allow this conversion to take account of price differences between countries. In that way GNI per capita (PPP US$) better reflects people's living standards. In theory, 1 PPP dollar (or international dollar) has the same purchasing power in the domestic economy of a country as US$1 has in the United States economy. The new PPP values have been used since 2008. The latest International Comparison Survey ICP, from which the PPPs are calculated, was done in 2005; 146 countries took part in the survey, which were 26 more than in the previous one. For further discussion on the PPP, see Human Development Indices – A statistical update 2008 (Section 2).

Mean years of schooling (MYS) for Andorra and Liechtenstein were based on the MYS of neighbouring countries Spain and Switzerland, respectively. For 25 countries, the MYS was estimated from nationally representative household surveys – UNICEF’s Multiple Indicator Cluster Surveys MICS) Demographic and Health Surveys (DHS, and the World Bank’s Income International Distribution Database. Expected years of schooling were estimated by cross-country regression in three countries – Montenegro, Singapore and Turkmenistan.

No. GNI per capita only reflects average national income. It tells nothing of how that income is spent, whether on universal health, education or military expenditure. Comparing rankings on GNI per capita and the HDI can reveal much about the results of national policy choices. For example, a country with a very high GNI per capita, such as Kuwait which has a relatively low mean years of schooling for its adult population, can have a lower HDI rank than, say, the Bahamas, which has less than 50% of the GNI per capita of Kuwait.

The previous cut-off points were set as absolute values, and were inevitably somewhat arbitrary. With the new classifications, the approach is explicitly relative -- based on quartiles. The new classification also reduces the amount of variation within each group: previously the medium human development group ranged from 0.500-0.799, whereas now the effective range is 0.488-0.669.

It does however mean that some countries have entered in a lower classification this year – even if they continue to make progress—this is the case for Bangladesh, Ghana, Kenya and Nepal for example. In these cases we would stress focusing on the change in the HDI value over time (as per Table 2), and underline that the classifications are relative, not absolute. The low group is the bottom 42 countries; medium next 42, and so on, while the high and Very high are in the top half – medium and low in the bottom half.

The changes in the indicators and method of aggregation have resulted in substantial changes for a number of countries. Adopting the geometric mean of aggregation produces lower index values for all countries because the extent to which a higher achievement in one dimension can be compensate lower achievement in other dimensions is reduced. The average decline is about 7% with the largest changes occurring in countries with uneven achievement across dimensions.

Comparable data are not available for many countries for all components of the HDI before 1980; so 1980 is the first year for which the HDI was calculated. Estimates for some indicators are available before this time, such as life expectancy, which is available since 1950.

This year, three indicators were available as estimates at source for 2010 – life expectancy, mean years of schooling and expected years of schooling. GNI was available for 2008 in the World Bank’s World development indicators. Estimated annual growth rates for GDP per capita were taken from the IMF’s World Economic Outlook for 2009 and 2010 to estimate GNI in 2010.

No. The concept of human development is much broader than can be captured in the HDI, or any other of the composite indices in the Human Development Report (Inequality-adjusted HDI, Gender Inequality Index and Multidimensional Poverty Index). The HDI, for example, does not reflect political participation or gender inequalities. The HDI and the other composite indices can only offer a broad proxy on some of the key issues of human development, gender disparity and human poverty. A fuller picture of a country's level of human development requires analysis of other indicators and information presented in the statistical annex of the report (see the HDR 2010 Readers Guide online http://hdr.undp.org/hdr4co/report/hdr/english/HDR_2010_EN_Readers.pdf).

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 22 percent – ranging from 6 percent (Czech Republic) to 45 percent (Mozambique). People in sub-Saharan Africa suffer the largest losses due to inequality in all three dimensions, followed by South Asia and the Arab States. South Asia suffers high inequality in health and education, while considerable losses in the Arab States can generally be traced to unequal distribution of education. Latin America and the Caribbean suffers the largest loss of any region due to inequality in income (38 percent). Generally countries with less human development also have more multidimensional inequality and thus larger losses in human development due to inequality, while people in developed countries experience the least inequality in human development. East Asia and the Pacific performs well on the IHDI, particularly in access to healthcare and education, and formerly socialist countries in Europe and Central Asia have relatively egalitarian distributions across all three dimensions.

No, because the IHDI is calculated for just one data point—2010. While we calculate HDI trends based on consistent time series data we are unable to do so for the IHDI due to lack of time series distribution data for most of the dimension indicators. Future versions of the IHDI will allow for comparisons over time.

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. (For details see Alkire and Foster (2010) and Technical Note 2 in HDR 2010)

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.

IHDI uses the HDI indicators that refer to 2010 and measures of inequality that are based on household surveys from 2000 to 2007 and life tables that refer to the 2005-2010 period. So, the logic was to 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, i.e., it does not account for overlapping inequalities -whether the same people experience r multiple deprivations. Also, the individual values of indicators such as income can be zero or even negative they have been adjusted to non-negative non-zero values uniformly across countries. The estimated inequality is sensitive to the approach we have taken.

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 HDR. The IHDI can be adapted to compare the inequalities in different subpopulations within a country, providing that the appropriate data are 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 be revised and improved in light of feedback and data availability.

This is the most difficult aspect as life expectancy data are aggregate indicators. However, between-groups inequality can be estimated from the abridged life table (usually five-year age cohort) data; this is what we have used. 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). However, the resulting index was not consistent for all subgroups. Moreover, the Gini index does not emphasize the lower part of the distribution, but instead places the same weight throughout the distribution.

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.

The Gender Inequality Index is a composite measure reflecting inequality in achievements between women and men in three dimensions: reproductive health, empowerment and the labour market. It varies between zero (when women and men fare equally) and one (when men or women fare poorly compared to the other in all dimensions). The health dimension is measured by two indicators: maternal mortality ratio and the adolescent fertility rate. The empowerment dimension is also measured by two indicators: the share of parliamentary seats held by each sex and by secondary and higher education attainment levels. The labour dimension is measured by women’s participation in the work force. The Gender Inequality Index is designed to reveal the extent to which national human development achievements are eroded by gender inequality, and to provide empirical foundations for policy analysis and advocacy efforts.

The world average score on the GII is 0.56, reflecting a percentage loss in achievement across the three dimensions due to gender inequality of 56 percent, Regional averages range from 32 percent in developed OECD countries, to 74 percent in South Asia. At the country level losses due to gender inequality range from 17 percent in the Netherlands, to 85 percent in Yemen. Sub-Saharan Africa, South Asia and the Arab States suffer the largest losses due to gender inequality. Regional patterns reveal that reproductive health is the largest contributor to gender inequality around the world – women in sub-Saharan Africa, with a massive 99 percent loss, suffer the most in this dimension, followed by South Asia (98 percent) and the Arab States and Latin America and the Caribbean (each with 96 percent loss). The Arab States and South Asia are both also characterized by relatively weak female empowerment.

The Gender Inequality Index is similar in method to the Inequality-adjusted Human Development Index (HDI) – see Technical Note 3 for details. It can be interpreted as a percentage loss to potential human development due to shortfalls in the dimensions included. Since the Gender Inequality Index includes different dimensions to the HDI, unlike the IHDI, it cannot be interpreted as the loss in HDI itself. Unlike the HDI, higher values of the GII indicate worse achievements.

The Gender Inequality Index faces very major data limitations, which constrained the choice of indicators. For example, we use national parliamentary representation that excludes participation at the local government level and elsewhere in community and public life. Also, the labour market dimension lacks information on incomes, employment and on unpaid work by women. The Index misses other important dimensions, such as time use – the fact that many women have the additional burden of care giving and housekeeping, which cut into leisure time and increase stress and exhaustion is not taken into consideration. Asset ownership, gender-based violence and participation in community-level decision making are also not captured, mainly due to limited availability of data in these areas.

The Gender Inequality Index relies on data from major publicly available databases, including maternal mortality ratio from UNICEF’s The State of the World’s Children, adolescent fertility rates from the UN Department of Economic and Social Affair’s World Population Prospects, educational attainment statistics from Barro-Lee data sets, parliamentary representation from the International Parliamentary Union, and labour market participation from the International Labour Organization’s LABORSTA database.

It is true that reproductive health indicators used in the Gender Inequality Index do not have equivalent indicators for males. So in this dimension, the reproductive health of girls and women is compared to what should be societal goals —no maternal death, and no adolescent pregnancy. The rationale is that safe motherhood reflects the importance society attaches to women’s reproductive role.

Reproduction is risky, and often begins too early, compromising health and future opportunities. Early childbearing, as measured by the adolescent fertility rate, is associated with greater health risks for mothers and infants; also, adolescent mothers often are forced out of school and into low-skilled jobs.

This year only the parliamentary representation of women in 2 out of 138 countries are equal to zero. We replaced the zero value with 0.1 percent to make the computation possible. The rationale is that while women may not be represented in parliament, they do have some political influence. The relative rank of these countries is sensitive to the choice of the replacement value. The lowest observed non-zero parliamentary representation was 0.7.

Yes. The introduction in 1995 of the Gender-related Development Index (GDI) and the Gender Empowerment Measure (GEM) coincided with growing international recognition of the importance of monitoring progress in the elimination of gender gaps in all aspects of life. While the GDI and the GEM have contributed immensely to the gender debate, they have conceptual and methodological limitations. In the 20^th anniversary edition of the Human Development Report, the Gender Inequality Index has been introduced as an experimental index. It is not a perfect measure. Just as the HDI continues to evolve, the Gender Inequality Index will also be refined.

The GDI is not a measure of gender inequality; it is the HDI adjusted for gender disparities in its basic components and cannot be interpreted independently of the HDI. The difference between the HDI and the GDI appears to be small because the differences captured in the three dimensions tend to be small, giving a misleading impression that gender gaps are irrelevant. In addition, gender-disaggregated incomes have to be estimated in a very crude way using not so realistic assumptions due to the lack of income data by gender for over three-fourths of countries.

Both the GDI and GEM combined relative and absolute achievements. The earned income component uses both—the income level and the gender-disaggregated income shares. However, income levels tend to dominate the indexes, and as a result, countries with low income levels cannot achieve a high score even with perfect gender equality in the distribution of earnings and other components of the indexes. Also, nearly all of the GEM indicators reflect a strong elite bias making the measure more relevant for developed countries and urban elites in developing countries. Further, the indicators used as proxy do not correspond to the underlying concept.

The Gender Inequality Index introduces methodological improvements and alternative indicators. It measures inequality between genders in three dimensions, with carefully chosen indicators to reflect women’s reproductive health status, their empowerment and labour market participation relative to men’s. The Gender Inequality Index combines elements of the GDI and the GEM. Income, the most controversial component of the GDI and GEM, is not a component of the Gender Inequality Index. Moreover, the new Index does not allow high achievement in one dimension to compensate for low achievement in another dimension. However, like the GDI, one cannot determine which of the sexes is better off by looking at the value.

The Gender Inequality Index provides insights into gender disparities in health, empowerment and labour market in almost 140 countries. It can be useful to help governments and others better understand the gaps between women and men.

The Inequality-adjusted HDI defines the loss in human development, as measured by the HDI, due to inequality in distribution of health, education and standard of living across a population. The Gender Inequality Index measures the loss in human development due to inequality in reproductive health, empowerment and labour market between women and men. Losses in HDI and the Gender Inequality Index are highly correlated (0.87), indicating that unequal distribution of human development is strongly associated with gender inequality.

Yes. The Gender Inequality Index, as any other global composite index, is constrained by the need of comparability. However, national teams can use the indicators that may make more sense. Also, the functional form allows easy extension to more indicators and dimensions.

The Gender Inequality Index is one of three experimental indices introduced in 2010, alongside the Inequality-adjusted Human Development Index and the Multidimensional Poverty Index. It will be revised and improved in light of feedback and data availability.

The World Economic Forum’s Global Gender Gap Index (GGI), released in October 2010, differs from the Human Development Report’s GII in many ways. First, the dimensions and indicators are different. Second, GGI measures gender gaps without taking into consideration a country’s level of development. In contrast, the GII shows the loss to potential achievement in a country due to gender inequality across reproductive health, empowerment and labour market participation.  Another recent gender index -the Economist Intelligence Unit’s Women’s Economic Opportunity Index (WEOI), launched earlier this year –focuses on laws and regulations about women’s participation in the labour market and social institutions that affect women’s economic participation. It has five dimensions– labour policy and practice, women’s economic opportunity, access to finance, education and training, women’s legal and social status, and general business environment. Each category or sub-category has four to five indicators. Like the OECD’s Social Institutions and Gender Index (SIGI), the WEOI complements the GII because it helps us to understand the underlying causes of gender inequalities in economic participation.

The Multidimensional Poverty Index (MPI) is a new measure designed to capture the severe deprivations that people face at the same time. The MPI reflects both the incidence of multidimensional deprivation, and its intensity – how many deprivations people experience at the same time. It can be used to create a comprehensive picture of people living in poverty, and permits comparisons both across countries, regions and the world and within countries by ethnic group, urban/rural location, as well as other key household and community characteristics. The MPI builds on recent advances in theory and data to present the first global measure of its kind, and offers a valuable complement to traditional income-based poverty measures. The 2010 Human Development Report (HDR), being launched on November 4, presents estimates for 104 countries with a combined population of 5.2 billion (92 percent of the population in developing countries). About 1.7 billion people in the countries covered – a third of their entire population - live in multidimensional poverty.

The MPI will replace the HPI, which had been published since 1997. Pioneering in its day, the HPI used country averages to reflect aggregate deprivations in health, education, and standard of living. It could not identify specific individuals, households or larger groups of people as jointly deprived. The MPI addresses this shortcoming by capturing how many people experience overlapping deprivations (incidence) and how many deprivations they face on average (intensity). The MPI can be broken down by indicator to show how the composition of multidimensional poverty changes for different regions, ethnic groups and so on—with useful implications for policy.

As the upcoming HDR states, the MPI identifies overlapping deprivations at the household level across the same three dimensions as the Human Development Index (living standards, health, and education) and shows the average number of poor people and deprivations with which poor households contend. For details see Alkire and Santos 2010.

One deprivation alone may not represent poverty. The MPI requires a household to be deprived in multiple indicators at the same time. A person is multidimensionally poor if the weighted indicators in which he or she is deprived add up to at least 30 percent.

We could not include income due to data constraints. Income poverty data come from different surveys, and these surveys often do not have information on health and nutrition. For most countries we are not able to identify whether the same people are income poor and also deprived in all the MPI indicators so could not include income.

We could not include empowerment due to data constraints. The DHS surveys collect data on womens’ empowerment for some countries, but not every DHS survey includes empowerment, and the other surveys do not have these data. Data on men’s empowerment or political freedom are missing.

The MPI relies on three main datasets that are publicly available and comparable for most developing countries: the Demographic and Health Survey (DHS), the Multiple Indicators Cluster Survey (MICS), and the World Health Survey (WHS).

We could not include other countries due to data constraints. Comparable data on each of the indicators were not available for other developing nations.

The MPI relies on the most recent and reliable data available since 2000. However surveys are taken in different years, and some countries do not have recent data. Sixty four countries’ data comes from 2005 or later; thirty countries are from 2003 or 2004, and ten countries from 2000-2002. The difference in dates limits direct cross country comparisons, as circumstances may have improved, or deteriorated, in the intervening years.

The MPI complements income poverty measures. It measures various deprivations directly. In practice, although there is a clear overall relationship between MPI and $1.25/day poverty, the estimates do differ for many countries. This is a topic for further research, but some possibilities can include public services, as well as different abilities to convert income into outcomes such as good nutrition.

The MPI, like the $1.25/day line, is a globally comparable measure of poverty. It measures acute multidimensional poverty, and only includes indicators that are available for many countries. National poverty measures are typically monetary measures, and thus capture something different. The fact that there are differences does not mean that the national poverty number, or the MPI headcount is wrong – these simply measure different conceptions of poverty. At the same time, just as national poverty measures, in contrast, are designed to reflect the national situation more accurately and often differ in very useful ways from the $1.25 measure, some countries may wish to build a national multidimensional poverty index that is tailored to their context, to complement this international MPI.

No. The MPI is intended to complement monetary measures of poverty, including $1.25 a day estimates. The relationship between these measures, as well as their policy implications and methodological improvement, are priorities for further research.

The MPI methodology shows aspects in which the poor are deprived and help to reveal the interconnections among those deprivations. This enables policymakers to target resources and design policies more effectively. This is especially useful where the MPI reveals areas or groups characterized by severe deprivation. Examples where this has been done in practice already include Mexico’s poverty targeting program, as described in the upcoming HDR.

The MPI reflects the severe deprivations that people face at the same time. Because it was designed to internationally compare across developing nations, it is most relevant to lesser developed countries. We have described the MPI as a measure of ‘acute’ poverty to avoid confusion with the World Bank’s measure of ‘extreme’ poverty that captures those living on less than $1.25 a day.

The MPI constitutes a family or set of poverty measures. These measures can be unpacked to show the composition of poverty both across countries, regions and the world and within countries by ethnic group, urban/rural location, as well as other key household and community characteristics. This is why OPHI describes the MPI as a high resolution lens on poverty: it can be used as an analytical tool to identify the most prevailing deprivations. The MPI measures are explained below:

Incidence of poverty: the proportion of people who are poor according to the MPI (those who are deprived in at least 30% of the weighted indicators).

Average intensity of poverty: the average number of deprivations people experience at the same time.

MPI value: The MPI value summarises information on multiple deprivations into a single number. It is calculated by multiplying the incidence of poverty by the average intensity of poverty.

The MPI indicators are drawn from the MDGs as far as the available internationally comparable data allow. The ten indicators of the MPI are identical, or relate, to MDG indicators: nutrition (MDG 1), child mortality (MDG 4), access to drinking water (MDG 7), access to sanitation facility (MDG 7) and use of an improved source of cooking fuel (MDG 9. The overall MPI can be broken down into its constituent parts, revealing the overlapping needs of families and communities across a range of indicators which so often have been presented in isolation. This helps policymakers to see where challenges lie and what needs to be addressed.

The MPI has some drawbacks, due mainly to data constraints. First, the indicators include both outputs (such as years of schooling) and inputs (such as cooking fuel) as well as one stock indicator (child mortality, which could reflect a death that was recent or long ago), because flow data are not available for all dimensions. Second, the health data are relatively weak and overlook some groups’ deprivations especially for nutrition, though the patterns that emerge are plausible and familiar. Third, in some cases careful judgments were needed to address missing data. But to be considered multidimensionally poor, households must be deprived in at least six standard of living indicators or in three standard of living indicators and one health or education indicator. This requirement makes the MPI less sensitive to minor inaccuracies. Fourth, as is well known, intra-household inequalities may be severe, but these could not be reflected. Fifth, while the MPI goes well beyond a headcount to include the intensity of poverty experienced, it does not measure inequality among the poor, although decompositions by group can be used to reveal group-based inequalities. Finally, the estimates presented here are based on publicly available data and cover various years between 2000 and 2008, which limits direct cross-country comparability.

The multidimensional poverty approach can be adapted using indicators and weights that make sense at the country level to create tailored national poverty measures. The MPI can be useful as a guide to helping governments tailor a poverty measure that reflects multiple local indicators and data. In 2009 Mexico, became the first country to adopt a multidimensional poverty measure reflecting multiple deprivations on the household level.

Yes. The global MPI estimates are constrained by need for comparability. National teams should use the indicators and weights that make sense. At the country level, however, the multidimensional poverty approach to assessing deprivations at the household level can be tailored using country-specific data and indicators to provide a richer picture of poverty at the country level.

Yes. The MPI methodology can and should be modified to generate national Multidimensional Poverty Measures that reflect local cultural, economic, climatic and other factors. The international MPI was devised as an analytical tool to compare acute poverty across nations.

We estimated the MPI over time and conducted trend analysis for a handful of countries for which suitable data are available for. For details see page 51 of Alkire and Santos 2010.

The effects of shocks are difficult to capture in any poverty measure. Because the standard survey data used to estimate the global measure are collected only every three years, the ability to detect changes is limited by the available data fed. The MPI will reflect the impacts of shocks as far as these cause children to leave primary education or to become malnourished, for example. If more frequent data is available at the country or local level, this can be used to seek to capture the effects of larger scale economic and other shocks.

The MPI is one of three new experimental series introduced in 2010, alongside the Inequality-adjusted Human Development Index and the Gender Inequality Index. It will be revised and improved in light of feedback and data availablility. Each annual report is expected to update estimates as data allows.

Background materials that provide the technical guidance needed to apply and adapt the MPI approach are available from the OPHI (www.ophi.org.uk) and HDRO (http://hdr.undp.org/en/) websites. OPHI’s website also advertises periodic short courses on multidimensional poverty.

This is presently under investigation.