Human Development Index

Published on 4 November 2010 (and updated on 10 June 2011), the 2010 Human Development Report calculated the HDI combining three dimensions:[7][8]

• A long and healthy life: Life expectancy at birth
• Education index: Mean years of schooling and Expected years of schooling
• A decent standard of living: GNI per capita (PPP international dollars)

In its 2010 Human Development Report, the UNDP began using a new method of calculating the HDI. The following three indices are used:

1. Life Expectancy Index (LEI) ${\displaystyle ={\frac {{\textrm {LE}}-20}{85-20}}}$

LEI is 1 when Life expectancy at birth is 85 and 0 when Life expectancy at birth is 20.

2. Education Index (EI) ${\displaystyle ={\frac {{\textrm {MYSI}}+{\textrm {EYSI}}}{2}}}$[9]

2.1 Mean Years of Schooling Index (MYSI) ${\displaystyle ={\frac {\textrm {MYS}}{15}}}$[10]

Fifteen is the projected maximum of this indicator for 2025.

2.2 Expected Years of Schooling Index (EYSI) ${\displaystyle ={\frac {\textrm {EYS}}{18}}}$[11]

Eighteen is equivalent to achieving a master's degree in most countries.

3. Income Index (II) ${\displaystyle ={\frac {\ln({\textrm {GNIpc}})-\ln(100)}{\ln(75,000)-\ln(100)}}}$

II is 1 when GNI per capita is $75,000 and 0 when GNI per capita is $100.

Finally, the HDI is the geometric mean of the previous three normalized indices:

${\displaystyle {\textrm {HDI}}={\sqrt[{3}]{{\textrm {LEI}}\cdot {\textrm {EI}}\cdot {\textrm {II}}}}.}$

LE: Life expectancy at birth
MYS: Mean years of schooling (i.e. years that a person aged 25 or older has spent in formal education)
EYS: Expected years of schooling (i.e. total expected years of schooling for children under 18 years of age)
GNIpc: Gross national income at purchasing power parity per capita

The HDI combined three dimensions last used in its 2009 report:

• Life expectancy at birth, as an index of population health and longevity to HDI
• Knowledge and education, as measured by the adult literacy rate (with two-thirds weighting) and the combined primary, secondary, and tertiary gross enrollment ratio (with one-third weighting).
• Standard of living, as indicated by the natural logarithm of gross domestic product per capita at purchasing power parity.

HDI trends between 1975 and 2004

OECD   Europe (not in the OECD), and CIS   Latin America and the Caribbean   East Asia

Arab League   South Asia   Sub-Saharan Africa

This methodology was used by the UNDP until their 2011 report.

The formula defining the HDI is promulgated by the United Nations Development Programme (UNDP).[12] In general, to transform a raw variable, say ${\displaystyle x}$, into a unit-free index between 0 and 1 (which allows different indices to be added together), the following formula is used:

• ${\displaystyle x{\text{ index}}={\frac {x-a}{b-a}}}$

where ${\displaystyle a}$ and ${\displaystyle b}$ are the lowest and highest values the variable ${\displaystyle x}$ can attain, respectively.

The Human Development Index (HDI) then represents the uniformly weighted sum with ​¹⁄₃ contributed by each of the following factor indices:

• Life Expectancy Index = ${\displaystyle {\frac {LE-25}{85-25}}}$
• Education Index = ${\displaystyle {\frac {2}{3}}\times ALI+{\frac {1}{3}}\times GEI}$
  • Adult Literacy Index (ALI) = ${\displaystyle {\frac {ALR-0}{100-0}}}$
  • Gross Enrollment Index (GEI) = ${\displaystyle {\frac {CGER-0}{100-0}}}$
• GDP = ${\displaystyle {\frac {\log \left(GDPpc\right)-\log \left(100\right)}{\log \left(40000\right)-\log \left(100\right)}}}$

The Inequality-adjusted Human Development Index (IHDI)[16] "equals the HDI when there is no inequality across people but is less than the HDI as inequality rises. In this sense, the IHDI is the actual level of human development (accounting for this inequality), while the HDI can be viewed as an index of 'potential' human development (or the maximum level of HDI) that could be achieved if there was no inequality. The 'loss' in potential human development due to inequality is given by the difference between the HDI and the IHDI and can be expressed as a percentage." Below is the list of countries or territories with very high and high human development:

Rank	Country	2019 estimates (2020 report)[17][18][19]
		IHDI	HDI	Overall loss (%)	Growth since 2010
1	Norway	0.899	0.957	6.1	0.021
2	Iceland	0.894	0.949	5.8	0.055
3	Switzerland	0.889	0.955	6.9	0.015
4	Finland	0.888	0.938	5.3	0.040
5	Ireland	0.885	0.955	7.3	0.066
6	Denmark	0.883	0.940	6.1	0.025
7	Sweden	0.882	0.945	6.7	0.033
8	Netherlands	0.878	0.944	7.0	0.036
9	Slovenia	0.875	0.917	4.6	0.047
10	Germany	0.869	0.947	8.2	0.016
11	Australia	0.867	0.944	8.2	0.011
12	Czech Republic	0.860	0.900	4.4	0.042
13	Belgium	0.859	0.931	7.7	0.026
14	New Zealand	0.859	0.931	7.7	NA
15	Austria	0.857	0.922	7.0	0.021
16	United Kingdom	0.856	0.932	8.2	0.032
17	Canada	0.848	0.929	8.7	0.025
18	Japan	0.843	0.919	8.3	0.053[lower-alpha 1]
19	Estonia	0.829	0.882	7.1	0.051
20	Luxembourg	0.826	0.916	9.8	0.009
21	Hong Kong	0.824	0.949	13.2	NA
22	Malta	0.823	0.895	8.0	0.033[lower-alpha 2]
23	France	0.820	0.901	9.0	0.022
24	South Korea	0.815	0.916	11.0	0.074
25	Israel	0.814	0.919	11.4	0.031
26	Singapore	0.813	0.938	13.3	NA
26	Poland	0.813	0.880	7.6	0.063
28	United States	0.808	0.926	12.7	0.004
29	Slovakia	0.807	0.860	6.2	0.032
30	Cyprus	0.805	0.887	9.2	0.048
31	Hungary	0.791	0.854	7.4	0.032
31	Lithuania	0.791	0.882	10.3	0.055
31	Greece	0.791	0.888	10.9	0.014
34	Italy	0.783	0.892	12.2	0.010
34	Latvia	0.783	0.866	9.6	0.050
34	Croatia	0.783	0.851	8.0	0.092
34	Spain	0.783	0.904	13.4	0.004
38	Belarus	0.771	0.823	6.3	0.050
39	Kazakhstan	0.766	0.825	7.2	0.105
40	Portugal	0.761	0.850	12.7	0.031
41	Montenegro	0.749	0.829	9.7	0.026
42	Russia	0.740	0.824	10.2	0.049
43	Romania	0.730	0.828	11.8	0.022
44	Argentina	0.729	0.845	13.7	0.063
45	Ukraine	0.728	0.779	6.4	0.035
46	Bulgaria	0.721	0.816	11.6	0.022
47	Georgia	0.716	0.812	11.8	0.093
48	Uruguay	0.712	0.817	12.7	0.055
49	Chile	0.709	0.851	16.7	0.058
50	Albania	0.708	0.795	10.9	0.058
51	Oman	0.706	0.813	13.2	NA
52	Serbia	0.705	0.806	12.5	0.021

The year represents the time period from which the statistics for the index were derived. In parentheses is the year when the report was published.

Economists Hendrik Wolff, Howard Chong and Maximilian Auffhammer discuss the HDI from the perspective of data error in the underlying health, education and income statistics used to construct the HDI. They identified three sources of data error which are due to (i) data updating, (ii) formula revisions and (iii) thresholds to classify a country's development status and conclude that 11%, 21% and 34% of all countries can be interpreted as currently misclassified in the development bins due to the three sources of data error, respectively. The authors suggest that the United Nations should discontinue the practice of classifying countries into development bins because: the cut-off values seem arbitrary, can provide incentives for strategic behavior in reporting official statistics, and have the potential to misguide politicians, investors, charity donors and the public who use the HDI at large.[22]

In 2010, the UNDP reacted to the criticism and updated the thresholds to classify nations as low, medium, and high human development countries. In a comment to The Economist in early January 2011, the Human Development Report Office responded[23] to a 6 January 2011 article in the magazine[24] which discusses the Wolff et al. paper. The Human Development Report Office states that they undertook a systematic revision of the methods used for the calculation of the HDI, and that the new methodology directly addresses the critique by Wolff et al. in that it generates a system for continuously updating the human-development categories whenever formula or data revisions take place.

In 2013, Salvatore Monni and Alessandro Spaventa emphasized that in the debate of GDP versus HDI, it is often forgotten that these are both external indicators that prioritize different benchmarks upon which the quantification of societal welfare can be predicated. The larger question is whether it is possible to shift the focus of policy from a battle between competing paradigms to a mechanism for eliciting information on well-being directly from the population.[25]