Emerging Markets Journal Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu RECOMPUTATION OF UNDP’S HDI RANKINGS BY DATA ENVELOPMENT ANALYSIS Mehmet Tolga Taner Bülent Sezen Lutfihak Alpkan Selim Aren e-mail: mtaner@gyte.edu.tr e-mail: bsezen@gyte.edu.tr e-mail: alpkan@gyte.edu.tr e-mail: aren@gyte.edu.tr Abstract The HDI has played an influential role in the debate on human development. No index is perfect and so is the Human Development Index of United Nations Development Program. This paper aims to measure the performance of 182 countries in terms of performance by means of non-parametric input oriented CRS employed Data Envelopment Analysis. In addition, it elaborates on the cut-off values assigned by UNDP to categorize the countries. By means of this research, countries will be able to choose those elements by benchmarking from other countries that are applicable and most likely to develop strategy formulation processes for human development and international growth. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License. This journal is published by the University Library System of the University of Pittsburgh as part of its D-Scribe Digital Publishing Program, and is cosponsored by the University of Pittsburgh Press Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu mailto:mtaner@gyte.edu.tr http://creativecommons.org/licenses/by-nc-nd/3.0/us/ http://www.library.pitt.edu/ http://www.pitt.edu/ http://www.library.pitt.edu/articles/digpubtype/index.html http://www.upress.pitt.edu/upressIndex.aspx http://emaj.pitt.edu/ Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 21 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu Recomputation Of Undp’s Hdi Rankings By Data Envelopment Analysis Mehmet Tolga Taner Bülent Sezen Lutfihak Alpkan Selim Aren I. Introduction Today, normalised measures of life expectancy, literacy, educational attainment, and GDP per capita are considered to be the main indicators of development for countries worldwide. These three indicators are unified to give a measure of development, namely the Human Development Index (HDI). HDI has been first used in the United Nations Development Program’s (UNDP) World Development Report. Since the first publication of this annual report in 1990, UNDP has been seeking to explore the concept and measurement of global human development. The Human Development Index (HDI) computes and assigns a single, scalar value to each country of the world based on three components of human development. This simple measure has changed the global debate on development and influenced public policy around the world. Criticism and proposed alternatives abound, yet the index has managed to maintain its popularity and simplicity with only minor modifications over the years of 1991, 1994, 1995, 1999 and 2005. The HDI was developed to measure “the basic concept of human development to enlarge people’s choices” (Ul Haq, 1995). It was also designed as an alternative to the use of GDP per capita alone as a measure of human development. To these ends, it must be concluded that the HDI has achieved overwhelming success. However, it is still prone to criticisms as it lacks the means to correctly measure and analyse the annual performance of countries. Ul Haq stated that the purpose of the HDI was to measure at least a few more choices besides income and to reflect them in a methodologically sound composite index. Indeed, the HDI has included only a limited number of indicators to keep it simple and manageable. This simple HDI algorithm is still being used today and calculated from regularly available data to produce a meaningful number that can be used to compare and rank countries across the world. Up-to-date, critics on HDI have claimed that it uses very few or the wrong indicators. Others allege that it presents an oversimplified view of human development and added that a pure economic model focusing on growth alone should set the tone on discourse regarding human development. In fact, some of these critics have developed their own novel indices or have resulted in the modification of HDI. But, collecting reliable data continues to be the major obstacle in the poorest countries (Harkness, 2004). Regarding health and longevity, Harkness notes that mortality data are most likely to be missing in countries where mortality is the highest. According to another critic, both the resources allocated throughout a country and the levels of inequality that may exist across the country are not taken into account in the HDI index (Foster, 2005; Ul Haq, 1995). In recent years, most critics have taken issue with the equal weights assigned to each of the respective indicators of the index (Mahlberg and Obersteiner, 2001; Chowdhury and Squire, 2006) but assigning differing weights have been proven to be unnecessary (Stapleton and Garrod, 2007). And yet, the HDI has been extensively criticised for its lack of desirable statistical properties. To overcome the deficiencies of previous traditional parametric approaches and weighing problems, Data Envelopment Analysis can be employed. To measure the HDI, this analysis has Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 22 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu been firstly used by Mahlberg and Obersteiner in 2001. The following year, Lozano and Gutierrez proposed a new DEA model that computes a range- djusted measure (RAM) of efficiency for HDI and Lee et al. (2006) made use of a fuzzy multiple objective DEA for the HDI. In 2005, the HDI of the Asian and Pacific countries were calculated by Despotis (2005). Having automatically overcome the subjectivity difficulties in weighing the component indices, this technique analyses the inherencies of the data by a different approach. II. METHOD Data Envelopment Analysis (DEA) is a data-oriented technique which has been proven to be an effective tool in evaluating relative efficiency. It is a nonparametric method of measuring the efficiency of a decisionmaking unit (DMU) such as a country, first introduced into Operations Research literature by Charnes, Cooper and Rhodes in 1978. Recent years have seen a great variety of applications of DEA for use in evaluating the performances of many different kinds of entities engaged in many different activities in many different contexts in many different countries such as sports, logistics, hospitals, universities, cities, business firms etc. Because it requires very few assumptions, DEA has opened up possibilities for use in cases which have been resistant to other approaches because of the complex and often unknown nature of relations between the multiple inputs and multiple outputs involved in the DMUs. Throughout the paper, we use decision making units (DMUs) to represent countries. Each DMU is assumed to have a constant input and represented by three outputs , i.e. HDI component indicators (life expectancy index (LEI), education index (EI) and GDP per capita index (GDPI)). The DEA model used assumes an input oriented radial CRS technology. The main advantages of DEA are: (1) Multiple inputs and outputs can be used effectively, while ascertaining efficiency, and a specific production function is not required; (2) The decision maker does not need prior information about weights of inputs and outputs; and (3) For each DMU, efficiency is compared to that of an ideal operating unit, rather than to the average performance. The HDI is based on three indicators: longevity, as measured by life expectancy at birth; educational attainment, as measured by a combination of adult literacy (two-thirds weight) and combined primary, secondary and tertiary enrollement ratios; and standard of living, as measured by real GDPI (Purchasing Power Parity in US$). To calculate the dimension indices, UNDP has assigned minimum and maximum values (goalposts) for each underlying indicators. Performance in each dimension is then calculated and expressed as a value between 0% and 100%. Then, the HDI is calculated as a simple average of the dimension indices by basic algebra. In UNDP’s approach, this was followed by assigning (equal) weights to each dimension index given as follows: HDI = x. (LEI) + y. (EI) + z. (GDPI) (where x = y = z = 1/3). Whereas, in our approach, the indices are analyzed by the use of linear programming methods to construct a non-parametric piece-wise surface over the data. The CRS surface is presented by a straight line that starts at the origin and passes through the first DMU that it meets as it approaches the observed population. The models with CRS envelopment surface assume that an increase in inputs will result in a proportional increase in outputs. Efficiency measures are then calculated relative to this surface. For the purpose of analyzing the data, Efficiency Measurement System (EMS) is used. The inherent weights for the inputs and outputs are assigned by the model itself. Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 23 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu The essence of the CRS model is the ratio of maximization of the ratio of weighted multiple outputs to weighted multiple inputs. Any country compared to others shold have an efficiency score of 100% or less. The efficiency score in the presence of multiple input and output indicators is defined as: Efficiency = Weighted sum of outputs / Weighted sum of inputs Assuming that there are n DMUs, each of with i inputs and j outputs, the relative efficiency score of a test DMU m is obtained by solving the following model proposed by [Charnes et. al., 1978]: The above model is run n times in identifying the relative efficiency scores of all DMUs. Each DMU selects input and output weights that maximize its efficiency score. In general, a DMU is considered to be efficient if it obtains an efficiency score of 100% and a score of less than 100% implies that it is inefficient. III. ANALYSIS Unlike the HDI, the DEA scores on Table 1 are relative measures. Each country is compared with the best practice countries when it assesses its composite performance on the human development indicators. As shown in Table 1, the EMS analysis has yielded differences in country rankings between the UNDP and DEA approaches. The DEA approach identified a group of 20 optimally performing countries that are defined as efficient and assigns them an efficiency score of 100%. These efficient countries are then used to create an “efficiency frontier” or “data envelope” against which all other countries are compared. In sum, countries that require relatively more weighted inputs to produce weighted outputs, or, alternatively, produce less weighted output per weighted inputs than do countries on the efficient frontier, are considered technically inefficient. They are given efficiency scores of less than 100%, but greater than 0%. Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 24 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu Table 1 HDR 2009 Data and DEA Rankings UNDP ranking DMU HDI Efficien cy Score GDP per capita index Education Index Life Expect ancy Index DEA ranking 1 Norway 0,971 100,00% 1,000 0,989 0,925 1 2 Australia 0,970 100,00% 0,977 0,993 0,940 1 3 Iceland 0,969 100,00% 0,981 0,980 0,946 1 4 Canada 0,966 99,85% 0,982 0,991 0,927 21 5 Ireland 0,965 100,00% 1,000 0,985 0,911 1 6 Netherlands 0,964 99,59% 0,994 0,985 0,914 22 7 Sweden 0,963 99,41% 0,986 0,974 0,930 24 8 France 0,961 99,09% 0,971 0,978 0,933 25 9 Switzerland 0,960 100,00% 1,000 0,936 0,945 1 10 Japan 0,960 100,00% 0,971 0,949 0,961 1 11 Luxembourg 0,960 100,00% 1,000 0,975 0,906 1 12 Finland 0,959 100,00% 0,975 0,993 0,908 1 13 United States 0,956 100,00% 1,000 0,968 0,902 1 14 Austria 0,955 98,86% 0,989 0,962 0,915 26 15 Spain 0,955 98,59% 0,960 0,975 0,929 29 16 Denmark 0,955 100,00% 0,983 0,993 0,887 1 17 Belgium 0,953 98,41% 0,977 0,974 0,908 30 18 Italy 0,951 98,71% 0,954 0,965 0,935 27 19 Liechtenstein 0,951 100,00% 1,000 0,949 0,903 1 20 New Zealand 0,950 100,00% 0,936 0,993 0,919 1 21 United Kingdom 0,947 97,83% 0,978 0,957 0,906 32 22 Germany 0,947 97,81% 0,975 0,954 0,913 33 23 Singapore 0,944 100,00% 1,000 0,913 0,920 1 24 Hong Kong 0,944 100,00% 1,000 0,879 0,953 1 25 Greece 0,942 98,71% 0,944 0,981 0,902 28 26 Republic of Korea 0,937 99,50% 0,920 0,988 0,904 23 Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 25 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu 27 Israel 0,935 97,58% 0,930 0,947 0,928 35 28 Andorra 0,934 100,00% 1,000 0,877 0,925 1 29 Slovenia 0,929 97,58% 0,933 0,969 0,886 34 30 Brunei 0,920 100,00% 1,000 0,891 0,867 1 31 Kuwait 0,916 100,00% 1,000 0,872 0,875 1 32 Cyprus 0,914 95,12% 0,920 0,910 0,910 47 33 Qatar 0,910 100,00% 1,000 0,888 0,841 1 34 Portugal 0,909 94,49% 0,906 0,929 0,893 52 35 United Arab Emirates 0,903 100,00% 1,000 0,838 0,872 1 36 Czech Republic 0,903 94,44% 0,916 0,938 0,856 53 37 Barbados 0,903 98,12% 0,866 0,975 0,867 31 38 Malta 0,902 94,70% 0,908 0,887 0,910 50 39 Bahrain 0,895 95,04% 0,950 0,893 0,843 49 40 Estonia 0,883 97,05% 0,887 0,964 0,799 38 41 Poland 0,880 95,88% 0,847 0,952 0,842 44 42 Slovakia 0,880 93,44% 0,885 0,928 0,827 57 43 Hungary 0,879 96,64% 0,874 0,960 0,805 42 44 Chile 0,878 94,00% 0,823 0,919 0,891 54 45 Croatia 0,871 92,17% 0,847 0,916 0,850 63 46 Lithuania 0,870 97,40% 0,863 0,968 0,780 36 47 Antigua and Barbuda 0,868 95,12% 0,873 0,945 0,786 48 48 Latvia 0,866 96,71% 0,851 0,961 0,788 40 49 Argentina 0,866 95,27% 0,815 0,946 0,836 46 50 Uruguay 0,865 96,17% 0,788 0,955 0,852 43 51 Cuba 0,863 100,00% 0,706 0,993 0,891 1 52 Bahamas 0,856 88,79% 0,886 0,878 0,804 93 53 Mexico 0,854 90,00% 0,826 0,886 0,850 78 54 CostaRica 0,854 93,19% 0,782 0,883 0,896 58 55 Libya 0,847 90,38% 0,829 0,898 0,814 74 56 Oman 0,846 90,63% 0,906 0,790 0,841 71 57 Seychelles 0,845 89,23% 0,851 0,886 0,797 89 58 Venezuela 0,844 92,70% 0,801 0,921 0,811 59 Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 26 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu 59 SaudiArabia 0,843 90,72% 0,907 0,828 0,794 70 60 Panama 0,840 89,49% 0,790 0,888 0,842 85 61 Bulgaria 0,840 93,59% 0,788 0,930 0,802 56 62 SaintKitts and Nevis 0,838 90,16% 0,830 0,896 0,787 76 63 Romania 0,837 92,07% 0,804 0,915 0,792 64 64 Trinidad and Tobago 0,837 91,13% 0,911 0,861 0,737 68 65 Montenegro 0,834 89,70% 0,795 0,891 0,817 81 66 Malaysia 0,829 86,60% 0,819 0,851 0,819 103 67 Serbia 0,826 89,70% 0,773 0,891 0,816 80 68 Belarus 0,826 96,77% 0,782 0,961 0,733 39 69 SaintLucia 0,821 89,52% 0,765 0,889 0,810 84 70 Albania 0,818 90,60% 0,710 0,886 0,858 72 71 Russian Federation 0,817 93,94% 0,833 0,933 0,686 55 72 TheFormerYug oslavRe publicofMaced onia 0,817 88,61% 0,753 0,880 0,819 96 73 Dominica 0,814 90,01% 0,729 0,848 0,865 77 74 Grenada 0,813 89,07% 0,717 0,884 0,838 92 75 Brazil 0,813 89,67% 0,761 0,891 0,787 83 76 Bosnia and Herzegovina 0,812 88,51% 0,726 0,874 0,834 98 77 Colombia 0,807 88,69% 0,743 0,881 0,795 94 78 Peru 0,806 89,70% 0,728 0,891 0,800 82 79 Turkey 0,806 83,36% 0,812 0,828 0,779 117 80 Ecuador 0,806 88,12% 0,719 0,866 0,833 99 81 Mauritius 0,804 84,45% 0,789 0,839 0,785 115 82 Kazakhstan 0,804 97,10% 0,782 0,965 0,666 37 83 Lebanon 0,803 86,32% 0,770 0,857 0,781 104 84 Armenia 0,798 91,49% 0,675 0,909 0,810 66 85 Ukraine 0,796 96,65% 0,707 0,960 0,720 41 Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 27 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu 86 Azerbaijan 0,787 88,67% 0,728 0,881 0,751 95 87 Thailand 0,783 89,37% 0,734 0,888 0,728 88 88 Iran 0,782 81,15% 0,784 0,793 0,769 126 89 Georgia 0,778 92,18% 0,641 0,916 0,777 62 90 Dominican Republic 0,777 84,50% 0,702 0,839 0,790 114 91 Saint Vincent and the Grenadines 0,772 82,30% 0,725 0,817 0,774 120 92 China 0,772 85,67% 0,665 0,851 0,799 106 93 Belize 0,772 88,52% 0,703 0,762 0,851 97 94 Samoa 0,771 91,10% 0,634 0,905 0,773 69 95 Maldives 0,771 89,07% 0,659 0,885 0,768 91 96 Jordan 0,770 87,56% 0,650 0,870 0,790 101 97 Suriname 0,769 85,58% 0,727 0,850 0,729 107 98 Tunisia 0,769 84,64% 0,721 0,772 0,813 112 99 Tonga 0,768 92,61% 0,605 0,920 0,778 60 100 Jamaica 0,766 83,91% 0,686 0,834 0,778 116 101 Paraguay 0,761 87,67% 0,633 0,871 0,778 100 102 SriLanka 0,759 85,88% 0,626 0,834 0,816 105 103 Gabon 0,755 84,99% 0,838 0,843 0,584 110 104 Algeria 0,754 81,86% 0,726 0,748 0,787 123 105 Philippines 0,751 89,41% 0,589 0,888 0,777 87 106 ElSalvador 0,747 81,31% 0,678 0,794 0,771 125 107 Syria 0,742 85,08% 0,636 0,773 0,818 109 108 Fiji 0,741 87,37% 0,628 0,868 0,728 102 109 Turkmenistan 0,739 91,25% 0,651 0,906 0,661 67 110 Occupied Palestinian Territories 0,737 89,22% 0,519 0,886 0,806 90 111 Indonesia 0,734 84,61% 0,603 0,840 0,758 113 112 Honduras 0,732 82,59% 0,607 0,806 0,783 119 113 Bolivia 0,729 89,77% 0,624 0,892 0,673 79 114 Guyana 0,729 94,56% 0,555 0,939 0,691 51 Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 28 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu 115 Mongolia 0,727 91,87% 0,580 0,913 0,687 65 116 VietNam 0,725 85,47% 0,544 0,810 0,821 108 117 Moldova 0,720 90,46% 0,541 0,899 0,722 73 118 Equatorial Guinea 0,719 95,54% 0,955 0,787 0,415 45 119 Uzbekistan 0,710 89,43% 0,532 0,888 0,711 86 120 Kyrgyzstan 0,710 92,38% 0,500 0,918 0,710 61 121 Cape Verde 0,708 80,87% 0,570 0,786 0,769 127 122 Guatemala 0,704 78,22% 0,638 0,723 0,752 130 123 Egypt 0,703 77,94% 0,664 0,697 0,749 131 124 Nicaragua 0,699 82,74% 0,542 0,760 0,795 118 125 Botswana 0,694 82,00% 0,820 0,788 0,473 121 126 Vanuatu 0,693 77,85% 0,601 0,728 0,748 132 127 Tajikistan 0,688 90,25% 0,478 0,896 0,691 75 128 Namibia 0,686 81,60% 0,658 0,811 0,590 124 129 SouthAfrica 0,683 84,84% 0,765 0,843 0,442 111 130 Morocco 0,654 79,83% 0,620 0,574 0,767 128 131 Sao Tome and Principe 0,651 81,87% 0,467 0,813 0,673 122 132 Bhutan 0,619 70,52% 0,647 0,533 0,678 141 133 Lao 0,619 69,70% 0,513 0,683 0,659 143 134 India 0,612 66,92% 0,553 0,643 0,639 151 135 Solomon Islands 0,610 70,91% 0,475 0,676 0,680 138 36 Congo 0,601 74,07% 0,594 0,736 0,474 134 137 Cambodia 0,593 70,85% 0,483 0,704 0,593 139 138 Myanmar 0,586 79,23% 0,368 0,787 0,603 129 139 Comoros 0,576 69,26% 0,407 0,655 0,666 145 140 Yemen 0,575 64,96% 0,526 0,574 0,624 153 141 Pakistan 0,572 71,44% 0,537 0,492 0,687 137 142 Swaziland 0,572 73,56% 0,646 0,731 0,339 135 143 Angola 0,564 67,26% 0,665 0,667 0,359 150 144 Nepal 0,553 71,64% 0,392 0,579 0,688 136 145 Madagascar 0,543 68,01% 0,373 0,676 0,582 148 Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 29 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu 146 Bangladesh 0,543 70,53% 0,420 0,530 0,678 140 147 Kenya 0,541 69,42% 0,457 0,690 0,477 144 148 Papua New Guinea 0,541 61,85% 0,507 0,521 0,594 160 149 Haiti 0,532 62,45% 0,408 0,588 0,600 158 150 Sudan 0,531 56,97% 0,507 0,539 0,548 165 151 Tanzania 0,530 67,76% 0,416 0,673 0,500 149 152 Ghana 0,526 62,59% 0,432 0,622 0,525 156 153 Cameroon 0,523 63,11% 0,510 0,627 0,431 155 154 Mauritania 0,520 55,46% 0,494 0,541 0,526 170 155 Djibouti 0,520 55,75% 0,505 0,554 0,501 168 156 Lesotho 0,514 75,80% 0,457 0,753 0,332 133 157 Uganda 0,514 70,29% 0,394 0,698 0,449 142 158 Nigeria 0,511 66,12% 0,497 0,657 0,378 151 159 Togo 0,499 64,47% 0,345 0,534 0,620 154 160 Malawi 0,493 68,96% 0,339 0,685 0,456 146 161 Benin 0,492 62,49% 0,430 0,445 0,601 157 162 Timor Leste 0,489 61,87% 0,329 0,545 0,595 159 163 Cote d’Ivoire 0,484 55,21% 0,472 0,450 0,531 171 164 Zambia 0,481 68,67% 0,435 0,682 0,326 147 165 Eritrea 0,472 59,33% 0,306 0,539 0,570 163 166 Senegal 0,464 52,64% 0,469 0,417 0,506 173 167 Rwanda 0,460 61,07% 0,360 0,607 0,412 162 168 Gambia 0,456 53,17% 0,418 0,439 0,511 172 169 Liberia 0,442 57,76% 0,215 0,562 0,548 164 170 Guinea 0,435 56,02% 0,406 0,361 0,538 167 171 Ethiopia 0,414 51,59% 0,343 0,403 0,496 174 172 Mozambique 0,402 48,17% 0,348 0,478 0,380 175 173 Guinea Bissau 0,396 55,60% 0,261 0,552 0,375 169 174 Burundi 0,394 56,25% 0,205 0,559 0,418 166 175 Chad 0,392 44,94% 0,449 0,334 0,393 177 176 Democratic Republic of the Congo 0,389 61,26% 0,182 0,608 0,377 161 Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 30 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu 177 Burkina Faso 0,389 48,10% 0,404 0,301 0,462 176 178 Mali 0,371 40,24% 0,398 0,331 0,385 181 179 Central African Republic 0,369 42,20% 0,328 0,419 0,361 179 180 Sierra Leone 0,365 40,54% 0,320 0,403 0,371 180 181 Afghanistan 0,352 39,31% 0,393 0,354 0,310 182 182 Niger 0,340 44,80% 0,307 0,282 0,431 178 We compared the DEA efficiency scores with HDI values. Pearson correlation coefficient of 0.958 shows that the two indices are highly correlated. Despite this strong correlation, there are also some notable differences between the two measurements. IV. DISCUSSION Benchmarks DEA analysis shows that Australia is the country that is the most frequently used as a reference by the inefficient countries (115 times or by the 63% of the inefficient countries). The corresponding frequencies for Denmark and Japan are 94 (52%) and 58 (32%), respectively. Therefore, both Australia and Denmark can be regarded as role model countries. Cluster Analysis The basis of UNDP’s classification of 182 countries into 4 groups (shown in Table 2) is based on a simple leveling structure. A better method for determining the real cut-offs between countries is the cluster analysis. In a previous research, Wolff et al. (2009) have examined the consequences of data error in data series used to construct aggregate indicators and found that up to 45% of developing countries were misclassified in HDR 2008. Our analysis of corrected HDI and DEA-based cutoffs are given in Table 3. Grouping of countries by means of cluster analysis is given in Table 4. In addition, the ranking results of DEA have also been examined by cluster analysis. The countries have again been classified in four groups. However, there are substantial differences between the groupings of HDI and DE Table 2 Classification of countries according to HDR, 2009 No. of countries UNDP’s lower cut-off (HDI) UNDP’s upper cut-off (HDI) Very High Human Development 0.900≤HDI≤1.000 38 0.902 0.971 High Human Development 0.800≤HDI<0.900 45 0.803 0.895 Medium Human Development 0.500≤HDI<0.800 75 0.511 0.798 Low Human Development 24 0.340 0.499 Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 31 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu HDI<0.500 Table 3 Corrected and DEA cutoffs classifying the 182 countries Group no. No. of countries lower cut-off Upper cut-off Corrected HDI 1 66 0.829 0.971 2 63 0.683 0.826 3 30 0.499 0.654 4 23 0.340 0.493 DEA 1 84 90.00% 100.00% 2 57 70.53% 89.49% 3 33 52.64% 70.29% 4 8 39.31% 48.17% Corrected groups of HDI has differed from the former one in many terms. Firstly, Group 1 now includes many of the recently EC-integrated countries such as Estonia, Poland, Slovakia, Hungary, Lithuania, Latvia, Bulgaria and Romania. Secondly, South and Central American countries has appeared in Group 1 for the first time. These countries include Chile, Argentina, Uruguay, Costa Rica, Venezuela, Panama and Trinidad Tobago. It should be noted that Argentina, Uruguay and Venezuela are full members of Mercosur. Thirdly, none of the African countries are categorized in Group 1. Next, Group 2 now includes the majority of Asian, Turkic and North African countries. Last, whereas Group 4 includes mostly the Central African countries. According to the classification by DEA, all ex-USSR countries except Azerbaijan and Uzbekistan have moved to Group 1 from Group 2 due to their high adult literacy rate. In return, Bahamas and Malaysia have moved to Group 2 from Group 1 due to their relatively low EI. Equatorial Guinea have moved to Group 1 from Group 2 due to its high GDP per capita of 30.627 USD. In return, Panama has moved from Group 1 to Group 2 due to its relatively low GDP per capita. Moving from Group 3 to Group 2 has required countries to have superiority over other countries in any of the two indicators. For instance, Pakistan has higher GDP per capita (0.537 versus 0.526) and life expectancy (0.687 versus 0. 624) indices than Yemen. Therefore, Pakistan has moved to the upper group whereas the group of Yemen has remained the same. It should be noted that high education index is proven to be the most important criterion while grouping the countries by DEA. All countries moving from Group 4 to Group 3 such as Malawi, Zambia and Rwanda have enjoyed relatively higher adult literacy rates. It is also observed that countries with the lowest efficiency scores are mainly from the Central African countries. Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 32 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu Group 1 Group 2 Group 3 Group 4 Corrected HDI Norway, Australia, Iceland, Canada, Ireland, Netherlands, Sweden, France, Switzerland, Japan, Luxembourg, Finland, United States, Austria, Spain, Denmark, Belgium, Italy, Liechtenstein, New Zealand, United Kingdom, Germany, Singapore, Hong Kong, reece, Republic of Korea, Israel, Andorra, Slovenia, Brunei, Kuwait, Cyprus, Qatar, Portugal, United Arab mirates, Czech Republic, Barbados, Malta, Bahrain, Estonia, Poland, Slovakia, Hungary, Chile, Croatia, Lithuania, Antigua and Barbuda, Latvia, Argentina, Uruguay, Cuba, Bahamas, Mexico, Costa Rica, Libya, Oman, Seychelles, Venezuela, Saudi Arabia, Panama, Bulgaria, Saint Kitts and Nevis, Romania, Trinidad and Tobago, Montenegro, Malaysia Serbia, Belarus, Saint Lucia, Albania, Russian Federation, the Former Yugoslav Republic of Macedonia, Dominica, Grenada, Brazil, Bosnia and Herzegovina, Colombia, Peru, Turkey, Ecuador, Mauritius, Kazakhstan, Lebanon, Armenia, Ukraine, Azerbaijan, Thailand, Iran, Georgia, Dominican Republic, Saint Vincent and the Grenadines, China, Belize, Samoa, Maldives, Jordan, Suriname, Tunisia, Tonga, Jamaica, Paraguay, Sri Lanka, Gabon, Algeria, Philippines, El Salvador, Syria, Fiji, Turkmenistan, Occupied Palestinian Territories, Indonesia Honduras, Bolivia, Guyana, Mongolia, Vietnam, Moldova Equatorial Guinea, Uzbekistan, Kyrgyzstan, CapeVerde, Guatemala, Egypt, Nicaragua, Botswana, Vanuatu, Tajikistan, Namibia, South Africa Morocco, Sao Tome and Principe, hutan, Lao, India, Solomon Islands, Congo, Cambodia, yanmar, Comoros, Yemen, Pakistan, Swaziland, Angola, Nepal, Madagascar, Bangladesh, Kenya, Papua New Guinea, Haiti, Sudan, Tanzania, Ghana, Cameroon, Mauritania, Djibouti, Lesotho, Uganda, Nigeria, Togo Malawi, Benin, Timor Leste, Cote d’Ivoire, Zambia, Eritrea, Senegal, Rwanda, Gambia, Liberia, Guinea, Ethiopia, Mozambique, Guinea Bissau, Burundi, Chad, Democratic Republic of the Congo, Burkina Faso, Mali, Central African Republic, Sierra Leone Afghanistan, Niger Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 33 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu DEA Norway, Australia, Iceland, Canada, Ireland, Netherlands, Sweden, France, Switzerland, Japan, Luxembourg, Finland, United States, Austria, Spain, Denmark, Belgium, Italy, Liechtenstein, New Zealand, United Kingdom, Germany, Singapore, Hong Kong, reece, Republic of Korea, Israel, Andorra, Slovenia, Brunei, Kuwait, Cyprus, Qatar, Portugal, United Arab mirates, Czech Republic, Barbados, Malta, Bahrain, Estonia, Poland, Slovakia, Hungary, Chile, Croatia, Lithuania, Antigua and Barbuda, Latvia, Argentina, Uruguay, Cuba, Mexico, Costa Rica, Libya, Oman, Venezuela, Saudi Arabia, Bulgaria, Saint Kitts and Nevis, Romania, Trinidad and Tobago, Montenegro, Serbia, Belarus, Saint Lucia, Albania, Russian Federation, Dominica, Brazil, Peru, Kazakhstan, Armenia, kraine, Georgia, Samoa, Tonga, Turkmenistan, Bolivia, Guyana, Mongolia, Moldova, Equatorial Guinea, Kyrgyzstan, Tajikistan Bahamas, Seychelles, Panama, , Malaysia, Namibia, South Africa, the Former Yugoslav Republic of Macedonia, Grenada, Bosnia and Herzegovina, Colombia, Turkey, Ecuador, Mauritius, Lebanon, Azerbaijan, Thailand, Iran, Dominican Republic, Saint Vincent and the Grenadines, China, Belize, Maldives, Jordan, Suriname, Tunisia, Jamaica, Paraguay, Sri Lanka, Gabon, Algeria, Philippines, El Salvador, Syria, Fiji, Occupied Palestinian Territories, Indonesia, Honduras, Vietnam, Uzbekistan, Cape Verde, Guatemala, Egypt, Nicaragua, Botswana, Vanuatu, Namibia, South Africa, Morocco, Sao Tome and Principe, Bhutan, Solomon Islands, Congo, Cambodia, Myanmar, Pakistan, Swaziland, Nepal, Bangladesh, Lesotho Lao, India, Comoros, Yemen, Angola, Madagascar, Kenya, Papua New Guinea, Haiti, Sudan, Tanzania, Ghana, Cameroon, Mauritania, Djibouti, Uganda, Nigeria, Togo Mozambique, Chad, Burkina Faso, Mali, Central African Republic, Sierra Leone Afghanistan, Niger Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 34 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu V. CONCLUSIONS It is true that the HDI has brought the global community closer and inspired a united effort in the common cause of improving the human condition for those dwelling in the darkest corners of the world. It is also true that HDI is a simple and universal index. However, this index has been very subjective and not been scientifically successful in correctly categorizing the countries. To overcome this problem, cluster analysis has been used. The proposed approach in this paper differs from the previous HDI assessments since it does not need to assign any subjective weights to EI, LEI and GDPI. It also differs from the previous DEA applications on HDI assessment by clustering countries by means of DEA-based cutoff points. Recomputation Of Undp’s HDI Rankings By Data Envelopment Analysis Emerging Markets Journal | P a g e | 35 Volume 1 (2011) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2011.10 | http://emaj.pitt.edu VI. References 1. 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