# Gender Development Index

The Gender Development Index (GDI) is an index designed to measure gender equality.
GDI, together with the Gender Empowerment Measure (GEM), was introduced in 1995 in the Human Development Report written by the United Nations Development Program. These measurements aimed to add a gender-sensitive dimension to the Human Development Index (HDI). The first measurement that they created as a result was the GDI. The GDI is defined as a "distribution-sensitive measure that accounts for the human development impact of existing gender gaps in the three components of the HDI" (Klasen 243). Distribution sensitivity means that the GDI takes into account not only the average or general level of well-being and wealth within a given country but focuses also on how this wealth and well-being is distributed between different groups within society. The HDI and the GDI (as well as the GEM) were created to rival the more traditional general income-based measures of development such as gross domestic product (GDP) and gross national product (GNP).

## Definition and calculation

The GDI is often considered a "gender-sensitive extension of the HDI" (Klasen 245). It addresses gender gaps in life expectancy, education, and income. It uses an "inequality aversion" penalty, which creates a development score penalty for gender wander gaps in any of the categories of the Human Development Index (HDI) which include life expectancy, adult literacy, school enrollment, and logarithmic transformations of per-capita income. In terms of life expectancy, the GDI assumes that women will live an average of five years longer than men. Additionally, in terms of income, the GDI considers income gaps in terms of actual earned income. The GDI cannot be used independently from the HDI score, and so, it cannot be used on its own as an indicator of gender gaps. Only the gap between the HDI and the GDI can actually be accurately considered; the GDI on its own is not an independent measure of gender gaps.

## Gender Development Index (2018)

Below is a list of countries by their Gender Development Index, based on data collected in 2018, and published in 2019. Countries are grouped into five groups based on the absolute deviation from gender parity in HDI values, from 1 (closest to gender parity) to 5 (furthest from gender parity). This means that grouping takes equally into consideration gender gaps favoring males, as well as those favoring females.
| 2018 rank | Country | Gender Development Index | Group | Human Development Index (women) | Human Development Index (men) |
| 1 | Kuwait | 0.999271313598908 | 1 | 0.802241545091312 | 0.802826553883562 |
| 2 | Kazakhstan | 0.998616111258415 | 1 | 0.814121946939387 | 0.815250162460792 |
| 3 | Trinidad and Tobago | 1.00211774602851 | 1 | 0.797989701033099 | 0.796303332812547 |
| 4 | Slovenia | 1.00257442927832 | 1 | 0.901787072451453 | 0.899471446823739 |
| 5 | Vietnam | 1.00272297523169 | 1 | 0.693389879484458 | 0.691506923259876 |
| 6 | Burundi | 1.00324890931813 | 1 | 0.421654103634997 | 0.420288624008154 |
| 7 | Dominican Republic | 1.00339001174288 | 1 | 0.744042111285307 | 0.741528321567516 |
| 8 | Philippines | 1.00369597615498 | 1 | 0.712223593546365 | 0.709600925446362 |
| 9 | Thailand | 0.995480861692473 | 1 | 0.762715746885023 | 0.766178212194142 |
| 10 | Panama | 1.00461251995559 | 1 | 0.793862458409325 | 0.790217564125534 |
| 11 | Ukraine | 0.995122669191676 | 1 | 0.745224174704749 | 0.748876694076404 |
| 12 | Brazil | 0.995109362655928 | 1 | 0.757109191363106 | 0.760830135636948 |
| 13 | Moldova | 1.00705674095832 | 1 | 0.713558080174709 | 0.70855797012558 |
| 14 | Bulgaria | 0.992621622836447 | 1 | 0.811903568014688 | 0.817938627706547 |
| 15 | Slovakia | 0.992371676979385 | 1 | 0.852080306845641 | 0.858630215484618 |
| 16 | Poland | 1.00854973881397 | 1 | 0.874194924380356 | 0.86678414632122 |
| 17 | United States | 0.99144743381844 | 1 | 0.914844606387427 | 0.922736370262227 |
| 18 | Namibia | 1.0094706476123 | 1 | 0.647427874518634 | 0.641353838321097 |
| 19 | Norway | 0.990437581014824 | 1 | 0.94564679665501 | 0.954776772187986 |
| 20 | Finland | 0.989817373600636 | 1 | 0.919751993696064 | 0.929213830982077 |
| 21 | Barbados | 1.01032361432783 | 1 | 0.816388101546477 | 0.808046144788592 |
| 22 | Belarus | 1.010339927488 | 1 | 0.819686875325532 | 0.811298111679611 |
| 23 | Botswana | 0.989531869461814 | 1 | 0.723041706146159 | 0.730690671478228 |
| 24 | Canada | 0.989058149729888 | 1 | 0.915888363975847 | 0.926020744307072 |
| 25 | Croatia | 0.98859213038971 | 1 | 0.832316431348996 | 0.841920955835336 |
| 26 | Singapore | 0.98814794506132 | 1 | 0.929356109430028 | 0.940503002687878 |
| 27 | Argentina | 0.987919014775328 | 1 | 0.817640023795134 | 0.827638714880978 |
| 28 | Venezuela | 1.01272311153934 | 1 | 0.728475070383083 | 0.719323043073244 |
| 29 | Brunei | 0.986891147195856 | 1 | 0.836720430865344 | 0.847834569438376 |
| 30 | Nicaragua | 1.01321583363332 | 1 | 0.654849103183038 | 0.646307609342023 |
| 31 | Colombia | 0.986296673191879 | 1 | 0.754714364824177 | 0.765200152588724 |
| 32 | Romania | 0.986261546538915 | 1 | 0.809420161886165 | 0.820695245319724 |
| 33 | Jamaica | 0.986030910048998 | 1 | 0.718965693897112 | 0.729151273626285 |
| 34 | Russia | 1.01499805083001 | 1 | 0.828317933961805 | 0.816078349396287 |
| 35 | France | 0.98439750467821 | 1 | 0.883037148032378 | 0.897033102822659 |
| 36 | Estonia | 1.01574985871536 | 1 | 0.885869263158098 | 0.872133287105225 |
| 37 | South Africa | 0.984153359434317 | 1 | 0.698296318804934 | 0.709540146473014 |
| 38 | Portugal | 0.984006569463407 | 1 | 0.842559344988258 | 0.856253780345916 |
| 39 | Uruguay | 1.01607193850868 | 1 | 0.809691228698831 | 0.79688376187934 |
| 40 | Hungary | 0.983855072217788 | 1 | 0.836374771060734 | 0.850099567180554 |
| 41 | Cape Verde | 0.98384439453558 | 1 | 0.644164225448235 | 0.654741978534431 |
| 42 | Cyprus | 0.983090727880394 | 1 | 0.864740933228215 | 0.879614575444782 |
| 43 | Czech Republic | 0.983021479607738 | 1 | 0.881578351276749 | 0.896804769340881 |
| 44 | Belize | 0.982811514946144 | 1 | 0.712983445231243 | 0.725452881237674 |
| 45 | Sweden | 0.981817713523961 | 1 | 0.927549412691099 | 0.944726704269694 |
| 46 | Spain | 0.98068365758681 | 1 | 0.881897607495364 | 0.899268179573288 |
| 47 | Denmark | 0.980461996197969 | 1 | 0.920118047343707 | 0.938453556498605 |
| 48 | Ecuador | 0.979876022499264 | 1 | 0.747701339556282 | 0.763057083128946 |
| 49 | Georgia | 0.978843828928938 | 1 | 0.774556381501532 | 0.791297200442139 |
| 50 | Costa Rica | 0.977136852016496 | 1 | 0.781504112645575 | 0.799789825788274 |
| 51 | Japan | 0.976487130681848 | 1 | 0.901210670433948 | 0.92291095511383 |
| 52 | Serbia | 0.976372480770375 | 1 | 0.789117394155053 | 0.808213473542829 |
| 53 | Australia | 0.975113503181452 | 1 | 0.925664958786577 | 0.949289447604262 |
| 54 | Ireland | 0.974930720274505 | 2 | 0.928842297989999 | 0.9527264642235 |
| 55 | Saint Lucia | 0.974776845288729 | 2 | 0.734104181262105 | 0.753099732323518 |
| 56 | Lesotho | 1.02554956311433 | 2 | 0.522151801801454 | 0.50914341011059 |
| 57 | Mauritius | 0.973598560971563 | 2 | 0.781958849986583 | 0.803163522762666 |
| 58 | Guyana | 0.973439493655793 | 2 | 0.655984723050024 | 0.673883407572098 |
| 59 | Armenia | 0.972097105538784 | 2 | 0.745713315885668 | 0.767118132166803 |
| 60 | Lithuania | 1.02801557456846 | 2 | 0.880350319739633 | 0.856358932216745 |
| 61 | Belgium | 0.971637285832976 | 2 | 0.904498199776896 | 0.93090108105668 |
| 62 | Suriname | 0.971619589838185 | 2 | 0.710079630808469 | 0.730820619751736 |
| 63 | Israel | 0.971565636624078 | 2 | 0.89085212219952 | 0.916924280375936 |
| 64 | Malaysia | 0.971535181068249 | 2 | 0.791500865872141 | 0.814690894674394 |
| 65 | Albania | 0.971302380112087 | 2 | 0.778864159321813 | 0.801876094684266 |
| 66 | Honduras | 0.970407383075693 | 2 | 0.611426703399936 | 0.630072188303048 |
| 67 | Luxembourg | 0.970263947573514 | 2 | 0.893206480322808 | 0.920580922909261 |
| 68 | Latvia | 1.03040141727652 | 2 | 0.86528356437401 | 0.839753856959034 |
| 69 | Mongolia | 1.03051247212425 | 2 | 0.745684609993285 | 0.723605613871095 |
| 70 | El Salvador | 0.969303900072772 | 2 | 0.65414310778579 | 0.67485863591045 |
| 71 | Germany | 0.968046731183915 | 2 | 0.922788125514936 | 0.953247499102003 |
| 72 | Paraguay | 0.968014313475195 | 2 | 0.710081665159304 | 0.733544592548527 |
| 73 | Italy | 0.967274986133354 | 2 | 0.865859235918938 | 0.895153134663575 |
| 74 | United Kingdom | 0.96671693364499 | 2 | 0.903526469774669 | 0.934633953672392 |
| 75 | Netherlands | 0.966586563190941 | 2 | 0.915682504422063 | 0.94733626484437 |
| 76 | Iceland | 0.966035360302579 | 2 | 0.921422694662473 | 0.953818806771077 |
| 77 | Montenegro | 0.965505839872185 | 2 | 0.800863981950797 | 0.829476062057601 |
| 78 | United Arab Emirates | 0.965148016786254 | 2 | 0.831679159131191 | 0.861711514364929 |
| 79 | Malta | 0.964573668396 | 2 | 0.867003905508653 | 0.898846748481537 |
| 80 | New Zealand | 0.963450079812055 | 2 | 0.901877659315533 | 0.936091737613916 |
| 81 | Switzerland | 0.963384994370094 | 2 | 0.924302891740428 | 0.959432518818482 |
| 82 | Hong Kong | 0.96331458591632 | 2 | 0.91883629861405 | 0.953827868951074 |
| 83 | Austria | 0.962992625875126 | 2 | 0.894949094941461 | 0.929341586731435 |
| 84 | Greece | 0.96272210220035 | 2 | 0.854140900297802 | 0.887214387563783 |
| 85 | Swaziland | 0.962280698092814 | 2 | 0.594969468404531 | 0.618290972253447 |
| 86 | Chile | 0.961896022109213 | 2 | 0.827637034592205 | 0.860422556668226 |
| 87 | China | 0.960737178700119 | 2 | 0.7411723134053 | 0.771462091649362 |
| 88 | Kyrgyzstan | 0.959354156976191 | 2 | 0.655758696158308 | 0.683541830084114 |
| 89 | Mexico | 0.957251775460597 | 2 | 0.747167434728433 | 0.780533871947035 |
| 90 | Qatar | 1.04338023447896 | 2 | 0.87328373892252 | 0.836975543588494 |
| 91 | Myanmar | 0.953281245175706 | 2 | 0.566167394183869 | 0.593914332259327 |
| 92 | Peru | 0.951068629111926 | 2 | 0.73835574021778 | 0.776343281249042 |
| 93 | Zambia | 0.949346763894446 | 3 | 0.575199531528163 | 0.60588981118823 |
| 94 | Cuba | 0.94847909440168 | 3 | 0.752740766990656 | 0.793629265456294 |
| 95 | North Macedonia | 0.946858477421388 | 3 | 0.736774749145141 | 0.778125524261687 |
| 96 | Madagascar | 0.946436637249011 | 3 | 0.504225253132795 | 0.532761764800671 |
| 97 | Tonga | 0.944301733548051 | 3 | 0.691914784976437 | 0.732726373779583 |
| 98 | Guatemala | 0.943001743676744 | 3 | 0.628457412659945 | 0.666443531917134 |
| 99 | Rwanda | 0.942983702163843 | 3 | 0.519691032216798 | 0.551113482687214 |
| 100 | Oman | 0.942644918586126 | 3 | 0.792879654368817 | 0.841122291899752 |
| – | World average | 0.941430799701876 | – | 0.706980962068851 | 0.750964343096414 |
| 101 | Azerbaijan | 0.94043401604125 | 3 | 0.728006586417231 | 0.774117666948894 |
| 102 | Maldives | 0.938974186367784 | 3 | 0.689217295551526 | 0.734010908454909 |
| 103 | Uzbekistan | 0.938530667537194 | 3 | 0.685437015702195 | 0.730329907599989 |
| 104 | Sri Lanka | 0.937501402709405 | 3 | 0.749425007262443 | 0.799385478354042 |
| 105 | Indonesia | 0.937278216882204 | 3 | 0.681319036769408 | 0.726912270548411 |
| 106 | Bahrain | 0.936580181665306 | 3 | 0.799753662146286 | 0.853908376242029 |
| 107 | Bolivia | 0.936071128421922 | 3 | 0.677681643411889 | 0.723963834408994 |
| 108 | Tanzania | 0.93556520183438 | 3 | 0.509116716427692 | 0.54418090308346 |
| 109 | South Korea | 0.933514804909621 | 3 | 0.869859990274136 | 0.931811671008637 |
| 110 | Kenya | 0.93334124890745 | 3 | 0.553446092043308 | 0.592972926773739 |
| 111 | Libya | 0.930834633256552 | 3 | 0.670350699455828 | 0.720160891640427 |
| 112 | Republic of the Congo | 0.930508381323755 | 3 | 0.590608226344738 | 0.63471564383389 |
| 113 | Malawi | 0.929979500928547 | 3 | 0.466256425669024 | 0.501362046371437 |
| 114 | Laos | 0.929388949637999 | 3 | 0.580896379268115 | 0.625030434775856 |
| 115 | Zimbabwe | 0.924865126473049 | 4 | 0.540217146902477 | 0.584103704896499 |
| 116 | Turkey | 0.923845887665176 | 4 | 0.770530112179602 | 0.834046156904971 |
| 117 | Bosnia and Herzegovina | 0.92376150833791 | 4 | 0.735305564655512 | 0.795990694587958 |
| 118 | Cambodia | 0.919132552991075 | 4 | 0.556669111249323 | 0.605646170879042 |
| 119 | Gabon | 0.917044836281997 | 4 | 0.668897563298245 | 0.72940551741197 |
| 120 | Ghana | 0.912066262295093 | 4 | 0.567120060412223 | 0.621796994206474 |
| 121 | Angola | 0.901852522177659 | 4 | 0.545524138209497 | 0.60489284533157 |
| 122 | Mozambique | 0.901399241057088 | 4 | 0.42171001631638 | 0.467839329243092 |
| 123 | São Tomé and Príncipe | 0.899721720272795 | 5 | 0.571432940029916 | 0.635121868411333 |
| 124 | East Timor | 0.899338643290567 | 5 | 0.589475390655512 | 0.655454310846352 |
| 125 | Liberia | 0.898619930984625 | 5 | 0.437938141035413 | 0.487345234548226 |
| 126 | Tunisia | 0.898516211947261 | 5 | 0.68930089658175 | 0.767154657218593 |
| 127 | Nepal | 0.897374748629354 | 5 | 0.548886325033576 | 0.611657867431575 |
| 128 | Bangladesh | 0.895463713494037 | 5 | 0.574538067712771 | 0.64160954715961 |
| 129 | Bhutan | 0.893345815434905 | 5 | 0.580503137357053 | 0.649807865361129 |
| 130 | Lebanon | 0.890577064263023 | 5 | 0.678454800871403 | 0.761814814344947 |
| 131 | Haiti | 0.890365827551326 | 5 | 0.477397671690552 | 0.536181485090781 |
| 132 | Comoros | 0.888069540927266 | 5 | 0.504017390629825 | 0.567542706288025 |
| 133 | Benin | 0.883486835760026 | 5 | 0.485715005319931 | 0.549770506656267 |
| 134 | Sierra Leone | 0.882483208929897 | 5 | 0.410599830153055 | 0.465277782056556 |
| 135 | Saudi Arabia | 0.879136805709795 | 5 | 0.784333088515893 | 0.892162725325372 |
| 136 | Egypt | 0.878316588012583 | 5 | 0.64266778257163 | 0.731704024884503 |
| 137 | Burkina Faso | 0.874690316250611 | 5 | 0.403149171515835 | 0.460905035789063 |
| 138 | Iran | 0.873999741121421 | 5 | 0.726849370286313 | 0.831635681440477 |
| 139 | Senegal | 0.87347139391351 | 5 | 0.475960252557682 | 0.544906514253643 |
| 140 | Palestine | 0.871346924588787 | 5 | 0.623519218495938 | 0.71558090227976 |
| 141 | Cameroon | 0.86892158600649 | 5 | 0.522007757584777 | 0.600753584663367 |
| 142 | Jordan | 0.868301159101109 | 5 | 0.654288917853024 | 0.753527633811249 |
| 143 | Nigeria | 0.867675972564795 | 5 | 0.491676192340555 | 0.566658761896094 |
| 144 | Algeria | 0.864588565403417 | 5 | 0.684971930096163 | 0.792251895879002 |
| 145 | Uganda | 0.86268775649487 | 5 | 0.48376445336274 | 0.56076425070444 |
| 146 | Mauritania | 0.852934961025278 | 5 | 0.479113168207732 | 0.561722980181056 |
| 147 | Democratic Republic of the Congo | 0.844045244422387 | 5 | 0.418857464866842 | 0.496250014599019 |
| 148 | Ethiopia | 0.843899175273984 | 5 | 0.42770052294657 | 0.506814718485429 |
| 149 | South Sudan | 0.838915228792041 | 5 | 0.368735499184939 | 0.439538449809623 |
| 150 | Sudan | 0.836500123073206 | 5 | 0.456500034277483 | 0.545726200972158 |
| 151 | Morocco | 0.832807050749792 | 5 | 0.602993983556629 | 0.724050046182658 |
| 152 | Gambia | 0.832110339375305 | 5 | 0.415697194375194 | 0.499569798264101 |
| 153 | India | 0.828659271423645 | 5 | 0.573650381208353 | 0.692263275136976 |
| 154 | Togo | 0.817890855118709 | 5 | 0.458991965749326 | 0.561189751513615 |
| 155 | Mali | 0.807099598839839 | 5 | 0.380140424771307 | 0.470995680480746 |
| 156 | Guinea | 0.80606657004618 | 5 | 0.41342656240414 | 0.512893820147453 |
| 157 | Tajikistan | 0.798555909314393 | 5 | 0.561341006774011 | 0.702945154154523 |
| 158 | Ivory Coast | 0.796251100904936 | 5 | 0.445376820642565 | 0.559342172508641 |
| 159 | Central African Republic | 0.795444752528615 | 5 | 0.335149259100481 | 0.421335684263534 |
| 160 | Syria | 0.79532319946114 | 5 | 0.457372222910504 | 0.57507718022106 |
| 161 | Iraq | 0.789324230426714 | 5 | 0.587352897134761 | 0.744121204561571 |
| 162 | Chad | 0.774452360811538 | 5 | 0.347398235861034 | 0.448572763723 |
| 163 | Pakistan | 0.746878273640409 | 5 | 0.464284284133844 | 0.621633136911112 |
| 164 | Afghanistan | 0.722861973965333 | 5 | 0.410756365978411 | 0.568236234263597 |
| 165 | Yemen | 0.457536126892644 | 5 | 0.244873082377673 | 0.5351994476168 |
| 166 | Niger | 0.298179843688684 | 5 | 0.129771161871938 | 0.435211046684383 |

## Controversies
### General debates

In the years since its creation in 1995, much debate has arisen surrounding the reliability, and usefulness of the Gender Development Index (GDI) in making adequate comparisons between different countries and in promoting gender-sensitive development. The GDI is particularly criticized for being often mistakenly interpreted as an independent measure of gender gaps when it is not, in fact, intended to be interpreted in that way, because it can only be used in combination with the scores from the Human Development Index, but not on its own. Additionally, the data that is needed in order to calculate the GDI is not always readily available in many countries, making the measure very hard to calculate uniformly and internationally. There is also worry that the combination of so many different developmental influences in one measurement could result in muddled results and that perhaps the GDI (and the GEM) actually hide more than they reveal.

### Criticism on Life Expectancy adjustment

More specifically, there has been a lot of criticism over the Life-Expectancy component of the GDI. As was mentioned previously, the GDI life expectancy section is adjusted by assuming that women will automatically live five years longer than men. This provision has been criticized on multiple grounds; e.g. it has been argued that if the GDI was really looking to promote true equality, it would strive to attain the same life expectancy for women and men, despite what might be considered a "normalized" advantage. In terms of policy, this could be achieved through providing better treatment to men, which women's rights organizations sometimes argue to be discriminatory against women. Critics also argue that the UN provides a number of strategies and plans giving preferential treatment to women and girls that are not seen as discriminatory towards men ─ not only for health issues but also for education and job opportunities. Furthermore, it has been argued that the GDI does not account for sex-selective abortion, meaning that the penalty levied against a country for gender inequality is smaller as it affects less of the population (see Sen, Missing Women).

### Debates surrounding income gaps

Another area of debate surrounding the GDI is in the area of income gaps. The GDI considers income-gaps in terms of actual earned income. This has been said to be problematic because often, men may make more money than women, but their income is shared. Additionally, the GDI has been criticized because it does not consider the value of care work as well as other work performed in the informal sector (such as cleaning, cooking, housework, and childcare). Another criticism of the GDI is that it only takes gender into account as a factor for inequality; it does not, however, consider inequality among class, region or race, which could be very significant. Another criticism with the income-gap portion of the GDI is that it is heavily dependent on gross domestic product (GDP) and gross national product (GNP). For most countries, the earned-income gap accounts for more than 90% of the gender penalty.

## Suggested alternatives

As was suggested by Halis Akder in 1994, one alternative to the Gender Development Index (GDI) would be the calculation of a separate male and female Human Development Index (HDI). Another suggested alternative is the Gender Gap Measure which could be interpreted directly as a measure of gender inequality, instead of having to be compared to the HDI as the GDI is. It would average the female-male gaps in human development and use a gender-gap in labor force participation instead of earned income. In the 2010 Human Development Report, another alternative to the GDI, namely, the Gender Inequality Index (GII) was proposed in order to address some of the shortcomings of the GDI. This new experimental measure contains three dimensions: Reproductive Health, Empowerment, and Labor Market Participation.