Gender Development Index

The Gender Related Development Index (GDI) is an index designed to measure gender equality.

GDI together with the Gender Empowerment Measure (GEM) were introduced in 1995 in the Human Development Report written by the United Nations Development Program. The aim of these measurements was to add a gender-sensitive dimension to the Human Development Index (HDI). The first measurement that they created as a result was the Gender-related Development Index (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 sensitive means that the GDI takes into account not only the averaged 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).[1]

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 incomes. It uses an "inequality aversion" penalty, which creates a development score penalty for gender gaps in any of the categories of the Human Development Index 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.[1] The GDI cannot be used independently from the Human Development Index (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.[2]

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.[3] 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.

World map showing countries in Group 1 to 5 of the Gender Development Index (based on 2018 data, published in 2019). Countries in Group 1 are closest to gender parity, while those in Group 5 are furthest (i.e. have the greatest gender disparity).
  Group 1
  Group 2
  Group 3
  Group 4
  Group 5
  Data unavailable
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.[1]

Debates surrounding the life expectancy adjustment

More specifically, there has been a lot of debate over the life-expectancy component of the Gender-related Development Index (GDI). As was mentioned previously, the GDI life expectancy section is adjusted to assume that women will live, normally, five years longer than men. This provision has been debated, and 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 biological advantage or not. However, this may seem paradoxical in terms of policy implications, because, theoretically, this could only be achieved through providing preferential treatment to males, effectively discriminating against females. Furthermore, it has been argued that the GDI doesn't account for sex-selective abortion, meaning that the penalty levied against a country for gender inequality is less because it affects less of the population (see Sen, Missing Women).[1]

Debates surrounding income gaps

Another area of debate surrounding the Gender-related Development Index (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.[1] 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-related Development Index 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 Human Development Index (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 Gender-related Development Index (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.[2]

gollark: I don't think that particularly matters. We define our perceptual up and down and such based on vision.
gollark: Also merging together information from saccades (rapid eye movements to look at more of a scene with the fovea) and correcting for orientation/vibrations/movement.
gollark: And the brain does a lot of fancy stuff to pretend to have a coherent visual field despite the blind spot and the fact that only a small region (the fovea) can actually sense color well.
gollark: I read that somewhere, I forgot where.
gollark: Apparently the retinas also do edge detection stuff onboard.

See also
Indices

References
  1. Klasen S. UNDP's Gender-Related Measures: Some Conceptual Problems and Possible Solutions. Journal of Human Development [serial online]. July 2006;7(2):243-274. Available from: EconLit with Full Text, Ipswich, MA. Accessed September 26, 2011.
  2. Klasen, Stephan1; Schuler, Dana. Reforming the Gender-Related Development Index and the Gender Empowerment Measure: Implementing Some Specific Proposals. Feminist Economics. January 2011 (1) 1 - 30
  3. "Gender Development Index (GDI)". United Nations Development Programme - Human Development Reports. Retrieved 12 December 2019.
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