Gender Development Index

  (Redirected from Gender-related 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 calculationEdit

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)Edit

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

ControversiesEdit

General debatesEdit

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 adjustmentEdit

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 gapsEdit

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 alternativesEdit

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]

See alsoEdit

Indices

ReferencesEdit

  1. ^ a b c d e 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. ^ a b 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.