Gender Development Index Explained

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 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 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.[2] 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
CountryGender Development IndexGroupHuman Development Index
(women)
Human Development Index
(men)
1 Kuwait0.99927131359890810.8022415450913120.802826553883562
2 Kazakhstan0.99861611125841510.8141219469393870.815250162460792
3 Trinidad and Tobago1.0021177460285110.7979897010330990.796303332812547
4 Slovenia1.0025744292783210.9017870724514530.899471446823739
51.0027229752316910.6933898794844580.691506923259876
6 Burundi1.0032489093181310.4216541036349970.420288624008154
7 Dominican Republic1.0033900117428810.7440421112853070.741528321567516
8 Philippines1.0036959761549810.7122235935463650.709600925446362
9 Thailand0.99548086169247310.7627157468850230.766178212194142
10 Panama1.0046125199555910.7938624584093250.790217564125534
11 Ukraine0.99512266919167610.7452241747047490.748876694076404
12 Brazil0.99510936265592810.7571091913631060.760830135636948
131.0070567409583210.7135580801747090.70855797012558
14 Bulgaria0.99262162283644710.8119035680146880.817938627706547
15 Slovakia0.99237167697938510.8520803068456410.858630215484618
16 Poland1.0085497388139710.8741949243803560.86678414632122
17 United States0.9914474338184410.9148446063874270.922736370262227
18 Namibia1.009470647612310.6474278745186340.641353838321097
19 Norway0.99043758101482410.945646796655010.954776772187986
20 Finland0.98981737360063610.9197519936960640.929213830982077
21 Barbados1.0103236143278310.8163881015464770.808046144788592
22 Belarus1.01033992748810.8196868753255320.811298111679611
23 Botswana0.98953186946181410.7230417061461590.730690671478228
24 Canada0.98905814972988810.9158883639758470.926020744307072
25 Croatia0.9885921303897110.8323164313489960.841920955835336
26 Singapore0.9881479450613210.9293561094300280.940503002687878
27 Argentina0.98791901477532810.8176400237951340.827638714880978
281.0127231115393410.7284750703830830.719323043073244
290.98689114719585610.8367204308653440.847834569438376
30 Nicaragua1.0132158336333210.6548491031830380.646307609342023
31 Colombia0.98629667319187910.7547143648241770.765200152588724
32 Romania0.98626154653891510.8094201618861650.820695245319724
33 Jamaica0.98603091004899810.7189656938971120.729151273626285
341.0149980508300110.8283179339618050.816078349396287
35 France0.9843975046782110.8830371480323780.897033102822659
361.0157498587153610.8858692631580980.872133287105225
37 South Africa0.98415335943431710.6982963188049340.709540146473014
38 Portugal0.98400656946340710.8425593449882580.856253780345916
39 Uruguay1.0160719385086810.8096912286988310.79688376187934
40 Hungary0.98385507221778810.8363747710607340.850099567180554
41 Cape Verde0.9838443945355810.6441642254482350.654741978534431
42 Cyprus0.98309072788039410.8647409332282150.879614575444782
43 Czech Republic0.98302147960773810.8815783512767490.896804769340881
44 Belize0.98281151494614410.7129834452312430.725452881237674
45 Sweden0.98181771352396110.9275494126910990.944726704269694
46 Spain0.9806836575868110.8818976074953640.899268179573288
47 Denmark0.98046199619796910.9201180473437070.938453556498605
48 Ecuador0.97987602249926410.7477013395562820.763057083128946
49 Georgia0.97884382892893810.7745563815015320.791297200442139
50 Costa Rica0.97713685201649610.7815041126455750.799789825788274
51 Japan0.97648713068184810.9012106704339480.92291095511383
52 Serbia0.97637248077037510.7891173941550530.808213473542829
53 Australia0.97511350318145210.9256649587865770.949289447604262
54 Ireland0.97493072027450520.9288422979899990.9527264642235
55 Saint Lucia0.97477684528872920.7341041812621050.753099732323518
56 Lesotho1.0255495631143320.5221518018014540.50914341011059
57 Mauritius0.97359856097156320.7819588499865830.803163522762666
58 Guyana0.97343949365579320.6559847230500240.673883407572098
590.97209710553878420.7457133158856680.767118132166803
60 Lithuania1.0280155745684620.8803503197396330.856358932216745
61 Belgium0.97163728583297620.9044981997768960.93090108105668
62 Suriname0.97161958983818520.7100796308084690.730820619751736
63 Israel0.97156563662407820.890852122199520.916924280375936
64 Malaysia0.97153518106824920.7915008658721410.814690894674394
65 Albania0.97130238011208720.7788641593218130.801876094684266
66 Honduras0.97040738307569320.6114267033999360.630072188303048
67 Luxembourg0.97026394757351420.8932064803228080.920580922909261
68 Latvia1.0304014172765220.865283564374010.839753856959034
69 Mongolia1.0305124721242520.7456846099932850.723605613871095
70 El Salvador0.96930390007277220.654143107785790.67485863591045
71 Germany0.96804673118391520.9227881255149360.953247499102003
72 Paraguay0.96801431347519520.7100816651593040.733544592548527
73 Italy0.96727498613335420.8658592359189380.895153134663575
74 United Kingdom0.9667169336449920.9035264697746690.934633953672392
75 Netherlands0.96658656319094120.9156825044220630.94733626484437
76 Iceland0.96603536030257920.9214226946624730.953818806771077
77 Montenegro0.96550583987218520.8008639819507970.829476062057601
78 United Arab Emirates0.96514801678625420.8316791591311910.861711514364929
79 Malta0.96457366839620.8670039055086530.898846748481537
80 New Zealand0.96345007981205520.9018776593155330.936091737613916
81 Switzerland0.96338499437009420.9243028917404280.959432518818482
82 Hong Kong0.9633145859163220.918836298614050.953827868951074
83 Austria0.96299262587512620.8949490949414610.929341586731435
84 Greece0.9627221022003520.8541409002978020.887214387563783
85 Swaziland0.96228069809281420.5949694684045310.618290972253447
86 Chile0.96189602210921320.8276370345922050.860422556668226
87 China0.96073717870011920.74117231340530.771462091649362
88 Kyrgyzstan0.95935415697619120.6557586961583080.683541830084114
89 Mexico0.95725177546059720.7471674347284330.780533871947035
90 Qatar1.0433802344789620.873283738922520.836975543588494
91 Myanmar0.95328124517570620.5661673941838690.593914332259327
92 Peru0.95106862911192620.738355740217780.776343281249042
93 Zambia0.94934676389444630.5751995315281630.60588981118823
94 Cuba0.9484790944016830.7527407669906560.793629265456294
950.94685847742138830.7367747491451410.778125524261687
96 Madagascar0.94643663724901130.5042252531327950.532761764800671
97 Tonga0.94430173354805130.6919147849764370.732726373779583
98 Guatemala0.94300174367674430.6284574126599450.666443531917134
99 Rwanda0.94298370216384330.5196910322167980.551113482687214
100 Oman0.94264491858612630.7928796543688170.841122291899752
World average0.9414307997018760.7069809620688510.750964343096414
101 Azerbaijan0.9404340160412530.7280065864172310.774117666948894
102 Maldives0.93897418636778430.6892172955515260.734010908454909
103 Uzbekistan0.93853066753719430.6854370157021950.730329907599989
104 Sri Lanka0.93750140270940530.7494250072624430.799385478354042
105 Indonesia0.93727821688220430.6813190367694080.726912270548411
106 Bahrain0.93658018166530630.7997536621462860.853908376242029
1070.93607112842192230.6776816434118890.723963834408994
1080.9355652018343830.5091167164276920.54418090308346
1090.93351480490962130.8698599902741360.931811671008637
110 Kenya0.9333412489074530.5534460920433080.592972926773739
111 Libya0.93083463325655230.6703506994558280.720160891640427
1120.93050838132375530.5906082263447380.63471564383389
113 Malawi0.92997950092854730.4662564256690240.501362046371437
1140.92938894963799930.5808963792681150.625030434775856
115 Zimbabwe0.92486512647304940.5402171469024770.584103704896499
116 Turkey0.92384588766517640.7705301121796020.834046156904971
117 Bosnia and Herzegovina0.9237615083379140.7353055646555120.795990694587958
118 Cambodia0.91913255299107540.5566691112493230.605646170879042
119 Gabon0.91704483628199740.6688975632982450.72940551741197
120 Ghana0.91206626229509340.5671200604122230.621796994206474
121 Angola0.90185252217765940.5455241382094970.60489284533157
122 Mozambique0.90139924105708840.421710016316380.467839329243092
1230.89972172027279550.5714329400299160.635121868411333
1240.89933864329056750.5894753906555120.655454310846352
125 Liberia0.89861993098462550.4379381410354130.487345234548226
126 Tunisia0.89851621194726150.689300896581750.767154657218593
127 Nepal0.89737474862935450.5488863250335760.611657867431575
128 Bangladesh0.89546371349403750.5745380677127710.64160954715961
129 Bhutan0.89334581543490550.5805031373570530.649807865361129
130 Lebanon0.89057706426302350.6784548008714030.761814814344947
131 Haiti0.89036582755132650.4773976716905520.536181485090781
132 Comoros0.88806954092726650.5040173906298250.567542706288025
133 Benin0.88348683576002650.4857150053199310.549770506656267
134 Sierra Leone0.88248320892989750.4105998301530550.465277782056556
135 Saudi Arabia0.87913680570979550.7843330885158930.892162725325372
136 Egypt0.87831658801258350.642667782571630.731704024884503
137 Burkina Faso0.87469031625061150.4031491715158350.460905035789063
1380.87399974112142150.7268493702863130.831635681440477
139 Senegal0.8734713939135150.4759602525576820.544906514253643
1400.87134692458878750.6235192184959380.71558090227976
141 Cameroon0.8689215860064950.5220077575847770.600753584663367
142 Jordan0.86830115910110950.6542889178530240.753527633811249
143 Nigeria0.86767597256479550.4916761923405550.566658761896094
144 Algeria0.86458856540341750.6849719300961630.792251895879002
145 Uganda0.8626877564948750.483764453362740.56076425070444
146 Mauritania0.85293496102527850.4791131682077320.561722980181056
1470.84404524442238750.4188574648668420.496250014599019
148 Ethiopia0.84389917527398450.427700522946570.506814718485429
1490.83891522879204150.3687354991849390.439538449809623
150 Sudan0.83650012307320650.4565000342774830.545726200972158
151 Morocco0.83280705074979250.6029939835566290.724050046182658
152 Gambia0.83211033937530550.4156971943751940.499569798264101
153 India0.82865927142364550.5736503812083530.692263275136976
154 Togo0.81789085511870950.4589919657493260.561189751513615
155 Mali0.80709959883983950.3801404247713070.470995680480746
156 Guinea0.8060665700461850.413426562404140.512893820147453
157 Tajikistan0.79855590931439350.5613410067740110.702945154154523
1580.79625110090493650.4453768206425650.559342172508641
159 Central African Republic0.79544475252861550.3351492591004810.421335684263534
1600.7953231994611450.4573722229105040.57507718022106
161 Iraq0.78932423042671450.5873528971347610.744121204561571
162 Chad0.77445236081153850.3473982358610340.448572763723
163 Pakistan0.74687827364040950.4642842841338440.621633136911112
1640.72286197396533350.4107563659784110.568236234263597
165 Yemen0.45753612689264450.2448730823776730.5351994476168
166 Niger0.29817984368868450.1297711618719380.435211046684383

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.[3] 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.[4]

See also

Indices

External links

Notes and 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. Gender Development Index (GDI). United Nations Development Programme - Human Development Reports. 12 December 2019. Nations . United .
  3. Web site: What we do. 2022-01-06. UN Women. en.
  4. 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