Abstract
This paper constructs a weighted measure of the multidimensional concept of gender inequality: the Multidimensional Gender Inequality Index (MGII). Multiple Correspondence Analysis is used to rank the separate forms in which gender inequality appears in developed and developing countries respectively. Eight dimensions were identified as relevant for economic purposes: identity, physical integrity, intra-family laws, political activity, education, health, access to economic resources, and economic activity. In the 109 developing countries considered, gender inequality in the identity and family dimensions are particularly severe for women: these dimensions hence have greater weight in the MGII. However, in OECD countries gender inequality occurs mainly in the political and family dimensions. Nevertheless, the family sphere remains particularly important for gender inequality, whatever the level of development. The MGII is a non-linear weighted composite indicator of gender inequality which yields a country ranking. The South-Asian region is calculated to be the most unequal.
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Notes
Source: UN Division for the Advancement of Women.
In much of West Asia and North Africa, women married to foreigners cannot transfer citizenship to their husbands, although men can to their wives.
Married women are under the permanent guardianship of their spouses and have no right to manage property in Botswana, Chile, Lesotho, Namibia, and Swaziland.
Husbands can restrict their wives’ employment outside the home in Bolivia, Guatemala, and Syria.
In some Arab countries, the husband’s consent is necessary for the wife to obtain a passport, but not vice-versa. Women cannot leave the country without their husband’s permission in Iran.
While Schnler (2006) considers GDI and GEM UNDP indices, this can be generalized to composite indicators.
To confirm an association between two variables, I do not consider a priori any correlation threshold. I only rely on the rejection of the null hypothesis at the 5 % significance level. The null hypothesis is that the Kendall Tau-b value is equal to zero, which implies no association. Rejection of the null hypothesis, when the Kendall Tau-b statistic is statistically different from zero, is thus sufficient to ensure significant association between two variables.
In addition, figures suggest that the MGII and the GII are the most correlated gender-specific measures relatively to the other ones, but interpretation of this strong relationship would be purely speculative. It could be due to the aggregation method, to the dimension included or the variables used to describe the female situation relative to the male one, etc.
Continuous variables are discretized using quantiles.
The prevalence and acceptance of violence, genital mutilation, physical security, prevalence of contraception, and adolescent fertility.
The Workshop was held at the Institute of Social Studies in the Hague (13th-18th January 1997).
In particular from Bhutan, Benin, Costa Rica, the Netherlands, the United Kingdom and Vietnam.
The last dimension put forward in this workshop was ‘time’, which includes relative access to leisure and sleep. Due to a lack of data, we are not able to take this dimension into account here. The last chapter of my PhD thesis considers time-allocation in the household.
This includes the right to enter into marriage on a basis of equality with men, to travel abroad, to obtain a passport, to confer citizenship to children or a husband, to initiate a divorce, to participate in social, cultural and community activities, and to choose a residence/domicile. “A score of 0 indicates that there were no social rights for women in comparison to men in law and that systematic discrimination based on sex may have been built into law. A score of 1 indicates that women had some social rights under law, but these rights were not effectively enforced. A score of 2 indicates that women had some social rights under law, and the government effectively enforced these rights in practice while still allowing a low level of discrimination against women in social matters. Finally, a score of 3 indicates that all or nearly all of women’s social rights were guaranteed by law and the government fully and vigorously enforced these laws in practice”, taken from the CIRI coding variables. As for other variables higher values mean more inequality, we will recode the CIRI variables accordingly.
“Freedom of movement measures the freedom of women to move outside the home. The following elements were considered: freedom to travel; freedom to join a club or association; freedom to do the groceries (and other types of shopping) without a male guardian; freedom to see one’s family and friends. A score of 0 means no restrictions on women’s movement outside the home in comparison to men; 0.5 indicates that some women can leave home sometimes, but with restrictions and 1 means that women can never leave home without restrictions”. Source: GID Coding, Jntting et al. (2006).
The sharing rule is the rule governing the distribution of resources between household members.
Female autonomy is defined as the ability of women to lead their own lives, make decisions and have influence on projects that affect them.
This includes equal rights to vote, run for political office, hold elected and appointed government positions, join political parties, and petition government officials.
Following Anand and Sen (1995) it is assumed in the life expectancy component that, given equal treatment and the apparent biological advantage of females, women would outlive men by an average of five years. If female life expectancy exceeds male life expectancy by less or more than five years, a gender gap is held to exist.
This index takes into account the two recent controversies surrounding the levels and trends in the number of ‘missing women’ in the world. See Klasen (2008).
This includes the right to equal pay for equal work, free choice of profession or employment without the need to obtain husband’s or male relative’s consent, right to gainful employment without the need to obtain husband’s or male relative’s consent, equality in hiring and promotion practices, job security (maternity leave, unemployment benefits, no arbitrary firing or layoffs, and so on), the right to work at night, the right to work in occupations classified as dangerous, and the right to work in the military and the police force.
A vast literature has risen up regarding missing data. Nevertheless, as criticisms have been put forward with respect to these methods, I prefer to exclude countries for which at least one of the 31 variables is not available.
Standardization means subtracting the mean and dividing by the standard deviation for continuous variables. The results from an ordered probit model are used to standardize the categorical variables.
The weights given by PPCA for sub-index construction do not differ from 1/n, where n is the number of variables included in each sub-index.
More than 1/8: the implicit weight for a simple sum.
Source: INSEE 2009.
It is worth noting that significant correlation between all sub-indices is a necessary condition for the efficient application of MCA.
Economies are divided according to their 2008 annual GNI per capita, Source: World Bank.
Controlling for region-specific characteristics and other determinant of growth and development (the level of education, capital accumulation, population growth etc.).
The first axis explains 74.16 % of the total inertia, which is more than satisfactory (nearly 3/4 of the inertia of the initial variables). The second factor explains 9.88 % of inertia. Therefore, the factorial map (f1, f2) explains 84.04 % of the dispersion of the scatter plot.
References
Abdul, A., Ben Abdallah, K., Ramaroson, S., Sidikou, M., & Van de Wiel, L. (2007). Accelerating girls’ education in Yemen: Rethinking policies in teachers’ recruitment and school distribution. UNICEF working paper.
Ainsworth, M., Fransen, L., Over, M. (1998). Confronting AIDS: Evidence from the developing world. Brussels: The European Commission and Washington DC: The World Bank.
Anand, S., & Sen, A. (1995). Gender inequality in human development: Theories and measurement. Human development report office occasional paper.
Bardhan, K., & Klasen, S. (1999). UNDP’s gender-related indices: A critical review. World Development, 27(6), 985–1010.
Bazillier, R. (2004). Core labour standards and economic growth. Cahiers de la Maison des Sciences Economiques.
Bazillier, R., & Gouret, F. (2004). Composite index and principal component analysis, Do we use the right method? Cahiers de la Maison des Sciences Economiques.
Behrman, J. R., Foster, A. D., Rosenzweig, M. R., & Vashishtha P. (1999). Women’s schooling, home teaching, and economic growth. Journal of Political Economy, 107(4), 682–714.
Benzecri, J.-P. (1992). Correspondence analysis handbook. New-York: Marcel Dekker.
Berenger, V., & Verdier-Chouchane, A. (2011). From the relative women disadvantage index to women’s quality-of-life. Journal of Human Development and Capabilities, 12(2), 203–233.
Bierstedt, P. R. (1963). The social order. New York: McGraw Hill.
Bloom, D. E., & Williamson, J. G. (1997). Demographic transitions and economic miracles in emerging Asia. NBER working paper.
Boone, P. (1996). Political and gender oppression as a cause of poverty. CEP discussion paper.
Boserup, E. (1970). Women’s role in economic development. New York: St Martin’s Press.
Branisa, B., Klasen, S., & Ziegler, M. (2009a). The construction of the social institutions and gender index (SIGI). Ibero America Institute for Econ. Research (IAI) discussion paper.
Branisa, B., Klasen, S., & Ziegler, M. (2009b). Why we should all care about social institutions related to gender inequality. Courant research centre: Poverty, equity and growth, discussion paper.
BTrenger, V., & Verdier-Chouchane, A. (2007). Multidimensional measures of well-being: Standard of living and quality of life across countries. World Development, 35, 1259–1276.
Broom, L., & Selznick, P. (1963). Sociology. New York: Harper and Row.
Charmes, J., & Wieringa, S. (2003). Measuring women’s empowerment: An assessment of the gender-related development index and the gender empowerment measure. Journal of Human Development and Capabilities, 4(3), 419–435.
Chiappori, P.-A., Fortin, B., & Lacroix, G. (2002). Marriage market, divorce legislation, and household labor supply. Journal of Political Economy, 110(1), 37–72.
Coleman, J. S. (1990). Foundations of social theory. Cambridge: Harvard University Press.
De Beauvoir, S. (1949). Le DeuxiFme Sexe. Paris: Gallimard.
Diewert, W. E. (1976). Exact and superlative index numbers. Journal of Econometrics, 4(2), 115–145.
Dijkstra, A. G. (2002). Revisiting UNDP’s GDI and GEM: Towards an alternative. Social Indicators Research, 57(3), 301–338.
Dijkstra, A. G. (2006). Towards a fresh start in measuring gender equality: A contribution to the debate. Journal of Human Development, 7(3), 275–283.
Dijkstra, A. G., & Hanmer, L. C. (2000). Measuring socio-economic gender equality: Toward an alternative for UNDP’s GDI. Feminist Economics, 6, 41–75.
Dollar, D., & Gatti, R. (1999). Gender inequality, income and growth: Are good times good for women? World Bank working paper.
Dollar, D., Fisman, R., & Gatti, R. (2001). Are women really the fairer sex? Corruption and women in government. Journal of Economic Behavior and Organization, 46(4), 423–429.
Dorlin, E. (2008) Sexe, Genre et Sexualités. Introduction à la théorie féministe. Paris: PUF.
Dwyer, D., & Bruce, J. (1988). A home divided: Women and income in their third world. Stanford: Stanford Univeristy Press.
Easterly, W. (2001). The elusive quest for growth. Cambridge: The MIT Press.
Elster, J. (1989). Social norms and economic theory. Journal of Economic Perspectives, 3(4), 99–117.
Escofier, B., PagFs, J. (1998). Analyses factorielles simples et multiples. Objectifs méthodes et interprétation. Paris: Dunod.
Esteve-Volart, B. (2004). Gender discrimination and growth: Theory and evidence from India. STICERD—Development economics papers.
Ferber, M. A., Nelson, J. (1993). Beyond economic man: Feminist theory and economics. Chicago: Chicago University Press.
Folbre, N. (1986). Cleaning house: New perspectives on households and economic development. Journal of Development Economics, 22(1), 5–40.
Forsythe, N., Korzeniewicz, R. P., & Durrant, V. (2000). Gender inequalities and economic growth: A longitudinal evaluation. Economic Development and Cultural Change, 48(3), 573–617.
Frances, M., & Russell, H. (2008). Gender inequalities in time use. The distribution of caring, housework and employment among women and men in Ireland. Equality Research Series.
Gajdos, T. (2001). Les fondements axiomatiques de la mesure normative des inégalités. Cahiers de la Maison des Sciences Economiques.
Greenacre, M. J. (1984). Theory and applications of correspondence analysis. London: Academic Press.
Harvey, E., Blakely, J., & Tepperman, L. (1990). Toward an index of gender equality. Social Indicators Research, 22(3), 299–317.
Hausmann, R., Tyson, L. D., Zahidi, S. (2007) The global gender gap report. World Economic Forum.
Heise, L., Ellsberg, M., & Gottemoeller, M. (1999). Ending violence against women. Population Reports, 11(4), 1–43.
Hill, M. A., & King, E. (1995). Women’s education and economic well-being. Feminist Economics, 1(2), 21–46.
Johnston, D. F. (1985). The development of social statistics and indicators on the status of women. Social Indicators Research, 16(3), 233–261.
Jones, N., Harper, C., & Watson, C. (2010). Stemming girls’ chronic poverty: Catalysing development change by building just social institutions. Manchester: Chronic Poverty Research Centre, University of Manchester.
Jütting, J. P., & Morrisson, C. (2005). Changing social institutions to improve the status of women in developing countries. OECD development centre working paper.
Jütting, J. P., Morrison, C., & Drechsler, D. (2006). The gender, institutions and development data base. OECD development centre working paper.
Klasen, S. (1999). Does gender inequality reduce growth and development? Evidence from cross-country regressions. World Bank working paper.
Klasen, S. (2002). Low schooling for girls, slower growth for all? Cross-country evidence on the effect of gender inequality in education on economic development. World Bank Economic Review ,16(3), 345–373.
Klasen, S. (2006). UNDP’s gender-related measures: Some conceptual problems and possible solutions. Journal of Human Development and Capabilities, 7(2), 243–274.
Klasen, S. (2008). Missing women: Some recent controversies on levels and trends in gender bias in mortality. Ibero America Institute for Econ. Research (IAI) discussion paper.
Klasen, S., & Lamanna, F. (2009). The impact of gender inequality in education and employment on economic growth: New evidence for a panel of countries. Feminist Economics, 15(3), 91–132.
Klasen, S., & Schüler, D. (2011). Reforming the gender-related development index and the gender empowerment measure: Implementing some specific proposals. Feminist Economics, 17(1), 1–30.
Kolm, S.-C. (1976). Unequal inequalities II. Journal of Economic Theory, 13(1), 82–111.
Lagerlof, N.-P. (2003). Gender equality and long-run growth. Journal of Economic Growth, 8(4), 403–426.
Lebart, L., Morineau, A., & Piron, M. (2004). Statistique exploratoire multidimensionnelle. Paris: Dunod.
Martineau, H. (1837). Society in America. London: Saunders and Otley.
Mcgillivray, M., & White, H. (1993). Measuring development? A statistical critique of the UNDP human development index. Journal of International Development, 5(135), 183–192.
Mohiuddin, Y. (1996). Country rankings by the status of women index. Paper presented at the conference of the international association for feminist economics.
Munda, G., & Nardo, M. (2005a). Constructing consistent composite indicators: The issue of weights. Technical Report European Comission.
Munda, G., & Nardo, M. (2005b). Non-compensatory composite indicators for ranking countries: A defensible setting. Technical Report European Comission.
Murthi, M., Guio, A.-C., & DrFze, J. (1995). Mortality, fertility and gender-bias in India. Population and Development Review, 21(4), 745–782.
North, D. C. (1991). Institutions. Journal of Economic Perspectives, 5(1), 97–112.
OECD (2005). Handbook on constructing composite indicators: Methodology and user guide. OECD statistics working paper.
Permanyer, Inaki. (2010). The measurement of multidimensional gender inequality: Continuing the debate. Social Indicators Research, 95(1), 181–198.
Perrons, Diane (2005). Gender inequalities in regional development. Regional Studies, 29(5), 465–476.
Podinovskii, V. V. (1994). Criteria importance theory. Mathematical Social Sciences, 27(3), 237–252.
Rodrik, D., Subramanian, A., & Trebbi, F. (2002). Institutions rule: The primacy of institutions over geography and integration in economic development. NBER working paper.
Rosenzweig, M. R., & Wolpin, K. I. (1994). Inequality among young adult siblings, public assistance programs, and intergenerational living arrangements. Journal of Human Resources, 29(4), 1101–1125.
Saith, R., & Harriss-White, B. (1999). The gender sensitivity of well-being indicators. Development and Change, 30, 465–497.
Schnler, D. (2006). The uses and misuses of the gender-related development index and the gender empowerment measure: A review of the literature. Journal of Human Development, 7(2), 161–181.
Sen, A. (1999). Development as freedom. New York: Oxford University Press.
Sen, Gita, & Grown, Caren (1987). Development, crises and alternative visions: Third world women’s perspectives. New York: Monthly Review Press.
Seth, S. (2009a). Inequality, interactions, and human development. Journal of Human Development and Capabilities, 10(3), 375–396.
Seth, S. (2009b). A class of association sensitive multidimensional welfare indices. OPHI working paper 27.
Sethuraman, K. (2008). The role of women’s empowerment and domestic violence in child growth and undernutrition in a tribal and rural community in south India. WIDER research paper.
Simons, M. A. (1995). Feminist interpretations of Simone de Beauvoir. Philadelphia: Pennsylvania State University Press.
Sugarman, D. B., & Straus, M. A. (1988). Indicators of gender equality for American states and regions. Social Indicators Research, 20(3), 229–270.
Swamy, A., Knack, S., Lee, Y., & Azfar, O. (2001). Gender and corruption. Journal of Development Economics, 64(1), 25–55.
Tauzin, A. (1988). Excision et identité féminine. L’exemple mauritanien. Anthropologie et Sociétés, 12(1), 29–37.
Thomas, Duncan (1993). The distribution of income and expenditure within the household. Annales d’Economie et de Statistique, 29(29), 109–135.
Thomas, D., & Strauss, J. (1997). Health and wages: Evidence on men and women in urban Brazil. Journal of Econometrics, 77(1), 159–185.
Tinker, I. (1990). Persistent inequalities: Women and world development. New York: Oxford University Press.
Udry, C. (1996). Gender, agricultural production, and the theory of the household. Journal of Political Economy, 104(5), 1010–46.
Udry, C., Hoddinott, J., Alderman, H., Haddad, L. (1995). Gender differentials in farm productivity: Implications for household efficiency and agricultural policy. Food Policy, 20(5), 407–423.
UNDP (1995). Human development report. New York: Oxford University Press.
UNICEF (2006). The state of the world’s children, women and children: The double dividend of gender equality. New York: UNICEF.
von Braun, J., Puetz, D., & Webb, P. (1989). Irrigation technology and commercialization of rice in the Gambia: Effects on income and nutrition. IFPRI working paper.
Wieringa, S. E. (1997). Report of the workshop on GDI/GEM indicators. The Hague. Institute of Social Studies Working Papers.
World Bank (2001). Engendering development through gender inequality in rights, resources and voice. Washington: World Bank.
World Health Organization. (2005). Addressing violence against women and achieving the millennium development goals. World Bank.
Acknowledgments
I thank the participants at the ISS Gender and Development Workshop, at the CSAE Conference and at the CES Development Economics and Gender Economics Seminars. I am also grateful to OECD Development Center members, especially Somali Cerise and Johannes Jutting, and Pr. Stephan Klasen for their help. Finally, I thank Alex Michalos and two anonymous referees for their useful comments.
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The paper was written while I were working at Centre d’Economie de la Sorbonne, Université Paris I Panthéon Sorbonne and Paris School of Economics.
Appendix
Appendix
1.1 Correlation Tests by Dimensions
1.2 Correlation Tests for Variables Included in Each Dimension
We present here the correlation test for the variables included in each dimension. The p values correspond to the null hypothesis that the two variables are independent of each other. *, **, *** represent significant correlations at the 10, 5 and 1 % levels, respectively. See Tables 7, 8, 9, 10, 11, 12, 13 and 14.
1.3 Stability Tests for MCA on the OECD European Sample
The small dataset of 21 OECD Europe countries may bias the MCA results. There is no consensus in the literature concerning the minimum dataset size required to apply the MCA, which has remained little discussed. Nevertheless, stability tests allow us to confirm the significance of the results. See Tables 15 and 16.
Dataset size is strongly linked to the question of its representativeness. Here, I focus on 21 European OECD countries for the sake of homogeneity. The exclusion of non-European OECD countries results from homogeneity concerns, as gender inequalities are strongly linked to culture and history. I assume that the European continent is homogeneous from this point of view, although there are differences between countries. There are 34 OECD member countries, including 24 European countries. However, the data were not available for three of these (Estonia, Slovakia and Slovenia) since they ratified the OECD Convention in 2010, 2000 and 2010, respectively. The addition of three new countries (unfortunately not possible since the data are not available) would increase the sample size by 14 %. Could this change the results?
Stability calculations are considered to be the most valid procedures by Lebart et al. (2004). From a theoretical point of view, we check the stability of the configurations (MCA factors and values, the orientation of the axes and its configurations) after various disturbances. Empirically, resampling methods are applied. Two methods are used here: the bootstrap and cross-validation to simulate random samples. MCA is then applied to each simulated sample. Finally, bootstrapping provides confidence intervals for eigenvalues.
Using Spad.7, MCA is applied to the simulated samples. The correlations between these factorial axes and those of the initial dataset are then computed, which show the stability of the axis. Tables 15 and 16 present the effects of the disturbances simulated by bootstrap and cross-validation methods, respectively. On the diagonals, we can see the stability of the first, second and third factors. Table 15 shows the confidence intervals for the eigenvalues of the first two axes obtained via the bootstrap method. Stability is confirmed, even if we only have 21 countries. Finally, I have checked a number of points in the MCA results: the countries have very different terms; all variables have nonzero terms; the axes are well defined with an inertia of about 78 % for the first axis and 10 % for the second for a total of 88 %.
These three steps allow us to confirm the relevance of the MCA applied to the 21 European OECD countries.
1.4 The MGII Ranking
Country | MGII | Rank | Country | MGII | Rank |
|---|---|---|---|---|---|
Afghanistan | 0.975 | 109 | Haiti | 0.264 | 54 |
Yemen | 0.886 | 108 | Morocco | 0.258 | 53 |
Chad | 0.869 | 107 | Madagascar | 0.229 | 52 |
Sudan | 0.844 | 106 | Sri Lanka | 0.213 | 51 |
Pakistan | 0.772 | 105 | Botswana | 0.207 | 50 |
Nigeria | 0.769 | 104 | Cambodia | 0.193 | 49 |
Bangladesh | 0.769 | 104 | Guatemala | 0.179 | 48 |
Niger | 0.767 | 102 | Laos PDR | 0.177 | 47 |
India | 0.751 | 101 | South Africa | 0.171 | 46 |
Sierra Leone | 0.691 | 100 | Tajikistan | 0.164 | 45 |
Guinea | 0.677 | 99 | Malaysia | 0.164 | 45 |
Iran, Islamic Rep. | 0.672 | 98 | Albania | 0.159 | 43 |
Benin | 0.669 | 97 | Tunisia | 0.156 | 42 |
Nepal | 0.66 | 96 | Fiji | 0.154 | 41 |
Cameroon | 0.659 | 95 | Namibia | 0.145 | 40 |
Saudi Arabia | 0.645 | 94 | China | 0.132 | 39 |
Congo, Dem, Rep. | 0.63 | 93 | Nicaragua | 0.125 | 38 |
Gambia, The | 0.629 | 92 | Honduras | 0.125 | 38 |
Iraq | 0.628 | 91 | Ecuador | 0.122 | 36 |
Mozambique | 0.628 | 91 | Georgia | 0.118 | 35 |
Uganda | 0.61 | 89 | Mauritius | 0.114 | 34 |
Mali | 0.599 | 88 | Bolivia | 0.112 | 33 |
Jordan | 0.596 | 87 | Dominican Republic | 0.11 | 32 |
Ivory Coast | 0.596 | 87 | El Salvador | 0.104 | 31 |
Zambia | 0.569 | 85 | Israel | 0.1 | 30 |
Ethiopia | 0.556 | 84 | Uzbekistan | 0.099 | 29 |
Gabon | 0.554 | 83 | Macedonia. FYR | 0.097 | 28 |
Central African Republic | 0.547 | 82 | Panama | 0.093 | 27 |
United Arab Emirates | 0.545 | 81 | Azerbaijan | 0.09 | 26 |
Togo | 0.533 | 80 | Chile | 0.089 | 25 |
Congo, Rep. | 0.507 | 79 | Peru | 0.085 | 24 |
Liberia | 0.498 | 78 | Armenia | 0.084 | 23 |
Libya | 0.497 | 77 | Costa Rica | 0.082 | 22 |
Burkina Faso | 0.486 | 76 | Russian Federation | 0.081 | 21 |
Zimbabwe | 0.483 | 75 | Brazil | 0.081 | 21 |
Malawi | 0.468 | 74 | Paraguay | 0.08 | 19 |
Egypt, Arab Rep. | 0.465 | 73 | Thailand | 0.075 | 18 |
Mauritania | 0.462 | 72 | Cuba | 0.069 | 17 |
Oman | 0.452 | 71 | Singapore | 0.066 | 16 |
Kuwait | 0.443 | 70 | Vietnam | 0.062 | 15 |
Senegal | 0.442 | 69 | Trinidad and Tobago | 0.059 | 14 |
Algeria | 0.425 | 68 | Colombia | 0.055 | 13 |
Bahrain | 0.4 | 67 | Kyrgyz Republic | 0.052 | 12 |
Kenya | 0.4 | 67 | Ukraine | 0.051 | 11 |
Papua New Guinea | 0.392 | 65 | Jamaica | 0.048 | 10 |
Swaziland | 0.389 | 64 | Mongolia | 0.043 | 9 |
Eritrea | 0.378 | 63 | Venezuela, RB | 0.042 | 8 |
Syrian Arab Republic | 0.374 | 62 | Philippines | 0.034 | 7 |
Ghana | 0.339 | 61 | Kazakhstan | 0.034 | 7 |
Indonesia | 0.338 | 60 | Uruguay | 0.031 | 5 |
Tanzania | 0.337 | 59 | Argentina | 0.027 | 4 |
Rwanda | 0.326 | 58 | Croatia | 0.025 | 3 |
Lebanon | 0.285 | 57 | Moldova | 0.021 | 2 |
Burundi | 0.28 | 56 | Belarus | 0.016 | 1 |
Bhutan | 0.272 | 55 |
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Ferrant, G. The Multidimensional Gender Inequalities Index (MGII): A Descriptive Analysis of Gender Inequalities Using MCA. Soc Indic Res 115, 653–690 (2014). https://doi.org/10.1007/s11205-012-0233-3
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DOI: https://doi.org/10.1007/s11205-012-0233-3