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Are UNDP Indices Appropriate to Capture Gender Inequalities in Europe?

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Abstract

This study critically investigates the suitability of United Nations’ composite indices and other related measures—among which the Gender Inequality Index just released in 2010—to capture gender inequalities in the context of “highly developed” countries, focusing on the case of Europe. Our results indicate that many of the gender gaps in health and education variables have either vanished or even reversed, thus questioning their appropriateness to capture women’s disadvantage in Europe and inviting to construct region-specific measures. Alternatively, parliamentary representation and labor force participation are variables with large gender gaps that highlight important dimensions where women disadvantage prevails. Different cross-section and cross-time associations between economic growth and gender equality are generally not statistically significant—or at most weakly correlated—at European level. This is basically due to the fact that the gender gaps included in UNDP gender-related indices reached their normatively desirable values long ago, therefore leaving no room for further improvement.

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Notes

  1. See, for instance, the Gender Inequality Index (Forsythe et al. 2000), the Relative Status of Women index (Dijkstra and Hanmer 2000), the Standardized Index of Gender Equality (Dijkstra 2002), the African Gender and Development Index (UNECA 2004), the Gender Equity Index (Social Watch 2004), the Gender Equality in Education Index (Unterhalter 2006), the Global Gender Gap Index (Hausmann et al. 2007), the Social Institutions and Gender Index (Jütting et al. 2008), the Multidimensional Gender Equality Index (Permanyer 2008), the Gender Relative Status and Women Disadvantage indices (Beneria and Permanyer 2010), the European Gender Equality Index (Bericat 2011) and the Gender Gap Measure (Klasen and Schüler 2011).

  2. However interesting these separate subcomponents might be, it is important to highlight that they are only showing the marginals of an inherently multivariate distribution whose internal structure is very important to understand. As is well known, the same set of marginals can be generated from alternative multidimensional distributions that are essentially different when exploring their corresponding welfare implications: therefore, relying on the marginals only can lead to a very misleading course of action.

  3. In Klasen and Schüler (2011) that index is called Gender Gap Measure (GGM).

  4. For more technical details on this discussion and a review of alternative gender inequality indices recently presented in the literature see Permanyer (2008), Beneria and Permanyer (2010) and Bericat (2011).

  5. The original GDI methodology uses an additive form: (2/3)ALR + (1/3)GER. However, given the fact that GRS is a multiplicative index, we keep the same formulation within each component to ensure methodological consistency.

  6. The 33 countries included in this study are: Albania, Austria, Austria, Belarus, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland and United Kingdom. Other European countries have not been included for lack of available data (the most notable cases being Balkan countries like Bosnia-Herzegovina, Macedonia, Montenegro or Serbia).

  7. The essence of the change was that the inequality aversion calculation is now applied after the log transformation of incomes (rather than applied to unadjusted incomes) to indicate that the declining human development benefit of incomes is not only true for average incomes (as in the HDI), but also for incomes earned by females and males. For more details see Bardhan and Klasen (1999) and the Technical notes of the Human Development Reports.

  8. According to the values reported in Beneria and Permanyer (2010), women’s average achievement at the world level represented 62.5% of men’s in 1995 and 74.3% in 2005.

  9. The only exception to that rule is found in Albania, which follows a slightly erratic pattern over time for which we have found no clear explanation.

  10. “Association sensitivity” is an axiom borrowed from Seth (2009) adapted to the context of gender inequality measurement. Originally, Seth (2009) introduced that axiom for the measurement of multidimensional welfare. Loosely speaking, an “association sensitive social welfare index” can be thought as a welfare index defined in a n-person (n ∈ \( {\mathbb{N}} \)) multidimensional framework that is responsive to changes in association between indicators, that is: penalizing or rewarding those distributions where some individuals perform better than others in all dimensions at the same time. By the same token, an “association sensitive gender inequality index” can be thought as an index that is responsive to those distributional changes that end up benefiting one gender over the other in all indicators at the same time.

  11. Since \( GRS = \prod\nolimits_{i} {(x_{i} /y_{i} )}^{{w_{i} }} \), one has that \( \ln (GRS) = \sum\nolimits_{i} {w_{i} \ln } (x_{i} /y_{i} ) \), where w i is the weight attached to dimension ‘i’. Therefore, the percent contribution of gap ‘i’ to the value of GRS can be computed as \( 100 \times \left( {{{w_{i} \ln (x_{i} /y_{i} )} \mathord{\left/ {\vphantom {{w_{i} \ln (x_{i} /y_{i} )} {\ln (GRS)}}} \right. \kern-\nulldelimiterspace} {\ln (GRS)}}} \right) \). Recall that, since the gender gaps can either favor women or men, the previous contributions can be positive or negative, so they do not add up to 100%: it is their absolute values that actually add up to 100%. For more details see Beneria and Permanyer (2010).

  12. Unfortunately, labor force participation rates miss important sectors like the informal and care economies; see Folbre (2006) and Beneria (2008).

References

  • Bardhan, K., & Klasen, S. (1999). UNDP’s gender-related indices: A critical review. World Development, 27(6), 985–1010.

    Article  Google Scholar 

  • Beneria, L. (2008). The crisis of care, international migration, and public policy. Feminist Economics, 14(3), 1–21.

    Article  Google Scholar 

  • Beneria, L., & Permanyer, I. (2010). The measurement of socio-economic gender inequality revisited. Development and Change, 41, 375–399.

    Article  Google Scholar 

  • Bericat, E. (2011) The European gender equality index: Conceptual and analytical issues. Social Indicators Research, doi: 10.1007/s11205-011-9872-z.

  • Dijkstra, A. G. (2002). Revisiting UNDP’s GDI and GEM: Towards an alternative. Social Indicators Research, 57, 301–338.

    Article  Google Scholar 

  • Dijkstra, A. G. (2006). Towards a fresh start in measuring gender inequality: A contribution to the debate. Journal of Human Development, 7(2), 275–283.

    Article  Google Scholar 

  • Dijkstra, A. G., & Hanmer, L. C. (2000). Measuring socio-economic gender inequality: Towards an alternative to the UNDP gender-related development index. Feminist Economics, 6(2), 41–75.

    Article  Google Scholar 

  • Dollar, D., & Gatti, R. (1999). Gender inequality, income and growth: Are good times for women? Washington DC: The World Bank.

    Google Scholar 

  • Dorius, S., & Firebaugh, G. (2010). Trends in global gender inequality. Social Forces, 88(5), 1941–1968.

    Article  Google Scholar 

  • Easterly, W. (1999). Life during growth. Journal of Economic Growth, 4, 239–276.

    Article  Google Scholar 

  • Esquivel, V., Budlender, D., Folbre, N., & Hirway, I. (2008). Explorations: Time-use surveys in the South. Feminist Economics, 14(3), 107–152.

    Article  Google Scholar 

  • Folbre, N. (2006). Measuring care: Gender, empowerment, and the care economy. Journal of Human Development, 7(2), 183–199.

    Article  Google Scholar 

  • Forbes, K. J. (2000). A reassessment of the relationship between inequality and growth. American Economic Review, 90(4), 860–887.

    Article  Google Scholar 

  • Forsythe, N., Korzeniewick, R., & Durrant, V. (2000). Gender inequalities and economic growth: A longitudinal evaluation. Economic Development and Cultural Change, 48(3), 573–617.

    Article  Google Scholar 

  • Gaye, A., Klugman, J., Kovacevic, M., Twigg, S., & Zambrano, E. (2010). Measuring key disparities in human development: The gender inequality index. Human Development Research Paper 2010/46.

  • Hausmann, R., Tyson, L., & Zahidi, S. (2007). The global gender gap report 2007. Switzerland: World Economic Forum.

    Google Scholar 

  • Hawken, A., & Munck, G. (2009) Cross-national indices with gender-differentiated data: What do they measure? How valid are they? Technical background paper for the forthcoming UNDP Asia Pacific Human Development Report on gender. UNDP.

  • Jütting, J., Morrisson, C., Johnson, J., & Drechsler, D. (2008). Measuring gender (In)equality: The OECD gender, institutions and development data base. Journal of Human Development, 9(1), 65–86.

    Article  Google Scholar 

  • Klasen, S. (1999). Does gender inequality reduce growth and development? Evidence from cross-country regressions. Policy research report on gender and development, Working paper series No.7, The World Bank.

  • Klasen, S. (2006). UNDP’s gender-related measures: Some conceptual problems and possible solutions. Journal of Human Development, 7(2), 243–274.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lutz, W., Goujon, A., KC, S., & Sanderson, W. (2007). Reconstruction of populations by age, sex and level of educational attainment for 120 countries for 1970–2000. Vienna Yearbook of Population Research, 2007, 193–235.

    Article  Google Scholar 

  • Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini E. (2005). Handbook on constructing composite indicators: Methodology and user guide. OECD Statistics Working Paper STD/DOC(2005)3, OECD, Paris.

  • Notzon, F., Komarov, Y., Ermakov, S., Sempos, C., Marks, J., & Sempos, E. (1998). Causes of declining life expectancy in Russia. Journal of the American Medical Association, 279(10), 793–800.

    Article  Google Scholar 

  • Permanyer, I. (2008). On the measurement of gender equality and gender-related development levels. Journal of Human Development, 9(1), 87–108.

    Article  Google Scholar 

  • Permanyer, I. (2010). The measurement of multidimensional gender inequality: Continuing the debate. Social Indicators Research, 95(2), 181–198.

    Article  Google Scholar 

  • Plantenga, J., Figueiredo, H., Remery, C., & Smith, M. (2009). Towards an EU gender equality index. Journal of European Social Policy, 19(1), 19–33.

    Article  Google Scholar 

  • Ravaillon, M. (2010). Mashup Indices of Development. World Bank Policy Research Working Paper No. 5432.

  • Ravaillon, M. (2011). On multidimensional indices of poverty. Journal of Economic Inequality, 9(2), 235–248.

    Article  Google Scholar 

  • Seguino, S. (2000). Gender inequality and economic growth: A cross-country analysis. World Development, 28(7), 1211–1230.

    Article  Google Scholar 

  • Seth, S. (2009). A class of association sensitive multidimensional welfare indices. OPHI WP27.

  • Smyth, I. (1996). Gender analysis of family planning: Beyond the feminist vs. population control debate. Feminist Economics, 2(2), 63–86.

    Article  Google Scholar 

  • Social Watch (2004). Social Watch Annual Report 2004. Montevideo: Instituto del Tercer Mundo.

  • UNDP (1995). Human Development Report 1995. Gender and Human Development, New York.

  • UNDP (2010). Human Development Report 2010. The Real Wealth of Nations: Pathways to Human Development, New York.

  • UNECA (United Nations Economic Commission for Africa). (2004). The African Gender and Development Index (Addis Ababa, Ethiopia: Economic Commission for Africa).

  • Unterhalter, E. (2006). Measuring gender inequality in education in South Asia. UNICEF and UNGEI.

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Acknowledgments

Financial support from the Spanish Ministry of Education “Juan de la Cierva” Research Grant Program and research projects ERC-2009-StG-240978, CSO2008-00654 and ECO2010-21668-C03-02 is gratefully acknowledged.

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  1. Centre d’Estudis Demogràfics, Campus de la Universitat Autònoma de Barcelona, 08193, Bellatera, Spain

    Iñaki Permanyer

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Permanyer, I. Are UNDP Indices Appropriate to Capture Gender Inequalities in Europe?. Soc Indic Res 110, 927–950 (2013). https://doi.org/10.1007/s11205-011-9975-6

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