Inequality, gender, and sustainable development: measuring feminist progress

ABSTRACT

This article considers progress made in research, theory, and measurement of feminist progress, it recalls the promises made at the Beijing Platform for Action (BPfA) and asks if there is potential for the Sustainable Development Goals (SDGs) and their commitment to gender mainstreaming to offer the measurement framework that the BPfA lacked. We start with a summary of the evolution of feminist thinking in the area of development. We then go on to assess how key global frameworks such as the Millennium Development Goals (MDGs), the 2030 Agenda and its SDGs, and the Leave No-One Behind principle have engaged or not with feminist approaches to measurement of development. We then move on to present an intersectional approach to the measurement of progress which recognises the role of inequalities not only between women and men but within groups of women and girls and across the various dimensions of sustainable development. We name this multi-dimensional and multi-sectoral approach Inequality, Gender, and Sustainable Development (IGSD).

Cet article considère les progrès effectués sur le plan des recherches, des théories et de la mesure des avancées féministes, rappelle les promesses faites lors du Programme d’action de Beijing (BPfA) et pose la question de savoir s’il est possible que les ODD et leur engagement en faveur de l’intégration du genre proposent le cadre de mesure qui manquait au BPfA. Nous commençons par un résumé de l’évolution de la pensée féministe dans le domaine du développement, puis nous évaluons en quoi des cadres mondiaux clés comme les Objectifs du Millénaire pour le développement (OMD), le Programme 2030 et ses Objectifs de développement durable (ODD) et le principe « Ne laisser personne de côté » (Leave No one Behind – LNOB) ont ou non établi des liens avec les approches féministes de la mesure du développement. Nous présentons ensuite une approche intersectionnelle de la mesure des progrès qui reconnaît le rôle des inégalités, non seulement entre les femmes et les hommes, mais au sein des groupes de femmes et de filles et au niveau de toutes les dimensions du développement durable. Nous nommons cette approche multidimensionnelle et multisectorielle « Inégalités, genre et développement durable » (Inequality, Gender and Sustainable Development – IGSD).

El presente artículo examina los avances que han tenido la investigación, la teoría y la medición del progreso feminista. Además, recuerda las promesas hechas en la Plataforma de Acción de Beijing, preguntándose si existen posibilidades de que los grupos de desarrollo sostenible comprometidos con la incorporación masiva de la perspectiva de género ofrezcan el marco de medición del que carecía dicha plataforma. Comenzamos realizando un resumen de la evolución seguida por el pensamiento feminista en el ámbito del desarrollo. Luego evaluamos la forma en que los marcos mundiales clave, como los Objetivos de Desarrollo del Milenio (odm), la Agenda 2030 y sus Objetivos de Desarrollo Sostenible (ods), además del principio de "No dejar a nadie atrás" (ndna), se han comprometido o no con los enfoques feministas para la medición del desarrollo. Finalmente presentamos un enfoque interseccional para medir el progreso que reconoce el papel que desempeñan las desigualdades no sólo entre mujeres y hombres, sino también al interior de los grupos de mujeres y niñas y en las diversas dimensiones del desarrollo sostenible. A este enfoque multidimensional y multisectorial le damos el nombre de Desigualdad, Género y Desarrollo Sostenible (igsd).

KEYWORDS:

• Beijing +25
• feminist progress
• Sustainable Development Goals
• inequality
• sustainable development
• Leave No-One Behind
• women’s rights

Acknowledgements

The authors would like to acknowledge the valuable comments of Caroline Sweetman and Guillem Fortuny; they are also grateful for the excellent research assistance provided by Julia Brauchle.

Notes on contributors

Ginette Azcona is a Research and Data Policy Specialist at UN Women. She leads, data and statistics for UN Women’s global reports. Postal address: UN Women, 405 East 42nd Street, New York, NY 10017, USA. Email: [email protected]

Antra Bhatt is a Statistics Specialist in the Research and Data Section of UN Women. Email: [email protected]

Notes

1 For further elaboration of this term in the context of the SDGs, see Chapter 2 in Azcona et al. (2018).

2 For more information on the Inter-Agency Expert Group, see UN (2017).

3 For an article analysing Leave No-One Behind from a feminist perspective, see Stuart and Woodroffe (2016).

4 Authors’ calculations based on 2016 Demographic and Health Survey for Timor Leste (General Directorate of Statistics (GDS), Ministry of Health, and ICF 2018).

5 Authors’ calculations based on 2016 Demographic and Health Survey for India (International Institute for Population Sciences (IIPS) and ICF 2017).

6 Authors’ calculations based on 2014 Demographic and Health Survey for Bangladesh (National Institute of Population Research and Training (NIPORT), Mitra and Associates, and ICF International 2016).

7 The ‘national average’ or the mean value for the population of a nation is an important metric for assessing broad patterns of progress, but without further disaggregation it runs the risk of concealing disparities in outcomes. Many criticisms were levelled at the MDG monitoring framework for failing to include disaggregated data. See OHCHR (2010).

8 Household surveys are often designed to assess national outcomes, but with sampling methodology that cannot always accommodate extensive subgroup analysis. Disaggregating by two dimensions, such as sex and age or sex and income, is largely possible, but more refined analysis of disadvantaged groups using multi-level disaggregation, e.g. women from ethnic minorities living in poor households and rural areas, is often not.

9 SDG 13.b.1 Least Developed Countries (LDCs) and Small Island Developing States (SIDS) receiving support for climate change-related planning and management.

10 For extensive coverage on this topic, see Gender & Development Vol. 27, No. 2, 187–201, 2019.

11 ‘Big data’ refers to data meeting three criteria: extreme volume, collected quickly or in real time, and covering a variety of topics beyond typical demographic data. Analysing big data can uncover new trends, details, and patterns not possible with traditional surveys and administrative datasets. For more information, see Grable and Lyons (2018).

12 GIS is a technique that allows researchers to analyse and view data spatially. For more information, see Galati (2006).