Theme 2: Improving Data Collection, and Reliability of Statistics and Indicators on Gender with Intersectionality Dimensions
This session tackles the historical lack of reliable data on gendered social behaviours, cultures, and processes, which impact in different ways on quality of interventions and of change for women and men. It also examines important interconnections between biological (sex) and social diversities (gender) with other human conditions such as age, ethnicity, education, and social status.
Moderator introducing Theme 2: 2-0 Insill Yi, Professor, Graduate School of Economics at Sogang University, 12th Commissioner of Statistics Korea
- 2-1 Papa Seck, Chief Statistician, UN Women
Counted and visible: Measurements of gender and intersecting inequalities across UN agencies - 2-2 Albert Motivans, Head of Data and Insights, Equal Measures 2030
Exploring the policy space and gender data, using data across the SDGs and unpacking disparities - 2-3 Cheryl Doss, Senior Lecturer and Associate Professor, U Oxford, UK
Lessons from the Gender Asset Gap Project - 2-4 Jonathan Dawes, Professor, Deputy Director, Centre for Networks and Collective Behaviour, University of Bath, UK
Prioritisation within the SDG network: which SDG linkages matter most? - 2-5 Jacqueline McGlade, Frank Jackson Foundation Professor of the Environment at Gresham College, Professor of Resilience and Sustainable Development at University College London and Professor at the Maasai Mara University in Kenya
Can gender equality help solve Climate Change
What you will learn
Modules in Theme 2: Improving Data Collection, and Reliability of Statistics and Indicators on Gender with Intersectionality Dimensions
Theme 2 Introduction
Prof Yi explains that good data is essential to effective action.
Can gender equality help solve Climate Change
Prof McGlade creates a context by referring to the names given to storms where tradition is to alternate male and female names.
Prioritisation within the SDG network: which SDG linkages matter most?
Prof Dawes compares different approaches to identify networks of interacting SDG targets that can support analysis of impacts and rates of change to choose the most effective SDGs implementation st
Lessons from the Gender Asset Gap Project
Prof Doss explains how assets are shared between women and men and that joint ownership does not imply equality. Legal frameworks affect how data can be interpreted, e.g. marital property rights.
Exploring the policy space and gender data, using data across the SDGs and unpacking disparities
Mr Motivans explains the mission of Equal Measures 2030 to close gaps in gender data and evidence through their SDG Gender Index, and to connect gender statistics with advocacy efforts and actions.