Research and data can influence crucial decisions in development projects. To strive for Agenda 2030’s aspiration to leave no one behind, development institutions need to know who is actually being left behind and, perhaps most importantly, how.
‘No two villages are alike…but when we collect data we paint the poor with one brush, we paint women with one brush, we paint immigrants with one brush (…) If you do not want to leave anyone behind that means that, number one, you have to count everyone in spite of their differences’ Al Kags, Exacutive Director Open Institute
The Foundation’s latest event “Who is counted is who counts: Intersectionality and data” addressed inclusion at the level of data collection, processing, and analysis. It gathered four speakers who apply intersectional perspectives in data gathering (see image caption) and around 20 participants from around the world. The roundtable was organised in partnership with the United Nations’ Inclusive Data Charter and was part of the Foundation’s current work on intersectionality, including a roundtable series and this year’s upcoming Development Dialogue Volume.
The speakers detailed techniques, models and practices for working with intersectional data, such as citizen-produced data and partnerships with civic society organisations to incorporate qualitative insights. They also engaged in a discussion about gathering and handling sensitive information, digital tools and inclusive data activism. Read below for the event’s key takeaways.
What intersectionality means in practice
The concept of intersectionality, introduced by Professor Kimberlé Crenshaw in 1989, provides a tool to understand discrimination and structural disadvantages. It is widely used in academia, development, and media to highlight the multidimensional nature of exclusion. In essence, the concept addresses the way structures of exclusion such as race, class or gender overlap on individuals and generate specific experiences and forms of discrimination.
Data disaggregation, the breakdown of statistical populations’ data by context, gender, sexual orientation or gender, has been one of the main ways to apply intersectionality. The Millennium Development Goals Report of 2015 highlights the importance of data disaggregation beyond the parameters of age and sex to include others such as immigrant status or disability. ‘The leave no one behind premise puts to the fore that disaggregated data is foundational for ensuring a respect for human rights and ensuring that there is freedom for every segment of the population,’ stated Sandile Simeline from the UN’s Population Fund (UNFPA).
However, ‘it is not enough to show cold tables that compare information,’ stated Karen García, Gender and Intersectional Statistics Adviser at the National Administrative Department of Statistics (DANE) in Colombia; it is important to keep an intersectional approach across data processes because often data is disaggregated only to be aggregated again for statistical analysis.
‘Intersectionality goes beyond desegregating data to asking, how can we ensure the inclusion and participation of different voices?’ Tichafara Chisaka, Programme Manager Inclusive Data Charter.
Tichafara Chisaka from the Inclusive Data Charter emphasised that using intersectionality implies ‘applying a lens across data process and practice’ and goes ‘beyond disaggregating data’ because it ‘involves asking critical questions.’ This means looking into the systems and institutions that produce data to achieve meaningful inclusion. García underlined this point on expertise: ‘awareness and training in differential and intersectional approaches must be permanent.’
Constructing inclusive data for development
The UN has recently tackled the gathering and management of inclusive data through its 2020 Data Strategy and the Secretary General’s Data Strategy for Action by Everyone, Everywhere. Intersectional approaches already in practice can provide a path forward.
National statistics are one of the crucial places where this approach can have a significant impact. The UN Population Fund provides support for countries to be able to conduct national censuses and works with the aim of ensuring that data collection and dissemination are representative of everybody in society, explained Sandile Simeline, technical specialist in the institution.
‘Accessibility of data is key, to make the data a public good for everyone even to the segments of society that are marginalized or excluded,’ Sandile Simeline, Technical Specialist UN Population Fund
García underscored the relevance of working with different disciplines like anthropology and sociology and incorporating qualitative research in statistical work. ‘While there is this ambition to collect this intersectional data,’ she explained, ‘it is not always feasible to collect data of every level of desegregation.” Diverse and updated expertise is necessary to make choices and prioritise. García also shared with participants DANE’s guidelines for intersectional data intended to support Colombia’s work to mainstream intersectionality approaches in statistics.
Al Kags from Open Institute in Kenya underlined that inclusive data also leads to more efficient and effective development projects. ‘We don’t take enough time to understand the diversity of the communities,’ he explained, which leads to problems and failures on the ground. ‘We need to get a lot more granular in the way we do our development projects,’ he stated.
From inclusion to empowerment through data
All speakers referred to intersectional approaches to data as a vehicle for meaningful change. Simeline, for example, expressed that working towards data as a common public good gives the opportunity to communities to see their position for themselves and organise to push for change. García underscored the use of inclusive language in data processes and to avoid reproducing damaging stereotypes when sharing the collected information.
‘Our data collection capacity must increase and move even faster (…) We can work with citizens and stop seeing them only as passive producers of data,’ Al Kags, Executive Director Open Institute
García also pointed at civil society organisations as a fruitful source of advancement to improve intersectional findings and develop data inclusively from the beginning. For example, at DANE she partnered with a feminist organisation to investigate a qualitative hypothesis about gender. ‘We construct data in conversation with them,’ to learn what the categories and findings may mean at the qualitative level, she explained.
Kags explained that the Open Institute ‘works with governments and with citizens to figure out how they can be active enough so they can collect data themselves and how they can be active enough to use the data for themselves so they can advocate.’ This approach to data at the grassroots level allows to grow the capacity and speed of data collection through very small investments.
Most importantly, Kags stated his conviction that involving citizens in data will lead to faster and more inclusive development. Citizens can identify information that others cannot because they know their communities best. They are the ones who can most accurately identify their own priorities and advocate for change. ‘If village by village are identifying their priorities,’ he stated, ‘then we will be moving much faster.’
Written by María Langa