4th IODC

Blog IODC 2016
Madrid. October 6-7, 2016

#IODC16

Sex-disaggregated data, a means towards gender equality

September 9, 2016 by Reyes Montiel

Reyes Montiel is expert in public affairs, has worked on formulation, management and evaluation of public policies. Additionally, she has participated in political incidence activities for public decision-making and in negotiation projects at national and international level. Trained journalist, she currently develops 2.0 communications plans for public and private companies and entities. She works in strategies on economic mobilization and citizen participation that make sense out of open data services. Transparency activist, she participates in research projects for the causes of corruption and the evaluation of action plans against it, as well as specific education plans.

Traditionally, science –especially economic science– has been treated within “gender-blind” categories; this is, it has not considered men and women’s behavior and condition to be different as a consequence of the different social, cultural and economic roles that they have been assigned, culturally and economically. This is far from a neutral position, and we can go further, it is overtly antisocial, as it leaves behind numerous realities we live with, especially the one that concerns gender equality.

Therefore, the reason behind counting on sex-disaggregated data is not just a technical matter. We measure what we value. We only act over what is valued and can only be adequately valued if we have data, as has been said by Eva Armendáriz, from Economistas sin Fronteras: “As traditional economy just takes into account what happens within the market, a separation is produced between the economic –the public sphere- and what is not considered economic –the private sphere-, hence resulting in the disappearance from the economic level of all those jobs realized in the home environment, community work or those realized through volunteering or citizenship participation.”

Many years of advocacy and work have made us go forward in what we have named “Gender Statistics.” Tools and methodologies that bring us to having this kind of statistics are already being implemented. The fight for equality has made institutions and entities look to women’s issues. On the website of Women’s Institute in Spain we have access to statistics about conciliation, violence, discrimination, recognition…(even though it has not been updated since 2013). However, we must distinguish between sex-disaggregated data and gender statistics:

  • Sex-disaggregated data are data whose main standard is based on whether people are men or women. They are not gender-based because they are limited to raw data. They feed gender analises.
  • Gender statistics, they drive us to present an image of the conditions, contributions, specific needs and problems of men and women.

What is the purpose of sex-disaggregated data? They raise awareness and promote change; they are the base for the formulation, implementation and evaluation of public policies, plans and programmes. Gender statistics are meant to help us measure impact and improve these data.

An example of how sex-disaggregated data foster progress is financial inclusion, defined by the Centre for Financial Inclusion as “a state in which everyone who can use them has access to a range of quality financial services at affordable prices, with convenience, dignity, and consumer protections.” Measuring who is included or excluded from the financial system is an essential consideration for regulators and political decision-makers. Sex-disaggregated data give us information about who is accessing which kind of products, their behaviors, which channels and which users, and what kind of financial providers are they using and at what level. For example, disaggregated data use in Rwanda made the financial inclusion index rise from 20% in 2008 to 42% in 2012. The National Bank of Rwanda has set the goal of reaching 80% by 2017. According to Global Findex, an index resource that measures global financial inclusion, the amount of women owning a bank account is 15% lower than men, and the estimate of neglected financial needs of women who own businesses is 320,000 million dollars. Being aware of these gaps is essential to overcome them, and this is impossible without sex-disaggregated data.

Beyond formal declarations, we need to start working while bearing in mind the following actions:

Knowing what is being done and who is doing it: It is important to support initiatives that are opening sex-disaggregated data in order to have healthy diagnostics, devise gender objectives and measure their impact. In this regard there are many interesting initiatives (see References). This is important, not only to know the reality of data in the world and how they are retrieved but also because it is crucial to agree on data standardization so that we can have efficient tools. For example, from the indicators that have been prepared to measure the achievements of the Objective for Development 5, the one covering gender equality, only 3 comply with the agreed international standards for measuring, and the collection of information about them is made systematically. For further information, see When data became cool.

Establishing cross-cutting strategies with a clear public leadership: A lack of information is serious enough, but it is even worse to have partial information that devalues the role of women and presents them as more dependent and less productive than they really are. We need to count on a cross-cutting strategy with objectives in all areas of government, and whose implementation is supported by objectives of generation of data related with action objectives.

Relying on awareness and education plans: We need to be aware that the information that supports the formulation of public, economic and social policies and their development must project the reality of women’s everyday life, and that corresponds with political values and priorities. Data use requires strong skills in searching, cleaning, verification and analysis. According to Mayra Buvinic and Ruth Levine on The Guardian, “the data gap has fed the myth that women working in the home have free time for training and other development interventions. Projects designed on this false premise have high dropout rates from female participants.” Gender education is fundamental.

Visualizing good practices: There is much to do yet, but it is important to be aware that what is being done is efficient. The effort that is being made to fulfil the OSD can give us a source of good practices (and a few errors) to keep on advancing. From this perspective, we can find them on the UN Women initiative Make every woman and girl count.

 

Cover photo by Stephen Di Donato.

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