24
Oct 17

Pingback: Have you #HeForShe’d yet?

Eraptis, October 24.

Tonight, Studentafton hosted an event with the head of #HeForShe, Elizabeth Nyamayaro, at Lund University, Sweden.

 

Here are some of the things being tweeted by #HeForShe during the event!

Elizabeth Nyamayaro emphasizes the need for all (both men and women) to engage in order to create lasting change:

To this day, there’s yet a country that has achieved gender equality. Thus, it’s not only a “Global South” problem – change needs to happen everywhere:

The need to act when required, to not be passive in the face of inequality and injustice:

But what about data?

DIAL* asked the same question – is all data created equal?

* DIAL (Digital Impact Alliance) is a “partnership amongst some of the world’s most active digital development champions” including: UN Foundation; Bill & Melinda Gates Foundation; SIDA (Swedish International Development Cooperation Agency); and USAID 

Ping: Have you #HeForShe’d yet? Data for women’s empowerment?

 


12
Oct 17

(Big) Data for women’s empowerment? – How does it work?

Eraptis, October 12

In my last post, I asked the question about how data could be used in order to measure the impact of the #HeForShe movement on women’s empowerment and argued that theory could guide us in the interpretation of such data. But through logical deduction data must first be generated before it can be analyzed, how does it work when data is generated in practice?

In accordance with Morten Jerven, a basic point of departure when we want to know something about a population is to first establish what the population is. Only after we have established this can we know something about other properties affecting that population. From a development perspective factors such as economic growth, agricultural production, education and health measurements are all predicated upon population data to be meaningful. Many times, however, the definite (real) population number is not actually known but estimated through a population counting process commonly referred to as a census. Jerven illustrates the possible implications of census-taking in a development context through a case study from Nigeria, saying that:

“Today, we can only guess at the size of the total Nigerian population. In particular, very little is known about the population growth rate. The history of census-taking in Nigeria is an instructive example of the measurement problems that can arise in sub-Saharan Africa. It is also a powerful lens through which the legitimacy of the Nigerian colonial and postcolonial state can be observed” – Morten Jerven

Without us getting into the particulars of the Nigerian census-taking case, Jerven points out an interesting aspect of this quote which he elaborates further elsewhere in his book: the involvement of the state in the production of data and official statistics. Thus, argues Jerven, if a particular state is interested in achieving development, we should expect that it also has an interest in measuring (that particular) development. If that’s the case, then the availability of state-generated data should reflect its statistical priorities, which is likely to mirror its political priorities. We, therefore, once again, arrive at the question asked in my first blog post – is all data created equal?

[youtube]href=”https://www.youtube.com/watch?=2&v=YABFcA8yHZ0″[/youtube]

Directing that question to Data2x seems to yield the simple answer “No”, at least not yet. Data2x is a joint initiative of the UN Foundation, the Bill & Melinda Gates Foundation, and the William & Flora Hewlett foundation dedicated to “improving the quality, availability, and use of gender data in order to make a practical difference in the lives of women and girls worldwide”. According to this report produced by Data2x, approximately eighty percent of countries produce sex-disaggregated data on education and training, labour force participation, and mortality. But only one third do the same on informal employment, unpaid work, and violence against women. Mapping the gender data gap across five development and women’s empowerment domains by using 28 indicators identified several types of gaps for each indicator as shown in this table:

big data womens empowerment

Source: Data2x

To close these gaps Data2x argues that existing data sources should be mined for sex-disaggregated data, and new data collection should be designed as a tool for social change that takes into account gender disaggregation already in the planning stages. But useful as they are, conventional data forms generated by household surveys, institutional records, and national economic accounts are not very well suited to capture a detailed account of the lives, experiences, and expressions of women and girls.

Can big data help close this gap? In this new report, Data2x shows it might by profiling a set of innovative approaches of harnessing big data to close the gender data gap even further. For example, have you ever wondered how data generated by 500 million daily tweets across 25,000 development keywords from 50 million Twitter users could be disaggregated by sex and location for analysis? I admit it, before reading the report; I cannot recall being struck by that thought. But, apparently, open data generated from social media platforms may not be sex-disaggregated from the outset. To solve this, the UN Global Pulse and the University of Leiden jointly collaborated together with Data2x to develop and test an algorithm inferring the sex of Twitter users. The tool takes into account a number of classifiers such as name and profile picture to determine the sex of the user producing a tweet and was developed so it could be applied on a global scale across a variety of languages. Comparing the gender classification results generated by the tool to that of a crowdsourced panel for which the correct results were assumed assessed the accuracy of the algorithm. In 74 % of the cases, the algorithm indicated the correct sex, a number that UN Global Pulse deems could be improved through further system development. The results of the project show great potential in generating new insights on development concerns disaggregated by both sex and location by using user-generated data from social media channels, as shown in this screenshot of the online dashboard (go and explore it for yourself!):

big data womens empowerment

Source: UN Global Pulse

Before ending this post I’d like to highlight three other relevant posts from our blog that takes up the relevant questions of bias in data generated from social media, the issue of privacy, as well as the geo-mapping and visualization of big data. Read, reflect, and tell us what you think in the comments field below!

 


02
Oct 17

Have you #HeForShe’d yet? – Data for women’s empowerment?

Eraptis, October 2

As the third anniversary of the global #HeForShe solidarity movement for gender equality just passed us by on September 20, I decided to renew my commitment to ”he for she” (apparently, I had already committed to the campaign two years ago). But now that I’ve committed (again), what do I do? As I browsed further on the website I discovered their action kit specifically targeting students, I felt compelled to act, the results so far? A tweet and a blog post (starting small…):

Since its inception, the #HeForShe campaign, which is organized by UN Women, has gathered an impressive 1.5 million commitments, of which 1.2 million are from men, sparked over 1.3 billion gender equality actions and generated 1.1 thousand offline events and counting. Using “online, offline, and mobile phone technology to identify and activate advocates in every city, community, and village around the world” surely this must generate a large amount of data, possibly even “big data”, for analysis of the contribution of the movement towards UN Women’s core strategic pillars. This is important because achieving gender equality everywhere is absolutely crucial, and perhaps not the least so in communities and villages in developing countries. However, with this is mind, the question from my previous blog post echoed loudly in my head when I saw the global distribution of commitments on the interactive map on the #HeForShe site. Is all data created equal?

Photo: heforshe.org (accessed September 28, 2017)

Although it looks like the question “Have you #HeForShe’d yet?” is mostly a phenomena asked among “Western” men, there are also some beacons shining brighter in Magenta (the color symbolizing the movement) than others. In Rwanda, over 200,000 commitments have been made, of which over 160,000 are from men. While its neighbor Uganda have less than 1,000 commitments. Why this difference? Perhaps part of the answer spells Rwanda’s President Paul Kagame. But does Kagame’s role as an IMPACT Champion for #HeForShe help to better position his country to empower women? It could, for a number of reasons – some which could be extrapolated from his quote:

Women and men are equal in in terms of ability and dignity, and they should be equal in terms of opportunity. As Rwandans, as a global community, we need every member of our society to use his or her talents to the fullest. – Paul Kagame

Kagame’s words can be elaborated further in terms of power by using Naila Kabeer’s distinction of positive and negative agency. The interpretation of the first sentence of the quote could be that of limiting men’s power over women (negative meaning of agency) by clearly making the statement of equality in both ability and dignity, coupled with a vision of equality in opportunity. Whereas the second sentence is more directed towards the positive meaning of agency aimed at nurturing the power to pursue ones own choices and goals in life. The latter aspect can be further traced to Amartya Sen’s capabilities approach and the notion of development as freedom. Although these elements are primarily aimed at altering the power balance of individual agency between men and women, there is also a structural aspect related to the issue of women’s empowerment. In this second dimension, Kagame’s position in Rwandan society offers the opportunity to greatly influence the structural and institutional barriers hindering such a development.

How could data generated by #HeForShe be used in order to measure the impact of the movement on women’s empowerment both in terms of agency and structure in Rwandan society? Here, we enter the domain of theory. A good starting point would be to depart from Dorothea Kleine’s depiction of the choice framework.

In her framework, the combination of individual resources (agency) and the structural dimensions of a particular society determine the degrees of empowerment of individuals in that society, and whether or not they can identify the various degrees of choice and use it to achieve a set of development outcomes, of which choice itself is the primary outcome. But choice is complex. In her paper, Kabeer points out the difficulties of qualifying choice itself referring to the conditions (access or absence of viable alternatives) and the consequences (the degree of importance) of choice. Furthermore, Kabeer has also demonstrated the conceptual difficulties of using indicators in order to measure empowerment due to a very complex and dynamic interrelation between choice and access to resources, achievements, and agency. However, combining the development outcomes suggested by Kleine with Kabeer’s insights from her “reflections on the measurement of women’s empowerment” could provide a practical blueprint for how theory could be used in order to analyze the large amount of data generated by #HeForShe and determine its impact on women’s empowerment in Rwanda and elsewhere.

Thus, the point I try to make here is similar to that of my previous post that data is “facts and statistics collected together for reference or analysis”, but to make sense of it all we need to view the data through a theoretical lens. Do you agree? Let me hear your thoughts in the comment field below and let’s engage in dialogue!