Claudia Wells at SDG Knowledge Hub: “A shocking increase in violence against women and girls has been reported in many countries during the COVID-19 pandemic, amounting to what UN Women calls a “shadow pandemic.”
The jarring facts are:
Globally 243 million women and girls have been subjected to sexual and/or physical violence by an intimate partner in the past 12 months.
The UNFPA estimates that the pandemic will cause a one-third reduction in progress towards ending gender-based violence by 2030;
UNFPA predicts an additional 15 million cases of gender-based violence for every three months of lockdown.
Official data captures only a fraction of the true prevalence and nature of gender-based violence.
The response to these new challenges were discussed at a meeting in July with a community-led response delivered through local actors highlighted as key. This means that timely, disaggregated, community-level data on the nature and prevalence of gender-based violence has never been more important. Data collected within communities can play a vital role to fill the gaps and ensure that data-informed policies reflect the lived experiences of the most marginalized women and girls.
Community Scorecards: Example from Nepal
Collecting and using community-level data can be challenging, particularly under the restrictions of the pandemic. Working in partnerships is therefore vital if we are to respond quickly and flexibly to new and old challenges.
A great example of this is the Leave No One Behind Partnership, which responds to these challenges while delivering on crucial data and evidence at the community level. This important partnership brings together international civil society organizations with national NGOs, civic platforms and community-based organizations to monitor progress towards the SDGs….
While COVID-19 has highlighted the need for local, community-driven data, public health restrictions have also made it more challenging to collect such data. For example the usual focus group approach to creating a community scorecard is no longer possible.
The coalition in Nepal therefore faces an increased demand for community-driven data while needing to develop a “new normal for data collection.”. Partners must: make data collection more targeted; consider how data on gender-based violence are included in secondary sources; and map online resources and other forms of data collection.
Addressing these new challenges may include using more blended collection approaches such as mobile phones or web-based platforms. However, while these may help to facilitate data collection, they come with increased privacy and safeguarding risks that have to be carefully considered to ensure that participants, particularly women and girls, are not at increased risk of violence or have their privacy and confidentiality exposed….(More)”.
Paper by Ridhi Kashyap, Masoomali Fatehkia, Reham Al Tamime, and Ingmar Weber: “Background: In recognition of the empowering potential of digital technologies, gender equality in internet access and digital skills is an important target in the United Nations (UN) Sustainable Development Goals (SDGs). Gender-disaggregated data on internet use are limited, particularly in less developed countries.
Objective: We leverage anonymous, aggregate data on the online populations of Google and Facebook users available from their advertising platforms to fill existing data gaps and measure global digital gender inequality.
Methods: We generate indicators of country-level gender gaps on Google and Facebook. Using these online indicators independently and in combination with offline development indicators, we build regression models to predict gender gaps in internet use and digital skills computed using available survey data from the International Telecommunications Union (ITU).
Results: We find that women are significantly underrepresented in the online populations of Google and Facebook in South Asia and sub-Saharan Africa. These platform-specific gender gaps are a strong predictor that women lack internet access and basic digital skills in these populations. Comparing platforms, we find Facebook gender gap indicators perform better than Google indicators at predicting ITU internet use and low-level digital-skill gender gaps. Models using these online indicators outperform those using only offline development indicators. The best performing models, however, are those that combine Facebook and Google online indicators with a country’s development indicators such as the Human Development Index….(More)”.
It reports on the availability of gender data in Colombia, Costa Rica, the Dominican Republic, Jamaica, and Paraguay at the international, national, and microdata levels, and it assesses the availability of 93 gender indicators, their disaggregations, and their frequency of observation in international and national databases and publications.
Additionally, with the assistance of the UN Economic Commission for Latin America (ECLAC), the report documents the availability of statistical indicators to support gender development plans in the five countries.
Through this report, we hope to help move the development community one step closer to producing high-quality and policy-relevant gender indicators to inform better decisions….Read the report.“
Press Release: “As part of efforts to identify priorities across sectors in which data and data science could make a difference, The Governance Lab (The GovLab) at the New York University Tandon School of Engineering has partnered with Data2X, the gender data alliance housed at the United Nations Foundation, to release ten pressing questions on gender that experts have determined can be answered using data. Members of the public are invited to share their views and vote to help develop a data agenda on gender.
The questions are part of the 100 Questions Initiative, an effort to identify the most important societal questions that can be answered by data. The project relies on an innovative process of sourcing “bilinguals,” individuals with both subject-matter and data expertise, who in this instance provided questions related to gender they considered to be urgent and answerable. The results span issues of labor, health, climate change, and gender-based violence.
Through the initiative’s new online platform, anyone can now vote on what they consider to be the most pressing, data-related questions about gender that researchers and institutions should prioritize. Through voting, the public can steer the conversation and determine which topics should be the subject of data collaboratives, an emerging form of collaboration that allows organizations from different sectors to exchange data to create public value.
The GovLab has conducted significant research on the value and practice of data collaboratives, and its research shows that inter-sectoral collaboration can both increase access to data as well as unleash the potential of that data to serve the public good.
Data2X supported the 100 Questions Initiative by providing expertise and connecting The GovLab with relevant communities, events, and resources. The initiative helped inform Data2X’s “Big Data, Big Impact? Towards Gender-Sensitive Data Systems” report, which identifies gaps of information on gender equality across key policy domains.
“Asking the right questions is a critical first step in fostering data production and encouraging data use to truly meet the unique experiences and needs of women and girls,” said Emily Courey Pryor, executive director of Data2X. “Obtaining public feedback is a crucial way to identify the most urgent questions — and to ultimately incentivize investment in gender data collection and use to find the answers.”Said Stefaan Verhulst, co-founder and chief research and development officer at The GovLab, “Sourcing and prioritizing questions related to gender can inform resource and funding allocation to address gender data gaps and support projects with the greatest potential impact. This way, we can be confident about solutions that address the challenges facing women and girls.”…(More)”.
Flavie Halais at Wired: “Public transportation is sexist. This may be unintentional or implicit, but it’s also easy to see. Women around the world do more care and domestic work than men, and their resulting mobility habits are hobbled by most transport systems. The demands of running errands and caring for children and other family members mean repeatedly getting on and off the bus, meaning paying more fares. Strollers and shopping bags make travel cumbersome. A 2018 study of New Yorkers found women were harassed on the subway far more frequently than men were, and as a result paid more money to avoid transit in favor of taxis and ride-hail….
What is not measured is not known, and the world of transit data is still largely blind to women and other vulnerable populations. Getting that data, though, isn’t easy. Traditional sources like national censuses and user surveys provide reliable information that serve as the basis for policies and decisionmaking. But surveys are costly to run, and it can take years for a government to go through the process of adding a question to its national census.
Before pouring resources into costly data collection to find answers about women’s transport needs, cities could first turn to the trove of unconventional gender-disaggregated data that’s already produced. They include data exhaust, or the trail of data we leave behind as a result of our interactions with digital products and services like mobile phones, credit cards, and social media. Last year, researchers in Santiago, Chile, released a report based on their parsing of anonymized call detail records of female mobile phone users, to extract location information and analyze their mobility patterns. They found that women tended to travel to fewer locations than men, and within smaller geographical areas. When researchers cross-referenced location information with census data, they found a higher gender gap among lower-income residents, as poorer women made even shorter trips. And when using data from the local transit agency, they saw that living close to a public transit stop increased mobility for both men and women, but didn’t close the gender gap for poorer residents.
To encourage private companies to share such info, Stefaan Verhulst advocates for data collaboratives, flexible partnerships between data providers and researchers. Verhulst is the head of research and development at GovLab, a research center at New York University that contributed to the research in Santiago. And that’s how GovLab and its local research partner, Universidad del Desarollo, got access to the phone records owned by the Chilean phone company, Telefónica. Data collaboratives can enhance access to private data without exposing companies to competition or privacy concerns. “We need to find ways to access data according to different shades of openness,” Verhulst says….(More)”.
Essay by Bapu Vaitla, Stefaan Verhulst, Linus Bengtsson, Marta C. González, Rebecca Furst-Nichols & Emily Courey Pryor in Special Issue on Big Data of Nature Medicine: “Women and girls are legally and socially marginalized in many countries. As a result, policymakers neglect key gendered issues such as informal labor markets, domestic violence, and mental health1. The scientific community can help push such topics onto policy agendas, but science itself is riven by inequality: women are underrepresented in academia, and gendered research is rarely a priority of funding agencies.
However, the critical importance of better gender data for societal well-being is clear. Mental health is a particularly striking example. Estimates from the Global Burden of Disease database suggest that depressive and anxiety disorders are the second leading cause of morbidity among females between 10 and 63 years of age2. But little is known about the risk factors that contribute to mental illness among specific groups of women and girls, the challenges of seeking care for depression and anxiety, or the long-term consequences of undiagnosed and untreated illness. A lack of data similarly impedes policy action on domestic and intimate-partner violence, early marriage, and sexual harassment, among many other topics.
‘Big data’ can help fill that gap. The massive amounts of information passively generated by electronic devices represent a rich portrait of human life, capturing where people go, the decisions they make, and how they respond to changes in their socio-economic environment. For example, mobile-phone data allow better understanding of health-seeking behavior as well as the dynamics of infectious-disease transmission3. Social-media platforms generate the world’s largest database of thoughts and emotions—information that, if leveraged responsibly, can be used to infer gendered patterns of mental health4. Remote sensors, especially satellites, can be used in conjunction with traditional data sources to increase the spatial and temporal granularity of data on women’s economic activity and health status5.
But the risk of gendered algorithmic bias is a serious obstacle to the responsible use of big data. Data are not value free; they reproduce the conscious and unconscious attitudes held by researchers, programmers, and institutions. Consider, for example, the training datasets on which the interpretation of big data depends. Training datasets establish the association between two or more directly observed phenomena of interest—for example, the mental health of a platform user (typically collected through a diagnostic survey) and the semantic content of the user’s social-media posts. These associations are then used to develop algorithms that interpret big data streams. In the example here, the (directly unobserved) mental health of a large population of social-media users would be inferred from their observed posts….(More)”.
Book edited by Tanu Priya Uteng, Hilda Rømer Christensen, and Lena Levin: “This book considers gender perspectives on the ‘smart’ turn in urban and transport planning to effectively provide ‘mobility for all’ while simultaneously attending to the goal of creating green and inclusive cities. It deals with the conceptualisation, design, planning, and execution of the fast-emerging ‘smart’ solutions.
The volume questions the efficacy of transformations being brought by smart solutions and highlights the need for a more robust problem formulation to guide the design of smart solutions, and further maps out the need for stronger governance to manage the introduction and proliferation of smart technologies. Authors from a range of disciplinary backgrounds have contributed to this book, designed to converse with mobility studies, transport studies, urban-transport planning, engineering, human geography, sociology, gender studies, and other related fields.
The book fills a substantive gap in the current gender and mobility discourses, and will thus appeal to students and researchers studying mobilities in the social, political, design, technical, and environmental sciences….(More)”.
Report by Data2X: “How can insights drawn from big data sources improve understanding about the lives of women and girls?
This question has underpinned Data2X’s groundbreaking work at the intersection of big data and gender — work that funded ten research projects that examined the potential of big data to fill the global gender data gap.
Big data offers unique insights on women and girls.
Gender-sensitive big data is ready to scale and integrate with traditional data.
Identify and correct bias in big datasets.
Protect the privacy of women and girls.
Women and girls must be central to data governance.
This report argues that the time for pilot projects has passed. Data privacy concerns must be addressed; investment in scale up is needed. Big data offers great potential for women and girls, and indeed for all people….(More)”.
Pew Research Center: “Machine vision tools like facial recognition are increasingly being used for law enforcement, advertising, and other purposes. Pew Research Center itself recently used a machine vision system to measure the prevalence of men and women in online image search results. This kind of system develops its own rules for identifying men and women after seeing thousands of example images, but these rules can be hard for to humans to discern. To better understand how this works, we showed images of the Center’s staff members to a trained machine vision system similar to the one we used to classify image searches. We then systematically obscured sections of each image to see which parts of the face caused the system to change its decision about the gender of the person pictured. Some of the results seemed intuitive, others baffling. In this interactive challenge, see if you can guess what makes the system change its decision.
Brief of the Data 2X Big Data and Gender Brief Series by The GovLab, UNICEF, Universidad Del Desarrollo, Telefónica R&D Center, ISI Foundation, and DigitalGlobe: “Mobility is gendered. For example, the household division of labor in many societies leads women and girls to take more multi-purpose, multi-stop trips than men. Women-headed households also tend to work more in the informal sector, with limited access to transportation subsidies, and use of public transit is further reduced by the risk of violence in public spaces.
This brief summarizes a recent analysis of gendered urban mobility in 51 (out of 52) neighborhoods of Santiago, Chile, relying on the call detail records (CDRs) of a large sample of mobile phone users over a period of three months. We found that: 1) women move less overall than men; 2) have a smaller radius of movement; and 3) tend to concentrate their time in a smaller set of locations. These mobility gaps are linked to lower average incomes and fewer public and private transportation options. These insights, taken from large volumes of passively generated, inexpensive data streaming in realtime, can help policymakers design more gender inclusive urban transit systems….(More)”.