Female Victims of Gendered Violence, Their Human Rights and the Innovative Use of Data Technology to Predict, Prevent and Pursue Harms


Paper by Jamie Grace: “This short paper has the objective of making the case for more investment to explore the use of data-driven technology to predict, prevent and pursue criminal harms against women. The paper begins with an overview of the contemporary scale of the issues, and the current problem of recording data on serious violent and sexual offending against women, before moving on to consider the current status and strength of positive obligations under UK human rights law to protect victims of intimate partner violence. The paper then looks at some examples of how data tech can augment policing of serious criminal harms against women, before turning to consider some of the legal problems concerning potential bias, inaccuracies and transparency that can dog ‘predictive policing’ in particular. Finally, a conclusion is offered up which explores the degree to which investment and exploration of the predictive policing of intimate partner violence must be pursued at the same time as better oversight mechanisms are also developed for the use of machine learning technology in public protection roles, since the two emphases go hand in hand…(More)”.

Selected Readings on Data, Gender, and Mobility


By Michelle Winowatan, Uma Kalkar, Andrew Young, and Stefaan Verhulst

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data, gender, and mobility was originally published in 2017, and updated in 2021.

This edition of the Selected Readings was  developed as part of an ongoing project at the GovLab, supported by Data2X, in collaboration with UNICEF, DigitalGlobe, IDS (UDD/Telefonica R&D), and the ISI Foundation, to establish a data collaborative to analyze unequal access to urban transportation for women and girls in Chile. We thank all our partners for their suggestions to the below curation – in particular Leo Ferres at IDS who got us started with this collection; Ciro Cattuto and Michele Tizzoni from the ISI Foundation; and Bapu Vaitla at Data2X for their pointers to the growing data and mobility literature. 

Introduction

Daily mobility is key for gender equity. Access to transportation contributes to women’s agency and independence. The ability to move from place to place safely and efficiently can allow women to access education, work, and the public domain more generally. Yet, mobility is not just a means to access various opportunities. It is also a means to enter the public domain.

Women’s mobility is a multi-layered challenge

Women’s daily mobility, however, is often hampered by social, cultural, infrastructural, and technical barriers. Cultural bias, for instance, limits women’s mobility in a way that women are confined to an area with close proximity to their house due to society’s double standard on women to be homemakers. From an infrastructural perspective, public transportation mostly only accommodates home-to-work trips, when in reality women often make more complex trips with multiple stops, for example, at the market, school, healthcare provider – sometimes called “trip chaining.” From a safety perspective, women tend to avoid making trips in certain areas and/or at certain times due to a constant risk of being sexually harassed n public places. Women are also pushed toward more expensive transportation – such as taking a cab instead of a bus or train – based on safety concerns.

The growing importance of (new sources of) data

Researchers are increasingly experimenting with ways to address these interdependent problems through the analysis of diverse datasets, often collected by private sector businesses and other non-governmental entities. Gender-disaggregated mobile phone records, geospatial data, satellite imagery, and social media data, to name a few, are providing evidence-based insight into gender and mobility concerns. Such data collaboratives – the exchange of data across sectors to create public value – can help governments, international organizations, and other public sector entities in the move toward more inclusive urban and transportation planning, and the promotion of gender equity.

The below curated set of readings seek to focus on the following areas:

  1. Insights on how data can inform gender empowerment initiatives,
  2. Emergent research into the capacity of new data sources – like call detail records (CDRs) and satellite imagery – to increase our understanding of human mobility patterns, and,
  3. Publications exploring data-driven policy for gender equity in mobility.

Readings are listed in alphabetical order.

We selected the readings based upon their focus (gender and/or mobility related); scope and representativeness (going beyond one project or context); type of data used (such as CDRs and satellite imagery); and date of publication.

Annotated Reading List

Data and Gender

Blumenstock, Joshua, and Nathan Eagle. Mobile Divides: Gender, Socioeconomic Status, and Mobile Phone Use in Rwanda. ACM Press, 2010.

  • Using traditional survey and mobile phone operator data, this study analyzes gender and socioeconomic divides in mobile phone use in Rwanda, where it is found that the use of mobile phones is significantly more prevalent in men and the higher class.
  • The study also shows the differences in the way men and women use phones, for example: women are more likely to use a shared phone than men.
  • The authors frame their findings around gender and economic inequality in the country to the end of providing pointers for government action.

Bosco, Claudio, et al. Mapping Indicators of Female Welfare at High Spatial Resolution. WorldPop and Flowminder, 2015.

  • This report focuses on early adolescence in girls, which often comes with higher risk of violence, fewer economic opportunity, and restrictions on mobility. Significant data gaps, methodological and ethical issues surrounding data collection for girls also create barriers for policymakers to create evidence-based policy to address those issues.
  • The authors analyze geolocated household survey data, using statistical models and validation techniques, and creates high-resolution maps of various sex-disaggregated indicators, such as nutrition level, access to contraception, and literacy, to better inform local policy making processes.
  • Further, it identifies the gender data gap and issues surrounding gender data collection, and provides arguments for why having  comprehensive data can help create better policy and contribute to the achievements of the Sustainable Development Goals (SDGs).

Buvinic, Mayra, Rebecca Furst-Nichols, and Gayatri Koolwal. Mapping Gender Data Gaps. Data2X, 2014.

  • This study identifies gaps in gender data in developing countries on health, education, economic opportunities, political participation, and human security issues.
  • It recommends ways to close the gender data gap through censuses and micro-level surveys, service and administrative records, and emphasizes how “big data” in particular can fill the missing data that will be able to measure the progress of women and girls well being. The authors argue that identifying these gaps is key to achieving SDG 5: advancing gender equality and women’s empowerment.

Catalyzing Inclusive Financial Systems: Chile’s Commitment to Women’s Data. Data2X, 2014.

  • This article analyzes global and national data in the banking sector to fill the gap of sex-disaggregated data in Chile. The purpose of the study is to describe the difference in spending behavior and priorities between women and men, identify the challenges for women in accessing financial services, and create policies that promote women inclusion in Chile.

Ready to Measure: Twenty Indicators for Monitoring SDG Gender Targets. Open Data Watch and Data2X, 2016.

  • Using readily available data, this study identifies 20 SDG indicators related to gender issues that can serve as a baseline measurement for advancing gender equality, such as percentage of women aged 20-24 who were married or in a union before age 18 (child marriage), proportion of seats held by women in national parliament, and share of women among mobile telephone owners, among others.

Ready to Measure Phase II: Indicators Available to Monitor SDG Gender Targets. Open Data Watch and Data2X, 2017.

  • The Phase II paper is an extension of the Ready to Measure Phase I above. Where Phase I identifies the readily available data to measure women and girls well-being, Phase II provides information on how to access this data and summarizes insights extracted from it.
  • Phase II elaborates the insights about data gathered from ready to measure indicators and finds that although underlying data to measure indicators of women and girls’ wellbeing is readily available in most cases, it is typically not sex-disaggregated.
  • Over one in five – 53 out of 232 – SDG indicators specifically refer to women and girls. However, further analysis from this study reveals that at least 34 more indicators should be disaggregated by sex. For instance, there should be 15 more sex-disaggregated indicators for SDG number 3: “Ensure healthy lives and promote well-being for all at all ages.”
  • The report recommends national statistical agencies to take the lead and assert additional effort to fill the data gap by utilizing tools such as the statistical model to fill the current gender data gap for each of the SDGs.

Reed, Philip J., Muhammad Raza Khan, and Joshua Blumenstock. Observing gender dynamics and disparities with mobile phone metadata. International Conference on Information and Communication Technologies and Development (ICTD), 2016.

  • The study analyzes mobile phone logs of millions of Pakistani residents to explore whether there is a difference in mobile phone usage behavior between male and female and determine the extent to which gender inequality is reflected in mobile phone usage.
  • It utilizes mobile phone data to analyze the pattern of usage behavior between genders, and socioeconomic and demographic data obtained from census and advocacy groups to assess the state of gender equality in each region in Pakistan.
  • One of its findings is a strong positive correlation between the proportion of female mobile phone users and education score.

Stehlé, Juliette, et al. Gender homophily from spatial behavior in a primary school: A sociometric study. 2013.

  • This paper seeks to understand homophily, a human behavior that characterizes interactions with peers who have similarities in “physical attributes to tastes or political opinions”. Further, it seeks to identify the magnitude of influence, a type of homophily applied to social structures.
  • Focusing on gender interaction among primary school aged children in France, this paper collects data from wearable devices from 200 children in the period of 2 days and measures the physical proximity and duration of the interaction among those children in the playground.
  • It finds that interaction patterns are significantly determined by grade and class structure of the school. This means that children belonging to the same class have most interactions, and that lower grades usually do not interact with higher grades.
  • From a gender lens, this study finds that mixed-gender interaction lasts shorter relative to same-gender interaction. In addition, interaction among girls is also longer compared to interaction among boys. These indicate that the children in this school tend to have stronger relationships within their own gender, or what the study calls gender homophily. It further finds that gender homophily is apparent in all classes.

Strengthening Gender Measures and Data in the COVID-19 Era: An Urgent Need for Change. Paris 21, 2021.

  • COVID-19 has exacerbated gender disparities, especially with regard to women’s livelihoods, unpaid labor, mental health, and risk of gender-based violence. Gaps in gender data impedes robust, data-driven, and effective policies to quantify, analyse, and respond to these issues. 
  • Without this information, the full effects of the COVID-19 pandemic cannot be understood. This report calls on National Statistical Systems, survey managers, funders, multilateral agencies, researchers, and policymakers to collect gender-intentional and disaggregated data that is standardized and comparable to address key areas of concern for women and girls. Additionally, it seeks to link non-traditional data sources, such as social media and news media, with existing frameworks to fill in knowledge gaps. Moreover, this information must be rendered accessible for all stakeholders to maximize the potential of the information. Post-pandemic, conscious collection and collation of gendered data is vital to preempt policy problems.

The Sex, Gender and COVID-19 Project: The COVID-19 Sex-Disaggregated Data Tracker. 2021.

  • This data tracker, produced by Global Health 50/50, the African Population and Health Research Center, and the International Center for Research on Women, tracks which countries and datasets have reported sex-disaggregated data on COVID-19 testing, confirmed cases, hospitalizations, and deaths.

Data and Mobility

Bengtsson, Linus, et al. Using Mobile Phone Data to Predict the Spatial Spread of Cholera. Flowminder, 2015.

  • This study seeks to predict the 2010 cholera epidemic in Haiti using 2.9 million anonymous mobile phone SIM cards and reported cases of Cholera from the Haitian Directorate of Health, where 78 study areas were analyzed in the period of October 16 – December 16, 2010.
  • From this dataset, the study creates a mobility matrix that indicates mobile phone movement from one study area to another and combines that with the number of reported cases of cholera in the study areas to calculate the infectious pressure level of those areas.
  • The main finding of its analysis shows that the outbreak risk of a study area correlates positively with the infectious pressure level, where an infectious pressure of over 22 results in an outbreak within 7 days. Further, it finds that the infectious pressure level can inform the sensitivity and specificity of the outbreak prediction.
  • It hopes to improve infectious disease containment by identifying areas with highest risks of outbreaks.

Calabrese, Francesco, et al. Understanding Individual Mobility Patterns from Urban Sensing Data: A Mobile Phone Trace Example. SENSEable City Lab, MIT, 2012.

  • This study compares mobile phone data and odometer readings from annual safety inspections to characterize individual mobility and vehicular mobility in the Boston Metropolitan Area, measured by the average daily total trip length of mobile phone users and average daily Vehicular Kilometers Traveled (VKT).
  • The study found that, “accessibility to work and non-work destinations are the two most important factors in explaining the regional variations in individual and vehicular mobility, while the impacts of populations density and land use mix on both mobility measures are insignificant.” Further, “a well-connected street network is negatively associated with daily vehicular total trip length.”
  • This study demonstrates the potential for mobile phone data to provide useful and updatable information on individual mobility patterns to inform transportation and mobility research.

Campos-Cordobés, Sergio, et al. Chapter 5 – Big Data in Road Transport and Mobility Research.” Intelligent Vehicles. Edited by Felipe Jiménez. Butterworth-Heinemann, 2018.

  • This study outlines a number of techniques and data sources – such as geolocation information, mobile phone data, and social network observation – that could be leveraged to predict human mobility.
  • The authors also provide a number of examples of real-world applications of big data to address transportation and mobility problems, such as transport demand modeling, short-term traffic prediction, and route planning.

Gauvin, Laetitia et al. Gender gaps in urban mobility. Humanities and Information Science. Humanities & Social Sciences Communications vol. 7, issue 11, 2020.

  • This article discusses how urbanization affects mobility of women in realizing their rights. It points out the historic lack of gender disaggregated data for urban planning, leading to transportation designs that do not best accommodate the needs of women.
  • Examining the case study of urban mobility through a gendered lens in the large and growing metropolitan area of Santiago, Chile, the article examines the mobility traces from Call Detail Records (CDRs) of an anonymized cohort of mobile phone users, sorted by gender, over 3 months. It then mapped differences between men and women with regard to socio-demographic indicators and mobility differences across the city and through the Santiago transportation network structure and identified points of interests frequented by either sex to inform gendered mobility needs in urban areas.

Lin, Miao, and Wen-Jing Hsu. Mining GPS Data for Mobility Patterns: A Survey. Pervasive and Mobile Computing vol. 12, 2014.

  • This study surveys the current field of research using high resolution positioning data (GPS) to capture mobility patterns.
  • The survey focuses on analyses related to frequently visited locations, modes of transportation, trajectory patterns, and placed-based activities. The authors find “high regularity” in human mobility patterns despite high levels of variation among the mobility areas covered by individuals.

Phithakkitnukoon, Santi, Zbigniew Smoreda, and Patrick Olivier. Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data. PLoS ONE, 2012.

  • This study used a year’s call logs and location data of approximately one million mobile phone users in Portugal to analyze the association between individuals’ mobility and their social networks.
  • It measures and analyze travel scope (locations visited) and geo-social radius (distance from friends, family, and acquaintances) to determine the association.
  • It finds that 80% of places visited are within 20 km of an individual’s nearest social ties’ location and it rises to 90% at 45 km radius. Further, as population density increases, distance between individuals and their social networks decreases.
  • The findings in this study demonstrates how mobile phone data can provide insights to “the socio-geography of human mobility”.

Semanjski, Ivana, and Sidharta Gautama. Crowdsourcing Mobility Insights – Reflection of Attitude Based Segments on High Resolution Mobility Behaviour Data. vol. 71, Transportation Research, 2016.

  • Using cellphone data, this study maps attitudinal segments that explain how age, gender, occupation, household size, income, and car ownership influence an individual’s mobility patterns. This type of segment analysis is seen as particularly useful for targeted messaging.
  • The authors argue that these time- and space-specific insights could also provide value for government officials and policymakers, by, for example, allowing for evidence-based transportation pricing options and public sector advertising campaign placement.

Silveira, Lucas M., et al. MobHet: Predicting Human Mobility using Heterogeneous Data Sources. vol. 95, Computer Communications , 2016.

  • This study explores the potential of using data from multiple sources (e.g., Twitter and Foursquare), in addition to GPS data, to provide a more accurate prediction of human mobility. This heterogenous data captures popularity of different locations, frequency of visits to those locations, and the relationships among people who are moving around the target area. The authors’ initial experimentation finds that the combination of these sources of data are demonstrated to be more accurate in identifying human mobility patterns.

Wilson, Robin, et al. Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLOS Current Disasters, 2016.

  • Utilizing call detail records of 12 million mobile phone users in Nepal, this study seeks spatio-temporal details of the population after the earthquake on April 25, 2015.
  • It seeks to answer the problem of slow and ineffective disaster response, by capturing near real-time displacement patterns provided by mobile phone call detail records, in order to inform humanitarian agencies on where to distribute their assistance. The preliminary results of this study were available nine days after the earthquake.
  • This project relies on the foundational cooperation with mobile phone operators, who supplied the de-identified data from 12 million users before the earthquake.
  • The study finds that shortly after the earthquake there was an anomalous population movement out of the Kathmandu Valley, the most impacted area, to surrounding areas. The study estimates 390,000 more people  than normal had left the valley.

Data, Gender and Mobility

Althoff, Tim, et al.Large-Scale Physical Activity Data Reveal Worldwide Activity Inequality. Nature, 2017.

  • This study’s analysis of worldwide physical activity is built on a dataset containing 68 million days of physical activity of 717,527 people collected through their smartphone accelerometers.
  • The authors find a significant reduction in female activity levels in cities with high active inequality, where high active inequality is associated with low city walkability – walkability indicators include pedestrian facilities (city block length, intersection density, etc.) and amenities (shops, parks, etc.).
  • Further, they find that high active inequality is associated with high levels of inactivity-related health problems, like obesity.

Borker, Girija. Safety First: Street Harassment and Women’s Educational Choices in India.Stop Street Harassment, 2017.

  • Using data collected from SafetiPin, an application that allows users to mark an area on a map as safe or not, and Safecity, another application that lets users share their experience of harassment in public places, Borker analyzes the safety of travel routes surrounding different colleges in India and their effect on women’s college choices.
  • The study finds that women are willing to go to a lower ranked college in order to avoid higher risk of street harassment. Women who choose the best college from their set of options, spend an average of $250 more each year to access safer modes of transportation.

Frias-Martinez, Vanessa, Enrique Frias-Martinez, and Nuria Oliver. A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records. Association for the Advancement of Artificial Intelligence, 2010.

  • Using encrypted Call Detail Records (CDRs) of 10,000 participants in a developing economy, this study analyzes the behavioral, social, and mobility variables to determine the gender of a mobile phone user, and finds that there is a difference in behavioral and social variables in mobile phone use between female and male.
  • It finds that women have higher usage of phone in terms of number of calls made, call duration, and call expenses compared to men. Women also have bigger social network, meaning that the number of unique phone numbers that contact or get contacted is larger. It finds no statistically significant difference in terms of distance made between calls in men and women.
  • Frias-Martinez et al recommends to take these findings into consideration when designing a cellphone based service.

Psylla, Ioanna, Piotr Sapiezynski, Enys Mones, Sune Lehmann. The role of gender in social network organization. PLoS ONE 12, December 20, 2017.

  • Using a large dataset of high resolution data collected through mobile phones, as well as detailed questionnaires, this report studies gender differences in a large cohort. The researchers consider mobility behavior and individual personality traits among a group of more than 800 university students.
  • Analyzing mobility data, they find both that women visit more unique locations over time, and that they have more homogeneous time distribution over their visited locations than men, indicating the time commitment of women is more widely spread across places.

The Landscape of Big Data and Gender. Data2X, February, 2021.

  • Under the backdrop of COVID-19, this report reaffirms that big data initiatives to study mobility, health, and social norms through gendered lenses have greatly progressed. More private companies and think tanks have launched data collection and sharing efforts to spur innovative projects to address COVID-19 complications.
  • However, economic opportunity, security, and civic action have been lagging behind. Big data collection among these topics is complicated by the lack of sex-disaggregated datasets, gender disparities in technology access, and the lack of gender-tags among big data.
  • Large technology firms, especially social networks like Facebook, LinkedIn, Uber, and more, create a large amount of gender-organized data. The report found that users and data-holding companies are willing to share this information for public policy reasons so long as it provides value and is protected. To this end, Data2X, alongside its partners, champion the use of data collaboratives to use gender sorted information for social good.

Vaitla, Bapu. Big Data and the Well Being of Women and Girls: Applications on the Social Scientific Frontier. Data2X, Apr. 2017.

  • In this study, the researchers use geospatial data, credit card and cell phone information, and social media posts to identify problems–such as malnutrition, education, access to healthcare, mental health–facing women and girls in developing countries.
  • From the credit card and cell phone data in particular, the report finds that analyzing patterns of women’s spending and mobility can provide useful insight into Latin American women’s “economic lifestyles.”
  • Based on this analysis, Vaitla recommends that various untraditional big data be used to fill gaps in conventional data sources to address the common issues of invisibility of women and girls’ data in institutional databases.

The Landscape of Big Data and Gender


Report by Data2X: “This report draws out six observations about trends in big data and gender:

– The current environment COVID-19 and the global economic recession is stimulating groundbreaking gender research.

– Where we’re progressing, where we’re lagging Some gendered topics—especially mobility, health, and social norms—are increasingly well-studied through the combination of big data and traditional data. However, worrying gaps remain, especially around the subjects of economic opportunity, human security, and public participation.

– Capturing gender-representative samples using big data continues to be a challenge, but progress is being made.

– Large technology firms generate an immense volume of gender data critical for policymaking, and researchers are finding ways to reuse this data safely.

– Data collaboratives that bring private sector data-holders, researchers, and public policymakers together in a formal, enduring relationship can help big data make a practical difference in the lives of women and girls….(More)”

Mining Twitter Data to Identify Topics of Discussion by Indian Feminist Activists


Brief by the Center on Gender Equity and Health at the University of California at San Diego (UC San Diego): “Over the past decade, social media platforms have become ubiquitous, serving as a democratic space for activism and providing new opportunities for social movements. Twitter has emerged as a popular tool used by feminist activists for spreading awareness and organizing. Research examining feminist movements on social media have highlighted the role of Twitter in emphasizing issues related to gender-based violence (GBV) victimization including the MeToo movement, as well as calling out male privilege and regressive gender norms.

Scholars have examined the high levels of engagement in Twitter discussions and debates by grassroots feminists, as well as the effect of this activity on advancing the feminist agenda in the digital space and amplifying minority voices. Studying Twitter conversations of feminist activists can help identify gender issues that need attention but are underprioritized politically. This brief presents findings from
our analysis of a corpus of tweets by 59 Indian feminist activists, tweeted between March and August 2020. The analysis examines how the feminist community in India has used Twitter as a tool for activism during the COVID-19 pandemic. In addition to providing insights related to mainstream gender issues in India, this analysis hopes to contribute to methodological advancement in gender research….(More)”.

A New Normal for Data Collection: Using the Power of Community to Tackle Gender Violence Amid COVID-19


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)”.

Monitoring global digital gender inequality using the online populations of Facebook and Google


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)”.

Where are there gaps in gender data in five Latin American and Caribbean countries?


Data2X: “This report builds on our 2019 technical report, Bridging the Gap: Mapping Gender Data Availability in Africabut shifts the geographic focus to Latin America and the Caribbean (LAC).

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.

10 transformative data questions related to gender


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)”.

Making Public Transit Fairer to Women Demands Way More Data


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)”.

The promise and perils of big gender data


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)”.