Several governments across Asia and the Global South have taken cognizance of the need for gender data in different fields, supported by national comparisons undertaken by international organisations.
Equally important are initiatives that involve women and other citizen groups in the use of open data, as well as monitor the impact of public initiatives on the lives of women.
Projects have used big data that utilises existing streams of data such as cellular and financial data, geospatial data, among others, to produce gender data in critical fields such as health - albeit excluding marginalised women without digital access.
Responsible and feminist design practices need to be adopted in projects that open and use gender data, in order to produce data that is usable, contextually relevant, and protects the rights to privacy, consent, and ownership.
Women and other marginalised groups are less likely to be counted in data, and their concerns are underrepresented in the design of data systems. This absence from datasets and systems can be addressed by both political as well as technical means. Apart from the need to implement legislation and policy promoting gender justice, there are technical solutions that can be adopted to bridge the gender data gap.1 Researchers have attempted to highlight areas which systematically lack data and point to actionable solutions that the data and policy communities can use to remedy the gap in information.
Databases have gender data that has not been analysed and could be mined to address some of the data gaps before engaging in collection of new data.2 Researchers have shown that gender data gaps can be closed using existing and new data sources - censuses and micro-level surveys, and service and administrative records.3
The recognition of the need for gender data in national statistics has resulted in incremental opening up of relevant data across policy outcomes. Gender data can be opened by creating new sources, or disaggregating existing data along relevant variables. The Beijing Platform for Action has been working towards mainstreaming a gender perspective in national statistics and producing gender statistics.4
The Gender Inequality Index (GII) is a notable initiative by the United Nations Development Organisation to strengthen gender analysis by using data from national statistical systems.5 The GII measures reproductive health, empowerment through participation in governance and education, and participation in the labour force.6 The Asia-Pacific Portal for Gender Equality provides resources, country profiles, and analyses of the GII in the Asian context.7
Opening up of gender data by governments has also demonstrated impact on policy outcomes for women and girls. In a case study from Nepal, collecting and opening data on the ownership of land supported policy change, which subsequently encouraged land ownership among women.8 Data collected by adding sub-fields on property, land and livestock under a field on female assets in the national census triggered legislation around land ownership.9
Several notable initiatives have been spearheaded by governments and international organisations to address gaps in some of the more critical areas with a data gap. To address some of the gaps in the sector of asset ownership, the Evidence and Data for Gender Equity project funded by the Asia Development Bank has conducted surveys that use a gender lens to measure assets and entrepreneurship in Georgia, Mongolia and the Philippines. They find a large gender gap in Mongolia when it comes to ownership of housing land, and in Georgia for agricultural land.10 The Uruguayan government has focused on publishing open data on gender-based violence, under a national observatory that will also document the use of public funds for implementation of legislation to curb violence against women.11
Similarly, the Brazilian government has been working towards developing a system to standardise and collection information on domestic and family violence against women, and enable civil society to monitor the government’s actions and policy.12 On the topic of sex-disaggregated labour data, in 2019 the Argentinian government has begun working with civil society and trade unions to produce more and better quality data on the participation of women in the labour force.13 The Indonesian government has made a commitment to bring a gender perspective into its planning around policy and budgeting.14 The Open Data Labs Jakarta has been monitoring the success of local government budgeting to capture the “most pressing needs of women”, as well as the impact of public spending on the lives of women and girls.15
Apart from opening data, initiatives to ensure that open data is being used by relevant stakeholders is also critical. The Open Data for Gender Inclusive initiative is a project that has emerged from a coalition of civil society organisations, including IDEA Association and Jakarta Open Data Labs, working towards promoting accountability, transparency and gender-responsivity in budgets at the level of villages in Indonesia.16 The initiative has been collecting data on budget allocation for gender-responsive programmes and activities in villages.17 The initiative also measures the participation of women at village-level meetings on governance and budgeting18, as well as management of gender data in village information systems.19
There are several other initiatives that work towards digital and technological capacity building amongst girls and underrepresented communities. Telecentre Foundation and ITU works globally to build content for women, by women, create peer-learning networks, and build leadership among women using digital tools.20 Technovation is a non-profit which offers tech education programs for girls and their families21, while Internet Saathi aims to build capacity among rural women in India.22 Passerelles Numeriques operates in three asian countries - Cambodia, Philippines, and Vietnam, to provide training in the digital sector to young underprivileged people.23 Project Girl Code works in Cambodia to provide IT training to girls and women who are vulnerable to sexual exploitation.24
Big data has been under focus for holding potential to close the global gender data gap by international development organisations.25 For example, UN Women has collaborated with UN Global Pulse to produce several case studies across the Global South using existing big data to plug data for policy making on several areas of development for women.26 The various case studies use geospatial data to infer and create detailed maps on patterns of health data, as the two are correlated; anonymised credit card and cell phone data to map women’s expenditure and mobility; and social media posts to identify priorities of women users across developmental issues.27
Data2x’s detailed mapping provides country-specific assessments which can spur concrete policy making and action, but also demonstrates that big datasets fail to represent those who do not have access to technology and disproportionately represent those who are capable of producing these trails.28 In the case study using social media posts for example, only the developmental concerns of women from higher socioeconomic groups, with digital literacy and fluency in certain languages will be represented. To extrapolate information from such databases to represent the concerns of women across different contexts replicates existing resource inequalities.29 As a result there is a risk that those who lack access to the means of producing data will be disenfranchised, as policy-making processes become configured to accommodate the needs and interests of a privileged minority who have access to technology and are disproportionately represented in big data.30
Further, international development organisations have been critiqued for uncritically using big data to create databases about populations in Asia and the Global South, without accounting for concerns around privacy and data ownership.31
Limitations of big data usage further reiterate the persisting importance of traditional methods of data collection, and the need to adapt existing data systems to local contexts and invest in qualitative data collection.32
The absence of standards in opening up gender data can lead to challenges in relevant and usable data. Technical issues in statistical databases include lack of granularity or sex-disaggregation, creating barriers for effective use in policy making. The lack of standards and best practices in the use of open data have created barriers for effective use by governments. In Cameroon for instance, even as the government regularly produces gender statistics, it has not been able to use it effectively to inform policy decisions yet.33 Some other barriers to data use can result from data being published in a non-machine readable format or in a format that requires additional tools and skills to conduct gendered analysis.34
Some databases might not pay adequate attention to metadata, which provides information about the data classification and collection.35 The absence of proper metadata can invisiblise critical context about the data, and is often cited as leading to misinterpretation or false and varied conclusions.36
The Responsible Data community describes one of the core values of responsible data as “prioritising people’s right to consent, privacy, and security and ownership when using data in social change and advocacy efforts”.37 They call for the practice of certain principles in data projects - such as taking precaution to evaluate risk and side-effects of projects, and qualifying innovation on the basis of whom it benefits.38 For example, even though using anonymised data for social good has become popular, the possibility of de-anonymisation or recognition of additional characteristics can create risks for those represented in the data.39
Data-driven projects will be considered responsible if they actively avoid gender unequal outcomes from the use of that data. Commonly used survey methods should be re-designed to account for gender inequalities. When it comes to gender-responsive design, the open data field can learn immensely from existing literature on feminist research methods and epistemology. Feminist research methods make the political ideology, positionality and goals of the project explicit in their design. Feminist methods entail the interrogation of the identities of researchers and respondents.40 They then call for integrating feminist principles of intersectionality and participation at different stages in the research design, including framing questions, administration of data collection tools, and dissemination. The Ladysmith Collective is an example of a group that takes an innovative approach through its design of open source technologies, methods and resources that apply a critical feminist lens to data-driven projects.41
The first step towards gender-responsive policy is to collect gender open data evaluating policy design and implementation. One of the tools available for such analysis, Gender-Based Analysis Plus, is a tool devised by the Government of Canada to assess the differential experience of policies and programmes by women, non-binary people, and men.42 Policy frameworks should promote opening gender data to improve transparency, participation and accountability in governance.43 Another point of critical absence in terms of national policy is the lack of regulation on privacy and data protection, which leaves women’s data vulnerable to being used in ways they cannot anticipate or did not consent to, and which may ultimately cause them harm - such as phishing44, doxxing45, etc.
Multi-stakeholder collaborations and community participation will play an instrumental role in making gender-responsive open data a reality.46 Policy frameworks need to be designed in consultation with women’s rights groups, digital rights groups, and gender experts to enable accessibility and use of open data by women.47 To enable participation, institutions need to provide a safe environment for people to participate and voice their opinions. TechMousso is an example of collaborative initiative from Côte d’Ivoire which consulted with digital rights and gender rights groups, as well as government, and used open data to design solutions to a variety of gender-based developmental concerns.48