State of Gender Data Financing 2021

The demand for more and better gender data is on the rise. Global, regional, and national initiatives cite the economic and social value of gender equality. Thanks to the efforts of many advocates, our collective recognition of the importance of gender data has inVeased. We now have a much better understanding of gender data gaps: where they are, what causes them, and what needs to be done to fill them. However, increased awareness of the importance of gender data and of gaps in statistical systems has not been met with increased financing. Monitoring of aid for data—and gender data in particular—shows that financial flows have stagnated in recent years. Persistent low levels of financing for gender data go hand in hand with recurring gender data gaps. How do we remedy this problem and break this vicious cycle?

This report takes a detailed look at the state of gender data financing, starting with a landscape assessment of the gender data ecosystem, the core elements of that system, its stakeholders, and the links of gender data to policy. The report then highlights existing funding for gender data, the gender data systems that this funding currently supports, and the gap between current financing and the level of financing that is needed to fully fund gender data systems from now until 2030. The report also provides an overview of the financing options available to fill the funding gap and it concludes with suggestions on six areas of action for the way forward.

The main messages from the study are:

Gender data instruments overlap significantly with those needed by robust statistical systems.
A review of over 100 gender data stakeholders shows that most efforts to support gender data take place at the international level, demonstrating a need for increased advocacy efforts at the regional, national, and local levels.
Most gender data stakeholders focus on production rather than the use of gender data. Increased focus on data use would support the virtuous cycle of demand leading to greater supply. The analysis also shows that while it is a crowded space with many organizations interested in gender data, few organizations are funding efforts themselves.
The level of development aid for gender data is not increasing sufficiently to build robust gender data ecosystems in low- and middle-income countries. Neither countries, through their domestic resource mobilization, nor international donors are providing the levels of investments needed. Despite added demands placed on statistical systems due to the COVID-19 pandemic, less than half a percent of international aid for COVID-19 relief as of August 2020 had a primary focus on data and statistics, of which only 11 percent had a gender statistics component according to PRESS2020 (PARIS21 2020b).
The research reveals more information on main sources of funding, donors, and recipients:
- The total amount of financing for gender data is driven by a small number of large projects. Overall funding is volatile, with substantial changes year-over-year.
- Development Assistance Committee (DAC) countries accounted for the most funding for gender data between 2014 and 2018, with Canada, Sweden, and the United Kingdom leading the way.
- Regional and multi-country projects account for roughly half of all the commitments for gender data.
The study examined the capacity of 74 IDA-eligible countries to produce core gender data instruments:
- Foundational systems in many countries are weak. Two-thirds of IDA-eligible countries have low to middling civil registration and vital statistics (CRVS) systems that cannot provide women with adequate visibility in population estimates or the documentation they need, such as birth certificates to access services and education. In addition, while only seven countries do not plan to conduct a census during the current round, the COVID-19 pandemic has led to significant delays in many others.
- Only two countries conduct enough household surveys and only ten conduct enough labor force surveys to provide data at adequate frequency, but most countries have conducted income/expenditure surveys at regular intervals.
- Administrative data systems are also weak. Over eighty percent of countries have low functioning education or health management information systems, showing a persistent need for increased investment in these systems.
To fully fund core gender data systems, donor support averaging $500 million a year is needed, or roughly twice the recent level of support. Recognizing that full support for core gender data systems cannot be achieved immediately, a “glidepath” to increased funding is suggested to achieve full funding by 2030.

Based on these findings, the report offers a way forward by calling on the gender data community to:

1. Build a coalition for more and better financing.
2. Increase demand for gender data.
3. Deliver on the promise of new sources of data.
4. Encourage a country-driven approach.
5. Prioritize core data systems.
6. Advocate for increased donor funding.

This is an ambitious agenda that tries to solve a significant and persistent problem. Working with partners through various networks, we will share our knowledge and work toward a joint plan of action to build a sustainable environment for more and better gender data financing.

1. Introduction

To make progress toward the promise of gender equality and the empowerment of women and girls as outlined in the 2030 Agenda for Sustainable Development, to measure progress toward that promise, and to meet the demand for data during and after the COVID-19 pandemic, there is a need for more and better gender data. Yet these demands for more granular and timely data rest on shaky foundations of ill-equipped, overworked, and underfunded statistical systems across the globe. Since the inception of the Sustainable Development Goals (SDGs), the capacity of countries to respond to the statistical needs to monitor this ambitious development agenda has not kept pace nor has sufficient financial support from donors. This has only been exacerbated by the COVID-19 pandemic. The United Nations Statistical Division (UNSD) and the World Bank, in coordination with the five UN Regional Commissions, conducted a global online survey to assess the impact of the current crisis on statistical offices, and to identify needs for financial and technical support (World Bank 2020). The survey found that statistical agencies in countries with the least resources are facing the greatest challenges.

Since the inception of the Sustainable Development Goals (SDGs), the capacity of countries with the statistics needed to monitor this ambitious development agenda has not kept pace nor has financial support from donors. This has only been exacerbated by the COVID-19 pandemic.

This paper builds on the development data financing reports of SDSN (2015) and GPSDD (2016) and Data2X and Open Data Watch (2019a). The latter report, State of Gender Data Financing, for the first time examined the cost of improving the gender data ecosystem and calculated a financing gap in aid funding. This paper broadens the scope of this discussion by providing a comprehensive overview of the gender data ecosystem. It lays out a path to gender data financing (See Figure 1) by first setting out to understand current gender data gaps, identifying instruments in a core gender data system, identifying active stakeholders working in this space, and reviewing the use and impact of gender data. Second, the paper analyzes financing flows and gaps by reviewing current funding levels for gender data, the level of progress that financing has—or has not—made in building statistical capacity for the core gender data systems in 74 IDA-eligible countries, costing estimates of how much additional funding is needed, and describing a menu of options of common funding modalities. Third, the paper concludes with a way forward section that outlines six ideas for enabling more sustainable financing of gender data.

Figure 1: The Path to Gender Data Financing

2. Understanding the Landscape of Gender Data

This section documents the current gaps in data coverage, describes the core sources of data needed to measure and monitor gender progress, and identifies the major stakeholders that collect, produce, use, and finance gender data. But understanding gender data instruments and stakeholders is only one part of the puzzle. A discussion of the enabling factors to ensure gender data are used and linked to policies such as openness and leadership capacity is also included.

2a. Highlighting the Importance of Gender Data and Identifying Gaps

Gender data are those data that are collected and presented by sex, reflect gender issues, are based on concepts and definitions that adequately reflect the diversity of women and men, and are developed in ways that are sensitive to methods that could introduce gender bias (UNSD 2016). The sources of gender data are censuses, surveys, and administrative records from which indicators are constructed. Many of these data are collected for both men and women, and the resulting indicators should be disaggregated to show differences by sex and by other important characteristics such as geographic location, race and ethnicity, disabilities, and immigration or refugee status. Other indicators are unique to women—particularly measures of their sexual and reproductive health—or concern issues that disproportionately affect women—such as intimate partner violence. To identify women’s needs, to build recognition of their economic and social roles, and to design policies and programs that overcome inequalities in their treatment, timely indicators are needed from multiple domains, derived from reliable data sources, constructed using appropriate methodologies at regular intervals, and made available in open databases.

The COVID-19 pandemic has demonstrated the need for more and better gender data. We know that the pandemic has affected men and women differently, but in many countries, we lack the granular information to design effective, gender-informed mitigation and recovery policies (Buvinic et al. 2020a). Women seem equally likely to contract COVID-19, but less likely to require hospitalization or die from the virus, though many countries are not reporting sex-disaggregated data on these crucial indicators (Global Health 50/50 2021). Beyond the primary impacts of the disease, gender roles will shape the adaptive response to the pandemic (Buvinic et al. 2020b). Women fill many crucial jobs in health care and in education, but they are also primary caregivers at home. How many have lost their jobs or have had to choose between employment and their responsibilities at home? Reports suggest that isolation from pandemic-related lockdowns has exposed women to increased domestic violence (Perez-Vincent et al. 2020), but we lack consistent data to understand the full extent of the problem and how best to respond to it to ensure women’s health and safety. And the economic contraction caused by the pandemic—even in countries without widespread infections—may increase poverty rates and contribute to further economic and social impoverishment of women and their families. Overall, the lack of reliable gender indicators leaves women and their children more vulnerable to the pandemic and at risk of being neglected in recovery efforts.

Gender statistics are often cited, but on closer examination, they are sparse, infrequent, and often out of date. In a review of women’s vulnerabilities to the COVID-19 pandemic, Buvinic et al. (2020b) identified 32 indicators that are needed at a minimum to assess women’s health, education, and economic status. That study found that while many women and girls live in countries where they are at risk due to economic or health crises brought on by the pandemic, there are just as many countries where the pandemic has not yet disrupted life but where data are not sufficiently available to monitor their wellbeing or identify mitigation and recovery strategies should the pandemic spread further.

The Open Data Inventory (ODIN) documents the availability of 22 categories of statistics, including ten that are relevant to measuring the wellbeing of women and children (Open Data Watch 2020b). The ODIN overall score measures both data coverage, by the availability of disaggregated data at the national and sub-national level over the previous ten years; and the openness of data, measured against the standards of the Open Definition (Open Knowledge Foundation n.d.). Scores are lowest in the poorest countries, including the 74 countries that qualify for concessional and grant funding from the International Development Association (IDA) as of World Bank fiscal year 2021 (World Bank n.d). Differences exist not just between countries but also between categories of indicators. As shown in Figure 2, scores for gender data categories lag economic categories,^[1] and the gap has been growing.

Figure 2: ODIN coverage scores for IDA-eligible countries

Source: Open Data Watch.

The ODIN assessments are consistent with recent studies by Data2X and Open Data Watch of 25 countries in Sub-Saharan Africa, Latin America and the Caribbean, and Asia and the Pacific that found large gaps in the availability of gender indicators included in the SDGs (Data2X and Open Data Watch 2019b, 2020, 2021). The studies included both low- and middle-income countries. As shown in Figure 3, roughly half of recommended SDG gender indicators were unavailable or lacked sex-disaggregated data in national or international databases. A recent appraisal of gender data in the SDG era (Grantham 2020) expands on the deficiencies of the gender data ecosystem that need to be addressed to achieve gender equality in the next decade. For example, sex disaggregation remains a challenge in part because many instruments collect data only at the household instead of the individual level. In addition, the production of gender data often lags behind in crucial sectors: for example, environmental measures for capturing the impact of climate change on women are still being conceptualized and data disaggregated to show the condition of vulnerable populations such as older women, youths, or ethnic minorities are rarely collected. Finally, both short- and long-term effects of outcomes and policies are often not detected due to gaps in gender data and the lack of gender data over time.

Figure 3: Availability of SDG gender indicators

Source: Open Data Watch.

The evidence of these and earlier studies (Buvinic et al. 2014) shows that despite strong advocacy for gender data, many gender indicators that were selected for their importance in advancing the SDGs remain unavailable. Would policies change, or would they be more effective if these indicators were consistently available? Data use and linking data to policies for impact are key drivers for filling data gaps.

Because gaps in high-level indicators are often evidence of weaknesses in the underlying censuses, surveys, and administrative systems that are the foundation of the core gender data system, the following section will examine the components of a core gender data system.

2b. Defining instruments of a core gender data system

In the 2019 report, we identified the instruments and administrative sources needed to produce the data required to construct the gender indicators included in the SDGs. These data sources, shown in Figure 4, make up the “core gender data system” (Data2X and Open Data Watch 2019a).

Figure 4: Building a Core Gender Data System

Censuses are the backbone to all demographic data and are complemented by CRVS systems. In addition, administrative data produced by the education and health systems are important sources of gender data. Not shown here, administrative records of the justice system, court records and police reports, are a potentially rich but largely untapped source for gender data.

Surveys of health and women’s wellbeing also provide crucial information at the household and individual level for gender data. These include Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) and household income and expenditure surveys, such as Living Standard Measurement Surveys (LSMS). Labor force surveys, time use surveys, and agricultural censuses and surveys provide additional information on women’s market and non-market economic activities.

The shared costs of producing gender and non-gender data cannot be estimated separately, but all data users will benefit from a high quality, reliable gender data system.

Together these surveys and administrative systems comprise the core gender data system and produce the data necessary to understand the realities of women’s and girls’ lives. In this report they form the basis for the cost estimates and calculations of the financing needed to ensure high-quality, core gender data systems that meet the recommended frequency and timeliness intervals and adhere to international standards. The requirements for the core gender data system overlap with those of a well-functioning statistical system. The same instruments and administrative systems that produce gender indicators produce a large array of other indicators needed to design, implement, and monitor development programs. The shared costs of producing gender and non-gender data cannot be estimated separately, but all data users will benefit from a high quality, reliable gender data system. Likewise, improving the structure and management of statistical systems will make gender data production easier. For example, the restructuring of the statistical system in Senegal to provide better gender and subnational data will benefit data users, whether or not they seek gender statistics. See Box 1 for more about the inclusion of gender in Senegal’s Third National Strategy for the Development of Statistics (ANSD 2019).

In addition to the traditional data sources discussed here, alternative data sources such as geospatial data, call detail records, and other forms of “digital exhaust”—which can be used to provide additional and more timely sex-disaggregated data—are garnering increased attention. But before so-called “big data” can be used to supplement traditional data sources in official statistics, significant issues concerning gender bias in big data sets and the protection of privacy of individuals must be resolved (Data2X 2019). In the costing section 3c, alternative sources are not included. To the extent that new sources and methods are used to produce the current set of gender data, we assume they will cost no more and may indeed be cheaper. However, if they produce new data on new topics or require additional facilities, technology, or training, new investments will be required.

Box 1: Senegal – Building gender data on a strong foundation

Senegal: Building gender data on a strong foundation

Senegal has laid the foundation for its core gender data system. Over the last five years it has met or has exceeded the recommended frequency of surveys, producing four health and wellbeing surveys, five labor force surveys, two agricultural surveys or censuses, and one income and expenditure survey between 2015 and 2019. Senegal completed its previous census in 2013 and plans to complete the 2020 round of population and housing censuses in 2023. The one gap in Senegal’s core gender data systems, shared with many countries as of Open Data Watch’s assessment in fall 2020, is the lack of a time-use survey, which is important for measuring women’s paid and unpaid labor.

Senegal is continuing to strengthen its gender data system. Much of that progress is due to the National Agency of Statistics and Demography’s (ANSD) growing role as a leader, coordinator, and advocate for better gender data. In 2020, Senegal adopted its third national strategy for the development of statistics (NSDS) over the period 2019–2023 (ANSD 2019). Senegal is one of the pilot countries of a joint PARIS21-UN Women project, where gender statistics are mainstreamed in NSDSs (PARIS21 2020a). Senegal’s NSDS includes a budget of 578 million West African CFA francs (~$1 million) for “implementing a strategy for better consideration of gender in the production and analysis of statistics” over the period 2019–2023. The budget relies on external donors to cover 99 percent of the cost (ANSD 2019, p. 115). Senegal is the 12th largest country-specific recipient of aid for gender data, with average project-based commitments between 2014 and 2018 of $360K, which represents a quarter of all of Senegal’s overall statistical capacity financing received over that period according to ODW analysis of PRESS2020 data for this report.

Senegal’s administrative systems are generally good, although there is room for improvement. Senegal’s Law on National Statistics “provides for the collection, compilation and dissemination of vital statistics on the basis of civil registers,” (UNICEF n.d.) and its civil registration and vital statistics system gets high marks. In Senegal, 77.4 percent of children under five years of age have their births registered with a civil authority. (UNSD n.d.) Its health management information systems (HMIS) and education management information systems (EMIS) are relatively strong but have room to improve. These systems need to be improved to deliver more timely data. The most recent year with data on enrollment rates by sex is 2016 (Open Data Watch 2020a), but there is ongoing international support for strengthening the EMIS (UNICEF 2019). In the case of HMIS, the latest year available on health facilities is from 2016 (Open Data Watch 2020a). A recent report by Measure Evaluation found that, “Senegal faces significant challenges that affect its healthcare system, including gaps and weaknesses in human resources and capacity, equipment, quality services, health information systems (HIS), and management of infectious and chronic diseases.” (Measure Evaluation n.d.)

Senegal needs to build upon this foundation to achieve a fully functional gender data system. Its published gender indicators still have many gaps. Among 15 sub-Saharan African countries evaluated in Bridging the Gap: Mapping Gender Equality in Africa (Open Data Watch and Data2X 2019b), Senegal ranked 13th with only 42 percent of 104 possible gender indicators available with sex-disaggregated data. Another view comes from the Open Data Inventory (ODIN), which rates countries on the coverage and openness of their official statistical systems. Although Senegal ranks well among low- and lower-middle income countries, its 2020 overall score of 49.3 indicates that Senegal’s national statistical system satisfies roughly half the criteria for data coverage and openness. Looking at 10 categories of statistics particularly important for monitoring gender outcomes, Senegal’s score of 49.5 on ODIN’s Open Gender Data Index placed it 9th among IDA-eligible countries. But there are signs of improvement: Senegal’s Open Gender Data Index has risen by 9 percent since 2016, while its coverage subscore that measures the availability of disaggregated data has risen by 21 percent (Open Data Watch 2020a).

2c: Mapping Gender Data Stakeholders

Figure 5: Mapping Gender Data Stakeholders

There are many notable organizations working to improve the availability, quality, and use of gender data that should be considered to understand the landscape for gender data financing. To this effect, Open Data Watch and Data2X conducted a stakeholder mapping exercise to identify important stakeholders and their interests. Over 125 stakeholders were included in the inventory and characterized along five dimensions (Figure 5). This provides a detailed picture of the gender data landscape to identify where there is good coverage in gender data efforts and where duplications or gaps exist. The stakeholder mapping exercise revealed who is doing what and where they are located in the gender data ecosystem. Five main findings across the five dimensions are shared below.

Role: Support for gender data comes from a wide range of organizations, with academic and civil society organizations being the most numerous, but more diversified support is needed among funders.

Most stakeholders serve multiple roles in the gender ecosystem. Many civil society organizations and NGOs that support gender data do so by conducting research. And many funders also serve as custodian agencies. While we can see that a wide range of organizations exist, understanding the impact of each organization can be a challenge. The mapping exercise identified 21 funders, but ODW analysis of PRESS 2020 data finds that close to 90 percent of funding for gender data between 2014 and 2018 comes from five top donors: Canada, Sweden, United Kingdom, the World Bank, and Australia. Because of the small group of funders, financing of gender data is subject to abrupt changes in level and direction and needs more diverse sources of funding.

Figure: Number of stakeholders that serve in each role with selected examples

Source: Open Data Watch.

Gender focus: Gender data is an incidental focus of most organizations in this analysis.

Most organizations that provide support for gender data do so as part of the broader scope of their work. Support for data in general will often require appropriate disaggregation, which in turn results in gender data. Some organizations have a partial focus on gender, including projects directly related to gender data. A smaller but still notable number of organizations have a strong focus on gender data. These labels do not correspond to the importance of these stakeholders to gender data, but rather represent how central gender data is to the stated objectives of each stakeholder.

Figure 7: Percent of stakeholders by gender focus

Source: Open Data Watch.

Geographic Focus: Most efforts to support gender data occur at the international level.

To understand their geographic focus, each stakeholder’s work was categorized as international, regional, or national. None of the organizations included had a local level (sub-national) focus on gender data. Most initiatives to support gender data occur broadly at the international level, both through support for international gender data initiatives and projects that occur across a wide range of countries. Some stakeholders have multiple projects at different levels. In Figure 8, stakeholders that participate at multiple geographic levels are counted more than once.

Figure 8: Proportion of stakeholders by geographic
focus

Source: Open Data Watch.

Data topic: Most stakeholders provide support for gender data broadly, with a focus on health and gender equality.
Gender data cover a wide range of thematic issues, such as health, education, financial inclusion, and others. Naturally, the organizations that provide support for gender data also have a wide range of topical interests. While most organizations either provide support for gender data in general or cover several topics, the most common specific areas of interest were health and gender equality in one or more domains. Only a small handful work on civil registration and vital statistics, despite the critical role that CRVS data serves for monitoring gender-related SDG indicators.

Figure 9: Number of stakeholders that cover each gender-data topic

Source: Open Data Watch.

Box 2: Measuring Women’s Economic Empowerment: The Need for a Collaborative Approach

Measuring Women’s Economic Empowerment: The Need for a Collaborative Approach

A prerequisite to adequately finance gender data at the country and global level is to dig deeper into how to use the individual power and competencies of different stakeholders and how to utilize formal or informal collaborative processes that work together toward a common set of goals or principles. In the case of the Women’s Economic Empowerment theme, a diverse range of stakeholders are needed to fill gender data gaps and ensure the data are used for positive impact. UN Women defines women’s economic empowerment (WEE) as women’s ability to participate equally in existing markets; their access to and control over productive resources, access to decent work, control over their own time, lives, and bodies; and increased voice, agency, and meaningful participation in economic decision-making. Achieving this is certainly not easy, and no one agency, organization, or country ministry can overcome the challenges of having the necessary data.

According to Data2X and Open Data Watch’s Bridging the Gap regional studies in Africa, Latin America and the Caribbean, and Asia and the Pacific, economic empowerment indicators such as, time spent on unpaid domestic and care work, growth of household expenditure or income, and populations covered by social protection systems are not sex-disaggregated at the national level or are missing all together. This lack of availability coupled with a lack of multi-lateral donor projects containing activities on gender statistics in the economic empowerment sector leave much room for improvement (See Figure A below). Luckily, women’s economic empowerment is an area where actors are coming together to collect data, close gaps, and use information to make progress towards gender equality.

Figure A. Multi-lateral donors’ project themes containing activities on gender statistics, 2018

Source: PRESS 2020 (PARIS21 2020b)

While the national statistical office may be in the lead to collect, publish, and disseminate official data on women’s employment, their work operates in a gender data ecosystem with other organizations also working to collect such information. For example, Data2X’s Measuring Women’s Economic Empowerment: A Compendium of Selected Tools selects and reviews tools for measuring women’s economic empowerment (or disempowerment) grouped into 20 population monitoring tools (PM) and 15 monitoring and evaluation tools (M&E) (Data2X 2020). The main objective is practical: helping readers understand how different measurement tools are built and select among the most well-known and widely (cross-culturally) applicable tools for different purposes. The compendium, citing works of the African Development Bank, Equal Measures 2030, USAID, J-PAL, and many others, highlights the complexity of the stakeholders in the space that are also working to measure the topic.

Who are the key stakeholders that must be included in a joint WEE gender data agenda setting?

In terms of the data production itself, it is possible to select a handful of critical actors that would be needed to produce the data and fill the gap. Despite the country and context, it is safe to say this group of stakeholders together could take on the collective action the sector requires. Government agencies such as national statistical offices and ministries of finance and labor are critical to identify country demand, policy priorities and therefore indicators, and mobilize the necessary domestic resources. Alongside them, philanthropic organizations such as the Hewlett Foundation and the Bill & Melinda Gates Foundation and donors like Statistics Norway’s Division for Development Cooperation and Department of Foreign Affairs and Trade of Australia have supported innovative statistical programs that deepen gender-sensitive and multi-dimensional measures of economic empowerment. Others such as the World Bank and ILO, through their Closing gender data gaps in the world of work – role of the 19th ICLS standards work, have been instrumental in providing technical assistance to countries to run the specific surveys and data collection efforts, as have UN Women and PARIS21 through the Making Every Women and Girl Count program (UN Women n.d.). The UN Statistical Commission has been spearheading efforts to set international standards on new data collection efforts on time use. Last but certainly not least—though less involved on the production side—civil society organizations such as WIEGO are paramount to using the data and advocating for change.

In the case of women’s economic empowerment, it takes everyone to leave no one behind.

Data Value Chain: Connecting users to gender data needs more attention among gender data stakeholders.

Stakeholders contribute to the publication and use of gender data at various stages. The data value chain, developed as a joint product between Data2X and ODW (2018), breaks down the process through which data move from production to use, covering the four stages of collection, publication, uptake, and impact. The collection stage involves gathering raw data through surveys and other sources and preparing it for analysis. The publication stage involves the analysis of raw data to make data publicly available. The uptake stage relies on data intermediaries to make it possible for users to access and use available data. The impact stage covers the ultimate impacts that improve the lives of women and girls. Many stakeholders cover multiple stages of the data value chain. Most focus on either the collection or publication of data. Fewer work to increase the uptake and impact of gender data among users. And few stakeholders were found that focus on impacts of gender data to directly improve the lives of women and girls, as the scope of this mapping exercise was on stakeholders that provided support for data itself.

Figure 10: Number of stakeholders providing support for each stage of the data value chain

Source: Open Data Watch.

This analysis of gender data financing stakeholders shows that a gap remains between support for the production and use of gender data. While sustained support for the collection and publication is needed to close critical gender data gaps, additional attention is needed to further promote the uptake, use, and ultimately impact of gender data. Gender data financing must support the full scope of the data value chain to ensure that the collection of gender data ultimately results in impacts that improve the lives of all individuals, especially women and girls.

2d: Painting a complete picture of the value of gender data: use and impact

The previous sections introduced the landscape of gender data through an inventory of the instruments used and the stakeholders that are active in the space. Yet understanding merely the mechanics of the production or financing of gender data will fail to close the gap between generating gender data and its impact. Although the costing exercise in Section 3 below will use the inventory of gender data instruments as its basis, the true value of the gender data ecosystem is captured only when the raw production of gender data is connected to gender data use through open gender data and policymaking. Gender data are not the goal in and of themselves; their true value comes from what they allow us to analyze and act upon regarding increasing gender equality.

Connecting Gender Data to Policy

To reach gender equality, gender data must be used to inform policies that can address and mitigate gender inequalities at the global, regional, and national level. At the global level, the Generation Equality Forum is commemorating the anniversary of the 1995 Beijing Declaration and Platform for Action by hosting a series of events in 2021 to mobilize around Action Coalitions that seek to address specific areas for gender equality. Gender data is a cross-cutting issue for these action coalitions and will inform the efforts to catalyze collective action and drive public and private investments for gender equality.

At the regional level, multilateral, and national stakeholders are sharing the need for more gender mainstreaming in statistics. ECLAC, for example, has organized events for countries in Latin America and the Caribbean. At such an event in August 2020, Open Data Watch was able to share results from the Data2X/ODW Bridging the Gap study of Latin American countries and what lessons these countries can provide for other countries in the region.

The Africa Gender Data Network (AGDN) fosters gender data expertise, facilitates cross-country learning, enables capacity building and training, and provides a platform for members to raise and solve challenges they face.

In a mix between the regional and national level approach to linking gender data to policy, in 2019, the African Centre for Statistics (ACS) at the United Nations Economic Commission for Africa (ECA), in partnership with Data2X and Open Data Watch, initiated the Africa Gender Data Network (AGDN) to improve the production and use of gender data within African national statistics systems. The main goal of the Network is to improve the standard of gender data production to bolster linkages with gender data demand, enhance the effectiveness of communication about gender data, and encourage gender data use across participating countries. The Network fosters gender data expertise, facilitates cross-country learning, enables capacity building and training, and provides a platform for members to raise and solve challenges they face. It is currently piloted in 15 countries, with plans for expansion in 2021. At each level of linking data to policy, the ability and capacity for leadership and their constituents to analyze and use gender data emerges as a key challenge for gender data and therefore for gender data financing moving forward.

Harnessing Open Data

Open data requires a digital as well as a legal infrastructure that facilitates the use of data.

One of the principal tools for enabling gender data use is making gender data openly available. A lot of data are made available through reports or even featured on portals, but this does not necessarily make them open. To be open, data must be accessible in machine-readable, non-proprietary formats with flexible download options, include metadata, and the data must be made available with terms of use that allow for the use and reuse of the data. Open data therefore requires a digital as well as a legal infrastructure that facilitates the use of data. Building this infrastructure will require investment, whether through domestic resource mobilization or in collaboration with partners, but it will increase the value of data, especially gender data, because open data can be used and reused—reaching wider audiences and creating positive feedback loops for more and better gender data. Open gender data is particularly relevant in the context of COVID-19, where it is crucial for revealing insights on the scourge of domestic violence amidst lockdowns. Open data is key to tracking these and other aspects of the pandemic, particularly as the world needs more answers on how COVID-19 affects various groups differently, such as men and women.

Promoting Gender Data Use

The link to policies that improve gender equality can only be made when there is more gender data use. Data use is defined by how data—in this case, gender data—are used by various audiences and for what purpose. Too often, discussions around gender data take place only with an emphasis on production, without consideration of how they are used. This is reflected in the above stakeholder analysis, which found the fewest number of stakeholders involved in data use. Operationally, data use covers how gender data are published on national and international data portals, as well as how policymakers and journalists engage with gender data. The landscape for gender data therefore encapsulates both the technical angle of building data portals, as well as the storytelling and procedural aspect of how stakeholders engage with and act on gender data.

Too often, discussions around gender data take place only with an emphasis on production, without consideration of how they are used.

On the technical aspects of gender data use, what works for disseminating official data on any topic also applies to gender data, while acknowledging that the stakeholders, such as those listed in the section above, may differ and will need to be engaged through various avenues. There are existing best practices for allowing data and gender data to be accessed, for example through ODW’s report, Understanding the Usability of National SDG Reporting Platforms. Gender data producers at all levels should ask themselves who their users are, how they can access the gender data portal, how they can find the data they are looking for, and whether the portal provides them with the necessary information to understand and use the data.

Countries currently face a wide range of challenges on data use. Previous work by ODW has found that many countries still struggle with maintaining website uptime. Countries who experience trouble maintaining their NSO site online will serve fewer data users. Work by EM2030 has shown that policymakers in low- and middle-income countries often lack the skills to “access, analyze and communicate data.” The former example highlights the need for gender data financing to go beyond financing surveys and to consider the ICT infrastructure that undergirds the national statistical system and that will enable data use online, while the latter shows that policymakers need more training on how to access and work with gender data. As strategies and best practices for external engagement of users are often not available, the PARIS21 Academy, with the help of Data2X, has begun an effort to work with statisticians and journalists on how to communicate gender statistics to increase gender data use for advocacy and policymaking.

Armed with the recognition of the power of data to tell compelling stories about the state of women and their children, many organizations have embraced data storytelling to inform their communications and infuse this crucial aspect of data use throughout their operations. Rather than display the wealth of data available in a multitude of tables and graphs without context, UNSD opted to tell the story of the World’s Women 2020 as a series of data-driven vignettes on the dimensions of gender equality. Though high-quality communication materials such as these may be out of reach for many lower-income countries to construct themselves, the trainings available through the PARIS21 Academy showcase how to use the resources available to effectively communicate gender statistics.

Countries have suffered both from a lack of production of these data and a lack of effective publishing practices that enable citizens and analysts to use the data to engage in policy conversations around measures to fight the pandemic. On the other hand, effective data use practices by some NSOs have led to a meteoric rise in how audiences engage with the NSO, which underscores the importance of data use for bolstering the NSO’s business case.”

The availability of gender data themselves is a story, as Data2X and ODW have shown, and as the World Health Organization described in its recent data story. UNICEF, meanwhile, builds on its Data for Children Strategic Framework which posits that “data demand, supply and use are equally important.” In a recent report, they add the importance of sex-disaggregating its data to present meaningful comparisons between girls and boys and to use gender data to communicate with and appeal to a wide range of stakeholders who may not be explicitly focused on gender equality.

The COVID-19 pandemic has brought into sharp relief just how crucial data use is for gender data, as less than half of all countries have been able to provide continuous and intersectional data on COVID-19 cases and deaths disaggregated by sex according to Global Health 50/50. Countries have suffered both from a lack of production of these data and a lack of effective publishing practices that enable citizens and analysts to use the data to engage in policy conversations around measures to fight the pandemic. On the other hand, effective data use practices by some NSOs, such as the Office of National Statistics (ONS) in Great Britain, have led to a meteoric rise in how audiences engage with the NSO, effectively underscoring the importance of data use for bolstering the NSOs’ business case (Waters 2020).

Gender data needs to be linked to policy if they are to reach their full value for gender equality. This can be done through global, regional, and national networks that emphasize the importance of mainstreaming gender in statistics, as well as advocating for gender data use, which can be enabled by making data openly available. Publishing open gender data, improving policymaker skills, and increasing investments in communication materials are just some of the additional aspects of gender data financing that must be considered alongside investments in surveyors and NSO statisticians.

3. Exploring current investments and gaps in gender data financing

3a. Financing flows for gender data: top donors and recipients

Existing funding, both domestic and international, is clearly not meeting the demands for gender data instruments that adequately represent the situation of men and women, boys and girls (see section 2). But where does the current funding stream for gender data come from?

Data on domestic funding for gender data does not exist at a globally comparable level, although the PARIS21 has published Country Reports on Support to Statistics (CRESS) that could be used to evaluate country support to gender data. However, presently there is evidence only for 14 countries, far less than the 74 IDA-eligible countries studied in this report.

The best source of information on external financing for statistical activities in the form of Official Development Assistance (ODA)—meaning in the form of grants or very favorable loans—is the Partnership Report for Support to Statistics (PRESS), published annually by PARIS21. Since 2018, PARIS21 has worked together with UN Women as part of the initiative Making Every Woman and Girl Count to include a special section on support to projects that support gender data efforts as a subset of all activities that support the national statistical system. This section uses both the results of this gender data section from the recent PRESS2020 report (PARIS21 2020b), as well as Open Data Watch analysis based on a dataset of gender data projects graciously made available by PARIS21.

PRESS2020 shows that support to gender data is low on the list of priorities for donors amidst COVID-19 relief efforts. Of the $64 billion in ODA committed to COVID-19 relief as of August 2020, only 0.4 percent is focused explicitly on data and statistics (~$250 million) amidst a pandemic that requires a data-driven response and only 11 percent of this amount (~$28 million) is focused on gender data (PARIS21 2020b). However, statistical and gender data activities specifically are often included alongside larger programs, so the true amount of money spent on gender data may be higher (the 0.4 percent of COVID-19 funding for specific statistical activities grows to 16 percent if any data collection dimension is included). We simply do not know enough about whether and how gender data have factored into 2020 ODA flows in response to the COVID-19 crisis, but they must be part of efforts to leave no one behind with an eye towards achieving Agenda 2030.

What the results from the PRESS projects from the Creditor Reporting System (CRS) of the OECD make clear is that support to gender data was hard to come by even before the pandemic. Not only were total levels largely unchanged between 2009–2013 and 2014–2018, they were also extremely volatile. The average commitment to a gender data project between 2014 and 2018—the last year for which data are available on a project basis—was $38 million, but this masks years in which commitments were just above $5 million, such as in 2014, as well as $84 million in 2018, with a host of values in the middle. Because of this volatility, 2014–2018 will be the most recently available period analyzed for the donor and recipient-level section of the report.

Over this 2014–2018 period, a new trend in financing for gender statistics emerged: DAC countries have invested considerably more in gender data compared to multilateral organizations who have traditionally dominated in this area. In fact, DAC countries accounted for around 80 percent of the total amount of gender data financing from 2014–2018.

This is also reflected in the list of the top five donors over the period, which were Canada, Sweden, the United Kingdom, the World Bank, and Australia. Just Canada, Sweden, and the UK alone made up almost 70 percent of all funding to gender statistics over this period. As another indicator of the volatility of gender statistics financing, the World Bank’s rank on this list is accounted for almost entirely by one $23 million project in 2015 across West African countries.

The changing top five list of donors reflects the fact that multilateral donors have, over time, been displaced at the top by bilateral donors. For example, UNFPA, EUROSTAT, and UNECA are all multilateral institutions whose gender data financing amounts drop off considerably between 2009–2013 and the most recent five-year period.

Table 1: Top ten donors providing support for gender statistics, 2009–2018

2009–2013		2014–2018
Donor	Share of total commitments (%)	Donor	Share of total commitments (%)
UNFPA	50	Canada	30
Canada	12	Sweden	24
EUROSTAT	10	UK	15
UNECA	9	World Bank	13
Sweden	9	Australia	6
UNDP	3	UNFPA	5
UNSD	2	Ireland	1
Australia	1	UNICEF	1
UNCTAD	1	EUROSTAT	1
Austria	1	Austria	1

DAC donors may designate the primary and non-primary purposes of their aid. Among DAC donors, the share of aid to statistics that focuses on gender data as a non-primary objective has increased over the last six years of data (PARIS21 2020b). This offers a potential bright light amidst the bad news on overall funding for gender statistics: DAC funding is increasingly accounted for by projects that embed gender data as an additional goal, which may be an indicator of greater mainstreaming of gender data. Though levels are still low, increased mainstreaming may lead to greater sustainability of gender data financing.

The World Bank’s place in the donor rankings as a result of one large project, as well as other similar projects by other donors, has repercussions for the recipient side of gender data flows. Regional and multi-country projects occupy around half of all the commitments for gender data between 2014 and 2018 and 40 percent of all recipients are either multi-country projects (tagged as “Bilateral, unspecified”) or Sub-Saharan African or Caribbean projects. The country receiving the highest amount of funding on a bilateral basis is Pakistan, which received most of its aid for gender data in 2018, followed by Mozambique and Guatemala.

A comparison with the previous five-year period 2009–2013 reveals that this concentration into multi-country projects is a new phenomenon. Between 2009–2013, only a quarter of funds were for multi-country or regional projects, compared to half between 2014–2018; the top ten recipients contained more countries, including Rwanda as the top recipient, Burkina Faso, Bangladesh, Vietnam, Peru, and Moldova.

Table 2: Top ten recipients of aid for gender statistics, 2009–2018

2009–2013		2014–2018
Recipient	Share of total commitments (%)	Recipient	Share of total commitments (%)
Rwanda	15	Bilateral, unspecified	16
Africa, regional	11	Pakistan	14
Burkina Faso	9	South of Sahara, regional	13
Bangladesh	9	Caribbean, regional	11
Bilateral, unspecified	5	Mozambique	8
Vietnam	5	Europe, regional	3
Latin America and Caribbean, regional	4	Guatemala	3
South of Sahara, regional	3	Africa, regional	2
Peru	3	Asia, regional	2
Moldova, Rep.	3	America, regional	2

There are two ways to count support for gender data. The first applies text mining to project descriptions written by donors and tags a project as gender-data-relevant when evaluated against a previously tagged set of gender data projects. In Tables 1 and 2, flows from donors and to recipients are summed from each individual project identified in the CRS database using this “project-based” method. However, donors may not always describe their projects in ways that allow researchers to determine their relevance to gender data from the project description itself.

The second method uses information provided by donors who tag their projects with specific markers indicating the type of statistical activity supported.^[2] These statistical activities do not describe specific gender data interventions, although they do group projects into useful aggregates, some of which are more gender-relevant than others. In the State of Gender Data Financing (Data2X and Open Data Watch 2019a), we developed a methodology for using these aggregate categories to estimate the total amount of gender-relevant funding in ODA. In Figure 11 below, we replicate this methodology^[3] by using the aggregate shares of funding for statistics to create a range of estimates of gender data-relevant funding flows and present these estimates in addition to the project-based method and total funding for statistics.

Figure 11: Donor funding for statistics, 2009–2019

Source: Open Data Watch calculations based on PRESS 2020 (PARIS21 2020b).

Aside from spikes in 2011 and 2013, total funding for statistics has not sustained significant increases and has, since 2017, experienced successive decreases. The upper and lower bounds of funding for gender data using the sector method show broadly similar trajectories in recent years, if at different levels. Examining funding by the project-based method yields low and widely varying estimates. To increase funding for gender data, donors will need a greater focus on statistical activities relevant to gender data, as well as a more targeted approach to countries that are most in need (See section 3b for a discussion on this group of countries). Section 3d describes the menu of financial options that are available. At the international level, initiatives like the Bern Network (see Box 4) can provide and encourage knowledge sharing and are working to support more and better financing to make sure countries have the support they need to leave no one behind.

As we saw in Figure 11 there is relatively little change in the past five years of data. To get a sense of average funding year-over-year and for the purposes of constructing an estimate of the gap in overall gender data financing in section 3c, we estimate the most recent levels based on the sector method, as shown in Table 3. For the most recent period with available data (2015–2019), gender data financing stood between $217 and $272 million per year on average.

Table 3: Annual financing for gender data

Averages 2015–2019, $ millions, 2019 prices	Upper bound estimate	Lower bound estimate
Total donor commitment to statistics	678
↳ of which, commitments to gender related activities	381	304
↳ of which, commitments to IDA-eligible countries	272	217

Where does this leave our assessment of the necessary support to gender data? As shown in Figure 11, funding has not meaningfully increased in recent years (by whichever method is used), even as the urgency of gender data has increased, especially during the COVID-19 pandemic. But how much do the amounts above fall short of the needs of a robust gender data ecosystem? The next section investigates the current capacity of countries to produce gender data-relevant instruments, and section 3c will describe what is needed to fund a robust statistical system able to produce the full range of gender data.

3b. Measuring the capacity of gender data systems

Censuses and surveys

To document the current capacity of countries to produce gender data, we compiled an inventory of the available survey and census instruments conducted from 2010–2020. The construction of the inventory occurred during the period of October 2020 to December 2020, although minor adjustments were made to the inventory up until February 2021. Consisting of 74 IDA-eligible countries, the inventory covers six types of censuses and surveys, as shown in Figure 12. Surveys were obtained from microdata catalogs (World Bank, IHSN, ILO, NADA catalogs); reports on NSO and relevant ministry websites; schedules and status of surveys found on IGO and NSO websites; and guidance from international partners. The inventory also recorded information on upcoming or delayed censuses and surveys from UNSD. The summary of the survey inventory covers the most recent five-year period, 2015–2019 (Because of time lags in reporting surveys, complete data for 2020 are not available; however, censuses planned for 2020–2024 are included on the assumption that they have secured funding). Together, these instruments provide us with an assessment of survey systems as well as a country’s foundational systems (CRVS systems will be introduced in the next section).

The inventory is grouped by population and housing censuses; household health and wellbeing surveys; income or expenditure and multi-topic household surveys; labor force surveys or modules; time use surveys or modules; and agriculture censuses or surveys. More information on the inventory is available in Annex 1. The detailed methodology of the survey inventory and its results are available here.

Figure 12: Types of census and survey instruments included in the inventory of gender data

The structure of the inventory is shown in Figure 12, and the results for 74 IDA-eligible countries are summarized in Table 4. The expected number of surveys is based on the Data for Development report (SDSN 2015) and updated in the State of Development Data Financing report (GPSDD 2016). Censuses are typically conducted once in a decade; their frequency as noted in the table assumes that planned censuses that were not conducted between 2015 and 2020 should be completed by 2024.

Figure 13 shows the number of countries that have accomplished the expected number of censuses or surveys over the period 2015–2019. Important gender data collection instruments are administered far less frequently than recommended: only two countries conducted four health and wellbeing surveys over the period; 10 countries conducted annual labor force surveys; and 51 countries conducted at least one income/expenditure survey. Agricultural censuses or surveys and time-use surveys were the least well represented in the inventory. Four countries have conducted two agricultural censuses or surveys over the period. Only one example of a freestanding time-use survey was found, although time-use modules were included in a few income/expenditure or multi-topic surveys.

Figure 13: Survey and census frequency, 2015–2019

Source: Open Data Watch.

Sixty-seven countries have conducted or have plans for conducting censuses during the 2020 round. Forty countries have completed or planned to complete their censuses by 2020, although some have been delayed by the COVID-19 pandemic. These and 27 more countries should complete their censuses by 2024. Only seven countries lack plans to conduct censuses in the 2020 round.^[4]

The count of surveys conducted over the period is shown in Table 4. Time use surveys are the least frequently conducted surveys, while income or expenditure surveys come closest to meeting the expected frequency. Supplemental surveys include other specialized surveys, such as victimization surveys on violence against women. In cases where a country conducted more than the expected number surveys, the excess surveys were counted as “supplemental” surveys.

Table 4: Gender data surveys in IDA-eligible countries, 2015–2019

Instrument	Frequency per 5 years	Expected number of surveys or censuses 2015-2019	Number conducted 2015-2019	Proportion conducted (%)
Health and wellbeing survey	4	296	76	25.7
Income/expenditure survey	1	74	51	68.9
Labor force surveys	5	370	106	28.6
Time use surveys ¹	1	74	1	1.4
Agricultural census or survey	2	148	26	17.6
Supplemental household surveys	2	148	29	19.6
¹ Includes one-time use survey completed in 2020

Administrative systems

CRVS systems record births, deaths, marriages, and divorces and are commonly grouped with censuses to form a country’s foundational data system. In addition to their statistical function, CRVS systems establish civil status through birth registration and certification, in addition to legal and marital rights.

Three administrative systems are important sources of gender data. CRVS systems record births, deaths, marriages, and divorces and are commonly grouped with censuses to form a country’s foundational data system. In addition to their statistical function, CRVS systems provide evidence of legal and marital rights. Recording of births along with the issuance of a registration certificate establishes a child’s civil status. Education management information systems (EMIS) record enrollment and progress through the educational system and measures of educational attainment: completion rates and subject-specific achievement scores. EMIS also include information on teachers and administrators, teaching materials, and the physical characteristics of schools. Health management information systems (HMIS) record information on patients and their illnesses and outcome along with the personnel and facilities of the healthcare system. For the purposes of grouping a country’s areas of improvement in Figure 14, we designate HMIS and EMIS as representing a country’s administrative system.

CRVS, EMIS, and HMIS systems should record information continuously, although data may be published at discrete intervals. Some indicators may be derived from surveys and others may be arduously assembled from decentralized or paper-based processes. Therefore, published data are not necessarily evidence of functioning information systems. In assessing the current status of administrative systems, a combination of quantitative information—the availability of certain characteristic indicators—and qualitative information was used to assess the functionality of these systems.

The functionality of CRVS, EMIS, and HMIS were characterized as high, medium, or low based on the following criteria:

Civil registration and vital statistics systems: The designation of low, medium, and high functionality was based on SDG indicator 16.9.1 (Proportion of children under 5 years of age whose births have been registered with a civil authority). This indicator is usually estimated from surveys that ask mothers whether their children’s births have been registered. In cases where birth registration was missing, qualitative information from independent sources was used to assign a functionality score.
Education management and information systems: The designation of low, medium, and high functionality was based on the availability of enrollment data (recorded as indicator 3.1 in the Open Data Inventory (ODIN)); the years available, and any available qualitative information on the capacity of the country’s EMIS to produce statistics. Qualitative information was primarily found on websites of international organizations (such as UNICEF, UNESCO), bilateral agencies (USAID), NGOs, and academic articles.
Health management and information systems: The designation of low, medium, and high functionality is based on ODIN indicator 4.1—the number of health facilities, the years available, and any qualitative information on the capacity of the country’s HMIS to produce statistics. Qualitative information on national HMISs were primarily from WHO, Measure Evaluation, bilateral aid agencies, governmental reports, and other publications from academia or from other NGOs and IGOs.

The results of the administrative systems assessment are shown in Table 5.

Table 5: Functionality of administrative data systems

Administrative systems	No. of countries
Administrative systems	High	Medium	Low
Civil registration and vital statistics	26	27	21
Education management information system	8	29	37
Health management information systems	12	14	48

The detailed methodology of the functionality scales along with individual country ratings is available here. The assumption in the subsequent cost analysis is that low functioning systems will need higher levels of support and incur higher costs than better functioning systems.

Targeting gender data interventions

The previous two sections have shown which gender data instruments are missing and which administrative systems function poorly, leaving gaps in a gender data ecosystem that cannot adequately describe the condition of all individuals. These gaps occur in clusters. In Figure 14 we have used the results to group IDA-eligible countries that lag farthest behind the standards set out in GPSDD (2016). The three groups are defined as follows:

Weak foundational systems: Countries that do not have a census planned or conducted during the 2020 round or if they have been rated as low or medium on their CRVS capacity, as even medium capacity means that a quarter of all births are not registered.
Weak administrative systems: Countries have been rated as having low or medium EMIS and HMIS capacity.
Weak survey systems: Countries that meet one or none of the established standards for frequency of the five categories of survey instruments (health, income/expenditure, labor, agricultural, supplemental).

Figure 14: Gender statistics systems of 74 IDA-eligible countries

Source: Open Data Watch.

As Figure 14 shows, 34 countries or just under half of the 74 IDA-eligible countries studied have weak foundational, administrative, and survey systems. Another 25 countries have weaknesses among two core gender data instrument groups, while 11 countries only need to improve on one aspect of their gender data systems, relative to other IDA-eligible countries.^[5]

The total number of countries within each of the three groups is also instructive. More countries clear the bar for having a census planned or implemented or having sufficiently robust CRVS systems than for survey systems or other administrative systems. It is heartening that more countries have focused on and received support for their foundational systems, but the overall number of countries that have weak foundational systems still constitutes almost two-thirds of IDA-eligible countries.

Although this analysis has grouped countries according to strengths and weaknesses in their production of gender data instruments, it cannot not tell us the cost of upgrading their systems or provide information on country budgets, which the following section will address.

3c. Estimating costs to complete the gender data system

What will it cost to provide regular and timely gender statistics? In the preceding analysis we have detailed shortfalls in surveys and censuses and the functionality of administrative systems. This suggests where donors and countries might focus their efforts. In this section we estimate the cost of filling the gaps in the production of gender data instruments and the level of donor support needed. This is not the full cost of improving statistical systems in IDA-eligible countries. It excludes additional investments needed in systems that are not directly concerned with the production of gender data, such as business registers and national accounts, money and banking data, balance of payments, government budget information, and geospatial and remote sensing systems. Some of these systems were included in earlier cost estimates (SDSN 2015; GPSDD 2016). The estimates also exclude the cost of ad hoc, specialized data collection that may be needed to respond to specific events, such as the COVID-19 pandemic, or project-specific data used for impact evaluations.

We proceed as follows:

1. Calculate the full cost of the core gender data system, 2015–2020.

We estimate the full cost of producing censuses and surveys and upgrading administrative systems for the core gender data system, using unit costs documented in the 2015 Data for Development report and converted to 2019 prices using the average of GDP deflators in high-income countries. The full cost shown in Table 6 is estimated to be $5.8 billion.

1. Estimate apparent spending on censuses, surveys, and administrative data and then calculate the shortfall.

Applying the same unit costs to the surveys and censuses that have been completed according to the inventory described in section 3b, we estimate apparent spending to be $2.2 billion. In addition, we estimate the value of spending on improvements to administrative systems to have been $975 million for a total of $3.1 billion or $600 million a year. This establishes a baseline for past spending on gender data. We call this apparent spending because it is based on outputs, not inputs. The shortfall from full funding, also shown in Table 6, shows $2.7 billion over the period 2015–2020.

1. Estimate the share of funding provided by donors.

The expected donor share of the funding is assumed to be 52 percent of the total, based on a previous analysis of national strategies for the development of statistics. This assumption is maintained in the projections for the period 2021–2030. Estimated donor support over the period 2015–2020, shown in Table 3, has averaged between $217 million (lower bound estimate) to $272 million (upper bound estimate). Donor shares of the full cost of and apparent spending on gender data systems are shown in Table 7 and Figure 15.

1. Project expenditures from 2021 to 2030.

Expenditures for the period 2021 through 2030 are projected in constant 2019 dollars. Countries that have completed the 2020 census round are assumed not to need additional support for their census programs until the 2030 census round is initiated in 2025. Countries with high-functioning administrative systems will require less support than those with medium or low functioning systems. The full cost of the core gender data system is expected to average slightly over $1 billion a year from 2021–2025 and $900 million a year from 2025–2030. Annual spending levels are shown in Figure 16.

1. Describe a path to full funding of core gender data systems.

To fully fund core gender data systems, donor support averaging $500 million a year is needed, or roughly twice the estimated level of support in recent years. Neither domestic nor donor support for statistics is likely to increase quickly to the level needed. We describe, instead, a “glidepath” that achieves full funding by the end of the decade. The glidepath is illustrated in Figure 17.

Costs and spending shortfall in 2015-2020

The inventory of surveys and the assessment of administrative systems have shown that the ambitions of the SDSN (2015) and the Data2X and Open Data Watch (2019a) estimates of funding needed to support the core gender data system have fallen short. Table 6 shows the cost of the full suite of instruments and the apparent spending from domestic and international sources on surveys and censuses that were conducted from 2015 to 2020. Spending on administrative systems was estimated from the apparent functionality of those systems as of 2020.

Estimating the full cost

The unit costs of surveys and censuses were the result of several rounds of consultation conducted with survey statisticians for the Data for Development report (SDSN 2015). They have been inflated to 2019 price levels using the GDP price deflator for OECD countries. The unit costs and costs for improvements to administrative systems have been similarly inflated.

In The State of Development Data Funding (SDDF) 2016 (GPSDD 2016), it was recommended that the unit cost of labor force surveys be increased by 20 percent to include a “light” time use module. Subsequent experience suggests that the cost of collecting time use data may be significantly higher. In the current estimates, time use surveys have been included as a separate line and the cost of labor force surveys returned to their original estimate (adjusted for inflation). The SDDF report included additional agricultural surveys based on the AGRIS model of annual, rotating survey modules costing $1.5 million each. Two of these survey modules are expected to include gender-relevant data on asset ownership and paid and unpaid labor.

The SDDF report also drew attention to the need for more information on violence against women. Even in high-income countries, administrative reporting on violent crimes other than homicide is erratic and unreliable. Well-designed surveys therefore provide the best evidence of assaults by intimate partners and others. Here we assume that at least one of the supplemental surveys could be used for this purpose, but it is arguable that additional surveys will be needed to improve data collection and reporting systems to provide a complete picture of violence against women.

Table 6: Costs and spending on core gender data systems, 2015–2020

Spending on core gender data ($millions 2019 prices)	Frequency per 5 years	Unit costs	Full cost 2015–2020	Apparent spending 2015–2020	Shortfall 2015–2020
Health and wellbeing surveys	4	1.4	507.5	130.3	377.2
Household income or expenditure	1	1.9	165.0	113.7	51.3
Labor force survey	5	0.5	221.2	63.4	157.8
Time use survey	1	1.3	116.1	1.3	114.8
Agricultural census or survey	2	1.7	295.1	51.8	243.3
Supplemental survey	2	0.5	84.8	16.6	68.2
Survey subtotal		7.2	1,389.7	377.2	1,012.5
Civil registration and vital statistics	continuous	3.5	1,531.8	672.8	859.1
Education management information systems	continuous	1.3	559.4	160.0	399.4
Health management and information systems	continuous	1.3	559.4	142.4	417.1
Administrative data systems subtotal		6.0	2,651.0	975.0	1,676.0
Census	1 per decade	44.6	1,784.5	1,784.5	–
Total core gender data systems 2015-2020		57.8	5,825.2	3,136.7	2,688.5

Spending on surveys was estimated by applying the unit costs to surveys completed and recorded in survey archives between 2015 and 2019. Because of reporting lags, the estimates of spending on surveys for 2020 were extrapolated from the 2015 to 2019 data. Spending on censuses is based only on the 40 countries that planned to complete their censuses by 2020, hence there is no shortfall, but the cost of censuses in the remaining 27 countries will be included in projections for the 2021–2025 period.

The apparent spending for administrative systems was estimated from their current functionality shown in table 5 above. Spending on low-functioning systems was assumed to be 10 percent of the recommended level. Spending on medium-functioning systems was estimated at 50 percent; and for high functioning systems, which nevertheless need further improvements, spending was estimated to be 90 percent of the recommended level.

Spending shortfall

The full cost of core gender data systems between 2015 and 2020 is estimated to be $5.8 billion or an average of $971 million a year (in 2019 prices). Apparent spending based on the availability of survey instruments, censuses, and quality of administrative systems comes to $3.1 billion or $523 million a year. The shortfall in spending is the difference between the full cost of operating the core gender data system and the estimated costs of producing the census and surveys conducted and the spending on improvements to administrative systems over the period 2015 to 2020. That difference is $2.7 billion or about $448 million a year.

Cost sharing: donor and domestic resources

The original Data for Development study and subsequent estimates of the cost of statistical systems in IDA-eligible countries expected that domestic resources were available to cover 48 percent of the total cost and that donors would underwrite 52 percent. We maintain that assumption here, as shown in Table 7. To cover the full cost, donors would have provided $505 million a year, matched by domestic resources of $466 million. But that did not happen: Given actual total spending of $523 million between 2015–2020, the donors’ share would have only been about $272 million under the 52 percent cost-sharing assumption.

Table 7: Average annual costs, spending, and donor support, 2015–2020, $ millions (2019 prices)

	Full cost of core gender data ecosystem	Estimated spending on surveys, censuses, and administrative systems
Estimated full cost of core gender data ecosystem	971	523
Of which Support from donors under 52% cost sharing assumption	505	272
Funded from domestic resources	466	251

The estimate of $272 million spent by donors is based on our assumption that donors provided 52 percent of the cost of building statistical systems, but what evidence do we have that donors actually spent this amount? From Table 3 above, our best estimate is that between 2015 and 2019, donors provided between $217 and $272 million a year for gender statistics (it is possible that support for censuses led to higher commitments in 2020 but those data are not yet available). As shown in Figure 15, the high estimate of donor commitments for gender statistics of $272 million is exactly their calculated share of apparent annual spending over the period. These results do not prove that donors’ commitments were used to fund surveys, censuses, or administrative systems. We know that some donor funding went to regional and global programs that may have had little direct impact on the production of gender data. Therefore, the actual share of donor funding available to support the production of gender data could have been less than 52 percent.

Figure 15: Full cost, estimated spending, and donor commitments, 2015–2020

Source: Open Data Watch.

The path to a complete gender data system

Core gender data systems have been underfunded by an average of $448 million a year from 2015 to 2020.

By our calculations, core gender data systems were underfunded by an average of $448 million a year in the preceding 6 years. The gap in funding is reflected in the low number of surveys undertaken and the weak state of administrative data systems. Censuses have been adequately funded, but gaps could appear if those delayed or postponed by the pandemic require additional funding. To fill these gaps and ensure that the data required to guide countries to the SDGs are available, funding for gender data will have to increase over the next ten years.

Projected costs 2021–2030

Costs will change depending on investments made now. While the number of censuses to be completed will taper off over the next few years, achieving functional administrative systems will require increased investments in low-functioning countries and continuing support even in medium- and high-functioning countries. If investments are made now, costs for building administrative systems will decline after 2025. Survey costs are assumed to remain constant (in real terms) over the decade. Although technological innovations could reduce unit costs, we expect that increasing demand for more finely disaggregated data will absorb any cost savings. And the cost of censuses will increase at the end of the decade when the 2030 census round hits its peak.

Figure 16: Projected costs of core gender data systems, 2021–2030

Source: Open Data Watch.

A glidepath to full funding

Achieving a fully functional core gender data system will require a substantial increase in donor funding and domestic resources over the next decade. With the estimated annual costs discussed above, the full cost over the period 2021 to 2025 is expected to be $5.2 billion or a little over $1 billion a year, approximately double the annual spending by donors and countries in the previous six years ($523 million). Total costs in the next five years, 2026–2030, are expected to be slightly less, at $4.5 billion, but still substantially above historical levels. Neither donors nor countries can be expected to suddenly double their spending on data systems, especially not while the COVID-19 pandemic continues to disrupt daily life. But the gaps in gender data will not be eliminated without a sustained effort to build and sustain the core gender data system.

There are many paths to achieve full funding of gender statistics. Here we take a cautious approach, assuming that countries and donors will start near their current spending levels and gradually ramp up to meet the total spending needed at the end of the decade. Figure 17 illustrates such a path. Several assumptions have been made:

1. To reflect uncertainties in the starting dates of censes, surveys, and improvements in administrative systems, total costs are calculated as a centered, three-year moving average of the total costs shown in Figure 16.
2. Expected spending from domestic and donor resources will increase from its average level of 53 percent of total cost to full funding in 2030.
3. The donor share of spending will increase from its average of 47 percent over the period 2015-2020 to 52 percent in 2030.

Figure 17: Possible paths to full funding of core gender data systems

Source: Open Data Watch.

In this scenario, some surveys or investments in administrative systems will have to be deferred until later in the decade. What activities to fund and what to defer are strategic questions that must be made by countries working with their development partners while responding to the needs of the user community. Donors may also choose to allocate more funding toward countries with greater need, postponing support for others in the meantime. But the goal, in keeping with SDG 17.18.3, should be to have fully functional core gender data systems in place within the current decade.

3d. Menu of Options for Gender Data Financing

It is not only a question of how much support is needed for gender data, but also of how it is provided.

This section outlines several of the most common funding modalities for statistics such as domestic resource mobilization, loans and grants, bilateral grants, technical assistance, and multi-donor or pooled trust funds, and emergency funds for COVID-19. However, regardless of the funding method, the goal is that all funding efforts are rooted in the needs outlined in countries’ national plans and are well linked to the demand for better data.

Domestic Resource Mobilization

Domestic resource mobilization is the most sustainable path for building an effective gender data infrastructure, including the provision of core gender data instruments covered earlier in this report. Some countries such as the Philippines and Mexico have proven self-sustainability is possible and are among governments who finance their development data modernization almost entirely from domestic resources (See PARIS21 (2016) for a breakdown of Philippines’ domestic and external financing for statistics). These examples highlight the importance of linking gender data efforts to the national demand for gender statistics. Gender data efforts that are owned and demanded by the country can also improve accountability and efficiency, while lowering the burden of donor activity coordination and additional reporting and procedural requirements. Therefore, the main goal should be countries fully financing their national gender data system modernization.

Loans and Grants

While countries that can raise domestic funds for gender data should do so over the next ten years, unfortunately not all countries have reached a level of financing maturity that allows for this. In many cases, additional funds are needed beyond domestic resources to meet gender data capacity-building efforts. One common way the financial gap for gender data capacity development has been closed is through loans and grants, either directly or indirectly. Multilateral lending instruments through organizations such as the World Bank and African Development Bank provide the largest proportion of aid to statistics (PARIS21 2020b). This could be an important option for countries that need a sizable loan or grant to overhaul their statistical system and provide funds for office infrastructure, purchase of equipment, staff training, cost of surveys, and census. Other loans and grants to low-income countries for building better statistical systems focus on data production through household surveys or a census.

The development of such foundational systems could greatly benefit the gender data agenda. However, without some direct discussions of needs, the development processes may miss gender data needs altogether. It would be hugely beneficial if initial plans and project discussions would incorporate gender data at the outset, for example when a new household survey or a labor force survey is designed and costed. Current opportunities for this type of fund include IDA-19 with its $82 billion dollar portfolio and opportunities for countries to use the new IDA19 Data for Policy (D4P) theme to improve data and statistics (See Box 3).

Bilateral Grants

In addition to organizations like the World Bank, donor government agencies such as those from the United Kingdom, Canada, and Sweden are major contributors to financial support for gender data financing at the country level through bilateral grants. Philanthropic organizations such as the Bill & Melinda Gates Foundation are also in this category. These funds are often targeted at specific data categories that donors prioritize such as violence against women, women’s economic empowerment, or time use surveys. The process of receiving funds may be faster than the options above but reporting requirements could be extensive, and some donors require strict monitoring of results and reporting.

Technical Assistance

Support to improve developing countries’ statistical production is traditionally delivered in the form of technical assistance (e.g., conducting training, designing surveys, building data management systems). For many years, the International Monetary Fund has been recognized for its technical assistance in statistics—even more so due to its Data for Decisions (D4D Trust Fund). UN Women, with support from the Bill & Melinda Gates Foundation, is also leading technical assistance for gender data work in several countries as part of its Women Count program. Another form of technical assistance has been the twinning arrangements between countries that form partnerships to exchange expertise sponsored by a donor. The UK’s Office of National Statistics currently has several such partnerships.

Multi-donor or Pooled Trust Funds

As noted above, sometimes technical assistance takes the form of multi-donor or pooled trust funds. In the past two decades, low-income countries have strongly benefited from funds such as the World Bank’s Statistical Capacity Trust Fund (TFSCB). Such funds have been especially useful in uniting donors around priority areas such as decennial censuses or surveys and improving coordination. As these funds often span several years, they can also help improve budget predictability. They also make it easier to trace results and document learnings on result-based capacity building. Multi-donor or pooled trust funds can also support an overall sector—for example, the 50×2030 initiative is funding and strengthening agriculture data—or a specific data instrument such as the census through UNFPA’s Population Data Thematic Fund.

Box 3: Building on Synergies with IDA-19

Building on Synergies with IDA-19

A new approach by donors is to encourage synergies between gender data improvements and larger statistical capacity building investment projects. One laudable example is support from the Bill and & Melinda Gates Foundation to forge connections between the World Bank’s IDA-19 replenishment and gender data capacity building efforts. Despite a shared commitment to strengthening statistical systems, the Gates Foundation, the World Bank, and NSO counterparts have identified as challenges a lack of technical expertise to improve gender data collection and analysis, competing demands leading to a lower prioritization of gender data, and a lack of overall bandwidth to engage on the topic.

The Gates Foundation’s initiative will help address and alleviate this constraint by providing specific technical assistance and flexible support to NSOs on gender statistics in a way that is targeted, detailed, and impactful in view of the range of demands of statistics programs. The overall expected outcome of their work is to narrow gender data gaps, especially those related to economic status and outcomes for at least 10 World Bank IDA countries who have received IDA-19 support and technical assistance from the World Bank. The initiative will also help leverage the World Bank’s existing engagement with NSOs on statistics lending through the Data for Policy initiative, which seeks to help countries improve National Statistical Systems (NSS) by enhancing the availability, timeliness, quality, and relevance of key data for evidence-based decision making.

Leveraging existing World Bank resources, staff/personnel, country presence, and government relationships, this project approach will provide a low-cost model to help reduce gender data gaps in IDA countries. This approach can also be easily integrated and scaled up with the World Bank’s existing technical assistance and statistical capacity building projects, and outreach with Ministries of Finance on resource allocation and prioritization for gender data.

While the selection of countries is still in development, the approach consists of three activities: a country assessment of gender data gaps that provide the status of gender data in detail to help identify opportunities for closing gaps through surveys and indicators; technical assistance to NSOs; and flexible funds and incentives (approximately 20 percent of the direct costs) to address miscellaneous needs and barriers that prevent NSOs from adopting and implementing changes to fill gender data gaps. This can include, for example, research analysts to support the NSOs statistics work; editorial support for a gender statistics report, or other specific technical assistance or activities that might emerge for the NSO.

Emergency Funds

The most recent funding methods to emerge are emergency funds for COVID-19 response and recovery efforts. Several opportunities to direct funds from existing funding mechanisms into statistical systems to improve gender data production are surfacing—such as the new funding mechanisms the UN and IMF created to support countries’ response to COVID-19. A noteworthy example of this is the African Development Bank, which approved $27.4 million in grants in July 2020 to boost the African Union’s efforts to mobilize a pandemic response—including funding specifically for the collection of gender-disaggregated data.

The options offered here have advantages and disadvantages depending on the country’s circumstances. If the speed of implementation and quick access to more timely data is a priority, domestic resource mobilization or aid through a bilateral donor may be fastest. However, if the long-term goal is to invest in a system-wide approach, supplementing domestic resources with a multilateral grant or loan may be the best way forward. In all cases, exploring how to build synergies with popular existing funds such as IDA19 and/or linking to emergency funds should be considered. In the future, tools such as the Bern Network’s Clearing House (See Box 4 below) will be useful in supporting decision makers to explore different options and different funding modalities, including for gender data.

4. Defining the Way Forward

We have less than 10 years to reach the shared global ambitions of the SDGs. The centrality of gender equality in achieving those goals—including but not limited to SDG 5 on gender equality—seems largely understood, or at least is given rhetorical due. However, a rhetorical commitment to gender equality is insufficient; we need the tools to collect and use quality gender data on all aspects of women’s and girls’ lives to drive action and advance gender equality.

The lessons learned through the COVID-19 pandemic confirm the importance of and need for better gender data while highlighting extensive gender data gaps and the immediate need for rethinking how to increase investments in gender data

It starts at birth by counting women and girls through national ID and CRVS systems and continues through the lifecycle by having the right data to ensure all have access to and equal opportunities for health, education, labor, family planning, protection against violence, representation in politics, and more. From birth to death to the stages in between, from her health to her economic opportunity, from her safety to her role as a leader in her country—we have limited insight into the full spectrum of women’s and girls’ lives worldwide (see Figure 18), which impedes progress on gender equality and women’s empowerment. These limited insights—caused by poor quality or non-existent data—hamper informed decision-making and can reinforce existing inequalities for women and girls. The lessons learned through the COVID-19 pandemic confirm the importance of and need for better gender data while highlighting extensive gender data gaps and the immediate need for rethinking how to increase investments in gender data, as detailed in Strengthening Gender Measures and Data in the Covid-19 Era: An Urgent Need for Change (Center on Gender Equity and Health et al. 2021).

This moment of improved attention on gender data gives the global community an opportunity to emerge with a stronger vision—motivated by the reality that in order to better respond to a global emergency, we need a stable data infrastructure. Such infrastructure requires investing in foundational gender data systems. Countries themselves should drive their own gender data capacity development based on national context and objectives, and there must be better alignment between global, regional, and national stakeholders to increase, improve, and match resources with needs. The roadmap displayed in Figure 18 provides an overview of countries’ readiness on five dimensions and can help countries evaluate where they stand, which instruments to prioritize in their national planning, and how they can help donors better prioritize and focus investments.

Figure 18: A roadmap to building a core gender data system

Source: Open Data Watch.

Gender data financing is a complex field, as demonstrated by this paper, and needs to remain on the agenda of agencies concerned with gender and development, which will require ongoing research, monitoring, and advocacy. Building on our 2019 report on this topic, this edition provides a significant update and delves deeper into gender data challenges and opportunities. As a way forward, we present six ideas for enabling sustainable financing of gender data.

Build a Coalition for More and Better Gender Data Financing

We suggest building a coalition of stakeholders that can collaborate on a common goal of building a sustainable environment for more and better gender data financing.

Gender data financing is a challenging area without quick, effortless fixes. It will require a strong multi-year action agenda, advocacy for more and better funding, and persistent champions to spread the message and systematically monitor progress. Because no single donor or international agency can take on the topic of gender data financing alone, there is a need to connect different stakeholder groups, capitalize on new opportunities and audiences—such as the energy behind the Generation Equality Forum and its focus on gender equality commitments—and tap into existing networks. The Bern Network is one such network that provides an opportunity to continue to build the case for more and better gender data financing within it.

Box 4: Advocating for More and Better Financing for Development Data: The Bern Network

Advocating for More and Better Financing for Development Data: The Bern Network

The Bern Network, an open, multi-stakeholder alliance to support the SDGs by promoting more and better financing for data, is developing a ‘Clearinghouse for Financing Development Data’ to help countries, donors and development partners identify funding opportunities, bring projects to scale, advocate for support to data and statistics, and connect to new partners.

The online platform will provide information and services to match the supply and demand of financing for data and statistics to foster transparency, accountability, and alignment, and ease coordination among donors and partner countries. While it will serve the needs of broader financing for the development data community, it has exciting potential for gender data. Donors will be able to use it to identify key data funding gaps in recipient countries, benchmark their country’s data funding, and highlight opportunities for joint projects with other donors. Recipient countries can use it to understand how much aid they are receiving for statistics across the board and plan investments; assess their funding gaps to lobby for more resources from government and donors; and access best practices to improve efficiency and effectiveness of investments in data and statistics.

The clearinghouse will meet the needs of diverse user groups. An official at a national statistical organization can discover the trends in international support for data, as well as opportunities for funding by various doors. A project manager at a donor agency can learn about which countries and sectors are well funded and which are underfunded, or deep dive into a range of specific country data. Researchers can analyze overall trends in financing data and forecast future trends. Civil society organizations advocating for better data can review the funding landscape and identify who the top donors/recipients are, and which countries are increasing their own investment in data and statistics.

A preview of the pilot clearinghouse was launched at the virtual UN World Data Forum in October 2020, and a fully operational clearinghouse is expected to be launched at the UN World Data Forum in October 2021. Components include visualizations of funding levels and opportunities based on analyses of financing flows and needs, including by sector and SDG area such as gender; now-casting and forecasting of resource allocations for data and statistics; and profiles that highlight the financing flows from and to countries around the world.

Increase Use of and Demand for Gender Data

One of the challenges of financing for gender data is demonstrating what changes in the world, and what changes in people’s lives, when you have gender data. This is made even harder due to the historic gaps that exist in gender data in every country. The key to achieving sustainability in this field is to better understand, document, and motivate the increased use and impact of gender data, and to ensure that gender data, when produced, is meaningfully linked to policy decisions. There have been promising signs as a result of COVID-19 data use, which has triggered an increased demand for better sex-disaggregated data on cases and causes of death and has encouraged countries to collect better data. However, there is still a dearth of examples in the use of gender data for policy impact and concerted effort is needed to document and then utilize that work to strengthen advocacy. This is an area which Data2X will prioritize over the next three to five years, and for which allies will be needed.

Deliver on the Promise of New and Alternative Gender Data Sources

It is critical that the gender data agenda benefits from data innovations in recent years and uses new and alternative gender data sources. Research by Data2X and other partners on big data point to several alternative sources that could be used to fill gender data gaps (Data2X 2021), but there is a need to strengthen connections between these new data sources and countries’ statistical systems. The use of alternative data sources should start with better national planning and efficient mainstreaming of gender data into national data systems. This requires further improvements to the gender data planning processes of National Statistics Offices (NSOs), and better use of National Strategies for the Development of Statistics (NSDS) for both planning and budgeting for gender data. This will also require building new innovative collaborations between country statistical systems, private sector data producers, civil society, and policymakers to demonstrate how alternative data sources can accelerate progress towards better, more gender-sensitive policymaking.

Encourage and Support Country-driven Approaches for Resource Mobilization

To build a sustainable gender data financing environment, the first step is for countries to clearly articulate their gender data demand and budget for gender data in national statistical plans and advocate for support.

It is essential that any efforts towards improved financing emphasize and ensure that countries are in the driver’s seat in articulating their own gender data needs and ensuring resource mobilization for gender data. There are three steps to this action: first, encourage countries to take charge of their gender data needs and priorities. Second, if countries request it, ensure technical support exists for assessing and articulating the resource needs in countries. Third, once the demand for gender data has been articulated, urge countries to follow through on these goals and either build them into budgeting from domestic resources or ensure it is included in resource requests from international sources. Our latest monitoring work shows that improvement is needed on all of these steps (see Box 5 on “Prioritizing Gender Data in National Statistical Strategies” below). More countries need to articulate national demand for improving the lives of women and girls with a corresponding gender data plan that is costed and ideally has identified sources of domestic or international funding. We believe that the above action item—to make better use of data—can directly assist countries in achieving this goal.

Box 5: Prioritizing Gender Data in National Statistical Strategies

Prioritizing Gender Data in National Statistical Strategies

An assessment to determine the extent to which IDA-eligible countries prioritize gender statistics within their national statistical planning shows that of the 74 IDA-eligible countries covered in this report, only 35 have current or recently expired national strategies for the development of statistics (NSDS) available. By scanning the 35 NSDSs for key words associated with gender statistics, such as “sex-disaggregated,” it was found that 30 NSDSs have refences to gender statistics, of which, only 19 NSDSs have a budget line for gender sex-disaggregated data (this includes NSDSs that have a budget of zero for sex-disaggregation, as in the case of Burundi). These results show some improvements compared to the 2019 preliminary findings but still present an area in need of further improvements.

Prioritize strengthening the core gender data system

In the near-term, the gender data community should prioritize strengthening the core gender data system, particularly in IDA-eligible countries (as shown in Fig 18). This should be a time-bound goal for national plans that is costed and articulated in budget requests from national sources and from development partners and donors supporting data and statistical capacity building. Countries have diverse needs and different absorptive capacities so there is a need to sequence and think through what parts of the core gender data system have the highest priority and most urgency. Our recent work on COVID-19 gender vulnerability assessments and the Bridging the Gap studies in 25 countries show that increased frequency and better coordination of surveys has a high return proving to be one of the most practical solutions for building an effective gender data system.

Fully funding the core gender data system will improve the overall demographic and social data system. And if by improving the gender data system, data to measure goals and conditions of other vulnerable groups underrepresented by current data are improved, then the gender equality and women’s empowerment movement will have helped lead the way to achieving the Sustainable Development Goals.

Advocate for improved ODA funding in support of gender data

The levels and types of aid for gender data do not look promising in terms of the distribution of more funding by international donors and domestic sources. In fact, due to the ongoing pandemic, maintaining current levels of investments may be challenging as donors shift resources. Due to this state of funding, it is essential to rethink and improve modes of support for gender data. Opportunities to do this include:

- - Gender data, as a cross-cutting topic, can benefit from linkage to other data investment plans such as improvements in administrative systems, CRVS, or surveys.
  - As countries improve their planning and budgeting for gender data, donors’ practices must be improved as well. Currently, only five donors make up the majority of funding for development data. That is a fixable problem, and we should improve the diversity of donors for gender data.
  - Donor support for specific one-off efforts (such as surveys) should be complemented with support to build a foundational system and technical support to build sustained capacity. Donors also need to support efforts beyond data production to ensure that data produced is analyzed, used, and made accessible.

Producing and using high quality gender data to advance gender equality and women’s and girls’ empowerment requires increased technical capacity, political will, and financing. Closing the gender data financing gap provides an opportunity and challenge for collective efforts by leaders at all levels to support this agenda and allocate the needed resources to enable us to build a better, stronger gender data system that is more suited to meet the needs of women and girls globally.

Annex: Survey Instrument Types

A list of survey instrument types based on Figure 12.

Population and housing censuses: Population and housing censuses enumerate the entire population of a country. Basic demographic information of the entire population (such as age, sex, residence) is also compiled. Censuses also provide information of the enumerated population’s housing and living conditions. The 2010 round of censuses and those completed or scheduled for the 2020 round (2015 to 2024) are included in the inventory. A number of scheduled 2020 round of censuses have been delayed as a result of the COVID-19 pandemic but are included in Table 3 as if completed.
Household health and wellbeing surveys: These surveys typically include Multiple Indicator Cluster Surveys (MICS), Demographic and Health Surveys (DHS) and any DHS-affiliated surveys (AIDS Indicator Survey, Malaria Indicator Surveys), and other household health and wellbeing surveys that are not sponsored by USAID/DHS and UNICEF/MICS. These surveys capture data on the wellbeing of women and children, including topics on reproductive health, contraceptives and fertility, child nutrition and health, and diseases such as HIV/AIDS and malaria.
Income or expenditure and multi-topic household surveys: These include Living Standards Measurement Study (LSMS) other surveys that collect information on household expenditures or sources of income and household assets that are the primary source of estimates of poverty status and the distribution of income or wealth. These surveys are essential to SDG indicators under Goal 1 (No Poverty) and Goal 10 (Reduced Inequalities).
Labor force surveys or survey modules: These include stand-alone labor force surveys or comprehensive labor force modules in existing income-expenditure and multi-topic household surveys are recorded. Labor force surveys or modules capture the status of the labor force population, including data on employment rate, employment by sector, unemployment rate, and other information surrounding the status of the labor force.
Time use surveys or modules: These include stand-alone time use surveys along with time use modules in existing labor force surveys or income-expenditure and multi-topic household surveys. Time use surveys collect basic information on the activities of household members that allows a better representation on how men and women use their time on paid work, household work, and other paid and unpaid activities. Most of the recorded time use surveys were conducted prior to 2015; one recent survey, completed in 2020, has been included in the 2015-19 tally.
Agriculture censuses or surveys: Agricultural censuses and surveys focus on farm production and income; however, FAO has piloted an integrated agricultural survey (AGRIS) that includes more detailed information about the paid and unpaid activities of family members in the agricultural labor force. These surveys have been implemented in a small number of countries; however, we count other censuses or surveys that collect basic information on household members or the agricultural population, as this forms the basis on which to collect basic gender data from agricultural censuses or surveys.

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Footnotes

↑ [1] The gender data categories include labor statistics, which are not included here in the average scores for economic statistics. Other categories in economic statistics include balance of payments, government finance, price indexes and others.

↑ [2] Classification of Statistical Activities used in the United Nations Economic Commission for Europe’s (UNECE)

Database of International Statistical Activities https://unstats.un.org/unsd/iiss/Classification-of-International-Statistical-Activities.ashx

↑ [3] The areas of statistical activities are displayed in Figure 15 of PRESS2020 (PARIS21 2020b). We created an upper bound for gender-relevant funding by summing the shares of support to demographic and social statistics and general statistical items and methodology of data collection, processing, dissemination, and analysis. The lower bound is created by summing support to demographic and social statistics and a fractional share of support to general statistical items based on the share of funding to demographic and social statistics among economic and environmental statistics. We also adjust both bounds for the share of ODA funding received by IDA-eligible countries. See Table 3 for the effect of these steps on total support to gender data.

↑ 4] Only seven countries out of 74 IDA-eligible countries have failed to complete or lack plans to complete the 2020 census round by 2024 (Afghanistan, Central African Republic, Eritrea, Somalia, South Sudan, Syrian Arab Republic, and Yemen). The World Bank and the United Nations Statistical Division (UNSD), in coordination with the five UN Regional Commissions, conducted a global online survey to assess the impact of the coronavirus crisis on statistical offices, and to identify needs for financial and technical support. According to their survey seventy-three percent of NSOs had a Population and Housing Census planned in 2020 or 2021 before the pandemic hit. In the low and lower-middle income group, 68 percent of the NSOs that were planning a census had to postpone it (World Bank 2020). The delayed censuses are treated as completed or to be completed within the 2020 census round for the purpose of assessing the need for additional funding.

↑ [5] Four countries (Kosovo, Mozambique, Pakistan, and Tajikistan) are not part of this graphic, as their gender data systems are not classified as weak under the conditions above. However, even these countries have room to improve. All four, for example, fall short of the recommended number of household health surveys.

About this report

This report was authored by a team at Open Data Watch (ODW) comprised of Shaida Badiee, Eric Swanson, Lorenz Noe, Tawheeda Wahabzada, Amelia Pittman, and Deirdre Appel with financial and content support from Data2X under the leadership of Emily Courey Pryor. We would like to acknowledge the peer reviewers who have strengthened this report through their invaluable feedback: Neeraja Penumetcha (Data2X), Mayra Buvinic (Center for Global Development (CGD) and Data2X), Jessica Espey (Senior Adviser to ODW), and Archita Misra (PARIS21). We especially want to thank the entire PARIS21 team for their support and partnership on better financing for development data. In addition, we would like to thank Kathleen Beegle (World Bank), Caren Grown (World Bank), and Diva Dhar (Bill & Melinda Gates Foundation) for helpful comments, as well as Kieran Walsh (ILO) and Yves Perardel (ILO) for their insights on labor force surveys. Amelia Pittman (ODW) created infographics and design. Elizabeth Black (Data2X) provided copyediting.

State of Gender Data Financing 2021

Contents

Executive Summary

1. Introduction

2. Understanding the Landscape of Gender Data

2a. Highlighting the Importance of Gender Data and Identifying Gaps

2b. Defining instruments of a core gender data system

2c: Mapping Gender Data Stakeholders

2d: Painting a complete picture of the value of gender data: use and impact

Connecting Gender Data to Policy

Harnessing Open Data

Promoting Gender Data Use

3. Exploring current investments and gaps in gender data financing

3a. Financing flows for gender data: top donors and recipients

3b. Measuring the capacity of gender data systems

Censuses and surveys

Administrative systems

Targeting gender data interventions

3c. Estimating costs to complete the gender data system

Costs and spending shortfall in 2015-2020

The path to a complete gender data system

3d. Menu of Options for Gender Data Financing

Domestic Resource Mobilization

Loans and Grants

Bilateral Grants

Technical Assistance

Multi-donor or Pooled Trust Funds

Building on Synergies with IDA-19

Emergency Funds

4. Defining the Way Forward

Build a Coalition for More and Better Gender Data Financing

Advocating for More and Better Financing for Development Data: The Bern Network

Increase Use of and Demand for Gender Data

Deliver on the Promise of New and Alternative Gender Data Sources

Encourage and Support Country-driven Approaches for Resource Mobilization

Prioritizing Gender Data in National Statistical Strategies

Prioritize strengthening the core gender data system

Advocate for improved ODA funding in support of gender data

Annex: Survey Instrument Types

A list of survey instrument types based on Figure 12.

Bibliography

Footnotes

About this report

Author: Open Data Watch and Data2x

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