State of Gender Data Financing 2019

Executive Summary

The 2030 Agenda for Sustainable Development, endorsed by the 193 member states of the United Nations, is an ambitious commitment to progress, articulated in 17 goals and 169 quantifiable targets measured by 232 indicators. Achieving these goals requires implementing polices based on evidence and sound analysis and systematically measuring progress. This will therefore require equally ambitious efforts on the part of statisticians and data users in governments, international agencies, and civil society, supported by domestic and external resources, to improve the availability, reliability, and use of data.

Spanning the Sustainable Development Goals is a promise “to achieve gender equality and the empowerment of all women and girls.” Because advancing gender equality and women’s empowerment touches many domains – from equitable labor opportunities to drastically reduced maternal mortality to increased representation in political life – 12 of the 17 goals contain targets of importance to women. Thirty-two of the SDG indicators have an explicit focus on women’s status and wellbeing; another 36 can and should be disaggregated by sex to provide a better understanding of the challenges women face. And yet the reality is that we are still missing much of the data needed to measure these indicators. Our collective and persistent inability to answer key questions about progress on these topics must change, and can, if we are motivated to do so.

There are notable organizations advocating for increased funding for development data. This project team supports those efforts. When it comes to the specific needs of gender data, there is also an opportunity to more deeply understand the challenges related to gender data financing and drive a greater sense of urgency to address it. Data2X’s intention in kicking off this work program is to understand the costs of funding a ‘core gender data system,’ layout the existing funding gaps for this system, and determine where and how those gaps can be filled. Presented here are the early findings, and after stakeholder consultation, the project team will present a more thorough study in Fall 2019. This paper finds that the current gap in financing needed to sustain a core gender data system in lower-income countries is between $170M-$240M a year between now and 2030. Filling this funding gap would enable data collection activities for all 68 gender-relevant indicators in the SDGs. It is important to note that 1) information on data financing is itself collected in a way that makes it difficult to account for what money goes to which data source, for what purpose, and from where; and 2) that the requirements for a high quality, reliable gender data system overlap with those of a well-functioning data system: for example, in the need for more timely surveys, complete civil registration and vital statistics systems, and decennial censuses. Achieving and fully funding the gender data agenda will indisputably improve data systems overall. And if by improving the gender data system, the information available to measure other goals and the conditions of other groups currently disenfranchised and underrepresented by current data is improved, then the gender equality and women’s empowerment movement will have helped lead the way to achieving the Sustainable Development Goals.

The paper concludes with initial thinking on a way forward – calling for a coalition willing to work together to problem-solve on how to increase total funding, improve existing funding flows for higher impact, have honest conversations about donor coordination, and engage with countries to encourage them to take charge of their own resource mobilization and prioritization of gender data.

1. Introduction

Three years ago, a forward-looking study of the statistical requirements of the 2030 Sustainable Development Agenda found that annual spending in excess of $1.1 billion was needed to ensure that the poorest countries of the world would be able to produce the statistics needed to set policies, manage development programs, and monitor their outcomes (GPSDD 2016). The demand for better development data has continued to grow, due to both the needs of the SDG agenda and requests from donors for better and more frequent statistics, but the capacity of countries to respond to those demands has not kept pace, nor has financial support from the donors. In 2016, the most recent year with data, PARIS21 reports that aid for statistics to all countries comprised only 0.33 percent of total official development assistance (PARIS21 2018).

To make progress toward the 2030 Agenda’s promise of gender equality and the empowerment of all women and girls, and to measure that progress, there is a need for more and better gender data. Recognizing this, the Cape Town Global Action Plan for Sustainable Development Data calls on all countries to “Promote the systematic mainstreaming of gender equality in all phases of planning, production and usage of data and statistics.” This paper reexamines and updates the earlier estimates of the support needed for statistics, specifically focusing on the data needed to calculate the gender indicators of the Sustainable Development Goals (SDGs). The sources of gender indicators are microdata collected through censuses, surveys, and administrative records. This paper describes the main gender data sources, defines what is included in a “core gender data system,” and presents early findings on the costs of building and maintaining a core gender data system in the 75 poorest countries and the gap between those costs and the international financing available for gender data.

The findings presented here are a first step – the project team will do further work to round out what is shown here, including: digging deeper into mapping of key actors i8n data production and the role they play such as UNFPA in Census, ILO in Labour Force Surveys, UN Women FPI, UNICEF MICS, World Bank LSMS, overview of the gender data funding landscape – major sources of funding and mechanisms to channel them, a menu of options for investment which would outline key sources of gender data and the gender data financing gap for each source, and, finally, additional areas of investments such as (un-costed) innovations, coordination, capacity  issues, and investments in users.

2. The main sources of gender data

Where do gender data come from? Bridging the Gap: Mapping Gender Availability in Africa (Open Data Watch 2019) examined the availability, frequency, timeliness, and adherence to standards of 104 gender indicators in 15 low- and lower-middle income Sub-Saharan African countries¹. Sixty-eight indicators were selected from the SDGs (IAEG-SDG 2018); the remaining 36 have been suggested by UN Women as part of their so-called Minimum Set or as supplemental indicators in their publication Turning Promises to Action (UN Women 2018). Excluded from the study set are seven Tier III SDG indicators that currently lack agreed standards and methodologies.

As part of the Bridging the Gap study, assessors recorded the microdata sources of the available indicators in each country. While these countries are not necessarily representative of all developing countries, a consistent picture of the main sources of gender data emerged that is likely to be representative of many low- and lower-middle income countries that qualify for financing from the World Bank and the International Development Association (IDA and Blend countries).

Table 1 shows the microdata sources of the 68 SDG gender indicators and frequency of occurrence within 12 goals of the SDGs. Among the most important sources are surveys of health and women’s wellbeing such as MICS and DHS and household income and expenditure surveys such as living standard measurement surveys (LSMS). Labor force surveys provide additional information on women’s economic roles. Although not often cited, censuses are the backbone to all demographic data, and civil registration and vital statistics (CRVS) systems remain largely incomplete and underutilized. Administrative systems of the education and health systems are important sources of gender data in their domains, although their benefits have not been fully realized as they are often incomplete or under resourced. Other administrative systems tabulated here are chiefly police or criminal justice reports. Taken together these surveys and administrative systems comprise the core gender data system.

¹ Botswana, Cote d’Ivoire, Ethiopia, Ghana, Kenya, Lesotho, Malawi, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe

Table 1. Proportion of SDG gender indicators, by goal, derived from microdata instruments

Click here to  expand table

* Other surveys include agricultural, transportation, crime/victimization, and miscellaneous household surveys.

Source: ODW calculations from Bridging the Gap: Mapping Gender Data Availability in Africa

3. What does it take to build a core gender data system?

In 2015, anticipating the expanded statistical demands of the SDGs, the Sustainable Development Solutions Network (SDSN), with Open Data Watch (ODW) and PARIS21, published Data for Development: A Needs Assessment for SDG Monitoring and Statistical Capacity Development (SDSN 2015), which included estimates of the cost of producing a representative set of indicators for 77 IDA and Blend countries. The full cost of data acquisition and improvements to statistical systems was estimated to be $900 to $940 million a year over the SDG period from 2016 to 2030 or a total of $13.5 to $14.2 billion. This included $760 to $810 million a year ($11.4 to $12.1 billion total) for surveys, censuses, and improvements to CRVS and education management information systems (EMIS). The unit costs of surveys and censuses and their frequencies were provided by institutions and experts familiar with the instruments and data collection process. The remaining $2.1 billion
was for improvements to real sector statistics and the development of geospatial and environmental monitoring capabilities. The cost estimation methodology is described in Data for Development.

After the complete set of SDG indicators was agreed in 2016, the State of Development Data Funding (SDDF) revised the Data for Development estimates to include additional surveys to monitor gender-based violence and literacy levels, add time-use modules to labor force surveys, complement agricultural censuses with a suite of annual  surveys, and support improvements to health management information systems (HMIS). The total cost over the SDG period was estimated to be $17.0 billion, of which $14.9 billion, or $990 million a year, was for surveys, censuses, CRVS, EMIS, and HMIS (GPSDD 2016).

3.1 Cost estimates for gender statistics

The Data for Development and SDDF reports provided cost estimates for producing a broad set of development data that are essential for achieving the SDGs. This report focuses on the core gender data indicators included in the SDGs. We include all the instruments shown in Table 1, which produce data on demographics, health, education, household welfare, and labor force statistics. As noted in section 2, we refer to this as the core gender data system.

Excluded are the costs assigned to  improvements in geospatial data systems and environmental monitoring because these  important systems do not generally provide  individual data. Also excluded are industrial surveys and improvements to statistics of the real economy. Although surveys of industrial establishments are potentially useful sources of information on employed persons, including their sex, occupation, and wages or salaries, they were not cited by any of the 15 Bridging the Gap countries. Where available, characteristics of the employed population typically come from labor force or other household surveys.

The surveys, censuses and administrative systems included in the core gender data system have many applications, and it is not possible to allocate the cost of data collection separately among gender- and non-gender-related purposes. We therefore attribute the full cost of data collection to the core gender data system, while recognizing that their benefits will be shared across many domains and by applications that do not require sex-disaggregated data. Improvements to any part of the core gender data system will increase the availability and quality of data disaggregated along other dimensions and used for other purposes, just as improvements made for other purposes can and should benefit gender data.

The costs of producing core gender indicators were derived from the 2016 SDDF estimates and the unit costs from the 2015 Data for Development report with the following adjustments:

1.    To account for changes in IDA membership, the annual costs for all components were rescaled from 77 countries to the 75 current IDA and Blend countries.

2.    Unit costs were increased by the weighted-average increase in the GDP deflator for the 75 countries from 2015 to 2016.

3.    Core gender data costs were taken to include all survey costs except for the eight additional agricultural surveys included in the 2016 SDDF study. Although agricultural surveys, like other establishment surveys, can and should produce important information about women’s participation in the sector, quinquennial surveys or censuses of agriculture combined with higher frequency household surveys
should yield adequate coverage of gender issues in this sector.

4.    Costs for improvements in real sector data, industrial surveys, and the implementation of geospatial systems and environmental monitoring were excluded from the estimate of gender data costs, although investments in these sectors may complement improvements to the core gender data systems.

With these assumptions, the annual average cost of producing a robust set of gender data capable of  generating the SDG gender indicators, shown in table 2, is $920 million, representing 79 percent of $1.1 billion spending on statistics required by the 75 IDA and Blend countries. The cost of other statistical activities is $230 million a year. These estimates include an allowance for training of staff and associated capacity improvements, but do not include additional investments in new systems and technologies or the acquisition of data from private or non-traditional sources.

Table 2. Estimated cost of core gender indicators

Source: ODW calculations

The 2015 Data for Development report reviewed national statistical plans for IDA and Blend countries and found that 52 percent of financing for statistics among low- and lower-middle income countries was expected to come from external sources. Applying this factor to the current estimates, annual support of $600 million is required, of which $480 million is needed to support the core gender data system.

3.2 Donor share of commitments supporting gender data

The most recent Partnership Report on Support for Statistics, PRESS 2018 (PARIS21 2018), compiles annual statistics through 2016 on aid commitments by donors to low- and middle-income countries. There is not a marker to flag commitments that contribute directly to improvements in gender data. However, according to PRESS, 64 percent of all ODA projects include a marker for gender equality, but only 4 percent are marked as having gender equality as the main objective. That is a wide range and it is not clear what proportion of these projects includes statistical activity. Furthermore, there may be gender-relevant projects that are not marked. So how else can one measure the proportion of donor commitments to gender data?

To attempt to answer this question, we used the PRESS breakdown of commitments by topical areas of statistical activity, and took the share of commitments going to “demographic and social statistics” along with a proportionate share of support for “general statistical items and methodology of data collection, processing, dissemination, and analysis” to arrive at the lower bound of support for gender data. Over the five years, 2012 – 2016, this has averaged 44 percent of total commitments. As an upper bound, we include all support for general statistical items with demographic and social statistics, which has averaged 57 percent of total commitments. The remaining amounts went to economic, environmental and multidomain statistics, and strategic and managerial issues.

Not all commitments go to the poorest or least creditworthy countries. PRESS data show that the share of gender-related commitments going to IDA and Blend countries has varied from 70 percent in 2012 to 96 percent in 2013, with an average share of 79 percent over the most recent five years.

Figure 1 shows total commitments to statistics and the high and low estimates of commitments to genderrelated statistical activities in IDA and Blend countries. All values have been converted to 2016 price levels. The series is lumpy. The highest year ever recorded by PRESS was 2013, when there was a major international effort to collect data to mark the end of the Millennium Development Goals (MDGs) period in 2015.

Figure 1. Donor commitments to gender-related statistical activities, 2012 – 2016

3.3 Measuring the financing gap

The year-to-year financing gap, shown below in figure 2, is computed as the difference between the high and low estimates of commitments to gender-related activities in IDA/Blend countries and the donor share of the cost of the core gender data system in 2016 dollars from table 2 above. In 2013, the year of the MDG surge, the gap measured at the higher level of commitments fell to just $10 million, but in the most recent year (2016) the shortfall was between $290 and $330 million, due to a drop in the share going to gender-related activities combined with a drop in the share of commitments to IDA and Blend countries.

Figure 2. Gender data financing gap, 2012 to 2016

This study has used the best available estimates of aid to statistics, but it is not possible to determine what part of the support recorded in PRESS was directed exclusively to the activities included in the costing of the SDGs or, specifically, to producing the sex-disaggregated data needed to construct gender indicators. We take the funding gap of $170 to $240 million per year to be a baseline estimate of the additional financial support needed to produce the full range of the core gender data needed to inform policies, guide programs, and measure progress toward gender equality and women’s empowerment.

Looking ahead, there will certainly be additional investments in data ollection and, more broadly, in statistical capacity to meet thedemands of the SDGs along with other timely, reliable data needed to address country-specific issues. Among the seven Tier III gender SDG indicators are two that require testing of the educational proficiency of preschool age children and of children in grades 2 or 3. These tests do not exist and will require funding for development and deployment. Other Tier III indicators may entail additional costs that cannot be estimated at present.

The cost estimates presented here in 2016 prices will certainly increase over time with the general increase in the price level. Extrapolating from the rate of inflation in the 75 IDA and Blend countries, and assuming no change in real values, the financing gap in 2019 could grow by more than 10 percent, given the average rate of inflation in IDA and Blend countries. Offsetting these price increases in the future, new technologies for data collection and processing and new sources of data are likely to reduce costs, and it is possible that domestic resource mobilization could increase over the SDG period, also reducing the financing gap. New information on the costs of recent MICS or DHS surveys or the experience of countries that have already undertaken censuses as part of the 2020 round could further alter these estimates.

4. The Way Forward

This paper has established what sources are needed to build a core gender data system in all 75 IDA countries and how much it will cost. Building an effective core gender data system in every IDA country is urgently needed but will not happen without political and financial support and collective action. The next step is to establish a plan that will raise the profile of gender data and suggest a way forward to finance a better gender data ecosystem that recognizes the importance of national capacity. This section outlines that pathway forward with focus on building a coalition of stakeholders for more and better gender data financing.

There are a range of opportunities for financing gender data that should be carefully weighed. One path forward is to build a coalition among donors and stakeholders to promote creativity and urgency around the true scope of the gender data challenges and the resources needed to address it.

The Bern Network, a group of donors and data producers led by the UN High-Level Group for Partnerships, Coordination and Capacity Building for Statistics (HLG-PCCB), the Swiss Federal Statistics Office, and the Swiss Agency for Development and Cooperation, is advocating for increased funding for statistics in order to implement the Cape Town Global Action Plan. However, there is a need for focused attention on gender statistics within these global efforts. The gender data coalition could build and link to other initiatives, harness opportunities of the data revolution, promote use and impact, and increase the value of data, thereby encouraging additional investments. More specifically, the coalition would center its efforts to accelerate gender data financing around the following actions.

4.1 Promoting & investing in the importance of gender data use and link to policies

Without presenting a clear ‘value case’ to governments and donors, financing will not be sustained over time. The goal of all the financing for gender data efforts is to ensure that data – whether derived from a household survey or big data – are used for policy and government decisions as well as research and innovations. The financing work going forward should focus on this and must expand beyond only data production and towards capacity and incentives for data use. Financing for gender data must include involvement, engagement, and inclusion of the expected users and recognize all stages of the data value chain, from production to use and impact. The aim is for funding efforts to provide users incentives and capacity to use data.

4.2 Modernizing statistical systems through partnerships and alternative gender data sources

There are many examples to share and exchange best practices and build partnerships among countries for modernization of gender data systems to make gender data production more efficient and its use more frequent. Alternative sources of data from the private sector and technological advancements could help with filling existing gender data gaps. Some of these may qualify for “leapfrogging” in some countries as well as cost-saving opportunities, and also as a signal that statistical systems are flexible and capable of adjustments and a smart bet for long-term investments. Other examples include exploring big data – satellite imagery, call records, mobile money transactions, or citizen-generated data – to fill gaps and identify policy-oriented interventions to improve the lives of women and girls.

4.3 Encouraging country-driven approaches for resource mobilization for gender data

Country demand is critical for ensuring the success of gender data financing. Better financing will be achieved by ensuring that donor financing efforts mirror country demand and relevancy, while at the same time countries clearly articulate national priorities on gender and hold those in charge of implementation accountable. The overall goal is to see an increase in domestic investments on gender data for which political buy-in is crucial. After all, financing for gender data is not only a technical issue but a political one as well.

National Strategies for Development of Statistics (NSDSs) are an opportune starting point for donors to understand the country demand and national plans for gender statistics. According to PARIS21, NSDSs are “expected to provide a country with a strategy for developing statistical capacity across the entire national statistical system.” Providing a vision for where the country’s statistical system should be in five to ten years, the NSDS presents a comprehensive framework of statistical priorities and capacity development needs alongside a framework for mobilizing and leveraging resources to achieve the outlined goals.

4.4 Prioritizing and advocating for increased frequency and better coordination of surveys on gender data

Building a core gender data system in every IDA country should be a priority for all stakeholders. Surveys provide a vast amount of the currently available information on women and girls. Because they remain a critical input to measuring and monitoring gender equality, surveys must be prioritized both in funding and frequency. Unfortunately, as the Bridging the Gap study revealed, many countries do not meet the recommended frequency intervals for surveys.

It is equally important to note the importance of coordination in the discussions on increasing frequency of surveys and better planning for their sequencing and implementation. Conducting a census or large-scale survey takes up a huge amount of limited resources of a country so they need to be carefully planned. To give an example: this year alone, Nepal is undertaking their Multiple Indicator Cluster Survey (MICS) alongside their Nepal Living Standard Survey (NLSS) and has plans to conduct a time-use survey. The fieldwork for the Nepal Labor Force Survey was also completed this year. All surveys are supposed to be conducted by the Central Bureau of Statistics in collaboration and partnership with relevant custodian agencies including ILO, UNICEF, World Bank and UN Women (UNESCAP 2018). Additionally, the Central Bureau of Statistics has to begin planning for the 12th National Housing and Population Census in 2021. While donors must share responsibility for coordinating their activities, countries should be empowered to take charge of coordination to avoid overcrowding of survey activities and overburdening of capacity-strained national statistics offices.

4.5 Rethinking and improving modes of donor support for gender data

There are many questions surrounding how to build a sustainable source of funding for gender data: What should be the target for domestic versus international aid for low-income countries? What funding mechanisms work best: bilateral, results-based program/project funding, grants, loans, a pooled global fund, or a multi-donor trust fund? Should gender data be earmarked in donors’ support to building countries’ statistical capacity? Even if the gender data coalition may not be able to tackle some of these issues initially, it is important to note and acknowledge them. Forums such as the Development Assistance Committee or the Financing for Development Forum may present opportunities for highlighting some of these issues.

The evidence strongly suggests that it is not only a question of how much support, but also of how it is given that matters. The results of a survey of DAC members’ policies and practices to support national statistical capacities and systems in developing countries show that DAC members provide support to improve developing countries’ statistical production mainly in the form of technical assistance (for example, conducting training, designing surveys, building data management systems). (Sanna and  Mc Donnell, 2017)  While approaches such as these may identify and fix a broken piece in the data machine, they fail to consider the broader enabling environment or to reinforce the ability of the system to self-repair in the future. An approach addressing technical bottlenecks is not enough; what is needed is a  demanddriven, holistic approach designed to strengthen the entire statistical system, with full commitment and accountability at national level.

And just as the SDGs call to Leave No One Behind, more and better gender statistics funding efforts should Leave No Country Behind. Calls for increasing financing for statistics must include how to address the countries who struggle most with the weakest core gender data system, such as those with failing civil registration systems or those overdue for a census. The financing for the data agenda must create a tide that lifts all boats. Findings from PARIS21’s PRESS 2018 found that five countries receive 30 percent of all country-specific commitments to funding for statistics, and the top five donors of development cooperation in statistics (UNICEF, IMF, European Commission/ Eurostat, UNFPA, World Bank) provided 69 percent of total commitments in 2016. In the previous year, the top five donors were similar except for Canada and AfDB taking the place of UNICEF and IMF. These findings highlight the need to diversify the number of donors and the
number of country recipients.

While there is a need to rethink current approaches to funding and what specifically is funded, there is also a need to improve the quality of the data behind financing for gender statistics. Current efforts to measure financial support to statistics have limitations and methodological shortcomings. The PARIS21 secretariat has outlined best practices for reporting, including efforts to avoid double counting in aid reporting; the need to report on actual disbursements and domestic resources invested in statistics in addition to commitments; and how to specify the respective share of the total project commitment that goes towards statistics. (OECD, 2017)

In addition to changes to current practices, new guidelines should be adopted and a marker for development data should be created in the Credit Reporting System (CRS). While there is a CRS sector code for statistical capacity building, it fails to identify multi-sector projects that include only a small statistics component. A distinct marker would improve the identification of funding towards statistics in ODA reporting while acknowledging the importance of statistical capacity building through its own distinct marker. Such efforts or increasing transparency of funding for development data and gender data should not be confined only to ODA-reporting countries. Philanthropic organizations should be encouraged as well to participate and provide data on their funding efforts.

Conclusion

This is the first time that a detailed costing and analysis of the volume of aid for building gender data systems has been presented. There are opportunities for further studies and deeper analysis of national capacity and investment issues and opportunities. It may be useful to have a periodic report on the state of gender data funding to inform the stakeholders  concerned with the status and progress made in national and international efforts for building better gender data systems.

This paper makes a case for increased investments in gender statistics by providing the first cost estimates for a core gender data system and highlighting a way forward to improve the gender data investment and funding ecosystem to produce the data required to measure and monitor gender equality. There is an urgent need to prioritize funding for statistics as current levels of ODA remain below what is necessary to meet the data demands of the SDGs. Achieving the SDGs – and collecting the data that underpin them – will depend on concerted actions by countries, donors, and other stakeholders to work together and share knowledge and invest in better gender data for better policies.

Acknowledgements

Data2X and Open Data Watch would like to thank PARIS21 and acknowledge support from Yu Tian and his team for extensive data preparation and analysis carried out for this report based on the PRESS database.

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