2 About Financing for Development Data

High-quality data are fundamental to effective policymaking and sustainable development. They underpin evidence-based decisions that improve lives and are central to achieving the 2030 Agenda for Sustainable Development. National statistical systems (NSS) collect, analyse and disseminate data on issues such as poverty, education, trade, health, and the environment, providing governments, businesses and civil society with the information needed to set priorities, target policies, and monitor progress.

Financing for development data refers to the financial resources dedicated to strengthening data and statistical systems in low- and middle-income countries. It is a subset of development finance, encompassing official development assistance (ODA) as well as private and philanthropic funding flows. According to the OECD Development Assistance Committee (DAC), ODA consists of government aid in the form of grants or concessional loans that promote the economic and social welfare of eligible countries. Broader development finance also includes private investment, remittances, and blended finance initiatives that mobilise additional resources for development objectives.

Since the Monterrey Consensus (2002) and the Addis Ababa Action Agenda (2015), the international community has recognised the critical role of data financing in achieving the Sustainable Development Goals (SDGs). Recent analyses highlight persistent gaps in financing for data and statistics, particularly in low-income and fragile settings. The World Development Report 2024 emphasises the need for stronger information systems to drive inclusive growth and decarbonisation, yet many systems remain under-funded. Meanwhile, OECD data show a real-terms 7.1 % decline in ODA in 2024 compared with 2023, raising concerns that support for statistical systems may also face cuts. Although donor funding for data rose from roughly US$734 million to US$1176 million between 2015–2023 (PRESS, 2025), the majority is concentrated among a few donors, leaving statistical systems vulnerable. The recent UN SDG report (UN, 2025) documents how fragile financing has already disrupted national data collection efforts and calls for more predictable, country-led investment in statistical capacity.

2.1 Financial Support to Statistics and Data

Financial support for statistics and data can come from bilateral or multilateral development partners. Bilateral financing refers to support provided directly from one country to another, while multilateral financing involves contributions pooled from several countries through institutions such as the World Bank, UN agencies, or regional development banks. Both types of support are critical to building sustainable national statistical systems, yet the complexity of reporting and coordination often makes it difficult to track funding flows comprehensively.

Development partners use a variety of instruments to provide this support, including grants and concessional loans. Some assistance is delivered as part of national budgets (“on-budget”), while other forms of support, such as technical training or equipment, are provided outside government systems (“off-budget”). Tracking both types of financing is important to ensure transparency and improve the alignment of donor funding with national priorities.

The Clearinghouse presents data on financial commitments and disbursements to statistics and data projects (definitions below are based on the DAC Glossary):

Commitments. A firm obligation, expressed in writing and backed by the necessary funds, which is undertaken by an official donor. It provides specified assistance to a recipient country or a multilateral organisation. Bilateral commitments are recorded in the full amount of the expected transfer, irrespective of the time required for the completion of disbursements. Commitments to multilateral organisations are reported as the sum of (i) any disbursements in the year reported on, which have not previously been notified as commitments, and (ii) expected disbursements in the following year.

Disbursements. The release of funds to or the purchase of goods or services for a recipient; by extension, the amount spent. Disbursements record the actual international transfer of financial resources, or of goods or services valued at the cost to the donor. In the case of activities conducted in donor countries, such as training, administration, or public awareness programs, disbursement is assumed to have occurred when the funds have been transferred to the service provider or recipient. These may be recorded as gross (the total amount disbursed over a given accounting period) or net (the gross amount, less any repayments of loan principal or recoveries on grants received during the same period).

This separation in reporting offers more explanation on the processes behind financing for development data. On the donor side, a delay between when commitments are finalized and when disbursements are made is common, because it takes time to implement the approved projects. Sometimes, it may take up to several years to disburse a commitment.

2.2 Funding Opportunities in Statistical Systems

Assessing the financial situation of national statistical systems remains challenging. Budgets are often fragmented across ministries and agencies, and transparency in public reporting varies widely. Many low- and middle-income countries depend on a combination of domestic and external funding to support data collection and analysis, which can lead to volatility and delays in implementation when donor disbursements are postponed.

To address these gaps, the Clearinghouse compiles and validates information from multiple international and national sources, working in partnership with organisations such as the World Bank and the UN Statistics Division. Pilot country assessments have further helped to capture the diversity of financing patterns and challenges on the ground.

Beyond current funding levels, the Clearinghouse also identifies the latent demand for financing, emerging needs such as digitalisation of data systems, capacity building, and improved gender-disaggregated data, that are not always visible in budget reports but are crucial for future investment planning.

2.3 Statistical Performance

Indicators of statistical performance, such as the World Bank’s Statistical Performance Indicators and PARIS21’s Statistical Capacity Monitor, serve as important benchmarks for assessing the health of national statistical systems. By integrating these metrics, the Clearinghouse provides a more holistic picture of data capacity across countries, helping development partners better target financial and technical assistance.

2.4 Gender Data Financing

Gender data is defined by the UN Statistics Division as data that are collected and presented by sex as a primary and overall classification, data that reflect gender issues, data that are based on concepts and definitions that adequately reflect the diversity of women and men and capture all aspects of their lives, and as data that are collected using methods that take into account stereotypes and social and cultural factors that may induce gender bias in the data. Research by several actors, including UN Women, Data2X, and ODW has shown that large gaps exist for gender data and that more and better data are needed in order to achieve the SDG promise of leaving no one behind.

Financing for gender data is defined here as the domestic and external support to building gender data systems. These do not exist separately from robust overall statistical systems but rather complement them. Just as there is a gap in the available data for women, men, girl, and boys, there is a large gap in the financing required to fill these data gaps.

To identify gender-focused projects in order to assess the current state of gender data financing, a gender classification system was developed using a comprehensive combination of criteria (see Chapter 3.4), including gender-related purpose codes, policy markers, keywords, and specialised funding channels such as UN Women. Projects were then refined to ensure thematic precision, focusing only on those explicitly centred on gender equality and statistical system objectives.