When the Earth formed about 4.5 billion years ago, the early years were pretty chaotic. Eventually, oceans formed, and simple organisms began to evolve, followed by more complex plants, animals and, finally, us. Statistics have also been evolving into more complex forms—granted over a much shorter period of time.
The earliest data collections took the form of census. Sampling was eventually discovered and gave rise to surveys and then multi-topic surveys. More recent years have seen a Cambrian explosion of technology: remote sensing, electronic data capture, and Big Data.
Alongside this evolution, support from development partners like the World Bank for countries around statistics has also been transformed. The focus has shifted from countries engaging in data collection that development partners could use for their analysis to investing in the capacity of countries to collect, analyze, and use the data for evidence-based policymaking. For countries to create their own development agenda and monitor progress, the capacity to collect, produce and use data is critical. Having reliable—and timely—data is particularly important for low-income countries to ensure that limited resources are allocated opportunely to their most efficient use—with applications from the targeting of social transfers for inclusive development, to understanding the implications of subsidy reform, to prioritizing infrastructure to maximize economic returns.
The Data for Policy (D4P) initiative exemplifies the next generation of engagement to support the development of national statistical systems (NSS) by the World Bank. It is supported by a policy commitment in IDA19, the latest round of funding by the International Development Association, the World Bank’s fund for the poorest countries. D4P aims to enhance the availability, timeliness, quality, and relevance of key data for evidence-based decision making.
Working at the national and regional levels, the D4P initiative translates in practice into supporting:
- Key NSS enabling factors to improving performance and productivity of statistical agencies, such as quality assurance, data integration, and institutional resources and management;
- The production of a core set of economic, social, and sustainability statistics utilized to monitor national development plans and the Sustainable Development Goals (SDGs), including five core statistical operations: household surveys and census, enterprise surveys and census, agricultural surveys, administrative data, and price data; plus two complementary data systems: national accounts and Big Data; and
- Access and use of data key for monitoring and evaluating public policies and programs.
These core operations and systems supported by D4P were defined as the data necessary to monitor the progress of policies, National Development Plans and the SDGs. Despite recent progress, further effort is still needed to close acute data gaps across the world’s poorest and conflict-affected countries. But beyond strengthening collection, the D4P promotes open access and data utilization—supporting activities that make data central in policymaking, such as linking data systems to the relevant national institutions charged with monitoring progress in national development plans and the 2030 SDG agenda; as well as managing disclosure risk for open data and encouraging dissemination efforts to inform the public.
The D4P approach is geared toward strengthening the capacity of National Statistics Offices to improve the national statistical systems to be fit-for-purpose for the data revolution—from having the adequate skill mix of technical staff to physical infrastructure and software to the legal frameworks necessary for statistics and data sharing—with an emphasis on investing in sustainable systems that can be maintained without donor assistance. Interested countries will be able to explore non-traditional data sources and methods: high-resolution satellite data, call records, crowdsourcing techniques, machine learning. And while remaining custom tailored to each country’s capabilities, objectives, and priorities, the D4P initiative encourages regional cooperation and harmonization, creating comparable statistics based on international methods and standards.
The seriousness of the pledge to improve the availability of more and better data for policymaking in developing countries is demonstrated in IDA19’s commitment to support at least 30 IDA countries under the D4P agenda. Impact will be measured by the number of IDA countries that are supported in the production of at least two out of the core statistics included in the D4P package. Two projects are already under way: one for the Pacific Island countries covering Kiribati and Tonga and one for West Africa that includes Burkina Faso, Cabo Verde, Cote d’Ivoire, Ghana, Liberia, Sierra Leone and Togo, with the potential to scale up to other countries and regions.
Summing up, evolution is good—evolution in how we think about the data requirements for policymaking, evolution in how we finance and collect that data, evolution in how that data is used. The Data for Policy initiative represents the most current thinking on how to support IDA countries in pursuing data-driven policy relevant for measuring and monitoring the progress toward the 2030 SDG agenda. It encourages these countries to look beyond a single statistical operation and into the core data sources needed to understand the constraints of the poor and vulnerable groups, thinking about statistical capacity building in a comprehensive way, and leveraging technology, country ownership, peer-to-peer learning, and expansion of the use of data to promote sustainability of these efforts.