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The Role of National Statistical Systems in the Data Revolution

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The Role of National Statistical Systems in the Data Revolution Development Co-operation Report 2017 Data for Development © OECD 2017 PART I Chapter 3 The role of national statistical systems in the data revolution by Shaida Badiee, Johannes Jütting, Deirdre Appel, Thilo Klein and Eric Swanson* The supply of relevant, timely and usable data is essential for countries to set priorities, make informed choices and implement better policies for sustainable development. This chapter looks at how national statistical systems in developing countries can and should harness the data revolution. It explores the opportunities, enablers and challenges countries face in using big data and other new sources of data. The chapter reviews developing country capacity, gaps and strategies for putting in place the right data for policy making. It also presents selected examples of how the data revolution is already fuelling better statistics in developing countries.Thechapterconsiderstheroleofgovernmentsaswellasthe opportunities offered by public-private partnerships. It enumerates the key conditions for building capable statistical systems and proposes steps to be taken by national statistical offices, policy makers and international development partners. * Shaida Badiee, Deirdre Appel and Eric Swanson from Open Data Watch; and Johannes Jütting and Thilo Klein from PARIS21. 55 I.3. THE ROLE OF NATIONAL STATISTICAL SYSTEMS IN THE DATA REVOLUTION Key facts ● The 2010 Population Census Round, conducted between 2005 and 2014, was one of the great successes of national and international statistical efforts. Only 21 countries did not conduct a census (UNFPA, 2016a). An estimated 6.4 billion people (93% of the world’s population) were enumerated (UNFPA, 2016b). ● The 2020 census round has already begun. Thirty-nine countries (including some that missed earlier rounds) are expected to prepare for or conduct censuses in 2017; some 200 more will need to complete censuses between 2018 and 2024. ● Many low and middle-income countries are using outdated base years for national accounts and price statistics while the lack of recent agricultural surveys or censuses limit their ability to produce reliable economic statistics. ● According to the World Health Organization’s Global Health Observatory, “Only 34 countries – representing 15% of the world population – produce high-quality cause-of-death data… A further 85 countries – representing 65% of the world population – produce lower quality cause-of-death data, while 75 countries lack such data altogether” (WHO, n.d.). ● To seize the opportunities presented by the data revolution, statistical offices will need to invest in new technology and production processes and establish partnerships with new actors. 56 DEVELOPMENT CO-OPERATION REPORT 2017 © OECD 2017 I.3. THE ROLE OF NATIONAL STATISTICAL SYSTEMS IN THE DATA REVOLUTION Advances in the ability to manage, exchange, combine and analyse data of all types, and to disseminate statistical information on line, are changing the way traditional statistical processes are carried out. National statistical offices can and should play a critical role in harnessing the data revolution for sustainable development. To be effective in stimulating data use and evidence-based decision making, they must also improve data accessibility by adopting open data policies. However, there are still large differences in the capabilities of statistical systems. Despite some progress made over the past decade, many countries still lack the means and infrastructure to produce high-quality data. To enable national statistical systems to respond to the demands of data users, notably policy makers, it is critical that providers of development co-operation and developing countries alike increase their support for national statistical offices, strengthen the use and production of statistics, and change their mind-sets towards producing and using more open, transparent and action-oriented data. The data revolution is fuelling better data in developing countries The size and scope of the data revolution can be gauged by the exponential increase of on line digital information; by the growth of new occupations described as data scientist, data activist or data evangelist; and by the manifold impacts of digital information on our daily lives. Revolutions are, by their nature, disruptive, and the data revolution has already disrupted traditional modes of production, human interaction and public discourse. Yet a revolution can also overcome enduring barriers and solve long-standing problems, bringing benefits to people previously left out, left behind or forgotten. The data revolution has the potential to transform the operations of national statistical systems in rich and poor countries alike. The data revolution has the potential to transform the operations of national statistical systems in rich and poor countries alike. It is often described in terms of the vast increase in the volume of digital data, called “big data”, but it is more than big data. Innovative technologies have decreased the cost and increased the speed of data collection and data dissemination, responding to the growing demand for actionable, empirical information. When, for example, a World Bank project in Guatemala used entry-level mobile phones and free web-based software for data collection, it cut the average cost per interview by 71%. The project could, as a result of lower costs, increase the survey’s sample size from 200 to 700 respondents, including from remote and marginalised areas highly populated by indigenous people, making the survey nationally representative.1 There are signs that the national statistical systems of developing countries are already embracing the data revolution and starting to make use of new technologies and methods. Far from being reluctant followers, many statistical offices are enthusiastic leaders. The following examples illustrate the exciting opportunities for development partners to engage in new and fruitful enterprises. Combining traditional and unconventional data sources can fill statistical gaps The United Nations (UN) Global Working Group on Big Data for Official Statistics is working with countries and their private sector partners to demonstrate the use of unconventional data sources to supplement official statistics. While many projects are still in the pilot phase, they are already demonstrating that insights can be obtained by combining data from traditional sources – such as censuses, surveys or administrative data – with information from new, big data sources. Statistics South Africa, for example, is assessing the use of detailed scanner data from retail chains as inputs to the consumer price index (GWG, 2017a). Statistics Canada is investigating the use of data from smart metres to track electricity consumption (GWG, 2017b). The World Bank Group is partnering with the government of Colombia to assess use of call detail records to measure income and inequality (GWG, 2017c). DEVELOPMENT CO-OPERATION REPORT 2017 © OECD 2017 57 I.3. THE ROLE OF NATIONAL STATISTICAL SYSTEMS IN THE DATA REVOLUTION Geospatial data can help to include people who have been overlooked The Data2X report, “Big data and the well-being of women and girls” (Data2X, 2017), illustrates the use of a large, geospatial database to improve the understanding of stunting, literacy and access to contraceptives in Bangladesh, Haiti, Kenya, Nigeria and the United Republic of Tanzania. Because many types of social and health data correlate with physical phenomena – such as elevation, land cover, and distance to roads and schools – it is possible to use geospatial data along with other sources of data to infer social and health conditions in communities not included in the sample design, ensuring that these groups are not left behind. Innovative use of big data can improve Sustainable Development Goal outcomes SDG Target 3.3 calls for the elimination of epidemic diseases, including malaria. Insecticide- treated bed nets offer a proven method of reducing malaria incidence, but it is expensive and ineffective to distribute bed nets widely in low-incidence areas. If measures are taken to protect privacy, big data can be used to identify target populations. In Namibia, the country’s largest cell phone service provider shared anonymised call detail records for 1.2 million subscribers. This permitted the construction of maps documenting patterns of internal migration. To pinpoint areas with high risk of malaria infection, the data from these maps were combined with remote sensing data – collected by the Namibia National Vector-borne Diseases Control Programme – tracing the factors affecting the location of mosquitos. With this information in hand, Namibia’s Ministry of Health can target the distribution of bed nets to the most likely sources of the spread of infections (Vaitla et al., 2017; Tatem et al., 2014). Monitoring progress on half of the SDG targets depends on the availability of environmental statistics. Citizen-generated data can help close gaps in environmental statistics Monitoring progress on half of the SDG targets depends on the availability of environmental statistics, yet a large portion of the indicators under these targets require data that are not regularly produced by countries. It may be possible to compensate for these significant gaps in environmental data by engaging citizens in data collection. A case study in the People’s Republic of China is exploring the use of citizen-generated data to address traditionally intractable gaps in environmental statistics,
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