Improving the Productivity of National Offices for Statistics (IPNOS)i

For decades, policy experts’ and practitioner consensus has been growing about the crucial role quality data plays in informing policy making. This has led to investment and projects to increase quality data availability. But progress has been slow, as reflected by slow improvement in country statistical capacity—many countries remain data deprived. The IPNOS toolkit is based on the notion that producing more and better statistics, while requiring adequate resources, should also be efficient. Various initiatives assess national statistical systems’ (NSS) and data production. The World Bank’s IPNOS initiative complements existing kits, providing in-depth analytical tools to evaluate the efficiency of national statistical offices (NSOs), including costs, data quality, and NSO management. IPNOS application in 3 countries has identified bottlenecks and areas for improvement to inform data policies.

Despite these efforts, progress has been limited across regions. Regional values of the statistical capacity index (SCI) ii have barely changed. Between 2004 and 2018, scores have increased slightly in some regions, by less than 1 percent in Europe and Central Asia (ECA) and a bit over 1 percent in Latin America and the Caribbean (LAC), for instance. SCI scores in some regions deteriorated, such as the Middle East and North African (MENA). In addition, lack of poverty data is widespread and persistent. Despite some advancements, as many as 67 countries are still poverty data deprived; that is, they have no data, or only up to two data points over 6-year intervals (see Figure 1). Other key data also remain scarce. Low correlation between investments and statistical results is largely due to poor management of data production and dissemination. Initiatives to assess the capacity of NSSs include the United Nations Economic Commission for Africa's (UNECA) self-assessment guidance questionnaire, the European Commission's Assessment Questionnaires, or the IMF's Data Quality Assessment Framework for National Accounts Statistics.iii However, these do not measure efficiency, productivity, or costs of creating and disseminating data, nor the quality of the data. As a result, statistical production has frequently been poor quality, expensive, or does not meet the information needs of policy makers.

IPNOS addresses the gap in data assessment
initiatives. IPNOS focuses on NSO costs, quality, and managerial processes, as well as on the promotion of data usage. NSO's require productivity analysis to assess their efficiency. Productivity is a consequence of many factors, including institutional context, inputs, processes, outputs, and dissemination. Improvement in NSO´s productivity therefore implies one or more simultaneous upgrades in the quantity, quality, timeliness, or unit costs of 3main statistical products: Census, Household Surveys (HHS), and administrative records.

Figure 2: The 3 main IPNOS pillars
Assessments consider 3 main pillars, leading to the completion of an action plan. The IPNOS package offers 3 main assessment tools for: • Budget and cost-efficiency of production.
• Quality of processes, products, and user´s satisfaction. • Institutional and organizational aspects. Assessments in all 3 areas in turn inform development of an action plan to strengthen the NSO functioning.

Quality analysis (IPNOS-Qual):
A second excelbased tool, along with other tools and software, assesses the quality of statistical operations and estimates underlying quality drivers in the life-cycle process. The tool identifies specific quality thresholds for the different products (Census, HHS, administrative records) for statistical production processes, outputs, and user satisfaction. The different measures and indicators used for this exercise (see Figure 3) depend on the specific product and aspect assessed.  and low), the cost, and the period of execution (long, medium, and short term). The Plan also makes a distinction between actions that are strictly internal and those that require intra-institutional management.

IPNOS in practice
NSOs in Costa Rica, El Salvador, and the Seychelles applied the IPNOS package to assess capacity. The exercise identified the most important areas for work in these countries, and NSOs are using assessment results to improve productivity and data quality.

Costa Rica Instituto Nacional de Estadistica y Censos (INEC). Costa Rica's NSO implemented the IPNOS
improvement plan from 2016-2020 to substantially improve its statistical capacity, as the WB SCI shows. Application of the IPNOS tool in Costa Rica's INEC indicates that the quantity of good quality data produced increased with no increase in costs. However, it also shows that quality of certain datasets may be decreasing, and that some institutional bottlenecks constrain productivity, including the inadequacy of the amount and/or training of staff, the lack of recognition of INEC´s work in budgetary terms, and IT and managerial challenges. Figure 3 shows that high staff turnover rates, for instance, affect the quality of Costa Rica's HHS.  Through an action plan derived from IPNOS implementation, NSOs can produce more and better statistics at lower cost, and also promote data use with key governmental analysis units or agencies.
Importantly, to promote data usage and dissemination, country NSOs must allow access to their data, as well as facilitate interviews with NSO personnel, statistical users, and allow access to NSO accounting books.
i IPNOS final case studies, presentations, guidelines, and the IPNOS toolkit will be released soon through the website. ii The World Bank's SCI. a composite score assessing capacity of a country's statistical system, is based on a diagnostic framework assessing data methodology, sources, periodicity, and timeliness. iii Others include for instance the Snapshot tool or the or the Pan-African Statistics Program of EUROSTAT, the Tool for Assessing Statistical Capacity of the US Census Bureau, UNECA´s Africa Statistical Development Indicators and Framework, or the AFDB Tool for Assessing Statistical Capacity.