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IMF technical assistance provided by the Statistics Department--toward assisting IMF member countries in developing the ability to provide reliable and comparable economic and financial data on a timely basis to policymakers and markets--has increased more than fourfold over the past decade. This assistance has proven critical in countries building their statistical capacity so as to come into line with international data standards in an increasingly globalized and electronically interconnected world. Statistical Capacity Building: Case Studies and Lessons Learned presents four case studies drawn from experience in three countries in transition to the market, two of which were also in postconflict situations, in the 1990s and early 2000s: Cambodia, Bosnia and Herzegovina, and Ukraine. Issues of setting, institutional and statistical arrangements, strategies, and implementation are examined, and lessons learned.
Few papers have attempted to assess the role of “capacity,” especially in the area of macroeconomic statistics. Consequently, we make an attempt to advance this literature through the construction of a “statistical capacity building index,” and then test its explanatory power on the cyclicality of government spending. Using panel data from 62 developing countries, we find evidence that improvements in this index are associated with less procyclicality of government spending over the period 1990–2012; with the significance of this relationship dependent upon the quality of administrative and technical capacity of budgetary institutions.
Why is there a disparity in the levels of technical and institutional capacity of national statistical offices (NSOs) in the Latin American and Caribbean region? There is a consensus about the importance of having up-to-date and quality official statistics. The data from censuses, household surveys, and administrative records are an essential input for decision-making, and for the design, implementation, and evaluation of public policies in a country. However, this recognition of the value of statistics does not necessarily translate into greater support for the institutions responsible for their production. To understand the disparity in the capacity of NSOs, the publication provides an innovative approach: it uses the theoretical framework of the study of State capacity, and it develops a methodological framework to compare the political economy factors that influence statistical capacity, through case studies in ten countries of the region: Argentina, Bolivia, Brazil, Colombia, Dominican Republic, Ecuador, El Salvador, Guatemala, Mexico, and Peru. Additionally, the publication offers a series of recommendations to strengthen the capacity of NSOs in the region, which include the implementation of institutional reforms to modernize the legal frameworks that govern NSOs in order to grant them more autonomy and allow them to assume a coordinating role of the national statistical system; the greater use of administrative records; the promotion of a dialogue between the NSOs and the community of data users; the establishment of links with non-governmental and international actors; and adherence to international standards and best practices for the production and dissemination of official statistics.
African countries need to improve the performance of their public sectors if they are going to achieve their goals of growth, poverty reduction, and the provision of better services for their citizens. Between 1995 and 2004, the Bank provided some $9 billion in lending and close to $900 million in grants and administrative budget to support public sector capacity building in Africa. This evaluation assesses Bank support for public sector capacity building in Africa over these past 10 years. It is based on six country studies, assessments of country strategies and operations across the Region, and review of the work of the World Bank Institute, the Institutional Development Fund, and the Bank-supported African Capacity Building Foundation.
Based on internal data, this paper finds that the capacity development program of the IMF’s Statistics Department has prioritized technical assistance and training to fragile and conflict-affected states. These interventions have yielded only slightly weaker results in fragile states than in other states. However, capacity development is constantly needed to make up for the dissipation of progress resulting from insufficient resources that fragile and conflict-affected states allocate to the statistical function, inadequate inter-agency coordination, and the pervasive impact of shocks exogenous to the statistical system. Greater coordination with other capacity development providers and within the IMF can help partially overcome low absorptive capacity in fragile states. Statistical capacity development is more effective when it is tailored to countries’ level of fragility.
This publication has been produced as part of a capacity building programme to strengthen the ability of national statistical systems to collect subnational demographic, socio economic and fiscal data. This is important in the design of public policy options, particularly as subnational governments in the transition economies of Central and Eastern Europe become responsible for the delivery of local services. This publication contains case studies from five countries in Central and Eastern Europe that are at different stages of fiscal decentralisation (covering Bulgaria, Romania, the Slovak Republic, Slovenia, and Ukraine).
One of the most urgent challenges in African economic development is to devise a strategy for improving statistical capacity. Reliable statistics, including estimates of economic growth rates and per-capita income, are basic to the operation of governments in developing countries and vital to nongovernmental organizations and other entities that provide financial aid to them. Rich countries and international financial institutions such as the World Bank allocate their development resources on the basis of such data. The paucity of accurate statistics is not merely a technical problem; it has a massive impact on the welfare of citizens in developing countries. Where do these statistics originate? How accurate are they? Poor Numbers is the first analysis of the production and use of African economic development statistics. Morten Jerven's research shows how the statistical capacities of sub-Saharan African economies have fallen into disarray. The numbers substantially misstate the actual state of affairs. As a result, scarce resources are misapplied. Development policy does not deliver the benefits expected. Policymakers' attempts to improve the lot of the citizenry are frustrated. Donors have no accurate sense of the impact of the aid they supply. Jerven's findings from sub-Saharan Africa have far-reaching implications for aid and development policy. As Jerven notes, the current catchphrase in the development community is "evidence-based policy," and scholars are applying increasingly sophisticated econometric methods-but no statistical techniques can substitute for partial and unreliable data.
A guide for constructing and using composite indicators for policy makers, academics, the media and other interested parties. In particular, this handbook is concerned with indicators which compare and rank country performance.
This book explores the reliability of official statistical data in the ASEAN (the Association of Southeast Asian Nations), and the benefits of a better vocabulary to discuss the quality of publicly available data to address the needs of all users. It introduces a rigorous method to disaggregate and rate data quality into principal factors containing a total of ten dimensions, which serves as the basis for a discussion on the opportunities and challenges for data quality, capacity building programs and data policy in Southeast Asia. Tools to standardize and monitor statistical capacity and data quality are presented, as well as methods and data sources to analyse data quality. The book analyses data quality in Indonesia, Malaysia, Singapore, the Philippines, Thailand, Vietnam, Brunei, Laos, Cambodia, and Myanmar, before concluding with thoughts on Open Data and the ASEAN Economic Community (AEC).
There is growing recognition of the need for new approaches to the ways in which donors support accountability, but no broad agreement on what changed practice looks like. This publication aims to provide more clarity on the emerging practice.