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.
Mr. Paul A Austin, Mr. Marco Marini, Alberto Sanchez, Chima Simpson-Bell, and James Tebrake
As the pandemic heigthened policymakers’ demand for more frequent and timely indicators to assess economic activities, traditional data collection and compilation methods to produce official indicators are falling short—triggering stronger interest in real time data to provide early signals of turning points in economic activity. In this paper, we examine how data extracted from the Google Places API and Google Trends can be used to develop high frequency indicators aligned to the statistical concepts, classifications, and definitions used in producing official measures. The approach is illustrated by use of Google data-derived indicators that predict well the GDP trajectories of selected countries during the early stage of COVID-19. To this end, we developed a methodological toolkit for national compilers interested in using Google data to enhance the timeliness and frequency of economic indicators.
Metodij Hadzi-Vaskov, Mr. Luca A Ricci, Alejandro Mariano Werner, and Rene Zamarripa
This paper investigates the performance of the IMF WEO growth forecast revisions across different horizons and country groups. We find that: (i) growth revisions in horizons closer to the actual are generally larger, more volatile, and more negative; (ii) on average, growth revisions are in the right direction, becoming progressively more responsive to the forecast error gap as horizons get closer to the actual year; (iii) growth revisions in systemic economies are relevant for growth revisions in all country groups; (iv) WEO and Consensus Forecast growth revisions are highly correlated; (v) fall-to-spring WEO revisions are more correlated with Consensus Forecasts revisions compared to spring-to-fall revisions; and (vi) across vintages, revisions for a given time horizon are not autocorrelated; within vintages, revisions tend to be positively correlated, suggesting perception of persistent short-term shocks.
International Monetary Fund. Strategy, Policy, & and Review Department
The first data and statistics strategy for the Fund comes at a critical time. A fast-changing data landscape, new data needs for evolving surveillance priorities, and persisting data weaknesses across the membership pose challenges and opportunities for the Fund and its members. The challenges emerging from the digital revolution include an unprecedented amount of new data and measurement questions on growth, productivity, inflation, and welfare. Newly available granular and high-frequency (big) data offer the potential for more timely detection of vulnerabilities. In the wake of the crisis, Fund surveillance requires greater cross-country data comparability; staff and authorities face the complexity of integrating new data sources and closing data gaps, while working to address the weaknesses noted by the IEO Report (Behind the Scenes with Data at the IMF) in 2016. The overarching strategy is to move toward an ecosystem of data and statistics that enables the Fund and its members to better meet the evolving data needs in a digital world. It integrates Fund-wide work streams on data provision to the Fund for surveillance purposes, international statistical standards, capacity development, and data management under a common institutional objective. It seeks seamless access and sharing of data within the Fund, enabling cloud-based data dissemination to support data provision by member countries (e.g., the “global data commons”), closing data gaps with new sources including Big Data, and improving assessments of data adequacy for surveillance to help better prioritize capacity development. The Fund also will work with policymakers to understand the implications of the digital economy and digital data for the macroeconomic statistics, including new measures of welfare beyond GDP.
Total factor productivity (TFP) of 14 manufacturing sectors in France has kept up with that of the United States during 1980-2002 and remained well above that of the United Kingdom. Estimates using a dynamic panel equilibrium correction model indicate that sectors further behind the technological frontier experience faster productivity growth and that spending on research and development and trade with technologically advanced economies positively influences TFP growth, but not the speed of convergence. Conversely, TFP growth is negatively related to some key labor market variables, namely the replacement ratio and the ratio of the minimum wage to the median wage.
We explore the role of business services in knowledge accumulation and growth and the determinants of knowledge diffusion including the role of distance. A continuous-time model is estimated on several European countries, Japan, and the United States. Policy simulations illustrate the benefits for EU growth of the deepening of the single market, the reduction of regulatory barriers, and the accumulation of technology and human capital. Our results support the basic insights of the Lisbon Agenda. Economic growth in Europe is enhanced to the extent that: trade in services increases, technology accumulation and diffusion increase, regulation becomes both less intensive and more uniform across countries, and human capital accumulation increases in all countries.
The paper estimates an empirical relation based on Krugman’s ‘technological gap’ model to explore the influence of the pattern of international trade and production on the overall productivity growth of a developing country. A key result is that increased import competition in medium-growth (but not in low- or high-growth) manufacturing sectors enhances overall productivity growth. The authors also find that a production-share weighted average of (technological leaders’) sectoral productivity growth rates has a significant effect on the rate of aggregate productivity growth.
Research shows that international trade is an important channel for the transfer of technology. Building on this evidence, this paper examines the effects of inter- and intraindustry trade on technology transfer. The paper develops and tests the hypothesis that intraindustry trade stimulates more technology transfer than interindustry trade because countries are likely to absorb foreign technologies more easily when their imports are from the same sectors as their production and export sectors. The results of empirical tests for 87 countries during 1970–93 support this hypothesis.
In recent years, the IMF has released a growing number of reports and other documents covering economic and financial developments and trends in member countries. Each report, prepared by a staff team after discussions with government officials, is published at the option of the member country.