This paper studies the determinants of repeated use of Fund-supported programs in a large sample covering virtually all General Resources Account (GRA) arrangements that were approved between 1952 and 2012. Generally, the revolving nature of the IMF’s resources calls for the temporary sup-port of member countries to address balance of payments problems while repeated use has often been viewed as program failure. First, using probit models we show that a small number of country-specific variables such as growth, the current account balance, the international reserves position, and the institutional framework play a significant role in explaining repeated use. Second, we discuss the role of IMF-specific and program-specific variables and find evidence that a country’s track record with the Fund is a good predictor of repeated use. Finally, we conduct an out-of-sample forecasting exer-cise. While our approach has predictive power for repeated use, exact forecasting remains challenging. From a policy perspective, the results could prove useful to assess the risk IMF programs pose to the revolving nature of the Fund’s financial resources.

This paper studies the determinants of repeated use of Fund-supported programs in a large sample covering virtually all General Resources Account (GRA) arrangements that were approved between 1952 and 2012. Generally, the revolving nature of the IMF’s resources calls for the temporary sup-port of member countries to address balance of payments problems while repeated use has often been viewed as program failure. First, using probit models we show that a small number of country-specific variables such as growth, the current account balance, the international reserves position, and the institutional framework play a significant role in explaining repeated use. Second, we discuss the role of IMF-specific and program-specific variables and find evidence that a country’s track record with the Fund is a good predictor of repeated use. Finally, we conduct an out-of-sample forecasting exer-cise. While our approach has predictive power for repeated use, exact forecasting remains challenging. From a policy perspective, the results could prove useful to assess the risk IMF programs pose to the revolving nature of the Fund’s financial resources.

Research work by the IMF’s staff on the effectiveness of the country programs the organization supports, which has long been carried out, has intensified in recent years. IMF analysts have sought to “open up the black box” by more closely examining program design and implementation, as well as how these influence programs’ effectiveness. Their efforts have also focused on identifying the lending, signaling, and monitoring features of the IMF that may affect member countries’ economic performance. This book reports on a large portion of both the new and the continuing research. It concludes that IMF programs work best where domestic politics and institutions permit the timely implementation of the necessary measures and when a country is vulnerable to, but not yet in, a crisis. It points to the need for a wider recognition of the substantial diversity among IMF member countries and for programs to be tailored accordingly while broadly maintaining the IMF’s general principle of uniformity of treatment.

This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical.

A recovery is underway, but the economic fallout from the global pandemic could be with us for years to come. With the crisis exacerbating prepandemic vulnerabilities, country prospects are diverging. Nearly half of emerging market and developing economies and some middle-income countries are now at risk of falling further behind, undoing much of the progress made toward achieving the UN Sustainable Development Goals.

Over the past two decades, many low- and lower-middle income countries (LLMICs) have improved control over fiscal policy, liberalized and deepened financial markets, and stabilized inflation at moderate levels. Monetary policy frameworks that have helped achieve these ends are being challenged by continued financial development and increased exposure to global capital markets. Many policymakers aspire to move beyond the basics of stability to implement monetary policy frameworks that better anchor inflation and promote macroeconomic stability and growth. Many of these LLMICs are thus considering and implementing improvements to their monetary policy frameworks. The recent successes of some LLMICs and the experiences of emerging and advanced economies, both early in their policy modernization process and following the global financial crisis, are valuable in identifying desirable features of such frameworks. This paper draws on those lessons to provide guidance on key elements of effective monetary policy frameworks for LLMICs.

Global growth remains moderate and uneven, and a number of complex forces are shaping the outlook. These include medium- and long-term trends, global shocks, and many country- or region-specific factors. The April 2015 WEO examines the causes and implications of recent trends, including lower oil prices, which are providing a boost to growth globally and in many oil-importing countries but are weighing on activity in oil-exporting countries, and substantial changes in exchange rates for major currencies, reflecting variations in country growth rates and in exchange rate policies and the lower price of oil. Additionally, analytical chapters explore the growth rate of potential output across advanced and emerging market economies, assessing its recent track and likely future course; and the performance of private fixed investment in advanced economies, which has featured prominently in the public policy debate in recent years, focusing on the role of overall economic weakness in accounting for this performance.