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Predicting Fiscal Crises: A Machine Learning Approach
In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.
Supply and Demand Effects of Unemployment Insurance Benefit Extensions: Evidence from U.S. Counties
I use three decades of county-level data to estimate the effects of federal unemployment benefit extensions on economic activity. To overcome the reverse causality coming from the fact that benefit extensions are a function of state unemployment rates, I only use the within-state variation in outcomes to identify treatment effects. Identification rests on a differences-in-differences approach which exploits heterogeneity in county exposure to policy changes. To distinguish demand and supply-side channels, I estimate the model separately for tradable and non-tradable sectors. Finally I use benefit extensions as an instrument to estimate local fiscal multipliers of unemployment benefit transfers. I find (i) that the overall impact of benefit extensions on activity is positive, pointing to strong demand effects; (ii) that, even in tradable sectors, there are no negative supply-side effects from work disincentives; and (iii) a fiscal multiplier estimate of 1.92, similar to estimates in the literature for other types of spending.
The Bankers’ New Clothes
A Wall Street Journal, Financial Times, and Bloomberg Businessweek Book of the Year Why our banking system is broken—and what we must do to fix it New bank failures have been a rude awakening for everyone who believed that the banking industry was reformed after the Global Financial Crisis—and that we’d never again have to choose between massive bailouts and financial havoc. The Bankers’ New Clothes uncovers just how little things have changed—and why banks are still so dangerous. Writing in clear language that anyone can understand, Anat Admati and Martin Hellwig debunk the false and misleading claims of bankers, regulators, politicians, academics, and others who oppose effective reform, and they explain how the banking system can be made safer and healthier. Thoroughly updated for a world where bank failures have made a dramatic return, this acclaimed and important book now features a new preface and four new chapters that expose the shortcomings of current policies and reveal how the dominance of banking even presents dangers to the rule of law and democracy itself.
Strengthening Social Protection to Pave the Way for Technological Innovation
This paper investigates the impact of automation on the U.S. labor market from 2000 to 2007, specifically examining whether more generous social protection programs can mitigate negative effects. Following Acemoglu and Restrepo (2020), the study finds that areas with higher robot adoption reduced employment and wages, in particular for workers without collegue degree. Notably, the paper exploits differences in social protection generosity across states and finds that areas with more generous unemployment insurance (UI) alleviated the negative effects on wages, especially for less-skilled workers. The results suggest that UI allowed displaced workers to find better matches The findings emphasize the importance of robust social protection policies in addressing the challenges posed by automation, contributing valuable insights for policymakers.
South Asia's Path to Resilient Growth
South Asia’s Path to Sustainable and Inclusive Growth highlights the remarkable development progress in South Asia and how the region can advance in the aftermath of the COVID-19 pandemic. Steps include a renewed push toward greater trade and financial openness, while responding proactively to the distributional impact and dislocation associated with this structural transformation. Promoting a green and digital recovery remains important. The book explores ways to accelerate the income convergence process in the region, leveraging on the still-large potential demographic dividend in most of the countries. These include greater economic diversification and export sophistication, trade and foreign direct investment liberalization and participation in global value chains amid shifting regional and global conditions, financial development, and investment in human capital.
Engine of Inequality
The first book to reveal how the Federal Reserve holds the key to making us more economically equal, written by an author with unparalleled expertise in the real world of financial policy Following the 2008 financial crisis, the Federal Reserve’s monetary policy placed much greater focus on stabilizing the market than on helping struggling Americans. As a result, the richest Americans got a lot richer while the middle class shrank and economic and wealth inequality skyrocketed. In Engine of Inequality, Karen Petrou offers pragmatic solutions for creating more inclusive monetary policy and equality-enhancing financial regulation as quickly and painlessly as possible. Karen Petrou is a leadi...
Australia
Australia’s post-pandemic recovery remained strong. However, growth is weakening on the heels of tighter macroeconomic policies and financial conditions. While inflation has peaked, it remains persistently high. Labor market shows signs of easing, and the positive output gap is narrowing. Increased cost of living started to weigh on household consumption. The economy remains resilient in the near term but confronts a secular productivity slowdown. Financial stability risks remain contained although pockets of vulnerability exist and risks of spillovers from global financial conditions have increased since the last Article IV.
Federal Democratic Republic of Ethiopia
This Selected Issues paper reviews the evolution of inequality in Ethiopia and discusses the role of various macroeconomic policies as well as structural factors. With a Gini coefficient of 30, Ethiopia remains among the most egalitarian countries in the world. The most vulnerable households seem to experience less benefit from growth than those in the higher income deciles. In terms of tax revenue collection, Ethiopia faces the typical challenges of a developing country. It is required that Ethiopia builds on its successful experience with the Productive Safety Net Program to address the growing needs of the urban poor.
Overfitting in Judgment-based Economic Forecasts: The Case of IMF Growth Projections
I regress real GDP growth rates on the IMF’s growth forecasts and find that IMF forecasts behave similarly to those generated by overfitted models, placing too much weight on observable predictors and underestimating the forces of mean reversion. I identify several such variables that explain forecasts well but are not predictors of actual growth. I show that, at long horizons, IMF forecasts are little better than a forecasting rule that uses no information other than the historical global sample average growth rate (i.e., a constant). Given the large noise component in forecasts, particularly at longer horizons, the paper calls into question the usefulness of judgment-based medium and long-run forecasts for policy analysis, including for debt sustainability assessments, and points to statistical methods to improve forecast accuracy by taking into account the risk of overfitting.
Contextual Computing
Recent advances in the fields of knowledge representation, reasoning and human-computer interaction have paved the way for a novel approach to treating and handling context. The field of research presented in this book addresses the problem of contextual computing in artificial intelligence based on the state of the art in knowledge representation and human-computer interaction. The author puts forward a knowledge-based approach for employing high-level context in order to solve some persistent and challenging problems in the chosen showcase domain of natural language understanding. Specifically, the problems addressed concern the handling of noise due to speech recognition errors, semantic ...
