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From a theoretical perspective the link between the speed and scope of rapid labor reallocation and productivity growth or income inequality is ambiguous. Do reallocations with more flows tend to produce higher productivity growth? Does such a link appear at the expense of higher income inequality? We explore the rich evidence from earlier studies on worker flows in the period of massive and rapid labor reallocation, i.e. the economic transition from a centrally planned to a market-oriented economy in Central and Eastern Europe. We have collected over 450 estimates of job flows from the literature and used these inputs to estimate the short-run and long-run relationship between labor market flows, labor productivity and income inequality. We apply the tools typical for a meta-analysis to verify the empirical regularities between labor flows and productivity growth as well as income inequality. Our findings suggest only weak and short term links with productivity, driven predominantly by business cycles. However, data reveal a strong pattern for income inequality in the short-run -- more churning during reallocation is associated with a level effect towards increased Gini indices.
The volume highlights the state-of-the-art knowledge (including data analysis) of productivity, inequality and efficiency analysis. It showcases a selection of the best papers from the 9th North American Productivity Workshop. These papers are relevant to academia, but also to public and private sectors in terms of the challenges that firms, financial institutions, governments, and individuals may face when dealing with economic and education related activities that lead to increase or decrease of productivity. The volume also aims to bring together ideas from different parts of the world about the challenges those local economies and institutions may face when changes in productivity are observed. These contributions focus on theoretical and empirical research in areas including productivity, production theory and efficiency measurement in economics, management science, operation research, public administration, and education. The North American Productivity Workshop (NAPW) brings together academic scholars and practitioners in the field of productivity and efficiency analysis from all over the world, and this proceedings volume is a reflection of this mission. The papers in this volume also address general topics as education, health, energy, finance, agriculture, transport, utilities, and economic development, among others. The editors are comprised of the 2016 local organizers, program committee members, and celebrated guest conference speakers.
The COVID-19 pandemic struck the global economy after a decade that featured a broad-based slowdown in productivity growth. Global Productivity: Trends, Drivers, and Policies presents the first comprehensive analysis of the evolution and drivers of productivity growth, examines the effects of COVID-19 on productivity, and discusses a wide range of policies needed to rekindle productivity growth. The book also provides a far-reaching data set of multiple measures of productivity for up to 164 advanced economies and emerging market and developing economies, and it introduces a new sectoral database of productivity. The World Bank has created an extraordinary book on productivity, covering a large group of countries and using a wide variety of data sources. There is an emphasis on emerging and developing economies, whereas the prior literature has concentrated on developed economies. The book seeks to understand growth patterns and quantify the role of (among other things) the reallocation of factors, technological change, and the impact of natural disasters, including the COVID-19 pandemic. This book is must-reading for specialists in emerging economies but also provides deep insights for anyone interested in economic growth and productivity. Martin Neil Baily Senior Fellow, The Brookings Institution Former Chair, U.S. President’s Council of Economic Advisers This is an important book at a critical time. As the book notes, global productivity growth had already been slowing prior to the COVID-19 pandemic and collapses with the pandemic. If we want an effective recovery, we have to understand what was driving these long-run trends. The book presents a novel global approach to examining the levels, growth rates, and drivers of productivity growth. For anyone wanting to understand or influence productivity growth, this is an essential read. Nicholas Bloom William D. Eberle Professor of Economics, Stanford University The COVID-19 pandemic hit a global economy that was already struggling with an adverse pre-existing condition—slow productivity growth. This extraordinarily valuable and timely book brings considerable new evidence that shows the broad-based, long-standing nature of the slowdown. It is comprehensive, with an exceptional focus on emerging market and developing economies. Importantly, it shows how severe disasters (of which COVID-19 is just the latest) typically harm productivity. There are no silver bullets, but the book suggests sensible strategies to improve growth prospects. John Fernald Schroders Chaired Professor of European Competitiveness and Reform and Professor of Economics, INSEAD
Despite stringent dismissal restrictions in most European countries, rates of job creation and destruction are remarkably similar across European and North American labor markets. This paper shows that relative-wage compression is conducive to higher employer-initiated job turnover, and argues that wagesetting institutions and job-security provisions differ across countries in ways that are both consistent with rough uniformity of job turnover statistics and readily explained by intuitive theoretical considerations. When viewed as a component of the mix of institutional differences in Europe and North America, European dismissal restrictions are essential to a proper interpretation of both similar patterns in job turnover and marked differences in unemployment flows.
Occupational specificity of human capital motivates an important role of occupational reallocation for the economy's response to shocks and for the dynamics of inequality. We introduce occupational mobility, through a random choice model with dynamic value function optimization, into a multi-sector/multi-occupation Bewley-Aiyagari model with heterogeneous income risk, liquid and illiquid assets, price adjustment costs, and in which households differ by their occupation-specific skills. Labor income is a combination of endogenous occupational wages and idiosyncratic shock. Occupational reallocation and its impact on the economy depend on the transferability of workers' skills across occupations and occupational specialization of the production function. The model matches well the statistics on income and wealth inequality, and the patterns of occupational mobility. It provides a laboratory for studying the short- and long-run effects of occupational shocks, automation and task encroaching on income and wealth inequality. We apply the model to the pandemic recession by adding an SIR block with occupation-specific infection risk and a ZLB policy and study the impact of occupational and aggregate labor supply shocks. We find that occupational mobility may tame the effect of the shocks but amplifies earnings inequality, as compared to a model without mobility.
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.