Knowledge that Transforms

At a time when standards of living have improved more than any time in history, this book makes a proposal for radical change. It is based—loosely—on market design principles. The plan for attacking overlapping ownership is reasonably well thought out. Most of the book, however, proposes to use mechanisms designed for a narrow purpose; to attack real or imagined problems that they are ill—suited to solve. I conclude that while market design has a lot to offer when properly applied, the proposals here are not sufficiently well thought out to constitute a serious plan of action.

Does academic economic research produce material of general scientific value, or do academic economists write only for peers? Is economics scholarship uniquely insular? We address these questions by quantifying interactions between economics and other disciplines. Changes in the influence of economic scholarship are measured here by the frequency with which other disciplines cite papers in economics journals. We document a clear rise in the extramural influence of economic research, while also showing that economics is increasingly likely to reference other social sciences. A breakdown of extramural citations by economics fields shows broad field influence. Differentiating between theoretical and empirical papers classified using machine learning, we see that much of the rise in economics’ extramural influence reflects growth in citations to empirical work. This growth parallels an increase in the share of empirical cites within economics. At the same time, some disciplines that primarily cite economic theory have also recently increased citations of economics scholarship.

We make the case for a shift in what students learn in a first economics course, taking as our exemplar Paul Samuelson’s paradigm-setting 1948 text. In the shadow of the Great Depression, Samuelson made Keynesian economics an essential component of what every economics student should know. By contrast, leading textbooks today were written in the glow of the Great Moderation and the tamed cyclical fluctuations in the two decades prior to 2007. Here, using topic modeling, we document Samuelson’s novelty and the evolution of the content of introductory textbooks since, and we put forward three propositions. First, as was the case in the aftermath of the Great Depression, new problems now challenge the content of our introductory courses; these include mounting inequalities, climate change, concerns about the future of work, and financial instability. Second, the tools required to address these problems, including strategic interaction, limited information, principal–agent models, new behavioral foundations, and dynamic processes including instability and path dependence, are available (indeed widely taught in PhD programs). And third, as we will illustrate by reference to a new open access introductory text, a course integrating these tools into a new benchmark model can be accessible, engaging, coherent and, as a result, successfully taught to first-year students. Deployed to address the new problems, following Samuelson’s example, the new benchmark provides the basis for integrating not only micro- and macroeconomics but also the analysis of both market failures and the limits of government interventions.

This paper brings together methodological, theoretical, and empirical analysis into the framework of linguistic diversity. It reflects both historical and contemporary research by economists and other social scientists on the impact of language on economic outcomes and public policies. We examine whether and how language influences human thinking (including emotions) and behavior, and analyze the effects of linguistic distances on trade, migrations, financial markets, language learning, and its returns. The quantitative foundations of linguistic diversity, which rely on group identification, linguistic distances as well as fractionalization, polarization, and disenfranchisement indices are discussed in terms of their empirical challenges and uses. We conclude with an analysis of linguistic policies and examine the trade-offs between the development of labor markets and the social costs that they generate in various countries.

This paper first documents trends in employment rates and then reviews what is known about the various factors that have been proposed to explain the decline in the overall employment-to-population ratio between 1999 and 2018. Population aging has had a large effect on the overall employment rate over this period, but within-age-group declines in employment among young- and prime-age adults also have played a central role. Among the factors with effects that we can quantify based on existing evidence, labor demand factors, in particular increased import competition from China and the penetration of robots into the labor market, are the most important drivers of observed within-group declines in employment. Labor supply factors, most notably increased participation in disability insurance programs, have played a less important but not inconsequential role. Increases in the real value of state minimum wages and in the share of individuals with prison records also have contributed modestly to the decline in the aggregate employment rate. In addition to the factors whose effects we roughly quantify, we identify a set of potentially important factors about which the evidence does not yet allow us to draw clear conclusions. These include the challenges associated with arranging child care, improvements in leisure technology, changing social norms, increased use of opioids, the growth in occupational licensing, and declining labor market fluidity. Our evidence-driven ranking of factors should be useful for guiding future discussions about the sources of decline in the aggregate employment-to-population ratio and consequently the likely efficacy of alternative policy approaches to increasing employment rates.

This paper reviews the literature in historical record linkage in the U.S. and examines the performance of widely-used record linking algorithms and common variations in their assumptions. We use two high-quality, hand-linked datasets and one synthetic ground truth to examine the direct effects of linking algorithms on data quality. We find that (1) no algorithm (including hand-linking) consistently produces representative samples; (2) 15 to 37 percent of links chosen by widely-used algorithms are classified as errors by trained human reviewers; and (3) false links are systematically related to baseline sample characteristics, showing that some algorithms may induce systematic measurement error into analyses. A case study shows that the combined effects of (1)-(3) attenuate estimates of the intergenerational income elasticity by up to 20 percent, and common variations in algorithm assumptions result in greater attenuation. As current practice moves to automate linking and increase link rates, these results highlight the important potential consequences of linking errors on inferences with linked data. We conclude with constructive suggestions for reducing linking errors and directions for future research.

In recent years, there has been widespread interest around the potential for technology to transform learning. As investment in education technology continues to grow, students, parents, and teachers face a seemingly endless array of education technologies from which to choose—from digital personalized learning platforms to online courses to text message reminders to submit financial aid forms. Amid the excitement, it is important to step back and understand how technology can help—or in some cases hinder—learning. This review article synthesizes and discusses rigorous evidence on the effectiveness of technology-based approaches to education in developed countries and outlines areas for future inquiry. In particular, we examine randomized controlled trials and regression discontinuity studies across the following categories of education technology: (i) access to technology, (ii) computer-assisted learning, (iii) technology-enabled behavioral interventions in education, and (iv) online learning. We hope this synthesis will advance academic understanding of how technology can improve education, outline key areas for new experimental research, and help drive improvements to the policies, programs, and structures that contribute to successful teaching and learning.

This paper advances the proposition that economics, as a discipline, gives rewards that favor the “hard” and disfavor the “soft.” Such bias leads economic research to ignore important topics and problems that are difficult to approach in a “hard” way—thereby resulting in “sins of omission.” This paper argues for reexamination of current institutions for publication and promotion in economics—as it also argues for greatly increased tolerance in norms for publication and promotion as one way of alleviating narrow methodological biases.

We examine the record of cross-country growth over the past fifty years and ask if developing countries have made progress on closing the income gap between their per capita incomes and those in the advanced economies. We conclude that, as a group, they have not and then survey the literature on absolute convergence with particular emphasis on that from the last decade or so. That literature supports our conclusion of a lack of progress in closing the income gap between countries. We close with a brief examination of the recent literature on cross-individual distribution of income, which finds that despite the lack of progress on cross country convergence, global inequality has tended to fall since 2000.

We review research that measures time preferences-i.e., preferences over intertemporal tradeoffs. We distinguish between studies using financial flows, which we call "money earlier or later" (MEL) decisions and studies that use time-dated consumption/effort. Under different structural models, we show how to translate what MEL experiments directly measure (required rates of return for financial flows) into a discount function over utils. We summarize empirical regularities found in MEL studies and the predictive power of those studies. We explain why MEL choices are driven in part by some factors that are distinct from underlying time preferences.