Knowledge that Transforms

In this essay I discuss potential outcome and graphical approaches to causality, and their relevance for empirical work in economics. I review some of the work on directed acyclic graphs, including the recent The Book of Why (Pearl and Mackenzie 2018). I also discuss the potential outcome framework developed by Rubin and coauthors (e.g., Rubin 2006), building on work by Neyman (1990 [1923]). I then discuss the relative merits of these approaches for empirical work in economics, focusing on the questions each framework answers well, and why much of the the work in economics is closer in spirit to the potential outcome perspective. (JEL C31, C36, I26)

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. (JEL H52, H75, I20, O33)

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. (JEL A11, B40)

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. ( JEL E01, E13, O11, O47, F41, F62)

The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical techniques to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas including growth economics, production modeling, finance and forecasting macroeconomic quantities. (JEL C11, C15, C20, C52, O47).

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.

This paper examines the relationship between placement of publications in top five (T5) journals and receipt of tenure in academic economics departments. Analyzing the job histories of tenure–track economists hired by the top 35 US economics departments, we find that T5 publications have a powerful influence on tenure decisions and rates of transition to tenure. A survey of the perceptions of young economists supports the formal statistical analysis. Pursuit of T5 publications has become the obsession of the next generation of economists. However, the T5 screen is far from reliable. A substantial share of influential publications appear in non-T5 outlets. Reliance on the T5 to screen talent incentivizes careerism over creativity.( JEL A14, I23, J44, J62)

Current Editorial Board:Olivier Coibion, University of Texas, AustinRaymond Fisman, Boston UniversityBenjamin R. Handel, University of California, BerkeleyRema N. Hanna (Co-Chair), Harvard UniversityBrian A. Jacob, University of MichiganShachar Kariv, University of California, BerkeleyAmit K. Khandelwal (Co-Chair), Columbia UniversityXiaoxia Shi, University of Wisconsin–MadisonRecently Retired from the Editorial Board:Amitabh Chandra, Harvard UniversityBrian S. Graham, University of California, BerkeleyAsim I. Khwaja, Harvard UniversityRohini Pande, Yale UniversityShort Paper Policy:Prior to 2017, Notes submitted were limited to 3,000 words and printed in their own section at the back of each issue. In early 2017, the policy regarding Notes changed: shorter papers were still considered, but there were no specific guidelines regarding length. Notes were not advertised on the Review of Economics and Statistics submission website (but were still allowed to be submitted). In the two years following the policy change, Note submissions dropped 30%: REStat published five Notes (compared to ten Notes between 2015 and 2017).In a desire to take advantage of the growing demand for shorter papers, REStat introduced Short Papers in December 2019. The former policies regarding Notes are no longer in effect. The hope is that this policy change will increase the number of quality Short Paper submissions that REStat receives and publishes.Short Papers are strictly held to a size limitation of 6,000 words and five exhibits. For each exhibit before the maximum five, the authors may add up to 200 words toward the word limit; thus, a paper with no exhibits must have 7,000 or fewer words. Online appendix materials are allowed of modest length—no more than 20 pages—but the papers should be self-contained. Short Papers will be held to the same quality standard as regular-length papers and undergo the usual review process and turnaround times. Since all published papers by REStat have the same quality threshold, Short Papers will be published alongside regular-length papers rather than in a separate issue.Table 1–Manuscripts Submitted and Published:This table shows the trends in papers submitted and published over the past five years. Paper submissions remained steady from 2015 to 2016. In the years following, the journal has seen increased growth in paper submission: 7% in 2017, 14% in 2018, and 11% in 2019. The number of published papers has remained steady, ranging from 66 to 78 papers per year.Table 2–Status of Manuscripts by Year of Submission:This table shows the status of manuscripts submitted over the past five years. The percent of papers summarily rejected remains steady, ranging from 61% to 64% of papers submitted. The percent of papers sent out for review but ultimately rejected also remains stable, ranging from 28% to 31% of papers. Acceptance rates range from 5% to 8% of papers submitted. Papers submitted in 2019 have not had enough time to accurately reflect the acceptance rate.Table 3–Decision Time for Manuscripts Sent for Review:This table reports the decision time for manuscripts sent out to referees for review over the past five years. The journal has greatly improved turnaround times. In 2015 and 2016, the average decision time for papers sent to referees ranged from 120 to 129 days. The average decision time in 2019 improved to 82 days. In addition, the decision time at the right tail improved substantially.Table 4–Distribution of First Decision Times, by Submission Year:This table includes all first-round paper submissions and shows the distribution of decision times in monthly increments for the past five years. This table also reflects the improvement in the journal's decision turnaround times. In 2019, 93% of manuscripts received a decision within four months and 99% within six months.Table 5–Subject Matter of Published Manuscripts, 2019:This table shows the distribution of the subject matter of papers published in 2019. Published papers covered a variety of subjects, but the most common were microeconomics and mathematical and quantitative methods.

This paper provides new insights into the relationship between structural change and the fertility transition. We exploit the spread of an agricultural pest in the American South in the 1890s as plausibly exogenous variation in agricultural production to establish a causal link between earnings opportunities in agriculture and fertility. Households staying in agriculture reduced fertility because children are a normal good, while households switching to manufacturing reduced fertility because of the higher opportunity costs of raising children. The lower earnings opportunities in agriculture also decreased the value of child labor, which increased schooling, consistent with a quantity-quality model of fertility.