Knowledge that Transforms

As Africa’s role on the global stage is rising, so does the need to understand the shadow of history on the continent’s economy and polity. We discuss recent works that shed light on Africa’s colonial and precolonial legacies. The emerging corpus is remarkably interdisciplinary. Archives, ethnographic materials, georeferenced censuses, surveys, and satellite imagery are some of the sources often combined to test influential conjectures put forward in African historiography. Exploiting within-country variation and employing credible, albeit mostly local, identification techniques, this recent literature has uncovered strong evidence of historical continuity as well as instances of rupture in the evolution of the African economy. The exposition proceeds in reverse chronological order. Starting from the colonial period, which has been linked to almost all of Africa’s postindependence maladies, we first review works that uncover the lasting legacies of colonial investments in infrastructure and human capital and quantify the role of various extractive institutions, such as indirect rule and oppression associated with concessionary agreements. Second, we discuss the long-lasting impact of the “Scramble for Africa,” which led to ethnic partitioning and the creation of artificial modern states. Third, we cover studies on the multifaceted legacy of the slave trades. Fourth, we analyze the contemporary role of various precolonial, ethnic-specific, institutional, and social traits such as political centralization. We conclude by offering some thoughts on what we view as open questions.

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.

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.

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.

We provide evidence on three mechanisms that can reconcile frequent individual price changes with sluggish aggregate price dynamics. To that end, we estimate a semistructural model that can extract information about real rigidities and the distribution of price stickiness from aggregate data. Hence, the model can also speak to the debate about the aggregate implications of sales. Our estimates indicate large real rigidities and substantial heterogeneity in price stickiness. Moreover, the cross-sectional distribution of price stickiness implied by aggregate data is in line with an empirical distribution obtained from microprice data that factors out sales and product substitutions.

We provide new evidence on the cyclicality of employers' real labor costs using BLS establishment job data for the 1982–2018 period. Average straight-time wages have become countercyclical since the financial crisis and the subsequent Great Recession. So have benefit expenditures and overall labor costs, as well as major benefit expenditures, including health insurance and Social Security. Consistent with prior literature, we find that total earnings—the sum of straight-time wages, bonuses, and overtime earnings—were procyclical before 2008; even earnings have become countercyclical since then. The increasing countercyclicality of labor costs is largely attributable to periods with below-trend GDP.

Aggregate price levels are positively related to GDP per capita across countries. We propose a mechanism that rationalizes this observation through sectoral differences in intermediate input shares. As productivity and income grow, so do wages relative to intermediate input prices, which increases the relative price of nontradables if tradable sectors use intermediate inputs more intensively. We show that sectoral differences in input intensities can account for about half of the observed elasticity of the aggregate price level with respect to GDP per capita. The mechanism has stark implications for industry-level real exchange rates that are strongly supported by the data.

The most commonly employed paradigms for decision making under risk are expected utility, prospect theory, and regret theory. We examine the simple heuristic of maximizing the probability of being ahead, which in some natural economic situations may be in contradiction to all three of the above fundamental paradigms. We test whether this heuristic, which we call probability dominance (PD), affects decisions under risk. We set up head-to-head situations where all preferences of a given class (expected utility, original or cumulative prospect theory, or regret theory) favor one alternative yet PD favors the other. Our experiments reveal that 49% of subjects' choices are aligned with PD in contradiction to any form of expected utility or prospect theory maximization; 73% are aligned with PD as opposed to preferences under risk aversion and under original and cumulative prospect theory preferences; and 68% to 76% are aligned with PD contradicting preferences under regret theory. We conclude that probability dominance substantially affects choices and should therefore be incorporated into decision-making models. We show that PD has significant economic consequences. The PD heuristic may have evolved through situations of winner-take-all competition.

Medicare reimburses health care providers for the drugs they administer. Since 2005, it has reimbursed based on the past price of the drug. Reimbursement on past prices could motivate manufacturers to set higher launch prices because providers become less sensitive to price and because provider reimbursement is higher if past prices were higher. Using data on drug launch prices between 1999 and 2010, we estimate that reimbursement based on past prices caused launch prices to rise dramatically. The evidence is consistent with the 2018 claim from Medicare's administrator that it “creates a perverse incentive for manufacturers to set higher prices.”