This essay reviews two recent books on economic growth: Mark Koyama and Jared Rubin’s How the World Became Rich and J. Bradford DeLong’s Slouching Towards Utopia. Both books offer rich and nuanced treatments of the long-run and proximate mechanisms underlying the global growth in the past centuries. I evaluate their arguments and conclude with some reflections on conceptual shortcomings in the growth literature and some suggestions for the future.

This paper explores the role of complementarities and incomplete markets in economic growth. We analyze the evolution of an economy composed of a countable set of industries. Individual industries exhibit non-convexities in production and are linked by localized technological complementarities. These complementarities, when strong enough, produce multiple equilibria in long-run economic activity. The equilibria have a simple probabilistic structure that demonstrates how local interactions can affect the aggregate equilibrium. The model generates interesting cross-sectional and intertemporal dynamics as coordination problems become the source of aggregate and individual industry volatility. The model also illustrates how the growth of leading sectors can cause a takeoff to a high aggregate production equilibrium.

This paper considers racial profiling in traffic stops as a public policy problem. Efficiency and equity considerations are characterized. I argue that while there is a strong argument that racial profiling produces a violation of fairness, specifically in the treatment of innocent black motorists, the efficiency effects of profiling are not known. One cannot assign probabilities to the possible magnitudes of either deterrent effects or the harms of profiling to individuals. This makes the assessment of profiling an example of decisionmaking under ambiguity. I defend a notion of a “Fairness Presumption” that requires a policymaker to be able to make an affirmative case if a policy is to be implemented that induces unfairness. On this basis, I reject racial profiling as a policy. Steven N. Durlauf Department of Economics University of Wisconsin 1180 Observatory Drive Madison, WI 53706-1393 [email protected]

This paper provides a model of individual decisionmaking in the presence of neighborhood effects. By neighborhood effects, we refer to interdependencies between individual decisions and the decisions and characteristics of others within a common neighborhood. As such, neighborhood effects are forms of social interactions; in fact the terms would seem to be interchangeable in many contexts. Within economics, there has developed an increasing recognition that neighborhood effects play a possibly major role in explaining a range of individual behaviors; rich empirical and theoretical literatures have developed. A failing of much of this work is the absence of strong connections between theory and empirics. This paper addresses this limitation by providing a model of multinomial choice with neighborhood effects. The model represents a generalization of the binary choice model of William Brock and Steven Durlauf (2001a,b). The model is econometrically implementable and so has the potential of allowing for structural estimation of neighborhood effects. Section I outlines a basic choice model with neighborhood effects. Section II specializes the structure using a multinomial logit framework. Section III considers econometric implementation. Section IV discusses some limitations of the framework and proposes some research directions. Proofs of all theorems may be found in Brock and Durlauf (2001c).

In this paper, we describe a set of trade-offs that policymakers face with respect to fluctua? tions at different frequencies. A complete treat? ment is given in Brock, Durlauf, and Rondina (2008). The presence of frequency-specific effects of policy choices has generally not been a focus of macroeconomic policy analysis, important exceptions include Alexei Onatski and Noah M. Williams (2003). Our analysis dif? fers from previous studies in that our objective is to understand to what extent a policymaker is forced, when choosing a feedback rule to sta? bilize the economy, to exacerbate volatility at some frequencies in order to reduce them at oth? ers, or whether it is possible to reduce volatil? ity at all frequencies. These trade-offs, known as design limits in the control theory literature, are important in understanding what stabiliza? tion policies do, and also possess implications for policy design in the presence of policymaker ignorance.

This paper studies the effects of spurious detrending in regression. The asymptotic behavior of traditional least squares estimators and tests is examined in the context of models where the generating mechanism is systematically misspecified by the presence of deterministic time trends. Most previous work on the subject has relied upon Monte Carlo studies to understand the issues involved in detrending data that are generated by integrated processes and our analytical results help to shed light on many of the simulation findings. Standard F tests and Hausman tests are shown to inadequately discriminate between the competing hypotheses. Durbin-Watson statistics, on the other hand, are shown to be valuable measures of series stationarity. The asymptotic properties of regressions and excess volatility tests with detrended integrated time series are also explored.

“An Empirical Analysis of Racial Differences in Police Use of Force” (Fryer 2019) addresses a fundamental aspect of racial inequality in America. The paper suggests that there is evidence of discrimination against African Americans in police use of force except for officer-involved shootings. Given the importance of the issues involved, clarity is needed in understanding how this or any other piece of research contributes to understanding the nature of contemporary racial inequality and injustice. In this comment, we focus on the paper’s failure to find empirical differences in police shootings by race. This finding has received the most publicity and is the one that the paper most strongly defends: “[T]he data do more to provide a more compelling case that there is no discrimination in officer-involved shootings than they do to illuminate the reasons behind racial differences in nonlethal uses of force” (Fryer 2019, 1216). In our judgment, this paper does not establish credible evidence on the presence or absence of discrimination against African Americans in police shootings. We applaud analyses of data about police use of force of any type. But it is important to state clearly what the available data do and do not prove. The evidence provided in this paper fails to give any reason to conclude that discrimination is absent.

This paper examines how estimates of the deterrent effect of capital punishment depend on alternate choices of assumptions concerning the homicide process. Specific models of the homicide process represent bundles of these assumptions, which involve the unobserved heterogeneity, the relevant penalty probabilities for homicide choices, possible cross-polity parameter variation, and exchangeability between polity-time pairs that do and do not experience positive numbers of murders. We demonstrate how various assumptions have driven the conflicting findings from studies on capital punishment, and isolate a particular set of assumptions that are required to find a positive deterrent effect.

This paper provides a systematic analysis of identification in linear social interactions models. This is a theoretical and econometric exercise as the analysis is linked to a rigorously delineated model of interdependent decisions. We develop an incomplete information game for individual choice under social influences that nests standard models as special cases. We consider identification of both endogenous and contextual social effects under alternative assumptions regarding an analyst’s a priori knowledge of social structure or access to individual-level or aggregate data. Finally, we discuss potential ramifications for identification of endogenous formation of social structure.