Journal of Economic Literature201957(2), 275-328open access
This overview examines critically the post-9/11 empirical literature on terrorism. Major contributions by both economists and political scientists are included. We focus on five main themes: the changing nature of terrorism, the organization of terrorist groups, the effectiveness of counterterrorism policies, modern drivers or causes of terrorism, and the economic consequences of terrorism. In so doing, we investigate a host of questions that include: How do terrorist groups attract and retain members? What determines the survival of terrorist groups? Is poverty a root cause of terrorism? What counterterrorism measures work best? In the latter regard, we find that many counterterrorism policies have unintended negative consequences owing to attack transference and terrorist backlash. This suggests the need for novel policies such as service provision to counter some terrorist groups’ efforts to provide such services. Despite terrorists’ concerted efforts to damage targeted countries’ economies, the empirical literature shows that terrorism has had little or no effect on economic growth or GDP except in small terrorism-plagued countries. At the sectoral level, terrorism can adversely affect tourism and foreign direct investment, but these effects are rather transient and create transference of activities to other sectors, thus cushioning the consequences. (JEL F21, F52, H56, K42, Z31)
Given a game with uncertain payoffs, information design analyzes the extent to which the provision of information alone can influence the behavior of the players. Information design has a literal interpretation, under which there is a real information designer who can commit to the choice of the best information structure (from her perspective) for a set of participants in a game. We emphasize a metaphorical interpretation, under which the information design problem is used by the analyst to characterize play in the game under many different information structures. We provide an introduction to the basic issues and insights of a rapidly growing literature in information design. We show how the literal and metaphorical interpretations of information design unify a large body of existing work, including that on communication in games (Myerson 1991), Bayesian persuasion (Kamenica and Gentzkow 2011), and some of our own recent work on robust predictions in games of incomplete information. ( JEL C70, D82, D83)
This paper reviews recent economic research in tax compliance and enforcement. After briefly laying out the economics of tax evasion, it focuses on recent empirical contributions. It first discusses what methodologies and data have facilitated these contributions, and then presents critical summaries of what has been learned. It discusses a promising new development—the analysis of randomized controlled trials mostly delivered via letters from the tax authority—and then reviews recent research using various methods about the impact of the principal enforcement tax policy instruments: audits, information reporting, and remittance regimes. I also explore several understudied issues worthy of more research attention. The paper closes by outlining a normative framework based on the behavioral response elasticities now being credibly estimated that allow one to assess whether a given enforcement intervention is worth doing. (JEL H26, H30)
An ever-increasing share of human interaction, communication, and culture is recorded as digital text. We provide an introduction to the use of text as an input to economic research. We discuss the features that make text different from other forms of data, offer a practical overview of relevant statistical methods, and survey a variety of applications. (JEL C38, C55, L82, Z13)
Journal of Economic Literature201957(1), 3-43open access
Digital technology is the representation of information in bits. This technology has reduced the cost of storage, computation, and transmission of data. Research on digital economics examines whether and how digital technology changes economic activity. In this review, we emphasize the reduction in five distinct economic costs associated with digital economic activity: search costs, replication costs, transportation costs, tracking costs, and verification costs. (JEL D24, D83, L86, O33, R41)
The Review of Economics and Statistics2019101(4), 742-742
IN an article published in this review (Antman & Duncan, 2015), we document how racial identity responds to state affirmative action policy. A coding error was recently brought to our attention that resulted in 0.55% of our sample being misclassified in terms of their African ancestry.1 We regret and apologize for this error. Although the error affected only a tiny percent of the overall sample, the correction changes the conclusion of how individuals with multiracial African ancestry respond to state affirmative action bans, from a negative and statistically significant effect to a positive and statistically significant effect. The corrected table 3 shows the updated results. The coefficients for college-aged individuals with African ancestry reported in table 5 are also now positive, but are no longer statistically significant at conventional levels. Correcting the error does not change the conclusions for individuals with only African ancestry or no African ancestry. None of the Asian ancestry classifications, and thus none of the results for individuals with Asian ancestry (table 4 and the last two columns of table 5), were affected by the coding error. There are no meaningful changes to the summary statistics in table 2 except in the column for those with multiracial black ancestry. The most notable change is the fraction of individuals with multiracial black ancestry who self-identify as black: 49.37% in the original table and 90.86% in the updated table.2 (For further explanation and a complete set of updated results, see Antman & Duncan, 2019.) We continue to find that racial identity responds to state affirmative action policy, albeit with a different conclusion for multiracial blacks, and we are now able to distinguish stronger effects for multiracial individuals with more distant connections to their minority group.This is the original abstract: We link data on racial self-identification with changes in state-level affirmative action policies to ask whether racial self-identification responds to economic incentives. We find that after a state bans affirmative action, multiracial individuals who face an incentive to identify under affirmative action are about 30% less likely to identify with their minority group. In contrast, multiracial individuals who face a disincentive to identify under affirmative action are roughly 20% more likely to identify with their minority group once affirmative action policies are banned.We modify this original abstract as follows: We link data on racial self-identification with changes in state-level affirmative action policies to ask whether racial self-identification responds to economic incentives. We find that after a state bans affirmative action, multiracial individuals who face an incentive to identify under affirmative action are about 2% to 5% more likely to identify with their minority group. In contrast, multiracial individuals who face a disincentive to identify under affirmative action are roughly 20% more likely to identify with their minority group once affirmative action policies are banned.
The Review of Economics and Statistics2019101(2), 264-278open access
The increasing popularity of regression discontinuity methods for causal inference in observational studies has led to a proliferation of different estimating strategies, most of which involve first fitting nonparametric regression models on both sides of a treatment assignment boundary and then reporting plug-in estimates for the effect of interest. In applications, however, it is often difficult to tune the nonparametric regressions in a way that is well calibrated for the specific target of inference; for example, the model with the best global in-sample fit may provide poor estimates of the discontinuity parameter, which depends on the regression function at boundary points. We propose an alternative method for estimation and statistical inference in regression discontinuity designs that uses numerical convex optimization to directly obtain the finite-sample-minimax linear estimator for the regression discontinuity parameter, subject to bounds on the second derivative of the conditional response function. Given a bound on the second derivative, our proposed method is fully data driven and provides uniform confidence intervals for the regression discontinuity parameter with both discrete and continuous running variables. The method also naturally extends to the case of multiple running variables.
The Review of Economics and Statistics2019101(5), 791-807
Abstract I introduce an empirical auction model where in addition to the private value that each bidder receives upon winning the auction, losing bidders incur a negative externality that depends on the identity of the rival winner. I show how the externalities and private value distributions can be identified and estimated in such a model. I then apply the model to U.S. Forest Service timber auctions. I find that mill bidders impose significant externalities on one another of between 10% and 22% of the heterogeneous portion of their valuation. This leads the timber tracts to be misallocated in 5.2% of the auctions in my sample.
The Review of Economics and Statistics2019101(3), 522-530open access
Local projections (LP) is a popular methodology for the estimation of impulse responses (IR). Compared to the traditional VAR approach, LP allow for more flexible IR estimation by imposing weaker assumptions on the dynamics of the data. The nonparametric nature of LP comes at an efficiency cost, and in practice, the LP estimator may suffer from excessive variability. In this work, we propose an IR estimation methodology based on B-spline smoothing called smooth local projections (SLP). The SLP approach preserves the flexibility of standard LP, can substantially increase precision, and is straightforward to implement. A simulation study shows that SLP can deliver substantial gains in IR estimation over LP. We illustrate our technique by studying the effects of monetary shocks where we highlight how SLP can easily incorporate commonly employed structural identification strategies.