I argue that there exists a coherent and relevant tradition in economic thought that I label “price theory.” I define it as neoclassical microeconomic analysis that reduces rich and often incompletely specified models into “prices” (approximately) sufficient to characterize solutions to simple allocative problems. I illustrate this definition by highlighting distinctively price theoretic approaches to prominent research practices (diagrams and problems sets) and substantive research topics (e.g. selection markets and media slant). I trace the origins of price theory from the early nineteenth century through its segregation into the Chicago School in the last quarter of the twentieth. I argue that price theory plays a valuable complementary role to two traditions, “reductionism” and “empiricism,” with which I contrast it and show how this contribution of price theory has fueled a resurgence in this style of research in fields ranging from market design to international trade. Approximations critical to price theory are less formally developed than tools used in other methodological traditions, suggesting a research agenda to clarify the accuracy and range of validity of these methods.
Over two dozen different terms for identification appear in the econometrics literature, including set identification, causal identification, local identification, generic identification, weak identification, identification at infinity, and many more. This survey: (i) gives a new framework unifying existing definitions of point identification; (ii) summarizes and compares the zooful of different terms associated with identification that appear in the literature; and (iii) discusses concepts closely related to identification, such as normalizations and the differences in identification between structural models and causal, reduced form models.
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.
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.
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.
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(5), 791-807
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(1), 146-159
This paper considers moment-based tests applied to estimated quantities. We propose a general class of transforms of moments to handle the parameter uncertainty problem. The construction requires only a linear correction that can be implemented in sample and remains valid for some extended families of nonsmooth moments. We reemphasize the attractiveness of working with robust moments, which lead to testing procedures that do not depend on the estimator. Furthermore, no correction is needed when considering the implied test statistic in the out-of-sample case. We apply our methodology to various examples with an emphasis on the backtesting of value-at-risk forecasts.
The Review of Economics and Statistics2019101(1), 107-120
Research has focused on why and when firms access external technology markets. Less is known about the reliability of patents attached to licensed technologies during litigation. Unreliable patents expose a firm to loss of downstream revenues. We address this by constructing a data set of patent litigation in the pharmaceutical industry and exploit a change in patent law that exogenously increased the probability of litigation. We find that licensed patents are more likely to fall during litigation. This effect is isolated to firms with fewer intellectual property capabilities and less patenting experience, suggesting that benefits from external technology are not shared equally.