Knowledge that Transforms

New technologies have recently led to a boom in real‐time pricing. I study the most salient example, surge pricing in ride hailing. Using data from Uber, I develop an empirical model of spatial equilibrium to measure the welfare effects of surge pricing. The model is composed of demand, supply, and a matching technology. It allows for temporal and spatial heterogeneity as well as randomness in supply and demand. I find that, relative to a uniform pricing counterfactual in which Uber sets the overall price level, surge pricing increases total welfare by 2.15% of gross revenue. Welfare effects differ substantially across sides of the market: rider surplus increases by 3.57% of gross revenue, whereas driver surplus and the platform's current profits decrease by 0.98% and 0.50% of gross revenue, respectively. Riders at all income levels benefit. Among drivers, those who work long hours are hurt the most, especially women.

Milgrom and Shannon (1994) provide necessary and sufficient conditions on parameterized optimization problems for their solution sets to be globally monotone in the parameter. We establish that their conditions may be significantly relaxed when focusing on discrete, binary comparisons between solution sets. Such binary comparisons are ubiquitous in economics and may involve comparing the same decision maker across two distinct regimes or two distinct decision makers with related objectives (e.g., a monopolist firm versus a social planner). While the single‐crossing property remains prominent in the theory, quasisupermodularity of the objective functions of interest is not needed. Our approach relies upon a novel method of embedding a new optimization problem with a quasisupermodular objective function “between” the two original problems of interest. In smooth problems, sufficient conditions for our new assumptions may be verified by elementary differential comparisons, making them well suited for applied work. We illustrate the relevance of this novel approach with several economic applications.

This study provides a structural analysis of detailed, alternating‐offer bargaining data from eBay, deriving bounds on buyers and sellers private value distributions and the gains from trade using a range of assumptions on behavior and the informational environment. These assumptions range from weak (assuming only that acceptance and rejection decisions are rational) to less weak (e.g., assuming that bargaining offers are weakly increasing in players' private values). We estimate the bounds and show what they imply for consumer negotiation behavior and inefficient breakdown. For the median product, bargaining ends in impasse in 37% of negotiations even when the buyer values the good more than the seller.

Policymakers and market intermediaries often use quality scores to alleviate asymmetric information about product quality. Scores affect the demand for quality and, in equilibrium, its supply. Equilibrium effects break the rule whereby more information is always better, and the optimal design of scores must account for them. In the context of Medicare Advantage, I find that consumers' information is limited, and quality is inefficiently low. A simple design alleviates these issues and increases total welfare by 3.7 monthly premiums. More than half of the gains stem from scores' effect on quality rather than information. Scores can outperform full‐information outcomes by regulating inefficient oligopolistic quality provision, and a binary certification of quality attains 98% of this welfare. Scores are informative even when coarse; firms' incentives are to produce quality at the scoring threshold, which consumers know. The primary design challenge of scores is to dictate thresholds and thus regulate quality.

While the mechanism design paradigm emphasizes notions of efficiency based on agent preferences, policymakers often focus on alternative objectives. School districts emphasize educational achievement, and transplantation communities focus on patient survival. It is unclear whether choice‐based mechanisms perform well when assessed based on these outcomes. This paper evaluates the assignment mechanism for allocating deceased donor kidneys on the basis of patient life‐years from transplantation (LYFT). We examine the role of choice in increasing LYFT and compare realized assignments to benchmarks that remove choice. Our model combines choices and outcomes in order to study how selection affects LYFT. We show how to identify and estimate the model using instruments derived from the mechanism. The estimates suggest that the design in use selects patients with better post‐transplant survival prospects and matches them well, resulting in an average LYFT of 9.29, which is 1.75 years more than a random assignment. However, the maximum aggregate LYFT is 14.08. Realizing the majority of the gains requires transplanting relatively healthy patients, who would have longer life expectancies even without a transplant. Therefore, a policymaker faces a dilemma between transplanting patients who are sicker and those for whom life will be extended the longest.

We provide a decision‐theoretic analysis of bandit experiments under local asymptotics. Working within the framework of diffusion processes, we define suitable notions of asymptotic Bayes and minimax risk for these experiments. For normally distributed rewards, the minimal Bayes risk can be characterized as the solution to a second‐order partial differential equation (PDE). Using a limit of experiments approach, we show that this PDE characterization also holds asymptotically under both parametric and non‐parametric distributions of the rewards. The approach further describes the state variables it is asymptotically sufficient to restrict attention to, and thereby suggests a practical strategy for dimension reduction. The PDEs characterizing minimal Bayes risk can be solved efficiently using sparse matrix routines or Monte Carlo methods. We derive the optimal Bayes and minimax policies from their numerical solutions. These optimal policies substantially dominate existing methods such as Thompson sampling; the risk of the latter is often twice as high.

Academic research investigating the economic consequences of tax avoidance is almost always interested in the consequences of intentional, deliberate actions undertaken to reduce taxes relative to income. Therefore, it is crucial that such research distinguishes between intentional and incidental tax avoidance, since failure to do so can create endogeneity concerns and lead to incomplete and incorrect economic inferences. In this paper, we first develop a framework that conceptually defines and distinguishes between intentional and incidental tax avoidance. We highlight that the endogeneity problem arises because intentional tax avoidance is not directly observable. We consider two approaches to mitigating endogeneity concerns and apply these approaches by reexamining two influential studies that investigate the economic consequences of tax avoidance. We show how controlling for past accounting losses eliminates the effect of tax avoidance on credit spreads (Hasan et al. 2014, Journal of Financial Economics, 113 (1), 109–130) and how using an instrumental variables approach changes the sign of the relation between tax sheltering and stock price crash risk (Kim et al., 2011, Journal of Financial Economics, 100 (3), 639–662). Overall, our paper punctuates the importance of both (1) conceptually distinguishing between incidental and intentional tax avoidance and (2) econometrically addressing the challenges that arise when empirical differentiation between incidental and intentional tax avoidance is important to the research question.

The Sarbanes‐Oxley Act of 2002 mandates the PCAOB to enforce compliance with its audit standards fairly. However, the enforcement process is not sufficiently transparent for public evaluation of its fairness, prompting a call by a former Board member for transparency of the process and for improvement suggestions from the public. Further, academic evidence on the PCAOB enforcement is limited. We address this call and the gap in the literature by interviewing 33 difficult‐to‐access participants about the enforcement process: 20 sanctioned auditors and 13 former PCAOB enforcement staff members. Using procedural justice theory as a lens in evaluating our data, we conclude the enforcement process lacks fairness in key components. Both auditors and former enforcement staff express concerns that staff use overly damning one‐sided language in public orders, do not assess investor harm, and face incentives to sanction auditors, particularly small firms that cannot afford costly defense. We contribute to the literature on PCAOB enforcement by offering new insights into the enforcement process from firsthand perspectives of sanctioned auditors and former enforcement staff, deepening understanding of how enforcement practices align with the PCAOB's mandate for fair procedures. We also discuss process improvement suggestions from our participants and important future research opportunities.

Recent research into the uses of accounting as a technology of government has used Foucault's notion of “counter‐conduct” to shed light on various ways in which the governed can seek to alter the regimes to which they are subjected. This paper unpacks the notion of counter‐conduct further in order to develop a clearer conceptualization of how regimes of government can change over time, with or without clearly identifiable attempts by the governed to influence such changes. We develop our argument based on a longitudinal field study of sustainable waste management practices in a municipality in the English East Midlands. We track the municipality's attempts to become more sustainable in the context of an evolving central government performance management regime that went through a series of legislative and administrative iterations—namely, Best Value, Comprehensive Performance Assessment, and Comprehensive Area Assessment. We conceptualize these iterations of central performance management and the related changes in local government practices and technologies of governing as a series of overlapping “modes of governing” (Bulkeley et al., 2007, Environment and Planning A , 39 (11), 2733–2753). We suggest that accounting research can benefit from the notion of modes of governing because it sheds light on the theoretically expected, but empirically underresearched, copresence of multiple rationales, programs, and technologies of governing, all operating at the same time.

Many CEOs engage in activism by publicly expressing their views on social, environmental, and political issues, while other CEOs refrain from doing so—a behavior we term CEO inactivism. We use two experiments to examine how CEO (in)activism impacts investor decisions. Our results are consistent with our theoretical predictions. When a CEO expresses an activist position that is consistent versus inconsistent with investors' views, investors invest more in the CEO's firm because they perceive the CEO more positively. We also find that CEO inactivism can lead to investment decisions that are as favorable as when the CEO expresses a position consistent with investors' views; our process evidence suggests that this may occur because CEO inactivism increases the likelihood that investors believe the CEO shares their position on a social issue. Finally, we do not find evidence that investor decisions are influenced by whether CEO (in)activism is in response to an external prompt. This study contributes to the emerging literature on CEO activism, a unique form of voluntary disclosure, by providing evidence about how CEO (in)activism influences investors. We also contribute to the literature examining the impact of social media disclosure on investor decisions. Finally, our findings have practical implications for CEOs, who increasingly face external pressures to engage in activism.