We estimate and compare the effect of increased time costs on consumer satisfaction and behavior. We are able to move beyond the existing literature, which focuses on satisfaction and intention, and estimate the effect of waiting time on return behavior. Further, we do so in a prosocial context and our measure of cost is the length of time a blood donor spends waiting. We find that relying on satisfaction data masks important time cost sensitivities; namely, it is not how the donor feels about the wait time that matters for return behavior, but rather the actual duration of the wait. Consistent with theory we develop, our results indicate that waiting has a significant longer-term social cost: we estimate that a 38% increase (equivalent to one standard deviation) in the average wait would result in a 10% decrease in donations per year.

Tax wash sale rules prohibit the recognition of capital losses when substantially identical securities are sold and immediately repurchased within short windows. This study examines whether institutional investors use exchange-traded funds (ETFs) to circumvent wash sale rules. Consistent with tax-motivated demand for ETFs, incumbent ETFs both create more shares and experience more trading volume upon the introduction of nearly identical ETFs, particularly when recent returns are negative. We show that tax-sensitive institutions’ investment in highly correlated ETFs has proliferated in recent years, exceeding a quarter of their assets under management. Furthermore, tax-sensitive institutions holding more ETFs are significantly more likely to engage in swapping nearly identical ETFs. This swapping behavior has become widespread, with tax-sensitive institutional investors swapping $417 billion of nearly identical ETFs since 2001. We estimate that tax-sensitive institutions realized more than $84 billion dollars in losses in highly correlated ETFs associated with the swapping activity since 2001.

We replicate 469 anomaly variables similar to those studied by Hou et al. (2020) using Chinese A-share data and a reliable testing procedure with mainboard breakpoints and value-weighted returns. We find that 83.37% of the anomaly variables do not generate significant high-minus-low quintile raw return spreads. Further adjusting risk increases the failure rate slightly to 84.22% based on CAPM alphas and 86.99% based on Fama–French three-factor alphas. We show that the conventional procedure using all A-share breakpoints with equal-weighted returns for the anomaly test is indeed problematic as it assigns too much weight to microcaps and has a very limited investment capacity. The CH3-factor, CH4-factor, and q-factor models show the best performance over the whole sample period. The q-factor model is the best performer in the post-2007 subsample period after significant improvements occurred in China’s financial market environment, such as the completion of the split-share structure reform and the implementation of new accounting standards conforming to the International Financial Reporting Standards. The non–state-owned enterprise subsample in the post-2007 period is a cleaner sample in which the CH4-factor and q-factor models are the best performers. This paper was accepted by Lukas Schmid, finance. Funding: Z. Li acknowledges financial support from the National Natural Science Foundation of China [Grant 72103043] and the Fundamental Research Funds for the Central Universities in UIBE [Grants 19QN01 and 22PY053-72103043]. L. X. Liu acknowledges financial support from the National Natural Science Foundation of China [Grants 71872006 and 72273006]. L. X. Liu and X. Liu acknowledge financial support from the Guanghua Thought Leadership Platform of Peking University. K. C. J. Wei acknowledges partial financial support from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant 15507320]. The authors acknowledge financial support from the Guanghua School of Management, Peking University; the School of Banking and Finance, University of International Business and Economics; and the Non-PAIR Research Centre “Research Centre for Quantitative Finance” at Hong Kong Polytechnic University.

We study the effects of positive and negative advertising in presidential elections. We develop a model to disentangle these effects on voter turnout and candidate choice. The central empirical challenges are highly correlated and endogenous advertising quantities that are measured with error. To address these challenges, we construct a large set of potential instruments, including interactions with incumbency that we demonstrate provide the critical identifying variation, and apply machine-learning causal inference methods. Using data from the 2000 and 2004 U.S. presidential elections, we find that positive and negative ads play fundamentally different roles. Negative ads are more effective at driving relative candidate shares, whereas positive ads stimulate turnout. These results indicate that a candidate geographically targeting tone trades off local relative share gains and local increases in turnout for localities with a strong base. Counterfactual simulations, where the candidates adjust the quantity of positive and negative advertising while budgets remain fixed, indicate that ad tone alone can impact the outcome of close elections. Our analysis also provides potential explanations as to why past studies have produced mixed findings on both ad-tone and turnout effects.

Scholars generally assume that consumer ratings reflect consumer satisfaction, but ratings can be influenced by the design of the rating system. We examine two rating designs—single-dimensional rating systems, which elicit overall ratings only, and multidimensional (MD) rating systems, which elicit both dimensional and overall ratings—and how they impact overall ratings. Drawing on the accessibility–diagnosticity framework, we argue that dimensional ratings in MD systems influence overall ratings based on how the dimensions have been rated. We support this explanation with seven experiments. Our results suggest that across various experimental settings, rating objects, dimensions, and numbers of dimensions, overall ratings are systematically influenced by the design of the rating system.

We study whether relative power in the chief executive officer (CEO)–chief financial officer (CFO) relationship influences CEO compensation. To operationalize relative power of a CEO over a CFO, we define CFO co-option as the appointment of a CFO after a CEO assumes office. We find that CFO co-option is associated with a CEO pay premium of about 10%, which is concentrated more in the early years of the co-opted CFO’s tenure and in components of compensation that vary with the achievement of analyst-based earnings targets. Our evidence also indicates that a primary channel through which CEO power over a co-opted CFO yields the achievement of earnings targets is the use of earnings management to inflate earnings. Co-opted CFOs rely primarily on using discretionary accruals to manage earnings prior to the Sarbanes–Oxley regulatory intervention and switch to real-activities manipulation afterward. The evidence thus suggests that the form of earnings management depends on costs imposed on the CFO to inflate earnings.

This research examines how an individual’s place in the status hierarchy affects their willingness to expend effort on group tasks, why this occurs, and a contingency governing this relationship. Among firefighter teams (Study 1), MBA student workgroups (Study 2), and undergraduates in the laboratory (Study 3), we find that the relationship between status and effort, through performance expectations, is contingent on the perceived visibility of one’s efforts (i.e., task visibility). When task visibility is high, greater status leads to higher performance expectations. When task visibility is low or absent, this relationship was not present. Overall, our findings help paint a more complete picture of the relationship between status, performance expectations, and effort in workgroups while also furthering our understanding of the psychological experience of status. Data are available at https://doi.org/10.1287/mnsc.2018.3103 .

Selecting among alternative projects is a core management task in all innovating organizations. In this paper, we focus on the evaluation of frontier scientific research projects. We argue that the "intellectual distance" between the knowledge embodied in research proposals and an evaluator's own expertise systematically relates to the evaluations given. To estimate relationships, we designed and executed a grant proposal process at a leading research university in which we randomized the assignment of evaluators and proposals to generate 2,130 evaluator-proposal pairs. We find that evaluators systematically give lower scores to research proposals that are closer to their own areas of expertise and to those that are highly novel. The patterns are consistent with biases associated with boundedly rational evaluation of new ideas. The patterns are inconsistent with intellectual distance simply contributing "noise" or being associated with private interests of evaluators. We discuss implications for policy, managerial intervention, and allocation of resources in the ongoing accumulation of scientific knowledge.

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting (i.e., not limited to geometric Brownian motion) for which European call-option prices are readily available. By approximating the value function with an appropriately chosen interpolation function, the pricing of an American-style derivative with arbitrary payoff function is converted to the pricing of a portfolio of European call options, leading to analytical expressions for those cases where analytical European call prices are available (e.g., the Merton jump-diffusion process). Furthermore, in many settings, the approach yields upper and lower analytical bounds that provably converge to the true option price. We provide computational results to illustrate the convergence and accuracy of the resulting estimators.

An important debated topic in strategic management concerns the so-called “chief executive officer (CEO) effect,” which quantifies the impact that CEOs have on the performance of the firms that they lead. Prior literature has empirically investigated the CEO effect and found support for both theses: a significant effect and no effect at all. We note, however, that virtually all prior studies have relied on an empirical specification that leverages in-sample data, which could be unreliable in certain circumstances. In this paper, we utilize machine learning models and predictive analytics based on out-of-sample data to revisit the CEO effect. In particular, we operationalize the CEO effect as the gain in the out-of-sample predictive accuracy by adding the CEO information to the model input in addition to the firm information. By analyzing 1,245 firms and 1,779 CEOs over 20 years, we demonstrate that the results of the approach from the literature have limited external validity. More specifically, we convey that the analyses are purely based on in-sample data and that the predictive effects of CEOs are not substantive when out-of-sample test data sets are used. Although our main analysis relies on optimized distributed gradient boosting, we also conduct extensive robustness tests spanning close to 100 models with alternative algorithms and specifications, all of which yield consistent results.