This paper investigates the implications of social rewards on the allocation of talent in society and consequently on the process of economic growth. The authors consider two sources of heterogeneity among workers: nonwage income and innate ability. A greater emphasis on status may induce the 'wrong' individuals, that is, those with low ability and high wealth, to acquire schooling, causing workers with high ability and low wealth to leave the growth-enhancing industries. This crowding-out effect, taken alone, discourages growth. Growth may be enhanced by a more egalitarian distribution of wealth, which reduces the demand for status.

Governments implement wealth transfers with different policy goals and distribution methods. Prior research examines the timing (lump sum/periodic) of transfers but fails to simultaneously consider payment delivery method (standalone/combined with other income). Based on the behavioral life-cycle model, we predict payment timing influences how recipients spend government transfers, but that this effect is muted when the transfer payment is combined with other income. In contrast to prior research, our experimental findings provide theory-consistent results and suggest recipients of a periodic transfer spend more of the transfer than recipients of a lump sum transfer, but only when the transfer is standalone and not combined. Our findings help to explain theory-inconsistent results of prior research and extend the literature on the behavioral life-cycle model and mental budgeting. Moreover, our results suggest policymakers can intentionally structure the distribution of government transfers to encourage household spending or saving consistent with policy goals.

Local-thinking bias, wherein agents overweight information that comes readily to mind, is a prominent finding in cognitive psychology. In this study, we investigate local-thinking bias in the context of sell-side analysts and measure each analyst’s “local” information as news stemming from their individual coverage portfolio. Tests examining multiple analysts forecasting on the same focal firm at the same time find that individual analysts overweight idiosyncratic local news and underweight news from economically linked firms that are not in their coverage portfolios. Market prices track the analyst bias from local news, leading to predictable and economically significant return reversal patterns in the future. A trading strategy that adjusts for analysts’ biases earns meaningful abnormal returns. We discuss the implications of these findings for three literatures: (1) cognitive psychology, (2) analyst behavior, and (3) behavioral asset pricing.

This research evaluates whether reporting incentives and consequences change under the new lease accounting standard. Under prior guidance (SFAS 13), we predict and find firms with high financing cost sensitivities to leverage have greater incentive to finance investments with operating leases. Under the new lease accounting standard (ASU 2016-02), we predict and find this leverage incentive remains but is reduced, consistent with the FASB’s objective to limit opportunities to structure lease contracts for balance sheet purposes. Under ASU 2016-02, we also predict and find that the leverage incentive encourages firms to reduce operating lease liabilities by decreasing the duration of minimum lease payments. Lastly, we predict and find that firms use fewer operating leases under both SFAS 13 and ASU 2016-02 when managers’ income objectives exclude depreciation and/or interest expense. These findings suggest reporting incentives remain post ASU 2016-02 that encourage firms to structure leases to achieve accounting outcomes. Data Availability: This study uses licensed data from a variety of sources (see Appendix A for a detailed list of providers).

Although q2 measures how well a pattern of means fits a custom contrast, there is no guidance for what values are “good.” We survey experimental accounting researchers who assess the fit between plots of means and contrast weights as poor, acceptable, good, or excellent. We find that graphical presentation effects and researchers’ individual attributes influence their assessments. This suggests that research needs an ex ante method for evaluating q2, grounded empirically in the wisdom of the crowd across many different presentations, rather than relying solely on the idiosyncratic assessments of individual researchers. Using fuzzy set theory, we develop such a method that researchers can use to characterize q2 = 0.100, for example, as mostly good fit, leaning toward acceptable. Our approach has significant advantages over bright-line cutoffs commonly used for other statistical indices. Overall, our study can improve our discipline’s assessments of fit between experimental results and custom contrast weights. Data Availability: Data are available from the authors upon request.

Global markets are moving toward requiring firms to track and report greenhouse gas emissions across their value chain. This includes self-generated emissions (i.e., Scope 1), emissions from power providers (i.e., Scope 2), and emissions from upstream suppliers and downstream parties (i.e., Scope 3). Yet, obtaining reliable emissions data is a complex task that often relies on cumbersome legacy processes and becomes especially challenging with upstream and downstream emissions. Using the Design Science Research Methodology, we propose and prototype (early-stage) a technological solution that uses blockchain, non-fungible tokens, and smart contracts to track and report greenhouse gas emissions across a firm’s value chain. The proposed model demonstrates how these tools can create a reliable classification and provenance of emissions that all value chain members can access for reporting purposes. Experts in the field evaluate the proposed model, find it technologically feasible, and call attention to some of its challenges.

Although activist short sellers can play a crucial role in fraud detection, they have come under scrutiny following accusations of systematically disseminating false negative information. We develop a framework delineating the roles of campaign-specific private information quality and short-selling dynamics in shaping disclosure incentives. We predict that the act of disclosure combined with pre-disclosure stock lending dynamics is informative about the quality of an activist’s private information. We find that increased pre-disclosure shorting intensity is associated with more negative post-disclosure returns, adverse media coverage, and consequential campaign outcomes, including auditor turnover, accounting restatements, class-action lawsuits, and performance-related delistings. Furthermore, elevated short-selling costs and risks magnify the association between pre-disclosure shorting intensity and post-disclosure underperformance. Finally, we examine V-shaped reversals and short covering following activists’ disclosures and find no evidence of systematic manipulation. We conclude that activists disclosing fraud allegations under their own names are discouraged from engaging in “short-and-distort” schemes. Data Availability: Data are available from the sources cited in the text.

Prior research shows that individuals exhibit a propensity to surrogate performance measures for their underlying strategy, resulting in suboptimal strategic decisions. We investigate whether the incorporation of flexibility in contemporary performance measurement systems (PMSs) reduces surrogation propensity in the context of product innovation and whether this effect varies depending on environmental dynamism. We conduct a 2 × 2 experiment and find that PMS flexibility significantly lowers managers' surrogation propensity when the business environment is more dynamic and when the investment decisions have opportunity costs. Our study contributes to the literature by identifying a viable way to reduce managers' surrogation propensity.