This study examines the effect of audit committee connectedness through director networks on financial reporting quality, specifically the misstatement of annual financial statements. Using network analysis, we examine multiple dimensions of connectedness and find that after controlling for operating performance and corporate governance characteristics, firms with well-connected audit committees are less likely to misstate annual financial statements. In addition, our study demonstrates that audit committee connectedness through director networks moderates the negative effect of board interlocks to misstating firms on financial reporting quality. We conduct several tests to address identification concerns and find similar results. Our findings suggest that firms with better-connected audit committees are less likely to adopt reporting practices that reduce financial reporting quality.

What motivates the rich and powerful to exhibit generosity? We explore this important question in a large field experiment. We solicit donations from 32,174 alumni of an Ivy League university, including thousands of rich and powerful alumni. Consistent with past psychology research, we find that the rich and powerful respond dramatically, and differently than others, to being given a sense of agency over the use of donated funds. Gifts from rich and powerful alumni increase by 100%–350% when they are given a sense of agency. This response arises primarily on the intensive margin with no effect on the likelihood of donating. Results suggest that motivating the rich and powerful to act may require tailored interventions.

Governments use consumer incentives to promote green technologies (e.g., solar panels and electric vehicles). Our goal in this paper is to study how policy adjustments over time will interact with production decisions from the industry. We model the interaction between a government and an industry player in a two-period game setting under uncertain demand. We show how the timing of decisions affects the risk sharing between the government and the supplier, ultimately affecting the cost of the subsidy program. In particular, we show that when the government commits to a fixed policy, it encourages the supplier to produce more at the beginning of the horizon. Consequently, a flexible subsidy policy is on average more expensive, unless there is a significant negative demand correlation across time periods. However, we show that the variance of the total sales is lower in the flexible setting, implying that the government’s additional spending reduces the adoption level uncertainty. In addition, we show that for flexible policies, the supplier is better off in terms of expected profits, whereas the consumers can either benefit or not depending on the price elasticity of demand. Finally, we test our insights with a numerical example calibrated on data from a solar subsidy program.

We obtain a closed-form solution for pricing European options under a general jump-diffusion model that can incorporate arbitrary discrete jump-size distributions, including nonparametric distributions such as an empirical distribution. The flexibility in the jump-size distribution allows the model to better capture leptokurtic features found in real-world data. The model uses a discrete-time framework and leads to a pricing formula that is provably convergent to the continuous-time price as the discretization is increased. The solution is easy to implement with fast convergence properties. Numerical results illustrate the efficiency and accuracy of the proposed model and highlight its robustness and flexibility.

Out-of-hospital cardiac arrest is a significant public health issue, and treatment, namely, cardiopulmonary resuscitation and defibrillation, is very time sensitive. Public access defibrillation programs, which deploy automated external defibrillators (AEDs) for bystander use in an emergency, reduce the time to defibrillation and improve survival rates. In this paper, we develop models to guide the deployment of public AEDs. Our models generalize existing location models and incorporate differences in bystander behavior. We formulate three mixed integer nonlinear models and derive equivalent integer linear reformulations or easily computable bounds. We use kernel density estimation to derive a spatial probability distribution of cardiac arrests that is used for optimization and model evaluation. Using data from Toronto, Canada, we show that optimizing AED deployment outperforms the existing approach by 40% in coverage, and substantial gains can be achieved through relocating existing AEDs. Our results suggest that improvements in survival and cost-effectiveness are possible with optimization.

Some environments constrain the information that managers and decision makers can observe. We examine judgment in censored environments where a constraint, the censorship point, systematically distorts the observed sample. Random instances beyond the censorship point are observed at the censorship point, whereas uncensored instances are observed at their true value. Many important managerial decisions occur in censored environments, such as inventory, risk taking, and employee evaluation decisions. In this research, we demonstrate a censorship bias—individuals tend to rely too heavily on the observed censored sample, biasing their belief about the underlying population. We further show that the censorship bias is exacerbated for higher degrees of censorship, higher variance in the population, and higher variability in the censorship points. In four studies, we find evidence of the censorship bias across the domains of demand estimation and sequential risk taking. The bias causes individuals to make costly decisions and behave in an overly risk-averse manner.

We introduce the pathwise optimization (PO) method, a new convex optimization procedure to produce upper and lower bounds on the optimal value (the “price”) of a high-dimensional optimal stopping problem. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the martingale duality approach. We demonstrate via numerical experiments that the PO method produces upper bounds of a quality comparable with state-of-the-art approaches, but in a fraction of the time required for those approaches. As a by-product, it yields lower bounds (and suboptimal exercise policies) that are substantially superior to those produced by state-of-the-art methods. The PO method thus constitutes a practical and desirable approach to high-dimensional pricing problems. Furthermore, we develop an approximation theory relevant to martingale duality approaches in general and the PO method in particular. Our analysis provides a guarantee on the quality of upper bounds resulting from these approaches and identifies three key determinants of their performance: the quality of an input value function approximation, the square root of the effective time horizon of the problem, and a certain spectral measure of “predictability” of the underlying Markov chain. As a corollary to this analysis we develop approximation guarantees specific to the PO method. Finally, we view the PO method and several approximate dynamic programming methods for high-dimensional pricing problems through a common lens and in doing so show that the PO method dominates those alternatives.

Prior research documents performance improvements following the implementation of pay-for-performance (PFP) bonus plans. However, bonus plans typically pay for performance relative to a goal, and the manager whose performance is to be evaluated often participates in setting the goal. In these settings, PFP affects managers' incentive to influence goal levels in addition to affecting performance effort. Prior field research is silent on the effect of PFP on goals, the focus of this paper. Using sales and sales goal data from 61 stores of a U.S. retail firm over 10 quarters, we find that the introduction of a performance-based bonus plan with participative goal setting is accompanied by lower goals that are more accurate predictors of subsequent sales performance. Statistical tests indicate that increased goal accuracy is attributable to managers “meeting but not beating” goals and to new information being impounded in goals. We further investigate how differences among managers are associated with goal levels. We find significant “manager effects” but no “supervisor effects.” In additional tests we find that cross-sectional differences among managers are related to differing marginal returns to slack-building effort. Turning to the role of new information on goals, we find that prior period performance has incremental power to explain goal levels in the postplan period. Our results provide field-based evidence that PFP and participative goal setting affect the level and accuracy of goals, effects that are associated with both information exchange and with managers' incentives to influence goals.

Estimating quantile sensitivities is important in many optimization applications, from hedging in financial engineering to service-level constraints in inventory control to more general chance constraints in stochastic programming. Recently, Hong (Hong, L. J. 2009. Estimating quantile sensitivities. Oper. Res. 57 118–130) derived a batched infinitesimal perturbation analysis estimator for quantile sensitivities, and Liu and Hong (Liu, G., L. J. Hong. 2009. Kernel estimation of quantile sensitivities. Naval Res. Logist. 56 511–525) derived a kernel estimator. Both of these estimators are consistent with convergence rates bounded by n −1/3 and n −2/5 , respectively. In this paper, we use conditional Monte Carlo to derive a consistent quantile sensitivity estimator that improves upon these convergence rates and requires no batching or binning. We illustrate the new estimator using a simple but realistic portfolio credit risk example, for which the previous work is inapplicable.

Most prior event studies find that the announcement of a new alliance is accompanied by a positive stock market response for the partners. This result has usually been interpreted as evidence for the prevailing view that alliances are effective vehicles for partners to acquire or access new skills and thus become stronger competitors. However, partners should also earn positive abnormal returns if alliances are used to shape competitive interactions, attenuating competitive intensity industry-wide. In this study, we disentangle these different mechanisms by examining how alliance announcements affect the stock market's evaluation of allying firms' rivals: if an alliance is expected to make partner firms more competitive, this should lead to negative abnormal returns for partners' rivals; if an alliance is expected to facilitate a reduction in competitive intensity, this should lead to positive abnormal returns for rivals. Results from an event study analysis of research and development alliances in the telecommunications and electronics industries during 1996–2004 provide evidence consistent with competition attenuation in some alliances. Our research thus challenges the increasingly narrow focus on learning and resource accumulation through alliances, and calls for broader consideration of the roles and effects of collaboration, both for individual firms and for industry structure.