Publications

The case explores the CEO’s response to an existential threat to Nielsen’s core business coupled with a challenge to the company’s core values. Nielsen’s highly profitable TV audience measurement and customer confidence in Nielsen’s main offering – trusted, accurate data - is threatened by shifting consumer viewing habits and emerging competitors using disruptive technology. The TV panel homes, Nielsen’s long standing  differentiator for audience measurement, is appearing to lose relevance to customers; at the same time, household recruiting challenges is contributing to salesforce income disparity, impacting morale and testing the company’s commitment to diversity, equity and inclusion. The company announced a radical new product, Nielsen ONE, powered by data and machine learning, but it would be differentiated, the company insisted, by the venerable Nielsen panel – households across America. At the same time, COVID, George Floyd’s murder and the Black Lives Matter movement tested Nielsen’s operations and the authenticity of its purpose and values. Can the CEO and his senior leaders use a multistakeholder lens to simultaneously address the compensation inequity issue, as well re-frame its customer value proposition and the role of the household panel to compete effectively against the new disruptors?

Economists widely agree that, absent sufficient efficiencies or other offsetting factors, mergers that increase concentration substantially are likely to be anticompetitive. Further, holding everything else equal, the magnitude of anticompetitive effects tends to be larger, the larger is the increase in concentration caused by the merger. As market concentration is more easily measured than the post-merger equilibrium (which is unobserved ex ante), the use of concentration screens in the antitrust review of mergers is sensible and economically well-founded.

It might seem natural to determine whether prices are positively related to measures of concentration, such as the Herfindahl-Hirschman Index (HHI), comparing across different geographic markets or time periods. This might be implemented by using a simple regression of price on the HHI. However, for reasons that we describe, regressions of price on the HHI should not be interpreted as establishing causation. That is, they do not inform how a change in concentration from a merger would affect prices. Courts and other policy-makers should not rely on regressions of price on the HHI for the purposes of antitrust merger review.

In the article, the affiliation information for Alixandra Barasch and Deborah A. Small has been updated and now appears in the author note. All versions of this article have been corrected.] The widespread demand for luxury is best understood by the social advantages of signaling status (i.e., conspicuous consumption; Veblen, 1899). In the present research, we examine the limits of this perspective by studying the implications of status signaling for cooperation. Cooperation is principally about caring for others, which is fundamentally at odds with the self-promotional nature of signaling status. Across behaviorally consequential Prisoner's Dilemma (PD) games and naturalistic scenario studies, we investigate both sides of the relationship between signaling and cooperation: (a) how people respond to others who signal status, as well as (b) the strategic choices people make about whether to signal status. In each case, we find that people recognize the relative advantage of modesty (i.e., the inverse of signaling status) and behave strategically to enable cooperation. That is, people are less likely to cooperate with partners who signal status compared to those who are modest (Studies 1 and 2), and more likely to select a modest person when cooperation is desirable (Study 3). These behaviors are consistent with inferences that status signalers are less prosocial and less prone to cooperate. Importantly, people also refrain from signaling status themselves when it is strategically beneficial to appear cooperative (Studies 4-6). Together, our findings contribute to a better understanding of the conditions under which the reputational costs of conspicuous consumption outweigh its benefits, helping integrate theoretical perspectives on strategic interpersonal dynamics, cooperation, and status signaling.

Nonprofit crowdsourcing platforms such as food recovery organizations rely on volunteers to perform time-sensitive tasks. Thus, their success crucially depends on efficient volunteer utilization and engagement. To encourage volunteers to complete a task, platforms use nudging mechanisms to notify a subset of volunteers with the hope that at least one of them responds positively. However, because excessive notifications may reduce volunteer engagement, the platform faces a tradeoff between notifying more volunteers for the current task and saving them for future ones. Motivated by these applications, we introduce the online volunteer notification problem, a generalization of online stochastic bipartite matching where tasks arrive following a known time-varying distribution over task types. Upon arrival of a task, the platform notifies a subset of volunteers with the objective of minimizing the number of missed tasks. To capture each volunteer’s adverse reaction to excessive notifications, we assume that a notification triggers a random period of inactivity, during which she will ignore all notifications. However, if a volunteer is active and notified, she will perform the task with a given pair-specific match probability that captures her preference for the task. We develop an online randomized policy that achieves a constant-factor guarantee close to the upper bound we establish for the performance of any online policy. Our policy and hardness results are parameterized by the minimum discrete hazard rate of the interactivity time distribution. The design of our policy relies on sparsifying an ex ante feasible solution by solving a sequence of dynamic programs. Furthermore, in collaboration with Food Rescue U.S., a volunteer-based food recovery platform, we demonstrate the effectiveness of our policy by testing it on the platform’s data from various locations across the United States.

Past research has found that employees who view themselves as overqualified for their jobs tend to hold negative job attitudes and be unwilling to go beyond the call of duty. In challenging situations such as during the COVID-19 crisis, when having “all hands-on deck” may be important to an organization’s survival, mitigating the negative tendencies of these employees becomes important. Adopting a sensemaking perspective on crisis management, we examine whether supervisors’ self-sacrificial leadership can mitigate these negative tendencies. First, we propose that employee perceived overqualification is associated with lower levels of felt obligation to the organization and thereby lower levels of extra-role behaviors (i.e., helping and proactivity). We next propose that supervisors’ self-sacrificial leadership during the COVID-19 crisis can evoke, especially when COVID-19 more strongly impacts the organization, a sense of collectivism toward the organization, which mitigates the negative association of perceived overqualification with felt obligation and thus extra-role behaviors. We tested our theorizing in samples from the UK (n = 121, Pilot Study) and US (n = 382, Main Study) in studies with a multi-wave, time-lagged design. Findings from both studies provide support for our theorizing. We discuss implications for research and practice concerning perceived overqualification during a crisis.

Innovations in statistical technology, including in predicting creditworthiness, have sparked concerns about distributional impacts across categories such as race. Theoretically, distributional consequences of better statistical technology can come from greater flexibility to uncover structural relationships, or from triangulation of otherwise excluded characteristics. Using data on US mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups; these changes are primarily attributable to greater flexibility.

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develop a two-stage sequential search model where, in the first stage, the consumer sequentially screens positions to observe the preference weight of the products placed in them and forms a consideration set. In the second stage, she observes the additional idiosyncratic utility that she can derive from each product and chooses the highest-utility product within her consideration set. For this model, we first characterize the optimal sequential search policy of a welfare-maximizing consumer. We then study how platforms with different objectives should rank products. We focus on two objectives: (i) maximizing the platform’s market share and (ii) maximizing the consumer’s welfare. Somewhat surprisingly, we show that ranking products in decreasing order of their preference weights does not necessarily maximize market share or consumer welfare. Such a ranking may shorten the consumer’s consideration set due to the externality effect of high-positioned products on low-positioned ones, leading to insufficient screening. We then show that both problems—maximizing market share and maximizing consumer welfare—are NP-complete. We develop novel near-optimal polynomial-time ranking algorithms for each objective. Further, we show that, even though ranking products in decreasing order of their preference weights is suboptimal, such a ranking enjoys strong performance guarantees for both objectives. We complement our theoretical developments with numerical studies using synthetic data, in which we show (1) that heuristic versions of our algorithms that do not rely on model primitives perform well and (2) that our model can be effectively estimated using a maximum likelihood estimator.

As Artificial Intelligence (AI) proliferates across important social institutions,many of the most powerful AI systems available are difficult to interpret forend-users and engineers alike. Here, we sought to characterize public attitudestowards AI interpretability. Across seven studies (N = 2475), we demonstraterobust and positive attitudes towards interpretable AI among non-experts thatgeneralize across a variety of real-world applications and follow predictablepatterns. Participants value interpretability positively across different levels ofAI autonomy and accuracy, and rate interpretability as more important for AIdecisions involving high stakes and scarce resources. Crucially, when AIinterpretability trades off against AI accuracy, participants prioritize accuracyover interpretability under the same conditions driving positive attitudestowards interpretability in the first place: amidst high stakes and scarceresources. These attitudes could drive a proliferation of AI systems makinghigh-impact ethical decisions that are difficult to explain and understand.

Fear of escalating input prices in response to retail success is a commonly discussed phenomenon affecting supply chains. Such a ratchet effect arises when a retailer feels compelled to modify its investments to better serve the end customers in order to hide positive prospects and restrain future wholesale price hikes. In a two-period model of supply chain interactions, the authors dem- onstrate that such an endogenous ratchet effect can have multifaceted reverberations. A retailer fearing price hikes may be tempted to curtail near-term profits to ensure favorable long-term pricing. In response, the supplier can use deep discounts in its initial wholesale prices to convince the retailer to focus on its short-term profits rather than long-term pricing concerns. These deep discounts not only encourage mutually beneficial investments but also alleviate double marginalization inefficiencies along the supply chain. In light of these results, the authors show that a mandatory information disclosure policy to reduce the ratchet effect decreases total channel efficiency compared with no information disclosure, precisely because mandatory disclo- sure interrupts the healthy tension among supply chain partners. Thus, the model presents a scenario in which ratcheting con- cerns can create a degree of self-enforcing cooperation that results in socially beneficial responses in supply chains