Publications

We study a retirement savings plan with a default contribution rate of 12% of income, which is much higher than previously studied defaults. Twenty-five percent of employees had not opted out of this default 12 months after hire; a literature review finds that the corresponding fraction in plans with lower defaults is approximately one-half. Because only contributions above 12% were matched by the employer, 12% was likely to be a suboptimal contribution rate for employees. Employees who remained at the 12% default contribution rate had average income that was approximately one-third lower than would be predicted from the relationship between salaries and contribution rates among employees who were not at 12%. Defaults may influence low-income employees more strongly in part because these employees face higher psychological barriers to active decision making.

A critical element of word of mouth (WOM) or buzz marketing is to identify seeds, often central actors with high degree in the social network. Seed identification typically requires data on the relevant network structure, which is often unavailable. We examine the impact of WOM seeding strategies motivated by the friendship paradox, which can obtain more central nodes without knowing network structure. Higher degree nodes may be less effective as seeds if these nodes communicate less with neighbors or are less persuasive when they communicate; therefore, whether friendship paradox–motivated seeding strategies increase or reduce WOM and adoption remains an empirical question. We develop and estimate a model of WOM and adoption using data on microfinance adoption across village social networks in India. Counterfactuals show that the proposed strategies with limited seeds are about 13%–30% more effective in increasing adoption relative to random seeding. These strategies are also on average 5%–11% more effective than the firm’s leader seeding strategy. We also find these strategies are relatively more effective when we have fewer seeds.

This chapter reviews major theories of emotion and decision making, concentrating on developments within the disciplines of psychology, economics, and decision science. These theories naturally cluster into two sets of theories – one set that views emotional valence (i.e., positivity versus negativity) as the primary factor for predicting decision outcomes, and a second set of theories that views valence as one of multiple factors for making predictions. Often known as “emotion-specific models”, theories in this latter set propose that emotions of the same valence can have opposing (rather than similar) effects on certain decisions. After describing strengths and weaknesses of each approach, the chapter offers a review of the Emotion-Imbued Choice model (EIC) – a unified, meta-level model of emotion and decision making.

How does market power in the corporate banking sector influence the effects of interest rate cap policies on credit allocation? We study this question using administrative credit registry data in Bangladesh, where the Central Bank capped the interest rate on corporate loans at 13% in 2009, relative to a pre-reform average interest rate of 14.5%. We apply a difference-in-differences design with variation in pre-regulation, branch-level interest rates as an exposure measure and find that a one percentage point cap-induced drop in rates increased lending amounts by 30% over the two years of the cap regime. This increase in lending is not driven by banks’ costs to supplying credit, as proxied by the riskiness of the borrower pool or deposit funding costs. Our results point to substantial credit under-provision due to banks’ market power in an emerging markets context, even in the presence of relationship lending.

Environmental jolts can trigger more conducive conditions for driving change in organizations. However, punctuated equilibrium theories of organizational change concentrate on top managers’ implementation of de novo radical changes after jolts. Existing research has not examined frontline-driven, incremental change efforts during these periods of disrupted stasis, despite the value of frontline change ideas. We develop a process model to explain how and when those on an organization’s front lines can leverage a jolt to opportunistically implement long-desired change ideas in ways that promote their retention. We conducted a two-year qualitative field study at a hospital during the Covid-19 pandemic, examining the trajectories of 33 premeditated change ideas raised by frontline staff. By comparing ideas that persisted to become part of normal operations with those that failed to be selected or retained, we identified practices and conditions that promoted the selection and retention of frontline change ideas. Our study suggests that frontline change advocates can seed the long-term retention of “shovel-ready” ideas—as opposed to de novo ideas—after a jolt by rapidly and opportunistically deploying a novel set of practices before the brief window of opportunity created by lessened constraints and increased managerial receptivity closes. Prior theories of change largely assume frontline-driven change to be slow and continuous, proceeding in a one-off fashion; we explain how and when frontline change can instead occur in rapid, opportunistic bursts. This study advances theories of punctuated equilibrium and bottom-up change in organizations by unearthing an alternative way that change can be intentionally accomplished in organizations.

We study the socially optimal level of illiquidity in an economy populated by house- holds with taste shocks and present bias with naive beliefs. The government chooses mandatory contributions to accounts, each with a different pre-retirement withdrawal penalty. Collected penalties are rebated lump sum. When households have homoge- neous present bias, β, the social optimum is well approximated by a single account with an early-withdrawal penalty of 1 − β. When households have heterogeneous present bias, the social optimum is well approximated by a two-account system: (i) an account that is completely liquid and (ii) an account that is completely illiquid until retirement.

A common-sense moral intuition is that bad acts should be condemned according to severity. Yet seven experiments (N = 6,075 U.S. adults) show that the extent to which people differentiate between transgressions hinges on the direction of comparison. When scaling up from a less severe transgression to a more severe one, people readily express stronger condemnation of the worse transgression. But when scaling down from a more severe transgression to a less severe one, they differentiate less, often condemning the lesser transgression just as strongly as one that is transparently worse. Indicating that one transgression is less bad than another can be construed as downplaying such transgressions, signaling bad moral character. Supporting this account, the asymmetry is larger for judgments that implicate moral character and for transgressions that seem especially important to condemn. Observers’ moral-character judgments reveal a similar pattern, suggesting that the asymmetry is reinforced by social incentives.

Online platforms are rife with racial discrimination, but current interventions focus on employers, rather than customers. We propose a customer-facing solution: changing to a two-point rating scale (dichotomization). Compared with the ubiquitous five-star scale, we argue that dichotomization reduces modern racial discrimination by focusing evaluators on the distinction between ‘good’ and ‘bad’ performance, thereby reducing how personal beliefs shape customer assessments. Study 1 is a quasi-natural experiment on a home-services labour platform (n = 69,971) in which the company exogenously changed from a five-star scale to a dichotomous scale (thumbs up or thumbs down). Dichotomization eliminated customers’ racial discrimination whereby non-white workers received lower ratings and earned 91 cents for each US dollar paid to white workers for the same work. A pre-registered experiment (study 2, n = 652) found that the equalizing effect of dichotomization is most prevalent among evaluators holding modern racist beliefs. Further experiments (study 3, n = 1,435; study 4, n = 528) provide evidence of the proposed mechanism, and eight supplementary studies support measurement and design choices. Our research offers a promising intervention for reducing customers’ subtle racial discrimination in a large section of the economy and contributes to the interdisciplinary literature on evaluation processes and racial inequality.

The world has seen a stunning rise in the willingness of great powers to use their trade and financial relationships for geopo- litical ends. This rise of “Geoeconomics” has the potential to reshape the interna- tional trade and financial system. Geoe- conomic policies include not only sanctions but also the strategic use of export controls, efforts to reshape supply chains for secu- rity purposes, the provision of foreign aid to secure political alignment, and the en- couragement or pressure on domestic and foreign firms to alter their business rela- tionships. While the foundation of a na- tion’s geoeconomic power is its economic strength, size and connections alone do not automatically translate into geoeconomic power. Instead, governments seeking to project geoeconomic power abroad need to be able to credibly co-opt or coerce their do- mestic firms and citizens, and perhaps crit- ical foreign allies, to take part in this power projection. Achieving this involves navigat- ing a range of political economy constraints at home, including legal restrictions, do- mestic political objectives, interest groups and other forces that limit a government’s ability to exert its influence. An important question for geoeconomic power projection is how far a government can push its own firms or allies to act against their private interests in pursuit of the country’s geopo- litical goals.

Consideration set models generalize discrete-choice models by relaxing the assumption that consumers consider all available options. Determining which options were considered has previously required either survey data or restrictions on how attributes affect consideration or utility. We provide an alternative route. In full-consideration models, choice probabilities satisfy a symmetry property analogous to Slutsky symmetry in continuous-choice models. This symmetry breaks down in consideration set models when changes in characteristics perturb consideration. We show that consideration probabilities are constructively identified from the resulting asymmetries. We validate our approach in a lab experiment where consideration sets are known and then apply our framework to study a “smart default” policy in Medicare Part D, wherein consumers are automatically reassigned to lower-cost prescription drug plans with the option of opting out. Full-consideration models imply that such a policy will be ineffective because consumers will opt out to avoid switching costs. Allowing for inattention, we find that defaulting all consumers to lower-cost options produces negligible welfare benefits on average, but defaulting only consumers who would save at least $300 produces large benefits.

This paper develops a theory-based, explainable deep learning convolutional neural network (CNN) classifier to predict the time-varying emotional response to music. We design novel CNN filters that leverage the frequency harmonics structure from acoustic physics known to impact the perception of musical features. Our theory-based model is more parsimonious, but it provides comparable predictive performance with atheoretical deep learning models while performing better than models using handcrafted features. Our model can be complemented with handcrafted features, but the performance improvement is marginal. Importantly, the harmonics-based structure placed on the CNN filters provides better explainability for how the model predicts emotional response (valence and arousal) because emotion is closely related to consonance—a perceptual feature defined by the alignment of harmonics. Finally, we illustrate the utility of our model with an application involving digital advertising. Motivated by YouTube’s midroll ads, we conduct a laboratory experiment in which we exogenously insert ads at different times within videos. We find that ads placed in emotionally similar contexts increase ad engagement (lower skip rates and higher brand recall rates). Ad insertion based on emotional similarity metrics predicted by our theory-based, explainable model produces comparable or better engagement relative to atheoretical models.

This paper examines the welfare implications of third-party informational interme- diation. A seller sets the price of a product that is sold through an intermediary, who discloses information about the product to consumers. In a model where the inter- mediary is consumer-minded—has a payoff that depends on both the seller’s revenue and the consumer surplus, we show that total welfare may decrease in the Pareto sense, as the intermediary’s consumer-mindedness increases. Furthermore, we show that consumer-mindedness emerges endogenously when a revenue-maximizing interme- diary is forward-looking and the consumer base is increasing in past consumer surplus.

This article develops a method to automatically discover and quantify human-interpretable visual characteristics directly from product image data. The method is generative, and can create new visual designs spanning the space of visual characteristics. It builds on disentanglement methods in deep learning using variational autoencoders, which aim to discover underlying statistically independent and interpretable visual characteristics of an object. The impossibility theorem in the deep learning literature indicates that supervision with ground truth characteristics would be required to obtain unique disentangled representations. However, these are typically unknown in real world applications, and are in fact exactly the characteristics we want to discover. Extant machine learning methods require ground truth labels for each visual characteristic, resulting in a task requiring human evaluation and judgment to both design and operationalize. In contrast, this method postulates the use of readily available product characteristics (such as brand and price) as proxy supervisory signals to enable disentanglement. This method discovers and quantifies human-interpretable and statistically independent characteristics without any specific domain knowledge on the product category. It is applied to a dataset of watches to automatically discover interpretable visual product characteristics, obtain consumer preferences over visual designs, and generate new ideal point designs targeted to specific consumer segments.

We propose a novel, theory-based approach to the reinforcement learning problem of maximizing profits when faced with an unknown demand curve. Our method, rooted in multi-armed bandits, balances exploration and exploitation across various prices (arms) to maximize rewards. Traditional Gaussian process bandits cap- ture one informational externality in price experimentation – correlation of rewards through an underlying demand curve. We extend this framework by incorporating a second externality, monotonicity, into Gaussian process bandits by introducing monotonic versions of both the GP-UCB and GP-TS algorithms. Through reduction of the demand space, this informational externality limits exploration and experimentation, out- performing benchmarks by enhancing profitability. Moreover, our approach can also complement methods such as partial identification. Additionally, we present algorithm variants that account for heteroscedastic noise in purchase data. We provide theoretical guarantees for our algorithm, and empirically demonstrate its improved performance across a broad range of willingness-to-pay distributions (including discontinuous, time-varying, and real-world) and price sets. Notably, our algorithm increased profits, especially for distribu- tions where the optimal price lies near the lower end of the considered price set. Across simulation settings, our algorithm consistently achieved over 95% of the optimal profits.

This paper proposes a new measure of technological obsolescence using detailed patent data. Using this measure, we present two sets of results. First, firms' technological obsolescence foreshadows substantially lower growth, productivity, and reallocation of capital. This finding applies mainly for obsolescence of core innovation and embodied innovation, and it is stronger in competitive product markets. Second, in stock markets, high-obsolescence firms under-perform low-obsolescence firms by 7 percent annually. Using analyst forecast data, we show this is due to a systematic overestimation of future profits of obsolescent firms. The measure contains incremental information about firm innovation relative to measures focusing on new innovation.

We conduct a systematic re-analysis of intervention-based studies that promote hygienic latrines and evaluate via experimental methods. We impose systematic inclusion criteria to identify such studies and compile their microdata to harmonize outcome measures, covariates, and estimands across studies. We then re-analyze their data to report metrics that are consistently defined and measured across studies. We compare the relative effectiveness of different classes of interventions implemented in overlapping ways across four countries: community-level demand encouragement, sanitation subsidies, product information campaigns, and microcredit to finance product purchases. In the sample of studies meeting our inclusion criteria, interventions that offer financial benefits generally outperform information and education campaigns in increasing adoption of improved sanitation. Contrary to a policy concern about sustainability, financial incentives do not undermine usage of adopted latrines. Effects vary by share of women in the household, in both positive and negative directions, and differ little by poverty status.

Healthcare is one of the largest global industries, encompassing a wide range of occupations, organizational designs, and advanced technologies within different types of systems and forms of ownership (public, for-profit, non-profit). The complexity, range, scale, and importance of the work conducted in the healthcare sector render it an ideal context for scholars engaged in management and organizational research to develop novel, generalizable, and useful theories aligned with the mission of Administrative Science Quarterly.

We are thrilled to feature seven insightful and impactful papers that showcase healthcare as an important context to advance the study of organizations and management.

This context was long the purview of scholars in other disciplines writing solely for healthcare audiences, but management scholars recently have given increased attention to studying healthcare organizations and their management. We were ecstatic to discover that the publication rate of ASQ articles featuring research conducted in healthcare contexts has increased in the last decade relative to prior decades.

We present a new database of banking-crisis interventions from the Roman Empire
to the present, covering 1,946 interventions in 20 categories across 143 countries.
We demonstrate that crisis-intervention patterns are significantly related to income
and fiscal variables and to measures of the political system and currency regime.
GDP losses following crises are economically significant and are larger for
wealthier countries, with some evidence that these losses are mitigated by
democratic political systems and liberal currency regimes. Finally, intervention
frequencies reached an apex during the post-Bretton Woods era, continuing a
secular increase since at least the late 17th century.

People have a more-nuanced view of misinformation than the binary distinction between “fake news” and “real news” implies. We distinguish between the truth of a statement's verbatim details (i.e., the specific, literal information) and its gist (i.e., the general, overarching meaning), and suggest that people tolerate and intentionally spread misinformation in part because they believe its gist. That is, even when they recognize a claim as literally false, they may judge it as morally acceptable to spread because they believe it is true “in spirit.” Prior knowledge, partisanship, and imagination increase belief in the gist. We argue that partisan conflict about the morality of spreading misinformation hinges on disagreements not only about facts but also about gists.