Publications

How does the exchange of employees between regulatory agencies and regulated firms (i.e., the firm-government revolving door) affect firm regulatory outcomes? Existing work has mostly found a positive impact of revolving door hiring on firm outcomes, but it has overlooked potential limitations of this corporate political activity (CPA) tactic. We argue that the advantages firms can gain from hiring former regulators are bound by the timing of revolving door employment relative to the regulatory process. Within the context of agribiotechnology and its main regulator, the U.S. Department of Agriculture, we study regulators who move to in-house and contract lobbying positions (i.e., exit revolving door). We find that firms receive better regulatory outcomes (i.e., faster regulatory approval for new crops) only prior to the regulators’ move to in-house lobbying, consistent with the regulatory capture perspective. Moreover, this revolving door was only valuable in the time period immediately before the mobility event. Additionally, contrary to the belief that former regulators provide firms with expertise and social capital as lobbyists, we find that firms did not gain any advantage after regulators became lobbyists. Taken together, our results suggest that revolving doors can be an effective business political mobilization strategy, albeit one that has limited success in shaping firm government outcomes, much like other types of CPA.

The role of demand curvature in determining firm behavior in symmetric oligopolistic product markets is well-understood. We consider the empirically relevant discrete choice differentiated product demand and point to two forces that drive curvature in logit demand: the impact of outside-good spending on the consumer’s indirect utility and the heterogeneity in this response across consumers. We use the canonical example of the ready-to-eat cereal market (Nevo, 2000) to contrast elasticity and curvature estimates across several workhorse models. We illustrate that the log-concave Multinomial Logit and Nested Logit demands yield significantly biased curvature estimates. In contrast, a Mixed Logit specification generates a wider range of curvatures, including curvatures larger than one. These results are of immediate relevance to the robust assessment of tax incidence and the pass-through of cost savings, such as from a horizontal merger, in differentiated product markets.

Market producers often seek to position themselves in different categories over time. Successful repositioning is difficult, however, as audiences often devalue offerings that depart from a producer’s past creations. Prior research suggests that this penalty arises as evaluators withhold opportunities for producers to reposition because of presumptions of a lack of competence in different categories. In this paper, we develop understanding of a novel evaluator-driven challenge to producers’ repositioning efforts: evaluators are prone to “categorical stickiness,” by which the categories they have come to associate with a producer through its prior offerings shape their perceptions of the producer’s subsequent offerings. The result is a systematic mismatch between what producers claim and what evaluators perceive when a producer repositions. We further propose that audience members who have the greatest prior experience with a producer are the least likely to recognize its repositioning efforts. We examine evidence for our theory using data from Goodreads.com on authors within the book publishing industry, 2007–2017. We first build a novel deep-learning framework to predict categorization of a given book based solely on an author’s description of its content. We then use data on how Goodreads users categorize and evaluate books as well as their past reading behavior to test for evidence of our proposed mechanism. Overall, our results extend understanding of the evaluative processes that generate categorical constraints and how these may differ among various types of audience members.

When organizational members violate important organizational standards, they may face termination, or they may instead be retained by the organization and given a second chance. Retained transgressors experience the tension of liminality: they maintain their affiliation to the organization, making them structural insiders, but they have committed a transgression, making them moral outsiders. How might transgressors attempt to reintegrate and feel like full organizational insiders once again? And what makes transgressors feel more, or less, reintegrated? Previous work has studied reintegration from victims’ or third-parties’ perspectives, but little is known about transgressor reintegration. To build theory on transgressor reintegration, I studied transgressors at a military service academy. Through waves of qualitative data collection and inductive analyses, I find that transgressions threaten transgressors’ integration, leading transgressors to feel precarious in their perceptions of membership and their feelings of belonging. Transgressors attempt to restore both elements, but use distinct approaches for each. Because belonging restoration requires positive interactions with many organizational actors, transgressors can—and, in my data, frequently do—experience restoration of membership but not belonging. Therefore, it may be relatively rare for transgressors to feel highly reintegrated following transgressions, even in organizations that devote considerable resources to reintegration.

We develop the first structural model of a multitasking salesforce to address questions of job design and incentive compensation design. The model incorporates three novel features: (i) multitasking effort choice given a multidimensional incentive plan; (ii) salesperson’s private information about customers and (iii) dynamic intertemporal tradeoffs in effort choice across the tasks. The empirical application uses data from a micro nance bank where loan officers are jointly responsible and incentivized for both loan acquisition repayment but has broad relevance for salesforce management in CRM settings involving customer acquisition and retention. We extend two-step estimation methods used for unidimensional compensation plans for the multitasking model with private information and intertemporal incentives by combining flexible machine learning (random forest) for the inference of private information and the first-stage multitasking policy function estimation. Estimates reveal two latent segments of salespeople-a “hunter” segment that is more efficient in loan acquisition and a “farmer” segment that is more efficient in loan collection. We use counterfactuals to assess how (1) multi-tasking versus specialization in job design; (ii) performance combination across tasks (multiplicative versus additive); and (iii) job transfers that impact private information impact firm profits and specific segment behaviors.

Incomplete vaccine uptake and limited vaccine availability for some segments of the population could lead policymakers to consider re-imposing restrictions to help reduce fatalities. Early in the pandemic, full business shutdowns were commonplace. Given this response, much of the literature on policy effectiveness has focused on full closures and their impact. But were complete closures necessary? Using a hand-collected database of partial business closures for all U.S. counties from March through December 2020, we examine the impact of capacity restrictions on COVID-19 fatality growth. For the restaurant and bar sector, we find that several combinations of partial capacity restrictions are as effective as full shutdowns. For example, point estimates indicate that, for the average county, limiting restaurants and bars to 25% of capacity reduces the fatality growth rate six weeks ahead by approximately 43%, while completely closing them reduces fatality growth by about 16%. The evidence is more mixed for the other sectors that we study. We find that full gym closures reduce the COVID-19 fatality growth rate, while partial closures may be counterproductive relative to leaving capacity unrestricted. Retail closures are ineffective, but 50% capacity limits reduce fatality growth. We find that restricting salons, other personal services and movie theaters is either ineffective or counterproductive.