Publications

This paper studies the welfare effects of encouraging rural-urban migration in the develop-
ing world. To do so, we build a dynamic incomplete-markets model of migration in which
heterogeneous agents face seasonal income fluctuations, stochastic income shocks, and disu-
tility of migration that depends on past migration experience. We calibrate the model to
replicate a field experiment that subsidized migration in rural Bangladesh, leading to signif-
icant increases in both migration rates and consumption for induced migrants. The model’s
welfare predictions for migration subsidies are driven by two main features of the model and
data: first, induced migrants tend to be negatively selected on income and assets; second, the
model’s non-monetary disutility of migration is substantial, which we validate using newly
collected survey data from this same experimental sample. The average welfare gains are
similar in magnitude to those obtained from an unconditional cash transfer, and greater than
from policies that discourage migration, though migration subsidies lead to larger gains for
the poorest households, which have the greatest propensity to migrate.

The ability of recent Large Language Models (LLMs) such as GPT-3.5 and GPT-4 to generate human-like texts suggests that social scientists could use these LLMs to construct measures of semantic similarity that match human judgment. In this article, we provide an empirical test of this intuition. We use GPT-4 to construct a measure of typicality—the similarity of a text document to a concept. We evaluate its performance against other model-based typicality measures in terms of the correlation with human typicality ratings. We conduct this comparative analysis in two domains: the typicality of books in literary genres (using an existing dataset of book descriptions) and the typicality of tweets authored by US Congress members in the Democratic and Republican parties (using a novel dataset). The typicality measure produced with GPT-4 meets or exceeds the performance of the previous state-of-the art typicality measure we introduced in a recent paper [G. Le Mens, B. Kovács, M. T. Hannan, G. Pros Rius, Sociol. Sci. 2023, 82–117 (2023)]. It accomplishes this without any training with the research data (it is zero-shot learning). This is a breakthrough because the previous state-of-the-art measure required fine-tuning an LLM on hundreds of thousands of text documents to achieve its performance.

Consensus analyst target prices are widely available online at no cost to investors. In this paper we examine how the amount of dispersion in the individual target prices comprising the consensus affects the predictive association between the consensus target price and future returns. We find that returns implied by consensus target prices and realized future returns are positively correlated when dispersion is low, but they become highly negatively correlated when dispersion is high. Further analyses suggest that the differing effect of dispersion stems from incentive-driven staleness in price targets by some analysts after bad news. As a stock performs poorly and some analysts are slow to update their target prices, dispersion increases, and the consensus target price becomes too high. This has important implications for how consensus analyst target prices should inform investment decisions. We show that a hedge strategy taking a long (short) position in stocks with the highest predicted returns among stocks with the lowest (highest) dispersion earns more than 11% annually. Finally, we show that the negative correlation between consensus-based predicted returns and future realized returns for high-dispersion stocks exists mainly for stocks with high retail interest, suggesting that unsophisticated investors are misled by inflated target prices that are available freely online.

Though organizational scholars have studied punishment for decades, recent examples of punishment in organizations cannot be fully explained by the scholarly literature. This may be because much of our prior understanding of punishment has been based on studies in highly bounded organizations, but with the shift to remote work arrangements and contract or freelance work, modern organizations are becoming increasingly boundaryless. Does punishment in bounded organizations look different than punishment in boundaryless organizations? To answer this question, we review and categorize the literature on punishment in organizations according to the boundedness of the organization it examined. We find that though there are similarities in punishment in bounded and boundaryless organizations, punishment in boundaryless organizations involves different actors, punishing different situations, for different reasons, using different methods. However, many questions about punishment in boundaryless organizations remain, including about the pervasiveness, motivations, and impacts that punishment has in boundaryless organizations.

We study the impact of changing choice set size on the quality of choices in health insurance markets. Using novel data on enrolment and medical claims for school district employees in the state of Oregon, we document that the average employee could save $600 by switching to a lower cost plan. Structural modelling reveals large “choice inconsistencies” such as non-equalization of the dollar spent on premiums and out of pocket, and a novel form of “approximate inertia” where enrolees are excessively likely to switch to other plans that are close to the current plan on the plan design spreadsheet. Variation in the number of plan choices across districts and over time shows that enrolees make lower-cost choices when the choice set is smaller. We show that a curated restriction of choice set size improves choices more than the best available information intervention, partly because approximate inertia lowers gains from new information. We explicitly test and reject the assumption that this is because individuals choose worse from larger choice sets, or “choice overload”. Rather, we show that this feature arises from the fact that larger choice sets feature worse choices on average that are not offset by individual re-optimization.

Two years after COVID-19 vaccine rollouts began, COVID-19 vaccination rates in low- and middle-income countries (LMICs) continue to lag. Tracing individual country experiences with vaccine procurement in the early stages of the pandemic suggests that international supply chain failures caused initial delays. High vaccine hesitancy in the population and last-mile delivery challenges within LMICs were other possible limiting factors. This paper summarizes descriptive and experimental research on vaccine demand and supply in LMICs to evaluate these competing claims. The weight of the evidence suggests that external supply restrictions and internal distribution challenges (rather than vaccine hesitancy) appear to be paramount.

Effective teachers are a vital input for schools and students. Teachers can have important and long-lasting impacts on students’ learning, college attendance, and eventual earnings. They can also reduce teen pregnancy or incarceration. Attracting effective teachers into public schools and retaining them is thus a first-order policy goal. Changes in teacher compensation, for example across-the-board raises in salaries or pay plans that directly tie salaries to performance, are often proposed as ways to achieve this goal. The debate on these reforms, though, is very much open; some opponents argue that these changes would be ineffective because teachers are not motivated by money.

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.