Knowledge that Transforms

We survey 153 empirical studies of family firms and document 192 operational definitions representing 25 distinct definition types. These definitions can be decomposed into five recurring, codable dimensions: ownership, management, board representation, embeddedness, and succession. We relate these dimensions to economic mechanisms including agency conflicts, residual control rights, transaction costs, and dynastic control. Alternative definitions select systematically different populations and yield materially different estimates of firm performance and innovation. Definition choice is largely unrelated to the research question, except in succession studies. Ownership thresholds track private enforcement of self-dealing rules, but not statutory shareholder rights or rule of law. We conclude that the family firm is a family of related constructs rather than a single latent construct. Definitions should therefore match the family-firm construct implied by the research question, and results should be reported across alternative classification rules.

We present a flexible and scalable computational framework integrating machine learning and optimization theory to solve dynamic adverse selection models with persistent private information and many types. Our approach reformulates the model into a numerically tractable structure that bypasses set-valued dynamic programming; we formally prove that, under verifiable conditions, this relaxation yields the solution to the original problem. The recast problem is solved via a parallelized value function iteration algorithm, where high-dimensional, nonlinear functions are approximated using Gaussian process regression combined with Bayesian active learning. We apply our framework to two previously intractable models: one with persistent hidden information involving up to ten types and another incorporating multiple persistent types and overreporting. Validation against known solutions and rigorous credibility measures confirms accuracy. Allowing overreporting significantly alters long-run contract outcomes, concentrating consumption away from extremes and smoothing utility promises over time.

Market definition is challenging in settings with network effects, where substitution patterns depend on changes in network size. We study these effects in the context of social media. We conduct an incentivized experiment comparing substitution in response to a proposed U.S. TikTok ban, in which all users simultaneously leave the app, with substitution when only a single user deactivates. We find substantially higher valuations of alternative social apps under a collective TikTok ban than under an individual TikTok deactivation. Mechanism evidence shows that both anticipated content-supply shifts and social coordination partly explain the wedge, with the relative importance of each channel varying across platforms. We then show that a collective time limit challenge, where peers jointly reduce TikTok and Instagram use, leads to more time spent on alternative social apps than has been observed in prior individual deactivation experiments. Together, our results suggest that individual-level substitution estimates can be an unreliable guide to market definition for network goods.

Recent advances have substantially improved our ability to predict disease risk with genetic data. In response, many countries have banned insurers from using genetic data, despite concerns about adverse selection. This paper measures adverse selection in a market where insurers can underwrite only on nongenetic information while consumers also have access to genetic information. We do so by combining methods from quantitative genetics, selection measures from economic theory, and genetic and electronic health record data on nearly 500,000 individuals in the UK Biobank. We focus on the critical illness insurance market and consider scenarios in which consumers have access to current or expected future genetic prediction technology. We find noticeable levels of selection with current prediction technology, and potentially crippling selection with expected future technology.