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.

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.

We study causal inference with high‐dimensional covariates, observed with various types of heteroscedastic noise. Our main assumption is that the true covariates are low rank, which we empirically evaluate with plots and interpret as approximate repeated measurements. This assumption delivers semiparametric inference on the causal parameter, as precisely as if the true covariates were available. A methodological contribution is to introduce an error‐in‐variable balancing weight for estimation and inference on cross‐sectional causal parameters with heterogeneous effects.

How costly is inflation to workers? Answers to this question have focused on the path of real wages during inflationary periods. We argue that workers must take costly actions (“conflict”) to have nominal wages catch up with inflation, meaning there are welfare costs even if real wages do not fall as inflation rises. We study a menu‐cost style model, where workers choose whether to engage in conflict with employers to secure a wage increase. We show that, following a rise in inflation, wage catch‐up resulting from more frequent conflict does not raise welfare. Instead, the impact of inflation on worker welfare is determined by what we call “wage erosion”—how inflation would lower real wages if workers' conflict decisions did not respond to inflation. As a result, using observed wage growth to measure worker welfare understates the costs of inflation. We conduct a survey showing that workers are willing to sacrifice around 1.75% of their wages to avoid conflict. Calibrating the model to survey data, we find that incorporating conflict significantly raises the costs of inflation for workers.

We propose a framework for making Bayesian parametric models robust to local misspecification. Suppose in a baseline parametric model, a parameter of interest has an interpretation in an encompassing semiparametric model. Bayesian and maximum likelihood estimators are generally biased under local misspecification. We propose to augment the baseline likelihood by a multiplicative factor that involves scores for the baseline model, the efficient scores for the encompassing semiparametric model, and an auxiliary parameter that has the same dimension as the parameter of interest. We show that the marginal posterior for the parameter of interest in the augmented model is asymptotically normal with mean equal to the semiparametrically efficient estimator and variance equal to the semiparametric efficiency bound. The suggested augmentation robustifies the baseline parametric model to local misspecification, while preserving the appeal of Bayesian inference. We develop an MCMC algorithm for the augmented model and illustrate the approach in applications.

Random Priority is a popular mechanism used to allocate a set of objects to a set of agents without the use of monetary transfers. Random Priority is appealing because it satisfies desirable efficiency, fairness, and incentive properties. Is it the only mechanism with these properties? We answer this long‐standing question in the positive: Random Priority is the unique mechanism that is Pareto efficient, symmetric, and obviously strategy‐proof.