From 1940 to 1990, a 10% increase in a metropolitan area's concentration of college-educated residents was associated with a 0.8% increase in subsequent employment growth. Instrumental variables estimates support a causal relationship between college graduates and employment growth, but show no evidence of an effect of high school graduates. Using data on growth in wages, rents, and house values, I calibrate a neoclassical city growth model and find that roughly 60% of the employment growth effect of college graduates is due to enhanced productivity growth, the rest being caused by growth in the quality of life. This finding contrasts with the common argument that human capital generates employment growth in urban areas solely through changes in productivity.

We cast the problem of communicating scientific uncertainty as one of reporting a posterior distribution on an unknown parameter to an audience of Bayesian decision‐makers. We establish novel bounds on the audience's regret when the analyst reports an approximation to a posterior that the audience treats as exact. Under a palatable restriction on the audience's decision problems, the bounds take an especially convenient form. Under a further restriction on the audience's priors, a bootstrap distribution can be used as a stand‐in posterior. We propose a practical recipe for checking whether a conventional statistical report (say, a normal parameterized by a point estimate and standard error) is a good approximation, and for improving the report if it is not. We illustrate our proposals using the articles in the 2021 American Economic Review that use a bootstrap for inference.

We construct a new index of media slant that measures the similarity of a news outlet's language to that of a congressional Republican or Democrat. We estimate a model of newspaper demand that incorporates slant explicitly, estimate the slant that would be chosen if newspapers independently maximized their own profits, and compare these profit-maximizing points with firms' actual choices. We find that readers have an economically significant preference for like-minded news. Firms respond strongly to consumer preferences, which account for roughly 20 percent of the variation in measured slant in our sample. By contrast, the identity of a newspaper's owner explains far less of the variation in slant.

We use heterogeneity in the timing of television's introduction to different local markets to identify the effect of preschool television exposure on standardized test scores during adolescence. Our preferred point estimate indicates that an additional year of preschool television exposure raises average adolescent test scores by about 0.02 standard deviations. We are able to reject negative effects larger than about 0.03 standard deviations per year of television exposure. For reading and general knowledge scores, the positive effects we find are marginally statistically significant, and these effects are largest for children from households where English is not the primary language, for children whose mothers have less than a high school education, and for nonwhite children.

A Bayesian consumer who is uncertain about the quality of an information source will infer that the source is of higher quality when its reports conform to the consumer's prior expectations. We use this fact to build a model of media bias in which firms slant their reports toward the prior beliefs of their customers in order to build a reputation for quality. Bias emerges in our model even though it can make all market participants worse off. The model predicts that bias will be less severe when consumers receive independent evidence on the true state of the world and that competition between independently owned news outlets can reduce bias. We present a variety of empirical evidence consistent with these predictions.

We propose a local measure of the relationship between parameter estimates and the moments of the data they depend on. Our measure can be computed at negligible cost even for complex structural models. We argue that reporting this measure can increase the transparency of structural estimates, making it easier for readers to predict the way violations of identifying assumptions would affect the results. When the key assumptions are orthogonality between error terms and excluded instruments, we show that our measure provides a natural extension of the omitted variables bias formula for nonlinear models. We illustrate with applications to published articles in several fields of economics.

We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We show that standard approaches suffer from a severe finite‐sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson's party from a single utterance. Our estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s after remaining low and relatively constant over the preceding century.

The controversy over how much to charge for health products in the developing world rests, in part, on whether higher prices can increase use, either by targeting distribution to high-use households (a screening effect), or by stimulating use psychologically through a sunk-cost effect. We develop a methodology for separating these two effects. We implement the methodology in a field experiment in Zambia using door-to-door marketing of a home water purification solution. We find evidence of economically important screening effects. By contrast, we find no consistent evidence of sunk-cost effects.

A large literature in cognitive science studies the puzzling “Flynn effect” of rising fluid intelligence (reasoning skill) in rich countries. We develop an economic model in which a cohort’s mix of skills is determined by different skills’ relative returns in the labor market and by the technology for producing skills. We estimate the model using administrative data from Sweden. Combining data from exams taken at military enlistment with earnings records from the tax register, we document an increase in the relative labor market return to logical reasoning skill as compared to vocabulary knowledge. The estimated model implies that changes in labor market returns explain 37% of the measured increase in reasoning skill, and can also explain the decline in knowledge. An original survey of parents, an analysis of trends in school curricula, and an analysis of occupational characteristics show evidence of increasing emphasis on reasoning as compared to knowledge.