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.

I use variation across markets in the timing of television's introduction to identify its impact on voter turnout. The estimated effect is significantly negative, accounting for between a quarter and a half of the total decline in turnout since the 1950s. I argue that substitution away from other media with more political coverage provides a plausible mechanism linking television to voting. As evidence for this, I show that the entry of television in a market coincided with sharp drops in consumption of newspapers and radio, and in political knowledge as measured by election surveys. I also show that both the information and turnout effects were largest in off-year congressional elections, which receive extensive coverage in newspapers but little or no coverage on television.

Popular accounts suggest that advertising revenue per unit of consumer attention is lower online than offline, and has fallen in traditional media as the Internet has made advertising markets more competitive. I assess these claims theoretically and empirically, and compare the patterns we observe for the Internet to trends in advertising around the introduction of television and radio. The evidence suggests that the price of attention for similar consumers is actually higher online than offline, and that the growth of new media is not robustly associated with a declining price of attention.

Many important economic questions hinge on the extent to which new goods either crowd out or complement consumption of existing products. Recent methods for studying new goods rule out complementarity by assumption, so their applicability to these questions has been limited. I develop a new model that relaxes this restriction, and use it to study competition between print and online newspapers. Using new micro data from Washington, DC, I estimate the relationship between the print and online papers in demand, the welfare impact of the online paper's introduction, and the expected impact of charging positive online prices.

We show that most U.S. food, drugstore, and mass-merchandise chains charge nearly uniform prices across stores, despite wide variation in consumer demographics and competition. Demand estimates reveal substantial within-chain variation in price elasticities and suggest that the median chain sacrifices $16 million of annual profit relative to a benchmark of optimal prices. In contrast, differences in average prices between chains are broadly consistent with the optimal benchmark. We discuss a range of explanations for nearly uniform pricing, highlighting managerial inertia and brand image concerns as mechanisms frequently mentioned by industry participants. Relative to our optimal benchmark, uniform pricing may significantly increase the prices paid by poorer households relative to the rich, dampen the response of prices to local economic shocks, alter the analysis of mergers in antitrust, and shift the incidence of intranational trade costs.

We use individual and aggregate data to ask how the Internet is changing the ideological segregation of the American electorate. Focusing on online news consumption, offline news consumption, and face-to-face social interactions, we define ideological segregation in each domain using standard indices from the literature on racial segregation. We find that ideological segregation of online news consumption is low in absolute terms, higher than the segregation of most offline news consumption, and significantly lower than the segregation of face-to-face interactions with neighbors, co-workers, or family members. We find no evidence that the Internet is becoming more segregated over time.

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.

A researcher can use a tightly parameterized structural model to obtain internally consistent estimates of a wide range of economically interesting targets. We ask how reliable these estimates are when the researcher’s model may be misspecified. We focus on the case of multivariate, potentially nonlinear models where the causal variable of interest is endogenous. Reliable estimates require that the researcher’s model is flexible enough to describe the effects of the endogenous variable approximately correctly. Reliable estimates do not require that the researcher has correctly specified the role of the exogenous controls in the model. However, if the role of the controls is misspecified, reliable estimates require a property we call strong exclusion. Strong exclusion depends on having sufficiently many instruments that are unrelated to the controls. We discuss how practitioners can achieve strong exclusion, and illustrate our findings with an application to a differentiated goods model of demand for beer.

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.