Public Radio in the United States: Does It Correct Market Failure or Cannibalize Commercial Stations?

Radio signals are pure public goods whose total value to society is the sum of their value to advertisers and listeners. Because broadcasters can capture only part of the value of their product as revenue, there is the potential for a classic problem of underprovision. Small markets have much less commercial program variety than larger markets, suggesting a possible underprovision problem. Public funding of radio broadcasting targets programming in three formats - news, classical music, and jazz - with at least some commercial competition. Whether public support corrects a market failure depends on whether the market would have provided similar services in the absence of public broadcasting. To examine this we ask whether public and commercial classical stations compete for listening share and revenue. We then directly examine whether public stations crowd out commercial stations. We find evidence consistent with the view that public broadcasting crowds out commercial programming in large markets, particularly in classical music and to a lesser extent in jazz. Although the majority of government subsidies to radio broadcasting are allocated to stations without commercial competition in their format (thereby possibly correcting inefficient market underprovision), roughly a quarter of subsidies support direct competition with existing commercial stations.

Identification in Differentiated Products Markets Using Market Level Data

We consider nonparametric identification in models of differentiated products markets, using only market level observables. On the demand side we consider a nonparametric random utility model nesting random coefficients discrete choice models widely used in applied work. We allow for product/market-specific unobservables, endogenous product characteristics (e.g., prices), and high-dimensional taste shocks with arbitrary correlation and heteroskedasticity. On the supply side we specify marginal costs nonparametrically, allow for unobserved firm heterogeneity, and nest a variety of equilibrium oligopoly models. We pursue two approaches to identification. One relies on instrumental variables conditions used previously to demonstrate identification in a nonparametric regression framework. With this approach we can show identification of the demand side without reference to a particular supply model. Adding the supply side allows identification of firms' marginal costs as well. Our second approach, more closely linked to classical identification arguments for supply and demand models, employs a change of variables approach. This leads to constructive identification results relying on exclusion and support conditions. Our results lead to a testable restriction that provides the first general formalization of Bresnahan's (1982) intuition for empirically discriminating between alternative models of oligopoly competition.

Mergers, Station Entry, and Programming Variety in Radio Broadcasting

Free entry into markets with decreasing average costs and differentiated products can result in an inefficient number of firms and suboptimal product variety. Because new firms and products draw their customers in part from existing products, concentration can affect incentives to enter as well as how to position products. This paper examines how product variety in the radio industry is affected by changes in ownership structure. While it is in general difficult to measure the effect of concentration on other factors such as the number of products and the extent of product variety, the 1996 Telecommunications Act substantially relaxed local radio ownership restrictions, giving rise to a major and exogenous consolidation wave. Between 1993 and 1997 the average Herfindahl index in major US media markets increased by almost 65 percent. Using a panel data set on 243 U.S. radio broadcast markets in 1993 and 1997, we find that concentration reduces entry and increases product variety. Our results are consistent with spatial preemption. Jointly owned stations broadcasting from the same market are more likely than unrelated stations - and more likely than jointly owned stations in different markets - to broadcast in similar formats.

Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers

We consider identification of nonparametric random utility models of multinomial choice using "micro data," i.e., observation of the characteristics and choices of individual consumers. Our model of preferences nests random coefficients discrete choice models widely used in practice with parametric functional form and distributional assumptions. However, the model is nonparametric and distribution free. It allows choice-specific unobservables, endogenous choice characteristics, unknown heteroskedasticity, and high-dimensional correlated taste shocks. Under standard "large support" and instrumental variables assumptions, we show identifiability of the random utility model. We demonstrate robustness of these results to relaxation of the large support condition and show that when it is replaced with a weaker "common choice probability" condition, the demand structure is still identified. We show that key maintained hypotheses are testable.Nonparametric identification, Discrete choice demand, Differentiated products

Identification in a Class of Nonparametric Simultaneous Equations Models

We consider identification in a class of nonseparable nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine standard exclusion restrictions with a requirement that each structural error enter through a "residual index" function. We provide constructive proofs of identification under several sets of conditions, demonstrating tradeoffs between restrictions on the support of the instruments, restrictions on the joint distribution of the structural errors, and restrictions on the form of the residual index function.Simultaneous equations, Nonseparable models, Nonparametric identification

Identification of a Heterogeneous Generalized Regression Model with Group Effects

We consider identification in a "generalized regression model" (Han, 1987) for panel settings in which each observation can be associated with a "group" whose members are subject to a common unobserved shock. Common examples of groups include markets, schools or cities. The model is fully nonparametric and allows for the endogeneity of group-specific observables, which might include prices, policies, and/or treatments. The model features heterogeneous responses to observables and unobservables, and arbitrary heteroskedasticity. We provide sufficient conditions for full identification of the model, as well as weaker conditions sufficient for identification of the latent group effects and the distribution of outcomes conditional on covariates and the group effect.Nonparametric identification, Binary choice, Threshold crossing, Censored regression, Proportional hazard model

Nonparametric Identification of Differentiated Products Demand Using Micro Data

We examine identification of differentiated products demand when one has “micro data” linking the characteristics and choices of individual consumers. Our model nests standard specifications featuring rich observed and unobserved consumer heterogeneity as well as product/market-level unobservables that introduce the problem of econometric endogeneity. Previous work establishes identification of such models using marketlevel data and instruments for all prices and quantities. Micro data provides a panel structure that facilitates richer demand specifications and reduces requirements on both the number and types of instrumental variables. We address identification of demand in the standard case in which non-price product characteristics are assumed exogenous, but also cover identification of demand elasticities and other key features when these product characteristics are endogenous and not instrumented. We discuss implications of these results for applied work

On the Nonparametric Identification of Nonlinear Simultaneous Equations Models: Comment on B. Brown (1983) and Roehrig (1988)

This note revisits the identification theorems of B. Brown (1983) and Roehrig (1988). We describe an error in the proofs of the main identification theorems in these papers, and provide an important counterexample to the theorems on the identification of the reduced form. Specifically, contrary to the theorems, the reduced form of a nonseparable simultaneous equations model is not identified even under the assumptions of those papers. We conclude the note with a conjecture that it may be possible to use classical exclusion restrictions to recover some of the key implications of the theorems

Identification of Nonparametric Simultaneous Equations Models with a Residual Index Structure

We present new identification results for a class of nonseparable nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine traditional exclusion restrictions with a requirement that each structural error enter through a “residual index.” Our identification results are constructive and encompass a range of special cases with varying demands on the exogenous variation provided by instruments and the shape of the joint density of the structural errors. The most important of these results demonstrate identification even when instruments have limited variation. A genericity result demonstrates a formal sense in which the associated density conditions may be viewed as mild, even when instruments vary only over a small open ball