This paper develops a framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous residential choice model, addressing the endogeneity of school and neighborhood characteristics. The model is estimated using restricted-access Census data from a large metropolitan area, yielding a number

This paper examines some of the recent literature on the empirical identification of production functions. We focus on structural techniques suggested in two recent papers, Olley and Pakes (1996), and Levinsohn and Petrin (2003). While there are some solid and intuitive indentification ideas in these papers, we argue that the techniques, particularly those of Levinsohn and Petrin, suffer from collinearity problems which we believe cast doubt on the methodology. We then suggest alternative methodologies which make use of the ideas in these papers, but do not suffer from these collinearity problems. 1

Conventional hedonic techniques for estimating the value of local amenities rely on the assumption that households move freely among locations. We show that when moving is costly, the variation in housing prices and wages across locations may no longer reflect the value of differences in local amenities. We develop an alternative discrete-choice approach that models the household location decision directly, and we apply it to the case of air quality in U.S. metro areas in 1990 and 2000. Because air pollution is likely to be correlated with unobservable local characteristics such as economic activity, we instrument for air quality using the contribution of distant sources to local pollution – excluding emissions from local sources, which are most likely to be correlated with local conditions. Our model yields an estimated elasticity of willingness to pay with respect to air quality of 0.34 to 0.42. These estimates imply that the median household would pay $149 to $185 (in constant 1982-1984 dollars) for a one-unit reduction in average ambient concentrations of particulate matter. These estimates are three times greater than the marginal willingness to pay estimated by a conventional hedonic model using the same data. Our results are robust to a range of covariates, instrumenting strategies, and functional form assumptions. The findings also confirm the importance of instrumenting for local air pollution.

This paper introduces a new equilibrium framework for analyzing residential sorting, designed to take advantage of newly available restricted-access Census microdata. The framework adds an equilibrium concept to the discrete choice framework developed by McFadden (1973, 1978), permitting a more flexible characterization of preferences than has been possible in previously estimated sorting models. Using data on nearly a quarter of a million households residing in the San Francisco Bay Area in 1990, our estimates provide a precise characterization of preferences for many housing and neighborhood attributes, showing how demand for these attributes varies with a household’s income, race, education, and family structure. We use the equilibrium model in combination with these estimates to explore the effects of an increase in income inequality, the findings indicating that much of the increased spending power of the rich is absorbed by higher housing prices.

The purpose of this article is to report a new approach for measuring the gen-eral equilibrium willingness to pay for large changes in spatially delineated public goods such as air quality. We estimate the parameters of a locational equilibrium model and compute equilibria for alternative scenarios characterizing the avail-ability of public goods within a system of communities. Welfare measures take into consideration the adjustments of households in equilibrium to nonmarginal changes in public goods. The framework is used to analyze willingness to pay for reductions in ozone concentrations in Southern California between 1990 and 1995. 1.

This chapter explains the logic of structural econometric models and compares them to other types of econometric models. We provide a framework researchers can use to develop and evaluate structural econometric models. This framework pays particular attention to describing different sources of unobservables in structural models. We use our framework to evaluate several literatures in industrial organization economics, including the literatures dealing with market power, product differentiation, auctions, regulation and entry.

This paper specifies and estimates a dynamic model of consumer preferences for new durable goods with persistent heterogeneous consumer tastes, rational expectations about future products and repeat purchases over time. Most new consumer durable goods, particularly consumer electronics, are characterized by relatively high initial prices followed by rapid declines in prices and improvements in quality. The evolving nature of product attributes suggests the importance of modeling dynamics in estimating consumer preferences. We estimate the model on the digital camcorder industry using a panel data set on prices, sales and characteristics. We find that dynamics are a very important determinant of consumer preferences and that estimated coefficients are more plausible than with traditional static models. We use the estimates to investigate the value of new consumer goods and intertemporal elasticities of demand.

Standard discrete choice models such as logit, nested logit, and random coef¿cients models place very strong restrictions on how unobservable product space increases with the number of products. We argue (and show with Monte Carlo experiments) that these restrictions can lead to biased conclusions regarding price elasticities and welfare consequences from additional products. In addition, these restric-tions can identify parameters which are not intuitively identi¿ed given the data at hand. We suggest two alternative models that relax these restrictions, both motivated by structural interpretations. Monte-Carlo experiments and an application to data show that these alternative models perform well in practice. 1 1

Abstract In this paper, we present an equilibrium model of the market for higher education. Our model simultaneously predicts student selection into institutions of higher education, financial aid, educational expenditures, and educational outcomes. We show that the model gives rise to a strict hierarchy of colleges that differ by the educational quality provided to the students. We develop an algorithm to compute equilibria for these types of models. We also develop a new estimation procedure that exploits the observed variation in prices within colleges. Identification is based on variation in differences in fundamentals such as endowments and technology, and not on the observed variation in potentially endogenous characteristics of colleges. We estimate the structural parameters using data collected by the National Center for Education Statistics and aggregate data from Peterson's and the National Science Foundation. Our empirical findings suggest that our model explains observed admission and tuition policies reasonably well. The findings also suggest that the market for higher education is very competitive.

We present methods to estimate marginal utility and marginal product functions that are nonadditive in the unobservable random terms, using observations from a single hedonic equilibrium market. We show that nonadditive marginal utility and nonadditive marginal product functions are capable of generating equilibria that exhibit bunching, as well as other types of equilibria. We provide conditions under which these types of utility and production functions are nonparametrically identified, and we propose nonparametric estimators for them. The estimators are shown to be consistent and asymptotically normal.