We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically endogenous market structure. We propose the use of Generalized Instrument Variables methods to identify those dynamic policy functions that are consistent with instrumental variable (IV) restrictions. Extending popular “two-step” methods, these policy functions then identify a set of structural parameters that are consistent with the dynamic model, the IV restrictions and the data. We provide computed illustrations to both single-agent and oligopoly examples. We also present a simple empirical analysis that, among other things, supports the counterfactual study of an environmental policy entailing an increase in sunk costs.
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 market-level 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.
Demand elasticities and other features of demand are critical determinants of the answers to most positive and normative questions about market power or the functioning of markets in practice. As a result, reliable demand estimation is an essential input to many types of research in Industrial Organization and other fields of economics. This chapter presents a discussion of some foundational issues in demand estimation. We focus on the distinctive challenges of demand estimation and strategies one can use to overcome them. We cover core models, alternative data settings, common estimation approaches, the role and choice of instruments, and nonparametric identification.
Job differentiation gives employers market power, allowing them to pay workers less than their marginal productivity. We estimate a differentiated jobs model using application data from Careerbuilder.com. We find direct evidence of substantial job differentiation. Without the use of instruments for wages, job applications appear very inelastic with respect to wages. Plausible instruments produce elastic firm level application supply curves. Under some assumptions, the implied market level labor supply elasticity is 0.5, while the firm level labor elasticity is 4.8. This suggests that workers may produce 21% more than their wage level, consistent with significant monopsony power.
In this paper we provide an algorithm for estimating characteristic based demand models from alternative data sources, and apply it to new data on the market for passenger vehicles. We find that, provided care is taken in constructing the demand system and rich enough data are available, the characteristic based model can both rationalize existing results and provide realistic out of sample predictions.