This paper examines the properties of instrumental variables (IV) applied to models with essential heterogeneity, that is, models where responses to interventions are heterogeneous and agents adopt treatments (participate in programs) with at least partial knowledge of their idiosyncratic response. We analyze two-outcome and multiple-outcome models including ordered and unordered choice models. We allow for transition-specific and general instruments. We generalize previous analyses by developing weights for treatment effects for general instruments. We develop a simple test for the presence of essential heterogeneity. We note the asymmetry of the model of essential heterogeneity: outcomes of choices are heterogeneous in a general way; choices are not. When both choices and outcomes are permitted to be symmetrically heterogeneous, the method of IV breaks down for estimating treatment parameters.

Understanding Instrumental Variables in Models with Essential Heterogeneity

This paper examines the properties of instrumental variables (IV) applied to models with essential heterogeneity, that is, models where responses to interventions are heterogeneous and agents adopt treatments (participate in programs) with at least partial knowledge of their idiosyncratic response. We analyze two-outcome and multiple-outcome models, including ordered and unordered choice models. We allow for transition-specific and general instruments. We generalize previous analyses by developing weights for treatment effects for general instruments. We develop a simple test for the presence of essential heterogeneity. We note the asymmetry of the model of essential heterogeneity: outcomes of choices are heterogeneous in a general way; choices are not. When both choices and outcomes are permitted to be symmetrically heterogeneous, the method of IV breaks down for estimating treatment parameters.

/pdf/understanding-instrumental-variables-in-models-with-39xxq7fgm0.pdf

This paper studies alternative empirical strategies for estimating the effects of organization design practices on performance, as well as the factors which determine organizational design, in a cross-section of firms. Our economic model is based on a firm where multiple organizational design practices are en endogenously determined, and these organizational design practices affect output through an 'organizational design production function.' The econometric model includes unobserved exogenous variation in the costs and returns to each of the individual practices. The model is used to evaluate how different econometric strategies for testing theories about complementarity can be interpreted under alternative assumptions about the economic and statistical environment. We identify plausible hypotheses about the joint distribution of the unobservables under which several different approaches from the existing literature will yield biased and inconsistent estimates. We show that the sign of the bias depends on two factors: whether the organzational design practices are complements, and the correlation between the unobserved returns to each practice. We find several sets of conditions under which the sign of the bias can be determined, and we provide economic interpretations. Our analysis shows that for a particular set of hypotheses, a variety of different procedures may all yield qualitatively similar biases, presenting a challenge for the identification of complementarity. We then propose a structural approach, which is based on a system of simultaneous equations describing productivity and the demand for organizational design practices. As long as exogenous variables are observed which are uncorrelated with the unobserved returns to practices, the structural parameters are identified, yielding consistent tests for complementarity as well as the cross-equation restrictions implied by static optimization of the organizatin's profit function.

/pdf/an-empirical-framework-for-testing-theories-about-3xpc5et1m4.pdf

An Empirical Framework for Testing Theories About Complimentarity in Organizational Design

A semiparametric model for observational data combines a parametric form for some component of the data generating process (usually the behavioral relation between the dependent and explanatory variables) with weak nonparametric restrictions on the remainder of the model (usually the distribution of the unobservable errors). This chapter surveys some of the recent literature on semiparametric methods, emphasizing microeconometric applications using limited dependent variable models. An introductory section defines semiparametric models more precisely and reviews the techniques used to derive the large-sample properties of the corresponding estimation methods. The next section describes a number of weak restrictions on error distributions — conditional mean, conditional quantile, conditional symmetry, independence, and index restrictions — and show how they can be used to derive identifying restrictions on the distributions of observables. This general discussion is followed by a survey of a number of specific estimators proposed for particular econometric models, and the chapter concludes with a brief account of applications of these methods in practice.

Chapter 41 Estimation of semiparametric models

https://www.nber.org/system/files/working_papers/w9881/w9881.pdf

The Effect of Schooling and Ability on Achievement Test Scores

Identification of Semiparametric Discrete Choice Models

We discuss competition among payment card networks, and in particular how antitrust practitioners might approach questions of market definition and market power in these markets. Application of the hypothetical monopolist test to define markets, and the use of traditional metrics to measure market power, may be less straightforward for card networks than for many markets. The "two-sidedness" of the market does not, however, overturn the basic logic of the hypothetical monopolist test or traditional measurements of market power. We demonstrate some practical ways to apply these antitrust principles to competition among payment card networks.

/pdf/market-definition-and-market-power-in-payment-card-networks-2my0mexyv9.pdf

T. Scott Thompson

Papers

Identification of Semiparametric Discrete Choice Models

Market Definition and Market Power in Payment Card Networks