Unobservable Product Differentiation in Discrete Choice Models: Estimating Price Elasticities and Welfare Effects

Discrete choice models used in statistical applications typically interpret an unobservable term as the interaction of unobservable horizontal differentiation and idiosyncratic consumer preferences. An implicit assumption in most such models is that all choices are equally horizontally differentiated from each other. This assumption is problematic in a number of recent studies that use discrete choice frameworks to evaluate the welfare effects from different numbers of goods (e.g. Berry and Waldfogel, 1999; Rysman, 2000). Researchers might think that it is possible for product space to "fill up" and that ignoring this issue might lead to an overestimate of welfare as the number of new products increases. This paper proposes a solution whereby the researcher estimates the decrease in value that agents receive from higher numbers of products as a result of the decreasing importance of horizontal differentiation. The paper reviews previous results on how a linear random utility model (LRUM) can be mapped into an address (Hotelling) model. The paper shows how realistic assumptions on differentiation in an address setting can be mapped into an LRUM. LRUM models imply that all choices are strong gross substitutes. In order to preserve that condition in an address model, n choices must be differentiated along at least $n-1$ dimensions. This paper proposes that utility drawn from different dimensions be weighted differently. Mapping this feature into an LRUM requires weighting the utility from each choice based upon the dimension along which it is differentiated from others. As researchers will typically be unwilling to make assumptions about which dimension products differ on, the paper discusses integrating over the different possibilities in a computationally inexpensive way that still allows the researcher to relax the assumption of symmetric differentiation.

Unobserved Product Differentiation in Discrete Choice Models: Estimating Price Elasticities and Welfare Effects

Standard discrete choice models such as logit, nested logit, and random coefficients 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 restrictions can identify parameters which are not intuitively identified 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.

Improved Jive Estimators for Overidentified Linear Models with and without Heteroskedasticity

We introduce two simple new variants of the Jackknife Instrumental Variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, signifi- cantly improving on its small sample bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator suggested in Donald and Newey (2001). These theoretical results are verified in a set of Monte-Carlo experiments and then applied to estimating the returns to schooling using actual data.

Asymptotic Variance Estimator for Two-Step Semiparametric Estimators

The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric variances using standard formulas derived in the already-well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations "as if" it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.Two-step semiparametrics

Low-Income Demand for Local Telephone Service: Effects of Lifeline and Linkup

This study evaluates the effect of the “Lifeline” and “Linkup” subsidy programs on telephone penetration rates of low-income households. It is the first to estimate low-income telephone demand across demographic groups using location-specific Lifeline and Linkup prices. The demand specifications use a discrete choice model aggregated across demographic groups. GMM estimators correct for the possible endogeneity of subsidized prices. A simulation predicts low-income telephone penetration would be 4.1 percentage points lower without Lifeline and Linkup. Results suggest that Linkup is more cost-effective than Lifeline, and that automatic enrollment in the programs increases penetration.telephone subsidies, low-income telephone usuers

Measuring the Relative Performance of Providers of a Health Service

A methodology is developed and applied to compare the performance of publicly funded agencies providing treatment for alcohol abuse in Maine. The methodology estimates a Wiener process that determines the duration of completed treatments, while allowing for agency differences in the effectiveness of treatment, standards for completion of treatment, patient attrition, and the characteristics of patient populations. Notably, the Wiener process model separately identifies agency fixed effects that describe differences in the effectiveness of treatment ('treatment effects'), and effects that describe differences in the unobservable characteristics of patients ('population effects'). The estimated model enables hypothetical comparisons of how different agencies would treat the same populations. The policy experiment of transferring the treatment practices of more cost-effective agencies suggests that Maine could have significantly reduced treatment costs without compromising health outcomes by identifying and transferring best practices.

Asymptotic Exit Location Distributions in the Stochastic Exit Problem

Consider a two-dimensional continuous-time dynamical system, with an attracting fixed point $S$. If the deterministic dynamics are perturbed by white noise (random perturbations) of strength $\epsilon$, the system state will eventually leave the domain of attraction $\Omega$ of $S$. We analyse the case when, as $\epsilon\to0$, the exit location on the boundary $\partial\Omega$ is increasingly concentrated near a saddle point $H$ of the deterministic dynamics. We show that the asymptotic form of the exit location distribution on $\partial\Omega$ is generically non-Gaussian and asymmetric, and classify the possible limiting distributions. A key role is played by a parameter $\mu$, equal to the ratio $|\lambda_s(H)|/\lambda_u(H)$ of the stable and unstable eigenvalues of the linearized deterministic flow at $H$. If $\mu<1$ then the exit location distribution is generically asymptotic as $\epsilon\to0$ to a Weibull distribution with shape parameter $2/\mu$, on the $O(\epsilon^{\mu/2})$ length scale near $H$. If $\mu>1$ it is generically asymptotic to a distribution on the $O(\epsilon^{1/2})$ length scale, whose moments we compute. The asymmetry of the asymptotic exit location distribution is attributable to the generic presence of a `classically forbidden' region: a wedge-shaped subset of $\Omega$ with $H$ as vertex, which is reached from $S$, in the $\epsilon\to0$ limit, only via `bent' (non-smooth) fluctuational paths that first pass through the vicinity of $H$. We deduce from the presence of this forbidden region that the classical Eyring formula for the small-$\epsilon$ exponential asymptotics of the mean first exit time is generically inapplicable.Comment: This is a 72-page Postscript file, about 600K in length. Hardcopy requests to [email protected] or [email protected]

A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators

The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric variances using standard formulas derived in the already-well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations “as if” it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures

Identification Properties of Recent Production Function Estimators

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116342/1/ecta1558_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116342/2/ecta1558.pd

Measuring the relative performance of providers of a health service

A methodology is developed and applied to compare the performance of publicly funded agencies providing treatment for alcohol abuse in Maine. The methodology estimates a Wiener process that determines the duration of completed treatments, while allowing for agency differences in the effectiveness of treatment, standards for completion of treatment, patient attrition, and the characteristics of patient populations. Notably, the Wiener process model separately identifies agency fixed effects that describe differences in the effectiveness of treatment ('treatment effects'), and effects that describe differences in the unobservable characteristics of patients ('population effects'). The estimated model enables hypothetical comparisons of how different agencies would treat the same populations. The policy experiment of transferring the treatment practices of more cost effective agencies suggests that Maine could have significantly reduced treatment costs without compromising health outcomes by identifying and transferring best practices