135 research outputs found

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

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    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 n1n-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

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    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

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    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.

    Measuring the Relative Performance of Providers of a Health Service

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    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.

    Identification Properties of Recent Production Function Estimators

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    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

    How Costly Is Hospital Quality? A Revealed-Preference Approach

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    Abstract We analyze the cost of quality improvement in hospitals, dealing with two challenges. Hospital quality is multidimensional and hard to measure, while unobserved productivity may in ‡uence quality supply. We infer the quality of hospitals in Los Angeles from patient choices. We then incorporate 'revealed quality' into a cost function, instrumenting with hospital demand. We …nd that revealed quality di¤erentiates hospitals, but is not strongly correlated with clinical quality. Revealed quality is quite costly, and tends to increase with hospital productivity. Thus, non-clinical aspects of the hospital experience (perhaps including patient amenities) play important roles in hospital demand, competition, and costs. We wish to than

    Advertising Bans and the Substitutability of Online and Offline Advertising

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    The authors examine whether the growth of the Internet has reduced the effectiveness of government regulation of advertising. They combine nonexperimental variation in local regulation of offline alcohol advertising with data from field tests that randomized exposure to online advertising for 275 different online advertising campaigns to 61,580 people. The results show that people are 8% less likely to say that they will purchase an alcoholic beverage in states that have alcohol advertising bans compared with states that do not. For consumers exposed to online advertising, this gap narrows to 3%. There are similar effects for four changes in local offline alcohol advertising restrictions when advertising effectiveness is observed both before and after the change. The effect of online advertising is disproportionately high for new products and for products with low awareness in places that have bans. This suggests that online advertising could reduce the effectiveness of attempts to regulate offline advertising channels because online advertising substitutes for (rather than complements) offline advertising.Google (Firm)WPP (Firm