Nikhil Agarwal

Toxic exposure in America: estimating fetal and infant health outcomes from 14 years of TRI reporting.

We examine the effect of exposure to a set of toxic pollutants that are tracked by the Toxic Release Inventory (TRI) from manufacturing facilities on county-level infant and fetal mortality rates in the United States between 1989 and 2002. Unlike previous studies, we control for toxic pollution from both mobile sources and non-TRI reporting facilities. We find significant adverse effects of toxic air pollution concentrations on infant mortality rates. Within toxic air pollutants we find that releases of carcinogens are particularly problematic for infant health outcomes. We estimate that the average county-level decreases in various categories of TRI concentrations saved in excess of 13,800 infant lives from 1989 to 2002. Using the low end of the range for the value of a statistical life that is typically used by the EPA of

Identification and Estimation in Two-Sided Matching Markets

1.8M, the savings in lives would be valued at approximately

Toxic Exposure in America: Estimating Fetal and Infant Health Outcomes

25B.

Latent indices in assortative matching models

We study estimation and non-parametric identification of preferences in two-sided matching markets using data from a single market with many agents. We consider a model in which preferences of each side of the market are vertical, utility is non-transferable and the observed matches are pairwise stable. We show that preferences are not identified with data on one-to-one matches but are non-parametrically identified when data from many-to-one matches are observed. The additional empirical content in many-to-one matches is illustrated by comparing two simulated objective functions, one that does and the other that does not use information available in many-to-one matching. We also prove consistency of a method of moments estimator for a parametric model under a data generating process in which the size of the matching market increases, but data only on one market is observed. Since matches in a single market are interdependent, our proof of consistency cannot rely on observations of independent matches. Finally, we present Monte Carlo studies of a simulation based estimator.

An Empirical Model of the Medical Match

We examine the effect of toxic exposure on U.S. infant and fetal mortality rates between 1989 and 2002 from toxic pollution released by facilities reporting to the Toxic Release Inventory (TRI). Unlike previous studies, we control for toxic pollution from mobile sources and from non-TRI reporting facilities. We find significant adverse effects of TRI exposure on infant mortality. There is evidence that health effects vary across media: air and water having a larger impact than land pollution. And, within air, we find that releases of carcinogens are particularly problematic for infant health outcomes. We estimate that the average county-level decreases in TRI concentrations between 1988 and 2002 saved in excess of 13,800 infant lives.

Skewed Bidding in Pay-per-Action Auctions for Online Advertising

A large class of two‐sided matching models that include both transferable and non‐transferable utility result in positive assortative matching along a latent index. Data from matching markets, however, may not exhibit perfect assortativity due to the presence of unobserved characteristics. This paper studies the identification and estimation of such models. We show that the distribution of the latent index is not identified when data from one‐to‐one matches are observed. Remarkably, the model is nonparametrically identified using data in a single large market when each agent on one side has at least two matched partners. The additional empirical content in many‐to‐one matches is demonstrated using simulations and stylized examples. We then derive asymptotic properties of a minimum distance estimator as the size of the market increases, allowing estimation using dependent data from a single large matching market. The nature of the dependence requires modification of existing empirical process techniques to obtain a limit theorem.