Applied Welfare Economics with Discrete Choice Models

Economists have been paying increasing attention to the study of situations in which consumers face a discrete rather than a continuous set of choices. Such models are potentially very important in evaluating the impact of government programs upon consumer welfare. But very little has been said in general regarding the tools of applied welfare economics indiscrete choice situations. This paper shows how the conventional methods of applied welfare economics can be modified to handle such cases. It focuses on the computation of the excess burden of taxation, and the evaluation of quality change. The results are applied to stochastic utility models, including the popular cases of probit and logit analysis. Throughout, the emphasis is on providing rigorous guidelines for carrying out applied work.

Estimation of causal effects using instrumental variables with nonignorable missing covariates: Application to effect of type of delivery NICU on premature infants

Understanding how effective high-level NICUs (neonatal intensive care units that have the capacity for sustained mechanical assisted ventilation and high volume) are compared to low-level NICUs is important and valuable for both individual mothers and for public policy decisions. The goal of this paper is to estimate the effect on mortality of premature babies being delivered in a high-level NICU vs. a low-level NICU through an observational study where there are unmeasured confounders as well as nonignorable missing covariates. We consider the use of excess travel time as an instrumental variable (IV) to control for unmeasured confounders. In order for an IV to be valid, we must condition on confounders of the IV---outcome relationship, for example, month prenatal care started must be conditioned on for excess travel time to be a valid IV. However, sometimes month prenatal care started is missing, and the missingness may be nonignorable because it is related to the not fully measured mother's/infant's risk of complications. We develop a method to estimate the causal effect of a treatment using an IV when there are nonignorable missing covariates as in our data, where we allow the missingness to depend on the fully observed outcome as well as the partially observed compliance class, which is a proxy for the unmeasured risk of complications. A simulation study shows that under our nonignorable missingness assumption, the commonly used estimation methods, complete-case analysis and multiple imputation by chained equations assuming missingness at random, provide biased estimates, while our method provides approximately unbiased estimates. We apply our method to the NICU study and find evidence that high-level NICUs significantly reduce deaths for babies of small gestational age, whereas for almost mature babies like 37 weeks, the level of NICUs makes little difference. A sensitivity analysis is conducted to assess the sensitivity of our conclusions to key assumptions about the missing covariates. The method we develop in this paper may be useful for many observational studies facing similar issues of unmeasured confounders and nonignorable missing data as ours.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS699 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modulation of Thermoelectric Power of Individual Carbon Nanotubes

Thermoelectric power (TEP) of individual single walled carbon nanotubes (SWNTs) has been measured at mesoscopic scales using a microfabricated heater and thermometers. Gate electric field dependent TEP-modulation has been observed. The measured TEP of SWNTs is well correlated to the electrical conductance across the SWNT according to the Mott formula. At low temperatures, strong modulations of TEP were observed in the single electron conduction limit. In addition, semiconducting SWNTs exhibit large values of TEP due to the Schottky barriers at SWNT-metal junctions.Comment: to be published in Phys. Rev. Let

Heart Strings and Purse Strings: Carryover Effects of Emotions on Economic Decisions

We examined the impact of specific emotions on the endowment effect, the tendency for selling prices to exceed buying or “choice” prices for the same object. As predicted by appraisal-tendency theory, disgust induced by a prior, irrelevant situation carried over to normatively unrelated economic decisions, reducing selling and choice prices and eliminating the endowment effect. Sadness also carried over, reducing selling prices but increasing choice prices—producing a “reverse endowment effect” in which choice prices exceeded selling prices. The results demonstrate that incidental emotions can influence decisions even when real money is at stake, and that emotions of the same valence can have opposing effects on such decisions

Time transfer between the Goddard Optical Research Facility and the U.S. Naval Observatory using 100 picosecond laser pulses

A horizontal two-way time comparison link in air between the University of Maryland laser ranging and time transfer equipment at the Goddard Optical Research Facility (GORF) 1.2 m telescope and the Time Services Division of the U.S. Naval Observatory (USNO) was established. Flat mirrors of 25 cm and 30 cm diameter respectively were placed on top of the Washington Cathedral and on a water tower at the Beltsville Agricultural Research Center. Two optical corner reflectors at the USNO reflect the laser pulses back to the GORF. Light pulses of 100 ps duration and an energy of several hundred microjoules are sent at the rate of 10 pulses per second. The detection at the USNO is by means of an RCA C30902E avalanche photodiode and the timing is accomplished by an HP 5370A computing counter and an HP 1000 computer with respect to a 10 pps pulse train from the Master Clock

Peculiar Broad Absorption Line Quasars found in DPOSS

With the recent release of large (i.e., > hundred million objects), well-calibrated photometric surveys, such as DPOSS, 2MASS, and SDSS, spectroscopic identification of important targets is no longer a simple issue. In order to enhance the returns from a spectroscopic survey, candidate sources are often preferentially selected to be of interest, such as brown dwarfs or high redshift quasars. This approach, while useful for targeted projects, risks missing new or unusual species. We have, as a result, taken the alternative path of spectroscopically identifying interesting sources with the sole criterion being that they are in low density areas of the g - r and r - i color-space defined by the DPOSS survey. In this paper, we present three peculiar broad absorption line quasars that were discovered during this spectroscopic survey, demonstrating the efficacy of this approach. PSS J0052+2405 is an Iron LoBAL quasar at a redshift z = 2.4512 with very broad absorption from many species. PSS J0141+3334 is a reddened LoBAL quasar at z = 3.005 with no obvious emission lines. PSS J1537+1227 is a Iron LoBAL at a redshift of z = 1.212 with strong narrow Mgii and Feii emission. Follow-up high resolution spectroscopy of these three quasars promises to improve our understanding of BAL quasars. The sensitivity of particular parameter spaces, in this case a two-color space, to the redshift of these three sources is dramatic, raising questions about traditional techniques of defining quasar populations for statistical analysis.Comment: 27 pages, 13 figures, Accepted to the Astronomical Journa

Perils and Prospects of Using Aggregate Area Level Socioeconomic Information as a Proxy for Individual Level Socioeconomic Confounders in Instrumental Variables Regression

A frequent concern in making statistical inference for causal effects of a policy or treatment based on observational studies is that there are unmeasured confounding variables. The instrumental variable method is an approach to estimating a causal relationship in the presence of unmeasured confounding variables. A valid instrumental variable needs to be independent of the unmeasured confounding variables. It is important to control for the confounding variable if it is correlated with the instrument. In health services research, socioeconomic status variables are often considered as confounding variables. In recent studies, distance to a specialty care center has been used as an instrument for the effect of specialty care vs. general care. Because the instrument may be correlated with socioeconomic status variables, it is important that socioeconomic status variables are controlled for in the instrumental variables regression. However, health data sets often lack individual socioeconomic information but contain area average socioeconomic information from the US Census, e.g., average income or education level in a county. We study the effects on the bias of the two stage least squares estimates in instrumental variables regression when using an area-level variable as a controlled confounding variable that may be correlated with the instrument. We propose the aggregated instrumental variables regression using the concept of Wald’s method of grouping, provided the assumption that the grouping is independent of the errors. We present simulation results and an application to a study of perinatal care for premature infants