Different Contexts, Different Risk Preferences?

We examine the stability of risk preferences across contexts involving different stakes. Using data on households\u27 deductible choices in three property insurance coverages and their limit choices in two liability insurance coverages, we assess the stability across the five contexts in the ordinal ranking of the households\u27 willingness to bear risk. We find evidence of stability across contexts involving stakes of the same magnitude, but not across contexts involving stakes of very different magnitudes. Our results appear to be robust to heterogeneity in wealth and access to credit, complicating seemingly ready explanations

Design, Synthesis, and Screening of Non-Estrogenic Bisphenol A mimics

Undergraduate Basi

A Galaxy Photometric Redshift Catalog for the Sloan Digital Sky Survey Data Release 6

We present and describe a catalog of galaxy photometric redshifts (photo-z's) for the Sloan Digital Sky Survey (SDSS) Data Release 6 (DR6). We use the Artificial Neural Network (ANN) technique to calculate photo-z's and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for ~ 77 million objects classified as galaxies in DR6 with r < 22. The photo-z and photo-z error estimators are trained and validated on a sample of ~ 640,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by SDSS, 2SLAQ, CFRS, CNOC2, TKRS, DEEP, and DEEP2. For the two best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than sigma_{68} =0.021 or $0.024. After presenting our results and quality tests, we provide a short guide for users accessing the public data.Comment: 16 pages, 12 figure