Monaural measurements of minimum audible angle ͑MAA͒ ͑discrimination between two locations͒ and absolute identification ͑AI͒ of azimuthal locations in the frontal horizontal plane are reported. All experiments used roving-level fixed-spectral-shape stimuli processed with nonindividualized head-related transfer functions ͑HRTFs͒ to simulate the source locations. Listeners were instructed to maximize percent correct, and correct-answer feedback was provided after every trial. Measurements are reported for normal-hearing subjects, who listened with only one ear, and effectively monaural subjects, who had substantial unilateral hearing impairments ͑i.e., hearing losses greater than 60 dB͒ and listened with their normal ears. Both populations behaved similarly; the monaural experience of the unilaterally impaired listeners was not beneficial for these monaural localization tasks. Performance in the AI experiments was similar with both 7 and 13 source locations. The average root-mean-squared deviation between the virtual source location and the reported location was 35°, the average slopes of the best fitting line was 0.82, and the average bias was 2°. The best monaural MAAs were less than 5°. The MAAs were consistent with a theoretical analysis of the HRTFs, which suggests that monaural azimuthal discrimination is related to spectral-shape discrimination.
In a government auction program where first‐price auctions generate significantly higher revenue than English auctions, I document evidence that bidders are uncertain about the number of auction entrants. Motivated by additional data evidence, I estimate a structural model of auctions in which rivals' participation is stochastic, allowing for bidders' risk aversion and asymmetry. Counterfactual simulations reveal that bidders' uncertainty about the number of entrants, combined with risk aversion, substantially softens the revenue impact of low competition in first‐price auctions. This explains the observed revenue patterns and uncovers an empirically important reason for sellers to favor first‐price auctions over English auctions.
In this paper, we conduct a structural analysis of multi-attribute auctions of contracts with a general allocation rule when private information is multidimensional. Upon modeling bidders’ contract value that accounts for their endogenous ex post actions, we nonparametrically identify bidders’ private information from their bids and estimate their joint distribution. Analyzing cash-royalty auctions of Louisiana oil leases, we find government revenue worse and development rates no better than in a cash auction with a fixed royalty in view of adverse selection and moral hazard. Our findings revise conventional wisdom on the optimality of multi-attribute auctions. (JEL D44, D82, D86, H82, Q35)
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