Atomic Resonance and Scattering

Contains reports on nine research projects.U.S. Energy Research and Development Administration (Contract EG-77-S-02-4370)U. S. Air Force - Office of Scientific Research (Contract F44620-72-C-0057)Joint Services Electronics Program (Contract DAAB07-76-C-1400)National Science Foundation (Grant PHY75-15421-AO1)National Science Foundation (Grant PHY77-09155)National Science Foundation (Grant CHE76-81750)U. S. Air Force - Office of Scientific Research (Grant AFOSR-76-2972A

A novel method for estimating heritability using molecular markers

Heritability is usually estimated with individuals of known relatedness generated using a controlled breeding programme or through response to selection. In this paper, we use two single-locus VNTR DNA fingerprint markers in conjunction with a maximum likelihood method to infer relatedness among pairs of individuals in a captive population of Pacific chinook salmon (Oncorhynchus tshawytscha). Patterns of relatedness inferred from the two DNA fingerprint markers were used to estimate heritability for, and genetic correlations among, several economically and ecologically important traits (weight, length, flesh colour and precocious male maturation). Heritabilities ranged from 0.20 for weight, 0.38 for length, 0.67 for precocious male maturation (‘jacking’) to 0.76 for flesh colour, which are in good agreement with estimates for salmonids generated using classical quantitative genetic methods. This molecular marker-based method allows for the estimation of heritability in wild, long-lived species not easily manipulated for study using controlled breeding programmes

Female Labor Supply Differences by Sexual Orientation: A Semi-Parametric Decomposition Approach

Using 2000 U.S. Census data we illustrate the importance of accounting for household specialization in lesbian couples when examining the sexual orientation gap in female labor supply. Specifically, we find the labor supply gap is substantially larger between married women and partnered lesbian women who specialize in market production (primary earners) than between married women and partnered lesbian women who specialize in household production (secondary earners). Using a semi-parametric decomposition approach, we further show that the role of children in explaining the mean labor supply gap by sexual orientation is greatly understated if the household division of labor between household and market production is not taken into account. Finally, we illustrate that controlling for children significantly reduces differences between married women and secondary lesbian earners both in terms of the decision to remain attached to the labor market (the extensive margin), as well as in terms of annual hours of work conditional on working (the intensive margin). Further, the effect of controlling for children is not uniform across the distribution of conditional annual hours; instead it primarily reduces the percentage of secondary lesbian earners working extremely high annual hours

Productivity, Wages, and Marriage: The Case of Major League Baseball

The effect of marriage on productivity and, consequently, wages has been long debated in economics. A primary explanation for the impact of marriage on wages has been through its impact on productivity, however, there has been no direct evidence for this. In this paper, we aim to fill this gap by directly measuring the impact of marriage on productivity using a sample of professional baseball players from 1871 - 2007. Our results show that only lower ability men see an increase in productivity, though this result is sensitive to the empirical specification and weakly significant. In addition, despite the lack of any effect on productivity, high ability married players earn roughly 16 - 20 percent more than their single counterparts. We discuss possible reasons why employers may favor married men

College Quality and Wages in the United States

Abstract We estimate the effects of the quality of the college a student attends on their later earnings using data from a cohort of U.S. college students from the late 1970s and early 1980s. We rely on a linear selection on observables identification strategy, which is justified in our context by a very rich set of conditioning variables. We find economically important earnings effects of college quality for men and women, as well as effects on educational attainment, spousal earnings and other demographic variables. These effects remain roughly constant over time and result primarily from effects on wages, rather than from effects on hours or labor force participation. We find that, over the lower part of the range of college quality, increases in college quality (which entail higher expenditures per student) pass a simple social cost-benefit test

Term Limits and Electoral Competitiveness: California\u27s State Legislative Races

California’s legislative term limits have dramatically reduced campaign expenditures. Real expenditures during the three general elections after the term limits initiative passed in 1990 were lower than in even 1976. This drop has occurred at the same time that races have become closer contests and more candidates are running for office. By any measure, term limits have coincided with large changes in the level of political competition, even before term limits have forcibly removed a single politician from office. The changes are so large that more incumbents are being defeated, races are closer, more candidates are running, and fewer single candidate races than occur at any other time during our sample period from 1976 to 1994

Analysis Of LVQ In The Context Of Spontaneous EEG Signal Classification

OF THESIS ANALYSIS OF LVQ IN THE CONTEXT OF SPONTANEOUS EEG SIGNAL CLASSIFICATION Learning Vector Quantization (LVQ) has proven to be an effective classification procedure. Since its introduction by Kohonen in 1990 several extensions to the basic algorithm have been proposed. This paper investigates what and how LVQ learns in the context of EEG signal classification. LVQ is shown to be comparable with other Neural Network algorithms for the task of classifying electroencephalograph (EEG) signals, yielding approximately 80% classification accuracy for three out of the four subjects tested when differentiating between two different mental tasks. The best classification accuracy was obtained with unnormalized, sixth-order autoregressive, AR(6), coefficients derived from raw, unfiltered EEG signals. The LVQ2.1 algorithm outperformed any of the other traditional LVQ algorithms tested, yielding a slightly higher classification accuracy than the LVQ3 algorithm. The highest classification accu..