Altruistic Behavior and Habit Formation

This paper examines whether altruistic behavior is habit forming. We take advantage of a data set that includes a rich set of information concerning individuals’ donations of cash and time as adults as well as information about whether they were involved with charitable activities when they were young. The basic premise is that if altruistic behavior when young is a good predictor of such behavior in adulthood, then this is consistent with the notion that altruistic behavior is habit forming. Using U.S. data, we examine both donations of money and time, and find that engaging in charitable behavior when young is a strong predictor of adult altruistic behavior, ceteris paribus. A major issue in the interpretation of this result is that the correlation between youthful and adult altruistic behavior may be due to some third variable that affects both. While it is impossible to rule out such a possibility, we are able to control for family influences that likely could affect lifetime attitudes toward altruism. We find that, even taking this factor into account, altruistic behavior as a youth plays a significant role in explaining adult behavior. This result applies to donations of money and time to a variety of types of non-profit organizations.altruistic behavior, donations, nonprofit fundraising

Aerospace Medicine and Biology: A continuing bibliography (supplement 229)

This bibliography lists 109 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1982

A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models

Constructing confidence intervals for the coefficients of high-dimensional sparse linear models remains a challenge, mainly because of the complicated limiting distributions of the widely used estimators, such as the lasso. Several methods have been developed for constructing such intervals. Bootstrap lasso+ols is notable for its technical simplicity, good interpretability, and performance that is comparable with that of other more complicated methods. However, bootstrap lasso+ols depends on the beta-min assumption, a theoretic criterion that is often violated in practice. Thus, we introduce a new method, called bootstrap lasso+partial ridge, to relax this assumption. Lasso+partial ridge is a two-stage estimator. First, the lasso is used to select features. Then, the partial ridge is used to refit the coefficients. Simulation results show that bootstrap lasso+partial ridge outperforms bootstrap lasso+ols when there exist small, but nonzero coefficients, a common situation that violates the beta-min assumption. For such coefficients, the confidence intervals constructed using bootstrap lasso+partial ridge have, on average, $50\%$ larger coverage probabilities than those of bootstrap lasso+ols. Bootstrap lasso+partial ridge also has, on average, $35\%$ shorter confidence interval lengths than those of the de-sparsified lasso methods, regardless of whether the linear models are misspecified. Additionally, we provide theoretical guarantees for bootstrap lasso+partial ridge under appropriate conditions, and implement it in the R package "HDCI.

A Variant of Uzawa's Theorem

Uzawa (1961) has shown that balanced growth requires technological progress to be strictly Harrod neutral (purely labor-augmenting). This paper offers a slightly more general variant of the theorem that does not require assumptions about savings behavior or factor pricing and is much easier to prove

Muon Identification with VERITAS using the Hough Transform

Imaging atmospheric Cherenkov telescope (IACT) arrays such as VERITAS are used for ground-based very high-energy gamma-ray astronomy. This is accomplished by the detection and analysis of the Cherenkov light produced by gamma-ray-initiated atmospheric air showers. IACTs also detect the Cherenkov light emitted by individual muons. Identification of these muons is useful because their Cherenkov light can be used to calibrate the telescopes. Muons create characteristic annular patterns in the cameras of IACTs, which may be identified using parametrization algorithms. One such algorithm, the Hough transform, has been successfully used to identify muons in VERITAS data. Details of this technique are presented here, including results regarding its effectiveness

A Variant of Uzawa's Theorem

Uzawa (1961) has shown that balanced growth requires technological progress to be strictly Harrod neutral (purely labor-augmenting). This paper offers a slightly more general variant of the theorem that does not require assumptions about savings behavior or factor pricing and is much easier to prove.Harrod bias technological progress

A Variant of Uzawa's Theorem

Uzawa (1961) has shown that balanced growth requires technological progress to be strictly Harrod neutral (purely labor-augmenting). This paper offers a slightly more general variant of the theorem that does not require assumptions about savings behavior or factor pricing and is much easier to prove.Harrod neutral; bias; technological progress

DAILY SOLAR RADIATION ESTIMATED FROM TKMPERA TURE RECORDS

Crop growth models and other environmental analyses require the input of daily global solar radiation values. Unfortunately many locations lack long-term solar radiation data. Most agricultural experiment stations, however, have daily temperature records. Also they are often the locations for which crop growth simulations are conducted. In an unpublished manuscript in the field of agricultural meteorology, researchers wanted to address this need. Specifically they wanted to estimate historical daily global solar radiation using daily air temperature data records by adapting a single published empirical intrinsically nonlinear model, a form of the Weibull curve. In order to help future research in the given field, this paper argues that the selected model is a poor choice. Two independent long-term data sets that come from a similar climate to that of the researchers\u27 are used, one for model development and the other for testing model prediction. Through the use of performance statistics on the cross-validation, three alternative models are offeredfor comparison (the performance statistics are accepted by researchers in the agricultural meteorology discipline). The results give no reason to favor the researchers\u27 selected model. Furthermore no model performed well under advective conditions. Future research should consider finding a better means to account for advection, developing and evaluating other models, and justifying the assumptions of the methodology to be employed