SIZE OF THE MILITARY SECTOR AND ECONOMIC GROWTH: A PANEL DATA ANALYSIS OF AFRICA AND LATIN AMERICA

We estimate the influence of defense spending and military labor use on economic growth in African and Latin American countries. Our model integrates disparate implications from the defense economics literature into a Barro-style model of economic growth that controls for political and economic institutional variation across countries. Our panel data analysis of 44 countries in Africa and Latin America from 1975 to 1989 also controls for cross-country variation in lost human capital and public sector production inefficiencies. We find empirical evidence that the defense burden on economic growth is non-linear, with low levels of military spending increasing economic growth but higher levels of military spending decreasing growth. We also find evidence that the influence of military labor use on growth is non-linear, and exhibits a greater drag on economic growth in those countries with relatively higher levels of adult male education attainment.defense burden; economic growth

GENERALIZED LINEAR MIXED MODELS: AN APPLICATION

The purpose of this paper is to present a specific application of the generalized linear mixed model. Often of interest to animal-breeders is the estimation of genetic parameters associated with certain traits. When the trait is measured in terms of a normally distributed response variable, standard variance-component estimation and mixed-model procedures can be used. Increasingly, breeders are interested in categorical traits (degree of calving difficulty, number born, etc.). An application of the generalized linear mixed to an animal breeding study of the number of lambs born alive will be presented. We will show how the model is determined, how the estimation equations are formed, and the resulting inference

GENERALIZED LINEAR MIXED MODELS - AN OVERVIEW

Generalized linear models provide a methodology for doing regression and ANOV A-type analysis with data whose errors are not necessarily normally-distributed. Common applications in agriculture include categorical data, survival analysis, bioassay, etc. Most of the literature and most of the available computing software for generalized linear models applies to cases in which all model effects are fixed. However, many agricultural research applications lead to mixed or random effects models: split-plot experiments, animal- and plant-breeding studies, multi-location studies, etc. Recently, through a variety of efforts in a number of contexts, a general framework for generalized linear models with random effects, the generalized linear mixed model, has been developed . The purpose of this presentation is to present an overview of the methodology for generalized mixed linear models. Relevant background, estimating equations, and general approaches to interval estimation and hypothesis testing will be presented. Methods will be illustrated via a small data set involving binary data

Size of the Military Sector and Economic Growth: A Panel Data Analysis of Africa and Latin America

We estimate the influence of defense spending and military labor use on economic growth in African and Latin American countries. Our model integrates disparate implications from the defense economics literature into a Barro-style model of economic growth that controls for political and economic institutional variation across countries. Our panel data analysis of 44 countries in Africa and Latin America from 1975 to 1989 also controls for cross-country variation in lost human capital and public sector production inefficiencies. We find empirical evidence that the defense burden on economic growth is non-linear, with low levels of military spending increasing economic growth but higher levels of military spending decreasing growth. We also find evidence that the influence of military labor use on growth is non-linear, and exhibits a greater drag on economic growth in those countries with relatively higher levels of adult male education attainment

HOW GOOD ARE SPATIAL GLM\u27S? A SIMULATION STUDY

An area of increasing interest to agricultural and ecological researchers is the analysis of spatially correlated non-normal data. A generalized linear model(GLM) accounting for spatial covariance was presented by Gotway and Stroup (1997). Their method included approximate inference based on asymptotic distributions. A simulation study was conducted to assess the small sample behavior of their proposed estimates and test statistics. This study suggests that the spatial GLM yields unbiased estimates of treatment means and differences for binomial data, that the spatial GLM improves precision, as measured by MSE, and that the approximate F-statistic is acceptable for hypothesis testing

Testing the Effectiveness of Lecture Capture Technology Using Prior GPA as a Performance Indicator

This empirical study examines whether making lecture capture technology available in a face-to-face lecture environment can improve students’ ability to learn the course material. We examine student performance in undergraduate principles courses in computer science and economics. However, rather than simply comparing average course grades between lecture capture and non-lecture capture classes, we use student grade point average (GPA) as a predictor of course grades earned in non-lecture capture classes and lecture capture classes taught by the same professors using the same course materials. Our results imply that making lecture capture technology available in face-to-face lectures does not appear to impact high GPA students’ ability to learn the course material one way or the other. However, low GPA students in one of the lecture capture courses earned significantly lower grades relative to low GPA students in the non-lecture capture class

The design of mixed-use virtual auditory displays: Recent findings with a dual-task paradigm

Presented at the 10th International Conference on Auditory Display (ICAD2004)In the third of an ongoing series of exploratory sound information display studies, we augmented a dual task with a mixed-use auditory display designed to provide relevant alert information for each task. The tasks entail a continuous tracking activity and a series of intermittent classification decisions that, in the present study, were presented on separate monitors that were roughly 90\,^{\circ} apart. Using a 2-by-3 design that manipulated both the use of sound in each task and where sounds for the decision task were positioned, the following principal questions were addressed: Can tracking performance be improved with a varying auditory alert tied to error? To what degree do listeners use virtual auditory deixis as a cue for improving decision reaction times? Can a previous finding involving participants' use of sound offsets (cessations) be repeated? And, last, are there performance consequences when auditory displays for separate tasks are combined? Respectively, we found that: Tracking performance as measured by RMS error was not improved and was apparently negatively affected by the use of our auditory design. Listener's use of even limited virtual auditory deixis is robust, but it is probably also sensitive to the degree it is coincident with the location of corresponding visual stimuli in the task environment. On the basis of manually collected head movement data, listeners do make opportunistic use of sound offsets. And, finally, a significant interaction, as measured by average participant reaction time, was observed between the auditory display used for one task and the manipulation of the degree of auditory deixis encoded in the auditory display used for the other task in our paradigm

NONLINEAR MODELS FOR MULTI-FACTOR PLANT NUTRITION EXPERIMENTS

Plant scientists are interested in measuring plant response to quantitative treatment factors, e.g. amount of nutrient applied. Response surface methods are often used for experiments with multiple quantitative factors. However, in many plant nutrition studies, second-order response surface models result in unacceptable lack of fit. This paper explores multi-factor nonlinear models as an alternative. We have developed multi-factor extensions of Mitscherlich and Gompertz models, and fit them to data from experiments conducted at the University of Nebraska-Lincoln Horticulture department. These data are typical of experiments for which conventional response surface models perform poorly. We propose design selection strategies to facilitate economical multi-factor experiments when second-order response surface models are unlikely to fit