Anthropogenic Carbon Dioxide Emmision in Asia: Effect of Population, Affluence and Energy Effeciency

This study was conducted to determine the effects of population, affluence and energy efficiency (the ratio of real GDP to commercial energy use) on the level of Anthropogenic Carbon Dioxide Emissions in Asia from 1980 to 2004. It also sought to provide empirical evidence for the Environmental Kuznets Curve (EKC) hypothesis in Asia. Results of the study showed that the level of anthropogenic carbon dioxide emissions increased by 265.20 percent from 1980 to 2004. CO2 emissions rose with increases in population and GDP per capita, and decreased with increasing energy efficiency. Results showed that 97 percent of the variation in the level of anthropogenic carbon dioxide emission could be explained by changes in population, GDP per capita and energy efficiency. Results also confirmed the existence of EKC in Asia, A monotonically upward trend in emissions with increasing income level was observed.Anthropogenic Carbon Dioxide Emissions; Energy Efficiency; Environmental Kuznets Curve

Factors Affecting the Magnitude of Poor Families Across the Philippines: A Cross Section Data Analysis

This study is conducted to determine the factors affecting magnitude of poor families in the Philippines and measure the effect of the variables presented. The model was estimated using the Ordinary Least Square (OLS) procedure and cross sectional data set consisting of the 16 regions in the Philippines in the year 2000. The four variables that are found to have significant coefficients are gross regional domestic product (GRDP), functional literacy rate of the population 10-64 years old, number of persons with disabilities, and percentage of household with at least one land owned. Specifically, a peso increase in GRDP decreases the magnitude of poor families by 1 family. When the functional literacy rate increases by one percent decreases the number of poor families by 10,426 families. A unit increase in the number of persons with disability increases the number of poor families by around 4 families. While a percentage increase in the number of family with access to land by at least one land decreases the magnitude of poor families by 5,633 families. Result of the estimation shows that 81% of the variability of the magnitude of poor families in the Philippines can be explained by the predictors of the Model.OLS, Poverty, Factors of Poverty

The Way to Become a Man : The Influence of Commercial Sex on Male Psychosocial Development

Hegemonic masculinity and the negative consequences that it has on men and women continues to gain increasing attention in research and in popular culture. Particular attention is paid to the sexual violence that is perpetrated by men towards women, however, the research on men’s sexual development and its relationship to this sexual violence focuses largely on biological explanations. The feminist literature, however, on sexual violence offers a differing perspective, which includes the socialization processes that men undergo in their sexual development that lead them towards normalizing sexual violence. One of these processes is the existence and normalization of the commercial sex industry. This study interviewed 12 participants, eight via individual interviews and four in a focus group, to ascertain the relationship between men’s sexual development and their engagement with the commercial sex industry. The findings within this study suggested a strong link between men’s sexual development, pornography, prostitution, and fitting in with all-male peer groups. Commercial sex served as a vehicle for participants to express their masculine identities and bond with peers over sexual topics. Literature and exploration on hegemonic masculinity may benefit from further analysis on how men’s sexual development, including their enactment or understanding of sexual violence, is interwoven into their experiences of commercial sex. This dissertation is available in open access at AURA, http://aura.antioch.edu/ and Ohio Link ETD Center https://etd.ohiolink.edu/etd

Matching estimators for the effect of a treatment on survival times

We perform inference on the effect of a treatment on survival times in studies where the treatment assignment is not randomized and the assignment time is not known in advance. We estimate survival functions on a treated and a control group which are made comparable through matching on observed covariates. The inference is performed by conditioning on waiting time to treatment, that is time between the entrance in the study and treatment. This can be done only when sufficient data is available. In other cases, averaging over waiting times is a possibility, although the classical interpretation of the estimated survival functions is lost unless hazards are not functions of the waiting times. To show unbiasedness and to obtain an estimator of the variance, we build on the potential outcome framework, which was introduced by J. Neyman in the context of randomized experiments, and adapted to observational studies by D. B. Rubin. Our approach does not make parametric or distributional assumptions. In particular, we do not assume proportionality of the hazards compared. Small sample performance of the estimator and a derived test of no treatment effect are studied in a Monte Carlo study

Thermal APU/hydraulics analysis program. User's guide and programmer's manual

The User's Guide information plus program description necessary to run and have a general understanding of the Thermal APU/Hydraulics Analysis Program (TAHAP) is described. This information consists of general descriptions of the APU/hydraulic system and the TAHAP model, input and output data descriptions, and specific subroutine requirements. Deck setups and input data formats are included and other necessary and/or helpful information for using TAHAP is given. The math model descriptions for the driver program and each of its supporting subroutines are outlined

Covariate selection for non-parametric estimation of treatment effects

In observational studies, the non-parametric estimation of a binary treatment effect is often performed by matching each treated individual with a control unit which is similar in observed characteristics (covariates). In practical applications, the reservoir of covariates available may be extensive and the question arises which covariates should be matched for. The current practice consists in matching for covariates which are not balanced for the treated and the control groups, i.e. covariates affecting the treatment assignment. This paper develops a theory based on graphical models, whose results emphasize the need for methods looking both at how the covariates affect the treatment assignment and the outcome. Furthermore, we propose identification algorithms to select at minimal set of covariates to match for. An application to the estimation of the effect of a social program is used to illustrate the implementation of such algorithms

Testing for nonparametric identification of causal effects in the presence of a quasi-instrument

The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption (exogeneity of the treatment). In this paper, we define variables which we call quasi-instruments because they allow us to test for the unconfoundedness assumption although they do not necessarily yield nonparametric identification of the average causal effect. A quasi-instrument is defined as an instrument except for that its relation to the treatment is allowed to be confounded by unobservables, thereby resulting in a wider range of potential applications. We propose a test for the unconfoundedness assumption based on a quasi-instrument, and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment

Multi-Use Facilities - Repurposing Facilities Infrastructure to Support the Multi-customer Environment

Since the early 90’s the space launch business has undergone significant change in customers, composition, capacity, and key players. These changes have a resultant effect upon the spaceport industry. Some spaceports, in their current state, may not survive the probable future market and business environment. To stay in the game, spaceports are reexamining their target launch vehicles, services, and operations philosophy. One of the strategies being developed to accommodate current and future changes is to repurpose facilities to support multiple customers. The new vision is that multiple commercial, as well as government entities, having multiple spacecraft and launch vehicle configurations will all operate within or upon one multi-purpose facility – including launch pads. One of the challenges is to develop capability for the “first to market” vehicles without increasing price or risk to future users. This presentation will provide the considerations involved in developing facility infrastructure to support the multi-customer environment