No description available.

No description available.

No description available.

No description available.

No description available.

This is a study of families that had pregnancies while they were recipients or potential recipients of public assistance.

The purpose of this study is to make an overall examination of the Texas Aid to Families with Dependent Children Program in relation to its structure, operation, and effectiveness. In this vein the Program is examined with regard to the Federal and State guidelines which direct and govern the Program.

No description available.

As offshore installations in the North Sea near the end of their economically viable life they will be decommissioned. There is limited experience of decommissioning the structures in the Northern North Sea. When decommissioning is being considered studies are undertaken to assess each option on such criteria as: environmental impact, technical feasibility, cost, health and safety and public acceptability. This thesis aims to explore the contribution that economics can make to the debate surrounding decommissioning with the introduction of a cost benefit analysis framework within which to compare each decommissioning option. Cost Benefit Analysis (CBA) considers the economic costs and benefits from any change in resource allocation within an economy. This would provide a structured assessment of all the impacts of decommissioning in monetary terms. CBA also allows social desirability to be considered. The CBA framework involves valuing costs and benefits. Whilst estimating costs raises important questions, the identification, measurement and valuation of (dis)benefits represents one the greatest challenges facing economists. This thesis will focus on how economics can be applied to this problem to obtain monetary valuations of the benefits of decommissioning. Two methods of valuing the (dis)benefits of decommissioning are considered - Contingent Valuation Method (CVM) and Discrete Choice Experiments (DCE). These are applied to valuing of one impact of decommissioning (drill cuttings) and the value of alternative decommissioning policies. As well as addressing the issue of decommissioning, these experiments also consider methodological issues in the application of CVM (dealing with Don't Know and Protest responses) and DCE (testing for compensatory decision making). Following this the costs of decommissioning are estimated using an existing financial simulation model developed for oil industry use. Recommendations are made for policy and future methodological work.

A research report submitted to the Faculty of Health sciences, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Epidemiology & Biostatistics Johannesburg, October 2014 / Introduction Disability is a major public health concern worldwide. The situation in Africa is serious. It is estimated that ten percent of the world’s population is living with a disability and close to two-thirds of all people with a disability live in low-income countries. The main objective of this study was to determine the spatial distribution of disability and disability grant allocation and to identify factors associated with disability within the Gauteng Province (2007). Materials and Methods An analytical cross-sectional study design was used to analyse secondary data from the 2007 South African survey data. The population of Gauteng was the focus of the study. The prevalence of disability in Gauteng was estimated. Chi-square test of proportions was used to analyse the distribution of social and demographic characteristics among participants. Poisson regression models were constructed to determine the association between disability and socio-demographics characteristics. Results Of a sample of 133 691 individuals in Gauteng Province, 4 492 (3.4%) reported being disabled, and of these, 2 333 (51,94%) were male and 2159 (48,06%) were female. The overall prevalence of disability or disability rate was 3.4%. Most of the disabled people were older individuals aged 40 to 64 years (51,51%), followed by those aged 18 to 39 years (33,17%); the rest were individuals aged over 64 years of age (retirement age category). Most of these disabled participants were black (77,8%), with whites contributing 15,69%. Almost half (42,72%) of the disabled participants were never married. More than half of the disabled participants (59,75%) had a high school level of education, followed by those with primary school as their level of education (25,31%). Almost 18% of the disabled people were employed and the remaining percentage was unemployed (82%). More than half of the disabled population in Gauteng resided in Johannesburg (34,93%) and Ekurhuleni (26,89%), followed by Tshwane (19.08%). There was a statistically difference in disability grant allocation between the disabled males (51,34%) and (48,66%) females. About 67,93% of the disability grant was given to the older working age category (40-64 years). More than 80% of the disability grants support was issued to the black population group. More than 45 % of the disability grants support issued was given to people who had never married. More than 80% of the disability grants issued was given to the non-economically active category of disabled people. More than 60% of the disability grants support went to those in Johannesburg, Tshwane and Ekurhuleni. Variables associated with disability in Poisson regression analysis included the following: Female participants in the study showed a lower risk (40%) of disability compared to males, and this difference was statistically significant (IRR 0.6, CI 0.59-0.67, p= <0.001). The older working age category (39 to 64 years) (IRR2.9, CI 2.6-3.1, p=<0001) and retirement age category (65 years and above) (IRR 3.0, CI 2.5-3.5, p=<0.001) were respectively associated with a higher risk of disability. Coloured (IRR 1.37,CI 1.2-1.6, p <0.001) and white (IRR 1.41, CI 1.3-1.6, p<0.001) participants showed a 1.4 times greater risk of having disability compared to individuals of the black community, and these differences were statistically significant. While Indians (IRR 1.13, CI 0.9-1.4, p=0.247) had 1.1 times the risk of having disability compared to black participants but the difference was not statistically different. The risk of disability in individuals living in Tshwane (IRR 0.87,CI 0.80-0.95, p=0.001) and the West Rand (IRR 0.86,CI 0.75-0.99, p=0.037) districts was lower by 10% relative to individuals staying in the city of Johannesburg. This risk was relatively lower by 20% in Metsweding (IRR 0.77,CI 0.63-0.94, p=0.012) compared to Johannesburg. These differences were statistically significant. On the other hand, although not significant, the risk of disability was higher by 7% in Sedibeng district (IRR 1.07,CI 0.97-1.18, p=0.187). Participants in a traditional marriage (IRR 1.1, CI 0.97-1.24, p =0.14) and those who were polygamous (IRR 1.0, CI 0.33-3.21, p= 0.96) were not associated with disability compared to civil/ religiously married participants. Others categories of marital status included living together as married (IRR 1.2, CI 1.06-1.37, p=0.006); never married (IRR 1.6, CI 1.49-1.78, p< 0.001); widow/widower (IRR 1.4, CI 1.2-1.6, p <0.001); separated (IRR 1,6, CI1.34-2.08, p<0.001 and divorced (IRR 1.9,CI 1.65-2.24, p<0.001) were associated with disability and the observed differences were statistically significant. Those who had attended high school (IRR 0.48, CI 0.44-0.53, p <0.001) and those who had post matric studies (higher school)(IRR 0.34, CI 0.27-0.42, p< 0.001) were less associated with disability compared to those who only had a primary school level of education (IRR 0.8, CI 0.76-0.93, p = 0.001). Participants classified as not economically active were 7.5 times at risk of being disabled (IRR 7.5, CI6.95-8.19, p < 0.001). The observed difference was statistically significant. The least poor households were 0.7 times at risk of having a disabled member (IRR 0.7, CI 0.62-0.75, p <0.001) while the poor households had a 0.9 times the risk of having a family member with any disability (IRR 0.9, CI 0.81-0.94, p <0.001) - compared to most poor households, and the difference was statistically significant. Conclusion Gauteng showed a prevalence of individuals living with a disability in South Africa. In fact, it was found that the overall prevalence of disability in the Gauteng Province was 3,6%. During the same period Statistics South Africa estimated the whole county disability rate to be 4%. Statistically significant risk factors associated with disability in Gauteng included males aged 39 years and older; the coloured and white population group; living in the Sedibeng district; living together as married, never married, widower/widow, separated and divorced; not educated; not economically active; and most poor households. The spatial distribution of grant allocation was proportional to the disability burden per district as well as well as per local municipality, with a statistically significant relationship between disability burden and grants allocation. A higher proportion of males disabled received a grant compared to disabled females. Sedibeng district was highly associated with any disability, whilst Metsweding was the safest district.