This thesis employs advanced econometric methods to understand the determinants of race inequalities in labour markets and in higher education in Brazil. It then investigates whether race diversity can be used as a policy to reduce existing inequalities in pay and college campuses. It uses data from the National Household Sample Survey PNAD of 2005 and the National Examination of Higher Education Courses (ENC/Provão) of 2003. The main methodological contributions of this thesis are: 1) extending wage models to include several variables which can explain more than 40 percent of the total variation in wages; 2) computing a proxy for parental education (this has not been possible to estimate using PNAD since 1996); 3) correcting wage equations for selection bias using a robust instrument (most studies ignore the sample selection problem by using employed males only); 4) implementing a new algorithm that combines Heckman Two-Step, complex sample weights and constrained least squares (this increases robustness of the detailed decomposition of the discrimination term). This is done in a generalized wage decomposition setting where the level of discrimination is invariant to the choice of the reference wage group. Results show an existing pay-gap and a significant level of discrimination against nonwhites even after corrections are made. Selection bias appears to underestimate the discrimination term considerably. This study also develops a theoretical framework for the study of the impact of diversity on labour productivity and on discrimination simultaneously. Results support policies which seek greater diversity in order to reduce the existing inequalities in labour markets and on higher education campuses. However, the outcome of policies aimed at increasing diversity on campuses can significantly differ depending on the existing level of diversity and the subject majors attended by students.

This paper investigates the impacts of non-traditional exports on household outcomes in rural Zambia. Traditionally, Zambia has been an exporter of copper and only recently has the increase in agricultural exports been observed. Potential products include cotton, tobacco, fruits, vegetables, food processing, and textiles. International markets for these nontraditional exports have generated new opportunities for vulnerable and poor households. While the current literature focuses more on the income dimension of adjustment, in this paper we explore non-monetary outcomes as well. Concretely, we study the impacts of export opportunities on income, child health and education in rural households. We find positive income differentials of households involved in market agriculture over subsistence agriculture. While we find that children living in households involved in cotton tend to show better longrun anthropometric outcomes, no systematic differences are observed in households engaged in other agricultural activities. Finally, we find that households in market agriculture tend to educate their children more than households in subsistence. There is some evidence that boys are benefited more than girls.

Thesis, PlanA (M.S.)--University of Wisconsin--Stout, 2006. / Includes bibliographical references.

Thesis (Ed.D.)--Teachers College, Columbia University, 1994. / Includes tables and appendices. Typescript; issued also on microfilm. Sponsor: John P. Allegrante. Dissertation Committee: Robert Crain. Includes bibliographical references (leaves 82-92).

Thesis (M.Ed. (Education)) - Rhodes University, 2008. / A half thesis submitted in partial fulfilment of the requirements of the degree of Master of Education: Environmental Education.

Philosophiae Doctor - PhD / The United Nations (UN) Millennium Development Goals (MDGs) drafted eight goals to be achieved by the year 2015, namely: eradicating extreme poverty and hunger, achieving universal primary education, promoting gender equality and women empowerment, reducing child mortality, improving maternal health, combating HIV/AIDS, malaria and other diseases, ensuring environmental sustainability and lastly developing a global partnership for development. Many public health studies often result in complicated and complex data sets, the nature of these data sets could be clustered, multivariate, longitudinal, hierarchical, spatial, temporal or spatio-temporal. This often results in what is called correlated data, because the assumption of independence among observations may not be appropriate. The shared genetic traits in the studies of illness or shared household characteristics among family members in the studies of poverty are examples of correlated data. In cross-sectional studies, individuals may be nested within sub-clusters (e.g., families) that are nested within clusters (e.g., environment), thus causing correlation within clusters. Ignoring the structure of the data may result in asymptotically biased parameter estimates. Clustered data may also be a result of geographical location or time (spatial and temporal). A crucial step in modelling correlated data is the speci cation of the dependency by choosing the covariance/correlation function. However, often the choice for a particular application is unclear and diagnostic tests will have to be carried out, following tting of a model. This study's view of developing countries investigates the prospects of achieving MDGs through the development of flexible predictor statistical models. The first objective of this study is to explore the existing methods for modelling correlated data sets (hierarchical, multilevel and spatial) and then apply the methods in a novel way to several data sets addressing the underlying MDGs. One of the most challenging issue in spatial or spatio-temporal analysis is the choice of a valid and yet exible correlation (covariance) structure. In cases of high dimensionality of the data, where the number of spatial locations or time points that produced the observations is large, the analysis of such data presents great computational challenges. It is debatable whether some of the classical correlation structures adequately reect the dependency in the data. The second objective is to propose a new flexible technique for handling spatial, temporal and spatio-temporal correlations. The goal of this study is to resolve the dependencies problems by proposing a more robust method for modelling spatial correlation. The techniques are used for di erent correlation structures and then combined to form the resulting estimating equations using the platform of the Generalized Method of Moments. The proposed model will therefore be built on a foundation of the Generalized Estimating Equations; this has the advantage of producing consistent regression parameter estimates under mild conditions due to separation of the processes of estimating the regression parameters from the modelling of the correlation. These estimates of the regression parameters are consistent under mild conditions. Thirdly, to account for spatio-temporal correlation in data sets, a method that decouples the two sources of correlations is proposed. Speci cally, the spatial and temporal e ects were modelled separately and then combined optimally. The approach circumvents the need of inverting the full covariance matrix and simpli es the modelling of complex relationships such as anisotropy, which is known to be extremely di cult or Lastly, large public health data sets consist of a high degree of zero counts where it is very di cult to distinguish between "true zeros" and "imputed" zeros. This can be due to the reporting mechanism as a result of insecurity, technical and logistics issues. The focus is therefore on the implementation of a technique that is capable of handling such a problem. The study will make the assumption that "imputed" zeros are a random event and consider the option of discarding the zeros, and then model a conditional Poisson model, conditioning on all cases greater than 0.

This study focuses on the role and contribution of women in the context of the social and economic development of two towns in Hertfordshire during the nineteenth century. Although the age saw an increase in urbanisation, Hertfordshire remained an agricultural county with long established land owners, a middle class with influence in the towns and its closeness to London attracting the newly wealthy in search of a country estate. The towns selected for this study, Hertford and Hitchin, changed little in their character and, compared with others which experienced industrial expansion, saw a modest population growth. This, however, brought the consequential pressures on housing and poverty. This research is unique in combining the study of the activities of women and the challenges faced by two market towns over a period of time of change and thus making a contribution to the debate on the concept of “separate spheres” by demonstrating that women had a place in the public arena. The daily life of a country town was reliant on a thriving economic environment. As this research demonstrates, many women had trades and businesses, contributed to good causes and were central to the education of children and adults. Their philanthropic efforts supported the building and maintenance of churches, schools, and hospitals. It charts the role of ordinary women, operating in a small town environment, before extension of the suffrage and Equal Opportunities legislation established their position as legitimate influencers of policy and practice. Little work has been done on how the English small town coped with its growth in population and the summons from central government on compliance with an increasing body of legislation on how the town should be run. It was men who undertook the necessary offices associated with this seed of local government but a micro-history of the people who inhabited these two towns demonstrates that women made a significant contribution to social and economic life of these towns.

Adult depression is a common consequence of adverse childhood experience. There is also a higher likelihood of being affected by economic burdens after having expierienced a traumatic event in childhood. As depression has been associated with economic burden, these long-term sequelae of childhood adversity are likely to interact. We investigated depression and economic consequences, such as unemployment, lower level of education, lower income as long term sequelae of adverse childhood experiences in adulthood and their interaction. Childhood Maltreatment was measured by the German version of the Adverse Childhood Experience (ACE) questionnaire. Depression was measured by the Patient Health Questionnaire (PHQ-2). Logistic regressions were applied to investigate the risks of suffering economic burdens, with depression as a moderator. Depressive symptoms increased with the number of ACEs and were highest in those reporting four or more ACEs, especially amongst those who experienced sexual and emotional abuse, as well as neglect. Moderation analysis showed a significant effect of depression incresing almost all economic burdens. Migration background additionally increased the risk of unemployment and working in a blue-collar job. Female gender decreased the risk of unemployment and working in a blue-collar job, but increased the risk of low income and part-time employment. The moderation effect of depression increased the negative impact of exposure to multiple ACEs on economic life in adulthood. Prevention of ACEs and early intervention are needed to prevent the mental health and economic consequences.