Exploring the interplay between Buddhism and career development : a study of highly skilled women workers in Sri Lanka

This article adopts a socio cultural lens to examine the role of Buddhism in highly skilled women workers’ careers in Sri Lanka. While Buddhism enabled women’s career development by giving them strength to cope with difficult situations in work, it also seemed to restrict their agency and constrain their career advancement. Based on our findings, we argue that being perceived as a good Buddhist woman worked as a powerful form of career capital for the respondents in our sample, who used their faith to combat gender disadvantage in their work settings

Livelihoods after land reform in South Africa

Over the past few decades, Zimbabwe, Namibia and South Africa have pursued redistributive land reform as a means to address rural poverty. The Livelihoods after Land Reform (LaLR) study was carried out between 2007 and 2009, to understand the livelihood and poverty reduction outcomes of land reform in each of the three countries. The South African component focused on Limpopo province, and investigated land reform processes, trajectories of change and outcomes in thirteen detailed case studies. This paper summarizes some of the main findings from the South African study, and briefly compares them with findings from Namibia and Zimbabwe. The paper argues that a fundamental problem affecting land reform in both South Africa and Namibia is the uncritical application of the Large-Scale Commercial Farming (LSCF) model, which has led to unworkable project design and/or projects that are irrelevant to the circumstances of the rural poor. Nevertheless, some ‘beneficiaries’ have experienced modest improvements in their livelihoods, often through abandoning or amending official project plans.Web of Scienc

'Parasitic invasions' or sources of good governance: constraining foreign competition in Hong Kong banking, 1956-81

This paper investigates the operation and impact of the moratorium on new banking licences imposed in Hong Kong in 1965 and the claims that foreign banks destabilised the banking system and drained resources from the colony. First it examines foreign banks' attempts to circumvent the moratorium through claims of special circumstances and buying interests in local banks, and secondly it examines the efforts of incumbents to extend barriers to non-bank financial institutions and to branches of foreign banks. The general conclusions are that while the moratorium was aimed at increasing the stability of the banking system, it had the effect of decreasing the regulatory breadth of the government, and reducing incentives for mergers and acquisitions that might have improved governance

Impact of natural disasters on income inequality in Sri Lanka

We explore the relationship between natural disasters and income inequality in Sri Lanka as the first study of this nature for the country. The analysis uses a unique panel data set constructed for the purpose of this paper. It contains district inequality measures based on household income reported in six waves of the Household Income and Expenditure Survey of Sri Lanka during the period between 1990 and 2013, data on disaster affected population and other economic and social indicators. Employing a panel fixed effects estimator, we find that contemporaneous natural disasters and their immediate lags significantly and substantially decrease inequality in per adult equivalent household income as measured by the Theil index. Findings are robust across various inequality metrics, sub-samples and alternative estimators such as Ordinary Least Squares and System GMM. However, natural disasters do not affect household expenditure inequality. Either households behave as if they have a permanent income or all households reduce their expenditure proportionately irrespective of their income level in responding to natural disasters. Natural disasters decrease non-seasonal agricultural and non-agricultural income inequality but increase seasonal agricultural income inequality. Income of richer households is mainly derived from non-agricultural sources such as manufacturing and business activities and non-seasonal agricultural activities. Poorer households have a higher share of agricultural income

Impact of intercensal population projections and error of closure on breast cancer surveillance: examples from 10 California counties

INTRODUCTION: In 2001, data from the California Cancer Registry suggested that breast cancer incidence rates among non-Hispanic white (nHW) women in Marin County, California, had increased almost 60% between 1991 and 1999. This analysis examines the extent to which these and other breast cancer incidence trends could have been impacted by bias in intercensal population projections. METHOD: We obtained population projections for the year 2000 projected from the 1990 census from the California Department of Finance (DOF) and population counts from the 2000 US Census for nHW women living in 10 California counties and quantified age-specific differences in counts. We also computed age-adjusted incidence rates of invasive breast cancer in order to examine and quantify the impact of differences between the population data sources. RESULTS: Differences between year 2000 DOF projections and year 2000 census counts varied by county and age and ranged from underestimates of 60% to overestimates of 64%. For Marin County, the DOF underestimated the number of nHW women aged 45 to 64 years by 32% compared to the 2000 US census. This difference produced a significant 22% discrepancy between breast cancer incidence rates calculated using the two population data sources. In Los Angeles and Santa Clara counties, DOF-based incidence rates were significantly lower than rates based on census data. Rates did not differ significantly by population data source in the remaining seven counties examined. CONCLUSION: Although year 2000 population estimates from the DOF did not differ markedly from census counts at the state or county levels, greater discrepancies were observed for race-stratified, age-specific groups within counties. Because breast cancer incidence rates must be calculated with age-specific data, differences between population data sources at the age-race level may lead to mis-estimation of breast cancer incidence rates in county populations affected by these differences, as was observed in Marin County. Although intercensal rates based on population projections are important for timely breast cancer surveillance, these rates are prone to bias due to the error of closure between population projections and decennial census population counts. Intercensal rates should be interpreted with this potential bias in mind

Long-term care cost drivers and expenditure projection to 2036 in Hong Kong

<p>Abstract</p> <p>Background</p> <p>Hong Kong's rapidly ageing population, characterised by one of the longest life expectancies and the lowest fertility rate in the world, is likely to drive long-term care (LTC) expenditure higher. This study aims to identify key cost drivers and derive quantitative estimates of Hong Kong's LTC expenditure to 2036.</p> <p>Methods</p> <p>We parameterised a macro actuarial simulation with data from official demographic projections, Thematic Household Survey 2004, Hong Kong's Domestic Health Accounts and other routine data from relevant government departments, Hospital Authority and other LTC service providers. Base case results were tested against a wide range of sensitivity assumptions.</p> <p>Results</p> <p>Total projected LTC expenditure as a proportion of GDP reflected secular trends in the elderly dependency ratio, showing a shallow dip between 2004 and 2011, but thereafter yielding a monotonic rise to reach 3.0% by 2036. Demographic changes would have a larger impact than changes in unit costs on overall spending. Different sensitivity scenarios resulted in a wide range of spending estimates from 2.2% to 4.9% of GDP. The availability of informal care and the setting of formal care as well as associated unit costs were important drivers of expenditure.</p> <p>Conclusion</p> <p>The "demographic window" between the present and 2011 is critical in developing policies to cope with the anticipated burgeoning LTC burden, in concert with the related issues of health care financing and retirement planning.</p

Indigenous well-being in four countries: An application of the UNDP'S Human Development Index to Indigenous Peoples in Australia, Canada, New Zealand, and the United States

<p>Abstract</p> <p>Background</p> <p>Canada, the United States, Australia, and New Zealand consistently place near the top of the United Nations Development Programme's <it>Human Development Index (HDI) </it>rankings, yet all have minority Indigenous populations with much poorer health and social conditions than non-Indigenous peoples. It is unclear just how the socioeconomic and health status of Indigenous peoples in these countries has changed in recent decades, and it remains generally unknown whether the overall conditions of Indigenous peoples are improving and whether the gaps between Indigenous peoples and other citizens have indeed narrowed. There is unsettling evidence that they may not have. It was the purpose of this study to determine how these gaps have narrowed or widened during the decade 1990 to 2000.</p> <p>Methods</p> <p>Census data and life expectancy estimates from government sources were used to adapt the Human Development Index (HDI) to examine how the broad social, economic, and health status of Indigenous populations in these countries have changed since 1990. Three indices – life expectancy, educational attainment, and income – were combined into a single HDI measure.</p> <p>Results</p> <p>Between 1990 and 2000, the HDI scores of Indigenous peoples in North America and New Zealand improved at a faster rate than the general populations, closing the gap in human development. In Australia, the HDI scores of Indigenous peoples decreased while the general populations improved, widening the gap in human development. While these countries are considered to have high human development according to the UNDP, the Indigenous populations that reside within them have only medium levels of human development.</p> <p>Conclusion</p> <p>The inconsistent progress in the health and well-being of Indigenous populations over time, and relative to non-Indigenous populations, points to the need for further efforts to improve the social, economic, and physical health of Indigenous peoples.</p

Application of Data Mining in Air Traffic Forecasting

The main goal of the study centers on developing a model for the purpose of air traffic forecasting by using off-the-shelf data mining and machine learning techniques. Although data driven modeling has been extensively applied in the aviation sector, little research has been done in the area of air traffic forecasting. This study is inspired by previous research focused on improving the Federal Aviation Administration (FAA) Terminal Area Forecasting (TAF) methodology, which historically assumed that the US air transportation system (ATS) network structure was static. Recent developments use data mining algorithms to predict the likelihood of previously un-connected airport-pairs being connected in the future, and the likelihood of connected airport-pairs becoming un-connected. Despite the innovation of this research, it does not focus on improving the FAA’s existing methodology for forecasting future air traffic levels on existing routes, which is based on relatively simple regression and growth models. We investigate different approaches for improving and developing new features within the existing data mining applications in air traffic forecasting. We focus particularly on predicting detailed traffic information for the US ATS. Initially, a 2-stage log-log model is applied to establish the significance of different inputs and to identify issues of endogeneity and multi-colinearity, while maintaining the simplicity of current models. Although the model shows high goodness of fit, it tested positive for both mentioned issues as well as presenting problems with causality. With the objective of solving these issues, a 3-stage model that is under development is introduced. This model employs logistic regression and discrete choice modelling. As part of future work, machine learning techniques such as clustering and neural networks will be applied to improve this model’s performance