'Dog Days' Full Employment without Depreciation: Can It Be Done?

Policy Forum: The Murray Financial System Enquir

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

What Explains the Gender Gap in Schlepping? Testing Various Explanations for Gender Differences in Household-Serving Travel*

Using data from the American Time Use Survey (ATUS), researchers at UCLA and Rutgers find the gender gap in household-serving travel still exists — women are indeed more likely to take these trips than men. To explain this persistent division in household-serving travel, researchers explore the ATUS data using three perspectives: (1) time availability, (2) microeconomic, and (3) gender socialization. Their findings can help transportation planners and researchers better understand travel patterns within households

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

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

Cost-effectiveness of Implementing Low-Tidal Volume Ventilation in Patients With Acute Lung Injury

Background: Despite widespread guidelines recommending the use of lung-protective ventilation (LPV) in patients with acute lung injury (ALI), many patients do not receive this lifesaving therapy. We sought to estimate the incremental clinical and economic outcomes associated with LPV and determined the maximum cost of a hypothetical intervention to improve adherence with LPV that remained cost-effective. Methods: Adopting a societal perspective, we developed a theoretical decision model to determine the cost-effectiveness of LPV compared to non-LPV care. Model inputs were derived from the literature and a large population-based cohort of patients with ALI. Cost-effectiveness was determined as the cost per life saved and the cost per quality-adjusted life-years (QALYs) gained. Results: Application of LPV resulted in an increase in QALYs gained by 15% (4.21 years for non-LPV vs 4.83 years for LPV), and an increase in lifetime costs of $7,233 per patient with ALI ($99,588 for non-LPV vs $106,821 for LPV). The incremental cost-effectiveness ratios for LPV were$22,566 per life saved at hospital discharge and $11,690 per QALY gained. The maximum, cost-effective, per patient investment in a hypothetical program to improve LPV adherence from 50 to 90$9,482. Results were robust to a wide range of economic and patient parameter assumptions. Conclusions: Even a costly intervention to improve adherence with low-tidal volume ventilation in patients with ALI reduces death and is cost-effective by current societal standards.NIH F32HL090220.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84154/1/Cooke - CEA LPV.pd

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