Ethel Yiranbon

The effect of herd formation among healthcare investors on health sector growth in China

BackgroundChina has become the world‘s second largest healthcare market based on a recent report by the World Health Organization. Eventhough China achieved universal health insurance coverage in 2011, representing the largest expansion of insurance coverage in human history achieved; health inequality remains endemic in China. Lessons from the effect of market crisis on health equity in Europe and other places has reignited interest in exploring the potential healthcare market aberrations that can trigger distributive injustice in healthcare resource allocation among China’s provinces. Recently, many healthcare investors in China have become more concerned about capital preservation, and are responding by abandoning long term investments strategies in healthcare. This investment withdrawal en mass is perceived to be influenced by herding tendencies and can trigger or consolidate endemic health inequality.MethodsOur study simultaneously employs four testing models (two state spaced models and two return dispersion models) to establish the existence of procyclical (herding) behavior among the stocks and its health equity implications. These are applied to a large set of data to compare and contrast results of herd formation among investors in fourteen healthcare sectors in China.ResultsThe study reveals that apart from the cross sectional standard deviation (CSSD) model, the remaining two models and our augmented state space model yields significant evidence of herding in all subsectors of the healthcare market. We also find that the herding effect is more prominent during down movements of the market.ConclusionHerding behavior may lead to contemporaneous loss of investor confidence and capital withdrawal and thereby deprive the healthcare sector of the much needed capital for expansion. Thus there may be obvious delay in efforts to bridge the gap in access to healthcare facilities, medical support services, medical supplies, pharmaceuticals, biotechnology, diagnostic substances, medical laboratory and advanced medical equipment across China. Moreover, a potential crash in the healthcare market is possible in the healthcare sector as a result of persistent herding tendencies among investors and that may have more damaging consequences for health inequality in China.

The Impact of Privatisation of Healthcare Equipment and Technology SOEs on Productivity in Africa

Upon the attainment of independence many African countries emerged with a new spirit of entrepreneurial governance and domestic industrialization. However with time, most of the state owned enterprises (SOEs) set up have been privatized largely because of mismanagement, huge deficits and operational inefficiencies created by many factors. In all material moments, the objective of divesture of SOEs was to stimulate efficiency, productivity and relieve the state of the huge financial burden they bring. Our study examines the methods of privatization of healthcare technology and equipment SOEs in Africa and their impact on post-divestiture productivity based on cases from Ghana, Nigeria, Tanzania and Kenya.We simultaneously collect and model privatization data from International Monetary Fund (IMF) and the World Bank relating to Ghana, Nigeria, Tanzania and Kenya. These were data submitted to the IMF and World Bank as part of the measures to implement the different forms of economic recovery and structural adjustment programs in the respective countries. Our empirical strategy follows the broader literature in estimating reduced form equations for firm performance as a function of ownership, while trying to account for potential problems of heterogeneity (observed and unobserved) and simultaneity bias. We note the insider/employee shareholding accounted for only 23.6 percent of privatization of healthcare equipments and technology manufacturing enterprises on average while mass privatization program accounts for 18.2 percent of the privatization mode. We note that each of these methods yield positive post divestiture labour productivity. However privatization of healthcare equipment and technology manufacturing enterprises by block sale to outside investors generated the highest form of labour productivity.

Forecasting Optimal Solar Energy Supply in Jiangsu Province (China): A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.