This study investigated the effect of economic growth on CO 2 emission using the dynamic panel threshold framework. The analysis is based on data from a panel of 31 developing countries. The results indicate that economic growth has negative effect on CO 2 emission in the low growth regime but positive effect in the high growth regime with the marginal effect being higher in the high growth regime. Thus our finding provides no support for the Environmental Kuznets Curve (EKC) hypothesis; rather a U-shaped relationship is established. Energy consumption and population were also found to exert positive and significant effect on CO 2 emission. Including financial development indicator in the model did not change the conclusion about EKC hypothesis. Employing panel causality methods, there is evidence of significant causal relationship between CO 2 emission, economic growth, energy consumption and financial development. The findings emphasize the need for transformation of low carbon technologies aimed at reducing emissions and sustainable economic growth. This may include energy efficiency and switch away from nonrenewable energy to renewable energy.

This study examines the socioeconomic characteristics that influence the decision to diversify and also the welfare effect of diversification on farm households in Makurdi, Benue State. A total of 120 farm households were sampled using a simple random technique. Structured questionnaires were used in collecting the data. The ordinary least square (OLS) model was used to analyze the welfare effect of diversification while the Logit model was used to analyze the determinants of diversification. The Logit results show that a male-headed household, education and credit increase the probability of diversification while farming experience and market access decrease the probability. The OLS result shows that diversification, age, education and credit have a positive and significant effect on household welfare while household size has a negative effect. These results have important implications for policy, economic growth and development.

This paper presents an efficiency assessment of selected OECD countries using a Slacks Based Model with undesirable or bad outputs (SBM-Undesirable). In this research, SBM-Undesirable is used first in a two-stage approach to assess the relative efficiency of OECD countries using the most frequent indicators adopted by the literature on energy efficiency. Besides, in the second stage, GLMM-MCMC methods are combined with SBMUndesirable results as part of an attempt to produce a model for energy performance with effective predictive ability. The results reveal different impacts of contextual variables, such as economic blocks and capital-labor ratio, on energy efficiency levels. IntroductionThis paper analyzes the energy efficiency of selected OECD countries using SBM- The paper is structured as follows: The nest Section presents the contextual setting, including a description of the energy sector across the OECD countries under investigation.The literature survey is then presented in Section 3, followed by the SBM-Undesirable methodology Section. Section 5 presents the data and the prediction of efficiency levels using MCMC generalized linear mixed models, followed by the discussion of the results and the conclusion. Contextual SettingEnergy is one of the major inputs in many production and related processes. Energy is needed in the industrial sector, transportation, street lighting, residential, commercial and government buildings, among others. The demand for energy is rising due to a rising population and the quest for economic growth, which has consequently led to rising energy Due to increasing globalisation and international competitiveness, more emphasis is being placed on reducing production costs, including those related to energy. Moreover, in addition to energy security issues, an increasing cause of concern over the increasing The reason is that energy efficiency is considered as one of the vital strategies to addressing the challenges posed by increasing energy demand (Ang, 2006;Zhou and Ang, 2008). Improving energy efficiency is important from various policy perspectives.Conservation of energy derived from fossil fuels in order to prevent their depletion in the 5 near future is a very crucial objective (Mukherjee, 2008a). Moreover, energy security can be enhanced by improving energy efficiency. Furthermore, reduction in energy use, especially coming from burning fossil fuels, is important for preventing a further deterioration of environmental quality, through increasing CO 2 emissions (Balachandra et al., 2010). Energy efficiency also plays a vital role in achieving the underlying economic objective of cost minimization. For cost effectiveness, it is very important to reduce energy use during periods of high energy prices and also to suitably substitute other inputs for energy (Mukherjee, 2008a). Energy efficiency makes available additional energy resources, which can help in addressing the issues of energy inadequacy or insecurity as well as equity concerns (Balachandra, et al. 2010). Ac...

Background: Forty-nine million people or 83 per cent of the entire population of 59 million rely on the public healthcare system in South Africa. Coupled with a shortage of medical professionals, high migration, inequality and unemployment; healthcare provision is under extreme pressure. Due to negligence by the health professionals, provincial health departments had medical-legal claims estimated at R80 billion in 2017/18. In the same period, provincial health spending accounted for 33 per cent of total provincial expenditure of R570.3 billion or 6 per cent of South Africa's Gross Domestic Product. Despite this, healthcare outcomes are poor and provinces are inefficient in the use of the allocated funds. This warrants a scientific investigation into the technical efficiency of the public health system. Methods:The study uses data envelopment analysis (DEA) to assess the technical efficiency of the nine South African provinces in the provision of healthcare. This is achieved by determining, assessing and comparing ways that individual provinces can benchmark their performance against peers to improve efficiency scores. DEA compares firms operating in homogenous conditions in the usage of multiple inputs to produce multiple outputs. Therefore, DEA is ideal for measuring the technical efficiency of provinces in the provision of public healthcare. In DEA methodology, the firms with scores of 100 per cent are technically efficient and those with scores lower than 100 per cent are technically inefficient. This study considers six DEA models using the 2017/18 total health spending and health staff as inputs and the infant mortality rate as an output. The first three models assume the constant returns to scale (CRS) while the last three use the variable return to scale (VRS) both with an input-minimisation objective. Results:The study found the mean technical efficiency scores ranging from 35.7 to 87.2 per cent between the health models 1 and 6. Therefore, inefficient provinces could improve the use of inputs within a range of 64.3 and 20.8 per cent. The Gauteng province defines the technical efficiency frontiers in all the six models. The second-best performing province is the North West province. Other provinces like KwaZulu-Natal, Limpopo and the Eastern Cape only perform well under the VRS. The other three provinces are inefficient. Conclusions:Based on the VRS models 4 to 6, the study presents three policy options. Policy option 1 (model 4): the efficiency gains from addressing health expenditure wastage in four inefficient provinces amounts to R17 billion. Policy option 2 (model 5): the potential savings from the same provinces could be obtained from reducing 17,000 health personnel, advisably, in non-core areas. In terms of Policy option 3 (model 6), three inefficient provinces should reduce 6940 health workers while the same provinces, inclusive of KwaZulu-Natal could realise health expenditure savings of R61 million. The potential resource savings from improving the efficiency of the inefficient provinces could...

Given the rapid rise and volatility of oil prices, the paper investigates the e¤ect of oil price uncertainty on the South African manufacturing production using monthly observations covering the period 1974:02 to 2012:12. In addition, we quantify the responses of manufacturing production to positive and negative oil price shocks. We examine the dynamic relationship using a bivariate GARCH-in-mean VAR simultaneously estimated with a full information maximum likelihood technique. The conditional standard deviation of the forecast of the growth of US crude oil imported acquisition cost by re…ners is used as a measure of oil price uncertainty. Our results show that oil price uncertainty negatively and signi…cantly impacts on South Africa's manufacturing production. We also …nd that the responses of manufacturing production to positive and negative shocks are asymmetric.JEL codes: C32, D24, E23, E32

This paper investigates the dynamic causal link between exports and economic growth using both linear and nonlinear Granger causality tests. We use annual South African data on real exports and real gross domestic product from 1911-2011. The linear Granger causality result shows no evidence of significant causality between exports and GDP. The relevant VAR is unstable, which undermines our confidence in the causality result identified by the linear Granger causality test. Accordingly we turn to the nonlinear methods to evaluate Granger causality between exports and GDP. First, we use Hiemstra and Jones (1994) nonlinear Granger causality test and find a unidirectional causality from GDP to exports. However, using a more powerful and less biased nonlinear test, the Diks and Panchenko (2006) test, we find evidence of significant bi-directional causality. These results highlight the risk of misleading conclusions based on the standard linear Granger causality tests which neither accounts for structural breaks nor uncover nonlinearities in the dynamic relationship between exports and GDP.JEL Classifications: C14, C32, F43, O40

We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas City Fed's financial stress index and the U.S. Economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons. Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme. JEL Classification codes: C11, C53, F37, F47, Q02 Abstract We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas CityFed's financial stress index and the U.S. Economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons.Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme.JEL Classification codes: C11, C53, F37, F47, Q02

This paper analyses the asymmetric volatility spillovers between the real exchange rate and stock returns in South Africa. A Multivariate Exponential Generalized Autoregressive Conditionally Heteroskedastic (EGARCH) model alongside other asymmetric GARCH models (GJR GARCH and APARCH) were estimated using monthly data from 1996 to 2016 to examine the relationship. The results show that there is a bi-directional volatility spillover effect between the two markets in the short-run. Also these effects are asymmetric. These findings suggest that while information in one market can be used to forecast changes in the other, these financial assets should not be included in the same portfolio when diversifying risk.