This study examines the determinants of educational expenditures by households in Nigeria. Data from the Nigerian General Household Survey, Panel 2012/2013, Wave 2 was used and a doublehurdle model was employed for the analysis. The results suggest household income, the age, education, gender of the household heads and urban versus rural residence have a significant impact on the decision to spend on education. Such expenditures are income elastic overall, but are very different in magnitude for low income compared to higher income families. It is found that the income elasticity of education expenditures are approximately four times greater for households in the bottom two-thirds of the income distribution than for those on the top one-third of the income distribution.
The displacement impacts of wind power generation on other generation technologies are estimated for Ontario. In addition, their annual financial benefits, costs, and international stakeholder impacts are measured. For every 100 MWh generated, almost 53 MWh of gas output and 23 MWh of hydro output is displaced, and 19 MWh of power is exported. Ontario loses 826.42 million USD annually from having wind power generation in the system, while the US gains 7.50 million USD through electricity exported from Ontario. Wind power generation has produced an estimated 108.98 million USD in reducing CO2 emissions in the US and Ontario through displacing thermal generation. Comparing the environmental benefits with the net cost to consumers shows the promotion of wind power generation to be largely a waste of Ontario's resources.
PurposeIn spite of the certain risk imposed by financial stress on the real economy, the relationship between financial stress and economic activity is complicated and underresearched, meaning that important gaps still remain in the authors’ understanding of this critical relationship. Therefore, the current study aims to answer the significant question regarding whether a stressful financial sector has predictive power on the real sector and vice versa. Hence, the study examines the causal interrelationship between financial stress index (FSI) and economic activity in Luxembourg as a sample country.Design/methodology/approachIn this study, accompanying the time domain Granger causality framework of Hacker and Hatemi-J (2012), the authors utilize the spectral causality technique of Breitung and Candelon (2006), which is based on the study of Geweke (1982) and Hosoya (1991). This method enables the researcher to measure the degree of a particular variation in time series. Moreover, it allows considering the nonlinearities and causality cycles. The authors further apply the recent method of Farné and Montanari (2018) that is a bootstrap framework on Granger-causality spectra, which allows for disambiguation in causalities.FindingsThe time-domain approach finds evidence of bidirectional causation between the variables. However, the spectral causality results indicate the causal linkages between the series are only valid under the medium-run frequency. This study’s findings emphasize covering the frequency causality to deliver a more comprehensive picture of the interrelationship between the variables.Originality/valueThere are many studies in this area that examine the nexus between financial stress and economic activity. However, the authors believe this paper is the first study in the context of Luxemburg. The authors focus on this country since its financial sector is designated as the most important pillar for the economy. Thus, a careful and reliable examination of the relationship between the financial sector and economic activity is likely to be of considerable interest to policymakers and researchers in this field.
The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African inflation by means of non-linear models and using a long historical dataset of seasonally-adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models' forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02-1966:09 and evaluate 24 step-ahead forecasts over an out-of-sample period of 1966:10-2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear models across various extreme economic environments and forecast horizons. In general, we find that no competing model consistently and significantly beats the LoLiMoT's performance in forecasting South African inflation.
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