does fiscal rule matter?

Thesis(Master) --KDI School:Master of Development Policy,2017For growth prospects, many oil dependent countries prepare their budget based on revenue from sales of crude oil. A number of studies have focused extensively on the relationship between growth and deficits, however, while controlling for other variables, the research investigated oil rent’s effect on fiscal balance in the presence of fiscal rules for oil dependent economies, and for selected net oil exporting countries, while controlling for the effects of some macroeconomic, budgetary, and political variables, such as government size, interest rate, unemployment rate, inflation rate, real GDP per capita, debt-to-GDP ratio, and control of corruption. The study relied on the strengths of past studies like those of Mika Tujula and Guido Wolswijk (2004) by establishing the specific effects of oil rents for crude oil endowed economies, as one of the main determinants of fiscal balance. This establishes the importance of oil rents, amongst other previously identified macroeconomic, budgetary, and political covariates. Thus, we have attempted to overcome the shortcomings of studies that make generalized conclusions for the main determinants of fiscal balance for all countries, without highlighting specific variables that takes a huge chunk of the effects for specific natural resource endowed economies. Given large macroeconomic panel dataset, our empirical analysis solved the possibility of endogeneity, simultaneity bias and unobserved heterogeneity of oil rents and fiscal balance by the main econometric technique i.e. using an instrumental variable approach based on Dynamic Panel estimators or the General Method of Moment (GMM). This is used in comparison with estimations from pooled OLS, LSDV fixed effects, and the IV/2SLS techniques (using each country’s share of world output as instrument). Our pre-estimation diagnostics showed that the GMM approach may not be applicable to the small sample, and we suspected that the IV/2SLS method may also be weak in testing our hypothesis for the oil dependent economies with N = 20 and T = 16, and therefore we maintained the LSDV Fixed effects estimations as our main result for this category of sample countries selected. We also utilized the Drisc/Kraay standard errors, as well as the robust standard errors, which are standard errors robust to cross-sectional dependence and heteroskedasticity of unknown forms respectively, that exists in large macroeconomic panel data where N > T. Our estimation results shows that in countries with fiscal rules, there is insignificant reaction of fiscal balance to changes in oil rents shocks, and the impact is weak. We find also that welfare spending, which was captured by the real GDP per capita, affects fiscal balance, and so does the budgetary variable, i.e. debt-to-GDP ratio, and the ability of the government to curb corruption and mismanagement of funds, which is politically motivated.1 INTRODUCTION 2 LITERATURE REVIEW 3 METHODOLOGY AND DATA 4 RESULTS AND DISCUSSION 5 CONCLUSION 6 APPENDICESOutstandingmasterpublishedFatai Festus ADEDOYIN

An empirical assessment of electricity consumption and environmental degradation in the presence of economic complexities

To a large extent, the theories and concepts behind the effect of ecological footprint have been the paramount concern of the recent literature. Since the rising and falling of environmental degradation have been a continuous issue since the first phase of development, determinants such as economic complexity may play a critical role in achieving long-term sustainable development in the framework of environmental Kuznets curve (EKC) paradigm. Therefore, this research expands on the notion of an EKC paradigm for the world’s top ten most complex economies by considering four variables, such as real GDP per capita, electricity consumption, trade openness, and a new putative factor of environmental obstacle, the economic complexity index (ECI). This is one of the first studies to look at the impact of ECI on the ecological footprint of a specific sample from 1998 to 2017. The findings demonstrate a continuous inverted U-shaped link between real GDP per capita, the square of real GDP per capita, and ecological footprint. The EKC hypothesis is found to be valid in the long term in the examined complex economies. The findings of the panel autoregressive distributed lag (ARDL) of the pooled mean group (PMG) and fully modified ordinary least squares (FMOLS) estimations demonstrate that in the long term, electric power usage contributed to the carbon footprints. Furthermore, the economic complexity index and trade openness increase environmental performance over time. To determine if there is causation between the variables, we employ the panel vector error correction model (VECM) framework. Particularly, the results show unidirectional causality running from electric power consumption to ecological footprint and bidirectional causal relationship between (1) economic growth and ecological footprint; (2) square of economic growth and ecological footprint; (3) economic complexity index and ecological footprint; and (4) trade openness and ecological footprint. © 2022, The Author(s)

Detection of Hyperpartisan news articles using natural language processing techniques

Yellow journalism has increased the spread of hyperpartisan news on the internet. It is very difficult for online news article readers to distinguish hyperpartisan news articles from mainstream news articles. There is a need for an automated model that can detect hyperpartisan news on the internet and tag them as hyperpartisan so that it is very easy for readers to avoid that news. A hyperpartisan news detection article was developed by using three different natural language processing techniques named BERT, ELMo, and Word2vec. This research used the bi-article dataset published at SEMEVAL-2019. The ELMo word embeddings which are trained on a Random forest classifier has got an accuracy of 0.88, which is much better than other state of art models. The BERT and Word2vec models have got the same accuracy of 0.83. This research tried different sentence input lengths to BERT and proved that BERT can extract context from local words. Evidenced from the described ML models, this study will assist the governments, news’ readers, and other political stakeholders to detect any hyperpartisan news, and also helps policy to track, and regulate, misinformation about the political parties and their leaders

The role of income, trade, and environmental regulations in ensuring environmental sustainability in MINT countries: Evidence from ecological footprint

Income alone cannot ensure environmental sustainability. As such, different economies have relied on environmental regulations to preserve the quality of their environment. The efficiency of such regulations on environmental degradation is still unclear in developing countries culpable for lax environmental regulations. As such, this study applies the Prais-Winsten regression, along with the Driscoll-Kraay panel-corrected standard errors approach to explores the effects of environmental regulations on the ecological footprint (EFP) in MINT (Mexico, Indonesia, Nigeria, Turkey) countries from 1980-2016. The results suggest that energy consumption, trade and GDP increase the EFP while environmental regulations reduce it thereby mitigating environmental degradation, though insignificantly. This indicates that environmental regulations are not totally successful in mitigating ecological distortions in MINT countries. The study applies the Fully Modified Ordinary Least Squares (FMOLS) estimator to obtain the country-wise results. There is evidence that energy consumption increases the EFP in all the countries. The same influence is exacted by trade on the EFP, except in Turkey. The abating role of environmental regulations on environmental degradation were confirmed in all the countries. It was significant in Nigeria and Turkey, but not in Mexico and Indonesia. Further findings revealed a bidirectional causality between GDP and EFP. A one-way causality flows from trade to energy consumption, and from energy consumption and EFP to environmental regulations. Policy directions are discussed within the framework of Sustainable Development Goals (SDGs)

Data-Driven Business Analytics for the Tourism Industry in the UK: A Machine Learning Experiment Post-COVID

The use of data-driven business analytic models has had a significant impact on several sectors of the economy. In the UK, the tourism industry has contributed significantly to the economy. The contribution of tourism to the UK economy is estimated to be £145.9 billion (7.2%) of UK GDP. Regardless of its economic value, tourism is also one of the most vulnerable sectors, as it is susceptible to natural disasters, civil unrest, crisis, and pandemics, all of which can fully shut down the industry. Hence, an accurate and reliable tourism demand forecast is important. Apart from COVID-19, no other occurrence in modern history has had such a broad impact on the economy, industries, everyone and businesses in the world (Galvani et al., 2020). However, with the impact of COVID19 on the industry, it is imperative to reassess potential recovery plans for the UK economy, particularly for local tourism businesses. Macroeconomic data is collected over many source markets for the UK and a machine learning algorithm is tested to assess the future of the industry

An empirical assessment of electricity consumption and environmental degradation in the presence of economic complexities.

To a large extent, the theories and concepts behind the effect of ecological footprint have been the paramount concern of the recent literature. Since the rising and falling of environmental degradation have been a continuous issue since the first phase of development, determinants such as economic complexity may play a critical role in achieving long-term sustainable development in the framework of environmental Kuznets curve (EKC) paradigm. Therefore, this research expands on the notion of an EKC paradigm for the world's top ten most complex economies by considering four variables, such as real GDP per capita, electricity consumption, trade openness, and a new putative factor of environmental obstacle, the economic complexity index (ECI). This is one of the first studies to look at the impact of ECI on the ecological footprint of a specific sample from 1998 to 2017. The findings demonstrate a continuous inverted U-shaped link between real GDP per capita, the square of real GDP per capita, and ecological footprint. The EKC hypothesis is found to be valid in the long term in the examined complex economies. The findings of the panel autoregressive distributed lag (ARDL) of the pooled mean group (PMG) and fully modified ordinary least squares (FMOLS) estimations demonstrate that in the long term, electric power usage contributed to the carbon footprints. Furthermore, the economic complexity index and trade openness increase environmental performance over time. To determine if there is causation between the variables, we employ the panel vector error correction model (VECM) framework. Particularly, the results show unidirectional causality running from electric power consumption to ecological footprint and bidirectional causal relationship between (1) economic growth and ecological footprint; (2) square of economic growth and ecological footprint; (3) economic complexity index and ecological footprint; and (4) trade openness and ecological footprint

An Investigation into the Role of Tourism Growth, Conventional Energy Consumption and Real Income on Ecological Footprint Nexus in France

Previously documented studies in the literature on how tourism leads to economic growth in the form of tourism-led growth hypotheses (TLGH) has been investigated. This study presents a new perspective on the growth of tourism by considering its impact on conventional energy consumption, real income level, and emission via the channel of globalization. Sequences of econometric tests were conducted to validate the hypothesized claims between tourism development and growth impact on conventional energy consumption and pollution proxy by ecological footprints, globalization GDP per capita, biocapacity, and tourists for the case of France. Empirical evidence from the Granger causality test presents a uni-directionalcausality from ecological footprints to GDP per capita and from biocapacity to ecological footprints. The correlation matrix shows interrelation amongst series with biocapacity significantly correlating with ecological footprints with tourist’s arrival having a positive correlation with ecological footprints and a negative one with biocapacity. GPD per capita was found to positively affect the ecological footprints and have a negative correlation with biocapacity and a significant relationship with tourists' arrivals. Additionally, globalization exerts a positive impact on ecological footprints, and its effect on biocapacity was found to be negative although globalization's effect on tourists’ arrivals and per capita GDP is significant. The ARDL estimation indicated biocapacity as a neutral agent for ecological footprints, tourist arrivals having a negative impact on ecological footprints, and globalization significantly affecting ecological footprints. From these findings, it is evident that tourism growth has a significant impact on energy consumption and pollution. Policy recommendations were also provided in this study accordingl

Firm-level pollution and membership of emission trading schemes

Several firms have joined emission trading schemes in response to the call for corporate climate action. Using a comprehensive international data set on corporate membership of emission trading schemes (ETSs), we find that members of the scheme emit more CO2 than non-participants. This result also holds when exploring the corporate discharge of sulphur and volatile organic compounds (VOCs). The magnitude of this relationship persists even in the long run showing little evidence of a reduction from the firms in polluting the environment. We also find that firms that select to exit the scheme continue to pollute at a higher rate in the following years. Firms that enter the scheme for the first time increase their pollution in the following years. Although we identify significant differences at a country and continental level on the effectiveness of ETSs, our results raise some concerns about ETSs’ role

Natural resource abundance, renewable energy, and ecological footprint linkage in MENA countries

Apart from being vulnerable to the menace created by climate change, the MENA countries consume more of non-renewable energy despite their resource endowments and great renewable energy potentials. Energy consumption, natural resources and urbanization may add to environmental degradation since ecological distortions mostly emanate from human activities. This study investigates the effects of the aforementioned variables on the ecological footprint (EFP) in MENA countries. The findings confirm the Environmental Kuznets Curve (EKC) hypothesis and further reveal the negative impact of natural resources and economic growth on the environment. Renewable energy and urbanization reduce EFP. The Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) were applied to obtain the country-specific results which reveal that urbanization surge promotes environmental degradation in all the countries except in Algeria, Bahrain, Tunisia, and Morocco were it is not particularly harmful. Natural resource rent increases the EFP in the UAE, Oman, and Lebanon. Further findings suggest a feedback causality between urbanization, economic growth and EFP. Policy directions based on the findings are extensively discussed

Determinants of green growth in developed and developing countries.

Considering the need for environmental sustainability while ensuring economic growth and development by 2030, this study uses data on 123 developed and developing countries to examine factors that influence green growth. The empirical results show that economic development positively influences green growth. However, trade openness is detrimental to green growth. Regarding energy-related factors, we find energy consumption negatively affecting green growth, but renewable energy consumption significantly improves green growth. In further analysis, we find that the influence of these factors differs between developed and developing countries. The result implies that countries at a different development level will require different strategies in achieving the Sustainable Development Goals in 2030. The results are robust to alternative identification strategies such as the System Generalised Method of Movement, which accounts for potential endogeneity