Financial Development and Governance: A Panel Data Analysis Incorporating Cross-sectional Dependence

This study investigates bidirectional causality between governance and financial development using panel data of 101 countries from 1984 to 2013. The financial development–governance nexus is explored using econometric methods robust to cross-sectional dependence, and the relationship between different levels of development and openness is analyzed. Long-run equation estimates show clear evidence that financial development positively affects governance, and this positive impact is found to be robust to three different measures of governance. Further analysis shows that improving governance quality has positive effects on financial development, while Granger causality tests demonstrate bidirectional causality between financial development and the governance measures. Last, the impact of financial development on governance is dependent on a country’s level of development and openness. These findings underscore the crucial role of financial development in bringing about good governance reforms and economic growth that, in turn, can further develop the financial sector. As such, a symbiotic and synergistic relationship can persist between good governance, growth, and financial development. The findings provide significant motivation for policymakers to encourage openness and financial sector development to lift the standard of living, especially in emerging economies

A Nonparametric Analysis of Energy Environmental Kuznets Curve in Chinese Provinces

Energy resources are an important material foundation for the survival and development of human society, and the relationship between energy and economy is interactive and complementary. This paper analyzes the energy consumption–economic growth nexus in Chinese provinces using novel and recent nonparametric time-series as well as panel data empirical approaches. The dataset covers 30 provinces over the period of 1980-2018. The empirical analysis indicates the presence of a nonlinear functional form and smooth structural changes in most of the provinces. The nonparametric empirical analysis validates the presence of a nonlinear unit root problem in energy consumption and economic growth, and nonlinear cointegration between the variables. Additionally, the nonparametric panel cointegration test reports evidence of convergence in energy consumption and economic growth patterns across the provinces. The nonparametric regression analysis finds economic growth to have a positive effect, on average, on energy consumption in all provinces, except for Beijing. Further, the energy environmental Kuznets curve exists between economic growth and energy consumption in 20 out of 30 Chinese provinces. The Granger causality analysis reveals the presence of a mixed causal relationship between economic growth and energy consumption. The empirical findings have important implications for Chinese authorities in planning for improving energy efficiency, decoupling between economic growth and energy consumption, and reducing the environmental footprint of provinces

The Dynamics of Financial Development, Globalization, Economic Growth and Life Expectancy in Sub-Saharan Africa

The importance of life expectancy is recognized in the development economics literature because of its increasing effects on labor productivity and economic growth in in long-run. However, no published study to date empirically examines the nonlinear relationships between globalization, financial development, economic growth and life expectancy in Sub-Saharan African (SSA) countries. Therefore, our study intends to fill this gap by using non-parametric cointegration test and multivariate Granger causality test towards a non-linear empirical understanding of the factors affecting the life expectancy. We consider the case of 16 Sub-Saharan African economies using annual data over the period 1970-2012. The empirical analysis indicates that financial development, globalization and economic growth appear to have a positive impact upon life expectancy in Sub-Saharan African economies, except for Gabon and Togo. Our empirical findings may provide insightful policy implications towards improving population health conditions which are vital for promoting the productivity of labor force and long-run economic growth in Sub-Saharan African countries. In light of these policy implications, governments should incorporate globalization, financial development and economic growth as key economic instruments in formulating sustainable developmental policy to promote life expectancy for the people in Sub-Saharan African countries

The Dynamics of Financial Development, Globalization, Economic Growth and Life Expectancy in Sub-Saharan Africa

The importance of life expectancy is recognized in the development economics literature because of its increasing effects on labor productivity and economic growth in in long-run. However, no published study to date empirically examines the nonlinear relationships between globalization, financial development, economic growth and life expectancy in Sub-Saharan African (SSA) countries. Therefore, our study intends to fill this gap by using non-parametric cointegration test and multivariate Granger causality test towards a non-linear empirical understanding of the factors affecting the life expectancy. We consider the case of 16 Sub-Saharan African economies using annual data over the period 1970-2012. The empirical analysis indicates that financial development, globalization and economic growth appear to have a positive impact upon life expectancy in Sub-Saharan African economies, except for Gabon and Togo. Our empirical findings may provide insightful policy implications towards improving population health conditions which are vital for promoting the productivity of labor force and long-run economic growth in Sub-Saharan African countries. In light of these policy implications, governments should incorporate globalization, financial development and economic growth as key economic instruments in formulating sustainable developmental policy to promote life expectancy for the people in Sub-Saharan African countries

Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks

[EN] Internet of things (IoT) is a very important research area, having many applications such as smart cities, intelligent transportation system, tracing, and smart homes. The underlying technology for IoT are wireless sensor networks (WSN). The selection of cluster head (CH) is significant as a part of the WSN's optimization in the context of energy consumption. In WSNs, the nodes operate on a very limited energy source, therefore, the routing protocols designed must meet the optimal utilization of energy consumption in such networks. Evolutionary games can be designed to meet this aspect by providing an adequately efficient CH selection mechanism. In such types of mechanisms, the network nodes are considered intelligent and independent to select their own strategies. However, the existing mechanisms do not consider a combination of many possible parameters associated with the smart nodes in WSNs, such as remaining energy, selfishness, hop-level, density, and degree of connectivity. In our work, we designed an evolutionary game-based approach for CH selection, combined with some vital parameters associated with sensor nodes and the entire networks. The nodes are assumed to be smart, therefore, the aspect of being selfish is also addressed in this work. The simulation results indicate that our work performs much better than typical evolutionary game-based approachesThe authors acknowledge the support of the Hankuk University of Foreign Studies Research Fund 2019 for this work.Sohail, M.; Khan, S.; Ahmad, R.; Singh, D.; Lloret, J. (2019). Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks. Sensors. 19(18):1-20. https://doi.org/10.3390/s19183835120191

The CO2-Growth nexus revisited: A nonparametric analysis for G7 economies over nearly two centuries

Using a two-century long dataset and some recently popularized nonparametric econometric techniques, this study revisits the nexus between economic growth and carbon dioxide (CO2) emissions for the G7 countries over nearly two centuries. The use of nonparametric modelling is warranted by the fact that long historical time series are often subject to structural breaks and other forms of nonlinearity over the course of time. We employ nonparametric cointegration and causality tests along with the cross-validated Local Linear technique analysis and validate the existence of the environmental Kuznets curve in six of the G7 countries – Canada, France, Germany, Italy, U.K. and the U.S.– and the only exception is Japan. Our empirical analysis also finds CO2 emissions and economic growth to be cointegrated and closely interrelated in the Granger sense. Our results are robust and highlight the nonlinear causal relationship between the two variables

The CO2-Growth nexus revisited: A nonparametric analysis for G7 economies over nearly two centuries

Using a two-century long dataset and some recently popularized nonparametric econometric techniques, this study revisits the nexus between economic growth and carbon dioxide (CO2) emissions for the G7 countries over nearly two centuries. The use of nonparametric modelling is warranted by the fact that long historical time series are often subject to structural breaks and other forms of nonlinearity over the course of time. We employ nonparametric cointegration and causality tests along with the cross-validated Local Linear technique analysis and validate the existence of the environmental Kuznets curve in six of the G7 countries – Canada, France, Germany, Italy, U.K. and the U.S.– and the only exception is Japan. Our empirical analysis also finds CO2 emissions and economic growth to be cointegrated and closely interrelated in the Granger sense. Our results are robust and highlight the nonlinear causal relationship between the two variables

Do stock markets play a role in determining COVID-19 economic stimulus? A cross-country analysis

This paper makes an innovative contribution to the extant literature by analysing the determinants of economic stimulus packages implemented by governments in response to the COVID-19 pandemic. In particular, we explore whether stock market declines observed in many countries can predict the size of COVID-19 stimulus packages. Moreover, we explore whether a country's level of income can augment the underlying relationship between stock market declines and stimulus packages. The findings reveal that a larger stock market decline results in a larger stimulus package; however, this effect is only observed in countries that have an income level greater than the mean and/or median per capita gross domestic product (GDP). Moreover, our results show that monetary policy is more responsive to a stock market decline than fiscal policy. Thus, our results underscore the importance of international donor agencies such as the World Bank and International Monetary Fund (IMF) in supporting less affluent countries in coping with the adverse impacts of the COVID-19 pandemic on their economies

On Bringing Robots Home

Throughout history, we have successfully integrated various machines into our homes. Dishwashers, laundry machines, stand mixers, and robot vacuums are a few recent examples. However, these machines excel at performing only a single task effectively. The concept of a "generalist machine" in homes - a domestic assistant that can adapt and learn from our needs, all while remaining cost-effective - has long been a goal in robotics that has been steadily pursued for decades. In this work, we initiate a large-scale effort towards this goal by introducing Dobb-E, an affordable yet versatile general-purpose system for learning robotic manipulation within household settings. Dobb-E can learn a new task with only five minutes of a user showing it how to do it, thanks to a demonstration collection tool ("The Stick") we built out of cheap parts and iPhones. We use the Stick to collect 13 hours of data in 22 homes of New York City, and train Home Pretrained Representations (HPR). Then, in a novel home environment, with five minutes of demonstrations and fifteen minutes of adapting the HPR model, we show that Dobb-E can reliably solve the task on the Stretch, a mobile robot readily available on the market. Across roughly 30 days of experimentation in homes of New York City and surrounding areas, we test our system in 10 homes, with a total of 109 tasks in different environments, and finally achieve a success rate of 81%. Beyond success percentages, our experiments reveal a plethora of unique challenges absent or ignored in lab robotics. These range from effects of strong shadows, to variable demonstration quality by non-expert users. With the hope of accelerating research on home robots, and eventually seeing robot butlers in every home, we open-source Dobb-E software stack and models, our data, and our hardware designs at https://dobb-e.comComment: Project website and videos are available at https://dobb-e.com, technical documentation for getting started is available at https://docs.dobb-e.com, and code is released at https://github.com/notmahi/dobb-