scholarly journals Impact of Incentive Policies and Other Socio-Economic Factors on Electric Vehicle Market Share: A Panel Data Analysis from the 20 Countries

2021 ◽  
Vol 13 (5) ◽  
pp. 2928
Author(s):  
Chenlei Xue ◽  
Huaguo Zhou ◽  
Qunqi Wu ◽  
Xueying Wu ◽  
Xingbo Xu
Keyword(s):  
Panel Data ◽  
Electric Vehicle ◽  
Market Share ◽  
Panel Data Analysis ◽  
Strong Support ◽  
Economic Factor ◽  
Economic Factors ◽  
Positive Effects ◽  
Incentive Policies ◽  
Socio Economic Factors

Under the strong support of policies and incentives, the global electric vehicle (EV) market has been developing rapidly. However, in the context of the overall EV market boom, the promotion policies and incentives for consumers to adopt EVs differ from country to country. It is worth exploring the key factors that affect market share and adoption of EVs, such as incentives, policies, and additional socio-economic factors. The data on EV market share and information on policies and incentives in 20 countries were collected from the published reports and online resources from 2015 to 2019. Random effects model analysis was conducted to explore the effect of various factors on EV market share. The innovation of this article is to combine incentive policies with socio-economic factors and use panel data to analyze the actual adoption behavior of the global EV market. Results show that the tax reduction policy, charger density, and income have significantly positive effects on the penetration of EVs. Thus, it suggested that government should still maintain tax incentives and focus on the deployment of charging infrastructure. Household income, as a socio-economic factor, also plays an important role in the adoption of EVs. This will help policymakers adjust and improve policy emphasis to promote the adoption of EVs.

scholarly journals Socio-Economic Factors Influencing on Total Fertility Rate in Iran: A Panel Data Analysis for the Period of 2002-2012

10.19082/2551 ◽  
2016 ◽  
Vol 8 (6) ◽  
pp. 2551-2556 ◽  
Author(s):  
Hasan Jafari ◽  
Ebrahim Jaafaripooyan ◽  
Abou Ali Vedadhir ◽  
Abbas Rahimi Foroushani ◽  
Bahman Ahadinejad ◽  
...  
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Total Fertility Rate ◽  
Panel Data Analysis ◽  
Fertility Rate ◽  
Economic Factors ◽  
Factors Influencing ◽  
Socio Economic Factors

scholarly journals Vulnerability Variables and Their Effect on Wildfires in Galicia (Spain). A Panel Data Analysis

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1004
Author(s):  
Jaime de Diego ◽  
Antonio Rúa ◽  
Mercedes Fernández
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Rural Areas ◽  
Social Vulnerability ◽  
Panel Data Analysis ◽  
Economic Factors ◽  
Low Density ◽  
Socio Economic Variables ◽  
Negative Impacts ◽  
Socio Economic Factors

This paper studied the effect of the socio-economic variables related to social vulnerability on wildfire characteristics (ignitions, hectares burned, and ratio hectares burned/ignitions) in Galicia, Spain. The study recognized that wildfires present threats to people and communities, so actions might be taken to address vulnerabilities in ways that mitigate the negative impacts of such fires. Our final aim was to identify those variables that are relevant to the starting and spreading of wildfires that can help improve the prevention and mitigation of wildfires. Panel data collected over 15 years (2001–2015) for the municipalities of Galicia were used in this study. The results show that vulnerability-related socio-economic factors affect the number of wildfires and the extent of the destruction they cause. Indeed, the progressive abandonment of rural areas is one of the most important problems that increases the occurrence of wildfires. This abandonment is connected to population factors such as aging or low density of population, economic factors such as the decrease in income or low cadastral value, and territorial factors such as the decrease in rustic hectares and ranches. We conclude that prevention and mitigation focused on areas prone to wildfires could be enhanced by taking into account these variables.

scholarly journals Socio-Economic Factors on Indonesia Education Disparity

2015 ◽  
Vol 8 (12) ◽  
pp. 218 ◽  
Author(s):  
Yuni Azzizah
Keyword(s):  
Standard Deviation ◽  
Panel Data ◽  
Education Level ◽  
Economic Conditions ◽  
Economic Factors ◽  
Education Management ◽  
Inequality In Education ◽  
The Difference ◽  
Socio Economic Factors ◽  
Regional Governments

<p class="apa">Since 1998, regional governments in Indonesia have had greater autonomy due to the commencement of a reformation movement across Indonesia. Large portions of education management were delegated to the regional governments. Because of this, the education level varies strongly across Indonesia’ provinces. Referring to the data provided by the Indonesian Bureau of Statistics, it is found that Eastern Indonesia generally has a higher rate of uneducated than Western Indonesia. We review the current condition of Indonesian education in terms of regional disparity among eastern and western provinces and study the correlation between inequality in education and other related aspects, such as social and economic conditions. We find that inequality issues on socio-economic conditions are reflected in the education disparity between Eastern and Western Indonesia. By employing panel data with provinces as units of observations, we find that the difference in regional development among Indonesian provinces influences education issues. By evaluating the standard deviation of the statistic we were able to identify socio-economic factors that influence the regional education disparity.</p>

scholarly journals Market Concentration, Market Share, and Profitability (Study at Indonesian Commercial Banking in the Period of 2001-2012)

2015 ◽  
Vol 11 (27) ◽  
pp. 18
Author(s):  
Sapto Jumono ◽  
Noer A. Achsani ◽  
Dedi B. Hakim ◽  
Muhamad Fidaus
Keyword(s):  
Panel Data ◽  
Market Share ◽  
Concentration Ratio ◽  
Panel Data Analysis ◽  
Positive Influence ◽  
Commercial Banking ◽  
Panel Data Regression ◽  
Data Regression ◽  
Individual Market ◽  
Market Channel

<p>The objective of this research is to examine the influence of market structure on Indonesian commercial banking performance by using concentration ratio and individual market share through deposits market channel and credits market channel. There were 101 banks chosen from 120 banks in a period of 2001-2012 as sampling of research by using purposive sampling. This research uses data panel that combines data cross section and data time series, therefore panel data regression is used in this research. The result of panel data analysis has allowed us to conclude that concentration ratio of deposits market has a significant and positive influence on ROA, meanwhile concentration ratio of credits market, individual market share of deposits, and individual market share of credits market have no significant effects on ROA.</p>

Determinants of External Indebtedness in Heavily Indebted Poor Countries: What Macroeconomic and Socio-Economic Factors Matter?

2020 ◽  
pp. 056943452093832
Author(s):  
Chukwuebuka Bernard Azolibe
Keyword(s):  
Panel Data ◽  
Population Growth ◽  
Causal Relationship ◽  
Government Expenditure ◽  
External Debt ◽  
Economic Factors ◽  
Poor Countries ◽  
Heavily Indebted Poor Countries ◽  
External Indebtedness ◽  
Socio Economic Factors

This study specifically examined whether macroeconomic and socio-economic factors such as corruption, foreign aid, government expenditure, external reserve, population growth, economic growth, and unemployment rate matter in increasing or reducing the level of external indebtedness in heavily indebted poor countries (HIPCs). Both static panel data and panel fully modified ordinary least squares (OLS) estimation techniques were employed. Using panel data set of all the 39 HIPCs covering period of 1996 to 2018, we found out that the factors that matter in increasing their external indebtedness are high rate of corruption that leads to mismanagement of public funds, high dependency on foreign aids, increase in government expenditure, population growth, and unemployment rate. However, external reserve and gross domestic product (GDP) has a reducing effect on their external indebtedness. In terms of causal relationship, only corruption, population growth, and GDP have a causal relationship with external debt, while other variables exhibited a zero causal relationship with external debt. JEL Classifications: F34, E6, E24

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