scholarly journals Socio-economic factors on haze pollution: based on panel data of 30 provinces in China

2021 ◽  
Vol 647 ◽  
pp. 012199
Author(s):  
Minjun Huang ◽  
Zhilin Guo ◽  
Yangli Guo
Keyword(s):  
Panel Data ◽  
Economic Factors ◽  
Haze Pollution ◽  
Socio Economic Factors

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>

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

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 Modelling Global Burden of Disease Measures in Selected European Countries Using Robust Dynamic Spatial Panel Data Models

2020 ◽  
Vol 2 (347) ◽  
pp. 109-127
Author(s):  
Agnieszka Orwat-Acedańska
Keyword(s):  
Panel Data ◽  
Burden Of Disease ◽  
Global Burden Of Disease ◽  
Global Burden ◽  
Years Of Life Lost ◽  
Economic Factors ◽  
Spatial Panel ◽  
Spatial Panel Data Models ◽  
Spatial Panel Data ◽  
Socio Economic Factors

The aim of the paper is to study relationships between selected socio‑economic factors and health of European citizens. The health level is measured by selected global burden of disease measures – DALYs (Disability Adjusted Life Years) and its two components: YLL (Years of Life Lost) and YLD (Years Lived with Disability). We identify which factors significantly affect these indicators of health. The empirical study uses a panel data comprising 16 countries mostly from the old‑EU in the period 2003–2013. Fixed‑effects dynamic spatial panel data (DSPD) models are used to account for autocorrelations of the dependent variables across time and space. The models are estimated with a novel, modified quasi maximum likelihood Yang method based on M‑estimators. The approach is robust on the distribution of the initial observations. The empirical analysis covers specification, estimation, and verification of the models. The results show that changes in YLD are significantly related to alcohol consumption, healthcare spending, social spending, GDP growth rate and years of education. Exactly the same set of factors is associated with variation in DALYs. Sensitivity of the YLL component to the socio‑economic factors is considerably weaker.

scholarly journals (RE) MAPPING EMPOWERMENT OUTCOMES AMONG MARRIED WOMEN IN RURAL INDIA: A PANEL DATA STUDY

2019 ◽  
Vol 7 (6) ◽  
pp. 585-595
Author(s):  
Asoke Howlader ◽  
Sidhartha Sankar Laha ◽  
Arindam Modak
Keyword(s):  
Panel Data ◽  
Rural Women ◽  
Participation Rate ◽  
Poverty Line ◽  
Married Women ◽  
Rural India ◽  
Economic Factors ◽  
Factors Influencing ◽  
Poverty Status ◽  
Socio Economic Factors

Purpose of the study: This paper endeavours to re-examine the socio-economic factors influencing empowerment among married women in rural India over two points of time, 2005 and 2012. It examines the interplay of the work status of rural married women and the poverty status of their household in influencing empowerment. Methodology: The study uses the nationally representative multi-topic India Human Development Survey (IHDS). IHDS panel data has been utilized to assess the entry and exit from a workforce of rural married women, to define the components of empowerment among rural married women and analyze the socio-economic factors influencing the empowerment among rural married women. Main Findings: The outcomes show the increase in the overall empowerment rates in spite of their mobility constraints seem to have badly risen during the period 2005-2012, especially in the context of deteriorating work input among rural women. Moreover, working rural married women from BPL (Below Poverty Line) rural households are less likely to be empowered as compared to working rural married women from APL (Above Poverty Line) households. Applications of this study: The rural female work participation rate is declining in the phase of rising economic growth and education. In this context, their empowerment would not only benefit their personal lives but also impact their economic lives, thus contribute to the country`s GDP. This makes it vital to analyze as to what comprises their empowerment in the first place so that it can be promoted through various schemes. Novelty/Originality of this study: Women’s economic empowerment and their participation in work are essential to bringing in the fullest demographic dividend for inclusive economic escalation and sustainable development in India. Thus, empowerment which may not necessarily be implied by employment is conditioned upon the poverty status of the household. However, the empowerment of rural married women is facilitated by higher education of self, husband and other family members.

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 Dynamic Spatial Panel Data Models in Identifying Socio-Economic Factors Affecting the Level of Health in Selected European Countries

2019 ◽  
Vol 26 (1) ◽  
pp. 195-211
Author(s):  
Agnieszka Orwat-Acedańska
Keyword(s):  
Panel Data ◽  
Data Models ◽  
European Countries ◽  
Likelihood Method ◽  
Economic Factors ◽  
Panel Data Models ◽  
Spatial Panel ◽  
Spatial Panel Data Models ◽  
Spatial Panel Data ◽  
Socio Economic Factors

The aim of the paper is to investigate the relationship between socio-economic factors and the level of health of citizens of selected European countries. Disability-adjusted life years (DALYs) were used as the measure of health. The author applied dynamic spatial panel data models with fixed effects and spatial autocorrelation of the error term. The models were estimated using a novel, modified quasi maximum likelihood method based on M-estimators. The approach is resistant to deviations from the assumptions on the distribution of initial observations. The estimation of initial observations is a severe weakness of standard methods based on the maximization of the quasi-likelihood function in the case of short panels. M-estimators are consistent and asymptotically normally distributed. The empirical analysis covers the specification, estimation, and verification of the models.

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