scholarly journals The Impact of Air Quality on Population Migration

2020 ◽  
Vol 15 (2) ◽  
pp. 23-32
Author(s):  
Ke Wang ◽  
◽  
Yiwei Wang ◽  
Chun-Ping Chang
Keyword(s):  
Air Quality ◽  
Panel Data ◽  
Negative Impact ◽  
Fixed Effect Model ◽  
Education Level ◽  
Population Migration ◽  
Control Factors ◽  
Effect Model ◽  
Per Capita ◽  
The Impact

Based on annual panel data of OECD countries from 1995 to 2014, this paper analyzes the impact of air quality (including per capita CO2, PM2.5, and SO emissions) on the immigrant population through a panel fixed-effect model, while employing control factors such as GDP, unemployment rate, and education level. Overall, we provide evidence that air quality is a key determinant of immigration in the selected countries, and in particular the host country’s emissions have a negative impact on immigrants. Greater emissions imply fewer immigrants, while fewer emissions denote more immigrants. Our findings provide countries with a way to more accurately estimate migrant inflow and offer an idea for OECD members on how to attract immigrants via an improvement in environmental quality.

scholarly journals FAKTOR FAKTOR YANG MEMPENGARUHI URBANISASI DI SUMATRA BARAT

2017 ◽  
Vol 6 (1) ◽  
pp. 15
Author(s):  
Febbi Wulandari
Keyword(s):  
Panel Data ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Education Level ◽  
Research Data ◽  
Health Facilities ◽  
Insignificant Effect ◽  
Effect Model ◽  
West Sumatra ◽  
The Impact

The purpose of this research was to knowing: (1) the impact  of income on urbanization in west Sumatra. (2) the impact  of education level on urbanization in west Sumatra. (3) the impact  of health facilities and infrastructure on urbanization in west Sumatra. (4) the impact  of education facilities and infrastructure on urbanization in west Sumatra. (5) the impact  of income, education level, health facilities and infrastructure, education facilities and infrastructure together on urbanization in west Sumatra.The type of this research is descriptive and associative. The type of research data is secondary data. This research uses panel data that is using 19 districts / cities in West Sumatra, By using approach Fixed Effect Model (FEM).  The results of this study show that: 1) income has a positive and significant effect on on urbanization in west Sumatra, (2) education level has a positive and insignificant effect on urbanization in West Sumatra, (3) Health facilities and infrastructure has negative and insignificant effect on urbanization in West Sumatera, (4) Education facilities and infrastructure has positive and insignificant effect on urbanization in West Sumatera, (5) Income, education level, health facilities and infrastructure, and education infrastructure facilities together have a significant effect on urbanization in West Sumatra.

scholarly journals Determinants of Structural Transformation in ASEAN

2020 ◽  
Vol 35 (2) ◽  
pp. 137
Author(s):  
Kalies Sirieh Puspitowati ◽  
Deden Dinar Iskandar
Keyword(s):  
Panel Data ◽  
Structural Transformation ◽  
Value Added ◽  
Fixed Effect Model ◽  
Dependency Ratio ◽  
Service Value ◽  
Income Per Capita ◽  
Effect Model ◽  
Per Capita ◽  
Asean Countries

This study aims to analyze the determinants of the structural transformation in ASEAN countries. This study uses quantitative panel data from 9 countries in ASEAN from 2000 to 2017, thus makes up for 162 observations. This study employs panel data regression analysis with fixed effect model approach. In this study, the shifting of sectoral value added away from agriculture sectors indicates structural transformation. In particular, sectoral value added consists of the industrial value added and service value added. The results of this study shows that dependency ratio, income per capita, education, and trade significantly affect the increase of industrial value added during observation period. On the other hand, total population, dependency ratio, income per capita, education, control of corruption, and trade significantly increase the service value added over time.

scholarly journals The Impact of Dividend Policy on Stock Price Volatility in Pakistan

2019 ◽  
Vol IV (I) ◽  
pp. 506-515
Author(s):  
Ziaullah Shah ◽  
Shehzad Khan ◽  
Muhammad Faizan Malik
Keyword(s):  
Panel Data ◽  
Stock Price ◽  
Dividend Policy ◽  
Price Volatility ◽  
Fixed Effect Model ◽  
Share Prices ◽  
Dividend Payments ◽  
Effect Model ◽  
Dividend Irrelevance ◽  
The Impact

The objective of this study is to inspect dividend policy influence on volatility of share prices. For investigation seven Non-financial segment/sectors have been selected. A sample of 137 firms who paid four dividend payments listed at PSX is analysed for the period of 2007-2017.Proxy for policy of dividend are earning per share, Payout ratio, dividend yield, while assets growth and firm size are taken as control variables. OLS regression model has been initially applied on panel data. The outcomes of fixed effect model are focused. Overall outcomes of the study confirmed that prices of stock is significantly influenced by policy of dividend and reject dividend irrelevance theory.

scholarly journals Determinants of Financial Performance of Nepalese Commercial Banks: Evidence from Panel Data Approach

2020 ◽  
Vol 32 (2) ◽  
pp. 45-59
Author(s):  
Purna Man Shrestha
Keyword(s):  
Panel Data ◽  
Financial Performance ◽  
Commercial Banks ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Lagrangian Multiplier ◽  
Effect Model ◽  
Specific Factors ◽  
The Impact ◽  
Bank Specific Factors

The impact of bank specific factors on the financial performance of Nepalese commercial banks is analyzed in this paper. The financial performance is measured by using return on assets (ROA). Similarly, managerial efficiency (ME), liquidity (LIQ), credit risk (CR), assets quality (AQ) and operational efficiency (OE) is used as proxy of bank specific factors. This study used panel data of 17 commercial banks for the period of 2010/11 to 2017/18. Breusch and Pagan Lagrangian multiplier test showed that Pooled Regression model is not appropriate and Hausman test concluded that Fixed Effect model is appropriate rather than Random Effect model. Using the Fixed Effect model; this study concludes that bank specific factors have significant impact on financial performance of Nepalese commercial banks. Finally, this study reveals that ME, AQ and OE have significant positive impact, and CR has negative impact on the financial performance of Nepalese commercial banks.  

scholarly journals Pengaruh Konsumsi Energi dan Deforestasi Terhadap Output Nasional Lima Negara Anggota ASEAN

2021 ◽  
Vol 10 (2) ◽  
pp. 105-112
Author(s):  
Neni Kristiana ◽  
Lorentino Togar Laut ◽  
Jalu Aji Prakoso
Keyword(s):  
Panel Data ◽  
Co2 Emissions ◽  
Negative Impact ◽  
National Income ◽  
Electricity Consumption ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Coal Consumption ◽  
Effect Model ◽  
National Output

The economic development aimed at improving people’s welfare often ignores the negative impact of the surrounding environment. The high use of energy aimed to increase the national income of the five ASEAN members hurts the environment by increasing CO2 levels in the air.  This research aims to analyze the effect of CO2 emissions, coal consumption, electricity consumption and deforestation on national output in five ASEAN members. The variable used in this research is national output as the dependent variable and CO2 emissions, coal consumption, electricity consumption and deforestation as the independent variables. This research uses secondary data. The data is the panel data of five ASEAN members (Indonesia, Malaysia, Thailand, Philippines, Myanmar) from 2002 until 2018.  The research method in this time is panel data regression, using Fixed Effect Model. This research shows that in five ASEAN members from 2002 until 2018, CO2 emissions harm national output, coal consumption, and electricity consumption positively affects national outcome, while deforestation does not affect national output.

scholarly journals Managing Climate Change for Wheat Production: An Evidence from Southern Punjab, Pakistan

2019 ◽  
Vol 1 (2) ◽  
pp. 48-58
Author(s):  
Muhammad Haseeb Raza ◽  
Allah Bakhsh ◽  
Muhammad Kamran
Keyword(s):  
Climate Change ◽  
Negative Impact ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Wheat Production ◽  
Panel Model ◽  
Effect Model ◽  
Tube Wells ◽  
Relationship Of ◽  
The Impact

The current research study was conducted to estimate the impact of climate change on wheat production by using panel data from 1998-2014.  For this purpose four districts were selected from southern Punjab, Pakistan. Panel model of fixed effect (FE) was estimated at region level for wheat productivity utilizing climatic and non-climatic variables based on season. The conclusion of the study showed that non-climatic, i.e. inputs, number of tractors, area under wheat, number of tube wells and fertilizer consumption in each district have significant impact on the wheat production. The fixed effect model results revealed that the increase in temperature has significance impact on the month of the November and January, while it showed negative impact in the month of April. The results also showed a non-linear relationship of precipitation for the months of April and November.

scholarly journals IMPACT OF MACROECONOMIC FACTORS ON FOREIGN DIRECT INVESTMENT IN SELECTED SOUTHEASTERN EUROPEAN COUNTRIES

TEME ◽  
2019 ◽  
pp. 1237 ◽  
Author(s):  
Jelena Andrašić ◽  
Vera Mirović ◽  
Branimir Kalaš
Keyword(s):  
Foreign Direct Investment ◽  
Direct Investment ◽  
Negative Impact ◽  
Positive Impact ◽  
Fixed Effect Model ◽  
European Countries ◽  
Market Size ◽  
Macroeconomic Factors ◽  
Effect Model ◽  
The Impact

Foreign direct investment has a significant role in Southeastern European countries. The aim of the paper is reflected in assessing the character and nature of the relationship between macroeconomic factors and foreign direct investment in Southeastern European countries. Further, the subject of paper includes the examination of the impact of selected macroeconomic variables on foreign direct investment in six countries for the period from 2000 to 2012. The selected countries are Albania, Bosnia and Herzegovina, Bulgaria, Macedonia, Romania and Serbia. The research includes an examination impact of market size, national competitiveness and employment on foreign direct investment. By using the Hausman test, it was confirmed that the fixed effect model is an appropriate model in panel analysis. Based on the result, it determined the positive impact of market size, while the industry's share of GDP and employment have a negative impact on this variable. Also, the results confirmed that only the market size of the countries significantly affected on the flow of foreign direct investment in Southeastern European countries.

The impact of international sanctions on energy security

2020 ◽  
pp. 0958305X2093768
Author(s):  
Jun Wen ◽  
Xinxin Zhao ◽  
Quan-Jing Wang ◽  
Chun-Ping Chang
Keyword(s):  
Regression Analysis ◽  
Energy Security ◽  
Negative Impact ◽  
Economic Sanctions ◽  
Fixed Effect Model ◽  
Policy Makers ◽  
Effect Model ◽  
Different Types ◽  
International Sanctions ◽  
The Impact

This study first investigates different types of sanctions on energy security by employing data from a panel of target countries covering the period 1996–2014 and using the panel fixed effect model. Our evidence indicates that international sanctions do significantly negatively influence the energy security of target countries in some cases. Specifically, unilateral sanctions, U.S. sanctions, economic sanctions, and the intensity of sanctions have a significantly negative impact on energy security. However, plurilateral sanctions, EU sanctions, UN sanctions, and non-economic sanctions have no significant impact on the energy security of target countries. The results of endogeneity concerns are also consistent with the results of the basic regression analysis. Overall, our empirical findings merit particular attention from policy makers of target countries to ensure their energy security when facing international sanctions.

scholarly journals Energy Utilization Efficiency of China Considering Carbon Emissions—Based on Provincial Panel Data

2021 ◽  
Vol 13 (2) ◽  
pp. 877
Author(s):  
Ge Huang ◽  
Wei Pan ◽  
Cheng Hu ◽  
Wu-Lin Pan ◽  
Wan-Qiang Dai
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Panel Data ◽  
Industrial Structure ◽  
Negative Impact ◽  
Positive Impact ◽  
Fixed Effect Model ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Effect Model

With the development of the economy, environmental pollution caused by energy consumption has become increasingly prominent. Improving the efficiency of energy utilization is an important way to solve this problem. Firstly, we used a data envelopment analysis (DEA) model to calculate the energy utilization efficiency of China’s provinces and regions from the perspective of environmental constraints, including four inputs—labor force, capital stock, energy consumption and carbon emission—and one output, GDP. Secondly, an entity fixed effect model of panel data was built to investigate the influence of openness, urbanization, marketization and industrial structure on energy utilization efficiency in the process of economic structure change. The results indicate that China’s energy efficiency shows a trend of first stabilizing and then declining from 2007 to 2017. Meanwhile, the comprehensive energy efficiency of all provinces and regions is not very ideal. Only Beijing, Shanghai and Guangdong constitute the forefront of China’s energy efficiency. The lack of pure technical efficiency in most provinces is the main reason for the low comprehensive efficiency, but there are also obvious differences among provinces and regions. In addition, urbanization, openness and industrial structure have a negative impact on energy efficiency, while marketization has a significant positive impact on energy efficiency. Finally, based on the regional differences, some suggestions were put forward to improve China’s energy utilization efficiency.

scholarly journals ANALISIS REGRESI DATA PANEL TERHADAP INDEKS PEMBANGUNAN MANUSIA (IPM) JAWA TIMUR TAHUN 2006-2015

2018 ◽  
Vol 18 (2) ◽  
pp. 69
Author(s):  
Muhammad Jamil Hidayat ◽  
Alfian Futuhul Hadi ◽  
Dian Anggraeni
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Cross Section Data ◽  
Section Data ◽  
Effect Model ◽  
Per Capita

Panel data is a combination of time series and cross section data. Panel data regression is used because in a time there is time researchers can’t perform analysis only by using time series data and cross section data only. This is because the number of factors used in the analysis phase, so that if the researcher only uses cross section data then the researcher can’t see the influence of factors that affect as well as on the growth of HDI that occurs from time to time in a certain period. Whereas it is quite possible that the conditions between one year and another will be different. Based on the model estimation, it is used with fixed effect model (FEM) approach. Modeling HDI with FEM in 2006-2015 period resulted in R2 value of 94.23%. The results showed that from 2006-2015 the ratio of student-teacher (RST), health facilities (HF), percentage of expenditure per capita by group of food (PPF) and regional per capita expenditure (PPE) have significant effect to HDI. Keywords: HDI, Panel, Fixed Effect Model

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