Shadow economy and economic development: a panel cointegration and causality analysis

2020 ◽  
Vol 13 (2) ◽  
pp. 1
Author(s):  
Inna Tiutiunyk ◽  
Sergij Lyeonov ◽  
Tatiana Vasylieva ◽  
Yuriy Bilan
Keyword(s):  
Economic Development ◽  
Shadow Economy ◽  
Panel Cointegration ◽  
Causality Analysis

Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

2020 ◽  
Author(s):  
Rıdvan Karacan
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fossil Fuels ◽  
Fine Particulate Matter ◽  
Panel Cointegration ◽  
Air Pollutant ◽  
Population Exposure ◽  
Causality Analysis ◽  
G7 Countries

<p>Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases.</p><p><br></p><p><br></p><p>[1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).</p>

scholarly journals The Causal Relationship between Inflation and Unemployment: A Panel Cointegration and Causality Analysis

2014 ◽  
Author(s):  
Ekrem Gül ◽  
Ahmet Kamacı ◽  
Serkan Konya
Keyword(s):  
Phillips Curve ◽  
Causal Relation ◽  
Panel Data Analysis ◽  
Consumer Price Index ◽  
Panel Cointegration ◽  
Causality Analysis ◽  
Causality Test ◽  
Panel Causality ◽  
Unemployment Rates ◽  
The Difference

Central Asian Republics have been facing high unemployment rates and inflation problems since they established. This work is based on the Phillips curve, which deals the opposite relationship between inflation and unemployment. In the article, unemployment rates and Consumer Price Index (CPI) are used. Within this work, the relationship between inflation and unemployment is examined by the panel data analysis for Azerbaijan, Kazakhstan, Kyrgyzstan, Macedonia and Turkey (1996-2012).We acquired the data of this work from the web site of the IMF. Panel Unit Root Tests are used in order to test stagnation of the data. Afterwards Cointegration Test and Panel Causality Test are used. After that panel cointegration and panel causality tests were made to learn if a cointegral relationship was occurred between inflation and unemployment rate or not. As a result of this study we done, the data level is not stable. Because of that reason, we took the difference of them. There is a one-sided causal relation from inflation to unemployment rates in Turkey and other countries.

The Effects of Health Expenditures to Decrease Infant Mortality Rates in OECD Countries

2020 ◽  
pp. 357-383 ◽  
Author(s):  
Funda Kara ◽  
İrfan Ersin
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Health Expenditure ◽  
Panel Cointegration ◽  
Mortality Rates ◽  
Oecd Countries ◽  
Infant Mortality Rate ◽  
Annual Data ◽  
Causality Analysis ◽  
Analysis Process

There is a positive relationship between the health level of the society and its economic development. The main reason is that improving quality of the lives and increasing lifetime has lead to higher economic performance. This evaluates the relationship between health expenditure and infant mortality rate in OECD countries. In the analysis process, 20 different countries in the OECD are selected and annual data of these countries for the years between 1980 and 2017 is evaluated with the help of Kao panel cointegration and Dumitrescu-Hurlin causality analysis. The findings show that there is long term relationship between health expenditure and infant mortality rates in OECD countries. Another important conclusion is that there is a causality analysis from health expenditure to the infant mortality rate. While considering these results, it is recommended that OECD countries should take some actions in order to increase health expenditure so that it can be possible to decrease infant mortality rate.

scholarly journals Trade and economic development: global causality and development- and openness-related heterogeneity

2020 ◽  
Vol 17 (4) ◽  
pp. 923-944
Author(s):  
T. Gries ◽  
M. Redlin
Keyword(s):  
Economic Development ◽  
Trade Openness ◽  
Panel Cointegration ◽  
Ordinary Least Squares ◽  
Development Stage ◽  
Long Run ◽  
Short Run ◽  
Method Of Moments Estimation ◽  
General Method Of Moments ◽  
High Degree

Abstract This paper reconsiders the classic relationship between trade and economic development. We examine the short-term and long-run dynamics between trade and income for 167 countries over the period 1970–2011 and assume that the effect is not homogenous for all countries but rather varies according to the development stage and the degree of trade openness. We apply panel cointegration, Granger causality and panel error correction in combination with Dynamic Ordinary Least Squares and General Method of Moments estimation to explore the causal relationship between these two variables. The results suggest a statistically significant positive short-run and long-run global relationship between trade and income. However, when splitting the panel into different income and trade openness groups, a long-run relationship is observed only for high-income countries and countries with a relatively high degree of trade openness.

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