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Showing 3 results for Economic Vulnerability
Dr Mozhgan Moallemi, Dr Yeganeh Moosavi Jahromi, Dr Alireza ُsharif Moghadasi, Maryam Ramezani,
Volume 23, Issue 2 (5-2023)
Volume 23, Issue 2 (5-2023)
Abstract
Aim and Introduction
Internal and external economic crises and shocks are inevitable in different countries. Many countries are unable to resist economic crises and witnessed undesirable economic events. On the other hand, some countries are highly resilient to domestic and foreign economic crises. The single-product countries are more vulnerable to economic crises than other countries. Resilience of the economy can help move the economy towards sustainable development.
Sustainable development is a development that meets the current human needs without harming the capabilities of the future generations to meet their needs. For sustainable development, four dimensions including governance, economic, environmental and social dimensions are considered. Analyzing the growth history of countries reveals the fact that international trade has been the engine of economic prosperity and expansion of most advanced and developing societies. In order to achieve sustainable development, it is necessary to have trade relations with other countries of the world.
Also, the country's distance from international trade centers due to specific geographical and political conditions will be an obstacle to the development of the economy. In this way, the peripheral dimension is proposed as the fifth dimension of sustainable development to show the political and geographical isolation of the country. In this article, first, the dimensions of sustainable development and its subgroup variables are determined. Then the impact of sustainable development dimensions on the state of economic vulnerability and resilience of different countries are analyzed. The statistical sample includes two groups of countries including G7 member countries and MENA countries. This study investigates the state of vulnerability and resilience (VR) of these two groups during 2017-2020.
Methodology
In this article, Graph theory and Tarjan's algorithm are used to analyze the relationships within the network of variables influencing sustainable development and to examine the relationship between these variables and economic vulnerability and resilience. Tarjan's algorithm is looking for a strongly connected graph that can identify the fundamental variables affecting economic vulnerability and resilience and finally determine the maximal graph. The final output of Tarjan's algorithm is n* variables for measuring VR. Tarjan's algorithm divides the variables into two general parts; The first group of variables that causes vulnerability and resilience and the second group of variables that is created as a result of resilience and vulnerability. The initial set of variables in the dimensions of sustainable development includes economic, social, environmental, governance and peripheral dimensions. After identifying the relationships between the 43 variables presented, a graph is drawn that expresses the relationships between the desired variables.
Findings
The results of the algorithm reveal the fact that the resilience of the model is due to the economic and governance dimensions. If the "economic" or the "governance” dimension are specifically removed, the capacity of the directed graph which is resilient to the strongly connected feature will definitely be lost. Since governance and economic dimensions directly affect other dimensions, they are called as control dimensions. On the other hand, social, environmental and peripheral dimensions are considered as contingent dimensions.
Therefore, contingent dimensions are directly dependent and influenced by control dimensions. In this research, the Net Vulnerability and Resilience Index (NVRI) is separated in all dimensions and calculated in the range of -1 and 1. The NVRI time series is shown during the period and based on the sample countries. The results indicate that in all periods, the status of the NVRI index of the G7 countries was better than the MENA countries, and all the G7 members had resilient economies.
Discussion and Conclusion
According to the index calculations, the countries are classified into four states of uncontrolled vulnerable, limited vulnerable, unstable resilient and sustainable resilient. The G7 countries are sustainable and resilient, which means that in these countries, resilience has surpassed vulnerability. The countries of Oman, Kuwait, Saudi Arabia, UAE, Occupied Palestine and Bahrain from the MENA group are also sustainable and resilient.
The main strength of MENA countries, which are in the group of sustainable resilient, is focused on the peripheral dimension and how these countries interact with the global economy. Among these countries, the UAE and occupied Palestine have a more suitable situation. MENA countries are mainly in the pure and uncontrolled vulnerable group. The governance dimension and then the economic dimension are the important factors of the vulnerability of these countries.
The temporal analysis of the index for the selected countries shows that the majority of the countries that are in the sustainable resilient group did not change their situation during the period under review. Iran is an uncontrolled vulnerable during the years 2017-2020, and in all periods, the index in governance and economic dimensions has been negative and vulnerability is more than resilience.
The analysis of the NVRI index examines the strengths and weaknesses of MENA and G7 countries with a sustainable development approach. It helps the policymakers to get strategic suggestions to improve the situation in weaker countries by following the example of the countries that have a better VR state. The goal of quantifying the state of vulnerability and resilience is to achieve sustainable and inclusive growth in accordance with international programs.
Keywords: Economic Vulnerability and Resilience, Sustainable Development, Graph Theory, Tarjan Algorithm, Composite Index
JEL Classification: C02, C60, O10
Internal and external economic crises and shocks are inevitable in different countries. Many countries are unable to resist economic crises and witnessed undesirable economic events. On the other hand, some countries are highly resilient to domestic and foreign economic crises. The single-product countries are more vulnerable to economic crises than other countries. Resilience of the economy can help move the economy towards sustainable development.
Sustainable development is a development that meets the current human needs without harming the capabilities of the future generations to meet their needs. For sustainable development, four dimensions including governance, economic, environmental and social dimensions are considered. Analyzing the growth history of countries reveals the fact that international trade has been the engine of economic prosperity and expansion of most advanced and developing societies. In order to achieve sustainable development, it is necessary to have trade relations with other countries of the world.
Also, the country's distance from international trade centers due to specific geographical and political conditions will be an obstacle to the development of the economy. In this way, the peripheral dimension is proposed as the fifth dimension of sustainable development to show the political and geographical isolation of the country. In this article, first, the dimensions of sustainable development and its subgroup variables are determined. Then the impact of sustainable development dimensions on the state of economic vulnerability and resilience of different countries are analyzed. The statistical sample includes two groups of countries including G7 member countries and MENA countries. This study investigates the state of vulnerability and resilience (VR) of these two groups during 2017-2020.
Methodology
In this article, Graph theory and Tarjan's algorithm are used to analyze the relationships within the network of variables influencing sustainable development and to examine the relationship between these variables and economic vulnerability and resilience. Tarjan's algorithm is looking for a strongly connected graph that can identify the fundamental variables affecting economic vulnerability and resilience and finally determine the maximal graph. The final output of Tarjan's algorithm is n* variables for measuring VR. Tarjan's algorithm divides the variables into two general parts; The first group of variables that causes vulnerability and resilience and the second group of variables that is created as a result of resilience and vulnerability. The initial set of variables in the dimensions of sustainable development includes economic, social, environmental, governance and peripheral dimensions. After identifying the relationships between the 43 variables presented, a graph is drawn that expresses the relationships between the desired variables.
Findings
The results of the algorithm reveal the fact that the resilience of the model is due to the economic and governance dimensions. If the "economic" or the "governance” dimension are specifically removed, the capacity of the directed graph which is resilient to the strongly connected feature will definitely be lost. Since governance and economic dimensions directly affect other dimensions, they are called as control dimensions. On the other hand, social, environmental and peripheral dimensions are considered as contingent dimensions.
Therefore, contingent dimensions are directly dependent and influenced by control dimensions. In this research, the Net Vulnerability and Resilience Index (NVRI) is separated in all dimensions and calculated in the range of -1 and 1. The NVRI time series is shown during the period and based on the sample countries. The results indicate that in all periods, the status of the NVRI index of the G7 countries was better than the MENA countries, and all the G7 members had resilient economies.
Discussion and Conclusion
According to the index calculations, the countries are classified into four states of uncontrolled vulnerable, limited vulnerable, unstable resilient and sustainable resilient. The G7 countries are sustainable and resilient, which means that in these countries, resilience has surpassed vulnerability. The countries of Oman, Kuwait, Saudi Arabia, UAE, Occupied Palestine and Bahrain from the MENA group are also sustainable and resilient.
The main strength of MENA countries, which are in the group of sustainable resilient, is focused on the peripheral dimension and how these countries interact with the global economy. Among these countries, the UAE and occupied Palestine have a more suitable situation. MENA countries are mainly in the pure and uncontrolled vulnerable group. The governance dimension and then the economic dimension are the important factors of the vulnerability of these countries.
The temporal analysis of the index for the selected countries shows that the majority of the countries that are in the sustainable resilient group did not change their situation during the period under review. Iran is an uncontrolled vulnerable during the years 2017-2020, and in all periods, the index in governance and economic dimensions has been negative and vulnerability is more than resilience.
The analysis of the NVRI index examines the strengths and weaknesses of MENA and G7 countries with a sustainable development approach. It helps the policymakers to get strategic suggestions to improve the situation in weaker countries by following the example of the countries that have a better VR state. The goal of quantifying the state of vulnerability and resilience is to achieve sustainable and inclusive growth in accordance with international programs.
Keywords: Economic Vulnerability and Resilience, Sustainable Development, Graph Theory, Tarjan Algorithm, Composite Index
JEL Classification: C02, C60, O10
Mr Farouq Mahmoudi-Razgeh, Dr Ali Rezazadeh, Dr Yousef Mohammadzadeh,
Volume 24, Issue 1 (3-2024)
Volume 24, Issue 1 (3-2024)
Abstract
رIntroduction
The tourism sector plays a pivotal role in national economic development because it promotes the development of related industries such as transportation. The boosting effect of tourism on economic growth is more obvious in developing countries with abundant tourism resources (Dieke, 2003). However, tourism development undergoes great dynamic changes due to complex and volatile external environments, such as global climate change and social disturbances with a high degree of uncertainty (Nguyen et al., 2020; Scott et al., 2019). thus, the tourism economy has become very fragile and has a weak ability to withstand risks from various sources (Wang et al., 2022). Therefore, this study attempts to examine the Indirect impact of tourism on economic vulnerability and other factors affecting economic vulnerability in selected developing countries over the period 1995-2021 by using a panel smooth transition regression model.
Methodology
In this study, the nonlinear threshold effect of tourism on economic vulnerability in selected developing countries is examined using a PSTR model. For this purpose, following Gonzalez et al. (2005) and Colletaz & Hurlin (2006), a PSTR model with two regimes and a transition function is defined. according to the study of Colletaz & Hurlin (2006), can be chosen among the explanatory variables, the lag of the dependent variable, or any other variable outside the model that is theoretically related to the model under study and causes a nonlinear relationship.
As mentioned earlier, another salient feature of the PSTR model is that it provides a parametric approach to cross-country heterogeneity and time instability of the slope coefficients, allowing the parameters to change smoothly as a function of the threshold variable
It is worth noting that the estimation of the parameters of the PSTR model consists in eliminating the individual effects by removing the individual means and then applying nonlinear least squares (NLS) to the transformed model (see for details, Gonzalez et al., 2005). This method is equivalent to maximum likelihood (ML) estimation in the case of normal errors.
Following Gonzalez et al. (2005), Colletaz & Hurlin (2006), and Jude (2010), the estimation steps of a PSTR model are as follows: First, the linearity test against PSTR is performed using Wald Tests (
Results and Discussion:
The results show that in the first regime, trade openness has a negative effect on economic vulnerability, which has decreased and turned positive after crossing the threshold location in the second regime. Government expenditure has a positive effect on economic vulnerability, and after crossing the threshold location and entering the second regime, its effect gradually decreased and became positive. Inflation coefficients in the regime had a negative and insignificant effect on economic vulnerability, which after crossing the threshold location and entering the second regime, its effect gradually decreased and became positive, but it was significant at the 10 percent level.
Also, the results show that before the threshold location and at low levels of tourism income, the logarithm of financial development has a negative and significant effect on economic vulnerability, and after the threshold location and entering the second regime, this effect is still negative and increases. The coefficients of the logarithm of total unemployment have a negative effect on economic vulnerability in the first regime and before the threshold location. By crossing the threshold location and entering the second regime, this effect decreases and becomes positive.
Conclusion
In this study, the threshold and Indirect effect of tourism on economic vulnerability in selected developing countries during 1995-2021 was investigated. For this purpose, the PSTR model provided and developed by Gonzalez et al. (2005) and Colletaz & Hurlin (2006) was used. The estimation results suggested a nonlinear relationship between trade openness, financial development, government spending, total unemployment, inflation and economic vulnerability. Moreover, considering a threshold with two regimes or a transition function is sufficient to investigate nonlinear behaviors. The results show that the threshold of the transition variable is equal to 3.1378 and the slope parameter is equal to 33.8978, which include only one transition function and only one threshold.
Considering the positive impact of tourism on financial development and government spending, it can be said that the development of tourism income can indirectly reduce the economic vulnerability of developing countries by increasing financial development and national income and adjusting industrial structures, while this mediating effect at the level Social does not appear. Therefore, it is suggested that considering that in developing countries where the overall economic strength of a country is weak, with low economic development, the development of international tourism should be cautious. The main task should be to create infrastructure and stimulate domestic consumption. Investment should be focused on industries such as manufacturing and financial development to increase the growth of GDP and improve people's quality of life. Physical needs are the most important factor to maintain economic stability and prevent economic vulnerability. For these countries, attention should be paid to domestic tourism by strengthening the construction of tourism service facilities, adjusting the structure of the tourism industry and ensuring the sustainable development of international tourism, while accelerating the development of domestic tourism.
From an institutional perspective, creating active employment policies to create preferential employment conditions for low-income people can further ensure the positive impact of low-level international tourism on economic vulnerability. Finally, regardless of the level of economic development, one should have a clear understanding of the performance of the tourism industry based on the state of the country. This is possible by correctly positioning the tourism industry and not exaggerating the role of tourism and not giving up on its development due to some negative factors. Economic vulnerability can be effectively reduced only by combining tourism with other industries and focusing on overall economic development.
Mrs Sedigheh Hossaini, Dr Saman Ghaderi, Dr Zana Mozaffari, Mr Ramin Amani,
Volume 24, Issue 2 (5-2024)
Volume 24, Issue 2 (5-2024)
Abstract
Introduction
The Covid-19 pandemic, as one of the recent world crises, has brought costs to the economies, which has drawn the attention of researchers and politicians to the concept of economic vulnerability in the form of a warning index to evaluate this external shock. The main aim of this study is to investigate the impact of the COVID-19 pandemic on economic vulnerability in high, medium, and low-income levels countries. This study was conducted for 150 countries using the Panel Smooth Transition Regression (PSTR) approach over 2020-2021. In this regard, the Briguglio method was used to calculate the Economic Vulnerability Index. The results of this research indicate that the COVID-19 pandemic has had a positive and significant effect on the economic vulnerability of countries. The linear test results confirm the non-linear relationship between the variables. Moreover, by considering a transfer function with a threshold parameter (the level of COVID-19 morbidity and mortality), a two-regime model is presented to specify the non-linear relationship between the pattern variables for three groups of high, medium, and low-income countries. The slope parameter (transfer rate) for these three groups of countries is 5.9876, 6.1569, and 3.9987, respectively. The model estimation results show that in both linear and non-linear regimes, COVID-19 has a positive impact on the economic vulnerability of countries with high, medium, and low incomes, meaning that an increase in the COVID-19 pandemic has led to a decrease in the economic vulnerability of these groups of countries.
Methodology
Through extensive research and data collection, a sample of 150 countries for the period 2020-2021 has been selected. The primary criterion for selecting countries and the period is the availability of data. The research database includes sources such as the World Bank, the International Monetary Fund, and the United Nations Development Organization. The dependent variables in this study are the Vulnerability Index. The Vulnerability Index is constructed based on the Briguglio method using four components: 1) Trade openness 2) Export concentration 3) Dependency on strategic imports, and 4) Exposure to natural disasters. Other variables included in the model are the number of COVID-19 deaths, per capita gross domestic product (GDP), foreign direct investment, and remittances as a percentage of GDP, which have been collected from the World Bank and other reliable sources. This study used Panel Smooth Transition Regression (PSTR) approach. PSTR is a statistical model that is commonly used to analyze the non-linear relationships between economic variables. This model is particularly useful for investigating the behavior of variables that exhibit non-linear patterns or changes in their behavior over time. PSTR is a flexible model that can be used to capture the complex relationships between different variables, making it a popular choice in various fields, such as economics, finance, and social sciences. The PSTR model is an extension of the Smooth Transition Regression (STR) model, which is a non-linear regression model that allows for the specification of the transition function between two different regimes. In the PSTR model, the transition function is extended to include panel data, which allows for the analysis of the non-linear relationships between variables across multiple units, such as countries or firms, over time. PSTR is a powerful tool for analyzing the impact of various economic factors on different regions or countries. For example, it can be used to investigate whether the impact of a particular economic policy or event is uniform across different countries or regions, or whether it varies depending on the level of economic development or other relevant factors. Additionally, PSTR can be applied to different types of data, including cross-sectional, time series, and panel data, making it a versatile tool for analyzing a wide range of economic phenomena.
Results and Discussion
the vulnerability model indicates that the slope parameter, which represents the speed of transition from one regime to another, is equal to 1191.414, and the regime change location is 435.6, with the logarithm of its anti-value being 2213094. Therefore, as long as the COVID-19 pandemic (mortality) value is less than the anti-logarithm values, the variables will behave according to the first regime. If the value of the COVID-19 pandemic exceeds the anti-logarithm values, the variables will follow the second regime. Based on the results of the two regimes, it is evident that the COVID-19 pandemic variable has had a positive and significant impact, both linear and nonlinear on countries. This means that the increase in the COVID-19 pandemic has led to an increase in the economic vulnerability of countries. In other studies, such as Brzyska & Szamrej (2021), Marti (2021), and Puertas, it has been demonstrated that the COVID-19 pandemic has had a positive and significant effect on the vulnerability of countries in the European Union, which mostly includes high-income countries.
Conclusion
This paper examines the impact of the COVID-19 pandemic on economic vulnerability in 150 countries during 2020-2021. The results obtained from the Panel Smooth Transition Regression (PSTR) model confirm a nonlinear relationship between the variables and the presence of two threshold regimes with a threshold for economic vulnerability and model. It also indicates that the COVID-19 pandemic has a positive effect on vulnerability. This means that an increase in the COVID-19 pandemic has led to an increase in vulnerability and a decrease in economic resilience in these countries.
The Covid-19 pandemic, as one of the recent world crises, has brought costs to the economies, which has drawn the attention of researchers and politicians to the concept of economic vulnerability in the form of a warning index to evaluate this external shock. The main aim of this study is to investigate the impact of the COVID-19 pandemic on economic vulnerability in high, medium, and low-income levels countries. This study was conducted for 150 countries using the Panel Smooth Transition Regression (PSTR) approach over 2020-2021. In this regard, the Briguglio method was used to calculate the Economic Vulnerability Index. The results of this research indicate that the COVID-19 pandemic has had a positive and significant effect on the economic vulnerability of countries. The linear test results confirm the non-linear relationship between the variables. Moreover, by considering a transfer function with a threshold parameter (the level of COVID-19 morbidity and mortality), a two-regime model is presented to specify the non-linear relationship between the pattern variables for three groups of high, medium, and low-income countries. The slope parameter (transfer rate) for these three groups of countries is 5.9876, 6.1569, and 3.9987, respectively. The model estimation results show that in both linear and non-linear regimes, COVID-19 has a positive impact on the economic vulnerability of countries with high, medium, and low incomes, meaning that an increase in the COVID-19 pandemic has led to a decrease in the economic vulnerability of these groups of countries.
Methodology
Through extensive research and data collection, a sample of 150 countries for the period 2020-2021 has been selected. The primary criterion for selecting countries and the period is the availability of data. The research database includes sources such as the World Bank, the International Monetary Fund, and the United Nations Development Organization. The dependent variables in this study are the Vulnerability Index. The Vulnerability Index is constructed based on the Briguglio method using four components: 1) Trade openness 2) Export concentration 3) Dependency on strategic imports, and 4) Exposure to natural disasters. Other variables included in the model are the number of COVID-19 deaths, per capita gross domestic product (GDP), foreign direct investment, and remittances as a percentage of GDP, which have been collected from the World Bank and other reliable sources. This study used Panel Smooth Transition Regression (PSTR) approach. PSTR is a statistical model that is commonly used to analyze the non-linear relationships between economic variables. This model is particularly useful for investigating the behavior of variables that exhibit non-linear patterns or changes in their behavior over time. PSTR is a flexible model that can be used to capture the complex relationships between different variables, making it a popular choice in various fields, such as economics, finance, and social sciences. The PSTR model is an extension of the Smooth Transition Regression (STR) model, which is a non-linear regression model that allows for the specification of the transition function between two different regimes. In the PSTR model, the transition function is extended to include panel data, which allows for the analysis of the non-linear relationships between variables across multiple units, such as countries or firms, over time. PSTR is a powerful tool for analyzing the impact of various economic factors on different regions or countries. For example, it can be used to investigate whether the impact of a particular economic policy or event is uniform across different countries or regions, or whether it varies depending on the level of economic development or other relevant factors. Additionally, PSTR can be applied to different types of data, including cross-sectional, time series, and panel data, making it a versatile tool for analyzing a wide range of economic phenomena.
Results and Discussion
the vulnerability model indicates that the slope parameter, which represents the speed of transition from one regime to another, is equal to 1191.414, and the regime change location is 435.6, with the logarithm of its anti-value being 2213094. Therefore, as long as the COVID-19 pandemic (mortality) value is less than the anti-logarithm values, the variables will behave according to the first regime. If the value of the COVID-19 pandemic exceeds the anti-logarithm values, the variables will follow the second regime. Based on the results of the two regimes, it is evident that the COVID-19 pandemic variable has had a positive and significant impact, both linear and nonlinear on countries. This means that the increase in the COVID-19 pandemic has led to an increase in the economic vulnerability of countries. In other studies, such as Brzyska & Szamrej (2021), Marti (2021), and Puertas, it has been demonstrated that the COVID-19 pandemic has had a positive and significant effect on the vulnerability of countries in the European Union, which mostly includes high-income countries.
Conclusion
This paper examines the impact of the COVID-19 pandemic on economic vulnerability in 150 countries during 2020-2021. The results obtained from the Panel Smooth Transition Regression (PSTR) model confirm a nonlinear relationship between the variables and the presence of two threshold regimes with a threshold for economic vulnerability and model. It also indicates that the COVID-19 pandemic has a positive effect on vulnerability. This means that an increase in the COVID-19 pandemic has led to an increase in vulnerability and a decrease in economic resilience in these countries.
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