nader mehrgan; Mohammadyusoaf khashee
Abstract
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Extended abstract
1- INTRODUCTION
In today's world, economic growth and development is more of a regional phenomenon than a national one. Countries, especially neighboring countries such as Europe or Southeast Asia, grow together; Because the proximity of countries in a region, due to shorter distances and reduced transportation costs on the one hand, and other commonalities such as culture, language, religion, etc., on the other hand, can achieve different economic integration for countries around the world. And made them more interested in economic and trade cooperation. Trade is the starting point of this economic cooperation. When a country's economy grows, an economic overflow occurs, and when an economic overflow is created, the economies of the surrounding countries practically change.
As Afghanistan is a drug producer and a hub for smuggled goods, Afghanistan's underground economy and war have transformed the legal economy throughout the region. This underground economy has led to the financing of terrorist groups in this country. Thus, lasting peace in Afghanistan requires not only political agreement, but also the transformation of the regional economy. Therefore, this study tries to measure the impact of economic growth of Afghanistan's neighbors and most important business partners on its economic growth by using annual GDP data (2002-2007) and self-regression model (VAR) and identify which one of the neighboring countries, it has the greatest impact on the economic growth of Afghanistan, according to which, through tariff policies and the expansion of trade, to provide the basis for further economic growth for the people of Afghanistan.
2- THEORETICAL FRAMEWORK
Many economic thinkers believe that a country's economic situation is not only influenced by its economic performance and behavior; It is also influenced by the performance of neighboring countries. Ignoring these relationships and ignoring spatial factors can have very negative effects on a country's performance. Because in international trade, spatial dependence can be justified in the course of trade through the overflow effects of neighboring countries. In such a way that some structural changes in the trade flow of a region affect the trade flow of the neighboring country. Because the structural changes that occur in a country, affect the flow of trade in that country and will also affect the flow of trade of geographical neighbors. Thus, in the real world, when a country's expenditures and incomes change, that change is transmitted to other countries through a change in that country's imports. When reactions occur in other countries, feedback is generated in the original country. Experience has also shown that the countries of the world are dependent on macroeconomic activity, and the income level of one country is positively dependent on the income level of other countries. Therefore, Afghanistan's economic growth is also subject to the economic growth of other countries. This was also seen in the global economic downturn that began in 2007-2008. When a boom or bust occurs in one country, it returns to the original country after being transferred to other countries.
3- METHODOLOGY
This research is performed by using annual GDP data (2017-2002) and self-regression vector model (VAR); Because contemporaneous equation models are based on an approach that assumes some variables are endogenous and some are exogenous. Defining variables into "endogenous" and "exogenous" may have theoretical underpinnings or may be a matter of taste. Even when it has theoretical support, doubts are raised about it, and the experimental results may contradict its theoretical foundations. However, the self-regression vector (VAR) model is used in cases where there is no certainty whether the variables are endogenous or exogenous.
4- RESULTS & DISCUSSION
The short-term results show that there is no causal relationship between Afghanistan's GDP growth and the economic growth of Tajikistan and Turkmenistan. But the relationship between Iran's and Afghanistan's economic growth is a one-way causal relationship on Iran's economic growth. Because Iran's exports to Afghanistan are often intermediate-capital goods that lead to production and economic growth; But Afghanistan's exports to Iran are mostly agricultural products, which are considered as consumer goods and have no effect on Iran's economic growth. The results also show that the relationship between the economic growth of Afghanistan and Pakistan is a causal two-way relationship. Because Afghanistan's exports to Pakistan are agricultural and livestock products. These goods are considered as consumer and final goods that due to the lack of necessary infrastructure for storage of agricultural products in Afghanistan, they are exported to Pakistan cheaply in the harvest season and re-enter Afghanistan in the winter. Pakistan exports to Afghanistan (compared to Iran) are mostly final and consumer goods. Hence, Iran's economic growth has a greater impact on Afghanistan's economic growth than Pakistan's. Long-term results based on the Johansson test also show that the growth of Afghanistan's GDP depends on the GDP growth of Iran and Pakistan.
The results obtained from the shock response functions for shocks to Afghanistan from Iran, Pakistan, Tajikistan and Turkmenistan show that shocks to Afghanistan from Iran, Tajikistan and Turkmenistan disappear over time and destabilize the Afghan economy. It is not possible; But the shock from Pakistan to Afghanistan is further destabilizing Afghanistan's economy. This is quite clear in the comparison between the graphs. The results of analysis of variance also show that in the short run, most of the changes in Afghanistan's GDP are self-sustaining. But in the long run, most of the changes are explained by Iran's GDP and the least by Tajikistan's GDP. In other words, Iran's economic growth compared to other countries has the greatest effect on Afghanistan's GDP growth.
5- CONCLUSIONS & SUGGESTIONS
The results of this study show that there is a positive and long-term relationship between the economic growth of Afghanistan and the countries of Iran and Pakistan. The results in the short run also show that there is a one-way causal relationship from Iran's GDP to Afghanistan's GDP. But there is no two-way causal relationship between Afghanistan and Iran GDP growth. There is also a two-way causal relationship between Afghanistan and Pakistan's GDP. While there is no causal relationship between the GDP of Afghanistan, Tajikistan and Turkmenistan. In addition, the long-term results show that Iran, Pakistan and Turkmenistan play a major role in explaining the fluctuations of Afghanistan's GDP, with Iran accounting for the largest share.
According to the results of the study, it is suggested that in order to ensure Afghanistan's economic growth, tariff barriers to the import of Iranian goods to Afghanistan should be removed or reduced, and in contrast, customs tariffs for other neighboring countries should be increased.
Mohammad Reza Eskandari Ata; nader mehregan; Alireza Pourfaraj; Saeed Karimi Petanlar
Abstract
Introduction Regional unbalanced growth and the factors affecting it are one of the most important economic issues in developing countries. One of the characteristics of developing countries is the presence of significant regional inequalities. The existence of this phenomenon is one of the main impediments ...
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Introduction Regional unbalanced growth and the factors affecting it are one of the most important economic issues in developing countries. One of the characteristics of developing countries is the presence of significant regional inequalities. The existence of this phenomenon is one of the main impediments to balanced development in these countries. One of its specific consequences is the creation of inequalities and the consolidation and expansion of deprivation. Inequality alongside widespread poverty can provide grounds for public discontent and thus be one of the concerns of socio-economic policymakers. Considering the importance of balanced regional development in the country and the environmental-spatial potentials and political characteristics of the provinces, this study considers the effects of environmental and political factors on the distribution of inequality in provinces of Iran, considering the neighborhood effects. Theoretical framework Environmental differences play a decisive role in the distribution of regional inequality. At the early stage of economic development, environmental conditions are one of the most determining factors. For example, favorable environmental conditions are often the basis for rapid growth in developing countries. Although the effects of environmental conditions on regional development at higher levels are less pronounced, the specific functions of these factors are still unknown in many countries. In economic literature, several environmental factors influence and are influenced by the distribution of regional inequality. Variables such as cities with coastal boundaries, commercial areas, tourism, water resources, railways, border areas and urban development are among the areas considered in regional studies. Modern governments, unwittingly or unwillingly, engage in various economic policies such as monetary policy, fiscal policy, and commercial policy. Applying these policies shift interests and the pattern of income distribution and create winners and losers across different segments and groups of society, thereby changing regional inequalities. Methodology Spatial inequality refers to situations in which different spatial or geographical units of some variables are at different levels. In the present study, after investigating the regional inequality with regard to the effects of spillover in the provinces, an assessment of the environmental and political factors on it during 2006 to 2015 has been examined. The explanatory variables were compiled according to the purpose of the study, based on environmental and political factors that cause regional imbalances and also according to the statistical constraints of the country. According to the theoretical foundations, identifying variables in previous studies as well as statistical feasibility in the country, from three models has been used to investigate the impact of environmental and political factors on regional inequality. The variables used include urban index, dummy variable for business areas, tourist and religious centers, the logarithm of GDP, ratio of government expenditure to GDP, Ratio of education cost to the government expenditure and the members of parliament. Results and discussion The evaluation of Population-Weighted Coefficient of Variation (PW-CV) indices show that Iranian provinces during the research period have been very inadequate. The results of estimating Spatial Autoregressive with Autoregressive Error (SARAR) regression models indicate a strong spatial dependence among the provinces. So that the inequality index of each province with an approximate coefficient of 45% is affected by the economic inequality in neighborhood provinces. In the analysis of environmental factors affecting regional inequality; urban development, water resources and tourism have a negative relationship with provinces' inequality and as each of these factors increases, the inequality index of the provinces will decrease. But religious and commercial provinces have a positive impact on economic inequality; as a result, inequalities are higher in these provinces. Results of the estimation of the impact of political variables on regional inequality show that the provinces with a more gross domestic product, have a higher inequality index. Moreover, the larger the size of the government in the provinces, the more economic inequality. Also, increasing the share of education costs from provincial budgets increase regional inequality and in the provinces where the number of members of parliament is higher, there are more economic inequalities. Conclusions and suggestions According to the results of the present study, the importance of the distribution of inequality in different provinces and the effects of neighborhoods with regard to environmental and political factors are overemphasized. Governments and trusted entities in different areas can be more successful in delivering social justice and reducing regional inequalities by designing and implementing management policies tailored to each province's environmental and political potentials. Managing water resources, paying attention to tourism, controlling suburbs in big and religious cities, and implementing income redistribution policies are some of the policies that can be implemented in environmental and operational areas. Reducing government tenure and administrative bureaucracy are also some of the factors that will be effective in reducing regional inequalities.
Nader Mehregan; Younes Teymourei
Abstract
Introduction
The main purpose of this study is the spatial investigation of the industrial structure among Iran’s provinces and the matters that can effect this formed structure. In fact, the underlying spatial structure of industry which shapes the economy is the result of the spatial dependence ...
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Introduction
The main purpose of this study is the spatial investigation of the industrial structure among Iran’s provinces and the matters that can effect this formed structure. In fact, the underlying spatial structure of industry which shapes the economy is the result of the spatial dependence on the different parts of that economy due to which the centripetal and centrifugal forces come to existence. This study analyses the spatial structure of industry in the Iranian economy. Therefore, the vital questions raising here are how is the spatial structure of the industrial activities in this economy? Whether or not this structure associates with the spatial inequality? What factors cause the formation of such structure in the present industry? And eventually, knowing whether spatial dependence on different provinces of Iran, could be an important channel to construct this structure. These various issues are addressed in this study. Geographers and economists alike have sought to develop indices that capture inequality across industries, time, and space. In the first section, we present the ideal index of spatial concentration i.e., Ellison and Glaeser index (1997). This will allow us to find out how spatial inequalities imply new and specific constraints. Then, the approach that consists of regressing industry-specific indices of spatial concentration on a number of explanatory variables suggested by theoretical models, such as the intensity of increasing returns, the level of trade costs, or the market potential is considered.
Methodology
In order to answer the questions of this study, Ellison and Glaeser (1997) concentration index has been used to evaluate the industrial spatial structure between the provinces of Iran. As this measurement has been done within the period of 1997-2013 and is based on the two-digit industries (ISIC classified), the effective factors forming the structure of industrial activities which have estimated the spatial econometric model of the time should be identified. The analytical model used in this study is Spatial Autoregressive Panel Data model (SAR Panel Data). An important point is that in spatial regression models each observation corresponds to a location or region. In other words, this model could consider the dependence between the regions or sections. Spatial dependence reflects a situation where values observed at one location or region, say observation i, depends on the values of the neighboring observations at nearby locations. Spatial regression models exploit the complicated dependence structure between observations which represent countries, regions, counties, etc. As a result, the parameter estimates contain a wealth of information on the relationships among the observations or regions. A change in a single observation (region) associated with any given explanatory variable will affect the region itself (a direct impact) which can potentially affect all the other regions indirectly (an indirect impact). In fact, the ability of spatial regression models to capture these interactions represents an important aspect of spatial econometric. An implication of this is that a change in the explanatory variable for a single region (observation) can potentially affect the dependent variable in all other observations (regions). This is of course a logical consequence of our SDM model, since the model takes into account other dependent regions and the explanatory variables. This type of development has wide-ranging implications for the interface of economic theory and econometrics. It suggests that spatial econometric models may be applicable to many situations where they have not previously been employed.
Results and Discussion
The results of EG indicator show that the spatial structure of industry in the economy is considerably unequal. Thus, based on this inequality, Azarbaijan sharghi, Qazvin, Markazi, and Tehran with values of .044, .051, .052 and .063 for spatial concentration index perspectively, are the most industrialized Provinces. In contrast, Bushehr, Hormozgan, and Ilam with values of .550, .244, and .317 are as the least developed provinces in the industry sector and have only certain industrial activities. In the next phase of the study, results of estimating spatial econometric model show that coefficients of market potential, return to scale, price per square meter of land in the province, and the transportation costs in spatial model, each one is estimated as -.174, -.041, -.023, and.038, respectively. In contrast, the coefficients of the variables of average wages and budget expenses of government is estimated with positive values. The estimation of the rate of dependence coefficient on provinces in spatial model (.430) shows that the centripetal forces in the provinces can dominate the centrifugal forces. In addition, due to existance of spatial dependence, we can identify extensive network of interdependences among provinces and their potentials impact on the structure of industry. Also, we can analyze the province's capacity to absorb changes in any of the mentioned factors that affect the structure. Results from the identified extensive network show that provinces of Tehran, Qom, and Qazvin have the highest coefficient of effectiveness and the highest ability to absorb the changes generated in the explanatory variable. Besides, Sistan and Baluchestan and Hormozgan provinces on average, have the lowest coefficient of effectiveness and the ability to absorb the changes generated in the explanatory variables.
hassan daliri; nader mehregan
Abstract
Per capita Ecological Footprint (EF) or Ecological Footprint Analysis (EFA), is a means of comparing the consumption and lifestyles while checking this based on the nature's ability to account for this consumption. The tool can inform the policymakers by examining to what extent a nation uses more or ...
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Per capita Ecological Footprint (EF) or Ecological Footprint Analysis (EFA), is a means of comparing the consumption and lifestyles while checking this based on the nature's ability to account for this consumption. The tool can inform the policymakers by examining to what extent a nation uses more or less than what is available within its territory, or to what extent the nation's lifestyle would be replicable worldwide. The footprint can also be a useful tool to educate the people about the nature capacity and over-consumption with the aim of altering personal behaviors. Ecological footprints may be used to argue that many current lifestyles are not sustainable. Such a global comparison also clearly shows the inequalities of the resource use on this planet at the beginning of the twenty-first century. The ecological footprint analysis is now widely used around the Earth as an indicator of the environmental sustainability. It can be used to measure and manage the use of resources throughout the economy. It can be used to explore the sustainability of the individual lifestyles, goods and services, organizations, industry sectors, neighborhoods, cities, regions, and nations.
Methodology
The ecological footprint accounting method at the national level is described in the Atlas Footprint 2010 or in greater detail in the calculation methodology for the National Footprint Accounts. The National Accounts Review Committee has also published a research agenda on how the method will be improved. There has been a difference in the methodology used by various ecological footprint studies. The examples include how the sea area should be counted, how to account for fossil fuels, how to account for the nuclear power, which data sources used, when average global numbers or local numbers should be used, when to look at a specific area, what areas for biodiversity should be included, and how imports/exports should be accounted for. However, as new footprint standards emerge, the calculation methodologies are converging.
The EF is an attempt to quantify sustainability. The EF is based on the fact that every human activity has an impact on the environment through the resources required by these activities and the wastes generated from them. The logic dictates that a certain area of land is required to produce resources and sequester the wastes. What differentiates EF from other methods of sustainability assessment is that all human enterprises are reduced to a single dimensional area. Ecological foot-printing itself is based on several assumptions, the primary ones being as follows: It should be possible to identify the resources required by an activity and quantify the wastes generated by it. These resources and wastes can then be converted to the land area values that are representative of the bio-productive land required to produce the resources and sequester the wastes. The EF represents the critical natural capital requirements of a defined economy or population in terms of the corresponding biologically productive areas (Rees & Wackernagel, 1992). Once the values for the resource consumption are generated, biological yield conversion factors are used to translate the resource flows into land values. These conversion factors can vary greatly depending on how they are calculated as well as the bio-productivity of the regions on which they are based. The resources themselves are divided into several sections such as housing, transport, consumer goods etc. while this can also vary based on the methodology which is used. Once calculated, the per capita footprint can be compared to the global Earth-share, which is the average land availability per person on the earth.
Any overshoot above this figure is termed the environmental deficit that indicates the degree to which a population is living beyond the nature’s means. An easy method for visualizing what the EF means is the example of the modern city with the associated resources and waste flows. A large dome covers the city and the only thing that can travel through this dome is light. Naturally, the inhabitants do not survive and the structure of their society breaks down. Imagine, if it were possible to stretch this dome, it encompassed the bio-productive area outside this city. The EF of the city/region is the total area the dome would have to cover to be able to sustain itself indefinitely with the same levels of consumption. That is, the total area required to provide all the resources and sequester all the wastes indefinitely. Thus, EFs are practical indicators for the impact or environmental overshoot of the region since high economic demand equates with an excessive resource requirement. This means more land is required to maintain production, which in turn, results in the depleted capital stocks. Productive land itself is a good proxy for the natural capital since it can supply the vital ecosystem services.
Results and Discussion
The article is classified in several sections, Calculation of agricultural land shows: South Khorasan has the highest ecological footprint and the Fars lowest agricultural ecological footprint among Iran provinces. Calculation of energy land shows: Isfahan has the highest ecological footprint and the Sistan and Bluchestan lowest energy ecological footprint among Iran provinces. Calculation of Pasture ground land shows: south khorasan has the highest ecological footprint and the Qazvin lowest Pasture ecological footprint among Iran provinces. Calculation of Forest land shows: Tehran has the highest ecological footprint and the Sistan and Bluchestan lowest Forest ecological footprint among Iran provinces. Calculation of Build up land shows: south Khorasan has the highest ecological footprint and the Western Azerbaijan lowest Build up ecological footprint among Iran provinces.
Conclusion
But this research results show that the inhabitants of Isfahan have footprint 2.412 acres for each person so Isfahan has the highest amount of the footprint between the provinces of Iran. Furthermore Sistan and Baluchestan's footprint was 1.962 hectares for each person that has the least amount of ecologic value between the provinces of Iran
