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Showing 4 results for Added Value
Mohammad Hadi Hajian, Sadegh Khalilian, Ahmad Sam Daliri,
Volume 7, Issue 4 (1-2008)
Volume 7, Issue 4 (1-2008)
Abstract
Monetary and fiscal policies are the main macroeconomic instruments, so the most important step in programming for national development is to know how they affect economic variables. On the other side, agricultural sector is the main sector in economic growth and development and a strategic sector for supplying food for growing population, so this sector has a great importance in development programs. In this research, the effect of monetary and fiscal policies on the major factors of agricultural sector in Iran i.e. added value, price, and export are investigated by using Vector Auto-regressive Model (VAR). Results show that monetary and fiscal policies have positive effect on added value, price and export in agricultural sector but negative effect on investment in this sector. Furthermore, short-run effect of monetary policies on added value, export and investment is more than that of fiscal policies but long-run effect of fiscal policies on the added value, export and investment in agricultural sector is more than that of monetary policies. Furthermore, a one-S.D. liquidity innovation has a positive decreasing effect on the added value and price in agriculture sector but its effect on investment is positive in the first year and will be nearly consistent in the next years. In addition to that the effect on the export is positive in the first year and negative in the next years. Each government budget innovation has a positive effect on added value, export and price in agriculture sector but its effect on investment is positive in the first year and negative in the next years.
Khadijeh Nasrollahi, Nematollah Akbari, Safoora Ramzani,
Volume 13, Issue 1 (4-2013)
Volume 13, Issue 1 (4-2013)
Abstract
Tourism plays a growing role in developing countries and is considered as a way of government deficit reduction, gaining foreign exchange and creating jobs. In addition, it is viewed as a channel of getting away from single product economies. In fact, tourism is an industry with multi-dimensional impacts on different sectors of economy. Therefore, it can also be viewed as an important development factor in Isfahan province. In this study, the impacts of international tourist expenditures on income generation, allocation, distribution, consumption and savings in different economic sectors of Isfahan province has been analyzed using Social Account Matrix (SAM) technique, supported by input-output matrix. The results show that financial sector has a very poor performance in this regard in terms of generating revenues coming from international tourists. Although the government sector has the highest share regarding the initial allocation of income, but due to the transactions happening among structural sectors, households have been the main gainers of this income generation. In fact, almost 84% of the generated revenues in this way have been consumed.
Kambiz Hozhabr Kiani, Mohammad Naghibi,
Volume 15, Issue 2 (6-2015)
Volume 15, Issue 2 (6-2015)
Abstract
Nowadays, different methods are used to calculate the capital stock in Iran. For example, the Central Bank uses permanent inventory method (PIM) to calculate the capital stock, however the researchers have used the production function method or the method of the ratio of capital to production in different periods. The present study aims at estimating the capital stock in Iran’s economic sectors. By comparing the results of permanent inventory method (PIM) and production function method, it is observed that the estimations by production function method are more suitable due to the data limitations. The same method was applied for the U.S. regarding data limitations and estimations were compared with the U.S capital stock; and once again the production function method was selected. It is found that the depreciation rates in agriculture, industry and mining, building, services, oil and gas, and water and electricity are 5.8, 4.9, 7.8, 3.5, 6.3, and 4.1 per cent, respectively. In order to make the depreciation rate more reliable, these rates were compared with the similar researches in Iran and a close relationship was found among them
Mr Ramin Amani, Mr Zanko Ghorbani, Dr Zana Mozaffari,
Volume 24, Issue 3 (9-2024)
Volume 24, Issue 3 (9-2024)
Abstract
Introduction
Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.
Methodology
The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:
y i = ˊ x i β τ + u τi . 0< τ <1
Quant τ ( y i | x i )= x i β τ
Quant τ ( u τi | x i )=0
In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:
As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:
min ( α . β ) Σ k =1 K Σ t =1 T Σ i =1 N w k ρ τk y it - α i - x it T β τ k + λ Σ i N α i
In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).
Results and Discussion
The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.
Conclusion
Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.
Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.
The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.Introduction
Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.
Methodology
The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:
y i = ˊ x i β τ + u τi . 0< τ <1
Quant τ ( y i | x i )= x i β τ
Quant τ ( u τi | x i )=0
In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:
As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:
min ( α . β ) Σ k =1 K Σ t =1 T Σ i =1 N w k ρ τk y it - α i - x it T β τ k + λ Σ i N α i
In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).
Results and Discussion
The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.
Conclusion
Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.
Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.
The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.
Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.
Methodology
The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:
In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:
As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:
In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).
Results and Discussion
The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.
Conclusion
Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.
Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.
The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.Introduction
Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.
Methodology
The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:
In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:
As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:
In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).
Results and Discussion
The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.
Conclusion
Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.
Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.
The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.
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