Search published articles
Showing 148 results for Regression
Volume 0, Issue 0 (1-2024)
Abstract
This study investigates customer loyalty in Iran's chain stores, with a particular emphasis on fresh food consumers. The research utilizes a combination of K-means clustering, a weighted RFM (Recency, Frequency, Monetary) model, and ordinal logistic regression to analyze customer behavior. Using real transaction data from 9,014 customers alongside questionnaire responses, the analysis categorizes customers into four distinct groups: very loyal, loyal, at-risk, and disloyal. The weighted RFM model indicates that recency is the most significant predictor of loyalty. Further, the ordinal logistic regression identifies several key factors influencing loyalty: age, marital status, income level, perceived food quality, preference for modern stores, and brand image. These all have positive affect on loyalty; on the contrary, the importance of price and a preference for packaging-free products negatively impact loyalty. These findings provide actionable insights for retail managers, enabling them to develop segment-specific strategies that enhance customer loyalty and strengthen competitiveness in Iran’s dynamic retail sector.
Volume 0, Issue 0 (1-2024)
Abstract
Probiotic juices have experienced a notable rise in popularity due to their potential health benefits, particularly for digestive health. This study examined the viability, physicochemical characteristics, and sensory qualities of kiwifruit juice containing Lacticaseibacillus paracasei B31-2. To analyze the data, Gaussian Process Regression (GPR) and Multi-Layer Perceptron (MLP) models were used to predict various factors, including pH, acidity, viable cell counts of L. paracasei B31-2, color differences (ΔE), and overall acceptance. Probiotic L. paracasei B31-2 was added to the kiwifruit juice at different concentrations (0%, 1% and 2%) and stored at 4 °C. The probiotic juices showed fewer changes in pH, acidity, and color compared to the control juice during storage at room temperature. The sample with a 2% probiotic concentration exhibited the highest viable cell count (7.98 log CFU/mL) and received the most sensory scores among the tested samples. A strong correlation between the predictions made by the GPR model and the actual observed data further validated its effectiveness in similar experimental contexts. This suggests that GPR could offer strategic benefits by lowering laboratory costs and improving analytical efficiency. The GPR model's precision in closely matching real-world data demonstrates its potential as a cost-effective and expedited tool for scientific inquiries. Overall, these findings indicate that kiwifruit juice serves as a promising substrate for carrier of L. paracasei B31-2
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Measurement and examination of unobservable variables directly such as inflation expectations or potential output, is really challenging. Inflation expectations have been considered a key variable in many macroeconomic models, particularly in the realm of monetary economics. Macroeconomic models assume that economic agents make consumption, savings, and labor market decisions based on their perception of future inflation levels, and these decisions play a great role in realizing economic variables, including inflation. The role of inflation expectations differs from other inflation-generating factors. While factors such as money supply, budget deficit, exchange rate, and to some extent, economic sanctions can be considered as policy tools. Inflation expectations normally result from the interaction of other factors and may potentially predict future inflation. For example, an increase in the budget deficit, if not addressed independently by the Central Bank, can lead to an increase in money supply, inflation, and intensification of inflation expectations. Thus, inflation expectations can be considered as a variable that evolves within society and changes due to other inflation-generating factors. However, once formed, these expectations themselves become significant factors in inflation and other economic variables. Unlike many countries, in Iran, despite the importance of inflation due to decades of double-digit inflation, no action has been taken to produce and provide survey data related to this variable. However, according to existing literature, comparing the results of alternative methods incorporating inflation expectations with survey data can provide valuable insights. In practice, incorporating inflation expectations can improve the performance of inflation prediction models.
Methodology
Empirical research indicates that methods that consider inflation expectations along with its fluctuations and dynamics outperform models that do not consider these dynamics. Therefore, paying proper attention to how inflation expectations form and fluctuate, as well as avoiding simple methods, is necessary in calculating inflation expectations. In this research, an attempt was made to calculate and present data related to this variable in the framework of rational expectations for the period of 1996 to 2021 using the random forest regression method, considering the strengths and weaknesses of each method of mapping inflation expectations. Subsequently, after learning the random forest-based model, by conducting an in-sample prediction, the data were extracted and the features related to rational expectations regarding these data were examined.
Findings
The coefficient of determination value for the test data was found to be 80%, indicating that, on average, 80% of inflation variations are correctly predicted by economic factors using the model inputs or features. Based on this and by examining the features related to estimation residuals, it was determined that economic factors in predicting inflation do not exhibit systematic errors and, with a sufficiently large time interval and having an adequate information set, can have a proper understanding of inflation behavior. Moreover, the results of comparing inflation expectations based on random forest regression-based predictions show superiority of this approach compared to competing methods such as the Hodrick-Prescott filter. After that, the importance of each of the factors in the basket of information related to inflation expectations was ranked. It should be noted that the selection of features for predicting inflation expectations was not based on the direct attention of households and economic factors to these features. Rather, economic factors and households may find the effect of these features in other evidence. For example, the effect of an increase in the exchange rate on the prices of goods that are somehow related to this variable may be apparent to households, and fundamentally, the prevalent interpretation of rational expectations in the literature of this field is based on this approach. The results of this ranking indicate that among the entire information set, factors such as inflation breaks, exchange rates, and economic sanctions had the highest importance in shaping inflation expectations.
Discussion and Conclusion
It is worth mentioning that inflation breaks have been identified as the most important factor among the entire information set as a manifestation of the adaptive section of inflation expectations. However, this does not mean that expectations are entirely adaptive. Based on the research findings, it is clear that if economic factors rely solely on the adaptive section to predict inflation, zero estimation error, unpredictability of errors, and consequently the formation of rational expectations will not be achieved. Using a combination of three approaches: gradient boosting algorithm, random forest algorithm, and linear regression, a voting regression was also performed, showing a 3% improvement in determination coefficient compared to random forest (83%). Moreover, other results, such as the order and intensity of feature importance, and predicted inflation values, are similar to the random forest method with slight variations which means, estimating rational expectations is reliable
Measurement and examination of unobservable variables directly such as inflation expectations or potential output, is really challenging. Inflation expectations have been considered a key variable in many macroeconomic models, particularly in the realm of monetary economics. Macroeconomic models assume that economic agents make consumption, savings, and labor market decisions based on their perception of future inflation levels, and these decisions play a great role in realizing economic variables, including inflation. The role of inflation expectations differs from other inflation-generating factors. While factors such as money supply, budget deficit, exchange rate, and to some extent, economic sanctions can be considered as policy tools. Inflation expectations normally result from the interaction of other factors and may potentially predict future inflation. For example, an increase in the budget deficit, if not addressed independently by the Central Bank, can lead to an increase in money supply, inflation, and intensification of inflation expectations. Thus, inflation expectations can be considered as a variable that evolves within society and changes due to other inflation-generating factors. However, once formed, these expectations themselves become significant factors in inflation and other economic variables. Unlike many countries, in Iran, despite the importance of inflation due to decades of double-digit inflation, no action has been taken to produce and provide survey data related to this variable. However, according to existing literature, comparing the results of alternative methods incorporating inflation expectations with survey data can provide valuable insights. In practice, incorporating inflation expectations can improve the performance of inflation prediction models.
Methodology
Empirical research indicates that methods that consider inflation expectations along with its fluctuations and dynamics outperform models that do not consider these dynamics. Therefore, paying proper attention to how inflation expectations form and fluctuate, as well as avoiding simple methods, is necessary in calculating inflation expectations. In this research, an attempt was made to calculate and present data related to this variable in the framework of rational expectations for the period of 1996 to 2021 using the random forest regression method, considering the strengths and weaknesses of each method of mapping inflation expectations. Subsequently, after learning the random forest-based model, by conducting an in-sample prediction, the data were extracted and the features related to rational expectations regarding these data were examined.
Findings
The coefficient of determination value for the test data was found to be 80%, indicating that, on average, 80% of inflation variations are correctly predicted by economic factors using the model inputs or features. Based on this and by examining the features related to estimation residuals, it was determined that economic factors in predicting inflation do not exhibit systematic errors and, with a sufficiently large time interval and having an adequate information set, can have a proper understanding of inflation behavior. Moreover, the results of comparing inflation expectations based on random forest regression-based predictions show superiority of this approach compared to competing methods such as the Hodrick-Prescott filter. After that, the importance of each of the factors in the basket of information related to inflation expectations was ranked. It should be noted that the selection of features for predicting inflation expectations was not based on the direct attention of households and economic factors to these features. Rather, economic factors and households may find the effect of these features in other evidence. For example, the effect of an increase in the exchange rate on the prices of goods that are somehow related to this variable may be apparent to households, and fundamentally, the prevalent interpretation of rational expectations in the literature of this field is based on this approach. The results of this ranking indicate that among the entire information set, factors such as inflation breaks, exchange rates, and economic sanctions had the highest importance in shaping inflation expectations.
Discussion and Conclusion
It is worth mentioning that inflation breaks have been identified as the most important factor among the entire information set as a manifestation of the adaptive section of inflation expectations. However, this does not mean that expectations are entirely adaptive. Based on the research findings, it is clear that if economic factors rely solely on the adaptive section to predict inflation, zero estimation error, unpredictability of errors, and consequently the formation of rational expectations will not be achieved. Using a combination of three approaches: gradient boosting algorithm, random forest algorithm, and linear regression, a voting regression was also performed, showing a 3% improvement in determination coefficient compared to random forest (83%). Moreover, other results, such as the order and intensity of feature importance, and predicted inflation values, are similar to the random forest method with slight variations which means, estimating rational expectations is reliable
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Economic globalization has many economic benefits, but it has also been accompanied by environmental challenges that have increased concern about the impact of these trends on the environment. Environmental welfare plays a key role in the organization of societies and drawing attention to environmental issues as one of the main dimensions of sustainability. This is also true for the development structures and decisions related to the environment. The purpose of the present study is to investigate the impact of economic globalization on environmental well-being in developed and developing countries during the years 2000 to 2020 using soft panel regression. The results show the existence of a non-linear relationship between the research variables. For developed and developing countries, a transfer function and two threshold limits, representing a two-regime model, were also chosen as the optimal model. The slope factor for developed and developing countries was equal to 1.28 and 159.78 respectively. The results of the model estimation indicate that in developed countries, the variable of economic globalization has a negative effect on environmental welfare in the first extreme regime and a positive and significant effect in the second extreme regime. In developing countries, the variable of economic globalization has also a negative and significant effect on environmental well-being in both regimes. On the other hand, in developed countries, for the first limit regime, economic globalization may lead to an increase in unsustainable use of resources and environmental pollution. But in the second extreme regime, it can promote the improvement of international cooperation in the field of environmental protection and the development of clean and green technologies. In developing countries, increased economic globalization may lead to increased industrial pressures and inappropriate use of natural resources, which causes damages to the environment and rampant pollution. Due to technical, financial, and regulatory constraints, these countries may not be able to take advantage of the benefits of globalization in a positive way for the environment and thus have a negative impact on environmental well-being. According to the research results, with the development of technology and industrial control, along with sustainable policies, it is possible to ensure the improvement of environmental well-being and strengthen the positive effect of economic globalization on environmental well-being.
Methodology
This study examines the impact of globalization on environmental well-being in developed and developing countries (133 countries) for the period 2000-2020 using the panel smooth transition regression (PSTR) model. Statistical tables, global databases, data from the Swiss Economic Institute KOF, and the Social Science Institute (SSI) - TH Köln website were used to collect statistics and quantitative information. The environmental welfare variable in this research as a dependent variable is the geometric mean of seven indicators of biodiversity, renewable water resources, energy consumption, energy efficiency, energy reserves, greenhouse gases and renewable energy. Economic globalization is considered as a transition variable, and to better explain the issues of GDP per capita growth (percentage per annum), general government final consumption expenditure (percentage of GDP), foreign direct investment, net inflows (percentage of GDP) and population growth (percentage per annum) were selected as influential factors. PSTR as a statistical model is usually used to analyze non-linear relationships between economic variables, especially to investigate non-linear patterns or changes in the behavior of variables over time. This flexible model can depict complex relationships between different variables and is known as a popular choice in various fields such as economics, finance and social science. The model is an extension of the smooth transition regression (STR) that allows the determination of the transition function between two different regimes. With PSTR, the transfer function is extended for panel data, which allows the analysis of nonlinear relationships between variables in 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 and can be used to examine the impact of a specific economic policy or event on different regions. PSTR can also be used for different types of data such as cross-sectional, time series and panel data, which makes it a versatile tool for analyzing various economic phenomena.
Findings
The research shows the estimated results of the model upon which the slope parameter, which expresses the speed of adjustment from one regime to another, is equal to 1.28 and 159.78 for developed and developing countries, respectively, i.e, the transition from linear regime to non-linear regime in developed countries is done at a much lower speed than in developing countries. The estimation of the model shows the nonlinear relationship in two threshold points for developed countries c_1=79.5617 and c_2=85.0326 and c = (79.56+85.03)/2 = 82.29 also for developing countries c_1= 50.6518 and c_2 = 62.4416 and c = (50.65+62.44) /2 = 56.54 and the transfer function is in two regimes. If the economic globalization exceeds 82.29 in developed countries and 56.54 in developing countries, the behavior of the variables will be according to the second regime, and if it is less than the above threshold, they will be in the first regime.
In developed countries, the coefficients are such that the variable of economic globalization has a negative and significant effect on environmental welfare in the first limit regime and a positive and significant effect in the second limit regime. GDP per capita growth has a positive and non-significant effect on environmental well-being in the first limit regime and a significant negative effect in the second limit regime. Government size and population growth have also a positive effect in the first limit regime and a negative and significant effect in the second limit regime. Foreign direct investment in both regimes has a negative and insignificant effect on environmental well-being.
In developing countries, the coefficients are such that the variable of economic globalization, the growth of GDP per capita in both marginal regimes has a negative and significant effect, as well as the size of the government and population growth in both marginal regimes have a negative and insignificant effect on the dependent variable (welfare). Foreign direct investment has also a positive and insignificant effect in the first limit regime and a negative and significant effect in the second limit regime on environmental well-being.
Discussion and Conclusion
The results of the research show that the impact of various factors on environmental well-being in developed and developing countries is different from each other. These differences may be due to different economic, social, and cultural conditions in these countries.
In developed countries in the first limit regime, economic globalization leads to an increase in economic pressures and international competition, which can cause more use of natural resources, increase the production of pollutants, and decrease the quality of the environment. Moreover, in the second extreme regime, the Economic globalization variable has a positive and significant effect on environmental well-being. This may be due to increased access to advanced technologies, higher environmental standards, and increased international cooperation in environmental protection.
In developing countries, economic globalization variables have a negative effect on environmental well-being in both regimes. In other words, the increase of these variables in both limit regimes leads to a decrease in the quality of the environment and environmental well-being. In other words, economic globalization leads to an increase in the per capita production and consumption of energy and natural resources, which can lead to air and water pollution, a decrease in biodiversity, and a reduction in air and water quality.
In general, it can be concluded that in developed countries, increasing economic growth, government size, and population growth lead to improved environmental conditions, but in developing countries, these factors usually cause a decrease in environmental quality and environmental well-being. For the optimal management of environmental welfare in any country, it is necessary to pay attention to the economic, social and cultural conditions of that country. It is also vitally important to formulate appropriate policies and strategies to deal with environmental challenges
Economic globalization has many economic benefits, but it has also been accompanied by environmental challenges that have increased concern about the impact of these trends on the environment. Environmental welfare plays a key role in the organization of societies and drawing attention to environmental issues as one of the main dimensions of sustainability. This is also true for the development structures and decisions related to the environment. The purpose of the present study is to investigate the impact of economic globalization on environmental well-being in developed and developing countries during the years 2000 to 2020 using soft panel regression. The results show the existence of a non-linear relationship between the research variables. For developed and developing countries, a transfer function and two threshold limits, representing a two-regime model, were also chosen as the optimal model. The slope factor for developed and developing countries was equal to 1.28 and 159.78 respectively. The results of the model estimation indicate that in developed countries, the variable of economic globalization has a negative effect on environmental welfare in the first extreme regime and a positive and significant effect in the second extreme regime. In developing countries, the variable of economic globalization has also a negative and significant effect on environmental well-being in both regimes. On the other hand, in developed countries, for the first limit regime, economic globalization may lead to an increase in unsustainable use of resources and environmental pollution. But in the second extreme regime, it can promote the improvement of international cooperation in the field of environmental protection and the development of clean and green technologies. In developing countries, increased economic globalization may lead to increased industrial pressures and inappropriate use of natural resources, which causes damages to the environment and rampant pollution. Due to technical, financial, and regulatory constraints, these countries may not be able to take advantage of the benefits of globalization in a positive way for the environment and thus have a negative impact on environmental well-being. According to the research results, with the development of technology and industrial control, along with sustainable policies, it is possible to ensure the improvement of environmental well-being and strengthen the positive effect of economic globalization on environmental well-being.
Methodology
This study examines the impact of globalization on environmental well-being in developed and developing countries (133 countries) for the period 2000-2020 using the panel smooth transition regression (PSTR) model. Statistical tables, global databases, data from the Swiss Economic Institute KOF, and the Social Science Institute (SSI) - TH Köln website were used to collect statistics and quantitative information. The environmental welfare variable in this research as a dependent variable is the geometric mean of seven indicators of biodiversity, renewable water resources, energy consumption, energy efficiency, energy reserves, greenhouse gases and renewable energy. Economic globalization is considered as a transition variable, and to better explain the issues of GDP per capita growth (percentage per annum), general government final consumption expenditure (percentage of GDP), foreign direct investment, net inflows (percentage of GDP) and population growth (percentage per annum) were selected as influential factors. PSTR as a statistical model is usually used to analyze non-linear relationships between economic variables, especially to investigate non-linear patterns or changes in the behavior of variables over time. This flexible model can depict complex relationships between different variables and is known as a popular choice in various fields such as economics, finance and social science. The model is an extension of the smooth transition regression (STR) that allows the determination of the transition function between two different regimes. With PSTR, the transfer function is extended for panel data, which allows the analysis of nonlinear relationships between variables in 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 and can be used to examine the impact of a specific economic policy or event on different regions. PSTR can also be used for different types of data such as cross-sectional, time series and panel data, which makes it a versatile tool for analyzing various economic phenomena.
Findings
The research shows the estimated results of the model upon which the slope parameter, which expresses the speed of adjustment from one regime to another, is equal to 1.28 and 159.78 for developed and developing countries, respectively, i.e, the transition from linear regime to non-linear regime in developed countries is done at a much lower speed than in developing countries. The estimation of the model shows the nonlinear relationship in two threshold points for developed countries c_1=79.5617 and c_2=85.0326 and c = (79.56+85.03)/2 = 82.29 also for developing countries c_1= 50.6518 and c_2 = 62.4416 and c = (50.65+62.44) /2 = 56.54 and the transfer function is in two regimes. If the economic globalization exceeds 82.29 in developed countries and 56.54 in developing countries, the behavior of the variables will be according to the second regime, and if it is less than the above threshold, they will be in the first regime.
In developed countries, the coefficients are such that the variable of economic globalization has a negative and significant effect on environmental welfare in the first limit regime and a positive and significant effect in the second limit regime. GDP per capita growth has a positive and non-significant effect on environmental well-being in the first limit regime and a significant negative effect in the second limit regime. Government size and population growth have also a positive effect in the first limit regime and a negative and significant effect in the second limit regime. Foreign direct investment in both regimes has a negative and insignificant effect on environmental well-being.
In developing countries, the coefficients are such that the variable of economic globalization, the growth of GDP per capita in both marginal regimes has a negative and significant effect, as well as the size of the government and population growth in both marginal regimes have a negative and insignificant effect on the dependent variable (welfare). Foreign direct investment has also a positive and insignificant effect in the first limit regime and a negative and significant effect in the second limit regime on environmental well-being.
Discussion and Conclusion
The results of the research show that the impact of various factors on environmental well-being in developed and developing countries is different from each other. These differences may be due to different economic, social, and cultural conditions in these countries.
In developed countries in the first limit regime, economic globalization leads to an increase in economic pressures and international competition, which can cause more use of natural resources, increase the production of pollutants, and decrease the quality of the environment. Moreover, in the second extreme regime, the Economic globalization variable has a positive and significant effect on environmental well-being. This may be due to increased access to advanced technologies, higher environmental standards, and increased international cooperation in environmental protection.
In developing countries, economic globalization variables have a negative effect on environmental well-being in both regimes. In other words, the increase of these variables in both limit regimes leads to a decrease in the quality of the environment and environmental well-being. In other words, economic globalization leads to an increase in the per capita production and consumption of energy and natural resources, which can lead to air and water pollution, a decrease in biodiversity, and a reduction in air and water quality.
In general, it can be concluded that in developed countries, increasing economic growth, government size, and population growth lead to improved environmental conditions, but in developing countries, these factors usually cause a decrease in environmental quality and environmental well-being. For the optimal management of environmental welfare in any country, it is necessary to pay attention to the economic, social and cultural conditions of that country. It is also vitally important to formulate appropriate policies and strategies to deal with environmental challenges
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Inequality is a multidimensional phenomenon that affects various aspects of households' lives. The economic well-being of individuals depends not only on their income but also on other factors such as access to healthcare, education, transportation, etc. Therefore, one-dimensional methods (income-focused) are insufficient for measuring inequality. The multidimensional approach to inequality considers different aspects of individual welfare, unlike the one-dimensional approach. The concentration of population and activities in some provinces of Iran, along with macroeconomic indicators (inflation and unemployment), exacerbates inequality. These inequalities affect various dimensions of people's lives and endanger their economic welfare. The primary aim of this study is to examine the effects of inflation and unemployment on multidimensional inequality in the provinces of Iran and their reciprocal effects on each other, using a multidimensional Gini coefficient estimated from the household budget microdata of the Statistical Center of Iran for the years 2000-2021.
Methodology
In this study, the multidimensional Gini coefficient by Kumar Banerjee (2010) has been estimated for 9 dimensions of welfare. Then, the effects of inflation and unemployment, along with variables such as per capita real government expenditure and per capita real financial facilities as indicators of financial development, will be analyzed using a spatial econometric model. The mathematical form of the multidimensional Gini coefficient (MGI) is as follows:
Here, the mathematical formula would be inserted) In this equation: represents the non-increasing rank of the unit under study in the individual's overall welfare vector, and represents the sample size. The range of this index fluctuates between zero (completely equal distribution) and one (completely unequal distribution). For measuring multidimensional inequality in this study, the multidimensional Gini coefficient by Kumar Banerjee (2010) has been used which is based on the microdata from the household expenditure (income) survey of the Statistical Center of Iran and involves data mining processes such as aggregating groups of beverages and tobacco, ready meals with food expenditure groups, and communications with transportation, and extracting data related to each household code in each province using R Studio 2020 software. The model is based on the spatial econometric method with spatial panel data, defined using a proximity method in which provinces sharing a border have an element of one and otherwise zero. The adjacency matrix (spatial weight) is normalized, where neighboring provinces carry the most weight, and distant provinces carry the least.
Findings
The results of estimating the multidimensional Gini coefficient for the provinces during 2000-2021 show that most provinces have experienced a high rate of inequality. Provinces such as Bushehr, Khuzestan, Kermanshah, Kurdistan, Markazi, Qazvin, Qom, Semnan, Sistan and Baluchestan, West Azerbaijan, Zanjan, and Yazd are in an unfavorable condition compared to the country, and most of these provinces are border regions. Over these 22 years, Sistan and Baluchestan with 77.66% have the highest rate of multidimensional inequality, while Isfahan with 60.85% has the lowest among the provinces. Additionally, the findings indicate that inflation, unemployment, per capita real government expenditure, and per capita real disbursed financial facilities have a significant positive effect on multidimensional inequality in the provinces of Iran. The proximity of provinces has also worsened the inequality conditions in the neighboring provinces.
Discussion and Conclusion
Four variables including unemployment, inflation, per capita real government expenditure, and per capita real disbursed financial facilities have a significant positive effect on the multidimensional Gini coefficient, worsening income distribution. The most significant impact is seen with per capita real government expenditure, which is not allocated effectively to enhance welfare and improve economic conditions, thus not improving income distribution and reducing inequality. The effects of the other variables are in the following order: per capita real disbursed financial facilities, unemployment, and inflation. It is recommended to consider all welfare dimensions in the household consumption basket, create equal conditions for access to bank facilities, allocate a specific quota of facilities to lessdeveloped provinces, allocate government expenditures to expand public services and infrastructure in deprived provinces, consider the interactive effects between provinces in policymaking, and implement effective policies to improve welfare conditions and balanced income distribution across all provinces
Inequality is a multidimensional phenomenon that affects various aspects of households' lives. The economic well-being of individuals depends not only on their income but also on other factors such as access to healthcare, education, transportation, etc. Therefore, one-dimensional methods (income-focused) are insufficient for measuring inequality. The multidimensional approach to inequality considers different aspects of individual welfare, unlike the one-dimensional approach. The concentration of population and activities in some provinces of Iran, along with macroeconomic indicators (inflation and unemployment), exacerbates inequality. These inequalities affect various dimensions of people's lives and endanger their economic welfare. The primary aim of this study is to examine the effects of inflation and unemployment on multidimensional inequality in the provinces of Iran and their reciprocal effects on each other, using a multidimensional Gini coefficient estimated from the household budget microdata of the Statistical Center of Iran for the years 2000-2021.
Methodology
In this study, the multidimensional Gini coefficient by Kumar Banerjee (2010) has been estimated for 9 dimensions of welfare. Then, the effects of inflation and unemployment, along with variables such as per capita real government expenditure and per capita real financial facilities as indicators of financial development, will be analyzed using a spatial econometric model. The mathematical form of the multidimensional Gini coefficient (MGI) is as follows:
Here, the mathematical formula would be inserted) In this equation: represents the non-increasing rank of the unit under study in the individual's overall welfare vector, and represents the sample size. The range of this index fluctuates between zero (completely equal distribution) and one (completely unequal distribution). For measuring multidimensional inequality in this study, the multidimensional Gini coefficient by Kumar Banerjee (2010) has been used which is based on the microdata from the household expenditure (income) survey of the Statistical Center of Iran and involves data mining processes such as aggregating groups of beverages and tobacco, ready meals with food expenditure groups, and communications with transportation, and extracting data related to each household code in each province using R Studio 2020 software. The model is based on the spatial econometric method with spatial panel data, defined using a proximity method in which provinces sharing a border have an element of one and otherwise zero. The adjacency matrix (spatial weight) is normalized, where neighboring provinces carry the most weight, and distant provinces carry the least.
Findings
The results of estimating the multidimensional Gini coefficient for the provinces during 2000-2021 show that most provinces have experienced a high rate of inequality. Provinces such as Bushehr, Khuzestan, Kermanshah, Kurdistan, Markazi, Qazvin, Qom, Semnan, Sistan and Baluchestan, West Azerbaijan, Zanjan, and Yazd are in an unfavorable condition compared to the country, and most of these provinces are border regions. Over these 22 years, Sistan and Baluchestan with 77.66% have the highest rate of multidimensional inequality, while Isfahan with 60.85% has the lowest among the provinces. Additionally, the findings indicate that inflation, unemployment, per capita real government expenditure, and per capita real disbursed financial facilities have a significant positive effect on multidimensional inequality in the provinces of Iran. The proximity of provinces has also worsened the inequality conditions in the neighboring provinces.
Discussion and Conclusion
Four variables including unemployment, inflation, per capita real government expenditure, and per capita real disbursed financial facilities have a significant positive effect on the multidimensional Gini coefficient, worsening income distribution. The most significant impact is seen with per capita real government expenditure, which is not allocated effectively to enhance welfare and improve economic conditions, thus not improving income distribution and reducing inequality. The effects of the other variables are in the following order: per capita real disbursed financial facilities, unemployment, and inflation. It is recommended to consider all welfare dimensions in the household consumption basket, create equal conditions for access to bank facilities, allocate a specific quota of facilities to lessdeveloped provinces, allocate government expenditures to expand public services and infrastructure in deprived provinces, consider the interactive effects between provinces in policymaking, and implement effective policies to improve welfare conditions and balanced income distribution across all provinces
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction:
Vulnerable employment, a segment of the informal economy, includes home-based businesses that emerge due to a lack of opportunities for formal employment. These businesses often operate without essential benefits such as medical insurance, social security, bonuses, and pensions, which exposes workers to economic instability. Consequently, many individuals engaged in vulnerable employment seek loans and financial assistance to expand their business activities and transition to the formal sector. Banks, as the primary providers of such loans, request collateral from borrowers – typically in the form of property documents – to ensure repayment and mitigate financial risk. Strengthening legal rights related to loan collateral enhances banks’ confidence in issuing loans, thereby increasing access to credit for vulnerable workers.
Due to the oil-dependent nature of OPEC economies and their reliance on oil revenues, many of these countries often lack robust production infrastructures capable of generating sufficient formal employment opportunities. This study aims to analyze the effect of strengthening loan-related legal rights on vulnerable employment in OPEC member countries, including Iran, Iraq, Algeria, Angola, Congo, Gabon, Kuwait, Saudi Arabia, the United Arab Emirates, Venezuela, Guinea, Libya, and Nigeria, during the period from 2013 to 2021.
Methodology:
Following the approach of Herkenhoff et al. (2021), this study employs a model in which the independent variables include the strength of legal rights related to loans, oil revenues, secondary school enrollment rates, and the urbanization ratio. Given the study’s objective of analyzing the threshold effects of legal loan rights on vulnerable employment, the Panel Smooth Transition Regression (PSTRmouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')">[A1] ) method is used to estimate the model.
Results and Discussion:
The analysis identifies a 6.22% threshold in the legal rights index, distinguishing two distinct regimes. In the first regime, the strength of legal loan rights does not significantly impact vulnerable employment. However, in the second regime, a higher index value reduces vulnerable employment, suggesting that more substantial legal loan rights facilitate the transition of workers from the vulnerable to the formal sector. Additionally, oil revenues and secondary school enrollment rates exhibit a negative effect on vulnerable employment, while the urbanization ratio has a positive effect.
Conclusion:
The findings of this study indicate that strengthening legal loan rights has contributed to a reduction in vulnerable employment, which is a subset of informal employment. This shift has contributed to growth in formal sector employment. Banking regulations and enhanced requirements for obtaining collateral have increased banks’ confidence in lending, as they are better able to mitigate the risk of non-repayment. However, this system primarily benefits individuals who can pledge valid collateral, such as real estate and housing documents. Given the high value of such collateralized assets, borrowers are more likely to invest their loans in business development, transitioning their employment from the informal to the formal sector. In addition to securing stable employment, they also gain access to social benefits such as insurance and social security. This financial stability enables them to make timely loan repayments, preventing defaults and preserving their financial credibility.
Based on these findings, it is recommended that governments and banking authorities in the investigated countries implement strict laws and regulations to guarantee loan security and identify factors contributing to bank insolvency. Such measures would help prevent financial resource mismanagement in the banking sector and reduce the probability of bank failures. Strengthening financial regulations and risk management strategies would facilitate lending, ultimately promoting employment growth in the formal sector and reducing the prevalence of vulnerable employment.
Furthermore, the study reveals that oil revenues negatively impact vulnerable employment, which may be attributed to increased government spending on productive investments and formal job creation. This suggests that redirecting oil revenues toward investment, production, and employment generation—rather than short-term expenditures—can facilitate the transition of workers from the informal to the formal sector. Thus, policymakers are encouraged to prioritize long-term economic strategies that allocate oil revenues to sectors that foster sustainable employment opportunities.
The findings also highlight the positive effect of education on labor force transition. Higher levels of education and training result in a more skilled workforce, increasing their acceptance and employability in formal job markets. Therefore, governments should allocate additional resources to public education, provide free schooling, and expand access to higher education for economically disadvantaged groups. Promoting scientific education and fostering a culture that values learning can further enhance workforce skills and economic mobility.
Finally, the study finds that urbanization has had a positive effect on vulnerable employment, indicating that increasing urbanization has not been accompanied by industrial advancements or skill development, thereby failing to support the expansion of the formal sector. Instead, urbanization in the studied countries has often been driven by unfavorable business environments, weak regulatory frameworks, and a lack of political transparency, contributing to the growth of the informal economy. To address these challenges, policymakers should focus on improving governance, strengthening legal and economic structures, and fostering a business-friendly environment that supports formal employment
Vulnerable employment, a segment of the informal economy, includes home-based businesses that emerge due to a lack of opportunities for formal employment. These businesses often operate without essential benefits such as medical insurance, social security, bonuses, and pensions, which exposes workers to economic instability. Consequently, many individuals engaged in vulnerable employment seek loans and financial assistance to expand their business activities and transition to the formal sector. Banks, as the primary providers of such loans, request collateral from borrowers – typically in the form of property documents – to ensure repayment and mitigate financial risk. Strengthening legal rights related to loan collateral enhances banks’ confidence in issuing loans, thereby increasing access to credit for vulnerable workers.
Due to the oil-dependent nature of OPEC economies and their reliance on oil revenues, many of these countries often lack robust production infrastructures capable of generating sufficient formal employment opportunities. This study aims to analyze the effect of strengthening loan-related legal rights on vulnerable employment in OPEC member countries, including Iran, Iraq, Algeria, Angola, Congo, Gabon, Kuwait, Saudi Arabia, the United Arab Emirates, Venezuela, Guinea, Libya, and Nigeria, during the period from 2013 to 2021.
Methodology:
Following the approach of Herkenhoff et al. (2021), this study employs a model in which the independent variables include the strength of legal rights related to loans, oil revenues, secondary school enrollment rates, and the urbanization ratio. Given the study’s objective of analyzing the threshold effects of legal loan rights on vulnerable employment, the Panel Smooth Transition Regression (PSTRmouseout="msoCommentHide('_com_1')" on
Results and Discussion:
The analysis identifies a 6.22% threshold in the legal rights index, distinguishing two distinct regimes. In the first regime, the strength of legal loan rights does not significantly impact vulnerable employment. However, in the second regime, a higher index value reduces vulnerable employment, suggesting that more substantial legal loan rights facilitate the transition of workers from the vulnerable to the formal sector. Additionally, oil revenues and secondary school enrollment rates exhibit a negative effect on vulnerable employment, while the urbanization ratio has a positive effect.
Conclusion:
The findings of this study indicate that strengthening legal loan rights has contributed to a reduction in vulnerable employment, which is a subset of informal employment. This shift has contributed to growth in formal sector employment. Banking regulations and enhanced requirements for obtaining collateral have increased banks’ confidence in lending, as they are better able to mitigate the risk of non-repayment. However, this system primarily benefits individuals who can pledge valid collateral, such as real estate and housing documents. Given the high value of such collateralized assets, borrowers are more likely to invest their loans in business development, transitioning their employment from the informal to the formal sector. In addition to securing stable employment, they also gain access to social benefits such as insurance and social security. This financial stability enables them to make timely loan repayments, preventing defaults and preserving their financial credibility.
Based on these findings, it is recommended that governments and banking authorities in the investigated countries implement strict laws and regulations to guarantee loan security and identify factors contributing to bank insolvency. Such measures would help prevent financial resource mismanagement in the banking sector and reduce the probability of bank failures. Strengthening financial regulations and risk management strategies would facilitate lending, ultimately promoting employment growth in the formal sector and reducing the prevalence of vulnerable employment.
Furthermore, the study reveals that oil revenues negatively impact vulnerable employment, which may be attributed to increased government spending on productive investments and formal job creation. This suggests that redirecting oil revenues toward investment, production, and employment generation—rather than short-term expenditures—can facilitate the transition of workers from the informal to the formal sector. Thus, policymakers are encouraged to prioritize long-term economic strategies that allocate oil revenues to sectors that foster sustainable employment opportunities.
The findings also highlight the positive effect of education on labor force transition. Higher levels of education and training result in a more skilled workforce, increasing their acceptance and employability in formal job markets. Therefore, governments should allocate additional resources to public education, provide free schooling, and expand access to higher education for economically disadvantaged groups. Promoting scientific education and fostering a culture that values learning can further enhance workforce skills and economic mobility.
Finally, the study finds that urbanization has had a positive effect on vulnerable employment, indicating that increasing urbanization has not been accompanied by industrial advancements or skill development, thereby failing to support the expansion of the formal sector. Instead, urbanization in the studied countries has often been driven by unfavorable business environments, weak regulatory frameworks, and a lack of political transparency, contributing to the growth of the informal economy. To address these challenges, policymakers should focus on improving governance, strengthening legal and economic structures, and fostering a business-friendly environment that supports formal employment
mouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')" style="text-align: justify;">
mouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')" style="text-align: justify;">
mouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')" style="text-align: justify;"> [A1]The written abbreviation is for “the Panel Smooth Transition Regression”
Volume 1, Issue 2 (6-2013)
Abstract
Logistic regression (LR) was used to model urban growth between the years 1987 and 2001 in Gorgan city, north east of Iran. Three groups of variables including economic-social, land use and biophysical variables were used in the modeling practice. Using covariance of the independent variables, distance to administrative and sporting centers plus distance to cities were removed. ROC (Relative Operating Characteristic) value for LR was 0.87 that confirmed success of the modeling method. Using maps of urban growth probability predicted by the LR model, urban distribution patterns for the years 2010, 2020, 2030, 2040 and 2050 were created. Land use maps for the years 2001-2050 were created using urban probability pattern maps and the base land use map of the year 1987. We used landscape metrics at class and landscape levels to compare the urban growth effects on other land use types present in the area. The comparison showed that urban development influences agriculture and pasture land use types more than other land uses. Also, we found that the landscape in the study area has undergone fragmentation and will become more fragmented and heterogeneous over time. Urban growth creates higher urban patchiness and increases the number of pasture and agricultural patches. The information thus obtained is helpful in more effective management of the area.
Volume 1, Issue 2 (2-2009)
Abstract
The identity of a society is a tool for distinguishing different nations from each other based on a common concept or predetermined concepts. The strong sense of identity can be considered as a social capital. In addition, social capital and social identity are the result of tangible social relationships, which are understandable by the society. They Also have strong affects on each other. With respect to this relationship, this paper verifies the relationship between identity and social capital. The data were gathered from the world values data of 70 countries according to the definitions of the variables. The results by logistic regression showed that there is a positive and significant relationship between social identity and social capital.
Volume 1, Issue 3 (9-2023)
Abstract
Following years of contamination, rivers may experience significant levels of heavy metal pollution. Our research aims to pinpoint hazardous areas in these rivers. In our specific case, we focus on the floodplains of the Meuse River contaminated with zinc (Zn). Elevated zinc concentrations can lead to various health issues, including anemia, rashes, vomiting, and stomach cramping. However, due to limited sample data on zinc concentrations in the Meuse River, it becomes imperative to generate missing data in unidentified regions. This study employs universal Kriging in spatial data mining to investigate and predict unknown zinc pollutants. The semivariogram serves as a valuable tool for illustrating the variability pattern of zinc. To predict concentrations in unknown regions, the model captured is interpolated using the Kriging method. Employing regression with geographic weighting allows us to observe how stimulus-response relationships change spatially. Various semivariogram models, such as Matern, exponential, and linear, are utilized in our work. Additionally, we introduce Universal Kriging and geographically weighted regression. Experimental findings indicate that: (i) the Matern model, determined by calculating the minimum error sum of squares, is the most suitable theoretical semivariogram model; and (ii) the accuracy of predictions is visually demonstrated by projecting results onto a real map.
Volume 1, Issue 4 (12-2023)
Abstract
Today, carbon dioxide emission is one of the concerns of all countries in the world, so in this paper, we examine the effect of export quality, energy efficiency, and economic complexity on CO2 emissions per capita during the period of 1990 to 2014 in emerging economies. For this purpose, first, energy efficiency is calculated using mathematical programming methods (DEA). Then, the effect of export quality, energy efficiency, and economic complexity on per capita carbon dioxide emissions in the panel of emerging economies is investigated using panel quantile regression. The energy efficiency results show that the average energy efficiency of the studied countries had been increasing from 1990 to 2014. The lowest efficiency score among the studied countries is related to China. The results of quantile regression indicate that the export quality and consumption per capita of fossil fuels have a positive and significant effect on CO2 emissions per capita in all quantiles. The results also show that the coefficient increases by moving in the level of quantiles, so that, the highest effect coefficient of export quality on CO2 emission is related to the quantile 90th and about 0.874. Energy efficiency has a negative and significant effect in all quantiles except 90th, and the highest coefficient of influence (0.133) is related to quantile 10th. The increase in economic complexity increases the co2 emissions in all quantiles except 10th, and the highest coefficient (about 0.487) is related to quantile 90th.
Volume 2, Issue 2 (9-2021)
Abstract
Sense of place is one of the new concepts in urban design field that plays an important role in creating the sustainability of urban communities and the quality of urban spaces. Many factors are effective in creating this feeling, one of the most important of which is the physical characteristics of urban spaces. These characteristics can affect the sense of place by affecting human perception, but the role of individual and social characteristics should not be ignored. This study seeks to investigate the relationship between physical-perceptual characteristics as well as individual-social characteristics as intervention variables. For this purpose, an urban space (sidewalk of Ayatollah Kashani Street) was studied as an example. The methods used were Spearman correlation coefficient and ordinal regression according to the type of data. The results obtained from the research indicate the high role of factors such as originality of meaning and visual comfort, etc. In general, all studied variables (physical-perceptual) have played a positive and significant role with a sense of place. Among the individual-social characteristics, the most important role in creating a sense of place have been factors such as age, gender and literacy. Therefore, it can be said that with increasing age and literacy level, the sense of place has increased and also the sense of place among women is more than men. Of course, as mentioned in the conceptual model of the research, this effect is indirect and is through the effectiveness of perception of these factors. The proposed regression model well showed
Volume 3, Issue 2 (9-2013)
Abstract
Managerial researches emphasize an organizational contingency elements rule at improving the performance. Although, few theorical and empirical researches were implemented upon the influencing elements at organizational performance. This paper is an effort to fill gaps of corresponding issue. For this, exploration and searching the literature and expert interviews (DELPHI) resulted in formulation of 14 contingent elements which impact the military organization performance. Then, an importance- performance analysis technique based on experts needs and expectations was used to prioritize the elements performance. This technique also presents weakness and strength of influencing performance elements. In this paper, to improve the validity and practical aspects of importance- performance analysis technique, experts and personnel of mentioned organization were interviewed. Then, two quantitative and qualitative techniques multiple regression and DEMATEL final values as integration of implicit and explicit importance respectively are used to extract the importance rates. Finally, the performance rate extracted through interviews based on likert values as well as importance rate applied to build the importance- performance matrix in four quadrants which based on two axes (importance, performance) to analysis the feature of located elements in each quadrant.
Volume 3, Issue 3 (7-2001)
Abstract
The impact of climatic variations on basal area growth of basswood (BA) (Tilia americana L.), American beech (BE) (Fagus grandifolia Enrh.), bitternut hickory (BH) (Caria cordiformis (Wang.) K. Koch), largetooth aspen (LA) (Populus grandidentata Michx.), red maple (RM) (Acer rubrum L.), red oak (RO) (Quercus rubra L.), sugar maple (SM) (Acer saccharum Marsh.), and white ash (WA) (Fraxinus americana L.) was studied in a southern province of Quebec, Canada (45o 25 ’ N, 73 o 57 ’ W). In total, forty-eight climatic variations of precipitation (P) (13 variables), temperature (T) (13 variables), heat index (H), (11 variables), and evapotranspiration (11 variables) from the current (C) and past three years (P1, P2, & P3) were tested in regression models to find the best model of the relationship between those independent variables and the last ten years (1985-1994) of basal area growth of the species. Simple individual linear and second degree, mixed, and combination of multiple regression models were used to develop the best regression model for each tree species, separately. The best models explained 79% , 80% , 99% , 91% , 71%, 99% , 49% , and 98% of the total variance of the growth in BA, BE, BH, LA, RM, RO, SM and WA, respectively. The growth in BH, LA, RM, RO, SM, and WA were more associated with the previous year’s climatic variations rather than the current year’s. Bitternut hickory, LA, RM, SM, and WA growth were more related to the first year rather than the second or third preceding year variables. The June heat index of the third previous year of variables explained only 7% of the growth of white ash. It was concluded that the impact of climatic variables on tree growth may vary and may depend on the species and other unknown variables. Also, the results suggested that the first and second previous climatic variables have an important role on the growth of some species. American beech, BH, RO, and WA seem to be a good species to use for the study in dendrochronological and dendroclimatological studies.
Volume 3, Issue 4 (12-2014)
Abstract
Nasonovia ribisnigri (Mosely) is one of the most important pests of the lettuce plant and it was reported for the first time in Ahvaz in 2008. In order to investigate the dominant species of its natural enemies and their population fluctuations, sample were taken arbitrarily from fifty plants twice a week during the growing season in 2010-2012. In this study, ten species of predators, three species of parasitoids and two species of hyperparasitoids were collected and identified. Hoverflies with a relative frequency of 55% were the dominant predators. Peaks of lacewings and subsequently ladybird beetles were more coincident with peaks of aphid population in mid-March in the first year of studies. But their densities in the second year were very low. Also, hoverflies and parasitoids were mainly observed in the high densities in late March-early April, in both years. Regression analysis indicated that populations of aphids were mainly affected by ladybird beetles and lacewings in the first year of study, as well as by ladybird beetles, hoverflies and parasitoids in the second year. Therefore, additional studies are required for further evaluation on the potential abilities of these natural enemies being a good candidates for the future biological control programs.
Volume 4, Issue 2 (6-2016)
Abstract
The effect of forest roads on the extent and type of fire damage occurred in forests and rangelands of the Neka County in Mazandaran province was investigated. For this purpose, all fire spots, stand number, percentage and type of injury and damage to tree species, and average diameter at breast height (DBH) were noted with 100% inventory; area and geographic location of access roads were determined using GPS. The results showed that distance from the main access road had a significant correlation with the area of fire spots, but no significant correlation existed between the distance from the strip roads, skid ways, town of Neka and the area of fire spots. The factors influencing fire severity were analyzed using the stepwise regression model. Model also showed that just the distance from the main access road affected the area of fire spots. For every one meter increase in the distance from the main access roads, the area of fire spot was increased by 1.545 m2. Further, the extent of fire can be controlled by reducing the distance from the main access roads.
Volume 4, Issue 3 (9-2016)
Abstract
Directional felling of trees plays a key role in reducing of damages to forest residual trees and can also facilitate skidding. The aim of this study was presents a practical linear model for estimation of tree falling direction error in an uneven-aged mixed stand in northern forests of Iran. To conduct the study a number of 95 trees of four species Fagus Orientalis Lipsky, Carpinus Betulus L., Alnus Subcordata C.A. May and Acer Platanoides were randomly selected,and assumed felling direction were marked on the trunk of these trees. The trees felled by experienced chainsaw operators, and the differences between the assumed and actual direction were measured as the felling error. The results showed that among the 12 effective factors, the elements of foot slope, diameter at the breast height (DBH), horizontal and vertical angles and area of the backcut surface (HABS, VABS, BA),vertical angle and area of undercut surface (VAUS, UA) significantly correlated with the felling error, and the determination coefficient (R2) of presented linear model was 52.0 % (P < 0.01). Among the model factors, DBH, VABS, and HABS had the three most pronounce impact on felling error.
Volume 5, Issue 1 (3-2019)
Abstract
Arthropods were sampled on an early- and late-season crop of watermelon in the 2016 cropping season using motorized suction sampler swept along 5m length of the middle row of 20 experimental plots at Federal University Wukari. Specimens were sorted to morphotypes, feeding guilds and as dominant based on percentage relative abundance (RA) and frequency of occurrence (FO). Different species diversity indices were computed. The collections made on the early- and late-sown crops were compared using Jaccard’s Similarity index (Cj). Spatial distribution pattern of the dominant arthropods were determined using Taylor’s power law and Iwao’s patchiness regression. Results showed that collections on both crops were similar (Cj= 0.83). A total of 14,466 specimens sorted to 1 order (Araneae) in the class Arachnida and 64 species in 41 families and 8 orders in the class Hexapoda were collected. Data showed moderately high species diversity (H = 2.8-3.0), richness (R = 6.0-7.2), but low evenness (E = 0.26-0.39). Coleopterous insects (22 species), dominated by chrysomelids, were the most diverse and species-rich followed by hymenopterans, mainly formicids. Dominant arthropods (RA≥1.0 and FO≥25.0%) included Asbecesta nigripennis, Aulacophora africana, Philanthus triangulum (parasitoid of bee), Pheidole sp., Camponotus sp., Rhynocoris nitidulus and spiders. Most dominant arthropods were aggregated; dispersion varied with model used and crop season. Only 27.3% of the diverse and rich arthropods on watermelon at Wukari require pest management intervention and validation of their dispersion pattern in large-scale watermelon production.
Volume 5, Issue 2 (6-2017)
Abstract
Background: Soil salinization is a world-wide land degradation process in arid and semi-arid regions that leads to sever economic and social consequences.
Materials and Methods: We analyzed soil salinity by two statistical linear (multiple linear regression) and non-linear (artificial neural network) models using Landsat OLI data in Agh-Ghala plain located in north east of Iran. In situ soil electrical conductivity (EC) of 156 topsoil samples (depth of 0-15cm) was also determined. A Pearson correlation between 26 spectral indices derived from Landsat OLI data and in situ measured ECs was used to apply efficient indices in assessing soil salinity. The best correlated indices such as blue, green and red bands, intensity indices (Int1, Int2), soil salinity indices (Si1, Si2, Si3, Si11, Aster-Si), vegetation Indices (NDVI, DVI, RVI, SAVI), greenness and wetness indices were used to develop two models.
Results: Comparison between two estimation models showed that the performance of ANN model (R2=0.964 and RMSE=2.237) was more reliable than that of MLR model (R2=0.506 and RMSE=9.674) in monitoring and predicting soil salinity. Out of the total area, 66% and 55.8% was identified as non-saline, slightly and very slightly saline for ANN and MLR models, respectively.
Conclusions: This shows that remote sensing data can be effectively used to model and map spatial variations of soil salinity.
Volume 6, Issue 1 (7-2016)
Abstract
In recent years, increased the sensitivity of the competition in the market, because nature of competition has changed since investments in tangible resources to invest in intangible resources. The company's success is their ability to adapt to rapid changes in technology and market conditions. Human capital can be strategic assets and create competitive advantage for companies. In this context, the aim of this study was to investigate the effect of human capital on wealth creation for shareholders. Sample of 146 firms during the period 2009 to 2013 is the Stock Exchange. Fuzzy regression was used to test the research hypothesis. Control variables consider for this study, including firm size, return on assets and financial leverage. Current research suggests there is a significant relationship between human capital and wealth creation for shareholders. Higher human capital is associated with higher- yielding assets. However, there is not a significant relationship between firm size and leverage with human capital.
Volume 6, Issue 1 (4-2018)
Abstract
Aims: Soil organic carbon (SOC) is contemplated as a crucial proxy to manage soil quality, conserve natural resources, monitoring CO2 and preventing soil erosion within the landscape, regional, and global scale. Therefore, the main aims of this study were to (1) determine the impact of terrain derivatives on the SOC distribution and (2) compare the different algorithms of topographic wetness index (TWI) calculation for SOC estimation in a small-scale loess hillslope of Toshan area, Golestan province, Iran. (3) Comparison between multiple linear regression (MLR) and artificial neural networks (ANN) methods for SOC prediction.
Materials & Methods: total of 135 soil samples were taken in different slope positions, i.e., shoulder (SH), backslope (BS), footslope (FS), and toeslope (TS). Primary and secondary terrain derivatives were calculated using digital elevation model (DEM) with a spatial resolution of 10 m × 10 m. To SOC estimation (dependent variable) was applied two models, i.e., MLR and ANN with terrain derivatives as the independent variables.
Findings: The results showed significant differences using Duncan’s test in where TS position had the higher mean value of SOC (25.90 g kg−1) compared to SH (5.00 g kg−1) and BS (12.70 g kg−1) positions. The present study also revealed which SOC was more correlated with TWIMFD (Multiple-Flow-Direction) and TWIBFD (Biflow-Direction) than TWISFD (Single Flow Direction). The MLR and ANN models were validated by additional samples (25 points) that can be explain 65% and 76% of the total variability of SOC, respectively, in the study area.
Conclusion: These results indicated that the use of terrain derivatives is a beneficial method for SOC estimation. In general, an accurate understanding of TWIMFD is needed to better estimate SOC to evaluate soil and ecosystem related effects on global warming of as this hilly region at a larger scale in a future study.