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Showing 8 results for Spatial Econometrics
Hamed Najafi Alamdarloo, Seyed Abolqasem Mortazavi, Katayoon Shemshadi Yazdi,
Volume 13, Issue 3 (9-2013)
Volume 13, Issue 3 (9-2013)
Abstract
According to trade theories, economic integration results in increasing trade and income among trade partners. This paper tries to test the major factors affecting the exports of agricultural products in ECO members using spatial econometric approach. For this purpose, the exports statistics of ECO members has been used in the form of panel data during 1992-2008. Agricultural exports function has been estimated using the Static (fixed and random effects) and Dynamic (generalized method of moments (GMM)) methods in panel data with classic and spatial econometric approaches. The estimated results indicate the existence of spatial dependence among the countries, so the using this estimation procedure is justified. GDP, Exchange rate and spatial variables (such as proximity) have positive effects and Population has negative effect on agricultural exports. Finally, it is suggested that the estimation equations should consider the proximity between the countries and with the increase in the exchange rate and GDP, increase exports in order to provide the necessary basis. Population control policies may also apply.
Alireza Shakibaee, Mohamadreza Ahmadinejad, Fatemeh Taleghani, Zahra Kamalaldini,
Volume 16, Issue 4 (12-2016)
Volume 16, Issue 4 (12-2016)
Abstract
Societies and governments have considered tax as one of the most important ways of public financing. Moreover, identifying new tax capacity to improve tax status and to increase tax incomes is assumed substantial. On the other hand, most economists believe that economic status of a country is affected not only by the economic performance of region but also by the adjacent zones. Therefore, ignoring such spatial factors and linkages may negatively affect the performance of that region. Accordingly, convergence and the related subjects are increasingly drawing the attention of more economists. The present study tries to examine the tax capacity convergence among Iran's provinces during 2001-2011 using Spatial Econometrics in MATLAB software. The findings indicate that meaningful and negative coefficient of the lagged dependent variable represents the convergence of the tax to value added (VA) ratio to its long-term path.
Mohamad Ali Kafaee, Atefeh Khosravi,
Volume 17, Issue 2 (6-2017)
Volume 17, Issue 2 (6-2017)
Abstract
Energy is one of the main production inputs, which indicates potential political and economic power in different countries. Due to higher energy prices, energy productivity is of special importance, and recent applied researches imply the effectiveness of energy productivity on economic growth. In addition, the process of regional economic growth is influenced by geographic conditions, regional and neighborhood specifications and functioning as well as usual regional economic variables including labor, capital, technology and energy. This paper estimates the effect of provincial energy productivity on provincial economic growth, using spatial econometric model and Maximum Likelihood method with applying provincial data from 2001 to 2011. In addition, it investigates β and σ convergence of energy productivity among Iran’s provinces. In spatial econometric method, we can consider the spillover effects and spatial relations among adjacent provinces with defining a distance-weight matrix between regions. Our findings indicate no σ convergence, but there is a conditional β convergence, which is a signal of existence of either spillover or neighborhood effect in provincial energy productivity growth. Meanwhile, the higher economic openness and lower government intervention in the economy improve energy productivity growth, but increasing energy price has no significant effect on the provincial energy productivity.
Dr. Gholamali Haji, Mr. Reza Keyhanihekmat, Dr. Sayed Abbas Najafizadeh, Dr. Nader Mehregan,
Volume 20, Issue 4 (12-2020)
Volume 20, Issue 4 (12-2020)
Abstract
This study attempts to investigate the effect of government spending on regional growth in Iran. The relationship between government spending and economic growth is one of the well-known topics in economic literature. One of the problems of developing countries is the failure to achieve sustainable economic growth, which not only causes economic problems such as recession and unemployment, but also cultural, political and social problems. The government economic stabilization policies can be used to narrow the gap between the potential and realized product and to maintain the product near its potential level. One key issue in the field of regional planning is to study and understand the geographical inequalities in different dimensions. In this paper, using the spatial econometric method, the relationship between government spending and regional growth is estimated by applying the regional data of Statistical Center of Iran during 2001-2017, and Excel and R software’s are used to perform the calculations. This study seeks to explain the growth of different regions using government spending, and to answer these questions: Does government spending have a significant effect on growth in the regions? Do the regions converge in terms of economic growth over time? The results indicate the negative effect of government spending, population growth and human capital on regional growth in Iran. In addition, the statistical significance of spatial correlation coefficient indicates the positive diffusion effects of regional economic growth.
Mr. Kazem Biabany Khameneh, Dr Reza Najarzadeh, Dr Hassan Dargahi, Dr Lotfali Agheli,
Volume 22, Issue 1 (3-2022)
Volume 22, Issue 1 (3-2022)
Abstract
Along with the increased trade integration of countries and the expansion of international production fragmentation, Global Value Chains (GVCs) amount to a huge part of trade today, and participation in a network of trade partners at downstream and upstream of the value chains brings about considerable potentials such as the improvement of the flow of knowledge and more advanced production technologies and techniques, particularly for developing countries. It would not be unexpected for GVCs and participating in them from an environmental aspect to have potential benefits for countries as well.
In this regard, the present study discusses the role that GVCs play in countries' environmental performance. For this purpose, a sample of 65 developing and 36 developed countries was investigated using spatial panel data econometrics, conditional convergence, spatial auto-correlation, and GVCs participation spillover and direct impacts for countries in the form of south-south, north-south, and north-north bilateral added-value trade. The results indicated that there was spatial auto-correlation and conditional convergence based on GVCs for all countries although they are more intense in the case of north-north trade in developed countries. Besides, participation in GVCs has spillover impacts on the trading partner countries if developed countries are included in the bilateral value-added trade but this impact is not statistically acceptable in south-south trade of developing countries according to estimations. Thus, establishing trading relations with developed countries through GVCs is a potentially beneficial policy to improve developing countries’ environmental performance.
Dr Hasan Heydari, Mr. Vahid Nikpey Pesyan,
Volume 23, Issue 1 (3-2023)
Volume 23, Issue 1 (3-2023)
Abstract
Aim and Introduction
Unemployment is a major issue in developing countries. The high rate of unemployment means that the country does not use the workforce effectively. Unemployment is the worst economic problem due to its negative impact on the individual and society and the speed of its spread in the world. Therefore, solving the problem of unemployment is one of the most important goals of institutions. Trade liberalization is one of the effective factors in reducing the unemployment rate.
Globalization is an inevitable process with different economic, social and political aspects. Meanwhile, trade liberalization is considered as the main symbol of globalization and its most important driving force. Therefore, considering the high unemployment rate in most of the provinces of Iran, with the increase in commercial liberalization in line with the economic growth and development, it can be seen in the sectors that have a relative advantage. Increasing national security and political and economic stability provides the necessary infrastructure to attract foreign investors. Therefore, by increasing trade liberalization, it is possible to overcome many economic and political problems in the provinces of Iran, and its effects will spill over to the provinces with the high unemployment rate and will increase economic growth, will increase gross domestic product and finally will reduce the unemployment rate in the provinces of the country.
Methodology
In spatial econometrics, spatial effects are added to the performance of periodic or complex regression models (panels). Therefore, in spatial econometrics, sample information has a spatial component. When data has a spatial component, two issues can be discussed: (1) Spatial dependence, and (2) Spatial heterogeneity. Before estimating spatial panel models, we need to perform spatial dependence tests and to check the existence of autocorrelation between disturbance terms. The existence of spatial coherence between observations and spatial autocorrelation between disturbance terms indicates the need to use spatial panel models. To do this, Moran, Jerry C, and Jetis Ord J tests are used. The Moran test examines the assumption of spatial autocorrelation between disturbance terms. In spatial econometric models, to model spatial reactions, it is necessary to select the numerical value of spatial directions. For this purpose, we have two sources of assumptions: (1) Position on the coordinate plane, which is expressed by latitude and longitude, so that the distance of any point in the location, or distance of any observation located at any point relative to fixed or central points or observations can be calculated. (2) The source of spatial information is neighborhood and neighborhood, which expresses the relative location in the space of an observed peripheral unit, compared to other such scales.
Findings
In this study, the impact of trade liberalization on the unemployment rate in the provinces of Iran over the period 2006-2019 was investigated with a spatial econometric approach. First, to check the spatial dependence, the spatial dependence of the provinces was confirmed by the Moran and Jerry C test, and based on the significance of the above tests, the research model was estimated in the spatial panel model. According to the results, trade liberalization shows positive effects on reducing the unemployment rate of these provinces, and this result is compatible with the results of other studies including Yanıkkaya (2008), Li et al (Li & et al., 2020), Adou (2021), Amini and Muradzad (2014) and Samimi et al (2014). The variables of gross domestic product and construction credits have positive and significant effects on reducing the unemployment rate. However, the inflation rate and the wage rate have negative and significant effects on reducing the unemployment rate of the provinces of Iran.
Discussion and Conclusion
Since according to the research literature, part of the unemployment rate is reduced through various channels such as trade liberalization, attracting foreign investment and export, considering that macroeconomic decisions are made in the center of the country, it is suggested It is possible, taking into account the increase in regional and provincial decision powers, it is possible to achieve favorable economic effects in the direction of improving macro-economic variables, policy and regional planning.
The cooperation between the central government and the provincial centers increases the security of the region (country). As a result, high security encourages foreign investment, increases the stability of the economic policy, facilitates the laws and removes obstacles. National security increases the degree of commercial openness, increases production and reduces the unemployment rate.
To achieve a balanced and convergent regional development and to resolve the spatial and financial disparities and heterogeneities of the country's provinces, it is suggested that the allocation of budget resources to the less developed provinces should be increased. And the share of allocation of budget resources of different provinces should be converged. As a result, the provinces of the country, especially the less developed provinces should be able to flourish as much as possible from the point of view of economic growth and unemployment rate reduction.
The lack of economic infrastructure in rural areas, especially in the industrial sector, increases the rate of emigration and the overflow of labor to urban areas. Therefore, the improvement of economic infrastructure and the increase of trade liberalization policies lead to the improvement of technology and innovation in the less developed regions, and ultimately lead to a decrease in the unemployment rate in the province level.
Dr. Maryam Khodaverdi Samani, Dr. Gholamreza Nemati, Dr. Alireza Kashefi, Dr. Parvaneh Salatin,
Volume 23, Issue 3 (8-2023)
Volume 23, Issue 3 (8-2023)
Abstract
Aim and Introduction
Today, planners and decision makers of countries need timely and accurate evaluation of their decisions and policies. The issue of time and precision is so important that it provides the possibility of implementing possible changes and modifications of patterns and plans, and prevents wasting resources and opportunities. Fortunately, various indicators have provided such a possibility to evaluate these policies and decisions. Misery index is one of the most important measures of social welfare. This index is obtained from the linear combination of inflation and unemployment. This index was introduced by Aokan (1999) and expanded by Barro (1996). An increase in the misery index is associated with many social and economic costs, such as an increase in crime, poverty, divorce, a decrease in social security, damage to mental health, the collapse of families, a decrease in health expenses, and a decrease in life expectancy. Inflation causes the imposition of welfare costs by reducing the value of people’s financial assets, and on the other hand, it harms production by creating uncertainty in the decisions of institutions for investment and creating other costs. Inflation leads to sub - optimal allocation of resources, economic inefficiency and social, cultural and political disorder of the society. Unemployment like inflation is the cause of chaos in the economic conditions of the society. Unemployment has caused people to suffer from social problems such as crimes, addiction and moral corruption. Unemployment causes people to be caught in social problems such as crime, addiction and moral corruption.
Methodology
Knowledge and awareness of the state of misery index in the regions of the country in certain time horizons are very important for the planners of the region and economic policy makers of the country. Considering the importance of the misery index, this question is raised: Which factors affect the convergence of the misery index in the provinces? In this regard, several studies have been conducted in the field of misery index. However, none of the studies have investigated the influence of the factors affecting the convergence of the misery index in the provinces using spatial econometrics.
In economic literature, there are several methods for investigating the convergence. Absolute beta convergence and conditional beta convergence have been used in this study. Absolute beta convergence is formed independently of initial conditions and other characteristics of an economy.
For this purpose, using theoretical foundations and empirical studies, the variables of economic growth, monetary indiscipline, human capital, and information and communications technology (ICT) were added to the convergence model as explanatory variables. Absolute beta convergence and conditional beta convergence models have been estimated using the spatial econometric method over the period 2006-2020.
In this study, after defining the spatial weight matrix, the unit root test is used to examine the "stationary" of the variables. Moran test and Lagrange multiplier test are used to detect spatial autocorrelation and examine the presence of spatial effects, respectively. Chow's test is used to determine whether the data is a panel, and Hausman's spatial test is used to use the fixed or random effect method. Finally, the model is estimated, and effects of space spillovers are analyzed with "spatial econometrics method" by accounting for direct and indirect effects in Stata software.
The calculations of the overflow coefficients of each province on other provinces and the drawing of maps were done using R software and Maptools, Spdep and IMPact function packages for the year 2019.
The statistical data including inflation and unemployment rates are used to calculate misery index. Gross domestic product, population, number of university graduates (as human capital index) are extracted from statistical yearbook of the provinces and Statistical Center of Iran. The penetration coefficient of the internet (as ICT indicator) is extracted from Ministry of Communications and Information Technology, as well as facilities and deposits after deducting legal trust are gathered from the Central Bank of Iran. The statistical population of this study is the provinces of Iran except for Alborz province.
The results of stationary test using Levin, Lin and Chu (2002) method showed that all variables are stationary at level. Also, the null hypothesis of Moran's test regarding the absence of spatial effects in absolute convergence model and conditional convergence model was rejected. Therefore, the presence of spatial effects in absolute and conditional convergence models was confirmed. According to the conducted tests, the spatial auto-regression method (SAC) was used in this study. The results of the spatial Hausman test also showed that the models should be estimated using the fixed effects method.
Findings
The results of estimating the models showed that economic growth and human capital have a negative and significant effect, ICT and monetary indiscipline of banks have a positive and significant effect on the convergence of the misery index in the provinces. According to the speed of convergence, in the case of annual absolute convergence of about 10.9 % and in the case of conditional convergence of about 12.6 % , the gap between the " current growth rate of the misery index " of the provinces and the " long - term equilibrium misery index " of the provinces will be resolved. In the case of conditional convergence, the time required to eliminate half of the aforementioned gap is about 5.5 years. It should be noted that in this study, the misery index is a negative variable. The interpretation of the beta coefficient means that there is an opposite relationship between the initial situation and the average growth rate of the misery index: That is, regions with a lower "misery index" move towards the average misery index with a higher speed and higher growth rate than other regions. This means that the economic situation of the provinces is getting worse. Therefore, it is expected that the provinces will converge to their long - term equilibrium misery index and the gap between the current growth rate of the province's misery index and its long - term equilibrium will be resolved.
Discussion and Conclusion
According to the positive and significant effect of the monetary indiscipline index on the convergence of the misery index in the provinces, it can be said that with the increase of monetary indiscipline in the banks, liquidity has increased at the community level. Consequently, it has caused an increase in the general level of prices and an increase in the misery index. On the other hand, due to the economic situation of Iran, the existence of economic and banking sanctions and the impossibility of financing and investing in foreign sectors, the government's credit facilities and debt to banks have increased, and the monetary indiscipline index of banks is increasing, and as a result, the liquidity risk of banks is increasing. As a result, the lending power of banks will decrease, that is, it is not possible to grant large bank loans to drive the productive and entrepreneurial sectors into spur the economic growth of the provinces, and this will cause a decrease in employment, a decrease in the level of production, and then an increase in unemployment. This is why the misery index increases in the provinces. The spatial coefficient of the interval of the dependent variable is positive and significant. The existence of a positive and significant coefficient of the spatial dependence variable shows the positive effect of the poverty index of neighboring provinces on each other, so the distance between the provinces of the country has an effect on the convergence of the poverty index.
Today, planners and decision makers of countries need timely and accurate evaluation of their decisions and policies. The issue of time and precision is so important that it provides the possibility of implementing possible changes and modifications of patterns and plans, and prevents wasting resources and opportunities. Fortunately, various indicators have provided such a possibility to evaluate these policies and decisions. Misery index is one of the most important measures of social welfare. This index is obtained from the linear combination of inflation and unemployment. This index was introduced by Aokan (1999) and expanded by Barro (1996). An increase in the misery index is associated with many social and economic costs, such as an increase in crime, poverty, divorce, a decrease in social security, damage to mental health, the collapse of families, a decrease in health expenses, and a decrease in life expectancy. Inflation causes the imposition of welfare costs by reducing the value of people’s financial assets, and on the other hand, it harms production by creating uncertainty in the decisions of institutions for investment and creating other costs. Inflation leads to sub - optimal allocation of resources, economic inefficiency and social, cultural and political disorder of the society. Unemployment like inflation is the cause of chaos in the economic conditions of the society. Unemployment has caused people to suffer from social problems such as crimes, addiction and moral corruption. Unemployment causes people to be caught in social problems such as crime, addiction and moral corruption.
Methodology
Knowledge and awareness of the state of misery index in the regions of the country in certain time horizons are very important for the planners of the region and economic policy makers of the country. Considering the importance of the misery index, this question is raised: Which factors affect the convergence of the misery index in the provinces? In this regard, several studies have been conducted in the field of misery index. However, none of the studies have investigated the influence of the factors affecting the convergence of the misery index in the provinces using spatial econometrics.
In economic literature, there are several methods for investigating the convergence. Absolute beta convergence and conditional beta convergence have been used in this study. Absolute beta convergence is formed independently of initial conditions and other characteristics of an economy.
For this purpose, using theoretical foundations and empirical studies, the variables of economic growth, monetary indiscipline, human capital, and information and communications technology (ICT) were added to the convergence model as explanatory variables. Absolute beta convergence and conditional beta convergence models have been estimated using the spatial econometric method over the period 2006-2020.
In this study, after defining the spatial weight matrix, the unit root test is used to examine the "stationary" of the variables. Moran test and Lagrange multiplier test are used to detect spatial autocorrelation and examine the presence of spatial effects, respectively. Chow's test is used to determine whether the data is a panel, and Hausman's spatial test is used to use the fixed or random effect method. Finally, the model is estimated, and effects of space spillovers are analyzed with "spatial econometrics method" by accounting for direct and indirect effects in Stata software.
The calculations of the overflow coefficients of each province on other provinces and the drawing of maps were done using R software and Maptools, Spdep and IMPact function packages for the year 2019.
The statistical data including inflation and unemployment rates are used to calculate misery index. Gross domestic product, population, number of university graduates (as human capital index) are extracted from statistical yearbook of the provinces and Statistical Center of Iran. The penetration coefficient of the internet (as ICT indicator) is extracted from Ministry of Communications and Information Technology, as well as facilities and deposits after deducting legal trust are gathered from the Central Bank of Iran. The statistical population of this study is the provinces of Iran except for Alborz province.
The results of stationary test using Levin, Lin and Chu (2002) method showed that all variables are stationary at level. Also, the null hypothesis of Moran's test regarding the absence of spatial effects in absolute convergence model and conditional convergence model was rejected. Therefore, the presence of spatial effects in absolute and conditional convergence models was confirmed. According to the conducted tests, the spatial auto-regression method (SAC) was used in this study. The results of the spatial Hausman test also showed that the models should be estimated using the fixed effects method.
Findings
The results of estimating the models showed that economic growth and human capital have a negative and significant effect, ICT and monetary indiscipline of banks have a positive and significant effect on the convergence of the misery index in the provinces. According to the speed of convergence, in the case of annual absolute convergence of about 10.9 % and in the case of conditional convergence of about 12.6 % , the gap between the " current growth rate of the misery index " of the provinces and the " long - term equilibrium misery index " of the provinces will be resolved. In the case of conditional convergence, the time required to eliminate half of the aforementioned gap is about 5.5 years. It should be noted that in this study, the misery index is a negative variable. The interpretation of the beta coefficient means that there is an opposite relationship between the initial situation and the average growth rate of the misery index: That is, regions with a lower "misery index" move towards the average misery index with a higher speed and higher growth rate than other regions. This means that the economic situation of the provinces is getting worse. Therefore, it is expected that the provinces will converge to their long - term equilibrium misery index and the gap between the current growth rate of the province's misery index and its long - term equilibrium will be resolved.
Discussion and Conclusion
According to the positive and significant effect of the monetary indiscipline index on the convergence of the misery index in the provinces, it can be said that with the increase of monetary indiscipline in the banks, liquidity has increased at the community level. Consequently, it has caused an increase in the general level of prices and an increase in the misery index. On the other hand, due to the economic situation of Iran, the existence of economic and banking sanctions and the impossibility of financing and investing in foreign sectors, the government's credit facilities and debt to banks have increased, and the monetary indiscipline index of banks is increasing, and as a result, the liquidity risk of banks is increasing. As a result, the lending power of banks will decrease, that is, it is not possible to grant large bank loans to drive the productive and entrepreneurial sectors into spur the economic growth of the provinces, and this will cause a decrease in employment, a decrease in the level of production, and then an increase in unemployment. This is why the misery index increases in the provinces. The spatial coefficient of the interval of the dependent variable is positive and significant. The existence of a positive and significant coefficient of the spatial dependence variable shows the positive effect of the poverty index of neighboring provinces on each other, so the distance between the provinces of the country has an effect on the convergence of the poverty index.
Dr Elham Nobahar, Dr Seyed Kamal Sadeghi, Mr Hadi Kheirollahi Zaki,
Volume 24, Issue 3 (9-2024)
Volume 24, Issue 3 (9-2024)
Abstract
Introduction
Crime is a multifaceted phenomenon that has always attracted the attention of economists, sociologists, lawyers and psychologists. Many experts and economic pioneers consider it necessary to achieve economic development to improve the level of security and reduce crime in the society. Since the occurrence of any phenomenon is affected by various factors, the occurrence of crime as an undesirable phenomenon is not excluded from this rule. Various economic, social and political factors affect crime in society. Identifying these factors can help a lot in the correct understanding and appropriate policy making in order to control and reduce the crime rate in the society. Meanwhile, one of the most important economic factors affecting crime is unemployment. Unemployment is one of the most important macroeconomic variables, which clearly affects many social phenomena, including crime. In this regard, the main goal of the present study is to investigate the relationship between unemployment and crime and to identify factors affecting crime in the Iranian cities. The statistical population of the current research is the cities of Iran and the time range under investigation is 2016.
Methodology
In this study, the spatial causality test was used to investigate the relationship between unemployment and crime. The first step in investigating the spatial causality relationship between the studied variables is to perform the spatial independence test of the variables. In the second step, the existence of spatial dependence between variables is examined. If both the investigated variables have a spatial structure and there is a spatial dependence between the two variables, then the spatial causality test is performed in the third step. In this study, the spatial econometric approach has also been used to estimate the crime model in the cities of Iran. In this regard, the presence of spatial effects in the model has been tested using Moran's I test, and then the most appropriate spatial regression model has been selected and estimated based on the Lagrange coefficient (LM) test and the LR diagnostic tests. The software packages used in this study are Matlab 2023, GeoDa 1.16 and Stata 15.
Findings
The results of the spatial tests show that both crime index and unemployment rate have a spatial structure and the spatial dependence between these two variables was also confirmed, so in the third step, the spatial causality has been tested. The results of spatial causality test indicate the existence of a two-way causality relationship between the crime index and the unemployment rate. In other words, unemployment was the cause of crime during the period under investigation, and unemployment also led to the occurrence and increase of crime. According to the results of the spatial causality test, the crime model of the Iranian cities was developed in terms of the unemployment variable and several control variables. In order to estimate the model, the presence of spatial effects was first investigated using Moran's I test. The results of this test indicate the presence of spatial effects in the model. Also, based on the results of the Lagrange coefficient and likelihood ratio tests, the Spatial Durbin Model (SDM) was chosen as the most appropriate method for estimating the model. The results of the estimation of the crime model indicate that the spatial lag coefficient is positive and significant at a high level, which indicates the existence of spatial dependence in the model. The positiveness of this coefficient shows that an increase in crime in one city causes an increase in crime in neighboring cities. Also, according to the results of the research, the variables of unemployment, industrialization, urbanization and divorce rate are the most important variables affecting crime rate. The results show positive and significant relationships between unemployment, urbanization, divorce rate, and crime. The industrialization variable also has a negative and significant effect on crime. Also, the spillover effect of the unemployment variable is negative and significant. Based on the results, the higher the unemployment rate and the urbanization rate in the cities, the crime rate will also increase in those cities. On the other hand, as cities move toward industrialization and the number of industrial enterprises in them increases, the rate of crime will decrease more.
Discussion and conclusion
The findings reveal that, unemployment is one of the most important variables affecting crime in the Iranian cities. So, it is recommended that authorities pay special attention to sustainable policies regarding employment and its proportional distribution in cities. Considering excessive growth of urbanization and its detrimental impact on rampant crime rates, it is suggested that statesmen and policy makers create more facilities and pay special attention to rural areas to provide reverse migration in order to prevent occurrence of various crimes, which are happenning due to population increase especially in informal settlements of larger cities.
Crime is a multifaceted phenomenon that has always attracted the attention of economists, sociologists, lawyers and psychologists. Many experts and economic pioneers consider it necessary to achieve economic development to improve the level of security and reduce crime in the society. Since the occurrence of any phenomenon is affected by various factors, the occurrence of crime as an undesirable phenomenon is not excluded from this rule. Various economic, social and political factors affect crime in society. Identifying these factors can help a lot in the correct understanding and appropriate policy making in order to control and reduce the crime rate in the society. Meanwhile, one of the most important economic factors affecting crime is unemployment. Unemployment is one of the most important macroeconomic variables, which clearly affects many social phenomena, including crime. In this regard, the main goal of the present study is to investigate the relationship between unemployment and crime and to identify factors affecting crime in the Iranian cities. The statistical population of the current research is the cities of Iran and the time range under investigation is 2016.
Methodology
In this study, the spatial causality test was used to investigate the relationship between unemployment and crime. The first step in investigating the spatial causality relationship between the studied variables is to perform the spatial independence test of the variables. In the second step, the existence of spatial dependence between variables is examined. If both the investigated variables have a spatial structure and there is a spatial dependence between the two variables, then the spatial causality test is performed in the third step. In this study, the spatial econometric approach has also been used to estimate the crime model in the cities of Iran. In this regard, the presence of spatial effects in the model has been tested using Moran's I test, and then the most appropriate spatial regression model has been selected and estimated based on the Lagrange coefficient (LM) test and the LR diagnostic tests. The software packages used in this study are Matlab 2023, GeoDa 1.16 and Stata 15.
Findings
The results of the spatial tests show that both crime index and unemployment rate have a spatial structure and the spatial dependence between these two variables was also confirmed, so in the third step, the spatial causality has been tested. The results of spatial causality test indicate the existence of a two-way causality relationship between the crime index and the unemployment rate. In other words, unemployment was the cause of crime during the period under investigation, and unemployment also led to the occurrence and increase of crime. According to the results of the spatial causality test, the crime model of the Iranian cities was developed in terms of the unemployment variable and several control variables. In order to estimate the model, the presence of spatial effects was first investigated using Moran's I test. The results of this test indicate the presence of spatial effects in the model. Also, based on the results of the Lagrange coefficient and likelihood ratio tests, the Spatial Durbin Model (SDM) was chosen as the most appropriate method for estimating the model. The results of the estimation of the crime model indicate that the spatial lag coefficient is positive and significant at a high level, which indicates the existence of spatial dependence in the model. The positiveness of this coefficient shows that an increase in crime in one city causes an increase in crime in neighboring cities. Also, according to the results of the research, the variables of unemployment, industrialization, urbanization and divorce rate are the most important variables affecting crime rate. The results show positive and significant relationships between unemployment, urbanization, divorce rate, and crime. The industrialization variable also has a negative and significant effect on crime. Also, the spillover effect of the unemployment variable is negative and significant. Based on the results, the higher the unemployment rate and the urbanization rate in the cities, the crime rate will also increase in those cities. On the other hand, as cities move toward industrialization and the number of industrial enterprises in them increases, the rate of crime will decrease more.
Discussion and conclusion
The findings reveal that, unemployment is one of the most important variables affecting crime in the Iranian cities. So, it is recommended that authorities pay special attention to sustainable policies regarding employment and its proportional distribution in cities. Considering excessive growth of urbanization and its detrimental impact on rampant crime rates, it is suggested that statesmen and policy makers create more facilities and pay special attention to rural areas to provide reverse migration in order to prevent occurrence of various crimes, which are happenning due to population increase especially in informal settlements of larger cities.
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