Search published articles
Showing 14 results for Unemployment
Dr Esmaiel Abounoori, Dr Anahita Roozitalab,
Volume 0, Issue 0 (12-2024)
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
Saleh Ghavidel,
Volume 8, Issue 1 (4-2008)
Volume 8, Issue 1 (4-2008)
Abstract
The goal of this paper is to examine Impact of the economical, cultural and Environmental variables such as unemployment rate, educational level and Share of rural, on the Iran self-employment rate. We use panel information data for all provinces in Iran. Using data provided by the 1996 - 2003 period from central statistic of Iran. We find for most provinces there is negative relationship between the unemployment rate and Self- employment rate, and negative for educational level. Thus unemployment and Self-employment are alternative for together in Iran Labour Market. In Iran, increase of educational cause the promotion employment in public or wage sector. Other result in case of Iran is Positive effect on Self-employment rate such as share of rural population. However, in this paper I argue that Self-employment sector have to high employment capacity in Iran Labour market.
Ebrahim Ali Razini, Amir Reza Soori, Ahmad Tashkini,
Volume 11, Issue 2 (8-2011)
Volume 11, Issue 2 (8-2011)
Abstract
The goal of this paper is investigating the relationship between unemployment rate and government size in Iran. For that, we have used some VAR models, which include the following variables: government size which is measured by total government outlays as a percentage of GDP, unemployment rate, real GDP growth rate, inflation rate, and minimum wage. The results reveal that a large government sector would raise unemployment, and an increase in GDP growth rate, inflation rate and minimum wages are likely to decrease unemployment rate.
Nader Mehregan, Saied Garshasbi Fakhr,
Volume 11, Issue 4 (1-2012)
Volume 11, Issue 4 (1-2012)
Abstract
This paper, using an economic analysis of crime, focuses on the relationship between crime and income inequality in Iran. The results indicate that there is a significant relationship between crime and income inequality in Iran. This means that by increasing income inequality in Iran the robbery as a crime will increase as well. In addition the findings reveal that there is a direct relationship between robbery and the proportion of people in urban areas, divorce and unemployment rate, but there is a negative relationship for households’ monthly income in Iran.
Abolfazl Shahabadi, Tayebeh Khatami,
Volume 13, Issue 1 (4-2013)
Volume 13, Issue 1 (4-2013)
Abstract
Self-employment is a crucially important subject in an economy, especially for a country with high unemployment rate. This paper studies the determinant factors of self employment in Iran during 1996-2006 using a panel data method. The results show that unemployment rate per province, the ratio of 15-65 age bracket to total population of the country, and the share of each province to total GDP aren't statically significant. While the ratio of urban population to total population for each province has a negative coefficient in self-employment equation, the ratio of R&D expenditure to GDP and literacy rate has a positive one.
Ali Akbar Gholizadeh, Behnaz Kamyab,
Volume 15, Issue 3 (11-2015)
Volume 15, Issue 3 (11-2015)
Abstract
The regional labor migration is responsive to regional differences of unemployment rate which is interrelated with fundamentals of housing market. This paper analyzes the relationship between labor and housing markets in 30 provinces of Iran. It uses Vermeulen and Ommeren (2005) model in a panel data for regions and an Autoregressive Distributed Lags (ARDL) model in the country over the period 1998-2011. The estimation results indicate that most of provinces with high housing prices on average have also low volatility in unemployment rates. The workers in high-unemployment regions are compensated through housing markets, since the prices of land and housing and living expenses are lower in these regions than ones with low unemployment. In addition, the propensity of households to get housing loan decreases and the housing price falls despite high risk of unemployment. The risk of getting unemployed raises the income uncertainty and reduces the housing price. This indicates a negative relationship between housing price and unemployment rate. The test for causality shows that this relationship is bidirectional in provincial level, so that provinces having high unemployment rates are compensated by better conditions of housing market which means low housing prices.
Mansour Zarranejad, Abdolkarim Hosseinpoor,
Volume 16, Issue 1 (5-2016)
Volume 16, Issue 1 (5-2016)
Abstract
One of the most controversial and relatively old subjects in economics is the optimal size of government and its impact on macroeconomic variables. Government size and extent of public enterprises are of crucial impacts on economy. Thus, one of the main objectives of the governments is to achieve full employment. This paper investigates the effects of government size on unemployment rate in Iran’s economy using annual data during 1959-2011. It applies Pesaran, Shin and Smith (PSS) bounds testing approach to estimate an Unrestricted Error Correction Model (UECM), which derives both dynamic and long-term relationships. The finding of the research shows that the government size has a significant positive effect on unemployment rate, indicating that reducing the size of government would lead to reduction of unemployment rate in Iran. With increases in size of government, the crowding out effect in the form of private investment is decreased. As a result, productivity growth and international competition are reduced, then the unemployment rate is increased. The estimation of the ECM model shows that the error term is negative and statistically significant. The Error Correction Term (ECT) is relatively low (-0.27) indicating a slow adjustment toward the equilibrium.
Mohammad Reza Kohansal, Zorar Permeh,
Volume 17, Issue 1 (4-2017)
Volume 17, Issue 1 (4-2017)
Abstract
This paper investigates the effect of reduction in agricultural subsidies in Iran. To this end, the social accounting matrix (SAM) as an analytical model is developed with dimensions of 78 × 78 for the year 2006. Since the amount of subsidies paid to the agriculture sector, based on Total Aggregate Measurement of Support (TAMS), is higher than amount set by the World Trade Organization, hence, in the case of applying for membership of Iran in this organization, Iran should reduce subsidies during a 10-year period, annually by reduction as much as 13600 billion Rials. This paper studies the reduction in agricultural subsidies by 13600 billion Rials. The findings indicate the reduction in gross domestic product (GDP) by more than 57000 billion Rials. One main effect of reduction in agricultural subsidies relates to reduction in output and employment, in such a way this shock results in reducing number of agricultural employees by 86000 people.
Sohrab Delangizan, Mohammad Karimi, Parastoo Amiriani,
Volume 17, Issue 1 (4-2017)
Volume 17, Issue 1 (4-2017)
Abstract
This research examines the effect of monetary policies on unemployment under inflation uncertainty in Iran using the annual data during 1974-2011. The basic model is selected according to the simultaneous equilibrium of dynamic aggregate demand and supply. In addition, inflation uncertainty is calculated using the GARCH family models including ARCH, GARCH and EGARCH. The generated data from a novel model is considered as a proxy for inflation uncertainty, and Generalized Method of Moments (GMM) is used to estimate this model. The estimation results show that inflation uncertainty reduces the unemployment rate, i.e. the effect of monetary policies on unemployment is decreased under inflation uncertainty, and there is a significant and positive relationship between unemployment and inflation rates. Henceforth, an increase in inflation uncertainty leads to an increase in unemployment rate, which is in line with Friedman's theory in this field
Dr Hassan Daliri,
Volume 18, Issue 4 (12-2018)
Volume 18, Issue 4 (12-2018)
Abstract
In recent years, declining fertility rate has been main problem in Iran. The theoretical framework shows that economic and social variables affect fertility rate. Household expected income is one of the most important variables influencing fertility rate. Expected income is directly related to the quality and quantity of future parents’ jobs. Using a panel data model, this research investigates the effect of labor market uncertainty on fertility rate in Iran's provinces during 2005-2013. Since there is no single index for labor market uncertainty, a proxy for job uncertainty was calculated for women and men. According to research findings, job uncertainty in both women and men is of important influence on fertility rate. Men's job uncertainty has a positive effect, but women's job uncertainty has a negative effect on fertility rate. As a result, the structure and expectations of the labor market among men and women are crucial factors in determining fertility rate. Hence, policymakers can increase fertility rates by improving the labor market structure
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. Mirhossein Mousavi, Dr. Musa Khoshkalam Khosroshahi, Mrs. Samira Torkashvand,
Volume 23, Issue 4 (12-2023)
Volume 23, Issue 4 (12-2023)
Abstract
The purpose of this study is to investigate the effect of oil price shocks on the components of the Iranian labor market and the role of government capital expenditure in this field. Labor market components include job vacancies, job finding rates, inflow rates to unemployment and unemployment rates. For this purpose, the structural vector autoregressive approach over the period 2005:2- 2019:3 has been used. The results of impulse response functions show that positive oil price shocks have significant effects on model variables. However, negative oil price shocks are not significant. The positive oil price shock increase government capital expenditure, but due to the inefficiency of government investment, job vacancies decrease and the inflow rates to unemployment increase. As a result, the unemployment rate has risen in response to positive oil price shocks. The results show the Dutch disease and the asymmetric effect of oil price shocks on the labor market.
Introduction:
In addition to creating economic problems, the problem of unemployment can be the source of behavioral disorders and political tensions and can be considered a threat to the health of a society. For this reason, analyzing the labor market and knowing the factors that lead to unemployment is one of the concerns of every economy. Oil is a source of income in exporting countries and an important production factor in importing countries. Therefore, oil price shocks are expected to have an impact on market developments in terms of income and cost effects. This study aims to answer the question of whether oil price shocks have an effect on the components of Iran's labor market. For this purpose, the effect of oil price shocks from the channel of construction expenditure on the rates of finding a job, entering unemployment, unemployment and job opportunities, which are adjustment factors and represent the components of the labor market, are investigated.
Methodology:
In this article, structural vector auto regression (SVAR) model is used to investigate the effect of oil price shock on labor market components. For this purpose, it is necessary to specify the VAR model first, and then analyze the SVAR model by applying restrictions on matrices A and B. Constraints are imposed on the relationships between the regression residuals and the disturbance terms of the structural equation system so that the structural form can be identified.
Results and Discussion:
The results related to the significance of the variables show that all the variables are at the significance level. In addition, the optimal lag for estimating the basic VAR model based on the Akaike criterion is 1. Examining the roots of the VAR equation system shows that all the roots are less than one and are inside the unit circle, so the effects of shocks disappear in the long-run and the system is stable. The findings show that one standard deviation shock to the series of positive changes in oil price causes its instant increase by 0.4 and its effect decreases in the second season and disappears from the third season onwards. Government construction expenditure has increased immediately in response to shocks (one-time lag amounts), but this increase is not lasting, and it has declined in the second season, and in the third season it disappears with a slight increase in shock effects that shows a strong dependence on oil revenues. Because the only source of construction expenditure is from oil revenues, job openings have declined due to these shocks, which last until the second season. Then it increases slightly, but less than the initial negative effect, and gradually the shock effect disappears. One standard deviation shock to the series of negative oil price changes will cause it to increase by 0.1 immediately, and its effect will disappear in the third quarter. The effect of negative oil price shocks on any of the variables is not significant. However, these shocks immediately reduce government construction spending. This again shows the dependence of construction expenditure on oil revenues because at the time of the drop in oil prices, construction expenditure is decreased and allocated to current expenditure.
Conclusion:
The aim of this study was to investigate the effect of oil price shocks on the labor market. For this purpose, the effects of positive and negative shocks of oil prices on construction expenditure, job opportunities, and unemployment rates, entering unemployment and finding jobs in a SVAR model were investigated. Positive (negative) shocks in oil prices immediately increase government construction spending. This increase (decrease) is not lasting and quickly decreases (increase) and from the third season onwards, the shock effects disappear. The positive shock of oil prices has a significant effect on four components of labor market, namely job opportunities, entering unemployment, job finding rate and unemployment rate. However, negative oil price shocks are not meaningful. Therefore, oil price shocks have asymmetric effects on the labor market. The results also confirm two problems of dependence on current expenditure and Dutch disease. Because shocks only affect the short term, while construction spending is expected to improve job opportunities in the long run.
Introduction:
In addition to creating economic problems, the problem of unemployment can be the source of behavioral disorders and political tensions and can be considered a threat to the health of a society. For this reason, analyzing the labor market and knowing the factors that lead to unemployment is one of the concerns of every economy. Oil is a source of income in exporting countries and an important production factor in importing countries. Therefore, oil price shocks are expected to have an impact on market developments in terms of income and cost effects. This study aims to answer the question of whether oil price shocks have an effect on the components of Iran's labor market. For this purpose, the effect of oil price shocks from the channel of construction expenditure on the rates of finding a job, entering unemployment, unemployment and job opportunities, which are adjustment factors and represent the components of the labor market, are investigated.
Methodology:
In this article, structural vector auto regression (SVAR) model is used to investigate the effect of oil price shock on labor market components. For this purpose, it is necessary to specify the VAR model first, and then analyze the SVAR model by applying restrictions on matrices A and B. Constraints are imposed on the relationships between the regression residuals and the disturbance terms of the structural equation system so that the structural form can be identified.
Results and Discussion:
The results related to the significance of the variables show that all the variables are at the significance level. In addition, the optimal lag for estimating the basic VAR model based on the Akaike criterion is 1. Examining the roots of the VAR equation system shows that all the roots are less than one and are inside the unit circle, so the effects of shocks disappear in the long-run and the system is stable. The findings show that one standard deviation shock to the series of positive changes in oil price causes its instant increase by 0.4 and its effect decreases in the second season and disappears from the third season onwards. Government construction expenditure has increased immediately in response to shocks (one-time lag amounts), but this increase is not lasting, and it has declined in the second season, and in the third season it disappears with a slight increase in shock effects that shows a strong dependence on oil revenues. Because the only source of construction expenditure is from oil revenues, job openings have declined due to these shocks, which last until the second season. Then it increases slightly, but less than the initial negative effect, and gradually the shock effect disappears. One standard deviation shock to the series of negative oil price changes will cause it to increase by 0.1 immediately, and its effect will disappear in the third quarter. The effect of negative oil price shocks on any of the variables is not significant. However, these shocks immediately reduce government construction spending. This again shows the dependence of construction expenditure on oil revenues because at the time of the drop in oil prices, construction expenditure is decreased and allocated to current expenditure.
Conclusion:
The aim of this study was to investigate the effect of oil price shocks on the labor market. For this purpose, the effects of positive and negative shocks of oil prices on construction expenditure, job opportunities, and unemployment rates, entering unemployment and finding jobs in a SVAR model were investigated. Positive (negative) shocks in oil prices immediately increase government construction spending. This increase (decrease) is not lasting and quickly decreases (increase) and from the third season onwards, the shock effects disappear. The positive shock of oil prices has a significant effect on four components of labor market, namely job opportunities, entering unemployment, job finding rate and unemployment rate. However, negative oil price shocks are not meaningful. Therefore, oil price shocks have asymmetric effects on the labor market. The results also confirm two problems of dependence on current expenditure and Dutch disease. Because shocks only affect the short term, while construction spending is expected to improve job opportunities in the long run.
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.
Volume 28, Issue 2 (4-2021)
Abstract
Some researches in the domain of women's employment show that gender is a significant factor in the professional position i.e. gender makes women less likely to achieve lucrative and high-paying jobs compared to their men folks. However, in many countries, increasing women's education and skills are key indicators of their growing participation in the labor market. In Iran, despite a decline in women's illiteracy and spectacular rise intheir presenceat universities, even in some traditionally masculine fields, the unemployment rate of educated women is higher than those of men. This study is aimed at understanding the female students' opinions about the gap between higher education and their career prospects. For that purpose, a qualitative research in four government universities from four regions of Iran has been conducted. The data of the study shows that despite theirgrowing number in these universities, most female students are worried about their professional future. In nutshell, it can be said that the massification of higher education has not resulted into more access of girls to the labor market, and there is much disappointment among them of finding a suitable job after graduating from universities.
| Page 1 from 1 |