zana mozaffari; hassan farazmand; zahra omidi
Abstract
1- INTRODUCTION
Economic growth is one of the goals that every economy pursues, and the reason for this is the achievement of many benefits and advantages that are realized in the process of growth. Policy makers in every country seek to achieve a higher growth rate in their country.
Among ...
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1- INTRODUCTION
Economic growth is one of the goals that every economy pursues, and the reason for this is the achievement of many benefits and advantages that are realized in the process of growth. Policy makers in every country seek to achieve a higher growth rate in their country.
Among the types of assets, housing is considered one of the most important socio-economic components in a country. It is also argued that in the new theories, the development of the housing sector is one of the drivers of economic growth, so in this article, the impact of investment in housing on the economic growth of Iran's provinces during the years 2006-2019 has been examined.
2- THEORETICAL FRAMEWORK
According to the point of view of macroeconomics, considering that between 21 and 24% of the country's GDP is related to the housing sector (during the construction and operation period), the housing sector is considered as the driving engine of economic growth and the factor that strengthens it in the country. From the point of view of consumption and investment, housing also plays a significant role in national accounts.
In general, there are three different views on the relationship between investment in real estate and economic growth. The first point of view emphasizes that investment in real estate affects economic growth. The second point of view shows that economic growth affects investment in real estate. According to the third perspective, there is a mutual causal relationship between investment in real estate and economic growth.
3- METHODOLOGY
To estimate the research model, the econometric method of generalized moments of GMM has been used. The important feature of this estimation method is that there is no need to know the exact distribution of disturbance sentences. The main assumption of this method is based on the fact that disturbance terms are not correlated with instrumental variables. The method of generalized moments, by choosing the correct instrumental variable, by applying a weight matrix, can create a compatibility estimator for the conditions of heterogeneity and unknown autocorrelations.
4- RESULTS & DISCUSSION
The results show that the economic growth coefficient of the previous period was evaluated as 0.73. Based on the probability level corresponding to the t-statistic of this coefficient, it can be stated that the economic growth of the previous period had a positive and significant effect on the economic growth in the provinces of Iran. This result is consistent with economic theories and most previous studies such as Kazerooni et al (2018) and Mozaffari (2021). The variable coefficient of human capital has been evaluated as 0.16 and according to the probability level corresponding to its t-statistic, it can be stated that human capital has had a positive and significant effect on the economic growth of Iran's provinces. This result is in line with endogenous growth theories and previous empirical studies such as Kazerooni et al (2018) and Mozaffari (2021).
The size of the government also has a negative and significant effect on economic growth. So that the elasticity of the economic growth of Iran's provinces in relation to the government's current expenses is equal to -0.32.
The urbanization variable has a negative and significant effect on the economic growth of Iran's provinces. The negative sign of the variable coefficient of urbanization can be caused by the fact that the rate of urbanization has increased regardless of the process of providing the necessary infrastructure in the cities and increasing the expertise of the migrant workforce.
5- CONCLUSIONS & SUGGESTIONS
Investment in housing has a positive and significant effect on the economic growth of Iran's provinces during the research period. Which is compatible with the growth models and is in line with the studies conducted in the field of the effect of investment in real estate on economic growth, it is recommended that the government, in addition to paying special attention to capital expenditures in the field of housing; It has provided the necessary incentives and conditions for the investment of the private sector in the field of housing, so that the investment in this sector has increased, which on the one hand has increased the welfare and economic growth of the society, and on the other hand, it can reduce the lack of housing supply in this way, the Iranian market can be partially compensated. The effect of capital stock on the economic growth of Iran's provinces also has been positive and significant, which is in line with the existing theory in this field and previous studies, it is recommended that the government provide a mechanism and in the budgeting of the provinces. The discussion of the number of credits for the acquisition of capital assets should be given special attention in order to improve the economic growth of different regions of the country. Human capital and industrialization index have a positive and significant effect on the economic growth of Iran's provinces. Aggregation of industrial activities leads to growth by creating savings resulting from local aggregation. The effect of urbanization on the economic growth of Iran's provinces was negative and significant. Although the previous theory and studies show a positive and significant effect of urbanization rate on economic growth. Governments should pay attention to prevent the increase in the size of the government, because based on the results of this research, the economic growth has decreased with the increase in the size of the government, and the large involvement of the government in the economy can have negative consequences. This result is consistent with the results of most previous studies in developing countries.
Habib Ansari Samani; masoume Rouzbahani
Abstract
Introduction The main keys that influence crime rates are examined from two economic and sociological points of view. Preventing crime is one of the requirements of any healthy society. Economic inequalities and unfair distribution can be the main reasons of crimes. The complexity of the relationship ...
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Introduction The main keys that influence crime rates are examined from two economic and sociological points of view. Preventing crime is one of the requirements of any healthy society. Economic inequalities and unfair distribution can be the main reasons of crimes. The complexity of the relationship between justice and fairness on the one hand and the indicators of the distribution of economic benefits from the other hand admit the essentiality of the examining relationship between income inequality and crime rates. Recent researches have shown that income inequalities and economic discrimination are among the top priority factors that affect social crimes. This research studies the effect of various economic justice indicators on crime rates in the provinces of Iran. Theoretical frame work Studies show that the economic situation has significant effects on individual activities, including crime. In addition, the feeling of being deprived of success and exacerbating this feeling in relation to successful people (the existence of inequality) can be a source of criminal behavior (Stolzenberg et al., 2006). People who are frustrated by their failures in their community become more annoying when confronted with successful people around them. According to this theory, poor people in a situation of high inequality are more likely to commit criminal acts (Enamorado, et al., 2016). The sense of deprivation can be due to various factors such as belonging to an ethnic minority, Ethnic heterogeneity, or income inequality. Runciman& Runciman, (1966) argues in the theory of relative deprivation that income inequality has created a sense of expropriation in one person and increases injustice, thereby increasing the amount of crime committed by increasing inequality of income (Rufrancos et al., 2013). Economic and social inequalities increase the crime rate by weakening social integration and increasing the social class gap (Wilkinson & Pickett, 2010). Methodology Choe (2008) shows that the amount of crime in the past period has a great effect on the crimes of the current period. Therefore, to test the hypotheses, the GMM Arellano and Bond (1991) method is used to estimate the model. The following models are estimated for 28 provinces of Iran during the period 2000-2015. (1) (2) (3) (4) Where is the number of crimes divided by province population, represents the Gini coefficient, is discrimination indicator from a capacity viewpoint, is discrimination indicator from a need viewpoint and is the average of and . Economic discrimination index is the province's current and capital government budget divided by the capacity share (population, value-added, and area), and share of needs (unemployment rate, illiteracy rate, and life expectancy) of each province[1]. expresses the unemployment rate, is per capita GDP, is government size (division of provincial government expenditures on provincial GDP) and is the urbanization rate (urban population divided by the total population). Results and Discussion The results of the estimation of regression models are presented in Table 1. Table 1: Regression coefficients by two stage stepwise generalized moments Model/Variable 1 2 3 4 Gini coefficient 10.838*** 0.000 Capacity Discriminative Index 8.343*** 0.000 Need Discrimination Index 8.035*** 0.000 Total index of discrimination 9.702*** 0.000 Unemployment rate 0.727*** 0.581*** 0.746*** 0.552*** 0.001 0.002 0.000 0.005 GDP per capita 150.871*** 118.595*** 160.182*** 117.769*** 0.000 0.000 0.000 0.000 Government Size -0.83 -0.115*** -0.099*** -0.113*** 0.064 0.000 0.000 0.000 Urbanization rate 1.153*** 1.443*** 1.324*** 1.433*** 0.000 0.000 0.000 0.000 CRIME (t-1) 0.562*** 0.506*** 0.523*** 0.508*** 0.000 0.000 000/0 0.000 Sargan chi-2 24.408 26.929 25.391 27.310 *** is 99% significance level and the values inside The results indicated in Table 1 show that the effect of all indicators of economic inequality on crime is positive and significant. The effect of the unemployment rate and GDP per capita on crime is positive and significant in all four estimated models. The effect of government size on the crime rate is negative and significant in models 2-4. That means government spending has been increasing welfare and reducing inequality. The impact of urbanization rates on crime in all models is positive and significant. Results show that the impact of migrations to cities and the marginalization of households increase crime. The causality test also shows that all indicators of economic inequality are the statistical cause of crime, but there is no inverse relationship. Conclusions and Suggestions The results show that there is a statistically significant relationship between crime and economic inequalities. Also, causality shows that in addition to statistical relationship, causality relationship also exists. Hence, reducing income inequality and economic discrimination is needed to reduce crime. Economic discrimination of government spending can also increase the rate of crime in the provinces. Therefore, governments should realize that the discriminatory spending of governments, in needs and talents viewpoint, can in addition to slow down economic progress, lead to social harms. The results show that government size had a negative impact on the crime rate. The relationship between unemployment and crime also indicates the importance of reducing unemployment in reducing crime rates. It seems that the positive relationship between economic growth and the crime rate was due to an increase in crime benefits because of rising incomes, and because the wealthy regions are due to the opportunities available for theft (which constitute a large amount of total crime) will attract more criminals (Khan et al., 2015). Finally, increasing the urbanization rate increases the crime rate. Immigration and marginalization of cities seem to have a damaging effect on the health of the community. [1]. See Ezzati (2013) for further study on this index.
Mohammad Reza Eskandari Ata; nader mehregan; Alireza Pourfaraj; Saeed Karimi Petanlar
Abstract
Introduction Regional unbalanced growth and the factors affecting it are one of the most important economic issues in developing countries. One of the characteristics of developing countries is the presence of significant regional inequalities. The existence of this phenomenon is one of the main impediments ...
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Introduction Regional unbalanced growth and the factors affecting it are one of the most important economic issues in developing countries. One of the characteristics of developing countries is the presence of significant regional inequalities. The existence of this phenomenon is one of the main impediments to balanced development in these countries. One of its specific consequences is the creation of inequalities and the consolidation and expansion of deprivation. Inequality alongside widespread poverty can provide grounds for public discontent and thus be one of the concerns of socio-economic policymakers. Considering the importance of balanced regional development in the country and the environmental-spatial potentials and political characteristics of the provinces, this study considers the effects of environmental and political factors on the distribution of inequality in provinces of Iran, considering the neighborhood effects. Theoretical framework Environmental differences play a decisive role in the distribution of regional inequality. At the early stage of economic development, environmental conditions are one of the most determining factors. For example, favorable environmental conditions are often the basis for rapid growth in developing countries. Although the effects of environmental conditions on regional development at higher levels are less pronounced, the specific functions of these factors are still unknown in many countries. In economic literature, several environmental factors influence and are influenced by the distribution of regional inequality. Variables such as cities with coastal boundaries, commercial areas, tourism, water resources, railways, border areas and urban development are among the areas considered in regional studies. Modern governments, unwittingly or unwillingly, engage in various economic policies such as monetary policy, fiscal policy, and commercial policy. Applying these policies shift interests and the pattern of income distribution and create winners and losers across different segments and groups of society, thereby changing regional inequalities. Methodology Spatial inequality refers to situations in which different spatial or geographical units of some variables are at different levels. In the present study, after investigating the regional inequality with regard to the effects of spillover in the provinces, an assessment of the environmental and political factors on it during 2006 to 2015 has been examined. The explanatory variables were compiled according to the purpose of the study, based on environmental and political factors that cause regional imbalances and also according to the statistical constraints of the country. According to the theoretical foundations, identifying variables in previous studies as well as statistical feasibility in the country, from three models has been used to investigate the impact of environmental and political factors on regional inequality. The variables used include urban index, dummy variable for business areas, tourist and religious centers, the logarithm of GDP, ratio of government expenditure to GDP, Ratio of education cost to the government expenditure and the members of parliament. Results and discussion The evaluation of Population-Weighted Coefficient of Variation (PW-CV) indices show that Iranian provinces during the research period have been very inadequate. The results of estimating Spatial Autoregressive with Autoregressive Error (SARAR) regression models indicate a strong spatial dependence among the provinces. So that the inequality index of each province with an approximate coefficient of 45% is affected by the economic inequality in neighborhood provinces. In the analysis of environmental factors affecting regional inequality; urban development, water resources and tourism have a negative relationship with provinces' inequality and as each of these factors increases, the inequality index of the provinces will decrease. But religious and commercial provinces have a positive impact on economic inequality; as a result, inequalities are higher in these provinces. Results of the estimation of the impact of political variables on regional inequality show that the provinces with a more gross domestic product, have a higher inequality index. Moreover, the larger the size of the government in the provinces, the more economic inequality. Also, increasing the share of education costs from provincial budgets increase regional inequality and in the provinces where the number of members of parliament is higher, there are more economic inequalities. Conclusions and suggestions According to the results of the present study, the importance of the distribution of inequality in different provinces and the effects of neighborhoods with regard to environmental and political factors are overemphasized. Governments and trusted entities in different areas can be more successful in delivering social justice and reducing regional inequalities by designing and implementing management policies tailored to each province's environmental and political potentials. Managing water resources, paying attention to tourism, controlling suburbs in big and religious cities, and implementing income redistribution policies are some of the policies that can be implemented in environmental and operational areas. Reducing government tenure and administrative bureaucracy are also some of the factors that will be effective in reducing regional inequalities.
marzieh dindar rostami; Shams alah shirinbakhsh
Abstract
The purpose of this study is to investigate asymmetric effects of house prices shocks on urban household consumption in Iran during the period of 1385-1393. For this purpose, by estimating the two models, effects of house price shocks on household consumption have been studied in urban households and ...
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The purpose of this study is to investigate asymmetric effects of house prices shocks on urban household consumption in Iran during the period of 1385-1393. For this purpose, by estimating the two models, effects of house price shocks on household consumption have been studied in urban households and investigating asymmetric effects with using the approach of Panel Vector Auto Regression (Panel VAR). The results of this study suggest that the effect of changes in house price on household's consumption of urban is negative and significant in provinces of Iran during the period under study. Then, the effects of asymmetric shocks of house price have shown that the positive shocks have negative and significant effect and negative shocks have positive and significant effect on household consumption. Orthogonalized Impulse response functions (OIRF) suggest that the positive impact of price shocks on consumption is more effective than negative price shocks. Generally, urban households will change saving behavior in the facing the potential gains and losses of capital. The results indicate that the saving tendency and uncertainty has been increased in urban households during this period.
Methodology
Panel VAR is composed of traditional VAR, which endogenously defines all the variables in the pattern and panel data method, which imports unobservable factors in the pattern.
In this study, after examining the stationary of the pattern variables and determining the optimal lag, the stability is checked in the desired pattern. The results of the table and the study of statistics values show that the null hypothesis based on the lack of stationary of variables like consumption, housing price, positive and negative changes in housing price and urban household's income are rejected in all three tests, in the sense that all variables in the model are static.
In the case of the lag order of the tests, the minimum value in MBIC, MAIC, and MQIC standards is the first lag order. In both studied patterns, eigenvalues are inside the unit circle which represents the stability of the pattern.
In this study, the coefficients are estimated using panel vector autoregressive pattern and based on GMM method. Some New software called Love (2015) and Abrigo and STATA12 have been used to estimate the coefficients.
Results and Discussion
The effect of housing price shock on urban household's consumption, housing price, urban inflation and urban household's income are shown in diagram 1. As can be seen, the positive housing price shock as large as one standard deviation will cause negative consumption response and the negative response will reach its peak in the second year, then it will be gradually converged to its initial level. According to the theory, a demand shock in housing price will lead to increase in housing price through an initial increase in mortgage rates. Therefore, the increase in real prices of estate caused increased household wealth and thereby increased demand for the goods. The increased household consumption expenditure will lead to inflationary pressure.
According to the results of the analysis in this study, the proposed theory has been violated, which could be due to various factors, including uncertainty in housing price changes, assuming it as a temporary matter by households, increased precautionary savings and retirement savings in order to deal with disability and disease and increased liquidity constraints in the collateral role and increased housing price.
diagram 1: Orthogonalized Impulse Response Function: The effect of housing price shock on models variables.
The effects of impulse response functions of positive price shock (hpp) on consumption are a little more than negative price shock (hpn). After positive housing price shock, urban household's consumption will be decreased in provinces and it will reach its lowest value in the first period. Then the behavior will be fluctuating and finally, it will be converged to its first route after 10 periods. In other words, by increasing the housing price, homeowners feel getting richer because of increased possible benefits of their wealth, and they will increase their consumption. Tenants and those who want to buy a house, feel getting poorer and therefore consumption reduction and increased savings will occur. According to diagram 2, the effect of reducing consumption will be higher than the effect of increasing consumption; therefore, the positive housing price shock will decrease the consumption.
diagram 2: Orthogonalized Impulse Response Function: The asymmetric effect of housing price shock
Conclusion
This study aimed to investigate the asymmetric effects of housing prices on urban household's consumption in provinces of Iran during 1393-1385. For this purpose, the effects of housing price shock on urban household's consumption are studied using two patterns then its asymmetric effects are examined using Panel vector autoregressive approach (Panel VAR).
According to the literature about the discussed issue, theoretically, the effect of increased housing price on total consumption expenditure looks ambiguous. In most studies, this relationship has been positive. The results of the study obtained from the estimation of the pattern and examination of orthogonalized Impulse response function show that the effect of housing price changes on urban household's consumption during the studied period is negative and significant in the provinces of Iran. In addition, the asymmetric effects of housing price shock have a negative and significant effect on positive housing price shock and also have a positive effect on negative housing price shock. Orthogonalized Impulse response function indicates that the effect of positive price shock on consumption is more effective and higher than negative price shock. Therefore, the asymmetric effect of housing shock on urban household's consumption has been approved.
