Macroeconomics
Zahra Sheikhali Zadeh; Jafar Haghighat; Zahra Karimi Takanlou; Seyed Saleh Akbar Mousavi
Abstract
The present study aimed to explore the impact of banking crisis on income distribution among various income classes in 60 world countries during 1990–2020. In this line, the Generalized Method of Moments (GMM) was used to estimate the six models with different dependent variables that depicted ...
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The present study aimed to explore the impact of banking crisis on income distribution among various income classes in 60 world countries during 1990–2020. In this line, the Generalized Method of Moments (GMM) was used to estimate the six models with different dependent variables that depicted income percentiles for the wealthy, middle, and poor classes. The findings indicated that during a banking crisis, the income share of the wealthy class decreases, while the middle class and the bottom 20% experience an increase in their income share. Consequently, banking crisis could contribute to income equality in the countries under study. In addition to the variable of banking crisis, other variables such as financial development and financial openness could lead to income inequality, while the variables like the ratio of public expenditure to GDP, trade openness, GDP, and GDP squared would cause income distribution equality in the countries. The results suggest that governments support lower-income percentiles through subsidies, support packages, more job opportunities, and provision of low-interest loans, in a bid to mitigate the detrimental effects of banking crisis and reduce income inequality. Furthermore, governments should levy taxes, such as capital gains tax, on higher-income percentiles.IntroductionThe literature offers various definitions for banking crisis. For instance, Liana et al. (2015) define banking crisis as the occurrence of simultaneous bankruptcies within the banking sector, resulting in substantial damage to the capital of the entire banking system, significant economic repercussions, and government intervention. According to Laeven and Valencia (2020), banking crisis occurs when two conditions are met: 1) “significant signs of financial distress within the banking system (indicated by significant bank runs, losses in the banking sector, and/or bank liquidations)” and 2) “significant intervention measures in banking policy in response to significant losses in the banking system.” The year in which both criteria are met is the year when crisis becomes systemic. Banking crisis exerts a myriad of effects, with one notable consequence being the issue of income inequality. There are two points of debate in this respect: the impact of banking crisis on income inequality and the reciprocal influence of income inequality on banking crisis. This research focused on the former. There are various channels through which banking crisis can adversely impact households and their income, including:(a) Loss of deposits in a failed banking institution(b) Loss of employment or earnings directly due to (i) disruption of the payments process, (ii) the bankruptcy of financial institutions (for employees and other stakeholders of these institutions) or (iii) the interruption of credit flows (for borrowing clients with information capital invested in the failed financial institutions)(c) Tax increases or curtailment of public spending due to fiscal cost of bail-outs of financial firms or their customers(d) Temporary or permanent changes in relative prices of (i) consumption goods, (ii) wage rates, (iii) production goods (iv) asset prices, that arise through knock-on effects on the rest of the economy(e) Involuntary unemployment if the crisis leads to a generalized economic downturn. (Honohan, 2005, pp. 6–7)In this context, the present study tried to answer the following questions: How does a banking crisis influence the income distribution of households and contribute to income inequality? Is the presumed impact the same across different income classes (i.e., wealthy, middle, and poor)?Materials and MethodsIn line with El Herradi and Leroy (2022), the present study used the following economic model:(1) In the model, refers to the income share of six different percentiles (p) including Top1%, Top10%, Top20%, Middle-class (21–79 percentile), Bottom20% and Bottom10% in the country i at the time t. is a dummy variable of the banking crisis (1 if a country i faces a banking crisis, otherwise 0). indicates the dependent variable of income distribution, with two lags to show the dynamics of the model. Finally, is a vector of lagged control variables, including GDP and GDP squared, financial development, trade openness, financial openness, the ratio of government public expenditures to GDP and political governance. Also, , and refer to country fixed effects, time fixed effects and an error term, respectively. , and k are model coefficients. The study sample comprised 60 countries worldwide, with annual data spanning the years 1990 to 2020.Results and DiscussionThe occurrence of a banking crisis is linked to significant yet varied effects across the income distribution. Consequently, during a banking crisis, the income shares of the top 1%, top 10%, top 20%, and bottom 10% experienced a decrease. Moreover, a banking crisis resulted in an increase in the income share of the middle-class population (21–79 percentiles) as well as the bottom 20% of individuals. Notably, the rise in the middle class was more substantial. Conversely, the lowest income group (the bottom 10%) exhibited a negative correlation between banking crisis and income share, mirroring the trend observed in the upper percentiles. However, the reduction in the income shares of the lowest income group (the bottom 10%) is considerably less than the losses suffered by higher income groups. According to the findings, the adverse impacts of banking crisis are more pronounced at the right end of income distribution. Therefore, the crisis could contribute to a reduction in income inequality.ConclusionThe findings indicated that a banking crisis adversely affects the income shares of the top 1%, top 10%, and top 20%. In simpler terms, a banking crisis diminishes the income share of these groups in the overall income of society. Notably, the reduction in the income shares of the top 10% (-0.426) is more pronounced compared to the top 1% and top 20% percentiles. Conversely, a banking crisis can increase the income share of the middle class (21–79 percentiles) and of the bottom 20% (i.e., the poor class), with a particularly substantial increase observed in the middle class. Turning to the lowest income group (the bottom 10%), a negative correlation exists between banking crisis and income share. Despite facing a decrease in income similar to the top income percentiles, the decline in their income share is considerably less than the losses experienced by the wealthy percentiles.In summary, a banking crisis could diminish the income share of the wealthy class and increase the income share of the middle and lower classes, contributing to a reduction in income inequality in the studied countries. Consequently, to mitigate the adverse effects of a banking crisis, governments can provide support to low-income percentiles through subsidies, support packages, more job opportunities, and low-interest loans. Additionally, taxes on high-income percentiles, such as capital gains tax, can be helpful. The measures can ultimately lead to a reduction in the income share of the wealthy percentiles and an increase in the share of the lower percentiles, improving income distribution and reducing income inequality.
Mohsen Mehrara; Mojtaba Mohammdian
Volume 19, Issue 61 , February 2015, , Pages 83-116
Abstract
This paper has tried to apply Bayesian Model Averaging (BMA) and Weighted Average Least Squares (WALS) approaches as methods of averaging in Bayesian econometrics in order to investigate the impact of 18 macroeconomic variables on Gini coefficient in Iran based on annual data from 1976 to 2010. The results ...
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This paper has tried to apply Bayesian Model Averaging (BMA) and Weighted Average Least Squares (WALS) approaches as methods of averaging in Bayesian econometrics in order to investigate the impact of 18 macroeconomic variables on Gini coefficient in Iran based on annual data from 1976 to 2010. The results of two approaches indicate that the growth rate of Gross domestic products is the most important determinant of the Gini coefficient, implying economic growth arising from oil booms leads to more unequal distribution of income. As regards to other variables, the results of BMA estimation indicate that second and third effective variables on Gini coefficient are respectively the ratio of government current expenditure to GDP and the ratio of oil revenue to GDP for which an increase in the ratios engenders inequality. Thus the distribution of oil rents and government expenditure is inconsistent with primary purposes of politicians to ameliorate income distribution. Also the results of WALS estimation rank openness and exchange rate as the most effective variables respectively with positive sign. Therefore economic liberalization moving along with oil revenue during the sample period, has been against the favor of lower income groups. According to aforementioned results, it is necessary to revise economic growth policies to the benefit of low-income groups of people. In addition, renovation of budgeting process and allocation of resources with aim to reduce rent-seeking opportunities and more privileges for low-income groups must be considered by policy makers.
Alireza Eghbali; Alireza Jorjorzadeh; Masoomeh Kiani
Volume 18, Issue 56 , October 2013, , Pages 187-203
Abstract
The purpose of this research is investigation of the relationship between income inequality and business cycles in Iran over the 1351 to 1389. In this study, Gini coefficient is the indicator of inequality, and business cycles were obtained by using the Hodrick- Prescott filter. Also In order to examine ...
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The purpose of this research is investigation of the relationship between income inequality and business cycles in Iran over the 1351 to 1389. In this study, Gini coefficient is the indicator of inequality, and business cycles were obtained by using the Hodrick- Prescott filter. Also In order to examine the effect of business cycle on inequality, autoregressive-distributed Lag (ARDL) approach was attempted. Our findings show that business cycles have negative impact on income inequality. During recession, income inequality increases and in the booms decreases. Also we find that higher minimum wages decrease income inequality, and oil incomes increase it.
Mohammad Noferesti; Fardin Mohammadi
Volume 13, Issue 38 , April 2009, , Pages 31-52
Abstract
This paper analyzes a macro-micro linkage using a vector autoregressive model (VAR) for Iran comprising following variables: Gross Domestic Product(GDP), Inflation, Exchange Rate, Oil Revenue, Government Expenditure, Money Supply, and a Micro-simulation model of household budget. The procedure is a top-down, ...
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This paper analyzes a macro-micro linkage using a vector autoregressive model (VAR) for Iran comprising following variables: Gross Domestic Product(GDP), Inflation, Exchange Rate, Oil Revenue, Government Expenditure, Money Supply, and a Micro-simulation model of household budget. The procedure is a top-down, macro to micro simulation, aiming at evaluating the impact of macroeconomic shocks on the Iranian rural and urban household’s distribution of income. A representative of the Iranian rural and urban households, which involves social characteristics, is linked with household budget equations. Income is a function of social characteristic and macro economic variables in the household budget model. The research work is done through three steps:
First in the macro-model, variables and shocks are simulated during ten years (2006-2016). Second, in the micro-model, the incomes of the entire households are simulated after and before the shocks. Finally, the Gini coefficient index, as an inequality indicator, is calculated using the results of the second step. We have achieved the following results: GDP, inflation, weighted exchange rate and oil shocks increased inequality, while money supply and government expenditures decreased inequality for urban households. For rural households, GDP, inflation, government expenditure and oil shocks decreased inequality, while weighted exchange rate and money supply did not have any effect on inequality.
Koohsar Khaledi; Saeed Yazdani; Andisheh Haghighatnejad Shirazi
Volume 11, Issue 35 , July 2008, , Pages 205-228
Abstract
Investment as one of the important factors that has a positive impact on economic growth is expected to alleviate poverty in the long run. This paper studies the role of agricultural investment on economic growth and rural poverty in the rural areas of Iran. To estimate the model, we use the seemingly ...
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Investment as one of the important factors that has a positive impact on economic growth is expected to alleviate poverty in the long run. This paper studies the role of agricultural investment on economic growth and rural poverty in the rural areas of Iran. To estimate the model, we use the seemingly unrelated regression equations "SURE" and the time series date for the period 1971-2003 The results show that although agricultural investment has a positive effect on economic growth of this sector, the gains from the growth have not had a positive impact on poverty alleviation. It seems Iran agri-economic growth is not at the level that could affect the rural poverty and the benefit resulted from current growth rate does not trickle-down to the rural poor.
Ebrahim Gorji; Mohammad Borhanipour
Volume 10, Issue 34 , April 2008, , Pages 99-124
Abstract
The impact of globalization on income distribution is one of the most important issues among economists regardless of their views on globalization. In this study we investigate the impact of globalization on income distribution in Iran using the data for the period 1968-2004 and Johanson - Joselius co-integration ...
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The impact of globalization on income distribution is one of the most important issues among economists regardless of their views on globalization. In this study we investigate the impact of globalization on income distribution in Iran using the data for the period 1968-2004 and Johanson - Joselius co-integration method. We use the index of trade intensive (the ratio of the sum of export and import to GDP) as a globalization measurement standard. The rate of inflation and unemployment, government public expenditure and per capita income as other effective variables on income distribution.The results of estimation confirms the Kuznets hypothesis in Iranian economy, and the rate of inflation and unemployment have direct relationship with income inequality. The ratio of government public expenditure to GDP has direct relationship with the improvement of income distribution, and globalization would increase income inequality..
Mohammad Ali Kafaei,; Dorostkar Ezzatolah
Volume 9, Issue 30 , April 2007, , Pages 53-76
Abstract
Income distribution is usually affected by social and economic structures and especially by factors such as inflation, unemployment rates and the government size. In addition, the level of literacy and education are also seen as important factors affecting the income gap among different groups. The ...
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Income distribution is usually affected by social and economic structures and especially by factors such as inflation, unemployment rates and the government size. In addition, the level of literacy and education are also seen as important factors affecting the income gap among different groups. The purpose of this article is to study the relationship between educational variables, measured by the literacy level and its dispersion, and income distribution in the Iranian economy for the period 1347-1380 (1968-2001). Our findings indicate that as the average level of education increases, income inequality will decrease; but with increasing the standard deviation of education among citizens, income equality worsens.
Javid Bahrami; Parvaneh Aslani
Volume 7, Issue 23 , July 2005, , Pages 119-145
Abstract
In this research, we test for the factors that determine private saving in the Iranian economy during 1968-2001 using auto regressive distributed lag model (ARDL). In this model, we examine the effects of factors such as disposable income, social security costs, unemployment rate, long term interest ...
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In this research, we test for the factors that determine private saving in the Iranian economy during 1968-2001 using auto regressive distributed lag model (ARDL). In this model, we examine the effects of factors such as disposable income, social security costs, unemployment rate, long term interest rate, inflation, Gini coefficient, ratio of the value of stocks exchanges to the terms of trade GDP, and a dummy variable for the post-war years. The results show positive effects of income, improvement of income distribution, and more developed financial markets, and negative effect of social security costs on the saving of private sector.
Our results also indicate that the best and the most secured way to increase private saving is to improve financial markets performance that leads to a better absurbtion of saving and to an increases in investment possibility.
