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 ...
Read More
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.
Monetary economy
Seyed Saleh Akbar Mousavi; Behzad Salmani
Abstract
The main purpose of this study is to identify the determinants of banking crisis losses for 49 sample countries over the period 1980-2019. In this regard, two sub-purposes are pursued. In the first preliminary step, we identify and date episodes of banking crises for 49 countries. The graphical analysis ...
Read More
The main purpose of this study is to identify the determinants of banking crisis losses for 49 sample countries over the period 1980-2019. In this regard, two sub-purposes are pursued. In the first preliminary step, we identify and date episodes of banking crises for 49 countries. The graphical analysis of crises showed that about half of the crises were occurred between 2008-2012 in which the share of high-income countries was higher than other country groups. Then, in the second preliminary step, we used the Hodrick-Prescott filter to extract different trends from countries' GDPs to calculate four alternative measures of real output losses. The investigated output losses showed that Angola and Greece had the highest and lowest losses among the four types of losses, respectively. Finally, to achieve the main purpose, we use the Poisson quasi-maximum likelihood (PPML) method to estimate model. The model was estimated without and with currency crisis variable. Our findings show the occurrence of a currency crisis is effective in intensifying output losses following banking crises. Also, the variables of inflation, bank credit to GDP, credit-to-GDP gap, public debt/GDP, with a positive effect and variables of financial openness, discretionary government spending and central bank assets with a negative impact, are important factors in output losses of banking crisis. Therefore, we recommend that the mentioned variables be considered in banking crisis management.
Monetary economy
Seyed Saleh Akbar Mousavi; Behzad Salmani; Jafar Haghighat; Hossein Asgharpour
Abstract
The main purpose of this study is to estimate the probability of banking crisis using the second generation of early warning systems (logit models), for 13 selected high-middle income countries over the period of 1980-2016. In this regard, two types of logit models; binomial and multinomial, are estimated. ...
Read More
The main purpose of this study is to estimate the probability of banking crisis using the second generation of early warning systems (logit models), for 13 selected high-middle income countries over the period of 1980-2016. In this regard, two types of logit models; binomial and multinomial, are estimated. The results of estimated binomial logit model show that three leading indicators of the crisis are broad liquidity ratio, stock price index and inflation, which are the main causes of crisis in the studied countries. These variables account for about 17 percent of the probability of a banking crisis. Then, to avoid post-crisis bias, the multinomial logit model is estimated. The empirical results confirm that above three leading indicators are warning. Also, among the above three variables, only stock price index variable with a probability of 12.68%, causes the economy to exit the banking crisis and change its situation from the crisis/recovery period to the tranquil period. The multinomial logit model exhibit significantly better in-sample predictive abilities than the binomial logit model.
Seyed Saleh Akbar Mousavi; Jafar Haghighat; Mohammdreza Salmani Bishak
Abstract
Recent technological advances have increased the importance of human capital over the past years. In this paper, we study the impact of human capital on economic growth in Iran using the nonlinear STR method for the period 1345-1389. To this end, we estimate a two regime Logistic Smooth Transition Dynamic ...
Read More
Recent technological advances have increased the importance of human capital over the past years. In this paper, we study the impact of human capital on economic growth in Iran using the nonlinear STR method for the period 1345-1389. To this end, we estimate a two regime Logistic Smooth Transition Dynamic Regression (LSTR) model in which the transition variable is the logarithmic change in human capital. The results show that the impact of human capital on growth is different in two regimes. In the first regime, if the human capital growth rate is below the threshold value, the effects of human and physical capital on economic growth will be negative and positive, respectively. In the second one, human capital has positive and significant impact on economic growth. The main conclusion of the study is that it is crucial to take the type of regime into account.
