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.
Financial Economics
Saman Hatamerad; Bahram Adrangi; Hossein Asgharpur; Jafar Haghighat
Abstract
The present research aimed to investigate the relationship between Iran’s stock price index and nine macroeconomic variables during 1996–2019. Three methods were employed to reduce uncertainty, namely three Bayesian averaging methods (BMA, BMS, BAS), weighted average least squares (WALS), ...
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The present research aimed to investigate the relationship between Iran’s stock price index and nine macroeconomic variables during 1996–2019. Three methods were employed to reduce uncertainty, namely three Bayesian averaging methods (BMA, BMS, BAS), weighted average least squares (WALS), and Vselect. The experimental results of the Bayesian methods and WALS showed that the exchange rate and the consumer price index are the most important variables among the nine macroeconomic variables considered in the model. Moreover, the results revealed that the exchange rate has a minor impact on the stock price index, while the stock price index exerts a substantial effect on the exchange rate. The findings of Vselect validated the conclusion that these two variables are the primary drivers of stock price estimation and are present in nearly all predictive modelsIntroductionThe harmonization of financial markets with the macroeconomic sector is crucial for stabilizing the economy and achieving the adopted policies. In recent years, several significant studies have been conducted on financial markets, particularly the stock market, highlighting their pivotal role in allocating capital resources efficiently in advanced economies. Empirical evidence supports the view that financial markets have evolved in tandem with all sectors of the economy. Therefore, it can be argued that financial markets constitute one of the most vital components of any country’s economy. Throughout history, major economic crises have resulted from the collapse of financial markets, which underscores their critical significance. The financial market comprises several components, with the stock market being a crucial part. Economists view it as a barometer of a country’s economic health due to its ability to reflect macroeconomic asset prices more accurately than other markets. The uncertainty surrounding stock prices in stock markets is a significant aspect of the entire economy, capable of generating and disrupting unsustainable growth. For investors, the risk of participating in an investment is a crucial consideration. To comprehend total risk, it is beneficial to examine two aspects: systematic and non-systematic risk.The present study aimed to examine the impact of economic factors on stock market prices in Iran with the high degree of risk involved. There is a consensus among economists that asset prices are responsive to economic news, and that stock prices and economic factors are strongly interconnected. Thus, this research investigated the potential impact of macroeconomic factors on the Iranian stock price index from 1996 to 2019 using Bayesian averaging methods, followed by an analysis of the effect size of each variable through the weighted average least square method (WALS).Materials and MethodsResearchers often draw conclusions based on the assumptions of their selected model, assuming that it can accurately predict real-world situations. However, this approach may overlook true uncertainty, leading to non-conservative conclusions. Statistical models comprise two parts: variables and assumptions, and the model selected based on these assumptions to estimate the variables. Uncertainty exists at both levels. For instance, a researcher estimating the impact of influential factors on an independent variable may choose a model based on their assumptions and report their estimates. But is this the best answer? Another researcher with different assumptions may opt for a different model with lower variance and error. In other words, numerous models may fit the sample data equally well but with different coefficient estimates and standard errors. Bayesian model averaging (BMA) is a robust method that aims to remove uncertainty. It assesses the robustness of results to alternative specifications by computing posterior distributions for coefficients and models. This study employed three models of BMA, BMS, and BAS, using various averaging methods to verify the reliability of the results. Moreover, two non-Bayesian methods, namely WALS and Vselect, were used to select the best variables for predicting the optimal models.ConclusionThis study tried to investigate the relationship between Iran’s stock market index and nine macroeconomic variables during 1996–2019 by using the models that identify and limit uncertainty. The models selected include three Bayesian averaging models as well as WALS and Vselect which were used to verify the results obtained. The results indicated that only two variables, the exchange rate and consumer price index, are statistically significant when assuming a uniform distribution of the prior distribution function, which is the assumption of the BMS method. The remaining variables are not statistically significant. Furthermore, the estimates derived from the BMA and BAS models were quite similar, with the exception of less important variables. However, the similarity decreased in the BAS method. Moreover, WALS and Vselect confirmed the results obtained from all the three methods.
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. ...
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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 ...
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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.
