Research Paper
Behavioral economics
Morteza Khorsandi; Mahnoush Abdollah Milani; Teimour Mohammadi; Pardis Hejazi
Abstract
The effect of income on subjective well-being, often used as a key measure of well-being, has been widely studied. However, various dimensions of this relationship remain unexplored. The current study aimed to examine the nonlinear effect of income on the subjective well-being of 58 countries over during ...
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The effect of income on subjective well-being, often used as a key measure of well-being, has been widely studied. However, various dimensions of this relationship remain unexplored. The current study aimed to examine the nonlinear effect of income on the subjective well-being of 58 countries over during 2005–2020. The analysis relied on two distinct scenarios. The Panel Smooth Threshold Regression (PSTR) model, derived from regime-switching models, was employed for the analysis. Additionally, the study investigated the effects of income, unemployment, inflation, life expectancy, and income inequality on subjective well-being. The findings revealed that in a nonlinear relationship, the effect of GDP on subjective well-being diminishes at a certain threshold value of income inequality. Consequently, while policymakers aim to increase national income and reduce income inequality to enhance well-being, it is crucial to recognize that further reductions in inequality beyond a certain threshold may reduce the effect of income on well-being. This suggests that after a certain threshold, governments should prioritize reallocating resources toward other essential needs rather than solely focusing on reducing income inequality.1.IntroductionWell-being is one of the primary indicators of development and a crucial element in social progress, making it a growing focus for policymakers. In a seminal 1974 article, Easterlin found that wealthy individuals are generally happier than their poorer countrymen. However, at a cross-national level, the average happiness in wealthier nations does not exceed that of poorer nations. Furthermore, despite significant economic growth in the United States between 1944 and 1970, no corresponding increase in average happiness was observed. These findings became known as the Easterlin Paradox. Easterlin contends that while economic growth may boost happiness in the short term, it has no lasting impact (over 10 years or more) on a nation’s happiness. Policymakers, seeking to address the question of what constitutes a fair level of income inequality, have thought of various policies. For some, the relationship between income inequality and economic growth is the primary focus of policymaking. Easterlin contends that while economic growth may boost happiness in the short term, it has no lasting impact (over 10 years or more) on a nation’s happiness. Policymakers, seeking to address the question of what constitutes a fair level of income inequality, have thought of various policies. For some, the relationship between income inequality and economic growth is the primary focus of policymaking. Research in the field of happiness economics has sought to explain the Easterlin Paradox and adjust macroeconomic policies accordingly. To date, the threshold factor (in the case of the effect of income on subjective well-being) has often been determined exogenously, visually, or based on the assumption of a linear relationship. The present study sought to answer the following question: Does income affect subjective well-being, taking into account the threshold factor of income and income inequality?2.Materials and MethodsThe present study used the Panel Smooth Threshold Regression (PSTR), which is a generalized version of the Panel Threshold Regression (PTR) model introduced by Gonzales et al. (2005). This nonlinear model extends regime-switching models, where regimes are determined by a threshold variable. The explanatory variables included inflation, unemployment, life expectancy, and gross domestic product (GDP) adjusted for purchasing power parity (PPP). The data for these variables was sourced from the World Bank, while the inequality dispersion ratio was obtained from the World Inequality Database. Numerous studies have investigated the effect of macroeconomic variables on subjective well-being indices. Such studies tend to examine inflation and unemployment together, with their potential interdependence typically overlooked. The dependent variable was subjective well-being, assessed using various components and scales. The data on subjective well-being was obtained from the World Happiness Report database. The report employs the life ladder scale, in which individuals rate their subjective well-being on a 1–10 scale.3.Results and DiscussionVarious factors influence the subjective well-being of countries, with income emerging as a key determinant that has been extensively studied. However, certain aspects of this relationship remain underexplored. Using income inequality as a threshold factor, the present study examined the nonlinear effect of income on subjective well-being across a sample of 58 countries. Two scenarios were analyzed to address the main research question. The first scenario examined the linear relationship between income and subjective well-being. The findings revealed that income has a positive and significant impact on subjective well-being, whereas income inequality exerts a significantly negative effect.The second scenario examined the nonlinear relationship using the PSTR model, which extends regime-switching models. The results indicated that while income continues to positively influence subjective well-being, the magnitude of this effect diminishes as income inequality increases.Drawing on the theory of relative deprivation, the study demonstrated that income inequality significantly affects subjective well-being. Moreover, in line with the tunnel effect theory, it was shown that changes in living conditions (e.g., increasing income inequality) can weaken the positive effect of income on subjective well-being.At an income inequality threshold of 2.16, the coefficient representing the effect of income on subjective well-being decreases from 0.1 to 0.09. Additionally, the findings from the first scenario confirmed that income inequality has a significantly negative effect on subjective well-being, with a coefficient of -0.058.4.ConclusionThe study of subjective well-being, alongside economic well-being, has garnered significant attention among economists. In economics, well-being is traditionally assessed through an individual’s capacity to purchase goods and services. However, subjective well-being encompasses a broader range of factors beyond income, focusing on overall quality of life. As a result, governments should consider subjective well-being as a critical aspect of policymaking, given its broader scope and its measurability through subjective and composite indicators. Equally important is addressing the social cost of inadequate subjective well-being. Mental illnesses are a leading cause of pain and suffering, significantly reducing productivity. Strengthening social connections can foster positive psychological effects, which, in turn, improve physical health. Thus, prioritizing subjective well-being could encourage governments to a shift in the reallocation of resources from solely physical health to mental health. In addition, enhancing subjective well-being can help reduce both psychological and physical costs in society. Rising income inequality has been shown to diminish the impact of income on subjective well-being. Consequently, if policymakers aim to promote well-being by fostering national income growth and reducing income inequality, it is essential to recognize that reducing inequality beyond a certain threshold may weaken the positive effect of income on subjective well-being. This suggests that after a certain threshold, governments should prioritize reallocating resources toward other essential needs rather than solely focusing on reducing income inequality.
Research Paper
Behavioral economics
Habib Morovat; Ali Asghar Salem; Shayan Mohammad Sharifi
Abstract
Several factors influence the growth and development of the stock market. One of these factors is the behavior and performance (investment return) of individual investors. Individual investors are motivated to invest in the stock market for various reasons, such as long-term capital growth, dividends, ...
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Several factors influence the growth and development of the stock market. One of these factors is the behavior and performance (investment return) of individual investors. Individual investors are motivated to invest in the stock market for various reasons, such as long-term capital growth, dividends, and hedging against the decline in purchasing power due to inflation. However, their performance in the market, in addition to general economic and stock market conditions, depends on individual characteristics. In this respect, the present study aimed to examine the significance of demographic, behavioral and investment-related factors on the performance of individual investors in the Tehran Stock Exchange. The demographic factors included age, gender, risk tolerance, patience level, etc. Behavioral biases included factors such as overconfidence and loss aversion, while the investment-related factors encompassed experience, skills, and the frequency of portfolio restructuring. Using a systematic sampling, the study collected the data from 240 questionnaires completed by individual investors in the Tehran Stock Exchange. The ordinal logit model was employed to analyze the data. The results showed that age, gender, and risk tolerance did not significantly affect the performance of individual investors. However, the patience level had a positive and significant effect on performance, with more patient investors achieving higher returns. Overconfidence and loss aversion were found to have a significantly negative effect on performance. Finally, investment experience and skills had a significantly positive effect on the investor performance.IntroductionTraditional finance assumed that investors make rational decisions in the stock market, particularly regarding risk-return trade-offs and utility maximization. However, psychologists have found that human behavior is not as rational as economists assume. Stock market anomalies and empirical research show that investors often act in ways that deviate from rational expectations. These anomalies can be explained by the emerging field of behavioral finance. Behavioral finance explores how psychological factors influence the actions of individuals or groups as investors, analysts, and portfolio managers. It also seeks to understand how emotions and cognitive biases affect the behavior of individual investors (Kengatharan & Kengatharan, 2014).Behavioral biases are defined as systematic errors in judgment. Recent studies have identified over 100 such biases in individual investor behavior. Some researchers refer to these biases as heuristic rules (rules of thumb), while others describe them as beliefs, judgments, or preferences. Psychological factors include heuristic rules or cognitive shortcuts related to information processing, memory errors, emotional and/or motivational influences, and social factors such as family upbringing or cultural norms (Pompian, 2021).The Tehran Stock Exchange (TSE) has been one of the most profitable markets (in terms of nominal yield) in the world in recent years. Compared to other parallel investment markets, it has achieved the highest nominal returns over the past 10 and 5 years. The present study aimed to assess the effect of individual factors on the performance of individual investors in the TSE. These factors were grouped into three general categories: economic–demographic characteristics (e.g., age, gender, education, income level, risk tolerance, and patience level), behavioral biases (e.g., loss aversion and overconfidence), and investment-related characteristics (e.g., investment skills, experience in the stock market, and the frequency of portfolio restructuring. The data was collected in 2021 and 2022. A systematic sample of 240 individual investors was surveyed both in-person and online. Multivariate regression and ordinal logit models were used for data analysis.Materials and MethodsUsing a systematic sampling, the study selected a sample of 240 investors active in the TSE. The data was collected through in-person and online surveys. Multivariate regression was applied to examine the causal relationship between the factors influencing the individual investors’ returns. Since the dependent variable was ordinal, the ordinal logit model was used to conduct the analysis.Results and DiscussionIn order to see if there is a significant relationship between explanatory and dependent variables, we used multiple regression. Since the dependent variable is an ordinal variable, the ordinal logit regression model was used to fit the model. The model fitting results are presented in the table below.Table 1. Model Estimation Results Using the Ordered Logit Model MethodProbabilityOdd ratioCoef*Variables0.3240.7533-0.2833Gender0.4040.9899-0.0101Age0.6331.07690.0760Education0.1591.18040.1659Income0.3790.9627-0.0380Risk tolerance0.0002.8739***1.0557Patience0.0130.7539**-0.2824Overconfidence0.0530.9867*-0.0136Loss aversion0.0031.4317***0.3589Experience0.0281.3268**0.2828Investment skill0.0571.2929*0.2561Stock share0.3290.9408-0.0610Portfolio restructuring0.2300.8534-0.1585Initial investment0.1903Pseudo R2***134.82LR Chi2(8)Source: research calculations based on STATA software *, **, *** Coefficients are significant at 10%, 5%, and 1% levels, respectively.All diagnostic control tests for goodness of fit were conducted, including tests for heteroscedasticity (Breusch-Pagan test), collinearity (VIF test), and parallel regressions across different categories (Brant’s test). The null hypotheses of no heteroscedasticity, no collinearity, and the parallel regression assumption were not rejected. In non-linear models such as the logit model, the parameter sign and p-value provide insights into the direction and significance of the effect of coefficients. However, interpreting results directly on the log-odds scale is often impractical. Similarly, expressing results in terms of odds ratios presents challenges, as odds ratios are frequently misunderstood. One common misconception is treating odds ratios as probabilities, which they are not. For these reasons, it is more meaningful to interpret logit models using predicted probability scale, as this approach aligns better with research questions focused on understanding how covariates affect the probability of an event occurring. Table 2 illustrates how the probability of being placed in each category of the dependent variable (investment performance) changes with variations in explanatory variables (patience, overconfidence, investment experience, and financial literacy)-assuming all other variables are held constant at their mean values. For instance, the probability that the most patient individual (patience = 5) falls into the category with the poorest investment performance (I have lost a lot) is 0.006. In contrast, the probability that the same individual belongs to the category with the best investment performance (I have made a lot of profit) is 0.285.Table 2. Final Effects of Changing Explanatory VariablesInvestment performanceI have lost a lot.I have lost.There is no change in my capital.I have made a profit.I have made a lot of profit. Explanatory variables0.275***0.338***0.278***0.102***0.006**1Patience0.117***0.24***0.385***0.24**0.016***20.044***0.118***0.34***0.451***0.046***30.016***0.048***0.198***0.616***0.122***40.006***0.018**0.87***0.604***0.285***50.057***0.145***0.367***0.293***0.035***1Overconfidence0.074***0.177***0.384***0.336***0.027***20.095***0.211***0.390***0.281***0.020***30.122***0.246***0.383***0.231***0.015***40.155***0.279***0.365***0.187***0.012**50.195**0.308***0.337***0.150***0.009*60.155***0.278***0.365***0.188***0.012**1Investment experience0.114***0.236***0.387***0.245***0.017***20.083***0.192***0.388***0.310***0.024***30.06***0.151***0.371***0.382***0.034***40.004***0.115***0.337***0.455***0.047***50.11***0.231***0.388***0.252***0.017***1Investment skills (financial literacy)0.084***0.194***0.389***0.308***0.023***20.064***0.158***0.375***0.368***0.032***30.048***0.127***0.35***0.430***0.042**40.036***0.1***0.315***0.490***0.056***5Source: Research calculations based on STATA software. *, **, *** Coefficients are significant at 10%, 5%, and 1% levels, respectively.These findings demonstrate that greater patience is associated with higher investment performance. However, the relationship between patience and the likelihood of belonging to specific investment performance categories is non-linear. For highly patient individuals, the probability of being in higher performance categories increases up to the third category and then declines. In contrast, for the variable of overconfidence, the probability of the most overconfident individual (overconfidence = 6) achieving the highest investment performance category (I have made a lot of profit) is only 0.009, which is statistically significant at the 10% level. Meanwhile, the probability of this same individual falling into the lowest performance category (I have lost a lot) is 0.195. Non-linear relationships are evident between all explanatory variables and the predicted probabilities of being in various investment performance categories. This non-linearity underscores the value of analyzing final effects over relying solely on the interpretation of coefficients and odds ratios in logit models.ConclusionThe current study aimed to examine the individual factors influencing the performance of individual investors in the TSE, incorporating new explanatory variables and a new model-fitting technique. The analysis using the ordinal logit model revealed that variables such as age, gender, and risk tolerance do not significantly impact the performance of individual investors. However, patience was found to have a positive and significant effect on investment performance, with more patient individuals achieving higher returns. Conversely, overconfidence and loss aversion exhibited negative and significant effects on performance. Additionally, investment experience and skills were shown to positively and significantly influence investors’ performance. Moreover, the findings indicated that TSE investors are affected by behavioral biases, which have a significantly negative impact on their performance, as well as on the overall efficiency and stability of the market. For example, loss aversion leads investors to hold onto losing investments for prolonged periods, reducing portfolio returns. Similarly, overconfidence causes investors to overestimate their ability to evaluate investment opportunities, often disregarding negative signals that could suggest avoiding or selling certain stocks. The study also demonstrated that investment skills (e.g., financial literacy) had a significantly positive impact on investment performance. In contrast, general literacy and unrelated educational backgrounds showed no significant effect on performance. Overall, individuals with greater investment experience and higher levels of patience tend to achieve better outcomes and earn higher returns in the TSE.
Research Paper
Monetary economy
Rana Abbasgholi Nezhad Asbaghi; Hosein Samsami
Abstract
Some monetary policymakers attribute the persistent high inflation in Iran’s economy solely to the lack of central bank independence, arguing that granting the central bank autonomy is necessary to reduce inflation. However, empirical studies reveal that central bank independence faces significant ...
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Some monetary policymakers attribute the persistent high inflation in Iran’s economy solely to the lack of central bank independence, arguing that granting the central bank autonomy is necessary to reduce inflation. However, empirical studies reveal that central bank independence faces significant structural challenges due to the endogeneity of money within Iran’s economic system. This article aimed to identify the key components of requirements of central bank independence, with a particular focus on the government structure in Iran’s economy. A comprehensive review of existing literature on central bank independence was conducted. Moreover, a grounded theory approach was used to achieve theoretical saturation concerning central bank independence in Iran. Then, the study relied on the Bayesian model averaging (BMA) and analyzed 21 variables to identify the key factors defining the requirements of central bank independence in Iran. The findings highlighted several key factors, including the deviation of the effective exchange rate from the appropriate exchange rate, the government budget deficit, oil revenues, and the government effectiveness index. Furthermore, the results suggested that increasing central bank independence alone, within the context of variables contributing to endogeneity of money under Iran’s current economic conditions, has a weak and fragile effect. Thus, it is essential to undertake structural reforms targeting these critical variables as a prerequisite to meaningful discussions and efforts toward central bank independence.IntroductionThe theory of time inconsistency proposed by Kydland and Prescott (1977) posits that central bank independence can reduce inflation rates without incurring economic costs while enhancing stability by lowering inflationary expectations. However, several empirical studies (e.g., Bauman et al., 2021) emphasize that the effectiveness of central bank independence depends on the unique structural and institutional characteristics of each country. As a result, central bank independence is not a universal solution and may vary depending on the specific structural conditions of each economy. The relationship between central bank independence and inflation rates can significantly differ when structural and institutional factors deviate from the ideal. Iran’s economy has recently undergone substantial fluctuations in inflation rates. Some monetary policymakers attribute these high inflation levels solely to the lack of central bank independence, asserting that greater independence is necessary to control inflation. However, structural factors unique to Iran’s economy complicate this view. Issues such as an inefficient tax system, reliance on oil revenues, underdeveloped financial markets, exchange rate markets, and the quality of governance indicate that money is largely determined endogenously. These structural challenges undermine the effective implementation of central bank independence as a tool to reduce inflation and promote economic growth. Given these complexities, the present study sought to identify the key components necessary for central bank independence within Iran’s economic system. Focusing on the government structure, the study employed a grounded theory approach and Bayesian model averaging (BMA) to identify the requirements for central bank independence in Iran.Materials and MethodsThis research identified categories related to central bank independence by reviewing the existing literature. It used a grounded theory approach to achieve theoretical saturation. As a result, four key categories were identified: the exchange rate market, the government budgeting system, the quality of governance, and the central bank independence. Specific variables were analyzed within each subcategory to uncover the robust components influencing central bank independence in Iran’s economy. To collect data for the analysis, the study used reliable sources, including databases from the Central Bank of Iran, the Statistical Center of Iran, and the World Bank. The concept of central bank independence was treated as a dependent variable, consistent with the methodology proposed by Giannone et al. (2011) and informed by studies such as Rogoff (2019) and Baumann et al. (2021). These studies define the concept in terms of liquidity under optimal conditions. The variables were tested for their significance and intensity of influence on the dependent variable, allowing for the identification of those that retained their effects even when other variables were included in the model.Results and DiscussionThe results presented in Table 1 are based on coefficient calculations and posterior probabilities from 340,000 regressions. They helped identify four variables as statistically robust and non-fragile even when accounting for the presence of all other variables. These variables included the deviation of the effective exchange rate from the appropriate exchange rate, the government budget deficit, oil revenues, and the government effectiveness index. The result is supported by their posterior probabilities, which exceed the prior probability threshold of 50%, as assumed under the uniform distribution in the Bayesian model selection (BMS) method.Table 1. The Results of the Sampling Process and BMS Estimation Calculations based on 340 Thousand RegressionsA proportion of regressions withCond. Pos. SignPost SDPost MeanPIPSymbol 0.9910.09870.54240.9999DEA10.9710.14890.50730.9880BDG20.9110.03950.11060.9417GOR30.8500.0554-0.10400.8832EFG40.610.99990.03890.02360.3293COC50.580.01981.0681-0.57120.2869RET60.560.88091.05530.56240.2839REO70.560.92340.25230.13440.2786REG80.280.89960.00480.00110.1355BAC90.250.09150.0733-0.01200.1247CUK100.210.14430.0777-0.00510.1219MAT110.200.28310.0411-0.00080.0970GRI120.180.35270.0634-0.00590.0899DUM130.190.86900.00650.00130.0890UR140.170.99800.00310.00070.0875EC150.150.21920.1140-0.00740.0805TINF160.140.49550.0148-0.00150.0787ECI170.150.15390.0088-0.00120.0676RLA180.130.12760.0179-0.00210.0610DECM190.120.83750.00550.00060.0594HI200.090.90910.00340.00030.0549UNC21* Source: Research resultsThe weighted average of posterior coefficients further revealed that the variable representing the deviation of the effective exchange rate from the appropriate exchange rate is the most influential in the model, exerting the strongest positive effect in terms of intensity. Following this, the government budget deficit, oil revenues, and the government effectiveness index ranked as the next most significant variables, respectively, based on their influence coefficients. Yet, the results indicated that in the presence of all variables, central bank independence indices (i.e., GRI, CUK, MAT, and DUM) are fragile and statistically insignificant. This is due to their lower posterior probabilities of inclusion compared to the prior probability, underscoring their limited relevance within the model.ConclusionSince central bank independence indices lose significance when the four key components are considered, simply enhancing central bank independence is not a viable long-term solution under the current conditions of Iran’s economy characterized by the endogeneity of money. Therefore, during the transition phase, policymakers must prioritize structural reforms in several key areas: reforming the government budgeting system, improving governance with a focus on efficiency and effectiveness, and developing a competitive foreign exchange market capable of establishing an optimal and efficient exchange rate. Only after addressing these foundational issues should efforts to enhance central bank independence proceed, supported by a robust legal framework and coordinated collaboration with the government.
Research Paper
Financial Economics
Ali Nassiri Aghdam; Mahtab Moradzadeh
Abstract
The leverage ratio reflects a company’s relative reliance on capital and debt. Higher leverage ratios, indicating greater dependence on debt relative to equity, ceteris paribus, increase the firm’s financial risks. The present study examined the effect of income tax and deductible financial ...
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The leverage ratio reflects a company’s relative reliance on capital and debt. Higher leverage ratios, indicating greater dependence on debt relative to equity, ceteris paribus, increase the firm’s financial risks. The present study examined the effect of income tax and deductible financial costs on the firm’s leverage ratios. The data was collected from companies listed on the Tehran Stock Exchange during the period 2011–2020. Theoretically, higher effective tax rates and deductible financial costs are expected to lead to higher leverage ratios. These hypotheses were tested using a dynamic panel data model and the generalized least squares (GLS) method. The findings revealed that, after accounting for control variables, the effective income tax rate has no significant impact on the leverage ratio. However, financial costs have a positive and significant relationship with the leverage ratio.IntroductionModern theories of capital structure are rooted in Modigliani and Miller’s (1958) theory of irrelevance of capital structure. According to their theory, under conditions of perfect competition, with no taxes, symmetric information, and the absence of bankruptcy and agency costs, a firm’s value is independent of its financing sources. However, in practice, a firm’s value does depend on its financing sources. In another study, Modigliani and Miller (1963) relaxed the assumption of no taxes and demonstrated that introducing corporate income tax affects the firm’s capital structures. Firms tend to use more debt than equity to optimize financing costs because interest expenses are deductible from taxable income. However, increasing debt also raises the risk of insolvency. This trade-off in leveraging a firm’s capital structure suggests that firms determine an optimal debt-to-equity ratio by balancing the tax savings and bankruptcy costs associated with higher debt levels (Fama & French, 2005). In other words, a higher corporate income tax rate increases the tax shield, incentivizing firms to use more debt. The motivation to take the advantage of tax shield, in turn, increases the risk of bankruptcy (Faccio & Xu, 2015). In Iran, corporate income is subject to a flat tax rate of 25%. Additionally, financial costs, including interest expenses, are treated as deductible, reducing the firm’s tax burdens. Moreover, dividends are tax-exempt due to the absence of personal income tax. These conditions create an incentive for companies to distribute profits and rely more heavily on debt to finance their operations. The present study aimed to test this hypothesis by relying on empirical data.Materials and MethodsThis study used the following regression model to examine the effect of income tax and financial costs on corporate capital structure. The dependent variable is the leverage ratio (lev), calculated as total debt divided by total assets (Chakrabarti & Gruzin, 2019; Rajan & Zingales, 1995). The independent variables are the effective tax rate (ETR) and financial costs (FC). The effective tax rate is determined by dividing tax payments by pre-tax income (Graham, 1996), while financial costs are calculated by dividing interest expenses by total debt (Hossain, 2015). To control for the effects of other factors, the research included several control variables: tangible assets (Gas, 2018; Rajan & Zingales, 1995), growth opportunities (Titman & Wessels, 1988), profitability (Li, 2020), company size (Gas, 2018; Panda & Nanda, 2020), and non-debt tax shields (Chakrabarti & Gruzin, 2019; Gas, 2018; Karadeniz et al., 2009). Before estimating the model using panel data, it was necessary to determine the appropriate data type by using the Limer’s F test. The Hausman test was conducted to decide whether fixed effects (FE) or random effects (RE) is the more suitable estimation method (Gujarati, 2022). Based on the Hausman test results, the fixed effects method was deemed the most appropriate for estimating the model. Moreover, the Wooldridge test and the Wald test were used to evaluate autocorrelation and heteroscedasticity, respectively. Since the p-values in both tests are below 0.05, the null hypothesis was rejected, indicating the presence of autocorrelation and heteroscedasticity in the model. Finally, the generalized least squares (GLS) method was applied to ensure the efficiency of the results (Baltagi, 2008).Results and DiscussionThe GLS method was used to estimate the panel model. As shown in Table 1, the probability of the Wald statistic is less than 0.05, indicating the statistical significance of the regressions (Torres Reyna, 2007). The results of the model estimation revealed that the coefficient for financial costs is significant, and its sign is as expected. Specifically, as financial costs increase, the capital structure becomes more leveraged. This finding aligns with the predictions of static trade-off theory and is consistent with the results of studies such as Akhtar and Massoud (2013). Table 1. Results of Model Estimation Based on Alternative Specifications (Dependent Variable: Leverage Ratio)(5)(4)(3)(2)(1)Variables1.08*(0.57)0.24***(0.095)0.17*(0.095)0.24***(0.095)0.20**(0.096)Financial Cost0.64*(0.34)0.04(0.041)0.05(0.041)0.04(0.04) 0.06(0.040)Effective tax rate -0.17***(0.028)-0.14*** (0.028)-0.18***(0.028)-0.14***(0.028)Tangible assets0.91(0.16)-0.03**(0.015) -0.03**(0.015)-0.02(0.015)Profitability 0.08***(0.02) -0.02***(0.004) -0.03***(0.004)Growth opportunities -0.06***(0.008)-0.05***(0.008)-0.06***(0.008) Size3.52(5.69)0.19(1.81)1.15(1.84) Non-debt tax shield0.59***(0.10) Leverage(-1)164.270.00120.530.00140.500.00121.650.00103.980.00Wald chi2P- value 1.007***(0.057)0.9***(0.057)1.00***(0.056)0.58***(0.012)InterceptP=0.00Arellano-bond test for AR(1) in first differencesP=0.132Arellano-bond test for AR(2) in first differencesP=0.07Hansen test15401540154015401540Observation*P<0.1, **P<0.05,***P<0.01 Standard errors in parenthesesTable 1 presents the model estimation results using the GLS method in Column 1. Columns 2, 3, and 4 display the results of robustness check, with additional control variables included. Column 5 shows the outcomes of the model estimation using the Difference GMM method. A key aspect to note is the consistency of the estimation results. AR1 and AR2, as well as the Hansen test, are related to the GMM estimation in the fifth column.Source: Research findingsThe coefficient for the effective tax rate is not significant, meaning that changes in the effective tax rate do not influence the firm’s decisions regarding their capital structure. Although this finding is unexpected, it is consistent with the results of studies such as Pinto and Silva (2021), Saeedi and Mahmoudi (2011), and Alipour et al. (2015). These researches found the effect of the effective tax rate on the financial leverage ratio to be insignificant. Therefore, the hypothesis that the effective tax rate has a positive effect on financial leverage cannot be confirmed.ConclusionThe estimation results indicated that financial costs have a positive and significant effect on financial leverage. This finding supports the idea that the deductibility of financial costs encourages economic agents to rely more heavily on debt. The results also suggested that the introduction of personal income tax and the taxation of dividends incentivizes companies to reconsider their profit distribution policies and rely more on internal financing rather than debt. This conclusion is consistent with the findings of Haji et al. (2022).
Research Paper
Economic Development
Amirhossein Pooreh; Abbas Hatami
Abstract
More than three decades have passed since the adoption of development policies in Iran following the Islamic Revolution. Despite these efforts, Iran remains in the fourth quarter of development and the first quarter of underdevelopment. While development and underdevelopment cannot solely be attributed ...
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More than three decades have passed since the adoption of development policies in Iran following the Islamic Revolution. Despite these efforts, Iran remains in the fourth quarter of development and the first quarter of underdevelopment. While development and underdevelopment cannot solely be attributed to policymaking, it is widely recognized as a critical factor influencing development. Employing the grounded theory approach, the present study sought to uncover the underlying reasons for the failure of development policy in Iran from the perspectives of experts and policymakers. First, semi-structured interviews were conducted with 22 experts and development policy managers. The collected data was analyzed through three stages of coding: open, axial, and selective. The analysis resulted in 629 primary concepts, which were refined and reduced into 78 subcategories, 24 main categories, and ultimately, one core category. The core category revealed that development policy in Iran suffers from a triangle of inefficiencies: the inefficiency of governance, the inefficiency of social structure, and the inefficiency of the elite order. These three inefficiencies are not isolated but interconnected, forming a kind of ominous triangle that undermines the efficiency of development policy in Iran. If these inefficiencies persist, Iran risks facing a double underdevelopment that can significantly hamper the pursuit of sustainable development. Introduction Development is one of the most pressing concerns for societies and nations worldwide. Various societies have pursued comprehensive and multidimensional progress by implementing diverse strategies, which are often structured as development policies. In the case of Iran, more than three decades have passed since the adoption of development policies following the Islamic Revolution. Yet, the country remains in the fourth quarter of development and the first quarter of underdevelopment, struggling to move beyond this phase. While development and underdevelopment cannot be attributed solely to policymaking, it is widely recognized as a critical factor influencing development. Relying on a grounded theory approach, this research sought to investigate the underlying reasons for the failure of development policy in Iran. It aimed to address the following questions: What are the causal conditions contributing to the inefficiency of development policy in Iran? What are the contextual conditions influencing the inefficiency of development policy in Iran? What are the intervening conditions affecting the inefficiency of development policy in Iran? What strategies can effectively improve the efficiency of development policy in Iran? What are the consequences of implementing strategies to improve the efficiency of development policy in Iran? Materials and Methods The current research used a grounded theory approach. The participants included experts, university professors, managers, and former administrators involved in development policy. A purposive sampling method with a homogeneous approach was applied to enrich the categories, dimensions, and components, as well as to achieve theoretical saturation. As a result, a total of 22 individuals were selected as participants. The data was collected through semi-structured interviews, and the interview process continued until theoretical saturation was reached. For data analysis, the three-stage coding method outlined by Strauss and Corbin (2015) was employed, encompassing open coding, axial coding, and selective coding. Results and Discussion The interviews were analyzed in three stages. During the open coding phase, 629 initial codes were extracted. At the next level of abstraction, these codes were organized into 78 subcategories, and finally categorized into 24 main categories. In the axial coding phase, the main and secondary categories were arranged according to paradigmatic dimensions, including causal conditions, contextual conditions, intervening conditions, strategies, and consequences. In the selective coding phase, a core category was identified, linking all the categories together. The causal conditions contributing to the inefficiency of development policy included: discourse conflicts, an anti-scenario futurism, a weak civil society coupled with a large mass society, the breakdown of elite communication, low institutional quality, an overly interventionist yet weak government, and the unrealistic, wishful thinking in program planning. The contextual conditions exacerbating development policy inefficiency were found to include poor timing, economic–political instability, and a lack of historical awareness. Intervening conditions identified were: epistemic foundations of anti-development, an absence of dialogue, an economic-focused and budget-oriented approach to planning, and an inappropriate composition of policy formulators.The strategies to enhance the efficiency of development policy in Iran, as identified by experts and policymakers, included: downsizing the government while empowering civil society, strengthening social capital, undertaking institutional and organizational reconstruction, adopting strategic foresight, achieving consensus among policymaking elites, and modernizing and screening development plans. If these strategies are implemented, Iran could experience multidimensional development, including improvements in life quality, social development, individual-cum-mental development, environmental livability, political development, and economic development. Conversely, failure to implement these strategies risks perpetuating multilayered underdevelopment, characterized by increasing unbalanced development, declining quality of life, reduced social satisfaction, rising costs, intensification of violence, and underdevelopment across economic, social, political, and environmental dimensions. Finally, the core category underlying these findings is the trinity of inefficiencies: incompetent governance, weak society, and elite disorder. Conclusion According to the research results, development policymaking in Iran suffers from a triangle of inefficiencies: the inefficiency of governance, the inefficiency of social structure, and the inefficiency of elite order. These inefficiencies are not isolated but are deeply interconnected, forming an ominous triangle that undermines development policymaking in Iran. If left unaddressed, it will entrench the country in a state of double underdevelopment and further delay the achievement of sustainable development.
Research Paper
Behavioral economics
Taha Shishegari; Farhad Ghaffari
Abstract
Conventional economics posits that the presence of arbitrage in financial markets forces market participants to act rationally in order to maximize profits. This assumption underpins the efficient market hypothesis (EMH). However, in recent years, behavioral economics has challenged the assumption of ...
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Conventional economics posits that the presence of arbitrage in financial markets forces market participants to act rationally in order to maximize profits. This assumption underpins the efficient market hypothesis (EMH). However, in recent years, behavioral economics has challenged the assumption of market efficiency and rational behavior by demonstrating the significant impact of seemingly irrelevant factors (e.g., weather conditions, air temperature, and pollution) on financial markets. The present research aimed to compare the explanatory power of these two perspectives by analyzing daily data from the Tehran Stock Exchange index over two periods: February 20th, 2022 to February 19th, 2023, and February 20th, 2023 to February 19th, 2024. The study relied on the daily data on the growth rate of the dollar as an explanatory variable for the total stock market index growth from a conventional economics perspective. From a behavioral economics viewpoint, the analysis incorporated variables such as air temperature, weather conditions, and the pollution index. Given the nature of financial markets, the study used the EGARCH method for analysis. The results indicated that during the period from February 20th, 2022 to February 19th, 2023, when the dollar rate exhibited a significant upward trend, the explanatory power of behavioral variables decreased, with some even losing their significance in explaining the total stock market index. However, during the period from February 20th, 2023 to February 19th, 2024-when the exchange rate remained relatively stable-behavioral variables had a significant impact on the total stock market index. Introduction In the conventional economics perspective, which has long dominated the analysis of financial markets, actors are assumed to behave rationally. This means that they adjust their beliefs accurately (according to Bayes’ rule), aligning their subjective probabilities with reality and making decisions based on expected utility. However, in recent decades, the deviation of conventional economics theories from empirical data—along with the emergence of large, persistent, and severe price bubbles in financial markets—has led a group of economists to question the explanatory power of conventional theories and the assumption of rational behavior in financial markets. The present study aimed to address the duality between conventional and behavioral economics within the context of Iran’s developing economy. Focusing the country’s unique economic conditions, the study sought to determine which perspective—conventional or behavioral economics—provides a better explanation for stock market behavior. Two distinct time periods were analyzed: 1) from February 20th, 2022 to February 19th, 2023, during which the exchange rate (U.S. dollar) nearly doubled as a representative variable of the macroeconomic situation; and 2) from February 20th, 2023 to February 19th, 2024, when the exchange rate remained relatively stable, increasing by about 40%. The impact of behavioral variables on stock market returns was examined in two scenarios: one characterized by significant changes in macroeconomic variables and the other by more moderate changes. Conventional economics suggests that humans act rationally when the data is clear and the analysis is straightforward. However, as complexity increases and data becomes less clear, individuals tend to deviate from rational behavior due to limited rationality (Thaler, 2009). This hypothesis was tested by focusing on the two time periods of the study. In the first period, when macroeconomic variables exhibited a clear and specific trend, conventional economic theories were expected to provide a more accurate explanation of stock market behavior, with the influence of behavioral variables likely to decrease. Conversely, in the second period, when macroeconomic variables lacked a clear direction, it was anticipated that behavioral economics—along with variables rooted in psychological influences and the internal states of actors—would offer a better explanation for stock market performance. In this study, environmental variables such as air temperature, atmospheric conditions, and air pollution were considered representative of behavioral variables. The analysis investigated the impact of behavioral variables on the Tehran Stock Exchange index. Materials and Methods Financial sector data often exhibit heteroskedasticity, which makes the use of linear structures for estimation and modeling problematic. Additionally, fluctuations in financial data tend to cluster, indicating that the variance is self-explanatory. These characteristics make ARCH and GARCH models particularly suitable for modeling in this context. When using ARCH and GARCH models, it is essential for the estimated coefficients to be non-negative, which can present challenges in the estimation process. To address this issue, EGARCH models, which is the logarithmic form of the GARCH model, can be employed. This approach eliminates the need to impose the non-negativity condition on the variance coefficients. The current study estimated the daily growth rate of the total index of Tehran Stock Exchange over two separate time periods: from February 20th, 2022 to February 19th, 2023, and from February 20th, 2023 to February 19th, 2024. The analysis applied the AR(1) model and incorporated both behavioral and conventional variables into the variance component of the model to explain fluctuations in the total index efficiency. Results and Discussion During the first period (February 20th, 2022 to February 19th, 2023), the exchange rate experienced a clear and significant increase of 100%. Market players, adhering closely to conventional economic theories, operated under the assumption of rational and optimizing behavior. As a result, the exchange rate variable became more effective in explaining market fluctuations, while some behavioral variables, such as climate and air pollution, lost their explanatory power in the variance equation. In the second period (February 20th, 2023 to February 19th, 2024), the conventional variable (currency growth rate) became less significant and transparent. Market players increasingly relied on behavioral variables, which offered a better explanation for fluctuations in the total stock market index. The estimated coefficient for the conventional variable (foreign exchange growth rate) lost its significance during this period. The results showed that air temperature had a negative and significant impact on fluctuations in the growth index during both periods, consistent with the findings of previous studies. Conclusion This study analyzed two distinct economic periods: one marked by significant growth in foreign exchange rates, and the other characterized by relative stability in the foreign exchange market. The objective was to examine the behavior of financial actors and compare the explanations provided by conventional and behavioral perspectives on financial markets using the available data. According to the results from the two estimated models, the exchange rate growth (as the representative variable of the conventional view) had a significant and positive impact on stock index fluctuations during the first period, when exchange rates exhibited a clearly upward movement. However, this variable lost its significance in the second period, when exchange rates remained relatively stable. During the second time, the explanatory power shifted to behavioral variables such as weather conditions, pollution, and air temperature.
Research Paper
Economic Development
Fariba Rashnoo; Ahmad Sarlak
Abstract
In an era of economic complexity, where goods and services are produced using advanced technologies and with significant diversity, achieving economic growth without environmental pollution has become one of the primary goals for nations worldwide. This objective necessitates measures such as investment ...
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In an era of economic complexity, where goods and services are produced using advanced technologies and with significant diversity, achieving economic growth without environmental pollution has become one of the primary goals for nations worldwide. This objective necessitates measures such as investment in knowledge-based production, which in turn relies heavily on investment in research and development. The present study aimed to examine the relationship between multidimensional economic complexity and inclusive green economic growth. Given the geographic proximity of some developed and developing countries, the research employed a spatial panel econometric method using data from these nations. The results indicated a significant relationship between inclusive green growth and economic complexity in both developed and developing countries. However, this relationship is relatively weaker in developing countries.IntroductionThe most crucial factor influencing the level of economic development in any country is the extent to which knowledge is generated and applied in its production processes (Kazemi, 2013). Moreover, the integration of knowledge into production significantly reduces greenhouse gas emissions (Barbieri, 2012). Economic complexity, through the knowledge channel, promotes resource efficiency, enhances the quality of production institutions, and facilitates the establishment of green productive structures (Hassan et al., 2022). Since developing countries often rely on the production of a limited range of goods, it is essential to examine their level of economic complexity. Furthermore, given the strong link between economic complexity and the technology required for renewable energy production, it is critical to examine the relationship between multidimensional economic complexity and economic growth through the technology production channel. In this context, countries like Iran must be analyzed in terms of economic complexity and compared with developed nations. Despite the importance of this issue, no study to date has explored the relationship between economic complexity and inclusive green economic growth across both developed and developing countries. The present research tried to address the following question: What is the relationship between economic complexity and green economic growth in developed and developing countries? To answer the question, the study first reviewed the theoretical foundations of green economic growth and economic complexity, followed by a discussion of the methods and models.Materials and MethodsThe present study relied on the model built upon the work of Mohammadi et al. (2023), who investigated the impact of economic complexity and renewable energy consumption on environmental pollution in developing countries. Spatial econometrics in Stata software was used to analyze the relationship between economic complexity and inclusive green economic growth from 2000 to 2022. This approach not only examines the relationship between independent and dependent variables but also incorporates the spatial characteristics of the locations involved, as highlighted in studies such as AbuGhunmi et al. (2023). Additionally, data from both developed and developing counties was used to conduct a comparative analysis. The first step in estimating the spatial panel model is to create the adjacency matrix. In this research, the proximity matrix for the seven OPEC member countries with common borders of spatial heterogeneity refers to the deviations in relationships between observations at different geographical locations. In this matrix, neighboring and non-neighboring countries are assigned a value of 1 and 0, respectively. Next, autocorrelation is tested through methods such as the Moran and Gray tests. Once autocorrelation is confirmed, the model type is determined through parent, multiple parent, and Akaike and Schwartz tests. Finally, the model is estimated.Results and DiscussionThe results of estimating the relationship between multidimensional economic complexity and green economic growth in developing countries are presented in Table 1, and those for developed countries are shown in Table 2.Table 1. Model Estimation Results With the Dependent Variable in Developing CountriesEffectsVariablesCoefficientProbabilityDirect effectsINF-0.080.000CO2-0.090.000R&D0.30.000TECHEX0.210.000EC0.120.000Indirect effectsINF-0.10.000CO20.110.000R&D0.240.000R&D(-1)0.210.000TECHEX0.200.000EC0.20.000Total effectsINF-0.110.000CO20.190.000R&D0.130.000TECHEX0.120.000EC0.210.000Spatial correlation coefficient-0.2360.039Hausman test7.110.81Source: Research findingsTable 2. Model Estimation Results With the Dependent Variable in Developed CountriesEffectsVariablesCoefficientProbabilityDirect effectsINF-0.060.000CO2-0.040.000R&D0.410.000TECHEX0.330.000EC0.240.000Indirect effectsINF-0.050.000CO2-0.060.000R&D0.360.000TECHEX0.210.000EC0.290.000Total effectsINF-0.030.000CO2-0.060.000R&D0.290.000TECHEX0.210.000EC0.390.000Spatial correlation coefficient-0.2490.029Hausman test6.990.78Source: Research findingsAs observed in the calculations, both the direct and indirect effects, as well as the total economic complexity, have a direct and significant impact on green economic growth. As expected, this effect is stronger in developed countries than in developing ones. The effect coefficient for total economic complexity in developing and developed countries is 0.21 and 0.39, respectively. These figures indicate that the overall impact of economic complexity on economic growth is greater in developed countries. This relationship can be explained using the Kuznets curve. According to the results, economic complexity fosters green economic growth by increasing the use of technology in production and reducing emissions.ConclusionThe results indicated that the impact of economic complexity on green growth is smaller in developing countries compared to developed countries. Additionally, since economic complexity reflects the use of advanced technologies and increased costs in the production process, the rise in technology use and in research and development expenditures will not only drive the production process towards greener, pollution-free methods but will also help reduce production costs over time. The coefficients presented in the table for both developed and developing countries showed the positive effect of economic complexity on green production and growth.
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