Research Paper
Political economy
Farshad Momeni; Hojjatollah Mirzaei; Ali Jafari Shahrestani
Abstract
AbstractBefore the 1990s, political economy theories typically presumed the security of property rights. The significance of property rights as an underlying factor in economic growth and development was first recognized by new institutional economists and has since gained further attention. The current ...
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AbstractBefore the 1990s, political economy theories typically presumed the security of property rights. The significance of property rights as an underlying factor in economic growth and development was first recognized by new institutional economists and has since gained further attention. The current research examined Marxist and methodological individualism approaches within political economy that have been used by several scholars to explain the main causes of underdevelopment in Iran, with a particular focus on property rights. A common weakness of these approaches is their neglect of Iran’s unique historical context and their failure to present an independent theory addressing the inefficiency of property rights in Iran’s history. The present study suggested that the new institutional approach, which incorporates historical analysis and the study of societal institutions, could offer a more comprehensive understanding of the role of property rights in underdevelopment. Rather than simply analyzing a series of historical events and geographical features, the proposed approach emphasizes several specific aspects of Iran that must be considered. These include the country’s unique climatic conditions, particularly the scarcity of water, which has led to significant tensions. Additionally, the impact of these climate conditions on political and economic systems (the theory of government) has shaped nomadic and tribal lifestyles and contributed to the formation of tribal governments. This, in turn, has influenced the underdevelopment of property rights (the theory of property rights) in Iran.1. IntroductionBefore the 1990s, political economy theories typically presumed the security of property rights. The significance of property rights as an underlying factor in economic growth and development was first recognized by new institutional economists and has since gained further attention. The current research examined Marxist and methodological individualism approaches within political economy that have been used by several scholars to explain the main causes of underdevelopment in Iran, with a particular focus on property rights.2.Materials and Methods Drawing on the institutional political economy framework, the present study employed a descriptive–qualitative method and library research to examine the theoretical models of Marxist political economy and the ideas grounded in methodological individualism. The former is represented through Iranian feudalism and the Asian mode of production, while the latter is expressed through the concept of unsuccessful libertarianism. These models have been used by several scholars to analyze the causes of Iran’s underdevelopment, with a particular focus on property rights.3. Results and DiscussionThe analysis focused not only on the theoretical inconsistencies of the models, but also on their contradictions with the historical records of Iran’s economy, as outlined in the following table: Table 1. Characteristics of Political Economy Approaches Used to Explain Iran’s UnderdevelopmentPolitical economy approachTheoryThinkerMost important featuresPosition of property rights in theoryCriticismsMarxismFeudalismPetrushevskyNomaniValiThe emergence of the autocratic government as the biggest ownerThe existence of the lord-serf systemAt the disposal of the royal family, nobles and dependentsThe absence of slavery in the history of IranThe existence of small ownership in the history of IranThe disproportion between the ownership of the state over the means of production and the ownership of the feudal lords over itThe absence of stable legal relationship between different classes and the government in IranThe absence of aristocracy in Iran and the complete dependence of property on the monarchyThe Asian mode of productionAshrafKatouzianSeifEmphasis on water scarcity and the unique role of the government to provide the needed resourcesSubjected ownership to the will of the autocratic governmentLack of formation /independence of social classesLack of market exchangesThe slow growth rate of productive forcesAt the disposal of the kingFailure to pay attention to the complexities and special conditions of each societyIncompatibility of the hypothesis of self-sufficient villages with autocratic governmentThe existence of other forms of ownership (private and endowment) in the history of IranNo historical record of government investment in water projects in IranMethodological individualismUnsuccessful libertarianismTabibian, Ghaninejad, AbbasiEmphasis on individual freedoms as the basis of property rights and intellectuals’ historical lack of attention to itRepeated emergence of tribal and patriarchal tyranny as the dominant model of all the events of contemporary Iranian historyHarmony between individual desires or interestsThe emergence of interventionist state due to the continuity of traditional tribal values and its affinity with socialist collectivist idealsIntellectuals’ lack of attention to the importance of the issue and repression by the interventionist governmentNeglect of the role of history and institutions on the decision-making of agentsLimited rationality, incomplete information and uncertainty about the futureExistence of transaction costsFailure to establish the assumption of a neutral contractual government and the history of the tribal government in form and content until the early Qajar period (the plundering government)The lack of application of neoclassical models in non-competitive conditions that form the major part of economic historySource: Research resultsBased on the theoretical models reviewed in the table above, several key theoretical flaws in were identified in the theories. First, given the existence of private ownership and endowment, state ownership has not been dominant throughout Iran’s history. Second, the models failed to consider the impact of Iran’s dry, water-scarce climate, which played a crucial role in shaping tribal life, collective agricultural cooperation, and the subsequent underdevelopment of private property. Third, these models overlooked the persistently tribal nature of government in Iran, which has influenced the political and economic landscape. Another significant flaw is the predatory nature of the government in Iran, which has historically focused on maximizing rents and self-preservation rather than investing in productive inputs. Finally, the models failed to address the lack of a stable and consistent relationship between different social classes and the government, as well as the absence of secure property rights for all classes.Table 2. A Comparison of Political Economy Theories Concerning Iran’s UnderdevelopmentTheoryWater scarcity and the dispersion of villagesSupremacy of collective/state ownershipThe use of lands for rent distributionThe existence of private property and endowmentTyranny and irregular relations between the government and social strataFailure to secure property rightsShareholding system in agricultureTribal structure of the political powerFeudalism * * The Asian mode of production** ** Unsuccessful libertarianism * ** *Source: Research results4. ConclusionA key weakness of these approaches is their failure to account for Iran’s unique historical context and their inability to present an independent theory explaining the inefficiency of property rights in Iran’s history. To address this gap, the new institutional approach, which emphasizes historical analysis and the study of societal institutions, can provide a more comprehensive understanding of the role of property rights in underdevelopment by going beyond simply examining a series of historical events and geographical features. The present analysis suggests focusing on specific aspects of Iran, such as its unique climatic conditions, particularly the scarcity of water, which has been a major source of tension. Additionally, the impact of these climatic conditions on the political and economic systems (the theory of government) has shaped nomadic and tribal lifestyles, as well as tribal governments. Finally, the influence of these political and economic structures on the underdevelopment of property rights (the theory of property rights) should also be considered.
Research Paper
Financial Economics
Reza Taleblou; Mohammad Mehdi Bagheri Todeshki
Abstract
AbstractThe current study examined the influence of sentiment as a key risk factor in capital markets, which contributes to behavioral deviations in the pricing of financial assets. The stochastic discount factor (SDF) framework was used to propose an estimation of the asset pricing model, incorporating ...
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AbstractThe current study examined the influence of sentiment as a key risk factor in capital markets, which contributes to behavioral deviations in the pricing of financial assets. The stochastic discount factor (SDF) framework was used to propose an estimation of the asset pricing model, incorporating both traditional and behavioral approaches. An attempt was made to extend the consumption-based capital asset pricing model (CCAPM) and incorporate sentiment into the utility function through Euler equations and the generalized method of moments (GMM). To measure sentiment, the analysis utilized the market turnover sentiment index as a reliable indicator. The data covered 18 stock exchange groups, including 63 companies listed on the Tehran Stock Exchange, over the years 2011 to 2020. The results showed that the behavioral SDF model demonstrates higher consistency and efficiency than the traditional model, aligning more closely with observed market dynamics in the Tehran Stock Exchange. Furthermore, the sentiment coefficient was found to be statistically significant. In terms of risk, the behavioral model demonstrated higher coefficients than the traditional model. Notably, both models indicated that market participants exhibit a high time preference factor and display patience in their investment behavior.IntroductionOne of the most important approaches to asset pricing is the asset pricing model based on the stochastic discount factor (SDF), from which most asset pricing models can be derived within a general framework combining macroeconomics, finance, and mathematics. The model is based on the concept of a random discount factor (Cochrane, 2000; Foldes, 2000). Shefrin (2008) derived the behavioral SDF model by introducing market sentiment as a random discount factor. A key premise of the behavioral SDF model is that, despite investor sentiment, mistakes are made. As long as sentiment remains in the market, stock prices will not reflect their true value; they may be either inflated or deflated. This has a significant impact on asset pricing and leads to fluctuations in the SDF, as investors exhibit mass behavior, such as excessive optimism or pessimism toward the market (Shefrin, 2008). The primary objective of this study was to address the following questions: Is the experimental SDF in the Tehran Stock Exchange traditional or behavioral? Does sentiment influence asset pricing? Which model better explains investor behavior, asset market fluctuations, market bubbles, and inflated or deflated stock values?Materials and MethodsIn general, the pricing patterns of capital assets can be analyzed using two approaches: Behavioral and traditional. In recent years, economists have introduced new traditional asset pricing models based on the SDF framework. However, these models often fail to align with real-world outcomes, primarily due to their reliance on rational assumptions and the lack of consideration for behavioral factors. Using Shefrin’s Log-SDF theorem and incorporating sentiment into the utility function, the present study estimated the empirical SDF through moment equations and the GMM with both traditional and behavioral approaches.Shefrin’s approach involves estimating the log-SDF as the sum of fundamental components and emotions (Λ). The fundamental variables included in the SDF are total consumption growth (g), the market’s relative risk aversion coefficient and the market’s time discount factor ). The formal equation relating to the Log-SDF and market behavior is as follows: In the framework of the traditional classical model, market sentiment is assumed to be zero and Ln(m) is equal to (Shefrin, 2008). In the present article, both the traditional and Shefrin’s behavioral models are estimated and compared. Shefrin presents the following figure, comparing the behavioral SDF with the traditional SDF:Figure 1. Comparison of Behavioral SDF and Traditional SDF Source: Shefrin (2008)The analysis relied on the seasonal data from 2011 to 2020. The macroeconomic data included private sector consumption, money supply, exchange rates in the free market, the volume of demand deposits in banks and credit institutions, and the gold price per ounce. Initially, the capital market data sample consisted of 18 stock groups, totaling 130 shares. However, due to missing data for some stocks during the timeframe, the final sample was reduced to 63 stocks, as shown in the table below. An attempt was made to include a diverse selection of stocks, representing both high and low volatility, as well as winner and loser stocks from each group. The stock prices were obtained from the TSE website based on the daily closing prices, and the average quarterly returns were calculated for all the stocks in the sample. For the sentiment data across the 18 stock groups, the market turnover sentiment index was calculated for each group. Finally, the average sentiment index of stock market turnover and the average sentiment index of stock price fluctuations across the groups in the sample were estimated.Results and DiscussionThe results of the behavioral SDF model with the assumption of the market turnover sentiment index by different stock market groups are as follows: Table 1. The Results of the Estimation of the Behavioral SDF Model of the Stock Groups in the Tehran Stock ExchangeStock groupThe results of the estimationThe statistic JThe probability of test statistic J βP-ValueMetallic minerals0.99 0.039 0.0204.020.67Sugar0.99 0.034 0.0183.60.73Ceramic Tile0.99 0.055 0.0116.040.41Rubber and plastic0.99 0.083 0.0055.230.51Financial intermediation0.99 0.047 0.0314.040.54Investment0.99 0.031 0.0135.520.35Metals0.99 0.030 0.0195.750.33Bank0.99 0.091 0.0495.640.22Transportation0.99 0.059 0.0211.50.90Car0.99 0.063 0.0305.390.36Metal products0.99 0.079 0.0114.350.49Equipment and machinery0.99 0.031 0.0035.060.53Non-metallic minerals0.99 0.021 0.0036.490.37Electrical devices0.99 0.055 0.0205.480.48Oil products0.99 0.061 0.0068.010.23Paper0.99 0.031 0.0014.630.59Cement0.99 0.035 0.0065.690.45Chemical products0.99 0.061 0.0046.990.32 Source: Shefrin (2008)The general tools used in the behavioral SDF test for all groups are as follows: GSKS (-1), Gexch (-1), Gm (-1), Ghesab (-1), Ggold (-1)The results and estimations indicated several key points. First, the time preference factor (β) is close to one in three cases, indicating that people in the society are patient and have a strong desire to save. Second, the risk tolerance coefficient (γ) in both behavioral SDF models is higher than in the traditional SDF model. This difference arises from the inclusion of the emotion variable in the behavioral model, which aligns with the theoretical expectations that investors do not always behave rationally and are often risk-seeking. Third, the ϵ coefficient, which represents the share of sentiment in the utility function (indicating optimism and pessimism), varies between 0.04 and 0.001 for different stock market groups in the sample when the turnover sentiment index is considered. Fourth, the results showed that the risk-reward ratio of the behavioral SDF model is higher than that of the traditional SDF model in the TSE. Fifth, when considering the average quarterly returns of the stock sample as a proxy for risky assets, the risk premium compared to short-term and one-year deposits is 0.039. The behavioral SDF model, assuming the turnover sentiment index, yields a risk premium of 0.035, suggesting that the behavioral SDF model’s risk-reward results are close to real-world observations. Sixth, the Hansen-Jonathan (HJ) distance criterion, used to compare the performance of non-linear models based on the GMM method, indicated that the SDF model performs better and more efficiently. Moreover, the coefficients of the model were calculated and placed into the LOG-SDF equation, and the two models were compared graphically. ConclusionAccording to the results, the behavioral SDF model aligns more closely with the realities of the TSE than the traditional model, with the sentiment coefficient being statistically significant. The risk tolerance factor in the behavioral model is higher than the traditional model, and in both models, individuals exhibit a high time preference and a degree of patience. When the charts of the behavioral and traditional SDF models intersect, it signifies a market where sentiment is zero, and stock prices are efficient. However, when the behavioral SDF exceeds the traditional SDF, it suggests that investors are overly optimistic, pushing prices above their true value. This scenario can lead to market overvaluation, potentially resulting in sales queues or a shift to negative sentiment after a large volume of transactions. For instance, in 2019 and early 2020, the market was inefficient, with prices falling below their intrinsic value. During this period, the behavioral SDF was lower than the traditional SDF, reflecting pessimism in the market. The two charts converged, signaling that prices were undervalued. As we approached the end of 2020, the gap between the behavioral and traditional SDF charts widened, indicating that investors became overly optimistic, thus driving prices beyond their intrinsic value. This reinforces the importance of considering emotional factors, which are absent in traditional models, and demonstrates how the behavioral model can help in asset pricing. Similar to Barbaris (2018), the findings of the present study suggest that by incorporating transaction volume as a sentiment indicator, we can better understand market fluctuations and identify market bubbles, in line with deviations from the inherent value of shares. Furthermore, like the results of Lu et al. (2000), this research showed that the experimental SDF in the TSE is behavioral and volatile.
Research Paper
Financial Economics
Masood Baghbani; GholamReza Keshavarz Haddad; Hossein Abdoh Tabrizi
Abstract
AbstractThis study examined the relationship between dividend policy (measured by dividend yield and dividend payout ratio) and stock price volatility in the Tehran Stock Exchange. Using fixed effects and random effects regression models developed by Baskin (1989) and Allen and Rachim (1996), the study ...
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AbstractThis study examined the relationship between dividend policy (measured by dividend yield and dividend payout ratio) and stock price volatility in the Tehran Stock Exchange. Using fixed effects and random effects regression models developed by Baskin (1989) and Allen and Rachim (1996), the study analyzed the data of 200 public firms listed on the TSE that consistently paid dividends from 2010 to 2020. The results indicated a significantly negative relationship between dividend policy and stock price volatility. Additionally, firm size was negatively correlated with stock price volatility, with this relationship proving statistically significant. Consequently, managers can partly control the stock risk and influence investors’ decisions through a firm’s dividend policies. Stock price volatility in emerging markets, particularly the Tehran Stock Exchange, is substantially influenced by macroeconomic fluctuations, so considering these factors notably affects the coefficient sizes.IntroductionDividend policy has long been a central focus in financial research, especially concerning its impact on stock price volatility. As a form of return to shareholders, dividend payments play a significant role in shaping investment decisions. In this respect, the present study aimed to investigate the effect of dividend policy—measured through the dividend payout ratio and dividend yield—on stock price volatility in the Tehran Stock Exchange (TSE), using the data of 200 firms over the period from 2010 to 2019. The research aimed to address the following questions: How does the dividend payout ratio affect stock price volatility in the TSE? What is the impact of dividend yield on stock price volatility in the TSE? How do seasoned offerings and macroeconomic factors influence the relationship between dividend policy (dividend payout ratio and dividend yield) and stock price volatility?Materials and MethodsThe research used a sample of 200 firms listed on the TSE from 2010 to 2020 in order to examine the effect of dividend policy on stock price volatility. Using panel regression analysis, the study evaluated the effects of the dividend payout ratio and dividend yield, with control variables such as firm size, earnings volatility, debt ratio, and growth. The data was sourced from Codal (www.codal.ir) and TSETMC (www.tsetmc.com). Stock price volatility was measured through the Parkinson estimator, which calculates volatility based on weekly high and low prices, thereby minimizing distortions from daily price limits on the exchange.Results and DiscussionThe empirical results revealed a statistically inverse relationship between dividend yield and stock price volatility, supporting the duration effect hypothesis. Higher dividend yields contribute to more stable prices, as stocks with larger dividends are less sensitive to changes in the discount rate. This finding is consistent with earlier studies by Baskin (1989), Hussainy (2011), and Mingli et al. (2016). The relationship remains robust across different model specifications. However, no significant relationship was found between the payout ratio and stock price volatility when both dividend yield and payout ratio were included, which is likely due to multicollinearity. When dividend yield was excluded, the payout ratio became significant at the 10% level, showing an inverse relationship with volatility (Table 1). Control variables such as firm size and growth significantly influenced volatility, with higher growth correlated with higher volatility. Debt levels, initially insignificant, became significant when total debt was considered. Adjusting for seasoned equity offerings reduced the effect of dividend yield on volatility but still maintained its significance. Macroeconomic volatility, measured by TEDPIX fluctuations, had the largest impact on stock price volatility, highlighting the sensitivity of TSE to Iran's unstable macroeconomic environment.Table 1. Multicollinearity of Payout Ratio and Dividend Yield, and Its Effect on Regression Results(5)(4)(3)Variable -0.094***(0.032)-0.088***(0.034)DivYield-0.0046*(0.0028) 0.0015-(0.0024)PayoutRatio0.019(0.070)0.023(0.071)0.023(0.070)EarningVol-0.015**(0.0065)-0.015***(0.0063)-0.015***(0.0061)Size0.020***(0.0070)0.021***(0.0069)0.021***(0.0070)Growth-0.035(0.053)-0.026(0.053)-0.026(0.053)DebtRatio0.650***(0.178)0.652***(0.178)0.526***(0.083)ConstantYesYesYesTime Fixed EffectYesYesYesIndustry Fixed Effect169216921801Num of Observation0.4890.5891692R2Source: Research resultsTable 1 shows the results of assessing the effect of dividend policy on stock price volatility, taking into account the multicollinearity between dividend yield and payout ratio. In Specification (3), where all explanatory variables are included, a significant negative relationship is found between dividend yield and stock price volatility, while the payout ratio remains insignificant. Specification (4), which excludes the payout ratio, shows that the dividend yield remains significantly negative. In Specification (5), when dividend yield is excluded, the payout ratio becomes significant at the 10% level, suggesting the presence of multicollinearity between the two variables. The dependent variable is stock price volatility, measured using Parkinson’s method, with the models controlling for firm size, growth, debt ratio, and fixed effects.ConclusionThis research examined the effect of dividend policy on stock price volatility in the TSE, using dividend yield and payout ratio as key indicators. A sample of 200 public firms listed that consistently paid dividends from 2010 to 2020 was selected, and a panel regression analysis was conducted to assess the effect of the indicators. The results revealed a significantly negative relationship between dividend yield and stock price volatility, while the payout ratio was not significant due to multicollinearity. However, when dividend yield was excluded, the payout ratio became significant at the 10% level, also showing a negative relationship with volatility. The findings support the duration, rate of return, and signaling effects, and are consistent with prior studies by Baskin (1989), Hussainy (2011), and Mingley et al. (2016). Among the control variables, firm size and growth were significant, and redefining the debt ratio to include total debt made it significant at the 10% level. The results from alternative specifications using net dividend yield and payout ratio were consistent, offering valuable insights for investors and managers in predicting and managing stock price volatility.
Research Paper
Monetary economy
Reza Alaei; Ahmad Salahmanesh
Abstract
AbstractThe present study examined the effect of uncertainty on specific monetary policy transmission mechanisms in Iran from the first quarter of 1990 to the fourth quarter of 2022. First, the three variables representing the Central Bank’s policy instruments were considered, namely monetary base ...
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AbstractThe present study examined the effect of uncertainty on specific monetary policy transmission mechanisms in Iran from the first quarter of 1990 to the fourth quarter of 2022. First, the three variables representing the Central Bank’s policy instruments were considered, namely monetary base (BM), money (M1), and liquidity (M2), along with two target variables of GDP and inflation. The VAR-X method was used to analyze the three monetary transmission channels: Interest rate, exchange rate, and credits. The findings indicated the central role of the credit channel, regardless of the Central Bank’s policy variable or target. However, the effectiveness of the interest rate and exchange rate channels varies depending on the type of policy instrument and the target variable. The study also explored the effect of different levels of uncertainty on the monetary transmission channels. The 90th and 10th percentiles of the optimal economic uncertainty index were used as proxies for high and low uncertainty, respectively. The analysis employed the interaction vector autoregression (IVAR) method and extracted impulse response functions (IRFs) for GDP and inflation. According to the results, monetary policy transmission functions operated differently under varying levels of uncertainty, indicating that the level of uncertainty significantly affects the monetary policy transmission channels.IntroductionAn examination of changes in Iran’s economy over the past few decades shows that it has faced multiple challenges, including chronic recessions, inflation, exchange rate fluctuations, economic reform plans, and severe international sanctions. These factors have contributed to heightened economic uncertainty. Meanwhile, monetary policies have been widely applied in Iran to stabilize the economy and achieve policymakers’ objectives, despite the theoretical and empirical evidence emphasizing the impact of uncertainty on monetary policy effectiveness. Concerning the literature on monetary policy transmission mechanisms in Iran, while some research has identified various transmission channels, no studies have specifically examined the performance of these channels under different uncertainty conditions. Understanding the transmission channels of monetary policy and the impact of uncertainty on them can help policymakers control the monetary policy and make its outcomes more predictable across varying economic conditions. In this respect, the current study aimed to analyze the three primary channels of monetary policy transmission: Interest rate, exchange rate, and credit. Then, it applied the optimal economic uncertainty index developed by Alaei et al. (2018) to evaluate the uncertainty effect on the transmission channels.Materials and MethodsThis study followed a three-step process. First, an uncertainty index was created by updating the optimal economic uncertainty index for Iran’s economy, originally developed by Alaei et al. (2018). Second, the two-stage approach used in studies by Poddar et al. (2006), Nyumuah (2018), and Anwar and Nguyen (2018) was applied—along with the extraction of impulse response functions (IRFs) from VAR and VAR-X models—to examine the monetary transmission mechanisms. Finally, the interactive vector autoregression (IVAR) method was employed to assess the effect of uncertainty on the monetary transmission mechanisms.Results and DiscussionThe analysis focused on the three policy instruments adopted by the Central Bank: The growth rate of monetary base, money growth, and liquidity. It thus examined the three monetary transmission channels: interest rate, exchange rate, and credit. These channels were analyzed in relation to two target variables, inflation (LCPI) and production (LGDP). The results indicate that the effectiveness of each transmission channel varies depending on the type of policy instrument used and the specific target variable, as shown in Table 1.Table 1. Results of Examination of Monetary Transmission ChannelsInstrumentTargetConfirmed Monetary Transmission Channels Logarithm of Monetary Base (LBM)Logarithm of Production (LGDP)CreditLogarithm of Consumer Price Index (LCPI)Real Exchange Rate, Credit Logarithm of Money ( )Logarithm of Production (LGDP)Real Exchange Rate, CreditLogarithm of Consumer Price Index (LCPI)Interest Rate, Credit Logarithm of Liquidity ( )Logarithm of Production (LGDP)Interest Rate, CreditLogarithm of Consumer Price Index (LCPI)Real Exchange Rate, Credit Source: Research resultsThe investigation into the effect of uncertainty on monetary transmission channels reveals that it varies depending on the type of policy instrument and the Central Bank’s target variable. During the period under study, when the logarithm of the monetary base (LBM) was used as a policy instrument with the growth rate of the logarithm of the consumer price index (LCPI) as the target variable, high levels of uncertainty would weaken both the exchange rate and credit channels. This results in less effective transmission of shocks from the LBM variable to the LCPI target, as uncertainty reduces the power of these monetary transmission channels. However, when the production variable (LGDP) served as the target, high uncertainty instead strengthened the credit channel. In this case, LGDP exhibited a heightened response to shocks on the LBM variable. Considering the logarithm of money (LM1) as the policy instrument, the analysis of the interest rate channel indicated that lower uncertainty strengthened this channel in transmitting shocks on LM1 toward the LCPI target. In contrast, differing levels of uncertainty did not significantly impact the effectiveness of the exchange rate channel in transmitting LM1 shocks to LGDP. For the credit channel, high uncertainty caused LGDP to respond more slowly to shocks, while uncertainty did not appear to affect the credit channel’s influence on the response of LCPI to LM1 shocks. Considering the logarithm of liquidity (LM2) as the policy instrument, not only did uncertainty lead to a change of the interest rate channel to LGDP, but also the response of the target variable at high uncertainty levels increased. Similarly, high uncertainty strengthened the exchange rate channel, resulting in an increased LCPI response to shocks on LM2. An analysis of the credit channel under high uncertainty revealed a stronger LGDP response to shocks in the 4th period; however, this response weakened over time. In contrast, with LCPI as the target, lower levels of uncertainty strengthened the credit channel, leading to a greater response of the target variable to shocks.ConclusionThe findings revealed that the credit channel remained valid regardless of the Central Bank’s policy instrument or target variable. However, the validity of other channels was sensitive to changes in the policy instrument or the target variable. Concerning the period under study, the monetary transmission channels operated differently across different levels of uncertainty. In fact, the impact of uncertainty on the monetary transmission channels proved to be significant, though its influence varied in degree across channels.
Research Paper
Welfare, poverty and income distribution
Hamidreza Navvabpour; Parya Torabi Kahlan
Abstract
AbstractMost countries define poverty simply as a lack of money, yet poor individuals themselves often view their experience of poverty more broadly. A person living in poverty can face multiple overlapping disadvantages simultaneously, so focusing on a single factor, such as income, does not fully capture ...
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AbstractMost countries define poverty simply as a lack of money, yet poor individuals themselves often view their experience of poverty more broadly. A person living in poverty can face multiple overlapping disadvantages simultaneously, so focusing on a single factor, such as income, does not fully capture the reality of poverty. In Iran, several studies have attempted to calculate the multidimensional poverty index, yet most rely on household income and expenditure survey data, which is limited in calculating the relevant indicators. The present study aimed to calculate and measure multidimensional poverty at the provincial level in Iran, assessing the contribution of each dimension to overall poverty and using the Alkire–Foster method to inform policymakers in their poverty alleviation efforts. The data was collected from the 2015 Multiple Indicator Demographic and Health Survey (MIDHS), encompassing 33,013 households and a wider range of indicators. The results indicated that, aside from Khuzestan and Qom Provinces, the multidimensional poverty index was particularly high in provinces along the eastern borders, while provinces along the northern, southern, and parts of the western borders experienced less poverty. Additionally, the contribution of each dimension to overall poverty revealed that the types of deprivation experienced by households varied across provinces in 2015.IntroductionIncome poverty is an important dimension of poverty, but it fails to capture the full reality of deprivation. The global Multidimensional Poverty Index (MPI) provides an internationally comparable measure of acute multidimensional poverty across more than 100 countries. The global MPI identifies acute deprivations in health, education, and living standards that affect individuals simultaneously, thus complementing the traditional monetary poverty measures—such as the World Bank’s extreme poverty line. The national MPI is a measure of multidimensional poverty within a specific country, aligned with that country’s definitions of poverty. It can identify poverty across different population groups, such as by age or gender. The national MPI reveals not only who falls below the poverty threshold but also highlights specific deprivations that may affect even those above it. This insight allows policymakers to understand how certain deprivations impact both poor and non-poor segments of society. Using the Alkire–Foster method, the present study aimed to assess Iran’s national MPI and examine the contribution of each dimension to the overall MPI across its provinces. The analysis relied on data from the 2015 Multiple Indicator Demographic and Health Survey (MIDHS).Materials and MethodsThe Alkire–Foster method assigns a deprivation score ( ) to each household, calculated as the weighted average of deprivation across all selected dimensions. Households with a deprivation score at or above the established poverty cut-off are considered multidimensionally poor. The incidence of poverty is the proportion of the population that is multidimensionally poor, calculated as ( ). MPI is the product of poverty incidence (H) and the intensity of poverty, which is measured as the average deprivation score among the poor ( ).All topics related to the national MPI were organized into seven dimensions, represented by 21 indicators. A poverty cut-off of 33% was applied, with equal weights assigned to each dimension and to all indicators within each dimension (see Table 1). Table 1. Deprivation Cut-offs, Dimensions, and Indicators of National MPI DimensionsIndicatorsCut-off: Household is deprived if …HealthChild mortalityAny child under the age of 18 years has died in the family in the five-year period preceding the survey.DisabilityAt least one household member suffers from one of the types of disabilities.Mental healthAt least one household member aged 15 or older suffers from severe mental illness according to Kessler 6 scale (the score greater than or equal 19).EducationSchool attendanceAny school-aged child is not attending school up to the age at which he/she would complete class eight.Level of educationNo household member aged 15 or older has completed primary schooling.Well-beingCooking fuelThe household cooks with dung, agricultural crop, shrubs, wood, charcoal or coal.SanitationThe household’s sanitation facility is not improved (according to SDG guidelines) or it is improved but shared with other households.Drinking waterThe household does not have access to improved drinking water (according to SDG guidelines) or improved drinking water is at least a 30-minute walk from home, round trip.ElectricityThe household has no electricity.AssetsThe household does not own more than one of these assets: Radio, television, telephone, computer, motorbike or refrigerator, and does not own a car.HousingThe household with inadequate housing; the housing is made of low-quality materials (clay and mud/wood)Overall life satisfactionAt least one household member aged 15 or older is dissatisfied or very dissatisfied with himself/herself, her/his family life, friends, current job, income or place of residence. EmploymentUnemploymentNo household member aged 15 or older is employed or has an income without work.InsuranceThere is at least one household member without health insurance.SecurityViolent disciplineAt least one child aged 1-14 has experienced some violent discipline.Domestic violenceAt least one woman aged 15 or older has agreed that her husband has the right to beat up his wife. CultureMass media and information technologyAt least one household member aged 15 or older does not read the newspaper or magazine, does not listen to radio or does not use the internet at all.Access to cultural activities for childrenAt least one child does not have access to sport, poetry, painting, or religious classes. Environment Disaster preparednessThe household has not done any action in the past year to deal with natural hazards and disasters.Drought-stricken people (1/21)More than 50% of the population in a particular area is affected by severe drought.Proximity to industrial pollutionAt least 50% of the average industrial waste of the country is generated in the proximity of the household’s place of residence.Source: Torabi, et al. (2021)Results and DiscussionAs shown in Table 2, in addition to Qom and Khuzestan provinces, all provinces bordering Afghanistan and Pakistan experience higher levels of multidimensional poverty. It also shows the contribution of each dimension to the overall MPI across Iran’s 31 provinces, ranked from the most prosperous to the poorest. Qom ranks highest in well-being and security, yet it is the most deprived in employment and environment. Hormozgan ranks best in health but is the most deprived in education. Ilam is the most deprived in security, while it ranks highest among provinces in environment and employment (with only a slight difference after East Azerbaijan).Table 2. The Contribution of Each Dimension in Percentage in MPI by Province and National Level and the p-Values of the Wald TestHealthEducationWell-beingEmploymentSecurityCultureEnvironmentPopulation ShareConfidence Interval (95%)MPIProvinces15.616.52.912.817.713.620.94.6[0.004,0.010]0.007Mazandaran6.7175.315.620.419.815.21.1[0.006,0.014]0.010Chaharmahal and Bakhtiari13.314.91.76.232.121.8100.7[0.006,0.014]0.010Ilam7.315.71.914.925.424.210.62.4[0.007,0.015]0.011Golestan6.714.91.714.323.819.918.71.3[0.008,0.016]0.012Boshehr9.9146.214.81711.426.73.6[0.010,0.019]0.015Gilan7.620.64.19.625.318.714.14[0.010,0.020]0.015Western Azerbaijan3.3212.611.824.320.816.21.7[0.010,0.020]0.015Hormozgan1017.84.414.224.517.611.52.4[0.011,0.022]0.017Kermanshah8.914.61.813.623.812.32517.3[0.010,0.024]0.017Tehran10.916.249.42718.813.72.3[0.014,0.025]0.019Hamedan10.714.42.815.618.312.425.86.9[0.015,0.025]0.020Esfahan9.815.23.26.130.918.316.54.8[0.015,0.026]0.021Eastern Azarbaijan8.2110.81714.816.631.63.6[0.014,0.027]0.021Alborz5.5172.717.116.215.625.91.9[0.017,0.028]0.022Markazi819.12.915.916.212.725.20.8[0.016,0.028]0.022Semnan8.5117.912.825.217.716.92.2[0.016,0.032]0.024Lorestan1115.73.48.127.117.317.41.6[0.018,0.029]0.024Ardebil1211.52.911.320.814.726.86.3[0.019,0.032]0.025Fars9.315.91.97.331.316.917.41.9[0.023,0.036]0.029Kordestan5.912.72.110.621.914.8321.5[0.023,0.035]0.029Yazd7.612.63.79.724.218.523.71.4[0.022,0.037]0.030Zanjan8.513.11.611.72014.1311.7[0.025,0.038]0.031Ghazvin7.810.92.811.93021.714.90.8[0.027,0.043]0.035Kohgilouye & Boyerahmad7.7124.110.826.721.617.13.6[0.024,0.048]0.036Kerman6.520.2410.325.716.916.40.9[0.028,0.044]0.036Southern Khorasan7.912.20.818.214.212.7341.4[0.028,0.045]0.037Ghom813.8212.722.913.826.88[0.031,0.045]0.038Razavi Khorasan6.314.839.922.716.3275.6[0.032,0.047]0.039Khuzestan6.515.74.96.721.916.228.11.2[0.039,0.055]0.047Northern Khorasan5.816.97.313.820.122.313.82.5[0.075,0.101]0.088Sistan & Balouchestan8.314.63.312.222.616.222.8100[0.023,0.026]0.025National level0.000.000.000.000.000.000.000.00--p-valuesSource: Research resultsConclusionOverall, the three dimensions of culture, security, and environment were found to be the most significant contributors to deprivation in Iran, accounting for 16.2%, 22.6%, and 22.8% of the MPI, respectively. Improved access to MIDHS micro-data and administrative data (e.g., air pollution and crime statistics), as well as the inclusion of relevant items into the MIDHS questionnaire (e.g., social protection, violence against women, and nutrition), would improve the MPI measurement in Iran.
Research Paper
Political economy
Vahid Azizi; Bakhtiar Javaheri; Fateh Habibi
Abstract
AbstractEconomic growth and development, as the primary goals of any country, play a crucial role in improving living standards and promoting sustainable development. Efforts to achieve these goals, and consequently increase per capita income, can ensure the enhancement of economic and social well-being ...
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AbstractEconomic growth and development, as the primary goals of any country, play a crucial role in improving living standards and promoting sustainable development. Efforts to achieve these goals, and consequently increase per capita income, can ensure the enhancement of economic and social well-being of a nation. However, natural and political crises can pose significant obstacles to achieving such objectives. Natural disasters and economic sanctions, in particular, can have devastating effects on economic growth and development, leading to a decline in per capita income. Using the Dynamic Ordinary Least Squares, the present study aimed to examine the effect of economic sanctions and natural disasters on non-oil per capita income in Iran from 1980 to 2022. The findings showed that, in the long-term, increases in natural disasters and economic sanctions had contributed to a decline in per capita income in Iran. Additionally, environmental innovation and the interaction between innovation and natural disasters positively influenced per capita income. The results also indicated that factors such as the labor force, physical capital, and trade openness had contributed to improvements in per capita income. In light of the findings, it is recommended that Iran implement effective plans and policies to mitigate the effects of sanctions and natural disasters, promote environmental innovations, and strengthen the development of fixed capital and the labor force, aimed at ensuring the continued growth of per capita income.IntroductionIn recent years, Iran has become a prominent case study and focal point in discussions about sanctions within global research and academic circles. This attention stems from Iran’s status as a target of both multilateral and unilateral sanctions campaigns, which have had adverse effects on its economy. The sanctions have led to currency devaluation; severe budgetary, commercial, and financial deficits; reduced foreign investment, skyrocketing inflation, and rising poverty rates. Natural disasters, meanwhile, are large-scale catastrophic events that intermittently strike, causing extensive human and infrastructural damage that impacts societies and economies alike. Natural disasters inflict significant damage on infrastructure, property, and industries, leading to reduced production, business disruptions, damage to manufacturing facilities, and interruptions in transportation systems. Due to their unpredictability, natural disasters have a substantial impact on the economy. The current study aimed to explore whether natural and political disasters pose genuine obstacles to economic growth and development in Iran. The primary research question is: What are the effects of economic sanctions, natural disasters, and environmental innovation on per capita income in Iran’s economy?Materials and MethodsTo meet the objectives, the study used an experimental model as defined in logarithmic form based on Equations (1) and (2) below. (1) (2)Per capita income (Y) was considered as the dependent variable, while the independent variables included economic sanctions (ES), natural disasters (ND), environmental innovation (EI), physical capital (K), labor force (L), trade openness (TO), and an interaction variable (ND×EI). In line with the research objectives, time series data spanning from 1980 to 2022 were utilized. The research model was analyzed using the Dynamic Ordinary Least Squares (DOLS) estimator in EViews software.Results and DiscussionTo analyze the results, a unit root test was first conducted to evaluate the reliability of the data. The results showed that the variables of trade openness (TO) and economic sanctions (ES) were at a stationary level, while the variables of per capita income (Y), physical capital (K), labor force (L), environmental innovation (EI), and natural disasters (ND) could be stationary with one time difference. Next, the Bayesian Information Criterion (BIC) was used to determine the optimal lag length. The presence of long-term relationships among the variables was then tested using the Augmented Engle-Granger cointegration test and the Cointegrating Regression Durbin-Watson (CRDW) test, both of which indicated at least one long-term relationship among the variables. The DOLS method was then employed to estimate the research model (see Table 1). The findings revealed that economic sanctions (ES) had a significant and negative effect on per capita income (Y) in Iran’s economy. Specifically, a one percent increase in ES in Models 1 and 2 reduces non-oil per capita income by 0.108% and 0.063%, respectively. Additionally, the frequency of severe natural disasters (ND) had a significantly negative correlation with non-oil per capita income. A one percent increase in ND results in a reduction of 0.161% and 0.158% in non-oil per capita income. Conversely, environmental innovation (EI) had a significantly positive effect on per capita income, with a one percent increase in EI leading to a 0.032% rise in non-oil per capita income. The interaction variable (ND×EI) was also positive and significant, where a one percent increase in this variable results in a 0.025% increase in non-oil per capita income (Y). Furthermore, physical capital (K) had a significantly positive effect on non-oil per capita income. In this case, a one percent increase in K is associated with an increase in Y by 0.185% and 0.25% in Models 1 and 2, respectively. Labor force (L) also had a positive and significant effect on non-oil per capita income, with a one percent increase in L leading to an increase in Y by 0.611% and 0.518% in Models 1 and 2, respectively. Finally, trade openness (TO) had a positive effect on per capita income, as a one percent increase in TO results in a rise of 0.253% and 0.208% in non-oil per capita income.Table 1. Estimation Results of Research ModelsVariablesModel 1Model 2Coefficientst statisticCoefficientst statisticK0.1852.483**0.2505.140***L0.6113.887***0.5182.257***TO0.2534.462***0.2085.516***ES-0.108-2.119**-0.063-1.883*ND-0.161-4.946***-0.158-8.704***EI0.0322.359**--ND×EI--0.0254.745***C3.5332.028*4.2963.994***Note: ***, ** and * represent significance levels of 1%, 5% and 10%, respectively.Source: Research resultsConclusionThe research findings suggested that repeated political and natural disasters could drive a country and its economy into a period of stagnation. On the one hand, these events lead to the destruction of physical, human, and natural resources. On the other hand, they disrupt trade processes, halting the transfer of technology through imports and impeding the modernization of domestic industries. As a result, both outcomes contribute to a decline in per capita income. Moreover, the study showed that adopting environmental innovations could not only increase per capita income but also positively influence the relationship between natural disasters and per capita production, thus helping to mitigate the impact of such disasters. Therefore, investing in research, development, and innovation will strengthen the country’s ability to cope with and adapt to such challenges, while reducing risk levels. In conclusion, this study demonstrated that the political and natural disasters observed during the analyzed period hah negatively impacted the country’s economic growth and development, leading to a decrease in Iran’s per capita income.
Research Paper
Growth Economy
Mohsen Namaei Ghasemi; Mehdi Fathabadi; Masood Soufi Majidpoor; Mahmoud Mahmoudzadeh
Abstract
AbstractThe total GDP of the MENA region is approximately $7 trillion (5.9% of the world economy), and its population is around 405 million (5.5% of the global population). This article aimed to evaluate the drivers of sustainability in 15 MENA countries from 1998 to 2019, examining different sub-periods. ...
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AbstractThe total GDP of the MENA region is approximately $7 trillion (5.9% of the world economy), and its population is around 405 million (5.5% of the global population). This article aimed to evaluate the drivers of sustainability in 15 MENA countries from 1998 to 2019, examining different sub-periods. Evidence indicated that 14 countries made strides in catch-up growth between 1999 and 2019. The correlations were also calculated in four periods: 1980–1989, 1990–1999, 2000–2009, and 2010–2019. In the first period, only Egypt and Morocco showed progress in catching up. During the second period, nine countries experienced catch-up, while six lagged behind. The third period saw substantial improvements in the catch-up across most countries, with the exception of the UAE. In the fourth period, most countries continued on a catch-up trajectory. The analysis revealed that capital deepening played a crucial role in the performance of 11 countries. The human resource participation was positive in most countries but was negative in Iraq, Syria, and Saudi Arabia—although overall, its contribution was less significant than capital deepening. None of the countries demonstrated a positive contribution of productivity, and there was no evidence of productivity growth supporting performance improvements, leaving a considerable gap compared to the United States (as an ideal example). Furthermore, the catch-up patterns of large economies mirrored those of smaller economies.IntroductionAmong the various explanations for the surge in per capita income growth during the quarter-century following World War II, the most prominent hypothesis is that industrialized Western countries were able to produce a large amount of unused technology. Most of these technologies included methods of production and systems of industrial and commercial organization that were already established in the United States but had yet to be widely adopted in other Western countries. According to the hypothesis, the United States is viewed as a leader, while other nations are followers with the opportunity to catch up. Following this perspective, the loss of catch-up opportunities is often cited as a reason for lagging behind concerning the per capita income in the follower countries. The concept of catch-up and follow-up suggests a broader hypothesis stating that per capita income levels among countries tend to converge. However, catch-up and convergence are distinct concepts. To address the issue, the present study tried to answer the following questions: How has the gap in per capita income among MENA countries evolved over recent decades? And is the catch-up phenomenon evident in these nations? At first glance, distinguishing between these questions may seem challenging, as both involve a progress toward more equitable living standards among countries—often referred to as convergence. This article, therefore, sought to assess the convergence and catch-up in MENA countries, shedding light on the factors driving the catch-up.Materials and MethodsThe first section of the methodology was to clarify the distinction between convergence and catch-up. It specifically examined how differences in growth rates across countries influenced both the average GDP per capita relative to the United States and the Mean Log Deviation (MLD) over time. The MLD reveals how variations in average growth relative to the United States, along with the distribution of growth rates among countries, impact these two metrics.To evaluate the catch-up performance over a given period, we defined the Catch-Up Index (CUI) as follows:(1) Where represents for the relative per capita income of country in year compared to the United States, which is defined as follows: (2) Note that and denote the per capita income of country and the United States in year , respectively, measured based on purchasing power parity (PPP) at constant prices. According to this definition, if the index is positive ( ), country has experienced catch-up; If the ( ) index is negative ( ), country has lagged behind. Moreover, if the index is zero ( ), country has neither caught up nor lagged behind. To analyze the factors driving the catch-up, the study employed the analytical framework of GDP growth developed by Jorgenson et al. (2005), as presented below: 3) Thus, the CUI of a country can be divided into three components, representing its performance relative to the United States across three sources of per capita growth:: The difference in the capital deepening rate.: The difference in the labor force participation rate.: The difference in total factor productivity (TFP).Results and DiscussionIn Figure 1, the left axis displays convergence, shown by the MLD of GDP per capita (blue line). The right axis displays catch-up, measured by the average GDP per capita of 15 Middle Eastern countries relative to that of the United States (red line). Note that GDP per capita is measured at constant 2017 prices based on purchasing power parity. The graph clearly demonstrates that convergence and catch-up are distinct concepts. A comparison between 1980 and 2019 revealed significant convergence, as the dispersion of per capita income across these countries in 2019 (MLD = 0.40) was significantly lower than in 1980 (MLD = 1.07). However, no catch-up was observed during this period, as the average per capita income of these countries relative to the United States declined from 1.12 in 1980 to 0.47 in 2019.Figure 1. Convergence and Catch-Up in MENA Countries Source: Research resultsRefer to Table 1 for further clarity. Concerning the entire 40-year period as a whole, the results indicated both convergence and a relative lagging behind.Table 1. Convergence and Catch-Up1980-20192010-20192000-20091980-1999 ConvergenceSlight convergenceConvergenceConvergenceDispersion among countriesLagging behindLagging behindCatch-upLagging behindGaps with the USASource: Research resultsEvidence suggests that capital deepening played a significant role in the performance of the 11 countries that experienced catch-up. In contrast, the impact of human resources appears to be less pronounced compared to that of capital deepening. Table 2. Drivers of Economic Catch-Up (1980–2019)Catch-up components (Share)Catch-up components (amount) Total factor productivityHuman resource participationCapital deepeningTotal factor productivityHuman resource participationCapital deepeningCatch-up Index (CUI)Country-166.012.7253.3-6.140.479.363.69Egypt-122.346.5175.8-2.961.134.252.42Lebanon-108.028.4179.6-1.60.42.61.4Iran22.7-2.679.90.26-0.030.91.13Iraq-784.4290.1594.3-7.962.956.031.02Oman-833.059.7873.3-69.10.497.240.83Morocco-702.7196606.7-4.41.23.80.6Jordan-706.9-94.0900.9-3.1-0.413.950.44Syria-204.75201.41946.1-6.40.616.10.31Tunisia-2583.01072.02610.8-4.571.373.330.13Bahrain-10541.33290.67350.7-4.691.463.270.04Qatar5412.4-3782.5-1529-9-2.631.840.74-0.05Kuwait199.136.1-135.2-1.16-0.210.79-0.58Saudi Arabia178.9-80.31.4-3.491.57-0.03-1.95Algeria18.1-5.687.5-0.860.26-4.14-4.74The EmiratesSource: Research resultsConclusionThe findings showed that MENA economies are heavily reliant on capital accumulation and deepening, with capital deepening serving as the primary driver of economic growth. This trend has continued for over 40 years and is likely to persist. There is no evidence to suggest that productivity growth has significantly contributed to the performance of MENA countries, leaving them substantially behind the United States in this respect. Additionally, the catch-up patterns seen in larger economies closely resemble those of smaller economies. To enhance economic stability and improve future prospects, it is essential for these countries to prioritize investment, increase the involvement of skilled labor (especially in knowledge-based industries), and gradually make a transition toward a knowledge-based economy. Producing more complex goods and fostering regional collaboration to reduce political tensions and economic risks are also crucial steps. By adopting these strategies, MENA countries can bolster their catch-up and pave the way for sustainable economic development in the future.
