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.
Financial Economics
Gholam Reza Keshavarz Haddad; Iman Sharifi
Abstract
The book-to-market ratio is known as an anomaly variable in the financial literature. This variable has a high explanatory power in predicting the returns of companies in different capital markets across world; But understanding why it has the power to explain is still a matter of debate. In this study, ...
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The book-to-market ratio is known as an anomaly variable in the financial literature. This variable has a high explanatory power in predicting the returns of companies in different capital markets across world; But understanding why it has the power to explain is still a matter of debate. In this study, we seek a clear understanding of the explanatory power of the ratio of book-to-market ratio in explaining the annual return of cross-sectional data of stocks on the Tehran Stock Exchange. Book value can be divided into two parts: retained earnings and contributed capital, which have different economic meanings for readers of financial statements. Our hypothesis is that the predictive power of the book-to-market ratio arises from a component of book value that could be a good proxy for underlying earnings yield. Using the method of Fama and Macbeth (1973), we regress the annual return of cross-sectional data of companies listed on the Tehran Stock Exchange for the years 2001-2019 on the ratio of book-to-market ratio and its two components. Neither component of book-to-market ratio could eliminate the predictive power of this ratio; however, the ratio of retained Earnings-to-market ratio could show predictive power along with the book-to-market ratio. We contribute to the literature by providing additional evidence from Tehran's Stock Exchange.1- IntroductionThe book-to-market ratio is known as an anomaly variable in the financial literature. It has appeared as a key explanatory variable with high explanatory power in predicting the returns of firms in capital markets across the world, however, understanding the mechanism through which this financial factor functions and its origin of the explanatory power is still a matter of research debates. Empirical researches on the returns and “book to market value” can be divided into two strands. The first group aims to examine the existence of abnormal returns on the ratio of "book to market value" in the stock markets. This stream of works aim to answer the question of whether the "book to market value" is able to predict companies' returns in capital markets or the returns is caused by other sources including random noise. Rosenberg et al. (1985) show, for instance, that in the US capital market, the strategy of the "book to market value" can yield abnormal returns for investors. In terms of this strategy, at the beginning of each month, the shares with a high "book to market value" are bought and the shares that have a low "book to market value" ratio are sold. A relationship between the ratio and average stock returns for the period 1981-1981 in the capital markets of Switzerland, France, Germany and the United Kingdom has also been observed by Coppole, Rollie and Sharp (1992). The second stream of studies on the "book to market value" seeks to understand the cause of its explanatory power. This issue is an active research area and is still subject of discussions and has been studied from various aspects. One of the most highly cited of them is Fama and French (1993), which attributes high returns in stocks with a higher magnitude of "book to market value", to higher systematic risk. In contrast, Daniel and Titman (1997) introduces the hypothesis of equity characteristics and by providing empirical evidence argues that the returns premia on high book-to-market stocks does not arises because of the co-movements of these stocks with pervasive factors. It is the characteristics of the share rather than the covariance structure of returns that appear to explain the cross-sectional variation in stock returns. So, these are not associated with greater risk tolerance. Ball, Gerakos, Linnaeus, and Nikolaev (2020) examines the "book to market value" through its components (retained earnings and contributed capital) in the US capital market. He argues that the ability of "book to market value" to predict the cross-sectional returns is not because of its intrinsic information contents, but it appears as an appropriate proxy for the actual profitability of the firms, because, the retained earnings component of the book value of equity includes the accumulation and, hence, the averaging of past earnings, instead the contributed capital-to-market has no predictive power. HypothesesWe contribute to the literature by providing additional evidence from Tehran's Stock Exchange. Our study aims to provide further evidence to clarify explanatory power of the ratio in predicting the variations of annual returns in cross-sectional data for stocks in the Tehran Stock Exchange. Our hypothesis is that the predictive power of the book-to-market ratio arises from a component of book value that could be an appropriate proxy for underlying earnings yield. Data and Identification methodologyWe use the annual returns and financial statements of all shares traded from the beginning of 2001 to the end of 2020 in Tehran Stock Exchange. Annual returns are calculated from price data recorded and reported in the “tseclient” software and accounting data are downloaded from “codal.ir” website. In this research, financial companies listed in the TSE have not been included in our working sample due to their special nature. Because, by nature of their activities, they have high financial leverage, which is normal for companies active in the financial field. The characteristics might be interpreted as a financially critical situation, whereas, the it is not so for firm that are active in financial fields. The information extracted from the financial statements is matched with the annual return of 1 month after the end of the financial year. The reason for this identification strategy is to make sure that the published financial information affects the share price. For example, if the company's financial year is at the end of March, we will assume that this information was available to the public at the end of April. Findings Following the statistical method of Fama and Macbeth (1973), we regress the annual return for cross-sectional data of companies listed on the Tehran Stock Exchange over the years 2001-2019 on the ratio of book-to-market ratio and its two components as well. Neither component of book-to-market ratio could eliminate the predictive power of book-to-market; however, the ratio of retained Earnings-to-market ratio could show predictive power along with the book-to-market ratio. Table (1) reports the Fama and Macbeth (1973) regressions in which, outcome of interest is returns and determinants of the regression are the log of "Book to Market Value", log of " Retained Earnings to the Market Value " and log of "Contributed Capital to Market value". We include a few controlling variables that are identified theoretically as determinants of returns.Table(1): Contributed Capital and Retained Earnings in the Fama and Macbeth Regression(1)(2)(3)(4)(5)(6)Variables-0.129**-0.128**-0.116**-0.0901**-0.126**-0.103**Log(Market Value)(-2.680)(-2.762)(-2.492)(-2.474)(-2.257)(-2.228)0.498** 0.210** 0.508** Log( Book-to-Maket)(2.744) (2.471) (2.342) 10.53***8.557** 9.914**Log(Retained Earnings to market Value) (2.992)(2.426) (2.890) 0.371***0.004060.255***Log(Contributed Capital) (3.446)(0.0438)(3.343) 0.619***0.560*** 0.415**Binary if profit>0 (3.382)(3.058) (2.256)2.973**-19.64***-15.47**2.429**2.959**-18.34**Constant(2.731)(-2.907)(-2.272)(2.825)(2.534)(-2.806) 3,7943,7943,7943,7943,7943,794#OBS0.1210.1440.1880.0990.1350.189R-Square212121212121# Groups*** p<0.01, ** p<0.05, * p<0.1, t-stats in parenthesisNote: the firms fixed effect regression over 2001- 2021 across 181 firms are reported in the columns. Contributed capital includes all of the book value accounts except retained earnings. Column (1) shows the regression of annual stock returns on the logarithm of "book to market value" in the presence of a control variable, logarithm of market value. The estimated coefficient for "logarithm of book to market value" equals to 0.498 with t-statistic t = 2.74, which is statistically significant at 5 percent critical region. The result is in the same direction with those in previous studies on the "book to market value". In column (2), "logarithm of retained earnings to market value" has been replaced for "logarithm book to market value". The coefficient of " logarithm of retained earnings to market value" is equal to 10.53 and is statistically different from zero at the 1 percent significance level with the estimated t = 2.99. In column (3), two variables "logarithm of book to market value" and "logarithm of retained earnings to market value" are included in the model. The coefficients of "logarithm of retained earnings on market value" and the "logarithm of book to market value" are significant at the conventional significance level. It suggesting that, "logarithm of book to market value" and "logarithm of retained earnings market value" are not able to fully represent the information contained in their competitors, as determinants of the firms' annual returns.The columns (4) and (5), report similar regressions by substituting "logarithm of contributed capital to market value" in place of "logarithm of retained earnings to market value". Once, we include this determinant alone, it significantly impacts (coefficient 0.371 with t = 3.446) annual returns, but if we add "logarithm of book to market value", to the specification "logarithm of contributed capital on market value" loses its significance and its t statistic drops to 0.0438. Meanwhile, the "logarithm of book to market value" remains significant at the 5 percent level. In the column (6), in addition to the "Book to market Ratio "we keep both "logarithm of retained earnings to market value" and "logarithm of contributed capital to market value" in the specification. The coefficient of "logarithm of retained earnings to market value" remains almost with no tangible change 9.914 with and significant, and the coefficient of "logarithm of contributed capital on market value" is appears significant as well.The inability of "logarithm of retained earnings to market value" to absorb the effect of "logarithm of book to market value" can be due to the weakness of this financial account in representing the companies' profitability information. This might originates in the fact that the retained earnings account is not an appropriate representative of the company's profitability. More specifically, this account is the balance of profits that have not been distributed among investors, it is not representative of all the company's acquired profits, and in each period that: (1) the company distributes profits among investors or (2) transfers an amount from this account to another account in equity, a part of the information in the accumulated profit will also be removed from this account. Consequently, this account cannot contain all the profitability information of the company. When the company distributes profits to shareholders, the company's profitability information is removed away from both the retained earnings balance and the book value. For this reason, we simply return the amounts transferred from the retained earnings account to other equity accounts to the retained earnings account and define the adjusted retained earnings account and the adjusted contributed capital as follows:Adjusted retained earnings = retained earnings + legal reserve + plan and development reserve + other reserves + total capital increase from retained earnings until the end of the reported year + total other transfers from retained earnings until the end of the reported yearAdjusted Contributed Capital = Equity - Adjusted Retained EarningsAdjusted retained earnings is the balance of all profits earned by the company during its life and not withdrawn from the company. The adjusted contributed capital is equal to the book value minus the adjusted retained earnings. To test our hypothesis, we separated "book to market value" into two parts (1) "adjusted retained earnings on market value" and (2) "adjusted contributed capital on market value". The significance level of the coefficient of "book to market value" decreases when it is included in the model beside to "adjusted retained earnings to market value", in contrast to the specification that includes the "retained earnings to market value", however, the coefficient of "book to market value" is still significant at the 5 percent significance level. The significance of the coefficient of "adjusted retained earnings to market value" also improves, in comparison to all similar regressions in which unadjusted "retained earnings to market value" are used as determinant. All in all, this evidence shows that a part of the information in "book value to market value" is caused by a variable that is related to the company's profitability, but not all the information in "book to market value" is caused by the company's profitability.
Gholamreza Keshavarz Haddad; Esmaiel Abounoori; Tahereh Jahani
Abstract
The IMF reports that, over 60% of foreign trade income and 40% of government revenue of Iran comes from the oil and gas sectors, which has always been a source of volatilities in the economy. The imposed sanctions on the Iranian economy also influence economic activities by reducing currency earnings ...
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The IMF reports that, over 60% of foreign trade income and 40% of government revenue of Iran comes from the oil and gas sectors, which has always been a source of volatilities in the economy. The imposed sanctions on the Iranian economy also influence economic activities by reducing currency earnings and restricting access to capital and intermediaries goods. Understanding extent of the shocks’ effects of sanctions and fluctuations in oil revenue, are key factors for the policymakers in foreseeable planning risks. In order to examine the effect of sanctions and oil price(revenue) fluctuations on the country's economy, this paper intends to quantify the effects of sanction by making use of a VARMAX GARCH-in-Mean Asymmetric BEKK model in terms of structural failure of the conditional variance. We use real non-oil GDP, Iranian heavy oil exports, exchange rates, total stock market index and sanctions index data over 1991:Q2 to 2018:Q1. The results show that a shock of oil revenue or sanctions index affects activities in all of three sectors. The increasing sanctions pressure leads to a spillover effect of uncertainty to all sectors under study and a decline in production activities and national currency depreciations; but in turn the relative share of the stock market in the portfolio of investors' choice increases. Strong evidence for asymmetric effects of impulses of sanction and oil revenue on the study sections is observed.
Gholam Reza K. Haddad; Shahla Ojaghi
Volume 19, Issue 60 , October 2014, , Pages 67-99
Abstract
This article intends to examine the time allocation of children among Iranian households. It is well established that parents have different attitudes towards male and female children’s wellbeing. By making use of mother’s bargaining power and households’ expenditures and income survey ...
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This article intends to examine the time allocation of children among Iranian households. It is well established that parents have different attitudes towards male and female children’s wellbeing. By making use of mother’s bargaining power and households’ expenditures and income survey (1390), we develop a bivariate probit procedure to model the children’s time allocation between work and going to school. Our finding confirm that mother’s bargaining power has positive and significant impact on the schooling decision for both male and female children, however magnitude of its marginal effect is larger for female than male children.
Gholam Reza K. Haddad; Mahboobeh Kabiri-Renani
Volume 18, Issue 57 , February 2014, , Pages 97-124
Abstract
This research investigates demand for child among Iranian urban households in an intra-household bargaining decision process. Using Household’s Expenditures Survey of Iran(2008), a count regression technique which takes into account the over-dispersion and under-dispersion characteristic of Poisson ...
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This research investigates demand for child among Iranian urban households in an intra-household bargaining decision process. Using Household’s Expenditures Survey of Iran(2008), a count regression technique which takes into account the over-dispersion and under-dispersion characteristic of Poisson regression is specified as a function of intra-household bargaining factors, extra-household environmental factors, and family’s characteristics. Findings confirm the significance of extra-household environmental factorsand household’s characteristics in demand for child. Mothers with higher opportunity cost of child caring, more particularly college educated women, tend to have less children and they substitute quality of children for their quantity. As the Mothers’ bargaining power goes up, their propensity to bring more children decreases, however fathers with higher non-labor earning prefer to have more children. Diagnostic checking confirms accuracy and appropriateness of the Generalized Poisson against its alternatives. To examine the exogeneity of the explanatory variables we re-estimated the proposed specification with Generalized Method of Moments (GMM), where the hypothesis of exogeneity is confirmed. Further robustness checking by Negative Binomial distribution of dependent variable and specifying the models by mothers’ age disaggregation show that the sign and significance of estimated coefficients are similar to those of the Generalized Poisson and GMM; however modest changes have been experienced in the magnitude of estimated coefficients.
Gholamreza Keshavarz Haddad; Mohammadreza Esfahani
Volume 18, Issue 56 , October 2013, , Pages 1-40
Gholamreza Keshavarz Haddad; Arash Alavian Ghavanini
Volume 17, Issue 53 , February 2013, , Pages 101-133
Abstract
In the recent years, females’ literacy rate has experienced a drastic improvement in Iran; accordingly a remarkable increase in their participation rate is reasonably expected. Education is considered as one of driving forces of females’ presence in the labor market; however they ...
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In the recent years, females’ literacy rate has experienced a drastic improvement in Iran; accordingly a remarkable increase in their participation rate is reasonably expected. Education is considered as one of driving forces of females’ presence in the labor market; however they are likely to be paid less than men in some occupations or activities. This phenomenon is called gender wage gap in the labor market literature. This wage gap occurs when a worker is paid less than her counterpart with the same level of marginal product. This study provides quantitative estimates of the gender gap in the labor market of Iran. To this end, using data on household income and expenditure, from 2005 through 2011, and applying the Oaxaca (1973) and Blinder (1973) wage decomposition, the wage differential between men and women that cannot be explained by human capital characteristics, is estimated as an indicator of gender wage discrimination. The results reveal the existense of the gender wage discrimination in the labor market of Iran, but its magnitude in professional job groups is less than low skilled jobs. Furthermore, the wage differentials in the private sector are much higher than the public sector.
Gholamreza Keshavarz Haddad; Seyed Babak Ebrahimi; Akbar Jafar Abadi
Volume 16, Issue 47 , July 2011, , Pages 129-162
Abstract
Long memory in asset returns and volatilities is a new research area, both in theoretical and empirical modeling of high frequent financial time series. The most popular techniques of time series modeling with long memory is the ARFIMA-FIGARCH, but this fractionality in the integration of time series ...
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Long memory in asset returns and volatilities is a new research area, both in theoretical and empirical modeling of high frequent financial time series. The most popular techniques of time series modeling with long memory is the ARFIMA-FIGARCH, but this fractionality in the integration of time series modeling has not been extended to the Multivariate GARCH models yet. The present paper aims to extend the BEKK’s MGARCH models to take into account the presence of long memory in daily financial time series. Although the proposed procedure is highly non-linear in the fractionality parameters with a serious computational burden, it estimates all the parameters of mean and variance equations in a nonlinear framework and finds a unique solution, by numerical optimization procedures. In the empirical part of the paper a multivariate FIGARCH is used to check the transmission of volatility among the automobile industry, machinery leasing and equipment indices in the Tehran Stock Exchange. The results confirm the existence of short memory in both conditional means and conditional variances, and moreover the magnitude of estimated d parameter is remarkably different from those of resulted from GPH and single ARFIMA-FIGARCH. Empirical findings of the MFIGARCH specification were compared with those of BEKK, and the comparison shows that MFIGARCH estimations are consistent with theoretical considerations. Moreover, our findings confirm the presence of lead and lag effects and information flow between the returns and volatilities of automobile industries and machinery leasing stock prices, and a multilateral information transmission from machinery leasing’s stock towards the Auto industry and machinery parts manufacturing share prices is observed.
Gholamreza Keshavarz Haddad; Mohammad Rezaei
Volume 15, Issue 45 , February 2011, , Pages 103-137
Abstract
this paper following Lakonishok (1992) and Sias (2004), we examine the presence of herding behavior among active institutional investors, test the presence of momentum strategy (as a determiner of herding behavior) and the correlation between herding behavior and weekly, monthly and quarterly stock return, ...
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this paper following Lakonishok (1992) and Sias (2004), we examine the presence of herding behavior among active institutional investors, test the presence of momentum strategy (as a determiner of herding behavior) and the correlation between herding behavior and weekly, monthly and quarterly stock return, using time series data (2006 – 2008) in the Tehran Stock Exchange. Our findings confirm the presence of herding behavior among the institutional investors and show that its intensity is higher than the developed countries, but they reject the presence of momentum strategy and its role as a determiner of herding behavior. Furthermore, the results show that the herding behavior of institutional investors does not affect the market return and has no correlation with the past and future returns.
Gholamreza Keshavarz Haddad; MohamadReza Satari
Volume 15, Issue 44 , October 2010, , Pages 135-171
Abstract
Following the fisher’s hypothesis about the relationship between asset returns and inflation, numerous studies have tried to test the hypothesis with various data sets. Contradiction in the findings resulted to the proxy hypothesis of Fama (1981). In present article, survey the theoretical and ...
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Following the fisher’s hypothesis about the relationship between asset returns and inflation, numerous studies have tried to test the hypothesis with various data sets. Contradiction in the findings resulted to the proxy hypothesis of Fama (1981). In present article, survey the theoretical and empirical literature, and conduct a test for inflation hedging ability of land, gold and stock in Iran. Considering the seasonal characteristics of the data (1385-1355), we use the HEGY (1990) unit root test, and VECM methodology to estimate long and short run relationships. Our findings show that in the long run, all three types of assets hedge against inflation. However, in the short run, we observe that money reserve, oil prices and real GDP are significant determinants of the assets returns.
Gholamreza Keshavarz Haddad; Hamed Mortezazadeh
Volume 14, Issue 42 , April 2010, , Pages 25-53
Abstract
Decreasing trend of gasoline real price accompanied with economic growth and increasing number of automobiles have resulted in high consumption of gasoline in Iran. The average growth rate of hidden subsidies in the recent years is amounted 8.5 percent, and the size of gasoline subsidies ...
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Decreasing trend of gasoline real price accompanied with economic growth and increasing number of automobiles have resulted in high consumption of gasoline in Iran. The average growth rate of hidden subsidies in the recent years is amounted 8.5 percent, and the size of gasoline subsidies has been more than government tax revenues for many years. The average growth rate of gasoline consumption has also been 10 percent in recent years. ,. The huge financial burden of imported gasoline and the amount of required subsidies have made it indispensable to make a serious change in the pricing policy of oil products. The main purpose of this paper is to evaluate the distortionary effects of gasoline price control on output, employment, prices, wages and income distribution, using a CGE model. Our findings show that, a 100% increase in the gasoline price will result in the increase of consumer’s and product’s prices and wage in short run, but it will decrease households’ income and the price of capital in the long run.
Gholamreza Keshavarz Haddad; Syed Hassan Maanavi
Volume 12, Issue 37 , February 2009, , Pages 155-177
Gholamreza Keshavarz Haddad; Mohammad Mirbagheri jam
Volume 9, Issue 32 , October 2007, , Pages 137-160
Abstract
The residential and commercial sectors are the main consumers of natural gas in Iran. The demand for natural gas in these sectors is on its peak in the cold seasons for the heating. In addition to changes in temperature, which is the main determinant of demand fluctuation in energy, other factors such ...
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The residential and commercial sectors are the main consumers of natural gas in Iran. The demand for natural gas in these sectors is on its peak in the cold seasons for the heating. In addition to changes in temperature, which is the main determinant of demand fluctuation in energy, other factors such as unobservable seasonal shocks affect the seasonal demand. Moreover, observable economic factors such as price and income, as well as non economic factors, like changes in consumer’s taste and technical progresses, affect the energy demand. In this paper, we use an applied structural time series model (STSM) approach, which considers both stochastic trend and stochastic seasonality, to estimate the price and income elasticities for the natural gas demand in Iran. We apply the kalman filter with a maximum likelihood estimation method to provide unbiased estimators for the parameters. According to our results, although the estimated demand for natural gas does not have a trend component, the nature of seasonal component is stochastic. The elasticity of demand with respect to temperature is -0.26 percent and the long-run income and price elasticities are 0.17, -0.13, respectively.
Gholamreza Keshavarz Haddad
Volume 6, Issue 21 , February 2005, , Pages 115-133
Abstract
In this Paper financial services in the Iranian economy, which is portioned into 41 industries, is considered as a sector with a Leontief production function. Financial Sectors, buy inputs from other sectors, and provide services to expedite the cash fellows and risk transformation for other sectors. ...
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In this Paper financial services in the Iranian economy, which is portioned into 41 industries, is considered as a sector with a Leontief production function. Financial Sectors, buy inputs from other sectors, and provide services to expedite the cash fellows and risk transformation for other sectors. The former relations are called Backward Linkage and the later relations through which the sectors provide output to meet required cash fellow and risk reduction of economic activities are called Forward Linkage. In quantification of the linkages, employment and output elasticity, the forward index of value added, and backward linkage of final demand are used. Furthermore, direct and indirect effects of a hypothetical extraction of the sectors on output and employment are calculated. The results show that, although the linkages between financial sectors and the rest of the economic activities are not strong, their extraction will result in 225246.8 decrease in job opportunity, according to the 1370 input-output table produced by the Statistical Center of Iran.
Gholamreza Keshavarz Haddad
Volume 6, Issue 18 , April 2004, , Pages 39-56
Abstract
Creating job opportunities for job seekers, who experience a growth of more than half a million per year, has become a crucial problem for Iranian policy makers. Estimations depict that on average the growth rate of labor will amount to 3.3 percent during 1381-1385. Also, continuation of current ...
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Creating job opportunities for job seekers, who experience a growth of more than half a million per year, has become a crucial problem for Iranian policy makers. Estimations depict that on average the growth rate of labor will amount to 3.3 percent during 1381-1385. Also, continuation of current economic situations will result in a 21 percent unemployment rate. The purpose of this paper is to identify those economic sectors whose potentials in job creation are higher than others. Our analytical framework is Input-Output technique which consists of backward linkage and employment creation elasticity. Furthermore, the cost of creating a full-time job in terms of final demand is calculated. According to backward linkage, findings indicate that Social and Religious Services, Agriculture, Business, Education Services sectors are ranked top and of the lowest cost job creation, respectively. The highest cost of creating a full-time job belongs to the R&D sector with the amount of 184.8 million Rials, and the lowest to the Social Services with the amount of 9.14 million Rials.
