Samira Nasiri; Parviz Davoodi; Hosein Samsami; Hossein Tavakolian
Abstract
To achieve the optimal monetary policy, attention must be paid to a key element: the credibility of monetary policy authorities. Credibility plays a crucial role in the effectiveness of these policies, as it facilitates the attainment of targeted variables with minimal fluctuation and social cost. Conversely, ...
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To achieve the optimal monetary policy, attention must be paid to a key element: the credibility of monetary policy authorities. Credibility plays a crucial role in the effectiveness of these policies, as it facilitates the attainment of targeted variables with minimal fluctuation and social cost. Conversely, in times of unexpected economic shocks, discretionary policy becomes optimal, providing policymakers with flexibility to make appropriate monetary decisions.Conversely, in times of unexpected economic shocks, the use of discretionary policy is optimal because it provids policy makers with more flexibility to make appropriate monetary decisions. In this study, we pursued two goals. First, we constructed a Dynamic Stochastic General Equilibrium (DSGE) model for an open and small economy according to the conditions of Iran's economy. We then estimated the of credibility of Iran’s monetary policy authority in managing both the inflation rate and exchange rate yielding figures of0.1019 and 0.0099 respectively. Then, in order to find the optimal discretionary monetary policy under the influence of two scenarios of low and high credibility of the monetary policy authorities, we utilized the minimum loss function approach. The results of the model indicated that the credibility of the monetary policy maker in Iran is very low in both the inflation rate and the exchange rate. Examining the loss function of the central bank showed that, in both scenarios, minimizing the exchange rate gap while assigning equal weight to other objectives leads to the lowest possible loss function value.IntroductionThe optimal monetary policy is the maximization of welfare or the minimization of social loss of economic factors that are applied according to the constraints governing a society. A very important point that governments should consider when implementing their economic policies is the ability of central banks to influence key macroeconomic variables through the implementation of these policies. The key element in the effectiveness of these policies is the credibility of the monetary policy authorities. The credibility of the monetary policy authorities depends on various factors and components, the most important of which is his commitment and ability to achieve well-defined goals. From the point of view of central banks and economists, the credit of the policymaker is important because it helps to achieve the variables targeted by the central bank with the least amount of volatility and the least social cost. The credibility of the authorities is an exogenous variable that the politician cannot influence quickly because the credibility, depends on his past behavior and his success rate in achieving the goals that he has clearly set. In this study, for the first time in the studies conducted in Iran, monetary policy was considered discretionary, and with the DSGE model, we estimated the credibility of the policymaker in the two fields of inflation and exchange rate. The difference between this study and other studies conducted so far considering the discretionary policy is that they had always first considered a rule for policy making, and then, in a discretionary policy, the parameters estimated with the assumption of the rule have been used, but in this study, no rule has been considered for monetary and exchange rate policy. After estimating the credit value of the policy authority, we entered the credit values of monetary policy authority in the central bank's loss function, which is also done for the first time in Iran.In this research, we seek to answer the following questions:How much is the credit of the monetary policy authority in the field of inflation rate and exchange rate in Iran?Optimal monetary policy by considering the credit of the monetary policy authorities, in which weights will be obtained from the central bank's loss function?MethodIn this research, we used a DSGE model for an open and small economy and we tried to adjust all parts of the model according to the conditions of Iran's economy. Then by using the Bayesian approach, we estimated the values of the model's parameters, including the credibility of the policy authorities. At last, we used the loss function approach to obtain the optimal monetary policy.Results and DiscussionWe obtained the values of the variables using the Bayesian method. The obtained values for the credibility coefficient in the two fields of inflation rate and exchange rate were obtained as 0.1019 and 0.0099, respectively. In order to check the validity of the estimates, the Geweke (1992) test was used, which makes the calculations much less and faster than the Brooks & Gelman test. Then we used an identification test to determine the values of 𝛻 and ∇_e. Since these parameters could not be identified, we had to calibrate and analyze their sensitivity. The estimation results showed that the credibility of the monetary policy maker in Iran is low in both the fields of inflation and exchange rate and it is worse in the field of exchange rate.In pursuit of finding the optimal monetary policy in discretionary policy, by assigning different weights to the goals of the central bank, under two scenarios of low and high credit of the monetary policy maker, we try to obtain the minimum amount of the loss function. The results are reported in tables (1) and (2).In both scenarios, the examination of different weightings to the coefficients of the central bank's goals in the loss function shows that in the case where the monetary policy authorities give the higher weight to the reduction of the exchange rate gap and equal weight for all three other goals, the amount of loss function of the central bank is less and in the scenario of low and high credit of the policy authority is equivalent to 0.0030 and 0.0036 respectively. Perhaps the reason for this can be found in the oil structure of Iran's economy.ConclusionWe designed a DSGE model for Iran's economy and we estimated the amount of credibility of the monetary policy authority in two fields of inflation rate and exchange rate which are very low. Then, in order to find the optimal discretionary monetary policy under the influence of two scenarios of low and high credibility of the monetary policy authorities, we used the approach of the minimum value of the loss function. To further investigate the model, we analyzed the impulse and response functions for the main variables of the model to productivity shocks, government spending and oil price inflation in two scenarios. In all the input impulses, we found that the high credit of the monetary policy authority causes fewer fluctuations in the key variables of the model compared to the low credit of the policy maker. The results show that when the policymaker has a favorable reputation, the members of the society consider the effects of shocks to be temporary and do not anchor their decisions to it; therefore, even if the policy authority does not implement a specific policy to mitigate the effects of the shock, the variables will stabilize faster than when the credibility of the monetary policy maker is low. Table 1. Variance values of key variables and loss function in different weights to different objectives of the central bank based on the low credibility of the monetary policy maker in IranLoss function The growth rate of moneyExchange rateProductionInflation0.00570.01070950.0001080.0488740.0002550.0001210.1998800.5522740.00006111110.00320.0108780.0000370.0491810.0002390.00012380.2009630.5568540.0000710.510.510.00310.0108370.0001140.0490030.0002470.0001200.2004290.5524500.0000620.50.50.510.00320.0105980.0001360.0484430.0002710.0001240.1971120.5470300.0000300.50.510.50.00300.0108390.00002340.0490700.0002450.0001250.2004830.5571800.0000700.5110.5Source 1: research findingsTable 2. Variance values of key variables and loss function in different weights to different objectives of the central bank under the high credibility of the monetary policy maker in IranLoss function The growth rate of moneyExchange rateProductionInflation0.00710.0109100.0000200.0491810.0002520.0001250.2008670.5545190.00007511110.00430.0109920.0000070.0492830.0002430.0001240.2011880.5528290.0000860.510.510.00420.0109750.0000230.0492140.0002440.0001220.2010130.5503740.0000790.50.50.510.00400.0107380.0000280.0490630.0002700.0001280.1992800.5535860.0000280.50.510.50.00360.0109280.0000060.0492520.0002480.0001270.2010520.5570540.0000810.5110.5Source 2: research finding
Elham Farzanegan
Abstract
The information diffusion and interactions within financial markets have a significant impact on the price discovery process and the sentiment and risk dispersion. Despite its importance, limited research has been conducted on information flow dynamics within the Tehran Stock Exchange, which is ...
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The information diffusion and interactions within financial markets have a significant impact on the price discovery process and the sentiment and risk dispersion. Despite its importance, limited research has been conducted on information flow dynamics within the Tehran Stock Exchange, which is a vital component of Iran's capital market. This study aims to fill this gap by examining the information flow dynamics among 39 major industries from March 27, 2010, to June 21, 2023. Effective transfer entropy is employed to quantify the intensity of information flow between industry indices. Sequence of information matrices are constructed using rolling one-week windows over one-year periods. Given the occurrence of critical events during the research period, their influence on information flow dynamics is analyzed using Frobenius distance-based k-nearest neighbor networks, Influence Strength analysis, and threshold networks. The findings reveal that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable throughout most periods. Furthermore, critical events significantly impact information flow dynamics, with abnormal values of Influence Strength associated with market volatility and major events. Additionally, the dominant source of information in the information flow network changes over time, highlighting the transient nature of industry dominance within the network.IntroductionThe diffusion of information and interactions within financial markets greatly influences the price discovery process and affects sentiment and risk dispersion. The potential for growth in the Tehran Stock Exchange (TSE) through the introduction of innovative financial instruments can offer investors additional investment opportunities. Therefore, understanding the dynamics of information transmission within the market aids investors in decision-making.Existing literature suggests that stock price volatilities are interconnected, and stocks within the same industry often exhibit high correlations. Additionally, industry stock price indexes within the market can serve as leading indicators of economic activity. Analyzing the information flow network at the industry index level holds significant implications for investors, portfolio managers, and policymakers seeking to devise appropriate risk-mitigating strategies, especially industry sector rotation strategies.Despite the Tehran Stock Exchange being a vital component of Iran's capital market, there has been limited research on the information flow network between industries and its time-varying characteristics. Furthermore, despite significant events occurring during the specified sample period, there is a lack of empirical evidence regarding their impacts on information flow within the Tehran securities market.Methods and MaterialIn this research, the dynamics of information flow between the 39 major industries are investigated from March 27, 2010, to June 21, 2023. Following Ni (2023), the Effective transfer entropy that measures the intensity of information flow between industries indices is calculated. Then the sequence of information matrices is created by rolling a one-week calculation window. In this paper, the calculation window of 237-trading day widths and the rolling window of 5-day widths are used to calculate the information matrices of length 591. Moreover, using quantiles of return series, and , the information matrix sequences are constructed.Given that the research period encompasses critical events, their influence on information flow is examined using various methodologies, including the Frobenius distance-based k-nearest neighbor network, Influence Strength (IS) analysis, and a threshold-directed network of information matrices. Results and DiscussionUpon depicting the Frobenius distance matrix based on Q1, significant shifts in the distance between the information matrices are observed. These shifts often coincide with critical events that have impacted the market.The IS series graph over the research period reveals several local peaks. For some peaks, no significant events occurred during the research period. Peak 2, however, corresponds to severe market fluctuations and turmoil, primarily stemming from the global impact of the 2008 financial crisis. Additionally, this time window aligns with the initial period of oil and petrochemical sanctions against Iran, leading to a decline in the total index of the TSE. Peak 4 reflects a decrease in the TSE's total index following Iran's nuclear agreement with the P5+1 in 2015 (post-JCPOA). During peak period 5, coinciding with the US withdrawal from the JCPOA and the re-imposition of all US sanctions, the TSE's total index experienced a drop. Peaks 1, 3, and 7 correspond to the bursting of stock price bubbles in 2009, 2013, and 2020, respectively.The findings also highlight that the window corresponding to the maximum value of IS (0.1757) is from 31/12/2012 to 7/1/2014, coinciding with the bursting of the stock price bubble in January 2014. Peak 6 corresponds to the window from 19/7/2020 to 7/7/2021, which includes the early days of the COVID-19 pandemic. Lastly, from 1/6/2022 to 3/6/2023, the government's decision to abolish the preferential exchange rate for importing basic goods negatively affected the prices of some listed companies in the TSE and the indexes of related industries. Comparing the patterns of IS calculated based on Q1 vs. Q2 demonstrates the correspondence between the local peaks.On the other hand, examining the Financial industry (node 37), the series reached its peak during 2/2/2016-25/1/2017. During this period, the TSE faced a significant decline in the total index due to uncertainty caused by the JCPOA. Analysis of the directional network of the information matrix, filtered with a threshold of 0.01, reveals that in the post-JCPOA period, there is an information flow between the Financial industry and all other industries except the Furniture industry (node 20) and Peymankari industry (node 26).Furthermore, aside from node 37, which serves as the central node during this period, node 34 (Banking industry, deg = 34), node 39 (EstekrajeNaft industry, deg=33), and node 35 (SayerMali industry, deg =32) also exhibit high degrees. Additionally, the network constructed from the information matrix corresponding to peak 6 indicates several central nodes. However, during the time window corresponding to peak 6, node 24 (Daroee industry) with the highest (0.0105) exerts the strongest influence on the network.The results also demonstrate that for certain industries, such as the Pharmaceutical industry, the value of increased during the 19/1/2016-11/1/2017 period, corresponding to the post-JCPOA era. However, for other industries, the maximum value of occurred mainly during other critical periods, such as the stock price bubble bursts in 2010 and 2014 and the imposition of new sanctions against Iran..ConclusionThe findings indicate that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable over the majority of periods. Additionally, critical events have notably impacted the dynamics of information flow, with abnormal values of Influence Strength correlating with market volatility and significant events. Moreover, the primary source of information in the sequence of the information flow network evolves over time, suggesting that the dominant industry in the network is not consistently sustainable.
Yazdan Gudarzi Farahani; Omidali Adeli
Abstract
This study aims to investigate the relationship between currency crises and fluctuations in banking credits in Iran. Utilizing a time-varying coefficients approach spanning from 1989 to 2022, alongside economic boom and recession indicators, the analysis assesses the impact of currency crisis occurrences ...
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This study aims to investigate the relationship between currency crises and fluctuations in banking credits in Iran. Utilizing a time-varying coefficients approach spanning from 1989 to 2022, alongside economic boom and recession indicators, the analysis assesses the impact of currency crisis occurrences on banking credit cycles. The currency crisis index, based on a dummy variable, and the credit cycle index, derived from banking credit booms and busts, are examined alongside the economic cycle, gauged by production fluctuations using intermediate filters. Findings suggest that currency crises influence the occurrence of credit cycles and production facility cycles, while shocks stemming from economic cycles exacerbate currency crises and credit cycles within the banking system. Given the bidirectional relationship observed between the currency crisis index and credit cycles, policymakers are advised to exercise caution in implementing drastic measures during economic fluctuations and credit cycles. Prudent management of currency markets can mitigate the adverse effects of currency crises on economic variables.
Introduction
Financial crises have profound and far-reaching implications, encompassing economic, political, and social spheres. They exact a heavy toll on society, manifesting in reduced welfare, heightened unemployment, and diminished public trust. Given their extensive repercussions across various sectors, financial crises have garnered considerable attention from economic policymakers. Among the diverse forms they take, currency crises stand out as particularly significant. These crises, marked by sudden depreciation or robust intervention by monetary authorities to bolster national currency values through foreign exchange reserve sales, exert widespread influence across the economy. They precipitate pressures on consumers, producers, and central banks, disrupting market dynamics for other assets and impinging on monetary policy frameworks. Moreover, they adversely impact credit allocation within the banking system, underscoring their multifaceted ramifications.
The main question investigated in this article pertains to the interplay between currency crises and credit cycles during the economic upswings and downturns in Iran. Given the nuanced nature of credit cycle delineation, coupled with the fluctuating dynamics of economic expansions and contractions, the vector autoregression (VAR) approach with time-varying coefficients has been employed to examine the evolving dynamics of this relationship spanning the period from 1989 to 2022. This methodological choice is motivated by its ability to yield more realistic findings, accounting for the temporal variability of coefficients and the dynamic interrelationships among variables. This contrasts with traditional time series models and conventional VAR frameworks, thereby enabling the formulation of more informed policy recommendations.
Methods and Material
In this study, credit cycles and currency crises spanning the period from 1989 to 2022 were extracted using the Cristiano and Fitzgerald filters. The relationship between these components and economic expansions and contractions was then explored. Additionally, the dynamic interplay among these variables was assessed using the vector autoregression method with time-varying coefficients (TVP-VAR). The study incorporates four primary variables: the currency crisis index, credit cycle, economic boom and recession periods, and liquidity growth. To compute the currency crisis index, a virtual variable was employed. The data utilized in this research were sourced from the Central Bank's database and statistical quarterly reports.
Results and Discussion
The findings from the TVP-VAR model reveal several significant dynamics. Initially, in response to a shock from the credit cycle, liquidity growth displays a positive reaction, with the impact dissipating over the long term. Conversely, the currency crisis initially reacts negatively to the credit cycle shock but eventually exhibits a positive response, indicating that the creation of the credit cycle contributes to the occurrence of currency crises. The economic cycle, when shocked by the credit cycle, responds negatively. On the other hand, when the credit cycle is shocked by the currency crisis, it initially reacts positively, followed by a subsequent negative reaction, with the long-term effect dissipating. Liquidity growth, in response to the currency crisis shock, demonstrates a positive reaction. Regarding the economic cycle, its response to the currency crisis shock is initially negative, then positive, and eventually negative again, suggesting that currency crises give rise to periods of economic expansion and contraction. In another aspect, the shock from the credit cycle prompts a positive reaction in liquidity growth, while the currency crisis variable responds negatively to the credit cycle shock. Initially, the credit cycle variable reacts positively to the credit cycle shock, but over time, it turns negative, with the impact fading in the long run. Finally, in response to the shock of liquidity growth, the currency crisis variable shows a positive reaction, the economic cycle reacts positively, and the credit cycle also responds positively, with the effect of the shock diminishing over time..
Figure 1. IRF diagram in TVP-VAR model format
Conclusion
The findings from this study highlight the adverse impact of currency crises on the economic cycle, leading to periods of recession. Conversely, economic downturns can exacerbate currency crises. Based on these results, it is advisable for monetary authorities and central banks to refrain from implementing contractionary policies, particularly in foreign exchange policies and credit restrictions, during economic downturns. Instead, they should expedite the process of foreign exchange allocation to economic enterprises for purchasing production inputs, thereby fostering an environment conducive to improving production and stimulating economic growth. Furthermore, during credit crises, commercial banks are encouraged to increase credit limits for commercial enterprises and streamline the loan repayment process in the micro-finance sector. These measures aim to prevent the economy from slipping into recession and promote sustainable economic development.
Hodjatollah Mirzaei; Narges Razban; Teymor Mohamadi; Habib Morovat
Abstract
Housing price shocks of one region may spread to the housing market of neighboring regions or geographical areas bounded by political border and lead to the formation of price shocks in shock-receiving areas. The housing policies may not be effective when implemented regionally and separately ...
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Housing price shocks of one region may spread to the housing market of neighboring regions or geographical areas bounded by political border and lead to the formation of price shocks in shock-receiving areas. The housing policies may not be effective when implemented regionally and separately if there is a confirmed network connection between the housing markets of regions. Price shocks to a housing market spreads with a delayto interconnected housing markets, ultimately resulting in the diffusion of the price shock across the entire of the housing network. This researchaims to investigate the housing network between selected cities (centers of the country's provinces) using the VAR model and Forecast Error Variance Decomposition (FEVD). The results of this research confirm the existence of a network connection between the housing markets of the country's provinces, and unlike previous studies, the results show that it is not only the city of Tehran that spreads price shocks to other regions, but also cities such as Karaj, Shiraz, and Arak spread price shocks to other cities. In addition, the results suggest that the recent price jump, since 2019 has significantly increased the density of the housing network in the country. Based on this, price shocks are expected to be distributed more quickly throughout the country.
Introduction
In addition to the fact that economic characteristics, macroeconomic policies, and external factors affect housing prices, housing price shocks in neighboring geographical areas also spread to housing prices in each region and can lead to the formation of price changes in the price-accepting region. Therefore, it is essential to investigate the network connection between the housing markets of the geographical regions within a country. This research aims to explore the network connections and dynamics between housing markets in provincial centers, as well as the relationships between all pairs of centers to form a comprehensive housing market network for the country. Specifically, the study seeks to identify: (a) the centers of the provinces whose housing price disturbances are most contagious to other provinces and (b) the centers of the provinces that are most affected by the housing price disturbances of other provinces should be identified.
Methods and Material
The study utilized data from the Statistics Center spanning period from 2009 to 2011.
The research methodology employed the vector autoregression (VAR) model. To address the identification problem in the model, the centers of the provinces were classified into four groups:
1: Tehran, Alborz, Mazandaran, Isfahan, Gilan, Khorasan-Razavi, Qom, Qazvin and East Azerbaijan.
2: Fars, Khuzestan, Golestan, Hormozgan, Bushehr, Zanjan and Hamedan.
3: Semnan, Yazd, Lorestan, North Khorasan, Kerman, South Khorasan, Kohgiluyeh and Boir Ahmad, Markazi and Kurdistan.
4: West Azerbaijan, Ardabil, Ilam, Kermanshah, Sistan and Baluchistan, and Chaharmahal and Bakhtiari.
In the network connection approach proposed Diebold and Yilmaz (2014), the vector autoregression model or VAR has been used.
In a country with three geographical regions A, B and C:
(1)
The VAR system of equations has three equations for housing prices in areas A, B, and C. The housing price in each region such as A at the current time (t) is a function of the price of the same region in previous periods ( ), and the price of other regions in previous periods ( and ) (k=1, 2, .. K). The effectiveness of the price of region A from the price of the same region and regions B and C in the previous periods are measured by β-11k, β-21k, and β-31k coefficients, respectively. The number of optimal breaks in equation (1) is determined by the Schwartz criterion.
In order to form a network connection and to check the amount of shock propagation from region i to j, variance analysis of prediction error is used. In this regard, Diebold and Yilmaz (2014) introduced four indicators:
(1)Shock received from others: The shock received by each region from other regions
FC=
(2)Shock sent to others: The shock sent by each region to other regions
OC=
(3)Total connections per network: average total shock per region
TC=
(4)Net communication or NC: the net shock sent by any region to other regions
NC=
Correlation between regions based on variance analysis
Shock received from other areas
areas
Shock sent to other areas
Results and Discussion
The reliability test of Becker et al. (2007) was conducted for all provinces, which was found to be significant in all cases.
Based on the results of VAR model and variance analysis:
First group: Isfahan and Qom are the biggest receivers and Mashhad is the weakest recipient. Karaj and Tehran are the biggest senders of shocks and Qom and Isfahan are the weakest senders.
Second group: Gorgan and Hamadan are the most important and Bushehr is the weakest recipient. Shiraz and Zanjan are the most important shock transmitters , while Bandar Abbas and Bushehr are the weakest.
Third group: Semnan and Sanandaj are the most important shock receiver, and Bojnord is the weakest receiver; Arak and Yazd are the most important sender of shocks and Sanandaj is the weakest sender .
Fourth group: Ardabil and Kermanshah are the most important senders and receivers of price shocks, respectively. The calculation of the total communication index in the housing network shows that the first group has the densest and the fourth group has the thinnest housing network.
In order to investigate the evolution of the housing network (changes in density over time), the Galtan's regression logic was used indicating an increase in the density of the housing network in the centers of the provinces over time.
Conclusion
The dynamics of real housing price changes demonstrate three distinct patterns.. During the years 2009 to 2012, the price of most centers decreased and remained almost constant from 2012 to 2018, and then all the centers had a sharp price increase. As a result:
(1) Karaj, Tehran, Shiraz, Arak and Ardabil sent the most price shocks;
(2) Isfahan, Gorgan, Semnan and Kermanshah received the most price shocks,
(3) the strongest housing network was observed between the cities of Mashhad, Sari, Qom and Tabriz, Isfahan, Karaj, Tehran, Qazvin and Rasht and
(4) the housing network among the provincial centers has increased during the years (2009 to 2010).
In times when the living conditions in the cities that are significant senders the shock become difficult, other cities within the network have become centers of population attraction and can change their roles. Consequently, it is advisable for housing market policies to consider the mutual influence between city prices. By doing so, when market price jumps occur, the extent of shock transmission from these driving centers can be somewhat controlled, thereby mitigating market excitement and excessive growth in prices. As an example, the policies on the supply side can be such that the supply in the shock-sending areas is facilitated. Preventive measures such as supporting the supply of semi-finished units, facilitating the conditions for issuing permits, facilitating access to construction loans, activating pre-sale tools, etc., should be adopted in leading areas so that when price jumps occur, shocks will be sent from these regions to other regions at a slower rate. it is advisable for housing market policies to consider the mutual influence between city prices. By doing so, when market price jumps occur, the extent of shock transmission from these driving centers can be somewhat controlled, thereby mitigating market excitement and excessive growth in prices.
Acknowledgment
In the end, we would like to express our gratitude to Dr. Nasser Khiabani, Dr. Ali Nasiri-Aghdam, Dr. Mirhossein Mousavi, and Dr. Taleblo, for their invaluable contributions to this paper.
Shahryar Zaroki; Sahar Nasrnejad Nesheli; Niloufar Gorgani Firoozjah
Abstract
In any society, the focus of statesmen, policymakers, and researchers on poverty reduction and enhancing economic welfare is necessary. Given that most government economic policies affect relative prices and their fluctuations, which in turn impact welfare, analyzing the welfare effects caused ...
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In any society, the focus of statesmen, policymakers, and researchers on poverty reduction and enhancing economic welfare is necessary. Given that most government economic policies affect relative prices and their fluctuations, which in turn impact welfare, analyzing the welfare effects caused by price changes in different product groups seems necessary The aim of this study is to investigate the role of inflation of different commodity groups on Iran's economic welfare from 1972 to 2021. The research model is estimated using Autoregressive Distributed Lag (ARDL) approach. Economic welfare is measured using a composite index of well-being. The trend of the well-being index fluctuated during the study period reaching its peak in 1975 and its minimum in 1990. The long-term results of the estimation of the research model reveal several key findings:
First, total inflation and inflation of different groups of goods have an unfavorable effect on economic well-being.
Second, among different product groups, inflation in the housing, fuel, and lighting group has the most adverse effect on economic welfare.
Third, considering that the share and weight of the group of food, beverages and tobacco in the consumption basket of households is larger than other groups of goods, but the size of the effect of inflation of this group of goods on economic welfare is in the fourth place. Fourth, the increase in the inflation of the health and treatment group has the least adverse effect on economic welfare. Another finding is that per capita income and economic growth have a favorable effect on economic welfare. According to the obtained results, the government should take measures such as adopting appropriate measures in line with monetary discipline and preventing the irrational increase of monetary variables in accordance with inflation targeting to control inflation in order to improve welfare.
Introduction
The provision of economic welfare across different segments of society is a key concern for politicians in the country. Article 43 of the Constitution aims to ensure the independence of society, eradicate poverty and deprivation, and fulfill the basic needs of individuals as they progress, including housing, food, clothing, health, education, and the necessary facilities for family formation, along with providing working conditions and opportunities for full employment. To attain a comprehensive understanding of welfare and how to measure it, it is crucial to clarify the concept itself. Identifying the impact of various sectors such as housing, education, nutrition, health, and treatment on changes in welfare is essential. This allows for prioritizing efforts in each of these fields to enhance societal welfare, growth, and development. Monroe asserts that the primary objective of a society is to allocate resources among its members to maximize their welfare. Achieving this goal involves allocating resources in a manner that generates the highest overall income for society. In a free market economy, this allocation is typically accomplished through prices, which play a crucial role in determining changes in household welfare. Inflation and its fluctuations should be recognized as significant factors affecting welfare. Understanding the relationship between inflation and welfare enables policymakers to implement effective measures to mitigate its adverse effects and promote overall societal well-being.
Method
The Index of Economic Well-Being (IEWB) serves as a comprehensive and inclusive measure utilized in the current research to assess economic welfare. This index encompasses various dimensions that contribute to overall well-being, including:effective per capita consumption flow, net social accumulation of reserves and wealth-generating resources, economic inequality and economic insecurity. Each dimension is assigned weights in a specific manner, reflecting their relative importance. Consequently, the weights allocated to each dimension may vary across different observations. (Ozberg and Sharp, 2009). The general form of this index is as follows:
The value of the economic welfare index is measured by four components, which are consumption flow (CF), productive asset balance (WS), individual income distribution (ID), and economic security level (ES) (Bakhtiari et al., 2013). In this research, the base year of 2015 was used to validate the variables.
In the following, in order to investigate the effect of inflation of the total basket and different groups of goods on economic well-being, the autoregressive approach with distribution breaks (ARDL) has been used. First, the research model is specified with the aim of explaining the effect of inflation in the total basket of goods and services on economic welfare. Then, with the aim of analyzing the effect of inflation in different commodity groups, the research model will be presented. So in these two specifications of the IEWB research model, economic welfare is expressed as a dependent variable, which is calculated with the combined index of welfare. Inf inflation of the entire basket of goods and services, inflation for each of the 7 product groups [including 1. Health and treatment group (Health), 2. Clothing and footwear group (Cloth), 3. Furniture, accessories and Services used at home (Furniture), 4. Food, beverages and tobacco group (Food), 5. Recreation, education, hotel and restaurant group (ECERH), 6. Transportation and communication group (Transport) 7. Housing, fuel and lighting group, RGDPPC per capita real GDP, EG is economic growth.
Based on the above model, it is possible to test the effect of inflation in the mentioned 7 groups on the welfare of Iran's economy in the short ـ term and long ـ term situation.
Result and Discussion
In the material dimension of welfare, people should have a balanced life that includes employment and sufficient income to meet their basic needs. However, in many societies, includingIran, inflation and the instability of real purchasing power often pose challenges to people's ability to maintain material welfare. This can directly impact household consumption patterns and overall economic welfare. Therefore, understanding the effects of inflation on economic welfare is crucial. Considering the necessity of explaining the effect of inflation on economic welfare in Iran, in the present study, an attempt was made to analyze the effect of inflation of total goods and services and inflation of different groups of goods on economic welfare. For this purpose, while calculating the economic welfare with the composite index of welfare in the period of 1973 ـ 2022, the research model was estimated with the autoregression approach with distribution breaks. Our findings reveal a fluctuating trend in Iran's economic welfare during the study period. Following an initial increasing trend, economic welfare experienced a decreasing trend from 1976 until the end of the war. It increased again after the war, but decreased again in the post ـ war period.
The maximum value of the welfare index with the value of 69.6 belongs to the year 1354 and the lowest value of the welfare index with the value of 1.16 belongs to the year 1991. The results of the model estimation in 8 different estimations indicate the existence of a negative effect of inflation (total and basket of goods) on economic welfare. Based on this, the comparative results in the long term indicate that firstly, the inflation of all goods and services and the inflation of different commodity groups have an adverse effect on welfare. Second, among the 7 product groups, the inflation of the housing, fuel, and lighting group has had the most adverse effect on economic welfare. Also, due to the fact that the share and weight of the group of food, beverages, and tobacco in the consumption basket of households is larger than other groups, but the size of the inflation effect of this group of goods on welfare is in the fourth place. The increase in the inflation of the health and treatment group has the least adverse effect on economic welfare. Per capita income and economic growth also have a favorable effect on welfare, as expected.
Roghaye Shojaeddin; Majid Sameti; Zahra Dehghan Shabani
Abstract
The significance of tax revenue as the primary source of government finance underscores the importance of accurately measuring tax efforts using unbiased methodologies. This study employs a state-space model and the Kalman filter algorithm to estimate tax effort as an unobservable variable within the ...
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The significance of tax revenue as the primary source of government finance underscores the importance of accurately measuring tax efforts using unbiased methodologies. This study employs a state-space model and the Kalman filter algorithm to estimate tax effort as an unobservable variable within the tax revenue equation in Iran from 1970 to 2021.The findings reveal a nuanced relationship between various factors and the tax ratio. Per capita income exhibits a positive impact, while the agriculture share in GDP exerts a negative influence. Interestingly, the coefficients of openness and monetization initially have negative elasticity but transition to positive after reaching a certain threshold, indicating a dynamic relationship with the tax ratio. Conversely, the services and industry share in GDP demonstrate a positive effect on the tax ratio before reaching a peak, after which their squared coefficients turn negative.Tax effort in Iran, throughout the studied period, has never been more than 0.25 highlighting a significant disparity between actual and potential tax revenue and underscores inefficiencies within the tax system.
Introduction
Due to dependence on oil and structural problems, attention to important tax indexes has been neglected in Iran’s Economy. Therefore, in order to achieve more accurate results, a new approach has been taken by the research to estimate tax effort as an indicator that shows the ability of the government to enhance tax revenues.
Traditionally, tax effort is calculated by dividing actual tax revenue by potential tax revenue. However, this method is inherently biased as it fails to account for the influence of economic, social, and political factors on tax revenue collection, alongside tax effort itself. To address this limitation, the study employs the Kalman Filter estimation technique, which treats tax effort as an unobservable variable within the tax revenue function, alongside other economic variables.
Methods and Material
In literature, tax effort is calculated by estimating the following equation:
(1)
F1 = = =
where T is tax share, F is tax effort, Z is a vector of other factors affecting tax share and ω is an error term. It is evident that the estimator F1 is a biased estimator for tax effort F. Considering the effect of tax effort on tax revenue, the index should be included as a dependent variable in the tax revenue function. Referring to the study of Kim (2007) and in order to overcome the bias, the research suggests a state-space approach and Kalman Filter Algorithm. The structural time series method allows tax effort to be taken into consideration in the tax revenue function as an unobservable variable.
In this context, the tax revenue function considered a linear form incorporating variables such as per capita income, the share of agriculture, services and industry, openness, and monetization . However due to the low coefficient of determination and the results of Ramsey Reset test, adopting a quadratic function became imperative. Consequently,the final equation was changed as follows:
(3)
Results and Discussion
In econometric analysis, the stationary test of data typically examined. However, According to Harvey, the stationary test holds less signifcance in the structural time series model.. The analysis of variables confirms that all variables exibite statistical normality. The results of estimating equation 3 are reported in Table 1, which shows that the variables are significant at one percent level.
Table 1. The results of estimating the square function of tax revenue using the STSM method
Prob
t-statistic
RMSE
Coefficients
Variables
0.0090
-2.7836
0.0647
-0.1801
Level break 1998
0.0000
5.3930
0.0586
0.3159
Level break 2005
0.0000
-5.8246
0.0569
-0.3314
Level break 2000
0.0000
6.4744
0.9254
5.9920
LPY
0.0000
-6.0820
0.0611
-0.3719
LPY^2
0.0031
-3.2082
2.4578
-7.8851
LMO
0.0030
3.2130
0.2998
0.9631
LMO^2
0.0449
-2.0900
0.5520
-1.1536
LOP
0.0626
1.9312
0.0766
0.1480
LOP^2
0.0000
5.9359
3.2214
19.1223
LIND
0.0000
-6.1448
0.4668
-2.8682
LIND^2
0.0007
3.7434
7.1427
26.7385
LSEV
0.0004
-3.9563
0.8990
-3.5566
LSEV^2
0.0106
-2.7203
0.1102
-0.2997
LAGR
Reference: Research calculations and software output
There were breaks in 1998, 2000, and 2005. The cause of these breaks can be attributed to the Asian financial crisis, the dot-com bubble, and oil fever, respectively.
"In addition to examining elasticities, the squared coefficients of variables hold significance in the analysis. Despite the elasticity of the agricultural sector share being -0.29, its squared coefficient was omitted from the model due to its low explanatory power. Notably, the tax exemption status of the agricultural sector in Iran contributes to a negative impact on tax revenues.
Regarding per capita income, its elasticity is positive, yet its squared coefficient is negative. Initially, an increase in per capita income enhances tax revenues, but subsequently leads to a decline in the tax ratio. This phenomenon arises because governments can only collect a specific portion of per capita income as taxes. Continued taxation may result in taxpayer resistance, consequently leading to a reduction in tax revenue.".
The share of industry and services, both, have negative elasticity and squared coefficient. Initially, an increase in these variables leads to a rise in tax revenue, followed by a subsequent decrease where the negative effect predominates. Notably, only in cases where production is efficient, the industry can generate a significant taxable surplus. Therefore, the inefficient industry sector will not result in higher tax revenue in Iran.
Due to the lack of a full database and since some economic activities in Iran are unregistered and consequently untraceable, a significant percentage of tax evasion occurs in the services sector. Hereupon, the increase of this sector in Iran will not lead to more tax revenue.
Both openness and monetization exibit positive quadratic coefficients. The effect of these variables on the tax ratio is negative at first and becomes positive after the minimum point. The negative elasticity of these two variables is respectively caused by the government's policies such as lower tariffs for essential goods and the adverse effects of inflation on monetization and tax revenue as a result.
The tax effort trend is shown in Figure 1. The unevenness of the trend is caused by the fluctuation in oil revenues in Iran. Tax effort in the last 50 years has always been lower than 0.25, which indicates the misutilization of tax capacities.
Figure 1. Tax Effort Trend in Iran during the years 1970-2021
Reference: Research Findings
Conclusion
Recognizing the significance of taxes as a primary source of government revenue, this research employs the Kalman Filter algorithm and a state-space model to calculate tax effort in a novel manner. In this approach, tax effort, treated as an unobservable variable, is incorporated into the tax revenue function alongside six other variables. Given the low coefficient of determination and the outcomes of the Ramsey reset test, the linear model was deemed unsuitable. Consequently, the quadratic form of the function was adopted to better capture the complex relationship between tax effort and tax revenue."The estimations showed that the effect of per capita income on tax ratio is positive due to the increase in the potential of citizens to pay taxes, and the impact of agriculture share is negative due to tax exemptions.
The elasticity of monetization is negative owing to high inflation in Iran and its adverse effects on sales tax. The effect of this variable on tax ratio is initially negative, but after the minimum point, it becomes positive due to the compliance of taxpayers with inflationary conditions. Openness also has a negative elasticity due to the negative effect of import promotion policies. The share of industry and services have a positive effect on the dependent variable before reaching the maximum point. However, due to a high rate of tax evasion within this sector and production inefficiency in manufacturing, this effect reverses after surpassing the maximum point..
The discrepancy in the signs of the elasticities for some variables can be attributed to utilization of different approaches in estimating the tax effort. The low tax effort in Iran reveals the necessity to make changes in government tax policies to make the most of tax capacities.
For the purpose of enhancing tax revenue, some measures should be taken to reduce tax evasion and increase the tax potential of economic sectors. Eliminating unnecessary tax exemptions can also improve tax performance. The exemptions should be gradually phased out until they are completely eliminated,as long-term tax exemptions in Iran create non-competitive structures.
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