Journal of Quantitative Economics
Year:2020 | Volume:16 | Issue:4|Papers Count:6

Governments mainly obtain income from a variety of sources so as to support their expenditures. Among them, receiving tax from goods and services is one of the most important sources of governments’ revenues. The Value Added Tax (VAT) has been thus considered in many countries around the world as a new tax method by creating a massive tax base. The VAT, as a multi-step tax applied on goods and services, is the taxation on the added value of the produced goods or provided services at the various steps of production and distribution. It is, indeed, obtained as a percentage of the price in each step in which the burden of taxation is on the final consumer. In effect, this tax is derived from the added value of firms as the difference between the earnings from the sale of goods and services and the total costs incurred in purchasing production inputs, excluding human resources. There are two general methods for calculating the VAT: cumulative and subtractive. Each of the cumulative and subtractive methods is, in turn, categorized in two ways: direct and indirect. On the other hand, the VAT can be applied in two different ways based on the principle of origin and destination. Depending on how the capital goods are purchased, in the base of VAT, this tax can be introduced in three different bases: the VAT on the basis of gross domestic production (production type); the VAT on the basis of net domestic production (income type); the VAT on the basis of consumption (consumption type). In Iran, in order to reform the tax structure, the VAT law has also been implemented experimentally since the second half of 2008, and many legal efforts have been made to convert it from an experimental legislation to a permanent one. The study of the effects of VAT on the macroeconomic variables has, thus, special importance. Accordingly, this paper investigates the implementation effects of the experimental law of VAT on the macroeconomic variables of Iran including inflation, Gross Domestic Production (GDP) and consumption. This quantitative research was, indeed, performed using a Computable General Equilibrium (CGE) model (Lafgren et al., 2002) which is often applied to examine a wide range of policy issues including tax policies. It can, in turn, provide a comprehensive framework for examining the effects of such policies. This model’ s equations were divided into four sections or blocks including price block, production and trade block, institution block, and system constraint block. Moreover, the necessary modifications were made to the standard model equations to align it with the Iranian economy. In the CGE model, the data source is usually a matrix called the social accounting matrix (SAM). SAM, indeed, as the square matrix where each account is placed in a row and column collision, examines the numerical information of the economy comprehensively. The last Iranian social accounting matrix is related to the year 2011, which was prepared by the Islamic Parliament Research Center of Iran in 2015. This matrix was, thus, used as a source of data in this paper. The economic activities were, then, divided into seven sectors as agriculture, oil, mining, industry, energy, buildings, and services. Policy analysis was, in turn, conducted in two scenarios including the VAT at the rate of 6% and 9%. In both scenarios, the VAT rate in the agricultural sector was set to zero. The results showed that the implementation of the VAT in Iran increased inflation, which, in turn, led to a decrease in the GDP and household consumption expenditure.

The frequent occurrence of foreign exchange crises in recent years has brought back early warning literature to the focus of researchers. The aim of this study is to introduce a warning pattern before the crisis of the foreign exchange crisis in Iran. The purpose of this study was to introduce a warning pattern before the onset of the currency crisis in Iran. In recent years, a concept has been developed for an Early Warning System (EWS), which should be able to identify costly events such as currency crises in order to create enough time to reduce the cost of the crisis for economic researchers. In this study, factors affecting the contingency of an Iranian currency crisis during the years 1981-2016 are examined using a discrete dependent variable model in the Logit model. This study uses a combination of exchange rate changes, foreign exchange reserves and bank interest rate to calculate the adjusted currency crisis index. The results of the estimate logit model shows that the increase in variable ratio of budget deficit to GDP ratio of current account deficit to GDP ratio of domestic credit to the private sector to the GDP and inflation, the probability of a currency crisis increase. In other words, the increase in these variables (as indicators of the advancement of the currency crisis) is causing an intensification in the occurrence of the currency crisis. Other research results suggest that an increase in the GDP growth rate will reduce the probability of a currency crisis. This study uses a combination of exchange rate changes, foreign exchange reserves and bank interest rate to calculate the adjusted currency crisis index. The elasticity of government deficit to GDP ratio indicates that a one percent increase in this variable, assuming other variables are constant. Increases the probability of a currency crisis by 0. 48 percent. On the other hand, the marginal Effect on the ratio of state budget deficit to gross domestic product (gdp) shows that in return for a single increase in this variable, the probability of a foreign exchange crisis will increase by 32 per cent. Thus an increase in the budget deficit is one of the leading factors in the occurrence of the currency crisis. It can be said that if the government finances part of its deficit from the reserve location, this would lead to a decrease in foreign exchange reserves and it increases the probability of a currency crisis in the country. On the other hand, the variable elasticity of the current account deficit to GDP ratio indicates that the probability of occurrence of a currency crisis increases by 0. 38 percent for one percent increase in this variable assuming the stability of other conditions. On the other hand, the marginal effect of this variable indicates that the probability of a currency crisis occurring during the period under review increases by about 14% per unit increase in the current account deficit. Another leading indicator of the currency crisis that has a negative impact on the currency crisis is the GDP growth rate. In fact, the elasticity of this variable indicates that the probability of a currency crisis is reduced by 0. 03% for a one percent increase in GDP growth rate. On the other hand, its marginal effect also indicates that for a unit increase in GDP growth rate, assuming the stability of other conditions, the probability of a currency crisis decreases by 0. 24%. The amount of the variable of financial development variable indicates that for one percent increase in this variable, assuming the stability of other conditions, the probability of currency crisis will increase 0. 007 percent. On the other hand, the marginal effect of this variable indicates that the probability of occurrence of currency crisis increases by 0. 004% per unit increase in financial development. It should be noted that this coefficient is not statistically significant. Marginal ly, inflation rate elasticity indicates that for one percent increase in inflation rate, assuming the stability of other variables, the probability of currency crisis increases by 1. 54 percent. The marginal effect of this coefficient also indicates that by one unit increase in inflation rate the probability of a currency crisis increases by 1. 33% Therefore, inflation is also one of the leading factors in the currency crisis.

The main purpose of this study was to estimate the shadow economy and tax evasion in Iran using the governmental financial discipline variables during the period 1967-2015. Considering the large size of the government in Iran and the lack of revenues, the Iranian government has faced a budget deficit for most of the periods under study. The continued provision of the government's budget deficit through borrowing from the central bank (printing new money) can lead to inflation which, in turn, increases the size of the shadow economy and its resulting tax evasion. On the other hand, in recent years, the implementation of targeted subsidies has created an additional pressure on the government's budgetary conditions and has forced the government to counter the budget deficit by selling oil dollars in the free market in addition to borrowing from the central bank. The latter case, in its own turn, led to high volatilities of the exchange rate and rising inflation, covering the increase of the size of the shadow economy in Iran more than ever. MIMIC method was used to estimate the shadow economy in the present study. Most methods of shadow economy estimation have considered only one index to cover all the effects of shadow economy. However, the effects of shadow economy tend to appear simultaneously in the production, labor and money markets. Thus, the model approach adopted considers the multiple causes of the existence and growth of the shadow economy as well as the multiple indicators of the shadow economy over time. In the present study, the variables of tax burden in the two indices of total tax burden and tax burden on imports, the income derived from natural resources, unemployment rate, exchange rate as well as the three indices of inflation rate, budget deficit and government volume taken as the three variables for the governmental financial discipline along with the dummy variable of targeted subsidies were considered as the cause variables. Besides, three indices of energy consumption, household expenditures and money demand were considered as the effects of shadow economy in the final models of estimating the shadow economy. To estimate the shadow economy, at first, eight models were selected from the best estimation models and the final model was selected using the Multiple Indicators Multiple Causes (MIMIC) approach and the general and comparative fitting criteria. Then, the relative and absolute size of the shadow economy was calculated on the base price in 2004, using the side information and calibrating the time series. The resulting tax evasion was, then, calculated as a ratio of shadow economy. The results showed that the average relative size of the shadow economy over the time period was 30. 24%. Moreover, the maximum relative size of the shadow economy in 2008 was found to be 43. 3% and the minimum value in 1968 was 18. 01%. The average absolute size of the shadow economy was, in turn, 388719. 5 billion rials. Besides, the maximum value of the absolute size of the shadow economy in 2011 was obtained 885642. 2 billion rials and the minimum value in 1967 was 88687. 6 billion rials. The average tax evasion was 19952. 85 billion rials, as well. In turn, the minimum value of tax evasion in 1967 was 3923. 8 billion rials, and the maximum value in 2015 was 56628. 6 billion rials. In the final model of shadow economy, the variables of tax burden on imports, the income derived from natural resources, unemployment rate, inflation rate, budget deficit and dummy variable of targeted subsidies were considered as the cause variables and the variables of money volume and household expenditures were, in turn, considered as the index variables. The results showed that the tax burden on imports and unemployment rate were the main causes of shadow economy in Iran. Considering the three variables of inflation rate, budget deficit and government volume as the variables of governmental financial discipline, the findings showed that the inflation and budget deficit had a positive effect on the shadow economy and its resulting tax evasion. As a result, the shadow economy can be moved by reducing the consumption costs and government size so as to reduce the government's budget deficit and provide more financial discipline. Thus, controlling the budget deficit and inflation rate is expected to increase the governmental financial discipline, and accordingly, decrease the size of shadow economy and the resulting tax evasion in Iran.

The relationship between the openness and economic growth is one of the controversial economic subjects and still there is no consensus among economists regarding the nature of their interaction. The existing disagreements are not only limited to theoretical areas but there are also some contradictory results in empirical studies. Empirical studies in this field can be divided into two general categories: The former is a group of studies which investigate the relationship between openness and economic growth using regression and cointegration techniques and the latter include studies which focus on the granger causality between these two variables. According to this classification, this study falls into the latter category. An important reason for the contradictions found in the empirical studies of the second category is that the cross-sectional correlations as well as the heterogeneity of the coefficients are not considered in these studies. In this regard, this study uses the Bootstrap Panel Granger Causality approach proposed by Konya (2006). In the Konya's approach, there is no need to test granger causality jointly for all cross sections and the causality test can be carried out for all cross sections separately. The main advantage of this method over the Granger causality method in the time series framework (causality analysis separately for each cross section) is that the Konya's method uses the cross-sectional dependence information and it leads to more efficient estimates. Konya uses the SUR method for estimation to use this extra information. The advantage of the SUR method is that it also takes into account the variance-covariance matrix information, which makes estimates more efficient. Another advantage of the Konya's method is that the nonstationarity of the variables cannot affect the accuracy of the results, and therefore, within the framework of this model, it is not required to do unit root tests. It should be noted that the data used in this study are related to the 9 selected countries of the MENA region from 1970 to 2016. The results of this study show that openness to international trade Granger-causes GDP growth in Algeria, Egypt, Iran, Morocco, Oman, Saudi Arabia and Tunisia while in Iraq and Malta there is no causality relationship between trade openness and economic growth. The causal relationship from economic growth to trade openness was not observed in any of the countries.

Given its important role in the development process of human resources, human health is one of the major economic concerns, especially in developing countries. There are different perspectives about the relationship between woman's health and economic growth. The question has, in turn, raised among economists as to whether the economic growth is affected by women's health or the economic growth causes women's health. Despite the high importance of health variables, especially women's health, many areas related to the women's health have received little attention in the field. In the meantime, investing in women's health is considered as an investment for future generations, which, in turn, contributes to the development of societies. In effect, women covering half of the world's population play a major role in the economic development, and therefore have an effective and constructive role in the conduct of different economic sectors. Women are, indeed, one of the most important social groups affected by and at the same time affecting the quality of life in the society. They are also responsible for accelerating the process of sustainable development and change in society. For this reason, developing countries have realized that the need to create a healthy society mainly depends on having effective healthy women in the society. Healthy women are actually better able to contribute effectively to the labor market with direct results to providing the effective labor, and hence accelerating the economic growth and output levels. In effect, the better the health, the higher the return on educational capital. Considering the importance of health in the economic growth as one of the essential components of human capital, we attempted to investigate the effects of life expectancy, fertility rate, and mortality rate on the economic growth in the D8 countries. This study, indeed, investigated the causal relationship between women's health indicators (i. e., life expectancy, fertility rate and mortality rate) and the economic growth over the period 1990– 2015 using the Konya (2006) Panel Causality approach for D8 countries. This method is based on Seemingly Unrelated Regression (SUR) and Wald tests with specific bootstrap critical values for each country. In addition, the Dmitrovskoe-Hurlin causality test was used to test causality in the D8 countries as a whole. The required data were extracted from the World Bank statistical database and the statistical analyzes were performed using the STATA 14, TSP and GAUSS 16 software. The findings indicated that in most of the D8 countries the hypothesis assuming no causal relationship between women's health indicators (i. e., life expectancy, fertility rate and mortality rate) and the economic growth cannot be rejected at the significance level of 5%. However, the hypothesis that there was no causal relationship between the economic growth and the women's health indicators was rejected. This means that there was no causal relationship between the variables of women’ s health indicators and the economic growth; however there was a causal relationship between the economic growth and the women’ s health indicators. Indeed, there was a one-way causal relationship between the economic growth and the women's health indicators. Finally, we concluded that there was a one-way causal relationship between the economic growth and the women's health indicators in these countries as a whole.

In recent years, many countries have experienced boom-bust cycles in credit and asset prices, some of which resulted in severe financial crises. Given that housing prices in both advanced and emerging countries have been heavily influenced by housing credit, the decline in housing prices is similar in both groups of countries. In response to these cycles, authorities in many countries have used macro prudential policies as the frontline of defense against the financial instability risks. Macro prudential measures are, indeed, policy tools that have been used intensively in recent years to target suppliers or applicants of financial services (e. g., households and firms). There is no consensus on the relationship between monetary stability and financial stability and their effects on each other. Some argue that while high-interest rates may control inflation (monetary stability), they are deemed to destroy banks’ balance sheets (financial instability). Another argument is that in the financial instability condition, the contraction monetary policy intensifies the likelihood of instability in the financial institutions. In turn, some believe that the financial policies adopted by monetary institutions, including the central bank, are the leading causes of financial instability. On the other hand, some believe that financial stability preservation is always one of the intrinsic duties of the central bank. In effect, the lack of cooperation between the macro prudential and monetary policy-maker authorities may lead to conflicting policies and, therefore nonoptimal results. Moreover, monetary and macro prudential policies as complements to each other can guarantee monetary and financial stability at the same time. This paper studies the effect of macro prudential policies and the interaction of monetary and macro prudential policies on the credit growth and housing prices using the generalized moment method. To that end, the first macro prudential tools index (including the capital requirements, loan-tovalue ratios, and debt-to-income ratios) are constructed for the advanced and emerging economies over the period 2000 to 2014. Then, an aggregate index was constructed under two scenarios of tightening (contraction) and easing (expansion) actions. In effect, the produced aggregate index showed the state of macro prudential policy in each country. To construct the macro prudential tools, IMF (2011) data used in the study of Lim et al. (2011) and Shim et al. (2013) were considered. Following Eckinsky and Ramsey (2017), this paper used seven macro prudential tools (i. e., loan-tovalue ratios, debt-to-income repayments and other housing sector tools, countercyclical capital requirements, loan-loss provision requirements, consumer loan limits, and credit growth limits), to construct the aggregate indicator for the macro prudential policy and sub-indices for housing and non-housing in the studied countries. The dummy variable was, in turn, used to construct the macro prudential tools. If the macro prudential tool was used or intensified in the desired season, the dummy variable was considered 1, and if the use of the macro prudential tool was reduced, the dummy variable was-1. Otherwise, if no macro prudential tool was used, the value was zero. The aggregate macro prudential policy index for each of the housing and non-housing sectors was considered as the sum of the dummy variables. In addition, by aggregating the dummy variables of the specific tools in each country, the non-housing and housing macro prudential policy indexes were constructed. The aggregate macro prudential index was also obtained from the sum of the housing and nonhousing indexes. The results showed that the macro prudential policy indexes (aggregate-housing) had no significant effect on the housing prices and credit growth. However, the simultaneous adoption of the macro prudential policies and monetary policies inhibited the growth of the credit and, consequently the rise in the housing prices. In turn, a comparison of the coefficients showed that the effect of these tools on the credit growth was greater than the housing price growth. This finding, consistent with the results of other research including Wenden Beach et al. (2012), Kotner and Shim (2012), Brno and Friedrich (2014), Bruno et al. (2017), showed that the effectiveness of macro prudential policies in controlling the credit growth was more than lowering the housing prices. On the other hand, the interaction of monetary policies with the non-housing and housing macro prudential policies produced results which were more effective in controlling the credit and housing prices, respectively. In other words, the macro prudential policies that targeted the housing sector were more effective in controlling the housing prices. However, non-housing macro prudential policies were more effective in reducing the credit growth.