Appropriate Labor income and Capital gain tax rates functions extraction based on Overlapping Generation Models: Dynamic Stochastic General Equilibrium (DSGE) approach

In this study, using the overlapping generation (OLG (model and the Stochastic Dynamic General Equilibrium (DSGE) approach, the optimal form of labor income tax rate and capital income tax functions is extracted for the economy of Iran using annual time series data during 1357 to 1397. The results of comparing the calibration and simulation of the designed model show that the optimal functions of labor income tax rate and capital gains tax are retrospective autoregressive form and also in terms of government debt to GDP ratio with the same coefficients. In fact, the tax policymaker should pay attention to the values ​​of the past period of these rates as well as the ratio of government debt to GDP with equal weight in determining current labor and income tax rates. The impulse-response functions of a positive shock to the labor income tax rate express the positive effect of this shock on savings, capital stock and GDP, and have a negative effect on the consumption of the working period, government debt and money supply. On the other hand, the impulse-response functions of the positive capital gains tax shock indicate the positive effect of this shock on consumption of working period and interest rates, but the consumption of retirement period, saving, capital stock, production, government debt and money supply have a negative reaction to this shock. In general, although expansionary tax policies such as capital gains and income tax rates increase have positive effects on reducing government debt and liquidity, however, there are  negative effects of the capital gains tax rate increase on savings, capital and production, as well as the negative effect of income tax rates increasing on consumption and household welfare that should be considered, and therefore, a sudden increase in these tax rates to compensate the government budget deficit is not recommended.