The Optimal Cigarette Excise Tax Rate in Thailand

Abstract

This study aimed : 1) to determine factors affecting the demand for cigarettes in Thailand; 2) to study the optimal cigarettes excise tax rate in Thailand; and 3) to compare of equilibrium in cases of government charged and did not charge the excise taxes at 90% rate as well as calculate tax revenue and tax burden in 2017. The research used secondary data collected from 1990 to 2015 which was analyzed by multiple regression technique. The parameters of the demand for cigarettes model were estimated by Ordinary Least Squares (OLS) method. The price elasticity of demand for cigarette was then calculated to find the optimal excise tax rate according to Ramsey’s rule. The results of the study showed that factors affecting the demand for cigarette in Thailand at the statistical significance level of 0.05 are cigarette demand in Thailand in the previous year, GDP per capita, average retail price of cigarettes in Thailand, cigarette excise tax policy and at the statistical significance level of 0.10 was warning policy with terrible images on cigarette packets. According to the estimated model, the price elasticity of demand for cigarettes was equal to - 0.63 which implied the inelastic of cigarette demand and the optimal cigarette excise tax rate in Thailand based on the Ramsey’s rule equal to 26.19 percent. The reason for the obtained optimal cigarette excise tax rate was lower than the actual rate in July 2017 was that the Ramsey’s rule took into account only minimum tax burden under expected tax revenue regardless other external costs. In addition, the tax rate in July 2017 is 90% based on the guidelines of current economic situation and the tobacco control policy. With the optimal ad valorem tax derived from this study, tax burden and tax revenue were equal 11,505.65 and 55,908.68 Million Baht, respectively. The result from this study was beneficial for policy makers to determine the cigarette excise tax rate in Thailand. Moreover, the model could be applied for considering impact of relevant variables including forecast analysis to control tobacco consumption at an appropriate level.