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Developing Models in Estimating the Revenue Impact of Proposed Reforms on Income Tax, July - August 2012

The paper presents the development of models/methodologies in calculating the revenue impact of tax proposals on income tax particularly on corporate and individual income tax. The paper is a requirement by the Philippine Australia Human Resource and Organizational Development Facility (PAHRODF) for a scholarship granted for one year. At present, there are various revenue forecasting models being used by concerned government agencies in estimating revenue impact of tax proposals in the Philippines. The most common method of estimating the tax revenue gain or loss from the proposed tax changes is the static revenue estimate which is a straightforward calculation of the revenue based on the previous year’s collection and the proposed tax changes. This type of revenue estimation assumes that there will be no change on the behavior of the affected economic agents. The development of forecasting models and methodologies in the estimation of the impact on revenues of proposed tax reforms for different types of tax is of paramount importance to the government, especially for the Department of Finance (DOF), which evaluates tax bills referred to by Congress, other government agencies and the private sector. The revenue impact of a tax bill is one of the major considerations of legislators enacting a bill into a law. Moreover, the developed models can be used in tax policy analysis as in the tax gap studies where the assessment of the performance of a certain type of tax is undertaken. With the developed models/methodologies in place, there will be an opportunity to review and further improve the same over time. A revenue estimate is the expected tax receipts as a result of a change in the tax law. As a process, it is closely related to revenue forecasting though insufficiently different. Forecasts are required even when there is no proposed change in the tax law while revenue estimates are often done for tax proposals which may or may not be adopted afterwards and therefore not considered in any revenue forecast. The revenue estimates of the tax proposal, however, may be factored in setting revenue targets especially if the enactment of such bill is certain during the budgetary process. In developing models in estimating the revenue impact of proposed tax reforms for the corporate income tax, the tax elasticity approach was employed wherein a set of variables was used in the estimation process covering the last twenty years, i.e., 1992-2011. As observed, corporate income tax collection (CITC) level during the period was determined mostly by the level of the GDP and the corporate income tax rate (CITR). Thus, it is assumed that CITC is a function of GDP and CITR. Following this relationship, the CITC was regressed with the GDP and the CITR and the results are as follows: (1) The coefficient of the corporate income tax collection with respect to GDP is 0.95 which means that for every 1% increase in the GDP, corporate income tax revenue increases by 0.95% holding the tax rate constant; (2) With respect to corporate income tax rate, the coefficient is 2.55 which means that for every 1% increase in the tax rate, the corporate income tax revenue increases by 2.55% holding the GDP constant. Similarly, for compensation income tax (CoIT), it is observed that the collection was determined mostly by the level of the GDP and the effective compensation income tax rate (ECoITR). Thus, the compensation income tax collection was regressed with GDP and ECoITR. The regression results show that CoIT collection increases by 0.93% per 1% increase in the GDP with no change in the tax structure and that for every 1% increase in the effective tax rate, CoIT collection increases by 1.12% holding the GDP constant. In the case of the individual business income tax, a dummy variable was included in the model in addition to the GDP and effective business/professional income tax rate (EBusITR) to reflect the effects of certain discretionary changes during the period that significantly influenced the tax performance, though these may be hard to quantify. The results of the regression reveal that for every 1% increase in the GDP, BusITC increases by 0.81% holding the effective tax rate (ETR) constant and for every 1% increase in the effective tax rate, BusITC increases by 0.61% holding the GDP constant. Moreover, the overall net effect of administrative measures undertaken to enhance income tax collection from self-employed and professionals brought in an increase of about 0.10%, on the average, in the BusITC holding the GDP and the ETR constant. The developed models for both the corporate income tax and the individual income taxes yielded more acceptable statistical results than other tested models, i.e., the models passed the statistical tests for goodness of fit and other statistical parameters indicating the forecasting capability of the models. The developed models show that the variation in both corporation and individual income taxes could very well be explained by the variation in the level of economic activities represented by GDP and the changes in tax structure, e.g., change in the effective tax rates and the over-all net effect of administrative measures aimed at improving collection efficiency or taxpayer’s compliance, if any. The developed models may, therefore be used in estimating revenue from proposed changes on corporate and individual income taxation subject to a period review for further improvement over time.


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