ABSTRAK
The thesis is a continuing forecast stiy of the two biggest taxes managed byIndonesian?s Directorate General of Tax (Ditjen Paak)--lncome Taxes (PPh) andValue Added Tax (PPN). In addition, the respond hopes to assist any revenue forecasteron forecast analysis by introducing new conventional and unconventional ways. Currently, the Ministry of Finance (DepKeu), in this case represented byDitjen Pajak and Agency for Fiscal Analysis (BAF) is responsible for revenueforecasting in Indonesia. The existing forecasting practices used by Ditjen Pajak andBAF based on a linear relationship between tax revenue and the macroeconomicaggregates such as GDP, inflation rate, exchange rate, and others. This approachseems to lack the fundamental economic relation that needs to show in any fiscalforecasting. The best alternative and common international practice is to relate taxrevenue with its proxy base. For example, logically national consumption can act as areliable PPN?s base. As people increasingly consume goods and services, then PPN,which is a tax on final domestic consumptiOn, will also increase. The economic theorybehind the movement can also assist any forecaster. For example, an increase in sayPPN rate will definitely affect the revenue. However, questions such as, theeflèctivenesS of the rate change needs to be addressed. A good revenue forecastershould take into account any changes in economic behavior. The thesis employs the most commOn method of revenue forecastingtechnique--Baseline forecasting, which is estimating of future revenues based oncurrent laws andlor decrees. The two types of baseline forecasting are macro models(aggregate models) and micro models. For PPh, the report will only forecast macromodels, because of lack of ?good and unbiased micro database (i.e., a clean andsufficient tax return database). On the other hand, PPN?s forecast analysis will haveboth micro model and macro model, because of its sufficient micro and macrodatabases.The macro methodologies for PPh are elasticity, time-series model, andmonitoring, whereas the macro models for PPN are only time-series model andmonitoring. A regression analysis between tax receipt and GDP is practiced to findthe elasticity. The elasticity model will employ a stable relationship between thegrowth of tax receipts and tir growth in the tax base. In addition, a dummy variable isused to discover whether a tax reform has any impact on revenue collection. By usingthe estimated of tax elasticity and forecasted growth rate of the tax base, a forecast ofthe change in tax revenue can be obtained by simply multiplying the growth rate inthe tax base by the elasticity. The elasticity approach is feasible if there have been nochanges in the tax system (in rates, exemptions, and compliance) during a sufficientlylong period to permit estimation of its value. An alternative way is to discard theconcepts of tax elasticity and buoyancy and the economic basis of the revenueequations in general. The new method is a time series analysis that will use aregression analysis to exploit trends and correlations in the series of data for revenueand the proxy base, including the autocorrelation in the tax revenue series. It does notinvolve the assumption of an absence of tax changes, and it requires modest types andqualities of data. The monitoring system works as a measurement of administrativeefficiency. Estimating the PPh?s elasticity, the writer employs annual PPh?s data from1984 to 1997, by taking into account the 1994 as the tax reform year. The results onthe pPh?s elasticity, the multiple regression analysis shows a linear relationshipbetween independent variables in the modeI?multicollinearity problem. This isindicated by a relatively high R2 in the regression equation with few significant tstatistics. The presence of multicollinearity implies that there is no effect of 1994 taxrefbrm in PPh collection. The new estimated PPh elasticity of tax revenue with itsproxy base will not take into account the 1994 tax reform (i.e. GDP).The time series model for both PPh and PPN will be a regression time seriesmodel, which will utilize a quarterly data from 1989 to 2000. The model provides amore sophisticated description of cause-and-effect relationship between the taxes andtheir proxy bases (i.e. GDP for PPh and national consumption for PPN) and therandom matute of the process that generated the sample observations of the two taxes.The result for PPh shows a significant relationship between PPh and GÐP. However,the PPN?s result looks logically inconsistent with no significant relationship betweenPPN and national consumption.. PPN?s regression time series model is not a fit model. The monitoring analysis for both PPh and PPN serves as a management toolthat Ditjen Pajak can employ. It provides a flmdarnental inputs to both short- andLong-term Ditjen Pajak planning. PPN is the only tax that can accommodate micro model. The writer employs amicro data that is input-output table, a statistical framework of indonesian economicactivities in a given period. Later, the writer will estimate the PPNs base beforeestimating future revenue. me shortcomings are lack of in depth economic study, statisticai problems(e.g. multicollinearity, simultaneous equation problem, low confidence interval, andfimited number of observations), and lack of scenario adjustments (e.g. impact ofWTC incident on the tax revenue). The recommendations to counter these shortcomings are: 1. Setup the economic framework that accompanies any revenue forecastinganalysis. 2. Expand the number observations to stabilize the elasticity3s multiple regressionmodel and regression time series model. 3. Setup a clean and reliable tax revenue database, which includes discretionarychanges eflèct for tax revenue. 4. Setup a statistics of income database, a database of sample of tax return. This isimportant for niicrosiniulation modeling. 5. Includes performance targeting measurement, such as audit rate, percentage ofcollection, and others as a monitoring tool. 6. Setup a sepaiate database for personal and corporate income taxes. These twotaxes have very different characters. 7. Take into account any endogenous and exogenous adjustments. |
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