Microsimulation models may be superior for analyzing the revenue effects of tax reform or redistributive policies in theory, yet often access to complete tax microdata is limited or unavailable – in such instances group simulation models offer an effective alternative solution.
However, grouped data often produces higher prediction errors compared to its microdata counterpart due to insufficient consideration of attributes and structural factors.
Behavioral Feedback
Behavioral feedback is the backbone of any effective tax model. People respond to changes in taxes and fiscal policies in ways that affect their behavior – investors might wait to cash out capital gains while low-income families drop out when EMT rates increase; such second-order effects are often ignored in calculator models but are essential in understanding the effects of reform on people.
PWBM’s comprehensive tax policy simulator enables users to build and test tax reform plans while viewing their budgetary and economic impacts. Users can alter 16 key tax provisions, with 4,096 policy combinations possible in total.
The model consists of functions which work on simulated tax microdata and configuration files to parameterize them, along with recipes in the Tax-Calculator Python Cookbook that describe them. The /sim folder holds functions governing scenario execution from reading input data through to output data writing; while /data and /misc contain core data structures used by functions (including lists of tax units and tax law information).
Economic Projections
Micro simulation models offer researchers many advantages over cross-country comparison methods in that they allow them to analyze the impact of policy changes on target variables (such as income, poverty or government budget) across their entire distribution. Such analyses require behavioral assumptions such as fertility models, household formation and dissolution rates and labor supply quotients to perform accurately.
Assuming complete microdata are available, microsimulation is the preferred analytical tool. For example, Urban-Brookings Tax Policy Center’s new tax module employs a sampled microdata model to assess redistributive effects of income taxes and federal fiscal issues.
Microanalytic Simulation
Microanalytic simulation can be used to enhance the accuracy of group model results. For instance, when investing income is tax deductible it may be vitally important to know who takes advantage of this deduction (TIig). Unfortunately, such information can rarely be found within tabulated group data so microsimulation must be used instead.
Microsimulation is an adaptable modeling approach that allows users to impose behavioral feedback assumptions without restricting functional form. Behavior modules, comprised of R scripts that operate on tax parameters in the model and return modified values, can be run at the beginning of non-static mode to make modifications before Tax-Simulator recalculates taxes.
Modifying the tax base decreases the relative deviation between group and microsimulation models, and note that deviations are larger for taxpayers with negative income, possibly as the group simulation interpolates a class sum of taxable income rather than individual TIigs.
Group Simulation
Group simulation is a modeling tool that utilizes user-written modules expressing behavioral feedback as function forms to dynamically update tax microdata at runtime. It is an ideal alternative to microanalytic simulation as it does not require accessing individual microdata records directly and allows for simplified yet flexible modeling of complex tax policy effects.
However, as income tax statistics published by the German Statistical Office are organized primarily based on attribute and class rather than taxable income levels, group simulation models must work with data that exhibit high amounts of information loss compared to its source; this leads to differences between results obtained using group simulation and those from microanalytic simulation.
This can be seen by the relative variations between group simulation based on income tax statistics and microsimulation results in regard to vertical loss offset restrictions on tax base deductions (Table 4). Herein, TI tables do not take account of taxpayers with negative taxable income who do not fall into any further subcategorization and thus shift away from tax statistics altogether.