Microsimulation modelling
Fiscal microsimulation, a crucial tool in public policy analysis, emerged in the mid-20th century, transforming how economists and policymakers understand the distributional impact of fiscal policies. The roots of fiscal microsimulation can be traced back to the 1960s, with the advent of computer technology and a growing need for more sophisticated policy analysis. In the early days, economists like Richard Orcutt played a role in introducing the concept of microsimulation. Orcutt, along with colleagues at the Urban Institute, developed the Tax Simulation Model in 1965, a pioneering effort that simulated the effects of tax policy changes at the individual or household level. This marked a departure from traditional aggregate modeling, emphasizing the importance of individual characteristics in understanding policy impacts. The Michigan Model of Income Maintenance Simulation, created by Robert Moffitt and Peter Gottschalk in the early 1970s, built upon Orcutt’s ideas and expanded the scope to include a broader range of social policies beyond taxation. As technology advanced, the 1980s and 1990s saw a proliferation of fiscal microsimulation models globally, with researchers leveraging microdata from surveys or administrative records to enhance precision and reliability.
Static vs dynamic
An important distinction in tax-benefit microsimulation modelling is that between static and dynamic microsimulation. These are two distinct approaches employed in economic modeling to analyze the effects of fiscal policies on individuals or households. In static fiscal microsimulation, the focus is on capturing a snapshot of the population at a specific point in time, often using a cross-sectional dataset. This method assumes that individuals' characteristics and behaviors remain constant over time, neglecting the potential changes that may arise due to policy adjustments or life events. On the other hand, dynamic fiscal microsimulation incorporates the temporal dimension by considering how individuals' circumstances evolve over time in response to policy changes or life events. This approach utilizes longitudinal data and accounts for behavioral responses, allowing for a more realistic representation of the dynamic nature of economic systems.
21st century
In the 21st century, fiscal microsimulation has become integral to policy analysis, helping governments, international organizations, and research institutions assess the distributional consequences of fiscal policies. These models have evolved to consider dynamic features, allowing analysts to simulate long-term effects and behavioral responses to policy changes across various economic domains. The global financial crisis of 2008 underscored the importance of fiscal microsimulation in assessing the impact of policy responses on different segments of the population. These models continue to be essential for designing socially equitable and economically efficient policies. Challenges persist, such as the need for high-quality microdata and accurate behavioral assumptions. However, ongoing research aims to refine and expand fiscal microsimulation models to address emerging policy questions and ensure their relevance in an ever-changing economic landscape.
Belgium
In Belgium, tax-benefit microsimulation is at present either performed on survey data or on administrative data. The former option has the advantage of simplicity, and the main exponent here is Euromod, the tax-benefit microsimulation under the patronage of the European Commission and running on the EU-SILC survey data (European Survey on Income and Living Conditions). An important limitation of this approach is that the limited number of variables allows only for a very stylized simulation of the tax-benefit system. Tax-benefit microsimulation on administrative data is generally more complicated, and also more rich. In Belgium, this is principally a matter of a few important public administrations and universities. We list a few important examples: the Federal Public Service (FPS) manages the Aurora model of the Personal Income Tax, the FPS Social Security manages the BelMod model of social policies, and the Federal Planning Bureau manages Expedition. All these models are static microsimulation models, and the latter two are based on the Euromod framework. Besides these static models, the Federal Planning Bureau also manages the dynamic MIDAS model, within the self-developed LIAM2 framework for dynamic microsimulation modelling. Finally, the Finance and Budget Department of the Vlaamse Overheid manages the Fantasi model of the personal income tax, which is also integrated into the Beamm platform. At the level of Belgian universities, 2 universities have a long tradition in tax-benefit microsimulation modelling: the University of Leuven (with prof. André Decoster) and the University of Antwerp (prof. Gerlinde Verbist). CAPE is a relative newcomer to this list, but has developed very rapidly during the last few years around the Beamm platform.