Microsimulare

Microsimulare, or microsimulation in English, is a modeling approach that represents individual units—such as people, households, or firms—and simulates their attributes, decisions, and interactions over time to estimate aggregate outcomes. Unlike macro models that describe systems with aggregated indicators, microsimulation operates at the micro level and can produce distributional results, such as how a policy affects different income groups.

Most microsimulation models rely on microdata, drawn from censuses, surveys, or administrative records, and codify policy rules or behavioral rules that map individuals’ attributes to outcomes. Models can be static, applying current attributes to compute immediate results, or dynamic, updating attributes and transitions over time to simulate life histories or evolving circumstances. Calibration and validation against observed outcomes are important to ensure credibility, and models are often linked to additional components such as tax systems, labor markets, or transport networks.

Applications include tax-benefit analysis and social policy evaluation, budget impact studies, transportation planning, urban economics, and public-health simulations. Static microsimulation is common for incidence and distributional analyses, while dynamic microsimulation supports long-run policy evaluation and scenario testing.

Related approaches include agent-based modeling, which emphasizes interactions among autonomous agents, and various microsimulation frameworks used for policy analysis.

Strengths include the ability to reveal distributional effects and equity implications and to test counterfactual policies before implementation. Limitations include data requirements, assumptions about behavior, computational demands, and potential biases if microdata are not representative or up to date.