Each box in the diagram includes a number of activities and submodels which rule these activities. All submodels within a box are called a module. Simulations are done sequentially in the order indicated in the diagram. Data are annual data and the time unit of SESIM is a year. SESIM thus starts with the base year data of 1999. Each individual in the SESIM population runs through all the boxes in the diagram starting with the demographic box. First a death probability is computed, and if a uniform random number on the interval $ (0,1) $ does not exceed the computed probability, the individual dies. Then the simulation stops for this individual. If however, the individual is simulated to live he/she continues to the submodels for adoption, migration, etc until all demographic models have been passed. The next individual then runs through the same loop until all individuals have run through the demographic module. The simulation then continues module by module until the models for health status, health care and social care have been passed by all individuals. The SESIM population has then become updated for one year, both in the sense that some people have died, children have been born, some have emigrated and some have immigrated, and in the sense that the properties of people have changed, i.e. they might have added a year of education, got a new job, increased their earnings, moved to another municipality, bought a house, etc. SESIM is thus a dynamic microsimulation model in the sense that the population is aged endogenously. It does not necessarily mean that the behavioral relations used are dynamic in the sense that they use information from a past year. Some of them are, but not all.

There is in general no interaction or stochastic dependence between different individuals with the exception of household members. In some models, for instance the models that determine the wealth distribution, and the models for geographical mobility and tenure choice, the decision unit is the household. There are also models that use “neighborhood” variables, such as the regional unemployment rate in the model that simulates regional moves, and the municipal tax rate in the module that computes taxes and disposable income.

The behavioral models do not have a uniform functional form, but we have chosen the model that fitted best to explain behavior and to simulate, given data available. There are thus a number of different types of models within SESIM. Common choices are probit and logit models, selection models and so called two part models. For instance, access to disability insurance is determined by a logit model, which gives the probability to get a disability pension as a function of health status and socio-demographic properties of the individual. (To become disabled is not seen as a choice subject to economic incentives.) Each individual who is simulated to get a disability pension then gets it according to the rules in the social security legislation, usually a percentage of past labor incomes.

For a description of functional form, explaining variables and population at risk for each behavioral model see Flood (2008a). A more detailed discussion of choice of model structure, estimation and estimation results for the more recently developed models is found in Klevmarken and Lindgren (2008).