- Our simulations are sensitive to changes in the death rates. Low death rates for “young old” may - depending on the health status of the elderly – result in an increasing number of very old and heavily care dependent persons.

### 4. Concluding evaluation of advantages and shortcomings of SESIM

A model is built to respond to certain scientific problems, or to policy problems. There is no model which can respond to all our problems, now or in the future. The design of a model is determined by its applications. This is true for microsimulation models as well as for other models. In the case of SESIM it was originally designed to analyze the properties of the Swedish student grant and loan system. It was then added to for studies of the properties of the pension system and finally further expanded for a study of the consequences of population ageing. In this gradual expansion of the model more and more submodels have been added to SESIM, such that it today can respond to more questions than its first version could. Is there any limit to the possibilities to add new structures and refine old ones? With unlimited time and resources, perhaps not, but in practice neither time nor resources are unlimited. In policy applications time might be the binding constraint, because policy makers have to take decisions and they need the output from a simulation in time for the decision, while in scientific work resources might be the binding constraint. But the key issue is what properties a model must have to answer a particular policy question. If it is possible to get an intelligent answer from a small model why use a large and complex one?

The idea behind microsimulation is to model the decisions of those who actually make them, not the make believe decisions of aggregates of decision makers. In SESIM the unit of analysis is thus the individual and the household. Another important idea is that the model should include the policy instruments that the policy makers actually use, for instance the true eligibility rules for tax deductions and all the details of the tax scales rather than just an average tax rate. SESIM thus includes (almost) all the details of the Swedish personal tax system, social security system and other benefits to the household sector. The model aims at capturing both individual longitudinal trajectories and cross-sectional distributions. Usually it is of interest to study both the snap-shot of poverty a cross-sectional distribution gives and to understand for how long people stay in poverty. To accomplish this we need both a demographic model that can age and renew the population and dynamic behavioral models which tell us when people take their decisions.

The basic time unit in SESIM is a calendar year. Most simulations are designed to replicate what happens to an individual in a year. One reason for this is that the tax system operates on an annual basis. There are, however, processes which run on a shorter time span. For instance, sickness benefits are given by the day and unemployment compensation by month and day. In these cases we simulate the number of days (months) with benefits within a year, but not when in a year the spells of sickness or unemployment take place. Mostly for practical reasons the model and the simulations have a hierarchical structure. We try to simulate in an order such that decisions which have immediate far-reaching consequences come first in the simulation sequence. This does not necessarily imply that individuals take decisions in this order. In principle one can think of joint decisions, and to some extent it might be possible to