Finance satisfied our demands, and we were fortunate to reach an agreement of co-operation with the ministry, which permitted us to use their model and also to extend it with new sub-models. Implicit in our agreement was that the Ministry could use any additions to SESIM that we would design.

The version of SESIM that existed at the turn of the century missed many of the behavioral models we needed to analyze the various aspects of population ageing. Our first task was thus to start a number of studies to design and estimate the models we needed. We have supplemented SESIM with new models which simulate the following aspects of individual or household behavior: a) the progression of health as people age, b) sickness absence, c) early retirement with disability benefits, d) old-age retirement, e) geographical mobility and tenure choice, f) incomes of the elderly, g) wealth of the elderly, h) utilization of hospital care, and i) utilization of old-age care. A detailed description of this work is given in Klevmarken and Lindgren (2008). Here we will only briefly review three models to give the reader an idea of the types of models used and of some of the special problems involved in designing models for microsimulation. The three models are the models for utilization of hospital care, retirement and the stocks of wealth.

#### 3.5.3 Utilization of hospital care

Almost all hospital care in Sweden is publicly provided at small user-fees. There is thus no regular market for the services of hospitals. Excess demand is manifested in queues. Politicians may or may not react to these queues by increasing resources to the health care sector. When estimating a model to capture the utilization of the services of hospitals the resulting estimates will thus in general depend both on the demand and supply of these services. When using such a relation for simulations one has to assume that the public will accommodate an increased demand in about the same way as they have done historically.

In SESIM we have estimated a model for the utilization of inpatient care, see Bolin et al (2008a) The dependent variable is the number of days a person has spent in a hospital in a year. Most people never spend time in hospital while for a few we record very many days. The distribution is thus much skewed with a high frequency at 0. The kind of model chosen was a so called zero-inflated negative binomial model. Explanatory variables were variables capturing health status, number of inpatient care days last year, age, schooling, if divorced, relative income (individual income relative to population mean), geographical region, if born in Sweden, gender and an interaction between gender and relative income, and finally if the respondent is female and had a child less than one year old. The choice of variables was guided by previous literature and experience, see Bolin et al (2008a). One might note that relative income was used rather than real income. In microsimulation it is often problematic to use income as explanatory variable, because if there is economic growth and the simulation is carried out for a long period, the income variable might drive the results completely at the end of the simulation period. For this reason we have frequently opted for a relative income measure.