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Microsimulation is a technique that uses the capacity of modern computers to make microunits act and interact in such a way that it is possible to aggregate to the level of interest. A microsimulation model can be seen as a set of rules, which operates on a sample of microunits such as individuals, households, and firms. Each microunit is defined and characterized by a set of properties (variables) and as the model is simulated these properties are updated for each and every microunit. The model might simply be a set of deterministic rules such as the income tax rules of a country operating on a sample of taxpayers, and used to compute the distribution of after-tax income, the aggregate income tax revenue, or other fiscal entities of interest. But the model could also include behavioral assumptions usually formulated as stochastic models. Examples are fertility models, models for household formation and dissolution, labor supply, and mobility.

It is not easy to get a long perspective on the distribution of wealth in Sweden because there is no single data source that gives a consistent view for a long period of time. The early estimates of the distribution of wealth were based on the concept of tax-assessed wealth which is the basis of the wealth tax. This definition has the disadvantage of not including assets that were not taxed, and no or very unreliable data were given for the majority of the tax payers who were below the taxation threshold. Furthermore, this variable was defined for individuals and for jointly taxed individuals, but no economically meaningful household concept was available. Register data have since then improved, in particular after the late 1990s when data became available directly from banks, brokers, and insurance companies without the filtering of the tax payers. The problem with the household definition remains, but in SESIM we have made corrections to get a useful definition (see Chapter 3). A relatively large survey (HEK) run by Statistics Sweden which combines survey information about the household with register data on assets estimates the median household wealth to 156000 SEK in 1999 and 197000 SEK in 2003.² The latter estimate is in the 1999 price level.³ These estimates apply to all households independent of age. As will be shown below, the level of wealth depends very much on age.

Since SESIM is of a fundamental importance for this analysis, we also give a short presentation of the income-generating mechanisms in the model, focusing on earnings and income from capital.

The challenge of an ageing population is a major concern to policymakers and researchers all over the world. As evident in Figure 1, the percentage of people aged 60 and above will increase substantially between 2000 and 2050 in all parts of the world. Europe has the highest proportion; only Japan has a similar age structure. The already high proportion of older people in Europe is expected to rise to an even higher level by 2050, from currently 19 percent to an estimated 34 percent.

In 1997, SESIM was developed as a tool at the Swedish ministry of finance to evaluate the Swedish system to finance higher education. Part of that work was documented in Ericson and Hussénius (2000). We refer to this as version I of SESIM. Focus then shifted from education to pensions. SESIM was used to evaluate the financial sustainability of the new Swedish pension system. This new application implied that SESIM was developed into a general micro-simulation model (MSM) that can be used for a broad set of issues. We refer to this as the second version of SESIM and the documentation is presented in Flood (2003). The present version, SESIM III, maintains the focus on pensions but extends the analyses to include health issues, regional mobility, and wealth.

There are many factors that may explain the number of spells and the number of days of absence from work reported as due to sickness. Health problems seem to be the most natural candidate to include among the explanatory factors, but individual health behavior could enter the scene in several ways. A day of reported sickness might primarily be due to the fact that a person's capacity to produce market goods and household commodities is so heavily reduced so the day is just spent at home with very little or no household commodities produced. It might also be a day when the person actively produces a restoration of his or her health, combining own time and healthcare of some kind. It might be a day when the person waits for a hospital treatment, for instance, a hip replacement, but his or her condition is an obstacle for taking part in market production (very much depending on the kind of job, in which the person would normally be involved).

In Sweden, responsibility for the public care of the frail elderly rests with three authorities acting at different levels. At national level, the Riksdag and the Government realize policy goals through legislation and financial control measures. At regional level, 18 county councils and two regions are responsible for the provision of health and medical care. At local level, Sweden's 290 municipalities have a statutory duty to meet the social service and housing needs of the elderly. Sweden's municipalities and county council have a high level of autonomy by international standards. Activities in caring services are ultimately controlled by politicians appointed to policy-making assemblies in municipalities and county councils through general elections. The decentralization of responsibility for elderly care makes it possible for local and regional conditions to be taken into account when policies for the elderly are formulated. The national authorities – the National Board of Health and Welfare and the 20 county/region administrative boards – are responsible for supervision, follow-up, and evaluation of municipal and county council caring services.