DREAM develop and maintain the dynamic micro-simulation model SMILE. The model starts with the entire Danish population in a base year (with approximately 5.7 million individuals) and simulates the further life course for each individual in this initial population. Transition probabilities depending on individual characteristics are estimated from observed transitions in a recent period.
The SMILE model (Simulation Model for Individual Lifecycle Evaluation) is a Danish dynamic, data-driven microsimulation model. The current version forecasts demography, household structure, education level, socioeconomic characteristics, housing demand, income, taxation, public benefits and labour market pensions.
The SMILE model is dynamic in the sense that an initial population (the entire Danish population of approximately 5.7 million persons) is forecasted into the future. The SMILE model is a data-driven model, based on rich Danish register data. The data cover the entire Danish population on annual basis in the period between 1986 and 2013. On each individual our dataset contains information about the person him-/herself (gender, age, educational background, labor market participation, income etc.), the person’s family situation (single/couple, number of children living at home etc.) and information about the dwelling that the person’s household occupies (location, owner/rental status, dwelling type and size etc.). We derive data from seven different sources made available through Statistics Denmark. The main data sources are the Danish Civil Registration System (CPR-registret), the Housing Register (Bygnings- og Boligregistret, BBR), the education register (Uddannelsesregistret) and the labor force statistics (Registerbaseret Arbejdsstyrkestatistik, RAS).
Demographic events such as death, birth, immigration, emigration etc. are modelled. Projections of death probabilities are based on the Lee-Carter econometric method (Lee & Carter, 1992). The model has been developed to include two regional models: in one the country is subdivided in 98 regions, while the other uses the more crude subdivision of 11 regions. The family structure is modelled by subdividing events of leaving home-events for adult children, couple-establishment and –splitting; to implement the couple establishment we deploy our matching algorithm called SBAM (Stephensen & Markeprand, 2013). To obtain a realistic family structure the model includes parity, in which the fertility coefficients includes a component that accounts for the previously births in the household.
The moving probability is determined by background characteristics of the household and by characteristics of the household's current dwelling.
The modelling of education decisions is based on a regionally subdivided transition probabilities calculated from Danish register data and it thus forecasts education levels by employing historical educational behaviour. The model establishes each person’s on-going education, duration of the current education spell and the highest attained education.
The modelling of income and labour market dynamics is subdivided into a labour supply model and an earned income model. The labour supply model firstly divide individual labour-supply event into gross labour force, retirement and study status. The gross labour force subdivides the status further into employment, unemployed, cash-benefit and no-income states, determined by a competitive risk model to model the annual in- and outflow that characterise the labour market dynamics. The model includes persistency of employment that should take into account the disparity in the distribution of attachment to the labour market in the population. The employment state is further subdivided into self-employment, assisting spouses and employed. Students also determine their employment status: a model based on annual transition probabilities. The retirement event consists of an early retirement scheme, a disability pension scheme and the age-retirement pension scheme.
Employed and students employed also determine their supply of weekly working hours, and for students a separate model determines the annual weeks of employment. Further a model for the wage rate is determined by a heterogenic dynamic model.
The income of self-employed and assisting spouses is determined by a dynamic model which has a heterogenic persistent term and duration of the employment spell.
Based on the earned income and the individual/family characteristics we can determine the rights of social- or income-replacing transfers and the tax obligations, and the contributions to the labor market pension schemes. The labor market pension schemes are personal and are basically life annuities.