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To promote transparency and provide information, the Federal Planning Bureau regularly publishes the methods and results of its works. The publications are organised in different series, such as Outlooks, Working Papers and Planning Papers. Some reports can be consulted here, along with the Short Term Update newsletters that were published until 2015. You can search our publications by theme, publication type, author and year.
The Belgian Study Group on Ageing of the High Council of Finance, in its Annual Report, publishes the results of research on the budgetary and social effects of ageing. In this context, the Federal Planning Bureau, in its capacity as secretariat and main research body of the Committee, has in recent years been stepping up its efforts to deve lop models based on socioeconomic micro data. The results of one of these models, de signed to make short-term projections of poverty indicators, are presented in this paper.
The Working Paper presents a study or analysis conducted by the Federal Planning Bureau on its own initiative.
The need to project poverty indicators arose from th e fact that the principal Belgian data source used to study poverty, the ‘Panel Survey on Belgian Households’ ( PSBH ), was discontinued in 2002. Its successor, the EU ‘Statistics on Income and Living Conditions’ ( SILC ), is available for 2002 and 2003, and is not sufficiently comparable with the PSBH to form a homogeneous panel data set. In order to gain insight into the recent evolution of the povert y risk of elderly people, a simple micro simulation model was developed that enabled us to estimate various poverty indicators for the entire 1994-2005 period.
Micro simulation in the social sciences typically in volves the adjustment of a set of data on micro- entities, be it individuals or households, to meet exogenous information. The motivation behind this modelling on the micro level is that social and fiscal policy measures intervene on the micro level, and the simulation should therefore be carried ou t on this level. Furthermore, micro simulation models allow simulation of the distributional e ffects of these developments. Unfortunately, these models usually come at a significant cost in de velopment and maintenance time. The goal of this paper is to show how a simple model can generate acceptable results. Secondly, this paper aims at reconsidering the use of the state that an individual occupies. Most micro simu lation models to date have as a common characteristic that every individual occupies one of multiple mutually exclusive states in each period of time. For example, one is either working, unemployed or out of the labour market. This state may change over time or not, but alternative states within one period are not considered and therefore do not affect the simu lation results. While this may be a realistic description of a person’s situation at any moment in time (insofar as states cannot be cumulated, like being employed and unemployed, or employed and retired), it is less so over the course of the time span typically covered by socioeconomic surveys (w hich are conducted annually at best, and often biannually). Indeed, it is far from unusual that individuals report having been in multiple states over the course of the period covered in the interview. Ob viously, if the states are mutually exclusive, this implies that the individual has spent a number of mo nths in different states consecutively. This point can be made even more forcefully when the unit of observation is the household, as in the present study (for reasons to be discussed below): hous eholds, except when they consist of a single individual, usually combine several states over the observation period, and almost invariably combine several sources of income. Consequently, th e concept of the ‘state’ of a household may not be very useful to describe its socioeconomic and demographic position, nor the associated income. In this paper, an alternative and very simple model of static simulation is proposed and applied to the Panel Set of Belgian Households dataset.
Social protection, demography and prospective studies > Income distribution and poverty