The aim has been to make the allocation model flexible enough for the users to choose the appropriate combination of background variables in each case of allocation. In addition, the model allows the user to choose the percentage of schools benefiting from the allocation of extra funds. A third variant is received by 'diluting' the values computed for those variables that the area statistics provide. This happened by mixing - in a proportion determined by the user of the model - the weight coefficient of the areas with their own logarithm. This option was provided for the reason that many local variables fluctuate strongly in the same direction, a fact which may bias the intended allocation of means for positive discrimination.

Positive discrimination is one of the basic concepts of the welfare society. In short: more support is given to those who need more support than others. This can apply to a certain group of people such as the disabled. In cities, however, debate on this issue often tends to focus on certain areas. This comes from the fact that the kind of information that reveals deviation often appears in information that concerns certain areas.

Out of the City of Helsinki's 1998 Programme against Social Exclusion and Segregation the idea was born that the city's administrative bodies could start allocating special funds to problematic housing areas. Many offices and departments then contacted the city's statistical authority. The city's school authority's section for general education came first, followed by the section for education in Swedish. The social welfare authority and the youth authority, too, addressed the issue - the latter even deciding to extend the application beyond mere positive discrimination against social exclusion to include the entire allocation of means for the local units. This model became quite different: today, not only statistics are consulted, but the scope of the activities of the youth centres, too. Later, the Health Board adopted this type of thinking for the resource allocation for the city wards. 

There are certain areas of pupil recruitment defined (former 'school districts') for the comprehensive school system for children in age 7-15 years. The division is not as binding as it used to be, and children may go to school in other than their home districts, providing the education plan is sufficiently similar and there is capacity in the school. Yet, the majority of children go to school in their home district. Therefore it is reasonable to use the properties of the area's inhabitants as criteria for the allocation of extra resources in the spirit of positive discrimination. Thus, our task was to select a number of property variables to help us identify the target areas.

A study of various statistical sources led to the following selection of variables, which we believe make local inequality to appear, particularly with regard to school children. The below variables are included in the calculation model developed by specialist Markku Lankinen:

It was vital for the selected variables to address to the problems of families with children and to be reasonably easy to update from various data bases.

The statistics are adapted to conform with the division into school districts. The annual number of children in schools are also noted.

The model constructed for the social welfare authority was given a core theme named 'early interference', which defines the children of immigrants as its most important target group and the municipal day care centres as the appropriate arena. This model uses almost the same area statistics as the model of the school authority. An additional feature is that the allocation of PD funds is finalized using a coefficient formed out of the proportion of immigrant children and the proportion of children of Finnish citizens.

The allocation model made for the youth authority resembles the ones seen above in terms of the variables used to describe local background, but it has some additional features. Namely: the model also wants to produce a more general assessment of the relationship between the youth authority and the areas in which it operates. This means that all the youth centres are included in the inventory, not just those in housing areas that are marked for PD allocation. Therefore, the allocation criteria include not only the parameter how many young people there are in the area, but also such data as youth centre frequentation, membership cards and the personnel's work input. Since the aim is to allocate the entire budget for local activities according to the PD weighting, it has been important to take the existing activities into account when allocating the means. The users may decide which of these facts should be included and with what emphasis. A comparison of the results yielded by various allocation criteria and the latest accounts showed a certain need to reorient the local activities.

The allocation models described above have been made for independent use by the various authorities. They contain enough flexibility and readiness for alternative solutions to prevent the impact of statistics from becoming too dominant. When allocation is carried out by the concerned authorities themselves using an applied implement model on the grounds of choices made by the authorities themselves, required consensus can be reached.

As far as the Swedish-speaking schools are concerned, we have to accept the fact that, in practice, local statistics describe the Finnish-speaking majority. Only the elementary schools among Swedish-language schools form a network that can be regarded as sufficient. Therefore, the model applied a special variable: the proportion of children with Swedish as their mother tongue in the area. The smaller it is, the greater weight it has for the coefficient of positive discrimination. In practice: the less Swedish-speaking children in an area, the greater its need for positive discrimination. The results of the allocation model pretty well corresponded to the idea that the school authority's section for education in Swedish had of their own schools.

City of Helsinki Urban Facts
Finland
http://www.hel2.fi/tietokeskus/
Mr. Markku Lankinen (Senior researcher), tel. +358 9 169 3111

01/06/2001

Markku Lankinen

research

Urban Policy > Social inclusion & integration > Education

Primary education