Differentiated financing of schools in French-speaking Belgium: prospectives for regulating a school quasi-market

Abstract

The school quasi-market in French-speaking Belgium is characterised by segregation. Efforts to apply measures that encourage greater social mixing have met with stiff resistance. In 2008 and 2009, turbulence was caused by the application of the “social mixing” law influencing the registration procedures. The purpose of this article is to present some results from a prospective research project that investigated the possibility of modifying the formula for financing schools. To do this, a generalised formula for allocating funds to schools according to need is proposed. Then, the solution tested is presented with a financing formula that takes into account indicators of the social composition of the school population. Various scenarios of differentiated financing are presented, through simulations on the effects of these scenarios for all schools. Finally, the implications of these scenarios are discussed and put into perspective with respect to the different solutions considered since 2005 in French-speaking Belgium.

Keywords:

• prospective
• social mixing
• regulation

Acknowledgements

The authors would like to thank the Government of the French Community of Belgium for giving them the opportunity to carry out this research. Their thanks also goes out to ETNIC for providing the statistics, to Christian Monseur, Simon Uyttendaele, and Thomas Herreman from the University of Liège for their participation in the research, and to Ramona Shelby for translating the article from French to English.

Notes

 1. This article presents certain results of an interuniversity research project commissioned by the Government of the French Community of Belgium in the framework of priority 9 of the “Contrat pour l'École”(Government of the French Community of Belgium, 2005). The research was carried out by Marc Demeuse, Antoine Derobertmasure, Nathanaël Friant, and Nathalie Verdale for the University of Mons-Hainaut; and Christian Monseur, Thomas Herremans, and Simon Uyttendaele for the University of Liège.

 2. [email protected]

 3. For example, the existence of compensatory policies favouring some populations considered as underprivileged, for instance, in the case of the famous “Title I” in the United States (Borman, Stringfield, & Slavin, 2001).

 4. Such as the possibility of closing certain schools that are not considered efficient (“failing schools”) or distributing “vouchers” that allow the most underprivileged students to leave these schools and go to schools that are considered more efficient.

 5. The acronyms are based on the English formulation of Ross and Levacic (1999): Basic student allocation (BSA), Curriculum enhancement (CE), Supplementary educational needs (SEN), School sites needs (SSN).

 6. For a description of the positive discrimination policy in French-speaking Belgium, see Friant, Demeuse, Aubert-Lotarski, and Nicaise (2008).

 7. For more information about the construction of the indicators see Friant, Derobertmasure, and Demeuse (2008).

 8. Equity function, directive function, and market regulation function (Ross & Levacic, 1999, pp. 29–30).

 9. In particular, the Entreprise des Technologies Nouvelles de l'Information et de la Communication (ETNIC)

10. The records were made anonymous by the administration, which only retained a random identification number assigned to each student to allow mergers over several years.

11. The socioeconomic index is based on the student's district of origin (the notion of district is a statistical division of the territory (Demeuse, 2002, p. 219). A synthetic socioeconomic index score is assigned to each district in Belgium, on the basis of 11 variables within the framework of six domains (income per inhabitant, level of the certificates, unemployment rate, employment rate and proportion of people receiving welfare, professions, comfort of housing). Thus, each student is assigned the socioeconomic index score of the district where he lives and somehow brings this index score to the level of the institution. From a statistical point of view, this is a normal distribution metric variable that varies between −3.5 and 3.5. It is recalculated every 3 years on the basis of the latest statistics available.

12. The simulation tool was made by Simon Uyttendaele, University of Liège.

13. The logistical function is mathematically written as follows:

a_g being the left asymptote, a_d being the right asymptote, a being the slope, and inf being the point of inflexion.

14. As a reminder, a negative value indicates that the pupils of the school are generally underprivileged. On the other hand, a positive value corresponds to the situation of a school whose pupils are mainly privileged.

15. This simulation is made without increasing the overall budget awarded by the department of education for financing schools.

16. This was made impossible in order to respect the anonymity of the schools.

17. As a reminder, the first simulation was made without a budget modification. In the case of financing scenario 2, the budget was increased by 1%.

18. The distribution of financing can be seen in Figure 6 (see arrow) by taking into account the space between the curve and the horizontal line.

19. The organising authority of an educational institution is the authority, the natural person(s) or corporate body or bodies that are responsible for it (law of 29 May 1959, called the “Pacte scolaire”).