Navigation – Plan du site
2016
97

Migratory movements and dynamics of neighbourhoods in Brussels

Mouvements migratoires et dynamiques des quartiers à Bruxelles
Migratiebewegingen en dynamische processen in de Brusselse wijken
Gilles Van Hamme, Taïs Grippa et Mathieu Van Criekingen
Traduction de Jane Corrigan
Cet article est une traduction de :
Mouvements migratoires et dynamiques des quartiers à Bruxelles
Autre(s) traduction(s) de cet article :
Migratiebewegingen en dynamische processen in de Brusselse wijken

Résumés

Cet article vise à cerner le rôle des mouvements migratoires dans les dynamiques de transformation socio-démographique des quartiers en région de Bruxelles-Capitale et à en tirer quelques réflexions en termes d’implications politiques. De cette analyse des mouvements migratoires complexes que connaît la Région, on peut tirer quelques éléments synthétiques forts. Les territoires les plus pauvres de la ville, dits du « croissant pauvre », sont à la croisée de mouvements migratoires divergents, marqués en particulier par l’arrivée de nouveaux immigrés issus de pays pauvres ou intermédiaires et le départ de populations résidentes. Néanmoins, l’analyse montre aussi que ces quartiers ne peuvent être entièrement réduits à une fonction de transit étant donné qu’une partie importante de leur population y demeure. Par opposition, les parties les plus riches de la ville, situées dans le cadrant sud-est de la Région, connaissent des mouvements migratoires beaucoup moins massifs. Ceux-ci n’accueillent ni les primo-arrivants, ni les ménages quittant les zones défavorisées de la ville. Ils apparaissent de la sorte largement fermés aux mouvements de population.

Haut de page

Texte intégral

Introduction

1The dynamics of neighbourhoods are at the centre of social and political concerns in big cities. Based on the observation that Brussels is a city marked by a strong social and territorial divide between poor and rich neighbourhoods [Van Hamme, 2010], since the beginning of the 1990s, the political response on behalf of the authorities of the Brussels-Capital Region has favoured targeted and multidimensional action in the poor territories of the city, in particular through Neighbourhood Contract programmes [Sacco, 2010]. These policies are based in particular on the idea that spatial segregation reinforces social inequalities via “neighbourhood effects” [Musterd et al., 2003]. Reinforcing the social mix in these poor neighbourhoods is seen as a means of reducing these “neighbourhood effects”. In the case of Brussels, it has been shown that the geographical concentration of social difficulties reinforced these difficulties, in particular as regards access to employment [Dujardin et al., 2008]. In other words, all other things being equal, a person from one of the poorest neighbourhoods has more difficulty in finding a job than a person from a rich neighbourhood. This “neighbourhood effect” may be due to inequalities in terms of social networks used by people or public services offered, such as education in particular, or to certain types of discrimination or social and territorial stigmatisation. It must nevertheless be borne in mind that these territorial effects have much less of an impact than social characteristics (gender, social background, nationality, etc.) on the future of individuals [Van Hamme et al., 2011] and that the geographic deconcentration of poverty is thus not a satisfactory response to the social issue in Brussels.

  • 1 Statistical sectors are the most detailed spatial entity for which data may be obtained. The analys (...)

2The evaluation of territorialised policies cannot be limited to a monitoring of social indicators over time [IBSA, 2015]. The evolution of statistical sectors1 depends on the future of “sedentary” populations during the period under study as well as the population movements between each statistical sector and the rest of the city, country or world. A recent study on the dynamics of neighbourhoods in difficulty in Belgian urban regions [Van Hamme et al., 2015] sought precisely to better understand local dynamics by focusing on migratory movements at a detailed scale [see also Marissal et al., 2015]. Based on data from Banque Carrefour de la Sécurité Sociale (BCSS), this study was able to measure the dynamics of statistical sectors between 2005 and 2010 as well as evaluate the weight of migratory movements in these dynamics. The major trends observed shall be examined and interpreted further in this article.

3We shall begin by presenting a brief review of the impact of residential migrations on territorial dynamics in cities (section 1). Next, we shall present a summary measurement of the socioeconomic dynamics of statistical sectors in the Brussels-Capital Region (BCR) between 2005 and 2010 (section 2). Finally, the third section presents an analysis of migratory movements and their impact on socioeconomic dynamics at a detailed scale within the BCR. In conclusion, we shall examine certain political implications of the results of the analyses.

1. Migratory movements and dynamics of neighbourhoods: three models

4There are different explanatory models of migratory movements at the scale of cities. These models are not necessarily contradictory and lead us to put forward a series of hypotheses and central questions in the study of migrations in Brussels. Here we place the accent on the disadvantaged neighbourhoods of the city, even if the processes described inevitably include the metropolitan area as a whole.

5The first of these models considers the disadvantaged neighbourhoods as neighbourhoods of transit for upwardly mobile migrant populations. According to this model, these neighbourhoods are home to new immigrants (nationals or foreigners) while the resident populations– sometimes from past immigration movements – tend to leave [Saunders, 2011]. This model was developed as of the 1920s by the Chicago School of Urban Sociology [Park et al., 1925] to describe the dynamics of the central neighbourhoods of big North American cities. It is often used to characterise the evolution in Brussels where– as in the United States – the disadvantaged neighbourhoods are located in the centre of the urban area and where there is a high level of immigration. The following questions are thus raised: What is the rate of entry and exit movements in disadvantaged neighbourhoods? Who enters and who leaves these neighbourhoods?

6The second explanatory model is that of suburbanisation, i.e. a migratory movement from the dense centres towards the residential suburbs associated with middle- and upper-class households. Beyond the questions regarding the continuation of this suburbanisation movement, we examine the existence of similar movements from disadvantaged neighbourhoods associated with less well-off social profiles. The recent literature on the evolution of suburban spaces points to a diversification of the social profile of households leaving the central parts of urban areas. Certain suburbanisation movements are thus associated with households from the lower classes [e.g. Bonard et al., 2009].

7Finally, the third explanatory model is that of gentrification, i.e. the reinvestment in certain run-down neighbourhoods of the urban centre by categories of the population with a high level of cultural and/or economic capital, to the detriment of the lower classes [Lees et al., 2007]. These dynamics are often accompanied by public urban renovation policies and involve a modification of the social composition of the population in central neighbourhoods.

8After describing the major trends in the evolution of statistical sectors in the Brussels-Capital Region (section 2), our analyses shall attempt to test these different explanatory models through the analysis of population movements between the poor central neighbourhoods and the rest of the city (section 3).

2. Measuring the dynamics of statistical sectors within the Brussels-Capital Region

9Our analysis is based on a set of socioeconomic and demographic variables at the scale of statistical sectors in 2005 and 2010. Most of the data were obtained from Banque Carrefour de la Sécurité Sociale (BCSS). The entire population of the urban spaces concerned is included. We have used 23 indicators covering four dimensions of insecurity: income, national origin, job insecurity and the dependence of households on social transfers. The dimension of income alone does not account for the social difficulties of individuals and households, as the origin of income (dependence on social transfers) and the types of participation in the job market may underline other types of social insecurity which often come in addition to low income. The same is true for national origin, in particular that of newcomers who must face the difficulties of integration (language, various forms of discrimination, access to social rights, etc.). Based on these 23 indicators, we have elaborated a single summary index measuring a level of socioeconomic difficulty at the scale of statistical sectors. The decision to combine several indicators is justified because, considered individually, each of the indicators presents more or less significant biases. For example, income is often measured inaccurately in the lower and upper levels of the social hierarchy, with the result that taking only this dimension into account would lead to a biased geography of socioeconomic difficulties.

  • 2 This analysis is carried out for the 22 urban regions in Belgium; here we have extracted the result (...)

10In order to carry out this combination of indicators, we made use of the technique of principal component analysis (PCA), as this method allows a “common dimension” of these multiple indicators to be identified. We were thus able to put forward a dimension which may be interpreted as a summary index of socioeconomic difficulty of neighbourhoods. However, as each of the four dimensions described above was covered by an unequal number of indicators, we first summarised the information for each of the dimensions before coming up with a single summary indicator of the evolution of statistical sectors between 2005 and 2010 which combines the four dimensions2 (Table 1). For example, we have eight indicators on the questions of national origin, and only two on income. The establishment of a summary indicator could thus have been influenced much more by the aspect of national origin than that of income if we had not used a two-stage approach. This is why we summarised each of the four dimensions before producing a summary index of socioeconomic difficulty.

11The methodology is described below and illustrated in Figure 1:

  • For each dimension of social insecurity, the different indicators calculated for 2005 and 2010 were introduced in a principal component analysis (PCA). This technique allows the available information to be summarised. When the indicators are closely correlated, a single variable (the first dimension of the PCA) allows all of the initial variables to be summarised efficiently (see the last two in Table 1 indicating the proportion of the information included in the first two dimensions of the analysis). Following this operation, we were left with four variables in 2005 and four in 2010, each corresponding to a dimension of social insecurity;

  • In a second stage, these four variables were introduced into a new PCA, which allowed most of their combined information to be summarised in a single component (in statistical terms: 87% of the total variance). This new summary component may be interpreted as a socioeconomic summary index at the scale of statistical sectors;

  • As each indicator has been duplicated in 2005 and 2010, our analysis allows us to obtain a socioeconomic summary index for each statistical sector in 2005 and 2010. The subtraction of the two values gives the average dynamics of the statistical sectors. Table 2 summarises these dynamics according to major type of statistical sector in the Brussels-Capital Region.

Figure 1. Elaboration of the summary index for the level of socioeconomic difficulty of statistical sectors

Figure 1. Elaboration of the summary index for the level of socioeconomic difficulty of statistical sectors

Table 1. List of socioeconomic indicators and proportion of variance in the summary index of the four dimensions

Table 1. List of socioeconomic indicators and proportion of variance in the summary index of the four dimensions

12The map of the summary index of socioeconomic difficulty (Figure 2) highlights the existence of the “poor area” which extends from the north (Saint-Josse) to the south (Saint-Gilles) of the pentagon along the west (Anderlecht, Molenbeek). Certain more peripheral statistical sectors also show high levels of insecurity: these correspond to large social housing developments. However, our index of difficulty is – as expected – low in the historically middle-class neighbourhoods of the city, in the south and the east. This east/west dichotomy has strong structural implications with respect to the space in Brussels [Vandermotten, 2014].

Figure 2. Level of socioeconomic difficulty of statistical sectors in the Brussels-Capital Region, 2010

Figure 2. Level of socioeconomic difficulty of statistical sectors in the Brussels-Capital Region, 2010
  • 3 The detailed maps may be consulted in the atlas of the dynamics of neighbourhoods in difficulty [Va (...)

13Given the complexity of territorial dynamics at a scale as detailed as a statistical sector, we chose to show the calculated evolution of our index according to group of statistical sectors. In practical terms, we defined five major types of statistical sector using a simple typology based on the socioeconomic level of statistical sectors and their location to the east or west of the canal (Figure 3).3 These five major types of statistical sector are represented in Figure 3. Based on this, Table 2 summarises the major social and demographic dynamics for the period analysed, i.e. 2005‑2010. The last column of the table shows the average evolution in all of the areas; a positive figure indicates an increase in the index of difficulty and therefore an average deterioration of the situation social. The average value for the Brussels-Capital Region indicates that the socioeconomic situation has deteriorated on average with respect to the other urban regions, in an unequal manner among the major types of neighbourhood.

Figure 3. The major types of statistical sector

Figure 3. The major types of statistical sector

14On average, the situation in the “poor area” has deteriorated less than the situation recorded at a regional scale (Table 2). However, the details show that there are contrasting dynamics, with strong variations between statistical sectors. For example, while the dynamics are positive along the canal, certain sectors of Cureghem or Vieux-Molenbeek which are not next to the canal have undergone a deterioration in their situation. In the statistical sectors at intermediate level which are adjacent to the poor area (in purple in Figure 3), the dynamics are much more homogeneous, especially in the western part of the outer ring as well as towards the northeast, beyond Boulevard Lambermont: the vast majority of them show a deterioration in the index of socioeconomic difficulties (Table 2). However, in the more eastern part (Ixelles, Saint-Gilles, Schaerbeek) of the dense inner ring of the city, the dynamics are not as unfavourable, and are close to the regional average. In the richest part of the city, i.e. the southeast quadrant, the evolution is favourable on average, indicating a relative improvement in the position of these neighbourhoods with respect to the other neighbourhoods. This average evolution masks strong variations from one statistical sector to the next, whose sources are not always easy to understand.

Table 2. Dynamics of the major types of statistical sector in the Brussels-Capital Region, 2005‑2010

Table 2. Dynamics of the major types of statistical sector in the Brussels-Capital Region, 2005‑2010

15These results provide us with an instrument for monitoring statistical sectors. However, the dynamics observed remain to be explained. They involve endogenous dynamics, i.e. related to the evolution of the situation of people who were initially residents of the statistical sector, as well as migratory dynamics, i.e. modifications of the composition of the population following migratory movements. Without any individual data to localise people at a detailed scale, we were not able to measure the endogenous dynamics; however, we were able to evaluate the entries and exits of all of the statistical sectors in Brussels.

3. The role of migratory movements in the social dynamics of neighbourhoods

16Thanks to the data from BCSS, we were also able to measure the population flows between the statistical sectors in Brussels and the rest of the world (making a distinction between the rest of the national territory and other countries), as well as according to the major types of statistical sector based on their socioeconomic level at the scale of 22 large urban regions. We also obtained the exits and entries of statistical sectors according to the social characteristics of people (national origin, position on the job market, etc.). It is however useful to underline the fact that we do not have all of the pertinent variables at individual level, but that we have certain intersected variables related to the place of residence (statistical sector) and the social characteristics of people. For example, it is not possible to know where the poor and rich individuals from the same poor neighbourhoods go. BCSS does not allow the extraction of data at this level of disintegration. However, the data we have allow us to better understand the migratory dynamics, in particular in the poorest neighbourhoods, and to understand the impact of these migratory movements on the socioeconomic dynamics of neighbourhoods as described in Table 2.

3.1. The exits from the “poor area”

17On the whole, the exit rates from the most disadvantaged statistical sectors do not appear to be much higher than average in the Region (Table 3). A significant proportion of these moves take place in the direction of statistical sectors of the same type, i.e. other disadvantaged sectors. The exit rate from the “poor area” towards less disadvantaged sectors reaches 30% for the five years considered (2005‑2010). This figure is high but does not allow this part of the city to be reduced to an area of transit, as a significant proportion of the population remains in the poor area. However, it should also be noted that these neighbourhoods are at the centre of very significant movements in the opposite direction: their migration rate reached –15.6% with the rest of the national territory between 2005 and 2010 and +18.4% with other countries. During the same period, the affluent neighbourhoods in the southeast of the city experienced much more moderate migrations (–1.2% towards the rest of the national territory and +4.2% with other countries). In the west, beyond the poorest neighbourhoods, the evolution was different once again, as the migrations were positive with respect to the rest of the national territory and other countries. As we shall see, these positive migrations with the national territory reflect migratory movements from the poor area.

Table 3. Exit rate and migrations according to type of statistical sector, between 2005 and 2010

Table 3. Exit rate and migrations according to type of statistical sector, between 2005 and 2010
  • 4 The exit rates for a neighbourhood are widely determined by the age structure: the younger the neig (...)

18The role of transit of poor neighbourhoods must also be evaluated in light of the other characteristics of these neighbourhoods. When these characteristics are taken into account– in particular the high rate of tenants – their exit rate may not be considered as high. A linear regression model based on the exit rates of statistical sectors indicates that when the tenant rate and age structure4 are taken into account, the exit rates of the most disadvantaged statistical sectors in the city are in fact lower than those in the rest of the city (Table 4). Once these factors are taken into consideration, the negative coefficient of the socioeconomic index of difficulty in the regression model shows us that the tendency to leave a statistical sector is less pronounced the more disadvantaged it is.

Table 4. Modelling of the exit rate according to statistical sector in the Brussels-Capital Region, 2005‑2010

Table 4. Modelling of the exit rate according to statistical sector in the Brussels-Capital Region, 2005‑2010

(1) Age structure is an expected exit rate given the age structure of the statistical sector and the average propensity to leave for each age category. A high index therefore signifies a high presence of people who belong to the most mobile age categories, i.e. young people (ages 18‑30 in particular). *, **, *** significant at 10, 5 and 1%.
Explanation of the table: The positive coefficient for the indicator “age structure” signifies that the younger statistical sectors have a higher exit rate. The positive coefficient for the “tenant rate with private landlord” signifies that in the statistical sectors with a high proportion of tenant households with a private landlord, the exit rates are higher, while the opposite is true for the “tenant rate with social housing company”. The negative coefficient for the “index of difficulty” signifies that the greater the difficulty faced by a statistical sector, the lower the exit rate. The weakness of this coefficient shows us that the impact of this variable is lower than “age structure” and “tenant rate”.

19However, our data do not allow us to measure the proportion of long-term residents of these neighbourhoods. The poorest statistical sectors of the capital are still characterised very clearly by their negative migrations with respect to the rest of the national territory and very positive migrations with other countries. Thus, while these neighbourhoods are– to a certain extent only – areas of transit for immigrants who have recently arrived from abroad, with a proportion of their inhabitants moving to other areas in Brussels or in Belgium, these neighbourhoods may also be represented in terms of blocking and/or rooting for a proportion of inhabitants. This underlines the material difficulties involved in leaving these neighbourhoods for the disadvantaged populations, as well as the necessity to live there for certain populations who take advantage of the resources (lower cost of living, social networks, etc.). The existence of many types of attachment to working-class neighbourhoods must not be overlooked either [Vignal, 2014; Chabrol & Rozenholc, 2016]. This dual reality of disadvantaged neighbourhoods in big European cities is also described in the study of residential trajectories in the urban area of Stockholm [van Ham et al., 2014]: while certain social groups live in this type of neighbourhood temporarily– such as young students or young households from privileged backgrounds – others are born there or live there for long periods of their adult life, or continue to go there very often after leaving. The central working-class neighbourhoods may therefore not be reduced to areas structured exclusively by a transit function.

20Finally, while the literature often suggests that the people who leave the poor territories of big cities are upwardly mobile, our analyses have not shown a social differentiation among the people who leave these neighbourhoods in Brussels. The poorest central neighbourhoods experience intensive and complex population flows, related to an appeal which is highly dependent on the social position of individuals, with a high level of heterogeneousness from one neighbourhood to the next. This leads to the exit of upwardly mobile households and disadvantaged households, which may be pushed away by gentrification processes at a local scale or simply choose to live in housing of a mediocre quality in other parts of the city with a higher status. Conversely, young socially privileged households or young people in a temporary situation of insecurity may live in these poor neighbourhoods – often for a limited period of time – benefiting from lower housing costs and easy access to urban amenities [Lenel, 2013].

21We are also able to provide a few indications as to the destinations of populations who have left the most disadvantaged neighbourhoods of the city. To that end, Figure 4 indicates the migrations of each statistical sector of the city with the “poor area”. The surface of the circle is proportional to the migrations measured in absolute terms (entries–exits), the colour of the circles distinguish the positive migrations in blue (more entries than exits from the “poor area”) and negative in red (opposite situation). The red circles in the poor area indicate significant exits towards other parts of the poor area. The reading of the map allows us to underline that the vast majority of the populations who leave the poorest statistical sectors in the city move to the adjacent intermediate territories in the west of the Region. This trend was already observed in the 1990s [Van Criekingen, 2006]. This process is very certainly at the origin of the widespread deterioration of socioeconomic indexes in the socially intermediate statistical sectors in the west of the city (see above – Table 2). A process of spatial extension of poverty towards the west is therefore observed. A much smaller proportion of those who leave the statistical sectors of the “poor area” participate in a movement of suburbanisation towards municipalities such as Ruisbroek in the south and Vilvorde in the north, i.e. municipalities located in the formerly industrialised area along the canal, towards Antwerp or Charleroi. However, the middle-class territories in the southeast of the city appear to be almost completely closed to the arrival of these populations.

Figure 4. Migrations between the statistical sectors of the poor area and the rest of the city, 2005‑2010

Figure 4. Migrations between the statistical sectors of the poor area and the rest of the city, 2005‑2010

3.2. The arrivals in the “poor area”

  • 5 The countries were classified according to the criterion of income per inhabitant. Among the rich c (...)

22In Brussels, the period from 2005 to 2010 was characterised by large influxes of populations from abroad. This immigration was concentrated in the dense central parts of the city, in particular the poorest areas (Figure 5). While it is not possible to know where these immigrants came from, their place of birth is known, whether or not they lived in Belgium before their arrival in the statistical sector. Taking this information into account, it appears that the populations born in countries which may be classified as “poor” or “intermediate”5 prefer to migrate towards the poor parts of the city, while those born in “rich” countries arrive in the eastern part of the inner ring (Figure 6). As with interior migrations, the rich southeast quadrant of the city rarely serves as the first place of residence of foreign migrants, even for those born in “rich” countries.

Figure 5. Migrations involving the rest of the world, 2005‑2010

Figure 5. Migrations involving the rest of the world, 2005‑2010

Figure 6. Migrations of people born in a rich country, 2005‑2010

Figure 6. Migrations of people born in a rich country, 2005‑2010

Conclusion

23Following this analysis, it must be borne in mind that the dynamics of neighbourhoods in Brussels must be understood at metropolitan level, as suburbanisation is widespread [De Maesschalck et al., 2015]. It is also important to note that the processes described for the metropolitan space in Brussels are also observed in the other big cities in Belgium, in particular Antwerp [Van Hamme et al., 2015]: the continued widespread suburbanisation, the complexity of the dynamics of poor central neighbourhoods and the deterioration of socioeconomic indicators in the territories adjacent to the poorest sectors of the city are also seen, often to a lesser extent.

24The role of migratory processes in these complex dynamics may be summarised in a few key features. Firstly, a significant proportion of inhabitants leave the poor territories of the city. Secondly, a large proportion of them move to the adjacent territories which are less poor, towards the west or the northeast of the Region. Thirdly, immigration from abroad is concentrated in the dense central parts of the city, and those from poor or intermediate countries settle in the poorest statistical sectors of these central parts. Fourthly, even if suburbanisation does not show signs of slowing, suburbanisation from the poor area only represents a small fraction of it. Fifthly, the rich southeast quadrant of the city appears to be largely outside these migratory dynamics, as it is neither a destination for immigrants from abroad, nor for those coming from the poor statistical sectors.

25What are the implications of these observations?

26The departure from the poorest statistical sectors is an important reality; this process leads to a dispersal of poverty towards the adjacent intermediate territories and, for a very small proportion, towards the suburbs. In terms of numbers, these exits are more than compensated for by the arrival of new immigrants, with the result that the migratory movements contribute to demographic pressure in these dense central areas due to high birth rates. Thus, in keeping with the model from the Chicago School, these territories are to a certain extent areas of transit. However, this image must be detailed in several respects. On the one hand, a significant proportion of the initial population remains in the most disadvantaged statistical sectors: in five years, 30% of the population left this type of statistical sector, the vast majority of whom went to adjacent territories which are similar to their original place of residence from a social point of view. On the other hand, this relative “spatial advancement” cannot be likened to social advancement. Our analyses have not shown a higher proportion of more privileged profiles leaving these poor territories; for example, being unemployed leads to an equally strong propensity to leave, compared with the other social categories (all other things being equal). Admittedly, due to the absence of individual data, our results remain unsatisfactory regarding the social profile of exits and call for more in-depth quantitative and qualitative analyses.

27Therefore, a significant proportion of disadvantaged populations continue to live in the poor statistical sectors, even when they become home to a significant proportion of new poor immigrants. Furthermore, public policies are aimed at increasing the social mix in these neighbourhoods, by encouraging the arrival of new middle-class residents. Therefore, in the end, the demographic pressure on these already dense territories is enormous, resulting in the multiplication of small dwellings (Figure 7).

Figure 7. Evolution of the population in the Brussels-Capital Region, 2005‑2010

Figure 7. Evolution of the population in the Brussels-Capital Region, 2005‑2010

28Faced with this situation, it should be noted that the southeast quadrant of the city experiences demographic pressure which is two times lower than the city’s average during the period from 2005 to 2010 (Figure 7). This part of the city does not receive new immigrants or inhabitants who leave the poor area of the city (see Figures 4 to 6), and the natural growth observed there is lower due to the older age structures. This is just an average, which masks the fact that the municipalities of Uccle (excluding the lower part of Uccle, extending from the poor neighbourhoods of Forest), Watermael-Boitsfort, Auderghem and Woluwe-Saint-Pierre have seen a decrease in their population, in particular in the less dense parts of these municipalities. This observation certainly calls for a proactive response in terms of housing policies [Romainville, 2010], so that the disadvantaged neighbourhoods do not have to deal with demographic pressure on their own.

Haut de page

Bibliographie

BONARD, Y., LORD, S., MATTHEY, L., ZANGHI, F., 2009. Splendeur et misère du périurbain. Introduction. In: Articulo – Journal of Urban Research, 5, http://articulo.revues.org/1479 (consulted on: 21 January 2015).

CHABROL, M., ROZENHOLC, C., 2016. Rester au centre-ville : ce(ux) qui résiste(nt) à la gentrification. In: Uzance, 4, forthcoming.

DE MAESSCHALCK, F., DE RIJCK, T., HEYLEN, V., 2015. Crossing Borders. Social-spatial Relations between Brussels and Flemish Brabant. In: Brussels Studies, No. 84, http://brussels.revues.org/1260 (consulted on: 21 January 2015).

DUJARDIN, C., SELOD, H., THOMAS, I., 2008. Residential Segregation and Unemployment: The Case of Brussels. In: Urban Studies, 45(1), 89‑113.

HEDMAN, L., MANLEY, D., VAN HAM, M., ÖSTH J., 2015. Cumulative exposure to disadvantage and intergenerational transmission of neighbourhood effects. In: Journal of Economic Geography, 15, 195‑215.

IBSA, 2015, Le Monitoring des Quartiers de la Région de Bruxelles-Capitale https://monitoringdesquartiers.irisnet.be/ (consulted on: 21 January 2015).

LEES, L., SLATER, T., WYLY, E., 2007. Gentrification, London, Routledge.

LENEL, E. 2013, Social mix in public urban action in Brussels. Project or political language? In: Brussels Studies, No. 65, http://brussels.revues.org/1134 (consulted on: 21 January 2015).

MARISSAL, P., VAN HAMME, G., VAN CRIEKINGEN, M., HAROU, R., DE KEERSMAECKER, M.-L., 2015. Gentrification dans les villes wallonnes ? Limites et risques d’une politique de densification. In: Territoires, 4 (online).

MUSTERD, S., MURIE, A., 2004. Social exclusion and opportunity structures in European cities and neighbourhood. In: Urban Studies, 41, 8, 1441‑1459.

MUSTERD, S., OSTENDORF, W., DE VOS, S., 2003. Neighbourhood effects and social mobility: a longitudinal analysis. In: Housing Studies, 18(6): 877‑92.

PARK, R.E., BURGESS, E., MCKENZIE, R., 1925. The City. University of Chicago Press.

ROMAINVILLE, A., 2010. Who benefits from home ownership support policies in Brussels? In: Brussels Studies, No. 34, http://brussels.revues.org/742 (consulted on: 21 January 2015).

SACCO, M., 2010. Cureghem: from demolition to revitalisation. In: Brussels Studies, No. 43, http://brussels.revues.org/815 (consulted on: 21 January 2015).

SAUNDERS, D., 2011. Arrival City: How the Largest Migration in History Is Reshaping Our World. Pantheon, 2011.

VAN CRIEKINGEN, M., 2006. What is happening to Brussels’ inner-city neighbourhoods? In: Brussels Studies, No. 1, http://brussels.revues.org/303 (consulted on: 21 January 2015).

VAN HAM, M., HEDMAN, L., MANLEY, D., COULTER, R., ÖSTH, J., 2014. Intergenerational transmission of neighbourhood poverty in Stockholm. An analysis of neighbourhood histories of individuals. In: Transactions of the Institute of British Geographers, 39: 402–417.

VAN HAMME, G., 2010. Social cohesion in European cities. In: FOCI, Scientific Final report. Chapter 3, pp. 68‑100.

VAN HAMME, G., GRIPPA, T., MARISSAL, P., MAY, X., WERTZ, I., LOOPMANS, M., 2015. Dynamiques des quartiers en difficulté dans les régions urbaines belges, SPP – Intégration sociale, 87 pp.

VAN HAMME, G., WERTZ, I., BIOT, V., 2011. Economic growth devoid of social progress: the situation in Brussels. In: Brussels Studies, No. 48, http://brussels.revues.org/857 (consulted on: 21 January 2015).

VANDERMOTTEN, C., 2014. Bruxelles, une lecture de la ville. Editions de l’Université de Bruxelles, 233 pp.

VIGNAL, C., 2014. L’ancrage local, une ressource pour les classes populaires des territoires désindustrialisés ? In: S. FOL, Y. MIOT, C. VIGNAL (dir.), Mobilités résidentielles, territoires et politiques publiques. Presses Universitaires du Septentrion.

Haut de page

Notes

1 Statistical sectors are the most detailed spatial entity for which data may be obtained. The analyses conducted for this article were carried out mainly at this scale.

2 This analysis is carried out for the 22 urban regions in Belgium; here we have extracted the results for the Brussels-Capital Region.

3 The detailed maps may be consulted in the atlas of the dynamics of neighbourhoods in difficulty [Van Hamme et al., 2015].

4 The exit rates for a neighbourhood are widely determined by the age structure: the younger the neighbourhood, the higher the exit rate, which is partly explained by the migrations of young adults leaving home. Furthermore, it is observed that the exit rates are closely related to the proportion of tenants in the private market in the neighbourhood.

5 The countries were classified according to the criterion of income per inhabitant. Among the rich countries, the former European countries of emigration to Belgium were distinguished (Italy, Spain, Portugal, Greece). The poor and intermediate countries therefore cover all of the countries which are not on the list in Figure 6.

Haut de page

Table des illustrations

Titre Figure 1. Elaboration of the summary index for the level of socioeconomic difficulty of statistical sectors
URL http://brussels.revues.org/docannexe/image/1338/img-1.jpg
Fichier image/jpeg, 52k
Titre Table 1. List of socioeconomic indicators and proportion of variance in the summary index of the four dimensions
URL http://brussels.revues.org/docannexe/image/1338/img-2.jpg
Fichier image/jpeg, 252k
Titre Figure 2. Level of socioeconomic difficulty of statistical sectors in the Brussels-Capital Region, 2010
URL http://brussels.revues.org/docannexe/image/1338/img-3.jpg
Fichier image/jpeg, 156k
Titre Figure 3. The major types of statistical sector
URL http://brussels.revues.org/docannexe/image/1338/img-4.jpg
Fichier image/jpeg, 160k
Titre Table 2. Dynamics of the major types of statistical sector in the Brussels-Capital Region, 2005‑2010
URL http://brussels.revues.org/docannexe/image/1338/img-5.jpg
Fichier image/jpeg, 108k
Titre Table 3. Exit rate and migrations according to type of statistical sector, between 2005 and 2010
URL http://brussels.revues.org/docannexe/image/1338/img-6.jpg
Fichier image/jpeg, 140k
Titre Table 4. Modelling of the exit rate according to statistical sector in the Brussels-Capital Region, 2005‑2010
Légende (1) Age structure is an expected exit rate given the age structure of the statistical sector and the average propensity to leave for each age category. A high index therefore signifies a high presence of people who belong to the most mobile age categories, i.e. young people (ages 18‑30 in particular). *, **, *** significant at 10, 5 and 1%. Explanation of the table: The positive coefficient for the indicator “age structure” signifies that the younger statistical sectors have a higher exit rate. The positive coefficient for the “tenant rate with private landlord” signifies that in the statistical sectors with a high proportion of tenant households with a private landlord, the exit rates are higher, while the opposite is true for the “tenant rate with social housing company”. The negative coefficient for the “index of difficulty” signifies that the greater the difficulty faced by a statistical sector, the lower the exit rate. The weakness of this coefficient shows us that the impact of this variable is lower than “age structure” and “tenant rate”.
URL http://brussels.revues.org/docannexe/image/1338/img-7.jpg
Fichier image/jpeg, 44k
Titre Figure 4. Migrations between the statistical sectors of the poor area and the rest of the city, 2005‑2010
URL http://brussels.revues.org/docannexe/image/1338/img-8.jpg
Fichier image/jpeg, 240k
Titre Figure 5. Migrations involving the rest of the world, 2005‑2010
URL http://brussels.revues.org/docannexe/image/1338/img-9.jpg
Fichier image/jpeg, 244k
Titre Figure 6. Migrations of people born in a rich country, 2005‑2010
URL http://brussels.revues.org/docannexe/image/1338/img-10.jpg
Fichier image/jpeg, 256k
Titre Figure 7. Evolution of the population in the Brussels-Capital Region, 2005‑2010
URL http://brussels.revues.org/docannexe/image/1338/img-11.jpg
Fichier image/jpeg, 230k
Haut de page

Pour citer cet article

Référence électronique

Gilles Van Hamme, Taïs Grippa et Mathieu Van Criekingen, « Migratory movements and dynamics of neighbourhoods in Brussels », Brussels Studies [En ligne], Collection générale, n° 97, mis en ligne le 21 mars 2016, consulté le 22 septembre 2017. URL : http://brussels.revues.org/1338  ; DOI : 10.4000/brussels.1338

Haut de page

Auteurs

Gilles Van Hamme

Gilles Van Hamme is an assistant professor of geography at Université libre de Bruxelles. His work is centred in particular on the impact of economic change on the European social and economic space. He recently published: Pain K., Van Hamme G., Vinciguerra S., David Q. (2015), Global networks, cities and economic performance: Observations from an analysis of cities in Europe and the USA, Urban Studies.
gvhamme@ulb.ac.be

Articles du même auteur

Taïs Grippa

Taïs Grippa has a master’s degree in geography from Université libre de Bruxelles and has been a researcher at IGEAT since 2013, where he conducted the study entitled Dynamiques des quartiers en difficulté dans les régions urbaines belges, published in 2015. He is currently working on a doctoral thesis in the field of geomatics.
tgrippa@ulb.ac.be

Mathieu Van Criekingen

Mathieu Van Criekingen is a geographer, lecturer and researcher at Université libre de Bruxelles. His work is centred in particular on the contemporary transformations of working-class neighbourhoods in the city centre, addressed from the point of view of relations of social domination. He has published several articles on the mechanisms and consequences of gentrification, such as La gentrification mise en politiques. De la revitalisation urbaine à Bruxelles, Métropoles, 13, www.metropoles.revues.org/4753
mvancrie@ulb.ac.be

Articles du même auteur

Haut de page

Droits d’auteur

Licence Creative Commons Licence CC BY

Haut de page
  • Logo BSI
  • Logo Innoviris
  • Logo Région Bruxelles-Capitale
  • Revues.org