Socio-economic impacts of migrant clustering on Dutch neighbourhoods: In search of optimal migrant diversity

Socio-Economic Planning Sciences 44 (2010) 231e239

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Socio-economic impacts of migrant clustering on Dutch neighbourhoods: In search of optimal migrant diversityq Thomas de Graaff a, *, Peter Nijkamp a, b a b

Department of Spatial Economics, Faculty of Economics and Business Administration, VU University Amsterdam, The Netherlands Tinbergen Institute, Amsterdam, The Netherlands

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 21 July 2010

The recent empirical literature on the impact of migrant clustering on socio-economic welfare indicators shows inconclusive and often even contradictory results. In this paper we argue that there is not an unambiguous empirical outcome of migrant or ethnic diversity, but that it depends on the level of migrant or ethnic composition itself. A low degree of socio-economic and cultural diversity may be beneficial for neighbourhoods, whereas an excessive degree of diversity may be harmful. We test this hypothesis by (i) constructing a migrant clustering index for all neighbourhoods in the Netherlands based on a gamma index; and, subsequently, (ii) incorporating it in a regression framework to assess three relevant socio-economic outcomes: neighbourhood income, number of students, and average housing value. We show that there is apparently an optimal level of migrant clustering, and that it is remarkably robust. For the Netherlands as a whole and for the ten largest Dutch cities as well, it is striking that largely similar effects were found. Our results suggest that population composition in neighbourhoods may vary up to about 40 per cent from the national average before migrant clustering generates negative effects.
2010 Elsevier Ltd. All rights reserved.

Keywords: Migrant diversity Migrant clustering Socio-economic outcomes

1. Introduction Many countries e and in particular, many cities e all over the world have recently experienced a large influx of foreign migrants of different origin. These movements have changed the ‘face’ of cities and have prompted vivid debates in most countries on the challenges and benefits of such drastic changes in population composition. Unsurprisingly, recent decades have witnessed a large amount of empirical research on the socio-economic advantages and disadvantages of large flows of migrants for cities and neighbourhoods.1 Especially in Europe, we have observed an unprecedented immigration flow from many countries all over the world. During the last three decades, the foreign-born population in Europe has

q We are much indebted to the late Cees Gorter, who contributed greatly to an earlier version of this paper and was an important source of inspiration. Further, the authors would like to thank Raymond Florax and Brigitte Waldorf for useful remarks. * Corresponding author. E-mail addresses: [email protected] (T. de Graaff), [email protected] (P. Nijkamp). 1 For a further analysis of socio-economic diversity and segregation patterns of migrants, we refer, amongst others, to Refs. [1e7]. 0038-0121/$ e see front matter
2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.seps.2010.06.002

risen more than in any other part of the world. At the same time, migration flows have become more heterogeneous in socioeconomic and ethnic-cultural terms. This migrant diversity e based on different origin and socio-economic cultural characteristics e has prompted much discussion on the socio-economic benefits of foreign migration. Clearly, heterogeneous immigration may affect localities in the host country through different channels. Workers with different cultural backgrounds may embody complementary skills, problem-solving abilities, ideas and aspirations. Bonding and bridging social capital in migrant communities is, therefore, of utmost importance [see, e.g., Ref. [8]], as a strong communication and cooperation among these migrants with the natives may increase the local productivity and socio-economic performance due to knowledge spillovers or other positive externalities, as argued also in the socio-cultural embeddedness literature [see, e.g., Ref. [9]]. Despite the advantages of socio-cultural diversity from immigration, it should also be recognized that a high degree of variety in socio-ethnic and cultural backgrounds may lead to socio-economic fractionalization accompanied by excessive transaction costs for communication [the ‘Babylon’ effect; see Ref. [10]]. Such a case may then reduce productivity and economic performance. Too much cultural diversity in an area may frustrate mutual understanding, leads to local stress situations, or even distort local identity. Thus,

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there may exist some sort of optimal cultural diversity in urban migrant neighbourhoods. This calls for fundamental and evidencebased research on cultural diversity conditions that strengthen the socio-economic profile of urban migrant districts. Apart from the broad concern on the increase and composition of migration flows, there are two more reasons for this surge in research. Firstly, more detailed migration data sets have become available on a wider scale: detailed not only in terms of number of individuals and their characteristics, but also in terms of their spatial distribution. Geographical information systems and remote sensing techniques have enabled researchers to study the effects of segregation on a level of spatial detail that was not imaginable until a few years ago. This increased availability of data also allows the use of more sophisticated econometric tools, such as instrumental variables and panel data methods [see, e.g., Ref. [11]]. Secondly, understanding the impact of migrant diversity e or of population composition, in general e on the socio-economic performance of individuals within specific spatial areas, such as urban neighbourhoods, is of crucial importance for the development of (local) policy. The question whether or not policy intervention is needed e and, subsequently, what the best policy instrument should be e is currently still heavily debated and not yet conclusively answered. Indeed, the literature regarding the impact of socio-cultural and ethnic diversity on socio-economic outcomes for cities and neighbourhoods still shows rather inconclusive and even contradictory results.2 In this paper, however, we argue that there is no single impact of a diverse migrant composition, but that the impact varies with the level of diversity among migrants. In economic terms, one may even speak of an optimal extent of cultural diversity. Thus, a small degree of diversity or segregation within a neighbourhood may be beneficial, but too much is harmful. This argument can be experienced to some extent in almost every large city in the world. Neighbourhoods that receive a moderate influx of migrants are typically known as dynamic and diverse neighbourhoods, and seem to prosper, in general, rather well. However, neighbourhoods in which large groups of migrants (usually of the same origin) cluster can perform significantly worse than the average, and might even change into ‘ghettos’. So, the size and nature of the interaction between cultural diversity and neighbourhood outcome depends on the amount of that very same segregation or migrant composition. To detect the impact of population composition on socioeconomic performance, first a proper tool is needed for measuring socio-cultural and spatial migrant clustering. The literature contains many indicators to represent migrant clustering and segregation. In our research, we will employ the gamma index of Ref. [12] to measure whether neighbourhoods in Dutch cities have a deviant population composition. The gamma index has two advantages: namely, that, in theory, it is both scale-independent and it is a result of utility maximization theory.3 The remainder of the paper is organized as follows. The next section will give a brief overview of the literature concerning the pros and cons of cultural diversity and segregation, and why it is

2 In the present paper we will broadly use the following definitions for the three basic concepts of clustering, segregation and diversity: (i) clustering of migrants emerges when a close group of migrants with similar cultural, language or ethnic characteristics is geographically concentrated; (ii) segregation takes place in case of an enforced or emerging separation of people of a common ethnic, socio-economic or racial group in a community; (iii) migrant diversity reflects a variety in population features on the basis of complementary socio-economic, cultural or ethnic characteristics. 3 Because we measure segregation on a neighbourhood level, we refrain from individually based segregation measures which are more network-oriented and offer more insight on the individual level of individual migrants (see, e.g., Ref. [13]).

plausible that there is an optimal level of migrant diversity. Thereafter, we present the specific migrant clustering index that we use in this paper. Subsequently, we discuss the data and show how the migrant clustering index varies over neighbourhoods in the Netherlands. Next, we use this index to measure the impact of migrant diversity on three distinct socio-economic outcomes, and argue that there does indeed seem to be an ‘optimal’ level of diversity. Remarkably, this ‘optimal’ point seems to be rather similar for socio-cultural diversity both within urban neighbourhoods and within all neighbourhoods in the country. The last section provides concluding remarks and discusses possible extensions of this research. 2. Theory and review The analysis of the socio-economic benefits (and costs) of cultural diversity e as a result of mass immigration e has been the subject of much recent research in economics, sociology and political science. Important contributions were made by, among others [7,14e16]. Nowadays, there is indeed a large body of literature concerning the socio-economic consequences of migrant clustering. Unfortunately, most of the results of this literature are at first sight inconsistent with each other. Especially the earlier studies e culminating in the seminal research of Ref. [4] e report significant negative effects from migrant clustering, while later studies, such as Ref. [17] find insignificant or even positive effects. Ref. [11] argues that this ambiguity is caused by an incorrect measurement of selection and sorting effects between immigrants. That is, without correcting for such mechanisms, at first sight, migrant clustering might seem to have a negative effect on the socio-economic outcomes of a neighbourhood. However, if correcting appropriately for this sorting mechanism, segregated areas contribute significantly to individual human capital, more or less in the same way as Ref. [18e20] proposed with his introduction of the concept of ethnic human capital. The benefits of migrant segregation can usually be labelled as migrant network effects [for a discussion on the working of network effects, see, e.g., Ref. [21]]. When more individuals from the same ethnic background cluster, access to information, transportation, employment opportunities, and (ethnic) goods becomes available or less costly. For example, as Ref. [22] has shown, having large socio-ethnic networks enables people to find jobs faster. Or, in the words of Ref. [11]: “. the benefits associated with segregation can be thought of as reducing the costs of assimilation to the host society, primarily by making that process less necessary to economic success.” They argue that segregated areas do indeed have a positive impact by providing a positive treatment effect: migrant concentration seems to have a positive impact on the earnings and language proficiency of young adults. In recent years much attention has been paid to the analysis of the complex interrelationships between cultural diversity and the economic performance of European regions [see, e.g., Ref. [23]]. This research finds that there is a causal link from migrant diversity to regional productivity in 12 EU countries. Moreover, for a specific set of local labour markets in western Germany it has been found that such diversity interacts with skills: even though a greater share of unskilled foreign workers may yield negative wage and employment effects, greater diversity among a given share of unskilled workers has positive productivity effects [see also Ref. [24]]. The recent literature on agglomeration economies is also important when considering migrant diversity. It has been shown

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that industrial diversity has positive effects on employment development [25] and this could be connected to cultural diversity. The size of a region and the concentration of economic activities and of interactions within the population might be very important for the optimal degree of diversity. This links directly to the literature on growth in cities, in which the roles of specialisation, competition and diversity have been recently identified by means of a meta-analysis [26]. On the other hand, there are many disadvantages associated with migrant clustering reported in the literature. Ref. [27] was one of the first to advocate this by formulating his spatial mismatch hypothesis, relating the relative position of ethnic enclaves within the city centre with employment opportunities outside the city centre. Most of the associated disadvantages seem to accrue over time. If migration costs truly do seem to be endogenous (the abovementioned access becomes more available and cheaper when more migrants are clustered e cf. [21]), then segregated areas face an increasing influx of migrants. Thus, migrant clustering in itself has the tendency to grow stronger over time. This causes the initial positive network externalities e or the positive influence of the ethnic human capital of Ref. [20] e to change into negative network externalities. Indeed, there is ample empirical evidence [see, e.g., Refs. [4,28,29]] that migrant clustering (albeit in low-skilled areas) is indeed disadvantageous for ethnic minorities. There is another line of research which stresses another negative impact of migrant clustering. This particular notion is indirectly derived from trade theory and argues that, in order to trade, individuals must trust each other. Trust may be derived from similar backgrounds, speaking a similar language, or sharing a similar ethnicity. As Refs. [30] and [10] show, there is indeed a negative effect of segregation on language proficiency (which seems to contradict the results of Ref. [11] above). If segregation becomes stronger, then the incentive to learn the native language seems to diminish. It is demonstrated that this diminished language proficiency has a profound effect on (future) earnings [31,32]. The observations presented above are mainly economicallyoriented arguments, while in sociology and geography other examples of the positive or negative impacts of migrant diversity and clustering have been found. For example, Ref. [33] found that in highly segregated areas a devaluation of social norms and values occurred, while Ref. [34] reported a lagging educational performance of schools in segregated areas. On the other hand, there is also a literature that stresses the beneficial influence of migrant diversity by bringing forward the importance of ethnic entrepreneurship [see, e.g., Ref. [35,36]]. Here, small ethnic enterprises may form a stepping-stone for job-opportunities, which are otherwise not available for individuals belonging to migrant minorities (due to discrimination, skill deficiencies, lack of information, et cetera). A recent systematic overview of all the advantages and disadvantages of diversity in a city can be found in Ref. [7] and [16]. Thus, the impact of migrant clustering seems to be e empirically considered e rather ambiguous. However, taking into account the endogenous nature of migrant diversity and the various socioeconomic externalities associated with diversity segregation, it might well be that migrant clustering might be both beneficial and harmful. To interpret this in an economic context, we refer here once again to Lazear’s (1999) trade-theory regarding the impact of ethnic segregation on language. With little ethnic segregation, ethnic minorities are still inclined to learn the majority’s language (because they have to trade with them), while the total pay-off increases with the level of segregation (more ethnic minorities indicates more ‘types’ of knowledge or goods and thus a higher potential pay-off). However, if the potential pay-off is high the level of segregation is high as well, which leads to a decreasing incentive for the ethnic minority to learn the majority’s language. Thus there

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seems to be a trade-off between the pay-off of trade and the inclination to trade. In this paper we aim to identify such an ‘optimal’ level of migrant clustering by looking at the impact of clustering at a neighbourhood level on three economic outcomes for which uniform data are readily available: the average value of the income level; the average value of the housing stock; and the percentage of individuals who follow an education at college or university. To find this empirical point, we use an empirical clustering index from Ref. [12] which has as a specific advantage that the index is scaleindependent, and that it is directly derived from economic theory. 3. An empirical index for migrant clustering This section presents the migrant clustering index (henceforth referred to as the gamma index) as developed by Ref. [12] and later adapted by Ref. [37]. As indicated above, the gamma index has two major advantages compared with other concentration indices. The first one is that the gamma index is the result of profit (or utility) maximizing behaviour by each individual immigrant (or household), which implies that the index originates directly from microeconomic theory.4 Secondly, Ref. [12] has proven that the index is scale-independent, which makes comparisons on different geographical scales possible. This is in contrast with, for example, Gini’s coefficient, which is sensitive to differences in geographical scale [42].5 The rationale behind the index is as follows. Assume that there are M migrant groups living in a country, and that the nationwide share of each migrant group m(˛ {1, ., M}) is xm. Now, if individuals are perfectly randomly distributed across the country, then the share xmr of individuals belonging to migrant group m in areas r(˛ {1, ., R}) is xm. If these area-wide shares of migrant group m deviate from nationwide shares of migrant group m, then there is an under or overrepresentation of that specific migrant group. This goes both ways. On the one hand, large cities usually contain many representatives of many migrant groups, and are therefore expected to display an overrepresentation of ethnic minority groups and an underrepresentation of the indigenous population. Thus, the index should be high in multicultural cities. On the other hand, rural areas are usually only inhabited by the indigenous population, and therefore show an overrepresentation of the indigenous population and an underrepresentation of ethnic minority groups. Therefore, the index should be at least different from zero in rural areas. To construct an operational index, we start with the choice of an individual k, belonging to an ethnic minority m, who has to choose a region r in which to live. We assume further that the utility of location r for individual k is given by:

logpkmr ¼ logpmr þ 3kmr ;

(1)

where the ekmr denote idiosyncratic factors e such as specific needs for open space, nature and public facilities e which are assumed to be distributed according to a Weibull distribution, and where logpmr reflects certain location-specific factors which might be

4 This in contrast to (in no particular order): the segregation index, which is often used in geography and is defined as the percentage of a population which has to move in order to obtain a homogeneous distribution of that population (for an application of this index to the Dutch situation, see, e.g.,Ref. [38]); the dissimilarity index, which bears a close resemblance to Krugman’s specialisation index [39]; and subsequent variations on the latter [40,41]. 5 However, as Ref. [37] show, when units such as neighbourhoods are spatiallycorrelated, then the scale-independency of the gamma index no longer holds. The scale-independency only holds when units are not correlated with respect to any particular aggregation scheme.

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particularly appreciated by ethnic minority group m. As such, these location-specific factors could be related to a large variety of reasons, such as:

P ðsmr  xm Þ2  : plimN/N m P 1  x2m m

local amenities: ethnic minority groups might differ in their valuation of specific local amenities; type of activity: ethnic minority groups may go to particular locations for specific activities, e.g. for work or study; size of the residing ethnic group: ethnic minority groups might differ in their preference to reside in those locations with large numbers of individuals from the same ethnic group.

Proof of Theorem (1). We start with:

plimN/N

i X X h ðsmr  xm Þ2 ¼ plimN/N E ðsmr  xm Þ2 m

i Xh X varðsmr  xm Þ þ ðEðsmr  xr ÞÞ2 ðsmr  xm Þ2 ¼ plimN/N m

Note that, in this study, we are not interested in the reason for an individual’s locational choice. Instead here, we want to separate idiosyncratic preferences from group preferences. Conditional on the realization of the random variables logp1r ; .; logpMr e that is, the distribution of ethnic minority groups m in region r e and our distributional assumption, the probability that individual k living in region r belongs to ethnic minority m is:

p

mr Prðmk ¼ mjlogp1r ; .; logpMr Þ ¼ P ¼ pmr ;

pir

(2)

which boils down to a conditional logit model (see [43]). Following [37], we impose the following assumptions on the distribution of pmr:

Eðpmr Þ ¼ mm

(3)

varðpmr Þ ¼ gr mm ð1  mm Þ;

(4)

where mm ˛ [0,1] and gr ˛ [0,1]. Now it is easy to see that E(xm) ¼ mm. Thus, mm actually reproduces the actual distribution of ethnic minorities observed in the data. This entails that it is expected, for example, that there are more individuals from the indigenous population in an area than from an immigrant population. The variance of pmr measures the sensitivity of the utility of an individual to region-specific factors. Obviously, for ethnic minorities in less denser areas the variance is likely to be higher than in denser areas. Basically, this is because location-specific factors pmr are high for a specific ethnic group and zero for the others. A particular example is formed by rural areas in which only the indigenous population resides. The parameter gr drives the variance. When gr ¼ 0, then the variance of pmr is at its minimum, and the location decision of ethnic groups is completely dominated by the idiosyncratic factors emr. This happens when there is a representation of ethnic groups in an area proportional to the nationwide average. When gr ¼ 1, then the variance of pmr is at its maximum, and the location decision of ethnic groups is totally dominated by the variation in location-specific factors logpmr . This happens when a region is completely inhabited by the indigenous population, or in areas completely dominated by one or more specific migrant groups, which might happen in exceptionally clustered ‘ghetto’s’. The parameter gr can therefore be interpreted as an index for the clustering of ethnic minorities. It indicates whether the locational choice of individuals is influenced by ideosyncratic or region-specific factors. Now we have to define an unbiased estimator for gr. To do so, we first define smr as the share of ethnic minority m living in area r. Then [for more details, see Ref. [37]]: Theorem 1. Consider the case with N individuals belonging to ethnic minorities m with nationwide share xm, who choose their residential areas according to (1), (3) and (4). Then a consistent estimator for gr is:

m

(6) X X ðsmr  xm Þ2 ¼ plimN/N ðpmr  mm Þ2 m

(7)

m

# " X X X 2 2 ¼ ðsmr  xm Þ ¼ E ðpmr  mm Þ varðpmr Þ m

m

m

(8)

m

! X X X 2 2 gr mm ð1  mm Þ ¼ gr 1  mm ; ðsmr  xm Þ ¼ m

i

(5)

m

(9)

m

where (5) uses the definition of the probability limit, (6) the identity var[X] ¼ E[X2]  (E[X])2, (7) plimN/N varðsmr  xm Þ ¼ 0, E P (smr) ¼ pmr, and E(xm ¼ mm), and (9) m mm ¼ 1. Rearranging terms and using E(xm) now yields the desired result for gr. In theory, gr should be scale-independent, but only if the values of logpmr are drawn independently of the aggregation scheme. In reality, this is usually not true because surrounding neighbourhoods usually share similar characteristics. Thus, neighbourhoods within certain areas, such as cities, are probably correlated, and therefore gr is probably biased for more aggregated areas. Indeed, as Ref. [37] convincingly show, aggregation tends to overestimate gr. Therefore, we intend to measure gr in the greatest detail possible.

4. Data, empirical measurement and exploratory interpretation To measure migrant clustering in the Netherlands at a detailed spatial scale level, we use an extensive neighbourhood database from 2005 provided by the Central Bureau of Statistics.6 We use data for seven distinct ethnic groups: namely, Moroccans, Dutch Antilleans, Turks, Surinamers, other Non-western groups, minority groups from Western countries, and the indigenous population. For our case, an individual belongs to an ethnic minority if at least one of his or her parents was born outside the Netherlands, otherwise he or she belongs to the indigenous population. For reasons of privacy, figures concerning the migrant groups are only provided when a neighbourhood contains more than 50 inhabitants and when there are at least ten individuals of nonWestern origin living in that particular neighbourhood. Further, the figures are rounded to whole percentages of total population within that neighbourhood. In total, there are 11,286 neighbourhoods as measured by the Central Bureau of Statistics in the Netherlands, but because of the imprecise recording we restrict the empirical research to neighbourhoods with more than 250 individuals. This leaves us with 7737 neighbourhoods with a mean population of about 2058 inhabitants.

6

See http://www.cbs.nl/statline/.

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Table 1 Shares of ethnic groups in the Netherlands in 2005. Ethnic group

Share

Moroccans Dutch Antilleans Turks Surinamers Other non-Western nationalities Western nationalities Indigenous population Total

0.0189 0.0073 0.0216 0.0197 0.0342 0.0872 0.8109 1

Table 1 gives the nationwide shares of each ethnic group. Obviously, the indigenous population (81 per cent) forms by far the largest ethnic group in the Netherlands. The second largest group (around 9 per cent) is formed by migrant groups that have a Western origin. Usually, individuals in these groups have parents from a Western country e usually Belgium and Germany. Japan and Indonesia also belong to this category. The third largest group (around 3.5 per cent) is formed by immigrants from non-Western countries. From the category typically referred to as ‘guestworkers’, the Turks form the largest group, closely followed by the Surinamese and the Moroccans (with these latter three groups around 2 per cent each). The ethnic group formed by migrants from the former colony the Netherlands Antilles is relatively small with a share of 0.7 per cent. The resulting gamma index e thus calculated over all neighbourhoods with more than 250 inhabitants e falls in the range [0.0001e1.3971]7 with a mean of 0.0218 and a variance of 0.0617. Thus, the measured empirical migrant clustering in the Netherlands is not very high. To illustrate this, Fig. 1 shows the mean gamma index for each municipality in the Netherlands. Obviously, most municipalities in the Netherlands do not display a high level of migrant diversity. Low diversity can especially be encountered in rural municipalities in the Western and Southern parts of the Netherlands. Here, the population compositions are similar to that of the Netherlands as a whole. Rural areas in the Eastern and Northern parts of the Netherlands display more ethnic diversity. Note that this may be caused by an overrepresentation of the Dutch population (there are no individuals from foreign origin at all) or an overrepresentation by some migrant groups (who seem to have a particular preference for rural areas). But in general, levels of migrant diversity seem to be fairly low in most parts of the Netherlands. There are, however, three exceptions to that. The first one is, as could be expected, migrant clustering in the big cities e most notably Amsterdam, Rotterdam, the Hague and to a lesser extent, Utrecht, Eindhoven, Almere, and some smaller cities. Diversity here is most likely caused by the influx of immigrants from non-Western countries, and then in particular from Surinam, the Netherlands Antilles, Morocco and Turkey. The second exception is the municipalities close to the Belgian and German border. This seems to be especially the case in the South-western part (called ZeeuwsVlaanderen) and the South-eastern part (in the province of Limburg). Here migrant clustering is most likely caused by immigrants from Belgium and Germany, respectively. The third exception is the high level of migrant diversity in some rural municipalities, which is due to Dutch localization policies regarding asylum seekers from non-Western countries. Because socio-cultural diversity in the Netherlands is most likely caused by several factors, it is insightful to look at the

7 Although it is theoretically not possible, in practice it might occur for g to be higher than 1. In our data set this happens six times with relatively small neighbourhoods, and it does not affect the results if the variable is restricted to the interval [0e1].

Fig. 1. Average gamma coefficient for each municipality in the Netherlands.

distribution of the gamma index. If the distribution displays several tops, then different causes of diversity are at work at distinct levels. Therefore, Fig. 2 shows a histogram of the gamma index above 0.05 (the majority of the index is just above zero, as displayed in Fig. 2, and is of less interest for us at the moment). Clearly, the distribution of the gamma index shows a decreasing slope, with only a few outliers towards 1. These outliers are formed by small neighbourhoods that have very specific circumstances, such as a large presence of asylum seekers. To illustrate the size of the gamma index: neighbourhoods that are generally considered as highly clustered in Amsterdam, the Hague, and Rotterdam usually have a gamma index between 0.4 and 0.7. In general, it does not seem that there are ‘clusters’ of cultural diversity. Most neighbourhoods are not segregated, and only very few face high levels of segregation. However, one might argue that migrant diversity is a purely urban phenomenon and that one should therefore focus on the larger cities. To do so, we took a subset from our data set containing only the neighbourhoods from the ten largest cities in the Netherlands (which, in 2005, were:

Fig. 2. Histogram of gamma coefficients for neighbourhoods in the Netherlands.

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measurable socio-economic indicators: namely, neighbourhood income levels; number of students within a neighbourhood; and the average housing value within a neighbourhood. Obviously, these outcomes are also determined by other exogenous variables, such as average age within a neighbourhood, average educational level, number and types of jobs available, and the degree of urbanization. Therefore, we aim to control as much as possible for these variables, as long as they are exogenous. To model the impact of segregation we adopt the following straightforward regression:

yr ¼ a þ d1 gr þ d1 g2r þ bXr þ 3r ;

Fig. 3. Histogram of gamma coefficients for neighbourhoods in the ten largest Dutch cities.

Amsterdam, Rotterdam, The Hague, Utrecht, Eindhoven, Tilburg, Groningen, Almere, Breda, and Nijmegen). Fig. 3 again shows a histogram of neighbourhoods with a gamma index above 0.05, but now only for the ten largest cities. Remarkably, the general shape of the distribution within the largest cities is the same as that for the whole country. Most neighbourhoods in the largest cities have low migrant clustering levels, and only a few are highly segregated with gamma indices between 0.4 and 0.7. Even the outliers close to 1 have now disappeared. Using this measure of migrant clustering the following section now relates this measure to three relevant socio-economic outcomes.

(10)

where y denotes the socio-economic outcomes as explained above; X is a vector of exogenous variables; e a residual; and a, d1, d2, and b are coefficients. We are especially interested in the sign and size of the coefficients d1 and d2. If a low level of migrant clustering is beneficial but a high level harmful for individuals living within such an area, then d1 should be positive and d2 negative. Note that these coefficients do not say anything about the direction of causality (see Ref. [44]). Migrant clustering may cause socio-economic outcomes, and socio-economic outcomes may cause clustering. Without proper instruments, we can only say something about the correlation. In the following two subsections, we estimate regression (10) twice, once for the Netherlands and once for the ten largest Dutch cities, in order to investigate whether there are structural differences between the urban and the national scale. 5.1. Impact of migrant clustering within the Netherlands

5. Empirical impact assessment of migrant clustering To research the effect of cultural diversity within neighbourhoods, we look at the effect of segregation on three relevant and

Table 2 presents the results for the impacts of migrant clustering on three economic outcomes in neighbourhoods in the Netherlands. The estimation technique itself is a weighted least

Table 2 Impact of segregation on various economic outcomes in Dutch neighbourhoods. Variable

Students  1000

Income levels

Housing value

Coeff.

Std. Err.

Coeff.

Std. Err.

Coeff.

6.347** 6.335** 0.112** 0.089**

1.381 1.821 0.009 0.005

11.041** 13.94** 0.029* 0.068**

1.872 2.344 0.013 0.009

351.609** 385.311** 3.916** 3.141**

64.783 90.921 0.460 0.231

0.020 0.009 0.007 0.010 0.034 0.000

0.133** 0.389** 0.011 0.080** 0.085* 0.000**

0.026 0.038 0.011 0.015 0.040 0.000

1.696* 3.098** 1.144** 1.998** 8.348** 0.001**

0.712 0.408 0.271 0.378 1.224 0.000

Households (left-outcategory: with children (in percentages)) One person (%) 0.072** 0.013 Without children (%) 0.082** 0.014 Size of households 2.210** 0.335

0.015 0.036** 0.933*

0.011 0.011 0.476

3.819** 2.096** 171.069**

0.697 0.621 20.629

0.082** 0.099** 0.030** 0.112** 0.076** 0.049** 0.060** 5.250** 7737 0.353 81.101

0.004 0.010 0.009 0.028 0.014 0.010 0.008 1.625

0.642** 0.536 6.538** 6.108** 1.397** 0.844** 3.396** 914.291** 7732 0.483 256.607

0.117 0.343 0.362 0.551 0.339 0.297 0.215 104.472

g g2 Western immigrants (%) Indiginous pop. (%)

Age (left-outcategory: >65 year (in percentages)) <15 years (%) 0.021 15e25 years (%) 0.155** 25e45 years (%) 0.028** 45e65 years (%) 0.053** Urbanization level 0.210** Population density 0.000*

Sector employment Industry Trade Service sector Education Health sector Other non-com. serv. Agriculture Constant N R2 F(20, 7716) Significance levels:

y: 10%

0.007* 0.003 0.168** 0.171** 0.018* 0.019* 0.012y 9.518** 7737 0.487 277.912 *: 5%

**: 1%.

0.003 0.019 0.008 0.013 0.008 0.008 0.006 2.095

Std. Err.

T. de Graaff, P. Nijkamp / Socio-Economic Planning Sciences 44 (2010) 231e239

Fig. 4. Impact of segregation in the Netherlands on various socio-economic outcomes.

237

The remainder of the coefficients in Table 2 conform to our intuition. We have statistically controlled for the (richer) indigenous population and the Western ethnic group, and these two variables have a large statistically significant impact on the average income level within a neighbourhood and the average value of a house. Further, more individuals within a neighbourhood above the age of 45 suggest higher income levels and larger housing values, and so do neighbourhoods with high employment levels in the service sector. Relatively large numbers of students can usually be found in more urban and densely populated areas without much industry and trade. Households without children can usually be found in richer neighbourhoods with higher housing values, and so are larger households. 5.2. Impact of migrant clustering within Dutch cities

squares approach, with the number of inhabitants of each neighbourhood used as weights. The most important result in Table 2 is that the coefficients for g and g2 are significant and have the hypothesized sign. Thus, d1 is positive and d2 is negative. Fig. 4 gives a graphical interpretation of the impact of the standardized coefficients on these three socioeconomic outcomes: neighbourhood income levels, number of students within a neighbourhood, and the average housing value within a neighbourhood. Clearly, the results imply that migrant diversity is associated with beneficial socio-economic outcomes until the gamma index reaches a maximum of about 0.4e0.5. More diversity then seems to correlate with less beneficial outcomes and even with negative outcomes. This indicates that low levels of diversity (as usually found within cities) tend to coincide with rather successful neighbourhoods. Only when neighbourhoods become highly clustered, which occurs mainly in the largest cities, does cultural diversity then coincide with less successful neighbourhoods.

Table 3 presents the results for the impacts of cultural diversity on the various economic indicators on neighbourhoods in the ten largest Dutch cities. Quantitatively, the values for the coefficients d1 and d2 have clearly changed (the impact on the housing values in the ten largest Dutch cities is much higher), but, qualitatively, they have not: d1 is still positive, and d2 is still negative (and significant). To compare the two sets of results better, we again give a graphical interpretation of the impact of the standardized coefficients on the three socio-economic outcomes in Fig. 5. Strikingly, the estimated values of d1 and d2 again imply that there seems to be a maximum around 0.4e0.5 (although for the number of students it seems to be slightly less). Thus the impact of urban migrant clustering does not seem to differ much from the impact of clustering on a nationwide level. Even within cities, it seems that a moderate degree of migrant diversity coincides with better socio-economic outcomes for the inhabitants. Only when

Table 3 Impact of segregation on various economic outcomes in neighbourhoods of the 10 largest Dutch cities. Variable

Students  1000

Income levels Coeff.

g g2

Std. Err.

10.697** 11.956** 0.206** 0.103**

2.676 3.474 0.029 0.010

Age (left-outcategory: >65 year (in percentages)) <15 years (%) 0.024 15e25 years (%) 0.114** 25e45 years (%) 0.021 45e65 years (%) 0.047* Urbanization level 0.189* population density 0.001**

0.044 0.019 0.015 0.023 0.078 0.000

Western immigrants (%) Indiginous pop. (%)

Coeff. 11.395* 15.77** 0.120 0.058* 0.138 0.422** 0.045 0.144y 0.557** 0.000**

Households (left-outcategory: with children (in percentages)) One person (%) 0.068y without children (%) 0.122** Size of households 2.263*

0.036 0.030 1.130

0.060 0.040 2.627

Sector employment Industry Trade Service sector Education Health sector Other non-com. serv. Agriculture

0.008 0.038 0.022 0.042 0.029 0.026 0.038

0.123** 0.339* 0.061 0.209 0.109 0.016 0.034

0.002 0.127** 0.194** 0.126** 0.041 0.021 0.056 13.026* 565 0.811 69.544

Constant N R2 F(22, 542) Significance levels:

y: 10%

*: 5%

**: 1%.

5.691

0.659 565 0.432 16.246

Housing value Std. Err.

Coeff.

Std. Err.

4.997 5.318 0.095 0.023

638.177** 683.527** 9.341** 3.996**

117.762 153.974 1.418 0.517

0.125 0.099 0.058 0.081 0.196 0.000

7.854** 0.367 0.684 2.813** 3.954 0.000

1.822 0.717 0.578 0.839 4.125 0.001

0.050 0.046 1.877

4.303** 4.668** 125.843**

1.257 1.056 43.399

0.023 0.144 0.057 0.185 0.080 0.077 0.085

0.223 4.867** 3.694** 2.680 2.044y 0.626 7.306**

0.338 1.350 0.825 1.817 1.066 1.031 2.514

9.624

993.323** 562 0.695 34.851

196.389

238

T. de Graaff, P. Nijkamp / Socio-Economic Planning Sciences 44 (2010) 231e239 Neighbourhood income

Number of students

House value

1

Standardised impact

0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6

Gamma index

Fig. 5. Impact of segregation in the ten largest Dutch cities on various socio-economic outcomes.

there is too much migrant clustering does the corresponding socioeconomic performance decrease. The remainder of the variables conform again to intuition and the discussion above applies here as well.

neighbourhoods are most likely segregated as well. And the effects of clustering might well be felt in surrounding neighbourhoods. To correct for this spatial correlation, ideally we should incorporate spatial autocorrelation in the regression analysis. This would then eventually result in less biased results. Clearly, this addition would figure high on the research agenda for the near future. And, finally, we have provided an operational statisticaleconomic analysis of clustering effects leading to issues such as optimal diversity levels. But the nature of this ‘mix’ calls for future applied research. Are all the ingredients of this ‘melting pot’ equal? Or is it possible to identify group-specific elements that have a differential impact on the migrant clustering index? To answer these questions would need a combination of research from various different disciplinary backgrounds. Migrant diversity is not an issue of sheer numbers, but of relative shares and interaction. But the general lesson from our analysis is that ‘too little’ and ‘too much’ is not optimal from a social perspective. Migration policy e with a view to the enhancement of social benefits - calls for a balance in shares and interactions, while taking into account intervening factors such as skill levels.

6. Conclusions and further research

References

In this paper we have investigated whether there is an ‘optimal’ point of migrant diversity: a level of diversity that coincides with optimal socio-economic neighbourhood outcomes. Indeed, there does seem to be such an ‘optimal’ level. Population composition in neighbourhoods may vary up to about 40 per cent from the national average before migrant clustering displays negative effects. In other words, neighbourhoods seem to prosper more if the population composition is slightly deviant from the national average. Only if the population composition deviates too much do negative externalities arise. This result is remarkably robust with respect to several economic outcomes and an urban versus a national approach. The latter is especially noteworthy. Ethnic clustering is usually regarded as an urban phenomenon, but its effects on urban neighbourhood outcomes do not differ much from other neighbourhood outcomes. This is probably caused by our level of spatial detail: namely, we have looked at almost 8000 neighbourhoods, and only a few are highly segregated. Most neighbourhoods in the Netherlands simply do not deviate that much from the average national or urban population composition. In fact, only 31 of the neighbourhoods have a gamma index above 0.4, and they consist of those highly clustered ethnic urban neighbourhoods that seem to perform less well (in terms of average income, education, and so forth). Note, therefore, that only these 31 neighbourhoods are responsible for the non-linear behaviour of the gamma index. Obviously, the analysis is subject to various sorting mechanisms. When neighbourhoods become more culturally diversified, higher skilled individuals tend to move out, while lower-skilled individuals tend to move in. Therefore, we refrain in this paper from speaking about causality issues regarding cultural diversity in cities. In fact, we are only looking at correlations without interpreting them by using some sort of causal process. For conclusions about the impact of clustering on individual performance, we at least need to find proper instruments or create a panel data set. And, even then, we need to find additional information about average skill levels within neighbourhoods in order to really address the impact of migrant clustering on individual performance. Nevertheless, our empirical results really show that there is an optimal point of cultural diversity for the average neighbourhood performance. The analysis might suffer from another bias, and that is spatial correlation: if neighbourhoods are segregated, their surrounding

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Thomas de Graaff is currently an assistant professor in the Department of Spatial Economics at the VU University Amsterdam. He holds a Master’s degree in business econometrics from the VU University in Amsterdam in 1997 and a Ph.D. in economics from the Department of Spatial Economics at the VU University, Amsterdam. His Ph.D. thesis dealt with ethnic cluster forming and endogenous dynamics in international migration patterns. After obtaining his Ph.D. in 2001, he continued working at the department of Spatial Economics as a researcher and subsequently as an assistant professor. He also has been a quest researcher at the Netherlands Bureau for Economic Policy Analysis (CPB) and the Netherlands Environmental Assessment Agency (PBL). Spatial location patterns of households and firms, spatial econometrics, and migration theory are among his main research interests.

Peter Nijkamp is professor in regional and urban economics and in economic geography at the VU University, Amsterdam. His main research interests cover quantitative plan evaluation, regional and urban modelling, multicriteria analysis, transport systems analysis, mathematical systems modelling, technological innovation, entrepreneurship, environmental and resource management, and sustainable development. In the past years he has focussed his research in particular on new quantitative methods for policy analysis, as well as on spatial-behavioural analysis of economic agents. He has a broad expertise in the area of public policy, services planning, infrastructure management and environmental protection. He is past president of the European Regional Science Association and of the Regional Science Association International. He is also fellow of the Royal Netherlands Academy of Sciences, and past vicepresident of this organization. From 2002 to 2009 he has served as president of the governing board of the Netherlands Research Council (NWO). In addition, he is past president of the European Heads of Research Councils (EUROHORCs). He is also fellow of the Academia Europaea, and member of many international scientific organizations. He has acted regularly as advisor to (inter)national bodies and (local and national) governments. In 1996, he was awarded the most prestigious scientific prize in the Netherlands, the Spinoza award.