Neighborhood-Based Networks, Social Resources, and Labor Market Participation in Two Dutch Neighborhoods

ABSTRACT:

Numerous neighborhood effect studies have reported on the negative consequences of living in disadvantaged neighborhoods for various employment outcomes, such as the duration of welfare dependence and level of income. One hypothesis for explaining this relationship is the social isolation hypothesis, which assumes that low-income residents in disadvantaged neighborhoods are worse off than their counterparts in mixed neighborhoods because they rely on other disadvantaged neighbors to find work. These ideas are addressed by comparing survey data on social resources in the social networks of residents in a low-income neighborhood and a socioeconomically mixed neighborhood in the Dutch city of The Hague. Findings show that living in a low-income neighborhood influences labor market participation indirectly by limiting residents’ access to job information. However, differences in access to job information cannot be explained by the high degree of neighborhood orientation in the social networks of residents in the low-income neighborhood.

Notes

1 This method was partially adapted from Volker and Flap (2002).

2 Based on the experiences in the test phase of the questionnaire, a distinction was made between first-degree family (parents/siblings/children) and extended family (cousins/aunts and uncles) in view of the importance attached to extended family relations by many ethnic minority residents. Moreover, in contrast to other studies on social capital, the categories of friends and acquaintances were combined because this distinction was not made and understood by respondents.

3 It is not possible to indicate to what degree the survey sample is representative for social housing tenants in the two neighborhoods, because no census data are available for subgroups at the local level due to privacy considerations. However, nonresponse was not geographically biased within the two neighborhoods and in the case of Transvaal—where social housing accounts for the majority of the housing stock—the ethnic composition of the research sample in broad lines reflects neighborhood statistics.

4 This is the group of respondents who score 0 on the socioeconomic diversity variable because they do not know anyone with an occupation from the position generator item list. Often, they are of minority background and, in particular, first-generation immigrants.

5 Other network characteristics were not added because (1) this model would only apply to those respondents who score more than 0 on the diversity indicator, the result of which is a reduction in N; (2) adding these variables does not improve the model or change the effect of personal characteristics; and (3) the relationship between network diversity and the other network variables was already discussed in the previous paragraph.