At which geographic scale does ethnic diversity affect intra-neighborhood social capital?

Sluiter, Roderick; Tolsma, Jochem; Scheepers, P.L.H.

doi:10.1016/j.ssresearch.2015.06.015

Cited by 21 publications

(17 citation statements)

References 47 publications

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“…Theoretically, it is often assumed that the negative effects of ethnic diversity are most pronounced in smaller contexts (Putnam, 2007) because people spend most of their free time in their immediate residential surroundings (Öberg, Oskarsson, & Svensson, 2011). Inhabitants might thus be more aware of the ethnic composition of smaller contexts (Sluiter et al, 2015). Consequently, we expect that streets and neighborhoods more accurately reflect people's daily experiences with ethnic heterogeneity than larger contexts, such as districts.…”

Section: A Multiscale Approachmentioning

confidence: 95%

“…With few exceptions, scholars in the European context tend to assume that the hypothesized effects of diversity are similar for both the native majority and ethnic minorities (e.g., Gijsberts et al, 2012;Scheepers et al, 2013;Völker et al, 2007) or only study the effects among native respondents (e.g., Dinesen & Sønderskov, 2015;Hooghe & De Vroome, 2016;Sluiter, Tolsma, & Scheepers, 2015;Tolsma & Van der Meer, 2017). 2 It is, however, reasonable to expect that the effects of living in diversity are different depending on whether an individual is a native or not.…”

Section: The Role Of Ethnic Backgroundmentioning

confidence: 99%

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The street level and beyond: The impact of ethnic diversity on neighborhood cohesion and fear of crime among Dutch natives and nonnatives

Glas

Engbersen

Snel

2018

Journal of Urban Affairs

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Ethnic diversity is increasing in most Western societies. Research suggests that these increasing levels of diversity could result in less neighborhood cohesion and more fear of crime. In this article, we examine both hypothesized outcomes of ethnic diversity, using survey data of the Dutch Safety Monitor 2014 in combination with detailed register data. The effects of diversity on neighborhood cohesion and fear of crime are simultaneously assessed at 3 spatial levels: districts, neighborhoods, and streets. The results of the multilevel analyses show that ethnic diversity is modestly related to less neighborhood cohesion and more fear of crime at specific spatial levels. The patterns are largely similar for natives and nonnatives. We found, in addition, that recent increases in diversity are unable to explain differences in neighborhood cohesion and fear of crime. Altogether, our study provides a nuanced understanding of diversity effects in the Dutch context.

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Section: A Multiscale Approachmentioning

confidence: 95%

Section: The Role Of Ethnic Backgroundmentioning

confidence: 99%

The street level and beyond: The impact of ethnic diversity on neighborhood cohesion and fear of crime among Dutch natives and nonnatives

Glas

Engbersen

Snel

2018

Journal of Urban Affairs

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“…In one of the early egohood applications, Hipp and Boessen (2013) studied crime in nine U.S. cities using egohoods with different sizes (see also Collins & Guidry, 2018). Dinesen and Sønderskov (2015) studied ethnic diversity and trust relationship in egohoods with a radius of 250 m while replicating the analysis with narrower egohoods with a radius of 80 m. Sluiter, Tolsma, and Scheepers (2015) studied similar effects in the Netherlands by using various size egohoods (200-400 m radius). Recently, Tolsma and van der Meer (2017) studied trust formation in egohoods of various sizes up to 4 km.…”

Section: The Definition Of Neighbourhoodmentioning

confidence: 99%

Egohoods that segregate. Immigrants, social contexts, and income penalties

Zampatti

Ballarino

Squazzoni

2019

Population Space and Place

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This paper looks at the causal impact of residential segregation on income among migrants in Brescia, a wealthy industrial city of about 200,000 inhabitants in North‐Eastern Italy, where almost one citizen out of five is born abroad. Using a new register‐like dataset including economic and demographic microdata from administrative sources, we geo‐localised each migrant household and created for each individual an “egohood” by imposing buffer circles with the same diameter (250 or 500 m) around his or her residential location. Within this spatial area of reference, we calculated indices of ethnic segregation and exposure to Italian natives, thus measuring the individual network of potential contacts with co‐ethnics and with Italians. Following social networks and social capital theories, we expected ethnic segregation to have a negative impact and exposure to natives to have a positive impact on income and tested our hypotheses with a set of ordinary least squares and two‐stage least squares with instrumental variables regression models. Results showed a robust and negative effect of ethnic segregation on income, whereas a small and positive effect of exposure to natives was not robust to endogeneity checks. We also found substantial heterogeneity among migrant groups defined by macroareas of origin: Exposure to Italians was found to have the expected impact only among migrants coming from Eastern Europe and from the Middle East and North Africa.

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“…In social science survey research, analyzing the context of social behavior is heavily supported by using areal information about respondents' 2 neighborhoods, especially on a small scale (Nonnenmacher and Friedrichs 2011;Sluiter, Tolsma, and Scheepers 2015). By using small-scale georeferenced survey data, researchers can answer questions about individual social behavior or attitudes (Förster 2018;Termorshuizen, Braam, and van Ameijden 2015) while also taking into account the geospatial patterns of social processes (Klinger, Müller, and Schaeffer 2017;Legewie and Schaeffer 2016;Tolsma and van der Meer 2017).…”

Section: Introductionmentioning

confidence: 99%

Documenting Georeferenced Social Science Survey Data: Limits of Metadata Standards and Possible Solutions

Jünger

Borschewski

Zenk-Möltgen

2019

Journal of Map & Geography Libraries

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In this article, we present documentation of the georeferenced social science survey data that are spatially linked to geospatial data attributes. We introduce the challenges of documentation, as different metadata standards are used for both data sources: social science survey data and geospatial data. In particular, we analyze the extent to which the social sciences metadata standard DDI Lifecycle is capable of incorporating the geosciences metadata standard ISO 19115. We find that the most challenging attributes to describe are those concerning the geographic structure of the geospatial data, especially if they stem from different sources. To navigate these issues, we developed and evaluated four workaround approaches which we demonstrate in a case study on the georeferenced German General Social Survey. Because not all of the approaches apply equally to every research project and institution, we provide a scheme to assist in making informed and weighted decisions.

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At which geographic scale does ethnic diversity affect intra-neighborhood social capital?

Cited by 21 publications

References 47 publications

The street level and beyond: The impact of ethnic diversity on neighborhood cohesion and fear of crime among Dutch natives and nonnatives

The street level and beyond: The impact of ethnic diversity on neighborhood cohesion and fear of crime among Dutch natives and nonnatives

Egohoods that segregate. Immigrants, social contexts, and income penalties

Documenting Georeferenced Social Science Survey Data: Limits of Metadata Standards and Possible Solutions

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