ESRA 2017 Programme

Tuesday 18th July      Wednesday 19th July      Thursday 20th July      Friday 21th July     

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Friday 21st July, 09:00 - 10:30 Room: F2 102

Quantitative Spatial Analysis of Micro and Macro Data: Methodological Challenges and Solutions 1

Chair Professor Henning Best (TU Kaiserslautern )
Coordinator 1Professor Corinna Kleinert (Leibniz Institute for Educational Trajectories)
Coordinator 2Mr Tobias Ruettenauer (TU Kaiserslautern)
Coordinator 3Dr Michaela Sixt (Leibniz Institute for Educational Trajectories )

Session Details

The session intends to bring together methodological experiences made when working with spatial data in quantitative empirical social research. On the one hand, spatial data offers the opportunity to investigate the relationship between regional characteristics on the macro level. On the other hand, spatial data can be used to enrich survey data with structural information on a certain regional level, either to control for context effects or to explicitly analyse these effects and their interplay with mechanisms on the individual level. By using GIS, addresses of survey participants can be linked with objective measures of their neighbourhood (e.g. pollution data) or proximity to institutions (e.g. of educational institutions or workplaces). Thus, these data allow investigating the relevance of infrastructure distances for social action as well as processes of spatial spillovers and diffusion.

In doing so, several methodological questions arise: What kind of regional level is adequate to what kind of question (“MAUP”)? And how can we handle social action at boarders of administrative units? To derive closer estimates of real individual distances and potential spaces of action a possible solution could be to weight the importance of neighbouring regions by information on actual traveling times with different means of transport. What are the challenges and limitations of these approaches and how can it be done reliably?

Furthermore, innovative statistical methods are necessary to adequately analyse spatial data. Various regression models (e.g. SAR, SARAR, SLX, Durbin and others) address the spatial dependence in different ways and offer alternative approaches to identify different types of spatial spillovers or spatial interdependences, in cross-sectional and longitudinal data. Which types of models are adequate for which type of questions? Which models can be used to simultaneously analyse individual and aggregate data?

In sum, in this session we are especially interested in methodological and applied studies dealing with topics of:
1. Choice of adequate regional level and handling of borders when using administrative data
2. Connection of individual data and spatially aggregate as well as infrastructural data
3. Spatial analysis of time-series and cross-sectional data
4. Modelling spatial relationships (e.g. commuting flows, distances, traveling times, social interactions)
5. Modelling spatial interaction, spillover or diffusion processes
6. Further challenges and solutions when using georeferenced data

Paper Details

1. Local social contexts and educational aspirations: spatial references and group-specific spatial effects of academic composition.
Mr Andreas Hartung (University of Tuebingen)
Professor Steffen Hillmert (University of Tuebingen)

A central idea of social sciences is that individuals are embedded in social contexts and are influenced by them. Many relevant social mechanisms can be expected to have spatial references. Researchers face the challenge of theoretically describing these spatial dimensions and their importance for the respective social processes. At the same time it has become evident that the conceptualisation of spatially structured contexts is nontrivial. In particular, research on the modifiable areal unit problem (MAUP) has shown that the definition of the area where the explanatory factors are measured typically affects the results, even when the definitions are relatively similar.

The aim of this paper is to theoretically specify adequate areas – given the particular research question – and to test these expectations by comparing different spatial operationalizations. Thereby we go beyond the classical approach of considering individuals within fixed structure of proximate contexts. While often justified, this approach is not suited for capturing flexible and overlapping individual contexts in terms of areas of individual action or perception. For this reason we make use of ego-centred areas of different radii to measure contextual indicators.

Applying this approach we assess the impact of social composition of the proximate living environment on young people’s educational aspirations. In previous research, neighbourhood conditions have frequently been considered as among the main explanatory factors for educational outcomes. Neighbourhood characteristics have often defined in terms of unfavourable neighbourhood conditions. Because of noticeable difference with regard to social policies and the level of socio-spatial segregation, research in (continental) Europe has studied neighbourhood effects rather from the perspective of living in advantaged neighbourhoods than from the perspective of consequences of living in the disadvantaged quarters. Our paper in particular investigates whether there is an effect on young people’s aspirations to attend higher education when living in an environment that is characterised by a high share of academics
Besides the substantial question on the existence of this effect we explicitly test our expectations of where to locate the assumed mechanisms. A further challenge is the possible heterogeneity of spatial context effects; previous studies have shown that there are significant differences in the degree to which various groups are susceptible to specific local context conditions. In our paper we investigate whether the effects of the living environment can be differentiated by individual characteristics such as social and migration background as well as sex.


2. Ethnic Diversity, Institutional Capacity, and Social Cohesion in European Cities
Dr Conrad Ziller (Universtiy of Cologne)
Professor Hans-Jürgen Andreß (Universtiy of Cologne)

Research on ethnic diversity effects suggests, by and large, negative relationships with indicators of social cohesion, such as social trust and civic engagement. Most of the studies in this regard have relied on cross-sectional survey data merged with neighborhood-level information on ethnic diversity. However, this type of research design remains short on the question whether residents are merely influenced by their neighborhood or rather gain information about ethnic diversity from contexts they spend time in other than their immediate residential environment. On the other side, studies on regional contexts have difficulties to convincingly link regional context and individual exposure. In this paper, we focus on cities as contextual units, which so far have largely been overlooked in research on contextual ethnic diversity. In addition, we consider residents’ assessment of city infrastructure as proxy of institutional capacity, which we expect to moderate the way immigration affects social cohesion. To do so, we use survey data on European cities from the European Commission Urban Audit and Large City Audit projects (5 waves; 2004-2015) combined with structural city-level information (e.g., proportion of immigrants). This empirical set-up further has two methodological advantages: (1) The large numbers of respondents per city-year allows aggregating individual-level information to reliable city-level indicators. (2) Using multi-level models with city fixed effects, we are able to gauge the longitudinal structure of macro-level indicators. Our results show that an increase in city-level immigration is negatively related to perceptions of neighborhood safety as well as trust in neighbors and community members. Moreover, the negative relationship is mitigated in cities with high institutional capacity.


3. The Halo Effect: Are people who live in homogenous neighborhoods that border ethnically diverse neighborhoods (or are even encircled by them) more xenophobic?
Professor Merlin Schaeffer (University of Cologne)
Mrs Julia Klinger (University of Cologne)
Mr Stefan Mueller (Gesis - Leibniz Institute for the Social Sciences)

Are people who live in homogenous neighborhoods that border ethnically diverse neighborhoods (or are even encircled by them) more xenophobic? This socio-spatial constellation, which is known as the ‘halo effect’ hypothesis, synthesizes two prominent explanations of xenophobia: as the direct neighborhood offers little opportunities for positive intergroup contact, the neighboring ethnically diverse neighborhoods can instill feelings of competition and group threat, which eventually result in xenophobia, with full force. Beyond classic hypotheses about the contextual effects of population shares (e.g., % immigrants, poor, or unemployed), this perspective emphasizes the importance of neighborhoods’ local embeddedness. Yet, analyses based on geo-coded ALLBUS 2014 data provide neither support for the halo effect hypotheses among the general population nor among xenophobia-minded subpopulations. Nevertheless, our study demonstrates the methodological characteristics and challenges of such a spatial analysis of the geocoded ALLBUS data and discusses plausible reasons why our results deviate from earlier American and European studies.


4. The Causes of Environmental Inequality: Evidence from Spatial Time-Series Analysis in Germany
Mr Tobias Rüttenauer (TU Kaiserslautern)
Professor Henning Best (TU Kaiserslautern)

The analysis of environmental quality and socioeconomic composition on the level of spatial units has become a standard approach of empirical research on environmental inequality. The majority of prior studies relies on cross-sectional spatial data and hence cannot adequately study the causal mechanisms leading to the unequal distribution of environmental. Thus, it remains a puzzle whether selective move-in, selective move-out, selective siting by the industry or a combination of these factors lead to an uneven distribution of environmental hazards across different socio-economic groups. In addition, most of the research has been conducted in the US and empirical results from continental Europe and especially Germany are rare.

In this paper, we study the spatial distribution of air pollution emissions and the connection to socio-economic factors in Germany between 2001 and 2012. The analysis relies on a combination of data from the European Pollutant Release and Transfer Register (EPRTR) and socio-economic data on the level of 4,521 German municipalities. The amount of pollution from the EPRTR facilities is matched to the communities within a 5 km radius of the facility location proportionate to its spatial overlap. To investigate the causal mechanisms producing the unequal distribution of environmental pollution, we use a fixed-effects time-series approach with a correction for spatial autocorrelation (FE-SAR). The results identify selective siting and selective move-in as a cause of the unequal distribution of environmental harm, while contradicting the selective move-out explanation. However, we need to be aware of potential problems due to the use of spatially aggregated data.