The neighbourhood in the cloud
How do neighbourhood networks develop? Who interacts with whom and through which channels? And how do such contacts and other spatial contexts influence individual decisions and values?
Factsheet
- Schools involved Business School
- Institute(s) Institute for Applied Data Science & Finance
- Research unit(s) Applied Data Science
- Strategic thematic field Thematic field "Humane Digital Transformation"
- Funding organisation SNSF
- Duration (planned) 01.08.2024 - 31.01.2026
- Head of project Prof. Dr. Christoph Zangger
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Project staff
Prof. Dr. Christoph Zangger
Amélie-Sophie Bank
Maret Rhea Korell - Keywords Neighbourhood effects, Networks, Survey experiments
Situation
The COVID-19 pandemic has clearly highlighted the importance of neighbourly networks: Throughout Switzerland, neighbours supported each other, for example by doing the shopping for people at risk or helping out with childcare. But even in normal times, spatial networks and contexts have an influence on our everyday lives. Be it when choosing a new home, thinking about changing jobs or deciding which secondary school to send our children to. Such influences are commonly referred to in science as neighbourhood effects. While a considerable amount of research and results have already been accumulated over the years, the field of research is still in its infancy in the Swiss context. In addition, the unbiased identification of such effects presents us with major challenges: When it comes to educational decisions, for example, the exchange between parents in the context of a school is often more decisive than the fact that these people also live in the same neighbourhood. However, such overlapping contexts and networks have so far been insufficiently taken into account. Translated with DeepL.com (free version)
Course of action
To investigate how neighbourhood and other local networks develop and how these, together with other spatial contexts, influence individual educational, labour market and political participation decisions, we combine different methodological approaches. On the one hand, we conduct two surveys with a representative sample throughout Switzerland. The repeated survey of the same people not only allows us to make statements about changes in the local context and how these are related to other life events, but also enables a methodologically robust approach to the otherwise difficult causal identification of contextual effects. On the other hand, we combine this survey data with survey and discrete choice instruments that allow us to gain a deeper understanding of the underlying decision-making processes of people in different contexts. Finally, we draw on a wide range of other data. On the one hand, extensive geocoded structural survey data and, on the other hand, web-scraped data from different sources and networks, which give us additional insights into the different neighbourhood and spatial contexts.
