Project 1.3

Public debates on migration often exaggerate divisions between ‘pro’ and ‘anti’ positions, fostering a polarization panic: the belief that society is deeply divided, despite limited empirical evidence. These misperceptions of others’ attitudes can undermine social cohesion. While such misperceptions are well documented, their consequences at the local level and effective ways to correct them remain unclear. This PhD project examines how perceived polarization around migration affects trust, belonging and cooperation in neighbourhoods. By comparing perceived and actual attitudes and developing interventions with local stakeholders, the project identifies how these attitudes affect social cohesion and how misperceptions can be reduced.

The aim of this project is to examine the size, structure and diversity of kinship networks among older migrants in the Netherlands. Using Statistics Netherlands (CBS) microdata, the project will assess how network characteristics and family configurations are associated with older migrants’ health, well-being and loneliness. The project will identify differences across migrant groups and between migrants and non-migrants, and analyse how kinship networks change over time and across institutional contexts. Where the expertise and interests of the candidate allow, the project will be complemented by a qualitative component aimed at understanding how older migrants themselves interpret, navigate and experience their family networks and social ties.

The research design combines quantitative survey methods, network analysis and a field experiment. The project starts with a pre-test in diverse neighbourhoods across the Netherlands. Residents will complete a survey measuring 1) their own migration attitudes; 2) perceptions of other neighbourhood residents’ migration attitudes, allowing us to assess misperceptions and polarization panic; 3) ego network composition at the neighbourhood level (e.g., discussion partners, practical support ties, frequency of contact); and 4) social cohesion outcomes, such as trust in neighbours, sense of belonging, neighbourhood involvement, willingness to cooperate and loneliness.

Neighbourhood-level and contextual data (e.g., demographic composition, socio-economic indicators and voting patterns) will be linked to individual responses to examine where misperceptions are most pronounced and how they relate to local contexts.

Following the pre-test, three of the six neighbourhoods will be assigned to an intervention condition, while the remaining neighbourhoods serve as controls. In collaboration with local stakeholders, an intervention will be co-developed and piloted that aims to correct misperceptions about local migration attitudes. The effectiveness of the intervention will be evaluated using a post-test survey identical to the pre-test.

In addition to self-reported outcomes, the post-test includes a behavioural indicator of social cohesion, collected through a small-scale observational or experimental task embedded in a local community activity (e.g., cooperation or contribution in a neighbourhood initiative).