Sepehr Ilami, Margherita Comola, Silvia Prina, Babak Heydari
arXiv 21 Jun 2026 · Econometrics
arXiv:2606.22599 · PDF · DOI · OpenAlex · Extracted main text
Risk perception is typically modeled as an individual cognitive readout of objective hazard, yet during crises what people judge as risky is shaped by what their peers do. Using weekly mobility data from 313 Massachusetts municipalities over the first year of the COVID-19 pandemic and a pre-pandemic inter-town mobility network that fixes interaction structure before the shock, we estimate two-way fixed-effects panel regressions that separate local case response, inter-town behavioral spillover along the mobility network, and within-town inertia; the pre-shock network and a lagged peer signal address the standard reflection and endogenous-group concerns. Three findings emerge. First, inter-town behavioral spillovers are substantial and localize almost entirely within mobility-defined communities, with effectively no propagation across community boundaries, the empirical referent of behavioral bubbles. Second, the within-community spillover carries behavioral content beyond peer-town case information: when network-exposure-to-cases and network-exposure-to-behavior are raced, the behavioral channel survives and the case-exposure channel goes null. Third, a joint mobility-by-demographic decomposition shows the spillover requires both routine connection and demographic similarity. It concentrates where towns are connected and similar, and vanishes between similar towns that are not connected, ruling out a shared-conditions confound and pointing to an observational and normative channel rather than a purely informational one. These results recast risk perception as a networked phenomenon and identify mobility-defined communities, rather than administrative units, as the operative scale of behavioral response. The pattern should generalize wherever exposure is uncertain, evolving, and socially negotiated, including climate adaptation and financial contagion.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bramoullé, Yann and Djebbari, Habiba and Fortin, Bernard (2009) Identification of peer effects through social networks | 1.000 | 5 | 4 | 100% |
| 2 | Manski, Charles F (1993) Identification of endogenous social effects: The reflection problem | 0.928 | 4 | 4 | 100% |
| 3 | Centola, Damon (2010) The spread of behavior in an online social network experiment | 0.843 | 3 | 3 | 100% |
| 4 | Centola, Damon (2018) How behavior spreads: The science of complex contagions | 0.843 | 3 | 3 | 100% |
| 5 | Jackson, Matthew O and others (2008) Social and economic networks | 0.843 | 3 | 3 | 100% |
| 6 | Blondel, Vincent D and Guillaume, Jean-Loup and Lambiotte, Renaud an… (2008) Fast unfolding of communities in large networks | 0.737 | 3 | 2 | 100% |
| 7 | Flaxman, Seth and Mishra, Swapnil and Gandy, Axel and Unwin, H Julie… (2020) Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe | 0.737 | 3 | 2 | 100% |
| 8 | Hsiang, Solomon and Allen, Daniel and Annan-Phan, Sébastien and Bell… (2020) The effect of large-scale anti-contagion policies on the COVID-19 pandemic | 0.737 | 3 | 2 | 100% |
| 9 | Balcan, Duygu and Colizza, Vittoria and Gon calves, Bruno and Hu, Ha… (2009) Multiscale mobility networks and the spatial spreading of infectious diseases | 0.644 | 2 | 2 | 100% |
| 10 | Bauch, Chris T and Galvani, Alison P (2013) Social factors in epidemiology | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 75 scored citations.