Job loss disrupts individuals' mobility and their exploratory patterns

TL;DR

This work investigates how job loss disrupts human mobility and exploratory patterns by integrating GPS-based mobility data with administrative employment datasets (LEHD, UI, LAUS, BLS QCEW) and sector teleworkability. It introduces a remote-workability–aware unemployment signal, defined through core quantities $r_u(t)$ and $rt_u(t)$ and sector-level measures $\tilde{R}_s(t)$ and $\tilde{r}_s(t)$, culminating in the unemployment probability $P_s(t)=1-\min\left(\frac{\tilde{R}_s(t)}{\tilde{r}_{s}(t)},1\right)$. The authors validate the approach against UI claims and LAUS data, achieving high correlations at state and county levels, and perform extensive demographic analyses revealing significant differences in mobility metrics (radius of gyration, entropy, capacity) across sex, age, income, race, and education. The results illuminate how employment shocks shape mobility regularities at scale and offer actionable insights for targeted interventions to support vulnerable populations.

Abstract

In recent years, human mobility research has discovered universal patterns capable of describing how people move. These regularities have been shown to partly depend on individual and environmental characteristics (e.g., gender, rural/urban, country). In this work, we show that life-course events, such as job loss, can disrupt individual mobility patterns. Adversely affecting individuals' well-being and potentially increasing the risk of social and economic inequalities, we show that job loss drives a significant change in the exploratory behaviour of individuals with changes that intensify over time since job loss. Our findings shed light on the dynamics of employment-related behavior at scale, providing a deeper understanding of key components in human mobility regularities. These drivers can facilitate targeted social interventions to support the most vulnerable populations.

Figures (24)

• Figure S1: State selection based on the primary, secondary, and tertiary workforce composition in the US states. The selected states are New York (NY), Wyoming (WY), Indiana (IN), Idaho (ID), Washington (WA), North Dakota (ND), and New Mexico (NM). The internal colour code distinguishes states with a high-tertiary workforce (in red) from those with a low-tertiary workforce (in blue). The intensity of the shaded circle surrounding states highlights those states with higher levels of the primary workforce (higher intensity corresponds to higher primary workforce levels).
• Figure S2: Geographical displacement of the countries included in this work analysis.
• Figure S3: Remote working mechanism with NAICS sectors that are less teleworkable: Accommodation and Food Services (CNS18); Agriculture, Forestry, Fishing and Hunting (CNS01); Retail Trade (CNS07); Construction (CNS04); and Transportation and Warehousing (CNS08). Given the less remote workability, the adjustment leaves the unemployed unaffected (middle and left panel). The share of jobs that can be performed at home are reported in Tab. \ref{['tab:telework']}.
• Figure S4: Remote working mechanism with NAICS sectors that are more teleworkable: Educational Services (CNS15); Professional, Scientific, and Technical Services (CNS12); Management of Companies and Enterprises (CNS13); Finance and Insurance (CNS10); and Information (CNS09). Given the more teleworkability the adjustment "removes" the unemployed since they are working from home (middle and left panel). The share of jobs that can be performed at home are reported in Tab. \ref{['tab:telework']}.
• Figure S5: Algorithm evaluation and average Pearson correlation using the Unemployment Insurance (UI) claims data for each state and month.