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Updated: Apr 11, 2026

Measuring the Switch Cost of Smartphone Use While Walking

Measuring the Switch Cost of Smartphone Use While Walking

Published on: April 30, 2020

Tracking employment shocks using mobile phone data.

Jameson L Toole¹, Yu-Ru Lin², Erich Muehlegger³

¹Engineering Systems Division, MIT, Cambridge, MA 02144, USA jltoole@mit.edu.

Journal of the Royal Society, Interface

|May 29, 2015

Summary

This summary is machine-generated.

Mobile phone data can track mass layoffs and unemployment. Researchers used call detail records (CDRs) to identify affected individuals and predict economic changes, offering new insights into microeconomic behavior.

Keywords:

complex systems computational social science mobility social networks unemployment

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Area of Science:

Socioeconomics
Data Science
Labor Economics

Background:

Traditional economic indicators often lack real-time granularity.
Observing the micro-level impacts of economic shocks is challenging.

Purpose of the Study:

To develop and validate methods for detecting economic shocks using mobile phone data.
To identify individuals affected by mass layoffs and predict aggregate unemployment rates.

Main Methods:

Utilized structural break models to detect mass layoff events from call detail records (CDRs).
Employed Bayesian classification to identify affected individuals based on behavioral changes in CDRs.
Aggregated micro-level behavioral changes to improve macroeconomic unemployment forecasts.

Main Results:

Successfully detected mass layoffs and identified affected individuals through changes in calling behavior.
Observed significant declines in social behavior and mobility among laid-off individuals.
Demonstrated that aggregated calling behavior changes improve unemployment rate predictions.

Conclusions:

Mobile phone data (CDRs) offer a novel resource for observing economic shocks at multiple scales.
These methods enhance the measurement of microeconomic behavior and the estimation of economic indicators.
The study highlights the potential of digital data for real-time economic monitoring.