Harvard Kennedy School, Harvard University, Cambridge, MA 02144, USALazer Laboratory, Department of Political Science and College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA
A schematic view of the relationship between job loss and call dynamics. We use the calling behaviour of individuals to infer job loss and measure its effects. We then measure these variables and include them in predictions of unemployment at the macroscale, significantly improving forecasts.
Identifying the layoff date. (a) Total aggregate call volume (black line) from users who make regular calls from towers near the plant is plotted against our model (blue). The model predicts a sudden drop in aggregate call volume and correctly identifies the date of the plant closure as the one reported in newspapers and court records. (b) Each of the top 300 users likely to have been laid off is represented by a row where we fill in a day as coloured if a call was made near the plant on that day. White space marks the absence of calls. Rows are sorted by the assigned probability of that user being laid off according to our Bayesian model. Users with high probabilities cease making calls near the plant directly following the layoff. (c) We see a sharp, sustained drop in the fraction of calls made near the plant by users assigned to the top decile in probability of being unemployed (red) while no affect is seen for the control group users believed to be unaffected (blue). Moreover, we see that laid-off individuals have an additional drop off for a two week period roughly 125 days prior the plant closure. This time period was confirmed to be a coordinated vacation for workers providing further evidence we are correctly identifying laid-off workers.
Changes in social networks and mobility following layoffs. We quantify the effect of mass layoffs relative to two control groups: users making regular calls from the town, who were not identified as laid off and a random sample of users from the rest of the country. We report monthly point estimates for six social and three mobility behaviours: (a) total calls, (b) number of incoming calls, (c) number of outgoing calls, (d) fraction of calls to individuals in the town at the time of the call, (e) number of unique contacts, (f) the fraction of individuals called in the previous month who were not called in the current month (churn), (g) number of unique towers visited, (h) radius of gyration, (i) average distance from most visited tower. Pooling months pre- and post-layoff yield statistically significant changes in monthly social network and mobility metrics following a mass layoff. (j) Reports regression coefficient for each of our nine dependent variables along with the 66 and 95% CIs.
Predicting unemployment rates using mobile phone data. We demonstrate that aggregating measurements of mobile phone behaviours associated with unemployment at the individual level also predicts unemployment rates at the province level. To make our forecasts, we train various models on data from half of the provinces and use these coefficients to predict the other half. (a) Compares predictions of present unemployment rates to observed rates and (b) shows predictions of unemployment one-quarter ahead using an AR1 model that includes covariates of behaviours measured using mobile phones. Both predictions correlate strongly with actual values while changes in rates are more difficult to predict. The insets show the per cent improvement to the RMSE of predictions when mobile phone covariates are added to various baseline model specifications. In general, the inclusion of mobile phone data reduces forecast errors by 5–20%.