Estimating regional unemployment with mobile network data for Functional Urban Areas in Germany

The ongoing growth of cities due to better job opportunities is leading to increased labour-related commuter flows in several countries. On the one hand, an increasing number of people commute and move to the cities, but on the other hand, the labour market indicates higher unemployment rates in urban areas than in the surrounding areas. We investigate this phenomenon on regional level by an alternative definition of unemployment rates in which commuting behaviour is integrated. We combine data from the Labour Force Survey with dynamic mobile network data by small area models for the federal state North Rhine-Westphalia in Germany. From a methodical perspective, we use a transformed Fay-Herriot model with bias correction for the estimation of unemployment rates and propose a parametric bootstrap for the mean squared error estimation that includes the bias correction. The performance of the proposed methodology is evaluated in a case study based on official data and in model-based simulations. The results in the application show that unemployment rates (adjusted by commuters) in German cities are lower than traditional official unemployment rates indicate.

Automated summary

The ongoing growth of cities and increased labor-related commuting flows have led to higher unemployment rates in urban areas compared to surrounding areas. In this study, we investigate this phenomenon on a regional level using an alternative definition of unemployment rates that integrates commuting behavior. By combining data from the Labor Force Survey with dynamic mobile network data and using a transformed Fay-Herriot model with bias correction, we find that unemployment rates (adjusted by commuters) in German cities are lower than traditional official rates indicate.