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

Abstract

The ongoing growth of cities due to better job opportunities is leading to increased labour-relatedcommuter flows in several countries. On the one hand, an increasing number of people commuteand move to the cities, but on the other hand, the labour market indicates higher unemployment ratesin urban areas than in the surrounding areas. We investigate this phenomenon on regional level byan alternative definition of unemployment rates in which commuting behaviour is integrated. Wecombine data from the Labour Force Survey (LFS) with dynamic mobile network data by small areamodels for the federal state North Rhine-Westphalia in Germany. From a methodical perspective, weuse a transformed Fay-Herriot model with bias correction for the estimation of unemployment ratesand propose a parametric bootstrap for the Mean Squared Error (MSE) estimation that includes thebias correction. The performance of the proposed methodology is evaluated in a case study based onofficial data and in model-based simulations. The results in the application show that unemploymentrates (adjusted by commuters) in German cities are lower than traditional official unemployment ratesindicate.