IAB-SMART: Collecting Data for Labor Market Research Through a Smartphone App

Project Description

Smartphones are multifunctional tools, which can be used for personal communication, planning, entertainment, information search, and many other things in our daily lives. Many people cannot imagine a life without their smartphones, and they carry them around with them all the time. The omnipresence of smartphones makes these devices interesting for researchers who want to collect data to measure human behavior through sensors built-in on a smartphone. Together with the Institute for Employment Research (IAB) we developed the IAB-SMART app to evaluate the opportunities and challenges when using smartphones for data collection in social research, more specifically on labor market research. The IAB-SMART app passively collects mobile data, such as geolocation of users, activities, social interactions, and online behavior, and launches in-app surveys. In addition, we are able to combine these data (given the user’s consents) with survey data from a longstanding panel survey (PASS) and administrative data from the Institute for Employment Research (IAB) containing the employment history of users. The passive measures allow researchers to take a wider perspective on labor market-related behavior such as home office productivity and job search strategies. Furthermore, the combination of sensor, survey, and administrative data will help us to understand how (un)employment affects daily life. In addition to these substantial questions, this project helps us answer methodological research questions on the quality of the data collected through this method.


  • Bähr, S., Haas, G.-C., Keusch, F., Kreuter, F. & Trappmann, M. (2020). Missing data and other measurement quality issues in mobile geolocation sensor data. Social Science Computer Review : SSCORE, 1–24. https://doi.org/10.1177/0894439320944118
  • Haas, G.-C., Kreuter, F., Keusch, F., Trappmann, M. & Bähr, S. (2020). Effects of incentives in smartphone data collection. In C. A. Hill (eds.), Big data meets survey science : a collection of innovative methods (S. 387–414). Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781118976357.ch13
  • Haas, G.-C., Trappmann, M., Keusch, F., Bähr, S. & Kreuter, F. (2020). Using geofences to collect survey data: Lessons learned from the IAB-SMART study. Survey Methods : Insights from the Field, 2020(10/12/20), 1–12. https://doi.org/10.13094/SMIF-2020-00023
  • Keusch, F., Bähr, S., Haas, G.-C., Kreuter, F. & Trappmann, M. (2020). Coverage error in data collection Ccombining mobile surveys with passive measurement using apps: Data from a German national survey. Sociological Methods & Research : SMR. https://doi.org/10.1177/0049124120914924
  • Kreuter, F., Haas, G.-C., Keusch, F., Bähr, S. & Trappmann, M. (2019). Collecting survey and smartphone sensor data with an App: Opportunities and challenges around privacy and informed consent. Social Science Computer Review : SSCORE, 38(5), 533–549. https://doi.org/10.1177/0894439318816389
  • Bähr, S., Haas, G.-C., Keusch, F., Kreuter, F. & Trappmann, M. (2018). IAB-SMART-Studie: Mit dem Smartphone den Arbeits­markt erforschen. IAB-Forum : Das neue Onlinemagazin des Instituts für Arbeits­markt- und Berufsforschung, 2018, 09.01.2018.