  By modeling macro-economical indicators using digital traces of human
activities on mobile or social networks, we can provide important insights to
processes previously assessed via paper-based surveys or polls only. We
collected aggregated workday activity timelines of US counties from the
normalized number of messages sent in each hour on the online social network
Twitter. In this paper, we show how county employment and unemployment
statistics are encoded in the daily rhythm of people by decomposing the
activity timelines into a linear combination of two dominant patterns. The
mixing ratio of these patterns defines a measure for each county, that
correlates significantly with employment ($0.46\pm0.02$) and unemployment rates
($-0.34\pm0.02$). Thus, the two dominant activity patterns can be linked to
rhythms signaling presence or lack of regular working hours of individuals. The
analysis could provide policy makers a better insight into the processes
governing employment, where problems could not only be identified based on the
number of officially registered unemployed, but also on the basis of the
digital footprints people leave on different platforms.
