Characterizing the Global Crowd Workforce: A Cross-Country Comparison of Crowdworker Demographics

Authors

  • Lisa Posch Graz University of Technology
  • Arnim Bleier GESIS - Leibniz Institute for the Social Sciences
  • Fabian Flöck GESIS - Leibniz Institute for the Social Sciences
  • Clemens M. Lechner GESIS - Leibniz Institute for the Social Sciences
  • Katharina Kinder-Kurlanda University of Klagenfurt
  • Denis Helic Graz University of Technology
  • Markus Strohmaier University of Mannheim, GESIS - Leibniz Institute for the Social Sciences, Complexity Science Hub Vienna

DOI:

https://doi.org/10.15346/hc.v9i1.106

Keywords:

Crowdworkers, Micro Tasks, Demographics, Cross-National Comparison

Abstract

Since its emergence roughly a decade ago, microtask crowdsourcing has been attracting a heterogeneous set of workers from all over the globe. This paper sets out to explore the characteristics of the international crowd workforce and offers a cross-national comparison of crowdworker populations from ten countries. We provide an analysis and comparison of demographic characteristics and shed light on the significance of microtask income for workers situated in different national contexts. With over 11,000 individual responses, this study is the first large-scale country-level analysis of the characteristics of workers on the platform Appen (formerly CrowdFlower and Figure Eight), one of the two platforms dominating the microtask market. We find large differences between the characteristics of the crowd workforces of different countries, both regarding demography and regarding the importance of microtask income for workers. Furthermore, we find that the composition of the workforce in the ten countries was largely stable across samples taken at different points in time.

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Published

2022-08-03

How to Cite

Posch, L., Bleier, A., Flöck, F., Lechner, C. M., Kinder-Kurlanda, K., Helic, D., & Strohmaier, M. (2022). Characterizing the Global Crowd Workforce: A Cross-Country Comparison of Crowdworker Demographics. Human Computation, 9(1). https://doi.org/10.15346/hc.v9i1.106