ABSTRACT. The current study supports previous research claiming that algorithmic control is key to the performance of online labor platforms. Using data from BloombergOpinion, Edelman Berland, Elance-oDesk, Ernst & Young, Freelancers Union, National Technology Readiness Survey, T. Rowe Price, and U.S. Bureau of Labor Statistics, we performed analyses and made estimates regarding the income gap between traditional and gig workers, on-demand economy consumers by technology readiness segment compared to the U.S. population, self-employed workers as a percentage of total employment, not seasonally adjusted, and on-demand economy consumers by age group. Empirical and secondary data are employed to support the claim that online labor platforms utilize algorithms to clarify work away from individuals with unsatisfactory ratings.
JEL codes: L14; L86

Keywords: algorithmic labor; platform economy; job quality; workplace surveillance

How to cite: Popescu, Gheorghe H., Irina Elena Petrescu, and Oana Matilda Sabie (2018). “Algorithmic Labor in the Platform Economy: Digital Infrastructures, Job Quality, and Workplace Surveillance,” Economics, Management, and Financial Markets 13(3): 74–79.

Received 22 February 2018 • Received in revised form 27 March 2018
Accepted 28 March 2018 • Available online 5 May 2018

doi:10.22381/EMFM13320184