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Productivity outcomes in online labor markets and within-task complexity and difficultly

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  • Mourelatos, Evaggelos
  • Giannakopoulos, Nicholas
  • Tzagarakis, Manolis

Abstract

We analyze the impact of within-task difficulty and complexity on workers' productivity in online labor markets. Using a randomized control quasi-experiment in AMT we are able to define the difficulty and complexity embodied in requested sub-tasks within a problem-solved task. We find that our productivity measures are negatively related to the difficulty and complexity of a specific sub-task. This finding is robust to several sources of workers' heterogeneity and to different pay schemes.

Suggested Citation

  • Mourelatos, Evaggelos & Giannakopoulos, Nicholas & Tzagarakis, Manolis, 2020. "Productivity outcomes in online labor markets and within-task complexity and difficultly," GLO Discussion Paper Series 739, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:739
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    References listed on IDEAS

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    1. John Horton & David Rand & Richard Zeckhauser, 2011. "The online laboratory: conducting experiments in a real labor market," Experimental Economics, Springer;Economic Science Association, vol. 14(3), pages 399-425, September.
    2. Maria Cubel & Ana Nuevo‐Chiquero & Santiago Sanchez‐Pages & Marian Vidal‐Fernandez, 2016. "Do Personality Traits Affect Productivity? Evidence from the Laboratory," Economic Journal, Royal Economic Society, vol. 0(592), pages 654-681, May.
    3. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    4. Evangelos Mourelatos & Nicholas Giannakopoulos & Manolis Tzagarakis, 2022. "Personality traits and performance in online labour markets," Behaviour and Information Technology, Taylor & Francis Journals, vol. 41(3), pages 468-484, February.
    5. Arindrajit Dube & Jeff Jacobs & Suresh Naidu & Siddharth Suri, 2020. "Monopsony in Online Labor Markets," American Economic Review: Insights, American Economic Association, vol. 2(1), pages 33-46, March.
    6. John J. Horton, 2017. "The Effects of Algorithmic Labor Market Recommendations: Evidence from a Field Experiment," Journal of Labor Economics, University of Chicago Press, vol. 35(2), pages 345-385.
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    Citations

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    Cited by:

    1. Evangelos Mourelatos & Jaakko Simonen & Simo Hosio & Daniil Likhobaba & Dmitry Ustalov, 2024. "How has the COVID-19 pandemic shaped behavior in crowdsourcing? The role of online labor market training," Journal of Business Economics, Springer, vol. 94(9), pages 1201-1244, November.
    2. Mourelatos, Evangelos & Krimpas, George & Giotopoulos, Konstantinos, 2022. "Sexual identity and Gender Gap in Leadership. A political intention experiment," GLO Discussion Paper Series 1187, Global Labor Organization (GLO).

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    More about this item

    Keywords

    Productivity; Online Labor markets; Task Difficulty and Complexity;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General

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