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The online laboratory: conducting experiments in a real labor market

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Abstract

Online labor markets have great potential as platforms for conducting experiments. They provide immediate access to a large and diverse subject pool, and allow researchers to control the experimental context. Online experiments, we show, can be just as valid—both internally and externally—as laboratory and field experiments, while often requiring far less money and time to design and conduct. To demonstrate their value, we use an online labor market to replicate three classic experiments. The first finds quantitative agreement between levels of cooperation in a prisoner’s dilemma played online and in the physical laboratory. The second shows—consistent with behavior in the traditional laboratory—that online subjects respond to priming by altering their choices. The third demonstrates that when an identical decision is framed differently, individuals reverse their choice, thus replicating a famed Tversky-Kahneman result. Then we conduct a field experiment showing that workers have upward-sloping labor supply curves. Finally, we analyze the challenges to online experiments, proposing methods to cope with the unique threats to validity in an online setting, and examining the conceptual issues surrounding the external validity of online results. We conclude by presenting our views on the potential role that online experiments can play within the social sciences, and then recommend software development priorities and best practices.

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References

  • Andreoni, J. (1990). Impure altruism and donations to public goods: a theory of warm-glow giving. The Economic Journal, 464–477.

  • Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science, 211(4489), 1390.

    Article  Google Scholar 

  • Bainbridge, W. S. (2007). The scientific research potential of virtual worlds. Science, 317(5837), 472.

    Article  Google Scholar 

  • Benjamin, D. J., Choi, J. J., & Strickland, A. (2010a). Social identity and preferences. American Economic Review (forthcoming).

  • Benjamin, D. J., Choi, J. J., Strickland, A., & Fisher, G. (2010b) Religious identity and economic behavior. Cornell University, Mimeo.

  • Bohnet, I., Greig, F., Herrmann, B., & Zeckhauser, R. (2008). Betrayal aversion: evidence from Brazil, China, Oman, Switzerland, Turkey, and the United States. American Economic Review, 98(1), 294–310.

    Article  Google Scholar 

  • Brandts, J., & Charness, G. (2000). Hot vs. cold: sequential responses and preference stability in experimental games. Experimental Economics, 2(3), 227–238.

    Google Scholar 

  • Camerer, C. (2003). Behavioral game theory: experiments in strategic interaction. Princeton: Princeton University Press.

    Google Scholar 

  • Chandler, D., & Kapelner, A. (2010). Breaking monotony with meaning: motivation in crowdsourcing markets. University of Chicago, Mimeo.

  • Chen, D., & Horton, J. (2010). The wages of pay cuts: evidence from a field experiment. Harvard University, Mimeo.

  • Chilton, L. B., Sims, C. T., Goldman, M., Little, G., & Miller, R. C. (2009). Seaweed: a web application for designing economic games. In Proceedings of the ACM SIGKDD workshop on human computation (pp. 34–35). New York: ACM Press.

    Chapter  Google Scholar 

  • Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation. Boston: Houghton Mifflin.

    Google Scholar 

  • Eckel, C. C., & Wilson, R. K. (2006). Internet cautions: experimental games with Internet partners. Experimental Economics, 9(1), 53–66.

    Article  Google Scholar 

  • Falk, A., & Heckman, J. J. (2009). Lab experiments are a major source of knowledge in the social sciences. Science, 326(5952), 535.

    Article  Google Scholar 

  • Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. Quarterly Journal of Economics, 114(3), 817–868.

    Article  Google Scholar 

  • Fehr, E., Schmidt, K. M., & Gächter, S. (2000). Cooperation and punishment in public goods experiments. American Economic Review, 90(4), 980–994.

    Article  Google Scholar 

  • Fischbacher, U. (2007). z- tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171–178.

    Article  Google Scholar 

  • Frei, B. (2009). Paid crowdsourcing: current state & progress toward mainstream business use. Produced by Smartsheet.com.

  • Gneezy, U., Leonard, K. L., & List, J. A. (2009). Gender differences in competition: evidence from a matrilineal and a patriarchal society. Econometrica, 77(5), 1637–1664.

    Article  Google Scholar 

  • Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.

    Article  Google Scholar 

  • Herrmann, B., & Thöni, C. (2009). Measuring conditional cooperation: a replication study in Russia. Experimental Economics, 12(1), 87–92.

    Article  Google Scholar 

  • Horton, J. (2010). Online labor markets. In Workshop on Internet and network economics (pp. 515–522).

    Chapter  Google Scholar 

  • Horton, J. (2011). The condition of the Turking class: are online employers fair and honest? Economic Letters (forthcoming).

  • Horton, J. & Chilton, L. (2010). The labor economics of paid crowdsourcing. In Proceedings of the 11th ACM conference on electronic commerce.

    Google Scholar 

  • Ipeirotis, P. (2010). Demographics of Mechanical Turk. New York University Working Paper.

  • Kagel, J. H., Roth, A. E., & Hey, J. D. (1995). The handbook of experimental economics. Princeton: Princeton University Press.

    Google Scholar 

  • Kittur, A., Chi, E. H., & Suh, B. (2008). Crowdsourcing user studies with Mechanical Turk.

  • Kocher, M. G., & Sutter, M. (2005). The decision maker matters: individual versus group behaviour in experimental beauty-contest games*. The Economic Journal, 115(500), 200–223.

    Article  Google Scholar 

  • Levitt, S. D., & List, J. A. (2009). Field experiments in economics: the past, the present, and the future. European Economic Review, 53(1), 1–18.

    Article  Google Scholar 

  • Little, G., Chilton, L. B., Goldman, M., & Miller, R. C. (2009). TurKit: tools for iterative tasks on Mechanical Turk. In Proceedings of the ACM SIGKDD workshop on human computation. New York: ACM Press.

    Google Scholar 

  • Lucking-Reiley, D. (2000). Auctions on the Internet: what’s being auctioned, and how? The Journal of Industrial Economics, 48(3), 227–252.

    Google Scholar 

  • Mason, W., & Watts, D. J. (2009). Financial incentives and the performance of crowds. In Proceedings of the ACM SIGKDD workshop on human computation (pp. 77–85). New York: ACM Press.

    Chapter  Google Scholar 

  • Mason, W., Watts, D. J., & Suri, S. (2010). Conducting behavioral research on Amazon’s Mechanical Turk. SSRN eLibrary.

  • Pallais, A. (2010). Inefficient hiring in entry-level labor markets.

  • Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5.

  • Resnick, P., Kuwabara, K., Zeckhauser, R., & Friedman, E. (2000). Reputation systems. Communications of the ACM, 43(12), 45–48.

    Article  Google Scholar 

  • Resnick, P., Zeckhauser, R., Swanson, J., & Lockwood, K. (2006). The value of reputation on eBay: a controlled experiment. Experimental Economics, 9(2), 79–101.

    Article  Google Scholar 

  • Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.

    Article  Google Scholar 

  • Selten, R. (1967). Die Strategiemethode zur Erforschung des eingeschrankt rationalen Verhaltens im Rahmen eines Oligopolexperiments. Beitrage zur experimentellen Wirtschaftsforschung, 1, 136–168.

    Google Scholar 

  • Shariff, A. F., & Norenzayan, A. (2007). God is watching you. Psychological Science, 18(9), 803–809.

    Article  Google Scholar 

  • Sheng, V. S., Provost, F., & Ipeirotis, P. G. (2008). Get another label? Improving data quality and data mining using multiple, noisy labelers. In Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 614–622). New York: ACM Press.

    Chapter  Google Scholar 

  • Sorokin, A., & Forsyth, D. (2008). Utility data annotation with Amazon Mechanical Turk. University of Illinois at Urbana-Champaign, Mimeo, 51, 61820.

    Google Scholar 

  • Suri, S., & Watts, D. (2011). A study of cooperation and contagion in web-based, networked public goods experiments. PLoS ONE (forthcoming).

  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453.

    Article  Google Scholar 

  • von Ahn, L., Blum, M., Hopper, N. J., & Langford, J. (2003). CAPTCHA: using hard AI problems for security. In Lecture notes in computer science (pp. 294–311). Berlin: Springer.

    Google Scholar 

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Authors and Affiliations

  1. Harvard University, Cambridge, USA

    John J. Horton, David G. Rand & Richard J. Zeckhauser

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  1. John J. Horton
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  2. David G. Rand
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  3. Richard J. Zeckhauser
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Corresponding author

Correspondence to Richard J. Zeckhauser.

Additional information

Thanks to Alex Breinin and Xiaoqi Zhu for excellent research assistance. Thanks to Samuel Arbesman, Dana Chandler, Anna Dreber, Rezwan Haque, Justin Keenan, Robin Yerkes Horton, Stephanie Hurder and Michael Manapat for helpful comments, as well as to participants in the Online Experimentation Workshop hosted by Harvard’s Berkman Center for Internet and Society. Thanks to Anna Dreber, Elizabeth Paci and Yochai Benkler for assistance running the physical laboratory replication study, and to Sarah Hirschfeld-Sussman and Mark Edington for their help with surveying the Harvard Decision Science Laboratory subject pool. This research has been supported by the NSF-IGERT program “Multidisciplinary Program in Inequality and Social Policy” at Harvard University (Grant No. 0333403), and DGR gratefully acknowledges financial support from the John Templeton Foundation’s Foundational Questions in Evolutionary Biology Prize Fellowship.

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Horton, J.J., Rand, D.G. & Zeckhauser, R.J. The online laboratory: conducting experiments in a real labor market. Exp Econ 14, 399–425 (2011). https://doi.org/10.1007/s10683-011-9273-9

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  • DOI: https://doi.org/10.1007/s10683-011-9273-9

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