The Online Laboratory: Conducting Experiments in a Real Labor Market

TL;DR

This paper demonstrates that online labor markets are effective platforms for conducting valid and cost-efficient experiments, replicating classic studies and confirming behavioral theories with high external validity.

Contribution

It provides empirical evidence that online experiments can match traditional methods in validity, discusses threats to validity, and offers best practices for conducting online research.

Findings

Subjects respond to framing, priming, and pro-social incentives as in traditional experiments.

Labor supply curves are upward sloping in online field experiments.

Online experiments can achieve high external validity, comparable or superior to traditional methods.

Abstract

Online labor markets have great potential as platforms for conducting experiments, as they provide immediate access to a large and diverse subject pool and allow researchers to conduct randomized controlled trials. We argue that online experiments can be just as valid---both internally and externally---as laboratory and field experiments, while requiring far less money and time to design and to conduct. In this paper, we first describe the benefits of conducting experiments in online labor markets; we then use one such market to replicate three classic experiments and confirm their results. We confirm that subjects (1) reverse decisions in response to how a decision-problem is framed, (2) have pro-social preferences (value payoffs to others positively), and (3) respond to priming by altering their choices. We also conduct a labor supply field experiment in which we confirm that workers have upward sloping labor supply curves. In addition to reporting these results, we discuss the unique threats to validity in an online setting and propose methods for coping with these threats. We also discuss the external validity of results from online domains and explain why online results can have external validity equal to or even better than that of traditional methods, depending on the research question. We conclude with our views on the potential role that online experiments can play within the social sciences, and then recommend software development priorities and best practices.