The Labor Economics of Paid Crowdsourcing (1001.0627v1)

Abstract: Crowdsourcing is a form of "peer production" in which work traditionally performed by an employee is outsourced to an "undefined, generally large group of people in the form of an open call." We present a model of workers supplying labor to paid crowdsourcing projects. We also introduce a novel method for estimating a worker's reservation wage--the smallest wage a worker is willing to accept for a task and the key parameter in our labor supply model. It shows that the reservation wages of a sample of workers from Amazon's Mechanical Turk (AMT) are approximately log normally distributed, with a median wage of $1.38/hour. At the median wage, the point elasticity of extensive labor supply is 0.43. We discuss how to use our calibrated model to make predictions in applied work. Two experimental tests of the model show that many workers respond rationally to offered incentives. However, a non-trivial fraction of subjects appear to set earnings targets. These "target earners" consider not just the offered wage--which is what the rational model predicts--but also their proximity to earnings goals. Interestingly, a number of workers clearly prefer earning total amounts evenly divisible by 5, presumably because these amounts make good targets.

• The paper introduces a novel method to estimate reservation wages, revealing a median of $1.38 per hour and a labor supply elasticity of 0.43.
• The paper demonstrates that workers on platforms like AMT are price-sensitive but show little response to variations in task difficulty.
• The paper highlights target earning behavior, showing that workers aim for rounded financial goals, which challenges traditional economic labor models.

The Labor Economics of Paid Crowdsourcing: A Methodological Analysis

The paper "The Labor Economics of Paid Crowdsourcing" by John J. Horton and Lydia B. Chilton provides an in-depth examination of labor supply dynamics within the sphere of paid crowdsourcing. This work is situated in the broader context of online labor markets, such as Amazon Mechanical Turk (AMT), and focuses on understanding workers' decision-making processes and how they respond to varying economic incentives.

Core Contributions and Methodology

Central to this paper is the investigation of workers' reservation wages, defined as the minimum compensation a worker requires for task engagement, and a model of labor supply elasticity. The authors present a novel approach for estimating reservation wages, demonstrating that on AMT, these wages are approximately log-normally distributed with a median of $1.38 per hour. The elasticity of labor supply at the median wage is calculated at 0.43.

The paper critically evaluates how workers react to different wage structures and task difficulties through two experiments. These experiments explore whether workers adjust their output based on task difficulty and price variation. Interestingly, the findings illustrate that while workers are price-sensitive, they exhibit insensitivity to task difficulty. This aspect is particularly evident as some workers set target earnings, focusing more on proximity to predetermined financial goals than on the actual wages offered.

Numerical Findings and Implications

The empirical results reveal profound insights: participants showed a discernible preference for earning rounded figures, indicative of target earning behavior. Such behavior deviates from predictions by classical rational models, suggesting that traditional economic interpretations of labor supply might require adjustment when applied to crowdsourcing contexts.

Additionally, the paper identifies significant differences in output levels between high and low wage offerings, confirming that higher wages incentivize greater labor supply. The insights gained through the modeled distribution and elasticity parameters offer pathways to predict labor supply responses in various pricing scenarios effectively.

Theoretical and Practical Implications

The work contributes to both theoretical and practical domains in crowdsourcing by highlighting behavioral idiosyncrasies such as target earning, which are not typically addressed by rational economic models. These findings necessitate a reconsideration of incentive design in crowdsourcing platforms, suggesting that incorporating clear, achievable financial targets may enhance worker motivation and output.

From a practical standpoint, the insights derived from this paper are invaluable for platform designers and managers seeking to optimize task design and compensation models. Understanding the nuanced responses of workers to pay structures can guide the development of more effective incentive mechanisms, ultimately improving task completion rates and enhancing the efficiency of crowdsourcing projects.

Future Directions

The exploration of behavioral economics within the crowdsourcing labor market opens several avenues for future research. Investigation into compensating differentials for various task types and understanding the correlates of reservation wages across demographic and economic variables could provide further clarity. There is also a critical need for deeper analysis of how time zone differences and task visibility impact labor supply decisions on platforms like AMT.

In conclusion, this paper extends the discourse on labor economics within crowdsourcing environments and challenges existing paradigms by presenting evidence of target earning behavior. It lays a foundational framework for further academic inquiry into the complexities of digital labor markets and offers practical guidance for optimizing labor supply in these settings.