Conformity under uncertainty: Reliance on gender stereotypes in online hiring decisions

2014 ◽  
Vol 37 (1) ◽  
pp. 103-104 ◽  
Author(s):  
Eric Luis Uhlmann ◽  
Raphael Silberzahn
Keyword(s):  
Labor Markets ◽  
Gender Stereotypes ◽  
Gender Discrimination ◽  
Confirmation Bias ◽  
Feedback Loops ◽  
Hiring Decisions ◽  
Homo Economicus ◽  
Social Stereotypes ◽  
Online Labor Markets ◽  
Online Labor

AbstractWe apply Bentley et al.’s theoretical framework to better understand gender discrimination in online labor markets. Although such settings are designed to encourage employer behavior in the northwest corner of Homo economicus, actual online hiring decisions tend to drift southeast into a “confirmation bias plus weak feedback loops” pattern of discrimination based on inaccurate social stereotypes.

scholarly journals A Deep Choice Model for Hiring Outcome Prediction in Online Labor Markets

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Yixuan Ma ◽  
Zhenji Zhang ◽  
Alexander Ihler
Keyword(s):  
Labor Markets ◽  
Logit Model ◽  
Choice Model ◽  
Hiring Decisions ◽  
Conditional Logit Model ◽  
Conditional Logit ◽  
Online Labor Markets ◽  
Non Linear ◽  
Linear Utility ◽  
Online Labor

A key challenge faced by online labor market researchers and practitioners is to understand how employers make hiring decisions from many job bidders with distinct attributes. This study investigates employer hiring behavior in one of the largest online labor markets by building a datadriven hiring decision prediction model. With the limitation of traditional discrete choice model (conditional logit model), we develop a novel deep choice model to simulate the hiring behavior from 722,339 job posts. The deep choice model extends the classical conditional logit model by learning a non-linear utility function identically for each bidder within of the job posts via a pointwise convolutional neural network. This non-linear mapping can be straightforwardly optimized using stochastic gradient approach. We test the model on 12 categories of job posts in the dataset. Results show that our deep choice model outperforms the linear-utility conditional logit model in predicting hiring preferences. By analyzing the model using dimensionality reduction and sensitivity analysis, we highlight the nonlinear combination of bidders’ features in impacting employers’ hiring decisions.

More for Less: Adaptive Labeling Payment for Online Labor Markets

2016 ◽  
Author(s):  
Tomer Geva ◽  
Harel Lustiger ◽  
Maytal Saar-Tsechansky
Keyword(s):  
Labor Markets ◽  
Online Labor Markets ◽  
Online Labor

Scoundrels or Stars? Theory and Evidence on the Quality of Workers in Online Labor Markets

2016 ◽  
Vol 92 (1) ◽  
pp. 93-114 ◽  
Author(s):  
Anne M. Farrell ◽  
Jonathan H. Grenier ◽  
Justin Leiby
Keyword(s):  
Labor Markets ◽  
Data Availability ◽  
Accounting Research ◽  
Research Participants ◽  
Large Numbers ◽  
Research Designs ◽  
Online Labor Markets ◽  
Online Labor ◽  
Low Pay

ABSTRACT Online labor markets allow rapid recruitment of large numbers of workers for very low pay. Although online workers are often used as research participants, there is little evidence that they are motivated to make costly choices to forgo wealth or leisure that are often central to addressing accounting research questions. Thus, we investigate the validity of using online workers as a proxy for non-experts when accounting research designs use more demanding tasks than these workers typically complete. Three experiments examine the costly choices of online workers relative to student research participants. We find that online workers are at least as willing as students to make costly choices, even at significantly lower wages. We also find that online workers are sensitive to performance-based wages, which are just as effective in inducing high effort as high fixed wages. We discuss implications of our results for conducting accounting research with online workers. Data Availability: Contact the authors.

scholarly journals Monopsony in Online Labor Markets

2018 ◽  
Author(s):  
Arindrajit Dube ◽  
Jeff Jacobs ◽  
Suresh Naidu ◽  
Siddharth Suri
Keyword(s):  
Labor Markets ◽  
Online Labor Markets ◽  
Online Labor

Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets

2021 ◽  
Author(s):  
Yili Hong ◽  
Jing Peng ◽  
Gordon Burtch ◽  
Ni Huang
Keyword(s):  
Labor Markets ◽  
Communication Channel ◽  
Message Content ◽  
Job Fit ◽  
Beneficial Effects ◽  
Online Labor Markets ◽  
Lower Status ◽  
Prospective Employer ◽  
Online Labor

This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exchange of online labor. We propose the effect of workers’ use of the direct messaging system on employers’ hiring decisions and conceptualize the information role of direct messaging. To empirically evaluate the information role of the direct messaging system, we leverage data on the direct messaging activities between workers and employers across more than 470,000 job applications on a leading online labor market. We report evidence that direct messaging with a prospective employer increases a worker’s probability of being hired by 8.9%. However, the degree to which workers benefit from direct messaging is heterogeneous, and the effect amplifies for workers approaching employers from a position of disadvantage (lacking tenure or fit with the job) and attenuates as more workers attempt to message the same prospective employer. The effects also depend on message content. In particular, we find that the benefits of direct messaging for workers depend a great deal on the politeness of the workers, and this “politeness effect” depends on several contextual factors. The beneficial effects are amplified for lower-status workers (i.e., workers lacking tenure and job fit) and workers who share a common language with the employer. At the same time, the beneficial effects weaken in the presence of typographical errors. These findings provide important insights into when and what to message to achieve favorable hiring outcomes in online employment settings.

scholarly journals Monopsony in Online Labor Markets

2020 ◽  
Vol 2 (1) ◽  
pp. 33-46 ◽  
Author(s):  
Arindrajit Dube ◽  
Jeff Jacobs ◽  
Suresh Naidu ◽  
Siddharth Suri
Keyword(s):  
Labor Supply ◽  
Labor Markets ◽  
Search Costs ◽  
Amazon Mechanical Turk ◽  
Large Dataset ◽  
On Demand ◽  
Online Labor Markets ◽  
Online Labor ◽  
Monopsony Power ◽  
Jel C44

Despite the seemingly low switching and search costs of on-demand labor markets like Amazon Mechanical Turk, we find substantial monopsony power, as measured by the elasticity of labor supply facing the requester (employer). We isolate plausibly exogenous variation in rewards using a double machine learning estimator applied to a large dataset of scraped MTurk tasks. We also reanalyze data from five MTurk experiments that randomized payments to obtain corresponding experimental estimates. Both approaches yield uniformly low labor supply elasticities, around 0.1, with little heterogeneity. Our results suggest monopsony might also be present even in putatively “thick” labor markets. (JEL C44, J22, J23, J42)

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