The Robert Wood Johnson Foundation (RWJF), known as the United States’ largest philanthropy focused solely on health, recently awarded a $120,024 grant to Assistant Professor of Information Systems Yili Hong and Professor of Information Systems Bin Gu. While the RWJF typically bestows awards for research and programs targeting some of America’s most pressing health issues, the foundation welcomes brief proposals for pioneering ideas to help anticipate the future of work.
The professors’ shared area of study that focuses on online labor markets will help the RWJF understand how this new nature of work in the U.S. affects health. Specifically, Hong and Gu examine the so-called gig economy — where people work job-to-job with little security and few employment rights.
“It is important to understand how participants in gig economy platforms make decisions and how such decisions affect their health,” Hong explains, “as these platforms promise to become the future workplace for hundreds of millions of citizens.”
Upwork.com and Freelancer.com are two such web-based digital platforms that connect employers and millions of workers completing jobs worth billions of dollars.
“As much as online labor platforms are accessible to employers and workers around the world, inefficiencies plague them,” says Hong. For instance, on Freelancer.com, it is difficult for companies to discern employees’ work ethic and quality. On the other hand, it’s tough for workers to determine the trustworthiness of the employer. Participants tend to rely on decision processes and consider observable attributes, such as race, gender, and reputation as criteria for the matching process.
Such biases — like employers against new workers — and the health consequences of work-hour flexibility and employment uncertainty, are what’s being addressed in the RWJF paper, “Toward a Fair Workplace With Equal Opportunities: Understanding Decision-Making Biases and Health Outcomes in Online Gig Economy Platforms.”
Working in a temporary, short-term employment relationship tends to be different from working in a contract-based, long-term one, which may lead to health-related issues such as technostress, according to Hong. “Because these are new markets, there are a lot of unanswered questions. How are these people going to pay for health insurance? Who are these people? Are they just doing moonlighting gigs or is it full-time employment? And is there any evidence that it will impact their health, mentally or physically?”
While the deadline for the RWJF research is in two years, Hong and Gu have already set out to study racial and gender biases. A paper they co-authored with PhD student Chen Liang investigates the effects of an IT-enabled monitoring system Freelancer.com implemented in August 2015. “Our first analyses show the monitoring system lowers the employers’ bias against new bidders, and thus lowers the entry barrier for contractors who have not yet established a reputation on the platform,” says Hong about the preliminary results. They’ll collect new data to validate these findings further and update RWJF in the working paper every six months.
As the data grows, the professors are building a tech tool to capture information, too. A fully automated program that’s in prototype phase will soon pose as employers or workers with different racial or gender identities but the same job requirements or qualifications. Then they’ll gather and analyze data on employers’ hiring choice and workers’ bidding patterns to estimate the biases. Additionally, they’ll collect archival data from Freelancer.com to identify how the platform’s reputation system and monitoring system mitigate potential racial and gender biases.
A survey approach is being used to find out the health implications of the gig economy. “Understanding health-related challenges faced by these workers will help us prescribe policy suggestions for online labor platforms to inform platform designs,” Hong explains.
When the research is complete, the results will not only shed light on health consequences and offer actionable insights for platform designs. They will lead to equal opportunities for all participants — managers and workers alike — with minimum racial and gender biases and entry barriers.