Cited papers
- Costly Capacity Signaling Increases Matching Efficiency: Evidence from a Field Experiment
- Preference Signaling in Labor Markets: Evidence from a Field Experiment
Authors
- Prasanna Parasurama, Assistant Professor of Information Systems & Operations Management
- Apostolos Filippas, Information, Technology, and Operations at the Fordham Gabelli School of Business and MIT IDE Digital Fellow
- John Horton, MIT IDE Research Lead and Chrysler Associate Professor of Management and an Associate Professor of Information Technologies at MIT Sloan
- Diego Urraca, unaffiliated
Integrating AI in the hiring process has changed the game for both employers and those on the job hunt. Companies can more easily sort resumes and qualified candidates, while workers can customize resumes at scale.
However, both employers and job seekers have reported mixed results—and frustration—with these AI-powered systems. Companies report being buried in applicants and are leaning on AI-tools to manage the volume. Applicants are finding it harder to get noticed. In turn they are applying to more jobs, even those that aren’t a great fit, making the volume problem worse.
A recent study from Standford University drove home the imprecise nature of this system. It revealed that 90% of companies are using AI to filter applications, but that these AI hiring tools have algorithmic biases—including racial bias—that are filtering out potentially qualified candidates.
“The way it used to work is that applicants would write really nice cover letters to stand out, but generative AI has completely eroded that signal,” said Prasanna Parasurama.
Parasurama and a team working within John Horton’s AI, Marketplaces, and Labor Markets research group at the MIT Initiative on the Digital Economy, have spent several years running field experiments on major online labor markets. They asked, what would happen if workers could pay a small cost to signal their interest or availability? Would this help them cut through the noise? Would their signal accurately show if a candidate is a good fit?
The results in both studies indicate yes, that costly signals help connect applicants and employers, and that these matches hold up over time.
Study 1: Boosting applications to the top of the list
The first experiment sought to uncover what would happen if applicants could bring their application to the top of the pack.
In a field experiment, the research team introduced “boosted applications” where workers could bid using platform coins to compete for one of three pinned slots at the top of an employer’s applicant list. The team then randomly showed when an applicant had been boosted, so that some employers (the treated group) knew the applicant had opted to boost, while others did not (the control group).
The Results
Boosting an application increased the likelihood of being hired by 40.8%. Within that group, the researchers found that appearing at the top of the list accounted for 79.8% of the lift. The presence of the boosted disclosure label accounted for the remaining 20.2%.
Employers selected the boosted candidates because they were truly stronger, the researchers found. They were more active, more sought-after by employers, and strategic about when they spent their signal, boosting only 17–18% of their applications on average.
“The more important question is, does it lead to better matches? Evidence so far says yes.” Parasurama said. The employers that were shown which applications were boosted were more likely to rehire the same worker for subsequent jobs than those who were not shown this information.
The researchers believe this system works in the long term due in large part to the multi-stage process of hiring. Even if an unqualified worker thinks boosting will help, they still have to compete against more qualified candidates in the interview process, which discourages them from boosting in the first place.
When the signal stops working
Next, the research team turned to gig economy platforms and how workers indicate their availability.
For years, platforms offered a free availability indicator where workers could self-report how many hours per week they had for new work. Early on, it was effective. Workers who signaled high availability received more employer invites and were more likely to sign contracts.
But over time, this indicator became less reliable. Researchers found that 88.6% of workers had marked themselves as available for 30+ hours per week regardless of how busy they were. Invite acceptance rates for “full-time available” workers were barely higher than for those who claimed limited availability.
This is the predictable outcome of costless signaling, the researchers argue. When there’s no downside to claiming you’re available, everyone does it.
Study 2: A badge that costs something
The researchers wondered if making that signal costly could restore its credibility.
In another field experiment, workers on the platform in select technical categories could rent an “Available Now” badge. It was displayed on their profile and in employer search results for two coins per week (roughly $0.30). Unlike the boosted application experiment, the badge had no effect on search rankings. It was a pure signal of capacity and openness to new work.
Employers were again randomly divided: treated employers could see the badge. Control employers could not. The experiment engaged 84,425 employers and 243,126 workers over 10 weeks.
The results reinforced the pattern from the previous study. Nearly 40% of active workers rented the badge by the end of the experiment. Workers who chose to display the badge were already stronger performers before the experiment began. They had more invites, higher acceptance rates and signed more contracts. Again, the signal attracted the workers who had the most to credibly signal.
For employers who could see the badge, matching efficiency improved. They were more likely to send recruiting invites, more likely to get a positive response, and signed about 2.6% more contracts than the control group.
For workers, renting the badge increased the probability of receiving an employer invite by roughly 4.8% per impression. The badge was not received as a sign of desperation. Employers read it as a credible indicator of genuine availability.
Two years after the experiment ended and the badge had been rolled out platform-wide, badge renters were receiving about 50% more employer invites than non-renters, indicating the signal’s value held.
What both studies tell us about digital matchmatching systems
Taken together, these two experiments make a consistent case. Costly signals work in hiring markets whether the cost is attached to a specific application bid or a weekly availability badge.
The costs here are low but not insignificant because they indicate scarcity. A signal that anyone can claim at any time for free will be claimed by everyone and mean nothing. A signal that requires money, coins, or a limited number of uses retains informational value because not everyone will use it, and those who do tend to mean it.
“One place this already happens is in online dating,” said Apostolos Filippas. The dating app Tinder offers “super liking,” indicating strong interest in a potential match. While costly, it sends a strong message when the other person sees they are “super liked” by someone. Philippas pointed to a Stanford University study that showed similar costly signaling in online dating apps improved the likelihood of an offer. This type of signaling can apply to other marketplaces too, he said, as long as there are enough people and the specific value in the exchange is subjective or variable.
The team’s research also speaks directly to where most hiring happens today. Nearly all job applications now flow through an applicant tracking system—Greenhouse, Lever, Workday, or similar. Some platforms are already testing versions of costly signaling.
For example, Greenhouse has piloted a premium application feature called “dream job” that gives candidates a limited number of “flags” they can use to indicate a role as a dream job. Any platform-issued boost earned through activity or a verified availability badge could function the same way, as long as they’re scarce enough that the signal stays meaningful.
The case for competitive signaling
In today’s job market, AI has simultaneously made it cheaper for candidates to apply and easier for those applications to blend together. It creates a system where motivated, well-matched candidates are routinely invisible. Signals that used to surface quality have been inflated to the point of uselessness.
What this research shows is that a small cost attached to a signal, whether it’s a bid, a badge, or a limited-use flag, doesn’t disadvantage strong candidates. It helps them stand out in a market that has, for the moment, made standing out nearly impossible.
The two papers also indicate that many of the challenges in early-stage hiring are rooted in the system’s design. When AI hiring tools and platforms add user friction to the system, that friction offers valuable information, and based on these two experiments, better outcomes.