With decades of experience helping organizations navigate change through technology, HRTech expert Ling-Yi Tsai has a unique perspective on the systems that underpin our work lives. When news broke of a major misconduct scandal within Japan’s public employment service, Hello Work—where an employee faked jobseeker identities to meet performance targets—we knew she was the perfect person to help us understand the deeper institutional failures at play. This incident, which has now triggered a nationwide probe, exposes the critical vulnerabilities that can arise when high-stakes performance metrics collide with human pressure, raising profound questions about accountability and trust in public institutions.
The article notes Hello Work offices face “guidance” if they fall below a 95% job placement target. From your experience, how does a rigid, high-stakes metric like this create an environment where misconduct becomes almost predictable, and what might this “guidance” actually feel like for the staff on the ground?
A 95% target is extraordinarily high for any placement-based organization and essentially sets people up for failure. When you tie performance to such an unforgiving number, the focus inevitably shifts from the quality of the work—finding sustainable, suitable employment for people—to simply hitting the target by any means necessary. This is a classic case of a well-intentioned goal creating perverse incentives. In practical terms, “guidance” is likely a bureaucratic euphemism for intense pressure. It would mean daily scrutiny from management, performance improvement plans, endless meetings to justify every shortfall, and a constant, looming threat to one’s career stability. This creates a culture of fear and desperation where an employee might rationalize that bending the rules is the only way to survive.
This employee managed to register false identities and have declined offers counted as official placements. Could you walk us through the likely systemic breakdowns that allowed this to happen? What fundamental verification processes must have failed for such a scheme to even be possible?
This points to a chain of catastrophic failures in the system’s basic architecture. First, the ability to register two entirely false identities suggests a shocking lack of robust identity verification at the front end. There should be digital or physical checks in place to confirm a person is who they say they are before they ever enter the system as a jobseeker. Second, and perhaps more critically, the fact that a declined offer can be counted as a placement is a massive loophole. It indicates the system’s definition of “success” is flawed. A successful placement should be a confirmed hire, not just an offer. This loophole suggests the system relies on self-reporting from its own staff without cross-verification from either the company or the applicant, creating a process built on a level of trust that, in this case, was completely broken.
The Labour Ministry is now investigating all 544 Hello Work offices. What would a probe of this magnitude actually entail? What kind of data anomalies and red flags would investigators be hunting for to uncover similar instances of placement inflation?
An investigation of this scale will be a massive data-mining operation. Investigators won’t be going through paper files one by one initially; they’ll be running algorithms across the entire national database, looking for statistical impossibilities and suspicious patterns. They’ll search for offices that consistently hover just above the 95% threshold, as that can be a red flag for manipulation. They will also look for a high volume of placements attributed to a single employee, an unusual number of offers that are quickly declined, or multiple “applicants” applying to the same nine or ten companies. Another key red flag would be a spike in placements recorded at the very end of a reporting period, as that’s when the pressure to meet targets is at its peak. It’s essentially a forensic audit of their performance data.
Considering this employee submitted applications to nine different companies, what kind of damage does an incident like this do to the trust between Japanese businesses and the Hello Work system? What long-term changes might we see in how hiring managers interact with and verify candidates from this public service?
The damage to trust is immediate and severe. For the nine companies involved, their time, resources, and energy were completely wasted interviewing a ghost applicant. This erodes the fundamental assumption that Hello Work is a reliable partner in the hiring process. In the long term, this could make businesses across the country more skeptical of any candidate coming through the Hello Work system. We might see hiring managers implementing their own redundant verification steps, demanding more direct proof of identity, or simply prioritizing candidates from other channels. It undermines Hello Work’s core function as a trusted intermediary and forces businesses to add layers of security and bureaucracy to their own hiring, slowing everything down for legitimate jobseekers.
What is your forecast for performance management within Japan’s public sector employment services following this scandal?
My forecast is for a period of significant, and likely painful, reform. The immediate response will be to plug the obvious loopholes with stricter controls, mandatory data verification, and more aggressive auditing. However, the real, lasting change will have to be a philosophical shift in how performance is measured. This scandal is a clear sign that a single, rigid quantitative target like a 95% placement rate is dangerously flawed. I predict a move towards a more balanced scorecard that includes qualitative metrics: things like the six-month job retention rate for placements, employer satisfaction surveys, and the suitability of the job match. This will be a difficult transition, as it’s much harder to measure quality than quantity, but it’s absolutely necessary to rebuild trust and re-center the system on its true mission of meaningful employment.