When Data Tells Three Different Stories: What the Overqualification Debate Really Reveals
- 7 days ago
- 5 min read
Recently, a study on overqualification in Singapore made its way across headlines, reports, and social media.
At first glance, it seemed straightforward:
Singapore has a highly educated workforce
Some workers are “overqualified” for their jobs
And depending on who you read, this is either a concern… or not
But here’s where it gets interesting.
Three different sources — the Ministry of Manpower (MOM), NTUC, and Channel News Asia — all reported on the same topic.
Yet, they told slightly different stories.
Not because anyone was wrong.
But because they were answering different questions.
The Same Topic, Three Different Lenses
Let’s start with what each source was actually doing.
1. MOM: A structural, data-driven view
MOM approached the issue from a national, statistical perspective.
They define overqualification as:
A worker having a higher level of education than what their job typically requires
This is based on an internationally aligned framework (ILO), and uses large-scale labour force data — covering about 33,000 households.
Their headline finding:
19.4% of Singapore’s resident workforce is overqualified
But importantly:
This is lower than the high-income country average of 21.6%
2. NTUC: A worker experience perspective
NTUC took a different approach.
Instead of just looking at job classifications, they asked workers directly:
Are your skills fully used?
Does your job match your qualifications?
Are you working in your field of study?
Their survey (about 1,100 respondents) found:
23% felt overqualified for their jobs
22.5% felt their skills were underutilised
31.4% were working outside their field of study
This paints a broader picture:Not just whether people are “technically overqualified” — but whether they feel underutilised.
3. Media: The narrative bridge
The media then stepped in to translate these findings for the public.
And naturally, the focus shifted toward:
What this means for workers
Whether this is a problem
And what people should take away
But here’s the challenge: When different definitions and datasets are presented side by side, it’s easy for readers to assume they are directly comparable.
They’re not.
The First Lesson: Not All Metrics Measure the Same Thing
This is where many discussions go off track.
Let’s simplify:
MOM is measuring job–qualification mismatch (objective, structured)
NTUC is measuring worker–job fit (subjective, self-reported)
Both are valid.
But they answer different questions:
“Is the job below your qualification level?”
vs
“Do you feel your capabilities are fully used?”
If you treat them as the same thing, the numbers will look inconsistent.
If you understand the definitions, they actually complement each other.
So What Does the Data Really Say?
Once we align the definitions, a clearer picture emerges.
1. Yes, overqualification exists
About 1 in 5 workers are in roles below their formal education level.
2. But it is not unusually high
Singapore is actually below the average for high-income countries.
3. Most of it is voluntary
This is perhaps the most overlooked insight.
MOM: ~90% of overqualification is voluntary
NTUC: 85.5% voluntary
In other words:Most people are not “stuck” — they are making trade-offs.
4. The labour market is still broadly balanced
64.0% of the workforce has tertiary education
64.2% of jobs require tertiary-level skills
That’s a remarkably close match.
Which suggests:This is not a case of widespread oversupply.
The Second Lesson: Correlation Is Not the Full Story
One of the more widely cited points is this:
Countries with more degree holders tend to have higher overqualification
At a high level, this is true.
But here’s the nuance.
Singapore is an exception:
Very high tertiary education levels (64%)
Yet lower-than-average overqualification
So what’s going on?
It tells us something important:
Education levels alone do not determine overqualification.
Other factors matter just as much, if not more:
The types of jobs available
How employers hire (skills vs credentials)
Career transitions and mobility
Worker preferences (flexibility, purpose, stability)
This is where simple narratives start to break down.
The Third Lesson: When Context Is Missing, Narratives Fill the Gap
Once the data was released, social media quickly filled in the blanks.
Some common reactions:
“Singaporeans are underachieving”
“Overqualified workers are taking jobs from fresh graduates”
“There are too many degree holders”
“AI is making jobs redundant”
These are not unreasonable questions.
But they are interpretations — not conclusions from the data.
Because the data itself does not say:
That people are underperforming
That jobs are being displaced
Or that education is the root problem
What it does say is:
Many people are making deliberate career choices
The labour market is still functioning relatively well
And the real challenge lies in skills alignment, not qualifications alone
So What Was the Report Actually Trying to Do?
This is where the message often gets lost.
The MOM report was not written to:
Criticise workers
Set performance targets
Or suggest people need to “do better”
Its purpose was much more practical:
To measure underemployment more accurately
To go beyond just unemployment rates
And to understand how the workforce is evolving
In fact, one of the key takeaways is this:
The issue is not too many qualifications — but whether skills are aligned with changing job needs.
Data does not speak for itself
It depends on:
How the problem is defined
What is being measured
And how the results are interpreted
When those pieces are not clearly aligned, even well-intentioned analysis can lead to confusion.
And when context is missing, people will naturally fill the gaps with their own assumptions.
Singapore does show some level of overqualification.
But the fuller picture is this:
It is not unusually high
It is largely voluntary
And it is not simply a result of “too many degrees”
More importantly, it reminds us of something bigger:
In today’s data-rich world, the real skill is not just analysing data —it is understanding what the data is actually telling us.
A Final Note from FYT
At FYT, this is exactly the kind of real-world scenario we use in our programmes.
Not because the topic itself is complex —but because it highlights how easily:
different definitions lead to different conclusions
numbers can be misunderstood when taken out of context
and narratives can quickly take over when the data story is not clearly told
In this article, we’ve deliberately deconstructed:
how the same study led to three different interpretations
how the metrics were defined and used
and how a clearer, more grounded data story could have been communicated
This is at the heart of what we teach —helping professionals move beyond just working with data, to thinking critically about what the data actually means.
If you’re interested in building these skills — whether for yourself or your organisation — we’d be happy to have a conversation.
Feel free to reach out to us to learn more about how we can help.
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