What Three Simple Productivity Numbers Teach Us About Data, Definitions, and Singapore’s Future
- Derrick Yuen, MBA
- 6 minutes ago
- 6 min read
Productivity is one of the most hotly debated topics in Singapore.Whether we’re discussing competitiveness, wages, or the future of work, productivity becomes the centre of every conversation.
Yet beneath these discussions lies a subtle but dangerous trap:
Different datasets use different definitions — and if we don’t understand what the numbers truly represent, we risk drawing the wrong conclusions.
In data analytics, small misunderstandings can lead to:
pointless debates
misdiagnosed problems
and even harmful recommendations
To illustrate just how easily this can happen, I analysed three datasets:
Value Added per Employee Dollar – across Singapore sectors
GDP per Employee Dollar – also across Singapore sectors
GDP per Employee Dollar – across Asia-Pacific economies
At first glance, these datasets look similar.But each reveals something completely different once we understand what the numbers actually mean.And together, they paint a powerful picture of Singapore’s economic structure, workforce future, and regional position.
1. Value Added per Employee Dollar: Who Really Receives the Economic Pie?
Value Added per Employee Dollar (VA/Employee$) measures:
How much value a sector generates for every $1 it pays its workers.
It is not a productivity measure. It is a distribution measure — showing how value is split between labour and capital.
Here’s what the data shows for Singapore:
High VA/Employee$ sectors
Wholesale & Retail Trade: 3.08
Transportation & Storage: 2.81
Finance & Insurance: 2.47
Manufacturing: 1.68
These sectors are capital- or asset-intensive:
Ports, airports, global logistics
High-tech factories with robotics and automation
Financial systems and risk models
Infrastructure for trade and supply chains
Here, every worker is backed by large capital investments — so each labour dollar is amplified by assets and technology.
Low VA/Employee$ sectors
Construction: 0.29
Other Services: 0.76
Accommodation & Food: 0.90
ICT: 1.23
These sectors are labour-intensive or wage-heavy, meaning labour accounts for a large share of value added.
For example:
Construction relies on large pools of manpower with limited capital leverage
ICT pays high wages, so labour captures a bigger portion of the economic pie (lowering the ratio)
The insight:High VA/Employee$ does not mean “high productivity.”It simply means labour receives a smaller share of the value created.
This is why understanding definitions is crucial.
2. GDP per Employee Dollar: A Different Metric That Tells a Different Story
GDP per Employee Dollar (GDP/Employee$) sounds similar, but measures:
How much total output a sector produces per $1 of labour cost.
It behaves differently because:
GDP includes taxes, subsidies, inventories
GDP reflects demand and output
It captures sector scale, not distribution
The ranking changes dramatically:
Highest GDP/Employee$ sectors
Real Estate: 5.45
Utilities: 5.22
Wholesale Trade: 4.77
Transportation & Storage: 3.77
Manufacturing: 3.84
What do these sectors have in common?
They are high-capital, high-scale sectors where output is driven by:
asset ownership
infrastructure
volume of goods and services moved
Why Finance & ICT drop here
Finance: 2.42
ICT: 1.61
These are high-wage sectors.Their labour costs are substantial, which suppresses the ratio even though output is strong.
The insight:VA/Employee$ tells us who captures value.GDP/Employee$ tells us how big the value is.
Mixing them leads to incorrect conclusions.
3. Asia-Pacific GDP per Employee Dollar: Why External Perspective Matters
Here is where things get interesting — and where incorrect definitions can really mislead.
Asia-Pacific GDP per Employee Dollar ranges:
Country | GDP/Employee$ |
Macao | 2.92 |
Brunei | 2.81 |
Philippines | 2.56 |
Mongolia | 2.69 |
Thailand | 2.11 |
Malaysia | 2.23 |
Vietnam | 2.34 |
Singapore | 1.99 |
Japan | 1.76 |
Korea | 1.68 |
At first glance, someone might conclude:
“Singapore’s productivity is lower than the Philippines, Vietnam, or Malaysia.”
But this would be a major misunderstanding.
Why Singapore’s number looks “low”
In countries with large informal sectors:
much labour income is not captured as "employee compensation"
this makes labour cost appear artificially small
the ratio becomes inflated
Singapore, Japan, and Korea — with fully formal and well-documented labour markets — naturally show lower GDP/Employee$ ratios.
What the ratio still tells us
Even with these distortions, we can still use it to understand:
where economies stand in their development stage
where labour-intensive growth is occurring
where capital-based growth dominates
structural shifts in Asia
competitive pressures for Singapore’s labour market
4. When We Combine All Three Datasets: A Clear Pattern of Singapore’s Economic Future Emerges
This is where the analysis becomes truly powerful.Across the datasets, a structural story emerges:
A. High-capital sectors will automate further
Sectors like:
Utilities
Transportation & Storage
Manufacturing
Wholesale Trade
Real Estate
show high values in both VA/Employee$ and GDP/Employee$, meaning:
capital already does most of the heavy lifting
labour plays supervisory or specialist roles
automation is a natural extension, not a disruption
These industries will:
automate faster
rely on smaller, more skilled teams
need workers who understand systems, not just tasks
B. Labour-intensive sectors face two possible futures
Sectors such as:
Construction
Accommodation & Food
Other Social Services
Education
Admin & Support
face the greatest change.Two scenarios are emerging:
Scenario 1 — Automation takes root
Drivers:
manpower shortages
rising wages
falling automation costs
Examples:
autonomous cleaning
digital concierge systems
robotics in F&B
prefabrication in construction
AI workflow orchestration
Scenario 2 — Human talent remains critical
In areas like:
healthcare
education
hospitality
human empathy, judgement, and creativity cannot be replaced.AI assists — but does not substitute — these workers.
These sectors need:
skills upgrading
job redesign
better tools
AI integration
Rather than replacement.
C. Asia-Pacific patterns reveal the forces Singapore must navigate
The region’s numbers — while imperfect — highlight a critical truth:
Younger, lower-cost economies are climbing the productivity ladder faster.
Countries like Vietnam, the Philippines, Cambodia, and Malaysia are:
expanding their labour force
industrialising quickly
offering lower labour costs
attracting labour-intensive work
improving their GDP/Employee$ at a fast pace
Meanwhile, Singapore faces:
falling labour force growth
rising wage expectations
competition for mid-skill roles
pressure to move up the value chain
This puts Singapore at a strategic economic inflexion point.
5. Three Lessons Every Leader, Analyst, and Decision-Maker Should Take Away
Lesson 1: Data definitions matter
VA per Employee$ and GDP per Employee$ sound similar — but tell completely different stories.Misplacing definitions leads to:
wrong conclusions
wrong arguments
wrong decisions
Definitions are the foundation of analytics.
Lesson 2: Layered data builds complete understanding
No single dataset reveals the full picture.But together, they show:
how value is created
how value is distributed
how sectors differ
where automation hits first
where human skills remain critical
Layering data is how analysts move from numbers to meaning.
Lesson 3: External benchmarking prevents false alarms
A number that looks “bad” in isolation may be normal — or even strong — in regional context.
External perspective helps us understand:
scale
urgency
whether a problem is real or imagined
where Singapore truly stands in Asia
This prevents wasted energy on the wrong issues.
6. Final Reflections: Singapore’s Workforce at the Inflexion Point
Singapore cannot compete on labour quantity.It cannot compete on low wages.It cannot compete on scale.
But it can compete on:
problem solving
human-AI collaboration
decision-making
systems thinking
strategic coordination
innovation
domain expertise
Singapore’s role in Asia’s economy is shifting from execution to orchestration —from doing the work to designing how the work gets done.
The future belongs to workers who can:
ask better questions
understand data deeply
integrate technology intelligently
solve messy problems
make sound decisions with imperfect information
This is why definitions matter.This is why context matters.This is why external perspective matters.
Because productivity is not just about output.It is about how we choose to think.
This Isn’t About Economics — It’s About Thinking Clearly in the Data Age
Again, I am not an economist by training.This analysis is not intended to predict macroeconomic outcomes.
It is intended to illustrate the analytical thinking process:
Clarify definitions
Layer adjacent datasets
Compare externally
Ask deeper questions
Draw objective insights
These are skills that anyone can learn — and they are essential in a world overflowing with data and noise.
Explore the Data, Ask Questions, Learn the Skills
If the charts in this article caught your interest, you can:
👉 Click on the charts to explore the interactive dashboardsDive deeper into sector patterns and regional comparisons.
👉 Contact us if you have questions about the data or analysisWe’re happy to share our methodology and thought process.
👉 Join us if you want to learn these analytics skills yourselfWhether you’re in HR, business, operations, finance, or government —the ability to think clearly with data is one of the most valuable skills today.
