Singapore employers face a tightening labour market. The resident workforce has passed its peak, and the number of working‑age citizens is now declining. Yet many organisations still make workforce decisions based on instinct rather than data – despite a wealth of public information available at no cost.
This technical note shows shows how publicly available data from SingStat, MOM and IRAS can be used to inform internal HR planning, from talent sourcing to pay benchmarking and succession management.
1. The Workforce Has Peaked – and That Is Not a Forecast
SingStat’s population and labour force data reveal a clear, mathematically certain trend: Singapore’s resident labour force (ages 20–64) peaked around 2023 and is now shrinking. The reason is not complex – birth rates have been below replacement level since 1976, and that pipeline has finally run dry.
For HR planning, this means the competition for local talent will intensify. Organisations can no longer assume a steady supply of young graduates. Using public data, HR teams can:
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Track the size of each age cohort entering and leaving the workforce over the next 5–10 years.
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Forecast attrition by age and occupation using published labour force participation rates.
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Model the impact of raising retirement age to 70 (by 2030) on their own workforce composition.
Example: The number of residents aged 55–64 is large and growing. Many are still working (40% of men over 65 remain in the labour force). That is not a problem – it is an underused talent pool.
2. Fertility and Race: No Group Is Replacing Itself
Fertility rate data by race (available from SingStat) shows that every major racial group is now below replacement level. The “Two is Enough” policy (1972) was highly effective; the baby bonus scheme (2001 onwards) has not reversed the decline.
For HR planning, this matters when organisations rely on specific ethnic or cultural competencies (e.g., language, community engagement). The data do not explain why fertility dropped – that requires hypothesis testing – but they clearly show the what. HR can use this to:
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Validate or challenge assumptions about future supply of specific talent segments.
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Benchmark their own workforce demographics against national trends.
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Avoid over‑reliance on a shrinking local pool for hard‑to‑fill roles.
3. Labour Force Participation – Room for Improvement Is Small
Overall labour force participation is ~70%, already high for a developed nation. The historic increase in female participation (from much lower levels in 1990) was driven by policy changes – childcare subsidies, foreign domestic worker schemes. Today, further increases are unlikely.
For HR planning, this means:
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The resident labour force cannot expand significantly.
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Any growth in headcount will require either foreign talent or productivity gains (including AI).
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Policies that keep older workers employed (flexible hours, job redesign) are not charity – they are strategic necessities.
The data show that 75% of women over 65 are retired, compared to 40% of men. That gap may narrow as retirement ages rise, but organisations should not wait for policy to force change.
4. Income Data – Benchmarking Without Expensive Surveys
IRAS annual income profiles and SingStat household income data provide objective benchmarks for pay planning.
Key public figures:
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Median household income: ~$12,000/month
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Median household income per capita: ~$4,000/month
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Earning >$50,000/year puts an individual above half of Singapore’s taxpayers
For HR planning, these numbers allow organisations to:
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Benchmark their pay scales against national distributions (not just industry surveys).
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Assess affordability of local hires against the actual income distribution of residents.
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Model the impact of salary changes on employee household income – especially relevant for policies like progressive wages.
The “second child” effect is also visible in the data: a median‑income household drops below the per‑capita median after adding a second child. That is arithmetic, not opinion. HR can use this to inform family‑friendly policy design.
5. Sector Employment Trends – Where the Workers Are
MOM’s sectoral employment data (by age, by occupation) show clear shifts: manufacturing is declining, while accommodation and food services employ mostly older workers. In essential roles like cleaning and dishwashing, the workforce is ageing rapidly.
For HR planning, this allows organisations to:
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Compare their own age profile with sector averages.
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Identify occupations where national supply is concentrated in older age groups – and plan for retirements.
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Spot emerging gaps before they become crises.
Example: If your organisation relies on local welders or machine operators, public data can tell you the number of qualified workers by age, their pay ranges, and whether supply is sufficient given competition from other firms.
6. The Analytics Framework – Start with a Problem, Not the Data
Public data is only useful if you ask the right question. The most common mistake in workforce analytics is skipping problem definition and jumping straight to charts.
A structured approach:
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State the problem – e.g., “We cannot hire mechanical engineers.”
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List hypotheses – pay too low? too few graduates? competition from other sectors?
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Identify public data that tests each hypothesis – graduate numbers from SingStat, pay benchmarks from IRAS, employment trends from MOM.
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Analyse – let the data disprove or support each hypothesis.
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Decide – present options, not just findings.
Without this discipline, public data remains just numbers.
Conclusion
Singapore’s public data agencies publish a wealth of information that is severely underused by employers. The data do not require expensive tools – Excel dashboards built from SingStat tables can reveal workforce trends years before they become crises.
The key is to start with a clear business problem, use public data to test hypotheses, and then act. In a shrinking labour market, that is not optional – it is survival.