Keywords: Singapore Labour Market, Hiring Equilibrium, Low Labour Mobility, AI Displacement, Global Uncertainty, SkillsFuture, MOM, Workforce Policy
Data sources: Ministry of Manpower (MOM), ManpowerGroup Employment Outlook Survey, IMF, Workforce Singapore (WSG), CSIS, SpringerLink
Date: February 2026
Executive Summary
Singapore’s labour market entered 2026 in a state of subdued but not destabilised equilibrium — one characterised by low firing, low hiring, and declining labour mobility. While headline unemployment remained at a historically low 2.0% for the full year 2025 and total employment grew by 57,300 — exceeding the 44,500 recorded in 2024 — the trajectory is one of deliberate caution rather than expansionary dynamism. Job vacancies fell from 81,100 in March 2025 to 69,200 by September 2025. Recruitment and resignation rates are now below their ten-year averages. The ManpowerGroup Net Employment Outlook (NEO) for Q1 2026 stands at +15%, the weakest reading since Q1 2022.
This case study examines the structural forces underpinning this equilibrium: global trade uncertainty amplified by U.S. tariff escalation, accelerated artificial intelligence (AI) adoption reshaping demand for labour, tightening of corporate hiring mandates, and the demographic constraints of a small, ageing city-state. It further analyses the macroeconomic and social impacts of prolonged low-hiring conditions, assesses Singapore’s policy outlook for 2026 and beyond, and proposes a framework of interventions to restore dynamic labour market participation and productivity-led employment growth.
- Introduction: The Anatomy of a Low-Hiring Equilibrium
Labour market equilibrium, in the classical sense, denotes a state in which the supply of and demand for labour are balanced at a prevailing wage. A “low-hiring equilibrium,” however, refers to a more nuanced condition: one in which both the rate of new job creation and the rate of separations (firings, resignations) are simultaneously suppressed, resulting in reduced labour market dynamism without necessarily generating headline unemployment. Firms manage headcount through natural attrition; workers, perceiving fewer opportunities, reduce job-switching behaviour. Mobility — both horizontal and vertical — declines. The market stabilises, but at a level below its productive potential.
Singapore’s labour market as of early 2026 exhibits precisely these characteristics. The Ministry of Manpower’s (MOM) Q3 2025 Labour Market Report noted explicitly that “firms are managing headcount through natural attrition rather than actively laying off workers” and that “employees, perceiving fewer opportunities, are also switching jobs less frequently — the result is lower labour mobility.” This represents a qualitative shift from the post-pandemic recovery period of 2022–2023, when both hiring and resignation rates were elevated and the labour market was characterised by high dynamism.
Understanding the drivers, implications, and appropriate policy responses to this condition is of significant importance. Singapore’s economy is uniquely exposed: it is a small, trade-dependent, high-cost city-state with no natural resources, meaning human capital productivity is the primary lever of national competitiveness. A prolonged period of low-hiring equilibrium risks suppressing wage growth, reducing skill acquisition through on-the-job learning, delaying graduate career formation, and, ultimately, eroding the innovation and adaptability that underpin Singapore’s economic model. - Empirical Landscape: Key Labour Market Indicators
2.1 Employment and Unemployment
The aggregate employment figures for 2025 present an ostensibly healthy picture. Total employment (excluding migrant domestic workers) grew by 57,300 over the year, exceeding the 44,500 gain recorded in 2024. Q3 2025 recorded a particularly strong quarterly addition of 25,100, driven by the Financial and Insurance Services sector (resident workers) and Construction (non-resident workers). This growth moderated to 19,600 in Q4 2025, still above the 7,700 recorded in Q4 2024.
Unemployment, on its seasonally adjusted overall measure, remained at 2.0% throughout H2 2025 — unchanged from 2024 and within the MOM’s declared non-recessionary range. The resident unemployment rate held at 2.9%, and the citizen rate at 3.0% in December 2025. These figures may obscure structural underemployment and skills mismatch, dimensions the headline rate does not capture.
Indicator Q1 2025 Q2 2025 Q3 2025 Q4 2025
Total Employment Growth (net) +2,300 +10,400 +25,100 +19,600
Overall Unemployment Rate (S.A.) 2.0% 1.9% 2.0% 2.0%
Resident Unemployment Rate 2.9% 2.7% 2.9% 2.9%
Retrenchments per 1,000 Employees 1.5 1.5 1.6 1.5
Job Vacancies (000s) 81.1 76.9 69.2 est. 65–68
Table 1: Singapore Key Labour Market Indicators, 2025. Sources: Ministry of Manpower Labour Market Reports Q1–Q4 2025; Trading Economics.
2.2 Vacancy Trends and Labour Mobility
Perhaps the most telling indicator of the evolving equilibrium is the trajectory of job vacancies. From a peak of 81,100 in March 2025, vacancies declined to 76,900 in June and 69,200 in September — the lowest in over two years. While the vacancies-to-unemployed ratio remained above parity at 1.35 (June 2025), suggesting no absolute shortage of openings, the pace of decline is significant. Vacancies in outward-oriented sectors such as Information and Communications and Professional Services were notably muted, reflecting the impact of global economic headwinds on Singapore’s internationally exposed industries.
Recruitment and resignation rates, both measures of labour market dynamism, fell below their ten-year averages. This is a dual signal: firms are less willing to hire and existing employees are less willing to voluntarily exit their current roles, reducing the natural churn through which skills are matched to opportunities. The consequence is a labour market that appears stable but is internally less productive than headline figures suggest.
2.3 Employer Sentiment
Forward-looking employer sentiment indicators corroborate the empirical trend. The ManpowerGroup Employment Outlook Survey for Q1 2026 reported a Net Employment Outlook (NEO) of +15% for Singapore — a deterioration of five percentage points from Q4 2025 and eleven points year-on-year, marking the lowest reading since Q1 2022. While 32% of employers planned headcount increases, 18% anticipated decreases and 46% intended to maintain current staffing levels. The Finance and Insurance sector reported the strongest sectoral outlook at +33%, partly supported by Singapore’s continued positioning as a regional financial hub.
MOM’s business expectations survey for Q1 2026 similarly indicated that the proportion of firms planning to hire fell from 44.1% (Q3 2025) to 43.3%, while those expecting to retrench rose from 2.3% to 4.3% — still low in absolute terms but directionally consistent with increased caution. Retrenchments for the full year 2025 reached 14,400, a modest increase from 13,020 in 2024, primarily attributed to business reorganisation and restructuring rather than closure-driven layoffs.
- Structural Drivers of the Low-Hiring Equilibrium
3.1 Global Trade Uncertainty and the Export-Sensitive Sector
As a small, highly open economy with trade-to-GDP ratios consistently exceeding 300%, Singapore is acutely sensitive to shifts in global trade architecture. The re-escalation of U.S. tariff policy in 2025 — characterised by tariffs on most countries in the 10–30% range — introduced the highest levels of protectionist pressure in nearly a century, according to Singapore’s Prime Minister Lawrence Wong at the 2025 National Day Rally. These tariffs directly affect Singapore’s trade-reliant industries, including electronics, semiconductors, petrochemicals, and precision engineering.
The resulting uncertainty has suppressed corporate investment appetite. When business leaders cannot reliably forecast demand, supply chain costs, or regulatory environments, hiring decisions are delayed or cancelled. This is particularly evident in outward-oriented industries — Information and Communications, Professional Services — which have seen stagnant or declining recruitment rates despite continued long-run demand for skilled labour. The IMF’s 2025 Singapore Article IV assessment similarly noted that greater uncertainties in external growth prospects were expected to soften the labour market going forward.
3.2 Artificial Intelligence Adoption and Structural Labour Displacement
The accelerating integration of AI and automation into Singapore’s economy represents a second-order structural force with dual, potentially contradictory effects. On one hand, AI adoption increases productivity and creates demand for high-skill workers capable of developing, deploying, and supervising AI systems. On the other, it displaces routine cognitive and manual tasks, suppressing demand for a large segment of the existing workforce.
According to the IMF’s AI Preparedness Index, Singapore is among the most AI-ready economies globally. The IMF’s 2024 Selected Issues Paper on Singapore estimated that approximately 77% of employed workers are highly exposed to AI — far above the advanced economy average of 60% and the emerging market average of 40%. This high exposure stems from Singapore’s economic structure, in which a substantial majority of workers occupy high- and semi-skilled roles. Critically, the IMF analysis found that while half of this highly exposed segment stands to benefit from AI complementarity, the other half faces elevated displacement risk, particularly in clerical support, business administration, and sales roles.
From a labour market equilibrium perspective, this creates a structural wedge: firms are reducing headcount in AI-automatable roles while being cautious about creating new roles because AI-driven productivity gains reduce the headcount required to maintain output. Springer’s analysis of the Singapore labour market noted that approximately one-fifth of the full-time workforce could be displaced by 2028 if present trends continue — a figure that underscores the urgency of structural adjustment. The World Economic Forum’s Future of Jobs Report 2025 projects that 39% of key skills required in the job market will change by 2030.
Sector / Role Category AI Exposure Level AI Complementarity Net Employment Risk
Managers, Science & Engineering Professionals High High (productivity gain) Low — benefit likely
Health, Legal & Education Professionals High High Low — benefit likely
Clerical Support & Administration High Low High — displacement risk
Sales & Business Services High Low–Medium High — displacement risk
ICT & Data Professionals Very High Very High Low — strong demand
Low-skilled Construction / Manufacturing Medium Low Medium — automation risk
Table 2: AI Exposure and Complementarity by Occupational Category in Singapore. Adapted from IMF Selected Issues Paper SIP/2024/040 (Khan, 2024) and Mavenside Consulting (2025).
3.3 Demographic Constraints and an Ageing Workforce
Singapore’s total resident population growth has slowed substantially, and its workforce is ageing. The proportion of residents aged 55 and above is increasing, while the working-age cohort (25–54) is under pressure from declining birth rates. This demographic profile has two implications for the labour market: first, it tightens the available labour supply over the medium term, limiting employers’ ability to freely substitute new workers for displaced ones; and second, it increases the proportion of the workforce in occupations identified as having lower AI complementarity — mid-career professionals in administrative and managerial roles who may require significant reskilling.
The IMF analysis found that women and younger workers are disproportionately exposed to AI’s disruptive effects, raising inequality concerns. A Workday research survey conducted in Singapore in early 2025 found that only 30% of business leaders were confident their organisations had the skills needed for long-term success, and 43% were worried about future talent shortages — a paradox of simultaneous hiring caution and skills anxiety that characterises the current equilibrium.
3.4 Polarisation of Opportunities: The Dual Labour Market
A fourth structural driver is the increasing segmentation of Singapore’s labour market into two distinct tiers. The upper tier — comprising roles in finance, technology, life sciences, advanced manufacturing, and professional services — continues to experience tight labour conditions, skills shortages, and relatively robust wage growth. The lower tier — encompassing routine professional, administrative, and support roles — faces mounting competition, elevated AI substitution risk, and stagnating wages. This polarisation is also visible in the graduate employment data: employment rates for fresh graduates improved from 47.9% to 51.9% between cohorts, but a significant proportion reported holding out for preferred roles and rejecting initial offers, suggesting a mismatch between graduate expectations and available positions.
Non-resident employment remains a structural component of Singapore’s labour supply, particularly in Construction and Manufacturing. These workers are excluded from SkillsFuture upskilling programmes, making them acutely vulnerable to displacement by automation. CSIS and SUSS economist Walter Theseira have noted that when displaced migrant workers return to their home countries, the social costs of unemployment are effectively exported to often lower-capacity welfare systems — a transnational labour market externality with both ethical and diplomatic dimensions.
- Sectoral and Macroeconomic Impact Analysis
4.1 Macroeconomic Implications
A sustained low-hiring equilibrium carries significant macroeconomic risks, even if it does not manifest in headline unemployment increases. First, constrained labour mobility suppresses the reallocation of human capital toward its highest-value uses, reducing aggregate total factor productivity (TFP). When workers remain in sub-optimal roles due to reduced external opportunity, and when firms resort to natural attrition rather than strategic recruitment, the labour market loses its function as an allocative mechanism.
Second, reduced hiring constrains wage growth. Compensation increases typically occur when workers change employers — inter-firm mobility is the most reliable mechanism for wage appreciation, particularly in high-skill sectors. A market in which such mobility is suppressed will exhibit wage stagnation even in conditions of low unemployment, with knock-on effects on household consumption, private savings behaviour, and aggregate demand. MOM data confirms that wage growth expectations have moderated, with only approximately 22% of firms planning wage increases in Q3 2025, down from 24.4% earlier in the year.
Third, lower hiring rates slow the absorption of new labour market entrants — fresh graduates, mid-career switchers, and retrenched workers. The re-entry rate for retrenched workers improved to 60.6% in Q1 2025, which is encouraging, but long-term unemployment rose marginally to 0.9%. For workers displaced by AI or restructuring, longer unemployment spells reduce human capital through skill atrophy, generating hysteresis effects that persist beyond the immediate downturn.
4.2 Sectoral Variation
The impact of the low-hiring equilibrium is not uniform. Financial and Insurance Services, Health and Social Services, and high-end Professional Services represent the relative bright spots — sectors with continued labour demand, robust wage growth, and strong alignment with Singapore’s comparative advantages. These sectors contributed the largest share of resident employment gains in 2025.
In contrast, Information and Communications — a sector central to Singapore’s digital ambitions — has experienced hiring stagnation as outward-oriented technology firms responded to global demand uncertainty. This represents a structural concern: the very sector expected to generate the AI-ready jobs of the future is temporarily suppressing its own hiring due to cyclical headwinds. The risk is that this cyclical pause compounds the structural adjustment problem by reducing the absorptive capacity for workers displaced from AI-automatable roles.
Construction and Manufacturing continue to rely heavily on non-resident labour under Work Permit arrangements. While employment in these sectors remains stable, the absence of upskilling pathways for this workforce creates a structural vulnerability: as automation penetrates construction (through robotics and prefabrication) and manufacturing (through Industry 4.0 technologies), a large population of workers will face displacement without recourse to publicly funded retraining programmes.
4.3 Social and Distributional Impacts
Beyond aggregate macroeconomic effects, the distributional consequences of a prolonged low-hiring equilibrium merit attention. The IMF estimates that women and younger workers in Singapore face above-average AI exposure with below-average complementarity, suggesting that income inequality could widen absent targeted intervention. The SkillsFuture Level-Up Programme’s focus on workers aged 40 and above, while addressing the most immediately at-risk cohort of mid-career individuals, leaves younger workers — who face long time horizons of potential disruption — relatively underserved.
Fresh graduates facing a more selective job market are more likely to accept roles misaligned with their qualifications, a phenomenon of “credential underemployment” with long-term wage suppression effects. Social mobility — particularly for graduates from lower-income households without parental employment networks — is constrained when hiring is selective and referral-dependent.
For the non-resident workforce, the social impacts are transnational. Displacement without retraining support effectively transfers unemployment costs to source countries in the ASEAN region, undermining Singapore’s stated commitments to regional inclusive development. - Forward Outlook: 2026 and the Medium Term
5.1 Near-Term (2026): Cautious Stabilisation
The consensus outlook for Singapore’s labour market in 2026 is one of cautious stabilisation. MOM’s advance release for Q4 2025 projects that the labour market will continue to expand, but that hiring will remain deliberate and selective. The ManpowerGroup NEO of +15% for Q1 2026 confirms that a majority of employers intend to maintain rather than expand headcount. Planned redundancies have risen from 2.3% to 4.3% of firms — still low in absolute terms, but trending upward.
Several external variables could shift this equilibrium in either direction. On the downside, further U.S. tariff escalation or a deterioration in global trade volumes would disproportionately affect Singapore’s export-oriented sectors and reduce hiring intentions in professional and business services. Conversely, a resolution of trade tensions, a resurgence in global technology investment, or Singapore’s successful positioning as an AI infrastructure hub (data centres, model development, AI governance) could catalyse a new cycle of employment growth in high-value sectors.
The data centre and digital infrastructure sector merits specific attention. Singapore has established itself as a leading data centre hub in Asia, and continued investment in AI computing infrastructure creates demand for engineering, operations, and cybersecurity talent. However, the industry’s capital intensity means that employment creation per dollar invested is lower than in previous technology cycles, moderating the macroeconomic employment multiplier.
5.2 Medium-Term (2027–2030): Structural Transition
Over the medium term, the primary determinant of Singapore’s labour market trajectory is the pace and breadth of structural adjustment to the AI-driven economy. The World Economic Forum estimates that AI will generate a net gain of 78 million jobs globally by 2030, but this positive aggregate masks significant distributional disruption. For Singapore, the structural challenge is three-fold: accelerating the supply of workers with high AI complementarity, managing the reallocation of workers from low-complementarity roles, and preserving labour market participation rates as demographic headwinds intensify.
Singapore’s National AI Strategy 2.0 aims to triple the number of AI practitioners to 15,000 over five years, backed by a $20 million investment in AI practitioner training through the IMDA-SSG partnership. The TeSA (TechSkills Accelerator) initiative and the AI Apprenticeship Programme — combining 3 months of AI engineering immersion with 6 months of industry placement — represent promising mechanisms for structured reskilling. However, the scale of displacement risk (potentially one-fifth of the full-time workforce by 2028) suggests that these targeted programmes will need to be substantially expanded to achieve population-level impact. - Policy Recommendations and Solutions Framework
6.1 Expanding Demand-Side Employment Incentives
To break the low-hiring equilibrium in the near term, the government should consider targeted demand-side interventions that reduce the cost and risk of hiring for firms considering workforce expansion. These could include time-limited wage co-investment credits for firms hiring workers displaced from AI-automatable roles, particularly in sectors critical to Singapore’s long-term competitiveness such as advanced manufacturing and clean energy. The Workforce Development Grant (WDG), launched in 2025 to streamline workforce transformation support, should be augmented with hiring subsidies that specifically incentivise net headcount addition, not merely training.
The Singapore Economic Resilience Task Force, chaired by Deputy Prime Minister Gan Kim Yong, should also consider sector-specific labour demand strategies. For export-sensitive industries most affected by tariff uncertainty, the task force should work with industry associations to identify critical skill nodes and fund transitional employment arrangements that preserve human capital during demand downturns, reducing hysteresis risk.
6.2 Scaling and Extending the SkillsFuture Ecosystem
SkillsFuture represents Singapore’s most comprehensive institutional response to structural labour market adjustment, and its enhancement should remain a central policy priority. Specific reforms warranted by the current labour market evidence include:
Extending SkillsFuture Level-Up eligibility to workers aged 25–39, who face a long time horizon of AI exposure and are currently underserved by existing programmes. The IMF explicitly recommends expanding the initiative to include younger workers.
Incorporating real-time labour market data — via AI-driven platforms such as MySkillsFuture.sg — into course recommendation engines, so that individual training pathways are informed by live vacancy and skills demand signals rather than static industry frameworks.
Piloting a scheme to extend access to subsidised digital skills training to Work Permit holders, in partnership with employers and source country governments. This addresses the transnational inequality dimension of AI displacement and strengthens Singapore’s international labour partnerships.
Mandating AI literacy modules within all SkillsFuture course tracks, so that workers across occupational categories develop baseline competency in working alongside AI systems regardless of their primary specialisation.
6.3 Strengthening Labour Mobility Infrastructure
A low-hiring equilibrium is self-reinforcing: workers who perceive fewer opportunities switch jobs less, reducing the labour market churn that enables efficient skill-job matching. Breaking this dynamic requires active labour market infrastructure. Career Health SG — launched to provide free career guidance and job matching — should be expanded and promoted, with particular emphasis on active outreach to workers in declining occupational categories rather than passive self-registration.
The SkillsFuture Jobseeker Support scheme, which provides up to SGD 6,000 over six months for involuntarily unemployed individuals, is a valuable safety net. Its extension and potential broadening to cover workers facing significant underemployment (rather than only unemployment) would reduce the cost of voluntary job switching for workers seeking better skill-job alignment.
Consideration should also be given to establishing a national “Labour Market Transition Fund” — modelled on elements of the Danish “flexicurity” model — that provides income support during structured retraining periods longer than six months, recognising that transitions out of AI-displaced roles may require extended skill acquisition beyond the current support window.
6.4 Addressing the Dual Labour Market through Inclusive Hiring Practices
Policy should explicitly target the polarisation of Singapore’s labour market between high-value and at-risk occupational tiers. This includes regulatory guidance encouraging firms to develop internal promotion and reskilling pathways rather than treating workers in at-risk categories as replaceable inputs. The Jobs Transformation Map (JTM) programme — which maps technological disruption impacts at the sector level and identifies skills transition pathways — should be accelerated and extended to cover all major employment sectors, with quarterly refresh cycles rather than periodic reviews.
For fresh graduates, a structured traineeship expansion programme should be co-developed by government and the Financial Services, Technology, and Healthcare sectors to provide structured on-ramps for new labour market entrants in a more selective hiring environment. This reduces the probability of prolonged credential underemployment in the graduate cohort.
6.5 Macroprudential and Monetary Policy Coordination
At the macroeconomic level, the Monetary Authority of Singapore (MAS) should continue its existing framework of monitoring for signs that the low-hiring equilibrium is becoming entrenched in a manner that generates wage stagnation and demand deficiency. Should such conditions emerge, coordinated fiscal stimulus targeted at sectors with high employment multipliers — construction, healthcare, public services — would provide a countercyclical buffer. Singapore’s fiscal reserves and strong balance sheet position afford substantial room for such intervention if required. - Comparative Context: Singapore in the Global Picture
Singapore’s experience of a low-hiring equilibrium is not unique. The United States labour market, as described in the article by Investopedia (February 2026) that contextualises this case study, exhibits analogous dynamics: job growth concentrated in healthcare, declining job openings, employer hesitancy driven by tariff and AI uncertainty. However, the policy levers and institutional contexts differ materially.
Singapore’s advantage over comparators lies in its institutional agility — its ability to mobilise government, industry, and educational institutions in a coordinated response — and its financial capacity to fund transitional support programmes. Japan faces similar demographic constraints but has historically deployed less comprehensive retraining infrastructure. The United Kingdom post-Brexit labour market has seen structural mismatches driven by migration policy changes analogous (in direction, if not scale) to Singapore’s foreign workforce management challenges.
The key lesson from comparative analysis is that countries which invest in active labour market policies — defined as proactive interventions in job search, training, and employment subsidies, as distinct from passive income support — demonstrate significantly faster and more equitable transitions out of structural adjustment periods. Singapore’s SkillsFuture ecosystem positions it well, provided the programmes are scaled to the magnitude of the structural challenge, particularly the AI displacement risk. - Conclusion
Singapore’s labour market as of early 2026 occupies a delicate equilibrium: resilient enough to avoid headline distress, but structurally constrained in ways that pose risks to long-term productivity, equity, and economic dynamism. The confluence of global trade uncertainty, accelerating AI adoption, demographic headwinds, and a dual labour market architecture has suppressed hiring ambitions among firms and job-switching behaviour among workers, producing a market that functions but does not flourish.
The structural challenge is substantial. An estimated 77% of Singapore workers are highly exposed to AI, with half of these facing displacement risk rather than productivity augmentation. One-fifth of the full-time workforce may see their roles fundamentally disrupted by 2028. The pace of skills change — with 39% of key job-market competencies expected to transform by 2030 — demands policy responses of proportionate scale.
Singapore possesses the institutions, fiscal capacity, and political will to navigate this transition successfully. The SkillsFuture ecosystem, the Career Health SG infrastructure, the National AI Strategy 2.0, and the Singapore Economic Resilience Task Force represent the building blocks of an effective response. The imperative is to scale these mechanisms to match the size of the challenge, broaden their reach to include younger workers and vulnerable non-resident labour, and sustain them through what is likely to be a prolonged period of structural transformation rather than a short-cycle adjustment.
The low-hiring equilibrium, properly managed, can serve as a period of productive reconfiguration rather than stagnation. The policy choices made in 2026 and 2027 will determine which of these outcomes prevails.
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