Sri Lanka Labour Force Participation Rebounds to 49.9% in Q3 2025; Unemployment Holds Steady at 4.3%

January, 21, 2026

Sri Lanka’s labour force participation rate climbed to 49.9% in the third quarter of 2025 (July–September), according to the latest quarterly bulletin released by the Department of Census and Statistics under the Ministry of Finance, Planning and Economic Development. This marks a notable recovery from 46.9% in the corresponding quarter of 2024 and 49.3% in Q2 2025.

The economically active population stood at approximately 8.47 million, with males comprising 63.3% and females 36.7%. Female participation showed particular strength, rising to 33.9% from 29.4% a year earlier, while male participation was 68.6%. The inactive population was roughly equal in size at 8.49 million, predominantly female (71.2%).

Employment reached 8.10 million persons, up from 7.84 million in 2024 Q3. The services sector continued to dominate, employing nearly half (49.8%) of the workforce, followed by industry (26.8%) and agriculture (23.4%). Both industry and services saw gains compared to the previous year, reflecting modest sectoral shifts away from agriculture.

The unemployment rate edged up slightly to 4.3% from 4.2% in 2024 Q3, affecting 367,000 individuals. Gender disparities persist: women faced a 6.2% unemployment rate versus 3.2% for men. Youth unemployment (ages 15–24) remained high at 19.2%, with females in this group at 22.8%. Unemployment is most acute among those with higher education (GCE A/L and above: 7.0%), especially educated women (9.0%).

Private sector employees formed the largest employment category, while own-account workers were prominent in agriculture. Over two-thirds of employed persons worked 40 or more hours per week.

These figures indicate a gradual post-pandemic stabilization in labour market participation, particularly among women, though challenges remain in reducing youth and female unemployment and fully absorbing educated workers. The Department of Census and Statistics noted that estimates should consider sampling variability when interpreting changes over time.