AI is reshaping Cambodia's labour market: low-skilled workers face disproportionate displacement while new industries create employment and spike demand for reskilling; policymakers must invest in targeted training and education.

Main Finding

AI adoption in Cambodia is reshaping the labor market: it displaces jobs—especially for low‑skilled workers—while simultaneously creating employment in emerging industries and driving up demand for new skills. Targeted reskilling, upskilling, and investments in education and training are essential to mitigate displacement and improve workforce adaptability.

Key Points

• Empirical evidence from a survey of 351 respondents analyzed with PLS‑SEM shows significant relationships among AI adoption, job displacement, skill demand, and employment outcomes.
• AI-driven displacement disproportionately affects low‑skilled workers, increasing inequality risks.
• At the same time, AI stimulates employment growth in some emerging sectors, creating new job opportunities.
• Job displacement is positively associated with heightened demand for new skills, implying strong reskilling/upskilling needs.
• Investments in education and training are identified as critical policy levers to reduce AI‑induced employment disruptions.
• The study contributes context‑specific insights from a developing economy, where socioeconomic and institutional factors mediate AI’s labor market effects.

Data & Methods

• Data: Cross‑sectional survey of 351 respondents in Cambodia (details on sampling frame, sectors, and respondent types not specified in the summary).
• Method: Partial Least Squares Structural Equation Modeling (PLS‑SEM) to estimate relationships among latent constructs (AI adoption, job displacement, demand for skills, employment growth).
• Key modeled relationships: AI adoption → job displacement; job displacement → demand for new skills; AI adoption → employment growth in emerging industries.
• Caveats (inferred from study design): moderate sample size, potential self‑reporting and cross‑sectional limitations (no causal time series), and limited generalizability beyond the Cambodian context without further sectoral or longitudinal study.

Implications for AI Economics

• Inequality and distributional effects: Results align with skill‑biased technical change frameworks — AI can reduce demand for routine, low‑skilled tasks and raise returns to new, higher‑level skills, worsening wage/ employment divides unless countered by policy.
• Human capital policy priority: Scaling reskilling/upskilling, vocational training, and education reforms is central to enabling labor reallocation toward AI‑complementary roles.
• Active labor market policies: Targeted interventions (training subsidies, retraining programs for displaced low‑skilled workers, job placement services) are crucial to smooth transitions and limit long‑term unemployment.
• Role of firms and public‑private collaboration: Incentives for firms to invest in worker training, and partnerships between industry and education providers, will accelerate skills alignment.
• Research agenda: Need for longitudinal and sectorally disaggregated studies in developing economies to quantify net job creation/losses, wage impacts, and the effectiveness of different policy responses; evaluation of heterogeneity across industries and regions.
• Policy trade‑offs: Policymakers should balance promoting productive AI adoption that creates new industries with protections and active support for workers most at risk of displacement.