What question did this study set out to answer?

This research aims to consolidate Vietnamese labour-market data to enhance major-selection decisions for students.

May 14, 2026Open Access

Vietnamese Labour-Market Signals 2024-2026 for Major-Selection Decisions: A Sector × Major × Education-Level Synthesis with AI-Disruption Overlay and a 17-Year-Old's Reality-Check Protocol

Key Points

This research aims to consolidate Vietnamese labour-market data to enhance major-selection decisions for students.
Synthesized publicly available data from various Vietnamese labour-market reports and surveys for 2024-2026.
Created a sector × major × education-level matrix categorizing signals into forward-leading, coincident, and lagging.
Developed a reality-check protocol for 17-year-olds based on the I3.1 framework and illustrated with five examples.
Established a detailed labour-market matrix across major sectors and education levels.
Outlined a wage-tier ladder for tier-1 cities versus regional areas based on the analysis.
Identified data-quality gaps in graduate employment surveys, emphasizing the need for triangulation of signals.

Abstract

The Vietnamese major-selection framework synthesised in I3.1 specifies a labour-market reality-check at Step 4 but defers the data consolidation. This paper supplies that consolidation. It synthesises the publicly available 2024-2026 Vietnamese labour-market evidence base — General Statistics Office (GSO) Niên giám Thống kê 2023 and 2024, GSO Labour Force Survey quarterly bulletins, MOLISA labour-market reports, the World Bank Skilling for the Future 2024 review, the ILO Vietnam Employment, Wages and Skills synthesis, the ADB Vietnam Skills Development series, the OECD Skills Strategy Vietnam line, the WEF Future of Jobs 2024 country breakdown, the Acemoglu 2024 macro-AI literature, MPI FDI registry data, and the major Vietnamese commercial labour-market sources (VietnamWorks Salary Report, JobStreet Vietnam, TopCV, JobsGO, Adecco Vietnam, TopDev) — into a sector × major × education-level descriptive matrix; classifies each signal class as forward-leading (job postings, wage trends, FDI flows, ngành đào tạo expansion), coincident (current employment by sector), or lagging (graduate-employment surveys 12-18 months postgraduation); produces a wage-tier ladder by sector and education level for tier-1 cities (Hà Nội, TP.HCM) versus regional flagships; overlays an AI-disruption profile per the I4.3 thesis applied to the top Vietnamese sectors using robustness-across-scenarios framing rather than occupation-disappearance prediction; and consolidates an operationally usable reality-check protocol for a 17-year-old completing Step 4 of the I3.1 framework. The protocol specifies which data sources to consult, what each source actually tells the chooser, what each source does not tell them, the triangulation rule across forward-leading and lagging signals, and the recommended refresh cadence. Five worked examples are produced for the sectors most-asked-about in Vietnamese career-guidance content: công nghệ thông tin (CNTT), kế toán, sư phạm tiểu học, y khoa, and ngôn ngữ Anh. A projection band 2026-2030 is delivered with explicit confidence registers (measured / plausible / speculative) so a chooser can distinguish what is known from what is projected. The paper documents honestly the data-quality gap in Vietnamese graduateemployment surveys — university self-reporting, methodological unevenness across institutions, response-rate variability, and a 12-18 month measurement lag — and recommends triangulation across forward-leading sources rather than reliance on any single survey. The contribution is not a major ranking and not a forecast of which jobs will exist; it is the descriptive substrate plus a triangulation discipline that converts noisy signals into a

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