Evaluating the Alignment Between Digital Vocational Training and Skill Demand and Its Wage Effects

This study examines how well digital vocational training aligns with the skill demands of the labor market and investigates the impact of such alignment on wage outcomes. The analysis focuses on the K-Digital Training (KDT) and K-Digital Basic Competency (KDC) programs. Skill-related keywords were extracted from training course descriptions and matched with occupational skill keywords from online job postings (OJPs). Using TF-IDF and LSA for text vectorization, cosine similarity scores were computed to quantify the degree of alignment between training and skill demand.

The results show that competencies in digital content and programming/development exhibit high alignment with new technology-related skills, while big data, network security, and hardware fields show relatively low alignment. Internet of Things (IoT) and metaverse-related courses demonstrate notably strong alignment. Regression analysis reveals that higher alignment between training and skill demand has a significant positive effect on wages: a one-standard-deviation increase in similarity corresponds to a 2.0–3.5% wage increase. However, when controlling for occupation, industry, and technology type, the significance of the coefficient weakens, suggesting partial overlap with high-wage occupational composition effects.

This study contributes to the literature by quantitatively measuring qualitative skill alignment using unstructured text data and linking it to labor market outcomes. Policy implications include incorporating alignment indicators into performance management, strengthening low-alignment training areas, expanding bridge programs for KDC participants, and establishing an integrated data feedback system. Nonetheless, endogeneity and sample limitations remain, underscoring the need for future research employing instrumental variables (IV) and panel DID approaches.