Determinants of Starting Salaries for Filipino Graduates: An Explainable Machine Learning Approach

Research employing an explainable machine learning approach to analyze starting salaries for Filipino graduates indicates a significant disconnect between educational preparation and labor market outcomes. The study identifies job role and industry as the primary determinants of starting salary, surpassing the influence of institutional prestige. This finding addresses a gap in current Philippine research, which predominantly relies on descriptive employment studies without explaining salary determinants.

Why it matters

Understanding the precise determinants of starting salaries is crucial for aligning educational outcomes with economic realities and optimizing human capital development. This insight can inform strategies to enhance graduate employability and ensure that educational investments translate into improved economic opportunities, fostering broader economic growth and stability.

Key insights

• Filipino graduates experience a persistent mismatch between their educational background and labor market results.
• Starting salary serves as a critical indicator of entry-level valuation within the labor market.
• Existing research in the Philippines largely consists of descriptive tracer studies, focusing on employment rates rather than the factors determining pay.
• An explainable machine learning approach was applied to a crowd-sourced, self-reported survey dataset of graduate responses.
• Job role and industry are identified as the most significant determinants of starting salaries, strongly outweighing institutional prestige.
• This central finding is robust, corroborated by three independent lines of evidence.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.21383