用可解释机器学习分析菲律宾毕业生起薪，发现职业与行业比学校名气更重要。
Determinants of Starting Salaries for Filipino Graduates: An Explainable Machine Learning Approach
- 通过可解释机器学习分析自报薪资数据，识别出职业和行业是核心影响因素。
- 三种独立验证方法一致表明：职业类型文本信息对预测贡献最大。
- 适合关注就业政策、职业指导及教育公平的研究者与决策者。
菲律宾毕业生在教育准备与劳动力市场结果之间存在持续脱节，起薪是衡量其初始价值的关键指标。现有菲律宾研究多为描述性追踪调查，仅记录就业率而未解析薪资决定因素。本文利用众包调查数据，针对噪音大、自报为主的薪资预测任务，应用机器学习方法，发现职业角色与行业是起薪的主导决定因素，显著超越院校声望的影响。该结论的可靠性来自三条独立证据：SHAP特征重要性分析、最优集成模型高度依赖职业文本特征、以及自然语言推理重构任务的验证。结果表明，职业指导与政策应侧重特定行业的技能培养，而非院校品牌。
原文摘要 · Abstract (English)
Filipino graduates face a persistent disconnect between educational preparation and labor market outcomes, where starting salary is a key signal of entry-level valuation. Current Philippine research is dominated by descriptive tracer studies that document employment rates but do not explain the determinants of pay. We address this gap using a crowd-sourced survey dataset of graduate responses whose noisy, self-reported nature makes it a challenging prediction target. Applying machine learning to this problem, we identify job role and industry as the dominant determinants of starting salary, significantly outweighing institutional prestige. The strength of this finding is its central contribution: it is corroborated by three independent lines of evidence, namely SHAP attributions, the heavy reliance of the best ensemble on occupational text, and a Natural Language Inference reformulation. These results suggest that career guidance and policy should prioritize sector-specific skills over institutional brand.
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