菲律宾毕业生起薪的决定因素：一种可解释的机器学习方法
Determinants of Starting Salaries for Filipino Graduates: An Explainable Machine Learning Approach
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中文总结 AI 辅助
本研究针对菲律宾毕业生教育与就业脱节问题，利用众包调查数据，通过可解释机器学习方法发现职位角色和行业是起薪主要决定因素，提出职业指导应侧重行业技能而非院校品牌。
中文摘要 AI 辅助
菲律宾毕业生面临教育准备与劳动力市场结果之间持续存在的脱节，而起薪是入门级岗位估值的关键信号。当前菲律宾的研究主要以描述性追踪研究为主，这类研究仅记录就业率，却未解释薪酬的决定因素。我们利用众包调查的毕业生反馈数据集开展研究，该数据集存在嘈杂、自我报告的特性，使其成为具有挑战性的预测目标。我们将机器学习应用于该问题，确定了职位角色和行业是起薪的主要决定因素，其影响远超院校声望。这一发现的价值在于其核心贡献，它得到了三条独立证据线的印证，分别是SHAP（SHapley Additive exPlanations，沙普利加性解释）归因、最优集成模型对职业文本的高度依赖，以及自然语言推理（Natural Language Inference）重构结果。这些结果表明，职业指导和政策应优先考虑特定行业的技能，而非院校品牌。
英文摘要
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.
发表机构
- Institute of Computer Science, University of the Philippines Los Baños（菲律宾大学洛斯巴诺斯分校计算机科学学院）
- Machine Learning and Artificial Intelligence Applications Lab, University of the Philippines Los Baños（菲律宾大学洛斯巴诺斯分校机器学习与人工智能应用实验室）
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