摘要翻译：
技能短缺是社会的一种消耗。它们阻碍了个人的经济机会，减缓了企业的增长，并阻碍了总体劳动生产率。因此，提前了解和预测技能短缺的能力对于政策制定者和教育者帮助减轻其不利影响至关重要。这项研究实现了一种高性能的机器学习方法来预测职业技能短缺。此外，我们还展示了分析短缺职业潜在技能需求的方法，以及预测技能短缺的最重要特征。为了这项工作，我们编制了一个独特的数据集，包括2012年至2018年澳大利亚劳动力需求和劳动力供给的职业数据。这包括来自770万个招聘广告(ads)和20个官方劳动力指标的数据。我们使用这些数据作为解释变量，并利用XGBoost分类器预测132个标准化职业的年度技能短缺分类。我们构建的模型实现了macro-F1的平均性能得分高达83%。我们的结果表明，招聘广告数据和就业统计数据是预测职业技能短缺逐年变化的性能最高的特征集。我们还发现，诸如“工作时间”、“教育年限”、“经验年限”和“工资中位数”等特征是预测职业技能短缺的非常重要的特征。这项研究为预测和分析技能短缺提供了一个强有力的数据驱动方法，可以帮助政策制定者、教育者和企业为未来的工作做好准备。
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英文标题：
《Predicting Skill Shortages in Labor Markets: A Machine Learning Approach》
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作者：
Nik Dawson, Marian-Andrei Rizoiu, Benjamin Johnston and Mary-Anne
  Williams
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Computer Science        计算机科学
二级分类：Computers and Society        计算机与社会
分类描述：Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  Skill shortages are a drain on society. They hamper economic opportunities for individuals, slow growth for firms, and impede labor productivity in aggregate. Therefore, the ability to understand and predict skill shortages in advance is critical for policy-makers and educators to help alleviate their adverse effects. This research implements a high-performing Machine Learning approach to predict occupational skill shortages. In addition, we demonstrate methods to analyze the underlying skill demands of occupations in shortage and the most important features for predicting skill shortages. For this work, we compile a unique dataset of both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018. This includes data from 7.7 million job advertisements (ads) and 20 official labor force measures. We use these data as explanatory variables and leverage the XGBoost classifier to predict yearly skills shortage classifications for 132 standardized occupations. The models we construct achieve macro-F1 average performance scores of up to 83 per cent. Our results show that job ads data and employment statistics were the highest performing feature sets for predicting year-to-year skills shortage changes for occupations. We also find that features such as 'Hours Worked', years of 'Education', years of 'Experience', and median 'Salary' are highly important features for predicting occupational skill shortages. This research provides a robust data-driven approach for predicting and analyzing skill shortages, which can assist policy-makers, educators, and businesses to prepare for the future of work.
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PDF链接：
https://arxiv.org/pdf/2004.01311