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摘要翻译：
在这篇文章中，我们描述了波兰统计局对劳动力需求调查的改进，其中包括从在线招聘广告中获得的技能。主要目标是提供对技能（能力）需求的估计，这在DL调查中是缺失的。为了实现这一目标，我们采用了一种将传统标定方法与套索辅助方法相结合的数据集成方法来校正在线数据中的表示误差。面对无法从DL调查中获得单位一级数据的情况，我们使用估计的人口总数，并提出了一种自举方法，以解释波兰统计局报告的总数的不确定性。我们表明，在标准误差方面，LASSO辅助的校准估计器优于传统的校准，并减少了在线招聘广告中观察到的技能表征偏差。我们的实证结果表明，在线数据显著高估了人际、管理和自组织技能，而低估了技术和身体技能。这主要是由于被归类为工艺及相关行业工人、工厂和机器操作员及装配工的职业人数不足。
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英文标题：
《Enhancing the Demand for Labour survey by including skills from online
job advertisements using model-assisted calibration》
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作者：
Maciej Ber\k{e}sewicz and Greta Bia{\l}kowska and Krzysztof
Marcinkowski and Magdalena Ma\'slak and Piotr Opiela and Robert Pater and
Katarzyna Zadroga
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最新提交年份：
2019
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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：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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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
In the article we describe an enhancement to the Demand for Labour (DL) survey conducted by Statistics Poland, which involves the inclusion of skills obtained from online job advertisements. The main goal is to provide estimates of the demand for skills (competences), which is missing in the DL survey. To achieve this, we apply a data integration approach combining traditional calibration with the LASSO-assisted approach to correct representation error in the online data. Faced with the lack of access to unit-level data from the DL survey, we use estimated population totals and propose a~bootstrap approach that accounts for the uncertainty of totals reported by Statistics Poland. We show that the calibration estimator assisted with LASSO outperforms traditional calibration in terms of standard errors and reduces representation bias in skills observed in online job ads. Our empirical results show that online data significantly overestimate interpersonal, managerial and self-organization skills while underestimating technical and physical skills. This is mainly due to the under-representation of occupations categorised as Craft and Related Trades Workers and Plant and Machine Operators and Assemblers.
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PDF链接：
https://arxiv.org/pdf/1908.06731
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