arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.06731econ.GNq-fin.ECstat.AP
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通过使用模型辅助校准,将在线招聘广告中的技能纳入其中,以加强劳动力需求调查

Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration

Maciej Beręsewicz, Greta Białkowska, Krzysztof Marcinkowski, Magdalena Maślak, Piotr Opiela, Robert Pater, Katarzyna Zadroga

摘要:

在这篇文章中,我们介绍了波兰统计局进行的劳动力需求(DL)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,这是DL调查中缺少的。为了实现这一目标,我们采用了传统的校准与LASSO辅助方法相结合的数据集成方法,以纠正在线数据中的表示错误。面对缺乏访问单位层面的数据从DL调查,我们使用估计的人口总数,并提出了一个~自助方法,占波兰统计局报告的总数的不确定性。我们表明,校准估计辅助LASSO优于传统的校准标准误差,并减少在网上招聘广告中观察到的技能的代表性偏见。我们的实证结果表明,在线数据显着高估了人际关系,管理和自我组织能力,而低估了技术和物理技能。这主要是由于被归类为工艺和相关行业工人以及设备和机器操作员和装配工的职业代表性不足。

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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