PDF(Pubmed)
Abstract：
Social soft skills are crucial for workers to perform their tasks, yet it is hard to train people on them and to readapt their skill set when needed. In the present work, we analyze the possible effects of the COVID-19 pandemic on social soft skills in the context of Italian occupations related to 88 economic sectors and 14 age groups. We leverage detailed information coming from ICP (i.e. the Italian equivalent of O*Net), provided by the Italian National Institute for the Analysis of Public Policy, from the microdata for research on the continuous detection of labor force, provided by the Italian National Institute of Statistics (ISTAT), and from ISTAT data on the Italian population. Based on these data, we simulate the impact of COVID-19 on workplace characteristics and working styles that were more severely affected by the lockdown measures and the sanitary dispositions during the pandemic (e.g. physical proximity, face-to-face discussions, working remotely). We then apply matrix completion-a machine-learning technique often used in the context of recommender systems-to predict the average variation in the social soft skills importance levels required for each occupation when working conditions change, as some changes might be persistent in the near future. Professions, sectors, and age groups showing negative average variations are exposed to a deficit in their social soft-skills endowment, which might ultimately lead to lower productivity.
摘要：
社交软技能对于工人执行任务至关重要，然而，很难对人们进行培训，并在需要时重新调整他们的技能。在目前的工作中,我们分析了在意大利与88个经济部门和14个年龄组相关的职业中,COVID-19大流行对社会软技能的可能影响.我们利用来自ICP的详细信息(即意大利相当于O*Net)，由意大利国家公共政策分析研究所提供，从微观数据中研究劳动力的连续检测，由意大利国家统计局（ISTAT）提供，以及ISTAT关于意大利人口的数据。基于这些数据,我们模拟了COVID-19对工作场所特征和工作方式的影响，这些特征和工作方式受到大流行期间封锁措施和卫生倾向的更严重影响(例如，身体接近，面对面的讨论,远程工作)。然后，我们应用矩阵完成-一种常用于推荐系统的机器学习技术-来预测工作条件变化时每个职业所需的社会软技能重要性水平的平均变化。因为一些变化可能会在不久的将来持续存在。专业,部门，显示负平均变化的年龄组面临着社会软技能禀赋的不足，这最终可能会导致生产率下降。
