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Data aggregation to better understand the impact of computerisation on employment

机译:数据聚集以更好地了解计算机化对就业的影响

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摘要

Data reduction methods are called for to address challenges presented by big data. Correlation of two variables may be less clear if data are organised at disaggregate levels in regression analysis. In this study, we apply data aggregation to regression analysis in the context of a study forecasting the impact of computerisation on jobs and wages. We show that data grouped by the ranked independent variable, versus random or other grouping schemes, provides a clearer pattern of the employment impacts of computerisation probability on job categories. The coefficient estimates are more consistent for groupings based on a ranked independent variable, than those provided by random grouping of the same independent variable. The improved estimations can have positive policy implications.
机译:调用数据减少方法以解决大数据提出的挑战。如果在回归分析中的分解电平以分解级别组织数据,则两个变量的相关性可能不太清楚。在这项研究中,我们在研究预测计算机化对工作和工资的影响时,将数据聚集应用于回归分析。我们显示由排名独立变量组分组的数据,随机或其他分组方案,为工作类别的计算机化概率的就业影响提供了更清晰的模式。基于排名独立变量的分组比由相同的独立变量的随机分组提供的分组更为一致。改进的估计可以具有积极的政策影响。

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