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Improving Stylised Working Time Estimates with Time Diary Data: A Multi Study Assessment for the UK

机译:使用时间日志数据改进程式化的工作时间估计:英国的一项多项研究评估

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摘要

Accurate working time estimates represent an important component of the statistical toolbox used for economics forecasting and policy-making. The relatively good availability of such estimates may sometimes induce researchers to take them for granted and see their reliability as largely unproblematic. There is however a growing body of evidence showing that measurement errors may affect their robustness and quality, especially as far as specific but policy relevant subgroups of the population such as part-time or atypical workers are concerned. Against this background, the goal of this paper is to investigate the reliability of paid weekly working-time measurement instruments commonly available in a key UK social survey, the Labour Force Survey. It focuses on the discrepancies between estimates obtained by self-assessed/aggregated instruments—also known as stylised—and those recorded using time diaries which have been found more truthful to the time spent working in ones paid job(s). It is also to explore ways to improve the reliability of stylised estimates in datasets for which no time diary instruments are available, contrasting those where ’usual’ and ’actual’ hours of work are recorded. It does so by creating calibration weights based on the Work Schedule recorded in the 2000 and 2015 UK Time Use Surveys and using them to up/down scale stylised estimates in the 2000 and 2015 UK Labour Force Survey using statistical matching. Such techniques could enable significant improvements of measurement errors in large scale social surveys at a minimal cost.
机译:准确的工作时间估算值是用于经济预测和决策的统计工具箱的重要组成部分。这种估计的相对较好的可用性有时可能会诱使研究人员将其视为理所当然,并认为其可靠性在很大程度上没有问题。但是,越来越多的证据表明,测量误差可能会影响其健壮性和质量,特别是就特定但与政策相关的人口子群体(例如兼职或非典型工人)而言。在此背景下,本文的目的是调查英国关键社会调查(劳动力调查)中普遍使用的有薪每周工作时间测量工具的可靠性。它着重于通过自我评估/汇总的工具(也称为风格化工具)获得的估计值与使用时间日志记录的估计值之间的差异,这些日志被发现与在有薪工作中花费的时间更为真实。它还将探索在没有时间日志工具可用的数据集中提高程式化估计值可靠性的方法,与记录“正常”和“实际”工作时间的数据集进行对比。它通过基于2000年和2015年英国时间使用情况调查中记录的工作时间表创建校准权重,并使用它们来使用统计匹配在2000年和2015年英国劳动力调查中对程式化的估计值进行向上/向下缩放。这种技术可以以最小的成本显着改善大规模社会调查中的测量误差。

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