Methodological Advancements on the use of Administrative Data in Official Statistics

官方统计中行政数据使用方法的进步

• 批准号: ES/V005456/1
• 负责人: Natalie Shlomo
• 金额: $ 20.48万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FV005456%2F1
• 关键词: Methodological; Advancements; use; Administrative; Data

National Statistical Institutes (NSIs) are directing resources into advancing the use of administrative data in official statistics systems. This is a top priority for the UK Office for National Statistics (ONS) as they are undergoing transformations in their statistical systems to make more use of administrative data for future censuses and population statistics. Administrative data are defined as secondary data sources since they are produced by other agencies as a result of an event or a transaction relating to administrative procedures of organisations, public administrations and government agencies. Nevertheless, they have the potential to become important data sources for the production of official statistics by significantly reducing the cost and burden of response and improving the efficiency of such systems. Embedding administrative data in statistical systems is not without costs and it is vital to understand where potential errors may arise. The Total Administrative Data Error Framework sets out all possible sources of error when using administrative data as statistical data, depending on whether it is a single data source or integrated with other data sources such as survey data. For a single administrative data, one of the main sources of error is coverage and representation to the target population of interest. This is particularly relevant when administrative data is delivered over time, such as tax data for maintaining the Business Register. For sub-project 1 of this research project, we develop quality indicators that allow the statistical agency to assess if the administrative data is representative to the target population and which sub-groups may be missing or over-covered. This is essential for producing unbiased estimates from administrative data. Another priority at statistical agencies is to produce a statistical register for population characteristic estimates, such as employment statistics, from multiple sources of administrative and survey data. Using administrative data to build a spine, survey data can be integrated using record linkage and statistical matching approaches on a set of common matching variables. This will be the topic for sub-project 2, which will be split into several topics of research. The first topic is whether adding statistical predictions and correlation structures improves the linkage and data integration. The second topic is to research a mass imputation framework for imputing missing target variables in the statistical register where the missing data may be due to multiple underlying mechanisms. Therefore, the third topic will aim to improve the mass imputation framework to mitigate against possible measurement errors, for example by adding benchmarks and other constraints into the approaches. On completion of a statistical register, estimates for key target variables at local areas can easily be aggregated. However, it is essential to also measure the precision of these estimates through mean square errors and this will be the fourth topic of the sub-project. Finally, this new way of producing official statistics is compared to the more common method of incorporating administrative data through survey weights and model-based estimation approaches. In other words, we evaluate whether it is better 'to weight' or 'to impute' for population characteristic estimates - a key question under investigation by survey statisticians in the last decade.

国家统计机构正在将资源用于推动官方统计系统使用行政数据。这是联合王国国家统计局的一个最高优先事项，因为它们正在对其统计系统进行改革，以便在今后的普查和人口统计中更多地利用行政数据。行政数据被定义为二级数据来源，因为它们是其他机构因与组织、公共行政部门和政府机构的行政程序有关的事件或交易而产生的。不过，这些系统有可能成为编制官方统计数据的重要数据来源，因为它们可以大大减少答复的费用和负担，并提高这些系统的效率。将行政数据纳入统计系统并非没有成本，而且了解何处可能出现错误至关重要。《行政数据总体误差框架》列出了将行政数据用作统计数据时所有可能的误差来源，具体取决于行政数据是单一数据源还是与调查数据等其他数据源相结合。就单一行政数据而言，误差的主要来源之一是覆盖率和对目标人口的代表性。这在行政数据是随着时间的推移而提供的情况下尤其重要，例如用于维持企业登记册的税务数据。对于本研究项目的分项目1，我们制定了质量指标，使统计机构能够评估行政数据是否代表目标人口，以及哪些分组可能缺失或覆盖过度。这对于从行政数据中得出无偏估计数至关重要。统计机构的另一个优先事项是根据多种来源的行政和调查数据编制人口特征估计数的统计登记册，如就业统计。使用行政数据建立脊柱，调查数据可以使用记录链接和统计匹配方法对一组共同的匹配变量进行整合。这将是分项目2的主题，分项目2将分为几个研究主题。第一个主题是添加统计预测和相关性结构是否会改善链接和数据集成。第二个主题是研究一个大规模插补框架，用于插补统计登记册中缺失的目标变量，其中缺失的数据可能是由于多种潜在机制造成的。因此，第三个专题的目标是改进总体估算框架，以减少可能出现的计量错误，例如在各种方法中增加基准和其他限制。在完成统计登记后，可以很容易地汇总地方关键目标变量的估计数。然而，还必须通过均方误差来衡量这些估计数的精确度，这将是该分项目的第四个专题。最后，将这种编制官方统计数据的新方法与通过调查加权和基于模型的估计方法纳入行政数据的更常见方法进行了比较。换句话说，我们评估是否更好地“加权”或“估算”人口特征估计-一个关键问题正在调查的调查统计学家在过去十年。