The Measurement of Social and Labour Market Policies - A Natural-Language-Processing-Based Approach

社会和劳动力市场政策的衡量——基于自然语言处理的方法

• 批准号: 2260225
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2260225
• 关键词: Measurement; Social; Labour; Market; Policies

My research will make both empirical contributions to the literature on the consequences as well as determinants of social and labour market policies and theoretical innovations to the methodology of policy measurement. I want to close this empirical gap by researching how such policy measurements should look like and how various disciplines and their data sources can be brought together in order to solve this highly policy-relevant issue. These new measures will capture timing, intensity and direction of social and labour market policy changes more accurately than existing ones, which will enable researchers from various disciplines to use the new dataset in panel, cross-section and time series settings. To make a starting point of such exercises, I want to test the quality of the new measures in standard regressions as dependent and independent variable.I am aware of the technical challenges of this project, which is why I would start to build the entire measurement pipeline for one country and one policy domain (for instance labour market regulation in the United Kingdom). I believe that my research experience and the interdisciplinary environment at the Department of Social Policy and Intervention will enable me to successfully conduct my intended research project. Because of the multi-disciplinary nature of the PhD thesis, I am happy that Professor Tim Vlandas (Department of Social Policy and Intervention) and Professor Taha Yasseri (Oxford Internet Institute; expert in Natural Language Processing) have expressed their interest to supervise the project.

我的研究将对有关社会和劳动力市场政策的后果和决定因素的文献作出实证贡献，并对政策衡量方法进行理论创新。我想通过研究这些政策措施应该是什么样子，以及如何将不同的学科及其数据来源结合在一起，以解决这个与政策高度相关的问题，来弥合这一经验差距。这些新措施将比现有措施更准确地捕捉社会和劳动力市场政策变化的时间、强度和方向，这将使来自不同学科的研究人员能够在面板、横截面和时间序列设置中使用新数据集。作为这种工作的起点，我想用标准回归作为因变量和自变量来测试新措施的质量。我知道这个项目的技术挑战，这就是为什么我会开始为一个国家和一个政策领域(例如英国的劳动力市场监管)建立整个衡量管道。我相信，我在社会政策和干预部的研究经验和跨学科环境将使我能够成功地进行我想要的研究项目。由于博士论文的跨学科性质，我很高兴Tim Vlandas教授(社会政策和干预系)和Taha Yasseri教授(牛津互联网研究所；自然语言处理专家)表示有兴趣监督该项目。