Mapping Labor Demand for Data‐Intensive Work in the United Kingdom, Canada, and the United States
开发了一种基于自然语言处理的方法，从招聘广告中识别数据密集型技能，并构建了职业和行业的数据密集度指标，为英国、加拿大和美国的数据专家招聘提供了可比较的跨国估计。
ABSTRACT The paper makes three main contributions. First, it develops an NLP‐based methodology to identify data‐intensive skills in job advertisements at scale. Second, it operationalizes a transparent indicator of data intensity for occupations and industries. Third, by applying the method to job advertisements from the United Kingdom, Canada, and the United States, it provides harmonized cross‐country estimates of data‐expert hiring, offering a key input to derive a proxy for investment in data assets. Empirical results show that although the ranking of data‐intensive occupations is broadly similar across countries, the industrial distribution of data‐intensive jobs differs, reflecting distinctive labor‐demand structures.