Combining job mobility patterns and vacancy data to better measure labour market opportunities and skill mismatch

Covid大流行带来了前所未有的劳动力市场冲击。酒店业等行业的就业机会一夜之间消失了，而驾车和医疗保健等其他行业的就业机会则扩大了。这种彻底的经济重组需要一种新的方式来衡量劳动力市场前景，这些人捕捉到了他们通过进入替代工作行业来适应冲击的能力差异很大。受此启发，我们开发了一种新的针对工人的劳动力市场机会衡量标准。我们的衡量标准是基于工作变动的历史模式，我们使用这种模式来推断特定工人对不同类型工作的适应程度。结合职位空缺的高质量数据，这使我们能够超越劳动力市场表现的总体衡量标准，并量化大流行病期间不同类型工人面临的劳动力市场前景的重要差异(Costa Dias等人2021年，Joyce等人2022年)。虽然我们的创新是由大流行引发的，但我们方法的实用性要广泛得多。我们的方法将经济某个部分的劳动力市场状况与经济的其他部分联系起来，这意味着它可以用来回答这样的问题：零售业的冲击如何影响送货司机的工资压力和职业发展？更广泛的劳动力市场的变化如何影响社会护理部门招聘和留住工人的能力？我们的方法进一步洞察了工人的技能在多大程度上与雇主要求的技能相一致--劳动力市场“错配”的程度--这对生产率和经济增长具有影响。例如，如果最近离开劳动力市场的老年工人重返劳动力市场，这将在多大程度上缓解特定职业的劳动力短缺？在经济中，技能短缺最严重的地方是什么？就总生产率而言，错配的经济成本是多少？这个项目的目的是扩展和改进我们的新方法，将其建立在一个理论搜索和匹配模型的基础上，并证明它对实质性问题的适用性以及它相对于传统方法的优势。我们会修订我们的指数，以涵盖不同类别求职者因竞争职位而产生的机会差异，以及雇主所需技能的差异(由我们现时的指数所涵盖)。然后，我们将通过将其应用于如上所述的经验性、与政策相关的问题来展示其效用--特别是在确定劳动力市场机会在不同工人和时间上的变化，以及在量化经济中劳动力市场错配的程度和经济成本方面。将这一创新嵌入到未来的研究和统计实践中的一个关键部分将是传播和与其他人接触--不仅是在项目完成后，而且是在项目制作期间，帮助我们最大限度地发挥其有用性。主要目标受众将包括研究界、统计从业者(例如国家统计局)和政策制定者--因为我们创新的最终目的是更好地指导决策，通过对政策制定者产生影响，我们将在未来的研究中进一步突出我们创新的潜力。

The Covid pandemic brought about an unprecedented labour market shock. Job opportunities in sectors like hospitality disappeared overnight, whilst opportunities expanded in other sectors like driving and healthcare. This radical restructuring of the economy called for a new way of measuring the labour market prospects facing individuals that captured their widely-varying ability to adapt to the shock by moving into alternative lines of available work. Motivated by this, we developed a new worker-specific measure of labour market opportunities. Our measure is based on historical patterns of job-to-job moves, which we use to infer how suitable a given worker is for different types of jobs. Combined with high-quality data on vacancy postings, this allowed us to look beyond aggregate measures of labour market performance and quantify important differences in the labour market prospects facing different types of workers over the pandemic (Costa Dias et al 2021, Joyce et al 2022). While our innovation was triggered by the pandemic, the usefulness of our method is much more general and wide-ranging. Our method links the state of the labour market in one part of the economy to the rest of the economy, meaning that it could be used to answer questions like: how do shocks in retail affect wage pressures and career progression for delivery drivers? How do changes in the wider labour market affect the ability of the social care sector to recruit and retain workers? Our method further provides insights into the extent to which workers' skills align with the skills demanded by employers - the extent of labour market 'mismatch' - which has implications for productivity and economic growth. For example, if the older workers who have recently left the labour market were to come back, to what extent could this ease labour shortages in specific occupations? Where in the economy are skills shortages most acute, and what is the economic cost of mismatch in terms of aggregate productivity?The aim of this project is to extend and refine our new method, grounding it in a theoretical search and match model, and demonstrate its applicability to substantive questions and its advantages over traditional methods. We will refine our index to capture variation in opportunities arising from competition for jobs from different types of jobseekers, as well as variation in the skills demanded by employers (captured by our current index). We will then demonstrate its utility by applying it to empirical, policy-relevant questions like the ones above - in particular, in identifying the variation in labour market opportunities across workers and over time, and in quantifying the extent and economic cost of labour market mismatch in the economy. A key part of embedding this innovation within future research and statistical practice will be dissemination and engagement with others - not only after the project is done, but during its production, helping us to maximise its usefulness. The main target audiences will include the research community, statistical practitioners (e.g. ONS), and policy-makers - since the ultimate purpose of our innovation is to better guide policy-making, and through achieving impact on policy-makers we will further highlight the potential of our innovation within future research.