The impact of COVID-19 on unemployment and earnings inequality.

当前的大流行使英国经济的许多部门萎缩，大量个人失业或面临失业风险。越来越明显的是，这一大流行病的经济影响将长期持续下去，特别是在受影响最严重的部门。因此，英国经济复苏的速度取决于失业者从遭受重创的行业重新分配到繁荣行业的程度。更多的再分配可能会让失业率的上升变得短暂，从而提高英国经济迅速复苏的可能性。英国政府最近宣布了一系列政策，鼓励个人从受covid - 19大流行影响严重的行业重新培训和重新分配。然而，有证据表明，在经济衰退期间，职业/行业流动性的程度会下降（参见Carrillo-Tudela， Hobijn， She和Visschers， European Economic Review, 2016）。这让人怀疑个人是否真的愿意和/或能够在这个困难时期换工作。事实上，对于许多转行的人来说，这仍然是一个艰难的决定：他们是否要冒着长期失业的风险，等待以前行业/职业的工作机会重新出现？或者他们接受现有的工作，即使他们失去了职业/行业特定技能，这可能意味着更不稳定的工作和更低的收入？这种权衡清楚地表明，公共政策鼓励这种重新分配可能是不可取的，因为它们可能在相当多的人口中产生“低薪无薪循环”。鉴于自金融危机以来，随着个人重新分配到低收入行业，工资水平一直在下降，鼓励进一步重新分配可能会加速这一趋势，并使生计恶化。在这项研究中，我们将首先记录个人如何跨职业/行业寻找工作。为此，我们将使用通过了解社会covid - 19研究获得的新收集的求职纵向数据。根据这些数据，我们将开发和估计多部门商业周期模型，在该模型中，工人的职业/行业流动性决策将其职业前景与各部门相对丰富的职位空缺进行权衡。这一框架将使我们能够量化求职者援助、再培训和工作保留计划对失业和收入不平等的有效性，通过它们对工人再分配和企业裁员和创造就业决策的影响。这将为当前关于如何最好地让人们重返工作岗位的辩论提供一个新的视角。为了展示我们的发现，除了学术文章外，我们还将免费提供一个在线“失业与不平等计算器”。该工具将提供不同模拟政策制度下失业和收入不平等的可能演变。用户将能够通过简单地改变描述上述策略的模型参数来分析不同的假设场景。在后台，我们的模型将被重新模拟以产生期望的输出。这将告知由于大规模裁员而持续高失业率的可能性；疫情是否会加剧与低薪部门再分配相关的不平等加剧；以及可以实施哪些政策措施来减少失业和不平等。

The current pandemic has left many sectors of the UK economy shrinking, with large number of individuals unemployed or at risk of unemployment. It is increasingly clear that the economic impact of the pandemic will persist over time, particularly in the worse affected sectors. The speed of the UK's economic recovery therefore depends on the extent to which the unemployed reallocate from harder hit sectors to those that are booming. More reallocation could make the increase in unemployment short-lived, boosting the UK's chance of a swift economic recovery. The UK Government has recently announced a set of policies to encourage individuals to retrain and reallocate away from sectors hard-hit by the COVID19 pandemic. Evidence shows, however, that the degree of occupational/industry mobility falls during recessions (see e.g. Carrillo-Tudela, Hobijn, She and Visschers, European Economic Review, 2016). This cast doubts on whether individuals will actually be willing and/or able to change occupations in this difficult time. Indeed, for many changing careers remains a difficult decision: do they wait for jobs to reappear in their previous industries/occupations, risking long periods of unemployment? Or do they accept available jobs, even if they lose their occupation/industry-specific skills which potentially means less job stability and lower earnings? This trade-off makes clear that it might not be desirable for public policy to encourage such reallocations as they might generate "low-pay-no-pay cycles" among a significant group of the population. Given that wages have been already falling since the financial crisis, as individuals reallocate to low-paying sectors, encouraging further reallocation could hasten this trend and worsen livelihoods. In this research we will first document how individuals search for jobs across occupations/industries. For this purpose, we will use newly collected longitudinal data on job search available through the Understanding Society COVID19 study. Informed by these data, we will develop and estimate multi-sector business cycle models in which workers' occupation/industry mobility decisions trade off their career prospects against the relative abundance of vacancies across sectors. This framework will allow us to quantify the effectiveness of e.g. job seekers assistance, re-training and job retention schemes on unemployment and earnings inequality through their effects on workers' reallocation and firms' layoff and job creation decisions. This will provide a new perspective to the current debate on how best to bring people back to work. To showcase our findings, in addition to academic articles we will make freely available an online "unemployment and inequality calculator". This tool will provide the likely evolution of unemployment and earnings inequality under different simulated policy regimes. Users will be able to analyse different what-if scenarios by simply changing the models' parameters that describe the aforementioned policies. In the background our models will be re-simulated to produce the desired output. This will inform about the likelihood of persistently high unemployment due to mass layoffs; whether the increase in inequality connected to reallocation to low-paying sectors will be exacerbated by the pandemic; and which policy measures could be implemented to reduce unemployment scarring and inequality.