Supporting fiscal policy decisions in the crisis

我们正在经历一场经济危机。作为回应，英国政府推出了史无前例的一揽子政策，在短短几个月内花费了数百亿英镑--支付员工休假费用、支付个体户工资、减免商业税率、增加公共服务支出，并让福利体系变得更加慷慨。在接下来的一年里，政府将不得不决定是否采取更多措施来保障家庭收入和防止企业倒闭，是否在健康危机消退时使用财政政策来启动经济，以及最终如何取消这些措施，或者是否将其中一些措施永久化。为了指导这些重大决策，我们提出了对新数据来源的有针对性的新分析，包括即将推出的大规模纵向家庭调查中的新冠肺炎模块，以及来自预算应用程序的实时银行账户数据。与IFS在税收和福利政策设计方面的世界领先专业知识以及对英国劳动力市场、税收和福利以及公共财政的制度和政策背景的无与伦比的熟悉相结合，将为未来12个月应如何调整财政政策提供信息。在这场危机中，我们正在与公务员和政府高层的政策顾问定期进行讨论和研讨会。这里提出的新工作将直接促进这种接触，使我们能够帮助政策制定者在这种非常不寻常和快速变化的经济环境中做出艰难的决定，包括通过利用新的和及时的数据来源来产生尽可能最佳的经验证据。

We are going through an economic crisis. The government has responded with an unprecedented policy package, costing many tens of billions of pounds over just a few months - paying for employees to be furloughed, making payments to the self-employed, forgiving business rates, increasing public service spending and making the benefit system much more generous. Over the coming year, the government will have to decide whether to do more to insure households' incomes and prevent business failure, whether to use fiscal policy to kickstart the economy as the health crisis subsides and, ultimately, how to unwind these measures or whether to make some of them permanent. To guide these momentous decisions we propose targeted new analysis of new data sources, including a soon-to-be-available covid-19 module in a large-scale longitudinal household survey and real-time bank account data from a budgeting app. Combined with IFS' world-leading expertise on tax and benefit policy design and unrivalled familiarity with the institutional and policy context around the UK's labour market, taxes and benefits and the public finances, will inform how fiscal policies should be adjusted in the coming 12 months. We are having regular discussions and seminars with civil servants and policy advisors at the top of government through this crisis. The new work proposed here would feed in to this engagement directly, allowing us to help policy-makers take difficult decisions in this highly unusual and fast-moving economic environment, including through taking advantage of novel and timely data sources to produce the best possible empirical evidence.