Governments deployed credit policies on a historically unprecedented scale in response to the COVID-19 pandemic. We estimate the effective size of credit policies for seven large advanced economies in terms of the incremental resources provided to firms and households—a measure that allows aggregation across credit support, forbearance, and traditional fiscal policies but that does not appear in traditional government statistics. These estimates are used to reassess the absolute and relative size of different governments' policy interventions and to evaluate whether taking credit policies into account can help explain the cross section of macroeconomic outcomes. Incremental resources increase from an average 14.5 percent of 2020 GDP when only fiscal policies are considered to 22 percent of 2020 GDP when credit policies are also taken into account. Incorporating credit policies also reduces the cross-country variation in the total size of policy interventions. With regard to fiscal cost, fair value estimates for these credit support programs average 37 percent of principal, with wide variation depending on program features. We also discuss several related measurement issues, the financial regulatory changes that accommodated these programs, the pros and cons of the different types of credit policies, and how in principle budgetary costs should be calculated versus how governments account for credit policies in practice.

为应对 COVID-19 大流行，各国政府以历史上前所未有的规模部署了信贷政策。我们根据向企业和家庭提供的增量资源来估计七个大型发达经济体信贷政策的有效规模，这一指标允许对信贷支持、宽容和传统财政政策进行汇总，但不会出现在传统政府统计数据中。这些估计用于重新评估不同政府政策干预的绝对和相对规模，并评估考虑信贷政策是否有助于解释宏观经济结果的横截面。增量资源从仅考虑财政政策时占 2020 年 GDP 的平均 14.5% 增加到同时考虑信贷政策时占 2020 年 GDP 的 22%。纳入信贷政策还可以减少政策干预总规模的跨国差异。就财政成本而言，这些信贷支持计划的公允价值估计平均为本金的 37%，根据计划特点存在很大差异。我们还讨论了几个相关的衡量问题、适应这些计划的金融监管变化、不同类型信贷政策的优缺点，以及原则上应如何计算预算成本以及政府在实践中如何核算信贷政策。