This paper addresses how to nowcast household income changes in a context of generalized but asymmetric economic shocks like the COVID-19 pandemic by integrating real-time data into microsimulation models. The analysis provides an accurate assessment of distributional impacts of COVID-19 and Italian policy responses during 2020, thanks to quarterly data on the turnover of firms and professionals and on costs (goods, services and personnel). Thanks to these data, we can nowcast both the income dynamics of the self-employed and entrepreneurs and the wage-supplementation scheme for working time reduction, as well as all the other interventions based on turnover variations. The nowcasting procedure applies the firm-level data to the TAXBEN-DF microsimulation model (Italian Department of Finance) already relying on a particularly rich and update database of survey and administrative data at individual level that makes it an almost unique model of its kind. Results suggest that policy measures in response to the first pandemic year have been effective in keeping overall income inequality under control, while not yet being able to avoid a concerning polarization of incomes and large heterogeneous effects in terms of both income losses and measures’ compensation.

本文探讨了如何通过将实时数据集成到微观模拟模型中，在像 COVID-19 大流行这样普遍但不对称的经济冲击背景下预测家庭收入变化。借助有关公司和专业人员营业额以及成本（商品、服务和人员）的季度数据，该分析准确评估了 2020 年 COVID-19 和意大利政策应对措施的分布影响。借助这些数据，我们现在可以预测自营职业者和企业家的收入动态、减少工作时间的工资补贴计划，以及基于营业额变化的所有其他干预措施。临近预报程序将公司级数据应用于 TAXBEN-DF 微观模拟模型（意大利财政部），该模型已经依赖于个人层面特别丰富且更新的调查和管理数据数据库，这使其成为同类模型中几乎独一无二的模型。结果表明，应对大流行第一年的政策措施有效地控制了总体收入不平等，但仍无法避免令人担忧的收入两极分化以及收入损失和措施补偿方面的巨大异质性影响。