The aggregate effects of fiscal stimulus: Evidence from the COVID-19 unemployment supplement
BibTeX
@techreport{casado2020aggregate,
title={The aggregate effects of fiscal stimulus: Evidence from the COVID-19 unemployment supplement},
author={Casado, Miguel Garza and Glennon, Britta and Lane, Julia and McQuown, David and Rich, Daniel and Weinberg, Bruce A},
year={2020},
institution={National Bureau of Economic Research}
}
Abstract
The current economic crisis has highlighted the need for data that are both timely and local so that the effects of fiscal policy options on local economies can be evaluated more immediately. This paper highlights the potential value of using two new sources of near real-time data to inform decisions about the appropriate stimulus approach to implement. The first data source is administrative records that provide universal, weekly, information on unemployment claimants. The second data source is transaction level data on economic activity that are available on a daily basis. We make use of discrete changes in stimulus payments to construct a framework for evaluating real-time effects of fiscal policy on local economic activity. In particular, we leverage cross-county and over-time variation in the relative size of the Federal Pandemic Unemployment Compensation (FPUC) COVID-19 supplement to Unemployment Insurance – from $0 to $600 to $300 between March and September 2020 - to estimate the local economic impact of unemployment, earnings replacement, and the interaction between the two. We find that higher earnings replacement rates lead to significantly more consumer spending, even with increases in the unemployment claimant rate, which is consistent with the goal of the fiscal stimulus.
Notes and Excerpts
In this paper we make use of the universe of weekly benefits data in the fifth largest state in the economy – Illinois – to estimate the links between [FPUC] and the aggregate spending in each county
Existing data can be combined to do so: in particularly, state administrative records on unemployment claims can aggregated at the county level and combined with county level credit- and debit- card transaction data. The first dataset, unemployment claims records, provides universal and weekly information on the claims, benefits, and previous earnings of claimants, and makes it possible to calculate individual-level replacement rates and unemployment claimant rates that can be aggregated to any level of geography, demographic group and industry
They have private data from Illinois DoL which “provides information at the individual level about weekly unemployment claims, benefits received, and previous earnings”.
Also uses Quarterly UI Wage Records
The construction of the unemployment claimant rate measure is conceptually different from the BLS unemployment measure, which directly asks individuals whether they are “actively looking for work in the survey week”. The unemployment claimant rate measure is directly based on the count of individuals who certified and received unemployment benefits for the weeks between that ending January 25, 2020 and that ending September 5, 2020. The BLS measure has the advantage of familiarity, but has been criticized for being atheoretical (Card 2011) and for not providing a measure that is useful for policymakers(Brandolini 2018; Brandolini and Viviano 2016).
We calculate the earnings replacement rates by creating a ratio of the total amount paid in UI benefits14 for each certified claimant - for each week from the week ending January 25, 2020 to the week ending September 5, 2020 - to their 2019 average weekly wage.
chetty2020economic cites this paper’s estimates of the FPUC bonus on spending levels to say that the response was stronger than what would be predicted to happen from a lump sum transfer to everyone.