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Income, liquidity, and the consumption response to the COVID-19 pandemic and economic stimulus payments

Scott Baker, R.A. Farrokhnia, Michaela Pagel, Steffen Meyer, Constantine Yannelis 17 June 2020

The rapid spread of the COVID-19 pandemic throughout the US necessitated shelter-in-place orders across the country. With a majority of the population staying home, and many routine services closed as ‘non-essential’, income and spending was drastically affected. The glaring economic shifts, including an upswell in unemployment, called for an unprecedented collection of fiscal stimulus measures from the US government.

Using transaction-level household data, we provide a comprehensive understanding of how households shifted spending as news about the virus spread, and the impact on certain geographic areas became more severe and far-reaching than others. Additionally, we study how income and account balances are affected by the shelter-in-place policies. Finally, we look at the distribution of stimulus checks that were part of the Coronavirus Aid, Relief, and Economic Security (or CARES) act and analyse how individuals spent their stimulus checks. Our related paper (Baker et al. 2020) studies household consumption during the onset of the pandemic in the US using the same data source. Several studies (Carvalho et al. 2020, Andersen et al. 2020, Bounie at al. 2020, and Chen et al. 2020) perform analyses similar to ours using transaction-level data from Spain, Denmark, France, and China.

We use high frequency transaction data from SaverLife, a non-profit that helps families to develop long-term savings habits and meet financial goals. Individuals can link their accounts to the service, and we have access to de-identified bank account transactions and balances data from August 2016 to May 2020 for these users. The fact that we observe inflows and outflows from individual accounts as well as balances in this dataset allows us to explore heterogeneity in levels of income, changes in income, and liquidity. Additionally, this dataset covers a population characterised by relatively low incomes living all over the US.

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Figure 1 Average annual household income by five-digit zip code in 1,000 USD

We find that households substantially changed their spending as news about COVID-19’s impact in their area spread. Overall, spending increased dramatically in an attempt to stockpile needed home goods and in anticipation of the inability to patronise retailers. Household spending increased by approximately 50% overall between 26 February and 11 March. Grocery spending remained elevated through 27 March, with a 7.5% increase relative to earlier in the year. We also see an increase in card spending, which is consistent with households borrowing to stockpile goods. As the virus spread and more households stayed home, we see sharp drops in restaurants, retail, air travel, and public transport in mid to late March.

Restaurant spending declined by approximately one third. The speed and timing of these decreases in spending varied significantly across individuals depending on their geographic location as state and local governments reacted to outbreaks of different sizes and with different levels of urgency. The overall drop in spending is approximately twice as large in states that issued shelter-in-place orders, while the increase in grocery spending is three times as large for states with shelter-in-place orders.

We explore heterogeneity among partisan affiliations, demographics, and education, which are closely tied to stated beliefs about the impacts of the virus. Across the board, we see drastic shifts in spending that do not appear to vary substantially with these observable characteristics.

Figure 2 Household spending response across categories, by predicted partisanship

Notes: Estimates are taken as the change in household spending from the first week of February to the first week of March. For each category, average response is plotted for three groups: the quartile of the sample with the highest predicted ‘democrat’ lean and the quartile of the sample with the highest predicted ‘republican’ lean and ‘independents’ who are in the middle two quartiles. Spending is measured in daily dollars.
Source: SaverLife.

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In response to the economic fallout of the COVID-19 pandemic, the US government enacted the CARES Act, with over $2 trillion in stimulus measures. Among its various provisions, US households under a certain income threshold qualify to receive direct payments in the form of stimulus checks.

Households responded rapidly to the receipt of stimulus payments, with spending increasing by $0.25 to $0.30 per dollar of stimulus during the first week. Households with lower incomes, greater income drops, and lower levels of liquidity display stronger responses, highlighting the importance of targeting. Liquidity plays the most important role, with no observed spending response for households with high levels of bank account balances. Relative to the effects of previous economic stimulus programs in 2001 and 2008, we see faster effects, smaller increases in durables spending, and larger increases in spending on food, likely reflecting the impact of shelter-in-place orders and supply disruptions. Additionally, we see substantial increases in payments such as rents, mortgages, and credit cards, reflecting a short-term debt overhang.

Figure 3 Impact of stimulus payments on household spending

Notes: This figure shows estimates of βi from , broken down by spending categories. The solid line shows point estimates of βi, while the dashed lines show the 95% confidence interval. Time to payment is equal to zero on the day of receiving the stimulus check.
Source: SaverLife.

Our heterogeneity results are important in terms of targeting stimulus policies towards groups most impacted by them. The theory behind stimulus payments rests on multipliers, which are determined by the marginal propensity to consume (MPCs) in most models. The results of this study suggest that targeting stimulus payments to households with low levels of liquidity during a recession in which large sectors of the economy are shut down will have the largest effects on MPCs, and hence on fiscal multipliers.

We then show in a macroeconomic model with multiple sectors that untargeted fiscal stimulus payments in environments like the 2020 COVID-19 pandemic may be less effective than the payments in response to the 2001 and 2008 economic downturns. Reflecting the current situation, we map out a three-sector model in which one sector employing lower-wage agents is shut down while a second low-wage essential sector remains operational alongside a higher-wage sector that can largely work from home.

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Due to the shutdown of one low-wage sector, those poorer and higher MPC agents are largely excluded from benefiting from additional spending induced by stimulus payments, thereby reducing the fiscal multiplier effect. Additionally, agents in the lower-wage sectors tend to accumulate more debt by borrowing from the higher-wage sector. Agents end up using the stimulus payments to repay debt to high-wage individuals who have the lowest MPCs out of income. In short, workers will spend their stimulus payment on mortgages and loan repayments as well as non-durable essentials which implies that cash flows immediately to agents with lower MPCs. This tends to make fiscal stimulus less effective overall.

References

Andersen, A L, E T Hansen, N Johannesen and A Sheridan (2020), “Consumer Reponses to the COVID-19 Crisis: Evidence from Bank Account Transaction Data”, Technical Report, Working Paper.

Baker, S R, R A Farrokhnia, S Meyer, M Pagel and C Yannelis (2020), “How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic”, NBER Working Paper.

Bounie, D, Y Camara and J W Galbraith (2020), “Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data”, Available at SSRN 3588373.

Carvalho, V M, J R Garcia, S Hansen, A Ortiz, T Rodrigo, J V Rodriguez Mora and Jose Ruiz (2020), “Tracking the COVID-19 Crisis with High-Resolution Transaction Data”, Technical Report, Working Paper.

Chen, H, W Qian and Q Wen (2020), “The Impact of the COVID-19 Pandemic on Consumption: Learning from High Frequency Transaction Data”, Available at SSRN 3568574.

Johnson, D S, J A Parker and N Souleles (2006), “Household Expenditure and the Income Tax Rebates of 2001”, American Economic Review, 96(5): 1589–1610.

Parker, J A, N S Souleles, D S Johnson and R McClelland (2013), “Consumer Spending and the Economic Stimulus Payments of 2008”, American Economic Review, 103(6): 2530–53.