[Paper Review] Pandemic, Shutdown and Consumer Spending: Lessons from Scandinavian Policy Responses to COVID-19

Asger Lau Andersen, Emil Toft Hansen+2 more|arXiv (Cornell University)|May 10, 2020

COVID-19 Pandemic Impacts93 citations

TL;DR

The paper uses Danish bank transaction data to compare Denmark’s shutdown with Sweden’s lighter approach, quantifying how policy response shaped consumer spending during COVID-19 and how effects vary by age health risk.

ABSTRACT

This paper uses transaction data from a large bank in Scandinavia to estimate the effect of social distancing laws on consumer spending in the COVID-19 pandemic. The analysis exploits a natural experiment to disentangle the effects of the virus and the laws aiming to contain it: Denmark and Sweden were similarly exposed to the pandemic but only Denmark imposed significant restrictions on social and economic activities. We estimate that aggregate spending dropped by around 25 percent in Sweden and, as a result of the shutdown, by 4 additional percentage points in Denmark. This implies that most of the economic contraction is caused by the virus itself and occurs regardless of social distancing laws. The age gradient in the estimates suggest that social distancing reinforces the virus-induced drop in spending for low health-risk individuals but attenuates it for high-risk individuals by lowering the overall prevalence of the virus in the society.

Motivation & Objective

• Assess how social distancing laws affected aggregate consumer spending during COVID-19 in Denmark versus Sweden.
• Isolate the additional spending impact of Denmark’s shutdown beyond the virus itself.
• Examine heterogeneity in spending effects across age groups reflecting health risk.
• Decompose spending by categories with varying social proximity to understand mechanism of effects.
• Relate findings to macroeconomic policy implications and prior literature.

Proposed method

• Use transaction data from Danske Bank for ~860,000 individuals in Denmark and Sweden.
• Construct daily total spending from cards, cash withdrawals, mobile wallets, and online invoices.
• Create four high-social-proximity spending categories to capture heterogeneity (social, personal care, public transport, high street/mall retail).
• Define a daily excess spending measure by comparing to the same weekday a year earlier to control for seasonality.
• Estimate country-specific spending changes with a saturated regression (Eq. 1) including demographics, then compute adjusted counterfactuals.
• Analyze age-specific effects by estimating age-group country-specific regressions (Eq. 2).

Experimental results

Research questions

• RQ1 What is the differential impact of COVID-19 shutdowns on aggregate consumer spending in Denmark versus Sweden?
• RQ2 How does shutdown-related spending change vary across age groups reflecting health risk?
• RQ3 Which spending categories with high social proximity show the strongest responses to policy restrictions?
• RQ4 To what extent does the virus itself versus policy measures drive observed spending changes?
• RQ5 What policy implications arise from the interaction between health risk and spending during a pandemic?

Key findings

• Denmark’s shutdown reduced aggregate spending by about 4 percentage points beyond the virus effect, while Sweden saw a ~25% drop.
• Overall Danish spending fell ~29% relative to a no-pandemic counterfactual, Swedish spending ~25%.
• The shutdown’s additional effect is larger for lower-health-risk individuals and smaller for higher-health-risk individuals due to virus containment effects.
• Spending declines are most pronounced in high-proximity categories such as social spending, personal care, and retail; high-risk groups may see preserved or increased spending in moderately proximal categories due to reduced infection risk.
• The results imply most economic contraction stems from the pandemic itself, with shutdowns adding relatively small incremental losses.
• The age gradient indicates negative effects for young adults and positive or less negative effects for the oldest cohort, driven by health risk considerations.