Savings rates among European households have reached record levels during the Corona recession (see Figure 1). Simultaneously, the average debt to GDP ratio of the euro area is expected to increase by 15% through 2020,¹ although the degree to which this is driven by increased spending, as opposed to decreased tax receipts, varies substantially by country. The sharp rise in household savings rates appears high enough to completely offset the increase in government deficit spending at the aggregate, while firm savings rates remain largely unchanged, such that countries’ economy wide savings rates (private and public) are roughly unchanged relative to their pre-COVID-19 levels.

Understanding why households are saving so much is crucial for understanding how and when we should expect aggregate demand to return to pre-COVID-19 levels. In this note, we propose three explanations for the sharp rise in savings and assess their ability to explain variation in household savings across European countries.

Perhaps the most obvious explanation for the increase in savings is reduced consumption opportunities related to lockdown measures. By shutting down restaurants, discouraging travel, and limiting retail capacity, governments reduced consumers’ opportunities to spend. While consumers may substitute these lost consumption opportunities with online spending, many may prefer to bide their time until they can travel or eat out again. This explanation can be thought as the supply hypothesis as, under this explanation, the decline in household consumption is not driven by demand. If households were unable to spend, a relaxation of measures may then result in a strong, perhaps short-lived consumption boom in the spring/summer of 2021. Especially those sectors that particularly suffered during the lockdown (tourism, restaurants, retail stores) would benefit.

Another intuitive explanation is that the increase in savings is driven by a decline in household demand. Fears of future employment income losses associated with increased economic uncertainty may have driven households to accumulate precautionary savings. It is well established in the theoretical and empirical literature that economic uncertainty associated with recessions drives households to reduce consumption.² This explanation would tie a recovery in consumption to the economic recovery: To the extent that economic uncertainty persists even after lockdown measures have been phased out and adjustments to economic preferences due to Corona imply substantial structural adjustments, we would expect the recovery in consumption to be weak in 2021.

The third explanation is related to the sharp increase in deficit spending associated with the fiscal response packages to the recession. As households are forward looking, debt-financed increases in government spending may induce households to save in anticipation of future tax hikes to pay off the government’s debt. In the academic literature, this is known as Ricardian Equivalence.³ If households are worried about future tax payments, the increase in savings will persist until governments substantially reduce their deficits and debt levels. Hence, the recovery in consumption in 2021 can be expected to be weak based on this explanation as well.

In the cross-sectional data, we observe a relationship that appears to be consistent with Ricardian Equivalence (see Figures 1 and 2). In other words, countries where the government is running a higher deficit also happen to be countries where households are saving more.

We compile a monthly panel of eurozone countries from January 2019 to August 2020 to measure the extent to which precautionary savings, reduced consumption opportunities, and Ricardian Equivalence can explain the rise in household savings. Because savings rates are only available at the quarterly level, we use monthly household deposits from the European Central Bank’s (ECB) Statistical Data Warehouse as a proxy that we then seasonally adjust.

While households can increase net savings by investing, reducing their debt, or holding currency, deposits should capture the majority of month-to-month changes in savings and are conveniently available on a monthly frequency for euro area members. Dossche and Zlatanos (2020) show that while currency in circulation has increased and household debt has declined, by far the most important driver of the increase in savings rates is household deposits.⁵

We measure government lockdown stringency using the Oxford COVID-19 Government Response Tracker (OxCGRT), which tracks comparable policy measures of government COVID-19 response measures across countries and time from January 2020 to today.⁶ We take the monthly average of all nine containment and closure policies by country and divide this value by the sample maximum, such that the country-month observation with the strictest set of containment policies equals one. We assign all observations prior January 2020 a lockdown stringency value of zero.

To capture variation in precautionary motivations for savings, similar to the approach of Dossche and Zlatanos (2020), we retrieve the seasonally adjusted households’ self-reported unemployment expectations over the next twelve months from the European Commission’s Consumer Survey Questionnaire. The index varies from –100 to +100 with a long-run average of 0. We argue that such a measure should be superior to using the realised level of unemployment for capturing precautionary motivations, as it captures households’ expectations about the future, which should be more important in explaining consumption and savings behaviour today.

We capture variation in the growth rate of government debt using total government debt securities divided by GDP. Total government debt securities are sourced from the ECB Statistical Data Warehouse. Using debt securities systematically underestimates the total level of debt in countries which rely on sources of financing beyond government bonds. However, over this period, there have been no international bailout loans to national eurozone governments that would not be captured by debt securities, so there should be little mismeasurement of changes in government debt for our sample.

Our sample consists of all eurozone members with populations greater than one million. All time series variables are seasonally adjusted using X-12-ARIMA. The sample period runs from January 2019 to August 2020, yielding 257 country-month observations with non-missing values for all variables.

Table 1 presents summary statistics for our sample. Both the stringency measure and death per million are strongly rightward skewed in their distribution, as the sample period covers at least 13 months of times before the COVID-19 crisis arrived in Europe.

Table 2 displays pairwise correlations of the various variables included in the model. Straightaway, we observe that the variable most correlated with the deposit rate growth is the lockdown stringency index, followed closely by unemployment expectations. Moreover, the highest pairwise correlation of variables in the table is between the stringency index and unemployment expectations, perhaps reflecting both the impact of the lockdown on economic activity, but also the lockdown’s importance in forming households perceptions. Interestingly, we observe that the correlation between unemployment expectations and the stringency measure is greater than that of unemployment expectations and GDP growth. This could suggest that the lockdown may be more salient in forming the public’s perception of changes in economic activity than actual changes in economic activity.

Table 3 presents our results with different levels of fixed effects for the following regression model:
ΔDeposits_i,t = β₀ + β₁Stringency_i,t + β₂ΔGovDebt_i,t + β₃E[U]_i,t + ω_t + δ_i + ϵ_i,t
where ΔDeposits_i,t refers refers to the monthly growth rate of in the household deposit share in country i at time t, Stringency_i,t refers to the monthly average of the government mobility restrictions index from OxCGRT in country i at time t, ΔGovDebt_i,t refers to the level of government debt, E[U]_i,t refers to employment expectations of households at time t in country i, ω_t capture monthly time fixed effects, δ_i captures country fixed effects and ϵ_i,t refers to the error term, which we cluster at the country-level.