The heterogeneous effects of household debt relief.
Date: 2025-03-04
Institutions: European Central Bank
Subjects: consumption, coronavirus disease, crisis management, debt reduction, financial stability, mortgage

Working Paper Series 

Manuel Adelino, Miguel A. Ferreira, 
Miguel Oliveira 

The heterogeneous effects of 
household debt relief 

No 3034 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

ABSTRACT

Large-scale debt forbearance is a key policy tool during crises, yet targeting is
challenging due to information asymmetries. Using transaction-level data from a
Portuguese bank during COVID-19, we find that financially fragile households are
more likely to enter forbearance, irrespective of income shocks. Mortgage payment
suspension increases consumption and savings, but effects differ across households.
Low liquid wealth and income are associated with a higher marginal propensity to
consume. Additionally, ineligible households accessing forbearance show a higher
propensity to consume than eligible ones. Our results suggest that observable
household characteristics can help in the design of effective debt relief policies.

JEL classification: E21, E62, G28, G50, H31
Keywords: Debt forbearance, Mortgages, Consumption, Income, COVID-19
pandemic

ECB Working Paper Series No 30341Non-Technical Summary

The COVID-19 pandemic brought unprecedented financial challenges to households

worldwide, prompting governments to implement swift policy interventions to prevent

widespread financial distress. One such measure was debt forbearance, which allowed

borrowers to temporarily pause mortgage payments. This policy provided immediate

liquidity to households, mitigating the risk of defaults and stabilizing financial markets.

In general, debt forbearance programs targeted households experiencing significant

income reductions or job losses due to the pandemic. However, the enforcement of

eligibility criteria was notably lenient, allowing many ineligible households to benefit

according to program rules.

This study leverages transaction-level data from a major Portuguese bank to

investigate the effects of debt forbearance on household behavior during the pandemic.

The analysis focuses on understanding the characteristics of the households that

accessed the program and evaluating how the temporary suspension of mortgage

payments impacted their consumption behavior. The findings provide crucial insights

into the program’s effectiveness and the broader implications for policy design.

Households who opted for forbearance were generally more financially fragile than

those who did not. They had lower incomes, fewer savings, and higher debt burdens even

before the pandemic. Interestingly, while the program was designed to support households

experiencing pandemic-related financial shocks, many eligible households chose not to

participate, while a substantial number of ineligible households did access the program.

This outcome highlights the challenges of implementing broad relief measures, where

lenient enforcement can lead to resource allocation beyond the intended targets.

Debt forbearance provided significant short-term relief, allowing households to

increase consumption. On average, households spent 15 cents per euro of deferred

mortgage payments in the first year, with spending rising to 22 cents in the long term.

Consumption responses were particularly pronounced among households with lower

liquid wealth and lower income, who spent a greater share of their deferred payments, at

approximately 30 cents per euro of postponed payment after entering forbearance. This

ECB Working Paper Series No 30342heterogeneity highlights the varying financial needs and behaviors across household

groups, indicating that a one-size-fits-all approach may not be optimal.

The program also reduced unsecured debt, such as credit card balances, with

reductions of 4 cents per euro of deferred mortgage payments. Thus, the liquidity

provided by forbearance was primarily for consumption and savings, rather than to pay

off debt. Despite these benefits, households struggled to fully adjust their spending

behavior after forbearance. Consumption remained elevated even as payments resumed,

raising concerns about the possible debt burden for participants in the long term.

A notable finding is

the significant consumption response among ineligible

households that accessed the program. On average, these households spent 18 cents per

euro deferred, compared to just 7 cents among eligible households in forbearance. This

difference suggests that the less stringent eligibility criteria allowed financially fragile

households, who could otherwise have been excluded, to benefit from the program.

However, this also diluted the program’s focus on households experiencing acute

pandemic-related financial shocks. Eligible households that did not take up forbearance

faced greater consumption reductions, highlighting the potential costs of

forgoing

participation in such relief measures.

The study concludes that while debt forbearance programs provided essential

support during the COVID-19 crisis,

they also raise important questions

for

policymakers. Designing effective relief measures requires balancing accessibility with

targeting to ensure that resources reach those most in need. Observable characteristics

such as household income, wealth, and debt-to-income ratios can play a crucial role in

identifying households that are likely to benefit from such interventions.

In addition,

understanding the heterogeneous responses of households to financial relief can help

tailor programs to address diverse needs effectively.

As governments consider future policy responses to crises, this paper underscores the

importance of integrating lessons from past interventions. Programs like debt forbearance

must be designed to address both immediate financial distress and support long-term

economic stability. Ensuring that relief measures are inclusive and targeted can help

achieve these objectives, maximizing the benefits for households and the broader economy.

ECB Working Paper Series No 303431

Introduction

Large-scale debt relief

for distressed borrowers is often hampered by information

frictions between lenders and borrowers (Adelino, Gerardi, and Willen (2013), Eberly

and Krishnamurthy (2014)),

institutional frictions such as securitization (Piskorski,

Seru, and Vig (2010), Agarwal, Amromin, Ben-David, Chomsisengphet, and Evanoff

(2011), Kruger

(2018)) and financial and organizational

constraints

faced by

intermediaries (Aiello (2022)). Thus, designing debt relief programs requires making

trade-offs between a slower and document-intensive approach that targets only “truly”

distressed households (that minimizes type I error) versus a quick-to-implement and

catch-all approach that reaches most households (that minimizes type II error). During

the Great Recession, the U.S. government chose the former approach. Despite the

implementation of

the Home Affordable Modification Program (HAMP), which

incentivized financial intermediaries to modify delinquent mortgages, up to two-thirds of

heavily indebted households received no debt relief (Noel (2021)). The failure to assist

more households contributed to employment losses and the slow economic recovery after

the crisis (Dynan, Mian, and Pence (2012), Mian and Sufi (2014), Piskorski and Seru

(2021)). Two key questions emerge from this literature: first, to what extent the

information accessible to lenders effectively characterizes the households that opt into

assistance measures, and second, how these households use the additional liquidity they

receive.

During the initial months of the COVID-19 pandemic, unlike the 2007-2009

foreclosure crisis,

governments worldwide swiftly implemented debt

forbearance

programs,

temporarily suspending debt payments to prevent widespread financial

distress and defaults. In Portugal, eligibility for mortgage payment suspensions required

households to be employed in sectors directly affected by lockdowns or to have

experienced an income reduction of at least 20% compared to pre-pandemic levels.

However, the enforcement of these eligibility criteria was typically lax, with limited

ECB Working Paper Series No 30344verification of households’ compliance. The focus on households facing pandemic-related

financial hardship and the practice of allowing easy access to the program was also

adopted by other European countries and the United States.1

This paper uses a transaction-level panel from a leading Portuguese bank to examine

the determinants of selection into forbearance and the effects of debt payment

suspension implemented between March 2020 and September 2021. The analysis focuses

on the impact of forbearance on household consumption, savings, and unsecured debt.

We measure the effect of the additional liquidity provided by forbearance by relying on:

(1) the high frequency of the data on checking and savings accounts, as well as credit

and debit card activity, which allows us to identify sharp changes in behavior around the

start of forbearance; (2) the ability to control for both levels and changes in income, the

main unobserved variables in similar studies, and performing the comparisons within

time-varying income-by-wealth household groups; and (3) the validation that household

consumption, saving, and unsecured debt were following parallel trends before the

forbearance.

In our analysis, we are interested not only in the changes in household behavior due

to the program but also in the selection of households into forbearance (whether they

met the formal criteria or not) and potential heterogeneous treatment effects. Thus,

our estimated average effects should be interpreted as the effect of forbearance on the

set of households who choose to suspend debt payments relative to otherwise similar

households who choose not to suspend payments. To the best of our knowledge, this is

the first study to use household-level data on income, consumption, and balance sheet

to explore who accesses large-scale forbearance programs and to measure the marginal

propensity to consume (MPC) and save out of liquidity, two key inputs into the design of

such programs.

1The U.S. CARES Act was targeted at borrowers experiencing “financial hardship,” but the program
did not require any proof or documentation of hardship (National Credit Union Administration (2020)).
The programs were similar across countries in the European Union (see, e.g., European Banking Authority
(2020)).

ECB Working Paper Series No 30345We show that, on average, households who entered forbearance were more financially

fragile than those who did not, as they had lower income, lower wealth, and higher debt

burden even before the pandemic, consistent with the experience in the United States

(Cherry, Jiang, Matvos, Piskorski, and Seru (2021), Gerardi, Lambie-Hanson, and Willen

(2022)). They also suffered a larger wage drop in March and April of 2020, although

total income was less affected than wages as these households received larger government

transfers.

Households in forbearance increased their consumption starting in the summer of 2020

compared to the pre-pandemic period by utilizing a significant fraction of their postponed

debt payments. We estimate an MPC out of postponed debt payments of about 15 cents

per euro during the first year after payment suspension, rising to about 22 cents in the

long term (i.e., more than one year post-suspension). These estimates align closely with

those reported in Johnson, Parker, and Souleles (2006) and Parker, Souleles, Johnson,

and McClelland (2013) regarding the effects of temporary income shocks, such as federal

income tax rebates and the 2008 economic stimulus payments, respectively. This similarity

is notable given the distinct nature and duration of the shocks. Unlike the one-time nature

of tax rebates, the forbearance program provides households with additional liquidity over

an extended period of 18 months. Moreover, it does not constitute a net transfer, as no

principal is forgiven, and missed interest payments are capitalized into the loan balance.

We include the change in income, relative to the 2019 average, as a control variable in

our regression analyses. Our findings indicate that the MPC from liquidity provided via

forbearance is of similar magnitude to the MPC observed in response to income changes

for the households in our sample. Specifically, the sensitivity of consumption to income

changes is approximately 0.08 euros per euro of income on average, increasing to around

0.13 euros per euro for households receiving forbearance.

Households in forbearance also increased their saving in deposits (checking and

savings accounts) by about 35 cents per euro in forbearance shortly after the program’s

initiation, stabilizing at about 23 cents in the long term. Mortgage payment suspension

ECB Working Paper Series No 30346also resulted in higher consumption and saving rates (relative to income) among

households in forbearance compared to those outside forbearance.

Specifically,

forbearance is associated with a 6 percentage point increase in the consumption rate and

a 5 percentage point increase in the saving rate.

The impact on unsecured debt was modest compared to consumption and saving.

Households reduced their unsecured debt (i.e., overdrafts and credit card borrowing) by

about 4 cents per euro in forbearance shortly after entering the program, while debt at

other banks decreased by 4 cents only more than one year after the start of the program.

These average estimates overlook substantial heterogeneity based on liquid wealth

(as proxied by total deposits),

income, and indebtedness (as proxied by the debt

payment-to-income ratio).

Households with below-median wealth increased their

consumption by about 30 cents per euro of postponed payment after entering

forbearance. In contrast, households with above-median wealth at the bank had a much

smaller increase in spending of about 7 cents per euro. While this may seem an intuitive

result, previous work has not always found a strong cross-sectional spread in MPC

estimates based on the level of assets (e.g., Parker, Souleles, Johnson, and McClelland

(2013)). Our finding of substantial heterogeneity in MPCs aligns with recent studies

using high-quality data on liquid assets. Notably, Ganong, Jones, Noel, Greig, Farrell,

and Wheat (2023), who examine consumption responses to income shocks using data

from the JPMorgan Chase Institute, and Fagereng, Holm, and Natvik (2021), who use

tax return data from the Norwegian Tax Administration. Although both studies focus

on income shocks rather than changes in available liquidity, as is the case in our analysis,

the observed heterogeneity in MPCs remains consistent across these different settings. A

similar pattern emerges when households are categorized by median income or

indebtedness.2 Additionally, we find that public sector employees who face less income

2Although, once again, we focus on changes in liquidity rather than income, our finding of
heterogeneous effects by income is consistent with Baker, Farrokhnia, Meyer, Pagel, and Yannelis (2020)
for the effects of the COVID-19 stimulus package by income level, as well as previous work using temporary
shocks to household income (e.g., Hall and Mishkin (1982), Johnson, Parker, and Souleles (2006), and
Di Maggio, Kermani, Keys, Piskorski, Ramcharan, Seru, and Yao (2017)).

ECB Working Paper Series No 30347uncertainty exhibit a higher MPC (25 cents) than private sector employees (11 cents).

Saving shows

the reverse pattern from consumption in the cross-section of

households. While the below-median wealth group only saved about 10 cents per dollar

out of postponed repayment, the above-median wealth group saved as much as 40 cents

after the start of the forbearance. We find qualitatively similar results when we split

households by median income or DTI ratio. Overall, our heterogeneous consumption

and saving responses raise questions about the need for a mortgage moratorium for less

fragile groups that opted for forbearance.

The design of the forbearance program, with its specific eligibility criteria, combined

with the richness of our data, allows us to measure the responses of both targeted

households and non-targeted households that nevertheless gained access.3 We construct

individual proxies for eligibility based on wage change and sector of employment, the

two primary criteria for the program. Our estimates indicate that around 10% of

households in our sample were eligible; however, actual enrollment diverged significantly

from these eligibility criteria. Notably, most eligible households opted not to participate

in the program (90%), while 80% of households that did suspend payments do not

appear to be formally eligible based on our proxies (we refer to this group as “ineligible”

below). This confirms that banks employed loose screening criteria, favoring households

proactively seeking forbearance. Consequently, the program missed many intended

targets but reached a broader range of households not originally designated as recipients.

Interestingly, ineligible households that entered forbearance were, on average, the

most financially fragile group before the pandemic, even though their income was not

disproportionately affected by the pandemic. This indicates that, although the program

was intended for households experiencing significant shocks during the pandemic, the

3Previous studies have considered the characteristics of borrowers accessing forbearance during the
COVID pandemic, including differences in credit scores, race, and income levels, as well as the effects on
delinquencies and debt usage (see Yannelis and Amato (2023) for a survey). Ganong and Noel (2020)
study the consumption response to mortgage modifications following HAMP using end-of-month credit
card balances and payments to calculate monthly expenditures. Albuquerque and Varadi (2024) consider
the consumption response around the U.K. mortgage moratoria during the pandemic using data from an
online personal budgeting application.

ECB Working Paper Series No 30348actual participants were more likely to be those with pre-existing low income,

low

wealth, or high debt burden. In fact, we show that the level of deposits at the bank is a

stronger predictor of forbearance status than the wage drop due to the pandemic. These

ineligible households also exhibit a higher likelihood of job loss one year into the

pandemic, suggesting they may have sought forbearance as a precaution against

anticipated future economic shocks.

When we break down the average consumption effect by eligibility status, we find

that ineligible households who opt for forbearance exhibit significantly higher marginal

propensities to consume, averaging about 18 cents per euro of postponed payment,

compared to just 7 cents for eligible households in forbearance, even controlling for

time-varying income-by-wealth fixed effects. This 11-cent difference between the two

groups indicates that the ease of access and the inclusion of ineligible borrowers in the

forbearance program substantially influenced the

estimated average

effect

of

forbearance. Additionally, eligible households that did not take up forbearance and

continued to make their mortgage payments reduced consumption by about 14 cents

compared to ineligible households that did not take up the program. This suggests that

choosing not to enter the program required significant adjustments for these households.

The pattern for saving in bank deposits is reversed. Eligible households in forbearance

increased their saving by approximately 50 cents per euro of postponed payment, while

ineligible households increased their saving by only 28 cents.

Finally, we examine the outcomes for households exiting the forbearance program and

the potential effects of additional debt relief provided by banks in September 2021 at

the direction of the regulator. Our findings indicate that households do not fully revert

the increased consumption observed during forbearance. Specifically, consumption per

euro of postponed payment declines from 23 cents to 13 cents once payments resume.

This consumption response is accompanied by a reduction in saving of similar magnitude

once payments resume.

It is surprising that households do not immediately adjust to

the resumption of payments, especially since much of the consumption response during

ECB Working Paper Series No 30349forbearance occurs in categories that are typically considered “adjustable” (Chetty and

Szeidl (2007)).

A small fraction of households that had entered forbearance in the first half of 2020

opted to take up additional relief measures offered by the bank in September 2021. This

additional relief

involved reduced or suspended payments,

including loan maturity

extensions,

interest

rate reductions, or

further payment

suspensions,

targeting

households that had been in forbearance over the previous 18 months and might still

require support. Like the government moratoria, the bank was explicitly instructed to

provide relief to any household that might need it, rather than limiting assistance to the

most distressed borrowers. This approach involved proactively contacting all borrowers

in forbearance to assess their need for further relief. During the initial program period,

households that took up these additional measures exhibited a somewhat lower

consumption sensitivity to forbearance and allocated more towards saving from

postponed payments. This behavior persisted even after the implementation of the

additional relief measures.

Overall, our findings provide new insights into the design of large-scale debt relief

programs. Optimal program design should account for the behavior of

ineligible

households that strategically enter the program and how they allocate the additional

resources between consumption and saving.

Our results indicate that ineligible

households substantially increase consumption and may face a heightened debt burden

after exiting forbearance due to postponed principal and interest payments without a

corresponding increase in savings. To the extent that debt relief policies are also aimed

at stimulating demand during times of crisis, understanding the MPC of those who opt

into forbearance is also important for policy design. We also show that consumption

does not fully adjust downwards after exiting forbearance. This indicates that the

program may inadvertently lead to more indebtedness among households that were not

the intended targets. Our findings suggest that observable household characteristics,

which are typically available to lenders (and not just unobservables, as earlier literature

ECB Working Paper Series No 303410might have implied), can effectively identify households more likely to opt for

forbearance when access criteria are less stringent, as well as predict how they are likely

to use the additional liquidity.

Our paper adds to the literature on the effects of government and private debt relief

programs. This literature focuses on information and institutional frictions, the impact

of loan modifications on delinquency and consumption, and optimal policy design.

In

research that is directly relevant for understanding the optimality of short-term

forbearance programs and the selection into these programs, Eberly and Krishnamurthy

(2014) develop a framework for assessing and designing efficient mortgage modification

programs. They show that a program with temporary payment reduction during a crisis

is

a

cheaper

alternative

than

principal

forgiveness when

borrowers

are

liquidity-constrained.4 At the same time,

lenders may find it optimal to perform

principal reductions to reduce the incentive for borrowers to default. The most often

cited concern about providing blanket debt relief to households is strategic behavior,

i.e., that “too many” households will request help, even though most do not need

assistance to remain current on their debts. Recent work shows that borrower default is

generally not consistent with pure strategic behavior,

i.e., borrowers do not default

purely due to negative equity (Guiso, Sapienza, and Zingales (2013), Gerardi,

Herkenhoff, Ohanian, and Willen (2018) and Ganong and Noel (2023)).5

The consumption and delinquency effects of the policies during the Great Recession

in the post-2008 period are already well documented (see, among many others, Agarwal,

Amromin, Ben-David, Chomsisengphet, Piskorski, and Seru (2017), Ganong and Noel

(2020), Abel and Fuster (2021), Agarwal, Amromin, Chomsisengphet, Landvoigt,

Piskorski, Seru, and Yao (2023)). For work on debt relief during the COVID-19

4Using a randomized trial that compares commonly employed debt relief measures, Aydin (2021) finds

that forbearance is more effective when applied to constrained households or late-cycle delinquencies.

5A notable exception is Mayer, Morrison, Piskorski, and Gupta (2014) who find that borrower
delinquency rates increase when Countrywide is forced by court decision to offer more generous
modification terms. Recent experimental work looking at forbearance programs in India by Fiorin, Hall,
and Kanz (2023) suggests that the effect on moral hazard may be muted, and that borrowers are more
likely to interact with the bank in the future if they are offered forbearance.

ECB Working Paper Series No 303411pandemic, Cherry,

Jiang, Matvos, Piskorski,

and Seru (2021) and Gerardi,

Lambie-Hanson, and Willen (2022) show that public and private forbearance programs

contributed to low delinquencies in the United States. Hong and Lucas (2023) show that

the credit policies implemented during the pandemic were an important source of

incremental resources for households, in addition to governments’ fiscal response, and

Lee and Maghzian (2023) link forbearance to better macroeconomic outcomes. The

reduction in delinquency rates was higher among low-income and minority individuals

(Gerardi, Lambie-Hanson, Willen, et al. (2021), An, Cordell, Geng, and Lee (2022), Shi

(2022)), but financial intermediary frictions may have prevented some borrowers from

receiving forbearance (Cherry, Jiang, Matvos, Piskorski, and Seru (2022), Kim, Lee,

Scharlemann, and Vickery (2024)).

Our paper is also related to the literature on the consumption response to the

COVID-19 pandemic. This literature focuses on the effects of (one-time or repeated)

transfers rather than debt forbearance. Baker, Farrokhnia, Meyer, Pagel, and Yannelis

(2020) studies the consumption response of households at different income levels and

with different shocks to income around the onset of the pandemic and as a function of

shelter-in-place orders. Ganong, Greig, Noel, Sullivan, and Vavra (2024) show that

unemployment benefits introduced at the height of the crisis had a large impact on

spending but a small impact on employment, and Cox, Ganong, Noel, Vavra, Wong,

Farrell, Greig, and Deadman (2020) investigate the heterogeneity across households and

consumption categories in the initial phase of the pandemic.6

Our paper provides new insights into the dynamics of household consumption, saving,

and unsecured debt around large-scale debt relief programs. Understanding the selection

patterns for debt forbearance and the heterogeneous effects of policy tools on different

6Intermediary frictions shaped the implementation of the CARES Act-driven debt relief during the
pandemic. Cherry, Jiang, Matvos, Piskorski, and Seru (2021) and Kim, Lee, Scharlemann, and Vickery
(2024) show that shadow banks provided mortgage forbearance at lower rates than banks, while Cherry,
Jiang, Matvos, Piskorski, and Seru (2022) show higher forbearance provision among better-capitalized
shadow banks. Research on other pandemic relief programs also finds variation in outcomes across
financial intermediaries (e.g., Granja, Makridis, Yannelis, and Zwick (2022)).

ECB Working Paper Series No 303412household groups is crucial for effectively designing financial stability policies that operate

through the household balance sheet channel.

2

Institutional Details

At the outbreak of the COVID-19 pandemic, governments and financial institutions

worldwide issued legislative and non-legislative moratoria on loan payments, targeting

households and non-financial corporations.7 By the end of March 2020, just a week after

the State of Emergency was declared and a national lockdown imposed, the Portuguese

government mandated a debt forbearance program, suspending principal and interest

payments for certain types of loans upon eligible borrowers’ applications.8 By then, the

measure’s

scope was

restrictive,

only including mortgage

loans

for acquiring

owner-occupied properties. As the loan maturity date was deferred according to the

duration of the forbearance, banks would bear the potential cost of the policy.

Access to the initial government program was limited to individuals: (1) infected

with COVID-19 or providing assistance to a relative infected with COVID-19 (which

represented a very small fraction of the individuals in the population during the early

months of the pandemic);9 (2) working in companies that reduced work hours due to the

pandemic and requested paycheck assistance (the “layoff” regime); (3) unemployed; (4)

eligible for financial support for self-employed; or (5) individuals working in industries

more affected by the COVID-19 lockdowns as defined in the government legislation. If

individuals satisfied one of these criteria, they could request a suspension of loan

payments for six months until September 2020. Moreover, the eligibility criteria

restricted forbearance to individuals who were not delinquent at the time (defined as

those not having payments 90 days past due) nor had outstanding tax or social security

7Figure IA.1 in the Internet Appendix provides an overview of the main events related to the

Portuguese government’s response to the pandemic, highlighting the debt forbearance program.

8Borrowers would restart making higher payments at the end of moratoria due to unpaid interest.

The number of payments would not change, so the loan maturity was effectively extended.

9About 42,000 cases in a population of about 10 million by the end of June of 2020,

https://coronavirus.jhu.edu/region/portugal).

ECB Working Paper Series No 303413liabilities. The program received widespread coverage across television and newspapers,

reducing the likelihood that mortgage holders were unaware of the availability of

assistance.

By April 2020, an interbank agreement led to a complementary and non-legislative

moratorium, expanding the set of loans eligible for forbearance by including other

mortgage loans, personal, and auto loans. The government also soon broadened the

legislative moratoria, and by mid-June, the measure was extended to all individuals

experiencing, or expecting, a 20% reduction in income due to the pandemic (the exact

timing or definition of income was not clear in the legislation). At the same time,

changes were made to the legislative program in order to include all types of mortgages

for residential property and student loans. As a result of these measures,

loans in

forbearance as a percentage of the total number of loans increased from around 13% in

April to 18% in June and then stabilized until 2021. In addition, the suspension of loan

payments was extended until the end of March 2021, which would be extended until

September 2021, or 18 months after the forbearance was first implemented.

Portugal was among the top three countries in Europe with the highest share of

mortgages on repayment moratoria. According to the European Banking Authority

(EBA), e365 billion in household loans (e268 billion of which were mortgages) entered

moratoria in the Euro area by June 2020, about 7% of household loans (Nicolaou

(2020)). In Portugal, e17 billion in mortgages, representing about 18% of all mortgages,

were under repayment moratoria by June 2020. The U.S. Government Accountability

Office reports that the use of forbearance peaked in the United States in May 2020 at

about 7% of single-family mortgages (about 3.4 million) and gradually declined to about

5% percent by February 2021 (Pendleton (2021)).

During 2021, concerns over households’ ability to resume payments led to new

regulatory guidelines on the prevention and management of arrears, demanding a more

proactive role for banks. In addition to closely monitoring borrowers, the local regulator

asked banks to offer additional assistance measures after September 2021 to individuals

ECB Working Paper Series No 303414at risk of defaulting. The exact nature of such measures was left at the banks’ discretion

and could include loan maturity extensions, interest rate reductions, or additional loan

payment suspension. As we will show below, despite the bank’s active effort to make the

additional assistance measures known to borrowers, only a small fraction of them took

advantage of this possibility.

3 Data

Our data comprises account-level transactions provided by a leading Portuguese bank.

We restrict our analysis to clients who have an outstanding mortgage with the bank.

We then group clients with a joint mortgage and who share checking accounts to define

a household. In addition, in order to identify households using this particular bank as

their primary bank, we focus on households who simultaneously satisfy the following

criteria: (1) at least one member of the household has direct deposit of wages, pensions,

or social security benefits (e.g., unemployment insurance);10 and (2) at least one member

of the household regularly uses debit and credit cards held at the bank for purchases and

payments (an average of at least ten transactions per month).11 The final sample includes

about 137,363 households between January 2018 and June 2022.

Our data include purchases and payments with debit or credit cards, cash withdrawals,

and electronic transactions from checking accounts at the transaction level. Given that

our sample is composed of households with direct deposit of wages, pensions, and other

social security benefits, we are able to estimate monthly household income using checking

account transfers. Thus, we can track income even if individuals change jobs or become

unemployed. We complement the data with third-party transfers, which include incoming

transfers such as within-household transfers from other banks, tax refunds, or rental

income. We can also identify the company where wage earners work.12

10Households are offered a reduction in the mortgage spread if they choose to have wages and pensions

deposited directly at the bank.

11On average, households in our sample made 39 monthly transactions, and the median is 35.
12Out of the 137,363 households, we find a valid employer match for about 100,000 of them. The

ECB Working Paper Series No 303415Our measure of consumption includes any purchases or payments using debit or

credit cards using data from point-of-sale transactions and cash withdrawals.13 We

complement the data with automatic payments of utilities and other services. Our

transaction-level data bypasses the concerns of using annual household wealth snapshots

to calculate imputed consumption discussed in Baker, Kueng, Meyer, and Pagel (2022).

Although we have an almost complete picture of the activity at the bank, we do not

observe outbound transfers to other banks or their intended recipients. This could

represent additional saving (if a transfer goes to accounts at other banks owned by the

household itself) or additional consumption (purchases of goods and services paid for by

bank transfer rather than by card, cash, or automatic withdrawal). We are able to

classify purchases by category starting in January 2020. We categorize transactions by

relying on point-of-sale terminal information, namely the reported Merchant Category

Code (MCC). This classifies merchants into categories based on the type of business and

the reported industry code according to Classifica¸c˜ao das Actividades Econ´omicas

(CAE) Revision 3.

The household balance sheet data include end-of-the-month balances for all checking

and savings accounts held at the bank, as well as balances for all liabilities, including

mortgages, personal loans, auto loans, credit cards, and overdrafts.14 The data also

include additional

liabilities information, such as interest rate (as of August 2021),

origination date, maturity, and monthly installment before the pandemic.

We merge the internal information of the bank with data from the Credit Register

remaining households include about 20,000 non-employed households (i.e., unemployed and retired), and
about 17,000 employed individuals with an unmatched employer. To achieve this, we consider the name
of the entity ordering the Single Euro Payments Area (SEPA) transfer and then use the Levenshtein
Distance string metric to match the employer with the universe of firms operating in Portugal. Firm
names and industry codes are drawn from SABI (Iberian Balance Sheet Analysis System).

13Including cash withdrawals is crucial for measuring consumption accurately, as a significant fraction
of retail transactions in Portugal (and across Europe) are still done in cash during this period. According
to the 2022 study on the payment attitudes of consumers in the euro area (SPACE) conducted by the
European Central Bank, 64% of in-person retail transactions are done in cash in Portugal, as opposed to
31% by card and 5% by other means.

14We are unable to compute account balances for financial assets (e.g., individual stocks, bonds, and
mutual funds) due to data limitations. However, we see that about 12% of our sample households hold
financial assets.

ECB Working Paper Series No 303416(Central de Responsabilidades de Cr´edito) managed by Bank of Portugal to obtain

outstanding loans at other banks for each household. By matching these databases, we

can fully track the liability side of the household balance sheet over the sample period

and delinquency. While we have daily information on loan-level delinquency for all

contracts held with this particular bank, we can only observe end-of-the-month overdue

debt in other banks using the Credit Register.

Our data allow us to determine which households applied for and received forbearance

and which did not. In addition, we use our data to infer whether a household was eligible to

obtain forbearance according to program rules. Specifically, we consider that a household

is formally eligible for forbearance if any household member: (1) suffered an income drop

of at least 20%, as proxied by the change in wages from the first quarter of 2020 (i.e., the

pre-pandemic period) to the second quarter of 2020 (i.e., the start of the pandemic); (2)

was working in more affected industries during the first quarter of 2020 as given by the

list of industries included in the program rules.15

3.1 Summary Statistics

Table 1 presents pre-pandemic (as of December 2019) averages of variables for our

sample of households, separately for households in and outside forbearance and eligible

and ineligible households. This allows us to examine whether selection on observable

characteristics plays an important role in applications for the government forbearance

program at the beginning of the pandemic. Table IA.1 in the Internet Appendix

presents detailed summary statistics for our full sample. Households in our sample

comprised 1.7 mortgagors on average, with negligible differences between those who got

forbearance and those who did not. The average household monthly total income in our

full sample is e2, 527, which is significantly higher than the average in the country,

15The list considers a broad industry definition, which we then match to the Portuguese industry

classification code list (CAE, Revision 3).

ECB Working Paper Series No 303417which in 2019 amounted to e1, 800.16 The 90th percentile of total income is e4, 659 per

month.17

The average wage is higher for households outside of forbearance, particularly in the

case of eligible households. Total income, including pensions (for 40,000 of the 137,363

households in the sample), social security benefits, and other inbound transfers (i.e., rents,

business or professional income) follow a similar pattern. For instance, the average total

income for eligible households outside forbearance is substantially higher at e2, 759 per

month than that of eligible households in forbearance at e2, 449. In addition, ineligible

households in forbearance are the group with the lowest average wage and total income

at e1, 416 and e2, 046, respectively.

Average household consumption is about e1, 500 per month (from 39 monthly

transactions per household, including cash withdrawals, on average). This compares to

an average consumption expenditure per household of about e1, 560 in the whole

country.18 Average household consumption follows a similar pattern to income across

groups of households, with households outside forbearance exhibiting higher spending

levels.

In addition, ineligible households in forbearance are the group with the lowest

average consumption at e1, 320 per month.

Households, on average, maintain checking account balances of e6,700 and savings

account balances of e17,300, conditional on having a savings account. However, median

balances are substantially lower, at e2,000 for checking accounts and e5,700 for savings

accounts, respectively (see Table IA.1). Importantly, households in forbearance exhibit

significantly lower average balances in both checking and savings accounts, which aligns

with the observation that these households are generally more financially fragile,

16Annual mean net income per household (e) by Deciles of income; INE - Instituto Nacional de
Estat´ıstica, Statistics on Income and Living Conditions (Inqu´erito `as Condi¸c˜oes de Vida e Rendimento).
17The group of homeowners with mortgage comprised around 30% of all Portuguese households in
2021 (INE, Population and Housing Census (Recenseamento da popula¸c˜ao e habita¸c˜ao), 2021), with
its median income being substantially higher (at least 25%) than the remaining households, per adult
equivalent (Xerez, Pereira, and Cardoso, 2019).

18Estimate for 2015, excluding actual or imputed rentals for housing (Peralta, Carvalho, and Esteves,

2021).

ECB Working Paper Series No 303418irrespective of their eligibility status. Figure IA.2 of the Internet Appendix shows that

total deposits are a particularly strong predictor of forbearance access.

Indeed, in a

penalized regression setting (LASSO), total deposits are more important in predicting

forbearance access than changes in wages, even though the government eligibility

criteria primarily focus on changes in wages and employer industry.

Table IA.2 of the Internet Appendix examines whether future outcomes differ

significantly between eligible and ineligible households. The findings indicate that

ineligible households in forbearance are the most likely to lose their job within the 12

months following the start of forbearance. Furthermore, among those who remain

employed,

ineligible households exhibit

lower wage growth compared to eligible

households. These results reinforce the notion that ineligible households in forbearance

represent a particularly fragile group of the population.

Mortgage balances are, on average, e69,000, and households in forbearance have higher

average balances. Almost all clients have a credit card or an overdraft, holding an average

balance of about e420.

In contrast, only about 1% of households in our sample hold

student or auto loans, and 7% hold other types of loans, such as personal loans. Finally,

most households in our sample have loans with other banks, with a balance of about

e7,500 on average.

The average total loan payment (mostly mortgage payments), including principal

and interest, is e315 per month, higher than the country’s average by the end of 2019

(e248).19 Moreover, households in forbearance have higher loan payment commitments

(e377) than households outside forbearance (e311). By entering forbearance, the

average household postpones e345 per month. Considering total income, we estimate an

average debt payment-to-income (DTI) ratio of about 19% in 2019, slightly above the

country’s average in 2022 (17%).20 As expected, the average DTI is higher for

households in forbearance,

in particular for ineligible households at 32%. Debt

19Press Release INE, Interest rates implied in housing loans, January 19, 2022.
20Banco de Portugal, Relat´orio de Estabilidade Financeira, November 2022.

ECB Working Paper Series No 303419delinquency is infrequent in our sample, with just 1% of households having payments

more than 30 days past due. We conclude that ineligible households in forbearance are

the most fragile group with lower income, consumption, total deposits, and higher DTI.

4 Empirical Methodology

We estimate a difference-in-differences regression to compare the marginal propensities

to consume, save, and borrow between households in forbearance and households outside

forbearance around the start of the debt forbearance program:

Yi,t − Y i,2019 = βF orbearance Amounti × P ostt + λXi,t + µg,t + εi,t,

(1)

where the outcome variable is either the change in monthly consumption expenditures

(∆Consumption); change in saving in total deposits (∆Saving in Deposits); change in

credit card and overdraft borrowing (∆Borrowing Credit Card & Overdraf t); and

change in unsecured debt borrowing at other banks (∆Borrowing Other Bank′s Debt)

for household i at time t. Changes are calculated relative to each variable’s 2019

average. Saving is the change in end-of-month checking and savings account balance

(∆T otal Deposits). Borrowing is the change in end-month credit card and overdraft

balance or unsecured debt outstanding at other banks.

F orbearance Amounti is the amount of postponed debt payments (mostly mortgages,

but it may include other loans for some households) for household i. P ostt is a dummy

variable that takes a value of one for the period starting when mortgage payments are

suspended and zero otherwise.21 The coefficient of interest is β, which captures whether

forbearance is associated with a differential effect on consumption, saving or borrowing

around the program’s start. The sample period is between January 2018 and September

2021 (i.e., the end of the government program).

21We drop the household subscript from P ostt for expositional purposes. The P ostt dummy variable
is household-specific since households entered forbearance between April and June 2020. However, most
households in our sample start forbearance in April 2020 (65%), plus 28% in May and just 7% in June.

ECB Working Paper Series No 303420We also estimate the effect at different horizons by replacing the Post dummy

variable with three dummy variables: (1) Immediate Effect for months 1 through 3 after

a household enters forbearance; (2) Short-run Effect for months 4 through 12 after

forbearance; and (3) Long-run Effect for the period starting 12 months after forbearance

and up to the end of the government program.

Xi,t

is a set of household-level time-varying controls, which includes changes in

monthly total income (∆Income) relative to average monthly income in 2019, and the

F orbearancei dummy variable that takes the value of one for households receiving

forbearance, and zero otherwise. All the regressions include month-year fixed effect (µt)

to

absorb shocks

that may

affect

all households

in a

given period or

group-by-month-year fixed effects (µg,t) to absorb time-varying shocks for households by

quartile of pre-pandemic income and wealth (2019 averages). Thus, the regressions

include a total of 16 group indicators (four quartiles of income by four quartiles of total

deposits) interacted with dummy variables for each month-year. Standard errors are

clustered two-way at the household and month-year levels.

We also estimate the dynamic effect of the forbearance by replacing the P ostt variable

with time dummies, 1(period = τ ):

Yi,t − Y i,2019 =

28
(cid:88)

τ =−29

βτ × F orbearance Amounti × 1(period = τ ) + λXi,t + µg,t + εi,t. (2)

The coefficients of interest are βτ , which measure the change in consumption, saving, or

borrowing due to postponed debt payments in each month around the start of forbearance.

To estimate the average change in the consumption and saving rates, defined as

monthly spending and saving divided by average total income, we estimate the following

difference-in-differences regression:

Yi,t − Y i,2019
Incomei,2019

= αF orbearancei × P ostt + λXi,t + µg,t + εi,t,

(3)

ECB Working Paper Series No 303421where the outcome variable is either the change in monthly consumption expenditures

or the change in saving in total deposits for household i at time t, relative to the

household’s 2019 average, divided by average income in 2019. The coefficient of interest

is α, which measures the average change in the consumption or saving rate associated

with forbearance.

5 Effects of Debt Forbearance on Consumption,

Saving, and Borrowing

In this section, we first show the evolution of the average income, consumption, and total

deposits before and after the government debt forbearance program initiated in March

2020.

Figure 1 shows the evolution of average wages, social security benefits, and total

income between July 2019 and the end of the program in September 2021 for households

in forbearance and outside forbearance. We adjust for seasonality using month dummies

but do not control for any other variables.

Panel A of Figure 1 shows that households in forbearance were, on average, more

exposed to the pandemic shock, losing about e130 of monthly wages, on average, at the

onset of the pandemic, compared with about e80 for households outside forbearance.

Notice that households in forbearance had lower average wages to begin with, as shown

in Table 1.

Panel B of Figure 1 shows that the evolution of social security benefits at the onset of

the pandemic disproportionately benefited households in forbearance and that this gap

persisted until 2022. Panel C shows total income evolution, including wages and social

security transfers. The figure shows that income supplements and other government

transfers were sufficient to stabilize total income during this period.

Figure IA.3 of the Internet Appendix shows the evolution of wages, social security

benefits, and total income separately for households receiving forbearance who meet the

ECB Working Paper Series No 303422eligibility criteria and for those who do not meet. We find that the large drop in wages for

households in forbearance happens almost exclusively for eligible households, with almost

no reduction for ineligible ones. This is somewhat mechanical, as one of the criteria for

eligibility is a drop in income of at least 20%. Notably, we do not see virtually any drop

for ineligible households who choose to enter forbearance. In addition, the figure shows

that even after social security benefits, eligible individuals in forbearance still suffered a

substantial drop in income.

Figure 2 shows the evolution of the average consumption and total deposits. Panel A

shows that both groups of households cut spending right after the start of the pandemic in

March 2020, likely due to a combination of demand and supply factors due to shutdowns.

However, we find a positive and statistically significant difference in consumption for

households in forbearance relative to households outside forbearance of about e200 per

month by the summer of 2020. Interestingly, households in forbearance end up with a

significantly higher average monthly consumption even compared to pre-pandemic levels.

This is particularly noteworthy given that the total income was lower for households in

forbearance (see Table 1).22

Figure IA.4 of the Internet Appendix shows the evolution of consumption and total

deposits separately for eligible and ineligible households in forbearance. We find that

ineligible households drive the positive gap in consumption that emerges for households

in forbearance, whereas the consumption of eligible households in forbearance mostly

tracks that of those outside of forbearance.

Panel B of Figure 2 shows the evolution of the average total deposits. The

pandemic’s beginning is associated with a slower growth in total deposits of households

in forbearance (mostly driven by checking accounts). By mid-2020, and even more so by

early 2021, households in forbearance started increasing their total deposits,

i.e.,

accumulating balances in their checking and savings accounts, faster than households

22The figure shows the evolution of consumption using equal weights among households. Table IA.3
of the Internet Appendix shows that the value-weighted evolution of consumption in our sample closely
matches the evolution of consumption in Portugal for this period.

ECB Working Paper Series No 303423outside forbearance.

5.1 Response of Marginal Propensity to Consume and Save

In this section, we present the estimates of the effect of the debt forbearance program on

household consumption and saving in deposits. Table 2 shows difference-in-differences

regression estimates based on equation (1). Columns (1)-(4) show the estimates for the

effect on the marginal propensity to consume (MPC) measured as the change in

consumption per euro of postponed debt payment in each month relative to the

household’s 2019 average, and columns (5)-(8) show the estimates for the effect on the

marginal propensity to save in total deposits. The main explanatory variable is the

amount of postponed debt payments in euros (F orbearance Amounti). Columns (1) and

(5) include month-year fixed effects. Columns (2)-(4) and (6)-(8) include 16 household

group indicators using quartiles of income-by-total deposits interacted with month-year

fixed effects. In columns (3), (4), (7), and (8), we control for ∆Income. Additionally,

since households in forbearance are on average more fragile, in columns (4) and (8) we

include the ∆Income × F orbearance interaction term to control for potential differences

in the propensities to consume/save out of income.

Column (1) of Table 2 indicates that the MPC to consume out of payments in

forbearance is both positive and statistically significant, amounting to 15 cents per euro

of postponed payment. This effect translates to approximately e52 at the average

forbearance amount. Columns (2)-(4), which include household group-by-month-year

fixed effects and total income as controls, yield similar estimates, ranging from 13 to 15

cents, all of which are also significant.

Column (5) shows a positive effect on the marginal propensity to save of 12 cents per

euro of postponed payment, although this effect is statistically insignificant. In contrast,

when household group-by-month-year fixed effects are included in column (6), the

coefficient increases to 18 cents per euro and becomes statistically significant. Further,

when changes in total

income are added as a control

in columns (7) and (8), the

ECB Working Paper Series No 303424marginal propensity to save in deposits rises to about 30 cents per euro.

Changes in total income exhibit a positive and significant impact on both consumption

and total deposits. We estimate an MPC out of income changes of about 9 cents (lower

than in Ganong, Jones, Noel, Greig, Farrell, and Wheat (2023) who obtain an MPC of 21

cents out of typical income fluctuations) and a marginal propensity to save of 70 cents per

euro. In column (4), we include the ∆Income × F orbearance interaction term and find

that the combined MPC estimate is about 0.13. This effect is comparable in magnitude

to that of forbearance, indicating that households adjust their consumption in response

to increased liquidity from suspended debt payments in much the same way they respond

to changes in income. It remains unclear whether households perceive these changes in

liquidity as temporary or permanent.

Table IA.4 of the Internet Appendix presents robustness tests of the effect of

forbearance on consumption and saving in Table 2. Column (1) shows that these results

are robust to the inclusion of household-by-month fixed effects to absorb unobserved

(time invariant) household-level heterogeneity and monthly seasonality. Moreover,

columns (2) and (3) show that the estimated coefficients are robust when we include

industry-by-month and municipality-by-month fixed effects,

thus controlling for

potential unobserved industry and regional shocks.

5.2 Response at Different Horizons

Next, we examine the response to the suspension of debt payments at different horizons

after the start of forbearance. We estimate the regression in equation (1), replacing the

Post dummy variable with three dummy variables: Immediate Effect (month 1 through

month 3); Short-run Effect (month 4 through month 12); and Long-run Effect (after

month 12).

Table 3 presents these estimates, which can be directly compared to those in Table 2.

Columns (1)-(3) presents the estimates for the effect of forbearance on consumption. In

column (1), we find that the immediate effect on the MPC is close to zero and statistically

ECB Working Paper Series No 303425insignificant. However, this estimate becomes significant and ranges from 13 to 15 cents

per euro in forbearance for the short-term horizon. The long-term effect on consumption

reaches approximately 22 cents per euro. This indicates that consumption did not respond

to the amount of forbearance during the early months of the pandemic (possibly due to

supply-side constraints) but increased over time.

Columns (4)-(6) of Table 3 presents the estimates for the effect of forbearance on saving

in deposits. Controlling for observable differences is crucial in the saving regressions, as

both income and wealth levels, along with changes in income, significantly impact the

estimated saving behavior of households in forbearance (which is apparent from comparing

columns (4)-(6)). Using our most comprehensive specification in column (6), we find that

households save up to 35 cents per euro of postponed payments immediately following the

onset of forbearance. Although the saving response to forbearance decreases to 32 cents

per euro of postponed payment in the short run and 23 cents in the long run, it remains

positive and strongly significant.23

Figure 3 shows the evolution of the marginal propensity to consume (Panel A) and

save in deposits (Panel B) for households in forbearance and outside forbearance from

12 months before the start of the program (March 2020) up to 12 months after. The

figure plots the estimates of the monthly βτ coefficients obtained from the regression in

equation (2). The coefficients measure the difference between households in forbearance

and households outside forbearance relative to the month before the start of the

forbearance. Despite significant differences in the average characteristics of the two

groups, Panel A shows no evidence of preexisting differential trends in the MPC prior to

the initiation of

forbearance.

Additionally,

there is a positive and significant

consumption response shortly after the forbearance program begins.

This effect

increases over the first six months of the forbearance period and is persistent in the long

23Table IA.5 in the Internet Appendix shows that the estimates of the consumption and saving response
to forbearance are similar when we restrict the sample to individuals working in the private sector (i.e.,
we exclude public servants and other individuals working in public entities and state-owned firms). This
sample includes households likely to have been more affected by the pandemic.

ECB Working Paper Series No 303426run.

Panel B shows the response of the marginal propensity to save in deposits around

the onset of forbearance. A positive and significant saving response is clear from the

beginning of the forbearance period. The effect becomes smaller in magnitude over the

short-term and long-term horizons but is still positive and significant.

5.3 Response of Consumption and Saving Rate

Our results are robust when we use alternative definitions of the consumption and saving

response to debt forbearance. Table 4 shows the effect of forbearance on consumption

and saving rates,

i.e., change in monthly consumption expenditure and change in

monthly saving as a fraction of pre-pandemic average income. Columns (1)-(3) present

the estimates using the consumption rate. Using the most stringent specification in

column (3), we find that households in forbearance increased the consumption rate by

about 6 percentage points. As before, the effect is stronger at 9 percentage points in the

long run, which compares with an unconditional average of 76% of income allocated to

consumption before the pandemic (and about 88% for households in forbearance).

Columns (4)-(6) report the estimates using the saving rate. We find that the average

saving rate response is positive and significant. Households increase the saving rate by

about 5 percentage points over the forbearance period. When we control for changes

in income in column (6), the impact on the saving rate is similar at different horizons,

ranging from 4 (immediate effect) to 5 percentage points (short-run and long-run effects).

5.4 Response of Marginal Propensity to Borrow

In this subsection, we estimate the marginal propensity to borrow (unsecured debt) out

of

postponed

debt

payments.

Table

5

presents

the

estimates

of

the

F orbearance Amount × P ost coefficient for two additional outcome variables: changes

in credit card and overdraft borrowing at the bank in columns (1)-(3) and changes in

ECB Working Paper Series No 303427unsecured debt borrowing from other banks in columns (4)-(6). Columns (1)-(3)

indicate that households in forbearance reduced borrowing in credit card and overdraft

compared to households outside forbearance. Although the overall effect on credit card

and overdraft borrowing is statistically insignificant, we find that households during the

first quarter of forbearance (immediate effect) allocated approximately 4 cents per euro

of postponed payments to reduce short-term liabilities.

Conversely, columns (4)-(6) suggest that households in forbearance paid down

unsecured debt held at other banks, compared to those outside forbearance, but this

effect is statistically significant only in the long term. Our estimates across all

specifications indicate that households reduced borrowing at other banks by about 4

cents per euro of postponed payment. Considering that all types of unsecured loans

from other banks are included, these results are consistent with the idea that households

initially

prioritize

deleveraging

high-cost

loans,

gradually

transitioning

to

longer-maturity (cheaper) loans.

5.5 Spending Categories

Our bank transaction-level data include merchant codes for debit and credit card

transactions, which allow us to categorize most of the consumption expenditures.24

Table 6 shows the estimates of the forbearance effect on the MPC by category. Panel A

presents the estimates of the overall effect and Panel B presents the estimates by

horizon (immediate, short run and long run). The sample period is from January 2020

to September 2021 (merchant codes are unavailable in 2018 and 2019). All regressions

include household group-by-month-year fixed effects, as well as ∆Income as a control.

Column (1) shows that the estimates of the total consumption response are similar

when we use the sample for which we can categorize consumption expenditures (i.e.,

starting in January 2020). The consumption effect is positive and significant at 10 cents

24This is similar to the consumption categories in Cox, Ganong, Noel, Vavra, Wong, Farrell, Greig,

and Deadman (2020) using credit card data from a large U.S. bank.

ECB Working Paper Series No 303428per euro of postponed payment, ranging between 9 cents in the short run and 17 cents in

the long run.

Columns (2)-(10) report the estimates of the consumption sensitivity to forbearance for

each category. During the first year after the forbearance start (immediate and short-run

effects), the main drivers of the consumption response to forbearance are “Groceries” and

“Clothing”, and to a smaller extent “House Maintenance”, “Furniture”, “Health Care”

and “Entertainment and Education”, which have a positive and significant effect. Notably,

“Restaurants” initially have a negative effect, possibly due to COVID-19-related closures.

Over the long term (beyond one year after the start of the forbearance), the effects are

positive and significant across all categories. The consumption response to forbearance

is more pronounced for “Groceries” at 6 cents per euro of postponed payments, and for

“Clothing”, “Transportation”, and “Restaurants” at about 2 cents.

5.6 Heterogeneous Effects

We investigate the extent to which the marginal propensity to consume or save out of

postponed debt payments is heterogeneous across households with different levels of

financial fragility as measured by wealth, income, and indebtedness. We estimate the

F orbearance Amount × P ost interaction term coefficient using the regression in

equation (1) separately for the sample of more fragile households and the sample of less

fragile households. Specifically, we split households at the median of pre-pandemic

wealth (proxied by total deposits), income, and indebtedness (proxied by the DTI ratio)

in Panels A-C. Figure 4 reports the effects on the MPC for these groups separately,

while Figure 5 provides the corresponding estimates for the marginal propensity to save

in deposits.

Panels A-C of Figure 4 show that the consumption response is concentrated primarily

in the most fragile households across all three financial fragility proxies. In Panel A, the

low-deposits group consumption response is positive and significant at about 30 cents

per euro of postponed payments, compared to about 7 cents for the high-deposits group.

ECB Working Paper Series No 303429In Panel B, the consumption response is stronger for low-income households than for

high-income households, with similar magnitudes to those in Panel A. Panel C shows a

stronger consumption response for households with a high DTI ratio than a low DTI ratio.

The effect is almost 20 cents for households with a high DTI ratio and only 6 cents for

households with a low DTI ratio.

We also show how households with differing levels of income volatility react to

forbearance: Panel D presents estimates for households with above- and below-median

income volatility, defined as the standard deviation of total monthly income normalized

by its average in 2019. Panel E presents estimates for employees working in the public

and private sectors, based on the primary earner in the household. This analysis

addresses whether the perception of income risk and unemployment risk influences the

response to forbearance. In Panel D, 4, the consumption response is lower, at about 10

cents, for households with more volatile income prior to the pandemic, compared to

about 21 cents for those households with low volatility of income. Similarly, in Panel E,

the consumption response is about 10 cents per euro for households in the private sector,

whereas it is significantly stronger for public sector households, at about 25 cents.

Conversely, Panels A-C of Figure 5 show that the average saving response in deposits

is primarily driven by high-wealth, high-income, and low-debt burden households,

respectively. The propensity to save per euro of postponed payment by less fragile

households is about four times higher than for the more fragile households. In Panel A,

the high-deposits group’s saving response is positive and significant at almost 40 cents

per euro of postponed payments, compared to about 10 cents for the low-deposits group.

In Panel B, the saving response is stronger for high-income households than for

low-income households, with similar magnitudes to those in Panel A. Finally, Panel C

shows a stronger saving response for households with a low DTI ratio than a high DTI

ratio. The effect is approximately 40 cents greater for the low-DTI group compared to

the high-DTI group.

Panels D and E of Figure 5 show that households with more volatile earnings or

ECB Working Paper Series No 303430employed by the private sector show a higher propensity to save, although the difference

between the groups is not statistically significant. These findings suggest that anticipated

income risk and employment risk, which is notably higher for private sector employees,

play a role in shaping household responses to the debt forbearance program.

In summary, forbearance induces heterogeneous responses among households with

different levels of financial fragility and income risk. The consumption response is

concentrated among more fragile households and those facing lower income risk, whereas

the saving response is concentrated among less fragile households and those facing

higher income risk.

6 Forbearance Eligibility and Selection

Access to the debt forbearance program during the COVID-19 pandemic was relatively

lenient, unlike the stringent criteria for loan modifications that characterized the 2008-

2009 foreclosure crisis in the United States. (Adelino, Gerardi, and Willen (2013)).

We estimate the effect of forbearance on consumption and saving in deposits using

the regression in equation (1), focusing on four groups of households based on their

forbearance status and program eligibility status:

(1) ineligible households outside

forbearance (the omitted group);

(2) eligible households outside forbearance;

(3)

ineligible households in forbearance; and (4) eligible households in forbearance.

Specifically, we define a Eligible dummy variable that takes the value of one for

households eligible to the forbearance program according to program rules, and zero

otherwise (see Section 3 for details on the eligibility definition). We then interact the

Eligible dummy variable with the Forbearance and the Post dummy variables.

6.1 Number of Eligible and Ineligible Households

Table 7 (at the bottom of the table) presents the number of observations for each of the

four groups. The distribution of households across these groups confirms that access to

ECB Working Paper Series No 303431the forbearance program was relatively lenient during the pandemic, with implementation

by banks favoring broader access to forbearance.

In fact, we find that about 10% of

households were eligible for forbearance, but only about 11% [= 1.1 ÷ (9.1 + 1.1)] of those

eligible households actually entered the program (representing 1.1% of the full sample).

In contrast, 5.3% [= 4.8 ÷ (85.0 + 4.8)] of the ineligible households entered the program

(representing 4.8% of the full sample). This implies that over 80% of the households in

forbearance were ineligible, while less than 20% met the eligibility criteria. These findings

align with the lack of rigorous eligibility checks during the pandemic and the absence of

strong incentives for bank officers to screen out ineligible forbearance applications.25

As discussed in Section 3.1,

there are significant differences

in observable

characteristics between households in and out of forbearance, as well as between eligible

and ineligible households. Ineligible households in forbearance exhibit the lowest levels

of total income, consumption, and total deposits, coupled with the highest DTI ratios,

among the four groups. A LASSO regression shows that total deposits are the most

important factor in determining access to forbearance, even more so than wage

reductions, which were part of the program’s eligibility criteria, as shown in Figure IA.2

of the Internet Appendix. This indicates that ineligible households in forbearance were

already the most fragile group at the onset of the pandemic crisis

6.2 Heterogeneus Effects by Eligibility Status

Table 7 shows substantial heterogeneity in the effects of forbearance on consumption

among the four household groups. We find that eligible households outside forbearance

exhibit a significantly lower MPC after the start of the forbearance period relative to

ineligible individuals also outside of forbearance (the omitted group in these regressions,

which constitutes 85% of the sample). This difference is about -14 cents per euro of

postponed payment when we include household group-by-month-year fixed effects and

25While we recognize that our eligibility measure may not be perfect, the proportion of the sample
identified as ineligible significantly exceeds what would be expected from merely assignment errors in our
measures.

ECB Working Paper Series No 303432total income as a control, as shown in column (3). This indicates that the burden of

higher debt payments led these eligible households, who opted not to access forbearance,

to significantly reduce consumption during the pandemic.26 Eligible households in

forbearance avoided this decline in consumption, showing a positive consumption

response of 7 cents per euro in column (3); however, this response is not statistically

different from that of ineligible households outside of forbearance.

In contrast,

ineligible households that nonetheless entered forbearance exhibit a

significantly higher MPC out of postponed payments, ranging from about 17 to 18 cents

per euro of forbearance relative to the omitted group, as shown in columns (2) and (3).

These findings indicate that the significant MPC reported in Table 2 is primarily driven

by ineligible households that applied for and received forbearance.

Furthermore,

concerns about future job loss may have influenced these households’ decision to seek

forbearance, as they are more likely to experience job loss within 12 months after

entering forbearance, as shown in Table IA.2 of the Internet Appendix.

Column (6) of Table 7 shows that ineligible households in forbearance have a marginal

propensity to save in deposits of about 28 cents per euro when controlling for changes in

total income. In contrast, we find no significant relationship between the saving behavior

of eligible households outside of forbearance and the amount of postponed payments,

unlike the results observed for consumption. Additionally, forbearance is associated with

a higher marginal propensity to save among eligible households, reaching about 49 cents

per euro of forbearance in column (6).

Figure 6 shows the evolution of the marginal propensity to consume (Panel A) and

the marginal propensity to save in deposits (Panel B) for households in forbearance,

distinguishing between eligible and ineligible households (the omitted group consists of all

households outside forbearance). In Panel A, the results indicate that ineligible households

26The decision by these households to forgo the forbearance option and instead adjust their
consumption to meet mortgage payments aligns with the model presented in Campbell, Clara, and Cocco
(2021). According to that model, opting for interest-only payments during recessions results in smaller
reductions in consumption, which is consistent with our findings for borrowers who choose forbearance.

ECB Working Paper Series No 303433in forbearance are the primary contributors to the observed increase in consumption in

response to forbearance. This effect increases during the first six months of the forbearance

period and remains significant in the long term. Conversely, the consumption response

among eligible households is statistically insignificant. Panel B depicts the response of

the marginal propensity to save around the start of forbearance. From the beginning of

the forbearance period, a positive and significant saving response is evident. Both eligible

and ineligible households exhibit a similar sensitivity to forbearance.

Table IA.6 of the Internet Appendix shows the effects for eligible and ineligible groups

on changes in credit card and overdraft borrowing at the bank, as well as unsecured

debt at other banks. We do not find significant differences in the marginal propensity

to borrow, except for eligible households who do not enter forbearance with a 4% effect

on unsecured debt at other banks. The other estimates in the table are not statistically

significant.

Selection plays an important role in our setting as over 80% of the households in

forbearance in our sample are identified as ineligible based on our proxy. The observed

differences in the sensitivity of consumption (as shown in column (3)) and saving (as

shown in column (6)) between eligible and ineligible households indicate that differences

in forbearance program participation, when access is lenient, significantly influence the

overall effects of

forbearance.

Specifically,

ineligible households that entered the

forbearance program exhibited greater sensitivity in their consumption to the amount of

postponed payments compared to eligible households. These findings imply that easier

access to forbearance programs may lead households with a higher sensitivity of

consumption to deferred debt payments to opt for forbearance.27

Table 8 examines the eligibility effects separately for more and less affected

household groups according to the industry they work in. The more affected subsample

comprises households whose primary employer industry had below-median revenue

27Table IA.7 in the Internet Appendix shows the response by spending category for each group, akin

to Table 6.

ECB Working Paper Series No 303434growth between 2019 and 2020. Even though this is not how the government selected

industries for eligibility, a much larger fraction of the workers in these industries are

eligible for forbearance according to the formal criteria (almost 30% in total).28 Still,

even for this group, only 14% of eligible households entered forbearance (4.2% of the

sample of households in these industries). This implies that ineligible households make

up a smaller fraction of households in forbearance in these industries (about 50%

instead of 80% in the full sample). Column (1) shows that, within this group of more

affected industries, the differential effect is similar to the average effect in the whole

sample, with eligible households outside forbearance cutting consumption by 12 cents

per euro of postponed payment; eligible households in forbearance avoiding to do so;

and finally ineligible households in forbearance exhibit a positive and statistically

significant MPC out of postponed payments of 14 cents.

Interestingly, a different pattern emerges when focusing on industries less affected by

the pandemic (column (2)), defined as those with above-median revenue growth between

2019 and 2020, or when focusing on public servants (column (3)), who experienced no

wage changes and were ineligible under the program rules.29 In these sectors, ineligible

households constitute a larger share of those in forbearance—76% in less affected sectors

and 90% among public servants—and their behavior closely mirrors that of eligible

households. Additionally, the perception of income risk plays a significant role across all

household groups, with the impact on consumption (saving) showing a monotonic

decrease (increase) with income risk.

28We measure revenue growth until December 2020, and this was only available after March 2020, the

date of eligibility definition by the government.

29In these less affected industries, the proportion of households meeting the eligibility criteria decreases
to 14%, and to just 5% among public servants. These public servants were eligible primarily due to meeting
eligibility criteria through their secondary employer, i.e., the household’s secondary source of income.

ECB Working Paper Series No 3034357 Effect of Exit and Additional Debt Relief Measures

We examine whether the effects of the forbearance program on household behavior

persisted after the program ended in September 2021. To conduct this analysis, we

extend the sample period through June 2022 and introduce a dummy variable, Exit

Effect, that takes the value of one between September 2021 and June 2022 and zero

otherwise. The regression also includes indicators for the three different horizons

presented before (Immediate Effect, Short-run Effect, and Long-run Effect).

Table 9 presents the results. Column (1) shows that consumption is affected by the

end of the forbearance program. The Long-run Effect dummy, which is set to one

starting twelve months after the initiation of forbearance and lasting until the program’s

end in September 2021, is associated with a 22 cents increase in consumption per euro of

forbearance. However, this effect diminishes to about 13 cents after the forbearance

period ends, as indicated by the coefficient on the Exit Effect dummy. The estimates for

saving in deposits in column (3) align with those for consumption. In fact, the long-run

effect of forbearance on saving changes significantly after the forbearance period, with

saving decreasing from 23 cents per euro to about -12 cents post-forbearance. These

findings suggest that households increased their consumption during the forbearance

period and did not fully adjust consumption levels downward after its termination,

resulting in a drawdown of savings (Chetty and Szeidl, 2007). Table IA.8 of the Internet

Appendix further extends the analysis of the propensity to consume upon exiting

forbearance, indicating that the rigidity in reducing consumption is primarily driven by

spending in “Groceries”, “Clothing”, and “House Maintenance and Utilities”.

In the summer of 2021, and at the regulator’s request, banks assessed the risk levels of

borrowers in forbearance. For those identified as having a higher risk of default at the end

of the forbearance based on a credit risk model, the bank conducted a survey to evaluate

whether they should receive additional debt relief measures due to perceived default risk.

Furthermore, all borrowers in forbearance were notified via SMS, email, and the bank’s

ECB Working Paper Series No 303436app that additional assistance was available if they had difficulties meeting their debt

obligations. Similiar to the government forbearance program, access to these additional

debt relief measures did not require on a formal verification of the borrower’s income or

financial hardship. In this section, we compare the evolution of consumption and saving

for households that did and did not receive additional debt relief measures implemented

by the bank in September 2021.

A more nuanced pattern emerges when examining the additional relief measures.

Each of the three horizon dummies is interacted with an additional indicator, Additional

Relief,

to separately identify the effects on households exiting forbearance after

September 2021 and and those receiving additional assistance. Columns (2) and (4) of

Table 9 present the results. Only about 7.4% of ineligible households in forbearance

requested additional relief, compared to about 6.1% of eligible households. Column (2)

shows that households receiving additional relief in September 2021 did not adjust their

consumption behavior after the forbearance period ended, as the point estimate for the

long-run effect remains nearly identical to the estimate after the end of forbearance (15

cents per euro of postponed payment). This is unexpected, given the temporary nature

of these debt relief measures, but it aligns with the “consumption commitments” model

proposed by Chetty and Szeidl (2007) for this subset of borrowers. Despite this, these

households that requested additional relief, on average, exhibit lower propensity to

consume from forbearance funds and higher propensity to save, as shown in columns (2)

and (4). This behavior continues after the transition from the initial government

forbearance program to bank-provided assistance, with the marginal propensity to save

being 52 cents higher for households that remain in forbearance compared to those who

exit the program.

ECB Working Paper Series No 3034378 Conclusion

Government debt relief programs during the COVID-19 pandemic provide a unique

laboratory to understand borrower selection into forbearance and borrower response to

preemptive interventions even before large-scale defaults. Using comprehensive bank

account transaction and balance sheet data, we show that forbearance programs have a

positive and significant impact on household consumption and saving. Households in

forbearance increased their spending by approximately 15 cents per euro of postponed

debt payments compared to those outside forbearance, and increased their saving by

around 30 cents. These effects are not only substantial but also persist for over a year

following the initiation of forbearance.

Importantly, our findings reveal heterogeneity in responses across different household

groups. Financially fragile households, characterized by lower wealth,

income, and

higher indebtedness, exhibit a higher marginal propensity to consume from postponed

payments. In contrast, less fragile households exhibit a greater marginal propensity to

save. The response is also heterogeneous based on program eligibility. Both eligible and

ineligible households in forbearance were financially more fragile even before the

pandemic.

Eligible households benefit from forbearance by avoiding consumption

reductions due to ongoing debt payments, while ineligible households in forbearance

show a relatively higher marginal propensity to consume compared to their counterparts

outside forbearance.

These findings have implications for the design of debt relief programs. Our research

is the first to examine the consumption and saving responses of both eligible and ineligible

households within a large-scale forbearance program, while accounting for key unobserved

factors such as changes in income. The results suggest that, although the Portuguese

government (and many others globally) aimed to assist those most directly affected by

the pandemic, the program was also accessed by households that were already financially

fragile before the crisis, who were not the intended targets. Financially fragile households

ECB Working Paper Series No 303438were also more likely to increase their consumption, leading many to exit forbearance

with higher debt burdens (due to postponed payments) and only modest improvements

in savings, potentially exacerbating their financial fragility.

Our study highlights a potential unintended consequence of broad-based forbearance

programs: while the comprehensive measures implemented at the start of the pandemic

provided temporary relief and helped stabilize household finances, likely preventing a wave

of defaults globally, these programs may have also increased indebtedness among already

fragile households. Policymakers could improve the effectiveness of debt relief measures by

using observable household characteristics, commonly available to lenders, and considering

how different groups respond to forbearance when designing future interventions. This

targeted approach could be complemented by additional policies aimed at supporting

household financial health, thereby reducing the risk of long-term financial instability and

ensuring that the primary goal of these programs—providing relief without exacerbating

debt burdens—is achieved.

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ECB Working Paper Series No 303445Figure 1: Evolution of Income by Forbearance Status

This figure shows the household average and 95% confidence interval for different components of monthly income from July
2019 to September 2021. All measures are reported in euros, seasonally adjusted and relative to a pre-pandemic baseline
(February 2020). Panel A shows the change in monthly wages. Panel B shows the change in social security benefits. Panel
C shows the change in total monthly income, computed as the sum between monthly wages, social security, and retirement
benefits. In all panels, the average change is presented separately for households who received forbearance and those who
never received forbearance. Standard errors are clustered by household.

ECB Working Paper Series No 303446Figure 2: Evolution of Consumption and Deposits by Forbearance Status

This figure shows the household average and 95% confidence interval for monthly consumption and total deposits from
January 2019 to September 2021. All measures are reported in euros, seasonally adjusted, and relative to the pre-pandemic
baseline (February 2020). Panel A shows the change in monthly consumption. Panel B shows the change in total deposits,
computed as the sum between end-of-the-month checking and saving accounts’ balances. In both panels, the average change
is presented separately for households who received forbearance and those who never received forbearance. Standard errors
are clustered by household.

ECB Working Paper Series No 303447Figure 3: Household Propensities to Consume and Save in Deposits

This figure shows the regression coefficients and 95% confidence intervals of the changes in monthly consumption and saving
in total deposits using the difference-in-differences regression in equation (2). Panel A shows the estimates of the change
in monthly spending per euro of postponed debt payment. Panel B shows the estimates of the change in saving in total
deposits per euro of postponed debt payment. The specification includes group-month-year fixed effects and includes as
control variables the changes in total income and the forbearance dummy variable that takes the value of one for households
receiving forbearance, and zero otherwise. Standard errors are two-way clustered by household and month-year.

ECB Working Paper Series No 303448Figure 4: Household Propensity to Consume: Wealth, Income, Indebtedness and Employer
Sector

This figure shows the regression coefficients and 95% confidence intervals of the changes in monthly consumption using the
difference-in-differences regression in equation (1). Panels A-D show the estimates for subgroups of households based on the
pre-pandemic median of assets, income, debt payment-to-income ratio, and income volatility (2019 average). Panel E shows
the estimates for subgroups of households whose primary employer is from the private and public sector. The specification
includes group-month-year fixed effects and includes as control variables the changes in total income and the F orbearance
dummy variable. Standard errors are two-way clustered by household and month-year.

ECB Working Paper Series No 303449Figure 5: Household Propensity to Save: Wealth, Income, Indebtedness and Employer Sector

This figure shows the regression coefficients and 95% confidence intervals of the changes in monthly saving in total deposits
using the difference-in-differences regression in equation (1). Panels A-D shows the estimates for subgroups of households
based on the pre-pandemic median of assets, income, debt payment-to-income ratio, and income volatility (2019 average).
Panel E shows the estimates for subgroups of households whose primary employer is from the private or public sector. The
specification includes group-month-year fixed effects and includes as control variables the changes in total income and the
F orbearance dummy variable. Standard errors are two-way clustered by household and month-year.

ECB Working Paper Series No 303450Figure 6: Household Propensities to Consume and Save in Deposits: By Eligibility Status

This figure shows the regression coefficients and 95% confidence intervals of the changes in monthly consumption and saving
in total deposits using the difference-in-differences regression in equation (2). The F orbearance Amount variable is further
interacted with the Eligibility dummy variable. Panel A shows the estimates of the change in monthly spending per euro
of postponed debt payment. Panel B shows the estimates of the change in saving in total deposits per euro of postponed
debt payment. The specification includes group-month-year fixed effects and includes as control variables the changes in
total income and the F orbearance dummy variable. Standard errors are two-way clustered by household and month-year.

ECB Working Paper Series No 303451Table 1: Average Household Characteristics by Groups

No Forbearance

Forbearance

Total Eligible

Ineligible

Total Eligible

Ineligible

Wages
Social Security Benefits
Total Income
Consumption
Consumption Rate (%)
Total Deposits
Mortgage Loans
Credit Cards and Overdraft
Other Banks’ Debt
Debt Payment
Debt Payment-to-Income (%)
Forbearance Amount

1,837
346
2,553
1,516
75.6
18,855
67,803
399
7,044
311
18.7
0

1,885
350
2,759
1,615
67.2
17,854
73,339
375
7,417
305
14.7
0

1,830
345
2,531
1,506
76.6
18,962
67,211
401
7,005
312
19.1
0

1,466
300
2,121
1,343
88.1
7,581
93,077
749
13,885
377
29.8
345

1,620
303
2,449
1,441
66.9
7,449
96,256
654
13,457
360
19.6
333

1,416
299
2,046
1,320
93.0
7,612
92,346
771
13,984
381
32.2
347

Observations

129,201

12,469

116,732

8,162

1,525

6,637

This table shows pre-pandemic (2019) averages for households who received forbearance and those who never received
forbearance, further dividing those two groups on whether they were eligible or not according to program rules. Income,
deposits, liabilities and consumption measures are winsorized at the top and bottom 1%.

ECB Working Paper Series No 303452e
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ECB Working Paper Series No 303453Table 3: Household Propensities to Consume and Save in Deposits by Horizon

∆Consumption

∆Saving in Deposits

(1)

(2)

(3)

(4)

(5)

(6)

Forbearance Amount ×

Immediate Effect (1m-3m)

0.038
(0.039)

-0.019
(0.032)

0.018
(0.035)

-0.165
(0.144)

0.045
(0.080)

0.345***
(0.055)

Short-run Effect (4m-12m)

0.153***
(0.029)

0.133***
(0.024)

0.146***
(0.023)

0.186**
(0.092)

0.213***
(0.056)

0.318***
(0.042)

Long-run Effect (>12m)

0.211***
(0.029)

0.215***
(0.025)

0.218***
(0.024)

0.173
(0.110)

0.206***
(0.062)

0.226***
(0.044)

∆Income

0.085***
(0.003)

0.695***
(0.009)

Month × Year FE
Group × Month × Year FE
R2
Observations

Yes
No
0.062
6,181,335

No
Yes
0.082
6,181,335

No
Yes
0.102
6,181,335

Yes
No
0.018
6,043,972

No
Yes
0.026
6,043,972

No
Yes
0.157
6,043,972

This table presents difference-in-differences estimates of regressions of changes in monthly consumption and saving in total
deposits using the difference-in-differences regression in equation (1) at the household-month level, but replacing the Post
indicator with three different time dummy variables: the Immediate Effect measures the impact over the first quarter
after the start of forbearance; the Short-run Effect measures the average effect from the 4th to the 12th month; and the
Long-run Effect measures the average effect after one year. The dependent variable in columns (1)-(3), ∆Consumption,
is the difference between monthly spending at time t, relative to the corresponding household average in 2019. The
dependent variable in columns (4)-(6), ∆Saving in Deposits, is the monthly saving in total deposits at time t, relative
to the corresponding household average in 2019. Forbearance Amount is the amount of postponed debt payments. All
specifications include the F orberance dummy variable as a control variable. In some specifications, the regressions include
group-month-year fixed effects, with group referring to quartiles of pre-pandemic deposits and income (2019 averages),
and changes in total income relative to the corresponding household average in 2019 as a control. The sample period is
from January 2018 to September 2021. Two-way standard errors clustered by household and month-year are reported in
parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

ECB Working Paper Series No 303454Table 4: Effect of Forbearance on Consumption and Saving Rates

Consumption Rate

Saving Rate

(1)

(2)

(3)

(4)

(5)

(6)

Panel A: Overall

Forbearance × Post

0.118***
(0.011)

0.064***
(0.008)

0.063***
(0.007)

0.040*
(0.020)

0.048***
(0.014)

0.045***
(0.010)

%∆Income

0.148***
(0.009)

0.486***
(0.021)

Forbearance Amount ×

Panel B: Horizons

Immediate Effect (1m-3m)

0.055***
(0.016)

0.017
(0.011)

0.024*
(0.013)

-0.023
(0.037)

0.013
(0.023)

0.037***
(0.013)

Short-run Effect (4m-12m)

0.111***
(0.008)

0.063***
(0.006)

0.062***
(0.006)

0.050**
(0.024)

0.051***
(0.018)

0.048***
(0.014)

Long-run Effect (>12m)

0.163***
(0.009)

0.092***
(0.008)

0.087***
(0.007)

0.060**
(0.029)

0.064***
(0.021)

0.045***
(0.013)

%∆Income

0.148***
(0.009)

0.486***
(0.021)

Month × Year FE
Group × Month × Year FE
R2
Observations

Yes
No
0.052
6,170,985

No
Yes
0.084
6,170,985

No
Yes
0.129
6,170,985

Yes
No
0.012
6,033,852

No
Yes
0.015
6,033,852

No
Yes
0.093
6,033,852

This table presents difference-in-differences estimates of regressions of changes in the consumption rate and saving rate using
the difference-in-differences regression in equation (3) at the household-month level. The dependent variable in columns
(1)-(3), Consumption Rate, is the difference between monthly spending at time t and the corresponding household average
in 2019, divided by the average income in 2019. The dependent variable in columns (4)-(6), Saving Rate, is the monthly
saving in total deposits at time t and the corresponding household average in 2019, divided by the average income in 2019.
Forbearance is a dummy variable that takes the value of one for households receiving forbearance, and zero otherwise.
Post is a dummy variable that takes a value of one for the period starting when mortgage payments are suspended and
zero otherwise. In Panel B, the Post indicator is replaced by three different time dummy variables: the Immediate Effect
measures the impact over the first quarter after the start of forbearance; the Short-run Effect measures the average effect
from the 4th to the 12th month; and the Long-run Effect measures the average effect after one year. All specifications include
the F orberance dummy variable as a control variable. In some specifications, the regressions include group-month-year
fixed effects, with group referring to quartiles of pre-pandemic deposits and income (2019 averages), and percentage changes
in total income relative to the corresponding household average in 2019 as a control. The sample period is from January
2018 to September 2021. Two-way standard errors clustered by household and month-year are reported in parentheses. *,
**, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

ECB Working Paper Series No 303455Table 5: Household Propensity to Borrow

Forbearance Amount × Post

∆Income

Forbearance Amount ×

Immediate Effect (1m-3m)

Short-run Effect (4m-12m)

Long-run Effect (>12m)

∆Income

∆Borrowing Credit Card & Overdraft

∆Borrowing Other Banks’ Debt

(1)

(2)

(3)

(4)

(5)

(6)

-0.009
(0.011)

Panel A: Overall
-0.009
(0.009)

-0.011
(0.009)

-0.053*
(0.026)

-0.033
(0.025)

-0.029
(0.025)

-0.007***
(0.001)

0.022***
(0.002)

Panel B: Horizons

-0.043**
(0.020)

-0.034
(0.020)

-0.001
(0.012)

-0.005
(0.015)

-0.005
(0.009)

-0.004
(0.011)

-0.037*
(0.019)

-0.006
(0.009)

-0.004
(0.011)

-0.007***
(0.001)

No
Yes
0.023
6,043,972

-0.036
(0.027)

-0.051
(0.037)

-0.006
(0.026)

-0.034
(0.035)

0.003
(0.028)

-0.031
(0.035)

-0.064***
(0.021)

-0.045**
(0.021)

-0.044**
(0.021)

0.022***
(0.002)

Yes
No
0.001
4,395,616

No
Yes
0.003
4,395,616

No
Yes
0.004
4,395,616

Month × Year FE
Group × Month × Year FE
R2
Observations

Yes
No
0.012
6,043,972

No
Yes
0.021
6,043,972

This table presents difference-in-differences estimates of regressions of changes in credit card and overdraft, and other
banks’ debt payments using the difference-in-differences regression in equation (1) at the household-month level. The
dependent variable in columns (1)-(3), ∆Borrowing Credit Card & Overdraft, is the difference in monthly changes in end-
of-the-month credit card and overdraft balances at time t at the bank, relative to the corresponding household average
in 2019. The dependent variable in columns (4)-(6), ∆Borrowing Other Banks’ Debt, is the difference between monthly
changes in liabilities at other banks at time t, relative to the corresponding household average in 2019. Forbearance Amount
is the amount of postponed debt payments. Post is a dummy variable that takes a value of one for the period starting
when mortgage payments are suspended and zero otherwise. In Panel B, the Post indicator is replaced by three different
time dummy variables: the Immediate Effect measures the impact over the first quarter after the start of forbearance; the
Short-run Effect measures the average effect from the 4th to the 12th month; and the Long-run Effect measures the average
effect after one year. All specifications include the F orberance dummy variable as a control variable. All specifications
include the F orberance dummy variable as a control variable. In some specifications, the regressions include group-month-
year fixed effects, with group referring to quartiles of pre-pandemic deposits and income (2019 averages), and changes in
total income relative to the corresponding household average in 2019 as a control. The sample period in columns (1)-(3)
is from January 2018 to September 2021. The sample period in columns (4)-(6), due to data limitations, is from January
2019 to September 2021. Two-way standard errors clustered by household and month-year are reported in parentheses. *,
**, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

ECB Working Paper Series No 303456y
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ECB Working Paper Series No 303457Table 7: Household Propensities to Consume and Save in Deposits by Eligibility and
Forbearance Status

∆Consumption

∆Saving in Deposits

(1)

(2)

(3)

(4)

(5)

(6)

Debt Payment ×

Forbearance=0 × Eligible=1 × Post

-0.242***
(0.038)

-0.152***
(0.024)

-0.135***
(0.021)

-0.131*
(0.069)

-0.133*
(0.068)

0.012
(0.050)

Forbearance=1 × Eligible=0 × Post

0.195***
(0.036)

0.169***
(0.033)

0.177***
(0.030)

0.144*
(0.085)

0.210***
(0.051)

0.275***
(0.044)

Forbearance=1 × Eligible=1 × Post

0.011
(0.056)

0.015
(0.052)

0.065
(0.048)

0.019
(0.112)

0.075
(0.105)

0.486***
(0.083)

Controls
Month × Year FE
Group × Month × Year FE
R2
Observations

Observations:

Forbearance=0 × Eligible=0

% of sample

Forbearance=0 × Eligible=1

% of sample

Forbearance=1 × Eligible=0

% of sample

Forbearance=1 × Eligible=1

% of sample

No
Yes
No
0.062
6,181,335

No
No
Yes
0.082
6,181,335

Yes
No
Yes
0.102
6,181,335

No
Yes
No
0.018
6,043,972

No
No
Yes
0.026
6,043,972

Yes
No
Yes
0.157
6,043,972

116,732
(85.0%)
12,469
(9.1%)
6,637
(4.8%)
1,525
(1.1%)

This table presents difference-in-differences estimates of regressions of changes in monthly consumption and saving in
total deposits using the difference-in-differences regression in equation (1) at the household-month date level. The
dependent variable in columns (1)-(3), ∆Consumption, is the difference between monthly spending at time t, relative
to the corresponding household average in 2019. The dependent variable in columns (4)-(6), ∆Saving in Deposits, is
the monthly saving in total deposits at time t, relative to the corresponding household average in 2019. Debt Payment is
the pre-pandemic amount of debt payments of each household. Forbearance is a dummy variable that takes the value of
one for households receiving forbearance and zero otherwise. Eligible is a dummy variable that takes the value of one if
the household satisfied the legal requirements for suspending mortgage payments. Post is a dummy variable that takes a
value of one for the period starting when mortgage payments are suspended and zero otherwise. All specifications include
the F orberance dummy variable as a control variable. In some specifications, the regressions include group-month-year
fixed effects, with group referring to quartiles of pre-pandemic deposits and income (2019 averages), and changes in total
income relative to the corresponding household average in January and February 2020 as a control. The sample period is
from January 2018 to September 2021. Two-way standard errors clustered by household and month-year are reported in
parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

ECB Working Paper Series No 303458Table 8: Propensity to Consume and Save in Deposits by Eligibility and Selection Groups:
More Affected versus Less Affected

∆Consumption

∆Saving in Deposits

More
Affected
(1)

Less
Affected
(2)

Public
Servants
(3)

More
Affected
(4)

Less
Affected
(5)

Public
Servants
(6)

Debt Payment ×

Forbearance=0 × Eligible=1 × Post

-0.123***
(0.033)

-0.085**
(0.036)

-0.076
(0.046)

0.013
(0.077)

0.072
(0.083)

-0.011
(0.107)

Forbearance=1 × Eligible=0 × Post

0.139***
(0.051)

0.190***
(0.061)

0.230***
(0.053)

0.180*
(0.094)

0.236**
(0.088)

0.269***
(0.081)

Forbearance=1 × Eligible=1 × Post

0.030
(0.069)

0.196**
(0.085)

0.299**
(0.114)

0.654***
(0.119)

0.135
(0.113)

0.273
(0.263)

Controls
Group × Month × Year FE
R2
Observations

Observations:

Forbearance=0 × Eligible=0

% of sample

Forbearance=0 × Eligible=1

% of sample

Forbearance=1 × Eligible=0

% of sample

Forbearance=1 × Eligible=1

% of sample

Yes
Yes
0.109
919,080

Yes
Yes
0.105
1,327,095

Yes
Yes
0.106
1,719,855

Yes
Yes
0.176
898,656

Yes
Yes
0.167
1,297,604

Yes
Yes
0.163
1,681,636

13,485
(66.0%)
5,176
(25.3%)
904
(4.4%)
859
(4.2%)

23,899
(81.0%)
4,103
(13.9%)
1,076
(3.6%)
413
(1.4%)

35,241
(92.2%)
1,775
(4.6%)
1,096
(2.9%)
107
(0.3%)

This table presents difference-in-differences estimates of regressions of changes in monthly consumption and saving in total
deposits using the difference-in-differences regression in equation (1) at the household-month level. The dependent variable
in columns (1)-(3), ∆Consumption, is the difference between monthly spending at time t, relative to the corresponding
household average in 2019. The dependent variable in columns (4)-(6), ∆Saving in Deposits, is the monthly saving in
total deposits at time t, relative to the corresponding household average in 2019. The More Affected subsample corresponds
to households whose primary employer operates in an industry with revenue growth from 2019 to 2020 below the median.
The Less Affected subsample corresponds to households whose primary employer operates in an industry with revenue
growth from 2019 to 2020 above the median. The Public Servants subsample is defined as households whose primary
employer operated in the public sector. Debt Payment is the amount of pre-pandemic debt payments made by households.
Forbearance is a dummy variable that takes the value of one for households receiving forbearance and zero otherwise.
Eligible is a dummy variable that takes the value of one if the household satisfied the legal requirements for suspending
mortgage payments. Post is a dummy variable that takes a value of one for the period starting when mortgage payments
are suspended and zero otherwise. All specifications include the F orberance dummy variable as a control variable. In some
specifications, the regressions include group-month-year fixed effects, with group referring to quartiles of pre-pandemic
deposits and income (2019 averages), and changes in total income relative to the corresponding household average in 2019
as a control. The sample period is from January 2018 to September 2021. Two-way standard errors clustered by household
and month-year are reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level,
respectively.

ECB Working Paper Series No 303459Table 9: Household Propensities to Consume and Save in Deposits on Exit

Forbearance Amount ×

Immediate Effect (1-3)

Short-run Effect (4-12)

Long-run Effect (>12)

Exit Effect

Immediate Effect (1-3) × Additional Relief

Short-run Effect (4-12) × Additional Relief

Long-run Effect (>12) × Additional Relief

Exit Effect × Additional Relief

Controls
Group × Month × Year FE
R2
Observations

Observations:

∆Consumption

∆Saving in Deposits

(1)

(2)

(3)

(4)

0.024
(0.036)

0.026
(0.038)

0.343***
(0.056)

0.336***
(0.058)

0.150***
(0.024)

0.157***
(0.025)

0.319***
(0.045)

0.296***
(0.045)

0.221***
(0.025)

0.228***
(0.026)

0.228***
(0.046)

0.206***
(0.047)

0.126***
(0.028)

0.124***
(0.029)

-0.120**
(0.054)

-0.164***
(0.056)

-0.029
(0.077)

-0.083
(0.084)

-0.075
(0.107)

0.026
(0.100)

0.080
(0.070)

0.273**
(0.123)

0.267*
(0.142)

0.522***
(0.152)

Yes
Yes
0.101
7,417,602

Yes
Yes
0.101
7,417,602

Yes
Yes
0.152
7,280,239

Yes
Yes
0.152
7,280,239

Forbearance=1 × Eligible=0
Forbearance=1 × Eligible=0 × Additional Relief=1

% of group

Forbearance=1 × Eligible=1
Forbearance=1 × Eligible=1 × Additional Relief=1

% of group

6,637
490
(7.4%)
1,525
93
(6.1%)

This table presents difference-in-differences estimates of regressions of changes in monthly consumption and saving in total
deposits using the difference-in-differences regression in equation (1) at the household-month level, but replacing the Post
indicator with four different time dummy variables: the Immediate Effect measures the impact over the first quarter after
the start of forbearance; the Short-run Effect measures the average effect from the 4th to the 12th month; the Long-run
Effect measures the average effect after one year and until the end of the payment suspension; and the Exit Effect measures
the average effect after the end of the payment suspension. The dependent variable in columns (1)-(2), ∆Consumption,
is the difference between monthly spending at time t, relative to the corresponding household average in 2019. The
dependent variable in columns (3)-(4), ∆Saving in Deposits, is the monthly saving in total deposits at time t, relative to
the corresponding household average in 2019. Forbearance Amount is the amount of postponed debt payments. Additional
Relief is a dummy variable that takes a value of one if the household requested additional relief after the forbearance end.
All specifications include the F orberance dummy variable as a control variable. All specifications include group-month-year
fixed effects, with group referring to quartiles of pre-pandemic deposits and income (2019 averages), and changes in total
income relative to the corresponding household average in 2019 as a control. The sample period is from January 2018 to
June 2022. Two-way standard errors clustered by household and month-year are reported in parentheses. *, **, and ***
indicate statistical significance at the 10%, 5%, and 1% level, respectively.

ECB Working Paper Series No 303460Internet Appendix for

“The Heterogeneous Effects of Household Debt Relief ”

ECB Working Paper Series No 303461s
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Mar 3 2020First Recorded CaseMay 15 2020Reopening of some non-essential servicesMar 18 2020Declarationof State ofEmergency(Lockdown) 050100150200250300350010,00020,00030,00040,00050,00060,00070,00002/202003/202004/202005/202006/202007/202008/202009/202010/202011/202012/202001/202102/202103/202104/202105/202106/202107/202108/202109/202110/202111/202112/202101/202202/202203/202204/202205/202206/202207/2022New Cases (Left Axis)New Deaths (Right Axis)Mar 14 2020First Recorded DeathMay 3 2020Declaration of State of CalamityJun 16 2020Changes to Forbearance ProvisionJul 1 2020Declaration of State of AlertJul 24 2020Changes to Forbearance ProvisionJul 31 2020Layoff Scheme EndSep 29 2020Extension of Forbearance until Sep 21Oct 14 2020Declaration of State of CalamityOct 31 2020Partial LockdownNov 6 2020Declaration of State of EmergencyDec 31 2020Minor Changes to Forbearance ProvisionJan 13 2021General LockdownMar 11 2021Beginning of Lockdown EasingMay 1 2021Declaration of State of CalamityAug 7 2021Provision for Additional ReliefAug 20 2021Declaration of State of ContingencySep 30 2021End of Forbearance SchemeOct 1 2021Declaration of State of AlertMar 31 2022End for most Additional ReliefApr 15 2020Private Forbearance MeasureMar 26 2020Layoff Scheme +First Forbearance ProvisionECB Working Paper Series No 303462Figure IA.2: Probability of Acessing Forbearance: Coefficient Path

This figure plots the coefficient path of the selected variables in a LASSO regression, considering a set of pre-pandemic
measures, for the probability of accessing forbearance. Coefficients of each variable are shown relative to λ, the overall
penalty level of the regularized regression. Variables are shown in the legend according to the order by which they are
selected.

ECB Working Paper Series No 303463Figure IA.3: Evolution of Income by Eligibility and Forbearance Status

This figure shows the household average and 95% confidence interval for different components of monthly income from
July 2019 to June 2022. All measures are reported in euros, seasonally adjusted, and relative to the pre-pandemic baseline
(February 2020). Panel A shows the change in monthly wages, while Panel B shows the change in social security benefits
received. Panel C shows the change in total monthly income, computed as the sum between monthly wages, social security,
and retirement benefits.
In all panels, the average change is presented separately for eligible households who received
forbearance, ineligible households who received forbearance, and households who never received forbearance. Standard
errors are clustered by household.

ECB Working Paper Series No 303464Figure IA.4: Evolution of Consumption and Deposits by Eligibility and Selection Status

This figure plots the household average and 95% confidence interval for monthly consumption and total deposits from
January 2019 to June 2022. All measures are reported in euros, seasonally adjusted, and relative to the pre-pandemic
baseline (February 2020). Panel A reports monthly consumption. Panel B shows total deposits, computed as the sum
between end-of-the-month checking and saving accounts’ balances. In all panels the average change is presented separately
for eligible households who received forbearance, ineligible households who received forbearance, and households who never
received forbearance. Standard errors are clustered by household.

ECB Working Paper Series No 303465Table IA.1: Summary Statistics of Household Characteristics

Variable

Mean

SD

p10

p25

p50

p75

p90 Observations

Average Age
Number of Mortgagors
Wages
Pensions
Social Security Benefits
Other Inbound Transfers
Total Income
Consumption
Consumption Rate
Total Deposits
Checking Accounts
Savings Accounts
Mortgage Loans
Credit Cards and Overdraft
Other Banks’ Loans
Total Debt Payment
Debt Payment-to-Income
7 Day Delinquency

47.9
1.7
1,816.1
1,313.9
341.7
663.2
2,527.1
1,505.9
0.76
18,185.0
6,710.8
17,356.5
69,304.6
419.6
7,451.0
315.3
0.19
0.02

9.1
0.5
1,067.3
928.4
439.8
976.0
1,754.6
932.2
0.75
30,787.9
12,490.4
28,834.9
52,000.4
784.2
17,692.4
170.1
0.25
0.13

37.0
1.0
756.1
390.2
33.6
25.6
870.0
559.0
0.32
302.0
195.2
0.0
15,173.5
0.0
0.0
148.7
0.06
0.00

41.5
1.0
1,075.3
629.2
63.4
107.1
1,394.4
860.2
0.46
1,159.5
696.8
431.5
30,724.2
0.0
0.0
207.4
0.09
0.00

47.0
2.0
1,566.6
1,056.7
163.9
297.5
2,090.1
1,298.3
0.62
5,567.4
2,010.5
5,757.9
57,206.5
87.5
470.0
279.4
0.14
0.00

54.0
2.0
2,250.9
1,767.7
446.1
803.0
3,176.0
1,915.2
0.80
20,738.0
6,528.3
20,541.7
95,048.2
473.3
7,507.7
378.1
0.21
0.00

60.5
2.0
3,216.9
2,539.4
882.3
1,693.8
4,658.7
2,704.5
1.13
51,399.2
17,853.7
49,559.6
137,247.8
1,224.8
19,463.5
523.5
0.32
0.00

137,363
137,363
111,979
40,258
42,761
137,363
137,363
137,363
137,133
137,363
137,363
90,241
137,363
137,328
137,363
137,363
137,133
137,363

This table shows pre-pandemic (2019) mean, standard deviation (SD), the 10% (p10), 25% (p25), 50% (p50), 75% (p75) and
90% (p90) percentiles, and number of households. Income, deposits, liabilities, and consumption measures are winsorized
at the top and bottom 1% by date.

ECB Working Paper Series No 303466Table IA.2: Forbearance, Eligibility and Future Outcomes

Forbearance

Eligible

Employment Status Change

Wage Growth

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

-0.009***
(0.003)

-0.008***
(0.003)

-0.008***
(0.003)

-0.007**
(0.003)

3.114***
(0.631)

2.962***
(0.634)

2.766***
(0.635)

2.044***
(0.631)

-0.006**
(0.003)

-0.006**
(0.003)

-0.006**
(0.003)

-0.003
(0.003)

21.310***
(0.541)

21.360***
(0.541)

21.210***
(0.540)

20.810***
(0.569)

Forbearance × Eligible

0.004
(0.009)

0.004
(0.009)

0.003
(0.009)

0.004
(0.009)

1.821
(1.679)

1.914
(1.678)

2.091
(1.676)

1.518
(1.655)

Additional Controls
Group FE
Location FE
Industry FE
R2
Observations

Yes
No
No
No
0.004
65,658

Yes
Yes
No
No
0.006
65,658

Yes
Yes
Yes
No
0.011
65,658

Yes
Yes
Yes
Yes
0.014
65,658

Yes
No
No
No
0.113
64,351

Yes
Yes
No
No
0.114
64,351

Yes
Yes
Yes
No
0.121
64,351

Yes
Yes
Yes
Yes
0.141
64,351

This table presents estimates of linear probability models of losing the job and wage growth between the first quarter
of 2020 and 2021, as a function of predetermined variables. Observations are at the household level and only include
households employed by a single employer during the first quarter of 2020. In columns (1)-(4) the dependent variable is
the difference between a dummy variable that takes the value of one if the household receives no wage payment, comparing
the dummy variable value during 2021:Q1 relative to 2020:Q2. In columns (5)-(8), the dependent variable corresponds to
the household wage growth between 2020:Q2 and 2021:Q1. Forbearance is a dummy variable that takes the value of one
for households receiving forbearance and zero otherwise. Eligible is a dummy variable that takes the value of one if the
household satisfied the legal requirements for suspending mortgage payments. In all specifications, the regressions include
pre-pandemic controls such as age, total income, total deposits, credit card and overdraft balances, mortgage loan balance,
balances for loans held at other banks, and debt payment. We also control for employment risk by including a private sector
dummy that takes the value of one if the household’s employer in the first quarter of 2020 operated in the private sector,
and a measure of employer’s industry volatility, computed as the standard deviation of sales at the industry level and for
the previous thee years, normalized by the industry’s total assets. Robust standard errors are shown in parentheses. *, **,
and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

ECB Working Paper Series No 303467Table IA.3: Household Consumption Growth: Sample and Population

2019
2020
2021

Sample

7.2%
-4.1%
14.8%

Population

4.2%
-4.7%
13.8%

This table shows the annual growth rate of consumption for the average household in our sample and the corresponding
statistic for the population. The population average is the yearly growth rate of the average consumption by all resident
households, measured as the final consumption expenditure divided by the number of households. National accounts data
are from INE.

ECB Working Paper Series No 303468Table IA.4: Household Propensities to Consume and Save in Deposits: Robustness

∆Consumption

∆Saving in Deposits

(1)

(2)

(3)

(4)

(5)

(6)

Forbearance Amount × Post

0.167***
(0.024)

0.150***
(0.028)

0.159***
(0.027)

0.282***
(0.030)

0.298***
(0.040)

0.294***
(0.041)

∆Income

0.091***
(0.003)

0.086***
(0.003)

0.085***
(0.003)

0.701***
(0.007)

0.694***
(0.009)

0.695***
(0.009)

Group × Month × Year FE
Household × Month FE
Industry × Month FE
Municipality × Month FE
R2
Observations

Yes
Yes
No
No
0.423
6,181,335

Yes
No
Yes
No
0.103
6,180,996

Yes
No
No
Yes
0.107
6,181,335

Yes
Yes
No
No
0.508
6,043,972

Yes
No
Yes
No
0.158
6,043,639

Yes
No
No
Yes
0.159
6,043,972

This table presents difference-in-differences estimates of regressions of changes in monthly consumption and saving in total
deposits using the difference-in-differences regression in equation (1) at the household-month level. The dependent variable
in columns (1)-(3), ∆Consumption, is the difference between monthly spending at time t, relative to the corresponding
household average in 2019. The dependent variable in columns (4)-(6), ∆Saving in Deposits, is the monthly saving in total
deposits at time t, relative to the corresponding household average in 2019. Forbearance Amount is the amount of postponed
debt payments. Post is a dummy variable that takes a value of one for the period starting when mortgage payments are
suspended and zero otherwise. Specifications in columns (2), (3), (5), and (6) include the F orberance dummy variable as a
control variable. All specifications include group-month-year fixed effects, with group referring to quartiles of pre-pandemic
deposits and income (2019 averages), and changes in total income relative to the corresponding household average in 2019
as a control. In columns (2) and (5) we define industry as the two-digit industry code of the primary employer, in case
of employed households; and for non-employed households, the current status (either unemployed or retired). The sample
period is from January 2018 to September 2021. Two-way standard errors clustered by household and month-year are
reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

ECB Working Paper Series No 303469Table IA.5: Household Propensities to Consume and Save in Deposits by Horizon: Sample of
Individuals Working in the Private Sector

∆Consumption

∆Saving in Deposits

(1)

(2)

(3)

(4)

(5)

(6)

Panel A: Overall

Forbearance Amount × Post

0.129***
(0.045)

0.110***
(0.040)

0.131***
(0.038)

0.061
(0.079)

0.120**
(0.058)

0.302***
(0.059)

∆Income

0.085***
(0.003)

0.711***
(0.010)

Forbearance Amount ×

Panel B: Horizons

Immediate Effect (1m-3m)

0.020
(0.049)

-0.032
(0.040)

0.013
(0.039)

-0.192*
(0.098)

0.015
(0.049)

0.396***
(0.054)

Short-run Effect (4m-12m)

0.136***
(0.044)

0.116***
(0.037)

0.137***
(0.036)

0.090
(0.093)

0.117*
(0.069)

0.294***
(0.065)

Long-run Effect (>12m)

0.175***
(0.045)

0.175***
(0.038)

0.185***
(0.036)

0.151
(0.123)

0.180**
(0.084)

0.262***
(0.077)

∆Income

0.085***
(0.003)

0.711***
(0.010)

Month × Year FE
Group × Month × Year FE
R2
Observations

Yes
No
0.062
2,246,175

No
Yes
0.086
2,246,175

No
Yes
0.106
2,246,175

Yes
No
0.017
2,196,260

No
Yes
0.026
2,196,260

No
Yes
0.170
2,196,260

This table presents difference-in-differences estimates of regressions of changes in monthly consumption and saving in total
deposits using the difference-in-differences regression in equation (1) at the household-month level. Panel A considers a single
Post indicator, while in Panel B this indicator is replaced with three different time dummy variables: the Immediate Effect
measures the impact over the first quarter after the start of forbearance; the Short-run Effect measures the average effect
from the 4th to the 12th month; and the Long-run Effect, measures the average effect after one year. The dependent variable
in columns (1)-(3), ∆Consumption, is the difference between monthly spending at time t, relative to the corresponding
household average in 2019. The dependent variable in columns (4)-(6), ∆Saving in Deposits, is the monthly saving in total
deposits at time t, relative to the corresponding household average in 2019. Forbearance Amount is the amount of postponed
debt payments. Post is a dummy variable that takes a value of one for the period starting when mortgage payments are
suspended and zero otherwise. All specifications include the F orberance dummy variable as a control variable. In some
specifications, the regressions include group-month-year fixed effects, with group referring to quartiles of pre-pandemic
deposits and income (2019 averages), and changes in total income relative to the corresponding household average in 2019
as a control. The sample is restricted to households whose primary employer in the first quarter of 2020 is in the private
sector. The sample period is from January 2018 to September 2021. Two-way standard errors clustered by household
and month-year are reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level,
respectively.

ECB Working Paper Series No 303470Table IA.6: Household Propensity to Borrow by Eligibility and Forbearance Status

Debt Payment ×

Forbearance=0 × Eligible=1 × Post

Forbearance=1 × Eligible=0 × Post

Forbearance=1 × Eligible=1 × Post

Controls
Month × Year FE
Group × Month × Year FE
R2
Observations

Observations:

Forbearance=0 × Eligible=0

% of sample

Forbearance=0 × Eligible=1

% of sample

Forbearance=1 × Eligible=0

% of sample

Forbearance=1 × Eligible=1

% of sample

∆Borrowing Credit Card & Overdraft

∆Borrowing Other Banks’ Debt

(1)

(2)

(3)

(4)

(5)

(6)

0.002
(0.009)

-0.011
(0.012)

0.000
(0.018)

0.001
(0.008)

-0.011
(0.009)

-0.000
(0.017)

-0.000
(0.007)

-0.012
(0.009)

-0.004
(0.017)

0.014
(0.017)

0.033*
(0.017)

0.039**
(0.017)

-0.051*
(0.028)

-0.109*
(0.054)

-0.029
(0.027)

-0.080
(0.054)

-0.028
(0.027)

-0.067
(0.054)

No
Yes
No
0.012
6,043,972

No
No
Yes
0.021
6,043,972

Yes
No
Yes
0.023
6,043,972

No
Yes
No
0.002
4,395,616

No
No
Yes
0.003
4,395,616

Yes
No
Yes
0.005
4,395,616

116,732
(85.0%)
12,469
(9.1%)
6,637
(4.8%)
1,525
(1.1%)

This table presents difference-in-differences estimates of regressions of changes in credit card and overdraft, and other
banks’ debt payments using the difference-in-differences regression in equation (1) at the household-month level. The
dependent variable in columns (1)-(3), ∆Borrowing Credit Card & Overdraft, is the difference in monthly changes in end-
of-the-month credit card and overdraft balances at time t at the bank, relative to the corresponding household average
in 2019. The dependent variable in columns (4)-(6), ∆Borrowing Other Banks’ Debt, is the difference between monthly
changes in liabilities found in the Credit Register held at other banks at time t, relative to the corresponding household
average in 2019. Debt Payment is the amount of pre-pandemic debt payments made by households. Forbearance is a
dummy variable that takes the value of one for households receiving forbearance and zero otherwise. Eligible is a dummy
variable that takes the value of one if the household satisfied the legal requirements for suspending mortgage payments.
Post is a dummy variable that takes a value of one for the period starting when mortgage payments are suspended and
zero otherwise. All specifications include the F orberance dummy variable as a control variable. In some specifications, the
regressions include group-month-year fixed effects, with group referring to quartiles of pre-pandemic deposits and income
(2019 averages), and changes in total income relative to the corresponding household average in 2019 as a control. The
sample period in columns (1)-(3) is from January 2018 to September 2021. The sample period in columns (4)-(6), due to
data limitations, is from January 2019 to September 2021. Two-way standard errors clustered by household and month-year
are reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

ECB Working Paper Series No 303471)
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ECB Working Paper Series No 303473Acknowledgements 

We thank Paul Goldsmith-Pinkham, Isaac Hacamo, Sasha Indarte, and Amit Seru; participants at the CEPR European Workshop on 
Household Finance, Finance Forum, Luso-Brazilian Finance Conference (Lubrafin), NBER Summer Institute Workshop on Capital 
Markets and the Economy, Nova SBE-WU Workshop, Stanford Institute for Theoretical Economics (SITE) Financial Regulation Session, 
University of Tennessee “Smokey” Mountain Finance Conference; and seminar participants at Bayes Business School, European 
Central Bank, Tinbergen Institute, University of Naples Federico II (CSEF), and Washington University in St. Louis for helpful comments. 
The contribution by Miguel Ferreira has been prepared under the Wim Duisenberg Fellowship Program sponsored by the ECB. Any 
views expressed are only those of the authors and do not necessarily represent the views of the ECB or Eurosystem 

Manuel Adelino 
Duke University’s Fuqua School of Business, North Carolina, United States; Centre for Economic Policy Research, London, United 
Kingdom; National Bureau of Economic Research (NBER), Massachusetts, United States; email: manuel.adelino@duke.edu 

Miguel A. Ferreira 
Nova School of Business and Economics, Lisbon, Portugal; Centre for Economic Policy Research, London, United Kingdom; European 
Corporate Governance Institute, Brussels, Belgium; email: miguel.ferreira@novasbe.pt 

Miguel Oliveira 
Nova School of Business and Economics, Lisbon, Portugal; email: miguel.oliveira@novasbe.pt 

© European Central Bank, 2025 

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This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
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ISBN 978-92-899-7116-4 

ISSN 1725-2806 

doi:10.2866/4885866 

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