Households' response to the wealth effects of inflation.
Date: 2024-02-14
Institutions: European Central Bank
Subjects: household consumption, household income, inflation, monetary policy, wealth

Working Paper Series 

Philip Schnorpfeil, Michael Weber, 
Andreas Hackethal 

Households' response to the wealth 
effects of inflation 

ECB – Lamfalussy 
Fellowship Programme 

No 2904 

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. 

 
 
ECB Lamfalussy Fellowship Programme 

This paper has been produced under the ECB Lamfalussy Fellowship programme. This programme was launched in 2003 in the context 
of the ECB-CFS Research Network on “Capital Markets and Financial Integration in Europe”. It aims at stimulating high-quality research 
on the structure, integration and performance of the European financial system.  

The Fellowship programme is named after Baron Alexandre Lamfalussy, the first President of the European Monetary Institute. Mr 
Lamfalussy is one of the leading central bankers of his time and one of the main supporters of a single capital market within the 
European Union. 

Each year the programme sponsors five young scholars conducting a research project in the priority areas of the Network. The 
Lamfalussy Fellows and their projects are chosen by a selection committee composed of Eurosystem experts and academic scholars. 
Further information about the Network can be found at http://www.eufinancial-system.org and about the Fellowship programme under 
the menu point “fellowships”. 

ECB Working Paper Series No 29041Abstract

We study the redistributive eﬀects of surprise inﬂation combining administrative
bank data with an information provision experiment during an episode of historic
inﬂation. On average, households are well-informed about prevailing inﬂation and are
concerned about its impact on their wealth; yet, while many households know about
inﬂation eroding nominal assets, most are unaware of nominal-debt erosion. Once they
receive information on the debt-erosion channel, households view nominal debt more
positively and increase estimates of their own real net wealth. These changes causally
aﬀect actual consumption and hypothetical debt decisions. Our ﬁndings suggest that
real wealth mediates the sensitivity of consumption to inﬂation once households are
aware of the wealth eﬀects of inﬂation.

JEL codes: D12, D14, D83, D84, E21, E31, E52
Keywords: Inﬂation Beliefs, Information Treatment, Consumption, Monetary Policy

ECB Working Paper Series No 29042Non-technical summary

Unexpected inﬂation erodes the real value of nominal savings and debt, redistributing wealth
from savers to borrowers. These wealth eﬀects often dwarf income eﬀects of surprise inﬂation
because households hold large nominal positions. Theoretically, this redistribution of wealth
can also boost aggregate demand, because those that beneﬁt, consumers with net debt, have
on average higher marginal propensities to consume than consumers who lose. Yet, little is
known about whether households are aware of the distributional consequences of surprise
inﬂation and how they adjust their economic decisions to the induced wealth eﬀects.

We study how households respond to the wealth eﬀects of inﬂation using a randomized
control trial on several thousand bank customers in Germany in July 2022 when inﬂation
was at a 70-year high. We ﬁrst ask questions on knowledge about the wealth-erosion channel
and respondents’ wealth positions. We then randomly assign respondents into two treatment
groups and one control group. We inform all groups about prevailing inﬂation. One treat-
ment additionally discusses the erosion of savings, whereas the other discusses the erosion of
loans. Speciﬁcally, we explain that unexpected inﬂation hurts savers / beneﬁts debtors be-
cause it erodes nominal positions and we calculate the change in real value of a savings prod-
uct / loan due to inﬂation. By comparing the treatment groups to the control group, we can
thus isolate the eﬀect of information about the erosion channel of inﬂation. Post-treatment,
we elicit beliefs about nominal positions, own wealth, and the economy. Moreover, we ask
respondents about their spending plans, they perform a hypothetical real-estate investment,
and we track their actual spending using bank data.

We report three ﬁndings. First, households exhibit asymmetric awareness of the erosion
channel. Households on average are well-informed about nominal-asset erosion; however,
knowledge about loan erosion is more limited. Limited awareness of debt erosion, including of
debtors, suggests muted short-run responses to the redistributive eﬀects of inﬂation. Limited
awareness is also surprising, because survey respondents are relatively well educated, hold
large nominal positions, and state they care about inﬂation and its wealth eﬀects.

Second, providing information on the erosion channel, in particular on debt, aﬀects be-
liefs. We ask respondents to rank household balance-sheet items in terms of their inﬂation
protection. The savings-erosion treatment group ranks savings products slightly worse than
the control group in terms of inﬂation protection. Respondents who receive the loan treat-

ECB Working Paper Series No 29043ment, instead, rank loans much more favorably. Moreover, treated respondents update their
estimates of real net wealth: those who receive the savings-erosion information on average
estimate their change in real net wealth over the past and next 12 months 1.6–1.8 percentage
points lower than respondents in the control group. Learning about inﬂation-induced loan
erosion increases wealth estimates relative to the control group by 2.5–2.9 percentage points.
Respondents with larger nominal positions drive the respective eﬀects.

Third, learning about inﬂation-induced nominal-debt erosion has real eﬀects both in
survey data and in actual account-transaction data from the partner bank. We study house-
holds’ spending plans as well as their actual spending over the weeks after our survey in-
tervention compared to the previous weeks. Learning about debt erosion leads to higher
planned and actual spending, suggesting changes in real wealth aﬀect the consumption re-
sponse to inﬂation when households aware of the wealth eﬀects of inﬂation.
In addition
to spending, we show learning about debt erosion also aﬀects debt choices. In the survey,
respondents engage in a hypothetical real-estate transaction, choosing the value of property
and the ﬁnancing of it. Respondents in the loan treatment choose real estate of similar value
compared to respondents in the control group. However, the loan-treatment group prefers a
signiﬁcantly higher debt/price ratio and tilts the mortgage choice towards ﬁxed- rather than
adjustable-rate mortgages. These choices likely reﬂect treatment-induced shifts in beliefs
about the inﬂation-hedging properties of long-maturity ﬁxed debt.

Overall, our results increase the understanding of redistributive eﬀects of surprise inﬂation
at the micro level and provide useful insights for recent theoretical work featuring heteroge-
neous consumers, such as Heterogeneous-Agent New Keynesian models. In this work, wealth
redistribution caused by surprise inﬂation boosts the economy because the beneﬁciaries of
it, net debtors, have higher spending propensities than the losers of it. Limited awareness
of inﬂation-induced debt erosion suggests muted debtor responses in the short run. Ex-
tending the modeling framework by allowing for deviations from full information rational
expectations would be a fruitful avenue for future research.

ECB Working Paper Series No 290441

Introduction

After having been dormant for decades, inﬂation in 2022 reached levels many households had

not witnessed during their lifetime. Unexpected inﬂation erodes the real value of savings and

debt with ﬁxed nominal interest rates, redistributing wealth from savers to borrowers (Fisher,

1933).1 These redistributive eﬀects can be sizable because households hold large nominal

positions (Doepke and Schneider, 2006); in fact wealth eﬀects dwarfed income eﬀects of

surprise inﬂation for households with signiﬁcant net nominal positions during the 2021–22

inﬂation surge (Pallotti et al., 2023). Theoretically, this redistribution of wealth can also

have sizable stimulative eﬀects on aggregate demand, because those that beneﬁt, consumers

with large negative net nominal positions, have on average higher marginal propensities to

consume (MPC) than consumers who lose (Auclert, 2019).

Yet, little is known about whether households are aware of the distributional consequences

of surprise inﬂation and how they adjust their economic decisions to the induced wealth

eﬀects. Households might be unaware of the wealth eﬀects of inﬂation because of money

illusion (e.g., Cohen, Polk, and Vuolteenaho, 2005; Modigliani and Cohn, 1979). Moreover,

even households that are aware of the wealth eﬀects might primarily adjust their consumption

to realized payments rather than unrealized capital gains (e.g., Di Maggio, Kermani, and

Majlesi, 2020; Lettau and Ludvigson, 2004). Because nominal wages take time to adjust

to inﬂation, it might also take time for households to adjust their consumption, savings,

investment and debt decisions, even if they become wealthier in real terms.

We study the extent to which households are aware of the wealth eﬀects of inﬂation, how

awareness aﬀects beliefs about nominal positions and own wealth, and how these beliefs feed

into debt choices, consumption plans, and actual consumption decisions using administra-

1See also Bhamra et al., 2023; Kang and Pﬂueger, 2015; and Leombroni et al., 2020.

ECB Working Paper Series No 29045tive data. To study these questions, we run a large-scale randomized control trial (RCT) on

customers of a major German bank in which participants receive information on inﬂation-

induced erosion of either nominal assets, or nominal debt. We ﬁnd participating households,

on average, are well-informed about the current inﬂation rate and concerned about its wealth

impact. However, whereas households are largely aware of nominal-asset erosion, they have

limited knowledge about the debt-erosion channel of surprise inﬂation. Consequently, re-

spondents who receive information about nominal-debt erosion form more positive beliefs

about debt, as well as increase their estimates of their own real net wealth. Households’

changes in beliefs have real eﬀects: they plan to spend more, update actual consumption ex

post, and choose a higher share of debt ﬁnancing in a hypothetical real-estate investment.

We implemented the RCT on several thousand bank customers in July 2022 when inﬂa-

tion in Germany was at a 70-year high of 8.7%. We ﬁrst ask questions on knowledge about

the erosion channel. Respondents also estimate the recent change in their real net wealth

and decompose their balance sheet, allowing us to calculate their net nominal position. The

subsequent information experiment builds the core of the survey. We randomly assign re-

spondents into two treatment groups and one control group. We inform all groups about

prevailing inﬂation. One treatment additionally discusses the erosion of savings, whereas

the other discusses the erosion of loans. Speciﬁcally, we explain that unexpected inﬂation

hurts savers / beneﬁts debtors because it erodes nominal positions and calculate the change

in real value of a savings product / loan due to inﬂation. By comparing the treatment

groups to the control group, we can thus isolate the eﬀect of information about the erosion

channel of inﬂation. Post-treatment, we elicit beliefs about nominal positions, own wealth,

and the economy. Moreover, we ask respondents about their spending plans, they perform

a hypothetical real-estate investment, and we track their actual spending using bank data.

ECB Working Paper Series No 29046We have three main ﬁndings. First, we document asymmetric awareness of the erosion

channel. Households on average are well-informed about nominal-asset erosion. For example,

75% of respondents believe the impact of unexpected inﬂation on ﬁxed-rate savings products

is very negative or negative. However, knowledge about loan erosion is more limited. Only

9% of respondents believe the impact of unexpected inﬂation on ﬁxed-rate loans is very

positive and 25% believe it is positive. Limited awareness of debt erosion, including of

debtors, suggests muted short-run responses to the redistributive eﬀects of inﬂation.

Limited awareness is surprising, because survey respondents are on average better edu-

cated than the average German, hold large nominal positions (e.g., 55% have outstanding

debt), and state they care about inﬂation and its wealth eﬀects. Which characteristics then

predict awareness of the erosion channel? First, awareness only weakly varies by households’

net nominal position. That is, debtors, for example, are not signiﬁcantly more aware of the

redistributive eﬀects of inﬂation. Second, business education, wealth, and stock ownership

correlate with awareness. Third, general knowledge about inﬂation, such as an accurate

perception of current inﬂation, predicts awareness of the wealth eﬀects of inﬂation.

Second, providing information on the erosion channel, in particular on debt, aﬀects beliefs

about nominal positions and own real net wealth. We ask respondents to rank household

balance-sheet items in terms of their inﬂation protection. The savings-erosion treatment

group assigns a slightly worse inﬂation-protection rank to savings products than the control

group. Respondents who receive the loan treatment rank loans more favorably. In addition,

learning about inﬂation-induced debt erosion reduces general debt aversion, suggesting that

information eﬀects extend beyond the context of inﬂation. Knowledge about the wealth

eﬀects of inﬂation hence impacts beliefs about nominal positions.

Treated respondents also update their estimates of real net wealth. Conditioning on

ECB Working Paper Series No 29047pre-treatment estimates of wealth changes, we ﬁnd respondents who receive the savings-

erosion information on average estimate their change in real net wealth over the past and

next 12 months 1.6–1.8 percentage points lower than respondents in the control group.

Learning about inﬂation-induced loan erosion, instead, increases wealth estimates relative to

the control group by 2.5–2.9 percentage points. This latter eﬀect is large: it corresponds to

around 50% of the average estimate of -5.6% in the control group. Respondents also report

they were less aware of the information provided in the loan as compared to the savings

treatment, suggesting the diﬀerential treatment eﬀects reﬂect asymmetric prior awareness.

We then study heterogeneity in the treatment eﬀect on perceived and expected real net

wealth. Respondents with a positive net nominal position (net savers) drive the negative

average eﬀect of the savings treatment, but the economic magnitude remains modest. The

positive wealth eﬀect of the loan treatment comes from respondents with a negative net

nominal position (net debtors), who on average estimate their past- and next-12-month

change in real net wealth ﬁve percentage points higher. Furthermore, we study heterogeneity

in respondents’ ability to map the loan-treatment information into their own wealth situation.

The sensitivity of the wealth eﬀect of the loan treatment to net nominal positions is strongest

for respondents with high cognitive abilities and strong interest in the topic of inﬂation

(Agarwal and Mazumder, 2013; D’Acunto et al., 2022).

Third, learning about inﬂation-induced nominal-debt erosion has real eﬀects both in

survey data and in actual account-transaction data from our partner bank. We focus on

the eﬀects of the debt-erosion treatment because only this treatment generates signiﬁcant

eﬀects on beliefs. We study households’ spending plans as well as their actual spending over

the weeks after our survey intervention compared to the previous weeks. Learning about

debt erosion leads to higher planned and actual spending, both in reduced-form and in

ECB Working Paper Series No 29048instrumental-variable estimations. The estimates map into MPCs of close to 3%, in line with

estimates from the literature on MPCs from unrealized capital gains (e.g., Chodorow-Reich,

Nenov, and Simsek, 2021; Di Maggio, Kermani, and Majlesi, 2020). Our results suggest

changes in real net wealth aﬀect households’ consumption response to inﬂation, conditional

on households being aware of the wealth eﬀects of inﬂation.

Finally, we show another real eﬀect of learning about debt erosion, on hypothetical debt

choices. In the survey, respondents engage in a hypothetical real-estate transaction. The

transaction encompasses choosing both the value of the property and the ﬁnancing of it,

using both equity and debt. Respondents in the loan treatment choose real estate of similar

value compared to respondents in the control group. However, the two groups diﬀer in

their preferred debt/price ratio, which is signiﬁcantly higher in the loan-treatment group.

Moreover, the loan treatment tilts the mortgage choice towards ﬁxed- rather than adjustable-

rate mortgages, with longer ﬁxation periods for nominal interest rates. These choices likely

reﬂect treatment-induced shifts in beliefs about the inﬂation-hedging properties of long-

maturity ﬁxed debt and an associated reduction in debt aversion.

Overall, our results increase the understanding of redistributive eﬀects of surprise inﬂation

at the micro level and provide useful moments to target for models featuring heterogeneous

consumers such as Heterogeneous-Agent New Keynesian (HANK) models (e.g., Kaplan, Moll,

and Violante, 2018). Auclert (2019) shows wealth redistribution caused by surprise inﬂation

boosts the economy because the beneﬁciaries of it, net debtors, have higher MPCs than

the losers of it. Limited awareness of inﬂation-induced debt erosion suggests muted debtor

responses in the short run. Extending the HANK framework by allowing for deviations

from full information rational expectations (FIRE), for example via information frictions,

would be a fruitful avenue for future research. More generally, a positive covariance between

ECB Working Paper Series No 29049individual MPCs and exposure to economic shocks is a core ampliﬁcation mechanism in

HANK models (e.g., Bilbiie, 2008; Patterson, 2023). Allowing additionally for heterogeneity

in the awareness of these shocks might help to better understand their aggregate consequences

(Pf¨auti and Seyrich, 2022; Pf¨auti, Seyrich, and Zinman, 2023).

Related literature We contribute to several strands of the literature. First, we build

on work on inﬂation-induced debt erosion that goes back to at least Fisher (1933). Doepke

and Schneider (2006) document the net nominal positions of diﬀerent economic actors in the

U.S. over time and study the redistributive eﬀects of various inﬂation scenarios within and

across these groups. A key ﬁnding is young households with large debt positions beneﬁt at

the expense of wealthy middle-aged households. Auclert (2019) and Pallotti (2023) study

theoretically the redistributive eﬀects of monetary policy on spending based on diﬀerential

MPCs of winners and losers of the wealth eﬀects of inﬂation. Coibion et al. (2017) document

inequality dynamics due to monetary policy, including by household net wealth. Brunner-

meier et al. (2023) and Gomes, Jermann, and Schmid (2016) analyze how unanticipated

inﬂation transmits to the real economy through nominal corporate debt. We are the ﬁrst to

study how households perceive and adjust to the redistributive eﬀects of inﬂation, using an

information provision experiment paired with administrative bank data.

Second, we contribute to a burgeoning literature on households’ response to inﬂation. Ev-

idence on the sensitivity of spending to inﬂation expectations is mixed. Some studies report

a positive eﬀect of inﬂation expectations on spending, consistent with an intertemporal-

substitution motive, whereas other studies ﬁnd households associate higher future inﬂation

with worse economic outcomes and hence spend less if researchers do not condition on eco-

nomic outlook (see D’Acunto, Malmendier, and Weber (2023) and Weber et al. (2022) for

recent surveys of work in this area). We add to this literature by showing that wealth eﬀects

ECB Working Paper Series No 290410of inﬂation mediate the consumption-inﬂation sensitivity once households are aware of the

eﬀects. Diﬀerences across studies in how households update their real-net-wealth estimates

to inﬂation can hence possibly also rationalize part of the mixed results in the literature.

Beyond eﬀects on spending, the literature studies how households seek protection from

inﬂation. Botsch and Malmendier (2020) ﬁnd that experiencing high inﬂation in the 1970s

is associated with an aversion to adjustable-rate mortgages. Leombroni et al. (2020) use an

asset-pricing model to link high inﬂation expectations in the 1970s to a portfolio shift toward

housing. Braggion, von Meyerinck, and Schaub (2023) ﬁnd investors purchase fewer stocks

when facing higher inﬂation during the hyperinﬂation in Germany in the 1920s. Schnorpfeil,

Weber, and Hackethal (2023) use an RCT to study the eﬀects of beliefs about inﬂation on

portfolio choices. We add to these papers by showing that learning about inﬂation-induced

debt erosion has a positive eﬀect on beliefs about debt and increases debt ﬁnancing.

Finally, a recent literature in economics employs information-provision experiments to

study how consumers, who make consumption, savings, and investment decisions in the

ﬁeld, perceive economic policies and phenomena, as well as their eﬀects (e.g., Andre et al.,

2022; D’Acunto et al., 2022).2 We contribute to this literature by analyzing how households

perceive the wealth eﬀects of inﬂation, how they map information about these eﬀects into

their own economic situation in an experimental setting, and how exogenous changes in their

economic situation feed into real-world choices. A methodological novelty is explaining an

economic mechanism rather than purely providing information about a single variable, such

as a point forecast for inﬂation, as is common in the macro literature (e.g., Coibion, Gorod-

nichenko, and Weber, 2022). We thereby manipulate the interpretation of the consequences

of inﬂation instead of exogenously varying inﬂation expectations.

2See Haaland et al. (2023) for a review on how to run surveys and implement information experiments.

ECB Working Paper Series No 2904112 Experimental design and data

In this section, we discuss the survey design and characteristics of our sample. Section 2.1 fo-

cuses on the survey design, emphasizing the information-provision part of the survey, whereas

Section 2.2 describes the bank data as well as the sample composition and characteristics.

2.1 Experiment

We implemented the RCT on more than 3,800 bank customers in July 2022. The survey

consists of three sections: a pre-treatment section on demographics, respondents’ balance

sheet, and economic beliefs; an information-provision section; and a post-treatment section

on economic beliefs, hypothetical and planned economic choices, and additional background

characteristics. Online Appendix B contains the survey questions translated to English.

Pre-treatment section Respondents start by answering two questions on their educa-

tional background. We then assess respondents’ marginal propensity to save, consume, and

pay down debt in the three months following a hypothetical one-time payment of e 10,000

(see, e.g., Fuster, Kaplan, and Zafar, 2020), framed as either a gain or a loss. In addition,

respondents receive a slight variation of this question asking about their hypothetical re-

sponse to noticing by chance that their own net wealth is e 10,000 higher or lower than

previously thought. We randomize the order of the two questions. The two questions allow

us to infer diﬀerential hypothetical spending responses to unexpected actual payments versus

unexpected changes in perceived net wealth with no actual cash ﬂows involved.

Respondents then answer questions about the economy and its impact on their net wealth.

The questions include the importance of macroeconomic factors, such as inﬂation and GDP,

for their own wealth; the consumption response to recent changes in these factors; the percep-

ECB Working Paper Series No 290412tion of current and forecasts of future inﬂation; and whether holding cash, ﬁxed-rate savings

products, stocks, real estate, and ﬁxed-rate loans provide a hedge against an unexpected

surge in inﬂation. These questions aim to elicit existing knowledge about inﬂation-induced

erosion of nominal assets and liabilities.

Furthermore, we ask respondents to provide a decomposition of their balance sheet into

nominal assets (we mention cash, bonds, and life insurances as examples), stocks, real estate,

other assets (we mention vehicles and gold as examples), and nominal liabilities (divided into

mortgages and consumer loans).3 Respondents state the value of each balance-sheet item as

a fraction of their gross wealth. Given the cognitive burden of this question, we carefully

explain the concept of gross wealth and provide several examples. Moreover, we provide

a warning message if the sum of respondents’ assets diﬀers from their gross wealth. The

median time survey participants spent on the balance-sheet-decomposition screen is more

than two minutes, indicating that they carefully answer the question.

Respondents also estimate the change in their real net wealth over the past 12 months.

We follow the design in the New York Fed Survey of Consumer Expectations (Armantier

et al., 2017) and ﬁrst elicit a directional estimate before asking for a point prediction, on a

scale from -60% to 60%. We familiarize respondents to net and gross wealth in the preceding

balance-sheet-decomposition task, by providing a simple sample balance sheet to diﬀerentiate

between gross and net wealth and by stating their net wealth as a fraction of their gross

wealth. As we discuss in Section 2.2 below, the elicited responses appear meaningful.

Treatment section The second part of the survey contains the information provi-

sion experiment. The objective of the intervention is to generate exogenous variation in

knowledge about inﬂation-induced erosion of nominal assets and liabilities. To generate this

3We follow Adam and Zhu (2015) in classifying life insurances as nominal claims, as insurance companies

in the euro area predominantly invest in nominal assets.

ECB Working Paper Series No 290413variation, we randomly split the sample into three equally-sized groups, two of which receive

information and one serves as a control group. The two treatments are similar, other than

that one focuses on the erosion of nominal assets, whereas the other focuses on the erosion

of nominal liabilities. Our speciﬁc treatments are:

The current inﬂation rate in Germany is 8.7%, the highest rate in more
than 70 years. That is, goods and services priced at e100 one year ago now
cost e108.7 on average. This price increase has a relatively negative eﬀect
on savers: the savings amount (e.g., checking account, bond, life insurance) is
unchanged nominally or lower, but worth less in real terms as a consequence of
money depreciation.
As an example, consider a e50,000 savings product with a three-year maturity
that you took out one year ago. The real value of the savings product has already
fallen sharply, and will depreciate further if inﬂation remains high:

e50,000 savings value one year ago ⇓ e38,800 real value today

The inﬂation-induced savings depreciation thus has a negative eﬀect on the
real net wealth of savers.

Note: the numbers come from current calculations by the Universities of Chicago
and Frankfurt (calculation details).

Respondents in the loan-erosion treatment group read the following text:

The current inﬂation rate in Germany is 8.7%, the highest rate in more
than 70 years. That is, goods and services priced at e100 one year ago now
cost e108.7 on average. This price increase has a relatively positive eﬀect on
borrowers: the loan amount is unchanged nominally, but worth less in real terms
as a consequence of money depreciation.
As an example, consider a e50,000 loan with a three-year maturity that you took
out one year ago. The real value of the loan has already fallen sharply, and will
depreciate further if inﬂation remains high:

e50,000 loan value one year ago ⇓ e38,800 real value today

The inﬂation-induced loan depreciation thus has a positive eﬀect on the real
net wealth of borrowers.

ECB Working Paper Series No 290414Note: the numbers come from current calculations by the Universities of Chicago
and Frankfurt (calculation details).

Respondents assigned to the control group receive only the ﬁrst two sentences on inﬂa-

tion. We provide this information, instead of having a fully passive control group, because

information on current inﬂation being historically high can have a confounding eﬀect on

post-treatment beliefs and choices, for example via eﬀects on income expectations or labor-

supply decisions. By comparing respondents in the treatment groups with respondents in

the control group, we can identify the eﬀect of learning about the inﬂation-induced erosion

of nominal assets and liabilities and absorb any eﬀect that the information about the current

inﬂation level might have.

The real-value numbers in the treatments come from a simple present-value calculation.

Real changes in the positions’ present value occur because future nominal cash ﬂows are

discounted at higher interest rates. Discount rates increase linearly over the course of one

year, because inﬂation in 2021–22 increased gradually. We disregard any reallocation or

reinvestment after the products’ maturity. We thereby assume exposure to nominal erosion

ends at maturity, or that inﬂation suddenly drops back to its baseline level.4 At the end of

the treatment text, respondents can click on a button to see calculation details.5

Post-treatment section Following the information intervention, we ﬁrst elicit 12-

month-ahead expectations for real-estate prices, the unemployment rate, interest rates, and

respondent’s income. If the treatments aﬀect some of these expectations, it might be through

these expectations that respondents alter their economic choices. For example, Coibion et al.

(2023) show that people’s macroeconomic outlook causally aﬀects their consumption. We

4This assumption is similar to the lower-bound scenario by Doepke and Schneider (2006), who calculate
wealth eﬀects of inﬂation assuming that households switch to inﬂation-indexed securities once their nominal
positions expire.

519% of respondents click on the button; they do not diﬀer in their survey responses.

ECB Working Paper Series No 290415investigate this possible alternative channel below.

We then ask survey participants about planned spending on nondurables and durables.

Following Roth and Wohlfart (2020), respondents state their consumption plans for multiple

nondurables categories over the next four weeks relative to the previous four weeks. Response

options, on a ﬁve-point scale, range from “much less” to “much more.” The short time window

mitigates concerns that changes in inﬂation or other economic news confound the responses.

On durables spending, we ask whether respondents plan to make major purchases over the

next 12 months, such as buying a car or an apartment. If they do, we ask them to state

the amount they plan to spend. These self-reported spending plans are useful because (i)

we cannot observe actual account transactions for all respondents, (ii) we can distinguish

between nondurables and durables spending, and (iii) we can corroborate treatment eﬀects

on actual spending with spending plans.

We again elicit respondents’ perception about the past-12-month change in their real net

wealth, as well as their expectation for the next 12 months. This time, respondents directly

provide a point estimate, with answer options between -60% and 60%. We elicit posteriors

for real net wealth changes in a diﬀerent format compared to priors to mitigate concerns of

survey fatigue and demand eﬀects, following Coibion, Gorodnichenko, and Weber (2022).6

We are therefore able to study the instantaneous revision in own-wealth perceptions and

expectations in response to the provided information.

The ﬁnal belief questions relate to nominal assets and debt. We elicit beliefs about the

relative wealth protection that savings products, stocks, real estate, and ﬁxed-rate loans

provide. In addition, we ask about debt aversion (Almenberg et al., 2021). Moreover, re-

spondents engage in a hypothetical real-estate transaction. They choose a preferred purchase

6De Quidt, Haushofer, and Roth (2018) show that demand eﬀects tend to be small in settings like ours.

ECB Working Paper Series No 290416price and mode of ﬁnancing, with up to e 500,000 equity and a mortgage of up to e 500,000.

Respondents also choose whether they prefer an adjustable- or a ﬁxed-rate mortgage, and if

they choose the latter, the length of the ﬁxation period.

The last section of the survey elicits respondents’ risk tolerance, money illusion (Shaﬁr,

Diamond, and Tversky, 1997), and the value of their nominal positions. After completion,

we ask respondents how interesting they found the survey and they can leave comments.

2.2 Data

Survey administration We run the survey experiment in partnership with a large German

bank. The bank oﬀers both retail-banking and brokerage services. In July 2022, the bank

sent out a short email to around 215,000 customers, inviting them to participate in a survey

on inﬂation administered by Goethe University Frankfurt. The survey was in the ﬁeld for

two weeks. After the ﬁrst week, the bank sent out a reminder email, informing customers

they have one week left to complete the survey. During the ﬁeld period, inﬂation in Germany

was at 8.7%, the highest rate in more than 70 years.

Overall, 3,846 bank customers complete the survey. 45% of respondents complete the

survey within a day after the bank sent the invitation email, and 43% of the responses

came in within a day after the reminder email. The overall response rate is 1.8%, which is

comparable to other surveys of the bank and the median response time is 18.3 minutes.7

Administrative bank data The partnering bank provides us with data on customer

demographics and, importantly, categorized account transactions. Demographic information

include age, gender, marital and employment status, and zip code. Account transactions

7When compared to bank customers who receive a survey invitation but do not participate, respondents
are more likely to be male, use the partnering bank’s online banking, have outstanding loans with the bank,
have a securities portfolio with the bank, and have higher income.

ECB Working Paper Series No 290417come from the bank’s personal-ﬁnancial-management (PFM) tool, which registers customers’

in- and outﬂows and classiﬁes them into more than 50 categories.8

Broad spending categories include expenses on living (e.g., groceries and clothing); hous-

ing (e.g., rent and furniture); leisure (e.g., restaurants and events); mobility (e.g., cars and

fuel); health (e.g., pharmacies and hospitals); occupation and education (e.g., oﬃce supplies

and tuition fees); insurances, loans, and investments; cash withdrawals; credit cards; and

online shopping.

Income categories include salaries, other forms of regular income (e.g.,

pension and rental income), additional sources of income (e.g., tax refunds and children’s

allowances), and capital income (e.g., dividends).

Around one third of transactions are uncategorized. These transactions usually con-

stitute payments to a party unknown to the categorization algorithm, such as peer-to-peer

transactions or transfers between accounts. We omit uncategorized transactions for our main

consumption measures, because we do not know whether these transactions reﬂect consump-

tion. In a robustness test, we incorporate uncategorized transactions identiﬁed by the bank

as likely representing consumption to our spending measures.

We consider three measures of consumption. Total spending subtracts from all cate-

gorized outﬂows those related to investments, insurances, and loans. We also split total

spending into discretionary and nondiscretionary spending, following D’Acunto, Rossi, and

Weber (2023). Discretionary spending includes categories such as clothing, leisure, cash

withdrawals, and online shopping. Nondiscretionary spending is the diﬀerence between to-

tal and discretionary spending. Appendix Table A1 details all categories as well as the

subcomponents of our diﬀerent measures of consumption.

8The bank deﬁnes categories based on the classiﬁcation used by the German Federal Statistical Oﬃce.
The tool is similar to personal-ﬁnance apps such as Mint; an important diﬀerence, however, is that the tool
is embedded in the bank’s online-banking environment.

ECB Working Paper Series No 290418Sample selection Together with the bank, we select the survey sample based on two

criteria. First, because of the importance of observing actual consumption choices, all bank

customers with an activated PFM tool and who had received any cash inﬂows over the

past six months receive a survey invitation. We randomly incentivize survey participation

with either a voucher, or participation in a lottery. All payoﬀs are in the form of online-

shopping vouchers.9 Second, bank customers who do not satisfy the above criteria but have

an outstanding mortgage or consumer loan at the partnering bank receive a survey invitation,

which comes with the lottery incentive (15% of the selected sample). Given their outstanding

debt, these customers should be particularly exposed to inﬂation-induced debt erosion.

Given noise in survey data (D’Acunto, Fuster, and Weber, 2021), we take two steps to

ﬁlter respondents. First, we omit 563 respondents who make wrong or implausible entries in

the balance-sheet-decomposition task: those who enter a negative share of a balance-sheet

item relative to gross wealth; a share greater than one, that is, the value of a single balance-

sheet item exceeds gross wealth; a share of nominal assets equal to zero; or a value of an

outstanding mortgage, consumer loan, or the sum of the two that is equal to gross wealth.10

Second, we drop respondents who take less than seven minutes or more than 120 minutes to

complete the survey, roughly corresponding to the 1.5th and 98.5th percentiles. After these

two steps, 3,190 individuals remain in the baseline sample. The results are robust to not

performing the two screening steps.

Our sample to analyze treatment eﬀects on spending using bank data comprises 2,671

bank customers. We arrive at this smaller sample because some survey participants with

9Survey responses do not systematically diﬀer by type of participation incentive.
10Most respondents (10%) that we screen out enter mortgage and/or consumer-loan values that sum up
to the value of their gross wealth. Because we ask respondents for the sum of assets to equal 100% of gross
wealth, some respondents might have mistakenly assumed that the sum of all liabilities should also equal
100% gross wealth. We are unable to calculate the net nominal position of these survey respondents.

ECB Working Paper Series No 290419outstanding debt at the bank have not activated the PFM tool, and hence we cannot observe

their account transactions. Moreover, we want to ensure that our bank-customer sample

actively uses the observed bank accounts. We therefore require the sample to receive at least

e 100 as average regular income per month on observable accounts.11

Sample characteristics Table 1 reports summary statistics for our sample, with basic

demographics in the top panel. 45% of respondents are female and the average age is 48

years. The educational level is relatively high, with 48% having completed college, around

20% of them in business. We benchmark our sample against the most recent (2017) wave

of the Bundesbank’s Panel on Household Finances (PHF), which is a representative survey

of German households’ ﬁnances and comprises the German data for the ECB’s Household

Finance and Consumption Survey. In the PHF, the share of the population with completed

college education is 29%. 72% of the sample is employed (54% in the PHF).

Regarding household ﬁnances, respondents’ average gross wealth is e 386,000 (e 238,000

in the PHF). Average nominal assets are 43% (37% in the PHF) and mean nominal debt is

17% of gross wealth (10% in the PHF). The average net nominal position is hence 26% of

gross wealth. We follow Auclert (2019) and Doepke and Schneider (2006) by deﬁning the net

nominal position as all nominal assets minus all nominal liabilities. Similar to Auclert (2019),

we exclude indirect nominal positions, which originate from investment intermediaries and

the ownership of ﬁrms, to reduce complexity for respondents. We operationalize this measure

by taking the sum of cash and ﬁxed-rate savings products minus the sum of mortgage and

consumer loans.12 Respondents estimate the value of each balance-sheet position relative to

their gross wealth, so the net nominal position is naturally scaled by gross wealth. 54% of

11The consumption results do not rely on the cutoﬀ choice.
12We do not distinguish between ﬁxed- and variable-rate loans in the balance-sheet-decomposition task
to reduce complexity and because around 90% of loans in Germany have a ﬁxed rate. Our bank partner
only oﬀers ﬁxed-rate loans (though mortgagors in the prolongation period can have a variable rate).

ECB Working Paper Series No 290420respondents own stocks and 59% own real estate (20% and 44% in the PHF, respectively).

The middle panel of Table 1 presents statistics on average monthly spending and income.

We calculate the individual-level average based on the six months preceding the survey. Total

spending amounts to e 1,844 for the average respondent, with around 50% of it being dis-

cretionary spending. Average regular income, constituting of, among other things, salaries,

pensions, children’s allowances, and rental income, is e 2,862. Gross individual income in

the PHF is e 2,274 on average.13

The bottom panel of Table 1 reports statistics for respondent beliefs, elicited prior to

the information provision. The average perceived inﬂation rate is 8.8%, close to the actual

inﬂation rate of 8.7%. 75% of respondents have a perception error of at most 1.5 percent-

age points, likely because they actively acquire information about inﬂation when it is high

(Cavallo, Cruces, and Perez-Truglia, 2017; Weber et al., 2023). Respondents expect inﬂation

to remain high, with a mean one-year forecast of 10.4% and a ﬁve-year forecast of 10.7%.

Cross-sectional dispersion increases with the forecast horizon. The estimated average change

in the respondents’ real net wealth over the past 12 months is -7.5%. Many respondents state

that inﬂation matters for their own wealth, and relatively more so than GDP growth and

the level of interest rates. Overall, respondents on average are relatively well-educated and

wealthy, have accurate inﬂation perceptions, and are subjectively (given their beliefs) and

objectively (given their large nominal positions) strongly exposed to changes in inﬂation.

Integrity of randomization Appendix Table A2 reports a balancing table for the two

treatment groups and the control group. Our sample is largely well-balanced across groups

for a wide array of demographic characteristics and perceptions and beliefs. The three groups

13In our bank data, we observe net ﬂows, such as salaries net of taxes. We restrict income to be at
least e 100 to ensure some account usage; nonetheless, our respondents can receive income on accounts
unobservable to us. We include the same restriction in the PHF for consistency.

ECB Working Paper Series No 290421are also statistically indistinguishable from each other in terms of actual average spending

and income. Overall, only a few imbalances occur, such as for gender and wealth. To address

the slight imbalances, we include a set of control variables in all speciﬁcations.

Reliability of survey responses We try to alleviate concerns about inaccurate or

untruthful reporting in our survey (Stantcheva, 2023). First, we attempt to mitigate in-

accuracies in responses by carefully explaining the concepts we aim to elicit, tracking time

spent on the questions, and ﬁltering the sample. In addition, we test for reliability of survey

responses using administrative data provided by the bank partner (Dutz et al., 2022). We

focus on the reliability of reported outstanding debt because (i) these data are particularly

important for our analysis, (ii) the balance-sheet-decomposition task is demanding, and (iii)

holding debt in Germany may be stigmatized (e.g., D’Acunto, Schnorpfeil, and Weber, 2022).

Appendix Figure A1 shows the association between outstanding debt reported in the

survey and at the partnering bank in the month of survey participation. For debt reported in

the survey, we take the mid point of the debt-balance range respondents select. Outstanding

debt at the partnering bank increases monotonically with survey-reported debt. Importantly,

virtually no survey respondent has outstanding debt with the partnering bank when stating

in the survey they have zero debt. The debt level we observe in the bank data is generally

lower than what survey participants report, indicating that other lenders originated some

of the debt and underscoring the importance of using surveys to elicit the overall household

balance-sheet positions. The correlation between survey-reported and bank debt is 0.49.

ECB Working Paper Series No 2904223 Prior knowledge about the wealth eﬀects of inﬂation

Level of knowledge We start by documenting respondents’ prior knowledge of the wealth

eﬀects of inﬂation. Figure 1 shows a large share of respondents appears aware of inﬂation-

induced erosion of nominal assets. Panel A displays the distribution of beliefs about how a

surprise increase in inﬂation aﬀects nominal positions. 88% of respondents believe that the

impact on cash on hand is “very negative” or “negative,” whereas that number is 75% for

ﬁxed-interest savings products. Panel B of Figure 1 reports respondents’ relative ranking

of nominal assets, stocks, real estate, and nominal loans in terms of wealth protection pro-

vided against unexpected inﬂation.14 70% of respondents ascribe relatively poor inﬂation

protection (rank 3 or 4) to nominal assets.15

Pre-existing knowledge about the erosion of nominal liabilities appears more limited,

however. 25% of respondents believe that the impact of unexpected inﬂation on those with

a ﬁxed-rate loan is “rather positive,” and only 9% believe it is “very positive” (top panel).

Similarly, 62% assign a relatively poor inﬂation-protection rank, of 3 or 4, to ﬁxed-rate loans

(bottom panel). Overall, the ﬁndings indicate asymmetric knowledge about the erosion of

nominal assets versus nominal debt. Incomplete knowledge is necessary for the information

intervention to plausibly have scope to aﬀect beliefs and hence choices. We therefore expect

stronger responses to the loan-erosion than to the savings-erosion treatment.

Heterogeneity in knowledge According to models of endogenous information acqui-

sition, awareness of the wealth eﬀects of inﬂation might vary with exposure to the eﬀects

(e.g., Kindermann et al., 2021; Ma´ckowiak, Matˇejka, and Wiederholt, 2023). Households

14We show statistics for the control group only, because we ask this question post-treatment. We discuss

how the treatments aﬀect responses to this question below.

15The majority of respondents assign the best inﬂation protection (rank 1) to real estate, consistent with
Malmendier and Wellsjo (2023), who show that inﬂation protection is a key motivation for homeownership.

ECB Working Paper Series No 290423with larger nominal positions might hence exhibit greater ex-ante awareness of the Fisher

channel. Figure 2 documents how prior knowledge about the wealth eﬀects of inﬂation varies

by respondents’ net nominal position. Consistent with greater awareness among those who

are more exposed, respondents with a negative net nominal position have a more positive

perception about the impact of unexpected inﬂation on ﬁxed-rate loans (left scale), but the

magnitudes are small.16 Beliefs about the impact on savings products do not vary by net

nominal position (also left scale).17 Small diﬀerences in knowledge by net nominal position

do not translate into diﬀerences in respondents’ self-reported consumption response to the

recent increase in inﬂation (right scale).18

Which other individual characteristics explain knowledge about the wealth eﬀects of in-

ﬂation? Table 2 shows results of regressing beliefs about the impact of unexpected inﬂation

on balance-sheet items, including nominal assets and debt.19 Two results stand out. First,

business education, higher wealth, and stockholdings predict knowledge about the inﬂation-

induced erosion of nominal assets (Columns 1–2) and nominal debt (Column 5). Second,

general knowledge about inﬂation correlates with awareness about the wealth eﬀects of in-

ﬂation. Especially respondents who consider inﬂation to be important for their wealth, have

an accurate perception of current inﬂation, or expect inﬂation to be lower in ﬁve years than

they perceive it today appear more knowledgeable. Appendix Table A3 shows results for a

more comprehensive set of correlates.

16We abstain from analyzing diﬀerences in the inﬂation-protection ranking (bottom panel of Figure 1) by

net nominal position because of the smaller sample size coming from a restriction to the control group.

17Appendix Table A3 conﬁrms these patterns in a multivariate analysis, controlling for a wide array of

demographic characteristics, perceptions and expectations, as well as wealth.

18Consistent with limited variation in prior knowledge by net nominal position, Appendix Figure A2 shows
that knowledge about the information provided in the treatments does not vary by net nominal position. If
anything, knowledge about savings erosion is lower for respondents with a positive net nominal position.

19The number of observations is lower than the 3,190 of the baseline sample because we trim the 1% tails

of perceived and expected inﬂation, elicited as point estimates.

ECB Working Paper Series No 2904244 The eﬀects of information treatments on beliefs

In this section, we investigate how providing information about the wealth eﬀects of inﬂation

in the survey experiment shifts beliefs of our participants. To characterize average eﬀects of

the information interventions, we estimate variants of the following equation:

posteriori = const +

2
(cid:88)

j=1

βj I{i ∈ treat j} + γ prior rnwi + controlsi + errori,

(1)

where posteriori is a post-treatment measure of beliefs of respondent i. I{i ∈ treat j}

indicates whether respondent i received treatment j. The omitted category is the control

group, so coeﬃcients {βj}2

j=1 can be interpreted as being relative to the control group.

Because we randomize the treatments, controls help with the precision of the estimates but

have no material eﬀect on the point estimates. prior rnwi refers to estimates of past real-

net-wealth change. We control for two wealth estimates because the elicitation occurs in

two steps, ﬁrst with a directional and then with a point estimate. Other controlsi include

a quadratic polynomial in the respondent’s age, risk tolerance measured on a 1–5 ordinal

scale, the log of gross wealth (1% tails winsorized), and a rich set of dummy variables for

a respondent’s gender, marital status, educational level (equal to one for college degree or

higher), a business degree, employment status, debt and stock holdings, net nominal position

relative to gross wealth (<-50%, -50% to <-25%, etc.), accuracy of perceived current inﬂation

(<1.5 pp deviation from actual rate), expectation that inﬂation will be lower in ﬁve years (1%

tails of inﬂation estimates trimmed), whether survey participation is based on the voucher

or lottery incentive, and whether participation is after the reminder email sent by the bank.

ECB Working Paper Series No 2904254.1 Eﬀects on beliefs about nominal positions

Table 3 presents the results of estimating Equation 1 with and without controls. In Columns

1–4, the dependent variable captures beliefs about relative inﬂation protection provided

by nominal assets and debt. The survey question reads: “With which of the following

ﬁnancial instruments would you expect the most positive real-net-wealth impact in times

of unexpectedly high inﬂation?” Respondents rank nominal assets, stocks, real estate, and

nominal liabilities. Hence, responses are on a 1–4 ordinal scale, which we reverse for simplicity

so that a higher number indicates a better hedge against unexpectedly high inﬂation.20 In

Columns 5–6, the dependent variable proxies debt aversion, based on reported agreement

with the statement “I am uncomfortable with taking on debt.” Five possible responses range

from “completely disagree” to “completely agree.” We standard the dependent variables.

Provision of information about the wealth eﬀects of inﬂation impacts beliefs about nom-

inal positions. Respondents who learn about inﬂation-induced erosion of nominal savings

attach a signiﬁcantly poorer inﬂation-protection ranking to nominal assets (Column 1–2).

Provision of the loan-erosion treatment has a signiﬁcantly positive eﬀect on the ranking of

nominal debt (Columns 3–4). We do not ﬁnd strong evidence of cross-learning, that is, re-

spondents who learn about the erosion of nominal assets due to unexpectedly high inﬂation

do not infer high inﬂation also erodes nominal debt, and vice versa.21 Moreover, learning

about inﬂation-induced loan erosion reduces individuals’ debt aversion, indicating that ef-

fects extend beyond the direct inﬂation context (Columns 5–6). Overall, knowledge about

the wealth eﬀects of inﬂation impacts beliefs about nominal assets and debt, opening up the

possibility to aﬀect economic choices, which we discuss in Section 5.2.

20The reduced sample size is due to the fact that respondents were not required to respond to the question.
21A statistically weakly signiﬁcant coeﬃcient on the loan-treatment indicator in Columns 1–2 is partially

mechanical, compensating for the treatment-induced eﬀect on the ranking of nominal debt.

ECB Working Paper Series No 2904264.2 Eﬀects on perceived and expected real net wealth

Average eﬀects Table 4 quantiﬁes the treatment eﬀects on the perceived and expected

change in real net wealth, again based on estimating Equation 1. Importantly, we control for

the pre-treatment estimate of the past-12-month change in real net wealth. Panel A displays

results from OLS estimations. The savings treatment has an insigniﬁcantly negative eﬀect

on the perceived wealth change over the past 12 months (Columns 1–2), the expectation

over the next 12 months (Columns 3–4), and the sum of the two changes (Columns 5–6).

The loan treatment, instead, signiﬁcantly increases perceived and expected real net wealth;

the treatment group reports a nearly three-percentage-point higher change in real net wealth

over the last and next 12 months. This eﬀect is economically sizable, corresponding to about

50% of the average estimate of -5.6% in the control group.

Panel B of Table 4 reports results from Huber-robust regressions. Huber regressions

allow us to systematically control for outliers and inﬂuential observations (see, e.g., Coibion,

Gorodnichenko, and Ropele, 2019), which is useful because of rounding, heaping, mean

reversion, and survey noise. We ﬁnd that estimates are quite similar to the ones using

OLS, in particular on wealth changes over the past and next 12 months (Columns 5–6), with

coeﬃcients generally more precisely estimated. In estimations below that involve perceptions

and expectations of wealth changes, we therefore focus on Huber regressions to mitigate the

impact of extreme observations and for increased precision.22

In addition to quantitative wealth-change estimates, we analyze treatment eﬀects on

qualitative wealth perceptions. Appendix Table A4 shows survey respondents in the loan-

treatment group have signiﬁcantly more positive perceptions about their wealth in the con-

22The variation in the number of observations across speciﬁcations is because (i) Huber regressions weight
observations, with particularly inﬂuential observations receiving a weight of zero, and (ii) regressions with
controls (Columns 2, 4, and 6) include trimmed inﬂation perceptions (1% tails).

ECB Working Paper Series No 290427text of unexpected inﬂation than survey participants in the control group. Speciﬁcally, the

loan treatment induces more positive responses to a question on the eﬀect of inﬂation on

the respondents’ real net wealth over the last twelve months (Columns 1–2). The loan

treatment also increases agreement to the statement that with their current balance sheet,

respondents are well-prepared for times of high inﬂation (Columns 4–5). The eﬀects of the

savings treatment are insigniﬁcant throughout.

To sum up, the loan-erosion treatment leads to higher perceived and expected real net

wealth, whereas the eﬀect of the savings-erosion treatment is limited. Success of the informa-

tion provision in shaping wealth estimates likely reﬂects respondents’ limited prior knowledge

about inﬂation-induced erosion of nominal debt. Diﬀerential prior knowledge about the ero-

sion of nominal assets versus debt also shows up in respondents’ stated knowledge about

the treatment information they receive. Appendix Figure A2 shows that respondents report

less awareness of the loan- than the savings-erosion information. Our ﬁndings imply that

consequences of the redistributive eﬀects of unexpected inﬂation are likely muted in the short

run in light of limited awareness of inﬂation-induced erosion of nominal debt.

The information provision on nominal-debt erosion thus induces signiﬁcant exogenous

variation in perceptions and expectations of real-net-wealth changes. As a result, this treat-

ment can serve as an instrument to help us identify how and whether wealth eﬀects of

inﬂation due to nominal-debt erosion aﬀect spending. Information on nominal-savings ero-

sion, instead, does not shift beliefs suﬃciently to serve as a strong ﬁrst stage. In Section 5.1,

we thus focus on the eﬀects of the loan treatment on spending, through changes in perceived

and expected real net wealth.

Heterogeneity by net nominal position Do treatment eﬀects on real-net-wealth

estimates vary with net nominal exposure? Table 5 reports results of regressing changes in

ECB Working Paper Series No 290428real net wealth on a treatment indicator interacted with respondents’ net nominal position.

Estimates of wealth changes again focus on the past 12 months (Columns 1–2), the next 12

months (Columns 3–4), and the sum of the two (Columns 5–6). The eﬀect of the savings

treatment is insigniﬁcant for respondents with a negative net nominal position and respon-

dents with a positive net nominal position have a signiﬁcantly lower wealth estimate than

those with a negative net nominal position. Respondents in the loan-treatment group with

a negative net nominal position relative to respondents in the control group with a negative

net nominal position report signiﬁcantly higher changes in real net wealth. In our preferred

speciﬁcation on perceived past and expected future wealth changes with the full set of con-

trols (Column 6), the diﬀerence between the two groups amounts to ﬁve percentage points.

This eﬀect nearly fully oﬀsets the reported average change in real net wealth of -5.8% of

respondents with a negative net nominal position in the control group.

Figure 3 graphically illustrates treatment eﬀects by net nominal position. Based on a

regression of changes in real net wealth over the past and next 12 months, we plot coeﬃcients

and 95% conﬁdence bounds on the interaction of a treatment indicator and respondents’ net

nominal position. We group the net nominal position into bins of less than -50% of gross

wealth, -50% to less than 0%, 0% to 50%, and more than 50%. The estimated change in real

net wealth of respondents with a net nominal position of less than -50% to gross wealth is

7.1 percentage points higher in the loan-treatment group than in the control group, whose

average estimate is -1.2% (Panel A). Moreover, the treatment eﬀect monotonically falls as

the net nominal position increases. The pattern is comparable albeit much weaker for the

savings-erosion treatment: respondents with a net nominal position of more than 50% of

gross wealth have a 2.9 percentage points lower estimate of real-net-wealth changes than

ECB Working Paper Series No 290429similar respondents in the control group.23 Appendix Figure A3 similarly shows treatment

eﬀects on wealth estimates, but decomposes the net nominal position into nominal debt and

savings as a fraction of gross wealth. We ﬁnd the loan treatment has a large, signiﬁcant

eﬀect on wealth estimates for highly levered respondents, whereas the savings-treatment

eﬀect increases only modestly with the fraction of wealth held in nominal savings.

Two additional pieces of evidence corroborate the mediating role of net nominal exposure

for the eﬀects of the loan treatment. First, Appendix Table A4 documents respondents in

the loan-treatment group with a negative instead of a positive net nominal position express

a more positive qualitative perception about the eﬀect of the recent rise in inﬂation on their

real net wealth (Column 3). Similarly, agreement to the statement that with their balance

sheet, respondents are well-positioned to cope with high inﬂation is stronger for those with

a negative net nominal position (Column 6).

In both cases, however, diﬀerences by net

nominal position are not statistically signiﬁcant.

Second, Appendix Figure A4 shows the cross-sectional raw-data relationship between

respondents’ prior estimate about the past-12-month change in real net wealth and posterior

estimates about the past- and future-12-month change in real net wealth.24

In Panel A,

we restrict the sample to respondents with a positive net nominal position. The panel

reveals that, among net savers, those who receive the savings treatment tend to have lower

posterior estimates of wealth changes. In Panel B, the sample includes only respondents with

a negative net nominal position. Among net debtors, posterior wealth-change estimates are

higher in the loan-treatment than in the control group. Moreover, the eﬀect of the treatment

does not appear to be driven by respondents with extreme prior real-net-wealth estimates.

23Positive coeﬃcients for those with a negative net nominal position are unlikely to reﬂect cross-learning,

because Table 3 shows that the savings-erosion treatment does not impact beliefs about nominal debt.

24We do not elicit the expected 12-month change in real net wealth pre-treatment to avoid survey fatigue

when asking the same question twice (Stantcheva, 2023).

ECB Working Paper Series No 290430Comprehension of treatment information Incorporating information about inﬂation-

induced nominal-debt erosion into estimates of changes in real net wealth is challenging. Two

features of our study likely help respondents to map the treatment information into their

own wealth situation. First, respondents are well-educated on average, interested in inﬂation,

and take their time to go through the survey. Second, we carefully introduce concepts such

as changes in real terms and net wealth, with respondents providing plausible responses on

questions involving these concepts. Nonetheless, variation in respondents’ ability to incorpo-

rate the treatment information into their estimates of real net wealth may exist. Speciﬁcally,

diﬀerential treatment reactions may reﬂect heterogeneity in cognitive abilities and interest

in the topic of inﬂation (Agarwal and Mazumder, 2013; D’Acunto et al., 2022).

We analyze heterogeneity in respondents’ ability to map the loan-treatment information

into their own wealth situation in Figure 4, displaying results of regressing real-net-wealth

changes on the loan-treatment indicator for diﬀerent subsamples. In Panel A, we limit the

sample to respondents with a negative net nominal position. Importantly, we additionally

split the sample by education and beliefs about inﬂation as proxies for cognitive abilities and

interest in inﬂation. We argue that respondents who completed higher education, expect

inﬂation to be lower in ﬁve years, have a relatively accurate perception of current inﬂation

(deviation from the actual rate of at most 0.5 percentage points), or consider inﬂation to be

relatively important should be better able to incorporate the loan-treatment information into

beliefs about their own real net wealth. We indeed ﬁnd treatment eﬀects on real net wealth

of highly educated respondents or those with general knowledge or interest in inﬂation are

relatively high and signiﬁcant (6.9–8.3 pp) compared to those without these characteristics

(2.1–3.6 pp).25 Importantly, on the subsample of respondents with a positive net nominal

25Appendix Table A5 displays the full regression output.

ECB Working Paper Series No 290431position (Panel B), the loan-treatment eﬀects on real net wealth are close to zero, independent

of the subsample we consider. These ﬁndings suggest that respondents with greater ability

and with a higher relative interest in the topic are better able to map the implications of

inﬂation-induced nominal-debt erosion to their own net nominal exposure.

5 Wealth eﬀects of inﬂation and economic choices

In this section, we study the real eﬀect of learning about the wealth eﬀects of inﬂation on

economic choices. In Section 5.1, we link exogenous variation in respondents’ knowledge of

the eﬀect to planned and actual spending. In Section 5.2, we study how knowledge feeds

into debt ﬁnancing in a hypothetical real-estate investment.

5.1 Eﬀects on spending

Spending data Do the changes in perceived and expected real net wealth generated by the

loan-erosion information treatment have any eﬀect on spending decisions? We investigate

this question using two data sets. First, in the survey, we elicit planned changes in spending

on a wide category of nondurable goods and services over the next four weeks relative to

the previous four weeks, following Roth and Wohlfart (2020). We elicit these plans via a

ﬁve-point Likert scale, ranging from “much less” to “much more.” Respondents additionally

state whether they plan to buy big-ticket items over the next 12 months, as in Coibion,

Gorodnichenko, and Weber (2022). Second, the partnering bank provides us with categorized

transaction-level account data. We use the bank data to study actual changes in spending

in the 30–90 days after survey participation relative to the 30–90 days before.

The two data sets are complementary to each other. All respondents report spending

ECB Working Paper Series No 290432plans, allowing us to test for eﬀects of the information treatment on intended spending

on both nondurable goods and services as well as larger durable goods. The survey data

are useful because the administrative bank data (i) do not cover all respondents, (ii) do

not necessarily comprise all spending decisions, as respondents may spend from accounts

unobservable to us, and (iii) cannot fully distinguish between nondurable and large/durable

spending because big spending categories are cash withdrawals, credit cards, and online

shopping, which can entail both. However, the bank data provide a useful check on whether

respondents actually follow through with their spending plans.

We ﬁrst verify that spending plans and actual spending are positively related. Naturally,

the correlation will be substantially below one because of survey noise, measurement error,

spending in accounts at other banks, other news after our survey intervention that aﬀect

actual spending, and the fact that we elicit planned spending via a Likert scale. Appendix

Table A6 shows results of regressing actual spending changes on planned changes in non-

durable (Panel A) and large/durable spending (Panel B). We consider actual changes in

spending over the 30 days (Columns 1, 4, and 7), 60 days (Columns 2, 5, and 8), and 90

days (Columns 3, 6, and 9) after survey participation relative to before. Planned changes in

nondurable spending are the average across categories such as groceries and restaurants. On

durable spending, we take the sum across categories such as real estate and cars (the average

sum is around one; that is, respondents plan to purchase one large/durable item over the

next 12 months). We standardize both survey-based measures.

In regressions of actual total and discretionary spending, coeﬃcients on planned spend-

ing tend to be signiﬁcantly positive. Moreover, the association strengthens with the length

of the event window, even for the four-week plan on nondurable goods and services as the

independent variable, suggesting that changes in planned spending only gradually trans-

ECB Working Paper Series No 290433late into actual changes in spending.

In terms of economic magnitude, a one-standard-

deviation increase in planned nondurable spending corresponds to e 37 (60 days) and e 98

(90 days) higher actual discretionary spending, and with e 59 (60 days) and e 88 (90 days)

for large/durable items. Overall, these results indicate that spending plans are consistent

with actual measures of spending, which is comforting given that self-reported spending is

the only type of information available for all respondents.

Planned spending Table 6 links the loan-erosion information treatment, through its

eﬀect on perceived and expected changes in real net wealth, to planned spending. We run

regressions of spending plans for each nondurable category (Columns 1–4) and, because of

its low frequency, the sum of planned purchases on durable goods (Column 5). To make

nondurable and durable spending comparable, we standardize both measures. As a ﬁrst

step, we show that spending plans are signiﬁcantly positively correlated with respondents’

post-treatment estimates of changes in their real net wealth over the past and next 12

months (Panel A).26 This result indicates that respondents who perceive and expect a more

positive change in their real net wealth plan to reduce spending by less, as we ﬁnd that the

control group on average intends to reduce spending on nondurables over the next four weeks.

Groceries is the single nondurable-spending category not signiﬁcantly correlated with wealth

changes. Respondents on average do not plan to alter spending on groceries as a function of

their real-net-wealth change, which likely reﬂects its largely non-discretionary nature.

In Panel B, we observe a positive reduced-form eﬀect of the loan-erosion treatment on

planned spending. Households in the loan-treatment group expect to spend signiﬁcantly

26We include all respondents in the estimation sample. We account for this choice by adding a treatment
indicator to the list of controls. Results are similar when restricting the sample to the control group.
Moreover, we abstain from controlling for the pre-treatment estimate of the past-12-month change in real
net wealth, because we do not want to relate spending plans to perceived and expected changes in real
net wealth that arise as respondents go through the survey. When we add the pre-treatment estimate as a
control, the coeﬃcient on the post-treatment wealth-change estimate is still signiﬁcantly positive but weaker.

ECB Working Paper Series No 290434more on restaurants (Column 2) and leisure (Column 3) than comparable respondents in

the control group. Economically, the treatment increases planned spending by 11% of a

standard deviation. The eﬀect on planned spending on clothing (Column 4) and durable

items (Column 5) is positive but statistically insigniﬁcant. As expected, planned spending

on groceries does not vary across groups (Column 1).

We then examine how treatment-induced knowledge about nominal-debt erosion caused

by unexpected inﬂation aﬀects planned spending through its impact on own real-net-wealth

perceptions and expectations. We estimate the following speciﬁcation:

spendi = β posterior rnwi + γ prior rnwi + controlsi + errori.

(2)

spendi measures either planned spending on nondurable or durable goods and services, or

actual spending. posterior rnwi is the sum of the post-treatment estimate of past- and

next-12-month change in real net wealth. We instrument this variable using Equation 1. By

using the loan-erosion treatment as a source of exogenous variation in perceived and expected

wealth, the instrumental-variables approach can resolve possible endogeneity. prior rnwi

again refers to the pre-treatment directional and point estimate of the past-12-month change

in real net wealth. controlsi are similar to Equation 1. We run Huber-robust regression in

the ﬁrst stage and OLS in the second stage when studying planned spending. Following

Coibion, Gorodnichenko, and Weber (2022), we use Huber regression in the ﬁrst stage and

a jackknife approach in the second stage to control for outliers and inﬂuential observations

in both stages when analyzing actual spending.

Panel C of Table 6 reports the results from estimating Equation 2. The Kleibergen-Paap

F-statistic for the ﬁrst stage is 10.3, which is non-homoskedasticity robust in settings like

ECB Working Paper Series No 290435ours with a single endogenous regressor (Andrews, Stock, and Sun, 2019). The coeﬃcient on

posterior real-net-wealth changes is signiﬁcantly positive for the planned change in spending

on restaurants (Column 2) and leisure (Column 3). A one-percentage-point increase in real

net wealth causes planned spending to go up by around 4% of a standard deviation.27 The

coeﬃcient is positive but insigniﬁcant for clothing (Column 4) and durables (Column 5).

Again, exogenous variation in changes in wealth does not causally aﬀect planned spending

on groceries (Column 1). The results indicate that knowledge about the wealth eﬀects of

inﬂation transmits to spending through its eﬀect on perceived and expected real net wealth.

Actual spending We now turn to the treatment eﬀects on actual spending. In Ta-

ble 7, we report eﬀects on total spending, as well as separately on nondiscretionary and

discretionary spending. Panel A presents reduced-form evidence of a signiﬁcantly positive

eﬀect of the loan-erosion information on total and discretionary spending. Comparing the

loan-treatment group with the control group in the 60 days following survey participation,

relative to the 60 days prior, the treatment group increases total spending by e 187 and dis-

cretionary spending by e 121. Economically, the eﬀect on discretionary spending constitutes

a 6% increase in spending over the average measured spending in the 60 days pre-treatment.

The magnitudes are roughly similar over a 90-day window. Eﬀects are weaker over a 30-day

horizon, likely because it takes time to adjust spending.

Panel B reports results from an IV regression based on Equation 2. The Kleibergen-

Paap F-statistic varies between 7.4 and 10.9, rejecting that the worst-case bias of two-stage

least squares exceeds 15–20% of the worst-case error of OLS. We ﬁnd a positive relation

27The economic magnitude of the coeﬃcient on real net wealth is substantially above the magnitudes
shown in Panel A. The diﬀerence possibly in part reﬂects that in the absence of news, wealth estimates
are sticky, and households are unlikely to make large adjustments to their short-term spending plans as a
consequence. Once households receive news about real net wealth instead, for example due to our information
intervention, the updating of perceptions and expectations can cause larger changes in spending plans.

ECB Working Paper Series No 290436between instrumented posterior real-net-wealth perceptions and expectations and total and

discretionary spending. Eﬀects are strongest when considering spending over the 60 days

post-treatment relative to pre-treatment: a one-percentage-point increase in real net wealth

raises total spending by e 92 and discretionary spending by e 70. Again, the eﬀect on

nondiscretionary spending is close to zero and insigniﬁcant. The patterns are hence similar

to the reduced-form evidence (Panel A), albeit statistical signiﬁcance is more sparse.

To gauge the economic magnitudes, we translate the average treatment eﬀects into MPCs

using back-of-the-envelope calculations. The loan-erosion treatment increases perceived and

expected real net wealth by 2.5 percentage points on average (Table 4). Average net wealth

of our respondents is e 326,000, corresponding to an increase of e 8,150. Hence, with an

average treatment eﬀect on total spending of e 187 over 60 days (Panel A of Table 7), the

estimates translate into an MPC of 2.3%. We calculate a comparable MPC of 2.8% based on

the IV regression (Panel B of Table 7), as a one-percentage-point increase in real net wealth

(e 3,260) induces e 92 more total spending. The magnitudes are consistent with estimates

from the literature on the stock-market-wealth eﬀect on consumption.28

Appendix Table A7 illustrates the robustness of the treatment eﬀects on actual spending.

In Panel A, we estimate treatment eﬀects using OLS instead of Huber-robust regressions.

Economic magnitudes remain non-trivial but estimates become a bit noisier. Panel B shows

estimates when ﬁxing the baseline time window to 60 days before the survey intervention.

Treatment eﬀects are stronger. In Panel C, we add uncategorized spending to our measures of

total and discretionary spending. We restrict transactions to uncategorized non-integer out-

ﬂows of below e 100, which according to the bank are more likely to constitute consumption

28Chodorow-Reich, Nenov, and Simsek (2021) calculate an MPC of three percent in a quantitative model,
a magnitude similar to the central scenario in the review by Poterba (2000). Di Maggio, Kermani, and
Majlesi (2020) report an MPC of below three percent for the top ten percent of the wealth distribution.

ECB Working Paper Series No 290437(rather than, e.g., peer-to-peer transfers). Treatment eﬀects are slightly larger on average.

Results are also robust to additionally controlling for changes in regular income, such as

salary, pension, and children’s allowances (Panel D), which we do because inﬂation-induced

wealth changes might alter the demand for leisure (Doepke and Schneider, 2006). Similarly,

in Panel E, we add other post-treatment expectations to our standard set of controls. These

additional controls include expectations about house prices, unemployment, own economic

situation, and interest rates, and could in principle confound the loan-treatment eﬀect on

spending.29 The loan-treatment coeﬃcients are slightly larger with the added controls.

Real-net-wealth change as the underlying mechanism We interpret our results as

reﬂecting how knowledge about the wealth eﬀects of inﬂation, through own real-net-wealth

changes, impacts spending. Our approach, based on Equation 2, yields a “total” estimate

of how exogenous variation in these changes ultimately aﬀects spending. However, the loan-

erosion information provision could alter other perceptions and expectations, which in turn

could shape spending choices. For example, households, realizing that debt erosion harms

banks’ proﬁts, might expect increases in interest rates. Table 8 reports insigniﬁcant loan-

treatment eﬀects on a set of other expectations: house prices, unemployment, own income,

general optimism, and interest rates.30 This result suggests knowledge about the wealth

eﬀects of inﬂation indeed aﬀects spending through perceived and expected wealth.

Our results suggest households spend out of wealth gains, likely because of increased

conﬁdence about their household balance sheet. Indeed, respondents state that their hypo-

thetical MPC out of such unrealized gains is smaller but close to their MPC out of realized

gains (Appendix Figure A5). 51% of respondents have a positive MPC out of realized gains

29As the loan treatment might alter these expectations, we sacriﬁce econometric rigor by estimating such

a model (Angrist and Pischke, 2009).

30The point estimate on expected house prices is marginally signiﬁcant but negative and therefore is

unlikely to drive the positive eﬀect of the loan-erosion treatment on consumption.

ECB Working Paper Series No 290438and 43% spend out of unrealized gains.31 Households that receive the loan-erosion informa-

tion report more positive beliefs about debt, both in the context of inﬂation and generally,

than those in the control group (Table 3). Treated respondents also agree more with the

statement that with their balance sheet they are well-positioned for high inﬂation (Appendix

Table A4). Higher household spending might thus reﬂect greater perceived ﬁnancial security

and conﬁdence about own wealth. This channel is consistent with Fisher (1933) who posits

changes in real debt feed into economic activity through consumer conﬁdence. More gener-

ally, this channel supports the view that ﬂuctuations in consumer sentiment causally aﬀect

consumption (e.g., Angeletos and La’O, 2013).

5.2 Eﬀects on debt ﬁnancing

Can more positive beliefs about nominal debt induced by the loan-erosion information provi-

sion aﬀect debt choices? Table 9 documents eﬀects of the loan-erosion treatment on choices

in a hypothetical real-estate investment task. Respondents purchase real estate with up

to e 500,000 in equity and e 500,000 in debt. We also elicit preferences on the mortgage

type: adjustable (ARM) versus ﬁxed rate (FRM) and, conditional on selection of a FRM,

the length of the ﬁxed-rate period.

Panel A reports reduced-form evidence that survey participants in the loan-treatment

and control group are similar in their preferred home value on average. However, the two

groups diﬀer signiﬁcantly in their preferred ﬁnancing structure. Information about inﬂation-

induced debt erosion increases the average mortgage size by nearly e 18,000. This increase

leads to a three-percentage-points higher debt/price ratio, relative to an average of 49%.

Moreover, the treatment group prefers a FRM over an ARM.

3110% of respondents would spend less, a fraction similar to Fuster, Kaplan, and Zafar (2020).

ECB Working Paper Series No 290439In Panel B, we analyze the eﬀect of the loan-erosion treatment through the perceived

inﬂation protection of debt. We therefore instrument beliefs about the relative inﬂation

protection provided by nominal debt using the loan-erosion-treatment dummy (Columns

3–4 of Table 3 report the ﬁrst stage). The Kleibergen-Paap F-statistic for the ﬁrst stage

is between 15 and 20. Treatment-induced increases in the perceived inﬂation protection

of nominal debt reduce the use of equity, increase the use of debt, and hence lead to a

higher debt/price ratio of the transaction. A one-unit increase in the perceived inﬂation

protection of debt (ranging from 1 to 4) increases this ratio by 16 percentage points. These

results indicate that knowledge about inﬂation-induced erosion of nominal liabilities aﬀects

hypothetical ﬁnancing choices through its positive impact on beliefs about debt.

6 Implications for theory

Our ﬁndings have implications for the HANK literature that focuses on the transmission

of economic policy, while at the same time being consistent with micro data on the wealth

distribution and the composition of wealth (e.g., Bayer, Born, and Luetticke, 2023; Kaplan,

Moll, and Violante, 2018). A key insight of this literature is that the impact of macroe-

conomic shocks on aggregate consumption depends on the covariance between individual

MPCs and exposure to the shock (e.g., Bilbiie, 2008; Patterson, 2023). Consider an expan-

sionary monetary policy that increases inﬂation. In HANK models, redistribution caused by

the Fisher channel ampliﬁes the eﬀect of monetary policy on aggregate consumption because

the beneﬁciaries, households with a negative net nominal position, have higher MPCs than

the losers (e.g., Auclert, 2019; Pallotti, 2023). This result hinges on the assumption that all

households have full information rational expectations; that is, they are aware of the Fisher

ECB Working Paper Series No 290440channel, and adjust their consumption accordingly.

We argue that taking into account heterogeneity in awareness of the eﬀects of macroe-

conomic shocks is important to assess their aggregate consequences (D’Acunto et al., 2023).

One key ﬁnding of our paper is that households with large nominal-debt positions, who typ-

ically have higher MPCs, are largely unaware of inﬂation-induced debt erosion. Based on

our ﬁndings, it would be fruitful to extend the HANK framework to allow for information

frictions and other deviations from FIRE. One possible avenue could be following the mod-

eling strategy of Auclert, Rognlie, and Straub (2020) who introduce information rigidity to

jointly get large impact MPCs at the micro level and humped-shaped macro responses.

We sketch how asymmetric awareness of the Fisher channel might alter its short-run

eﬀects. Suppose for example that nominal prices unexpectedly rise by 10%. A nominal

saver with N N P = e100k experiences a real wealth eﬀect of −e10k. Conversely, a nominal

debtor with N N P = −e100k gains the equivalent of e10k. Assume a one-year MPC of

0.3 for the nominal saver and of 0.5 for the nominal debtor. Under FIRE, the saver then

reduces consumption by e3k, whereas the debtor spends e5k more. Aggregate consumption

increases by e2k. Limited awareness of debt erosion alters this calculation and the eﬀects

on aggregate demand. Assume only 50% of borrowers know about debt erosion, whereas

everyone knows about savings erosion, roughly consistent with our evidence. This assump-

tion cuts the increase in consumption of net debtors to e2.5k, leading to a fall in aggregate

consumption by e0.5k. While highly stylized, this simple example illustrates the possible

importance of heterogeneity in awareness for the eﬀects of macroeconomic shocks and for

the redistributive eﬀects of surprise inﬂation on aggregate demand in particular.

Two additional points are noteworthy. First, as we show in Appendix Figure A5, MPCs

out of balance-sheet revaluations akin to the Fisher channel may be lower than MPCs out

ECB Working Paper Series No 290441of realized gains. If lower MPCs out of the wealth eﬀects of inﬂation compress the MPC gap

between debtors and savers, eﬀects on consumption due to redistribution might be muted.

Second, limited awareness adds an intertemporal-substitution component: even unaware

debtors may raise consumption once they repay ﬁxed nominal obligations with higher future

nominal income. High impact MPCs, as in FIRE, hence frontload consumption that might

occur later. One implication of frontloading is that inﬂation can feed on itself and trigger

inﬂationary spirals, as in Pallotti (2023).

7 Conclusion

We causally study the wealth eﬀects of inﬂation in an information-provision experiment on

customers of a large German bank during a historic inﬂation episode. On average, households

are well-informed about prevailing inﬂation and are highly concerned about the impact of

inﬂation on their wealth. Yet, they know surprisingly little about the reduction in the real

value of nominal debt due to surprise inﬂation. Once we inform respondents about the debt-

erosion channel, they increase estimates of their real net wealth and causally increase their

planned and actual consumption. Moreover, treated respondents diﬀer in leverage choices

in a hypothetical real-estate transaction. Our results document the redistributive nature of

surprise inﬂation across households and provide causal estimates for how individuals adjust

behavior following inﬂation-induced redistribution of wealth.

Our ﬁndings inform a recent class of HANK models. A core ampliﬁcation mechanism

in these models comes from a positive covariance between exposure to economic shocks

and propensities to spend. Applied to the Fisher channel, ampliﬁcation occurs because the

beneﬁciaries of unexpected inﬂation, households with a negative net nominal position, have

ECB Working Paper Series No 290442higher MPCs than the losers (Auclert, 2019). We show the beneﬁciaries are largely unaware

of inﬂation-induced debt erosion, which suggests eﬀects of redistribution might be limited in

the short run. Based on our ﬁndings, it would be interesting to extend the HANK framework

to consider how shock exposure varies with shock awareness.

On the policy side, understanding optimal monetary policy in general and the implica-

tions for the optimal inﬂation target in particular remain largely unexplored in a framework

that deviates from FIRE (Coibion, Gorodnichenko, and Wieland, 2012; D´avila and Schaab,

2023). Moreover, our results suggest suboptimal household debt choices given a large frac-

tion of our survey population was initially uninformed about inﬂation-induced debt erosion.

In addition, wealthier, more educated survey participants, which are overrepresented in

our sample relative to the German population, are better informed about inﬂation-induced

nominal-position erosion, which might raise concerns about possible redistribution from parts

of the population that are less well informed to those that are better informed (D’Acunto

et al., 2023). This concern could possibly be counteracted through information campaigns

and robo-advise (D’Acunto and Rossi, 2023).

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ECB Working Paper Series No 290448Figures and Tables

Figure 1: Prior knowledge about the wealth eﬀects of inﬂation

Panel A. Impact of unexpected inﬂation on nominal positions

0
5

0
4

0
3

0
2

0
1

s
t
n
e
d
n
o
p
s
e
r

f

o
%

0

Very negative

Rather negative

Neither nor

Rather positive

Very positive

Impact of unexpected inflation increase

Cash on hand

Fixed income

Fixed-rate loans

Panel B. Wealth protection by nominal positions during unexpected inﬂation

0
6

s
t
n
e
d
n
o
p
s
e
r

f

o
%

0
4

0
2

0

Worst

Rank 3

Rank 2

Best

Wealth protection when unexpected increase in inflation

Savings

Stocks

Real estate

FR loans

Notes: The ﬁgures show the distribution of beliefs about how ﬁnancial instruments fare during an
unexpected increase in inﬂation. The top panel shows the distribution of responses to “What do you
think, is an unexpected increase in inﬂation positive or negative for owners of the following ﬁnancial
products?” For ﬁxed income, we list savings accounts, bonds, and life insurances as examples. The
bottom panel shows the distribution of responses to “With which of the following ﬁnancial products
would you expect the highest real-net-wealth return during unexpectedly high inﬂation? Please
state “1” for the product with the best inﬂation protection, “2” for [...]” We plot responses of the
control group only, as we pose this question post-treatment. We list examples of savings products.

ECB Working Paper Series No 290449 
 
 
 
Figure 2: Prior knowledge about the wealth eﬀects of inﬂation by NNP

Notes: Referring to the left vertical axis, we plot average responses by respondents’ net nominal
position to the question “What do you think, is an unexpected increase in inﬂation positive or
negative for owners of the following ﬁnancial products?” FR loans refers to ﬁxed-rate loans. Savings
are the average of cash on hand and ﬁxed income. For ﬁxed income, we list savings accounts, bonds,
and life insurances as examples. Referring to the right vertical axis, we plot average responses by
respondents’ net nominal position to the question “Have you consumed rather more or less as a
consequence of changes in the following factors over the past 12 months, such as abstained from a
purchase or purchased something extra?” One of the factors we ask about is the rate of inﬂation.

Much lessRather lessNo changeRather moreMuch moreConsumption over past 12 monthsVery negRather negNeither norRather posVery posImpact of unexpected inflation<-50%[-50%, -25%)[-25%, 0%)[0%, 25%)[25%, 50%)[50%, 75%)>=75%Net nominal position / gross wealthFR loans (left scale)Savings (left)Effect of inflation (right)ECB Working Paper Series No 290450Figure 3: Treatment eﬀect on changes in real net wealth by NNP

Panel A. Loan-erosion treatment

%
5
1

%
0
1

%
5

%
0

%
5
-

%
0
1
-

%
5
1

%
0
1

%
5

%
0

%
5
-

%
0
1
-

<-50%

-50% to <0%
Net nominal position / gross wealth

0% to 50%

>50%

Panel B. Savings-erosion treatment

t

)
s
h
n
o
m
2
1
e
r
u
u

t

f

+

t
s
a
p
(
e
g
n
a
h
c
h

t
l

a
e
w

-
t
e
n
-
l
a
e
R

t

)
s
h
n
o
m
2
1
e
r
u
u

t

f

+

t
s
a
p
(
e
g
n
a
h
c
h

t
l

a
e
w

-
t
e
n
-
l
a
e
R

<-50%

-50% to <0%
Net nominal position / gross wealth

0% to 50%

>50%

Notes: The ﬁgures report coeﬃcients and 95% conﬁdence bounds from regressions of respondents’
post-treatment perceptions and expectations of changes in real net wealth over the sum of the past
and next 12 months. We regress these wealth changes on a loan-treatment (Panel A) or savings-
treatment indicator (Panel B), interacted with respondents’ net nominal position as a fraction of
their gross wealth. We describe the treatments in detail in Section 2.1. Regressions include the
standard set of controls. Results are from Huber-robust regressions to control for outliers and
inﬂuential observations. Standard errors are robust to heteroscedasticity.

ECB Working Paper Series No 290451 
 
 
 
 
 
 
 
 
 
 
 
Figure 4: Heterogeneity in loan-treatment eﬀect on real net wealth

Panel A. Subsample of households with negative net nominal position

t

)
s
h
n
o
m
2
1
e
r
u
u

t

f

+

t
s
a
p
(
e
g
n
a
h
c
h

t
l

a
e
w

-
t
e
n
-
l
a
e
R

%
5
1

%
0
1

%
5

%
0

%
5
-

Education high Inflation down 5 yrs

Inflation right

Inflation important

No

Yes

Panel B. Subsample of households with positive net nominal position

t

)
s
h
n
o
m
2
1
e
r
u
u

t

f

+

t
s
a
p
(
e
g
n
a
h
c
h

t
l

a
e
w

-
t
e
n
-
l
a
e
R

%
5
1

%
0
1

%
5

%
0

%
5
-

Education high

Inflation down

Inflation right

Inflation important

No

Yes

Notes: The ﬁgures report coeﬃcients and 95% conﬁdence bounds on the loan-treatment indicator
from regressions of respondents’ post-treatment estimates of changes in real net wealth over the
past and next 12 months. We describe the treatment in detail in Section 2.1. In Panel A, these
regressions are limited to households with a negative NNP; in Panel B, regressions are limited to
households with a positive NNP. Moreover, we split the sample by another respondent characteristic:
Education high, which equals one for completed higher education and zero otherwise; Inﬂation down
5 yrs, which is one if the rate of inﬂation expected in ﬁve years is below currently perceived inﬂation;
Inﬂation right, which is one if currently perceived inﬂation deviates by at most 0.5 pp from actual
inﬂation; and Inﬂation important, which is one if respondents perceive inﬂation to be substantially
more important for their own wealth than the average of GDP growth, stock prices, and interest
rates. Regressions include the standard set of controls. Results are from Huber-robust regressions
to control for outliers and inﬂuential observations. Standard errors are robust to heteroscedasticity.

ECB Working Paper Series No 290452 
 
 
 
 
 
 
 
 
 
 
 
Table 1: Descriptive statistics

Data sources:

Statistics:

Demographic characteristics
Female (0/1)
Age (years)
University completed (0/1)
Business at university (0/1)
Employed (0/1)
Gross wealth (e k)
Nominal assets / gross wealth (%)
Nominal debt / gross wealth (%)
Homeowner (0/1)
Stockholdings (0/1)

Spending and income
Total spending (avg./month)
Nondiscretionary spending (avg./month)
Discretionary spending (avg./month)
Regular income (avg./month)

Perceptions and expectations
Inﬂation rate today (%)
Inﬂation rate in 12 months (%)
Inﬂation rate in ﬁve years (%)
Real-net-wealth change past 12 months (%)
Inﬂation important for own wealth (0–4)
GDP growth important for own wealth (0–4)
Interest rates important for own wealth (0–4)

PHF

Bank sample

Mean Mean

SD

P25

P50

P75

0.50
52.97
0.29

0.54
238.13
37.21
10.12
0.44
0.20

0.45
48.08
0.48
0.10
0.72
385.57
42.67
16.78
0.59
0.54

0.50
15.08
0.50
0.30
0.45
659.37
33.66
22.88
0.49
0.50

2,274

1,844
818
978
2,862

8.78
10.39
10.67
-7.45
2.37
1.73
1.34

1,202
584
763
2,002

6.24
9.80
15.07
14.91
1.02
1.06
1.14

0.00
36.00
0.00
0.00
0.00
25.00
10.00
0.00
0.00
0.00

1,001
397
432
1,457

7.00
6.00
3.00
-14.00
2.00
1.00
0.00

0.00
48.00
0.00
0.00
1.00
120.83
30.00
5.00
1.00
1.00

1.00
60.00
1.00
0.00
1.00
463.02
79.00
30.00
1.00
1.00

1,604
702
785
2,381

7.90
8.50
5.00
-6.00
2.00
2.00
1.00

2,385
1,106
1,329
3,826

8.00
10.00
10.50
0.00
3.00
2.00
2.00

Notes: This table reports summary statistics for respondents’ characteristics (survey data), spend-
ing and income (bank data), and perceptions and expectations (survey data). We present the
variables’ mean, standard deviation (SD), 25th percentile (P25), median (P50), and 75th per-
centile (P75). We compare our respondents to a representative German sample from the 2017 wave
of the Bundesbank’s Panel on Household Finances. In our sample, we winsorize the 1% tails of gross
wealth. Spending and income measures are monthly averages spanning the six months preceding
the survey, and are winsorized at the 97.5th percentile. The belief variables reported in the table
refer to priors elicited before the treatment. Priors on inﬂation and own real net wealth are point
estimates, with the 1% tails trimmed. The baseline number of observations is 3,190.

ECB Working Paper Series No 290453Table 2: Correlates of beliefs about balance-sheet eﬀects of unexpected inﬂation

Dependent variable:

Unexpected inﬂation increase positive or negative for...

Female (0/1)

Age group

cash

(1)

−0.049
(0.035)

ﬁxed income

stocks

real estate

FR loans

(2)

0.001
(0.036)

(3)

(4)

(5)

−0.125*** −0.038
(0.036)
(0.036)

−0.116***
(0.035)

>65 years

>55 to 65 years

>45 to 55 years

>35 to 45 years

University completed (0/1)

Business at university (0/1)

0.000
(0.057)
−0.003
(0.058)
−0.022
(0.060)
0.078
(0.064)
−0.144***
(0.041)
−0.119**
(0.052)
−0.084*** −0.036***
(0.012)
(0.012)
−0.114*** −0.021
(0.041)
(0.040)
0.101**
0.141***
(0.046)
(0.045)
−0.152*** −0.054
(0.038)
(0.037)
Accurate inﬂation perception (0/1) −0.151*** −0.064
(0.043)
0.063
(0.040)

−0.150*** −0.208*** −0.024
−0.059
(0.056)
(0.053)
(0.057)
(0.057)
−0.135** −0.131** −0.055
−0.128**
(0.055)
(0.058)
(0.058)
(0.058)
−0.085
−0.255*** −0.216*** −0.107*
(0.057)
(0.059)
(0.060)
(0.060)
−0.072
−0.059
−0.280*** −0.096
(0.061)
(0.063)
(0.061)
(0.061)
0.051
0.087** −0.014
0.046
(0.039)
(0.039)
(0.040)
(0.040)
0.214***
0.053
−0.013
−0.035
(0.062)
(0.060)
(0.060)
(0.066)
0.100***
0.079***
0.016
(0.012)
(0.012)
(0.012)
0.135***
−0.007
−0.029
(0.039)
(0.041)
(0.042)
−0.052
0.071
0.000
(0.042)
(0.045)
(0.045)
0.074**
0.108***
0.006
(0.037)
(0.038)
(0.038)
0.023
0.085*
0.040
(0.040)
(0.044)
(0.043)
0.250***
0.100**
0.133***
(0.039)
(0.041)
(0.042)

(0.046)
−0.131***
(0.042)

Inﬂation lower in ﬁve years (0/1)

Inﬂation relatively important

Stockholdings (0/1)

Homeowner (0/1)

Log gross wealth

Observations
R-squared

3,134
0.06

3,134
0.02

3,134
0.02

3,134
0.04

3,134
0.09

Notes: This table reports estimates of regressions of beliefs about the impact of an unexpected
inﬂation increase on various balance-sheet items, measured on an ordinal scale from 0 (“very neg-
ative”) to 4 (“very positive”). We standardize the outcome variables using the mean and standard
deviation of our sample. For ﬁxed income, we list savings accounts, bonds, and life insurances as
examples. FR loans refers to ﬁxed-rate loans. Inﬂation relatively important measures respondents’
beliefs about the importance of inﬂation for own wealth relative to the average importance of GDP
growth, interest rates, and stock prices. Accurate inﬂation perception is a dummy equal to one if
respondents’ estimate of current inﬂation is at most 1.5 pp oﬀ actual inﬂation. Inﬂation lower in
ﬁve years indicates the expectation that inﬂation will be lower in ﬁve years than it is perceived
today. Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290454Table 3: Treatment eﬀects on beliefs about nominal positions

Dependent variable:

Inﬂation-protection ranking

Nominal assets

Nominal debt

Debt aversion

(1)

(2)

(3)

(4)

(5)

(6)

Treat: savings erosion −0.124*** −0.126***

Treat: loan erosion

(0.045)
−0.080*
(0.045)

(0.043)
−0.080*
(0.044)

0.051
(0.044)
0.200***
(0.045)

0.037
(0.043)

0.056
(0.044)
0.190*** −0.119*** −0.123***
(0.046)

0.046
(0.041)

(0.044)

(0.042)

Controls
Observations
R-squared

N
2,977
0.00

Y
2,928
0.11

N
2,977
0.01

Y
2,928
0.04

N
3,190
0.00

Y
3,134
0.11

Notes: This table reports regression estimates of beliefs about nominal positions in each treatment
group relative to the control group. To elicit the beliefs about inﬂation protection (Columns 1–4), we
ask: “With which of the following ﬁnancial products would you expect the highest real-net-wealth
return during unexpectedly high inﬂation?” Respondents rank nominal assets, stocks, real estate,
and nominal debt relative to each other, with higher numbers indicating better performance. For
nominal assets, we list cash on hand, savings accounts, bonds, and life insurances as examples. In
Columns 5–6, the dependent variable measures agreement with the statement “I am uncomfortable
with taking on debt.” Five possible responses range from “completely disagree” to “completely
agree.” We standardize the outcome variables using the mean and standard deviation of the sample.
A detailed description of the treatments is in Section 2.1. The list of controls is in Section 4.
Observations are lower in Columns 1–4 because respondents may skip this question and lower in
odd-numbered columns because we trim the 1% tails of inﬂation beliefs that serve as controls.
Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290455Table 4: Treatment eﬀect on changes in real net wealth

Dependent variable:

Change in real net wealth

Last 12 months

Next 12 months

Last + next 12 months

(1)

(2)

(3)

(4)

(5)

(6)

Panel A. Estimation based on OLS

Treat: savings erosion

Treat: loan erosion

Observations
R-squared

−0.932
(0.648)
1.490**
(0.676)

3,190
0.17

−0.807
(0.646)
1.743***
(0.666)

−0.835
(0.769)
1.260
(0.769)

3,134
0.19

3,190
0.10

−0.981
(0.773)
1.204
(0.761)

3,134
0.13

−1.767
(1.217)
2.749**
(1.222)

3,190
0.18

−1.788
(1.222)
2.947**
(1.207)

3,134
0.20

Panel B. Estimation based on robust regression

Treat: savings erosion

Treat: loan erosion

Observations
R-squared

Controls for prior estimate
Controls for demographics
Avg. Y control group

−1.428*** −1.247*** −0.741
(0.508)
(0.386)
(0.391)
1.025**
0.602
0.441
(0.517)
(0.392)
(0.398)

−0.737
(0.511)
1.543***
(0.517)

−1.809** −1.606**
(0.786)
1.793**
(0.794)

(0.781)
2.495***
(0.787)

3,123
0.46

Y
N
-2.51

3,059
0.48

Y
Y
-2.55

3,165
0.24

Y
N
-3.11

3,112
0.26

Y
Y
-3.01

3,161
0.38

Y
N
-5.62

3,099
0.40

Y
Y
-5.56

Notes: This table reports the perception and expectation of changes in the real net wealth in each
treatment group relative to the control group. Columns 1 and 2 consider real-net-wealth changes
estimated over the past 12 months, Columns 3 and 4 refer to changes estimated over the next 12
months, and Columns 5 and 6 show estimates of changes over the last plus next 12 months. Panel A
reports estimates from ordinary least squares regressions; results in Panel B are from Huber-robust
regressions to control for outliers and inﬂuential observations. We describe the treatments in detail
in Section 2.1. The list of controls is in Section 4. Observations are lower in odd-numbered columns
because we trim the 1% tails of inﬂation beliefs that serve as controls. Robust standard errors are
in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290456Table 5: Treatment eﬀect on changes in real net wealth by NNP

Dependent variable:

Change in real net wealth

Last 12 months

Next 12 months

Last + Next 12 months

(1)

(2)

(3)

(4)

(5)

(6)

NNP≥0

Treat: loan erosion

Treat: savings erosion

−0.092
(0.956)
2.254**
(1.001)
0.502
(0.804)
Treat: savings erosion × NNP≥0 −2.278*** −2.529*** −0.764
(1.126)
−1.500
(1.167)

0.559
(0.730)
1.658**
(0.752)
1.208**
(0.603)

0.283
(0.748)
1.155
(0.769)
0.971
(0.606)

Treat: loan erosion × NNP≥0

(0.859)
−1.513*
(0.880)

(0.879)
−0.942
(0.900)

0.517
(0.953)
2.895***
(0.984)
0.999
(0.820)
−1.673
(1.125)
−1.880
(1.154)

0.711
(1.523)
3.859**
(1.559)
1.388
(1.222)
−3.204*
(1.773)
−2.606
(1.808)

Controls for prior estimate
Controls for demographics
Avg. Y omitted group
Observations
R-squared

Y
N
-2.52
3,127
0.46

Y
Y
-2.74
3,059
0.48

Y
N
-3.15
3,165
0.24

Y
Y
-3.22
3,112
0.26

Y
N
-5.56
3,158
0.38

1.632
(1.508)
5.017***
(1.528)
2.280*
(1.241)
−4.447**
(1.762)
−3.546**
(1.784)

Y
Y
-5.82
3,101
0.39

Notes: This table reports the perception and expectation of changes in the real net wealth in each
treatment group relative to the control group. Columns 1 and 2 consider changes estimated over
the past 12 months, Columns 3 and 4 refer to changes estimated over the next 12 months, and
Columns 5 and 6 show estimates of changes over the past plus next 12 months. We describe the
treatments in detail in Section 2.1. NNP≥0 equals one if the respondent has a non-negative net
nominal position and is zero otherwise. Controls for prior estimate include a directional and a point
estimate of past-12-month changes in real net wealth. Controls for demographics include a quadratic
polynomial in the respondent’s age, risk tolerance, the log of gross wealth, and dummy variables
for the respondent’s gender, marital status, educational level, business-related university degree,
employment status, beliefs about inﬂation (trimmed at 1% tails), voucher instead of lottery survey-
participation incentive, and whether participation follows a reminder email sent to encourage survey
participation. Results are from Huber-robust regressions to control for outliers and inﬂuential
observations. Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290457Table 6: Changes in real net wealth and planned spending

Dependent variable:

Planned spending

Groceries Restaurants

Leisure

Clothing

Durables

(1)

(2)

(3)

(4)

(5)

Panel A. Endogenous regression

0.001
(0.001)

N
3,134
0.04

0.004***
(0.001)

0.004***
(0.001)

0.004***
(0.001)

0.003***
(0.001)

N
3,134
0.16

N
3,134
0.13

N
3,134
0.10

N
3,134
0.08

RNW change

Controls for prior RNW
Observations
R-squared

Panel B. Reduced form

Treat: loan erosion

−0.007
(0.043)

0.110***
(0.041)

0.108**
(0.042)

0.042
(0.042)

Controls for prior RNW
Observations
R-squared

Y
2,088
0.04

Y
2,088
0.16

Y
2,088
0.12

Y
2,088
0.10

Panel C. Instrumental variable

0.069
(0.043)

Y
2,088
0.09

RNW change

−0.010
(0.017)

0.037*
(0.019)

0.041**
(0.020)

0.015
(0.017)

0.027
(0.019)

Controls for prior RNW
Observations
1st stage F-stat

Y
2,065
10.31

Controls for demographics

Y

Y
2,065
10.31

Y

Y
2,065
10.31

Y

Y
2,065
10.31

Y

Y
2,065
10.31

Y

Notes: This table reports estimates from regressions of planned spending. In Columns 1–4, we
measure changes in planned spending on various nondurables over the next four weeks relative to
the last four weeks on a ﬁve-point scale, ranging from “much less” to “much more.” In Column
5, the dependent variable is the number of durable items expected to purchase over the next 12
months. We standardize the outcome variables. RNW change is the post-treatment point estimate
of last- and next-12-month changes in own real net wealth. In Panels B and C, we omit the savings-
erosion treatment group. In Panel C, we instrument RNW change using the loan-erosion treatment.
The list of controls is in Section 4. Results are from OLS regressions (Huber robust in ﬁrst stage
in Panel C). Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290458Table 7: Changes in real net wealth and actual spending

DV:

Window:

Total

60

(2)

30

(1)

Panel A. Reduced form

Nondiscretionary

Discretionary

90

(3)

30

(4)

60

(5)

90

(6)

30

(7)

60

(8)

90

(9)

T: loan

N
R2

65.6*
(36.4)

1,465
0.03

186.5***
(59.6)

166.9**
(78.4)

1,513
0.03

1,477
0.02

Panel B. Instrumental variable

RNW

N
F-stat

Controls
Avg. Y

29.8
(24.1)

1,460
8.49

Y
-267.0

92.4**
(44.5)

1,469
8.83

Y
-308.4

42.6
(46.2)

1,452
8.12

Y
-22.8

−4.8
(15.8)

1,431
0.03

0.9
(8.7)

1,451
10.92

Y
-92.6

21.7
(24.3)

1,414
0.02

7.8
(13.8)

1,441
10.51

Y
-46.6

38.2
(33.1)

1,405
0.03

18.0
(20.3)

1,429
9.01

Y
58.7

40.8*
(24.7)

1,451
0.03

33.2*
(19.4)

1,447
8.00

Y
-147.2

121.0***
(40.8)

132.8**
(55.8)

1,488
0.04

1,497
0.03

70.3**
(34.2)

1,465
8.18

Y
-222.9

55.8
(38.7)

1,458
7.35

Y
-240.3

Notes: This table reports estimates from regressions of actual spending. We study total (Columns
1–3), nondiscretionary (Columns 4–6), and discretionary (Columns 7–9) spending, as described in
Section 2.2. We compare individual-level aggregate spending in the 30, 60, and 90 days following
survey participation relative to the same time window pre-participation respectively. RNW is
the post-treatment point estimate of the sum of last- and next-12-month changes in own real net
wealth. We instrument this estimate using the loan-erosion information treatment. We omit the
savings-erosion treatment group from the estimations. Section 2.1 entails detailed descriptions of
the treatments. All regressions include the standard set of controls, described in Section 4. Results
in Panel A are from Huber-robust regressions to control for outliers and inﬂuential observations. In
Panel B, we use Huber regressions in the ﬁrst stage and a jackknife procedure in the second stage.
Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290459Table 8: Treatment eﬀects on other expectations

Dependent variable:

12-month expectation

Change in interest rates

House prices Unemployment Own income Optimism One year

Five years

Treat: loan erosion

Controls
Avg. Y
Observations
R-squared

(1)

−0.078*
(0.043)

Y
0.00
2,088
0.09

(2)

−0.063
(0.044)

Y
0.00
2,088
0.04

(3)

(4)

(5)

(6)

−0.040
(0.042)

−0.005
(0.042)

−0.036
(0.101)

−0.274
(0.183)

Y
0.00
2,088
0.11

Y
0.00
2,088
0.11

Y
1.47
2,067
0.04

Y
2.48
2,065
0.11

Notes: This table reports expectations of the loan-erosion treatment group relative to the control
group. In Columns 1–3, we study 12-month-ahead expectations using a ﬁve-point scale, ranging
from “much lower” to “much higher.” The dependent variable in Column 4 measures agreement, on
a ﬁve-point scale ranging from “completely disagree” to “completely agree,” to the statement “I am
optimistic about the future.” We standardize the outcome variables in Columns 1–4. In Columns
5–6, we compare point estimates of expected interest rates relative to the perceived current interest
rate (we trim the 1% tails of the outcome variable). Section 2.1 entails detailed descriptions of the
treatments. The list of controls is in Section 4. Results are from OLS regressions. Robust standard
errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290460Table 9: Perceived inﬂation protection of debt and real-estate investment

Dependent variable:

Price (e k) Equity (e k) Debt (e k) Debt/price

(1)

(2)

(3)

(4)

FRM

(5)

Panel A. Reduced form

Treat: loan erosion

Observations
R-squared

5.474
(7.574)

2,082
0.19

−9.829
(6.254)

2,088
0.11

17.770***
(6.202)

0.031***
(0.009)

0.073*
(0.039)

2,088
0.12

2,082
0.06

2,088
0.06

Panel B. Instrumental variable

Debt as inﬂation hedge

0.067
(44.590)

−67.138*
(36.228)

83.528**
(40.753)

0.160**
(0.065)

Observations
1st stage F-stat

Controls
Avg. Y control group

1,914
17.70

Y
542.79

1,940
20.29

Y
280.25

1,933
16.89

Y
260.48

1,913
18.46

Y
0.48

0.321
(0.268)

1,910
15.12

Y
2.22

Notes: This table reports estimates from regressions of features of a hypothetical real-estate trans-
action. Column 1 is on the preferred hypothetical purchase price, capped at e 1,000,000; Column
2 considers the equity stake, capped at e 500,000; Column 3 refers to the size of the mortgage, also
capped at e 500,000; Column 4 features the debt-to-price ratio; and Column 5 is on an ordinal-scale
variable that takes on 0 if respondents choose an adjustable-rate mortgage, 1 in case of a mortgage
with a ﬁve-year ﬁxed-rate period, 2 for a 10-year ﬁxed-rate period, 3 for a 20-year ﬁxed-rate period,
and 4 for a 30-year ﬁxed-rate period. Section 2.1 entails detailed descriptions of the treatments. To
elicit beliefs about Debt as inﬂation hedge, we ask: “With which of the following ﬁnancial products
would you expect the highest real-net-wealth return during unexpectedly high inﬂation?” Respon-
dents rank nominal assets, stocks, real estate, and nominal debt relative to each other, with higher
numbers indicating higher relative returns. We instrument the perceived inﬂation protection of
nominal debt using the loan-erosion information treatment. The list of controls is in Section 4. In
Panel A, results are from Huber-robust regressions to control for outliers and inﬂuential observa-
tions. In Panel B, we use OLS in the ﬁrst stage and a jackknife procedure in the second stage.
Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290461Online Appendix:

Households’ Response to the Wealth Eﬀects of Inﬂation

Philip Schnorpfeil, Michael Weber, and Andreas Hackethal

Not for Publication

ECB Working Paper Series No 290462A Appendix ﬁgures and tables

Figure A1: Association between debt reported in survey and data from bank

Notes: This ﬁgure documents respondents’ average debt balance with our bank partner in the month
of survey participation for each mid point of outstanding-debt bandwidth reported by respondents
in the survey. The debt balance at the bank includes consumer and mortgage debt. In the survey,
households select the value of their outstanding debt, if any, from various ranges we provide. We
take the respective mid point of the ranges we ask about.

050k100k150k200kOutstanding debt with bank02.5k7.5k17.5k37.5k62.5k87.5k125k175k275k425k600kMid point of survey-reported range of outstanding debtECB Working Paper Series No 290463Figure A2: Knowledge about information provided by treatment and NNP

Notes: The ﬁgure shows respondents’ stated knowledge about the treatment information. We plot
averages by treatment (savings erosion vs. debt erosion) and net nominal position as a fraction of
gross wealth. Naturally, the control group does not receive this question.

Not at all234FullyKnow about information provided<-50%[-50%, -25%)[-25%, 0%)[0%, 25%)[25%, 50%)[50%, 75%)>=75%Net nominal position / gross wealthSavings erosionLoan erosionECB Working Paper Series No 290464Figure A3: Treatment eﬀect on changes in real net wealth by nominal position

Panel A. Loan-erosion treatment

t

)
s
h
n
o
m
2
1
e
r
u
u

t

f

+

t
s
a
p
(
e
g
n
a
h
c
h

t
l

a
e
w

-
t
e
n
-
l
a
e
R

t

)
s
h
n
o
m
2
1
e
r
u
u

t

f

+

t
s
a
p
(
e
g
n
a
h
c
h

t
l

a
e
w

-
t
e
n
-
l
a
e
R

%
5
1

%
0
1

%
5

%
0

%
5
-

%
5

%
0

%
5
-

%
0
1
-

%
5
1
-

0%

>0% to 20%

>20% to 40%

>40%

Debt / gross wealth

Panel B. Savings-erosion treatment

<=20%

>20% to 40%

>40% to 70%

>70%

Savings / gross wealth

Notes: The ﬁgures report coeﬃcients and 95% conﬁdence bounds from regressions of respondents’
post-treatment estimates of the change in their real net wealth over the past and next 12 months.
In Panel A, we regress the wealth changes on the loan-treatment indicator, interacted with respon-
dents’ debt as a fraction of gross wealth. In Panel B, we regress real-net-wealth changes on the
interaction of the savings-treatment indicator and respondents’ nominal savings as a fraction of
gross wealth. We describe the treatments in detail in Section 2.1. Regressions include the standard
set of controls. Results are from Huber-robust regressions to control for outliers and inﬂuential
observations. Standard errors are robust to heteroscedasticity.

ECB Working Paper Series No 290465 
 
 
 
 
 
 
 
 
 
 
 
Figure A4: Treatment eﬀects on beliefs about RNW changes by prior and NNP

Panel A: Eﬀect of savings-erosion treatment on positive-NNP subjects

t

)
s
h
n
o
m
2
1
e
r
u
u

t

f

+

t
s
a
p
(
e
g
n
a
h
c
W
N
R

r
o
i
r
e
t
s
o
P

0
2

0
1

0

0
1
-

0
2
-

0
3
-

-40

-30

-20

-10
Prior RNW change (past 12 months)

0

10

20

Control group

Savings-treatment group

Panel B: Eﬀect of loan-erosion treatment on negative-NNP subjects

t

)
s
h
n
o
m
2
1
e
r
u
u

t

f

+

t
s
a
p
(
e
g
n
a
h
c
W
N
R

r
o
i
r
e
t
s
o
P

0
2

0
1

0

0
1
-

0
2
-

0
3
-

-40

-30

-20

-10

0

10

20

Prior RNW change (past 12 months)

Control group

Loan-treatment group

Notes: This ﬁgure reports binscatter plots of respondents’ post-treatment beliefs about changes in
their real net wealth over the last 12 months plus next 12 months, conditional on pre-treatment
estimates of past-12-month RNW changes. In Panel A, the sample comprises respondents with a
positive net nominal position. In Panel B, the sample contains respondents with a negative NNP.

ECB Working Paper Series No 290466 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A5: Marginal propensity to consume out of realized vs. unrealized gain

Notes: This ﬁgure shows a histogram of MPCs for two gain scenarios. On realized gains, we pose the
following question: “Please consider a hypothetical situation where you unexpectedly receive a one-
time payment of e 10,000 today. How would you change your spending behavior over the next three
months as a consequence of that?” On the unrealized-gain scenario, we ask: “Please consider that
today you carefully calculate your total wealth, from which you subtract the value of outstanding
loans. You are surprised to realize that you own e 10,000 more than previously believed. How
would you change your spending behavior over the next three months as a consequence of that?”
Respondents use a slider to indicate a spending response between e -10,000 and e 10,000.

01020304050% of respondents<00(0, 0.25)[0.25, 0.5)[0.5, 0.75)[0.75, 1]Realized gainUnrealized gainECB Working Paper Series No 290467Table A1: Consumption categories

Total

Discretionary

Nondiscretionary

spending

spending

spending

Living

Groceries

Clothing

Multimedia

Hairdresser/wellness

Drugstore

Pets

Cafeteria

Gifts

Other living expenses

Housing

Rent

Energy and water

Furniture / home accessories

Housing fees

Domestic help

Property taxes

Renovations

Other housing expenses

Leisure

Restaurants/cafes/bars

Events/tickets

Sports/ﬁtness

Hobbies/clubs/associations

Vacation / travel expenses

Books/music/movies/apps

Electronics/computers/games

Subscriptions

Other leisure expenses

Transportation

(1)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(2)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(3)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

continued

ECB Working Paper Series No 290468Table A1 continued

Total

Discretionary

Nondiscretionary

spending

spending

spending

Health

Pharmacy

Physician

Glasses / contact lenses

Hospital

Other health expenses

Children

Activities and toys

Children’s clothing

Childcare

School fees

Other expenses on children

Career

Oﬃce supplies / teaching material

Business travel

Tuition

Continuing education

Other career-related expenses

Other outﬂows

Donations

Cash withdrawals

Credit card

Online purchases

(1)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(2)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(3)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Notes: This table reports spending categories coming from the partner bank’s PFM tool, and how

we assign them to the three spending measures. Details on these measures are in Section 2.2.

ECB Working Paper Series No 290469Table A2: Balancedness across treatment arms

Demographic characteristics
Female (0/1)
Age (years)
University completed (0/1)
Business at university (0/1)
Employed (0/1)
Gross wealth (e k)
Nominal assets / gross wealth (%)
Nominal debt / gross wealth (%)
Homeowner (0/1)
Stockholdings (0/1)

Spending and income
Total spending (avg./month)
Nondiscretionary spending (avg./month)
Discretionary spending (avg./month)
Regular income (avg./month)

Perceptions and expectations
Inﬂation rate today (%)
Inﬂation rate in 12 months (%)
Inﬂation rate in ﬁve years (%)
Real-net-wealth change past 12 months (%)
Inﬂation important for own wealth (0–4)
GDP growth important for own wealth (0–4)
Interest rates important for own wealth (0–4)

Control Treat:
savings
group
(2)
(1)

p-value Treat:
loan
(1)=(2)
(4)
(3)

p-value
(1)=(4)
(5)

p-value
(2)=(4)
(6)

0.48
48.34
0.51
0.10
0.71
407.25
43.09
17.06
0.60
0.54

1,870
837
985
2,847

8.62
10.22
10.50
-7.61
2.39
1.76
1.40

0.45
47.82
0.47
0.10
0.71
401.13
41.55
15.94
0.60
0.55

1,840
799
994
2,899

8.87
10.55
10.77
-7.12
2.31
1.73
1.29

0.20
0.43
0.07*
0.90
0.96
0.84
0.29
0.25
0.99
0.41

0.63
0.21
0.81
0.62

0.34
0.44
0.67
0.44
0.09*
0.41
0.03**

0.42
48.06
0.47
0.11
0.73
348.09
43.37
17.35
0.57
0.53

0.01***
0.66
0.10*
0.57
0.32
0.03**
0.85
0.77
0.25
0.91

1,808
813
949
2,813

8.85
10.41
10.73
-7.63
2.42
1.69
1.33

0.32
0.42
0.36
0.74

0.40
0.65
0.72
0.97
0.52
0.11
0.19

0.14
0.71
0.88
0.65
0.34
0.06*
0.21
0.16
0.25
0.35

0.60
0.64
0.25
0.40

0.93
0.76
0.95
0.43
0.02**
0.42
0.41

Notes: This table shows means for diﬀerent observable characteristics of respondents in each treat-
ment arm (Columns 1, 2, and 4). We provide a check of balance of means across arms in Columns
3, 5, and 6. We winsorize the 1% tails of gross-wealth estimates. Spending and income measures
are monthly averages spanning the six months preceding the survey, and are winsorized at the
97.5th percentile. The belief variables reported in the table refer to priors elicited before the in-
formation treatment. Priors on inﬂation and own real net wealth are point forecasts, with the 1%
tails trimmed. The baseline number of observations is 3,190.

ECB Working Paper Series No 290470Table A3: Full set of correlates of beliefs about balance-sheet eﬀects of inﬂation

Dependent variable:

Unexpected inﬂation increase positive or negative for...

Female (0/1)

Age group

>35 to 45 years

>45 to 55 years

>55 to 65 years

>65 years

East Germany (0/1)

University completed (0/1)

Business at university (0/1)

Married (0/1)

Retired (0/1)

Student (0/1)

Employee (0/1)

Craftsman (0/1)

Risk tolerance

Money illusion (0/1)

Inﬂation relatively important

Accurate inﬂation perception (0/1)

Inﬂation lower in ﬁve years (0/1)

cash

ﬁxed income

stocks

real estate

FR loans

(1)

(2)

(3)

(4)

(5)

−0.026
(0.038)

0.013
(0.039)

−0.122*** −0.043
(0.039)
(0.039)

−0.143***
(0.037)

−0.011
(0.061)
−0.021
(0.064)
−0.054
(0.067)
0.050
(0.093)
0.016
(0.045)
−0.129***
(0.042)
−0.117**
(0.053)
−0.010
(0.038)
0.010
(0.082)
−0.051
(0.096)
0.010
(0.042)
0.191**
(0.086)
0.001
(0.020)
0.040
(0.040)
−0.148***
(0.038)
−0.150***
(0.046)
−0.109**

−0.055
(0.063)
−0.115*
(0.064)
−0.226***
(0.066)
−0.231***
(0.088)
−0.006
(0.046)
0.050
(0.042)
−0.046
(0.067)
0.038
(0.040)
−0.014
(0.080)
0.009
(0.088)
0.012
(0.047)
−0.096
(0.084)
0.031
(0.020)
0.004
(0.041)
−0.055
(0.039)
−0.070
(0.044)
0.064

−0.101
(0.062)
−0.086
(0.064)
−0.165**
(0.067)
−0.085
(0.091)
0.001
(0.047)
0.077*
(0.041)
−0.033
(0.060)
0.000
(0.040)
0.022
(0.085)
0.039
(0.091)
−0.003
(0.045)
−0.185**
(0.088)
0.060***
(0.019)
−0.037
(0.040)
0.009
(0.039)
0.035
(0.043)
0.125***

−0.136**
(0.063)
−0.042
(0.065)
−0.025
(0.067)
0.018
(0.087)
0.021
(0.044)
−0.023
(0.040)
0.051
(0.060)
0.038
(0.039)
−0.043
(0.076)
0.198**
(0.092)
−0.029
(0.045)
−0.221**
(0.087)
0.019
(0.019)
0.030
(0.039)
0.096**
(0.038)
0.087**
(0.044)
0.084**

0.039
(0.057)
0.028
(0.061)
0.020
(0.063)
−0.001
(0.088)
−0.037
(0.044)
0.049
(0.040)
0.200***
(0.062)
−0.007
(0.038)
−0.039
(0.081)
0.088
(0.085)
−0.117***
(0.043)
−0.197***
(0.076)
0.050***
(0.019)
−0.163***
(0.040)
0.075**
(0.037)
0.017
(0.040)
0.218***

continued

ECB Working Paper Series No 290471Table A3 continued

Dependent variable:

Unexpected inﬂation increase positive or negative for...

Log gross wealth

Stockholdings (0/1)

Homeowner (0/1)

Net nominal position / gross wealth

-50% to <-25%

-25% to <0%

0% to <25%

25% to <50%

50% to <75%

≥75%

Participation following reminder (0/1)

Participation incentive voucher (0/1)

Observations
R-squared

cash

ﬁxed income

stocks

real estate

FR loans

(1)

(2)

(3)

(4)

(5)

(0.043)
−0.082***
(0.013)
−0.134***
(0.042)
0.155***
(0.054)

(0.041)
−0.047***
(0.013)
−0.045
(0.043)
0.039
(0.055)

0.026
(0.096)
0.102
(0.087)
0.131
(0.082)
0.150*
(0.091)
0.205**
(0.099)
0.070
(0.100)
0.041
(0.035)
−0.032
(0.037)

3,091
0.07

−0.079
(0.115)
0.027
(0.102)
−0.107
(0.097)
−0.081
(0.105)
−0.152
(0.109)
−0.171
(0.112)
0.006
(0.036)
0.015
(0.038)

3,091
0.02

(0.042)
0.014
(0.013)
−0.065
(0.044)
0.036
(0.056)

−0.074
(0.107)
0.002
(0.094)
0.065
(0.091)
0.011
(0.098)
0.001
(0.105)
0.107
(0.107)
0.018
(0.036)
0.047
(0.038)

3,091
0.03

(0.042)
0.081***
(0.013)
−0.020
(0.043)
0.073
(0.054)

−0.020
(0.107)
−0.200**
(0.096)
−0.064
(0.092)
−0.087
(0.099)
−0.181*
(0.106)
−0.015
(0.107)
−0.071**
(0.035)
−0.002
(0.037)

3,091
0.05

(0.039)
0.096***
(0.012)
0.116***
(0.041)
−0.085*
(0.049)

−0.115
(0.106)
−0.144
(0.094)
−0.248***
(0.090)
−0.237**
(0.097)
−0.260**
(0.101)
−0.160
(0.103)
−0.010
(0.034)
−0.008
(0.036)

3,091
0.11

Notes: This table reports regression estimates of beliefs about the impact of unexpected inﬂation
on various balance-sheet items, measured on an ordinal scale from 0 (“very negative”) to 4 (“very
positive”). We standardize the outcome variables. For ﬁxed income, we list savings accounts, bonds,
and life insurances as examples. FR loans refers to ﬁxed-rate loans. Inﬂation relatively important
measures respondents’ beliefs about the importance of inﬂation for own wealth relative to the
average importance of GDP growth, interest rates, and stock prices. Accurate inﬂation perception
is a dummy equal to one if respondents’ estimate of current inﬂation is at most 1.5 pp oﬀ actual
inﬂation. Inﬂation lower in ﬁve years indicates the expectation that inﬂation will be lower in ﬁve
years than it is perceived today. Participation following reminder indicates survey participation
after the bank sent a reminder email to the selected respondents. Participation incentive voucher
equals one if the respondent received a guaranteed voucher (rather than a lottery ticket) for survey
participation. Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290472Table A4: Treatment eﬀects on beliefs about own wealth

Dependent variable:

Inﬂation positive eﬀect on RNW Strong balance sheet when inﬂation

(1)

(2)

(3)

(4)

(5)

(6)

Treat: savings erosion

Treat: loan erosion

NNP≥0

−0.012
(0.043)
0.129***
(0.044)

−0.019
(0.043)
0.142***
(0.044)

Treat: savings erosion × NNP≥0

Treat: loan erosion × NNP≥0

Controls
Observations
R-squared

N
3,190
0.00

Y
3,134
0.03

−0.025
(0.088)
0.232**
(0.096)
−0.028
(0.071)
0.008
(0.101)
−0.118
(0.108)

Y
3,134
0.03

0.042
(0.044)
0.071
(0.043)

0.026
(0.041)
0.093**
(0.040)

N
3,190
0.00

Y
3,134
0.18

0.116
(0.077)
0.141*
(0.075)
−0.031
(0.068)
−0.112
(0.091)
−0.064
(0.089)

Y
3,134
0.17

Notes: This table reports beliefs about own wealth in the context of inﬂation of respondents in
each treatment group relative to those in the control group. Columns 1–3 consider beliefs about
the impact of inﬂation on real net wealth. The question reads: “What has been the impact of the
following factors on the change of your real net wealth over the past 12 months?” Five response
options range from “very negative” to “very positive.” From the response to inﬂation we subtract
the average response to the other factors we ask about (Ukraine conﬂict, COVID-19, climate change,
economic growth). Columns 4–6 refer to agreement to the statement that the respondent’s balance
sheet is well-suited for high inﬂation. Five response options range from “completely disagree” to
“completely agree.” We standardize all outcome variables. Section 2.1 entails detailed descriptions
of the treatments. NNP≥0 is a dummy equal to one if the respondent has a non-negative net
nominal position and zero otherwise. The list of controls is in Section 4. Results are from OLS
regressions. Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290473Table A5: Heterogeneity in loan-treatment eﬀect on change in RNW

Dependent variable:

Real-net-wealth change over last + next 12 months

Characteristic:

Education high Inﬂation down in 5

Inﬂation accurate

Inﬂation important

(1)

(2)

(3)

(4)

Panel A. Subset with negative NNP and characteristic satisﬁed

Treat: loan erosion

Observations
R-squared

7.852***
(1.877)

270
0.45

6.850***
(1.699)

353
0.42

7.630***
(1.851)

270
0.57

8.302***
(2.785)

199
0.46

Panel B. Subset with negative NNP and characteristic not satisﬁed

Treat: loan erosion

Observations
R-squared

2.115
(2.619)

255
0.45

3.317
(3.956)

166
0.35

2.836
(2.507)

249
0.36

Panel C. Subset with positive NNP and characteristic satisﬁed

Treat: loan erosion

Observations
R-squared

−0.532
(1.111)

751
0.47

2.101**
(0.999)

927
0.43

0.290
(1.227)

735
0.46

Panel D. Subset with positive NNP and characteristic not satisﬁed

Treat: loan erosion

3.655**
(1.503)

−0.858
(1.923)

Observations
R-squared

Controls
Avg. Y control group

831
0.30

Y
-5.62

621
0.31

Y
-5.62

2.130
(1.388)

819
0.34

Y
-5.62

3.615*
(1.872)

327
0.40

2.520
(1.544)

667
0.39

0.586
(1.185)

914
0.32

Y
-5.62

Notes: This table reports estimates of regressions of RNW changes on various subsamples based
on respondent characteristics: Education high, which equals one for completed higher education
and zero otherwise; Inﬂation down in 5, which is one if the rate of inﬂation expected in ﬁve
years is below currently perceived inﬂation; Inﬂation accurate, which is one if currently perceived
inﬂation deviates by at most 0.5 pp from actual inﬂation; and Inﬂation important, which is one
if respondents perceive inﬂation to be substantially more important for their own wealth than the
average of GDP growth, stock prices, and interest rates. In Panels A and C, we run regressions
on the subsample of respondents who satisfy a characteristic, respectively.
In Panels B and D,
we consider the subsamples of respondents not satisfying a characteristic. Moreover, we split the
sample based on respondents’ net nominal position (negative in Panels A and B, non-negative in
Panels C and D). Section 2.1 entails detailed descriptions of the treatment. The list of controls
is in Section 4. Results are from Huber-robust regressions to control for outliers and inﬂuential
observations. Standard errors are robust to heteroscedasticity. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290474Table A6: Correlation between planned and actual changes in spending

Dep. var.:

Window:

Total

60

(2)

30

(1)

Nondiscretionary

Discretionary

90

(3)

30

(4)

60

(5)

90

(6)

30

(7)

60

(8)

90

(9)

Panel A. Planned nondurables spending

Nondurables

N
R2

44.6**
(18.2)

1,466
0.03

62.4**
(30.6)

111.9***
(41.2)

18.2**
(8.4)

1,514
0.03

1,472
0.02

1,432
0.04

Panel B. Planned durables spending

Durables

N
R2

Controls
Avg. Y

17.9
(18.6)

1,465
0.03

Y
-217.2

76.7**
(30.0)

1,514
0.03

Y
-174.5

54.7
(40.9)

1,476
0.02

Y
91.8

6.4
(8.0)

1,431
0.03

Y
-91.8

18.2
(12.6)

1,413
0.02

5.0
(12.3)

1,414
0.02

Y
-37.2

9.4
(17.1)

1,407
0.03

−5.7
(16.2)

1,405
0.03

Y
84.1

17.6
(12.4)

1,455
0.03

4.8
(12.5)

1,453
0.03

Y
-130.2

36.9*
(20.5)

1,489
0.04

97.6***
(30.1)

1,493
0.04

58.8***
(21.3)

88.3***
(29.2)

1,490
0.04

Y
-124.4

1,494
0.04

Y
-153.5

Notes: This table reports estimates from regressions of actual spending on planned spending.
We study total (Columns 1–3), nondiscretionary (Columns 4–6), and discretionary (Columns 7–9)
spending, as described in Section 2.2. We compare individual-level aggregate spending in the 30,
60, and 90 days following survey participation relative to the same time window pre-participation,
respectively. In Panel A, Nondurables is the average planned change in spending across nondurables
categories (groceries, restaurants, leisure, clothing) over the next four weeks relative to the past
four weeks. Five response options range from “much less” to “much more.” In Panel B, Durables is
the sum across indicators of planned purchases of large/durable items (real estate, vehicles, large
household items, large vacations, luxury goods, others) over the next 12 months. We standardize
the two measures of planned spending. The list of controls is in Section 4. Results are from Huber-
robust regressions to control for outliers and inﬂuential observations. Robust standard errors are
in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290475Table A7: Robustness of treatment eﬀects on actual spending

Dep. var.:

Window:

Total

60

(2)

30

(1)

Nondiscretionary

Discretionary

90

(3)

30

(4)

60

(5)

90

(6)

30

(7)

60

(8)

90

(9)

Panel A. Estimation using OLS

T: loan

N
R2

117.9*
(68.2)

1,514
0.02

264.1**
(102.5)

136.2
(136.8)

1,514
0.02

1,514
0.02

Panel B. Fixed pre-treatment baseline of 60 days

Treat: loan erosion

N
R2

180.2***
(68.2)

186.5***
(59.6)

132.5*
(79.1)

1,469
0.07

1,513
0.03

1,482
0.04

44.3
(31.2)

1,514
0.02

34.3
(31.7)

1,464
0.07

85.9*
(51.6)

1,514
0.02

21.7
(24.3)

1,414
0.02

48.8
(72.0)

1,514
0.02

39.5
(38.4)

1,456
0.04

Panel C. Adding uncategorized spending to total and discretionary spending

T: loan

N
R2

66.1*
(37.0)

1,466
0.03

190.6***
(60.6)

177.8**
(79.7)

1,513
0.03

1,483
0.02

Panel D. Adding change in income as control

T: loan

N
R2

69.6*
(37.0)

1,466
0.03

171.8***
(59.9)

162.3**
(76.6)

1,512
0.04

1,480
0.03

Panel E. Adding controls for other expectations

T: loan

N
R2

Controls
Avg. Y

73.4**
(36.8)

198.8***
(60.2)

166.0**
(79.5)

1,448
0.04

Y
-267.0

1,494
0.03

Y
-308.4

1,459
0.02

Y
-22.8

−4.8
(15.8)

1,431
0.03

−4.0
(15.8)

1,435
0.04

−1.5
(16.0)

1,414
0.04

Y
-92.6

21.7
(24.3)

1,414
0.02

21.1
(24.0)

1,411
0.03

22.8
(24.7)

1,402
0.03

Y
-46.6

38.2
(33.1)

1,405
0.03

34.4
(32.9)

1,404
0.03

42.4
(33.7)

1,390
0.03

Y
58.7

56.2
(51.7)

1,514
0.02

72.0*
(42.5)

1,460
0.05

39.3
(25.6)

1,455
0.03

42.0*
(25.1)

1,455
0.03

44.4*
(25.5)

1,437
0.03

Y
-147.2

177.6**
(74.6)

1,514
0.03

116.1
(96.3)

1,514
0.03

121.0***
(40.8)

185.8***
(54.3)

1,488
0.04

1,473
0.05

130.9***
(41.6)

141.1**
(56.4)

1,489
0.04

1,493
0.03

117.9***
(41.1)

129.3**
(55.5)

1,491
0.04

1,498
0.04

130.5***
(41.3)

131.8**
(55.8)

1,467
0.04

Y
-222.9

1,471
0.04

Y
-240.3

Notes: This table shows estimates from regressions of actual spending. We study total (Columns
1–3), nondiscretionary (Columns 4–6), and discretionary (Columns 7–9) spending, as described in
Section 2.2. We compare individual-level aggregate spending in the 30, 60, and 90 days following
survey participation relative to the same time window pre-participation respectively. In Panel A,
we use OLS rather than Huber-robust regressions. In Panel B, we vary the post-treatment time
window but hold ﬁxed the pre-participation window at 60 days. In Panel C, we add uncategorized
non-integer outﬂows worth less than e 100 to total and discretionary spending. In Panel D, we add
regular income (such as salary, pension, children’s allowances) to the list of controls. In Panel E,
we add to the list of controls the 12-months-ahead expectation about house prices, unemployment,
and own income; general optimism; and expectations about changes in interest rates over the next
12 months and 5 years. Section 2.1 entails detailed descriptions of the treatment. The list of
controls is in Section 4. Results are from Huber-robust regressions (other than in Panel A). Robust
standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

ECB Working Paper Series No 290476B Experimental instructions

This appendix provides the survey instructions translated from German into English. We use

green text in parentheses to highlight aspects of the survey design. We show non-numerical

response options to the questions using a), b), c), and so forth.

B.1 Welcome screen

Welcome to this survey by Goethe University Frankfurt!

The survey will take about 10 minutes. Your responses will be aggregated and only be used for
scientiﬁc research. If you feel you are unfamiliar with some of the survey topics, that is ﬁne. We
just ask you for your best guess. Since we are interested in your unﬁltered opinion, please refrain
from using external sources (e.g., Google).
In return for your completed participation, you will receive an Amazon voucher worth e 10 [if
voucher version] / get the chance to win one of 50 Amazon vouchers worth e 50 [if lottery
version]. To receive the voucher [if voucher version] / To receive the voucher in case of a win [IF
lottery version], you can enter your e-mail address at the end of the survey. Your email address
will only be used to send you the voucher.

Please do not use the “Back” button in your browser, as this may require restarting the survey.

Do you have questions? Please contact us at umfrage@ﬁnance.uni-frankfurt.de.

B.2 Pre-treatment section

Q1: What is your highest level of educational attainment?

a) Currently pursuing apprenticeship or studies (no degree yet)

b) Finished apprenticeship at training college or company

c) Finished apprenticeship at technical school or university of cooperative education

d) Obtained Bachelor, graduated from university of applied sciences or technical college

e) Diploma or Master, ﬁnished teacher training

f) Finished doctorate studies

g) Other professional degree

h) No degree (and not currently pursuing apprenticeship or studies)

[Ask if Q1=d), e), or f)]

Q1.1: In what ﬁeld of study did you obtain your highest educational attainment?

ECB Working Paper Series No 290477a) Economics and Business Administration

b) Computer Science

c) Law

d) Medicine or Psychology

e) Engineering

f) Other ﬁeld of study

[Randomized order of Q2 and Q3]

[Randomized assignment to either gain frame, or loss frame in Q2 and Q3]

Q2: Please imagine that you unexpectedly receive [if gain frame] / have to make [if loss frame] a
one-time payment of e 10,000 today. How would this change your spending over the next
three months?
Please click and drag the sliders below. [Range is from e -10,000 to e 10,000, in e 100 increments]

How much more/less would you save?

How much more/less would you spend?

How much higher/lower would your outstanding loans be?

Q3: Please imagine that today you carefully calculate the total value of your assets and subtract
from it the value of possible outstanding loans. You realize to your own surprise that you have
e 10,000 more [if gain frame] / less [if loss frame] than previously assumed. How would this
change your spending over the next 3 months?
Please click and drag the sliders below. [Range is from e -10,000 to e 10,000, in e 100 increments]

How much more/less would you save?

How much more/less would you spend?

How much higher/lower would your outstanding loans be?

Q4: To what extent does your wealth situation depend on the following factors?

Response options: Not at all – Very little – Somewhat – Strongly – Very strongly

[Factors presented in randomized order]

Inﬂation rate

Economic growth

Stock prices

Interest rates

Q5: Have you consumed rather more or less as a consequence of changes in the following factors
over the past 12 months, such as abstained from a purchase or purchased something extra?

ECB Working Paper Series No 290478Response options: Much less – Rather less – No change – Rather more – Much more

[Factors presented in randomized order]

Inﬂation rate

Economic growth

Stock prices

Interest rates

Q6: What do you think is the current rate of inﬂation in Germany, and what will it be in the
future?

Note: the rate of inﬂation is the percentage change in overall prices in the economy in the last 12
months, most commonly measured by the Consumer Price Index. A falling price level is commonly
known as “deﬂation”.

If you think there was deﬂation, please enter a negative value. You may enter up to one decimal
point.

Current rate of inﬂation:

%

Expected rate of inﬂation in one year:

Expected rate of inﬂation in ﬁve years:

%

%

Q6.1: How certain are you about your responses?

Response options: 1 (“Not at all certain”) – 2 – 3 – 4 – 5 (“Very certain”)

Current rate of inﬂation

Expected rate of inﬂation in one year

Expected rate of inﬂation in ﬁve years

Q7: What is the composition of your household’s gross wealth? Gross wealth includes all assets,
without deducting outstanding loans. For example, if you bought property ﬁnanced by a loan, only
the current value of the property is part of gross wealth, without deducting the outstanding loan
amount.
Example: a household has e 20 of cash, e 80 of real estate, and e 40 of loans. Gross wealth is
therefore e 100. Cash constitutes 20% and real estate 80% of gross wealth.

Please enter “0” if a category does not apply to you. The sum of your entries should equal 100%.

Cash and ﬁxed-rate savings (e.g., saving accounts, bonds, life insurances):

%

Stocks:

%

Real estate (owner-occupied or investment):

%

Other (e.g. vehicles, gold):

%

Total (the sum should equal 100): [Sum of values above] %

ECB Working Paper Series No 290479[If the total of the asset entries does not equal 100%, the following message is displayed: “Please
make sure that the sum of your entries equals 100%.”]
How large are outstanding loans as a share of your gross wealth? Example: a household has e 100
of gross wealth and e 40 of loans. Loans have a 40% share of gross wealth.

Enter a “0” if you do not have any outstanding debt.

Mortgages:

%

Consumer loans:

%

[As soon as the respondent makes a loan entry, the following text is displayed.]

Your outstanding loans make up [sum of share of mortgages and consumer loans]% of your gross
wealth. Thus, your net wealth is [1 - (sum of share of mortgages and consumer loans)]% of your
gross wealth.

Q8: For some of the following questions, we kindly ask you to provide information about real-
value changes. These changes take inﬂation into account. For example, an investor who earns
a 5% annual return would achieve a real return of -2% with 7% inﬂation. Thus, despite having a
positive nominal return, this investor experiences a loss in purchasing power.

Now, please think about the net wealth of your household. This is your gross wealth minus
your outstanding loans. How has the real value of your net wealth changed in the past twelve
months?

Real value gain: Net worth has increased by more than inﬂation.

Net wealth has increased similar to inﬂation.

Real value loss: Inﬂation has increased by more than net wealth.

[If a) or c) in Q8, ask the following question]

Q8.1: Please estimate how positively [if a)] / negatively [if c)] your net wealth has changed in
real terms.

Please click and drag the slider below.

Positive real change in % [if a)] / Negative real change in % [if c)]. [Slider ranging from 1% to 60%]

Q9: What do you think, is an unexpected increase in inﬂation rather positive or negative for
owners of the following ﬁnancial instruments?

Response options: Very negative – Rather negative – Neither nor – Rather positive – Very positive

Cash on hand

Fixed-rate products (e.g., time deposits, bonds, life insurances)

Stocks

Real estate

Fixed-rate loans

ECB Working Paper Series No 290480Q9.1: How certain are you about your responses?

Response options: 1 (“Not at all certain”) – 2 – 3 – 4 – 5 (“Very certain”)

B.3 Treatment section

[Control group]

The current inﬂation rate in Germany is 8.7%. This is the highest rate in more than 70 years.
That is, goods and services priced at e 100 one year ago now cost e 108.70 on average.

[Savings-erosion treatment group]

The current inﬂation rate in Germany is 8.7%, the highest rate in more than 70 years. That
is, goods and services priced at e100 one year ago now cost e108.7 on average. This price increase
has a relatively negative eﬀect on savers: the savings amount (e.g., checking account, bond,
life insurance) is unchanged nominally or lower, but worth less in real terms as a consequence of
money depreciation.

As an example, consider a e50,000 savings product with a three-year maturity that you took out
one year ago. The real value of the savings product has already fallen sharply, and will depreciate
further if inﬂation remains high: e50,000 savings value one year ago ⇓ e38,800 real value
today

The inﬂation-induced savings depreciation thus has a negative eﬀect on the real net wealth
of savers.

Note: the numbers come from current calculations by the Universities of Chicago and Frankfurt
(calculation details).

[Loan-erosion treatment group]

The current inﬂation rate in Germany is 8.7%, the highest rate in more than 70 years. That
is, goods and services priced at e100 one year ago now cost e108.7 on average. This price increase
has a relatively positive eﬀect on borrowers: the loan amount is unchanged nominally, but
worth less in real terms as a consequence of money depreciation.
As an example, consider a e50,000 loan with a three-year maturity that you took out one year ago.
The real value of the loan has already fallen sharply, and will depreciate further if inﬂation remains
high: e50,000 loan value one year ago ⇓ e38,800 real value today

The inﬂation-induced loan depreciation thus has a positive eﬀect on the real net wealth of
borrowers.

Note: the numbers come from current calculations by the Universities of Chicago and Frankfurt
(calculation details).

[Text presented to respondents who click on “calculation details” button]

Calculation details: “To determine the present value of a ﬁxed-rate savings product [if savings
treatment] / loan [if loan treatment], we calculate the present value of future interest payments and

ECB Working Paper Series No 290481principal repayments (discounted cash ﬂow). The present value calculation involves discounting
each payment using a discount rate. This step allows to make comparable payments that occur at
diﬀerent points in time. The discount rate takes into account the inﬂation expectation prevailing
at the respective time periods.

In our calculation, the discount rate is set at 3% one year ago (1% + 2% inﬂation expectation)
and then increases linearly to 9.7% today (1% + 8.7% inﬂation expectation). This increase in the
discount rate signiﬁcantly reduces the present value of future payments. The inﬂation data used
is based on the Harmonized Consumer Price Index from the German Federal Statistical Oﬃce. [If
savings treatment] Additionally, the calculation assumes that the savings product every month pays
interest at an annualized rate of 0.3%. [If loan treatment] Additionally, the calculation assumes a
nominal interest rate of 3%, an annual repayment rate of 3%, and monthly payments.”

[Treatment groups only]

Q10: Have you known about the information on the impact of inﬂation on loans [if loan treatment]
/ savings [if savings treatment]?

Response options: 1 (“Completely unknown”) – 2 – 3 – 4 – 5 (“Completely known”)

B.4 Post-treatment section

Q11: What do you expect the following economic factors to be in twelve months?

Response options: Much lower – Rather lower – No change – Rather higher – Much higher

Stock prices

Real-estate prices

Unemployment rate

Own net income

Q12: What do you estimate to be the interest rate for a newly contracted loan with a ﬁve-year
maturity and ﬁxed interest rate currently? And what interest rate do you expect for the future?

Please enter up to one decimal place.

Current annual interest rate:

%

Annual interest rate in one year:

Annual interest rate in ﬁve years:

%

%

Q13: Compared to the past four weeks, do you plan to spend more or less in the next four
weeks on the following:

Response options: Much less – Somewhat less – Similar amount – Somewhat more – Much more

Groceries

ECB Working Paper Series No 290482Eating out at restaurants

Leisure activities (e.g., movies/theater, vacations, hobbies)

Clothing, shoes

Q14: Do you plan to purchase any of the following products or take a major vacation within the
next twelve months?

Multiple choices are possible.

a) House or apartment

b) Car or other vehicle

c) Household item or electronic device (e.g., refrigerator, sofa, mobile phone)

d) Major vacation

e) Luxury item (e.g., watch, jewelry)

f) Other

g) None of the above

[If Q14 is not answered with g), ask Q14.1 in the next screen.]

Q14.1: How much do you plan to spend on the house or apartment [if a)], the car or other vehicle
[if b)], the household item or electronic device [if c)], the major vacation [if d)], the luxury item [if
e)], other major products [if f)]?
a) e

%

b) I do not know or prefer not to answer

Q15: We would like to ask you again about the net wealth of your household. There is no right
or wrong answer here. Please estimate the real-value change of your net wealth in the past
twelve months, as well as the expected change in the next twelve months.

Please click and drag the sliders below. [Sliders ranging from -60% to 60%]

Real change of net wealth in the past twelve months

Expected real change of net wealth in the next twelve months

Q15.1: How certain are you about your estimates?

Response options: 1 (“Not at all certain”) – 2 – 3 – 4 – 5 (“Very certain”)

Real change of net wealth in the past twelve months

Expected real change of net wealth in the next twelve months

Q16: In your opinion, what impact did the following factors have on the real value of your net
wealth in the past twelve months?

Response options: Very negative – Rather negative – None – Rather positive – Very positive

ECB Working Paper Series No 290483[Randomized order]

Inﬂation

Ukraine conﬂict

COVID-19

Climate change and policies

Economic development

Q17: With which of the following ﬁnancial products would you expect the highest real-net-
wealth return during unexpectedly high inﬂation?

Please assign a “1” to the ﬁnancial product with the best inﬂation protection, a “2” to the second-
best protection, a “3” to the third-best, and a “4” to the fourth-best inﬂation protection.

Savings products (e.g., cash, savings account, bonds, life insurance)

Stocks

Real estate

Fixed-rate loans

Q18: Imagine you want to buy property. You can use up to e500,000 in borrowed capital (bank
loan) and e500,000 in equity (savings) for the purchase. How much money would you use to buy
the property, and how would you ﬁnance it?

Please click and drag the sliders below.
Purchase price [Slider ranging from e0 to e1,000,000]
Equity used [Slider ranging from e0 to e500,000]
You would ﬁnance the property using e[purchase price - equity used] of borrowed capital.

Fixed-rate period [Response options: “None” – “5 years” – “10 years” – “20 years” – “30 years”]

Note: the mortgage term describes the period for which the agreed interest rate in the loan contract
is ﬁxed.

Q19: To what extent do you agree with the statements below?

Response options: “Strongly disagree” – “Somewhat disagree” – “Neither agree nor disagree” –
“Somewhat agree” – “Strongly agree”

[Randomized order]

a) One should use savings to buy something for themselves.

b) Taking on loans makes me uncomfortable.

c) I am concerned about high inﬂation.

d) With my balance sheet, I am well-equipped for times of high inﬂation.

e) I am optimistic about the future.

ECB Working Paper Series No 290484f) I expect to work more in the coming months than in the previous months.

g) Unexpected high inﬂation leads to a redistribution of wealth from savers to borrowers.

Q20: You are now nearing the end of the survey. We just have a few more questions
about you.

When making personal savings or investment decisions, how would you generally describe your risk
tolerance?

Response options: 1 (“Not at all willing to take risks”) – 2 – 3 – 4 – 5 (“Very risk tolerant”)

Q21: Imagine that your income and expenses are about 10% higher than they were a year ago. At
that time, you were planning to sell a high-quality watch but did not get around to it due to lack
of time. The price of this watch has since increased from e10,000 to e11,000. Would you now be
more inclined to sell your watch compared to a year ago?

a) Yes, more inclined to sell

b) No, more inclined to keep

c) Unchanged

Q22: How would you estimate the combined value of the cash holdings and interest-bearing assets
(such as savings accounts, bonds, life insurance) of your household?

[Dropdown menu]
a) 0 to under e2,500
b) e2,500 to under e5,000
c) e5,000 to under e10,000
d) e10,000 to under e25,000
e) e25,000 to under e50,000
f) e50,000 to under e75,000
g) e75,000 to under e100,000
h) e100,000 to under e150,000
i) e150,000 and above

Q23: How would you estimate the outstanding value of all the loans of your household?

[Dropdown menu]
a) e0 (no loans)
b) e1 to under e5,000
c) e5,000 to under e10,000
d) e10,000 to under e25,000

ECB Working Paper Series No 290485e) e25,000 to under e50,000
f) e50,000 to under e75,000
g) e75,000 to under e100,000
h) e100,000 to under e150,000
i) e150,000 to under e200,000
j) e200,000 to under e350,000
k) e350,000 to under e500,000
l) e500,000 and above

Q24: How interesting did you ﬁnd this survey?

Response options: 1 (“Not at all interesting”) – 2 – 3 – 4 – 5 (“Very interesting”)

Q25: Do you have any suggestions or feedback regarding our survey? Please share them here
(optional).

[Text ﬁeld]

Q26: Thank you for participating in our survey!
As a token of appreciation for your participation, you will receive an Amazon voucher worth e10
[if voucher] or a chance to win one of 50 Amazon vouchers worth e50 [if lottery]. To receive the
voucher, simply conﬁrm that you would like to be contacted by us for the purpose of voucher
delivery and provide your email address in the next step.

a) Yes, I would like to receive the voucher [if voucher] / participate in the voucher lottery [if lottery]

b) No, I do not want to receive the voucher [if voucher] / do not want to participate in the voucher
lottery [if lottery]

[If Q26 is answered with a), present Q26.1 in the same screen]

Q26.1: Please provide your email address for voucher delivery:

Enter email address:

Conﬁrm email address:

[Closing text below]

Thank you once again for your participation! Your answers have been saved. You may now close
this window in your browser.

ECB Working Paper Series No 290486Acknowledgements 

We thank our partners at a bank for their collaboration and the data necessary to conduct this research. The bank data were obtained 
under a non-disclosure agreement. The bank did not review the results of the paper prior to public dissemination. We are also grateful 
for helpful comments from Adrien Auclert, Sebastian Blesse, Paola Boel, Andreas Fuster, Katrin Gödker, Mete Kilic, Nora Lamersdorf, 
Christine Laudenbach, Stephan Luck, Chris Roth, Jan Toczynski, Emil Verner, and Johannes Wohlfart, and from conference and 
seminar participants at Deutsche Bundesbank, Erasmus University Rotterdam, Georgetown University, Goethe University Frankfurt, the 
Heidelberg-Karlsruhe-Mannheim seminar, Maastricht University, Nova SBE, Radboud University Nijmegen, UT Austin, the 2023 
Bundesbank International Conference on Household Finance, 2023 Central Bank Research Association Conference, 2023 Dolomites 
Summer Finance Conference, 11th Helsinki Finance Summit, 13th ifo Conference on Macroeconomics and Survey Data, 7th SAFE 
Household Finance Workshop, 2023 Swiss Winter Conference on Financial Intermediation, and 2023 Western Finance Association 
Conference. The experiment received IRB approval from the German Association for Experimental Economic Research (Number: 
5hjDTgn8) and is preregistered in the AEA RCT Registry (ID: 9735). We gratefully acknowledge research support from the efl - the Data 
Science Institute and Leibniz Institute for Financial Research SAFE. The contribution by Philip Schnorpfeil has been prepared under the 
Lamfalussy Fellowship Programme sponsored by the ECB. 
Any views expressed are only those of the authors and do not necessarily represent the views of the ECB or the Eurosystem. 

Philip Schnorpfeil 
Goethe University, Frankfurt am Main, Germany; email: schnorpfeil@nance.uni-frankfurt.de 

Michael Weber 
University of Chicago, Chicago, United States; Centre for Economic Policy Research (CEPR), London, United Kingdom; 
National Bureau of Economic Research (NBER), Massachusetts, United States; email: michael.weber@chicagobooth.edu 

Andreas Hackethal 
Goethe University, Frankfurt am Main, Germany; Leibniz Institute for Financial Research (SAFE), Frankfurt am Main, Germany; 
email: hackethal@safe-frankfurt.de 

© European Central Bank, 2024 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

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www.ecb.europa.eu 

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

ISBN 978-92-899-6384-8 

ISSN 1725-2806 

doi:10.2866/055338 

QB-AR-24-021-EN-N