Loss aversion, economic sentiments and international consumption smoothing.
Date: 2019-05-08
Institutions: European Stability Mechanism
Subjects: consumer behaviour, consumption, international trade, macroeconomics

Working Paper Series  | 35 |  2019

Loss aversion, economic sentiments and
international consumption smoothing

 This paper demonstrates that loss-averse behaviour weakens 
international consumption smoothing

Daragh Clancy
 European Stability Mechanism 
Lorenzo Ricci
 European Stability Mechanism 

Disclaimer
This Working Paper should not be reported as representing the views of the ESM.
The views expressed in this Working Paper are those of the author(s) and do not
necessarily represent those of the ESM or ESM policy.

  
 
Working Paper Series  |  35  |  2019

Loss aversion, economic sentiments and 
international consumption smoothing

Daragh Clancy 1 European Stability Mechanism 
Lorenzo Ricci 2 European Stability Mechanism 

Abstract
We examine an unexplored connection between loss aversion and international consumption 
smoothing.  In  the  face  of  expected  income  declines,  loss-averse  behaviour  implies  that  any 
adjustments  in  consumption  are  delayed  until  they  are  necessary.  However,  if  the  expected 
fall  in  income  materialises,  the  eventual  adjustments  in  consumption  are  larger.  Therefore, 
loss  aversion  could  weaken  international  consumption  smoothing.  We  test  this  mechanism 
using  measures  of  economic  sentiment.  These  provide  a  sense  of  the  expected  direction  of 
future  income  changes.  We  demonstrate  that  prevailing  confidence  and  uncertainty,  our 
proxies for economic sentiment, reduce the degree of international consumption smoothing. 
We  provide  evidence  that  this  effect  arises  from  loss-averse  behaviour,  using  a  test  of  the 
asymmetric response of consumption to positive and negative output fluctuations that occur 
following  periods  of  weak  economic  sentiment.  Our  findings  have  important  implications 
for  public  institutions  and  private  initiatives  that  aim  to  improve  cross-country  risk  sharing. 

Keywords: confidence, consumption smoothing, loss aversion, uncertainty. 

JEL codes: E21, E71, F41, F44

1 Email: d.clancy@esm.europa.eu

2 Email: l.ricci@esm.europa.eu

Disclaimer
This  Working  Paper  should  not  be  reported  as  representing  the  views  of  the  ESM.  The  views 
expressed in this Working Paper are those of the author(s) and do not necessarily represent those of 
the ESM or ESM policy. No responsibility or liability is accepted by the ESM in relation to the accuracy 
or completeness of the information, including any data sets, presented in this Working Paper.

© European Stability Mechanism, 2019 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 European Stability Mechanism.

ISSN 2443-5503 
ISBN 978-92-95085-67-1

doi:10.2852/018764
EU catalog number DW-AB-19-002-EN-N

Loss aversion, economic sentiments and
international consumption smoothing*

Daragh Clancy†

Lorenzo Ricci‡

April 8, 2019

Abstract

We examine an unexplored connection between loss aversion and international
consumption smoothing. In the face of expected income declines, loss-averse be-
haviour implies that any adjustments in consumption are delayed until they are
necessary. However, if the expected fall in income materialises, the eventual ad-
justments in consumption are larger. Therefore, loss aversion could weaken in-
ternational consumption smoothing. We test this mechanism using measures of
economic sentiment. These provide a sense of the expected direction of future in-
come changes. We demonstrate that prevailing conﬁdence and uncertainty, our
proxies for economic sentiment, reduce the degree of international consumption
smoothing. We provide evidence that this effect arises from loss-averse behaviour,
using a test of the asymmetric response of consumption to positive and negative
output ﬂuctuations that occur following periods of weak economic sentiment. Our
ﬁndings have important implications for public institutions and private initiatives
that aim to improve cross-country risk sharing.

JEL classiﬁcation: E21; E71; F41; F44

Keywords: Conﬁdence, Consumption smoothing, Loss aversion, Uncertainty

*The views contained here are those of the authors and not necessarily those of the European Sta-
bility Mechanism (ESM). We are grateful to Pierfederico Asdrubali, Giancarlo Corsetti, Harris Dellas,
Aitor Erce, Massimo Giuliodori, and Georgios Palaiodimos for comments, as well as participants at the
8th International Ioannina Meeting on Applied Economics and Finance and seminars at the ESM and
Universitat de Barcelona. We thank Kimi Jiang for his help with the Bloomberg data.

†European Stability Mechanism, 6a Circuit de la Foire Internationale, L-1347 Luxembourg.

Email: d.clancy@esm.europa.eu

‡European Stability Mechanism, 6a Circuit de la Foire Internationale, L-1347 Luxembourg.

Email: l.ricci@esm.europa.eu

1

1

Introduction

Open economies experiencing country-speciﬁc (or the asymmetric effect of global) out-

put ﬂuctuations can smooth consumption through both domestic and cross-border

trading. This provides a measure of risk sharing across different states of nature (Cole

and Obstfeld, 1991) and could increase the synchronisation of international business

cycles (Kose et al., 2003). Greater business cycle correlations are particularly impor-

tant amongst members of a monetary union to compensate for the loss of independent

monetary policy (Frankel and Rose, 1998).1 However, starting with Backus et al. (1992),

a large body of empirical evidence shows that international consumption correlations

are signiﬁcantly lower than unity and are even lower than cross-country output cor-

relations. This ﬁnding represents one of the major puzzles in international economics

(Obstfeld and Rogoff, 2000).

In this paper, we argue that loss-averse behaviour can at least partly explain this

puzzle. The connection between loss aversion and international consumption smooth-

ing is quite intuitive. Loss aversion implies that expected consumption gains induce

risk-averse behaviour, while expected losses induce risk-loving behaviour (Tversky and

Kahneman, 1991). Therefore, an expected decrease in future income should initially

lead to greater consumption smoothing in the presence of loss aversion, as agents de-

lay adjusting their consumption until they absolutely must. However, if their gamble

fails and the expected fall in income materialises, the eventual adjustment in consump-

tion is much larger. Overall, the presence of loss-averse behaviour should weaken in-

ternational consumption smoothing.

Economic sentiments provide a sense of the expected direction of future aggregate

income changes. Measures of economic sentiment therefore provide us with a basis

upon which to test this, to the best of our knowledge, unexplored, connection between

1Of course, the incomplete synchronisation of international business cycles is not necessarily dam-
aging. Callen et al. (2015) argue that a certain degree of heterogeneity across business cycles is poten-
tially an asset, as it increases the room for international diversiﬁcation. If output growth rates across
countries were perfectly aligned, there would be no potential to diversify income sources across bor-
ders. Devereux and Smith (1994) demonstrate that greater international risk sharing could even result
in lower economic growth.

2

loss aversion and international consumption smoothing. Because economic sentiment

is such a nebulous concept, we decompose it into two parts: conﬁdence and uncer-

tainty.2 We proxy these measures of economic sentiment using consumer sentiment

surveys and the output growth errors of professional forecasters. We empirically test

the importance of the strength of conﬁdence and uncertainty prior to the realisation

of an output ﬂuctuation on a country’s subsequent degree of consumption smoothing,

using a panel of 20 advanced economies.3

We ﬁnd that international consumption smoothing is weaker when an output ﬂuc-

tuation materialises following periods of weak economic sentiment. We use a test of the

asymmetric response of consumption to provide evidence that this effect arises at least

partly due to loss-averse behaviour. Our asymmetric test also allows us to demonstrate

that loss aversion plays a greater role in international consumption smoothing than al-

ternative theories of consumer behaviour that focus on myopia or liquidity constraints.

Our ﬁndings on the relevance of economic sentiments are robust to the inclusion

of alternative determinants of international consumption smoothing, such as trade

and ﬁnancial openness, long-term interest rates and ﬁnancial crisis periods. We also

demonstrate that economic sentiments have an effect on international consumption

smoothing after explicitly controlling for the state of the business cycle, global out-

put ﬂuctuations and the persistence of idiosyncratic output ﬂuctuations. Overall, to

the extent that greater international consumption smoothing boosts the correlation of

cross-border business cycles, our results imply a desynchronisation following periods

of weak economic sentiment.

2See Nowzohour and Stracca (2017) for a recent review of the related literature and empirical mea-
sures of economic sentiment, as well as some stylised facts (based on international evidence) regarding
these measures. They describe conﬁdence “as a subjective feeling of certainty or strong belief in positive
future economic developments”. They state that uncertainty “could be either the range of possible out-
comes of future economic developments, and/or the lack of knowledge of the probability distribution
from which future economic developments are drawn” (pp. 8). Importantly, they also note that these
concepts of sentiment may be observationally equivalent.

3Kalemli-Ozcan et al. (2013) note that the different types of shocks experienced by countries means
that the pooling of developed, emerging and underdeveloped countries in empirical estimations is
not ideal. We therefore restrict our sample to only advanced economies. Future work could exam-
ine whether economic sentiments play a role in international consumption smoothing and the business
cycle synchronisation of emerging economies.

3

Literature review:

Our paper is related to the substantial literature on international consumption risk

sharing. Sørensen and Yosha (1998) demonstrate that full risk sharing implies per-

fect consumption smoothing. There are many suggested theoretical explanations for

why risk sharing is lower than predicted by the classical one-good complete-markets

model.4 These include the presence of non-traded goods (Tesar, 1993), taste shocks

(Stockman and Tesar, 1995), capital market restrictions (Baxter and Crucini, 1995),

preference shocks (Sørensen and Yosha, 1998), transaction costs (Obstfeld and Ro-

goff, 2000), transport costs (Kraay and Ventura, 2002), insufﬁcient ﬁnancial instruments

(Heathcote and Perri, 2002), productivity shocks (Corsetti et al., 2008), contract enforce-

ment (Broner and Ventura, 2011), default risk (Bai and Zhang, 2012) and the persistence

of consumption risk (Lewis and Liu, 2015).

The empirical macroeconomic literature generally focuses on quantiﬁcation of the

degree of international consumption risk sharing and the examination of the channels

through which it takes place. A common ﬁnding is that consumption risk sharing

is larger, although still imperfect, within regions of a country than between different

countries (Sørensen and Yosha, 1998; Crucini, 1999; M´elitz and Zumer, 1999; Kalemli-

Ozcan et al., 2003; Asdrubali and Kim, 2004). Greater country size (Head, 1995), indus-

trial specialisation (Kalemli-Ozcan et al., 2003), trade and ﬁnancial openness (Imbs,

2004), foreign direct investment ﬂows (Albuquerque, 2003), habit formation (Fuhrer

and Klein, 2006), international asset holdings (Sørensen et al., 2007) and ﬁnancial glob-

alisation (Kose et al., 2009) are all positively related to international consumption risk

sharing. Fratzscher and Imbs (2009) show that international risk sharing is negatively

related to the quality of borrowing institutions, while Kalemli-Ozcan et al. (2014) show

that consumption risk sharing collapsed in the European countries most affected by

the recent sovereign debt crisis.

Our paper adds to this empirical literature by exploring whether loss aversion is

4Alternatively, Brandt et al. (2006) argue that the smoothness of exchange rates implies the existence
of substantial international consumption risk sharing. Fitzgerald (2012) shows that risk sharing amongst
developed economies is close to optimal, considering the large costs of trade.

4

another mechanism that could potentially affect the degree of observed international

consumption smoothing. To do so, we will use measures of economic sentiment. These

capture expectations of future income changes. Many studies have demonstrated the

empirical relevance of shocks to economic sentiments in propagating domestic busi-

ness cycle ﬂuctuations (Beaudry and Portier, 2006; Jaimovich and Rebelo, 2009; Barsky

and Sims, 2011; Angeletos et al., 2018) and their international transmission (Jaimovich

and Rebelo, 2008; Dees, 2017; Levchenko and Pandalai-Nayar, 2019). We instead ex-

amine whether prevailing economic sentiments affect the degree of international con-

sumption smoothing in the face of (exogenous) country-speciﬁc output ﬂuctuations.

The benchmark theoretical framework used as the basis for empirical tests of in-

ternational consumption risk sharing assumes risk-averse behaviour by the expected-

utility maximising representative agent. Obstfeld and Rogoff (1996) note that it is the

concavity of the utility function that makes the expected-utility maximiser risk averse

and interested in purchasing insurance to smooth their consumption. However, this

behaviour is inconsistent with experimental evidence (Kahneman et al., 1991; Camerer,

1995) that people care more about reductions in their consumption, relative to a refer-

ence point, than gains. Therefore, it is unsurprising that the high degree of interna-

tional consumption smoothing predicted by the benchmark theoretical model ﬁnds

little empirical support.

Instead, there is empirical evidence (Shea, 1995a; Bowman et al., 1999; Foellmi et al.,

2018) that aggregate consumption time series react to ﬂuctuations in income in a man-

ner qualitatively consistent with loss aversion. These studies broadly follow the ap-

proach used at the household level (Altonji and Siow, 1987; Shea, 1995b) to test the life

cycle / permanent income hypothesis of consumption behaviour and ﬁnd no evidence

in favour of myopia and liqudity constraints. According to the consumption-saving

model of Bowman et al. (1999), utility is concave only when consumption increases

above a reference level and convex when consumption declines below. This implies

that consumers are willing to risk greater losses in order to have a chance at avoiding

losses altogether. Our measures of economic sentiment provide an insight into ex-

5

pected changes in aggregate income. Therefore, we use these measures as a means to

assess whether loss-averse behaviour is a quantitively important factor in explaining

lower-than-expected international consumption smoothing.

Our paper is structured as follows. Section 2 summarises the benchmark theoreti-

cal framework for international consumption smoothing and discusses how economic

sentiments could affect the degree of smoothing observed, focusing on the potential

role of loss aversion. Section 3 introduces our empirical strategy. We describe the

construction and interpretation of our economic sentiment measures in Section 4. We

detail the empirical results in Section 5 and summarise and conclude in Section 6.

2 Consumption smoothing

The consumption smoothing condition imposed by the intertemporal Euler equation

plays a key role in modern open economy macroeconomics (Obstfeld and Rogoff,

1996). The benchmark two-country, two-period model with complete ﬁnancial mar-

kets allows individuals in different countries to use the same state-contingent security

prices to equate their marginal rates of substitution between current consumption and

state contingent future consumption:

π(s)βU (cid:48)[CH,t+1(s)]
U (cid:48)(CH,t)

=

π(s)βU (cid:48)[CF,t+1(s)]
U (cid:48)(CF,t)

=

1
1 + r

,

(1)

where U represents utility derived from per-capita consumption C in the home (H)

and foreign (F) country, s denotes different states of nature that occur with probability

π and 1

1+r is the (gross) riskless interest rate from period t to period t+1 that reﬂects the

relative price of a certain unit of consumption in both periods. Assuming a constant

relative risk aversion utility function, it is straightforward to show that home country

consumption is a constant fraction γ of world output YW regardless of the state of the

world:

CH,t+1(s)
YW,t+1(s)

= γ =

CH,t
YW,t

,

6

(2)

and analogously for the foreign country with a 1 − γ share of world output. Therefore,

consumption growth rates are the same across countries in every state and are equal

to the growth rate of world output. Uncertainty over future world output means that

consumption is not constant across states. However, because of international diversiﬁ-

cation, changes in consumption are entirely due to world, rather than country-speciﬁc

(or the asymmetric effect of global), output ﬂuctuations.

The role of loss aversion

The prediction of substantial international consumption smoothing in the benchmark

theoretical model relies on a number of key assumptions. These include complete mar-

kets and the existence of ﬁnancial products (with fully enforceable contracts) that allow

agents to insure against all idiosyncratic risks.5 These assumptions are made for ana-

lytical convenience and allow for closed-form solutions of the model environment. We

focus on the effect that the typical assumption of a von Neumann-Morgenstern utility

function has on international consumption smoothing.6 Speciﬁcally, the concavity of

this utility function ensures that the risk-averse representative agent purchases sufﬁ-

cient insurance to enable them to smooth consumption.

However, the use of this class of utility function to explain consumption behaviour

has long been questioned (Koopmans, 1960; Ryder and Heal, 1973; Laibson, 1997).

Prospect theory (Kahneman and Tversky, 1979) suggests that there is an asymmetry

in the perceived losses and gains from decreases and increases in consumption. While

the assumption of risk aversion is appropriate in consumption gains, it is not compat-

ible with consumption losses (Tversky and Kahneman, 1991, 1992). This is because

changes in consumption are not considered in abstract, but instead are judged rela-

tive to a reference point. Shea (1995a) was the ﬁrst to provide empirical evidence of

this consumption asymmetry at the aggregate level (for the United States). He did

so by amending the test for the life cycle / permanent income hypothesis suggested

5Baxter and Crucini (1995) show that perfect consumption smoothing, but not full risk sharing, is

possible even with incomplete markets.

6See Van Wincoop (1994, 1999) for studies assessing the impact of nonexpected utility and habit

formation preferences on international risk sharing.

7

by Campbell and Mankiw (1990) to examine consumption smoothing for positive and

negative expected income ﬂuctuations.

Bowman et al. (1999) expand upon the theoretical underpinnings of this asymmetry,

and provide empirical evidence of its relevance in explaining consumption smoothing

in ﬁve advanced economies (Canada, France, West Germany, Japan and the United

States). Rosenblatt-Wisch (2008) provide evidence of loss-averse behaviour in US ag-

gregate macroeconomic time series. Santoro et al. (2014) embed loss aversion in a gen-

eral equilibrium framework and demonstrate that this behaviour can rationalise the

asymmetric effect of monetary policy on output and prices. More recently, and most

closely related to our study, Foellmi et al. (2018) examine the differences in the degree of

loss aversion across a broad range of OECD countries. They ﬁnd it is negatively related

to per-capita income (GDP) and consumption levels. They also provide some evidence

(in the form of bivariate correlations) of a higher degree of consumption smoothing

in countries with higher degrees of loss aversion. These results are in line with the

theoretical predictions of Foellmi et al. (2011).

We use economic sentiments as a means to test whether loss aversion can help ex-

plain the lower-than-expected degree of international consumption smoothing. The

importance of economic sentiment in driving business cycle ﬂuctuations has long been

recognised (Pigou, 1929; Keynes, 1936). We instead analyse the role of economic senti-

ments as ampliﬁers of output ﬂuctuations via their effect on international consumption

smoothing, rather than being the source of the ﬂuctuation itself.

Economic sentiments provide a sense of the expected direction of future aggregate

income changes. Weak sentiments signalling an expected decrease in future income

should initially lead to greater consumption smoothing in the presence of loss aver-

sion, as agents delay adjusting their consumption (possibly to avoid falling below a

reference level) until they absolutely must. As such, countries display risk-loving ten-

dencies when faced with potential consumption losses. However, if their gamble fails

and the expected fall in income materialises, the eventual adjustment in consumption

is larger. Therefore, in the presence of loss-averse behaviour, international consump-

8

tion smoothing should be weaker for negative output ﬂuctuations that follow periods

of weak economic sentiments.7

3 Empirical strategy

Our empirical strategy to ascertain whether loss aversion affects the degree of inter-

national consumption smoothing in our panel of advanced economies builds upon

the framework developed by Asdrubali et al. (1996). This approach has served as a

benchmark for a large number of empirical studies in this and connected literatures

(Sørensen and Yosha, 1998; M´elitz and Zumer, 1999; Sørensen et al., 2007; Kose et al.,

2009) and allows us to test the degree of consumption smoothing. In particular, we

estimate the following panel regression:

∆ log ci,t = νt + µi + β1∆ log yi,t + εi,t,

(3)

where ci,t and yi,t denote the per-capita consumption and output of country i in year

t. We include time ﬁxed effects νt to control for any other undiversiﬁable aggregate

ﬂuctuations.8 The inclusion of country ﬁxed effects µi ensures that we account for all

country-speciﬁc inﬂuences. We estimate the model using a two-step generalised least

squares (GLS) procedure with heteroskedastic and autocorrelation robust standard er-

rors.9

In this speciﬁcation, the β1 coefﬁcient captures the average degree of consumption

7Throughout our analysis, we deﬁne periods of weak economic sentiment as occurring if conﬁdence
is low and/or uncertainty is high. We chose our deﬁnition because the mechanism through which loss
aversion affects consumption behaviour relies on uncertainty. However, it could be that low conﬁdence
with more certainty (i.e. lower uncertainty) has a larger effect on the degree of international consump-
tion smoothing. We therefore used a model speciﬁcation that includes triple interactions to test this and
found that it is not the case. The results are available upon request.

8Idiosyncratic components can be also estimated as the deviation of consumption and output from
their OECD aggregations (Ostergaard et al., 2002). This methodology is equivalent to estimating equa-
tion (3) with time dummies (Ventura, 2003).

9The presence of heteroscedasticity and serial correlation in our data means that we follow Sørensen
and Yosha (1998)’s use of the Generalised Least Squares (GLS) estimator rather than Ordinary Least
Squares (OLS). However, we perform robustness tests using alternative estimators such as OLS with
panel-corrected standard errors (Beck and Katz, 1994), an Arellano-Bond dynamic panel (Arellano and
Bond, 1991) and an interactive ﬁxed effect panel (Bai, 2009). Our results are not altered using these
alternative estimators. We provide these results in Appendix A.

9

smoothing following countries’ idiosyncratic (or the asymmetric response to global)

output ﬂuctuations at time t. Sørensen and Yosha (1998) note that, according to the

benchmark theoretical framework discussed earlier, this coefﬁcient is equal to zero if

consumption in an individual country does not vary with idiosyncratic output ﬂuctu-

ations. In the case of a non-zero coefﬁcient, larger coefﬁcients signal less consumption

smoothing. This framework can also be used to examine the channels through which

consumption is smoothed.10 Because our interest lies in assessing whether loss-averse

behaviour plays a role in the total degree of international consumption smoothing, our

main analysis does not further disaggregate amongst the various channels.

We extend the benchmark empirical model in Eqs. (3) to examine the role of loss

aversion on the degree of international consumption smoothing. We test this connec-

tion using measures of economic sentiment as a indication of expected income changes.

We ﬁrst examine the effect that economic sentiments have on the degree of interna-

tional consumption smoothing, without establishing the exact mechanism behind this

relationship. To do so, we include the interaction of the idiosyncratic component of

output with the prevailing strength of our economic sentiment measures, θi,t−1, in the

panel regression:

∆ log ci,t = νt + µi + β1∆ log yi,t + β2∆ log yi,t × θi,t−1 + β3θi,t−1 + εi,t,

(4)

where β2 captures the degree of consumption smoothing that depends on the level of

economic sentiment in the period before the country-speciﬁc output ﬂuctuation hits.

We include our economic sentiment variable with a lag to avoid contemporaneous cor-

relations with the idiosyncratic output ﬂuctuations. A positive β2 coefﬁcient suggests

that, on average, weaker economic sentiments in the period prior to the realisation of

10A common ﬁnding from the empirical literature is that the “credit channel” is the primary (and
in many cases, the only) source of international consumption risk sharing (Sørensen and Yosha, 1998;
Asdrubali and Kim, 2004, 2008a,b). This use of borrowing and lending to smooth consumption in the
face of temporary output ﬂuctuations is consistent with the permanent income hypothesis proposed
by Friedman (1957). The empirical evidence suggests that intra-temporal risk sharing through income
insurance mechanisms playing a relatively minor role. For completeness, we also assess the role of ex-
ante and ex-post smoothing channels and ﬁnd that conﬁdence and uncertainty play very little role in
the degree of smoothing achieved via income insurance mechanisms. See Section 5.3 for details.

10

a country-speciﬁc output ﬂuctuation leads to a worsening in consumption smoothing.

We also include the lagged economic sentiment variable separately to ensure that our

β2 is not biased and to capture the linear relation between lagged economic sentiment

and international consumption smoothing.

To assess whether loss aversion is the mechanism through which economic sen-

timents affect the degree of international consumption smoothing, we incorporate a

modiﬁed version of the asymmetric test proposed by Shea (1995a) into the Asdrubali

et al. (1996) framework. Speciﬁcally, we test for an asymmetric response of interna-

tional consumption smoothing by constructing two distinct series based on ∆ log yi,t.

Following Cover (1992), we use a nonlinear interactive variable to represent the posi-

tive and negative of ∆ log yi,t:11

while

negi,t = −

1
2

[| ∆ log yi,t | −∆ log yi,t] ;

posi,t =

1
2

[| ∆ log yi,t | +∆ log yi,t] .

We then modify Eqs. (4) in order to examine whether positive and negative idiosyn-

cratic output ﬂuctuations have symmetric effects on international consumption smooth-

ing depending on the strength of economic sentiment prior to the realisation of the

ﬂuctuation:

∆ log ci,t = νt + µi + β1∆ log posi,t + β2∆ log negi,t

+ β3∆ log signi,t × θi,t−1 + β4θi,t−1 + εi,t,

(5)

where signi,t is alternatively posi,t or negi,t depending on the hypothesis we are test-

11An equivalent approach, also used by Cover (1992), is that the series neg is deﬁned as the output
ﬂuctuation if it is negative, otherwise zero: negi,t = min(∆ log yi,t, zero). The series pos equals the
output ﬂuctuation if the ﬂuctuation is positive, otherwise zero: posi,t = max(∆ log yi,t, zero). Pierucci
and Ventura (2010) examine the asymmetric effect of risk sharing by assessing the differential responses
to positive and negative realisations of GDP.

11

ing.12 Eqs. (5) provides a simple test of alternative hypotheses of consumption be-

haviour.13 A ﬁnding of no statistical difference in international consumption smooth-

ing following positive and negative output ﬂuctuations (βpos

1 = βneg

2

) would be consis-

tent with standard rational-expectations behaviour or myopia. Given that liquidity con-

straints should only impede borrowing but not saving (Shea, 1995a), if they are present,

international consumption smoothing should be weaker for expected income increases

than decreases. We can access if this is the case from testing βpos

1 + βpos

3 = βneg

2

, where

a rejection of the null hypothesis means that the degree of international consumption

smoothing is at least somewhat affected by liquidity constraints.

Finally, we can test whether loss aversion affects international consumption smooth-

ing by assessing βneg

2 + βneg

3 = βpos

1

, where a rejection indicates that consumption

smoothing is weaker for expected income decreases than increases. We can gain some

further insights into the possible presence of loss aversion by examining the effect of

prevailing sentiments alone (i.e. not interacted with subsequent output ﬂuctuations).

By checking the estimated signs of βneg

3

and β4 from Eqs. (5), we can assess whether

the initial adjustment of consumption is consistent with subsequent behaviour after ex-

pectations were realised (or not). Speciﬁcally, a positive (and signiﬁcant) coefﬁcient for

βneg
3

and a negative (and signiﬁcant) β4 provide evidence of loss-averse behaviour.

4 Measures of economic sentiment

We assess whether loss aversion affects the degree of international consumption smooth-

ing in a set of 20 advanced (OECD) economies: Australia, Austria, Belgium, Canada,

Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, the Netherlands,

New Zealand, Portugal, Spain, Sweden, Switzerland, the United Kingdom and the

12We also tested a model speciﬁcation that includes the interactions of both positive and negative
output ﬂuctuations with prevailing economic sentiment. Our results are robust to this alternative spec-
iﬁcation, and are available from the authors upon request.

13In addition to the tests of myopia, liquidity constraints and loss aversion, we also test for evidence
supporting precautionary savings. We do so by decomposing consumption smoothing into ex-ante and
ex-post channels. If precautionary savings were the dominant mechanism through which economic sen-
timents affect international consumption smoothing, the ex-ante channel should be more prominent
than the ex-post channel. We ﬁnd that this is not case. See Section 5.3 for details.

12

United States. Our sample includes the majority of the world’s advanced economies

and the time period is sufﬁciently long to permit a detailed economic analysis.14

We aim to maximise data consistency across countries. We therefore collect most

of our variables from the OECD’s Economic Outlook database (Table 1). We retrieve

output (gross domestic product) and consumption (household ﬁnal consumption ex-

penditure) from the annual national accounts main aggregate. We use consumer price

indices and the populations of the countries to transform our series into real per-capita

terms. We use annual growth rates to characterise the business cycle because of dif-

ﬁculties identifying them using ﬁltering techniques (Canova and Ravn, 1996; Canova,

1998). Our maximum sample runs from 1970 to 2015, and contains substantial cyclical

variation across time and countries.

Economic sentiments capture the expected direction of future income changes, al-

lowing us to formally test the connection between loss aversion and international con-

sumption smoothing. Given the wide-ranging interpretations of what constitutes eco-

nomic sentiments, we empirically test two of the more widely-agreed upon compo-

nents: conﬁdence and uncertainty. As these are latent variables, we use observable

proxies in their place. Our conﬁdence measure is based on the OECD’s consumer

opinion (tendency) surveys, which provide qualitative information on households’ as-

sessments of the current economic situation and expectations for the immediate fu-

ture.15 Consumers are questioned about their perceptions of the economic situation

now compared with the recent past, their intentions concerning major purposes and

their expectations for the immediate future.16

14To make sure that no one country is driving our results, we rerun our analysis excluding one coun-
try at a time. This should also reduce the impact of any outliers. Our results are robust to the exclusion
of any given country in the sample. The results are available upon request.

15The surveys, therefore, have both a contemporaneous and a forward-looking component. There
is substantial empirical evidence that consumer surveys are useful predictors for consumption growth
(Acemoglu and Scott, 1994; Bram and Ludvigson, 1998; Ludvigson, 2004). As we are testing the effect
of prevailing economic sentiments on the degree of international consumption smoothing, we prefer to
use the contemporaneous component as this reﬂects consumers’ perceptions on the realised (but still
unknown to the survey respondent) state of the economy (Merella and Satchell, 2014). However, as a
robustness check, we repeat our analysis with the forward-looking component for countries where it
was available. Although this substantially reduces the number of years in our sample, our results are
qualitatively unchanged. The results of these robustness tests are presented in Table 13.

16As our regressions are based on annual data, we compute the yearly average of the OECD’s con-
sumer survey that is provided at monthly or quarterly frequency (depending on the country). The

13

Responses in these surveys are relative to a “normal” state, so a respondent reports

whether their conﬁdence has increased sharply / increased slightly / remained the

same / fallen slightly / fallen sharply. The time series of these surveys only present

the balance. This is computed by ignoring the “same” answers and taking the dif-

ference between the percentage of respondents giving favourable and unfavourable

answers. This can make it difﬁcult to interpret the meaning of the estimated coefﬁ-

cients from regressions using the raw consumer conﬁdence series. We therefore use

the consumer conﬁdence indices to construct an indicator variable for conﬁdence θi,t

that varies between 0 and 1 according to the strength of consumer conﬁdence sit:

θit =

exp (−γsi,t)
1 + exp (−γsi,t)

, γ > 0,

(6)

where sit is a two-year moving average of the (raw) consumer conﬁdence indicator and

a higher θi,t represents lower conﬁdence (i.e. weaker sentiment). Auerbach and Gorod-

nichenko (2012) use this approach to deﬁne the state of the business cycle when assess-

ing the state-dependency of ﬁscal multipliers. In the absence of any prior knowledge

of what value γ should take, we calibrate it to 1.5. This is the same value used by

Auerbach and Gorodnichenko (2012) to ensure their measure of the US business cycle

indicates a recession about 20 percent of the time.17

We plot our constructed conﬁdence measure for each country in our sample in Fig-

ure 1. There is substantial heterogeneity in the conﬁdence series across countries and

time. Our conﬁdence measure tends to be high during economic downturns, such as in

the European periphery during the recent sovereign debt crisis and in Finland during

the early-1990s banking crisis. It tends to be low during economic expansions, such as

in the euro area following the introduction of the new currency and in Japan during

the late 1980s real-estate boom.

The other component of economic sentiment we examine is uncertainty. There is

OECD choose surveys from each country so as to maximise cross-country consistency. Despite this,
they may not all be exactly equivalent. For all EU countries, the use of a harmonised consumer survey
methodology ensures they are fully comparable across all EU countries.
17Our results are robust to reasonable variations in the value of γ.

14

considerable evidence on the importance of uncertainty in business cycle ﬂuctuations

(Bloom, 2009; Basu and Bundick, 2017). We proxy uncertainty with the output growth

errors of professional forecasters.18 Forecasts are quite literally the expectations for fu-

ture economic activity of those making the forecasts. Analysing these errors can allow

one to assess the degree of uncertainty regarding the current (for nowcasts) and future

(for forecasts) economic situation. More speciﬁcally, we use the output (GDP) growth

errors from the International Monetary Fund’s World Economic Outlook. This ensures

extensive cross-country coverage and relatively long time series.19 Each vintage (pub-

lished in the Spring and Fall of each year) contains forecasts for the current year and

year ahead. Spring forecasts are performed at the beginning of the year without ex-

haustive information for the previous year for some countries. We therefore use the

Fall forecasts as they contain the ﬁnal release data from the previous year. Our sample

of output growth errors runs from 1990 to 2015 for all countries.

We construct our measure of uncertainty using the absolute value of current year

output growth errors. This ensures consistency with the time period covered by our

conﬁdence measure. To facilitate interpretation of the estimated coefﬁcients, we trans-

form the output growth errors into an indicator variable that varies between 0 and 1

following the same procedure as in Eq.

(6). We set up our uncertainty series such

that larger values indicate larger errors and higher uncertainty and therefore weaker

economic sentiments. Low values indicate periods of low uncertainty (i.e. stronger

sentiment). This also has the beneﬁt of easing comparisons with the results from our

conﬁdence measure.

Figure 2 demonstrates that our uncertainty measure also displays considerable het-

erogeneity across countries and time. Our measure tends to be high during economic

18For our analysis using quarterly data (see Section 5.5), we also used the economic uncertainty index
(Bloom, 2009). Our results are robust to this alternative measure of uncertainty. However, the coverage
(in terms of business cycle ﬂuctuations and countries included) is more limited than the consensus
forecast data used for the regressions reported in this paper. The results are available from the authors
upon request.

19Blanchard and Leigh (2013), Blanchard et al. (2017) and Di Bella and Grigoli (2018) also exploit IMF
output growth errors as a measure of expectations. Similar to our consumer conﬁdence measure, our
analysis differs from the existing literature by using uncertainty as an ampliﬁer of an output ﬂuctuation
rather than the source of the disturbance itself.

15

downturns, such as in the United States in the global ﬁnancial crisis and in the Euro-

pean periphery during the sovereign debt crisis. Our measure tends to be low dur-

ing normal times, when the economy is neither expanding nor contracting by a large

amount.

5 Results

We report the results from the regressions in Eqs.

(3) and (4) in Table 3. The ﬁrst

column contains the results from the benchmark empirical model. As is commonplace

in the literature, the predictions of the benchmark theoretical framework also do not

ﬁnd empirical support in our sample. We ﬁnd that a large proportion (around 60%) of

output ﬂuctuations go unsmoothed.

The second and third columns of Table 3 contain the results from our extended

model with the interaction between our economic sentiment measures and idiosyn-

cratic output ﬂuctuations (as well as the lagged level of the economic sentiment mea-

sures).20 We ﬁnd that weak prevailing economic sentiment on its own slightly improves

consumption smoothing. This is in line with the results of Foellmi et al. (2018). How-

ever, when we interact prevailing economic sentiments with subsequent output ﬂuctua-

tions, we ﬁnd that consumption smoothing is signiﬁcantly worse: there is quite a large

adjustment of consumption.

Carroll (2003) demonstrates that households’ expectations are derived in part by

news reports of the views of professional forecasters. This suggests there may be a link

between our measures of economic sentiment. To make sure that our measures are

independently relevant, we estimate our regressions with both measures of economic

sentiment (and their interactions with output) included simultaneously. The fourth

column of Table 3 shows that these measures are both statistically signiﬁcant drivers

of the degree of international consumption smoothing. Therefore, both measures have

a distinct role to play and are worth investigating separately. To the extent that greater

20Our results are robust to the use of alternative measures of consumer conﬁdence and uncertainty.

The results are presented in Table 13. See Appendix B for more details.

16

international consumption smoothing boosts the correlation of cross-border business

cycles, our results imply a desynchronisation following periods of weak economic sen-

timent.

The point estimates presented so far represent the average degree of international

consumption smoothing over our sample period. We assess how these point estimates

have evolved through time using 10-year rolling regressions of Eqs (3) and (4). We

ﬁnd that international consumption smoothing tends to strengthen during periods of

economic expansion and worsen during economic downturns.21 This is the case for the

benchmark model and the model speciﬁcations that control for prevailing economic

sentiments. However, our analysis reveals that controlling for economic sentiments

ampliﬁes these swings in the degree of international consumption smoothing.

We now examine why weak economic sentiments worsen international consump-

tion smoothing following subsequent output ﬂuctuations. To do so, we modify Eqs.

(3) and (4) to (alternatively) include negi,t and posi,t instead of ∆ log yi,t. This asymmet-

ric test allows us to assess the mechanism through which economic sentiments affect

international consumption smoothing. Tables 4 and 5 detail the asymmetric test re-

sults using conﬁdence and uncertainty respectively as our measures of sentiment. Our

estimated coefﬁcients (in the ﬁrst column) are statistically different, and therefore neg-

ative output ﬂuctuations have a greater effect on the degree of consumption smoothing

than positive output ﬂuctuations. This lack of symmetry in the responses indicates that

weak international consumption smoothing does not arise from rational-expectations

or myopic behaviour.

Columns (2) and (3) in Tables 4 and 5 show positive and negative output ﬂuctu-

ations respectively interacted with prevailing economic sentiments. As explained in

Section 3, we consider Column (2) as a test for evidence supporting liquidity con-

straints, while Column (3) tests for the presence of loss aversion. When controlling for

prevailing economic sentiments, we ﬁnd that the adjustment in consumption following

positive output ﬂuctuations are much smaller than those from a negative ﬂuctuation.

21As a robustness test, we control for the state of the business cycle in assessing the effect of economic

sentiments on international consumption smoothing. See Section 5.2 for details.

17

Our F-tests conﬁrm this ﬁnding for conﬁdence, suggesting that the large adjustment

in consumption is the result of loss-averse behaviour rather than due to the presence

of liquidity constraints. The evidence is not as clear cut for uncertainty, suggesting

that this measure of sentiment may be capturing both loss averse behaviour and the

existence of liquidity constraints.

Overall, our results provide evidence for the importance of loss aversion in the

lower-than-expected degree of observed international consumption smoothing. This

could be due to, for example, cross-country differences in consumption reference lev-

els. However, our empirical approach does not allow us to identify the exact reason

for the loss-averse behaviour. By failing to reduce consumption in anticipation of in-

come reductions, the adjustment in consumption when the decrease materialises is far

greater (i.e. consumption smoothing is even weaker). Loss aversion, therefore, pro-

vides a partial explanation for the international consumption correlation puzzle.

5.1 Consumption smoothing dynamics

We also assess the dynamics of consumption smoothing of idiosyncratic output ﬂuctua-

tions, depending on prevailing economic sentiments. This will also allow us to explic-

itly control for persistence in consumption smoothing. Asdrubali and Kim (2004) show

the importance that the dynamic response to output shocks has on international risk

sharing and consumption smoothing using impulse response functions from vector au-

toregressions (VARs). We instead employ an equivalent approach proposed by Jord`a

(2005). He demonstrates that an impulse response function estimated via local projec-

tions is robust to a misspeciﬁcation of the data generating process. It can accommodate

the nonlinearities in our model speciﬁcation that would be impractical or infeasible

using a VAR. These nonlinearities can be estimated in a simple univariate framework,

preserving valuable degrees of freedom.22 Local projections are based on sequential

regressions of the endogenous variable shifted forward:

22Although the local projection method allows for more ﬂexibility by imposing weaker assumptions

on the dynamics, its nonparametric nature comes at an efﬁciency cost.

18

∆ log ci,t+h = µh

i + βh

1,1∆ log yi,t + βh

2,1∆ log yi,t × θi,t−1 + βh

3,pθi,t−1 + βh

4,p∆ log ci,t−1 + ...

1,p∆ log yi,t−p + βh
βh

2,p∆ log yi,t−p × θi,t−1−p + βh

3,pθi,t−1−p + βh

4,p∆ log ci,t−1−p + εi,t+h,

(7)

where the effect of idiosyncratic output ﬂuctuations on consumption smoothing at

horizon h are given by βh

1,1 + βh

2,1θi,t−1. Therefore, the persistence in the degree of inter-

national consumption smoothing following an output ﬂuctuation is also a function of

economic sentiments in the period prior the ﬂuctuation materialising.23

Figure 4 shows the impulse response functions estimated using the local projections

method. All output ﬂuctuations are scaled to be 1% of GDP and the ﬁgures report the

impulse responses for four years after. Our standard error bands represent the 90%

conﬁdence level.

The top-left and bottom left panels represent the dynamics of consumption smooth-

ing following an output ﬂuctuation that materialises after a period of weak economic

sentiment (i.e. low conﬁdence and high uncertainty). The top-right and bottom-right

panels show the response when economic sentiment was weak prior to the realisa-

tion of the output ﬂuctuation. We deﬁne weak sentiment as corresponding to the 90th

percentile of our economic sentiment measures. We deﬁne strong sentiment as corre-

sponding to the 10th percentile of our economic sentiment measures, with these im-

pulse response functions (IRFs) plotted on the top-left and bottom-left panels of Figure

4.

We ﬁnd less consumption smoothing on impact following a period of weak eco-

nomic sentiment.24 This is in line with the results from our static analysis. Thereafter,

23We also include a time trend, country ﬁxed effects and two lags (p) of the variables in our dynamic
regressions. We selected the lag order (p) by looking at different statistics, such as the Bayesian Infor-
mation Criterion and Akaike’s Information Criterion. Our results are qualitatively robust to the use of
alternative lag structures. Due to the inherent serial correlation in the local projections approach, we use
Newey-West standard errors.

24We also compared the differences between the weak and strong sentiment IRFs following the ap-
proach of Broner et al. (2018). We ﬁnd a statistically signiﬁcant difference on impact. However, the
difference between the degree of consumption smoothing following periods of strong and weak eco-
nomic sentiment becomes statistically insigniﬁcant at the ﬁrst projection horizon (i.e. after one year).
The results are available from the authors upon request.

19

there is no discernible difference in consumption smoothing following a period of high

or low consumer conﬁdence, with an output ﬂuctuation being fully smoothed in three

years regardless. However, our results indicate that output ﬂuctuations that occur fol-

lowing a period of low uncertainty take longer (three years instead of two) to fully

smooth than ﬂuctuations occurring after a period of high uncertainty.

5.2 Confounding factors

We test the robustness of our ﬁndings by including alternative determinants of interna-

tional consumption smoothing found in the literature into our empirical framework.25

Formally, we adjust Eqs. (4):

∆ log ci,t = νt + µi + β1∆ log yi,t + β2∆ log yi,t × θi,t−1 + β3θi,t−1

+ βj∆ log yi,t × Ωi,t−1 + βkΩi,t−1 + εit,

(8)

where Ω represents additional explanatory variables (added one at a time) including

trade and ﬁnancial openness, interest rates and periods of ﬁnancial crisis. We con-

trol for trade openness using the sum of exports and imports to gross domestic prod-

uct (Dejuan and Luengo-Prado, 2006) and ﬁnancial openness using the Chinn–Ito in-

dex (Chinn and Ito, 2008). We use long-term interest rates from the OECD economic

database and a ﬁnancial crisis dummy from the Laeven and Valencia (2013) database,

with updates from Detken et al. (2014). We display our results in Table 6. The co-

efﬁcients on our economic sentiment variables (i.e. both in levels and interacted with

output) are not signiﬁcantly altered by the introduction of these additional explanatory

variables.

It is also possible that our sentiment measures simply reﬂect the state of the busi-

ness cycle. Chauvet and Guo (2003) provide empirical evidence of the differing re-

sponses of economic agents to consumer (and business) conﬁdence shocks, depending

25Because our dynamic analysis demonstrated that the effect on consumption smoothing from pre-
vailing economic sentiments is predominantly on impact, our robustness tests focus on the static model.

20

on the state of the economy. Our rolling regressions results in Figure 3 showed that the

effect of economic sentiments on the degree of international consumption smoothing

varies with the business cycle. To make sure there is an independent effect on inter-

national consumption smoothing from our sentiment measures, we include a business

cycle indicator as an explanatory variable in our regressions. We follow Auerbach

and Gorodnichenko (2012) and create an index of the business cycle zi,t based on a

backward-looking (Alloza, 2017) three-year moving average of the output growth rate.

Normalising the variable to have zero mean and unit variance allows us to interpret

the variable as a measure of the probability of being in a recession at time t based on

a measure of the state of the business cycle. As with all the other explanatory vari-

ables, we include the business cycle indicator with a lag to avoid contemporaneous

correlations with the idiosyncratic output ﬂuctuations.

We ﬁnd that our results are not signiﬁcantly altered by controlling for the state of

the business cycle. Our results are therefore quite robust to a range of confounding

factors found to be important in the literature. Economic sentiment, be it conﬁdence

or uncertainty, in the period prior to the realisation of an output ﬂuctuation plays an

important role in the degree of international consumption smoothing.

5.3 Consumption smoothing channels

Our results thus far demonstrate that economic sentiments affect the degree to which

output ﬂuctuations are absorbed, and provide evidence that this ﬁnding is due to the

presence of loss aversion. Our empirical approach, however, does not disentangle the

exact channels through which this (stronger or weaker, depending on the prevailing

economic sentiment) consumption smoothing takes place. It could be that expected

future income changes spur agents to use ex-ante insurance mechanisms, such as port-

folio diversiﬁcations and net international transfers, rather than using ex-post smooth-

ing mechanisms as we have implicitly assumed until now. Following Asdrubali et al.

(1996), many studies have used a variance decomposition scheme to quantify the de-

21

gree of risk sharing achieved through different channels.26

Given that the focus of our paper is not on the individual channels, we limit our

decomposition to the aggregate ex-ante income smoothing and ex-post consumption

smoothing channels. The ex-ante channel is represented by the difference between out-

put (GDP) and disposable income (GDI) growth, that the literature further decomposes

into smoothing via net factor income and net international transfers.27 The ex-post

channel decouples consumption and GDI and represents intertemporal consumption

smoothing through saving and dissaving. We run the following regression to measure

the importance of economic sentiments for the ex-ante smoothing channel:

∆ log yi,t − ∆ log inci,t = νt + µi + β1∆ log yi,t + β2∆ log yi,t × θi,t−1 + β3θi,t−1 + εi,t,

(9)

where ∆ log inci,t is the change in the logarithm of real per-capita disposable income.

We measure the effect of prevailing economic sentiments on ex-post smoothing channel

through the following regression:

∆ log inci,t − ∆ log ci,t = νt + µi + β1∆ log yi,t + β2∆ log yi,t × θi,t−1 + β3θi,t−1 + εi,t. (10)

The sum of the two channels should be equal to one minus the coefﬁcients from the

aggregate smoothing found in section 3. Table 9 reports our results. We ﬁnd that

only a small share of output ﬂuctuations are smoothed by the ex-ante channel and

that economic sentiments seem to matter little for its effectiveness. This suggests that

economic sentiments do not affect international consumption smoothing via ex-ante

insurance mechanisms that could be more closely related to precautionary saving mo-

tives. Prevailing economic sentiments predominantly have an effect on the degree of

26See Asdrubali et al. (2018) for a recent survey of this literature, as well as several advancements in

the empirical approach.

27We also tested the effect of prevailing economic sentiments on the effectiveness of smoothing via
these more disaggregated channels. Our results are robust to the level of disaggregation of the channels
and are available from the authors upon request.

22

international consumption smoothing via the ex-post channel, which provides some

further support for our contention that this effect arises due to lumpy consumption

adjustment as a result of loss-averse behaviour.

5.4 Global factors

Our approach thus far has focused on idiosyncratic output ﬂuctuations. However, it is

also possible that these country-speciﬁc output ﬂuctuations are actually best described

as the response to global (or aggregate or common) factors. Kalemli-Ozcan et al. (2013)

note that cross-sectional patterns, though informative, do not identify causal effects,

as they might be driven by global shocks and/or unobserved country-pair hetero-

geneity. Kose et al. (2003) estimate a dynamic factor model and ﬁnd that world and

region-speciﬁc factors account for a larger share of output growth ﬂuctuations, while

country-speciﬁc factors explain more of the consumption growth than the common

factors. Stock and Watson (2005) attribute the reduction in output volatility during the

1980s and 1990s to a reduction in the magnitude of common international shocks. Kose

et al. (2012) use a dynamic factor model to decompose output ﬂuctuations into a global

factor, factors speciﬁc to country groups, and country-speciﬁc factors. They ﬁnd that

the international business cycle is largely driven by the global factor. Jos Jansen and

Stokman (2014) shows that output developments will be more correlated if common

shocks happen to be predominant, while they will be more asymmetric if idiosyncratic

shocks are the most important.

Our approach so far using time ﬁxed effects can only properly capture comove-

ment if global factors have the same effect across countries. Otherwise, our estimated

coefﬁcients are inconsistent because our speciﬁcation may not take into account the po-

tentially heterogeneous transmission channel of global output ﬂuctuations. To assess

the importance of this issue for our results, we can extract global factors directly from

consumption and output by cross-country aggregation. Since the idiosyncratic com-

ponents are driven by non-pervasive (i.e. country-speciﬁc) ﬂuctuations, by worldwide

aggregation they are ruled out and only the common factors inﬂuencing our variables

23

in all countries remain.

We extract a global factor from our macroeconomic variables using a linear panel

model with the (unknown) factors as interactive ﬁxed effects (Bai, 2009).28 The inter-

action between the state and time ﬁxed effects captures the heterogeneity in the data

in a more ﬂexible way, since it allows for common time-varying factors to affect the

cross-sectional units with individual speciﬁcity. Figure 5 shows the percentage of the

variance of consumption and output explained by the global factor. The impact of this

factor varies considerably across countries.

In the fourth column of Table 8, we report the results from the regressions with the

interactive ﬁxed effect. They are broadly similar to our other results. This indicates that

our measures of economic sentiment affect the degree of international consumption

smoothing following country-speciﬁc output ﬂuctuations, even after explicitly con-

trolling for individual country responses to a global factor.

5.5 Fluctuation persistence

The permanent income hypothesis predicts that agents’ capacity to smooth consump-

tion weakens in response to persistent shocks (Friedman, 1957). Becker and Hoffmann

(2006) demonstrate the importance of the persistence of shocks in quantifying the

degree of consumption risk sharing. Asdrubali et al. (1996) ﬁnd that consumption

smoothing (via credit markets) is considerably lower following more persistent shocks.

Corsetti et al. (2012) use spectral analysis to assess international consumption risk shar-

ing at different frequencies. We assess whether the effect of economic sentiments on

international consumption smoothing is stronger in the face of less persistent output

ﬂuctuations.

To do so, we assemble a quarterly dataset. By following the same estimation pro-

cedure as before, by construction the output ﬂuctuations are less persistent (quarterly

rather than annual frequency). To maintain consistency with the annual data, we re-

28Bai (2009) estimates a model: Yi,t = Xi,tβ + µi,t and µi,t = Λ(cid:48)

i is a vector of factor
loadings and Ft is a vector of common factors. Giannone and Lenza (2010) also use a similar approach
to analyse the Feldstein-Horioka puzzle.

iFt + εi,t, where Λ(cid:48)

24

trieve most of our variables from the OECD’s Economic Outlook database (see Table

1 for details). Respondents to consumer tendency surveys in many countries are re-

quested to take seasonal factors into consideration in their qualitative opinions.

In

spite of this, the OECD provide seasonally-adjusted series due to the residual season-

ality that remains in a number of series. The series published for EU member and

candidate countries are seasonally adjusted by the European Commission using the

DAINTIES seasonal adjustment method. Consumer conﬁdence series for other coun-

tries are seasonally adjusted by their national statistical institutes or by the OECD, if

their seasonal pattern is found to be signiﬁcant. The X-12 Reg-ARIMA method is used

by the OECD for seasonal adjustment. As before, we use real private consumption ex-

penditure and real gross domestic product divided by population as our variables of

interest. We transform our quarterly growth rate variables into annual rates of change.

For our uncertainty measure, we now use current year output growth errors from

Consensus Forecasts, rather than those from the IMF. However, Loungani (2001) shows

a high degree of similarity between private sector growth forecasts and those of inter-

national organisations.29 Consensus Forecasts produce output growth estimates at a

monthly frequency, ensuring that we have a different observation for each quarter.

This information is summarised with the mean forecast. Our sample runs from 1990

to 2015 for all countries in our annual sample except for Austria, Denmark and New

Zealand. Similar to the annual IMF series of output growth errors, we transform the

absolute value of the output growth errors from Consensus Forecasts into a variable

varying between 0 and 1.30

In Table 10, we report the results from the regressions in Eqs. (3) and (4) estimated

using quarterly data.31 Our ﬁndings for the conﬁdence measure are broadly consistent

with the annual results, with low conﬁdence greatly weakening the degree of con-

sumption smoothing following an output ﬂuctuation. However, we ﬁnd no evidence

29Some deviations could occur because of strategic behaviour from the private sectors forecasters,

but Frenkel et al. (2013) ﬁnd that this strategic behaviour only lasts roughly three months.
30In our quarterly framework, si,t, from Eqs. (6) is an eight-quarter moving average.
31The quarterly frequency allows us to estimate the model using OLS with panel-corrected standard
errors. Our results are robust to estimation using GLS. The results are available from the authors upon
request

25

that the degree of uncertainty prior to an output ﬂuctuation affects international con-

sumption smoothing after it materialises. This suggests that uncertainty only plays a

role in the degree of international consumption smoothing following persistent output

ﬂuctuations.32

The consumption smoothing dynamics for our quarterly sample are provided in

Figure 6. As with the annual results, we plot the IRFs for strong economic sentiment

(i.e. high conﬁdence and low uncertainty) on the left-hand side. These are represented

by the 10th percentile of our economic sentiment measures. The IRFs for weak eco-

nomic sentiment (i.e. low conﬁdence and high uncertainty) are on the right-hand side.

These are represented by the 90th percentile of our economic sentiment measures. It ap-

pears that weak economic sentiments worsen the degree of international consumption

smoothing on impact, but this difference fades quickly.

6 Conclusions

The substantial empirical evidence that international consumption smoothing is weaker

than predicted by theory represents a major puzzle in international economics. We as-

sess whether loss aversion can partly explain this puzzle. The risk-loving behaviour

induced by loss aversion means that expected income declines are met with a smaller

initial adjustment in consumption. However, if the expected drop in income materi-

alises, an even larger adjustment in consumption becomes necessary. This mechanism

should weaken international consumption smoothing.

To test this unexplored connection, we use measures of economic sentiment to pro-

vide a sense of the expected direction of future aggregate income changes. Because

economic sentiment is such a nebulous concept, we decompose it into two parts: con-

ﬁdence and uncertainty. These are proxied using consumer sentiment surveys and the

output growth errors of professional forecasters. We then test the importance of the

32We also conducted some tests using model speciﬁcations with a more detailed lag structure. These
show that uncertainty can still affect international consumption smoothing following less persistent (i.e.
quarterly) output ﬂuctuations. In particular, a model speciﬁcation with four lags is highly signiﬁcant.
Future work could examine this aspect in more detail. Our main focus is on annual data to allow for a
comparison with the literature.

26

strength of these sentiment measures prior to the realisation of an output ﬂuctuation

on a country’s subsequent degree of consumption smoothing, using a panel of 20 ad-

vanced economies.

We ﬁnd that international consumption smoothing is weaker when an output ﬂuc-

tuation materialises following periods of weak economic sentiment. Using a test of the

asymmetric response of consumption to positive and negative output ﬂuctuations fol-

lowing periods of weak consumer sentiment, we ﬁnd evidence that this effect is due to

loss-averse behaviour: countries where weak economic sentiment implies that income

is expected to decline, initially smooth their consumption to a greater degree. However,

if the expected drop in income materialises, an even larger adjustment in consumption

becomes necessary. The effect is largely on impact and is not very persistent.

Our results on the relevance of economic sentiments are robust to the inclusion

of alternative determinants of international consumption smoothing, such as trade

and ﬁnancial openness, long-term interest rates and ﬁnancial crisis periods. We also

demonstrate that economic sentiments have an effect on international consumption

smoothing after explicitly controlling for the state of the business cycle, global output

ﬂuctuations and the persistence of idiosyncratic output ﬂuctuations. Overall, to the ex-

tent that greater international consumption smoothing boosts the correlation of cross-

border business cycles, loss-averse behaviour implies a desynchronisation following

periods of weak economic sentiment.

Our ﬁndings on the relevance of loss aversion have important implications for the

design of public institutions and private initiatives whose task is to help improve the

degree of risk sharing and international consumption smoothing. Mendoza (2010)

notes that suboptimally low levels of precautionary savings can lead to sudden stops

that require large adjustments in consumption. Aizenman (1998) shows that loss averse

behaviour can lead to much larger stabilisation funds than those designed on the basis

of agents maximising the conventional expected utility. Future work could consider

whether this ﬁnding, which considers the (domestic) stabilisation funds of commodity

producers, extends to an international dimension. It would also be interesting to con-

27

sider the special case of a monetary union, where nominal interest and exchange rates

react to area-wide rather than country-speciﬁc developments.

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Annual
Quarterly

Annual
Quarterly

Annual
Quarterly

Monthly

Annual
Quarterly

Annual
Quarterly

Annual
Quarterly

Annual
Quarterly

Annual

Monthly

Tables

Variable

Gross domestic product

Private consumption

Population (national concept)

TABLE 1. Data

Source

Frequency

OECD Annual National Accounts
OECD Quarterly National Accounts

OECD Annual National Accounts
OECD Quarterly National Accounts

OECD Annual National Accounts
OECD Quarterly National Accounts

Consumer price index

OECD Key Short-Term Economic Indicators

GDP deﬂator

Private consumption deﬂator

Exports of good and services

Imports of good and services

Gross Disposable Income

Long term interest rates

OECD Annual National Accounts
OECD Quarterly National Accounts

OECD Annual National Accounts
OECD Quarterly National Accounts

OECD Annual National Accounts
OECD Quarterly National Accounts

OECD Annual National Accounts
OECD Quarterly National Accounts

OECD Annual National Accounts

OECD Main Economic Indicators

Consumers Opinion: Consumer conﬁdence OECD Business Tendency and Consumer Opinion Surveys (MEI)

Monthly

Consumers Opinion: Future tendency

OECD Business Tendency and Consumer Opinion Surveys (MEI)

Monthly

Current year forecast error

Year-ahead forecast error

IMF World Economic Outlook and authors’ calculations
Consensus forecasts and authors’ calculations

Semi-Annual
Monthly

IMF World Economic Outlook and authors’ calculations
Consensus forecasts (for quarterly) and authors’ calculations

Semi-Annual
Monthly

Stock market returns volatility

Bloomberg; authors’ calculations

Financial liberalisation

Trade openness

Chinn and Ito (2008)

Authors’ calculations based on OECD’s data

Daily

Annual

Annual

Notes: All variables in the regressions are transformed into real per-capita terms, expressed in logarithms where
possible. We convert nominal variables into real terms using the relevant deﬂators (see Section 4 for details). For
quarterly data, we use population (domestic concept) to complete the panel data in case of unavailable data.

38

TABLE 2. Summary Statistics

Consumers Conﬁdence
Future tendency
Current year forecast error
Market volatility
∆ log ci,t
∆ log cOECD
i,t
∆ log yi,t
∆ log yOECD

i,t

Obs. Mean
-0.013
725
-0.000
513
0.001
503
0.010
514
0.015
725
0.013
725
0.016
725
0.011
725

S.D. Min
-2.831
0.996
-3.120
0.992
-0.071
0.020
0.004
0.004
-0.112
0.024
-0.015
0.015
-0.119
0.030
-0.046
0.020

p25
-0.708
-0.631
-0.007
0.007
0.003
0.004
0.001
0.001

p75 Max
p50
2.657
0.691
0.062
0.682
2.774
0.113
0.010 0.252
0.002
0.009
0.013 0.028
0.016 0.029 0.092
0.014 0.018 0.056
0.017 0.032 0.295
0.012 0.024 0.055

Notes: This table reports the summary statistics of the main variables used in our empirical analysis. Our set of
advanced economies includes 20 OECD countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France,
Germany, Greece, Ireland, Italy, Japan, Netherlands, New Zealand, Portugal, Spain, Sweden, Switzerland, the United
Kingdom and the United States. ∆ log ci,t represents country-speciﬁc real consumption growth per capita, ∆ log yi,t
is the country-speciﬁc real GDP growth per capita. ∆ log cOECD
indicate the aggregate OECD20
real consumption growth per capita and OECD20 real GDP growth. Our maximum sample runs from 1971 to 2015.
See Section 4 for details on the construction of all our variables. Obs.: Observations; S.D.: Standard Deviation; Min:
Minimum. p25: 25th precentile; p50: Median; p75: 75th precentile; and Max: Maximum.

and ∆ log yOECD

i,t

i,t

39

TABLE 3. Economic sentiments and consumption smoothing

(1)

(2)

(3)

(4)

Output

Output × θC

i,t−1

0.61***
(0.02)

θC
i,t−1

Output × θU

i,t−1

θU
i,t−1

0.42***
(0.04)
0.28***
(0.06)
-0.01***
(0.00)

0.27***
(0.05)

0.40***
(0.08)
-0.01***
(0.00)

0.10*
(0.06)
0.38***
(0.08)
-0.02***
(0.00)
0.32***
(0.08)
-0.00**
(0.00)

Observations
Country FE
Year FE

725
YES
YES

687
YES
YES

480
YES
YES

463
YES
YES

Notes: This table shows the results of panel regressions using annual data, with consumption (∆ log ci,t) as the
dependent variable. Output refers to ∆ log yi,t. Larger estimated coefﬁcients represent less consumption smoothing.
The HAC-robust standard errors are in parentheses ( *** p<0.01, ** p<0.05, * p<0.1). The strength of economic
sentiment, proxied by our measures of consumer conﬁdence (θC
i,t), varies between 0 and 1. See
Section 4 for details on the construction of our economic sentiment measures.

i,t) and uncertainty ( θU

40

TABLE 4. Asymmetries: Conﬁdence

(1)

0.51***
(0.03)
0.74***
(0.04)

posi,t

negi,t

θC
i,t−1 × posi,t

θC
i,t−1

θC
i,t−1 × negi,t

Observations
Country FE
Year FE

687
YES
YES

(2)

0.43***
(0.06)
0.70***
(0.04)
0.18*
(0.09)
-0.01***
(0.00)

687
YES
YES

Test - H0:
F-stat

1 = βneg
βpos
15.60

2

1 + βpos
3 = βneg
βpos
1.44

2

(3)

0.51***
(0.03)
0.28***
(0.11)

-0.01***
(0.00)
0.64***
(0.14)

687
YES
YES

2 + βneg
βneg
35.41

3 = βpos

1

Notes: This table shows the results of panel regressions using annual data, with consumption (∆ log ci,t) as the
dependent variable. Output refers to ∆ log yi,t. The HAC-robust standard errors are in parentheses ( *** p<0.01,
** p<0.05, * p<0.1). The strength of economic sentiment is proxied by our measure of consumer conﬁdence θC
i,t
and varies between 0 and 1. See Section 4 for details on the construction of this economic sentiment measure. We
analyse the asymmetric effects of consumption smoothing in response to positive and negative output ﬂuctuations.
See Section 3 for details on the construction of the positive and negative output ﬂuctuation series.

41

TABLE 5. Asymmetries: Uncertainty

(1)

0.32***
(0.04)
0.83***
(0.04)

posi,t

negi,t

θU
i,t−1 × posi,t

θU
i,t−1

θU
i,t−1 × negi,t

Observations
Country FE
Year FE

480
YES
YES

Test - H0:
F-stat

1 = βneg
βpos
68.44

2

(2)

0.16***
(0.06)
0.81***
(0.04)
0.33***
(0.11)
-0.01***
(0.00)

480
YES
YES

(3)

0.32***
(0.03)
0.52***
(0.11)

0.00
(0.00)
0.47***
(0.14)

480
YES
YES

1 + βpos
βpos
12.99

3 = βneg

2

2 + βneg
βneg
84.77

3 = βpos

1

Notes: This table shows the results of panel regressions using annual data, with consumption (∆ log ci,t) as the
dependent variable. Output refers to ∆ log yi,t. The HAC-robust standard errors are in parentheses ( *** p<0.01,
** p<0.05, * p<0.1). The strength of economic sentiment is proxied by our measure of uncertainty θU
i,t and varies
between 0 and 1. See Section 4 for details on the construction of this economic sentiment measure. We analyse the
asymmetric effects of consumption smoothing in response to positive and negative output ﬂuctuations. See Section 3
for details on the construction of the positive and negative output ﬂuctuation series.

42

TABLE 6. Confounding factors: Conﬁdence

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Output

Output × θC

i,t−1

θC
i,t−1

Output × Dcrisis

Dcrisis

Output × Financial Openness

Financial Openness

Output × LIR

Long term interest rates (LIR)

Output × Trade Openness

Trade Openness

Output× F(zi,t−1)

F(zi,t−1)

Observations
Country FE
Year FE

0.47***
(0.05)
0.27***
(0.06)
-0.01***
(0.00)

0.27***
(0.05)
0.14**
(0.07)
-0.01***
(0.00)

0.63***
(0.05)
0.17***
(0.06)
-0.01***
(0.00)

0.33***
(0.05)
0.29***
(0.07)
-0.01***
(0.00)

0.60***
(0.02)

0.42***
(0.04)
0.28***
(0.06)
-0.01***
(0.00)

0.41***
(0.04)
0.24***
(0.06)
-0.01***
(0.00)
0.12***
(0.04)
-0.00**
(0.00)

-0.02
(0.02)
0.00
(0.00)

0.03***
(0.01)
-0.00***
(0.00)

-0.00***
(0.00)
-0.00***
(0.00)

687
YES
YES

687
YES
YES

687
YES
YES

687
YES
YES

626
YES
YES

687
YES
YES

0.16**
(0.08)
0.00
(0.00)

609
YES
YES

Notes: This table shows the results of panel regressions using annual data, with consumption (∆ log ci,t) as the depen-
dent variable. Output refers to ∆ log yi,t. The HAC-robust standard errors are in parentheses ( *** p<0.01, ** p<0.05,
* p<0.1). The strength of economic sentiment is proxied by our measure of consumer conﬁdence θC
i,t and varies be-
tween 0 and 1. See Section 4 for details on the construction of this economic sentiment measure. F(zi,t−1) represents
an indicator of the state of the business cycle, broadly based on the approach of Auerbach and Gorodnichenko (2012).
See Section 5.2 for details on the construction of the business cycle state indicator.

43

TABLE 7. Confounding factors: Uncertainty

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Output

Output × θU

i,t−1

θU
i,t−1

Output × Dcrisis

Dcrisis

Output × Financial Openness

Financial Openness

Output × LIR

Long term interest rates (LIR)

Output × Trade Openness

Trade Openness

Output× F(zi,t−1)

F(zi,t−1)

Observations
Country FE
Year FE

0.30**
(0.13)
0.40***
(0.08)
-0.01***
(0.00)

0.19***
(0.05)
0.21***
(0.08)
-0.00
(0.00)

0.47***
(0.06)
0.41***
(0.07)
-0.01***
(0.00)

0.16***
(0.06)
0.44***
(0.08)
-0.01***
(0.00)

0.48***
(0.03)

0.27***
(0.05)
0.40***
(0.08)
-0.01***
(0.00)

0.21***
(0.05)
0.30***
(0.07)
-0.01***
(0.00)
0.34***
(0.04)
-0.00***
(0.00)

-0.01
(0.05)
-0.00
(0.00)

0.03***
(0.01)
-0.00***
(0.00)

-0.00***
(0.00)
-0.00*
(0.00)

480
YES
YES

480
YES
YES

480
YES
YES

480
YES
YES

472
YES
YES

480
YES
YES

0.24***
(0.08)
-0.00
(0.00)

480
YES
YES

Notes: This table shows the results of panel regressions using annual data, with consumption (∆ log ci,t) as the
dependent variable. Output refers to ∆ log yi,t. The HAC-robust standard errors are in parentheses ( *** p<0.01,
** p<0.05, * p<0.1). The strength of economic sentiment is proxied by our measure of uncertainty θU
i,t and varies
between 0 and 1. See Section 4 for details on the construction of this economic sentiment measure. F(zi,t−1) represents
an indicator of the state of the business cycle, broadly based on the approach of Auerbach and Gorodnichenko (2012).
See Section 5.2 for details on the construction of the business cycle state indicator.

44

TABLE 8. Linear panel model with interactive ﬁxed effects

(1)

(2)
Benchmark Conﬁdence Uncertainty

(3)

Output

θi,t−1 × Output

0.68***
(0.03)

θi,t−1

0.52***
(0.06)
0.23**
(0.09)
-0.01***
(0.00)

0.41***
(0.08)
0.36***
(0.11)
-0.01***
(0.00)

Observations

894

686

480

Notes: This table shows the results of a linear panel model with the (unknown) factors as interactive ﬁxed effects using
annual data, with consumption (∆ log ci,t) as the dependent variable. Output refers to ∆ log yi,t. The HAC-robust
standard errors are in parentheses ( *** p<0.01, ** p<0.05, * p<0.1). The strength of economic sentiment θi,t varies
between 0 and 1. See Section 4 for details on the construction of our economic sentiment measures.

TABLE 9. Economic sentiments and consumption smoothing: Channels

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Ex ante

Ex post

(10)
(11)
Consumption

(12)

Output

θC
i,t−1 × Output

0.03**
(0.01)

θC
i,t−1

θU
i,t−1 × Output

θU
i,t−1

0.08***
(0.03)
-0.09**
(0.04)
0.00
(0.00)

0.04
(0.04)

0.01
(0.06)
-0.00
(0.00)

0.07
(0.04)
-0.06
(0.05)
-0.00
(0.00)
0.02
(0.06)
-0.00
(0.00)

0.31***
(0.03)

0.46***
(0.05)
-0.23***
(0.07)
0.01***
(0.00)

0.56***
(0.06)

-0.27***
(0.10)
0.01**
(0.00)

0.73***
(0.07)
-0.43***
(0.09)
0.02***
(0.00)
-0.20**
(0.10)
0.00
(0.00)

0.60***
(0.02)

0.42***
(0.04)
0.28***
(0.06)
-0.01***
(0.00)

0.27***
(0.05)

0.40***
(0.08)
-0.01***
(0.00)

0.10*
(0.06)
0.38***
(0.08)
-0.02***
(0.00)
0.32***
(0.08)
-0.00**
(0.00)

Observations
Country FE
Year FE

689
YES
YES

655
YES
YES

461
YES
YES

461
YES
YES

655
YES
YES

655
YES
YES

478
YES
YES

461
YES
YES

687
YES
YES

687
YES
YES

480
YES
YES

463
YES
YES

Notes: This table shows the results of panel regressions using annual data, with Ex ante (∆ log yi,t − ∆ log inci,t),
Ex post (∆ log inci,t − ∆ log ci,t) and consumption (∆ log ci,t) as the dependent variables. Output refers to ∆ log
yi,t. The HAC-robust standard errors are in parentheses ( *** p<0.01, ** p<0.05, * p<0.1). The strength of economic
sentiment, proxied by our measures of consumer conﬁdence (θC
i,t), varies between 0 and 1. See
Section 4 for details on the construction of our economic sentiment measures.

i,t) and uncertainty ( θU

45

TABLE 10. Economic sentiments and consumption smoothing: Quarterly

(1)

(2)

(3)

(4)

Output

Output ×θC

i,t−1

0.34***
(0.03)

θC
i,t−1

Output ×θU

i,t−1

θU
i,t−1

0.16***
(0.05)
0.38***
(0.07)
-2.63***
(0.31)

Observations
R-squared
Country FE
Year FE

1,866
0.42
YES
YES

1,831
0.47
YES
YES

0.32***
(0.05)

0.06
(0.08)
-0.58*
(0.34)

1,375
0.40
YES
YES

0.15**
(0.06)
0.49***
(0.09)
-2.65***
(0.36)
-0.03
(0.08)
-0.00
(0.32)

1,367
0.48
YES
YES

Notes: This table shows the results of panel regressions using quarterly data, with consumption (∆ log ci,t) as the
dependent variable. Output refers to ∆ log yi,t. The HAC-robust standard errors are in parentheses ( *** p<0.01, **
p<0.05, * p<0.1). The strength of economic sentiment, proxied by our measures of consumer conﬁdence (θC
i,t) and
uncertainty (θU
i,t), varies between 0 and 1. See Section 4 for details on the construction of our economic sentiment
measures.

46

Figures

FIGURE 1. Strength of economic sentiment: Consumer conﬁdence

Notes: This ﬁgure plots our constructed measure of consumer conﬁdence, θC
i,t. This measure varies between 0 and
1, with higher values indicating lower consumer conﬁdence and therefore weaker economic sentiment. See Section 4
for a detailed discussion of the construction and interpretation of this sentiment measure.

47

0.510.510.510.510.510.510.510.51.2.4.6.810.510.510.510.510.510.510.510.510.510.510.51197019801990200020102020197019801990200020102020197019801990200020102020197019801990200020102020197019801990200020102020ATAUBECACHDEDKESFIFRGBGRIEITJPNLNZPTSEUSyearsFIGURE 2. Strength of economic sentiment: Uncertainty

Notes: This ﬁgure plots our constructed measure of uncertainty, θU
i,t. This measure varies between 0 and 1, with higher
values indicating high uncertainty and therefore weaker economic sentiment. See Section 4 for a detailed discussion
of the construction and interpretation of this sentiment measure.

48

0.510.510.510.51.2.4.6.810.510.51.2.4.6.810.510.51.2.4.6.81.2.4.6.81.2.4.6.810.510.510.510.510.510.51.2.4.6.81197019801990200020102020197019801990200020102020197019801990200020102020197019801990200020102020197019801990200020102020ATAUBECACHDEDKESFIFRGBGRIEITJPNLNZPTSEUSyearsFIGURE 3. Rolling consumption smoothing

Benchmark

Low conﬁdence

High uncertainty

Notes: The solid lines represent the estimated degree of consumption smoothing using 10-year rolling windows,
while the shaded areas contain the 95% conﬁdence intervals. Low conﬁdence and high uncertainty corresponds to
the 90th percentile of our economic sentiment measures. See Section 4 for details on the construction of our economic
sentiment measures.

49

.3.5.7.92000200520102015years.3.5.7.92000200520102015years.3.5.7.92000200520102015yearsFIGURE 4. Dynamic consumption smoothing

High conﬁdence

Low conﬁdence

Low Uncertainty

High Uncertainty

Notes: The solid lines represent the estimated degree of consumption smoothing, while the shaded areas contain the
90% conﬁdence intervals. Low conﬁdence and high uncertainty correspond to the 90th percentiles of our economic
sentiment measures, while high conﬁdence and low uncertainty correspond to the 10th percentiles.

FIGURE 5. Estimated global factor

Note: The bars represent the percentage of GDP (blue bar) and Consumption (red bar) variance explained by our
estimated global factor. See Section 5.4 for a discussion on the extraction of this global factor.

50

-.4-.20.2.4.6.8101234years-.4-.20.2.4.6.8101234years-.4-.20.2.4.6.8101234years-.4-.20.2.4.6.8101234years0.2.4.6.81ATAUBECACHDEDKESFIFRGBGRIEITJPNLNZPTSEUSFIGURE 6. Quarterly dynamic consumption smoothing

High conﬁdence

Low conﬁdence

Low uncertainty

High uncertainty

Notes: The solid lines represent the estimated degree of consumption smoothing, while the shaded areas contain the
90% conﬁdence intervals. Low conﬁdence and high uncertainty correspond to the 90th percentiles of our economic
sentiment measures, while high conﬁdence and low uncertainty correspond to the 10th percentiles.

51

-.4-.20.2.4.6.8104812quarters-.4-.20.2.4.6.8104812quarters-.4-.20.2.4.6.8104812quarters-.4-.20.2.4.6.8104812quartersA Alternative estimators

TABLE 11. Alternative estimators: Conﬁdence

(1)

(2)
GLS SE Panel-corrected SE GMM

(3)

Output

θC
i,t−1 × Output

θC
i,t−1

Observations

0.42***
(0.04)
0.28***
(0.06)
-0.01***
(0.00)
687

0.28***
(0.06)
0.45***
(0.08)
-0.02***
(0.00)
687

0.29***
(0.10)
0.40***
(0.09)
-0.01***
(0.00)
666

Notes: This table shows the results of panel regressions with annual data, with consumption (∆ log ci,t) as the de-
pendent variable. GLS and Panel-corrected SE estimations include country- and time-ﬁxed effects, GMM estimations
include time-ﬁxed effects. Robust standard errors are in parentheses ( *** p<0.01, ** p<0.05, * p<0.1). Output refers
to ∆ log yi,t. The strength of economic sentiment is proxied by our measure of consumer conﬁdence θC
i,t and varies
between 0 and 1. See Section 4 for details on the construction of this economic sentiment measure.

TABLE 12. Alternative estimators: Uncertainty

(1)

(2)
GLS SE Panel-corrected SE GMM

(3)

Output

θU
i,t−1 × Output

θU
i,t−1

Observations

0.27***
(0.05)
0.40***
(0.08)
-0.01***
(0.00)
480

0.18***
(0.06)
0.47***
(0.09)
-0.01***
(0.00)
480

0.20
(0.13)
0.39***
(0.13)
-0.01*
(0.00)
460

Notes: This table shows the results of panel regressions with annual data, with consumption (∆ log ci,t) as the de-
pendent variable. GLS and Panel-corrected SE estimations include country- and time-ﬁxed effects, GMM estimations
include time-ﬁxed effects. Robust standard errors are in parentheses ( *** p<0.01, ** p<0.05, * p<0.1). Output refers
to ∆ log yi,t. The strength of economic sentiment is proxied by our measure of uncertainty θU
i,t and varies between 0
and 1. See Section 4 for details on the construction of this economic sentiment measure.

52

B Alternative economic sentiment measures

We assess the robustness of our ﬁndings to alternative measures of economic senti-

ment. For our consumer conﬁdence measure, we use the future tendency component

of the OECD’s consumer conﬁdence surveys rather than the contemporaneous com-

ponent. For our uncertainty measure, we use stock market volatility (Bloom, 2014) as an

alternative proxy to IMF output growth errors. We retrieve the data from Bloomberg

and compute the standard deviation of the stock market return. In this case, Ireland is

not included in the sample due to data availability. As before, higher values of these

measures indicate higher volatility (i.e. weaker economic sentiment). The results are

detailed in Tables 13. Our results are qualitatively unaffected by these alternative ap-

proaches to constructing our conﬁdence and uncertainty measures.

TABLE 13. Alternative economic sentiment measures

(1)

(2)

(3)

(4)

(5)

Benchmark Conﬁdence Uncertainty Future tendency Market volatility

Output

Output × θi,t−1

0.60***
(0.02)

θi,t−1

Observations
Country FE
Year FE

894
YES
YES

0.43***
(0.04)
0.28***
(0.06)
-0.01***
(0.00)

688
YES
YES

0.27***
(0.05)
0.40***
(0.08)
-0.01***
(0.00)

480
YES
YES

0.23***
(0.05)
0.53***
(0.07)
-0.02***
(0.00)

479
YES
YES

0.51***
(0.05)
0.24***
(0.08)
-0.01*
(0.00)

508
YES
YES

Note: This table shows the results of panel regressions with annual data, with consumption (∆ log ci,t) as the depen-
dent variable. The HAC-robust standard errors are in parentheses ( *** p<0.01, ** p<0.05, * p<0.1). Output refers to
∆ log yi,t. The strength of economic sentiment θi,t) varies between 0 and 1. See Section 4 and Appendix B for details
on the construction of our economic sentiment measures.

53

C European monetary union

Our results show that loss aversion can materially affect the degree of consumption

smoothing following an output ﬂuctuation. In particular, weak economic sentiments,

that signal the expected direction of future income changes, reduce consumption smooth-

ing and could lead to a desynchronisation of cross-border business cycles. Members of

a monetary union do not have direct control over their nominal interest and exchange

rates, and therefore have a reduced set of macroeconomic stabilisation tools available

to them (Afonso and Furceri, 2008).33 Therefore, we assess the degree to which eco-

nomic sentiments affect the degree of consumption smoothing following subsequent

output ﬂuctuations in the 11 euro area countries in our sample in isolation. We then

test whether this effect is due to loss aversion.

The baseline results for the euro-area subsample are reported in Table 14. The ex-

tent of consumption smoothing with and without controlling for prevailing economic

sentiments is very similar to that uncovered in the full sample. This is contrary to the

notion that these countries’ participation in a monetary union should, in principle, lead

to a higher degree of business cycle synchronisation (Giannone et al., 2010; De Grauwe

and Ji, 2017). One notable difference in the baseline results is that uncertainty is no

longer signiﬁcant when also controlling for consumer conﬁdence. This indicates that

the consumer conﬁdence channel may play a larger role in international consumption

smoothing than the uncertainty channel (at least according to our measure of uncer-

tainty) in a monetary union.

The results of our asymmetry tests aimed at uncovering the mechanism through

which economic sentiments affect the degree of international consumption smoothing

are reported in Tables 15 and 16 for conﬁdence and uncertainty respectively. We ﬁnd

strong evidence of loss aversion from our measure of conﬁdence, with the effect larger

in the euro area countries than in the full sample. However, while the sign of the

coefﬁcient of our uncertainty measure is consistent with loss-averse behaviour, it is

33The recent introduction of macroprudential regulation offers a new national policy tool that can
help stabilise small open economies in monetary unions (Clancy and Merola, 2017), who have such little
weight in area-wide aggregates that nominal interest and exchange rates are effectively exogenous.

54

statistically insigniﬁcant.

The importance of our economic sentiment measures are robust to the inclusion of

the same set of confounding factors used in the full sample analysis. The main differ-

ence in results is that trade openness and the business cycle are more important factors

in international consumption smoothing in the euro area. We also ﬁnd qualitatively

and quantitatively similar results for our analysis of consumption smoothing dynam-

ics, confounding variables, global output ﬂuctuations, the persistence of ﬂuctuations

and alternative measures of economic sentiment. To save space, we do not report the

results here, but they are available from the authors upon request.

55

TABLE 14. Euro area economic sentiments and consumption smoothing

(1)

(2)

(3)

(4)

Output

Output × θC

i,t−1

0.59***
(0.03)

θC
i,t−1

Output × θU

i,t−1

θU
i,t−1

0.32***
(0.06)
0.44***
(0.08)
-0.02***
(0.00)

0.35***
(0.08)

0.34***
(0.12)
-0.00
(0.00)

0.06
(0.08)
0.69***
(0.10)
-0.02***
(0.00)
0.09
(0.11)
-0.00
(0.00)

Observations
Country FE
Year FE

489
YES
YES

393
YES
YES

264
YES
YES

264
YES
YES

Notes: This table shows the results of panel regressions using annual data for the euro area countries in our sample,
with consumption (∆ log ci,t) as the dependent variable. The HAC-robust standard errors are in parentheses ( ***
p<0.01, ** p<0.05, * p<0.1). The strength of economic sentiment, proxied by our measures of consumer conﬁdence
(θC
i,t), varies between 0 and 1. See Section 4 for details on the construction of our economic
sentiment measures.

i,t) and uncertainty (θU

56

TABLE 15. Euro area Asymmetries: Conﬁdence

(1)

(2)

(3)

0.45***
(0.05)
0.82***
(0.06)

posi,t

negi,t

θC
i,t−1 × posi,t

θC
i,t−1

θC
i,t−1 × negi,t

0.32***
(0.07)
0.75***
(0.06)
0.29**
(0.12)
-0.02***
(0.00)

0.45***
(0.04)
0.15
(0.14)

-0.01**
(0.00)
0.87***
(0.18)

Observations
Country FE
Year FE

393
YES
YES

393
YES
YES

393
YES
YES

Notes: This table shows the results of panel regressions using annual data, with consumption (∆ log ci,t) as the
dependent variable. The HAC-robust standard errors are in parentheses ( *** p<0.01, ** p<0.05, * p<0.1). The strength
of economic sentiment is proxied by our measure of consumer conﬁdence θC
i,t and varies between 0 and 1. See
Section 4 for details on the construction of this economic sentiment measure. We analyse the asymmetric effects of
consumption smoothing in response to positive and negative output ﬂuctuations. See Section 3 for details on the
construction of the positive and negative output ﬂuctuation series.

57

TABLE 16. Euro area Asymmetries: Uncertainty

(1)

(2)

(3)

posi,t

negi,t

θU
i,t−1 × posi,t

θU
i,t−1

θU
i,t−1 × negi,t

0.30***
(0.05)
0.90***
(0.05)

0.29***
(0.10)
0.90***
(0.06)
0.02
(0.17)
0.00
(0.00)

Observations
Country FE
Year FE

264
YES
YES

264
YES
YES

0.29***
(0.05)
0.71***
(0.15)

0.00
(0.00)
0.28
(0.19)

264
YES
YES

Notes: This table shows the results of panel regressions using annual data, with consumption (∆ log ci,t) as the
dependent variable. The HAC-robust standard errors are in parentheses ( *** p<0.01, ** p<0.05, * p<0.1). The strength
of economic sentiment is proxied by our measure of uncertainty θU
i,t and varies between 0 and 1. See Section 4 for
details on the construction of this economic sentiment measure. We analyse the asymmetric effects of consumption
smoothing in response to positive and negative output ﬂuctuations. See Section 3 for details on the construction of
the positive and negative output ﬂuctuation series.

58

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