Wealth shocks, unemployment shocks and consumption in the wake of the Great Recession.
Date: 2015-07-06
Institutions: European Central Bank
Subjects: consumption, economic recession, financial loss, household consumption, unemployment

Dimitris Christelis,  
Dimitris Georgarakos 

and Tullio Jappelli   

Working Paper Series 

Wealth shocks, 
unemployment shocks and 
consumption in the wake of 
the Great Recession  

No 1762 / March 2015 

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

 
 
Household Finance and Consumption Network 
This paper contains research conducted within the Household Finance and Consumption Network (HFCN). The HFCN consists of 
survey specialists, statisticians and economists from the ECB, the national central banks of the Eurosystem and a number of national 
statistical institutes. 
The HFCN is chaired by Oreste Tristani (ECB) and Carlos Sánchez Muñoz (ECB). Michael Haliassos (Goethe University Frankfurt ), 
Tullio Jappelli (University of Naples Federico II), Arthur Kennickell (Federal Reserve Board) and Peter Tufano (University of Oxford) and 
act as external consultants, and Sébastien Pérez Duarte (ECB) and Jiri Slacalek (ECB) as Secretaries. 
The HFCN collects household-level data on households’ finances and consumption in the euro area through a harmonised survey. The 
HFCN aims at studying in depth the micro-level structural information on euro area households’ assets and liabilities. The objectives of 
the network are: 
1) understanding economic behaviour of individual households, developments in aggregate variables and the interactions between the 
two;  
2) evaluating the impact of shocks, policies and institutional changes on household portfolios and other variables; 
3) understanding the implications of heterogeneity for aggregate variables; 
4) estimating choices of different households and their reaction to economic shocks;  
5) building and calibrating realistic economic models incorporating heterogeneous agents;  
6) gaining insights into issues such as monetary policy transmission and financial stability. 
The refereeing process of this paper has been co-ordinated by a team composed of Oreste Tristani (ECB), Pirmin Fessler 
(Oesterreichische Nationalbank), Michalis Haliassos (Goethe University Frankfurt) , Tullio Jappelli (University of Naples Federico II), 
Sébastien Pérez-Duarte (ECB), Jiri Slacalek (ECB), Federica Teppa (De Nederlandsche Bank), Peter Tufano (Oxford University) and 
Philip Vermeulen (ECB).  
The paper is released in order to make the results of HFCN research generally available, in preliminary form, to encourage comments 
and suggestions prior to final publication. The views expressed in the paper are the author’s own and do not necessarily reflect those of 
the ESCB. 

Acknowledgements 
We thank Chris Carroll, Jonathan Heathcote, Michael Hurd, Urban Jermann, Jian Li, Marco Pagano, Luigi Pistaferri and an anonymous 
referee, as well as seminar participants at the 7th CSEF-IGIER Symposium on Economics and Institutions, the Economics of Household 
Saving Workshop at the 2011 NBER Summer Institute, the Household Finance and Consumption Network at the ECB, the 2014 
European Economic Association, the Board of Governors of the Federal Reserve System, the NETSPAR International Pension 
Workshop, the Bank of Greece, the Reserve Bank of Australia, the CESifo Conference on Macroeconomics and Survey Data in Munich, 
the 10th CRETE Conference, the Macro and Financial Econometrics Conference in Heidelberg, the Conference on Macroeconomic 
Policy in Istanbul, the DIW in Berlin, the universities of Leicester, Mannheim, Monash, New South Wales, Stirling and Wollongong for 
helpful comments. Financial support from NETSPAR is gratefully acknowledged. Christelis acknowledges financial support from the 
European Union and the Greek Ministry of Education under program Thales (Grant MIC 380266). Georgarakos acknowledges financial 
support under ERC Advanced Grant 22992. Jappelli acknowledges financial support from the Italian Ministry of University and Research 
(PRIN project). The usual disclaimer applies. 

Dimitris Christelis 
CSEF, CFS and CEPAR; e-mail: dimitris.christelis@gmail.com 

Dimitris Georgarakos 
Goethe University Frankfurt, CFS and University of Leicester; e-mail: dimitris.georgarakos@hof.uni-frankfurt.de 

Tullio Jappelli 
University of Naples Federico II, CSEF and CEPR; e-mail: tullio.jappelli@unina.it 

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ISSN 
ISBN 
DOI 
EU catalogue number 

1725-2806 (online) 
978-92-899-1575-5 
10.2866/101323 
QB-AR-15-002-EN-N 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Abstract 

Data  from  the  2009  Internet  Survey  of  the  Health  and  Retirement  Study  show  that  many  U.S. 
households  experienced  large  capital  losses  in  housing  and  financial  wealth,  and  that  5%  of 
respondents  lost  their  job  during  the  Great  Recession.  As  a  consequence  of  these  shocks,  many 
households  reduced  substantially  their  expenditures.  For  every  10%  loss  in  housing  and  financial 
wealth,  the  estimated  drop  in  household  expenditure  is  about  0.56%  and  0.9%,  respectively.  In 
addition, those who became unemployed reduced spending by 10%. We also distinguish the effect 
of perceived transitory and permanent wealth shocks, splitting the sample between households who 
think that the stock market is likely to recover in a year’s time, and those who do not. In line with 
the  predictions  of  standard  models  of  intertemporal  choice,  we  find  that  the  latter  group  adjusted 
much more than the former its spending in response to financial wealth shocks.   

Keywords: Wealth Shocks; Unemployment; Consumption; Great Recession. 

JEL Classification Codes: E21, D91. 

ECB Working Paper 1762, March 20151 
 
 
 
Non-technical summary  

During the Great Recession households in the US were hit by three different shocks: a large 

drop  in  house  prices,  a  strong  decline in  the stock  market, and  a  dramatic  worsening  of  the  labor 

market conditions. In this paper, we estimate the separate impact of these three shocks on household 

expenditure. In addition, we shed light on the influence of heterogeneous asset price expectations on 

the response of household consumption to these shocks.  

We  use  microdata  from  the  2009  Internet  Survey  of  the  US  Health  and  Retirement  Study, 

which  samples  individuals  aged  50  and  older,  i.e.  the  population  segment  that  owns  by  far  the 

largest  share  of  household  wealth.  Importantly,  this  survey  is  the  first  one  (to  the  best  of  our 

knowledge) that provides at the same time information on consumption, capital gains on financial 

assets  and  housing,  and  labor  force  status.  In  addition,  it  records  household  expectations  on  the 

persistence  of  stock  losses,  which  in  turn  allows  us  to  evaluate  how  these  expectations  affect  the 

response of household consumption to such losses.  

We  find  that  for  every  loss  of  10%  in  housing  and  financial  wealth,  the  estimated  drop  in 

household  expenditure  is  about  0.56%  and  0.9%,  respectively.  We  then  introduce  shocks  to  the 

return on capital in a standard life cycle consumption model and in a model of buffer-stock saving, 

and  find  that  the  results  from  simulating  both  these  models  are  consistent  with  our  empirical 

estimates. In addition, we find that those who become unemployed reduced spending by 10%. We 

obtain  similar  empirical  estimates  of  the  effect  of  housing  wealth  losses  and  unemployment  on 

household expenditure when using the Panel Study of Income Dynamics, which is a representative 

sample  of  the  whole  US  population.  Finally,  in  line  with  predictions  of  standard  intertemporal 

choice models, we find that households who perceive the stock market shock to be permanent adjust 

spending much more than those who perceive the shock to be temporary.  

Our results imply that higher housing and stock prices and an improved job market are needed 

for  a  rebound  in  US  household  expenditure,  especially  at  a  time  when  households  may  desire  to 

ECB Working Paper 1762, March 20152 
 
 
save more in order to rebuild their asset position. Given the finding that the effect of financial losses 

on  expenditure  depends  on  whether  they  are  perceived  as  temporary  or  permanent,  an  important 

factor  for  boosting  final  consumption  is  the  confidence  that  households  have  in  the  economy’s 

prospects  in  the  near  future.  Hence,  if  policy  makers  could  steer  into  a  positive  direction 

households’ expectations about asset prices, then the economy might get back on track faster due to 

a larger final demand. 

ECB Working Paper 1762, March 20153 
 
1. Introduction 

In  2008,  American  households  experienced  a  loss  of  13.6  trillion  in  wealth,  compared  to  a 

disposable  income  of  11  trillion.  Between  October  2007  and  October  2008  the  stock  market 

declined  by  almost  40  percent,  and  house  prices  by  almost  20  percent.  The  unemployment  rate, 

which  throughout  2007  averaged  4.8  percent,  doubled  in  less  than  two  years,  from  5  percent  in 

January  2008  to  10.1  percent  in  November  2009.  Many  analysts  link  this  large,  unexpected  and 

unprecedented  fall  in  the  market  value  of  household  wealth  and  the  dramatic  increase  in 

unemployment  to  the  drop  in  consumption  that  took  place  in  the  second  half  of  2008  and  2009. 

Indeed, real consumption expenditures dropped from 10.078 trillion dollars (in constant 2009 prices, 

seasonally adjusted at an annual rate) in the second quarter of 2008 to 9.806 trillion dollars in the 

second quarter of 2009, i.e., a decline of about 2.7 percent. All these figures suggest that a special 

feature of the Great Recession is that households were hit by three different shocks: a large drop in 

house  prices,  a  strong  decline  in  the  stock  market,  and  a  dramatic  worsening  of  the  labor  market 

conditions.  

This  paper  attempts  to  estimate  the  separate  impact  of  these  three  shocks  on  households’   

expenditures, using recently available microdata. In particular, the paper makes three contributions. 

First, we take advantage of the first (to the best of our knowledge) household dataset that provides 

at  the  same  time  information  on  consumption,  capital  gains  on  financial  assets  and  housing,  and 

labor force status, and use it in order to assess the impact of wealth losses and unemployment on 

consumption. The use of directly elicited information on stock capital gains/losses in particular is an 

important  breakthrough  in  the  literature  on  wealth  effects.  This  is  the  case  because  typically  in 

microdata  surveys  one  has  information  on  the  level  of  stock  holdings  in  two  or  more  waves,  and 

thus a change in the value of such holdings can be due not only to changes in stock prices but also 

to purchases or sales of stocks, mutual funds, etc. As a result, measures of stock capital gains/losses 

typically found in the literature are contaminated by the effect of transactions, while our measure is 

ECB Working Paper 1762, March 20154 
 
not.  Having  in  our  dataset  information  on  housing  capital  gains/losses  is  also  fundamental,  given 

that the house is the main component of most households’ wealth.  

Second, we take into account household heterogeneity in exposure to each of the three shocks 

in  order  to  show  the  fundamental  role  that  capital  losses  on  stocks  and  housing,  as  well  as 

unemployment  shocks  played  for  the  reduced  consumption  of  older  Americans  during  the  Great 

Recession, which is the most serious economic crisis affecting the US economy since the 1930s. 

Third,  we  use  available  information  on  household  expectations  on  the  persistence  of  stock 

losses  in  order  to  show  how  these  expectations  affect  households’  consumption  response  to  such 

losses.  After  documenting  the  considerable  heterogeneity  in  these  expectations,  we  show  that 

households  that  perceive  wealth  losses  to  be  more  long-lasting  reduce  their  consumption  by  a 

greater percentage than their counterparts that expect a rebound in the stock market. This finding is 

in line with predictions from standard economic theory, and points to the importance of household 

expectations for consumption adjustments during the Great Recession. 

The microdata that we use in this paper come from the 2009 Internet Survey of the Health and 

Retirement Study (HRS), and refer to the population aged 50 or older. Hence, they are particularly 

well suited to analyze the impact of wealth shocks on consumption. Indeed, older households have 

accumulated significant amounts of wealth over the lifecycle and therefore control a large fraction 

of  society's  resources;1 thus  their  decisions  have  pronounced  aggregate  implications.  Those  aged 

fifty and above typically have higher stock market participation rates than the rest of the population, 

and  a  higher  fraction  of  their  wealth  is  invested  in  risky  financial  assets.  Furthermore,  over  90 

percent of households in the sample own their home. Hence, our analyses are less likely to suffer 

from the endogeneity bias that arises when one examines consumption responses to housing wealth 

losses  over  homeowners,  and  the  heterogeneity  of  responses  with  respect  to  wealth  losses 

experienced by owners and renters. Finally, recent studies (e.g., Attanasio et al., 2009) emphasize 

1 Using  information  from  the  2007  and  2010  waves  of  the  US  Survey  of  Consumer  Finances,  we  calculate  that 
households in which the head is aged 50 and above have about 62% of total gross housing wealth, 78% of all equity 
wealth, and 75% of total net worth. 

ECB Working Paper 1762, March 20155 
 
                                                 
that  co-movements  in  consumption  and  house  prices  may  be  driven  by  a  common  factor  such  as 

income expectations. Given that the elderly typically face a relatively flat future income profile, this 

problem  may  be  less  severe  in  our  sample.2 On  the  other  hand,  the  unemployment  rate  and  the 

probability of job loss tend to be lower among older households. 

We  find  that  capital  losses  on  housing  and  financial  assets,  as  well  as  the  income  loss  from 

becoming unemployed, do indeed lead households to reduce their spending, and that these effects 

are net of the influence of a number of important socio-economic characteristics including family 

size,  health  deterioration,  and  change  in  working  and  retirement  status.  When  we  examine 

disaggregated  financial  assets  we  find  that  the  effects  of  financial  losses  come  primarily  through 

losses experienced from directly held stocks and individual retirement accounts (IRAs). 

More  specifically,  we  estimate  that  the  elasticity  of  consumption  to  financial  wealth  losses 

experienced in 2008-2009 is about 0.09, implying a marginal propensity to consume with respect to 

financial wealth equal to 3.3 percentage points. In addition, households in which at least one of the 

two partners in the  main couple (or  the single head) became unemployed  in 2008  and  early 2009 

reduced consumption by 10 percent in 2009. Finally, we find that the fall in house prices between 

the  summer  of  2006  and  the  first  half  of  2009  also  has  an  important  impact  on  consumption  (the 

estimated  elasticity  is  about  0.06  and  the  associated  marginal  propensity  to  consume  reaches  1 

percentage point). Furthermore, we generate artificial data from both a buffer stock and a permanent 

income model, and use them to calculate the implied elasticities of consumption to wealth. We find 

that our empirical estimates of the elasticities are in line with those generated by these two standard 

intertemporal consumption models. 

It should be noted that, while we study the consumption response to capital losses using data 

from 2008-2009, the economic relevance of this issue is more general, given that large asset price 

movements have by now become the norm in the U.S. economy. In Figure 1, we plot capital gains 

2  Indeed,  Attanasio  et  al.  (2009)  find  that  younger  households  (most  of  which  are  renters)  have  higher  wealth-
consumption correlations than older households, and take this as evidence that the co-movement between consumption 
and house prices is driven by income expectations, rather than a genuine wealth effect. 

ECB Working Paper 1762, March 20156 
 
                                                 
and  active  saving  accruing  to  the  US  household  sector  (both  are  measured  as  a  share  of  personal 

disposable income) from 1990 to 2010. As the graph makes it clear, during this period capital gains 

and  losses  form  a  much  larger  part  of  households’  year-to-year  asset  accumulation  than  active 

saving;  in  fact,  the  median  yearly absolute  ratio of  capital gains to  active  saving  is  equal  to  5.43. 

Furthermore,  the  accumulated  real  (in  2009  prices)  capital  gains,  after  subtracting  real  losses,  are 

equal to about 35.99 trillion dollars during this period, while the accumulated real household saving 

is equal to about 8.14 trillion dollars. As we shall see in Section 3 below, very large capital losses 

will show up also in the microdata that we will use for our analyses.  

According  to  several  models  of  intertemporal  choice,  the  impact  of  wealth  shocks  on 

consumption depends on the nature of the shocks (permanent or transitory) and the opportunities to 

smooth them through credit and insurance markets. We attempt to distinguish between permanent 

and transitory shocks to financial wealth by relying on subjective expectations elicited in the fall of 

2008  about  stock  market  gains  or  losses  expected  one  year  ahead.  We  split  the  sample  between 

households that expected the stock market to recover in a year’s time, and those who did not. We 

expect the consumption response to wealth shocks to be larger for the latter group, who are likely to 

perceive the negative shock to their financial wealth as permanent. Indeed, we find that the response 

of consumption to this shock is very strong for this group of households, while it is insignificant for 

the other group. Finally, we investigate the separate role that increased income uncertainty plays in 

the drop in consumption. We find that our measures of income risk based on subjective expectations 

do not have a statistically significant effect on consumption. 

The remaining of the paper is organized as follows. Section 2 reviews previous literature on 

the effect of wealth and unemployment shocks on consumption. Section 3 presents the data. Section 

4 presents estimates of the effect of wealth shocks and unemployment on consumption. In Section 5 

we  compare  our  results  to  those  obtained  from  two  standard  models  of  intertemporal  choice  (the 

permanent income model and the buffer stock model) in which we introduce shocks to the return on 

capital.  Section  6  takes  into  account  heterogeneity  in  stock  market  expectations  and  presents 

ECB Working Paper 1762, March 20157 
 
estimates  of  the  response  of  consumption  to  transitory  and  permanent  wealth  shocks.  Section  7 

presents various robustness checks to corroborate the empirical findings. Section 8 presents results 

from an additional source, namely the Panel Survey of Income Dynamics. Section 9 concludes.  

2. Wealth and unemployment shocks 

Standard  models  of  intertemporal  choice  suggest  that  unexpected  and  permanent  drops  in 

wealth reduce consumption, and that this reduction equals the annuity value of the drop in wealth. 

There  is,  however,  much  disagreement  about  the  magnitude  of  the  impact  of  wealth  shocks  on 

consumption.  Most  of  the  literature  attempting  to  estimate  this  impact  is  based  on  two  implicit 

assumptions: (i) wealth shocks (whether due to house price changes or movements in stock prices) 

are  not  predictable,  and  therefore  not  anticipated  by  consumers;  (ii)  current  prices  are  the  best 

predictors of future asset prices, and therefore changes in asset prices constitute a permanent wealth 

shock.  According  to  the  permanent  income  hypothesis,  it  follows  from  (i)  and  (ii)  that  wealth 

shocks should have a relatively large impact on consumption, equivalent to the annuity value of the 

wealth shock (in the order of 2 to 5 percent, depending on the assumed real interest rate). 

Several  studies,  relying  on  macroeconomic  or  regional  data,  regress  the  logarithm  of 

consumption,  consumption  growth  or  saving  on  shocks  to  housing  or  financial  wealth,  but  no 

consensus  has  yet  emerged  on  the  link  between  house  prices  and  consumption. 3 Studies  using 

microeconomic  data  allow  researchers  to  dig  deeper  into  this  link.  While  changes  in  stock  prices 

imply  unambiguous  wealth  effects  on  consumption,  as  discussed  in  Sinai  and  Souleles  (2005), 

Campbell and Cocco (2007) and Attanasio et al. (2009), the consumption response to a house price 

decline  is  quite  heterogeneous  across  the  population.  Most  empirical  analyses  using  micro-data 

3 Davis and Palumbo (2001) estimate that the MPC out of total wealth is in the range of 0.04-0.06. Case et al. (2005) 
provide estimates from a panel of developed countries and a panel of U.S. states. In both datasets, they find an MPC out 
of housing wealth of around 0.03-0.04 and a small and insignificant MPC out of stock market wealth. Ludwig and Sløk 
(2004)  found  a  larger  effect  of  stock  wealth  than  housing  wealth  in  a  panel  of  OECD  countries.  In  a  recent  study, 
however, Carroll et al. (2011) estimate the longer run effects on consumption from housing wealth changes, as opposed 
to the immediate ones (e.g., those of the next quarter), to be larger than the effects of financial wealth shocks. 

ECB Working Paper 1762, March 20158 
 
 
                                                 
refer to the U.S. and the U.K. Engelhardt (1996) estimated an MPC of 0.03 or higher for the U.S. in 

the 1980s,  and Juster et al. (2001) found an even higher MPC out of stock price  changes. On the 

other  hand,  Hoynes  and  McFadden  (1997)  found  that  households  who  had  experienced  housing 

capital gains increased their saving rather than their spending, and Hryshko et al. (2010) find that 

after a job loss homeowners can smooth consumption easier than renters in times of higher house 

prices. In the UK Disney et al. (2010) find a relatively low MPC out of housing wealth (of the order 

of 0.01), while Campbell and Cocco (2007) a relatively strong response for older households that 

own their home. Attanasio et al. (2009) conclude that the co-movements in consumption and house 

prices  are  not  generated  by  a  causal  link  running  from  the  latter  to  the  former,  but  by  common 

factors, contradicting the findings in Campbell and Cocco (2007).   

On  balance,  results  based  on  micro-data  are  also  mixed,  with  some  papers  finding  large 

responses of expenditure to house and stock prices shocks, while others find smaller effects. This 

literature generally suffers from some limitations. First and foremost, house and stock price changes 

are likely correlated with other economic events, and therefore have an impact on expectations of 

future income. A second limitation is that most studies rely on aggregate measures of house price 

changes  (either  at  the  national,  regional  or  county  level),  while  house  price  risk  has  also  an 

idiosyncratic component specific to each dwelling. A third limitation of current studies is that they 

usually  do  not  distinguish  between  transitory  and  permanent  wealth  shocks,  which  should  have 

different impact on consumption. As we shall see, our survey provides information that allows us to 

provide some evidence on this issue. Furthermore, most evidence refers to house price booms (as in 

the UK in the 1990s), while the present paper focuses on wealth losses during the Great Recession, 

which allows us to estimate the impact of very large losses in both housing and financial wealth on 

consumption.  As  noted  in  Browning  and  Collado  (2001),  consumers  may  tend  to  smooth 

consumption  when  income  or  wealth  changes  are  large,  but  are  less  likely  to  do  so  when  the 

changes are small  and the  cost of  adjusting  consumption is  not  trivial. Indeed,  it is quite possible 

ECB Working Paper 1762, March 20159 
 
that  the  literature  has  not  been  able  to  obtain  more  precise  estimates  of  the  MPC  out  of  wealth 

shocks because some of the shocks are small, and consumers might react mostly to large shocks.4  

During the Great Recession households also experienced negative income shocks, particularly 

those  who  became  unemployed.  The  consumption  response  to  unemployment  shocks  depends  on 

the extent to which the shock is anticipated, on the persistence of the shock, and on the degree of 

imperfections  of  credit  and  insurance  markets  (Jappelli  and  Pistaferri,  2010).  According  to  the 

permanent income hypothesis, the impact should be strongest when the shock is not anticipated (as 

is most likely the case for those who became unemployed in 2007-08), when the shock is perceived 

to be permanent, and when consumers are liquidity constrained. One should also bear in mind that 

unemployment  shocks  may  be  partially  insured  through  unemployment  insurance.  Therefore,  a 

complete  analysis  of  the  impact  of  unemployment  requires  explicit  modeling  of  the  type  of 

insurance available to individuals as well as of the possible interactions between public and private 

insurance.5 

One of the earlier attempts to look at the effect of unemployment shocks on consumption is 

Gruber  (1997).  Using  the  PSID,  he  constructs  a  sample  of  workers  who  lose  their  job  between 

period t1 and period t, and regresses the change in food spending over the same time span against 

the unemployment insurance (UI) replacement rate an individual is eligible for. Gruber finds a large 

smoothing  effect  of  UI,  in  particular  that  a  rise  in  the  replacement  rate  by  10  percentage  points 

reduces the fall in consumption upon unemployment by about 3 percent. He also finds that the fall 

in  consumption  at  a  zero  replacement  rate  is  about  20  percent,  suggesting  that  consumers  face 

liquidity constraints. Browning and Crossley (2001) estimate a small elasticity of expenditures with 

4 In  quite  different  contexts,  this  “magnitude  hypothesis”  has  been  tested  by  Coulibaly  and  Li  (2006)  and  Scholnick 
(2013), who argue that the final mortgage payment represents a large expected disposable income shock (that is, income 
net  of  pre-committed  debt  service  payments).  The  test  of  the  magnitude  hypothesis  looks  at  whether  the  response  of 
consumption  to  expected  income  increases  depends  on  the  relative  amount  of  mortgage  payments.  Stephens  (2008) 
studies  consumption  adjustments  due  to  an  expected  rise  in  income  following  the  last  repayment  of  a  vehicle  loan. 
Shapiro and Slemrod (2003) and Agarwal et al. (2007) examine consumption responses to the receipt of a tax rebate. 
5 Some of these interactions stem from the fact that most welfare programs are means- and asset-tested. For example, in 
the US individuals with more than $2,000 in liquid assets are not eligible to receive Food Stamps, Medicaid and other 
popular welfare programs even if they have no income. The disincentives to save (self-insure) induced by the presence 
of public insurance (which in most cases are not subject to time limits) have been studied by Hubbard et al. (1995). 

ECB Working Paper 1762, March 201510 
 
                                                 
respect to UI benefit (equal to 0.05) in Canada. But this small effect masks substantial heterogeneity, 

with low wealth households at the time of job loss exhibiting elasticities as high as 0.2. This finding 

is also consistent with the presence of liquidity constraints. 

Some recent papers study the implications of unemployment shocks and changes in wealth on 

consumption during the Great Recession. Shapiro (2010) uses data from the Cognitive Economics 

Study (CogEcon), conducted via Internet, in order to assess the effect of the financial crisis on the 

well-being  of  older  Americans.  The  initial  wave  of  CogEcon  was  fielded  shortly  before  the 

financial  crisis  that  began  in  the  fall  of  2008,  and  provides  baseline  wealth  measurements  and 

information  about  the  structure  of  households’  portfolios  for  a  representative  sample  of  almost 

1,000 US individuals aged 50 years and older. The second wave was completed in summer 2009. 

Shapiro  finds  that  financial  wealth  fell  by  about  15  percent  for  the  median  household,  and  that 

financial losses were concentrated among households with high levels of wealth, who tend to have 

higher  exposure to the stock  market. Nonetheless, households with  little  financial wealth suffered 

declines in consumption as large as households with substantial exposure to the stock market. Tight 

credit market conditions and adverse labor market outcomes account for much of the effect of the 

financial crisis on the consumption of these low-wealth households. 

Hurd  and  Rowhedder  (2010b)  use  the  American  Life  Panel,  an  ongoing  Internet  survey  of 

about  2,500  respondents,  which  was  fielded  at  the  beginning  of  November  2008,  immediately 

following the large declines in the stock market associated with the collapse of Lehman Brothers. 

They find that between November 2008 and April 2010 almost 40 percent of American households 

have  been  affected  either  by  unemployment,  negative  home  equity,  arrears  on  their  mortgage 

payments,  or  foreclosure.  A  third  study,  also  by  Hurd  and  Rowhedder  (2010a)  combines 

longitudinal data from the Health and Retirement Study (HRS) with the 2009 HRS Internet Survey 

to  provide  an  overview  of  the  effects  of  the  financial  crisis  on  the  population  aged  50  or  older. 

According  to  the  descriptive  statistics  reported  by  Hurd  and  Rowhedder,  the  majority  of  older 

households  have  suffered  substantial  losses  in  stocks  and/or  housing  wealth,  while  some  of  them 

ECB Working Paper 1762, March 201511 
 
have  extracted  home  equity  (and,  as  a  result,  increased  their  indebtedness).  They  also  find  that 

almost  30  percent  of  households  reduced  spending  between  2007  and  2009,  and  that  the  average 

decline was larger than 8 percentage points. 

Using the 2007–09 Survey of Consumer Finance panel, Bricker et al (2011), find substantial 

heterogeneity in changes in wealth among households. Furthermore, these changes appear to reflect 

changes in asset values (particularly the value of homes, stocks, and businesses) rather than changes 

in the level of ownership of assets and debts or in the amount of debt held. The study also finds that 

families appear more cautious in 2009 than in 2007, as most families reported greater desired buffer 

savings,  and  many  of  them  expressed  concern  over  future  income  and  employment.  Petev  et  al. 

(2011) point out that the consumption of the wealthy fell more than that of the less wealthy during 

the recession. Using the typical estimates of the wealth effect available in the literature, they show 

that  this  factor  can  explain  a  significant  fraction  of  the  fall  in  consumption  experienced  by  the 

wealthy. 

A  related  issue  is  that  the  recession  increased  insecurity  about  the  future.  Indeed,  the 

Consumer  Sentiment  Index  declined  dramatically  in  the  second  half  of  2007.  Petev  et  al.  (2011) 

suggest  that  increased  uncertainty  may  have  reduced  spending  through  precautionary  saving,  and 

that the credit crunch that followed the financial crisis may have prevented some households from 

purchasing  goods  that  are  typically  acquired  through  borrowing.  Deaton  (2011)  analyzes  self-

reported well-being questions collected by the Gallup Organization. Between the fall of 2008 and  

the  spring  of  2009  (at  which  point  the  stock  market  hit  bottom),  Americans  became  much  more 

negative when evaluating their lives, were much more worried and stressed, and exhibited declines 

in positive affect. As we shall see, in our robustness analysis we address these issues by looking at 

the consumption response to household liabilities and to measures of income risk.  

ECB Working Paper 1762, March 201512 
 
 
 
3. The data 

In our investigation we use information from two micro-data surveys. Our first data source is 

the  HRS,  which  is  a  longitudinal,  nationally  representative  micro  survey  interviewing  those  aged 

fifty and above in the US. The survey, conducted on a biannual basis since 1992, provides extensive 

information  on  households’  socioeconomic  characteristics,  income,  and  assets  holdings  (for  a 

detailed description of the survey see Hauser and Willis, 2005). 

Wave  9  of  the  HRS,  which  was  conducted  between  February  2008  and  February  2009, 

interviewed 16,477 individuals belonging to 11,187 different households. In 2009, the HRS asked a 

subset of the Wave 9 respondents to participate in an Internet survey (our second data source), with 

the  aim  to  collect  information  on  households’  experiences  and  circumstances  during  the  ongoing 

recession. Most of the sampled individuals had participated in wave 9 of the HRS and had reported 

having Internet access, while the few who had not appeared in wave 9 had participated in previous 

waves of the Internet Survey (2003, 2006, or 2007). The 2009 Internet Survey was conducted from 

March 2009 through August 2009, and its sample consists of 4,415 respondents belonging to 3,438 

households (the sample response rate was about 77 percent).6 The survey provides information on 

the  wealth  losses  that  respondents  have  experienced,  on  the  adjustments  they  have  made  in  their 

consumption, on changes in their labor status, and on how they cope with financial difficulties. In 

our analysis we merge the 2009 Internet Survey with the 2008 main survey, thus ending up with a 

sample of 3,328 households. 

For our purposes, a most important feature of the Internet Survey is that respondents are asked 

about changes in their total spending compared to the previous year (i.e., 2008). They are first asked 

to indicate  whether their current spending  is lower, higher, or has stayed the same. Subsequently, 

they are asked to report the percentage change in their total spending. In our analysis, we are going 

6 In order to reduce the possibility that our estimates are affected by outliers, we do not use any observations for which 
the absolute value of the percentage change in consumption is larger than 0.8, and thus we drop 26 households from our 
sample. 

ECB Working Paper 1762, March 201513 
 
                                                 
to examine both the continuous (percentage) and the qualitative (categorical) change in expenditure 

as our outcomes of interest.7  

Furthermore,  the  Internet  Survey  asks  a  series  of  questions  aiming  to  measure  the  wealth 

losses that households have suffered. Specifically, households are asked whether their own home is 

worth more, less or about the same compared to its value in the summer of 2006, which is the year 

in which house prices peaked in the US. Then, they are also asked to report the change in the value 

of  their  house,  both  as  an  amount  and  as  a  percentage.  We  will  use  as  a  forcing  variable  in  our 

specifications the answer to the percentage change question, given that the questions on changes in 

spending  and,  as  we  will  see  below,  in  the  value  of  financial  assets  are  also  asked  in  percentage 

terms.8   

Finally, the Internet Survey also asks a series of questions regarding the percentage losses in 

the  value  of  the  following  financial  assets:  employer  retirement  saving  plans  (incl.  401k’s); 

individual  retirement  accounts  (IRAs)  or  Keogh  plans;  investment  trusts;  mutual  funds;  directly 

held  stocks;  and  stocks  held  through  other  assets.9 For  each  of  these  assets  owners  are  asked  to 

report  the  percentage  decline  of  the  asset  value  since  September  2008,  which  was  the  month  in 

which  Lehman  Brothers  collapsed,  resulting  in  a  major  upheaval  in  financial  markets  worldwide. 

Unlike the questions on the change in the value of the house, the questions on changes in the value 

of  financial  assets  ask  only  about  losses,  and  hence  the  values  of  the  corresponding  variables  are 

censored at zero. However, given the fact that financial markets went in a tailspin in the fall of 2008, 

7 The Internet Survey also asks about current spending on some basic consumption items. Furthermore, one can recover 
information  on  spending  in  2008  by  using  information  from  the  Consumption  and  Activities  Mail  Survey  (CAMS), 
which  is  a  supplemental  mail  survey  conducted  in  2009,  and  in  which  a  sub-sample  of  2008  HRS  respondents  were 
asked  about  their  expenditures  over  the  past  12  months.  In  principle,  one  could  examine  changes  in  consumption  by 
also using this additional information. In practice, however, it is very difficult to use either of these additional sources 
of  data  on  expenditure.  First,  there  are  very  few  observations  (less  than  400  households)  for  which  the  information 
needed from all three surveys (i.e., 2008 HRS, 2009 CAMS and the Internet Survey) exists. This is the case because the 
vast majority of households participating in 2009 CAMS do not participate in the Internet Survey. Second, the Internet 
Survey does not provide any information on a number of major expenditure items (e.g., housing expenses, recreation, 
personal care).  
8 For  cases  in  which  the  percentage  change  in  the  value  of  the  home  is  missing  we  calculate  it  by  using  information 
from the amount change in the home value, and the current value, which are related to the percentage change by the 
equation p=DV/(V-DV), where p denotes the percentage change, DV the change in value, and V the current value. 
9 There are no questions in the Internet Survey about less risky financial assets like checking or savings accounts and 
bonds.  

ECB Working Paper 1762, March 201514 
 
                                                 
and  that  the  US  stock  market  in  particular  hit  bottom  in  March  2009  (i.e.,  one  month  before  the 

Internet  Survey  began),  we  think  that  very  few,  if  any,  households  in  the  survey  may  have 

experienced  any  financial  gains.  In  any  case,  in  order  to  test  the  sensitivity  of  our  results  to  this 

feature  of  the  data,  we  also  tried  as  an  alternative to  the  continuous percentage  change variable  a 

four-level categorical variable, the top level of which denotes no losses (or gains), while the other 

three levels the terciles of financial losses. As we discuss below, using this alternative categorical 

variable did not change our results in any significant way.  

One may wonder to what extent the reported capital losses accurately reflect the actual losses 

households suffered on their assets. However, our empirical results are unlikely to be due to such a 

measurement  error.  First,  households  report  the  gain/loss  that  they  perceive  to  have  incurred  on 

their house and on their equity holdings. These perceived price changes might be different from the 

ones that would be recorded if, say, there were an actual auction of the households’ main home or if 

they  sold  their  equity  holdings.  However,  what  should  matter  for  households’  consumption 

response is precisely this perceived loss and not the hypothetical accurately recorded one. After all, 

it is reasonable to assume that households act on what they think has occurred. On the other hand, 

measurement  error  would  be  an  issue  in  our  case  if  a  respondent  knowingly  misreports  to  the 

interviewer the value of the gain/loss, as in this case the household would act based on a value of 

the  relevant  variable  that  is  different  from  the  one  observed  by  the  econometrician.  However,  we 

know of no evidence that such deliberate misreporting is common in the HRS.  

Second,  in  the  linear  models  we  estimate,  measurement  error  in  the  regressors  has  an 

attenuating  effect  on  the  associated  coefficients,  and  thus  the  bias  that  it  potentially  induces  goes 

against  us.  On  the  other  hand,  measurement  error  in  the  dependent  variable  does  not  affect  the 

consistency of the estimates; rather, it increases their standard errors. Third, as already mentioned, 

we estimate a number of models that use functional forms and variable formats (e.g., models with a 

categorical dependent variable and/or dummies denoting quartiles of housing and financial wealth 

gains/losses)  that  are  much  more  robust  to  possible  measurement  error.  The  results  from  these 

ECB Working Paper 1762, March 201515 
 
models, as discussed in sections 4 and 6 below, are entirely consistent with those from our baseline 

specification. 

As an additional check of the quality of our data, we compared our measures of housing and 

capital  losses  to  those  recorded  in  other  sources,  namely  the  2007-2009  panel  of  the  Survey  of 

Consumer Finances and the Flow of Funds. As reported in detail in Appendix A, results from these 

two external sources are reasonably close to those obtained from our data.   

Our  primary  objective  is  to  examine  the  relationship  between,  on  the  one  hand,  changes  in 

consumption  and,  on  the  other  hand,  capital  losses  in  housing  and  financial  assets,  as  well  as 

unemployment.  Losses  in  financial  assets  will  be  expressed  either  as  a  weighted  average  of  the 

percentage change in the aforementioned six financial assets, or as six separate percentage change 

variables.  We  construct  the  weighted  average  of  the  percentage  change  in  the  value  of  financial 

assets,  by  weighing  the  percentage  change  in  each  of  the  six  asset  categories  with  the  financial 

portfolio share of the respective asset, as recorded in the Internet Survey. As we will discuss below, 

we  have  also  tried  an  unweighted  average  of  the  changes  in  the  value  of  the  individual  financial 

assets,  and  this  change  left  our  results  unaffected. 10  In  order  to  avoid  problems  with  sample 

selection, we will include in our estimation sample also households that do not own a house and/or 

financial assets. As expected, the value of the capital gains variables will be equal to zero for those 

households. 

Table  1  summarizes  changes  in  consumption,  both  in  percentages  and  in  categorical  form 

(lower, same, or higher compared to the previous year), by quartiles of percentage changes in asset 

values.  Descriptive  statistics  suggest  a  negative  association  between  asset  capital  losses  and 

spending.  While  the  median  household  has  not  reduced  its  consumption,  households  that  have 

10 We should note that the Internet Survey asks households to give an estimate of the current value of the six financial 
assets in question. It is not possible, however, to combine this information with asset values reported in the 2008 HRS 
in order to calculate percentage losses for each financial asset. This is the case because changes in asset values do not 
distinguish  between  active  saving  and  changes  in  market  prices.  Furthermore,  there  is  not  an  exact  correspondence 
between financial assets about which questions are asked in HRS 2008, and those in the Internet Survey (e.g., there is 
no information on employer provided plans and trusts in the 2008 HRS). As a result, we have to use the Internet Survey 
question on percentage changes in asset values in order to measure asset losses. 

ECB Working Paper 1762, March 201516 
 
                                                 
 
suffered the largest losses in housing have reduced their spending by 5.2 percent on average, while 

the  corresponding  drop  for  those  with  the  largest  losses  in  financial  assets  is  7.2  percent.  On  the 

other  hand,  households  with  the  smallest  losses  (i.e.,  those  in  the  4th  quartile),  reduce  on  average 

their  spending  by  2.8  percent  and  3.3  percent  due  housing  and  financial  losses,  respectively.  The 

results  on  qualitative  consumption  changes  suggest  a  similar  picture,  as  the  fraction  of  those 

reporting a decline (increase) in consumption increases (decreases) when losses are higher (i.e., at 

the lower quartiles).  

In  Table  2  we  show  statistics  on  losses  on  housing  and  total  (weighted)  financial  assets,  as 

well  as  for  each  financial  asset  separately.  It  is  immediately  clear  that  a  significant  fraction  of 

households  have suffered losses in housing (54 percent) and in their financial  assets (94 percent), 

conditional on ownership. The prevalence of losses is also very severe (between 73 percent and 92 

percent) in all six financial assets.  

About half of the households that have experienced a drop in their housing wealth have lost at 

least  18  percent  of  the  value  of  their  main  home  between  the  summer  of  2006  and  the  spring  of 

2009. This implies a considerable hit to household net worth, given that the house is typically the 

dominant asset in household portfolios. The drop in households’ financial wealth has also been very 

striking. Among those who have suffered losses the median percentage loss with respect to the four 

major asset categories (i.e., employer-based pension plans, IRAs, mutual funds, and direct stocks) is 

about  28  percent  since  September  2008.  Furthermore,  one  out  of  four  households  with  losses  has 

witnessed a decline of at least 36 percent in the value of its investments in the aforementioned four 

assets.  

We  then  calculate  what  the  percentage  losses  shown  in  Table  2  imply  in  dollar  terms  by 

applying  the  reported  losses  in  percentages  to  the  values  of  the  assets  as  reported  in  the  HRS 

Internet  Panel.  We  find  that  the  median  amount  of  the  sum  of  losses  from  housing  and  financial 

assets  was  about  50,300  dollars  (in  2009  prices)  for  the  whole  sample.  These  large  capital  losses 

recorded  in  our  microdata  are  congruent  with  the  aggregate  capital  losses  during  the  Great 

ECB Working Paper 1762, March 201517 
 
Recession  that  are  shown  in  Figure  1.  The  magnitude  of  the  capital  losses  suffered  by  the 

households in our data is likely to have a negative impact on their spending. 

Apart from changes in housing and financial wealth, we will use in some of our specifications 

variables denoting a variety of socio-economic characteristics, information on which is taken from 

the 2008 HRS. These include age, household size, marital status, being in fair/poor health, working 

status,  education,  and  race.  Moreover,  we  use  the  number  of  correct  answers  to  a  numeracy  test 

(five successive subtractions of the same number) as an indicator of cognitive ability.11 Furthermore, 

we take into account households’ resources in 2008 by controlling for total household income, and 

net worth.12 Finally, we include dummy variables representing a transition into unemployment, an 

exit  into  retirement,  and  a  deterioration  in  health  status  between  HRS  2008  and  the  Internet 

Survey.13  

Table  3  provides  summary  statistics  on  the  aforementioned  socioeconomic  characteristics. 

The mean age is about 63 years, while households in which there are two partners form 75 percent 

of the sample. In about half of the households at least one member was employed full time, hence 

facing  a  potential  risk  of  unemployment.  On  the  other  hand,  in  34  percent  of  cases  both  partners 

were retired. In a non-trivial fraction of older households (5 percent) at least one of the two partners 

(or the single head) became newly unemployed between the 2008 HRS and the Internet Survey (as 

opposed to almost 6 percent for the population at large in the same period). In the same period, the 

rate  of  exit  into  retirement  was  11  percent.  Roughly  7  percent  of  households  have  at  least  one 

member  declaring  deterioration  in  health  status  in  comparison  to  2008,  while  one  out  of  four 

11 Shapiro (2010) also associates cognition with changes in consumption. 
12 We  control  for  net  income  and  net  wealth,  which  both  have  highly  skewed  distributions,  by  using  the  inverse 
hyperbolic sine transformation (hereafter IHS): log(x+(x2+1)1/2), which allows for nonlinear effects and is defined for 
zero  and  negative  values.  The  IHS  function  is  asymptotic  to  the  logarithmic  one  (with  a  difference  equal  to  the 
logarithm  of  two)  starting  from  values  of  x  that  are  very  close  to  zero  (Burbidge  et  al,  1988).  Hence  an  estimated 
coefficient of an IHS-transformed variable can be interpreted essentially in the same way as a coefficient of a variable 
in logarithms. 
13 In the case of couples characteristics represent a combination of the information from the two partners. In particular 
we use  average age,  worse  reported health  status, and  the maximum  of  educational  level and  of  the numeracy score. 
Furthermore, the couple is determined to be in the labor force if any of the two partners is working and retired if both 
are  retired.  With  reference  to  changes  in  occupation,  a  couple  with  at  least  one  newly  unemployed  or  newly  retired 
member between HRS 2008 and the Internet Survey is classified as becoming unemployed or retired, respectively. 

ECB Working Paper 1762, March 201518 
 
                                                 
households  declares  health  problems  in  2008.  The  median  household  income  was  about  70,000 

dollars, while the corresponding numbers for financial and net real assets are 81,800 and 193,100 

dollars, respectively (the latter figure is mainly due to the high home ownership rate and relatively 

low amounts of outstanding mortgages observed in our sample). 

Figure 2 highlights graphically our main results. It plots the change in the value of financial 

assets and the home against consumption growth, with the data aggregated in bins. Both relations 

are  positive,  suggesting  sizeable  wealth  effects.  The  response  of  consumption  to  financial  losses 

appears, however, to be much stronger. In particular, the left panel of Figure 2 shows that a drop in 

the value of housing wealth of 25 percent  is associated with a decrease  in expenditure of about 2 

percent.  On  the  other  hand,  the  right  panel  of  Figure  2  shows  that  financial  wealth  losses  of  25 

percent are associated with a reduction in consumption of about 4 percent. 

4. Model specification and empirical results 

We  will  study  the  effect  that  capital  gains  on  housing  and  financial  assets  have  on 

consumption by using a linear specification, in which the percentage change in consumption C will 

be associated to the percentage changes in the values of housing and financial wealth (denoted by 

HW  and  FW,  respectively)14,  to  becoming  unemployed  (denoted  by  ΔU)  as  well  as  to  various 

changes  over  time  in  a  vector  of  demographic  and  economic  variables  X.    Thus,  we  estimate  the 

following equation: 

C
it


C
it

1






HW
it


HW
it

1






FW
it


FW
it

1





U




X




it

,

it

it

(1) 

where i denotes the household and itan error term. This specification has been often used in the 

literature in order to capture the effect of various impulses on the growth rate of consumption. As 

Souleles (1999) notes, equation (1) nests the linearized Euler equation of Zeldes (1989) and Lusardi 

14 In  the  variables  denoting  percentage  changes,  negative  values  will  denote  capital  losses;  in  other  words,  these 
variables will effectively denote capital gains. 

ECB Working Paper 1762, March 201519 
 
 
                                       
 
 
 
                                                 
(1986)  when  β  and  γ  are  equal  to  zero.15 Due  to  differencing,  estimation  is  not  affected  by  any 

household fixed effects that could influence the expenditure in levels (Parker, 1999). 

In this framework, the coefficients of the variables denoting percentage changes in the values 

of the two assets (i.e., β and ) have a straightforward economic interpretation: they represent the 

elasticity of consumption with respect to those assets. Similarly, δ represents the semi-elasticity of 

consumption  to  becoming  unemployed.  As  we  will  discuss  in  Section  6  below,  we  check  the 

robustness of our results to the assumption of linearity by re-estimating all our specifications using 

the fractional variable framework of Papke and Wooldridge (1996, henceforth PW). 

We always include a constant α in our specification, which captures the effects of aggregate 

shocks to consumption growth. Hence, our estimates of β, γ and δ reflect the response of household 

consumption to idiosyncratic shocks to their wealth and employment status. 

We will estimate four different variants of each model that will include four different sets of 

covariates, in addition to those denoting capital gains. The first set includes age and household size, 

i.e.,  we  have  a  basic  specification  as  used  in  Zeldes  (1989).  The  second  set  includes  in  addition 

variables that capture changes in the households’ circumstances between the  main HRS survey of 

2008  and  the  2009  Internet  Survey:  whether  at  least  one  of  the  two  partners  (or  the  single  head) 

becomes  unemployed,  retires,  or  reports  a  deterioration  in  their  health.  In  the  third  set  we 

additionally control for economic resources by adding net real and financial assets, as recorded in 

the  main  HRS  survey  in  2008.  Finally,  in  the  fourth  set  we  add  further  controls  from  the  2008 

survey  in  order  to  check  the  sensitivity  of  our  results  and  capture  potential  heterogeneous 

consumption  responses  of  different  population  groups.  These  controls  include:  being  in  a  couple, 

educational attainment, the score in a numeracy test, being in fair or bad health, working status, and 

race.16 

15 Other papers that use the same framework include Parker (1999), Johnson et al. (2006), Agarwal et al. (2007), and 
Disney et al. (2010). 
16 We use two dummies denoting unemployment in the 2008 HRS, as well as becoming unemployed between that time 
and  the  2009  Internet  Survey  interview,  given  that  if  one  is  already  unemployed  in  2008,  then  the  transition  to 

ECB Working Paper 1762, March 201520 
 
                                                 
We first show in Panel A of Table 4 the elasticities derived from associating the percentage 

change  in  consumption  to  the  percentage  changes  in  the  values  of  the  house  and  in  the  weighted 

percentage change in financial assets. We observe that the elasticity of consumption with respect to 

the value of the house is roughly equal to 0.056 across all four specifications and significant at the 

10  percent  level.  Gains  on  financial  assets  appear  to  have  a  strong  positive  association  with  the 

change  in  consumption,  as  the  estimated  elasticity  equals  0.089  and  is  also  significant  at  the  1 

percent level. Obviously, a constant elasticity does not imply that rich and poor households change 

their expenditure by the same amount in response to a given percentage drop in their wealth. In fact, 

as rich households generally spend more than poor ones, a constant elasticity implies that they will 

reduce  their  consumption  by  a  greater  amount. 17 We  should  note,  however,  that  the  estimated 

elasticities of consumption to housing and financial wealth are not strictly comparable, given  that 

the  associated  capital  gains  variables  reflect  asset  price  changes  taking  place  over  different  time 

periods (i.e., since the summer of 2006 for housing and since September 2008 for financial assets); 

we will return to this issue below. 

When we look at the remaining variables in our specification we find very strong associations 

of  the  percentage  change  in  consumption  with  the  transitions  into  unemployment  and  into 

retirement  (the  semi-elasticities  are  equal  to  0.1  and  0.026,  respectively).18 The  strong  effect  of 

unemployment  suggests  that  it  represents  a  shock  that  is  at  least  partly  unanticipated  and  against 

which  the  household  can  only  partially  insure.  The  negative  association  of  consumption  with 

retirement points to the lack of perfect consumption smoothing, as well as to the possible existence 

of consumption items that are complementary to working (Banks et al., 1996). 

Having thus calculated the elasticity of consumption with respect to the values of the house 

and  of  financial  assets,  we  can  subsequently  calculate  the  marginal  propensity  to  consume  out  of 

unemployment dummy will be equal to zero. Therefore, using both variables gives us more information on the effects 
of unemployment on spending. Analogous arguments apply for the transitions into bad health and into retirement. 
17 This  is  consistent  with  the  evidence  presented  by  Petev  et  al.  (2011),  who,  using  CEX  data,  find  that  during  the 
recession the consumption of the rich fell more than that of the poor. 
18 We  also  find  a  positive  association  of  the  growth  rate  of  consumption  with  age.  We  cannot  distinguish,  however, 
between age and cohort effects in our framework. 

ECB Working Paper 1762, March 201521 
 
                                                                                                                                                                  
those two assets (shown in Table 4, Panel B), which is equal to the elasticity divided by the ratio of 

the value of the associated asset to consumption expenditure. For housing, we use the value of the 

house as recorded in the 2006 HRS, as the question is about changes in the price of the house since 

the summer of 2006. For financial assets, we use the value of risky financial assets as recorded in 

the 2008 HRS, as respondents in the Internet Survey are asked about their losses since September 

2008. For the associated consumption expenditure, we use the values of total expenditure recorded 

in  the  2007  and  2009  CAMS  surveys,  which  also  partly  cover  the  previous  calendar  year.  As 

already discussed, however, when we merge the CAMS surveys with the Internet Survey we have 

information on total expenditure only for relatively few households (367 for CAMS 2007, and 386 

for CAMS 2009). The values of the marginal propensities to consume that we obtain (shown Panel 

B of Table 4) using the asset to consumption ratios recorded for households in the Internet Survey 

are equal to 0.009 for housing and to 0.033 for financial assets.19As is the case for the underlying 

elasticities, the two MPC estimates are not strictly comparable due to the different time frames in 

reported  gains,  yet  they  both  fall  within  the  range  of  estimates  found  in  previous  literature 

(reviewed in Section 2).  

The small magnitude of our estimated MPC out of housing could be due to the fact that not all 

homeowners  may  reduce  their  consumption  in  response  to  a  house-price  decrease.  For  example, 

homeowners who expect to remain in their current dwelling for a very long time are hedged against 

fluctuations  in  rents  and  house  prices.  Furthermore,  in  the  absence  of  any  substitution  effects  or 

credit constraints, a change in house prices is less likely to affect their consumption. On the other 

hand, a decline in house prices might induce a decline in consumption for homeowners planning to 

trade  down,  or  stay  in  the  same  home  and  access  their  housing  wealth  through  an  equity  release 

19 For the calculation of the MPC out of financial assets we included bond holdings recorded in the 2008 HRS because: 
(i) the single question on them therein also includes bond holdings in mutual funds; (ii) in the Internet Survey, we have 
information on the capital losses on mutual funds only for all of them combined. When we repeated our calculations 
excluding bond holdings, the calculated MPC out of financial assets was only slightly higher at 0.034. 

ECB Working Paper 1762, March 201522 
 
                                                 
scheme.20 For homeowners wishing to trade up in the future, however, the effect is ambiguous, as 

the value of both their current property and of any future dwelling will have unexpectedly declined. 

One  should  also  keep  in  mind  the  possibility  that  the  long-term  effect  of  housing  losses  on 

consumption could be larger than the short-term one (Carroll et al., 2011). 

In order to check whether our results on the MPCs are affected by the relatively small number 

of  observations  used  in  their  calculation,  we  applied  the  estimated  elasticities  (i.e.,  the  regression 

coefficients)  not  only  to  the  households  in  the  Internet  Survey  that  also  appear  in  the  main  HRS 

surveys  in  2006  and  2008,  but  rather  to  all  households  in  the  2008  (2006)  HRS  for  which 

expenditure information exists from the 2009 (2007) CAMS. We can do this because the elasticities 

are  fixed  numbers,  i.e.,  they  do  not  depend  on  any  of  our  independent  variables  on  which 

information can be found in the Internet Survey but not in the 2006 and 2008 HRS. The advantage 

of  using  these  alternative  samples  is  that  we  end  up  with  much  larger  numbers  of  households  on 

which  we  can  calculate  the  MPCs  (1,846  households  for  the  MPC  out  of  housing,  and  1,294 

households for the MPC out of financial assets). We found that the calculated MPC out of housing 

remained  the  same  at  0.009,  while  the  MPC  out  of  financial  assets  was  slightly  lower  at  0.03. 

Therefore,  we  conclude  that  our  MPC  estimates  from  the  Internet  Survey  are  not  significantly 

affected by the relatively small number of observations used for their calculation. 

As already discussed, the estimated MPCs out of financial assets and housing are not directly 

comparable to each other, given that the underlying reported gains used in their calculation refer to 

different  periods.  One  way  to  address  this  issue  is  to  change  the  period  of  reference  of  housing 

capital gains so that it starts from September 2008, as is the case with financial asset gains. In order 

to do this one would need to calculate the part of the total reported housing capital gain (i.e., from 

the summer of 2006 to the time of the interview) that occurred from September 2008 to the time of 

the  interview.  In  order  to  do  this  apportioning  we  have  to  make  an  assumption  about  the  rate  of 

20 As  suggested  by  Aoki  et  al.  (2001)  a  fall  in  house  prices  might  also  affect  access  to  credit  in  the  form  of  equity 
withdrawal.  In  fact,  a  reduction  in  house  prices  reduces  collateral  available  to  homeowners,  discouraging  them  to 
borrow. 

ECB Working Paper 1762, March 201523 
 
                                                 
change  in  housing  prices  from  the  summer  2006  on.  It  turns  out  that  the  different  housing  price 

indices give conflicting results for this rate. The Case-Shiller US house price index implies that the 

drop in housing prices slowed down from September 2008 on compared to the interval between the 

summer of 2006 and September 2008. On the other hand, the US house price index produced by the 

Federal  Housing  Finance  Administration  (FHFA)  leads  to  the  opposite  conclusion.21 Hence,  we 

proceed  with  our  calculations  by  assuming  that  the  rate  of  change  of  housing  prices  was  roughly 

constant  from  the  summer  of  2006  to  the  time  of  the  interview.  It  is  important  to  note  that  our 

apportioned housing capital gain/loss varies across households because the latter are interviewed at 

different points (chosen randomly) in the first half of 2009. 

We use this calendar time-based apportioning scheme both for households reporting housing 

gains and for those reporting losses. It turns out that the so-apportioned (i.e., since September 2008) 

housing  price  change  is,  on  average,  about  one  fourth  of  the  actual  reported  one,  although,  as 

already  mentioned,  this  proportion  varies  across  households.  As  a  result,  when  we  substitute  this 

apportioned housing price change for the actual one in our empirical specifications, the associated 

regression coefficient is about four times larger, i.e. the elasticity changes from about 0.055 to about 

0.23. This is to be expected, given that the dependent variable and all other regressors retain their 

original  values;  hence,  dividing  one  regressor  by  a  factor  of four  on  average  results  in  an  inverse 

adjustment of its estimated impact.  

Given that the MPC out of housing is equal to the estimated elasticity multiplied by the ratio 

of  consumption  to  the  housing  value,  it  also  becomes  roughly  four  times  larger.  Hence,  it  is 

approximately equal to 0.04, i.e. a bit larger than the MPC out of stock capital gains, but still within 

the range of estimates usually found in the literature. The same reasoning obviously implies that if 

the apportioned housing capital loss since September 2008 is smaller on average than one fourth of 

21 The  FHFA  index  declined,  on  a  seasonally  adjusted  basis,  by  9.6%  in  9  quarters  (i.e.,  from  221.98  in  the  second 
quarter of 2006 to 202.88 in the third quarter of 2008). The same index fell by 4.5% between the third quarter of 2008 
and the second quarter of 2009 (=193.67), which represents an accelerated pace compared to the previous period. On 
the other hand, the Case-Shiller index for the US declined from 189.93 to 139.41, i.e., by about 27%, during the first 
period, while it declined by 4.5% in the second period (its value was equal to 133.18 in the second quarter of 2009).  

ECB Working Paper 1762, March 201524 
 
                                                 
the  actually  reported  total  loss  since  the  summer  of  2006,  then  the  upward  adjustment  of  its 

associated  coefficient  will  be  larger.  Notably,  the  estimated  elasticity  of  consumption  to  stock 

wealth  remained  essentially  unchanged  when  the  modified  measure  of  housing  capital  gains  was 

used.  

In  order  to  check  whether  our  results  are  sensitive  to  any  outliers  in  the  variable  denoting 

consumption  growth,  we  re-estimated  our  model  using  as  a  dependent  variable  the  categorical 

change  in  consumption  relative  to  the previous  year  instead of the  continuous  percentage  change. 

As there are three possible values (lower, the same, higher) to this categorical variable, we show in 

Table  5  the  marginal  effects  on  the  three  associated  probabilities,  derived  from  an  ordered  probit 

(more  details  about  the  calculation  of  marginal  effects  are  given  in  Appendix  B).  We  note  that  a 

capital  gain  of  15  percent  (our  assumed  perturbation  of  the  capital  gains  variables)  lowers  the 

probability of reducing consumption by about 1.5 percentage points and 2.2 percentage points in the 

case  of  housing  and  financial  assets,  respectively.  Analogously,  this  capital  gain  makes  the 

probability  of  increased  spending  higher  by  1.6  percentage  points  and  2.4  percentage  points. 

Importantly, the housing capital gain is statistically significant at the 5 percent level in most cases, 

while the p-value of the financial capital gain is always below 1 percent. Becoming unemployed has 

a  large  negative  impact  on  consumption,  as  it  increases  that  probability  of  smaller  spending  by 

roughly 21 percentage points, while it decreases the probability of higher spending by roughly 14 

percentage  points.  Therefore,  we  conclude  that  our  results  obtained  by  using  the  continuous 

consumption growth as the dependent variable are robust to the presence of outliers. 

As  we  have  detailed  information  on  the  composition  of  financial  assets,  we  repeat  our 

analysis  using  as  separate  controls  the  percentage  changes  in  the  asset  values  of  the  six  financial 

assets found in the Internet Survey (as before, positive values of these six variables denote financial 

gains).  This  allows  us  to  estimate  to  which  financial  assets  in  particular  we  should  attribute  the 

strong effect of changes in total financial wealth on consumption displayed in Table 4. The results 

of this disaggregated analysis are shown in Table 6, and it is clear that the association of financial 

ECB Working Paper 1762, March 201525 
 
wealth shocks to consumption is to a large extent due to directly held stocks (the estimated elasticity 

is  0.088).  It  is  also  worth  noting  that  in  this  specification  the  estimated  elasticity  of  changes  in 

housing  wealth  (0.068)  is  slightly  larger  than  the  one  estimated  from  the  specification  that  uses 

changes in the value of aggregated financial wealth. Importantly, this elasticity is now statistically 

significant  at  the  5  percent  level,  which  indicates  that  the  value  of  the  home  quite  likely  has  a 

considerable effect on consumption expenditure. Losing one’s job during the crisis has essentially 

the same large negative impact as before. 

One notable feature of the results shown in Tables 4 and 6 is that the household’s net worth as 

recorded  in  the  main  HRS  survey  in  2008  is  not  associated  with  the  subsequent  change  in 

consumption,  after  controlling  for  capital  gains.  Households’  indebtedness  could,  however,  affect 

the  response  of  consumption  to  capital  losses;  a  household  with  large  debts  might  have  more 

difficulties  in  adjusting  consumption  smoothly  to  any  changed  circumstances  due  to  the  financial 

crisis.  Therefore,  instead  of  using  total  net  worth  in  the  third  and  fourth  specifications  shown  in 

Tables 4-6 above, we disaggregated in these two specifications net worth into its three components: 

gross  real  assets,  gross  financial  assets,  and  total  debt.  Our  results  our  shown  in  Appendix  Table 

A.1 for the specifications corresponding to Tables 4 (columns (1)-(4)) and 6 (columns (5)-(8)), and 

in Table A.2 for the specifications corresponding to Table 5. We find that larger debts are indeed 

negatively  associated  with  the  change  in  consumption,  with  an  elasticity  of  about  -0.002  in  the 

specifications  shown  in  Table  A.1.  In  addition,  the  results  in  Table  A.2  imply  that  an  increase  of 

10,000 dollars in total debt increases the probability of lower consumption by about 1.1 percentage 

point.  One  possible  interpretation  of  this  effect  is  that  households  with  more  debt  were  more 

affected  by  tightening  credit  conditions,  and  therefore  cut  back  on  their  consumption  more 

strongly.22 The results for changes in the values of the home and financial assets are affected very 

little by the disaggregation of net worth into its components. 

22 This result is qualitatively consistent with the findings of Mian et al. (2013), who find that in zip codes with poorer 
and more levered households have a significantly higher MPC out of housing wealth. 

ECB Working Paper 1762, March 201526 
 
                                                 
5. Simulation results from two models of intertemporal choice 

To gain insights about the potential size of wealth effects on consumption and to motivate our 

empirical specification,  we simulate the  consumption  elasticity with respect to  a wealth shocks  in 

two standard models of intertemporal choice, namely the Permanent Income Hypothesis (PIH) one 

and the Buffer Stock Model (BSM) one. In both models households maximize the expected value of 

an intertemporally additive constant relative risk aversion utility function over a finite time horizon. 

The  labor  income  process  is  standard,  with  a  permanent  and  a  transitory  component  that  are 

distributed lognormally. The rate of return on wealth is the sum of a deterministic component and a 

stochastic  component,  which  is  meant  to  capture  shocks  to  wealth.  In  our  context,  these  shocks 

represent capital gains/losses. The crucial condition that differentiates the BSM from the PIH is the 

existence  of  a  non-negativity  constraint  on  wealth,  which  generates  buffer  stock  saving  (Deaton, 

1991).  

We calibrate the stochastic process for capital gains using data from the US Flow of Funds. 

All  details  about  the  models’  specification,  calibration  and  solution  methods  are  reported  in 

Appendix C. 

We run the simulations for both the BSM and PIH models for three periods for approximately 

10,000  households,  taking  as  an  initial  condition  for  wealth  the  cross-sectional  distribution  of 

wealth  of  the  households  in  the  HRS  Internet  Survey.  The  youngest  age  in  our  sample  at  the 

beginning  of  the  simulation  is  50  and  the  oldest  one  90.  In  each  period  we  generate  for  every 

household the values of consumption, capital gains, wealth and labor income and then estimate the 

same  empirical  model  that  we  run  in  our  HRS  sample.  In  particular,  we  estimate  the  following 

equation: 

C


C
it

1


it



l

it
it



,

,  

(2) 

ECB Working Paper 1762, March 201527 
 
 
 
 
 
 
where lit  denotes capital gains/losses as a percentage of the value of the underlying asset. Hence the 

coefficient β in equation (2) can be interpreted as the elasticity of consumption to wealth. 

Given  that  both  the  BSM  and  the  PIH  models  imply  a  nonlinear  relationship  between 

percentage  changes  in  consumption  and  wealth  (or  cash-on-hand)  we  also  estimate  a  version  of 

equation  (2)  in  which  our  capital  gains  variable  is  interacted  with  wealth  and  income,  while 

including  in  our  specification  those  two  variables  also  as  independent  terms.23 Given  that  now 

capital  gains  are  interacted  with  income  and  wealth,  we  calculate  again  the  elasticity  of 

consumption  to  wealth,  which  is  now  equal  to  the  total  derivative  of  percentage  consumption 

growth with respect to  percentage capital gains, taking into  account both the uninteracted  and the 

interacted terms in which these gains appear. This derivative varies across observations, as it now 

depends on the values of income and wealth; hence, we calculate its average across the sample, i.e., 

we calculate the average marginal effect of capital gains on percentage consumption growth. 

Our  results  are  shown  in  Table  7,  for  both  the  BFS  and  PIH  model,  with  and  without 

interactions with income and wealth, and for both definitions of the capital gains variable discussed 

above. When there are no interactions, the elasticity from the BSM is 0.098 when capital gains are 

calibrated  to  match  data  only  from  equities,  and  0.084  when  using  a  weighted  average  of  capital 

gains  on  housing  and  equities.  The  corresponding  magnitudes  from  the  PIH  model  are  0.080  and 

0.079, respectively. In all cases the elasticity is very precisely estimated. We also experiment with a 

version  of  the  PIH  model  in  which  both  the  permanent  income  shock  and  the  transitory  income 

shock are switched off, and thus labor income evolves deterministically. We find that the elasticity 

is 0.077 for both cases of capital gains. Therefore, it seems that in our simulation the presence of 

income shocks in the PIH model does not affect the estimated elasticity of consumption to wealth. 

When  using  interactions  of  capital  gains  with  income  and  wealth,  it  turns  out  that  the 

interaction terms are statistically significant in all cases. As can be seen from Table 7, however, the 

23 We transform both variables using the inverse hyperbolic sine transformation, given that in levels they are both very 
skewed.  

ECB Working Paper 1762, March 201528 
 
                                                 
magnitude  of  the  elasticity  remains  essentially  identical  to  the  one  from  the  uninteracted  models. 

Therefore, interaction terms in both simulated models, while individually statistically significant, do 

not affect the overall estimated impact of capital gains on consumption growth. 

To summarize, the simulations reveal that the implied elasticities are somewhat higher in the 

BSM than in the PIH model, and that our estimated response of consumption to wealth in the data 

fall  in  between  this  range.  This  suggests  that  our  empirical  estimates  of  the  sensitivity  of 

consumption  to  wealth  shocks  are  broadly  consistent  with  standard  intertemporal  consumption 

models. 

6.  Permanent vs. transitory wealth shocks 

One  of  the  core  predictions  of  the  life-cycle  theory  of  consumption  is  that,  when  hit  by 

unexpected wealth or income shocks, households should adjust their consumption much more when 

they  consider  the  shock  to  be  permanent  rather  than  transitory.24 In  order  to  determine  whether 

shocks are transitory or permanent, one can estimate the process generating the shocks, or rely on 

subjective  expectations.  Contreras  and  Nichols  (2010)  follow  the  first  approach.  They  exploit 

regional  variability  in  house  price  dynamics  and  estimate  that  the  consumption  responses  to 

permanent shocks to housing wealth is between 3.5 and 9.2 percentage points, while in the case of 

responses  to  transitory  shocks  the  MPC  is  between  0.5  and  3.7  percentage  points.  The  second 

strategy,  forcefully  endorsed  by  Manski  (2004),  is  to  use  subjective  expectations  as  recorded  in 

survey data in order to elicit information on the distribution of future shocks.25 In the case of stock 

24 Several studies have examined this prediction using aggregate or regional data (Lettau and Ludvigson, 2004; Luengo-
Prado and Sorensen, 2008). There are also studies that use survey data in order to examine consumption responses to 
income  shocks,  and  to  distinguish  between  the  effect  of  permanent  and  transitory  shocks  (Blundell  et  al.,  2008). 
Recently, Campbell and Cocco (2007) have used survey data to investigate the impact of housing wealth fluctuations on 
consumption, distinguishing between anticipated and unanticipated changes in housing prices.  
25 Other  papers  that  rely  on  subjective  expectations  to  distinguish  between  transitory  and  permanent  income  shocks 
include  Hayashi  (1985),  who  used  a  four-quarter  panel  of  Japanese  households  containing  respondents’  expectations 
about expenditure and income in the following quarter, and Pistaferri (2001), who combined income realizations and 
quantitative subjective income expectations contained in the Italian Survey of Household Income and Wealth (SHIW). 

ECB Working Paper 1762, March 201529 
 
 
                                                 
market expectations this is actually the only feasible approach, because stock market prices do not 

vary among individuals or geographical districts. 

We follow the latter approach, and thus examine households’ expectations about the course of 

the stock market in the near future in order to understand whether they consider the financial losses 

experienced  during  the  crisis  as  permanent.26 These  expectations, even  if  not  fulfilled,  can  induce 

substantial  consumption  adjustments.  We  would  expect  financial  wealth  losses  to  have  a  stronger 

effect  on  consumption  for  households  that  perceive  the  stock  market  decline  to  be  permanent, 

compared to those that anticipate stock prices to recover relatively fast. 

This heterogeneity in expectation formation among households can be properly studied only 

by using micro survey data. To that effect, we exploit the fact that in both the 2008 main survey and 

the Internet Survey households are asked to report the probability that blue chips shares (like those 

in the Dow Jones Industrial Average) will be higher in a year’s time. The distribution of answers to 

this question in the 2008 HRS is as follows: the first quartile is equal to 30 percent, the median is 50 

percent,  and  the  third  quartile  equal  70  percent  (the  mean  is  49  percent).  The  corresponding 

quartiles  computed  from  the  2009  Internet  Survey  are  10,  30,  and  60  percent  (the  mean  is  37 

percent).  The  shift  of  the  distribution  to  the  left  suggests  that  many  households  became  more 

pessimistic  in  the  second  interview  about  the  future  course  of  stock  prices.  On  the  other  hand,  a 

non-trivial fraction of households in our sample (32 percent) become more positive about the stock 

market between the two surveys, in the sense that they reported a larger probability of a rise in the 

stock  market  in  2009  than  in  2008.  This  upward  revision  in  the  reported  probabilities  likely 

indicates  that  these  households  consider  the  decline  in  stock  prices  to  be  temporary.  Hence,  their 

spending should be less affected by financial capital losses compared to that of households with a 

more  pessimistic  outlook  on  the  stock  market  (i.e.,  those  that  report  the  same  or  a  smaller 

probability in 2009 compared to 2008). 

26 There are no questions in the 2008 HRS on households’ expectations about housing prices. 

ECB Working Paper 1762, March 201530 
 
                                                 
To check our intuition, we re-estimate our baseline model after splitting our sample between 

these two types of households. The results are shown in Table 8, Panels A.1 and A.2. We find that, 

in  line  with  our  expectations,  households  that  consider  the  stock  market  decline  as  non-transitory 

respond  quite  strongly  to  financial  capital  losses.  Indeed,  the  estimated  elasticity  equals  0.12, 

substantially higher than the one found in our basic specification for the  whole sample (shown in 

Table  4),  which  was  about  0.09.  On  the  other  hand,  we  estimate  much  weaker  and  statistically 

insignificant  consumption  adjustments  by  households  that  in  2009  revise  their  expectations  about 

stock prices upwards compared to 2008. 

An alternative way to check the effect of permanent and transitory wealth shocks is to split 

the sample based only on the expectation about higher stock prices reported in the main HRS survey 

in  2008.  We  consider  households  that  reported  a  probability  larger  than  50  percent  as  likely  to 

believe that the drop in stock prices is temporary, whereas those that reported a probability less or 

equal to 50 percent were considered as more likely to think of the drop as a lasting one. Once more, 

our estimates (shown in Panels B.1 and B.2 of Table 8) strongly suggest that households that view 

the  stock  market  slump  as  more  likely  to  persist  respond  strongly  to  financial  capital  losses  (the 

elasticity is equal to 0.134), whereas the response of those that expect a rebound in stock prices is 

again weak and not significant. 

It  is  well  documented  (see,  e.g.,  Fischhoff and  Bruine  de  Bruin, 1999)  that  respondents  in 

household surveys who cannot answer a question about the probability of a future event sometimes 

give  an  answer  of  50  percent  instead  of  admitting  their  inability  to  answer.  In  order  to  check  the 

robustness  of  the  results  discussed  in  this  Section  to  this  pattern  of  answers,  we  repeated  all  our 

analyses after excluding all households who gave an answer equal to 50 percent. None of our results 

were affected by this exclusion. 

ECB Working Paper 1762, March 201531 
 
 
7. Robustness checks 

To check the robustness of the results presented in Sections 4 and 5 we performed a number 

of robustness checks. Due to space constraints, we show only some of the results discussed in this 

Section. All results are available from the authors upon request.  

First, given  that  the values of the percentage  change in  consumption lie between  minus one 

and plus one, we redo our estimation using the PW fractional variable model (discussed in further 

detail  in Appendix D), which features a conditional  mean that  is nonlinear in the regressors. This 

nonlinearity could be important because the closer this mean gets to the variable bounds, the less it 

should be influenced by changes in the regressors. In contrast, a linear model produces a constant 

effect  of  the  regressors  across  all  ranges  of  the  conditional  mean,  hence  potentially  leading  to  an 

overestimation of the effect for sample units with predicted means close to the bounds. In addition, 

nothing  prevents  a  linear  model  from  predicting  out  of  range.  The  results  from  the  PW  model, 

however, prove to be essentially identical both in sign and in magnitude to those obtained from the 

linear model. We conclude, therefore, that the linearity of our main statistical model is unlikely to 

lead to any bias in our results. 

Second, in order to check the sensitivity of our findings to possible outliers we perform robust 

regressions  using  Huber’s  (1973)  M-estimator.  The  estimated  impact  of  the  variables  denoting 

becoming  unemployed  and  stock  capital  gains  remains  unchanged,  while  that  of  the  variable 

denoting  housing  capital  gains  was  slightly  reduced  from  about  0.055  to  0.045.  This  latter  effect, 

however, is much more precisely estimated; its p-value was below 0.02 in all specifications. 

Third, instead of using as forcing variables the percentage changes in the values of the home 

and of financial assets, we use: (i) the quartiles of the capital gains in housing; (ii) the four levels of 

capital gains in total financial  assets, which we  described  in Section 2 above. Using a categorical 

variable is a natural way to check whether our estimates are affected by the fact that in our data the 

financial  capital  gains  variables  are  censored  at  zero.  The  results  of  our  estimation  are  shown  in 

ECB Working Paper 1762, March 201532 
 
Table 9, and we observe that the association of housing capital gains with the percentage change in 

consumption is strong and statistically significant at the top quartile: households that experience the 

largest  capital  gains  (or  smallest  losses)  increase  their  spending  by  roughly  2.4  percentage  points 

compared  to  those  with  the  lowest  gains  (or  largest  losses).  The  fact  that  we  find  a  statistically 

significant association only for the top quartile of gains is indeed an indication of a non-linearity in 

the effect of housing capital gains. On the other hand, all levels of financial gains have a positive 

effect on the change in consumption (e.g., the effect of the highest level of financial gains is roughly 

equal to 3.4 percentage points across the four specifications). The effects of all remaining variables 

(including the transition into unemployment) are essentially identical to those shown in Table 4. In 

Appendix  Table  A.3  the  analysis  is  repeated  with  the  categorical  change  in  consumption  as  the 

dependent  variable,  and  the  results  are  essentially  the  same  as  those  shown  in  Table  5:  housing 

gains again matter at the highest quartile, while financial gains matter at all levels. As a result, we 

conclude that expressing our gains variables as categorical variables largely confirms our findings 

up to now; in particular, the censoring of the financial gains variable at zero has no apparent effect 

on our estimates. 

We also estimate a specification with the categorical change in consumption as the dependent 

variable  that  includes  disaggregated  financial  assets.  Our  results  (shown  in  Appendix  Table  A.4) 

confirm those shown in Table 6 for the continuous variable denoting change in consumption, i.e., 

gains  on  both  housing  and  direct  stocks  are  associated  with  increases  in  consumption,  while  the 

opposite is true for becoming unemployed. Importantly, we find in three out of four specifications 

an  additional  positive  and  economically  significant  association  of  changes  in  consumption  with 

capital gains in IRAs: a 15 percent increase in the latter raises by more than 1.5 percentage points 

the probability that households spend more. Given that the prevalence of IRA ownership is larger 

than that of stocks, capital losses in IRAs are likely to be an important transmission channel of the 

effect of the financial crisis on household spending. 

ECB Working Paper 1762, March 201533 
 
The 2009 HRS Internet Panel does not collect information on income. This is the reason why 

in  our  baseline  regressions  we  control  for  income  from  2008  HRS  in  levels  and  changes  in 

employment status, instead of including change in income between the 2009 survey and the 2008 

HRS wave as a separate covariate. The only other measure of income that is available to us is the 

income  reported  in  the  2010  HRS  main  wave,  which  refers  to  calendar  year  2009.  We  have  thus 

matched  households  in  the  2010  HRS  that  report  their  incomes  from  2009  with  our  data.27 This 

allows us to compute the percentage change in income between the 2009 and 2008 waves, and use it 

as a regressor in our specification. This measure of income change is not ideal, given that it covers 

the whole calendar year 2009, while the interviews in our sample take place in the first half of that 

year.  It  also  represents  a  change  over  two  years,  i.e.  from  2007  to  2009.  In  any  case,  when  we 

include  this  regressor,  it  is  statistically  significant  and  the  associated  elasticity  is  about  0.028. 

Importantly,  the  estimated  elasticities  with  respect  to  both  housing  and  stock  wealth  remain 

unaffected by the inclusion of the percentage income change. 

We then want to check whether the association of capital gains with consumption differs by 

whether  household  members  were  retired  or  not.  As  already  discussed,  while  households  with 

members  that  still  work  might  feel  a  stronger  drop  in  their  permanent  income  because  of  the 

recession, older households have less time to adjust their spending to any negative shocks; therefore, 

which  of  the  two  effects  prevails  is  an  empirical  issue.  When  we  interact  our  retirement  dummy 

with  our  variables  denoting  gains,  the  interaction  term  is  insignificant,  and  the  same  is  true  for  a 

dummy denoting that both partners (or the single household head) are less than 65 years old. In all 

cases, our results are unaffected by the inclusion of these interacted terms. 

One  factor  that  could  possibly  affect  our  results  could  be  the  perception  (especially  by  the 

younger households in our sample) that permanent income has taken a negative hit during the Great 

Recession. This negative development could be reflected at the local level (e.g., due to the closing 

27 This is the case for almost 74% of households in the 2010 HRS that were interviewed in 2010, while the rest were 
interviewed in 2011. 

ECB Working Paper 1762, March 201534 
 
                                                 
of  a  factory),  and  thus  could  affect  the  value  of  one’s  home.  In  order  to  control  for  perceived 

changes in permanent income, we use a question that asks the persons in our sample who work to 

report  the  probability  that  they  will  become  unemployed  in  the  next  year.28 Our  results  remain 

unaffected by the inclusion of this additional variable, which has a negative sign as expected but is 

not statistically significant. 

The same probability p, when added in the specification in the form p(1-p), could be used as a 

measure of uncertainty that households face about their future income prospects (Guiso et al., 1999). 

Such uncertainty has been proposed as one of the reasons for the drop in consumption in the US. 

We  find that  the  coefficient  of  our proxy  for  uncertainty is statistically  insignificant  and  does  not 

change  the  estimated  effect  of  the  financial  capital  gains  on  consumption.  As  for  housing  capital 

losses,  their  effect  now  becomes  insignificant  in  our  baseline  specification,  but  it  remains  highly 

significant  when  expressed  in  quartiles  and  also  when  financial  capital  losses  are  disaggregated. 

This  is  true  when  consumption  growth  is  expressed  both  as  a  continuous  variable  and  as  a 

categorical  one.  As  a  result,  we  still  think  that  the  weight  of  the  evidence  indicates  that  housing 

capital gains have an economically and statistically significant effect on consumption growth. 

We  also  try  to  account  for  negative  permanent  income  developments  and  increased 

uncertainty by including information at the regional level. To that effect, we use the change in the 

GDP  per  capita  and  in  the  unemployment  rate  from  the  2nd  quarter  of  2008  to  the  corresponding 

quarter in 2009 for each Census Division, which is the most disaggregated regional level for which 

information is available in the data. We find that a negative change in the regional GDP per capita 

has  a  strong  negative  effect  on  the  growth  in  household  consumption  (a  1  percent  decrease  in 

regional GDP per capital implies a 0.4 percent decrease in consumption), while we find a negative 

but statistically insignificant effect of an increase in regional unemployment (possibly because we 

28 We set this probability equal to zero for retirees. We tried two approaches to deal with the value of this probability for 
the  unemployed:  (i)  given  that  they  are  asked  about  the  probability  that  they  will  find  a  job  next  year,  we  used  one 
minus this probability; (ii) we took the unemployed out of our sample. In neither case did our results change. 

ECB Working Paper 1762, March 201535 
 
                                                 
already control for unemployment at the household level). In any case, the inclusion of these two 

regional-level variables leaves our main results unchanged. 

We also check whether the elasticity of consumption with respect to assets varies by the level 

of the assets that the household possesses (as already noted, the MPC does so because it is equal to 

the  elasticity  multiplied  by  the  consumption  to  asset  ratio).  When  we  interact,  however,  our 

variables  denoting  capital  gains  with  the  corresponding  assets,  the  interaction  terms  are  not 

significant.  The  same  is  true  of  the  interaction  of  the  gains  with  the  amount  of  household  debt, 

although,  as  already  mentioned,  the  coefficient  of  the  uninteracted  debt  term  is  negative  and 

statistically significant. The inclusion of these interaction terms does not change the coefficients of 

the uninteracted capital losses terms. 

We  then  check  whether  our  results  are  affected  by  time  effects.  For  example,  there  were 

considerable fluctuations in asset prices during our sample period (the S&P 500 Index increased by 

about 22 percent from between March and June 2009). When we include dummies for the interview 

month, however, our results do not change. 

Given that consumption could be affected not only by financial capital gains and losses, but 

also by any buying or selling of financial assets, we include in our specification both dummies that 

denoted  buying  and  dummies  that  denote  selling  of  each  of  the  financial  assets  recorded  in  the 

survey.29 Once more, our results are not affected by taking into account these financial transactions. 

Finally,  we  check  the  sensitivity  of  our  results  to  the  weighting  procedure  that  we  use  to 

calculate  the  weighted  percentage  financial  gain,  as  described  in  Section  3.  To  that  effect,  we 

calculate  the  unweighted  percentage  capital  gain  on  financial  assets  for  any  given  household  by 

taking the simple arithmetic average of the percentage gains in all the financial assets owned by that 

household.  The  estimation  results  obtained  from  using  this  unweighted  magnitude  are  essentially 

29 There are trivially few households in our sample who changed their home between the 2008 HRS main survey and 
the 2009 Internet Survey. Furthermore, the precise amounts of financial assets bought or sold are not known. 

ECB Working Paper 1762, March 201536 
 
                                                 
identical to those shown in Table 4. We thus conclude that the particular weighting we use to derive 

the overall financial capital gain variable does not affect our results. 

8. Results from the Panel Study of Income Dynamics 

Given  that  our  dataset  consists  of  individuals  aged  50  and  above  we  want  to  repeat  our 

analysis  in  a  sample  representative  of  the  whole  US  population.  For  that  purpose,  we  choose  the 

Panel Study of Income Dynamics (hereafter PSID), which is a panel survey that has started in 1968, 

and since 1997 is conducted every two years. The recent PSID waves contain detailed information 

on  consumption  expenditures,  as  well  as  on  the  value  of  the  house  and  risky  financial  assets.30 

Unfortunately,  the  survey  provides  no  information  on  capital  gains  or  losses  on  financial  assets. 

Hence, the change in the value of those assets from one wave to the next is the result of both asset 

price changes and active saving. On the other hand, we can deduce home capital gains or losses by 

calculating  the  cross-wave  difference  in  the  reported  housing  value  for  respondents  who  do  not 

move  between  waves.  We  choose  the  2007,  2009  and  2011  waves  for  our  estimation  in  order  to 

cover the period corresponding to the Great Recession, and keep those households who either own 

the home they live in and do not move between waves or households who rent in all waves. Hence, 

we end up with a sample size of about 10,600 households. 

We  proceed  to  estimate  equation  (1)  in  this  sample  using  the  same  four  specifications 

reported  in  Table  4  above.  Our  results  are  shown  in  Table  10,  and  we  note  that  the  elasticity  of 

consumption  with  respect  to  capital  gains  on  housing  wealth  is  about  0.051,  which  results  in  an 

MPC  of  about  0.009.  Both  these  estimates  are  very  close  to  the  ones  obtained  from  the  HRS 

Internet Panel and shown in Table 4. The estimated elasticity with respect to risky financial assets is 

about  0.024,  and  the  resulting  MPC  about  0.01.  Hence,  these  estimates  are  smaller  than  those 

30 Our measure of consumption consists of the sum of all expenditures that households report in the 2007-2011 waves, 
after  excluding those  expenditures  that  would  not  be  considered  as  part  of consumption,  namely  property taxes paid, 
and  mortgage  and  car  loan  repayments.  Risky  financial  assets  refer  to  stocks  in  publicly  held  corporations,  mutual 
funds,  or  investment  trusts,  and  any  money  in  private  annuities  or  IRAs.  For  a  detailed  overview  of  the  PSID,  see 
McGonagle et al. (2012). 

ECB Working Paper 1762, March 201537 
 
 
                                                 
obtained from the HRS data, but as we already discussed the variable denoting changes in financial 

assets is not comparable in the two surveys. Finally, the semi-elasticity of consumption with respect 

to a transition to unemployment is estimated to be about -0.0935, which is very close to the results 

obtained in the HRS. 

All  in  all,  we  find  the  results  from  PSID  to  be  comparable  to  those  from  the  HRS  for  the 

variables whose definitions can be matched between the two surveys. As a result, it seems that our 

results  obtained  for  the  population  aged  50  and  above  could  be  applicable  to  the  whole  US 

population. 

9. Conclusions   

We have examined the effects of the recent crises in the US housing, stock and labor markets 

on household spending, using recently available HRS data for the population aged fifty and above. 

The  dataset  records  capital  losses,  employment  transitions,  and  consumption  changes  at  the 

household level, as well as stock market expectations between 2008 and 2009. We find that housing 

and  financial  wealth  losses  have  a  substantial  negative  effect  on  household  consumption,  and  the 

same is true if someone in the household loses his/her job. In particular, the estimated elasticities of 

consumption  with  respect  to  housing  and  financial  wealth  are  equal  to  about  5.6%  and  9%, 

respectively, while the corresponding marginal propensities to consume are equal to about 1 and 3.3 

percentage  points,  respectively.  The  effects  of  financial  losses  stem  primarily  from  directly  held 

stocks, while there is some evidence that losses on IRAs matter as well.  

Our  estimates  are  very  robust  to  numerous  variations  in  specifications,  outcome  variables, 

and  forcing  variables.  Importantly,  the  derived  marginal  propensities  to  consume  out  of  both 

housing  and  financial  assets  are  economically  significant  and  fall  within  the  range  of  estimates 

previously  found  in  the  literature  on  the  effects  of  housing  and  financial  wealth  on  consumption. 

Moreover,  our  estimated  elasticities  are  in  line  with  the  corresponding  elasticities  implied  by 

ECB Working Paper 1762, March 201538 
 
 
standard  intertemporal  consumption  models.  We  also  find  that  results  from  the  PSID  are  broadly 

comparable to those from the HRS for the variables whose definitions can be matched between the 

two surveys. 

Our  results  imply  that  higher  housing  and  stock  prices  and  an  improved  job  market  are 

needed  for  an  increase  in  US  household  expenditure,  especially  at  a  time  when  households  may 

desire  to  save  more  in  order  to  rebuild  their  asset  position.  This  process  is  unlikely  to  be  brief 

because many households lost a large chunk of their wealth during the Great Recession, while still 

being saddled with considerable debt. 

Finally, the finding that the effect of financial losses on expenditure depends on whether they 

are perceived as temporary or permanent implies that a key factor for boosting final consumption is 

the  confidence  that  households  have  in  the  economy’s  prospects  in  the  near  future.  Optimistic 

expectations about the stock market are likely to increase spending, thus helping the economy and 

the stock market, to recover. In turn, this could make households even more optimistic, leading to 

further  increases  in  spending.  All  these  imply  that  if  policy  makers  could  steer  households’ 

expectations  about  asset  prices  into  a  positive  direction,  then this  could  generate  a  virtuous  circle 

that could help the economy get back on track faster. 

ECB Working Paper 1762, March 201539 
 
 
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Appendix 

A.  Comparison  of  housing  and  equity  wealth  losses  recorded  in  HRS  and  in  other  data 

sources 

With respect to housing gains/losses, we consider households in the Survey of Consumer Finances 

(2007-2009  panel)  who  owned  their  home  in  both  waves  and  did  not  move  in-between,  and  in 

which the financial respondent was aged 50 and above. In our dataset the weighted mean (median) 

housing  loss  for  such  households  is  equal  to  -19.7%  (-18%),  while  in  the  SCF  the  corresponding 

magnitudes  are  equal  to  -13.3  (-12.2).  However,  there  is  a  difference  in  timing  between  the  two 

datasets: ours records housing losses since the summer of 2006, while the SCF since mid-2007 on 

average. If one takes into account the fact that according to the Case-Shiller US housing price index 

there  was  a  drop  in  home  prices  of  about  3.4%  between  the  second  quarter  of  2006  and  the 

corresponding quarter in 2007, and assuming that this aggregate number would have been reflected 

in  the  SCF  data  had  they  covered  2006,  then  the  reported  housing  losses  match  reasonably  well 

between the HRS Internet Survey and the SCF panel. 

As regards losses on equity, there is no variable in the SCF panel that corresponds to the one found 

in our dataset. In the SCF there is a question (named P5712) only on realized capital gains/losses on 

mutual funds combined with net gains/ losses from the possible sale of stocks, bonds and other real 

estate and without any reference to when these assets were bought or sold. On the other hand, the 

questions  in  our  dataset  are  about  capital  losses  since  September  2008  in  various  kinds  of  equity 

holdings only. Therefore, and in order to assess the quality of our measure of stock capital losses, 

we turned to aggregate data. Hence, we used data from the US Flow of Funds to calculate the losses 

on all forms of equity incurred between the third quarter of 2008 and the second quarter of 2009 by 

the US household sector and non-profit institutions (the latter cannot be separated from the former 

in  the  Flow  of  Funds).  These  losses  were  equal  to  -14.1%.  Given  that  the  Flow  Funds  records 

aggregate  data,  this  number  represents  an  average  loss  not  conditional  on  ownership.  In  addition, 

ECB Working Paper 1762, March 201546 
 
the losses recorded in the Flow of Funds are incurred by the whole population rather by only those 

aged  over  50,  as  is  the  case  in  the  HRS.  In  any  case,  the  measure  of  stock  capital  losses  in  our 

dataset  that  most  closely  corresponds  to  the  one  from  the  Flow  of  Funds  is  the  unconditional 

weighted  mean  loss,  which  is  equal  to  about  -18%.  Hence,  we  again  conclude  that  the  variable 

denoting stock capital losses in our data records these losses reasonably well.  

B. Calculation of magnitudes of interest via Monte Carlo simulation 

Given  that  marginal  effects,  elasticities,  and  marginal  propensities  to  consume  are  nonlinear 

functions of the estimated parameters  ˆ, we compute their point estimates and standard errors via 

Monte Carlo simulation (Train, 2003) by using the formula: 

(
gE

(



))



g

(
(


d

)

)

f

(B.1) 

where 

g   denotes the magnitude of interest and 

(

)

f  the joint distribution of all the elements in 
(

)

. We implement this simulation estimator by drawing 1,000 times from the joint distribution of the 

estimated vector of parameters  ˆ under the assumption that it is asymptotically normal with mean 

and  variance-covariance  matrix  equal  to  the  maximum  likelihood  estimates.  Then,  for  a  given 

parameter  draw j we generate  the  magnitude of interest 

ˆ(
g  .  We  first  calculate  this  magnitude 

)j

for  each  household  in  our  sample,  and  then  calculate  the  average  (median)  marginal  effect  as  the 

average (median) of the effect across all households in our sample. We then estimate 

E g   and 
))

( (

its standard error as the mean and standard deviation, respectively, of the distribution of 

ˆ(
)j
g  over 

all parameter draws. 

C. Simulations from a Permanent Income (PIH) and a Buffer Stock (BSM) Model 

In  both  models  households  maximize  the  expected  value  of  a  standard  intertemporally  additive 

CRRA utility function over a finite time horizon, i.e. 

ECB Working Paper 1762, March 201547 
 
 
                                                            
                                              
 
E

0

T



t



0






1
t C
t
1 


(C.1) 

where  β  denotes  the  discount  rate  and  σ  the  coefficient  of  relative  risk  aversion  (assumed  to  be 

equal to 0.96 and 2, respectively). Households survive with probability 1 till period T, and then all 

perish. In our simulations we will assume T=100. 

End-of-period wealth W evolves according to the law of motion 

~

WRW

t

t



CY


t

t

1

(C.2) 

~
where  Y denotes  labor  income  and  C  denotes  consumption.  The  rate  of  return  on  wealth  R

 is  the 

sum of a deterministic component  R  and a stochastic component lt, i.e.  

~
R
t



R

t



l

t

(C.3) 

The  stochastic  component  is  meant  to  capture  shocks  to  wealth,  which,  in  our  case,  would  mean 

capital  gains/losses.  We  calibrate  stochastic  process  for  lt using  data  from  the  US  Flow  of  Funds. 

More specifically, we calculate the real capital gains on all forms of equity as a percentage of the 

value of such equity, and it turns out that for the period 1952-2010 this series has a mean of 0.0243 

and a standard deviation of 0.08. As an alternative, we calculate for the same period the weighted 

average of the capital gains on equity and residential real estate, with the weights being equal to the 

share  of  each  asset  in  the  sum  of  equity  and  housing  wealth.  This  second  series  has  a  mean  of 

0.0185 and a standard deviation of 0.0653. Both series exhibit very little autocorrelation, and thus 

we model both of them as normal i.i.d. variables. We assume that the deterministic component of 

the rate of return  R is equal to 1.02. 

Labor income Y is equal to a permanent component P multiplied by a transitory shock ξ, i.e. 

Y
t P

t
t

(C.4) 

and  the  permanent  component  P  grows  deterministically  at  the  rate  G  and  is  also  subject  to  a 

permanent shock η , i.e. 

PGP

t
t

t

1
t

(C.5) 

ECB Working Paper 1762, March 201548 
 
  
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
 
 
 
 
We  assume  that  both ξ and η  are  distributed  lognormally, and  the  parameters  of  their  distribution 

are calibrated as in Coco, Gomez and Maenhout (2005). The deterministic rate of income growth G 

is modeled as a step function depending on age, as in Carroll (1997). 

The crucial condition that differentiates a BSM after Deaton (1991) from the PIH is the existence of 

a non-negativity constraint on wealth, i.e.  

  0 tW
t

(C.6) 

In  Deaton’s  (1991)  this  non-negativity  constraint  generates  buffer  stock  saving.  In  our  baseline 

simulations the only difference between the BSM and the PIH will be this non-negativity constraint. 

After defining cash-on-hand X as the sum of  end-of-previous-period wealth, its associated capital 

income and labor income, i.e. 

X

t



~
WR
t
t



Y
t

1

one can write the Bellman equation of the household’s optimization problem as 


V X P
t

,

t

t





max
C
t

1



C
t

1








E V
t
t

1




X

,

P
t

1




t

1






subject to 

and, for the BSM model, 

X

t



1



~
R
t



X

t



1



C

t





Y
t



1

(C.7) 

(C.8) 

(C.9) 

t CX
 t

  0 
t

(C.10) 

Following Carroll (2006), and in order to reduce the number of state variables, we reformulate the 

household’s optimization problem by normalizing various variables by the permanent income P and 

then solve the dynamic problem using the endogenous grid method as suggested by Carroll. 

D. The Papke-Wooldridge fractional variable model 

In the PW model the mean of the dependent variable conditional on the regressors X is assumed to 

be  equal to  G(Xβ), where G denotes a function  the range of  which  matches that of the dependent 

ECB Working Paper 1762, March 201549 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
variable, and β a vector of parameters. The usual practice for variables that lie in [0,1] is to use the 

cumulative statistical  distribution  as  the form  of G. In  our  case,  and  since  our  dependent  variable 

denoting percentage changes in consumption lies in [-1,1], we rescale it to lie in [0,1] by adding one 

to it and then multiplying it by one half. This linear transformation of the dependent variable simply 

results in a rescaling of the estimated coefficients and does not affect the results in any way. Having 

thus  transformed  our  dependent  variable,  we  choose  the  cumulative  standard  normal  function  to 

model G. 

PW  use  a  quasi-maximum  likelihood  estimation  strategy  that,  under  the  assumption  that  the 

dependent  variable  has  G(Xβ)  as  a  conditional  mean,  results  in  consistent  estimates  (Gourieroux, 

Monfort and Trognon, 1984). The quasi ML estimation needs to be performed by using a member 

of  the  linear  exponential  family  of  distributions,  and  we  follow  PW  in  choosing  the  Bernoulli 

distribution. Hence, the log likelihood of a household i reporting a percentage change yi is given by: 

l y
(
i

)



y
i

ln



G X
(

)


i



(1
 

y
i


) ln 1



G X
(

)


i



                                (D.1) 

The  quasi  ML  approach  proposed  by  PW  has  been  found  to  perform  very  well  in  estimation 

problems  involving  fractional  variables  (Kieschnick  and  McCullough,  2003)  and  requires  no 

additional assumptions about other features of the data generating process (e.g. about the variance 

of the errors, which are heteroskedastic as the conditional mean approaches zero or one). Therefore, 

standard errors of the estimates need to be corrected for possible misspecifications of the likelihood, 

and hence we obtain them by using 500 bootstrap replications. As the PW model is a nonlinear one, 

we calculate the marginal effects and their standard errors as described in Appendix B above.  

ECB Working Paper 1762, March 201550 
 
                                         
 
Figure 1. Capital gains and saving, 1990-2010  

1995

2000

2005

2010

100%

50%

0%

1990

‐50%

‐100%

‐150%

Cap. Gains/Disposable Income

Saving/Disposable Income

Notes: Saving and disposable income as measured in the National Income and Product Accounts. The values of 
capital gains/losses in housing and risky financial assets are taken from the Flow of Funds of the United States. 
In order to compute capital gains on housing, we use data on capital gains on real estate owned by households 
and non-profit institutions (Table R.100, line 10). Table R.100 does not break down these capital gains/losses by 
residential and non-residential real estate, and the Flow of Funds does not provide separate data on capital gains 
for non-profit institutions. Therefore, our calculations rest on the assumptions that percentage capital gains/losses 
on  residential  real  estate  are  similar  to  those  on  non-residential  real  estate,  and  that  non-profit  institutions 
experienced  roughly  the  same  capital  losses  on  real  estate  (in  percentage  terms)  as  households.  In  order  to 
compute  the  percentage  capital  losses  in  housing  we  divide  the  accumulated  capital  losses  from  2006Q3  to 
2009Q2  with  the  value  of  real  estate  owned  by  households  and  non-profit  institutions  at  the  end  of  2006Q2 
(Table B.100, line 3).  
Our  data  on  financial  capital  gains  and  losses  come  from  the  capital  gains  on  corporate  equities,  mutual  fund 
shares, equity in non-corporate business and life insurance and pension fund reserves as recorded in Table R.100 
(lines 11-14). In order to compute the percentage capital losses in risky financial assets we cumulate the changes 
in asset values from 2008Q3 to 2009Q2, and then divide them by the sum of the values of corporate equities, 
mutual fund shares, life insurance reserves, pension fund reserves, and equity in non-corporate business at the 
end of 2008Q2, as recorded in Table B.100 (lines 24, 25, 27-29).  
Source: Board of Governors of the Federal Reserve System (2011), BEA (2011).  

ECB Working Paper 1762, March 201551 
 
 
 
 
 
Figure 2. Growth rates of consumption and of the value of assets 

Note: The bins are constructed by first dividing the range of values of the capital gains into intervals with a width of 5 
percentage  points,  except  for  values  denoting  very  heavy  stock  capital  losses  (worse  than  -50%),  for  which  the 
interval width was 10 percentage points due to the low number of observations exhibiting such values. Subsequently, 
we calculated the mean capital gain and consumption growth over all observations in each interval, and plotted the 
latter against the former. 

ECB Working Paper 1762, March 201552 
 
 
Table 1. Changes in consumption and capital gains 

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Percentage Change in Consumption (Unconditional)

Qualitative Change in Consumption

25th 
quantile

50th 
quantile

75th 
quantile

Mean

Lower

Same

Higher

‐0.150

‐0.150

‐0.100

‐0.050

‐0.150

‐0.100

‐0.100
‐0.050

0.000

0.000

0.000

0.000

0.000

0.000

0.000
0.000

Panel A. Housing
0.000

‐0.052

0.000

0.000

0.000

‐0.060

‐0.043

‐0.028

Panel B. Total Financial Assets

0.000

0.000

0.000
0.000

‐0.072

‐0.053

‐0.045
‐0.033

0.298

0.314

0.239

0.209

0.288

0.256

0.234
0.215

0.482

0.523

0.579

0.575

0.554

0.557

0.557
0.544

0.220

0.163

0.182

0.217

0.157

0.188

0.208
0.240

Gains in 
Assets

1st quartile
2nd quartile
3d quartile
4th quartile

1st level
2nd level
3d level
4th level

Notes: The 4th level of gains in financial assets denotes zero or positive appreciation. The remaining three levels denote 
the terciles of financial losses (e.g., the 1st level denotes the largest losses). All figures are calculated using sampling 
weights from the 2008 HRS main survey (the 2009 Internet Survey does not contain any sampling weights). 
Source: 2009 HRS Internet survey, 2008 HRS main survey.  

ECB Working Paper 1762, March 201553 
 
 
 
 
Table 2. Capital losses in housing and financial assets 

(1)

(2)

(3)

(4)

(5)

(6)

Asset

Ownership 
Prevalence

Prevalence of 
Losses, 
Conditional on 
Ownership

Quantiles of Losses, Conditional on Having Any Losses

25th 
quantile

50th 
quantile

75th 
quantile

Mean

Main Residence
Financial Assets

Employer‐Provided 
Pension Plans
Individual Retirement 
Accounts
Mutual Funds
Directly Held Stocks
Trusts 
Other Assets Invested in 
Stocks

Panel A. Main Residence and All Financial Assets
0.893
0.692

‐0.250
‐0.357

0.537
0.944

‐0.180
‐0.275

‐0.111
‐0.176

‐0.197
‐0.275

Panel B. Financial Assets in Detail

0.402

0.406

0.443
0.321
0.104

0.245

0.878

‐0.400

‐0.300

‐0.200

‐0.304

0.921

0.917
0.839
0.807

0.730

‐0.400

‐0.400
‐0.400
‐0.330

‐0.330

‐0.300

‐0.300
‐0.250
‐0.250

‐0.205

‐0.200

‐0.200
‐0.175
‐0.150

‐0.125

‐0.310

‐0.297
‐0.308
‐0.256

‐0.254

Note:  Lower quantiles of losses denote larger losses (more negative gains). All figures are calculated using sampling 
weights from the 2008 HRS main survey (the 2009 Internet survey does not contain any sampling weights). 

Source: 2009 HRS Internet survey, 2008 HRS main survey. 

ECB Working Paper 1762, March 201554 
 
 
 
 
Table 3. Demographics and economic characteristics in the sample 

Variable

Statistic

Age 
Household Size 
Becomes Unemployed Between 2008 and 2009
Becomes Retired Between 2008 and 2009
Health Deterioration Between 2008 and 2009
Couple
High School Education 
More than High School 
Self‐reported Health Fair or Bad 
Numeracy Score (max. 5)
Working 
Retired 
White 
Household net real assets (median)
Household net financial assets (median)
Household income (median)

62.60
2.20
0.05
0.11
0.07
0.75
0.50
0.49
0.25
4.56
0.61
0.34
0.90
193,108
81,799
70,034

Notes:  Figures  reflect  average  age,  household  size,  numeracy  score,  and 
median  net  real  and  financial  assets  and  household  income.  The  remaining 
figures denote prevalence. All figures are calculated using sampling weights 
from the 2008 HRS main survey (the 2009 Internet Survey does not contain 
any sampling weights).All magnitudes are measured at the household level as 
discussed in the text.  
Source: 2009 HRS Internet Survey, 2008 HRS main survey. 

ECB Working Paper 1762, March 201555 
 
 
 
 
 
Table 4. Elasticities and marginal propensities to consume  

Variable

Model 1

Model 2

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Age/100
Household Size
Becomes Unemployed
Becomes Retired
Health Deterioration
Household Net Worth (IHS)
Household Income (IHS)
Couple
High School Education
More than High School
Bad Health 
Numeracy Score
Working
Retired
White
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets
Constant

Panel A. Regression Estimates

0.3515 0.0540 ***
0.0084 0.0049 *

0.3302 0.0550 ***
0.0094 0.0050 *
‐0.1014 0.0277 ***
‐0.0267 0.0123 **
‐0.0123 0.0174

0.3160 0.0569 ***
0.0091 0.0050 *
‐0.1018 0.0277 ***
‐0.0278 0.0124 **
‐0.0113 0.0175
0.0019 0.0013
0.0025 0.0048

0.2416 0.0713 ***
0.0065 0.0053
‐0.0990 0.0277 ***
‐0.0241 0.0128 *
‐0.0143 0.0176
0.0020 0.0014
0.0057 0.0050
0.0189 0.0137
‐0.0215 0.0443
‐0.0359 0.0446
‐0.0008 0.0112
‐0.0105 0.0063 *
‐0.0077 0.0253
0.0148 0.0245
‐0.0127 0.0180

0.0578 0.0305 *

0.0537 0.0307 *

0.0572 0.0309 *

0.0541 0.0308 *

0.0887 0.0277 ***

0.0862 0.0279 ***

0.0997 0.0290 ***

0.0838 0.0294 ***

‐0.2564 0.0410 ***

‐0.2363 0.0412 ***

‐0.2761 0.0727 ***

‐0.1939 0.0895 **

Number of Observations

1,915

1,883

1,883

1,881

Implied Marginal Propensity to 
Consume with Respect to the Value 
of the Main Residence
Implied Marginal Propensity to 
Consume with Respect to the Value 
of Financial Assets

Panel B. Marginal Propensities to Consume 

0.0094 0.0050 *

0.0090 0.0051 *

0.0094 0.0052 *

0.0091 0.0050 *

0.0323 0.0102 ***

0.0319 0.0107 ***

0.0370 0.0110 ***

0.0321 0.0112 ***

Notes: The implied marginal propensity to consume out of the value of the main residence and out of financial assets is 
computed  as  the  corresponding  elasticity  (which  is  equal  to  the  regression  coefficient)  divided  by  the  ratio  of  the 
associated  asset  to  total  expenditure.  This  ratio  is  computed  using  information  recorded  in  the  main  HRS  surveys of 
2006 and 2008, and in the CAMS surveys of 2007 and 2009.*,**,*** denote statistical significance at 10%, 5% and 1%, 
respectively. We calculate robust standard errors. 

ECB Working Paper 1762, March 201556 
 
 
 
 
Table 5. Categorical change in consumption 

Variable

Model 1

Model 2

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Becomes Unemployed
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets

Becomes Unemployed
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets

Becomes Unemployed
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets

Probability that Consumption is lower

‐..‐

‐..‐

0.2107 0.0531 ***

0.2128 0.0534 ***

0.2063 0.0525 ***

‐0.0153 0.0068 **

‐0.0149 0.0069 **

‐0.0151 0.0070 *

‐0.0146 0.0067 *

‐0.0240 0.0063 ***

‐0.0227 0.0064 ***

‐0.0246 0.0066 ***

‐0.0204 0.0066 ***

Probability that Consumption is the same

‐..‐

‐..‐

‐0.0720 0.0303 **

‐0.0732 0.0309 **

‐0.0691 0.0295 **

‐0.0006 0.0008

‐0.0008 0.0008

‐0.0008 0.0009

‐0.0008 0.0008

‐0.0015 0.0013

‐0.0017 0.0013

‐0.0020 0.0014

‐0.0014 0.0012

Probability that Consumption is higher

‐..‐

‐..‐

‐0.1387 0.0241 ***

‐0.1396 0.0238 ***

‐0.1373 0.0243 ***

0.0159 0.0073 **

0.0156 0.0075 **

0.0159 0.0075 **

0.0153 0.0072 **

0.0255 0.0072 ***

0.0244 0.0073 ***

0.0266 0.0075 ***

0.0218 0.0073 ***

Number of Observations

1,940

1,907

1,907

1,905

Notes:  Marginal  effects  of  the  percentage  changes  in  the  values  of  the  main  residence  and  of  financial  assets  are 
computed after assuming a change of 15 pp in the two underlying variables. *,**,*** denote statistical significance at 
10%, 5% and 1%, respectively. We calculate robust standard errors. 

ECB Working Paper 1762, March 201557 
 
 
 
 
Table 6. Elasticities of consumption obtained using disaggregated financial assets 

Variable

Model 1

Model 2

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Age/100
Household Size
Becomes Unemployed
Becomes Retired
Health Deterioration
Household Net Worth (IHS)
Household Income (IHS)
Couple
High School Education
More than High School
Bad Health 
Numeracy Score
Working
Retired
White
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Employer‐Provided Pension Plans

0.3255 0.0514 ***
0.0065 0.0047

0.3049 0.0523 ***
0.0077 0.0048
‐0.1005 0.0263 ***
‐0.0161 0.0116
‐0.0041 0.0170

0.2882 0.0535 ***
0.0073 0.0048
‐0.1008 0.0263 ***
‐0.0172 0.0116
‐0.0028 0.0170
0.0022 0.0013 *
0.0028 0.0046

0.2378 0.0670 ***
0.0051 0.0051
‐0.0994 0.0263 ***
‐0.0145 0.0121
‐0.0037 0.0172
0.0024 0.0013 *
0.0060 0.0049
0.0142 0.0129
‐0.0177 0.0435
‐0.0321 0.0439
0.0052 0.0103
‐0.0107 0.0058 *
‐0.0029 0.0242
0.0127 0.0235
‐0.0163 0.0167

0.0704 0.0285 **

0.0665 0.0289 **

0.0692 0.0290 **

0.0668 0.0288 **

0.0107 0.0274

0.0126 0.0277

0.0171 0.0282

0.0119 0.0283

Percentage Change in Value of IRAs

0.0372 0.0277

0.0329 0.0275

0.0417 0.0276

0.0316 0.0274

Percentage Change in Value of 
Mutual Funds
Percentage Change in Value of 
Stocks Directly Held
Percentage Change in Value of 
Trusts 
Percentage Change in Value of 
Other Assets Invested in Stocks
Constant

0.0208 0.0288

0.0136 0.0286

0.0218 0.0288

0.0179 0.0289

0.0880 0.0252 ***

0.0776 0.0251 ***

0.0830 0.0252 ***

0.0785 0.0254 ***

‐0.0181 0.0403

‐0.0029 0.0413

‐0.0008 0.0414

‐0.0014 0.0421

0.0052 0.0345

0.0100 0.0352

0.0103 0.0353

0.0044 0.0349

‐0.2319 0.0387 ***

‐0.2158 0.0391 ***

‐0.2608 0.0680 ***

‐0.1959 0.0837 **

Number of Observations

2,235

2,193

2,193

2,191

Notes:  *,**,*** denote statistical significance at 10%, 5% and 1%, respectively. We calculate robust standard errors. 

ECB Working Paper 1762, March 201558 
 
 
 
 
 
Table 7. Elasticity of consumption to wealth, estimated using simulated data 

from the buffer-stock and permanent income models 

Specification

Capital Gains in Equity

Weighted Average of 
Capital Gains in 
Residential Real Estate 
and Equity

Elasticity

Std. Error

Elasticity

Std. Error

Buffer‐stock Model

Without interactions of capital gains 
with wealth and income
With interactions of capital gains 
with wealth and income

0.0986 0.0085 ***

0.0838 0.0123 ***

0.0963 0.0075 ***

0.0840 0.0108 ***

Permanent Income Model

Without interactions of capital gains 
with wealth and income
With interactions of capital gains 
with wealth and income

0.0804 0.0016 ***

0.0787 0.0020 ***

0.0822 0.0009 ***

0.0786 0.0015 ***

Note:  With  no  interactions  of  capital  gains  with  income  and  wealth,  the  elasticity  is  equal  to  the  regression 
coefficient of the capital gains variable. With interactions, the elasticity is equal to the average marginal effect 
of  the  capital  gains  variable,  which  is  equal  to  the  total  derivative  of  the  percentage  growth  in  consumption 
with respect  to  capital  gains,  taking  into account  all  interaction  terms  and  averaging across all  sample  units. 
*,**,*** denote statistical significance at 10%, 5% and 1%, respectively. 

ECB Working Paper 1762, March 201559 
 
 
 
 
 
 
Table 8. Consumption and changes in expectations about the stock market 

 between the 2008 and 2009 surveys 

Variable

Model 1

Model 2

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Panel A1. Negative or zero change in the reported probability of a rise in stock prices 

Percentage Change in Value of 
Financial Assets
Number of Observations

Percentage Change in Value of 
Financial Assets
Number of Observations

0.1192 0.0359 ***

0.1129 0.0357 ***

0.1291 0.0363 ***

0.1214 0.0367 ***

1,015

1,001

1,001

1,000

Panel A2. Positive change in the reported probability of a rise in stock prices 

0.0739 0.0537

0.0799 0.0546

0.0643 0.0576

0.0665 0.0555

483

473

473

472

Panel B1. Reported probability in 2008 of a rise in stock prices equal to .5 or lower

Percentage Change in Value of 
Financial Assets
Number of Observations

Percentage Change in Value of 
Financial Assets
Number of Observations

0.1390 0.0394 ***

0.1403 0.0394 ***

0.1369 0.0414 ***

0.1195 0.0414 ***

916

904

904

903

Panel B2. Reported probability in 2008 of a rise in stock prices higher than .5 

0.0378 0.0398

0.0335 0.0399

0.0536 0.0406

0.0496 0.0414

765

748

748

747

Notes:  *,**,*** denote statistical significance at 10%, 5% and 1%, respectively. We calculate robust standard errors. 

ECB Working Paper 1762, March 201560 
 
 
 
 
 
Table 9. Changes in consumption using quartiles of changes in asset values 

Variable

Model 1

Model 2

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Age/100
Household Size
Becomes Unemployed
Becomes Retired
Health Deterioration
Household Income (IHS)
Household Net Worth (IHS)
Couple
High School Education
More than High School
Bad Health 
Numeracy Score
Working
Retired
White
2n d Quartile of Percentage Change 
in Value of the Main Residence
3d Quartile of Percentage Change 
in Value of the Main Residence
4th Quartile of Percentage Change 
in Value of the Main Residence
2n d Level of Percentage Change in 
Value of Financial Assets
3d Level of Percentage Change in 
Value of Financial Assets
4th Level of Percentage Change in 
Value of Financial Assets
Constant

0.3525 0.0543 ***
0.0082 0.0049 *

0.3311 0.0554 ***
0.0093 0.0050 *
‐0.1008 0.0277 ***
‐0.0263 0.0124 **
‐0.0134 0.0174

0.3136 0.0572 ***
0.0089 0.0050 *
‐0.1017 0.0278 ***
‐0.0273 0.0124 **
‐0.0123 0.0174
0.0020 0.0014
0.0026 0.0048

0.2386 0.0716 ***
0.0065 0.0053
‐0.0982 0.0278 ***
‐0.0232 0.0129 *
‐0.0151 0.0175
0.0021 0.0014
0.0058 0.0050
0.0177 0.0137
‐0.0227 0.0442
‐0.0388 0.0446
0.0011 0.0114
‐0.0105 0.0064
‐0.0076 0.0254
0.0159 0.0245
‐0.0141 0.0181

0.0023 0.0154

0.0032 0.0155

0.0023 0.0156

0.0039 0.0155

0.0216 0.0152

0.0212 0.0155

0.0214 0.0155

0.0234 0.0155

0.0307 0.0138 **

0.0305 0.0138 **

0.0327 0.0138 **

0.0329 0.0137 **

0.0260 0.0118 **

0.0254 0.0118 **

0.0247 0.0118 **

0.0234 0.0119 **

0.0378 0.0117 ***

0.0373 0.0117 ***

0.0371 0.0117 ***

0.0358 0.0117 ***

0.0346 0.0126 ***

0.0331 0.0126 ***

0.0401 0.0134 ***

0.0305 0.0137 **

‐0.3216 0.0422 ***

‐0.3000 0.0427 ***

‐0.3460 0.0753 ***

‐0.2537 0.0921 ***

Number of Observations

1,915

1,883

1,883

1,881

Notes:  *,**,*** denote statistical significance at 10%, 5% and 1%, respectively. We calculate robust standard errors. 

ECB Working Paper 1762, March 201561 
 
 
 
 
 
Table 10. Elasticities and marginal propensities to consume, PSID data 

Variable

Model 1

Model 2

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Age/100
Household Size
Becomes Unemployed
Becomes Retired
Health Deterioration
Household Net Worth (IHS)
Household Income (IHS)
Couple
High School Education
More than High School
Bad Health 
Working
Retired
White
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets
Constant

Panel A. Regression Estimates

‐0.0414 0.0225 *
0.0194 0.0023 ***

‐0.0673 0.0237 ***
0.0195 0.0023 ***
‐0.1117 0.0124 ***
‐0.0168 0.0141
‐0.0072 0.0196

‐0.1001 0.0247 ***
0.0159 0.0023 ***
‐0.0975 0.0124 ***
‐0.0147 0.0141
0.0031 0.0196
0.0012 0.0004 ***
0.0165 0.0028 ***

‐0.1268 0.0294 ***
0.0138 0.0027 ***
‐0.0713 0.0135 ***
‐0.0290 0.0150 *
‐0.0159 0.0270
0.0011 0.0004 ***
0.0125 0.0033 ***
0.0110 0.0087
‐0.0121 0.0118
‐0.0137 0.0114
0.0250 0.0199
0.0555 0.0126 ***
0.0678 0.0163 ***
‐0.0084 0.0073

0.0482 0.0200 **

0.0448 0.0199 **

0.0554 0.0200 ***

0.0558 0.0201 ***

0.0219 0.0074 ***

0.0233 0.0074 ***

0.0257 0.0074 ***

0.0256 0.0074 ***

‐0.0680 0.0141 ***

‐0.0452 0.0145 ***

‐0.2163 0.0345 ***

‐0.1922 0.0368 ***

Number of Observations

10,686

10,580

10,580

10,488

Implied Marginal Propensity to 
Consume with Respect to the Value 
of the Main Residence
Implied Marginal Propensity to 
Consume with Respect to the Value 
of Financial Assets

Panel B. Marginal Propensities to Consume 

0.0083 0.0034 **

0.0077 0.0034 **

0.0095 0.0034 ***

0.0095 0.0034 ***

0.0094 0.0032 ***

0.0100 0.0032 ***

0.0110 0.0032 ***

0.0109 0.0031 ***

Notes: The implied marginal propensity to consume out of the value of the main residence and out of financial assets is 
computed  as  the  corresponding  elasticity  (which  is  equal  to  the  regression  coefficient)  divided  by  the  ratio  of  the 
associated  asset  to  total  expenditure.  *,**,***  denote  statistical  significance  at  10%,  5%  and  1%,  respectively.  We 
calculate robust standard errors. 

ECB Working Paper 1762, March 201562 
 
 
 
 
Table A1. Elasticities of consumption obtained after disaggregrating net worth, with the 

percentage change in consumption as the dependent variable 

Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Aggregated Financial Assets

Model 3

Model 4

Disaggregated Financial Assets

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Age/100
Household Size
Becomes Unemployed
Becomes Retired
Health Deterioration
Gross Financial Assets (IHS)
Gross Real Assets (IHS)
Total Debts (IHS)
Household Income (IHS)
Couple
High School Education
More than High School
Bad Health 
Numeracy Score
Working
Retired
White
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets
Percentage Change in Value of 
Employer‐Provided Pension Plans

Percentage Change in Value of IRAs

Percentage Change in Value of 
Mutual Funds
Percentage Change in Value of 
Stocks Directly Held
Percentage Change in Value of 
Trusts 
Percentage Change in Value of 
Other Assets Invested in Stocks
Constant

0.3029 0.0602 ***
0.0090 0.0051 *
‐0.1007 0.0276 ***
‐0.0277 0.0123 **
‐0.0108 0.0174
‐0.0020 0.0020
0.0026 0.0027
‐0.0024 0.0008 ***
0.0054 0.0049

0.2348 0.0732 ***
0.0063 0.0054
‐0.0973 0.0276 ***
‐0.0248 0.0128 *
‐0.0149 0.0175
‐0.0013 0.0021
0.0023 0.0027
‐0.0022 0.0008 **
0.0071 0.0051
0.0223 0.0140
‐0.0161 0.0446
‐0.0286 0.0451
‐0.0036 0.0113
‐0.0102 0.0063
‐0.0054 0.0254
0.0151 0.0245
‐0.0087 0.0178

0.2661 0.0569 ***
0.0074 0.0049
‐0.1000 0.0262 ***
‐0.0172 0.0116
‐0.0028 0.0170
‐0.0014 0.0019
0.0021 0.0026
‐0.0026 0.0008 ***
0.0057 0.0048

0.2215 0.0690 ***
0.0051 0.0052
‐0.0979 0.0262 ***
‐0.0153 0.0121
‐0.0044 0.0171
‐0.0005 0.0020
0.0020 0.0026
‐0.0025 0.0008 ***
0.0072 0.0050
0.0175 0.0131
‐0.0126 0.0439
‐0.0254 0.0445
0.0033 0.0103
‐0.0107 0.0058 *
0.0002 0.0243
0.0129 0.0235
‐0.0134 0.0165

0.0520 0.0312 *

0.0507 0.0310

0.0639 0.0289 **

0.0631 0.0286 **

0.0830 0.0299 ***

0.0740 0.0299 **

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

‐..‐

0.0110 0.0281

0.0087 0.0282

0.0322 0.0280

0.0256 0.0278

0.0177 0.0290

0.0168 0.0291

0.0804 0.0254 ***

0.0776 0.0256 ***

0.0013 0.0416

0.0013 0.0422

0.0085 0.0349

0.0053 0.0346

‐0.2739 0.0736 ***

‐0.1951 0.0908 **

‐0.2466 0.0689 ***

‐0.1845 0.0851 **

Number of Observations

1,883

1,881

2,193

2,191

Notes: The coefficients of IHS-transformed variables denote elasticities. *,**,*** denote statistical significance at 10%, 
5% and 1%, respectively. We calculate robust standard errors. 

ECB Working Paper 1762, March 201563 
 
 
 
Table A2. Elasticities of consumption obtained after disaggregrating  

net worth, with the categorical change in consumption 

 as the dependent variable 

Variable

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Probability that Consumption is lower

Becomes Unemployed
Gross Financial Assets (IHS)
Gross Real Assets (IHS)
Total Debts (IHS)
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets

0.2086 0.0529 ***
0.0010 0.0030
‐0.0010 0.0016
0.0108 0.0050 **

0.2014 0.0522 ***
‐0.0003 0.0031
‐0.0002 0.0016
0.0104 0.0050 **

‐0.0140 0.0069 **

‐0.0135 0.0073 *

‐0.0225 0.0068 ***

‐0.0188 0.0068 ***

Probability that Consumption is the same

Becomes Unemployed
Gross Financial Assets (IHS)
Gross Real Assets (IHS)
Total Debts (IHS)
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets

‐0.0708 0.0302 **
0.0002 0.0003
0.0000 0.0003
0.0014 0.0009

‐0.0670 0.0289 **
0.0000 0.0004
0.0001 0.0003
0.0013 0.0008

‐0.0007 0.0009

‐0.0006 0.0008

‐0.0016 0.0013

‐0.0011 0.0010

Probability that Consumption is higher

Becomes Unemployed
Gross Financial Assets (IHS)
Gross Real Assets (IHS)
Total Debts (IHS)
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Financial Assets

‐0.1378 0.0242 ***
‐0.0011 0.0032
0.0010 0.0018
‐0.0122 0.0057 **

‐0.1344 0.0244 ***
0.0002 0.0034
0.0000 0.0018
‐0.0117 0.0057 **

0.0147 0.0074 **

0.0141 0.0078 *

0.0241 0.0076 ***

0.0199 0.0075 ***

Number of Observations

1,907

1,905

Notes:  Marginal  effects  of  the  percentage  changes  in  the  values  of  the  main 
residence and of financial assets are computed after assuming a change of 15 pp 
in the two underlying variables. Marginal effects of the variables denoting gross 
financial assets, gross real assets and total debts are computed after assuming a 
change  of  10,000  dollars  in  all  three  underlying  variables.  *,**,***  denote 
statistical  significance  at  10%,  5%  and  1%,  respectively.  We  calculate  robust 
standard errors. 

ECB Working Paper 1762, March 201564 
 
 
 
 
Table A3. Categorical changes in consumption using quartiles of changes in asset values 

Variable

Model 1

Model 2

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Becomes Unemployed
2nd Quartile of Percentage Change in 
Value of the Main Residence
3d Quartile of Percentage Change in 
Value of the Main Residence
4th Quartile of Percentage Change in 
Value of the Main Residence
2nd Level of Percentage Change in 
Value of Financial Assets
3d Level of Percentage Change in 
Value of Financial Assets
4th Level of Percentage Change in 
Value of Financial Assets

Becomes Unemployed
2nd Quartile of Percentage Change in 
Value of the Main Residence
3d Quartile of Percentage Change in 
Value of the Main Residence
4th Quartile of Percentage Change in 
Value of the Main Residence
2nd Level of Percentage Change in 
Value of Financial Assets
3d Level of Percentage Change in 
Value of Financial Assets
4th Level of Percentage Change in 
Value of Financial Assets

Becomes Unemployed
2nd Quartile of Percentage Change in 
Value of the Main Residence
3d Quartile of Percentage Change in 
Value of the Main Residence
4th Quartile of Percentage Change in 
Value of the Main Residence
2nd Level of Percentage Change in 
Value of Financial Assets
3d Level of Percentage Change in 
Value of Financial Assets
4th Level of Percentage Change in 
Value of Financial Assets

Probability that Consumption is lower

‐..‐

‐..‐

0.2067 0.0531 ***

0.2095 0.0534 ***

0.2022 0.0524 ***

0.0230 0.0300

0.0234 0.0305

0.0242 0.0310

0.0199 0.0295

‐0.0197 0.0298

‐0.0170 0.0297

‐0.0171 0.0292

‐0.0204 0.0294

‐0.0563 0.0233 **

‐0.0526 0.0240 **

‐0.0549 0.0238 *

‐0.0556 0.0247 *

‐0.0620 0.0243 **

‐0.0605 0.0239 **

‐0.0601 0.0239 *

‐0.0537 0.0232 *

‐0.0894 0.0247 ***

‐0.0824 0.0244 ***

‐0.0843 0.0252 ***

‐0.0755 0.0245 ***

‐0.0670 0.0225 ***

‐0.0642 0.0228 ***

‐0.0708 0.0248 ***

‐0.0524 0.0240 *

Probability that Consumption is the same

‐..‐

‐..‐

‐0.0697 0.0302 **

‐0.0714 0.0305 **

‐0.0670 0.0294 **

‐0.0042 0.0060

‐0.0038 0.0057

‐0.0042 0.0062

‐0.0033 0.0057

0.0021 0.0039

0.0017 0.0037

0.0017 0.0039

0.0020 0.0038

0.0016 0.0038

0.0009 0.0036

0.0009 0.0037

0.0012 0.0036

0.0079 0.0048 *

0.0066 0.0045

0.0074 0.0049

0.0050 0.0041

0.0053 0.0054

0.0043 0.0053

0.0054 0.0055

0.0027 0.0048

0.0079 0.0047 *

0.0066 0.0045

0.0072 0.0048

0.0052 0.0040

Probability that Consumption is higher

‐..‐

‐..‐

‐0.1370 0.0243 ***

‐0.1381 0.0242 ***

‐0.1352 0.0243 ***

‐0.0188 0.0245

‐0.0196 0.0255

‐0.0200 0.0254

‐0.0166 0.0243

0.0176 0.0267

0.0154 0.0270

0.0153 0.0262

0.0184 0.0266

0.0547 0.0210 ***

0.0517 0.0220 **

0.0541 0.0216 **

0.0545 0.0226 **

0.0542 0.0211 **

0.0539 0.0212 **

0.0527 0.0208 **

0.0488 0.0207 **

0.0841 0.0234 ***

0.0781 0.0233 ***

0.0790 0.0236 ***

0.0728 0.0234 ***

0.0591 0.0192 ***

0.0576 0.0197 ***

0.0637 0.0216 ***

0.0472 0.0210 **

Number of Observations

1,940

1,907

1,907

1,905

Notes:  Marginal  effects  of  the  percentage  changes  in  the  values  of  the  main  residence  and  of  financial  assets  are 
computed after assuming a change of 15 pp in the two underlying variables. *,**,*** denote statistical significance at 
10%, 5% and 1%, respectively. We calculate robust standard errors. 

ECB Working Paper 1762, March 201565 
 
 
 
Table A4. Changes in consumption (categorical) using changes in the values of disaggregated 

financial assets 

Variable

Model 1

Model 2

Model 3

Model 4

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Marg. Eff.

Std. Error

Becomes Unemployed
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Employer‐Provided Pension Plans

Probability that Consumption is lower

‐..‐

‐..‐

0.2090 0.0513 ***

0.2109 0.0513 ***

0.2059 0.0510 ***

‐0.0146 0.0061 **

‐0.0136 0.0063 **

‐0.0139 0.0063 *

‐0.0137 0.0060 *

‐0.0033 0.0068

‐0.0028 0.0070

‐0.0029 0.0069

‐0.0030 0.0069

Percentage Change in Value of IRAs

‐0.0133 0.0067 **

‐0.0128 0.0070 **

‐0.0146 0.0068 *

‐0.0119 0.0072 *

Percentage Change in Value of Mutual 
Funds
Percentage Change in Value of Stocks 
Directly Held

‐0.0080 0.0075

‐0.0056 0.0077

‐0.0069 0.0075

‐0.0050 0.0076

‐0.0269 0.0064 ***

‐0.0243 0.0063 ***

‐0.0255 0.0065 ***

‐0.0251 0.0063 ***

Percentage Change in Value of Trusts 

0.0007 0.0123

‐0.0042 0.0121

‐0.0037 0.0126

‐0.0032 0.0119

Percentage Change in Value of Other 
Assets Invested in Stocks

Becomes Unemployed
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Employer‐Provided Pension Plans

0.0020 0.0095

0.0008 0.0097

0.0009 0.0096

0.0013 0.0092

Probability that Consumption is the same

‐..‐

‐..‐

‐0.0699 0.0295 **

‐0.0709 0.0292 **

‐0.0679 0.0287 **

‐0.0008 0.0008

‐0.0009 0.0008

‐0.0009 0.0008

‐0.0009 0.0007

‐0.0002 0.0004

‐0.0002 0.0004

‐0.0002 0.0004

‐0.0002 0.0004

Percentage Change in Value of IRAs

‐0.0007 0.0008

‐0.0009 0.0009

‐0.0010 0.0010

‐0.0008 0.0008

Percentage Change in Value of Mutual 
Funds
Percentage Change in Value of Stocks 
Directly Held

‐0.0004 0.0007

‐0.0004 0.0006

‐0.0004 0.0006

‐0.0003 0.0006

‐0.0024 0.0015

‐0.0023 0.0015

‐0.0025 0.0016

‐0.0024 0.0014 *

Percentage Change in Value of Trusts 

‐0.0004 0.0007

‐0.0005 0.0009

‐0.0005 0.0009

‐0.0005 0.0008

Percentage Change in Value of Other 
Assets Invested in Stocks

Becomes Unemployed
Percentage Change in Value of the 
Main Residence
Percentage Change in Value of 
Employer‐Provided Pension Plans

‐0.0002 0.0005

‐0.0002 0.0005

‐0.0002 0.0005

‐0.0002 0.0005

Probability that Consumption is higher

‐..‐

‐..‐

‐0.1392 0.0230 ***

‐0.1400 0.0233 ***

‐0.1379 0.0235 ***

0.0154 0.0066 **

0.0145 0.0068 **

0.0147 0.0069 **

0.0146 0.0065 **

0.0035 0.0070

0.0030 0.0072

0.0031 0.0071

0.0032 0.0071

Percentage Change in Value of IRAs

0.0140 0.0073 *

0.0137 0.0077 **

0.0156 0.0075 **

0.0127 0.0078

Percentage Change in Value of Mutual 
Funds
Percentage Change in Value of Stocks 
Directly Held

0.0084 0.0080

0.0059 0.0081

0.0074 0.0079

0.0053 0.0080

0.0293 0.0075 ***

0.0266 0.0074 ***

0.0280 0.0077 ***

0.0275 0.0074 ***

Percentage Change in Value of Trusts 

‐0.0004 0.0124

0.0047 0.0127

0.0042 0.0131

0.0037 0.0123

Percentage Change in Value of Other 
Assets Invested in Stocks

‐0.0018 0.0095

‐0.0006 0.0099

‐0.0007 0.0098

‐0.0011 0.0093

Number of Observations

2,267

2,223

2,223

2,221

Notes:  Marginal  effects  of  the  percentage  changes  in  the  values  of  the  main  residence  and  of  financial  assets  are 
computed after assuming a change of 15 pp in the variable of interest. *,**,*** denote statistical significance at 10%, 
5% and 1%, respectively. We calculate robust standard errors. 

ECB Working Paper 1762, March 201566