DEPARTMENT OF ECONOMICS HOUSEHOLD DEBT AND FINANCIAL ASSETS: EVIDENCE FROM GREAT BRITAIN, GERMANY AND THE UNITED STATES

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1 DEPARTMENT OF ECONOMICS HOUSEHOLD DEBT AND FINANCIAL ASSETS: EVIDENCE FROM GREAT BRITAIN, GERMANY AND THE UNITED STATES Sara Brown, University of Leicester, UK Karl Taylor, University of Leicester, UK Working Paper No. 05/5 Marc 2005

2 Houseold Debt and Financial Assets: Evidence from Great Britain, Germany and te United States Sara Brown and Karl Taylor Department of Economics University of Leicester University Road, Leicester Leicestersire LE1 7RH Great Britain Abstract: We explore te determinants of debt and financial asset accumulation at te ouseold level using survey data for Great Britain, Germany and te United States (US). Given tat debt and assets are bot components of ouseold s financial portfolio, we explore te degree of inter-dependence between ouseolds assets and liabilities by jointly modelling tese two aspects of te portfolio. Indeed, our empirical findings for bot countries support ig degree of inter-dependence between debt and asset olding. Furtermore, te nature of tis inter-dependence varies across income ranges and age groups wit te weakest correlation between financial assets and debt being found for te lowest income groups in Great Britain, suggesting tat suc groups may be particularly vulnerable to adverse financial socks. Evidence supporting inter-dependence between assets and debt no longer remains, owever, once we focus on debtors wic suggests tat ouseolds in debt may potentially face difficulties following adverse canges in teir financial situation. Key Words: Debt; Financial Assets; Houseold Financial Portfolio; Random Effects; Tobit Estimator. JEL Classification: D14, G11; H31 Acknowledgements: We are particularly grateful to te Leverulme Trust for providing financial support under grant F/00212/J. We would like to tank te Data Arcive at te University of Essex for supplying te Britis Houseold Panel Surveys 1991 to 2000; te Department of Policy and Management, Cornell University and te German Institute for Economic Researc (DIW), Berlin for supplying te German Socio Economic Panel, 1990 to 2001; and te Institute for Social Researc, University of Micigan for supplying te Panel Study of Income Dynamics 1968 to We are especially grateful to Bina Prajapati for excellent researc assistance. We are also grateful to William Greene, Dolores Garcia-Crespo and Ricardo Pagan-Rodiriguez for sending us te amended Limdep programme for te estimation of bivariate tobit models. Te normal disclaimer applies. Marc 2005

3 I Introduction Over te last decade tere as been an explosion in consumer credit on bot sides of te Atlantic. In te U.K., for example, te amount of unsecured borrowing accumulated by individuals and ouseolds, as a proportion of GDP, as more tan doubled since 1993 to 16 per cent. At te end of te tird quarter of 2003, te total amount of unsecured debt was nearly 168 billion, or more tan 4,000 for every adult of working age. Te level of ouseold debt (secured and unsecured) relative to income in te U.K. as increased from approximately 90% to 115% over te last five years (Hamilton, 2003). Similarly, for Germany, te Bundesbank as sown concern over te growt in debt (see te Bundesbank Montly Report, January 1999). By te end of 1997, ouseolds in Germany ad accumulated around 140 billion (394 billion DM) troug borrowing for consumption purposes. 1 Te US Federal Reserve as also expressed concern about debt levels revealing tat te value of consumer credit stood at nearly $135 billion by te end of 2000 an increase of around 10% on Moreover, families oldings of total outstanding debt rose by 9.6% from 1998 to Furtermore, Aizcorbe et al. (2003) found tat te growt in debt over te period 1998 to 2001 in te US was outweiged by te growt in financial assets. Alan Greenspan, Cairman of te US Federal Reserve Board, as recently commented tat unless one simultaneously considers financial assets along wit liabilities it is difficult to assess te true burden of debt. 2 1 Te accumulation of debt as also been noted in a number of oter European countries. For example, te European Central Bank (ECB) reported tat falling interest rates ave allowed ouseolds to borrow more and accumulate more debt. As a consequence, ouseold indebtedness in te euro area as increased significantly in recent years. In 2004 it was estimated at 54% of GDP. See ttp:// for te speec by Lucas Papademos, Vice-President of te ECB, delivered at te Nomura annual Euro Conference A Callenging Future for Europe, Tokyo, 11 November Remarks made by te Cairman of te Federal Reserve Broad Alan Greenspan Understanding Houseold Debt Obligations at te Credit Union National Association, Governmental Affairs Conference, Wasington, D.C. February 23,

4 Similarly, te Monetary Policy Committee in Great Britain as acknowledged te importance of establising weter te same ouseolds ave been accumulating financial assets as well as debt over recent years. As recorded in te Minutes of te Monetary Policy Committee, June 2002: te aggregate expansion of bot sides of te ouseold sector balance seet concealed a risk at a disaggregated level; to te extent tat some ouseolds were accumulating liabilities wilst oters were increasing teir assets, tere was a risk tat indebted ouseolds migt ave to adjust teir balance seets and consequently reduce teir consumption in te event of an adverse sock. Clearly, ascertaining te distribution of financial assets and liabilities at te ouseold level is of paramount importance for economic policy-making since it indicates te extent of financial stress at te ouseold level. Cox et al. (2002) explore te distribution of financial pressure across ouseolds in Great Britain. Teir findings, wic are drawn from a descriptive analysis of te data, suggest tat ouseolds wit te igest absolute levels of debt also tend to ave te igest income and net wealt. Tis implies tat ouseolds may be relatively well disposed towards dealing wit adverse financial socks given tat tey ave financial assets to draw upon. In addition, suc findings reveal interesting insigts into te beaviour of ouseolds wit respect to asset and debt accumulation. For example, te accumulation of debt is often associated wit iger interest rate tan tat received from, for example, savings. 3 Tus, joint olding of debt and financial assets suggests tat some ouseolds may be disinclined to dis-save in order to repay debts. Given te degree of Government concern over debt accumulation on bot sides of te Atlantic, te scarcity of researc into te determinants of debt at te ouseold level in te economics literature is somewat surprising. Te aim of tis paper is to furter explore weter te concerns raised by te Federal Reserve Board, te Monetary Policy Committee and te Bundesbank are warranted. To be specific, we expand te descriptive analysis of Cox et al. (2002) and Aizcorbe et al. (2003) by 3 It may be te case, owever, tat ouseolds are taking advantage of te numerous interest free credit arrangements on offer in order to enance current liquidity. 3

5 conducting econometric analysis of te determinants of debt and asset accumulation at te ouseold level. Moreover, given tat debt and financial assets are bot components of te ouseold s financial portfolio, we model teir accumulation jointly. In order to ascertain te extent to wic ouseolds can absorb financial socks, suc as reductions in teir income or increases in te interest rate, it is important to consider financial assets and liabilities simultaneously at te ouseold level. We explore ouseold data from Great Britain, Germany and te US in order to ascertain weter differences exist in te relationsip between ouseolds assets and debts across countries. Houseold level analysis is clearly appropriate since, as argued by Leece (1995), te use of aggregate time series data may mask ouseold responses to canges in te economic environment. In addition, suc aggregate data does not indicate wic ouseolds ave accumulated te most debt (Cox et al., 2002). II Background Tere is a growing empirical literature on ouseolds financial portfolios (see, for example, Guiso et al., 2002, for a compreensive review of tis area). In general, economists ave focused on specific aspects of te financial portfolio suc as te demand for risky financial assets (e.g. stocks and sares), debt or savings. 4 Turning initially to te literature on risky financial assets, Guiso et al. (1996) recognise te influence of earnings risk on ouseold s demand for risky financial assets and report an inverse relationsip between investment in risky financial assets and income risk. Te implications of ouseold asset market participation and nonparticipation for inter-temporal consumption ave been explored using US data by Vissing-Jørgensen (2002) and Vissing-Jørgensen and Attanasio (2003). Differences in estimates of te elasticity of inter-temporal consumption between asset olders and non-asset olders are found to be large and statistically significant. Attanasio et al. 4 One exception is Hocguertel et al. (1997) wo adopt a trivariate tobit approac to model te portfolio coice of ouseolds. 4

6 (2002) report consistent U.K. evidence. Wilst, Guiso et al. (2003) ave sown tat stock market participation is correlated wit ouseold wealt across countries. Hocguertel et al. (1997) argue tat te typical ouseold s portfolio consists of only a few different assets. Given tat a wide range of financial assets are available on te stock market tey pose an interesting question as to wy risk averse ouseolds do not diversify to a greater extent. Using cross-section data for te Neterlands in 1988 tey sow tat te portfolio coice of ouseolds, i.e. allocation between risky (stocks and bonds) and risk-free assets (savings accounts), is influenced by overall financial wealt and te marginal tax rate. Recent empirical studies of debt accumulation at te ouseold level include Godwin (1997) wo explores ouseolds use of credit using US panel data. Te findings support considerable mobility in debt status during te 1980s, wit te majority of ouseolds being in a different debt quintile in 1989 relative to In a more recent study, Crook (2001) aims to ascertain te factors tat explain US ouseold debt over te period using data from te Survey of Consumer Finances. Income, ome ownersip and family size all impact positively on ouseold debt. In one of te few papers based on U.K. data, Bridges and Disney (2004) explore access to credit, default and arrears among low-income U.K. ouseolds. Te results indicate tat differences in te incidence of credit and default across ouseolds are influenced by labour market status, age, access to social security benefits and ouseold composition. Brown et al. (2005) present a teoretical framework were optimistic financial expectations impact positively on te quantity of unsecured debt at te individual and te ouseold level. Teir empirical analysis based on Britis panel data confirms tat financial expectations are an important determinant of unsecured debt. Furtermore, te empirical results indicate tat it is optimistic financial expectations per se tat are 5

7 important in influencing unsecured debt, rater tan te accuracy of individuals predictions regarding teir future financial situation. Leece (1995) explores mortgage demand at te ouseold level using crosssection data from te Britis Family Expenditure Survey (FES) and focuses on weter financial deregulation in te 1980s affected mortgage demand. Leece found tat suc structural canges were evident in te data. Oter factors found to influence mortgage demand were income, te size of te property, regional location and age. Leece (2000) investigated weter te factors tat influenced overall mortgage demand differ by te type of mortgage undertaken suc as an endowment mortgage (interest only) versus a repayment mortgage (annuity). Te results from te 1986 FES suggest tat financial deregulation and credit market rationing ad differing impacts on eac type of mortgage demand. Saving at te ouseold level as been analysed from bot a teoretical and an empirical perspective. For example, Dynan et al. (2004) report a positive relationsip between savings rates and lifetime income. From a teoretical perspective, life cycle models ave been used to explain ow saving and dis-saving are associated wit consumption smooting over te life cycle. Te notion of precautionary saving introduces an additional role for saving as a type of insurance against future unforeseen events suc as job loss or illness. Lusardi (1998) explores te importance of precautionary saving exploiting US data on individuals subjective probabilities of job loss from te Healt and Retirement Survey. Te evidence is consistent wit precautionary savings motives in tat individuals facing iger income risk save more, altoug te findings suggest tat te contribution of precautionary saving to wealt accumulation is not particularly large. Jappelli and Pistaferri (2000) provide a test of bot te precautionary saving model and te excess sensitivity of consumption to predicted income using Italian ouseold panel data over te period Consumption growt is found to be 6

8 uncorrelated wit expected income growt, wilst income risk is correlated wit consumption risk partially supporting te precautionary savings motive. Similarly, Guariglia (2001) analyses te extent to wic Britis ouseolds save in order to selfinsure against uncertainty. Te findings support a significant relationsip between earnings uncertainty and savings. Moreover, te results imply tat ouseolds save more if tey expect teir financial situation to deteriorate. Guariglia and Rossi (2002) report furter U.K. evidence supporting precautionary saving motives. III Data and Metodology In te remainder of te paper, we explore te empirical determinants of te amount of debt and financial assets accumulated at te ouseold level in Great Britain, Germany and te US. For Great Britain, we exploit information contained in te 2000 wave of te Britis Houseold Panel Survey (BHPS), wic is te most recent wave containing information about ouseolds debt and financial investments. 5 Te BHPS is a random sample survey, carried out by te Institute for Social and Economic Researc, of eac adult member from a nationally representative sample of more tan 5,000 private ouseolds (yielding approximately 10,000 individual interviews). For wave one, interviews were conducted during te autumn of Te same individuals are re-interviewed in successive waves te latest available being wave twelve, collected in In 2000, individuals were asked ow muc in total tey owed. Tis question relates to non-mortgage debt as details about mortgages are asked in a separate question. Te answers tus provide information about te amount of outstanding unsecured debt. Wit respect to secured debt, eac ead of ouseold was asked to state ow muc in total is owed wit respect to te total amount of mortgage borrowed at purcase as well as te amount of any additional mortgage taken on. 5 In te BHPS, tere are unfortunately only two waves (1995 and 2000), wic include questions relating to unsecured debt and financial assets. 7

9 Tus, in order to calculate te total liabilities of eac ouseold, d, we sum te information related to secured and unsecured debt, were total unsecured debt at te ouseold level is derived by summing eac ouseold member s revealed level of unsecured debt. Turning to financial assets, individuals were asked to state te total value of financial investments eld including sares, personal equity plans, unit trusts, oter investments (including government and company securities), premium bonds, National Savings and building society accounts, tax exempt special savings accounts, investment savings accounts and te total value in savings accounts. Again, in order to ascertain te amount of financial assets at te ouseold level,, we aggregate te financial assets of eac ouseold member. For Germany, we use te German Socio-Economic Panel (GSOEP), a representative longitudinal study of private ouseolds wo ave been surveyed annually since 1984, funded by te German National Science Foundation. Te GSOEP as followed approximately 13,500 individuals, living in around 7,000 ouseolds, eac year since We concentrate on te 2002 wave since it is te most recent year tat respondents are asked detailed questions about oldings of financial assets and debt. 6 To be specific, individuals were asked to specify te total value of financial assets over te value of 2500 euros, wic are eld in te form of a savings balance, savings bonds, bonds, sares or investments. We obtain a measure of by summing te information provided by eac ouseold member. Turning to ouseold unsecured debt, we focus on credit obtained as a private individual from a bank, similar institution or anoter individual. Tis data concerns debt tat is greater tan 2500 euros before interest. Te value of total mortgage debt is defined as te remaining mortgage debt on te first property as well as any mortgages on second 6 Information on debt and assets at te ouseold level is only available in 1988 and 2002 in te GSOEP. In addition, tere are some discrepancies across te questions suc tat less detailed information is available wit respect to financial assets and no information is available wit respect to unsecured debt in

10 owned properties excluding interest. As in te case of te BHPS, in order to obtain information on total debt at te ouseold level ( d ), we sum total secured and unsecured debt witin eac ouseold. For te US, we use te Panel Study of Income Dynamics (PSID), wic began in 1968, and is a longitudinal study of a representative sample of US individuals (men, women, and cildren) and te family units in wic tey reside. It empasises te dynamic aspects of economic and demograpic beaviour. Te sample size as grown from 4,800 families in 1968 to more tan 7,000 families in We concentrate on te 2001 wave since it is te most recent year tat ouseolds are asked detailed questions about teir oldings of financial assets and debt. 7 To be specific, turning to financial assets, te ead of family is asked to specify te amount of sares of stock in publicly eld corporations, mutual funds, investment trusts, money in cecking or savings accounts, money market funds, certificates of deposit, and government savings bonds or treasury bills. In terms of debt te ead of family is asked to specify te amount remaining on first mortgage, second mortgage, credit card carges, student loans, medical or legal bills, or oter loans. Since bot of tese questions are asked to te ead of te family, te responses yield ouseold level totals. 8 By definition and d cannot be negative and so we treat tem as censored variables in our econometric analysis. Following Bertaut and Starr-McCluer (2002), we employ a tobit model to ascertain te determinants of debt and assets at te ouseold level, wic allows for te fact tat a number of ouseolds report zero 7 Te questions on financial assets and debt were first asked in te PSID in 1999 in Section W of te questionnaire called Wealt and Active Savings. Prior to tis period tere was no exact matc to te questions posed in te subsequent periods. 8 It is apparent tat differences exist wit respect to te specific questions in te GSOEP, BHPS and PSID regarding assets and debts. Te key difference is te specification tat assets and debts are over 2500 euros in te case of Germany. In terms of te figures for average debt levels, Table 1 reveals tat tere is no significant difference in tat reported in te two European countries, altoug te figure for te US is relatively ig. In terms of financial assets, it is apparent tat te mean level is igest for Germany wic accords wit a priori expectations given te nature of te GSOEP question. 9

11 assets and/or debt. 9 Tis is apparent in Figures 1 to 6, wic sow te distributions of ln ( ) and ( d ) ln for eac country. Hence, we estimate te following in te case of ouseold debt, were we specify a logaritmic dependent variable following Gropp et al. (1997): 10 * ( d ) ln = β X + ε (1) ln ln 1 * ( d ) = ln( d ) ( d ) = 0 oterwise if d * > 0 were te debt of ouseold is given by d suc tat =1,,n, (2) X denotes a vector of ead of ouseold and ouseold caracteristics and ε represents te random error term. Wit respect to ouseold asset accumulation, we repeat te tobit analysis replacing d wit. Tus, we initially model d and independently assuming tat te correlation between te random error terms across te two equations equals zero. Our samples drawn from te BHPS, te GSOEP and te PSID comprise 3,887, 8,956 and 4,885 ouseolds respectively wit an employed ead of ouseold as our unit of observation. <<FIGURES 1 TO 6 HERE>> We draw upon Guiso et al. (1996, 2002, 2003) and Brown et al. (2005) in order to specify X. We include demograpic caracteristics of te ead of ouseold suc as age, gender, marital status, etnicity, region and igest educational qualification as well as labour market caracteristics suc as occupation, industrial 9 For Germany te number of ouseolds reporting zero assets is 2,865 (32%) and te number of ouseolds reporting zero debt is 4,704 (53%). Te figures for Great Britain are 1,310 (34%) and 1,382 (36%) respectively, wilst te corresponding figures for te US are 1,200 (25%) and 1,265 (26%) respectively. Tose reporting bot zero assets and zero debts in Germany, Great Britain and te US are 2,008 (22%), 618 (16%) and 598 (12%) respectively. For tose wit positive ouseold debt and zero assets te figures in Germany, Great Britain and te US are 857 (10%), 692 (18%) and 602 (12%) respectively. 10 For ouseolds reporting zero financial assets or debts, ( ) d ln and ( ) ln were recoded to zero, as tere is no reported debt or assets between zero and unity. Trougout te analysis we refer to debt and assets as logged variables due to te fact tat te distributions of debt and assets are bot igly skewed towards zero. 10

12 affiliation and aving a second job. We also control for a number of ouseold caracteristics including te number of cildren in te ouseold, ouseold size, te value of te ouse and weter te ouse is owner occupied. We control for te ead of ouseold s income, te income of is/er spouse, unearned income, te amount of any windfall and te amount of past total income observed in te survey, as a proxy for lifetime income, because one would expect te amount of debt and financial assets accumulated to vary over te life cycle. For te GSOEP, tis is income accumulated over , for te BHPS tis is income accumulated over , wilst for te PSID tis is income accumulated over Full summary statistics of te variables used in our empirical analysis are presented in Table 1. Te monetary figures are all expressed in real terms and ave been converted, were appropriate, into US Dollars. Given tat <<TABLE 1 HERE>> d and represent two components of te ouseold s financial portfolio, we contrast our findings from modelling tem independently wit te findings from modelling tem jointly by employing a bivariate tobit estimator. Suc an approac allows for te possibility of inter-dependent decision-making wit respect to financial assets and liabilities. Te bivariate tobit model is specified as follows: ln ln * ( d ) = β X + ε (3) ( a ) * = β 2 X + ε 2 (4) 2 were ε and ε are te stocastic disturbance terms, ε ε ~ N ( 0, 0, σ, σ, ρ ), and te covariance is σ = ρσ σ. In te bivariate tobit model, te disturbance terms, ε 1 and ε 2, are jointly normally distributed wit variances σ and 1 σ 2. If 11 Te time period over wic we aggregate is determined by a variety of factors. For te BHPS, te first wave was conducted in For GSOEP, we aggregate from te year after unification. For PSID te income question is only comparable across waves 1993 troug to

13 te correlation term, ρ, is zero, ten te asset and debt decisions are independent. If ρ 0, ten joint estimation is caracterised by greater efficiency and implies a degree of inter-dependence between d and. Te parameters of te bivariate model are estimated by full information maximum likeliood. IV Results Te Determinants of Houseold Debt and Financial Assets Te results from estimating equations (1) and (2) are presented in Table 2 below. Tese findings inform us about te determinants of debt and financial assets at te ouseold level maintaining te assumption tat te accumulation of tese two components of ouseold s financial portfolio are independent from eac oter. It is apparent tat in Germany and te US debt accumulation increases wit age but at a diminising rate wilst tere is no life-cycle effect in Great Britain. Being male is positively associated wit te accumulation of financial assets across countries, but is insignificantly related to te accumulation of debt apart from in te US were males ave less debt tan females, ceteris paribus. Tere are interesting differences wit respect to te effect of marital status on asset and debt accumulation across te tree countries. In Germany being married is positively associated wit ouseold debt and financial assets, wilst in Great Britain, tere is an inverse relationsip between being married and bot asset and debt accumulation. For te US marital status as no significant influence upon eiter asset or debt accumulation. 12 Income of te ead of ouseold and tat of is/er spouse are positively associated wit bot financial assets and liabilities across eac country. Te same olds for unearned income wit te exception of te significant inverse relationsip between debt and unearned income for Great Britain and te US. In eac country, te 12 Te positive influence of being married on debt and assets in Germany may reflect te possibility tat tey could be eld jointly between spouses. Unfortunately, tere is not a common question across countries to enable us to explicitly control in a consistent manner for weter debt or assets are eld at te individual level witin te ouseold or weter tere is joint financial responsibility. Brown et al. (2005) investigating debt in Great Britain using te BHPS found tat, even controlling for joint responsibility for debt, married couples ad lower levels of debt. 12

14 number of cildren is inversely related to financial asset oldings and debt levels wit te exception of Germany, wereas ouseold size only as a significant positive effect on debt and assets for Great Britain, and debt accumulation in te US. In contrast to Great Britain, te value of te ouse as a relatively large and igly significant positive influence on financial assets and debt in bot Germany and te US. In general, iger levels of educational attainment are positively related to financial asset accumulation in Germany and Great Britain, but interestingly education yields an insignificant effect in te US te only exception is te influence of a college degree yet tis effect is only significant at te 10 percent level. 13 <<TABLE 2 HERE>> Inter-dependence between Houseold Debt and Financial Assets In Panel B of Table 2, we explore te relationsip between debt and financial assets at te ouseold level by augmenting equations (1) and (2) to include debt in te asset equation and vice versa. It is apparent tat, across countries, ouseold debt (financial assets) is positively related to ouseold financial assets (debt). Hence, our findings suggest tat assets and debt are not accumulated independently from one anoter. 14 Moreover, our findings support a positive association between assets and debt at te ouseold level suggesting tat te concerns of te Federal Reserve Board, te Monetary Policy Committee and te Bundesbank about te nature of ouseolds balance seets may be unwarranted. In order to explore suc considerations more fully, we estimate te determinants of debt and assets by specifying a bivariate tobit model tat allows for possible inter-dependence between tese two components of te ouseold s financial portfolio. Tese results are presented in Table 3 and generally concur wit tose 13 Bot te BHPS and GSOEP also ask questions about ouseolds financial concerns and teir financial optimism for te future. Tese controls are omitted since corresponding questions are not asked in te PSID. Te results for Great Britain and Germany are robust to teir inclusion. In accordance wit Brown et al. (2005), financial optimism is positively associated wit te level of ouseold debt. 14 In addition, te correlation between te residuals from te asset equation and tat from te debt equation for Germany, Great Britain and te US is relatively ig and statistically significant. 13

15 sown above. Te correlation parameter, ρ, is statistically significant for Germany, Great Britain and te US indicating tat debt and asset accumulation are interdependent and so a joint modelling approac is appropriate for eac country. It is apparent tat te correlation term is positive across countries implying, conditional on te covariates, tat debt accumulation takes place simultaneously wit asset accumulation. Te relatively large ρ parameter in Germany suggests a greater degree of complementarity (i.e. symmetry) between ouseold debt and financial asset accumulation in Germany relative to Great Britain and te US (were te order of magnitude is similar for te ρ parameter). Tus, our findings support a positive association between ouseold assets and liabilities, wic may be a consequence of ouseolds olding debt and assets simultaneously. 15 One could argue tat it is irrational to old bot debt and assets simultaneously since debt usually attracts iger interest rate tan, for example, te rate of return on savings. A possible reason wy it migt be optimal for ouseolds to jointly old debt and assets is to overcome sort-term cas flow problems (see Cox et al., 2002, and Banks et al., 2002). For instance, in te face of cas flow problems it could be easier to use savings rater tan to arrange credit. Conversely, it migt be easier to arrange credit tan to liquidate financial assets suc as stocks and sares. A furter reason for jointly olding debt and assets migt be tat in te sort term debt may attract zero interest payments for a limited period. Te existence of a positive relationsip between assets and liabilities suggests tat ouseolds may be able to absorb adverse financial socks providing furter evidence suggesting tat te concerns of te Monetary Policy Committee, te Bundesbank and te Federal Reserve may be unfounded. However, it may be te case tat certain types of ouseolds are vulnerable to financial socks, i.e. not all 15 Following Flavin and Yamasita (2002), we ave explored te robustness of our findings by furter analysing te role of ousing wealt by including ousing wealt in te definition of ouseold assets. Our findings, wic are available on request, are largely uncanged. Te correlation parameter, ρ, remained positive and statistically significant in eac country. 14

16 ouseolds may be caracterised by suc a strong symmetry in oldings of debt and assets. In order to explore suc issues more fully, we ascertain te nature of te relationsip between assets and debt, firstly, across different income groups and, secondly, across different age groups. <<TABLE 3 HERE>> Does te Degree of Inter-Dependence vary wit Income or across te Life Cycle? We furter investigate te symmetry between ouseold debt and financial asset accumulation in Great Britain, Germany and te US by estimating a bivariate tobit model: firstly by splitting eac sample into quartiles according to ouseold income, defined as ouseold labour income plus ouseold non labour income; and secondly by age quartiles, given tat debt and asset accumulation may differ over te life cycle (see Banks et al., 2002, and Guiso et al., 2002). As suc, we estimate te following across eiter income or age quartiles: ln ln * ( d ) ( a ) * = β X ~ 1 2 = β X ~ ε + ε 1 2 < q, q 1 1 and < q, q 2 2 and < q, q were q 1 is te 25 t quartile, q 2 is te 50 t quartile, q 3 is te 75 t quartile and 3 3 X ~ (5) is te same as X, as defined above, but excludes income of te ead of ouseold, income of is/er spouse and unearned income in te case of estimating equation (5) by income quartiles and age in te case of estimating equation (5) by ead of ouseold age quartiles. Specifically, we are interested in te sign, magnitude and significance of ρ across income and age groups. 16 To be specific, we explore te nature of te relationsip between ouseold debt and financial assets across ouseold income as well as across age groups. <<TABLES 4 AND 5 HERE>> 16 Te results are robust to controlling for income (age) witin income (age) quartiles, but are omitted for brevity. 15

17 Te results of estimating equation (5) across ouseold income quartiles are summarised in Table 4, were Panel A refers to Germany, Panel B to Great Britain and Panel C to te US. Eac panel sows ρ across eac income quartile as well as te raw correlation in te data between debt and financial assets, and average debt and financial asset values. Clearly, across countries average ouseold debt and average financial assets increase monotonically from te lowest to te igest income quartiles. Tis is also evident for te correlation in te raw data between debt and assets for Germany. Interestingly, te opposite is evident in te US wit te raw correlations decreasing monotonically across income quartiles. No clear pattern is evident from te raw correlations in te case of Great Britain. Te correlation coefficients derived from te bivariate tobit estimates generally increase monotonically from te lowest to te igest income quartiles in Germany and Great Britain. However, in te US te evidence from te bivariate tobit estimates sows a monotonic fall in te correlation coefficients akin to te pattern in te raw data. For Germany and Great Britain, tis suggests, conditional on te covariates, tat te interdependence between ouseold debt and financial assets is greater for iger income groups. In oter words a greater degree of complementarity appears to exist at iger levels of ouseold income wereas in te US te opposite appears to old. It sould be noted tat tere is always a symmetry between tese two components of te ouseolds financial portfolio, i.e. a positive correlation between assets and liabilities, altoug te degree of correlation in Great Britain is relatively modest for te two lowest income quartiles. Te results of estimating equation (5) across ead of ouseold age quartiles are summarised in Table 5, were te Panels are arranged as in Table 4. Clearly, in Germany and Great Britain average ouseold debt decreases monotonically moving from te youngest to te oldest age quartiles tat is younger ouseolds are te large debt olders. Te opposite is evident in te US in tat older ouseolds ave 16

18 more debt. For ouseold financial assets te pattern is te same as for ouseold income quartiles, see Table 4, in tat tere is a monotonic increase in asset accumulation wit age. Te raw correlation coefficients for Germany sow tat ouseold debt and assets are greater complements for te lowest age quartile (less tan 37 years of age) and exibit te lowest correlations between te first and second age quartiles (aged 37 to 44). Te same is evident for Great Britain, were ig correlations exist below te first age quartile (aged 32) and te lowest complementarity exists between te second and tird age quartiles (aged 40 to 48). For te US te igest correlations appear in te middle quartile ranges (aged 31 to 47). Te correlation coefficients derived from te bivariate tobit model do not increase monotonically across ead of ouseold age quartiles. Rater, our results suggest tat, conditional upon te covariates, te inter-dependence between ouseold debt and financial assets is greater at iger age groups aged 44 (q 2 ) to 57 (q 3 ) for Germany, 48 (q 3 ) or over in Great Britain, and aged 39 (q 2 ) to 47 (q 3 ) in te US. It sould be noted tat altoug tere is always a symmetry between tese two components of te ouseold s financial portfolio across age quartiles, in Great Britain tis falls over eac quartile until te final one, wilst in Germany te opposite is evident in tat te correlation rises ten falls to its lowest level by te tird age quartile, a similar pattern is evident in te US. Tis suggests tat tere are different life cycle effects across te tree countries wic drive te inter-dependence observed in te data, and sould be borne in mind wen considering ow suc ouseolds can deal wit adverse financial socks. Houseolds reporting Positive Debt It is apparent, owever, tat te positive and significant relationsip between assets and debt migt reflect tat te fact tat a number of ouseolds report zero assets and zero debt (see Figures 1 to 6). Tus, in order to explore te issue of financial pressure, 17

19 it seems appropriate to focus on tose ouseolds reporting positive levels of debt. We specify a sequential (nested) tobit model, wic controls for sample selection (see Lee, 1992, and Howe et al., 1994), and serves to select on tose ouseolds reporting a strictly positive level of debt. In terms of equations (3) and (4), ln ( ) and ln. d only observed wen ( ) > 0 X 2 are Te results, wic are obtained via full information maximum likeliood, are presented in Table 6 and are similar to tose of te bivariate tobit model (Table 3) in terms of te statistical significance of te covariates. However, it is apparent tat ρ becomes insignificant for Germany, Great Britain and te US, indicating tat tose ouseolds in debt may well be vulnerable to canges in teir financial situation. Indeed, based upon our results if a common macroeconomic sock led to unemployment so labour income fell by 100% in eac country, ten debt would fall by 30%, 46% and 21% in Germany, Great Britain and te US respectively, ceteris paribus. Tis may imply tat current levels of international debt are not sustainable if suc socks were to occur, for instance in te advent of a recessionary period, as current debtors would arguably ave problems in maintaining teir current liabilities. Moreover, Table 7 sows tat tis pattern exists across all income quartiles, wit average debt exceeding average assets across all income quartiles. Te finding tat tere is no significant ρ across quartiles is potentially worrying as it suggests tat ouseolds in debt are vulnerable to economic socks. Furtermore, tis is especially te case for low income ouseolds. 17 Te pattern is less clear in te case of age quartiles since for Germany (Great Britain), ρ is insignificant across all quartiles except te igest age quartile were tere is a positive (negative) and 17 For te US, data from te 1998 Survey of Consumer Finances sows tat more tan 20% of families earning below $50,000 ad 40% of teir income set aside to cover debt payments. Indeed in 2001 tose families wit repayment difficulties on loans were typically in te lowest quartile of te income distribution, Aizcorbe et al. (2003). 18

20 significant relationsip between assets and debt respectively. For te US tere are no significant correlation coefficients across age quartiles. <<TABLES 6 TO 8 HERE>> Tus, our findings suggest tat joint olding of assets and debt may not be prevalent once we restrict our sample to tose ouseolds reporting positive debt. Our estimates of ρ, owever, do not inform us about te level of asset accumulation vis a vis debt accumulation. An insignificant correlation parameter, for example, may reflect a situation were te distribution of assets differs from tat of debt yet lies to te rigt of te debt distribution ence rendering financial pressure minimal. In order to compare te distribution of assets and debt, Figures 7 to 9 present kernel density plots of te two distributions for eac country, for tose ouseolds in debt. It is apparent tat especially for Great Britain and te US debt outweigs financial assets for a significant proportion of ouseolds. Moreover, one migt argue tat te problem may be abated if we include ousing wealt in te definition of assets. Altoug Cox et al. (2002) argue tat ouseolds experiencing a financial sock may find it difficult in te sort term to realise some of teir ousing equity to ease financial pressure. However, abstracting from suc liquidity issues, it is apparent from Figures 10 to 12 tat even wen including ousing equity in te definition of financial assets, tere still remains again especially in Great Britain and te US a significant proportion of ouseolds wit debt in excess of financial assets. <<FIGURES 7 TO 12 HERE>> V Conclusion Gaining an insigt into te factors tat influence debt accumulation at te ouseold level is an important issue for economic policy making. Furtermore, as Alan Greenspan, Cairman of te Federal Reserve Board, as recently argued weter ouseolds ave te financial assets to cover debt repayments is a crucial economic question. 19

21 Our results suggest tat ouseold debt and financial assets sould be modelled simultaneously, wic ties in wit our a priori expectations given tat debt and assets represent two components of te ouseold s financial portfolio. Our empirical analysis supports a positive association between debt and assets at te ouseold level, altoug te nature of tis relationsip varies over bot ouseold income and age quartiles. Te positive relationsip between financial assets and liabilities at te ouseold level may be indicative of ouseolds aiming to reduce financial risks by olding a diversified financial portfolio. However, tis positive association between assets and debts dissipates once we focus on te sample of ouseolds in debt, wit te poorest and te youngest ouseolds being te most vulnerable to canges in teir financial circumstances. Tus, our findings suggest tat te concerns of te Monetary Policy Committee, te Bundesbank and te Federal Reserve over te potential degree of financial stress at te ouseold level are well founded. References Aizcorbe, A. Kennickell, A. and K. Moore (2003) Recent Canges in US Family Finances: Evidence from te 1998 and 2001 Survey of Consumer Finances, Federal Reserve Bulletin, ttp:// Alessie, R. and A. Lusardi (1997) Saving and Income Smooting: Evidence from Panel Data, European Economic Review, 41, Attanasio, O. and J. Banks (2001) Te Assessment: Houseold Saving Issues in Teory and Policy, Oxford Review of Economic Policy, 17(1), Attanasio, O., Banks, J. and Tanner, S. (2002). Asset Holding and Consumption Volatility. Journal of Political Economy, 110, Banks, J., Smit, Z. and Wakefield, M. (2002). Te Distribution of Financial Wealt in te UK: Evidence from 2000 BHPS Data. Institute of Fiscal Studies, WP02/21. Bertaut, C. and M. Starr-McCluer (2002) Houseold Portfolios in te US in Houseold Portfolios Guiso, L., Haliassos, M. and T. Jappelli (eds). MIT Press. Bridges, S. and Disney, R. (2004). Use of credit and debt among low-income families in te UK: an empirical analysis, Fiscal Studies, 25, Brown, S., Garino, G., Taylor, K. and S. Weatley Price (2005) Debt and Financial Expectations: An Individual and Houseold Level Analysis, Economic Inquiry, 43(1), Browning, M. and A. Lusardi (1996) Houseold Saving: Micro Teories and Micro Facts, Journal of Economic Literature, XXIV, Cox, P., Witley, J. and P. Brierley (2002) Financial Pressures in te UK Houseold Sector: Evidence from te Britis Houseold Survey, Bank of England Quarterly Bulletin, Winter,

22 Crook, J. (2001) Te Demand for Houseold Debt in te USA: Evidence from te 1995 Survey of Consumer Finance, Applied Financial Economics, 11, Flavin, M. and Yamasita, T. (2002). Owner-Occupied Housing and te Composition of te Houseold Portfolio. American Economic Review, 92, Follain, J. and R. Dunsky (1997) Te Demand for Mortgage Debt and te Income Tax, Journal of Housing Researc, 8, Godwin, D. D. (1997) Dynamics of Houseolds Income, Debt and Attitudes Towards Credit, Te Journal of Consumer Affairs, 31, Gropp, R., Scolz, J.K. and M.J. Wite (1997) Personal Bankruptcy and Credit Supply and Demand, Te Quarterly Journal of Economics, 112, Guariglia, A. (2001) Saving Beaviour and Earnings Uncertainty: Evidence from te Britis Houseold Panel Survey, Journal of Population Economics, 14, Guariglia, A. and M. Rossi (2002) Consumption, Habit Formation, and Precautionary Saving: Evidence from te Britis Houseold Panel Survey, Oxford Economic Papers, 54, Guiso, L., Jappelli, T. and D. Terlizzese (1996) Income Risk, Borrowing Constraints and Portfolio Coice, American Economic Review, Vol. 86, Guiso, L., M. Haliassos and T. Jappelli (2002) Houseold Portfolios, MIT Press. Guiso, L., Haliassos, M. and Jappelli, T. (2003). Equity Culture: Teory and Cross-Country Evidence, Economic Policy, Hamilton, R. (2003) Trends in Houseolds Aggregate Secured Debt, Bank of England Quarterly Bulletin, Autumn, Hocguertel, S., Alessie, R. and Van Soest A. (1997) Savings Accounts versus Stocks and Bonds in Houseold Portfolio Allocation, Scandinavian Journal of Economics, 99, Howe, C., Lee, B-J., and L. Bennett (1994) Design and Analysis of Contingent Valuation Surveys using te Nested Tobit Model, Review of Economics and Statistics, Vol.76, Jappelli, T. and L. Pistaferri. (2000) Using Subjective Income Expectations to Test for Excess Sensitivity of Consumption to Predicted Income Growt. European Economic Review, 44(2), Jappelli, T. and L. Pistaferri (2002) Incentives to Borrow and te Demand for Mortgage Debt: An Analysis of Tax Reforms, CSEF Working Paper No. 90. Lee, B-J. (1992) A Nested Tobit Analysis for a Sequentially Censored Regression Model, Economics Letters, Vol.38, Leece, D. (1995) Rationing, Mortgage Demand and te Impact of Financial Deregulation, Oxford Bulletin of Economics and Statistics, Vol.57, Leece, D. (2000) Coice of Mortgage Instrument, Liquidity Constraints and te Demand for Housing Debt in te UK, Applied Economics, Vol.32, Ling, D. and G. McGill (1998) Evidence on te Demand for Mortgage Debt by Owner-Occupants, Journal of Urban Economics, Vol. 44, Lusardi, A. (1998) On te Importance of te Precautionary Saving Motive, American Economic Review, Papers and Proceedings, 88(2), Vissing- Jørgensen, A. (2002). Limited Asset Market Participation and te Elasticity of Intertemporal Substitution. Journal of Political Economy, 110, Vissing-Jørgensen, A. and Attanasio, O. (2003). Stock-Market Participation, Intertemporal Substitution and Risk-Aversion. American Economic Review, Papers and Proceedings, 93,

23 Figure 1: Distribution of Houseold Debt for Germany Figure 2: Distribution of Houseold Financial Assets for Germany Density Density D e b t F in a n c ia l_ A s s e ts Figure 3: Distribution of Houseold Debt for Great Britain Figure 4: Distribution of Houseold Financial Assets for Great Britain Density Density D e b t F in a n c ia l_ A s s e ts Figure 5: Distribution of Houseold Debt for te US Figure 6: Distribution of Houseold Financial Assets for te US Density Density Debt Financial_Assets

24 Figure 7: Kernel Density of Houseold Financial Assets and Debt for Germany Figure 8: Kernel Density of Houseold Financial Assets and Debt for Great Britain Figure 9: Kernel Density of Houseold Financial Assets and Debt for te US x kdensity ltdebt2 kdensity lervas x kdens ity ltdebt1 kdensity lrjnve x kdensity Debt kdensity Financial_Assets Figure 10: Kernel Density of Houseold Financial Assets (including ouse value) and Debt for Germany Figure 11: Kernel Density of Houseold Financial Assets (including ouse value) and Debt for Great Britain Figure 12: Kernel Density of Houseold Financial Assets (including ouse value) and Debt for te US x x x kdensity ltdebt2 kdensity lervas2 kdens ity ltdebt1 kdensity lrjnve2 kdensity Debt kdensity Financial_Assets Key: Houseold Debt Houseold Financial Assets

25 Table 1: Summary Statistics GERMANY GREAT BRITAIN US Variable Mean Standard Deviation Variable Mean Standard Deviation Variable Mean Standard Deviation ln ( ) ln ( d ) ln ( ) ln ( d ) ln ( ) ln ( d ) Age Age Age Age Age Age Male Male Male Married Married Married Immigrant Non-Wite Non-Wite Ln (Income) Ln (Income) Ln (Income) Ln (Unearned Income) Ln (Unearned Income) Ln (Unearned Income) No. Cildren No. Cildren No. Cildren Houseold Size Houseold Size Houseold Size Ln (Spouse Income) Ln (Spouse Income) Ln (Spouse Income) Ln (House Value) Ln (House Value) Ln (House Value) Second Job Second Job Second Job Ln (Windfall) Ln (Windfall) Ln (Windfall) ED1 (Realscule) ED1 (O Levels) ED1 (GED) ED2 (Arbitur) ED2 (A Levels) ED2 (Hig Scool) ED3 (Vocational) ED3 (Furter Education) ED3 (Vocational Degree) ED4 (Degree) ED4 (Degree) ED4 (College Degree or iger) Healt Healt Healt Home Owner Home Owner Home Owner Ln (Life Time Income) Ln (Life Time Income) Ln (Life Time Income) OBSREVATIONS 8,956 3,887 4,885 Note: For reasons of brevity, we ave omitted summary statistics on region, mont of interview, industry and occupation. Tey are available from te autors on request.

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