Debt and Distress - Evaluating the Psychological Cost of Credit

Journal of Economic Psychology 26 (2005) 642–663

www.elsevier.com/locate/joep

Debt and distress: Evaluatingthe psychological cost of credit

Sarah Brown *, Karl Taylor, Stephen Wheatley Price

Department of Economics, University of Leicester, University Road, Leicester LEI 7RH, UK

Received 25 April 2004; received in revised form 9 December 2004; accepted 26 January 2005

Available online 4 May 2005

Abstract

In this paper we explore the association between debt and psychological well-being

amongst heads of households using the British Household Panel Survey. Our principle finding

is that those household heads who have outstanding (non-mortgage) credit, and who have

higher amounts of such debt, are significantly less likely to report complete psychological

well-being. The average increase in the psychological distress is greater when outstanding

credit is measured at the individual, as opposed to household, level. No such significant asso-

ciation is found in the case of mortgage debt. Our results highlight the psychological cost asso-

ciated with the consumer credit culture in Britain.

� 2005 Elsevier B.V. All rights reserved.

JEL classification: G11; 131

PsycINFO classification: 3900; 3920

Keywords: Debt; Credit; Psychological well-being; Ordered probit models

0167-4870/$ - see front matter � 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.joep.2005.01.002

* Corresponding author. Tel.: +44 116 2522827; fax: +44 116 2522908.

E-mail address: [email protected] (S. Brown).

mailto:[email protected]

S. Brown et al. / Journal of Economic Psychology 26 (2005) 642–663 643

�My other piece of advice, Copperfield,� said Mr. Micawber, �you know.Annual income twenty pounds, annual expenditure nineteen and six, resulthappiness. Annual income twenty pounds, annual expenditure twenty poundsought and six, result misery.�

(David Copperfield, Chap. 12, by Charles Dickens)

1. Introduction

There was a consumer credit explosion in the UK between 1994 and 2004. This

accompanied the sustained economic boom during this period and followed on from

the gradual relaxation of credit constraints in the late 1980s and early 1990s. The in-

creased availability of unsecured credit is clear from the massive rise in the number

of different credit cards available (over 1300 in the UK, in 2000) and the broadening

of the range of financial institutions offering unsecured loans. From once being pri-

marily the preserve of the major banks, loans can also now be readily obtained from

building societies, UK and overseas-based finance companies and, even, supermar-kets. In addition, the advent of telephone and internet-banking, and the availability

of credit at the point of purchase, has increased the accessibility of consumer credit

and the speed with which loan contracts are made.

Fig. 1 illustrates the dramatic escalation in the total value of outstanding con-

sumer credit in the UK, between 1982 and 2002 (measured in 1995 pounds sterling

and excluding mortgage debt). Less than 1% of this change can be explained by the

5% growth in the size of the UK population during this period. Most of the increase

has arisen from the rise in the value of loans arranged directly (e.g. personal loans) orindirectly (e.g. hire purchase agreements) with financial institutions (the Other cate-

gory). A growing proportion of outstanding consumer credit has been obtained

through the use of credit cards. As a percentage of GDP, the amount of unsecured

borrowing accumulated by individuals and households doubled, between 1993 and

2002, to 16%. By the end of 2004 the total amount of outstanding (non-mortgage)

credit was over £185 billion (in current prices), an average of more than £4800 for

every adult of working age in the UK.

Monetary policymakers have become concerned about the extent of personalindebtedness, its sustainability and impact on aggregate economic performance

(e.g. Bank of England, 2004, pp. 9–10). However, there is also considerable concern

from social welfare lobbyists, amongst others, about the associated increase in the

number of individuals and families with problematic levels of personal debt. For

example, members of the National Association of Citizens Advice Bureau in the

UK dealt with approximately one million new personal debt enquiries during 2002

(NACAB, 2003). Over two-thirds of these contacts were associated with consumer

credit arising from bank loans, credit and store cards (whose interest rates are typ-ically two or three times those of the banks), catalogue debts and hire purchase

agreements. Many of their clients were also in arrears with regard to housing rent,

council tax and utilities bills. Additionally, they report a 47% increase in the number

Fig. 1. Outstanding consumer credit (1995 prices). Notes: The data were obtained from the UK

Government�s National Statistics �Time Series Data� website at http://www.statistics.gov.uk/.

644 S. Brown et al. / Journal of Economic Psychology 26 (2005) 642–663

of new contacts in this area over the period 1997–2002 (and a 25% increase in thenumber of personal insolvencies, to 30,587). A quarter of these clients reported anx-

iety, depression and stress problems that resulted in them seeking medical treatment

(NACAB, 2003).

In this paper we examine the extent to which having outstanding credit influences

the psychological well-being of household heads in the population as a whole, using

data from the 1995 and 2000 waves of the British Household Panel Survey. Our main

hypothesis is that debt may be associated with increased levels of psychological dis-

tress, a relationship which is most likely to hold amongst the principal financial deci-sion-makers in a household. Furthermore, we anticipate that unsecured debt is likely

to have a greater influence on psychological well-being than secured debt. It is there-

fore crucial that we can, at least broadly, differentiate between these two types of

debt in our empirical analyses. It is also critical that we distinguish between financial

liabilities and financial assets, rather than aggregate them together into an overall

measure of net wealth, allowing us to explore whether their associations with psycho-

logical well-being might be asymmetric.

http://www.bankofengland.co.uk


It is important to clarify, at this point, that we use the terms borrowing, credit,

debt and indebtedness interchangeably in this paper. However, as Webley and Ny-

hus (2001, p. 426) point out these terms have distinct meanings in the psychology lit-

erature (and elsewhere). Specifically, whilst borrowing is planned and intended and

may involve the granting of credit, it ‘‘is possible to have a debt problem withoutever having borrowed money’’. In contrast ‘‘Debt is unplanned and unintended

and may be . . . a stage (for some) en route to default and bankruptcy’’.

The principal empirical finding in this paper is that those heads of households

who have outstanding credit, and who have higher amounts of such debt, are signif-

icantly less likely to report complete psychological well-being. The average increase

in the psychological distress associated with this form of indebtedness is greater

when outstanding credit is measured at the individual, as opposed to household, le-

vel and exceeds that from mortgage debt. The paper is organised as follows. In Sec-tion 2 we review the contribution of previous studies, from both the economics and

psychology literatures, to our understanding of psychological well-being and indebt-

edness. In Section 3, we introduce our data source, define the measures we employ

and describe our sample. Our empirical methodology is explained in Section 4 and

the estimates from our statistical models are discussed in Section 5. We summarise

our findings and present our conclusions in Section 6.

2. Literature review

2.1. Psychological well-being

The investigation of the factors affecting human well-being is central to the disci-

pline of psychology (see Kahnemann, Diener, & Schwarz, 1999, for a detailed review

of this literature). It is recognised that the best method to gain information about a

person�s perspective on their life or work is to ask them directly. Economists havetraditionally been more reluctant to use self-reported subjective measures of well-

being (Bertrand & Mullainthan, 2001) due to concerns about the interpretation of

such variables, the validity of inter-personal comparisons and the difficulty of mod-

elling such psychological outcomes (Jahoda, 1982, 1988).

Whilst life satisfaction measures are now more widely used by economists, in the

UK literature to date (e.g. Clark & Oswald, 1994; Shields & Wheatley Price, forth-

coming) individual well-being measures have been mainly based on the General

Health Questionnaire (see Appendix B for details). The ordered ranking of re-sponses, known as the GHQ12 score (Goldberg, 1972), is widely recognised as a reli-

able measure of psychological well-being (Argyle, 1989). From the perspective of

economic research, these psychological well-being (or, less precisely, �happiness�)measures provide directly observable proxies for individuals� well-being or �utility�.Recent years have seen a large number of economic studies using such variables

(see Frey & Stutzer, 2002; Oswald, 1997).

An enormous literature, throughout the social sciences, has focussed attention on

the associations between individual well-being and economic outcomes. Economists


have been mainly interested in the effects of unemployment and income. The obser-

vation that being unemployed generally leads to a significant deterioration in an indi-

vidual�s well-being has become a �stylised fact�, validated across countries, time

periods and data sources (e.g. Clark & Oswald, 1994). Importantly, the causal direc-

tion from unemployment to lower levels of self-reported well-being has been con-vincingly demonstrated using longitudinal and panel data (e.g. Clark, 2003). This

arises, not primarily due to the fall in income (Clark & Oswald, 2002), but mainly

due to the loss of the psychological benefits from �work�, such as social recognition,

self-respect and the opportunities for social interaction (Darity & Goldsmith, 1996;

Jahoda, 1982; Lane, 1991). Crucially, unobservable individual heterogeneity, which

potentially could explain variations in such measures (see Kahnemann et al., 1999),

is relatively unimportant in these studies (Clark & Oswald, 2002).

Economic theory assumes a strong positive influence of income on individualwell-being, but consistent empirical evidence of this is lacking which is supportive

of Lane�s (1991) contrasting view. For example Campbell, Converse, and Rodgers

(1976) and Easterlin (1974, 1995) found that income is a poor predictor of many

measures of individual well-being, whilst Clark and Oswald (1994) found no robust

association between income and the GHQ12 score in the UK. In contrast, most

European-based empirical studies find a small positive effect on self-reported life sat-

isfaction (e.g. Frey & Stutzer, 2000; Winkelmann & Winkelmann, 1998). Perhaps the

most convincing evidence that higher income levels may lead to significant improve-ments in individual well-being is provided by Frijters, Haisken-DeNew, and Shields

(2004), who follow the income and life satisfaction levels of East Germans after

reunification. An alternative hypothesis, that �relative� rather than �absolute� income

matters, has considerable empirical support (e.g. Clark & Oswald, 1996; Van Praag

& Frijters, 1999). Headey and Wooden (2004) and Headey, Muffels, and Wooden

(2004) argue that net wealth and non-durable consumption expenditures have

greater positive impacts on life satisfaction than income.

A number of other consistent correlates of individual well-being are reported. Forinstance, a U-shaped association with age has been found for many countries (e.g.

Clark, 2003) whereas marital dissolution and ill-health have significant adverse

effects on individual well-being (e.g. Kahnemann et al., 1999; Shields & Wheatley

Price, forthcoming). No consistent empirical support for the associations between

gender, educational attainment or the presence of children and measures of individ-

ual well-being has been found.

2.2. Personal debt: Causes

The economic psychology literature represents one area where there has been sig-

nificant interest in the determinants of personal debt. Livingstone and Lunt (1992)

investigated the determinants of the level of debt and repayments across individuals

and found that attitudinal factors, such as whether individuals are pro or anti debt,

were key correlates. Davies and Lea (1995) analysed student attitudes towards debt

and found that as students increased borrowing levels, in order to finance invest-

ments in human capital, their attitudes became more tolerant towards credit and


debt. Lea, Webley, and Levine (1993) also found that debt levels are strongly asso-

ciated with attitudinal factors and warned that the adaptation of these attitudes to

higher debt levels, in combination with the increased availability of credit could lead

to ‘‘a self-sustaining �culture of indebtedness�’’ (p. 118). They also demonstrated the

importance of economic circumstances in determining debt outcomes and found thatthose with a tendency to have one form of debt were more likely to have many other

forms of debt as well.

Research by economists into personal debt issues is surprisingly scarce, especially

in the UK. Godwin (1997) has explored the dynamics of US household credit use

and found considerable mobility in debt status during the 1980s. More recently,

Crook (2001) has shown that income, home ownership and family size all impact

positively upon the level of debt in US households, whilst expectations of future

interest rate changes appear to have no influence. Cox and Jappelli (1993) have esti-mated that on average, desired debt levels are 75% higher than actual levels amongst

US households, highlighting the role of credit constraints (Jappelli, 1990). Whilst

some of the latent demand for credit may be met from private transfers (Cox & Jap-

pelli, 1990), Gross and Souleles (2002) observed that debt levels rise in response to

increases in credit (card) limits.

One intriguing puzzle is the apparent targeting of a specific credit utilisation rate

by credit card holders (Gross & Souleles, 2002) thus failing to eliminate costly debt

using available liquid assets. Bertaut and Haliassos (2002) have proposed an accoun-tant-shopper model to explain such �debt-revolvers� and provide corroborating evi-

dence from the 1995 and 1998 US Surveys of Consumer Finance. They argue that

consumption decisions are dissociated from portfolio allocations within the house-

hold. The �accountant�, who is in charge of household financial decision-making, at-

tempts to control consumption expenditure by the �shopper�, through holding credit

card balances as a fixed proportion of their limit. Hence a certain level of debt is tol-

erated in order to prevent additional spending, despite high levels of liquid savings –

we address this potential asymmetry in the empirical results.In one of the few pieces of economic research on debt using UK data, Bridges and

Disney (2004) find that differences in the incidence of credit and default among low-

income households, are influenced by labour market status, age, access to social

security benefits and household composition. More recently, Brown, Garino, Taylor,

and Wheatley Price (2005) have shown that individuals and households with more

optimistic financial expectations incur more debt in the UK.

2.3. Personal debt: Consequences

A key issue is whether psychological attributes are determinants of observed debt

outcomes or whether they are the result of being indebted. In addition to the studies

noted above there is considerable evidence of a strong statistical association between

financial distress and severe psychological problems in the general population

(Weich & Lewis, 1998), including depression amongst British Civil servants (Mar-

mot, Ryff, Bumpass, Shipley, & Marks, 1997). There are also several studies noting

that indebted students are more likely to be exhibiting symptoms of psychological


distress (e.g. Roberts, Golding, & Towell, 1998; Stradling, 2001, Chap. 5). Roberts

et al. (1998) argue that this may be because psychologically distressed individuals

are more likely to get into monetary problems. Alternatively, the financial strain

and worry of being in debt may lead to a decline in psychological well-being.

Webley and Nyhus (2001) argue that, in many of the cross-sectional psychologicalstudies cited above, the causality is unclear. Their analyses of panel data from the

Netherlands find some evidence of causality from debt to psychological outcomes.

Further support for this contention is found in longitudinal studies, such as Marmot

et al. (1997) and Stradling (2001, Chap. 5). A final concern is that if the credit is ob-

tained in order to finance the purchase of consumer durables this might lead to an

increase in psychological well-being. Hence it is important to control for the poten-

tial utility gains from the presence of such goods in empirical models of psycholog-

ical well-being.

3. Data

3.1. Data

In this paper, we explore the empirical determinants of individual psychological

well-being in Great Britain, focusing on the influence of outstanding debt. Our anal-ysis is based on a sample of household heads drawn from the British Household Pa-

nel Survey (BHPS). Potential alternative UK datasets such as the Family Resources

Survey or the Family Expenditure Survey contain information on only one form of

debt each (mortgage repayments and personal loans, respectively) but include no

measure of psychological well-being. Uniquely, the BHPS contains information

on both the total outstanding amount of credit and individuals� psychological

well-being.

The BHPS is a nationally representative random sample longitudinal survey, car-ried out by the Institute for Social and Economic Research, of every adult in more

than 5000 private households in Great Britain. The first interviews were conducted

during the autumn of 1991 and annual re-interviews have taken place ever since.

Our sample consists of a balanced panel of 2193 heads of households, of working

age (16–65), who responded to both the 1995 and 2000 waves of the BHPS. We focus

on household heads as they, typically, have final responsibility for household finan-

cial decision-making (the �accountant� role – see Bertaut & Haliassos, 2002) and are

thus expected to bear the main psychological burden of the household�s financial sit-uation. A descriptive portrait of the main financial features of our sample is provided

in Table A1, in Appendix A.

3.2. Key variable definitions

As with previous studies of individual psychological well-being in the UK we

examine the inverse �caseness� version of the GHQ12 score, which sums the binary

values to the responses from each question (1 indicating a high level of psychological


well-being and 0 signifying otherwise). This results in scores of 0–12, where higher

numbers indicate increased psychological well-being.

Our definition of outstanding debt is based on the question, asked only in the

1995 and 2000 waves of the BHPS, ‘‘How much in total do you owe?’’ The question

clearly relates to outstanding (non-mortgage) credit as details about mortgage debtare asked in a separate question. This information is also used later as a point of

comparison. Similar self-reported measures of debt have been shown to be reliable

indicators of actual debt (Lea et al., 1993; Lea, Webley, & Walker, 1995). All finan-

cial measures used are deflated to 1995 pounds sterling. If the household head re-

ports a non-zero value of debt they are coded as 1 in a dummy variable indicating

the individual has outstanding credit.

A left-censored measure of the natural logarithm of the individual level of out-

standing credit is also constructed taking the value of zero for non-debtors andthe log of amount of outstanding (non-zero) debt otherwise. Similar household mea-

sures of debt are defined, based on summing the underlying individual responses

across all adult members within the household. The main limitations of these out-

standing credit measures are that the contract starting date, expected duration of

the loan, applicable rate of interest and the type of creditor are not explored in

the BHPS.

The BHPS does record information on the amount saved each month, which we

use to create measures of annual savings at both the individual and household level.Further questions on the amount held in investments, the receipt of a lump sum

windfall within the past year and the home owners subjective valuation of their

house provide the basis for our wealth controls. We also include controls for individ-

ual labour income or total household income in our empirical models. Finally, given

the importance of attitudinal factors in determining responses to psychological well-

being questions and financial expectations for debt levels we include dummy

variables indicating the perception of the household head of their financial situation,

relative to one year previously, and their expectation of the direction of change overthe forthcoming year (Katona, 1975). These variables implicitly incorporate a syn-

thesis of the respondents� personal financial outlook (e.g. income and labour market

position and prospects), their macroeconomic expectations (e.g. future interest and

taxation rates) and their general personality traits of optimism or pessimism.

3.3. Descriptive analysis

In our sample of household heads the mean value of the GHQ12 score is around10, which is close to the maximum of 12, and this does not vary much across waves

(see Table A1). Reflecting the overall growth in consumer credit in the UK (illus-

trated earlier in Fig. 1), the mean levels of individual and household outstanding

credit have increased by over 50%, between 1995 and 2000, in our sample. The indi-

vidual outstanding credit levels, of those household heads in debt, have risen from an

average of £1957, in 1995, to £3192 in 2000. Mortgage debt also grew, by nearly 30%,

over this period. It is also interesting to note that household heads are, on average,

the predominant debtors within the household, individually responsible for over 85%


of total debt. However, the proportion of individuals and their households in debt,

and with a mortgage, has declined over these five years (from 47% to 43% for indi-

vidual level debt and from 56% to 51% for household level debt), perhaps reflecting

their movement through the life-cycle.

Of particular relevance to this paper we find that those household heads withoutany debt have significantly higher (t-statistic = 3.09, p-value < 0.001) mean levels of

psychological well-being (10.17) than those with some non-zero level of debt (9.87 –

not shown in Table A1). This finding holds for both individual and household mea-

sures of debt and across both waves of data. Later we explore whether this finding is

robust to controlling for potentially confounding factors.

Average labour income for these household heads grew by just over 4%, in real

terms, between 1995 and 2000 whilst household income grew by over 12%. Interest-

ingly, individual annual savings grew by 20%, and investments declined by nearly20%, during this period. Household savings grew more slowly. The value of windfalls

received more than doubled though the proportion of household heads receiving

them fell substantially and house values rose by over 60%. Importantly, these finan-

cial variables are not highly correlated. Indeed these correlations are very low, typ-

ically around 0.1. In both 1995 and 2002 around 30% of household heads had

optimistic financial outlooks, both retrospectively and prospectively, whilst across

these five years a declining share had a negative opinion of their relative financial

position.

4. Empirical methodology

4.1. Psychological well-being

Given our ordered dependent variable, the 0–12 ranking in the GHQ12 score,

the statistical model we employ is the standard ordered probit model (see, forexample, Greene, 2003, pp. 736–740 for details) with constant thresholds. This

approach, as opposed to treating our dependent variable as continuous and fitting

a linear model, is standard in the economics literature on psychological well-

being. As noted by Fielding (1999), the linear model requires a number of restric-

tive assumptions, in particular, the assumption of cardinality, which is difficult to

accept in the present situation. Importantly, since we have two observations on

the same household heads, in 1995 and in 2000, we ensure the standard errors of

the estimated coefficients are corrected both for the clustering of observations andfor heteroskedasticity.

Our primary interest is in estimating the association between debt and psycholog-

ical distress in our sample of household heads. Firstly we explore whether the signif-

icant difference in psychological well-being, between debtors and non-debtors, which

we observed in the raw data still exists once other potentially confounding factors

are accounted for. We estimate this, and all subsequent, models using both individ-

ual and household measures of debt (with individual level and household level finan-

cial control variables, respectively). Secondly we examine the impact of the level of


outstanding credit (using the log variables defined earlier – with non-debtors as-

signed the value zero) on the psychological well-being of household heads. In these

empirical models, whose results are presented in Tables 1 and 2, we control for

monthly income, annual savings, investments, windfall payments received over the

previous year, outstanding mortgage loans and a subjective estimate of house value(in the case of home owners). Since our data comprises information on the same indi-

viduals in both 1995 and 2000 we additionally include a dummy variable for the 2000

wave, and its interaction with the outstanding credit measure, to explore whether the

estimated associations have changed significantly over this period.

Following previous studies of individual psychological well-being, particularly

those undertaken using data from the United Kingdom (e.g. Clark, 2003; Clark &

Oswald, 1994), we also include a number of standard explanatory variables in our

statistical models, namely age, gender, marital status, the number of dependent chil-dren and of adults in the household, ordinal indicators of self-reported health status,

labour market status, housing tenure, car ownership, educational attainment, ethnic-

ity and region of residence. Following Taylor (2002) we include variables indicating

whether individuals expect their financial situation to improve or worsen over the

following year as well as their assessments of their current financial position, relative

to a year ago. Lastly we control extensively for recent purchases of consumer durable

goods using dummy variables to indicate the purchase of a colour television, video-

recorder, freezer, washing machine, tumble dryer, dishwasher, microwave, computeror CD-player, within the past year.

4.2. Endogenous measures of debt and savings

In the specifications outlined above, we treat our measures of outstanding credit

level and annual savings as exogenous. However, it may be the case that the coeffi-

cients on our measures of debt underestimate the true magnitude of the association

between outstanding credit levels and psychological well-being if there exist unob-served individual-specific factors which determine both the extent of indebtedness

and reported psychological distress. For instance, the presence of credit rationing

will not only lower the debt levels of the affected individuals but may also impact

upon their levels of psychological well-being. Therefore, in the estimates reported

in Table 3, we relax this assumption and explore the importance of predicted mea-

sures of the outstanding credit level and a predicted measure of savings. In each case

we a use Tobit specification to estimate predicted debt and savings, whilst controlling

for a range of potentially important determinants. The predicted debt and savingsmeasures, calculated at both the individual and household levels as appropriate, then

replace the exogenous variables in the instrumented ordered probit models reported

in Table 3.

The model specification and main control variables for these predicted models

build on the determinants of debt models estimated in Brown et al. (2005). Log debt

and log savings are assumed to be determined by the same set of personal character-

istics and other control variables used in the psychological well-being models with

the following over-identifying covariates: in Models A & B the debt variable is

Table 1

Head of households� psychological well-being and financial behaviour

Covariates Individual financial

behaviour

Household financial

behaviour

b S.E. M.E. b S.E. M.E.

Aged 25–34 years old .0147 .0944 .0059 .0251 .0951 .0100

Aged 35–44 years old .0640 .1008 .0255 .0738 .1016 .0294

Aged 45–54 years old .1188 .1039 .0473 .1211 .1047 .0482

Aged 55–64 years old .3536** .1154 .1392 .3377** .1154 .1331

Male .1973** .0475 .0785 .2110** .0474 .0840

Log(individual labour income last month) .0329** .0101 .0131 – – –

Log(total household income last month) – – – .0314 .0270 .0125

Individual has outstanding credit �.1567** .0492 �.0625 – – –

Individual has outstanding credit in

2000 wave

.0546 .0689 .0218 – – –

Household has outstanding credit – – – �.1184* .0497 �.0472

Household has outstanding credit in

2000 wave

– – – .0324 .0689 .0129

Individual saves money each year .0928* .0400 .0370 – – –

Household saves money each year – – – .1104** .0402 .0440

Individual has investments .0217 .0411 .0086 .0236 .0408 .0094

Individual received a lump sum windfall .0347 .0394 .0138 .0303 .0393 .0121

Individual has an outstanding

mortgage loan

�.0666 .0507 �.0266 �.0628 .0509 �.0251

Log(value of house – home owners only) �.0016 .0105 �.0006 �.0028 .0108 �.0011

Believes financial situation is better than

1 year ago

�.0130 .0440 �.0052 �.0047 .0437 �.0018

Believes financial situation is worse than

1 year ago

�.4088** .0443 �.1614 �.4158** .0442 �.1641

Expects financial situation to improve

in next year

.0157 .0421 .0063 .0123 .0422 .0049

Expects financial situation to worsen

in next year

�.1912** .0613 �.0760 �.1875** .0612 �.0745

Observation from 2000 wave .0127 .0478 .0051 .0324 .0689 .0056

Log likelihood (constant only model) �7370.97 �7370.97

Log likelihood �6932.60 �6939.09

LR Model v2 (d.f. 60) 849.13** 831.78**

Sample size 4186

Notes:

1. Fitted values from ordered probit models with the inverse �caseness� version of the GHQ12 score as the

dependent variable. Balanced panel sample of heads of households present in both the 1995 and 2000

waves of the British Household Panel Study.

2. b is the coefficient of the covariate included in the model and S.E. is the estimated standard error of the

reported. b, M.E. is the simulated marginal effect of the change in the probability of an average indi-

vidual reporting complete psychological well-being (i.e. a GHQ12 score of 12), due to a change in the

explanatory variable.


Table 1 (continued)

3. Omitted categories: aged less than 25 years old, female, believes financial situation is the same as 1 year

ago and expects financial situation to remain the same in the next year.

4. * and ** indicate statistical significance at the 5% and 1% levels, respectively.

5. Controls for marital status, ethnicity, self-reported general health status, educational qualification

level, labour market status, living in rented accommodation, car ownership, the number of children

in the household, the number of adults in the household, region of residence and the type of consumer

goods possessed are also included in each model. These results are not reported for the sake of brevity.

6. Eleven constant thresholds were also estimated.


instrumented by whether the individual has a credit card whereas the individuals� so-cial class is used in Models C & D; savings is instrumented, in Models B & D ; only,

by whether the individual is part of an employer pension scheme and whether the

individual contributes to a private pension plan but remains exogenous in Models

A & C. The different instruments used and the different combinations with exoge-

nous and endogenous measures of savings provide some confidence in the robustness

of these results, given the inherent difficulties in finding valid instruments for both

debt and savings simultaneously.

4.3. Marginal effects

The coefficient estimates from ordered probit models indicate the change in the

latent variable arising from a change in the respective correlates. However, it is

also useful to evaluate the impact of a change in each explanatory variable, on

the cumulative probability of psychological well-being (Fielding, 1999). Therefore

we simulate and report these marginal effects (M.E.) for each estimated model.For the binary independent variables we report the change in the predicted prob-

ability of an otherwise average individual, reporting a GHQ12 score of 12 rather

than a lower score on either scale, when a particular characteristic holds compared

to when relevant base characteristic is present. These numbers show the separate

effect of each explanatory variable on an average individual�s probability of having

the highest level of self-reported psychological well-being, compared to lower lev-

els. This is to be the primary threshold of interest, with 49.70% and 52.88% of indi-

viduals reporting this category in 1995 and 2000 respectively. For the non-binaryindependent variables, which have all been included in natural logarithmic form,

the marginal effect indicates the change in the probability of reporting a GHQ12

score of 12 arising from a 1% increase in the underlying continuous variable from

its mean value.

5. Empirical results

The parameter estimates, associated standard errors and marginal effects from

our ordered probit models of psychological well-being are presented in Tables 1–3.

Models are fitted using both individual and household measures of the financial vari-

ables. In the reported models it is clear that the null hypotheses, of the Likelihood

Table 2

Head of households� psychological well-being – individual and household levels of credit and income

Covariates Individual credit and income Household credit and income


Aged 25–34 years old .0158 .0943 .0063 .0248 .0950 .0099

Aged 35–44 years old .0654 .1006 .0261 .0738 .1014 .0294

Aged 45–54 years old .1159 .1037 .0462 .1178 .1046 .0469

Aged 55–64 years old .3489** .1156 .1374 .3332** .1156 .1314

Male .2034** .0476 .0810 .2144** .0474 .0854

Log(individual labour

income last month)

.0336** .0101 .0134 – – –

Log(total household

income last month)

– – – .0324 .0272 .0129

Log(individual level of

outstanding credit)

�.0231** .0071 .0092 – – –

Log(individual outstanding

credit) in year 2000

.0121 .0095 .0048 – – –

Log(household level of

outstanding credit)

– – – �.0163* .0070 �.006:5

Log(household outstanding

credit) in year 2000

– – – .0081 .0093 .0032

Log(individual amount

saved each year)

.0126* .0060 .0050 – – –

Log(household amount

saved each year)

– – – .0154** .0059 .0062

Log(total amount held

in investments)

�.0017 .0055 .0007 .0016 .0054 �.0006

Log(amount received as a

lump sum windfall)

�.0018 .0065 .0007 .0006 .0065 �.0002

Log(value of outstanding

mortgage loans)

�.0077 .0049 .0031 �.0075 .0049 �.0030

Log(value of house – home

owners only)

�.0006 .0106 .0003 �.0040 .0105 �.0007

Believes financial situation

is better than 1 year ago

�.0133 .0441 .0053 �.0047 .0438 �.0019

Believes financial situation

is worse than 1 year ago

�.4089** .0444 .1615 �.4147** .0444 �.1637

Expects financial situation

to improve in next year

.0172 .0422 .0069 .0134 .0423 .0053

Expects financial situation

to worsen in next year

�.1925** .0613 .0765 �.1891** .0613 �.0752

Observation from 2000 wave �.0021 .0463 .0008 �.0021 .0504 �.0008

Log likelihood (constant only model) �7370.97 �7370.97

Log likelihood �6933.32 �6939.88

LR Model v2 (d.f. 60) 847.49** 831.45**

Sample size 4186

Notes: As Table 1.


Table 3

Parameter estimates using endogenous measures of outstanding credit and annual savings

Model Log(outstanding credit) Log(annual savings)


Individual credit and income

Estimates from Table 2 for comparison �.0231** .0071 �.0092 .0126* .0060 .0050

Model A – endogenous credit only �.0265* .0123 �.0106 .0140* .0060 .0056

Model B – endogenous credit and savings �.0256* .0122 �.0102 �.0113 .0177 .0045

Model C – endogenous credit only �.0448** .0159 �.0179 .0134* .0060 .0054

Model D – endogenous credit and savings �.0450** .0153 �.0180 .0093 .0177 .0037

Household credit and income

Estimates from Table 2 for comparison �.0163* .0070 �.0065 .0154** .0059 .0062

Model A – endogenous credit only �.0279# .0150 �.0111 .0165** .0059 .0066

Model B – endogenous credit and savings �.0277* .0150 .0110 .0446** .0172 .0178

Model C – endogenous credit only �.0334* .0171 �.0133 .0159** .0059 .0064

Model D – endogenous credit and savings �.0358* .0170 �.0143 .0447** .0172 .0178

Sample size 4186

Notes:

1. All covariates as in Table 2. The full results are not reported for the sake of brevity.

2. #, * and ** indicate statistical significance at the 10%, 5% and 1% levels, respectively.

3. Model A replaces the relevant log(outstanding credit) variable with its predicted value estimated from

a debt equation with the following over-identifying covariate: whether the individual has a credit card.

The log(annual savings) variable is treated as exogenous.

4. Model B replaces the relevant log(outstanding credit) variable with its predicted value estimated from a

debt equation with the following over-identifying covariate: whether the individual has a credit card.

The log(annual savings) variable is also replaced by its predicted value from a savings equation with

the following over-identifying covariates: whether the individual is part of an employer pension scheme

and whether the individual contributes to a private pension plan.

5. Model C replaces the relevant log(outstanding credit) variable with its predicted value estimated from a

debt equation with the following over-identifying covariates: dummy variables for the individuals�social class (occupational status). The log(annual savings) variable is treated as exogenous.

6. Model D replaces the relevant log(outstanding credit) variable with its predicted value estimated from

a debt equation with the following over-identifying covariates: dummy variables for the individuals�social class (occupational status). The log(annual savings) variable is also replaced by its predicted

value from a savings equation with the following over-identifying covariates: whether the individual

is part of an employer pension scheme and whether the individual contributes to a private pension

plan.


Ratio test that all estimated coefficients are equal to zero (Greene, 2003, pp. 390–

391), are clearly rejected. Our simple tests for any statistical difference in the size

of the association between psychological well-being and outstanding credit, or in

the level of psychological well-being, across the two waves (see the coefficients on

the 2000 wave shift and interaction controls) are also clearly rejected in all estimated

models. This suggests that there has been no attenuation in the psychological impact

of debt (as Davies & Lea, 1995, found amongst students) over this period, despite the

dramatic increase of the levels of outstanding credit amongst these household heads.


This argument is reinforced by the decline in the proportion of household heads with

debt over the two waves if it is assumed that those for whom debt caused the greatest

psychological distress were able to pay it off.

Given the focus of this paper we report and discuss only the estimated associ-

ations between the financial variables and psychological well-being in our sampleof household heads. The general determinants, such as older individuals and males

having significantly higher GHQ12 scores, are clearly in line with the findings of

previous UK studies of representative samples of the entire adult population. Of

particular relevance is the influence of income at both the individual and house-

hold levels. For household heads the level of their own labour income clearly

has a significantly positive influence on their reported levels of psychological

well-being.

However, the level of household income is not significantly associated with house-hold heads reported GHQ12 scores, despite an estimated coefficient of only a slightly

smaller magnitude. We explored directly how the income of other household mem-

bers affected our dependent variable by separately including such a variable (results

not reported). Its estimated coefficient was positive, but close to zero, and clearly

insignificant. Hence the average impact, of the household heads own labour income

and the contribution from the rest of the household, gives us the reported overall

insignificant result. We also explored whether this result was sensitive to the defini-

tion of household income and found that estimates using household equivalencemeasures (of income and other household financial variables) gave qualitatively

equivalent results (not reported).

5.1. Psychological well-being, debtors and non-debtors

Household heads who have some outstanding credit, at either the individual level

or within their household, report significantly lower levels of psychological well-

being than those with no debt. As reported in Table 1 the presence of individual(household) debt reduces the probability of scoring the maximum on the GHQ12

score by over 6% (nearly 5%). Interestingly household heads with secured debt, in

the form a mortgage loan, do not report significantly different levels of psychological

distress confirming our contention that these different forms of debt may have dis-

tinct psychological affects.

Household heads who save themselves, or whose households save, on a regular

basis are found to be around 4% more likely to report complete psychological

well-being than non-savers. Interestingly the positive benefit from being a saver isoutweighed by the negative effect of being in debt which, together with the differen-

tial impact of outstanding credit and savings being much greater when measured at

the individual, rather than household, level, suggests a clear asymmetry in the way

these financial behaviours influence psychological well-being. However, other finan-

cial assets appear to have little effect as those with more valuable houses, who have

investments or who have received a lump sum windfall within the past year are no

more likely to report higher levels of psychological well-being.


In common with Taylor (2002), we find a strong statistical association between

individuals� financial expectations and their levels of psychological well-being. In

particular, those who have a pessimistic view of their relative financial position re-

port significantly lower GHQ12 scores than otherwise equivalent household heads.

Interestingly the association with psychological well-being is more than twice asstrong for those who view their current financial position as worse than one year pre-

viously (M.E. � �0.16) as compared to those are pessimist about future financial

conditions (M.E. � �0.075).

5.2. Psychological well-being and the quantity of outstanding credit

In the estimates reported in Table 2 we have replaced the outstanding credit, sav-

ings, investments, windfalls and mortgage with their level measures (the log variablesdefined earlier) in our estimated models. Since the significance and magnitude of the

other estimated parameters are unaffected we focus our discussion on the effect of

these quantity measures. We find evidence of a negative statistical association be-

tween the levels of individual and household outstanding credit and household

heads� GHQ12 scores, though the size of the marginal effect of the former is approx-

imately 50% larger.

In order to appreciate the magnitude of these effects we consider how much addi-

tional monthly income would be required in order to offset the negative impact onthe probability of reporting complete psychological well-being, for an otherwise

average individual, of a 10% rise in the average level of outstanding credit. At the

individual level mean monthly labour income, over the whole sample, is £936.5.

The average level of outstanding credit, for those with some debt, is £2574.75 so

the average debt level over the whole sample is £1153.5 as only 45% of household

heads in the sample are in debt. A 10% increase in the level of outstanding credit

(i.e. an additional £115.35) would reduce the probability, of a household head with

otherwise mean characteristics, reporting a maximum GHQ12 score by 0.092(10 · �0.0092). To eliminate this effect monthly labour income would have to rise

by £64.30, nearly 7% (.092/.0134). Similarly average annual savings, of the whole

sample (£635.5), would need to increase by £116.93 or over 18% (.092/.005) in order

to maintain the average probability of complete psychological well-being. It is

important to emphasise that these are average effects over the whole sample of

household heads. Amongst those in debt the marginal increase in psychological

well-being would be larger and the corresponding offsetting effects would need to

be much more substantial.A clear limitation of these findings is that we only observe our measures of out-

standing credit in two time periods, 1995 and 2000, and so cannot firmly establish

that our parameter estimates are the �causal� effects of debt on psychological well-

being. However, a number of recent UK studies have demonstrated that the causal-

ity runs from many of our covariates to self-reported GHQ12 scores (e.g. Clark,

2003). Furthermore, the studies by psychologists, which have incorporated a longi-

tudinal element, support our contention that causality is primarily from increased


levels of debt to reduced levels of psychological well-being. Nevertheless, we

acknowledge that the possibility of reverse causality, where debt results from behav-

iour associated with psychological distress, in our sample.

The amount of regular (annual) savings is significantly associated with increased

levels of psychological well-being, amongst heads of households in our sample (M.E.of a 10% increase in annual savings �.05). As with the dummy variable indicators,

neither the quantity of total investments, the size of lump sum windfalls nor home-

owners� house valuations have any significant association with our self-reported psy-

chological well-being measure. Our disaggregated findings, for heads of households�GHQ12 scores, thus contrast with those of Headey and Wooden (2004) (using Aus-

tralian data) and Headey et al. (2004) who find that households net worth impacts

positively on adult life satisfaction scores in a number of countries, including Britain.

There are a number of possible explanations for the differences in our findings tothose of Headey et al. (2004), and the British data they use: Headey et al. (2004) fo-

cus upon a single cross section – the year 2000 only; they measure life satisfaction (a

7-point scale) rather than the GHQ12 inverse caseness score; and finally their mea-

sure of net worth is defined as assets minus debt. By definition net worth is a linear

combination of variables which we find insignificant (assets) and significant (debt).

As such it is perhaps not surprising that Headey et al. (2004) find that net worth

is significant – our detailed disaggregation of net worth suggests that debt may drive

this relationship.

5.3. Psychological well-being and endogenous measures of debt and savings

The results of fitting our ordered probit models using predicted measures of out-

standing credit levels and annual savings are presented in Table 3. In every case the

parameter estimate of the association between outstanding credit, at both the indi-

vidual and household levels, increases in magnitude. This confirms our contention

that the exogenous debt parameter estimates should be treated as lower bounds ofthe true effect. The coefficients on the exogenous savings variables change little when

predicted measures of debt are used. However, when we attempt to simultaneously

control for the potential endogeneity of savings, as well as our debt measures, we

find that the coefficient on predicted savings becomes insignificant at the individual

level. In contrast the household level estimates retain their statistical significance lev-

els and increase in magnitude. Hence, our estimates of the impact of our debt mea-

sures on psychological distress are robust to whether savings is also instrumented or

not.

6. Conclusions

In this paper we have explored the impact of debt on the self-reported psycholog-

ical well-being of household heads in Great Britain. Our ordered probit estimates are

based on a balanced panel sample from the 1995 and 2000 waves of the British

Household Panel Surveys. The evidence confirms our main hypothesis, that debt


is associated with increased levels of psychological distress. We also find that

unsecured debt, as measured by outstanding (non-mortgage) credit, has a greater

negative influence on psychological well-being than secured (mortgage) debt, for

whom no significant statistical relationship is found. Furthermore, we have

shown that the psychological effects of being in debt and being a regular saver arenot only opposing but asymmetric at the individual level. Our estimated marginal

effect of having outstanding credit is nearly double that of being a regular saver.

Finally, we find no positive psychological benefit from investments, windfalls or

house values justifying our disaggregated approach to controlling for assets and

liabilities.

Simple simulations have revealed that plausible proportionate changes in out-

standing credit levels are associated with a non-trivial decrease in the probability

of reporting the highest level of-psychological well-being. For an otherwise averageindividual a 10% increase in the level of individual outstanding credit would need a

7% increase in monthly income, or a 18% increase in annual savings, to offset the

negative impact on their psychological well-being. Additionally, we have presented

some econometric evidence, which suggests that our estimates of the size of the exog-

enous outstanding credit effects are downwardly biased. We conclude that there may

be a substantive psychological cost associated with consumer credit culture in Brit-

ain. Future government policy perhaps ought not to just focus on the potential mac-

roeconomic consequence of the rising levels of consumer indebtedness in the UK butalso take consider the more general welfare effects of increased psychological distress

amongst debtors.

Acknowledgements

We are indebted to the editor, Simon Kemp, two anonymous referees and Mike

Shields for their constructive comments on earlier drafts, which have helped usgreatly improve this paper. We are also grateful to the Data Archive at the Univer-

sity of Essex, for supplying the 1995 and 2000 waves of the British Household Panel

Surveys, and to the principal investigator (Institute for Social and Economic

Research, University of Essex), the data collectors (NOP Market Research Ltd

and the Office for National Statistics) and sponsors (Economic and Social Research

Council, Health Education Authority, Office for National Statistics and Eurostat).

This paper was written whilst Stephen Wheatley Price was on Study Leave

from the University of Leicester, whose financial support he gratefully acknowl-edges. All views expressed and any remaining errors are the authors� joint

responsibility.

Appendix A

See Table A1.

Table A1

Descriptive statistics for heads of householdsa

Variable 1995 2000

Mean or

proportionbStandard

deviation

Mean or

proportionbStandard

deviation

GHQ12 score 10.00 3.00 10.07 3.05

Aged 16–25 years oldb .089 .285 .017 .128

Aged 25–34 years oldb .267 .443 .191 .393

Aged 35–44 years oldb .275 .447 .297 .457

Aged 45–54 years oldb .259 .438 .267 .442

Aged 55–64 years oldb .110 .313 .228 .420

Malec .525 .499 .525 .499

Log(individual labour income last month) £916.8 894.9 £955.5 1033.2

Log(total household income last month) £1791.7 1263.7 £2021.0 1358.1

Individual has outstanding creditb .469 .499 .427 .495

Individual amount of outstanding creditc £1957.1 3631.3 £3192.4 4500.8

Household has outstanding creditb .564 .496 .511 .500

Household amount of outstanding creditc £2309.1 3938.6 £3645.1 5478.9

Individual saves money each yearb .404 .491 .417 .493

Individual amount saved each yearc £1412.0 2094.6 £1695.5 2648.0

Household saves money each yearb .513 .500 .521 .500

Household amount saved each yearc £1755.4 2351.4 £1973.1 2757.4

Individual has investmentsb .310 .463 .334 .472

Individual amount held in investmentsc £12078 42482 £9789.2 23449

Individual received a lump sum windfallb .452 .498 .262 .440

Individual amount received as a lump sum windfallc £2362.8 9438.6 £5383.3 17971

Individual has an outstanding mortgage loanb .603 .489 .562 .496

Value of outstanding mortgage loanc £39351 39864 £50626 50034

Individual lives in own homeb .756 .430 .797 .402

Value of housec £74048 48561 £119887 91157

Believes financial situation is better

than 1 year agob.293 .455 .314 .464

Believes financial situation is worse

than 1 year agob.316 .465 .239 .426

Expects financial situation to

improve in next yearb.318 .465 .290 .454

Expects financial situation to

worsen in next yearb.125 .330 .081 .273

Sample size 2193 2193

a Balanced panel sample of heads of households present in both the 1995 and 2000 waves of the British

Household Panel Study.b The proportion of the sample is reported, not the mean, where indicated.c All means reported are for those who have non-zero values of each financial variable. The means for

the whole sample can be obtained by multiplying by the proportion who have non-zero values of the

relevant variable.



Appendix B. The relevant British Household Panel Survey questions

These are self-completion questions which respondents undertake in the presence

of an interviewer.

In this paper we use the inverse of the GHQ12 caseness score (see Goldberg &Williams, 1988, pp. 11–12, for a detailed discussion). Therefore we assign a score

of 1 to a response indicating a high level of psychological well-being (i.e. the first

two categories) and a score of 0 otherwise.

The General Health Questionnaire 12 Score Questions

Instruction: We should like to know how your health has been in general over the

past few weeks.

Please answer ALL questions (indicating) which (choice of answer given in brack-

ets below each question) you think most applies to you.

HAVE YOU RECENTLY:

1. been able to concentrate on whatever you�re doing?

(better than usual; same as usual; less than usual; much less than usual)

2. lost much sleep over worry?

(not at all; no more than usual; rather more than usual; much more than usual)

3. felt that you are playing a useful part in things?(more so than usual; same as usual; less so than usual; much less than usual)

4. felt capable of making decisions about things?

(more so than usual; same as usual; less so than usual; much less than usual)

5. felt constantly under strain?


6. felt you couldn�t overcome your difficulties?


7. been able to enjoy your normal day-to-day activities?(more so than usual; same as usual; less so than usual; much less than usual)

8. been able to face up to your problems?

(more so than usual; same as usual; less so than usual; much less than usual)

9. been feeling unhappy and depressed?


10. been losing confidence in yourself?


11. been thinking of yourself as a worthless person?(not at all; no more than usual; rather more than usual; much more than usual)

12. being feeling reasonably happy; all things considered?

(more so than usual; same as usual; less so than usual; much less than usual).

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Debt and Distress - Evaluating the Psychological Cost of Credit

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