Corresponding author: John Gathergood, School of Economics, Sir Clive Granger Building, University of Nottingham, Nottingham NG7 2RD, UK. Email: email@example.com
Debt and Depression: Causal Links and Social Norm Effects*
Article first published online: 9 MAR 2012
© 2012 The Author(s). The Economic Journal © 2012 Royal Economic Society
The Economic Journal
Volume 122, Issue 563, pages 1094–1114, September 2012
How to Cite
Gathergood, J. (2012), Debt and Depression: Causal Links and Social Norm Effects. The Economic Journal, 122: 1094–1114. doi: 10.1111/j.1468-0297.2012.02519.x
I thank the Economic and Social Research Council for providing funding for my post-doctoral research fellowship, under grant number PTA-026-27-1664. The data and tabulations used in this publication were made available through the ESRC Data Archive. The data were originally collected by the ESRC Research Centre on Micro-social Change at the University of Essex (now incorporated within the Institute for Social and Economic Research). Neither the original collectors of the data nor the Archive bear any responsibility for the analyses or interpretations presented here.
- Issue published online: 3 SEP 2012
- Article first published online: 9 MAR 2012
- Accepted manuscript online: 12 JAN 2012 03:14PM EST
- Submitted: 21 January 2011 Accepted: 28 November 2011
Individuals exhibiting problems repaying their debt obligations also exhibit much worse psychological health. Selection into problem debt on the basis of poor psychological health accounts for much of this difference. The causality between problem debt and psychological health may be two way. Using individual level UK panel data, local house price movements exogenous to individual households are used to establish the causality from problem mortgage debt to psychological health. In addition, the social norm effects of problem debt are investigated using local bankruptcy and repossession rates. Results indicate there are sizeable causal links and social norm effects in the debt–psychological health relationship.
This article examines the relationship between household problem debt, otherwise known as over-indebtedness, and psychological health, using the UK’s household panel survey. Access to credit improves household welfare by facilitating consumption smoothing. However, inability to repay debts can result in drastic welfare losses arising from bankruptcy or the seizure of collateral such as housing. Psychiatrists commonly report problem debt as a source of severe anxiety and psychological distress (Fitch et al., 2007). In the medical literature small-scale studies based on individuals exhibiting poor mental health find problem debt to be a common correlate with depression, anxiety and even self-harm (Hatcher, 1994; Maciejewski et al., 2000; Reading and Reynolds, 2001).
There is a well-documented statistical association between problem debt and poor psychological health at the individual level. Studies based on large samples of cross-sectional survey data using self-reported health data show that the positive association between high levels of debt or usage of high-cost credit and poor psychological health is not readily explained by covariates such as demographic and related characteristics or other existing health conditions (Bartel and Taubman, 1986; Lea et al., 1995; Hamilton et al., 1997; Drentea, 2000; Brown et al., 2005; Lenton and Mosley, 2008). There is also evidence that high-debt households exhibit more prevalent adverse health behaviours which may be related to the formation of psychological health such as smoking and obesity (Grafova, 2007).
What is more difficult to establish is causality between problem debt and psychological health. The positive relationship between the two might be explained by unobserved factors not captured in cross-section analysis, or alternatively a two-way causality.1 Also, an individual’s perception of the severity of their debt problems may be affected by their psychological health state. An individual with poor psychological health might be more, or less, inclined to subjectively report they are struggling with debts compared to an individual with good psychological health in the same financial situation. Bridges and Disney (2010) find evidence from UK household survey data that objective measures of debt problems correlate more weakly with subjective evaluations of poor psychological health than subjective measures of debt problems.
This study investigates the relationship between problem debt and psychological health for the UK by using the British Household Panel Survey (BHPS) (previously used by Wildman, 2003 and Brown et al., 2005 in related studies).2 This study makes the following contributions. First, it documents the large cross-sectional inequality in psychological health between those with and without problem debts. This is shown for both subjective and objective measures of psychological health. Households who report they face ‘difficulty’ meeting their housing payments (mortgage or rent), or are at least two months late on their housing payments, or who report that meeting their consumer credit repayments presents a ‘heavy burden’ to their household, exhibit poorer psychological health. To establish this result is not driven by perceptions alone local-level delinquency rates are used as an instrument for self-reported debt problems. We also present evidence of poorer psychological health among the spouses or partners of respondents reporting debt repayment difficulties, suggesting the reporting of debt difficulties is not driven by the psychological health state of the respondent.
Second, this study shows that selection into problem debt on the basis of poor psychological health accounts for much of the observed cross-sectional variation in psychological health between those with and without problem debts. Individuals who move into arrears on their housing payments or into reporting a heavy burden of debts between two waves of data exhibited, on average, worse psychological health in the first wave of data compared with those not moving into debt problems. This positive selection into problem debt on the basis of poor psychological health accounts for most of the observed difference in health in the cross-section comparison between the two groups.
Third, this study estimates the causal impact of worsening problem debt on psychological health by using movements in local-level house prices as exogenous variation in the severity of the consequences of inability to meet mortgage debt commitments. It does so in the following manner: it is shown that mortgage holders who enter into arrears on their mortgage debts in localities where house prices are growing (and so their home equity ‘buffer’ is increasing) suffer less deterioration in psychological health compared to individuals who enter into arrears in localities where house prices are falling (and so their home equity ‘buffer’ is decreasing). This identification strategy also allows a natural comparison group – renting households – for whom the impact of rent arrears on psychological health is shown to be unaffected by local house price movements. This allows us to rule out the possibility that local house price movements simply act as a proxy for local economic conditions in these regressions.
Home equity buffers have been shown to be important forms of consumption insurance for households facing adverse income shocks (Hurst et al. 2004; Benito, 2009). We show that households with mortgage payment problems who suffer house price falls are less likely to be able to refinance or extract housing equity and, furthermore, incur higher future mortgage payment costs. This primarily arises due to households who experience mortgage payment problems typically doing so in the early years of their mortgage contracts when they are most likely to seek to refinance to avoid reset rates arising after short-term mortgage deals elapse. House price falls reduce the option to do so and increase the chances of households facing the reset rate.
Fourth, the impact of the onset of problem debt on psychological health is shown to demonstrate a ‘social norm’ effect.3 Individuals who exhibit the onset of problems repaying their unsecured debts in localities with a higher bankruptcy rate are shown to experience less deterioration in psychological health. In the context of a uniform bankruptcy law across localities (and so little reason to believe that non-payment on unsecured debts is more likely to result in a bankruptcy filing in one region compared with another) this result is interpreted as evidence of a reduced social stigma associated with problem debt in areas in which problem debt is more prevalent. Individuals who exhibit the onset of problems repaying their secured debts in localities with higher mortgage repossession rates also show less deterioration in psychological health.
1.1. The BHPS
The BHPS is a well-known long-running UK household panel survey which started in 1991 and has been conducted annually; the most recent available data is for the year 2008. All 18 available waves of data are used for this study. The BHPS is a general household survey which began with approximately 5,500 households with 10,000 individuals from England and Wales in 1991, interviewing adults in the household on a range of socioeconomic topics including their finances, labour market activity and health. As the health and debt data are central to this study, these are now considered in more detail.
1.2. Psychological Health Data
The BHPS includes two survey instruments related to psychological health. First, in the health module of the survey all adult respondents in the household are asked to identify the health problems or disabilities which they currently suffer from among those on a list, the most relevant of which for this analysis is ‘Anxiety, depression, bad nerves, psychiatric problems’. Respondents are asked to ignore temporary conditions when answering this question. We use answers to this question to construct an indicator variable which takes a dummy form with a value of 1 for yes and 0 for no.
Second, the BHPS also includes the General Health Questionnaire (GHQ) in each wave. The GHQ comprises a series of 12 questions in which respondents are asked to identify how frequently they currently feel, relative to their normal state, depression, anxiety leading to insomnia, inability to cope and a number of related feelings (details of the particular questions asked are provided in Appendix A). Responses to the GHQ forms the basis for the ‘GHQ Caseness Score’, also known as the ‘Caseness GHQ’, a well-known scale measure of psychological health used in the medical and psychological literature and increasingly in the economics literature as a measure of ‘mental’ or ‘psychological’ health or ‘wellbeing’ (such as Clark, 2003).4 The GHQ Caseness score is ordered between 0 and 12, with 12 representing the poorest state of mental health.
1.3. Data on Household Finances and Problem Debts
The BHPS asks respondents detailed questions on their household finances every five years (in waves 5, 10 and 15) so it is not possible to construct balance sheet information for each wave of the survey.5 In addition, the head of household is asked the following questions about their household’s financial situation. In all waves all households which either own a home via a mortgage or who rent a home are asked: ‘Many people these days are finding it difficult to keep up with their housing payments. In the last twelve months would you say you have had any difficulties paying for your accommodation?’ (Yes/No) followed by the question ‘In the last twelve months have you ever found yourself more than two months behind with your rent/mortgage’? (Yes/No). In addition, in each wave since 1995 all households with outstanding unsecured credit on which they are making repayments are asked: ‘To what extent is the repayment of such debts and the interest a financial burden on your household? Would you say it is.... A heavy burden, Somewhat of a burden, Not a problem’?
From answers to the first question we construct a (1/0) dummy variable for the respondent’s evaluation of their difficulty paying for their housing based on the yes/no answer. We consider this answer to be a ‘subjective’ response as the interpretation of the term ‘difficult’ might vary between households. From the second question we construct a (1/0) dummy variable for the respondent’s objective housing arrears position based on their yes/no answer. This is designated as an ‘objective’ measure because whether or not an individual is two months late on payments is not a matter of interpretation. From the third question we construct a (1/0) dummy variable for the respondent’s subjective evaluation of their difficulty meeting their unsecured debt payments which takes a value of 1 is the respondent reports ‘A heavy burden’ or ‘somewhat of a burden’ and a value of 0 if they report ‘not a problem’.
1.4. Summary Statistics
Summary statistics for household demographic characteristics, education, employment and income are provided in Table 1. Individual characteristics and psychological health data are provided for the head of the household. As the housing payment questions are asked in every wave but the consumer credit payment question is asked only for 1995 onwards, summary statistics are shown for two periods separately 1991–2008 and 1995–2008. Comparing households in the whole sample 1991–2008 (Column 1) with those with housing payment problems (Column 2, 9.7% of the sample) and those 2+ months late on housing payments (Column 3, 2.3% of the sample), households with payment problems of either type are typically younger, more likely to have a male household head, more likely to have children, be less educated, be in unemployment and have lower income. From Columns 4 and 5, those with consumer credit payment problems (16.2% of the sample) are typically more likely to have a male household head and have children, but there are less noticeable differences in other variables (particularly in household income).
|1. Whole sample||2. Housing payment problems||3. 2+ months late on housing payments||4. Whole sample||5. Consumer credit payments a heavy burden|
|Percentage of sample||100||9.7||2.3||100||16.2|
|Male = 1||0.41||0.48||0.50||0.42||0.46|
|Married = 1||0.67||0.56||0.51||0.67||0.67|
|Has children = 1||0.47||0.54||0.58||0.48||0.58|
|Ethnic minority group = 1||0.14||0.14||0.16||0.17||0.17|
|Home owner = 1||0.66||0.47||0.39||0.66||0.34|
|Highest educational qualification|
|GCSE = 1||0.32||0.35||0.38||0.32||0.34|
|A-level = 1||0.20||0.19||0.15||0.21||0.20|
|Degree = 1||0.16||0.10||0.06||0.17||0.16|
|Employed = 1||0.67||0.57||0.48||0.68||0.67|
|Self-employed = 1||0.10||0.10||0.09||0.09||0.07|
|Unemployed = 1||0.05||0.10||0.16||0.04||0.05|
|Household income (monthly, £s)||£2,100||£1,400||£1,200||£2,100||£2,000|
|GHQ12 Score (0–12)||2.03||3.50||4.04||2.04||2.87|
|Suffers anxiety = 1||0.08||0.16||0.21||0.09||0.14|
Comparing average measures of psychological health between heads of households with payment difficulties to those without payment difficulties: heads of households with housing payment problems exhibit GHQ scores which are on average 1.63% points higher and are 6% points more likely to report an anxiety-related illness. For the 2+ months late on housing payments indicator the differences are larger at 2.05% points and 12% points respectively and for the ‘heavy burden’ consumer credit payments indicator the differences are 0.98% and 6% points. These differences in means are in each case statistically significantly different from zero at very high levels of confidence. By way of comparison, these differences are also larger than the average difference in GHQ score between individuals in employment and those unemployed, which are 1.6% points and 4% points respectively.
2.1. Panel Evidence on Problem Debt and Psychological Health
This subsection explores whether these observed differences in psychological health between those with and without housing and consumer credit payment problems arise due to covariates, selection or psychological health-driven biases in the perception of problem debt. First, to address the role of associated covariates and selection multivariate panel regression, estimates are presented. Second, to test whether the differences arise due to perceptions, the self-reported problem debt indicators (which may be biased by perception) are instrumented using local-level data on delinquency rates.
First, those households with and without problem debts differ in a range of associated characteristics, as illustrated in Table 1. Also, the average differences between groups might arise due to selection into problem debt on the basis of poor psychological health, or alternatively selection out of problem debt on the basis of better psychological health. Table 2 presents a transition matrix of before and after average GHQ Caseness Scores and rates of reporting anxiety for individuals entering the housing and consumer credit problem debt states compared with those not entering problem debt states.6 A comparison suggests that, in each case most of the observed difference in psychological health by problem debt status in the cross-section arises due to positive selection into problem debt on the basis of poor psychological health. Those households exhibiting the onset of problem debts already had worse psychological health in the first wave.
|GHQ12 Score||Anxiety-related illness|
|Mean (SD) at t||Mean (SD) at t + 1||% (SD) at t||% (SD) at t + 1|
|No payment problems at t, payment problems at t + 1 (N = 2,413)||2.97 (3.59)||3.40 (3.87)||0.14 (0.35)||0.16 (0.37)|
|No payment problems at t, no payment problems at t + 1 (N = 42,134)||1.78 (2.90)||1.80 (2.95)||0.07 (0.26)||0.07 (0.26)|
|Not 2+ months late t, 2+ months late at t + 1 (N = 648)||3.48 (3.88)||4.06 (4.16)||0.19 (0.39)||0.24 (0.43)|
|Not 2+ months late t, Not 2+ months late at t + 1 (N = 43,899)||1.93 (3.03)||1.94 (3.07)||0.08 (0.27)||0.08 (0.27)|
|Consumer credit repayments|
|Not a heavy burden at t, heavy burden at t + 1 (N = 3,561)||2.42 (3.37)||2.64 (3.53)||0.12 (0.32)||0.12 (0.33)|
|Not a heavy burden at t, not a heavy burden at t + 1 (N = 31,949)||1.78 (2.94)||1.78 (2.96)||0.08 (0.27)||0.08 (0.27)|
Panel regression estimates are presented in Table 3. The vector of control variables includes a broad range of demographic, education and employment, financial and related controls, details of which are provided in the notes accompanying the Table, plus regional dummies and year dummies. In each case, the dependent variable is the psychological health measure (the GHQ Caseness Score in Columns 1 and 2, the indicator variable of anxiety-related illness in Columns 3 and 4). Pooled panel estimates are presented alongside household fixed-effects estimates. Each model includes the three indicators of payment problems and is estimated for the 54,731 households present in at least two consecutive waves of the survey between 1995 (the first year in which the consumer credit question was asked) and 2008. In the pooled panel estimates in Columns 1 and 3, psychological health improves with employment and self-employment and is worse for men, the unemployed, those divorced and older individuals (nonlinearly). The coefficients on each of the problem debt measures are positive and significant at the 1% level. The coefficients on these variables are in each case considerably smaller than the unconditional differences in means between those with and without problem debts provided in Table 2.
|Dependent variable: GHQ-12 Score (O.L.S.)||Dependent variable: Anxiety-related illness (LPM)|
|(1) Housing payment ‘problems’||1.15** (0.05)||0.62** (0.05)||0.05** (0.01)||0.02** (0.001)|
|(2) 2+ months late housing payment||0.52** (0.11)||0.47** (0.11)||0.06** (0.01)||0.03** (0.01)|
|(3) Consumer credit ‘heavy burden’||0.75** (0.04)||0.33** (0.04)||0.04** (0.01)||0.01** (0.003)|
|(4) Age (years)||0.11** (0.01)||0.08** (0.02)||0.01** (0.00)||0.01** (0.00)|
|(5) Age squared (years)||−0.001** (0.00)||−0.001** (0.00)||−0.001** (0.00)||−0.001** (0.00)|
|(6) Male = 1||0.48** (0.03)||–||0.04** (0.00)||–|
|(7) Married = 1||0.12** (0.05)||−0.02 (0.09)||0.01 (0.01)||0.01 (0.01)|
|(8) Divorced = 1||0.48** (0.05)||0.43** (0.10)||0.04** (0.01)||0.03** (0.01)|
|(9) Employed = 1||−1.52** (0.05)||−0.85** (0.07)||−0.16** (0.01)||−0.07** (0.01)|
|(10) Unemployed = 1||0.32** (0.07)||0.31** (0.08)||0.11** (0.01)||0.05** (0.01)|
|(11) Self-employed = 1||−1.61** (0.06)||−0.86** (0.09)||−0.17** (0.01)||−0.07** (0.01)|
|Mean in sample||2.04||2.04||0.09||0.09|
|SD in sample||3.16||3.16||0.29||0.29|
In the models including household fixed effects (Columns 2 and 4) many of the covariates remain statistically significant. The coefficients on the problem debt variables are reduced in magnitude but remain statistically significant at the 1% level. The associations between problem debt and GHQ scores in Column 2 compared with the unconditional comparison (values given here in parenthesis) are: subjective difficulty paying for housing 0.62 (1.63), 2+ months late with housing payment 0.47 (2.05), subjective difficulty paying for consumer credit 0.33 (0.92). In Column 4, the equivalent values are 0.02 (0.08), 0.03 (0.12) and 0.01 (0.06) respectively. Therefore, in each case the magnitude of the association between problem debt and poor psychological health falls by at least two-thirds. These fixed-effects estimates establish there is a clear association between the onset of problem debt and the worsening of psychological health at the household level controlling for time-invariant heterogeneity but that the estimated effects are weaker than in the unconditional comparisons. Of course, these results do not establish the direction of causality between these contemporaneous changes.
Secondly, one difficulty with using the subjective measures of problem debt is the possibility that self-reported measures of problem debt or arrears might themselves be biased by an individual’s mental health state. If a respondent’s mental health state impacts upon their perception of their problem debt state, subjective measures of ‘problems’ and ‘burdens’ reported by respondents could be unreliable. Ideally, we would use lender-provided debt data, or data externally validated by some other means, which is not possible in the BHPS. Instead we present evidence against this bias by using two alternative approaches.
First, we instrument subjective responses using lender-provided measures of local-level mortgage and consumer credit delinquency, exploiting geographic variation in non-payment of debts. These local-level measures will correlate with actual debt problems but not purely perceived problems; although there is the possibility that poor mental health may affect the perceptions of individuals such that they report their own debt state to be something closer to the problematic state they observe in others in their locality. Results show the geographic concentration of housing and consumer credit payment problems are strong instruments for self-reported payment problems. Table 4 presents IV estimates in which these local-level rates are used as instruments alongside OLS estimates.7 In each case the IV procedure is implemented using two-stage least squares.8 In each case the instruments are precisely defined at the 0.001% level. Results from the second stage regressions return coefficients of very similar magnitude to those in Table 3.
|GHQ12 Score||Anxiety-related illness|
|(1) Housing payment problems||(2) Credit burden||(3) Housing payment problems||(4) Credit burden|
|Dependent variable: GHQ12 Score (0–12)||OLS||IV 2nd stage||OLS||IV 2nd stage||LPM||IV 2nd stage||LPM||IV 2nd stage|
|(1) Housing payment ‘problems’||0.77** (0.04)||0.76** (0.10)||–||0.02** (0.001)||0.02** (0.003)||–|
|(2) Consumer credit ‘heavy burden’||–||0.67** (0.04)||0.57** (0.06)||–||0.01** (0.001)||0.01** (0.002)|
|IV 1st stage||IV 1st stage||IV 1st stage||IV 1st stage|
|(3) Local mortgage arrears rate||–||0.85** (0.04)||–||–||–||0.85** (0.04)||–||–|
|(4) Consumer credit delinquency rate||–||–||–||0.90** (0.04)||–||–||0.90** (0.04)|
|Mean in sample||2.03||2.03||2.04||2.04||0.09||0.09||0.09||0.09|
|SD in sample||3.12||3.12||3.16||3.16||0.28||0.28||0.29||0.29|
Second, we examine the relationship between the payment ‘problems’ and ‘burdens’ responses given by the household head and the psychological health of the household head’s spouse or partner. If the household head’s perception of a payment difficulty arises due to his or her mental health state and not due to an actual difficulty, we would not expect to find a positive relationship between the head of household’s answers to the payment difficulty questions and the psychological health of the household head’s spouse or partner. If the payment difficulty is an actual problem and not purely a perception, we would expect to observe the psychological health effects for a partner or spouse who shares in the household’s financial situation. Of course, in the latter case we would not expect the household head’s psychological health to correlate perfectly with the psychological health of a spouse or partner as observed psychological health arises due to combinations of genetic, historical and environmental factors not all shared by household members. As we have individual level data for households in our sample, we can examine this relationship.
Table 5 presents estimates from models in which the partner/spouse’s psychological health data are the dependent variable and the household head’s responses to the payment difficulty questions enter as a dependent variable. For completeness, we include the full set of household head control variables together with control variables for partner/spouse characteristics. As can be seen from Table 5, in each case the coefficients on the payment problem and burden variables are positive, statistically significant and have magnitudes similar to those in Table 3. Hence head of household reported payment difficulties are associated with poorer psychological health on the part of his or her spouse or partner. On this basis, we conclude that the self-reported data on payment difficulties is not severely affected by a perception-bias.9
|Dependent variable: Spouse GHQ-12 Score (O.L.S.)||Dependent variable: Spouse anxiety-related illness (LPM)|
|(1) Housing payment ‘problems’||0.90** (0.09)||0.59** (0.10)||0.06** (0.01)||0.02** (0.01)|
|(2) 2+ months late housing payment||0.60** (0.20)||0.71** (0.21)||0.03** (0.01)||0.02** (0.01)|
|(3) Consumer credit ‘heavy burden’||0.57** (0.06)||0.25** (0.05)||0.04** (0.01)||0.01** (0.004)|
|Mean in sample||1.94||1.94||0.08||0.08|
|SD in sample||3.02||3.02||0.26||0.26|
2.2. Evidence From House Price Changes
The results from the previous subsection document that the onset of problem debt is associated with deterioration in psychological health but the causality between these two might run in either direction. An obvious identification strategy is to exploit exogenous variation in sources of psychological health or problem debt, that is, a variable correlated with psychological health which is exogenous to changes in individual indebtedness or, conversely, a variable correlated with problem debt which is exogenous to individual changes in psychological health. This study uses a source of exogenous source of variation in the severity of an individual’s problem debt: housing equity shocks arising from movements in local-level house prices which make the consequences of arrears on mortgage payments more or less severe.
The rationale for this is as follows. Unlike mortgage debt, movements in the value of an individual’s property are largely exogenous to the actions of the individual household. However, house price movements do impact upon the severity of late or non-payment of mortgage debts via their effect on the housing equity a homeowner owns in their home. If faced with difficulty paying a mortgage it is unambiguously better for an individual to face such a scenario with more rather than less housing equity.10 The null hypothesis under such an exercise is that individuals who exhibit the onset of problems paying for their housing but contemporaneously benefit from a positive housing equity shock will see less deterioration in their psychological health as the effects of their payment problems are mitigated in part by their equity gain.
Using local-level house price shocks as an instrument for the severity of problem mortgage debt also has the attraction of presenting a natural comparison group: renters, who experience late payment of their housing payments but do not benefit from increases in the value of the home in which they are resident. Of course, assignment into housing tenure is not exogenous to the individual household (unlike the value of house price changes in the locality). Renters are typically younger, with lower incomes and less likely to have children, so these and related covariates need to be included as additional controls in the econometric model. There is also an added advantage to the homeowners – renters comparison: one objection to interpreting house price shocks as a proxy for housing equity movements is that positive house price shocks might also reflect positive local income shocks (which increase housing demand and so cause house values in the locality to increase). Comparing the outcomes for renters with homeowners allows us to exploit renters as a comparison group who experience the effects of local income shocks correlated with house price movements but not the shocks in home equity.
To show the relevance of house price movements to homeowner’s mortgage activity and the particular relevance of changes in housing equity for homeowners with mortgage arrears, a series of models are first estimated to quantify the impact of house price movements on household payment problems, mortgage refinancing and equity extraction plus mortgage costs. This is done to substantiate the idea that house price movements are relevant for the psychological health of households, particularly those with payment difficulties. Local-level house price data are obtained from the Halifax Building Society (now Lloyds-Halifax Bank of Scotland) Mix-Adjusted House Price Index (2011), which is available at the county level.
Table 6 presents estimates from a number of panel data models for household mortgage activity. In the first column, the dependent variable is whether the household is 2+ months late on mortgage payments in the next wave. The coefficient on the house price term is statistically insignificant. Local house price movements have no effect on the likelihood of future payment arrears. However, the coefficient on the interaction term (in the third row) implies that households who are 2+ months late on their housing payments at t and experience a subsequent decline in local-level house prices of £10,000 are, compared to the baseline predicted probability, twice as likely to be 2+ months late on their payment at t + 1. The estimates in the subsequent columns show that those households who are 2+ months late on their mortgage payments at t and experience house price falls between t and t+1 are less likely to refinance (Column 3), less likely to withdraw home equity (Column 4) and, crucially, experience higher future mortgage payments (Column 5). Therefore, households with mortgage arrears who experience price falls are more likely to face future arrears, higher mortgage costs and less scope to refinance, including equity withdrawal. We would, therefore, expect such households to suffer increased psychological stress.11
|Whole sample||Mortgage holders only|
|(1) 2+ months late on housing payment t + 1||(1) Refinances mortgage t + 1||(2) Withdraws equity t + 1||(3) Change in monthly mortgage payments (%) t + 1|
|(1) 2+ months late||0.09** (0.01)||−0.07** (0.02)||−0.02** (0.003)||0.09** (0.03)|
|(2) Δ house price (£’0,000s)||−0.01 (0.01)||0.01** (0.002)||0.01** (0.006)||0.01 (0.01)|
|(3) 2+ months late × Δ house price (£’0,000s)||−0.02** (0.004)||0.02** (0.004)||0.01** (0.002)||−0.03** (0.003)|
|Mean in sample||0.02||0.15||0.06||−0.02|
|SD in sample||0.15||0.31||0.12||0.10|
Following on from these results, we now present estimates of the impact of house price movements on psychological health for households with and without payment problems. Results are presented in Table 7 (in which the GHQ score is the dependent variable) and Table 8 (in which anxiety as a medical condition is the dependent variable). In each case results are presented firstly for the sample of owners only (Column 1), then in a model including the renters comparison group (Column 2) and finally also for the sample in which consumer credit payments are observed and interacted with the house price movement (Column 3).
|Dependent variable: GHQ12 Score||(1) Owners only||(2) Renters comparison group||(3) Consumer credit problems|
|(1) 2+ months late||1.18** (0.12)||1.24** (0.21)||–|
|(2) Δ house price (£’0,000s)||−0.006 (0.01)||−0.01 (0.01)||−0.01 (0.01)|
|(3) 2+ months late × Δ house price (£’0,000s)||−0.42** (0.11)||0.11 (0.12)||–|
|(4) 2+ months late × owner||–||0.21** (0.03)||–|
|(5) Δ house price (£’0,000s) × owner||–||−0.05 (0.03)||–|
|(6) 2+months late × Δ house price (£’0,000s) × owner||–||−0.64** (0.18)||–|
|(7) Heavy burden||–||–||0.47** (0.07)|
|(8) Heavy burden × Δ house price (£’0,000s)||–||–||0.01 (0.04)|
|(9) Heavy burden × owner||–||–||0.15 (0.09)|
|(10) Δ house price (£’0,000s) × owner||–||–||−0.05 (0.03)|
|(11) Heavy burden × Δ house price (£’0,000s) × owner||–||–||−0.21 (0.12)|
|Average in sample||1.76||2.04||2.04|
|SD in sample||2.89||3.12||3.16|
|Dependent variable: Whether suffers anxiety (1/0)||(1) Owners only||(2) Renters comparison group||(3) Consumer credit problems|
|(1) 2+ months late||0.03** (0.006)||0.02** (0.008)||–|
|(2) Δ house price (£’0,000s)||0.001 (0.001)||0.001 (0.001)||0.001 (0.001)|
|(3) 2+ months late × Δ house price (£’0,000s)||−0.004** (0.001)||0.01 (0.02)||–|
|(4) 2+ months late × owner||–||0.02** (0.007)||–|
|(5) Δ house price (£’0,000s) × owner||–||−0.01 (0.01)||–|
|(6) 2+months late × Δ house price (£’0,000s) × owner||–||−0.005** (0.001)||–|
|(7) Heavy burden||–||–||0.01** (0.003)|
|(8) Heavy burden × Δ house price (£’0,000s)||–||–||0.002 (0.003)|
|(9) Heavy burden × owner||–||–||0.02 (0.01)|
|(10) Δ house price (£’0,000s) × owner||–||–||−0.003 (0.006)|
|(11) Heavy burden × Δ house price (£’0,000s) × owner||–||–||−0.01 (0.006)|
|Average in sample||0.06||0.09||0.09|
|SD in sample||0.23||0.28||0.30|
In Table 7, Column 1 the coefficient on the change in the county-level house price is statistically insignificant. Hence house price changes do not affect the psychological health of homeowners. The interpretation of the coefficient on the interaction term is that an individual who experiences the onset of housing payment arrears but a simultaneous positive increase in local-level house prices of £10,000 experiences a deterioration in their GHQ score of 0.42 points less than an individual who does not experience a positive house price gain.
In Column 2, the renters comparison group is introduced into the model. Results indicate the negative impact of falling house prices on GHQ scores is specific to homeowners only. The interaction term between late payment and the homeowner dummy implies homeowners with late payments suffer worse GHQ scores compared with renters. The interaction term between late payment, homeownership and the house price change indicates homeowners with late payments who suffer house price falls experience an increase (worsening) of their GHQ score. This effect is limited to homeowners only, with no impact for the renter comparison group. The magnitude of the interaction term implies a homeowner in late payment who suffers a £10,000 fall in house price experiences an increase in their GHQ score of 0.64 points.
Column 3 presents estimates for the same model as Column 2 but with consumer credit payment problems as the problem debt variable and is estimated over the 1995–2008 sample of households. Homeowners with consumer credit payment problems will also see their financial situation worsen if house prices fall as their scope for extracting home equity to repay outstanding consumer credit will diminish. The direction and magnitudes of the homeowner interaction terms are similar to before: homeowners who are late with payments have worse GHQ scores; homeowners late with payments who experience house price falls also have worse GHQ scores. However, the coefficients on these interactions are not statistically significantly different from zero.
Table 8 presents results from models with the (1/0) dummy variable for whether the individual suffers from anxiety or a related condition as the dependent variable. Results here reveal the same pattern as in Table 7. The pattern in the coefficients in the model for homeowners only (Column 1) shows negative local house price movements which accompany the onset of housing payment problems result in increased likelihood of suffering an anxiety-related medical condition for homeowners who are late on their housing payments. In Column 2, the relationship to the reference renters group is the same as before with no effect of house price movements on renters. The model for consumer credit in Column 3 returns expected signs on the homeowner and consumer credit burden interaction terms but these are again not statistically significantly different from zero.
Taken together, these results show that exogenous variation in the severity of arrears on housing payment arising from local-level house price movements causally impact the extent of deterioration in psychological health (by either of the measures used). This effect is stronger for homeowners with late payments on their housing debts than those who experience a heavy burden of consumer credit. An explanation for this difference is that the financial characteristics of households late on housing payments appear much worse than those households facing a heavy burden of consumer credit such that the housing equity buffer is more important for the latter group than for the former (see Table 1).
2.3. Social Norm Effects in the Debt–Depression Relationship
This final subsection in the analysis investigates the existence of social norm effects in the relationship between problem debt and psychological health. To the author’s knowledge, such effects have not been investigated elsewhere. This is perhaps surprising: a growing empirical literature in economics finds that individual perceptions and choices are influenced by those of others.12 This raises the prospect that social norm effects might be present in the relationship between problem debt and psychological health.
The particular social norm effect hypothesised here is the impact of social stigma arising from problem debt. The literature on the social stigma of individual indebtedness and adverse debt outcomes such as bankruptcy presents evidence that higher reference group bankruptcy rates diminish the social stigma associated with being declared bankrupt (Fay et al., 2002; Cohen-Cole and Duygan-Bopp, 2008). The falling stigma of bankruptcy has been widely cited as a reason why the bankruptcy filing rate increased rapidly in both the UK and US during the early 2000s, despite little change in the number of individuals who might benefit financially from filing. As with unemployment, the negative psychological effect of high debt might arise in large part due to the perceived stigma of problem debts rather than the material losses incurred by over-indebtedness and bankruptcy.
The existence of reference group effects is investigated in the following manner. Two contexts for problem debts are considered: problem housing debt in the context of the prevailing local housing repossession rate; and problem consumer credit debts in the context of the prevailing local personal insolvency rate. County-level repossessions data are provided by the Council for Mortgage Lenders. For bankruptcy data, we use data on the bankruptcy orders issues by courts in England and Wales provided by the Insolvency Service. So in both cases the ‘reference group’ level of bankruptcy is defined at a relatively broad ‘local’ definition.
Tables 9 and 10 present results for models in which these reference group rates of mortgage repossessions among mortgage holders and individual bankruptcies (cases per 100) are included in the specification in an interaction term to capture the impact of the local bankruptcy/repossession rate on the psychological health of those with problem debt. In Table 9 the GHQ score is the dependent variable, in Table 10 anxiety as a medical condition is the dependent variable. Column 1 presents estimates for a model estimated on a sample of all individuals. The reference group effect is captured by interacting the dummy variable for individuals exhibiting 2+ months late on payments with the local repossessions rate.
|Dependent variable:GHQ12 Score||(1) Repossession rate||(2) Bankruptcy rate|
|(1) 2+ months late||1.06** (0.09)||–|
|(2) Repossession rate||0.001 (0.002)||–|
|(3) 2+ months late × repossession rate||−0.005 (0.003)||–|
|(4) 2+ months late × owner||0.34** (0.07)||–|
|(5) Repossession rate × owner||0.004 (0.003)||–|
|(6) 2+ months late × repossession rate × owner||−0.024* (0.010)||–|
|(7) Heavy burden||–||0.41** (0.05)|
|(8) Bankruptcy rate||–||0.045 (0.053)|
|(9) Heavy burden × bankruptcy rate||–||−0.010** (0.003)|
|(10) Heavy burden × owner||–||0.06 (0.04)|
|(11) Bankruptcy rate × owner||–||0.01 (0.01)|
|(12) Heavy burden × bankruptcy rate × owner||–||−0.014** (0.004)|
|Average in sample||2.04||2.04|
|SD in sample||3.12||3.16|
|Dependent variable:GHQ12 Score||(1) Repossession rate||(2) Bankruptcy rate|
|(1) 2+ months late||0.02** (0.006)||–|
|(2) Repossession rate||−0.001 (0.001)||–|
|(3) 2+ months late × repossession rate||−0.001 (0.001)||–|
|(4) 2+ months late × owner||0.02** (0.004)||–|
|(5) Repossession rate × owner||−0.001 (0.001)||–|
|(6) 2+ months late × repossession rate × owner||−0.001* (0.0005)||–|
|(7) Heavy burden||–||0.02** (0.004)|
|(8) Bankruptcy rate||–||0.001 (0.001)|
|(9) Heavy burden × bankruptcy rate||–||−0.001** (0.0003)|
|(10) Heavy burden × owner||–||0.01 (0.01)|
|(11) Bankruptcy rate × owner||–||0.001 (0.001)|
|(12) Heavy burden × bankruptcy rate × owner||–||−0.001* (0.0005)|
|Average in sample||0.09||0.09|
|SD in sample||0.28||0.30|
Results firstly reveal the rate of local repossession rate has no impact on wellbeing independent of late payments (row 2). However, the coefficient on the interaction term (row 6) implies that individuals experiencing the onset of mortgage arrears in regions in which mortgage arrears are more prevalent see less deterioration in their psychological health scores compared with individuals who exhibit an onset of mortgage arrears in regions with lower mortgage arrears rates. The coefficient value implies this effect is small. The mean repossession rate across all region-year observations is 0.89% and range from the 25th to the 75th percentile is 0.53%. The coefficient value of 0.024 implies a 0.5% point increase in the repossession rate is associated with a 0.012 point reduction in the GHQ Caseness Score. On this basis, a very high regional repossession rate of 10% would be required to offset approximately one quarter of the negative effect of late payment on the GHQ Caseness Score (a 10% rate would reduce the GHQ Score by 0.24 points, the coefficient on the late payment variable is 1.06 (row1)).
In Column 2, a similar exercise is undertaken for the case of the subjective consumer credit payments burden question and the regional bankruptcy rate. The interaction term between the two is again statistically significant (at the 1% level) both for renters (row 9) and owners (row 12), and stronger for owners. The magnitudes imply the onset of consumer credit problem debt in a region with a bankruptcy rate of 10% leads to approximately half the deterioration in psychological health which would be experienced at a bankruptcy rate of 0%. Table 10 repeats the exercise from Table 9 with the objective psychological stress measure as the dependent variable. In these specifications the interaction terms on reference-level mortgage arrears and the bankruptcy rate are both negative but are much less statistically significant.
These results suggest some evidence in favour of the existence of social norm effects with stronger and more statistically significant effects for the subjective measure of psychological health. The results indicate that the psychological impact of problem debt, both mortgage debt and consumer credit debt, is less severe for individuals who live in localities in which problem debt is more widespread. This result is in keeping with the finding from the unemployment literature that the effect of unemployment on psychological health is less severe in localities in which unemployment is more prevalent (Clark, 2003). One interpretation of these results is that the social norm of problem debt, through peer group effects in localities in which problem debt is more prevalent, lessens the anxiety and worry caused by an individual’s problem debt position.
This study has investigated the relationship between problem debt and psychological health. Results demonstrate that much of the cross-sectional variation in problem debt and psychological health is attributable to omitted variables and selection. However, results show that exogenous factors which make the consequences of problem debt more severe do impact upon respondents’ psychological stress. Furthermore, results provide strong evidence that respondents’ reactions to problem debt have a non-negligible social dimension in which the prevailing local level of indebtedness impacts on individual psychological stress. These results suggest a role for policy towards helping individuals who suffer both problem debt and depression, both in terms of recognition that those individuals with problem debts may need psychological help and that peer effects might help to mitigate the impact of problem debt on an individual’s psychological health. Policy initiatives have emerged in recent years. For example, beginning in 2005, The Money Advice Liaison Group, a group of credit counselling agencies and representatives from the credit industry, have developed (voluntary) guidelines for creditors dealing with debtors with mental health problems.13
The key advantage of using the BHPS for this study is that it includes data on the geographical location of the household, not available in the Families and Children Survey (FACS) used in Bridges and Disney (2010). These data are crucial for the instrumental variable strategy used to identify the causal impact of problem debt (which uses local-level house price movements) and to establish reference group effects (which are defined at the local level) later in the article. The BHPS also has the advantage of including the General Health Questionnaire (GHQ) as an alternative to self-reported data on anxiety-related medical conditions. Also, the BHPS has the advantage of being more representative of the population as a whole. Whereas FACS surveys only family units with children and in the vast majority of cases interviews the mother (with women twice as likely to report depression compared with men in the UK), the BHPS is a representative sample of all UK households and adopts the typical convention of allowing the household to self-assign the household head and interviews all members of the household. Finally, the BHPS is a long-running household panel and so provides a usable number of observations of individuals with very severe debt problems.
The GHQ Caseness Score is calculated by counting the number of cases in which an individual reports experiencing six negative feelings ‘rather more than usual’ or ‘much more than usual’, or experiencing six positive feelings ‘less so than usual’ or ‘much less so than usual’. Hence a score of 12 indicates the individual reported they feel each of the six negative feelings at least ‘rather more than usual’ plus each of the six positive feelings less or much less than usual, and a score of 0 indicates the individual feels each of the six negative feelings not more than ‘no more than usual’ and each of the positive feelings at least as much as usual. On this basis, a score of 12 represents the lowest level of psychological wellbeing (worst mental health) and a score of 0 represents the highest level of psychological wellbeing (best mental health). Some studies invert this 12-point score, known as the ‘inverted GHQ’ such that a higher value represents a better level of psychological health.
However, in each wave respondents are asked some questions relating to their finances: detailed questions about their income, the amount they save from their current income, an estimate of the value of their home and any debts secured against it together with the type of mortgage they currently hold and the cost of their monthly housing payment (mortgage or rent).
For example, whereas in the cross-section those reporting difficulty paying for housing exhibited on average GHQ Caseness scores 1.63 points higher than those not reporting problems, in the transition data the deterioration in GHQ score among those developing difficulties paying for housing is 0.43 points. In the case of those 2+ months late with housing payments and those who face a ‘somewhat or heavy burden’ of consumer credit the difference in the transition is 0.58 (compared with 2.05 in the cross-section) and 0.24 (compared with 0.99 in the cross-section).
Measures of the local-level mortgage and consumer credit delinquency rate at available at the county level from the Council of Mortgage Lenders (CML) and Experian. CML provide data on the proportion of outstanding home loans at least three months in arrears. Experian provide data on the proportion of consumer credit products at least three months delinquent.
The dependent variables are as before. Separate models are estimated which include housing payment problems and consumer credit a heavy burden. In the housing payment regressions the sample is comprised of all years for which the housing payment question was included in the BHPS, in the case of the consumer credit payment regressions the sample is the years 1995–2008 only as before.
On this basis, we continue to use the self-reported measures of problem debt in the subsequent analysis.
Households who experience equity falls will suffer increases in their leverage and have less scope to refinance (and so face higher future payments), withdraw equity or sell their home without incurring a capital loss. More housing equity increases the likelihood of being able to refinance a mortgage onto more favourable terms, and increases the equity buffer if an individual is forced to sell their home. Hurst and Stafford (2004) present evidence that households use housing equity as a source of insurance when faced with income shocks.
It is perhaps not surprising that we find house price falls for those in arrears lead to negative outcomes. The majority of households who experience mortgage arrears do so in the early years since purchase (for an examination of the dynamics of mortgage arrears in the BHPS see Gathergood (2009)). At this stage in the life-cycle households are likely to be highly leveraged, so house price movements can have substantial effects on refinancing opportunities.
For example, Clark (2003) shows the impact of unemployment on psychological health is less severe for individuals who live in localities in which the unemployment rate is higher, and hence is more of a ‘social norm’ among the population. This finding is contrary to a standard labour market analysis in which higher local unemployment is indicative of fewer job opportunities and would result in increased psychological stress.
For example, the MALG have developed a ‘Debt and Mental Health Evidence Form’ for use by individual creditors dealing with clients with debt problems as a means of recording and recognising symptoms of mental health problems exhibited by debtors and providing guidance on referrals to medical professionals. This pro-forma is accompanied by a set of ‘Good Practice Mental Health Guidelines’ for creditors.
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Appendix A. BHPS General Health Questionnaire
‘Here are some questions regarding the way you have been feeling over the last few weeks. For each question please ring the number next to the answer that best suits the way you have felt’.
The first question is:
‘Have you recently been able to concentrate on whatever you’re doing?’
With four possible answers:
‘Better than usual ... Same as usual ... Less than usual ... Much less than usual...’
The next six questions are:
‘Have you recently lost much sleep over worry? Have you recently felt constantly under strain? Have you recently felt you couldn’t overcome your difficulties? Have you recently been feeling unhappy or depressed? Have you recently been losing confidence in yourself? Have you recently been thinking of yourself as a worthless person’
With the four possible answers:
‘Not at all ... No more than usual ... Rather more than usual ... Much more than usual ...’
The next five questions are:
‘Have you recently felt that you were playing a useful part in things? Have you recently felt capable of making decisions about things? Have you recently been able to enjoy your normal day-to-day activities? Have you recently been able to face up to problems? Have you recently been feeling reasonably happy, all things considered?’
With four possible responses:
‘More so than usual ... About same as usual... Less so than usual ... Much less than usual ...’