WORK-IN-PROGRESS: PLEASE DO NOT CITE OR CIRCULATE WITHOUT PERMISSION Mental health, work incapacity and tax contributions: an analysis of the British Household Panel Survey
William Whittaker, Matt Sutton Health Economics Health Methodology Research Group Community-Based Medicine University of Manchester Contact:
[email protected]
Abstract The Government has embarked on a programme to increase the availability of psychological therapies. This followed high-profile reports on the economics of such a programme claiming that the programme would “cost the Exchequer nothing”. In part, these analyses relied on two statistics: the proportion of Incapacity Benefit (IB) claimants diagnosed with mental and behavioural disorders; and estimates of the costs to the Exchequer of periods on IB. These are cross-sectional associations that have been used to support a case for intervention and constitute forms of cost-of-illness and human capital arguments. We subject these two statistics to more rigorous longitudinal analysis using nationally representative data from the first seventeen waves (1991-2007) of the British Household Panel Survey. The panel structure of the survey enables us to model the effect of depression on the probability of being on IB whilst controlling for various sources of endogeneity that bias cross sectional inference. The detailed income and benefits questions allow us to derive and analyse an individual-level proxy measure of ‘contributions to the Exchequer’. In addition, we can control for covariates and unobservable heterogeneity in our estimations. Our results reveal that simple estimates of the effects of depression on IB claims and on contributions to the Exchequer are confounded substantially by observable covariates and unobservable heterogeneity. We also find the effect of depression on IB claims is only a partial assessment of the impact of depression on Exchequer contributions.
Background Cognitive Behavioural Therapy (CBT) was recommended by the National Institute for Health and Clinical Excellence (NICE) as an effective treatment for depression in 2004 (NICE, 2004 (amended 2007)). In the 2007 Comprehensive Spending Review the Government committed itself to improving access to psychological therapies for those with depression or anxiety as a Public Service Agreement (PSA) target. The NHS programme, Improving Access to Psychological Therapies [http://www.iapt.nhs.uk/], is the process by which this PSA target will be delivered. This programme is currently in its second phase of roll out across a number of Primary Care Trust areas.
The high-level commitment to this programme follows influential reports by Layard and colleagues of The Centre for Economic Performance’s Mental Health Policy Group at the London School of Economics. In 2006 Layard made the case for expanding availability of psychological therapies in the British Medical Journal (Layard, 2006a). This was supported by a more substantial report (Layard, 2006b) and a number of other papers published on the LSE Programme website [http://cep.lse.ac.uk/research/mentalhealth/] including a draft costbenefit analysis (Layard et al, 2006c).
The case described in the cost-benefit analysis relies on two key empirical findings: 1. that the proportion of Incapacity Benefit (IB) claimants diagnosed with mental and behavioural disorders is 40%; and 2. that the treatment will result in higher contributions to the Exchequer.
The second finding was calculated using predicted tax gains earned in employment minus the benefits previously paid to people with depression on IB. Based on a (one-off) £750 cost per treatment cycle, Layard et al. (2006c) estimate the returns to the Exchequer are almost immediate with the treatment paying for itself within a year. Overall, these findings lead Layard et al (2006c) to suggest that increasing the availability of psychological therapies “would cost the Exchequer nothing” (p.1; emphasis in original).
Both findings are cross-sectional associations but are used to estimate the effects of changes in the level (and severity) of depression. Finding 1. is a form of cost-of-illness argument.
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Finding 2. is a human capital formulation of the cost-benefit framework and is a partial assessment of the ways in which individuals contribute to the Exchequer. Neither is an approach favoured in health economics (Sheill et al, 1997; Byford et al, 2000) or traditionally used in NHS decision-making.
In this paper we subject these two simple empirical findings to a more rigorous longitudinal analysis. The Layard argument for the rolling out of CBT therapy rests on the premise that treating depression will prevent transits onto, and expedite transits off of IB. In the analysis that follows we investigate the relationship between depression and IB claiming, and test if reducing depression will have the desired effect of reducing the probability of claiming IB. We estimate a random effects probit model with unobserved heterogeneity to control for several sources of endogeneity.
The second stage of our analysis addresses more comprehensively how depression influences the contribution that individuals make to the Exchequer. As above, we use longitudinal data and capture information on contributions made via income tax and National Insurance on earnings and claims made on a wide range of state benefits including IB.
Data To model depression and IB, we require panel data. Panel data allows us to observe changes to IB claiming and correlate these with changes of depression. In addition, panel data allows us to control for unobserved individual factors that may influence the decision to work and/or claim IB and thus pay taxes (e.g. ability).
We use the British Household Panel Survey (BHPS) to model IB, depression, and contributions to the Exchequer. The BHPS was designed as an annual survey of each adult (16+) member of a nationally representative sample of more than 5,000 households, making a total of approximately 10,000 individual interviews. The same individuals are re-interviewed in successive waves and, if they split-off from original households, all adult members of their new households are also interviewed. Children are interviewed once they reach the age of 16. Thus the sample should remain broadly representative of the population of Britain as it changes through time (Taylor et al. 2001).
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The panel nature of the data enables us to construct profiles of individuals over time, recording IB status, as well as changes in personal characteristics; in particular, within the BHPS are a range of questions about health and benefits received.
To model IB we use the variable f125. f125 asks respondents: ‘Have you yourself or jointly with others since 1.9.xx received Incapacity Benefit?’ Before 1995, IB was known as National Insurance Sickness Benefit (f134) and/or Invalidity Pension (f117), for waves 1-4 we use these measures. There are several important points to note here. First, this measure is retrospective. Second, the timing of interviews in the BHPS vary and as such periods covered by the question varies across observations. Third, this measure is rather crude in that it gives us no information on the number or duration of claim spells.
To model contributions and IB claiming we restrict our sample to those of working age. This is because IB applies to people of working age. As the IB claiming question is retrospective, we sample women until the age of 61, and men to the age of 66.
To measure depression we use variable hlprbi which asks respondents: ‘Do you have any of the health problems or disabilities listed on this card: Anxiety, depression or bad nerves?’ there are several alternative measures for depression in the BHPS, these include GHQ Caseness and the GHQ question asking if the respondent has recently been feeling depressed. We replicated our analysis that follows with each measure and the results are essentially the same. It may be the case that the depression measure would pick up other health problems in our later analysis and so we also include a wide range of health problems asked in the BHPS which includes (amongst others) heart and blood problems, diabetes, arm and leg problems, and sight and hearing problems.
Methodology and Results IB Claiming There are 3 important methodological concerns with modelling IB claiming and depression. First, there are likely to be many unobservable individual characteristics that influence whether someone claims IB, for example, attitudes to health and/or the State. Second, it is
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also likely that these unobserved characteristics are correlated with our controls in our model, for example, attitudes to health or State may be correlated with social class and/or age. Third, IB claiming is likely to be persistent in that claims for ill health may persist for a number of year for those with long-term conditions, it is important to control for this to remove any bias in the error term and estimates of our model.
To control for these issues, we follow Wooldridge (2005) in estimating a dynamic probit model with unobserved effects as represented by equation (1) below:
P( y it = 1 | y it −1 ,..., y i 0 , z i , ci ) = Φ( z it γ + λt + ρy it −1 + ci )
(1)
Here y it is a binary indicator for claiming IB at some point in the next period t + 1 , y i 0 is a binary variable indicating whether individual i had claimed IB in the past year of their first observation (initial condition), and ci is an individual specific time-invariant error term. z it is a vector of covariates that we think could have an effect on claiming IB, these include: dummies for other health problems, gender, age, region of residence, marital status, whether respondent is from an ethnic minority group, education, and children dummies. Finally, λt are wave/year dummies.
Equation (1) relies on several assumptions, firstly, that the dynamics are correctly specified as first order, having conditioned on λt , z it and ci . Secondly that ci is additive in the standard normal cumulative distribution function, and finally, that z it and λt are strictly exogenous.
To model the unobserved effect we assume:
(
ci | y i 0 , z i ~ Normal α 0 + α 1 y i 0 + z iα 2 , σ a2 and
)
(
)
(2)
ci = α 0 + α 1 y i 0 + z iα 2 + ai where ai | ( y i 0 , z i ) ~ Normal 0, σ a2 .
Our model is thus: y it = β 0 + β1 z it + β 2 λt + β 3 y it −1 + β 4 y i 0 + β 5 z i + ai + u it
(3)
Where z i contains the time averages of the exogenous explanatory variables. Specifying ci in this way means we can use standard random effects probit estimations in STATA, the only difference between the dynamic random effects model and a standard random effects probit model are the additional terms y it −1 , y i 0 and z i in our model.
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Contributions to the Exchequer Our next stage of our analysis is to test and quantify the effects of depression on contributions to the Exchequer. The BHPS contains questions on the usual gross and net pay the respondent receives per month. From this we can approximate the tax each respondent usually pays from their employment. The BHPS also contains a measure for the total amount of benefits received in the past month (including family benefit, housing benefit, and council tax benefit as well as IB). An individual’s contribution (C) to the Exchequer at time t can be estimated as: C it = Tit − Bit
(4)
in which T is the difference between gross and net pay and B is the sum of state benefits. Our contribution measure does not represent total individual payments to the Exchequer as we only have data on employment taxes/National Insurance payments, however, data from the Office of National Statistics reveal that income tax and National insurance benefits accounted for approximately 52% of Public Sector receipts and social benefits accounted for approximately 35% of Public Sector expenditure over the period 1992-2002 (ONS).
We use pooled OLS to estimate the following equation: C it = β k xitk + vit
(5)
Where C it is monthly contribution per individual, and xitk is a range of covariates that may affect the amount of contributions made by individuals. These include: depression, gender, age, marital status, ethnic minority, region, numbers of children and dummies for other health problems.
To ensure we have reliable estimates, we need to control for potential bias in the model. The first possible source of bias occurs were there to be reverse causality between the dependent variable (contributions) and one of our independent variables. Our prime interest is with the depression variable, reverse causality here would require depression to be caused by contributions to the Exchequer - we believe this causal pathway is unlikely. The second potential source of bias stems from unobserved heterogeneity; certain individuals may be unobservably more or less likely to contribute than others and these unobservables may be correlated with other independent variables. To correct for this potential source of bias, we estimate (5) using fixed effects assuming that the unobserved component is time invariant. Fixed effects would also control for any time invariant measurement error in the sample. Strictly speaking there is another form of bias/endogeneity, this arises where we have selected
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samples from the population, this may be important as attrition may be more prevalent for those with health problems, however, one past study found negligible effects from controlling for attrition bias in a model of self-assessed health using the BHPS (Contoyannis, Jones, & Rice, 2004b) .
Results Descriptive statistics We need data in the BHPS to be representative of Great Britain. Figure 1 plots the proportion of working-age adults claiming IB in the BHPS and rates from the Department of Work and Pensions (DWP) (5% sample of Great Britain during quarter three of each year) for the period 1997-2007. The DWP have data for those who receive monies for IB, and those who receive National Insurance credits only. Figure 1 plots the sum of these ‘IB claimants’ and the rates for those only receiving monies. Our BHPS measure lies between the two DWP measures. This is for two reasons. First, the BHPS question asks respondents if they have received incapacity benefit, and it may be that respondents interpret this as whether they have received monies under incapacity benefit and not National Insurance contributions (respondents are also asked how much they received, further supporting the view that these claimants were for payments rather than credits). Second, the IB measure is retrospective for the past year while the DWP measure is at a moment in time, this should make the number of IB claimants claiming greater in the BHPS than DWP data.
As our analysis also looks at the depression rates of IB claimants, Figure 2 plots the proportions of IB claimants whose primary reason for claiming IB is due to mental and behavioural disorders for the DWP sample, and those with depression for the BHPS sample. The two follow a similar increasing trend. 45% of those claiming IB in the BHPS reporting having depression problems, and 45% of those on IB in the DWP sample state their primary reason for claiming IB as down to mental and behavioural problems by 2007.
From the sample size of roughly 224,000 person-year observations, restricting our sample to those of working age reduces the sample to 182,000 and using forward looking IB claimant rates cuts our sample down to 145,000, with each individual being observed for at least two
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consecutive waves. Our panel is unbalanced and individuals can enter or leave the sample at any wave.
Table 1 provides summary statistics on rates of depression and IB claiming in the next period. Respondents reported depression at approximately 7% of interviews and 5% of respondents claimed IB in the following period. Depression is thus more prevalent than IB claiming. Approximately 33% of those who claim IB in the next period are depressed, this figure is slightly lower than the 40% figure found by WHO (2004) (for Western Europe) and Layard et al. (2006b) for the proportions with mental illness. However, as Figures 1 and 2 show (where BHPS IB and depression rates are compared to figures from the DWP), the average rates here disguise the similar trend/rates in depression and IB claiming between the BHPS and DWP over time. Table 1 also shows that the depressed are more likely to claim IB in the next period (22% compared with 4%).
Table 2 provides average rates of IB claiming, depression, and both depression and IB claiming across a range of stratifying variables. It is important to note that these are ‘raw’ estimates/ sample averages, these differences may not remain once we control for other factors in a multivariate model. There is a clear distinction between males and females, both for being IB claiming and being depressed. Females are less likely to claim IB, but more likely to be depressed.
IB claiming and depression monotonically increase with age, and are more prevalent for ethnic minorities. There are large and significant IB and depression rates for those with other health problems, these rates are also significant (and positive) in the third column for the depressed who claim IB in the next wave, this suggests there would be significant confounding were we to exclude these other problems in our model(s).
Table 2 also contains average rates by marital status. The group most likely to claim IB are the divorced, who are more than twice as likely to claim IB as married respondents. The divorced also have the highest rates of depression. The third column of Table 2 reveals that the divorced are significantly more likely than any other group to be depressed and claim IB in the next year. Furthermore, this effect is particularly large at almost triple that of the married group (base).
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The rates by region show a clear North-South divide, with regions in the north of Great Britain being almost twice as likely to be claiming IB than London, East Anglia, the South East, and South West. Of those who claimed IB, the rates of depression by region here reflect roughly the regional IB claiming differences, that is, that northern regions are, on the whole, significantly more likely to have claimed IB and been depressed in the past year.
We find degree-level educational attainment is associated with lower rates of IB claims and depression and no formal qualifications is associated with higher rates of both variables.
IB claiming The results from multivariate analyses are provided in Table 3. The estimates reveal signs and significance for the variables, for example, in the first column of results being depressed increases the probability of claiming IB in the next wave. The results in the pooled model suggest a lower but still strongly significant positive effect of reporting depression on the probability of claiming IB in the next wave. The effects of the covariates are qualitatively consistent with the univariate relationships shown in Table 2.
The third set of results are where we control for unobserved heterogeneity by modelling random effects and including time averages of our explanatory variables to allow for some correlation of the heterogeneity with the covariates. While we cannot compare directly the size of the estimates, controlling for individual specific and time invariant heterogeneity drives the marital status dummies to insignificance, and this is also true for those with degrees (relative to base of some qualifications), the East and West Midlands (relative to base of London), and hearing, skin and epilepsy conditions (relative to none). Our estimate of interest, depression, is positive and still strongly significant even after we control for unobservable characteristics. The estimate for the time-average of the depression dummy is positive and significant, this estimate is also larger than the depression dummy. One way of interpreting these estimates is to regard current depression as a measure for transitory depression shocks, and mean depression as a measure of permanent depression (similar interpretations for income are used in Contoyannis, Jones, & Rice, 2004a and 2004b; and Frijters, HaiskenDeNew, & Moffitt, 2003). This approach is valid where the mean of depression is exogeneous, which may or may not hold.
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The final set of results make our model dynamic by including IB claiming in the current wave and the initial status of IB claiming for individuals, these terms are significant, and for the current IB claim dummy, particularly large suggesting there is persistence in IB claiming. There is a drop in the both the ‘transitory’ and ‘permanent’ estimate for depression compared to the third set of results, which suggests the third set of results, where we do not include the lag of IB claiming or initial condition, were picking up the dynamic persistence in IB claiming.
Contributions to the Exchequer The distribution of the variable we have calculated is plotted in Figure 3. Given the previous focus on IB claiming, we also plot contributions for those on IB. As expected, the large majority of IB claimants make a negative contribution to the Exchequer.
The results from estimating equation (5) with pooled OLS and fixed-effects models are given in Table 4. In the pooled model the estimated effect of depression on contributions to the Exchequer is -£195. In a model estimated on the same sample that excludes the other covariates, this coefficient equals -£363. Thus, £168 of the difference in the Exchequer contributions of the depressed and non-depressed is attributable to (a limited range of) observable covariates and the crude difference suffers substantially from omitted variable bias. In a model with only depression and an IB claim dummy, the estimate for depression falls from -£363 to -£250, thus depression and IB claiming are linked in their effects on contributions to the Exchequer, but it is ambiguous whether depression caused the IB claim or visa versa, it is clear however, that IB claiming reflects only a partial impact of depression on contributions to the Exchequer. To model the complete effect of depression we omit IB claiming from our analysis.
The final column is where we control for unobserved heterogeneity, the estimate for rho of 0.492 is interpreted as 49.2% of the unobserved variation in contributions to the Exchequer is explained by the unobserved heterogeneity term. A test of the null that the unobserved effects are not significant was rejected (p-value
