2.1.1. Health variables
The ECHP has a variety of health-related questions. These include a measure of general self-assessed health (SAH) status as well as more specific questions related to limitations in daily activities, the presence of recent illness due to physical, emotional or mental health problems as well as admission to hospital.
The SAH variable is a simple five-point scale based on answers to the question “How is your health in general?” The available answers were very good/ good/ fair/ bad/ very bad. The question wording in France was “Pourriez vous indiquer, sur une échelle allant de 1 (pas satisfait du tout) à 6 (très stisfait) votre degree de satisfaction en ce qui concerne les points suivants? …… Votre santé” the answer was on a 6 point scale and has been recoded in the UDB to a five point scale. This variable was recoded to be increasing in good health.
The remaining health questions predominantly focus on impairment or limitation in normal daily activities. These consist of a set of self-report questions firstly based on the question “are you hampered in your daily activities by any physical or mental problem, illness or disability? coded: 1 yes, severely; 2 yes, to some extent; 3 no; −9 missing. In France the question was worded “gene par une maladie chronique, un handicap?” Binary variables for each of the three possible answers had been created i.e., severely hampered for answer =1; moderately hampered for =2; not hampered for =3. The next two questions were yes/no answers related to recent health problems and again binary dummy variables had been created. The questions were worded “during the past two weeks, have you had to cut down things you usually do about the house, at work or in free time because of illness or injury?” and “during the past two weeks, have you had to cut down things you usually do about the house, at work or in free time because of an emotional or mental health problem?”. The final health indicator, also coded as a binary variable, was worded “during the past 12 months, have you been admitted to a hospital as an in-patient?”.
It was emphasised earlier that using measures of self-reported health that are recorded prior to an individual’s exit from the labour market should help to mitigate problems of justification bias. However there may be anticipation effects and measurement errors may still occur. To address this possibility the method of estimating a model of SAH as a function of a set of health indicators is used to define a latent ‘health stock’. This follows the approach of [
32] as implemented in [
21] and more recently used by [
23] and [
24]. There are differences in how the latter two studies have created their latent health variable. [
23] use personal characteristics as well as health indicators while [
24] only include the health indicators when constructing the health stock. In this study we adopt the approach of [
23]. The idea of constructing this health stock variable is analogous to using the health indicators as instrumental variables to purge measurement error in the SAH variable. More conservatively it can be seen as a way of reducing the dimensionality of the problem by forming a single linear combination from a set of health indicators.
To construct each individual’s latent health stock we consider the aspect of health that influences an individual’s decision to retire, H
Rit, to be a function of the health indicators, Z
it, such that:
where ɛ
it is a time varying random error term that is assumed to be uncorrelated with Z
it. H
Rit can not be directly observed but instead we have a measure of self-assessed health (SAH), H
Sit and we can specify the latent counterpart of this as H
*it such that:
where the random error term η
it represents measurement error in the mapping of H
*it to H
Rit and is uncorrelated with H
Rit. Substituting (1) into (2) gives:
The presence of ηit in (3) is a potential source of bias if H*it is used directly when estimating the impact of health on retirement. This can either be distributed independently of labour market status or be a function of the labour market status of the individual. If it were distributed independently it would represent classical measurement error and could attenuate the effect of health on retirement. Alternatively, if it is a function of the labour market status i.e., individuals rationalise early labour market exit by reporting ill-health, then this would overestimate the effect of health on retirement. To avoid this bias a predicted health stock is used.
Combining (3) with the observation mechanism linking the categorical indicator, H
sit, to the latent measure of health H
*it, and assuming a distributional form for ν
it we can estimate the coefficients, β. For example, in the case of the categorical self-assessed health measure the observation mechanism can be expressed as:
where μ
0 = −∞, μ
j ≤ μ
j+1, μ
m = ∞. Assuming ν
it is normally distributed, model (3) can be estimated as an ordered probit using maximum likelihood. The predicted values for the health stock can then be used in our retirement model.
The ECHP does not have the same extensive range of objective health variables as the BHPS, as used by [
23] and [
24], but does have a small set of measures relating to limitation in daily activities, recent illness or mental problem and a history of in-patient stay in hospital. The latent health stock measure was thus created using the health indicators and demographic variables, using the method of [
23]. They estimated the health stock for each wave of the data using the wave specific values of the objective health variables but also included some demographic variables. These estimated values are then normalised as a deviation of the individual’s health index from the cohort mean for each year. This predicted individual ‘health stock’ thus creates a health stock for each individual in relation to the year on year average for the sample. The normalised variable has a mean of 0 and a standard deviation of 1 for each wave of the sample. This normalisation is carried out separately for males and females in each county in order to address concerns about cross-country and gender differences in self-reporting of health. This process was performed on the full sample.
Table 1 shows the coefficients of the health variables for the ordered probit models estimated on the first wave for both sexes in all countries for the health stock. The presence of mental health problems is the only adverse health event variable which is inconsistent in its effect. However, in those models were it is statistically significant it always has a negative coefficient. The remaining factors all have the expected negative coefficient and demonstrate variability between the sexes and between countries. The cut-points from the ordered probit models were then used to create an adjusted derived SAH for each individual in each wave.
Having the health stock variable allows us to specify different dynamic models for the impact of health on the hazard of early retirement. Our main results are based on three specifications for the health variable:
The first specification includes the current level of the health stock variable. The second uses lagged health stock with the main results presenting models with a one-year lag and the robustness analysis experimenting with additional lags. The third specification uses the health stock variable to construct measures of discrete ‘health shocks’, reflecting acute deteriorations in health. All specifications also condition on the initial level of health stock. This allows the estimated effect of the health shocks to represent the deviation from the underlying health stock and has the advantage of helping to control for individual-specific unobserved health-related heterogeneity.
We construct measures of discrete acute health shocks by considering the differences between consecutive waves in an individual’s normalised health stock value, their reported SAH and their adjusted SAH. This is similar to the concept of an acute health shock described by [
33], although she uses only reported health rather than a purged measure. Binary dummy variables were created for these shocks for each predicted health stock measure. Each is based on a decrement in the measure between consecutive waves:
The first is based on a 1 standard deviation or greater decrement in the normalised health stock.
The second is based on the reported (unadjusted) SAH: we compare the reported categories of SAH in waves t-1 and t and create an indicator for a reduction in SAH of 1 category or greater. This does not change between the three stock samples.
The third is based on the adjusted SAH. The measure of health shocks for the adjusted data mimics that for the raw data. The (normalised) latent health stock is calculated for each individual at each wave. The (normalised) cut-points are then used to predict which of the 5 categories of SAH the individual is assigned to at each wave. The indicators of health shocks are then computed in the same way as step 2, but using predicted rather than actual SAH category.
The use of the second and third discrete acute health shocks means that the estimated quantitative effects of the health shocks can be compared directly for the unadjusted and adjusted measures of SAH. These binary dummy variables were then used separately in the hazard models, conditioned on the initial normalised health stock value using the final censored stock sample. Conditioning on the initial level of health stock allows us to control for heterogeneity in the retirement decision caused through individuals in worse health on entering the stock sample retiring at a greater rate than more healthy comparators. This permits us to identify better the seperate effect of a health shock.
To assess whether these measures do indeed capture acute health shocks and to ensure comparability across countries the number of adverse health events associated with each shock were counted and compared as shown in
Table 2. Each acute health shock is associated with adverse health events. The health shock based on a standard deviation decrement has the highest number of adverse health events and the smallest coefficient of variation in that number across countries. This suggests that this would be the best measure in comparing countries as regards the effects of ill health on retirement decisions and this measure is used in our main results section (Section 4). However, the other two measures allow a direct comparison of the magnitude of the impact of health shocks, with and without the adjustment for reporting bias. These two measures are therefore used for robustness checks in Section 5. Comparison of the number of adverse health events associated with the unadjusted measure of SAH and the adjusted measure implies that, on average, using the unadjusted measure of decrements in SAH tends to over -report the incidence of health shocks. Not surprisingly therefore in the majority of countries there are many more health shocks recorded using the unadjusted SAH than the adjusted SAH.
2.1.2. Other explanatory variables
The ECHP asked a broad range of questions related to employment. The one used in this study is based on the self-defined main activity status with individuals classifying their status as one of the following: (1) working with an employer in paid employment (15+ hours/week); (2) working with an employer in a paid apprenticeship (15+ hours/week); (3) working with an employer in training under special schemes related to employment (15+ hours/week); (4) self-employment (15+ hours/week); (5) unpaid work in a family enterprise (15+ hours/week); (6) in education or training; (7) unemployed; (8) retired; (9) doing housework, looking after children or other persons; (10) in community or military service; (11) other economically inactive; (12) working less than 15 hours. A binary variable was created based on whether the individual had selected the 8th category, “retired”.
In addition, the question on the individual’s main activity status was used to generate another binary variable based on whether options 1–5, 7 or 12 were selected. This second variable uses the transition between reported activity in the labour market and inactivity as a measure of retirement: labour market activity therefore encompasses full and part-time employment, apprenticeships and training, self-employment and unemployment. This was chosen because of doubts raised about the accuracy of the self-reported ‘retired’ [
25] and also because transitions from activity to inactivity have been used frequently as outcome measures in analysing the effect of health on retirement [
2,
21]. For both the narrow and broad definitions, retirement is recorded when the first transition occurs and the discrete time hazard model only uses observations up to that wave.
A range of income variables are used. The ECHP-UDB includes some imputation to deal with item non-response, especially for non-labour components of total income. [
34] describe the imputation procedures and find that they are generally reliable in the 2003 version of the UDB. The starting point is household income from all sources which is used across all waves in which an individual is observed. This is then split into personal and other income. To permit comparisons across countries and across time the income variables are adjusted for the consumer price index (CPI) and purchasing power parities (PPPs), then equivalised by the modified-OECD scale to adjust for household size and composition. In order to reduce concerns over reverse causality we have used the one-period lagged value of these variables in all models. Other household income is used to capture the influence of the spouse’s income. In addition to income, household wealth is proxied by home ownership which is included as a binary variable.
The following socio-demographic variables are used in the analysis. Educational attainment graded using the highest grade of education achieved on the 3 level ISCED scale—completed third level secondary education; completed second stage of secondary education; completed less than second stage of secondary education—converted to binary variables; age dummy variables; the number of children living in the household; and cohabitational status as a binary variable.
2.1.3. Creation of samples
The focus of this study is on the role of health in the decision to retire and thus we need to observe individuals who are active at the start of the sample and are tracked over a period when they are at a risk of retiring. This defines a stock sample [
35].
For the purposes of our analyses we created three stock samples. We first selected those individuals who at wave 1 had the following characteristics: responded to the survey questionnaire; were aged 45–59 years and had measures of health and employment activity recorded for all subsequent waves. This age group was perceived to be at risk of retirement at the first wave of observation. This sample is used for the estimation of the health stock. Then, once an individual retired or was missing (lost to follow-up) their data after that point was excluded. We next selected from that stock sample those individuals who were employed or self-employed in wave 1 and finally we right censored that sample using the standard public retirement ages for each sex in each country (taken from [
7]). This means that people leave the risk set when they hit the official retirement age, so that the focus of analysis is on early retirement. This defines the final estimation stock sample.
Table 3 presents summaries of the samples broken down by country and gender. The nine countries selected for study have a total of 105,613 participants in the ECHP. Of these 23,405 (11,346 males; 12,059 females) met the age and complete data requirements selection criteria with 13,766 (8,928 males; 4,850 females) meeting the additional employment criteria at wave 1. Our final estimation sample presented for analysis regarding retirement totalled 12,153 (52% of age group). This varied by country with a high of 76% in Greek and Portugese males of the eligible age group compared to the low in Irish and Spanish females of 21% and 22% respectively. The proportion of those analysed who retired during the eight-year time period varied by country and by definition of retirement with a high of 40% of Greek males self-reporting retirement compared with a low of 13% in Irish males. Just under a quarter of the sample reported retiring during the study period. However more became inactive in the labour market with 29% doing so during the study period. Overall, across the nine countries, there was a 22% increase from 2,828 to 3,464 individuals retiring depending on the definition though there was great variation between countries and between sexes.
The mean age of individuals in each country’s stock sample is similar though there are differences in the distribution across the age groups. The differences in employment status, in the reported self-assessed health status and the proportion having some degree of limitation because of health problems are similar to those reported by [
36] for all age groups. This is true between the sexes in all countries as well as between countries. For all countries there was a general decline in the health status of the stock sample as individuals aged during the study period. This deterioration in health was accompanied by the occurrence of acute health shocks, though these did not increase in prevalence across the waves.