6.3  Adjusted self-rated health

In the previous section it was found that there was a significant relationship between health and participation even after accounting for unobserved variables. However, these results may occur owing to respondents using their health status to rationalise their participation; that is, reporting their health to be worse than it actually is to justify the fact that they are not participating. In previous studies, for example Disney et al (2003), one approach to try to remove this rationalisation bias from health measures has been to model self-rated health using more objective health related variables. Estimates from such a model have then been standardised and included in models to estimate the relationship between health and labour force participation in place of self-rated health. This approach was therefore used to try to rid the self-rated measure of health in SoFIE of its potential rationalisation bias. Full details of how the adjusted health measure was calculated and used in the models can be found in Appendix C. These results complement the findings in the previous section. The key finding is that, even when self-rated health is adjusted to account for potential rationalisation bias, a highly significant relationship is still found between health and labour force participation. This approach also leads to the fixed effects model being identified as the preferred model. The results strengthen the conclusions made in the previous section, in that it seems that the relationship identified between health and labour force participation is not owing to rationalisation bias.

6.3.1  Calculation of adjusted health measure

The following measures are available for each respondent in every wave of SoFIE: whether a respondent has ever smoked; the presence of each individual chronic disease; and the receipt of a health or illness related benefit.[29][30] Table 14 shows for each health state the proportion of people who report each health related measure. For example, 38% of those in excellent health have been diagnosed with one or more chronic diseases, compared to 84.8% of those in poor health. It shows that all three measures are correlated to some extent with health. These three measures are also more objective than self-rated health. These variables will therefore be termed “objective health measures”.

While Table 14 shows that only 4%, 7.2% and 14.8% of those who consider their health to be excellent, very good and good, respectively, receive a health related benefit. However, it is important to remember that these groups account for a large proportion of those who receive health related benefits once the relative size of these health states is considered. Table 7 shows that around 94% of the population consider themselves to be in excellent, very good or good health. Combining the figures from Table 14 and Table 7 indicates that around seven-tenths of those receiving a health related benefit consider themselves to be in good, very good or excellent health. This is well below the level for the population as a whole but it is still higher than may have been expected. This highlights the possible issues with the survey questions that measure self-rated health (discussed in Section 4.2.2) and the mis-match between health and disability; for example, a person who is blind may be eligible for a disability benefit (included here within health related benefit) but may consider themselves to be in excellent health. This finding is along similar lines to international evidence that suggests that on average one in three qualified recipients of a disability related benefit claim to have no subjectively perceived disability that limits their daily activity (OECD, 2003).

Source: SoFIE Waves 1-3 Version 4, standard longitudinal weights (* adjusted longitudinal weight), Statistics New Zealand

Notes:

1. The figures in each cell are the proportion in a certain health state that report the health related measure.

2. See footnotes Table 5.

It was shown previously that chronic disease presence is correlated with participation. There is also correlation between health benefit receipt and labour market participation and a weak correlation between whether a person has ever smoked and labour market participation. However, it is sensible to assume that if true health was measured correctly these objective health measures should only affect participation through this health measure once other factors are controlled for.

Given this relationship, these objective health measures (along with a set of other health related variables) were used to model self-rated health for each year. The results of these models can be found in Appendix Table G1.

Looking at the model results indicates that all of the objective health measures are highly significant in explaining self-rated health. However, overall, the models only explain around 11% of the variation in the data. In terms of interpreting the model results, a higher value of self-reported health means poorer health. This means that positive coefficients on the objective health measure, for example 0.418 for those who have cancer in Wave 1, are associated with an increase in the predicted probability that an individual will be in poor health and a decrease in the predicted probability that they will be in excellent health. With this in mind the largest health impact is seen from those receiving health related benefits (a coefficient of 1.286 in Wave 1) while the most influential health condition is diabetes (a coefficient of 1.086). The least influential health condition is high cholesterol (a coefficient of 0.177). Looking at the non-health coefficients indicates that health is generally predicted to be poorer for those outside Auckland; those born outside of New Zealand; those of non-NZ/European ethnicity; older respondents; those with no qualifications; and those with no partner relative to the reference categories. Health is generally predicted to be better for females; those with tertiary education; those who are undertaking some form of study; and those with higher household income.

The results of these models were used to create an adjusted health stock. The probability of being in poor health was predicted for each person. This probability was then standardised across all respondents to give a continuous measure of adjusted health status (or adjusted health stock). For all respondents (including those over working age) this adjusted measure therefore had a mean zero and standard deviation of one. As with self-rated health, a higher adjusted health stock indicates poorer health. This is illustrated in Table 15 where the mean and standard deviation of the adjusted health stock are presented for each self-rated health state. The mean and standard deviation of this health stock for those of interest (working age non-students) is less than zero. This is because those with generally poorer health (respondents aged 65 and over) are included in the model to create the adjusted health stock, but are excluded from the analysis to determine the relationship between health and participation. As with unadjusted self-rated health, this was done to ensure the total distribution of the adjusted health measure reflected that of health in the total population.

Source: SoFIE Waves 1-3 Version 4, standard longitudinal weights, Statistics New Zealand

Notes:

1. The health measure is derived based on the standardised probabilities of poor health for all longitudinal respondents from the data in Appendix Table G1. For full footnotes see that table. These means and standard deviations are for those of working age who aren't students.
2. The total figures are for the sample from the pooled and random effects regression. For the fixed effects regression the mean was -0.062 and standard deviation 0.750 indicating those who change participation status during the period are slightly less healthy than those who do not change.

6.3.2  Standard pooled regression

The adjusted health stock was then included in the standard pooled logistic regression in place of individual chronic diseases or self-rated health. Full results can be found in Table G2. This model explains a similar amount of variation in the data as the model including the unadjusted self-rated health and the model including the individual chronic diseases (32% compared to 32.3% and 30.9% respectively).

The coefficients of the non-health variables in this model are little changed from those in the pooled models, including chronic diseases or unadjusted self-rated health. Health is still highly significant in affecting participation even after attempting to adjust for possible incorrect measurement of self-rated health. The coefficient for adjusted health indicates that a one unit increase in the level of health (a move to poorer health) is associated with a 57% reduction in the odds of participating. The adjustment of self-rated health results in difficulties interpreting what a unit change in this measure actually means in the real world. To give an indication of the dispersion of the adjusted health measure for the sample used in analysis, the average adjusted health level was -0.167. The standard deviation was 0.652 indicating that, while a one unit increase in health reduces the odds of participating by around 57%, many respondents will not experience a one unit change in adjusted health. It is therefore more sensible to consider a one standard deviation increase in adjusted health; this is associated with a 42% reduction in the odds of participating. While the categories of self-rated health are subjective and have no definite boundaries, it is easier to relate to a change from excellent to poor health than to a one unit change in the adjusted health stock. However, the fact that this health measure is still significant in impacting on participation illustrates that health is significantly related to participation even allowing for possible rationalisation.

6.3.3  Fixed and correlated random effects panel models

The adjusted health measure was then included in the fixed and correlated random effect models. The results can be found in Appendix Tables G3 and G4 respectively. The coefficients for the non-health variables were similar to the models for unadjusted self-rated health (Tables F1 and F2).

The key thing to note from the fixed effects model (Appendix Table G3) is that a one standard deviation increase in adjusted health stock (so a poorer health shock) is associated with a 31% increase in the odds of not participating. This is in line with what was found when comparing the pooled and fixed effects model using unadjusted self-rated health (again the odds are not directly comparable as the fixed effects model only considers within person variation).

Turning to the correlated random effects model, both the health shocks (a change in adjusted health) and the average level of adjusted health are significantly related to participation. A one standard deviation increase in adjusted health is associated with a 31% reduction in the odds of participating. Further, the higher the average adjusted health state over a period is (ie, the poorer a person's longer term health) the less chance there is they will participate and this impact is larger than that for a health shock (a one standard deviation increase in the average adjusted health stock is associated with a 52% reduction in the odds a person will participate). Again these results are similar to what was found in the correlated random effects model including unadjusted self-rated health. This illustrates that health is significantly related to participation even allowing for possible rationalisation.[31]

As with the unadjusted health models a likelihood-ratio test for the random effects model indicates that the panel variation is significant and thus a panel model is preferred. A significant Hausman test, comparing the fixed effects and uncorrelated random effects model, indicated that the fixed effects estimator should be used instead of the random effects as the unobserved individual level effects were correlated with the other covariates. This correlation remains even after the correlated random effects model is used. This indicates that the preferred model is the fixed effects model.