10.2  Influence of chronic diseases on participation

We turn now to the impact that the presence of chronic diseases have on the likelihood that an individual is in the labour force. One way to assess this is to calculate the odds of working. These findings are summarised in Figure 21. In each case the bar represents the odds ratio, where if the confidence interval includes one, then the odds ratio is not significant. This is interpreted as the factor by which the odds of working are changed owing to the presence of the particular illness. For example, the odds that a woman with high blood pressure would be working are only 70% of the odds of a woman with identical characterisitics who is not afflicted with high blood pressure. In this context this means that there is no specific allowance for the presence of multiple illnesses or the possible impact of interaction effects between various illnesses in the one individual.

For males the presence of cancer (other than skin), epilepsy, heart problems and epilepsy all lower the odds that the individual would be working relative to the odds that an individual with identical characteristics who did not suffer from the chronic illness in question would be working. Perhaps perversely, asthmatics are more likely to be working relative to those who do not have asthma. For females the conditions that lower the odds of working are diabetes, high blood pressure and liver conditions. Those with skin conditions are more likely to be working.

Figure 21: Odds ratios for working and retired: by chronic illness and sex

Source:  Health, Work and Retirement Survey

Notes:
¹ Bars coloured red are based on statiistically significant coefficients in the underlying logistic regressions. For each illness, the upper and lower 90% confidence intervals are plotted.
² The graph is stopped after 1.5, although the value and confidence intervals may have larger values.
³ The data for the odds ratios are given in Appendix Table C. 20.

In a USA study using males aged 51 to 61, Dwyer and Mitchell (1998) find that severe physical limitations, stroke and heart problems reduce the expected age of retirement by one to two years. Paralleling the present study, a wide range of other chronic conditions had no significant effect.

Some caution is needed in interpreting these results. In addition to the fact that for both males and females the confidence intervals are in many cases relatively wide, there can be questions about the accuracy of the self-reported incidence. Baker, Stabile and Deri (2004) examine Canadian data in an attempt to assess the accuracy of self-reported disease incidence. They match the self-reported answers to data for the corresponding individual held by the Ontario Health Insurance Plan. They take the medical records in the insurance plan as their reference point (the “truth”) and quantify the incidence of false positives and false negatives in the self-reports for all the major disease categories. False positives are cases where the individual reports the presence of an illness but there is no corresponding entry in the insurance records. In contrast, false negatives are those cases in which the presence of the disease is recorded in the insurance files but not in the self-reported response. Their findings are somewhat disturbing – for many diseases the error rate in the self-reports was up to 50%. They demonstrate that this can lead to significant biases when using these measures as explanatory variables in regression models.

We conclude this section by analysing the marginal impact of particular chronic conditions on the probability of being in the workforce. The results are summarised in Table 10-4. Note that only those variables whose underlying coefficients were significant are included. These results demonstrate the absolute magnitudes of effect on labour force participation of up to 19 chronic conditions. For males, epilepsy, cancer (other than skin) and ulcers are three conditions that have the greatest impact in reducing the probability of working. Table 10-4 also includes two other conditions for males which were almost significant; namely liver conditions and high blood pressure. For females, liver problems, diabetes and high blood pressure are the chronic conditions that had a significant depressing effect on the probability of working.

Notes:
¹ Only variables whose coefficients were statistically significant are listed in the table.
² The full results are in Appendix Tables C.20(a) and (b).

In interpreting the findings in Table 10-4 it must be emphasised that the marginal effects on labour force participation of a particular disease are calculated holding all other variables at their mean values. This allows for the effect of multiple disease occurrence at the population level. In other words the marginal effect of epilepsy in males is to reduce the probability of working by 23 percentage points, assuming that the occurrence of other conditions is at their average levels. An alternative approach is to measure the marginal effect of a given disease by assuming no other diseases are present. This method was tested, and the differences were found to be minor, typically of the order of 1 to 2 percentage points.

Finally in this section we report the multiple occurrence of disease for those diseases that had a significant depressing effect on labour force participation. The results are summarised in Table 10-6 and Table 10-5 for females and males respectively. The bold numbers on the diagonal are the percentages of those reporting that they have or have had a given disease but report no other disease. The remaining numbers in the columns are the percentages of those with a given disease who also have another condition. For example, of all the females who report having a liver condition, 10% also have diabetes and 45% also have high blood pressure. The tables do not show those with more than two conditions.

Note:
The bold numbers on the diagonal refer to those who report solely having the given condition.