6.2 Unadjusted self-rated health

First, basic descriptives are considered. Table 7 shows the distribution of self-rated health across the population. Around three-quarters of the people consider themselves to be in excellent or very good health. A further 18% feel they are in good health. The remaining 6% feel they are in fair or poor health.

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

Note: Results are for those aged 15-64 and are not full-time students. Data for all three waves is pooled together to create an average rate.

Table 7 also shows that, as with the individual diseases, participation decreases as health declines. Around 88% of those in excellent health participate in the labour market, compared to just 29% of those in poor health.

6.2.1  Standard pooled regression

The odds ratios for the pooled logistic regression model where the chronic disease variables have been replaced by the self-rated health variable are shown in Figure 4. The participation rates for those in excellent health appear to be above those for people in very good health. However, the odds of participating for those of very good health are not significantly different from those of excellent health, once other factors are controlled for. Being in good, fair or poor self-rated health is associated with a reduction in the odds of participating compared to those of excellent self-rated health, by 46%, 76% or 92% respectively.

The equivalent marginal effects indicate that being of good, fair or poor health reduces the probability of participating by 6, 22 and 50 percentage points respectively (see Table 13).[24] The impact of being in these health states is significantly different from being in excellent health but also the impact of each health state is significantly different from one another (ie, the magnitude of the relationship between being in fair health and participation is less than that between poor health and participation). The R² for the self-rated health model is slightly higher than that for the individual diseases (0.3227 compared to 0.3090), suggesting self-rated health explains slightly more of the variation. An alternative test statistic to compare the models is the area under the Receiver Operating Characteristic(ROC) curve.[25] As with the R² these diagnostics indicate that the model including self-rated health performs slightly better than the model including individual diseases, with the area under the ROC curve of 0.871 and 0.864 respectively.

The only other variable that has odds of participating in the labour force of a similar magnitude to those for fair or poor health is having a young child for females (a reduction in the odds of participating of around 90%). This indicates the relative magnitude of the relationship between fair/poor health and participation.

Figure 4 - Estimated odds ratios of participating in the labour force - pooled logistic regression - self-rated health: 2002/03 to 2004/05

Source: SoFIE Waves 1-3 Version 4, unweighted, Statistics New Zealand

Notes:

1. Odds ratios are derived from Appendix Table E2 and are relative to excellent health. The footnotes from that table apply to this chart.

2. The following factors were held constant: gender; region; age (and whether 50 years of age or above); highest qualification; study status; marital status; place of birth; ethnicity; children; household income less personal income; years in paid employment; and unemployment rate at the time of the interview.

Table 8 indicates that around 18% of those in excellent health are participating in part-time work compared to 31% of those in poor health. As self-rated health decreases, the likelihood of working full-time appears to fall and the likelihood of working part-time to increase. This is consistent with the earlier observation that those who have been diagnosed with a chronic disease are relatively more likely to work part-time.

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

Note: See footnotes Table 5.

Table 9 shows the odds ratios from a multinomial logistic regression when other factors are controlled for. Even when other factors are held constant, being of good, fair or poor health is associated with a larger reduction in the odds of working full-time (relative to being inactive) as opposed to part-time (relative to being inactive).[26] For example, being in fair health rather than excellent is associated with an 83% reduction in the odds of working full-time (relative to being inactive), compared to a 61% reduction in working part-time (relative to being inactive).

Source: SoFIE Waves 1-3 Version 4, unweighted, Statistics New Zealand

Notes:

1. These odds are derived from the data in Appendix Table E3. For full footnotes see that table.

2. The following factors were held constant: gender; region; age (and whether 50 years of age or above); highest qualification; study status; marital status; place of birth; ethnicity; children; household income less personal income; years in paid employment; and unemployment rate at the time of the interview.

3. *Significant at the 90% level. **Significant at the 95% level. ***Significant at the 99% level.

Table 10 shows the marginal effects from the same model. The results show that, for an average person, being in any health state other than excellent is associated with a reduced chance of working full-time. For the majority of health states (other than poor health) this reduction in the chance of working part-time is balanced by increases (both significant and not significant) in the chance of working part-time, being unemployed or being inactive. For those in poor health the chance of working part-time is also reduced compared to someone of excellent health. An average person in poor health, compared to an average person in excellent health, is 49.1 percentage points less likely to work full-time, 4 percentage points less likely to work part-time and 51.9 percentage points more likely to be inactive.

Source: SoFIE Waves 1-3 Version 4, unweighted, Statistics New Zealand

Notes:

1. These marginal effects are derived from the data in Appendix Table E3. For full footnotes see that table.

2. The following factors were held at the mean value for the whole sample: gender; region; age (and whether 50 years of age or above); highest qualification; study status; marital status; place of birth; ethnicity; children; household income less personal income; years in paid employment; and unemployment rate at the time of the interview.

3. *Significant at the 90% level. **Significant at the 95% level. ***Significant at the 99% level.