9.3  Measuring the marginal effects on labour force participation

Table 9-2 provided a ready overview of those factors significantly related to the decision to work. However, these results, taken alone, do not indicate the magnitude of the effects. It is possible that a variable has a significant effect, yet compared to another, its absolute effect on the probability of working might be quite small. To assess the magnitudes, we calculate the marginal effects.[30]

The probability of remaining in the workforce (either full- or part-time or actively seeking work) as distinct from being retired (with no paid work) is calculated by setting all variables except the one of interest to their mean values in the case of continuous variables, and to zero in the case of binary variables. The mean values were chosen to correspond to the group of interest (eg, mean values for male and female splits are different).

The calculation is then repeated with a change made in the variable of interest. In the case of a continuous variable such as age, the second calculation is made with age increased by one year while all other variables are held at their means. In the case of a binary variable such as whether or not the respondent was separated, the variable is set to one. Exceptions to this apply in the case of the physical and mental health scores (both continuous) where the results are reported for a five unit change deemed to be clinically significant. In addition, for migrants the number of years in New Zealand is increased by five rather than one, simply to scale up what would otherwise be a very small, but significant, effect.

Notes:
¹ Only variables whose coefficients were statistically significant are listed in the table.
² The complete results are given in Appendix Table C.14(a).

Notes:
¹ Only variables whose coefficients were statistically significant are listed in the table.
²The complete results are given in Appendix Table C.14(b).

The results for males and females are shown in Table 9-5 and Table 9-6 respectively, and shown graphically in Figure 15. Overall, the pattern of results is broadly similar for males and females. The largest absolute effects on increasing the probability of working stem from a respondent’s marital status and whether the spouse is in the workforce. For example, the probability that males who are not separated or divorced are in the workforce is 76%. For those who are separated or divorced the probability of working rises to 91%, so the marginal effect is +14 percentage points. The corresponding figures for females are 50% rising to 92% for those separated or divorced, a marginal effect of +42 percentage points.

Figure 15: A summary of the factors that change the probability that a person will be retired: by sex

Source:  Health, Work and Retirement Survey

Note: The numerical scores refer to positive and negative effects at the 1% (=3), 5% (=2) and 10% (=1) levels of statistical significance.

In contrast a clinically significant unit improvement in physical health only raises the probability by +2 percentage points for both males and females. However, it is not surprising that improvements in health above the mean level have a modest effect on participation, which is already reasonably high. Also, given that there is some correlation between mental and physical health scores, it is reasonable to expect that the combined effect would be greater than changes in either alone.

To more fully explore the effect on participation rates we estimated the impact of simultaneous changes in physical and mental health scores above and below their respective means. The results are given in Table 9-7and Figure 16. An increase of five units in both scores now leads to a 3 percentage point increase in participation rates for males and females. In contrast, consider a 20 point decrease in the health scores. This corresponds approximately to a change in self-reported health from excellent to poor. At this level, male participation rates fall by 26 percentage points, from 90% to 64% (assuming all other variables are set at their mean values). The corresponding fall for females is from 81% to 68%.

Participation rates drop off at an increasing rate as health scores deviate further below their mean values. However, the decline is much more marked for males. For example, if the mean health scores fall from 10 to 20 units below their means, female participation rates decline only by 7 percentage points. In contrast, male participation for the same decline in health scores falls by 16 percentage points. So while male participation rates are higher than those for females, they decline more rapidly as health scores fall further below their means (Figure 16).

Figure 16: Health scores and participation rates