9 Labour force participation (continued)

9.5  Using a self-reported health measure

To this point we have analysed the impact of health status on labour force participation using the SF-36 measures of physical and mental health. An alternative approach is to use the self-reported health measure based on the response to the question:

In general would you say your health is: Excellent; Very Good; Good; Fair; or Poor?

Previous researchers have undertaken studies aimed at assessing the relative merits of so-called “objective” measures and self-rated measures. Bound (1989) identifies four reasons to be suspicious of the survey responses to a question soliciting the indivdual’s own rating of their health status. First, as this is a subjective judgement, there is no reason to suppose that each person would use the same subjective scale, thus raising the possibility that the results are not strictly comparable across respondents. Second, the responses may be influenced by the very labour market outcomes that are to be explained. Third, a person may use limitations owing to health as a reason for their not participating (the so-called justification bias). Finally, some may have a financial incentive to qualify for a disability allowance as a means of taking early retirement.[31]

A number of studies have explored the justification bias. Anderson and Burkhauser (1985) use early mortality data derived from longitudinal panel data, and find this more objective measure had a smaller effect on labour supply than did self-rated health status.

One approach has been to use objective measures as an instrumental variable, hoping thereby to overcome the problem of endogeneity; eg, Stern (1989). However Bound (1989) reports that while this has appeal, it leads to understating the effect of economic variables on retirement. He concludes that neither of the measures of health status will generate reliable estimates of the effect of health on labour force attachment of older males. However, as each tends to produce estimates biased in opposite directions, results from utilising both measures may well bound the actual effect.

Arguably, the risk of justification bias is lower with the SF-36 measures, given they are based on an extensive set of questions. In contrast, the single question soliciting self-reported health status may be subject to a much greater risk of bias by those who feel they need to justify their withdrawal from the labour force on health grounds. In other words the apparent health status based on a self-reported measure may not necessarily be the true underlying health status of the respondent.

Notwithstanding, it is of interest to test whether labour force participation is associated with the self-reported health measure. The results are summarised in Table 9-9. The estimated coefficients show that there is a significant association between health status and the probability of working. This reinforces the results obtained using the SF-36 measures. In both cases, health status has a significant association with the decision to retire or remain in the workforce.

For each health status, bar “excellent”, there is an odds ratio based on the estimated coefficients from the logit regressions. These ratios give the amount by which the odds of working must be discounted relative to the odds of working when self-reported health is excellent. For example, the odds of a female in fair health being in the workforce are 0.36 times those of a female in excellent health. Both males and females reporting either fair or poor health have significantly lower odds of working than those with excellent health. In the case of males, those reporting good health the odds are only half those in excellent health. Recall that these estimates are based on holding all other factors constant (eg age, income, education, marital status, ethnicity, etc).

Notes:
¹ ns = not significant at the 10% level; ** = significant at the 5% level; *** = significant at the 1% level.
² The odds ratios are found as where is the estimate of the k-th coefficient from the logit regression.
³Complete results are given in Appendix Table C 15.

We now supplement our earlier analysis of the marginal effects on participation (Section 9.3) by incorporating self-reported health measures. Recall that while the marginal effects of a five unit change in the component scores for physical and mental health were typically significant, their magnitude was apparently small (of the order of 1 to 2 percentage points).

As a first step we repeat the estimation of equation (13) replacing the health scores with the self-reported measures (denoted SR):

Pr(W) = W(SR,Z) (15)

while maintaining the same set of control variables (Z). As the self-reported measures are a set of categorical variables, it is necessary to delete one category (in this case Excellent) such that the estimates for the remaining categories are measured with respect to the deleted category. The results are summarised in Table 9-10, showing that males in poor health have a participation rate 11 percentage points lower than those in excellent health with a corresponding 12 points for females.

We now estimate the marginal effects on labour force participation by utilising the changes in health scores associated with the self-reported categories, and applying these changes to the logit model estimated on the basis of health component scores (ie, equation (13)). For example, for males, the average physical health component score for those reporting excellent health is 57 (see the first cell of Table 8-1), while those reporting very good health have a mean score of 53.

Note:
** = significant at the 5% level; *** = significant at the 1% level.

Based on this four point fall in the physical component score, participation would fall by an estimated 2 percentage points (Table 9-11). Note that this result closely mirrors that found for the five point change in the physical score described in Section 9.5. In the right-hand panel of Table 9-11, the marginal effects on participation are shown for the combined effect of both physical and mental scores. This is done as when respondents provide an estimate of their self-reported health status they presumably give an assessment derived from their perception of both their mental and physical health.

9.6  Using estimated wage rates

It is critical that the effect of the economic variables be captured accurately in order that the underlying relation between health and participation in the labour force is correctly revealed. If the model is misspecified, some of the effect owing to economic forces might be incorrectly attributed to health status. Up to this point the analysis has used either respondent income or the income of other family members.

Anderson and Burkhauser (1984) demonstrate that different measures of health status can alter the interpretation of economic variables. In this case they were concerned with the effect of wage rates. Two questions arise: Are wage rates an alternative to the income measures we have used to this point? Is the effect of wage rates modified by using different health measures?

To incorporate wage rates in our analysis we undertook the following steps. First we estimated an average hourly rate (w) based on the respondent's reporting of hours worked (h) and their total pre-tax income (Y).

(16)

We then fitted a wage equation of the form

w = α + Σβ_kZ_k + ε (17)

to all observations that reported non-zero hours, and from this used the predicted values (^ŵ) as the reservation wage for those not in the workforce. The vector Z of explanatory variables included age, education, marital status, etc. The final step was to use the log wage as an independent variable in the univariate logistic model for labour force participation.

On theoretical grounds alone, it is not certain whether a higher wage would increase or decrease the probability of working as distinct from retirement. There is both an income and a substitution effect. A higher wage raises the opportunity cost of leisure and would therefore tend to diminish the demand for leisure (ie, increase the propensity for labour market work). However, a higher wage also implies a higher income (for any given hours worked) with a greater opportunity to accumulate wealth for retirement. Whether the phenomenon of the “backward bending supply of labour” prevails is then an empirical question.

Notes:
¹ ns = not significant at the 10% level; ** = significant at the 5% level; *** = significant at the 1% level.
² Complete results are given in Appendix Tables (C.14-17)

The results are summarised in Table 9-12. Four separate logit regressions for labour force participation were run for both males and females. The first two, reported in the upper half of the table, used the estimated wage rate as the economic variable. It is intended to capture the effect of the wage on the decision to retire or not. In addition, separate runs were made using the SF-36 health measures and the self-reported health status. The latter results are given relative to excellent.

In the lower half of the table the runs of the logit model are repeated but this time using the income of other family members as a proxy for the effect of economic incentives on the decision to retire. There are a number of important conclusions to be drawn from this analysis. First, in all cases health has a significant influence on the decision to remain in the workforce or retire. Second, when the health measures from the SF-36 are used, both physical and mental health scores are significant for males; higher scores (meaning better health) are associated with a higher probability of remaining in the workforce. However for females, there is no significant effect on labour force participation from mental health scores. Third, based on the self-reported health status, both males and females reporting good (when the wage rate model is used for females), fair or poor health are significantly less likely to be in the workforce than those reporting excellent health.

Fourth, the wage effects are similar regardless of which measure of health status is used. For both males and females, higher wage rates lower the probability that the person would remain in the workforce. Finally, the overall conclusion that health has a significant influence on the decision to remain working holds for males and females regardless of which measure of health or which economic variable is used. It appears to be a robust finding.

9.7  Comparisons with results from SoFIE

In this section we present a brief comparison of the factors that affect the probability of working drawn from the present study based on the HWR survey and a similar study using data from the Survey of Family Income and Employment (SoFIE).[32] Data in SoFIE cover a wide range of ages so that the first step was to select only those respondents aged 55 to 70 years, thereby matching the sample in the HWR survey. Similarly, using SoFIE it is not easy to distinguish between “not in the labour force” and “retired”. As a result, the HWR population was widened to include the others in the survey, who were all placed in the “not in labour force” group with the retired group. This involved those identifying themselves as students, homemakers and others. A logit regression for the probability of working was estimated for this group using a set of explanatory variables common to both surveys. This inevitably meant that some of the explanatory variables used earlier in this study could not be included, as there was no comparable measure in the SoFIE data set.

In broad measure the results, summarised graphically in Figure 17, are comparable. Marital status (having a working spouse and being single), better health (physical and mental) and being educated to the tertiary level significantly increase the probability that a person will remain in the workforce. Being on a benefit reduces the probability in both surveys.

Figure 17: Factors influencing the probability of working: a comparison of HWR (Massey) with SoFIE

Source:  Health, Work and Retirement Survey and Survey of Family, Income and Employment

Notes:
¹ The numerical scores refer to positive and negative effects at the 1% (=3), 5% (=2) and 10% (=1) levels of statistical significance.
² The complete results are given Appendix Tables C.21 and C.22