11 Conclusions and discussion
This study has drawn on the first wave of the Health, Work and Retirement survey (HWR). The comprehensive nature of the data collected in this survey has allowed an extensive analysis of the factors that influence labour supply decisions and by implication retirement decisions of older New Zealanders. The survey has national coverage of those aged 55 to 70 and has heavy over-sampling of Māori.
The primary objective of the study is to assess the effect of health and wealth on the retirement decisions of older workers. In addition, the rich data set allowed for a range of other questions to be explored.
Analysis of living standards was undertaken using a range of measures. Higher values of the Economic Living Standards Index (ELSI) were associated with higher income, wealthier, better educated people with better health. The results confirm that those working had a lower living standard than those retired (Ministry of Social Development, 2007). Those who were Māori, those working and those on NZ Superannuation (NZS) had lower scores on this measure. Likewise, Māori, those working, on a benefit or NZS and in poorer health were forced to reduce costs on essential items more frequently. Among both working and retired, better health measures are associated with greater satisfaction with current material living standards. Similarly, better health is associated with the expectation of higher living standards in retirement. Furthermore, relative to working Europeans, working Māori expect to have higher living standards in retirement. This reflects the fact that moving from a low wage to NZS for many in the lower income brackets constitutes a rise in real income.
The study makes extensive use of logit regression models to analyse the factors associated with whether or not a respondent is in the labour force. The objective is to isolate the effects of health and wealth while controlling for a wide range of other influences. The overall pattern of results is broadly similar for males and females. In all the estimated models, health status is significantly associated with the decision to work. This result holds regardless of which measure of health was used. In contrast, wealth was not identified as having a significant effect, although this may reflect the limitations of the data more than the true underlying effect of net wealth.
A 10% decline in health below the mean score is associated with a fall in labour force participation of 3 to 4 percentage points. A decline of this magnitude is clinically significant. At 20% below the mean score, participation falls by 10 points for males and 6 for females. A 40% fall in the health scores would correspond approximately to a self-reported assessment of poor health. At this level, male participation falls 26 percentage points and females by 13 percentage points. The drop in participation is more than proportional for males, but less for females; in other words, while male participation rates are higher, they decline more rapidly as health deteriorates.
In addition to the effect of health, substantial absolute effects on the probability of working stem from a respondent's marital status. Being divorced, separated or widowed, or having a spouse working all increase the probability that a person remains in the workforce.
The probability that a person in the workforce would chose full-time over part-time employment was not significantly related to either the physical or mental health scores. While physical health status has a significant effect on whether to join the workforce, the evidence is that, given a person is employed, their choice about full- or part-time work is not a function of their health status.
Both males and females have a lower probability of working full-time as they age, receive a benefit or have income from superannuation. In contrast they are more likely to be in full-time employment if they are widowed or have dependants.
There is a marked reduction in labour force participation when respondents receive NZS, typically at age 65. The results suggest that there is a significant “deterrence effect” on labour force participation of NZS, once the effect of a wide range of other influences has been controlled for.
A core model on labour force participation was estimated using data from the HWR survey as well as similar data from SoFIE. In broad measure the results are consistent; for males, both surveys confirm that poorer physical and mental health reduces the probability of labour force participation. Notably, mental health conditions do not appear to influence the labour force participation decisions of females.
There is wide debate about the appropriate measure of health status. In large part this study has used the physical and mental scores from the international standard SF-36 survey. In addition, however, self-reported health status was tested as an alternative. It is generally argued that this measure may suffer from a reporting bias as those not in the workforce may justify their decision by reporting a health status worse than their actual condition. While acknowledging this drawback, the results indicated that for both males and females, those reporting lower standards of health were less likely to remain in the workforce. The odds of a person working if they report fair or poor health status are very much lower than those reporting excellent health. This finding was repeated using two different measures of the key economic variable: the respondent's wage rate and the income of other family members.
Another approach to measure health status is to ascertain if the respondent had ever been diagnosed with a particular chronic condition. The HWR survey identified 19 such illnesses. This measure is unlikely to be biased by the so-called “justification” effect. For males the probability of being retired is much greater where they report cancer (other than skin), epilepsy, blood pressure, heart conditions, ulcers or liver conditions. For females the critical chronic conditions influencing labour force participation are diabetes, high blood pressure and liver conditions.
The impact on the probability of an individual working and the impact on the labour supply of older workers are related but distinct questions. The latter requires information about the prevalence of each chronic illness. From the perspective of health policy, it is important to recognise the extent of the illness in the population as well as its effect on labour force participation. A given illness might be highly debilitating but only affect a very small proportion of the population. When the marginal effects of chronic illnesses were weighted by their prevalence in the survey sample, it was found that high blood pressure was the single most significant condition for males and females. For males, cancer and heart problems were important while, for females, diabetes was the next most important condition reducing the overall labour supply of older females. A limitation of the chronic disease measure is that the person responding positively to having once been diagnosed, may or may not be currently inflicted. No information on the date of diagnosis or whether the condition persisted was available.
Underlying the debate about appropriate measures of health status is the fact that what is really required is a measure of an individual's capacity for work. Their physical and mental health status and the presence of chronic illness are all attempts to provide a proxy for the unobservable capacity to work.
A further way to measure the effect of health is to ask whether a respondent expects to be in full-time employment once they reach a certain age. Two ages were specified: 62 and 65. Key factors that were associated with a significantly greater probability of expecting to be in the labour force at these ages were: being male; separated or widowed, Māori and the health of family members.
Few studies of retirement decisions capture the effects of the macroeconomic and policy environment. For example, how does the expected rate of inflation or the parameters of a public pension scheme influence individual decisions? French (2005) finds that the tax structure of the Social Security system in the USA has a greater effect on explaining the age of retirement than do the level of the pension or the health status of the individual. The experience in New Zealand of raising the age of eligibility from 60 to 65 and the resulting increase in the labour force participation rates of older workers is a clear reminder that the retirement decision is strongly influenced by social policies (Hurnard, 2005).
In all studies of the effect of health on retirement there is a question of causality; specifically is it possible that workforce status influences health. If so health status cannot be treated as a truly independent explanatory variable. Undoubtedly there is some reverse influence; the challenge is whether or not it can be corrected for by appropriate statistical methods. As in many other studies, attempts were made to find suitable instrumental variables that might determine health status but not influence the labour supply decision. These attempts proved unsuccessful. However, unless an unequivocally robust instrumental variable can be identified, it is unclear that the statistical properties of the resulting estimates are necessarily superior to those from a single equation.
This study has been based on the first wave of a longitudinal panel study. As a consequence it is a cross-sectional analysis. This has at least two consequences. First, it is possible that health status in earlier periods may be associated either directly or indirectly with current labour force status. In absence of data over time it has not been possible to allow for this effect. A better understanding of the relationship requires the use of longitudinal panel data.
Clearly there is an association between contemporaneous health status and labour force participation. The interaction between health and retirement is potentially a complex and dynamic process. The work of Bound, Schoenbaum, Stinebrickner and Waidman.(1998) shows that it is the decline in health status that has an equally important effect. The response to a decline could reflect the nature and rapidity of the decline, the expected persistence of a lower health status and the individual’s preference for consumption over leisure time together with their family and financial position.
Second, the results reported here for individuals of different ages assume no cohort effects; ie, a 60-year-old today is assumed to behave in 10 years time as a 70-year-old observed today. Soldo, Mitchell, Tfaily and McCabe (2006) find that, using USA data, there are significant differences in the health status prior to retirement of different cohorts. Hyslop and Dixon (2008) use the Linked Employee-Employer Dataset (LEED) to analyse the employment activity of older New Zealanders born in 1937, 1938, 1939 and 1940. Wage employment rates at age 63 rose consistently across the four one-year birth cohorts, suggesting that even within this short span there may well be significant cohort effects. Fortunately, as the present study is focused on a relatively narrow age range, this problem is minimised. Again, future waves of the HWR survey will allow the use of longitudinal panel data which largely overcomes this limitation.
A further strength of longitudinal data is its value in reducing or eliminating the effect of unobserved individual heterogeneity. In any cross-sectional survey, there are inevitably many personal characteristics of individuals which, while important in shaping their decisions, are simply not observed. By using longitudinal panel data one compares the same individuals through time, largely removing the effect of the unobservable characteristics.
A potentially important influence on the health status of an individual as a child is the socio-economic status (education, income and occupation) of their parents. A second related question is the extent to which childhood health status influences the subsequent education and labour market outcomes of adults. Currie (2009) finds strong evidence of both these links, “suggesting that health could play a role in the intergenerational transmission of economic status.” Clearly only with extensive longitudinal data sets is it possible to address these questions.
