3  Overview

This section provides a series of basic tabulations with the aim of providing the reader with an overview of the survey results for key variables relevant to this study.

3.1  Wealth

Respondents were asked to indicate which assets they owned (real estate, farms, businesses, holiday homes, financial investments etc). Where the respondent failed to indicate either yes or no, for this analysis the assumption is made of “no”. The present value of expected income from New Zealand Superannuation (NZS) is a legitimate part of the wealth of an individual and could be counted as part of total wealth; this has not been done in this study.[5] The results are summarised in Figure 1, for both those working and retired.[6]

Figure 1: Asset ownership

Source:  Health, Work and Retirement Survey

Home ownership is at high levels for working and retired respondents. The ownership of farms, businesses and rental properties declines after retirement. This result is consistent with the findings of Coile and Milligan (2006) for the USA. They note that there are marked shifts in the composition of asset holdings with ageing. The share of assets held in banks and term deposits rises from 11% of total assets at ages 60 to 64 to 28% at ages 80 to 84. They find that the effect is more pronounced where the person has suffered a health shock. They conclude that the standard risk versus return models of portfolio selection need to be augmented with ageing and health status. Older people and those with physical or mental disabilities tend to hold their assets in a more liquid form to be able to more readily meet unexpected costs. In addition, older people place more emphasis on the ease of portfolio management.

However, Love and Smith (2007) have questioned whether in fact there is a causal relation between health status and portfolio choice, such that those in poorer health tend to chose less risky assets. They argue that both health status and financial decisions are “driven by characteristics such as risk preference and impatience that are unobserved by the researcher” (p2). The consequence of this unobserved heterogeneity, if inadequately accounted for, is to severely bias the estimates of the demand for different classes of assets. Using data from the Health and Retirement Study for the USA, they attempt to correct for this unobserved heterogeneity and as a result find no statistically significant relation between health status and asset choice. Their findings underscore the difficulties in establishing robust relations between health status and other variables of interest.

Respondents in the HWR survey were given an option of providing an approximate value for each of the asset classes they owned. These were summed to give an estimate of total wealth. As not all respondents either indicated which assets were owned or chose not to provide estimates of values, the total number of observations was 3,966. The means, medians and inter-quartile ranges by age groups are summarised in Table 3-1.

Note: IQR is the inter-quartile range defined as the difference between the observations at the 25th and 75th percentile points of the distribution and is one measure of the dispersion of wealth.

In order to increase the sample size available for analysis, a value for total wealth was imputed for those cases where the values were missing. This was done by first estimating a regression of total wealth on an extended series of explanatory variables, and then using the estimated coefficients, values of total wealth were predicted for those individuals with missing values. This results in an expanded set of observations which are summarised in the right hand side of Table 3-1. Owing to not all variables in the imputation model being present for all observations, this increased the sample size to 5,093 out of the 5,339 observations available.

In making imputations there is a risk that the group who did not answer the question are systematically different from those who did. To test for this, a comparison was made of the characteristics of those with actual observations with the group for which total wealth was imputed by this method; for observable characteristics no significant differences were found.[7] The mean wealth level for the original and expanded samples is very similar, although the median is higher once the imputed values are included, indicating the distribution became somewhat more skewed toward higher wealth values.

In addition to the results in Table 3-1, total wealth levels for the working and retired groups were calculated. The average wealth is $676,000 for the working group and $427,000 for the retired group.

Unfortunately, it is not possible to compute net wealth based on the survey data as there is no corresponding estimate of total liabilities. The only information available is whether the respondent had a mortgage or a loan. This applied to 52% of the working group and 15% of the retired group, indicating that many people plan to pay off any outstanding debt by the time they retire.