Category: Retirement

Factors that Influence Retirement

Posted on By economica

by Derek Aldridge

This article first appeared in the Autumn 2011 issue of the Expert Witness.

We often note in our reports that a plaintiff’s loss of income can be sensitive to assumptions regarding his retirement age. (The retirement age assumption will generally be more important for a plaintiff who is close to retirement than for one who is still many years away.) In addition, it is also sometimes reasonable to incorporate early retirement scenarios, in which we assume that due to his residual deficits, a plaintiff will retire earlier than he would have if uninjured. This can have a substantial impact on the plaintiff’s future loss of income, especially if he is already close to retirement age.

The reason that an early with-accident retirement scenario is often sensible is because a person’s health is an important factor in his decision to retire. That is, poor health tends to hasten retirement, all else being equal. Of course, there are other factors that also influence a person’s decision to retire (financial considerations being most important, but also job satisfaction, spousal influences, and so forth), but health is an important factor, especially with respect to unplanned retirement.

Two articles that examine health impacts on retirement were recently published in Statistics Canada’s Perspectives on Labour and income. The first article (Heath factors and early retirement among older workers) used 12 years of longitudinal data1 to examine the various health factors that caused people to retire early. The second article (Retirement, health and employment among those 55 plus) used a cross-sectional survey2 of Canadians age 55 and over, to examine the reasons why they were either working (and not previously retired), partially retired, fully-retired, or previously retired but now working. Below, I will discuss some of the findings in these articles that are relevant to estimating a person’s loss of income due to an injury.

Health factors and early retirement among older workers (Park, 2010)

The first article relied on data from a group of workers who were age 40–52 in 1994/95. The survey interviewed this group every two years, through 2006/07, by which time they had reached age 52–64. By the end of the 12-year period, approximately 20 percent of the workers had left the labour force. However, among those who reported that they had fair or poor health, 35 percent had stopped working. Among those workers in poor health who stopped working, almost half reported that illness or disability was the main reason they had exited the labour force.

More specifically, the article found that when compared with men who reported that they had excellent health, men who reported fair or poor health were nearly five times more likely to exit the labour force. The researchers found a weaker health effect among women, and this effect was judged to be not statistically significant.3 The stronger effect among men was indeed found to be statistically significant. Among men, it was found that eye problems, back pain, ulcers, and migraine headaches were especially likely to increase the likelihood of retirement.

The researchers also examined health behaviours, and found that among men, daily smoking and heavy drinking also increased the chance of labour force exit (relative to males who did not practice these behaviours). In particular, male heavy drinkers were almost twice as likely to exit the labour force early, versus those who did not drink heavily. Among women, the one health behaviour that was judged to be statistically significant was obesity. Obese women were found to be 1.6 times more likely to exit the labour force early.

The article reported that if health status was controlled for, the effects of smoking and obesity disappeared. That is, smoking and obesity directly impact health, which in turn impacts retirement. Thus, the link between smoking/obesity and retirement is an indirect one. However, heavy drinking had a direct influence on retirement, even when overall health was controlled for. That is, heavy drinking was observed to be associated with early retirement among men, independent of the impact of drinking on the men’s health.

Although it is not especially relevant in the context of personal injury claims, the article also noted that working conditions had a significant impact on the likelihood of labour force exit. In particular, high job strain (stress), job dissatisfaction, and low supervisor support were all found to have statistically significant effects on retirement behaviour.

Retirement, health and employment among those 55 plus (Park, 2011)

The second article relied on a survey of a group of “older” Canadians, aged 55–85 in 2009. The survey group was divided into four subgroups: those who had never retired, those who were partially retired, those who were fully-retired, and those who had previously retired but had returned to work. The characteristics of the four groups were examined, in an effort to determine which characteristics were associated with a person’s retirement status.

The study reported some unsurprising results (for example, women were more likely to be retired than men; most of the workers who had never retired were under age 65, and so forth), and then addressed some issues that are much more interesting from our perspective.

Even when the age differences were accounted for, the study found that the retirees had worse health than those who were not retired. More than half of the retirees reportedly had three or more chronic conditions4, while more than a fifth had five or more chronic conditions. This effect was more pronounced among women than men.

The study also considered physical inactivity and found that retirees were much more likely to be considered physically inactive than those who were working.

Another factor considered by the study that is interesting from a personal injury perspective is financial status. The study reported that nearly 40 percent of the “never retired” workers reported that they had inadequate financial plans for retirement. More specifically, more than a third of them reported that they had less than $25,000 in savings and investments. In addition, among the groups who had retired at least once, the most common reason they reported for their decision to retire was that it was financially possible. These findings reinforce the notion that (especially for older plaintiffs), a person’s financial ability to retire should be considered when estimating when that person would have retired, but for the accident.

Comment

The two articles discussed above provide additional support for our common practice of assuming that a plaintiff with substantial residual deficits will likely retire earlier than she would have in the absence of the accident. The articles also identified some factors to be considered when making an assumption concerning a plaintiff’s without-accident retirement age. However, while we know that poor health tends to hasten retirement, we will continue to face the difficulty of knowing the specific effect on a particular plaintiff. That is, for most cases it will remain unclear whether it is more reasonable to assume that a plaintiff’s injuries will cause her to retire (say) three years early or two years. We are left with offering assumptions that are reasonable, providing multiple scenarios, and ultimately leaving it up to the Court to make a specific finding.

References

1. Park, Jungwee. 2010. “Health factors and early retirement among older workers.” Perspectives on Labour and Income. June 2010. Statistics Canada Catalogue no. 75-001-X. p. 5–13.

2. Park, Jungwee. 2011. “Retirement, health and employment among those 55 plus.” Perspectives on Labour and Income. January 2011. Statistics Canada Catalogue no. 75-001-X. p. 1–12.

Footnotes

1. Longitudinal data is collected over a period of time from the same set of people. [back to text]

2. Cross-sectional data is collected from a group of people at one point in time. [back to text]

3. By “not statistically significant”, I mean that the researchers believed that it might have been random chance that caused it to appear as though women with poor health were more likely to exit the labour force than women with excellent health. [back to text]

4. The number of chronic conditions was calculated based on respondents’ answers to questions about whether they had been diagnosed by professionals as having any of the following chronic conditions: asthma, arthritis, osteoporosis, high blood pressure, back problems, migraine headaches, chronic bronchitis, emphysema, chronic obstructive pulmonary disorder (COPD), diabetes, stroke, heart disease, cancer, stomach or intestinal ulcers, urinary incontinence, Alzheimer’s disease or other dementia, bowel disorder/Crohn’s disease or colitis, Parkinson’s disease, thyroid conditions, cataracts, glaucoma, mood disorders, and anxiety disorders. [back to text]

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Derek Aldridge is a consultant with Economica and has a Master of Arts degree (in economics) from the University of Victoria.

The Impact of Poor Health on Retirement Age

Posted on By economica

by Christopher Bruce

This article first appeared in the autumn 2007 issue of the Expert Witness.

In personal injury cases, plaintiffs often argue that their disabilities will induce them to retire early and, therefore, cause a reduction in lifetime earnings. As such claims are largely based on the plaintiff’s own testimony, it is often difficult for the courts to determine whether the claim is credible and, if so, to identify the number of years by which retirement will be accelerated.

The purpose of this article is to assist the court in these determinations by providing a survey of the academic literature on the effects that health limitations have on the age of retirement. As the first edition of my textbook, Assessment of Personal Injury Damages (Butterworths, 1985) contains a survey of the early literature on this topic, I concentrate in this paper on articles published since 1990.

This yields a set of eight studies. Of these, two (Disney, for Britain, and Campolieti, for Canada) reported only that a negative change in health or disability status among individuals over 50 had a “significant” negative effect on the age at which those individuals retired.

Of the remaining studies, two provided data concerning the impact of alternative levels of health status on the probability that 50-65 year-olds would be working. Au, Crossley, and Schellhorn, using Canadian data from 2000-2001, reported that even a minor change in health status, from “very good” to “good,” would reduce the probability of employment by 10 percent. (See Table 1.) And a change from “excellent” to “poor/fair” could reduce employment by as much as 40 percent (among males).

Table 1

Similarly, Cai and Kalb, using Australian data from 2001, found that a change in health status from “excellent” to “poor” would reduce the probability that individuals would be in the labour force by approximately 16-18 percent. (See Table 2.)

Table 2

At age 55, these reductions in probabilities imply that individuals in poor health will retire between one and two years earlier than those in very good health. This is consistent with Gustman and Steinmeier’s finding, for the United States, that individuals who were “limited in the kind or amount of work” in which they could engage could be expected to retire two years earlier than those not so-limited.

Berger and Pelkowski, for the United States, and Jimenez-Martin, Labeaga, and Prieto, for Spain, also found impacts that were similar to those found by Campolieti and Au, et. al., but using somewhat different measures of health status.

Jimenez-Martin et. al. reported that 55-65 year-old individuals with “severe disability” were 14.6 percent less likely to be employed than were the non-disabled, and that those with “very severe disability” were 28.5 percent less likely to be employed than were the non-disabled.

Berger and Pelkowski found that among 51-61 year-old couples in which both the husband and wife had (at the beginning of the study period) been healthy and employed, the effect of a health problem was to reduce the probability that the wife would be working by 19 percent and that the husband would be working by 35 percent.

Finally, McGarry found that a change in health status from “good” to “fair” would reduce the probability that a 62 year-old would be working from approximately 45 percent to 40 percent.

To summarise, regardless of the country that is investigated, the evidence is clear: a reduction in health, from “good” to “fair or poor” will have a significant, negative impact on the probability that 50-65 year-old individuals will be working. Although the precise effect of such a reduction varies from study to study, there appears to be fairly consistent evidence that the average effect is to reduce the age of retirement by approximately two years (for example, from age 61 to age 59).

References

Au, D. W., T. Crossley, and M. Schellhorn (2005) “The effect of long-term health on the work activity of older Canadians.” 14 Health Economics, 999-1018.

Berger, M., and J. Pelkowski (2004) “Health and family labor force transitions.” 43 Quarterly Journal of Business and Economics, 113-138.

Cai, L., and G. Kalb (2006) “Health status and labour force participation: Evidence from Australia.” 15 Health Economics, 241-261.

Campolieti, M. (2002) “Disability and the labor force participation of older men in Canada.” 9 Labor Economics, 405-432.

Disney, R., C. Emmerson, and M. Wakefield (2006) “Ill health and retirement in Britain: A panel data-based analysis.” 25 Journal of Health Economics, 621-649.

Gustman, A., and T. Steinmeier (2000) “Retirement in dual-career families: A structural model.” 18 Journal of Labor Economics, 503-545.

Jimenez-Martin, S., J. Labeaga, and C. Prieto (2006) “A sequential model of older workers’ labor force transitions after a health shock.” 15 Health Economics, 1033-1054.

McGarry, K. (2004) “Health and retirement: Do changes in health affect retirement expectations?” 39 Journal of Human Resources, 624-648.

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Christopher Bruce is the President of Economica and a Professor of Economics at the University of Calgary. He is also the author of Assessment of Personal Injury Damages (Butterworths, 2004).

Death and Retirement: Allowing for Uncertainty

Posted on By economica

by Christopher Bruce

This article first appeared in the autumn 2005 issue of the Expert Witness.

Assume that a plaintiff has begun to recuperate following a serious accident. If her injuries stabilize at their current level, she will suffer a loss of earnings of $20,000 per year. If, however, she has a relapse, her losses will increase to $40,000 per year. Her doctors tell you that there is a 50 percent chance that there will be a relapse (and a 50 percent chance that there will be no relapse).

How should the annual value of her loss be calculated? If damages are set equal to $20,000 per year, there is a 50 percent chance that she will be under compensated; whereas if she is paid $40,000, there is a 50 percent chance that she will be over compensated. (And if she is paid some amount between $20,000 and $40,000, there is a 100 percent chance that she will be incorrectly compensated.)

This conundrum, in which there is uncertainty about the outcome of future events, is common in the assessment of personal injury (and fatal accident) damages. Not only is there uncertainty about the future course of the plaintiff’s injuries, as in the example above, we also face uncertainty concerning the age at which the plaintiff will retire, the plaintiff’s life expectancy, the probability that the plaintiff would have (and will be) unemployed, and a host of other factors.

The general approach that virtually all financial experts take in such cases is to calculate the average outcome that would arise from the uncertain event, if the event could be repeated a large number of times. For example, if the injury described above was to be repeated 100 times (for example, if there were 100 plaintiffs with that same injury), we would expect that the plaintiff’s injuries would remain stable in approximately 50 cases, leading to a loss of $20,000 per case. In the other 50 cases, the plaintiff would suffer a relapse and her loss would rise to $40,000. Thus, the total annual loss, across all 100 cases, would be ((50 × $20,000) + (50 × $40,000) =) $3,000,000. The average annual loss would be $30,000; which could also be calculated by multiplying 50% times $20,000 and adding 50 % times $40,000. That is, the average value of a loss can be calculated by multiplying each of the possible losses by its probability and then adding the resulting numbers together.

But, as was noted above, $30,000 is guaranteed to be the “wrong” amount in 100 percent of cases. How, then, can it be justified? One simple answer is this: if the event in question is truly uncertain, the plaintiff should be able to use the $30,000 to purchase insurance that will compensate her fully regardless of which value turns out to be her true loss – either $20,000 or $40,000. The reason for this is that if the insurer issues, say, 100 such policies, it can expect to pay out $20,000 in 50 cases and $40,000 in the other 50, for an average of $30,000. (It will have collected $3,000,000 [= 100 × $30,000] and will have paid out $3,000,000 [= 50 × $20,000 + 50 × $40,000].)

Risk of Mortality

This type of calculation is most commonly used when dealing with the uncertainties associated with mortality. Take the extreme case in which there is a ? probability that a plaintiff will live exactly one year (and then die), a ? probability that he will live exactly two years, and a ? probability that he will live exactly three years. If he would have earned $60,000 per year but has now been left unable to work, his loss can be calculated using the technique described above. That is, there is a ? chance that he has lost one year’s income ($60,000), a ? chance he has lost two years’ income ($120,000), and a ? chance he has lost three years’ income ($180,000); for an average of $120,000 (=? × $60,000 + ? × $120,000 + ? × $180,000).

Alternatively, in such cases, it is sometimes possible to use a “rule of thumb” to estimate the loss. Given the probabilities in the preceding example, it can be shown that, on average, the plaintiff will live two more years before dying. (2 = ? × 1 + ? × 2 + ? × 3) That is, his life expectancy is two years. His expected loss can then be calculated as the sum of his losses over that life expectancy, or $120,000 (= 2 × $60,000). Note, however, that this approximation works best if the losses are approximately the same in each year, as it was here. (If the annual loss is significantly different in the first year than, say, the third year, this approach yields a biased estimate.)

What is clear is that it would be inappropriate to mix together the two calculation techniques. It is not appropriate, for example, to estimate the loss by multiplying each of the first two years’ losses by their associated probabilities and assuming that the loss continues for only two years. That would produce an “estimated” loss of only $60,000 (=? × $60,000 + ? × $120,000), $60,000 less than the true loss.

Retirement Age

The techniques described here can also be used to estimate the effect of uncertainties about the plaintiff’s retirement age. Assume, for example, that there was a ? probability that a 63 year-old plaintiff would have worked for exactly one year (i.e. to his 64th birthday) and then retired, a ? probability he would have worked two years, to his 65th birthday, and a ? probability he would have worked three years, to his 66th birthday. If he would have earned $60,000 per year while working, his loss, again, can be found from the formula: ? × $60,000 + ? × $120,000 + ? × $180,000 = $120,000; or by multiplying the average number of years to retirement by his annual earnings, to produce $120,000 = 2 × $60,000.

As with the mortality example, it would clearly be incorrect to multiply each year’s earnings by the probability it would occur and assume the individual would have retired at the average age, of 65. That would produce an “estimate” of, (? × $60,000 + ? × $120,000 =) $60,000, again, only half of the correct estimate.

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Christopher Bruce is the President of Economica and a Professor of Economics at the University of Calgary. He is also the author of Assessment of Personal Injury Damages (Butterworths, 2004).

Retirement trends in Canada

Posted on By economica

by Kelly Rathje

This article first appeared in the spring 2003 issue of the Expert Witness.

The value of an individual’s potential earning capacity depends in part on how long she will be in the labour force. That is, for the calculation of lost earnings, it is necessary to make assumptions concerning the age at which the individual would have retired (and will now retire).

The focus of this article is to compare retirement ages of Canadians over the five year period 1991-95 to retirement ages of Canadians over the five year period 1996-2000.* The findings show that for most educational and industry categories, Canadians are retiring earlier than they did even five years ago.

In the early 1980s, the median retirement age was close to age 65. However, retirement ages decreased steadily from 1986 to approximately 1993. Notably, in 1987, the Government of Canada reduced the age (from age 65 to age 60) at which Canada Pension Plan benefits could be collected (albeit with a reduced pension amount). I compare the retirement patterns of Canadians over the 1991-95 period to the retirement patterns of Canadians over the 1996-2000 year period to see if the trend for earlier retirement has continued. In Table 1, the distribution of retirement ages in Canada over the two five-year periods (1991-95 and 1996-2000) is summarized.

As shown in Table 1, in the five year period 1991-95, the highest percentage of individuals retired in the 60 to 64 year age category. In the five year period 1996-2000, 60 to 64 still remained the most popular age group for retirement, however the percentage of individuals retiring within the age category had dropped by 6 percentage points (37 percent to 31 percent). The percentage of individuals retiring “earlier”, in the 50 to 54 age category and 55 to 59 age category both increased – the former having the largest increase (9 percent to 15 percent, or 6 percentage points). This suggests that although many Canadians are still choosing to retire at a “normal” retirement age (60 to 64), there is a shift to earlier ages.

Table 1

Although the percentage of people retiring in their 60s decreased within the time period considered, the percentage of individuals retiring at age 70 or above remained unchanged.

Table 2 compares median retirement ages in Canada over the periods 1991-95 and 1996-2000. (The “median” retirement age is defined such that 50 percent of individuals retire at ages above that age and 50 percent below it.)

As shown in Table 2, the overall median retirement age has decreased by one year, from approximately age 62 (1991-95) to 61 (1996-2000). The median retirement ages of private employees also decreased by one year (from age 63 to age 62), however the retirement ages of public employees decreased by two years (from 60 to 58). Self-employed individuals experienced no change in retirement trends from 1991-95 to 1996-2000. The earlier retirement ages of public employees are likely due to the generous pension plans available to most public employees, which often offer incentives to retire at earlier ages.

Table 2

Table 3 summarises retirement ages by industry. As shown in the table, many industry categories also experienced decreases in median retirement ages between 1991-95 and 1996-2000. However, self-employed individuals did not alter their retirement patterns in most industries and, if anything, the retirement age of self-employed individuals may be increasing.

Table 3

Considering the “employees” category for the goods-producing occupations, retirement ages remained constant for all industries except for utilities and construction. These two experienced a decline in retirement ages of 2 years. For service-producing industries, all declined except management, which remained constant at 65. Educational services showed the largest decline in retirement ages over the two five year periods – from age 61 to age 57, a drop of four years. This may have resulted from restructuring within the education sector that led to the offering of early retirement packages to many teachers. If so, there may be a reversal of this decline in the future (as fewer early retirement incentives are offered).

Self-employed individuals again show higher retirement ages than employees. Within the goods-producing industries, most show an increase in the age of retirement (there was a decline in manufacturing), which goes against the overall trend for retirement patterns of Canadians. The service-producing industries, however, showed a one year decrease in retirement ages for the trade, management, and other industries; constant retirement ages for transportation and professional industries; and a one year increase in retirement ages for health care and accommodation industries. The largest increase was in the finance, insurance, and real estate industries, which saw a four-year increase in retirement ages over 1991-95.

The above tables show retirement trends by industry, and the statistics combine both male and female statistics, and also do not consider specific levels of education. The statistics for males and females, by education levels are summarized in Table 4.

Table 4

Over 1991-95, men and women overall had similar retirement patterns. That is, there was only one year difference in the retirement ages at each education level. In 1996-2000, the male retirement patterns saw very little change from 1991-95, with most education levels having unchanged retirement ages. Only males at the high school diploma level experienced a decline in retirement (age 61 to 60).

Females, on the other hand, have followed a decline at each level by at least one year. Females with a university education resulted in the lowest median retirement age (57) over 1996-2000. Also note that at the high school diploma level, males and females experienced the same retirement age (60).

I note, however, that the female retirement trends may be underestimated. Retirement trends are based on historical retirement, and may not reflect the trends of future generations. That is, young women in the labour force now may experience different retirement patterns than women who were of retirement age in the last five years. The current generation of women are obtaining higher levels of education, and are participating in the labour force more, as compared to those women of retirement age now. This suggests that their labour force attachment may be greater than the attachment of the older cohorts. Therefore, it may be theorized that young women now in the labour force will retire later than women who faced the retirement decision in recent years.

Also, there is evidence to suggest that in the future, there may be pressure for Canadians, both male and female, to delay retirement. For example, in a paper entitled “Future Age of Retirement”,** Brown argued that as the baby boom generation moves into the retirement ages, they will attempt to liquidate assets in order to buy goods and services. This will reduce the value of the assets due to the number of retirees attempting to do this. Also, the smaller “baby bust” generation will be the source of labour within the economy. The production in the economy may slow due to the smaller labour force, resulting in price inflation. This may force some potential retirees to postpone retirement since the value of their assets will have decreased. Thus, Brown’s prediction for retirement in Canada is that the median retirement age will fluctuate between age 60 and 61 over the next 47 years. That is, the overall median age of retirement and the trend of decreasing retirement ages may not continue on into the future.

Conclusion

Based on the above information, it seems that the median retirement age in Canada has fallen from age 62 to 61 and, depending on educational attainment and gender, the average retirement of an individual may be as low as 57, or as high as 65. Self-employed individuals continued to retire at approximately age 65.

For the purposes of loss of income calculations, it seems reasonable to consider the education level of the individual, since there are statistics available for both males and females. In addition, if a career path has been established, one should also consider retirement patterns of the specific industry. For minors, the overall or educational statistics would be appropriate.

Footnotes

* The source of the data provided in this article is the Statistics Canada publication Perspectives on Labour and Income, Summer 2002 Vol. 14, No. 2, and Summer 1997 Vol. 9, No. 2. [back to text of article]

** Brown, Robert L. “Future Age of Retirement” Canadian Investment Review, Fall 2002, pages 32-37. [back to text of article]

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Kelly Rathje is a consultant with Economica and has a Master of Arts degree (in economics) from the University of Calgary.

Complementarity in the Retirement Behaviour of Older Married Couples: An Update

Posted on By economica

by Daryck Riddell & Christopher Bruce

This article first appeared in the spring 2002 issue of the Expert Witness.

When forecasting the earnings streams of individuals over 50, one of the most important factors is predicted age of retirement. For example, changing the projected retirement age from 63 to 60, when the individual is currently 57, will decrease the future loss of earnings by approximately 50 percent.

It is often argued that one indicator of likely retirement age among individuals in this age group is the retirement decision of the plaintiff’s spouse. If a 57 year-old woman’s husband has already retired, that could indicate that she will retire earlier than would otherwise have been predicted.

Economists have observed three factors that might suggest a correlation between the retirement ages of spouses. These we refer to as: similarity of profiles, sharing of household finances, and complementarity of leisure.

Similarity of Profiles

Sociologists, psychologists, and economists have long observed that individuals choose mates who have socio-economic profiles similar to their own. If professionals marry professionals or high school leavers marry high school leavers, then the retirement ages of spouses will be similar, not because the retirement decision of one spouse affected the retirement decision of the other, but because the spouses’ decisions were affected by similar work-related influences.

Spouses who both worked in physically demanding jobs might both retire earlier than the population average, for example. Or spouses who were both self-employed – say, doctors or lawyers – might both retire later than average. In such cases, one might be tempted to conclude that because one retired soon after the other that the retirement of the first had “caused” the retirement of the second when, in fact, what had happened is that they had both been affected by the same external factors.

Sharing of Income

It has long been recognised in the economics literature that the likelihood that one spouse will leave the labour market will increase as the income of the other spouse increases. That is, the spouses of high income earners are more likely to be retired at any age than are the spouses of low income earners.

This observation suggests two hypotheses. The first of these is that if one spouse’s social security benefits increase, the “other” spouse will be more likely to retire. Evidence for this hypothesis has recently been obtained in two studies. Both Coile (1999) and Baker (2002) found that both wives and husbands were more likely to retire when the wives were eligible for income supplements than when the wives were not. It appears that wives’ retirement ages, however, were not strongly influenced by husbands’ availability of income supplements.

The second implication of “sharing of income” is that spouses’ retirement ages will be negatively correlated. That is, if one spouse has retired, the other will be less likely to retire. The reason for this is that when one spouse retires, that spouse’s income decreases (often, dramatically), thereby decreasing the probability that the other spouse will leave the labour force.

Complementarity of Leisure

A third hypothesis is that spouses will obtain greater pleasure from retirement if they retire together. In economic terminology, the benefits that one spouse obtains from leisure are complementary to the amount of leisure enjoyed by the other. For example, if the wife plans to spend her retirement travelling, she may expect to obtain more pleasure from her retirement if she anticipates that her husband will also be retired and will travel with her.

Clearly, this hypothesis suggests that spouses’ retirement ages will be positively correlated. That is, if one spouse retires, the other will be more likely to retire, as the second spouse will expect to obtain greater benefits from retirement leisure than if the first spouse had not retired.

Blau (1998) has recently provided evidence that this complementarity is an important factor in determining spouses’ retirement ages. His study examines the joint labour force behaviour of older married couples in the United States.

Using the Retirement History Survey (RHS), a longitudinal study that followed men and women who were age 58-63 in 1969, Blau constructs labour force histories for each married couple from the time the husband turned 55. The joint labour force status of the couple in any given time period is characterized by four possible states: both employed, neither employed, husband employed but wife not, wife employed but husband not.

The data set has some interesting features. Foremost among them is that the labour force transitions of one spouse are strongly associated with the labour force status of the other spouse. The wife’s exit rate from the labour force is 63 percent higher when the husband is not employed than when he is employed. Similarly, the husband’s exit rate when his wife is not working is 53 percent higher than when she is employed. Conversely, quarterly entry rates for both husband and wife are larger if the other spouse is employed rather than not employed.

Another feature is that the incidence of joint retirement is quite large. Between 11.4 percent and 15.7 percent of all couples exit the labour force in the same quarter and between 30.3 percent and 40.6 percent exit in the same year.

The key conclusion from this paper is that there is strong evidence of the preference to share leisure. This sample from the 1960s and 1970s shows a high incidence of joint retirement and a positive effect of non-employment of one spouse on the other spouse’s labour force exit rate, as well as a negative influence of non-employment of one spouse on the other’s entry (or re-entry) rate.

Summary

Economists have put forward three hypotheses concerning the likelihood that the retirement ages of spouses will be correlated. The first of these – similarity of profiles – suggests that, on average, spouses will retire at similar times because spouses tend to have similar socio-economic profiles. That is, the factors that act on retirement age independently of marital status will affect husbands and wives in similar manners.

The second hypothesis is that individuals will be more likely to retire, the higher is their spouse’s income. This hypothesis suggests that there will be a negative correlation between spouses’ retirement ages. When one spouse retires, family income will decrease and the second spouse will be provided with an incentive to remain in the labour force.

Finally, if the leisure activities of husband and wife are complementary, there will be a positive correlation between spouses’ retirement ages. Recent evidence suggests that this effect has been a significant determinant of retirement ages in the United States.

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Daryck Riddell was a graduate student in Economics at the University of Calgary

Christopher Bruce is the President of Economica and a Professor of Economics at the University of Calgary. He is also the author of Assessment of Personal Injury Damages (Butterworths, 2004).

Spousal Influence on the Decision to Retire

Posted on By economica

by Scott Beesley

This article was originally published in the spring 1997 issue of the Expert Witness.

The decision to retire is influenced by several factors including income level, the available pension, company policy, legislation, employment opportunities, the need to help care for family members, health and the status of the spouse. Some interesting Canadian survey data and analysis regarding this last factor can be found in a book entitled The Road to Retirement, by Grant Schellenberg for the Canadian Council on Social Development (1994). A detailed statistical treatment of the issue, using U.S. data, is provided in the working paper Retirement in a Family Context: A Structural Model for Husbands and Wives by Alan Gustman and Thomas Steinmeier (National Bureau of Economic Research: Working Paper #94-4). We provide here a brief summary of the results reported in these studies and their implications for the calculation of lost future income.

Schellenberg listed four particular items which together constituted the spouse’s influence in the decision to retire. They were: the timing of the spouse’s retirement; the spouse’s health; the spouse’s income; and finally, pressure from the spouse to retire. His survey noted, for each item, the percentage of retired men and women who said that issue had been important in their decision to retire. The most notable finding was that for all four items, men were far less influenced by their spouse’s situation than were women. Less than 5 percent of retired men, for example, said that the timing of their spouse’s retirement had influenced their own timing, yet 22 percent of retired women had considered their husband’s situation in making their choice. The three remaining spousal issues were important to about 10 percent of women and an even smaller 2 to 6 percent of men.

An interesting change appeared when the same questions were put to men and women who, unlike the group discussed above, had not yet retired. This sample put a much higher weight on spousal considerations than those who had already left the workforce. Forty-five percent of women said that they expected their spouse’s time of retirement to affect their own, up from 22 percent, while the number for men rose from approximately 3 to 14 percent. The fraction of wives listing their husband’s health and income as important determinants rose even more, to about 40 percent. Similarly, the number of men who listed spousal health rose from 6 to 22 percent, while spousal income was expected to be important by 12 percent, which, while still small, is a significant change from the minuscule 2 percent reported by the retired group. The data quoted clearly reflect, in our view, the much increased importance of women’s income in total family income. One implication is that studies of the factors which determine retirement age will probably underestimate spousal influence, to the extent they are based on older data.

The American study by Gustman and Steinmeier (G & S) was a sophisticated attempt to quantify the effect of one spouses’ retirement decision on the other. In a somewhat striking contradiction of the results given for Canada by Schellenberg, G & S state that “There is some suggestion in the data that the wife’s retirement decision is not strongly influenced by the husband’s, but the husband’s decision is more strongly influenced by the wife’s.” One possible explanation is familiar to those who analyse survey data: Individuals do not necessarily do as they say they will, or (in hindsight) they report reasons for decisions which do not accurately reflect the real choices they made. Hence, while men (in the Canadian survey) might report that their wife’s decision to retire was or will be an insignificant factor in their own decision, the U.S. data, based on actual behavior rather than survey responses, suggests they are influenced, to a statistically meaningful degree, by their wives’ situation. It is perhaps not surprising that men would prefer to say their decision was independent of their wives’ status, if the alternative is to grant that they did not want to be alone at home while their wives continued to work. The authors of the U.S. paper suggest explicitly that perhaps men are unwilling to face housework alone, and they estimate that the effect of “wife being retired” is that husbands then behave as if they were two years older, and are hence more likely to retire themselves. The average change in time of retirement is found in a simulation to be only five months, however.

Another finding of G & S was that when the retirement decisions of couples are treated as jointly determined, a moderate tendency to retire together (or closer than would otherwise be expected) is evident. The alternative to joint determination would be assuming each spouse takes the other’s retirement age as given when determining their own, but this tends to lead to an overestimation of spousal influence.

Finally, we note that, though the tendency to retire at times which are closer together than the couple’s age difference was statistically significant in G & S, this factor is still much less important than the major issues listed at the beginning of this article, such as current income, available pensions, company and government policy and so on. It is these issues which we have historically considered when setting the retirement age in our calculations of lost income. No change in methodology is warranted as yet, though the Canadian survey suggests that spousal influence is increasing and may have to be accounted for in some future cases. If further research suggests that the “spousal effect” is (or will be) likely to produce differences of over a year on average, we can justify changing our assumptions at times. For example, if there is a strong financial incentive for the woman to retire at 58, and her husband would then be 60, we might plausibly assume he would retire immediately, rather than waiting until age 62, if his own income vs. pension calculation was not very age dependent. While this change would be minor for a 30 year old plaintiff (or survivor, in a fatal accident case), it could be quite important for someone in their 50s.

Of course, it would also be interesting (to an economist, at least!) to see if the above-mentioned difference between the opinions expressed in survey responses and the behavior found in real data is resolved.

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Scott Beesley is a consultant with Economica and has a Master of Arts degree (in economics) from the University of British Columbia.