
Securing a certain income level to maintain quality of life after retirement is an important personal and social problem, as human life expectancy is increasing. Public pensions are a major source of retirement income providing monthly payments until the death of the beneficiary. Reverse mortgages and home pensions, which provide a certain amount of money each month until the contractor dies as collateral for the house, can provide additional retirement income to homeowners. Regular payments, which is called annuity in financial term, from public pensions and reverse mortgages are generally made to married couples until both parties are dead.
A life table indicating the probability of death at each age divided by sex is used to determine the amount of regular payments in annuity-type contracts such as pensions and reverse mortgages; other variables, such as interest rate and salary level, are also considered in an actuarial model. It is implicitly assumed that age and sex are the only variables that affect mortality. However, many studies have found that additional factors other than age and sex are significantly associated with human mortality. These include socio-economic/demographic characteristics such as income level, education level, and marital status; health behavioral factors such as smoking, alcohol intake, and physical activity; and other factors such as religion and ethnicity.
Brown and McDaid (2003) summarized the risk factors affecting retirement mortality and suggested that they should be considered in the risk management of pensions and individual annuities; marital status was one of the important mortality risk factors. Since Gove (1973) explored mortality difference according to marital status and discussed how marriage lowers mortality, various experience studies have been conducted on the relationship between marital status and mortality.
In particular, marital status has been found to be an important demographic mortality risk factor. Many experience studies including Hu and Goldman (1990), Murphy
One group of studies focused on the relationship between marital status and morbidity associated with adult diseases. Trovato and Lauris (1989) analyzed Canadian data on mortality due to cancer and cardiovascular disease and found benefits to marriage in the cause-specific mortality they examined. In addition, Ramezankhani
Kposowa (2000) and Park
Other studies have explored how the relationship between marital status and mortality is mediated by other factors. Hedel
Brockmann and Klein (2004) addressed the effect of biography (in terms of marital status) on mortality based on an analysis of longitudinal German data. Whisman
Frees
Since the beneficiaries of annuity-type contracts have various marital statuses, uncertainty associated with mortality can be evaluated separately for various risk groups classified by marital status if the required information is available. If a married couple are pension beneficiaries, the expected payment period derived from a life table based only on age and sex is likely to underestimate the actual payment period since married people have a longer life expectancy than do people with other marital statuses owing to the economic and social benefits of marriage. This underestimation can damage the financial soundness of pension funds since total payment amount is likely to be larger than expected.
This study intends to quantify the expected pension payment period for married couples by constructing life tables organized by marital status. Since the beneficiaries consist of two individuals, a multiple life function called “last survivor status” is used. Furthermore, as the death of either person in a married couple changes the marital status of the remaining spouse, an appropriate model allowing for this change of marital status should be considered. The results of this study are expected to provide practical implications for the mortality risk management of pensions and reverse mortgages.
The rest of this paper is organized as follows. The literature on the relationship between marital status and mortality is briefly reviewed in Section 2. Experience mortality rates by marital status and the data used for this study are described in Section 3. A detailed explanation of the construction of the study’s mortality model for married couples is presented in Section 4. Section 5 discusses the calculation of expected pension payment periods based on the proposed model and its implications for the mortality risk management of pensions and reverse mortgages. Finally, the paper closes with concluding remarks in Section 6.
Statistics Korea provides a variety of demographic data through the Korean Statistical Information Service (KOSIS) and the Microdata Integrated Service (MDIS). Population data by age, gender, and marital status are available from 1925 to 2015; these are obtained from Korea’s Population and Housing Census, which has been conducted every five years. Data on the numbers of deaths by age, gender, and marital status from 1997 to 2019 can also be obtained. In both the population and deaths data, marital status comprises four categories: “single,” “married,” “divorced,” and “widowed.”
Therefore, mortality rates by marital status, categorized by age and gender, can be calculated for 2005, 2010, and 2015. Tables 1 and 2 summarized the population and the number of deaths by marital status for age groups. Figure 1 illustrates the proportions of the Korean population by marital status. As expected, the relative proportion of married and widowed people by age differ by gender due to the mortality gap between males and females. Although the overall shapes of the graphs look similar for each gender, some noticeable differences can be observed. Observing the proportions for those in their early 30s by year reveals that the average age of marriage has increased over time.
In addition, the proportion of single people has increased as well, which has contributed to Korea’s low fertility rate in recent years. It can be also observed that the divorce rate has increased. As the proportion of married people aged around 60 is much higher than that of other marital status groups, most of those who begin receiving pension benefits or who enter reverse mortgages are mostly married.
Figure 2 presents crude mortality rates by marital status from age 30 to 84. As is in the previous studies, we find distinct mortality differences between the four marital status groups. The mortality rates used to determine pension, annuity, or reverse mortgage benefits are derived using experience mortality data drawn from the Korean population as a whole. As marital status has been found to be a significant risk factor for mortality, the risk associated with mortality can be evaluated more accurately if the mortality differences according to marital status are considered.
Mortality differences are observed at younger ages in males than in females. However, the differences between the four marital status groups are more distinct for females. Since the married and widowed groups show lower mortality levels, the mortality assumption based on the entire population likely overestimates the mortality levels among groups of annuitants since most are married or widowed, as indicated in Figure 1. This underestimation can be expanded to include annuities for married couples since annuity payments are made until both spouses die.
Therefore, this study constructs a mortality model for the lifetime of married couples that reflects mortality differences by marital status and also allows for changes in marital status due to the death of the spouse. The model can be used to quantify how an expected annuity payment period for a married couple reflecting mortality differences by marital status deviates from the case where only the mortality of the entire population is considered. This is discussed in the following sections.
Normally, annuity-type contracts such as a pension or reverse mortgage for married couples provides continued benefits to a spouse who becomes bereaved. As annuity benefits are usually made until both partners in a married couple die, a multiple life function called “last survivor status” can be used to measure how long annuity payments will be made. In this function, the future lifetime of two persons aged
Consider a married couple comprising a man aged
A multi-state model allows us to define various states indicating the survivorship of a married couple so that the appropriate mortality rates according to marital status and indicated by a state can be applied. The possibility of a change in state due to the death of a man or a woman can also be modeled as a transition probability. The possibility of divorce is not considered in this study since continued benefits for bereaved spouses is not available if the married couple has divorced. Therefore, a multi-state model consisting of the following four states was considered for a married couple:
State 1: Both are alive
State 2: Only the woman spouse is alive
State 3: Only the man spouse is alive
State 4: Both are dead
The structure of the multi-state model used in this study based on the defined states is illustrated in Figure 3, in which the possible transitions between the two states are indicated by arrows. Annuity contracts for a married couple start from State 1. While both partners of the married couple are alive, the mortality of married people applies. We denote the probability of death within one year for a male and a female of the couple at age
Since the annual mortality rate for each integer age by marital status is available for this study, a discrete time multi-state model can be applied. The model is specified with a transition matrix that includes the one-year probability of a transition from any state to another. As the ages of the married couple increase over time until death, the transition matrix varies according to the age of the annuity contract. As shown in Figure 3, five types of transitions are possible in each policy year of the annuity contract. We let
Based on the transition probabilities obtained in
Calculating the transition probabilities in
The approach based on the multi-state model allows for a more detailed actuarial analysis when the payment conditions of an annuity depend on the status of the annuitants. In most of public pension contracts for married couples, the amount of regular annuity payments generally depends on the survival status of the two spouses. Therefore, the multi-state model allows the future cash flows of an annuity for a married couple to be analyzed more comprehensively.
First, the expected payment period of an annuity is investigated. The payment period can be divided into two cases within the framework of the multi-state model described in the previous section. One is the period in which both spouses in a married couple are alive, corresponding to State 1; here, the full annuity payment is made. The other case is the period in which only one spouse is alive, corresponding to States 2 and 3; here, a reduced amount is paid. Since mortality rates are available up to age 84, the maximum year of observation should be considered. Following Dickson
where
In order to extend
where
If mortality differences by marital status aren’t considered,
Comparing Tables 3 and 4 reveals that the lengths of the period in which the couple stay in State 1, 2, or 3 are very similar. In the beginning, the lower mortality of the married couple relative to the general population results in a longer State 1 period. After one of the spouses dies, the mortality of widowed people which is higher than that of the general population applies to the bereaved spouse. This results in a shorter State 2 or State 3 period. As a result, the total expected length of the State 1, 2, or 3 period becomes similar. However, the length of period for State 1 differs depending on the age of the married couple.
This result can be illustrated in terms of the probabilities of staying in a certain set of states by elapsed year in the two cases depending on whether marital status is reflected in the mortality; this is presented in Figure 4. As expected from the previous discussion, the gap between the probabilities of staying in State 1, 2 or, 3 by elapsed year is small, although the gap increases over time. However, the gap of probabilities staying in the State 1 is distinct and increases over time.
The difference observed in Table 2 affects the value of an annuity for married couples. Consider a typical
The present value of the annuity paid to the married couple depends on the future lifetimes of the two spouses. Let
where
Since mortality rates separated by marital status are being used, appropriate mortality rates depending on the survivorship of spouses should be applied. When both spouses die before the last annuity payment is made and
For the case of
The superscripts in the notations for the probabilities of survival and death in
The corresponding expected present values based on the mortality of Korea’s population as a whole are 21.2034, 18.8584, and 16.8992. This implies that the actual risk evaluated by considering marital status in mortality assumptions is nearly 0.5% higher than the result based on the population’s mortality. This difference is considerable in the case of large public pensions such as special occupation (government employees, teachers, service person, etc.) The difference decreases as interest rates increase due to the effect of interest rate discounting. The Whittaker-Henderson graduation method produces similar results as the
The market of individual annuity products for married couple has been very small in Korea since insurers providing individual annuity products are reluctant to expand individual annuity market concerning longevity risk, interest rate risk, and possibly low profitability for multiple life pension. However, the importance of individual annuity for securing retirement income is addressed recently as the proportion of elderly population has been increasing fast.
According to the results of this study, there are implications on designing multiple life insurance or annuity products. When life annuity for married couple is considered, longevity risk should be carefully managed since population mortality table underestimate actual survival probabilities of married couple. From the perspective of annuitants, married couples have advantages for purchasing multiple life annuity products as they can expect longer benefit periods than assumed based on population mortality table. On the other hand, for life insurance product, which is usually purchased by married person for his/her spouse, mortality risk is reduced since population mortality table overestimate actual probability of death of married persons.
Mortality risk management is very important given the low interest rates in today’s economic environment. Since the expected present values based on the mortality of the general population are lower than the values based on mortality when marital status is considered, the mortality risk management of annuity-type contracts is expected to improve when marital status is considered. The results also imply that an analysis based on a risk factor evaluates actual risk more accurately, offering useful insight into mortality risk management.
This study proposed a method of considering the differences in mortality by marital status to evaluate the value of annuity payments of a typical pension contract for married couples, who own the largest share of pension portfolios. A multi-state model was constructed based on graduated Korean experience mortality data that allows changes in the survival status of married couples, which are associated with annuity payment amounts. The expected payment periods depending on payment conditions and annuity values were analyzed.
The results from the proposed model were compared with the results obtained without considering mortality differences by marital status. The analysis revealed that the obligation for married pension beneficiaries was underestimated when marital status was not considered. Based on the observed difference, risk can be adjusted to improve mortality risk management and ensure the financial soundness of pension funds.
This study is limited by the available age ranges in the life table. Since mortality rates by marital status were available only up to age 84, the term of annuity was confined to 30 years. Future studies will be able to calculate the value of annuity payments for whole life when sufficient experience data for advanced age groups become available. Future studies could also examine the behavior of mortality difference patterns among the advanced age groups. Further, this study used population data for its analysis. However, the degree and pattern of mortality differences by marital status found in this study may not be applicable to a group of people under various specific pension schemes. Especially, mortality difference pattern associated with marital status may differ in experience data of reverse mortgage due to anti-selection. Therefore, future experience studies could examine data drawn from the specific groups under consideration.
This study could be extended to other identified mortality risk factors. If differences in mortality by risk factor can be quantified, the overall risk for a group of policyholders can be evaluated more accurately and be compared with the risk through analyses in which the risk factor is not considered. The risk can then be adjusted based on the difference observed in the comparison. Performing mortality risk analyses based on risk factors requires the construction of a database on mortality associated with various risk factors as well as vital experience studies.
Expected payment periods (consideration of mortality difference)
Age | In State 1–3 | In State 1 | |||
---|---|---|---|---|---|
Male | Female | Expected value | Standard deviation | Expected value | Standard deviation |
55 | 52 | 29.20 | 2.53 | 23.19 | 7.63 |
55 | 53 | 29.11 | 2.65 | 23.05 | 7.65 |
55 | 54 | 29.00 | 2.78 | 22.89 | 7.67 |
55 | 55 | 28.88 | 2.91 | 22.71 | 7.68 |
54 | 55 | 28.97 | 2.80 | 23.12 | 7.60 |
53 | 55 | 29.06 | 2.69 | 23.50 | 7.51 |
52 | 55 | 29.14 | 2.59 | 23.85 | 7.42 |
Expected payment periods (without consideration of mortality difference)
Age | In State 1–3 | In State 1 | |||
---|---|---|---|---|---|
Male | Female | Expected value | Standard deviation | Expected value | Standard deviation |
55 | 52 | 29.17 | 2.59 | 22.45 | 8.02 |
55 | 53 | 29.07 | 2.72 | 22.29 | 8.03 |
55 | 54 | 28.95 | 2.85 | 22.12 | 8.03 |
55 | 55 | 28.83 | 2.99 | 21.93 | 8.03 |
54 | 55 | 28.91 | 2.89 | 22.32 | 7.98 |
53 | 55 | 29.00 | 2.79 | 22.69 | 7.92 |
52 | 55 | 29.08 | 2.69 | 23.02 | 7.86 |
Expected payment periods (without consideration of mortality difference)
Interest rate | Annuitant | Spouse | EPV | Standard deviation | EPV | Standard deviation |
---|---|---|---|---|---|---|
2% | 55(M) | 52(F) | 21.2968 | 2.3211 | 21.1305 | 2.4606 |
55(M) | 53(F) | 21.2677 | 2.3604 | 21.1009 | 2.5004 | |
55(M) | 54(F) | 21.2347 | 2.4039 | 21.0672 | 2.5447 | |
55(M) | 55(F) | 21.1975 | 2.4518 | 21.0293 | 2.5936 | |
55(F) | 52(M) | 21.9795 | 1.9927 | 21.8887 | 2.0741 | |
55(F) | 53(M) | 21.9542 | 2.0376 | 21.8652 | 2.1172 | |
55(F) | 54(M) | 21.9269 | 2.0854 | 21.8399 | 2.1628 | |
55(F) | 55(M) | 21.8976 | 2.1359 | 21.8128 | 2.2109 | |
3% | 55(M) | 52(F) | 18.9372 | 1.9280 | 18.7966 | 2.0532 |
55(M) | 53(F) | 18.9144 | 1.9588 | 18.7734 | 2.0844 | |
55(M) | 54(F) | 18.8885 | 1.9930 | 18.7471 | 2.1192 | |
55(M) | 55(F) | 18.8593 | 2.0308 | 18.7173 | 2.1577 | |
55(F) | 52(M) | 19.4958 | 1.6345 | 19.4222 | 1.7037 | |
55(F) | 53(M) | 19.4759 | 1.6702 | 19.4037 | 1.7380 | |
55(F) | 54(M) | 19.4545 | 1.7082 | 19.3838 | 1.7744 | |
55(F) | 55(M) | 19.4315 | 1.7485 | 19.3625 | 1.8128 | |
4% | 55(M) | 52(F) | 16.9661 | 1.6143 | 16.8466 | 1.7273 |
55(M) | 53(F) | 16.9481 | 1.6386 | 16.8283 | 1.7519 | |
55(M) | 54(F) | 16.9278 | 1.6656 | 16.8075 | 1.7793 | |
55(M) | 55(F) | 16.9048 | 1.6955 | 16.7841 | 1.8097 | |
55(F) | 52(M) | 17.4259 | 1.3511 | 17.3658 | 1.4103 | |
55(F) | 53(M) | 17.4102 | 1.3795 | 17.3513 | 1.4377 | |
55(F) | 54(M) | 17.3933 | 1.4099 | 17.3356 | 1.4669 | |
55(F) | 55(M) | 17.3752 | 1.4422 | 17.3187 | 1.4978 |
Population by marital status
Male | Female | ||||||||
---|---|---|---|---|---|---|---|---|---|
Year | Ages | Single | Married | Divorced | Widowed | Single | Married | Divorced | Widowed |
2005 | 30–39 | 1,229,605 | 2,790,443 | 97,421 | 8,112 | 543,426 | 3,370,772 | 141,725 | 27,563 |
40–49 | 264,347 | 3,513,248 | 228,921 | 37,770 | 120,087 | 3,427,558 | 273,553 | 158,456 | |
50–59 | 48,787 | 2,298,807 | 138,429 | 67,571 | 36,268 | 2,046,823 | 135,138 | 361,912 | |
60–69 | 11,521 | 1,494,147 | 41,896 | 105,769 | 12,812 | 1,176,504 | 39,912 | 686,359 | |
70–79 | 2,964 | 665,578 | 7,590 | 108,741 | 5,781 | 392,239 | 10,553 | 826,158 | |
80–84 | 502 | 98,174 | 697 | 37,332 | 1,550 | 32,117 | 1,534 | 260,353 | |
2010 | 30–39 | 1,489,578 | 2,350,237 | 86,259 | 5,242 | 790,162 | 2,917,760 | 144,611 | 17,903 |
40–49 | 453,957 | 3,367,774 | 276,936 | 30,026 | 195,259 | 3,423,950 | 356,916 | 117,455 | |
50–59 | 111,238 | 2,833,579 | 250,797 | 74,138 | 69,583 | 2,631,893 | 272,929 | 348,852 | |
60–69 | 20,896 | 1,697,660 | 89,440 | 110,029 | 21,782 | 1,379,277 | 81,260 | 633,732 | |
70–79 | 5,342 | 958,459 | 20,498 | 139,682 | 10,055 | 620,593 | 20,882 | 944,303 | |
80–84 | 741 | 150,396 | 1,649 | 48,653 | 2,846 | 61,738 | 2,775 | 363,068 | |
2015 | 30–39 | 1,667,542 | 2,038,651 | 63,381 | 3,597 | 1,014,429 | 2,471,460 | 112,034 | 10,931 |
40–49 | 779,122 | 3,199,458 | 281,287 | 21,111 | 369,618 | 3,321,181 | 411,382 | 84,175 | |
50–59 | 257,006 | 3,307,007 | 380,794 | 68,733 | 130,958 | 3,062,057 | 456,229 | 335,980 | |
60–69 | 51,165 | 2,025,917 | 173,654 | 105,810 | 47,703 | 1,654,133 | 174,338 | 628,480 | |
70–79 | 11,330 | 1,131,077 | 49,819 | 145,458 | 18,674 | 728,345 | 49,593 | 974,899 | |
80–84 | 1,436 | 211,297 | 4,504 | 55,597 | 5,009 | 99,063 | 6,456 | 422,211 |
Number of deaths by marital status
Male | Female | ||||||||
---|---|---|---|---|---|---|---|---|---|
Year | Ages | Single | Married | Divorced | Widowed | Single | Married | Divorced | Widowed |
2005 | 30–39 | 2,741 | 2,271 | 551 | 54 | 645 | 1,737 | 350 | 61 |
40–49 | 2,925 | 8,655 | 2,539 | 283 | 413 | 3,567 | 855 | 370 | |
50–59 | 1,267 | 14,235 | 2,652 | 765 | 308 | 4,454 | 682 | 1,240 | |
60–69 | 629 | 26,089 | 1,643 | 2,683 | 386 | 7,142 | 550 | 6,164 | |
70–79 | 310 | 26,264 | 570 | 5,895 | 623 | 6,516 | 529 | 21,917 | |
80–84 | 87 | 8,953 | 142 | 4,364 | 303 | 1,728 | 225 | 17,251 | |
2010 | 30–39 | 2,633 | 1,570 | 454 | 29 | 851 | 1,356 | 336 | 28 |
40–49 | 3,325 | 6,595 | 2,508 | 193 | 508 | 3,161 | 941 | 261 | |
50–59 | 2,155 | 13,986 | 4,320 | 571 | 399 | 4,643 | 1,102 | 1,007 | |
60–69 | 783 | 22,340 | 2,879 | 1,746 | 347 | 6,347 | 832 | 4,398 | |
70–79 | 405 | 32,542 | 1,377 | 6,037 | 714 | 8,165 | 665 | 19,528 | |
80–84 | 98 | 10,985 | 223 | 4,125 | 402 | 2,280 | 269 | 17,975 | |
2015 | 30–39 | 2,155 | 977 | 236 | 12 | 880 | 889 | 232 | 18 |
40–49 | 3,401 | 4,357 | 1,935 | 105 | 635 | 2,628 | 858 | 153 | |
50–59 | 3,167 | 12,162 | 5,323 | 580 | 430 | 4,749 | 1,360 | 804 | |
60–69 | 1,256 | 19,269 | 4,321 | 1,405 | 394 | 5,760 | 1,093 | 2,978 | |
70–79 | 482 | 35,150 | 2,809 | 5,811 | 655 | 9,587 | 1,027 | 17,438 | |
80–84 | 122 | 15,252 | 541 | 4,969 | 537 | 3,574 | 446 | 19,513 |
Expected payment periods (consideration of mortality difference)
Age | In State 1–3 | In State 1 | |||
---|---|---|---|---|---|
Male | Female | Expected value | Standard deviation | Expected value | Standard deviation |
55 | 52 | 29.20 | 2.51 | 23.24 | 7.62 |
55 | 53 | 29.11 | 2.63 | 23.10 | 7.64 |
55 | 54 | 29.00 | 2.76 | 22.95 | 7.65 |
55 | 55 | 28.89 | 2.90 | 22.77 | 7.67 |
54 | 55 | 28.97 | 2.79 | 23.18 | 7.59 |
53 | 55 | 29.06 | 2.68 | 23.55 | 7.50 |
52 | 55 | 29.14 | 2.58 | 23.90 | 7.40 |
Expected payment periods (without consideration of mortality difference)
Age | In State 1–3 | In State 1 | |||
---|---|---|---|---|---|
Male | Female | Expected value | Standard deviation | Expected value | Standard deviation |
55 | 52 | 29.19 | 2.53 | 22.68 | 7.87 |
55 | 53 | 29.09 | 2.66 | 22.52 | 7.88 |
55 | 54 | 28.98 | 2.79 | 22.35 | 7.88 |
55 | 55 | 28.86 | 2.93 | 22.15 | 7.88 |
54 | 55 | 28.95 | 2.82 | 22.55 | 7.82 |
53 | 55 | 29.03 | 2.72 | 22.92 | 7.75 |
52 | 55 | 29.11 | 2.61 | 23.26 | 7.68 |
Expected payment periods (without consideration of mortality difference)
Interest rate | Annuitant | Spouse | EPV | Standard deviation | EPV | Standard deviation |
---|---|---|---|---|---|---|
2% | 55(M) | 52(F) | 21.3061 | 2.3117 | 21.2034 | 2.3899 |
55(M) | 53(F) | 21.2767 | 2.3510 | 21.1743 | 2.4294 | |
55(M) | 54(F) | 21.2437 | 2.3945 | 21.1415 | 2.4733 | |
55(M) | 55(F) | 21.2068 | 2.4424 | 21.1049 | 2.5217 | |
55(F) | 52(M) | 21.9861 | 1.9780 | 21.9033 | 2.0330 | |
55(F) | 53(M) | 21.9605 | 2.0225 | 21.8794 | 2.0774 | |
55(F) | 54(M) | 21.9330 | 2.0699 | 21.8537 | 2.1243 | |
55(F) | 55(M) | 21.9037 | 2.1201 | 21.8266 | 2.1734 | |
3% | 55(M) | 52(F) | 18.9449 | 1.9199 | 18.8584 | 1.9905 |
55(M) | 53(F) | 18.9219 | 1.9507 | 18.8356 | 2.0213 | |
55(M) | 54(F) | 18.8961 | 1.9848 | 18.8099 | 2.0557 | |
55(M) | 55(F) | 18.8672 | 2.0225 | 18.7812 | 2.0937 | |
55(F) | 52(M) | 19.5014 | 1.6219 | 19.4343 | 1.6690 | |
55(F) | 53(M) | 19.4813 | 1.6571 | 19.4155 | 1.7043 | |
55(F) | 54(M) | 19.4598 | 1.6947 | 19.3954 | 1.7416 | |
55(F) | 55(M) | 19.4367 | 1.7346 | 19.3740 | 1.7809 | |
4% | 55(M) | 52(F) | 16.9726 | 1.6075 | 16.8992 | 1.6714 |
55(M) | 53(F) | 16.9545 | 1.6316 | 16.8813 | 1.6956 | |
55(M) | 54(F) | 16.9342 | 1.6585 | 16.8611 | 1.7227 | |
55(M) | 55(F) | 16.9115 | 1.6883 | 16.8385 | 1.7526 | |
55(F) | 52(M) | 17.4307 | 1.3403 | 17.3760 | 1.3809 | |
55(F) | 53(M) | 17.4149 | 1.3682 | 17.3612 | 1.4091 | |
55(F) | 54(M) | 17.3979 | 1.3981 | 17.3453 | 1.4390 | |
55(F) | 55(M) | 17.3797 | 1.4300 | 17.3284 | 1.4705 |