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Demography. 2010 May; 47(2): 313–326.
How Does the Age Gap Between Partners Bear on Their Survival?
Abstract
I employ hazard regression methods to examine how the age deviation between spouses affects their survival. In many countries, the age difference between spouses at marriage has remained relatively stable for several decades. In Kingdom of denmark, men are, on average, nearly 3 years older than the women they marry. Previous studies of the age gap between spouses with respect to mortality found that having a younger spouse is benign, while having an older spouse is detrimental for one's ain survival. Almost of the observed effects could not exist explained satisfactorily until at present, mainly because of methodological drawbacks and insufficiency of the data. The virtually mutual explanations refer to option effects, caregiving in later life, and some positive psychological and sociological effects of having a younger spouse. The present study extends earlier piece of work past using longitudinal Danish register data that include the entire history of fundamental demographic events of the whole population from 1990 onward. Controlling for confounding factors such as education and wealth, results suggest that having a younger spouse is beneficial for men but detrimental for women, while having an older spouse is detrimental for both sexes.
In recent years, the search for a single determinant of lifespan, such equally a unmarried cistron or the decline of a key body organization, has been superseded past a new view (Weinert and Timiras 2003). Lifespan is now seen as an upshot of complex processes with causes and consequences in all areas of life, in which different factors impact the individual lifespan simultaneously. Today's standard of knowledge is that about 25% of the variation of the human lifespan tin can be attributed to genetic factors and nearly 75% can exist attributed to nongenetic factors (Herskind et al. 1996). Research focusing on nongenetic determinants of lifespan has suggested that socioeconomic status, education, and smoking and drinking behavior take a major impact on private survival (due east.g., Christensen and Vaupel 1996). Bloodshed of individuals is also afflicted by characteristics of their partnerships. Partnership, as a basic principle of human society, represents i of the closest relationships individuals experience during their lifetimes. Regarding predictors of their bloodshed, partners usually share many characteristics, such every bit household size, fiscal state of affairs, number of children, and quality of the relationship, just several factors might affect partners differently—for example, pedagogy and social status. A cistron that might influence partners in different ways is the age gap between them.
Background
To describe age dissimilarities betwixt spouses, 3 dissimilar theoretical concepts have evolved over recent decades. The most common concept is homogamy or assortative mating, which presumes that people, predisposed through cultural conditioning, seek out and marry others like themselves. One assumption is that a greater age gap is associated with a higher marital instability. A further prominent concept is union clasp, which states that the supply and need of partners forces the individuals to broaden or narrow the age range of acceptable partners. A third and less common concept is the double standard of aging, which assumes that men are mostly less penalized for aging than women. This assumption is supported by a greater frequency of partnerships of older men with younger women and much more variability in men's historic period at marriage than in women's (Berardo, Appel, and Berardo 1993).
The age difference betwixt spouses at marriage has remained relatively stable for several decades in many countries, a fact that was described by Klein (1996) as an almost historical pattern. An example for such a stable pattern is shown in Figure one. Information technology shows that, considering all marriages, Danish men are, on boilerplate, iii years older at the time of their spousal relationship than women. If only showtime marriages are considered, the gap between the sexes is a little smaller. While the mean age at marriage increased by about six years during the twentieth century, peculiarly since the end of the 1960s, the age difference between the sexes increased only slowly in the first fifty years of the twentieth century and started to decrease again in the 2d half of the century. Today, the difference between the mean age at marriage of Danish men and women is but slightly smaller than it was at the beginning of the twentieth century.
Mean Age at Spousal relationship in Kingdom of denmark, 1920–2007
Source: Compiled by author from data in Statbank Kingdom of denmark (2007).
At the same time, matrimony beliefs in Denmark changed dramatically in nearly all other aspects, especially because cohabitation without marriage and divorce became more widespread. In 1901, the Danish Statistical Role counted 376 divorces. From then on, the number of divorces increased steadily and reached its peak in 2004 with 15,774 registered divorces. This increase in the number of divorces as an alternative to finish a marriage is of import considering it reflects dramatic changes in the fashion marriages are dissolved. Until the early 1920s, more than 90% of all marriages in Kingdom of denmark were dissolved by the decease of 1 of the spouses. This proportion decreased with time. Today, only about 55% of all marriages are dissolved by the death of a spouse, and well-nigh 45% end in divorce.
More often than not, most marriages that are dissolved by the decease of one of the spouses cease by the death of the husband. This is a universal blueprint because men are not only older at the time of marriage just likewise dice younger every bit compared with women (Luy 2002). At the first of the twentieth century, about 58% of all Danish marriages dissolved by expiry concluded due to the expiry of the husband, and about 42% ended by the decease of the wife. In the class of the twentieth century, Danish life expectancy increased for both sexes but rose more quickly for women. While the divergence in life expectancy between the sexes at age 18 was near ii.5 years in 1900, information technology was virtually four.3 years in 2005 (Human Mortality Database 2008). This increase led to an increase of about 10% in the proportion of marriages that were dissolved by the expiry of the husband. Today, about ii-thirds of all marriages that are dissolved by death end due to the death of the married man, and only one-third cease past the death of the wife.
Studies because the bear upon of age differences between the partners on their mortality are rare and relatively dated. Rose and Benjamin (1971) fabricated ane of the get-go attempts to quantify the influence of a spousal age gap on men's longevity. The authors found a correlation between longevity and having a younger married woman, which was the 13th highest among all 69 variables they studied in their analysis.
The first study that considered the touch of an age gap on both sexes was conducted past Fox, Bulusu, and Kinlen (1979). The authors ended that "conformity to the social norm, of the homo being older than his wife, is associated with relatively lower mortality for both parties," while differences from this norm, especially if they are farthermost, lead to higher bloodshed (p. 126). They speculated that this pattern might be driven by the different characteristics of those who grade these unusual partnerships.
In the 1980s, 2 studies provided further insights into this topic. Foster, Klinger-Vartabedian, and Wispé (1984) studied the effect of historic period differences on male mortality, and Klinger-Vartabedian and Wispé (1989) focused on females. Both studies used the same data and by and large supported earlier findings. They conceded that results regarding larger historic period gaps should be interpreted with caution, mainly due to insufficient data. Considering the direction of the observed furnishings were near the same, Foster et al. (1984) and Klinger-Vartabedian and Wispé (1989) drew like conclusions. The commencement possible explanation, that healthier or more active individuals are selected by younger men or women, was already mentioned past Fob et al. (1979). Such individuals would have lived longer whomever they married because physical vitality and health normally coincides with an increased longevity. Some other possible outcome of pick is that physical needs are ameliorate taken care of in later life for persons married to younger spouses. The 2d possible explanation refers to spousal interaction. It is speculated that there might be something psychologically, sociologically, or physiologically benign about a relationship with a younger spouse. Furthermore, it could be that intimate involvement with a younger spouse enlivens everyone'due south chances for a longer life. This caption directly refers to psychological determinants of mortality such equally social and interpersonal influences, happiness, self-concept, and social status.
The major drawbacks of all these studies are that their data were limited to 5-year age groups, that the authors did not include any data about additional variables (such every bit duration of the marriage), and that they were express to married couples. The missing data on the duration of the marriage could lead to a selection bias because it is uncertain whether the marriages in the samples were of sufficient duration to allow for any effects on mortality. Foster et al. (1984) stated that an unobserved significant relationship between spousal relationship elapsing and historic period of the spouse could question the generality of the observed mortality differentials.
In two more-contempo publications, historical information were used to identify a mortality pattern by the age of a spouse. Williams and Durm (1998) basically replicated the results of the studies mentioned earlier, but their study also faced the same limitations. Kemkes-Grottenthaler (2004) used a set of ii,371 family-related entries dating from 1688 to 1921 from two neighboring parishes in Deutschland. She showed that the mortality differentials were not but determined by the age gap itself but were likewise affected by several covariates, such as socioeconomic status and reproductive output. Regarding socioeconomic status, she found that age heterogamy was much more than prevalent in upper classes. In dissimilarity, the reviews of Berardo et al. (1993) and Atkinson and Glass (1985) concluded that age heterogamy was more common amidst lower classes and not more than common amid the more highly educated. All the same, although findings are mixed, research indicates that confounding factors like socioeconomic status are of critical importance for the analysis of the mortality differentials owing to the age gap between spouses.
In sum, previous inquiry found that having a younger spouse is benign, while having an older spouse is detrimental for the survival chances of the target person. Near of the observed effects could not exist explained satisfactorily until now, mainly because of methodological drawbacks and insufficiency of the data. The most common explanations refer to health pick effects, caregiving in later on life, and some positive psychological and sociological effects.
Inquiry QUESTIONS AND HYPOTHESES
In this section, I develop some hypotheses well-nigh the relationship betwixt the spousal age gap and the risk of dying. In my model, exposure to adventure of bloodshed depends on the individual'due south ain resources, those of their spouse, and their gender. Previous limitations are addressed by using detailed Danish register information in a time-dependent framework using gamble regression.
For men, the findings regarding the age gap to the spouse are relatively consistent: namely, that male bloodshed increases when the wife is older and decreases when the wife is younger. Previous research likewise indicated that bloodshed past the age gap to the spouse differs betwixt the sexes, but none of the authors proposed reasons for this effect (Kemkes-Grottenthaler 2004; Williams and Durm 1998). The well-nigh common explanations of mortality differences past age gap to the spouse—health selection, caregiving in later life, and positive psychological effects of having a younger spouse—do non propose large differences betwixt the sexes. Thus, I hypothesize a like pattern for women: namely, that the run a risk of dying increases when the husband is older and decreases when the husband is younger.
I also hypothesize that the elapsing of spousal relationship has an impact on the mortality differentials past the age gap to the spouse. Previous studies speculated that marriages should be of sufficient duration to allow for any effects on mortality. This reasoning suggests that the mortality reward of individuals who are younger than their spouses should not be observable in marriages of short duration.
In addition, I clarify the touch on of socioeconomic status. Previous research (eastward.g., Kemkes-Grottenthaler 2004) indicated that the frequency of age heterogamy differs by social class. Generally, more highly educated persons and individuals with greater wealth are known to feel lower mortality, but no study has analyzed whether these socioeconomic variables might have an impact on the survival differentials by the age gap to the spouse. If the frequency of age heterogamy differs by social class, it could partially explain these survival differentials. Thus, I hypothesize that the socioeconomic characteristics of the target person and his or her spouse volition modify the issue of the historic period gap to the spouse on the target person's mortality.
Previous research has argued that social norms and cultural groundwork can explain the mortality differentials. Although Denmark is known to be a very homogeneous state, it is likely that social norms may differ between Danish and non-Danish as well as between rural and urban areas. Thus, I hypothesize that bloodshed past historic period gap to the spouse might differ past place of residence and by citizenship of the target person.
DATA AND METHODS
Data
Denmark is among the countries with the almost sophisticated administration systems worldwide (Eurostat 1995). All persons living in Denmark have a personal identification number that is assigned at nascence or at the time of immigration. This personal identification was a crucial part of the 1968 Population Registration Human activity, which introduced a computerized Primal Population Register. This register serves as the source register for almost all major authoritative systems in Denmark, which means that most registers tin can exist linked by using the personal identification number. Today, many unlike authorities maintain about ii,800 public personal registers on almost all aspects of life. While the bulk of these registers are administrative, a small proportion can exist used for statistical or research purposes. Generally, the Danish registers are considered a source of detailed and exact information with a very low pct of missing data. For this study, individual-level data from five dissimilar registers are linked with 1 another through the personal identification number. An overview of registers that are used for this analysis is shown in Table 1.
Table 1.
Registers and Variables That Are Used in This Assay
| Name of Register | Variables |
|---|---|
| Family Register | Sexual activity, date of birth, marital status, personal identification number of the partner, citizenship, municipality of residence |
| Register of Deaths | Date of expiry |
| Migration Annals | Date of migration, type of migration |
| Education Register | Highest achieved educational caste |
| Income Register | Wealth |
The register extract I use hither covers the period betwixt 1990 and 2005. The information from the Register of Deaths and the Migration Register are given on a daily footing, meaning that the exact mean solar day of the event is known. The information from the Family Annals, the Educational activity Annals and the Income Annals is only updated annually, which means that the data are based on the individual's status at January 1 of each twelvemonth during the observation menstruation.
The variables personal identification number of the partner, wealth, municipality of residence, and citizenship were coded every bit time-varying covariates. The covariate historic period gap to the spouse is also time-varying only was computed from existing variables. The variable sex is a fourth dimension-constant covariate by nature, while education was assumed to be time-constant despite its inherently time-varying nature. My data set includes merely people aged 50 and over. At these avant-garde ages, educational activity is unlikely to change, so this approach should give approximately the same results. The remaining variables, marital status, date of migration, and type of migration, also every bit date of birth and date of death, were used to define the time periods under risk.
The base population of my analysis is all married people aged 50 years and older living in Denmark between January 1, 1990, and December 31, 2005. There are three ways for individuals to enter the study: (1) being married and 50 years old or older on January 1, 1990; (2) being married and condign 50 years old between January 2, 1990, and December 31, 2005; and (3) immigrating to Denmark betwixt Jan 1, 1990, and Dec 31, 2005, and being married, and existence 50 years or older.
At that place are five possible means to leave the study: (1) dying betwixt January ane, 1990, and December 31, 2005; (two) divorcing betwixt January 1, 1990, and December 31, 2005; (iii) condign widowed between January 1, 1990, and December 31, 2005; (4) existence live on December 31, 2005; and (v) emigrating from Kingdom of denmark betwixt January ane, 1990, and Dec 31, 2005.
Methods
I apply hazard regression models to examine the influence of the age gap to the spouse on the individual's mortality. Hazard regression, as well called event-history assay or survival analysis, represents the most suitable belittling framework for studying the fourth dimension-to-failure distribution of events of individuals over their life course. The general proportional hazards regression model is expressed by
(i)
where h(t | X i,…,X yard ) is the run a risk rate for individuals with characteristics X i,…,10 k at time t, h 0(t) the baseline risk at time t, and β j , j = 1,…,grand, are the estimated coefficients of the model.
Since the failure consequence in our analysis is the decease of the individual, the baseline hazard of our model h 0(t) is historic period, measured as time since the 50th birthday. It is assumed to follow a Gompertz distribution, defined every bit
h 0(t) = exp(γt)exp(β0),
(2)
where γ and β0 are ancillary parameters that command the shape of the baseline chance. The Gompertz distribution, proposed by Benjamin Gompertz in 1825, has been widely used by demographers to model human mortality information. The exponentially increasing hazard of the Gompertz distribution is a useful approximation for ages between thirty and 95. For younger ages, bloodshed tends to differ from the exponential curve due to infant and accident mortality. For advanced ages, the increase in the take chances of death tends to decelerate then that the Gompertz model overestimates mortality at these ages (Thatcher, Kannisto, and Vaupel 1998). I assume that the bear upon of this deceleration on my results is negligible because the number of married people over age 95 is extremely low.
The research plan was to exam whether the age difference between the spouses afflicted both sexes in the aforementioned fashion. Therefore, all regression models were calculated for females and males separately. It should exist noted that the male and female models do non necessarily include the aforementioned individuals. If both spouses are aged 50 or older, a couple is included in all models. If only the husband is 50 years or older, a couple is included only in the male models. Correspondingly, a couple is only included in the female models if the wife is 50 years or older and the husband is 49 years or younger.
RESULTS
In total, 1,845,956 married individuals anile fifty and older are included in the data set; 958,997 of them are male, 886,959 female. The distribution of all persons in the data set past age gap to the spouse is presented in Figure 2. It shows that nearly men are between two and iii years older than their wives, while almost women are two years younger than their husbands.
Distribution of Age Gap to the Spouse
Source: Compiled past author with information from Statistics Denmark (2007).
Approximately 75% of all married men anile 50 and over are married to women who are more than one year younger than themselves; only 10% of all men are at to the lowest degree one year younger than their wives. In contrast, the bulk of married women (65%) aged 50 years and over are married to men who are older than themselves, and simply 15% accept a spouse who is more than than one twelvemonth younger.
Tabular array 2 gives descriptive information on all covariates. It shows the distribution of fourth dimension at risk measured in days for men and women. In total, I observed 3,271 million person-days for men and 2,907 million person-days for women. The proportion of missing data is highest for duration of matrimony. This is because the engagement of marriage is unknown for all couples who married before January 1, 1990. These couples were assigned to the category unknown for 1,000 days when entering the study population and to the category ≥ i,000 days thereafter. A large number of missing values is also found for the variables highest achieved didactics and highest accomplished education of the spouse, with the proportion missing data increasing for older cohorts. I find no indication that this event influenced the outcome of the regression models.
Table 2.
Distribution of Time at Risk for Men and Women
| Covariate | Men | Women |
|---|---|---|
| Elapsing of Marriage | ||
| Unknown | fifteen.92 | 15.76 |
| < 1,000 days | 1.55 | 1.16 |
| ≥ 1,000 days | 82.53 | 83.08 |
| All | 100.00 | 100.00 |
| Menses | ||
| 1990–1994 | 29.37 | 29.09 |
| 1995–1999 | 31.xix | 31.10 |
| 2000–2005 | 39.44 | 39.81 |
| All | 100.00 | 100.00 |
| Highest Achieved Education | ||
| Low | 32.42 | 48.12 |
| Medium | 37.31 | 28.55 |
| High | xvi.80 | 13.28 |
| Unknown | thirteen.47 | 10.05 |
| All | 100.00 | 100.00 |
| Wealth in Danish Krones | ||
| < 0 | 17.57 | 18.12 |
| > 0 and ≤ 100,000 | 17.53 | 44.09 |
| > 100,000 | 64.47 | 30.81 |
| 0 or Unknown | 0.43 | half-dozen.98 |
| All | 100.00 | 100.00 |
| Highest Accomplished Didactics of the Spouse | ||
| Low | 47.15 | 32.21 |
| Medium | 29.57 | 35.98 |
| High | 14.17 | 16.x |
| Unknown | 9.12 | 15.71 |
| All | 100.00 | 100.00 |
| Residential Area | ||
| Copenhagen | 84.81 | 84.47 |
| Remaining Kingdom of denmark | 15.19 | 15.53 |
| All | 100.00 | 100.00 |
| Citizenship | ||
| Danish | 98.40 | 98.41 |
| Non-Danish | one.60 | 1.59 |
| All | 100.00 | 100.00 |
In the following paragraphs, I present the results of four estimated hazard regression models. For men, the relative gamble of dying by the age gap to the spouse and the standard errors of the fourth model are shown in Effigy 3. The corresponding results for women are shown in Figure 4. Both figures consist of four separate curves showing the relative risk of dying by age gap to the spouse. The reference category, represented by a dotted vertical line, includes all persons who are less than one year younger or older than their spouses. The function of each curve to the left of the reference category relates to individuals with older spouses, the right role relates to individuals with younger spouses. I evidence but the standard errors of the quaternary model because they were near the same for all iv models. For both sexes, the results of the additional covariates are presented in Table 3.
Relative Risk of Dying for Men, by Historic period Gap to the Spouse
Source: Compiled by author with data from Statistics Denmark (2007).
Relative Risk of Dying for Women, by Age Gap to the Spouse
Source: Compiled by author with data from Statistics Kingdom of denmark (2007).
Table iii.
Effect of the Boosted Covariates on the Chance of Mortality
| Covariate | Men | Women | ||||
|---|---|---|---|---|---|---|
| Model two | Model iii | Model 4 | Model 2 | Model 3 | Model 4 | |
| Duration of Marriage | ||||||
| Unknown | 0.78*** (0.018) | 0.78*** (0.018) | 0.76*** (0.018) | 0.79*** (0.029) | 0.74*** (0.028) | 0.75*** (0.028) |
| < 1,000 days | ane | 1 | 1 | ane | 1 | ane |
| ≥ ane,000 days | 0.77*** (0.017) | 0.89*** (0.020) | 0.87*** (0.020) | 0.eighty*** (0.029) | 0.79*** (0.028) | 0.80*** (0.029) |
| Period | ||||||
| 1990–1994 | 1.09*** (0.007) | 1.07*** (0.007) | 1.07*** (0.007) | 1.07*** (0.010) | 1.00 (0.010) | 1.00 (0.010) |
| 1995–1999 | 1 | one | 1 | i | 1 | ane |
| 2000–2005 | 0.85*** (0.004) | 0.86*** (0.005) | 0.86*** (0.005) | 0.87*** (0.006) | 0.95*** (0.007) | 0.95*** (0.007) |
| Highest Achieved Didactics | ||||||
| Low | 1.07*** (0.007) | 1.04*** (0.007) | 1.13*** (0.010) | 1.12*** (0.011) | ||
| Medium | one | 1 | 1 | 1 | ||
| High | 0.83*** (0.007) | 0.87*** (0.008) | 0.86*** (0.013) | 0.89*** (0.014) | ||
| Wealth | ||||||
| Low | 1.39*** (0.010) | 1.39*** (0.010) | 1.18*** (0.012) | ane.18*** (0.012) | ||
| Medium | 1 | ane | ||||
| High | 0.58*** (0.003) | 0.57*** (0.003) | 0.64*** (0.005) | 0.64*** (0.005) | ||
| Highest Achieved Didactics of the Spouse | ||||||
| Low | 1.08*** (0.007) | 1.05*** (0.010) | ||||
| Medium | one | 1 | ||||
| High | 0.88*** (0.009) | 0.91*** (0.012) | ||||
| Residential Surface area | ||||||
| Copenhagen | 0.97*** (0.005) | i.12*** (0.009) | ||||
| Remaining Kingdom of denmark | 1 | 1 | ||||
| Citizenship | ||||||
| Danish | 1.29*** (0.028) | one.xix*** (0.038) | ||||
| Non-Danish | 1 | one | ||||
| Constant | –16.34 | –16.52 | –xvi.79 | –16.56 | –xvi.52 | –sixteen.70 |
| Gamma | 0.00028 | 0.00029 | 0.00030 | 0.00027 | 0.00027 | 0.00027 |
As a first pace, I estimated a model (Model ane) that allowed me to replicate the results of previous studies by including age gap to the spouse every bit sole covariate. Thus, this model controls but for the age of the target person and age gap to the spouse.
Figure 3 shows that the risk of dying in men decreases as the age gap increases. The younger the married woman is compared with her spouse, the lower the mortality of the married man; the older the wife is compared with her spouse, the higher the mortality of the husband. Compared with the reference category, an excess mortality of more than xxx% can be constitute in married men who are more than 7 years but less than 17 years younger than their wives. Married men who are more 15 years simply less than 17 years older take a take chances of dying that is four% lower.
Figure 4 shows that, similar to that of men, female mortality is higher if the married woman is younger than her husband. Women who are more than than vii years but less than 17 years younger have an backlog mortality of about 10%. In dissimilarity to the pattern for men, women also have an elevated risk of dying when they are older than their spouses. Compared with the reference category, an backlog mortality of 40% is observed in women who are more than 15 years but less than 17 years older than their spouses. The lowest risk of dying is constitute in women who are about the same age as their husbands, which is the reference category.
These outset results provide strong testify that the age difference between the spouses affects individual survival chances. It besides shows that the effects are essentially different between the sexes. Side by side, in Models two, 3, and 4, I examine the impact of the age gap to the spouse in the presence of additional covariates.
Previous research had no information on the duration of spousal relationship, which could lead to a possible pick bias. Model ii, which includes elapsing of union, allows me to test for the confounding event of duration of marriage. A comparison of the coefficient for the historic period gap to the spouse in Model one and Model 2 shows that including the measure of marriage duration does non change the coefficients for the age gap to the spouse, suggesting that elapsing of marriage does not business relationship for the mortality differences of age-discrepant marriages. In results not shown here, I tested an additional model that included an interaction between historic period gap to the spouse and elapsing of marriage. None of the combinations between the two variables were statistically significant (at the .01 level).
In Model 3 of Table 3, I test the hypothesis that socioeconomic status affects the mortality differentials past the age gap to the spouse. This model includes measures of the target person's highest educational caste and wealth besides equally the variables already included in Model 2. The results evidence that both socioeconomic variables are important predictors of survival differences. Individuals with low education or low wealth face higher mortality rates. Comparing the relative risk past age gap to the spouse in Model 3 with the relative risk by historic period gap to the spouse in Model two reveals that holding the socioeconomic variables constant changes the effects for both sexes. For men, calculation these measures to the model reduces the relative run a risk of dying when they are younger than their wives, but it increases the survival advantage when they are older than their wives. For women, adding measures of socioeconomic status has virtually no affect when they are younger than their husbands but slightly increases the gamble of dying when they are older than their husbands. In results not shown here, I tested another model that included an interaction between the socioeconomic variables and the age gap to the spouse. One of the combinations was statistically significant (at the .01 level): men with high wealth and who are older than their wives experienced a significantly elevated risk of dying of about 5%. All remaining combinations between the variables were non statistically pregnant (at the .01 level).
Finally, I investigate the upshot of the remaining variables residential area, citizenship, and highest achieved education of the spouse, which are introduced into the analysis in Model 4 of Table iii. In this model, I wanted to test the supposition that cultural differences and social norms—represented by the two variables residential expanse and citizenship—account for some of the differences in the hazard of mortality by the age gap to the spouse. Once again, comparing the relative hazard by historic period gap to the spouse in Model 4 with the relative risk past age gap to the spouse in Model 3 reveals differences past sex. For men, the hazard of mortality increases when they are younger than their wives and decreases farther when they are older than their wives. In contrast, the run a risk of bloodshed for women does not change for women who are younger than their husbands but decreases considerably for women who are older than their husbands.
DISCUSSION
The present study addresses an underdeveloped research area. Using Danish population information, I used hazard regression methods to exploit fifteen years of age-specific information to investigate the effect of the age difference between the spouses on the individual's survival. I showed for the commencement time that survival differences by age gap to the partner are non limited to extreme cases but are statistically significant for small age differences. Individuals who are most ane to three years older than their spouses have a significantly different survival charge per unit than individuals who are upward to i twelvemonth older or younger than their spouses.
My hypothesis that the effect would exist the same in men and women receives no back up from these analyses. My results suggest that having a younger spouse is beneficial for men merely detrimental for women. Information technology also shows that controlling for additional covariates affects the pattern for men substantially, while it has very trivial outcome for women. The start possible reason for sex differences could be differences in health option. The selection hypothesis argues that healthier individuals are able to attract younger partners. Therefore, married people who are older than their spouses should experience a lower bloodshed. It was also proposed in the literature that a younger spouse is somehow beneficial in terms of health intendance support besides every bit in some positive psychological and sociological ways. Both arguments should hold for both sexes similarly. The sex differences could indicate that health selection is weaker in women. Women are much less likely to marry a younger husband, which suggests that uncommonly healthy women are less able than their male person counterparts to attract a younger partner. However, future analysis should include wellness indicators to investigate the pathway of a possible health selection in more than detail.
A 2d reason for sexual practice differences by age differences to the spouse is related to social support. A large body of research has plant that women accept generally more social contacts than men. This suggests that women are probably less dependent on the health support and social support of a younger spouse than are men, which means that a younger spouse would exist less beneficial for women'south survival than for the survival of men.
In nearly marriages, men are older than their wives. Given my results, this limerick favors men. Thus, the age gap between the spouses may in function explain why marriage is more than benign for men than for women. My results also suggest that the possible selection bias caused by an insufficient length of partnership is of no importance in explaining the effects of the survival differences by the age gap to the spouse.
Previous inquiry has suggested that selection and discrepancy from the social norm cause the mortality difference by the age gap to the spouse. This explanation was proposed in the 1970s, when social norms for mating behavior in general and particularly for the age difference betwixt partners were probably much stronger than today. My investigation supports this caption for men simply not for women. If social norms for the historic period gap to the spouse were the driving force of the observed mortality differentials, female bloodshed could be assumed to be lowest at ages where women are a few years younger than their spouses. Here, I observe that mortality in women is lowest when a woman is the same age every bit her husband and increases with increasing age discrepancy.
I extend previous inquiry of this area in several aspects. First, I utilise a longitudinal arroyo. By using the Danish registers, it is possible to track all individuals from the date of their wedlock until their engagement of decease and to incorporate all life events—such as the death of the spouse, a divorce, or a remarriage—into the analysis of events within the observed flow. The longitudinal approach avoids some of the drawbacks of earlier studies.
Some other limitation of previous research that I overcame in this study is the historic period grouping into five-yr historic period groups. Considering of the age grouping in earlier studies, each of the spouse-historic period-difference intervals covered an eight-year menstruation. Spouses who were stated as being in the aforementioned historic period grouping could differ plus or minus four years, while the difference for an individual who is married to a spouse in the neighboring age group varies from i to 9 years. Thus, the age groups are not simply broad only also overlapping. In my data set, the exact date of nascence is known for every private; thus, age and the age gap to the spouse are measured in days.
A further extension of previous research is also related to the data set. My study uses population data/annals data, not samples as were used in previous enquiry, to examination these hypotheses. I was thus able to avoid many problems related to sampling methods while substantially increasing the statistical power.
Information technology can be concluded that the driving strength of the observed mortality differences past the age gap to the spouse remain unclear. Further research is necessary using models that test for boosted multiplicative effects as well as for unobserved heterogeneity. A brusque coming of this study is that it does non include whatsoever behavioral or psychological aspects of the married couple considering the information came from administrative registers. Futurity research should point in this management as it is assumed to be of importance to business relationship amend for social values and norms as well equally certain behavioral aspects.
Farther enquiry directions are of possible involvement. In general, the age gap to the partner should affect the survival chances of members in all kinds of longtime partnerships between two individuals. Due to information limitations, studies, including the nowadays one, have had to focus on married couples exclusively. In a side by side step, it might be of interest to know whether the effects of the age gap to the partner can also exist observed in longtime cohabiting couples or other kinds of partnerships, specially in same-sexual practice couples. The Danish data that are now available let such analyses.
Acknowledgments
This piece of work was conducted at the Max Planck Plant for Demographic Enquiry, Rostock. I would also like to thank the Plant of Public Health, Academy of Southern Denmark, Odense. I am especially grateful to James W. Vaupel for his support and guidance and Heiner Maier for his helpful comments on this manuscript. A previous version of this manuscript was presented at the 2008 annual meeting of the Population Association of America in New Orleans, LA.
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Articles from Demography are provided here courtesy of The Population Association of America
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3000022/
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