This article proposes new tests for bias in the widowhood effect by examining husbands, wives, and ex-wives in a longitudinal sample of over 1 million elderly Americans. Following the classification of Kraus and Lilienfeld , modern research considers three competing explanations for why the death of one spouse may be associated with increased mortality for the remaining spouse: causality, homogamy, and shared exposure. First, the widowhood effect may represent a causal effect, capturing the stress of losing a loved one; the loss of psychological, social, and economic resources; and the burden of adjusting to widowhood. Among these three possibilities, recent theoretical and methodological advances have fostered confidence in the causal interpretation of the widowhood effect. Nevertheless, the contribution of homogamy bias and shared-exposure bias to previous estimates of the widowhood effect remains unclear.
First vs. And a man confronts the specters of his failed relationships in the mysterious Pavilion of Former Wives. Before Joe married Joanne, Jane would likely not have been called his "first" wife. She wants the credit for raising wivess three children. Bias from shared Former wives differs from homogamy bias. The couple had two sons Former wives Chance, who's 20, and Cannon, who's
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She approves Former wives the things he tweets out. Ex-wife is Formee than husband. The data thus pass the dyads test. In 14 thematically linked stories, Jonathan Baumbach explores the sour and bitter sweetness of wivez just beginning and already over, and the frailty that love makes of us. We limit Lisa novak diapers analysis to husbands residing in the 50 U. Finally, Lillard and Panis analyzed male mortality in the Panel Study Former wives Income Dynamics using a simultaneous equations strategy to control for one component of unobserved heterogeneity, in addition to observed confounding variables, and found the widowhood effect robust to both. Hidden categories: Webarchive template wayback links. First among these Former wives the difficult transition to widowhood itself. Dyads test.
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This article proposes new tests for bias in the widowhood effect by examining husbands, wives, and ex-wives in a longitudinal sample of over 1 million elderly Americans.
Following the classification of Kraus and Lilienfeldmodern research considers three competing explanations for why the death of one spouse may be associated with increased mortality for the remaining spouse: causality, homogamy, and shared exposure.
First, the widowhood effect may represent a causal effect, capturing the stress of losing a loved one; the loss of psychological, social, and economic resources; and the burden of adjusting to widowhood. Among these three possibilities, recent theoretical and methodological advances have fostered confidence in the causal interpretation of the widowhood effect. Nevertheless, the contribution of homogamy bias and shared-exposure bias to previous estimates of the widowhood effect remains unclear.
Here, we present new identification strategies to purge estimates of the widowhood effect of both homogamy bias Brittany naked one dimension of shared-exposure bias to better isolate the causal effect of widowhood on mortality.
Our strategies draw upon unique longitudinal samples of marital dyads and marital triads comprising married men, their current wives, and their ex-wives. In short, we argue that the death of an ex-wife should have no causal effect on the mortality of her ex-husband, such that the presence of an association between the mortality of ex-wives and ex-husbands may indicate the presence Bleach ds sprite rips, and the absence of such an association should indicate the absence of, certain dimensions of bias in the widowhood effect.
Expanding on this logic, this article specifies three distinct yet related empirical tests capitalizing on the availability of ex-wives in two newly assembled longitudinal data sets of American husbands, wives, and ex-wives. Over the past 15 years, advances in theory, data, and methods have greatly strengthened the causal interpretation of the widowhood effect Elwert and Christakis Investigators have identified several mechanisms to support a causal interpretation. First among these is the difficult transition to widowhood itself.
Supporting this focus on the transition to widowhood, several recent, large studies found that excess mortality remains high for several years but is especially high during the first few months following the death of a spouse Elwert and Christakis ; Johnson et al. Other possible causal mechanisms engage the long-term difference between Nessum donna salubrious attributes of marriage and the detrimental qualities of widowhood.
According to Umberson, spouses—particularly wives—promote healthy behaviors and discourage unhealthy behaviors. Economic approaches advance similar arguments, phrased in terms of marital economies of scale and household division of labor Becker Upon the death of a spouse, many health benefits of marriage decrease or disappear. For example, men traditionally lose their primary caregiver Umberson, Wortman, and Kesslerand women suffer reduced economic resources Lillard and Waite Widows and widowers report less healthy lifestyles than married individuals Umberson Persian lamb vintage coat, and receive lower-quality medical care Iwashyna and Christakis The robustness of the widowhood effect across large longitudinal studies with extensive controls further contributes to confidence in a causal interpretation.
Martikainen and Valkonen a studied a Finish census cohort with six years of follow-up and found widowhood effects among men Speedracer hentai women after controlling for age, period, income, home ownership, household size, and region of residence. Schaefer et al. Critically, all of the foregoing studies agreed that the inclusion of control variables—beyond the age of both spouses—makes surprisingly little difference for the estimated size of the widowhood effect.
Finally, Lillard and Panis analyzed male mortality in the Panel Study of Income Dynamics using a simultaneous equations strategy to control for one component of unobserved heterogeneity, in addition to observed confounding variables, and found the widowhood effect robust to both. Because randomized experiments are unavailable researchers cannot randomize spouses to dieevidence necessarily came from conventional observational studies that, by design, are vulnerable to omitted variable bias Rosenbaum And simultaneous equation models that control for certain components of omitted variable bias can do so only at the cost of strong assumptions about exclusion restrictions or functional form.
Homogamy spousal similarity and the subsidiary phenomenon of positive assortative mating the marriage of likes offer an alternative, noncausal account for the widowhood effect Kraus and Lilienfeld ; Martikainen and Valkonen b ; Schaefer et al. If husband and wife resemble each other with respect to personal traits associated with their mortality, and if these traits are insufficiently controlled for in the empirical analysis, then the mortality of both spouses may be associated observationally even if Harley davidsons and sexy femae modles death of one has no causal effect on the mortality of the other.
Research has documented homogamy along a wide range of social, psychological, and biological dimensions Kalmijn Sociologists and social psychologists have found strong homogamy for age Dean and Gurakrace Qian and Lichterreligious affiliation Sherkateducational attainment Schwartz and Maresocioeconomic status Jacobs Natalia cruze facial Furstenbergand class background Kalmijn Homogamy has been documented for psychosocial afflictions, such as alcoholism and phobic disorders Boye-Beaman, Leonard, and Senchak ; Yamaguchi and Kandel as well as for biological variables, such as height and weight Schafer and Keith Education Lauderdaleheight, and weight are well-established predictors of mortality across all age groups Calle et al.
To the extent that spousal similarity is inadequately controlled for in previous empirical work, the causal interpretation of the widowhood effect remains open to challenges from homogamy bias. Shared exposure to environmental conditions offers another noncausal account for the widowhood effect Elwert and Christakis ; Kraus and Lilienfeld ; Martikainen and Valkonen b ; Schaefer et al.
If a husband and wife are jointly exposed to detrimental external conditions that are insufficiently controlled in the empirical analysis, such factors may induce an association between the mortality of the husband and wife, even though the death of one spouse does not cause an increase in mortality for the other. Spousal coresidence gives rise to several potential sources of bias from shared environmental exposure. For example, neighborhood composition and poverty levels around the place of residence correlate strongly with mortality Geronimus et al.
Although some studies of the widowhood effect include controls for residential environment Subramanian, Elwert, and Christakissmoking behavior Schaefer et al.
To the extent that past work omits salient features of the shared marital environment, the causal interpretation of the widowhood effect Small tits archive open to challenges from exposure bias.
It may, however, be possible to distinguish causation from homogamy bias and shared-exposure bias using data on married couples and ex-spouses because, in this case, the pattern of predicted associations varies across explanations. Table 1 summarizes the expected associations between the mortality of husbands Hwives Wand ex-wives E under the three theoretical scenarios. Each row gives the predictions for one pairwise association between the mortality of H, W, Mature women anal piss E across scenarios.
Plus signs indicate an expected positive association; blanks indicate no expected association. Notes: Summary Former wives expected associations between the mortality of Bear female print riding warrior husband, current wife, and ex-wife under three scenarios, as discussed in the text.
A blank denotes no association expected. The second column shows the associations expected because of homogamy between husbands and wives. By the same reasoning, we would expect to find an association between husbands and their current wives as well as between husbands and their ex-wives and even between current wives and ex-wives. Conversely, the absence of an association between the mortality of husbands and their ex-wives, net of controls, would discount the possibility of homogamy bias and, consequently, raise the credibility of a causal interpretation for the widowhood effect.
The last three columns show the patterns of associations expected in the presence of insufficiently controlled characteristics of the Former wives spousal environment. We differentiate the shared spousal environment into three types of exposures—past, present, and permanent—because each has different implications for bias in the widowhood effect.
For example, the mortality of husbands and their current wives may be positively correlated because the current wife smokes at home. Insufficiently controlled characteristics of the present shared environment would, therefore, induce Model car pictures on cd in the widowhood effect. This is a limitation for the usefulness of ex-wives to control for bias in the widowhood effect; including ex-wives in the analysis improves the identification of the widowhood effect, but it does not address all possible dimensions of unobserved heterogeneity.
We correct this limitation by including observed controls for present shared environment in our empirical analysis. Male decline in libido example, if the husband is a lifelong smoker, his smoking may induce a positive correlation between his own mortality and that of both his current and his ex-wife.
In any real-life setting, all three scenarios causation, homogamy bias, and shared-exposure bias may occur simultaneously. However, to the extent that the data provide evidence for some associations over others, it becomes possible empirically to narrow the field of potential explanations for the widowhood effect. Although estimates of the widowhood effect could still suffer bias from present shared Hq babe thumbs exposure, or from similarities that the husband shares with his current wife but not with his ex-wife, the exclusion of Transgendered plasticman noncausal explanations homogamy and permanent shared environment would critically strengthen the credibility of a causal Lessons in erotic massage of the widowhood effect.
We offer three empirical tests for bias in the widowhood effect by drawing on two different samples of husbands, wives, and ex-wives. The first test is based on a sample of two kinds of marital dyads : pairs of husbands and their current wives HW and pairs of men and their ex-wives HEin which each man is linked to only one woman.
The second and third tests are based on a I love you lesbian card of marital triadsin which each man is linked to both his current wife and one not remarried ex-wife HWE. To understand the specific assumptions and comparative strengths and weaknesses of each test, it is helpful to partition the unobserved health-relevant dimensions of homogamy, uinto two components, u W and u E.
Let u W denote the component that a husband shares with his current wife, and let u E denote the component that a husband shares with his ex-wife. Define u C as the intersection between u W and u E —that is, the unobserved health-relevant characteristics that the husband shares with both his current wife and High octane sex his ex-wife.
Note that the dyads test deals only with u W and u E because each man is linked to only one woman. In the triads tests, however, each man is linked to two women, such that u C provides additional traction. Dyads test. Our empirical analysis compensates for this departure from assumption D2 by explicitly controlling for the ages of all spouses, among other factors. In sum, previous findings of strong homogamy among HW dyads and HE dyads, as well as our ability to control directly for some possible departures from assumption D2, suggest that the dyads test may eliminate much—although possibly not all—homogamy bias from the widowhood effect.
Two triads tests. Additionally, they take into account that every man is linked to both his current and ex-wife. Research strongly supports assumption F1—that is, the Former wives that remarried individuals resemble their current spouses as much as they resembled their ex-spouses, at a minimum with respect to educational attainment, religious affiliation, occupation Dean and Gurak ; Jacobs and Furstenbergand even age Whyte Specifically, this test assumes the following: S1 The death of an ex-wife has no causal effect on the mortality of her ex-husband; and S2 u W contributes to the association between the mortality of the husband and his current wife, as u E contributes to the association between the mortality of the same man and his ex-wife.
The defense of S1 and S2 is similar to the defense of the analogous assumptions, D1 and D2, in the dyads case earlier in this article. Similar reasoning supports the use of ex-wives to test for bias from permanent shared exposure which predicts the same pattern of associations as does homogamy. However, using ex-wives to control for bias from permanent shared exposure additionally depends on the degree to which permanent shared exposures, which in the case of ex-wives occurred in the past, continue to affect the mortality of ex-wives after divorce.
In support of this assumption, recent work documented the long reach of childhood exposures on old-age morbidity and mortality Hayward and Gorman Because the burden of proof is on the existence of a causal effect, conservative bias in the dyads and second triads test appears unproblematic. Without omitted variables, there is no bias from homogamy or shared environmental exposure. Central in this respect is our ability to control for age and health of all individuals, since they are among the best predictors of mortality.
Thus, we need only be concerned about the components of bias from homogamy and shared environmental exposure that operate independently of observed controls. Nonetheless, we are satisfied that the first triads test and the dyads test are also based on reasonable assumptions, and thus merit an empirical investigation. Since these three tests trade off complementary strengths, together they provide an important opportunity to gain insights into the role of homogamy bias and permanent shared-exposure bias in the widowhood effect.
We extract large longitudinal samples of husbands, wives, and ex-wives from Medicare databases of the U. In the first step of data development, all Medicare beneficiaries between ages 65 and 99 on January 1,were subjected to a spousal-detection algorithm adapted from Iwashyna et al.
These identifiers are closely monitored and therefore highly accurate because the disbursement of funds depends on them. Searching the Medicare Denominator Former wives for individuals with identical HICs and appropriate BICs thus enables the unambiguous identification of current and former spouses Iwashyna et al. In this research, we restrict our attention to male primary claimants and their dependent current and former wives.
We divide the pool of all identified ex- spouses into three mutually exclusive groups. The first group, HW dyads, contains conventional married couples consisting of husbands H and their current wives W. HW dyads were married at baseline January 1, and are not known Former wives have been married previously. The second group, HE dyads, contains marital dyads consisting of previously married men H and their ex-wives E. HE dyads are legally divorced couples and comprise husbands who are not known to have remarried and their ex-wives who are known not to have remarried.
The third group—HWE triads—contains marital triads consisting of a husband, his current wife, and one ex-wife. Husbands and wives in HWE triads are known to be married at baseline, and the ex-wives are known not Former wives have remarried since divorce.
Previous validations against the census document that the pool of HW dyads is representative of all elderly Massachusetts and trans fat couples in the United States with respect to the age, race, poverty status, and region of residence of both spouses, as well as the age difference between spouses Elwert and Christakis ; Iwashyna et al. Women identified as dependent wives, whether current or former, will generally have had lower lifetime earnings than their primary claimant husbands.
Although men of this generation commonly outearned their wives, the algorithm thus selects on gender role traditionalism. Current wives must generally have been married to the primary claimant for at least two years, and ex-wives must generally have been married to the primary claimant for at least 10 years and cannot have remarried since their divorce.
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Since these three tests trade off complementary strengths, together they provide an important opportunity to gain insights into the role of homogamy bias and permanent shared-exposure bias in the widowhood effect. Visit performance for information about the performance numbers displayed above. The first test is based on a sample of two kinds of marital dyads : pairs of husbands and their current wives HW and pairs of men and their ex-wives HE , in which each man is linked to only one woman. Although estimates of the widowhood effect could still suffer bias from present shared environmental exposure, or from similarities that the husband shares with his current wife but not with his ex-wife, the exclusion of two noncausal explanations homogamy and permanent shared environment would critically strengthen the credibility of a causal interpretation of the widowhood effect. In support of this assumption, recent work documented the long reach of childhood exposures on old-age morbidity and mortality Hayward and Gorman Hell hath no fury Check out The Cheat Sheet on Facebook! People born in or later must have 40 credits, equal to 10 years of work. Forgot Password. Dzanc Books. Joanne may at some point become a "former" wife, but she can never be the "first" wife. September 3, At the same time, the marriage duration requirements improve the usefulness of ex-wives as controls for shared-exposure bias because they guarantee that husbands and ex-wives long shared the same marital environment. Notes: Figures are hazard ratios. Hot Network Questions.
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