Correlates of Mortality in a U.S. Cohort of Youth, 1980–98: Implications for Social Justice

Social Justice Research [sjr] pp656-sore-453315 October 29, 2002 20:1 Style file version June 4th, 2002

Social Justice Research, Vol. 15, No. 3, September 2002 (C© 2002)

Correlates of Mortality in a U.S. Cohort of Youth,1980–98: Implications for Social Justice

Richard K. Caputo1

This paper reports results of a study based on a nationally representative sam-ple of U.S. youth (N = 11,549) that asked two questions: (1) How does familystructure affect the likelihood of adolescent death beyond that of race/ethnicity,sex, socioeconomic status, personal behavior, and other structural factors and (2)under what conditions might appeals for social justice be warranted for relativemortality statuses and for absolute gains in mortality? The study found that mar-ital instability increases the likelihood of dying when controlling for a variety ofother factors including class, race/ethnicity, sex, and unemployment rate in areaof residence. The author argues that this finding lends support to social justicearguments to redistribute resources in such a way as to ensure the likelihood ofabsolute gains in mortality. The study also found, however, that race/ethnicity/sexalso accounted for the likelihood of dying independently of family structure whencontrolling for socioeconomic and other factors. The author argues that this find-ing lends support to social justice arguments to redistribute resources on the basisof relative mortality statuses.

KEY WORDS: Adolescent mortality; social justice; health inequalities.

This paper reports results of a study that examines the influence of adolescent fam-ily structure, sociodemographic characteristics, and judgment-impairing substanceuse on mortality in a U.S. cohort of youth and it considers their implications forsocial justice. Identifying adolescent family structure, socioeconomic class, use ofjudgment-impairing substances, and other factors associated with the likelihoodof youths’ mortality influences the appropriateness of programmatic responsesand policy options aimed at improving chances to attain more normalized life ex-pectancies. At issue, in part, is what, if any, role government or society-at-large

1To whom correspondence should be addressed at Yeshiva University, Wurzweiler School of So-cial Work, Belfer Hall, 2495 Amsterdam Avenue, New York, New York 10033-3299. E-mail:[email protected]

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vis-a-vis adolescents and/or their parents can and should play in situations whererace/ethnic/sex disparities in mortality can be shown to exist independently ofadolescent’s early family structure, as well as cumulative exposure to poverty anduse of judgment-impairing substances over time.

This issue stems from identifying more precisely than is currently the casea more precise nature of injustice, that is, what wrongs might be thought to lie atthe heart of the injustice, and then assessing the merits of justice-based argumentsin favor of public or collective action to redress these wrongs in light of empiricalevidence (Marchand et al., 1998; Wikler, 1979). To the extent race/ethnicity/sexand/or socioeconomic disparities in mortality remain when controlling for otherfactors such as personal behavior and family structure, related claims based onsocial justice for government to take steps to increase the likelihood of normalizedmortality among youth are strengthened. That is, social justice claims might bemore appropriately focused on relative mortality or health statuses vis-à-vis ab-solute gains in mortality or health or respective decreases. If adolescents’ earlyfamily structure or behavioral lifestyle still influence the likelihood of mortal-ity when controlling for other factors like race/ethnicity/sex and/or socioeconomicinequalities, then justice-related claims, whether relative or absolute, might be mit-igated and further debate about the role of parents, family structure, and personalresponsibility in the well-being of the youth is warranted.

In particular, the study focuses on the influence of family structure,race/ethnicity, sex, socioeconomic status (SES), and use of substances like al-cohol, marijuana, and cocaine on the likelihood of dying among the youth in thecohort between 1980 and 1998. The author seeks to determine how adolescentfamily structure and sociodemographic characteristics like race, ethnicity, and sexinfluence the likelihood of mortality in light of variations in cumulative exposure topoverty and use of judgment-impairing substances over time (Oh, 2001). Povertyand use of drugs like crack cocaine are associated with the worsening gap in lifeexpectancy between Blacks and Whites throughout the 1980s and early 1990s. Therole of family structure, though much discussed in the context of single parent-hood and provision of public assistance in the United States, is much less certain(Caputo, 1997; Castro, 1993; Katz, 2001; Kochanek et al., 1994; National Centerfor Health Statistics, 1994b; Williams and Collins, 1995).

The study focuses on mortality because it is a definitive outcome and becausemany scholars of health inequalities use it as a primary, if not the most important,health-related outcome measure (e.g., Geronimus et al., 2001; Kaplan and Lynch,2001; Sen, 1992; Woodward and Kawachi, 2000). Mortality, however, also encom-passes more than health-related issuesper seas accidents, suicides, and homicidesare among the leading proximate causes of adolescent death (Singh and Yu, 1996;Zill and Rogers, 1988). Nonetheless, use and/or abuse of such substances as alco-hol and other judgment-impairing drugs often contribute to these more proximatecauses of adolescent death and they are a concern of public health, welfare, aswell as law-enforcement officials. The study focuses on maturing youth in part


Mortality in a U.S. Cohort of Youth 273

because there are relatively few studies that focus exclusively on adolescent andyoung adult mortality. Studies that do focus on the relationship between familystructure and adolescent behavior and well-being often rely on aggregated ratherthan individual- or family-specific data (e.g., Furstenberg and Condran, 1988;Uhlenberg and Eggebeen, 1986). Adolescents and young adults are entering theprime years of their lives in terms of forming families and establishing careers.They have survived well beyond infant and early childhood fatal diseases and othermaladies. For the most part these adolescents could have expected to live out anormal life span. In general, their loss terminates their personal and/or professionaldevelopment. It not only adversely affects their immediate families and friends butalso prematurely ends their direct economic, social, and related contributions tosociety.

Specifically, this study seeks to answer the following questions:

1. How does family structure affect the likelihood of dying among the youthin the cohort beyond that of race/ethnicity/sex, SES, personal behavior,and other structural factors?

2. Under what conditions might appeals for social justice be warranted forrelative mortality or health statuses and for absolute gains in mortality orhealth?

Answers to these questions will provide policymakers and others interestedin adolescent well-being ways of assessing the merits of arguments for remedialactions.

LITERATURE REVIEW

Related Theories of Social Justice

Interest in mortality/health inequalities has been in part fueled by activistsand scholars who view disparities in the health of Blacks versus Whites, of menversus women, of psychiatric patients versus others, and the like, in terms of op-pression, that is, discrimination and bias, and with appeals to social justice andreform (Satel, 2001). Daniels et al. (1999) link justice and mortality/health, andthey do so using Rawls’ notion of justice as fairness (Daniels, 1981; Rawls, 1971;see also Rawls, 2001). Basically, Rawls’ theory of justice is egalitarian in orienta-tion and yet justifies certain inequalities that might contribute to mortality/healthinequalities. The free and equal original contractors behind the veil of ignoranceRawls posits would not insist on a strictly egalitarian distribution of social goods, asthey might insist on equal basic liberties and equal opportunity, if doing so wouldmake them worse off. Specifically, Rawls argues that these contractors wouldchoose his Difference Principle, which permits inequalities provided they work tomake the worst-off groups in society as well as possible. This argument, known


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as themaximinprinciple, suggests that relative inequality is less important thanabsolute well-being, although the priority Rawls gives to the principles of politicalliberty and fair equality of opportunity helps constrain inequality and preserve thesocial bases of self-respect for all.

In light of evidence reviewed and supplemented below, Daniels et al. (1999)take social determinants of mortality/health for granted, agreeing with Dahlgrenand Whitehead (1991) about resulting inequities being both avoidable and unfair.That is, Daniels et al. view mortality/health as a product of cumulative experiencesacross the life course and reform to improve related inequalities as “intersectoral,”spanning beyond personal behavior and the health sectorper seand recognizingthe primacy of social conditions. An “intersectoral” perspective is strikingly rele-vant in regard to adolescents and young adults, because deaths due to homicidesor motor vehicle accidents, for example, require remediation across service areas.The theoretical issues boil down to whether it would ever be reasonable and ra-tional for contractors in the original position to accept a tradeoff in which somemortality/health inequality is (1) allowed to produce some nonhealth benefits forthose with the worst mortality/health prospects or (2) reduced to produce mortal-ity/health benefits for those with the worst mortality/health prospects. In regard tothe first, Daniels et al. answer affirmatively and make a case for extending Rawls’theory to include mortality/health care in the broad sense, incorporating all sociallycontrollable determinants of mortality/health through equal opportunity. Doing somitigates some of the criticisms of Arrow (1973) and Sen (1992) about Rawls’index of social goods, the particulars of which are beyond the scope of this pa-per. Daniels et al. draw a limit, however, given that a residue of mortality/healthinequalities would still be expected even if such a Rawlsian distribution flattenedmortality/health gradients further than what is observed in the most egalitarian,industrially developed countries. This is so because adequate knowledge of all therelevant causal pathways or effective interventions is unavailable, or because whatit might take to modify certain causal pathways or effectively intervene mightundermine principles of political liberty or conflict with other aspects of equalopportunity. Daniels et al. would not further reduce those socioeconomic inequal-ities if doing so reduces productivity to the extent that the institutional measuresemployed to promote life expectancy and health and to reduce mortality/healthinequalities can no longer be supported.

The second theoretical issue is whether it would ever be reasonable and ra-tional for contractors in the original position to accept a tradeoff in which somemortality/health inequality is reduced to produce life expectancy and health ben-efits for those with the worst mortality/health prospects. On this regard, Danielset al. are silent. In their review of four alternative views of equity and health,Marchand, Wikler, and Landesman (1998) indirectly address this issue. In theirdiscussion of “equity as priority to the sickest,” equity has its own minimum po-sition, namely, those who are threatened with the worst harms—who have theshortest life expectancy and most serious diseases and illnesses—should count as



“the worst-off.” For such a position to be plausible, it must claim that the urgencyof needs should have relative, not absolute priority. The issue here is not that re-sources should be expended on the sickest people, without any limitation on theground of cost or lack of benefit, which could lead to a bottomless pit as addressedby Arrow (1973). Rather, this view calls for a relative weighting: more urgent needsreceive more weight when needs are balanced against other factors, including costand efficiency in policy decisions. The merit of “equity as priority to the sickest,”for purposes of this paper, is its justification of relative health statuses vis-à-visabsolute gains in health. Likewise, Nathanson (1998) argues that the DifferencePrinciple can be justly violated in those situations where the situations that arediminished from a redistribution of resources benefiting the worst-off nonethelessremain relatively well-off. In matters of life and death, remaining relatively well-off translates into what might still be considered socially acceptable reduced risksor odds of mortality.

Theoretically, grounds can be found for both relative mortality/health sta-tuses and absolute gains in mortality/health. To the extent this is so, the empiricalquestion then becomes: under what conditions might appeals for social justice bewarranted for relative mortality/health statuses and for what absolute gains in mor-tality/health? Hence, it is to a review of the empirical literature regarding correlatesof mortality/health we now turn.

CORRELATES OF MORTALITY

Although specific proximate causal factors are disputed (Marmot andWilkinson, 2001), as noted above there seems to be agreement about the disparateimpact that demographic and socioeconomic factors have on mortality/health out-comes (Daniels et al., 1999). The link between SES and mortality/health, forexample, is fairly well acknowledged and established (Cattell, 2001; Greenwald etal., 1996; Kaplan et al., 1996; Kennedy et al., 1998; Kreiger and Fee, 1994; Leon etal., 2001; Lynch et al., 1998; Pappas et al., 1993; Rodgers, 1976; Shea et al., 1996).

In general, there are four central findings in the literature that serve as the basisof the present study, guiding its methodology and shaping its implications for socialjustice and remedial action. First, as Daniels et al. (1999, p. 218) note, the observedincome/health gradients do not result from fixed or determinate laws of economicdevelopment, but are influenced by policy choices. Second, the income/healthgradients operate across the whole socioeconomic spectrum within societies, suchthat the steepness of the gradient is affected by the degree of inequality. Third,relative SES is as important as, and perhaps even more important than, the absolutelevel of income in determining mortality/health status; at least once societies havepassed a certain threshold, which the United States easily does. And fourth, thereare identifiable determinants of mortality/health, in addition to socioeconomicstatus, amenable to policy choices that can be guided by considerations of justice(Daniels, 1981).


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In addition to socioeconomic status, race and ethnicity also remain potentpredictors of variations in health status (Livingston, 1994; Williams and Rucker,2000), with age-adjusted death rates for Blacks much higher than those of Whites,while some groups like Asian and Pacific Islander Americans having lower deathrates and others like Hispanics, albeit with intra-ethic variation, roughly equitableto those of Whites (National Center for Health Statistics, 1994a; Vega and Amaro,1994). Adjusting for SES substantially reduces but does not eliminate racial dispar-ities in health (Cooper, 1993). That is, within each level of SES, Blacks generallyhave worse health status than do Whites (Kreiger et al., 1993; Rogers et al., 1996;Schoendorf et al., 1992).

Williams and Collins (1995, p. 373) also note that individual behavior orlifestyle accounts for half of the annual number of deaths in the United States,compared with about 20% due to environmental factors, 20% to genetics, and10% to inadequate medical care. Aside from linking increased sexually trans-mitted diseases to the use of crack cocaine in the 1980s, Williams and Collinsmake no mention of lifestyle issues related to use of substances like alcohol, co-caine, and marijuana. They do provide evidence suggesting that early life socioeco-nomic and health conditions have long-term consequences for an adult’s mortality/health status (Elo and Preston, 1992), although noticeably absent is the influenceof family structure. Smith, Hart, Blane, and Hole (1998a) and Smith, Neaton,Wentworth, Stamler, and Stamler (1998b) also note the link between adverse so-cioeconomic conditions in childhood and adult mortality by specific causes, butthey confine their studies to men aged 35–64 at the time of examination, recruitedfrom workplaces in the west of Scotland between 1970 and 1973 and they clas-sified SES by father’s occupation (professional, managerial, skilled nonmanual,skilled manual, semiskilled, and unskilled manual), not by income or povertystatus.

Finally, in their study of adolescents and young adults, Singh and Yu (1996)showed that between 1979 through 1985 male youth had 2.7 times the risk of mor-tality than their female counterparts and that the risk of mortality was 1.4 timeshigher for Blacks than for non-Hispanic Whites. Family income and educationwere both inversely related to mortality. Education had a more powerful effect onmale mortality, whereas income was more strongly linked to female mortality.Ce-teris paribus, young men aged 20–24 years with 8 or fewer years of education had3.3 times the mortality risk of those with 13 or more years of education. Youngwomen in the lowest family income brackets experienced 2.2 times the mortalityrisk of their high-income counterparts. Finally, compared with married subjects,divorced, separated, or widowed youth had a 2.2 times higher risk of mortality.Singh and Yu’s study omitted early childhood and family background characteris-tics, as well as variables related to lifestyle behaviors. They nonetheless concludedthat the future course of youth mortality in the United States would depend greatlyon the extent to which the nation can influence deaths from unintentional injuries,



violence, and HIV/AIDS infection among its youth, factors that family structureand lifestyle behaviors associated with substance use can affect.

The study reported here differs from most other studies that have examinedthe relationship between socioeconomic factors and individual mortality/healthin several ways. First, unlike Grant et al. (2000), Hebert et al. (1998), Kaplanet al. (1996), and Rodgers (1976), for example, who used aggregated data, thisstudy relies primarily on individual-specific variables, while incorporating oneaggregated measure about the communities (the unemployment rate) in which thesubjects lived at the time of survey. In doing so, it nonetheless considers incomeinequality as a structural characteristic of the economy and family forms (e.g.,married, unmarried) as structural characteristics of society (Lynch et al., 1998).Second, the study examines both male and female youth, whereas many studiesthat rely on aggregated data focus on race or class to the exclusion of issues ofgender (e.g., Grant et al., 2000) and many that employ individual-specific variablesfocus primarily on men (e.g., Smith et al., 1996a,b, 1998b).

The third way this study differs from others is by using, as Williams andCollins (1995) suggest, longitudinal panel data, rather than cross-sectional data,which form the basis of many related studies such as those by Duncan et al. (1994)and Miller and Korenman (1994), who rely on single-year indicators of economicstatus. In doing so, this study takes advantage of information obtained about rela-tively recent events from the same individuals at the time of each survey, therebyminimizing inaccuracies due to recall and enabling construction of cumulativevariables that are both personal (age, education) and structural (family form, SES,unemployment rate in area of residence) in nature. In their longitudinal studiesof working men in Scotland, Smith et al. (1997) and Hart et al. (1998) used onlyone cumulative variable, a measure of social class, and found that socioeconomicfactors acting over the lifetime affect health and risk of premature death. Havinglongitudinal data with multiyear or long-term measures of income is importantbecause the larger effect for long-term deprivation may reflect families’ uses ofassets or credit to cushion the impact of short-term economic losses, as othershave found among older men (e.g., Mare, 1990). In addition, mediating factorsbetween income and mortality/health need to be taken into account. In their lon-gitudinal study of British households, Benzeval and Judge (2001) examined onlythe relationship between income and health without the benefit of controlling forany mediating factors.

The fourth way this study differs from others is that it incorporates lifestylebehaviors associated with the use of judgment-impairing substances like alcohol,cocaine, and marijuana, thereby affording the opportunity to establish a causal linkbetween the cumulative use of such substances and mortality when controlling forother factors. In their pooled cross-sectional study of demographic, socioeconomic,and behavioral factors affecting mortality, Rogers et al. (1996) employed only onebehavioral measure, namely smoking, constructed into categories of never, former


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light (used to smoke less than a pack of cigarettes a day), former moderate-to-heavy (a pack or more a day), current light (less than a pack a day), and currentmoderate-to-heavy (a pack or more a day). Although the study reported here doesnot include smoking as a behavioral measure, primarily because related questionswere not asked until survey year 1994, the judgment-impairing behavioral mea-sures it does include have deleterious effects on both physical and mental health.Fifth, the present study identifies and controls for exposure to both distal (i.e., earlylife exposures like parents’ family stability) and proximate measures thought toaffect mortality. Spencer (2001) has called for more life course studies that exam-ine the role of early life exposures as well as multiple exposures of other socialdeterminants of health and mortality.

METHODS

Data and Subjects

Data for the study were obtained from the 1979 cohort of the National Longi-tudinal Survey of Youth (NLSY79), a nationally representative sample of 12,686noninstitutionalized youth in the United States aged 14–21 as of December 31,1978. Respondents were interviewed annually between 1979 and 1994, and in 1996and 1998, and asked a range of questions regarding labor market experiences andfamily characteristics. For the 1998 survey, 8399 respondents were interviewed, a66.2% unweighted retention rate (79% weighted). Respondents in 1998 differed onseveral sociodemographic measures from those in 1979, with the major differencein annual family income ($16,726 vs. $10,195). In 1979 they were also slightlyyounger (17.6 vs. 17.9 years old), less educated (10.3 vs. 10.5 years of school-ing), from larger families (4.70 vs. 4.26 members), with proportionately moreBlacks (14.3% vs. 13.6%, weighted) and proportionately more women (51.4% vs.49.2%, weighted). Differences in part reflected cessation of interviews with the1643 members of the economically disadvantaged, non-Black, non-Hispanic sup-plemental sample beginning in 1991. Results and recommendations were madewith these differences in mind. Documentation about the national U.S. samplewas found in theNLS Handbook 1999(Center for Human Resource Research,1999a) and theNLSY79 User’s Guide 1999(Center for Human Resource Research,1999b).

By 1998, the most recent year of available data, 295 respondents, 2.3% of theoriginal population sample (weighted), were not interviewed because they weredeceased. Study samples varied by survey year and in any given year includedinterviewees and those not interviewed because of death about whom all rele-vant information in previous survey years, except as noted, was available. Missingvalues on several background measures defined below resulted in a total study



population sample of 11,549 respondents, of whom 271 were reported as nonin-terviewees in 1998 because of death.

Measures

Table 1 identifies and defines the measures used in this study. The dependentmeasure, Deceased, is dichotomous, with 1 (“noninterviewee because of death”)and 0 (“interviewee”). There are three major categories of correlates or independentvariables reflecting measures found to be significant determinants of mortality inthe literature: background, socioeconomic, and behavioral. Most measures are self-explanatory, although some elaboration regarding several measures within eachcategory is necessary. Among the background measures, of particular interestfor purposes of this study because it has been neglected in the literature, is thefamily structure at the time respondents were 14 years of age. Because there hasbeen so much public and scholarly debate over the past several decades about theimportance of family structure on children and adolescents in the United States,this study employs three dummy variables to assess the influence of both single-parent families and nonbiological two-parent families compared with biologicaltwo-parent or intact families on mortality.

Other background measures are used as controls. These include whether ornot respondents lived in an urban environment at age 14, lived in the South withits composition of economically poorer, albeit less unequal, states vis-à-vis thosein the Northeastern, North Central, and western United States, or were born in theUnited States, as well as the unemployment rate of the respondent’s area of resi-dence, level of education of respondent’s mother, number of siblings in the familyat the time of the first interview, age of respondent, race/ethnicity, and sex. Theuse of urban environment at age 14 is consistent with the work of Geronimus et al.(2001), which shows that residents of urban poor areas in the United States fareworse than their race- and sex-specific national average and worse than residentsof rural poor areas matched on race and gender. LeClere et al. (1997) also showthat the pathway between residential segregation and mortality is routed throughpoorer neighborhood economic conditions for men and high levels of female head-ship in segregated neighborhoods for women, both of which are found in urbanareas throughout the United States. The use of the South as a dummy measure isconsistent with the work of Lochner et al. (2001) and Kaplan et al. (1996), whichshowed that individuals living in high income-inequality states in the United Stateswere at a slightly increased risk of death and other adverse consequences (e.g., vi-olent crime rates, per capita medical care expenditures) compared with individualsliving in states with the lowest income inequality.

Two measures are used to capture a respondent’s SES, namely, respo-ndent’s highest grade completed and family poverty status. The latter is measured


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Table 1. Definitions of Study Variables

Variables Definition

Deceased 1= yes, 0= aliveBackground

Age of respondent Respondent’s age in 1979Respondent’s education Highest grade completed by 1979Respondent’s mother’s education Highest grade completed by 1979

Respondent’s family structure at age 14Biological two-parent family 1= at age 14 respondent lived with

biological mother and father, 0= other,Reference

Nonbiological two-parent family 1= at age 14 respondent lived with twoparents, one or both of whom are not therespondent’s biological parent, 0= other

Single-parent family 1= at age 14 respondent lived with a singleparent, 0= other

Siblings The number of siblings in the respondent’sfamily in 1979

Poverty status—reported in 1979 but basedon 1978 family income

1= family income fell at or below the U.S.poverty threshold, 0= above thethreshold

Race/ethnicity/sexWhite male 1= yes, 0= no, ReferenceBlack male 1= yes, 0= noHispanic male 1= yes, 0= noWhite female 1= yes, 0= noBlack female 1= yes, 0= noHispanic female 1= yes, 0= no

Region lived in 1979South 1= yes, 0= no, ReferenceNortheast 1= yes, 0= noNorth Central 1= yes, 0= noWest 1= yes, 0= no

Respondent’s marital status in 1979 1= married, 0= otherUnemployment rate in 1979 The unemployment rate in respondent’s area

of residenceUrban environment at age 14 1= at age 14 respondent lived in a city or

town, 0= other (country, farm, or ranch)U.S. born 1= yes, 0= noSocioeconomic status

Education level Highest grade completed by respondent todate

Years of poverty Number of years respondent lived in afamily whose income fell below the U.S.poverty threshold

Behavioral–substances used, survey yearMarijuana/hashish in past year, 1980 1= no use, 0= some useOther drugs to get high in past year, 1980 1= no use, 0= some useAlcohol, 6+ drinks within past 30 days,

1983–891= no use, 0= some use

Drugs without a doctor’s prescriptionwithin past 30 days, 1984

1= no use, 0= some use

Marijuana within past 30 days, 1984 1= no use, 0= some useCocaine within past 30 days, 1984 1= no use, 0= some use



Table 1. (Continued)

Variables Definition

Marijuana within past 30 days, 1988 1= no use, 0= some useCocaine within past 30 days, 1988 1= no use, 0= some use

Alcohol, number of drinks per daywithin past 30 days, 1992


Marijuana within past 30 days, 1992 1= no use, 0= some useCocaine within past 30 days, 1992 1= no use, 0= some useAlcohol, 6+ drinks within past 30 days,


OtherAverage unemployment rate The average annual unemployment rate in

respondent’s area of residence at the timeof survey

Years married The number of survey years respondentsreported their marital status as married

Note. The drugs used without a doctor’s prescription in 1984 included stimulants, sedatives,tranquilizers, psychedelics, cocaine, heroin, other narcotics, inhalants, and unspecified others. Theywere collapsed into one dichotomous variable because of the small number of respondents whoreported use of any specific drug. In survey year 1994, cocaine use in 1988 was omitted from theregression model because of too few respondents reporting such use to allow for sufficient separationbetween those dead and alive.

cumulatively and reported as the number of years respondents lived in familieswhose income fell below the official U.S. poverty thresholds. Related data arelagged, such that this measure reflects the economic circumstances of familiesduring the year preceding the survey year. Another cumulative measure, yearsmarried, appears in the last row of Table 1. A related community measure, theunemployment rate in respondent’s area of residence at the time of survey, alsoappears at the bottom of Table 1. The measure is also used in a cumulative sense,as an average.

Finally, the behavioral measures capture a range of substances that respon-dents intermittently reported to have used in varying amounts between 1980 and1994. The NLSY79 used different sets of questions about the nature and extent ofuse of these substances, at times asking about different periods of use (e.g., withinthe past year or within the past 30 days) and at other times collapsing amounts con-sumed into different categories, thereby making summation or averaging acrossyears of questionable validity. Hence, as Table 1 shows, dichotomous measuresare used to capture nonuse versus use of alcohol, marijuana, and other drugs likecocaine by year.

Procedures

Chi-square,t test, and ANOVA statistics are used to provide descriptive sum-maries comparing background characteristics of dead versus living respondents


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in survey year 1998. For dichotomous variables, the Cochran–Mantel–Haenszelstatistic is used with the chi-square to determine if deceased respondents are morelikely to vary by sociodemographic and other characteristics such as living in abiological two-parent family, being U.S. born, or living in an urban environment atage 14 than were living respondents without such characteristics (Cody and Smith,1997).

Logistic regression analysis is used initially to determine if Respondent’sFamily Structure at Age 14 adds to the explanatory power of the other backgroundmeasures thought to influence the likelihood of dying on those in the study sam-ple in survey year 1998. Subsequently, it is used to determine (1) if behavioral,socioeconomic, and other measures add to the explanatory power of all back-ground characteristics thought to influence the likelihood of dying and (2) howthe addition of these variables affect the influence of Respondent’s Family Struc-ture at Age 14 on the likelihood of dying. Separate analyses are conducted inselect survey years (namely 1984, 1988, 1992, and 1994) for these subsequentanalyses. The selection of survey years and the appropriate measures includedfor analysis is a function of when substance-use questions were asked and whenrespondents were first reported as noninterviewees because of death. For example,drug-related questions were asked about in survey year 1984. The regression mod-els incorporating all behavioral, socioeconomic, and other measures or correlatesbefore and inclusive of 1984 is designated 1985 because that is the subsequentsurvey year. The 1985 models capture those who were alive in the 1984 survey butwho were reported for the first time as noninterviewees because of death in the1985 survey or afterwards. In the 1985 models, the dependent measure, Deceased,excludes all those who were identified as noninterviewees because of death before1985.

For all regression models, correlates are grouped into two models. In the initialanalysis, the first or Main Effects Model comprises all the background measures,except Respondent’s Family Structure at Age 14, and the family’s poverty statusand respondent’s marital status in 1979. The second or Expanded Model includesmeasures in the Main Effects Model and adds Respondent’s Family Structure atAge 14. The residual score statistic,QRS (Breslow and Day, 1980; Stokes et al.,1995), is used to determine what effects, if any, Respondent’s Family Structure atAge 14 has on the overall effect of the Main Effects Model as well as on individualmeasures of the Main Effects Model. In the subsequent analyses used for selectedsurvey years, the first or Main Effects Model comprises all background measures,including Respondent’s Family Structure at Age 14. The second or ExpandedModel includes variables in the Main Effects Model and adds the behavioral,socioeconomic, and other measures. The residual score statistic,QRS, is used hereto determine what effects, if any, behavioral, socioeconomic, and other measureshave on the overall effect of the Main Effects Model as well as on individualvariables of the Main Effects Model.



The Main Effects Model fits adequately when theQRS statistic fails to meetstatistical significance (p > 0.05). The Hosmer and Lemeshow Goodness-of-FitTest is used to assess how well the data fit the Expanded Model, a good fit sig-nified by higherp-values. Both theQRS statistic and the Hosmer and LemeshowGoodness-of-Fit statistic are set up to reject the null hypothesis that the data fitthe specified model. Hence, for theQRS statistic, ap-value < 0.05 signifies thatthe Expanded Model is the better of the two models; conversely, ap-value≥ 0.05signifies that the Main Effects Model is the better of the two. The Hosmer andLemeshow Goodness-of-Fit Test is used as an additional support for the ExpandedModel’s adequacy for the data. In this case, we do not want to reject the null hy-pothesis that the data fit the specified model, so the higher thep-value the better(see Cody and Smith, 1997, p. 243; Stokes et al., 1995, pp. 192–193).

RESULTS

Background Characteristics of the Study Sample

As noted, the total study population sample comprised 11,549 youth, of whom271 were reported as noninterviewees in 1998 because of death. ANOVA resultsshowed differences by age in the total study population sample in 1979 (F= 8.59,p < 0.001). Duncan post hoc results indicated that White females (n= 3513) andmales (n= 3595) were older (18.03 and 18.01 years, respectively) to a statisticallysignificant degree than Black females (n= 1398), Hispanic males (n= 797), Blackmales (n= 1438), and Hispanic females (n= 808), roughly 17.7 years each (p <0.05). In regard to those reported as noninterviewees in 1998 because of death, nosignificant differences were found by race/ethnicity in the survey year that deathwas reported as the reason for noninterview.

Student’st test results showed that noninterviewees in 1998 because of deathwere younger (17.6 vs. 17.9 years old,p > 0.05), had more siblings (4.38 vs. 3.84,p > 0.01), were less educated (10.0 vs. 10.5 years of completed schooling,p >0.001), and had less educated mothers (10.4 vs. 10.8 years of completed schooling,p> 0.05) than interviewees. No difference was found in regard to the unemploymentrate of the area of residence.

Chi-square results showed that, compared with interviewees’ backgroundcharacteristics, noninterviewees because of death between 1980 and 1998 were3.1 times as likely than not to be a Black male (p < 0.001), 1.6 times as likelyto be a Hispanic male (p < 0.05), 2.5 times less as likely to be a White female(p < 0.001), 1.7 times less likely to belong to a biological two-parent family at age14 (p < 0.001), 1.6 times as likely to belong to a nonbiological two-parent family(p < 0.01), 1.4 times as likely to belong to a single-parent family (p < 0.01), 1.5times as likely to have lived in a poor family (p < 0.001), and 2 times less likely


284 Caputo

to have been married (p < 0.01). No differences were found by region, born in theUnited States, or lived in an urban environment at age 14.

Predictive Capacity of Background Factors on Mortalityin the Study Sample

As can be discerned from Table 2, the Main Effects Model fit the data ade-quately, signifying that the Expanded Model (not shown) that incorporated familypoverty and marital status did not affect the explanatory power of the background

Table 2. Odds of Death by Background Characteristics (N= 11,549)

Main Effects Model

ParameterBackground characteristics Odds estimate Standard error

Age of respondent 1.013 0.0126 0.0376Mother’s education 0.988 −0.0116 0.0242Respondent’s education in 1979 0.920 −0.0829 0.0437Respondent’s family structure at age 14

Biological two-parent family ReferenceNonbiological two-parent family 1.614 0.4785 0.1653Single-parent family 1.267∗∗ 0.2370 0.1568

Siblings 1.033 0.0322 0.0233Poverty status—reported in 1979 — — —

but based on 1978 family incomeRace/ethnicity/sex

White male ReferenceBlack male 2.454∗∗∗ 0.8977 0.1734Hispanic male 1.563 0.4468 0.2588White female 0.525∗∗∗ −0.6436 0.1940Black female 0.762 −0.2718 0.2420Hispanic female 0.862 −0.1489 0.3041

Region lived in 1979South ReferenceNortheast 1.594∗∗ 0.4661 0.1795North Central 1.588∗∗ 0.4622 0.1674West 1.080 0.0767 0.2049

Respondent’s marital status in 1979 — — —Unemployment rate in 1979 0.954 −0.0469 0.0987Urban environment at age 14 0.883 −0.1246 0.1571U.S. born 1.313 0.2724 0.2796

Max-rescaledR2 0.0494QRS chi-square 4.4816, df= 2,

p= 0.11Hosmer and Lemeshow chi-square 9.1860, df= 8,

p= 0.33

Note. Definitions appear in Table 1. Respondent’s education in 1979 and Hispanic male weresignificant at the 0.05≤ p < 0.10 level.∗p < 0.05;∗∗p < 0.01;∗∗∗p < 0.001.



characteristics (QRS= 4.4816, df= 2, p= 0.11). When controlling for all back-ground characteristics, family structure, race/ethnicity/sex, and region were foundsignificant. Compared with White males, Black males were 2.5 times as likely tohave died, whereas women were 1.9 times less likely (1/0.525). Compared withthose who resided in the South in 1979, those who resided in Northeastern andNorth Central United States were 1.6 times as likely to have died. Compared withthose who resided in biological two-parent families at age 14, those who residedin nonbiological two-parent families were 1.3 times as likely to have died. Severalbackground measures with significant bivariate relationships with the likelihoodof dying lost significance in the multivariate logistic regression model. These wereage of respondent in 1979, respondent’s education level, respondent’s mother’seducation level, number of siblings, being an Hispanic male, being married, resid-ing in single-parent families, and living in poor families. It should be noted thatthe relatively lowR2 (0.0494) signifies that the overall explanatory power of themodel attributed to the background characteristics, though statistically adequate, issomewhat limited, suggesting that other socioeconomic and behavioral measuresoccurring over the life course may better account for the likelihood of dying.

The Effects of Behavioral, Socioeconomic, and Other Factors on thePredictive Capacities of Background Characteristics, Especially

Respondent’s Family Structure at Age 14, on Mortality,Selected Survey Years

As can be seen in Table 3, the Main Effects Model failed to fit the data ad-equately (QRS = 15.9888, df= 6, p = 0.0138), signifying that the behavioral,socioeconomic, and other measures added to the explanatory power of the back-ground characteristics to account for those noninterviewees because of death be-tween survey years 1981 and 1998. In particular, inverse relationships were foundbetween level of education, years of marriage, and the use of drugs other thanmarijuana to get high within the past year and morality. Those with less education,fewer years of marriage, and users of drugs (vs. nonusers) at the time of the 1980survey were more likely to have died between 1981 and 1998. Several backgroundcharacteristics were significant in both models. Compared with White males,Black males were 2.4 times as likely to die, whereas White females were half aslikely. Compared with those living in the South, those living in the Northeast were1.5 times as likely to die, whereas those in the North Central United States were1.7 times as likely.

In subsequent survey years, as additional behavioral measures were added toall the measures in the previous survey year’s models, the Main Effects Modelsfit the data adequately. Table 4 shows the results of those variables in each of theMain Effects Models that were found significant by survey year, as well as therelevant model statistics, that is,R2, QRS chi-square, and Hosmer and Lemeshow


286 Caputo

Table 3. Odds, Parameter Estimates ( ), and Standard Errors [ ] for Background, Behavioral, Socioe-conomic, and Other Measures on Mortality, 1981 Regression Models

Measures Main Effects Model Expanded Model

Background characteristicsAge of respondent 0.964 1.028

(−0.0365) [0.0284] (0.0272) [0.0379]Respondent’s mother’s education 0.975 (−0.0183) [0.0254]

(−0.0249) [0.0246]Respondent’s family structure at age 14

Biological two-parent family Reference ReferenceNonbiological two-parent family 1.723∗∗ 1.664∗∗

(0.5443) [0.1720] (0.5095) [0.1733]Single-parent family 1.327 1.284

(0.2832) [0.1657] (0.2500) [0.1664]Siblings 1.033 1.029

(0.0324) [0.0240] (0.0287) [0.0244]Race/ethnicity/sex

White male Reference ReferenceBlack male 2.388∗∗∗ 2.396∗∗∗

(0.8707) [0.1805] (0.8737) [0.1825]Hispanic male 1.482 1.521

(0.3937) [0.2732] (0.4191) [0.2740]White female 0.461∗∗∗ 0.501∗∗∗

(−0.7736) [0.2036] (−0.6906) [0.2050]Black female 0.725 0.787

(−0.3211) [0.2510] (−0.2389) [0.2536]Hispanic female 0.725 0.811

(−0.3218) [0.3312] (−0.2092) [0.3325]Region lived in 1979

South Reference ReferenceNortheast 1.514∗ 1.448∗

(0.4150) [0.1854] (0.3702) [0.1862]North Central 1.719∗∗ 1.693∗∗

(0.5416) [0.1731] (0.5266) [0.1732]West 1.167 1.136

(0.1540) [0.2081] (0.1275) [0.2090]Urban environment at age 14 0.791 0.793

(−0.2339) [0.1602] (−0.2320) [0.1607]U.S. born 1.147 1.173

(0.1372) [0.2865] (0.1599) [0.2913]Socioeconomic status

Education level 0.912∗(−0.0923) [0.0456]

Years of poverty nsBehavioral–substances used, survey year

Marijuana/hashish in past year, 1980 nsOther drugs to get high in past year,

19800.719∗

(−0.3296) [0.1598]Other

Average unemployment rate nsYears married 0.688∗

(−0.3741) [0.1608]



Table 3. (Continued)

Measures Main Effects Model Expanded Model

Max-rescaledR2 0.0502 0.0564QRS chi-square 15.9888,

df = 6, p= 0.0138Hosmer and Lemeshow chi-square 8.6267,

df = 8, p= 0.3748

Note.Definitions appear in Table 1.N= 11,120, of whom 246 were reported as nonintervieweesbecause of death between survey years 1981 and 1998. In the main effects model, single-parentfamily was significant at the 0.05≤ p < 0.10 level.∗p < 0.05;∗∗p < 0.01;∗∗∗p < 0.001.

chi-square. (Regression results of all variables in each of the models by surveyyear are available upon request from the author.) As can be seen in Table 4, themost robust and consistent predictors of mortality between 1984 and 1998 werenonbiological two-parent family, single-parent family, Black male, White female,and years married. Compared with those who lived with their two biological parentsat age 14, those who lived within nonbiological two-parent families were 1.7–2.1 times likely to die, whereas those who lived within single-parent families were1.4–1.9 times as likely to die. Compared with White males, Black males were2.1–2.5 times as likely to die, whereas White females were about 50–60% lesslikely to die.

As can also be discerned from Table 4, after 1981 the influence of drug usein 1980 lost significance, whereas the use of drugs without a doctor’s prescriptionbecame significant in survey year 1993. Those who reported that they used drugswithout a doctor’s prescription in 1992 were more likely to die between 1993and 1998 than were those who reported no such usage. After 1984, education lostsignificance, whereas after 1985, the region of the country where respondents livedwhen first interviewed lost significance.

DISCUSSION: IMPLICATIONS FOR SOCIAL JUSTICE

This study shows that adolescents’ early family structure is a robust predic-tor of mortality in the youth cohort. Whether viewed over the entire span of thestudy, thereby incorporating all those who died between 1980 and 1998, or as thecohort aged, thereby excluding from the study sample those who died before eachselected survey year, 14-year-old youths whose biological parents lived togetherwere consistently more likely to live than die when controlling for lifestyle andrace/ethnicity/sex of the youth and the SES of the youth’s family. This findingin part supports those social conservatives and other scholars who have arguedover the past several decades that the disproportional shift from intact to alter-native family forms has deleterious consequences on the well-being of children


288 Caputo

Tabl

e4.

Odd

s,P

aram

eter

Est

imat

es(

),an

dS

tand

ard

Err

ors

[]fo

rS

igni

fican

tMea

sure

son

Mor

talit

y,S

elec

ted

Sur

vey

Yea

rs,M

ain

Effe

cts

Reg

ress

ion

Mod

els

Sel

ecte

dsu

rvey

year

s

1984

1985

1987

1989

1993

Mea

sure

s(N=

10,13

1)(N=

9700

)(N=

8541

)(N=

7844

)(N=

5955

)

Ba

ckg

rou

nd

cha

ract

erist

ics

Res

pond

ent’s

fam

ilyst

ruct

ure

atag

e14

Bio

logi

calt

wo-

pare

ntR

efer

ence

Ref

eren

ceR

efer

ence

Ref

eren

ceR

efer

ence

fam

ilyN

onbi

olog

ical

two-

pare

ntfa

mily

1.74

2∗∗

1.75

8∗∗

1.83

0∗∗

1.79

6∗2.

130∗

(0.5

552)

[0.2

048]

(0.5

641)

[0.2

146]

(0.6

044)

[0.2

242]

(0.5

856)

[0.2

694]

(0.7

562)

[0.3

326]

Sin

gle-

pare

ntfa

mily

1.487∗

ns1.

546∗

1.74

1∗1.

935∗

(0.3

968)

[0.1

961]

(0.4

355)

[0.2

124]

(0.5

547)

[0.2

446]

(0.6

602)

[0.3

123]

Rac

e/et

hnic

ity/s

exW

hite

mal

eR

efer

ence

Ref

eren

ceR

efer

ence

Ref

eren

ceB

lack

mal

e2.3

47∗∗∗

2.53

5∗∗∗

2.19

4∗∗∗

2.26

4∗∗

ns(0.8

530)

[0.2

121]

(0.9

302)

[0.2

256]

(0.7

859)

[0.2

389]

(0.8

171)

[0.2

831]

Whi

tefe

mal

e0.4

29∗∗∗

0.48

4∗∗∗

0.44

2∗∗

0.51

3∗ns

(−0.

8461

)[0.2

545]

(−0.

7247

)[0.2

669]

(−0.

8154

)[0.2

797]

(−0.

6680

)[0.3

309]

Reg

ion

lived

in19

79S

outh

Ref

eren

ceR

efer

ence

Nor

thC

entr

alns

1.604∗

nsns

ns(0.4

725)

[0.2

197]

Soc

ioec

onom

icst

atus

Edu

catio

nle

vel

0.901∗

nsns

nsns

(−0.

1040

)[0.0

461]

Beh

avio

ral–

subs

tanc

esus

edsu

rvey

year

Dru

gsw

ithou

tado

ctor

’sns

nsns

ns0

.484∗

pres

crip

tion

with

in(−

0.72

62)

[0.3

017]

past

30da

ys,1

984

Oth

erY

ears

mar

ried

0.834∗

0.86

1∗0.

865∗∗

0.90

4∗0.

935∗

(−0.

1712

)[0.0

706]

(−0.

1501

)[0.0

612]

(−0.

1452

)[0.0

559]

(−0.

1007

)[0.0

426]

(−0.

0677

)[0.0

342]

Max

-res

cale

dR2

0.06

660.0

676

0.068

60.0

765

0.080

0Q

RS

chi-s

quar

e2.0

521,

df=

1,6.

1238,df

=4,

1.13

70,df

=1,

4.54

36,df

=2,

5.36

47,df

=3,

p=

0.15

20p=

0.19

01p=

0.28

63p=

0.10

31p=

0.14

70H

osm

eran

dLe

mes

how

11.037

2,df

=8,

3.18

95,df

=8,

11.4

503,

df=

8,9.

4472,df

=8,

11.0

505,

df=

8,ch

i-squ

are

p=

0.19

96p=

0.92

19p=

0.17

75p=

0.30

60p=

0.19

89

*p<

0.05

;**p

<0.

01;*

**p

<0.

001.



and adolescents (e.g., Gingrich, 1995; Murray, 1984; Popenoe, 1988), but it alsosuggests that marital instability more so than never married parenthoodper seis problematic. This is so because the measure single-parent family used in thisstudy incorporated both those who never married and those who were separated,divorced, or widowed but not married when the subjects of the study were 14 yearsof age. This finding suggests that there is something about intact families that keepsadolescents from harm’s way, whether it be from homicide, suicide, accidents, orother maladies associated with mortality, even when controlling for a variety ofsocial conditions, personal behaviors, SES, race, and sex. Whether that somethingis the level or steadiness of time, attention, understanding, love, or other factors isa subject for future research.

The finding about family structure thereby corroborates the work of Cherlin(1992), who described the adverse and lasting social and behavioral consequencesof marital instability on both the parents and the children. It also provides em-pirical evidence, notably lacking in the literature (e.g., Furstenberg and Condran,1988) linking marital instability with a specific measure of adolescent well-being,namely mortality, perhaps the ultimate measure of well-being. In doing so, thisfinding about the stability of family structure presents a formidable challenge tothose advocates of social justice who view the “traditional” or “intact” familystructure as oppressive to women and children and seek increased social and gov-ernmental support for alternative family forms. To the extent that the link betweenmarital instability and likelihood of death found in this study applies to familiesthroughout the United States in general, then the merits of arguments based onsocial justice, whether on grounds of paternalism, fairness in the distribution ofburdens, or public welfare (Wikler, 1979), or on grounds of equity as maximiza-tion, equality, maximin, or priority to the least advantaged (Marchand et al., 1998),are questionable and need to be subject to greater scrutiny. Although social condi-tions and other factors amenable to public policy might exacerbate the likelihoodof dissolution of intact families, the historical reluctance of the United States toimplement a family policyper seand its reliance on the nongovernmental organi-zations and personal responsibility contribute to placing such matters consideredwithin the private sphere of society beyond the scope of social justice. This isnot to say that appeals for government remedial action should not be made, onlyto suggest that such appeals might be better made on grounds other than socialjustice.

Other findings of this study further complicate appeals to social justice basedon gender. Although women have a greater life expectancy than do men in generalin the United States, findings of this study show that White women are morelikely to live than White men throughout adolescence and young adulthood asthey form their own families and begin their careers. White men are perhaps themost privileged group in terms of SES and economic mobility in the United Statesin general and in this cohort of youth in particular (Caputo, 1999). Compared with


290 Caputo

White men, the White women in this study constitute the most privileged groupwhen it comes to life versus death. This finding supports that of Marchand et al.(1998), who argued that documentation of class and, by extension, gender andrace inequities in health do not in themselves identify the source of the natureof the moral problem whose existence they evidentially demonstrate. Most of thecommonly accepted perspectives of social justice would permit a diminution oflife expectancy among White women that others may live longer (Sterba, 1999).As previously noted, however, Nathanson (1998) argued that the welfare of thosebest off might be mitigated if such a diminution still left them relatively well off atsome socially acceptable, but unspecified level and if those whose life expectancywere to be improved by a redistribution of resources had mortality rates that weresocially unacceptable. Whether or not agreement could be obtained regardingthese socially acceptable levels is problematic, given the gravity of mortality asthe outcome. If anything, the finding about White women in this study wouldsupport arguments for social justice emphasizing absolute gains in life expectancyand health, rather than relative statuses, the position Marchand et al. hold. Thisposition avoids the contentious debates regarding socially acceptable levels ofmortality between groups.

Other findings of the study, however, support advocates of social justice whoclaim that race/ethnicity/sex matters in regard to mortality and health-related out-comes and by extension that the relative status of groups can form the basis oflegitimate governmental and social interventions on their behalf. Although thestudy corroborated prior research showing that the marital stability mattered inregard to the likelihood of adolescent death independently of other factors likeSES and race/ethnicity/sex, so too did race/ethnicity/sex matter independently ofother factors including the adolescents’ family structure at age 14. Black men aremore likely to die than White men throughout adolescence and young adulthoodas they form their own families and begin their careers, even when family-specificSES, measured by years of family poverty, and aggregated inequality, measured bythe background characteristic region of residence at age 14, are taken into account.Nothing in this study demonstrates a direct link between the greater likelihood ofadolescent mortality among Black vis-à-vis White males and racismper se. Givenhistorical and contemporary suspicions, caricatures, and treatment of many Blackmen in the United States, however, such an inference can be made and appealsto social justice for government action to increase the life expectancy of matur-ing adolescent Black men are warranted. As other studies show a socioeconomicgradient as a decisively more important factor than raceper sein regard to mortal-ity/health outcomes in general and this study found no relationship between yearsof poverty and likelihood of adolescent mortality, race-based vis-à-vis class-basedappeals for affirmative actions in the name of social justice can be justified. Giventhat homicide rates are higher for Black adolescent men than for their White coun-terparts and are in part a function of living in more densely populated urban areas,



arguments in the name of social justice to redistribute resources to the inner citieswhere these homicides are most likely to occur have merit.

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Documents

Correlates of Mortality in a U.S. Cohort of Youth, 1980–98: Implications for Social Justice