A broader perspective on education and mortality: Are we influenced by other people's education?

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Social Science & Medicine 66 (2008) 620–636 www.elsevier.com/locate/socscimed

A broader perspective on education and mortality: Are we influenced by other people’s education? Øystein Kravdal Department of Economics, University of Oslo, P.O. Box 1095, Blindern, 0317 Oslo, Norway Available online 19 November 2007

Abstract The objective of this study was to find out whether the educational achievements of family members and people in the municipality have an impact on a person’s mortality, net of the well-known strong influence of his or her own education. Using register data, discrete-time hazard models for all-cause mortality in 1980–2003 were estimated for all Norwegian men and women born between 1950 and 1973 (i.e. age 30–53). There were 23,692 deaths during the 19.1 million personyears of follow-up. The education of a former or current spouse had the clearest beneficial effect, although own education was more important. Mortality was also negatively associated with the education of the oldest sibling and to a lesser extent with that of the sibling-in-law and father-in-law. The average education in the municipality was not generally related to mortality, but a beneficial effect was seen among men with college education. In contrast to this, parents’ education affected mortality adversely, especially among women. The data did not allow causal pathways to be identified, but possible mechanisms were discussed. For example, it was argued that others’ education may affect mortality favourably through transmission of knowledge, imitation of behaviour, economic support, and the quality of health services. In some societies, childhood health might also be an issue. On the other hand, having better-educated family members or living in a community with many better-educated people, who typically also have higher incomes, may trigger psychosocial stress. However, one should be careful to interpret the observed relationships as reflecting purely causal effects. Various unobserved factors may influence the person’s choice of spouse and place of residence as well as mortality, and having parents with higher (lower) education may signal that the person has had special problems (resources) during childhood or adolescence, which also may have implications for later health. r 2007 Elsevier Ltd. All rights reserved. Keywords: Education; Family; Municipality; Parents; Sibling; Spouse; Mortality; Norway

Introduction Socio-economic differentials in health and mortality have attracted massive research interest over many years. Most of this work has taken an individual perspective, without necessarily being based on individual data: the focus has largely been Tel.: +47 22855158; fax: +47 22855035.

E-mail address: [email protected] 0277-9536/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi: 10.1016/j.socscimed.2007.10.009

on how a person’s own socio-economic resources influence his or her own health and mortality. For example, it has been firmly established that people with high education have lower mortality than those with little education, and much is also known about the causal pathways (e.g., Kunst & Mackenbach, 1996; Ross & Mirowsky, 1999; Zajacova, 2006). In recent years, however, there has been a growing interest in finding out how the socio-economic

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resources of other people may affect a person’s health, net of his or her own resources. Such studies help us build up a more complete picture of the social inequalities in health. The present study brings together three strands of this type of research, one that deals with the importance of the spouse’s resources, one that deals with the importance of the parents’ resources, and one that deals with the importance of the resources in the community. The focus is on education, which is a readily available, often used, and theoretically meaningful indicator. While it has been reported repeatedly that the married have lower mortality than the non-married, probably because of both selection (e.g. Goldman, 1993; Lillard & Panis, 1996) and truly causal effects of marriage and marital status changes (e.g. Lillard & Waite, 1995; Smith & Zick, 1996; Stolzenberg, 2001; Umberson, 1992a, 1992b), there is less certainty about the effects of the characteristics of the partner. Some studies of men’s health and mortality have shown that wife’s education has a beneficial effect (Bosma, Appels, Sturmans, Grabauskas, & Gostautas, 1995; Egeland, Tverdal, Meyer, & Selmer, 2002; Jaffe, Eisenbach, Neumark, & Manor, 2006; Monden, van Lenthe, de Graaf, & Kraaykamp, 2003; Strogatz, Siscovick, Weiss, & Rennert, 1988), although opinions differ about its strength compared to that of the man’s own education (Jaffe et al., 2006; Monden, van Lenthe et al., 2003). Other studies, largely from earlier periods, have shown adverse effects of wife’s education, at least for certain groups of men (Carmelli, Swan, & Rosenman, 1985; Eaker, Haynes, & Feinleib, 1983; Haynes, Eaker, & Feinleib, 1983; Suarez & Barrett-Connor, 1984). Low self-esteem among these men and a less supportive home environment because of the wives’ work were among the suggested explanations. The few investigations of this type that have addressed women’s health and mortality have concluded that it is an advantage to have a husband with good education, though her own education may be more important (Monden, van Lenthe et al., 2003). The impact of childhood socio-economic position has received far more attention, although the control for own resources has not always been very good, and there has been some concern about the quality of the parental resource indicators. Most of these studies have focused on men’s health and shown beneficial effects of parental

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resources (Galobardes, Lynch, & Davey Smith, 2004; Hayward & Gorman, 2004), but such effects have been reported for women also (Beebe-Dimmer et al., 2004). However, there are also investigations pointing in the opposite direction: in a recent Norwegian analysis of relatively young people, adverse effects of parents’ education appeared (Strand & Kunst, 2007). Over the last 10–15 years, there has been a rapidly increasing interest in multilevel analysis of socioeconomic health inequalities (Pickett & Pearl, 2001; Robert, 1999), also in Nordic countries (Blomgren, Martikainen, Ma¨kela¨, & Valkonen, 2004; Chaix, Rosvall, & Merlo, 2007a, 2007b; Gerdtham & Johannesson 2004; Martkainen, Kauppinen, & Valkonen, 2004; Osler et al., 2003; Sundquist, Malmstro¨m, & Johansson, 2004). These studies have largely dealt with the importance of aggregate income, poverty concentration and unemployment, and there has been large variation in the results. Much less attention has been devoted to average education, which of course is closely related to economic resources (Geronimus & Bound, 1998), but also may pick up other factors. It may be just as relevant to include in a multilevel analysis as various income indicators. In fact, two studies that included measures of both aggregate income and aggregate education showed that the effects of the latter were the strongest (Kravdal, 2006; Wen, Browning, & Cagney, 2003). Because there is mixed evidence about these effects of other people’s education, or quite few earlier studies on which to base conclusions, a new investigation based on a large data set with precise information on education should be valuable. It would be even more valuable if all the education variables could be considered in the same analysis. That would make it easier to compare the effects, which might provide some clues about the underlying mechanisms. Earlier studies have either included only the spouse’s education, only the parents’ education, or only the average education in the community (in addition to the person’s own education, of course). The present analysis is based on data from Norway, which has a nation-wide population register, and where educational differentials in mortality have been reported to be no less pronounced than in other rich countries (Mackenbach, Kunst, Cavelaars, Groenhof, & Geurts, 1997). The population registration system allows identification of family members and includes

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highly reliable information about municipalities of residence, date of death, and education. More precisely, a discrete-time hazard regression model is estimated to describe how the average educational level in the municipality and the education of parents, sibling, spouse, former spouse, parents-inlaw, and sibling-in-law is related to men’s and women’s all-cause mortality at the relatively low ages of 30–53, net of their own education. The educational levels of former spouse, sibling and inlaws have never been included in mortality models before, but may well have an influence, although not for all the same reasons as the other education variables. Unfortunately, the data do not allow statistical identification of the causal pathways that the different education variables may operate through, but it is discussed how various mechanisms may contribute to the education-mortality relationships. The consideration of a broad set of education variables should help to enrich this discussion. Some reasons why others’ education may be important A person’s own education has been thought to affect his or her mortality through, for example, the specific health knowledge obtained at school, consciousness about being able to influence one’s health and life situation, the ability to process new information of relevance for health, and the higher incomes resulting from the skills taught in school and the credentials (e.g. Ross & Mirowsky, 1999). A strong economy may in turn facilitate the engagement in activities that make life pleasant or are more directly health-promoting, and in many societies it increases the chance of receiving highquality health services. Also the education of other people may operate through the person’s knowledge, attitudes and economic resources, but other mechanisms may be involved as well. The main pathways are reviewed below. Learning and imitation Let us start with the possible ‘spill-over’ effect of other people’s education through social interaction: health knowledge and attitudes, which are linked with education, may be transmitted directly to other people through observation and communication, and there may also be a more passive imitation of health behaviour driven by a desire to gain others’ approval (Helleringer & Kohler, 2005; Montgomery

& Casterline, 1996). In marital relationships in particular, there is typically an exchange of knowledge about prevention and treatment of illnesses, and the partners may adopt each other’s life style and attitudes, though the process is not easy to measure (Monden, de Graff, & Kraaykamp, 2003; Tambs & Moum, 1992). While it is especially spouses’ education that may operate through such a channel, one may indeed learn from and imitate parents also (Farkas, Distefan, Choi, Gilpin, & Pierce, 1999; Wickrama, Conger, Wallace, & Elder, 1999). Much of this influence from parents may come at a low age, but with long-term consequences. In addition, siblings (Needle et al., 1986) and other family members may send signals that are important, and one typically interacts with friends, colleagues, and neighbours, who in turn interact with others, so that the education in a wider community may have an impact.

Effects of others’ health problems One may also be affected by family members’ actual health, which is influenced by their education. This is not because of transmission of fatal infections, which is no longer a big issue in rich countries, but for more social reasons. In particular, those struggling with serious diseases may be a burden to their partners (Chaix, Navaie-Waliser, Viboud, Parizot, & Chauvin, 2006; Christiakis & Allison, 2006) and children, who may get very worried and stressed or find too little time for their own needs. As elaborated on below, also the health of people outside the nearest family may be of some importance.

Adding to the pool of resources Sharing of resources is yet another channel that others’ education may operate through. In particular, a better-educated spouse may contribute much to the family’s pool of economic resources (Smith & Zick, 1994), and many socially advantaged parents may provide economic assistance even when the child is an adult (Cooney & Uhlenberg, 1992). In addition, there may be direct economic benefits from living in a community with a high average level of education. This is further dealt with below.

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Childhood health The socio-economic resources in the family of origin, which in principle may affect the person’s mortality through childhood health, are determined or signalled especially by the parents’ education. The factors discussed in the literature are that people who have parents with little resources on average may have been weaker already at the time of birth because of the mother’s health and life style during pregnancy; a larger proportion of them may have got inadequate nutrition through childhood; they may more often have been exposed to secondhand smoke or other environmental hazards; or (unless outweighed by a beneficial impact of lesseducated mothers’ lower rate of employment (Coreil, Wilson, Wood, & Liller, 1998)) insufficient measures to prevent illness may have been taken. These and other factors may have led to childhood infections and other diseases, which may be linked with certain potentially fatal diseases at adult age (Blackwell, Hayward, & Crimmins, 2001; Cohen, Doyle, Turner, Alper, & Skoner, 2004; Galobardes et al., 2004). On the other hand, there is a possibility of an opposite effect (in societies with high child mortality): survivors of poor conditions at low age may be selected for good health or have acquired immunity (Preston, Hill, & Drevenstedt, 1998). Quality of health services and other structural factors The education of people in the community is potentially important because it may influence society in ways that affect everyone, in addition to operating through the social interaction mechanism just mentioned. For example, most hospitals and health centres in Norway are public, with financing from national sources, and are subjected to national quality regulations, but when many local people are well educated, it is perhaps easier to recruit qualified health personnel. Besides, education is a major determinant of income. A strong public economy resulting from a high average education may allow more generosity with respect to social support (which is the municipalities’ responsibility), and high individual incomes may trigger the establishing of some smaller private health services. Further, when other people have better health because of better education, and thus present less competing demand for health services, the individual under consideration may receive better help. Another possibility is that a higher level of education may

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increase the chance that the individual has a wellpaid job in the advanced service sector, which may offer some health advantages. Such effects may not be immediate. For example, a person’s health is influenced by the quality of both current and earlier health services in the community, which in turn reflect socio-economic resources even further back in time. The focus in this paper, however, is on the current level of education in the municipality where the person currently lives. Harmful effects of low relative education All the mechanisms suggested so far would contribute to a low mortality among those who have well-educated family members or live in a community with many well-educated people, but harmful effects are also possible. For example, given one’s own education, those who are surrounded by well-educated people have a lower relative education than others. We do not know much about the effect of that, but it has been argued that a low income compared to others in the community may produce a psychosocial stress that increases mortality, though the evidence is not very solid (Elstad, Dahl, & Hofoss, 2006; Kawachi, Subramanian, & Almeida-Filho, 2002; Lynch et al., 2004; Wagstaff & van Doorslaer, 2000; Wilkinson, 1997). Perhaps a low education compared to others has a similar impact. A low relative education is at least linked with a low relative income. Whether there is an effect beyond that depends partly on how visible the education of other people in the community is. Besides, people make comparisons with single individuals (Buckingham & Alicke, 2002), and it has been suggested that a ‘status discrepancy’ vis-a`-vis a spouse, whether related to income or other status indicators, may create harmful stress (Vernon & Buffler, 1988). In line with this idea, it was pointed out in some of the studies reporting adverse effects of wife’s education that men with well-educated wives may have lower self-esteem than other men at the same educational level. Others have suggested that it may be problematic to have a partner with lower education (Pearlin, 1975). In countries where women generally have almost as high education as men, if not higher, gender-neutral versions of such hypotheses would seem most plausible, but one may well question the relevance of any hypothesis of this nature. After all, one has chosen to marry and remain married, which signals some acceptance of a discrepancy, and if such issues were important, one

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might expect to see sharper links between heterogamy and divorce (Lyngstad, 2004). The same types of arguments may be applied with respect to parents’ and other family members’ education. Under-achievement compared to parents may perhaps be perceived as particularly problematic, given parents’ special position as role models and educators (while there possibly may be a corresponding pleasure to be derived from overachievement). A somewhat different hypothesis, involving the absolute level of parents’ education rather than the relative, might be that well-educated parents tend to expect much from their children, which may have harmful consequences regardless of the children’s own education. It is indeed documented that parental expectations can be an important source of stress (Wang & Heppner, 2002). However, it is by no means obvious that well-educated parents are particularly critical and demanding of their adult children (Umberson, 1992b).

to perhaps being influenced by it) and also affect individual mortality. In this study, only a control for the population size of the municipality is included (see below). Similarly, spouse’s, sibling’s or parents’ intellectual resources and interests have been key factors behind their education, and may have an impact on the health of the person in focus. A related problem is that own education is determined by unobserved factors that are associated with parents’ education. For example, among those with little education, having well-educated parents, which typically is linked with high education for the child, may be a signal that the person has had health or behavioural problems in adolescence, which could have implications for mortality at adult ages. Conversely, among those with high education, coming from poorly educated families may be an indication of a strong personal drive. For similar reasons, such characteristics may also be picked up by sibling’s education, but to a lesser extent.

Potential confounders Relevance for both young and older adults Unfortunately, an association between individual mortality and other people’s education may not only reflect causal mechanisms such as those discussed above. One trivial reason is that people are not assigned randomly to places of residence and spouses. Marriage and (lack of) migration reflect choices that are influenced by the person’s own characteristics. For example, given own education, having a well-educated spouse may be a result of own resources beyond education and even a good health, assuming that a high education is highly valued by potential partners, so that the spouse has been in a position to ‘pick and choose’ (Becker, 1991). In principle, it is also possible that there is a special group of people with certain characteristics linked to good health that have migrated to or remained in a municipality with high average education. Another issue is that we do not know whether it is other people’s education itself that is potentially important, through for example the skills or attitudes developed at school or the credentials earned, or whether the associations are partly or fully a result of certain characteristics of these people (or places) affecting both their education and the individual’s mortality. For example, there are a number of economic, cultural, political and environmental characteristics of municipalities that may contribute to a high average education (in addition

These mechanisms should be of importance for mortality also at the quite low ages considered here, although perhaps not as much as at the higher ages, when various lifestyle factors have had more time to make themselves felt. A relatively large proportion of the deaths at age 30–53 are a result of suicides (9% at age 35–54 in 2004, as opposed to 1% for the total population, according to Statistics Norway, 2007a) and other violent deaths (16%, as opposed to 5%). Some of these deaths may be traced back to psychosocial stress or lifestyle factors such as alcohol and drug abuse (Brismar & Bergman, 1998; Giner et al., 2007; Leistikow, Martin, Jacobs, Rocke, & Noderer, 2000). Also cardiovascular diseases are a quite common cause of death among these relatively young adults (16% at age 35–54), though not as common as in the total population (38%). Cancer is responsible for the largest number of deaths (34% at age 35–54, compared to 25% in the total population). Lung, breast, colorectal, lymphatic/haemapoietic and pancreatic cancers are the most common types (in descending order). For some of these malignancies and the cardiovascular diseases, smoking, high fat intake, little physical activity and various other lifestyle factors may be involved in the aetiology. All these risk factors may in turn be affected by other people’s education through some of the channels discussed above. In

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addition, the chance of dying at young adult age depends on whether people seek and get good professional help for their diseases or lifestyle problems, which may also be linked to others’ education. However, the mechanism involving childhood health may be of particularly little relevance for the youngest cohorts, who grew up when children’s health was generally good even among the least advantaged.

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dance with that, Smith and Waitzman (1994) showed more harmful effect of own poverty among the non-married than among the married. On the other hand, the spouse may also buffer any discomfort stemming from high education among other people. In this analysis, the possibility of marital status differentials is taken into account, but they do not receive much attention.

Interactions with own education Material and methods Some of the effects that are reviewed above may depend on the person’s own educational level. For example, it would seem likely that men and women with little education are those who benefit most from economic contributions from others and from the various types of community support. To the extent that the argument about low relative education is relevant, one would expect to see the most adverse effect of others’ education among the least educated. However, if there is a similar advantage to be drawn from having a superior position, an adverse effect (beneficial effect of low education among others) would also show up among the better educated. To shed light on these issues, models are estimated separately for four different levels of own education in this analysis. Variations across sex and marital status The effects of others’ education may depend on the person’s sex as well. For example, some authors have suggested that boys and girls respond differently to signals from parents (Flay et al., 1994), and others have pointed to differences in the concern about relative economic deprivation (Yngwe, Fritzell, Lundberg, Dideriksen, & Burstro¨m, 2003). Besides, there are typically differences between the wife’s and husband’s economic contributions, and perhaps the strength of their voices when it comes to health issues in the family, and there are definitely sex differences in the causes of death. It is impossible to predict what all this should add up to. Moreover, it is possible that education effects vary across marital status. Unfortunately, few studies have addressed this and theoretical predictions can point in either direction. On the one hand, benefits from own, parents’ or other people’s resources may be less important for a person who is married and may enjoy some of these advantages anyway through the spouse’s support. In accor-

Data The data cover the period up through 2003, and the main source is the Norwegian population register, which includes everyone living in Norway after 1960. In addition, information has been extracted from the 1970 census and Statistics Norway’s education files. For each person, there is a unique anonymous identification code, as well as information on marital status 1 January each year from 1974 and spouse’s identity, highest educational level achieved as of 1 October every year from 1980 and as of 1970, municipality of residence every month since 1964, dates of any in- and outmigration, and date of death, if any. Parents are identified for almost everyone born in Norway after 1965, while, for those born earlier, they are identified largely on the basis of co-residence in the 1970 census. These parent–child links were used to find siblings. The education of various family members was added through a series of record linkages. Unfortunately, we do not know the composition of the person’s household. Most importantly, a large proportion of the non-married live in consensual unions and their partners are not identified. For example, figures from 1993–1995, which is in the middle of the period under analysis, show that about 40% of the non-married at age 30–54 were cohabiting (Statistics Norway, 2007b). The analysis is restricted to ages above 30, i.e. those born 1973 or earlier, because of the very low mortality at lower ages. The 1950 cohort is chosen as the oldest, because of the limitations on identification of parents. Among men, there are 15,764 deaths during the 9.8 million person-years of follow-up (from 1980 to 2003), while the corresponding numbers among women are 7928 deaths and 9.3 million person-years.

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Estimation of discrete-time hazard models For each person, a series of one-year observations is created, beginning in January the year the person turns 30, and ending in 2003 or the time of death or last emigration, whatever comes first. (About 2% are censored because of emigration.) Experimentation showed that one year was a sufficiently short observation length. These oneyear observations include various independent variables referring to the situation at the start of the year, and the outcome variable is whether the person died within the year. All observations for all persons are pooled together, and a logistic model is estimated from the resulting sample, using the SAS software. Mathematically, the model is logðpijt =ð1  pijt ÞÞ ¼ b1 X ijt þ b2 Z jt , where pijt is the probability that person i in municipality j dies within the year t. Xijt is a vector of characteristics of person i in municipality j at the start of t, and Zjt is a vector of characteristics of the municipality. b1 and b2 are the corresponding effect vectors. One-year observations starting at a time when the person under consideration is temporarily abroad or before first immigration are ignored. Ideally, one should include a random term at the municipality level to account for unobserved characteristics at that level. Without such a term, the standard error of the municipality-level effects will be biased downwards, so that one gets an exaggerated impression of their importance (Goldstein, 2003). Unfortunately, it is difficult to handle multilevel models with so many observations and variables as in this study with the commercially available software. For example, one runs into space constraints with MLwiN and numerical underflow with aML. When a model was estimated in NLMIXED/SAS for the sub-group of bettereducated men (see motivation for that limitation below), convergence was still not attained after 200 h with a well-equipped PC and reasonable starting values. However, a very simple model including only age, period, individual education, average education and a dummy for large city could be estimated. In this model, the standard error of the effect of average education was 19% larger than it was without inclusion of the municipality-level error term.

Variables The education variables refer to the number of years of schooling normally needed for the educational level recorded as the highest attained as of the year in focus, the last year alive in the country before that (if that year was in the 1980s), or 1970 (if last year alive in the country was 1970–1979). Five educational levels are defined, according to Statistics Norway’s standard: (i) compulsory (10 years), (ii) lower-secondary (11–12 years), (iii) higher-secondary (13 years), (iv) some college (14–17), and (v) higher education (18 years or more). Both categorical and continuous education variables are used in the models. When constructing the latter, years of education in the five categories are set to 10, 11, 13, 15, and 18, respectively. (This choice is not critical. For example, using 16 and 19 instead of 15 and 18 gave almost the same results.) There is missing information on education both in 1970 and the 1980s for about 2% of the relevant persons. A dummy (0/1 variable) for ‘own education unknown’ is therefore included. In addition, two dummies are included for each of the relatives. One signals whether the relative is unidentified (identification number not found, perhaps because the person does not exist) or not relevant (as for a spouse when the person is not married). The other signals unknown education. In either case, the education is set to 10, but any number would do. Marital-status indicators are also included, but are not important for the results. Dummies indicating whether the relative was dead or emigrated, and whether 1970 information was used, were added in preliminary models, without affecting other estimates. Some authors have shown that such a missingindicator approach may give biased estimates even when the missing values appear ‘completely at random’, and argued that it would be better to leave out all observations with at least one missing variable (Greenland & Finkle, 1995; Jones, 1996). As part of the present analysis, it was experimented with the exclusions of persons with missing values for the education of family members that were identified (existing). That gave almost the same results. Norway includes 433 municipalities, which are the lowest political- administrative units in the country and the lowest aggregate level defined in the data. The size of the municipalities differs greatly. Oslo, the capital, has about half a million inhabitants, and there are four other large urban

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municipalities with a population of 100 000–250 000. Among the other municipalities, the average population size is about 7000, with a variation from 200 to 75 000. Two municipality-level variables are considered: average education among women and men aged 30–69 in the municipality in which the person lived at the beginning of the one-year observation, and the population size of that municipality (grouped into 10 categories), which in the Norwegian context should also reflect density quite well. The latter variable is included because high average education may be partly a result of an urban environment (see earlier discussion about confounders), which may also affect health through different mechanisms (Chaix et al., 2006). Other potentials confounders were not available in the data. Taking averages over 30–49 or 30–89 instead gave similar results, and controlling for differences in age composition did not matter either. Leaving out the person in focus when calculating the average has no impact on the results, of course. Age and year are additional continuous control variables. A fine categorization of both variables was tried and gave the same results. One might perhaps hesitate to include so many potentially correlated education variables in one model. However, the correlations are actually not very strong (between 0.2 and 0.5; correlation table can be obtained from the author), and because the data set is also very large, identification of separate effects does not seem to be a problem. Several model specifications were tried and the effect of one education variable did of course change when others were added, but very modestly—except when own education was added—and the standard errors did not increase much.

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Table 1 Effects of various education variables on mortality at age 30–53 among Norwegian women and men born 1950–1973 (odds ratios with 95% confidence intervals)a Men Own education. Effects for the married 0 10 yearsb 11–12 years 0.794**** (0.733–0.860) 13 years 0.652**** (0.597–0.712) 14–17 years 0.568**** (0.515–0.626) 18+ years 0.456**** (0.392–0.530)

Women

0 0.764**** (0.701–0.832) 0.590**** (0.523–0.667) 0.566**** (0.505–0.634) 0.572**** (0.454–0.720)

Own education. Additional effect for the never marriedc 10 yearsb 0 0 11–12 years 0.933 0.750**** (0.848–1.025) (0.657–0.856) 13 years 0.579**** 0.651**** (0.519–0.647) (0.543–0.781) 14–17 years 0.505**** 0.503**** (0.446–0.572) (0.426–0.593) 18+ years 0.433**** 0.237**** (0.352–0.533) (0.159–0.354) Own education. Additional effect for the previously marriedc 10 yearsb 0 0 11–12 years 1.019 0.911 (0.909–1.142) (0.798–1.040) 13 years 0.724**** 0.753**** (0.630–0.833) (0.614–0.922) 14–17 years 0.722**** 0.685**** (0.615–0.847) (0.569–0.825) 18+ years 0.649**** 0.502**** (0.484–0.872) (0.310–0.813) Education of mother 10 yearsb 11–12 years 13 years

Results

14–17 years

Estimates from a model with categorical variables for own and family members’ education are shown in Table 1, while those from a corresponding model with continuous education variables are shown in Table 2. Because the latter model seems to reveal the main patterns well enough, continuous education variables are used in the remaining analysis for simplicity. An interaction between own education and marital status is included. Preliminary model experimentation showed that it was strongly significant, and that the effect of spouse’s education, which is only relevant for the married, would have

18+ years Education of father 10 yearsb 11–12 years 13 years 14–17 years 18+ years

0 0.998 (0.959–1.039) 0.958 (0.871–1.054) 0.993 (0.910–1.083) 0.964 (0.727–1.278)

0 1.042 (0.984–1.104) 1.155** (1.015–1.314) 1.311**** (1.170–1.470) 1.224 (0.843–1.778)

0 1.068*** (1.023–1.114) 1.107**** (1.044–1.174) 1.121*** (1.041–1.208) 1.197*** (1.071–1.339)

0 1.010 (0.949–1.075) 1.165**** (1.073–1.265) 1.207**** (1.092–1.335) 1.218*** (1.052–1.411)

Education of oldest sibling 10 yearsb 0

0

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628 Table 1 (continued )

Table 1 (continued ) Men

Women

0.935*** (0.891–0.981) 0.902**** (0.852–0.955) 0.889**** (0.838–0.943) 0.804**** (0.725–0.891)

0.924** (0.862–0.992) 0.873*** (0.803–0.949) 0.891*** (0.818–0.970) 0.798*** (0.693–0.918)

0.960 (0.910–1.012)

0.959 (0.891–1.032)

0 0.893**** (0.822–0.971) 0.786**** (0.708–0.874) 0.730**** (0.656–0.813) 0.618**** (0.490–0.779)

0 0.915* (0.830–1.009) 0.864*** (0.777–0.961) 0.825*** (0.734–0.927) 0.683**** (0.577–0.809)

Education of former spouse 10 yearsb 0 11–12 years 0.864*** (0.794–0.940) 13 years 0.749**** (0.667–0.841) 14–17 years 0.750**** (0.670–0.839) 18+ years 0.726** (0.542–0–974)

0 0.942 (0.844–1.053) 0.892* (0.786–1.013) 0.788*** (0.681–0.912) 0.706*** (0.552–0.903)

Education of mother-in-law 0 10 yearsb 11–12 years 0.935* (0.869–1.004) 13 years 0.901 (0.756–1.074) 14–17 years 0.943 (0.807–1.103) 18+ years 0.931 (0.550–1.576)

0 0.964 (0.884–1.050) 1.102 (0.905–1.343) 1.031 (0.855–1.244) 0.840 (0.428–1.646)

Education of father-in-law 10 yearsb 0 11–12 years 0.923** (0.854–0.997) 13 years 0.939 (0.845–1.043) 14–17 years 0.932 (0.816–1.064) 18+ years 0.965 (0.788–1.180)

0 0.940 (0.858–1.030) 0.921 (0.808–1.051) 0.943 (0.803–1.107) 0.778** (0.612–0.989)

Education of oldest sibling-in-law 10 yearsb 0 11–12 years 0.985 (0.872–1.051)

0 0.983 (0.882–1.097)

11–12 years 13 years 14–17 years 18+ years Average education in municipality Education of spouse 10 yearsb 11–12 years 13 years 14–17 years 18+ years

13 years 14–17 years 18+ years

Men

Women

0.954 (0.857–1.062) 0.893** (0.798–0.999) 0.929 (0.778–1.111)

0.940 (0.824–1.071) 0.945 (0.829–1.077) 1.114 (0.919–1.349)

*po0.10; **po0.05; ***po0.01; ****po0.001 two-tailed test. a In addition to including all these education variables, the models include age (continuous), period (continuous), size of the population (in 10 categories) and dummies for never-married, widowed, divorced and separated. Besides, they include indicators for each family member of whether he or she is identified or relevant and whether an educational code is found. The confidence intervals for the municipality-level effects are somewhat too small because it was impossible to add a municipalitylevel random term to the intercept. See text for further details. b Reference category. c To be multiplied with the effect for the married: to get the effects for this group.

been much weaker if this interaction had not been included. This is because having no such interaction in the model means that one assumes the same effect of own education for all marital status groups, and does not allow the effect for the married to be weaker. (For example, while the effect of own education for married men is 0.907, it is 0.9070.863 ¼ 0.783 among the never-married. The corresponding interaction effect for the previously married is 0.918. A very similar interaction pattern appeared if only the education variables that are relevant for all marital status groups were included, and not the education of spouse and inlaws: 0.894 for the never-married and 0.935 for the previously married, rather than 0.863 and 0.918. This was the case for women also). There are also significant, but weaker, interactions between marital status and some other education variables. These are described below. Both mother’s and father’s education are positively associated with women’s mortality, while only father’s education has such an effect on men’s mortality. (Throughout this presentation of results, the dividing line between ‘effect’ and ‘no effect’ is set at a significance level of 5%.) Moreover, the education of the oldest sibling, the spouse and the former spouse reduces mortality for both sexes, though less than own education. The same results appeared when the youngest sibling was considered

ARTICLE IN PRESS Ø. Kravdal / Social Science & Medicine 66 (2008) 620–636 Table 2 Effects of various education variables on mortality at age 30–53 among Norwegian women and men born 1950–73 (odds ratios with 95% confidence intervals)a Men

Women

0.907**** (0.893–0.920)

0.914**** (0.896–0.932)

Additional effect for the never married

0.863**** (0.847–0.880)

0.871**** (0.847–0.897)

Additional effect for the previously married Education of mother

0.918**** (0.895–0.942) 0.999 (0.984–1.014) 1.025**** (1.015-1.036) 0.978**** (0.969–0.987) 0.956* (0.907–1.008) 0.943**** (0.927–0.960) 0.953**** (0.935–0.972) 0.986 (0.959–1.014) 0.992 (0.973–1.011) 0.983** (0.967–1.000)

0.912**** (0.882–0.942) 1.051**** (1.030–1.073) 1.037**** (1.022–1.052) 0.975**** (0.963–0.988) 0.947 (0.880–1.019) 0.961**** (0.945–0.978) 0.958**** (0.937–0.979) 1.007 (0.974–1.041) 0.980* (0.957–1.003) 0.999 (0.980–1.018)

Own education For the married

Education of father Education of oldest sibling Average education in municipality Education of spouse Education of former spouse Education of mother-in-law Education of father-in-law Education of oldest siblingin-law

*po0.10; **po0.05; ***po0.01; ****po0.001 two-tailed test. a The model are the same as those shown in Table 1, except that the education variables are continuous. The confidence intervals for the municipality-level effects are somewhat too small because it was impossible to add a municipality-level random term to the intercept. See text for further details.

instead of the oldest. With respect to the average education in the municipality, a beneficial effect is only rather weakly indicated (p ¼ 0.09 for men and p ¼ 0.15 for women in these model where standard errors are biased downwards and we therefore get an exaggerated impression of significance). The education of the in-laws appears to be of little importance. There is some evidence for a negative relationship between the education of the oldestsibling-in-law and men’s mortality, but no clear trend appears in the coefficient across educational levels. For men, there is also a significantly reduced mortality among those who have a father-in-law with some secondary education compared to those whose father-in-law has only compulsory education,

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while for women, a significantly reduced mortality is seen for those with a father-in-law who has a higher education. Inclusion of interactions with marital status or stratified estimation reveals that there are some variations in the education effects across marital status. These variations are not central to the analysis, but let us briefly note the main patterns: (i) the effect of father’s education is somewhat sharper among the never-married than among the other groups, (ii) the effect of average education on men’s mortality is sharpest among the married (significant only in a model estimated for married men exclusively; 0.900 with CI 0.820–0.988), (iii) the effect of former spouse’s education is strongly significant according to a model estimated separately for the previously married (about 0.95 for both sexes, with CI 0.93–0.98), while it is insignificant when the model is restricted to the currently married (about 0.97, with CI 0.92–1.02). As explained above, it is possible that the effects of others’ education vary with the person’s own education, for example because a feeling of inferiority has a harmful influence while there is no advantage to be drawn from superiority. Model experimentation revealed that some cross-level interactions were significant, which supports the idea of such variations. To learn more about them, models are estimated separately for the three lowest educational levels and the two highest combined. The clearest variation is seen for the effect of average education on men’s mortality: the point estimate declines from higher than 1 to below 1 with increasing level of own education, and a significant negative relationship appears only in the highest educational category (Table 3). In these models without a municipality-level random term, one gets an exaggerated impression of significance, but the effect would have remained significance even with a 37% increase of the standard error, which is more than the 19% seen in experiments with a corresponding multilevel model with fewer variables (see above). There are indications of an opposite pattern for women, but the interaction between own education and average education is not significant. In addition, there are indications of a sharper effect of spouse’s education at low than at high levels of own education, primarily for men. A particularly interesting result is that the effects of mother’s and father’s education do not vary systematically with own education.

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Table 3 Effects of various education variables on mortality at age 30–53 among Norwegian women and men born 1950–73 (odds ratios with 95% confidence intervals), by level of own educationa 10 years of education

11–12 years of education

13 years of education

14+ years of education

Men Education of mother Education of father Education of oldest sibling Average education in municipality Education of spouse Education of former spouse Education of mother-in-law Education of father-in-law Education of oldest sibling-in-law

0.972 (0.931–1.015) 1.024* (0.997–1.053) 0.996 (0.976–1.017) 1.052 (0.944–1.172) 0.950** (0.904–0.997) 0.941*** (0.900–0.983) 0.944 (0.866–1.029) 1.014 (0.961–1.070) 0.968 (0.928–1.010)

1.019 (0.992–1.047) 1.041**** (1.021–1.061) 0.961**** (0.945–0.976) 0.995 (0.912–1.085) 0.924**** (0.892–0.957) 0.917**** (0.887–0.949) 1.001 (0.946–1.059) 0.988 (0.951–1.026) 0.991 (0.961–1.022)

1.007 (0.974–1.042) 1.007 (0.983–1.031) 0.999 (0.979–1.020) 0.920 (0.809–1.045) 0.966* (0.931–1.002) 0.974 (0.930–1.019) 0.977 (0.918–1.039) 1.001 (0.961–1.042) 0.955** (0.920–0.990)

0.993 (0.967–1.021) 1.022** (1.003–1.042) 0.977** (0.958–0.996) 0.839*** (0.739–0.953) 0.952*** (0.923–0.982) 0.989 (0.950–1.030) 1.002 (0.958–1.047) 0.985 (0.953–1.017) 1.014 (0.983–1.046)

Women Education of mother Education of father Education of oldest sibling Average education in municipality Education of spouse Education of former spouse Education of mother-in-law Education of father-in-law Education of oldest sibling-in-law

1.068** (1.000–1.014) 1.023 (0.980–1.067) 1.007 (0.975–1.040) 0.828** (0.702–0.978) 0.959* (0.916–1.003) 0.910**** (0.863–0.960) 1.069 (0.969–1.179) 0.982 (0.917–1.052) 0.970 (0.922–1.021)

1.064**** (1.025–1.104) 1.044**** (1.018–1.070) 0.970*** (0.950–0.991) 1.013 (0.903–1.136) 0.947**** (0.921–0.975) 0.980 (0.947–1.015) 1.030 (0.972–1.092) 0.984 (0.946–1.025) 1.004 (0.974–1.034)

1.061** (1.007–1.119) 1.031 (0.993–1.071) 0.994 (0.959–1.030) 0.996 (0.803–1.235) 1.004 (0.958–1.053) 0.948 (0.882–1.018) 1.014 (0.929–1.107) 1.003 (0.945–1.064) 0.981** (0.930–1.034)

1.032* (0.999–1.066) 1.037*** (1.012–1.062) 0.958**** (0.934–0.982) 0.913 (0.779–1.070) 0.959*** (0.930–0.988) 0.962* (0.921–1.005) 0.980 (0.929–1.034) 0.969 (0.933–1.007) 1.021 (0.985–1.058)

*po0.10; **po0.05; ***po0.01; ****po0.001 two-tailed test. a The models are the same as those shown in Table 2, except that own education is left out for those with 10–13 years of education. The number of deaths in the four education categories for men are 3960, 5724, 2729 and 2551, while the corresponding numbers for women are 1870, 3194, 900, 1536. The confidence intervals for the municipality-level effects are somewhat too small because it was impossible to add a municipality-level random term to the intercept. See text for further details.

Discussion

resources behind their schooling) that are transmitted to the person under consideration. If there really is a stressful effect operating through low relative education (or beneficial effect of high relative education), it is at least more than outweighed by effects contributing in the opposite direction, given the negative estimates for betterand less-educated alike. The very modest evidence for an effect of the education of the sibling-in-law is hardly surprising, as that person is more distant. Effects of siblings’ and sibling-in-law’s education might depend on age differences and distances between places of residence, but such variations are not explored in this analysis.

The education of the oldest sibling and sibling-in-law

Average education in the community

Earlier mortality studies have not checked the importance of siblings’ education. The effects that appear here are consistent with the ideas about learning and imitation: better-educated siblings may have health knowledge and behaviour (whether a result of their schooling itself or the individual

There are several reasons why one should expect a beneficial effect of average education, which in this study reflects the importance of the education of other people than the spouse (and other close family members perhaps living in the municipality), whose education is controlled for. Just as one apparently

Because the effect of average education on men’s mortality was found to differ between the married and the non-married when men in all educational categories were considered together, the models shown in Table 3 were also estimated for the married and non-married separately. The estimates generally pointed towards more beneficial effects of average education for the married than the nonmarried, and the difference was most pronounced for those with high education (0.791 CI 0.659–0.949 among the married, as opposed to 0.885 CI 0.740–1.057 among the non-married).

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learns from and imitates siblings, one may benefit from social interaction with friends, colleagues or neighbours in the municipality (while this causal channel would have appeared less likely if there were no effect of sibling’s education). In addition, the average education may, for example, affect the quality of health services and some other structural factors. However, a beneficial effect is seen only among men who themselves have high education or are married. This interaction with own education would be consistent with an idea that those with little education may feel stressed by being surrounded by people with higher education, who probably also have higher incomes (which may be a more visible characteristic), while there is no corresponding advantage of high relative education among the better-educated. Such a stress component among the less educated may outbalance the beneficial effect stemming from other factors. It is more difficult to understand why a beneficial effect should be restricted to men, and especially married men. Earlier studies provide no clues. Marital status differentials in the effect of community socioeconomic resources have never been addressed before, and no clear conclusions can be drawn from the few investigations that have considered sex differentials in such effects (Kavanagh, Bentley, Turrell, Bromm, & Subramanian, 2006). Anyway, it should be kept in mind that selective migration and common community factors behind average education and mortality in principle may have biased the estimates in any direction. Thus, one should be careful to give the estimates a causal interpretation and conclude that it, for example, would help for a better-educated man to move to a municipality with many well-educated people.

Education of spouse The education of the spouse or, for the previously married, the former spouse is negatively associated with mortality according to the Norwegian data. This is in line with other recent studies. In contrast to what has been reported by some authors, however, spouse’s education has less importance than own education, both for women and men. For example, one year of additional education for a spouse is associated with a 6% reduction of a man’s mortality, while the corresponding effect of his own education is 9%. Sibling’s education has weaker impact, however (2%).

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As pointed out above, there are several plausible reasons why partner’s education may contribute positively to a person’s health, even after a marital disruption. One might argue that a high education of a spouse also could be problematic because the person under consideration might feel inferior (perhaps depending on whether the situation was foreseen when the couple decided to marry), but this effect seems at least not to be strong enough to outweigh all the advantages of having a welleducated spouse. Once again, however, selection may be involved as well: having a well-educated spouse may to some extent be a result of the person’s own resources, beyond his or her education, and it is of course possible that the characteristics that have stimulated a spouse to take high education may be responsible for much of the lower mortality. Thus, we cannot be really sure that any given person can add years to life by choosing a spouse with much rather than with little education, or that health could be improved by encouraging the spouse to return to school to finish a degree. Education of parents The adverse effects of mother’s or father’s education stand in contrast to the beneficial effects of spouse’s and sibling’s education and average education (for some groups). Such adverse education effects were estimated also in another Norwegian study (Strand & Kunst, 2007), based on almost the same young cohorts and the same type of data, but have not been reported from other countries. (That study did not include the education of other family members or people in the municipality, but if these variables were left out of the present analysis, very similar effects of parents’ education appeared. See Appendix Table A1 for results from a simple analysis.) Other investigations, which have typically also considered older age groups and often used other indicators of socio-economic status and poorer control for own position, have pointed in the opposite direction. A good control for own position is indeed important, as reported also by others. As an illustration of that, there were beneficial effects of parents’ education in the Norwegian data when own education was left out (see Appendix Table A1), because of the clear positive association between the own and parents’ education. In other words, the newborn child of a well-educated couple has a particularly good chance of a long life, but this is because it tends to get high

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education. Net of that, the parents’ education is adversely linked with mortality. Parents’ education should operate through the same mortality-depressing factors as spouse’s education, although not necessarily with exactly the same strength. It is also possible, though probably not very likely in these Norwegian cohorts, that parental resources improve childhood health, with possibly beneficial long-term consequences. Why then do we see these adverse effects? One might perhaps suspect that those who attain a low level of education compared to their parents suffer from a feeling of inferiority. However, the effects of parents’ education were not particularly sharp among the least educated, which suggests that there either is no such relative-education effect or that there is also a corresponding benefit of a high relative education among the better-educated. Another possibility, though without substantiation in the literature, may be that well-educated parents are more demanding and have high expectations that are perceived as burdening for the children regardless of their own achievements. Alternatively, the effect is partly or fully a result of selection: as explained earlier, parents’ education may be correlated with the person’s own endowments, behaviour and interests in adolescence. For example, among those who themselves have low education, the subgroup with better-educated parents may have had serious problems with implications both for their educational careers and their mortality as adults. Similarly, upward educational mobility may signal special resources. The sex differences in the effects of parents’ education, which are seen especially with respect to mother’s education, are hard to understand. In principle, if mother’s education tends to be a particularly strong determinant of a daughter’s education, rather than that of a son, and there is not a correspondingly pronounced link between father’s and son’s education, the observed differences might be due to selection. Documentation of such a pattern is lacking, though. A more adverse effect for women might also reflect that they are more influenced, for better or worse, by how they have achieved relative to their parents, but this would be inconsistent with some recent findings about sex-specific responses to economic deprivation (Yngwe et al., 2003). Moreover, there is some evidence that girls are more influenced by parents’ health behaviour than boys (Flay et al., 1994). If that also means that they feel more stressed by

parental demands, the prediction could go in either direction. Finally, the results might reflect that the harmful effects of parents’ education tend to be most dominant for the causes of death that are most relevant for women, such as cancer. Unfortunately, the literature provides a weak basis for further speculation about these possibilities. Education of parents-in-law With respect to parents-in-law’s education, two types of mechanisms might be relevant. First, the spouse’s health and health behaviour may be affected adversely through the same channels as just described, which in turn may have implications for the partner’s mortality. Second, one may be more directly affected by the parents-in-law, though probably less than by parents, through for example learning about health behaviour or comparison. It would seem reasonable that such effects are weak, which is also supported by the data, as few significant effects of parents-in-laws’ education appear. Conclusion In addition to confirming the sharply protective effect of one’s own education on mortality, this study based on a large, high-quality data material documents that the education of close family members also matters. Most importantly, mortality is lower among those who have or have had a welleducated spouse than among those with a less educated spouse, although one’s own education is more important. This has not always been found in other studies. There are also effects of the education of the oldest sibling and, less clearly, of the siblingin-law. The average education in the municipality is not generally associated with mortality, but a beneficial effect appears among men with college education. On the other hand, parents’ education affects mortality adversely, as seen also in another Norwegian study (Strand & Kunst, 2007). One implication of these findings is, for example, that the overall mortality of a couple with high education is even lower compared to that of a lesseducated couple than one might assume from estimated effects of own education, because both partners benefit for the other’s education. Such knowledge may be valuable both for health personnel directly involved in support and treatment, and for politicians and planners who are

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concerned about social inequalities in health and mortality and need as complete picture as possible about these inequalities. The patterns may be quite similar in other countries, as many of the mechanisms suggested here as potentially responsible for the links between others’ education and mortality seem broadly relevant. However, the strength of the various relationships may well differ. In particular, education may have less impact on people’s economic resources in a Nordic egalitarian welfare country with generally low income inequality (United Nations, 2006) than elsewhere, and economic resources may be less important for health. Moreover, differences in educational levels between communities may have a particularly modest effect in Norway because of national regulation of social and health services and intra-national economic transfers, and the childhood-health argument may be even less relevant for young Norwegians than for their contemporaries in other countries. Unfortunately, the study has four major limitations. One is that only spouses could be identified, and not cohabitants. Another is that the data only allowed relatively young adults to be considered, rather than the entire population. Other effects might have been seen in the older population. Thirdly, the municipality is the lowest possible level of aggregation. The educational level among people in a smaller community might have had another effect than the municipality-level average. Fourthly, and in common with most other studies of the education-mortality relationship, one cannot know whether the estimates reflect only causal effects. In this case, three main types of confounders seem plausible, as discussed above. One of them is linked in particular to parents’ education and may explain

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why the estimated effect of that education variable is different from those of all others. Moreover, given the possibility of confounding, a person should not necessarily expect that finding a well-educated partner or moving to a community with a high average education will be beneficial to his or her health, and the estimates should not be used to predict how a further expansion of education in Norway will affect the level of mortality. It is certainly possible that those who get a good education themselves may benefit from that, and that there are advantageous ‘spill-over’ effects on other as well, but there is uncertainty even about the first of these causal effects. An additional complicating factor in such a prediction would be that a higher education for some people would increase the chance of others taking more education.

Acknowledgement This study was largely carried out while the author was at the Centre for Advanced Studies at the Norwegian Academy of Science. The support from the centre, the additional support from the Norwegian Research Council, and the helpful comments from Emily Grundy and four anonymous reviewers are greatly appreciated.

Appendix A Effects of own and parents’ education on mortality at age 30–53 among Norwegian women and men are shown in Table A1.

Table A1 Effects of own and parents’ education on mortality at age 30–53 among Norwegian women and men born 1950–1973, according to different models (odds ratios without confidence intervals)a Men Own education Education of mother Education of father Women Own education Education of mother Education of father

0.807****

0.799**** 0.995 1.033****

0.805**** 1.015*

0.949****

0.932**** 0.970****

0.838**** 1.045**** 1.036****

0.845**** 1.067****

0.985**

0.983 0.991

0.912****

0.859**** 0.976**

0.799**** 1.032**** 0.844**** 1.048****

*po0.10; **po0.05; ***po0.01; ****po0.001 two-tailed test. a Persons for whom own, mother’s or father’s education are missing are excluded. The models only include age and period in addition to the education variables for which estimates are shown in the column of estimates.

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