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Longevity and Lifespan Variation by Educational Attainment in Spain: 1960–2015

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Demography

Abstract

For a long time, studies of socioeconomic gradients in health have limited their attention to between-group comparisons. Yet, ignoring the differences that might exist within groups and focusing on group-specific life expectancy levels and trends alone, one might arrive at overly simplistic conclusions. Using data from the Spanish Encuesta Sociodemográfica and recently released mortality files by the Spanish Statistical Office (INE), this is the first study to simultaneously document (1) the gradient in life expectancy by educational attainment groups, and (2) the inequality in age-at-death distributions within and across those groups for the period between 1960 and 2015 in Spain. Our findings suggest that life expectancy has been increasing for all education groups but particularly among the highly educated. We observe diverging trends in life expectancy, with the differences between the low- and highly educated becoming increasingly large, particularly among men. Concomitantly with increasing disparities across groups, length-of-life inequality has decreased for the population as a whole and for most education groups, and the contribution of the between-group component of inequality to overall inequality has been extremely small. Even if between-group inequality has increased over time, its contribution has been too small to have sizable effects on overall inequality. In addition, our results suggest that education expansion and declining within-group variability might have been the main drivers of overall lifespan inequality reductions. Nevertheless, the diverging trends in longevity and lifespan inequality across education groups represent an important phenomenon whose underlying causes and potential implications should be investigated in detail.

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Notes

  1. One exception is the study by Majer et al. (2011), which included Spain. The countries Van Raalte et al. (2012) analyzed are Sweden, Norway, Finland, Belgium, Switzerland, France, Slovenia, the Czech Republic, Poland, Estonia, and Lithuania. Italy is the only southern European country Murtin et al. (2017) analyzed.

  2. According to Luy (2012:611), adoption effects and wrong age reporting on parents by offspring are in any case “unlikely to bias orphanhood-based estimates in modern populations from developed countries. Moreover, the biases caused by multireporting and various kinds of selection are to some extent mutually offsetting and therefore considered to be small and rather unimportant, as demonstrated in detail by Palloni et al. (1984).”

  3. In the online appendix, Figs. A1 and A2 show the distribution of single-year birth years and age of alive and dead parents according to the ESD.

  4. As noted earlier, the 1960–1989 data come from children who reported their parental educational level (i.e., it excludes the childless population). A comparison of the ESD data with the 1991 census that included a question on parity shows that the proportion of women aged 35+ who at most finished primary school education was slightly higher for mothers than for childless women (see Table A3 in the online appendix). Other than that, the proportion of primary or less-, secondary, and tertiary educated ESD respondents’ parents aged 35+ were between the proportions for the same age according to the 1981 and 1991 censuses.

  5. Because of the small relative size of the population of highly educated individuals in the 1960s and 1970s, education-specific life expectancy levels might not always go in the expected direction when individuals with secondary education and those with college education are compared.

  6. Because of the relatively small size of the population of highly educated individuals in the 1960s and 1970s, we do not observe the expected relationships between some contiguous education categories.

  7. To be sure, it is the interaction between these factors that actually drove the changes in lifespan inequality: without the emergence of group differences in education mortality, education expansion alone would not suffice to explain any of the observed changes. The counterfactual analysis shown here is helpful to indicate which factors might have been more influential in explaining the observed changes.

  8. The findings reported in Murtin et al. (2017) are particularly difficult to compare with ours because (1) the age ranges are different; (2) the analyzed years do not coincide; (3) they used three education categories rather than four; and more importantly, (4) they did not disaggregate their inequality results by sex as we do.

  9. Even if the inequality indicators differ, it is still interesting to compare trends over time.

  10. Hendi (2017) argued that this was due to numerator-denominator bias because Sasson (2016b) used unlinked data sources (National Vital Statistics System and U.S. Census Bureau). However, in Sasson’s (2017) reply, he stated that this discrepancy was within sampling variation. With regard to the two data sources used in our study, we would like to highlight that potential numerator-denominator bias is not relevant. As described earlier, the ESD pertains to a retrospective survey in which children reported on the survival status and educational attainment of their parents. Regarding the more recent INE mortality data, each mortality registry is linked to a population database that obtained the educational attainment of all individuals.

  11. Attempts to model the potential effects of measurement error would need to specify how one would expect the measurement error to vary across education groups. Although one might a priori expect the less-educated to report the age at death and the educational attainment of their parents with lower accuracy than their highly educated counterparts, it is unclear what the direction of such error should be. Are the less-educated more likely to over- or underestimate the age at death of their parents? Might it be the case that the overestimation of some and the underestimation of others cancel each other? Because there does not seem to be a clear-cut answer to such questions, we consider that the benefits of performing a measurement error simulation exercise are not warranted. In this line, recent studies delving with related issues (i.e., estimating adult mortality indirectly based on data from close relatives) have concluded that it is unclear whether further attempts to account for the potential biases associated with indirect mortality estimation will result in more accurate estimates (Masquelier 2013).

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Acknowledgments

The research leading to these results has received funding from the European Research Council (ERC-2014-StG-637768, EQUALIZE project); the Spanish Ministry of Economy and Competitiveness Ramón y Cajal and Juan de la Cierva Research Grant Programs (RYC-2013-14196, RYC-2013-14851 and JdlC-I-2014-21178); and the Spanish Ministry of Economy, Industry and Competitiveness, National R&D&I Plan CRISFAM (CSO2015-64713-R). Amand Blanes acknowledges support from the CSO2016-77449-R R+D project.

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    Iñaki Permanyer, Jeroen Spijker, Amand Blanes & Elisenda Renteria

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Permanyer, I., Spijker, J., Blanes, A. et al. Longevity and Lifespan Variation by Educational Attainment in Spain: 1960–2015. Demography 55, 2045–2070 (2018). https://doi.org/10.1007/s13524-018-0718-z

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