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19.05.2021

Does educational expansion decrease suicide rates in European countries? The compositional effect in educational stratification of suicides

verfasst von: Tomáš Katrňák, Barbora Hubatková

Erschienen in: Quality & Quantity | Ausgabe 3/2022

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Abstract

The aim of the text is to analyse the relationship between educational expansion and suicide rates. To examine this relationship, we analyse data from 24 European countries from 1994 to 2014. First, we analyse data from an age-period-cohort (APC) perspective using the intrinsic estimator (IE) approach to identify all three effects separately. The results show that the changes in suicide rates are driven by the birth cohort effect rather than the period effect, with each successive cohort born after 1960 having a lower suicide rate than its predecessor. This finding implies a cohort replacement explanation in suicide trends. Second, we approach our data from a multilevel (hierarchical) perspective using a three-level negative binomial regression model (suicides, nested in years, nested in countries) and analyse the direct effect of educational expansion on suicides by age groups. The results show that the decline in suicide rates in European countries does not occur because of a change in suicidal behaviour, but because of a change in the educational composition of populations. Educational expansion increases the proportion of young people with higher education who have a lower propensity to commit suicide; this mechanism decreases the suicide rates in European countries between 1994 and 2014.

Vorheriger Artikel Change in occupational tasks and its implications: evidence from a task panel from 1973 to 2011 for Western Germany

Nächster Artikel When Methods Meet Motives: methodological pluralism in Social Work research

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The first educational expansion occurred in Western European countries in the 1970s, when the number of university students more than doubled (Trow 1973). In the socialist countries, the class and political affiliations of the parents, as well as the low capacity of universities, limited access to higher education before 1989. In these countries, university study was subject to central planning (similar to other aspects of society). After the fall of communism in 1989, most of the limits were ended and the number of university students in those countries began to rise.

The research so far has been overwhelmingly psychological, with some studies finding U-shaped relationships between education and suicide (cf. Shah and Bhandarkar 2009; Shah 2012). However, the psychological perspective tends to confuse education with intelligence, to see mental illness as the main cause of suicide, and to understand education as a factor affecting mental health (cf. summary in Shah and Bhandarkar 2009).

Stack summarized that ‘cultural emphases placed on being male increase lethal suicidality and include competitiveness, impulsiveness-decisiveness, and being “strong”’ (2000a: 146).

Based on our own calculations.

This may also apply if only some proportion of suicides are due to psychological issues. Women tend to be more motivated by psychological issues than men, and women are more likely to seek and accept help (Stack 2000a). The decline in suicide rates may therefore be driven mainly by a decline in female suicide.

There is a slight difference in the information reported by WHO and Eurostat databases. WHO defines ‘intentional self-harm’ as X60-X84; in Eurostat it is X60-X84_Y87. The case group Y87 is ‘Sequelae of intentional self-harm, assault and events of undetermined intent’. The data from 2004 from Italy are missing; we therefore used the data from 2003.

Age 25–64 is used in social stratification research to delimit the labour force population with completed education (cf. Erikson and Goldthorpe 1992; Breen 2004).

ISCED11 (International Standard Classification of Education), a UNESCO tool used to compare education levels across countries and over time. 5–8 levels are 5) short-cycle tertiary education; 6) bachelor’s tertiary education; 7) master’s tertiary education, and 8) doctoral tertiary education.

For the 24 analysed countries, the correlation between the proportion of people with tertiary education in 1994 and the increase of people with tertiary education between 1994 and 2014 is -0.55. The regression equation is then y(proportion of increase) = 23.87–0.38*(initial proportion).

Given that our data are delimited by ages 25–64, some cohorts are omitted from some years. For example, cohort 1 (born 1930–1934) can only be found in 1994, whereas cohort 8 (born 1965–1969) is presented across all years. All cohorts are never presented together in all of the data. The result is a triangular dataset of birth cohorts.

For an overview cf. Kupper et al. (1985) or Robertson et al. (1999).

For more on algebraic, geometric, and verbal descriptions of parameter estimates cf. Yang et al. (2008).

The APC model identified by the IE method was estimated in STATA 16 (adofile apc_ie). We can provide both data and do-files on request.

Statistical models for count data with exposure population are generally called proportional intensity models and estimates of parameters are then incidence rate ratios (IRR) (Hilbe 2014).

The unconditional suicide mean is lower than its variance (mean = 82.45, SD = 127.07). The overdispersion test (module ‘overdisp’ in Stata) in which H0 is equidispersion suggests to reject H0 (coef. = 0.055; SE = 0.002; t = 27.48; p < 0.001).

The NBR model has two variants: NB1 and NB2. These models differ by the specification of variance, even though both are based on the Poisson-gamma mixture distribution. Because NB2 model is often used in applied research (Long and Freese 2006; Hilbe 2014), we use NB2 model.

All models are estimated in STATA 16 (adofile menbreg, option dispersion (mean)).

We also tried modelling the effect of age as both continuous and quadratic (age and age + age²), but the fit of both these models the data was significantly worse than the model with age as categorical. The LR test for age continuous vs. age categorical is 291.20 (df = 6; p < 0.001); and the statistic of the LR test for age quadratic vs. age categorical is 41.57 (df = 5; p < 0.001).

Within these multilevel models, statistical significance is seen as model based. In this case, a statistical model can be understood as a ‘data-generating mechanism’ and the randomness of the parameters comes from a distribution of responses and not from sampling units of finite population (cf. Rabe-Hesketh and Skrondal 2012).

LR statistics for all 7 tests was between 0.14 and 3.15.

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Titel: Does educational expansion decrease suicide rates in European countries? The compositional effect in educational stratification of suicides
verfasst von: Tomáš Katrňák
Barbora Hubatková
Publikationsdatum: 19.05.2021
Verlag: Springer Netherlands
Erschienen in: Quality & Quantity / Ausgabe 3/2022
Print ISSN: 0033-5177
Elektronische ISSN: 1573-7845
DOI: https://doi.org/10.1007/s11135-021-01160-4

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