The Education Effect: Higher Educational Qualifications are Robustly Associated with Beneficial Personal and Socio-political Outcomes

Abstract

Level of education is a predictor of a range of important outcomes, such as political interest and cynicism, social trust, health, well-being, and intergroup attitudes. We address a gap in the literature by analyzing the strength and stability of the education effect associated with this diverse range of outcomes across three surveys covering the period 1986–2011, including novel latent growth analyses of the stability of the education effect within the same individuals over time. Our analyses of the British Social Attitudes Survey, British Household Panel Survey, and International Social Survey Programme indicated that the education effect was robust across these outcomes and relatively stable over time, with higher education levels being associated with higher trust and political interest, better health and well-being, and with less political cynicism and less negative intergroup attitudes. The education effect was strongest when associated with political outcomes and attitudes towards immigrants, whereas it was weakest when associated with health and well-being. Most of the education effect appears to be due to the beneficial consequences of having a university education. Our results demonstrate that this beneficial education effect is also manifested in within-individual changes, with the education effect tending to become stronger as individuals age.

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Notes

  1. 1.

    These categories represent increasingly higher educational qualifications. Described briefly, each (increasing) category label represents: No qualifications: no formal qualifications; CSE: a lower secondary educational qualification; O Level: a higher secondary educational qualification; A Level: a further education qualification; Higher Vocational: a higher education qualification below a bachelors’ degree; Degree: a bachelors degree (similar categories are used in the other surveys). Although later waves of the BSA also included a ‘postgraduate’ qualification category, we included this in the ‘degree’ category due to its absence from the earlier waves and the very small number of respondents at any wave with postgraduate qualifications.

  2. 2.

    Rather than make assumptions about whether a respondent’s HEQ to date or the qualification they were studying for was the most appropriate to use for the HEQ variable, we excluded respondents who were in education during the time of the survey. This resulted in the removal of only 1,880 persons, a reduction of only .2 % of the sample.

  3. 3.

    We also investigated a model that additionally included year2 to investigate whether there were quadratic effects of year. These models were never a better fit than the models without this term, so we do not discuss them further.

  4. 4.

    Year was recoded so that its value represented number of years deviation from the most recent wave (2011 for the BSAS), with negative numbers indicating years in the past (for example, 2008, three years earlier than 2011, was coded as −3). Thus, a positive main effect for year indicates that the outcome variable has become more positive over time. The effect of year in a model that also includes the year by education dummy interactions represents the effect of an increase of one year on the outcome variable for the ‘no qualifications’ education reference category; the year by education dummy interactions represent the change in the effect associated with each HEQ with an increase of 1 year, and the effects associated with the education dummy variables shift from main effects to simple effects for the most recent wave.

  5. 5.

    The categories used for HEQ varied slightly over the course of the 15 surveys, but we used this categorisation for all the waves to ensure equivalence.

  6. 6.

    The larger sample size of the ISSP in comparison to the BSA enabled us to include these additional categories for respondents’ employment status in the ISSP analyses.

  7. 7.

    We did not include income as a control variable in these analyses because the survey from each nation recorded this variable in its own currency and the values were therefore not equivalent.

  8. 8.

    We also investigated a model that additionally included year2 to investigate whether there were quadratic effects of year. This model was never a better fit than the model without this term, so we do not discuss it further.

  9. 9.

    As with the BSAS, year was recoded such that the most recent wave was indicated by 0 and all other waves were represented by a negative number indicating its deviation in years from the most recent wave. For variables that were only included in two waves of the survey, we ran models A and B before moving to models D and E, including year of survey as a dummy variable, with the most recent year of the survey as the reference category.

  10. 10.

    The size of the sample in each analysis is only a small proportion of this total because it includes only data from respondents who responded to all items included in the analyses, giving an indication of how the response to the outcome variables change over time.

  11. 11.

    Although the BHPS also included a ‘postgraduate’ qualification category, we incorporated this into the ‘degree’ category due to the very small number of respondents with postgraduate qualifications, especially in the earlier waves.

  12. 12.

    Because the BHPS follows the same individuals over time, some respondents’ HEQ changed across the waves of the survey. Our statistical approach was to treat HEQ as a time-invariant variable, which we did to reduce the complexity of our latent growth curve models in order to aid convergence. Rather than make assumptions about whether a respondent’s initial or final HEQ was the most appropriate to use, we excluded respondents whose HEQ changed over the course of the survey from the analyses. This resulted in the removal of 179 respondents, a reduction of only .005 % of the sample.

  13. 13.

    We specified the growth factors so as to take account of the length between measurement occasions of the outcome variable, as well as dividing these values by 10 to aid convergence. For example, if the outcome variable was measured at Wave 1, Wave 4, and Wave 6, the growth factor would be specified as ‘i s | Wave1@0 Wave4@.3 Wave 6@0.5’, thus retaining the appropriate distance between the time points.

  14. 14.

    For example, estimates for the effect associated with degree ranged between −.19 to .38, with p values < .001–.857.

  15. 15.

    We used latent growth modelling for dichotomous outcomes for the BHPS social trust analyses (Múthen and Múthen 1998–2011).

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Easterbrook, M.J., Kuppens, T. & Manstead, A.S.R. The Education Effect: Higher Educational Qualifications are Robustly Associated with Beneficial Personal and Socio-political Outcomes. Soc Indic Res 126, 1261–1298 (2016). https://doi.org/10.1007/s11205-015-0946-1

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Keywords

  • The education effect
  • Education
  • Well-being
  • Social attitudes