Social class / Subjective social class

Subjective social class

Previous sections have focused on objective measures of social class and other measures of someone's social position. Here, we return to our initial question of the importance of someone's subjective class awareness. We ask how important this is in shaping social attitudes. We measure subjective social class using responses to the following questions (the first of which we report on in Table 7.17):

Which class would you place yourself in, middle class or working class?

Among which group would you place yourself, high income, middle income, or, low income?

To what extent do you think a person's social class affects his or her opportunities in Britain today? A great deal, quite a lot, not very much, or not at all

undefinedWe reported earlier, in Table 7.3, that there has been very little change in the proportion of people identifying as "middle" or "working class" over the last 30 years. In 2012, 35 per cent of the public sees itself as "middle class" and 60 per cent view themselves as "working class". Table 7.17 shows a similarly flat trend regarding the income group that people perceive themselves to be in, although there are signs of a slight increase in propensity to view oneself as middle rather than low income. In 2012, half (51 per cent) of people think they have a middle income, 44 per cent think low income and only four per cent perceive themselves as having a high income. Likewise there has been little movement since 1983 in whether the public perceives that someone's class affects their opportunities. Throughout the period, a majority of people (around seven in ten) think that social class does affect opportunities, either a great deal or quite a lot.

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As we discussed in the introduction, people's subjective awareness of social class may have followed a different trajectory over time from their objective one (measured by their socio-economic group), and it may be the subjective side that is more closely related to social attitudes. Thus we might see a sharper decline over time in the relationship between subjective class and attitudes than was the case with objective socio-economic group. And it could also be that people's subjective sense of where they stand in terms of income has become relatively more important. To explore these possibilities we add measures of subjective class, self-rated income and class awareness into our regression analyses on which we report. Table 7.18 shows the results for these three subjective measures only. All the other measures from Table 7.16 were also included in the model, but the coefficients are not shown as they were little affected by the inclusion of the new measures.

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This analysis confirms the view that the subjective significance of class has declined considerably over the past 30 years. In 1984, one's self-rated class affected both welfare and liberal attitudes, even controlling all other factors we have looked at so far. To this extent, subjective class was more important than objective class in the early 1980s. By 2012, however, subjective class makes no significant difference in attitudes to tax and spend, and nor does one's self-rated income position. It is true that class awareness remains significant and of identical magnitude, but here the causality is especially complex as it might be the case that those in favour of 'tax and spend' might be more predisposed to thinking that class matters in shaping opportunities.

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Notes
  1. The reduction in the strength of the association between socio-economic group and party identification is clear from the Cramer's V score in each year. Cramer's V is a chi-square based measure of association. While a chi-square coefficient depends both on the strength of the relationship and on sample size, Cramer's V eliminates the effect of sample size by dividing chi-square by N, the sample size, (together with a further adjustment) and taking the square root. V may be interpreted as the association between two variables expressed as a percentage of their maximum possible variation. In 1984, the Cramer's V was 0.180 (Chi2 = 179.7 (20 df), p < 0.0001). In 2012, it was 0.125 (Chi2 = 181.4 (20 df), p < 0.0001).
  2. The seven classes identified by Savage et al. (2013) are the elite; the established middle class; new affluent workers; the technical middle class; the traditional working class; emergent service workers and the precariat.
  3. Our analysis of the responses to the items on the first and the second priority for government spending (cross-tabulating the two variables and inspecting the adjusted standardised residuals) indicated that the responses "health" and "education" were highly significantly associated, while the responses "defence" and "police and prisons" were also significantly associated. None of the other responses showed a distinctive pattern of association. In our analysis we have therefore constructed three categories: health and education; defence and police; other.
  4. Factor analysis (see Technical details for more information) confirms that the questions we selected do indeed belong (in both periods) to two distinct ideological dimensions, the structure remaining largely unchanged over time. See the appendix to this chapter for the results of the factor analysis. 
  5. Chi-square is very sensitive to the sample size, and sample sizes vary both between surveys and within surveys (since some items were asked only of randomly chosen subsets of respondents). We cannot therefore use chi-square to tell us about the strength of association, only about its statistical significance. As a measure of strength of association we use Cramer's V (explained in note 1). 
  6. We also explored alternative 'objective' measures of class and reached the same conclusion.
  7. Since the factor analyses indicated that attitudes towards tax and spending and towards premarital sex had the strongest loadings on the two ideological dimensions (both in 1984 and in 2012 - see the appendix to this chapter), we focus on these two issues in our more detailed cross-tabular and regression analysis.
  8. The 1984 and 2012 datasets were pooled and a loglinear model fitted to the data. The model was one which assumed that there were relationships between social cleavage and attitude, between social cleavage and year, and between year and attitude, but that there was no three-way inter-relationship. In effect this tested whether the relationship between cleavage and attitude was the same in both years (allowing for changes in the marginal frequencies over time). It is analogous to the 'constant social fluidity model' in social mobility research. If the model does not give a good fit to the data, as judged by the deviance, then the null hypothesis of a constant relationship has to be rejected.
  9. Deviance 14.0 with 8 df, p > 0.05.
  10. Null hypothesis that the relationship is unchanged is rejected: Deviance = 9.9 with 2 df, p < 0.01.
  11. Deviance 76.9 with 16 df, p > 0.001.
  12. The measure of education level is different in the two years, so we therefore hesitate to interpret the changing pattern.
  13. The only measure available in 1984 was age when education completed, namely 19 and over (plus "still at college or university", equated to degree), 18 (equated with A levels), 17 (equated with GCSE), 16 (equated with CSE) and 15 or less (equated with CSE). These are very crude equivalences but do capture the hierarchical nature of education.
  14. Deviance 76.9 with 16 df, p > 0.001.
  15. We used ordered logit modelling, which is the appropriate technique when we have dependent variables such as attitudes towards premarital sex which are ordered (responses ranging from strongly agree to strongly disagree).
  16. Variance explained, or R squared, is a statistical measure of "the proportion of the total variability of the outcome that is accounted for by the model". It is used in OLS regression, where continuous, normally-distributed variables are assumed. The OLS interpretation has no formal equivalent in logistic regression (which does not assume that variables are either continuous or normally distributed). However, if some heroic assumptions are made, a statistic that looks like R-squared, and which has the same range from - to 1, can be developed. (They are essentially counterfactuals - what might the variance explained have been if this were a continuous normally distributed variable?) Lots of different pseudo R-squareds have been developed, and none has become standard. We use the Nagelkerke version. These measures should not be used to compare different datasets but only really to compare goodness of fit of different models within the same dataset. 
  17. See note 8.
  18. We also found some evidence, from the measures of variance explained (the pseudo R2 statistic) that the overall explanatory power of the predictors has declined somewhat between 1984 and 2012. We have to be a little cautious here, since the multivariate analyses reported in Table 7.16 only cover two of our nine attitude measures. To check our results we constructed composite measures of the two main ideological dimensions, using all the available attitude items. This composite analysis confirmed our individual analysis of government spending on the welfare state (R2 for the government spending dimension falling from 0.061 to 0.022) but it did not confirm a decline in explanatory power for the liberal dimension (R2 actually increasing when a composite measure was constructed from 0.264 to 0.301).
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  • Notes
    1. The reduction in the strength of the association between socio-economic group and party identification is clear from the Cramer's V score in each year. Cramer's V is a chi-square based measure of association. While a chi-square coefficient depends both on the strength of the relationship and on sample size, Cramer's V eliminates the effect of sample size by dividing chi-square by N, the sample size, (together with a further adjustment) and taking the square root. V may be interpreted as the association between two variables expressed as a percentage of their maximum possible variation. In 1984, the Cramer's V was 0.180 (Chi2 = 179.7 (20 df), p < 0.0001). In 2012, it was 0.125 (Chi2 = 181.4 (20 df), p < 0.0001).
    2. The seven classes identified by Savage et al. (2013) are the elite; the established middle class; new affluent workers; the technical middle class; the traditional working class; emergent service workers and the precariat.
    3. Our analysis of the responses to the items on the first and the second priority for government spending (cross-tabulating the two variables and inspecting the adjusted standardised residuals) indicated that the responses "health" and "education" were highly significantly associated, while the responses "defence" and "police and prisons" were also significantly associated. None of the other responses showed a distinctive pattern of association. In our analysis we have therefore constructed three categories: health and education; defence and police; other.
    4. Factor analysis (see Technical details for more information) confirms that the questions we selected do indeed belong (in both periods) to two distinct ideological dimensions, the structure remaining largely unchanged over time. See the appendix to this chapter for the results of the factor analysis. 
    5. Chi-square is very sensitive to the sample size, and sample sizes vary both between surveys and within surveys (since some items were asked only of randomly chosen subsets of respondents). We cannot therefore use chi-square to tell us about the strength of association, only about its statistical significance. As a measure of strength of association we use Cramer's V (explained in note 1). 
    6. We also explored alternative 'objective' measures of class and reached the same conclusion.
    7. Since the factor analyses indicated that attitudes towards tax and spending and towards premarital sex had the strongest loadings on the two ideological dimensions (both in 1984 and in 2012 - see the appendix to this chapter), we focus on these two issues in our more detailed cross-tabular and regression analysis.
    8. The 1984 and 2012 datasets were pooled and a loglinear model fitted to the data. The model was one which assumed that there were relationships between social cleavage and attitude, between social cleavage and year, and between year and attitude, but that there was no three-way inter-relationship. In effect this tested whether the relationship between cleavage and attitude was the same in both years (allowing for changes in the marginal frequencies over time). It is analogous to the 'constant social fluidity model' in social mobility research. If the model does not give a good fit to the data, as judged by the deviance, then the null hypothesis of a constant relationship has to be rejected.
    9. Deviance 14.0 with 8 df, p > 0.05.
    10. Null hypothesis that the relationship is unchanged is rejected: Deviance = 9.9 with 2 df, p < 0.01.
    11. Deviance 76.9 with 16 df, p > 0.001.
    12. The measure of education level is different in the two years, so we therefore hesitate to interpret the changing pattern.
    13. The only measure available in 1984 was age when education completed, namely 19 and over (plus "still at college or university", equated to degree), 18 (equated with A levels), 17 (equated with GCSE), 16 (equated with CSE) and 15 or less (equated with CSE). These are very crude equivalences but do capture the hierarchical nature of education.
    14. Deviance 76.9 with 16 df, p > 0.001.
    15. We used ordered logit modelling, which is the appropriate technique when we have dependent variables such as attitudes towards premarital sex which are ordered (responses ranging from strongly agree to strongly disagree).
    16. Variance explained, or R squared, is a statistical measure of "the proportion of the total variability of the outcome that is accounted for by the model". It is used in OLS regression, where continuous, normally-distributed variables are assumed. The OLS interpretation has no formal equivalent in logistic regression (which does not assume that variables are either continuous or normally distributed). However, if some heroic assumptions are made, a statistic that looks like R-squared, and which has the same range from - to 1, can be developed. (They are essentially counterfactuals - what might the variance explained have been if this were a continuous normally distributed variable?) Lots of different pseudo R-squareds have been developed, and none has become standard. We use the Nagelkerke version. These measures should not be used to compare different datasets but only really to compare goodness of fit of different models within the same dataset. 
    17. See note 8.
    18. We also found some evidence, from the measures of variance explained (the pseudo R2 statistic) that the overall explanatory power of the predictors has declined somewhat between 1984 and 2012. We have to be a little cautious here, since the multivariate analyses reported in Table 7.16 only cover two of our nine attitude measures. To check our results we constructed composite measures of the two main ideological dimensions, using all the available attitude items. This composite analysis confirmed our individual analysis of government spending on the welfare state (R2 for the government spending dimension falling from 0.061 to 0.022) but it did not confirm a decline in explanatory power for the liberal dimension (R2 actually increasing when a composite measure was constructed from 0.264 to 0.301).
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