Social class / Social class versus social attitudes

The relationship between social attitudes and measures of objective social class or social cleavage

In this section, we take the first of a series of steps to assess the relationship between someone's social class, or their social position, and their social attitudes - in the early 1980s and then in 2012. Step one is to look at the strength of the associations between the range of objective social class and cleavage measures above, and people's attitudes. (We turn later in the chapter to discuss people's subjective social class, that is, how they view themselves in terms of class and income levels.) So, without reporting at this stage on the actual percentages of who holds which views across the different social groups, we look at the overall pattern of the relationship between social position and attitudes, and where those relationships are strongest.

Tables 7.4 to 7.7 show how strongly people's attitudes are associated with each of our measures of social class or social position, firstly in 1984, then in 2012. In each case, we have measured the strength of the association using a Cramer's V (a statistical chi-square based measure of association), where the association between the two variables is expressed as a score between 0 and 1. The larger the V score, the more strongly the two variables are associated. So, for example in Table 7.4, someone's social class is more strongly associated with someone's views on the NHS (0.151) than with their views on opportunities for higher education (0.054). Using asterisks, we also show the level of statistical significance in the difference between the two variables (as measured by a chi-square test).[5]

undefinedWe look firstly at the relationship between someone's social class, and their position across other social cleavages, and their attitudes to welfare and redistribution in 1984 (Table 7.4) and in 2012 (Table 7.5). In 1984, someone's class, measured by socio-economic group, was significantly associated with four out of the five welfare state attitudes (with the working classes being more positive towards government spending). Likewise, other measures of someone's social class, such as their current economic activity, their education level and trade union membership were also strongly associated with attitudes to welfare, on at least three of our five attitudinal measures. It is striking that someone's attitudes to welfare issues were often less strongly associated with someone's income level. Other measures of social cleavage were associated with a preference for taxation and spending, but were often not related to the other welfare questions. The exception to this was someone's age, which was significantly associated with their views on welfare across all five of our attitudinal questions. But overall, in 1984, social class appeared to be significantly more important in structuring most attitudes to welfare than our other social cleavage measures.

By 2012, people's attitudes to welfare are less strongly related to their social class (measured by someone's socio-economic group) and other measures of their social position are somewhat more significant. People's housing tenure, economic activity and educational attainment are all more important now to people's attitudes to welfare than they were in 1984, having statistically significant associations with four of the five welfare attitudes questions. Religion, sex, age and ethnicity are all more important now too. They have statistically significant associations with four of our five welfare attitudes, whereas social class is associated with three, income with one, and trade union membership with two. To this extent, there is some evidence that social class has become somewhat less dominant in structuring welfare attitudes in the past 30 years.

undefinedSo, having established where there are statistically significant associations between people's attitudes and different measures of their social position, we turn to the strength of that association (as measured by Cramer's V). Overall, there is no clear trend that suggests that the strength of the associations we see have become more or less strong over time. For example, we see that in both 1984 and 2012, socio-economic group has a slightly stronger association with the welfare attitudes than income level does, and there is little sign that income has supplanted socio-economic group as the main economic driver of attitudes in 2012. Overall, economic activity remains as strong a predictor of people's attitudes to welfare as it had been in 1984, as does housing tenure. Just as in the early 1980s, private health and private education do not prove to be highly associated with people's attitudes in 2012. In contrast to theorists such as Peter Saunders (1990) who predicted that these 'consumption sectors' would become increasingly significant in a more marketised environment, in fact they have negligible significance. However, there does seem to be an important decline in the strength of the association between trade union membership and welfare attitudes. This may be due to the change in profile of trade union membership during this period, when there has been a shift from the majority of members being manual workers to non-manual workers, with the rise in professional trade union membership.

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Tables 7.6 and 7.7 show the changing relationship between liberal attitudes (those listed earlier) and people's social class and other measures of their social position, again comparing 1984 and in 2012. In 1984, someone's socio-economic group was not as significant in shaping these liberal attitudes as most of the other social class measures, especially education. By 2012, these measures of social class have also declined in importance, and there are much closer associations between liberal attitudes and the other social cleavages, notably religion, attendance at a place of worship, age and ethnicity. In 2012, as in 1984, religion and attendance at a place of worship have the strongest associations of all (measured by the Cramer's V score). This is especially the case with attitudes towards premarital sex (and related issues like ease of divorce). The relationship between liberal attitudes and religiosity has, if anything, got stronger over time, especially with respect to the acceptability of same-sex relationships. But educational level also remains a powerful predictor of liberal attitudes.

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To generalise, it is clear that, even in 1984, social class (measured by someone's socio-economic group) was not necessarily the key factor affecting people's attitudes and values. Several other measures of economic position, notably economic activity, housing tenure and membership of a trade union were associated with attitudes as much as, or possibly even slightly more than, socio-economic group. It would be wrong, therefore to think that class (objectively measured by socio-economic group) was predominant even at this period in the 1980s of apparent class polarisation.[6] In the early 1980s, there was a differentiation between welfare attitudes, which generally appear oriented on a left-right axis in which class, housing, economic activity, and trade union membership were important, and liberal attitudes, which were more closely related to age, education and religion. However, class and class-related factors such as economic position come over as the most significant predictor of attitudes across the board at that time - so to this extent we can usefully talk about socio-economic position being a fundamental driver of attitudes in the 1980s.

The patterns for 2012 reveal that there are many similarities and only modest changes since the 1980s. The overall pattern of associations between social position and social attitudes is broadly similar to 30 years ago, with class-related factors significantly related to the various welfare questions, and age, education, and religion, as before, being more strongly associated with liberal attitudes. However, age, education, ethnicity and religion now appear to have significant associations with many of the welfare issues in a way that was not apparent in the earliest period. Across all the measures of someone's social position, relationships between ethnicity and people's attitudes have shifted most in this 30 year period. Ethnicity now vies with economic activity as the single most important driver of attitudes across the board, more important than socio-economic group or sex. Its importance for same-sex relations and premarital sex is especially marked, which may well be associated with the religious views of some of the ethnic groups.

As one might expect, there is a considerable amount of fluctuation in levels of significance and magnitude of the associations over time, reflecting changing historical contexts - and also reflecting methodological issues such as sampling errors and changing sample sizes - but the overall patterns look pretty similar in the two periods. In order to illustrate some of these patterns in more detail, and the changes between the early 1980s and now, we next present some simple tables showing the relationship between people's attitudes and various measures of their social position. For simplicity, we focus on one question on attitudes to welfare - attitudes towards taxation and government spending - and one question on liberal attitudes - towards premarital sexual relationships. We chose these because earlier factor analyses had indicated that these were the most central items in both periods for each of the two ideological dimensions. We show the full set of response categories to each of these two questions enabling us to flesh out the findings above on exactly how people's attitudes on these two measures are associated with their position in society.

In Table 7.8, we begin with the relationship between someone's socio-economic group and their attitudes to taxation and government spending, in 1984 and 2012. We show the proportions from each socio-economic group who prefer increased taxation and increased spending on health, education and social benefits.[7] As we can see, both now and 30 years ago, business-owners and self-employed people (often termed the petty bourgeoisie) are the least likely to support increased taxation (28 per cent supported it in 1984, and 27 per cent did so in 2012, due to small sample size of this group the 1984 figure should be treated with caution), with managers not far behind in their views in 1984. Those more likely to benefit from income redistribution, in the lower socio-economic groups are most likely to support increased taxation and social spending. For instance, among the semi- and unskilled-manual classes, 40 per cent supported increased taxation and spending in 1984 and 36 per cent do so in 2012. However, we can also see that the gaps between the classes have reduced somewhat in 2012, compared with 1984, largely because the working classes have become less supportive of greater spending. This may reflect the effects of the recession of 2008, the subsequent austerity measures, and the consequent squeeze on the incomes of ordinary working people, which has perhaps made them more reluctant to support government spending. However, the change in the strength of relationship does not reach statistical significance, so we are careful not to over-interpret the change.[8]

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Table 7.9 shows the pattern of responses to the same question on taxation and spending, across people with different household incomes, divided roughly into four quartiles. In both years, the relationship between support for increased taxes and welfare spending and one's own income level is weak, with no statistically significant difference between the two time points.[9] At least on this particular issue, income is not associated with attitudes on taxation and spending in either year.

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In Table 7.10 we show the differences in attitudes to increased taxation and spending comparing those in full-time education, in employment (including self-employment), unemployed, and economically inactive (for simplicity grouping together people who are retired, homemakers and other inactivity). In both 1984 and 2012, there is a clear distinction between people who are employed and those who are unemployed, with unemployed people 10 percentage points more likely to prefer greater government spending. Once again, a formal test indicates that there has been no statistically significant change over time in the extent to which people who are unemployed differ from those who are employed.

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Next we turn to trade union membership. Here for the first time we see a major change over time with a significant weakening in the strength of association between a measure of social class and someone's attitudes to taxation and spending. In 1984, there was an 18 point difference in support for greater spending between trade union members and non-members (the largest we have seen so far), with trade union members, not surprisingly, being much more likely to support greater spending. The relationship remained significant in 2012 but was sharply reduced to only six points. Formal testing indicates that the change in strength of relationship is highly significant.[10] However, as we mentioned earlier, these findings need to be taken in the context that the profile of trade union membership during this period has shifted from majority membership from manual workers to non-manual workers, with the rise in professional trade union membership.

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We now move on to look at the relationships between someone's social class or social position, and their attitudes to premarital sexual relationships, a core question among a set of British Social Attitudes questions about liberalism. We begin in Table 7.12 with looking at the relationship between someone's age and their attitudes to premarital sex. As we can see, there was a very strong relationship between the two in 1984, much stronger than any involving the class-related issues or cleavages. But there is an interesting change by 2012: older age groups have become much more liberal, while the attitudes of the youngest group have barely changed.[11] This may well reflect processes of generational change (with younger more liberal cohorts replacing older ones with more traditional views), rather than individuals becoming more liberal as they age. Indeed, people who were aged between 25 and 34 in 1984 will broadly be concentrated in the 45 to 54 year old age group in 2012: and as we can see the attitudes of this cohort in 2012 are rather similar (67 per cent in favour of premarital sex) to the attitudes of the 25 to 34 year olds in 1984 (65 per cent in favour). One plausible interpretation therefore is that these kinds of attitudes are learned while young, and then change little over the course of life.

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We see a rather different pattern when it comes to the attitudes of people with different levels of education (Table 7.13). In 1984 we see a strong 'curvilinear' relationship (with people at the two ends of the spectrum holding similar views and those in between holding different ones) in which graduates and those with no educational qualifications were less likely to approve of premarital sex than those with intermediate qualifications. However by 2012, there is general acceptance of premarital sex across all groups.[12]

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We find a very powerful relationship between attendance at a place of worship (church, mosque, temple or gurdwara for example) and attitudes to premarital sexual relations (Table 7.14). If anything the relationship has strengthened over time;[14] in 2012 the gap between the level of acceptance of premarital sexual relations of people who attend a place of worship weekly and of people with no religion had widened to a massive 62 percentage points.

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Finally we turn to ethnicity, which we saw in Tables 7.5 and 7.7 had quite strong associations with attitudes across the board in 2012. Since the number of ethnic minority respondents in 1984 was very small, it does not make a great deal of sense to explore change over time in any detail, we therefore focus only on 2012. Table 7.15 shows the relationship between ethnicity and our two key measures of attitudes on welfare and liberal issues.

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The fact that ethnic minorities are less tolerant of premarital sexual relationships is no surprise. But it may surprise, given that minorities have very high levels of support for the Labour Party, that they are not supportive of the left-wing policy of increasing taxation and government spending. However, this pattern has been found before (Dancygier and Saunders, 2006; Heath et al., forthcoming) using independent data sources. One possible interpretation is that many people from ethnic minorities originate from countries with much less developed welfare states than Britain, and therefore are relatively satisfied with Britain's, in comparison, rather generous arrangements.

 

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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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