Survey Variable: Ranked Indicators of Status

Being asked to select all of the indicators of status that they think matter allows people to cast the net wide and indicate everything that they think acts as any sort of indicator of status. By contrast, being asked to rank those indicators requires people to be more discriminating and indicate what they think are the most important signs of people’s statuses. The survey asked respondents to do this ranking after they had selected indicators of status, and the results are shown in Figure 1 (above, using weighted data).[1]

Matching the most popularly selected indicators, the most highly ranked indicators are education (panel G) and occupation (panel F), which are each ranked first by a fifth of people (19.3% and 19.9% respectively) and placed in the top three by approaching half of people (47.4% and 46.4% respectively). The third highest ranked indicator of status is income (panel J), which is seen as the most important by one in seven people (14.3%) and in the top three by slightly less than two fifths (38.3%). None of the other indicators are ranked in the top three by more than a third of people, with the order being as follows: location of residence (30.4%, panel I); speech (20.8%, panel B); pastimes and interests (19.4%, panel D); ideas (17.4%, panel C); acquaintances (17.2%, panel H); appearance and dress (13.6%, panel A); possessions (10.2%, panel E).

People largely believe that the indicators of status that are most widely used in public discourse are the most important: education, occupation, and income. This may be because those are the indicators that they are most familiar with and encounter most often in commentary, because they are in fact the most important indicators, or some combination of the two. At the same time, however, many people are willing to entertain other indicators of status as important, and between three in ten and a fifth rank location of residence, speech, and pastimes and interests in their top three. It remains to be seen whether the indicators that people choose are related to each other but there are things beyond the most common indicators that at least some people take as important signs of the statuses of those they interact with.

Variable namespr_sia_b, pr_sis_b, pr_sii_b, pr_sic_b, pr_sip_b,
pr_sij_b, pr_sie_b, pr_siw_b, pr_sil_b, pr_sim_b,
pr_sin_c, pr_sio_b, priv_isgrankles_appear,
priv_isgrankles_speech, priv_isgrankles_ideas,
priv_isgrankles_intact, priv_isgrankles_possess,
priv_isgrankles_occup, priv_isgrankles_educ,
priv_isgrankles_whoknow, priv_isgrankles_wherelive,
priv_isgrankles_income, priv_isgrankles_other,
priv_isgrankmor_appear, priv_isgrankmor_speech,
priv_isgrankmor_ideas, priv_isgrankmor_intact,
priv_isgrankmor_possess, priv_isgrankmor_occup,
priv_isgrankmor_educ, priv_isgrankmor_whoknow,
priv_isgrankmor_wherelive, priv_isgrankmor_income,
priv_isgrankmor_other
Number of cases1,405
Number of categories2-3
Categories to code as missingNone
Cases to code as missingNone
Recoded variable namespr_ria_cir, pr_ris_cir, pr_rii_cir, pr_ric_cir, pr_rip_cir,
pr_rij_cir, pr_rie_cir, pr_riw_cir, pr_ril_cir, pr_rim_cir,
pr_rio_cir
Number of cases1,405
Number of categories5
New and old categoriesThe binary and original ranking variables (respondents
saw differently worded ranking questions depending
on how many reasons they selected) were combined
to create the new ranking variables. If a respondent
did not select an indicator of status, they were coded
as 0 (‘Not selected’) on the new variable. If they selected
an indicator but did not place it in their top three then
they were coded as 1 (‘Selected’). Those who ranked an
indicator were coded as 2 (‘Ranked third’), 3 (‘Ranked
second’), or 4 (‘Ranked first’), meaning that higher
values on the new variable equate to higher rankings
of that indicator of status.
Details of the original and recoded indicators of status variables.

[1] See also Table A1.

Published by joegreenwoodhau

Joe Greenwood-Hau is a Lecturer in Politics in the School of Social and Political Science at the University of Edinburgh, where his teaching focuses on Introduction to Political Data Analaysis and he is wrapping up the Capital, Privilege and Political Participation in Britain and Beyond project.

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