There has been a lot of press lately for a paper recently published in Proceedings of the National Academy of Sciences by Paul Piff (a PhD student at UC Berkeley) and three of his colleagues at Berkeley along with Stéphane Côté at the University of Toronto. The paper – “Higher social class predicts increased unethical behavior” – discusses numerous studies the authors conducted in order to better understand the behavioral differences between “upper-class” and “lower-class” people. They conducted both in-situ research as well as laboratory experiments looking at such things as the likelihood of breaking the law and participation in unethical behavior. As the title of their paper suggests, they concluded that upper-class individuals actually do behave differently in that they are more likely to break laws, cheat, and lie – that is, behave more unethically – than lower-class people and that this stems in part from their attitudes toward greed.
Socioeconomic status is an important factor which may carry a great deal of weight in health-related research but is largely ignored in other disciplines such as marketing research. However the fact that one’s socioeconomic standing impacts their behavior and attitudes (e.g., toward greed) – and how they think – makes this a critical component in our research designs.
But what are the design elements that effectively measure socioeconomic status? In 1958, Charles L. Vaughn published a paper in Public Opinion Quarterly titled “A scale for assessing socio-economic status in survey research.” Back then, door-to-door interviews was the most likely mode of data collection and the “common method” for assessing socioeconomic status was (remarkably) by way of the interviewers’ subjective ratings of each respondent’s “dwelling unit” as well as “personal care and speech.” Vaughn discusses several problems with this method, not the least of which is that the results were “not very reliable.” To address these problems, Vaughn designed seven questions (with the seventh question split into two questions about the household) to measure socioeconomic status by which respondents’ answers were scaled against interviewers’ ratings. While socioeconomic measures typically focus on income, occupation, and education, Vaughn omitted income in part because “the subject tends to irritate respondents and thus demoralize interviewers.” Instead, Vaughn asked about occupation and education as well as status symbols of the time such as automobile ownership, telephone service, and housing (although he later dropped that last two questions because there was “so much respondent resistance”).
Vaughn’s Socioeconomic Status Questions
1. Are you or is somebody else the chief wage earner in your home?
2. What is (your, his, her) occupation?
3. About how far did (you, he, she) go in school?
4. Is there a car in your home? Was it bought new or used?
5. Is there a telephone in your home? Is it a party or private line?
6. Do you rent or own the place where you are living?
7a.How many bedrooms are there in your home?
7b.How many people live there?
Piff et al. utilized a modern-day scale to gauge social status, the MacArthur Scale of Subjective Social Status. This scale was developed by Nancy Adler, PhD, professor of medical psychology at the University of California, San Francisco, in order to evaluate where people place themselves relative to others in terms of money, education, and job situation. Respondents are presented with a “social ladder” and asked to mark the ladder depending on where they see themselves relative to the top and bottom rung. Another version of this ladder is the “community ladder” which pegs an individual’s perceived status to their standing in the community.
Socioeconomic indicators are an important ingredient to our research outcomes. How (or if) people respond to our research questions is greatly impacted by where they find themselves in a world made increasingly more complicated by social and economic disparities. Research designs that better reflect the important role of socioeconomic data will come closer to understanding how people think which will ultimately lead to more targeted, effective decision making.