Dave Krantz pointed me to a paper by Kahan, Braman, Gastil, Slovic, and Mertz on “Gender, race, and risk perception: the influence of cultural status anxiety,” which explores the “white male effect,” which is the “tendency of white males to fear all manner of risk less than women and minorities,” a pattern first noted by Slovic and others in the early 1990s. Finucane and Slovic (1999) wrote that “the white-male effect seemed to be caused by about 30 percent of the white male sample that judged risks to be extremely low.”
Here’s the abstract of the new paper:
Why do white men fear various risks less than women and minorities? Known as the “white male
effect,” this pattern is well documented but poorly understood. This paper proposes a new explanation:
cultural status anxiety. The cultural theory of risk posits that individuals selectively credit and dismiss
asserted dangers in a manner supportive of their preferred form of social organization. This dynamic, it is
hypothesized, drives the white male effect, which reflects the risk skepticism that hierarchical and individualistic
white males display when activities integral to their status are challenged as harmful. The paper
presents the results of an 1800-person survey that confirmed that cultural worldviews moderate the
impact of sex and race on risk perception in patterns consistent with status anxieties. It also discusses the
implication of these findings for risk regulation and communication.
The paper is interesting, and I’m sympathetic to its general arguments–it certainly makes sense to me that risk perceptions, and perceptions about uncertainties in general, will be influenced by cultural values. But I have a couple of concerns relating to how the data were collected and analyzed.
The findings of the article come from regression analyses of responses to a national survey. They aksed people about their perceptions of risks of environmental danger, guns, and abortion. They also asked some cultural world view and personality questions, along with demographics. They found that the cultural worldview questions were predictive of risk attitudes.
I’m just a little worried that they may be measuring political views as much as risk attitudes. For example, one of the agree/disagree statements is “Women who get abortions are putting their health in danger.” Statistically, my impression is that the health risk from abortion itself is low, but a person who opposes abortion might answer Yes to the question, on the grounds that a lifestyle associated with frequent abortions is risky. My point here is that the answer to the question itself could have a political twist to it. Although the question is nominally about risks, I don’t know how much it’s really telling us about risk perception.
I’m not saying that this is a devastating critique. Understanding the “white male effect” is a challenge, and cultural world view, etc., has got to be relevant. But this particular study maybe could be interpreted in other ways.
The paper would be clearer if the tables were made into graphs. Table 1, for instance, is dominated by a weird visual effect having to do with the lengths of the labels. It also includes irrelevant information such as that the sd of the ages in the data is 16.99.
More importantly, Tables 2,3,4,5,7,8,10,11 could be nicely combined into a single display that conveys what is happening and allows the groups to be compared. Table 6,9,12,13 could be combined also.
Figures 2,3,4,5 are ok, but there’s a real opportunity missed to throw in some data. Also, the lines could be labeled directly rather than through different dottings.
This is an interesting paper, so it might be a good example for my statistical graphics class. One of the assignments will be to take a tabular presentation from an article of interest, redo as graphs, and discuss the effect on how the information is conveyed.
P.P.S. See here for more on this topic from Dan Kahan, the first author of the paper under discussion.
Colin Camerer briefly discussed gender effects on financial risk taking in field experiments at his CU biz school lecture last week. While he admitted that their experimental designs were far from optimal, they did look for this effect, but didn't see much. As in the survey data, there are lots of other possible confounding effects.
Keith Chen's monkey studies might be a better place to start looking for gender effects.