Jaime Napier is an assistant professor of psychology at Yale University. Her research focuses on political and religious ideologies. We invited our Facebook and Twitter followers, as well as students, to submit questions based on Napier’s research, and here is what she had to say.
In reference to the research article, “Why Are Conservatives Happier Than Liberals?”:
Are there any significant differences between the gaps in happiness in the United States and other countries? Was the gap larger or smaller in other nations or relatively similar?
There was a significant amount of random variance between countries, which means, yes, the gap between liberals and conservatives did differ from country to country. We examined whether a country’s “quality of life” (measured by the United Nations’ Human Development index, or HDI) related to the happiness gap and found that it did: The gap between liberals and conservatives was bigger in countries with a relatively low quality of life (e.g., Czech Republic, Slovakia) as compared to countries with a relatively high quality of life (e.g., Norway, Sweden). This should be interpreted with caution, however, because the countries that we had data for were ones that had pretty good quality of life on average. The HDI ranges from 0 to 1, where 1 is the highest quality of life and 0 is the lowest. In our sample, the lowest HDI was in the Czech Republic, which was .850. Although this is substantially lower than the United States (HDI=.930), Sweden (HDI=.933), and Norway (HDI=.936), it still denotes a relatively good standard of living. Indeed, the UN categorizes countries with HDI of more than .700 as “high human development.” Thus, we can’t tell from our data what the gap would look like in nations with medium or low levels of human development.
During the experiment, what were the extraneous variables that you had to account for, and how did you reduce their effect on the outcome of the experiment? Extraneous variables such as how people perceive scales differently (from 1-7) or how other countries may have different definitions of liberal and conservative (when asked to locate themselves on a scale from 1-10).
We used a multi-level model, with individuals nested in countries. This means that we analyzed the relationships between the variables for each country, independent of different levels between countries. So, if people in, say, Switzerland tend to use scale endpoints more than people in, say, Finland, this wouldn’t have affected our results because the model that we used adjusted for differences in levels and reports on the correlations (or relationships) between the variables.
The question of how people’s different notions of ideology might influence the results is an interesting one. The words “liberal” and “conservative” do have different meanings throughout the world. The scale labels presented to our participants were actually “left-wing” versus “right-wing,” which has a somewhat more standard interpretation internationally than liberal vs. conservative. And, again, using the two-level model (with people nested in countries) helped us to examine just the relationship between ideology and happiness, without having to be concerned about absolute levels.
It is quite conceivable that someone in the United States who chooses a 1 (“extremely left-wing”) on the scale is actually much more conservative (or right-wing) than someone who chooses a “1” in Norway, for example. What our data show, though, is that the relative difference within a population is a reliable predictor of happiness. More importantly, although the political “hot” topics likely do vary from country to country, what we found was that, across all countries, (1) people who are relatively more right- (vs. left-) wing are more likely to endorse meritocratic beliefs and (2) meritocratic beliefs are positively associated with life satisfaction. This held regardless of the nuances of any particular political context: Thus, conservatives (or right-wingers) are happier than liberals (or left-wingers) because they are more likely to hold meritocratic beliefs.
How did you avoid having a bias towards conservatives being happier (because you already had some information indicating that result beforehand), when examining the results of your studies?
Yes, we knew (from the Pew Research Center poll, and others) that conservatives reported higher life satisfaction. The goal of our work was to find out why that was the case, and we didn’t have a bias for the “why” question. We examined three hypotheses — whether the gap in happiness was due to (1) demographic differences, (2) difference in cognitive style, or (3) differences in system justification (or meritocratic beliefs). We found support for the third hypothesis. (And, actually, when we started the work, our first hypothesis was that the happiness gap was due to difference in cognitive style. But we found that to not be supported.)
How did Study 1 account for any possible differences in answers people gave via phone and in person? Also, how did you make sure people were not biased in agreeing to answer the questions? Maybe conservatives were more willing to discuss the presidential election than liberals or vice-versa. How was this taken into account?
We are quite thankful to have benefited from the hard work of the institutions that administered the surveys that we used — including the National Election Studies, the World Values Survey, and the General Social Survey. They have spent considerable time and money figuring out the complex issues involved in obtaining representative samples, and this type of research would not be possible without these institutions. The survey statisticians also provide users with weights that can adjust for variation in responses (including, for instance, taking the survey on the phone vs. in person), which we used in our analyses.
In each of the studies the researchers “adjust for demographic variables that could affect happiness” such as sex, marital status, employment status, church attendance, etc. While being employed or unemployed is a fairly straight-forward factor that could influence one’s happiness, I was wondering how factors such as gender and marital status were adjusted. What do the numbers assigned to them mean (0=male, 1-female)? Was one of the sexes deemed more likely to consider themselves happier?
The numbers assigned to these types of categories (0 vs. 1) are arbitrary; it could just have easily have been 1=male and 0=female. The only thing that would change is the sign of the coefficient generated by the model. So, for instance, if we coded 0=male and 1=female, and the model showed that the effect for sex was .6, it would mean that women are .6 points higher on happiness than men. (If we reversed the coding, such that 0=female and 1=male, the coefficient would be -.6. If the coefficient is b, and the variable for gender is X, the outcome is bX.)
In our results, we did not find that gender was related to happiness — so, no, there were no sex differences. There was an effect of marital status — married people are happier than people who are single, divorced, or widowed. And, yes, employed people are happier than the unemployed.
Do you think a three-point scale is a sufficient and valid measure of life satisfaction? I would be tempted to guess that most individuals choose the value of 2, considering it is in the middle of the three-point scale, therefore not creating any true variability in the results. Is this three-point measure robust enough?
Ideally, we would have a more fine-tuned measure (as was in the case in Study 2). However, there was considerable variability in the 3-point measures. We used “robust standard errors,” which can correct for violations in normality assumptions in statistical tests; thus, we were confident our results were reliable.
Greater rationalization of inequality by conservatives was proposed as the source of greater happiness among conservatives. This was true even when other factors like education, sex, employment status, and income were accounted for. To me, it seems counter-intuitive that even those individuals in bad economic situations would be comforted by a rationalization of inequality, because then they would experience self-blame and perhaps a loss of self-esteem. How can one account for these individuals’ greater happiness?
It’s a good question, and one that we are still trying to fully understand in our research! We think that rationalizations are palliative because they frame the world as ordered and meaningful. Thus, even if you are at the disadvantaged end of the spectrum, it might be better to have an explanation for your disadvantage (even if it constitutes self-blame) than to see the world as random and meaningless. We didn’t have measures of self-esteem in these studies, but you bring up another interesting question for future work — namely, the relationship between self-esteem and life satisfaction. It might be the case that these two measures of well-being are less correlated for the disadvantaged than they are for the advantaged. For instance, I might blame myself for bad outcomes (and thus have poor self-esteem) but also think that my life is pretty satisfactory.
That said, our data do not show that ideology trumps everything in terms of wellbeing — not at all. We find that a poor conservative is happier than a poor liberal, but it doesn’t mean that poverty has no effect on happiness (it does).
Do you think there is a liberal bias in your research, as some conservatives say?
All researchers are partial to their hypotheses, whether the topic is a political one or not. Using the scientific method, though, we can guard against letting our own beliefs drive any effects. This paper is actually a perfect example of this. Around the same time that we published this paper, Arthur Brooks (a conservative) published almost the exact same findings in his book, Gross National Happiness. Brooks independently found the same thing that John Jost and I discovered — that conservatives are happier than liberals because they are higher on meritocratic beliefs, which are associated with increased subjective wellbeing. Although one’s ideology might lead one to put a particular valence on the meaning of these findings, the effects are the same regardless.
Thus, there might be a bias in what message researchers take away from their work. For instance, I think that the palliative effect of meritocratic beliefs could be problematic: I worry that meritocratic explanations are often the result of people being motivated to perceive inequalities as legitimate, even when they are not, and thus can perpetuate and maintain the inequality. Brooks, however, highlights the other side of things, which is also true: If/when a system is truly unbiased and outcomes are based on meritocratic principles, this not only promotes a sense of fairness but also promotes our individual subjective wellbeing.