Does Life Seem Better on a Sunny Day?
We examine the association between daily weather conditions and life satisfaction in a representative sample of over 1 million Americans from all 50 states who were assessed (in a cross-sectional design) over a 5-year period.
Most daily weather conditions were unrelated to life satisfaction judgments, and those effects that were significant reflect very small effects that were only detectable because of the extremely high power of these analyses. These results show that weather does not reliably affect judgments of life satisfaction.
The goal of the current article is to examine whether the effects of weather conditions extend to judgments about one’s life as a whole. In other words, we seek to determine whether life seems better when the weather is good.
For instance, Lucas (2007b) showed that across two national panel studies, the onset of a severe disability was associated with a substantial drop in life satisfaction and that levels of satisfaction did not return to their baseline levels, even after a period of many years.
The goal of these analyses is to determine whether daily weather conditions are associated with life satisfaction scores. To accomplish this goal, we link each person’s responses to historical weather data from the location and date on which the survey took place. Multilevel modeling strategies are then used to isolate daily weather effects from seasonal or regional effects.
Over 1.9 million respondents participated in the survey during these 5 years (the data are cross-sectional, and thus, each respondent only participated once).
Across the full sample, the mean level of life satisfaction was 3.39. The between-person standard deviation was .63, the between-county standard deviation was .09, and the between metro-area standard deviation was .04. Estimated weather effects can be compared to these standard deviations to determine how big they are relative to the variance that exists in this sample. However, it is important to note that although regions do vary in meaningful ways (see, e.g., Lawless & Lucas, 2011; Oswald & Wu, 2010), the amount of absolute variance across metro areas is very small relative to variance across individuals (just 6% of the size). Thus, using this standard deviation will make most effects look large.
Tables 4, 5, and 6 show the results for barometric pressure, wind speed, and humidity. High barometric pressure is typically associated with clear, calm weather, and Keller et al. (2005 found that this was the primary factor predicting mood in their study. Table 4 shows that none of the effects for barometric pressure were significant in this sample. Similarly, Table 5 shows that none of the effects for wind speed were significant. Finally, Table 6 shows that of the humidity effects tested, only one emerged as being significantly different than zero: the effect for change from the previous day. In this case, regardless of when in the year it occurred and what the absolute humidity was, an increase in humidity was associated with higher levels of life satisfaction. Again, this effect did not replicate in the county-level analyses,