I am grateful for the insightful comments by five anonymous reviewers of this journal, which triggered a revision and extension of the earlier version. Financial support from the 21st Century Center of Excellence (COE) and Global Center of Excellence (GCOE) programs of Osaka University is gratefully acknowledged.
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Recall the famous lyric by the Carpenters: “Rainy days and Mondays always get me down.”
Additional direct evidence for the fact that people regard a pleasant climate as essential for a good life and for personal satisfaction is obtained from a large-scale survey conducted by Osaka University in 2007 and 2008. In that survey, respondents were asked to select a prefecture in which they wished to live and to select the reasons for their choices. Of the 16 possible reasons, “Nice climate and rich natural environment” was tied for the most common.
Several articles analyze the effect of climate on life patterns. For example, Maddison (2003) demonstrated that climate is a significant determinant of expenditure patterns. Hoch and Drake (1974) investigated the relation between climate and wages, under the hypothesis that higher money wages compensate for a lower quality of life. Maddison and Bigano (2003) studied the relationship between climate and wage and home price differentials in Italy. Van Praag (1988) researched the dependence of household costs on climatic conditions, and estimated a climate index and climate equivalence scales for European cities. Bigano et al. (2008) analyzed the effect of climate on sea levels and tourism flows.
Motivated by these psychology studies, economists investigated the relationship between weather and stock returns and risks (Hirshleifer and Shumway 2003; Brahmana et al. 2012; Wang et al. 2012; Hasan et al. 2011; Kang et al. 2010; Levy and Galili 2008; Jacobsen and Marquering 2008; Yoon and Kang 2009). Most of them reported some evidence for the weather effect; for example, Hirshleifer and Shumway (2003) found that sunshine is strongly significantly correlated with stock returns. However, Jacobsen and Marquering (2008) argued that the correlation between weather and stock returns might be spurious.
Subjects were recruited on campus grounds as well as through a website, so that they are not random or representative samples of Osaka University students.
Respondents were also asked to report their happiness every hour over one day (of their choice) per month. Answering this hourly survey was a necessary condition for the monthly bonus.
In section 4d, I investigate the effect of weather on affect measures other than self-reported happiness.
Out of 75 total subjects, 38 belong to Suita campus, 32 to Toyonaka, and five are unknown.
Sei Shonagon described beautiful scenes set in both of these seasons in her famous book Makurano Soshi (The Pillow Book), written in the 10th and 11th centuries.
I also estimated the specification that included monthly dummies. In that regression, temperature lost its significance, and the monthly dummy for August was significantly negative. This is probably because monthly dummies and temperature are highly correlated.
Since happiness is an ordered variable, it should be estimated with an ordered probit (logit). However, because it is difficult to interpret the effect sizes in such models, and because controlling for individual-level effects is important, I use fixed and random effects models. Ferrer-i-Carbonel and Frijters (2004) show that assuming ordinality or cardinality of happiness scores makes little difference, while allowing for fixed effects does change results substantially.
When robust standard errors are estimated, a Hausman test cannot be applied. Thus, I report the result of the model that was selected when estimated with nonrobust standard errors.
Estimates of coefficients on 23-h dummies are not shown to avoid making the table too large.
The coefficient on sunshine takes negative sign when hour dummies are not included. The reason for this result may be that SUNSHINE takes a zero value during nighttime, during which people tend to be happier (Kahneman et al. 2004). When the equation without hour dummies is estimated using data from 7:00 to 17:00 LT, SUNSHINE becomes insignificant under the usual standard errors (the number of observations is reduced to 12 187).
TEMP_40 is for over 35°C.
In these estimations, a Hausman test reported negative χ2 values, implying that the test cannot be applied for this case. Thus, I arbitrarily reported the results of the fixed effects model, which were quite similar to those of the random effects model.
Daily weather is the averaged data on the day when respondents made the reports. Although it is more appropriate to use the average for 24 h preceding the survey response, calculation of this proved difficult. The use of daily data of this paper, therefore, may underestimate the effect of daily weather.
See footnote 17 for the case of sadness.