- Affective meanings of 1,469 Bengali concepts
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- The Feedback Fallacy
- How We Learn
We set up our regression equation as follows:. The explanatory variables Culture i are a set of cultural indices, which measure different dimensions of culture. Over the past few decades, the World Values Surveys have interviewed representative national samples of scores of countries, with an average sample size of 1, respondents in a country. Over 1, publications have been based on these data. There are three categories of variables related to happiness in the World Values Survey, namely happiness, life satisfaction and SWB.
Affective meanings of 1,469 Bengali concepts
Life satisfaction was assessed by asking respondents to indicate how satisfied they were with their life as a whole, using a scale that ranged from 1 not at all satisfied to 10 very satisfied. Happiness was assessed by asking respondents to indicate how happy they were, using four categories: very happy, rather happy, not very happy, and not at all happy. Inglehart et al.
If happiness and life satisfaction were evenly balanced, the country would get a score of zero. Happiness reflects the sentimental part of happy like delight, satisfaction and other passive sentiments Andrews and Mckennell The explanatory variables are cultural indices. We also use the Hofstede culture indices for robustness check. As there are many factors affecting happiness, we must control for these factors to overcome the bias of omitted variables.
Following the steps outlined in previous literature Arrindell et al.
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National income is proxied by GDP, which represents the income level. In general, income level is related positively to SWB see Diener et al. Growth of income was proxied by the growth rate of GDP. According to Diener et al. Education EDU is measured by the enrollment ratio in tertiary education of each nation, which is predicted to be positively related with SWB. Since freedom and security are good for people, this variable is predicted to be positively related with SWB Diener et al.
Population density is the percentage of the population of each country living in urban areas of one million or more individuals World Development Report. A higher population density may mean a poorer quality of life and hence lowered SWB.
On the other hand, higher density may be related to a higher provision of public amenities and contribute to SWB. According to the comparison theory, comparison of income can predict SWB better than income itself and is negatively related with SWB, but Arrindell et al.
Our explanatory variable culture i comes from House et al. Control variables mainly come from Arrindell et al. We use a panel data set of 48 countries over the years from to to estimate Eq.
SWB also significantly related to culture indices. This table presents the Pearson correlation coefficients between variables. Given the fact that our model contains so many variables, possible serious multi-collinearity is an issue that has to be considered. Note that, the correlation coefficient of control variables e. In most cases, the correlation coefficients are less than 0. In later regression analysis, we calculate the variance inflation factors VIF of the various variables. This indicates that we can safely ignore the problem of multi-collinearity and use these variables to regress.
Due to space limitation, the results for VIF are not reported in the paper; interested readers may email us for them. We try two kinds of regression. Secondly, we do GMM estimation using language dummies as instruments to tackle the problem of possible endogeneity. We do the OLS regressions in three steps. In the first step, we run an OLS regress between SWB and national income, growth of income, human rights, population density, education, and social comparison of income to test the power of traditional variables in explaining SWB.
In the second step, we add country dummy variables into the regression in order to investigate whether there exist fixed effects which cannot be explained by the traditional variables. In the third step, we replace country dummy variables with cultural variables in the regression to test the effects of cultural variables on SWB. The possible double directions of causality may exist between culture and happiness, which induces the endogeneity problem.
Markus and Kitayama point out that, culture and the self are mutually determined. Happiness is a major experience of the self. Thus, though culture may have important effects on happiness, happiness may also affect cultural affiliation. For example, happier people value life more, and are more willing to maintain the current situation and hence less willing to take risks.
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Their degrees of risk aversion are thus likely higher. The survey of , subjects by Goudie et al. Cahit also points out that, SWB is important in influencing risk aversion and financial behaviour. Happier people are more conscious of risks and more willing to choose safe investment. Also, happier people have better future expectation and longer life expectation Cahit ; Veenhoven ; people scoring high on Future and with higher SWB are more generous Kirchsteiger et al.
We take the language dummy variables as instruments because language dummies are closely related to culture but not necessarily related to SWB. In some studies of cross-culture comparative research, language dummies are usually used as proxies or instruments of culture Diener et al. Language data are taken from Stulz and Williamson Column 1 shows that among traditional explanatory variables, Income and Social Comparison of Income SCI are significantly positively related to happiness.
These are basically the same as Arrindell et al. We can see that the R 2 of the regression is low at the level of 0. Column 2 shows the result of regression with country dummy variables, where R 2 sharply increases to 0.
The Feedback Fallacy
The robust t statistics are in parentheses. The country fixed effects do not change over time, and cannot be explained by traditional variables like income. To explore the details of these fixed effects, we add cultural dimensions into the regression equation. The results are shown in Columns 3 to As for R 2 , we find a great increase, from 0.
This contribution to R 2 is the highest among all culture variables.
How We Learn
It also means that culture is a very important predicting factor of SWB levels across countries. As discussed in Sect.
police-risk-management.com/order/iphone/nuce-network-dati.php This is consistent with Diener et al. These two results are consistent with our hypothesis 1 and hypothesis 2. This means that there exist big differences between the two collectivism indices in the GLOBE culture indices. These results are all consistent with our hypotheses 4—9 and also consistent with Diener et al. The coefficient of UAI is significantly positive, which is consistent with our hypothesis 5 but inconsistent with Arrindell et al.
It measures the degree of order in life and therefore is positively related to SWB. As our focus is the different effects of different culture characteristics on SWB, we are more concerned with the relative importance of the nine culture indices.