3. Explaining happiness by socio-demographic and economic variables
The individual-level determinants of life-satisfaction are analyzed in a pooled cross-section
ordered logit regression and the results given in Table 2. Note that in the regression analysis,
Austria is not included due to missing explanatory variables. The first column of results refers
to the full model containing all available regressors in the surveys. Following the general-to-
specific modeling strategy advocated by Hendry (1993), a consistent testing-down process has
been applied to this model, leading to the reduced model in the right part of the table.
Leamer (1978) argues that in large statistical samples there is the danger that even slight and
economically meaningless deviations from the null hypothesis lead to a rejection of the test.
In view of our sample size of more than 5600 observations, a significance level of 1% has
been used throughout the analysis. In the interpretation of the variables, we generally
concentrate on the statistically significant effects. The pseudo-R2 value of our regression,
below 9%, is not very high in absolute terms. This is an indication that we do not understand
happiness at an individual level very well. However, the fit of the regression is at least as high
as in comparable studies on Western countries. Thus, the determinants of happiness
considered in the literature are important for Eastern Europe even in the turbulent early period
of transition.
Regarding the estimates of country dummies, with the Czech Republic as a reference
category, we confirm the ranking in Table 1. It follows that the observed differences in the
average happiness values of countries cannot be explained by the individual-level explanatory
variables in our data set. There are not enough observations to study the determinants of these
cross-country differences in average life-satisfaction extensively. However, from the point of
view of economics it is particularly interesting to see whether these variations in average
national happiness are related to per capita income differentials within this group of countries.
The Pearson’s correlation coefficient between estimated country dummies and national GDP
per capita values in US Dollars is 0.40. Therefore, in a bivariate context inter-country income
variations can explain only about 16% of the variation in national happiness. This suggests
that per capita income will only play a moderate role in explaining inter-country happiness
differences in Eastern Europe. The analysis of cross-country variations in happiness will be
continued below.
We employ normal standard errors (SE) in the analysis, as they are the most efficient variance
estimators. It is apparent from Table 2 that heteroscedasticity-robust standard errors (HCSE)
do not lead to noticeable differences, except for the category “Apprentice”. However, the