(2002a) argue that the ordered logit us to analyze each factor that is correlated “allows with reported subjective well-being separately”, which is exactly what this paper wants to look at (406). In its roughest form, the model can be written as: SWBit=α+ + + β*incomeit γ*Xit εit where Xit is a vector of known socio-demographic characteristics, as well as different attitudes of the individual at time (i.e. year of the survey wave) and captures i t εit measurement errors and unobserved characteristics (such as personality traits). Specific variables that are included in the Xit vector are, for reasons outlined in previous sections: age, gender, marital status, employment status, educational health status, location (urban/rural), religion (I am currently inclined to include the answers to the you believe in God?” question), satisfaction with democracy (“On “Do the whole are you or very satisfied, rather satisfied, not very satisfied not at all satisfied with the way democracy is developing in our country?”), confidence in the government (a or confidence in the health care great deal, quite a lot, not very much none at all), system (a or great deal, quite a lot, not very much none at all). Both the variable and most of the variables included in the Xit vector are income categorical. In order to capture the effect that each category has on the dependent variable, I will break them down into dummy variables. With regards to the dependent variable, as I have mentioned before, the “life satisfaction” and are usually used interchangeably because they tend to be “happiness” highly correlated. It is thus fair to assume that it wouldn’t make much of a difference which of these two variables is going to be used as the dependent variable. However, my
(2002a) argue that the ordered logit “allows us to analyze each factor that is correlated with reported subjective well-being separately”, which is exactly what this paper wants to look at (406). In its roughest form, the model can be written as: SWBit=α+ β*incomeit + γ*Xit + εit where Xit is a vector of known socio-demographic characteristics, as well as different attitudes of the individual i at time t (i.e. year of the survey wave) and εit captures measurement errors and unobserved characteristics (such as personality traits). Specific variables that are included in the Xit vector are, for reasons outlined in previous sections: age, gender, marital status, employment status, educational health status, location (urban/rural), religion (I am currently inclined to include the answers to the “Do you believe in God?” question), satisfaction with democracy (“On the whole are you very satisfied, rather satisfied, not very satisfied or not at all satisfied with the way democracy is developing in our country?”), confidence in the government (a great deal, quite a lot, not very much or none at all), confidence in the health care system (a great deal, quite a lot, not very much or none at all). Both the income variable and most of the variables included in the Xit vector are categorical. In order to capture the effect that each category has on the dependent variable, I will break them down into dummy variables. With regards to the dependent variable, as I have mentioned before, the “life satisfaction” and “happiness” are usually used interchangeably because they tend to be highly correlated. It is thus fair to assume that it wouldn’t make much of a difference which of these two variables is going to be used as the dependent variable. However, my
romanianHappiness.docx