Econometric analyses in the happiness literature typically use subjective well-being (SWB) data to compare the mean of observed or latent happiness across samples. Recent critiques show that comparing the mean of ordinal data is only valid under strong assumptions that are usually rejected by SWB data. This leads to an open question whether much of the empirical studies in the economics of happiness literature have been futile. In order to salvage some of the prior results and avoid future issues, we suggest regression analysis of SWB (and other ordinal data) should focus on the median rather than the mean. Median comparisons using parametric models such as the ordered probit and logit can be readily carried out using familiar statistical softwares like STATA. We also show a previously assumed impractical task of estimating a semiparametric median ordered-response model is also possible by using a novel constrained mixed integer optimization technique. We use GSS data to show the famous Easterlin Paradox from the happiness literature holds for the US independent of any parametric assumption.
The mean rank of happiness outcomes between groups has often been estimated using ordered probit and logit models. However, it has recently been highlighted that such ranking is not identified in most applications. Can we then learn anything from a mean rank between groups that is reported by standard statistical softwares such as STATA? We argue it can instead be interpreted as the median rank, which is identified even when the mean rank is not. We thus suggest focusing on ranking happiness outcomes (and other ordinal data) by the median rather than the mean. The median ranking can also be performed semiparametrically and we provide a new constrained mixed integer optimization procedure for implementation. To illustrate, we use General Social Survey data to show the well-known Easterlin Paradox in the happiness literature holds for the US over the period 1972 to 2006.
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Econometric analyses in the happiness literature typically use subjective well-being (SWB) data to compare the mean of observed or latent happiness across samples. Recent critiques show that comparing the mean of ordinal data is only valid under strong assumptions that are usually rejected by SWB data. This leads to an open question whether much of the empirical studies in the economics of happiness literature have been futile. In order to salvage some of the prior results and avoid future issues, we suggest regression analysis of SWB
The mean rank of happiness outcomes between groups has often been estimated using ordered probit and logit models. However, it has recently been highlighted that such ranking is not identified in most applications. Can we then learn anything from a mean rank between groups that is reported by standard statistical softwares such as STATA? We argue it can instead be interpreted as the median rank, which is identified even when the mean rank is not. We thus suggest focusing on ranking happiness outcomes
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should focus on
by
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Median comparisons using parametric models such as the ordered probit and logit can be readily carried out using familiar statistical softwares like STATA. We also show a previously assumed impractical task of estimating a semiparametric median ordered-response model is also possible by using a novel
The median ranking can also be performed semiparametrically and we provide a new
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technique. We use GSS
procedure for implementation. To illustrate, we use General Social Survey
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famous Easterlin Paradox from
well-known Easterlin Paradox in
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independent of any parametric assumption.
over the period 1972 to 2006.