Robust Ranking of Happiness Outcomes: A Median Regression Perspective
Le-Yu Chen (),
Ekaterina Oparina (),
Nattavudh Powdthavee () and
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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.
Date: 2019-02, Revised 2021-04
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