Robust Ranking of Happiness Outcomes: A Median Regression Perspective
Le-Yu Chen (),
Ekaterina Oparina (),
Nattavudh Powdthavee () and
Papers from arXiv.org
Ordered probit and logit models have been frequently used to estimate the mean ranking of happiness outcomes (and other ordinal data) across groups. However, it has been recently highlighted that such ranking may not be identified in most happiness applications. We suggest researchers focus on median comparison instead of the mean. This is because the median rank can be identified even if the mean rank is not. Furthermore, median ranks in probit and logit models can be readily estimated using standard statistical softwares. The median ranking, as well as ranking for other quantiles, can also be estimated semiparametrically and we provide a new constrained mixed integer optimization procedure for implementation. We apply it to estimate a happiness equation using General Social Survey data of the US.
Date: 2019-02, Revised 2022-06
New Economics Papers: this item is included in nep-hap, nep-hpe and nep-ltv
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11) Track citations by RSS feed
Downloads: (external link)
http://arxiv.org/pdf/1902.07696 Latest version (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1902.07696
Access Statistics for this paper
More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().