Robust determinants of income distribution across and within countries.

Multicollinearity widely exists in empirical studies, which leads to imprecise estimation and even endogeneity when omitted variables are correlated with any regressors. We apply an innovative strategy, different from the usual tools (instrumental variable, ridge regression, and least absolute shrin...

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Autor principal:	Liang Frank Shao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/87c270a2cfa449d7a65c54e40c77d225
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spelling	oai:doaj.org-article:87c270a2cfa449d7a65c54e40c77d2252021-12-02T20:05:14ZRobust determinants of income distribution across and within countries.1932-620310.1371/journal.pone.0253291https://doaj.org/article/87c270a2cfa449d7a65c54e40c77d2252021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253291https://doaj.org/toc/1932-6203Multicollinearity widely exists in empirical studies, which leads to imprecise estimation and even endogeneity when omitted variables are correlated with any regressors. We apply an innovative strategy, different from the usual tools (instrumental variable, ridge regression, and least absolute shrinkage and selection operator), to estimate the robust determinants of income distribution. We transform panel data into (quasi-) cross-sectional data by removing country and time effects from the data so that all variables become zero mean and orthogonal to the country dummies and time variable, and multicollinearity becomes very low or even disappears with the quasi-cross sectional data in any specifications regardless of country dummies and time variable being included or not. Our contribution is threefold. First, we build a general method to address the multicollinearity issue in panel data, which is to isolate the common contents of correlated variables and ensures robust estimates in different specifications (dynamic or static specifications) and estimators (within- or between-effects estimators). Second, we find no evidence for the Kuznets hypothesis within and across countries; investment is economically and statistically the most robust determinant of income inequality; meanwhile, labor income share shows robustly and consistently positive effects on income inequality, which challenges the related literature. Last, simulations with our estimates show that the total marginal effects of development (regarding GDP, capital stock and investment) on income inequality are very likely to be positive within and between countries except that the impacts on middle-60% and top-quintile income shares are not so likely to increase income inequality across countries.Liang Frank ShaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253291 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Liang Frank Shao Robust determinants of income distribution across and within countries.
description	Multicollinearity widely exists in empirical studies, which leads to imprecise estimation and even endogeneity when omitted variables are correlated with any regressors. We apply an innovative strategy, different from the usual tools (instrumental variable, ridge regression, and least absolute shrinkage and selection operator), to estimate the robust determinants of income distribution. We transform panel data into (quasi-) cross-sectional data by removing country and time effects from the data so that all variables become zero mean and orthogonal to the country dummies and time variable, and multicollinearity becomes very low or even disappears with the quasi-cross sectional data in any specifications regardless of country dummies and time variable being included or not. Our contribution is threefold. First, we build a general method to address the multicollinearity issue in panel data, which is to isolate the common contents of correlated variables and ensures robust estimates in different specifications (dynamic or static specifications) and estimators (within- or between-effects estimators). Second, we find no evidence for the Kuznets hypothesis within and across countries; investment is economically and statistically the most robust determinant of income inequality; meanwhile, labor income share shows robustly and consistently positive effects on income inequality, which challenges the related literature. Last, simulations with our estimates show that the total marginal effects of development (regarding GDP, capital stock and investment) on income inequality are very likely to be positive within and between countries except that the impacts on middle-60% and top-quintile income shares are not so likely to increase income inequality across countries.
format	article
author	Liang Frank Shao
author_facet	Liang Frank Shao
author_sort	Liang Frank Shao
title	Robust determinants of income distribution across and within countries.
title_short	Robust determinants of income distribution across and within countries.
title_full	Robust determinants of income distribution across and within countries.
title_fullStr	Robust determinants of income distribution across and within countries.
title_full_unstemmed	Robust determinants of income distribution across and within countries.
title_sort	robust determinants of income distribution across and within countries.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/87c270a2cfa449d7a65c54e40c77d225
work_keys_str_mv	AT liangfrankshao robustdeterminantsofincomedistributionacrossandwithincountries
_version_	1718375493150965760

Robust determinants of income distribution across and within countries.

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