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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248696/ https://www.ncbi.nlm.nih.gov/pubmed/34197494 http://dx.doi.org/10.1371/journal.pone.0253291 |
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author | Shao, Liang Frank |
author_facet | Shao, Liang Frank |
author_sort | Shao, Liang Frank |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8248696 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82486962021-07-09 Robust determinants of income distribution across and within countries Shao, Liang Frank PLoS One Research Article 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. Public Library of Science 2021-07-01 /pmc/articles/PMC8248696/ /pubmed/34197494 http://dx.doi.org/10.1371/journal.pone.0253291 Text en © 2021 Liang Frank Shao https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Shao, Liang Frank Robust determinants of income distribution across and within countries |
title | 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_short | Robust determinants of income distribution across and within countries |
title_sort | robust determinants of income distribution across and within countries |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248696/ https://www.ncbi.nlm.nih.gov/pubmed/34197494 http://dx.doi.org/10.1371/journal.pone.0253291 |
work_keys_str_mv | AT shaoliangfrank robustdeterminantsofincomedistributionacrossandwithincountries |