Remeasurement of Income Opportunity Inequality of Chinese Residents:New Discoveries from Machine Learning
Narrowing the inequality of opportunities and eliminating the source of the widening income gap to achieve equitable distribution are the only ways to achieve common prosperity and the starting point of governance to promote Chinese-style modernization.In this study,a machine learning model based on an integrated regression tree algorithm is adopted to overcome the important defects of traditional methods in measuring opportunity inequality.Moreover,a quantile regression forest is introduced to expand the opportunity inequality of average income to the opportunity inequality of income distribution to provide a new method and perspective for measuring income opportunity inequality.Based on data from the China General Social Survey from 2010 to 2021,the results reveal that the opportunity inequality of the average income of the whole sample measured by the Gini coefficient is about 0.244-0.307,accounting for 38.1％-52.4％ of the total inequality.The calculation result of this nonlinear machine learning model is significantly higher than that of the traditional method,which relies on a linear model.The income opportunity inequality of urban residents is higher than that of rural residents,and environmental factors contribute the most to the income gap between urban and rural areas.The difference between the observable characteristics of individuals and their fathers tends to widen the income gap,while those of their mothers tend to do the opposite.In addition,the measurement results of opportunity inequality of income distribution reveal that environmental factors significantly affect the income risk of offspring,and a good environmental foundation is more inclined to give the right advantage of income distribution to offspring.From the perspective of distribution structure,the inequality of the lower income limit,upper income limit,accidental income,and income risk is significantly higher than the inequality of opportunity of average income.The child income limit and income risk inequality caused by uncontrollable environmental factors need more attention.The innovation of this study is reflected in the two aspects of the research method and research content.(1)In terms of research methods,this study adopts cutting-edge machine learning models to address the main theoretical defects in the existing methods and to measure the opportunity inequality of Chinese income and the contribution of environmental factors more scientifically,while continuously advancing the technology to achieve this goal.Based on this,this study also reveals some new important findings and obtains a new dimension of empirical explanation.(2)This study uses an integrated machine learning model to recover individual income distribution and describes the child generation income distribution gap caused by environmental factors from the four aspects of income lower limit,income upper limit,accidental income,and income risk in a more complete manner.It broadens the extension of opportunity inequality,an important concept in the field of income distribution to assess the opportunity inequality of income distribution.Based on the research conclusions,the study puts forward the following policy suggestions from the perspective of opportunity inequality and common prosperity.First,eliminating income inequality caused by environmental factors is essential for promoting future common prosperity.Second,the inequality of opportunity in income distribution should be included in the statistical monitoring system of common prosperity.Third,improve the governance system for common prosperity from the perspective of the dynamic distribution of income and wealth.Fourth,establish an expected management system for residents'income distribution and income risk based on scientific calculation.
CircumstancesInequality of OpportunityDistribution InequalityMachine Learning
NETL
NSTL
万方数据
