{"title":"规模很重要:衡量不平等和增长冲击的影响","authors":"Sanghamitra Bandyopadhyay , Rui Sun","doi":"10.1016/j.chieco.2025.102429","DOIUrl":null,"url":null,"abstract":"<div><div>Understanding the relationship between income inequality and economic growth is of utmost importance to social scientists, but the empirical evidence is inconclusive. We use a Bayesian structural vector autoregression approach to estimate the relationship between inequality and growth for two large economies, China and the USA, from 1978 to 2018. We find that a growth shock is inequality-increasing, and an inequality shock is growth-reducing. However, the size of the effects of these shocks is extremely small, accounting for under 2% of the variance for both countries, suggesting other important socio-economic determinants of growth and inequality.</div></div>","PeriodicalId":48285,"journal":{"name":"中国经济评论","volume":"93 ","pages":"Article 102429"},"PeriodicalIF":5.5000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Size matters: Measuring the effects of inequality and growth shocks\",\"authors\":\"Sanghamitra Bandyopadhyay , Rui Sun\",\"doi\":\"10.1016/j.chieco.2025.102429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Understanding the relationship between income inequality and economic growth is of utmost importance to social scientists, but the empirical evidence is inconclusive. We use a Bayesian structural vector autoregression approach to estimate the relationship between inequality and growth for two large economies, China and the USA, from 1978 to 2018. We find that a growth shock is inequality-increasing, and an inequality shock is growth-reducing. However, the size of the effects of these shocks is extremely small, accounting for under 2% of the variance for both countries, suggesting other important socio-economic determinants of growth and inequality.</div></div>\",\"PeriodicalId\":48285,\"journal\":{\"name\":\"中国经济评论\",\"volume\":\"93 \",\"pages\":\"Article 102429\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2025-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国经济评论\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1043951X25000872\",\"RegionNum\":1,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国经济评论","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1043951X25000872","RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Size matters: Measuring the effects of inequality and growth shocks
Understanding the relationship between income inequality and economic growth is of utmost importance to social scientists, but the empirical evidence is inconclusive. We use a Bayesian structural vector autoregression approach to estimate the relationship between inequality and growth for two large economies, China and the USA, from 1978 to 2018. We find that a growth shock is inequality-increasing, and an inequality shock is growth-reducing. However, the size of the effects of these shocks is extremely small, accounting for under 2% of the variance for both countries, suggesting other important socio-economic determinants of growth and inequality.
期刊介绍:
The China Economic Review publishes original works of scholarship which add to the knowledge of the economy of China and to economies as a discipline. We seek, in particular, papers dealing with policy, performance and institutional change. Empirical papers normally use a formal model, a data set, and standard statistical techniques. Submissions are subjected to double-blind peer review.