{"title":"技能之间的总替代弹性:来自宏观经济方法的估计","authors":"Michał Jerzmanowski, Robert Tamura","doi":"10.2139/ssrn.3614518","DOIUrl":null,"url":null,"abstract":"\n We estimate the elasticity of substitution between high-skill and low-skill workers using panel data from 32 countries during 1970–2015. Most existing estimates, which are based only on US microdata, find a value close to 1.6. We bring international data together with a theory-informed macro-approach to provide new evidence on this important macroeconomic parameter. Using the macro-approach, we find that the elasticity of substitution between tertiary-educated workers and those with lower education levels falls between 1.7 and 2.6, which is higher than previous estimates but within a plausible range. In some specifications, estimated elasticity is above the value required for strong skill-bias of technology, suggesting strong skill-bias is possible.","PeriodicalId":123778,"journal":{"name":"ERN: Theoretical Dynamic Models (Topic)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Aggregate Elasticity of Substitution between Skills: Estimates from a Macroeconomic Approach\",\"authors\":\"Michał Jerzmanowski, Robert Tamura\",\"doi\":\"10.2139/ssrn.3614518\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n We estimate the elasticity of substitution between high-skill and low-skill workers using panel data from 32 countries during 1970–2015. Most existing estimates, which are based only on US microdata, find a value close to 1.6. We bring international data together with a theory-informed macro-approach to provide new evidence on this important macroeconomic parameter. Using the macro-approach, we find that the elasticity of substitution between tertiary-educated workers and those with lower education levels falls between 1.7 and 2.6, which is higher than previous estimates but within a plausible range. In some specifications, estimated elasticity is above the value required for strong skill-bias of technology, suggesting strong skill-bias is possible.\",\"PeriodicalId\":123778,\"journal\":{\"name\":\"ERN: Theoretical Dynamic Models (Topic)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Theoretical Dynamic Models (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3614518\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Theoretical Dynamic Models (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3614518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Aggregate Elasticity of Substitution between Skills: Estimates from a Macroeconomic Approach
We estimate the elasticity of substitution between high-skill and low-skill workers using panel data from 32 countries during 1970–2015. Most existing estimates, which are based only on US microdata, find a value close to 1.6. We bring international data together with a theory-informed macro-approach to provide new evidence on this important macroeconomic parameter. Using the macro-approach, we find that the elasticity of substitution between tertiary-educated workers and those with lower education levels falls between 1.7 and 2.6, which is higher than previous estimates but within a plausible range. In some specifications, estimated elasticity is above the value required for strong skill-bias of technology, suggesting strong skill-bias is possible.