{"title":"专业复杂性指数与高校技能产出","authors":"Xiaoxiao Li, S. Linde, Hajime Shimao","doi":"10.2139/ssrn.3791651","DOIUrl":null,"url":null,"abstract":"We propose an easily computable measure called the Major Complexity Index (MCI) that captures the latent skills taught in different majors. By applying the Method of Reflections to the major-to-occupation network, we construct a scalar measure of the relative complexity of majors. Our measure provides strong explanatory power of major average earnings and employment. Further evidence suggests that the MCI is strongly associated with advanced skills such as quantitative problem-solving, and the use of computing technology. We also provide a two-stage algorithm to partial out selection on observables which opens up possibilities of applying the complexity measure in various contexts.","PeriodicalId":210669,"journal":{"name":"Labor: Human Capital eJournal","volume":"19 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Major Complexity Index and College Skill Production\",\"authors\":\"Xiaoxiao Li, S. Linde, Hajime Shimao\",\"doi\":\"10.2139/ssrn.3791651\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an easily computable measure called the Major Complexity Index (MCI) that captures the latent skills taught in different majors. By applying the Method of Reflections to the major-to-occupation network, we construct a scalar measure of the relative complexity of majors. Our measure provides strong explanatory power of major average earnings and employment. Further evidence suggests that the MCI is strongly associated with advanced skills such as quantitative problem-solving, and the use of computing technology. We also provide a two-stage algorithm to partial out selection on observables which opens up possibilities of applying the complexity measure in various contexts.\",\"PeriodicalId\":210669,\"journal\":{\"name\":\"Labor: Human Capital eJournal\",\"volume\":\"19 4\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Labor: Human Capital eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3791651\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Labor: Human Capital eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3791651","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Major Complexity Index and College Skill Production
We propose an easily computable measure called the Major Complexity Index (MCI) that captures the latent skills taught in different majors. By applying the Method of Reflections to the major-to-occupation network, we construct a scalar measure of the relative complexity of majors. Our measure provides strong explanatory power of major average earnings and employment. Further evidence suggests that the MCI is strongly associated with advanced skills such as quantitative problem-solving, and the use of computing technology. We also provide a two-stage algorithm to partial out selection on observables which opens up possibilities of applying the complexity measure in various contexts.