단행본NBER Working Paper 24951
Occupational classifications: a machine learning approach
- 청구기호
- WP 24951
- 발행사항
- Cambridge : NBER, 2018
- 형태사항
- 47 p. :. PDF file ;. 438.5 KB
- 분류기호
- 듀이십진분류법->WP
소장정보
위치 | 등록번호 | 청구기호 / 출력 | 상태 | 반납예정일 |
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이용 가능 (1) | ||||
E0003143 | 대출가능 | - |
이용 가능 (1)
- 등록번호
- E0003143
- 상태/반납예정일
- 대출가능
- -
- 위치/청구기호(출력)
책 소개
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.