Occupational classifications: A machine learning approach

Akina Ikudo · Julia Ingrid Lane · Joseph Staudt · Bruce A. Weinberg

16 citations0/0 files storedJournal of Economic and Social Measurement

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Computational and Text Analysis Methods Labor market dynamics and wage inequality Data Quality and Management

Abstract

Characterizing people’s occupations is important for both policy and research. However, as large scale administrative records are increasingly being used to describe labor market activity, it will become important to find new automated approaches to describing occupations. We apply new machine learning techniques to new sources of data and investigate the potential of using algorithms to classify occupations. We find that job titles are both inherently noisy and inconsistent across organizations, but a subset of them can be assigned algorithmically, with little impact on accuracy.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 16 citations exist upstream.

References

2001Measurement Error in Survey Data 2017Human Decisions and Machine Predictions 2017Human Decisions and Machine Predictions 2019The Impact of Machine Learning on Economics 2014Science Funding and Short-Term Economic Activity

Cited by

2022The ripple effects of funding on researchers and output

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.