CREAT: Census Research Exploration and Analysis Tool

Understanding Criminal Record Penalties in the Labor Market

June 2025

Written by: Evan Rose, Yotam Shem-Tov

Working Paper Number:

CES-25-39

Abstract

This paper studies the earnings and employment penalties associated with a criminal record. Using a large-scale dataset linking criminal justice and employer-employee wage records, we estimate two-way fixed effects models that decompose earnings into worker's portable earnings potential and firm pay premia, both of which are allowed to shift after a worker acquires a record. We find that firm pay premia explain a small share of earnings gaps between workers with and without a record. There is little evidence of variable within-firm premia gaps either. Instead, components of workers' earnings potential that persist across firms explain the bulk of gaps. Conditional on earnings potential, workers with a record are also substantially less likely to be employed. Difference-in-differences estimates comparing workers' first conviction to workers charged but not convicted or charged later support these findings. The results suggest that criminal record penalties operate primarily by changing whether workers are employed and their earnings potential at every firm rather than increasing sorting into lower-paying jobs, although the bulk of gaps can be attributed to differences that existed prior to acquiring a record.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
earnings, employee, employ, employed, effect wages, wage gap, effects employment, earn, wage earnings, employment earnings, earnings employees, crime

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Internal Revenue Service, Social Security Administration, Ordinary Least Squares, National Longitudinal Survey of Youth, North American Industry Classification System, American Community Survey, Longitudinal Employer Household Dynamics, AKM, Protected Identification Key, University of Michigan, Census Bureau Disclosure Review Board, Federal Statistical Research Data Center, COVID-19

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'Understanding Criminal Record Penalties in the Labor Market' are listed below in order of similarity.