CREAT: Census Research Exploration and Analysis Tool

Job-to-Job Flows and Earnings Growth*

January 2017

Working Paper Number:

CES-17-08

Abstract

The U.S. workforce has had little change in real wages, income, or earnings since the year 2000. However, even when there is little change in the average rate at which workers are compensated, individual workers experienced a distribution of wage and earnings changes. In this paper, we demonstrate how earnings evolve in the U.S. economy in the years 2001-2014 on a forthcoming dataset on earnings for stayers and transitioners from the U.S. Census Bureau's Job-to-Job Flows data product to account for the role of on-the-job earnings growth, job-to-job flows, and nonemployment in the growth of U.S. earnings.

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.
:
quarterly, earnings, employ, labor, recession, workforce, salary, employment wages, employment dynamics, unemployment rates, earn, earner, wage earnings, employment earnings, earnings growth

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.
:
Center for Economic Studies

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 'Job-to-Job Flows and Earnings Growth*' are listed below in order of similarity.