CREAT: Census Research Exploration and Analysis Tool

Within and Between Firm Changes in Human Capital, Technology, and Productivity Preliminary and incomplete

December 2001

Working Paper Number:

tp-2001-03

Abstract

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.
:
econometrically, econometric, estimating, statistical, data census, census data, survey, employee, employ, employed, labor, longitudinal, expenditure, economic census, workplace, workforce, census years, census bureau, decade, employer household, aging, research census, use census, censuses surveys

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.
:
Annual Survey of Manufactures, Standard Industrial Classification, Bureau of Labor Statistics, National Science Foundation, National Bureau of Economic Research, Financial, Insurance and Real Estate Industries, Urban Institute, Business Services, Cornell University, Economic Census, Department of Labor, National Institute on Aging, Alfred P Sloan Foundation, Longitudinal Employer Household Dynamics, AKM, Cornell Institute for Social and Economic Research

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 'Within and Between Firm Changes in Human Capital, Technology, and Productivity Preliminary and incomplete' are listed below in order of similarity.