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Who Moves Up the Job Ladder?*

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January 2017

Written by: John Haltiwanger, Erika McEntarfer, Henry Hyatt

Working Paper Number:

CES-17-63

Abstract

In this paper, we use linked employer-employee data to study the reallocation of heterogeneous workers between heterogeneous firms. We build on recent evidence of a cyclical job ladder that reallocates workers from low productivity to high productivity firms through job-to-job moves. In this paper we turn to the question of who moves up this job ladder, and the implications for worker sorting across firms. Not surprisingly, we find that job-to-job moves reallocate younger workers disproportionately from less productive to more productive firms. More surprisingly, especially in the context of the recent literature on assortative matching with on-the-job search, we find that job-to- job moves disproportionately reallocate less-educated workers up the job ladder. This finding holds even though we find that more educated workers are more likely to work with more productive firms. We find that while highly educated workers are less likely to match to low productivity firms, they are also less likely to separate from them, with less-educated workers both more likely to separate to a better employer in expansions and to be shaken off the ladder (separate to nonemployment) in contractions. Our findings underscore the cyclical role job-to-job moves play in matching workers to better paying employers.

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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. :
economist, employee, employ, employed, endogenous, job, employment growth, heterogeneous, heterogeneity, hiring, workforce, worker, hire, matching, career

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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. :
Total Factor Productivity, Current Population Survey, Longitudinal Business Database, Employer Identification Numbers, Longitudinal Employer Household Dynamics, AKM, Census Bureau Business Register, Quarterly Workforce Indicators, Census 2000, Quarterly Census of Employment and Wages, Society of Labor Economists

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