Labor Market Dynamics: A Hidden Markov Approach

December 20, 2019

Preview Citation

Format: Chicago

Ippei Shibata. Labor Market Dynamics: A Hidden Markov Approach, (USA: International Monetary Fund, 2019) accessed November 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

This paper proposes a hidden state Markov model (HMM) that incorporates workers’ unobserved labor market attachment into the analysis of labor market dynamics. Unlike previous literature, which typically assumes that a worker’s observed labor force status follows a first-order Markov process, the proposed HMM allows workers with the same labor force status to have different history-dependent transition probabilities. I show that the estimated HMM generates labor market transition probabilities that match those observed in the data, while the first-order Markov model (FOM) and its many-state extensions cannot. Even compared with the extended FOM, the HMM improves the fit of the empirical transition probabilities by a factor of 30. I apply the HMM to (1) calculate the long-run consequences of separation from stable employment, (2) study evolutions of employment stability across different demographic groups over the past several decades, (3) compare the dynamics of labor market flows during the Great Recession to those during the 1981 recession, and (4) highlight the importance of looking beyond distributions of current labor force status.

Subject: Employment, Employment rate, Labor, Labor force, Labor markets, Unemployment

Keywords: Attachment state, Employment, Employment rate, Employment stability, Labor force, Labor force history, Labor force status, Labor market attachment state, Labor market dynamics, Labor market transition, Labor markets, Transition dynamics, Transition probability, Unemployment, Unemployment state, Unobserved state, WP

Publication Details

Pages:

71
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2019/282
Stock No:

WPIEA2019282
ISBN:

9781513519265
ISSN:

1018-5941

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