Labor Market Dynamics: A Hidden Markov Approach

72 Pages Posted: 23 Jan 2020

See all articles by Ippei Shibata

Ippei Shibata

International Monetary Fund (IMF)

Date Written: December 2019


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.

Keywords: Labor force, Labor market characteristics, Labor markets, Human capital, Labor force participation, Unemployment Business Cycle, Unemployment, Great Recession, WP, FOM, transition probability, nonparticipant, mean absolute deviation

JEL Classification: E24, E32, J64, E2, E01, Z13, F16, I3

Suggested Citation

Shibata, Ippei, Labor Market Dynamics: A Hidden Markov Approach (December 2019). IMF Working Paper No. 19/282, Available at SSRN:

Ippei Shibata (Contact Author)

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

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