A Locally Stationary Markov Chain Model for Labor Dynamics

10.6339/JDS.2009.07(1).417

Enrique E. Alvarez；Francisco J. Ciocchini；Kishori Konwar

Markov Ccain ； sationarity ； unemployment

Journal of Data Science

7卷1期（2009 / 01 / 01）

27 - 42

英文

Labor market surveys usually partition individuals into three states: employed, unemployed, and out of the labor force. In particular, the Argentine ”Encuesta Permanente de Hogares (EPH)” follows a rotating scheme so that each selected household is interviewed four times within two years. Each time, the current labor state of individuals is recorded, together with extensive demographic information. We model those labor paths as consecutive observations from independent Markov chains, were transition matrixes are related to covariates through a multivariate logistic link.Because the EPH is severely affected by attrition, a significant fraction of the surveyed paths contain just one single point. Instead of discarding those observations, we opt to base estimation on the full data by (i) assuming the Markov chains are stationary and (ii) incorporating the chronological time of the first interview as an additional covariate for each individual. This novel treatment represents a convenient approximation, which we illustrate with data from Argentina in the period 1995-2002 via maximum likelihood estimation. Several interesting labor market indexes, which are functionally related to the transition matrixes, are also presented in the last portion of the paper and illustrated with real data.

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