This paper describes a dynamic random effects econometric model from which inferences on earnings mobility may be made. It answers questions such as, given some initial level of observed earnings, what is the probability that an agent with certain characteristics will remain below a specified level of earnings (for example the poverty level) for a specified number of time periods? Existing research assumes that the distributions of the unobserved permanent and transitory shocks in the model are known up to finitely many parameters. However, predictions of earnings mobility are highly sensitive to assumptions about these distributions. The present paper estimates the distributions of the random effects nonparametrically. The results are used to predict the probabilities of remaining in a low state of earnings. The results from the nonparametric distributions are contrasted to those obtained under a normality assumption. Using the nonparametrically estimated distributions gives estimated probabilities that are smaller than those obtained under the normality assumption. Through a Monte Carlo experiment and by examining unconditional predicted earnings distributions, it is demonstrated that the nonparametric method is likely to be considerably more accurate, and that assuming normality may give quite misleading results.
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