Markov chain theory, which has frequently been applied to analyze income convergence, imposes restrictive assumptions on the data-generating process. In most empirical studies, it is taken for granted that per capita income follows a stationary first-order Markov process. To examine the reliability of estimated Markov transition matrices, the authors propose Pearson X 2 and likelihood ratio tests of the Markov property, spatial independence, and homogeneity over time and space. As an illustration, it is shown that per capita income in the forty-eight contiguous U.S. states did clearly not follow a common stationary first-order Markov process from 1929 to 2000.
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