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100			_aKang, Wei _956515
245			_aInference for Income Mobility Measures in the Presence of Spatial Dependence/
260			_bSage, _c2020.
300			_aVol 43, Issue 1-2, 2020( 10–39 p.)
520			_aIncome mobility measures provide convenient and concise ways to reveal the dynamic nature of regional income distributions. Statistical inference about these measures is important especially when it comes to a comparison of two regional income systems. Although the analytical sampling distributions of relevant estimators and test statistics have been asymptotically derived, their properties in small sample settings and in the presence of contemporaneous spatial dependence within a regional income system are underexplored. We approach these issues via a series of Monte Carlo experiments that require the proposal of a novel data generating process capable of generating spatially dependent time series given a transition probability matrix and a specified level of spatial dependence. Results suggest that when sample size is small, the mobility estimator is biased while spatial dependence inflates its asymptotic variance, raising the Type I error rate for a one-sample test. For the two-sample test of the difference in mobility between two regional economic systems, the size tends to become increasingly upward biased with stronger spatial dependence in either income system, which indicates that conclusions about differences in mobility between two different regional systems need to be drawn with caution as the presence of spatial dependence can lead to false positives. In light of this, we suggest adjustments for the critical values of relevant test statistics.
700			_aRey, Sergio J. _956516
773	0		_011129 _917016 _dSage, 2019. _tInternational regional science review
856			_uhttps://doi.org/10.1177/0160017619826291
942			_2ddc _cEJR
999			_c14098 _d14098