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This thesis also proposes a new type of model: the panel data model with a nonhomogeneous block-wise factor structure. Extensive literature exists in macroeconomics and finance on block-wise factor models; however, these block-wise factor structures are homogeneous, and the subjects do not change the blocks that they belong to. For example, in research about how business cycle variations are driven by different types of shocks related to regional or country-specific events, the macroeconomic variables of the United States will always belong to the North American block. However, we have a nonhomogeneous block-wise factor structure inside wage dynamics: as workers have different returns, or may be subjected to different productivity shocks for their unobserved skills in different regions (blocks), the regions where workers reside could also change over time. According to our balanced data set from NLSY79 for more than 20 years, 306 of 1831 (16.72%) workers moved across regions during the survey period, which cannot simply be ignored. This thesis proposes a set of identification conditions and estimation methods for this new type of model, and the Monte Carlo simulation yields very good estimation results. I also apply this model to study the NLSY79 balanced panel data, and find that the Northeast and the South have similar regional value patterns, while the Midwest and the West share similar patterns.