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Abstract
The purpose of this investigation is to determine if popular convergence models that have been applied to international data and to states of the U.S. can be applied to regions within the U.S. Two large regions, the Great Plains and Plains, are analyzed. For the most part, results support the basic convergence notion. Some of the estimates of convergence rates for the regions are similar to those obtained in leading studies.
In the investigation, two concepts of per capita income are used. The first is the traditional measure, the ratio of total personal income to population. The second measure is called net per capita income and is formed by removing transfer payments from total personal income before the division by population. We argue that the rate of convergence may be aided by transfer payments and the results obtained support this concept. Estimates of the convergence rate in models for total per capita income are larger than those obtained in corresponding models where net per capita income is used.
Industry and population variables are used to control for shocks in the convergence models. In the Great Plains data, the variables add little to the explanatory power of the models. In the Plains data, the variables are significant as a group. In the Plains study, a metropolitan area dummy variable is used to test whether there are differences between metropolitan and nonmetropolitan counties in rates of convergence. It is shown that metropolitan areas converge more slowly than nonmetropolitan areas but the difference is slight.
Our evidence does suggest that convergence is occurring among the counties in both regions. The process of moving populations from agriculturally dominated areas to areas in need of more workers is all part of the process. At the heart of this movement in the Plains is advancing technological progress in agriculture. Fewer people are needed to sustain production. Excess population moves to where their skills are in greater demand.
