In an effort to analyze the interdependences among small business growth, migration behavior, local public services, and median household income, this study developed a simultaneous-equation system under the assumptions of profit maximization of firm and utility maximization of households as well as the neoclassical assumption of equilibrium growth in a partial lag-adjustment growth-equilibrium framework. This model is an extension of the "jobs follow people or people follow jobs" literature and it improved previous models in the growth-equilibrium tradition by explicitly modeling local government and regional income in the growth process. It also explicitly modeled gross in-migration and gross out-migration separately in order to spell out the differential effects, which used to be glossed over under net population change in previous studies.;Generally, the results from these model estimations are consistent with the theoretical expectations and empirical findings in the equilibrium growth literature and provide support to the basic hypotheses of this study. First, both the spatial and nonspatial models estimates showed the existence of feedback simultaneities among the endogenous variables of the models. This is especially true for the spatial panel model where the coefficients on the endogenous variables in almost all equations of the model are statistically significant atleast at the five percent levels. This indicates that the interdependences among employment growth rate, gross in-migration growth rate, gross out-migration growth rate, median household income growth rate and direct local government expenditures growth rate are very strong. The directions of causation as indicated by the signs of the coefficients are also consistent with the theoretical expectations.;Second, the results from both the spatial and the non-spatial model estimations also showed the existence of conditional convergence with respect to the respective endogenous variable of each equation of the models. This is indicated by the negative and statistically significant coefficients on the lagged dependent variables of the models. This implied that the rates of growth of employment, gross in-migration, gross out-migration, median household income, and direct local government expenditures were higher in counties that had low initial levels of employment, gross in-migration, gross outmigration, median household income, and direct local government expenditures, respectively compared to counties with high initial levels of the same.;Third, the results from the parameter estimation of spatial models and from the exploratory spatial data analysis indicated the existence of spatial autoregressive lag effects and spatial cross-regressive lag effects with respect to the endogenous variables of the models. Besides, the results for Global Moran's I statistics indicated the existence of spatial spillover effect with respect to the error terms of the spatial models. These results would imply that employment growth rate, gross in-migration growth rate, gross outmigration growth rate, median household income growth rate, and direct local government growth rate in a given county are dependent on the averages of employment growth rates, gross in-migration growth rates, gross in-migration growth rates, median household income growth rates, and direct local government growth rate of neighboring counties in the study area. These results are also important from the economic and policy perspectives because they indicate that each of the dependent variables in the model is not only dependent on the characteristics of that county but also on the characteristics of those of its neighbors. Thus, spatial effects should be tested for in empirical works involving growth rate of employment (EMPR), growth rate of gross in-migration (INMGR), growth rate of gross out-migration (OTMGR), growth rate of median household income (MHYR), and growth rate of direct local government expenditures per capita (DGEXR). The existence of spatial dependences in the error terms is an indication that random shocks into the system with respect to each of these endogenous variables do not only affect the county/counties where the shock originated and its/their neighbors, but also create shock waves across the study area (Appalachia). This is possible because of the structure of the autoregressive error model. (Abstract shortened by UMI.).