Death Taxes and Elderly Interstate Migration
Abstract
This study was undertaken to test the proposition that the relative levels of death taxes in the different states contribute to the interstate migration flows of the U.S. elderly population. A statistical model incorporating state-to-state elderly migration flows and the state death tax levels for each of the 50 states and the District of Columbia was tested. The results of the analysis point to a separation of the decision made to leave a state of origin from the decision regarding where to move. The data do not support the idea that the level of death taxes within a given state contributes to the out-migration of elderly residents; however, once the decision to migrate has been made, there is evidence that the destination area will have lower death tax rates, but the effect is considerably less important than the impact of climate and other factors measured by the model.
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1.
1. Each variable employed in this research was first reviewed for its theoretical and conceptual relevance and selected from a set of similar variables assembled for this research. While not all of the selected variables have been used in previous empirical studies, each is believed relevant to the general process of elderly migrant decision making as revealed by the literature on this topic. Second, each set of variables (e.g., we began with nine different climate measures) was subjected to a principal components factor analysis, with the variable having the highest factor loading with the principal factor taken as the most plausible single variable for use in the regression analysis. Finally, a regression test (with Mij as the dependent variable) was performed on combinations of variables, and on the individual variables, to determine which contributed most to the regression sum of squares. Those variables that made the smallest contributions were dropped from the model in the interest of parsimony and to reduce the extent of multicollinearity among the independent variables. The models in the analysis are multiple regression equations in which the independent variables are measured as close to the beginning of the migration period (1975) as possible to reduce potential spurious correlations that might arise from the effects of migration on end-of-period variables.
2.
2. To keep streams having no sample migrants in the analysis, a negligible, small positive number was added to each zero cell so that its logarithm could be taken. In the text that follows, variables are described in their nonlogged form. It should be remembered, however, that in the fitted models all variables are in logged form.
3.
3. We do, however, acknowledge one hint of an exception to this important conclusion. When the models in Table 2 were rerun omitting the null migration streams, a relatively strong (yet statistially insignificant at p < .15) negative elasticity emerged for variable DTHd among the most wealthy. This small elasticity (-.065) has the proper sign and emerges as the numerically largest regression coefficient for variable DTHd when compared with the other four wealth strata. Although we have dismissed the coefficient as not being significantly different from zero, it nevertheless stands as a hint that our conclusion is well founded. Elderly migration patterns do appear to be influenced by death tax levels in areas of designation (even if ever so slightly, net of the other stronger factors), and this influence further appears to be guided by the destination choices of the most wealthy among the elderly migrants. This makes intuitive sense; the effect simply is too weak (when using these particular census data) to emerge with statistical confidence.
4.
4. As indicated above, when we reran our models omitting the null streams, our conclusions held steady. The stronger coefficient consistently emerge with the correct (or hypothesized) sign.
References
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Article first published: September 1988
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