"A Collection of Regional Economies"

I thought Lewis’ last chapter and particularly his discussion on the Whitney controversy was particularly interesting (though like many of you I enjoyed his anecdotes and storytelling as well). Unlike Caroline and Shane, however, I do not know very much about California’s politics. Rather, Whitney’s discussion of the emergence of “regional strength and weaknesses” and view of “the U.S. national economy as a collection of regional economies” resonated with my personal experience (Lewis 177, 175). I am not certain the extent to which people move explicitly because of financial stability (though it isn’t difficult to imagine); however, there is definitely plenty anecdotal evidence of individuals who move between towns or states to avoid high taxes as well as to access economic opportunity. Being from New England, I know plenty of people from Vermont and Massachusetts who cross over into New Hampshire to do tax-free shopping (indeed, New Hampshire has capitalized on their sales tax free status as they have NH State Liquor Stores on either side of the highway when you enter and leave the state). Similarly, there are many people who move out of New England to southern states with lower tax rates (or, like Texas, with no income tax whatsoever), as well as for job opportunities. Regional “strengths and weaknesses” in terms of offering more preferable tax policies or opportunities thus seem to exist.

 Within Connecticut itself, in my experience it seems like there is a divergence among towns such that the towns with lower tax rates tend to attract those who are able to move, which are usually wealthier individuals. Towns with lower tax rates also seem to have higher median home values – it’s difficult to say whether the competition for lower tax rates would cause home values to rise or if the higher home values allow for a lower tax rate to maintain the same amount of revenue, but the correlation seems to exist. Thus, those who are wealthier and own nicer homes live in towns with lower tax rates, but because of the relationship between tax rates and property prices, they may be able to get the same level (or better) of municipal services . Imaginably, such towns will have a relatively easier time balancing their budget and will be at a much lower risk for a debt or budget crisis.

I wanted to see what the data says about my experience and to see if there is any sort of divergence among towns based on income or wealth, so I did a few quick and dirty regressions (data source: CT census data available through the Hartford Courant here). As the dependent variable, I used the mill rate (town property tax on real estate, the main, if not only, source of tax revenue for most towns in CT). I ran four different linear regressions, the first with all independent variables I gathered data for (average income, percent of family households with children, percent of residents over 65, percent of residents foreign born, percent of residents that were white, and median home value of homes where the owner lived in it). Interestingly, only the percent with children (positive correlation), percent white (negative correlation), and median home value (negative correlation) were statistically significant at the 5% level (all of them were significant at 1% as well; no variables were significant at 10%). A regression with only those three variables confirmed those results. I also regressed those three variables and average income as well as percent children, median home value, and average income, but in neither case was average income statistically significant at even the 10% level.

These results are interesting because they suggest that, although there is a negative correlation between median home value and tax rates, there is not a correlation – or at least no linear relationship – between income and tax rates. (I attempted to run a regression including average income squared, but there did not seem to be a quadratic relationship either – neither average income nor average income squared was statistically significant at even 10%). Of course, income and wealth are different, as are income and home ownership – many seniors on fixed incomes, for example, own homes of high values, and many people, including well off young professionals, rent.  Conceivably if we had a better measure of personal wealth, especially pertaining to homeowners specifically, that might show different results. Nonetheless, it does not seem like those with higher incomes – without taking into account home ownership status (a huge deficiency of this analysis) - automatically gravitate towards towns with lower property taxes.

Nonetheless, the negative correlation between home values and property tax rates seems to suggest that there may be a “divergence” among towns, in that those that are poorer and have less valuable homes tend to have higher taxes, which they may require simply to operate. When taxes are already high, they may be harder to raise (as Lewis’ analysis in the book suggests – people seem very loathe to tax increases, even if they’re not Greek) and thus “poorer” towns (defined by real estate values) might have greater difficulty balancing their budgets. If there is enough of a divergence between property values, poorer towns might not even be able to keep up with richer towns no matter how high their property taxes are. Thus, in terms of fiscal stability, there may be some degree to which towns with higher property values are more “stable” than others (& potentially able to provide more services for the given tax rate), though obviously this theory could use much better and more in depth data and analysis.