Archive for the ‘Dependency’ category

Who are the 47% and Who Did they Really Vote For?

December 6, 2012

I know a lot of people who voted for President Obama (and about as many and maybe more who voted for Mitt Romney).  None of the people who voted for the President fit the famous “47%” profile of individuals dependent on government for support.  In fact, very much the opposite was the case.  Nevertheless, the notion that a dependent population was largely responsible for the President’s re-election seems popular in some circles.  My small circle of acquaintances is not a  valid sample from which to accept or reject the dependency theory of  the election so here is one small step toward empirical verification or rejection.

I chose ten states from various regions of the country (NH,MA,NY,IN,KS,GA,FL,TX,AZ,OR), half of whom were won by President Obama and half by Mitt Romney.   I compiled a county-level dataset that includes the percentage of votes won by each candidate, the percentage of the population age 25 and older in the county that has a bachelor’s degree or higher, and the percentage of the population in the county that is white and non-Hispanic.   For my dependency measure I used the percentage of total personal income in the county that comes from government transfer payments.  The largest government transfer payments are for Social Security, Medicare and Medicaid (see chart below).  Of those, only Medicaid is for low-income individuals (and thus more closely fitting the profile of dependency) and income support payments like disability, supplemental income, food stamps and other (see chart below).

transfer payments

The ten states are not random and perhaps not a valid sample and there are many more demographic variables I could have included but this is all I could accommodate in the span of a Boston Celtics game and a couple of glasses of wine.  The ten states represent 814 counties, or about 26% of all counties in the U.S.  Using a simple regression model that analyzes the impact of the educational, race, and dependency variables on the percentage of the vote in each county received by the President, results were significant but still only explain about 25% of the variation in the percentage of the vote received by the President.  A larger percentage of income in a county  from government transfer payments is, in fact,  positively related to higher percentage of the vote for the President (although the simple correlation is small), and a higher percentage of the population that is white is negatively related to the vote received by the President (no surprise that we are a long ways from being color blind).  Its no great epiphany that users and supporters of government assistance  would be more likely to vote for a Democrat or that white voters might be less likely to vote for the President.  What is most interesting, however, is that the strongest relationship is a positive one between the percentage of persons age 25 and above in a county who have at least a bachelor’s degree, and the percentage of the vote received by the President.  Republicans may be right about not being able to win as many individuals who rely on government assistance as will Democrats but over the next few decades the percentage of the population that will be receiving the largest share of government benefits (Social Security and Medicare) is going to skyrocket and the percentage of the population that has a bachelor’s degree or higher is likely to increase as well.

I guess you can dismiss election results when they appear to be an aberration driven by the “great unwashed” who depend on government benefits, but what do you say if  the results were more influenced by the voting behavior of the most educated?

Anyone interested in the limited dataset I have, feel free to contact me.  I’d love to include all 50 states and many more demographic and economic variable but I doubt I will ever get to that.  For the truly nerdy who might want the stats from the regression models, you are welcome to those as well.

Its the Dependency Ratio That Matters Most

November 8, 2012

There is a good deal of fretting (warranted) about the impact on national and state-level government spending of a population that is growing older.  It is relatively easy to project a path for age-affected expenditures both nationally and in NH and to model how changes in spending programs and policies could alter the projected path of those expenditures.   Getting agreement on which policies to alter to influence the spending path is a much more difficult task.  What is missing from most discussions is an understanding that aging isn’t the only important demographic trend.  The dynamics of an increasing number of older individuals and median age of the population are largely misunderstood, but that is a subject for another post.  From a fiscal perspective, the most important indicator of spending pressures resulting  from the age structure  of the population is the “dependency ratio.”   The dependency ratio measures the ratio of working-age individuals in a population to those who are generally more ‘dependent” in a population (that is are likely to draw greater resources from governments then they give to governments).  Generally dependency is defined as age groups least likely to be in the labor force (children and those age 65+ – which may be unrealistic as individuals are healthier and for a variety of reasons stay longer in the labor force).  The dependency ratio affects both spending and revenues (revenue impacts are mostly missing from the demographic discussion and are the subject of tomorrow’s post).  A lot of government spending is directed at these groups – young people via schools and older citizens via things like Medicare, Medicaid, Social Security etc.  The chart below shows the rise in the projected dependent population in NH.  The chart shows that the past decade has been a “sweet spot” for the dependency ratio in NH, with an overall decline in the percentage of the population in “dependent” years (albeit with an increase in older dependency).  I produced a similar chart in the early 2000’s and suggested state government make good use of the state’s time in the “sweet spot” by adopting policies to minimize the impacts of future increase in the dependency ratio in the state (it wasn’t the first nor will it be the last time my thoughts were ignored by lawmakers – in fairness, it’s not always unreasonable for them to do so).  Certainly some policies have looked to reduce the impacts of an increasing older population.  But with limited, and in some years declines in the youth dependency, less attention has been given to innovative ways to slow the growth in spending (largely education expenditures) in a way that is proportional to growth in the youth population.   Effectively managing changes in spending pressures without producing unacceptably large overall increases in spending or unacceptable reductions in services requires that resources not be locked in specific spending categories or programs, but rather be allowed to rise and fall and flow to and from programs programs and services most influenced by demographic and economic pressures.  Tomorrow: The other side of the ledger – demographic influences on revenues.


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