The field of economics can be divided into two camps: positive and normative. Positive economics focuses solely on the objective: what is, and nothing more. Normative economics introduces subjectivity: why are the numbers what they are and what can be done about them.
For reasons that should be apparent, it is much safer to stick to positive economics and dryly report numbers that are publically available and can be found through a variety of sources. For the past month, that has been the focus of this column.
Normative economics is much more precarious because it is easy to disagree with another’s understanding or interpretation of the data. Personally, though, I can think of few situations where I would rather have someone just telling me the condition I am in and not telling me what they think can be done to help it. With that in mind, and building from the past four weeks of numbers, here are four thoughts for helping to turn future numbers more favorable.
First, create a definition of “economic development” and then stay true to it. Approximately $1 million is raised each and every year for economic development through the food and beverage tax. We are one of a very few counties that collect this optional tax and if it were directed to what most would consider economic development instead of trail markers and facade repairs, think of the dent it could make. Calling other things economic development does not make them so and if it doesn’t lead to economic development, it needs to be called something else.
Second, start a massive retraining program: it does no good to bring any new jobs in to the county if those who are here are not qualified to fill them. Third, use incentives to attract individuals in the prime wage-earning age range of 25-64 into the county. Fourth, above all else concentrate on one single statistic: median household income. If you can bring MHI up, you will reduce the number of residents on food stamps, bring in more high-end retailers, increase tax revenues, and pull everything else up with it.
If you use linear regression to look at the relationship between the 92 Indiana counties and the median household income for 2011, statistical significance can be found for the following: the unemployment rate of the county (a negative); the percentage of residents over the age of 25 with a bachelor’s degree or greater (a positive); the percentage of the population between the ages of 25 and 64 (a positive); the number of housing units per square mile (a negative), and the population per square mile (a positive).
The regression equation is able to account for approximately 73 percent of the variation in the median household income within each county at a 95 percent confidence interval level by focusing only on these five variables, the most significant of which is the age group. A number of incentives could be used in a targeted campaign to appeal to this demographic: more recreational offerings, low-cost housing loans, forgivable student loans, and so on. If you can change any of the other variables at the same time (bring in individuals with more education, with a higher rate of employment, etc.) then it is possible to make an even greater impact.
Emmett Dulaney is an Anderson resident and the author of several books on technology. His column appears Tuesdays.