While much research exists at the national level examining the dynamics between the economy and energy markets, few studies have been able to effectively model energy policies at both the state and local level. Yet information about sub-national and, in particular, state and local economic effects of policies is important, not just because most U.S. energy policies adopted over the past 20 years are sub-national policies, but also because the economic effects of policies vary across states and municipalities. This paper presents a model aimed at addressing this research gap. While the model was developed for the state of Indiana, its structure and data inputs are such that it can be adapted for any other state.

The Indiana Scalable Economic and Energy Model (IN-SEEM) is a data-intensive, dynamic, simultaneous econometric model depicting the state’s economy and energy system at both the state and multi-county regional level. Using data from the BEA, BLS, Census, NOAA, and the EIA—along with an allocation model that estimates energy consumption at the sub-state level—the model describes the energy sector using prices and consumption of electricity, natural gas, and motor gasoline across residential, commercial, industrial, and transportation end-use sectors. Employment and earnings in ten economic sectors, as well as gross state and regional product, unemployment, and non-wage income comprise the economic component of the model. The “scalable” aspect of IN-SEEM allows the model to be adapted based on researchers’ sub-state regional definitions. The iteration of IN-SEEM presented in this paper examines ten sub-state Indiana regions defined based on their counties’ shared economic characteristics. Each of these regions contributes 30 equations to the overall model for a total of 300 equations. Thirty additional identities summing the 30 endogenous variables for each region are used to represent the state as a whole. The structure of the model allows estimates at the state level to feed back into the regional equations, an important feature given the significant relationship state-level activity plays in the economic performance of certain sectors in certain regions.

Along with providing a “proof-of-concept” of a sub-state energy and economic model, this study uses IN-SEEM to estimate the impact of a carbon dioxide tax at various levels. The policy analysis produces a set of results describing the effect of such a tax at the state and sub-state level on energy consumption, employment, earnings, gross state (and regional) product, unemployment, and non-wage income. Economic impacts are not only presented by region (and the state), but by industry for the ten sectors included in the dataset. Estimates are also produced for carbon dioxide emissions reduction and potential revenue from the carbon dioxide tax.