We simulated these changes in the STAMP® model as a percentage price increase on electricity to measure the dynamic effects on the state economy. The model provides estimates of the proposals’ impact on employment, wages and income. Each estimate represents the change that would take place in the indicated variable against a “baseline” assumption of the value of that variable for a specified year in the absence of the RES policy.
Because the RES requires Michigan households and firms to use more expensive “green” power than they otherwise would have under a baseline scenario, the cost of goods and services will increase under the RES. These costs would typically manifest through higher utility bills for all sectors of the economy. For this reason we selected the sales tax as the most fitting way to assess the impact of the RES. Standard economic theory shows that a price increase of a good or service leads to a decrease in overall consumption, and consequently a decrease in the production of that good or service. As producer output falls, the decrease in production results in a lower demand for capital and labor.
BHI utilized its STAMP® model to identify the economic effects and understand how they operate through a state’s economy. STAMP® is a five-year dynamic CGE (computable general equilibrium) model that has been programmed to simulate changes in taxes, costs (general and sector-specific) and other economic inputs. As such, it provides a mathematical description of the economic relationships among producers, households, governments and the rest of the world. It is general in the sense that it takes all the important markets (such as the capital and labor markets) and flows into account. It is an equilibrium model because it assumes that demand equals supply in every market (goods and services, labor and capital). This equilibrium is achieved by allowing prices to adjust within the model. It is computable because it can be used to generate numeric solutions to concrete policy and tax changes.[*]
In order to estimate the economic effects of an RES we used a compilation of six STAMP® models to garner the average effects across various state economies: New York, North Carolina, Washington, Kansas, Indiana and Pennsylvania. These models represent a wide variety in terms of geographic dispersion (northeast, southeast, Midwest, the plains and west), economic structure (industrial, high-tech, service and agricultural), and electricity sector makeup.
First, we computed the percentage change to electricity prices as a result of three different cost scenarios. We used data from the EIA from the state electricity profiles, which contains historical data from 1990-2010 for retail sales by sector (residential, commercial, industrial, and transportation) in dollars and MWh and average prices paid by each sector. We inflated the sales data (dollars and MWh) though 2020 using the historical growth rates for each sector for each year. We then calculated a price for each sector by dividing the dollar value of the retails sales by kWh. Then we calculated a weighted average kWh price for all sectors using MWh of electricity sales for each sector as weights. To calculate the percentage electricity price increase we divided our estimated price increase by the weighted average price for each year. For example, in 2015 for our medium-cost case we divided our medium price of 10.78 cents per kWh by our estimated price increase of 0.85 cents per kWh for a price increase of 7.9 percent.
Graphic 13: Elasticities for the Economic Variables
Using these three different utility price increases — 1 percent, 4.5 percent and 5.25 percent — we simulated each of the six STAMP models to determine what outcome these utility price increases would have on each of the six states’ economies. We then averaged the percent changes together to determine what the average effect of the three utility increases would be. Graphic 13 displays these elasticities, which were then applied to the calculated percent change in electricity costs for the state of Michigan discussed above.
We applied the elasticities to the percentage increase in electricity price and then applied the result to Michigan’s economic variables to determine the effect of the RES. These variables were gathered from the Bureau of Economic Analysis Regional and National Economic Accounts, as well as the Bureau of Labor Statistics Current Employment Statistics.[†]
[*] For a clear introduction to CGE tax models, see John B. Shoven and John Whalley, “Applied General-Equilibrium Models of Taxation and International Trade: An Introduction and Survey,” Journal of Economic Literature 22 (September, 1984): 1008. Shoven and Whalley have also written a useful book on the practice of CGE modeling entitled Applying General Equilibrium (Cambridge: Cambridge University Press, 1992).
[†] For employment, see the following: “State and Metro Area Employment, Hours, & Earnings,” (U.S. Bureau of Labor Statistics, 2012), http://bls.gov/sae/ (accessed April 1, 2012). Private, government and total payroll employment figures for Michigan were used. For investment, see “National Income and Product Account Tables,” (U.S. Bureau of Economic Analysis, 2012), http://www.bea.gov/itable/ (accessed April 1, 2012); “Gross Domestic Product by State,” (U.S. Bureau of Economic Analysis, 2012), http://www.bea.gov/regional/ (accessed April 1, 2012). We took the state’s share of national GDP as a proxy to estimate investment at the state level. For state disposable personal income, see “State Disposable Personal Income Summary,” (U.S. Bureau of Economic Analysis, 2012), http://www.bea.gov/regional/ (accessed April 1, 2012).