Cornell University DEEP-GREEN-RADAR Research Associate Dr. Karen Thome's structural dynamic game model of ethanol investment was presented at the IO Fest Berkeley-Stanford Conference in Industrial Organization at Stanford University, Duke University, Rice University, the Berkeley Bioeconomy Conference, the American Economic Association (AEA) annual meeting, and a conference on Statistical Methodology in the Social Sciences.
Ethanol has attracted considerable policy attention both for its use as a gasoline substitute, and as a way to enhance profits in rural areas. In her research, Dr. Thome examines how economic factors, government policies, and strategic interactions affect decisions about whether and when to invest in building a new ethanol plant. She models the decision to invest in ethanol plants at the county level using a structural model of a dynamic game. She focuses on investment in corn-ethanol plants in the Midwestern United States, where the majority of corn in the US is grown, over the period 1996-2008.
The structural model has several advantages over the reduced-form model. First, the structural model explicitly models the dynamic investment decision, including the continuation value to waiting. As seen in the theoretical model, a potential entrant invests if the payoff from investment exceeds the continuation value from waiting.
A second advantage of the structural model is that she is able to estimate the effect of each state variable on the expected payoff from investing in an ethanol plant. The parameters in reduced-form models are confounded by continuation values. In contrast, in the structural model, she models the structural relationship between the continuation value from waiting and the payoff from investment, and use it to estimate parameters in the payoff from investing in the ethanol plant.
A third advantage of the structural model is that she is able to better estimate the strategic interaction between potential entrants. In the structural econometric model, potential entrants base their decisions in part on expectations of the future, including their expectations of how many plants will be built by the next year, which depend on what they expect other potential entrants to do in a given period.
A fourth advantage of the structural model is that the parameter estimates from the structural model can be used to simulate counterfactual scenarios. Dr. Thome uses the estimated parameters from the structural model to run counterfactual simulations to explore the effects of alternative policies on ethanol investment.
According to the results of the structural model the intensity of corn production; government policies, particularly the MTBE ban and the 2007 Renewable Fuel Standard (RFS2); and private information shocks all have significant effects on ethanol investment payoffs and decisions. Dr. Thome uses the estimated structural parameters to simulate counterfactual policy scenarios to disentangle the impacts of state and national policies on the timing and location of investment in the industry. She finds that, of the policies analyzed, the MTBE ban and the RFS2 led to most of the investment during this time period.
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