Specific Objectives of the Project:

Create and validate a structural econometric methodology to jointly estimate crop choice and yield response functions using satellite measurements of field level outcomes. The method will account for unobserved heterogeneity in field quality and the dependence on lagged dependent variables---two factors which characterize California agriculture. The method will allow us to separate the intensive and extensive margin responses to prices.

Summary of Results:
Estimation of a new structural model is a time-consuming process, so we have yet to complete the project. However, we have completed an intensive data-gathering and validation process and are wrapping up descriptive results that motivate and inform the structural model. We have begun simulation and validation exercises for the econometric model. The ultimate objective is to work on California crops, but since the set of crops in California is so complex, we focus our initial work on the simpler setting of corn and soy in the Midwest.

We have a number of findings that are consistent with the structural model of crop choice and yield that we plan to estimate. These include:

• Unobserved, persistent heterogeneity in corn and soy yields seems to be present in the satellite data. We are finding that the unobserved heterogeneity in corn and soy yields is correlated, but not perfectly.
• Fields seem to be specializing and making rotation decisions based on this unobserved heterogeneity.

Once we estimate a structural model of the production and profit functions, we’ll be able to simulate how rotation policies affect supply, crop choice, and nitrogen use.

Specific Objectives of the Project
Study whether and how three issues—selection on unobservable heterogeneity, learning about resource quality, and depletion—affect firms’ behavior in shale extraction. Understand what the implications are for energy prices in California agriculture and how unconventional shale resources might develop in California.

Project Report/Summary of Results
We often link increasing productivity in resource extraction to innovation in how firms extract. Yet resource quality—where—firms extract—is a key driver of productivity. Using a structural model and data from Louisiana's Haynesville shale, I disentangle the impacts of how and where firms extract natural gas. Mineral lease contracts, learning about geology, and prices actually explain more than half of growth in output per well—not just technological change. Neglecting this may lead to over-optimistic long-run supply forecasts. I also show that growth in output per well masked large distortions caused by mineral lease contracts, which reduced resource rents.
This work emphasizes the importance of accounting for interactions between institutions and unobserved resource quality in the supply of natural resources. It suggests that should shale resources be developed in California, we should be cautious about extrapolating productivity gains into the future.