Specific Objectives of the Project:

1. Estimate the effects of the recent agricultural boom in the value of agricultural production on farm income, land values, land rental rates, and other input prices.
2. Estimate the effects on employment, incomes and other aspects of the rural economy.

Project Report/Summary of Results:
US crop farmers experienced a large increase in crop revenue from 2007-13 due to high prices caused in large part by strong demand for corn from ethanol producers and for soybeans from China. Revenues have dropped since that time, although high government payments have cushioned the decline. How much of these high revenues flows to farmers, landowners, other factor owners, and the broader community. Using county data, we estimate that a 10% increase in crop revenue raises net farm income by about 10% in the current year and by 5% in the ensuing two years. Production expenses, especially fertilizer and seed increase by 2.5% in the current year and about 1.2% in the subsequent two years, implying significant returns to factor owners. Using data on Iowa farmland values we find that a 10% increase in crop revenue raises farmland values by 2.5% in the current and ensuing two years. We find similar, but less precise effects on land rental rates. Outside of agriculture, we find that increases in crop revenue have insignificant negative effects on non-farm income in the county. To obtain our estimates, we develop a novel instrumental variables approach to identify exogenous price shock using temperature and precipitation shocks.

Specific Objectives of the Project

1. Create an econometric forecasting model that uses spatially and temporally disaggregated electricity consumption data along with weather and economic variables to predict a baseline level of regional electricity consumption in California.
2. Determine the feasibility of using the counterfactual baseline consumption as a tool for measuring the performance of energy efficiency programs.

Summary of Results

We have obtained detailed electricity consumption data from the Sacramento Municipal Utility District (SMUD) and merged these data with hourly weather data and block-level census data. These data include the following:

1. Hourly consumption by almost all households in the SMUD region (approximately 500,000 premises) for all of 2012 and 2013.
2. Monthly electricity bills for almost all households in the SMUD region (approximately 500,000 premises) for 2005-2013.
3. County assessor information on house cha racteristics.
4. Energy efficiency program participation (89,000 records)

After several delays, we obtained these data in March 2014. To preserve individual privacy, we do not know the address of any premises in our sample.

The strongest predictor of household electricity use is temperature, which follows from the fact that air conditioning is a major source of household electricity demand. However, there is a lot of variation in the response of electricity consumption to temperature depending on the day of the week, the size and age of the home, and other characteristics. We have formulated an econometric approach to incorporate this heterogeneity and are in the early stages of testing our model.

So far, in testing our model, we have focused on the e nergy efficiency programs related to air conditioning (AC). AC programs are the largest operated by SMUD in terms of number of participants, and they potentially have the largest effects on electricity use. AC programs constitute just over a quarter of our energy efficiency program observations (about 27,200 records).

In preliminary analysis, we observe large reductions in peak electricity use for homes that replace an old air conditioning unit with a new one under an energy efficiency program. We obtain these results by conditioning on temperature, e.g., we estimate how much electricity is used when the temperature is 100° F before and after the energy efficiency intervention. This finding suggests that a forecasting model that conditions on temperature ha s the potential to provide an effective estimate of counterfactual baseline consumption.

There is significant variation across homes in the response to temperature, the response to the energy efficiency program, and propensity to participate in the program. Our next steps are to model this heterogeneity so as to obtain precise quantitative estimates of the baseline, and to expand our analysis to all of SMUD's energy efficiency programs.

Ethanol made from corn comprises 10% of U.S. gasoline, up from 3% in 2003. This dramatic increase was spurred by recent policy initiatives such as the Renewable Fuel Standard and state‐level blend mandates and supported by direct subsidies such as the Volumetric Ethanol Excise Tax Credit. Some proponents of ethanol have argued that ethanol production greatly lowers gasoline prices, with one industry group claiming it reduced gasoline prices by 89 cents in 2010 and $1.09 in 2011. The 2010 figure has been cited in numerous speeches by Secretary of Agriculture Thomas Vilsack. We show that these estimates were generated by implausible economic assumptions and spurious statistical correlat ions. To support this last point, we use the same statistical models and find that ethanol production "decreases" natural gas prices, but "increases" unemployment in both the United States and Europe. We even show that ethanol production "increases" the ages of our children. Overall, we see no compelling reason to believe that the effect of ethanol use on gasoline prices has been more than $0.10 per gallon. In California, ethanol has an even smaller negative effect on gasoline prices, partly because it is relatively expensive to ship ethanol from the Midwest to California and partly because the LCFS encourages imports from Brazil of ethanol made from sugarcane.