Specific Objectives of the Project:
The Biden administration’s goal is to eliminate fossil fuel electricity generation by 2035. Further, the Department of Energy’s Solar Futures Study projects that 40% of U.S. electricity generation could come from solar by 2035, which would require installing 30 GW/year until 2025 and 60 GW/year between 2025 and 2030 (DOE, 2021). The administration’s renewable energy targets are estimated to require an area larger than the Netherlands for solar energy (Rystad Energy, 2021).

These targets raise concerns about renewable energy development’s impact on land use and agricultural productivity. Many developers prefer to build solar farms on flat, clear agricultural land with low construction costs. Solar developers lease the land from the farmers. This lease price is typically much higher than what farmers would receive from leasing their land for agriculture. This project addresses the factors that influence whether farmers lease their land for solar and any unexplained cost farmers face by leasing their land for solar.

Summary of Results:
Illinois passed a law in 2016 and then a follow-on law in 2021 to allocate funding toward renewable energy development and addressing climate change. One program created by these laws was the Adjustable Block Program, which guaranteed prices of renewable energy credits (RECs) for community solar (≤ 2 MW) projects. The program received significantly higher demand than the allocated funding could support, so a lottery was held in 2019 to choose 112 projects out of 919 applications. The Agency published the lottery results, including the winning and losing projects’ sizes, locations, and developers. An application required that the developer had site control (lease agreement/option) of the area listed. Thus, we observe many farmers who agreed to lease their land for solar and many sites that developers thought were suitable for solar development.

We supplemented this field-level dataset with the published lottery results, estimates of lease value provided by a company specializing in new energy development on farmlands, and multiple spatial layers that include climate data, approximations of productivity, and proximity to the nearest infrastructure. We restricted our sample to fulfill the requirements established through conversations with multiple developers, agricultural land information platforms, and an officer at Illinois Power Agency. We then used linear fixed effects models and instrument-based estimation methods to analyze the data.

According to our analysis, a 10% increase in leasing prices is associated with a 4% increase in the likelihood that farmers are willing to lease their land for solar purposes. Not surprisingly, lower-productivity fields are more likely to be leased for solar projects. A 10% increase in field productivity results in a 28% decrease in the probability a farmer agrees to have a solar project installed.

Additionally, farmers who experience higher levels of climate risk are more likely to contract to lease. For instance, if a field experiences an average of 10% more extreme degree days or excessive rainfall during the planting season over the previous three years, the probability of leasing increases by 11% and 15%, respectively.

In summary, this study finds that lease prices, field productivity, and climate risk strongly affect farmers’ decisions to lease land for solar energy projects. These findings have important implications for policymakers, farmers, and other stakeholders in the renewable energy sector.

Specific Objectives of the Project
We quantified the short-term impact of tariffs and the corresponding relief payments on soybean futures prices.

Summary of Results
Due to the 2018–2019 trade wars, U.S. agricultural and food products have suffered eight waves of retaliatory tariffs from Canada, China, Mexico, the EU, and Turkey. The COVID-19 pandemic further isolated the economies in 2020. We studied the futures market effects of these tariffs on various U.S. crops, particularly soybeans.

We estimated reduced-form regressions of real futures prices or spreads on retaliatory tariffs and a set of event indicators to quantify the short-term impact of retaliatory tariffs and the corresponding relief payments on soybean prices. We controlled for the COVID-19 epidemic, related U.S. government direct payments, weather shocks, and information from USDA reports. Our analysis used price data from the Barchart website, tariff data from official documents published by the tax bureaus of each country and the World Trade Organization (WTO) Tariff Download Facility (TDF) database, USDA World Agriculture Supply and Demand Estimation Reports, and weather data from the Google earth engine from 2004 to 2020.

We found that a 25% increase in a retaliatory tariff, holding projections and weather variables constant, decreased the real futures price by 14.25% while the tariff was in place. The effect on the futures price spread grew with the length of the spread. It reached its peak at a one-year spread. Thus, the price pass-through of the tariff increase was large, and farmers suffered from the retaliatory tariff in the short run.

Specific Objectives of the Project:
We investigated the tradeoff facing hired, seasonal agricultural workers between wages and various benefits, particularly health care insurance.

Project Report/Summary of Results:
The Affordable Care Act (ACA) made health insurance more available to low-wage people, such as documented hired-seasonal-agricultural workers. Were workers who gained government health insurance more likely to take jobs that offered high wages and few benefits rather than jobs with lower wages that offered more benefits? Based on data from the National Agricultural Worker Survey from 2010 through 2016, the on-the-job benefits of these workers did not change significantly after the ACA went into effect. Incentive benefits, season-ending benefits, and holiday benefits did not change at all. Agricultural jobs provided health insurance at the same rate as prior to ACA. However, documented workers, particularly those with pre-existing medical conditions, were much 11.4% more likely to have government health insurance and 5.6% less likely to rely on employer insurance.

Specific Objectives of the Project
We investigated whether hired, seasonal agricultural workers have health care insurance and use it conditional on the workers’ legal status, the type of their employer, Medicaid Expansion, and the Affordable Care Act.

Project Report/Summary of Results
Farmworkers, regardless of their legal status, are more likely to have health insurance after the Affordable Care Act (ACA) went into effect in 2014. However, only farmworkers newly eligible for Medicaid increased their use of healthcare services. Farmworkers eligible for the ACA subsidy did not change their use of healthcare services.

After the ACA went into effect, farmworkers covered by Medicaid and those eligible for the ACA subsidy (except immigrant farmers with green cards) were less likely to visit the Emergency room and more likely to use healthcare providers such as private doctors/clinics, hospitals, community centers, dentists, and other healthcare providers.

In addition, after the ACA went into effect, employers are less likely to cover the healthcare expenses of farmworkers who are covered by Medicaid or eligible for the ACA subsidy. The healthcare expenses of these farmworkers are more likely to be covered by Medicaid, public clinics, out-of-pocket and other financial sources.

Specific Objectives of the Project
We will predict the probability of crop disasters and large crop insurance payouts in response to weather shocks and show how global climate change might amplify such conditions. Virtually the entire existing literature examines how weather affects mean yield. Mean yield is a key outcome measure for some questions such as the effect of weather on global food prices. However, farmers and policy makers concerned about crop insurance and disaster payment are more interested in predictions of the frequency with which yields fall below the critical levels that trigger crop insurance or disaster payments or cause financial disaster. That is, they are not concerned about how weather affects the mean (average) outcome; they care about how it alters the lower part of the distribution of yield outcomes. We will use a novel approach to estimate these tail events reliably. This approach has not previously been used to analyze agricultural outcomes.

Project Report/Summary of Results
Corn and soy crop yields vary nonlinearly with both temperature and precipitation. Using county-level data over many decades, we estimate the joint distribution of yield, temperature, and precipitation conditional on other factors that affect yield and then infer the conditional yield distribution given temperature and precipitation. This nonparametric approach allows us to avoid arbitrarily imposing rigid structural relationships as in traditional regression analyses and fits the data much better both within sample and out of sample. Moreover, this approach is much more likely to correctly identify the likelihood of catastrophic crop yields in response to adverse weather conditions.

Specific Objectives of the Project

We planned to test competing economic and psychological hypotheses on whether greater choice increases or decreases sales and welfare. We wanted to examine how consumers’ ice cream consumption adjust in response to a major change in the choice of products at a large grocery store chain, with headquarters in California.

Summary of Results

Standard economic theory argues that consumer benefit from more choices, while some psychologists have argued that more choices overwhelm consumers, resulting in reduced consumption. We used a natural experiment to test these competing hypotheses. Once a year, a major grocery chain changes its selection of ice cream. Because the chain introduces these changes at varying times across its stores, we were able to compare purchases at stores where the change had occurred to others where the change had not yet happened. Our results provide some support for the standard economic hypothesis and no support for the alternative hypothesis.

Specific Objectives of the Project

A crucial question for farmers and agricultural economists is how weather affects yield and risk. Unfortunately, relatively little is known about the joint distribution of yield and weather. We propose estimating the joint distribution of corn yield, temperature, and precipitation (during the flowering season).

Summary of Results

We have collected the data and have worked on the econometric techniques to estimate the trivariate distribution of corn yield, temperature, and precipitation. These techniques have been used to demonstrate feasibility with artificial data. However, we have not yet estimated using actual data.

Do changes in food commodity prices have asymmetric effects on retail grocery prices? For years, farmers, consumer advocates and others have argued that grocery store prices rise rapidly in response to increases in commodity prices, but do not fall equally rapidly when commodity prices drop. We examine pricing in both stores that have regular sales and those that use everyday low pricing and rarely have sa les. For both types of stores, we find very little evidence for asymmetric price adjustment. We believe that some earlier studies found asymmetric adjustments because they did not use narrowly defined products, control for all the main factor prices, and u se state-of-the-arts estimation procedures.

California farmers have been incensed over the years, complaining that when commodity prices rise, supermarket prices rise disproportionately, and when commodity prices fall, supermarket prices remain relatively constant or fall slowly. A large literature purports to find that grocery store prices adjust asymmetrically, with prices rising rapidly when input prices rise, but falling more slowly when input prices fall. However, these studies suffer from a variety of problems including aggregation bias (due to combining many goods into a single measure), statistical problems (failing to account for unit roots), using weak proxies for commodity prices, failing to note that some supermarkets that have frequent sales while others use an everyday-low-price approach. Our more careful studies fail to find evidence of asymmetric price adjustment. Moreover, we find little evidence of a tight-link between commodity prices and retail prices in general.

Consumers choose from among the varieties of two brands and an outside good using order statistics. We analytically derive demand functions conditional on their valuations of the varieties being distributed independently uniform. Demands are functions of higher-order polynomials, where polynomial order is increasing in variety. Curvatures (derivatives) of the demands are themselves demands (over lower numbers of varieties). Based on this theory, we estimate a three-parameter empirical version of the model for the retail soft-drink market. These estimates are used to determine the effects cost shocks on optimal prices a nd varieties of Coke and Pepsi for a grocery store. We provide estimates on how much an increase in variety affects welfare and determine the optimal number of varieties. Because the demand curves have convex and concave sections around an inflection point, firms are more likely to respond and make large price adjustments to increases in cost than to comparable decreases in costs.

A grocery store that sells to brand-name loyal customers and to price-sensitive customers must decide whether to carry both name-brand and private-label products and how much to charge. Introducing a private label may either raise or lower the price that a store charges for a brand-name product. As the number of customers that are price sensitive increases, a store may be more likely to carry a private label, but the prices of both the private label and brand-name may rise. As the reservation price of price-sensitive consumers rises, the price differential between the brand name and private label first falls; but if the reservation price rises further, the store may stop carrying the private label. Given price-sensitive consumers, a store may charge a higher price for the brand name than that which would maximize profit if it sold to only brand-loyal customers.

Under the U.S. Special Nutrition Program for Women, Infants, and Children (WIC) program, three major infant formula manufacturers compete for the supply contracts.eTo explain the striking differential between contracted WIC price and non-WIC price, we propose a spillover model where winning the WIC contract gains non-WIC market share. We use store level scanner data and exploit WIC contract changes to test the model predictions and show how a WIC contract change affects the market shares of the winner and other firms. We also find winning the contract does not affect the actual (not estimated) retail price markup