UC Berkeley

Agriculture has played a profound and unique role in humanity's development. We are dependent on agriculture for the vast majority of our food supply, and have so far been successful at increasing agricultural production to meet rising demand. At the same time, agriculture is the largest and most direct way that humans have altered our planet's landscape and natural environment. Indeed, over half of all land in the United States is used for some agricultural purpose. In this dissertation, I explore three different aspects of human-environmental-agricultural interdependency in the United States. In the first chapter, I study agricultural workers' wage-responsiveness under different environmental conditions on California blueberry farms. In the second chapter, Fiona Burlig and I study the effect of human social networks on agricultural technology adoption in three upper-Midwestern states. Finally, in the third chapter, I study how the location of ethanol refineries in the US Corn Belt affects crop choice decisions and nutrient runoff. Each of these chapters highlights a different interaction between human economic systems (wages, social networks, renewable fuel policy), environmental conditions (temperature, nitrogen application/runoff), and agricultural enterprises (specialty crop labor productivity, adoption of fertilizer, crop rotations). I utilize a similar empirical strategy in each chapter, employing fixed effects and other panel data techniques to control for time-invariant determinants of productivity, technology adoption, and optimal crop choice, respectively. This dissertation highlights the benefits of panel data methods in agriculture, especially in the modern era of abundant micro-level data.

In the first chapter, I study how agricultural laborers' productivity responds to changes in the piece rate wage they are paid: a wage paid per unit of output rather than per unit of time. Specifically, I exploit quasi-experimental variation to estimate the elasticity of labor productivity with respect to piece rate wages by analyzing a high-frequency panel of over 2,000 blueberry pickers on two California farms over three years. To account for endogeneity in the piece rate wage, I use the market price for blueberries as an instrumental variable. I find that picker productivity is very inelastic on average, and I can reject even modest elasticities of up to 0.7. However, this average masks important heterogeneity across outdoor working conditions. Specifically, at temperatures below 60 degrees Fahrenheit, I find that higher piece rate wages do in fact induce increases in labor productivity. This is suggestive evidence consistent with a model where at moderate to hot temperatures, workers face binding physiological constraints that prevent them from exerting additional effort in response to higher wages. This insight has important implications for understanding how climate change will affect the agricultural labor sector.

In the second chapter, Fiona Burlig and I use historical data and a natural experiment to study the effect of social networks on agricultural technology adoption. We present a model of the effects of social network size on information and technology take-up and test its implications using a unique natural experiment in the mid-20th century US Midwest. We find that social network expansions, in the form of mergers between congregations of the American Lutheran Church, led to increased rates of agricultural technology adoption among farmers. In counties that experienced a merger, the number of farms using nitrogen fertilizer increased by over 7% and the total fertilized acreage increased by over 13% relative to counties without a merger. We provide evidence that these effects are driven by increased information sharing between farmers as a result of these congregational mergers.

In the third chapter, I study how the location of ethanol refineries within the US Corn Belt affects farmers' land use decisions. Ethanol production in the United States, driven by federal renewable fuel policy, has exploded over the past two decades and has prompted the construction of many ethanol refineries throughout the US Corn Belt. These refineries have introduced a new inelastic demand for corn in the areas where they were built, reducing basis for nearby farmers and effectively subsidizing local corn production. I explore whether and to what extent the construction of new ethanol refineries has actually increased local corn acreage. I also explore some environmental effects of this acreage increase. Using a thirteen year panel of over two million field-level observations in Illinois, Indiana, Iowa, and Nebraska, I estimate a net increase of nearly 300,000 acres of corn in 2014 relative to 2002 that can be attributed to the placements of new ethanol refineries. This increase comprises approximately 0.75% of the total 2014 corn acreage within my dataset. Furthermore, this effect is separate from the general equilibrium effect of ethanol policy increasing aggregate demand for corn. Back-of-the-envelope calculations suggest that over 21,000 tons of the nitrogen applied to fields in my sample in 2014 can be attributed to refinery location effects. Essentially all of these observed effects occur only in areas within 30 miles of an ethanol refinery, suggesting that refineries have meaningful localized impacts on land use and environmental quality such as nitrate runoff.