| Summary: | This thesis consists of three chapters, two in Industrial Organization and one in Labor Economics. The first and second chapters study industrial technologies: the first explores how machine learning changes decision-making of heavy duty truck technicians, while the second studies technological switching in the shale industry. The third chapter studies wage garnishment in the United States.
The first chapter (joint with Adam Harris) uses observational data to explore how a predictive algorithm changes human decision-making. Using a novel, rich decision-level data set from the maintenance of heavy-duty trucks, we document how skilled technicians' decision-making is changed by the introduction of an algorithm designed to predict the risk of truck breakdowns. We develop and estimate a model of technician decision-making that accounts for variation in monetary and non-monetary costs. Using an embedded neural network, we flexibly estimate technicians' beliefs about the probability of truck breakdowns both before and after the introduction of the algorithm. Comparing these estimated beliefs with an objective breakdown probability, we find that the algorithm significantly improves technicians' ability to predict breakdowns: the algorithm narrows the gap between actual and optimal costs by 79%. All of this gain comes from decreased repair costs, suggesting that the algorithm primarily helps technicians avoid low value repairs.
The second chapter studies technology in a different setting: the U.S. shale industry. In late 2014, global oil prices dropped precipitously, driving U.S. shale producers out of the market. As the number of new wells completed dwindled, productivity began to rise sharply, beginning a steepened upward descent that continued through 2019. This chapter draws on detailed well-level data from the Bakken shale play in North Dakota to tease apart several classic explanations for these trends, including Schumpeterian creative destruction and technological improvement. I document firm-level jumps from gel-based completions to slickwater after the price shock, with earlier jumps for mid cap and private firms. However, I find that improved geological targeting (or "high-grading") and slickwater adoption fail to account for over 70% of the productivity increase.
The third chapter (joint with Anthony DeFusco and Brandon Enriquez) uses administrative data to investigate wage garnishment in the United States. Wage garnishment allows creditors to deduct money directly from workers' paychecks to repay defaulted debts. We document new facts about wage garnishment between 2014 and 2019 using data from a large payroll processor who distributes paychecks to approximately 20% of U.S. private-sector workers. As of 2019, over one in every 100 workers was being garnished for delinquent debt. The average garnished worker experiences garnishment for five months, during which approximately 11% of gross earnings is remitted to their creditor(s). The beginning of a new garnishment is associated with an increase in job turnover rates but no intensive margin change in hours worked.
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