About Me

Welcome! I am a PhD Candidate in the Economics department at UC Santa Barbara specializing in energy economics, environmental economics, and law and economics. I will be joining the University of Oklahoma as an Assistant Professor of Economics in Fall 2023.

In my current work, I use applied empirical methods and causal inference to understand the effect of assigning liability for low probability, high severity events on firm precaution to prevent such events. To further this research agenda, I work with detailed administrative data on California’s electricity distribution infrastructure and tools from atmospheric science to develop a causal empirical framework relating a firm’s precaution to the level of liability it faces.


    Working Papers

    The Precautionary Consequences of Wildfire Liability: Evidence from Power Shutoffs in California
    Across all sectors of the U.S. economy, regulators use liability regulations to encourage firms to take actions that reduce the costs associated with low probability, high severity events such as powerline-ignited wildfires and production defects. Despite the widespread use of these regulations, there is limited evidence of their effectiveness across many sectors of the economy. This study identifies a new channel through which liability regulation influences firm behavior and provides causal evidence of firm responses to the entire distribution of potential liability by studying a regulation in California’s electric utility sector. Using exogenous variation in the replacement cost of structures that lie downwind of powerlines, I find that firms increase their precaution by 130% in response to a $680 million increase in liability. In the short run, the estimates from this study imply that the implemented liability regulation had welfare costs between $17 million and $7 billion. [Job Market Paper]

    Causal Effects of Renewable Portfolio Standards on Renewable Investments and Generation: The Role of Heterogeneity and Dynamics
    with Olivier Deschênes and Gavin McDonald
    Despite a 30-year long history, Renewable Portfolio Standards (RPS) remain controversial and debates continue to surround their efficacy in leading the low-carbon transition in the electricity sector. Contributing to the ongoing debates is the lack of definitive causal evidence on their impact on investments in renewable capacity and generation. This paper provides the most detailed analysis to date of the impact of RPSs on renewable electricity capacity investments and generation. We use state-level data from 1990-2019 and recent econometric methods designed to address dynamic and heterogeneous treatment effects in a staggered adoption panel data design. We find that, on average, RPS policies increase wind generation capacity by 600-700 MW, a 21% increase, but have no significant effect on investments in solar capacity. Additionally, we demonstrate that RPSs have slow dynamic effects: most of the capacity additions occur 5 years after RPS implementation. Estimates for wind and solar electricity generation mimic those for capacity investments. We also examine the possibility of policy spillover where the introduction of an RPS in one state leads to a change in capacity mix in the neighboring states, but find no systematic evidence for such spillovers. [Under Review]

    Can the Low-Carbon Transition Energize Labor Markets? Evidence from Wind Electricity Investments in the U.S.
    with Olivier Deschênes

    Most western countries have made commitments or enacted policies aiming to transform their economies to become carbon-neutral by 2050. Many of the proposed policies to reduce carbon emissions are also promoted as engines of job creation and local economic development. While low-carbon transition policies continue to be debated and proposed, none have been implemented for a long enough period of time to permit an empirical evaluation of their impact.

    This paper uses the natural experiment provided by the rapid deployment of wind electricity projects in the United States over the period 2000-2019 to shed light on whether the low-carbon transition can deliver long-lasting and high-quality jobs. We compile detailed data on the location and operation date of 55,000 wind turbines, combined with county-level data on employment and earnings to estimate the impact of wind projects on employment rates and earnings. Our research design uses two-way fixed effects regression, stacked difference in differences (Cengiz et al. 2019), and the doubly robust estimator proposed by Callaway and Sant’Anna (2021). The empirical analysis points to a small, but durable positive effect of wind electricity investments on local labor market outcomes. Overall, the results suggest that the deployment of 100 GW of wind electricity production capacity over the last two decades created close to 150,000 jobs.


Policy Work

Contact Me

2015 North Hall, UC Santa Barbara
Santa Barbara, CA 93106
Twitter: @cmalloy_econ