Research

The economic cost of carbon pricing depends on the ability and incentives of firms to switch towards cleaner fuels. Yet, many fundamental economic forces that drive firms' decisions to use different fuels are unobserved, causing significant uncertainty over the effectiveness of carbon policies. In this paper, I propose a new dynamic production model with multidimensional unobserved heterogeneity that underly technology differences and captures how firms' fuel choices respond to price changes. These differences cause heterogeneity in abatement costs, which generates heterogeneous responses to carbon pricing. Leveraging minimal assumptions about optimal input choice and the technology frontier, I quantify the model from a detailed panel of Indian steel establishments. Based on these estimates, implementing a carbon tax equivalent to 2,000 INR/ton (25 USD/ton) of carbon dioxide equivalent leads to a 70% reduction in emissions. But only 18% of this reduction comes from fuel-switching within existing firms. I find that the larger reductions come from reallocation of output across firms (58%) and costly reduction in aggregate output (24%). Substantial heterogeneity in the fuel efficiency of existing furnaces coupled with the limited geographical reach of natural gas pipelines towards high-emission firms explains the prevalence of output reallocation relative to fuel switching.

Presented at the 2023 EEA-ESEM Congress (Barcelona), the 2023 European Association for Research in Industrial Economics (EARIE, Rome), the 57th Annual Conference of the Canadian Economics Association (Winnipeg), UM-UWO-MSU Labo(u)r Day (2023, East Lansing),  The 21st Annual International Industrial Organization Conference (IIOC 2023, Washington DC), Young Economist Symposium (2022, Yale University), the 56th Annual Canadian Economics Association Meetings (2022, Ottawa), and the 17th CIREQ PhD Students' Conference (2022, Montreal) 

This paper studies how plants reorganize their production when faced with asymmetric carbon pricing. When plants compete across areas, asymmetric regulation can lead to carbon leakage, shifting emissions from regulated to unregulated areas. I build a production model with multiple fuel inputs, imperfect competition, and region-specific carbon taxes. Using publicly available Canadian plant-level data on a wide range of air pollutants, I invert the chemical reactions from combustion to back out plants' fuel usage. I then estimate the model by exploiting variation in the British Columbia (B.C.) and Quebec carbon taxes implemented in 2008 and 2007, respectively. Findings indicate substantial emissions reductions in British Columbia, with 95% confidence intervals ranging from 18% to 45%, and 4% reductions in Quebec. Contrary to theoretical predictions of carbon leakage, the analysis reveals no statistically significant shift in production towards unregulated provinces. A detailed decomposition highlights that the absence of leakage was primarily due to regulated plants' ability to absorb the tax by switching from oil to natural gas and due to aggregate price increases, which suppressed overall consumer demand and inhibited the ability of unregulated plants to increase output. 

This research uses the method developed by Huang and Salmon (2004) to estimate marketherding dynamics in a CAPM framework, exploiting time variation in betas and cross-sectional dispersion of individual assets. Their model is adapted to the cryptocurrencymarket in order to shed some lights on the recent surge in prices and volatility. Using high frequency data at 5 minutes intervals, I find significant evidence of increasing market herding over the high volatility period between August 2017 and March 2018. 

The increase in high-heat days, droughts, and floods resulting from climate change will cause a reduction in yields for crops critical to the livelihoods of hundreds of millions of people in sub-Saharan Africa. Developing and widely adopting climate-resilient crop varieties would mitigate billions of dollars of agricultural production losses. However, climate-resilient crop traits are not immediately valued by farmers, who may only trust these traits after experiencing severe climate impacts, limiting innovators’ ability to charge higher prices. An advance market commitment that pays innovators if they successfully develop and distribute heat-tolerant varieties of staple crops could generate over $24 in economic benefits for every $1 spent. We identify crops in sub-Saharan Africa with high social returns from heat resilience that are neglected by crop variety innovation markets. We calculate the net social return by combining future climate projections with data on crop yields in relation to temperature changes, prices, variety releases, and innovation costs. We model crop variety innovation through two channels: conventional breeding and advanced technologies that leverage recent scientific advances such as CRISPR and RNA methylation. Our results show that increasing crops' heat resilience has large economic benefits. We model heat resilience by reducing the negative impact of high temperatures between 29 and 40 degrees Celsius on crop yields. Heat-resilience improvements for maize and sorghum provide the largest benefits. However, the benefits from groundnuts and soybeans are also large, with each generating more than $100 million in economic benefits in the advanced variety development scenario.