Research
Research
As climate change amplifies more volatile weather patterns, water utilities face increasing difficulty in simultaneously ensuring revenue feasibility, promoting water conservation, and protecting low-income consumers. This paper tests and concludes that price alone cannot achieve these competing policy goals under different weather patterns. Using granular household data from Austin, TX, and a structural demand model enhanced with satellite imagery-derived vegetation index, I find that because high-water users exist across all income levels, traditional tiered pricing doesn’t work as intended. Furthermore, higher-income households—who are both weather-sensitive and surprisingly price-elastic—complicate the utility's ability to achieve its distributional objectives while meeting the conservation target. When high-demand conditions (e.g., drought) make conservation measures necessary, low-income families experience an average welfare loss of $74 per month. This highlights the necessity of complementary policies to achieve distributional goals when demand increases. For example, a program encouraging households to convert 30% of their lawns to water-saving landscapes (zeroscaping/xeriscaping) could generate approximately $72.07 per month in welfare for the lowest-income families, nearly offsetting the financial burden imposed by conservation policies during droughts.
Many economic research studies have been focusing on the demand and welfare estimation of the ride-hailing market, specifically for platforms like Uber and Lyft. In this paper, I estimate the welfare effect of UberPool as a new product in the ride-hailing market accounting for heterogeneous preferences within and across locations by using a discrete-type random coefficient nested logit model. I find that, relative to the counterfactual worlds without UberPool, UberPool can increase consumer surplus by 31.58% - 33.51%. Even a partially accessible UberPool by location is 2.57% higher on consumer surplus, compared to if only UberX were provided but with lower prices, which shows the magnitude of the variety effect in the ride-hailing market.
Tolled roads in the US have been using a dynamic pricing mechanism to react to fluctuating demand, which essentially forms price discrimination on the value of time (VOT), incentivizing high VOT users to use the more expensive toll lanes to save time and vice versa. In this paper, I explored this price discrimination on VOT with two common infrastructure constraints: 1. a fixed free price on the slow lanes, and 2. the fast and slow lanes are not perfect substitutes in terms of their exits. Following existing research, I developed a structural model for optimal tolls to maximize consumer welfare. In addition, I also conducted a counterfactual analysis to explore the welfare effects of different policies on toll designs and infrastructural investments.
Served as an Assistant Instructor for sections with 100+ undergraduate students on average, with bi-weekly lectures of 75 minutes in the long semester. This course is a required upper-level economics course for economics majors with the target grade of at least a C-. The content of the course includes interpretation of economic data, introduction to statistical models, estimation, basic data analysis in R, and inference in economics.
Quotes from Students
"Prof. Ji always reviews lectures before each exam which helps us a lot in preparing."
"The instructor was very approachable and available at all times. He used a wide variety of problems to allow us to complete the learning objectives and become acquainted with course problems."
"He was very good at explaining confusing topics."
"Mr. Ji always looked willing to help. He was very friendly and helped a lot during office hours. He didn't beat around the bush when it came to explaining concepts and showing what needed to be done. Fast and effective."
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