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Essays on the Economics of Private and Social Insurance
No full textThe first chapter, joint with Sylvia Klosin, studies the 'scope' of insurance. Distinct risks are typically insured separately. A single 'aggregate' contract that pays more when many shocks occur simultaneously, but less when positive shocks offset negative shocks, is utility-increasing absent moral hazard. However, an aggregate contract discourages diversification, leading to a novel insurance-incentive trade-off. We study the US Federal Crop Insurance Program (FCIP), where farmers can choose the `scope' of their policy - whether to insure each field separately, or all fields of the crop as an aggregate unit. Starting in 2009, the FCIP introduced a large subsidy increase for aggregate insurance. We show that farms that moved to aggregate insurance reduced crop diversity and irrigation, farmed less and conserved more land, and insured price risk --- all reducing the diversification of their risks. This increased the variability of farm yield by 14%, raising the fiscal cost of aggregate insurance by about $1.5 billion per year. We derive and estimate a formula for the optimal contract scope. We find that an aggregate policy is never welfare maximizing, but that the optimal policy lies partway between separate and aggregate. More generally, we discuss scope's widespread relevance in insurance design.
The second chapter, proceeds from the fact that increasing climate risk has caused insurance in many locations to become unaffordable or unavailable. I study a novel policy response in Australian home insurance: government provided, mandatory, actuarially fair, reinsurance for cyclone damage. In this scheme, the government reinsures the cyclone risk, while the private market covers the remaining idiosyncratic risk. I find that public reinsurance leads to a 21% decrease in home insurance premiums and an 11% increase in the probability of insurance being offered at all. In terms of mechanisms, I rule out subsidization and show that the ambiguity of the risk has a minimal impact on premiums and insurance offerings. Instead, the entirety of the increase in insurance offered, and much of the decrease in premiums, comes from reducing the implicit costs associated with insuring spatially correlated risk. Increased competition due to insurer entry explains the remaining premium reductions. This isolates the cause of market dysfunction - correlated risk - and suggests that public reinsurance is a cost-effective policy to rehabilitate insurance markets for catastrophic climate risks.
The third chapter, studies bundling in insurance contracts. Every insurance contract bundles risks, and explicit bundling discounts are common. I show theoretically that bundling arises in a competitive market whenever correlation between risk types enables insurer 'cream-skimming': willingness-to-pay for insurance against one risk must be negatively correlated with expected costs from the other risk. I analyze long-term care insurance, in which both-spouse bundles are discounted by 20-35%. I show that cream-skimming incentives are sufficient to explain these discounts, and model-predicted equilibrium bundling discounts closely match empirical discounts. I rule out standard economies-of-scale and differential contract lapsation as alternate explanations of the offered discounts. Counterfactually, banning bundling would raise welfare by 10% by correcting separate-market unraveling, while mandatory family bundling would reduce welfare by 15% by exacerbating advantageous selection.Ph.D
High-resolution profiling reveals coupled transcriptional and translational regulation of transgenes
No full textConcentrations of RNAs and proteins provide important determinants of cell fate. Robust gene circuit design requires an understanding of how the combined actions of individual genetic components influence both messenger RNA (mRNA) and protein levels. Here, we simultaneously measure mRNA and protein levels in single cells using hybridization chain reaction Flow-FISH (HCR Flow-FISH) for a set of commonly used synthetic promoters. We find that promoters generate differences in both the mRNA abundance and the effective translation rate of these transcripts. Stronger promoters not only transcribe more RNA but also show higher effective translation rates. While the strength of the promoter is largely preserved upon genome integration with identical elements, the choice of polyadenylation signal and coding sequence can generate large differences in the profiles of the mRNAs and proteins. We used long-read direct RNA sequencing to define the transcription start and splice sites of common synthetic promoters and independently vary the defined promoter and 5′ UTR sequences in HCR Flow-FISH. Together, our high-resolution profiling of transgenic mRNAs and proteins offers insight into the impact of common synthetic genetic components on transcriptional and translational mechanisms. By developing a novel framework for quantifying expression profiles of transgenes, we have established a system for building more robust transgenic systems
Essays on Economic Growth and Innovation
No full textA foundational observation by Robert Solow holds that long-run economic growth is primarily driven by the innovation and adoption of new technologies (Solow, 1957). This set of essays provides new theory and evidence to explain how firms choose which technologies to innovate and adopt. A point of emphasis, particularly in the first two chapters, is that complementarities across firms play an important role in determining the rate and direction of technological change. These complementarities arise as firms build shared knowledge by innovating (Chapter 1) and from joint consumption of new products (Chapter 2). They provide a new channel through which market structure and property rights affect long-run technological change.
Chapter 1. The first chapter is motivated by the observation that the direction of innovation shapes both current technologies and future innovation opportunities, as firms acquire expertise and create public knowledge through discovery. But how do firms choose which technologies to develop? Do they ever fail to exploit new technological paradigms? I build a new model of innovation and firm dynamics to study a novel link between market structure, the direction of innovation, and economic growth: Expertise in a current technology gives incumbents a comparative advantage at innovating it relative to entrants, who instead favor a new technology with higher growth potential. Each firm’s innovation decisions influence others through knowledge spillovers, so the initial market structure can affect the long-run direction of innovation. Concentrating R&D resources in a small number of firms allows faster accumulation of expertise, raising growth when all firms innovate the same technology. But it can lower growth when firms face a technology choice, amplifying the influence of incumbents and potentially delaying or preventing the emergence of the new technology. I provide empirical evidence for the theory using data on firm patenting and R&D expenditures. I also show that it explains the historical development of mRNA vaccines, and I explore its implications for the highly concentrated innovation of artificial intelligence.
Chapter 2. In the second chapter, joint with Rebekah Dix, we observe that innovations often combine several components to achieve outcomes greater than the “sum of the parts.” We argue that such combination innovations can introduce an understudied inefficiency—a positive market expansion externality that benefits the owners of the components. We demonstrate the importance of this externality in the market for pharmaceutical cancer treatments, where drug combination therapies have proven highly effective. Using data on clinical trial investments, we document several facts consistent with inefficiently low private innovation: firms are less likely than publicly funded researchers to trial combinations, firms are less likely to trial combinations including other firms’ drugs than those including their own drugs, and firms often wait to trial combinations including other firms’ drugs until those drugs experience generic entry. Using microdata on drug prices and utilization, we quantify the externalities that arise from new combinations and find that the market expansion externality often dominates the standard negative business stealing externality, suggesting too little innovation in combination therapies. As a result, firms may have incentives to free ride off others’ innovation, which we analyze with a dynamic structural model of innovation decisions. We use the model to design cost-effective policies that advance combination innovation. Redirecting publicly funded innovation toward combinations with high predicted market expansion or consumer surplus spillovers minimizes crowd out of private investments, increasing the rate of combination innovation and total welfare while remaining budget neutral.
Chapter 3. The final chapter, joint with Daron Acemoglu, considers incentives to adopt transformative technologies that promise to accelerate productivity growth across many sectors but also present new risks from potential misuse. We develop a multi-sector technology adoption model to study the optimal regulation of transformative technologies when society can learn about these risks over time. Socially optimal adoption is gradual and typically convex. If social damages are large and proportional to the new technology’s productivity, a higher growth rate paradoxically leads to slower optimal adoption. Equilibrium adoption is inefficient when firms do not internalize all social damages, and sector-independent regulation is helpful but generally not sufficient to restore optimality.Ph.D
Leveraging Metal Complexes for Optical Read-out of Magnetic Fields
No full textOptical detection of magnetic phenomena offers a compelling pathway toward the development of highly sensitive and versatile molecular sensors. This thesis investigates the design of metal complexes tailored for magnetic field read-out through light–matter interactions, focusing on two strategies. The first section explores magnetochiral dichroism (MChD), an optical effect that emerges from the interplay between molecular chirality and magnetism. By systematically varying the metal centers within a series of chiral lanthanide complexes—specifically, Tb³⁺ and Dy³⁺—we examine how differences in magnetic moment modulate the MChD response. This comparative study reveals fundamental chemical design principles for enhancing MChD intensity and deepens our understanding of how structural and electronic factors jointly shape this directional optical effect. The second section addresses the challenge of engineering optically addressable molecular qubits based on Ni²⁺ complexes. Realizing effective spin-state read-out in these systems requires precise control over both magnetic and photophysical properties. To this end, we investigate ligand modification strategies aimed at enhancing luminescence while preserving an S = 1 ground state suitable for quantum applications. Collectively, we hope these studies contribute to a better understanding of the design space for spin–photon coupled molecular systems, offering new tools for magnetooptical sensing.Ph.D
Changes of the adsorption parameters under the influence of static magnetic field
No full textDuring the study, the influence of the external magnetic field on the adsorption of copper, nickel and cadmium were analysed. For that purpose, two different types of charcoals were used, paramagnetic and ferromagnetic. Obtained results showed clear changes in the effectiveness of adsorption processes under the influence of the strong magnetic field, but only for the ferromagnetic activated carbon. The stimulating nature of the modification on the copper removal processes has been proved while for nickel adsorption systems, an inhibiting effect of the same kind of field was demonstrated. For cadmium removal, there were no statistically significant changes in the processes before and after magnetic modifications. Besides that, additional parameters were studied like kinetic and thermodynamic analyses of the systems and it also proved the important influence of the static magnetic field, such as increase of the initial adsorption rate or changes in entropy, and the average Dubinin–Radushkevich energy of the process
Affect in Resiliency Planning: A Conversation with Broad Channel
No full textPlanning for climate change is more relevant than ever, as the earth continues to warm, sea levels rise, and no global policy or political will is in sight. In order to plan under hostile circumstances, it is of the utmost importance that planners turn our attention to the hyper-local scale, continuing momentum in our personal and professional relationships. In this thesis, I argue that centering affective experiences of place is essential in conversations about the future of places under climate change, especially in communities and neighborhoods resistant to the conversation about climate change’s impacts on their futures in the first place. This project focuses on Broad Channel, the only inhabited island community in New York City’s Jamaica Bay, which is on the front lines of sea level rise and tidal flooding in the city. I interviewed city leaders, community members, artists, planners, and activists to understand how we can move through and with affect when considering the future of a place. This can open up conversation about climate change previously inaccessible. These conversations also surfaced the need for planners to regroup and understand how their own affective positions impact difficult conversations about climate change. I offer these insights and recommendations for future resiliency planning work, reflecting both inward and outward.M.C.P
Energy Flow in Particle Collisions
No full textIn this thesis, I introduce a new bottom-up approach to quantum field theory and collider physics, beginning from the observable energy flow: the energy distribution produced by particle collisions. First, I establish a metric space for collision events by comparing their energy flows. I unify many ideas spanning multiple decades, such as observables and jets, as simple geometric objects in this new space. Second, I develop a basis of observables by systematically expanding in particle energies and angles, encompassing many existing observables and uncovering new analytic structures. I highlight how the traditional criteria for theoretical calculability emerge as consistency conditions, due to the redundancy of describing an event using particles rather than its energy flow. Finally, I propose a definition of particle type, or flavor, which makes use of only observable information. This definition requires refining the notion of flavor from a per-event label to a statistical category, and I showcase its direct experimental applicability at colliders. Throughout, I synthesize concepts from particle physics with ideas from statistics and computer science to expand the theoretical understanding of particle interactions and enhance the experimental capabilities of collider data analysis techniques.Ph.D
High-throughput tools for decoding T cell receptor specificity
No full textT cells play a central role in adaptive immunity by recognizing specific antigens through their T cell receptors (TCRs). These receptors bind to peptides presented by major histocompatibility complex (pMHC) proteins, driving immune responses in cancer, infection, and autoimmunity. Understanding how TCRs recognize antigens is crucial for developing cancer immunotherapies and identifying therapeutic targets in autoimmunity, infectious disease, and allergy. However, large-scale mapping of TCR-antigen interactions remains a challenge due to the vast diversity of both TCRs and antigens, as well as the limitations in current screening technologies in cost, throughput, and accessibility.
This work presents two advances in large-scale TCR-antigen screening. The first aim introduces a scalable and cost-effective platform for synthesizing tens of thousands of TCRs from sequence data to create synthetic TCR libraries. We integrate this approach with a high-throughput antigen discovery platform that leverages pMHC-pseudotyped viruses to identify TCR-pMHC pairs. Using this system, we screen 3,808 vitiligo patient-derived TCRs against 101 antigens, and synthesize 30,810 TCRs from patients with pancreatic ductal adenocarcinoma (PDAC). By streamlining TCR assembly and antigen screening, this pipeline has the potential to advance immunotherapy, accelerate vaccine design, and deepen our understanding of TCR recognition.
The second aim presents a new method that couples pMHC-displaying virus-like particles with yeast display, enabling efficient screening of millions of TCR variants against ~100 pMHCs at once. Yeast display is a powerful tool for studying TCR-antigen interactions but is constrained by its reliance on recombinant protein production. Our approach overcomes this limitation by replacing recombinant protein with barcoded lentiviral particles, allowing large-scale, multiplexed screening of TCR libraries. By overcoming key technical barriers, these tools significantly expand our ability to study TCR specificity and engineer new antigen-specific therapeutics.Ph.D
Transforming geospatial textual data into narrative storytelling visualization
No full textCurrent large language models (LLMs) often struggle to integrate geospatial data into dynamic, interactive visualizations, relying instead on text-based outputs. This limitation hinders the full potential of geospatial data to convey complex information through narrativedriven communication, making it difficult for users to interpret the data easily. Meanwhile, existing data visualization tools typically depend on static dashboards and rigid scientific formats, which have a steep learning curve and lack engagement through narrative elements. Audiences, however, are increasingly drawn to story-driven presentations, as seen in platforms pioneered by the MIT Senseable City Lab, and widely popularized by The New York Times and the Washington Post, which use narrative data visualization formats to attract and immerse readers. This gap between the capabilities of current LLM-based tools and users’ preferences presents a unique opportunity to develop a narrative-based geospatial visualization tool that meets these needs. This tool could transform how we communicate spatial data, particularly in fields such as journalism, travel planning, and urban planning, where the ability to convey complex patterns in an engaging manner is essential.S.M
Predicting Ridership and Travel Time Impacts of Bus Service Changes Using Sketch Planning Methods
No full textBus service changes range in scale, and understanding their impacts on ridership and travel times can inform decision-making as changes are considered for the bus network. Budgetary limitations are at the heart of service change decisions, resulting in the need for analysts to assess different scenarios and accommodate quick turnarounds. This thesis provides a sketch planning framework for predicting ridership and travel time impacts of bus service changes, with a focus on direct demand models and the use of an open-source multimodal routing algorithm. The framework is designed to be streamlined with the use of data sources and capabilities, such as exporting a General Transit Feed Specification (GTFS) feed of a given bus network scenario, that agencies may have access to through existing transit planning tools.
Direct demand models are developed to estimate bus ridership at the level of approximately one-mile route-segments and time-of-day periods. This level of analysis provides a more disaggregated evaluation of bus ridership than past direct demand models. The models are sensitive to both route and network improvements. New variables designed to capture the relationship between bus routes, including the competitive and complementary nature of routes, are introduced and incorporated in the model development process. These models are developed for the Washington Metropolitan Area Transit Authority (WMATA). A case study analyzing two scenarios in WMATA's Better Bus Network Redesign (BBNR) is presented, with selected route examples to illustrate how the models capture different types of service changes. These routes fall under three categories: routes with no major service changes, routes with improvements in frequency, and routes with re-routing and other improvements.
An open-source multimodal routing algorithm, available through an R package called r5r, is used for travel time analysis. r5r calculates a distribution of door-to-door travel times for a given origin-destination (OD) matrix and returns a selected percentile value from the distribution for each OD pair. The percentile parameter is calibrated through a comparison of estimated travel times and actual travel times recorded in origin-destination-interchange inference (ODX) data. Low percentile values were found to provide travel times close to actual travel times. Additional guidance is provided for interpreting travel times from r5r, and use cases related to calculating travel time impacts between scenarios and evaluating rail competitiveness for a given bus network are explored.S.M