UC Santa Barbara

Socioeconomic status (SES) is a key covariate in analyses by the Programme for International Student Assessment (PISA). In its technical documentation, PISA offers evidence for the Economic, Social, and Cultural Status instrument (ESCS) as a valid measure of SES. This dissertation, however, offers both conceptual and empirical arguments that ESCS does not meet the realist and pragmatic requirements set forth by modern measurement and validity theories. I further demonstrate how the adoption of non-measurement models in ESCS has undermined the trustworthiness of PISA’s most recent headline findings. Finally, I offer guidance for improving the validity of SES measurement in future PISA cycles.

Protein aggregation has long been a topic of interest within the scientific community due to its connection to various neurodegenerative disorders such as Alzheimer’s Disease and Parkinson’s Disease. While amyloidogenic fibrils were long thought to be the primary toxic species involved in these disorders, in recent years it has been shown that smaller, intermediary oligomers are more toxic than the mature fibrils. This has prompted additional study into these intermediary oligomers, and subsequently the potential aggregating behaviors of small metabolites such as nucleobases and individual amino acids. In this dissertation I will focus on the aggregation of select amino acids, and examine the effects of various alkali metal cations on the oligomer formation pathway and their potential role in stabilizing and promoting these aggregates. This has been done through a combination of experimental and computational techniques, namely ion mobility mass spectrometry and molecular dynamics.

Commodity thermosets are generally landfilled or incinerated due to the presence of covalent crosslinks that are effectively irreversible due to their high bond dissociation energy. This poses a pressing environmental concern. An emerging class of thermoset alternatives is covalent adaptable networks (CANs), which are crosslinked by dynamic covalent bonds that can be cleaved and regenerated upon application of an external stimulus (such as heat or light). By taking advantage of the on-demand reversibility of these CAN crosslinks, the material can undergo more environmentally sustainable end-of-life fates, such as reprocessing, upcycling, and chemical recycling. The activation and efficiency of the stimuli-responsive crosslink exchange of many CANs are contingent on externally added catalysts, which are not covalently bound to the polymer network. Catalyst loss via pathways such as leaching and degradation transforms reversible crosslinks into (effectively) irreversible linkages. This thesis tells three stories about developing polymer network platforms with catalyst-free reversible bonds. In the first section, a sulfur heteroatom capable of undergoing neighboring group participation (NGP) is embedded in the structure of a secondary alkyl halide bicyclo[3.3.1]nonane (BCN) crosslinker and used to generate ionic networks. This NGP—often called internal catalysis—affords thermally-induced solid-state bond exchange in crosslinkers that would otherwise be unreactive. Moreover, the use of externally added catalysts is avoided. This synthetically modular crosslinker platform is leveraged in the second story to incorporate other NGP heteroatoms (selenium and nitrogen) into the ionic CANs. The rate of the crosslink transalkylation exchange is controlled by the identity of the NGP atom [S, Se, or N-R] within the BCN crosslinker. The trivalent nitrogen atom in the aza-BCN crosslinkers enables further control of the anchimeric assistance efficiency by altering the steric parameters of the amine substituent. The temperature-dependent reversibility of these crosslinks allows the ionic CANs to be thermally reprocessed and chemically recycled. The final story incorporates dynamic boronic ester bonds into a vat photopolymerization 3D-printing resin to explore the application of catalyst-free reversible crosslinks in emerging manufacturing techniques. The photopolymerizable additive manufacturing resin combines dynamic boronic ester linkages (to provide reactive sites for post-printing modification) and a non-exchangeable crosslink diluent (to give the growing 3D-printed part dimensional stability). By capitalizing on the exchangeable crosslink, the 3D-printed constructs can undergo a series of post-manufacture modifications, such as tunable swelling and welding of separately printed parts to generate more structurally elaborate materials. As a result, this boronic ester-containing resin offers pathways to extend the lifecycle and utility of the parent 3D-printed polymer network.

The advent of wide-field surveys has led to an exponential increase in the number of supernovae discovered each year. As the sample sizes of these objects have grown, we have discovered supernovae that show greater diversity within their spectroscopic classes than expected, as well as objects that do not fit into traditional classification schemes altogether. Studying these ``extreme" supernovae in greater detail is crucial to understanding the poorly-understood end stages of stars' lives; early-time observations within hours of the supernova explosion probe the progenitor star's stellar structure as well as its circumstellar environment, which can be used to test current theories of stellar evolution.

Here I present studies utilizing photometric and spectroscopic observations, primarily taken by Las Cumbres Observatory, of supernovae that occupy poorly-understood or unpopulated regions of parameter space. First, I examine an object with extreme ejecta velocities. Observations of this Type Ia supernova, SN 2019ein, within days of explosion show some of the fastest-moving ejecta of any supernova. The potential sources of this high-velocity ejecta in the context of the poorly-understood progenitor channels and explosion mechanisms of Type Ia supernovae are explored.

Next, I investigate the powering mechanisms and progenitor systems of supernovae with rapidly-evolving luminosities. High-cadence observations of objects within this region of supernova phase space reveal significant diversity in their circumstellar environments and powering mechanisms. In particular, I show evidence connecting luminous, rapidly-evolving unclassified transients with supernovae powered by interaction with circumstellar material. I also present the first sample study of a new class of supernovae, Type Icn supernovae, with rapidly-evolving light curves powered by interaction with circumstellar material that is both hydrogen- and helium-poor. Studying these unique and rare objects reveals that some are likely the explosions of stars less massive than expected from our current understanding of mass-loss in massive stars. Finally, I present evidence of diversity in the powering mechanisms and progenitors of the well-understood class of Type IIb supernovae. Each of these major findings challenges our understanding of supernova physics as well as theories of mass loss during the final stages of stellar evolution.

This dissertation consists of three works which use econometric techniques to estimate how shifting liability regimes and green energy transitions impact firm behavior, making contributions to public economics.

In the first chapter, I develop a novel empirical strategy that causally estimates the relationship between firm precautions and the level of liability each firm faces. 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 power line-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 power lines, I find that firms increase their precaution by 130% in response to a $680million 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.

The second chapter, joint with Olivier Deschenes, uses recently developed econometric techniques to estimate how Renewable Portfolio Standards incentivize investments in solar and wind generation across the U.S.. 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 dateof the impact of RPSs on renewable electricity capacity investments and on 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 find similar results using a modified empirical model that allows states to meet their RPS requirements with pre-existing renewable generation and renewable generation from nearby states.

In the third chapter, also joint with Olivier Deschenes, we quantify how investments in wind generation reshape regional economies across the U.S. and which workers are impacted the most. Most western countries have made commitments or enacted policies aiming to transform their economies to become carbon-neutral by 2050. Many of the leading 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, few have been implemented, and none have operated 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, earnings, GDP, and per capita income to estimate the impact of wind projects on regional economies. Our research design uses two-way fixed effects regression and empirical strategies robust to concerns about heterogeneous treatment effects. The empirical analysis points to a small, but durable positive effect of wind electricity investments on regional economies. Overall, the results suggest that the projected additional150 GW of wind electricity production capacity from the Inflation Reduction Act willcreate close to 164,000 jobs.

This work provides a comprehensive review of techniques for solving the discrete algebraic Riccati equation and guidance for selecting an appropriate method for various user-specified DAREs. Motivated by process control, chemical engineering practitioners are driven by safety, profit optimization, and reduced process variability. The solution to the DARE offers a way to regulate and accurately track process behavior through the linear quadratic regulator and the linear quadratic estimator. This study offers a comparison between the iterative Riccati equation (IRE), generalized real-Schur vector methods (GSV), and Newton's method as algorithms for computing the solution to the DARE. Through numerical studies, we find Newton's method struggles as a stand-alone method as it requires information on the solution a priori. For large system sizes (more than 1000 states), the IRE seems to suffer from numerical instability and the GSV severely scales with system size. We offer two switch method alternatives by utilizing Newton's method as a refinement technique on IRE iterations. The A+BK stability switch (ABKSS) prioritizes Newton's method's reliability close to the solution and switches once a stable closed-loops state transfer matrix is found via the IRE. We also pose the IRE proximity switch (IREPS), which computes the iterations to the discrete Riccati equation until iterates are sufficiently close to the stabilizing solution and refined through iterations of Newton's method. For randomly generated systems of 1000 or more states, both switch methods offer a high-accuracy alternative that surpasses the GSV in computation time and circumvents the numerical instability experienced by the IRE. Furthermore, we find that the GSV and IRE are highly susceptible to systems that are close to being unstabilizable (nearly unstable directions of state matrix or nearly zero influence in the control matrix). IREPS and ABKSS offer significant improvements for computing the solution to barely stabilizable systems. Lastly, we initiate an investigation into utilizing Newton's method for DAREs with singular R. While not as general as positive semidefinite R, we prove that Newton's method can compute the solution to DAREs with R equal to zero as a special case. The purpose of this study is to revisit methods for the DARE with modernized computation tools. We find that as we pose more challenging problems, standard algorithms for computing the solution to the DARE become unviable and require a new approach with these old tools.

This dissertation consists of three essays. The first essay documents an empirical study of how the growth of imports from China changes post-secondary education decisions in Korea. As China has become “the world's factory'', the availability of low-skilled jobs has become worse in countries that import products from China. I exploited cross-industry and cross-local labor market variations in import growth from China to investigate how Korean students' post-secondary education decisions were influenced by more intensive import competition from China. Using administrative statistics from high schools between 2000 and 2015, I found that in regions more affected by Chinese imports, more students pursued a college degree after finishing high school. Consistent with the enrollment pattern, I found that in Korea, a surge in Chinese imports caused huge job losses, especially for low-educated workers, which decreased the opportunity cost and increased the marginal benefit of a college education. However, there is heterogeneity in the enrollments based on gender and type of educational institution. In the more affected regions, male students tended to enroll in 4-year universities rather than 2-year colleges, while female students were more likely to choose 2-year colleges. The findings of this study suggest that negative social norms and smaller employment losses for working women led to a more limited increase in educational investment among women.

The second essay recounts an investigation into how parents change child-related investments in rural China after introducing a pension program that provides them with an alternative to adult children as a source of support. Exploiting regional variation in the timing of the New Rural Pension Scheme (NRPS) in China, I found that parents who enrolled in the NRPS spent more on their children's education. Specifically, the increase in educational spending is observed for the parents of sons. In terms of the number of children, although the effect on the probability of giving birth to a baby is not statistically significant, I found that parents enrolled in NRPS were more likely to give birth to a boy. Considering the introduction of a pension scheme as lowering the price for future incomes, this implies that the income effect is more significant than the inter-temporal substitution effect leading to higher investments in their children. Moreover, these results suggest that NRPS does not weaken son preference in China, although this program can provide old-age support for parents instead of sons who traditionally did this.

The third essay revisits literature estimating the effect of unilateral divorce law on the divorce rate with US panel data. This literature could not have a consensus on the impact of this law on the divorce rate because two-way fixed effects and event study regressions are not robust to heterogeneous treatment effects. In this case, estimates cannot be interpreted as a causal effect. By using alternative estimators to address this issue (e.g., de Chaisemartin and D’Haultfuille 2020),I found that the divorce rate rose after the adoption of this law, and this rise reversed afterward. The average of the dynamic effects is smaller than previous papers found.

The hippocampal formation plays an important role in spatial navigation and episodic memory. At the core of the neuroscience community's understanding of this role, are classes of functional cell types, such as place cells and grid cells. Despite being the subject of intense study for over half a century, how the complex recurrent circuitry of the hippocampal formation generates these functional classes, and how they interact with each other to support the critical cognitive processes the hippocampal formation underlies, is an area of open research. In this thesis, we present experimental and computational work aimed at addressing some aspects of these questions.

First, we develop a novel technique to optically access the transverse plane of the hippocampus in vivo, allowing - for the first time - the neural activity of multiple subregions of the hippocampus to be simultaneously recorded. We utilize this tool to characterize functional and structural properties across the hippocampal circuit, finding stable and unstable populations of dendritic spines on the apical dendrites of CA1 pyramidal neurons and heterogeneity in the amount of spatial information encoded by place cells along the CA1-CA3 axis. Second, we test a long held assumption on properties of grid cells in medial entorhinal cortex, analyzing recent cutting-edge experimental data. We find evidence for the distribution of grid spacings and orientations in individual modules to be non-uniform, having small, but significant variation. Computational modeling leads us to conjecture that a grid code with such heterogeneity in its properties enables robust encoding of local spatial information. And third, we study the emergence of localized responses in artificial neural network models. We find that, as these local features form, the statistics of the internal representations of the network become increasingly non-Gaussian, in-line with theoretical work that has suggested this to be a general mechanism for driving localized responses. Sparsifying the network, via pruning or regularization, amplifies the non-Gaussian statistics, emphasizing the role of sparsification on internal representations. Each of these experimental and computational approaches motivates exciting avenues of future direction to shed light on the computations performed by the hippocampal formation.

Assumptions about human mating psychology can range from a lay person’s entertaining intuitions to ideas that are taken for granted by mating researchers but that, upon closer examination, rest on precarious theoretical and empirical foundations. Dealbreakers and short-term mate preferences are two topics that most people feel like they understand, but the research investigating these topics is limited to a handful of studies, leaving some important questions untested. Here, my aim is to offer some clarity about dealbreakers and mate preferences through the precision of computational models and the power of a large, cross-cultural dataset. In chapter one I ask two questions. First, what are dealbreakers? And second, how do dealbreakers influence attraction and mate choice? I use an evolutionary perspective to generate hypotheses about how dealbreakers might be used by the mind. I propose that dealbreakers can either be disqualifiers or preferences. By disqualifiers, I mean traits that cause us to eliminate people as potential mates. By preferences, I mean traits that influence how attractive we find a potential mate. I use agent-based modeling, a method where computer simulated agents serve as avatars for real life participants and interact in a simulated mating market, to test between these two possibilities. Here, I found evidence that many of the traits we consider to be dealbreakers, such as smoking status, height, and religion, are not used by the mind as disqualifiers, rather they act like preferences and are integrated into overall assessments of mate value. A person’s sex, on the other hand, acts like a disqualifier. If a person is not our preferred sex, we do not consider them to be a potential mate. In chapter two I examine patterns of short-term mate preferences in over 50,000 participants from 56 countries around the world. I test whether men and women have different short-term preferences, and if short-term preferences vary across countries due to disease prevalence, gender equality levels, and sex ratio. Additionally, I examine patterns of long-term mate preferences, to see if previous findings replicate. Overall, I found that both men and women, on average, prefer short-term sexual partners who are kind, healthy, and attractive, but women had higher ideal preferences for short-term mates than men did. There was one exception to that pattern— both men and women preferred the same level of physical attractiveness in a short-term mate, on average. In chapter three I explore whether short-term mate preferences and long-term mate preferences are different from each other, indicating distinct short-term and long-term mating strategies. I used the same cross-cultural data from chapter two and a machine learning technique to explore whether individuals have different short-term and long-term mate preferences. I found that there are differences between short-term and long-term preferences, on average. Physical attractiveness preferences are higher, while kindness and resources preferences are lower, for short-term mates compared to long-term mates. However, I found that most participants (59-80%) preferred the same type of ideal mate for short-term and long-term relationships. Overall, this dissertation helps to bolster the theoretical and empirical foundations of dealbreakers and mate preferences research.

This thesis explores the history of Black menstruation through enslavement and its afterlives and posits Black menstruation as an erotic and autonomous project that resists the culture of disembodiment from the Black reproductive body. I argue that ancestral trauma has conditioned a disembodied relationship to the body as Black people with uteri must internalize and negotiate the deviancy white supremacy, nationhood, patriarchy, and capitalism projects onto Black reproduction. To demonstrate this ancestral trauma, I analyze menstruation in the Gold Coast prior to European colonization and during chattel slavery in the U.S to reveal how menstruation has undergirded efforts to surveil Black reproduction and disrupt kinship paradigms in African and American cultural landscapes. I shift to current representations of Black menstruation including Audre Lorde’s “My Mothers Mortar” essay and Michela Coel’s drama HBO series I May Destroy You, to unravel how menstruation can cultivate erotic autonomy and resist violent disembodiment as they foster intimacy with Black menstruators and their bodies. Overall, my thesis demonstrates how Black menstrual history connects narratives of menstruation from precolonial Gold Coast and the American plantation to the current plight of Black menstruators and offers what I call the bloody erotic as an intervention to disrupt racialized and gendered disembodiment.