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Impact evaluation: an essential tool to improve FDI promotion
No full textThis Perspective explores how impact evaluation on IPA’s activities and projects can substantially improve the effectiveness and the competitive position of attracting FDI to the host country
Life, Death, and Pigouvian Taxes: Three Essays on Climate Change Economics
No full textThis dissertation analyzes the impact of climate change on temperature-related mortality and the subsequent welfare and policy implications and considerations.
In chapter 1, I assess the global mortality impacts of climate change, and I estimate both the mortality cost of carbon (MCC) and the social cost of carbon (SCC). The SCC is arguably the most important concept in climate economics. Until recently, climate-mortality damages were either negligible or excluded in SCC models. Now, they constitute the majority of damages in latest-generation models, which also project damages at much higher spatial resolution. Previously, discounting (how to value the future) was considered the most consequential choice in determining the SCC. Here, I demonstrate that valuing lives in poorer versus richer countries is now the most consequential choice. I provide the first estimate of the MCC—the number of deaths from emitting an additional tonne of CO₂—in a latest-generation climate-economy mortality model broken down by country. 83% of the deaths in the MCC occur in low and lower middle-income countries. I then calculate the SCC by monetizing these deaths and adding other damage categories. The highly unequal distribution of deaths makes the SCC extremely sensitive to how lives are valued in poor versus rich countries. I calculate the SCC across four approaches to valuing lives and livelihoods with support in the literature. Among approaches sanctioned in U.S. policymaking, the 2025 SCC varies from 3,567 (U.S. income weighting). These results empower decision-makers to choose their preferred approach while understanding sensitivity to alternative approaches. Applying these estimates, the Inflation Reduction Act saves an estimated 2.8M lives through GHG reductions, with monetary benefits ranging from 74T, depending on the approach.
Chapter 2 assesses the theory behind different approaches to Benefit Cost Analysis (BCA) and evaluates how they fare when applied to global externalities like climate change. Valuing deaths caused by climate change in BCA is complex and controversial, having caused disagreement and acrimony in past high-profile settings. Furthermore, it is of first order consequence to the value of the social cost of carbon (SCC). Despite this, the underlying considerations remain under-analyzed. The pure Kaldor-Hicks approach to BCA – measuring costs in market dollars unadjusted for diminishing marginal utility and valuing premature deaths in rich areas more than poor areas – relies on assumptions that are debated in domestic contexts, but clearly do not hold in the context of climate change. This approach is equivalent to defining a Negishi weighted social welfare function, which applies weights so that diminishing marginal utility is no longer accounted for. Furthermore, if costs are measured in purchasing power parity adjusted money – as is typical for the SCC – then the Kaldor-Hicks potential compensation criterion no longer necessarily holds. The first-best BCA approach in the climate context is welfare weighting. This approach accounts for diminishing marginal utility using empirical estimates for the curvature of the utility function, and it better captures what a social planner naturally cares about: real net benefits and the welfare people get from those net benefits. The current U.S. practice – identical to the pure Kaldor-Hicks approach except that it gives a uniform population average value to all premature deaths – is preferred over the pure Kaldor-Hicks approach because it implicitly welfare weights premature mortality costs. However, the fully welfare weighted approach is first-best because it accounts for diminishing marginal utility across all costs, not just premature mortality risk.
Chapter 3 uncovers the distribution of temperature-mortality impacts across demographic groups in Mexico, a country with exceptionally rich microdata that enables a deeper exploration of distributional impacts across demographic groups than the global analysis in chapter 1. We study heat and mortality in Mexico, a country that exhibits a unique combination of universal mortality microdata and among the most extreme levels of humid heat. Combining detailed measurements of wet-bulb temperature with age-specific mortality data, we find that it is younger people who are particularly vulnerable to heat: people under 35 years old account for 75% of recent heat-related deaths and 87% of heat-related lost life years while those 50 and older account for 96% of cold-related deaths and 80% of cold-related lost life years. We develop highresolution projections of humid heat and associated mortality and find that under the end-ofcentury SSP 3-7.0 emissions scenario, temperature-related deaths shift from older to younger people. Deaths among under-35-year-olds increase 32% while decreasing by 33% among other age groups
Contagious Places, Curative Spaces: Disease in the Making of Modern Chinese Architecture, 1894–1949
No full textAt the turn of the twentieth century, sources near and far characterized greater China as a “Sick Man of the Far East 東方病夫.” The phrase suggested a far-reaching, trans-regime backwardness, and it crystallized into the following question: Was greater China—customs, lands, peoples—inherently disease-ridden and unhygienic? This conflation of the body with the body politic contained medical and architectural dimensions.
Indeed, in 1894, as outbreaks of the Third Plague Pandemic ravaged British colonial Hong Kong, medical experts working on the ground uncovered the disease’s bacterial mechanics, and the first significant public health changes on the heels of this development targeted the built environment. In this way, bacterial transmission prompted architects, physicians, land-surveyors, and engineers across the region to integrate new understandings of disease into their work.
This dissertation traces the merging of medical and architectural expertise throughout greater China, first in Hong Kong, a colonial site adjacent to the ailing Qing empire (1644–1912), and eventually in the new Republic of China (1912–1949). Elsewhere within architectural history, hygiene’s stakes are well-established but predominantly address miasmic understandings of disease. In contrast, this project examines how, in the age of germ theory, medical experts and professional architects managed outbreaks, modernized architecture and infrastructure, and modulated between tradition and modernity
Effect of High-fidelity Simulation on Competence and State Anxiety Related to IV Medication Administration in Baccalaureate Nursing Students
No full textBackground
Medication administration is a significant responsibility of nurses, yet it remains one of the most challenging skills for nursing students to master. Students have limited experiential clinical learning opportunities during which to learn medication administration, specifically administering IV medication. These limited opportunities could possibly contribute to student anxiety and influence their medication administration competence. The purpose of this study was to explore the effects of High-Fidelity Simulation (HFS) versus unstructured practice sessions (UPS) on competence and state anxiety related to IV medication administration in baccalaureate nursing students.
Methods
A two-group pre-posttest interventional quasi-experimental study was implemented to explore the impact of a high-fidelity simulation based on the Constructivist Learning Theory on competence and state anxiety. Competence was assessed using the Medication Safety Critical Element Checklist (MSCEC), and state anxiety by the State-Trait Anxiety Inventory-Y1 (STAI-Y1). Twenty-nine full-time baccalaureate students enrolled in a nursing pharmacology course at a large private university participated. Students were excluded if they were repeating this course or were part-time. Participants were assigned to an intervention group (n = 11) or a comparison group (n = 18). Twelve students were in the accelerated nursing program [15 months], while 17 students were in the traditional program [4 years.]
Data were collected between March 12 and April 5, 2024. Students had the opportunity to participate in nine practice sessions over a three-week period. All students practiced IV medication administration 2-3 times a week. Students in the intervention group participated in one high-fidelity simulation during which they administered IV medications via primary or secondary lines, partook in a 30-minute simulation experience, and were required to attend the immediate debriefing. The comparison group attended unstructured practice sessions of IV medication administration 2-3 times a week or more without the simulated experience or debriefing.
Three instruments were used: The MSCEC objective measure by which the trained evaluators assessed student competence, the STAI-Y1 to assess state anxiety, and a demographic data form. Students completed the demographic questionnaire and the STAI-Y1 before participating in practice sessions. Student competence was evaluated by trained evaluators using the MSCEC at Week 1 and Week 3 [the first and last weeks of the study.] Students completed the posttest MSCEC and STAI-Y1 during the last week of the study.
Results
There were no statistically significant differences between groups (p > .05) for competence (Independent = t(27) = -.258); the mean posttest competence score for the intervention group was 8.63 (SD = 1.91), while the mean posttest competence score for the comparison group was 8.83 (SD = 2.03). There were no statistically significant differences between groups (p > .05) for anxiety (Independent = t(27) = -1.50); the mean posttest state anxiety score for the intervention group was 29.72 (SD = 7.39), while the mean posttest state anxiety score for the comparison group was 34.27 (SD = 8.18). Inspection of paired t-test results for the intervention group indicated their state anxiety decreased from a mean of 32.90 (SD = 9.32) before the intervention to a mean of 29.72 (SD = 7.39) following the intervention; conversely, state anxiety in the comparison group increased from a mean of 30.16 (SD = 6.2) to 34.27 (SD = 8.18). However, neither increase nor decrease in state anxiety was statistically significant.
There were no statistically significant differences in outcome variables by type of educational program. The mean posttest competence score for accelerated students was 8.08 (SD = 2.31), while the mean posttest competence score for traditional students was 9.23 (SD = 1.56). The mean posttest state anxiety score for accelerated students was 30.66 (SD = 7.02), while the mean posttest state anxiety score for the traditional students was 33.88 (SD = 8.69).
Conclusion
While sample size is a limitation, the finding that anxiety decreased in the intervention group could lend support for adding a high-fidelity simulation experience to existing student learning activities. High-fidelity simulation aligns with the constructivist principles of immersing learners in realistic or interactive scenarios, thus allowing the student to engage in problem solving, decision making, and teamwork. fix presents realistic scenarios with which students can interact safely and provides students with the opportunity to reflect on their performance. It is possible that incorporating time for reflection outside of the HFS experience may help decrease student state anxiety
Feed-om of Speech: The Technical Design and Legal Interpretation of Social Media Recommender Systems
No full textSocial media platforms have become the modern American town square, spaces where public discourse unfolds dynamically and ideas propagate outward. The Founding Fathers could not have imagined that, instead of cobblestones, machine learning models would form the foundations of personal expression in this new square. This work explores the technical foundations of personalized recommender systems that curate content and shape user experience on social media. By unpacking the architectural and algorithmic principles behind such systems, it examines the intersection of technical design and the legal frameworks governing speech and liability. Central to this inquiry is the question of how to legally classify recommender systems, whether as First Amendment-protected speech or neutral conduct. The paper argues that this dichotomy oversimplifies the issue, and instead advocates for a nuanced understanding that accounts for algorithmic intervention, editorial control, and financial interests. Bridging the gap between technical and legal perspectives is essential for developing informed regulatory and cultural responses in an increasingly social media-dependent society.
Keywords: social media, Section 230, recommender systems, free speech online, information ecosystem, attention econom
Pursuing Opportunities to Balance Efficiency and Effectiveness in Data Review Practices
Get PDFThe Astromaterials Data System (Astromat) is continually striving to improve the user experience. Continuously improving Astromat enables diverse communities to increase their knowledge of our solar system by using open data that have been produced from the laboratory analysis of extraterrestrial samples. Such samples include, among others, meteorites, moon rocks, space dust, and recently returned samples from the asteroid Bennu. Data users across a variety of disciplines can benefit from the availability of open data on astromaterials samples. Curation of astromaterials samples data enables reuse beyond the team that analyzed the samples to produce the data in their laboratories. Data peer review offers opportunities to improve data quality and usability so that the scientific community and the public can study the data and obtain knowledge from the investments in collecting, returning, and analyzing the astromaterials samples and in producing and disseminating the data. A data review typology is depicted to improve understanding about opportunities for data repositories to improve the data review process. A data review use case is presented to illustrate relationships among the actors and the actions performed during the data review process. Challenges for pursuing efficient and effective data peer review are also described
Essays in Financial Economics and Machine Learning
No full textThis dissertation comprises three essays in financial economics and machine learning. It leverages expansive datasets and modern empirical techniques to analyze the dynamics and origins of macroeconomic announcement risk premia, time variation in high-frequency return predictability, and stochastic components of factor risk prices.
In Chapter 1, I examine the dynamics and economic drivers of a persistent market phenomenon: stock returns are disproportionately high on days of scheduled macroeconomic announcements. Leveraging recent methodological advances and the availability of index options with frequent expiration dates, I estimate the conditional distributions of daily market returns, which enable dynamic analyses of daily risk premia and their origins in return space. I demonstrate that within my sample from 2016 to 2023, announcement risk premia were relatively muted in the initial years but began to spike sharply following pandemic-related surges in inflation, regularly exposing short-horizon investors to severe tail risks. Drawing on macroeconomic news data and interest rate forecasts, I propose a mechanism whereby heightened inflation triggers increased uncertainty about future monetary policy paths; this uncertainty, in turn, amplifies market sensitivity to various macroeconomic releases and ultimately leads to elevated announcement risk premia. Extrapolating this mechanism beyond my sample, my estimations suggest that monetary policy uncertainty has accounted for a substantial excess in announcement-day returns between 1983 and 2024.
In Chapter 2, I explore the dynamic properties of high-frequency stock return predictability across machine learning models of varying complexity. The results show that complex non-linear machine learning models based on gradient boosted trees or neural networks substantially and consistently outperform simpler linear models like ridge or partial least squares. Among non-linear models, both the general level of predictability and the predictive benefits of increased model complexity vary systematically across time and are strongly associated with a small set of aggregate market variables. In particular, retail trade volume emerges as a key driver of increased predictability, while return volatility is strongly associated with diminishing benefits to model complexity. Moreover, I demonstrate that gaps of as little as one day between estimation and prediction samples lead to significant losses in predictive accuracy, illustrating the substantial structural dynamics in high-frequency financial markets and evidencing the need for frequent model re-estimation as commonly practiced in academic applications. Finally, analysis of predictor importance using Shapley values reveals that buy-sell trade imbalances and bid-ask spreads are consistently the most potent high-frequency return predictors within my feature set.
In Chapter 3, I develop a strategy for the identification and robust estimation of continuous, predictably discontinuous (overnight), and unpredictably discontinuous (jump) factor risk premia from the cross section of stock returns. Recovering the continuous and respective discontinuous factor spaces via principal component analysis, I obtain the corresponding risk premia of observable factors through a sequence of regressions allowing for latent factors of different stochastic types. Empirically, I employ my novel risk premium estimator to various potentially priced equity risk factors at intraday frequency in a sample spanning from 2004 to 2022. My analysis corroborates that the market risk premium can primarily be attributed to jumps and further extends this finding to a large number of other well-known risk factors
Exploring Executive Function, Theory of Mind, and Vocabulary for Deaf and Hard of Hearing Children who use Listening and Spoken Language
No full textThis dissertation consisted of two studies investigating the development of deaf and hard of hearing children who utilize listening and spoken language. The first study examined cross-sectional, concurrent data to define the relationship between individual factors in recall, receptive vocabulary, and expressive vocabulary, for deaf and hard of hearing children.
Demographic information as well as standardized scores for recall, receptive vocabulary, and expressive vocabulary were extracted from a large dataset of deaf and hard of hearing children between the ages of 3;0 and 5;11 years from 35 specialized LSL education preschool programs. Significant group differences in recall as well as receptive and expressive vocabulary were revealed by type of hearing technology, number of children in the household, age of intervention, and primary home language among deaf and hard of hearing preschool-age children.
The second study explored the relations between theory of mind, recall, and executive functioning in 31 preschool-age deaf and hard of hearing children, when controlling for receptive vocabulary and chronological age. Family environment, including family relationships, personal growth, and system maintenance, were also investigated in relation to theory of mind, recall, and executive functioning. Participants’ theory of mind, recall, executive functioning, and receptive vocabulary were measured and each participant’s caregiver completed a questionnaire regarding their respective family environment.
Significant relations were demonstrated between executive functioning and theory of mind as well as recall and theory of mind. Family relationships, personal growth, and system maintenance within family environment accounted for a significant amount of variance in theory of mind and executive functioning
From Screen to Reality: Turning Faces into Reinforcers
No full textAttending to a person’s face is a significant developmental milestone for young children’s social and communication development. Across two experiments, the researcher evaluated whether a screen-based intervention could establish faces as reinforcers across different settings and contexts.
In Experiment 1, the researcher developed and conceptually validated a virtual face conditioning program by answering whether conditioned reinforcement to adult faces can be established through reinforcing attention to virtual faces and, if so, whether the stimulus control transfers to in-person faces and other real-life situations. The dependent variables of Experiment 1 were attention to virtual faces (faces presented on a screen), attention to live faces, and generalized observing probes for faces and voices in naturalistic settings. A seven-phased screen-based intervention, which gradually faded from the filters with three changes to the face down to no filter, was used as an intervention for six young children with disabilities. During the intervention, participants’ observing responses for virtual faces were synchronously reinforced using a reinforcement package that included preferred snacks, praise, and tactile reinforcement. The results showed that following the intervention, although the degree of increase varied, all participants showed an increase in all three dependent variables.
In Experiment 2, the researcher further investigated the effectiveness of the virtual face conditioning intervention by systematically replicating Experiment 1 with another six young children with disabilities. The researcher aimed to demonstrate further implications of conditioned reinforcement for faces in early social behaviors and daily instructions.
More specifically, Experiment 2 incorporated two additional dependent variables, the Early Social Communication Scales (ESCS; Mundy et al., 2013) - a tool designed to assess joint attention, behavioral requests, and social interaction behaviors in young children - and learning rate. The results replicated the effectiveness of virtual face conditioning intervention by showing similar increases for the same three dependent variables from Experiment 1. The results showed the greatest increase in initiating social interaction and initiating behavior request skills compared to the other domains. However, limited gains were observed in joint attention, suggesting that conditioned reinforcement for faces may be necessary for establishing joint attention, but it is not sufficient. Additionally, the participants showed an increased learning rate in the five-week period following the intervention. This suggests that virtual face conditioning was effective in establishing a crucial preverbal foundational cusp that influences the participants’ daily learning
The arithmetic of del Pezzo surfaces and Hilbert schemes of points
No full textMotivated by the Cassels–Swinnerton-Dyer Conjecture for cubic surfaces, this thesis investigates the stable birational class of Hilbⁿ_ , the Hilbert scheme of length closed subschemes on a given surface . The primary focus is to determine for which pairs of positive integers (,′) the varieties Hilbⁿ_ and Hilbⁿ′_ are stably birational, specifically when is a surface with irregularity () = 0.
After establishing general results for such surfaces, the study narrows its scope to geometrically rational surfaces. In this case, it is shown that, among the Hilbⁿ_’s, there exist only finitely many stable birational classes.
A corollary of this finding is the rationality of the motivic zeta function _mot (, ) in ₀ (Var/)/([A¹_]) [[]] over fields of characteristic zero.
Returning to cubic surfaces, the thesis further examines the stable birational types of Hilbⁿ_ both asymptotically and for small values of