Current research projects supported by the Zorro HPC System — organized below by reverse chronology and on right alphabetically by researcher:
Terrorist Attacks during Ramadan
Suat Cubukcu, Instructor, SPA/JLC
In this project, I would like to analyse the distribution of terrorist attacks during holy month Ramadan and other times.
Truth Table Generator in Python
Our artificial intelligence project is focused on the visualization of truth tables and the evaluation of truth expressions. It currently uses threading and parallelism processes to evaluate the expression and determine the values. Our project requires data collection that can determine the usefulness, speed and performance of the program itself and whether or not it can be improved.
Assessing the impact of inflation targeting in developing countries: A synthetic control approach
The objective of this paper is to measure the impact of inflation targeting (IT) on real GDP per capita in developing and emerging countries. To that end, we apply a methodology borrowed from the impact evaluation literature, the synthetic control method (SCM). This technique allows us to conduct data-driven comparative studies at the country level by comparing the macroeconomic trajectory of a given IT country with that of a combination of non-IT economies, i.e. the synthetic control. More precisely, the SCM provides an algorithm that finds a convex combination of comparison units, such that the chosen combination best resembles the values of predictors of the outcome variable for the affected unit before the adoption of IT. After constructing the synthetic control, we can simulate the evolution of GDP per capita in each targeting country, had it not implemented IT, and compare it to the actual outcome, thereby inferring the impact of the policy shift. Evidence on the impact of IT is mixed. Evidence on the impact of IT on output is especially mixed. In Chile, Ghana, and Peru, real GDP is higher than that of their respective synthetic controls after the adoption of IT, suggesting a positive impact of IT. However, the opposite is found in other countries, such as Mexico and Guatemala, and inconclusive results are found for various other countries.
Analysis of Consumer Demand for Health-related
Products Using Nielsen Scanner Data
This project uses weekly scanner data on sales of health-related products to study how consumers value product attributes and respond to new information on them. Nielsen Retail Scanner data are made available to academic researchers via the University of Chicago's Kilts Center for Marketing. The data consist of weekly pricing, volume, and store-level information for 2.6 million individual products grouped into 1,100 categories, from 2006 through the present. Data are collected via point-of-sale scanner systems from 90+ retail chains with outlets in 52 market areas across the U.S. The project will use both traditional demand-system and hedonic methods of estimating how consumers value attributes of products that are or may be subject to regulation by the U.S. Food and Drug Administration. Specific applications include: valuing menthol and nicotine as attributes of combusted cigarettes, assessing the extent to which prices of dietary supplements correlate with third-party quality ratings, and estimating consumer surplus from weight-loss products.
Sentiment Analysis Towards Gender in Twitter in Tanzania
I will be doing a text mining project in R for a Big Data Analytics course. I will be using the Rtm module to look at popular sentiment regarding gender in Tanzania for a five-week period in December 2015 through February 2016.
This paper uses a database of primarily small, local news sources to assemble US state-level economic uncertainty measures. We report the quantity of news related to both uncertainty and a collection of economic terms as a share of the total news volume in the given time and place. The economic terms reflect local concerns focusing mainly on regionally-important industries, jobs and budgeting. This improves upon existing economic uncertainty measures in several ways. Existing measures are generally presented at the national level, which has the advantage of accommodating the use of common macroeconomic indicators, such as stock market volatility or GDP and inflation forecasts. The disadvantage is the loss of information inherent in such aggregation, particularly in a country as large and diverse as the United States where conditions in one state may vary widely from conditions in another. While news based indices do not necessarily suffer from this data constraint, in practice all existing measures we are aware of are national in scale, and maintain a focus on policy and finance that we show misses important signals of economic uncertainty. The data covers all 50 states from 1995 to 2015, and the results track well with both reasonable expectations and, in the case of non-war and non-terrorism related economic uncertainty, act as a leading indicator of state unemployment rates. When the process is re-run with more traditional policy and finance search terms, neither is true.
The goal of merit awards is to attract talented students to campus, yet little is known about the efficacy of such awards and how they affect student behavior, decision making, and performance once on campus. The current study begins to fill this gap.
The project will address the question of whether the demographic match between law school instructors and students affects students’ course grades, future course-taking, or specialization decisions.
Impact of Marriage Quality on Self and Proxy Reporting of Agricultural Work
Existing surveys of agricultural work performed by women in Sub-Saharan Africa may underestimate the hours of work performed by women. This is primarily due to the fact that, quite often, estimates of the work performed rely on reporting by household heads, most often males. This research is based on the surveys conducted in Ghana, which contain both self and proxy reports on the hours of work performed. It is our goal to examine the differences between self and proxy reports, and to examine how quality of marriage, measured by responses of men and women in each household, impact the reporting of work.
On the roots of truncated hypergeometric series
over prime fields
We consider natural truncations of the hypergeometric functions 2F1, 3F2, and the Kummer hypergeometric functions 1F1 over the finite field Fp, for primes p. For the associated polynomials Q (of degree proportional to p), we obtain bounds for the number of roots of various congruences Q modulo p, which correspond to rational point counts where methods from algebraic geometry fail. Via a correspondence to families of elliptic curves, we obtain sharp bounds in some cases. We also show that the functions 2F1 and 3F2 are associated with K3 surfaces, including some previously studied for their modularity. For the Kummer hypergeometrics, we obtain a power saving for a large class of the parameter values within an algebraic closure of Fp by modification of methods from transcendence theory.
Topological Data Modeling for Cyber Data Analytics
We will explore and demonstrate the significance of mathematical methods based in topological data modeling (TDM) for analyzing big data with high complexity. We will ground our demonstrations in cyber applications and data sets, and address the scalability of TDM, both in terms of data volume and complexity, amongst the first to do so on these exciting new methods.
Revolutionary Pathways: The Effects of Domestic Revolutions
How much and in what ways do individual leaders matter for international politics? This paper sheds new light on these questions by considering the consequences of domestic revolutions in international relations. We argue that revolutions have international effects due to two separate pathways, one associated with the event and one associated with the new leader’s administration. In the first pathway, a revolutionary event disrupts established relationships and perceptions, both within the state and abroad. In the second pathway, revolutions put individuals into office who are more willing to challenge the status quo, and who have publicly committed to a sustained shift in policies during their administration. These two distinct pathways suggest that the important question about revolutions is not whether leaders or events matter most, but rather the conditions under which they matter. Consequently, we studied these pathways on three phenomena: international economic sanctions, domestic economic growth, and interstate alliances. We find that revolutionary events have a short-term negative effect on domestic economic growth, while revolutionary leaders have a long-term effect on the probability that a revolutionary state is targeted for sanctions. Both the revolutionary leader and its immediate events alter the state’s international alliances. Our findings suggest that no single level of analysis completely dominates, and the answer depends on the outcome of interest.
Non-equilibrium dynamics of quantum transverse-field Ising model
In this project, we numerically study the steady state of a transverse-field Ising model under dissipation. Prior work suggests that the combination of the dissipation and transverse-field leads to a non-equilibrium state whose properties resembles a classical Ising model under the influence of thermal fluctuation. An effective temperature is derived based on the dissipation, interaction and transverse field parameter of the model. With the quantum Monte Carlo method, We numerically calculate various quantities of the model to test the validity of the theoretical prediction and evaluate the effective temperature in various parameter regions.
Police performance: The Case of Mexico
This dissertation examines the effect of exogenous factors on police performance at the municipality level in Mexico. The influence of factors outside the control of police organizations affect their overall performance and thus, their ability to provide efficient public services and, in turn, spending far more resources to provide the same level of service. The author employs frontier methods to analyze police performance. In particular, the author employs partial non-parametric frontier methodologies like order-m and oder alpha to estimate the efficiency scores. The scores are then regressed against a set of exogenous variables to analyze their effects on police performance.
Ideological or Strategic? Using Electoral Loss to Understand Contribution Motivations
Political scientists have long noted the ideological nature of individual campaign contributors. Relative to PAC contributors, individuals are motivated by policy goals and seek to support candidates closest to their ideological ideal point. Yet, a similar line of research also argues that individual contributors tend to disperse their money across the country to ideological/partisan friends running in competitive elections. This project seeks to disentangle these two related, yet often times distinct motivations for individual campaign contributions: ideology and strategy. I argue that one can better understand the motivations of contributors by examining how contributors respond (in subsequent cycles) to losing elections. If contributors continue to contribute in the aftermath of loss (particularly persistent loss over time), then we can take this as an indicator of ideological motivations. By answering the open empirical question about how contributors respond to electoral loss, we can better understand campaign contributors. With an innovative empirical approach and an exhaustive behavioral measure of motivations, this research project is uniquely positioned to answer this question.
Image features that are essential to estimate mechanical properties of deformable objects in dynamic scenes
Humans visually estimate material properties of objects (is the sweater heavy or light?) when deciding how to interact with them (e.g. grip strength).Nonetheless, very little is known about how humans estimate mechanical properties from visual information. Lack of this knowledge prevents further understanding on how material perception is used by the visual system for action planning. Our goal is to seek image features that allow humans to estimate mechanical properties of deformable objects in dynamic scenes. We aim to solve this problem using three main strategies: (1) Create high- delity cloth simulations using physics engine in graphics software. (2) Manipulate image cues and use psychophysical experiments to systematically measure material perception. (3) Build a deep neural network to learn image features that can be used to estimate material properties.
Teacher-Student Match and Student
Disciplinary Outcomes in North Carolina
Many studies have detailed the significant negative relationship between student disciplinary actions and subsequent student outcomes (e.g.: Skiba, Michael, Nardo, and Peterson, 2002). Fewer studies have empirically assessed the role of teacher characteristics in determining student disciplinary actions; teachers serve as gatekeepers who report the severity and frequency of disciplinary actions. Given research that indicates that teacher race and gender can matter for minority students, this gatekeeper role may have implications for minority students’ outcomes (Dee, 2005; Clotfelter, Ladd & Vigdor, 2006; Bates & Glick, 2013). In particular, same race and/or same gender teachers may report student disciplinary actions differently. The racial composition of the teaching workforce is vastly mismatched to the current public school student population. Therefore, exploring this critical relationship between teacher demographic characteristics and student discipline outcomes is essential. This paper proposes to model predictors of student disciplinary actions, based on teacher characteristics, and their interactions with student characteristics.
This study uses statewide administrative data from North Carolina to address these research questions. North Carolina offers roughly 20 years of data where students can be matched to individual teachers at the classroom level. The state also collects disciplinary records for each student, including offenses committed by students and whether expulsion/suspension consequences are attached to each disciplinary incident. The state includes demographic data for both students and teachers. This is crucial in order to identify the effects we study here.
We use multiple strategies to identify the effects of teacher-student demographic match on student outcomes. First, following past researchers (Dee, 2005) we will conduct student fixed effects comparisons that uncover whether students are disproportionately likely to face disciplinary consequences in years (K-5) when their home classroom teacher is of a different race/sex group from them, compared to when they share their teachers’ characteristics. We will supplement these analyses with a second, novel strategy in this literature: identifying match effects off of twin differences. This strategy will take advantage of the fact that until the mid-2000s, North Carolina state policy encouraged that twins be assigned to separate classes. In some cases, this will mean that one twin is assigned to a same-race (gender) teacher while another twin is assigned to a different-race (gender) teacher. Exploring whether these different assignments are then associated with different discipline rates provides a strong alternative estimation of match effects.
To explore these issues at the secondary level, we adapt an analytic strategy used in past literature on the effects of students’ being matched to tenured vs. adjunct college instructors (Bettinger & Long, 2010). This strategy uses deviations from the long-term trends in the race/sex composition of faculty at a given school-grade level to determine whether, for instance, male students have lower disciplinary incident rates in years when a higher share of teachers in their specific school-grade combination are male compared to the historical average.
These analyses should have important implications for debates over the importance of diversifying the teaching workforce.
What's So Spatial about Diversification in Nigeria?
Paul Corral Graduate Student (CAS/Econ)
As time passes the importance of income diversification, even within farm households, is increasingly being noted. The belief that farm households rely mostly on own production in order to achieve food security has given way to the perception that diversification is the norm (Barrett et al., 2001; Davis et al., 2009). In fact some authors (Davis et al., 2009) suggest that in most countries most households diversify, and the majority of household income comes from off-farm activities. While several studies have been carried out which have found the determinants to household choice to diversify, no study to date has taken into account spatial dependence or the "neigborhood effect." Ignoring spatial relations may lead to biased or inconsistent estimates (Holloway et al., 2002). Thus taking into account spatial dependence could aide policy makers in order to ensure that resources are directed to areas where these would yield the highest returns.
Transcriptome profiling of the amphipod Gammarus minus
The cave realm is an ideal system for studying the evolution and genetics of regressive morphological traits. Gammarus minus, a freshwater amphipod living in the cave and surface streams in the eastern United States, provides an opportunity to examine the evolution of troglomorphic (= cave specific) traits. In G. minus, multiple pairs of genetically related, physically proximate cave and surface populations exist which exhibit an astounding degree of intraspecific morphological divergence. The morphology, ecology, and genetic structure of these populations are well characterized, yet the genetic basis of morphological divergence remains unknown. RNA-Seq data from one pair of morphologically distinct sister populations inhabiting surface and cave habitats was collected (four individuals from each population) and used to identify genes associated with the evolution of troglomorphic traits. Candidate genes were identified as those exhibiting either large difference in expression level or in their rates of evolution between cave and surface populations. Genes identified to be of interest vis-à-vis the RNA-Seq results will then be characterized more extensively at the population level through DNA sequencing and through quantitative analysis of gene expression. Population level characterization will include at least one additional cave/surface pair of populations, with convergent patterns of loss-of-function or gain-of-function mutations in cave populations to be taken as strong evidence of adaptive evolution.
Multi-sensory inference of material properties of deformable objects in dynamic scenes
Humans are good at recognizing objects as well the materials they are made of. Most previous research has focused on understanding optical properties from static images. We want to understand how humans perceive mechanical and tactile material properties of objects in dynamic scenes by observing how objects interact with external forces. Our long-term goal is to develop a computer vision system to recognize and classify material properties from real videos and images. To achieve our goals, we use computer graphics software to simulate videos that contain dynamic behavior of objects in complex scenes with physical-realistic dynamics. The fast development of computer graphics and physical simulation allow realistic rendering of deformable objects in dynamic scenes. Such stimuli also allow us to systematically isolate scene and image parameters and figure out the mapping between perception and physics. The dataset can also be used as training dataset to train algorithms to learn relevant features and classify and recognize materials in images and videos that don't have ground truth data. In addition, we also aim to facility computer rendering user-interface by identifying important perceptual dimensions of mechanical and tactile attributes. Currently, the computer capacity in the lab is limited. Hence, my team and I are planning to use the cluster to batch render our stimuli.
Comorbidity of ASD and ADHD in the Brain
Devon Shook, Independent Research Student (BCAN/CAS)
Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder are two of the most commonly diagnosed developmental disorders and often co-occur in the same child. Neuroimaging research on both disorders has consistently found the cerebellum to be a site of structural and functional abnormality. However, until the DSM-5 allowed for a comorbid diagnosis, study of their shared etiology has been a challenge. In the present study, we investigate the structural and functional abnormalities of comorbid ASD and ADHD with a particular focus on the cerebellum.
Disasters, Federal Disaster Relief Policy,
and Bankruptcy Filings
Bankruptcy is an important social program that annually results in the transfer of $42 billion from creditors to debtors, and its use has been increasing. According to the adverse events hypothesis, bankruptcy filings are triggered by a shock that pushes individuals "over the edge." Natural disasters are an important example of such a shock that affects many Americans. It has been noted that the bankruptcy rate increases in the period following hurricanes (Lawless, 2005). It is currently unknown how large this effect is, whether it pertains to other disaster types, or whether public funds expended to aid recovery following large disasters have an appreciable mitigating effect on such increases. This research project uses a new data set combining information on disasters, disaster declarations, and bankruptcy filings to determine the extent to which natural disasters increase bankruptcy rates and the extent to which different types of government aid mitigate increases.
Credit Ratings and the Cost of Municipal Financing
Kim Cornaggia (KSB/Finance and Real Estate), Jess Cornaggia, and Ryan Israelsen
Moody’s recalibrated its municipal bond rating scale in 2010, resulting in upgrades of zero to four notches on $2.2 trillion of bonds. We find the upgraded bonds earn abnormal returns, increasing in upgrade magnitude. Upgraded municipalities subsequently issue more bonds, relative to non-upgraded municipalities, and the new issues have lower relative offer yields. Additional tests indicate that ratings affect bond prices and debt capacity both because ratings provide information and because higher ratings reduce regulatory compliance costs. Overall, this recalibration event sheds light on the information environment in the municipal bond market and on the real effects of ratings.
Revolving Doors on Wall Street
Kim Cornaggia (KSB/Finance and Real Estate), Jess Cornaggia, and Han Xia
Credit analysts often leave rating agencies to work at firms they rate. These analyst transfers provide a unique laboratory for studying revolving door effects. Benchmark rating agencies provide counterfactuals which allow us to measure rating inflation in a difference-in-differences framework. We find that analysts transitioning to managerial positions and to top banks become more favorable to their future employers prior to their transitions. Further, these conflicted ratings become less responsive to changes in market-based measures of hiring firms’ credit quality. Our results reveal the presence of previously untested forces that affect information production by credit analysts.
Home Bias in Credit Ratings: Evidence from Municipal Bonds
Kim Cornaggia (KSB/Finance and Real Estate), Jess Cornaggia, and Ryan Israelsen
We identify detailed characteristics of lead credit analysts on municipal bond rating reports generated by Moody’s and Standard & Poor’s (S&P). We employ the lead analysts’ state of origin, and separately state of education, to test for home bias in ratings. Controlling for analyst education, age, gender, and tenure, along with bond-month fixed effects, we find that home analysts award significantly higher ratings (more favorable to the issuer) compared to ratings from outside analysts. The effect is increasing in credit risk and expands affected municipalities’ debt capacity. The effect on offer yields varies by rater. Because the effect is driven by analysts’ states of origin, but not by where analysts reside at the time they produce ratings, we conclude that our results reflect favoritism by home analysts rather than superior information.
Fundamentals, Derivatives Market Information,
and Oil Price Volatility
Jonathan Wallen and Michael Robe (KSB/Finance and Real Estate)
We investigate price volatility in the West Texas Intermediate (WTI) and Brent crude oil markets between 2000 and 2014. We provide empirical evidence of a relationship between the term structure of option-implied volatilities and global macroeconomic conditions, physical market fundamentals (OPEC surplus output capacity, oil storage) and economy-wide financial uncertainty (captured by the equity VIX). Based on public data regarding trader positions in U.S. futures markets, the intensity of oil speculation is statistically insignificant. Unexpected disruptions in the crude oil space are associated with large regression residuals. Our findings suggest that derivatives (“paper”) markets contain information on the magnitude and duration of major oil market disruptions.
We examine the potential for a simple auction to allocate arrival slots during Ground Delay Programs (GDPs) more efficiently than the currently used system. The analysis of these auctions uses Predictive Game Theory (PGT) , a new approach that produces a probability distribution over strategies instead of an equilibrium set. Furthermore, the game we consider is one of imperfect information and we show how averaging over priors still generates quantities of interest from the PGT distribution. We find that the second-price slot auction has the potential to lower social costs but further analysis is needed to determine which pre-GDP schedules are best suited for an auction.
Decision-Theoretic Prediction and Policy Design of GDP Slot Auctions,” American Institute of Aeronautics and Astronautics 2014-2163, J. Bono, D. Wolpert, D. Xie, and J. Grana, June 2014.”
Modern Value Chains and the Organization of Agrarian Production
Empirical studies of agrarian production in developing countries often find that smallholders possess a productivity advantage over large farms. Eswaran and Kotwal famously derive this inverse farm-size/productivity relationship from the structure of agrarian production. Their model predicts that in otherwise equivalent economies a more egalitarian land distribution raises output and producer welfare. However, developing countries have experienced the rapid emergence of modern value chains. Recent research provides evidence that this transformation alters the welfare possibilities of agrarian economies. We therefore extend the Eswaran-Kotwal model by incorporating a modern value chain. In our model, the inverse farm-size/productivity relationship persists, but we contradict previous sanguine conclusions about egalitarian redistributions of the means of production. We find a potential equity/efficiency tradeoff in the distribution of land.
The goal of this project is to develop a network model of financial contagion in the presence of a real sector. We add a non-bank corporate sector to a model of the banking sector based on Gai, Haldane, Kapadia (2011). We use agent-based methods in order to identify firm-level and bank-level heterogeneities that play a crucial role in the propagation of liquidity problems. Agents in the non-bank corporate sector need to borrow from financial intermediaries. We show that the interactions between the two sectors can generate and propagate financial fragility. We uncover a tendency for the banking sector to become increasingly concentrated over time.
Eastern Economic Association 40th Annual Conference, Boston, MA. 8 March 2014.
We study the extent to which differences between states in the standards of proof required at the various stages of a case of child abuse or neglect influence the outcomes of the case.We use administrative data on all reports of child abuse and neglect as captured in the National Child Abuse and Neglect Data System, and we study the substantiation decision, the filing of a juvenile petition, and the decision to place children into foster care.
Genomics of a deep terrestrial subsurface nematode,
The surprising discovery of a nematode from the deep terrestrial subsurface, Halicephalobus mephisto, fundamentally altered our perception of the adaptability and ecological range of metazoa. This finding has profound implications for our understanding of the evolution of life on earth and the search for life on exoplanets. In this project, we will assemble the genome of this organism and determine the mechanisms by which it adapted to an extreme environment containing very little O2, high pressure, and elevated temperature. Halicephalobus mephisto is a moderately thermophilic (35-41°C), parthenogenetic, bacteriofagous nematode discovered within a fluid-filled fracture, 1.4 km beneath the surface in South Africa. The high pCH4 (2.5 bars), high sulfide (~1 ppm) and low pO2 (0.07 bars) present significant environmental challenges to this aerobic metazoan. Nematodes are notoriously adept at adapting to novel environments, having explored a wide range of free-living, zooparasitic, or phytoparasitic environments. The process of environmental adaptation and selection leaves a lasting imprint on genomes, giving us a powerful approach to study the adaptation of this metazoan to its extreme environment.
Milieu: Defining Racial Context with Geolocation Data
Across disciplines, scholars strive to better understand individuals' milieus—the people, places, and institutions individuals encounter in their daily lives. In particular, political scientists argue that racial contexts shape individuals' attitudes about candidates, policies, and people of various races and ethnicities. Yet the current standard of measuring milieus is to place survey respondents in one or two geographic containers like counties or census tracts and then to ascribe all of that container's characteristics to the individual's milieu. Using a new dataset of over 2.6 million GPS records from over 400 individuals, we compare conventional static measures of racial context to dynamic and precise measures of their milieus. In particular, we demonstrate how low-level static measures (such as census block) tend to overstate how extreme individuals' racial contexts are, and how this overstatement can lead to underestimates of racial contextual effects.
Blocking before treatments are assigned can improve the precision of causal estimates, make experimental estimates more robust to unlucky randomizations or inadequate parametric adjustment, and preserve power, even in relatively small group-randomized trials. But how should experimentalists best incorporate valuable covariate information into blocked designs? We focus on the critical question of setting the covariate weights that define the extent to which experimental units are similar or different. Using simulated and applied data, we assess the relative performance of covariate weights derived from two sources: first, from genetic matching algorithms which explicitly seek to optimize balance; second, from Mahalanobis distances without explicit balance-seeking. We show that balance in randomized experiments that use genetic covariate reweighting is superior to that obtained by random allocation, but also by Mahalanobis-metric blocking alone, even when done optimally. Further, we show that a genetic algorithm can directly improve the robustness of a design to unobserved confounding when tuned to do so. Our results provide guidance for experimentalists and offer new insights about how to improve practice in experimental design.
Cost-Effectiveness of a Lifeline Telephone Crisis Center for Reducing Emergency Department Visits and Preventing Suicide, 2009-2014
Suicide is a serious public health problem with considerable societal costs. However, few previous studies have compared the costs of suicide prevention programs to positive outcomes for society as well as the individual. Suicide prevention hotlines are widespread and provide suicide prevention for callers in crisis. However, the cost-effectiveness of such hotlines is unknown. This study will obtain data from a large Lifeline call center serving a tri-state area in the United States for the period January 1, 2009 to January 1, 2014. We will test whether the empirically-based Lethality Assessment conducted by Lifeline workers leads high-risk callers to seek emergency department (ED) services. As EDs can be over-utilized by suicidal persons, we hypothesize that Lifeline can triage only those callers who need immediate medical care. Lifeline is a low-cost option to ED care for low-risk callers. This research includes the immediate and direct costs of preventable ED visits related to suicide. Additionally, we hypothesize that Lifeline is a cost-effective suicide prevention program in that it increases Quality Adjusted Life Years (QALYs) for high-risk callers who receive ED care. The cost-utility analysis of QALYs will examine, whether the cost of operating Lifeline yields an economic benefit a low incremental cost ratio for service operations compared to QALYs gained.
Our work focuses on extracting information from periodic point processes. These problems arise in numerous situations, from radar pulse repetition interval analysis to bit synchronization in communication systems. We divide our analysis into two cases: periodic processes created by a single source, and those processes created by several sources. We wish to extract the fundamental period of the generators, and, in the second case, to deinterleave the processes. We are developing efficient algorithms for extracting the fundamental period from a set of sparse and noisy observations of single and multiple source periodic processes. The algorithms are computationally straightforward, stable with respect to noise, and converges quickly.
Many solutions abound for the well-known Rubik's Cube puzzle. Similarly, any "twisty puzzle" made up of regular corner/edge/center pieces can be solved using basic tools of abstract algebra. However, if a sufficient number of pieces are fused together, a "bandaged puzzle" emerges, and the mathematics behind this is very complicated. In fact, it is known to be at least NP-hard, as a class of puzzles. All bandaging patterns for the standard Rubik's Cube have been solved, but beyond this, there is still much which is unknown about this class of puzzles. For a further explanation of the ideas in this paragraph, please see Jaap Scherphuis' explanation at: http://www.jaapsch.net/puzzles/pspace.htm
The project we are currently working on is to count (or estimate) the color-free configurations of a particular bandaging pattern for the Megaminx. The Megaminx is a dodecahedral puzzle with each face having a center, 5 edges, and 5 corners. The bandaging pattern we are investigating is one we found a pattern for on an internet chat board for twisty puzzles. (The thread is presently at: http://www.twistypuzzles.com/forum/viewtopic.php?f=9&t=18677)
Fast functional integrals with applications to dynamical systems
John Tillinghast (CAS/Mathematics)
Functional integrals (also called "path integrals") are integrals over function spaces, such as the set of Brownian motion paths from one point to another. They have been used for decades in probability and physics. As high-dimensional integrals they tend to be computation-intensive, generally requiring Monte Carlo sampling. This talk explains how to use sparse Laplace approximation, including higher-order Laplace approximation, for functional integrals. We introduce a way to calculate the higher-order terms in O(N) time, where N is the number of time points. For an infectious disease data set, we use functional integrals to estimate the model parameters. We compare the parameter estimates and approximate integrals to results from importance sampling. For this example, the higher-order approximation is extremely accurate, and even the basic approximation gives very good estimates of the model parameters. Speed is significantly greater than for STAN (a new, general-purpose tool for Hamiltonian MCMC) while getting near-identical parameter estimates.
Three Essays on the Interaction of Fiscal Shocks and Budgetary Constraints on the Public Education Sector
Michael Hayes Independent research student
My dissertation examines how budgetary constraints on school districts create a host of challenges for public managers, including higher levels of teacher turnover, as well as an unequal distribution of state funding across school districts. I plan to use the HPC to run bootstrapped quantile regressions.
Migration, Population imbalance and Decentralization in Indonesia
Smriti Tiwari Independent research student (CAS/Economics)
Most studies on migration focus on the impact of migration and/or remittances on the economic development of the migrant sending countries. But would better opportunities at home generated through economic development deter migrants? By using the unique features of decentralization in Indonesia, this paper aims to get at the role of local development on migration behaviors. In the case of Indonesia, it will also shed light on its implications, if any, on the differences in population pressures.
Characterization of Felid SINEs
Kathryn Walters-Conte (CAS/Biology)
Short-interspersed nuclear elements (SINEs) comprise a class of nuclear DNA that can define evolutionary history. Abundant in mammalian genomes, these transposable elements can characterize lineages. A SINE family‘CanSINE’ has been described in the order Carnivora. We are pursing examination of these motifs in the Feliformia suborder (cat-like animals) and Felidae family (cats) with respect to the distribution of conserved and non-conserved insertion sites, the utility of these sequences as markers for hybridized cats and the implementation of SINEs as tools in undergraduate education.
Internal grant proposal
"Transposable Elements as Markers of Hybrid Felines and Beacons of Cellular Diversity" 2014.
Gender Impact of Malawi Input Subsidy
Paul Winters (CAS/Economics) and Wendy Karamba (CAS/Economics)
Alleviating gender differences in agricultural productivity is not only a matter of equity but it is vital for poverty reduction. While a number of studies suggest that gender differences in agricultural productivity are a result of female farmers having limited access to resources, few studies investigate the role of agricultural interventions in alleviating the constraints to input use and subsequently the gender gap in productivity. For the first time, this study investigates whether there are gendered gains in agricultural productivity from participating in an input subsidy program. Using nationally-representative data that is disaggregated at the plot level, this study analyzes the large-scale voucher-based Farm Input Subsidy Program in Malawi. Focusing on the total value of output per hectare, the relationships are evaluated using weighted estimators where the weights are constructed from propensity scores since beneficiaries are not randomly selected. Spatial fixed effects are employed to deal with the unobservables that may confound the relationship between program participation and productivity.
Exchanging Fire: Trade, Conflict, and the Strategic Incentives of Indirect Economic Interdependence
David Ohls (SIS)
How do indirect economic ties—participation in common global networks of production and trade—dampen conflict incentives between antagonistic pairs of states? Using a formal model of resource allocation in the context of dyadic conflict, I show that latent economic interdependence reduces fighting incentives. I then test this empirically using dyad-year panel data from 1948 to 2000. I find evidence consistent with the hypothesis that mutual reliance on the same outside trading partner and densely-linked trade networks strongly decreases the likelihood of interstate disputes and increases scores on conflict-cooperation scales. This effect is particularly strong in rivalrous dyads, which frequently come into conflict; although such pairs of states tend to have have limited direct economic engagement, they often share great underlying structural potential for cooperation. These results have important implications for theoretical research on the links between economics and security, dyadic rivalries, and the role of third parties in international disputes.
Land Titling and Investment in Sub Saharan Africa
Woubet Kassa Independent research student (CAS/Economy)
The role of property rights in resource allocation has been one of the central themes in development economics. There has existed extensive theoretical arguments that property rights in land are closely associated with the allocative efficiency of agricultural resources as well as investment decisions. However, empirical findings have not been conclusive. This has been complicated due to possible endogeneity of titles, unobserved hetrogeneities and the non-experimental nature of the data. This study employs various econometric tools to address these challenges using the Living Standards Measurement Study surveys of six Sub-Sahran African countries.
Stochastic Demand Theory of Gene Regulation
Corinne Abolafia (MA Candidate, Mathematics)
Dr. Tuncay Alparslan (CAS/Mathematics and Statistics)
(Thesis for MA in Mathematics) We develop a stochastic model based on continuous-time Markov chains for selection between different modes of gene regulation.
Information Theoretic Modeling
Dr. Amos Golan (CAS/Economics/Info-Metrics Institute)
Dr. Heath Henderson (Research Associate, Info-Metrics Institute)
Skipper Seabold (PhD Candidate, Economics)
This project, joint with Heath Henderson and Skipper Seabold, develops an improved information-theoretic estimator. It is a computational semi-intensive method that has proved to dominate other traditional methods for all finite and complex data.
The Determinants of Student Retention at a Private Selective Post-Secondary Institution
Despite progress in leveling the playing field for disadvantaged college students, there continue to be gaps in college completion rates between students of different backgrounds. These gaps have the potential to perpetuate socioeconomic disparities, as four-year degrees become more of a prerequisite for labor market success. Existing research suggests that differential completion rates are driven almost entirely by differences in the persistence of admitted students, as opposed to differences in admission rates. Accordingly, the proposed research project seeks to identify the determinants of persistence at a selective private post-secondary institution.
Actionable University Responses to Undergraduate Student Persistence &Completion, IES, $225,000 ($5,000 for HPC)
Large Truck Crashes & State Roadside Inspection Practices:
A Spatial Analysis
Janine Bonner (Independent Researcher)
This project will combine the practices of analyzing large data, mapping, and drawing horizontal conclusions, while utilizing the statistical practices of linear regression and spatial analysis. The goal of the project is to draw correlations between individual state inspection practices and fatal crash rates by state. The intention is to show whether or not better regulatory compliance of state inspection practices helps reduce crash rate and/or severity.
Harnessing the power of parallel computing
for computer-aided drug discovery
My research revolves around the study of the cellular targets of drugs, the identification and pharmacological characterization of molecules that modulate their activity, and the examination of the cellular consequences resulting from such pharmacological intervention. Through the application of computational and experimental biochemical pharmacology techniques, the ultimate goal of my laboratory is to rationally identify molecules potentially endowed with a desired pharmacological activity and subsequently test their biological effect on mammalian cells that express the target of interest either naturally or artificially. The main research focus is on the discovery of compounds that act through G protein-coupled receptors (GPCRs), the single family of cellular targets most exploited by currently marketed medicines.
"Computer-aided discovery of biased agonists and allosteric modulators of the β2-adrenergic receptor," American University Faculty Research Support Grant, AY 2014-2015
This project seeks to estimate the effect of misbehavior on academic performance, the effect of teachers and peers on misbehavior, and the implications of omitting measures of misbehavior from value-added models of the education production function for rankings and estimates of teacher effectiveness.
"Panel data evidence on the effect of school size on student achievement" (joint with Laura Langbein) at the 2013 AEFP and AERA Annual Meetings
“Linking Teacher Quality, Student Attendance, and Student Achievement,” presented at 2014 Association for Education Finance & Policy Annual Conference and 2014 American Educational Research Association Annual Meeting
“Are Student Absences Worth the Worry in U.S. Primary Schools? (joint with Alison Jacknowitz, Alison and Andrew Brannegan),” presented at 2013 Southern Economic Association Annual Conference and 2014 American Educational Research Association Annual Meeting
Spencer Foundation Research Grant for "Linking Teacher Quality, Student Attendance, and Student Achievement," 2013-14, $39,427.
W.E. Upjohn Institute Early Career Research Grant. “The Effect of High-Stakes Accountability Policies on Teacher Absences,” 2014-15
Estimation and Inference in Value-Added Models when the Relationship between True Academic Achievement and Test Scores Varies Across Time and Teachers, IES, $225,000 ($5,000 for HPC)
The Soft Bigotry of Low Expectations: Estimating the Impact of Stigmatization on Student Outcomes, Spencer Foundation, $40,000 ($2,000 for HPC)
The Implications of Summer Learning Loss for Measuring Teacher and School Effectiveness, IES, $200,000 ($5,000 for HPC)
Gershenson, Seth. 2014. “Linking Teacher Quality, Student Attendance, and Student Achievement.”
Gershenson, Seth, Jacknowitz, Alison, & Brannegan, Andrew. 2014. “Are Student Absences Worth the Worry in U.S. Primary Schools?
Development of a Local Spatial Indicator of Association Based on Modified Moran's I
Jess Chen (CAS/Economics)
Local indicators of spatial association (LISAs) are used to detect clusters in spatial data. I derive and implement a LISA based on Jackson et al.'s Modified Moran's I (2010). I also conduct a simulation study to compare the power of this test against that of existing LISAs under various scenarios of underlying populations, spatial weight matrices, local and global clusters, and various degrees of data sparseness. This is one part of my dissertation, which will be on methods and applications in spatial statistics.
"A New Local Indicator of Spatial Association," Robyn Rafferty Mathias Student Research Conference, March 2014 and Women in Stats Conference, May 2014
Listening to Noise
Justin Grana (CAS/Economics)
Faculty Sponser: Alan Isaac (CAS/Econ)
Attempting to see if noise in trader activity influences the behavior of institutional traders.
Geospatial Determinants of Voting Behavior
Andrew Breza (SPA/Public Policy)
Faculty Sponsor: Alan Ford (CAS/GIS/Computer Science)
Does the distance that an individual lives from a polling place affect his or her likelihood of voting? Scholars and practitioners have written thousands of articles and books on why some people choose to vote while others do not, but many of them ignore local geography or focus on individual cities. This study seeks to calculate the decision to vote based on the distance that an individual lives from a polling place. Instead of focusing on a single city, as past researchers have done, this study uses the voter registration data and 2008 and 2012 voter histories from six states in order to generalize results. Because of the diversity of the sample, distance is regressed with several other independent variables, including access to and use of public transportation, income, unemployment, dominant local industries, and several other factors. Regressions will include a logistic regression with "Voted in person" as its dependent variable, and a multinomial logit regression with three dependent variables: voted in person, voted absentee, and did not vote. This study represents a unique contribution to voting choice literature due to the size and diversity of its sample, block-level demographic data, and use of a Geographic Information System (GIS).
There is a growing body of literature in dealing with interval data. Most of the focus is on only the first and second moments of the interval, then apply the classical regression type estimation. This project will try to look at it from Information-Theoretic perspective and considers higher moments in estimating and predicting such interval-valued data.
The Future of the Property Tax:
Institutional Factors which Shape its Acceptability, Yield and Burden Distribution
The Role of Local Revenue and Expenditure Limitations in Shaping the Composition of Debt and Its Implications for Efficiency, and Intergenerational and Intergovernmental Equity in Local Public Finance
Population Sorting, Economic Segregation and Growing Fiscal and
Budgetary Disparities in U.S. Communities
Paper 1: This study seeks to first update the findings of Mullins and Mikesell (2009) in an effort to better understand the lag properties of subnational tax bases with particular scrutiny placed upon property tax dynamics. Specifically, we assess the relative performance of tax bases, both vertically (local vs. state jurisdictions) and horizontally (across tax instruments).
Paper 2: The proposed paper is the first study to examine whether the enactment of TELs have changed the composition of debt in local governments. Particularly, we are interested in whether governments constrained by binding TELs have experienced increases in their ratio of debt to general revenue for financing government services and whether there has been a shift toward non-guaranteed debt.
Paper 3: This paper uses Census of Population and Housing Data, the American Community Survey and the Government Finance Series to identify the factors that lead to increased disparity and economic sorting as well as those that provide a revenue net to communities with the most limited resource capacities. It concludes that economic sorting is becoming more pronounced and that such sorting artificially and geographically constrains the resource pool available for addressing public policy needs across all communities.
The objective of this project is to measure the impact of Mexico's recently established non-contributory health insurance program "Seguro Popular" on the country's infant mortality and neonatal mortality rates, especially for the most vulnerable population. Controlling for municipality level fixed effects, the estimations will take advantage of the program's staggered roll-out as an exogenous variation of assignment into treatment.
Tobias Pfutze. 2014. "The Effects of Mexico's Seguro Popular Health Insurance on Infant Mortality: An Estimation with Selection on the Outcome Variable," World Development, Volume 59, July 2014, pp. 475-486, ISSN 0305-750X
Banks, Market Organization, and Macroeconomic Performance:
An Agent-Based Computational Analysis
This project is an exploratory analysis of the role that banks play in supporting the mechanism of exchange. It considers a model economy in which exchange activities are facilitated and coordinated by a self-organizing network of entrepreneurial trading firms. Collectively, these firms play the part of the Walrasian auctioneer, matching buyers with sellers and helping the economy to approximate equilibrium prices that no individual is able to calculate. Banks affect macroeconomic performance in this economy because their lending activities facilitate entry of trading firms and also influence their exit decisions. Both entry and exit have conflicting effects on performance, and we resort to computational analysis to understand how they are resolved. Our analysis sheds new light on the conflict between micro-prudential bank regulation and macroeconomic stability. Specifically, it draws an important distinction between "normal" performance of the economy and "worst-case" scenarios, and shows that micro prudence conflicts with macro stability only in bad times. The analysis also shows that banks provide a "financial stabilizer" that in some respects can more than counteract the more familiar financial accelerator.
Affine Models of the Term Structure
Barton Baker, PhD candidate (CAS/Economics)
My dissertation topic is fitting and solution methods of affine models of the term structure. The first two chapters will focus on extending the applicability of informing macroeconomic variables to the pricing kernel of the time series of yields on government bonds of various maturities, focusing specifically on aggregate uncertainty (Chapter 1), and real time data (Chapter 2). This added information will be used to tested with out-of-sample tests in the U.S and possibly Europe. Chapter 3 will present a solution class written in Python and C that I wrote from scratch for affine models of the term structure and possibility present a broad theoretical approach to solving affine models of the term structure, with and without unobserved factors. Many of these calculations involve complex operations and many iterations, so the resources provided by the HPC will benefit me immensely.
Informal Employment in Egypt:
Learning from Modeling with Essential Heterogeneity
The paper focuses on the wage differentials between formal/informal sectors on the Egyptian labour market. The objective is to analyse the nature of the informal employment (involuntary engagement of workers in a segmented labor market versus voluntary choice of workers). The Egyptian labor market has significantly evolved since major structural adjustments were implemented in 1991. In particular the public sector, which employed the majority of highly educated workers, has decreased significantly. The growth in private formal employment has not been however sufficient to absorb the growing labour force and the arrival of large cohorts of civil servants. Consequently, informal employment has grown significantly in the last two decades. All these trends are likely to increase or modify labour market segmentation. At the same time, they are likely to influence worker's behaviour and induce behavioural changes regarding the informal sector. We thus seek to investigate this issue by focusing on sectorial wage gaps using recently developed non parametric methods to estimate the model with essential heterogeneity. In particular, the marginal treatment approach is used to investigate whether wage gaps are due to compensatory differentials or to segmentation between non-competing groups.
Radchenko, Natalia. "Heterogeneity in Informal Salaried Employment: Evidence from the Egyptian Labor Market Survey." World Development 62 (2014): 169-188.
We investigate how salespeople use the information provided to them to set the prices in business-to-business transactions. Of particular interest to us is how salespeople use price recommendations coming from a decision support tool. We do this by building reduced-form models and testing those on a data set obtained by a grocery products distributor.
Donor Disasters or Disaster Donors:
Analysis of Data from the American Red Cross
There is a close relationship between donor behavior and marketing communications for every non-profit organization that relies on gifts from its donors to fund its services: Marketing communications and interactions may influence individual donor behavior and vice versa. Yet, in the end, what matters is not an individual donor and his gift but the total amount that has been collected. We participated in a research proposal competition and won an award, in the form of a data set, from the American Red Cross. In this project, we will build "explanatory" and "predictive" models to study donor behavior. Specifically, we will investigate what factors influence repeat donations, and how marketing communications influence the frequency and magnitude of giving.
This project explores the role that organizational learning processes play in state HIV/AIDS policy development. The puzzle addressed is the large degree of variation in policy output across states that are similar in terms of political or economic character. Although one can tell individual stories about each country, the overall variation defies the cross-applicability of many typical explanations. Where states better draw lessons from experience we should expect two results. First, structural characteristics of the state or of the set of HIV policy responders affects the character and degree of learning: the configuration of decision-making authority and information analytics interacts with the learning process, affecting the lessons drawn and policies pursued. Second, over time we observe some degree of policy convergence among states due to comparison and adaptation from others. The dissertation employs a mixed-methods approach. As a plausibility probe, econometric analysis tests for such patterns. The research employs an original dataset of 72 countries over 6 years and approximately 25 variables. To address data missingness, multiple imputation techniques were used. There were statisti- cally and substantively significant relationships and patterns, indicating further exploration of the underlying processes.
The Effect of Spatial Dependence on the Empirical Likelihood
The empirical likelihood is a nonparametric likelihood function that is analogous to its parametric counterpart. In particular, observations are assumed to be independent, and identically distributed.There are several empirical likelihood research papers regarding dependent data. However, there is no literature concerning the effect of spatial dependence on empirical likelihood procedures that are carried out assuming independence. This research presents the effect of such a violation on the asymptotic distribution of the empirical likelihood ratio. To determine whether the sampling distribution follows the specified distribution, we propose a spatially weighted Kolmogorov-Smirnoff Goodness-of-Fit test.
Accelerating Social Science Analysis for a New Age (ASSANA): Moving from Traditional Methods for Analyzing Large Scale Text-Based Data to Socially Intelligent High-Performance Computational Methods
The purpose of this project is to develop, test, refine and disseminate a repeatable interdisciplinary methodology and a related software tool for the computer-assisted analysis of large-scale text-based social science data. In keeping with the recent MOSAIC report , our overarching goal is to stimulate the next generation of social science research by providing analytical resources and interdisciplinary training to conduct textual analysis in PC and HPC environments. Key deliverables will be: 1) procedures for the ASSANA methodology; 2) an open-source HPC software tool; and 3) capacity building on ASSANA through workshops, seminars, and publications.
The objective of this research project is to develop and demonstrate a new data-driven modeling approach to provide long-term forecasts of streamflow. The modeling approach will incorporate wavelet-based analysis techniques used in statistical signal processing and a multivariate relevance vector machine (MVRVM) that uses a Bayesian regression method. We will develop a methodology that detects patterns in changes in Pacific sea surface temperature (SST), snowpack and streamflow using wavelet decomposition. This information will then be used to improve the forecasting potential of the MVRVM.
In the multiple testing literature, Zaykin et al. (2002) developed a truncated product method that combines only those p-values less than some pre-specified threshold, but the lack of a clear choice of truncation point becomes a major obstacle to its more widespread use. We solve this problem by proposing an adaptive truncated product method that optimizes the selection of the truncation point among a set of candidate cut-off values. We then develop a bootstrap re-sampling procedure to efficiently estimate the distribution of the adaptive method. We illustrate the performance of the proposed method through Monte Carlo simulation and an empirical example in the context of panel cointegration tests.
Improving Measurements of Neighborhood Attributes at Multiple Spatial Scales Using the Geostatistical Method of Kriging
With the growth of interest in accurately measuring neighborhood environments to study the influence of neighborhoods on individual-level outcomes, investigators have focused on improving two aspects of measurement: developing methods to create theoretically relevant measures and defining neighborhoods with relevant boundaries at appropriate spatial scales. Unfortunately, advancements made to improve the theoretical relevance of measures have been largely incompatible with defining appropriate neighborhood boundaries and vice-versa. In this paper, we argue that many neighborhood characteristics that social scientists are interested in studying should be conceptualized as changing from block-to-block rather than changing according to a patchwork of predefined discrete ecological units. We describe how a geostatistical method known as kriging can be combined with the existing econometric framework—an innovative method for measuring theoretically relevant attributes of discrete, ecological units—at small scales to develop city-block level estimates of theoretically nuanced measures that can then be flexibly reconfigured to multiple definitions of neighborhood boundaries. Using a cross-validation study with data from a 2002 systematic social observation of physical disorder on 1,663 city-blocks in Chicago, we show that this method creates valid results under assumptions of normality. We then demonstrate, using neighborhood measures aggregated to three different spatial scales, that the relationship between residents' perceptions of fear and neighborhood characteristics varies substantially across different spatial scales.
Forecasting Financial Data with Agent Based Models
PhD Candidate Georgi Panterov (CAS/Economics)
The purpose of this research is to build a rich, multi-agent artificial stock market where agents have endogenous expectations. Agents are able to act as sophisticated econometricians employing modern methods like Artificial Neural Networks and Genetic Algorithms. Unlike in standard financial models, agents are able to learn and update expectations using various Bayesian and non-Bayesian rules. This project will build a model that replicates some of the standard characteristics of financial markets such as volatility clustering and fat tail return distributions. At the end, there will be an attempt to calibrate the model using some real world price/orders data.
Exploiting Entanglement for Simulation of Few Body Systems
The goal of this project is to characterize entanglement in few body systems, and then to use this knowledge to optimize calculation and computation of few body dynamics. Few body systems are important at many physical scales, but this project will focus on atomic systems because implementations of quantum information processing devices, like ultracold atoms in optical lattices, require a precise understanding of few body dynamics. For example, few body effects are limiting sources of decoherence and loss in atomic interferometer experiments. The key to this method is that it finds a natural basis for efficient computation by choosing observables to describe the system such that energy eigenstates have minimal entanglement. For each additional particle in a simulation, complexity grows rapidly, and increases in efficiency become critical. More generally, studying entanglement is a theoretical probe that exposes kinematic and dynamical symmetries in both bound state and scattering problems. In this light, characterizing entanglement in few body systems shows how preferred physical observables are selected by the interactions even in complicated multiparticle systems. The outcomes of this project will include specific computational applications to cold atom systems and general results about entanglement in few body systems.
The objective of this research is to investigate the role of cofactor infections in understanding the heterosexual spread of HIV. Recent research applies agent-based models to the epidemics of HIV in sub-Saharan Africa. The use of agent-based modeling in epidemiology, including in the epidemiology of HIV, is relatively recent but has been very influential. Agent-based methods allow researchers to readily model the social determinants of disease incidence and prevalence, including the detailed networks of sexual partnership. Many sub-Saharan African countries have HIV prevalence that is one to four dozen times the prevalence in other countries. Explaining this enormous difference has consumed substantial research effort, since it has important implications for treatment and prevention. Conventional explanations of high prevalence of HIV in sub-Saharan Africa have focused almost exclusively on sexual behavior. However, there are strong reasons to explore other explanations. Specifically, a more detailed examination of the disease environment is likely to contribute more to understanding HIV prevalence in the region rather than variance in sexual behavior has been able to. One reason to expect this is the research that show that sexual behavior in high prevalence countires often appears to be more conservative than in many lower-incidence countries, like the US and the UK. Another reason to expect this emerged in recent research by Sawers, Isaac, and Stillwaggon (SIS). SIS found a substantial difficulty in the agent-based literature that has proposed that a high prevalence of sexual-partnership concurrency underpins high HIV prevalence. SIS showed that the agent-based research had ignored the implications of a broad empirical literature on coital diluation. This research extends the SIS model to explore how cofactors can make sexual networks more effective at spreading HIV. For this research, our working hypothesis is that cofactor infections are key to understanding the heterosexual spread of HIV in sub-Saharan Africa and perhaps elsewhere.
Research on Multivariate Probability Distributions
Some of my current research is related to classes of probability distributions. The stable laws and the extreme value laws generally do not have closed form expressions for their densities and cdfs. I am developing programs to compute these quantities, and some of them are computationally intensive. Some of these formulas are parallelizable, and I would like to experiment with this.
The Effect of Federal Subsidies on the
Outcomes of Children in Foster Care
Drs. Hansen and Reynolds will simulate the effect of extending federal subsidies to all children in foster care on the health outcomes of child victims of abuse and neglect. Currently, state child welfare systems cannot claim the same federal support for all of the children in foster care. States therefore have an incentive to provide more services to children who are eligible for federal support. This project will be the first to measure the extent to which eligibility for federal support influences services provided and health outcomes, and it will be the first to simulate the effect of broadening federal support to all children in foster care. It is critically important to understand how the structure of federal incentives affects outcomes because over $25 billion in child welfare services are provided annually to nearly a million children (DeVooght, Allen, and Geen, 2008). Dr. Hansen's expertise is in the economics of foster care policy. Hansen and Hansen (2006) and Hansen (2007, 2008) have shown that children who are eligible for federal subsidies for adoption after foster care get higher levels of support. The proposed project extends the work to consider health outcomes and services provided while children are still in foster care. Dr. Reynolds brings to the project her expertise in structural modeling (Feinberg and Reynolds, 2010; Reynolds (nee Olson, 2004), which is the preferred, but computationally-intensive, method for estimation of underlying policy invariant parameters in the policy simulation (Heckman, 2000).
This project will construct a long-run micro-level data set from original bankruptcy case files. Despite considerable study of personal bankruptcy, we still do not understand why personal bankruptcy rates have risen so dramatically in the late 20th century, nor do we understand the relationship between the business cycle and business and personal bankruptcy filings. The accumulated scholarship in law, economics, and sociology leads us to believe that these relationships are complex. They are mediated by both the letter and practice of the bankruptcy law, other credit laws, the liquidity of financial institutions, and the regulations imposed on credit markets. The creation of a micro-level data set on bankruptcy that covers a long time period will accelerate research that has been held back by data that are weak relative to the importance of the problem. Initial stages of this research have been funded by the Institute for New Economic Thinking and the Alfred P. Sloan Foundation.
The project has significant intellectual merit; it will transform the interdisciplinary dialog between economics and social work by providing evidence-based predictions for a change in policy. The project's broader impact on at-risk children cannot be overemphasized. Policy that improves outcomes for abused and neglected children can have very high rates of return; for example, the return on society's investment on adoption of children from foster care exceeds 100 percent (Hansen, 2008).
Mary Eschelbach Hansen "Sources of Credit and the Extent of the Credit Market: A View from Bankruptcy Records, Mississippi 1929-1936," Enterprising America (William Collins and Robert Margo, editors) NBER/Chicago University Press, forthcoming in 2015.
Institute for New Economic Thinking, "Emergency Preservation of Federal Bankruptcy Court Records," (co-PI Michelle Miller, Rutgers Business School) August 1, 2011-April 30, 2012
Alfred P. Sloan Foundation (Grant Number 2011-6-16), "Digital Preservation of Bankruptcy Court Records, 1898-2000," (co-PI Michelle Miller, Rutgers Business School) September 15, 2011-October 31, 2013
National Conference of Bankruptcy Judges Endowment for Education, "Opening New Views into Bankruptcy and Credit Markets Using Court Records," January 1, 2013-December 31, 2013
National Science Foundation, Economics Program Dissertation Improvement Grant (SES-1324468), "Doctoral Dissertation Research in Economics: The Impact of Medicare on Bankruptcy," dissertation supervisor for Megan Fasules, August 15, 2013-July 31, 2014
National Conference of Bankruptcy Judges Endowment for Education, "Continuation Funding forOpening New Views into Bankruptcy and Credit Markets Using Court Records," January 1, 2014-June 30, 2014
National Science Foundation, "Collaborative Research: Opening New Views into Bankruptcy and Credit Markets Using Court Records," June 2014-June 2016.
"Financial System Liquidity and Bankruptcy: Mississippi 1929-31," Economic History Association Annual Meeting, September 2012.
"Interactions between Social Insurance Programs: The Impact of Medicare on the Characteristics of Petitioners for Bankruptcy" (with Megan Fasules) Society for Government Economists Annual Meeting, November 2013, and American Economic Association Annual Meeting, January 2014
"Sources of Credit and the Extent of the Credit Market: A View from Bankruptcy Records, Mississippi 1929-1936," Enterprising America NBER Conference, December 14, 2013
Poster presentation for Economic History Association Annual Meeting (September 2014)"The Impact of Medicare on Personal Bankruptcy" (with Megan Fasules)
Mary Eschelbach Hansen and Nicolas L. Zeibarth. 2014. "The Relationship Between Banking and Business Distress: Evidence from Exits and Bankruptcies during the Great Depression"
The objective of Dr. Irvine Belson's project is to design applications that allow teachers and students with behavioral disorders to collect behavioral data using handheld devices and then to manipulate the data in a virtual, 3D environment. While self-monitoring and self-graphing have been proven to support students' acquisition of socially appropriate and academically useful behaviors (Cartledge et al., 2008, Maag 1999), there has been little development of tools that allow teachers and students to study behaviors over time and across multiple settings. The study of behaviors across settings is critical because students who fail to meet teacher expectations of social behavior are at an increased risk for unfavorable school outcomes. For example, these students have poor interactions with teachers and peers, poor academic performance, and high rates of disciplinary problems (Nowicki, 2003). Dr. Irvine Belson has experience directing school-based research projects examining implementation of high-end technology and telecommuni-cations in the classroom. She has trained pre-service and in-service teachers in electronic communication and technology integration, and she serves as a consultant to schools and businesses on design, implementation, and analysis of technology-based applications for instruction.
The intellectual merit of this project lies in its pedagogical innovation. The use of handheld devices to generate data on behaviors that can be quickly and cheaply visualized has the potential to transform instruction in special education. The broader impact of this program lies in the contributions to the research based on technology in education and to broader society in the effect of these tools to support long-term student achievement.
Spatial and Mixed Method Orientations in Native American Histories
Dr. Lawrence's work aims to show how teachers from the Bureau of Indian Affairs (BIA), their supervisors, and Native Americans acted on (or appropriated) federal Indian policy within on-reservation day schools. Specifically, Dr. Lawrence asks: How have the actions of BIA day school personnel and Native communities adapted and affected federal Indian policy locally over time? What relationships have existed between actions of BIA day school personnel, tribal members, and other BIA officials? There are only a handful of studies on the day school system, despite the fact that teachers and supervisors were effectively the agents of the U.S. government and were responsible for implementing a wide range of policies (Carter, 1995; Gere, 2005). The small amount of research does not reflect the wealth of primary source material. To effectively organize the material so that the specific aims can be addressed, Dr. Lawrence employs a metaphor from geography—sediment and sedimentation—to visualize the cumulative effects of individual action, group decisions, social ecologies, and the physical environment across space and over time. Dr. Lawrence will develop an open source digital tool that will allow researchers to incorporate geospatial, qualitative and quantitative source material. Dr. Lawrence is uniquely trained to accomplish this work. She is an education historian who specializes in federal Indian policy and has published in the fields of policy analysis, history, and ethnography (Lawrence, 2008, 2009; Lawrence & Cooke, 2010; Winstead, Lawrence, Brantmeier & Frey, 2008).
The intellectual merit of Lawrence's project lies in its potential to untangle epistemological questions stemming from the differences between the spatial orientation of Native scholars and the temporal-historical orientations of non-Native scholars (Deloria, 1992; Meyer, 2008). The reconfiguration of the history of federal Indian policy through simultaneous sedimentary (corresponding to temporal-historical) and spatial analyses has the potential to reshape the study of education history by facilitating the incorporation of rich qualitative data. The broader impact of this research will be felt in the field of educational policy studies because sedimentary analysis could, for example, help make successful school programs more easily replicable.
Dr. Malloy will examine the utility of computationally-intensive, data-adaptive approaches to splines and other smoothers in Cox regression models. Smoothing avoids a priori specification of the functional form of the relationship between exposure and a response. Because inferences about the relative risk of a response are drawn from the final model, selection of the model is critical. Dr. Malloy's preliminary studies show that there is excessive variation in model fit when different standard smoothing methods are applied to the same data (Govindarajulu, et al. 2009; Malloy et al. 2009). In this project, Dr. Malloy proposes to (1) implement model averaging methods within a given class of smoothers, and (2) extend the "super learner" algorithm (van der Laan et al. 2007) to estimate the dose-response from a set of candidate smoothers within Cox models based on cross-validated risk.
Dr. Malloy's research has intellectual merit because it will investigate the benefits of model averaging and the feasibility of using the super learner algorithm in estimation with smoothing methods. The broader impact of the research is in its application in epidemiology. The work will inform understanding of the underlying factors related to disease incidence or mortality. For example, Dr. Malloy and her collaborator, Dr. Ellen A. Eisen (School of Public Health, UC – Berkeley) plan to use the results of the research to estimate the incidence of skin cancer in relation to cumulative exposure to oil-based metalworking fluids in a cohort study of 23,650 autoworkers.
"The Super Learner for Estimating Nonlinear Associations in the Cox Regression Model," Malloy, E.J., Gautier, P., Cook, C., and Bergeron, M., Joint Statistical Meetings, poster, 2013
Malloy, E.J., Gautier, P., Cook, C. and Bergeron, M. 2013."The Super Learner for Estimating Nonlinear Associations in the Cox Regression Model."
A Scientific Computing Toolkit for the Volunteer Grid
Dr. Black will implement a mathematics and scientific computing toolkit that uses volunteer grid computing resources. The toolkit will equip the proposed HPC server with software to solve common mathematics problems using large-scale volunteer parallel resources and an interface allowing researchers to submit problems to the server. The server will divide the problems into sub-problems and dispatch the sub-problems to volunteers' computers to solve while they would otherwise sit idle. The toolkit will utilize the volunteer grid using the Berkeley Open Infrastructure for Network Computing (BOINC) framework (Anderson, 2004). Existing parallel solutions to these applications generally require researchers to dedicate their own resources to the problem and to have experience with software development (Ghuloum, 2007). On the other hand, most existing BOINC projects are specialized. Software in the proposed toolkit will be designed to solve satisfiability equations, calculate gradient descent, and compute graph coloring—very common problems. Dr. Black is prepared to complete this toolkit. He has published on porting the BOINC framework to new platforms (Black and Edgar, 2009) and has recently completed "proof-of-concept" satisfiability and gradient-descent solvers that use the BOINC framework.
The intellectual merit of this research lies in its innovative code to port the sub-problems through BOINC. The broader impact of this research lies in the potential of the toolkit to harness unused computing resources to help the worldwide community of researchers quickly solve important scientific problems.
Dr. Black's long-term research goal is to create a "meta BOINC project," not tied to any application, allowing less computer-savvy researchers to use the volunteer grid in their research and giving volunteers a single place they can go to contribute to many different research projects. The proposed toolkit is the first step in this meta-project.
Designing Novel Many-Body Quantum States of Ultracold Atoms using Dynamically Transforming Optical Lattices
The objective of Dr. Johnson's research is to develop and simulate a new method for creating and probing quantum states of ultracold matter not ordinarily existing in nature. He will use dynamical transformation of optical lattice potentials. Optical lattices are, essentially, crystals of light that can hold and control atoms suspended at the potential minima in a vacuum. The shapes of optical lattices can be dynamically transformed by manipulation of laser beams. Dr. Johnson will simulate the creation of quantum states by a sequence of single-well splitting and double-well merging operations on arbitrary pairings of adjacent lattice sites. Experimental tests of the simulations are within reach of leading experimental groups, including Dr. Johnson's collaborators at the Joint Quantum Institute of NIST and the University of Maryland, but experiments have not been performed because of the complexity of the modeling required to understand the physics. Dr. Johnson's previous work using numerical simulations of the macroscopic quantum mechanics of superconducting qubits uses similar methods (Johnson, 2003; Berkley 2003) and he has the requisite knowledge of optical lattices to carry out this work (Spielman, 2007; Johnson, 2009).
This research has intellectual merit because it designs a fundamentally new method for studying many-body states with optical lattices and because it is likely to yield explicit predictions that can be tested in the lab. Optical lattices have great promise as analogs for studying quantum phase transitions; therefore, the broader impact of the work is that it will contribute to the important goal of designing revolutionary materials such as high-temperature superconductors.