Open Access Publications

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Open access publications by faculty, postdocs, and graduate students in the Department of Applied Economics and Statistics.


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Now showing 1 - 17 of 17
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    Knowledge gaps about micronutrient deficiencies in Tanzania and the effect of information interventions
    (Global Food Security, 2024-02-02) Kilasy, Pius; McFadden, Brandon R.; Davidson, Kelly A.; Palm-Forster, Leah H.
    There were knowledge gaps about the severity of deficiencies and biofortified foods.Reducing micronutrient malnutrition (“hidden hunger”) in low-income countries is a global challenge, particularly among women, children, and high-poverty households. Countries like Tanzania have developed diverse strategies to combat malnutrition, including the biofortification of staple foods. However, broad awareness and knowledge of micronutrient deficiencies and beneficial foods are needed for these strategies to be effective. The objectives of this study were to (i) examine Tanzanian consumers' initial awareness and knowledge of deficiencies for four micronutrients and associated biofortified foods, and (ii) to examine the effectiveness of targeted communication approaches (i.e., information and branding) to improve knowledge. Data were collected from 1029 respondents in Tanzania using an online survey. Respondents were randomly assigned to treatments across two experiments in the survey. One experiment examined the effect of information about susceptibility and severity of micronutrient deficiencies and foods that reduce the risk of deficiency, and the other experiment examined the impact of ‘branding’ biofortified foods. The combination of providing information and branded biofortified crops most effectively reduced knowledge gaps about negative health outcomes and risk-reducing foods. Results suggest a need for evidence-based interventions that provide broad nutrition education and financial assistance for purchasing food. Highlights • Knowledge gaps were identified for deficiency in iron, vitamin A, and zinc. • Information interventions were used to identify knowledge gaps. • No information was provided for iodine to determine internal validity of results. • The at-risk subpopulation, women of reproductive age, were oversampled. • There were knowledge gaps about the severity of deficiencies and biofortified foods.
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    Integrative data analysis to identify persistent post-concussion deficits and subsequent musculoskeletal injury risk: project structure and methods
    (BMJ Open Sport & Exercise Medicine, 2024-01-19) Anderson, Melissa; Claros, Claudio Cesar; Qian, Wei; Brockmeier, Austin; Buckley, Thomas A
    Concussions are a serious public health problem, with significant healthcare costs and risks. One of the most serious complications of concussions is an increased risk of subsequent musculoskeletal injuries (MSKI). However, there is currently no reliable way to identify which individuals are at highest risk for post-concussion MSKIs. This study proposes a novel data analysis strategy for developing a clinically feasible risk score for post-concussion MSKIs in student-athletes. The data set consists of one-time tests (eg, mental health questionnaires), relevant information on demographics, health history (including details regarding the concussion such as day of the year and time lost) and athletic participation (current sport and contact level) that were collected at a single time point as well as multiple time points (baseline and follow-up time points after the concussion) of the clinical assessments (ie, cognitive, postural stability, reaction time and vestibular and ocular motor testing). The follow-up time point measurements were treated as individual variables and as differences from the baseline. Our approach used a weight-of-evidence (WoE) transformation to handle missing data and variable heterogeneity and machine learning methods for variable selection and model fitting. We applied a training-testing sample splitting scheme and performed variable preprocessing with the WoE transformation. Then, machine learning methods were applied to predict the MSKI indicator prediction, thereby constructing a composite risk score for the training-testing sample. This methodology demonstrates the potential of using machine learning methods to improve the accuracy and interpretability of risk scores for MSKI.
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    Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure
    (Machines, 2024-01-17) Primera, Ernesto; Fernández, Daniel; Cacereño, Andrés; Rodríguez-Prieto, Alvaro
    Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit.
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    Discovering Communication Pattern Shifts in Large-Scale Labeled Networks Using Encoder Embedding and Vertex Dynamics
    (IEEE Transactions on Network Science and Engineering, 2023-11-29) Shen, Cencheng; Larson, Jonathan; Trinh, Ha; Qin, Xihan; Park, Youngser; Priebe, Carey E.
    Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies, and predicting trends. In particular, the scalability of graph analysis is a critical hurdle impeding progress in large-scale downstream inference. To address this challenge, we introduce a temporal encoder embedding method. This approach leverages ground-truth or estimated vertex labels, enabling an efficient embedding of large-scale graph data and the processing of billions of edges within minutes. Furthermore, this embedding unveils a temporal dynamic statistic capable of detecting communication pattern shifts across all levels, ranging from individual vertices to vertex communities and the overall graph structure. We provide theoretical support to confirm its soundness under random graph models, and demonstrate its numerical advantages in capturing evolving communities and identifying outliers. Finally, we showcase the practical application of our approach by analyzing an anonymized time-series communication network from a large organization spanning 2019–2020, enabling us to assess the impact of Covid-19 on workplace communication patterns.
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    Are consumers no longer willing to pay more for local foods? A field experiment
    (Agricultural and Resource Economics Review, 2023-08-22) Davidson, Kelly A.; Khanal, Badri; Messer, Kent D.
    Government programs promoting locally produced foods have risen dramatically. But are these programs actually convincing consumers to pay more for locally produced food? Studies to date, which have mostly relied on hypothetical stated preference surveys, suggest that consumers will pay premiums for various local foods and that the premiums vary with the product and presence of any geographic identity. This study reports results from a large field experiment involving 1,050 adult consumers to reveal consumers’ willingness to pay (WTP) premiums for “locally produced” foods – mushrooms and oysters. Despite strong statistical power, this study reveals no positive effect of the locally produced label on consumer WTP. These null results are contrary to most of the existing literature on this topic. The finding that consumers are not willing to pay more for local foods has important implications for state and federal agencies that promote labeling campaigns that seek to increase demand and generate premiums for locally produced foods.
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    Learning from Lending in the Interbank Network
    (Data Science in Science, 2023-01-30) Laux, Paul; Qian, Wei; Zhang, Haici
    Empirical analysis of a major overnight-funding network of European banks shows that, when liquidity disruptions occur in a part of the network, lending banks in other parts of the network broaden their cohorts of borrowers in the part of the network that is subject to the disruptions. Measures of this broadening are useful new statistics for the amount of information conveyed from one part of the network to another. In our setting, we call this broadening “counterparty sampling,” and present evidence that it improves the network’s stock of information about future interest rates. By comparing to linkages forecast by an LSTM deep learning model for counterparty linkages, we find that the extent of surprising new linkages predicts lower future rates. Our evidence supports the idea that interbank funding networks provide benefits of learning and information aggregation, and our measures suggest new ways of looking at sparse networks with stable structures but dynamically-changing linkages.
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    Promoting Spatial Coordination in Flood Buyouts in the United States: Four Strategies and Four Challenges from the Economics of Land Preservation Literature
    (Natural Hazards Review, 2023-02-01) Dineva, Polina K.; McGranaghan, Christina; Messer, Kent D.; Palm-Forster, Leah H.; Paul, Laura A.; Siders, A. R.
    Managed retreat in the form of voluntary flood-buyout programs provides homeowners with an alternative to repairing and rebuilding residences that have sustained severe flood damage. Buyout programs are most economically efficient when groups of neighboring properties are acquired because they can then create unfragmented flood control areas and reduce the cost of providing local services. However, buyout programs in the United States often fail to acquire such efficient, unfragmented spaces, for various reasons, including long administrative timelines, the way in which buyout offers are made, desires for community cohesion, and attachments to place. Buyout programs have relied primarily on posted price mechanisms involving offers that are accepted or rejected by homeowners with little or no negotiation. In this paper, we describe four alternative strategies that have been used successfully in land-preservation agricultural–environmental contexts to increase acceptance rates and decrease fragmentation: agglomeration bonuses, reverse auctions, target constraints, and hybrid approaches. We discuss challenges that may arise during their implementation in the buyout context—transaction costs, equity and distributional impacts, unintended consequences, and social pressure—and recommend further research into the efficiency and equity of applying these strategies to residential buyout programs with the explicit goal of promoting spatial coordination.
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    One-Hot Graph Encoder Embedding
    (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022-11-28) Shen, Cencheng; Wang, Qizhe; Priebe, Carey E.
    In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC — making it an ideal candidate for huge graph processing. It is applicable to either adjacency matrix or graph Laplacian, and can be viewed as a transformation of the spectral embedding. Under random graph models, the graph encoder embedding is approximately normally distributed per vertex, and asymptotically converges to its mean. We showcase three applications: vertex classification, vertex clustering, and graph bootstrap. In every case, the graph encoder embedding exhibits unrivalled computational advantages.
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    Nudge or Sludge? An In-Class Experimental Auction Illustrating How Misunderstood Scientific Information Can Change Consumer Behavior
    (Applied Economics Teaching Resources, 2022-03-16) Paul, Laura A.; Savchenko, Olesya M.; Kecinski, Maik; Messer, Kent D.
    Scientific information can be used to help people understand and describe the world. For example, consumers regularly seek out information about their food and drink to help inform their purchasing decisions. Sometimes, however, consumers can respond negatively to this information, even when the information did not intend to convey a negative signal. These negative responses can be the result of misunderstandings or strong, visceral, emotional behavior, that can be challenging to foresee and once arisen, difficult (and expensive) to mitigate. In this paper, we show how educators can use an in-class economic experiment to introduce the power of a sludge—a small behavioral intervention that leads to worse outcomes. We provide a step-by-step guide to take students through a demand revealing design using a second-price, willingness-to-accept (WTA) auction that tests preferences for tap water and bottled water when students receive total dissolved solids (TDS) information. Additional classroom discussion topics are presented, including comparing nudges and sludges, the public response to the treatment of tap water, and the role of safety information in consumer response.
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    Demand for an Environmental Public Good in the Time of COVID-19: A Statewide Water Quality Referendum
    (Journal of Benefit-Cost Analysis, 2022-02-10) Parsons, George; Paul, Laura A.; Messer, Kent D.
    Due to COVID-19, many households faced hardships in the spring of 2020 – unemployment, an uncertain economic future, forced separation, and more. At the same time, the number of people who participated in outdoor recreation in many areas increased, as it was one of the few activities still permitted. How these experiences affect the public’s willingness to pay (WTP) for environmental public goods is unknown. During the early months of the pandemic, we conducted a stated preference survey to value statewide water quality improvements in Delaware. While a majority of participants report experiencing hardship of some sort (economic, emotional, etc.), mean household WTP declined by only 7 % by May 2020.
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    Impact of teaching methods on learner preferences and knowledge gained when informing adults about gene editing
    (Advancements in Agricultural Development, 2022-02-02) Thiel, Robert; Bowling, Amanda; Rumble, Joy; McFadden, Brandon; Stofer, Kathryn; Folta, Kevin
    Consumer acceptance of gene-editing technologies is a major hurdle to technology use, and opposition to gene-editing technologies may accompany a lack of knowledge by consumers. The purpose of this mixed-method study was to describe which method of instruction, behaviorism or constructivism, consumers preferred when learning about gene-editing and determine which method resulted in the highest amount of knowledge gained. Data were collected from eight focus groups across the country through a multiple-choice knowledge scale and open-ended questions. The qualitative results indicated that the participants preferred the behaviorism style over constructivist style due to the clarity of materials, the efficiency of time, and individual work. A large portion of participants felt the exposure to both teaching methods gave them more knowledge, that the information was interesting, and that they wanted more information. The quantitative results indicated that the behaviorist teaching method scores were significantly higher than the constructivist style of teaching. We recommend that practitioners align the appropriate teaching method with the amount of time allowed for the lesson, to use a variety of strategies when using behaviorist methods, and provide guidance and structure when using constructivist methods.
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    Private costs of carbon emissions abatement by limiting beef consumption and vehicle use in the United States
    (PLoS ONE, 2022-01-19) McFadden, Brandon R.; Ferraro, Paul J.; Messer, Kent D.
    A popular strategy for mitigating climate change is to persuade or incentivize individuals to limit behaviors associated with high greenhouse gas emissions. In this study, adults in the mid-Atlantic United States bid in an auction to receive compensation for eliminating beef consumption or limiting vehicle use. The auction incentivized participants to reveal their true costs of accepting these limits for periods ranging from one week to one year. Compliance with the conditions of the auction was confirmed via a random field audit of the behavioral changes. The estimated median abatement costs were greater than $600 per tCO2e for beef consumption and $1,300 per tCO2e for vehicle use, values much higher than the price of carbon offsets and most estimates of the social cost of carbon. Although these values may decline over time with experience or broader social adoption, they imply that policies that encourage innovations to reduce the costs of behavior change, such as meat alternatives or emission-free vehicles, may be a more fruitful than those that limit beef consumption or vehicle use.
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    Nudge to insure: Can informational nudges change enrollment decisions in pasture, rangeland, and forage rainfall index insurance?
    (Applied Economic Perspectives and Policy, 2021-11-25) Davidson, Kelly A.; Goodrich, Brittney K.
    Through a framed field experiment with livestock farmers in the Northeast and Southeast United States, this research explores whether an informational nudge changes producers' selection of two-month intervals and/or increases the likelihood of enrollment in pasture, rangeland, and forage (PRF) insurance. We find no evidence that a nudge influences interval choices; however, producers are more likely to enroll when PRF is framed as a risk management decision regarding forage loss. Risk aversion, familiarity with other United States Department of Agriculture livestock programs, and higher risk exposure increase the likelihood of enrollment. Past PRF and crop insurance participation decrease the amount insured during growing-season months.
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    Mitigating stigma associated with recycled water
    (American Journal of Agricultural Economics, 2021-10-07) Ellis, Sean F.; Savchenko, Olesya M.; Messer, Kent D.
    Stigmatization of water and food products can constrain markets and prevent the implementation of scientifically safe solutions to environmental problems, such as water scarcity. Recycled water can be a cost-effective, dependable, and safe solution to water shortages. However, consumers generally either require a large reduction in price to purchase products made with recycled water or reject such products outright. If emerging sustainable agricultural technologies, such as recycled water, are to be used to address growing water shortages worldwide, policymakers, water managers, and industry stakeholders must identify effective strategies for mitigating the stigma associated with recycled water. Using field experiments involving 1420 adult participants, we test the effectiveness of two stigma-mitigating techniques. We also demonstrate a novel twist to the collection of representative samples in non-hypothetical field experimental settings and then compare the results to a more traditional field experiment that recruited participants at large public gatherings. The analysis of these two different samples suggests a common finding: passing recycled water through a natural barrier, such as an aquifer, removes the stigma consumers would otherwise attach to it. We also find that the trophic level an organism occupies in the food chain influences stigmatizing behavior. The greater the steps in the food chain between an organism and the use of recycled water, the less it is stigmatized by consumers. These results have important implications for efforts to promote large-scale potable and non-potable water recycling projects and the use of recycled water in the agricultural industry.
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    The Chi-Square Test of Distance Correlation
    (Journal of Computational and Graphical Statistics, 2021-07-19) Shen, Cencheng; Panda, Sambit; Vogelstein, Joshua T.
    Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only if independence, making it an ideal choice to discover any type of dependency structure given sufficient sample size. One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data. To overcome the difficulty, in this article, we propose a chi-squared test for distance correlation. Method-wise, the chi-squared test is nonparametric, extremely fast, and applicable to bias-corrected distance correlation using any strong negative type metric or characteristic kernel. The test exhibits a similar testing power as the standard permutation test, and can be used for K-sample and partial testing. Theory-wise, we show that the underlying chi-squared distribution well approximates and dominates the limiting null distribution in upper tail, prove the chi-squared test can be valid and universally consistent for testing independence, and establish a testing power inequality with respect to the permutation test. Supplementary files for this article are available online.
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    Hypomethylation coordinates antagonistically with hypermethylation in cancer Development: a case study of leukemia
    (Biomed Central Ltd, 7/25/16) Kushwaha,Garima; Dozmorov,Mikhail; Wren,Jonathan D.; Qiu,Jing; Shi,Huidong; Xu,Dong; Garima Kushwaha, Mikhail Dozmorov, Jonathan D. Wren, Jing Qiu, Huidong Shi and Dong Xu; Qiu, Jing
    Background: Methylation changes are frequent in cancers, but understanding how hyper- and hypomethylated region changes coordinate, associate with genomic features, and affect gene expression is needed to better understand their biological significance. The functional significance of hypermethylation is well studied, but that of hypomethylation remains limited. Here, with paired expression and methylation samples gathered from a patient/control cohort, we attempt to better characterize the gene expression and methylation changes that take place in cancer from B cell chronic lymphocyte leukemia (B-CLL) samples. Results: Across the dataset, we found that consistent differentially hypomethylated regions (C-DMRs) across samples were relatively few compared to the many poorly consistent hypo-and highly conserved hyper-DMRs. However, genes in the hypo-C-DMRs tended to be associated with functions antagonistic to those in the hyper-C-DMRs, like differentiation, cell-cycle regulation and proliferation, suggesting coordinated regulation of methylation changes. Hypo-C-DMRs in B-CLL were found enriched in key signaling pathways like B cell receptor and p53 pathways and genes/motifs essential for B lymphopoiesis. Hypo-C-DMRs tended to be proximal to genes with elevated expression in contrast to the transcription silencing-mechanism imposed by hypermethylation. Hypo-C-DMRs tended to be enriched in the regions of activating H4K4me1/2/3, H3K79me2, and H3K27ac histone modifications. In comparison, the polycomb repressive complex 2 (PRC2) signature, marked by EZH2, SUZ12, CTCF binding-sites, repressive H3K27me3 marks, and "repressed/poised promoter" states were associated with hyper-C-DMRs. Most hypo-C-DMRs were found in introns (36 %), 3' untranslated regions (29 %), and intergenic regions (24 %). Many of these genic regions also overlapped with enhancers. The methylation of CpGs from 3'UTR exons was found to have weak but positive correlation with gene expression. In contrast, methylation in the 5'UTR was negatively correlated with expression. To better characterize the overlap between methylation and expression changes, we identified correlation modules that associate with "apoptosis" and "leukocyte activation". Conclusions: Despite clinical heterogeneity in disease presentation, a number of methylation changes, both hypo and hyper, appear to be common in B-CLL. Hypomethylation appears to play an active, targeted, and complementary role in cancer progression, and it interplays with hypermethylation in a coordinated fashion in the cancer process.
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    A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data
    (BioMed Central Ltd, 2016-01-11) Wang, Henan; He, Chong; Kushwaha, Garima; Xu, Dong; Qiu, Jing; Henan Wang, Chong He, Garima Kushwaha, Dong Xu and Jing Qiu; Qiu, Jing
    BACKGROUND: DNA methylation is an epigenetic modification that plays important roles on gene regulation. Study of whole-genome bisulfite sequencing and reduced representation bisulfite sequencing brings the availability of DNA methylation at single CpG resolution. The main interest of study on DNA methylation data is to test the methylation difference under two conditions of biological samples. However, the high cost and complexity of this sequencing experiment limits the number of biological replicates, which brings challenges to the development of statistical methods. RESULTS: Bayesian modeling is well known to be able to borrow strength across the genome, and hence is a powerful tool for high-dimensional- low-sample- size data. In order to provide accurate identification of methylation loci, especially for low coverage data, we propose a full Bayesian partition model to detect differentially methylated loci under two conditions of scientific study. Since hypo-methylation and hyper-methylation have distinct biological implication, it is desirable to differentiate these two types of differential methylation. The advantage of our Bayesian model is that it can produce one-step output of each locus being either equal-, hypo- or hyper-methylated locus without further post-hoc analysis. An R package named as MethyBayes implementing the proposed full Bayesian partition model will be submitted to the bioconductor website upon publication of the manuscript. CONCLUSIONS: The proposed full Bayesian partition model outperforms existing methods in terms of power while maintaining a low false discovery rate based on simulation studies and real data analysis including bioinformatics analysis.
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