Open Access Publications - Department of Applied Economics and Statistics

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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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A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes
(Sports Medicine, 2025-03-27) Claros, Claudio C.; Anderson, Melissa N.; Qian, Wei; Brockmeier, Austin J.; Buckley, Thomas A.
Background Emerging evidence indicates an elevated risk of post-concussion musculoskeletal injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated. Objective The purpose of this study was to model post-concussion musculoskeletal injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning. Methods A risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant’s heath and athletic history, concussion injury and recovery-specific criteria, and outcomes from a diverse array of concussion assessments. The machine learning approach involved transforming variables by the weight of evidence method, variable selection using L1-penalized logistic regression, model selection via the Akaike Information Criterion, and a final L2-regularized logistic regression fit. Results A model with 48 predictive variables yielded significant predictive performance of subsequent musculoskeletal injury with an area under the curve of 0.82. Top predictors included cognitive, balance, and reaction at baseline and acute timepoints. At a specified false-positive rate of 6.67%, the model achieves a true-positive rate (sensitivity) of 79% and a precision (positive predictive value) of 95% for identifying at-risk athletes via a well-calibrated composite risk score. Conclusions These results support the development of a sensitive and specific injury risk model using standard data combined with a novel methodological approach that may allow clinicians to target high injury risk student athletes. The development and refinement of predictive models, incorporating machine learning and utilizing comprehensive datasets, could lead to improved identification of high-risk athletes and allow for the implementation of targeted injury risk reduction strategies by identifying student athletes most at risk for post-concussion musculoskeletal injury. Key Points - There is a well-established elevated risk of post-concussion subsequent musculoskeletal injury; however, prior efforts have failed to identify risk factors. - This study developed a composite risk score model with an area under the curve of 0.82 from common concussion clinical measures and participant demographics. - By identifying athletes at elevated risk, clinicians may be able to reduce injury risk through targeted injury risk reduction programs.
From lab to field to farm: Applying behavioral science insights to agri-environmental programs
(Journal of Soil and Water Conservation, 2025-03-19) Fleming, Patrick M.; Palm-Forster, Leah H.; Connuck, Hannah; Fodor, Alice E.
Experimentation in environmental policy is often lacking because of an assumption that scientific research stops when implementation starts. However, researchers and policymakers increasingly recognize that environmental policy would benefit greatly from a more robust culture of experimentation and innovation, as has begun to develop in the areas of health and education policy (Ferraro et al. Citation2023). In the context of achieving agri-environmental policy goals, trialing new management programs is critical to generating measurable improvements that have remained elusive for decades. For example, the recent Comprehensive Evaluation of System Response (CESR) report for the Chesapeake Bay recommends more widespread use of policy implementation trials or “sandboxing” to test and evaluate the efficacy of new program rules and approaches in the face of insufficient progress on achieving Chesapeake Bay goals (Stac Citation2023). In this article, we apply insights gained from behavioral economic experiments in the lab and field to a policy implementation trial at a multifarm scale. Approaches for forming and incentivizing the policy trial are motivated by behavioral science results, such as those that demonstrate the benefits of pay-for-performance incentive structures (Schilizzi Citation2017), incentives for spatial coordination (Banerjee et al. Citation2021; Kuhfuss et al. Citation2016), information framing in terms of social norms and peer comparisons (Allcott Citation2011; Fleming, Palm-Forster, and Kelley Citation2021; Palm-Forster et al. Citation2022), and the necessity of building trust for stakeholder-engaged resource management (Ostrom Citation2010). In what follows, we describe resource councils as a form of stakeholder organization conducive to policy implementation trials. Then we illustrate the formation of a particular resource council in the Chesapeake Bay watershed in south-central Pennsylvania tasked with the goal of reducing streambank sediment pollution. We describe how insights from behavioral and experimental economics are applied to create incentives and supply information to allow the council to meet a shared environmental goal. Finally, we present key outcomes from the first year of the council’s work and discuss lessons learned from this approach.
Staging an Experience of Cultural Heritage Preservation: Consumers' Willingness to Pay for Heirloom Rice in the Philippines
(Agribusiness, 2025-01-22) Britwum, Kofi; Demont, Matty
The Cordillera Administrative Region in the Philippines is home to terraced rice embedded in centuries of cultural heritage. However, weak market incentives threaten sustained production, jeopardizing indigenous communities' cultural heritage and the in situ biodiversity of rice genetic resources. Demand-side policy interventions have been proposed to address these challenges. Drawing on the experience economy, we staged an experience with urban consumers, offering them the opportunity to participate in cultural heritage preservation through purchasing heirloom rice. Participants first self-selected into white or brown rice market segments as a benchmark. Subsequently, each market segment was invited to (i) identify their preference between their benchmark and heirloom rice, and (ii) bid to upgrade their non-preferred to their preferred rice through a Becker-DeGroot-Marschak (BDM) mechanism. The sample was randomly split between a control group and two “experience treatment” groups exposed to gain- or loss-framed narratives about rice terrace preservation. Results reveal that a subset of consumers in each market segment switched to heirloom rice. White rice consumers were more reluctant to transition to heirloom rice, although they responded positively to the gain-framed narrative, paying the highest price premiums for heirloom rice (PhP 92.55 or US $2.03 per kilogram). Brown rice consumers were more willing to switch but willing to pay lower premiums for heirloom rice, altogether suggesting the need for a segmented marketing strategy. Highlighting nutritional benefits and quality is crucial, but positioning heirloom rice within a gastronomic system that showcases its use in specific dishes and occasions is equally important for enhancing consumer appeal.
Building a multistate model from electronic health records data for modeling long-term diabetes complications
(Journal of Clinical and Translational Science, 2024-09-23) Li, Riza C.; Ding, Shanshan; Ndura, Kevin; Patel, Vishal; Jurkovitz, Claudine
Objective: The progression of long-term diabetes complications has led to a decreased quality of life. Our objective was to evaluate the adverse outcomes associated with diabetes based on a patient’s clinical profile by utilizing a multistate modeling approach. Methods: This was a retrospective study of diabetes patients seen in primary care practices from 2013 to 2017. We implemented a five-state model to examine the progression of patients transitioning from one complication to having multiple complications. Our model incorporated high dimensional covariates from multisource data to investigate the possible effects of different types of factors that are associated with the progression of diabetes. Results: The cohort consisted of 10,596 patients diagnosed with diabetes and no previous complications associated with the disease. Most of the patients in our study were female, White, and had type 2 diabetes. During our study period, 5928 did not develop complications, 3323 developed microvascular complications, 1313 developed macrovascular complications, and 1129 developed both micro- and macrovascular complications. From our model, we determined that patients had a 0.1334 [0.1284, .1386] rate of developing a microvascular complication compared to 0.0508 [0.0479, .0540] rate of developing a macrovascular complication. The area deprivation index score we incorporated as a proxy for socioeconomic information indicated that patients who reside in more disadvantaged areas have a higher rate of developing a complication compared to those who reside in least disadvantaged areas. Conclusions: Our work demonstrates how a multistate modeling framework is a comprehensive approach to analyzing the progression of long-term complications associated with diabetes.
Cross-species regulatory network analysis identifies FOXO1 as a driver of ovarian follicular recruitment
(Scientific Reports, 2024-12-28) Kramer, Ashley E.; Berral-González, Alberto; Ellwood, Kathryn M.; Ding, Shanshan; De Las Rivas, Javier; Dutta, Aditya
The transcriptional regulation of gene expression in the latter stages of follicular development in laying hen ovarian follicles is not well understood. Although differentially expressed genes (DEGs) have been identified in pre-recruitment and pre-ovulatory stages, the master regulators driving these DEGs remain unknown. This study addresses this knowledge gap by utilizing Master Regulator Analysis (MRA) combined with the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) for the first time in laying hen research to identify master regulators that are controlling DEGs in pre-recruitment and pre-ovulatory phases. The constructed ARACNe network included 10,466 nodes and 292,391 edges. The ARACNe network was then used in conjunction with the Virtual Inference of Protein-activity by Enriched Regulon (VIPER) for the MRA to identify top up- and down-regulated master regulators. VIPER analysis revealed FOXO1 as a master regulator, influencing 275 DEGs and impacting pathways related to apoptosis, proliferation, and hormonal regulation. Additionally, CLOCK, known as a crucial regulator of circadian rhythm, emerged as an upregulated master regulator in the pre-ovulatory stage. These findings provide new insights into the transcriptional landscape of laying hen ovarian follicles, offering a foundation for further exploration of follicle development and enhancing reproductive efficiency in avian species.
Smart connected farms and networked farmers to improve crop production, sustainability and profitability
(Frontiers in Agronomy, 2024-08-08) Singh, Asheesh K.; Balabaygloo, Behzad J.; Bekee, Barituka; Blair, Samuel W.; Fey, Suzanne; Fotouhi, Fateme; Gupta, Ashish; Jha, Amit; Martinez-Palomares, Jorge C.; Menke, Kevin; Prestholt, Aaron; Tanwar, Vishesh K.; Tao, Xu; Vangala, Anusha; Carroll, Matthew E.; Das, Sajal K.; DePaula, Guilherme; Kyveryga, Peter; Sarkar, Soumik; Segovia, Michelle; Silvestri, Simone; Valdivia, Corinne
To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. The aim of this article is to provide a comprehensive overview of the state of the art in SCF including the advances in engineering, computer sciences, data sciences, social sciences and economics including data privacy, sharing and technology adoption. More specifically, we provide a comprehensive review of key components of SCFs and crucial elements necessary for its success. It includes, high-speed connections, sensors for data collection, and edge, fog and cloud computing along with innovative wireless technologies to enable cyber agricultural system. We also cover the topic of adoption of these technologies that involves important considerations around data analysis, privacy, and the sharing of data on platforms. From a social science and economics perspective, we examine the net-benefits and potential barriers to data-sharing within agricultural communities, and the behavioral factors influencing the adoption of SCF technologies. The focus of this review is to cover the state-of-the-art in smart connected farms with sufficient technological infrastructure; however, the information included herein can be utilized in geographies and farming systems that are witnessing digital technologies and want to develop SCF. Overall, taking a holistic view that spans technical, social and economic dimensions is key to understanding the impacts and future trajectory of Smart and Connected Farms.
How Does Cultural and Colonial Heritage Affect Optimal Branding Strategies? Evidence From the Rice Sector in Senegal
(Agribusiness, 2024-09-29) Britwum, Kofi; Demont, Matty
Africa's cultural and colonial heritage has profoundly segmented rice markets. Whereas in ancient centers of rice domestication, consumers maintained preferences for local rice consistent with their cultural heritage, preferences have shifted toward imported Asian rice in coastal areas around seaports, due to prior exposure to colonial import substitution policies. To enhance the competitiveness of locally produced rice relative to imported versions, it is necessary to tailor new local rice products to both market segments. A study was conducted in Senegal to test branding strategies for local rice in a country where both market segments coexist. Brands that mimic local and international labels were developed for local rice, and urban consumers bid to upgrade non-preferred to preferred brands through the Becker–DeGroot–Marschak (BDM) mechanism. Contrary to expectations, results from an endogenous switching regression show that descendants from ancient Senegalese rice domesticators placed premiums on local rice with foreign-looking brands, indicating that foreignness is perceived as a quality cue even in market segments rooted in cultural heritage. Thus, branding local rice using a combination of local and foreign cues could be an effective strategy in promoting domestic rice to both segments shaped by cultural and colonial heritage.
Dare to Experiment: The Synergistic Relationship Between Undergraduate Research and Experimental Economics
(Applied Economics Teaching Resources, 2024-09-25) Nelson-Poteet, Carl; Xie, Lusi; Messer, Kent D.; Palm-Forster, Leah H.
Little attention has been given to the synergistic relationship that can exist between experimental economics research and undergraduate research experiences. In this article, we highlight the successes and challenges from working with more than 70 undergraduate research assistants at the University of Delaware’s Center for Experimental and Applied Economics (CEAE) since 2007. We describe our approaches for funding and engaging undergraduate students and efforts, including our layered mentorship network, to support CEAE’s mission to cultivate a diverse and inclusive research community. We present the results of a survey of CEAE’s alumni to understand how their research experiences influenced their undergraduate education and their post-graduate educational and career endeavors. Synthesizing the reflections of students and the experiences of lead researchers, we outline ten key recommendations regarding how faculty and administrators in agricultural and applied economics programs can design and implement successful undergraduate research experiences, strengthening the pipeline of researchers in our field.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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