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Consumers increasingly demand extraordinary experiences and businesses want to provide such experiences to build loyalty and increase customer lifetime value. One of the most significant aspects of consumer experiences is employee-customer interactions. We hypothesized that the value of customers' experiences would be reflected in the neurophysiology of sales associates and that these data would predict eventual purchases. We tested this hypothesis by measuring neurologic Immersion of sales associates serving customers (N=49) in a field study in two luxury retail stores with actual customers. A synthetic dataset was generated from these data and showed that sales associates' peak Immersion was positively associated with the time customers spent shopping, which, in turn, positively scaled with how much customers spent. Estimating a machine learning model using sales associates' peak Immersion predicted which customers purchased with between 64% and 80% accuracy. Our results demonstrate that the neurophysiologic Immersion of one person can be used to predict the behavior of another person with whom they are interacting even when their goals may not be perfectly aligned. Moreover, we have shown that such a field study is feasible with real customers who are spending nontrivial amounts of money (M=$323, range: $0-$2,734). More generally, measuring the contagion of Immersion from one side of an interaction may be an effective way to assess and improve the quality of social engagements of many types.
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This OPENICPSR site contains data deposits for manuscripts published from the Baby's First Years study. More information on Baby's First Years can be found at: https://www.icpsr.umich.edu/web/ICPSR/studies/37871#
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This dataset describes the number and density of health care services in each census tract in the United States. The data includes counts, per capita densities, and area densities per tract for many types of businesses in the health care sector, including doctors, dentists, mental health providers, hospitals, nursing homes, and pharmacies.
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We describe alignment between high school career and technical education (CTE) and local labor markets across five states—Massachusetts, Michigan, Montana, Tennessee, and Washington. We find that CTE is partially aligned with local labor markets. A 10-percentage-point higher share of local jobs related to a CTE career cluster is associated with a 3-point higher rate of CTE concentration in that cluster. Women and students from racial or ethnic minority groups are better aligned with local employment than men, in part due to their selection of CTE fields like Education & Training, Health Science, and Hospitality & Tourism, which correspond with a large portion of the workforce in almost every metro area. We find more limited evidence of dynamic, short-term adjustments in CTE after changes in local labor markets. A small degree of realignment lags the labor market by two-to-three years and is only observed following changes in college-level employment.
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Prospective cohort hospital-based study on 539 individuals suspected to carry SARS-COV-2
The past five decades have seen a remarkable convergence in the economic roles of men and women in society. Yet, persistently large gender gaps in terms of labor supply, earnings, and representation in top jobs remain. Moreover, in countries like the U.S., convergence in labor market outcomes appears to have slowed in recent decades. In this article, we focus on the role of children and show that many potential explanations for the remaining gender disparities in labor market outcomes are related to the fact that children impose significantly larger penalties on the career trajectories of women relative to men. In the U.S., we document that more than two-thirds of the overall gender earnings gap can be accounted for by the differential impacts of children on women and men. We propose a simple model of household decision-making to motivate the link between children and gender gaps in the labor market, and to help rationalize how various factors potentially interact with parenthood to produce differential outcomes by gender. We discuss several forces that might make the road to gender equity even more challenging for modern cohorts of parents, and offer a critical discussion of public policies that seek to address the remaining gaps.
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this is a data-set of 462 respondents (finance faculty) from Indian B-schools
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We present results from a small-scale randomized controlled trial that evaluates the impact of just-in-time interventions on the academic experiences and outcomes of N=65 undergraduate students in a STEM course. Intervention messaging content was based on machine learning forecasting models of data collected from 427 students in the same course over the preceding 3 years. Trial results show that the intervention produced a statistically significant increase in the proportion of students that achieved a passing grade. The outcomes point to the potential and promise of just-in-time interventions for STEM learning and the need for larger fully-powered randomized controlled trials.
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Replication datasets and R script for the forthcoming publication, entitled "Measuring public opinion about autonomous vehicles using data from Reddit, Public Deliberation, and Surveys", in Public Opinion Quarterly, Special Issue on New Data in Social and Behavioral Research.
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The Response2covid19 dataset tracks governments’ responses to COVID-19 all around the world. The dataset is at the country-level and covers the January 2020 - June 2021 period; it is updated on a monthly basis. It tracks 20 measures – 13 public health measures and 7 economic measures – taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset (“Response2covid19”) and the reference: Porcher, Simon "Response2covid19, a dataset of governments' responses to
COVID-19 all around the world", Scientific Data, 7, 423, 2020. https://doi.org/10.1038/s41597-020-00757-y" title="Link: https://doi.org/10.1038/s41597-020-00757-y">https://doi.org/10.1038/s41597-020-00757-y
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This public dataset contains transcripts of 8 in-depth semistructured interviews with drag artists. Student Destiny Baxter conducted these interviews during Spring 2020. These interviews use the same instrument as a set of 22 interviews with drag artists conducted in Spring 2019, also available via ICPSR at https://www.openicpsr.org/openicpsr/project/118483/.2020 dataset (Drag artist name, interview date, drag artist's location):Amoura Teese, April 13, San Francisco, CABella Noche, February 12, New York, NYDie Anna, February 12, Los Angeles, CAGigi Gemini, February 12, Las Vegas, NVMick Douch, February 18, Chicago, ILTomahawk Martini, April 16, Albuquerque, NMTwinkie LaRue, February 23, Toronto, OntarioWendy Warhol, April 28, Montreal, Quebec
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Data and replication files for:Buckles, Kasey, Melanie Guldi, and Lucie Schmidt. and Elizabeth L. Munnich. "The Great Recession's Baby-less Recovery: The Role of Unintended Births." Journal of Human Resources, forthcoming.
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Fine particulate matter (PM2.5) is a leading environmental cause of mortality. Droughts can worsen air quality in regions that rely on hydropower by shifting energy production to combustion power plants. This study quantifies drought-induced excess PM2.5 in Latin America and the Caribbean, where over 443 million people live within 50 km of a combustion power plant. Leveraging a monthly plant-level panel spanning 20 years, we link hydrological droughts, measured as negative runoff anomalies in hydropower watersheds, to changes in PM2.5 concentrations near combustion power plants. Our analysis reveals that these droughts lead to an average increase of 0.83 μg m-3 in PM2.5 levels. Counterfactual simulations for the region reveal that this excess PM2.5 results in up to 10,000 premature deaths annually. Combining our estimates with climate, demographic, and combustion power plant phase-out projections, we demonstrate that this health burden will persist over the next four decades without targeted interventions.
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The Real-Time Population Survey (RPS) is a national labor market survey of US adults aged 18-64 following the core questionnaire of the Current Population Survey (CPS), supplemented with additional questions (e.g., about commuting behavior and employer tenure). Separate waves of the survey were conducted each month between April 2020 and June 2021. Since then the RPS has been run two to three times per year.
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Curated dataset of COVID-19 pandemic policies and communications in the state of Illinois.
This project includes the data appendix, and the replication data and code for the paper "Inside the Box: Safety, Health, and Isolation in Prison." The appendix, a readme file, code, and data are included in a zip file.
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This study explores the factors that influence the data reuse behaviors of scientists and identifies the generalized patterns that occur in data reuse across various disciplines. An online survey was distributed to the scientists through Qualtrics. The initial email invitation to the survey was sent to 15,703 scientists within academic institutions on October 5, 2015, with a reminder sent on November 10, 2015. The survey closed on November 30, 2015. 1,987 email messages (12.65%) were returned and a total of 13,716 participants (87.35%) received the email invitation to participate in the survey. This research used the National Science Foundation (NSF) STEM discipline codes (2014) for the respondents to indicate their specific academic disciplines based on their current research activities. Of these participants, 1,528 scientists from 94 specific disciplines (as categorized by NSF STEM discipline codes (2014)), completed the survey with less than 5% of missing values (response rate: 11.14%).Reference:NSF. (2014, February 9). Crosswalk of NSF Discipline Codes to CASPAR Academic Discipline Codes. Retrieved from https://ncsesdata.nsf.gov/nsf/srs/webcasp/data/gradstud.htm
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Mental health professionals have relied primarily on clinical evaluations to identify in vivo pathology. As a result, mentalhealth is largely reactive rather than proactive. In an effort to proactively assess mood, we collected continuous neurophysiologicdata for ambulatory individuals 8-10 hours a day at 1Hz for 3 weeks (N=24). Data were obtained using a commercial neuroscience platform (Immersion Neuroscience) that quantifies the neural value of social-emotional experiences. These data were related to self-reported mood and energy to assess their predictive accuracy. Statistical analyses identifiedneurophysiologic troughs by the length and depth of social-emotional events with low value and neurophysiologic peaks as thecomplement. Participants in the study had an average of2.25 (SD = 3.70, Min = 0, Max = 25) neurophysiologic troughs per day and 3.28 (SD = 3.97, Min= 0, Max = 25) peaks. The number of troughs and peaks predicted daily mood with 90% accuracy using least squares regressions and machine learning models. The analysis also showed that men were more prone to low mood compared to women. Our approach indicates that a simple count variable derived from a commercially-available technology may be aviable way to assess low mood and low energy in populations vulnerable to mood disorders. In addition, peak Immersion events, which are mood-enhancing, may be an effective measure of thriving in adults.
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This paper examines the reliability of survey data on business incomes, valuations, and rates of return, which are key inputs for studies of wealth inequality and entrepreneurial choice. We compare survey responses of business owners with available data from administrative tax records, brokered private business sales, and publicly traded company filings and document problems due to nonrepresentative samples and measurement errors across several surveys, subsamples, and years. We find that the discrepancies are economically relevant for the statistics of interest. We investigate reasons for these discrepancies and propose corrections for future survey designs.
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The COVID-19 Coping Study is a national, longitudinal cohort study of 6,938 US adults aged ≥55 enrolled from April 2nd through May 31st, 2020 in all 50 US states, the District of Columbia, and Puerto Rico. Participants were recruited through a non-probability multi-frame sampling strategy, and completed data collection through online questionnaires administered via the University of Michigan Qualtrics in English (N=6,886) and Spanish (N=52). Data were collected on a variety of demographic, social, and health-related topics including COVID-19 symptom and testing history, COVID-19-related stressors and worries, self-isolation and social distancing practices, behavior changes and coping mechanisms, mental health symptom scales, and living arrangements. A sub-set of the baseline sample (N=4,401) were sent monthly follow-up questionnaires over the following 12 months. The included files contain baseline through 6-month of follow-up data from the COVID-19 Coping Study. Data are available in Stata (C19CS.dta), a CSV file with value labels (C19CS Labelled.csv), and a CSV file with numeric values (C19CS Numeric.csv).
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Consumers increasingly demand extraordinary experiences and businesses want to provide such experiences to build loyalty and increase customer lifetime value. One of the most significant aspects of consumer experiences is employee-customer interactions. We hypothesized that the value of customers' experiences would be reflected in the neurophysiology of sales associates and that these data would predict eventual purchases. We tested this hypothesis by measuring neurologic Immersion of sales associates serving customers (N=49) in a field study in two luxury retail stores with actual customers. A synthetic dataset was generated from these data and showed that sales associates' peak Immersion was positively associated with the time customers spent shopping, which, in turn, positively scaled with how much customers spent. Estimating a machine learning model using sales associates' peak Immersion predicted which customers purchased with between 64% and 80% accuracy. Our results demonstrate that the neurophysiologic Immersion of one person can be used to predict the behavior of another person with whom they are interacting even when their goals may not be perfectly aligned. Moreover, we have shown that such a field study is feasible with real customers who are spending nontrivial amounts of money (M=$323, range: $0-$2,734). More generally, measuring the contagion of Immersion from one side of an interaction may be an effective way to assess and improve the quality of social engagements of many types.