On the value(s) of time: Workers’ value of their time depends on mode of valuation

In this paper, we investigate how individuals make time–money tradeoffs in labor contexts in which they are either asked to work to earn money or to pay money to avoid work. Theory predicts that exchange rates between time and money are invariant to the elicitation method. Results from our experiments, however, show otherwise, highlighting inconsistencies in how individuals consider their time. In the first two experiments, participants work to earn money, and we compare two incentivized elicitation methods. In the first, “Fixed-Time mode,” we fix the amount of time participants need to work and elicit the minimum dollar amount they require to do the job. In the second, “Fixed-Money mode,” we fix the amount of money we pay participants and ask for the maximum amount of time they are willing to work for that pay. We similarly vary elicitation procedures in Experiment 3 for paying money to avoid work. Translating the results into pay per hour, we find that in Fixed-Time mode, valuation of time is stable across durations, based on an analytical approach. By contrast, in Fixed-Money mode, participants increase their pay-per-hour demand when the amount of money increases, indicating a less calculated and more emotional view of time. Our results demonstrate that individuals’ value of their time of labor can be fluid and dependent on the compensation structure. Our findings have implications for theories of time valuation in the labor market.

Variation in Trauma Team Response Fees in United States Trauma Centers: An Additional Undisclosed Variable Cost in Trauma Care

Background: The rising costs associated with trauma care in the United States is an important topic in today’s healthcare environment. Factors such as innovations in technology, increasing governmental and organizational regulations, and the specialization of care have led to increasing costs to the patient. A component of trauma cost is the one-time trauma team response fee (TTRF). The determination process of TTRF’s dollar amount is elusive as no apparent standardized process exists and the literature is scant regarding this aspect of trauma care. Methods: A nationwide cross-sectional convenience sample was conducted using Survey Monkey. Surveys were sent to 525 trauma centers in the continental U.S, including Alaska and Hawaii. Responses obtained from October 8, 2019 through March 11, 2020 Results: Only 46 out of 525 trauma centers, or 18.2% of those surveyed shared their scheduled fees. Comparisons of TTRF’s among different trauma centers, activation levels, and geographical locations were not statistically significant. Conclusions: Understanding the true costs of trauma care and fees for patients in the U.S remains elusive due to inadequate data and low response rates. Trauma centers struggle to maintain financial viability as regulatory agencies and the public push for transparency of TTRF’s. Collaboration between TC’s and regulatory agencies is needed to ensure a balance between providing quality trauma care with justified associated charges and financial sustainability.

Venture Capital Investment in Orthopaedics: Has the Landscape Changed over the Past Two Decades (2000–2019)?

Background. Innovations in orthopaedic technologies often require significant funding. Although an increasing trend has been observed for third-party investments in other medical fields, no study has examined the influence of venture capital (VC) funding in orthopaedics. Therefore, this study analyzed trends in VC investments related to the field of orthopaedic surgery, as well as the characteristics of recipients of these investments. Methods. Venture capital investments into orthopaedic-related businesses were reviewed from 2000 to 2019 using Capital IQ, a proprietary intelligence platform documenting financial investments. Metrics categorized were investments by year, investment amount, and subspecialty domain as per the American Academy Orthopaedic Surgeons website. The compound annual growth rate (CAGR) for both quantity and dollar amount of investments was calculated over the study period and the two decade-long periods (2000–2009 and 2010–2019). Results. Over two decades, 673 VC investments took place, involving a total of US$3.5 billion. Both the number and dollar value of investments were greater in the second decade (440, US$1.9 billion), compared to the first decade (233, US$1.6 billion). Both quantity and dollar amount of VC investments grew over the first decade, with a CAGR 9.53% and 4.97%, respectively. However, investment growth declined in the latter decade. The largest and most frequent investments took place within spine surgery and adult reconstruction. Conclusion. An initially rising trend in VC investment in orthopaedic-related businesses may have plateaued over the past decade. These findings may have important implications for continued investment into orthopaedic innovations and collaboration between the surgical community and private sector.

Wikipedia for multilingual COVID-19 vaccine education at scale

We present the design of a project to develop Wikipedia content on general vaccine safety and the COVID-19 vaccines, specifically. This proposal describes what a team would need to distribute public health information in Wikipedia in multiple languages in response to a disaster or crisis, and to measure and report the communication impact of the same. Researchers at the School of Data Science at the University of Virginia made this proposal in response to a February 2021 call from a sponsor which was seeking to share public health information to respond globally to vaccine hesitancy related to the COVID-19 vaccines. This proposal was not selected for funding, and now the research team is sharing the proposal here with an open copyright license for anyone to reuse and remix. Most of the text here is from the original proposal, but there are modifications to remove the names of the funder, named partners, and for other details to make this text more reusable. The budget in this proposal has been converted from a dollar amount to equivalent descriptions in terms of labor hours, and the timeline was adapted from absolute to relative months.

Wikipedia for multilingual COVID-19 vaccine education at scale

We present the design of a project to develop Wikipedia content on general vaccine safety and the COVID-19 vaccines, specifically. This proposal describes what a team would need to distribute public health information in Wikipedia in multiple languages in response to a disaster or crisis, and to measure and report the communication impact of the same. Researchers at the School of Data Science at the University of Virginia made this proposal in response to a February 2021 call from a sponsor which was seeking to share public health information to respond globally to vaccine hesitancy related to the COVID-19 vaccines. This proposal was not selected for funding, and now the research team is sharing the proposal here with an open copyright license for anyone to reuse and remix. Most of the text here is from the original proposal, but there are modifications to remove the names of the funder, named partners, and for other details to make this text more reusable. The budget in this proposal has been converted from a dollar amount to equivalent descriptions in terms of labor hours, and the timeline was adapted from absolute to relative months.

Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L1, L2, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.

Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L1, L2, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.

State Minimum Wage Laws as a Response to Federal Inaction

This article analyzes state legislative and ballot measure activity related to the minimum wage between 2003 and 2020. The analysis distinguishes proposals to raise the minimum wage from those to index it to the annual rate of inflation, and examines the proposed dollar amount, the process used (legislation vs. ballot measure), and the measure’s success or failure. The analysis suggests that state activity tends to increase when the minimum wage rises on the federal policy agenda, and that partisanship and ideology also play a central role in efforts to raise and index state minimum wages.

Will a green color and nature images make consumers pay more for a green product?

Purpose Would a green color label increase the dollar amount consumers are willing to pay for a green product? Would nature images (such as a leaf or flower) on the label have the same effect? This paper aims to examine the role of these labeling strategies in influencing consumer willing to pay. Design/methodology/approach Using a 2 × 3 experiment, the authors empirically test the research questions across two studies: in the controlled-lab setting with 160 students (Study 1) and in a field-setting with 268 consumers shopping at a grocery store (Study 2). Findings Results are consistent across both studies. Surprisingly, participants are willing to pay more for the product when it has a white-toned label rather than a green-toned one. Follow-up path analysis, with Study 2 data, reveals that a white-toned label has both an indirect (through more favorable evaluations of the product’s environmental friendliness), as well as a direct impact on willingness to pay. In providing a post hoc explanation, it is argued that a white-toned label better directs attention towards the claim signaling the product’s eco-friendliness, while providing a “clean”, “high-quality” look. In both studies however, nature images on the label did not have a significant effect. Practical implications Insights are particularly interesting for practitioners seeking to better label/package green products. Originality/value This investigation is the first to empirically examine how color and images on the label influence the dollar amount consumers are willing to pay for a green product. Findings reveal that counter to common belief, the heavy use of the color green on eco-friendly product labels might not be appropriate; a predominantly white-toned label works better.