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Transboundary pollution poses a major threat to environment and human health. An effective approach to addressing this problem is the adoption of long-term abatement technology; however, many developing regions are lacking in related technologies that can be acquired by licensing from developed regions. This study focuses on a differential game model of transboundary pollution between two asymmetric regions, one of which possesses advanced abatement technology that can reduce the abatement cost and licenses this technology to the other region by royalty or fixed-fee licensing. We characterize the equilibrium decisions in the regions and find that fixed-fee licensing is superior to royalty licensing from the viewpoint of both regions. The reason is that under fixed-fee licensing, the regions can gain improved incremental revenues and incur reduced environmental damage. Subsequently, we analyze the steady-state equilibrium behaviors and the effects of parameters on the licensing performance. The analysis indicates that the myopic view of the regions leads to short-term revenue maximization, resulting in an increase in total pollution stock. Moreover, a high level of abatement technology or emission tax prompts the licensee region to choose fixed-fee approach, which is beneficial both economically and environmentally for two regions.

Compulsory licensing allows firms in developing countries to produce foreign-owned inventions without the consent of foreign patent owners. This paper uses an exogenous event of compulsory licensing after World War I under the Trading with the Enemy Act to examine the effects of compulsory licensing on domestic invention. Difference-indifferences analyses of nearly 130,000 chemical inventions suggest that compulsory licensing increased domestic invention by 20 percent.

PurposeThe use of AI-powered technology, particularly OpenAI’s ChatGPT, holds significant potential to reshape healthcare and medical education. Despite existing studies on the performance of ChatGPT in medical licensing examinations across different nations, a comprehensive, multinational analysis using rigorous methodology is currently lacking. Our study sought to address this gap by evaluating the performance of ChatGPT on six different national medical licensing exams and investigating the relationship between test question length and ChatGPT’s accuracy.MethodsWe manually inputted a total of 1,800 test questions (300 each from US, Italian, French, Spanish, UK, and Indian medical licensing examination) into ChatGPT, and recorded the accuracy of its responses.ResultsWe found significant variance in ChatGPT’s test accuracy across different countries, with the highest accuracy seen in the Italian examination (73% correct answers) and the lowest in the French examination (22% correct answers). Interestingly, question length correlated with ChatGPT’s performance in the Italian and French state examinations only. In addition, the study revealed that questions requiring multiple correct answers, as seen in the French examination, posed a greater challenge to ChatGPT.ConclusionOur findings underscore the need for future research to further delineate ChatGPT’s strengths and limitations in medical test-taking across additional countries and to develop guidelines to prevent AI-assisted cheating in medical examinations.

In most cities, the taxi industry is highly regulated and has restricted entry. Ride sharing services, such as Uber and Lyft, which use mobile internet technology to connect passengers and drivers, have begun to compete with traditional taxis. This paper examines the efficiency of ride sharing services vis-a-vis taxis. In most cities with data available, UberX drivers spend a significantly higher fraction of their time, and drive a substantially higher share of miles, with a passenger in their car than do taxi drivers. Reasons for this efficiency advantage are explored.

This study examines occupational licensing in the United States using a specially designed national labor force survey. Estimates from the survey indicated that 35% of employees were either licensed or certified by the government and that 29% were licensed. Another 3% stated that all who worked in their job would eventually be required to be certified or licensed, bringing the total that are or eventually must be licensed or certified by government to 38%. We find that licensing is associated with about 18% higher wages but that the effect of governmental certification on pay is much smaller.

A large part of my work is to negotiate and issue blanket music publishing licenses that cover the availability for use of full catalogs of music on behalf of the songwriters and rightsholders my company represents. It should not come as a surprise if I were to say that licensing negotiations often center around what is the appropriate value for the use of music (i.e., ample consideration for the rights granted under contract), and that the economics of a deal take the spotlight. After all, the goal is to get music professionals paid for their work. However, equally important to the success of a deal, and therefore a key component of negotiating a blanket license, is operational—how is the use of music managed, and what are the responsibilities of each of the contracting parties to administer the license and pay the underlying rightsholders. Administering an individual synch license is straightforward—the licensee knows what music will be used and how and relays that to licensor, licensor issues the license, collects payment, and administers the royalties. The thought exercise becomes more complicated with digital service providers (DSPs) whose services and platforms host and make available seemingly limitless quantities of music, where the volume of usage is high or the extent of usage unknowable (or both).

The competence of ChatGPT (Chat Generative Pre-Trained Transformer) in non-English languages is not well studied. This study compared the performances of GPT-3.5 (Generative Pre-trained Transformer) and GPT-4 on the Japanese Medical Licensing Examination (JMLE) to evaluate the reliability of these models for clinical reasoning and medical knowledge in non-English languages. This study used the default mode of ChatGPT, which is based on GPT-3.5; the GPT-4 model of ChatGPT Plus; and the 117th JMLE in 2023. A total of 254 questions were included in the final analysis, which were categorized into 3 types, namely general, clinical, and clinical sentence questions. The results indicated that GPT-4 outperformed GPT-3.5 in terms of accuracy, particularly for general, clinical, and clinical sentence questions. GPT-4 also performed better on difficult questions and specific disease questions. Furthermore, GPT-4 achieved the passing criteria for the JMLE, indicating its reliability for clinical reasoning and medical knowledge in non-English languages. GPT-4 could become a valuable tool for medical education and clinical support in non-English-speaking regions, such as Japan.

Background ChatGPT, a recently developed artificial intelligence (AI) chatbot, has demonstrated improved performance in examinations in the medical field. However, thus far, an overall evaluation of the potential of ChatGPT models (ChatGPT-3.5 and GPT-4) in a variety of national health licensing examinations is lacking. This study aimed to provide a comprehensive assessment of the ChatGPT models’ performance in national licensing examinations for medical, pharmacy, dentistry, and nursing research through a meta-analysis. Methods Following the PRISMA protocol, full-text articles from MEDLINE/PubMed, EMBASE, ERIC, Cochrane Library, Web of Science, and key journals were reviewed from the time of ChatGPT’s introduction to February 27, 2024. Studies were eligible if they evaluated the performance of a ChatGPT model (ChatGPT-3.5 or GPT-4); related to national licensing examinations in the fields of medicine, pharmacy, dentistry, or nursing; involved multiple-choice questions; and provided data that enabled the calculation of effect size. Two reviewers independently completed data extraction, coding, and quality assessment. The JBI Critical Appraisal Tools were used to assess the quality of the selected articles. Overall effect size and 95% confidence intervals [CIs] were calculated using a random-effects model. Results A total of 23 studies were considered for this review, which evaluated the accuracy of four types of national licensing examinations. The selected articles were in the fields of medicine ( n  = 17), pharmacy ( n  = 3), nursing ( n  = 2), and dentistry ( n  = 1). They reported varying accuracy levels, ranging from 36 to 77% for ChatGPT-3.5 and 64.4–100% for GPT-4. The overall effect size for the percentage of accuracy was 70.1% (95% CI, 65–74.8%), which was statistically significant ( p  < 0.001). Subgroup analyses revealed that GPT-4 demonstrated significantly higher accuracy in providing correct responses than its earlier version, ChatGPT-3.5. Additionally, in the context of health licensing examinations, the ChatGPT models exhibited greater proficiency in the following order: pharmacy, medicine, dentistry, and nursing. However, the lack of a broader set of questions, including open-ended and scenario-based questions, and significant heterogeneity were limitations of this meta-analysis. Conclusions This study sheds light on the accuracy of ChatGPT models in four national health licensing examinations across various countries and provides a practical basis and theoretical support for future research. Further studies are needed to explore their utilization in medical and health education by including a broader and more diverse range of questions, along with more advanced versions of AI chatbots.

The case for occupational licensing, whether for physicians or teeth whiteners, is highly problematic. This paper makes the case for a free market system in certification of quality in medical and dental care, and much else. Purpose To explore occupational licensure as an infringement of liberty. Design/methodology/approach a logical and empirical analysis. Findings teeth whitening regulations serve as an anti-competitive barrier to entry. Originality/value applying the concept of entry restrictions is not original; applying them to teeth whitening, is. This is of value in that for economic liberty to be promoted, schemes against it such as this one must be analyzed and exposed.

ChatGPT, a language model developed by OpenAI, uses a 175 billion parameter Transformer architecture for natural language processing tasks. This study aimed to compare the knowledge and interpretation ability of ChatGPT with those of medical students in China by administering the Chinese National Medical Licensing Examination (NMLE) to both ChatGPT and medical students. We evaluated the performance of ChatGPT in three years' worth of the NMLE, which consists of four units. At the same time, the exam results were compared to those of medical students who had studied for five years at medical colleges. ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was related to the year in which the exam questions were released. ChatGPT’s knowledge and interpretation ability for the NMLE were not yet comparable to those of medical students in China. It is probable that these abilities will improve through deep learning.