International Studies of Economics最新文献

We apply machine learning models to a universe of 20,185 finance articles published between 1976 and 2015 on 17 finance journals, and objectively identify 38 research topics. The financial crisis, hedge/mutual fund, social network, and culture were the fastest growing topics, while market microstructure, initial public offering, and option pricing shrank most from 2006 to 2015. We also list each topic's most cited papers, and present the fastest-growing topics among the universe of 130,547 SSRN working papers. Moreover, we find a bibliometric regularity: the number of researchers covering n topics is about twice the number of researchers covering n + 1 topics.

Based on annual observations of heavily polluting enterprises of A-share listed companies from 2012 to 2019, this paper analyzes the impact of officials' preference for enterprises' investment in environmental protection in the officials' hometowns. It is found that when the Secretary of the municipal Party Committee takes office in his or her hometown, environmental protection investment by enterprises is higher, indicating that the preference of the municipal Party Secretary for their hometown has a positive promoting effect on enterprises' environmental governance behavior. At the same time, officials' hometown preferences promote environmental investment by strengthening environmental supervision. It is further found that the longer the municipal Party Secretary works in his or her hometown, the higher his or her education level, and the older he or she is, the greater the impact of his or her hometown preference on enterprises' environmental protection investment. Compared with female Party Secretaries, male Party Secretaries have more significant influence on corporate behavior. At the same time, corporate characteristics such as enterprise scale and regional characteristics such as economic development have a certain regulatory effect on this promoting effect.

First-class universities play an extremely important role in cultivating high-quality talents and technological innovation, serving as a significant indicator of a country's level of higher education development, developmental strength, and potential. However, there is little literature studying the long-term impact of first-class universities on a country's economic development. To better understand this long-term influence, our study examines the effect of first-class universities on per capita income based on cross-national samples, particularly their role in overcoming the “middle-income trap,” and analyzes whether general higher education can bring about equivalent effects. The main research conclusions are: First, both general higher education and first-class universities can significantly improve a country's per capita income, but compared to general higher education, first-class universities have greater marginal effects on national per capita income, and can more effectively enhance domestic average income levels and promote sustainable economic growth over time; Second, first-class universities have the greatest marginal effect on improving per capita income in middle-income countries, and compared to general higher education, first-class universities play a larger role in helping developing countries break through the “middle-income trap”; Third, both general higher education and first-class universities positively affect innovative activities, but first-class universities play a more significant role in promoting technological innovation, which can better facilitate high-quality economic development. Our study not only enhances the understanding of the effects and differences between general higher education and first-class universities on long-term economic development, but also contributes to the understanding of the economic miracle that China has created since its reform and opening up. It also provides clear policy implications.

Entrepreneurs and entrepreneurship are important for economic growth and development. Yet insufficient attention has been paid to psychological characteristics such as personality characteristics as factors for women entrepreneurship in emerging economies. This study aims to investigate the associations between women entrepreneurs' business intentions and their personality traits. This study utilizes binomial logistic regression for hypothesis testing using the unique data set from a survey of small and medium manufacturing enterprises located in nine cities and provinces from three main geographical regions of Vietnam. The findings show that personality factors can be important for women entrepreneurs' business intentions. Unlike some other studies, the personality trait conscientiousness is found negatively correlated with women's entrepreneurial intentions. External factors such as local institutional quality and business networks have been found to stimulate women entrepreneurial intentions. The finding also raises concerns over the undergraduate training programs that need to be improved to make future students more confident in planning their business intentions if entrepreneurship is their career choice. The findings provide a key contribution to the existing literature of entrepreneurship in the context of an emerging economy where studies on women's entrepreneurship are scarce.

I utilize latent Dirichlet allocation and dynamic topic model that are machine learning algorithms across a data set encompassing 25,990 financial accounting articles issued from 1956 to 2023 in 16 accounting journals, and impartially ascertain 20 research topics. The topics of mergers and acquisitions, disclosure and internal control, and political connection exhibited the most rapid expansion, whereas management control systems, earnings management, and valuation experienced the greatest contraction from 2014 to 2023. I also catalog the most referenced papers for each topic and highlight the most swiftly expanding and contracting topics within the realm of 21,620 SSRN working papers. Additionally, my analysis reveals a declining trend in the concentration of research interests within published articles over the preceding seven decades. This research on topic classification itself will aid accounting investigators in bypassing superfluous efforts and fostering increased interdisciplinary research.

Most of peer-to-peer (P2P) online borrowers are small business managers. The learning behavior of borrowers in the P2P market is rarely studied. The aim of this paper is to identify the existence of borrowers' learning behavior in the P2P market using a large sample from renrendai.com, which is one of the largest P2P lending platforms in China. The loan description written by the borrower is an important way to disclose the borrower's information. We analyze changes in loan descriptions in multiple borrowings with text mining techniques and investigate whether a borrower has a learning behavior in writing loan descriptions. Empirical results show that after accumulating enough experience, borrowers can optimize the loan description to make it easier to obtain loans at lower interest rates.

In conducting an extensive examination, we scrutinize the efficacy of algorithmic trading strategies applied to Futures CopperMainContinuous in the Shanghai Futures Exchange, utilizing a comprehensive data set spanning from January 2020 to December 2022. To mitigate the potential risk of data-snooping bias—the probability that any favorable results may inadvertently arise from random events rather than the inherent value of the strategies employed to generate these results—our study prudently conducts a reality check and advanced assessments. Throughout the evaluated period, the benchmark demarcation between the in-sample and out-of-sample stages is established in February 2022. Regrettably, our meticulous exploration fails to identify any successful or advantageous algorithmic trading strategies within these categories, particularly following the systematic elimination of data snooping bias. These results underscore the intrinsic challenges in accurately identifying and implementing profit-generating algorithmic trading strategies within the volatile and intricate futures market.

This study investigates a novel challenge within the digital economy: scrounging, which entails the exploitation of false identities to capitalize on benefits offered by digital platforms, such as sales promotions and new user incentives. As a form of fraud, scrounging can substantially affect platform efficiency and has emerged as a critical issue in the contemporary digital economy and Web 3.0 sector. This underscores the necessity to model scrounging behavior and implement strategies to mitigate it. In this study, we develop a model that characterizes scrounging behavior in digital platform promotions and provides a theoretical explanation. We also introduce Palm as Decentralized Identifiers (DIDs), offering Proof-of-Human (PoH) to reduce fake identity prevalence. Unlike traditional blockchain technology, Palm as Decentralized Identifiers (DIDs) utilizes the Human Chain approach, ensuring equitable treatment of all users within the system. We demonstrate the effectiveness of our system in mitigating scrounging and explore its prospective applications in tackling real-world challenges in the digital and Web 3.0 economies.