International Studies of Economics最新文献

{"title":"Dynamic Henry George Theorem and Optimal City Sizes","authors":"Shihe Fu","doi":"10.1002/ise3.70013","DOIUrl":"https://doi.org/10.1002/ise3.70013","url":null,"abstract":"<p>The Henry George Theorem (HGT) in static models states that when a city has an optimal population size, aggregate urban differential land rents exactly cover costs of pure public goods. This paper extends the static HGT to dynamic settings. Through a series of dynamic models, the paper tentatively concludes that the HGT holds in dynamic settings in terms of present value—the present value of urban differential land rents equals the present value of public goods expenditure. In urban economies with congestion externalities or production externalities, the dynamic HGT still holds if externalities are priced correctly.</p>","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"20 2","pages":"211-223"},"PeriodicalIF":0.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the Special Issue: Cities and Economic Development","authors":"Shihe Fu,&nbsp;Junfu Zhang","doi":"10.1002/ise3.70014","DOIUrl":"https://doi.org/10.1002/ise3.70014","url":null,"abstract":"&lt;p&gt;To a great extent, economic development is urban development. As economies grow, people and firms cluster in cities to take advantage of agglomeration economies, better infrastructure, access to markets and labor, and consumption amenities. Urbanization not only drives productivity gains and enhances consumer welfare but also introduces challenges such as housing affordability, traffic congestion, pollution, and inequality. Understanding these dynamics is essential for promoting efficiency and sustainable development. The evolution of cities is therefore not only a reflection of economic progress but also a key driver of it.&lt;/p&gt;&lt;p&gt;The field that examines these dynamics—urban economics—focuses on cities and the spatial organization of economic activities. It covers a wide range of topics, including but not limited to the determinants of city size and growth, the functioning of urban housing and labor markets, transportation systems, land use, public goods provision, urban poverty, segregation, and environmental challenges. Researchers in this field investigate how individuals and firms make location decisions and how their decisions shape the economic and social structure of urban areas.&lt;/p&gt;&lt;p&gt;A defining feature of research in urban economics is its methodological diversity. Scholars employ both partial and general equilibrium models, conduct reduced-form and structural estimations, and rely on mathematical modeling as well as computer simulations. On the empirical side, quasi-experimental strategies—such as difference-in-differences, spatial regression discontinuity, and instrumental variables—are widely used to identify causality. With the growing availability of geocoded data, remote sensing imagery, granular administrative records, and online big data, researchers increasingly adopt spatial econometrics, machine learning tools, and GIS techniques to uncover complex spatial patterns and effects.&lt;/p&gt;&lt;p&gt;The field has seen remarkable growth in recent years, propelled by greater data availability, advances in computing power, and methodological innovations inspired by neighboring disciplines such as microeconometrics, labor economics, industrial organization, and international trade. These developments have enabled scholars to revisit classic theories, uncover new stylized facts, and produce sharper, policy-relevant insights.&lt;/p&gt;&lt;p&gt;What makes urban economic research especially valuable is its direct relevance to real-world challenges. The insights it generates often inform how cities are planned, governed, and improved. For example, research on housing markets informs zoning and land-use regulation; studies of transportation systems guide infrastructure investment and congestion pricing; and work on crime and inequality has implications for public safety and social policies. Several papers in this special issue—including those on fiscal transparency, pandemic-era rental markets, and crime—offer concrete evidence that can help policymakers design","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"20 2","pages":"136-137"},"PeriodicalIF":0.5,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Income Polarization on Crime: Evidence From Court Judicial Documents in China","authors":"Jingqi Liu,&nbsp;Chen Wang,&nbsp;Yuzhou Wang","doi":"10.1002/ise3.70012","DOIUrl":"https://doi.org/10.1002/ise3.70012","url":null,"abstract":"<p>Crime affects social stability and people's safety, and directly increases the cost of urban development. Existing literature shows that income distribution affects crime, but it mainly focuses on the effect of income inequality and poverty, with scant evidence addressing the impact of income polarization on crime. Applying data from the China Household Finance Survey (CHFS) and judicial documents from 2014 to 2018, this paper demonstrates that rising income polarization significantly increases crime in cities. The result still holds after a series of robustness checks. Heterogeneity analyses show that income polarization has a more pronounced effect on criminal activities related to violence, robbery and stealing, drug and financial fraud. Moreover, cities with a higher proportion of young and migrant populations would be more adversely affected by income polarization. Mechanism analyses indicate that income polarization exacerbates crime by increasing alienation, reducing job-seeking willingness and happiness of residents.</p>","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"20 2","pages":"195-210"},"PeriodicalIF":0.5,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does National Auditing Improve Local Fiscal Transparency? Evidence From China","authors":"Zhuo Chen,&nbsp;Mingzhi Hu","doi":"10.1002/ise3.70007","DOIUrl":"https://doi.org/10.1002/ise3.70007","url":null,"abstract":"<p>This paper examines whether and how national audits affect local fiscal transparency in China. Using panel data from 30 provinces between 2009 and 2018, we find that national audits significantly improve local fiscal transparency after controlling for various economic and institutional factors. The effect of national audits on fiscal transparency varies significantly by region, which is stronger and statistically significant in eastern regions and in regions with high land finance, while not significant in central-western regions or in those with low land finance. Furthermore, the positive impact is primarily driven by the disclosure and defense functions. These findings suggest that national audits are valuable for improving fiscal transparency, but their effectiveness varies depending on regional economic development and local government financing mechanisms.</p>","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"20 2","pages":"153-161"},"PeriodicalIF":0.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Localized and Short-Term Effects of Lockdowns on Urban Rental Markets: Evidence From Shanghai","authors":"Yanpeng Jiang,&nbsp;Xiaochi Shen","doi":"10.1002/ise3.70005","DOIUrl":"https://doi.org/10.1002/ise3.70005","url":null,"abstract":"<p>This paper investigates the localized and short-term effects of COVID-19-induced lockdowns on Shanghai's rental market in the second half of 2022. Using Difference-in-Differences methodologies, we find that rental prices declined by 1.5% following the lockdowns on average, with areas characterized by a high density of companies falling by 2.0% and no significant change in suburban areas. Event-study analysis further reveals that this decline peaked at 3.0% 2 months post-lockdown. This decline was temporary with rental prices returning to pre-lockdown levels within 12 months. Robustness checks and comparisons with housing sale prices confirm the absence of significant spillover effects or structural shifts. These findings underscore the localized and temporary nature of lockdown-induced disruptions in the rental market and may be valuable to housing policymakers considering responses to short-term crises.</p>","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"20 2","pages":"177-194"},"PeriodicalIF":0.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special issue on machine learning and artificial intelligence in business and economics","authors":"Ye Luo","doi":"10.1002/ise3.106","DOIUrl":"https://doi.org/10.1002/ise3.106","url":null,"abstract":"&lt;p&gt;In academic studies, the integration of artificial intelligence (AI) and machine learning in the field of economics and finance has revolutionized research methodologies and enhanced the understanding of complex economic phenomena. Researchers can now analyze vast amounts of data more efficiently, identify patterns and trends, and develop predictive models with greater accuracy. This enables academics to delve deeper into economic theories, test hypotheses, and make more informed policy recommendations. Furthermore, the use of AI and machine learning algorithms in academic studies can lead to new insights, innovative research approaches, and interdisciplinary collaborations.&lt;/p&gt;&lt;p&gt;The leading article of this special issue, “Finance research over 40 years: What can we learn from machine learning?”, investigated on the topic distributional features of the research in finance over the past 40 years. This is the most thorough investigation on such a large field about the research topics, authorship distributions, using the method of machine learning. The authors have conducted a study applying machine learning models to analyze a data set comprising 20,185 finance articles published across 17 finance journals from 1976 to 2015. Through this analysis, they have objectively identified 38 research topics within the field. Among these topics, the financial crisis, hedge/mutual fund, social network, and culture emerged as the fastest-growing areas, while market microstructure, initial public offering, and option pricing experienced a decline in interest from 2006 to 2015. The authors also find a very interesting exponential decay rule for the number of topics that authors are covering.&lt;/p&gt;&lt;p&gt;A similar paper “Topic modeling of financial accounting research over 70 years” investigated topic distributions and time series patterns in financial accounting research in the past 70 years using machine learning methods. The author finds that 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. This research on topic classification itself will aid accounting investigators in bypassing superfluous efforts and fostering increased interdisciplinary research.&lt;/p&gt;&lt;p&gt;Beyond the topic modeling, there are two papers in this special issue regarding using machine learning in asset pricing. The paper “Investigating the profit performance of quantitative timing trading strategies in the Shanghai copper futures market, 2020–2022” investigates the time series signals using machine learning methods in the Shanghai copper futures market. The authors prudently conduct a reality check and advanced assessments to avoid data snooping problem. The basic conclusion of the paper demonstrates that after eliminating the data snooping bias, the time series signal within the class of ","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"19 4","pages":"470-471"},"PeriodicalIF":0.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Round-Tripping FDI from Firm-Level Data in China","authors":"Zeyi Qian,&nbsp;Junfu Zhang,&nbsp;Qiangyuan Chen","doi":"10.1002/ise3.102","DOIUrl":"https://doi.org/10.1002/ise3.102","url":null,"abstract":"<p>When capital leaves a country and then flows back as foreign direct investment (FDI), we call it round-tripping FDI. It is widely suspected that China's official FDI statistics contain a substantial amount of round-tripping FDI. However, it is difficult to quantify the round-tripping FDI due to the lack of data. In this paper, we propose two methods to identify round-tripping FDI. The first one tracks capital flows at the firm level. If a firm in China invests in a foreign firm and this foreign firm makes an investment back to China shortly after, then we consider this investment to China as round-tripping FDI. Our second measure of round-tripping FDI adds to the first measure by including investments in China made by Chinese investors registered in tax havens. The first estimate of round-tripping FDI accounts for up to 3% of China's total FDI from 1999 to 2015, whereas the second estimate accounts for up to 70% in the period. Our firm-level analysis shows that industrial firms facing higher tax burdens are more likely to make round-tripping FDI. We also show that at the city level, adjusted FDI statistics by subtracting the estimated round-tripping FDI are better predictors of imports and exports. Finally, we show that provinces receiving higher shares of round-tripping FDI are more likely to be punished for illegal financial activities. Taken together, these findings suggest that our measures of round-tripping FDI, although noisy, are indicative of real transactions.</p>","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"20 2","pages":"138-152"},"PeriodicalIF":0.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal and Spatial Effects of Mass Shootings on Gun Demand","authors":"Yuan Chen,&nbsp;Xun Li,&nbsp;Lisi Shi,&nbsp;Rui Wang,&nbsp;Qikexin Yu","doi":"10.1002/ise3.101","DOIUrl":"https://doi.org/10.1002/ise3.101","url":null,"abstract":"<p>Mass shootings in the U.S. have been at the center of the public crisis debate for a long time. Combining information on mass shootings with background check reports from the Federal Bureau of Investigation, this study applies mass shootings as exogenous shocks and reveals that the demand for guns is especially strong in the month in which a shooting occurs, and it decays with time. In addition, results confirm a spatial spillover effect of mass shootings, in which a mass shooting in one state affects gun demand in other states. The magnitude of the effect depends on the distance between the states. Our analysis also explores the difference in the effects between states with loose regulations on handguns and long guns and those with strict regulations. In the former, gun demand increases significantly after mass shootings, whereas in the latter the increase is insignificant. Finally, this study shows that consumers respond heterogeneously given the different characteristics of mass shootings, such as the number of victims and location.</p>","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"20 2","pages":"162-176"},"PeriodicalIF":0.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new era of financial services: How AI enhances investment efficiency","authors":"Zhiyi Liu,&nbsp;Kai Zhang,&nbsp;Hongyi Zhang","doi":"10.1002/ise3.97","DOIUrl":"https://doi.org/10.1002/ise3.97","url":null,"abstract":"&lt;p&gt;Financial investment is an important part of the modern economy, promoting economic growth and wealth accumulation through the efficient allocation of capital. However, with the rapid development of global financial markets, the investment environment has become increasingly complex. Investors not only need to cope with a large amount of data and information, but also to capture market opportunities and avoid risks in a timely manner. Traditional investment analysis methods and tools are often overwhelmed when dealing with these complexities.&lt;/p&gt;&lt;p&gt;Over the past period of time, the rapid advancement of artificial intelligence (AI) technology has brought new hope to financial investment (Holzinger et al., &lt;span&gt;2023&lt;/span&gt;). Through its powerful data processing capabilities, pattern recognition, and predictive analytics, AI is able to cope with the complexity and dynamics of the financial market, effectively enhancing the efficiency of traditional financial institutions and demonstrating great potential and broad application prospects.&lt;/p&gt;&lt;p&gt;Financial complex systems are networks of multiple interconnected financial entities and activities that exhibit complex interactions and dependencies. These systems typically exhibit nonlinear behavior, dynamic evolution, and have self-organizing features. Traders, financial firms, and investors, as the core elements of financial complex systems, together constitute the operating mechanism of financial investment markets through complex interactions and information exchange.&lt;/p&gt;&lt;p&gt;In this study, we will discuss how AI technology can empower financial investments (Ahmed et al., &lt;span&gt;2022&lt;/span&gt;) to enhance their efficiency from the perspective of financial complex systems and analyze their limitations and potential drawbacks from a new perspective. The rapid development and application of AI technology, especially in the sector of financial investment, not only foretells a fundamental change in the way the financial market operates, but also strengthens the technological foundation and clarifies the potential direction for the future development of the financial industry. Digital intelligence (Vijayakumar et al., &lt;span&gt;2022&lt;/span&gt;) finance will accelerate into a new era.&lt;/p&gt;&lt;p&gt;The wide application of AI in financial investment has significantly enhanced the efficiency of interconnected financial entities and markets within the financial ecosystem, injecting new vitality into the financial sector. For traders, AI technology aids in trend prediction, portfolio optimization, and real-time decision-making, greatly simplifying complex trading activities in an information-intensive era. For financial institutions, AI-driven intelligent customer service systems and RPA effectively enhance service efficiency while substantially reducing operational costs. For investors, large models improve the ability to collect and analyze financial information and data, thereby enhancing the quality of participation and decisio","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"19 4","pages":"578-588"},"PeriodicalIF":0.5,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.97","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143186295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finance research over 40 years: What can we learn from machine learning?","authors":"Po-Yu Liu,&nbsp;Zigan Wang","doi":"10.1002/ise3.95","DOIUrl":"https://doi.org/10.1002/ise3.95","url":null,"abstract":"<p>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 <i>n</i> topics is about twice the number of researchers covering <i>n</i> + 1 topics.</p>","PeriodicalId":29662,"journal":{"name":"International Studies of Economics","volume":"19 4","pages":"472-507"},"PeriodicalIF":0.5,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ise3.95","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}