Caught Stealing: Debunking the Economic Case for D.C. Baseball

District of Columbia mayor Anthony Williams has convinced Major League Baseball to move the Montreal Expos to D.C. in exchange for the city's building a new ballpark. Williams has claimed that the new stadium will create thousands of jobs and spur economic development in a depressed area of the city. Williams also claims that this can be accomplished without tax dollars from D.C. residents. Yet the proposed plan to pay for the stadium relies on some kind of tax increase that will likely be felt by D.C. residents. Our conclusion, and that of nearly all academic economists studying this issue, is that professional sports generally have little, if any, positive effect on a city's economy. The net economic impact of professional sports in Washington, D.C., and the 36 other cities that hosted professional sports teams over nearly 30 years, was a reduction in real per capita income over the entire metropolitan area. A baseball team in D.C. might produce intangible benefits. Rooting for the team might provide satisfaction to many local baseball fans. That is hardly a reason for the city government to subsidize the team. D.C. policymakers should not be mesmerized by faulty impact studies that claim that a baseball team and a new stadium can be an engine of economic growth

Go west for fame and fortune? The role of internationalization in the growth of Chinese telecom firms

This paper focuses on the role played by internationalization in the growth of Chinese telecom firms by comparing the three cases from China’s telecom sector – Huawei Technologies (Huawei), Zhongxing Telecom Equipment Corporation (ZTE), Datang Telecom Technology (Datang). Faced with a global market that was strongly oligopolistic and dominated by Western firms, we show that internationalization strategies triggered by resource seeking played different roles in the growth strategies of these three firms. The contrasting fortunes of these firms also underscores the fact that the success of internationalization strategies of firms from emerging markets cannot be understood without reference to the global competitive environment faced by firms

Economic and Political Consequences of the 1996 Telecommunications Act

See Crandall and Hazlett for a more recent analysis. This paper investigates the economic and political consequences of the 1996 Telecommunications Act by examining relevant marketplace data. In key segments of the telephone and cable television industries, the reform appears to be encouraging competition. Interestingly, stock price data indicate that the wave of "mega-mergers" in telecommunications, an unannounced and possibly unanticipated result of the Telecommunications Act, appears to be associated with consumer benefits. These improvements in competitiveness are modest by some standards, but impressive when judged against the results of other legislation with the announced goal of increasing market rivalry (e.g., the 1984 and 1992 Cable Acts). Federal policy makers also appear to be reaping benefits from the Telecommunications Act. The "deregulation"-which very cautiously opened markets, mandating extensive FCC rulemaking in the transition to competition-is associated with a sharp increase in political contributions to federal policymakers from telecommunications firms and executives. This is seen as an intended consequence of the act's major reform: Removing policy jurisdiction from Judge Harold Green's divestiture oversight and placing it in the hands of the Federal Communications Commission, a regulatory agency answerable to Congress.

Large Language Models for Telecom: The Next Big Thing?

The evolution of generative artificial intelligence (GenAI) constitutes a turning point in reshaping the future of technology in different aspects. Wireless networks in particular, with the blooming of self-evolving networks, represent a rich field for exploiting GenAI and reaping several benefits that can fundamentally change the way how wireless networks are designed and operated nowadays. To be specific, large language models (LLMs), a subfield of GenAI, are envisioned to open up a new era of autonomous wireless networks, in which a multimodal large model trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for dedicated AI models for each task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks. In this article, we aim to unfold the opportunities that can be reaped from integrating LLMs into the Telecom domain. In particular, we aim to put a forward-looking vision on a new realm of possibilities and applications of LLMs in future wireless networks, defining directions for designing, training, testing, and deploying Telecom LLMs, and reveal insights on the associated theoretical and practical challenges

Overseas Mergers and Acquisitions by Indian Enterprises: Patterns and Motivations

This paper examines the patterns and motivations behind the overseas M&As by Indian enterprises. It is found that a large majority of overseas M&As originated within services sector led by software industry and in overwhelming cases were directed towards developed countries of the world economy. The main motivations of Indian firm’s overseas acquisitions have been to access international market, firm-specific intangibles like technology and human skills, benefits from operational synergies, overcome constraints from limited home market growth, and survive in an increasingly competitive business environment. Further it has been found that overseas acquirers in the case of manufacturing sector tends to be large sized and research intensive, while they are older, large sized and export-oriented in the case of software sector.Indian firms; Overseas M&As

A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

Abstract The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered

Data Collaboratives as a New Frontier of Cross-Sector Partnerships in the Age of Open Data: Taxonomy Development

Data collaboratives present a new form of cross-sector and public-private partnership to leverage (often corporate) data for addressing a societal challenge. They can be seen as the latest attempt to make data accessible to solve public problems. Although an increasing number of initiatives can be found, there is hardly any analysis of these emerging practices. This paper seeks to develop a taxonomy of forms of data collaboratives. The taxonomy consists of six dimensions related to data sharing and eight dimensions related to data use. Our analysis shows that data collaboratives exist in a variety of models. The taxonomy can help organizations to find a suitable form when shaping their efforts to create public value from corporate and other data. The use of data is not only dependent on the organizational arrangement, but also on aspects like the type of policy problem, incentives for use, and the expected outcome of data collaborative