Telecommunications Policy最新文献

The COVID-19 pandemic has spurred global change from traditional office-based work to remote work, driven by policy interventions, and resulting in a significant number of employees worldwide transitioning to working from home. Existing research presents conflicting evidence regarding the relationship between remote work and productivity. However, few have examined the specific mechanisms through which remote work affects productivity. Given this, the present study bridges this gap by examining changes in job assignment as one mechanism in which working from home affects labor productivity. We empirically examine the impact of work-from-home (WFH) on job tasks using original data collected before and during the pandemic. Our results demonstrate that WFH increases non-routine analytic tasks and decreases routine manual tasks, while non-routine interactive, routine cognitive, and non-routine manual tasks remain unchanged. Furthermore, the negative impact of WFH on routine manual tasks is more significant for the groups that had more routine manual tasks before the pandemic. Meanwhile, the positive impact on non-routine analytic tasks is more pronounced for employees with fewer non-routine analytic tasks before the pandemic. Finally, through such task changes, WFH leads to higher wage rates. Our findings suggest that the switch to WFH and increased use of information and communication technology capital increases the input for non-routine tasks performed by labor, and, in turn, increases labor productivity.

An open-access market design is presented to manage network congestion and optimize network use and value. Open access eliminates the walled-garden approach; instead, it commoditizes communications network capacity while decentralizing access to a transparent wholesale market. It ensures that scarce capacity is put to its best use by providing a platform for efficient trade. The market operates without friction using flow trading. It allows participants to bid persistent piecewise-linear downward-sloping demand curves for portfolios of products, gradually adjusting positions toward targeted needs. Flow trading allows fine granularity of products in time and location, creating complete markets. Liquidity and computational feasibility are maintained despite trading millions of interrelated forward and real-time products. Participants manage risk and adverse price impact through trade-to-target strategies. The market operator clears the market every hour, finding unique prices and quantities that maximize as-bid social welfare. Prices, aggregate quantities, and the slope of the aggregate net demand are public. The market operator observes positions, enabling it to optimize collateral requirements to minimize default risk. Priority pricing is used to manage real-time imbalances. An application of the model is developed for intersatellite wholesale communications with optical (laser-beamed) mesh networks in space, showing several efficiency gains.

This study examines the challenges related to the music industry's digital transformation and potential role of blockchain from the perspective of collective management organizations (CMOs). Building on desk research and primary data from semi-structured surveys conducted with C-level executives and managers, this empirical analysis identifies major projects, their state of development, and prospects for digital transformation based on blockchain in the music industry. The findings reveal that there are a limited number of blockchain projects led by and/or with the relevant participation of CMOs. However, most are just research projects, proofs of concept, or pilots, showing that blockchain is currently in an experimental phase of development on the periphery of the music industry's digital transformation. This is not very different from analysts' understanding of the current situation and perspectives on the mass adoption of blockchain in other industries. In summary, blockchain is neither at the core of the music industry's digital transformation nor a priority for CMOs leading this process from the perspective of intellectual property rights management. The limited quality of music metadata, sparsity of sound data sources, and absence of a common data governance framework among different stakeholders within the industry are the main impediments to transforming blockchain into a global solution with transformative potential. Overall, the results provide a snapshot of the current status and possible future trajectories of blockchain adoption as a paradigm for intellectual property rights exchange within the music industry.

Telecom operators allocate a significant amount of resources to retain their customers as the organic growth in the number of customers is slowing down. Gamified loyalty programs, branded apps, and over-the-top (OTT) services emerged as ways to develop customer acquisition and retention strategies. Despite these strategies, some mobile customers still churn; therefore, churn prediction plays an essential role in the sustainable future of telecom businesses. Churn prediction is used both to detect customers with a high propensity to churn and to identify the reasons behind their churn behavior. This study examines several features affecting the churn behavior of mobile customers, including branded apps, gamified loyalty programs, and OTT services. In this study, the secondary data is provided by a telecom operator and contains the attributes of both churner and non-churner mobile customers. Logistic regression and random forest classifiers are compared in terms of their predictive power, and we used the latter as the machine learning algorithm in the churn prediction model. To understand the variable importance, mean decrease in impurity and permutation importance are performed. The key findings of this research reveal that while gamified loyalty giveaways and branded app strategies are effective, OTT service strategies show lower importance in predicting mobile customer churn behavior.

In this paper, we study the impact of high-speed internet access on student performance. Our empirical analysis leverages a unique dataset that combines information on ultra-broadband (UBB) diffusion in Italy with data on student performance in 2nd, 5th, and 8th grades for the period 2012–2017. We exploit the staggered roll-out of UBB, starting from 2015. Through an event study approach, we find evidence of endogeneity between student performance and UBB diffusion. We deal with this issue through an instrumental variable approach that exploits plausibly exogenous variation in the diffusion of the essential UBB input. Our results suggest that advanced internet connections significantly decrease student performance in Mathematics and Italian language in the 8th grade. In contrast, we do not find any significant effect in the 2nd and 5th grades. Male students from low-educated parental backgrounds are those more adversely affected, especially if they attend schools with a low IT usage.

This study explores spatial patterns of the digital divide and its determinants at the sub-district level in Thailand, uncovering nuanced patterns often overlooked in the in prior literature. To our knowledge, no studies in the digital divide field have offered such detailed estimations. Based on the Spatially Aware Technology Utilization Model, we constructed a conceptual model tailored to the digital divide's geospatial context in Thailand. Using a spatial econometric approach, we analyzed the 2021 data set on basic household necessities provided by Thailand's Community Development Department. The results show a positive spatial autocorrelation of the household internet access divide (HIAD) across Thailand's sub-districts. This spatial clustering tended to be high–high in the sub-districts located in the northeastern and northern regions, with low–low agglomerations in the central region of Thailand. In addition, the spatial econometric analysis results indicated that the HIAD may be influenced by various factors including demographic, economic, educational, government ICT prioritization, social capital, transportation, and disaster-related variables. This study provides both theoretical and policy implications to enrich geographic knowledge of the digital divide, specifically in the context of developing countries.

We analyze the effects of different data selling mechanisms of a monopolistic Data Broker (DB) who sells consumer data to firms in a downstream market with free entry, where data can be used for consumer price discrimination. We consider three possible data selling mechanisms, namely auctions with and without reserve prices, and Take-It-Or-Leave-It offers, which exhibit decreasing levels of DB’s bargaining power towards firms. We highlight the emergence of an entry barrier effect in the downstream market, regardless of the data selling mechanism. Moreover, we show that the auction-based selling mechanisms, and particularly the auction with reserve prices, induce the DB to sell the lowest quantity of data, implying the lowest level of consumer surplus. Conversely, under TIOLI, the DB floods the market for data, selling to all firms data partitions that overlap over subsets of consumers. Imposing the sale of non-overlapping partitions to all firms would improve consumer surplus and welfare.

Scholars and policymakers have long identified stakeholders whose activities should help reduce digital inequalities; these include “outreach initiatives,” where entities proactively go beyond their traditional boundaries to reach marginalized citizens. However, few studies have attempted to evaluate the potential impact of this approach quantitatively.

Our research fills this gap, employing a static and dynamic analysis of a pseudo-panel dataset relating to Italy. We aggregate data from a representative national survey for the years 2014–2020, and we proxy outreach through the number of public events promoted to spread digital literacy.

The static model highlights the role of systemic variables: education, employment, broadband coverage, and social connectedness. The dynamic model shows that outreach and library activism create positive fluctuations around the trend but reach a plateau.

We conclude that a policy mix is needed: outreach is a helpful tool to stimulate communities in the short term, but other, more structural interventions are required to close the digital skills gap. These results are relevant especially for countries, like Italy and the US, that are now experimenting with outreach-oriented policies to boost basic digital skills.

We use confidential firm-level data to examine the association between export intensity and subscription to cloud computer services – a technology that often requires very high-speed broadband. Our focus on rural nonfarm exports is motivated by the increasing concentration of manufacturing—the dominant export sector—in nonmetropolitan counties and the large public investment in extending high-speed broadband to underserved parts of the U.S. We find that cloud computing is associated with higher export intensity that is consistent with exporting activity placing new demands on IT-enabled functions such as order fulfillment and tracking, marketing, or document control.