Telecommunications Policy最新文献

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.

Ubiquitous high-speed fiber-based and mobile broadband availability is a key economic policy goal in the European Union and other developed countries. Policymakers seek to boost economic growth, productivity, and employment, especially in remote areas, through ambitious broadband targets and substantial public funding. This paper reviews the existing (empirical) literature on high-speed broadband Internet and its socioeconomic impact on key outcome variables. Our main findings are that (i) the socioeconomic benefits in terms of economic growth, productivity, house prices, and education, as well as economic resilience, are high but are also subject to diminishing returns beyond a certain broadband quality level, (ii) the effects on employment are more ambiguous, (iii) the positive socioeconomic effects take hold only after broadband adoption on the demand side, but not with mere availability on the supply side, and (iv) the effects of broadband differ significantly for urban vs. rural and high-skilled vs. low-skilled workers for some of the outcome variables. Based on these findings, we develop recommendations for a cost-effective and efficient broadband policy and identify an agenda for research.

There is an intrinsic relationship between innovation and digitalization, marked by the specific and structural socioeconomic characteristics of our regions and countries. These characteristics explain the territory heterogeneity and determine in many cases specific paths for the next digital transformations in the EU.

But there are some issues countries cannot ignore: are all these economical efforts around innovation well addressed to foster digital transformation? To what extent a country or a region offers a different behavior across this intime relationship between digitalization and innovation?

Innovation is a complex concept built by a multitude of interacting aspects that do not necessarily work synergistically: a framework of conditions, innovation activities, investment, and economics impacts. The consequential results of cross-cutting drivers, such as digitalization, are often difficult to assess, as the achievement of certain targets may also inadvertently hinder progress towards others. Our study describes a comprehensive and systematic European country-based analysis of statistical associations between both, digitalization and innovation indicators operating at several different levels to understand better these apparent contradictions.

The Affordable Connectivity Program (ACP) represents a historic investment by the federal government to address the digital divide. Its goals are both to increase the rate of broadband adoption and make it easier for low-income households to sustain service. But what have been the drivers of ACP enrollment? We know a great deal about trends in adoption and the geography of enrollment among eligible households. But those metrics – especially a focus on enrollment rates among eligible households – are only so useful in explaining ACP sign-ups.

This invites analysis that investigates the drivers of ACP enrollment in a comprehensive way, which this paper proposes to do. The paper hypothesizes that ACP enrollment decisions are not solely individual (i.e., that a household's enrollment decision is structured only by income level), but also influenced by community-wide considerations, such as housing costs, share of occupied houses, presence of anchor institutions such as public libraries, and population density (i.e., whether a place is urban or rural). The paper develops a regression model that predicts ACP enrollment rates among eligible households at the 5-digit zip code geography as a function of the variables discussed above, as well as others such as the racial and ethnic make-up of a zip code area. The analysis also controls for existing levels of broadband subscriptions and computer ownership in a given area. The difference between predicted and actual ACP enrollment rates at the 5-digit zip code level is proposed as a metric of ACP performance. These findings are supplemented with a geographically weighted regression (GWR) model that allows the relationship between each explanatory variable and ACP enrollment to vary by locality. This allows for analyzing and visualizing how these relationships vary across space.

The empirical findings show that high levels of economic distress are a strong determinant of ACP enrollment in a given area, but social and community-wide indicators are important as well. The GWR results demonstrate that important differences in these relationships exist across geographies. Understanding spatial variations in ACP performance can help policymakers and other stakeholders better target resources to address the digital divide.

Indices to measure the extent and penetration of information in an economy are static measures. Information, however, is a dynamic, expanding resource. Information is produced when data from digital computers articulate with human-generated systems where they are transformed into information. Economic information is effective when it fuels technological innovations contributing to total factor productivity (TFP) growth. This paper develops a theoretical model to measure effective economic information (EEI) as a dynamic process. Using systemic design thinking, design systems in data value chains are embedded within their TFP-information absorption ecosystem. The system's characteristics of human capital, economic, institutional, and regulatory factors determine the quality and amount of EEI produced. The EEI measurement model uses differential equations allowing for the dynamic expansion of information to include information-knowledge spillovers. Empirical tests for EEI on TFP for EU countries show that EEI is the key driver for TFP growth, while IT investment alone is only a necessary condition. EEI-type measures direct policy attention towards improving TFP-information absorption ecosystems and supporting the adaption of design processes that are more suitable for their TFP-information absorption ecosystems. Both are necessary to connect IT investment to TFP growth.

This project examines how state broadband policies diffused among the states in the United States over the last 30-year period utilizing a network approach and the State Broadband Explorer dataset curated by the Pew Charitable Trusts’ Broadband Access Initiate. The 621 valid state broadband policies in the U.S. (until January 2021) have been categorized into six main themes: broadband programs, competition and regulation, definitions, funding and financing, infrastructure access, and legislative intent. Our analytical strategy follows a two-step process: (1) to identify the latent network of broadband policy diffusion across the states using the NetInf algorithm; (2) to identify the nodal and dyadic variables that predict the observed diffusion flows. Our objective for the second step is to test out two competing hypotheses: the geographic learning model and the (co-)partisan learning model, which privilege geographic proximity and ideological affiliation respectively as the primary drivers of policy diffusion. The results show that geographic contiguity is the most significant factor predicting broadband policy diffusion. However, the results also identify the low salience of political factors in predicting broadband policy diffusion. Among nodal factors, only one namely divided government (of sender states) is a significant predictor of a diffusion tie. Among dyadic factors, there is one variable that supported political homophily as a significant predictor of diffusion flows (i.e., both states sharing the same type of legislative control). Partisanship appears to be much less of a driver of broadband policy in the U.S. context.

As regulators began prohibiting online platforms from collecting personal data based on the “take-it-or-leave-it” basis, platform firms must adopt more refined user consent rules such as the pay-or-consent approach. Ensuring sufficient user options could increase the welfare of privacy-sensitive users but reduce the efficiency of data-driven business models. To balance the benefits and costs of enhanced privacy protection, regulators should understand the diversity in users' attitudes toward behavioral data collection in free online platforms. Tradeoffs among privacy, conveniences, and free services based on users' heterogeneous preferences are considered to investigate the user's different privacy attitudes in free online platforms. Three distinct user groups were found: the first one reluctantly accepts the “take-it-or-leave-it” condition because of the lack of alternatives, the second one accepts it for free services, and the third one accepts it because it does not matter. These three user segments constituted 32.9%, 47.0%, and 20.1% of all the respondents, respectively. The pay-or-consent approach can be justifiable in terms of balancing the benefits and costs of the privacy regulations if it properly reflects privacy-sensitive users' willingness to pay for privacy.