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on Information and Communication Technologies |
| By: | Samantha Bates; John Bowers; Shane Greenstein; Jordi Weinstock; Jonathan Zittrain |
| Abstract: | This paper analyzes the extent to which the Internet’s global domain name resolution (DNS) system has preserved its distributed resilience given the rise of cloud-based hosting and infrastructure. We explore trends in the concentration of the DNS space since at least 2011. In addition, we examine changes in domains’ tendency to “diversify” their pool of nameservers – how frequently domains employ DNS management services from multiple providers rather than just one provider – a comparatively costless and therefore puzzlingly rare decision that could supply redundancy and resilience in the event of an attack or service outage affecting one provider. |
| JEL: | L2 L22 L86 |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24317&r=ict |
| By: | Krieger-Boden, Christiane; Sorgner, Alina |
| Abstract: | Digitalization offers a variety of opportunities for female empowerment and for a more equal female participation in labor markets, financial markets, and entrepreneurship. Currently, digitalization seems to favor female labor force, since women face on average lower risk of being replaced by machines, as compared to men. Women's often superior social skills represent a comparative advantage in the digital age, and this is particularly so when social skills are complemented with higher education and advanced digital literacy. However, the same barriers and deficits that obstruct women's current advancement in many countries may deprive them from many beneficial opportunities in the digital age, including new entrepreneurial opportunities. Major efforts by policy makers are required to invalidate these barriers. New digital technologies should be used more decisively to achieve the goal of gender equality. |
| Keywords: | digitalization,gender equality,labor markets,entrepreneurship,financial inclusion |
| JEL: | O3 J7 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwedp:201818&r=ict |
| By: | Glenda Quintini |
| Abstract: | This study focuses on the risk of automation and its interaction with training and the use of skills at work. Building on the expert assessment carried out by Carl Frey and Michael Osborne in 2013, the paper estimates the risk of automation for individual jobs based on the Survey of Adult Skills (PIAAC). The analysis improves on other international estimates of the individual risk of automation by using a more disaggregated occupational classification and identifying the same automation bottlenecks emerging from the experts’ discussion. Hence, it more closely aligns to the initial assessment of the potential automation deriving from the development of Machine Learning. Furthermore, this study investigates the same methodology using national data from Germany and United Kingdom, providing insights into the robustness of the results. The risk of automation is estimated for the 32 OECD countries that have participated in the Survey of Adult Skills (PIAAC) so far. Beyond the share of jobs likely to be significantly disrupted by automation of production and services, the accent is put on characteristics of these jobs and the characteristics of the workers who hold them. The risk is also assessed against the use of ICT at work and the role of training in helping workers transit to new career opportunities. |
| JEL: | J20 J21 J23 J24 |
| Date: | 2018–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:oec:elsaab:202-en&r=ict |
| By: | Chia-Lin Chang (National Chung Hsing University); Michael McAleer (Asia University, University of Sydney Business School, Erasmus University Rotterdam); Wing-Keung Wong (Asia University, China Medical University Hospital, Hang Seng Management College) |
| Abstract: | This paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models. Moreover, they could then conduct simulations to examine whether the estimators or statistics in the new theories on estimation and hypothesis have small size and high power. Thereafter, academics and practitioners could then apply their theories to analyze interesting problems and issues in the six disciplines and other cognate areas. |
| Keywords: | Decision sciences; economics; finance; business; computing; and big data; theoretical models; econometric and statistical models; applications. |
| JEL: | A10 G00 G31 O32 |
| Date: | 2018–03–14 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20180024&r=ict |