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on Information and Communication Technologies |
| By: | Asongu, Simplice; Odhiambo, Nicholas |
| Abstract: | This research assesses the relevance of information and communication technology (ICT) in primary education quality in a panel of 49 Sub-Saharan African countries for the period 2000-2012. The empirical evidence is based on Two Stage Least Squares (2SLS) and Instrumental Quantile regressions (IQR). From the 2SLS: (i) mobile phone and internet penetration rates reduce poor quality education and enhancing internet penetration has a net negative effect of greater magnitude. From the IQR: (i) with the exception of the highest quantile for mobile phone penetration and top quantiles for internet penetration, ICT consistently has a negative effect on poor education quality with a non-monotonic pattern. (ii) Net negative effects are exclusively apparent in the median and top quantiles of internet-related regressions. It follows that enhancing internet penetration will benefit countries with above-median levels of poor education quality while enhancing internet penetration is not immediately relevant to reducing poor education quality in countries with below-median levels of poor education quality. |
| Keywords: | ICT; Primary school education; Development; Sub-Saharan Africa |
| JEL: | F24 L96 O30 O55 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:93531&r=all |
| By: | Caroline Paunov; Sandra Planes-Satorra |
| Abstract: | How are OECD countries supporting digital innovation and ensuring that benefits spread across the economy? This paper explores the current landscape of strategies and initiatives implemented in OECD countries to support innovation in the digital age. It identifies common trends and differences in national digital, smart industry and artificial intelligence (AI) strategies. The paper also discusses policy instruments used across OECD to support digital innovation targeting four objectives: First, policies aimed at enhancing digital technology adoption and diffusion, including demonstration facilities for SMEs. Second, initiatives that promote collaborative innovation, including via the creation of digital innovation clusters and knowledge intermediaries. Third, support for research and innovation in key digital technologies, particularly AI (e.g. by establishing testbeds and regulatory sandboxes). Fourth, policies to encourage digital entrepreneurship (e.g. through early-stage business acceleration support). |
| Keywords: | digital innovation, digital technologies and artificial intelligence (AI), innovation and research policy, innovation strategies |
| JEL: | O30 O31 O33 O38 O25 I28 |
| Date: | 2019–05–06 |
| URL: | http://d.repec.org/n?u=RePEc:oec:stiaac:71-en&r=all |
| By: | Otello Ardovino; Jacopo Arpetti; Marco Delmastro |
| Abstract: | The Artificial Intelligence paradigm (hereinafter referred to as "AI") builds on the analysis of data able, among other things, to snap pictures of the individuals' behaviors and preferences. Such data represent the most valuable currency in the digital ecosystem, where their value derives from their being a fundamental asset in order to train machines with a view to developing AI applications. In this environment, online providers attract users by offering them services for free and getting in exchange data generated right through the usage of such services. This swap, characterized by an implicit nature, constitutes the focus of the present paper, in the light of the disequilibria, as well as market failures, that it may bring about. We use mobile apps and the related permission system as an ideal environment to explore, via econometric tools, those issues. The results, stemming from a dataset of over one million observations, show that both buyers and sellers are aware that access to digital services implicitly implies an exchange of data, although this does not have a considerable impact neither on the level of downloads (demand), nor on the level of the prices (supply). In other words, the implicit nature of this exchange does not allow market indicators to work efficiently. We conclude that current policies (e.g. transparency rules) may be inherently biased and we put forward suggestions for a new approach. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.12134&r=all |
| By: | Acemoglu, Daron (MIT); Restrepo, Pascual (Boston University) |
| Abstract: | Artificial Intelligence is set to influence every aspect of our lives, not least the way production is organized. AI, as a technology platform, can automate tasks previously performed by labor or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labor can be productively employed. The consequences of this choice have been stagnating labor demand, declining labor share in national income, rising inequality and lower productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the "right" kind of AI with better economic and social outcomes. |
| Keywords: | automation, artificial intelligence, jobs, inequality, innovation, labor demand, productivity, tasks, technology, wages |
| JEL: | J23 J24 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12292&r=all |
| By: | Richard Baron (Univ Lyon, UJM Saint-Etienne, GATE UMR 5824, F-42023 Saint- Etienne, France); Magali Chaudey (Univ Lyon, UJM Saint-Etienne, GATE UMR 5824, F-42023 Saint- Etienne, France) |
| Abstract: | This paper is interested in the analysis of Blockchains and Smart-contracts applied to inter-firms relationships, in particular the franchise networks. After defining the Blockchain technology and the Smart-contract as a particular type of contract stored in blockchains, we question the theory of contracts and its conception(s) of transactions, information asymmetries, firm or inter-firm relations. To better understand the challenges of blockchain for franchise networks and identify opportunities for implementation in these networks, we present some relevant applications of this technology. We identify different ways where blockchain technology could improve the network management and therefore their performance: the supply-chain, the brand-name protection, security and transparency in the payment of fees and royalties, access to reliable information via an oracle. |
| Keywords: | Blockchain, Smart-Contract, Transaction cost, Network, Franchise |
| JEL: | D86 L14 L81 O33 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:gat:wpaper:1917&r=all |
| By: | Acemoglu, Daron (MIT); Restrepo, Pascual (Boston University) |
| Abstract: | We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor – the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production – due to automation and new tasks – can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades. |
| Keywords: | automation, displacement effect, labor demand, inequality, productivity, reinstatement effect, tasks, technology, wages |
| JEL: | J23 J24 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12293&r=all |
| By: | Timothy DeStefano; Richard Kneller; Jonathan Timmis |
| Abstract: | The arrival of the cloud has enabled a shift in the nature of ICT use, from investment in sunk capital to a pay-on-demand service that allows firms to rapidly scale up. This paper uses new firm-level data to examine the impact of cloud on firm growth in the UK, using zipcode-level instruments of the timing of high-speed fibre availability and expected speeds. We find cloud leads to the growth of young firms in terms of employment and productivity, but they become more concentrated in fewer plants. For older firms we find no scale or productivity growth, but instead disperse activity by closing plants and moving employment further from the headquarters. In addition, the plants that close tend to be those without access to fibre broadband. |
| Keywords: | firm growth; the cloud; ICT use; employment; productivity |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:not:notgep:2019-09&r=all |
| By: | Yu Zheng; Yongxin Yang; Bowei Chen |
| Abstract: | In this paper, we propose a gated deep neural network model to predict implied volatility surfaces. Conventional financial conditions and empirical evidence related to the implied volatility are incorporated into the neural network architecture design and calibration including no static arbitrage, boundaries, asymptotic slope and volatility smile. They are also satisfied empirically by the option data on the S&P 500 over a ten years period. Our proposed model outperforms the widely used surface stochastic volatility inspired model on the mean average percentage error in both in-sample and out-of-sample datasets. The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a framework of integrating data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.12834&r=all |