nep-pay New Economics Papers
on Payment Systems and Financial Technology
Issue of 2019‒07‒22
43 papers chosen by

  1. The evolving liaisons between the transaction networks of Bitcoin and its price dynamics By Alexandre Bovet; Carlo Campajola; Francesco Mottes; Valerio Restocchi; Nicol\`o Vallarano; Tiziano Squartini; Claudio J. Tessone
  2. How are digital technologies changing innovation?: Evidence from agriculture, the automotive industry and retail By Caroline Paunov; Sandra Planes-Satorra
  3. Information-theoretic measures for non-linear causality detection: application to social media sentiment and cryptocurrency prices By Z. Keskin; T. Aste
  4. Les apports de l'analyse de contenu hybride à l'étude des activités productives du consommateur : une application aux vidéos « retour de courses » By Loic Comino
  5. The demand for Swiss banknotes: some new evidence By Katrin Assenmacher; Franz Seitz; Jörn Tenhofen
  6. Accounting for Innovations in Consumer Digital Services: IT still matters By David M. Byrne; Carol Corrado
  7. The Effects of the Introduction of Bitcoin Futures on the Volatility of Bitcoin Returns By Wonse Kim; Junseok Lee; Kyungwon Kang
  8. Micro-work, artificial intelligence and the automotive industry By Paola Tubaro; Antonio Casilli
  9. The Bahamas; Financial Sector Assessment Program-Technical Note on Financial Inclusion, Retail Payments, and SME Finance By International Monetary Fund
  10. Consumers’ perception on human-like artificial intelligence devices By Pelau, Corina; Ene, Irina
  11. Índice de Avisos Laborales de Internet By Erika Arraño; Katherine Jara
  12. An Empirical Comparison Between Discrete Choice Experiment and Best-worst Scaling: A Case Study of Mobile Payment Choice By Qinxin Guo; Junyi Shen
  13. Opportunities and Challenges of Sharia Technology Financials in Indonesia By Mujahidin, Muhamad
  14. How Clean is our Taxpayer Register? Data Management in the Uganda Revenue Authority By Mayega, Jova; Ssuuna, Robert; Mubajje, Muhammad; I. Nalukwago, Milly; Muwonge, Lawrence
  15. The Mobile Phone, Information Sharing and Financial Sector Development in Africa: A Quantile Regressions Approach By Simplice A. Asongu; Nicholas M. Odhiambo
  16. Is internet on the right track? The digital divide, path dependence, and the rollout of New Zealand’s ultra-fast broadband By Apatov, Eyal; Chappell, Nathan; Grimes, Arthur
  17. Invoice Financing of Supply Chains with Blockchain technology and Artificial Intelligence By Sandra Johnson; Peter Robinson; Kishore Atreya; Claudio Lisco
  18. Mobile Phones and Mozambique Traders: What is the Size of Reduced Search Costs and Who Benefits? By Wouter Zant
  19. Foreign Direct Investment, Information Technology and Economic Growth Dynamics in Sub-Saharan Africa By Simplice A. Asongu; Nicholas M. Odhiambo
  20. Automation and occupational mobility: A data-driven network model By R. Maria del Rio-Chanona; Penny Mealy; Mariano Beguerisse-D\'iaz; Francois Lafond; J. Doyne Farmer
  21. Increasing access to Information and Communications Technology (ICT) By Achara Jantarasaengaram
  22. Roles and responsibilities of actors for digital security By OECD
  23. On the (in)efficiency of cryptocurrencies: Have they taken daily or weekly random walks? By Apopo, Natalay; Phiri, Andrew
  24. From Learning to Doing: Diffusion of Agricultural Innovations in Guinea-Bissau By Rute Martins Caeiro
  25. O Bitcoin e os sentimentos dos agentes By Chemalle, Thierry Dayr Leandro
  26. Artificial Intelligence Alter Egos: Who benefits from Robo-investing? By Catherine D'Hondt; Rudy De Winne; Eric Ghysels; Steve Raymond
  27. Potenziale der Blockchain-Technologie für die Handelsintegration von Entwicklungsländern By Schwab, Jakob; Ohnesorge, Jan
  28. Terrorism and social media: global evidence By Simplice A. Asongu; Stella-Maris I. Orim; Rexon T. Nting
  29. E-commerce as a Potential New Engine for Growth in Asia By Tidiane Kinda
  30. Does Social Media Promote Democracy? Some Empirical Evidence By Chandan Kumar Jha; Oasis Kodila-Tedika
  31. Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks By Hung Ba
  32. Will this time be different? A review of the literature on the Impact of Artificial Intelligence on Employment, Incomes and Growth By Bertin Martens; Songül Tolan
  33. MOOC to OC (online courses) : The rhetoric of openness By Célya Gruson-Daniel; Olivier Aïm; Karl-William Sherlaw; Anneliese Depoux
  34. Switzerland; Financial Sector Assessment Program; Technical Note-Supervision and Oversight of Financial Market Infrastructures By International Monetary Fund
  35. P2P Loan acceptance and default prediction with Artificial Intelligence By Jeremy D. Turiel; Tomaso Aste
  36. Machine learning and behavioral economics for personalized choice architecture By Emir Hrnjic; Nikodem Tomczak
  37. Deep learning calibration of option pricing models: some pitfalls and solutions By A Itkin
  38. The impact of data access regimes on artificial intelligence and machine learning By Bertin Martens
  39. Financial Time Series Data Processing for Machine Learning By Fabrice Daniel
  40. The role of distance and social networks in the geography of crowdfunding: evidence from France By Sylvain Dejean
  41. Machine learning with kernels for portfolio valuation and risk management By Lotfi Boudabsa; Damir Filipovic
  42. Random Forest Estimation of the Ordered Choice Model By Michael Lechner; Gabriel Okasa
  43. Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing? By Michael Allan Ribers; Hannes Ullrich

  1. By: Alexandre Bovet; Carlo Campajola; Francesco Mottes; Valerio Restocchi; Nicol\`o Vallarano; Tiziano Squartini; Claudio J. Tessone
    Abstract: Cryptocurrencies are distributed systems that allow exchanges of native tokens among participants, or the exchange of such tokens for fiat currencies in markets external to these public ledgers. The availability of their complete historical bookkeeping opens up the possibility of understanding the relationship between aggregated users' behaviour and the cryptocurrency pricing in exchange markets. This paper analyses the properties of the transaction network of Bitcoin. We consider four different representations of it, over a period of nine years since the Bitcoin creation and involving 16 million users and 283 million transactions. By analysing these networks, we show the existence of causal relationships between Bitcoin price movements and changes of its transaction network topology. Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of nodes activity.
    Date: 2019–07
  2. By: Caroline Paunov; Sandra Planes-Satorra
    Abstract: Digital technologies impact innovation in all sectors of the economy, including traditional ones such as agriculture, the automotive industry, and retail. Similar trends across sectors include that the Internet of Things and data are becoming key inputs for innovation, innovation cycles are accelerating, services innovation is gaining importance and collaborative innovation matters more. Sector-specific dynamics are driven by differences in opportunities such technologies offer for innovation in products, processes and business models, as well as differences in the types of data needed for innovation and the conditions for digital technology adoption. The analysis calls for revisiting innovation policy mixes to ensure these remain effective and address emerging challenges. A sectoral approach is needed when designing innovation policies in some domains, especially regarding data access and digital technology adoption policies. The current focus of innovation policies on boosting R&D to meet R&D intensity targets also requires scrutiny.
    Date: 2019–07–18
  3. By: Z. Keskin; T. Aste
    Abstract: Information transfer between time series is calculated by using the asymmetric information-theoretic measure known as transfer entropy. Geweke's autoregressive formulation of Granger causality is used to find linear transfer entropy, and Schreiber's general, non-parametric, information-theoretic formulation is used to detect non-linear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect causality between social sentiment and cryptocurrency prices. We perform significance tests by comparing the information transfer against a null hypothesis, determined via shuffled time series, and calculate the Z-score. We also investigate different approaches for partitioning in nonparametric density estimation which can improve the significance of results. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, in directions of both sentiment to price and of price to sentiment. We report the scale of non-linear causality to be an order of magnitude greater than linear causality.
    Date: 2019–06
  4. By: Loic Comino (CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine)
    Abstract: Digital labor is gaining traction on social networks. Ranging from "likes" generation to the creation of more complex contents, it demonstrates the active role of internet users on these platforms. Marketing research pays close attention to these productive activities and the way they transforms consumption practices. Nevertheless, we acknowledge that only few research works pay attention to the nature of the contents produced. In our view, that situation is largely related to the lack of tools conceived to match the specificities of content analysis on social networks. In that respect, our research aims to outlines the contours of a methodological protocol designed to collect and analyze contents posted by internet users on social networks. This presentation also seeks to highlight the main challenges and opportunities related to content analysis in a digital environment. To assess the potential of this approach, we complete our presentation with an application focused on "Grocery Haul" phenomena.
    Abstract: Particulièrement visible sur les réseaux socionumériques, le digital labor des internautes prend des formes variées, allant du simple « like » à la création de contenus complexes. Si la recherche en marketing prête une oreille attentive à ces activités productives, nous relevons que peu de travaux interrogent la nature même des contenus produits. Selon nous, cette situation est en grande partie liée à l'absence d'outils adaptés aux spécificités du web participatif. Dans ce contexte, cet article esquisse les grandes lignes d'un protocole de collecte, de traitement et d'analyse des productions diffusées par les internautes sur les réseaux socionumériques. La présentation de ses modalités de mise en oeuvre s'accompagne d'une réflexion portant sur les challenges et opportunités qui traversent l'analyse de contenu hybride. Afin de préciser les potentialités d'une telle approche, nous la complétons par une application centrée sur l'étude des vidéos « retour de courses ».
    Date: 2018–03
  5. By: Katrin Assenmacher; Franz Seitz; Jörn Tenhofen
    Abstract: Knowing the part of currency in circulation that is used for transactions is important information for a central bank. For several countries, the share of banknotes that is hoarded or circulates abroad is sizeable, which may be particularly relevant for large-denomination banknotes. We analyse the demand for Swiss banknotes over a period starting in 1950 to 2017 and use different methods to derive the evolution of the amount that is hoarded. Our findings indicate a sizeable amount of hoarding, in particular for large denominations. The hoarding shares increased around the break-up of the Bretton Woods system, were comparatively low in the mid-1990s and have increased significantly since the turn of the millennium and the recent financial and economic crises.
    Keywords: Currency in Circulation, Cash, Demand for Banknotes, Hoarding of Banknotes, Banknotes Held by Non-residents
    JEL: E41 E52
    Date: 2019
  6. By: David M. Byrne; Carol Corrado
    Abstract: This paper develops a framework for measuring digital services in the face of ongoing innovations in the delivery of content to consumers. We capture what Brynjolfsson and Saunders (2009) call "free goods" as the capital services generated by connected consumers' stocks of IT digital goods; this service flow augments the existing measure of personal consumption in GDP. Its value is determined by the intensity with which households use their IT capital to consume content delivered over networks, and its volume depends on the quality of the IT capital. Consumers pay for delivery services, however, and the complementarity between device use and network use enables us to develop a quality-adjusted price measure for the access services already included in GDP. Our new estimates imply that accounting for innovations in consumer content delivery matters: The innovations boost consumer surplus by nearly $1,800 (2017 dollars) per connected user per year for the full period of this study (1987 to 2017) and contribute more than 1/2 percentage point to US real GDP growth during the last ten. All told, our more complete accounting of innovations is (conservatively) estimated to have moderated the post-2007 GDP growth slowdown by nearly .3 percentage points per year.
    Keywords: Consumer Digital Services ; Consumer Durables ; Consumer Surplus ; Digital Transformation ; Information And Communication Technology ; Ict ; Price Measurement ; Productivity
    JEL: L86 L16 D13 O33 O47
    Date: 2019–06–27
  7. By: Wonse Kim; Junseok Lee; Kyungwon Kang
    Abstract: This paper investigates the effects of the launch of Bitcoin futures on the intraday volatility of Bitcoin. Based on one-minute price data collected from four cryptocurrency exchanges, we first examine the change in realized volatility after the introduction of Bitcoin futures to investigate their aggregate effects on the intraday volatility of Bitcoin. We then analyze the effects in more detail utilizing the discrete Fourier transform. We show that although the Bitcoin market became more volatile immediately after the introduction of Bitcoin futures, over time it has become more stable than it was before the introduction.
    Date: 2019–06
  8. By: Paola Tubaro (LRI - Laboratoire de Recherche en Informatique - UP11 - Université Paris-Sud - Paris 11 - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - CNRS - Centre National de la Recherche Scientifique, TAU - TAckling the Underspecified - LRI - Laboratoire de Recherche en Informatique - UP11 - Université Paris-Sud - Paris 11 - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - CNRS - Centre National de la Recherche Scientifique - UP11 - Université Paris-Sud - Paris 11 - Inria Saclay - Ile de France - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique, CNRS - Centre National de la Recherche Scientifique); Antonio Casilli (I3, une unité mixte de recherche CNRS (UMR 9217) - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique - Télécom ParisTech - MINES ParisTech - École nationale supérieure des mines de Paris, Télécom ParisTech)
    Abstract: This paper delves into the human factors in the "back-office" of artificial intelligence and of its data-intensive algorithmic underpinnings. We show that the production of AI is a labor-intensive process, which particularly needs the little-qualified, inconspicuous and low-paid contribution of "micro-workers" who annotate, tag, label, correct and sort the data that help to train and test smart solutions. We illustrate these ideas in the high-profile case of the automotive industry, one of the largest clients of digital data-related micro-working services, notably for the development of autonomous and connected cars. This case demonstrates how micro-work has a place in long supply chains, where tech companies compete with more traditional industry players. Our analysis indicates that the need for micro-work is not a transitory, but a structural one, bound to accompany the further development of the sector; and that its provision involves workers in different geographical and linguistic areas, requiring the joint study of multiple platforms operating at both global and local levels.
    Keywords: Artificial intelligence,Micro-work,Automotive industry,Digital platform economy,Organization of work
    Date: 2019–06–05
  9. By: International Monetary Fund
    Abstract: The CBOB considers increased financial inclusion as a critical reform area. In this regard, the FSAP assessed developments in financial inclusion for individuals and enterprises (SME finance), retail payments and provides recommendations for improvements. A review of the market was undertaken using the Payment Aspects of Financial Inclusion (PAFI) framework covering areas such as the legal/regulatory framework and oversight, retail payment systems and instruments, access to transaction accounts and use cases, as well as SME policy, credit infrastructure, economic empowerment funds and consumer protection and financial literacy.
    Date: 2019–07–01
  10. By: Pelau, Corina; Ene, Irina
    Abstract: The presence of Artificial Intelligence in our everyday life has become one of the most debated topics nowadays. In opposition to the past, nowadays, in the age of broadband connectivity, it is difficult for individuals to imagine their everyday life, at work or in their spare time, without computers, internet, mobile applications or other devices. Most of these devices have had a contribution to the improvement of our everyday life by being more efficient and having a higher convenience. Few people are aware of the fact that, by continuously developing and improving these technologies, they might become more intelligent than we are and that they will have the potential to control us. In the attempt to make these devices friendlier to consumers, they have started to take human-like aspect and even having own identities. We have nowadays call center answering machines with names or robots with names and citizenship. The objective of this article is to determine the acceptance and preference of consumers for personalized or human-like robots or devices. For four different cases, the respondents had to choose between a classic device and a human-like robot. The results of the research show, with a high significance, that consumers still prefer the classic devices over anthropomorphic robots.
    Keywords: Artificial intelligence, robots, consumer, anthropomorphism, perception
    JEL: M0 M31
    Date: 2018
  11. By: Erika Arraño; Katherine Jara
    Abstract: The monitoring of the evolution of job ads posted by companies is used to estimate the economy’s labor demand, as it relates to economic activity and constitutes a matter of interest, for both business cycle evaluation and structural economic analysis. The digitalization of the economy, together with the massification of Internet use and access, has transformed the way potential employees are attracted. Companies have gone from posting job ads in the printed press, to doing so on specific web sites. Most recently, the state of the art in recruitment technology allows to collect, store and handle large volumes of unstructured information, by the use of specialized software. This document presents an Online Job Ad Index, based on public information posted on the main online recruitment web sites. This index seeks to complement the analysis of the labor market, as it is available before the results of employment surveys. The index is available at the Statistics Database of the Central Bank of Chile, at Employment chapter.
    Date: 2019–07
  12. By: Qinxin Guo (Graduate School of Economics, Kobe University, JAPAN); Junyi Shen (Research Institute for Economics and Business Administration, Kobe University, JAPAN)
    Abstract: As an alternative method to discrete choice experiments, best-worst scaling provides additional information about consumers, slightly lessens the burden of mental process, and shows better quality. However, its advantages were ambiguous in previous literature, since each case of the best-worst scaling contained distinct information, and results from comparisons with discrete choice experiment varied with different data. In this study, we applied a goodness of fit statistic named count R square in evaluating the best-worst scaling profile case, the discrete choice experiment, and the best-worst scaling multi-profile case by using data from a survey of preference for mobile payment. The results suggest that the best-worst multi-profile case surpasses other methods. We also compared the mixed logit model and the latent class model using three non-nested tests. The results indicate that the mixed logit model is superior to the latent class model in all three tests.
    Keywords: Discrete Choice Experiment; Best-worst Scaling; Goodness of Fit; Latent Class Model; Mixed Logit Model
    Date: 2019–07
  13. By: Mujahidin, Muhamad
    Abstract: This article describes how the opportunities and challenges of FinTech Sharia in the face of the industrial revolution 4.0. By using a negation approach, this study concludes that FinTech Sharia which is the development of technological innovations that are in accordance with sharia provisions and becomes a solution to avoid interest transactions. The synergy between the Islamic financial sector and information technology innovation should also be a challenge as well as an opportunity for all actors in the Islamic finance industry to catch up with the conventional financial industry.
    Keywords: Financial Technology, FinTech, FinTech Sharia, Islamic Finance
    JEL: A10 E40 G21 G23 G30 O14 O32
    Date: 2019–07–04
  14. By: Mayega, Jova; Ssuuna, Robert; Mubajje, Muhammad; I. Nalukwago, Milly; Muwonge, Lawrence
    Abstract: Revenue administrations collect large amounts of data on individuals and firms in the course of their work. Increasingly, this data is digitised. The use of digital technologies has the potential to greatly improve the efficiency and effectiveness of tax administration, by: Reducing the cost of routine operations for both taxpayer and tax collector; Reducing the need for face-to-face interactions between taxpayers and tax collectors,thereby shrinking opportunities and incentives for collusion and corruption; Making it possible to select taxpayers for audit easily and cheaply on the basis of riskanalysis; Opening up new opportunities to undertake statistical analysis to assess theeffectiveness of existing operational procedures, and to design improvements. The Uganda Revenue Authority (URA) uses automated digital processes to a higher degree than most tax administrations in Africa. These processes nevertheless suffer from a range of problems. We report here on an assessment that the URA undertook of one important aspect of its own data management practices: the management and accuracy of one of its most important data bases, the taxpayer register. We discovered considerable problems of inaccurate data and, primarily as a result of the activities of tax agents, a high level of duplication of the same telephone numbers and email addresses, and possession of multiple taxpayer identification numbers. These inaccuracies reflect a number of factors, including inadequate design of registration forms and procedures, and the low priority given to verification and the accuracy of the register.
    Keywords: Economic Development, Finance, Governance,
    Date: 2019
  15. By: Simplice A. Asongu (Yaoundé/Cameroon); Nicholas M. Odhiambo (Pretoria, South Africa)
    Abstract: This study investigates linkages between the mobile phone, information sharing offices (ISO) and financial sector development in 53 African countries for the period 2004-2011. ISO are private credit bureaus and public credit registries. The empirical evidence is based on contemporary and non-contemporary quantile regressions. Two main hypotheses are tested: mobile phones complement ISO to enhance the formal financial sector (Hypothesis 1) and mobile phones complement ISO to reduce the informal financial sector (Hypothesis 2). The hypotheses are largely confirmed. This research adds to the existing body of literature by engaging hitherto unexplored dimensions of financial sector development and investigating the role of mobile phones in information sharing for financial sector development.
    Keywords: Information sharing; Banking sector development; Africa
    JEL: G20 G29 L96 O40 O55
    Date: 2019–01
  16. By: Apatov, Eyal; Chappell, Nathan; Grimes, Arthur
    Abstract: data on internet access for New Zealand’s 46,637 meshblocks, we examine issues of path dependence and the digital divide. We test whether areas that had the best railway access in the 1880s also have best access to new fibre internet infrastructure. Results suggest strong path dependence with respect to topography: people in areas that lacked 19th century rail due to remoteness or terrain are much less likely to have prioritised fibre access and slightly less likely to have current or (planned) future fibre access. Next, we examine path dependence with respect to ethnicity, given that 19th century railways deliberately avoided predominantly Māori areas. The results suggest weak path dependence: countrywide, Māori are slightly less likely to get fibre access than other New Zealanders, though are slightly more likely to have access within urban areas. Finally, we examine whether the rollout of fibre is increasing or decreasing the digital divide in access between rich and poor. Results show that those in more deprived areas are the most likely to benefit from fibre access, because these areas also tend to be denser and density was a factor in determining the path of the fibre rollout.
    Keywords: Labor and Human Capital, Teaching/Communication/Extension/Profession
    Date: 2018–03
  17. By: Sandra Johnson; Peter Robinson; Kishore Atreya; Claudio Lisco
    Abstract: Supply chains lend themselves to blockchain technology, but certain challenges remain, especially around invoice financing. For example, the further a supplier is removed from the final consumer product, the more difficult it is to get their invoices financed. Moreover, for competitive reasons, retailers and manufacturers do not want to disclose their supply chains. However, upstream suppliers need to prove that they are part of a `stable' supply chain to get their invoices financed, which presents the upstream suppliers with huge, and often unsurmountable, obstacles to get the necessary finance to fulfil the next order, or to expand their business. Using a fictitious supply chain use case, which is based on a real world use case, we demonstrate how these challenges have the potential to be solved by combining more advanced and specialised blockchain technologies with other technologies such as Artificial Intelligence. We describe how atomic crosschain functionality can be utilised across private blockchains to retrieve the information required for an invoice financier to make informed decisions under uncertainty, and consider the effect this decision has on the overall stability of the supply chain.
    Date: 2019–05
  18. By: Wouter Zant (Vrije Universiteit Amsterdam)
    Abstract: We investigate to what extent the roll-out of the mobile phone network in Mozambique reduced transport costs and search costs, and thereby decreased spatial price dispersion and improved market efficiency. Estimations are based on data of transport costs of maize grain and maize market prices. The mobile phone rollout explains a 10%-13% reduction in maize price dispersion. Around half of this reduction is associated with search costs related to transport, the other half with other search costs, for example for the collection of maize in source markets. Search costs are substantial and also a substantial component of total transport costs. Benefits of increased market efficiency are biased towards consumer markets. Results are robust for non-random rollout of the mobile phone network and several other threats.
    Keywords: search costs, transport costs, mobile phones, agricultural markets, maize prices, Mozambique, sub-Sahara Africa
    JEL: Q13 O13 O33 Q11
    Date: 2019–07–13
  19. By: Simplice A. Asongu (Yaoundé/Cameroon); Nicholas M. Odhiambo (Pretoria, South Africa)
    Abstract: The research assesses how information and communication technology (ICT) modulates the effect of foreign direct investment (FDI) on economic growth dynamics in 25 countries in Sub-Saharan Africa for the period 1980-2014. The employed economic growth dynamics areGross Domestic Product (GDP) growth, real GDP and GDP per capita while ICT is measured by mobile phone penetration and internet penetration. The empirical evidence is based on the Generalised Method of Moments. The study finds that both internet penetration and mobile phone penetration overwhelmingly modulate FDI to induce overall positive net effects on all three economic growth dynamics. Moreover, the positive net effects are consistently more apparent in internet-centric regressions compared to “mobile phone”-oriented specifications. In the light of negative interactive effects, net effects are decomposed to provide thresholds at which ICT policy variables should be complemented with other policy initiatives in order to engender favorable outcomes on economic growth dynamics. Practical and theoretical implications are discussed.
    Keywords: Economic Output; Foreign Investment; Information Technology; Sub-Saharan Africa
    JEL: E23 F21 F30 L96 O55
    Date: 2019–01
  20. By: R. Maria del Rio-Chanona; Penny Mealy; Mariano Beguerisse-D\'iaz; Francois Lafond; J. Doyne Farmer
    Abstract: Many existing jobs are prone to automation, but since new technologies also create new jobs it is crucial to understand job transitions. Based on empirical data we construct an occupational mobility network where nodes are occupations and edges represent the likelihood of job transitions. To study the effects of automation we develop a labour market model. At the macro level our model reproduces the Beveridge curve. At the micro level we analyze occupation-specific unemployment in response to an automation-related reallocation of labour demand. The network structure plays an important role: workers in occupations with a similar automation level often face different outcomes, both in the short term and in the long term, due to the fact that some occupations offer little opportunity for transition. Our work underscores the importance of directing retraining schemes towards workers in occupations with limited transition possibilities.
    Date: 2019–06
  21. By: Achara Jantarasaengaram (Macroeconomic Policy and Financing for Development Division, ESCAP)
    Abstract: SDG Target 9.C calls for a significant increase in access to information and communications technology (ICT) and universal and affordable access to the Internet in least developed countries by 2020. Sustainable Development Goal (SDG) Indicator 9.c.1 sets the SDG progress evaluation benchmark as the “proportion of population covered by a mobile network, by technology”.
    Date: 2019–04
  22. By: OECD
    Abstract: This report provides a summary of the Inaugural Event of the OECD Global Forum on Digital Security for Prosperity (“Global Forum”) held on 13-14 December 2018 in Paris, France. The event gathered 240 experts and 50 speakers from governments, businesses, civil society, the technical community and academia of 40 countries. They examined the roles and responsibilities of actors for cybersecurity, with a focus on good practice for the governance of digital security risk in organisations, and how to improve digital security of technologies throughout their lifecycle. They discussed issues such as whether organisations can “hack back” in response to an attack, how to encourage “digital security by design” in products’ development, the role of certification, as well as how to foster the responsible disclosure of vulnerabilities by security researchers.
    Date: 2019–07–18
  23. By: Apopo, Natalay; Phiri, Andrew
    Abstract: The legitimacy of virtual currencies as an alternative form of monetary exchange has been the centre of an ongoing heated debated since the catastrophic global financial meltdown of 2007-2008. We contribute to the relative fresh body of empirical research on the informational market efficiency of cryptomarkets by investigating the weak-form efficiency of the top-five cryptocurrencies. In differing from previous studies, we implement random walk testing procedures which are robust to asymmetries and unobserved smooth structural breaks. Moreover, our study employs two frequencies of cryptocurrency returns, one corresponding to daily returns and the other to weekly returns. Our findings validate the random walk hypothesis for daily series hence validating the weak-form efficiency for daily returns. On the other hand, weekly returns are observed to be stationary processes which is evidence against weak-form efficiency for weekly returns. Overall, our study has important implications for market participants within cryptocurrency markets.
    Keywords: Efficient Market Hypothesis (EMH); Cryptocurrencies; Random Walk Model (RWM); Flexible Fourier Form (FFF) unit root tests; Smooth structural breaks.
    JEL: C22 C32 C51 E42 G14
    Date: 2019–06–26
  24. By: Rute Martins Caeiro
    Abstract: This paper analyzes the role of social networks in the diffusion of knowledge and adoption of cultivation techniques, from trainees to the wider community, in the context of an extension project in Guinea-Bissau. In order to test for social learning, we exploit a detailed census of households and social connections across different dimensions. More precisely, we make use of a village photo directory in order to obtain a comprehensive and fully mapped social network dataset. We find evidence that agricultural information spreads across networks from project participants to non-participants, with different networks having different importance. The most relevant connection is found to be between the network of people from which individuals would ‘borrow money’. We are also able to disentangle the relative importance of weak and strong ties: in our context, weak ties are as important in the diffusion of agricultural knowledge as strong ties. Despite positive diffusion effects in knowledge, we found limited evidence of network effects in adoption behavior. Finally, using longitudinal network data, we document improvements in the network position of treated farmers over time.
    JEL: O13 O31 O33 Q16
    Date: 2019–07
  25. By: Chemalle, Thierry Dayr Leandro
    Abstract: O presente trabalho visa analisar a possível influência de sentimentos, como medo e confiança, indentificados nas decisões dos agentes através de índices estatísticos, na composição do logartimo natural dos retornos observados do Bitcoin no período entre Outubro de 2010 e Fevereiro de 2019. Em outras palavras, procurou-se verificar se além da suposição factível de influência nas condutas dos agentes, os sentimentos podem ter correlação direta nos processos de valorização e desvalorização do Bitcoin na janela temporal contemplada.
    Date: 2019–07
  26. By: Catherine D'Hondt; Rudy De Winne; Eric Ghysels; Steve Raymond
    Abstract: Artificial intelligence, or AI, enhancements are increasingly shaping our daily lives. Financial decision-making is no exception to this. We introduce the notion of AI Alter Egos, which are shadow robo-investors, and use a unique data set covering brokerage accounts for a large cross-section of investors over a sample from January 2003 to March 2012, which includes the 2008 financial crisis, to assess the benefits of robo-investing. We have detailed investor characteristics and records of all trades. Our data set consists of investors typically targeted for robo-advising. We explore robo-investing strategies commonly used in the industry, including some involving advanced machine learning methods. The man versus machine comparison allows us to shed light on potential benefits the emerging robo-advising industry may provide to certain segments of the population, such as low income and/or high risk averse investors.
    Date: 2019–07
  27. By: Schwab, Jakob; Ohnesorge, Jan
    Abstract: Die Blockchain-Technologie (BT), die durch ihren Einsatz in digitalen Währungen bekannt wurde, bietet auch auf anderen Gebieten neue Möglichkeiten. Eines ist die Handelsintegration. Besonders Entwicklungsländer können von verstärkter Handelsintegration mit BT profitieren, da die Technologie z.B. Defizite in den Bereichen Zugang zum Finanzsystem, Schutz geistigen Eigentums und Steuerverwaltung mindern kann. BT ermöglicht es, Transaktionen und andere Daten auf dezentralen Computernetzwerken nahezu manipulationssicher zu speichern. Aber es können nicht nur Daten manipulations¬sicher gespeichert werden, sondern auch ganze Programme: sogenannte Smart Contracts ermöglichen die Automatisierung privater Trans¬aktionen und administrativer Prozesse. Dieser Artikel fasst den Stand der Forschung zur Anwendung der BT bei der Handelsintegration zusammen, indem er fünf zentrale, teils miteinander verbundene Anwendungsgebiete genauer beleuchtet. Dies ist erstens die Handelsfinanzierung, bei der BT kreditgewährende Intermediäre überflüssig machen kann, was direkte Kostensenkungen für Ex- und Importeure bedeutet. Zweitens kann die Dokumentation der Lieferkette durch die manipulationssichere Speicherung von Güterinformationen zu Herkunft und Zusammensetzung gestärkt werden. Auf dieser Basis kann die Einhaltung von Nachhaltigkeitsstandards auch bei global produzierten Gütern zuverlässiger nachgewiesen werden. Voraussetzung für eine wahrheitsgemäße Information in Blockchains ist allerdings deren korrekte Eintragung (die dann manipulationssicher ist), welche daher überwacht werden muss. Drittens kann BT im Bereich Handelserleichterung den Zugriff auf Güterinformationen durch die Grenzbehörden erleichtern und so den Berichtsaufwand für exportierende Unternehmen senken. Indem BT die Abhängigkeit von zentralen Datenbankbetreibern reduziert, kann sie bestehenden digitalen Technologien im Handelsbereich zum Durchbruch verhelfen. Viertens kann der leichtere Zugriff auf Güterinformationen auch die Prozesse im Bereich Zölle und Steuern vereinfachen und weniger anfällig für Korruption und Betrug machen. Dies geht sowohl mit Kostensenkungen für Exporteure als auch mit einer besseren Mobilisierung einheimischer Ressourcen für öffentliche Haushalte einher. BT ermöglicht fünftens im Bereich Digitaler Handel auch in Umfeldern, in denen geistiges Eigentum institutionell bedingt wenig geschützt ist, ein Rechtemanagement von digitalen Dateien. Dies kann zur Verbreitung von digitalen Industrien in Entwicklungsländern beitragen. Besonders für einen Einsatz in Grenz- und Zollsystemen ist aber eine frühe Einbindung der entsprechenden Behörden unabdingbar. Gleichzeitig sollten einheitliche technische Standards bei der Dokumentation von Lieferketten gefördert werden, sodass die Interoperabilität der verschiedenen Systeme über Akteure und Ländergrenzen hinweg gesichert werden kann, um die Kostenvorteile wirklich auszunutzen. Wenn diese Vorgaben eingehalten werden, kann BT nachhaltige Handelsintegration von und in Entwicklungsländern wirksam unterstützen.
    Keywords: Digitalisierung,Handel und Investitionen
    Date: 2019
  28. By: Simplice A. Asongu (Yaoundé/Cameroon); Stella-Maris I. Orim (Coventry University, UK); Rexon T. Nting (University of Wales, London, UK)
    Abstract: The study assesses the relationship between terrorism and social media from a cross section of 148 countries with data for the year 2012. The empirical evidence is based on Ordinary Least Squares, Negative Binomial and Quantile regressions. The main finding is that there is a positive relationship between social media in terms of Facebook penetration and terrorism. The positive relationship is driven by below-median quantiles of terrorism. In other words, countries in which existing levels of terrorism are low are more significantly associated with a positive Facebook-terrorism nexus. The established positive relationship is confirmed from other externalities of terrorism: terrorism fatalities, terrorism incidents, terrorism injuries and terrorism-related property damages. The terrorism externalities are constituents of the composite dependent variable.
    Keywords: Social Media; Terrorism
    JEL: D83 O30 D74
    Date: 2019–01
  29. By: Tidiane Kinda
    Abstract: The use of e-commerce around the world has accelerated in recent years, with Asia, led by China, spearheading the rise. Using cross-country enterprise survey data, this paper shows that firms engaged in e-commerce have higher productivity and generate a larger share of their revenues from exports than other firms. This is particularly true in Asia, where firms have 30 percent higher productivity and generate about 50 percent more of their revenues from exports. The results presented in this paper are robust to the use of instrumental variables, which highlight possible larger effects of e-commerce on Asian productivity and exports when essential elements are in place for its effective use, such as reliable electricity, telecommunication, and transport infrastructure. Despite the rapid growth of e-commerce in recent years, gaps persist in digital infrastructure and legislation, preventing many Asian countries from fully reaping the potential benefits of e-commerce.
    Date: 2019–07–01
  30. By: Chandan Kumar Jha (Madden School of Business, New York, USA); Oasis Kodila-Tedika (University of Kinshasa, The DRC)
    Abstract: This study explores the relationship between social media and democracy in a cross- section of over 125 countries around the world. We find the evidence of a strong, positive correlation between Facebook penetration (a proxy for social media) and democracy. We further show that the correlation between social media and democracy is stronger for low-income countries than high-income countries. Our lowest point estimates indicate that a one-standard deviation (about 18 percentage point) increase in Facebook penetration is associated with about 8-point (on a scale of 0–100) increase for the world sample and over 11 points improvement for low-income countries.
    Keywords: Democracy; Information; Facebook; Internet; Social Media
    JEL: D72 D83 O1
    Date: 2019–01
  31. By: Hung Ba
    Abstract: In this study, we employ Generative Adversarial Networks as an oversampling method to generate artificial data to assist with the classification of credit card fraudulent transactions. GANs is a generative model based on the idea of game theory, in which a generator G and a discriminator D are trying to outsmart each other. The objective of the generator is to confuse the discriminator. The objective of the discriminator is to distinguish the instances coming from the generator and the instances coming from the original dataset. By training GANs on a set of credit card fraudulent transactions, we are able to improve the discriminatory power of classifiers. The experiment results show that the Wasserstein-GAN is more stable in training and produce more realistic fraudulent transactions than the other GANs. On the other hand, the conditional version of GANs in which labels are set by k-means clustering does not necessarily improve the non-conditional versions of GANs.
    Date: 2019–07
  32. By: Bertin Martens (European Commission – JRC - IPTS); Songül Tolan (European Commission – JRC)
    Abstract: There is a long-standing economic research literature on the impact of technological innovation and automation in general on employment and economic growth. Traditional economic models trade off a negative displacement or substitution effect against a positive complementarity effect on employment. Economic history since the industrial revolution as strongly supports the view that the net effect on employment and incomes is positive though recent evidence points to a declining labour share in total income. There are concerns that with artificial intelligence (AI) "this time may be different". The state-of-the-art task-based model creates an environment where humans and machines compete for the completion of tasks. It emphasizes the labour substitution effects of automation. This has been tested on robots data, with mixed results. However, the economic characteristics of rival robots are not comparable with non-rival and scalable AI algorithms that may constitute a general purpose technology and may accelerate the pace of innovation in itself. These characteristics give a hint that this time might indeed be different. However, there is as yet very little empirical evidence that relates AI or Machine Learning (ML) to employment and incomes. General growth models can only present a wide range of highly diverging and hypothetical scenarios, from growth implosion to an optimistic future with growth acceleration. Even extreme scenarios of displacement of men by machines offer hope for an overall wealthier economic future. The literature is clearer on the negative implications that automation may have for income equality. Redistributive policies to counteract this trend will have to incorporate behavioural responses to such policies. We conclude that that there are some elements that suggest that the nature of AI/ML is different from previous technological change but there is no empirical evidence yet to underpin this view.
    Keywords: labour markets, employment, technological change, task-based model, artificial intelligence, income distribution,
    JEL: J62 O33
    Date: 2018–08
  33. By: Célya Gruson-Daniel (Centre Virchow-Villermé - UPD5 - Université Paris Descartes - Paris 5, LabCMO - Laboratoire de communication médiatisée par ordinateur - UQAM - Université du Québec à Montréal , COSTECH - Connaissance Organisation et Systèmes TECHniques - UTC - Université de Technologie de Compiègne); Olivier Aïm (GRIPIC - Groupe de recherches interdisciplinaires sur les processus d’information et de communication - UP4 - Université Paris-Sorbonne); Karl-William Sherlaw (Centre Virchow-Villermé - UPD5 - Université Paris Descartes - Paris 5); Anneliese Depoux (Centre Virchow-Villermé - UPD5 - Université Paris Descartes - Paris 5, GRIPIC - Groupe de recherches interdisciplinaires sur les processus d’information et de communication - SU - Sorbonne Université)
    Date: 2019–06–13
  34. By: International Monetary Fund
    Abstract: Financial Sector Assessment Program; Technical Note-Supervision and Oversight of Financial Market Infrastructures
    Date: 2019–06–26
  35. By: Jeremy D. Turiel; Tomaso Aste
    Abstract: Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection, while the second one predicts default risk for approved loans. Logistic Regression was found to be the best performer for the first phase, with test set recall macro score of $77.4 \%$. Deep Neural Networks were applied to the second phase only, were they achieved best performance, with validation set recall score of $72 \%$, for defaults. This shows that AI can improve current credit risk models reducing the default risk of issued loans by as much as $70 \%$. The models were also applied to loans taken for small businesses alone. The first phase of the model performs significantly better when trained on the whole dataset. Instead, the second phase performs significantly better when trained on the small business subset. This suggests a potential discrepancy between how these loans are screened and how they should be analysed in terms of default prediction.
    Date: 2019–07
  36. By: Emir Hrnjic; Nikodem Tomczak
    Abstract: Behavioral economics changed the way we think about market participants and revolutionized policy-making by introducing the concept of choice architecture. However, even though effective on the level of a population, interventions from behavioral economics, nudges, are often characterized by weak generalisation as they struggle on the level of individuals. Recent developments in data science, artificial intelligence (AI) and machine learning (ML) have shown ability to alleviate some of the problems of weak generalisation by providing tools and methods that result in models with stronger predictive power. This paper aims to describe how ML and AI can work with behavioral economics to support and augment decision-making and inform policy decisions by designing personalized interventions, assuming that enough personalized traits and psychological variables can be sampled.
    Date: 2019–07
  37. By: A Itkin
    Abstract: Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we consider a classical problem of mathematical finance - calibration of option pricing models to market data, as it was recently drawn some attention of the financial society in the context of deep learning and artificial neural networks. We highlight some pitfalls in the existing approaches and propose resolutions that improve both performance and accuracy of calibration. We also address a problem of no-arbitrage pricing when using a trained neural net, that is currently ignored in the literature.
    Date: 2019–06
  38. By: Bertin Martens (European Commission – JRC - IPTS)
    Abstract: Digitization triggered a steep drop in the cost of information. The resulting data glut created a bottleneck because human cognitive capacity is unable to cope with large amounts of information. Artificial intelligence and machine learning (AI/ML) triggered a similar drop in the cost of machine-based decision-making and helps in overcoming this bottleneck. Substantial change in the relative price of resources puts pressure on ownership and access rights to these resources. This explains pressure on access rights to data. ML thrives on access to big and varied datasets. We discuss the implications of access regimes for the development of AI in its current form of ML. The economic characteristics of data (non-rivalry, economies of scale and scope) favour data aggregation in big datasets. Non-rivalry implies the need for exclusive rights in order to incentivise data production when it is costly. The balance between access and exclusion is at the centre of the debate on data regimes. We explore the economic implications of several modalities for access to data, ranging from exclusive monopolistic control to monopolistic competition and free access. Regulatory intervention may push the market beyond voluntary exchanges, either towards more openness or reduced access. This may generate private costs for firms and individuals. Society can choose to do so if the social benefits of this intervention outweigh the private costs. We briefly discuss the main EU legal instruments that are relevant for data access and ownership, including the General Data Protection Regulation (GDPR) that defines the rights of data subjects with respect to their personal data and the Database Directive (DBD) that grants ownership rights to database producers. These two instruments leave a wide legal no-man's land where data access is ruled by bilateral contracts and Technical Protection Measures that give exclusive control to de facto data holders, and by market forces that drive access, trade and pricing of data. The absence of exclusive rights might facilitate data sharing and access or it may result in a segmented data landscape where data aggregation for ML purposes is hard to achieve. It is unclear if incompletely specified ownership and access rights maximize the welfare of society and facilitate the development of AI/ML.
    Keywords: digital data, ownership and access rights, trade in data, machine learning, artificial intelligence
    JEL: L00
    Date: 2018–09
  39. By: Fabrice Daniel
    Abstract: This article studies the financial time series data processing for machine learning. It introduces the most frequent scaling methods, then compares the resulting stationarity and preservation of useful information for trend forecasting. It proposes an empirical test based on the capability to learn simple data relationship with simple models. It also speaks about the data split method specific to time series, avoiding unwanted overfitting and proposes various labelling for classification and regression.
    Date: 2019–07
  40. By: Sylvain Dejean (CE.RE.GE - CEntre de REcherche en GEstion - ULR - Université de La Rochelle - IAE Poitiers - Institut d'Administration des Entreprises (IAE) - Poitiers - Université de Poitiers - Université de Poitiers)
    Abstract: This article aims to estimate the cost of distance in the geographical flow of crowdfunding, and to show how social ties between the 94 French metropolitan regions shape the geography of funding. Our analysis draws upon a unique database provided by the French leader in rewards-based crowdfunding. The main result is that the elasticity of distance remains important (around 0.5), and that social ties between regions determine the flow of funding. Doubling the number of immigrants in a region increases the number of investments by 24% and reduces the impact of distance.
    Keywords: Crowdfunding,economic geography,social networks,gravity
    Date: 2019–06–19
  41. By: Lotfi Boudabsa; Damir Filipovic
    Abstract: We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is given in closed form thanks to a suitable choice of the kernel. We develop an asymptotic theory and prove convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small training sample size.
    Date: 2019–06
  42. By: Michael Lechner; Gabriel Okasa
    Abstract: In econometrics so-called ordered choice models are popular when interest is in the estimation of the probabilities of particular values of categorical outcome variables with an inherent ordering, conditional on covariates. In this paper we develop a new machine learning estimator based on the random forest algorithm for such models without imposing any distributional assumptions. The proposed Ordered Forest estimator provides a flexible estimation method of the conditional choice probabilities that can naturally deal with nonlinearities in the data, while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference thereof and thus providing the same output as classical econometric estimators based on ordered logit or probit models. An extensive simulation study examines the finite sample properties of the Ordered Forest and reveals its good predictive performance, particularly in settings with multicollinearity among the predictors and nonlinear functional forms. An empirical application further illustrates the estimation of the marginal effects and their standard errors and demonstrates the advantages of the flexible estimation compared to a parametric benchmark model.
    Date: 2019–07
  43. By: Michael Allan Ribers; Hannes Ullrich
    Abstract: Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and microbiological laboratory data from Denmark to predict diagnostic test outcomes for urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and policy implementation when patient distributions vary over time. The proposed policies delay antibiotic prescriptions for some patients until test results are known and give them instantly to others. We find that machine learning can reduce antibiotic use by 7.42 percent without reducing the number of treated bacterial infections. As Denmark is one of the most conservative countries in terms of antibiotic use, this result is likely to be a lower bound of what can be achieved elsewhere.
    Date: 2019–06

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.