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on Payment Systems and Financial Technology |
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Issue of 2019‒09‒02
forty-six papers chosen by |
| By: | Said, Ahmed |
| Abstract: | The huge rapid growth of using internet and technology has been affecting all economies whether emerging or developed across the world. The financial sector is one of the sectors that has been directly influenced by technology due to the growth of electronic commerce and electronic payments. The emergence of digital currencies such as Bitcoin and the underlying blockchain as well as the distribution ledger technology have attracted significant interest. These developments have raised the possibility of considerable impacts on the financial system and perhaps the wider economy. The huge price leaps that happened to Bitcoin towards the end of 2017 until it reached its highest ever price, (19000 USD) since the beginning of its trading, followed by the significant fall that took place afterwards till it fell under the level of 4000 USD in 2018, made the Central banks more worried about the future of this market. In addition to that, the increase of developing new cryptocurrencies as well as the lack of control over it, made the central banks very alert to the futuristic view of this sector keeping their eyes wide open to this rapid growth. As a result, over the past few years, public authorities and central banks around the world have been monitoring developments of digital currencies and studying their implications. A question that has been raised frequently is whether central banks themselves should issue digital currency that could be used by the general public or not. The legal status of cryptocurrencies was always in question. Some administrations have banned them and other had implicit bans. In many other countries they are still under study and only official warnings from using and investing in cryptocurrencies were announced. The idea of issuing the central bank cryptocurrencies or Digital Fiat currencies has been studied by central banks in order to offer a formal/legal substitute for the consumer that is trusted and protected by central banks. Transitioning from private Cryptocurrencies to a legally issued digital currency will enhance the suite of financial inclusion tools that are already in place, offer "cash"-only households a leap into digital transactions, and increase the consumer choices of how to manage their household income and expenditures. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201744&r=all |
| By: | Hasbi, Maude; Dubus, Antoine |
| Abstract: | Using micro-level data coming from household surveys over 5 years, from 2013 to 2017, we analyse what are the determinants of mobile broadband adoption in developing economies. We provide empirical evidence on the presence of a learning effect stemming from mobile money use, which by providing a higher experience in using mobile phone increases mobile broadband use. The ownership of a mobile phone is also positively correlated with mobile broadband use. However, for those not owning a mobile phone the ownership of an active SIM card is a prerequisite for using mobile broadband. We highlight that the population left behind is mainly composed of poor households living in rural areas. |
| Keywords: | Mobile Broadband Use,Developing Economy,Inequality,Economic Growth |
| JEL: | I30 O12 L50 L96 O55 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201730&r=all |
| By: | Mahmoud, Zeinab |
| Abstract: | Mobile Money (MM) services are growing very fast in the developing countries as an important tool for the financial inclusion. Many critical challenges surrounds mobile money services adoption with the most important challenges to improve the service quality, attract and retain more customers, and reduce dealing with cash. Overcoming all these challenges would allow all citizens to have full access to financial services or in other words being financially included. Recently, Financial Inclusion has received higher priority to improve financial existing and reduce poverty on large scale. It is clear that due to the large scale of mobile phone access and the existing network, mobile money on of the most key enabler to financial inclusion. However, the differences of mobile money countries, characteristics, economy, adoption variables. This Paper analyzes the mobile money success factors from seven developing countries (Egypt, Kenya, Ghana, Tanzania, Uganda, Zimbabwe, and Rwanda) where there has been successful penetration of mobile money services in order to extract the determinants of mobile money services adoption. Mobile money adoption is affected by several factors that includes country specific characteristics, regulatory considerations, and service provision characteristics as a result nine independent variables were selected to be included in this research. Two dependent variables are chosen to present the mobile money adoption, these are registered subscribers ratio and active subscribers ratio. The analysis is based on the data collected from the central banks published statistics in each country of the above-mentioned seven countries. The analysis is achieved using panel data analysis for a sample of seven African countries for the period from 2013 to 2017. Data is analyzed using the linear regression model for each dependent variable of the mobile money adoption using nine explanatory variables. The statistical analysis is done using Eviews and least square (LS) estimation techniques are used to provide further strength for the results. The paper aims to define a model for measuring mobile money adoption and defining the impact of each of the mobile money adoption determinants on the adoption level. This could be used to define recommendations or strategic decisions for policy makers or mobile money service providers in Egypt to improve mobile money adoption. |
| Keywords: | Financial Inclusion,Mobile Money (MM),Mobile Money Services,Mobile Money Determinants,Registered subscribers ratio,Active subscribers ratio |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201742&r=all |
| By: | Luis Aguiar Wicht (European Commission – JRC) |
| Abstract: | With relatively small screens and limited display, smartphones significantly affect users' online browsing experience relative to fixed devices like the desktop. As consumers increasingly access the Internet through mobile devices, this paper explores the effects of a shift towards smartphone Internet access on the consumption of online content. Using data on the clickstream activity of over 2,900 individuals on both their smartphone and desktop, I estimate the effect of smartphone usage on users' allocation of time across various categories of websites, as well as their diversity and depth of online content consumption. Employing an instrumental variables approach based on updates of the smartphone operating system, the results show an increase in the usage of game and social networking domains at the expense of news and shopping domains - among others - as mobile usage increases relative to desktop. I also find that the diversity of consumption decreases within several categories, whereas consumption depth increases for games and social networking categories and decreases for search and news domains. Results show limited differences across consumer demographics. These results have important implications for website publishers, advertisers, and online competition. |
| Keywords: | Internet consumption, smartphones |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:decwpa:201907&r=all |
| By: | Jain, Rekha |
| Abstract: | Mobile phones have had one of the fastest adoption rates for any technology globally. Mobile services and mobile broadband have contributed to the economic growth and are increasingly seen as vehicles for development, especially in developing countries. As is the trend globally, spectrum has become a critical resource for further growth in the sector, especially with greater demand for data. The environment and hence the context of spectrum management varies significantly across developed and developing countries. Spectrum management in most developed countries is driven by the need to make the telecom sectors competitive and exploit technological advances for innovative cutting-edge services. The citizens and enterprises have a high propensity to pay. This is in an environment where there is near universal coverage of high-end services, both on wired and wireless infrastructure. On the other hand, in developing countries, wired infrastructure for broadband and backhaul services is very limited. In the wireless domain, supply side constraint of low spectrum availability prevails, as often institutional mechanisms for refarming, trading and sharing are inadequate. On the demand side, operators are obliged to serve large populations who are unable to migrate to newer technologies due to high cost of devices and services and lack of digital literacy in the population. The customers also have a lower propensity to pay, thus making it commercially demanding for operators to introduce new technologies. Most developing country leaderships also recognize that growth in broadband and economy is a twosided relationship. (...) |
| Keywords: | Spectrum management,market orientation,command and control,transition,spectrum trading,competition agencies |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201758&r=all |
| By: | Oliver, Miquel; Majumder, Sudip |
| Abstract: | Emergence of digital broadcasting is the key index of the new horizons of communication and media environment. In this paper we will discuss how TVWS, the resultant of digital switchover is approaching for becoming a financially rewarding solution in terms of rural areas than the other cellular technologies i.e 3G and 4G or even for upcoming 5G. We will analyze the standpoint of digital switchover around the globe and will try to the look at the white space capacity. There has been several TVWS pilot testing all over the world, specially in Africa region and we will try to evaluate their inclusive performance as they match the needs to deploy mobile broadband in rural and low-density areas. After shortly presenting the case, we will try to measure the potential of TVWS technology in terms of the other regions around the world which share the same teletraffic profile and socio-economic condition. Also we will try to present the importance of regulatory issues with ICT strategies and market development with propos |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201733&r=all |
| By: | Hartmann, Monika; Gijsel, Lola Hernandez-van; Plooij, Mirjam; Vandeweyer, Quentin |
| Abstract: | As a result of technological advancements, instant delivery of digital services has become the norm in today’s society. Yet, until recently, this trend did not extend to retail payment services, which normally took one or up to a few working days from the end user's perspective. Following Europe’s recent launch of its own SEPA-wide instant payment platform, now is the time to ask the question: will instant payment services become “the new normal” and what would this new normal look like? This paper assesses the overall prospects of instant payments in the euro area. It identifies structural drivers and blockers to the adoption of instant payments based on the analysis of country cases where instant payments became operational in the last few years. JEL Classification: E41, E42, E58 |
| Keywords: | instant payments, money demand, payment system |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbops:2019229&r=all |
| By: | Mark Weber; Giacomo Domeniconi; Jie Chen; Daniel Karl I. Weidele; Claudio Bellei; Tom Robinson; Charles E. Leiserson |
| Abstract: | Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency has introduced an intriguing paradox: pseudonymity allows criminals to hide in plain sight, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. Meanwhile advances in learning algorithms show great promise for the AML toolkit. In this workshop tutorial, we motivate the opportunity to reconcile the cause of safety with that of financial inclusion. We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency. We share results from a binary classification task predicting illicit transactions using variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of special interest as an emergent new method for capturing relational information. The results show the superiority of Random Forest (RF), but also invite algorithmic work to combine the respective powers of RF and graph methods. Lastly, we consider visualization for analysis and explainability, which is difficult given the size and dynamism of real-world transaction graphs, and we offer a simple prototype capable of navigating the graph and observing model performance on illicit activity over time. With this tutorial and data set, we hope to a) invite feedback in support of our ongoing inquiry, and b) inspire others to work on this societally important challenge. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.02591&r=all |
| By: | Stephany, Fabian; Lorenz, Hanno |
| Abstract: | The uniqueness of human labour is at question in times of smart technologies. The 250 years-old discussion on technological unemployment reawakens. Frey and Osborne (2013) estimate that half of US employment will be automated by algorithms within the next 20 years. Other follow-up studies conclude that only a small fraction of workers will be replaced by digital technologies. The main contribution of our work is to show that the diversity of previous findings regarding the degree of job automation is, to a large extent, driven by model selection and not by controlling for personal characteristics or tasks. For our case study, we consult Austrian experts in machine learning and industry professionals on the susceptibility to digital technologies in the Austrian labour market. Our results indicate that, while clerical computer-based routine jobs are likely to change in the next decade, professional activities, such as the processing of complex information, are less prone to digital change. |
| Keywords: | Classification,Employment,GLM,Technological Change |
| JEL: | E24 J24 J31 J62 O33 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:202035&r=all |
| By: | Rüdiger Fahlenbrach (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Marc Frattaroli (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute) |
| Abstract: | We conduct a detailed analysis of investors in successful initial coin offerings (ICOs). The average ICO has 4,700 contributors. The median participant contributes small amounts and many investors sell their tokens before the underlying product is developed. Large presale investors obtain tokens at a discount and flip part of their allocation shortly after the ICO. ICO contributors lack the protections traditionally afforded to investors in early stage financing. Nevertheless, returns nine months after the ICO are positive on average, driven mostly by an increase in the value of the Ethereum cryptocurrency. |
| Keywords: | Initial Coin Offering, FinTech, Individual Investors |
| JEL: | G11 G14 G24 H31 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1937&r=all |
| By: | Galenianos, Manolis; Gavazza, Alessandro |
| Abstract: | We build a framework to understand the effects of regulatory interventions in credit markets, such as caps on interest rates and higher compliance costs for lenders. We focus on the credit card market, in which we observe U.S. consumers borrowing at high and very dispersed interest rates, despite receiving many credit card offers. Our framework includes two main features that may explain these patterns: endogenous effort of examining offers and product differentiation. Our calibration suggests that these patterns occur because borrowers do not examine most of the offers that they receive. The calibrated model implies that interest rate caps reduce credit supply modestly and curb lenders' market power significantly, leading to large gains in consumer surplus, whereas higher compliance costs unambiguously decrease consumer surplus. |
| JEL: | D14 D83 G28 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13807&r=all |
| By: | Abdel Ghafar, Ahmed Ismail; Vazquez Castro, Ágeles; Essam Khedr, Mohamed |
| Abstract: | IoT is a coin term recently used in ICT research and industrial community to express the involvement of devices of different capabilities and functionalities in the daily activities of people and organizations. With the enormous amount of data generated by highly dynamic users, the problem of storing, looking up, validating and manipulating data becomes crucial for the success of future networks. A multidimensional chord peer-peer network as extension to the successful chord technology is proposed to cope with the dynamism of IoT networks. Novel approaches have been developed to tackle the high frequency of nodes joining and leaving/failure the network and to deal with Big data, similarity of data, filtering and Geo data. |
| Keywords: | Internet of Things,peer to peer networks,multidimensional chord networks,distributed resource sharing |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201736&r=all |
| By: | Christoph Basten (University of Zurich); Steven Ongena (University of Zurich - Department of Banking and Finance; Swiss Finance Institute; KU Leuven; Centre for Economic Policy Research (CEPR)) |
| Abstract: | We analyze how banks’ allocations of mortgage credit across regions change when an online platform enables them to offer to regions where they have no branches, staff or legacy. Unique data from an online platform with offers from different banks to each mortgage application yield three novel findings. First, banks offer more and cheaper credit to borrowers in less competitive offline markets. Second, banks offer more credit to more distant locations, where house prices appear less over-heated, and past price growth is less correlated with that in their existing portfolio. Third, over time offers become more automated, lowering operational costs. |
| Keywords: | Mortgage Lending, Spatial Competition, Credit Risk, Diversification, Automation of Banking, FinTech, Online Pricing |
| JEL: | G2 L1 R2 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1939&r=all |
| By: | Howell, Bronwyn E.; Potgieter, Petrus H.; Sadowski, Bert M. |
| Abstract: | Blockchains are the most well-known example of a distributed ledger technology (DLT). Unlike classic databases, the ledger is not maintained by any central authority. The integrity of the ledger is maintained automatically by an algorithmic consensus process whereby nodes vote and agree upon the authoritative version. In effect, the consensus algorithm operates in the manner of a decision-making process within a governance system. The technological characteristics of blockchain systems are well documented (Narayanan, Bonneau, Felton and Miller, 2016). We propose that one of the reasons why it has so far proved very difficult to seed large-scale commercial DLT (blockchain) projects lies in the arena of project ownership and governance. Unlike classic centralised database systems, DLTs have no one central point of "ownership" of any of the system's infrastructure or data. In this piece of exploratory research, we propose applying theories of club governance to both the technical design and operational development of a range of DLT (blockchain) systems, including (but not necessarily limited to) cryptocurrencies and enterprise applications to explore how they can explain the development of (or lack of development of) sustainable solutions to real business problems. There are many parallels to the governance arrangements observed historically in the origins of complex distributed telecommunications networks. |
| Keywords: | blockchain,distributed ledger,governance,club governance,distributed consensus |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201737&r=all |
| By: | Damian Zięba (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | Lévy processes are very often used in financial modelling since they address various characteristics of financial data. One of those characteristics is the heavy-tailedness of probability density functions - a very common empirical stylized fact on the cryptocurrency market. The aim of this study was to determine which type of Lévy motion fits the data of cryptocurrencies better, namely Alpha-Stable distribution or one of distributions from the family of generalized hyperbolic motions. The log-returns of 227 cryptocurrencies, standardized by the realized volatility estimated with the GARCH (1,1), were fitted to 11 types of distributions. The results show that the generalized hyperbolic motions fit the cryptocurrency data much more accurately than the Alpha-Stable distribution, similarly as in the case of TOP100 NASDAQ stocks. In the further stage of the analysis, it is shown how the distribution of cryptocurrency data varies over time, i.e. before, during, and after the ‘boom-period’ of 2017/2018. |
| Keywords: | cryptocurrency market, distribution fitting, Generalized Hyperbolic distribution, Alpha-Stable distribution, Lévy process |
| JEL: | C10 C30 G15 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2019-15&r=all |
| By: | Tatsuo Tanaka (Faculty of Economics, Keio University) |
| Abstract: | In this study, the effects of internet book piracy in the case of the Japanese comic book market were examined using direct measurement of product level piracy ratio and a massive deletion project as a natural experiment. Total effect of the piracy is negative to the legitimate sales, but panel regression and difference-in-difference analysis consistently indicated that the effect of piracy is heterogeneous: piracy decreased the legitimate sales of ongoing comics, whereas increased the legitimate sales of completed comics. The latter result is interpreted as follows: piracy reminds consumers of past comics and stimulates sales in that market. |
| Keywords: | copyright, comic, piracy, Internet, DID |
| JEL: | D12 L82 M3 O34 |
| Date: | 2019–08–08 |
| URL: | http://d.repec.org/n?u=RePEc:keo:dpaper:2019-016&r=all |
| By: | Cong Peng |
| Abstract: | Traditional retail involves traffic both from warehouses to stores and from consumers to stores. E-commerce cuts intermediate traffic by delivering goods directly from the warehouses to the consumers. Although plenty of evidence has shown that vans that are servicing e-commerce are a growing contributor to traffic and congestion, consumers are also making less shopping trips using vehicles. This poses the question of whether e-commerce reduces traffic congestion. The paper exploits the exogenous shock of an influential online shopping retail discount event in China (similar to Cyber Monday), to investigate how the rapid growth of e-commerce affects urban traffic congestion. Portraying e-commerce as trade across cities, I specified a CES demand system with heterogeneous consumers to model consumption, vehicle demand and traffic congestion. I tracked hourly traffic congestion data in 94 Chinese cities in one week before and two weeks after the event. In the week after the event, intra-city traffic congestion dropped by 1.7% during peaks and 1% during non-peak hours. Using Baidu Index (similar to Google Trends) as a proxy for online shopping, I found online shopping increasing by about 1.6 times during the event. Based on the model, I find evidence for a 10% increase in online shopping causing a 1.4% reduction in traffic congestion, with the effect most salient from 9am to 11am and from 7pm to midnight. A welfare analysis conducted for Beijing suggests that the congestion relief effect has a monetary value of around 239 million dollars a year. The finding suggests that online shopping is more traffic-efficient than offline shopping, along with sizable knock-on welfare gains. |
| Keywords: | e-commerce, traffic congestion, heterogeneous consumers, shopping vehicle demand, air pollution |
| JEL: | R4 O3 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1646&r=all |
| By: | Proeger, Till; Thonipara, Anita; Bizer, Kilian |
| Abstract: | Um den Digitalisierungsgrad im Bereich der Kundenwerbung und -bindung im Handwerk zu analysieren, wurde eine Webscraping-Analyse durchgeführt. Hierbei wurden Daten der Gelben Seiten sowie Handwerker-Homepages abgerufen und analysiert, wobei Informationen zur Branche, zur regionalen Verortung, zur Aktualität und zur Social-Media-Einbindung von Betrieben abgerufen und mit regionalökonomischen und soziodemografischen Daten verknüpft wurden. Insgesamt können auf Basis von rund 345.000 Betriebseinträgen und 105.000 damit verknüpften Homepages die grundlegenden Strukturen der Digitalisierung des Online-Marketings im Handwerk präsentiert werden. Es zeigen sich starke branchenspezifische Unterschiede bei der Verfügbarkeit von Homepages: Das Gesundheitsgewerbe weist mit 44%den höchsten Anteil an Betrieben mit einer Homepage auf, das Lebensmittelgewerbe und die Handwerke für den privaten Bedarf mit rund 20% den niedrigsten Anteil. Die höchste Aktualität ihrer Homepages zeigen Betriebe aus dem Gesundheits-, Lebensmittel-und Kraftfahrzeuggewerbe. Social-Media-Einbindungen auf den Seiten sind verbreitet, wobei Facebook relativ häufig und in vielen Branchen genutzt wird, Twitter und Instagram nur in einzelnen Branchen. Die aus dieser Analyse resultierenden Durchschnittszahlen von rund 30%Homepage-Nutzung und ca. 10% Social-Media-Nutzung fügen sich inhaltlich sinnvoll in die bisherigen Umfrageergebnisse zur Digitalisierung im Handwerk ein. Auf regionaler Ebene zeigt sich, dass die Homepage-Häufigkeit in Städten bis zu doppelt so hoch ist wie in ländlichen Räumen. Es kann gezeigt werden, dass die Bevölkerungsdichte eine zentrale Erklärung für den Digitalisierungsgrad dieser Form des Online-Marketings darstellt. Unter Berücksichtigung weiterer soziodemografischer Variablen zeigt sich: Die höchste Wahrscheinlichkeit, Homepages zu haben, weisen Kreise mit hoher Bevölkerungsdichte, relativ junger Bevölkerung, hohen Zuzugsraten, höherem durchschnittlichen Bildungsniveau bei den Beschäftigten und hohem Handwerksumsatz auf. Die Verfügbarkeit von Breitbandinternet hat in ländlich geprägten Kreisen einen positiven Zusammenhang mit der Homepage-Wahrscheinlichkeit, während sich in Kreisen mit Verstädterungsansätzen ein negativer Zusammenhangzeigt. Es gibt folglich viele ländliche Kreise mit schnellem Internet und stärker ausgeprägtem Digitalmarketing; kausale Zusammenhänge können jedoch aus den Daten nicht abgeleitet werden. Dieses nach Branchen und Regionstypen differenzierte Bild des Online-Marketings im Handwerk kann als betriebswirtschaftlich sinnvolle Reaktion auf Markterfordernisse, aber auch als Aufholbedarf im Wettbewerb um Kunden und Fachkräfte interpretiert werden. Eine Verstärkung der Bemühungen zur Ausweitung der digitalen Präsenz des Handwerks kann einen zweckmäßigen Einstieg in weitere digitale Transformationsprozesse darstellen. |
| Keywords: | Digitalisierung,Handwerk,Homepages,Regionalität,Social Media,digitization,German craft sector,regionality,social media webpages |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifhgbh:27&r=all |
| By: | Yao, Becatien H.; Shanoyan, Aleksan |
| Keywords: | Agribusiness |
| Date: | 2019–06–25 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea19:290715&r=all |
| By: | Andrea Potgieter (University of Johannesburg); Chris Rensleigh (University of Johannesburg) |
| Abstract: | Mobile application (app) usage has become a universal trend. Paramount in most app designers' focus, is what mobile app users want in terms of features offered by an app. Forming part of a larger study which aims to determine the most desirable app features for a mobile blood donation app, this paper reports on one section of that study's exploratory sequential mixed method research strategy.The paper illustrates how the use of Google Scholar alerts, over a period of six months, was systematically employed to inform the researchers of the most current research on app features. Abstracts and keywords from 47 academic articles which were included in the alert emails, were analysed through the natural language analysis software Leximancer. The findings aimed at highlighting prominent concepts and themes related to the development, selection, and application of mobile app features. Findings showed a prevalence of research articles focused on mobile health apps, specifically apps that support self-management of various illnesses. |
| Keywords: | Google Scholar; Leximancer; mobile app features; research trends |
| JEL: | L31 L86 O32 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:sek:ibmpro:8511148&r=all |
| By: | Gotsch, Matthias; Kelnhofer, Anton; Jäger, Angela |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fisisi:s072019&r=all |
| By: | G. Gulsun Akin (Department of Economics, Bogazici University); Ahmet Faruk Aysan (Department of Economics, Istanbul Sehir University); Sezgim Dasdogen (Department of Economics, Istanbul Sehir University); Levent Yildiran (4 Department of Economics, Bogazici University) |
| Abstract: | This paper attempts to assess whether the driving factor behind the rising credit card indebtedness of consumers in Turkey is financial illiteracy. Using the results of a nationwide survey, the authors conclude that even though credit card borrowing frequency and debt amount are affected by components of financial literacy, being credit-constrained has a very pronounced impact. An exploratory analysis finds that the probability of irrational credit card borrowing is increased by being credit-constrained but not affected by financial literacy. These findings suggest that credit card debt is at least as much a result of necessity as nescience. |
| Date: | 2019–08–21 |
| URL: | http://d.repec.org/n?u=RePEc:erg:wpaper:1315&r=all |
| By: | Simplice A. Asongu (Yaoundé/Cameroon); Joseph I. Uduji (University of Nigeria, Nsukka, Nigeria); Elda N. Okolo-Obasi (University of Nigeria, Nsukka, Nigeria) |
| Abstract: | This study investigates the relationship between social media and homicide in a cross section of 148 countries for the year 2012. The empirical evidence is based on Ordinary Least Squares, Tobit and Quantile regressions. The findings from Ordinary Least Squares and Tobit regressions show a negative relationship between Facebook penetration and the homicide rate. The negative relationship is driven by the 75th quantile of the conditional distribution of the homicide rate. The negative nexus is also driven by upper middle income countries and “Europe and Central Asia”. Three main implications are apparent when the findings are compared and contrasted. First, established findings from OLS and Tobit regressions are driven by countries with above-median levels of homicide. Second, such above-median countries are largely associated with upper middle income countries and nations in “Europe and Central Asia”. Third, modelling the relationship between Facebook penetration and homicide at the conditional mean of homicide may be misleading unless it is contingent on initial levels of homicide and tailored differently across income levels and regions of the world. |
| Keywords: | Homicide; Social media |
| JEL: | K42 D83 O30 D74 D83 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:agd:wpaper:19/049&r=all |
| By: | Jakub Cerveny (Medical University Vienna); Jan van Ours (Erasmus University Rotterdam) |
| Abstract: | This paper examines prices of cannabis sold over the anonymous internet marketplace AlphaBay. We analyze cannabis prices of 500 listings from about 140 sellers, originating from 18 countries. We find that both listing characteristics and country characteristics matter. Cannabis prices are lower if sold in larger quantities, so there is a clear quantity discount. Cannabis prices increase with perceived quality. Cannabis prices are also higher when the seller is from a country with a higher GDP per capita or higher electricity prices. The internet based cannabis market seems to be characterized by monopolistic competition where many sellers offer differentiated products with quality variation causing a dispersion of cannabis prices and sellers have some control over the cannabis prices. |
| Keywords: | Cannabis prices, Dark Web |
| JEL: | K42 D43 |
| Date: | 2019–08–19 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20190059&r=all |
| By: | Cerveny, Jakub; van Ours, Jan C. |
| Abstract: | This paper examines prices of cannabis sold over the anonymous internet marketplace AlphaBay. We analyze cannabis prices of 500 listings from about 140 sellers, originating from 18 countries. We find that both listing characteristics and country characteristics matter. Cannabis prices are lower if sold in larger quantities, so there is a clear quantity discount. Cannabis prices increase with perceived quality. Cannabis prices are also higher when the seller is from a country with a higher GDP per capita or higher electricity prices. The internet based cannabis market seems to be characterized by monopolistic competition where many sellers offer differentiated products with quality variation causing a dispersion of cannabis prices and sellers have some control over the cannabis prices. |
| Keywords: | Cannabis prices; Dark web |
| JEL: | D43 K42 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13933&r=all |
| By: | Calò, Silvia (Central Bank of Ireland); Herzberg, Valerie (Central Bank of Ireland) |
| Abstract: | This note presents a set of possible directions for the future of London and other financial centres in Europe after Brexit. It does so by building scenarios framed by the current landscape of financial services in the EU. We find that given the sizeable gap between London and other financial centres in Europe and London’s international orientation London is likely to remain a very large global financial centre even in more adverse scenarios. According to our analysis, the impact of fundamental factors on London could be very small due to the 'premium' it enjoys, not captured by size or productivity. Yet, the premium could be eroded and it may be sensitive to 1) a possible realignment of perceptions, and 2) abrupt changes in regulation and centrality due to new trading arrangements, disruption in global value chains, and institutional reshaping and associated uncertainty. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:cbi:fsnote:9/fs/19&r=all |
| By: | Anoek Castelein (Erasmus University Rotterdam); Dennis Fok (Erasmus University Rotterdam); Richard Paap (Erasmus University Rotterdam) |
| Abstract: | We develop a dynamic Bayesian model for clickthrough and conversion probabilities of paid search advertisements. These probabilities are subject to changes over time, due to e.g. changing consumer tastes or new product launches. Yet, there is little empirical research on these dynamics. Gaining insight into the dynamics is crucial for advertisers to develop effective search engine advertising (SEA) strategies. Our model deals with dynamic SEA environments for a large number of keywords: it allows for time-varying parameters, seasonality, data sparsity and position endogeneity. The model also discriminates between transitory and permanent dynamics. Especially for the latter case, dynamic SEA strategies are required for long-term profitability. We illustrate our model using a 2 year dataset of a Dutch laptop selling retailer. We find persistent time variation in clickthrough and conversion probabilities. The implications of our approach are threefold. First, advertisers can use it to obtain accurate daily estimates of clickthrough and conversion probabilities of individual ads to set bids and adjust text ads and landing pages. Second, advertisers can examine the extent of dynamics in their SEA environment, to determine how often their SEA strategy should be revised. Finally, advertisers can track ad performances to timely identify when keywords’ performances change. |
| Keywords: | Clickthrough, Conversion, Search engine advertising, Dynamic, Endogeneity, Time-varying parameters, Bayesian |
| JEL: | C32 C33 C11 |
| Date: | 2019–08–19 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20190056&r=all |
| By: | Seng, Kimty |
| Abstract: | This article analyses the effects of financial inclusion on poverty in terms of household income per capita in Cambodia, with data from the FinScope Survey carried out in 2015. The analysis describes the effects via financial literacy, accounting for endogenous selection bias resulting from unobserved confounders and for structural differences between users and non-users of financial services in terms of income functions. The findings suggest that the use of financial services is very likely to make a great contribution to reducing household budget deficit and poverty if the users, female in particular, have at least basic financial knowledge. |
| Keywords: | Poverty, financial inclusion, financial literacy, endogenous, Cambodia |
| JEL: | O1 O12 |
| Date: | 2019–08–26 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95726&r=all |
| By: | Albanesi, Stefania; Vamossy, Domonkos |
| Abstract: | We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation. |
| Keywords: | Consumer default; credit scores; deep learning; macroprudential policy |
| JEL: | C45 D1 E27 E44 G21 G24 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13914&r=all |
| By: | Lindquist, Matthew; Zenou, Yves |
| Abstract: | In this article, we argue that social network analysis can be used in a meaningful way to help us understand more about the root causes of delinquent behavior and crime and also to provide practical guidance for the design of crime prevention policies. |
| Keywords: | Co-offending; crime; Criminal networks; key player; peer effects; Social Networks |
| JEL: | A14 K42 Z13 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13823&r=all |
| By: | TAMURA Suguru |
| Abstract: | This study discusses the results of a survey on standardization activities to provide valuable information on this topic. Currently, standardization is similar to platform formation in that it serves as a firm's central theme for creating a strategy. Furthermore, the management structure of standardization is of interest to understand the organizational structures of firms better. Data from selected Japanese institutions' standardization activities in 2017 are collected using a questionnaire survey. The survey contains three main categories: (1) degree of standardization activities, (2) knowledge sources for standard formation, and (3) organization of standardization activities. Particular focus is on standardization activities with regard to artificial intelligence. To the best of my knowledge, this comprehensive survey related to standardization activities is the first of its kind. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:eti:polidp:19013&r=all |
| By: | Stefania Albanesi; Domonkos F. Vamossy |
| Abstract: | We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation. |
| JEL: | C45 D14 D18 E44 G0 G2 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26165&r=all |
| By: | CASELLA, BRUNO; FORMENTI, LORENZO |
| Abstract: | The digital economy is becoming an ever more important part of the world economy. It is revolutionizing the way we do business, and it has important implications for foreign direct investment (FDI). However, little systematic analysis has been done to investigate the investment patterns of digital multinational enterprises (MNEs). This study, conducted in the context of UNCTAD’s World Investment Report 2017 (WIR17), is an attempt to fill some of the gap in knowledge and to provide an impetus for future research. It proposes a new interpretative framework for the digital economy, builds an extensive sample of digital and ICT MNEs, and profiles their international operations. Its main findings are that MNEs in highly digitalized industries have a “lighter” FDI footprint than traditional MNEs; they tend to concentrate their operations in a few highly developed countries and their investment patterns are shaped by fiscal and financial motives more than those of traditional MNEs. As digital technologies and business models tend to disseminate across the broader economy, this may suggest the onset of a new era of international production and MNE internationalization paths. This paper sheds light on the methodology underpinning the analysis in WIR17 to ensure full replicability and to prepare the ground for further work in the area. It also builds further on the discussion in WIR17, proposing broader implications for international business and new avenues for future research. |
| Keywords: | FDI, digital economy, multinational enterprises, ICT |
| JEL: | F21 F23 |
| Date: | 2018–04–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95201&r=all |
| By: | Bailey, Michael; Farrell, Patrick; Kuchler, Theresa; Ströbel, Johannes |
| Abstract: | We use anonymized and aggregated data from Facebook to explore the spatial structure of social networks in the New York metro area. We highlight the importance of transportation infrastructure in shaping urban social networks by showing that travel time and travel costs are substantially stronger predictors of social connectedness between zip codes than geographic distance is. We also document significant heterogeneity in the geographic breadth of social networks across New York zip codes, and show that much of this heterogeneity is explained by the ease of access to public transit, even after controlling for socioeconomic characteristics of the zip codes' residents. When we group zip codes with strong social ties into hypothetical communities using an agglomerative clustering algorithm, we find that geographically non-contiguous locations are grouped into socially connected communities, again highlighting that geographic distance is an imperfect proxy for urban social connectedness. We also explore the social connections between New York zip codes and foreign countries, and highlight how these are related to past migration movements. |
| Keywords: | Agglomeration externalities; Social Connectedness; Transportation Infrastructure |
| JEL: | R1 R2 R3 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13822&r=all |
| By: | Bertin Martens (European Commission – JRC); Songul 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 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:decwpa:201808&r=all |
| By: | Hong, Yeon A; Kim, Sang Hyo |
| Keywords: | Food Consumption/Nutrition/Food Safety |
| Date: | 2019–06–25 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea19:290947&r=all |
| By: | Zöhre Akyol (Ege University); Mehmet Tokatl? (Ege University) |
| Abstract: | Changing marketing practices, legal regulations and new media channels push brands to use different marketing tactics. In all changing marketing tactics, stealth marketing shines out due to restrictive law that forbids some brands and sectors to run a marketing campaign in Turkey. As a term stealth marketing is a technique to deliver the brand's message to the people who should not realize the message is received as a marketing or sales purpose. In this way, brands are able to deliver desired messages for their target publics without getting caught by any restrictive laws. As the main channel to be used under this purpose is social media that shines out compared to traditional. The main reason for that is when traditional media is easy to control by the laws but social media doesn't. In this paper, we made a research about how these banned brands run a stealth marketing campaign in Turkey. Three brands that run a clear stealth marketing campaign from the alcoholic beverages sector are chosen and their campaign and it's social media site (Instagram) are analyzed with content analysis method for six months of duration. Analyzes show that brands use made up names and identities for running their campaigns to avoid getting caught from laws. Also, it is clear that all made up names that brands use have very similar corporate identities with the original brand. According to social media analyzes, storytelling shines out as a main structure of brands use in their Instagram posts and creating an interaction is also shines out as the main strategy that brands use in their stealth marketing campaigns. |
| Keywords: | Stealth marketing, Turkey, Alcohol Brands |
| JEL: | M31 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:sek:ibmpro:8511442&r=all |
| By: | Sandra Bestakova (Czech Technical University in Prague) |
| Abstract: | Prague has for a long time been struggling with the problem of constantly increasing housing prices and their lack. Offer available apartments is extremely low and is manifested by significant price growth and also the limited supply of apartments for sale and rent. One of the factors influencing the price of flats in Prague may be short-term rentals. Today there is an increasing number of flats in the total offer of short-term rentals and the number of hosts with more than one offer is also rising. Airbnb will deviate from its original idea of sharing "extra beds". |
| Keywords: | short-term rental; Prague; sharing economy; Airbnb |
| JEL: | R10 R21 R31 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:sek:ibmpro:8512235&r=all |
| By: | Konstantinos Gkillas (Department of Business Administration, University of Patras, Patras, Greece); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); David Roubaud (Montpellier Business School, Montpellier, France) |
| Abstract: | In this paper, we extend existing studies by considering the relationships across crude oil, gold, and Bitcoin markets. Using high-frequency data from December 2, 2014 to June 10, 2018, we analyze spillovers in volatility jumps and realized second, third, and fourth moments across crude oil, gold, and Bitcoin markets via Granger causality and generalized impulse response analyses in daily frequency. Results suggest evidence of predictability and emphasize, among others, the need of jointly modeling linkages across those three markets with higher-order moments; otherwise, inaccurate risk assessment and investment inferences may arise. The responses of realized volatility shocks and volatility jump are generally positive. Furthermore, results indicate evidence of a weaker relationship between gold – crude oil, and Bitcoin – crude oil compared to the case of Bitcoin - gold. Practical implications are discussed. |
| Keywords: | crude oil, gold, Bitcoin, realized moments, spillover effect |
| JEL: | C46 G10 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201965&r=all |
| By: | Bertin Martens (European Commission – JRC) |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:decwpa:201809&r=all |
| By: | David Byrd; Tucker Hybinette Balch |
| Abstract: | In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profitable machine learning-based trading strategies. However, the effectiveness of both approaches diminish over time, and neither of them are profitable after 2009. We present our implementation and results in detail for the period 2003-2017 and propose a novel idea: the use of such flexible machine learning methods as an objective measure of relative market efficiency. We conclude with a candidate explanation, comparing our returns over time with high-frequency trading volume, and suggest concrete steps for further investigation. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.08168&r=all |
| By: | Lisa-Cheree Martin (Department of Economics, Stellenbosch University) |
| Abstract: | This study employs traditional autoregressive and vector autoregressive forecasting models, as well as machine learning methods of forecasting, in order to compare the performance of each of these techniques. Each technique is used to forecast the percentage change of quarterly South African Gross Domestic Product, quarter-on-quarter. It is found that machine learning methods outperform traditional methods according to the chosen criteria of minimising root mean squared error and maximising correlation with the actual trend of the data. Overall, the outcomes suggest that machine learning methods are a viable option for policy-makers to use, in order to aid their decision-making process regarding trends in macroeconomic data. As this study is limited by data availability, it is recommended that policy-makers consider further exploration of these techniques. |
| Keywords: | Machine learning, Forecasting, Elastic-net, Random Forests, Support Vector Machines, Recurrent Neural Networks |
| JEL: | C32 C45 C53 C88 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:sza:wpaper:wpapers326&r=all |
| By: | Michael Zimmert; Michael Lechner |
| Abstract: | This paper considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a selection-on-observables framework where the number of possible confounders is very large. We propose a two-step estimator for which the first step is estimated by machine learning. We show that this estimator has desirable statistical properties like consistency, asymptotic normality and rate double robustness. In particular, we derive the coupled convergence conditions between the nonparametric and the machine learning steps. We also show that estimating population average treatment effects by averaging the estimated heterogeneous effects is semi-parametrically efficient. The new estimator is an empirical example of the effects of mothers' smoking during pregnancy on the resulting birth weight. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.08779&r=all |
| By: | Bazzi, Samuel; Blair, Robert; Blattman, Christopher; Dube, Oeindrila; Gudgeon, Matthew; Peck, Richard |
| Abstract: | Policymakers can take actions to prevent local conflict before it begins, if such violence can be accurately predicted. We examine the two countries with the richest available sub-national data: Colombia and Indonesia. We assemble two decades of finegrained violence data by type, alongside hundreds of annual risk factors. We predict violence one year ahead with a range of machine learning techniques. Models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best. Rich socio-economic data also substitute well for these histories. Even with such unusually rich data, however, the models poorly predict new outbreaks or escalations of violence. "Best case" scenarios with panel data fall short of workable early-warning systems. |
| Keywords: | Civil War; Colombia; conflict; Forecasting; Indonesia; Machine Learning; prediction |
| JEL: | C52 C53 D74 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13829&r=all |
| By: | Victor Chernozhukov; Whitney Newey; Vira Semenova |
| Abstract: | This paper gives a consistent, asymptotically normal estimator of the expected value function when the state space is high-dimensional and the first-stage nuisance functions are estimated by modern machine learning tools. First, we show that value function is orthogonal to the conditional choice probability, therefore, this nuisance function needs to be estimated only at $n^{-1/4}$ rate. Second, we give a correction term for the transition density of the state variable. The resulting orthogonal moment is robust to misspecification of the transition density and does not require this nuisance function to be consistently estimated. Third, we generalize this result by considering the weighted expected value. In this case, the orthogonal moment is doubly robust in the transition density and additional second-stage nuisance functions entering the correction term. We complete the asymptotic theory by providing bounds on second-order asymptotic terms. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.09173&r=all |
| By: | Songul Tolan (European Commission – JRC) |
| Abstract: | Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that‘objective’ machines base their decisions solely on facts and remain unaffected by human cognitive biases, discriminatory tendencies or emotions. Yet, there is overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are based on data that contains biased human decisions. This has led to a call for fairness-aware machine learning. However, fairness is a complex concept which is also reflected in the attempts to formalize fairness for algorithmic decision making. Statistical formalizations of fairness lead to a long list of criteria that are each flawed (or harmful even) in different contexts. Moreover,inherent tradeoffs in these criteria make it impossible to unify them in one general framework. Thus,fairness constraintsin algorithms have to be specific to the domains to which the algorithms are applied. In the future, research in algorithmic decision making systems should be aware of data and developer biases and add a focus on transparency to facilitate regular fairness audits. |
| Keywords: | fairness, machine learning, algorithmic bias, algorithmic transparency |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:decwpa:201810&r=all |