nep-pay New Economics Papers
on Payment Systems and Financial Technology
Issue of 2019‒05‒06
nineteen papers chosen by

  1. Regulating AI: do we need new tools? By Otello Ardovino; Jacopo Arpetti; Marco Delmastro
  2. The digital innovation policy landscape in 2019 By Caroline Paunov; Sandra Planes-Satorra
  3. Blockchain and Smart-contract: a pioneering Approach of inter-firms Relationships? The case of franchise networks By Richard Baron; Magali Chaudey
  4. Economists (and Economics) in Tech Companies By Athey, Susan; Luca, Michael
  5. Rough volatility of Bitcoin By Tetsuya Takaishi
  6. Optimal Commissions and Subscriptions in Networked Markets By Birge, John R.; Candogan, Ozan; Chen, Hongfan; Saban, Daniela
  7. Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network By Du, Ruihuan; Zhong, Yu; Nair, Harikesh S.; Cui, Bo; Shou, Ruyang
  8. Midline Effects of a Randomized Controlled Trial to Increase the Utilization of Financial Services by Women Business Owners in Rural Indonesia By James C. Knowles
  9. Is an Army of Robots Marching on Chinese Jobs? By Giuntella, Osea; Wang, Tianyi
  10. Distance Learning in Higher Education: Evidence from a Randomized Experiment By Cacault, M. Paula; Hildebrand, Christian; Laurent-Lucchetti, Jérémy; Pellizzari, Michele
  12. Traveler segmentation through Social Media for intercultural marketing purposes: The case of Halkidiki By Mavragani, Eleni; Nikolaidou, Paraskevi; Theodoraki, Efi
  13. The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand By Acemoglu, Daron; Restrepo, Pascual
  14. Robots and firms By Michael Koch; Ilya Manuylov; Marcel Smolka
  15. A New Organizational Chassis for Artificial Intelligence - Exploring Organizational Readiness Factors By Pumplun, Luisa; Tauchert, Christoph; Heidt, Margareta
  16. Price Promotions in “Freemium†Settings By Runge, Julian; Nair, Harikesh S.; Levav, Jonathan
  17. Digital Waste? Unintended Consequences of Health Information Technology By Böckerman, Petri; Kortelainen, Mika; Laine, Liisa T.; Nurminen, Mikko; Saxell, Tanja
  18. Determining the number of factors in a forecast model by a random matrix test: cryptocurrencies By Andr\'es Garc\'ia Medina; Graciela Gonz\'alez-Far\'ias
  19. Automation and New Tasks: How Technology Displaces and Reinstates Labor By Acemoglu, Daron; Restrepo, Pascual

  1. By: Otello Ardovino; Jacopo Arpetti; Marco Delmastro
    Abstract: The Artificial Intelligence paradigm (hereinafter referred to as "AI") builds on the analysis of data able, among other things, to snap pictures of the individuals' behaviors and preferences. Such data represent the most valuable currency in the digital ecosystem, where their value derives from their being a fundamental asset in order to train machines with a view to developing AI applications. In this environment, online providers attract users by offering them services for free and getting in exchange data generated right through the usage of such services. This swap, characterized by an implicit nature, constitutes the focus of the present paper, in the light of the disequilibria, as well as market failures, that it may bring about. We use mobile apps and the related permission system as an ideal environment to explore, via econometric tools, those issues. The results, stemming from a dataset of over one million observations, show that both buyers and sellers are aware that access to digital services implicitly implies an exchange of data, although this does not have a considerable impact neither on the level of downloads (demand), nor on the level of the prices (supply). In other words, the implicit nature of this exchange does not allow market indicators to work efficiently. We conclude that current policies (e.g. transparency rules) may be inherently biased and we put forward suggestions for a new approach.
    Date: 2019–04
  2. By: Caroline Paunov; Sandra Planes-Satorra
    Abstract: How are OECD countries supporting digital innovation and ensuring that benefits spread across the economy? This paper explores the current landscape of strategies and initiatives implemented in OECD countries to support innovation in the digital age. It identifies common trends and differences in national digital, smart industry and artificial intelligence (AI) strategies. The paper also discusses policy instruments used across OECD to support digital innovation targeting four objectives: First, policies aimed at enhancing digital technology adoption and diffusion, including demonstration facilities for SMEs. Second, initiatives that promote collaborative innovation, including via the creation of digital innovation clusters and knowledge intermediaries. Third, support for research and innovation in key digital technologies, particularly AI (e.g. by establishing testbeds and regulatory sandboxes). Fourth, policies to encourage digital entrepreneurship (e.g. through early-stage business acceleration support).
    Keywords: digital innovation, digital technologies and artificial intelligence (AI), innovation and research policy, innovation strategies
    JEL: O30 O31 O33 O38 O25 I28
    Date: 2019–05–06
  3. By: Richard Baron (Univ Lyon, UJM Saint-Etienne, GATE UMR 5824, F-42023 Saint- Etienne, France); Magali Chaudey (Univ Lyon, UJM Saint-Etienne, GATE UMR 5824, F-42023 Saint- Etienne, France)
    Abstract: This paper is interested in the analysis of Blockchains and Smart-contracts applied to inter-firms relationships, in particular the franchise networks. After defining the Blockchain technology and the Smart-contract as a particular type of contract stored in blockchains, we question the theory of contracts and its conception(s) of transactions, information asymmetries, firm or inter-firm relations. To better understand the challenges of blockchain for franchise networks and identify opportunities for implementation in these networks, we present some relevant applications of this technology. We identify different ways where blockchain technology could improve the network management and therefore their performance: the supply-chain, the brand-name protection, security and transparency in the payment of fees and royalties, access to reliable information via an oracle.
    Keywords: Blockchain, Smart-Contract, Transaction cost, Network, Franchise
    JEL: D86 L14 L81 O33
    Date: 2019
  4. By: Athey, Susan (Stanford Graduate School of Business); Luca, Michael (Harvard Business School)
    Abstract: As technology platforms have created new markets and new ways of acquiring information, economists have come to play an increasingly central role in tech companies--tackling problems such as platform design, strategy, pricing, and policy. Over the past five years, hundreds of PhD economists have accepted positions in the technology sector. In this paper, we explore the skills that PhD economists apply in tech companies, the companies that hire them, the types of problems that economists are currently working on, and the areas of academic research that have emerged in relation to these problems.
    Date: 2018–09
  5. By: Tetsuya Takaishi
    Abstract: Recent studies have found that the log-volatility of asset returns exhibit roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using the multifractal detrended fluctuation analysis, we obtain the generalized Hurst exponent of the log-volatility increments and find that the generalized Hurst exponent is less than $1/2$, which indicates log-volatility increments that are rough. Furthermore, we find that the generalized Hurst exponent is not constant. This observation indicates that the log-volatility has multifractal property. Using shuffled time series of the log-volatility increments, we infer that the source of multifractality partly comes from the distributional property.
    Date: 2019–04
  6. By: Birge, John R. (Booth School of Business, University of Chicago); Candogan, Ozan (Booth School of Business, University of Chicago); Chen, Hongfan (Booth School of Business, University of Chicago); Saban, Daniela (Graduate School of Business, Stanford University)
    Abstract: Two salient features of most online platforms are that they do not dictate the transaction prices, and use commissions/subscriptions for extracting revenues. We consider a platform that charges commission rates and subscription fees to sellers and buyers for facilitating transactions, but does not directly control the transaction prices, which are determined by the traders. Buyers and sellers are divided into types, and we represent the compatibility between different types using a bipartite network. Traders are heterogeneous in terms of their valuations, and different types have possibly different value distributions. The platform chooses commissions-subscriptions to maximize its revenues. We provide a convex optimization formulation to calculate the revenue-maximizing commissions/subscriptions, and establish that, typically, different types should be charged different commissions/subscriptions depending on their network positions. We establish lower and upper bounds on the platform’s revenues in terms of the supply-demand imbalance across the network. Motivated by simpler schemes used in practice, we show that the revenue loss can be unbounded when all traders on the same side are charged the same commissions/subscriptions, and bound the revenue loss in terms of the supply-demand imbalance across the network. Charging only buyers or only sellers leads to a (bounded) revenue loss, even when different types on the same side can be charged differently. Under mild assumptions, we establish that a revenue-maximizing platform achieves at least 2/3 of the maximum achievable social welfare. Our results highlight the suboptimality of commonly used payment schemes, and showcase the importance of accounting for the compatibility between different user types.
    Date: 2018–09
  7. By: Du, Ruihuan (?); Zhong, Yu (?); Nair, Harikesh S. (Stanford University Graduate School of Business); Cui, Bo (?); Shou, Ruyang (?)
    Abstract: This paper describes a practical system for Multi Touch Attribution (MTA) for use by a publisher of digital ads. We developed this system for, an eCommerce company, which is also a publisher of digital ads in China. The approach has two steps. The first step (“response modeling†) fits a user-level model for purchase of a product as a function of the user’s exposure to ads. The second (“credit allocation†) uses the fitted model to allocate the incremental part of the observed purchase due to advertising, to the ads the user is exposed to over the previous T days. To implement step one, we train a Recurrent Neural Network (RNN) on user-level conversion and exposure data. The RNN has the advantage of flexibly handling the sequential dependence in the data in a semi-parametric way. The specific RNN formulation we implement captures the impact of advertising intensity, timing, competition, and user-heterogeneity, which are known to be relevant to ad-response. To implement step two, we compute Shapley Values, which have the advantage of having axiomatic foundations and satisfying fairness considerations. The specific formulation of the Shapley Value we implement respects incrementality by allocating the overall incremental improvement in conversion to the exposed ads, while handling the sequence-dependence of exposures on the observed outcomes. The system is under production at, and scales to handle the high dimensionality of the problem on the platform (attribution of the orders of about 300M users, for roughly 160K brands, across 200+ ad-types, served about 80B ad-impressions over a typical 15-day period).
    Date: 2019–01
  8. By: James C. Knowles
    Abstract: This is the report of a midline evaluation of a randomized controlled trial to increase the utilization of saving and other financial services by women business owners in Indonesia. The trial was motivated by a recent law in Indonesia supporting the development of branchless banking services for a large unbanked rural population and by the results of several studies suggesting that it is possible to stimulate savings and improve a range of downstream outcomes with suitable interventions targeted to under-banked rural populations. The trial was conducted in 400 purposively selected rural and semi-urban villages in five districts of East Java province in which branchless banking services (including basic savings accounts accessible through mobile phones) were available. The randomized interventions supported by this trial include both supply-side treatments (higher agent incentives) and demand-side treatments (training and mentoring of female business owners). The data analyzed include both baseline and midline survey data on female and male business owners and branchless banking agents. Implementation of the trial was delayed due to difficulties in recruiting suitable agents in all 400 trial villages. Numerous supply-side problems, both technical and logistical, were also reported in the monitoring data. However, the midline results indicate that the interventions were successfully delivered, resulting in significant positive effects on key intermediate outcomes, including knowledge and use of mobile banking services and initial take up of a mobile basic savings account. Downstream effects indicate that the supply- and demand-side interventions, particularly in combination, increased women business owners’ savings, empowerment, self-confidence, and economic welfare.
    Date: 2019–03–26
  9. By: Giuntella, Osea (University of Pittsburgh); Wang, Tianyi (University of Pittsburgh)
    Abstract: A handful of studies have investigated the effects of robots on workers in advanced economies. According to a recent report from the World Bank (2016), 1.8 billion jobs in developing countries are susceptible to automation. Given the inability of labor markets to adjust to rapid changes, there is a growing concern that the effect of automation and robotization in emerging economies may increase inequality and social unrest. Yet, we still know very little about the impact of robots in developing countries. In this paper we analyze the effects of exposure to industrial robots in the Chinese labor market. Using aggregate data from Chinese prefectural cities (2000-2016) and individual longitudinal data from China, we find a large negative impact of robot exposure on employment and wages of Chinese workers. Effects are concentrated in the state-owned sector and are larger among low-skilled, male, and prime-age and older workers. Furthermore, we find evidence that exposure to robots affected internal mobility and increased the number of labor-related strikes and protests.
    Keywords: emerging economies, labor markets, robots
    JEL: J23 J24 O33
    Date: 2019–04
  10. By: Cacault, M. Paula (University of Geneva); Hildebrand, Christian (University of St. Gallen); Laurent-Lucchetti, Jérémy (University of Geneva); Pellizzari, Michele (University of Geneva)
    Abstract: Using a randomized experiment in a public Swiss university, we study the impact of online live streaming of lectures on student achievement and attendance. We find that (i) students use the live streaming technology only punctually, apparently when random events make attending in class too costly; (ii) attending lectures via live streaming lowers achievement for low-ability students and increases achievement for high-ability ones and (iii) offering live streaming reduces in-class attendance only mildly. These findings have important implications for the design of education policies.
    Keywords: EduTech, distance learning, live streaming
    JEL: I20 I21 I23
    Date: 2019–04
  11. By: Hughes, Joseph P. (Rutgers University); Jagtiani, Julapa (Federal Reserve Bank of Philadelphia); Moon, Choon-Geol (HANYANG UNIVERSITY)
    Abstract: We compare the performance of unsecured personal installment loans made by traditional bank lenders with that of LendingClub, using a stochastic frontier estimation technique to decompose the observed nonperforming loans into three components. The first is the best-practice minimum ratio that a lender could achieve if it were fully efficient at credit-risk evaluation and loan management. The second is a ratio that reflects the difference between the observed ratio (adjusted for noise) and the minimum ratio that gauges the lender’s relative proficiency at credit analysis and loan monitoring. The third is statistical noise. In 2013 and 2016, the largest bank lenders experienced the highest ratio of nonperformance, the highest inherent credit risk, and the highest lending efficiency, indicating that their high ratio of nonperformance is driven by inherent credit risk, rather than by lending inefficiency. LendingClub’s performance was similar to small bank lenders as of 2013. As of 2016, LendingClub’s performance resembled the largest bank lenders — the highest ratio of nonperforming loans, inherent credit risk, and lending efficiency — although its loan volume was smaller. Our findings are consistent with a previous study that suggests LendingClub became more effective in risk identification and pricing starting in 2015. Caveat: We note that this conclusion may not be applicable to fintech lenders in general, and the results may not hold under different economic conditions such as a downturn
    Keywords: marketplace lending; P2P lending; credit risk management; lending efficiency
    JEL: C58 G21 L25
    Date: 2019–04–02
  12. By: Mavragani, Eleni; Nikolaidou, Paraskevi; Theodoraki, Efi
    Abstract: This paper aims to present a methodology for the segmentation of travelers by studying social media profiles and extracting information on their preferences and demographic traits. Through the study of the sample’s social media profiles (Instagram, Facebook, and Twitter), information about travelers’ demographics and preferences are combined for the segmentation of the tourists visiting a Greek region. From the analysis of the data, 10 preference-based segments occur, while the cultural-based division corresponds to the main national groups visiting the region.
    Keywords: customer segmentation; customer profiling; digital marketing; social media; intercultural marketing
    JEL: L83 M3 M31 Z1
    Date: 2019–04–15
  13. By: Acemoglu, Daron (MIT); Restrepo, Pascual (Boston University)
    Abstract: Artificial Intelligence is set to influence every aspect of our lives, not least the way production is organized. AI, as a technology platform, can automate tasks previously performed by labor or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labor can be productively employed. The consequences of this choice have been stagnating labor demand, declining labor share in national income, rising inequality and lower productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the "right" kind of AI with better economic and social outcomes.
    Keywords: automation, artificial intelligence, jobs, inequality, innovation, labor demand, productivity, tasks, technology, wages
    JEL: J23 J24
    Date: 2019–04
  14. By: Michael Koch; Ilya Manuylov; Marcel Smolka
    Abstract: We study the implications of robot adoption at the level of individual firms using a rich panel data-set of Spanish manufacturing firms over a 27-year period (1990-2016). We focus on three central questions: (1) Which firms adopt robots? (2) What are the labor market effects of robot adoption at the firm level? (3) How does firm heterogeneity in robot adoption affect the industry equilibrium? To address these questions, we look at our data through the lens of recent attempts in the literature to formalize the implications of robot technology. As for the first question, we establish robust evidence that ex-ante larger and more productive firms are more likely to adopt robots, while ex-ante more skill-intensive firms are less likely to do so. As for the second question, we find that robot adoption generates substantial output gains in the vicinity of 20-25% within four years, reduces the labor cost share by 5-7%-points, and leads to net job creation at a rate of 10%. These results are robust to controlling for non-random selection into robot adoption through a difference-in-differences approach combined with a propensity score reweighting estimator. Finally, we reveal substantial job losses in firms that do not adopt robots, and a productivity-enhancing reallocation of labor across firms, away from non-adopters, and toward adopters.
    Keywords: automation, robots, firms, productivity, technology
    JEL: D22 F14 J24 O14
    Date: 2019
  15. By: Pumplun, Luisa; Tauchert, Christoph; Heidt, Margareta
    Date: 2019–06–08
  16. By: Runge, Julian (Humboldt University); Nair, Harikesh S. (Stanford University); Levav, Jonathan (Stanford University)
    Abstract: The “freemium†model for digital goods involves selling a base version of the product for free, and making premium product features available to users only on payment. The success of the model is predicated on the ability to profitably convert free users to paying ones. Price promotions (or “sales†) are often used in freemium to induce the conversion. However, the causal effect of exposing consumers to such inter-temporal price variation is unclear. While sales can generate beneficial short-run conversion, they may be harmful in the long-run if consumers inter-temporally substitute purchases to periods with low prices, or use them as signals of low product quality. These long-run concerns may be accentuated in freemium, where the base version is sold for free, so that sales form extreme price cuts on the overall product combination. We work with the seller of a free-to-play video game to randomize entering cohorts of users into treatment and control conditions in which promotions for in-game purchases are turned on or off. We observe complete user behavior for half a year, including purchases and consumption of in-game goods, which, in contrast to much of the extant literature, enables us to assess possible substitution over time in consumption directly. We find that conversion and revenue improve in the treatment group; and detect no evidence of harmful inter-temporal substitution or negative inferences about quality from exposure to price variation, suggesting that promotions are profitable. We conjecture that the zero price of the base product that makes its consumption virtually costless, combined with the complementarity between the base product and premium features can help explain this. To the extent that this holds across freemium contexts, the positive effects of promotions documented here may hold more generally.
    Date: 2019–03
  17. By: Böckerman, Petri (Labour Institute for Economic Research); Kortelainen, Mika (VATT, Helsinki); Laine, Liisa T. (University of Pennsylvania); Nurminen, Mikko (Turku School of Economics); Saxell, Tanja (VATT, Helsinki)
    Abstract: We exploit a large-scale natural experiment – the rollout of a nationwide electronic prescribing system in Finland – to study how digitization of prescriptions affects pharmaceutical use and health outcomes. We use comprehensive administrative data from patients treated with benzodiazepines, which are globally popular, effective but addictive psychotropic medications. We find no impact on benzodiazepine use on average, but among younger patients e-prescribing increases repeat prescription use. Younger patients' health outcomes do not improve but adverse outcomes, such as prescription drug abuse disorders and suicide attempts, increase dramatically. Improving access to medication through easier ordering may thus increase medication overuse.
    Keywords: health information technology, electronic prescribing, repeat prescriptions, inefficiency, medication overuse
    JEL: H51 H75 I12 I18
    Date: 2019–04
  18. By: Andr\'es Garc\'ia Medina; Graciela Gonz\'alez-Far\'ias
    Abstract: We determine the number of statistically significant factors in a forecast model using a random matrices test. The applied forecast model is of the type of Reduced Rank Regression (RRR), in particular, we chose a flavor which can be seen as the Canonical Correlation Analysis (CCA). As empirical data, we use cryptocurrencies at hour frequency, where the variable selection was made by a criterion from information theory. The results are consistent with the usual visual inspection, with the advantage that the subjective element is avoided. Furthermore, the computational cost is minimal compared to the cross-validation approach.
    Date: 2019–05
  19. By: Acemoglu, Daron (MIT); Restrepo, Pascual (Boston University)
    Abstract: We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor – the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production – due to automation and new tasks – can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.
    Keywords: automation, displacement effect, labor demand, inequality, productivity, reinstatement effect, tasks, technology, wages
    JEL: J23 J24
    Date: 2019–04

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