nep-pay New Economics Papers
on Payment Systems and Financial Technology
Issue of 2022‒09‒26
24 papers chosen by
Bernardo Bátiz-Lazo
Northumbria University

  1. Market Power of Digital Platforms By Jens-Uwe Franck; Martin Peitz
  2. Does Occupational Licensing Reduce Value Creation on Digital Platforms? By Peter Q. Blair; Mischa Fisher
  3. Competition, Alignment, and Equilibria in Digital Marketplaces By Meena Jagadeesan; Michael I. Jordan; Nika Haghtalab
  4. Online video sharing and revenues during the Pandemic. Evidence from musical stream data By Mourelatos, Evangelos; Mourelatos, Haris
  5. Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing By Danial Saef; Yuanrong Wang; Tomaso Aste
  6. Interbank Networks and the Interregional Transmission of Financial Crises: Evidence from the Panic of 1907 By Matthew Jaremski; David C. Wheelock
  7. Community currency systems: Basic income, credit clearing, and reserve-backed. Models and design principles By Criscione, Teodoro; Guterman, Eve; Avanzo, Sowuelu; Linares, Julio
  8. Using online vacancy and job applicants’ data to study skills dynamics By Bennett, Fidel,; Escudero, Verónica,; Liepmann, Hannah.,; Podjanin, Ana,
  9. The Steering Incentives of Gatekeepers in the Telecommunications Industry By Brian McManus; Aviv Nevo; Zachary Nolan; Jonathan W. Williams
  10. Short Term Cost of Cash and Mobile Financial Services: Evidence from a natural experiment in India By Costa, Helia; Pisu, Mauro; Shreeti, Vatsala
  11. Many-to-Many Matching on a Skill-Sharing Platform By Masaki Aoyagi
  12. Enabling the Metaverse. Whitepaper on international user preferences, business models and innovation processes in the Metaverse By Duwe, Daniel; Busch, Malte; Weissenberger-Eibl, Marion A.
  13. Nowcasting Brazilian GDP with Electronic Payments Data By Raquel Nadal Cesar Gonçalves
  14. A literature review on firm digitalization: drivers and impacts By Juan Jung; Gonzalo Gómez-Bengoechea
  15. Using Online Vacancy and Job Applicants' Data to Study Skills Dynamics By Bennett, Fidel; Escudero, Veronica; Liepmann, Hannah; Podjanin, Ana
  16. Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial By Keshav Agrawal; Susan Athey; Ayush Kanodia; Emil Palikot
  17. Macroeconomic Predictions using Payments Data and Machine Learning By James T. E. Chapman; Ajit Desai
  18. Amazon's Three Major Lines of Business By Snyder, Edward A.; Canaday, Jason; Hughes, Marley
  19. Broadband Internet and the Stock Market Investments of Individual Investors By Hans K. Hvide; Tom G. Meling; Magne Mogstad; Ola L. Vestad
  20. Big Techs vs Banks By Leonardo Gambacorta; Fahad Khalil; Bruno Maria Parigi
  21. llicit Financial Flows - The illusion of a common denominator By Johnny Flentø; Leonardo Santos Simao
  22. Investing in Bank Lending Technology: IT Spending in Banking By Zhiguo He; Sheila Jiang; Douglas Xu; Xiao Yin
  23. Redundancy of Centrality Measures in Financial Market Infrastructures By Constanza Martínez-Ventura; Ricardo Mariño-Martínez; Javier Miguélez-Márquez
  24. Do Twitter Sentiments Really Effective on Energy Stocks? Evidence from Intercompany Dependency By Yılmaz, Emrah Sıtkı; Ozpolat, Aslı; Destek, Mehmet Akif

  1. By: Jens-Uwe Franck; Martin Peitz
    Abstract: Digital platforms have reshaped many product markets and play an increasingly important role in economies around the globe. Some of these platforms have become powerful players and may possess a lot of market power. Economists use a number of indicators to assess market power. In this article we discuss to which extent these indicators are helpful in the context of digital platforms. In particular, we focus on assessing entrenched market power and the role of potential competition to constrain this power. Finally, we discuss some cross-border issues of platform market power.
    Keywords: market power, digital platforms, Big Tech, potential competition, Brussels effect
    JEL: K21 L40 L13
    Date: 2022–08
  2. By: Peter Q. Blair; Mischa Fisher
    Abstract: We test whether occupational licensing undercuts a key goal of digital marketplaces— to increase social surplus by increasing the effectiveness of customer search. Our setting is a large online marketplace in the $500B home services industry where a platform converts customer search into sales leads that are accepted for purchase by service providers on the platform. For each of the 21 million observations in our data set, we observe task-level variation in the state licensing requirements that service providers must meet to operate on the platform. Exploiting two natural experiments, we find that licensing reduces the accept rate of sales leads by an average of 25 percent. The accept rate drops because licensing reduces the aggregate labor supply of workers on the platform and not because licensing increases the volume of customer search. We develop a model and derive analytic expressions for the impact of licensing on the welfare of consumers, service providers and the platform as a function of seven sufficient statistics which we estimate from the data. We find that licensing a task reduces service provider surplus and platform surplus without increasing consumer surplus.
    JEL: D60 J44 L51 L86
    Date: 2022–08
  3. By: Meena Jagadeesan; Michael I. Jordan; Nika Haghtalab
    Abstract: Competition between traditional platforms is known to improve user utility by aligning the platform's actions with user preferences. But to what extent is alignment exhibited in data-driven marketplaces? To study this question from a theoretical perspective, we introduce a duopoly market where platform actions are bandit algorithms and the two platforms compete for user participation. A salient feature of this market is that the quality of recommendations depends on both the bandit algorithm and the amount of data provided by interactions from users. This interdependency between the algorithm performance and the actions of users complicates the structure of market equilibria and their quality in terms of user utility. Our main finding is that competition in this market does not perfectly align market outcomes with user utility. Interestingly, market outcomes exhibit misalignment not only when the platforms have separate data repositories, but also when the platforms have a shared data repository. Nonetheless, the data sharing assumptions impact what mechanism drives misalignment and also affect the specific form of misalignment (e.g. the quality of the best-case and worst-case market outcomes). More broadly, our work illustrates that competition in digital marketplaces has subtle consequences for user utility that merit further investigation.
    Date: 2022–08
  4. By: Mourelatos, Evangelos; Mourelatos, Haris
    Abstract: This study examines how instant online video sharing affects artists' musical streams during the pandemic. On average, the use of the TikTok app significantly increases artists' streams, by approximately 5%. This increase is even higher for male, European and dj Mag 2020 new entry artists.
    Keywords: Covid-19,Streams,Online video sharing
    JEL: I1 L82 Z10
    Date: 2022
  5. By: Danial Saef; Yuanrong Wang; Tomaso Aste
    Abstract: The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM). Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we applied this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options at the popular trading platform Deribit. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion.
    Date: 2022–08
  6. By: Matthew Jaremski; David C. Wheelock
    Abstract: This paper provides quantitative evidence on the interbank network’s role in transmitting the Panic of 1907 across the United States. Originating in a few New York City banks and trust companies, the panic led to payment suspensions and emergency currency issuance in many cities. Data on the universe of correspondent relationships shows that i) suspensions were more likely in cities whose banks had closer ties to New York, ii) banks with correspondents at the Panic’s center were more likely to close, and iii) banks responded to the panic by rearranging their correspondent relationships, with implications for network structure.
    Keywords: banking panics; interbank networks; contagion; bank closures; panic of 1907
    JEL: E42 E44 G01 G21 N11 N21
    Date: 2022–09
  7. By: Criscione, Teodoro; Guterman, Eve; Avanzo, Sowuelu; Linares, Julio
    Abstract: This paper briefly introduces models and basic design principles of community currency systems from economic and network analytical perspectives. Policymakers, grassroots organizations, and activists can find in this paper the necessary analytical and practical tools to start and enhance their own community currency projects.
    Keywords: community currency systems,complementary currency systems,basic income,monetary innovation,economic network analysis,circulation analysis,currency analysis,currency systems
    Date: 2022
  8. By: Bennett, Fidel,; Escudero, Verónica,; Liepmann, Hannah.,; Podjanin, Ana,
    Abstract: This paper finds that big data on vacancies and applications to an online job board can be a promising data source for studying skills dynamics, especially in countries where alternative sources are scarce. To show this, we develop a skills taxonomy, assess the characteristics of such online data, and employ natural language processing and machine-learning techniques. The empirical implementation uses data from the Uruguayan job board BuscoJobs, but can be replicated with similar data from other countries.
    Date: 2022
  9. By: Brian McManus; Aviv Nevo; Zachary Nolan; Jonathan W. Williams
    Abstract: We study trade-offs faced by multiple-system operators (MSOs), the gatekeepers in the provision of internet service, when setting prices and quality for internet access and TV service. In response to improvements in over-the-top video (OTT), MSOs choose between accommodating OTT to share in the surplus it provides consumers, or steering consumers towards TV. We augment the standard mixed bundling model to show that in some cases MSOs have incentives to steer consumers towards TV, but that these incentives vary with the available pricing tools. We then estimate the distribution of model parameters using household panel data on subscription choices and internet usage. Our estimates imply that if MSOs can set different prices for different internet content, under many cost circumstances MSOs discount the OTT usage price. Furthermore, we find that the ability to charge prices based on internet usage strengthens the MSOs' incentive to improve OTT quality.
    JEL: L11 L13 L96
    Date: 2022–08
  10. By: Costa, Helia; Pisu, Mauro; Shreeti, Vatsala
    Date: 2022–08
  11. By: Masaki Aoyagi
    Abstract: Each agent in a market needs to supplement his skill with a particular skill of another agent to complete his project. A platform matches the agents and allows members of the same match to share their skills. A match is valuable to an agent if he is matched with any agent who possesses a skill complementary to his own skill. When the platform uses the divide-and-conquer pricing strategy, we study the properties of incentive compatible mechanisms in relation to the reciprocal property of the complementary relationships among different skills, and when the market expands in its size.
    Date: 2022–08
  12. By: Duwe, Daniel; Busch, Malte; Weissenberger-Eibl, Marion A.
    Abstract: The Metaverse is on everyone's lips. After all, it is being treated as the successor to the Internet. Companies around the world are investing millions in its development. But so far it is unclear whether the preferences of people from different parts of the world about the Metaverse coincide or diverge. Therefore, over 1,500 people from Germany, the US and China were surveyed about their preferences in using the Metaverse. The results are presented in this whitepaper. In addition, an introductory overview of relevant functions and technologies is given and, following the survey results, it is shown what impact the Metaverse can have on companies' business models and innovation processes and how they can approach the Metaverse.
    Keywords: Metaverse,Metaversum,Preferences,Business Models,Innovation Process
    Date: 2022
  13. By: Raquel Nadal Cesar Gonçalves
    Abstract: Electronic payments data are usually available on a more timely basis than other coincident economic indicators and can be disaggregated into the level of economic divisions, by number of transactions and value, being potentially useful to anticipate the pace of economic activity. This paper seeks to measure how data from electronic payment instruments contribute to improving the nowcasting accuracy of GDP and its sectoral components. To do so, the nowcasting accuracy of complete models, with economic indicators and payments data, is compared with the accuracy of base models, without payments data, in two horizons: right after the closure of the quarter to be predicted, when payments data are already available; and about 15 days before the GDP release, when data from other coincident economic indicators are also known. The results show payments data contribute significantly to improving GDP nowcast accuracy in both horizons, but mainly just after the closure of the quarter.
    Date: 2022–08
  14. By: Juan Jung; Gonzalo Gómez-Bengoechea
    Abstract: The digital revolution is radically changing how modern business is conducted, leading to several transformations at the firm-level, and capturing the attention of practitioners, policymakers, and academics alike. Considering the ever-changing nature of digital technologies, the aim of this article is to provide an up-to-date review that describes the state of the art on this literature.
  15. By: Bennett, Fidel; Escudero, Veronica (ILO International Labour Organization); Liepmann, Hannah (ILO International Labour Organization); Podjanin, Ana (ILO International Labour Organization)
    Abstract: We assess whether online data on vacancies and applications to a job board are a suitable source for studying skills dynamics outside of Europe and the United States, where a rich literature has examined skills dynamics using online vacancy data. Yet, the knowledge on skills dynamics is scarce for other countries, irrespective of their level of development. We first propose a taxonomy that systematically aggregates three broad categories of skills – cognitive, socioemotional and manual – and fourteen commonly observed and recognizable skills sub-categories, which we define based on unique skills identified through keywords and expressions. Our aim is to develop a taxonomy that is comprehensive but succinct, suitable for the labour market realities of developing and emerging economies and adapted to online vacancies and applicants' data. Using machine-learning techniques, we then develop a methodology that allows implementing the skills taxonomy in online vacancy and applicants' data, thus capturing both the supply and the demand side. Implementing the methodology with Uruguayan data from the job board BuscoJobs, we assign skills to 64 per cent of applicants' employment spells and 94 per cent of vacancies. We consider this a successful implementation since the exploited text information often does not follow a standardized format. The advantage of our approach is its reliance on data that is currently available in many countries across the world, thereby allowing for country-specific analysis that does not need to assume that occupational skills bundles are the same across countries. To the best of our knowledge, we are the first to explore this approach in the context of emerging economies.
    Keywords: online data, job board, skills dynamics, skills taxonomy, natural language processing
    JEL: C81 J24 O33 O54
    Date: 2022–08
  16. By: Keshav Agrawal; Susan Athey; Ayush Kanodia; Emil Palikot
    Abstract: We study the impact of personalized content recommendations on the usage of an educational app for children. In a randomized controlled trial, we show that the introduction of personalized recommendations increases the consumption of content in the personalized section of the app by approximately 60% and that the overall app usage increases by 14%, compared to the baseline system of stories selected by content editors for all students. The magnitude of individual gains from personalized content increases with the amount of data available about a student and with preferences for niche content: heavy users with long histories of content interactions who prefer niche content benefit more than infrequent, newer users who like popular content. To facilitate the diffusion of personalized recommendation systems, we provide a framework for using offline data to develop such a system.
    Date: 2022–08
  17. By: James T. E. Chapman; Ajit Desai
    Abstract: Predicting the economy's short-term dynamics -- a vital input to economic agents' decision-making process -- often uses lagged indicators in linear models. This is typically sufficient during normal times but could prove inadequate during crisis periods. This paper aims to demonstrate that non-traditional and timely data such as retail and wholesale payments, with the aid of nonlinear machine learning approaches, can provide policymakers with sophisticated models to accurately estimate key macroeconomic indicators in near real-time. Moreover, we provide a set of econometric tools to mitigate overfitting and interpretability challenges in machine learning models to improve their effectiveness for policy use. Our models with payments data, nonlinear methods, and tailored cross-validation approaches help improve macroeconomic nowcasting accuracy up to 40\% -- with higher gains during the COVID-19 period. We observe that the contribution of payments data for economic predictions is small and linear during low and normal growth periods. However, the payments data contribution is large, asymmetrical, and nonlinear during strong negative or positive growth periods.
    Date: 2022–09
  18. By: Snyder, Edward A.; Canaday, Jason; Hughes, Marley
    Abstract: Since its founding in 1995 Amazon has become a leader in eCommerce, cloud computing services, and interactive devices for individuals and homes. In this study, we document the critical steps in Amazon's development in each line of business. Our review yields insights on (i) how Amazon responded to changes in demand, (ii) the importance of economies of scale, economies of scope, and network effects in Amazon's efforts to build out its lines of business, and (iii) interrelationships among these three apparently distinct commercial operations. This case study thereby provides insights how Amazon's Firm Specific Advantages (FSAs) contributed to its successes within and across lines of business. Our analysis further suggests that Amazon developed Dynamic Capabilities (DCs) capabilities that contributed to Amazon's superior performance. Our analysis is, however, necessarily interim in nature. Given changing market and regulatory conditions, whether Amazon will be able to sustain its performance in which lines of business and in which countries is uncertain.
    Keywords: entrepreneurship and business strategy,transaction cost economics,market entry,market power,dynamic capabilities,firm specific advantages
    JEL: L26 L7 L86 M21
    Date: 2022
  19. By: Hans K. Hvide; Tom G. Meling; Magne Mogstad; Ola L. Vestad
    Abstract: We study the effects of broadband internet use on the investment decisions of individual investors. A public program in Norway provides plausibly exogenous variation in internet use. Our instrumental variables estimates show that internet use causes a substantial increase in stock market participation, driven primarily by increased fund ownership. Existing investors tilt their portfolios towards funds, thereby obtaining more diversified portfolios and higher Sharpe ratios, and do not increase their trading activity in stocks. Overall, access to high-speed internet seems to spur a “democratization of finance”, with individuals making investment decisions that are more in line with the advice from portfolio theory.
    JEL: D04 D14 D15 G00 G11 G40
    Date: 2022–08
  20. By: Leonardo Gambacorta; Fahad Khalil; Bruno Maria Parigi
    Abstract: We study an economy in which large technology companies, big techs, provide credit to firms operating on their platforms. We focus on two advantages that big techs have with respect to banks: better information on their clients and better enforcement of credit repayment since big techs can exclude a defaulting firm from their ecosystem. While big techs have both superior enforcement and complete and private information of the firm type big techs can encroach on banks' turf only if they guarantee some privacy to firms by tempering their drive to collect information about firm characteristics and leaving some rents to them. The way big techs share information i.e. by providing information publicly or in a private way entails different outcomes in terms of efficiency.
    Keywords: big techs, credit markets, privacy, information sharing
    JEL: E51 G23 O31
    Date: 2022–08
  21. By: Johnny Flentø (Development Economics Research Group, University of Copenhagen); Leonardo Santos Simao (Former Minister of Health, Former Minister of Foreign Affairs & Cooperation, Government of Mozambique)
    Abstract: Illicit financial flows (IFFs) grew significantly with the accelerated liberalization of financial markets during the 1980s and 1990s. In Africa, IFFs out of the continent are believed to equal combined official development assistance and direct foreign investment into the continent. Work to conceptualize and measure IFFs is progressing and many methods for estimating them are being tested. Such methods are inherently crude, and some are questionable. A more fundamental question is whether the concept of IFFs as sometimes used is useful for analytical purposes. The definition adopted by the United Nations in 2020 includes a host of different flows and transactions that have very little in common other than that they are deemed illicit, primarily according to western norms, and are not based on the development effects of flows. The term “illicit financial flows” contains very many and too different types of things, which in combination with the challenges in estimating such flows makes the concept prone to instrumentalization. Defining what is illicit is ultimately a political choice. The world should take care that the work to create an institutional framework for reducing IFFs is not reduced to a platform for the most powerful alliance of governments to impose their version of what is illicit on the rest of the world.
    Keywords: Illicit Flows, Crime, International Economics and Cooperation, Tax Evasion, Drug Trafficking,
    JEL: F02 F51 F52 F53 F54 K34
    Date: 2022–08–07
  22. By: Zhiguo He; Sheila Jiang; Douglas Xu; Xiao Yin
    Abstract: This paper studies the economics behind the investment in information technologies (IT) by U.S. commercial banks in the past decade. By linking banks’ IT spending to their lending technologies, we analyze the distinctive natures of banks’ dealings with information across various lending activities. Investment in communication IT is shown to be associated more with improving banks’ ability of soft information production and transmission, while investment in software IT helps enhance banks’ hard information processing capacity. We exploit polices that affect geographic regions differentially to show causally that banks respond to an increased demand for small business credit (mortgage refinance) by increasing their spending on communication (software) IT spending. We also find that the entry of fintech induces commercial banks to increase their investment in IT—more so in the software IT category.
    JEL: G21 G51 O12 O32
    Date: 2022–08
  23. By: Constanza Martínez-Ventura; Ricardo Mariño-Martínez; Javier Miguélez-Márquez
    Abstract: The concept of centrality has been widely used to monitor systems with a network structure because it allows identifying their most influential participants.This monitoring task can be difficult if the number of system participants is considerably large or if the wide variety of centrality measures currently available produce non-coincident (or mixed) signals. This document uses principal component analysis to evaluate a set of centrality measures calculated for the financial institutions that participate in four financial market infrastructures of Colombia. The results obtained are used to construct general indices of centrality, using the strongest measures of centrality as inputs, and leaving aside those considered redundant. RESUMEN: El concepto de centralidad ha sido ampliamente utilizado para monitorear sistemas con estructura de red ya que permite identificar a los participantes más influyentes. Las labores de monitoreo pueden ser difíciles de realizar si el número de participantes en esos sistemas es considerablemente amplio o si las medidas de centralidad producen resultados no coincidentes o generan señales mixtas. Este documento usa el análisis de componentes principales para evaluar un conjunto de medidas de centralidad calculadas para las instituciones financieras que participan en cuatro infraestructuras de los mercados financieros en Colombia. Los resultados obtenidos son utilizados para construir índices generales de centralidad, utilizando como insumos las medidas de centralidad más fuertes y dejando de lado aquellas consideradas redundantes.
    Keywords: centrality, principal component analysis, redundancy analysis, clustering analysis, centralidad, análisis de componentes principales, análisis de redundancia, análisis de clustering
    JEL: G20 C38 C23
    Date: 2022–08
  24. By: Yılmaz, Emrah Sıtkı; Ozpolat, Aslı; Destek, Mehmet Akif
    Abstract: The study aims to examine the effects of social media activities on stock prices of the energy sector. In this respect, the sample covers the monthly period from 2015m6 to 2020m5 has been observed. Energy stocks as S&P 500 index (SP), stock market volatility index (VIX), trade-weighted USD index (USD) and Brent oil prices (OIL) have been used as independent variables. Accordingly, three different models have been created to analyze the link between returns, volatility and trading volume and Twitter sentiments by using Augment mean Group. As a result, we found that Twitter sentiment values have no significant impact on the returns and volatility of the companies. Tweets, on the other hand, appear to have a favorable impact on company trading volume values.
    Keywords: Social media; Twitter; Energy Sector; Stock Prices
    JEL: G0 Q4
    Date: 2022–03–01

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