Measuring retail trade using card transactional data

En este trabajo se presenta un Índice de Comercio Minorista (ICM) de alta dimensionalidad construido para España, estimado a partir del uso de datos masivos. La información utilizada se corresponde con la que surge de las transacciones con tarjetas de crédito y de débito de los clientes de BBVA en terminales de punto de venta (TPV) españoles. Los índices obtenidos son robustos cuando se comparan con los que publica el Instituto Nacional de Estadística (INE), tanto para el conjunto de España como para las distintas regiones y los diferentes canales de distribución. Dando un paso más, se calculan los índices mensuales por provincias y por sectores (información no publicada por el INE) y se construye un índice general diario. A partir de este último índice, se analizan además las pautas de consumo en alta frecuencia a través de un modelo estructural de series temporalesIn this paper we present a high-dimensionality Retail Trade Index (RTI) constructed to nowcast the retail trade sector economic performance in Spain, using Big Data sources and techniques. The data are the footprints of BBVA clients from their credit or debit card transactions at Spanish point of sale (PoS) terminals. The resulting indexes have been found to be robust when compared with the Spanish RTI, regional RTI (Spain’s autonomous regions), and RTI by retailer type (distribution classes) published by the National Statistics Institute (INE). We also went one step further, computing the monthly indexes for the provinces and sectors of activity and the daily general index, by obtaining timely, detailed information on retail sales. Finally, we analyzed the high-frequency consumption dynamics using BBVA retailer behavior and a structural time series mode

Some implications of new data sources for economic analysis and official statistics

Artículo de revistaOn the backof new technologies, new data sources are emerging. These are of very high frequency, with greater granularity than traditional sources, and can be accessed across the board, in many cases, by the different economic agents. Such developments open up new avenues and new opportunities for official statistics and for economic analysis. From a central bank’s standpoint, the use and incorporation of these data into its traditional tasks poses significant challenges, arising from their management, storage, security and confidentiality. Further, there are problems with their statistical representativeness. Given that these data are available to many agents, and not exclusively to official statistics institutions, there is a risk that different measures of the same phenomenon may be generated, with heterogeneous quality standards, giving rise to confusion among the public. Some of these sources, which consist of unstructured data such as text, require new processing techniques so that they can be integrated into economic analysis in an appropriate format (quantitative). In addition, their use entails the incorporation of machine learning techniques, among others, into traditional analysis methodologies. This article reviews, from a central bank’s standpoint, some of the possibilities and implications of this new phenomenon for economic analysis and official statistics, with examples of recent studie

Structural inequalities emerging from a large wire transfers network

We aim to explore the connections between structural network inequalities and bank’s customer spending behaviours, within an entire national ecosystem made of natural persons (i.e., an individual human being) and legal entities (i.e., private or public organisations), different business sectors, and supply chains that span distinct geographical regions. We focus on Italy, that is among the wealthiest nations in the world, and also an example of a complex economic system. In particular, we had access to a large subset of anonymised and GDPR-compliant wire transfer data recorded from Jan 2016 to Dec 2017 by Intesa Sanpaolo, a leading banking group in the Eurozone, and the most important one in Italy.Intesa Sanpaolo wire transfers network exhibits a strong heavy-tailed behaviour and a giant component that grows continuously around the same core of the 1% highest degree nodes, and it also shows a general disassortative pattern, even if some ranges of degrees’ values stand out from the trend. Structural heterogeneity is explored further by means of a bow-tie analysis, that shows clearly that the majority of relevant, in terms of transferred amount, transactions is settled between a smaller set of nodes that are associated to legal entities and that mostly belong to the strongly connected component. This observation brings to a more comprehensive inspection of differences between Italian regions and business sectors, that could support the detection and the understanding of the interplay between supply chains.Our results suggest that there is a general flow of money that seems to stream down from higher degree legal entities to lower degree natural persons, crossing Italian regions and connecting different business sectors, and that is finally redistributed through expenses sharing within families and smaller communities. We also describe a reference dataset and an empirical contribution to the study on financial networks, focusing on finer-grained information concerned about spending behaviour through wire transfers

Endogenous gentrification and housing price dynamics

Using a unique dataset of interest rates offered by a large sample of U.S. banks on various retail deposit and loan products, we explore the rigidity of bank retail interest rates. We study periods over which retail interest rates remain fixed ("spells") and document a large degree of lumpiness of retail interest rate adjustments as well as substantial variation in the duration of these spells, both across and within different products. To explore the sources of this variation we apply duration analysis and calculate the probability that a bank will change a given deposit or loan rate under various conditions. Consistent with a nonconvex adjustment costs theory, we find that the probability of a bank changing its retail rate is initially increasing with time. Then as heterogeneity of the sample overwhelms this effect, the hazard rate decreases with time. The duration of the spells is significantly affected by the accumulated change in money market interest rates since the last retail rate change, the size of the bank and its geographical scope.Housing - Prices ; Gentrification

Alternative Economic Indicators

Policymakers and business practitioners are eager to gain access to reliable information on the state of the economy for timely decision making. More so now than ever. Traditional economic indicators have been criticized for delayed reporting, out-of-date methodology, and neglecting some aspects of the economy. Recent advances in economic theory, econometrics, and information technology have fueled research in building broader, more accurate, and higher-frequency economic indicators. This volume contains contributions from a group of prominent economists who address alternative economic indicators, including indicators in the financial market, indicators for business cycles, and indicators of economic uncertainty.https://research.upjohn.org/up_press/1283/thumbnail.jp

Consumer intent to disclose personal information in ecommerce: a comparison of Estonia and the United States

2014 Fall.An online survey conducted among participants in the US (n=248) and Estonia (n=225) examined willingness to disclose and perceived risks pertaining to disclosing personally identifying information (PII, also referred to as personal data in Europe) in ecommerce, as well as attitude toward disclosure in general, and anxiety disclosing personal data. Additionally, the study investigated how willingness to disclose and perceived risk of disclosing personal data were affected by demographic variables, trust in the Internet and trust in institutions, the Big Five personality dimensions found in the psychology literature (neuroticism, openness, agreeableness, conscientiousness, and extraversion), and four sets of perceived shopping benefits (opportunity benefits, bargain benefits, purchase benefits, and expected privacy benefits). Despite Estonia's advanced adoption and progressive policies and practices toward the Internet, Americans were more willing to disclose, exhibited more positive attitudes, demonstrated less anxiety, and were less concerned about perceived risks. For Estonians, ecommerce experience, perceived purchase benefits, and trust in the Internet and institutions were significant predictors of willingness to disclose personal data. Americans who perceived purchase benefits were found to be the most likely to disclose PII, while Americans with lower levels of education were also more willing to disclose. The study utilized a 17-item list of potential disclosure items (name, email address, etc.) and showed these can be categorized reliably into six sub-indices: contact information, payment information, life history information, financial/medical information, work-related information, and online account information. Further, a reliable efficient, 20-item scale was developed that can be deployed in future studies investigating the Big Five personality traits. Online disclosure consciousness (ODC) was introduced as a framework to conceptualize and empirically measure the gap between one's willingness to disclose and perceived risk pertaining to the overall 17-item index used in the study, the sub-indices, and particular items. Using 7-point Likert-type measures, the results showed significant gaps among participants both within and across nations. A 5-scenario online disclosure consciousness model is presented to explain the tradeoffs involved in making a disclosure decision, with absolute willingness to disclose and absolute perceived risk on the two extremes and theoretical midpoint where the two competing motivations cancel themselves out. Changes in a person's position along the continuum are posited to be influenced by marketers' initiatives, personal experiences, and external factors. Implications for theory, consumers, marketing practice, and public policy are discussed. The findings suggest that willingness to disclose and risk aversion can and should be analyzed empirically together. Thus, the ODC model provides an alternative conceptualization to the ideas of the privacy paradox, privacy calculus, and privacy cost-benefit ratios found in the literature. The study suggests consumers have a responsibility to educate themselves about online disclosure practices and how to protect their privacy. The findings also suggest marketers and policy makers should recognize that data disclosed online are not all equally sensitive to consumers. However, fostering trust, reducing risks, and promoting benefits are essential to the future of ecommerce