|
on Big Data |
| By: | Daron Acemoglu (MIT); David Autor (MIT); Jonathon Hazell (Princeton University and LSE); Pascual Restrepo (Boston University) |
| Abstract: | We study the impact of AI on labor markets, using establishment level data on vacancies with detailed occupational information comprising the near-universe of online vacancies in the US from 2010 onwards. We classify establishments as "AI exposed" when their workers engage in tasks that are compatible with current AI capabilities.We document rapid growth in AI related vacancies over 2010-2018 that is not limited to the Professional and Business Services and Information Technology sectors and is significantly greater in AI-exposed establishments. AI-exposed establishments are differentially eliminating vacancy postings that list a range of previously-posted skills while simultaneously posting skill requirements that were not previously listed.Establishment-level estimates suggest that AI-exposed establishments are reducing hiring in non-AI positions even as they expand AI hiring. However, we find no discernible impact of AI exposure on employment or wages at the occupation or industry level,implying that AI is currently substituting for humans in a subset of tasks but it is not yet having detectable aggregate labor market consequences. |
| Keywords: | artificial intelligence, displacement, labor, jobs, tasks, technology, wages |
| JEL: | J23 O33 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:pri:cepsud:279&r= |
| By: | Pytka, Krzysztof; Gulyas, Andreas |
| JEL: | J01 J3 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc21:242402&r= |
| By: | Michel Alexandre; Thiago Christiano Silva; Colm Connaughton; Francisco A. Rodrigues |
| Abstract: | The purpose of this paper is to assess the role of financial and topological variables as determinants of systemic risk (SR). The SR, for different levels of the initial shock, is computed for institutions in the Brazilian interbank market by applying the differential DebtRank methodology. The financial institution(FI)-specific determinants of SR are evaluated through two machine learning techniques: XGBoost and random forest. Shapley values analysis provided a better interpretability for our results. Furthermore, we performed this analysis separately for banks and credit unions. We have found the importance of a given feature in driving SR varies with i) the level of the initial shock, ii) the type of FI, and iii) the dimension of the risk which is being assessed – i.e., potential loss caused by (systemic impact) or imputed to (systemic vulnerability) the FI. Systemic impact is mainly driven by topological features for both types of FIs. However, while the importance of topological features to the prediction of systemic impact of banks increases with the level of the initial shock, it decreases for credit unions. Concerning systemic vulnerability, this is mainly determined by financial features, whose importance increases with the initial shock level for both types of FIs. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:556&r= |
| By: | Mark Braverman (Princeton University); Sylvain Chassang (Princeton University and NBER) |
| Abstract: | This paper explores whether Big Data, taking the form of extensive high dimensional records, can reduce the cost of adverse selection by private service providers in government-run capitation schemes, such as Medicare Advantage. We argue that using data to improve the ex ante precision of capitation regressions is unlikely to be helpful. Even if types become essentially observable, the high dimensionality of covariates makes it infeasible to precisely estimate the cost of serving a given type: Big Data makes types observable, but not necessarily interpretable. This gives an informed private operator scope to select types that are relatively cheap to serve. Instead, we argue that data can be used to align incentives by forming unbiased and non-manipulable ex-post estimates of a private operator’s gains from selection. |
| Keywords: | adverse selection, big data, capitation, health-care regulation, detail-free mechanism design, delegated model selection |
| JEL: | C55 D82 H51 I11 I13 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:pri:cepsud:282&r= |
| By: | Frederic Boissay; Torsten Ehlers; Leonardo Gambacorta; Hyun Song Shin |
| Abstract: | The business model of big techs rests on enabling direct interactions among a large number of users on digital platforms, such as in e-commerce, search and social media. An essential by-product is their large stock of user data, which they use to offer a wide range of services and exploit natural network effects, generating further user activity. Increased user activity completes the circle, as it generates yet more data. Building on the self-reinforcing nature of the data- network-activities loop, some big techs have ventured into financial services, including payments, money management, insurance and lending. The entry of big techs into finance promises efficiency gains and greater financial inclusion. At the same time, it introduces new risks associated with market power and data privacy. The nature of the new trade-off between efficiency and privacy will depend on societal preferences, and will vary across jurisdictions. This increases the need to coordinate policies both at the domestic and international level. |
| JEL: | E51 G23 O31 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:970&r= |
| By: | Riccardo Degasperi; Seokki Simon Hong; Giovanni Ricco (Departement of Economics - University of Warwick - University of Warwick [Coventry]) |
| Abstract: | We quantify global US monetary policy spillovers by employing a high-frequency identification and big data techniques, in conjunction with a large harmonised dataset covering 30 economies. We report three novel stylised facts. First, a US monetary policy tightening has large contractionary effects onto both advanced and emerging economies. Second, flexible exchange rates cannot fully insulate domestic economies, due to movements in risk premia that limit central banks' ability to control the yield curve. Third, financial channels dominate over demand and exchange rate channels in the transmission to real variables, while the transmission via oil and commodity prices determines nominal spillovers. |
| Keywords: | monetary policy,trilemma,exchange rates,monetary policy spillovers |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03373749&r= |
| By: | Salazar, Lina; Palacios, Ana; Selvaraj, Michael; Montenegro, Frank |
| Keywords: | Research and Development/Tech Change/Emerging Technologies, Crop Production/Industries |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae21:315408&r= |
| By: | Sofia Patsali (Université Côte d'Azur, France; CNRS, GREDEG); Michele Pezzoni (Université Côte d'Azur, France; CNRS, GREDEG); Fabiana Visentin (Maastricht University; UNU-MERIT) |
| Abstract: | This study investigates the effect of research independence during the PhD period on students' career outcomes. We use a unique and detailed dataset on the French population of STEM PhD students who graduated between 1995 and 2013. To measure research independence, we compare the PhD thesis content with the supervisor's research. We employ advanced neural network text analysis techniques evaluating the similarity between student's thesis abstract and supervisor's publications during the PhD period. After exploring which characteristics of the PhD training experience and supervisor explain the level of research similarity, we estimate how similarity associates with the likelihood of pursuing a research career. We find that the student thesis's similarity with her supervisor's research work is negatively associated with starting a career in academia and patenting probability. Increasing the PhD-supervisor similarity score by one standard deviation is associated with a 2.1 percentage point decrease in the probability of obtaining an academic position and a 0.57 percentage point decrease in the probability of patenting. However, conditional on starting an academic career, PhD-supervisor similarity is associated with a higher student's productivity after graduation as measured by citations received, network size, and probability of moving to a foreign or US-based affiliation. |
| Keywords: | Research independence, Early career researchers, Scientific career outcomes, Neural network text analysis |
| JEL: | D22 O30 O33 O38 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2021-35&r= |
| By: | Steinhart, Alexander; Zerres, Thomas; Zerres, Christopher |
| Abstract: | Die mit dem Stichtag 25. Mai 2018 in Kraft getretene Datenschutz-Grundverordnung (DS-GVO) hat zu einem enormen Anstieg der Aufmerksamkeit auf diesem Gebiet, sowohl bei den betroffenen Personen als auch bei den datenverarbeitenden Unternehmen geführt. Gemäß einer Aussage des Landesbeauftragten für den Datenschutz und die Informationsfreiheit Baden-Württemberg, Stefan Brink, konnte die Quote der aktiven Umsetzung der datenschutzrechtlichen Vorschriften in den Betrieben von bisher einem Drittel auf zwei Drittel mit positivem Trend angehoben werden. Seitdem die DSGVO Gültigkeit erlangt hat, werden Umfragen zur praktischen Umsetzung der DS-GVO durchgeführt. Kaum ein Ergebnis einer dieser Umfragen besagt, dass die Umsetzung bei allen Betrieben nahezu abgeschlossen wäre. Der Digitalverband Bitkom hat eine repräsentative Befragung unter 500 Unternehmen in ganz Deutschland durchgeführt. Fast eineinhalb Jahre nach dem Geltungsbeginn der DS-GVO hatten zwar zwei Drittel der Befragten die DS-GVO größtenteils umgesetzt, bei lediglich 25 Prozent war die Umsetzung bereits zum Zeitpunkt der Umfrage vollständig abgeschlossen. Auch daran lässt sich erkennen, dass sich in den ersten zwei Jahren einige Herausforderungen in der betrieblichen Praxis ergeben haben. Zum einen kennen die betroffenen Personen ihre Rechte besser als zuvor und zum anderen haben hohe Bußgelder bei Nichteinhaltung der gesetzlichen Vorschriften eine abschreckende Wirkung. Bisher sind diese zwar meist von erheblichen Bußgeldern und einer Abmahnwelle verschont geblieben, dennoch ist ein konsequentes Vorgehen der Aufsichtsbehörden zu beobachten. An der Verdreifachung des Arbeitsvolumens und der Aufstockung der Datenschutzbereiche sowohl bei den Behörden als auch in den Unternehmen lässt sich die gestiegene Bedeutung dieses Bereichs deutlich erkennen. Die Verarbeitung von personenbezogenen Daten stellt für viele Unternehmen einen äußerst hohen wirtschaftlichen Wert dar. Sie spielen eine derart große Rolle, dass sie bereits als "Währung der Zukunft" bezeichnet werden. Gerade in Zeiten von Big Data und vielen neuen technischen Möglichkeiten im Bereich der Verarbeitung personenbezogener Daten sollen durch die DS-GVO Verbraucherrechte und die Privatsphäre geschützt werden. Die rechtlichen Anforderungen stellen die Unternehmen vor komplexe Aufgaben. Dadurch entsteht eine neue Art der Zusammenarbeit zwischen den Unternehmen, ihren Kunden, Lieferanten und sämtlichen externen Geschäftspartnern. |
| Keywords: | Datenlöschkonzept |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ouwpmm:55&r= |
| By: | Joëlle Noailly, Laura Nowzohour, Matthias van den Heuvel |
| Abstract: | We build a novel news-based index of US environmental policy and examine how it relates to clean investments. Extracting text from ten leading US newspapers over the last four decades, we use text-mining techniques to develop a granular index measuring the salience of US environmental policy (EnvP) over the 1981-2019 period. We develop further a set of additional measures, namely an index of sentiment on environmental policy, as well as various topic-specic indices. We validate our index by showing that it correctly captures trends and peaks in the evolution of US environmental policy and that it has a meaningful association with clean investments, in line with environmental regulations supporting growing opportunities for clean markets. In firm-level estimations, we find that the salience of environmental policy in newspapers is associated with a greater probability of cleantech startups receiving venture capital (VC) funding and reduced stock returns for high-emissions firms most exposed to environmental regulations. At the aggregate level, we find in VAR models that a shock in our news-based index of renewable energy policy is associated with an increase in the number of clean energy VC deals and in the assets under management of the main benchmark clean energy exchange-traded fund. Overall, our EnvP index provides a lot of substantial information on environmental policy and can help assist the policy and financial community in understanding how these regulations are perceived by investors — providing many avenues for future research. |
| Date: | 2021–11–02 |
| URL: | http://d.repec.org/n?u=RePEc:gii:ciesrp:cies_rp_70&r= |
| By: | Mr. Yan Carriere-Swallow; Mr. Vikram Haksar; Emanuel Kopp; Gabriel Quiros; Emran Islam; Andrew Giddings; Kathleen Kao |
| Abstract: | The ongoing economic and financial digitalization is making individual data a key input and source of value for companies across sectors, from bigtechs and pharmaceuticals to manufacturers and financial services providers. Data on human behavior and choices—our “likes,” purchase patterns, locations, social activities, biometrics, and financing choices—are being generated, collected, stored, and processed at an unprecedented scale. |
| Keywords: | Data, finance, bigtech, competition, privacy, trade, policy coordination, global principles |
| Date: | 2021–10–06 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfsdn:2021/005&r= |
| By: | Kummer, Michael E.; Laitenberger, Ulrich; Rich, Cyrus E.; Hughes, Danny R.; Ayer, Turgay |
| Abstract: | We show that review platforms reduce healthcare interruptions for patients looking for a new physician. We employ a difference-in-differences strategy using physician retirements as a 'disruptive shock' that forces patients to find a new physician. We combine insurance claims data with web-scraped physician reviews and highlight a substantial care-gap resulting from a physician's retirement. We then show that online physician reviews reduce this gap and help patients find a new physician faster. Our results are robust to including a variety of controls and various instruments for the availability of physician reviews, but are not found for patients of nonretiring physicians. By reducing interruptions in care, reviews can improve clinical outcomes and lower costs. |
| Keywords: | Healthcare,Online physician ratings,Online physician reviews,Care-gap |
| JEL: | I1 I11 I2 O33 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:21075&r= |