David
Bounie
Professor at Télécom Paris, Institut Polytechnique de Paris
Visiting Professor, McGill University
Professor at Télécom Paris, Institut Polytechnique de Paris
Visiting Professor, McGill University
"Leveraging granular, high-frequency data to understand how money moves, consumers behave, markets respond, and regulation adapts."
I am a Full Professor at Télécom Paris and Institut Polytechnique de Paris, and currently a Visiting Professor at McGill University for the 2026 academic year. I am also affiliated with the Center for Research in Economics and Statistics (CREST), a joint research center involving École Polytechnique, ENSAE, and Télécom Paris.
My research focuses on digital economics, digital finance, and computational statistics, with a distinctive emphasis on leveraging granular, high-frequency data (e.g., card transactions, mobile phone data) to address questions in urban and spatial economics, public policy, and computational statistics.
I collaborate with interdisciplinary teams—spanning economics, statistics, computer science, and law—from universities, public institutions, and private research companies. My work is published not only in academic journals but also in working paper series by central banks, including the European Central Bank, Federal Reserve Bank of Boston, Bank of Canada, De Nederlandsche Bank, Deutsche Bundesbank, Austrian Central Bank, and Colombian Central Bank.
I co-founded the Research Chair in Digital Finance (with Marianne Verdier, Université Paris 2 Panthéon-Assas), in partnership with the Groupement des Cartes Bancaires CB, Caisse des Dépôts, INSEE, and Institut Louis Bachelier.
This article uses geo-located card transactions to build consumer mobility and inter-regional trade measures, allowing comparison of online and offline inter-regional purchases on a common sample of merchants which each conduct both offline and online card transactions.
This article analyzes how take-it-or-leave-it offers (TIOLI) and auctions impact the selling strategy of a data intermediary, the price of information and the amount of consumer data collected.
This article analyzes the relationship between privacy protection and market competition. Firms strategically choose the number of consumers on whom they collect data — the extensive margin of privacy — as well as the precision of information — the intensive margin of privacy.
This paper investigates the effects of the pandemic containment periods in France on individuals' movements, expenditure and adaptation to the shock, using billions of French bank card transactions measured before and during the COVID-19 pandemic.
This paper investigates the strategies of a data broker in selling information to one or to two competing firms that can price-discriminate consumers.
Using anonymised French transaction and bank data, this paper tracks consumption and savings dynamics from the onset of the pandemic: a severe drop during lockdown, a strong summer rebound, and a sharp rise in aggregate household savings that was highly concentrated at the top of the income distribution.
Using score-matching and difference-in-differences on roughly 275,000 French merchants, this paper shows that accepting contactless card payments in 2018 raised card-sales value by 15.3% on average, with a positive spillover of 1.3% on contact card sales at the same merchants.
This paper studies how competing data intermediaries collect and sell consumer information to firms for price discrimination. It shows that competition among intermediaries benefits consumers, both by intensifying downstream competition and by curbing the volume of data collected, with implications for how merger policy should treat large data intermediaries.
This paper introduces a new algorithmic framework, Refined Random Search, for computing projection-based statistical depths at scale in high-dimensional settings. Run on GPUs, the method delivers up to a 7,000-fold speedup over existing approaches while improving precision, and is released as an open-source Python library.
Using geo-located card transaction data, this paper evaluates the local economic impact of Paris's large-scale cycling infrastructure investment, the Plan Vélo. Areas that gained better access to the new network saw brick-and-mortar spending rise by 4.4% per quarter, with the largest gains for smaller and younger businesses, alongside a measurable drop in car traffic.
Combining high-frequency mobile phone and card transaction data from the Lyon metropolitan area, this paper identifies two opposing demand shocks: a 1-point increase in working from home raises local spending by 1%, while a 1-point rise in workplace absence reduces it by 1.3%, clarifying whether telework redistributes or genuinely reduces local demand.
This paper combines high-frequency mobile phone location data with card transaction records to study how the number of people present in a zone at a given moment ("real-time population") relates to local spending, estimating this elasticity across more than a thousand zones in the Lyon metropolitan area.
Using high-frequency card transaction and reimbursement data spanning 2018–2023, this paper estimates weekly and annual return rates for online and in-store purchases, isolating the effects of e-commerce growth and the COVID-19 shock, and finds lasting changes in online return behavior for durable goods with potential environmental costs.
Address19 Place Marguerite Perey
Office 3A483, 91120 Palaiseau, France
Emaildavid.bounie@telecom-paris.fr
Phone+33 1 75 31 98 80