Which firms and consumers benefit from the increasing use of data for targeted advertising? This paper develops a model of search frictions in product markets where heterogeneous firms use data to target different consumers. Firms endogenously adopt Big Data technologies that lower the cost of targeting. Using firm-level balance sheet data combined with a technology survey for France, I show that firms analyzing Big Data from social media (i) use it for advertising, (ii) are larger, (iii) are more productive, and (iv) face higher fixed costs. I calibrate the model to France in the late 2010s and show that it replicates the positive correlation between Big Data use, firm size, and fixed costs. The model implies that a technological improvement increasing the availability of consumer data benefits only large firms, which allocate more resources to fixed targeting costs, and shifts consumption toward high-valuation consumers. The equilibrium is inefficient and expenditure in targeting is excessive because firms do not internalize that targeting high-valuation consumers crowds out competitors from reaching them. Welfare in the competitive equilibrium rises with a digital advertising tax, which reduces the intensive use of targeted advertising by large firms. Finally, I use the model to evaluate the aggregate effects of lifting the GDPR. Relaxing consumer data regulation would encourage Big Data adoption but increase firm concentration.

Presented at: UCL Macro Reading Group (2024), LSE Student Seminar (2024), BSE PhD Jamboree (2024), Universitat de València (2024), 22nd ZEW Conference on the Economics of ICT (2024) , XXVII Workshop on Dynamic Macroeconomics (2024), CREI Macroeconomics Lunch Workshop (2024),  SAEe (2024), VIII Winter Bellaterra Macroeconomics Workshop (2025), Lausanne PhD Macro Conference (2025)*   

Previous title: Firms and Big Data: Adoption, Use and Impacts. (* scheduled). 

We study how manufacturing establishments in India adapt to flood risk. Combining establishment-level data with geo-coded flood records and regional economic indicators, we examine how production and investment decisions respond to flood events conditional on historical exposure. We find that investment is more resilient in high-risk areas, consistent with forward-looking adaptation. To rationalize these findings, we propose a firm dynamics model featuring flood risk and private insurance to floods through a flood preventing capital. To overcome the course of dimensionality in this dynamic spatial model with aggregate uncertainty, we resort to Deep Learning techniques. We employ the model to quantify the aggregate economic impact of floods and evaluate the effectiveness of adaptation in mitigating climate-induced damages, and find that the proposed mechanism can replicate the patterns in the data.

Presented at: CREI International Lunch (2023), BSE PhD Jamboree (2023), PSE Summer School on Climate Change (2023), SAEe (2023)

Previous title: The Transmission of Climate Shocks: The Case of Floods in India

This paper explores how data protection regulations influence innovation across the firm distribution. Using evidence from the EIB Investment Survey—which links firms’ technology use with balance sheet information—we show that data use is associated with higher productivity. Data-intensive firms are more likely to innovate, invest in product development, and introduce products new to global markets. Motivated by this relationship, we study the impact of the General Data Protection Regulation (GDPR) on European firms’ innovation activities. To capture heterogeneous effects, we exploit cross-country variation in the enforcement intensity of national data protection authorities. Following the introduction of the GDPR, firms reduce software investment at the extensive margin, and R\&D expenditures decline for data-intensive firms. The link between data use and innovation is stronger for small firms, which cut innovation spending more sharply after the GDPR’s implementation.