Journal of Applied Economics

Beyond Conventional Frameworks: AI and Internet Data Reshaping Applied Economics

Artificial intelligence and internet-based data are transforming how economists measure, model, and explain economic behaviour. Advances in machine learning, causal inference, and Large Language Models let researchers extract high-dimensional information from unstructured text and exploit web sources at unprecedented scale. Search-engine queries, social-media streams, scraped web content, and geospatial and mobile data complement traditional micro- and macro-datasets, enabling new indicators of sentiment, activity, and expectations, alongside high-frequency nowcasting and forecasting. This Article Collection brings together rigorous applied-economics research that uses these methods and data sources to address contemporary economic questions, combining methodological innovation with sound empirical design and clear economic interpretation across micro- and macroeconomic fields.

These developments matter because they widen both the questions economists can ask and the evidence available to answer them. Internet data are timely and granular, and often available where official statistics are delayed or absent, supporting near real-time monitoring of activity, labour markets, prices, and well-being. AI methods can uncover non-linear patterns, improve prediction, and support credible causal analysis when applied transparently. Realising this potential demands attention to data quality, representativeness, reproducibility, and the interpretability and fairness of algorithms. By pairing strong applications with honest discussion of limitations, the Collection seeks to strengthen evidence-based policy on sustainability, inequality, gender equality, and development, and to guide researchers, statistical agencies, businesses, and policymakers working in data-rich environments.

We welcome original research articles, including empirical studies, methodological contributions, and rigorous applied work that fit the scope of the Journal of Applied Economics in micro- and macroeconomics. Submissions developed from work presented at the CARMA conference (Internet and in Economics and Social Sciences) are particularly encouraged, though the call is open to all. Topics include, but are not limited to:

• Machine learning econometrics and causal ML
• Large language models and generative AI in economics
• NLP and text based economic indicators
• Google Trends and search data for nowcasting
• Social media analytics and opinion mining
• Web scraping and online content analysis
• Financial markets analysis and prediction
• Labour markets and skills intelligence from online platforms
• Consumer behaviour and sentiment from digital traces
• Sustainability, ESG and development indicators from alternative data
• Official statistics enhanced with web data and AI
• Tourism, mobility and urban economics
• Algorithmic bias, fairness and responsible AI
• Digital platforms, data economy governance and regulation
• Inequality, the digital divide and gender in digital contexts

When submitting to this Article Collection, please be sure to select the name of the Collection when prompted in the submission portal!

Lisa Crosato is Associate Professor of Economic Statistics in the Department of Economics at Ca’ Foscari University of Venice, Italy. She holds a PhD in Quantitative Models for Economic Policy from the Catholic University of the Sacred Heart and previously held a position at the University of Milano-Bicocca. She is a member of the steering committee of the International Conference on Advanced Research Methods and Analytics (CARMA). Her research focuses on business and economic statistics, with particular attention to the use of website and web-based data for small and medium-sized enterprises, supervised and unsupervised learning, and robust statistical methods. Her work has appeared in journals including Statistical Papers, Economics Letters, Annals of Operations Research, and Technological Forecasting and Social Change.

Caterina Liberati is Associate Professor of Statistics for Economics at the University of Milano-Bicocca, where she directs the WebSight Observatory. She is a Council Member of the Statistical Measurement for Economic Analysis group (Italian Statistical Society) and a former Council Member of the International Society for Business and Industrial Statistics (ISBIS). Her research focuses on supervised and unsupervised machine learning, unconventional data sources, and the digital economy, with particular emphasis on the use of web-scraped and internet-based data to study firm behaviour, credit risk, and innovation in SMEs. She has worked extensively on credit-risk and default prediction for SMEs, and her work has appeared in journals including Annals of Operations Research, Technological Forecasting and Social Change, and Applied Stochastic Models in Business and Industry. Since 2022 she has been a member of the Steering Committee of CARMA, the International Conference on Advanced Research Methods and Analytics, a conference devoted to AI, internet data, and computational methods in the social sciences.

Josep Domènech is Full Professor of Applied Economics in the Department of Economics and Social Sciences at the Universitat Politècnica de València, Spain, with a multidisciplinary background in business, economics, and computer engineering. He is a founder and steering-committee member of the International Conference on Advanced Research Methods and Analytics (CARMA). He has served as principal investigator of numerous national research projects on web-based economic indicators and the digital footprint of firms. His research interests include internet economics, search-engine data such as Google Trends, the digital economy, and web mining, and his work has been widely published and cited in the field.

Konstantinos P. Tsagarakis is Professor of Environmental Economics and Technology at the School of Production Engineering and Management, Technical University of Crete, Greece. His research spans environmental and resource economics, the circular economy, sustainability, and water policy, and increasingly draws on internet-based and data-mining methods, including search-engine and social-media analytics. He has extensive editorial and peer-review experience with international journals and is active in organising scientific conferences in the field. He has published widely and collaborates with research groups across Europe on data-intensive approaches to economic and environmental questions.

The Guest Advisors acknowledge the use of Claude in the creation of this proposal.

The Guest Advisors do not have any conflicts of interest to disclose.

For more information regarding this Collection, please contact the Commissioning Editor, Dr. Molly Cole, at molly.cole@taylorandfrancis.com.