About the project
The world is moving in the direction of a data-driven economy: data creates more and more economic value. The data owners can earn large profits, influence society and contribute to a good life in the digital economy.
A data-driven economy requires the use of artificial intelligence (AI). Machine learning (ML) offers new tools for data processing and analysis and is necessary to turn data into something that creates economic value for the data owner. The goal of this project is to develop and use ML to improve the way social scientists can answer classic as well as emerging questions in economics that require the use of large datasets.
The first contribution of the research I propose is a significant theoretical advance which establishes that available matched employer-employee data can reveal the latent characteristics of individual workers and firms without invoking standard but strong assumptions on human behavior. The assumptions can be relaxed since the project uses artificial intelligence methods to analyse the data.
The second contribution of the proposed research is to develop the machine learning algorithms for the proposed identification strategy. The objective is to develop computational tools that would enable researchers to estimate latent worker and firm productivities with no more complexity then is involved in estimating wage regressions with worker and firm fixed effects. The first step of the proposed computational algorithm builds on algorithms currently used by Airbnb, Netflix, Microsoft, etc. Here Google's 2013 breakthrough in Language Processing seems most promising. The heart of the algorithm will be a novel hierarchical clustering strategy that is transparent and driven by the local properties of the network connecting workers and firms implied by economic theory
The third contribution of the proposed research will be in applying the proposed methods to register matched employer-employee data from Denmark/Norway to obtain substantive answers to a number of important empirical questions,
including employer-size wage differences, inter-industry wage differences, misallocation and aggregate income, mismatch over the business cycle, international trade and unemployment insurance.
Finally, the computational tools help understanding firms' smart pricing strategies and developing the appropriate responses for government regulation to the challenges of the 21st century digital economy.
Financing
The project is financed by the Research Council under the FRIHUMSAM program with 12 million NOK over a six-year period from 2023 to 2029.
