Companies make decisions about hiring, pricing, and investment based on incomplete and potentially misleading data about how their own production processes work. Current economic models for studying firms rely on shaky assumptions—for example, that managers choose inputs like labour and machinery at exactly the right moment, or that markets can be neatly divided into competitors. These assumptions can skew estimates of productivity, market power, and inequality, which in turn shape policy debates on everything from antitrust enforcement to macroeconomic stability. This project will develop new econometric tools that exploit richer data sources—such as firms’ own expectations and business network connections—to fix these flaws. It will also clarify what data are needed to model negotiations between firms, and combine machine learning with economic theory to define markets more rigorously. If successful, the research will give regulators and policymakers more reliable metrics for competition policy and economic forecasting. It will not change daily life directly, but it will improve the statistical infrastructure behind decisions that affect wages, prices, and market structure—systems most people never see.
View original technical description
The research this proposal describes will tackle existing challenges with the empirical assessment of firms' production processes and interactions. Firms' decisions and interactions lie in the background of the current debate on important themes such as inequality, market power, productivity, and the origins of macroeconomic fluctuations. The quantification of the main drivers of firms' behaviour is a key ingredient to many metrics colouring those debates. The identification of the causal links between firms' inputs and outputs and their market behaviour is thus fundamental as imprecisions here will steer conclusions on those big societal themes. Yet, (a) modern procedures to estimate production possibilities from conventional data rest on critical assumptions on how and when firms choose inputs that may unreliably drive estimates; (b) important multi-firm negotiation models lack clear guidelines on the data requirements for their empirical validity; and (c) systematic market definition protocols - key for research and competition policy - remain a challenge. This proposal offers ground-breaking research addressing these issues. The proposal incorporates new and increasingly available data to address the issues above; develops the necessary tools to take on the econometric challenges that these new data bring; and shows through substantive applications how this approach helps. The proposal covers methodological considerations and empirical applications on the analysis of firm interaction models in four workstreams that complement and build on one another. The workstreams will: (i) incorporate data on firm expectations and business networks to address the issues in (a) in two separate workstreams; (ii) formally explore the data demands for the accurate assessment of firm-to-firm relationship models noted in (b); (iii) and bring together machine learning methods and economics for market segmentation to address (c).
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know