Estimating Idea Production: A Methodological Survey

TL;DR

This paper surveys various methods for estimating idea production functions, providing guidance for researchers and highlighting challenges in empirical validation across different contexts.

Contribution

It offers a comprehensive review of methodologies for estimating idea production, including practical guidance and identification of key obstacles.

Findings

Different estimation methods suit various data scenarios

Case studies demonstrate practical application of methods

Highlights need for further empirical validation

Abstract

Accurately modeling the production of new ideas is crucial for innovation theory and endogenous growth models. This paper provides a comprehensive methodological survey of strategies for estimating idea production functions. We explore various methods, including naive approaches, linear regression, maximum likelihood estimation, and Bayesian inference, each suited to different data availability settings. Through case studies ranging from total factor productivity to software R&D, we show how to apply these methodologies in practice. Our synthesis provides researchers with guidance on strategies for characterizing idea production functions and highlights obstacles that must be addressed through further empirical validation.