Sergio Pelaez

Ph.D. Student

Member Of:
  • School of Public Policy
Email Address:


Personal Pronouns:
He / Him

Sergio is a doctoral student in Public Policy at Georgia Tech and a Graduate Research Assistant at STIP. His research examines how policy, incentives, and social networks shape researchers' scientific and technological outcomes and the societal impacts of such activities. He is also interested in unraveling the determinants of business innovation and identifying its effects on firms' performance.

  • MA Economics of Public Policy, Universidad del Rosario
  • BA Economics, Universidad Autonoma de Manizales
  • BA Business, Universidad Autonoma de Manizales
Areas of
  • Business Innovation
  • Causal Inference
  • Development Economics
  • Industrial Organization
  • Machine Learning
  • Scientometrics


Research Fields:
  • Applied Microeconomics
  • Development Economics
  • Industrial Organization
  • Science, Technology, and Innovation Policy
  • Technology and Innovation

Recent Publications

Journal Articles

  • Analyzing research outcomes and spillovers at a US nanotechnology user facility
    In: Journal of Nanoparticle Research [Peer Reviewed]
    Date: November 2022


    This paper maps research outcomes and identifies spillover effects at a US University Research Center (URC) that offers user facilities for nanotechnology research. We use scientometric and network science approaches to analyze measures of topical orientation, productivity, impact, and collaboration applied to URC-related Web of Science abstract publications records. A focus is on the analysis of spillover effects on external organizations (i.e., non-affiliated users). Our findings suggest the URC’s network relies on external organizations acting as brokers, to provide access to the facilities to other external organizations. Analysis of heterophily indicates that collaboration among internal and external organizations is enhanced by the facilities, while articles written by a mix of co-authors affiliated with internal and external organizations are likely to be more cited. These results provide insights on how URCs with user facilities can create conditions for diverse collaboration and greater research impact.


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  • Taxation and innovation: evidence from Colombia
    In: Economics of Innovation and New Technology [Peer Reviewed]
    Date: November 2022


    We use firm-level data from a Colombian manufacturing survey, complemented with data from the tax department, to test the effect of firms’ total tax and contribution rate (TCR) on the ratio of innovation expenditures to sales. We construct a data panel from 2003 to 2018 comprising 104,762 observations and implement fixed effects and instrumental variables estimation methods. Our results suggest that an increase of one percentage point in direct taxation leads to a decrease of 0.10% in the probability that firms engage in innovation investments, and market power moderates this effect. We discuss distinctive features of the effect of taxation on innovation in emerging economies—one being the inability of local innovation clusters to temper it. Policy implications include considering modifications to the magnitude and composition of the TCR as an alternative to R&D tax credits.

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Working Papers

  • Large-Scale Text Analysis Using Generative Language Models: A Case Study in Discovering Public Value Expressions in AI Patents
    In: arXiv
    Date: May 2023

    Labeling data is essential for training text classifiers but is often difficult to accomplish accurately, especially for complex and abstract concepts. Seeking an improved method, this paper employs a novel approach using a generative language model (GPT-4) to produce labels and rationales for large-scale text analysis. We apply this approach to the task of discovering public value expressions in US AI patents. We collect a database comprising 154,934 patent documents using an advanced Boolean query submitted to InnovationQ+. The results are merged with full patent text from the USPTO, resulting in 5.4 million sentences. We design a framework for identifying and labeling public value expressions in these AI patent sentences. A prompt for GPT-4 is developed which includes definitions, guidelines, examples, and rationales for text classification. We evaluate the quality of the labels and rationales produced by GPT-4 using BLEU scores and topic modeling and find that they are accurate, diverse, and faithful. These rationales also serve as a chain-of-thought for the model, a transparent mechanism for human verification, and support for human annotators to overcome cognitive limitations. We conclude that GPT-4 achieved a high-level of recognition of public value theory from our framework, which it also uses to discover unseen public value expressions. We use the labels produced by GPT-4 to train BERT-based classifiers and predict sentences on the entire database, achieving high F1 scores for the 3-class (0.85) and 2-class classification (0.91) tasks. We discuss the implications of our approach for conducting large-scale text analyses with complex and abstract concepts and suggest that, with careful framework design and interactive human oversight, generative language models can offer significant advantages in quality and in reduced time and costs for producing labels and rationales.

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