Novelty and Impact of Economics Papers

TL;DR

This paper introduces a multidimensional framework for assessing scientific novelty in economics papers, distinguishing between spatial and temporal novelty, and linking these to different impact outcomes using semantic metrics derived from large language models.

Contribution

It develops a novel semantic isolation framework to measure a paper's position in the research landscape, revealing distinct effects of spatial and temporal novelty on impact.

Findings

Temporal novelty predicts citation counts

Spatial novelty predicts disruptive impact

Four archetypes of research impact profiles identified

Abstract

We propose a framework that recasts scientific novelty not as a single attribute of a paper, but as a reflection of its position within the evolving intellectual landscape. We decompose this position into two orthogonal dimensions: \textit{spatial novelty}, which measures a paper's intellectual distinctiveness from its neighbors, and \textit{temporal novelty}, which captures its engagement with a dynamic research frontier. To operationalize these concepts, we leverage Large Language Models to develop semantic isolation metrics that quantify a paper's location relative to the full-text literature. Applying this framework to a large corpus of economics articles, we uncover a fundamental trade-off: these two dimensions predict systematically different outcomes. Temporal novelty primarily predicts citation counts, whereas spatial novelty predicts disruptive impact. This distinction allows us to construct a typology of semantic neighborhoods, identifying four archetypes associated with distinct and predictable impact profiles. Our findings demonstrate that novelty can be understood as a multidimensional construct whose different forms, reflecting a paper's strategic location, have measurable and fundamentally distinct consequences for scientific progress.