Novelty and Impact of Economics Papers

TL;DR

This paper reframes novelty as a paper’s position in the evolving knowledge landscape, decomposing it into spatial novelty (semantic distance from prior work) and temporal novelty (engagement with a moving frontier). Using LLM-derived semantic isolation metrics on full-text economics articles, it demonstrates that temporal novelty mainly predicts citations while spatial novelty predicts disruption, revealing a structural trade-off between the two. It introduces a four-archetype typology (Consolidating, Outlying, Trendy, Trailblazing) and shows that Trailblazing papers—high in both dimensions—have a disproportionate likelihood of being both highly cited and disruptive. The work provides a scalable, two-dimensional toolkit for evaluating novelty and its multifaceted impact, with important implications for science policy and research strategy.

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.

Figures (16)

• Figure 1: Illustration of point-in-time isolation metrics. The blue point is the focal paper, and the green points are other papers.
• Figure 2: Illustration of dynamic isolation metrics. Given the focal paper published in year $Y$, $M_2(k, t)$ represents the $k-$NN distance using papers that published in or before year $Y+t$. For example, $M_2(k, -3)$ is calculated with papers that published in or before year $Y-3$.
• Figure 3: Feature importance (mean absolute SHAP) for predicting log citations.
• Figure 4: Feature importance (mean absolute SHAP) for predicting disruption.
• Figure 5: A stylized illustration of the four novelty archetypes. The blue point is the focal paper; green points are prior work. The orange circle represents the paper's isolation level (e.g., its 5-NN distance) at publication ($t=0$) and three years prior ($t=-3$). High spatial novelty is indicated by a large radius at $t=0$. High temporal novelty is indicated by a large change in the radius between $t=-3$ and $t=0$.