Causal Claims in Economics

We analyze over 44,000 NBER and CEPR working papers from 1980–2023 using a custom language model to construct knowledge graphs that map economic concepts and their re- lationships. We distinguishes between general claims and those documented via causal inference methods (e.g., DiD, IV, RDD, RCTs). We document a substantial rise in the share of causal claims—from roughly 4% in 1990 to nearly 28% in 2020—reflecting the growing influence of the “credibility revolution.” We find that causal narrative complexity (e.g., the depth of causal chains) strongly predicts both publication in top-5 journals and higher cita- tion counts, whereas non-causal complexity tends to be uncorrelated or negatively associated with these outcomes. Novelty is also pivotal for top-5 publication, but only when grounded in credible causal methods: introducing genuinely new causal edges or paths markedly increases both the likelihood of acceptance at leading outlets and long-run citations, while non-causal novelty exhibits weak or even negative effects. Papers engaging with central, widely recognized concepts tend to attract more citations, highlighting a divergence between factors driving publication success and long-term academic impact.

Introduction. Economic research has undergone a significant transformation over the past few decades, Leading journals now prioritize studies employing these methods over traditional corre- Despite extensive discussions on methodological advancements, there is a lack of compre- We introduce a novel approach by constructing a knowledge graph for each paper in our

Discussion / Conclusion. This paper introduces a graph-based approach to quantifying the structure and content of First, the “credibility revolution” in economics has reshaped both how researchers establish Second, the drivers of top-tier journal publication often differ from those of long-term Overall, our results highlight the evolving nature of academic evaluation in economics.