Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308358 
Year of Publication: 
2024
Series/Report no.: 
CESifo Working Paper No. 11462
Publisher: 
CESifo GmbH, Munich
Abstract: 
We analyze over 44,000 economics working papers from 1980–2023 using a custom language model to construct knowledge graphs mapping economic concepts and their relationships, distinguishing between general claims and those supported by causal inference methods. The share of causal claims within papers rose from about 4% in 1990 to 28% in 2020, reflecting the "credibility revolution." Our findings reveal a trade-off between factors enhancing publication in top journals and those driving citation impact. While employing causal inference methods, introducing novel causal relationships, and engaging with less central, specialized concepts increase the likelihood of publication in top 5 journals, these features do not necessarily lead to higher citation counts. Instead, papers focusing on central concepts tend to receive more citations once published. However, papers with intricate, interconnected causal narratives—measured by the complexity and depth of causal channels—are more likely to be both published in top journals and receive more citations. Finally, we observe a decline in reporting null results and increased use of private data, which may hinder transparency and replicability of economics research, highlighting the need for research practices that enhance both credibility and accessibility.
Subjects: 
knowledge graph
credibility revolution
causal inference
narrative complexity
null results
private data
large language models
JEL: 
A10
B41
C18
C80
D83
Document Type: 
Working Paper
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