Causal Claims in Economics - Prashant Garg; Thiemo Fetzer (Institute for Replication (I4R), 2024)
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.
Keywords: A10; B41; C18; C80; D83; 330; knowledge graph; credibility revolution; causal inference; narrative complexity; null results; private data; large language models