Knowledge Graph Predictors of Publication and Citations

We use three categories of knowledge‐graph measures—(1) narrative complexity, (2) novelty and gap filling, and (3) conceptual importance and diversity—to analyze how papers in economics fare in terms of publication outcomes and subsequent citation counts. The findings reveal important differences between what top journals reward at the submission stage and what ultimately drives long‐run academic influence.

Key Findings

Causal Complexity is Rewarded: Deep, well‐structured causal arguments (for example, multiple causal paths or longer causal chains) significantly boost both the likelihood of placing in a top journal and earning more citations over time. Non‐causal complexity, by contrast, does not improve outcomes and can even be negatively associated with acceptance and citations.
Novelty Helps With Publication, Especially When It’s Causal: Papers that introduce genuinely new causal edges or paths are more likely to appear in elite journals. However, this “causal novelty” does not necessarily translate into higher citation counts. In other words, novelty helps in getting published but does not guarantee wider influence.
Gap Filling Matters When It’s Causal: Connecting underexplored topic pairs (so‐called “gap filling”) can help secure top‐tier publication—but only if these links are substantiated by credible causal methods. Neither causal nor non‐causal gap filling shows a clear link to higher long‐term citations.
Central Topics Drive Citations: Papers that engage with well‐established, high‐visibility concepts (like “wage inequality” or “health and education”) tend to receive more citations once published. However, top journals tend to favor specialized or frontier topics if they are backed by strong causal evidence, suggesting an editorial preference for new territory.
Constructing Persuasive Causal Narratives Is Essential: Causal “depth” matters. Papers that weave together multiple rigorously identified factors into a focused narrative (many sources, fewer outcomes) do better both in top‐tier outlets and in citations. Conversely, layering many correlational results without causal identification offers little payoff.

Narrative Complexity and Research Outcomes

Having more causal edges, more unique causal paths, and longer causal chains is strongly associated with higher publication success in top 5 outlets and with more citations.
Non‐causal complexity—simply adding more correlational relationships—does not boost outcomes and sometimes correlates negatively with citation counts.

Novelty, Gap Filling, and Editorial Reception

Novel causal edges or paths are consistently associated with greater chances of top‐tier acceptance.
Novelty in non‐causal claims does not show a strong relationship with either publication or citations.
Gap filling (introducing rare or underexplored concept pairs) helps mainly at top 5 journals, but mostly when it involves credible causal evidence.
Neither causal nor non‐causal gap filling has a consistent effect on long‐term citation counts.

Conceptual Importance and Diversity

Papers focusing on high‐centrality concepts (well‐established, widely connected topics in economics) accumulate more citations, even if they are not favored by top 5 journals at the submission stage.
Mixing central and niche concepts can appeal to diverse audiences and help citations but does not clearly boost top 5 acceptance.
A higher ratio of causal “sources” to “sinks” (many causes, fewer outcomes) is linked to both better top‐tier placement and stronger citation performance.

Some Context on the Top 5

Types of papers published in top 5 journals

With fixed supply of top 5, and an increasing demand for publication avenues, the share of papers total papers published in top 5 has reduced.
This also increases the status of a Top-5
Some fields have gained disproportionate interest by the Top-5: Behavioural and Development.

Distribution of Citation Percentiles by Journal Category

This figure displays kernel density plots of the citation percentiles for papers published in Top 5, Top 6-20, and Top 21-100 journals.
The plot shows that while papers published in higher-ranked journals tend to receive more citations on average, the most highly cited papers are more evenly distributed across journal categories.
This suggests that exceptionally influential papers can emerge from a wide range of journals.

Concept Centrality in Economics

Our analysis leverages eigenvector centrality to measure how “influential” different economic concepts (JEL codes) are within two separate knowledge graphs: one built from non‐causal claims, the other built only from edges supported by causal inference methods. This approach reveals which topics dominate each domain of economic research and how these patterns have shifted over time.

Top 20 JEL Codes in Non‐Causal vs. Causal Subgraphs

Most Central in Non‐Causal Subgraph: G21 (Banks and Mortgages), J31 (Wage Structure), and I24 (Education and Inequality) lead in overall centrality. They appear repeatedly in correlational or theoretical discussions, making them prominent across large swaths of the economics literature.
Most Central in Causal Subgraph: I24 (Education and Inequality) ranks at the very top followed by J13 (Fertility and Family) and I21 (Analysis of Education). These areas tend to use well‐identified methods such as natural experiments or RCTs, reflecting the popularity and feasibility of credible causal designs in education and family/health topics.
Skewed Distribution: A handful of JEL codes capture most of the centrality, especially in the causal graph. This skew suggests that many causal efforts focus on a relatively narrow set of issues while other topics remain on the periphery.

Rise and Fall of Concept Centrality Over Time

(A) Non‐Causal Subgraph Over Time

In the non‐causal subgraph, we observe a marked decline in the centrality of traditional macro/finance concepts such as Growth & Productivity (O49), Interest Rates (E43), and Foreign Exchange (F31).

Meanwhile, topics connected to inequality, behavior, and health—like Behavior & Decisions (D91), Minorities & Discrimination (J15), and Health & Inequality (I14)—consistently gain prominence.

This shift suggests that while classic macroeconomic and financial themes remain part of the discourse, the field’s overall attention is pivoting toward more applied or “micro” topics.

(B) Causal Subgraph Over Time

For the causal subgraph, the same broad pattern emerges but with an even stronger tilt toward education, family, and health.
Codes such as Education & Inequality (I24) and Health & Inequality (I14) rise steeply in centrality, underscoring how the credibility revolution has steered economists toward policy‐relevant areas amenable to rigorous identification.
Traditional macro/finance codes, by contrast, see flatter or declining trajectories in causal research, reflecting greater challenges in finding quasi‐experimental or experimental settings in those domains.

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