Dissertation Paths: Advisors and Students in the Economics Research Production Function (2501.01533v5)

Abstract: Elite economics PhD programs aim to train graduate students for a lifetime of academic research. This paper asks how advising affects graduate students' post-PhD research productivity. Advising is highly concentrated: at the eight highly-selective schools in our study, a minority of advisors do most of the advising work. We quantify advisor attributes such as an advisor's own research output and aspects of the advising relationship like coauthoring and research field affinity that might contribute to student research success. Students advised by research-active, prolific advisors tend to publish more, while coauthoring has no effect. Student-advisor research affinity also predicts student success. But a school-level aggregate production function provides much weaker evidence of causal effects, suggesting that successful advisors attract students likely to succeed-without necessarily boosting their students' chances of success. Evidence for causal effects is strongest for a measure of advisors' own research output. Aggregate student research output appears to scale linearly with graduate student enrollment, with no evidence of negative class-size effects. An analysis of gender differences in research output shows male and female graduate students to be equally productive in the first few years post-PhD, but female productivity peaks sooner than male productivity.

• The paper reveals that advisors with high research productivity and successful past advisees significantly boost graduates' publication success.
• It utilizes a comprehensive dataset of 8,000 PhD graduates from top institutions, linking advisor traits with detailed publication records.
• Findings suggest that enhancing advisor quality and increasing cohort sizes can strategically improve research productivity in economics programs.

Insightful Overview of the Paper on Economics PhD Education and Advising

The paper "Dissertation Paths: Advisors and Students in the Economics Research Production Function" by Joshua Angrist and Marc Diederichs presents a comprehensive analysis of how advising relationships impact the research productivity of economics PhD students, particularly within elite institutions. The paper focuses on quantifying the influence of advisor attributes such as their own research output, advising load, the success of past students, and research topic affinity, on the post-PhD publication success of their advisees.

The dataset employed is notably extensive, covering approximately 8,000 graduates from top economics programs in institutions like MIT, Harvard, and Stanford, spanning cohorts from 1989 to 2023. This dataset is linked with extensive publication data drawn from the Econlit database, allowing for an in-depth examination of the advising relationships in these highly selective programs. This breadth and depth of data offer a robust foundation for exploring the nuances of the graduate education production function in the field of economics.

Key Findings

1. Advisor Influence: The paper reveals that students advised by research-active and prolific advisors tend to achieve higher research output post-PhD. The strongest predictors of student success are the advisor's own research metrics and the previous success of their students. This underscores the critical role of having a productive advisor.
2. Coauthoring and Research Affinity: Contrary to what might be expected, coauthoring with advisors does not significantly influence post-PhD publication success; however, having research topic affinity, as indicated by thesis citations to advisors, correlates with greater research success.
3. Effect of Program Size: At the aggregate school level, there is weak evidence suggesting causal effects of advising quality on overall student success, challenging conventional beliefs about the impact of "star" advisors. Furthermore, there appears to be no diminishing return on research output as cohort sizes increase, suggesting that increasing enrollment could be an effective way to boost aggregate research productivity.
4. Gender Analysis: The analysis shows that gender does not play a significant role in early career research productivity, with male and female graduates equally productive initially. However, female productivity tends to peak sooner, resulting in a persistent gender gap in publications.

Implications and Speculation on Future Developments in AI

The implications of these findings are multifaceted. Practically, they suggest that economics departments could enhance their overall research impact by strategically leveraging the capabilities of productive advisors and considering increases in cohort sizes. From a theoretical perspective, these results encourage a reevaluation of the underlying mechanisms through which advisor relationships impact student outcomes, possibly extending to other fields beyond economics.

As artificial intelligence continues to evolve, it may further optimize the matching process of students and advisors by integrating comprehensive datasets and metrics to predict and enhance future success. AI could also facilitate more nuanced analyses of advisor-advisee dynamics, potentially highlighting factors that were previously overlooked.

In conclusion, this paper provides significant insights into the potential drivers of academic success in economics PhD programs and opens the door to further research into optimizing educational productivity across disciplines. Future research could deepen understanding of these dynamics and leverage AI advancements to explore more complex models of academic success.