A dynamical model of the U.S. mathematics graduate degree pipeline

TL;DR

This paper introduces a latent-stock compartmental model to analyze the U.S. mathematics graduate degree pipeline using only completion data, revealing structural shifts and dynamic trends over nearly five decades.

Contribution

It develops a novel latent stock dynamical framework that infers unobserved enrollment states from completion flows, advancing methodology for pipeline analysis with limited data.

Findings

The master's pathway's importance to PhD production increased then declined.

Completion hazards for degrees have risen, indicating faster turnover.

The model achieves about 4% error in fitting degree counts.

Abstract

We present a latent-stock compartmental framework for modeling degree production systems when only completion flows, rather than enrollments, are observed. Applied to U.S.\ mathematics degrees from 1969 to 2017, the model treats master's and PhD populations as latent compartments -- unobserved state variables that are inferred indirectly because they generate the observed completion flows -- with time-varying routing fractions and completion hazards. Using information-criterion model comparison across a grid of specifications, we find strong support for smooth nonlinear time variation in routing fractions and hazards, while models with explicit international forcing are disfavored. The preferred model achieves a log-scale root mean squared error of approximately 0.036, corresponding to a typical multiplicative error of about 4\% in fitted degree counts, and highlights key structural shifts in the graduate pipeline: the master's pathway became increasingly central to PhD production through the late twentieth century before weakening, while direct bachelor's-to-PhD entry remained small but persistent. Estimated completion hazards for both degrees rise over time, indicating faster effective turnover in the graduate compartments. Methodologically, our main contribution is a latent stock dynamical approach that recasts linked degreecompletion time series as a coherent stock-flow system when intermediate enrollments are unobserved, making explicit both what features of pipeline dynamics are identifiable from completion data alone and what limitations such data impose.