Staff Picks

"If the country is to continue leaning on research universities as the drivers of discovery, innovation, workforce development, and economic competitiveness, a national conversation about the future of these institutions is necessary. Unfortunately, the prevailing methods for comparing universities are insufficient for informing this discussion. Rankings that list universities like sports teams are arbitrarily weighted by the ranker, focused on branding the “best” for an arbitrary consumer and increasing traffic to the ranker’s website. Rankings that list universities like sports teams are arbitrarily weighted by the ranker, focused on branding the “best” for an arbitrary consumer and increasing traffic to the ranker’s website. To catalyze a more data-driven conversation, I developed an analytical framework for understanding the similarities and differences across top US research universities. Using principal component analysis, a technique that combines possibly correlated features (variables) in large datasets into components to visualize the drivers of variance, makes it possible to map universities by the relative influence of over a dozen characteristics shaping them." MLH

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cg

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"Productivity growth has shown a notable pickup since the fourth quarter of 2019, and some commentators cite artificial intelligence and other factors as reasons why technological progress can sustain this faster pace. Motivated by this consideration, we use a model designed to detect trend shifts to examine the behavior of productivity growth in the postwar period. The model allows for shifts between high- and low-growth productivity regimes and estimates the probability of being in one regime or the other. We find that recent data provide tentative support for a higher trend growth rate, with the model estimating about a 40 percent probability that the economy is in a high-growth productivity regime." MLH

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CG

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