Jeremy M. Berg, a former director of the National Institute of General Medical Sciences and editor of the Science journals, delivers a sobering mathematical reality check on the state of American biomedical research. While public discourse often fixates on political rhetoric, Berg cuts through the noise to reveal a disturbing trend: the shift toward "multiyear funding" is not a benevolent reform, but a mechanism that could drastically reduce the number of scientists receiving support. His analysis is essential reading because it exposes how a policy framed as "increasing flexibility" might actually function as a stealth cut to the research enterprise.
The Math of Scarcity
Berg begins by dismantling the assumption that more money automatically means more grants. He traces the history of the National Institutes of Health (NIH) budget, noting that while the appropriation "doubled" from 1998 to 2003, the success rate for researchers plummeted shortly after. "The success rate did increase somewhat during the 'doubling' but then it plummeted from > 0.30 (30%) to at or below 0.2 (20%) within a couple of years after the doubling ended," Berg writes. This counterintuitive drop occurred because the number of applications surged faster than the funding could keep up, a dynamic that has persisted for two decades.
The author constructs a predictive model based on inflation-corrected dollars and application volume, finding a strong correlation between these factors and grant success rates. He argues that the system is currently operating under a "mortgage" model, where the NIH commits funds for future years of a grant while only spending the first year's allocation from the current budget. "In essence, for each multi-year grant funded, NIH takes out a mortgage for the subsequent years of the grant," he explains. This historical practice has tied up approximately three-quarters of the available funds in legacy commitments, leaving little room for new investigators.
In essence, for each multi-year grant funded, NIH takes out a mortgage for the subsequent years of the grant.
This framing is crucial. It reframes the budget not as a simple checkbook, but as a complex ledger of future obligations. Berg's model successfully predicts the sharp drop in success rates following the budget doubling, validating his approach. However, one might argue that his model assumes a linear progression of grant sizes and application rates that may not account for sudden, non-linear shifts in scientific priorities or geopolitical disruptions.
The Multiyear Trap
The core of Berg's argument targets a specific policy shift: the move from incremental annual funding to upfront multiyear funding. The administration has proposed funding half of all competing research grants entirely in the year they are awarded, claiming this will "increase NIH budget flexibility by no longer encumbering large portions of each year's appropriation." Berg dismantles this logic with cold arithmetic. If the NIH funds a four-year grant upfront, it consumes four years' worth of budget in a single fiscal year.
"Suppose NIH funds a 4-year research grant using multiyear funding. This commits approximately 4 times as much funding as would have been committed had the grant been funded on traditional, annual basis," Berg writes. The consequence is stark: for every grant fully funded upfront, three other potential grants of the same size must be rejected. The author points out that the only real benefit to investigators is the ability to spend money faster, which is a minor administrative convenience compared to the loss of opportunities for other scientists.
He further notes that the administrative burden of non-competitive renewals is negligible, as these awards are historically granted 98-99% of the time. "These awards are historically awarded 98-99% of the time (and there are reasons when they are not) so this is not really much of a benefit and does give NIH less real oversight capabilities," he observes. This critique suggests that the policy removes a layer of accountability without providing a meaningful benefit to the research community.
The funds are not actually transferred from the treasury until they are spent, so there is a loss of cash flow to institutions as more spending authority sits in treasury accounts and not elsewhere.
Critics of this view might argue that multiyear funding provides stability that allows researchers to pursue riskier, long-term projects without the anxiety of annual renewals. While true in theory, Berg's data suggests that in a zero-sum budget environment, the stability of the few comes at the expense of the many. The trade-off is a reduction in the total number of independent investigators supported.
The Hidden Agenda
Berg's most provocative claim is that this policy shift is not a genuine efficiency measure, but a pretext for deeper cuts. He contextualizes the proposal within the administration's broader fiscal strategy, noting the timing is suspicious. "It would be possible to responsibly manage the transition to multiyear funding by adding funds to the NIH appropriation during the transition years to pay for the potential loss of projects," he writes. "But, it is noteworthy that the administration proposed this transition in the context of proposing a 40% cut in appropriations."
This observation aligns with the concept of Campbell's Law, which posits that when a quantitative measure becomes a target, it ceases to be a good measure. Here, the metric of "budget flexibility" becomes a tool to obscure the reduction in the number of funded projects. Berg suggests that the rationale for multiyear funding is "actually just a pretext for cutting funds to the extramural scientific enterprise and for other purposes." This reframing is powerful because it shifts the debate from administrative mechanics to the fundamental health of the scientific ecosystem.
The author's use of historical data from the 2000-2006 period, where programmatic changes during the budget doubling prevented success rates from rising too high, serves as a cautionary tale. It shows that when the NIH has excess funds, it often creates new, expensive programs rather than simply funding more individual grants. "These new programs allowed NIH to experiment with new approaches to supporting science and prevented grant success rates to rise well above historical norms that may have been deemed to be politically problematic," Berg notes. This historical parallel suggests that the current push for multiyear funding might be another way to manage political optics rather than scientific output.
Bottom Line
Jeremy M. Berg's analysis is a masterclass in using data to expose the hidden costs of administrative reform. His strongest argument is the mathematical inevitability that upfront funding reduces the total number of grants awarded, a fact that is obscured by the language of "flexibility." The piece's biggest vulnerability is its reliance on historical trends that may not hold if the scientific community radically changes how it applies for funding. However, the warning is clear: without a significant increase in the overall appropriation, the move to multiyear funding will shrink the American biomedical workforce, regardless of the administration's stated intentions.
The rationale for multiyear funding is actually just a pretext for cutting funds to the extramural scientific enterprise and for other purposes.