The views presented in On Balance are those of the authors and do not represent the views of the Society, its Board, or its members. 

In December 2024, I stepped down—on my own terms—from a long and deeply rewarding career as an economist in the federal government. Since then, I’ve been observing the policy world from the outside, immersing myself in current debates, and reflecting on the evolving role of benefit-cost analysis (BCA). Old habits die hard, and I often find myself thinking about how I might approach today’s policy challenges—and what advice I’d offer to former colleagues and decision-makers navigating them.

One development that caught my attention was the January 2025 executive order establishing the Department of Government Efficiency (DOGE), with its promise to “maximize governmental efficiency and productivity.” The formation of DOGE teams—each composed of an engineer, a human resources specialist, an attorney, and a team lead—signaled a renewed focus on operational performance. Having spent decades inside government, I’m intimately familiar with the inefficiencies that persist. As I began exploring ideas, I reconnected with a former colleague whose insights on efficiency I deeply respect. Together, we wrote a paper to look at a central claim of a proposed Office of Personnel Management (OPM) rule: that reclassifying federal positions to at-will status would yield large gains in agency efficiency. Our working paper, “From Rhetoric to Reform: Civil Service Employment Structures and the Measurement of Government Efficiency” is now available.

The proposed “Schedule Policy/Career” classification would convert career federal employees in policy-influencing roles into at-will positions, effectively removing civil service protections. OPM argues that this shift would enhance managerial discretion—particularly in dismissing underperforming staff—and thereby improve efficiency. However, their analysis lacks quantification and rests on a baseline assumption: that merit-based protections inherently reduce efficiency. This contrasts with an alternative theory—that such protections help guard against inefficiencies stemming from politicized hiring and retention.

Using state-level data and established methods for assessing production efficiency, we found that while efficiency scores vary across states, the differences between employment systems are not statistically significant. In short, there’s no evidence that at-will systems outperform merit-based ones. While our results are very preliminary, they suggest that employment structure alone is not a reliable lever for improving government performance. If policymakers are serious about efficiency, they must move beyond assumptions and focus on empirically grounded interventions—those that address input composition, labor quality, and organizational design.

Recent essays in On Balance offer a timely lens through which to view these findings. Cary Coglianese critiques the performance metrics used during the first Trump administration, arguing they lacked rigor and were oftenmisleading. Don Kenkel, by contrast, cautiously supports regulatory budgets, recognizing their potential to promote discipline and cost-awareness inrulemaking. Both perspectives are valid—and together, they underscore a central tension in performance management: metrics can mislead, but they can also motivate. I’ve seen that firsthand. At the Department of Transportation, the regulatory budget exercise helped prioritize projects and align strategic goals. It wasn’t perfect, but it gave people something to rally around. When used judiciously, quantitative target-setting can sharpen priorities and foster accountability—even if the metrics themselves are incomplete. And when those targets are grounded in a structured economic framework, they become more than symbolic—they become testable claims about efficiency, as our paper illustrates.

Susan Dudley’s critique of the 2023 revisions to Circular A-4 resonated strongly. In my former role, one of my responsibilities was to implement those revisions, and one change posed immediate challenges: the increased elevation of unquantified economic impacts, driven by a concern that such effects might otherwise be overlooked. From where I sat, the issue wasn’t a failure to consider unquantified impacts—it was a failure to quantify impacts that could be measured. This shift in emphasis had an unintended consequence: it encouraged analytical complacency. I saw this dynamic play out in rulemaking, where complexity was sometimes used as a shield against deeper inquiry. When benefits were labeled “difficult to quantify,” the conversation often stopped. But that’s precisely where BCA must push further. If benefits are hard to quantify, we should ask why. Are they inherently elusive—or has the agency simply not done its homework? That question shaped much of my work in government. OPM’s analysis continues a troubling trend: assuming that if something isn’t quantified, it must be difficult to quantify—rather than considering that the impacts may be negligible or nonexistent. As our paper demonstrates, efficiency benefits aren’t inherently difficult to measure. The methods exist. It’s simply a matter of assembling the right data.

Lisa Robinson warns that BCA’s credibility and usefulness are at risk if agencies lack the staffing and funding to conduct high-quality analyses. It’s a valid concern—and one I encountered repeatedly during my career. I often worked with limited resources and tight timelines, yet found that even preliminary analysis could shape decisions when it was clear, empirical, and strategically framed. The work rarely speaks for itself; we must demonstrate its value first. That often means making do with what we have. We cannot lead with “we need more resources”—we must first show what thoughtful analysis can accomplish under constraint. Once that value is demonstrated, resources may follow. While we acknowledge the exploratory nature of our work, urgency and limited resources should never be an excuse to forego rigorous thinking—especially in the context of a proposed rule like OPM’s, where public comment is both expected and essential.

Stuart Shapiro’s call to explore alternative institutional homes for BCA beyond the regulatory domain is one I wholeheartedly support. His essay prompted a renewed appreciation for the role BCA can play in federal grant administration—as I observed at the Department of Transportation. Over the past few months, I’ve followed the impoundment and elimination of funding for numerous federal programs and found myself wondering whether those funds would have been awarded in the first place had the projects undergone rigorous benefit-cost scrutiny. Expanding BCA to other discretionary grantmaking activities—like how DOT handles infrastructure investment—is a natural and necessary evolution. While our paper did not apply BCA directly, it reflects the kind of empirical scrutiny that BCA encourages and points to another potential area of expansion: performance management and strategic planning. These domains, like regulation, are rife with assumptions about effectiveness and impact. BCA offers a framework for testing those assumptions—and for grounding public decisions in evidence rather than aspiration.

While I understand that many of my former colleagues are feeling uncertain—and that the current policy environment may seem like an existential threat to the role of economists and to our remarkable tool, BCA—I remain optimistic. The pendulum will swing back, because no other framework offers the same disciplined, evidence-based foundation for public decision-making. My advice is simple: stay constructive and look for opportunities to contribute—even in areas that may seem peripheral or unglamorous. BCA has the power to reshape how we think about policy, anchoring it in rigor, transparency, and clarity. I have faith that the demand for our work will return—and then some. Not only will it be restored, but I believe we’ll emerge with a broader charge and a deeper impact than ever before.

Deborah Vaughn Aiken retired from the federal government in December 2024, after more than 30 years of service. All opinions are her own and should not be attributed to any current or former employer.