70% of Policies Now Rely on Data-Driven Evidence

By VirentaNews Staff — May 16, 2026

In a quiet corner of the British Library, tucked between treatises on economics and dusty volumes of parliamentary proceedings, sits a dog-eared 1998 government white paper titled ‘Modernising Government’. Within its unassuming covers lies a revolutionary declaration: policy should be shaped not by ideology or tradition, but by evidence. At the time, it was a radical notion—one that would ripple across continents, reshape institutions, and ignite fierce debate. Today, that idea underpins everything from vaccine rollouts to carbon taxation. Yet as science journalist Helen Pearson reveals in her compelling new book, ‘Beyond Belief: How Evidence Conquered Policy and Politics,’ the journey from principle to practice has been anything but linear. The promise of objective, data-driven governance remains tantalizingly out of reach, not because of a lack of data, but because of the messy, human realities of power, belief, and institutional inertia.

The Rise of the Evidence Movement

The idea that policy should be informed by scientific research is now so widely accepted it borders on cliché. Governments boast of ‘what works’ units, agencies commission randomized trials, and ministers cite meta-analyses in press conferences. But as Pearson illustrates, this paradigm is surprisingly recent. The term ‘evidence-based policy’ only entered the lexicon in the mid-1990s, inspired by the earlier success of evidence-based medicine. The UK’s New Labour government, eager to distance itself from ideological dogma, championed the approach as a hallmark of modernity. Soon, similar initiatives emerged in the U.S., Canada, Australia, and the EU. By the 2010s, the World Bank and OECD were promoting evidence-based frameworks globally. Yet Pearson argues that even as the infrastructure for evidence expanded—through bodies like the UK’s What Works Network—the actual impact remained inconsistent. In some areas, such as early childhood education and crime reduction, rigorous trials led to measurable improvements. In others, especially complex social issues like homelessness or inequality, the results were murkier, revealing the limits of data in the face of deep-seated structural challenges.

From Medicine to Policy: A Borrowed Framework

The roots of evidence-based policy lie in medicine, where clinicians began systematically reviewing clinical trials in the 1970s and 1980s to determine which treatments actually worked. This movement, led by figures like David Sackett and later popularized by the Cochrane Collaboration, emphasized randomized controlled trials (RCTs) as the gold standard. When policymakers adopted this model, they hoped to bring similar rigor to governance. But Pearson highlights a critical flaw: unlike medical interventions, which can often be isolated and tested in controlled settings, social policies operate within complex, dynamic systems. An education program’s success in one city may fail in another due to cultural, economic, or demographic differences. Moreover, RCTs are expensive, time-consuming, and often impractical for large-scale initiatives. Despite these challenges, the allure of ‘scientific’ policymaking proved irresistible. Institutions like the Abdul Latif Jameel Poverty Action Lab (J-PAL) began conducting large-scale field experiments in development economics, influencing programs from microfinance to deworming. Yet even these celebrated successes sparked debate over whether reducing human behavior to quantifiable outcomes risked oversimplifying the very problems they sought to solve.

The People Behind the Data

At the heart of Pearson’s narrative are the scientists, civil servants, and advocates who have fought to embed evidence into policymaking. Figures like Sir Michael Marmot, whose work on health inequalities reshaped public health strategies, and Sir Peter Gluckman, the former chief science advisor to the Prime Minister of New Zealand, emerge as quiet architects of change. These individuals navigated bureaucratic resistance, political skepticism, and public mistrust to build institutions that could generate and use evidence effectively. But Pearson also profiles lesser-known figures: data analysts in local councils, researchers in think tanks, and NGO workers who quietly compile reports that rarely make headlines but often shape decisions. Their motivations vary—some driven by idealism, others by frustration with wasteful or ineffective programs. Yet all share a belief that decisions affecting millions should not be made on instinct or ideology alone. Still, the book reveals how easily evidence can be sidelined when it contradicts political priorities, as seen during the early stages of the COVID-19 pandemic when scientific advice was sometimes ignored or selectively cited.

Consequences of a Half-Fulfilled Promise

The uneven implementation of evidence-based policy has real-world consequences. In public health, reliance on data has led to effective smoking cessation programs and improved maternal care. In criminal justice, some jurisdictions have reduced recidivism through rehabilitation models backed by research. But where evidence is ignored or manipulated, the costs are high: ineffective welfare programs, poorly targeted climate initiatives, and public distrust in institutions. Pearson warns that when evidence is used selectively—cherry-picked to justify pre-determined outcomes—it undermines the very credibility it seeks to build. Moreover, the emphasis on quantifiable metrics can lead to ‘metric fixation,’ where success is defined narrowly, and important but hard-to-measure outcomes, like community well-being or social cohesion, are overlooked. This creates a paradox: the more we rely on evidence, the more we may fail to capture the full picture of human experience.

The Bigger Picture

Pearson’s book matters because it forces us to confront a fundamental question: can governance ever be truly rational in a world governed by emotion, ideology, and power? The answer, she suggests, is not to abandon evidence, but to integrate it more thoughtfully. This means recognizing that data is not neutral—it is shaped by who collects it, how it is interpreted, and which questions are asked in the first place. It also means building systems where evidence is not just generated, but listened to, especially when inconvenient. In an age of misinformation and polarized discourse, the struggle for evidence-based policy is not just about efficiency—it’s about democratic integrity.

What comes next may not be a triumph of data over dogma, but a more mature understanding of their interplay. As Pearson shows, evidence is not a magic bullet, but a tool—one that must be wielded with humility, transparency, and a deep awareness of its limits. The future of governance may depend not on more studies or bigger datasets, but on cultivating a culture willing to listen, adapt, and, when necessary, admit uncertainty.

Source: New Scientist