Economists and policymakers alike rely on empirical methods to understand how public policies shape our lives. Whether a government raises the minimum wage, invests in healthcare, or reforms immigration rules, the central question is always whether a policy causes meaningful changes—or merely coincides with broader trends. Econometric methods, with their careful use of statistical tools, help disentangle genuine cause-and-effect relationships from mere correlations. By doing so, they ensure that policy decisions rest on solid evidence rather than on guesswork.
For policymakers, the stakes could not be higher. Allocating public resources to ineffective programs not only wastes money but can also undermine public trust. Strong empirical analysis lets governments focus on what genuinely works—whether that means improving job prospects, boosting public health, or alleviating poverty. Moreover, the ability to demonstrate a clear causal link between a policy and its outcomes often determines whether the initiative can be expanded or replicated.
One of the most direct and clearest ways to verify that a policy truly causes an outcome is through a randomized controlled trial (RCT). By randomly assigning participants to either a treatment group or a control group, RCTs balance out differences in both observed and unobserved characteristics. For instance, in the early 1960s, psychologist David Weikart and his team launched the Perry Preschool Project to examine whether high-quality early childhood education could change life outcomes for disadvantaged children. The researchers employed an RCT, assigning one group of low-income preschoolers to an intensive education program while another similar group did not receive the intervention. Over time, children who participated in the Perry Preschool program consistently showed stronger academic performance, higher graduation rates, and improved earnings in adulthood.
Similarly, in 2008, a research team including Heidi Allen, Katherine Baicker, Mira Bernstein, Amy Finkelstein, Jonathan Gruber, Joseph P. Newhouse, Eric Schneider, Jae Song, Sarah Taubman, Bill Wright, and Alan Zaslavsky leveraged a unique opportunity in Oregon where state officials used a lottery to distribute a limited number of Medicaid enrollment slots. This lottery effectively created an RCT, as some individuals randomly received the chance to sign up for Medicaid while others did not. By comparing outcomes—such as healthcare utilization, financial strain, and mental health—between the insured and uninsured groups, the study provided rigorous evidence of how gaining coverage affects people’s lives. The study found that Medicaid coverage increased health care use, including use of preventive services and visits to emergency departments; reduced financial strain; reduced depression, and improved self-reported health. In both cases, policymakers gained solid evidence that these programs could substantially improve people’s lives, guiding their decisions on where to invest public funds. By showing not only whether a policy works but also how much it can matter, these RCTs help officials allocate resources effectively and justify expansions or reforms to gain broader political and community support.
Yet large-scale randomization is not always feasible, especially for policy decisions affecting entire cities, regions, or countries. In these scenarios, researchers turn to quasi-experimental methods, which strive to approximate random assignment using observational data. Options abound: instrumental variables, regression discontinuity designs, synthetic control, and difference in differences are among the most popular. Each tackles the challenge of selection bias in a distinct way, ensuring that any measured effect can be plausibly credited to the policy rather than to confounding factors.
For example, in 1980, approximately 122,000 Cuban refugees arrived in Miami over a few short months, an event known as the Mariel boatlift. Economist David Card seized on this unexpected influx as a natural experiment to investigate how a rapid increase in the supply of lower-skilled workers might affect wages and employment for existing residents. To isolate the impact of the boatlift from other economic factors, Card compared labor market trends in Miami to those in similar four US cities that had no comparable surge in immigration—effectively using these “control” cities as a baseline. Contrary to expectations rooted in standard supply-and-demand theory, his analysis revealed that native Miami workers did not experience a substantial drop in wages or employment, prompting a major re-examination of how labor markets absorb new arrivals. For policymakers, the study’s implications are profound. This evidence helps inform decisions about immigration policy, social services, and economic planning by demonstrating that even large-scale inflows of people may not necessarily depress local labor conditions, depending on factors such as industry mix, local demographics, and broader economic trends deaths relative to states that did not enact such reforms.
Working successfully with practitioners to apply these insights in real-world settings requires thorough engagement from the start of any study. Collaborating with government agencies, nonprofits, and community organizations early on ensures that the research questions align with urgent on-the-ground needs, while regular communication keeps all parties informed about progress and any pivots in methodology if local conditions change. Practitioners benefit from clear, user-friendly interpretations of findings, including tangible estimates of cost, feasibility, and impacts on budgets or staffing. When initial results are shared, inviting practitioner feedback about what worked, what did not, and how the intervention might be improved, it fosters a cycle of ongoing refinement. With each iteration, interventions can be more precisely targeted, and subsequent analyses can build a deeper evidence base. Over time, such collaborations can become standing partnerships or working groups, continuously matching rigorous econometric methods to pressing policy challenges and ensuring that new research remains relevant to real-world decision-making.
Ultimately, the hallmark of sound econometric research is the ability to separate correlation from causation—even in messy real-world environments. Good policy depends on understanding why an outcome occurs, not just whether it does. Through continuous collaboration, evaluation, and refinement, researchers and practitioners can build an evidence base that supports policies truly capable of improving lives. And by communicating these findings clearly—showing which policies drive outcomes and which do not—policymakers can make more confident decisions about where to allocate resources.
In an era marked by rapid economic shifts and pressing social challenges, rigorously evaluating policy outcomes has never been more vital. As governments and communities strive to address our most difficult issues, econometric tools offer a clear path toward evidence-based decision-making. By grounding policies in robust data and transparent analysis, stakeholders can foster innovation, improve accountability, and ensure that limited resources deliver meaningful benefits where they matter most.
Reading Suggestion: For those eager to explore these themes further, I highly recommend Mostly Harmless Econometrics by Joshua Angrist. This accessible guide breaks down the core techniques behind causal analysis and offers practical advice for applying them in real-world scenarios.
About the Author
- Sezen Ozcan Onal is a researcher at the Center for Public Health Systems. She is interested in applying a modern econometric toolkit to address policy-relevant questions in public health research. Dr. Ozcan holds a PhD in Economics from the University of Wisconsin-Milwaukee and a master’s degree in Economics from the University of Missouri-Columbia.
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