INSTITUTE FOR POLICY RESEARCH

New IPR Research: December 2025

This month’s new research from our faculty experts examines how state reproductive rights policies relate to preterm birth rates and which kinds of messages most effectively encouraged people to adopt COVID-19 precautions during the pandemic. It also looks at how policymakers can better judge whether a new policy will improve overall welfare and how researchers can strengthen the tools used to measure policy impacts over time.

Health Inequalities

How Reproductive Rights Policies Relate to Preterm Birth Rates

Preterm birth is a major contributor to infant illness and death in the United States, but rates vary widely across states. In Women’s Health Issues, community health scholar and IPR associate Joe Feinglass and co-authors examine links between preterm birth rates and state reproductive rights policies. Using the 2022 National Vital Statistics System Natality File, the researchers analyzed more than 3.5 million births. They grouped states by their reproductive rights policy environment as least restrictive, somewhat restrictive, and most restrictive, based on the Institute for Women’s Policy Research index. They found differences in preterm birth rates across these categories. States with the least restrictive policies had an average preterm birth rate of 9.4%, compared with 11.2% in the most restrictive states. Some of the highest rates were in Mississippi (14.3%), Louisiana (13.3%), and West Virginia (13.3%), all of which had highly restrictive policies. Births in the most restrictive states had 20% higher odds of being preterm compared with births in the least restrictive states—even after accounting for mothers’ health and demographics, as well broader state characteristics like welfare spending and median income. Although the study cannot establish causation, the authors suggest that restricted reproductive rights may increase risks through pathways like higher unintended pregnancy rates and more severe maternal health complications. The findings add to evidence linking restrictive reproductive policies to poorer birth outcomes.

Methods for Policy Research

Improving How Researchers Measure Policy Impacts Over Time

When researchers want to estimate the impact of a policy or program but can’t randomly assign participants, they often use a difference-in-differences (DiD) design. This method compares how outcomes change over time in a treatment group versus a comparison group. In Evaluation Review, IPR education researcher and statistician Larry Hedges and co-author E. C. Hedberg offer new tools to help researchers calculate how much statistical power they have to detect real effects in these studies. The authors show that several common ways of analyzing DiD data—including gain score analysis, split-plot analysis of variance, and regression with cluster-robust standard errors—are mathematically equivalent and produce the same results. They caution, however, that standard ordinary least squares (OLS) regression is not appropriate for this design because it assumes individuals’ scores at different time points are uncorrelated, which is rarely true in practice. Hedges and Hedberg also introduce a clear way to define effect size and show how the minimum detectable effect—the smallest difference a study can reliably identify—changes based on sample size, covariates, and the number of time points measured. Their framework allows researchers to plan more rigorous, efficient studies and better understand how design choices influence the reliability of findings in education, policy, and other applied settings.

Policy Discourse & Decision-Making

What Persuades People During a Pandemic?

Governments often try to convince people to take actions that protect collective health and safety, but many widely used tactics have shown little evidence of changing behavior. In PNAS Nexus, IPR political scientist Alexander Coppock and colleagues examine which types of messages most effectively encouraged people to adopt COVID-19 mitigation behaviors, especially vaccination and masking. Their conclusions draw on 10 survey experiments conducted with more than 85,000 respondents. They compared three common strategies: endorsements from political leaders or celebrities; guidance or mandates from government agencies; and straightforward factual information delivered by a friend, their doctor, or the Centers for Disease Control. The study finds that information and guidance are the most consistently effective tools, increasing prosocial intentions by similar amounts across political parties. By contrast, endorsements often had unintended consequences, reducing willingness among some groups and sometimes even increasing polarization. Endorsements from Presidents Trump, Biden, and Obama produced strong negative or polarized effects; nonpolitical experts like Dr. Anthony Fauci were helpful early in the pandemic but not later on. Simple cues—like noting that a mask recommendation came from the CDC—or stating the facts about rising case counts or booster benefits reliably increased support for mitigation measures and did so without widening partisan divides. Overall, the research suggests that during public health crises, neutral guidance and clear information can encourage broad, bipartisan compliance, while relying on political figures to promote health behaviors can unintentionally undermine those efforts.

Rethinking How We Decide Whether a New Policy Improves Welfare

How should policymakers decide whether to keep an existing policy or adopt a new one? In Social Choice and Welfare, IPR economist Charles Manski explores this question by comparing two approaches: majority voting, where policies are chosen based on how many people prefer them, and utilitarian welfare, which seeks to maximize the total well-being of everyone affected. Manski develops a nonparametric analysis—a method that avoids strong assumptions about data—to study what can be inferred when citizens vote between a status quo policy (A) and a proposed policy (B). Even if people vote for the option that gives them greater personal benefit, the policy that wins a majority may not yield the highest total welfare. By combining what is known about the welfare under the current policy with the share of voters who prefer the new one, Manski derives informative bounds—a precisely defined range that narrows down the possible welfare outcomes for the proposed policy. These bounds help identify where the new policy’s welfare likely falls, but because the welfare of the current policy always lies within that range, it remains uncertain whether the new policy is truly better than the status quo. Manski argues that decision-makers must therefore confront genuine uncertainty. Under a minimax-regret or Bayesian framework, a new policy should only be adopted if its vote share exceeds a threshold that rises with how well the current policy is already performing.

Photo credit: iStock

Published: December 15, 2025.