About

Katherine Baicker serves as the 15th Provost of the University of Chicago and is the Emmett Dedmon Distinguished Service Professor at the Harris School of Public Policy. She is responsible for academic and research programs across the University and oversees its budget. Baicker is a leading scholar in the economic analysis of health care policy, with research focusing on the effectiveness of public and private health insurance, including the impact of reforms on the distribution and quality of care. Her large-scale research projects include the Oregon Health Insurance Experiment, a randomized evaluation of Medicaid coverage. Her work has been published in prominent journals such as the New England Journal of Medicine, Science, Health Affairs, JAMA, and the Quarterly Journal of Economics. Prior to her current role, she was the C. Boyden Gray Professor of Health Economics at Harvard T.H. Chan School of Public Health. Baicker is a research associate at the National Bureau of Economic Research, a director of Eli Lilly, and a trustee of the Mayo Clinic. She is an elected member of the National Academy of Medicine, the National Academy of Social Insurance, the Council on Foreign Relations, and the American Academy of Arts and Sciences. She has served on various advisory panels, including the Congressional Budget Office’s Panel of Health Advisers and the Advisory Board of the National Institute for Health Care Management. From 2005 to 2007, she was a Senate-confirmed member of the President’s Council of Economic Advisers, where she played a leading role in health policy development. Baicker earned her B.A. in economics from Yale and her Ph.D. in economics from Harvard.

Research topics

• Political Science
• Social Science
• Sociology
• Psychology
• Medicine
• Public relations
• Psychiatry
• Social psychology
• Engineering
• Virology
• Clinical psychology
• Law

Selected publications

• AI Will Accelerate Drug Discovery by Accelerating Clinical Evidence

  JAMA Health Forum · 2026-04-16

  articleOpen access1st authorCorresponding

  This JAMA Forum discusses the possibilities of using artificial intelligence to speed up the clinical trial process and accelerate the discovery and delivery of new treatments.

  PublisherDOI

• Intensive Nurse Home Visiting and Early Childhood Outcomes

  JAMA Pediatrics · 2025-06-16 · 7 citations

  article

  Importance: Programs that provide home visiting in early life have been proposed as a way to reduce early childhood adversity and improve child health outcomes. More evidence is needed to understand these programs' impact when delivered at scale. Objective: To evaluate how receiving home visits through the Nurse-Family Partnership (NFP), a program designed to support young and low-income families, impacted children's utilization and health outcomes in the 2 years after birth. Design, Setting, and Participants: The NFP is a home visiting program designed with the aim of reducing the incidence of adverse health outcomes in early childhood. In this study, we used data from a randomized clinical trial that enrolled 5670 Medicaid-eligible pregnant people in South Carolina who were randomly assigned at a 2:1 ratio to the NFP treatment (n = 3806) or usual care (n = 1864) between 2016 and 2020. The trial was conducted in 9 NFP-implementing authorities. Participants were eligible if they were fewer than 28 weeks pregnant with their first child, aged 15 years or older, and income eligible for Medicaid (income <200% of the federal poverty level). Data analysis was performed from June 2023 to July 2024. Intervention: The treatment group was offered NFP home visits during pregnancy and 2 years postpartum, while the control group received usual care. Main Outcomes and Measures: The primary outcome was a composite measure that included child mortality and claims related to major injury or concern for abuse or neglect within the first 2 years of life. Secondary outcomes included emergency department utilization and preventive health care measures, such as well-child visits and their components, including screenings for cognitive development, blood lead levels, fluoride varnish application, and dental health. We used an intent-to-treat approach with a linear regression model to estimate the treatment effect of NFP on early childhood outcomes by comparing participants assigned to the control and treatment group, regardless of whether they used NFP services. Results: Among enrolled participants, 4932 individuals were tracked to a live birth (3295 in the intervention group and 1637 in the control group) and were analyzed for child health and utilization outcomes once their child turned 2 years old. Mean (SD) participant age was 22.5 (4.7) years. The incidence of the composite adverse outcome was 27.3% and 26.8% in the intervention and control groups, respectively (adjusted between-group difference, 0.4 percentage points; 95% CI, -2.3 to 3.0), with no statistically significant differences between elements of the composite primary outcome. Among participants assigned to receive NFP, their children were less likely to use the emergency department by 2.9 percentage points (95% CI, -5.5 to -0.3), a 4% reduction relative to the rate of 72.8% in the control group. Once we adjusted for multiple hypothesis testing, this outcome was no longer statistically significant. Assignment to NFP did not significantly impact the likelihood of receiving the guideline number of well-child visits or preventive services. Conclusions and Relevance: In this randomized clinical trial, assignment to intensive nurse home visiting services did not reduce the likelihood of adverse outcomes in early childhood measured through administrative data. More evidence is needed to understand whether delivering intensive home visiting services at scale to a Medicaid population influences other child outcomes, including longer-term developmental outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT03360539.

  PublisherDOI

• Impact of nurse home visiting on take-up of social safety net programs in a Medicaid population

  Health Affairs Scholar · 2025-03-30 · 2 citations

  articleOpen access

  Abstract Childhood poverty can affect health and development across the life course. Access to social safety net programs may alleviate poverty-related hardships like food insecurity among low-income families, yet many eligible households do not enroll. We used a randomized controlled trial (n = 5670) to evaluate the impact of the Nurse–Family Partnership (NFP) home visiting program during pregnancy and the first 2 years after delivery on take-up of social programs including the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and Supplemental Nutrition Assistance Program (SNAP). The NFP services were funded as part of a Medicaid Section 1915(b) waiver in South Carolina. We found that NFP participants were more likely to be enrolled in SNAP or WIC during pregnancy (87.8% vs 86.0%) and were enrolled in SNAP for 0.6 months longer in the first 2 years after delivery than control participants. Nurse home visiting moderately increased take-up of social safety net programs in pregnancy and the first years of life, even in a context with already high rates of participation. This study contributes important evidence on the effectiveness of Medicaid-funded initiatives for addressing social needs of low-income families. Clinical Trial Registration ClinicalTrials.gov; ID NCT03360539 (https://clinicaltrials.gov/study/NCT03360539).

  PublisherDOI

• Can Prevention Save Money?

  JAMA Health Forum · 2025-04-03 · 5 citations

  articleOpen access1st authorCorresponding

  This JAMA Forum discusses some key considerations for addressing the question of whether and when prevention interventions are worth the cost.

  PublisherOA PDFDOI

• Evidence of Policy Effects on Infrequent but Important Outcomes—Lessons From the Medicaid Mortality Debate

  JAMA Health Forum · 2025-09-25

  articleOpen access1st authorCorresponding

  This JAMA Forum discusses the importance of scientific evidence to support health policy in the context of the recent passage of the One Big Beautiful Bill Act and its cuts to Medicaid.

  PublisherDOI

• The Effect of Medicaid on Crime: Evidence from the Oregon Health Insurance Experiment

  The Review of Economics and Statistics · 2025-10-29

  articleSenior author

  Abstract Those involved with the criminal justice system have disproportionately high rates of mental illness and substance-use disorders, prompting speculation that health insurance, by improving treatment of these conditions, could reduce crime. Using the 2008 Oregon Health Insurance Experiment, which randomly made some low-income adults eligible to apply for Medicaid, we find no statistically significant impact of Medicaid coverage on criminal charges or convictions. These null effects persist for high-risk subgroups, such as those with prior criminal cases and convictions or mental health conditions. In the full sample, our confidence intervals can rule out most quasi-experimental estimates of Medicaid’s crime-reducing impact.

  PublisherDOI

• The Effect of Medicaid on Crime: Evidence from the Oregon Health Insurance Experiment

  National Bureau of Economic Research · 2024-12-01

  reportOpen accessSenior author

  Those involved with the criminal justice system have disproportionately high rates of mental illness and substance-use disorders, prompting speculation that health insurance, by improving treatment of these conditions, could reduce crime.Using the 2008 Oregon Health Insurance Experiment, which randomly made some low-income adults eligible to apply for Medicaid, we find no statistically significant impact of Medicaid coverage on criminal charges or convictions.These null effects persist for high-risk subgroups, such as those with prior criminal cases and convictions or mental health conditions.In the full sample, our confidence intervals can rule out most quasiexperimental estimates of Medicaid's crime-reducing impact.

  PublisherDOI

• Heterogeneous effects of Medicaid coverage on cardiovascular risk factors: secondary analysis of randomized controlled trial

  BMJ · 2024-09-23 · 18 citations

  articleOpen access

  ABSTRACT Objectives To investigate whether health insurance generated improvements in cardiovascular risk factors (blood pressure and hemoglobin A 1c (HbA 1c ) levels) for identifiable subpopulations, and using machine learning to identify characteristics of people predicted to benefit highly. Design Secondary analysis of randomized controlled trial. Setting Medicaid insurance coverage in 2008 for adults on low incomes (defined as lower than the federal-defined poverty line) in Oregon who were uninsured. Participants 12 134 participants from the Oregon Health Insurance Experiment with in-person data for health outcomes for both treatment and control groups. Interventions Health insurance (Medicaid) coverage. Main outcomes and measures The conditional local average treatment effects of Medicaid coverage on systolic blood pressure and HbA 1c using a machine learning causal forest algorithm (with instrumental variables). Characteristics of individuals with positive predicted benefits of Medicaid coverage based on the algorithm were compared with the characteristics of others. The effect of Medicaid coverage was calculated on blood pressure and HbA 1c among individuals with high predicted benefits. Results In the in-person interview survey, mean systolic blood pressure was 119 (standard deviation 17) mmHg and mean HbA 1c concentrations was 5.3% (standard deviation 0.6%). Our causal forest model showed heterogeneity in the effect of Medicaid coverage on systolic blood pressure. Individuals with lower baseline healthcare charges, for example, had higher predicted benefits from gaining Medicaid coverage. Medicaid coverage significantly lowered systolic blood pressure (−2.93 mmHg (95% confidence interval −5.82 to −0.32)) for people predicted to benefit highly. No evidence showed that Medicaid coverage lowered HbA 1c for people with high predicted benefits. Conclusions Although Medicaid coverage did not improve cardiovascular risk factors on average, improvements were noted in blood pressure among a subset of individuals with higher predicted benefits. These individuals were more likely to have no or low prior healthcare charges, for example. The findings suggest that Medicaid coverage leads to improved blood pressure for some people, but those benefits may be diluted by individuals who did not benefit. Although the effect size may be of limited clinical significance for any individual, at a broad population level that includes individuals who are both hypertensive and normotensive, the findings may be of public health importance for policy interventions.

  PublisherOA PDFDOI

• Machine learning for detection of heterogeneous effects of Medicaid coverage on depression

  American Journal of Epidemiology · 2024-02-22 · 4 citations

  articleOpen access

  In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage lowered the risk of depression. However, this effect may vary between individuals, and the identification of individuals likely to benefit the most has the potential to improve the effectiveness and efficiency of the Medicaid program. By applying the machine learning causal forest to data from this experiment, we found substantial heterogeneity in the effect of Medicaid coverage on depression; individuals with high predicted benefit were older and had more physical or mental health conditions at baseline. Expanding coverage to individuals with high predicted benefit generated greater reduction in depression prevalence than expanding to all eligible individuals (21.5 vs 8.8 percentage-point reduction; adjusted difference = +12.7 [95% CI, +4.6 to +20.8]; P = 0.003), at substantially lower cost per case prevented ($16 627 vs $36 048; adjusted difference = -$18 598 [95% CI, -156 953 to -3120]; P = 0.04). Medicaid coverage reduces depression substantially more in a subset of the population than others, in ways that are predictable in advance. Targeting coverage on those most likely to benefit could improve the effectiveness and efficiency of insurance expansion. This article is part of a Special Collection on Mental Health.

  PublisherOA PDFDOI

• Investing in Long-Term Health

  JAMA Health Forum · 2024-02-08

  articleOpen access1st authorCorresponding

  This JAMA Forum discusses treatment and health care delivery, innovative use of risk analytics, and spending and coverage priorities in the US health insurance system.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R03AG027821

  NIH · $125k · 2009

• Core C - Measurement and Methods in ADRD and Racial Disparities Research

  NIH · $78.7M · 1997–2029

• NIH Grant RC2AG036631

  NIH · $11.5M · 2012

• The Impact of Employee Wellness Programs

  NIH · $2.2M · 2016–2021

• What Does Health Insurance Do? Evidence from the Oregon Health Insurance Lottery

  NIH · $9.1M · 2010–2025

Frequent coauthors

• Julia Adler‐Milstein

  University of California, San Francisco

  81 shared

• Lawton R. Burns

  California University of Pennsylvania

  81 shared

• Heidi Allen

  Stanford University

  81 shared

• James Joo

  University of Chicago

  81 shared

• Jin Kim

  College of Saint Rose

  81 shared

• Michelle M. Mello

  Stanford University

  81 shared

• Amitabh Chandra

  Dana-Farber/Harvard Cancer Center

  79 shared

• Amy Finkelstein

  National Bureau of Economic Research

  78 shared

Labs

• Urban LabsPI

Awards & honors

• Elected Member of the National Academy of Medicine
• Elected Member of the National Academy of Social Insurance
• Elected Member of the American Academy of Arts and Sciences