Keywords: Cost Growth Targets, Healthcare Expenditure, Policy Evaluation

Background: In 2023, national health expenditures exceeded $4.9 trillion, accounting for nearly 18% of the US GDP—an unsustainable trajectory that demands urgent reform (CMS, 2023). While healthcare spending growth has slowed in recent years, it continues to outpace economic growth, placing a significant burden on patients (Buntin, 2025). In response, several states implemented cost-growth target models, setting annual growth caps to limit spending increases. However, existing literature remains limited to descriptive analyses, with no empirical evaluations of the effectiveness of these models in aggregate. This study aims to provide a rigorous, quasi-experimental assessment of the impact of cost-growth models on total per capita healthcare spending.

Objective: This study evaluates the impact of state-level cost-growth target models on total per capita health care spending from 2010-2020 using CMS State Health Expenditure Accounts (SHEA) data. 

Methods: We evaluated the impact of state-level cost-growth target models using SHEA data from 2010 to 2020. Five treatment states with cost-growth policies in varying years (Massachusetts, Maryland, Vermont, Delaware, and Rhode Island) were compared to states without these policies. Pre-policy total per capita expenditure trends for each treatment state were compared to control states to ensure parallelism. We used a fixed-effects regression model to account for unobserved heterogeneity, within-state variability, and time-specific shocks, with each state serving as its own control over time. Demographic controls (age, sex, race, and ethnicity) were included using U.S. Census data, and robust standard errors were clustered by state.

Results: The analysis included 561 state-year observations. While significant year-to-year increases in per capita healthcare expenditure were observed across states, cost-growth target models did not result in significant reductions in total per capita healthcare spending (95% CI: -175.09, 246.87, p=0.734). Given non-parallel pre-policy trends in payer-specific data, we cannot definitively conclude the impact of cost-growth policies on Medicare, Medicaid, and commercial spending.

Discussion: The results of this study suggest that cost-growth models may not be sufficient to curb healthcare spending, especially given that these models generally rely on weak policy levers limited to public reporting and voluntary compliance, with only Maryland implementing stringent penalties (up to 2% of inpatient revenue). These findings indicate that stronger financial incentives and more robust enforcement mechanisms are necessary to achieve meaningful cost containment. 

Conclusion: State cost-growth target policies were not associated with significant reductions in personal healthcare spending. 

Limitations: We used aggregated data, which lacks within-state variations. Short post-policy periods for DE and RI, and heterogeneity in policy designs may not fully capture long-term effects, particularly for payer-specific spending.