Authors

Reese Conti, Anna Xu, Yuan Zou

Research Question

How does government spending on social welfare, community development and law enforcement affect suicide rates in the United States? The project applies longitudinal causal inference methods to national suicide and public funding data to identify the spending approaches best positioned to prevent suicide effectively and sustainably.

Background

Suicide is a major and growing public health crisis in the US. In 2024, approximately 50,000 lives were lost to suicide, 14.3 million people reported seriously considering suicide, and 2.2 million reported an attempt. Suicide is deeply intertwined with the social and structural determinants of health. How governments allocate public funds shapes access to, and the quality of, these determinants, which can either perpetuate or interrupt cycles of violence.

Research has linked historical underinvestment to present-day suicide rates, but there is little agreement on whether reallocating budgets toward root causes can meaningfully change outcomes. Governments may have limited control over total budget size but considerable discretion over how funds are distributed. Ongoing collaboration between NYU McSilver Institute and CUSP has found initial state-level links between public funding and suicide rates, and this project extends that work toward the local level to directly inform policy.

Methodology

The project involves:

• Leveraging existing CDC and other government datasets on suicide rates and public funding.
• Exploring whether these sources can be obtained at the local level (zip code, city, county).
• Applying advanced longitudinal causal inference methods, such as g-computation, inverse probability of treatment weighting, and Longitudinal Targeted Maximum Likelihood Estimation, to account for time-varying confounding.
• Estimating the effects of spending on social welfare, community development and law enforcement on suicide rates.
• Using statistical, geospatial and computational methods to examine variation across scales.

Deliverables

• Academic paper
• Algorithm development and design
• Data visualizations
• Machine learning model
• Technical report
• Interactive data dashboard
• Policy recommendations

Data Sources

• Centers for Disease Control and Prevention (CDC) suicide data
• Public funding data from other government sources
• Local-level versions of these sources