Exploring the spatial pattern of mental health expenditure

Area of Science:

• Public Health
• Spatial Economics
• Econometrics

Background:

• Growing interest in cross-sectional variations in municipality mental health expenditure.
• Limited empirical work on links between expenditure variability and demand/supply factors, especially spatially.
• Need to understand spatial influences on local government spending decisions.

Purpose of the Study:

• Examine if local authority mental health spending responds to neighboring authorities' decisions.
• Investigate potential interdependence effects: demonstrative, market leader, contextual, directive, shared resource, and inducement.
• Analyze sources of spending variation using spatial econometrics.

Main Methods:

• Exploratory spatial data analysis to detect spatial structure.
• Reduced form demand and supply model incorporating policy interaction.
• Spatial econometric techniques to account for data interdependence, avoiding classical regression assumptions.
• Comparison with non-spatial models to assess bias from spatial patterns.

Main Results:

• Significant positive spatial correlation in per capita mental health spending.
• Spending clusters observed in metropolitan areas like London, Manchester, and Birmingham.
• Spatial autocorrelation confirms policy interdependence between neighboring municipalities.
• Non-spatial models yield biased estimates due to unaddressed spatial patterns.

Conclusions:

• Spatial interdependence is a key feature influencing local mental health spending decisions.
• Results aid policymakers in understanding factors affecting local spending and performance targets.
• Further research should utilize panel data and disaggregated data with spatial multilevel techniques.