Article Abstract Online ISSN: 1099-176X    Print ISSN: 1091-4358 The Journal of Mental Health Policy and Economics Volume 8, Issue 4, 2005. Pages: 205-217 Published Online: 10 December 2005 Copyright © 2005 ICMPE.

* Correspondence to: Francesco Moscone, PSSRU, London School of Economics, Houghton Street, London, WC2A 2AE, UK.
Tel.: +44-20-7955 6087
Fax: +44-20-7955 6131
E-mail: f.moscone@lse.ac.uk

Source of Funding: None declared.

Abstract

There are marked variations in mental health expenditure across localities, but it is possible that those variations are related in ways not usually explored in empirical research. The paper examines a hitherto ignored source of variation in spending: the underlying spatial pattern of either unobservable risk factors or genuine policy interdependence. Exploratory data analyses test for the existence of spatial interaction and spatial structures of data. Drawing hypotheses from these initial exploratory analyses, a spatial autoregressive model is then specified and confronted with data to explore the sources of spending variation between municipalities. The estimation results when specifying a spatial structure are compared with those from a classical (‘non-spatial’) model. There are some strong regional patterns in mental health expenditure that should be factored into allocation mechanisms and performance monitoring. Spatial econometrics methods can contribute to our understanding of why costs of ostensibly similar services vary from one locality to another.

Background: Recent years have witnessed growing interest in cross-sectional variations in municipality mental health expenditure. However, empirical work to date has not examined the links between such variability and demand and supply factors, particularly in the spatial domain. Aims of the Study: The aim is to examine whether a local authority's spending decisions in the mental health field respond to neighbouring expenditure decisions. We explore a number of reasons why there might be interdependence between local authorities' decisions, labelling them the demonstrative, market leader, contextual, directive, shared resource and inducement effects. Methods: Exploratory techniques from spatial data analysis are used to test for the existence of spatial structure. Drawing hypotheses from these initial exploratory analyses, we then adopt a reduced form demand and supply model, extended to incorporate possible policy interaction. The analysis of expenditure and cost variations has traditionally been based on regression models under the classical assumption that the observations are independent. But omitting the recognition that observations are interdependent might lead to erroneous statistical conclusions. Hence, we use spatial econometric techniques that explicitly take into account the potential interdependence of data in order to study the sources of spending variation between municipalities. Results: The exploratory data analyses reveal the presence of positive significant spatial correlation. Per capita mental health spending distributes in clusters, with the highest concentrations in metropolitan areas such as Greater London, Greater Manchester and Birmingham. The estimated spatial regression models indicate that spatial autocorrelation characterises local expenditure decisions, consistent with some degree of policy interdependence between neighbouring municipalities. Comparing the results from our spatial model with those from a classical (`non-spatial') model suggests that the differences in the regression coefficients could be explained by the evident spatial pattern of the phenomenon, since the omission of the lagged dependent variable induces bias in the OLS estimates. Implications for Health Policies: These results help central and local decision makers understand the factors that influence local spending levels, including variations between municipalities in their achievement of expenditure-related and perhaps other performance targets. The actual patterns of spatial interaction may well be more complex than simple contiguity (the structure assumed here), but there seems little doubt that positive interdependence is an important feature of decision making. Implications for Further Research: Statistical interrogation of a panel dataset would allow exploration of both time-series and cross-municipality variation in mental health expenditure. Subsequent analysis would also benefit from more disaggregated data (e.g. at a census ward level) and the accompanying use of  spatial multilevel techniques.

Received 23 May 2005; accepted 9 September 2005

Copyright © 2005 ICMPE