The fundamental aim of public services is to improve the quality of life of citizens. The success of public service organisations (PSOs) is often judged in terms of the degree to which they are able to improve aspects of the quality of life of citizens in their jurisdiction. The main objective of this study was to investigate the influence of PSOs on aspects of quality of life (very broadly defined) at a local level. In doing so we addressed three main issues:
1. We considered the degree to which different PSOs can influence a range of aspects of the quality of life of citizens across a broad range of measures both within and outside their usual domains of influence.
2. We examined the degree to which factors outside the control of PSOs (e.g. socio-demographic population characteristics) influence quality of life outcomes.
3. In most public sector service areas, administrative organisations are arranged in a hierarchical manner. Large organisations such as strategic health authorities are at the top, with lower level Primary Care Trusts nested within these boundaries and smaller geographical areas below these. We investigated at which level there appears to be most scope to influence quality of life of citizens.
The objectives of the study were to develop statistical models to explain the link between public service organisations (PSOs) and quality of life indicators in order to:
1) examine the degree of variation in quality of life indicators associated with different PSOs;
2) explore the extent to which factors beyond the control of PSOs influence the quality of life of citizens;
3) explore the correlation in quality of life indicators across PSOs; and
4) examine the level in the organisational hierarchy which exerts the most influence on quality of life measures.
We assembled a rich database using 20 of the 45 quality of life measures developed by the Audit Commission. Those we selected covered broad areas of quality of life such as safety, housing, health, education, and transport and were available at “small area” level. Small areas include electoral wards which are the units used to elect local government councillors. They constitute the lowest administrative units in the UK. There are 8797 electoral wards in England. Small areas also include lower super output areas (LSOAs) which have an average population of 1,500. There are 32,482 LSOAs in England.
We added data on indicators of deprivation (to measure “needs” of the local population) and on the performance of PSOs. In addition to the small area levels, we considered Governmental Region, Strategic Health Authority, Primary Care Trust, and Local Authority area hierarchical levels.
We used a range of advanced statistical methods to analyse the relationships between PSOs and quality of life measures at different hierarchical levels. The techniques were selected to be robust when making comparisons between levels and when looking at associations between quality of life measures. A large number of models were constructed, varying according to the level considered, the way in which needs were taken into account, and whether or not the performance of organisations was included. Our approach allowed us to consider simultaneously the interactions that may exist between quality of life measures and levels, rather than looking at each model in isolation.
Comprehensive details of all our results can be found in our research paper (number 46) on the website of the Centre for Health Economics (http://www.york.ac.uk/inst/che/publications/index.htm).
Our findings illustrate the level at which variation in quality of life indicators is most apparent, and present a fairly consistent picture across all the models used. In general, there is a set of indicators that tend to have a large variation at small area level, including the standardised mortality ratio, educational attainment and the percentage of individuals living rough; and another set such as measures of air quality, election turnout and method of transport to work, for which the majority of the variation appears at the higher levels (Primary Care Trust, Strategic Health Authority or Local Authority area).
The identification of the degree of variation in quality of life indicators apparent at each level is important. It suggests that where those variations are large, there may be scope to influence outcomes at that level to a greater extent than where the variations are small. So where we find large variation in indicators such as the number of teenage conceptions at the higher level where healthcare organisations such as Strategic Health Authorities and Primary Care Trusts exist, we suggest that these organisations should be able to influence that outcome. On the contrary, because we find small variations at this level in indicators such as overall life expectancy, we suggest that these are less amenable to influence by higher level organisations.
The large degree of variation found in many quality of life indicators at small area level is also important. Whilst there are no obvious PSOs with responsibility for quality of life at that level, it suggests that organisations need to be aware of the potential impact of their policies at this level. Moreover, recent policy highlights the importance of local communities and neighbourhoods and PSOs have been encouraged to become more responsive to local needs and to devolve to communities a greater role in decision-making, including the handling of resources at neighbourhood, group and community level. Our results suggest that this approach is likely to be fruitful.
Our research provides methodological and policy insights. From a methodological perspective, our work makes a distinctive contribution to the literature and as far as we are aware, this is the first study of its kind to provide evidence on the sources of variation in quality of life indicators at small area level and to use advanced methods to disentangle this variation.
From a policy perspective, it provides both national and local policymakers with a deeper understanding of the role of public sector services in promoting the quality of life of citizens, contributes to a central area of public policy debate concerning neighbourhoods and quality of life and offers evidence on the influence that PSOs can exert on outcomes at different hierarchical levels and across public sector organisation boundaries.
This was a study to investigate the influence of public service organisations (PSOs) on broadly defined aspects of quality of life for citizens. Questions to be asked included how much variance in health outcomes is there due to the influence of local authorities outside the health services, such as education, housing and community safety? Is there a correlation between different quality of life measures or does achievement on one measure mean less achievement at another? How much of this can be put down to the social or economic circumstances of the individual rather than to the actions of PSOs? At what level in the PSO’s organisational structure can the variation of quality of life measures be explained?
• How do PSOs have an impact of the quality of life of citizens across a broad range of measures?
• How do factors outside the control of PSOs influence citizens’ quality of life?
• What is the correlation between quality of life indicators across PSOs?
• Bearing in mind the different levels and hierarchies at which PSOs operate, at which level is there the greatest scope for influence on quality of life?
• Quality of life can be very broadly interpreted at both individual and community level, taking into account concepts of happiness and the individual understanding of what wellbeing means.
• Many aspects of the social and environmental context in which people find themselves have a bearing on happiness and wellbeing.
• There was a correlation between environmental deprivation and lower quality of life
• The data showed a set of indicators that tend to have a large variation at small area level and another set for which the majority of the variation appears at the higher levels. These results are consistent despite varying the models used.
• The results suggest that the organisational level at which we find large variations is the level at which PSOs may have most influence over quality of life.
• PSOs at higher levels may therefore have an important role in influencing quality of life. However, the large variation found in many quality of life indicators at small area level is also important. Whilst there are no PSOs with responsibility for quality of life at this level, it indicates the importance of policies that operate at neighbourhood and community level.
We draw out two sets of key findings. First, from a methodological perspective, our work makes a distinctive contribution to the literature. It provides new evidence on the complex interactions between public service organisations (PSOs) and the potential influence they may have on the quality of life of citizens at a local level. So far as we are aware, this is the first study of its kind to provide evidence on the sources of variation in quality of life indicators at small area level and to use advanced methods to disentangle this variation. We provide insights into whether the three approaches: multilevel models (ML), seemingly unrelated regression (SUR) models, and an integration of both these approaches, namely the multivariate multilevel model (MVML model) are suitable methods to examine the complex interplay between different hierarchical levels that are commonplace in all public services and point the way forward for future analysis in this area.
Second, from a policy perspective we demonstrate that it is important to consider the influence of PSOs on quality of life in areas that fall outside their traditional domains. Moreover, our results give a flavour of the relative influence that health care and local government organisations may have on measures that span health, education, environment, safety, housing and others. We also illustrated the potential significance of considering the small area level in public policy making. The existence of substantial variation in quality of life measures at this level suggests that PSOs with responsibilities at higher level should be aware of the variation that exists at this level within their area and the differential impact their policies may have locally. Government policy highlights the importance of local communities and neighbourhoods and although there are no obvious PSOs that have responsibility for quality of life at small area level, the thrust of policy has been to encourage PSOs to become more responsive to local needs and to devolve to communities a greater role in decision-making, including the handling of resources at neighbourhood group and community level. Also, as the literature suggests, fostering social capital can enhance the quality of life of citizens and protect them from social exclusion. Neighbourhood and community networks and relationships appear to play an important role in the creation and maintenance of social capital. Our results therefore suggest that policy attention to the local level may well be a fruitful approach if the aim is to enhance the overall well-being of citizens.