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Street cleanliness in deprived and better-off neighbourhoods: a clean sweep

An exploration of why affluent neighbourhoods tend to have higher levels of street cleanliness than deprived neighbourhoods and what local authorities can do to narrow this gap.

Written by:
Annette Hastings, Nick Bailey, Glen Bramley, Rob Croudace and David Watkins
Date published:

This study brings together evidence from across England and Scotland on the relationships between street cleanliness, neighbourhood characteristics and local authority service provision and expenditure. It also includes three local authority case studies, which examine cleanliness in relation to different neighbourhood contexts and levels of public service.The study focuses on:

  • how local government street cleansing services contribute to the creation of contrasting levels of cleanliness between affluent and disadvantaged neighbourhoods;
  • how street cleanliness is linked to the particular characteristics and needs of neighbourhoods;
  • how to identify and target need; and
  • ideas, strategies and tools that service providers can use to form more targeted approaches.


This research explores why affluent neighbourhoods tend to have higher levels of street cleanliness than deprived neighbourhoods and how local authorities can narrow this gap and achieve desired standards in all areas.

Key points

  • More affluent neighbourhoods tend to have higher levels of street cleanliness than deprived neighbourhoods, but this difference is smaller in some local authorities than others.
  • Some neighbourhoods need higher levels of service because of their social or physical characteristics, particularly those with low-income households and high-density housing. Many of the risk factors associated with deprived neighbourhoods affect street cleanliness wherever they occur.
  • The three case study authorities included in this research all spent more money in deprived neighbourhoods but they did this in different ways. The targeting of routine or programmed services appeared to be more effective and offer better value than reliance on special initiatives or responsive services.
  • Efforts to produce more equal outcomes sometimes received weak political support, with service providers facing substantial demands from less needy areas. Therefore, covert approaches to targeting need may be necessary, as well as strategies that ensure standards in more affluent areas are not compromised.
  • Front-line operational staff are a key source of intelligence about the needs of different neighbourhoods and this information can be used to allocate workloads more fairly. This can have a real impact on outcomes.
  • Effective targeting also involves identifying inappropriate or inefficient forms of service. By doing this, more equal outcomes can be achieved within existing budgets.

The research
By a team from the Department of Urban Studies at Glasgow University.


Recent UK policy has seen an increasing emphasis on narrowing the gap in public service outcomes between deprived and better-off neighbourhoods. However, most research has pointed to a range of difficulties involved in delivering greater redistribution in practice. This study shows that, in relation to street cleaning, authorities can reduce the gap in service outcomes and achieve desired levels of cleanliness across all neighbourhoods. Furthermore, this may be achievable within existing resources.

The project brings together national-level evidence from England and Scotland on the relationships between cleanliness outcomes, the characteristics of neighbourhoods and expenditure patterns across local authorities. It also reports on three local authority case studies, exploring outcomes in relation to neighbourhood contexts and service inputs. The research highlights the importance of identifying and targeting need, and provides practical insights into how local authorities can narrow the gap between more and less deprived areas.

Is there a gap and is it narrowing?

Across the country, more affluent neighbourhoods tend to show a level of cleanliness above the standard expected in national performance monitoring, while less affluent neighbourhoods perform much worse. Some local authorities are achieving high average scores but failing to maintain more deprived neighbourhoods to the desired standard. Indeed, there is evidence of a trade-off for local authorities between achieving higher average grades and achieving equal outcomes across neighbourhoods.

There is some evidence of a national narrowing of this gap, although there is significant variation between authorities. In the two English case study local authorities there was clear evidence of a narrowing gap. In the remaining Scottish case study, the evidence was of an improvement in overall outcomes, but a slight widening of the gap between poor and better-off areas.

The study highlights the role of national performance monitoring and audit systems such as Best Value in influencing outcomes. These systems have helped raise overall standards in England and in Scotland. In England, however, a focus on the number of streets that fail to meet the target appears to have led to greater gains in more deprived areas. In Scotland, by contrast, a focus on the overall average across each authority has meant less attention to worst-performing areas.

What puts places at risk of poor street cleanliness?

Area deprivation (as measured by official indexes) is an important predictor for environmental problems but it also masks a range of factors that contribute to poor outcomes. The study identified a number of other risk factors for poor street cleanliness relating to the demographic and social composition of neighbourhoods. These included low income, number of children and proportion of young adult households. Physical characteristics of neighbourhoods such as high housing density, small or no gardens and disused buildings were also important. The two most prominent risk factors for environmental problems were the presence of low-income households and higher-density housing (irrespective of deprivation). The analysis therefore helps to explain what it is about deprived neighbourhoods that makes them more likely to have environmental problems.

Does resource allocation follow risk?

The evidence on this key question is complex. There was evidence that resources were skewed towards deprived neighbourhoods, both within the case studies and at a national level. In one case study, the authority spent five times as much on the most deprived streets than on the least deprived. Resources also tended to be skewed towards areas with high-density housing but only in one case study was expenditure targeted towards streets with lower income households.

While relative levels of expenditure suggest that authorities are trying to reduce the gap between deprived and better-off neighbourhoods, closer analysis produced some surprising results.

In one case study, when routine programmed expenditure was distinguished from responsive ('catch up') expenditure, it was apparent that more affluent streets tended to have a bigger share of programmed expenditure than less affluent streets. Thus, what appeared to be a progressive pattern of expenditure relative to deprivation was actually related to the provision of expensive responsive services. In the same case study, when the workloads of operational staff were examined, deprived streets tended to be serviced by staff with larger workloads than those operating in more affluent areas.

The allocation of staff workloads relative to area needs emerged as a key issue in the research. In one case study, there was an attempt to engineer the relative workloads of street cleaners to take account of the diversity of needs across neighbourhoods. Thus, staff operating in areas with higher levels of 'risk factors' associated with environmental problems had fewer dwellings or shorter street lengths to service. This authority also spent 35 per cent more on achieving nationally approved levels of cleanliness in the most deprived areas than on streets of average deprivation. It had the most equal outcomes of the three case studies.

Absolute levels of service appeared to be as important as relative resource allocation. One case study had a high relative difference between areas, spending five times as much in its most deprived streets compared to the least, but the absolute level of routine, programmed services delivered to these areas was still below the level of service provided to similar streets in the other two case studies. In this case, acceptable outcomes were achieved via special initiatives and responsive top-up services.

Finally, the appropriateness of the mode of service provision to the local context emerged as a key aspect of targeting need. Street cleaning can be provided manually or by mechanical vehicles, or a combination of the two. In general terms, mechanised systems often appeared less appropriate for busy, densely-built deprived areas with narrower streets and pavements. Conversely, they appear to be an efficient and effective mode of service delivery in areas with lower traffic and less litter.

Overall, the research suggests the need for a broad understanding of the different aspects of targeting need. Skewing resources towards more deprived areas is a start but may not be enough. Effective targeting involves providing the right level and the right kind of services. The existence of inappropriate or inefficient forms of service suggests that more equal outcomes could be achieved within existing budgets.

Achieving fairer outcomes in street cleanliness

The research identified three main ways to narrow the gap in outcomes and achieve desired levels of cleanliness across all neighbourhoods. Each of these routes negotiates the significant political and practical challenges inherent in targeting needs in different ways.

  • Programmed service frequencies are fairly standard across the authority and workloads for operatives are also fairly standard. To achieve reasonable cleanliness levels in areas with higher needs, extensive use is made of responsive services.
  • Programmed service frequencies are fairly standard across the authority but workloads vary significantly, so staff can spend more time in areas with higher needs. This relies much less on top-up services.
  • Programmed services are skewed towards areas with higher needs, with additional top-up provided via responsive services, funded in part by short-term income streams.
  • Each of these is a response not only to the variety of risk factors and needs within neighbourhoods, but also to a range of other pressures and influences including the demands of less needy affluent neighbourhoods as well as national performance and audit systems.

The demands of affluent neighbourhoods for environmental services clearly impacted on resource allocation and on day-to-day service provision in all three case studies. In the authority which had engineered the workloads of its staff to reflect needs, the targeting of resources was not obvious to the public. All residential streets therefore had the same reported frequency of service, but staff in more challenging areas had the capacity built into their workload to tackle the additional problems likely to arise. This can be understood as a stealth approach to targeting.


Local authorities can take a range of practical steps to more effectively target existing resources towards need. This includes identifying and mapping risk factors, and assessing how well street cleaning services relate to these. The views of front-line staff can be a ready and cost-effective source of information. A way of targeting need can then be developed which takes account of local and political sensitivities. Small adjustments to national performance measures and targets could incentivise local authorities considering a more equitable approach to service provision.

About the project

The research drew on a range of data sources to understand the relationships between cleanliness outcomes, levels of need and service inputs at the small area level. Data from large-scale household interview surveys, the Local Environmental Quality Survey for England and the Best Value Performance Survey, were used to measure outcomes. These were linked to the Government's official indices of neighbourhood deprivation and to databases capturing physical and social characteristics at the small area level to understand neighbourhood context. For national-level analysis, data on service inputs was only available at the level of whole local authorities. The three case studies involved detailed mapping and analysis of expenditure down to the level of the street. Within each case study, interviews were undertaken with senior managers and extensive shadowing and interviewing of street cleaning operatives was undertaken.