Will setting tax rates locally help to drive economic growth?

Whatever could be in that box? Image: Getty.

In its ongoing discussions about business rates devolution, the government is exploring the possibility of allowing local authorities to set their own rates.

This would probably be a popular move among local leaders: the latest communities department (DCLG) consultation on the subject reported that a significant number of local authorities were in favour of being granted powers to reduce the business rates in their areas, in order to gain more flexibility and encourage growth locally. But what impact might this have for local economic growth – and what issues might it raise?

While the What Works Centre for Local Economic Growth has not looked at the economic impact of setting taxation at the local level, it has looked at the impact of policies that offer local tax breaks, such as enterprise zones. Lessons can be drawn from these findings to help guide the localisation of the business rates multiplier.

The evidence found by the What Works Centre shows that enterprise zones can have a positive impact on both boosting employment and addressing unemployment inside the area they cover: out of 27 studies looking at the impact of enterprise zones on employment, 15 found a positive impact of enterprise zones, while seven out of nine studies looking at unemployment also found a positive impact. However, evidence of their impact on poverty and wages was mixed, with only half the studies investigating this issue finding a positive impact.

What’s missing from the existing studies is an assessment of the impact of the specific characteristics of different types of enterprise zones. For instance, some enterprise zones in the US and France only offer tax rebates on the basis that businesses hire a certain proportion of staff locally; but as there is no counterfactual (i.e. another similar area where firms would not have specific hiring requirements), there is no way to assess the influence of this precise characteristic on the overall success of the programme.

There are also potential risks associated with place-based tax rebates, although they are not fully understood. One major concern is displacement. Are enterprise zones successful at creating new activity? Or do they simply attract nearby companies, at the expense of those surrounding areas that do not benefit from the programme? Research seems to suggest that displacement effects are indeed common, meaning that the headline impacts of enterprise zones must be treated with caution.

Overall this suggests that local tax rebates can have a positive impact locally, but their precise factors of success and wider impact are not entirely clear.

Coming back to the current debate on business rates, this suggests that allowing local authorities to reduce the business rates multiplier could be investigated and implemented as a way to foster economic growth. But this would need to take into account a number of potential issues.


Firstly, there is a risk of job displacement between local authorities, with potential effects on neighbouring areas. Making sure there are specific safeguards to ensure good policy coordination (for instance, a limit on the multiplier reduction, a cross-authority pooling of revenues, etc.) will be critical.

Rates reduction should also be implemented under a strict assessment process, laying out clear objectives and risks. Local authorities are unlikely to provide the level of tax reduction that enterprise zones do – and in some areas a business rates rebate might be too modest to have a substantial impact of the economy, but large enough to significantly erode local revenues.

Ultimately, the effect of any fiscal incentive is unlikely to be significant, especially if applied in sluggish economies. The reason why some areas are less attractive to firms relates to their local characteristics, such as the level of skills of their population and the quality of their infrastructure.

Although fiscal incentives can act as an “extra push”, they do not tackle the core issues that must be addressed to make places more prosperous – and should not be considered as the Alpha and Omega for local economic growth.

Hugo Bessis is a researcher for the Centre for Cities, on whose blog this article originally appeared.

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Smart cities need to be more human, so we’re creating Sims-style virtual worlds

The Sims 2 on show in 2005. Image: Getty.

Huge quantities of networked sensors have appeared in cities across the world in recent years. These include cameras and sensors that count the number of passers by, devices to sense air quality, traffic flow detectors, and even bee hive monitors. There are also large amounts of information about how people use cities on social media services such as Twitter and foursquare.

Citizens are even making their own sensors – often using smart phones – to monitor their environment and share the information with others; for example, crowd-sourced noise pollution maps are becoming popular. All this information can be used by city leaders to create policies, with the aim of making cities “smarter” and more sustainable.

But these data only tell half the story. While sensors can provide a rich picture of the physical city, they don’t tell us much about the social city: how people move around and use the spaces, what they think about their cities, why they prefer some areas over others, and so on. For instance, while sensors can collect data from travel cards to measure how many people travel into a city every day, they cannot reveal the purpose of their trip, or their experience of the city.

With a better understanding of both social and physical data, researchers could begin to answer tough questions about why some communities end up segregated, how areas become deprived, and where traffic congestion is likely to occur.

Difficult questions

Determining how and why such patterns will emerge is extremely difficult. Traffic congestion happens as a result of personal decisions about how to get from A to B, based on factors such as your stage of life, your distance from the workplace, school or shops, your level of income, your knowledge of the roads and so on.

Congestion can build locally at pinch points, placing certain sections of the city’s transport networks under severe strain. This can lead to high levels of air pollution, which in turn has a severe impact on the health of the population. For city leaders, the big question is, which actions – imposing congestion charges, pedestrianising areas or improving local infrastructure – would lead to the biggest improvements in both congestion, and public health.

We know where – but why? Image: Worldoflard/Flickr/creative commons.

The irony is, although modern technology has the power to collect vast amounts of data, it doesn’t always provide the means to analyse it. This means that scientists don’t have the tools they need to understand how different factors influence the way cities function and grow. Here, the technique of agent-based modelling could come to the rescue.

The simulated city

Agent-based modelling is a type of computer simulation, which models the behaviour of individual people as they move around and interact inside a virtual world. An agent-based model of a city could include virtual commuters, pedestrians, taxi drivers, shoppers and so on. Each of these individuals has their own characteristics and “rules”, programmed by researchers, based on theories and data about how people behave.

After combining vast urban datasets with an agent-based model of people, scientists will have the capacity to tweak and re-run the model, until they detect the phenomena they’re wanting to study – whether it’s traffic jams or social segregation. When they eventually get the model right, they’ll be able to look back on the characteristics and rules of their virtual citizens, to better understand why some of these problems emerge, and hopefully begin to find ways to resolve them.

For example, scientists might use urban data in an agent-based model to better understand the characteristics of the people who contribute to traffic jams – where they have come from, why they are travelling, what other modes of transport they might be willing to take. From there, they might be able to identify some effective ways of encouraging people to take different routes or modes of transport.


Seeing the future

Also, if the model works well in the present time, then it might be able to produce short-term forecasts. This would allow scientists to develop ways of reacting to changes in cities, in real time. Using live urban data to simulate the city in real-time could help to inform the managers of key services during periods of major disruption, such as severe weather, infrastructure failure or evacuation.

Using real-time data adds another layer of complexity. But fortunately, other scientific disciplines have also been making advances in this area. Over decades, the field of meteorology has developed cutting-edge mathematical methods, which allow their weather and climate models to respond to new weather data, as they arise in real time.

The ConversationThere’s a lot more work to be done before these methods from meteorology can be adapted to work for agent-based models of cities. But if they’re successful, these advancements will allow scientists to build city simulations which are driven by people - and not just the data they produce.

Nick Malleson, Associate Professor of Geographical Information Systems, University of Leeds and Alison Heppenstall, Professor in Geocomputation, University of Leeds.

This article was originally published on The Conversation. Read the original article.