Students flock to cities – but how can they retain graduates?

Yep, that image again: graduation day at London South Bank University. Image: Getty.

Every autumn, hundreds of thousands of students begin their journey at university. For many, this will also mean moving away from home. As our report The Great British Brain Drain showed, students dominate migration patterns in the UK, with those who moved to go to university accounting for a fifth of all internal migration in 2014.

As a result, each year the movement of young people going to university cause a surge in the population of cities at the expense of non-urban areas. This is particularly true in large and medium-sized cities, with Sheffield, Leeds and Nottingham being the biggest student gainers. And this inflow of students brings benefits to the local economy, as students tend to spend their money where they study.

Interestingly, London, in spite of its 49 higher education institutions, saw a large outflow of students to the rest of the country – though this did not represent a net loss of students, thanks to the larger inflow of foreign students the capital attracted.

The ability of a place to attract students from other parts of the country will therefore affect the strength of its economy. But as the UK continues to specialise in more high-skilled, knowledge intensive activities, the extent to which cities can attract and retain skilled graduates will have a bigger impact on their economic performance. And when we look at the movement of graduates in UK cities, the patterns are significantly different to the movement of students.

Graduate gain 2014-2015.

For a start, London saw the largest net inflow of graduates, with a quarter of all graduates working in the capital six months after graduation. Its pull is even more pronounced for new Oxbridge graduates, over half of whom (52 per cent) moved to London after finishing university.


By contrast, medium-sized and large cities experienced a large outflow of graduates to non-urban areas and to London. And while many cities experienced a gain in graduates from other places, they still saw a net graduate loss when we consider the number of students who studied in those places but subsequently left after graduating.

This variation in patterns across the country poses a key question: why are some places better at attracting graduates than others?

Our research suggests that cities that gained the most graduates did so because of their strong economies and the opportunities they can offer. For example, successful places like Basildon and Crawley attracted significant numbers of graduates despite not having a university campus.

Indeed, our analysis shows that it is not graduate salaries which explain the differences in graduate gains in cities across the country, but instead the job opportunities and career progression that they can provide. And this applies in terms of both retaining graduates who studied locally, and attracting others from other places. Looking at the type of jobs, our data shows that graduate gains were generally larger in places where knowledge intensive businesses employment accounted for a larger share of all new graduate positions.

Click to expand.

As such, if a city wants to attract and retain a greater number of graduates, then it needs to focus on wider economic growth and job creation policies that support the creation of more jobs, and particularly high-skilled knowledge jobs, rather than focus on policies that are specifically targeted at graduate attraction and retention. 

Ultimately, this comes down to strengthening the local economy, by investing in transport, housing and supporting high-knowledge businesses. This will help create high-knowledge jobs and make these places more attractive to both new graduates and other skilled workers.

Gabriele Piazza is a researcher at the Centre for Cities. This post was originally published on the think tank's blog.

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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.