Around 70% of demographic change in cities and communities is the result of short-distance flows of people between cities and rural districts. It is the single largest influence on future developments. The level of migration and its nature in demographic and social terms are just some of the factors affecting population growth, so this has an influence on fundamental economic and social aspects in society – for example, it affects tax revenues, the need for hospitals, jobs, housing requirements, and thus land prices and rent. At the same time, the degree to which people are willing to move depends strongly on their age. Apprentices and students are around ten times more likely to move than old people. The Steinbeis Transfer Center for Applied System Analysis (STASA) has been analyzing these trends with a “regional opportunity monitor” called RCM.
Whether the total population of a city or community is rising or falling can be seen by looking at the official statistics. It’s different with the reasons why people move, which vary tremendously and depend strongly on the age group. Apprentices and students tend to move to cities offering the best education opportunities, but at the same time, they need somewhere affordable to live. A key factor affecting moves after training or studies is the number of employment opportunities, which are typically found in higher numbers in urban areas. Starting a family often causes people to move again, especially to rural areas or city outskirts. With each stage of life, the demand changes for local services. This affects things like regional transportation, services like day care facilities, the availability of doctors, and shopping. Local transportation, regional links, and networks also play an important role in where people decide to move and these factors are central to how people weigh up the alternatives to a certain city or community. Major cities have close ties to one another through the networks of business (such as company subsidiaries), and as a result, they often compete head-on. Smaller cities and communities tend to have more local networks and this can make them appealing as a place to live, a bit like bubbles which can sometimes separate themselves off from competing cities. One drawback with this, however, is that they can lose inhabitants due to demographic changes, especially if they don’t succeed in raising their appeal as a region.
One reliable way to measure population preferences and the strength of regional networks between cities and communities is to use the internationally recognized decision-oriented migration model developed by Weidlich and Haag. Their model provides a migration matrix for the whole of Germany which can be used to arrive at index values denoting regional “preference levels,” even down to individual communities. Population flows are broken down by age groups making it possible to make detailed statements about factors such as education migration or employment migration. The model captures around 160,000 migration flows for local districts, with roughly 22,000 additional community calculations tallying up which people come and go.
The results of the migration analysis are updated continuously and fed into the RCM regional opportunity monitor developed by STASA. The RCM defines popularity levels calculated by the migration model as above or below average, resulting in a net migration figure. For example, if more people come to an area than leave it, it has positive net migration. Opposite population flows give a negative balance. This provides four kinds of areas and there are specific recommendations for communities belonging to each. The RCM gives decision-makers who work for district authorities as well as economic development experts a powerful instrument for positioning their region (city, district, community) and defining its strategic direction.
The following four areas have been defined by the STASA team, each relating to communities’ positioning within an overall region:
Opportunity areas:
Negative net migration and above average popularity, showing that a city or community may be losing population but it still has a clear opportunity to encourage positive migration by making suitable structural changes and, if necessary, work together with other cities and communities.
Risk areas:
Positive net migration is no guarantee that things will remain positive in the future. If the preference score of the population is below average versus other areas, the positive net migration level will not last forever. The priority here is to analyze the causes early enough to be able to take preventative action.
Top level areas:
Positive net migration and positive preference levels are ideal for the overall sustainable development of a region. Nonetheless, a detailed analysis of structural factors can still help in order to keep positioning an area in a positive light versus other regions.
Low level areas:
Low level areas have a combination of negative net migration and a below-average preference level. In such areas it is particularly challenging working on future developments. It will be necessary to analyze structural factors and identify potential development parameters. To do this, the current status has to be examined in detail by looking at population preferences and other structural indicators in comparison to other areas. The aim should be to diminish negatives in the medium to long term versus other cities and communities.
The maps in this article show the RCM for cities and rural districts in the whole of Germany, plus the results for local communities versus the overall population. They clearly show the distinct regional structures for individual administrative districts and communities. The first RCM scores regions on the criteria outlined above. The method for scoring is complex, based on backgrounds and root causes, and it involves a detailed analysis of a large number of structural factors as well as accessibility. Benchmarking areas against similar regions is also useful and this is also accounted for in recommendations.
To provide an example of how this works, an analysis of Stuttgart is shown below. For the younger population (18 to 25-year-olds and 25 to 30-year-olds), net migration is high and so is the preference level. This makes Stuttgart a Top Level area for this section of the population. As people age, however, net migration becomes negative and preference levels recede. Overall, the RCM defines Stuttgart as a risk area because it has positive net migration but the preference level overall is just below average.
STASA has been working on the analysis in close collaboration with the Cologne-based German economic institute IW-Consult. The Steinbeis experts are conducting detailed regional analyses based on the results of the RCM and augmenting this with other regional indicators relating to the economy and local infrastructure. The regional profiles this provides help with understanding strengths and weaknesses and recommending actions to be taken in each area. Results can be depicted and processed by STASA in web apps on interactive maps and diagrams, thus going beyond conventional reports and drawing on state-of-the-art presentation and evaluation tools.
Dr. Philipp Liedl
STASA Steinbeis Angewandte Systemanalyse GmbH (Stuttgart)
su1390@stw.de