Urban Scaling of Cities in the Netherlands Anthony F.J. van Raan1*, Gerwin van der Meulen2, Willem Goedhart2 1 Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, The Netherlands 2 Decisio | Economic Consulting, Amsterdam, The Netherlands * corresponding author
[email protected]
Abstract We investigated the socioeconomic scaling behavior of cities in the Netherlands and found significant nonlinear correlations between gross urban product as well as number of jobs with population size. This nonlinearity manifested by a super-linear power law scaling is found for both the cities defined as municipalities and for the agglomerations of major cities. We used two types of agglomerations as defined by the Netherlands Central Bureau of Statistics: the direct agglomeration, i.e. the central city (municipality) with the adjacent suburbs (which are separate municipalities); and the larger urban area of the same major cities. The exponents are all similar, with values around 1.20. Remarkably, the agglomerations of cities underperform as compared to a city which is one municipality with the same population as the agglomeration. This effect is larger for the second type of agglomerations, the urban areas. We think this finding has important implications for the current Dutch urban policy. A residual analysis suggests that cities with a municipal restructuring recently and in the past decades have a higher probability to perform better than cities without municipal restructuring. In this study we also performed a diachronic, time-dependent analysis of cities. Also here nonlinearities are found which reflect scaling for rapidly growing cities.
Introduction and political context In recent years there is a rapidly growing interest in the role of cities in our global society. Cities are regarded as the main locations of human activity and, particularly, creativity [1]. Population size is an important determinant of the intensity of many socioeconomic [2, 3, 4, 5], infrastructural and knowledge production activities [6, 7, 8] in cities. Indicators representing these activities appear to scale nonlinearly with the number of inhabitants of cities and urban areas. The theoretical basis of this scaling behavior is provided by the theory of complex, adaptive systems [9] in which as the system grows networked structures reinforce nonlinearly, particularly more than proportional, i.e. superlinearly, described by a power law [10]. Moreover, the density of the population –which determines the average distance of interaction- is discussed as an important variable in explaining superlinear scaling [11]. This discussion relates to the problem of the relevant spatial unit of analysis in complex systems. Also for universities superlinear scaling behavior is found in which size is given by the number of publications and the impact of the publications as the dependent variable [12, 13, 14]. Again, distance of interaction appears to play an important role [15, 16]. Recent research in the US on the development of meaningful urban metrics based on a quantitative understanding of cities shows a more than proportional (superlinear) increase of socio-economic performance of cities with increasing population [2, 3, 4]. A city that is twice as large (in population) as another city can be expected to have a factor of about 2.15 larger socio-economic performance, for instance in terms of gross urban product. This urban scaling phenomenon is important for new insights into and policy for urban development and, particularly in the Netherlands, municipal restructuring of urban areas. Different from the usual focus on measures for cutting down expenses, the urban scaling phenomenon opens new vistas toward socio-economic progress. Possible effects
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could amount to hundreds of millions of euros and thousands of jobs per year and per urban area [17]. The US research on urban scaling is about urban areas (MSA’s, metropolitan statistical areas) that have grown autonomously to a specific number of inhabitants, regardless of the formal boundaries of municipalities within an urban area. Thus, it is a synchronic, ‘static’ measurement that has a predicting value for what happens with socioeconomic variables if, for instance, a city (i.e., urban area) doubles in population in the course of time. This is, of course, different from a situation in which a city defined as a municipality and being the central city of the urban area, doubles in population by a formal restructure of all municipalities within the urban area into one new municipality. Nevertheless it is probable that after some time the newly formed city municipality scaling should meet the scaling values as predicted by its new size of population. Crucial is however the interesting policy question: would these scaling values for the doubled population (‘created’ by municipal reconstruction) not already be attained for the urban agglomeration as a whole, simply because the urban agglomeration regardless of the formal municipal boundaries already has this double population. The answer to the above question is the key element in our study: Does an urban area having one formal municipality with a strong governmental, social, economic and cultural coherence gain more from the superlinearity as compared to an urban area with the same population size but less, governmental, social, economic and cultural coherence, for instance by a lack of cooperation between the autonomous municipalities within an urban agglomeration? It is plausible that in urban areas where the different autonomous municipalities do not cooperate optimally, the reinforcing, non-linear effects of the (central) city dynamics will be hampered. We hypothesize that for such non-optimal urban areas the scaling rules will apply, but less than what can be expected on the basis of the size of the total population. In this study our main goal is to find out how an increase in number of inhabitants either by municipal restructuring of the urban region or by growth of the city itself affects socioeconomic variables and, particularly, whether the socioeconomic variables show a scaling behavior as a function of city population. We apply two different types of analyses: a time-dependent (diachronic) analysis and a ‘static’ (synchronic) analysis. Below we describe for both parts of the study the data, method, results and conclusions.
Time-dependent analysis We collected 1 for 17 cities (defined as municipalities) as well as for 8 of them the agglomerations (which mostly consists of several autonomous municipalities) covering in total 62 cities for the period 1988-2013: (1) number of inhabitants (population); (2) gross urban product (index 2013 ^ 100); and (3) employment (number of jobs). Particularly the number of jobs is generally considered as a crucial socioeconomic indicator given the general problem of underemployment. We count jobs in all economic sectors (agriculture, industry, wholesale, retail, transport, food, financial, education, research, health etc.) We analyzed both the gross urban product as well as the number of jobs as a function of population (if applicable: before and after municipal restructuring, otherwise the city as 1
The source of the data is CBS, the Netherlands Central Bureau of Statistics.
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well as its agglomeration). In fact, in this time-dependent analysis cities are compared with themselves over a period of about 25 years. Because we investigate the scaling behavior of cities on the basis of their population, a time-dependent analysis of a specific city will only yield reliable results if there is a considerable population growth in the time period considered. Thus, there is a major difference between cities that have grown rapidly in the last 25 years, and cities that have increased only moderately or even decreased in population. We first focus on the latter case. Here a statistically significant measure of the gross urban product and the number of jobs as a function of population is hardly or not possible. For instance, the acquisition of a major company or the opposite, business closure, may change significantly the number of jobs in a city while the population of the city does not change. Thus, scaling of the gross urban product and the number of jobs as a function of population is not applicable. Generally one can expect that, because of the continuous reinforcing of wealth in a country, the gross urban product of cities will increase slowly as a function of time with an average annual rate (in the Netherlands) of 4.3 per cent in the period 1987-2012. In the case of cities with a slowly increase of population, this situation will be characterized by an ‘artificially’ very large power law exponent for the gross urban product as a function of population, as explained mathematically below (see textbox at the end of this section). As an example we show in Figure 1 the correlation of the gross urban product and population of Eindhoven, one of the major industrial centers in the Netherlands. In the period covered by our study (1988-2013) the population of the city (municipality) of Eindhoven (around 200,000 inhabitants) increased with 14 percent and the population of the Eindhoven metropolitan area (around 450,000 inhabitants) with 21 per cent. The GUP (inflation corrected, index 2013) however increased in the same period for the city with 60 percent and for the metropolitan area with 82 percent. As a consequence, we find very high exponents: 3.92 for the city and 3.01 for metropolitan area. Thus it is clear that for cities with a small increase or even decrease in population the measurement of the scaling behavior of socioeconomic variable with population and resulting exponents is meaningless.
Figure 1. Slowly growing cities: as an example the correlation of the gross urban product of Eindhoven (city/municipality, blue diamonds; metropolitan areas, red squares) with the number of inhabitants.
For cities that have grown rapidly in the covered time period the scaling has lower exponents as can be expected. We present in Figures 2 and 3 as an example Almere, the new city in the Amsterdam metropolitan area that more than tripled in population from 60,000 in 1988 to about 200,000 now. We find a scaling power law exponent of 1.16 for
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the gross urban product and an exponent of 1.43 for the number of jobs. Nevertheless, this new city underperforms considerably as compared to other cities, see Section 3. Other fast growing cities show similar and even higher scaling exponents. These fast growing cities are characterized by a typical residential role in the beginning (most of these fast growing cities are within the larger urban areas of major cities). But as these cities became larger they started to attract business companies and thus reinforced their socioeconomic position in a relatively great pace, often in a situation where the population was not growing that rapidly anymore. So also in this case, the high superlinear exponents can be explained, at least a part of it, by a similar simple mathematical model as used above (see text box).
Figure 2. Rapidly growing cities: as an example the correlation of the gross urban product (GUP) of Almere (city/municipality, blue diamonds) with the number of inhabitants.
Figure 3: Correlation of the number of jobs in Almere (city/municipality, blue diamonds) with the number of inhabitants.
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We conclude that the diachronic analysis of a single city indeed reveals scaling behavior, but the exponents must be corrected for an the underlying general increase of wealth in a nation and by growing characteristics of, in particular, new cities. It illustrates the difficulty to investigate the scaling behavior with time series data. In this context we refer to the remark of Bettencourt, Lobo, and Youn [18] “What scaling properties can be expected from the mixture of local and national-level effects?”. Given our aim to find out the socioeconomic performance of cities as a function of population, the synchronic analysis of a large set of cities is a more appropriate approach. Mathematical explanation of large power law exponents Suppose we have a slowly growing gross urban product αt
G(t) ~ e
(1)
and an even slower growing population ẞt
P(t) ~ e
,β
