Abstract
The aim of this paper is to present an overview of the results of a recent research project by the Bank of Italy. The paper analyses the interplay between historical origins, congestion costs, and agglomeration benefits in shaping the Italian urban system. It shows that urban agglomeration externalities (on wages, productivity, or innovation) tend to be smaller in Italy than in other developed countries; it also shows that the costs of congestion are relatively high and that high housing cost—explained by both physical constraints and public administration inefficiencies—discourage mobility. These features have a relevant impact on the development of an advanced urban system with possible negative consequences on the country’s ability to grow.
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Source: authors’ elaborations on data drawn from OECD. Each area represents the share of the urban area(s) GDP on the national GDP
Source: Caldera and Johansson (2013)
Source: authors’ elaboration on data drawn from Istat. Each dot represents a LLM
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Notes
Between 1920 and 2010 the population in the metropolitan areas of the United States grew by 17.9 per cent every 10 years on average, exceeding the nation-wide rate by 5.3 percentage points. Over the same period, urban areas in Spain grew by 18.1 per cent every 10 years, twice the national average. In France urban population growth was 7.7 per cent every 10 years in the period 1937–2007, 2 percentage points above the national average. In Italy, the population of the municipalities that in 1911 had more than 20,000 inhabitants rose by 7.2% every 10 years between 1911 and 2011, against 4.9 in the national average (Giffoni et al. 2017).
Unless otherwise specified, ‘urban areas’ are LLMs—as defined by Istat in 2011 on the basis of commuting patterns—with a population density above 1500 inhabitants per square kilometre. See Lamorgese and Petrella (2019) in this issue for more details.
See, for example, the role that ‘smart cities’ play in the European Commission’s development agenda.
According to Pleiades, in late Roman times Italy had 2.5 settlements per 1000 square kilometre, twice the density in France (1.2) and Spain (1.0) and five times that in Germany or Great Britain (0.5 for both); a settlement here means a city (with walls, aqueducts, theatres etc.) or village of Roman or earlier origin.
The share of population living in the main urban area (‘urban primacy’) is 7% in Italy, comparable to that in Germany, but is significantly higher in the United Kingdom, France and Spain; considering the share of the population living in an urban area with at least half a million inhabitants, Italy is by far the country with the lowest share (31%), not only in comparison with France and the United Kingdom (41% for both) and Spain (38%), but also compared with Germany (40%).
Considering a large sample of characteristics relating to geology, physical geography and seismicity, geography explains about 30 per cent of the differences between cities in terms of population, and about 50 per cent of the differences in term of population density.
Up to the 19th century, Naples was one the largest cities in Europe.
The data presented by Bosker et al. (2008) show how in the South urban primacy calculated on cities with more than 10,000 inhabitants was significantly higher than in the Centre-North (15 and 9 per cent respectively), but the average population per city was lower (21,000 versus 32,000 inhabitants).
Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, the United Kingdom and Spain. The highest values were recorded in France and the United Kingdom (where the ratio of house prices and per capita GDP was more than 12), which are affected by the high real estate values of Paris and London respectively.
Geographical differences remain significant if house prices are compared with income, especially between areas displaying different degrees of urbanization. The cost of a 100 square metre house is equal to about eight times the average income declared by taxpayers: the value is more than 10 for metropolitan labour market areas and more than 12 for the centroid municipality of the labour market area..
Calculations performed using data from the Survey on Household Income and Wealth survey, which make it possible to gain a better understanding of the qualitative differences between buildings, confirm these findings. The prices by square metre are almost 60% higher in the Centre-North than in the South. The gap narrows by 10 percentage points when controlling for cadastral category, year of construction, availability of a heating system, and the presence of two or more bathrooms. Within cities, house prices are 20% lower in the suburbs than in the city centre (only just over 10% when taking account of the features of the houses). These figures are about twice as high in municipalities with more than 200,000 inhabitants.
Differences between the various areas of a given city can reflect, among other things, a different composition of the population living there, both in terms of economic conditions (Manzoli and Mocetti 2019) and of other socio-demographic characteristics, for example citizenship (Accetturo et al. 2014).
See OECD (2018), ‘Metropolitan areas’, OECD Regional Statistics (database).
This component is due to sectoral composition, firm size or any other observable or non-observable characteristic of individuals and firms located in a given city, i.e. the effects stemming from the fact that cities attract—on a disproportionate scale relative to the average—faster-growing sectors of firms with a higher growth potential.
Another distinction made by the literature is between the advantages due to this city-specific component and that benefit firms and workers as soon as they move into the city versus those that are acquired gradually. In general, the former disappear as soon as the worker or firm relocates elsewhere, while the latter are a sort of knowledge capital that firms or workers carry with them when they move elsewhere (see De la Roca and Puga 2017).
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Accetturo, A., Lamorgese, A., Mocetti, S. et al. Local Development, Urban Economies and Aggregate Growth. Ital Econ J 5, 191–204 (2019). https://doi.org/10.1007/s40797-019-00095-y
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DOI: https://doi.org/10.1007/s40797-019-00095-y