A Research Programme on Urban Dynamics
7
endowment of these factors. This is what has been termed long-term structural
dynamics (Camagni et al. 2015a).
This implies that some large cities escape agglomeration diseconomies, despite
their large dimensions; by the same token, some small ones may face diseconomies
if unable to implement innovative strategies and functional upgrading or to broaden
their networks with other cities across short but also long distances through cooperative agreements relating to infrastructure, top public services or R&D facilities.
Within CPT, there is still considerable room for further advances. Particularly,
there seems to be a general lack of consensus regarding the very definition of urban
ranks. What do “large” and “medium” mean when defining urban ranks? While,
from a general equilibrium perspective, city sizes are distributed along a continuum
of functions and roles, structural breaks still seem to characterize urban systems,
thus strengthening the case for the existence of different production functions, and
different stocks of production factors for cities of different ranks. Ideally, theoretical
models should follow suit and accommodate rank thresholds.
An important step forward in this sense is the critique of a number of theoretical shortcuts in neoclassical urban economics (Camagni et al. 2016) which assume
that agglomeration economies (i.e. city size) automatically lead to urban growth
(Krugman 1991; Glaeser et al. 2001; Glaeser 2011). Henderson (2010) argues that
the “association between urbanization and development (…) is an equilibrium not
causal relation” (p. 518) and that “urbanization per se does not cause development”
(p. 515). The point made by the authors is that “along an average productivity curve
rising with urban size, reading the size-derivative as a time-derivative will be mistaken and, beyond that, implies a circular reasoning: ‘if a city grows demographically
it will grow economically’” (Camagni et al. 2016, p. 134). A second critique also
posed by the authors suggests the use of net rather than gross measures of urban efficiency when measuring agglomeration economies. This implies reaching beyond per
capita GDP, productivity and wages in order to also include urban costs (as argued
in Richardson 1978). Thirdly, in their empirical estimates (based on European metro
areas), Camagni et al. (2016) find that:
(i) In static terms, net overall urban benefits (urban land rent) display a U-shaped
relationship with urban size, suggesting the presence of net increasing returns
to urban scale;
(ii) On the other hand, from a dynamic perspective, this relationship fails when
it comes to interpreting urban growth. In fact, urban dynamics as measured
by net benefit growth rates show no relation to initial urban size or density.
Instead, results suggest that growth is positively associated with the upgrading
of urban functions, the development of the nearby urban system and, once again,
the capability of establishing long-distance cooperative agreements with other
cities. These results call for a dynamic approach to explaining agglomeration
economies (Camagni et al. 2016).
Despite consistent efforts, urban economics still has a long way to go. As frequently advocated (see e.g. Duranton and Puga 2004), the relative strength of agglomerative forces is still not fully understood. More specifically, there seems to be room
7
endowment of these factors. This is what has been termed long-term structural
dynamics (Camagni et al. 2015a).
This implies that some large cities escape agglomeration diseconomies, despite
their large dimensions; by the same token, some small ones may face diseconomies
if unable to implement innovative strategies and functional upgrading or to broaden
their networks with other cities across short but also long distances through cooperative agreements relating to infrastructure, top public services or R&D facilities.
Within CPT, there is still considerable room for further advances. Particularly,
there seems to be a general lack of consensus regarding the very definition of urban
ranks. What do “large” and “medium” mean when defining urban ranks? While,
from a general equilibrium perspective, city sizes are distributed along a continuum
of functions and roles, structural breaks still seem to characterize urban systems,
thus strengthening the case for the existence of different production functions, and
different stocks of production factors for cities of different ranks. Ideally, theoretical
models should follow suit and accommodate rank thresholds.
An important step forward in this sense is the critique of a number of theoretical shortcuts in neoclassical urban economics (Camagni et al. 2016) which assume
that agglomeration economies (i.e. city size) automatically lead to urban growth
(Krugman 1991; Glaeser et al. 2001; Glaeser 2011). Henderson (2010) argues that
the “association between urbanization and development (…) is an equilibrium not
causal relation” (p. 518) and that “urbanization per se does not cause development”
(p. 515). The point made by the authors is that “along an average productivity curve
rising with urban size, reading the size-derivative as a time-derivative will be mistaken and, beyond that, implies a circular reasoning: ‘if a city grows demographically
it will grow economically’” (Camagni et al. 2016, p. 134). A second critique also
posed by the authors suggests the use of net rather than gross measures of urban efficiency when measuring agglomeration economies. This implies reaching beyond per
capita GDP, productivity and wages in order to also include urban costs (as argued
in Richardson 1978). Thirdly, in their empirical estimates (based on European metro
areas), Camagni et al. (2016) find that:
(i) In static terms, net overall urban benefits (urban land rent) display a U-shaped
relationship with urban size, suggesting the presence of net increasing returns
to urban scale;
(ii) On the other hand, from a dynamic perspective, this relationship fails when
it comes to interpreting urban growth. In fact, urban dynamics as measured
by net benefit growth rates show no relation to initial urban size or density.
Instead, results suggest that growth is positively associated with the upgrading
of urban functions, the development of the nearby urban system and, once again,
the capability of establishing long-distance cooperative agreements with other
cities. These results call for a dynamic approach to explaining agglomeration
economies (Camagni et al. 2016).
Despite consistent efforts, urban economics still has a long way to go. As frequently advocated (see e.g. Duranton and Puga 2004), the relative strength of agglomerative forces is still not fully understood. More specifically, there seems to be room