Highways, local economic structure and urban development Marco Percoco 1 Università Bocconi May 2013 Abstract Transport costs are widely considered as a key driver of competitive advantage of coun- tries, regions and cities. Their relevance is even greater when scale economies are at work since production is concentrated and goods must be shipped. Recent literature has found that highways, by decreasing transport costs, are crucial in influencing agglomera- tion economies and ultimately urban development. In this paper we contribute to this lit- erature by studying the effect of highway construction on the structure of local economies. In particular, we consider the effect of highways in Italian cities in terms of firm location by explicitly recognizing the pivotal role played by the transport sector and by intersectoral linkages in promoting development. The main research hypothesis is that the location of an highway exit in a given city attracts firms operating in the transport service sector and consequently transport-intensive firms. Our empirical evidence concerns Italian cities over the period 1951-2001 and exploits variation in employment, population and plants induced by the construction of the highway network. To deal with the endogeneity of the geography of highways exits, we propose as an instrument the geography of Roman roads. To this end, we have coded the whole network of Roman roads in Italy. We have found that the loca- tion of highway exits increases employment and the number of plants and that this growth is concentrated in transport service-intensive sectors. This result is robust to a number of checks, including eventual instrument non-validity and selection into treatment. Keywords: Highways, Urban Development, Accessibility. JEL Classification Numbers: L91, N70, R11, R49. 1 I am grateful to Gianmarco Ottaviano, Michel Serafinelli, Dirk Stelder and audiences at the Università di Modena e Reggio Emilia, LSE, 2012 NARSC Conference in Ottawa for useful comments. Francesca Cattaneo and Francesca Scaturro provided superb research assistance. Please, address correspondence to: Marco Percoco, Università Bocconi, Department of Policy Analysis and Public Management, via Rontgen 1 Milano (ITALY). Email to: [email protected]. 1
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Highways, local economic structure and urban development
Marco Percoco1
Università Bocconi
May 2013
Abstract
Transport costs are widely considered as a key driver of competitive advantage of coun-tries, regions and cities. Their relevance is even greater when scale economies are atwork since production is concentrated and goods must be shipped. Recent literature hasfound that highways, by decreasing transport costs, are crucial in influencing agglomera-tion economies and ultimately urban development. In this paper we contribute to this lit-erature by studying the effect of highway construction on the structure of local economies.In particular, we consider the effect of highways in Italian cities in terms of firm locationby explicitly recognizing the pivotal role played by the transport sector and by intersectorallinkages in promoting development. The main research hypothesis is that the location ofan highway exit in a given city attracts firms operating in the transport service sector andconsequently transport-intensive firms. Our empirical evidence concerns Italian cities overthe period 1951-2001 and exploits variation in employment, population and plants inducedby the construction of the highway network. To deal with the endogeneity of the geographyof highways exits, we propose as an instrument the geography of Roman roads. To this end,we have coded the whole network of Roman roads in Italy. We have found that the loca-tion of highway exits increases employment and the number of plants and that this growthis concentrated in transport service-intensive sectors. This result is robust to a number ofchecks, including eventual instrument non-validity and selection into treatment.
1I am grateful to Gianmarco Ottaviano, Michel Serafinelli, Dirk Stelder and audiences at the Università diModena e Reggio Emilia, LSE, 2012 NARSC Conference in Ottawa for useful comments. Francesca Cattaneoand Francesca Scaturro provided superb research assistance. Please, address correspondence to: Marco Percoco,Università Bocconi, Department of Policy Analysis and Public Management, via Rontgen 1 Milano (ITALY).Email to: [email protected].
1
1 Introduction
Transport costs are generally considered as an important driver of economic development
and of economic geography. The recent World Development Report (World Bank, 2009) has
effectively summarized the literature and argued that the effects of the reduction in transport
costs occurred over the past two centuries has resulted in an increase in international trade and
in spatial concentration of production as a consequence of tougher competition. In case of scale
economies in production, the effect of a change in transport costs is non-linear and the net
effect on development depends on initial conditions (Martin and Rogers, 1995; Fujita and Mori,
1996). For developing countries and lagging regions, the impact of transport policies designed
for decreasing freight costs can be unclear or even negative if interventions are not such that
treated areas move from one equilibrium to another. Similarly, the reduction in transport costs
may generate unclear effects also at local level. Most of the recent literature has in fact focused
on the impact of expansion of infrastructure network (as a proxy for transport cost reduction)
on urban development.
Baum-Snow (2006) studied the effect of highway expansion on urban sprawl in a large sam-
ple of US Metropolitan Statistical Areas. Among several sources of endogeneity of the shape
of highway network, the author identifies political bargaining as one of the most problematic
and difficult to deal with. To identify causally the effect of highways he ingeniously proposes
to use the map of the initial project of highway network in the US as an instrument for actual
road development. This choice is justified by the fact that the map used is a representation of
the planned network before political bargaining took place and hence is a good approximation
of how the network would have been. A similar approach was adopted by Duranton and Turner
(2012a) who consider the effect of road and transport service on urban growth. They find that
infrastructure cause growth, although given their construction costs, the effectiveness of their
further expansion can be questioned2. Donaldson (2013) finds that the expansion of railroads in
India has promoted international and interregional trade as well as price convergence between
2A recent strand of literature has also focused on the impact of infrastructure on interregional trade and even-tually on price convergence (Donaldson, 2010; Duranton and Turner, 2012b; Michaels, 2008).
2
districts.
According to the geographical economics literature, the effect of a change in transport costs
can be nonlinear in a world characterized by multiple equilibria. Interestingly, Bleakley and Lin
(2012) address this issue empirically by exploiting a natural experiment related to portage sys-
tem in US counties. The authors have interestingly found that portage sites became prosperous
and specialized in the commercial sector at the beginnings of XIX century; their prosperity was
maintained also when portage technology lost its competitiveness with respect to other modes
of transport such as railways. Despite this persistence, Bleakley and Lin (2012) could not find
evidence of multiple equilibria. By using firm-level data, Gibbons et al. (2012) analysed the
effect of road transport innovation on firm behaviour in the UK. Interestingly enough, they have
found that improvements into road viability affects firm location in terms of entry and exit into
local markets, while they could not find any effect on employment growth in firms located in
treated arease before the treatment was introduced.
In this paper, we contribute to the literature by studying the effect of highway construction
on firm location behavior and on the structure of local economies. In particular, we consider the
effect of highways on Italian cities in terms of firm location by explicitly recognizing the crucial
role played by the transport sector and by intersectoral spillovers in promoting development. In
particular, we study the case of the construction of the highway network in Italy occurred in the
period 1950-1970 in a quasi-experimental setting. By assembling a large dataset on all Italian
cities, we have estimated the effect of the location of an highway exit on the urban economy in
terms of population, number of plants and employment growth. Contrary to previous literature,
which uses as a proxy for accessibility the extension of the highways within a territory, we make
use of a novel dataset containing information on the location of exits and on their catchment
area. This choice has been made because we think that accessibility is better measured in
terms of actual access to the infrastructure. To deal with the endogeneity of the geography
of highway exits, we propose as an instrument the network of Roman roads built about 2,000
years before. To this end, we have coded the whole network of Roman roads, both the main
and the secondary ones. We further contribute to the literature by testing and finding support to
3
the hypothesis that the impact of increased accessibility works through co-agglomeration forces
driven by location decision of firms in the transport sector. This evidence is corroborated by
a number of robustness checks and deviations from baseline models, including selection into
treatment, placebo regressions and multiple regimes of growth.
The paper is organised as follows. In section 2 we present a brief history of the highway
network in Italy, wherease section 3 contains a description of our methodological approach.
Section 4 presents the choice of our instrument, namely the geography of ancient Roman roads
and section 5 contains results and robustness checks. Section 6 concludes.
2 The highway network in Italy
An efficient highway system is probably amongst the prominent needs for industrialized
economies as most of freight is carried by trucks. In Italy, massive extension of highways
took place during the fifties and sixties of XX century and coincided with a period of sustained
growth and mass diffusion of cars, although some highways were built well before (during
the twenties) in Lombardy, between Milan and the lakes on the border with Switzerland and
between Naples and its suburban town Capua.
A significant effort in the extension of the highway network came in the aftermath of World
War II and in May 1955 when the Romita law was approved. This act planned to build more
than 1,200 kilometers of new highways, with the most important being the A1 Milan-Naples,
the so-called “Autostrada del Sole” (Maggi, 2009). Figure 1 reports the temporal evolution of
the highway network in Italy. It shows how it more than doubled between 1955 and 1960. In
1972 the quantity of kilometers was more than ten times the one in 1955. Along this period,
almost 208 km were built every year, whereas Germany built 170 and France 127. At the end
of 1974 the Italian network of highways was almost double than the one of France and UK and
was smaller only than US and German ones. Figure 2 shows the highway system as in 2001, in
terms of geography of the network.
[Figures 1 and 2]
The most important highway was certainly the A1 Milan-Naples whose construction lasted
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five years between 1959 and 1964 to build almost 700 km of roads. In San Donato Milanese,
municipality located in South of Milan, May 19, 1956 it laid the foundation stone of the Au-
tostrada del Sole that day in the presence of President Giovanni Gronchi and Archbishop Gio-
vanni Battista Montini of Milan a marble stone with the inscription, which linked the motorway
to the roads of ancient Rome (Menduni, 1999). In July 1959 the trait Milan-Bologna was com-
pleted and the following year, in December 1960, the highway touched Florence and, finally,
in October 1964, arrived in Naples. Within eight years, then, was an artery in the light of 755
kilometers, for a long time to become the main transport axis of the peninsula, through it, it
was thought, would have met the conditions for osmosis in " hundred cities of Italy, "because
not only was breaking the physical border between North and South, but would also eventu-
ally loose economic and cultural ones that still separated the two poles of the nation (Menduni,
1999). The Highway of the Sun was the carrier through (and long), which came to life the
incredible economic development that marked those years, although today there are few histo-
rians and economists who remember the role it played, and still less the literary evidence and
film, the footprints it left on the land are important and are still evident (Cardinale, 2000; Maggi,
2003; Menduni, 1999). Its construction, and the grafts that followed, helped to trigger social
phenomena (including mass tourism and commuting) and economic (primarily the re-location
space industries) which were accompanied by important changes to zoning and the industrial
fabric of the country.
[Figure 3]
The construction of the highway network made accessible and competitive for firm location
soma areas of the country (especially in the Center), that before that policy intervention were
relatively underdeveloped. Accessibility to highways is granted by the location of exits. In
figure 3 the opening of highway exits across time is shown. In conformity with the pattern of
investment in figure 1, figure 3 shows that the vast majority of highway exits was opened during
the Sixties and Seventies. In our analysis, exits play a crucial role since we will assume that the
opening of an exit in the surrounding of a city is a good proxy for a change in the accessibility.
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If we consider this assumption as plausible, then figure 3 shows the timing of the treatment. For
reasons related to the endogeneity of the treatment (i.e. the decition to locate an exit) we will
not exploit fully the temporal pattern of the treatment, although in section 5.3.1 we will also
present results of a panel difference-in-difference model.
3 Methodology
The objective of this study is to estimate the impact of a change in highway accessibility on
urban development indicators such as population, employment and the number of plants. For
Prima del 1950 Anni '50 Anni '60 Anni '70 Anni '80 Anni '90 Anni 2000
Source: CERTeT (2006). Figure 4: Highways and Roman roads in the province of Trento
Table 1: Descriptive statistics Mean and standard deviation With highway exit Without highway exit Cumulative Employment Growth
0.851 (1.131)
0.715 (1.022)
Cumulative Plant growth 0.640 (0.946)
0.525 (0.719)
Roman roads 0.253 (0.529)
0.053 (0.252)
Table 2: Expansion of the highway network and urban growth (OLS estimates) (1) (2) (3) (4) (5) 1951-2001 1951-1981 1951-1971 1971-2001 1981-2001 Panel A: Population growth Exit -0.00 0.01 0.02* 0.02* 0.03 (0.007) (0.008) (0.010) (0.011) (0.015) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.24 0.50 0.50 0.48 0.46 Panel B: Employment growth Exit 0.04*** 0.03*** 0.08*** 0.01** 0.06** (0.010) (0.002) (0.015) (0.004) (0.002) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.32 0.44 0.39 0.36 0.34 Panel C: Plants growth Exit 0.01** 0.01** 0.01** 0.03** 0.04** (0.004) (0.004) (0.004) (0.002) (0.002) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.54 0.52 0.44 0.48 0.51 Notes: In Panel A dependent variable is average decadal population growth; in Panel B and C dependent variables are employment per capita growth and plants per capita growth respectively. All specifications present OLS estimates and include a constant, surface, altitude, the initial level of the dependent variable, city population in 1861 and a full set of province-specific fixed effects, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Table 3: Expansion of the highway network and urban growth (OLS estimates; Only Center-North) (1) (2) (3) (4) (5) 1951-2001 1951-1981 1951-1971 1971-2001 1981-2001 Panel A: Population growth Exit 0.00 0.01 0.03 0.01 0.02 (0.009) (0.010) (0.014) (0.011) (0.016) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.26 0.50 0.50 0.08 0.05 Panel B: Employment growth Exit 0.04** 0.02*** 0.07** 0.01** 0.06* (0.012) (0.001) (0.025) (0.001) (0.024) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.36 0.45 0.45 0.39 0.37 Panel C: Plants growth Exit 0.01** 0.02** 0.02** 0.01** 0.04** (0.001) (0.008) (0.008) (0.001) (0.016) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.27 0.58 0.44 0.49 0.45 Notes: In Panel A dependent variable is average decadal population growth; in Panel B and C dependent variables are employment per capita growth and plants per capita growth respectively. All specifications present OLS estimates and include a constant, surface, altitude, the initial level of the dependent variable, city population in 1861 and a full set of province-specific fixed effects, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Table 4: Road network and urban growth (OLS estimates; reduced forms) (1) (2) (3) (4) (5) 1951-2001 1951-1981 1951-1971 1971-2001 1981-2001 Panel A: Population growth Roman road 0.09* 0.16*** 0.26*** 0.33*** 0.48*** (0.044) (0.044) (0.055) (0.059) (0.069) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.24 0.50 0.50 0.48 0.46 Panel B: Employment growth Roman road 0.11*** 0.26*** 0.38*** 0.06* 0.11* (0.022) (0.031) (0.038) (0.032) (0.048) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.32 0.44 0.44 0.39 0.36 Panel C: Plants growth Roman road 0.37*** 0.59*** 0.88*** 0.03 0.04 (0.025) (0.027) (0.034) (0.034) (0.055) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.24 0.52 0.33 0.44 0.48 Notes: In Panel A dependent variable is average decadal population growth; in Panel B and C dependent variables are employment per capita growth and plants per capita growth respectively. All specifications present OLS estimates and include a constant, surface, altitude, the initial level of the dependent variable, city population in 1861 and a full set of province-specific fixed effects, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Table 5: Road network and urban growth (OLS estimates; reduced forms; only Center-North) (1) (2) (3) (4) (5) 1951-2001 1951-1981 1951-1971 1971-2001 1981-2001 Panel A: Population growth Roman road 0.02 0.32*** 0.47*** 0.28** 0.42*** (0.065) (0.064) (0.072) (0.091) (0.101) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.26 0.50 0.50 0.38 0.45 Panel B: Employment growth Roman road 0.10** 0.31*** 0.44*** 0.13** 0.21*** (0.033) (0.042) (0.052) (0.046) (0.058) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.36 0.45 0.38 0.38 0.47 Panel C: Plants growth Roman road 0.36*** 0.64*** 0.89*** 0.67*** 0.48* (0.027) (0.032) (0.047) (0.022) (0.022) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.57 0.58 0.44 0.59 0.35 Notes: In Panel A dependent variable is average decadal population growth; in Panel B and C dependent variables are employment per capita growth and plants per capita growth respectively. All specifications present OLS estimates and include a constant, surface, altitude, the initial level of the dependent variable, city population in 1861 and a full set of province-specific fixed effects, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Table 6: Road network and roman roads (First stage regressions) Whole sample
Only Center-North (1) (2) (3)
(4) (5) (6) Population Employment Plants
Population Employment Plants
Roman road 0.11*** 0.14*** 0.14***
0.13*** 0.15*** 0.16*** (0.021) (0.023) (0.024)
(0.029) (0.033) (0.035)
F-statistics 27.12 29.22 31.49
22.89 27.39 32.55 Observations 7,478 7,214 7,214
4,153 3,984 3,984 R-squared 0.50 0.47 0.66
0.35 0.51 0.67 Notes: All specifications present first stage estimates and include a constant, surface, altitude, the initial level of the dependent variable, city population in 1861 and a full set of province-specific fixed effects, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Table 7a: Road network and urban growth (Second stage regressions) (1) (2) (3) (4) (5) 1951-2001 1951-1981 1951-1971 1971-2001 1981-2001 Panel A: Population growth Exit 0.06 0.06 0.12 0.08* 0.04 (0.052) (0.062) (0.071) (0.031) (0.022) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.23 0.50 0.47 0.40 0.41 Panel B: Employment growth Exit 0.03** 0.02*** 0.01*** 0.01* 0.01* (0.012) (0.002) (0.001) (0.004) (0.004) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.44 0.55 0.61 0.36 0.33 Panel C: Plants growth Exit 0.03* 0.03** 0.03*** 0.02 0.05 (0.013) (0.012) (0.005) (0.011) (0.140) Observations 7,480 7,480 7,480 7,480 7,480 R-squared 0.30 0.41 0.52 0.28 0.29 Notes: In Panel A dependent variable is average decadal population growth; in Panel B and C dependent variables are employment per capita growth and plants per capita growth respectively. All specifications present IV estimates and include a constant, surface, altitude, the initial level of the dependent variable, city population in 1861 and a full set of province-specific fixed effects although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Table 7b: Road network and urban growth (Second stage regressions; Only Center-North) (1) (2) (3) (4) (5) 1951-2001 1951-1981 1951-1971 1971-2001 1981-2001 Panel A: Population growth Exit 0.01 0.03 0.04 0.04 0.02 (0.057) (0.172) (0.181) (0.290) (0.134) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.22 0.29 0.21 0.25 0.22 Panel B: Employment growth Exit 0.03*** 0.04*** 0.05*** 0.01* 0.01* (0.004) (0.002) (0.007) (0.004) (0.004) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.55 0.58 0.61 0.31 0.30 Panel C: Plants growth Exit 0.04** 0.05** 0.06*** 0.02* 0.02* (0.017) (0.019) (0.026) (0.015) (0.017) Observations 4,154 4,154 4,154 4,154 4,154 R-squared 0.41 0.42 0.59 0.29 0.29 Notes: In Panel A dependent variable is average decadal population growth; in Panel B and C dependent variables are employment per capita growth and plants per capita growth respectively. All specifications present IV estimates and include a constant, surface, altitude, the initial level of the dependent variable, city population in 1861 and a full set of province-specific fixed effects, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Table 8a: Road network and urban growth (Second stage regressions) (1) (2) (3) (4) (5) 1951-2001 1951-1981 1951-1971 1971-2001 1981-2001 Panel A: Employment growth Exit 0.11** 0.12** 0.13*** 0.11* 0.11 (0.004) (0.007) (0.001) (0.005) (0.009) Observations 225,766 225,766 225,766 225,766 225,766 R-squared 0.55 0.57 0.67 0.31 0.27 Panel B: Plants growth Exit 0.11* 0.12*** 0.14*** 0.12 0.12 (0.005) (0.005) (0.002) (0.189) (0.201) Observations 225,766 225,766 225,766 225,766 225,766 R-squared 0.56 0.62 0.71 0.23 0.22 Notes: In Panel A and B dependent variables are employment per capita growth and plants per capita growth respectively. All specifications present IV estimates and include the initial level of the dependent variable, a full set of city-specific fixed effects and province-sector interaction dummies, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Table 8b: Road network and urban growth (Second stage regressions; Only Center-North) (1) (2) (3) (4) (5) 1951-2001 1951-1981 1951-1971 1971-2001 1981-2001 Panel A: Employment growth Exit 0.11** 0.12** 0.13*** 0.11 0.11 (0.004) (0.007) (0.001) (0.009) (0.011) Observations 175,299 175,299 175,299 175,299 175,299 R-squared 0.58 0.59 0.62 0.32 0.21 Panel B: Plants growth Exit 0.11* 0.12*** 0.13*** 0.11 0.12 (0.005) (0.006) (0.003) (0.219) (0.411) Observations 175,299 175,299 175,299 175,299 175,299 R-squared 0.32 0.61 0.69 0.21 0.21 Notes: In Panel A and B dependent variables are employment per capita growth and plants per capita growth respectively. All specifications present IV estimates and include the initial level of the dependent variable, a full set of city-specific fixed effects and province-sector interaction dummies, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Notes: All models are estimated via IV regressions. Specifications for the aggregated outcomes include a constant, surface, altitude, the initial level of the dependent variable, city population in 1861 and province-specific dummies. Specifications for sector-based outcomes include a full set of city-specific fixed effects and province-sector interaction dummies.. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
Observations 4,154 4,154 4,154 4,154 4,154 R2 0.41 0.42 0.59 0.29 0.29 Notes: All models present OLS estimates. All specifications include a constant, surface, altitude, city population in 1861 and a full set of province-specific fixed effects, although their coefficients are not reported. Significance values: *** p<0.001, ** p<0.01, * p<0.05. Robust standard errors clustered by province are in parentheses.
(0.001) Note: Oaxaca-Blinder regressions include a constant, surface, altitude, city population in 1861 and a full set of province-specific fixed effects. Robust standard errors clustered by province are in parentheses.