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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.
Keywords: Highways, Urban Development, Accessibility.JEL Classification Numbers: L91, N70, R11, R49.
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: marco.percoco@unibocconi.it.
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
4
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.
5
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
this purpose, let us consider the function:
yit = αi +dt +βaccessibilityit + γcontrolsit + εit (1)
Where yit is either population, the number of plants per capita or jobs per capita in city i at
time t; accessibilityit is an accessibility indicator, whereas controlsit is a set of control variables.
Parameters αi,dt indicate city-specific and year-specific fixed effects. Taking first differences
we obtain:
4yit = dt +β4accessibilityit + γ4controlsit +4εit (2)
Note that a function like that in (2) eliminates any fixed effects from (1). However, it should
be noted that the elimination of city-specific fixed effects holds only if those parameters are
stable. We will consider the period 1951-2001, which is long enough not to consider this as-
sumption as plausible. However, we will consider also narrower time windows to obtain more
precise estimates as well as time-invariant controls to proxy changes in city-specific fixed ef-
fects.
In our case we assume that the opening of an highway exit is a significant change in the
accessibility of the city, so that we estimate the following version of (2):
6
4yit = dt +βexitit + γ4controlsit +4εit (3)
Where we assume that4accessibilityit ∼= exitit . Variable exitit indicates whether an highway
exit has been located in the surrounding of city i between t and t-1.
It is worth noting that exit is defined in terms of catchment area, ie the variable takes the
value 1 is not only the town name or the highway, but also for all municipalities served. The
definition of the basins is contained in CERTeT (2006) and consider as treated (i.e. with an
highway exit) cities within 15 kilometers arc distance from an exit. This coding of the variable
is particularly useful because it als captures effects of spatial spillovers.
The geography of highways is probably endogenous with respect to development, so that,
our parameter of interest, β , might be biased. To deal with this issue, a recent strand of literature
has proposed the use of historical instrument to identify the parameter. In particular, Baum-
Snow (2007), Duranton and Turner (2012), Michaels (2011) propose the use of the initial 1947
project for the US highways system to estimate the impact of highways on a series of outcomes
in US cities. In the case of Italy, the road network has an important antecedent represented by
the network of Roman roads, originally built to provide an efficient postal and communication
service throughtout the Empire.
This complex arterial system was the geographical basis on which the Italian motorway
system was designed. Therefore, for the purposes of the present study, we consider the presence
of a Roman road on the urban territory as an instrument for exit, so that the estimated model
was a system of equations:
4yi1951−2001 = βexiti + γcontrolsi + εi (4)
exiti = δcontrolsi +ϖZi +υi (5)
where the instrument is Z = 1[RomanRoad], that is it takes the value of 1 if a trait of a
Roman road was located in city i and 0 otherwise. As for controls, we make use of the city
7
surface, altitute, the initial level of the dependent variable and a full set of province-specific
fixed effects. All data are from the Atlante Statistico dei Comuni by ISTAT. It should be noted
that we have excluded all cities that in 1951 already had a highway exit, hence the ones located
on the Milano-Laghi and Napoli-Capua highways.
The estimation of model (4)-(5) implies the acceptance of the identifying restriction that Z
influences the current level of development of the Italian cities only through the variable exit,
or through a change in transport costs. This assumption cannot be tested explicitly, however,
in section 4 we will discuss extensively the choice of the instrument as well as possible threats
to its validity in section 5. Finally, as the variable exit is a dichotomous variable, so that the
estimation of model (4)-(5) is possible only through the estimation of a linear probability model
in the first stage.
In this paper we are interested in estimating also the distribution of the effect of highway
exits across sectors within cities and towns. Our hypothesis is that the fundamental player in the
process of firm location is the transport sector. However, through a process of co-agglomeration
also firms belonging to other sectors but with high demand for transport services will preferably
locate in cities with a highway exit as in those locations transport costs are lower and firms
in the transport service sector have higher productivity (Ellison et al., 2010). In such simple
framework a pivotal role is played by the technological linkages between sectors, i.e. by input-
output coefficients. To test this simple proposition, we have estimated an equation in the form:
4yis1951−2001 = αi +αs + exiti ·aT SPs + controls+ εis (6)
where the dependent variable measures the change in the number of plants or in the number
of employees per capita in city i and sector s between 1951 and 2001. Variable aT SPs is the
technological coefficient for transport inputs in sector s as reported in the input-output tables
made available in Rampa (2001). It should be noted that this coefficient has been computed as
in 1959, i.e. at the early available year. Furthermore, we make use a 44 sector NACE classifi-
cation, whereas I-O tables are available with an ESA1979 classification, so that we have made
a concordance between the two classifications and imputed consequently transport technolog-
8
ical coefficient. The technological coefficient in specification (6) measures the dependence of
a given sector on the output of the road transport service sector. Hence, it does not capture
precisely the quantity of transport services effectively used by firms since auto-produced trans-
port inputs are not accounted for. However, we may reasonably think at transport technical
coefficients as a good measure for the relevance of transport usage for a given sector.
The rationale behind specification in (6) is that the location of a highway exit affects posi-
tively firms operating in the transport service sector and then, throigh them also other firms. In
other words, we are testing whether or not there is a co-location pattern between transport firms
and other firms (Ellison et al., 2010).
As regards the aforementioned endogeneity issues, in the case of a city-by-sector panel, the
first stage regression we have estimated was:
exiti = αi +αs +ϖZi ·aT SPs + controls+υi (7)
To deal with province-specific specialization patterns, equations (6) and (7) also include a
series of province-sector interactions as well as the initial level of the dependent variable.
4 The instrument
To deal with the problem of endogeneity in our regression, we make use of an instrument
that measures the presence of an intercity Roman road in the town. The complex of Roman
roads in Italy consisted of thirteen principal routes (Basso, 2009): 1) The Appian Way was built
in 312 BC by the Consul Appius Claudius was initially drawn to Capua to be then extended
to Beneventum, Venosa, Tarantum and Brundisium. 2) The Via Aemilia, which was the con-
tinuation of the Via Flaminia north-west, joining with Ariminum Placentia, touching Caesena,
Forum Livi, Bononia Mutina, Regium lepidum and Parma. 3) The Way of Capua-Rhegium,
which, starting from the Via Appia to Capua, went up to Rhegium, through Consentia and Vibo
Valentia. 4) The Via Aurelia, which connected Rome to Go Sabatia (Vado Ligure), through
Pisae, Moon and Genua. 5) The Way Domitiana that is separated from the Via Appia in Sin-
uessa (Mondragone) and finally to Neapolis. 6) The Popilia-Via Annia was another continua-
9
tion of the Via Flaminia and started from Ariminum through Rabenna, Atria (Adria), Patavium
(Padua), Altinum, Aquileia, Tergeste (Trieste). 7) The Latin Way connected Rome to Capua
through Anagnia, Frusino, Casinum. 8) The Via Flaminia linking Rome with Ariminium (Ri-
mini), touching Fanum Fortunae (Fano) and Pisuarum. 9) The Via Salaria was the main track
on which ran the supply and sale of salt, one of the most valuable materials for the time. The
route started from Rome and came to Castrum Truentinum (Porto d’Ascoli), and through Reate
Asculum. 10) The Way Postojna joined Genua and Aquileia, through the Po Valley. 11) The
Via Valeria connected the Roman coast to Aterni (Pescara), past Tibur (Tivoli) and Teate Mar-
rucinorum (Chieti). 12) The Via Cassia, linking Rome to northern Italy, touching Arretium,
Florentia, Pistoia, Luke. 13) Finally, the Via Clodia connected Rome to Saturnia.
Besides those major routes, a vast network of secondary roads was developed between IV
century B.C. and IV century A.C.. Our instrument is then a sort of long lag of our endogenous
variable and as such it could be considered as reasonably exogenous. Roman empire was ex-
tremely centralized on the city of Rome so that to maintain the control on peripheral areas, a
wide network of roads was built. Before the Romans, several civilization used to build roads
to connect the territory. However, Romans re-organized the space through a well established
network of roads on which they applied new engineering solutions in order to meet three main
features (Basso, 2009):
1. firmitas (solidity of the construction);
2. utilitas (social efficiency of the investment);
3. venustas (monumental appeherance).
Roman roads were built to last as for the first time in history, they have been paved (viae stratae)
with stones meant to last forever, in the intentions of the builders (Berechman, 2003; Staccioli,
2010). In the first period of network construction, i.e. in the IV century B.C., Roman roads were
crucial for the “romanization” of outermost provinces as they facilitated contacts with Rome.
They also contributed to economic development through trade growth and to urban develop-
ment through the concentration of population near principal roads. In a second period (IV-III
10
centuries B.C.), roads were essential for military expansion in the Italian peninsula. The third
period starts with the investment planned by Augustus between I B.C. and the beginning of I
A.C. during which the road network was extended out of Italy and probably reached 120,000
kilometers in length. After Augustus, his successors engaged in a large programme of main-
tainence up to IV century A.C. in which roads were used only as defence infrastructure to move
armies to the borders. There were three different types of roads (Basso, 2009): a) viae publicae,
i.e. roads connecting different parts of the urban space; b) viae vicinales, i.e. roads connecting
different parts of the urban space; c) viae privatae, i.e. roads used to have access to rural areas.
We think at the presence of Roman roads in cities as a measure of their long run accessibility
upon which the economic geography of Italy was built. Once the new technology of highways
was made available, we assume that all cities with a Roman road were eligible to receive the
treatment which in our case is measured through the location of an highway exit. Given this
assumption, the coefficient associated to the exit in our second stage regression measures locally
an Average Treatment on the Treated and should be thought as the impact of a reduction of
transport costs within the set of compliers, i.e. the cities already connected with a Roman road.
The rationale behind the choice of such instrument is hence that the network of Roman
roads has been important for the spatial organization of production for a wide range of countries
(Von Hagen, 1966). The increase in the accessibility of certain cities led the early distribution
of population and this, in the long run through forms of path dependence, drove, along with
production factors, the location of firms and industries. Hence, in our empirical analysis, we aim
to verify whether a further change in transport cost has resulted in a change in the equilibrium
and hence in firm relocation.
[Figure 4]
Figure 4 reports, only as an example, a map of the Province of Trento with the two highways
“Brennero-Modena” and “Vicenza-Schio”, as well as traits of the Roman roads. For the sake of
clarity, it must be stated that Roman road traits within city boundaries are purely indicative, so
that it is not possible to quantify precisely the kilometers of roads in each city. If we had to use
11
this information as an instrument, we would have incurred in a serious problem of measurement
error. On the other hand, whether a city was located on a Roman road or not is known without
uncertainty.
A possible problem with the proposed instrument is that if Romans located roads to serve
fast-growing cities and if those cities have continued to grow until present days, then our instru-
ment may be not valid. To deal with this issue, we have excluded from our analysis all province
chief-towns as Roman roads connected the most important cities of the Empire and those cities
seem to be modern chief-towns. In doing so, we think that the proposed instrument, although
potentially endogenous for major cities, is exogenous to minor ones. Furthermore, in section
5.3.3 we propose some robustness checks to deal with a potential selection into treatment threat.
5 Results
5.1 Baseline results
We start our empirical analysis with the descriptive statistics reported in table 1. The employ-
ment and plants cumulative growth rates are both found to be larger in cities with an highway
exit. Furthermore, it is interesting to note that about 25% of cities with an highway exit have had
also a Roman road, whereas only 5% of cities with Roman road do not actually have highway
access.
[Table 1]
In tables 2 and 3, estimates of equation (2) are reported for Italy and for only Center-
Northern regions respectively. As from table 2, it seems that the opening of an highway exit has
not produced any significant effect on population growth in any of the considered time periods,
whereas the increase in accessibility has generated an increase in the employment growth rate
by 4% over the period 1951-2001. As expected, the effect of exit location is stronger for the
period 1951-1971, i.e. during the effective development of the road network, with an estimated
12
impact of 8%. Results for the growth in the number of plants qualitatively confirm those ob-
tained in the case of employment, although point estimates are smaller and amount, on average,
to 1%, with a slightly larger effect for the period 1951-1971. In table 3 we report estimates
of equation (2) when the sample is restricted to Northern cities. These results are important as
policy impact estimates in table 2 might be driven by the low level of development of Southern
cities as only a relatively small portion of which received the treatment, and hence may not be
the appropriate counterfactual. In general, results are confirmed, although with relatively lower
point estimates with respect to table 2.
[Tables 2 and 3]
As argued in previous sections, OLS estimates of equation (2) may be biased by endogeneity
of the treatment variable. To deal with this issue we have estimated the system of equations in
(4)-(5) by using the location of a Roman road as an instrument for exiti. Tables 4 and 5 report
estimates of reduced form equations in which the instrument is used in place of the endogenous
variable. As from those tables, it seems verified our assumption of relevance of Roman roads to
explain the pattern of growth of employment and plants.
[Tables 4 and 5]
In table 6 we report estimates of first stage regressions in which the dependent variable is
exit and is regressed on the same variables as equation (2) and on Roman road. In this case,
we document a strong correlation between Roman road and exit with coefficients of magnitude
0.11-0.16. Tables 7a and 7b report estimates of second stage regressions. Interestingly enough,
in both tables, estimates follow the same pattern across sample periods as in tables 2 and 3,
as road accessibility improvemente has not caused population growth, whereas the impacts
on employment and plants growth are estimated in 1% and 3% respectively. These estimates
indicate that the construction of the highway network has had a larger impact on plant location
than on the growth of existing firms, a result that goes in the opposite direction of the one found
by Gibbons et al. (2012)
13
[Tables 6, 7a, 7b]
5.2 The effect on economic structure
Results in the previous section are to be interpreted as average treatment effect across sectors.
However, the decision to locate an exit in a given territory may have heterogeneous impacts
depending on the initial economic structure and may also influence the economic structure itself.
Tables 8a and 8b report estimates of (6)-(7) in which we assume that the impact of highways on
a given sector depends on the technology of the sector.
In particular, table 8a reports estimates for the whole sample. In Panel A, results for the
whole sample indicate that the point estimate is 0.11 for the period 1951-2001 and 0.13 for the
period 1951-1971. The technical coefficient for transport services ranges from 0.0015 for the
Real Estate sector to 0.1282 for Postal services, implying that the location of an highway exit
increased the growth rate of employment in the Real Estate sector by about 0.02%, whereas the
Postal services sector benefits by 1.66% increase in employment. Similar results were found
also in the case of growth in the number of plants and in models for which we use only Centre-
Northern cities.
[Tables 8a, 8b]
If we assume that tradable sectors have larger technical coefficients for transport services,
hence, our results indicate a larger impact of a reduction in transport costs on the production
of tradables. Furthermore, given the substantial similarity between estimates using the whole
sample of data and those for only Center-Northern cities, we could not find differential effects of
the highways across the country. These results seem hence to confirm the positive role played
by highways for urban development and that this effect acts through the co-location of firms
with firms operating in the transport service sector. In the following sub-section we provide a
number of robustness checks for our general models.
14
5.3 Robustness checks and extensions
5.3.1 Difference-in-difference estimates
As a first extension and check of the robustness of our results, we exploit the full temporal
variation in our sample. To this end, we have estimated a slightly different model in the form:
yit = αi +dt +βexitit + γcontrolit + εi (8)
and
yist = αi +αs +dt +βexitit ·aT SPs + γcontrolit + εis (9)
where the treatment variable is 1 after the treatment and zero before for the treated cities,
whereas it is zero for all control cities. Furthermore, in vector controlit we have used the same
controls used in models presented in sections 5.1 and 5.2. It should be noted that, by using
a difference-in-difference approach as in (8) and (9), we control for unobserved heterogeneity
through fixed effects in αi, capturing all time-invariant potential confounders.
[Table 9]
Table 9 reports estimates of both model (8) and (9) for the whole sample. Interestingly
enough, we have found that the average treatment on the treated generally decreases over time
in most of the cases, both for the employment rate and for the number of plants. This result
points at a confirmation of the positive impact of highways on local economic growth, although
it seems that the highway network has decreasing marginal productivity since the last treated
cities have generally experienced a lower treatment effect.
5.3.2 Placebo regressions for instrument validity
One of the main concerns on instrument validity is whether it meets the imposed exclusionary
restriction. We have already dropped from the dataset major cities as those were connected by
Roman roads probably bacause of their geographical advantage which may have had a substan-
tial role also in subsequent development. However, to address further concern, in table 10 we
15
report a number of placebo regressions. In particular, models (1)-(4) present linear probability
models in which the dependent variable is Z = 1[RomanRoad] and explanatory variables in-
clude the growth rate of employment rate or of plants per capita in two different time windows,
i.e. 1951-1971 and 1951-1981. Other control variables are altitude, surface, city population
in 1861 and province-specific fixed effects. Also in this case standard errors are clustered by
province. Interestingly coefficients are never significant and equal to 0.01-0.02 in both the
whole sample and the sample including only Center-Northern cities. Finally, model (5) tests
whether the instrument is correlated with the quantity of non-highway roads in the city. In
this case, the dependent variable is the quantity of kilomenters of roads per capita in 2001 and
Z = 1[RomanRoad] is used as a regressor. Also in this case we could not find any significant
correlation. However, a concern should be expressed on this last regression as the dependent
variable is measured as in 2001 and not in 1951 (to be noted is the fact that it was not used
as a control in the main IV regressions). To address this last concern, in the next subsection
we consider the possibility of selection into treatment, hence we avoid the use of instrument to
estimate the impact of exit location decision.
[Table 10]
5.3.3 Selection into treatment as a treat to identification
Although in our regressions we control for a number of predetermined variables, some pre-
treatment differences may still remain unobservable (although this concern is minor in the case
of difference-in-differences estimates). Furthermore, one may argue tha our instrument could be
not completely exogenous because of morphological characteristics of territory. To deal with
those issues, we have estimated counterfactual changes for treated cities via Oaxaca-Blinder
regressions (Kline, 2011).
The procedure runs as follows. We first fit regression model to control cities of the form
4yi1951−2001 = α + γcontrolsi + εi . We then use the vector estimated coefficients for pre-
program characteristics to predict the counterfactual mean for the treated cities.
16
The Oaxaca-Blinder regression has the advantage over standard regression methods of iden-
tifying the average treatment on the treated in the presence of treatment effect heterogeneity,
hence it may account for another potential source of endogeneity, such as unobserved hetero-
geneity. In addition, Kline (2011) has proven that the Oaxaca-Blinder regression method can
be interpreted as a propensity score reweighting estimator, so that it accounts also for selection
into treatment.
[Table 11]
Table 11 reports estimate for the average effect across sector for the whole sample. Interes-
tigly enough, also in this case, results are largely confirmed with estimates very close to the ones
obtained by standard IV regressions in section 5.1. However, although in the first stage we have
used exactly the same regressors as in model (4), treatment effects obtained via Oaxaca-Blinder
regressions are generally very sensitive to model specification.
6 Conclusion
The reduction of transport costs is at the heart of major policy interventions, althought their
effect is not always clear. In this paper, we have analysed an important policy experiment in
Italy, namely the construction of the whole highway network. In particular, we have focused on
the location of highway exit in Italian cities as a policy treatment and conducted an econometric
analysis in which the growth rates of population, employment rate and plants per capita have
been considered to be the main outputs. To take into account the endogeneity of the location
decision of exits, we have coded the geography of ancient Roman roads and used it as an
instrument. Our results indicate that access to an highway for a city has a positive impact on
urban development, however, contrary to Gibbons et al. (2012), transport innovations seem to
have a larger impact on firm growth than on firm entry. Furthermore, we have found that one of
the transmission channels through which a decrease in transport costs works is an improvement
in the competitive advantage of the transport service sector which, through co-location patterns,
propagates to other sectors.
17
References
[1] Aschauer, D.A. (1989), Is public expenditure productive?, Journal of Monetary Eco-
nomics, 23(2):177–200.
[2] Basso, P. (2009), Strade romane: storia e archeologia, Carocci, Roma.
[3] Baum Snow, N. (2007), Did highways cause suburbanization?, Quarterly Journal of Eco-
nomics, 122(2):775–805.
[4] Berechman Y. (2003), “Transportation – Economic Aspects of Roman Highway Develop-
ment: The Case of Via Appia”, Transportation Research A, 37(5):453-478.
[5] Cardinale, A. (2000), Aspetti sociologici dell’organizzazione del lavoro. Da Adam Smith
alla globalizzazione dell’economia, CUEM, Milano.
[6] Cattaneo, F. e M. Percoco (2010), Analisi Costi Benefici di grandi infrastrutture di
trasporto e Wider Economic Effect: una rassegna, Politica Economica, 2011(1):125-150.
[7] CERTeT Bocconi (2006), Rapporto AISCAT – L’impatto territoriale dei caselli au-
tostradali: un’indagine sui comuni, Università Bocconi.
[8] Donaldson, D. (2013), Railroads of the Raj: Estimating the Impact of Transportation In-
frastructure, American Economic Review, forthcoming.
[9] Ellison, G., E. Glaeser, W. Kerr (2010), What Causes Industry Agglomeration? Evidence
from Coagglomeration Patterns, American Economic Review, 100 (3), 1195-1213.
[10] European Commission (2008), Guide to Cost-Benefit Analysis of Investment Projects,
Structural Funds, Cohesion Fund and Instrument, European Commission, Directorate
General Regional Policy, Bruxelles.
[11] Fogel, R. W. (1964): Railroads and American Economic Growth: Essays in Economic
History, Johns Hopkins University Press, Baltimore.
18
[12] Fujita, A. and Mori, T. (1996), The role of ports in the making of major cities: self-
agglomeration and hub-effect, Journal of Development Economics, 49:93-120.
[13] Gibbons, S., T. Lyytikäinen, H. Overman, R. Sanchis-Guarner (2012), New Road Infras-
tructure: the Effects on Firms, London School of Economics, Spatial Economics Research
Center, SERC DP n. 0117.
[14] Ginsborg, P. (1989), Storia d’Italia dal dopo guerra ad oggi, Torino, Einaudi.
[15] Golinelli, R. e M. Monterastelli (1990), Un metodo di ricostruzione di serie storiche com-
patibili con la nuova contabilità nazionale (1951-89), Nota di lavoro di Prometeia, No
9001
[16] Graziani, A. (1972), L’economia italiana dal 1945 ad oggi, Bologna, Il Mulino.
[17] Hoover, M. (1948), The Location of Economic Activities, McGraw Hill, New York. Isard,
W. (1956), Location and Space Economy, New York, Wiley.
[18] Krugman, P. (1991), Increasing Returns and Economic Geography, Journal of Political
Economy, 99:483–499. Maggi, S. (2003), Storia dei trasporti in Italia, il Mulino, Bologna.
[19] Maggi, S. (2009), Storia dei trasporti in Italia, Bologna: il Mulino.
[20] Martin, P., and Rogers, CA (1995), Industrial Location and Public Infrastructure, Journal
of International Economics, 39:335-361.
[21] Menduni, E. (1999), L’Autostrada del Sole, Bologna, Il Mulino.
[22] Michaels, G. (2008), The effect of trade on the demand for skill Evidence from the Inter-
state Highway System. Review of Economics and Statistics, 90(4):683–701.
[23] Moses, L. (1958), Location and the Theory of Production, Quarterly Journal of Eco-
nomics, 72(2):259-272.
[24] Pesavento Mattioli, S. e Basso, P. (2004), Le strade dell’Italia romana, Touring Club Ital-
iano, Milano.
19
[25] Picci, L. (1999), "Productivity and infrastructure in the Italian regions" Giornale degli
Economisti e Annali di Economia, Vol. 58, N. 3-4, pp. 329-353, December 1999.
[26] Quilici, L. (1990), “Vita e costumi dei Romani antichi. Le strade. Viabilità tra Roma e
Lazio”, Rome: Edizioni Quasar.
[27] Quilici, L and S. Quilici Gigli (2004), “Introduzione alla topografia antica”, Bologna: Il
Mulino.
[28] Rampa G. (2001), “Yearly series of Input-Output tables (ESA1979) for the Italian econ-
omy, 1959-1987”, in Rivista Internazionale di Scienze Sociali, 59(4):449-478;
[29] Schmid, C., K.W. Steininger e A. Braumann (2007), New Road Transport Infrastruc-
ture and Sectoral Regional Growth: A SCGE Analysis for the Extension to the Aus-
trian–Hungarian Border, mimeo.
[30] Shefer, D. and H. Aviram (2005), Incorporating agglomeration economies in transport
cost-benefit analysis: The case of the proposed light-rail transit in the Tel-Aviv metropoli-
tan area, Papers in Regional Science, 84(3): 487-508.
[31] Staccioli, R.A. (2010), “Strade romane”, Rome: L’Erma di Bretschneider.
[32] Venables, A. e M. Gasiorek (1999), The Welfare Implications of Transport Improvements
in the Presence of Market Failure, Report to SACTRA, London, DETR.
[33] Von Hagen V.W. (1966), Roman Roads, World Publishing.
[34] Weber, A. (1909), Alfred Weber’s Theory of the Location of Industries, Chicago: Univer-
sity of Chicago Press.
20
Figure 1: The expansion of the highway network over time
0
1000
2000
3000
4000
5000
6000
7000
1955 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990
Source: Maggi (2009)
Figure 2: The highway network in Italy (2001)
Source: ISTAT, Atlante Statistico dei Comuni.
Figure 3: Openings of highway exits
51
12
219
162
11 104
0
50
100
150
200
250
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.
Table 9: Robustness check: Difference-in-difference
Aggregate outcomes Sector-based outcomes Employment Plant Employment Plant 1951-2001 0.07***
(0.013) 0.06*** (0.011)
0.03*** (0.001)
0.02*** (0.001)
1951-1981 0.04*** (0.012)
0.03*** (0.011)
0.02*** (0.000)
0.01*** (0.001)
1951-1971 0.02** (0.009)
0.03** (0.012)
0.02** (0.010)
0.01** (0.010)
1971-2001 0.02 (0.021)
0.02 (0.036)
0.01 (0.19)
0.01* (0.010)
R2 0.77 0.68 0.62 0.59 Observations 44,880 44,880 1,354,596 1,354,596
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.
Table 10: Placebo regressions (1)
Roman roads 1951-1971
(2) Roman roads 1951-1981
(3) Roman roads 1951-1971
(4) Roman roads 1951-1981
(5) Roads
in 2001 Panel A: Whole sample Employment growth
0.01 (1.02)
0.01 (1.12)
Plant growth 0.02 (1.29)
0.02 (1.42)
Roman roads 0.01 (0.99)
Observations 7,480 7,480 7,480 7,480 7,480 R2 0.53 0.51 0.42 0.38 0.29 Panel B: Only Center-Northern cities Employment growth
0.01 (0.99)
0.01 (0.96)
Plant growth 0.02 (1.22)
0.02 1.23)
Roman roads 0.02 (0.66)
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.
Table 11: Oaxaca-Blinder regression estimates (1)
1951-2001 (2)
1951-1981 (3)
1951-1971
Panel A: Employment growth – Whole sample Exit 0.02
(0.001) 0.03
(0.001) 0.03
(0.001)
Panel B: Plant growth – Whole sample Exit 0.02
(0.003) 0.02
(0.006) 0.03
(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.
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