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.

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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).

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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

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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

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):

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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

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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-

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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-

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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

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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

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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

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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)

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[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.

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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

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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.

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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

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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.