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Highways and productivity in urban and rural locations

Adelheid Holl1

Work in progress - February, 2014

Abstract:

I estimate the effect of access to highways on firm-level productivity using a micro level panel

data set for Spanish manufacturing and business service firms from 1997 to 2007. To estimate

the causal relationship between firm level productivity and access to highways, I use

instrumental variables estimation together with panel data techniques. The results show a

significant positive effect of access to highways on firm-level productivity for both

manufacturing and business service firms. However, the results also show that the gains from

access to new highways are not evenly distributed across space and type of firms. Firms that

benefit most are very small manufacturing firms and larger service sector firms located in

urban core areas as well as suburban manufacturing firms and to some extent also larger rural

manufacturing firms.

Keywords: highways, transport infrastructure, firm-level productivity, urban, suburban, rural.

JEL: C23, D24, R12, R3, R4;

1 Institute of Public Goods and Policy (IPP), CSIC – Spanish National Research Council, c/ Albasanz 26-28, 28037 Madrid, Spain; e-mail: a.holl@csic.es Acknowledgements: This research has received financial support from the projects SEJ2006-08063 and ECO2010-17485 (Ministerio de Ciencia e Innovacio) and CSIC [200910I105]. I thank Sabine D'Costa, Alexander Lembcke and Ricardo Mora for their useful comments and suggestions. I am also grateful to conference and seminar participants at the Urban Economics Association Meeting 2013 and the 2013 Barcelona Workshop on Regional and Urban Economics. .

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1. Introduction

The effect of infrastructure investment on economic outcomes is of central concern to policy

makers and academics as well. The subject has attracted increasing attention over the last

decades. Traditionally the emphasis has been on macro-economic studies (for a recent review

see Melo et al. 2013), but recently the focus is shifting to micro-economic analyses. However

the exact effects, and most importantly, whether there are causal effects still remains a matter

of debate (Funderburg et al. 2010; Crescenzi and Rodríguez-Pose 2012; Leduc and Wilson

2012).

Using data for Spain from 1997 to 2007, I focus on the direct effect of access to highways

and its improvement on firm-level productivity of manufacturing and business service firms.

A better accessible location can provide important transport and travel cost savings leading to

improved market access, and thereby allowing greater exploitation of economies of scale and

specialisation. Highways not only reduce direct transportation costs but also time costs (such

as, for example, delivery uncertainty) and facilitate travel for labour and information flows.

Together, this can create opportunities for new forms of organization of production,

improvements to supply chains and client services leading also to greater productivity via the

optimisation of production and input and output market relations. Shirely and Winston

(2004) show, for example, that highways reduce firms’ inventories and consequently logistics

costs. Similar evidence is found in Datta (2012) and Li and Li (2013) for India and China

respectively. This is consistent with transport investment induced changes in the organisation

of production towards an increasing reliance on transport that substitutes for traditional

inventory holding (Hesse and Rodrigue 2004). Transport, travel cost, and time savings from

improved access to highways and the resulting changes in production organisation will reflect

on firms’ productivity.

The impact of access to transportation networks has been studied by Banerjee et al. (2012)

for county economic outcomes in China. They find a small positive effect on GDP per capita

in levels but no effect on GDP per capita growth. They also find that an increase in distance

reduces average firm profits and the number of firms in counties. Faber (2014) finds that the

expansion of China’s National Trunk Highway System has reinforced concentration of

economic activity in central nodes with negative growth effects in peripheral counties along

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the new routes. Ghani et al. (2012) find for India's Golden Quadrilateral Program, a major

highway project, higher entry rates and increases in average plant productivity for non-nodal

districts within a distance of 10km from the project compared to non-nodal districts further

away. There are also studies that have focused specifically on the role of access to transport

infrastructure for firm location (Guimarães et al. 1998; Coughlin and Segev 2000; Gabe and

Bell 2004; Holl 2004a, 2004b), firm relocation (Holl 2004c; de Bok and van Oort 2011) or

firm level exports (Volpe Martincus et al. 2012; Albarran et al. 2013). 2

Few studies have investigated the impact of transport infrastructure on firm-level

productivity. Lall (2004), Graham (2007a, 2007b), Gibbons et al. (2012) and Holl (2012) are

recent examples. However, these studies use area market potential measures to capture road

improvement effects. In this paper, I use the variation in the distance from each firm to its

nearest limited access highway (autovías and autopistas) and pay particular attention to the

heterogeneity of impacts.

My study is close in spirit to Faber (2014), Banerjee et al. (2012), and Ghani et al. (2012).

However, these studies analyse the effects of access to transportation networks on county and

district outcomes. Ghani et al. (2013) further presents aggregate impacts separated between

urban and rural parts of districts. My study differs in so far that I estimate the effect of access

to highways at the firm level. This allows controlling for heterogeneity across firms.

Furthermore, I directly explore the time variation in the construction of a major limited

access highway network and therefore I am able to use distances that vary over time.

To establish a causal effect it is necessary to address the potential endogeneity of access to

highways. Endogeneity can stem from either the sorting and selection of firms via their initial

location decision or from government policy to specifically allocate new infrastructure to

places with higher or lower expected productivity growth. Governments may respond to

higher productivity growth in some areas or help to stimulate growth in others. There may

also be omitted variables that explain both highway access and firm level productivity. To

2Another related literature studies the effects of highways on growth (Chandra and Thompson, 2000) and the labour market (Michaels, 2008) in rural counties, on urban growth (Duranton and Turner, 2012), the effect of access to railroads on urbanisation (Atack et al., 2010), and the effect of highways on population suburbanization (Baum-Snow, 2007, Baum-Snow and Turner, 2012, Baum-Snow et al. 2013).

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address these issues, I rely on different fixed effects specifications, instrumental variable

estimation and panel data techniques.

I find that firms’ productivity is indeed significantly influenced by access to highways but

there is also important heterogeneity in impacts across firms. As suggested by economic

geography (NEG) as well as some previous empirical studies (Rephann and Isserman 1994;

Funderberg et al. 2010) effects can be different in urban and rural locations. My preferred

estimates show that improving access to highways benefits mostly very small manufacturing

firms and larger service sector firms located in urban core areas as well as suburban

manufacturing firms and to some extent also larger rural manufacturing firms.

The paper contributes to a better understanding of effects of transport infrastructure

investment on economic outcomes in two important ways. First, the results suggest a

significant role for highway access in shaping the economic geography of a country through

facilitating specialisation in different productive activities. This can improve national growth

performance and lead to important welfare gains wherever such changes result in a more

efficient spatial organisation of production. Second, the paper provides new insights into the

heterogeneity of productivity impacts of major highway projects. Improving our knowledge

of transport infrastructure investment is crucial to transport policy, for ensuring efficient

allocation of resources, and for improving our understanding of the development of cities

and regions.

The paper is organised as follows. Section 2 covers data. Section 3 presents the estimation

approach and variable definition. Section 4 presents the results of the empirical estimations.

Section 5 concludes.

2. Data

2.1. Firm-level data

The firm-level dataset used to calculate total factor productivity (TFP) is the SABI data base.

SABI (Sistema de Análisis de Balances Ibéricos) is generated by INFORMA and Bureau Van

Dyck and contains financial accounts of Spanish companies. I use data for firms in

manufacturing and the business service sectors in mainland Spain. The database contains

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exhaustive balance sheet information as well as other firm characteristics since 1997. Most

importantly, the data base includes the geographic co-ordinates of firms. I use this

information to relate firms to the highway network.

The information available from the SABI database does not permit outputs and inputs to be

assigned correctly among plants of multi-plant companies. Thus, I have applied two filters.

First, I use only firms with unconsolidated accounts. Second, I have dropped all firms that

report delegations. In also exclude firms that have relocated over the period of analysis and

firms that have changed their industrial sector. To identify those firms, I have extracted

information on firms’ previous location and sector from all earlier editions of the SABI

database. This way, I have identified 8,569 firms that changed location beyond the city level

over the period of analysis and 7,418 firms that changed their industrial sector.

After cleaning the data set, the final sample consists of an unbalanced panel of 126,820 firms.

Table A1 in the Appendix provides information on the distribution of firms by year in the

final sample used for estimation.

2.2. Limited Access Highways

The limited access highway network in Spain has been extended drastically over the last

decades making it the longest network among European Union member countries (Holl

2011). At the same time roads are the dominant transport mode in Spain. The share of roads

in total inland freight transport has actually been growing steadily and today accounts for well

over 90%.3 This is significantly above EU average and indicates the important role of

highways for freight transport in Spain. Roads also represent about 90% of overall passenger

transport in Spain.

To calculate access to highways, I use geographical information systems (GIS). First, I create

digital vector maps of the Spanish road network for the period 1997-2007. I use detailed

information obtained from the Ministry of Public Works regarding the opening of new

3 EU statistics on inland freight transport exclude air and maritime freight. Their weight is greater in international trade flows, but still roads are the dominant mode for exports in Spain and roads account for over 80% of total freight movements.

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limited access highway segments together with the annual official roadmaps published by the

Ministry of Public Works. Figure 1 shows the highway network for 1997 and for 2007.

Second, I overlay the digital vector maps with a point layer of the geographic coordinates of

all manufacturing and business service firms included in the analysis. With these two layers, I

calculate the distance from each company location to its nearest limited access highway.

Figure 2 shows the limited access highway network for 2007 and its relation to the 2007

sample of manufacturing and business service firms. Since I have information on the year of

opening of new highways, I can calculate this distance for each year of the period of analysis.

This allows me to analyse the relationship between highway access and firm level productivity

in a cross-sectional as well as in a panel data setting.

2.3. Urban and rural areas

Mainland Spain is divided into nearly 8,000 municipalities constituting separate political and

administrative units. Unlike other countries, Spain has no official definition of urban and rural

areas. Some studies distinguish urban and rural areas according to population thresholds (e.g.

municipalities with less than 2,000 or 10,000 inhabitants are sometimes classified as rural).

Here, I use the information from the open data project AUDES (Áreas Urbanas de España;

see Ruiz 2010) which provides a typology of Spanish municipalities.4 For mainland Spain,

AUDES defines 129 urban areas. These include a densely populated central city and its

adjacent municipalities which are selected based on land use continuity and commuting data

(totalling 1,357 municipalities). The remaining municipalities are defined as rural. 5

Figure 3 shows the AUDES urban areas defined according to Ruiz (2010) and their relation

to the 2007 highway network. It is well documented that urban areas are major contributors

to national economies but rural areas are not neglectable either. In my sample, nearly 30% of

4 See, http://alarcos.esi.uclm.es/per/fruiz/audes/ 5 Recently, the OECD in collaboration with the EU has developed a new approach to classifying functional urban areas (OECD, 2012). For mainland Spain, their classification defines 101 functional urban areas with hinterlands made up by 2,378 municipalities. Using this definition, results are qualitatively similar and available upon request.

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the manufacturing firms are located outside urban areas. Although, in the case of business

service firms this is just about 8%.

For transport planners, urban planners, and regional policy makers, an important question is

whether there are spatial variations in highway improvement effects on firm level productivity

and whether or not urban and rural areas are affected equally. There is evidence in the

literature that effects may depend on the geographical context (Funderberg et al. 2010), such

as the degree of prior urbanisation (Rephann and Isserman 1994). New economic geography

(NEG) shows that in the presence of increasing returns to scale, the spatial distribution of

benefits from falling trade costs between asymmetric markets depends on the initial size of

the costs (Puga 1999). With high initial transport costs, new highways can reinforce the

concentration of economic activity (see, Faber 2014; Roberts et al. 2012, for recent empirical

evidence on the Chinese highway system) but as shown in Kilkenny (1998) at relative low

average industrial transport costs, further reductions can also positively relate to rural

development. For the organized manufacturing sector in India, Ghani et al. (2013) find a

higher effect on entry rates in rural parts of districts that were in a distance of up to 10 km

from the highway upgrades compared to the urban parts of the districts. Regarding

productivity effects they find however very similar results in both rural and urban locations.

Rural and urban studies have largely developed separately with a focus on different issues

(Kilkenny 2010). But as Partridge et al. (2008) show, urban and rural areas should not be

viewed in isolation. Since highways can re-enforce rural-urban interdependences, there are

insights to be gained from approaches that consider both types of areas and the links between

them.

3. Estimation

I estimate a reduced-form equation where the logarithm of total factor productivity (TFP) of

firm i in sector s, province p, and year t is a function of the distance to the nearest highway

disit, firm-specific characteristics cit, industry-year fixed effects st, and province-year fixed

effects pt.

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isptptstititispt ecdisTFP 21log (1)

To calculate firm-level TFP, I apply the standard approach of Levinson and Petrin (2003).

This approach uses intermediate inputs as proxy for unobserved productivity shocks to

account for the possible endogeneity arsing from such unobserved shocks. I use the Stata

routine levpet provided by Petrin et al. (2004) and estimate firm-level production functions

separately for 23 manufacturing and business service sectors.6

The main variable of interest is the log distance to the nearest limited access highway (disit).

This distance measure is open to different endogeneity sources. First, higher productivity

firms may self-select into cities and areas with better access to highways through their initial

location decision (Baldwin and Okubo 2006; Nocke 2006). Second, governments may place

new limited access highways specifically in higher productivity areas to respond to the needs

of future growth or the government may place new highways in lower productivity areas to

stimulate growth. As highways in Spain where partly financed with the EU Structural Funds

and the Cohesion Fund, new highway placement could have been biased to poorer regions

and specifically to their urban areas. Moreover, the main productive areas in Spain are in the

centre of the country and along the north and eastern coastal regions. By linking those areas,

the new highways inevitably crossed regions with lower economic activity so that an

important part of the new infrastructure investments also went to areas of lower economic

development. Third, there may be omitted variables that explain both highway access and

firm-level productivity. I address potential endogeneity with different fixed effects

specifications, instrumental variables, and IV panel data regression.

First, I use industry-year and province-year fixed effects. The industry-year fixed effects

absorb industry-specific dynamic shocks to productivity. The province-year fixed effects

capture time-varying specificities at the province level and thus time varying productivity

advantages of some areas over others. In particular, Spain’s provinces relate fairly closely to

labour market areas and the province-year fixed effects should therefore capture any labour

market factors influencing firm-level productivity. More generally, the province-year fixed

6 Results from the estimation of the production function are not included here, but are available upon request.

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effects control for regional dynamic factors that might have a simultaneous effect on the

placement of new highways and firm-level productivity.

Second, to deal more generally with the potential two-way causality between productivity and

highway access I further use instrumentation. In choosing the instruments for estimation, I

follow the recent related literature which has used historical road networks as instruments to

account for the potential endogeneity of current highway improvements (see, Baum-Snow et

al. 2013; Duranton and Turner 2012; and Volpe Martincus et al. 2012, amongst others).

Specifically, as in Holl (2012) I use the 1760 Spanish postal route network. The Spanish

highway network presents a strongly radial outlay that dates back to the 18th century when

king Carlos III (1716-1788) ordered the construction of radial roads linking “as direct as

possible” Madrid to Valencia, Andalusia, Catalonia and Galicia. Later, routes to Extremadura

and the Basque Country were added, the latter linking on to France. The road network of that

time is reflected in Thomas Lopez’s map of postal routes, which he draw in 1760 for the

book by Pedro Rodríguez Campomanes entitled “Itinerario Real de Postas de dentro y fuera de

España” (Postal route itinerary in Spain and outside Spain) and published in 1761 (see also,

Menéndez-Pidal 1992). Thomas Lopez distinguished two types of postal routes: “montadas”

(or “sobre ruedas” - referring to routes for transport on wheels) and “no montada” (or “a la ligera”

- referring to routes for transport on horseback). The former are the precursors of the six

main radial highways and together with the latter they are good predictors of an important

part of the current Spanish highway network. I use this map and the itineraries described in

Campomanes (1761) to create a geo-referenced map of both types of 1760 postal routes

(Figure 4). Based on this map, I calculate the distance from each firm to the nearest of the

two types of 1760 postal routes.

The instrumental variable seeks to eliminate the bias from reversed causality. The instrument

is valid as long as it predicts recent highway improvements, but is otherwise uncorrelated with

current firm-level productivity. While it is plausible, that the 1760 postal routes were not

designed in response to current productivity patterns, there could still be factors that

influenced their location in the past and that continue to exert and influence on modern

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productivity (e.g. geographical characteristics or institutions). Such enduring location

advantages could cause correlation between the 1760 postal routes and factors that cause

modern productivity to be higher. The province-year fixed effects included in all estimations

help to avoid any correlation between the 1760 postal routes and firm level productivity other

than related to new highway construction.

Third, to further control for the existence of unobserved firm-level characteristics that might

jointly determine highway access and firm-level productivity, I also estimate IV panel data

models. For these within regressions, the instrument needs to have a time dimension. Here, I

follow in the spirit of Hornung (2012) and introduce postal routes within a 10km corridor

from the new highways that opened to traffic during that year. The IV panel data estimates

further mitigate bias stemming from omitted firm-level variables and the potential self-

selection of firms through their initial location decision.

4. Results

OLS estimation results

Results in Table 1 are based on OLS estimation of equation (1) where I regress firm-level

TFP on distance to the nearest highway together with firm level controls, industry-year, and

province-year fixed effects. In these estimations, the effect of highway access is driven by

differences across firms within province-year and industry-year combinations. Panel A shows

results for manufacturing. Panel B shows results for business service firms.

The elasticity between distance to highways and firm-level TFP is statistically significant and

negative (-0.013) for the pooled sample of manufacturing firms (column 1). Column 2 shows

the estimated effect for manufacturing firms in urban areas. Column 3 and 4 distinguish

further between micro firms with less than 10 employees and small- and medium-sized firms

with 10 to 250 employees. The Spanish productive system is characterised by its small firm

structure. According to the “Directorio Central de Empresas” (DIRCE- Central Directory of

Companies from the National Institute of Statistics) 99.8% of Spanish companies have less

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than 250 employees and 94% of all firms (88% of firms with paid employees) have less than

10 employees. Firms with more than 250 employees are somewhat overrepresented in the

SABI data base but still only account for approximately 0.5% of total observations in my

sample. I therefore do not report results separately for this size category. Columns 5 to 7

show the respective results for rural manufacturing firms. The estimated effect is always

statistically significant and higher in rural areas than in urban areas. The strongest effect is

observed for small rural manufacturing firms.

For the pooled sample of business service firms, the elasticity between distance to highways

and firm-level TFP is also statistically significant and negative (-0.007). However, estimations

for firms of different size in urban and rural areas show only significant results for rural firms,

regardless of size.

OLS pooled cross section estimations could, however, be biased whenever access to highways

is endogenous. For example, if more productive firms locate closer to highways or when the

government places new highways in response to productivity differences across locations,

reverse causation could be in place.

IV estimation results

Results of IV estimations exploiting cross sectional variation are presented in Table 2 for

manufacturing and for business service firms respectively. Table 2 also reports the

Kleibergen–Paap F test statistics for weak instruments in the presence of robust clustered

standard errors (Kleibergen and Paap 2006). The statistics show that the instrument is very

strong and that weak-instrument bias is not a problem.

The IV point estimate in column (1) for the pooled sample of manufacturing firms is again

negative and significant (panel A) and of the same magnitude as the corresponding OLS

coefficient. However, important differences arise when the sample is separated into urban

and rural firms. For urban manufacturing firms, TFP appears to be negatively affected by

improving highway access (column 2-4). In contrast, reducing the distance to the nearest

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highway has positively affected firm-level productivity for manufacturing firms in rural

locations. Here the IV point estimates are significant and larger in absolute value than the

OLS estimates: -0.023 for the pooled sample of rural manufacturing firms (column 5), -0.023

for micro rural manufacturing firms (column 6) and -0.017 for small to medium sized rural

manufacturing firms (column 7).

Panel B shows the corresponding results for business service firms. For the pooled sample

the coefficient is significant and markedly larger than the OLS coefficient (-0.043).

Controlling for endogeneity also results in a markedly larger coefficient for business services

compared to manufacturing. Separate estimations for firms of different size in urban and

rural areas show that the firms that benefit the most are those in urban areas. Moreover, there

is no statistically significant effect for rural business service firms once endogeneity has been

controlled for via instrumentation.

Recent empirical evidence also suggests that highways may change the spatial organization of

economic activity within urban areas (Baum-Snow 2014). In Table 3, I extend my IV analysis

by separating between the central-city-municipality of my urban areas and the sub-urban

parts. I find that the negative productivity effect in Table 2 for urban manufacturing firms is

only observed for those firms that are located in central city municipalities. In contrast, for

those firms located in suburban parts of urban areas, particularly the medium-sized firms,

improved highway access also shows a significant positive effect on their firm-level

productivity. In contrast, for business service firms the separation into urban core and sub-

urban parts yield weak results with only a marginally significant productivity effect for firms

in central city locations (see column 1, panel B).

IV panel data estimation results

The panel structure of the data allows investigating the effect of access to highways on firm-

level productivity taking into account time-invariant unobserved firm heterogeneity. The

within IV regressions explore short-run variations of variables and thus can be interpreted as

short-run effects of improving access to highways on firm-level productivity.

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Table 4 shows the estimation results for manufacturing and for business service firms, with

industry-year, and province-year fixed effects. The within IV results differ form the cross-

sectional ones. For the manufacturing firms, the coefficient for access to highways is now

higher (-0.022) but only marginally statistically significant at the 10% level (column 1).

Improving access to highways now shows a stronger effect for urban firms, particularly for

very small ones (column 3), while the coefficient for mediums sized urban manufacturing

firms is not significant. The significantly positive coefficients of distance to highways in

columns (2) to (4) of Table 2 (panel A) are therefore driven by differences across urban firms.

In rural areas only the medium sized firms benefit from highway access improvements

(column 7), but there is no statistically significant effect on very small rural manufacturing

firms (column 6) once unobserved firm-level heterogeneity is also controlled for.

For business services firms (Table 4, panel B), I only find a significant positive productivity

effect for medium sized firms in urban areas (column 4). The within IV point estimate is large

(-0.140) and highly significant. In contrast, for very small business service firms, the increase

in accessibility seems to have a negative effect on productivity. Improved access can increase

competition from distant firms that also experience improved accessibility and those

competition effects can dominate own gains in market access. Making cities more accessible

may also raise urban costs. Improved highway access may lead to a rise in agglomeration and

with it to increased congestion or rising land prices and wages that are bearing more heavily

on small firms.

Table 5 shows the within IV regressions separating between firms located in central cities and

in suburban areas for manufacturing and for business services. These estimations indicate that

the results in Table 4 for urban areas are again driven by those firms located in central cities.

Improved highway access impacts differently on suburban firms. In suburban locations, all

manufacturing firms, regardless of size, witness increases in TFP when access to highways

improves. For suburban business service firms the estimated coefficients indicate negative

productivity effects. However, for these estimations, the Kleibergen- Paap F-statistic suggests

that the time-varying instrument is weak.

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Overall, the within IV regression results suggest that, at least in the short run, new highways

benefit more firms in urban areas, with the exception of very small business service firms in

central cities. For larger rural manufacturing firms, the results also show positive productivity

effects, albeit somewhat lower. Improved highway access may have enabled these firms to

expand their market areas. In contrast, for business services and very small manufacturing

firms in rural areas the benefits may be off-set in the short run from resulting increased

competitive pressure as new highways integrate markets and not only reduce transport costs

from rural locations but also to rural locations (Kilkenny 1998). 5. Conclusion

The contribution of transport infrastructure to productivity is still a debated issue. I analyse

the effect of access to highways on firm-level productivity. The results show that firm-level

productivity is significantly related to highway access. An important policy concern is the

distribution of benefits from highway investment. The data and setting in this paper makes it

possible to directly analyse whether and to what extent highways affect productivity in urban,

suburban, and rural locations. The results show important heterogeneity of highway impacts.

Not all firms benefit equally from improved highway access. The preferred estimates, that

take into account firm-level unobserved heterogeneity as well as endogeneity of highway

improvements, show that firms that benefit most, at least in the short run, are very small

manufacturing firms and larger service sector firms located in urban core areas as well as

suburban manufacturing firms and to some extent also larger rural manufacturing firms. This

is an important finding for a better understanding of distributional effects of transport

infrastructure investment.

Improved highway access leads to change in urban and rural areas as well as to change within

urban areas and to change in the relationship between the areas. Together, the results suggest

a significant role for highways in shaping the economic geography of a country. Finally, the

results also highlight that transport improvements interact with agglomeration economies and

suggest that they can change the balance between urban costs and benefits.

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Table 1: OLS estimates of the effect of highway access on firm TFP

All firms Urban firms Rural firms all <10 10-250 all <10 10-250 (1) (2) (3) (4) (5) (6) (7) Panel A: manufacturing

log (distance to nearest highway) -0.013***

(0.001) -0.006**

(0.001) -0.005*** (0.002)

-0.004** (0.002)

-0.015*** (0.002)

-0.018*** (0.003)

-0.008*** (0.003)

R2 0.39 0.38 0.30 0.41 0.41 0.33 0.41 Observations 385244 277864 160686 115803 107380 64274 42758 Panel B: business services

log (distance to nearest highway) -0.007***

(0.002) 0.001

(0.002) 0.001 (0.002)

-0.007 (0.005)

-0.021*** (0.007)

-0.019*** (0.007)

-0.053*** (0.020)

R2 0.39 0.38 0.30 0.41 0.41 0.33 0.41 Observations 385244 277864 160686 115803 107380 64274 42758 Note: Robust standard errors corrected for clustering at the firm level are reported in parenthesis. Significant coefficients are indicated by ***, **, *, for significance at the 1%, 5% and 10% level, respectively. Firm level controls include foreign ownership status, age of the company, and dummies for five size-categories based on total deflated assets. All estimations include industry-year and province-year fixed effects.

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Table 2: IV estimates of the effect of highway access on firm TFP

All firms Urban firms Rural firms all <10 10-250 all <10 10-250 (1) (2) (3) (4) (5) (6) (7) Panel A: manufacturing

log (distance to nearest highway) -0.013***

(0.003) 0.013**

(0.006) 0.018** (0.008)

0.017* (0.009)

-0.023*** (0.004)

-0.023*** (0.006)

-0.017*** (0.006)

Kleibergen–Paap F-statistic 7641.3 2059.4 1654.7 645.9 3444.9 2318.9 1396.8 Observations 385244 277864 160686 115803 107380 64274 42758 Panel B: business services

log (distance to nearest highway) -0.043***

(0.008) -0.057***

(0.014) -0.052*** (0.015)

-0.065* (0.035)

-0.019 (0.013)

-0.015 (0.014)

-0.068 (0.045)

Kleibergen–Paap F-statistic 2775.6 1107.2 1089.5 140.8 743.4 698.7 49.7 Observations 178778 164600 130143 33818 14178 12778 1393 Note: Robust standard errors corrected for clustering at the firm level are reported in parenthesis. Significant coefficients are indicated by ***, **, *, for significance at the 1%, 5% and 10% level, respectively. Firm level controls include foreign ownership status, age of the company, and dummies for five size-categories based on total deflated assets. All estimations include industry-year and province-year fixed effects.

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Table 3: IV estimates of the effect of highway access on firm TFP in urban agglomerations

Central city location Suburban location all <10 10-250 all <10 10-250 (1) (2) (3) (4) (5) (6) Panel A: manufacturing

log (distance to nearest highway) 0.040***

(0.008) 0.036*** (0.010)

0.054*** (0.012)

-0.025*** (0.009)

-0.017 (0.012)

-0.028** (0.014)

Kleibergen–Paap F-statistic 1432.1 1086.1 491.4 763.2 604.9 243.2 Observations 129884 79206 50050 147980 81480 65753 Panel B: business services

log (distance to nearest highway) -0.034*

(0.020) -0.032 (0.021)

-0.036 (0.047)

-0.001 (0.017)

0.002 (0.020)

-0.001 (0.036)

Kleibergen–Paap F-statistic 591.9 576.6 80.7 549.9 509.8 87.5 Observations 131573 103519 27475 33027 26624 6343 Note: Robust standard errors corrected for clustering at the firm level are reported in parenthesis. Significant coefficients are indicated by ***, **, *, for significance at the 1%, 5% and 10% level, respectively. Firm level controls include foreign ownership status, age of the company, and dummies for five size-categories based on total deflated assets. All estimations include industry-year and province-year fixed effects.

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Table 4: Panel IV estimates of the effect of highway access on firm TFP

All firms Urban firms Rural firms all <10 10-250 all <10 10-250 (1) (2) (3) (4) (5) (6) (7) Panel A: manufacturing

log (distance to nearest highway) -0.022*

(0.011) -0.049**

(0.025) -0.109*** (0.038)

-0.002 (0.031)

-0.021 (0.015)

0.024 (0.017)

-0.069*** (0.021)

Kleibergen–Paap F-statistic 1547.1 489.1 325.0 169.7 489.1 325.0 169.7 Observations 388176 279937 159973 112936 279937 159973 112936 Panel B: business services

log (distance to nearest highway) 0.024

(0.020) 0.081

(0.053) 0.136** (0.065)

-0.140*** (0.054)

0.017 (0.048)

0.020 (0.051)

-3.123 (10.898)

Kleibergen–Paap F-statistic 885.5 193.8 121.0 48.0 207.3 211.6 0.06 Observations 164059 150659 117867 28875 13400 12007 1208 Note: Robust standard errors are reported in parenthesis. Significant coefficients are indicated by ***, **, *, for significance at the 1%, 5% and 10% level, respectively. All estimations include industry-year and province-year fixed effects.

22

Table 5: Panel IV estimates of the effect of highway access on firm TFP in urban agglomerations

Central city location Suburban location all <10 10-250 all <10 10-250 (1) (2) (3) (4) (5) (6) Panel A: manufacturing

log (distance to nearest highway) -0.020

(0.034) -0.106** (0.054)

0.068 (0.046)

-0.090*** (0.032)

-0.113** (0.047)

-0.091** (0.039)

Kleibergen–Paap F-statistic 376.0 211.5 201.8 121.2 95.3 36.9 Observations 130541 78702 48645 149396 81271 64291 Panel B: business services

log (distance to nearest highway) 0.055

(0.052) 0.115* (0.063)

-0.146*** (0.054)

0.623** (0.320)

0.622* (0.328)

-4.455 (3.162)

Kleibergen–Paap F-statistic 240.4 144.6 47.8 3.8 3.6 1.8 Observations 120147 93662 23287 30512 24205 5588 Note: Robust standard errors corrected for clustering at the firm level are reported in parenthesis. Significant coefficients are indicated by ***, **, *, for significance at the 1%, 5% and 10% level, respectively. All estimations include industry-year and province-year fixed effects.

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Table A1: Sample: number of firms by year and total number of observations

Year Number of firms

1997 14716

1998 18384

1999 37591

2000 42980

2001 54543

2002 62757

2003 66313

2004 68049

2005 71863

2006 76051

2007 69850

Total no of observations 583097

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Figure 1: The mainland Spanish highway network in 1997 and in 2007

Source: own elaboration.

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Figure 2: SABI companies and the highway network in 2007

Source: own elaboration.

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Figure 3: Urban and rural areas in mainland Spain

Source: own elaboration, with urban areas based on Ruiz (2010).

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Figure 4: Thomas Lopez’s map of 1760 postal routes

Source: own elaboration based on the map of Thomas Lopez “Mapa de las carreras de Postas de España” (1760) - Real Academia de la Historia, and

Campomanes (1761).