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How persistent is public goods provision in a comparative perspective? We explore the link between infrastructure investments made during antiquity and the presence of infrastructure today, as well as the link between early infrastructure and economic activity both in the past and in the present, across the entire area under dominion of the Roman Empire at the zenith of its geographical extension (117 CE). We find a remarkable pattern of persistence showing that greater Roman road density goes along with (a) greater modern road density, (b) greater settlement formation in 500 CE, and (c) greater economic activity in 2010. Interestingly, however, the degree of persistence in road density and the link between early road density and contemporary economic development is weakened to the point of insignificance in areas where the use of wheeled vehicles was abandoned from the first millennium CE until the late modern period. Taken at face value, our results suggest that infrastructure may be one important channel through which persistence in comparative development comes about.
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Roman Roads to Prosperity:
Persistence and Non-Persistence of
Public Goods Provision
Carl-Johan Dalgaard
Nicolai Kaarsen
Ola Olsson§
Pablo Selaya
February 16, 2018
Abstract
How persistent is public goods provision in a comparative perspective? We ex-
plore the link between infrastructure investments made during antiquity and the
presence of infrastructure today, as well as the link between early infrastructure
and economic activity both in the past and in the present, across the entire area un-
der dominion of the Roman Empire at the zenith of its geographical extension (117
CE). We find a remarkable pattern of persistence showing that greater Roman road
density goes along with (a) greater modern road density, (b) greater settlement for-
mation in 500 CE, and (c) greater economic activity in 2010. Interestingly, however,
the degree of persistence in road density and the link between early road density
and contemporary economic development is weakened to the point of insignifi-
cance in areas where the use of wheeled vehicles was abandoned from the first
millennium CE until the late modern period. Taken at face value, our results sug-
gest that infrastructure may be one important channel through which persistence
in comparative development comes about.
Keywords: Roman roads, Roman Empire, public goods, infrastructure, persistence.
JEL classification codes: H41, O40.
We have received useful comments on various versions of the present study from seminar partici-
pants in Belfast, Bergen, Copenhagen, Gothenburg, London School of Economics, Manchester, Tokyo,
Sheffield and the workshop “Deep-Rooted Factors in Comparative Development” at Brown University.
The usual disclaimer applies.
University of Copenhagen, CAGE (Warwick) and CEPR (London). carl.johan.dalgaard@econ.ku.dk
Danish Economic Council. nik@dors.dk
§University of Gothenburg. ola.olsson@economics.gu.se
University of Copenhagen. pablo.selaya@econ.ku.dk
1
1 Introduction
One of the most remarkable facts in the literature on comparative development is just
how persistent relative development differences are over time. On average, societies
that were relatively economically developed during the pre-industrial era tend to be
comparatively successful today.1As an illustration of such persistence, Figure 1 depicts
the conditional relationship between the intensity of Roman settlements in 500 CE and
contemporary population density, within the area under Roman control ca. 117 CE,
after controlling for country fixed effects. This insight has led researchers to search
for the origins of comparative development in (geographic) initial conditions, or in
historical processes that have shaped cultural traits and the institutional infrastructure
of individuals societies.2
Meanwhile much less attention has been devoted to the study of persistence in
proximate determinants of growth, factors that could be the channel or the transmis-
sion mechanism connecting fundamentals to economic development in the past and
in the present. Yet a deeper understanding of the channels through which persistence
in comparative development comes about may leave important clues as to which fun-
damentals are important, and how to potentially stimulate development in situations
where important fundamentals are lacking.
Figure 1
The present study explores the persistence of physical infrastructure across time
and space, starting in antiquity with the establishment of the Roman road network.
As discussed below, Roman road construction did not follow the rules of infrastruc-
ture planning in the contemporary era: The roads were build chiefly with a military
purpose in mind, and geographic obstacles in the landscape were usually surmounted
rather than evaded. Despite this, our analysis uncovers a remarkable degree of per-
sistence in road density across time and space: areas that attained greater road den-
sity during antiquity are characterized by a significantly higher road density today.
Moreover, the Roman roads were strongly linked to economic activity by the end of
1See, for example, Olsson and Hibbs (2005); Comin et al. (2010); Chanda et al. (2015); Maloney and
Valencia (2016).
2See Spolaore and Wacziarg (2013); Nunn (2014) and Ashraf and Galor (forthcoming) for recent re-
views.
2
antiquity, and they remain a strong positive correlate with prosperity today. Overall,
our analysis provides evidence that public goods provision is an important channel
through which persistence in economic development, as depicted in Figure 1, may
arise.
In our analysis we confine attention to localities, grid cells measuring one degree
latitude by one degree longitude, that were part of the Roman Empire by the second
century CE and were treated by at least one Roman road. By omitting areas that fell
outside the Empire, as well as those completely unconnected to the network within the
Empire, we hope to disentagle the influence of the physical infrastructure on economic
outcomes from the legacy of Roman rule more broadly.3To further limit the risk of
confounding the impact of the Roman roads with Roman influence more broadly, and
to filter out the impact of modern day institutions and (sub-national) differences in cul-
tural values on economic outcomes of interest, we control for country fixed effects as
well as language fixed effects throughout our entire econometric analysis. We believe
this strategy makes it unlikely that our results are driven by the legacy of Roman rule
in the broadest terms. However, a natural concern is that areas receiving more Roman
roads may differ in various geographic dimensions that by themselves may have in-
fluenced comparative economic development. We attempt to surmount this challenge
in several different ways.
For starters it is important to observe that the risk of confounding the influence of
roads with geography may not be as great as one might think, as alluded to above and
elaborated upon below. According to the historical literature, it is conventional wis-
dom that major Roman roads were built to facilitate the movement of troops across the
empire, rather than with the objective of enhancing economic development. Moreover,
the roads were arguably only to a limited extent dictated by geographic circumstances.
In our analysis we examine these arguments statistically. The role of many geographic
characteristics does in fact seem limited. In the end, however, we find a claim of or-
thogonality between geography and Roman road location to be untenable, for which
reason we control for a rich set of geographical characteristics when exploring the per-
sistence of early road infrastructure over time, and its predictive power vis-a-vis eco-
3See Landes (1998) on the legacy of Roman rule. More recent research has shown that areas under
Roman influence arguably developed different institutions from areas outside Roman direct influence
(e.g. Glaeser and Shleifer, 2002).
3
nomic development.
Still, doubts may linger whether our control strategy is fully sufficient. Therefore,
our second strategy to assess the importance of potentially omitted geographic char-
actaristics consists in exploiting the remarkable timespan of abandonment of wheeled
transportation in North Africa and the Middle East. According to the landmark study
by William Bulliet (1990 [1975]), wheeled transport disappeared in North Africa and
the Middle East somewhere between the fourth and sixth century CE. Eventually,
wheeled transport vehicles had to be reintroduced with the ascent of the automobile.4
Consequently, following the fall of the Western part of the Roman Empire, the roads
fell into disrepair, and ultimately went out of use in North Africa and the Middle East.
In contrast, Roman roads continued to be maintained and in use in Europe after the
fall of the Western Roman Empire (Glick, 2005 [1979]; Hitchener, 2012).
From the point of view of the present study, this natural experiment has two im-
portant implications. First, as the ancient roads fall into disuse and thus are left un-
maintained, it becomes much less likely that modern roads are built in their place.
Consequently, one would expect to see a far weaker link between Roman road den-
sity and modern road density outside the European part of the Empire. Second, if
the roads create persistence in economic development, one would expect to find a far
weaker link between ancient infrastructure and modern-day economic activity within
the regions where roads temporarily lost relevance. Consistent with these conjectures,
we find that there is no significant link between ancient infrastructure and modern
infrastructure within North Africa and the Middle East. Moreover, within these two
regions the ancient infrastructure is not a significant predictor of economic activity to-
day. In contrast, in Europe – the region where roads continued to be used and therefore
maintained – ancient roads predict modern roads as well as prosperity.
This differentiated effect of Roman road density is revealing from the point of view
of identifying channels of influence. Naturally, the fundamental principles govern-
ing the construction of the Roman roads were the same throughout the Empire. If our
baseline results are tainted by omitted variable bias, for example due to missing ge-
ographic characteristics that matter both for road location and subsequent economic
activity, one would expect to see evidence of an apparent persistence of infrastructure
4See also Chaves et al. (2014) on the absence of wheeled transport in Sub-Saharan Africa and its
introduction during the early colonial period.
4
density as well as a persistent impact from ancient infrastructure on modern economic
activity throughout the Empire. Accordingly, in light of our findings, the "abandon-
ment of the wheel"-experiment provides fairly compelling evidence of the mechanism
under scuntiny: persistence in public goods provision leads to persistence in economic
activity.
The present paper is related to several strands of literature. First, it is related to
the literature on long-run persistence in economic development summarized in Spo-
laore and Wacziarg (2013); Nunn (2014) and Ashraf and Galor (forthcoming). This
literature has largely been concerned with the influence and origins of fundamental
determinants of productivity that ultimately can explain persistence in comparative
development.5In contrast, the present paper focuses on channels of persistence, or
the persistence of proximate sources of growth. In particular, the focus is on whether
public goods provision appears to be persistent across long periods of time. From this
perspective our work is related to Chen, Kung and Ma (2017), who document within
China a remarkable persistence of another proximate source of growth: education. The
authors argue persuasively that the observed persistence is (in part, at least) explained
by the emergence of a pro-education culture, prompted by early educational invest-
ments. The argument is importantly supported by the fact that persistence in schooling
weakens markedly in areas particularly exposed to the anti-intellectual Cultural Revo-
lution. Our study documents that the provision of public goods – road infrastructure –
is similarly persistent over very long periods of time, except in areas where roads lose
economic value early on. By carefully controlling for the influence of (countrywide)
formal institutions and (within-country) informal institutions, our findings indicate
that this persistence is unlikely to be mediated by the emergence of cultural values or
institutions. Instead, our analysis suggests that later roads likely were built on top of
older roads, thus creating persistence in road density.6Of course, in places where the
signs of the early roads disappear, modern roads would be less likely to be located
along the ancient trajectories.
Second, our paper also contributes to a small literature on the economics of the Ro-
5Recent contributions include Galor and Özak (2016), Gorodnichenko and Roland (2017) on culture
and development; Angelucci et al (2017) on institutions and Andersen et et al. (2016) on geography.
6Our finding of a persistency in road investments over time is also consistent with the presence of
local increasing returns to scale in infrastructure investments, as emphasized in a literature inspired by
Krugman (1991).
5
man period (Finley, 1973; Temin, 2006; Bowman and Wilson, 2009; Michaels and Rauch,
2016). A striking feature of the classic work by Finley (1973), for instance, is its relative
neglect of the general importance of roads. A common theme is that road transport
was inferior to shipping in terms of efficiency and hence of less importance. Recent
research however has started to re-examine the influence of the Roman road network
on long-run develeopment. Bosker et al. (2013), studying determinants of city growth
between 800 CE and 1800, document that cities located at intersection points between
Roman roads grew bigger in Europe, but not in North Africa and the Middle East.7
The present study focuses on the persistence of infrastructure over time, which is not
discussed in Bosker et al. Moreover, we study the impact of ancient infrastructure on
21st century economic activity rather than urban population size at the eve of the late
Modern period. Also related is the work of Wahl (2017), who investigates the Roman
limes inside Germany and finds that contemporary development is more advanced on
the old Roman side of the border using a regression discontinuity design. Wahl iden-
tifies the road system as an important explanatory factor, which is consistent with our
findings that pertain to the European part of the Empire more generally. At the same
time, our analysis provides evidence on how shocks in the past, such a the abandon-
ment of the wheel, can importantly perturb development trajectories with long-run
implications for comparative development.
Third, in recent years, a surge of interest in the economic effects of infrastructure has
resulted in a number of important studies. The results indicate that infrastructure in-
vestments often have a strong positive influence on population growth and economic
activity.8A major difference with the present study obviously consists of the time
horizon over which the consequences of infrastructure investments are assessed; with
a two millennia perspective we believe the present study has the longest observation
window hitherto explored. At the same time it is important to stress that the objective
of the present study is not to estimate the productivity gains from infrastructure per
se. In fact, our approach does not allow us to distinguish whether areas that received
more infrastructure investments outgrew areas with less infrastructure because of pro-
ductivity benefits from public goods, or if more public goods in a particular location
7On the link between city location and Roman roads within Europe, see also Bosker and Buringh
(2017).
8On roads see Fernald (1999), Michaels (2008), and Bird and Straub ( 2015). On railways see Banerjee
et al (2012), Donaldson (2012), Jedwab and Moradi (2015), and Hornung (2015).
6
simply attracted activity from other locations. As should be clear, the main focus of the
present study is rather to assess the persistence of public goods provision and thereby
whether public goods provision has a significant role to play in the observed pattern
of persistence of comparative development.
The paper proceeds as follows. In section 2, we present our data on Roman roads,
and our outcome variables. In section 3, we outline an historical background on the
assignment of Roman roads as well as formal tests of the geographical determinants of
Roman road density. In section 4, we present and discuss the main empirical results.
Section 5 concludes.
2 Data
In this section we describe the central independent variable in the regressions to fol-
low, the Roman roads variable, as well as the main dependent variables that appear
below. The appendix contains a description of remaining (control) variables, as well as
summary statistics.
2.1 Independent variable: Roman Roads
The raw data for the Roman roads come from a digitized map version of the road
network illustrated and documented in Talbert’s (2000) "Barrington Atlas of the Greek
and Roman World".9We focus on all roads identified in the digitized map as being
of major importance, and drop all roads identified as minor because of the difficulties
involved in getting precise traces of small ancient roads.10 In terms of timing and
geographic coverage, we concentrate on roads within the borders of the Roman Empire
in the year 117 CE, which is around the time when the empire attained its maximum
9The digitization was done by McCormick et al (2013), as part of the Digital Atlas of Roman and
Medieval Civilization (DARMC) project of the Center for Geographic Analysis at Harvard University,
http://darmc.harvard.edu.
10The Barrington Atlas notices, in fact, that the roads system is perhaps “the most difficult element
to map”. Page 262 in the map-by-map directory that accompanies Talbert’s (2000) Barrington Atlas
describes, for example, the difficulty of mapping minor roads that had neither milestones nor paving in
the outline of Augustan roads or in routes through the Alpine valleys (Map 18). Page 169 describes, as
another example, the use of tree-ring dating methods to overcome the difficulties in tracing parts of the
Via Claudia Augusta (Map 12). See http://press.princeton.edu/B_ATLAS/B_ATLAS.PDF for a detailed
description of all roads and other features contained in the Barrington Atlas.
7
territorial expansion.11
To construct a measure of the influence that Roman roads have had, we draw areas
(buffers) of 5 km around the trace of each road in the whole network, and compute the
percentage of area of these buffers within the contours of country-cells, or the portion
pixels of 1x1 degrees of latitude by longitude that lie within the borders of each modern
country that the Roman Empire covered in 117 CE. As an illustration of our procedure
to measure the degree of influence of Roman roads, Figure 2 shows the buffer around
all major roads in the Roman Empire, and Figure 3 zooms in on the road system and
the buffer around the main Roman roads crossing the area of Lutetia (contemporary
Paris) in France.
Fig 2-3
At first sight, a 5 km buffer may seem large, and it is certainly possible to construct
a smaller buffer. The choice is made so as to accomodate the fact that early Roman
infrastructure was associated with a range of adjacent investments, as discussed be-
low, including drainage and more. In order to be sure to envelope the total treatment
resulting from the road construction, we use the said buffer size. This still allows a lot
of variation since our unit of observation are areas of about 10,000 km2 (if measured at
the equator).
As mentioned in the introduction, we confine our attention to country-cells that
were treated by at least one Roman road. That is, we focus the analysis exclusively on
the intensive margin of infrastructure investments. Also, in order to disentagle the in-
fluence of the physical infrastructure on economic outcomes from the legacy of Roman
rule more broadly, we drop areas that fell outside the Empire or were unconnected
to the road network within the Empire. Still, in the interest of completeness, we also
report, in the appendix, results where we measure Roman roads along the extensive
margin, within the borders of the Roman Empire.
2.2 Dependent variable I: Modern roads
Data for modern roads are taken from the Seamless Digital Chart of the World (SDCW)
Base Map version 3.01, which is one of the most comprehensive global GIS databases
11Data on the extent of the Roman Empire in the year 117 AD are from the Ancient World Mapping
Center at the University of North Carolina at Chapel Hill, http://awmc.unc.edu.
8
that is freely available.12
The road network data in the SDCW version 3.01 was published in 2000 and include
various characteristics of roadways, which are classified according to whether they
are operational or under construction, including a median (the central area reserved
to separate opposing lanes of traffic in divided roads) or not, and covering primary,
secondary or unknown (unexamined or not surveyed) routes.13
As the modern (2000) counterpart to the major Roman Roads in the Barrington
Atlas, we select all primary and secondary modern roads, with or without a median,
and with known (examined/surveyed) characteristics (that is, we drop all unexamined
or not surveyed modern roads). Primary modern roads are defined as hard surface, all
weather roads with two or more lanes in width, and maintained for automobile traffic.
Secondary roads are defined as all other roads maintained for automobile traffic.14
Just as we do for the Roman roads, we build our indicator of the intensity of modern
roads by constructing a buffer of 5 km around the network of modern roadways, and
computing the percentage of the buffer area within each country-cell of 1x1 degrees of
latitude and longitude within each country within the contours of the Roman Empire
in the year 117.
2.3 Dependent variable II: Roman settlements
As a measure of economic development at the end of antiquity we use the number of
Roman settlements in the year 500 CE. We take these data from the Digital Atlas of the
Roman Empire (DARE) constructed by Åhlfeldt (2017).15 The dataset was compiled
using Talbert (2000) and other sources, and contains the location of settlements, mines,
12The SDCW dataset, built by Global Mapping International, is a collection of shapefile layers for a
variety of geographic and population features. The underlying data are from the U.S. Government’s
Digital Chart of the World (DCW), which is a comprehensive and consistent cartographic global data-
base at a scale of 1:1,000,000 (Langas 1995), developed and maintained by the US National Imagery
and Mapping Agency. The underlying US Government’s DCW data in SDCW version 3.01 is based on
the Vector Map (VMap) Level 0, Edition 5 database published in September 2000 by the US National
Imagery and Mapping Agency. See World GeoDatasets (2017) for full documentation of the SDCW
database.
13Specifically, the road categories in te SDCW are: (1) road with a median (for primary, secondary
or unknown routes), (2) primary route, operational (including all primary routes except those under
construction and without a median), (3) primary route under construction (including all primary routes
and under construction roads), (4) secondary route or unknown, operational, without a median, (5)
secondary route or unknown, under construction or doubtful, (6) unexamined/unsurveyed.
14See Langas (1995) for details.
15The DARE data is continuously updated. Our version was downloaded from the Pleiades data site
https://pleiades.stoa.org/home on August 16, 2017.
9
forts, villas and various other localities. The observed time span of existence is indi-
cated for each locality. We compute the number of large settlements that existed in year
500 CE within each country-cell.
2.4 Dependent variable III and IV: Nightlights and population den-
sity in 2010
As proxies of local economic activity today we rely on the intensity of lights at night
and the level of population density, both measured in 2010.
The raw data for lights data come from satellites and sensors operated by the US
Department of Defense’s Version 4 Defense Meteorological Satellite Program Opera-
tional Linescan System (DMSP-OLS).16 We use nighttime lights raw imagery at a res-
olution of 30 arc seconds and compute averages within each 1x1 country-cell within
the contours of the Roman Empire in 117 CE. Figure 2 above shows the contempo-
rary geographical distribution of nightlights in the area former covered by the Roman
Empire.
For population density we use the UN-adjusted 2010 population count from the
Gridded Population of the World version 4 database, that adjusts gridded population
numbers to United Nations (UN) estimates of national population counts.17 To con-
struct population densities, we simply sum population numbers within each country-
cell and divide the sum by the total country-cell area.18
3 The Roman Roads
3.1 Historical Introduction
The Roman road construction program during antiquity is generally considered to
have been initiated in 312 BCE when censor Claudius Appius started the construc-
tion of a paved, all-weather road, subsequently named Via Appia, from Rome to Capua
16Data available at
http://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html
17The alternative are unadjusted population levels, which are based on individual countries censuses
and population registers, which we avoid with the aim of having more comparable data.
18We use the finest resolution data of 30 arc seconds to compute our variables. The raw data are
available at http://sedac.ciesin.columbia.edu/data/collection/gpw-v4.
10
(Figure 4). The immediate reason for the construction of Via Appia was the ongoing
Second Samnite War in which the Roman armies were trapped around Capua due to
shortage of supplies from Rome. It is believed that the road was completed in 308 BCE.
With the new and more efficient supply lines, the Romans defeated the Samnites in 304
BCE and Via Appia was eventually extended all the way to the southeastern port city
of Brundisium a few decades later (Laurence, 1999).
Fig 4
Via Appia was neither the first road in the Mediterranean area (the Persians under
Darius the Great had for instance constructed extensive royal roads in the 5th cen-
tury BCE), nor in Roman territory (earlier, non-paved roads are mentioned by ancient
Roman sources). Nonetheless, it would come to serve as a model for future road con-
structions, first on the Italian peninsula and later in the broader empire.19 At the peak
of the Roman empire at the death of Trajan in 117 CE, it is estimated that the empire
hosted about 80,000 km of paved road (Gabriel, 2002). As Figure 2 shows, the road
system connected regions in current Britain, Western Europe, Eastern Europe, North
Africa and the Near East.
Because of their military purpose for achieving effective Roman control of a terri-
tory, the construction of these public highways (viae publicae) was carried out by Roman
legions, and it was typically commissioned by a censor and administered by curatores
in Rome.20 Most of these roads were paved with stone and cement. Road building also
included supporting public goods such as bridges, tunnels, guest houses, and drainage
systems which required substantial engineering skills. Scholars have suggested that
the construction of roads also fostered the use of ground surveys and maps (Davies,
1998). Ordinary citizens sometimes had to pay tolls at city gates and bridges, and the
military always had priority. The viae publicae network was complemented by local
roads, viae vicinales, which typically linked the major roads to a town or to other ma-
jor roads. These roads were mainly the responsibility of local governments (Laurence,
1999).
19In Italy, for instance Via Flaminia (completed in 220 BCE) connected Rome with the Adriatic coast,
whereas Via Aemilia (187 BCE) cut through the Po plain and made that imporant agricultural area avail-
able for Roman colonization.
20Censor and curator were public offices in Republican Rome. During Imperial times, road construc-
tion was mainly carried out by the emperors.
11
3.2 The Assignment of Roman Roads: Historical Priors
There are three main reasons why the Roman road construction program almost presents
itself as a natural experiment, from the vantage point of the historical literature: i) The
military purpose of the roads, ii) the preferred straightness of the construction, and iii)
their construction in newly conquered and often undeveloped regions.
First, just as mentioned above with the early experience with the Appia during the
war with the Samnites, the purpose of the roads was to increase the speed and the ease
with which the legions could reach locations of military interest – including territories
of ongoing campaigns, army bases and Roman colonies that provided the army with
essential supplies. Viae publicae also played a key role for the consolidation of power
and hegemony in newly conquered areas.21 When the Roman limes stabilized along
its northern and eastern frontiers, the road system was used to transport marching
troops to the their legionary bases along the border. Very soon, the roads were also
used by traders and for transportation of agricultural goods, but this was not the main
intention.
Second, Roman roads were typically very straight over extensive distances. The
ambition of the road engineers was typically to connect an existing point A in an area
under Roman control with a specific point B in an area where power was less consoli-
dated. The example of contemporary Rimini and Piacenza in Figure A1 in the Appen-
dix illustrates this tendency. An obvious reason for choosing a straight road was the
shorter distance and the lower costs in terms of building material and soldier efforts.22
The straightness of the roads implied that they often passed right over hills and across
difficult terrain. Via Appia, for instance, passed like a straight line right from Rome to
the existing colony in Terracina through the Pontine marshes (Figure 4). Malaria was
prevalent in this area and the Romans had to construct drainage systems in order to be
able to get through. The marching armies were not necessarily much constrained by
these difficult conditions, but it has been claimed that the steepness of the roads often
made them unsuitable for commercial ox-drawn carts with agricultural goods (Mokyr,
21Laurence (1999) argues that a broader objective with the roads was also to demonstrate the gen-
eral technological superiority and political commitment of the Romans to the peoples in neighboring
areas. The construction of roads signalled an ability to even change the geography of landscapes, which
presumably greatly impressed many of Rome’s rivals.
22See Davies (1998) for an account of how the Romans managed to keep the roads straight between
two points without modern surveying tools.
12
1990).
The fact that the major roads tended to be straight also suggests that in between
the two connecting points A and B, the highway system often was not adjusted to take
into account pre-existing local economic or other social characteristics. For instance,
the Romans consciously avoided linking Via Appia to a number of existing Latin set-
tlements in the vicinity (Laurence, 1999). In this sense, we argue that the straightness
of the roads gives the road construction program the character of a random treatment
on the pre-Roman countryside.
This relates to the third argument, namely that the Roman roads were often con-
structed in newly conquered areas without any extensive, or at least not comparable,
existing network of cities and infrastructure. The Roman roads were laid out in territo-
ries in which they had limited prior knowledge and where they had the aim of quickly
securing Roman hegemony.
As an illustration of this point, consider the case of Lugdunum (contemporary
Lyon). Julius Caesar’s conquest of Gaul north of the Mediterranean coast was com-
pleted relatively rapidly during a frantic campaign in the 50s BCE. There were many
existing towns and cities in Gaul when the Romans arrived, but there was not a state
in any sense comparable to the Roman polity, and most scholars refer to Gaul as proto-
urban at the time (Woolf, 1998). In year 47 BCE, Caesar created a Roman colony in
the important town of Vienne, 30 km south of contemporary Lyon in the Rhone valley
and, at the time, the main settlement (referred to by Caesar as oppida) of the Gallic Al-
lobroges tribe (see Figure A2 in the Appendix). In 43 BCE, the Romans were expelled
from Vienne by the Allobroges. According to the Roman historian Dio Cassius, the
Roman Senate then ordered the governor of Gallia Transalpina to found a new city
for the refugees from Vienne to the north at the intersection of the Rhone and Saone
rivers. This city became the Roman town of Lugdunum. According to Åhlfeldt (2017),
this location was not an important existing oppida.
Shortly after the establishment of Lugdunum, Marcus Vipsanius Agrippa, the gov-
ernor of Gallia Transalpina, initiated an extensive road building project in order to
consolidate Roman rule in Gaul. Lugdunum was connected southwards along the
Rhone to the important cities of Vienne, Avignon and Massilia. Agrippa also built ex-
tensive roads towards the Atlantic to the west, towards the North Sea, and towards the
13
Rhine to the east, thereby making Lugdunum a key hub in Roman Gaul (Gros, 1991).
The city experienced a rapid growth as a result and soon eclipsed even the old Greek
colonies to the south. It became the capital of and gave name to the Roman province of
Gallia Lugdunensis, and served as the primary Roman city in Gaul for more than two
centuries.
The example indicates that the Roman decision to make Lugdunum a hub of road
construction was probably a combination of good geographical fundamentals (the
Rhone and Saone intersection), the historical accident related to the hostility to Ro-
mans in the previously much more significant town of Vienne, and the need to quickly
consolidate power in Gaul. We do not have strong reasons to believe that Roman road
construction was based on an already existing network of prosperous towns in the
area. Figure A2 shows the pre-Roman oppida in the Lugdunum area, as well as the
subsequent Roman roads and settlements. At least around this key city, there are no
indications that the Romans consciously tried to connect to older settlements.23
3.3 The Assignment of Roman Roads: Formal tests
In Table 1, we investigate determinants of road density. As described in Section 2.1,
our units of analysis are country-cells, or grid cells of 1x1 latitude-longitude degrees
within the borders of modern countries and territories covered by the extent of the
Roman empire in 117 CE. The dependent variable is (log) Roman road density (or the
percentage of a 5 km buffer around the Roman road system that lies wihin the total
area of a country-cell). Accordingly, only cells featuring at least one road are in the
sample.
The question we examine in this part is essentially the extent to which the received
perception from the historical literature, suggesting a very limited influence of geogra-
phy and pre-roman development on road investments, is accurate. Naturally, if geog-
raphy does not play a significant role for road assignment, it lessens the need to control
for it in the regressions to follow.
Table 1
23Michaels and Rauch (2016) find however that the location of existing oppida – pre-roman fortified
towns – does seem to predict the location of Roman towns in Gaul.
14
In a number of instances the historical priors seem to be confirmed. Terrain rugged-
ness does not limit road density; on the contrary the correlation is positive and sig-
nificant (column 2). Similarly, in sub-samples where we have proxies for pre-roman
development (oppidas, in the case of Europe, and the timing of the Neolithic), we find
very little evidence that such factors influence road density (columns 7 and 8). Even
areas featuring mining activity during Roman times are not characterized by greater
road density (column 9).
The expected militaristic motivation for road construction can also be confirmed.
Areas further away from the borders of the empire feature less road density, and dis-
tance to Rome also matters in the expect way (column 6). We also observe from column
1 that road density was greater in the northern part of the empire, and to the east. This
pattern is most likely found because these areas were more contested than the south-
ern border areas. Finally, the fact that road density declines when moving away from
navigable rivers may also be related to the needs of the military. Of course, roads ulti-
mately linked up to army outpost which needed to be supplied with provisions. The
transport of food and other necessities would be cheaper by sea transport, which may
create the link between road density and distance to navigable rivers.
In other cases the historical priors seem to ring less true. For example, there appears
to be a clear positive correlation between various measures of agricultural productivity
and road density (column 4). Similarly, grid cells found at greater levels of elevation
feature significantly lower levels of road density. Since these geographic features natu-
rally may influence economic development, and the location of modern roads, in their
own right they are essential controls in the remaining. Also significant are our various
distance measures to waterways. While the significance of waterways may be consis-
tent with a military motivation for road construction it is obvious that waterways may
influence development in their own right.
Finally, in column 10 we study the collective explanatory power of geography on
roman road density in our full sample. As can be seen geography does seem to mat-
ter: it accounts for about 40% of the variation in road density. Hence, while Table 1
does confirm important aspects of the historical priors it also clearly shows that geog-
raphy needs to be controlled for when examining the persistence of roads and the link
between ancient road density and economic activity today.
15
4 Ancient roads, Modern roads and Economic Activity
4.1 Empirical specification
We take to the data the following cross-sectional specification:
log yprc=δc+δr+βRRDprc +X0
prcγ+eprc. (1)
Our dependent variable, pertaining to pixel pin language region rand country c
is denoted yprc. The dependent variables of interest are, respectively, (log) modern day
road density and (log) economic activity during antiquity and today. In the latter case
we employ both nightlights (following Henderson et al, 2012) and population den-
sity (e.g., Rappaport and Sachs, 2003) in 2010. The independent variable of particular
interest is log (1+) Roman road density, RRDprc.24
In an effort to control for countrywide institutions we include a full set of coun-
try fixed effects, δc. In addition, since past research has documented important within-
country variation in culture that affect economic activity (e.g., Tabellini, 2010; Michalopou-
los and Papaiannou 2013), we rely on a full set of language fixed effects as a proxy, δr,
following Andersen et al. (2016).
Xprc contains additional controls, which can broadly be partitioned into three cat-
egories. First, geographic variables that involve latitude, longitude, ruggedness, ele-
vation and controls for soil quality. Second, proximity to waterbodies which involves
distances to coast, major rivers and natural harbors. Third, a set of variables that con-
trol for distances to Rome, the border of the empire and the current capital. In addition,
we also control for the location of historical mines. Finally, in all specifications we con-
trol for country-cell (prc-cell) area, as it varies with latitude and longitude due to the
earth’s curvature, modern country limits, and the borders of the former Roman Em-
pire.
Finally, in terms of statistical inference, we follow Abadie et al. (2017), who argue
that cluster adjustments for the standard errors should only be performed if there are
strong theoretical priors to do so. In particular, the authors argue that clustering is
only relevant to address an experimental design issue and/or a sampling design issue.
24Accordingly, since log(1+x)xthe coefficient βstrictly speaking has the interpretation of a semi-
elasticity.
16
Briefly, the former issue arises if the treatment focus occurs at a higher level of ag-
gregation than the unit of observation, whereas the second one emerges if multi-level
sampling is taking place (e.g., first in a sample of countries and then in a sample of
regions within those countries). In the present case our sample consists of all the pixels
within the Roman Empire that where treated by Roman roads, which means neither
of the two issues arises. Accordingly, we rely on standard errors that are robust to
heteroskedasticity throughout the empirical analysis in the paper.
4.2 Baseline results
In Table 2 we explore the link between road density during antiquity, and road density
today, in our full sample.
Table 2
As column 1 of Table 2 shows, on average ancient roads can account for about 12
percent of the current differences in modern-day road density within our sample. At
the level of raw partial correlation, an increase in Roman road density by one percent
is associated with an increase in modern day road density of about 0.24 percent.
In column 2 we introduce country fixed effects and in column 3 we introduce simul-
tanously country fixed effects as well as language fixed effects so as to partial out the
influence from institutions and cultural value variation within nations. The economic
significance of Roman road density declines, but only to a minor extent.
Adding the first set of geographic controls, involving e.g. measures of agricultural
potential, makes more of a difference. Collectively, the controls adds about eight per-
cent in explanatory power, and reduces the elasticity of Roman roads to about 0.15.
As seen from the rest of the columns, the apparent persistent influence of ancient in-
frastructure on modern infrastructure remains when we add further controls, and all
of the controls collectively. In total, historical road networks and geography account
for about one fifth of the variation in contemporanous road network across grid cells.
Figure 5
Figure 5 demonstrates the partial correlation, corresponding to the model estimated
in Table 2, column 7, using a binned partial residual plot in order better to assess the
17
partial correlation in the present large sample. Consistent with our estimation ap-
proach, we use a linear fit to summarize the relationship between Roman roads and
modern roads.25 The positive link appears well determined.
In the Appendix, we further examine the robustness of the link between ancient
roads and modern roads. In particular, we perturb the sample in various ways. We
examine whether the results are affected by omitting Italy; all areas within 100 km
of the ocean or the Roman border, respectively; if we add further climatic variables
such as frost days or if we control for pre-Roman economic activity. Overall the results
reported in Table 2 carry over.
Turning to the link between ancient roads and economic activity, Table 3 examines
the link between Roman roads and economic activity around the collapse of the West-
ern Roman Empire at the end of the fifth century. As a measure of economic activity we
use the density of major settlements. The control strategy is similar to that invoked in
Table 2. The general message from the table is that Roman road density is statistically
strongly correlated with early economic activity, featuring elasticities between 0.5 and
1; roads are significant at the one percent level or better, regardless of which controls
are added.
Table 3.
The controls themselves appear to enter in a meaningful way. Briefly, our results
indicate that by the fifth century CE we find more major settlements at low levels of el-
evation and in areas with productive agriculture (column 4); close to the coast (column
5), and close to Rome (column 6). We also find that the density of major settlements
declines as one moves from the southern parts of the empire and to the north, probably
testifying to the importance of the Mediterranean basin during antiquity (Column 4,
7). In addition, the results indicate, more surprisingly, a weak tendency towards lower
settlement density in the eastern part of the empire.
In the two subsequent tables, 4 and 5, we shift focus to contemporaneous economic
activity, measured by nightlights (Table 4) and population density (Table 5).
Table 4, Table 5
25After partialing out controls we then divide the sample into 20 equal sized bins, average the resid-
ualized Roman road density and Modern road density within these bins, and plot the resulting reduced
sample.
18
Once again, we find a statistically strong signal from ancient roads on economic
activity. Regardless of controls, or exact choice of measure of economic activity, Roman
road density is significant at the one percent level or better. Figure 6 and 7 depict the
binned added variables plot for contemporaneous nightlights and population density,
respectively, in the context of our full specification (Column 7 in the two tables). The
strong positive (partial) correlations do not appear to be driven by outliers.
Figure 6 & 7
The point estimate for the controls are broadly consistent with priors. In both tables
we find that economic activity tends to decline as one moves away from the ocean or
navigable rivers. Also, economic activity tends to decline at higher levels of elevation,
and with distance to the current capital. More unexpected is the positive correlation
with ruggedness, which is found in both tables as well. These commonalities are con-
sistent with the notion that both measures are reasonable proxies for economic activity.
At the same time, the result do not always line up. When we examine the determi-
nants of nightlights we find a positive latitude gradient, but this is not the case when
population density is used. On the other hand, the potential supply of calories seem to
matter to population density, but does not help to explain the variation in nightlights.
A potential explanation could be, that there need not be a perfect match between where
people live and where they work. To see what this implies, suppose population den-
sity captures place of residency to a relatively greater extent than nightlights. Then
the positive correlation with caloric suitablity could be due to the fact that cities his-
torically usually were located near rich agrarian hinterlands (Henderson et al., 2016).
The location of, say, a factory is potentially less path dependent than a city. Indeed,
in recent times production has moved out of city centers to capitalize on lower land
prices. Under a similar logic, latitude apparently influences productivity on-the-job
more than the location of population centers.
Overall, Roman road density appears strongly associated with economic activity,
both in the past and in the present. In every specification, statistical significance at the
1 percent level is attained, and the economic significance is quite substantial. In our full
specification we find that economic activity during antiquity rises by about 0.6 percent
for every percentage point increase in road density; in the modern day context we find
elasticities in the range 0.5 - 1 depending on the indicator.
19
4.3 Exploring the channel: Persistence and Non-Persistence
A key question regarding the results above is whether they reflect a causal impact of
ancient roads on modern roads, and ultimately economic activity today. Naturally, the
Roman roads are strongly predetermined, so reverse causality is not a concern. But it
seems hard to rule out that underlying structural charactaristics, perhaps notably of a
geographic nature, could be driving both the intensity of Roman road treatment and
the outcomes in focus. That is, despite our best efforts, the results may suffer from
omitted variable bias.
In the present section we explore the likelihood that our results can be accounted
for in this manner, by exploiting the remarkable abandonment of wheeled transport
in the Middle East and North Africa (MENA) during the second half of the first mil-
lenium CE (Bulliet, 1990 [1975]). This event is an astonishing fact of world history.
Perhaps especially since wheeled transport has had a very long history in the Mid-
dle East before its abandonment. The first instances of primitive two-wheeled carts,
drawn by oxen or later by horses were found already in the earliest civilizations of an-
cient Mesopotamia, for example. Such transportation was clearly facilitated by roads.
As mentioned above, notable roads were built in Persia during the Achaemenid pe-
riod around 500 BCE. But during the Roman era the roads became more pervasive and
better constructed. This frames the puzzle: why did wheeled transport decline and
disappear under those circumstances?
4.3.1 Empirical strategy: The regional loss of wheeled transport
Bulliet (1990 [1975]) argues that the key proximate reason for the abandonment of the
wheel was the emergence of the camel caravan as a more cost effective mode of trans-
port of goods in the region. The cost advantage during antiquity can be supported by
data from Diocletian’s price edict in 301 CE, which suggests a roughly 20 percent cost
advantage in transport of goods by way of camel, relative to oxen.26 To an economist,
this seems like a very reasonable explanation. But it immediately prompts the question
of why the ox-carriage then continued to dominate land-based transport until the first
26The objective of the edict was to stabilize prices in the region, which makes it probable that the
relative prices, stipulated by the edict, were based on relative cost differences. From the edict it can
be calculated that the price of transporting a given amount of goods (in Roman pounds) over a given
distance was 20 percent higher per oxen than per camel. See Bulliet (1990, Ch. 1) for further discussion.
20
half of the first millenium CE? After all, the domestication of the camel on the Arabian
Peninsula pre-dates the Roman era by millenia (Almathen et al, 2016).
Bulliet’s argument is that a series of developments had to take place before the
camel could emerge as the dominant mode of transport in MENA. First, the emergence
of a new type of camel saddle by 100 BCE made it possible for camel hearding tribes-
men to utilize new types of effective weapons. This improved the military strength
of ethnic groups that centuries earlier had perfected camel breeding, which allowed
them to gradually gain control of the trade routes and, as a consequence, gain politi-
cal power as well. Second, another important factor was the decline of Rome and the
ultimate rise of Islam as a key power factor in the Mediterranean. As also forcefully ar-
gued by Henri Pirenne (2012 [1937]), the ensuring decline in long distance trade across
the Mediterranean allowed for an increasing importance of inland trade routes within
the former Roman empire, which, in the case of the MENA supported caravan trans-
port. However, horse or ox-drawn carts remained the main mode of inland transport
in Europe. Therefore it is not surprising that while the Roman roads continued to be
maintained and in use in Europe (Glick, 2005 [1979}; Hitchener, 2012), where wheeled
vehicles dominated land-based transport, the same does not seem to be have been the
case in the remaining regions of the Roman empire where the caravan took over (Bul-
liet, 1990).
The implication of these developments is that since ancient roads fall into disrepair
in the MENA region, to a much greater extent than in Europe, one should expect to see
much less persistence in infrastructure density. The argument is simply that more than
a millenium of disrepair most likely would erase the traces of the ancient infrastruc-
ture to a considerable extent, and when the importance of maintaining or building
roads reappears in North Africa and the Middle East – with the advent of the auto-
mobile – the principles underlying road planning almost certainly differed from those
that directed the planning of the Roman roads. In Europe, where the ancient roads
persisted to a greater extent, modern roads are more likely to be built in place of the
ancient roads. As a result, it would seem highly unlikely that modern road density
would line up with ancient road density in the Middle East and North Africa whereas
persistence would be more likely a priori in Europe.
The potentially differentiated degree of persistence in road density across regions of
21
the Empire holds stark implications for the influence of Roman roads on comparative
development: one should expect an influence from Roman road density on economic
activity today only where persistence in infrastructure is found. Hence Roman roads
should be of little importance to contemporary comparative development within the
MENA region, while holding explanatory power within Europe. At the same time, one
should expect a positive influence of Roman roads on economic activity in all regions
during antiquity, before the abandonment of the wheel in North Africa and the Middle
East.
These considerations lead to a straightforward test. We re-estimate equation (1)
on subsamples: Europe and MENA, respectively. In this setting we expect to see per-
sistence of an influence of Roman roads only within the European part of the Empire.
This testing strategy allows us to assess the likelihood that our results above are driven
by omitted variable bias. If indeed Roman roads do not predict modern road density
in the MENA region, there is little reason why Roman roads should hold explanatory
power vis-a-vis contemporary comparative development. If a significant link between
past infrastructure investments and current economic activity arises in spite of this, the
link is likely spurious or driven by unobserved geographic determinants of roads and
economic development. Naturally, one might imagine that Roman roads could influ-
ence long run development through some type of cultural or institutional channel. But
in light of our extensive controls for current institutions and cultural variation, through
country fixed effects and language group fixed effects, such an account would seem to
stretch the imagination. Accordingly, the abandonment of the wheel experiment in
effect allows us to explore the channel through which our baseline results come about.
Before we turn to the results one further issue is worth raising. Today the MENA
region is considerably poorer, on average, than Europe. Perhaps the factors that sti-
fled economic development in this region would also serve to mollify the explanatory
power of the past? That is, perhaps an absence of a “signal” from the past, in this re-
gion, would have little to do with the mechanism in focus: that the abandonment of
the wheel diminished the persistence of infrastructure and therefore diminished the
persistence in comparative economic development. The key aspect to notice, however,
is that this concern involves comparative development across regions. The strategy em-
ployed below involves looking within regions; regions that ultimately followed seper-
22
ate development trajectories overall. While the persistence of infrastructure in Europe
relative to, say, the Middle East, may have many causes, the tests conducted below
involve asking if infrastructure was persistent within the MENA region and, by ex-
tension, whether ancient infrastructure predicts comparative development within the
MENA.
4.3.2 Empirical results
In Table 6 we begin by examining the correlation between Roman road density and our
measure of economic activity by the end of antiquity. The control set is the one in our
full specification (cf column 7 in Tables 2-5).
Table 6
As is evident, across country-cells within Europe and within MENA, repectively,
there is more economic activity in places with greater density of Roman roads. A nat-
ural interpretation of these findings is that by the end of antiquity areas more con-
nected to the Roman road network benefitted on net terms. Hence, prior to the aban-
donment of wheeled vehicles there is a positive influence from roads on comparative
development, regardless of which region of the empire we focus on. If anything, the
economic significance of the link appears stronger in MENA than within Europe.
If we then turn attention to contemporary outcomes, results change markedly. As
seen from column 3-4 Roman road density holds statistically significant predictive
power within Europe, with respect to modern-day road density, whereas the same is
not true for MENA. The economic significance also declines, but the main result is that
we can no longer reject the null that the observed positive link is a matter of chance.
This is consistent with what one would expect in the aftermath of the abandonment of
the wheel-experiment. As ancient roads are left to decay they ultimately become a less
reliable predictor of modern road location in the MENA.
In the remaining columns we turn attention to modern day economic activity. It
is evident that whereas Roman roads hold strong predictive power over comparative
development within Europe, both the economic and statistical significance are dramat-
ically smaller within the MENA sample. In light of the absence of persistence in road
density, these results are revealing, strongly suggesting that the explanatory power
23
of Roman roads on current economic development is driven by the persistence of the
road network.
Overall the results provide interesting perspectives on the roots of comparative de-
velopment. While previous research has demonstrated how the observed persistence
in economic development can arise due to variations in geographic initial conditions,
either directly or indirectly via cultural or institutional change, the above results draw
attention to an important role for shocks with persistent influence. From the point of
view of any given geographical sub-region the emergence of the Roman Empire, and
with it the Roman road network, is best viewed as external. The modest importance of
geography in dictating the location of roads (cf Section 3) illustrates that second nature
processes can, to some extent independently of geography (or first nature processes),
have a substantial impact on long-run comparative development. In the present con-
text the persistence of the shocks, and thereby in comparative development, arise via
a remarkable degree of persistence in road density across several millennia, in re-
gions where the roads were deemed economically useful. Evidently, persistence in
infrastructure investments is a potential source of persistence in comparative develop-
ment.
5 Concluding remarks
The existing literature on comparative development has drawn attention to a remark-
able pattern of persistence in economic activity: places featuring comparatively high
levels of economic development long before the industrial revolution often seem to fea-
ture high levels of comparative development today. In the present project we examine
the persistence of an important proximate source of economic activity: Infrastructure
investments.
Our analysis reveals that, within regions that used to be part of the Roman empire,
infrastructure density is highly persistent. That is, Roman road density is generally a
strong predictor of modern day road density. Moreover, Roman road density is gen-
erally a predictor of contemporary economic activity. These results are statistically
strong and resillient to extensive controls including for contemporary institutions and
cultural values. Taken at face value, these results suggest that infrastructure may be
24
one important channel through which the persistence in comparative development
comes about.
In examining whether our core results, linking early infrastructure to current-day
infrastructure and economic activity, are likely to reflect causal relationships, we exam-
ine the remarkable historical case of the abandonment of the wheel that occurred in the
Middle-East and North Africa (MENA) during the second half of the first millenium
CE. We find that in the MENA region, Roman roads lose predictive power vis-a-vis
modern day roads. Moreover, Roman road density does not predict current day eco-
nomic activity within the MENA region. In contrast, in the European region, where
the roads were maintained, our baseline results carry over. These results suggest quite
strongly that our reduced form results, linking Roman road density to current compar-
ative development, are importantly caused by the persistence of infrastructure over a
remarkable period of 2000 years.
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28
Figures
Figure 1: Conditional relationship between population in 2010 and the extent of Roman
settlements in 500 CE within the former Roman Empire
Note: The figure shows the conditional binned residual scatter plot of the relationship between
population size (in logs) in 2010 and number of Roman settlements (in logs) in 500 CE for 693
country-cells within the former Roman empire. The binned scatter plot groups the x-axis variable into
equal-sized bins, computes the mean of the x-axis and y-axis variables within each bin, then creates a
scatterplot of these data points. The underlying regression controls for contemporary country fixed
effects and hence estimates the within-country impact of historical Roman settlements on
contemporaneous population levels.
-2 -1 0 1 2
Population in 2010 (log)
-1 -.5 0 .5 1 1.5
Roman settlements in 500 CE (log)
29
Figure 2: Roman roads and contemporary night light intensity among 1000 country-cells
within the Roman empire in 117 CE
Note: The map shows major Roman roads (red lines) within the boundaries of the Roman Empire
(green lines) in 117 CE with nightlights intensity in 2010 indicated by white color.
30
Figure 3: Roman roads and contemporary nightlights intensity around Lutetia (Paris)
Note: The figure shows the rectangular 1x1 degree latitude/longitude country-cells including the hub
of Roman roads around Lutetia (contemporary Paris Ile de France), marked in light green. Buffer
zones of 5 km on either side are shown around the roads. White color indicates the strength of modern
nightlights intensity.
31
Figure 4: Via Appia from Rome to Capua in 312 BCE
Note: The thick black line shows Via Appia whereas the red lines show other, later Roman roads.
Source: Created on the basis of data in Talbert (2000)
32
Figure 5: Conditional relationship between modern road density in 2000 and Roman road
density in 117 CE within the former Roman Empire
Note: The figure shows the conditional binned residual scatter plot of the relationship between modern
road density (in logs) in 2000 and Roman road density (in logs) in 117 CE for 675 country-cells within
the former Roman empire. The binned scatter plot groups the x-axis variable into equal-sized bins,
computes the mean of the x-axis and y-axis variables within each bin, then creates a scatterplot of
these data points. The underlying regression in Table 2, column 7 controls for the full set of
geographical controls, as well as for country and language fixed effects, and hence estimate the within-
country impact of historical Roman settlements on contemporaneous population levels. See text and
Appendix for exact variable definitions.
-.06 -.04 -.02 0 .02 .04
Modern road density(log)
-.3 -.2 -.1 0 .1 .2
Roman road density(log)
33
Figure 6: Conditional relationship between nightlight intensity in 2010 and Roman road
density in 117 CE within the former Roman Empire
Note: The figure shows the conditional binned residual scatter plot of the relationship between
nightlight intensity (in logs) in 2010 and Roman road density (in logs) in 117 CE for 675 country-cells
within the former Roman empire. The binned scatter plot groups the x-axis variable into equal-sized
bins, computes the mean of the x-axis and y-axis variables within each bin, then creates a scatterplot
of these data points. The underlying regression in Table 4, column 7 controls for the full set of
geographical controls, as well as for country and language fixed effects, and hence estimate the within-
country impact of historical Roman settlements on contemporaneous population levels. See text and
Appendix for exact variable definitions.
-.2 -.1 0 .1 .2
Nightlights 2010 (log)
-.3 -.2 -.1 0 .1 .2
Roman road density (log)
34
Tables
Table 1: Determinants of Roman Roads
Dependent variable: Roman roads
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Latitude 0.165 -0.122-0.406 0.224
(0.040) (0.069) (0.374) (0.142)
Longitude 0.031 -0.009 -0.076 -0.009
(0.007) (0.013) (0.112) (0.025)
Ruggedness 0.025 -0.000 0.024 0.006
(0.010) (0.010) (0.021) (0.017)
Elevation -0.099 -0.044 -0.098 -0.083
(0.010) (0.013) (0.034) (0.026)
Africa dummy -0.0360.039
(0.019) (0.028)
Middle East -0.034 0.075 0.092
dummy (0.015) (0.030) (0.056)
Pre-1500 0.009 0.014 -0.026 0.020
caloric suitability (0.003) (0.003) (0.010) (0.008)
Agricultural 0.104 -0.016 0.026 -0.022
suitability (0.036) (0.033) (0.068) (0.071)
Distance to -0.031 -0.017 -0.014 -0.013
major river (0.005) (0.005) (0.010) (0.011)
Distance to -0.052 -0.037 0.035 0.013
coast (0.005) (0.007) (0.016) (0.012)
Distance to 0.024 0.019 -0.058 -0.027
natural harbor (0.007) (0.008) (0.024) (0.013)
Distance to -0.043 -0.063 -0.059 -0.047
Rome (0.011) (0.011) (0.159) (0.020)
Distance to -0.022 -0.033 -0.048 0.006
Roman border (0.005) (0.008) (0.013) (0.014)
Number of 0.591 0.743
oppidas (0.603) (0.451)
Years since 0.017 0.242
Neolithic transition (0.060) (0.092)
Number of mines -0.014 0.003 0.005 0.005
(0.008) (0.008) (0.012) (0.012)
Observations 693 693 693 675 693 693 183 182 693 675 183 176
R20.034 0.193 0.011 0.056 0.186 0.095 0.005 0.001 0.002 0.363 0.365 0.287
Notes: This table documents that the density of Roman roads only to a limited extend was determined by geography and pre-existing development. Roman roads
is the log of one plus the fraction of a 5 km buffer around the Roman road system that lies wihin the total area of a country-cell. The analysis is performed on
country-cells within the Roman empire containing non-zero Roman roads. All variables are in logs. Heteroskedasticity robust standard errors are reported in
parentheses. *** denotes statistical significance at the 1 pct. level, ** at the 5 pct. level, and * at the 10 pct. level.
35
Table 2: Roman Roads and Modern Roads
Dependent variable: Modern roads
(1) (2) (3) (4) (5) (6) (7)
Roman roads 0.240 0.183 0.197 0.148 0.175 0.198 0.149
(0.035) (0.037) (0.041) (0.043) (0.039) (0.042) (0.042)
Area -0.003 -0.003 -0.006 -0.005 0.001 -0.006 -0.001
(0.003) (0.003) (0.004) (0.005) (0.005) (0.005) (0.005)
Latitude -0.059 -0.004
(0.116) (0.135)
Longitude -0.015 -0.010
(0.021) (0.022)
Ruggedness 0.0130.007
(0.008) (0.009)
Elevation -0.025 -0.009
(0.011) (0.013)
Post-1500 caloric 0.0090.009
suitability (0.005) (0.005)
Agricultural 0.097 0.100
suitability (0.033) (0.032)
Distance to coast -0.020 -0.016
(0.004) (0.006)
Distance to major 0.001 0.002
river (0.005) (0.005)
Distance to natural 0.0110.011
harbor (0.006) (0.007)
Distance to Rome 0.006 0.015
(0.012) (0.013)
Distance to Roman 0.003 -0.004
border (0.007) (0.008)
Distance to capital -0.009 -0.006
(0.007) (0.007)
Number of mines -0.007 -0.007
(0.006) (0.006)
Country FE No Yes Yes Yes Yes Yes Yes
Country-language FE No No Yes Yes Yes Yes Yes
Observations 693 693 693 675 693 693 675
R20.123 0.465 0.548 0.581 0.570 0.551 0.592
Notes: This table documents the positive, statistically significant correlation between Roman roads
and modern roads when accounting for geographical characteristics and country-language fixed ef-
fects. Roman roads and modern roads are defined as the log of one plus the fraction of a 5 km buffer
around, respectively, the Roman and modern road system that lies wihin the total area of a country-
cell. The analysis is performed on country-cells within the Roman empire containing non-zero Roman
roads. All variables are in logs. Heteroskedasticity robust standard errors are reported in parentheses.
*** denotes statistical significance at the 1 pct. level, ** at the 5 pct. level, and * at the 10 pct. level.
36
Table 3: Roman Roads and Settlements
Dependent variable: Settlements in 500 CE
(1) (2) (3) (4) (5) (6) (7)
Roman roads 0.998 0.814 0.831 0.577 0.733 0.781 0.536
(0.170) (0.178) (0.192) (0.207) (0.198) (0.173) (0.188)
Area 0.165 0.171 0.198 0.209 0.224 0.200 0.229
(0.019) (0.021) (0.017) (0.020) (0.020) (0.022) (0.023)
Latitude -2.476 -3.620
(0.820) (0.826)
Longitude -0.122 -0.275
(0.147) (0.147)
Ruggedness 0.133 0.108
(0.037) (0.043)
Elevation -0.180 -0.160
(0.056) (0.066)
Pre-1500 caloric 0.0340.033
suitability (0.019) (0.019)
Agricultural 0.612 0.312
suitability (0.190) (0.178)
Distance to coast -0.068 -0.005
(0.024) (0.032)
Distance to major -0.026 -0.023
river (0.026) (0.023)
Distance to natural -0.068 -0.023
harbor (0.044) (0.046)
Distance to Rome -0.833 -0.863
(0.149) (0.161)
Distance to Roman -0.026 -0.116
border (0.030) (0.032)
Distance to capital 0.006 0.018
(0.037) (0.037)
Number of mines 0.062 0.051
(0.043) (0.043)
Country FE No Yes Yes Yes Yes Yes Yes
Country-language FE No No Yes Yes Yes Yes Yes
Observations 693 693 693 675 693 693 675
R20.135 0.324 0.395 0.450 0.412 0.475 0.535
Notes: This table documents the positive, statistically significant correlation between Roman roads
and Roman settlements when accounting for geographical characteristics and country-language fixed
effects. Roman roads is log of one plus the fraction of a 5 km buffer around the Roman road system that
lies wihin the total area of a country-cell. Roman settlements is log of one plus the number of major
settlements within the country-cell in CE 500. The analysis is performed on country-cells within the
Roman empire containing non-zero Roman roads. All variables are in logs. Heteroskedasticity robust
standard errors are reported in parentheses. *** denotes statistical significance at the 1 pct. level, ** at
the 5 pct. level, and * at the 10 pct. level.
37
Table 4: Roman Roads and Nightlights
Dependent variable: Nightlights in 2010
(1) (2) (3) (4) (5) (6) (7)
Roman roads 1.635 1.349 1.055 0.580 0.914 0.963 0.524
(0.186) (0.161) (0.153) (0.152) (0.153) (0.152) (0.153)
Area -0.004 0.014 -0.018 0.019 0.014 -0.025 0.013
(0.021) (0.022) (0.019) (0.019) (0.019) (0.021) (0.020)
Latitude 1.504 1.182
(0.515) (0.547)
Longitude -0.089 -0.009
(0.143) (0.128)
Ruggedness 0.105 0.117
(0.030) (0.035)
Elevation -0.356 -0.344
(0.041) (0.049)
Post-1500 caloric 0.022 0.010
suitability (0.016) (0.015)
Agricultural 0.200 0.136
suitability (0.129) (0.135)
Distance to coast -0.089 -0.013
(0.021) (0.024)
Distance to major -0.050 -0.009
river (0.019) (0.018)
Distance to natural -0.042 -0.054
harbor (0.033) (0.034)
Distance to Rome 0.083 0.155
(0.088) (0.097)
Distance to Roman -0.033 -0.012
border (0.035) (0.036)
Distance to capital -0.169 -0.169
(0.033) (0.032)
Number of mines -0.007 0.011
(0.028) (0.027)
Country FE No Yes Yes Yes Yes Yes Yes
Country-language FE No No Yes Yes Yes Yes Yes
Observations 693 693 693 675 693 693 675
R20.160 0.576 0.663 0.732 0.685 0.685 0.751
Notes: This table documents the positive, statistically significant correlation between Roman roads
and nigthlights when accounting for geographical characteristics and country-language fixed effects.
Roman roads is log of one plus the fraction of a 5 km buffer around the Roman road system that lies
wihin the total area of a country-cell. Nightlights is log of the average light intensity measured at night
by satelite in 2010. The analysis is performed on country-cells within the Roman empire containing
non-zero Roman roads. All variables are in logs. Heteroskedasticity robust standard errors are reported
in parentheses. *** denotes statistical significance at the 1 pct. level, ** at the 5 pct. level, and * at the 10
pct. level.
38
Table 5: Roman Roads and population in 2010
Dependent variable: Population in 2010
(1) (2) (3) (4) (5) (6) (7)
Roman roads 3.231 2.639 2.559 1.174 2.202 2.349 1.078
(0.421) (0.426) (0.400) (0.339) (0.381) (0.374) (0.331)
Area 1.004 1.029 1.094 1.137 1.178 1.061 1.116
(0.061) (0.069) (0.040) (0.035) (0.038) (0.041) (0.038)
Latitude 1.382 1.018
(1.255) (1.379)
Longitude -0.467 -0.250
(0.302) (0.269)
Ruggedness 0.335 0.383
(0.069) (0.078)
Elevation -0.802 -0.810
(0.109) (0.128)
Post-1500 caloric 0.242 0.208
suitability (0.061) (0.059)
Agricultural 0.281 0.069
suitability (0.351) (0.343)
Distance to coast -0.234 -0.014
(0.057) (0.061)
Distance to major -0.112 -0.026
river (0.048) (0.045)
Distance to natural -0.092 -0.118
harbor (0.086) (0.078)
Distance to Rome 0.098 0.248
(0.168) (0.174)
Distance to Roman 0.031 0.054
border (0.075) (0.079)
Distance to capital -0.487 -0.442
(0.083) (0.077)
Number of mines -0.046 -0.031
(0.064) (0.056)
Country FE No Yes Yes Yes Yes Yes Yes
Country-language FE No No Yes Yes Yes Yes Yes
Observations 693 693 693 675 693 693 675
R20.561 0.701 0.769 0.823 0.783 0.786 0.835
Notes: This table documents the positive, statistically significant correlation between Roman roads
and population density when accounting for geographical characteristics and country-language fixed
effects. Roman roads is log of one plus the fraction of a 5 km buffer around the Roman road system that
lies wihin the total area of a country-cell. The analysis is performed on country-cells within the Roman
empire containing non-zero Roman roads. All variables are in logs Heteroskedasticity robust standard
errors are reported in parentheses. *** denotes statistical significance at the 1 pct. level, ** at the 5 pct.
level, and * at the 10 pct. level.
39
Table 6: Sample Split: Roman Roads and Development in Europe and MENA
Dependent variable:
Settlements
in 500 CE Modern roads
Nightlights
in 2010
Population
in 2010
(1) (2) (3) (4) (5) (6) (7) (8)
Sample: Europe MENA Europe MENA Europe MENA Europe MENA
Roman roads 0.3990.855 0.160 0.129 0.760 0.109 1.394 0.668
(0.222) (0.395) (0.044) (0.086) (0.185) (0.245) (0.387) (0.624)
Area 0.240 0.210 0.005 -0.0250.058 -0.100 1.177 1.062
(0.026) (0.052) (0.005) (0.013) (0.022) (0.032) (0.038) (0.098)
Latitude -3.901 -6.080 0.068 -0.698 1.505 -0.163 0.353 0.977
(0.995) (1.807) (0.108) (0.297) (0.729) (1.053) (1.425) (3.427)
Longitude -0.2830.005 -0.047 0.186 -0.082 0.581 -0.334 0.537
(0.159) (0.389) (0.019) (0.073) (0.152) (0.233) (0.298) (0.602)
Ruggedness 0.015 0.105 0.009 -0.011 0.140 -0.013 0.467 -0.005
(0.051) (0.085) (0.010) (0.016) (0.048) (0.045) (0.092) (0.140)
Elevation -0.138-0.100 -0.033 0.046 -0.380 -0.271 -0.908 -0.666
(0.079) (0.128) (0.014) (0.023) (0.073) (0.079) (0.146) (0.260)
Pre-1500 caloric -0.015 0.029
suitability (0.020) (0.023)
Agricultural -0.151 1.256 0.043 0.205 0.094 0.827 0.089 1.661
suitability (0.183) (0.584) (0.033) (0.100) (0.167) (0.293) (0.378) (0.924)
Distance to coast 0.021 -0.030 0.000 -0.031 0.011 -0.006 0.029 -0.065
(0.042) (0.064) (0.005) (0.015) (0.029) (0.043) (0.062) (0.148)
Distance to major -0.002 -0.017 0.010 0.003 0.025 -0.0630.052 -0.285
river (0.021) (0.068) (0.004) (0.017) (0.020) (0.037) (0.044) (0.107)
Distance to natural -0.034 0.063 0.001 0.003 -0.048 -0.140 -0.076 -0.250
harbor (0.058) (0.097) (0.008) (0.015) (0.045) (0.062) (0.096) (0.197)
Distance to Rome -0.811 -1.859 -0.006 0.107 0.061 0.649 0.119 1.001
(0.155) (0.551) (0.011) (0.093) (0.095) (0.326) (0.177) (1.147)
Distance to Roman -0.113 0.161 -0.002 -0.110-0.034 -0.019 0.001 0.310
border (0.035) (0.400) (0.008) (0.057) (0.041) (0.217) (0.085) (0.681)
Distance to capital -0.016 0.006 -0.012-0.013 -0.142 -0.280 -0.356 -0.763
(0.042) (0.081) (0.007) (0.014) (0.043) (0.049) (0.100) (0.164)
Number of mines 0.010 0.184 -0.018 0.037-0.029 0.094 -0.1080.151
(0.041) (0.152) (0.006) (0.020) (0.028) (0.070) (0.056) (0.178)
Post-1500 caloric 0.009 0.012 0.011 0.018 0.148 0.205
suitability (0.007) (0.005) (0.026) (0.016) (0.041) (0.079)
Country-language FE Yes Yes Yes Yes Yes Yes Yes Yes
Observations 457 218 457 218 457 218 457 218
R20.557 0.541 0.509 0.655 0.744 0.732 0.906 0.724
Notes: This table documents that the positive, statistically significant correlation between Roman roads and devel-
opment persists in Europe but not in MENA (the Middle East and North Africa). Roman roads and modern roads
are defined as the log of one plus the fraction of a 5 km buffer around, respectively, the Roman and modern road
system that lies wihin the total area of a country-cell. Roman settlements is log of one plus the number of major
settlements within the country-cell in 500 CE. Nightlights is log of the average light intensity measured at night by
satelite in 2010 CE. The analysis is performed on country-cells within the Roman empire containing non-zero Roman
roads. All variables are in logs. Heteroskedasticity robust standard errors are reported in parentheses. *** denotes
statistical significance at the 1 pct. level, ** at the 5 pct. level, and * at the 10 pct. level.
40
A Appendix: Additional figures
Figure A.1: Via Aemilia from Ariminum to Placentia in 187 BCE
Note: The figure shows Via Aemilia from Ariminum to Placentia, completed in 187 BCE. It also shows
other later confirmed roads as thick red lines and unconfirmed roads as dotted lines. Quadratic
symbols denote later Roman towns and settlements during antiquity.
Source: Created on the basis of data in Talbert (2000).
41
Figure A.2: Roman towns, pre-Roman towns and roads emanating from Lugdunum (Lyon)
around 20 BCE
Note: The figure shows Roman towns and settlements after Caesars conquest in 58-50 BCE as dark
and white circles and pre-Roman Celtic settlements (oppida) as green arrows. Red lines show Roman
roads, including Via Agrippa from hub city Lugdunum (Lyon) south along the Rhȏne.
Source: Created on the basis of data in Åhlfeldt (2015)
42
B Appendix: Tables
Table B.1: Summary Statistics
Mean Std Min Max Obs
Main variables:
Roman roads 27.8 21.6 0.0 100.0 675
Modern roads 73.5 18.6 0.2 100.0 675
Number of major settlements in 500 AD 1.4 2.8 0.0 28.0 675
Nightlights 15.0 11.0 3.0 63.0 675
Population in 2010 (1000s) 706.0 1085.6 0.0 10513.0 675
Geography:
Area 5312.3 3530.3 0.0 11226.4 675
Latitude 41.6 6.1 24.5 55.1 675
Longitude 14.3 13.2 -9.2 39.3 675
Ruggedness (1000s) 169.8 159.9 1.0 1086.8 675
Elevation 488.3 446.4 0.0 2736.6 675
Post-1500 caloric suitability 7870.0 3426.8 0.0 15080.3 675
Pre-1500 caloric suitability 7153.1 2802.0 0.0 10549.4 675
Agricultural suitability (pct.) 55.9 30.6 0.0 99.8 675
Temperature 12.6 4.3 -1.0 24.8 675
Precipitation 58.9 28.1 0.2 189.5 675
Number of frost days 6.0 3.8 0.0 21.3 675
Waterways:
Distance to coast 121.9 111.9 0.1 508.5 675
Distance to major river 174.6 248.9 0.1 1213.2 675
Distance to natural harbor 276.9 168.1 0.8 850.0 675
Location:
Distance to Rome 1177.8 583.6 2.7 2731.8 675
Distance to Roman border 759.0 608.2 0.1 2246.2 675
Distance to capital 282.4 186.5 0.0 1126.1 675
Number of mines 0.7 1.8 0.0 17.0 675
Historical:
Number of oppidas 0.9 1.7 0.0 11.2 183
Years since Neolithic transition 6844.8 1290.8 4790.0 10220.0 176
Notes: This table shows the summary statistics of the variables included in the analy-
sis based on the observations included in the full specification of Tables 1 through 5.
Variables are not in logs.
43
Table B.2: Roman Roads and Modern Roads - Robustness
Dependent variable: Modern roads
(1) (2) (3) (4) (5)
No Italy No coastal Roman road
dummy No border More controls
for climate
Roman roads 0.163 0.155 0.131 0.130 0.150
(0.045) (0.069) (0.040) (0.046) (0.042)
Area -0.001 0.012 -0.001 0.002 -0.001
(0.006) (0.009) (0.004) (0.006) (0.005)
Latitude -0.031 -0.194 -0.171 0.054 -0.003
(0.164) (0.281) (0.152) (0.156) (0.168)
Longitude -0.012 -0.113 -0.031 -0.000 -0.006
(0.025) (0.045) (0.025) (0.023) (0.023)
Ruggedness 0.005 0.003 0.0180.003 0.005
(0.010) (0.018) (0.010) (0.009) (0.010)
Elevation -0.004 -0.025 -0.002 -0.003 -0.008
(0.014) (0.027) (0.012) (0.015) (0.019)
Post-1500 caloric 0.008 0.006 0.017 0.0090.007
suitability (0.005) (0.007) (0.004) (0.005) (0.006)
Agricultural 0.105 0.037 0.106 0.110 0.085
suitability (0.034) (0.046) (0.034) (0.035) (0.039)
Distance to coast -0.020 0.009 -0.024 -0.020 -0.016
(0.006) (0.022) (0.006) (0.007) (0.006)
Distance to major 0.002 0.008 0.002 0.000 0.002
river (0.005) (0.006) (0.005) (0.006) (0.005)
Distance to natural 0.014 0.021 0.0150.009 0.012
harbor (0.008) (0.021) (0.009) (0.007) (0.007)
Distance to Rome 0.007 0.013 0.043 0.017 0.017
(0.041) (0.069) (0.018) (0.013) (0.013)
Distance to Roman -0.004 -0.017 -0.005 0.013 -0.003
border (0.008) (0.013) (0.009) (0.019) (0.008)
Distance to capital -0.006 0.001 -0.011 -0.003 -0.007
(0.007) (0.010) (0.007) (0.008) (0.007)
Number of mines -0.008 -0.0160.006 -0.005 -0.007
(0.007) (0.008) (0.007) (0.007) (0.006)
Roman road dummy 0.026
(0.014)
Number of frost days 0.005
(0.028)
Temperature 0.033
(0.050)
Precipitation 0.017
(0.021)
Country-language FE Yes Yes Yes Yes Yes
Observations 621 318 964 583 675
R20.582 0.642 0.679 0.616 0.593
Notes: This table documents that the conclusion from Table 2 that Roman roads are postively
and significantly correlated to modern roads is robust to changing the sample and including
additional control variables. (1): Excludes Italy. (2): Excludes country-grid cells that lie within
100 km. of the coast. (3): Includes country-cells with zero Roman roads and adds a Roman
road dummy that equals one if Roman roads is positive. (4): Excludes country-grid cells
that lie within 100 km. of the border of the Roman empire. (5): Adds further controls for
climate. All variables are in logs. Heteroskedasticity robust standard errors are reported in
parentheses. *** denotes statistical significance at the 1 pct. level, ** at the 5 pct. level, and *
at the 10 pct. level.
44
Table B.3: Roman Roads and Ancient Settlements - Robustness
Dependent variable: Settlements in 500 CE
(1) (2) (3) (4) (5)
No Italy No coastal Roman road
dummy No border More controls
for climate
Roman roads 0.650 0.684 0.193 0.577 0.531
(0.183) (0.247) (0.172) (0.229) (0.190)
Area 0.199 0.146 0.086 0.246 0.233
(0.020) (0.026) (0.015) (0.029) (0.022)
Latitude -4.155 -2.892-2.343 -3.194 -4.575
(0.969) (1.549) (0.604) (1.008) (1.030)
Longitude -0.231 -1.027 -0.243 -0.265 -0.217
(0.159) (0.297) (0.110) (0.163) (0.156)
Ruggedness 0.079 0.073 0.0500.147 0.085
(0.040) (0.053) (0.026) (0.053) (0.046)
Elevation -0.142 -0.182 -0.058 -0.194 -0.209
(0.066) (0.086) (0.038) (0.077) (0.089)
Pre-1500 caloric 0.0330.010 0.017 0.030 0.014
suitability (0.017) (0.022) (0.011) (0.019) (0.021)
Agricultural 0.3260.3940.322 0.430 0.271
suitability (0.183) (0.222) (0.144) (0.217) (0.192)
Distance to coast -0.035 0.018 0.009 0.009 0.004
(0.032) (0.133) (0.025) (0.033) (0.036)
Distance to major -0.012 0.002 -0.011 -0.017 -0.023
river (0.024) (0.033) (0.022) (0.026) (0.023)
Distance to natural 0.001 -0.300 -0.053 -0.011 -0.017
harbor (0.048) (0.131) (0.041) (0.048) (0.046)
Distance to Rome -0.938 -0.891 -0.655 -0.922 -0.819
(0.212) (0.345) (0.160) (0.175) (0.162)
Distance to Roman -0.100 -0.072-0.044 -0.009 -0.123
border (0.031) (0.043) (0.027) (0.113) (0.033)
Distance to capital -0.001 0.024 -0.0690.033 -0.000
(0.036) (0.059) (0.036) (0.042) (0.038)
Number of mines 0.0770.086 0.118 0.041 0.051
(0.045) (0.054) (0.044) (0.050) (0.043)
Roman road dummy 0.086
(0.056)
Number of frost days 0.070
(0.167)
Temperature 0.028
(0.211)
Precipitation 0.264
(0.125)
Country-language FE Yes Yes Yes Yes Yes
Observations 621 318 964 583 675
R20.488 0.541 0.480 0.543 0.541
Notes: This table documents that the conclusion from Table 3 that Roman roads are postively
and significantly correlated to Roman settlements is robust to changing the sample and in-
cluding additional control variables. (1): Excludes Italy. (2): Excludes country-grid cells that
lie within 100 km. of the coast. (3): Includes country-cells with zero Roman roads and adds
a Roman road dummy that equals one if Roman roads is positive. (4): Excludes country-grid
cells that lie within 100 km. of the border of the Roman empire. (5): Adds further controls
for climate. All variables are in logs. Heteroskedasticity robust standard errors are reported
in parentheses. *** denotes statistical significance at the 1 pct. level, ** at the 5 pct. level, and
* at the 10 pct. level.
45
Table B.4: Roman Roads and Nightlights - Robustness
Dependent variable: Nightligts in 2010
(1) (2) (3) (4) (5)
No Italy No coastal Roman road
dummy No border More controls
for climate
Roman roads 0.558 0.645 0.614 0.406 0.523
(0.160) (0.201) (0.141) (0.171) (0.151)
Area -0.000 0.052 0.014 0.006 0.009
(0.020) (0.023) (0.010) (0.024) (0.019)
Latitude 0.750 1.174 0.8860.380 2.097
(0.630) (1.091) (0.458) (0.676) (0.718)
Longitude -0.011 -0.133 -0.003 0.016 0.046
(0.134) (0.200) (0.100) (0.130) (0.132)
Ruggedness 0.136 0.110 0.0480.109 0.107
(0.036) (0.052) (0.029) (0.039) (0.038)
Elevation -0.349 -0.259 -0.214 -0.364 -0.257
(0.050) (0.080) (0.038) (0.053) (0.073)
Post-1500 caloric 0.008 0.010 0.016 0.015 0.006
suitability (0.015) (0.018) (0.011) (0.015) (0.018)
Agricultural 0.069 0.097 0.171 0.030 0.048
suitability (0.143) (0.157) (0.114) (0.150) (0.148)
Distance to coast -0.025 -0.018 -0.043 -0.016 -0.005
(0.026) (0.084) (0.021) (0.027) (0.026)
Distance to major -0.009 -0.030 -0.018 -0.012 -0.015
river (0.018) (0.022) (0.017) (0.019) (0.017)
Distance to natural -0.028 0.157-0.037 -0.072 -0.053
harbor (0.040) (0.087) (0.031) (0.036) (0.034)
Distance to Rome -0.004 -0.152 0.169 0.2010.144
(0.159) (0.232) (0.086) (0.108) (0.096)
Distance to Roman -0.012 -0.041 -0.023 -0.153 -0.008
border (0.038) (0.040) (0.030) (0.101) (0.036)
Distance to capital -0.187 -0.180 -0.172 -0.196 -0.173
(0.031) (0.048) (0.028) (0.036) (0.033)
Number of mines 0.012 -0.010 0.032 -0.004 0.010
(0.027) (0.030) (0.025) (0.030) (0.027)
Roman road dummy 0.026
(0.046)
Number of frost days -0.181
(0.123)
Temperature 0.092
(0.175)
Precipitation -0.001
(0.096)
Country-language FE Yes Yes Yes Yes Yes
Observations 621 318 964 583 675
R20.752 0.826 0.755 0.751 0.753
Notes: This table documents that the conclusion from Table 4 that Roman roads are postively
and significantly correlated to nightlights is robust to changing the sample and including
additional control variables. (1): Excludes Italy. (2): Excludes country-grid cells that lie within
100 km. of the coast. (3): Includes country-cells with zero Roman roads and adds a Roman
road dummy that equals one if Roman roads is positive. (4): Excludes country-grid cells
that lie within 100 km. of the border of the Roman empire. (5): Adds further controls for
climate. All variables are in logs. Heteroskedasticity robust standard errors are reported in
parentheses. *** denotes statistical significance at the 1 pct. level, ** at the 5 pct. level, and *
at the 10 pct. level.
46
Table B.5: Roman Roads and Population - Robustness
Dependent variable: Population in 2010
(1) (2) (3) (4) (5)
No Italy No coastal Roman road
dummy No border More controls
for climate
Roman roads 1.061 1.011 0.528 1.045 1.064
(0.356) (0.392) (0.434) (0.374) (0.328)
Area 1.105 1.153 0.907 1.142 1.102
(0.043) (0.041) (0.078) (0.047) (0.037)
Latitude 0.515 0.680 3.451 0.009 4.327
(1.693) (2.978) (2.594) (1.674) (1.864)
Longitude -0.322 -0.685 0.078 -0.111 -0.042
(0.295) (0.463) (0.259) (0.277) (0.272)
Ruggedness 0.410 0.334 0.362 0.398 0.345
(0.085) (0.105) (0.104) (0.091) (0.084)
Elevation -0.846 -0.741 -0.463 -0.892 -0.486
(0.136) (0.157) (0.182) (0.144) (0.177)
Post-1500 caloric 0.201 0.189 0.132 0.208 0.205
suitability (0.060) (0.036) (0.040) (0.060) (0.072)
Agricultural -0.074 -0.159 0.266 -0.167 -0.137
suitability (0.353) (0.377) (0.323) (0.376) (0.349)
Distance to coast -0.028 -0.167 -0.020 -0.028 0.022
(0.070) (0.237) (0.092) (0.069) (0.060)
Distance to major -0.035 -0.055 -0.092 -0.043 -0.049
river (0.046) (0.054) (0.071) (0.050) (0.044)
Distance to natural -0.049 0.402 -0.271 -0.176 -0.125
harbor (0.100) (0.251) (0.086) (0.079) (0.075)
Distance to Rome -0.349 -0.755 0.602 0.373 0.194
(0.416) (0.621) (0.212) (0.188) (0.176)
Distance to Roman 0.042 0.002 0.111 -0.093 0.060
border (0.085) (0.078) (0.103) (0.223) (0.077)
Distance to capital -0.466 -0.444 -0.603 -0.515 -0.453
(0.077) (0.128) (0.083) (0.090) (0.080)
Number of mines -0.026 -0.047 0.146-0.079 -0.031
(0.058) (0.069) (0.083) (0.063) (0.055)
Roman road dummy 0.590
(0.226)
Number of frost days -0.797
(0.273)
Temperature -0.021
(0.492)
Precipitation -0.072
(0.273)
Country-language FE Yes Yes Yes Yes Yes
Observations 621 318 964 583 675
R20.821 0.917 0.803 0.807 0.838
Notes: This table documents that the conclusion from Table 5 that Roman roads are postively
and significantly correlated to population in 2010 is robust to changing the sample and in-
cluding additional control variables. (1): Excludes Italy. (2): Excludes country-grid cells that
lie within 100 km. of the coast. (3): Includes country-cells with zero Roman roads and adds
a Roman road dummy that equals one if Roman roads is positive. (4): Excludes country-grid
cells that lie within 100 km. of the border of the Roman empire. (5): Adds further controls
for climate. All variables are in logs. Heteroskedasticity robust standard errors are reported
in parentheses. *** denotes statistical significance at the 1 pct. level, ** at the 5 pct. level, and
* at the 10 pct. level.
47
C Appendix: Additional data definitions and sources
Oppida were Celtic settlement sites that functioned as economic and political centres during the last two
centuries BCE continuing to the first century CE before in Britain and Europe. The settlement area of oppidas
was often hundreds of hectares, and they typically accommodated thousands of regular inhabitants. They
were also characterized by being heavily fortified, and having a timber-laced stone-faced murus gallicus or a
‘Gallic wall’.
We take data for the location of oppida during the La Tène CD period preceding the beginning of Roman
expansion, from the Archaeology Data Service at the University of York.1Given that oppida were present
only in Britain and continental Europe, we create an envelope of the buffer of 100 km around all oppida, as an
estimate of the influece area of the La Tène culture. Within this area, similar to our procedure to estimate the
area of influence of roads, we construct a buffer of 5 km around each oppidum, and compute the percentage
of that area within each country-cell.2
Language areas are a complete set of language fixed effects, or indicator variables of ethnic languages
recorded in the World Language Mapping System Database version 3.01 (WLMS, which is a dataset containing
polygons for the linguistic homelands of more than 7,000 ethnic languages around the globe). We follow
Andersen et al (2016) in the construction of these variables and, basically, we consider the predominant ethnic
language to be the one with the largest area in cases where a country-cell has more than one ethnic language;
and we assign a separate dummy variable that represents the excluded language category in each country, in
country-cells where there are no specific ethnic languages recorded in WLMS.
Ruggedness is drawn from Nunn and Puga (2012) and reflects small-scale variations in elevation. They
compute the index as the sum of the squared differences in elevation between the cell in question and eight
adjacent cells. The ruggedness index is available at 30 arc-second cells. We aggregate the index by averaging
across smaller cells within each 1 by 1 degree cell.
Elevation is computed using the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). This
data set contains the average elevation in meters at the level of 30 arc-second cells. We aggregate elevation by
averaging across smaller cells within each 1 by 1 degree cell.
Caloric suitability is computed by Galor and Özak (2016) as the maximum potential crop yield in calories
per hectare per year. It is constructed based only on geographical characteristics and not actual historical yields
which makes suitable as an exogenous control variable. We compute the average of caloric suitability across
smaller grid cells within each 1 by 1 degree grid cell. Since the types of crops available in different regions
across the globe changed markedly after the Columbian Exchange, Galor and Özak (2016) provide two types
of variables, one for the pre-1500 era and one for the post-1500 era. We use the first measure for regressions
pertaining to the period prior to 1500 AD and the second measure for the regressions pertaining to the modern
period.
Agricultural suitability is an index reflecting the suitability of the climate and geography for agriculture
computed by Ramankutty et. al. (2002). The index takes on values between 0 and 1 indicating the probability
1The complete documentation can be found at http://archaeologydataservice.ac.uk/archiveDS/archiveDownload?t=arch-408-
1/dissemination/zip/Atlas/GIS_data/do cumentation/ATLAS_ METADATA.pdf.
2A buffer of 5 km for an oppidum is broadly consistent an area of about 7000 ha of agricultural land, outside a wall of 6 km lenght
securing an area of 300 ha.
48
of cultivation. It is computed as follows: First, the relationship between actual cultivation and exogenous data
on soil and climate is estimated in a statistical model. Then the probability of cultivation is predicted for each
grid cell based on climatic and geographical characteristics. The index is available across 0.5 by 0.5 decimal
degree grid cells. We average this to the level of 1 by 1 degree grid cells.
Distance to nearest river is computed using the location of rivers provided by CIA World Databank II. We
compute the distance in km. from the centroid of each 1 by 1 degree grid cell to the nearest major river.
Distance to coast is based on the location of shorelines from Natural Earth (2017). We compute the distance
in km. from the centroid of each 1 by 1 degree grid cell to the nearest coast.
Time since Neolithic transition is the number of years elapsed since the earliest evidence of agriculture in
the grid cell. We compute it based the list of archaeological Neolithic sites compiled by Pinhasi et al (2005) who
use it to trace the spread of the transition from hunting and gathering to agriculture throughout Europe and
the Middle East. Each site is provided with a radio-carbon date measured in years before present. We define
the time since the Neolithic transition as the years since the earliest Neolithic site within each 1 by 1 degree
grid cell.
Climatic variables (temperature, precipitation, frost days) are computed using data of New et al (2002).
They use data from weather stations across the globe to construct climate variables at the level of 10 by 10
arc-minute grid cells. Each variable is computed as monthly means of the period from 1961-1990. We first
compute the yearly average of temperature in degrees Celsius, precipitation in mm pr. month and the number
of days with ground frost per month. We then average across 10 by 10 arc-minute cells within each 1 by 1
degree cell unit.
Ancient mines. We use the Digital Atlas of the Roman Empire (DARE) database constructed by Åhlfeldt
(2017) to compute the number of ancient mines in each cell. This variable is used as a control to ensure the
estimated effect of roads on economic activity is not confounded by mineral deposits that were constructed
before or simultaneously with the roads. For each country-pixel, we count the number of mines that existed
prior to year 500 AD.
References
[1] Andersen, T. B., Dalgaard, C. J., & Selaya, P. (2016). Climate and the emergence of global income differences. Review of
Economic Studies, 83(4), 1334-1363.
[2] Galor, O. and Ö. Özak. (2016) "The agricultural origins of time preference." The American Economic Review 106(10):
3064-3103.
[3] Natural Earth (2017) http://www.naturalearthdata.com/
[4] New, M., D. Lister, M. Hulme and I. Makin (2002). A high-resolution data set of surface climate over global land areas.
Climate research, 21(1), 1-25.
[5] Nunn, N., and D. Puga (2012). Ruggedness: The blessing of bad geography in Africa. Review of Economics and
Statistics, 94(1), 20-36.
[6] Pinhasi, R., J. Fort, and A. Ammerman (2005) "Tracing the Origins and Spread of Agriculture in Europe" PLOS Biology
3(12), 2220-2228.
[7] Åhlfeldt, J. (2017) Digital Atlas of the Roman Empire. Web publication available at http://dare.ht.lu.se/. Lund University,
Lund.
49
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Article
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Dromedaries have been fundamental to the development of human societies in arid landscapes and for long-distance trade across hostile hot terrains for 3,000 y. Today they continue to be an important livestock resource in marginal agro-ecological zones. However, the history of dromedary domestication and the influence of ancient trading networks on their genetic structure have remained elusive. We combined ancient DNA sequences of wild and early-domesticated dromedary samples from arid regions with nuclear microsatellite and mitochondrial genotype information from 1,083 extant animals collected across the species’ range. We observe little phylogeographic signal in the modern population, indicative of extensive gene flow and virtually affecting all regions except East Africa, where dromedary populations have remained relatively isolated. In agreement with archaeological findings, we identify wild dromedaries from the southeast Arabian Peninsula among the founders of the domestic dromedary gene pool. Approximate Bayesian computations further support the “restocking from the wild” hypothesis, with an initial domestication followed by introgression from individuals from wild, now-extinct populations. Compared with other livestock, which show a long history of gene flow with their wild ancestors, we find a high initial diversity relative to the native distribution of the wild ancestor on the Arabian Peninsula and to the brief coexistence of early-domesticated and wild individuals. This study also demonstrates the potential to retrieve ancient DNA sequences from osseous remains excavated in hot and dry desert environments.
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We argue that a more individualist culture leads to more innovation and to higher growth because of the social status rewards associated with innovation in that culture. We use data on the frequency of particular genes associated with collectivist cultures, as well as a measure of distance in terms of frequencies of blood types, and historic prevalence of pathogens to instrument individualism scores. The relationship between individualism and innovation/growth remains strong even after controlling for institutions and other potentially confounding factors. We also provide evidence consistent with two-way causality between culture and institutions. © 2017 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
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The importance of evolutionary forces for comparative economic performance across societies has been the focus of a vibrant literature, highlighting the roles played by the Neolithic Revolution and the prehistoric "out of Africa" migration of anatomically modern humans in generating worldwide variations in the composition of human traits. This essay surveys this literature and examines the contribution of a recent hypothesis regarding the evolutionary origins of comparative economic development, set forth in Nicholas Wade's "A Troublesome Inheritance: Genes, Race and Human History," to this important line of research.
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This paper contributes to the understanding of the long-run consequences of Roman rule on economic development. In ancient times, the area of contemporary Germany was divided into a Roman and non-Roman part. The study uses this division to test whether the formerly Roman part of Germany show a higher nightlight luminosity than the non-Roman part. This is done by using the Limes wall as geographical discontinuity in a regression discontinuity design framework. The results indicate that economic development - as measured by luminosity - is indeed significantly and robustly larger in the formerly Roman parts of Germany. The study identifies the persistence of the Roman road network until the present as an important factor causing this development advantage of the formerly Roman part of Germany both by fostering city growth and by allowing for a denser road network.
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The effect of keju — China’s imperial examination system (607-1905) — on human capital outcomes persists to this day. Using the variation in the density of jinshi — the highest qualification — across 248 Chinese prefectures to proxy for the keju effect, and river distance to a prefecture’s nearest printing center as instrument, we find that a 1% increase in jinshi density increases years of schooling by 6.6%. After controlling for the effects of human capital of both ancestors and parents, the Chinese culture of valuing education — bred likely by the exceptional social status of the jinshi — represents the other channel in accounting for the observed persistence. A quasi-experiment of college students from all over China studying in Beijing further reveals that the jinshi density in their hometowns bears significantly upon their cognitive skills and non-cognitive performance. Finally, cultural transmission is aided by clans and weakened by the Cultural Revolution.
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Do fixed geographic features such as coastlines and rivers determine town locations, or can historical events trap towns in unfavourable locations for centuries? We examine the effects on town locations of the collapse of theWestern Roman Empire, which temporarily ended urbanization in Britain, but not in France. As urbanization recovered, medieval towns were more often found in Roman-era town locations in France than in Britain. The resetting of Britain's urban network gave it better access to natural navigable waterways, which mattered for town growth from 1200-1800. We conclude that history trapped many French towns in suboptimal locations. This article is protected by copyright. All rights reserved.
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This paper empirically investigates why, between 800 and 1800, the urban center of gravity moved from the Islamic world to Europe. Using a large new city-specific data set covering Europe, the Middle East, and North Africa, we unravel the role of geography and institutions in determining long-run city development in the two regions. We find that the main reasons for the Islamic world's stagnation and Europe's longterm success are specific to each region: any significant positive interaction between cities in the two regions hampered by their different main religious orientation. Together, the long-term consequences of a different choice of main transport mode (camel versus ship) and the development of forms of local participative government in Europe that made cities less dependent on the state explain why Europe's urban development eventually outpaced that in the Islamic world.
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We exploit the construction and eventual demise of the colonial railroads in Ghana, and most of the rest of Africa, to study the impact of transportation investments in poor countries. Using new data on railroads and cities spanning over one century, we find that railroads had large effects on the distribution of economic activity during the colonial period and these effects have persisted to date, although railroads collapsed and road networks expanded considerably after independence. Initial transportation investments may thus have large effects in poor countries. As countries develop, increasing returns solidify their spatial distribution, and subsequent investments may have smaller effects. © 2016 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.