OF MICE AND MERCHANTS: CONNECTEDNESS AND THE LOCATION
OF ECONOMIC ACTIVITY IN THE IRON AGE

Jan David Bakker, Stephan Maurer, Jörn-Steffen Pischke, and Ferdinand Rauch*

Abstract—We study the causal relationship between geographic connect-
edness and development using one of the earliest massive trade expansions:
the first systematic crossing of open seas in the Mediterranean during the
time of the Phoenicians. We construct a geography-based measure of con-
nectedness along the shores of the sea. We relate connectedness to economic
activity, which we measure using the presence of archaeological sites. Noi
find an association between better-connected locations and archaeological
sites during the Iron Age, at a time when sailors began to cross open water
routinely on a large scale. We corroborate these findings at the world level.

IO.

introduzione

WE investigate to what degree trading opportunities af-

fected economic development at an early juncture of
human history. In addition to factor accumulation and techni-
cal change, Smithian growth due to exchange and specializa-
tion is one of the fundamental sources of growth. An emerg-
ing literature on the topic is beginning to provide compelling
empirical evidence for a causal link from trade and market
access to growth. We contribute to this literature and focus on
one of the earliest massive expansions in maritime trade: IL
systematic crossing of open seas in the Mediterranean at the
time of the Phoenicians from about 900 BC. We relate trading
opportunities, which we capture through the connectedness
of points along the coast, to early development as measured
by the presence of archaeological sites. We find that loca-
tional advantages for sea trade matter for the presence of
Iron Age cities and settlements, and thus helped shape the
development of the Mediterranean region, and the rest of the
mondo.

A location with more potential trading partners should have
an advantage if trade is important for development. The shape
of a coast matters little for how many neighboring points can
be reached from a starting location within a certain distance
as long as ships sail mainly close to the coast. Tuttavia, once
sailors begin to cross open seas, coastal geography becomes

Received for publication January 18, 2019. Revision accepted for publi-

cation December 16, 2019. Editor: Rohini Pande.

∗Bakker: Bocconi University, University College London, and CEP;
Maurer: University of Konstanz and CEP; Pischke: LSE and CEP; Rauch:
University of Oxford and CEP.

We thank Juan Pradera for excellent research assistance, Tom Elliott for
help with and advice on the Pleiades database, and Rohini Pande, the edi-
tor, four referees, David Abulafia, Neeraj Baruah, Tim Besley, Andrew Be-
van, Francesco Caselli, Jeremiah Dittmar, Hannah Friedman, Avner Greif,
Vasiliki Kassianidou, Damian Kozbur, Carl Knappett, Jeffrey Lin, Andrea
Matranga, Guy Michaels, Dennis Novy, Luigi Pascali, Dominic Rathbone,
Tanner Regan, Corinna Riva, Susan Sherratt, Pedro CL Souza, Peter Temin,
John van Reenen, Ruth Whitehouse, David Yanagizawa-Drott, and partici-
pants at various seminars and conferences for their helpful comments and
suggestions. This research has been supported by a grant from the Economic
and Social Research Council (ES/M010341/1) to the Centre for Economic
Performance at the LSE.

A supplemental appendix is available online at https://doi.org/10.1162/

rest_a_00902.

more important. Some coastal points are in the reach of many
neighbors, while others can reach only a few. The shape of
the coast and the location of islands matter for this. We cap-
ture these geographic differences by dividing the Mediter-
ranean coast into grid cells and calculating how many other
cells can be reached within a certain distance. Parts of the
Mediterranean are highly advantaged by their geography—
Per esempio, the island-dotted Aegean and the “waist of the
Mediterranean” at southern Italy, Sicily, and modern Tunisia.
Other areas are less well connected, like most of the straight
North African coast, parts of Iberia and southern France, E
the Levantine coast.

We relate our measure of connectivity to the number of
archaeological sites found near any particular coastal grid
point. This is our proxy for economic development. It is based
on the assumption that more human economic activity leads
to more settlements and particularly towns and cities. When
these expand and multiply, there are more traces in the ar-
chaeological record. We find a pronounced relationship be-
tween connectivity and development in our data set for the
Iron Age around 750 BC, once the Phoenicians had begun to
systematically traverse the open sea. We have less evidence
whether there was any relationship between connectivity and
sites for earlier periods when the data on sites are poorer.
Connectivity might already have mattered during the Bronze
Age when voyages occurred at some frequency, maybe at
more intermediate distances. Our interpretation of the results
suggests that the relationship between coastal geography and
settlement density, once established in the Iron Age, persists
throughout classical antiquity. While our main results per-
tain to the Mediterranean, where we have good information
on archaeological sites, we also corroborate our findings at a
world scale using population data for 1 AD from McEvedy
and Jones (1978) as outcome.

Humans have obtained goods from faraway locations for
many millennia. Some of the early trade involved materials
useful for tools (like the obsidian trade studied by Dixon,
Cann, & Renfrew, 1968). As soon as societies became more
differentiated, a large part of this early trade involved lux-
ury goods undoubtedly consumed by the elites. Such trade
might have raised the utility of the beneficiaries, but it is
much less clear whether it affected productivity as well. Al-
though we are unable to measure trade directly, our work
sheds some light on this question. Since trade seems to have
affected the growth of settlements even at an early juncture,
this suggests that it was productivity enhancing. The view
that trade played an important role in early development has
been gaining ground among both economic historians and
archaeologists—for example, Temin (2006) for the Iron Age
Mediterranean, Algaze (2008) for Mesopotamia, Barjamovic

The Review of Economics and Statistics, ottobre 2021, 103(4): 652–665
© 2020 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0
Internazionale (CC BY 4.0) licenza.
https://doi.org/10.1162/rest_a_00902

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OF MICE AND MERCHANTS

653

et al. (2019) for Assyria, and Temin (2013) for ancient
Rome.

Our approach avoids issues of reverse causality and many
confounders by using a geography-based instrument for
trade. Infatti, we do not observe trade itself but effectively
estimate a reduced-form relationship, relating opportunities
for trade directly to economic development. This means that
we do not necessarily isolate the effect of the exchange of
goods per se. Our results could be driven by migration or the
spread of ideas as well, and when we talk about “trade," Noi
interpret it in this broad sense. While we cannot be sure ex-
actly how connectivity mattered, we show that it did not sim-
ply proxy for a variety of other geographic conditions. Both
our measure of connectedness and our outcome variable are
doubtlessly crude proxies of both trading opportunities and
economic development. This will likely bias us against find-
ing any relationship and hence makes our results only more
remarkable.

The periods we study, the Bronze and Iron Ages, were
characterized by the rise and decline of many cultures and
local concentrations of economic activity. Many settlements
and cities rose during this period, only to often disappear
Ancora. This means that there were ample opportunities for
new locations to rise to prominence, while path dependence
and hysteresis may have played a lesser role compared to
later ages. The political organization of the Mediterranean
world prior to the Romans was mostly local. The Egyptian
kingdoms are the main exception to this rule, but Egypt was
mostly focused on the Nile and less engaged in the Mediter-
ranean. Di conseguenza, institutional factors were less important
during the period we study.

There is a large literature on trade and growth. Canonical
studies are the investigations by Frankel and Romer (1999)
and Redding and Venables (2004). These papers use distance
from markets and connectivity as measured by gravity re-
lationships to capture the ease with which potential trading
partners can be reached. Tuttavia, these measures do not
rely purely on geography but conflate economic outcomes
like population and output, which are themselves affected by
the development process.

The more recent literature has circumvented this by an-
alyzing exogenous events related to changes in trade. Most
similar to our study are a series of papers that also exploit new
trade relationships arising from discoveries, the opening of
new trade routes, and technological change. Acemoglu, John-
figlio, and Robinson (2005) link Atlantic trade starting around
1500 AD to the ensuing shift in the focus of economic activ-
ity in Europe from the south and center of the continent to the
Atlantic periphery. Redding and Sturm (2008) focus on the
division and reunification in Germany, which changed access
to other markets sharply for some locations but not others.
Similar natural experiments are employed by Feyrer (2021)
and Maurer and Rauch (2019), who use exogenous variation
in sea distance created by the temporary closure of the Suez
Canal and the opening of the Panama Canal, rispettivamente. Var-

ious papers exploit the availability of new transport technolo-
gies; Donaldson (2018) and Donaldson and Hornbeck (2016)
study railroads, Pascali (2017) steamships, and Feyrer (2019)
air transport. These papers generally find that regions whose
trading opportunities improved disproportionately saw larger
income growth. That we find similar results for a much ear-
lier trade expansion suggests that the productivity benefits of
trade have been pervasive throughout history.

Our paper also relates to a literature on determinants and
dynamics of city locations (Davis & Weinstein, 2002; Bleak-
ley & Lin, 2012; Bosker & Buringh, 2017; Hanlon, 2017;
Michaels & Rauch, 2018). Our contribution stresses the role
of market access as a locational fundamental. In a world with
multiple modes of transport, it is typically hard to measure
market access and changes of a city’s market access. Nostro
measure relates to a world where much long-distance trade
took place on boats, which makes it easier to isolate a measure
related to market access.

Closely related is the paper by Ashraf and Galor (2011UN).
They relate population density in various periods to the rel-
ative geographic isolation of a particular area. Their interest
is in the impact of cultural diversity on the development pro-
cess, and they view geographic isolation effectively as an
instrument for cultural homogeneity. Similar to our measure,
their geographic isolation measure is a measure of connectiv-
ity of various points around the world. They find that better-
connected (cioè., less isolated) countries have lower population
densities for every period from 1 A 1500 AD, which seems to
contradict our result. Our approach differs from Ashraf and
Galor (2011UN) in that we look only at locations near the coast
and not inland locations. They control for distance to water-
ways in their regressions, a variable that is strongly positively
correlated with population density. Hence, our results are not
in conflict with theirs.

Our paper is also related to a number of studies on pre-
historic Mediterranean connectivity and seafaring. McEvedy
(1967) creates a measure of “littoral zones” using coastal
shapes. He produces a map that closely resembles the one
we obtain from our connectivity measure but does not relate
geography directly to seafaring. This is done by Broodbank
(2006), who overlays the connectivity map with archaeolog-
ical evidence of the earliest sea crossings up to the end of
the last Ice Age. He interprets the connections as nursery
conditions for the early development of nautical skills, Piuttosto
than as market access, as we do for the later Bronze and Iron
Ages.

Also related is a literature in archaeology using network
models connecting archaeological sites; Knappett, Evans,
and Rivers (2008) is an example for the Bronze Age Aegean.
Barjamovic et al. (2019) conduct a similar exercise for As-
syria based on a gravity model. None of these papers relate
to the changes arising from open sea crossings, the focus of
our analysis. Temin (2006) discusses the Iron Age Mediter-
ranean through the lens of comparative advantage trade but
offers no quantitative evidence as we do.

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654

THE REVIEW OF ECONOMICS AND STATISTICS

II. Brief History of Ancient Seafaring

in the Mediterranean

The Mediterranean is a unique geographic space. The large
inland sea is protected from the open oceans by the Strait of
Gibraltar. The tectonics of the area, the African plate descend-
ing under the Eurasian one, have created a rugged northern
coast in Europe and a much straighter one in North Africa.
Volcanic activity and the more than 3,000 islands also tend to
be concentrated toward the north. The climatic conditions in
the Mediterranean are generally relatively favorable to agri-
culture, particularly in the north. The Mediterranean is the
only large inland sea with such a climate (Broodbank, 2013).
Its east-west orientation facilitated the spread of agriculture
from the Levant (Diamond, 1997). The size of the Mediter-
ranean and an uneven distribution of natural resources also
implies great diversity. The geography and climate made the
region prone to risks such as forest fires, earthquakes, plagues
of locusts, droughts, floods, and landslides. Horden and Pur-
cell (2000) stress that the combination of these factors creates
ample opportiunties for trade networks to mitigate shocks and
exploit comparative advantage. Trade has played a central
role since the early history of the Mediterranean.1

Clear evidence of the first maritime activity of humans
in the Mediterranean is elusive. Crossings to islands close
to the mainland were apparently undertaken as far back as
30,000 BC (Fontana Nuova in Sicily), but Broodbank (2006)
dates more active seafaring to around 10,000 BC based on the
distribution of obsidian (a volcanic rock) at sites separated
by water (see Dixon, Cann, & Renfrew, 1965, 1968). Questo
points to the existence of active seafaring of hunter-gatherer
societies and suggests that boats must have traveled distances
Di 20 A 35 kilometers around that time. We have no evidence
on the first boats, but they were likely made from skin and
frame or dugout canoes.

Agriculture around the Mediterranean began in the Levant
some time between 9500 BC and 8000 BC. From there it
spread initially to Anatolia and the Aegean. Signs of a fairly
uniform Neolithic package of crops and domesticated ani-
mals can be found throughout the Mediterranean. The distri-
bution of the earliest evidence of agriculture, which includes
islands before reaching more peripheral parts of the main-
land, suggests a maritime transmission channel.

The Neolithic revolution did not reach Iberia until around
5500 BC. By that time, many islands in the Aegean had been
settled; there is evidence for grain storage, and metalworking
began in the Balkans. Because of the uneven distribution of
ores, metals soon became part of long-range transport. IL
first archaeological evidence of a boat also stems from this
period: a dugout canoe, Di 10 m long, at La Marmotta,
north of Rome. A replica proved seaworthy and allowed travel
Di 20 A 25 km per day in a laden boat.

1The following discussion mainly draws on Abulafia (2011) and Brood-

bank (2013).

The Levant, home to the first cities, remained a techno-
logical leader in the region, yet there is little evidence of
seafaring even during the Copper Age. This changed with
the rise of large-scale political entities in Mesopotamia and
Egypt. Growth in these first states created rich elites, who
soon wished to trade with each other. Being at the crossroads
between these two societies, the Levant quickly became a key
intermediary.

Two important new transport technologies arrived in the
Mediterranean around 3000 BC: the donkey and the sail. IL
donkey was uniquely suited to the climatic conditions and
rugged terrain around the Mediterranean (better than camels
or horses). Donkeys are comparable in speed to canoes. Sail-
boats of that period could be around five to ten times faster in
favorable conditions, ushering in a cost advantage of water
transport that would remain intact for millennia to come. IL
land route out of Egypt to the Levant was soon superseded
by sea routes leading up the Levantine coast to new settle-
ments like Byblos, with Levantine traders facilitating much
of Egypt’s Mediterranean trade. Coastal communities began
to emerge all the way from the Levant via Anatolia to the
Aegean and Greece.

There is no evidence of the sail spreading west of Greece
at this time. Canoes, though likely improved into high-
performance watercraft, remained inferior to sailboats but
kept facilitating maritime transport in the central and west-
ern Mediterranean. The major islands there were all settled
by the early Bronze Age. While not rivaling the maritime ac-
tivity in the eastern Mediterranean, regional trade networks
arose also in the west. The Beaker network and the Cetina
culture in the Adriatic during the third millennium BC are
examples. Occasional sea crossings up to 250 km were un-
dertaken during this period.

A drying spell around 2200 BC and decline in Egypt dis-
rupted the active maritime network in the eastern Mediter-
ranean and the population it supported. The oldest known
shipwreck in the Mediterranean at the island of Dokos in
southern Greece dates from this period. IL 15 m longboat
could carry a maximum weight of 20 tons. The wreck con-
tained largely pottery, which was likely the cargo rather than
carrying liquids, and also carried lead ingots. The ship prob-
ably was engaged in local trade.

Decline in the eastern Mediterranean soon gave rise to new
societies during the second millennium BC: palace cultures
sprang up all over the eastern Mediterranean. Minoan Crete
and Mycenae in Greece were notable examples, but similar
cities existed along the Anatolian coast and in the Levant.
The palaces did not simply hold political power; they were
centers of religious, ceremonial, and economic activity. A
least initially, craftsmen and traders most likely worked for
the palace rather than as independent agents. Sailboats still
constituted an advanced technology, and only the concen-
tration of resources in the hands of a rich elite made their
construction and operation possible. The political reach of
the palaces at coastal sites was local; larger polities remained

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OF MICE AND MERCHANTS

655

confined to inland areas as in the case of Egypt, Babylon, E
the Hittite Empire.

An active trade network arose again in the eastern Mediter-
ranean stretching from Egypt to Greece during the Palace
period. The Anatolian land route was replaced by sea trade.
Some areas began to specialize in cash crops like olives and
wine. A typical ship was still a 15-m, 20-ton, one-masted
vessel, as evidenced by the Uluburn wreck found at Kas in
Turkey, dating from 1450 BC. Such vessels carried diverse
cargoes including people (migrants, messengers, and slaves),
though the main goods were likely metals, textiles, wine, E
olive oil. Evidence for some of these was found on the Ulubu-
run wreck; other evidence comes from archives and inscrip-
tions akin to bills of lading. Broodbank (2013) suggests that
the cargo of the Uluburun ship was such that it was sufficient
to feed a city the size of Ugarit for a year. Ugarit was the
largest trading city in the Levant at the time, with a popula-
tion of about 6,000 A 8,000. This highlights that sea trade
still largely consisted of high-value luxury goods. The Ugarit
archives also reveal that merchants operating on their own ac-
count had become commonplace by the middle of the second
millennium. Levantine rulers relied more on taxation than
central planning of economic activities. Trade was both risky
and profitable; the most successful traders became among the
richest members of their societies.

Around the same time, the Mycenaeans traded as far as
Italy. Sicily and the Tyrrhenian got drawn into the network.
While 60- to 70-km crossings to Cyprus or Crete and across
the Otranto Strait (from Greece to the heel of Italy) were com-
monplace, coast hugging still prevailed among sailors during
the second millennium BC. After crossing the Otranto Strait,
Greek sailors would continue along the coast of the Bay of
Taranto, the instep of Italy’s boot, as is suggested by the distri-
bution of Greek pottery at coastal sites. Indigenous sea-farers
from the central Mediterranean now joined these routes, E
the sail finally entered the central Mediterranean around 1200
BC. While there were no big breakthroughs, naval technology
also improved in the late second millennium. Better caulking
and keels added to seaworthiness (Abulafia 2011), while brail
rigging and double prows improved maneuverability. Most
notably, latitude sailing was developed and allowed sailors to
steer a straight east-westerly course.

Before these changes could develop their full force, a new
period of decline around 1200 BC reduced the power of
Egypt, wiped out cities like Ugarit, and ended the reign of
the last palace societies in the eastern Mediterranean. Nel
more integrated world that the eastern Mediterranean had be-
come, troubles spread quickly from one site to others. IL
Bronze Age came to an end with iron coming on the scene.
Rather than being technologically all that much superior to
bronze, iron ore was far more abundant and widespread than
copper and hence much more difficult to monopolize. As
was the case many times before, decline and change opened
up spaces for smaller players and more peripheral regions.
Cyprus flourished. Many Levantine cities recovered quickly.
Traders from the central Mediterranean also expanded. Tra-

ditionally, decline during the Bronze Age collapse was often
blamed on the anonymous Sea Peoples. Modern scholarship
seems to challenge whether these foreigners were simply just
raiders and pirates, as the Egyptians surely saw them, Piuttosto
than also entrepreneurial traders who saw opportunities for
themselves to fill the void left by the disappearance of impe-
rial connections and networks.

The Levantine city-states that had taken in migrants from
the central Mediterranean during this period were the origin
of a newly emerging trade network. Starting to connect the
old Bronze Age triangle formed by the Levantine coast and
Cyprus, they began to expand throughout the entire Mediter-
ranean after 900 BC. The Phoenician city-states were much
more governed by economic logic than was the case for royal
Egypt. One aspect of their expansion was the formation of
enclaves, often at nodes of the network. Carthage and Gadir
(Cadiz) are prime examples, but many others existed. At least
initially, these were not colonies; the Phoenicians did not try
to dominate local populations. Invece, locals and other set-
tlers were invited to pursue their own enterprise and con-
tribute to the trading network. The core of the network con-
sisted of the traditional seafaring regions, the Aegean and the
Tyrrhenian. The expanding trade network of the early first
millennium BC did not start from scratch but encompassed
various regional populations. Tyrrhenian metal workers and
Sardinian sailors had opened up connections with Iberia at
the close of the second millennium. But the newly expand-
ing network not only stitched these routes together; it also
created its own long-haul routes.

These new routes began to take Phoenician and other
sailors over long stretches of open sea. While this had long
been conjectured by earlier writers like Braudel (2001, writ-
ing in the late 1960s) and Sherratt and Sherrat (1993), contem-
porary scholars are more confident. Cunliffe (2008) writes
about the course of a Phoenician sailor: “Beyond Cyprus, for
a ship’s master to make rapid headway west there was much
to be said for open-sea sailing. From . . . the western end of
Cyprus he could have sailed along the latitude to the south
coast of Crete . . . where excavation has exposed a shrine built
in Phoenician fashion. Traveling the same distance again . . . ,
once more following the latitude, would have brought him
to Malta” (pag. 275–276), a route that became known as the
Route of the Isles. Abulafia (2011) describes their seafar-
ing similarly: “The best way to trace the trading empire of
the early Phoenicians is to take a tour of the Mediterranean
sometime around 800 BC. . . . Their jump across the Ionian
Sea took them out of the sight of land, as did their trajectory
from Sardinia to the Balearics; the Mycenaeans had tended
to crawl round the edges of the Ionian Sea past Ithaka to the
heel of Italy, leaving pottery behind as clues, but the lack of
Levantine pottery in southern Italy provides silent evidence
of the confidence of Phoenician navigators” (P. 71).

These new routes involved crossing 300 A 700 km of open
sea. One piece of evidence for sailing away from the coast are
two deep-sea wrecks found 65 km off the coast of Ashkelon
(Ballard et al., 2002). Of Phoenician origin and dating from

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656

THE REVIEW OF ECONOMICS AND STATISTICS

Di 750 BC, the ships were 14 m long, and each carried
Di 400 amphorae filled with fine wine. These amphorae
were highly standardized in size and shape. This highlights
the change in the scale and organization of trade compared
to the Uluburun wreck with its diverse cargo. It also suggests
an early form of industrial production supporting this trade.
An unlikely traveler offers a unique lens on the scale of the
expansion of seafaring and the density of connections forged
during this period. The house mouse populated a small area
in the Levant until the Neolithic revolution. By 6000 BC, Esso
had spread into southern Anatolia before populating parts of
northeastern Africa and the Aegean in the ensuing millennia
(there were some travelers on the Uluburun ship). There were
no house mice west of Greece by 1000 BC. But within a few
centuries, the little creature turned up on islands and on the
mainland throughout the central and western Mediterranean
(Cucchi, Vigne, & Auffray, 2005).

The Phoenicians might have been at the forefront of spread-
ing mice, ideas, technology, and goods all over the Mediter-
ranean, but others were part of these activities. On the eve of
classical antiquity, the Mediterranean was constantly criss-
crossed by Greek, Etruscan, and Phoenician vessels, anche
as smaller ethnic groups. Our question here is whether this
massive expansion in scale led to locational advantages
for certain points along the coast compared to others, E
whether these advantages translated into the human activity
preserved in the archaeological record.

III. Data and Key Variables

For our Mediterranean data set we compute a regular grid
Di 10 × 10 km that spans the area of the Mediterranean and
the Black Sea based on a coastline map of the earth from
Bjorn Sandvik’s public domain map on world borders.2 We
use a Lambert Azimuthal Equal Area projection, with the
coordinates 39N, 18.5E as a reference point, which is close
to the center of the area we study. No projection avoids dis-
tortions completely, but this one works well for the study of
a limited geographical area. The distances of the edges of
our 10 × 10 km grid are close to the true distances. Even at
points farthest from the reference points, such as Gibraltar in
the west and Sinai in the east, the measurement error of both
vertical and horizontal lines remains within less than 2% Di
true distances.

We define a grid cell as coastal if its centroid is within 5 km
of a coastline. Grid cells whose centroid is more than 5 km
away from a landmass are classified as sea; the remaining
cells are classified as land. Our estimation data set consists
of all coast cells and all land cells within 50 km of a coast
cell. Each cell is an observation. There are 12,013 cells in
this data set, 3,352 of them coastal.

2We use version 3, available from http://thematicmapping.org/downloads

/world_borders.php.

We compute the distance between coastal point i and
coastal point j moving only over water di j.3 Our key vari-
able in this study, cdi, measures the number of other coastal
cells that can be reached within shipping distance d from
cell i. Destinations may include islands but we exclude is-
lands that are smaller than 20 km2. We also create separate
measures, one capturing only connectedness to islands and a
second measuring connectedness to other points on the main-
land coast. While we use straight lines or shortest distances,
we realize that these would have rarely corresponded to actual
shipping routes. Sailors exploited wind patterns and currents
and often used circular routes on their travels (Arnaud, 2007).
Our measure is not supposed to mimic sailing routes directly
but simply capture opportunities.4

Figura 1 displays the measure c500 for a distance of 500 km;
darker points indicate better-connected locations. Measures
for other distances are strongly positively correlated, E
maps look roughly similar. The highest connectedness ap-
pears around Greece and Turkey partly due to the islands, Ma
also western Sicily and the area around Tunis. The figure also
highlights substantial variation of the connectedness measure
within countries. The grid of our analysis allows for spatial
variation at a fine scale.

We interpret the measure cd as capturing connectivity. Of
course, coastal shape could proxy for other amenities. For
esempio, a convex coastal shape forms a bay, which may
serve as a natural harbor. Notice that our 10 × 10 km grid
is coarse enough to smooth out many local geographic de-
tails. We will capture bays 50 km across but not those 5 km
across. It is these more local features that are likely more rel-
evant for locational advantages like natural harbors. Our grid
size also smooths out other local geographic features, like
changes in the coastline that have taken place over the past
millennia, due, Per esempio, to sedimentation. The broader
coastal shapes we capture have been roughly constant for the
period since 3000 BC, which we study (Agouridis, 1997).

Another issue with our measure of connectivity is whether
it only captures better potential for trade or also more ex-
posure to external threats like military raids. Overall, it was
probably easier to defend against coastal attacks than land-
based ones (Cunliffe, 2008) so this may not be a huge concern.

3For this computation, we use the cost distance command in ArcGIS. Questo
tool calculates least-cost paths between points based on a cost raster that
assigns travel costs to the area in between the points. In our case, the cost
raster consists of a regular grid of 10 × 10 km of cells that are either over
water or coastal. We assign the same cost value to every grid cell, so that the
cost-distance calculation boils down to finding the shortest distance between
points via the cost raster cells (cioè., water or coast). We then treat one coastal
cell as origin and calculate the minimum distance from this origin cell to all
other coastal cells. We repeat this exercise using each of our coastal cells
as an origin cell to obtain the full matrix of pairwise distances.

4We do not attempt to use wind patterns to calculate sailing times. Lei-
dwanger (2013), combining modern data on wind speeds and prevailing
directions with the sailing logs from sea trials with the replica of a third-
century BC wreck on a Piraeus-to-Cyprus route, is an attempt to do this for
a small area a few hundred kilometers across off the Turkish coast. He dis-
cusses shortcomings and problems with this approach. His work illustrates
how far we still are from being able to extend an exercise like this to an area
like the entire Mediterranean.

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OF MICE AND MERCHANTS

657

FIGURE 1.—CONNECTEDNESS FOR 500 KM DISTANCE AND PLEIADES NARROW SITES, 750 BC

The different shades of gray indicate deciles of the connectedness distribution. Darker points show better-connected areas. The circles display archaeological sites for 750 BC from the Pleiades narrow definition.

FIGURE 2.—CONNECTEDNESS IN THE WORLD FOR A 500 KM DISTANCE MEASURE

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The different shades of gray indicate deciles of the connectedness distribution. Darker points show better-connected areas.

But at some level, it is obvious that openness involves oppor-
tunities as well as risks. In this respect, we measure the net
effect of better connectivity.

We also compute a global data set based on a global grid,
using a cylindrical equal area projection. We increase the cell
size to 50 × 50 km. This is for computational convenience,
but also our outcome variable at the global level varies only
at the country level and thus spatial precision is less rele-
vant than in the Mediterranean data set. While we define
our global connectedness measure for the whole world, our
analysis focuses on the part of the world between -60 Di-
grees and 60 degrees latitude, as units outside that range are
unlikely candidates for early urbanization for climatic rea-
sons. In the Southern Hemisphere, there is no landmass apart
from the Antarctic below 60 degrees, while in the Northern
Hemisphere, 60 degrees is close to Helsinki, Aberdeen, E
Anchorage, well north of climatic conditions particularly fa-
vorable to early settlement. We again compute the distance
from each coastal grid point to each other coastal grid point
by moving only over water. Figura 2 shows the global con-

nectedness measure c500. The most connected coastal points
are located again near Greece, but also in Southeast Asia,
Chile, Britain, and northern Canada, while western Africa
and eastern South America have few well connected coastal
points.

We measure economic development by counting archae-
ological sites of settlements. Historians and archaeologists
have long debated to what extent the material evidence that
has been discovered is representative of actual historical con-
ditions. On one end of the spectrum are warnings like that of
Equipaggio (2018, P. 64) that “archaeological evidence, espe-
cially for settlement history, is extremely uneven for the first
millennium BCE.” The idea of a “positivist fallacy” of “mak-
ing archaeological prominence and historical importance into
almost interchangeable terms: in equating what is observ-
able with what is significant” goes back to at least Snodgrass
(1987, P. 38). At the other end are optimists such as Brood-
bank (2013), who concludes that “only a single imbalance is
so devastating that it threatens to undermine the integrity of
the overall study of the Mediterranean. This is the dearth of

658

THE REVIEW OF ECONOMICS AND STATISTICS

information on the early societies of the Mediterranean North
Africa” (P. 37). We deal with the North African exception-
alism by showing results excluding the North African coast.
But Broodbank concludes that “the low archaeological pro-
file of much of Mediterranean North Africa may not entirely
be due to a lack of prospection. . . . In the coming chapters
we shall encounter several indications that this was indeed
the case” (P. 39).

Whether the archaeological record is representative of his-
tory is one issue, another is to obtain a quantitatively useful
snapshot of the archaeological record. Our data on settle-
ments for our period of investigation come from the Pleiades
Project, an electronic database (Bagnall et al. 2014) at the
Università della Carolina del Nord, the Stoa Consortium, and the
Institute for the Study of the Ancient World at New York
University maintained jointly by the Ancient World Map-
ping Center.5 The Pleiades data set is a gazetteer for ancient
history. It draws on multiple sources to provide a comprehen-
sive summary of the current knowledge on geography in the
ancient world. The starting point for the database is the Bar-
rington Atlas of the Greek and Roman World (Talbert, 2000),
but it is an open source project, and material from multiple
other scholarly sources has been added.6

The Pleiades data consist of three databases; we use the
pleiades-places data set. It offers a categorization as well as
an estimate of the start and end date for each place. We keep
only units that have a defined start and end date and limit the
data set to units that have a start date before 500 AD. We use
two versions of these data, one more restricted (which we
refer to as “narrow”) and the other more inclusive (“wide”).
In the narrow one, we keep only units that contain the word
urban or settlement in the categorization. These words can ap-
pear alongside other categorizations of minor constructions,
such as bridge, cemetery, lighthouse, temple, and villa. Nel
wide measure, we include any man-made structure, exclud-
ing only natural landmarks (per esempio., rivers) and administrative
units.7 Figure 1 displays the sites that appear in the narrow
data set in 750 BC as circles. The figure gives a first glimpse as
to the relationship between connectedness and the presence
of sites.

Some of the entries in the Pleiades data set are located more
precisely than others. The data set classifies the confidence
into the location as precise, rough, and unlocated. We only
keep units with a precisely measured location.8 For both data
sets, as we merge the Pleiades data onto our grid, we round
locations to the nearest 10 × 10 km and are thus robust to
some minor noise.

5Available at pleiades.stoa.org. We use a version of the data set down-

loaded in September 2017.

6Various historians have assured us that the Barrington Atlas is probably

the most representative source for the period we are studying.

7The raw Pleiades data set contains some sites that are duplicates or have
been moved to the errata section of Pleiades. We drop those sites from our
analysis.

8An exception to this are roads and canals, which typically cannot be
interpreted as a single point and where we therefore also include rough
locations.

Since the Pleiades data are originally based on the Bar-
rington Atlas, they cover sites from the classical Greek and
Roman period as well, and adequate coverage seems to extend
back to about 750 BC. Coverage of older sites seems much
more limited as the number of sites with earlier start dates
drops precipitously. Per esempio, our wide data set has 1,565
sites in 750 BC and 5,707 In 1 AD but only 142 In 1500 BC.
While economic activity and populations were surely lower
in the Bronze Age, there are likely many earlier sites missing
in the data. As a consequence, our estimation results with the
Pleiades data for earlier periods may be less reliable.9

Our measure of urbanization for a given cell is the number
of sites that exist at time t and fall into that cell. We prefer a
count of sites over an indicator given that it is scale invariant
with respect to the grid size. The maximum number of sites
in a cell for the narrow Pleiades measure is 5, but for 98.5%
of the cells, the value is either 0 O 1.

For our global results, we have only a single early out-
come measure: population in 1 AD from McEvedy and Jones
(1978). These are the same data that Ashraf and Galor (2011B)
used for a similar purpose. Population density is measured at
the level of modern countries, and our sample includes 122
countries.

IV.

Specification and Results

We run regressions of the following type:

uit = βdt cdi + Xiγt + eit ,

(1)

where uit is the urbanization measure for grid point i, cdi is
the log of the connectivity measure for distance d, and Xi is a
vector of grid point control variables. For coastal cells, con-
nectivity is simply the connectivity of the respective coastal
cells. For inland cells, we assign the connectivity level of the
closest coastal cell. We only measure connectivity of a lo-
catione, not actual trade. Hence, when we refer to trade, Questo
may refer to the exchange of goods but could also encompass
migration and the spread of ideas. uit measures the number
of archaeological sites in each cell and year, which we view
as a proxy for the GDP of an area. Growth manifests itself in
terms of both larger populations as well as richer elites in a
Malthusian world. We would expect that the archaeological
record captures exactly these two dimensions.

We start by using only linear variables for latitude and lon-
gitude as control variables. Latitude captures climatic varia-
tion due to the north-south gradient of the region. Climatic
conditions also vary in the east-west orientation since prox-
imity to the Atlantic moderates weather variability (Equipaggio,
2018), and the longitude variable controls for this. Since some
of our cells are up to 50 km inland, we also consider dis-
tance to the coast as an additional control variable, as well as

9In the online appendix we present some alternative estimates based on the
much earlier Archaeological Atlas of the World (Whitehouse & Whitehouse,
1975), which is more focused on the preclassical era but has problems of
its own.

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Dependent Variable

Agricultural productivity

(following Galor & Özak, 2016)

Ruggedness

(following Nunn & Puga, 2012)

River proximity

Mines proximity

Wind

Land connectedness

Observations
Controls

Longitude and latitude
Distance to coast and Fertile Crescent

Dropping Aegean
Dropping North Africa

OF MICE AND MERCHANTS

659

TABLE 1.—BALANCING CHECKS

(1)

0.46
(0.08)
0.19
(0.14)
−3.00
(1.72)
−0.37
(0.37)
0.31
(0.16)
−0.43
(0.22)
12,013

X

(2)

0.00
(0.10)
0.14
(0.19)
−2.90
(2.14)
0.10
(0.74)
1.05
(0.23)
−0.30
(0.24)
12,013

X
X

(3)

0.54
(0.14)
0.06
(0.29)
−4.32
(2.96)
−0.14
(1.22)
−0.53
(0.30)
−0.30
(0.15)
10,064

X

X

(4)

0.07
(0.16)
−0.05
(0.28)
−3.82
(3.33)
0.40
(1.48)
0.24
(0.34)
−0.04
(0.16)
10,064

X
X
X

(5)

0.16
(0.11)
−0.29
(0.16)
−2.45
(2.09)
−1.96
(0.74)
0.67
(0.17)
−0.24
(0.24)
9,464

X

X

(6)
−0.17
(0.09)
−0.13
(0.16)
−2.99
(2.19)
−0.04
(0.67)
1.20
(0.22)
−0.24
(0.24)
9,464

X
X

X

Each coefficient in this table is from a different regression. For each row, a different dependent variable is regressed on the log connectedness measure for 500 km, with controls as indicated at the bottom of each

column. Standard errors are clustered at the level of 200 × 200 km cells, in parentheses.

distance to the Fertile Crescent. The latter may be important
because agriculture spread from the Fertile Crescent through-
out the Mediterranean basin, and various authors have linked
the timing of the Neolithic Revolution to later development
(Diamond, 1997; Hibbs & Olsson, 2004; Comin, Easterly,
& Gong, 2010). We explore dropping the Aegean to address
concerns that our results may be driven exclusively by devel-
opments around the Greek islands, by far the best-connected
area in the Mediterranean. We also show results dropping
North Africa to address concerns that there may be fewer
archaeological sites in North Africa due to a relative lack of
exploration. This may spuriously correlate with the fact that
the coast is comparatively straight. We cluster standard er-
rors at the level of a grid of 200 × 200 km following Bester,
Conley, and Hansen (2011). Using a 400 × 400 km grid as
cluster variable results in very similar standard errors. Kelly
(2019) warns against spurious inference resulting from spa-
tially correlated but coincident variation on the left and right
of spatial regressions, but clusters of that size should largely
guard against this.

Our measure of connectedness depends only on coastal
and maritime geography and therefore is plausibly exoge-
nous. Tuttavia, it might be correlated with other factors that
affect early growth, such as agricultural productivity, topo-
graphic conditions, or rivers, which provide inland connec-
zioni. Those factors are hard to measure precisely. Hence,
instead of including them on the right-hand side of our re-
gression equation as control variables, we follow the sug-
gestion of Pei, Pischke, and Schwandt (2019) and show that
they are not systematically related to our measure of coastal
connectivity.

The results of these balancing regressions are shown in
table 1. In the first row, we relate connectedness to agri-
cultural productivity, which we construct using data from
the FAO-GAEZ database (FAQ/IIASA, 2010), following the
methodology of Galor and Özak (2016). We convert agrocli-

matic yields of 48 crops in 5(cid:3) × 5(cid:3) cells under rain-fed irri-
gation and low levels of input into caloric yields and assign
the maximal caloric yield of the closest 5(cid:3) × 5(cid:3) to our grid
cells. In the second row, we use Nunn and Puga’s (2012) mea-
sure of ruggedness, averaged over our 10 × 10 km cells. Both
ruggedness and agroclimatic conditions are standardized to
have mean 0 and standard deviation 1. The third row looks
at distance to the nearest river. For this, we used Wikipedia
to create a list of all rivers longer than 200 km and geocoded
their paths from FAO Aquamaps, dropping tributaries. Noi
then calculate the distance from each cell to the nearest river,
capping it at 50 km. To make the interpretation easier, we
take the negative of this measure, so that a positive coefficient
on connectedness would mean that well-connected cells are
closer to rivers. We use distance to the nearest mine with data
from the OXREP Mines Database (2017), coding distance
in the same way as for rivers. For wind, we use the AMI
Wind on ERS-1 Level 4 Monthly Gridded Mean Wind Fields
provided by the Centre de Recherche et d’Exploitation Satel-
litaire (CERSAT) at IFREMER, Plouzané (France). This data
set contains monthly average wind speeds over oceans on a
1 × 1 degree grid. We average wind speed over the sailing
period from March to October, using data for 1993. Each
coast cell is then assigned the value of the closest wind grid
cell. For the sixth row, we constructed a measure of land con-
nectedness that mimics our sea connectedness. Specifically,
for all the cells in our sample, we calculated how many other
cells in our data set on the same landmass can be reached
within 500 km, going only over the land or coast cells in our
sample.

Column 1 in table 1 starts by showing the results of bal-
ancing regressions just controlling for latitude and longitude.
Column 2 also adds controls for distance to the Fertile Cres-
cent and distance to the coast. Neither agricultural produc-
attività, ruggedness, nor distance to rivers or mines, nor land
connectedness has a large association with our measure of

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660

THE REVIEW OF ECONOMICS AND STATISTICS

TABLE 2.—MAIN RESULTS

Dependent
Variable
Mean

(1)

(2)

(3)

Dependent Variable

UN. Basic results

Pleiades wide 750 BC

0.130

Pleiades narrow 750 BC

0.103

Observations

12,013

0.208
(0.056)
0.156
(0.048)
12,013

0.103
(0.044)
0.075
(0.035)
10,064

B. Results excluding coastal cells from outcome definition

Pleiades wide 750 BC

0.100

Pleiades narrow 750 BC

0.081

Observations

C. Results excluding short connections
0.130

Pleiades wide 750 BC

Pleiades narrow 750 BC

0.103

Observations

Controls
Dropping Aegean
Dropping North Africa

0.176
(0.063)
0.131
(0.053)
8,661

0.201
(0.052)
0.152
(0.045)
12,013
X

0.095
(0.048)
0.073
(0.041)
7,567

0.103
(0.042)
0.076
(0.034)
10,064
X
X

0.204
(0.056)
0.156
(0.048)
9,464

0.184
(0.062)
0.140
(0.053)
6,647

0.197
(0.053)
0.152
(0.045)
9,464
X

X

Coefficients from regressions of the number of sites on 500 km log connectedness and controls. Controls
include longitude, latitude, distance to the coast, and distance to the Fertile Crescent. The dependent variable
counts the number of sites in a cell based on either the wide or the narrow Pleiades measure. Panel B excludes
coastal cells from the sample, and panel C uses log connectedness from 100 A 500 km as main regressor.
Standard errors are clustered at the level of 200 × 200 km cells, in parentheses.

connectedness once we control for the distance to the coast
and the Fertile Crescent. The exception is wind speed, Quale
correlates positively with connectedness.

Columns 3 E 4 show that dropping the Aegean from the
sample sometimes leads to bigger associations but also im-
pairs precision. When we control for distance to the coast
and Fertile Crescent in the sample without the Aegean, asso-
ciations between the balancing variables and connectedness
tend to be small and insignificant, including for wind speed.
The only exception is distance to rivers, but this relationship
is very imprecise. Outside of North Africa, a slight negative
association between connectedness and agricultural produc-
tivity arises with controls. We are comforted by the fact that
our measure of connectedness does not appear to be related
to the six variables examined in the table in a systematic way
across subsamples. This is especially true once we control
for distance to the coast and the Fertile Crescent. Di conseguenza,
we use all of latitude, longitude, and distance to the coast and
Fertile Crescent as controls in the analyses that follow.10

UN. Basic Results

In panel A of table 2, we start by showing results for con-
nections within 500 km and the settlement counts in 750 BC.
At this time, we expect sailors to make extensive use of direct
sea connections, and hence the coefficients βdt from equation

10In table A.1 in the online appendix, we add the variables used as out-
comes in table 1 as control variables to the main specification and find that
the coefficient does not change significantly with this inclusion.

TABLE 3.—2SLS REGRESSIONS FOR MARKET ACCESS INSTRUMENTING
WITH CONNECTEDNESS

Dependent Variable

Pleiades wide 750 BC

First-stage F -statistic
Pleiades narrow 750 BC

First-stage F -statistic
Observations
Controls
Dropping Aegean
Dropping North Africa

(1)

0.225
(0.056)
32
0.178
(0.050)
30
12,013
X

(2)

0.100
(0.038)
17
0.074
(0.031)
16
10,064
X
X

(3)

0.251
(0.064)
37
0.214
(0.060)
32
9,464
X

X

Coefficients from a 2SLS regression of the number of sites in a cell, computed for either the wide
or narrow Pleiades measure as indicated, on log market access computed for the 500 km connectedness
measure. Controls include longitude, latitude, distance to the coast, and distance to the Fertile Crescent. In
the first stage, market access is instrumented using 500 km log connectedness. Standard errors clustered
at the level of 200 × 200 km cells in parentheses.

(1) should be positive. This is indeed the case for all speci-
fications. We find stronger results in the wide Pleiades data,
and the association is highly significant. The magnitude of
these estimates is large. Increasing the connectedness of a
cell by 10% increases the number of archaeological sites by
around 0.02. The coefficients are larger than the means of the
dependent variables, also reported in the table, suggesting an
elasticity above 1. The coefficient is slightly lower for the
narrow site definition, but so is the mean of the site count.
Coefficients decrease in magnitude when we drop the Aegean
in column 2, but they remain positive and substantial, indi-
cating that the Aegean alone was not driving the results in
column 1. Dropping North Africa in column 3 makes little
difference compared to the original results.11

A potential concern with our results might be that we are
not capturing growth and urbanization, but simply the loca-
tion of harbors. To address this, panel B of table 2 repeats
the analysis of panel A but drops coastal cells themselves
from the sample. Here we are investigating whether a better-
connected coast gives rise to more settlements farther inland.
The results are similar to those from the previous panel, In-
dicating that the effects we observe are not driven by coastal
locations but also manifest themselves in the immediate hin-
terland of the coast. This bolsters the case that we are seeing
real growth effects of better connections. The same is true
when we exclude short connections within 100 km from the
connectedness variable in panel C of table 2. This is important
as we are primarily interested in the longer-range connections
that opened up with open sea crossing.12

Coastal points are only a proxy for market access. A more
direct measure would be to measure how many settlements a
ship can reach rather than how many coastal points. In table 3

11We find very similar results using a measure of eigenvector centrality
instead of our connectedness variable, which adds weighting to connecting
cells, but it is highly correlated to the original connections measure.

12The results in panel C of table 2 are unchanged if we separately control
for short connections up to 100 km, as we show in table A.1 in section 1.3
of the online appendix. This further strengthens the case that we are not
simply picking up some other effects of coastal shape.

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we use such a more direct measure of market access by
counting the number of sites within distance d. To account
for the endogenous location of settlements, we instrument
this market access measure with the connectedness variable,
both in logs. The first-stage F -tests we report show that con-
nectedness is strongly correlated with market access. IL
magnitude of the 2SLS effect is similar for all of these spec-
ifications to the one seen in the connectedness estimation.13
This effect is large compared with existing estimates of the
impact of market access. Per esempio, it is about twice as
large as the estimate for the land value elasticity in Donald-
son and Hornbeck (2016). This may reflect the relatively long
timescale over which these effects would have materialized.
It may also reflect the greater importance of connections in
the Iron Age Mediterranean, where there were few other pre-
existing long-range trading possibilities other than sea routes,
in contrast to a United States without railroads. It may also
show that in a less technologically advanced economy, mar-
ket access mattered more relative to other fundamentals.14

Our regressions relate the location of sites to geographic
connectedness or market access. We do not directly ob-
serve the channels through which connections might lead to
growth, like trade, migration, and the spread of ideas. Why
should we be confident that urbanization arose because of
these channels rather than something else, or that the asso-
ciation is merely spurious? While connectedness is clearly
related to other geographic features of an area, we find no
systematic lack of balance with respect to other measurable
factors. We find that it is longer-range connections that seem
to matter most, which is exactly the type of connections that
became important for sea travel during the Iron Age. The his-
torical literature on the period is adamant that longer-range
trade expanded massively during this period, as suggested by
the analysis of the origins of particular archaeological arti-
facts (Abulafia, 2011; Braudel, 2001; Broodbank, 2013; Sher-
ratt & Sherratt, 1993). In section 1.7 in the online appendix,
we also provide suggestive evidence that connectedness is re-
lated to a proxy for the interaction between locations. In par-
ticular, we show that better connections are associated with
lower genetic distance around 1500 AD (as measured by Spo-
laore & Wacziarg, 2018) at the world level. This holds on the
bilateral level, connected locations are less genetically dis-
tant, and on average, better-connected locations have lower
genetic distance to the rest of the world. While each of these
pieces of evidence might be merely suggestive, they point in
a consistent direction.

A somewhat different concern might be that connections
are indeed associated with the presence of sites, maybe be-
cause of trade, but the locations of the sites come about be-
cause individuals settled at favored localities. In the absence

13Table A.3 in the online appendix contrasts these estimates with an OLS
estimator. Magnitudes are similar when we exclude the Aegean. Otherwise
the 2SLS estimates are larger.

14Our results so far pertain to connections within a 500-km radius. Nel
online appendix, we also show results for other distances, which tend to
look fairly similar.

of good connections, sites might have simply arisen in some
other place. Our cross-sectional data cannot rule out this al-
ternative explanation. Tuttavia, this was a period of relatively
rapid growth in population (Scheidel, 2007) and social devel-
opment (Morris, 2010, figure 3.8). Maybe this too was just
coincidental. But this seems like a convoluted story: people
moved toward locations conducive to trade but not because
trade was productive. Allo stesso tempo, some other factor
did cause growth, which we observe manifesting itself in the
trading locations. Occam’s razor seems to favor an explana-
tion where connections and trade are the driving force behind
development and settlement patterns.

B. Timing and Persistence

So far we have shown that connectedness is related to the
presence of archaeological sites in 750 BC. This relationship
should have first emerged around this time but should be ab-
sent in 1000 BC or earlier. Inoltre, the period from the
Iron Age until the decline of the Roman Empire was one of
relative continuity; centers of gravity in the Mediterranean
might have shifted, but there were no further disruptions like
the Bronze Age collapse. Trade and seafaring kept expand-
ing during this period until the height of the Roman Empire.
Di conseguenza, we expect that the relationship between connec-
tivity and settlement density remained intact or grew even
stronger. Settlements existing in 750 BC should have contin-
ued (and possibly grown), while more intense trade should
have spurred the establishment of additional sites at similar
locations in the coming centuries.

In order to investigate these ideas, figure 3 shows results
from the narrow data set over time using the 500 km connect-
edness measure. The total number of sites differs by year. A
enable comparison over time, we divide the left-hand side by
the average number of sites per cell in each year, turning the
estimates effectively into elasticities. The figure has various
caratteristiche. Elasticities are positive and sizable but insignificant
during the second millennium BC. They increase in 750 BC,
consistent with the Iron Age expansion of open sea routes.
From 500 BC, the effects of connectivity decline continually
until no effect is left by the end of the Roman Empire. IL
results for the period before 750 BC are roughly in line with
our expectation, but the ones for the subsequent period are
non.

In figure 3, the relationship between settlements and con-
nections first emerges during the middle Bronze Age around
2000 BC. This may reflect the earlier trade networks that ex-
isted during this period, particularly in the eastern Mediter-
ranean. Tuttavia, estimates are noisy for this period. Questo
is a result of the fact that there are relatively few sites in
the Pleiades data set for the period before 750 BC. More-
Sopra, given the way the database was constructed, coverage
in the earlier periods is less complete, and the earlier sites are
largely ones that happened to have persisted into the classi-
cal period. This selection could mean that the results for the

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FIGURE 3.—SCALED COEFFICIENTS FOR DIFFERENT YEARS

This plot shows the main coefficient of our regression (see equation 1) for narrow Pleiades sites and 95% confidence intervals for different years. In these regressions, the left-hand-side variable is the number of sites
in each cell divided by the average number of sites in each period to allow for a comparison over time. The right-hand-side variable is log connectedness computed for a distance of 500 km. The regressions include
controls for latitude, longitude, distance to the coast, and distance to the Fertile Crescent. Standard errors are clustered at the level of 200 × 200 km cells.

earlier period are just an attenuated image of the later ones.
Therefore, we don’t want to overinterpret these findings.

The decline in the elasiticy between site density and con-
nectivity after 500 BC is not consistent with our expectations.
A large literature in urban economics and economic geogra-
phy has studied the dynamics of city locations and largely
found substantial persistence, sometimes across periods of
major historical disruption (Davis & Weinstein, 2002; Bleak-
ley & Lin, 2012; Bosker & Buringh, 2017, among others).
An exception is Britain after the fall of the Roman Empire,
which saw a substantial reorientation of the location of its
cities during the Middle Ages (Michaels & Rauch, 2018).
Tuttavia, the latter case is characterized by both a major
disruption of the entire urban network in Britain, as well as
a change in transportation modes. Given the relative con-
tinuity in the Mediterranean after 500 BC and the further
expansion of seafaring, we would not expect such a drastic
change in our setting. Invece, we would expect the locations
of new cities to be driven by the same forces as the previous
ones.

One possible explanation for the observed pattern could be
that the role of maritime connectivity declined—for example,
if sailors and ships got better and distance played less of a
role, or other modes of transport, such as on Roman roads,
became cheaper. But these were marginal changes, and the
cost advantage of water transport remained intact for the fol-
lowing millennia.

We suspect that the real explanation is a different one and
has to do with site density in our data growing too much, so

that our grid cells are becoming saturated with archaeological
find spots. In 750 BC, there are 1,565 sites in the wide data
set and this number increases to 5,707 In 1 AD at the height of
the Roman Empire.15 There are only 12,013 cells in our data
set. Di conseguenza, our grid quickly fills up with sites after the
start of the Iron Age. This eliminates a lot of useful variation
given our lack of an intensive margin measure. By the height
of the Roman Empire, many grid points will be the location
of archaeological sites.

A distinct and possibly complementary explanation is that
the first sites may be concentrated in the best-connected lo-
cations. New settlements after 750 BC, on the other hand,
might have arisen farther away from existing cities in unoc-
cupied locations, which are slightly less well connected. Questo
is consistent with the results of Bosker and Buringh (2017)
for a later period, who find that having a previously existing
city close by decreases a location’s chance of becoming a city
seed itself. In order to investigate this, we split the sites in
the Pleiades data into those that existed already in 750 BC
but remained in the data in subsequent periods and those that
first entered at some date after 750 BC. Figura 4 shows re-
sults for the period 500 BC to 500 AD. As in figure 3, we
show coefficients divided by the mean number of sites in the
period. The solid line shows the original elasticities for all
sites. The line with long dashes shows elasticities for sites
present in 750 BC that remained in the data, while the line

15See table A.5 in the online appendix for more details on the numbers of

sites in each data set and time period.

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FIGURE 4.—SCALED COEFFICIENTS FOR NARROW PLEIADES SITES: TOTAL, EXISTING, AND NEW ENTRY

This plot shows the main coefficient of our regression, equation (1), for narrow Pleiades sites divided by the average number of sites in a cell and 95% confidence intervals for different years. “Entry” refers to sites that
are present in a year but not in 750 BC. “Remain” refers to sites that were also present in 750 BC. “Total” refers to all sites. The regressions include controls for latitude, longitude, distance to the coast, and distance to
the Fertile Crescent.

with short dashes refers to sites that have newly entered since
750 BC. The elasticity for remaining sites is more stable (Esso
only falls because site density rises), while the relationship
between connectedness and the location of entering sites be-
comes weaker and even turns negative toward the end of the
period. Because the new entrants make up an increasing share
of the total over time, the total coefficients (solid line) are be-
ing dragged down by selective site entry toward the end of
the Roman era.

C. Results for a World Scale

Finalmente, we corroborate our findings for the Mediterranean
on a world scale. While Mediterranean trade is very well doc-
umented, there were several other prehistoric trade networks
around the world. Paine (2013) describes substantial trade
on the Arabian Sea, Persian Gulf, and the Red Sea and be-
tween the Mediterranean world, East Africa, and India. India
also traded with Southeast Asia, and evidence suggests long-
distance trade from Ecuador to Guatemala and Mexico, COME
well as trading relationships in the Caribbean and between
Southeastern Alaska and the Strait of Juan de Fuca.

For our global analysis, we use population in 1 AD from
McEvedy and Jones (1978) as the outcome variable. Popula-
tion density is measured at the level of modern countries, E
the sample includes 122 countries. Recall that we compute
connectivity for coastal cells based on a grid of 50 × 50 km
cells for this exercise.

We aggregate coastal connectivity to the level of countries,
which is the unit at which the dependent variable is measured
anyway.16 Figure 5 is a scatter plot of c500 against log popu-
lation density at the country level. The weights in this figure
correspond to the number of coastal grid points in each coun-
try. The line in the figure comes from a standard bivariate
regression and has a slope of 1.30 (1.02). This estimate is
very similar to the implied elasticity for the Mediterranean in
table 2, although the nature of the dependent variable is dif-
ferent. Note that many Mediterranean countries can be found
in the upper-right quadrant of this plot, highlighting how ex-
ceptional connectivity in the basin may have contributed to
the early development of this region.

Additionally, we regress log population density in 1 AD
on log 500 km connectedness, controlling for absolute values
of latitude and its square and again weighting by the num-
ber of coastal grid points in each country.17 This results in a
point estimate for connectivity of 2.35 with a standard error
Di 0.72.

16We drop Fiji from this analysis. Our projection does not allow travel
across the 180th meridian, and at a 500 km radius, Fiji is the one coun-
try that is affected by this, as its coast cells are on both sides of this
meridian.

17East-west orientation and distance from the Fertile Crescent are not par-
ticularly meaningful covariates for the world scale. Unlike for the Mediter-
ranean, there were various centers of early development around the world.
The squared term is introduced to capture potential nonlinearities of abso-
lute latitude.

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THE REVIEW OF ECONOMICS AND STATISTICS

FIGURE 5.—CONNECTEDNESS AND POPULATION DENSITY AROUND 1 AD AT THE WORLD SCALE

The graph shows a scatter of log population density in 1 AD against log connectedness at 500 km. The weights reflect the length of the coast of countries. The slope coefficient is estimated to be 1.30, with a standard
error of 1.02. The figure omits Bermuda, which is an outlier in terms of connectedness. The weighted slope (robust standard error) with Bermuda is 1.28 (1.00). When we include a control variable for the absolute
latitude and its square, the slope becomes 2.35 (0.72) with Bermuda and 2.40 (0.73) without it.

V. Conclusione

Connectedness matters for human development. Some ge-
ographic locations are advantaged because it is easier to reach
a larger number of neighbors. We exploit this idea to study
the relationship between connectedness and early develop-
ment around the Mediterranean. We argue that this associa-
tion should emerge most potently when sailors first started
crossing open seas systematically. This happened during the
time when Phoenician, Greek, and Etruscan sailors and set-
tlers expanded throughout the Mediterranean between 800
E 500 BC. Barry Cunliffe (2008) calls this period at the
eve of classical antiquity “The Three Hundred Years That
Changed the World” (P. 270).

This is not to say that sea trade and maritime networks
were unimportant earlier. We find clear evidence of a signifi-
cant association between connectedness and the presence of
archaeological sites for 750 BC. Our results are more difficult
to interpret as to whether this relationship began to emerge
at that period because the data for earlier periods are shakier.
Once locational advantages emerged, the favored locations
mostly retained their urban developments over the ensuing
centuries, in line with a large literature on urban persistence.

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