Trade and the End of Antiquity∗
Johannes Boehm†
Geneva Graduate InstituteThomas Chaney‡
USC

August 2024
Preliminary draft

Abstract
What caused the end of antiquity, the shift of economic activity away from the Mediterranean towards northern Europe? We assemble a large database of coin flows between the
4th and 10th century and use it to document the shifting patterns of exchange during thistime period. We build a dynamic model of trade and money where coins gradually diffuse
along trade routes. We estimate the parameters of this model and recover time-varying bilateral trade flows and real consumption from data on the spatial and temporal distributionof coins. Our estimates suggest that technical progress, increased minting, and to a lesser
degree the fall in trade flows over the newly formed border between Islam and Christianity
contributed to the relative growth of Muslim Spain and the Frankish lands of northern Europe and the decline of the Roman-Byzantine world. Our estimates are consistent with theincreased urbanization of western and northern Europe relative to the eastern Mediterranean
from the 8th to the 10th century.
JEL Classification: F1, O1, N73.
Keywords: Gravity Models, International Trade, Market Access, Diffusion.
∗

We thank Marek Jankowiak, Reka Juhasz, Ezra Oberfield, Kevin O’Rourke, and Pseudoerasmus for helpfulcomments. Malo Cadudal-Illy, Jinpei Tuchel, and in particular Ye Sun provided excellent research assistance.
Boehm gratefully acknowledges financial support from the Banque de France/Sciences Po partnership, and Chaneyfrom the European Research Council (ERC grant 884847). All remaining mistakes are our own.
†johannes.boehm@graduateinstitute.ch.
‡thomas.chaney@gmail.com.

Introduction
We quantify the contribution of disruptions in technology, coin production, and trade to the endof classical antiquity —the decline of Roman and Greek civilizations in the Mediterranean, andsubsequent shift of political and economic power to northwestern Europe and the Middle East.
What caused the end of antiquity has been a central question for centuries (see for instanceMontesquieu, 1734; Voltaire, 1756; Gibbon, 1789). Most contemporary historians believe that theconquest of Rome by Germanic invaders in the fifth century did not lead to an immediate endof Roman institutions and commerce, as local institutions remained largely in place (Pirenne,
1927, 1939; Findlay and O’Rourke, 2009; McCormick, 2001). The archaeological evidence points
to a shift in the economic activity to the north-west of Europe and away from the Mediterranean
between the fifth and the eighth century. The timing, extent, and reasons remains debated, butwhen Charlemagne was crowned as Emperor at the end of the eighth century, the political andeconomic power in Europe is no longer centered around the Mediterranean, but in the Frankishlands of northwestern Europe. Famously, historian Henri Pirenne proposes that the expansionof the Arab Caliphate along the southern Mediterranean coast and into the Iberian peninsula
disrupted commerce and political ties in the Mediterranean, and turned the emerging Carolingian
Empire into a northern European power (“without Mohammed, Charlemagne would have been
inconceivable,” Pirenne, 1939, p.234). The evidence for these disruptions brought forward by
Pirenne is mainly related to the disappearance of certain luxury goods north of the Mediterranean.1
In this paper, we study the changing economic geography during Late Antiquity using thetools of modern quantitative trade models and novel data on the circulation of ancient coins. Ourevidence suggests that Mediterranean trade was indeed disrupted by the emergence of the ArabCaliphate. This had a large negative impact on the heartlands of the Byzantine empire, initiallyvery open to trade. But this trade disruption played only a minor role in northwestern Europe,
which was not very open to trade. Instead, the growth of northwestern Europe is almost entirely
fueled by improved technology and an increase in seigniorage-financed consumption. The relative
decline of the Mediterranean world is therefore shaped by a combination of all three forces.
Our first contribution is to assemble a large database of coin finds from hoards that were deposited between ad 325 and ad 950, with observations from hundreds of thousands of coins foundin hoards across Europe, North Africa, and the Middle East. Coins offer rich quantitative information in a generally data-scarce setting,2 as numismatists and archaeologists have deciphered,
1

Pirenne’s argument is the near absence of mentions of silk and spices in historical texts written north of theMediterranean, and the disuse of gold for coinage and papyrus for writings. These fragments of evidence, along withnew archaeological findings, have been extensively studied and discussed by historians since Pirenne. See Lopez(1943), Ashtor (1970), Hodges and Whitehouse (1983), and, in particular, McCormick (2001)’s monumental workthat synthesizes the existing literary and archaeological evidence on changes around the Mediterranean, including
patterns of change in the flows of communications, objects, and travellers. The synthesis of Wickham (2006)
interprets the evidence through the lens of social structures.
2
Due to the fact that no comprehensive data on production, consumption, trade, or demographics exists for the1catalogued, and classified ancient coinage for over 200 years. We collect information about where
and when coins were minted and buried, and present three stylized facts: (i) bilateral coin flows
are disrupted by distance and political borders just like trade flows are, (ii) unlike traded goods,
coins are in use over many years and older coins tend to travel farther, and (iii) the geography ofcoin flows across the Mediterranean changes abruptly around the time of the Arab conquests.
Our second contribution is to build and estimate a dynamic model of trade where agents use
coins for transactions. Within each period, trade is governed by comparative advantages as in
Eaton and Kortum (2002), and coins flow in opposite direction to trade. After being minted, thesame coin can then be saved as a store of value and re-used for subsequent transactions. We firstshow that with saving, coin flows within a period inherit the same gravity structure as trade flows,
up to a single multiplicative constant. We then characterize the full dynamics of coin flows, ascoins are minted, saved, used for multiple transactions, and gradually percolate through the trade
network.3 Those results allow us the estimate the parameters governing coin creation (minting)and trade in goods (trade and production costs).4 Our estimates for minting output are in linewith known historical evidence, and our estimate for the travel time elasticity of trade is similar
to that for Roman trade in ceramics (Flückiger et al., 2022). Our estimates reveal a large costassociated with crossing the newly erected border between Islamic and non-Islamic regions.
Our third contribution is to reconstruct time series for real consumption per capita for every
20-year period from the 4th to the 10th century for every region, fully partitioned into three economically meaningful terms: trade openness (as in Eaton and Kortum, 2002; Arkolakis et al., 2012),
technology, and seigniorage-financed trade deficits (similar to Dekle et al., 2007). We are able torecover real consumption series using solely data on coins, because coin flows contain informationon (nominal) trade flows, which contain information on (relative) prices. Our estimates suggestthat real consumption in the heartlands of the Byzantine empire collapsed in part due to the fallin trade flows in and out of regions newly conquered by the Arabs. But outside of Byzantium,
fluctuations in trade openness contributed relatively little to changes in real consumption, simply
because ancient regions were not open enough for trade to play a large role.5 We attribute instead
most of the variations in real consumption to changes in technology and seigniorage-financed trade
deficits. For instance, western and northern Europe including Islamic Spain witness a spectacular
first millennium ad, historians of this period rely to a large extent on literary sources.
3This dynamic model allows us to identify trade flows even with sparse coin data. Our estimation leverages thefact that a coin used for multiple transactions contains information on trade flows over multiple periods.
4
As recognized by numismatists, whether a coin hoard is created (deliberately or accidentally buried), foundby archaeologists, and documented by numismatists, depends on a series of endogenous events. To purge ourestimation from those endogenous events we use only information on the shares of different coins within a hoard.
5
Our estimates show a surprisingly large degree of trade openness in the first millennium ad; on average, regions
import 20% of their consumption in ad 460-620, and 15% in ad 700-900. Yet unless the trade elasticity were verylow (we use θ = 4 from Simonovska and Waugh, 2014), a fall in import shares from 20 to 15% has only a small
impact on consumption. We also note that our evidence comes solely from coins, so corresponds solely to monetizedexchanges. Any non-monetized (barter) transaction is missing. To the extent that non-monetized exchanges are
more likely to be local, we possibly underestimate aggregate consumption, and over-estimate trade openness.

2

rise in real consumption fueled by technical progress and a commensurate increase in minting
output. Finally, in the absence of virtually any systematic evidence on ancient production, consumption, or trade, we show that our estimates on real consumption changes from pre- to post-ad
700 are remarkably consistent with measures of European urbanization post-ad 700.
While our paper is focused primarily on ancient trade, this does not necessarily mean that
trade was a primary driver of economic activity in Late Antiquity. We use (estimates of) trade asa tool to learn about the economic geography of the ancient world. Our results suggest that whileancient trade was substantial, it had a minor impact on real consumption outside of Byzantium.
Related literature. Our paper relates to the literature on the role of market access in shaping
economic outcomes across space, specifically in historical settings. Fogel (1964), Donaldson andHornbeck (2016) and Hornbeck and Rotemberg (forthcoming) evaluate the impact of the US
railroad on economic growth, Donaldson (2018) the impact of railroads in colonial India on relative
welfare, and Nagy (2023) the impact of the westward expansion of the US on growth; Pascali(2017) evaluates the impact of steamships on maritime trade and relative development; Redding
and Sturm (2008) study the impact of the iron curtain on comparative development in Germany,
and Ahlfeldt et al. (2015) the impact of the Berlin wall on the urban structure of Berlin; Juhász(2018) studies the impact of the trade disruption brought by the Napoleonic blocade on industrial
development; Flückiger et al. (2022) study the impact of the Roman transportation networkon trade in ceramics (terra sigillata) from the 1st century bc to the 3rd century ad, and thepersistent impact of the Roman network on European economic integration, while Michaels andRauch (2018) study changes in the transportation and urban networks after the fall of the Western
Roman Empire; Barjamovic et al. (2019) use shipment records from Assyrian merchant archives
in Bronze Age Anatolia to estimate the location of ancient lost cities and their size. Within
this literature, our paper is closest to several which use a structural approach (in particular
Donaldson and Hornbeck, 2016; Hornbeck and Rotemberg, forthcoming; Donaldson, 2018; Nagy,
2023; Redding and Sturm, 2008; Ahlfeldt, Redding, Sturm, and Wolf, 2015; Barjamovic, Chaney,Coşar, and Hortaçsu, 2019). In contrast to this literature, we do not observe prices, trade costs,
or even trade flows. Instead, we recover trade flows and relative factor prices from data on the
movement of coins over space and time, which we structurally interpret through the lens of a
dynamic model of trade and money. Liu and Tsyvinski (2024) feature a related mechanism where
the dynamic transmission of shocks in an input-output network is subject to adjustment costs.
Finally our paper speaks to a literature in economic history on the changes in Late Antiquity andearly medieval times. This literature frequently uses numismatic evidence, mostly in a descriptive
manner. A notable exception is Noonan (1980)’s study of Islamic coin finds of different vintages
and origins in Eastern Europe and Scandinavia, which is similar in spirit to our exercise, but stops
short of using a formal econometric model. Closest to our paper Persson and Sharp (2015) discuss

3

Pirenne’s thesis and economic integration in Europe through the lens of a gravity model.6
The remainder of the paper is structured as follows. Section 1 lays out the historical context,
describes our data, and presents three stylized facts on ancient coin flows. Section 2 presents andestimates our dynamic model of trade and money. Section 3 discusses our empirical results.

1

Historical context, data, and stylized facts

1.1

Historical context

We study the economic and political developments that occurred in the Mediterranean between
the 4th and the 1th century ad. At the start of this period, the fourth century, the Mediterranean
was still entirely under control of the Roman Empire, albeit at times with multiple emperors andconflict between them, and under mounting pressure from Germanic invasions. The death of theeastern emperor Theodosius I in 395 divided the Roman Empire into a western and an eastern
half. The fifth century saw increased Germanic incursions in the east and west, culminating with
the Ostrogothic king Odoacer deposing the last West Roman emperor Romulus Augustulus in
476 and ending the Western Roman Empire. Italy was ruled by the Ostrogoths until the 550s,
and later by the Lombards; Spain was taken over by the Visigoths, France by the Merovingian
dynasty of the Franks, and North Africa, Sicily, and Sardinia by the Vandals. The Eastern Roman(Byzantine) Empire at times reconquered parts of the former Western Roman territory, but inthe sixth century became increasingly under pressure by wars with the Sasanian Empire in theeast. The Byzantine-Sasanian wars of 602–628 depleted the resources of both empires, leaving avacuum that was filled by the emerging Arab caliphate.
Figure 1 shows the rapid Arab expansion, starting in 622. By 634 the Arabs controlled theentire Arabian Peninsula. The Levant followed in the late 630’s and Egypt in the 640’s. By theend of the Rashidun Caliphate in 661, the Arabs controlled a territory from Tripoli in the west to
Balkh in the east. The expansion continued under the Umayyad dynasty. In 698 the Arab army
razed Carthage and by 709 they had fully conquered the Maghreb. In 711 they crossed the Straitof Gibraltar and defeated the Visigoths at the Battle of Guadalete. In 732 they were stopped by
Charles Martel at Tours, and driven back across the Pyrenees. When the Abbasid family overthrew
the ruling Umayyads in 750, the Umayyads retained control of most of Iberia (al-Andalus). While
the Arab conquest ended Sasanian rule in the east, advances into Byzantine territory in Anatolia
did not lead to sustained shifts in the land border. Meanwhile the Arabs strengthened their naval
capabilities, ended the Byzantine naval control of the western Mediterranean and contested its
control of the east. Arab sea raids on Mediterranean cities became frequent.
Two other notable (and overlapping) events have been linked to economic and political changes:
the Plague of Justinian (541-549), which, according to contemporary literary sources led to large
6

See Shatzmiller (2018) for a qualitative application of the gravity model to medieval trade in the Middle East.

4

Byzantine Empire, ad 700

ad 634

ad 661

ad 661-750

ad 632, under Mohammed

ad 644

ad 661-700

ad 700-750

Figure 1: The Arab Conquests, ad 623-750
declines in population, and the temperature anomaly known as the “Late Antique Little Ice Age”
(536–560), likely due to volcanic eruptions, which caused temperatures in the northern hemisphereto drop by about one degree Celsius (Peregrine, 2020). The size and quantitative relevance of these
two events is heavily debated among historians. We will think of both these events as potentiallyaffecting population and productivity levels, which our estimation strategy accounts for.

1.2

Data

We construct a large dataset on the flows of coins around the Mediterranean between ad 325
and ad 950.7 For the period from ad 325 to ad 725 we mostly rely on data from the Framing
the Late Antique and Early Medieval Economy project (FLAME, 2023b),8 a large-scale effort
by historians and numismatists to record harmonized information on the location, dating, andcomposition of coin finds up to the year 725. FLAME covers hoards9 from the Mediterranean
and beyond, contributed by specialists working on the coinage of their geographical and temporalexpertise. We use the most recent release of FLAME (January 2023) which covers 9,831 coinhoards. We remove hoards that fall outside our area of interest, continental western Europe up
to the modern-day German-Polish border and including Bohemia, southern Europe up to the line
7

Appendix B contains extensive information on the assembly, harmonization, and cleaning of the coin flow data.
https://coinage.princeton.edu/
9
FLAME also includes finds from excavations and single finds. Unless explicitly mentioned, we will treat all
records in the same way and just use the word “hoard” to describe deposits of any size.
8

5

between Vienna and Odessa, and North Africa and the Middle East up to the maximum extent
and area of influence of the Umayyad Caliphate (stretching from the Maghreb in the West to theIndus in the east, and up to Bulgar in the north).10 We also remove all hoards that only consistof incompletely described coins (no mint location or mint date information).
We supplement FLAME’s data, in particular for the period after ad 725, with hand-coded
records of 106,373 coins from 824 finds, which we assemble using hoard catalogues from thenumismatic literature, similarly to the source documents that underlie FLAME. These additional
records include the time period of the Caliphate, so that we can assess the impacts of these changes
on the patterns of exchange in the Mediterranean. Together these data cover the vast majority of
published information on coin finds in our geographic and temporal scope.

Figure 2: Coin hoard data, an example from al ’Ush (1972)
Notes: The figure shows an excerpt of an original publication from which we assemble hoard data: al ’Ush (1972) gives the content of
the Damascus silver hoard in tabular form. From left to right, for the first row: the record number (51), the mint (al-Andalus), thedate (year 114 of the Hijri calendar), diameter (29mm), weight (2.93g), and the number of coins with these attributes (4). The issuingdynasty (Umayyad) is given in the table headings and the denomination and material (silver dirham) is stated in the text.

The structure of the coin hoard data is ideally suited for an analysis of dynamic bilateral spatial
flows. Each unit of observation —a coin— contains the following attributes: (i) the location where
the coin has been minted, “mint” (birth place), (ii) a year interval when the coin was minted,“mint date” (birth date), (iii) the identifier and the location of the hoard that the coin is part of(death place), (iv) a year interval when the hoard —and therefore the coin— was deposited (deathdate). These pieces of information are typically recorded by the author of the original numismaticor archaeological publication. Figure 2 shows an example. Mints are usually inferred from mint
marks on the coins.11 The mint date is often indicated on the coin. When this is not the case, it
can be approximated from the ruler (or dynasty or empire) under whose authority the coin was
issued and other information, like the mint mark. Finally, we follow the common approach of
historians to estimate the date of deposit of each hoard using the terminus post quem, or tpq for
short, the date of the youngest object in the hoard that can be dated. In our case that is typically10

See figure 6 below for a map of our area of study. The precise definition and construction is given in Appendix C.
We exclude the Viking lands, and therefore do not speak to the discussion on the potential role of the Vikings (andthe inflow of Islamic silver through trade via eastern and northern Europe) in the changing economic geographyduring Late Antiquity (Bolin, 1953). Recent archaeometric studies indicate that Carolingian silver is largely not of
Arab origin (Sarah, 2008; Sarah et al., 2008; Kershaw et al., 2024), suggesting a limited role for silver inflows via
the Viking route in affecting Carolingian mint output.
11
Mint marks have been in use since ancient Greek times to monitor the weight and metal content of coins.

6the most recent end year of the time intervals of the coins in the hoard.
In coding the mint location and date we typically follow the coding of the author of the original
publication which catalogues the hoard.12 In some cases this information is imprecise: the authorof the publication may not have been able to inspect the coin or inspected only a fragment. We
conduct robustness checks to investigate whether our findings are driven by endogenous selection.
We have data on 5,609 hoards and 494,311 coins that fall into our geographic boundary andhave tpq between 325 and 950. After removing from FLAME large hoards found in the 19th
century or earlier for which not much besides rough coin counts is known, 270,500, or 54.7% ofcoins are complete with a mint and minting year interval; on average 86% of coins in a hoard arecomplete. We define the age of coins at time of deposit as the difference between the midpoint of
the coin’s minting interval and the tpq of the hoard. Figure 3a shows the temporal distributionsof the number of hoard by tpq, and figure 3b shows the distribution of coin ages within a hoard.13

250
.02

200

DensityFrequency

.015
150

.01

100

.005

50

0

0
400

600

800

1000

0

(a) Distribution of hoards by tpq, ad 325-950

100

200

300+

(b) Coin age at time of deposit (tpq), in years

Figure 3: Number of hoards over time and ages of coins within hoards
Notes: Panel (a) shows the number of hoards per 20-year period. Panel (b) displays the (annual) density of coins of different ages.
Coin age is defined as the difference between the midpoint of the coin’s minting interval and the tpq of the hoard it is found in.

Coins are deposited on average 47 years after they are struck, with some coins that are hundredsof years old. On average, hoards contain fewer older coins than younger coins. Appendix tables
B.1 and B.2 contain additional summary statistics on coins and hoards.
Discussion. The interpretation of coin flows as relating to trade, despite having a long tradition
among numismatists and historians,14 deserves some discussion. The Roman and subsequently
Byzantine empire were generally fairly monetized economies, with coinage taking a pre-eminent
12

Sometimes these interpretations are critically evaluated and corrected by subsequent scholars. Appendix B
lists the extensive sources we use for each of the hand-coded hoards.
13
To further support the view that the hoards in our data reflect coin circulation during Late Antiquity, Appendix
figure A.1 compares the coin age distribution in our hoards with the one in twelve Byzantine hoards that Banaji(2016) labels as circulation hoards, i.e. that are known to have originated from coins that were in circulation.
14
See, in particular, the discussions by Grierson (1959) and, more recently, Naismith (2014).

7

role and credit being very limited (Morrisson, 2002).15 The situation was similar in the caliphate(Bessard, 2020) and in the Carolingian empire (Coupland, 2014). The fact that coins were light,
durable, and —because they were made from precious metal, which could be easily reminted—
accepted within and across borders made them particularly suitable for long-distance trade.16 Thisis particularly the case for gold coins, traded throughout the Mediterranean and valued for theirweight in gold (Banaji, 2016).17 Of course coins did not travel solely because of commerce; theft,
gift-exchange, tribute, dowry, and ransom are other explanations for coin flows. We subsume thosealternative motives for exchanges within a model of trade driven by comparative advantages.18
A potential source of bias comes from the fact that our data do not cover the universe ofcoin flows, but instead only hoards that have been created (i.e. the coins were either deliberately
or accidentally deposited, which may depend on warfare, natural disasters, and property rights
protection), found (which may depend on modern-day institutions, such as whether metal detecting
is allowed, and modern-day market prices for historical coins), and documented by experts (which
may depend on the local presence of experts, the ‘novelty’ of the hoards’ contents, and the demand
for research on these topics).19 Our model-based estimation in section 2 is designed to correct
those statistical biases, and differs from the more descriptive methods employed by historians.
Finally, an important characteristic of our data is that —in contrast to standard trade data—
we do not observe flows at each point in time, but only when and where a coin was minted,
and where and when a coin is deposited into a hoard. Our structural model in section 2 is
specifically designed to identify the parameters governing trade flows from data on coin stocks,
and to reconstruct the possibly numerous successive trips a coin took throughout its life.

1.3

Three stylized facts on ancient coins

We present reduced-form evidence on three stylized facts, which inform our model in section 2.
Distance and political borders disrupt trade. The bilateral structure of our dataset, witha mint-origin and hoard-destination for each coin, allows us to explore the geography of coin flows
in reduced form. We aggregate hoard (h) and mint (m) to 1◦ × 1◦ cells across all periods, and15

A possible exception was the eighth century, where Byzantine mint output collapsed.
Examples of the use of currency in foreign empires abound. Bates (1991) discusses how Byzantine coins kept
circulating (and even being minted) in Egypt following the Arab takeover in 641. Tribute and ransom payments
between the Arabs and Byzantines following periods of conflict often included domestic currency. See also Chapter
12 of McCormick (2001), who discusses the circulation of Arab and Byzantine coins in the west.
17
We conduct robustness checks of our main results that restricting the sample to gold coins.
18
Other data-driven approaches used by economic historians to measure economic activity include urbanization
rates, the flows of consumption goods, notably ceramics (Wickham, 2006, Flückiger et al., 2022), communication
flows and movements of people (McCormick, 2001), pollen grain measurements (Izdebski et al., 2016), and ice corereadings (McConnell et al., 2018, Loveluck et al., 2018). Coin flows bring several econometric advantages and areplausibly more directly related to comprehensive patterns of exchange than ceramics or communications flows. We
estimate similar distance elasticities from gravity regressions on coin and ceramic flows in appendix figure A.2.
19
FLAME (2023a) discusses potential sources of biases, which also apply to our combined data.
168

model coin flows between cells as a function of distance and a political border dummy,
countmhp = exp (amp + ah + b1 log distancemh + b2 PoliticalBorderhp + umhp ) .

(1)

We estimate this model by PPML using data on all triplets (m, h, p) for mint cell m in political
block b and hoard cell h. The political border dummy is one if the region where the center of thehoard cell h is located in has never and to no extent been under the political control of p.20
Table 1: Distance and Border Effects in Coin Flows
Dep. var.: # Coinsmdh
Log Distance

Dep. var.: Valuemdh

(1)

(2)

(3)

(4)

(5)

(6)

-1.138∗∗∗(0.12)-1.002∗∗∗(0.13)-1.139∗∗∗(0.10)-0.954∗∗∗(0.077)-1.146∗∗∗(0.076)-0.991∗∗∗(0.069)-1.945∗∗∗(0.62)

Political border-2.071∗∗∗(0.47)-1.516∗∗∗(0.27)

Hoard Cell FE
Mint × Empire Cell FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

SampleAll

All

Gold onlyGold onlyGold and Silver

Gold and Silver

Estimator

PPML

PPML

PPML

PPML

PPML

PPML

Pseudo-R2
Observations

0.767
2177480.778
217748

0.809
57457

0.824
57457

0.800
1467660.810
146766

Standard errors in parentheses, clustered at mint cell × empire and hoard cell level.
∗p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: This table presents various specifications of equation (1). The dependent variable is the number of coinsin a hoard cell h from a mint cell m issued by a political entity p in columns 1-4, and the value of coins in columns
5-6. Hoard and mint cells are 1◦ × 1◦ . Political entities here are categorized into fourteen divisions. Columns 1
and 2 use all coins. Columns 1-2 use data on all coins, columns 3-4 gold only, and columns 6-7 gold and silver.

Table 1 shows the results. Distance between mint and hoard is negatively correlated with coinflows, and crossing a political border is equivalent to a ten-fold increase in distance.21 Columns 3
and 4 show almost identical results when restricting the sample to gold coins, which were universally valued throughout the Mediterranean for their metal content and were therefore particularly
favored for long-distance trade. Despite accounting for only 7% of the coins in our sample, distance and border effects for gold coins are remarkably similar. Column 5 and 6 also show almost
identical results when using silver and gold coins and weighing them by their relative value.22
20

See appendix C for our division of the combined Arab and Mediterranean world into 13 regions.
The distance and border effects are robust to using only the intensive margin of coin flows see appendix table
A.1. An alternative explanation for the significance of the border effect would be that coins first get administratively
redistributed within a political entity before entering circulation. Appendix table A.2 shows gravity regressions
with hoard × empire fixed effects that suggest that this is unlikely to be the case at a large scale.
22
We calculate the equivalent gold weight of silver coins in two steps. First, we code the reference weights ofcoins of different denominations in our data, noting that coins are often clipped, broken, debased, or abraded.
Second, we convert this reference weight into a gold-equivalent weight assuming a constant conversion ratio of 12g
of silver for 1g of gold, which should be seen as a rough approximation given the fluctuations of the gold-silver
21

9While purely reduced form, those results suggest that coin flows contain information related
to trade costs (e.g. distance and border effects).23 The key contribution of our model in section 2
is to isolate features of the geography of coin flows that are driven by trade.
Older coins travel further. Coins are found, on average, in hoards 800 kilometers from theirmint. But within hoards, older coins are also coins that have on average travelled farther. Table 2
shows results from a regression of log distance between a coin’s mint and hoard place on the log age
between minting and the hoard’s tpq, with hoard fixed effects to isolate within hoard variations.
Table 2: Coin age and distance travelled
Dependent variable: Log Distance between Mint and Hoard(1)
Log Age of Coin

∗∗∗

0.146
(0.044)

(2)

(3)
∗∗∗

0.0831(0.026)(4)∗∗0.0750(0.030)SampleHoard FE
Mint × 50-year-interval FE
Mint × 25-year-interval FE
R2
Observations

Yes

Yes
Yes

Yes

∗∗∗

0.160
(0.043)0.0486∗∗(0.020)

No non-hoards

No non-hoards

Yes

Yes

Yes
0.762
2872570.863
2870400.869
286884

(5)

Yes
0.775
2501610.898
249835

Standard errors in parentheses, clustered at the hoard level.
∗p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: The dependent variable is the log distance between the mint location and the location of
the hoard. The independent variable is the log age of the coin at the date of the tpq of the hoard(where the age is defined as the difference between the midpoint of the minting interval and the
maximum of the endpoints of the minting intervals). In the rare cases where this age is zero (theyoungest coin in the hoard is dated to a precise year) we set the log age to zero. Mints are identified
as all Nomisma or FLAME-recorded entities that have been geocoded to the same 0.1◦ × 0.1◦ cell.
Columns (4) and (5) exclude FLAME finds that are tagged as not being hoards.

The coeﬀicient of coin age is positive and significant, even when including mint × mint time
fixed effects to control for the average distance travelled and age of coins of a particular mint
and issue. This suggests that older coins are used on average for more transactions, with eachtransaction taking them further away from their mint origin. Our structural model is designed todisentangle the many transactions of old coins from the few transactions of young coins.
ratio between 1:10 and 1:16 (Bolin, 1953). With this value metric, gold represents 80% of the resulting value inour data. Since the price of copper/bronze fluctuates heavily during Late Antiquity (see Banaji, 2016, Ch. 5) andcopper denominations frequently traded at values different from their intrinsic value based on their metal content,
we only use silver and gold coins. We also note that FLAME does not record weights of coins, resulting in only
approximate calculations.
23
To further investigate the informativeness of coins for trade flows, Appendix figure A.2 compares the distanceelasticity from coin flows with distance elasticities obtained from flows of Roman Terra Sigillata ceramics, the only
tradable good from Antiquity on which substantial amounts of data are available (Flückiger et al., 2022). We findsimilar but slightly higher distance elasticities for Terra Sigillata, possibly due to coins being more durable andhence being more frequently re-used in exchange (see Section 2.2).

10

The geography of coin flows changes sharply around the Arab conquests. We show
descriptives of the movements of coins during Late Antiquity. Figure 4 illustrates the changes in
the flow of coins across the Mediterranean before and after the Arab conquests.

(a) Before the Arab conquests: ad 450-630(b) After the Arab conquests: ad 713-900

Figure 4: Changes in coin flows in the ancient worldNotes: The figure shows coin flows, indicated by a straight line, between mints and find spots. The sample consists of all coin groups
where both the lower end of the mint interval and the tpq of the hoard lie between ad 450 and ad 630 (panel (a)) and ad 713 and ad
900 (panel (b)). Hoards from outside the shaded area are excluded.

Panel (a) shows flows from ad 450 to ad 630. Constantinople, Thessalonica, Rome, Ravenna,11and Carthage are important mints whose coins flow across the entire Mediterranean. Coins from
Carthage cross the sea into Europe, and coins from Rome and Constantinople cross into Africa andthe Middle East. Panel (b) shows that the patterns of coin flows change abruptly after 713, whenthe Arabs conquer the eastern Mediterranean coast (up to and including Antioch), the southern
Mediterranean coast, and most of the Iberian peninsula. Most coins flow east to west within theIslamic Caliphate, within the Arab heartlands of Syria, Mesopotamia, and Egypt, and within the
Frankish lands of northern Europe. The coin flows emanating from the remaining Byzantine mints
in Constantinople, Syracuse, and Italy are much smaller than in the earlier period.24 Besides a few
coins from the mints of Ifriqiya and al-Abbasiyya that end up in the hoards of Ilanz and Steckborn(McCormick, 2001), there are almost no north-south flows across the Mediterranean. Flows thatcross the border between Christianity and Islam primarily do so in the the West across the Pyrenees(Parvérie, 2014, 2018). Appendix figure A.3 and table A.3 present additional evidence that theflow of coins across the Mediterranean dropped after the Arab conquests, and that Islamic coinscrossing the sea replace Roman coins. Despite the fact that in some historical sources the Arabs
called the Mediterranean the “Sea of the Romans” (Baḥr al-Rūm(ī)), after the Arab conquests
the Mediterranean became, at least when it comes to coin flows, an Arab dominated sea.25
While only descriptive, figure 4 suggests that the Arab conquest induced a substantial change
in the economic geography of the ancient world, in the middle of our sample period. The structuralmodel in the next section is designed to quantify those changes.

2

Model and estimation

We now introduce a quantitative model of trade, money, and the diffusion of coins. This model
forms the basis for the estimation of trade costs, mint outputs, and technology, from which we
can quantify the importance of political changes for the economic geography of the ancient world.
This model hinges on two key assumptions. First, we assume that coins are used for transactions. This assumption is natural in a heavily monetized ancient world (Henry, 1967; Bessard,2020). It is plausible: using alternative means to finance transactions would require complex financial contracts. It is also necessary for our analysis: if coins are not used to clear gross bilateral
trades, then data on coin flows would not contain any information on bilateral trade flows. Second,24

The changes in the magnitude and location of Byzantine coin production has been the topic of a large literature.
Kazhdan (1954) was the first to argue for a decline of Byzantine cities in the 8th and 9th century based onarchaeological evidence. Several authors (including Kazhdan, Zavagno, 2022, and Pennas, 1996) relate these changes
to Arab military pressure. Grierson (1973) notes that the eastern mints of Nicomedia, Cyzicus, Thessalonica,
Cyprus, as well as Catania, were closed in 629-630, before the Arab conquests, and production was relocated toConstantinople. Nevertheless, a number of provincial mints, including Syracuse, Ravenna, and Rome, remained
active until at least the mid-8th century (in the case of Sycracuse until 878 when it fell to the Aghlabids).
25
Paraphrasing Pirenne (1939). We take the Arab conquests as a proximate cause for these changes, and do not
attempt to explain why the Arabs were successful. The commonly held view is that the Byzantine-Sasanian warof 602–628 exhausted the forces of both empires and paved the way for Arab military success (Foss, 1975).

12

we assume that coins are fungible. This assumption is natural with coins valued for their precious
metal content, and is supported by historical evidence on the wide circulation of foreign-origin
coins (Bates, 1991; McCormick, 2001).

2.1

Model

Set up. There are N locations, denoted by a subscript. Time is discrete, denoted in square
brackets. Each time period is decomposed into three sub-periods: beginning, middle, and end.
At the end of period t − 1, location n sets aside Sn [t] coins for consumption and saving at t.
At the beginning of period t, an exogenous fraction λn [t] of the coins in this stock Sn [t] ceases
to circulate, either lost or melted into fresh new coins.26 In addition, some locations own a mint
which exogenously generates fresh new coins if it is active in period t, Mn [t] ≥ 0.
In the middle of period t, Ln [t] identical workers save a fraction sn [t] of their coins, and spend
the rest on consumption, Xn [t]. Importantly, expenditures contain spending on all goods, possibly
including capital goods. What we label saving, sn [t], solely captures saving into nominal financial
assets (coins), not investment into physical capital. Workers face the following budget constraint,

Xn [t] = (1 − sn [t]) (1 − λn [t]) Sn [t] + Mn [t] , with sn [t] ≥ 0,(2)

where we assume workers cannot borrow (sn [t] ≥ 0). This is a ‘coin-in-advance’ economy where
consumption is financed by available coins, and not by promised future income.27
At the end of period t, workers earn a competitive wage wn [t] selling goods in exchange forwn [t] Ln [t] worth of coins. The stock of coins set aside for the subsequent period evolves recursively,
Sn [t + 1] = (1 − λn [t]) Sn [t] + Mn [t] + wn [t] Ln [t] − Xn [t] .

(3)Trade. Within each period trade is shaped by comparative advantages as in Eaton and Kortum(2002). Consumers in n spend a fraction πni [t] of their expenditures on imports from i, Xni [t],πni [t] =Xni [t]Ti [t] (wi [t] dni [t])−θ=P−θXn [t]k Tk [t] (wk [t] dnk [t])

26

(4)

We think of λ primarily as coins melted into bullion for their precious metal content, possibly as a segnioriage
tax to be re-minted into fresh new coins. A very small fraction of λ is literally lost, buried into a hoard andforgotten. Some of those lost coins will eventually be found by archaeologists and become part of our dataset.
27
If this assumption were relaxed, workers in one location would send their income (coins) to pay for consumption
on goods from another location; those coins would become the income of workers in those locations, which theywould send to other locations, etc, all within the same period. Coins would travel infinitely many times withineach period and data on coin holdings would contain no information on trade. Similarly, if coins were used only toclear bilateral imbalances, data on coins would contain information on net trade but not on gross trade flows.

13

Inter-temporal allocations.
"
Un [t] = Et

X

Workers have log-utility over real consumption,β τ −t ln

τ ≥tXn [τ ]pn [τ ]

#, with pn [t] = γ

X

!−1/θ
Tk [t] (wk [t] dnk [t])−θ

.

k

β is the discount rate and pn [t] the ideal price index in n at t as in Eaton and Kortum (2002).
Each location chooses a sequence of coins stocks to maximize utility, given wages, and subject tothe no borrowing constraint (2), the budget constraint (3), the optimal within-period allocation
across imports (4), and a transversality condition which prevents holding coins forever,






X τ −t  (1 − λn [τ ]) Sn [τ ] + Mn [τ ] + wn [τ ] Ln [τ ] − Sn [τ + 1] 
max Et β ln 
P
−1/θ{Sn [τ ]}τ ≥t
−θ
τ ≥t
γ
k Tk [τ ] (wk [τ ] dnk [τ ])s.t. Sn [τ + 1] ≥ wn [τ ] Ln [τ ] , ∀(τ ≥ t), and lim β τ
τ →∞

(5)

Sn [τ + 1]
= 0.
Xn [τ ]In this model, saving (sn [τ ] > 0 ⇔ Sn [τ + 1] > wn [τ ] Ln [τ ]) is used for consumption smoothing.
Equilibrium. Wages are determined by market clearing each period, given the trade equilibrium, πni [t] from equation (4), and the coin stock policy function, Sn [t] from equation (5),wi [t] Li [t] =Xπni [t] (1 − λn [t]) Sn [t] + Mn [t] + wn [t] Ln [t] − Sn [t + 1] , ∀(i, t).

(6)nIn a useful benchmark where agents save little to none, sn [t] ≈ 0, expenditures on the right handside simplify into Xn [t] = (1 − λn [t]) wn [t − 1] Ln [t − 1] + Mn [t].
Steady state equilibrium. We use the following steady state when simulating counterfactual
equilibria in section 3. All aggregate variables are constant, in particular wn [t] Ln [t] = wn Ln andMn [t] = Mn , ∀(n, t). If agents correctly anticipate they are in a steady state, there is no consumption smoothing motive for saving, sn = 0 and Xn = (1 − λn ) wn Ln + Mn . If agents incorrectly
anticipate shocks and save for precautionary motives, the same equality between expenditure andn((1 − λn ) wn Ln + Mn ) withincome (inclusive of minting) holds to a first order, Xn = 1−s1−sn +λn sn
1−sn
28
≈ 1. Equilibrium wages jointly clear markets,
1−sn +λn sn
w i Li =28
Ti (wi dni )−θ
πni (1 − λn ) wn Ln + Mn , and πni = P
,
−θ
T
(w
d
)
k
k
nk
k
n

X

We estimate λ = 1.7% p.a. (section 2.3), and Scheidel (2015) calculates s = 1.5% for Roman times.

14

(7)

such that the aggregate stock of coins in circulation is constant,

2.2

P

n Mn =

Pn λn w n Ln .

29

Dynamic accumulation in coin hoards

Our aim is to match the quantities in our model to our data on ancient coins, which contains
information on mint/hoard locations and dates. To do so, we explicitly follow the movements ofcoins of different vintages, minted in different locations, as they travel through the trade network.
We denote by Smi [t, τ ] the number of coins minted in location m at time t which are part ofPP
the coin stock of location i at time τ , with Si [τ ] = N
m=1t≤τ Smi [t, τ ]. Coins start their ‘coin
life’ when they are minted, so Smm [t, t] = Mm [t]. Subsequently, they circulate across locations as
they are used for transactions or saved. Smi (t, τ ) evolves recursively,
Smi [t, τ + 1] =NX

(1 − sn [τ ]) (1 − λn [τ ]) Smn [t, τ ] πni [τ ] + si [τ ] (1 − λi [τ ]) Smi [t, τ ] , ∀(τ ≥ t).

n=1

(8)
At time τ , each location n has a stock of coins set aside. A fraction (1 − sn [τ ]) is spent on
goods (non-saved). Of those coins, (1 − λn [τ ]) Smn [t, τ ] were minted in location m at time t.
Consumers in n send a fraction πni [τ ] of their non-saved coins to i to pay for imported goods.
We assume that coins are fungible so that buyers draw from their coin stock at random, and(1 − sn [τ ]) (1 − λn [τ ]) Smn [t, τ ] πni [τ ] coins minted in location m at time t move from n to i attime τ in expectation. Summing across all (coin) origins we derive the first term (sum) in equation(8). In addition, a fraction si [τ ] of coins is saved locally and remains in region i, the second termin equation (8). We can express the dynamic evolution of the composition of coin stocks in acompact matrix form and solve it forward,S [t, t] = M [t] , and S [t, τ + 1] = S [t, τ ] (I − λ [τ ]) Π̃ [τ ] , ∀τ > t,with Π̃ [τ ] ≡ (I − s [τ ]) Π [τ ] + s [τ ] ,⇒ S [t, T ] = M [t]

T
−1
Y

!

(I − λ [τ ]) Π̃ [τ ] ∀T ≥ t. (9)τ =tS [t, T ] is the square N ×N matrix of coin stocks with (n, i)th element Sni [t, T ]. M [t] is a diagonal
N × N matrix of minting with nth element Mn [τ ]. I is the N × N identity matrix and λ [τ ] isa diagonal N × N matrix of coin loss with nth element λn [τ ]. Π̃ [τ ] is the square N × N matrix,
P
We do not impose any constraint on using arbitrarily small coin denominations: if aggregate minting, n Mn ,increases (decreases), wages denominated in coins will increase (decrease), leading to inflation (deflation).
Note that this model is analogous to Dekle et al. (2007), where some locations run a trade deficit and some a trade
surplus. The trade deficit of location n, equal to the net creation of coins, is Dn ≡ Xn − wn Ln = Mn − λn wn Ln .
Any non-mint location runs a trade surplus (Dn < 0), and a mint location runs a trade deficit if minting is large
enough, with at least one mint location running a trade deficit.
29

15

which governs bilateral coin flows. This ‘augmented’ trade matrix Π̃ [τ ] is a function of s [τ ], thediagonal N × N matrix of net saving rates with nth element sn [τ ], and Π [τ ], the trade matrix
with (n, i)th element πni [τ ], the classical Eaton and Kortum (2002) trade share from equation (4).
Equation (9) forms the basis of our estimation. Before describing our estimation strategy,
we isolate two original features of our model, which helps gain intuition on how we can extract
information about trade from coins and clarifies the distinctions between data on coins and trade.
Coins as a medium of exchange versus a store of value. The stock of coins S in equation (9) diffuses across locations not according to the trade matrix Π, but to the ‘augmented’
trade matrix Π̃. Both matrices have almost the same structure, with one distinction: coins, unlike goods, have an additional tendency to stay locally, because they are also used as a store ofvalue for saving. To make this distinction explicit, we decompose the ‘augmented’ trade share π̃ni
into three multiplicative terms: a buyer term, α̃n , a seller term, β̃i , and a bilateral term δ̃ni ,(10)π̃ni [τ ] = α̃n [τ ] β̃i [τ ] δ̃ni [τ ] ,1α̃n [τ ] = P
,
β̃
[τ
]
δ̃
[τ
]
k
nk
k
β̃i [τ ] = Ti [τ ] (wi [τ ])−θ ,−θ
1(dni [τ ])δ̃ni [τ ] =×(dnn [τ ])−θ (1 − sn [τ ]/π̃nn [τ ])if n = i,if n ̸= i.

The classical Eaton and Kortum (2002) trade matrix Π [τ ] has almost the exact same structure:P
πni [τ ] = αn [τ ] βi [τ ] δni [τ ], with buyer and seller terms αn [τ ] = 1/ k βk [τ ] δnk [τ ] and βi [τ ] =Ti [τ ] (wi [τ ])−θ , and bilateral term δni [τ ] = (dni [τ ])−θ / (dnn [τ ])−θ . In the absence of saving, sn =0, both matrices are identical. But if sn > 0 the home bias for coins flows is magnified comparedto the home bias in trade flows, i.e. the gap between the (high) within-location flows versus the(low) between-location flows increases. In practice this distinction is of little consequence, as theancient saving rate into coins was likely very low: the savings rate of 1.5% that Scheidel (2020)
calculates for Roman times is likely an upper bound for Late Antiquity, where property rights
were weaker and conflict was widespread.
Equation (10) shows that coin and trade flows have the same gravity structure, and only differ
by a multiplicative constant, so coin flows and the saving rate are suﬀicient to recover trade flows.
Coin stocks versus coin flows. The second key distinction between coin and trade flows
is that coins do not travel just once: they may be used for multiple transactions throughout
their stochastic lifespan. This is made explicit by the product of ‘augmented’ trade matrices inequation (9). Our structural estimation unpacks the different elements of the product of matrices
in equation (9), leveraging the overlapping yet distinct information contained in ‘young’ coins16

—which have only traveled through a few iterations of the ‘augmented’ trade matrix— and in‘old’ coins —which have traveled through many iterations of the ‘augmented’ trade matrix.
A naive estimation that would wrongly ignore the dynamic nature of coin flows, simply combine
all coins of different ages, and run a gravity regression on coin shares would not identify theparameters of the ‘augmented’ trade matrix. This can be most easily seen in a stationary versionof our model, though the result extends to non-stationary cases. In a stationary equilibrium withno net saving, s [τ ] = 0, ∀τ , the trade matrix and ‘augmented’ trade matrix are identical, Π̃ = Π,and the dynamics of coin stocks in equation (9) simplify intoaS [t, t + a] = S [a] = M (I − λ) Π , ∀ (t, a) .

(11)

In this stationary steady state, only age, a, matters. Combining coins of different ages, we getAX
a=0

S [a] = MA X(I − λ) Π

a

!



=

A→+∞

a=0

M I − (I − λ) Π

−1

.

(12)

The share of coins from different mint origins (M ) in different locations depends not on the trade
matrix Π, but on the Leontief inverse of the trade matrix discounted by (I − λ): (I − (I − λ) Π)−1 .
The reason is simple: newly minted coins percolate through the trade network, just as value added
shocks percolate through the input-output network in the work of Wassily Leontief (Leontief, 1941,1944). The intensity with which coins flow from one location to another depends on bilateral tradeshares, just as the intensity with which one upstream sector affects the production of a downstream
sectors depends on bilateral input shares. The same coin will travel multiple times through thetrade network (until hit by a Poisson death shock λ), just as value added travels multiple times
through the input-output network. However, unlike in conventional static models of input-output
linkages, coins take time to percolate through the system, as inputs do in Liu and Tsyvinski (2024).
Figure 5 illustrates the potential bias from wrongly interpreting coin stocks as coin flows.
Within the first period of their life, coin flows mirror trade flows (Π in figure 5). The same tradeelasticity θ governs both coin and trade flows, Sni [t, t + 1] ∝ πni ∝ (dni )−θ . In the second periodof their life, coins have traveled twice through the trade network (Π2 in figure 5). Trade costs
have a weaker impact over short distances as coins have traveled longer, and have started diffusing
within nearby destinations. The trade elasticity falls below θ. As coins age, their flows graduallyescape the negative effect of trade costs; coins diffuse through the trade network and converge
towards a uniform distribution.30 The trade elasticity falls towards zero (see the flattening slopesof Π, Π2 , · · · Π100 in figure 5). A naive estimation using coins of all ages combined, i.e. wronglyinterpreting the Leontief inverse (I − (I − λ) Π)−1 as if it were the trade matrix Π, would infer
incorrect parameters. In our numerical example, if we assume that trade costs depend on travel30

Our model of gradual diffusion of coins through a trade network is intimately related to the model of diffusionof information through a trade network in Chaney (2018).

17

0
Log Coin Flows
-6
-4

-2

Π100

Π10
Coins of age 1: Π

-8

Coins of age 2: Π2

Π3
Π2

Coins of age 3: Π3
Coins of age 10: Π10-10

Coins of age 100: Π100

ΠCoins of all ages: (I-(I-λ)Π)-1

0

1

2
Log Travel times

3

Figure 5: Flows of coins of different ages
Notes: This figure presents a numerical illustration of the flow of coins of different ages as a function of trade costs. We use equations(11) and (12), and the trade model (4) to simulate an economy with 50 locations around a regular polygon, with same technology
Tn = T, ∀n, a trade elasticity θ = 4, and an annual coin
∑ loss rate λ = 0.017. Locations are symmetric so wages are equalized andtrade shares simplify to πni = α (dni )−θ with α = 1/ k (dnk )−θ . We parameterize (dni )−θ = (T ravelT imesni )−ζ with ζ = 3.06
(see our structural estimation in section 3). Log travel times are on the x-axis; and log flows of coins of different ages on the y-axis(ln Sni [t, t + a]) for age a). To ease comparisons, we normalize the largest log flows and smallest log travel times to zero, so all curves
start at (0,0). ‘Naively’ treating the flows of coins of all ages combined as trade flows, i.e. treating the Leontief inverse (I − (I − λ) Π)−1
as if it were the trade matrix Π, gives a misspecified trade times elasticity of 1.17, substantially below the true ζ = 3.06.

times, (dni )−θ = (T ravelT imesni )−ζ , with a true elasticity ζ = 3.06, we would wrongly estimate
a travel times elasticity of 1.17. This corresponds approximately to the discrepancy between our
reduced form estimate combining coins of all ages (1.138 in table 1) and our upcoming structural
estimate (3.06 in section 3).31 Appendix figure A.4 shows reduced form evidence suggestive of thisphenomenon: across separate gravity regressions (as in table 1) for different vintages of coins, thereduced form travel time elasticity falls towards zero as we move from younger to older coins.
Equation (11) and figure 5 illustrate how to combine information on coins of different ages torecover trade flows. If we had perfect data on coin stocks, we could directly recover trade flows
from the distribution of age 1 coins, discarding all other information. Unfortunately, our datais sparse. Information on those youngest coins is insuﬀicient, and we need to combine it withinformation on coins of different vintages. For instance, if no coins are minted in location n attime t, we simply cannot observe them flowing out of n at t. Instead, we can use coins minted attime t − 1, t − 2, etc, that are still in circulation at time t to recover trade flows to n at t.
31
Figure 5 is not a structural exercise. Our numerical example is a stylized example, with symmetric locations
around a regular polygon at a steady state. It is meant to build intuition, not to resemble the real ancient world.

18

2.3

Mapping the model to the data

To map the model to the data, we need to define the empirical counterparts to time periods, t,
locations, n, the coin loss rate, λ, and we parameterize trade costs as a function of observables.
Definition of time periods. We aggregate mint and hoard dates (tpq) to 20-year intervals.
Definition of locations. We partition the world into N = 13 regions. In the Islamic world theycorrespond to regions associated with graph nodes in al-Ṯurayyā (Romanov and Seydi, 2022). In
the Roman world they are based partly on Roman provincial borders, and partly on 9th-century
political borders (see appendix C for details). To facilitate the identification of the parameters,
we choose a level of aggregation such that each region has some minting and hoarding activity
before and after the conquests. Figure 6 maps the partition of the ancient world into 13 regions.

Figure 6: Region Definitions
Notes: Regions are based on Roman provincial boundaries and Medieval kingdom boundaries within non-Islamic Europe, and alṮurayyā regions within the maximum extent of the Umayyad caliphate. See appendix C for details.

Assumption: constant loss rate. With a constant loss rate, any collection of coins minted attime t will gradually disappear from the monetary system as those coins are (randomly) ‘lost’ at
a rate λ. At time t + 1 only a fraction (1 − λ) remains, at time t + 2 a fraction (1 − λ)2 , etc. The
19

same exponential decay holds for any starting date t, and it holds for the random sample foundby archaeologists. We aggregate all the coins in our dataset, and express the density of coins ofage a as f (a) ∝ (1 − λ)a , corresponding to figure 3b. Taking logs, we estimate λ by OLS,
ln f (a) = constant + ln (1 − λ) × a + ε (a) .

(13)

We estimate a coin loss rate of 1.7% per year, or 30% per 20-year interval, λ̂20−year = 0.301.32
Parameterization of trade costs. We assume that bilateral trade costs properly scaled by
the trade elasticity θ solely depend on (directed) bilateral travel times, T ravelT imeni , and on a
possible proportional penalty incurred when crossing political and religious boundaries, ∀(n ̸= i, t),ln (dni [t])−θ = γ0 − ζ ln (T ravelT imeni ) − κ1 P oliticalBorderni [t] − κ2 ReligiousBorderni [t] .
(14)
We normalize dnn [t] = 1, ∀n, t, as in Eaton and Kortum (2002). P oliticalBorderni [t] is a dummy
equal to 1 if regions n and i are separated by a political border in period t. ReligiousBorderni [t] isa dummy equal to 1 if in period t one region, n or i, is in the Islamic world and the other is not. γ0
is a scaling constant which adjusts travel time units, and governs the home bias in trade.33 Fromour ‘augmented’ trade model, which describes coin flows driven both by transactions (trade) andsaving, we derive the bilateral determinants of coin flows, the δ̃ni [t]’s in equation (10), ∀(n ̸= i, t),ln δ̃ni [t] = γ̃0 − ζ ln (T ravelT imeni ) − κ1 P oliticalBorderni [t] − κ2 ReligiousBorderni [t] ,(15)and δ̃nn [t] = 1, ∀(n, t). The bilateral determinants of external trade flows, (dni [t])−θ , and coinflows, δ̃ni [t], only differ by a multiplicative scalar, eγ̃0 −γ0 , due to saving.
Given estimates for within region coin flows, π̃nn [t], this scalar maps into the saving rates,
sn [t] = π̃nn [t] 1 − eγ̃0 −γ0 .

(16)π̃nn [t] controls the home bias in coins, and 1 − eγ̃0 −γ0 adjusts for the discrepancy (due to saving
sn [t]) between the home bias in coins (governed by γ̃0 ) and the home bias in trade (governed by
γ0 ). In the absence of direct evidence on ancient trade, we cannot directly estimate γ0 . Instead, wechoose γ0 to match the average ancient saving rate into nominal assets of 1.5% (Scheidel, 2020).
32

See appendix table A.4 for the formal estimation results.
Our parameterization for trade costs is potentially inconsistent with the assumption of arbitrage trade because
we compute optimal travel routes once and for all, without taking into account the additional costs associated withpotentially multiple border crossings. In practice, this does not happen: we manually verify that the estimatedcosts in equation (14) cannot be lowered by taking a longer route avoiding unnecessary border crossings.
33

20

Travel times. To compute (optimal) travel times given the transportation network and technology we use two geo-spatial models constructed by historians to provide quantitative estimatesof (shortest) distances, trade routes, and trade costs. The first is the Orbis (Scheidel, 2015), a
directed graph of cities and trade routes of the Roman world (i.e. from Britannia in the northwest to Egypt, Palestine, and Syria in the south-east) along with a calibrated model of trade
costs, in monetary units and units of time, along the edges to allow for the calculation of shortest
paths. The second is al-Ṯurayyā (Romanov and Seydi, 2022), a digitalization of the Atlas of theIslamic World of Cornu (1983), which, similarly, contains the coordinates of cities and trading
posts connected by trade routes, but without estimates of travel times. We combine the nodesof al-Ṯurayyā and Orbis and extend Scheidel (2015)’s methodology from Orbis to calculate travel
times for the Islamic world (see appendix C for details). Figure 7 shows the combined graph. We
validate the resulting travel times by comparing them to those reported by the 10th-century Arabgeographer Al-Muqaddasī (1994) (see appendix figure A.5).

Figure 7: The combined geospatial model
Notes: The figure shows the combined geospatial models from Orbis and al-Ṯurayyā. Edges in blue (red) indicate faster (slower) speeds.

For each region, we calculate the weighted average of mint locations (with the shares of eachlocation in total coin output as weights) and project it to the closest vertex on the road network
graph. The shortest travel time between n and i is our time-invariant measure of T ravelT imeni .
21

Political and religious borders. We construct political border and religious border dummies
by coding the start and end years of the presence of political entities across regions. We set
P oliticalBorderni [t] to one if the set of political entities that occupy at least some part of the regions n and i for some part of the 20-year time interval is completely disjoint. ReligiousBorderni [t]captures the border between the emerging religion of Islam and the rest of the world. We use twoalternative specifications for the religious border effect. The first (simple) specification is a dummy
equal to one if all political entities in region n are Islamic and none in i are, or vice versa. The
second (detailed) specification distinguishes the eastern, western, and Mediterranean borders ofIslam; the eastern land border is between the Byzantine heartlands and the Caliphate regions eastof Egypt, the western land border is between al-Andalus and Aquitaine or Francia/Germania, andthe Mediterranean maritime border is between all other region pairs.
Coin hoard data generating process. We assume that our hoard dataset H is a random
sample from the stocks of coins in each region and period. We group all coin hoards within aregion and period, with Hh [T ] the total number of coins found in region h and buried at timeT (tpq), which we decompose into coin types, with Hm,h [t, T ] the number of coins minted in m
at time t within that hoard. Our random sampling assumption means that the expected share ofcoins of different types within a hoard is equal to the share of coins of different types within a coinstock in our model (9). Formally, we assume
Hm,h [t, T ]Sm,h [t, T ].
E=
Hh [T ]
Sh [T ]

(17)

As we discuss in section 1, we recognize that the probability that a coin ends up in our dataset
may vary systematically between regions and periods, depending on whether coins were lost anddeposited in the ground, found by archaeologists, and documented by experts. By using only
information on the composition of coins within hoards, we condition on those events being realized(lost, found, documented), and we purge any variation in the probability of those events.

2.4

Estimation

We estimate the structural parameters of our model by maximum likelihood. Given our assumptionof random sampling in equation (17), the probability of observing (· · · , Hm,h [t, T ] , · · · )m,t coinsminted in different regions (m’s) at different times (t’s) among the total of Hh [T ] coins within ahoard buried in region h at time (tpq) T is multinomial, and depends on coin stocks,
Y  Sm,h [t, T ] Hm,h [t,T ]Hh [T ]!
Pr (· · · , Hm,h [t, T ] , · · · ) = Q.
′
Sh [T ]
m′ ,t′ Hm′ ,h [t , T ]! m,t

22

It depends on the model parameters through the predicted share of coin types, Sm,h [t, T ]/Sh [T ].
Assuming a constant loss rate λ (13), the stocks of coins evolve recursively as in equation (9):S [t, T ] = M [t]

T
−1
Y

!
(1 − λ) Π̃ [τ ] ∀(T ≥ t).

τ =t

We decompose the elements of the coin flow matrix (‘augmented’ trade matrix) Π̃ [τ ] into seller (β̃i )and bilateral terms (δ̃ni ) according to equation (10):β̃i [τ ] δ̃ni [τ ], ∀(n, i, τ ).
π̃ni [τ ] = Pk β̃k [τ ] δ̃nk [τ ]We parameterize the bilateral component of coin flows in equation (15): δ̃nn [t] = 1, ∀(n, t), andln δ̃ni [t] = γ̃0 − ζ ln (T ravelT imeni ) − κ1 P oliticalBorderni [t] − κ2 ReligiousBorderni [t] ,∀(n ̸= i, t).
The vector Θ collects all parameters: time-varying minting output Mn [t], time-varying seller terms
β̃n [t], and the parameters governing saving and trade costs, γ̃0 , ζ, κ1 , and κ2 ,
Θ = (· · · , Mn [t] , · · · )n,t , (· · · , β̃n [t] , · · · )n,t , γ̃0 , ζ, κ1 , κ2 .
As we target coin shares within hoards, we can never recover the total number of coins minted
over 320-950. We normalize mint output Mn0 [t0 ] = 100 for an arbitrary region n0 (northern Italy)
and period t0 (320-340). Similarly, only relative origin terms matter for coin flow shares, so wenormalize β̃n0 [t] = 100, ∀t, for region n0 (northern Italy).
b by maximizing the log-likelihood of observing a sample of coin hoards H,
We estimate Θb = arg max
Θ
Θ

XX



X
Hm,h [t, T ] ln Smh [t, T ] (Θ) − ln
Sm′ h [t′ , T ] (Θ) .

(18)

m′ ,t′

h,T m,tGiven those structural estimates, we recover the parameter γ0 governing bilateral trade costs(possibly distinct from the parameter γ̃0 governing bilateral coin flows in the presence of saving),
using equation (16) and an average net ancient saving rate into coins of 1.5% (Scheidel, 2020),
γ0 s.t. 1 − eγ̃0 −γ0hiEn,t π̃nn [t] = 0.015.

With those estimates, we can compute all equilibrium variables, including real consumption.

23

(19)

3

Trade and the end of antiquity

3.1

Parameter estimates

Ancient trade costs. Table 3 shows the estimates of the parameters governing ancient trade
costs. We consider two specifications for the religious border effect: either a single parameter
governing the cost of crossing from Islamic to non-Islamic regions (colums 1 and 3), or different
costs of crossing the religious border overland in the east (in and out of Byzantium), in the west
(in and out of al-Andalus), or over the Mediterranean in and out of its non-Islamic northern shore(columns 2 and 4). In addition, we use two accounting methods: we either use a simple count ofcoins (columns 1 and 2), or use only gold and silver coins and measure their value, accounting forweight or denomination, and metal content (columns 3 and 4).
Table 3: Determinants of ancient trade costs
Log Trade Costs

Log Travel Time
Political Border
Religious Border

(1)

(2)

(3)

(4)

3.04
(0.01)
0.64(0.02)
3.85(0.11)

3.06(0.02)
0.47(0.02)

1.41(0.04)
2.51(0.05)
2.94(0.16)

1.09(0.04)
3.19(0.05)

Religious Border: East

1.99
(0.12)
4.69(0.21)
5.20(0.19)

Religious Border: West
Religious Border: Mediterranean

0.12(0.30)
14.63(147.22)2.72(0.19)

SampleCoin Accounting

All
NumberAll
Number

Gold/Silver
Value

Gold/Silver
Value

Estimator

MLE

MLE

MLE

MLE

Observations

4,389

4,389

2,010

2,010

Notes: The table shows the coeﬀicient estimates in the trade cost function, equation (14)). “Political Border” is one if the sets of political entities that occupy at least
some part of the regions during the 20-year time period are completely disjoint, and zerootherwise. “Religious Border” is one if all political entities in one region are Islamic andall are non-Islamic in the other region, and zero otherwise. “Religious Border: East” is
one iff the religious border dummy is one and the regions are al-Andalus and Aquitaine
or Francia/Germania, or vice versa. “Religious Border: West” is one iff the religious
border dummy is one and the regions are the Byzantine Heartlands and one of theCaliphate regions east of Egypt, or vice versa. “Religious Border: Mediterranean” is
one for all other region pairs where the religious border dummy is one. “Observations”
denotes the number of observations (m, h, t, T ) in equation (18) where Hm,h [t, T ] > 0,i.e. which enter the loglikelihood.

In our simpler specification (column 1), the travel time elasticity of trade, ζ = 3.04 (s.e.
0.01), is somewhat larger but close to the 2.05-2.89 range of estimates from Flückiger et al. (2022)
24

using bilateral trade in terra sigillata in ancient Rome and optimal travel times along the Romantransportation network, and to the 1.9 distance elasticity from Barjamovic et al. (2019) using
merchant records in Bronze Age Anatolia. This proximity to estimates using actual (thoughpartial) ancient trade data is reassuring, as we do not use any direct information on trade, but
only indirect information from coins. Interestingly, ζ is also larger than the 1.1 elasticity estimatedin table 1 using the same data on coin hoards. The reason is that in table 1 we use a naive gravity
model, combining coins of all ages, and ignore the fact that older coins have a tendency to travel
longer distances. Our structural model (9) corrects this mis-specification (see section 2.2).
This travel time elasticity is robust to alternative specifications for the religion border effect,ζ = 3.06 in column 2 versus ζ = 3.04 in column 1. Our estimate is significantly lower (but stillof the same sign) when we restrict our sample to gold and silver coins, and measure their relativevalues, e.g. ζ = 1.41 in column 3 versus ζ = 3.04 in column 1. We conjecture that the stronger
reliance of Byzantium on gold, and the exclusion of bronze coins, induces a systematic bias in ourestimates.
In our simpler specification (column 1), the political (κ1 ) and religious (κ2 ) border effects arelarge, but of the same magnitude as estimates for modern border effects. Arbitrarily assuminga trade elasticity θ = 4 (Simonovska and Waugh, 2014), those correspond to a 17% tax forcrossing a political border (dacross /dwithin = eκ1 /θ = 1.17), and a 155% tax for crossing a religious
border (dacross /dwithin = eκ2 /θ = 2.55), similar to the estimated 49% cost of crossing the modern
US-Canada border (Anderson and van Wincoop, 2003).34
Changing the specification of the religious border effect, distinguishing the eastern land border,
the Mediterranean border, and the western land border (column 2) does not affect our estimateof the political border effect, which remains relatively small (0.64 in column 1 versus 0.47 in column 2). It does however reveal different estimated penalties associated with crossing from Islamic
to non-Islamic regions. The religious border effect is strongest for crossing the Mediterranean
ed.
est
(κM
= 5.20, s.e. 0.19) and for the western border from al-Andalus (κW= 4.61, s.e. 0.21), and
2
2
M ed.
lowest for the eastern border into Byzantium (κ2= 1.99, s.e. 0.12).
Columns 3 and 4 drop bronze coins and compute the value shares within hoards for gold andsilver coins using their weights or denominations.35 Under this accounting of the coins data, thepolitical border effect is larger (κ1 = 2.51 in column 3 versus 0.64 in column 1). The religious
border effect instead is smaller (κ2 = 2.94 in column 3 versus 3.85 in column 1). We also estimate
statistically insignificant eastern and western religious border effects, and a smaller Mediterraneaned.
= 2.72 in column 4, versus 5.20 in column 2). As for the travel time
religious border effect (κM2elasticity, we conjecture that selectively dropping bronze coins biases our estimates.
To conclude, our structural estimation confirms the reduced form evidence in section 1. The
Anderson and van Wincoop (2003) estimate that trade is exp(1.59) ≈ 5 times larger within the US or Canada
than between them. For an elasticity θ = 4 it corresponds to a dacross /dwithin − 1 = e1.59/4 − 1 = 49% border tax.
35
We assume a constant exchange rate of 12g of silver for 1g of gold for this exercise; see Appendix ?? for details.
34

25

200
100

Estimated Mint Output

25
10
5
1

1/10

1/100
Roman/Byz
Arab

1/1000Carolingian
Sasanian

320
340
360
380
400
420
440
460
480
500
520
540
560
580
600
620
640
660
680
700
720
740
760
780
800
820
840
860
880
900

Other

Year

Figure 8: Estimated mint output, ad 320-900
Notes: The figure shows the estimated coin output Mn [t] by time t (horizontal axis) and region n, using specification in column (2)of Table 3, and broken down by the political entities that locations are — at that time — primarily associated with. The units are
relative to northern Italy in 320-340, which we normalize to have a mint output of 100.

border between Islamic and non-Islamic regions becomes costlier to cross after the birth of Islam.
Minting output. Figure 8 shows estimates of mint output by region and time interval.
Our estimates line up with several patterns described in the numismatic literature: (i) the
decline of coin production in the western Mediterranean following the demise of the West Roman
Empire in the late 5th century; (ii) the large decline of Byzantine mint output in the ‘Byzantine
dark ages’ of the eighth century; (iii) the gradual increase in Arab and Carolingian mint output
starting from the late seventh century. It is important to note we are not merely counting coins;
we estimate minting output from the shares of coins from different mints found across hoards.

3.2

Real consumption in the ancient world

Our full set of estimates further allows us to recover all equilibrium variables in our model.
From the parameters of the trade cost function (γ0 , κ1 , κ2 ), data on the determinants of trade(travel times, political and religious borders as in column 2 of table 3), and the seller terms(β̃n [t]), we recover bilateral trade shares (πni [t]). Using the goods market clearing condition inequation (6), estimated bilateral trade shares, the coin loss rate (λ), and estimates of minting
26

output (Mn [t]), we recover aggregate regional income (wn [t]Ln [t]). Finally, in the absence of any
direct evidence on population, technology, or wages, we assume a simple Malthusian benchmark,Ln [t] = Tn [t], somewhat arbitrarily set the trade elasticity θ to 4 (Simonovska and Waugh, 2014),
and we recover population (Ln [t]) and technology (Tn [t]) from the seller terms (β̃n [t]).36
We can then fully characterize real consumption per capita in any equilibrium, realized orcounterfactual, and partition real consumption into three economically meaningful components,
Xn [t]/pn [t]
Mn [t] − λwn [t]Ln [t]
−1/θ
1/θ
−1
= γ (πnn [t])(Tn [t])1+.
Ln [t]
wn [t]Ln [t]| {z }|{z} | {z } |{z}Real Consumption

Openness

Technology(20)

Trade Deficit

1/θ

A better technology (higher Tn ) transforms labor into goods more eﬀiciently and improves realconsumption. As in Eaton and Kortum (2002) and its generalization in Arkolakis et al. (2012),−1/θ
trade openness (higher πnn ) further increases real consumption, leveraging the gains from trade.
As in Dekle et al. (2007), trade deficits financed by net coin creation (higher Xn/(wn Ln ) = 1 +(Mn − λwn Ln )/(wn Ln )) allow a region to consume more than it produces.37
While ‘openness’ and ‘trade deficit’ are unit-free ratios, technology depends on arbitrary units.
We choose those units such that average real consumption is normalized to one each period. Ourmodel informs us on cross-sectional differences in real consumption between regions. This is true
despite the fact that we only have information on nominal variables (coins); bilateral trade flows
reveal real differences in factor prices, which our structural estimation is able to recover. But as
any other trade model, our model offers no guidance on the absolute levels of real consumption.
The case of Byzantium and northern Europe. Figure 9 presents the time series of estimatedreal consumption per capita and its components, for each 20-year period over ad 380-880. We
highlight those two regions because they undergo some of the most striking reversals of fortune.38
The heartlands of the Byzantine empire are initially the wealthiest region of the ancient westernworld, with a real consumption per capita four times larger than the rest of the world. Ourestimates suggest that Byzantium is also, by a huge margin, the region that benefits the mostfrom international trade: it imports as much as 70% of its consumption. This reliance on foreignimports is in large part financed by seigniorage from a very large minting output. Around thetime of the Arab conquests to its east and south, Byzantium is hit by a triple shock; Byzantine
minting collapses until the ‘Byzantine dark ages’ of the eighth century so that Byzantium must
increasingly rely on exports to acquire foreign coins, trade drops sharply because of the higher
36

We explain in detail how to recover all equilibrium variables from our estimates in the technical appendix.
The first two components of the decomposition of consumption in equation (20) are the same as in equation (15)on page 1756 in Eaton and Kortum (2002). The last term is the same as in the (all important) unnumbered equation
on page 354 in Dekle et al. (2007), where they label trade deficits as Dn . In our model, trade deficits are financedby minting output in excess of coin losses, Dn = Mn − λwn Ln , so that so 1 + Dn /Yn = 1 + (Mn − λwn Ln ) /(wn Ln ).
38
See appendix figure A.6 for the time series of all 13 regions.
37

27

(a) Real consumption per capita(b) Openness(c) Technology(d) Trade deficit

Figure 9: Real consumption per capita ad 380-900: Byzantine Heartlands vs Francia & Germania
Notes: This figure shows the time-series of (logged) real consumption per capita (panel a) from ad 380 to ad 900 for the Byzantine
Heartlands and for Francia and Germania, and its partition into ‘openness’ (panel b), ‘technology’ (panel c), and ‘trade deficits’(panel d) as in equation (20). The technical appendix provides details on the computation of each term, using the dynamic model inequations (4), (6), (10), and the parameter estimates from (18)-(19). Units for technology are chosen such average real consumption
per capita equals one for each period. 95% confidence intervals (shaded areas) are computed from fully re-estimating our model on 100
bootstrapped samples from our coin hoard data. Time series for all 13 regions are in appendix figure A.6.

trade barriers into Islamic regions, and technology collapses. Byzantine real consumption is halved
following the Arab conquests.
In contrast, the northwest European Frankish lands of Francia and Germania grow from thepoorest to one of the wealthiest regions of the ancient world. Given their peripheral position, they
trade little, so that variations in trade openness have only a marginal impact on real consumption.
Our estimates suggest instead that the growth in real consumption is fueled almost entirely bytechnological improvements, and to a lesser extent by the very large increase in minting output
which allows them to acquire foreign goods with Carolingian silver coins around ad 700.
The time series in figure 9 (and appendix figure A.6) showcase the promise of recovering economically meaningful information from data on ancient coins, but they also reveal that estimates
can be imprecise for individual 20-year periods. We turn next to a less granular but also less28

noisy description of consumption changes between the first and second halves of our sample, byaggregating across time periods within each half.
The economic geography of the ancient world before and after the Arab conquests.
To explore the changes in real consumption from before to after the rise of Islam, we split out
sample between ad 460-620, just after the fall of Rome but before the birth of Islam, and ad
700-900, after the Arabs have conquered a territory that stretches from the Indus to the Atlantic.
For each period, we average our estimated parameters and solve a full stationary equilibrium using
equations (4), (7), (10). We then use equation (20) to compute real consumption per capita andits components for each region and period. The results are presented in table 4 (see appendix
table A.5 for additional details). We focus our discussion on a few important regions.
Table 4: Real consumption in the ancient world from ad 460-620 to ad 700-900
Real consumption
(

∆ log

(1)
al-Andalus
Aquitaine and Basque Country
Francia and Germania
Northern Italy and Balkans
Southern Italy
Byzantine Heartlands
al-Sham (Greater Syria)
Northern Syria and Caucasus
al-Iraq, al-Jibal, Khuzistan, Kirman
Eastern Caliphate
Jazirat al-arab and al-Yaman
Misr (Egypt)al-Maghrib

0.62
1.28
1.96
-0.31
-0.20
-1.56
-0.32
0.22
0.06
0.37
1.16
-0.36
0.28

Openness

Technology

(2)

(3)

(4)

(5)

(6)

(7)

(8)

( 0.25 )( 0.23 )

-0.06
-0.05
-0.05
-0.08
0.19
-0.23
-0.04
-0.01
-0.00
-0.00
-0.01
0.09
0.13

( 0.04 )( 0.01 )

0.77
1.22
1.80
-0.10
-0.94
-0.44
-0.11
0.15
0.06
0.39
0.66
-0.82
-0.49

( 0.32 )( 0.23 )

-0.09
0.11
0.20
-0.13
0.55
-0.89
-0.17
0.08
-0.00
-0.02
0.51
0.37
0.65

( 0.18 )( 0.06 )Xn /pnLn)( 0.24 )( 0.24 )( 0.34 )( 0.33 )( 0.27 )( 0.30 )( 0.27 )( 0.33 )( 0.34 )( 0.72 )( 0.33 )()−1/θ∆ log πnn

( 0.01 )( 0.03 )( 0.18 )( 0.14 )( 0.02 )( 0.03 )( 0.01 )( 0.00 )( 0.04 )( 0.23 )( 0.07 )()
1/θ∆ log Tn

( 0.26 )( 0.26 )( 0.37 )( 0.41 )( 0.29 )( 0.37 )( 0.29 )( 0.34 )( 0.45 )( 0.50 )( 0.27 )Trade Deficit()−λwn Ln∆ log 1 + Mnw
Lnn( 0.04 )( 0.10 )( 0.40 )( 0.54 )( 0.11 )( 0.12 )( 0.04 )( 0.04 )( 0.26 )( 0.90 )( 0.30 )Notes: This table shows (log) changes between ad 460-620 and ad 700-900 of real consumption per capita
1/θ−1/θ((Xn /pn )/Ln , column 1), openness (πnn , column 3), technology (Tn , column 5), and trade deficits (1 +(Mn − λwn Ln )/(wn Ln ), column 7), as in equation (20). We solve steady state equilibria for the ad 460-620 and
ad 700-900 periods separately, using equations (4), (7), (10), and the parameter estimates from (18)-(19) averaged
for each period (see the technical appendix). Units for technology are chosen such that average real consumption
per capita equals one for each period. Standard errors, in parentheses in even columns, are computed from reestimating our model on 100 bootstrapped samples from our coin data. See appendix table A.5 for details on levels.

Byzantine heartlands: the heartlands of the Byzantine empire suffer from the most dramatic
drop in relative real consumption, caused by a fall in trade, a large drop in technology, and acollapse in minting output. The fall in Byzantine trade is worth emphasizing; imports fall from
64% to 12% of consumption. While ancient trade decreases in most regions following the Arabconquests, this decrease is milder than for Byzantium, with imports falling on average from 20%
to 15% of consumption. This fall is concentrated in regions north of the Mediterranean.
Western and northern Europe: both Islamic (al-Andalus) and non-Islamic (Aquitaine and the29Basque country, and the Frankish lands of Francia and Germania) Western Europe experiencethe most spectacular relative rise in real consumption. Initially among the poorest regions theygrow to become among the wealthiest over the course of a few centuries. This growth is almost
entirely fueled by improvements in technology. Regional minting grows substantially, but justenough to keep up with the large increase in aggregate income. Even though trade openness falls
due to the newly formed religious border over the Pyrenees and the Mediterranean (imports as ashare of consumption fall from around 20% to around 2%), this has only a small impact on realconsumption because those regions are not very open to trade before the Arab conquests.
Egypt (Misr): the Arab conquest of Egypt has little impact on real consumption. Although
Egypt is partially cut off from trade across the Mediterranean by the Arab conquest, its proximity
to central regions of the eastern Caliphate more than compensates this loss. Interestingly, ourestimates suggest that Egypt goes from running a small trade surplus under Roman rule, torunning a trade deficit under Islamic rule, possibly due to an increased minting of Islamic coins.
Syria (Northern Syria and the Caucasus): as a province of the Byzantine empire, Syria is oneof the wealthiest regions before the rise of Islam. Similarly to Egypt, the loss of trade access to
Byzantium and Europe is partly compensated by its privileged access to the heart of the eastern
Caliphate. Syria also benefits from an improved technology and a larger minting output.
Arabian peninsula (Jazirat al-Arab and al-Yaman): the birthplace of Islam, the Arabian peninsula, initially the poorest region of the ancient world, experiences a sustained growth in realconsumption, primarily driven by improved technology and a larger minting output.
We note however that the point estimates for Egypt, Syria, and the Arabian peninsula areimprecise, with wide standard errors (columns 2, 4, 6, and 8 of table 4).
Counterfactual changes. We then leverage our fully specified structural model to explore
the causal impact of specific shocks to real consumption changes. They are causal in the sensethat we simulate counterfactual equilibria, changing only one set of parameters at a time andkeeping all other parameters fixed at their estimated levels. The results are presented in table 5.
Column 1 shows (log) real consumption per capita in the estimated ad 460-620 equilibrium.
We then compute (log) changes in real consumption between this initial equilibrium and various
counterfactual equilibria. In column 3 we turn on the religious border to its 700-900 AD level,
keeping all other parameters unchanged. In column 5, we only change technology to its 700-900 AD
level. And in column 7, we only change minting output to its ad 700-900 level.
The increase in trade costs associated with crossing the border in and out of Islam has an
asymmetric impact on real consumption (column 1). Non-Islamic regions see a large fall in trade,which contributes to substantial reductions in real consumption. The most severely hit region
is Byzantium (50% drop in real consumption), the region that benefits the most from access totrade before the Arab conquests. In a counterfactual equilibrium where Byzantine trade to thesouth and east is severed by the religious border, Byzantium suffers not just from a large drop30

Table 5: Counterfactual changes in real consumption per capita after ad 700
Log consumption

al-Andalus
Aquitaine and Basque Country
Francia and Germania
Northern Italy and Balkans
Southern Italy
Byzantine Heartlands
al-Sham (Greater Syria)
Northern Syria and Caucasus
al-Iraq, al-Jibal, Khuzistan, Kirman
Eastern Caliphate
Jazirat al-arab and al-Yaman
Misr (Egypt)al-MaghribCounterfactual log consumption change if:

All parametersad 460-620(1)(2)

Religious borderad 700-900(3)(4)

Technology
ad 700-900(5)(6)

Mintingad 700-900
(7)
(8)

-0.70
-1.04
-1.55
0.07
-0.25
1.22
0.30
-0.34
0.28
-0.44
-1.80
0.32
0.12

0.09
-0.15
-0.07
-0.24
-0.11
-0.69
0.04
0.02
0.01
0.01
0.26
0.02
0.01

0.55
0.99
1.68
-0.24
-0.60
-0.57
-0.18
0.15
0.03
0.38
0.66
-0.71
-0.46

1.57
3.93
6.17
-0.21
-0.03
-1.41
-0.22
0.19
0.03
0.34
2.71
-0.09
-0.05

( 0.10 )( 0.08 )( 0.09 )( 0.04 )( 0.06 )( 0.11 )( 0.04 )( 0.11 )( 0.08 )( 0.08 )( 0.18 )( 0.07 )( 0.06 )( 0.02 )( 0.03 )( 0.02 )( 0.05 )( 0.02 )( 0.08 )( 0.01 )( 0.02 )( 0.00 )( 0.00 )( 0.09 )( 0.00 )( 0.00 )( 0.10 )( 0.09 )( 0.11 )( 0.08 )( 0.13 )( 0.13 )( 0.10 )( 0.22 )( 0.08 )( 0.16 )( 0.40 )( 0.24 )( 0.17 )( 0.31 )( 0.30 )( 0.47 )( 0.07 )( 0.02 )( 0.19 )( 0.08 )( 0.19 )( 0.06 )( 0.26 )( 0.84 )( 0.02 )( 0.06 )Notes: This table shows levels of (log) real consumption per capita in ad 460-620 (column 1), and (log) changes
in real consumption per capita in counterfactual equilibria where we set the religious border to its ad 700-900
level while keeping all other parameters to their ad 460-620 levels (column 3), technology to its ad 700-900 level(column 5), or minting to its ad 700-900 level (column 7). For column 1, we solve for a steady state equilibriumusing the ad 460-620 average of parameters estimated from (18)-(19), and use equations (4), (7), (10), and(20) to recover real consumption. For columns 2-4, we set parameters to their counterfactual levels (technology
T ′ , population L′ , trade costs d′ , and minting M ′ ), solve for endogenous wages as a fixed point (Alvarez andLucas, 2007) using the trade equilibrium and market clearing conditions in equation (7), and use equation (20)to compute real consumption (see the technical appendix for details). Standard errors, in parentheses in evencolumns, are computed from re-estimating our model on 100 bootstrapped samples from our coin data.

in trade openness, but also from a sharp reduction in the contribution of trade deficits to realconsumption; the massive Byzantine minting output in ad 460-620 can no longer buy foreignimports, and contributes instead to inflation within Byzantium. Other European regions also
experience a sharp reduction in consumption (15% drop), as those relatively poor regions benefit
from trading with more developed regions south and east of the Mediterranean before the Arabconquests. In contrast, Islamic regions are almost unaffected by the reduced access to trade.
Changes in technology (column 5) and minting (column 7) induce more heterogeneous changes.
Western and northern Europe (including Islamic Spain) would have benefited from large technological improvements. Given their relatively small initial size, a counterfactual increase in minting
to post-Arab conquests levels would also have allowed those regions to finance large trade deficits,
contributing to large gains in real consumption. Byzantine real consumption would have dropped
if technology moved to its post Arab conquests level for all regions. Interestingly, this is notjust because Byzantine technology itself deteriorates; under the pre-Arab conquests trade costs,
Byzantium loses export market shares to competitors from northwestern European and Middle
Eastern regions who benefit from improved technology. On the other hand, the collapse of minting

31

output during the ‘Byzantine dark ages’ would have prevented Byzantium from acquiring foreign
wares using seignioriage-financed trade deficits, and would have severely hurt real consumption.

3.3

Urbanization and trade in ancient Europe

We conclude by confronting our estimates for changes in real consumption to realized changes inurbanization in Europe. While our model does not feature any explicit notion of urbanization, we
conjecture that a higher real consumption per capita allows to sustain a larger urban population.
This exercise is illustrative, meant to verify that our estimates for real consumption derived solely
from information on coin flows are in line with independent evidence on economic growth.
Δ log real consumption / capita> 1.5(1,1.5](.5,1](0,.5](-.2,0](-.3,-.2](-1,-.3](-2,-1]< -2(a) Relative real consumption changes: pre- to post-conquests
Increase in urban population, 700-900, %> 100(65,100](60,65](50,60](25,50](5,25]<5(b) Urban Population Growth in European Regions, ad 700-900

Figure 10: Real consumption and urbanization
Notes: Panel (a) shows the relative real consumption change from the pre-conquest period to the post-conquest period, column 1 ofTable 4. Panel (b) shows the percentage growth of the urban population between ad 700 and ad 900. City size data from Buringh(2021), except for Byzantine Anatolia, which is not covered. We construct measures of urban decline in Anatolia (calculations available
upon request) based on the shrinking surface area of cities described in Brandes (1989). The resulting figure of a 10% decline in urban
population over this time interval seems to be a conservative estimate in light of the fact that many coastal cities saw large amountsof destruction and depopulation as a consequence of Arab raids.

Figure 10 shows our estimates for changes in real consumption per capita (top panel) together
32with the patterns of urban population growth between ad 700 and ad 900 in the regions northof the Mediterranean, aggregated from city size data (bottom panel).39 Comparing both maps
suggests that our estimates for real consumption are qualitatively in line with independent evidence
on urbanization. Our estimated drop in real consumption in the heartlands of the Byzantine
empire, and substantial increase in western and northern Europe are in line with the decline inurban population in Asia Minor and Cyprus, low urban growth in Greece, Thracia, and Dacia,
medium urban growth in the Balkans, Italy, and Aquitaine, and strong urban growth in Iberia
and Francia/Germania.

Conclusion
In this paper we study the patterns of change in economic geography around the Mediterranean
during Late Antiquity through the lens of coin flows. We propose a dynamic model of trade
where agents use coins to make transactions, so that coins gradually diffuse over space and timein proportion to trade flows. We estimate this model using numismatic data on ancient coins. We
are then able to use these parameters to recover granular time series for relative real consumption
per capita from the fifth to the tenth century. Our estimates for changes in real consumption are
consistent with observed measures of relative urbanization across European regions.
Our evidence from coin finds indicate that trade flows declined following the Arab conquests.
This can be explained by a newly formed trade barrier between the emerging Arab Caliphate andthe Christian West. These changes in trade patterns are in line with the claims of a trade disruption
in the Mediterranean by Pirenne (1939). Pirenne, however, believed that these disruptions in tradeflows also led to a vast reduction in economic activity and exchange within the Frankish lands.
While our estimates reveal that Francia and Germania did experience a 15% drop in trade asa share of consumption, this cannot have had any meaningful impact on the Frankish economy
because pre-conquest trade shares were already low. Our estimates suggest instead that theFrankish economy enjoyed net welfare gains from running trade deficits financed by seigniorage
revenues, and strong increases in relative productivity, which far outweigh any reductions in foreign
market access. In contrast the Byzantine Empire experienced the largest declines in economic
activity in the seventh century, being faced with a triple shock of a lowered access to trade,reductions in relative productivity, and a drop in seigniorage revenues. Any view that attributes
most of the changing economic geography of Late Antiquity to the Arab conquests would need toestablish their role in driving the changes in relative productivity and in mint output.

39

While estimates of city sizes from this period are naturally imprecise, the broad patterns that emerge inpanel (b) of Figure 10 are in line with the consensus view of increased urbanization in northwestern Europe andstagnation in the Eastern Mediterranean.

33

Technical Appendix
Recovering real consumption. We explain here how to recover all equilibrium variables fromour structural estimation. Throughout, we approximate the savings rate into coins to zero (informed by the low saving rate in the Roman period of 1.5% computed by Scheidel, 2020), and wearbitrarily set the trade elasticity to θ = 4 (Simonovska and Waugh, 2014).
step 1. Parameters. Our structural estimation (18)-(19) delivers the following parameters: seller
terms β̃i [t], bilateral trade costs d−θ
ni [t], minting outputs Mi [t], and the coin loss rate λ.
Note: It is important to stress two important normalizations in our estimation. First,
minting is identified only up to a (single) scaling constant, so we normalize Mn0 [to ] = 1
for reference region n0 and period t0 . Second, the seller terms enter the numerator anddenominator of trade shares each period, they are therefore only identified up to a scaling
factor each period, so we are free to normalize them each period.
step 2. Trade shares are recovered from the seller and bilateral terms using equations (4) and (10),β̃i [t]δni [t]πni [t] = P, ∀(n, i, t)k β̃k [t]δnk [t]|{z}step 1

Note: Those are estimated trade shares, as we do not observe actual trade. As in Eaton
et al. (2013), they are also predicted trade shares, strictly between 0 and 1, even if realized
trade shares may equal 0 or 1. And they are invariant to any normalization of β̃i [t].
step 3. Aggregate nominal incomes are solved for using the dynamic market clearing conditions,
given trade shares, minting, and the coin loss rate. Given that markets clear dynamically,
we need to make an assumption for the period t0 − 1 before our sample starts. Absent any
guidance, we simply assume that aggregate incomes for period t0 − 1 are the same as forperiod t0 . We need to solve for one (single) system of linear equations for initial period t0 ,and we then recursively solve for incomes in all subsequent periods t > t0 :(a) We solve (once) for incomes in period t0 from the system of linear equations (7),wi Li [t0 ] =

X
n



πni [t0 ] (1 − λ) wn Ln [t0 ] + Mn [t0 ] , ∀i
| {z }| {z } | {z }step 2step 1step 1(b) We then solve recursively for incomes in all subsequent periods using equation (6),wi Li [t + 1] =

X
n



πni [t + 1] (1 − λ) wn Ln [t] + Mn [t + 1] , ∀i, t > t0
| {z } | {z } | {z } | {z }step 2step 134

step 3step 1

Note: Our estimates for nominal incomes inherit our minting normalization.
step 4. Effective labor supply (technology-augmented) is recovered by combining aggregate income
and seller terms, assuming a specific value for the trade elasticity θ, and using equation (10),β̃i = Ti [t]wi−θ [t]
1/θ

1/θ

Li Ti [t] = wi Li [t] β̃i [t]| {z } | {z }step 3step 1

Note: our estimation does not allow us to identify absolute levels of effective labor supply,
1/θ
only relative levels within period; Li Ti [t] inherits the normalizations on β̃i [t] and Mi [t].
step 5. Technology. Using the assumption Li [t] = Ti [t], we separate technology from labor supply,
1

 1+θ
1/θ
1/θ
Ti [t] = Li Ti [t]
.
|
{z
}

step 4step 6. Real consumption per capita. We are finally in a position to recover real consumption, bothaggregate and per capita, using the normalization dnn = 1 as in Eaton and Kortum (2002),
Tn (wn )−θ
(1 − λ) wn Ln + Mn
−1P
−1/θ = γP−θ−θk Tk (wk dnk )γk Tk (wk dnk )
Mn − λwn Ln
−1/θ
1/θ
−1
Ln T n1+, and= γ (πnn )w n Ln{z}| {z } | {z } |step 2step 4steps 1 and 3
Xn /pnMn − λwn Ln
−1/θ
1/θ
−1
= γ (πnn )
(Tn )
1+
.
Ln
w n Ln| {z } | {z } |
{z
}
Xn
=
pn

step 2
Openness

step 5
Technology!−1/θ(1 − λ) wn Ln + Mn

−1/θ
−θ
Tn (wn )steps 1 and 3
Trade Deficit

Note: Our normalizations for the seller terms and for minting do not affect the ‘openness’and ‘trade deficit’ terms, as both are unit-free ratios. They do however affect our measureof technology (step 4), so real consumption is only defined in relative terms within eachperiod.40 We choose units for technology such that Et [(Xn [t]/pn [t])/Ln [t]] = 1, ∀t.
Given the inherent sparsity of our ancient coin hoard data, our time series estimates for seller
terms and minting are noisy. In order to smooth out some of this noise, we use a simple movIf we re-scale β̃n [t], ∀t, by an abitrary positive multiplicative constant (κ[t])θ(1+θ) , then we must re-scale technology and population by (κ[t])θ , aggregate consumption by (κ[t])1+θ , and real consumption per capita by κ[t].
40

35

P
ing average. Formally, for any period t ∈ [380, 880],41 we use 51 t+2
τ =t−2 β̃i [τ ] instead of β̃i [t],Pt+21
and 5 τ =t−2 Mi [τ ] instead of Mi [t]. Our estimates for bilateral trade costs suffer less from thisestimation noise and are not transformed.
To compute a steady state equilibrium, we follow the same procedure but use only step 3(a).
Computing real consumption across counterfactuals. We compute counterfactual steadystate equilibria from the trade equilibrium and market clearing (7) as in Alvarez and Lucas (2007),X
Ti (wi )−θ (dni )−θπni = Pandw
L=
πni ((1 − λ)wn Ln + Mn ) .
i
i
−θ
−θ
k Tk (wk ) (dnk )n(equation (7) reminded)

For any choice of population L′ , technology T ′ , trade costs d′ , and minting M ′ , we solve for
equilibrium wages w′ using an iterative algorithm, imposing the trade equilibrium and market
clearing conditions: for any (n)th starting guess w(n) for wages, we impose the trade equilibrium,Ti′ (wi(n) )−θ (d′ni )−θ(n)πni = P ′ (n) −θ ′ −θ ,k Tk (wk ) (dnk )and update our guess for wages to wi(n+1) by solving the linear system of market clearing conditions,wi(n+1) L′i =

X


(n)
πni
(1 − λ)wn(n+1) L′n + Mn′ .

nWe iterate this mapping until convergence to find equilibrium wages w′ . We can then readily
compute counterfactual real consumption and its constituent parts using equation (20).

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40

Online Appendix for
Trade and the End of Antiquity
by Johannes Boehm and Thomas Chaney
A Additional results
A.1 Comparing hoards with circulation hoards of Banaji (2016). . . . . . . . . . . . . . . . . . . . . . .
A.2 Comparing coin flows to the flows of West Roman Terra Sigillata. . . . . . . . . . . . . . . . . . . .
A.3 Within-empire coin redistribution before entering circulation? . . . . . . . . . . . . . . . . . . . . .
A.4 Arab conquests and the Mediterranean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.5 Coin stocks versus coin flows, a numerical exploration. . . . . . . . . . . . . . . . . . . . . . . . . .
A.6 Estimation of λ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.7 Validating the geospatial model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.8 Changes in real consumption in the ancient world. . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.9 Tables and figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1
1
1
1
1
2
2
2
2
3

B Coin hoard data
B.1 Hand-collected data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.2 FLAME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.3 Locating mints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.4 Political entities and the geography of hoards and mints . . . . . . . . . . . . . . . . . . . . . . . .
B.5 Tables and references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14
14
15
16
16
19

C Mapping the Data to the Model
C.1 Constructing regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C.2 Defining the coin sample for the structural analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .
C.3 Constructing the geospatial model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C.4 Technical details on operations in spherical geometry . . . . . . . . . . . . . . . . . . . . . . . . . .
C.5 Other utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C.6 Tables and references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

46
46
46
47
52
55
57

A Additional results
A.1

Comparing hoards with circulation hoards of Banaji (2016).

To support the argument that the coins in our hoards are broadly reflective of coin circulation during Late Antiquity,
we compare the age distribution of the hoards in our data with a sample of Byzantine circulation hoards describedby Banaji (2016), Chapter 6. These are twelve hoards containing between 12 and 751 Byzantine solidi. Figure A.1
shows the average fraction of coins in each 10-year age bin in these hoards, alongside the distribution of coin ages
in Figure 3b, showing a similar age profile. Banaji (2016) also reports that on average 44% of the coins in thesehoards are older than 33 years at time of deposit; in our data the corresponding share (for hoards with more thanten coins) is 38%.

A.2

Comparing coin flows to the flows of West Roman Terra Sigillata.

Figure A.2 compares the relationship between distance and coin flows in our data with the relationship between
distance and flows of Terra Sigillata in the data of Flückiger et al. (2022) using binned scatterplots.1 The distanceelasticity is similar but slightly lower for coins, which is potentially due to the fact that naive gravity regressions
using coin stocks will exhibit a distance elasticity that is biased towards zero (see Section 2).

A.3

Within-empire coin redistribution before entering circulation?

One potential explanation for why coins flow in particular within empires (i.e. the observed border effect) is thatcoins could be redistributed across different mints first before they enter circulation. If that were the case, theprecise place of minting of a coin should not matter beyond the empire in which it has been minted. Table A.2
investigates this by including hoard cell × empire (that mints the coin) fixed effects in the specification of equation(1), and finds that distance matters almost to the same degree as in the baseline specification. It it therefore
unlikely that a lot of redistribution within an empire happens before coins enter circulation.

A.4

Arab conquests and the Mediterranean.

Figure A.3 shows the number of coins crossing the Mediterranean, and their composition. The time of the Arabconquests, indicated by two dashed vertical lines, corresponds to a decline of north-south flows (almost entirely
flows of Roman/Byzantine coins), and an increase in east-west flows. The new flows along both axes are almost
entirely made of Islamic coins.
We employ a set of descriptive regressions to further decompose these changes. We estimate by PPML
countmhpt = exp amh + amp +
b1 Mediterraneanmh × Aftert + b2 Mediterraneanmh × Aftert × Islamicp + umhpt .

(A.1)

We aggregate all hoard (h) and mint (m) locations to 1◦ × 1◦ cells, separately for each time period (t), and notefor each coin which one of fourteen aggregate political blocks p had issued it.2 Countmhpt is the number of coinsissued in cell m under empire/dynasty p and found in a cell h, both within time period t. Mediterraneanmh is
a dummy that is one if the geodetic line between cells m and h intersects the Mediterranean; Aftert is a dummy
equal to one if t is between 713 and 900, and zero if between 400 and 630; Islamicp is one if the coin is of Islamic
issue (any dynasty); amh and amp denote mint cell × hoard cell and mint-cell × dynasty/empire fixed effects,
respectively. The objective is to investigate whether the Mediterranean acts differentially as a barrier to coin flows
after the Arab conquests, and if so, for coins of which issue. Table A.3 presents the results. We drop all mint
cell × empire/dynasty combinations that did not produce coins. Column (1) shows a negative coeﬀicient on theinteraction of the Mediterranean and post-conquest dummies, so that after the Arab conquests coin flows declined in
1Comparing the pairwise flows of objects in the two datasets directly does not make sense since Terra Sigillata
are produced in locations that are very different from coin mints (see Figure 4 in their paper).
2
These political blocks are: Eastern Roman Empire, Western Roman Empire, Roman Empire (pre-division),
Sasanians, Umayyads, Spanish Umayyads, Abbasids, Fatimids, Samanids, Visigoths, Ostrogoths, Vandals, Merovingians, and Carolingians. See Appendix Figure B.1 for a breakdown of these and more aggregate political entities.

1cell pairs across the sea. Column (2) shows a positive coeﬀicient on the triple interaction: Islamic coins were facing
disproportionately lower barriers on sea routes in the post-conquest world, conditional on origin and destination
characteristics. Column (3) contrasts this with Roman/Byzantine coins, which experience disproportionately higher
barriers. Column (4) shows similar estimates with hoard cell × time and mint cell × time fixed effects, neutralizing
potential location-time-specific confounders. All specifications point to the same facts highlighted by Figure 4:
there are relatively fewer coins flowing across the Mediterranean in the 8th and 9th century than before; the dropis particularly strong for Roman coins, and the emergence of flows of Islamic coins partly make up for this drop.

A.5

Coin stocks versus coin flows, a numerical exploration.

We describe below the stylized numerical model used to generate figure 5.
Figure A.4 uses our data to empirically explore the hypothesis that gravity regressions with flows of durables
over longer horizons bias the distance elasticity towards zero. It shows a coeﬀicient plot of the following regression:(countmthτ = expX)′βτ ′ log distancemh × 1 (t − τ = τ ) + αmt + αhτ + εmthτ(A.2)

τ ′ ∈T

where T = {0, 20, 40, 60, 80, 100} and mint and hoard tpq dates are rounded to 20-year intervals. Coins with longer
timespans between mint and hoard tpq dates are omitted. We estimate the coeﬀicients using PPML.
The results confirm that the distance elasticity for coins that have travelled for longer is lower (i.e. closer tozero) than for coins that have travelled for shorter periods. Section 2.2 and Figure 5 provide the intuition for thisresult.

A.6

Estimation of λ.

To estimate λ, we divide coins by their age of deposit (using the tpq as the date of deposit) into n-year bins (forn = 10 and n = 20). We calculate the fraction f (n) (k) of coins that are in bin [k, k + n), and estimate the parameterof exponential decay fromklog f (n) (k) = λ̃(n) + εk .
nTable A.4 shows the OLS estimation results using 10-year and 20-year bins. The estimates of λ can be recoveredfrom λ = 1 − exp(λ̃), yielding, respectively, λ̂10 = .15 and λ̂20 = .301.

A.7

Validating the geospatial model.

We compare the implied travel times from our geospatial model (section C.3) to those reported by the 10th-century
Arab geographer al-Maqdisī in his work The Best Divisions for the Knowledge of the Regions (Al-Muqaddasī,1994).3 Figure A.5 shows the comparison. Our model generates travel times that are slightly larger for shorter
distances, and on average similar for longer routes.

A.8

Changes in real consumption in the ancient world.

Table A.5 shows the levels of real consumption per capita and its components before and after the Arab conquests.
Figure A.6 plots, for all 13 regions, the time series of real consumption per capita from ad 380 to ad 900, and thecomponents that partition real consumption (openness, technology, and trade deficits).

3

Al-Maqdisī reports cities and (unsystematically) distances (in travel stages, post stages, and farsakhs) or travel
times (in days, or nights in the desert) between cities in different parts of the Islamic lands. Historians note that it
is unlikely that al-Maqdisī did indeed travel to all these regions, and some distances and travel times are unrealistic.
We exclude the most egregious outliers.

2

A.9

Tables and figures

TablesAppendix Table A.1: Gravity and border effects: # coins vs values of coins
Dep. var.: # Coinsmdh
Log Distance

(1)

(2)

(3)

(4)

-1.138∗∗∗(0.12)-1.002∗∗∗(0.13)-0.727∗∗∗(0.10)-0.694∗∗∗(0.10)-1.945∗∗∗(0.62)

Political border
Hoard Cell FE
Mint × Empire Cell FE

Yes
Yes

Yes
Yes

-1.541∗∗∗(0.41)
Yes
Yes

Yes
Yes

SampleIntensive and Extensive Margins

Intensive Margin only

Estimator

PPML

PPML

PPML

PPML

Pseudo-R2
Observations

0.767
2177480.778
217748

0.737
6312

0.744
6312

Standard errors in parentheses, clustered at mint cell × empire and hoard cell level.
∗p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: This table presents variations of equation (1). The dependent variable is thenumber of coins in a hoard cell h from a mint cell m issued by a political entity p.
Columns 1 and 2 reproduce columns 1 and 2 from table 1 (intensive and extensive
margins of coin flows combined), while columns 3 and 4 exploits only the intensive
margin of coin flows (restricting the sample to m × h pairs where some coins from mint
cell m were found in hoard cell h).

3Appendix Table A.2: Do coins get redistributed within empires before entering circulation?
Dependent variable: # Coinsmdh(1)
Log Distance
Empire × Hoard Cell FE
Mint × Empire Cell FE

(2)
∗∗

(3)
∗∗

-0.709(0.092)-0.924(0.17)-0.669(0.11)-0.839∗∗(0.068)

Yes

Yes
Yes

Yes

Yes
Yes

Gold onlyGold only

SampleEstimator

(4)
∗∗

PPML

PPML

PPML

PPML

41443

41443

11367

11348

2

R
Observations

Standard errors in parentheses, clustered at mint cell × empire and hoard cell level.
+p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01
Notes: This table presents variations of equation (1). The dependent variable is thenumber of coins in a hoard cell h from a mint cell m issued by a political entity p. The
regression drops all (m, d) combinations that have no emitted coins. Hoard and mint
cells are 1◦ × 1◦ . Observations only include those that remain after dropping singletonsand separated observations. Political entities here are categorized into fourteen divisions.

4Appendix Table A.3: The Mediterranean Before and After the Conquests
Dependent variable: Number of Coins
Crossing Mediterranean × After Conquests

(1)

(2)

(3)

(4)

-1.774∗∗(0.46)-3.141∗∗(0.53)-0.712(0.66)-1.751(1.24)

7.171∗∗(0.91)

4.835∗∗(0.97)

8.382∗∗(1.15)-3.108∗∗(0.79)-2.976∗∗(0.71)

Yes
Yes
Yes

Yes
Yes

Crossing Mediterranean × After Conquests × Islamic Coin
Crossing Mediterranean × After Conquests × Roman Coin
Mint Cell × Empire FE
Mint Cell × Hoard Cell FE
After Conquests FE
Mint Cell × After Conquests FE
Hoard Cell × After Conquests FE

Yes
Yes
Yes

Estimator
Observations

PPML
10350

Yes
Yes
Yes

Yes
Yes
PPML
10350

PPML
10350

PPML
6023

Standard errors in parentheses, clustered at the hoard × era and mint × era level.
+p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01
Notes: This table presents various specifications of equation (A.1). The dependent variable is the numberof coins in a hoard cell from a mint cell × dynasty × era (where era is before vs after the conquests). The
regression drops all mint × dynasty combinations that have zero emitted coins. Hoard and mint cells are1◦ × 1◦ . Flows before the conquests are those with mint date after 400 and tpq before 630; flows after
the conquests are those with mint date after 713 and tpq before 900. Observation counts only include
those that remain after dropping singletons and separated observations. “Crossing Mediterranean” is adummy that is one if the geodesic line between hoard and mint cell intersects with the Mediterranean.
“Islamic Coin” and “Roman Coin” are dummies equal to one if the coin is of Islamic issue (any dynasty)
or Roman/Byzantine issue, respectively. “Empires” here are categorized as Sasanian, Roman-Byzantine,Franks, Islamic, Germanic Tribes, and Other Christian.

Appendix Table A.4: Estimation of λDependent variable: Log share of coins in bin [k, k + n)
(1)

(2)

-0.163∗∗(0.010)-0.358∗∗(0.032)Bin size n

10

20

R2
Observations

0.829
55

0.815
31

k/n

Standard errors in parentheses.

5Appendix Table A.5: The components of real consumption, ad 460-620 and ad 700-900
Consumption

Import share
1 − πnn

Xn /pnLnal-Andalus
Aquitaine and Basque Country
Francia and Germania
Northern Italy and Balkans
Southern Italy
Byzantine Heartlands
al-Sham (Greater Syria)
Northern Syria and Caucasus
al-Iraq, al-Jibal, Khuzistan, Kirman
Eastern Caliphate
Jazirat al-arab and al-Yaman
Misr (Egypt)al-Maghrib

AverageTechnology
1/θTn

Trade deficits
Xn
wn L n460-620700-900

460-620700-900

460-620700-900

460-620700-900

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.50

0.93

0.23

0.01

0.47

1.03

0.99

0.90

( 0.08 )( 0.13 )( 0.08 )( 0.01 )( 0.05 )( 0.15 )( 0.39 )( 0.04 )

0.35

1.27

0.22

0.05

0.35

1.20

0.94

1.05

( 0.03 )( 0.17 )( 0.04 )( 0.02 )( 0.03 )( 0.16 )( 0.06 )( 0.02 )

0.21

1.49

0.18

0.00

0.25

1.52

0.80

0.98

( 0.02 )( 0.20 )( 0.04 )( 0.01 )( 0.03 )( 0.20 )( 0.03 )( 0.01 )

1.07

0.79

0.31

0.05

0.87

0.79

1.12

0.99

( 0.05 )( 0.11 )( 0.07 )( 0.04 )( 0.10 )( 0.12 )( 0.11 )( 0.04 )

0.78

0.64

0.41

0.72

0.91

0.35

0.75

1.31

( 0.04 )( 0.24 )( 0.06 )( 0.23 )( 0.06 )( 0.11 )( 0.04 )( 0.60 )

3.40

0.71

0.64

0.12

0.96

0.62

2.73

1.11

( 0.44 )( 1.08 )( 0.06 )( 0.13 )( 0.10 )( 0.12 )( 0.68 )( 19.33 )

1.34

0.97

0.17

0.02

1.36

1.22

0.94

0.80

( 0.05 )( 0.15 )( 0.08 )( 0.02 )( 0.16 )( 0.19 )( 0.10 )( 0.04 )

0.71

0.89

0.08

0.04

0.93

1.08

0.75

0.81

( 0.07 )( 0.15 )( 0.05 )( 0.09 )( 0.13 )( 0.23 )( 0.04 )( 0.12 )

1.32

1.40

0.08

0.07

1.20

1.27

1.08

1.08

( 0.09 )( 0.21 )( 0.02 )( 0.03 )( 0.09 )( 0.20 )( 0.02 )( 0.04 )

0.64

0.93

0.02

0.00

0.63

0.93

1.02

1.00

( 0.05 )( 0.18 )( 0.01 )( 0.00 )( 0.07 )( 0.18 )( 0.03 )( 0.01 )

0.17

0.53

0.17

0.15

0.23

0.44

0.70

1.16

( 0.03 )( 0.11 )( 0.07 )( 0.12 )( 0.04 )( 0.23 )( 0.00 )( 0.30 )

1.38

0.96

0.04

0.32

1.74

0.77

0.78

1.13

( 0.09 )( 1.29 )( 0.03 )( 0.29 )( 0.13 )( 0.24 )( 0.02 )( 3.42 )

1.12

1.49

0.09

0.45

1.16

0.71

0.95

1.82

( 0.06 )( 0.36 )( 0.02 )( 0.17 )( 0.04 )( 0.14 )( 0.04 )( 0.58 )

1.00

1.00

0.20

0.15

0.85

0.92

1.04

1.09

( 0.00 )( 0.00 )( 0.02 )( 0.02 )( 0.05 )( 0.13 )( 0.08 )( 1.50 )Notes: This table shows the levels (not logs) of all equilibrium variables we use to compute changes in real capita
and its components in table 4. Columns 1 and 2 show real consumption per capita normalized to one on average
within each period ((Xn /pn )/Ln ), for ad 460-620 and ad 700-900 respectively. Columns 3 and 4 show a measureof trade openness, imports as a share of consumption (1 − πnn ), for ad 460-620 and ad 700-900. Columns 5
1/θ
and 6 show technology (Tn ) for ad 460-620 and ad 700-900. And columns 7 and 8 show trade deficits, i.e.
n
) for ad 460-620 and ad 700-900. Standard errors (in parentheses underconsumption relative to income ( wXn Lneach point estimate) are computed from re-estimating our model on 100 bootstrapped samples from our coin data.

6

Figures.2.02

Share of hoard's coins.15

Density

.015

.01

.1

.05
.005

0

0
0

100

200

300+

0

100

Coin age at time of deposit (tpq)

200

300

Age of coins in circulation hoard

Appendix Figure A.1: Comparison with Circulation Hoards in Banaji (2016)1

Log # objects
1.5
2

2.5

Notes: The left panel reproduces Figure 3b. The right panel shows the average share of coins in each 10-year age bin in the circulation
hoards of Banaji (2016), Chapter 6, who reports the issuing emperors (but not mint dates) of the coins in these hoards. We draw mint
dates uniformly from the ruling years of these emperors.

8.6

8.8

9
9.2
Log Effective Distance
Terra Sigillata

9.4

9.6

Coins

Appendix Figure A.2: Comparison with flows of West Roman Terra Sigillata (Flückiger et al.,2022)
Notes: The figure shows a binscatter of the log number of objects flowing (either coins or number of Terra Sigillata) between two 0.5 ×
0.5 degree cells, against the log effective distance between cells. Both are de-meaned by origin and destination location. Cell definitions
and effective distances are from Flückiger et al. (2022). The coin data is restricted to hoards with tpq up to 450 and that lie within the
aforementioned cells.

7Number of coins crossing the Mediterranean

20000
5000
1000
500
200
100
25
10
5
1

0
375 - 400

500 - 525

625 - 650

750 - 775

875 - 900

Year of minting
North-South

North-South: Roman/Byz.

North-South: Islamic

East-WestEast-West: Islamic

Appendix Figure A.3: Number of coins flowing across the Mediterranean
Notes: The figure shows the number of coins minted in the 25-year interval on the horizontal axis, that are minted on one side of theMediterranean, and are found on the other. Flows include both directions. The north is defined to go from the Pyrenees to Byzantine
Turkey, east from Byzantine Turkey to Egypt, south from Egypt to the Maghreb, and west from Maghreb to Aquitaine. Border regions
are included in these definitions, so regions are partly overlapping.

-.4

Distance Elasticity

-.6

-.8

-1

-1.2
0

20

40

60

80

100

Time difference bins: hoard tpq minus mint date, years

Appendix Figure A.4: The distance elasticity declines as coins age
Notes: The figure shows the distance elasticity estimates when estimating equation (A.2) using PPML.

8

40

Travel time in days, our model

30
20

10

5

2

1
Routes45-degree line
1

2

5

10

20

30

40

Travel time in days, al-Maqdisi

Appendix Figure A.5: Comparison of travel times, geo-spatial model versus Al-Muqaddasī (1994)
Notes: Each dot refers to a city-to-city connection for which Al-Muqaddasī (1994) lists the travel time in days. We
exclude desert routes (where he reports travel times in nights or in watering stations, routes with cities cannot befound in al-Thurayya, as well as the route between Tahart and Fes, which he claims can be travelled in three days
despite it being a distance of more than 600 kilometers (our model predicts 22 days).

910(b) Aquitaine and Basque Country(c) Francia and Germania(d) Northern Italy and Balkans

Notes: This figure complements figure 9, showing for all 13 regions the time-series of (logged) real consumption per capita (panel a) from ad 380 to ad 900 for the Byzantine Heartlands
and for Francia and Germania, and its partition into ‘openness’ (panel b), ‘technology’ (panel c), and ‘trade deficits’ (panel d) as in equation (20). The technical appendix provides
details on the computation of each term, using the dynamic model in equations (4), (6), (10), and the parameter estimates from (18)-(19). Units for technology are chosen such averagereal consumption per capita equals one for each period. 95% confidence intervals (shaded areas) are computed from fully re-estimating our model on 100 bootstrapped samples fromour coin hoard data. Our estimates are imprecise for some periods because we have too few coins, as evidenced by the spikes of the confidence intervals.

Appendix Figure A.6: Real consumption per capita ad 380-900, openness, technology, and trade deficits(a) Al-andalus

11(f) Byzantine Heartlands(g) al-Sham (Greater Syria)(h) Northern Syria and Caucasus

Appendix Figure A.6: Real consumption per capita ad 380-900, openness, technology, and trade deficits (cont.)(e) Southern Italy

12(j) Eastern Caliphate(k) Jazirat al-Arab and alYaman(l) Misr (Egypt)(m) al-Maghrib

Appendix Figure A.6: Real consumption per capita ad 380-900, openness, technology, and trade deficits (cont.)(i) al-Iraq, al-Jibal Khuzistan, Kirman

References
Al-Muqaddasī (1994): Aḥsan al-taqāsīm fi maʾarfat al-aqalīm (The Best Divisions for Knowledge of the Regions),
Garnet Publishing (transl. B.A. Collins).
Banaji, J. (2016): Exploring the Economy of Late Antiquity: Selected Essays, Cambridge University Press.
Flückiger, M., E. Hornung, M. Larch, M. Ludwig, and A. Mees (2022): “Roman transport network
connectivity and economic integration,” The Review of Economic Studies, 89, 774–810.

13

B Coin hoard data
Our numismatic data consists of two datasets: first, the set of hoards from the current release of the Framing
the Late Antique and Early Medieval Economy project (FLAME, 2023). FLAME is a large collaborative effort ofhistorians and numismatists that records data on coin hoards around the Mediterranean and Europe from between
ad 325 and ad 725. We use the most recent release (January 2023) which has data on about 1.7m coins belonging
to more than 9,000 hoards. Since the temporal and spatial focus of our study does not entirely overlap with thatof FLAME, we complement their data by constructing a hand-coded dataset on hoards between ad 700 and ad
900, and hoards with a heavier emphasis on near eastern coins. We describe the hand-collected data and FLAME’s
data in turn.

B.1

Hand-collected data

We search the numismatic and archaeological literature for descriptions of coin hoards or coin finds with a terminus
post quem (= date of the most recent content) of roughly between ad 700 and ad 950, that were discovered inEurope, North Africa, or the Middle East. For the sake of brevity we will refer to a single coin or a collection ofcoins that was found together in one place as a “hoard” (i.e. unless specifically mentioned, we do not distinguishbetween single finds, stray finds, mini-hoards, or full hoards). We exclude hoards that largely contain silver thatwas brought via the Viking route or that clearly have a Viking connection.4 We likewise exclude records fromexcavations, unless they are described as a hoard or constitute a set of coins that were found together in the same
location (e.g. in the same room of an excavated building).
An (at least approximate) findspot must be known for a hoard to be of use in our analysis. For each hoard
we record the latitude and longitude of its findspot. When the findspot is known only with a low level of precision(e.g. at the country or region level) we code this in a separate dummy variable. Importantly, we do not record
coins in museums or collections that have unknown findspots. While we digitize many descriptions of hoards thatare incomplete, we omit hoards of which no information on the vast majority of coins has been published.
For each coin (or group of coins with identical properties) in a hoard we record, if documented by the authorsof the hoard catalogue:• The mint where the coin was minted, or believed to have been minted. When a coin is believed to have been
an imitation, we note this separately.
• A time interval (consisting of a start year and end year) during which the coin was minted or is believed
to have been minted. For some coins, such as most Islamic dirhams, this information is imprinted on thecoin. For others, we code this as the shortest time interval during which the coin could have been minted,
taking into account the denomination of the coin, the ruler under whose authority it has been issued, as wellas his/her dynasty, and other information about coin types (e.g. pre/post-reform coinage). When the coinhas been dated through the regnal year of the ruler or in the Islamic calendar, we convert this to Gregoriancalendar years.
Beyond the attributes above, we record denomination, material, and issuing rulers and dynasties (mostly withdating of the coins in mind). This information, if known, is typically furnished by the authors of hoard catalogues
in the numismatic literature. We do not distinguish between fragments and entire coins.
The geocodes of the hoards and mints are only approximate. We code Nomisma IDs for the mints based on
the proximity of the place of minting, not based on the dynasty, e.g. “Siqilliyah” (Sicily) can be also used for
non-Islamic issue.

B.1.1

Hoards in the Near East and North Africa

Table B.5 shows the list of hand-coded hoards from the Near East and North Africa, along with references. Thesehoards consist mainly of Sasanian and/or Islamic coins, and sometimes Byzantine issue. We code approximate
4

Among the list from Appendix 3 of McCormick (2001), these are the hoards in Britain, Scandinavia, andSchleswig-Holstein (Germany). We also digitized the 10th century Máramaros county hoard (Fomin and Kovács,
1987), but drop it as its content (consisting of many imitations, as well as dirhams from the Samarkand andal-Shash mints) indicate that it was clearly brought in from the east.

14

mint locations based on the proposals in the literature, typically giving preference to the suggestions of the authorsof the original hoards.
A couple of notes on specific hoards:• We digitize the Umm-Hajarah hoard based on the description by al ’Ush (1972a) but follow Noonan (1980)
in treating the isolated Seljuk coin that Al-’Ush dates to 689-690 AH as not belonging to the hoard.
• We digitize Hoge (1997)’s description of a hoard from “North Africa (or Spain?)”, and assign Kairouan as
approximate location (and note that the precise location of the hoard is immaterial to our exercise). We
treat the Safavid dinar that is 650 years younger than the other coins (Hoge: “no doubt added to the otherpieces ’in trade’.”) as extraneous to the hoard.

B.1.2

Islamic hoards in Spain and France

Tables B.6 and B.7 show the hand-digitized hoards from Islamic Spain (al-Andalus) and Islamic coin finds fromsouthern France.

B.1.3

Other Islamic and Byzantine hoards in Europe

We digitize the hoards, mini-hoards, and stray finds from McCormick (2001)’s survey of Arab and Byzantine coinsin Europe (Appendix 3) between 668 and 900. We add those to our dataset, except when already covered in ourother sources. We update hoard descriptions for which newer catalogues are available.5 Finally, we exclude thecontested Odoorn/Zuidbarge (1859–60) hoard, as the identity of it as a single hoard is not clear, some of the coins
had been converted into jewellery, and the contents are not well described.6

B.1.4

Byzantine hoards

The hoards reported in the corpora by Pennas (1991), Füeg (2007), and Nikolaou and Touratsoglou (2019) form
the basis of our collection of Byzantine hoards (the corpus on earlier finds by Morrisson et al. (2006) is mostly
already incorporated into FLAME). Information on particular regions come from Mirnik (1981) (Balkans), Arslan(2005) (Italy), Kovács (1989) (Hungary), and Wołoszyn (2009) (Central Europe). Hoard catalogues typicallyrefer to collection catalogues (Sabatier, 1862, Wroth, 1908, Grierson, 1968, 1973) which we use to retrieve mint
date intervals and likely mints.7 We exclude coin finds from running excavations, unless the coins were found as
individual parcels in a specific location. Tables B.8 and B.9 show our hand-coded byzantine hoards.

B.1.5

Carolingian hoards

We follow Simon Coupland’s Checklist (Coupland, 2011a, 2014, 2020) and digitize hoards and finds primarily basedon the corpora presented by Völckers (1965), Duplessy (1985), and Haertle (1997), giving priority to more recent
descriptions. Tables B.10 to B.14 show details. We follow the mint codings of Louis the Pious’ Christiana religio
coins given by Coupland (2011b). As mentioned above, we exclude the contested Odoorn/Zuidbarge hoard.

B.2

FLAME

FLAME records their data in three different tables: coin finds, coin groups, and mints. In the coin find table eachobservation is a find that contains one or more coin groups; in the coin group table each observation is a set ofcoins with common recorded attributes (and linked to the coin find ID), including a mint and an interval for theyear of minting. In the mint table each observation is a mint, and the mint name string allows these to be matched
to coin groups. Mints and coin finds are geocoded.
5

A35 (Steckborn): Ilisch (2005), A8 (Cagliari): Saccocci (2005), who also mentions an Aghlabid semi-dirham of
Muhammad I found in Crotone, Sicily. We update A28 (Porto Torres, Sardinia) based on the number and datings
reported in Füeg (2007)’s corpus, likewise the dates from A34 (Reno River).
6
See Coupland (2011a) for a discussion of these issues.
7
For a large part of the time interval that is not covered by FLAME, Byzantine gold and silver coins are believed
to have been exclusively issued at Constantinople (Grierson, 1968).

15

The records in FLAME thus include a superset of the attributes in the hand-coded data above, except (i) the
material of the coin, which we code based on the denomination; (ii) the weight and dimensions of the coins, which
are sometimes (but not systematically) coded in the comments. We convert the FLAME data to the same structure
as our handcoded data, including the following cleaning steps:• A small number (6) of coin groups has a start year that’s after the end year; we switch those around.
• FLAME contains start and end dates for the coin find itself. For a small number of coin groups the end
date of the coin find falls in between the start and end dates of the coin group. This is often the case whenvery broad ranges have been given for the coin group, and so we truncate the coin group interval at the end
date of the find.
• For Sasanian coins, we adhere to the mint codings in FLAME. A number of coins report the mint abbreviation
but not the mint, we code and locate them analogously to how we coded them in the hand-coded coins (seebelow).
• A number of coin groups record a mint string that is not included in FLAME’s mint file. We code Nomisma
ID’s for those mints, wherever possible.
• A large fraction of FLAME coins don’t have mints or dates: often large hoards are not recorded by coin(just the total number of coins). Out of 1.7m coins, about 340k have mint and dates.

B.3

Locating mints

For FLAME data, we follow the attribution of mint locations done by the authors of the respective FLAME entries.
For hand-collected data, we attempt to map the hand-coded mints to Nomisma (2023) IDs for the mints (nmo:Mint).
Whenever a geocode for a mint is not available in Nomisma, or whenever the mint is not represented in Nomisma,
we hand-code the geocodes. These geocodes should only be regarded as approximate and with a degree of precision
required for our particular application in mind. Table B.3 shows the mints we add to Nomisma, along with ourcodings, and Table B.4 shows the codings for existing Nomisma mints without geocodes.

B.3.1

Sasanian mints

The location of Sasanian mints and the identification of Sasanian mint signatures are contested. We generally follow
the reading of the original hoard descriptions, except in situations where these are dated and the literature nowadays
prefers different readings. Regarding the approximate location of the mints, we decided to code the approximate
location for most signatures following the consensus in the literature; in some cases where the literature only agrees
up to the region we chose Nomisma IDs from mints of that region. As with the other codings, the Nomisma IDs
should only be seen as approximating the location of the mint, and do not carry any information on dating. Table
B.16 summarizes our signature codings with their approximate mint locations.

B.4

Political entities and the geography of hoards and mints

B.4.1

Dynasties/Empires

We record dynasties/empires through the dynastyname field of FLAME data, and an equivalent field of the handcoded data. We aggregate these to 10 more aggregate (“level 1”) dynasties/empires, and seven most aggregate(“level 2”) dynasties/empires. Figure B.1 shows the breakdown of recorded dynasties in our final sample.

B.4.2

Location of hoards and mints

Figure B.2 show the location of mints and hoards of our final dataset. Only locations corresponding to coins thatwere minted after ad 400 are shown. Figure B.3 shows details for western Europe and the eastern Mediterranean.

16Eastean E

rn Romom(54,19

nR

ter

mpire

s
We
an

Um

West
Rest ofFrancia (2Franks ,083)(1,966)ns (12,30

1)

am

ic

ks (

42,5

Fra
n
n (12,34

(4

72)

8)

Visigoths (5,934)

Vandals (2,568)
Ostrogoths (1,909)
Rest of Other Christian (1,000)

)
Is
l

5)

2)

Sasania

37
9,3
24
e(

5)

ntin

,9
1

yza

3)

(3,
16

Germanic Tribes (10,51

5)
Other Christian (1,000)

ks (

42,5

n-B

,52

ic

8,
15

ma

lam

Fra
n

Sasania

s(

17

r Is

Ro

(38

sid

8)

ns

ba

81
6,

Ab

5)

Ro

(2

(3,
16

Ot
he
ngia

,1
9

d

roli

ma

ya

ic

84
(1
re

37
6)

72)

Sasanian (12,34
8)
Germanic Tribes (10,51
5)
Other Christian (1,000)

Appendix Figure B.1: Dynasties/Empires

Region boundaries
Mint
Hoard

Appendix Figure B.2: Mints and Hoards

17

pi
an
om
R

ntin
ay

9)

r Is

lam

Ca

yza

Um

,7
8

n-B

s(

e(

9)

24

6
,8

sid

Em

(8

4)

ds

9,3

ya

51
7,

)

(1

ay

ba

17

fO
the

7)

ain

,94

Sp

10

of

m

st
o

9)

ds

e(
pir

ya

Em

ay

U

Ab
Re

Region boundaries
Mint
Hoard

(a) Western Europe

Region boundaries
Mint
Hoard

(b) North-eastern Mediterranean

Appendix Figure B.3: Mints and Hoards: Details
Note: Maps show coins minted after ad 400

18

B.5

Tables and references
Appendix Table B.1: Summary statistics: CoinsHas mint date interval
Has mint location
Has mint location and date interval
Mint date interval, years
Mint date interval, start year
Mint date interval, end year
Age at tpq
Has material
Coin is gold
Coin is silver
Coin is copper/bronze
Has denomination
Has some empire/dynasty information
Geodesic distance mint to hoard, km

count

mean

sd

min

p10

p50

p90

max

494,311494,311494,311
419,008
419,008
419,008
419,008494,311494,311494,311494,311494,311494,311
273,343

0.85
0.55
0.55
29.48
465.45494.93
59.00
0.98
0.07
0.18
0.74
0.99
0.69
769.74

0.36
0.50
0.50
41.66
186.70184.48
81.54
0.15
0.25
0.38
0.44
0.10
0.46
784.00

0
0
0
-19
34
79
0
0
0
0
0
0
0
0

0
0
0
1
306
333
6
1
0
0
0
1
0
59

1
1
1
20
375
395
29
1
0
0
1
1
1
503

1
1
1
58
815
840
154
1
0
1
1
1
1
1,631

1
1
1
432
949
950
805
1
1
1
1
1
1
6,302

Notes: Sample consists of all coins from hoards with tpq between 325 and 950. “Age at tpq” is definedas tpq of the hoard minus the midpoint of the mint date interval.

Appendix Table B.2: Summary statistics: Hoards

Hoard tpq
Number of coins in hoard
Fraction of coins with mint date interval
Fraction of coins with mint location
Fraction of coins with mint date interval and mint location
Average mint date interval
Average age of coins at tpq
Fraction of coins with material
Fraction of coins that are gold
Fraction of coins that are silver
Fraction of coins that are bronze
Fraction of coins with denomination
Fraction of coins with empire/dynasty information
Average distance of coins from mint, km

count

mean

sd

min

p10

p50

p90

max

5,503
5,503
5,503
5,503
5,503
5,503
5,503
5,503
5,503
5,503
5,503
5,503
5,503
5,400

590.85
89.83
0.98
0.87
0.86
23.24
25.44
0.99
0.29
0.15
0.55
0.99
0.80
684.90148.14823.96
0.12
0.27
0.28
32.66
41.76
0.08
0.45
0.35
0.49
0.08
0.38
610.64

325
1
0
0
0
0
0
0
0
0
0
0
0
0

376
1
1
0
0
1
0
1
0
0
0
1
0
88

578
1
1
1
1
11
10
1
0
0
1
1
1
533

782
81
1
1
1
81
50
1
1
1
1
1
1
1,462

950
43,867
1
1
1
377
522
1
1
1
1
1
1
6,124

Notes: Sample consists of all coins from hoards with tpq between 325 and 950. “Age at tpq” is defined as tpq of the hoard
minus the average coins’ midpoint of the mint date interval.

19

Mint

id

Location

LatitudeLongitude

Notes

Abarqubadh

abarqubadh

31.28027

47.49266

GanzakKufa

37.012332.05114

46.201944.44017

Mayadin35.00530.65388

40.4235
50.27472

34.46545.855

Nihavand, Iran

37.2310534.18879-3.70848
48.37046

Dinawar, Iran

34.583333

47.43333

33.52303

46.86539

NaysanPanjshir Valley
Bushehr

30.809335.254095
28.911947.5628
69.456014
50.8367roda
sarakhs

Sarakhs, Iran

42.26478
36.5449

3.17887
61.1577

oman

Oman

23.51234

58.27000“This mint was in the district of Khusra-shadh Bahmân (the district of the Tigris) in Irâq, between Wâsit
and al-Basra and near the border with Khuzistân.”
Lloyd (2023)
Sasanian mint (AT)Rare Abbasid mint during al-Mansur’s reign, situatedclose to Kufah (138-146 AH).
A mint in Syria, on the Euphrates“[Bizamqubadh] was an alternative name for Arrajânin Fars, and also appears to have struck Arab-Sasanian
issues.” Lloyd (2023)“This mint-name is that of a district (astân) in Irâq,
which covered an area to the north-east of Baghdad.
Le Strange notes that this district was also known as
Shâd Fîrûz - presumably its former Sasanian name.
The town of Hulwân itself evidently lay just over theborder in Jibâl province, although at this period it
appears to have been included with ‘Irâq for administrative purposes.” Lloyd (2023)
The archaeological site of Madinat Ilbira.
“The term is the Arabic name for Nihavand.” (British
Museum x107840)“Mah al-Kufah = Dinawar (sometimes incorrectly
written Daynawar) in the middle ages was one of themost important towns in Djibal (Media); it is now inruins. The exact location is 34 degrees 35 minutes Lat.
N. and 47 degrees 26 minutes E. Long. (Greenwich).”Lockhart (2012)
A district with capital al-Sirawan; the location of alSirawan is from Cornu (1983)’s atlas.
Sasanian mint Maysan (MY)Panjshir Valley, modern-day Afghanistan
Sasanian mint (LYW/ LYWARTHST/KWN LYW/GNC LYW); the location is from FLAME
A carolingian mint in Rosas, Spain“A town in Khurâsân located roughly midway between
Marw and Abrashahr. Sarakhs lay on the eastern bankof the Mashhad river, about forty or fifty miles northof its confluence with the Herât river.” Lloyd (2023)
Lloyd (2023): “Modern Oman on the Persian Gulf.”

Adurbadagan
al Hashimiyah

adurbadaganal-hashimiyahal Rahba
Arrajanal-rahba
arrajanHulwanhulwanMadinat Elvira
Mah al Basrahmadinat_elvira
mah-al-basrah

Mah al Kufah

mah-al-kufah

Masabadhanmasabadhan

MaysanPanjshir
Rev-Ardashir

maysanpanjshir
rev-ardashir

Roda
Sarakhs

Uman

Appendix Table B.3: Manual mint codings I: new mints

20Nomisma ID

Nomisma Note

LatitudeLongitude

Note

al-Abbasiyah“Earfly Abbasid site in North
Africa”“In the district of Shadh Bahman
in Iraq, but its exact location unknown. Klat, 16.”
“al-Madinat al-Mutawakkiliyah isjust north of Sammara and was
built by the Abbasids.”“The name of two districts, withtehir chief-towns, named Greater
Manadhir & Little Manadhir in
Khuzistan, Iran”

35.62183407

10.18089991

30.53269083

47.87593421

According to Abdul Wahab (2012),
three miles south-east of Kairouan.
Geocodes based on Fig. 11 in Morony(1982).

34.2621862

43.85500034

Close to Samarra, Iraq

31.97753445

48.69644554“Some place in North Africa”

36.30565739

10.13850323“Al-Samiya was in the Shatt alArab area of lower Iraq.
“Lower Bihqubach (sic) in Iraq on
the Euphrates”

30.6617666

47.78548511

Lloyd (2023): ”Manâdhir was a district within the province of Khuzistân,
situated between the Dizfûl and Dujayl rivers above their confluence northof Ahwâz. It was apparently divided
into two parts named Greater and Little Manâdhir, each containing a chief
town with the same name.”
Unknown location, coding it tomodern-day Tunisia
Coding to Shatt al-Arab.

31.56718959

45.22725183

Dastawa
Ma’din Bajunays“South of Qazvin”“Province north of Lake Van”

35.75554989
40.223509

50.08839336
43.8355181

Mani

Klat is uncertain of its location although the prefix Mah occurs inolder names for Dinavar & Nihavand. Quarter of Jibal. Klat”“Exact location on the river or
canal of the same name in Khuzistan not know. Klat, p.. 18”“A small province which stretches
along the foot of the Great Alburz
chain of mouintains. Klat, p. 17.”
“Surraq or DAWRAQ (or Dawraq
al-Fors), name of a district (k�´ra;
Moqaddasƒ´, pp.
406-07), also
known as Sorraq, and of a townthat was sometimes its chef-lieu inmedieval Islamic times.”“Tabaristan,also known as
Tapuria, was the name of the former historic region in the southerncoasts of Caspian Sea roughly inthe location of the northern andsouthern slopes of Elburz range in
Iran.”“Unknown location in Morroco”

34.38582341

47.97904114

30.8755

49.7131

Location very approximate in western
Armenia.
Lloyd (2023) puts it either at Mah al
Basrah (Nihavand) or Mah al Kufa
(Dinavar).
Our chosen geocode ishalfway between the two.
From FLAME.

35.96088616

54.03571139

Wikipedia “Qumis (region)”

30.65094882

48.67463446

Coding to Shadegan, Iran.

36.5656

53.0588

From FLAME

31.523-5.5313

al ’Ush (1982) identifies it with“Todr’a”, and cites Renou (1846) (incorrectly as authored by “Lavoix”) saying that it was located fourty kilometers west of Sijilmasa, at a river of thesame name. That would place it close
to Tinghir, Morocco.

al-Furat

al-Madinat
Mutawakkiliyahal-al-Manadhiral-Mubarakahbasid)
al-Samiyah(Ab-Bihqubadh af-Asfal

Nahr Tira

Qumis

SurraqTabaristan

Tudghah

Lloyd (2023): ”The three districts ofUpper, Middle and Lower Bihqubâdhwere located in ‘Iraq to the west of
the Euphrates. Bihqubâdh is takenfrom the Persian meaning ”the good
land of king Qubâdh. Al-Asfal means‘the lower,’ and covered the land next
to the Euphrates where it entered theGreat Swamp.” Coordinates based on
Fig. 8 of Morony (1982).

Appendix Table B.4: Manual mint codings II: geocoding existing Nomisma mints

21

Hoard Name

Date

#
Coins
described

Reference

Location

LatitudeLongitude

Abu Saida
Afaq
Afghanistan
Agrigenta
Al Raqqa
Al Wajh
Al-Khobar
Amman
Amūda I
Amūda II
Awarta (Nablus I )Bab Tuma
Babylone, Egypt
BuseyraCapernaum
Damascus
Damascus
Denizbaji
Diyarbakir
En NebkGaziraGodhlaniya
Hamah
Huszt
Iran 1970
IsfahanJarashJazira (Illisch)
KermanKhdir Elias
KhorasanKhirbat al-Minya
Kufah
Marv
Near Fez
Nippur (Bates)
Nippur (Sears)
North Africa (Spain?)Orif, Nablus
OuenzaQamishliyyah
Ra’s al-Khaimah
Sinaw
Tabaristan
TiflisUmm Hajarah
Utaifiyah
Volubilis
Yarubiyyah
Zahu/Zakho

ca. 721773-932
86-112 AH
699-828698-750

15
1674
131
370
1187
35
42
12
646
643
29
854
114
3108
288
3815
546
2496
224
102
2820
127
214
368
668
582
36
48
43
2865
196
2
178
855
36
76
97
87
19
12
1519
43
948
810
112
408
294
232
1415
3306

Royal Numismatic Society (1975)
Gachet (1993)
Album (1971)
Lagumina (1904)
Sears (2000)
Hakiem (1977)
Noonan (1980)
Kirkbride (1951)
Ilisch (1990)
Ilisch (1990)
Dajani (1951)
Gyselen and Kalus (1983)
Jungfleisch (1949)
Al Chomari (2020)
Wilson (1989)
al ’Ush (1972b)
al ’Ush (1954-1955)
Artuk (1966)
Ilisch (1979)
Royal Numismatic Society (1977)
Gyselen and Nègre (1982)
American Numismatic Society (2023)
Ilisch (1990)
Fomin and Kovács (1987)
Noonan (1980)
Lowick (1975)
Treadwell and Rogan (1994)
Ilisch (1990)
Heidemann et al. (2014)
Al-Naqshbandi (1954)
Hebert (1966)
Schneider (1952)
Noonan (1980)
Khodzhaniyazov and Treadwell (1998)
Royal Numismatic Society (1978)
Bates (1978)
Sears (1994)
Hoge (1997)
Ma’ayeh (1962)
Troussel (1942)
Gyselen and Kalus (1983)
Lowick and Nisbet (1968)
Lowick (1983)
Malek (1996)
Bartolomei (1857)
al ’Ush (1972a)
al Bakri (1973)
Eustache (1956)
American Numismatic Society (2023)
Al-Naqshbandi (1949, 1950, 1951, 1952)

Qaryat Abū Şaydā aş Şaghīrah, Iraq
Afak, Iraq
Afghanistan
Agrigento, Italy
Ar Raqqah, Syria
Al Wajh, Saudi Arabia
Khobar, Saudi Arabia
Amman, Jordan‘Āmūdā, Syria‘Āmūdā, Syria‘Awartā, Palestine
Damascus, Syria
Cairo, Egypt
Al Buşayrah, Syria
Kfar Nahum, Israel
Damascus, Syria
Damascus, Syria
Denizbacı, Turkey
Diyarbakır, Turkey
An Nabk, Syria
Al Jazīrah, Iraq
Syrian Arab Republic, Syria
Hamāh, Syria
Khust, Ukraine
Islamic Republic of Iran, Iran
Isfahan, IranJarash, Jordan
Al Jazīrah, Iraq
Kerman, Iran
Republic of Iraq, Iraq
Mashhad, IranHorbat Minnim, Israel
Kufa, Iraq
Mary, Turkmenistan
Fès, MoroccoAţlāl Nafar, IraqAţlāl Nafar, Iraq
Kairouan, Tunisia
Urif, Palestine, West Bank
Ouenza, Algeria
Al Qāmishlī, Syria
Ras Al Khaimah City, UAE
Sināw, Oman
Mazandaran Province, Iran
Tbilisi, Georgia
Umm Hajarah, Syria
Baghdad, Iraq
Oualili, Morocco
Al Ya‘rubīyah, Syria
Zaxo, Iraq

33.92432.06433.00037.31135.95326.24626.27931.95537.10437.10432.16133.51030.06335.15632.88133.51033.51037.13937.91434.02436.00035.00035.13248.17232.00032.65232.28136.00030.28333.00036.29832.86532.05137.59434.03332.13632.13635.67832.15935.95337.05225.79022.50136.25041.69436.19533.34134.07336.81137.14944.76145.24766.00013.57739.00836.45250.20835.94540.93040.93035.28436.29131.25040.42735.57536.29136.29138.39040.21736.72842.00038.00036.75823.29853.00051.67535.89942.00057.07944.00059.60635.53644.44061.830-5.00045.22145.22110.09635.2248.129
41.23155.94358.03052.33344.83441.07444.401-5.55542.06242.686

tpq 784/85
AH 79-125
tpq 874779-941602-685
tpq 748
157 AH - 241 AH
769-943548-736679-721
tpq 811802-902
tpq 747
3rd to 9th century
tpq 950
tpq 820
777-936
tpq 886
about 632-651
tpq 1014
705-774716-734
tpq 808/09
tpq 815704-794597-743
tpq 860691-742789-798
tpq 816921-975
589-841
about 718-760
ca. 280-330 AH
tpq 808/09
154-193 AH
tpq 125 AH (742)
tpq 815/816
tpq 808-9

Appendix Table B.5: Near East and North Africa Hoards

Hoard nameAlcaudete
Algeciras
AlhamaArrabal Occidental
AzanuyBadajoz
Baena
Barrio de los Olivos Borrachos
Benferri
BormujosCalle San Jose
Calle San Pedro
Calle Santa Julia
Campo de la Verdad
Carmona
Castillejos de Quintana
Castro Marim
Cerro da Villa
Crevilllent
CihuelaConsuegra
Cordoba I
Cordoba II
Cordoba III
Cordoba IV
Cova del Randerro
Cuba
Domingo Perez
ElcheElectromecanicas I
Electromecanicas II
El Pedroso
El Pedroso III
El Rebollar
Finca la Marquesa
Fontanar
Fuente de Cantos
Hospital Militar
HuescaIzcar
Iznajar
Jaen
Jerez de los Caballeros
La Almagra
La FuensantaLantejuela
La Rinconada
Las Torres
L’ElcaLleidaLora del Rio
Los Villares

Date698-734
710-727770-876929-1021
699-733
927-1011
699-754941-1004
941-958929-965936-950
967-1031
929-1012
775-912698-753933-1010
788-885831-900
770-1269912-1016
835-1010
817-1010
933-953933-1021
708-796768-835
932-1010
767-865841-1173941-1005
928-1016
928-1021
832-1021
810-818
941-1036
941-977
837-883970-1032
710-756778-886768-912711-713770-782820-822770-812773-887
770-912757-976933-950
770-1463
941-1021
942-1028# Coins described
14
29
459
373
6
99
160
165
12
11
16
19
263
176
146
39
53
239
34
296
173
25
328
379
119
54
9
367
316
169
102
144
144
5
246
764
15
23
100
50
1047
4
277
7
18
175
315
18
31
40
165
112

Reference
Cano Ávila (1989)
Canto García and Martín Escudero (2009)
Codera y Zaidín (1892)
Canto García et al. (2020a)
Codera y Zaidín (1913)
Prieto (1934)
Martín Escudero (2001)
Marcos Pous and Vicent Zaragoza (1992)
Doménech Belda (1997)
Cano Ávila (2016)
Doménech Belda (1997)
Canto García and Jablońska (2019)
Segovia Sopo (2014)
Martín and Martín (2006)
Canto García and Escudero (2012)
Cravioto (2016)Rodrigues Marinho (1995)
Heidemann et al. (2018)
Doménech Belda and Trelis (1990)
Navascués y de Palacios (1961a)
Martín Escudero (2011)
Navascués y de Palacios (1961b)
Navascués y de Palacios (1958)
Navascués y de Palacios (1958)
Canto García (1988)
Doménech Belda (1997)
Martín Escudero (2011)
Martín and Martín (2002)
Doménech Belda (1992)
Marcos Pous and Vicent Zaragoza (1992)
Marcos Pous and Vicent Zaragoza (1992)
Cano Ávila and Martín Gómez (2006)
Cano Ávila and Gómez (2008)
Salido Domínguez et al. (2020)
Doménech Belda (1997)
Canto García and Martín Escudero (2007)
Segovia Sopo (2006)
Martín Escudero (2003)
Martín Escudero (2012)
Ariza Armada (1988)
Canto García and Marsal Moyano (1988)
González García and Martínez Chico (2017)
Canto García (2019)
Museo Arqueológico de Murcia (2014)
Cravioto and Ayala (1995)
Ruiz Asencio (1967)
Cano Ávila and Martín Gómez (2005)
Martínez Enamorado (2004)
Doménech Belda (1997)
Soler Balaguero (1993)
Pellicer i Bru (1985)
Valle (1987)

Location

LatitudeLongitude

Alcaudete
Algeciras
AlhamaCordobaAzanuyBadajoz
Baena
Cordoba
Benferri
BormujosXàtivaMurciaMéridaCordoba
Carmona
Castillejos de Quintana
Castro-Marim
Cerro da Villa
Crevillent
CihuelaConsuegra
Cordoba
Cordoba
Cordoba
Cordoba
Pedreguer
Cuba
Domingo Pérez
Elche
Cordoba
Cordoba
Hacienda Montegil, El Pedroso
El Pedroso
Boalo
Montilla
Cordoba
Fuente de Cantos
Zaragoza
Huesca region
Cortijo de Izcar
IznajarJaen region
Jerez de los Caballeros
La Almagra
Cerro la Fuensanta
La Lantejuela
La Rinconada
Gavilanes
Oliva
LleidaLora del Rio
Caudete de las Fuentes

N 37◦ 35’ 27”

N 36◦ 7’ 59”
N 37◦ 0’ 24”
N 37◦ 53’ 29”
N 41◦ 59’ 10”N 38◦ 52’ 40”
N 37◦ 39’ 22”
N 37◦ 53’ 29”
N 38◦ 8’ 28”
N 37◦ 21’41.9”
N 38◦ 59’ 25”
N 37◦ 59’ 13”
N 38◦ 54’ 58”
N 37◦ 53’ 29”
N 37◦ 28’ 17”N 36◦ 46’58.7”
N 37◦ 13’ 14”
N 37◦ 4’ 48”
N 38◦ 14’ 59”
N 41◦ 24’ 26”
N 39◦ 27’ 44”
N 37◦ 53’ 29”
N 37◦ 53’ 29”
N 37◦ 53’ 29”
N 37◦ 53’ 29”
N 38◦ 47’ 35”
N 38◦ 10’ 24”
N 37◦ 29’ 45”
N 38◦ 15’ 43”
N 37◦ 53’ 29”
N 37◦ 53’ 29”
N 37◦ 43’51.9”
N 37◦ 51’ 0”
N 40◦ 42’ 57”
N 37◦ 36’07.2”
N 37◦ 53’ 29”
N 38◦ 15’ 0”
N 41◦ 39’ 21”

W 4◦ 4’ 56”
W 5◦ 27’ 1”
W 3◦ 59’ 22”
W 4◦ 46’ 21”
E 0◦ 18’ 58’
W 6◦ 58’ 14”
W 4◦ 20’ 4”
W 4◦ 46’ 21”
W 0◦ 57’ 43”
W 6◦ 06’ 38.1”
W 0◦ 31’ 6”
W 1◦ 7’ 48”
W 6◦ 20’ 37”
W 4◦ 46’ 21”
W 5◦ 38’ 46”
W 4◦ 41’ 30.9”
W 7◦ 26’ 36”
W 8◦ 7’ 13”
W 0◦ 48’ 35”
W 1◦ 59’ 59”
W 3◦ 36’ 28”
W 4◦ 46’ 21”
W 4◦ 46’ 21”
W 4◦ 46’ 21”
W 4◦ 46’ 21”
E 0◦ 2’ 2”
W 7◦ 53’ 46”
W 3◦ 30’ 33”
W 0◦ 42’ 3”
W 4◦ 46’ 21”
W 4◦ 46’ 21”
W 5◦ 51’39.8”
W 5◦ 46’ 0”
W 3◦ 54’ 59”
W 4◦ 37’11.7”
W 4◦ 46’ 21”
W 6◦ 18’ 0”
W 0◦ 52’ 38”

N 37◦ 39’56.1”
N 37◦ 15’ 27”

W 4◦ 23’41.6”
W 4◦ 18’ 30”

N 38◦ 19’ 14”
N 38◦ 2’ 15”36◦ 55’13.7”N
N 37◦ 19’17.5”
N 37◦ 29’ 10”N 40◦ 15’ 44”
N 38◦ 55’ 10”N 41◦ 37’ 7”N 37◦ 39’ 32”
N 39◦ 33’ 34”

W 6◦ 46’ 21”
W 1◦ 25’ 57”
4◦ 23’23.7”W
W 5◦ 13’27.6”
W 5◦ 58’ 51”
W 4◦ 51’ 30”
W 0◦ 7’ 9”
E 0◦ 34’ 29”
W 5◦ 31’ 39”
W 1◦ 16’ 42”

Appendix Table B.6: Hoards in al-Andalus, Part I

Hoard name
Madinat Iyyuh
Marroquies Altos
Marroquies Bajos
MartosMeridaMertolaMijas Costa
Montellano
MoralejaMoreria
NieblaOsuna
Parque Cruz condePartida de Atzbares
Pascul de Gayangos
Pinos Puente
Pozoblanco
Priego de Cordoba
Puebla de Cazalla
Puente de Miluze
Recopolis
Sagrada Familia
San Andres de Ordoiz
SaqundaSevillaSierra Cazorla
SilvesSinarcas
Solar del Museo Arqueologico
South France
Spain single finds (felus)
Tarancon
Teatro romano
TignarTijan
Trujillo
Valencia de Ventoso
Valeria
Valle de Guadajoz
Vega Baja
Vera
Villaviciosa
Yecla
Zafra
ZamoraDate711-856933-1010
941-1015
817-875
726-901
932-1036
932-976949-1010
767-854857-1015
805-884954-1022852-1021
941-970778-1204
770-816948-976770-856
770-892934-1057
772-785945-1012782-908
707-930711-1011
928-1021
770-875
942-1037
953-1007
692-886699-901
929-1014
805-819864-913976-1021
711-1014
933-1006
936-1009
931-1013-200-1500
941-1024705-817705-726789-892943-999# Coins described
20
270
201
24
60
81
533
23
16
134
36
3
3341
26
159
169
15
54
911
164
9
316
167
467
497
237
79
57
16
204
57
451
25
35
377
384
7
250
204
184
370
1361
5
43
10

Reference
Doménech Belda and Gutiérrez Lloret (2006)
Asencio (1962)
Canto García et al. (1997)
Canto García (1993)
Rodríguez Palomo and Martín Escudero (2022)
Poiares (2000)
Ayala Ruiz and Gozalbes Cravioto (1996)
Cano Ávila (2014)Álvarez (1993)
Palma García and Segovia Sopo (2007)
Cano Ávila and Martín Gomez (2011)
Alfaro Asins (1992)
Canto García et al. (2020b)
Doménech Belda (1997)
Marinho (1993)
Martín Escudero (2011)
Marcos Pous and Vicent Zaragoza (1992)Ávila and Pareja (1999)
Ibrahim and Canto García (1991)
Canto García (2001)
Priego and Enciso (2016)
Marcos Pous and Vicent Zaragoza (1992)
Uranga (1950)
Martín Escudero et al. (2023)
Saenz-Diéz (1993)
Pellicer i Bru (1982)
Miles (1960)
Arroyo Ilera (1989)
Marcos Pous and Vicent Zaragoza (1992)
Parvérie (2014, 2019)
Martín Escudero (2012)
Canto García (2014)
Segovia Sopo and Jiménez (2011)
Motos Guirao and Díaz García (1985)
Fontenla Ballesta (1998)
Navascués y de Palacios (1957)
Grañeda Miñón (2021)
Puertas (1982)
Ortega et al. (2006)
Priego (2020)
Doménech Belda (1997)
Peña Martín and Vega Martín (2007)
Codera y Zaidín (1913)
Canto García (2019)
Cerrato and Esquivel (2019)

Location

LatitudeLongitude

El Tolmo de Minateda
Jaen
Jaen
Cortijo del Mimbre
MeridaMertola
La Cala de Mijas
Montellano
Moraleja
MeridaSierra de Alcantara
Osuna
Cordoba
Atzavares Baix (Elche)
Algarve
Pinos Puente
Pozoblanco
Priego de Cordoba
La Puebla de Cazalla
Pamplona
Recopolis
Cordoba
Estella-Lizarra
Cordoba
SevillaSierra Cazorla
SilvesSinarcas
CordobaSouth France
Spain
Tarancon
MeridaAlbolote
Sierra de Cabrera
Trujillo
Valencia del Ventoso
Valeria
Fuentiduena (Baena)
ToledoVera
Villaviciosa de Cordoba
Yecla
Zafra
ZamoraN 38◦ 28’ 34”

N 37◦ 46’ 9”N 37◦ 46’ 9”N 37◦ 38’26.0”
N 38◦ 54’ 58”
N 37◦ 38’ 34”
N 36◦ 33’ 56”
N 37◦ 00’06.1”
N 40◦ 0’ 58”
N 38◦ 54’ 58”
N 37◦ 28’33.8”
N 37◦ 14’ 15”
N 37◦ 53’ 29”
N 38◦ 15’ 43”

W 1◦ 36’ 20”
W 3◦ 47’ 25”
W 3◦ 47’ 25”
W 3◦ 57’04.9”
W 6◦ 20’ 37”
W 7◦ 39’ 40”
W 4◦ 40’ 11”
W 5◦ 33’02.1”
W 6◦ 41’ 51”
W 6◦ 20’ 37”
W 6◦ 38’36.2”
W 5◦ 6’ 11”
W 4◦ 46’ 21”
W 0◦ 42’ 3”

N 37◦ 15’ 3”N 38◦ 22’ 44”
N 37◦ 26’ 17”
N 37◦ 13’ 17”
N 42◦ 49’ 0”
N 40◦ 19’15.1”
N 37◦ 53’ 29”
N 42◦ 40’ 19”
N 37◦ 53’ 29”
N 37◦ 22’ 58”
N 37◦ 54’ 45”
N 37◦ 11’ 21”
N 39◦ 44’ 0”
N 37◦ 53’ 29”

W 3◦ 44’ 58”
W 4◦ 50’ 53”
W 4◦ 11’ 42”
W 5◦ 18’ 41”
W 1◦ 38’ 35”
W 2◦ 53’37.7”
W 4◦ 46’ 21”
W 2◦ 01’ 56”
W 4◦ 46’ 21”
W 5◦ 58’ 23”
W 2◦ 58’ 34”
W 8◦ 26’ 17”
W 1◦ 14’ 0”
W 4◦ 46’ 21”

N 40◦ 0’ 30”
N 38◦ 54’ 58”
N 37◦ 13’ 51”
N 37◦ 06’30.3”
N 39◦ 27’ 28”
N 38◦ 16’ 0”
N 39◦ 47’ 0”
N 37◦ 43’42.1”
N 39◦ 51’ 29”
N 37◦ 14’ 36”
N 38◦ 5’ 0”
N 38◦ 39’ 18”
N 38◦ 25’ 31”
N 41◦ 30’ 22”

W 3◦ 0’ 26”
W 6◦ 20’ 37”
W 3◦ 39’ 18”
W 1◦ 55’49.2”
W 5◦ 52’ 55”
W 6◦ 28’ 0”
W 2◦ 9’ 0”
W 4◦ 16’54.3”
W 4◦ 1’ 21”
W 1◦ 51’ 32”
W 5◦ 1’ 0”
W 1◦ 7’ 46”
W 6◦ 25’ 2”
W 5◦ 44’ 40”

Appendix Table B.7: Hoards in al-Andalus, Part II

Hoard NameCountry

Reference

LatitudeLongitude

Ankara 1960?
Argos 1983
Ayies Paraskies/Crete 1962
Bajagic
Balchik Stray Find I
Berezeni
Bratimir
But
Byala 1954
Calarasi 1947
Camarina
Camarina ed. 18
Camarina ed. 1aCamarina ed. 6
Capo Schiso 1950
Chryse/Edhessa 1935
Cleja
Constanta Stray I
Constanta Stray II
Corinth 15 May 1934 (South Basilica)
Corinth 1934
Corinth 1965 (Roman Bath)Corinth 1965 (Roman Bath)Corinth (St John’s monastery)
Didyma (single find)Drobeta - Turnu Severin
Drobeta 1928
Dubravice
Dubrovnik 1982
ElazigElbistanEskisehir
Gabrica
GovoraHadrianoupolis Acropolis Kimistene
Hadrianoupolis Basilica A
Hadrianoupolis Bath A
Hadrianoupolis Bath B
Hadrianoupolis Building 4
Hadrianoupolis Domus
Hagios Nikolaos, Hydra (Greece)
Iatrus 1962
Iatrus 1975
IpsalaIstria Stray Finds 869-877
Kavakli
Kenchreai 1963
Kozojedy, Bohemia
Kyme near Aliaga
Kyulevcha Grave
Lagbe
Libice, Bohemia
Liopesi (around 1946)
Ljubimets
Luka Krnicka
Macvanska Mitrovica
Malthi (Dorion)
Maluk Povorets 1934
Matera Piazza S. Francesco
MediasMelito Porto Salvo
MikulciceMonemvasia Stray Find
Naxos
Nea Syllata/Chalkidiki 1977
Nin
Odartsi
Osava near Ram
Osvetimany

TurkeyGreeceGreeceCroatia
Bulgaria
Romania
Bulgaria
Italy
Bulgaria
Romania
Italy
Italy
Italy
Italy
Italy
GreeceRomania
Romania
Romania
GreeceGreeceGreeceGreeceGreeceTurkeyRomania
Romania
Croatia
Croatia
TurkeyTurkeyTurkeyBulgaria
Romania
TurkeyTurkeyTurkeyTurkeyTurkeyTurkeyGreeceBulgaria
Bulgaria
TurkeyCroatia
TurkeyGreeceCzechia
Croatia
Bulgaria
TurkeyCzechia
GreeceBulgaria
Croatia
SerbiaGreeceRomania
Italy
Romania
Italy
Czechia
GreeceGreeceGreeceCroatia
Bulgaria
SerbiaCzechia

Pennas (1991) 122
Pennas (1991) 8
Pennas (1991) 59 / Füeg (2007)
Mirnik (1981)
Curta (2005)
Oberländer-Târnoveanu (2001) p.67
Pennas (1991) 90
Arslan (2005) 2280
Pennas (1991) 73
Pennas (1991) 111, Dimian (1957)
Arslan (2005) 6170
Arslan (2005) 6185
Arslan (2005) 6181
Arslan (2005) 6182
Arslan (2005) 6910
Pennas (1991) 50 / Füeg (2007)
Pennas (1991) 113, Dimian (1957)
Dimian (1957)
Dimian (1957)
Pennas (1991) 3
Pennas (1991) 7
Pennas (1991) 1
Pennas (1991) 4 (BCH 90, 1966, 751, 754)
Pennas (1991) 9
Baldus (2006)
Oberländer-Târnoveanu (2001) p.68
Oberländer-Târnoveanu (2001) p.67
Mirnik (1981)
Mosser (1935) p.71 (“Ragusa”), Mirnik (1981) 359
Füeg (2007)
Füeg (2007)
Füeg (2007)
Sophoulis (2011)
Oberländer-Târnoveanu (2001) p.68
Laflı et al. (2016)
Laflı et al. (2016)
Laflı et al. (2016)
Laflı et al. (2016)
Laflı et al. (2016)
Laflı et al. (2016)
Pennas (1996), p. 270
Pennas (1991) 77
Pennas (1991) 75
Füeg (2007)
Miškec (2002)Ünal (2018)
Pennas (1991) 2
Profantova (2009)
Carroccio, cited by Morrisson (2017)
Curta (2005)
Füeg (2007), Newell (1945)
Profantova (2009)
Pennas (1991) 35 / Vryonis (1971)
Dimian (1957), Sophoulis (2011)
Miškec (2002)
Pennas (1991) 72
Pennas (1991) 6
Pennas (1991) 74
Arslan (2005) 4140
Oberländer-Târnoveanu (2001) p.68, Dimian (1957)
Arslan (2005) 0450
Profantova (2009)
Pennas (1996), p. 270
Füeg (2007)
Pennas (1991) 52
Mirnik (1981)
Sophoulis (2011)
Füeg (2007)
Profantova (2009)39.9388
37.6353
35.20943.758143.411946.37843.8682
46.4768
42.873944.202936.8279
36.8279
36.8279
36.8279
37.824440.81
46.401944.177744.1777
37.9373
37.9373
37.9373
37.9373
37.9373
37.373144.642544.642543.8506
42.6489
38.6747
38.201639.774343.5082
45.068140.923140.923140.923140.923140.923140.923137.301143.6262
43.626240.920145.143937.75537.8833
50.2548
38.7592
43.2559
36.8276
50.1285
37.954541.8466
44.972344.9655
37.26743.7133
40.665446.162137.9197
48.8167
36.687637.056740.3275
44.2392
43.44
44.8006
49.0562

32.859422.7277
25.2041
16.6657
28.162828.152326.704413.0246
27.888627.3115
14.5241
14.5241
14.5241
14.5241
15.2684
22.0446
26.942728.644228.644222.93222.93222.93222.93222.93227.263922.6587
22.6587
15.9398
18.09439.222937.192430.5138
26.9736
24.230232.4867
32.4867
32.4867
32.4867
32.4867
32.4867
23.3967
25.58725.58726.3828
13.825928.30522.9873
13.8153
26.9367
27.11130.411215.181523.852126.078114.0171
19.5975
21.8824
26.7652
16.608724.3567
15.7857
17.051623.0559
25.4638
23.13615.1791
27.9616
21.3433
17.2496

Appendix Table B.8: Hoards with Byzantine coins, Part I
25

Hoard NameCountry

Reference

LatitudeLongitude

Oszony, Komarom
PiranPliskaPrague, Tynsky dur
Rakvice (Breclav)
Rasova 1934
Reggio Calabria
Rhodos Stray Find 859
Rhodos V.12 (Kattavia)
Rome / Tiber
Salamis (South of Amphitheatre, 1964-1974)
Santorini (Thira) 1895-1902
Sicily (Fagerlie)
Songurlu / Mosser
Stare Mesto
Stimanga 1955
Streda nad Bodrogom
Syracuse Via G. Di Natale
Tegani/Samos 1914
Telerig Stray Miliaresion
Thessaloniki
Thessaloniki 1891
Tichilesti
Tralleis/Aydin
Trilj
Unknown Provenance (Turkey) 1987
Urluia 1936
Velul lui Trajan
Velul lui Trajan 1999/2000
Voila, Romania
Vukovar - Lijeva Bara
Yakimovo (Progorelets) 1960
Yunak

Hungary
Italy
Bulgaria
Czechia
Czechia
Romania
Italy
GreeceGreeceItaly
TurkeyGreeceItaly
TurkeyCzechia
GreeceSlovakia
Italy
GreeceBulgaria
GreeceGreeceRomania
TurkeyCroatia
TurkeyRomania
Romania
Romania
Romania
Croatia
Bulgaria
Bulgaria

Oberländer-Târnoveanu (2001) p.68
Arslan (2005) 2808
Füeg (2007)
Profantova (2009)
Profantova (2009)
Pennas (1991) 112, Dimian (1957)
Arslan (2005) 0670
Kasdagli (2018)
Kasdagli (2018)
Morrisson and Barrandon (1988)
Füeg (2007)
Pennas (1991) 57
Fagerlie (1974)
Füeg (2007) / Mosser (1935)
Profantova (2009)
Pennas (1991) 5 (BCH 80, 1956, 256)
Profantova (2009)
Arslan (2005) 7335
Pennas (1991) 58
Curta (2005)
Füeg (2007)
Pennas (1991) 51
Dimian (1957)Ünal (2015)
Mirnik (1981)
Pennas (1991) 123
Dimian (1957), Sophoulis (2011)
Pennas (1991) 105
Mănucu-Adameşteanu (2016)
Dimian (1957)
Mirnik (1981)
Pennas (1991) 91
Pennas (1991) 76

47.7295
45.527943.36250.07348.855944.2403
38.0947
36.43635.9534
41.8882
35.191436.4058
37.5732
40.162749.072737.90948.378537.0724
37.690443.8457
40.65240.65245.1291
37.859143.6187
39.296344.1016
44.164744.164745.81845.3382
43.633743.0763

18.1751
13.569427.1228
14.4286
16.81327.941415.6455
28.222127.7683
12.4768
33.8979
25.4588
14.211434.3767
17.4463
22.6989
21.75815.2845
26.941727.67122.9304
22.9304
27.9045
27.8335
16.724132.932727.9132
28.462128.462124.8405
19.0079
23.362127.6109

Appendix Table B.9: Hoards with Byzantine coins, Part II26

Hoard Name

Date

Reference

Location

LatitudeLongitude

Aalst
Aalsum
Achlum
Alfocea
Amerongen
Ampurias
Andalusia
Angeac-Champagne
Angers I
Angers II (Saint-Julien)
AnglureANS findAnse I
Anserall
Apremont
Aquitaine
ArdresArras
AshdonAspres-lès-Corps
Assebroek
Assen
AuxerreAuzeville
AvallonAvignonBakonyszombathely
BallooBarbentane
Barcelona
Bassenheim
Bátorove Kosihy
Beaumont

840-855814-855768-840943-977768-877768-814814-848840-877814-840819-877864-887768-922818-823768-815793-822814-887
888-923843-922843-898
901-924843-877
800-911813-877814-848843-877843-887898-973843-855814-840814-840814-876888-950
843-877

Bijsterveld et al. (2000)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Parvérie (2018)
Coupland (2014)
Doménech-Belda et al. (2013)
Parvérie (2018)
Duplessy (1985)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Guillemain (1993)
Doménech-Belda et al. (2013)
Morrison and Grunthal (1967)
Coupland (1991)
Haertle (1997)
Morrison and Grunthal (1967)
Blackburn (1989)
Schulze (1984)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Haertle (1997)
Sarah et al. (2016)
Coupland (2020)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Doménech-Belda et al. (2013)
Coupland (2019)
Kovács (1989)
Morrison and Grunthal (1967)

51.39611
53.340353.14779
41.724097
52.0025
42.134477

5.477
6.00538
5.48239-0.953131
5.46024
3.111418

45.60769
47.470747.470748.58345-0.29771-0.55324-0.55324
3.8135645.937639
42.37829
46.9064.717512
1.456511
3.048

50.856432
50.29039
52.05544
44.80162
51.18793
52.99421
47.796587
43.5257
47.488712
43.95344
47.47208
54.472363
43.89948
41.395937
50.359028
47.83083
48.409016

1.978355
2.778414
0.31373
5.98217
3.273636.55957
3.570535
1.493423.907758
4.80601
17.96018-5.69076
4.74635
2.174552
7.462443
18.41083
2.042742

Bel-Air
Bellpuig
Belvézet
Bikbergen
Bjerndrup
Blendecques
BlignyBlois
BondenoBonnevaux
Borne
Bourges
Bourges
Bourgneuf
Bourgneuf-en-Retz
Bray-sur-Seine
Bressuire
Breuvery-sur-Coole
Brion
Brioux-sur-Boutonne
Bruère-Allichamps
BurgumCaden
Caen
Calatrava la vieja
Campeaux
Carcassonne
Castelsarasin

768-814887-928768-840814-855
817-924814-840814-887898-940768-814800-887794-813840-877
800-887814-888843-877
840-877814-840768-813814-840814-840814-954843-877843-877936-954

46.57957
41.626531
44.08433
52.287933
54.93391
50.716982
48.172547.58696
44.89098
44.367837
52.30137
47.08585
47.08585
46.167624
47.04229
48.41451
46.84008
48.86311
47.4425
46.14349
46.7695
53.19527
47.630822
49.183512
39.074099
48.952844
43.206463
44.039071

6.605807
1.011607
4.36426
5.196186
9.328672.282169
4.61721.33139
11.41096
4.030289
6.75779
2.392932.39293-1.022216-1.95433.24057-0.49253
4.31164-0.1553-0.21823
2.43255.98694-2.287131-0.363489-3.833274-0.93197
2.363268
1.106969

Catalonia

768-905

Cauroir
Cerdanyola
Cerveník

843-882814-840826-950Morrison and Grunthal (1967)
Doménech-Belda et al. (2013)
Morrison and Grunthal (1967)
Cruysheer and der Veen (2015)
Coupland (2020)
Coupland (2020)
Morrison and Grunthal (1967)
Moesgaard (1997)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2011a)
Morrison and Grunthal (1967)
Coupland (2020)
Morrison and Grunthal (1967)
Coupland (2010)
Vandenbossche and Coupland (2012)
Coupland (1995)
Dhénin (1989)
Denais (1908)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Haertle (1997)
Coupland (2020)
Coupland (2020)
Parvérie (2018)
Haertle (1997)
Coupland (2014)
Morrison and Grunthal (1967) and Lafaurie(1965)
Balaguer (1999) and Doménech-Belda et al.
(2013)
Coupland (2011a)
Doménech-Belda et al. (2013)

Aalst
Aalsum
Achlum
Alfocea
Amerongen
Ampurias
Andalusia
Angeac-Champagne
AngersAngersAnglureFranceAnse
Anserall
Apremont-sur-Allier
Aquitaine
ArdresArras
AshdonAspres-lès-Corps
Assebroek
Assen
AuxerreAuzeville
AvallonAvignonBakonyszombathely
BallooBarbentane
Barcelonabassenheim
Bátorove Kosihy
Beaumont(ChaloSaint Mars)
Lausanne
Bellpuig
Belvézet
Bikbergen
Bjerndrup
Blendecques
BlignyBlois
BondenoBonnevaux
Borne
Bourges
Bourges
Bourgneuf
Bourgneuf-en-Retz
Bray-sur-Seine
Bressuire
Breuvery-sur-Coole
Brion
Brioux-sur-Boutonne
Bruère-Allichamps
BurgumCaden
Caen
Calatrava la Vieja
Campeaux
Carcassonne
Castelsarasin

50.17283
41.49201
48.45

3.30174
2.137338
17.75

813-877768-814888-898

Calalonia
Cauroir
Cerdanyola
Cerveník

Appendix Table B.10: Carolingian Hoards, Part I
27

Hoard Name

Date

Reference

Location

LatitudeLongitude

ChaleyChalo-Saint-Mars
Chalon-sur–Saône I
Chalon-sur-Saone II
Charente-Maritime
Chartes
Chartres II
Château Roussillon
Chateauneuf sur Cher
Chaumoux-Marcilly
Chauvigny
Chef-Boutonne
Chester
Chézy-sur-Marne
Choisy-au-Bac
Ciney Dinant
Clermont FerrandCompiègne I
Compiègne II
Corrèze
Cosne d’Allier
Cosne-Cours-sur-Loire II
Cosne-Cours-sur-Loire III
CroydonCsornaCuerdale
Dalen
DauphinéDeux-Sèvres

936-954840-877
800-887
800-887
888-898
923-977751-768
793-877843-954814-877843-877
800-922888-924768-814888-898
898-922843-918877-882843-882843-877814-840814-877877-840814-877
888-947
843-922843-976814-848814-877

Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Haertle (1997)
Coupland (2011a)
Duplessy (1985)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Société des antiquaires de l’Ouest (1982)
Haertle (1997) and Rondier (1869)
Webster et al. (1953)
Duplessy (1985)
Haertle (1997)
Coupland (2020)
Coupland (2020)

ChaleyChalo-Saint-Mars
Chalon-sur-Saône
Chalon-sur-Saône
Charente-Maritime
Chartres
Chartres
Château Roussillon
Chateauneuf sur Cher
Chaumoux-Marcilly
Chauvigny
Chef-Boutonne
Chester
Chézy-sur-Marne
Choisy-au-Bac
Ciney
Clermont-FerrandCompiègne
Compiègne
Corrèze
Cosne d’Allier
Cosne-Cours-sur-Loire
Cosne-Cours-sur-Loire
CroydonCsornaCuerdale
Dalen
DauphinéDeux-Sèvres

45.9552
48.4267
46.782132
46.782132

5.531222.067
4.858459
4.858459

48.446659
48.446659
42.710278
46.857333
47.12628
46.56974
46.10934
53.1903
48.989611
49.4477750.286773
45.778063
49.41762
49.41762

1.488596
1.488596
2.946667
2.320522
2.778840.64345-0.06806-2.89437
3.366294
2.88097
5.098966
3.083696
2.825132.8251346.474799
47.40983
47.40983
51.379287
47.6167
53.755352.69847

2.830127
2.92425
2.92425-0.09975
17.25-2.6386.75641

Dijon
Dommartin-Lettrée
Dordives
Dorestad
Drantum
Eichstetten
Ejstrup
EkerenEllikon an der Thur
Emmen
Entrammes
Espana 1-4
EtampesEtréchy
EvreuxExtremadura
Eyguières
FécampFlaceyFlanders
Florange
Foissy-lès-Vézelay
Fontaines
Frankfurt
Freiburg im Breisgau
Fresnes
Fridolfing
FrisiaGannatGelderland
GiekauGlisy
Gnadendorf
GoutumGrisebjerggård
Groningen
Guardamiglio

770-780923-936750-950768-877814-840
911-922814-840819-877
887-915814-877814-877
800-1009
843-882
832-877
840-95447.3268
48.7669
48.144081
51.97212
52.81942
48.094296
55.503525
51.276405
47.56253
52.49784
47.999133
41.352868
48.434768
48.88411
49.02754

5.046194.29933
2.766333
5.344769
8.195377.745429
9.377413
4.417467
8.823866.23039-0.716154-1.641101
2.162027
3.94374
1.1502843.696133
49.75765
48.147247

5.030134
0.37632
1.349598

49.32743
47.43637
46.85083
50.11208
47.99853
48.75043
47.998573
53.11035
46.10192

6.12273
3.76447
4.773055
8.683417.84965
2.322063
12.826917
5.848604
3.1969254.31793
49.8756
48.61549
53.178037
55.3028
53.25713
45.11055

10.50529
2.39788
16.39885
5.806018
11.2647
6.93525
9.68215814-840
900-999814-840814-877864-877814-877814-840898-922768-814814-855
800-887768-814814-911800-922898-905
814-877
898-922814-877843-884

Morrison and Grunthal (1967)
Coupland (2014)
Coupland (2014)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Kovács (1989)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2014)
Société de statistique, sciences, lettres et artsdu département des Deux-Sèvres (1882)
Bompaire and Depierre (1989)
Duplessy (1985)
Coupland (2014)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Coupland (2020)
Haertle (1997)
Zäch (2001)
Morrison and Grunthal (1967)
Coupland (2014)
Parvérie (2018)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Duplessy (1985) and Moesgaard (2003)
Parvérie (2018)
Coupland (2020)
Duplessy (1985)
Coupland (2020)
Coupland (2020)
Duplessy (1985) and Simmer (2000)
Duplessy (1985)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Duplessy (1985)
Coupland (2020)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Wiechmann (2004)
Morrison and Grunthal (1967)
Daim and Lauermann (2006)
Coupland (2020)
Morrison and Grunthal (1967)
Coupland (2011a)

Dijon
Dommartin-Lettrée
Dordives
Dorestadt
Drantum
Eichstetten
Ejstrup
EkerenEllikon an der Thur
Emmen
Entrammes
Calatayud
Etampes
Etrechy
EvreuxExtremadura
Eyguières
FécampFlaceyFlanders
Florange
Foissy-lès-Vézelay
Fontaines
Frankfurt am Main
Freiburg im Breisgau
Fresnes
Fridolfing
Grou
Gelderland
GiekauGlisy
Gnadendorf
GoutumSlagelse
Groningen
Guardamiglio

Appendix Table B.11: Carolingian Hoards, Part II28

Hoard Name

Date

Reference

Location

LatitudeLongitude

Györ I
Györ II
Halimba
HäljarpHarkirke
Harlingen
Haute Isle
Haza de Carmen
Hermenches
Hoen
Hole
Holy Family

888-950
888-951902-947814-840
843-905
840-855814-922888-954822-840814-855796-840
800-887

Györ
Györ
Halimba

47.69739
47.69739
47.03345
55.85578
53.48919
53.1735
49.083426
37.881495
46.640456
60.2204
58.897156
37.888028

17.652717.652717.53546
12.910919-3.048081
5.42461.65697-4.776125
6.757567
10.25852
6.018229-4.7734

Hradec Hilfort
Huriel

768-814800-887

Kovács (1989)
Kovács (1989)
Kovács (1989)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Coupland (2020)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2020)
Parvérie (2018) and Morrison and Grunthal(1967)
Coupland (2020)
Morrison and Grunthal (1967)

50.209703
46.37468

15.832231
2.47842

Ibaneta
Ibersheim
Ilanz I
Ilanz II

800-888768-814843-905
664-814

Hradec-Kralove
Le
Moulin-Gargot(Huriel)Puerto d’Ibaneta
Ibersheim
Ilanz
Ilanz

43.020083
49.72085
46.77451
46.77451-1.324207
8.40065
9.20463
9.20463

Île AgoisÎle-de-France
Imbleville
Imphy
Indre
Indre II
Indre-et-Loire
Indre-et-Loire II
Indre-et-Loire III
Isle-Aumont I
Isle-Aumont II
Issy l’Evêque
Jedomelice
JelsumJuaye-Mondaye
Jura
KardenKaros-Eperjesszög I
Karos-Eperjesszög II
Kättilstorp
Katwijk I
Katwijk II
Kecel
Kenézlő
Kimswerd-Pingjum I
Kimswerd-Pingjum II
Kiskundorozsma-Hosszúhát
Kiskunfélegyháza
Koblenz
Krinkberg
La Cornouaille
La Couvertoirade
La Roche en Ardenne
La Tessoualle
La Tour-de-Peilz
Ladánybene
LamairéLamotte Beuvron
LangonLangres I
Langres II
LarinoLauterach
LauzèsLavelanet
Laxfield
Leiderdorp

864-877
888-936864-877
751-814814-865814-848814-877
888-910888-898814-840864-898
843-922814-840768-814800-922768-814814-822888-915900-911814-877
800-922794-800
888-924826-950814-877814-878826-950
881-918823-830768-814814-877881-898
750-950814-877755-768888-922843-877814-877814-877843-922864-884768-840
840-924814-877888-898
843-877768-840

Île Agois
Ile de France
Imbleville
Imphy
Indre
Indre
Indre-et-Loire
Indre-et-Loire
Indre-et-Loire
Isle-AumontIsle-Aumont
Issy l’Evêque
Jedomelice
JelsumJuaye-Mondaye
Jura
KardenKaros
Karos
Kättilstorp
KatwijkKatwijk
Kecel
Kenézlő
Kimswerd
Kimswerd
SzegedKiskunfelegyhaza
Koblenz
Pöschendorf
La Cornouaille
La Couvertoirade
La-Roche-en-Ardenne
La Tessoualle
La-Tour-de-Peilz
Ladánybene
LamairéLamotte-Beuvron
LangonLangres
Langres
LarinoLauterach
LauzèsLavelanet
Laxfield
Leiderdorp

49.24935-2.18641

49.71539
46.934537

0.951983.259903

48.2113148.21131
46.70818
50.23411
53.23455
49.20803

4.12459
4.12459
3.973413.971234
5.783862-0.68508

50.179051
48.32959
48.32959
58.041694
52.195273
52.195273
46.5264448.2
53.128953.1289
46.27546.71246
50.359618
54.03055
47.578279
43.9112750.183528
47.00535
46.45302
47.03333
46.75707
47.602363
44.55389
47.85816
47.85816
41.7968
47.474547.470742.932652
52.30114
52.151653

7.299583
21.73712
21.73712
13.711198
4.421091
4.421091
19.24647
21.53333
5.43875.438720.06278
19.85279
7.59383
9.472156-0.797543
3.31355
5.575243-0.8494
6.8568619.45-0.12632.025245-0.24833
5.331135.3311314.91289.730031-0.55324
1.848583
1.36237
4.529015

Doménech-Belda et al. (2013)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Bernareggi (1977, 1983), Völckers (1965), McCormick (2001)
Johnston (1986)
Dhénin (2006)
Haertle (1997)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2014)
Coupland (2011a)
Coupland (2011a)
Coupland (2020)
Haertle (1997)
Haertle (1997)
Morrison and Grunthal (1967)
Coupland (2020)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Révész (1996)
Gedai (1993)
Morrison and Grunthal (1967)
Kluge (1993)
Van der Velde (2008)
Huszár (1955)
Huszár (1955)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Múzeum Móra Ferenc (2002)
Kovács (1989)
Reinhold Fischer Auktionshaus (2010)
Morrison and Grunthal (1967)
Coupland (2020)
Coupland (2011a)
Coupland (2014)
Haertle (1997)
Geiser (1990)
Huszár (1955)
Baigl et al. (1995)
Coupland (2020)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2011a)
De Benedittis and Lafaurie (1998)
Zäch and Tabernero (2002)
Morrison and Grunthal (1967)
Coupland (2020)
Morrison and Grunthal (1967)
Coupland (2020)

CrosbyHarlingen
Haute Isle
Cordoba
Hermenches
Hoen
Hole
CordobaAppendix Table B.12: Carolingian Hoards, Part III
29

Hoard Name

Date

Reference

Location

LatitudeLongitude

Lésigny-sur-Creuse
Levice-Géňa
Lillebonne
Limoux
Lisówek
Llanbedrgoch
LleridaLoire River Bank
LoiretLokerenLongjumeau
Loppersum
Lucca
Lussac-les-Châteaux
Lutkesaaxum
Luzancy
Lyon
Maine et Loire
MarçayMarssum
MarsumMatha
Melle I
Melle II
Melle IV
Mercurey
Méréville
Metz
Meurthe et Moselle
MidlarenMidlumMikulčice

814-898
926-950814-877849-877848-922814-878887-928814-840
843-1027
843-864
843-884814-877947-961845-848843-864
814-877751-771751-878840-898814-855814-887778-877875-877843-877823-825822-877814-877843-877
898-922814-877
900-961887-900

Lésigny-sur-Creuse
Levice-Géňa
Lillebonne
Limoux
Lisówek
Llanbedrgoch
LleidaLoire River
LoiretLokerenLongjumeau
Loppersum
Lucca
Lussac-les-Châteaux
Lutkesaaxum
Luzancy
Lyon
Maine-et-Loire
MarçayMarssum
Marsum
Matha
Melle
Melle
Melle
Mercurey
Méréville-Saint-Pierre
Metz
Meurthe-et-Moselle
MidlarenMidlumMikulčice

46.84996
48.21639
49.5180243.053658
51.9
53.300117
41.61879

0.7642118.60806
0.536812.217421
20.9333-4.236622
0.621737

51.10473
48.69173
53.33276
43.8420146.403093
53.364638
48.97205
45.758973

3.98652.29005
6.74398
10.51534
0.723563
6.489072
3.18654.830895

47.10002
53.21056
53.339476
45.867625
46.221471
46.221471
46.221471
46.833364
48.59069
49.11566

0.21706
5.730085.73008-0.321187-0.147358-0.147358-0.147358
4.7221196.15058
6.173253.111153.1820448.81667

6.67616
5.44716
17.05

Molliens-Vidame
Monchy-au-Bois
MontmainMontrieux-en-Sologne IIMontrieux-en-Sologne III
Moreria
Mourlieu
MuizenMullaghboden
Muret
Nagyszokoly
Nagyvázsony
Neufchateau I
Neufchateau II
Neuvy-au-Houlme

817-877
840-922768-814800-922864-898

Molliens-Dreuil
Monchy-au-Bois
MontmainMontrieux-en-Sologne
Montrieux-en-Sologne
Moreria
Mourlieu
MuizenMullaghboy
Muret
Nagyszokoly
Nagyvázsony
Neufchateau
Neufchateau
Neuvy-au-Houlme

49.883950.17999505
49.410716
47.55408
47.55408
38.916776
46.564931
51.01056
54.83536
43.460924
46.72132
46.9835
48.356071
48.356071
48.81812.02
2.656698281
1.252625
1.72638
1.72638-6.349645
0.512703
4.514722-5.72671
1.327252
18.21182
17.69408
5.692627
5.692627-0.19966

Niederlahnstein
Nourray
Nr.Trier

855-869
843-877768-855

Niederlahnstein
NourrayTrier

50.315193
47.71903
49.755513

7.598382
1.060236.640075

OdoornOrléans
Oudwoude
Palma de MajorquePaule
Pilligerheck

843-961814-864
814-877
800-888843-877814-877

OdoornOrléans
Oudwoude
Palma de MajorquePaule
Muenstermaifeld

52.85033
47.90143
53.27968
39.570589
48.235953
50.20461

6.847823
1.90496
6.114132.648991-3.444348
7.31152

Pingjum
Place Unknown
Plessé
Poitou Charentes
Pommern
Pont Saint-Pierre
Postsaal
Pouzauges

900-911954-986875-877814-877
887-924864-877814-1024875-898

Jeanne-Rose (1996)
Minarovicova (2007)
Coupland and Moesgaard (2012)
Haertle (1997)
Morrison and Grunthal (1967)
Coupland (2020)
Doménech-Belda et al. (2013)
Coupland (2014)
Duplessy (1985)
Haertle (1997)
Moesgaard (2010)
Morrison and Grunthal (1967)
Saccocci et al. (2004)
Haertle (1997)
Haertle (1997)
Sombart (2008)
Coupland (2020)
Coupland (2014)
Morrison and Grunthal (1967)
Coupland (2011a)
Morrison and Grunthal (1967)
Coupland (2014)
Haertle (1997)
Haertle (1997)
Coupland (2018)
Duplessy (1985) and Haertle (1997)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2014)
Morrison and Grunthal (1967), Haertle (1997)
Morrison and Grunthal (1967)
Slovenská akadémia vied. Archeologickỳ ústav
(1979)
Haertle (1997)
Morrison and Grunthal (1967)
Coupland (2020)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Parvérie (2018)
Caron (1882)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2020)
Kovács (1989)
Kovács (1989)
Coupland (2014)
Coupland (2014)
Morrison and Grunthal (1967), Duplessy(1985)
Coupland (2020)
Morrison and Grunthal (1967)
Coupland (2014) and Morrison and Grunthal(1967)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Doménech-Belda et al. (2013)
Coupland (2014)
Petry and Wittenbrink (2021), Coupland(2011b)Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Haertle (1997)
Coupland (2020)
Coupland (2020)
Coupland (2011a)
Coupland (2020)
Haertle (1997)

Pingjum

53.11519

5.44004

Plessé
Poitou-Charente
PommernPont-Saint-Pierre
BavièrePouzauges

47.54109-1.88812

50.169368
49.33388

7.269726
1.274546.7822-0.8361

900-925822-877814-877814-840926-947902-947
800-922814-848814-877

Appendix Table B.13: Carolingian Hoards, Part IV30

Hoard Name

Date

Reference

Location

LatitudeLongitude

Questembert
RaalteRegensburg
Rennes
Rijs
Rijswijk
Rochefort
Roches l’Evêque
Roermond

814-877814-877843-877843-922814-877814-840
900-911814-922222-877

Questembert
RaalteRegensburg
Rennes
Rijs
Rijswijk
Rochefort
Roches l’Evêque
Roermond

47.66097
52.38724
49.016213
48.10761
52.86298
52.039942
45.935077
47.7772
51.193179-2.45216.27462
12.097468-1.68448
5.49838
4.325633-0.962458
0.89225.98624

Rome I (Forum)
Rome II (Vatican)
Rosas
Roswinkel
Rotterdam
Saint Bris le Vineux
Saint Ponc
Saint Yrieix la PercheSaint-BrieucSaint-CalaisSaint-Cyr-en-Talmondais

887-950898-922814-840768-882814-840814-877884-887
888-898864-875768-877814-877

Haertle (1997)
Coupland (2011a)
Haertle (1997)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2020)
Coupland (2020)
Morrison and Grunthal (1967)
Haertle (1997), Coupland (2011b), Zuyderwyk
and Besteman (2010)
Metcalf (1992)
Morrison and Grunthal (1967)
Doménech-Belda et al. (2013)
Morrison and Grunthal (1967)
Coupland (2020)
Coupland (2020)
Doménech-Belda et al. (2013)
Coupland (2020)
Haertle (1997)
Paty (1848)
Morrison and Grunthal (1967)

41.90509
41.90509
42.265002
52.83787
51.919909
47.74291
41.963245
45.51359
48.513647.921146.4614

12.46194
12.461943.178593
7.038434.475443.651349
1.603627
1.203618-2.76530.7439-1.3356Saint-Denis
Saint-Martin-sur-le-Pré

793-875

Haertle (1997)
Coupland (2014)

48.936448.97782.35474.3394Saint-Même-le-Tenu
Saint-Michel-de-Chavaignes

814-877

Coupland (2014)
Haertle (1997)

47.020808
48.018584-1.794104
0.570918

Saint-Pierre-de-MailléSaint-Pierre-des-Fleurs I
Saint-Pierre-des-Fleurs II
Saint-Seine-l’Abbaye
Santa Elena
Santiago de Compostela

814-840823-877888-898

Benoit and Braunstein (1983)
Coupland and Moesgaard (2012)
Cardon et al. (2008)
Coupland (2014)
Doménech-Belda et al. (2013)
Doménech-Belda et al. (2013)46.679749.251449.251447.440003
43.337137
42.880265

0.84440.96670.96674.788637-1.786251-8.543118

SarlatSarzana
Saumeray
Saumur-Thouars
SaverneSavigné-sous-le-Lude
Savigny en Véron
Seiches sur le Loir
SéranonSevilla region’s-Hertogenbosch
SigeanSilverdale
Minor Finds
Søndre BøStrasbourg-Basel
Szabadbattyán
Szabadegyháza
Szedeg-othalom
Szekszárd
Tarrega
Taizy
TelochéTer Apel
Ter Heijde
Terslev
ThoiryThouars
Tiel
Tiszaeszlàr I
Tiszaeszlàr II
Tiszanána

814-877768-814843-877843-898

Rome
Rome
Rosas
Roswinkel
Rotterdam
Saint-Bris-le-Vineux
Saint-PonçSaint-Yrieix-la-PercheSaint-BrieucSaint-CalaisSaint-Cyr-enTalmondais
Saint-Denis
Saint-Martin-sur-lePré
Saint-Même-le-Tenu
Saint-Michel-deChavaignes
Saint-Pierre-de-MailléSaint-Pierre-des-FleursSaint-Pierre-des-FleursSaint-Seine-l’Abbaye
Irun
SantiagodeCompostela
Sarlat-la-Canéda
Sarzana
Saumeray
SaumurSaverneSavigné-sous-le-Lude
Savigny-en-Véron
Seiches-sur-le-Loir
SéranonSevilla’s-Hertogenbosch
SigeanSilverdale

44.889865
44.11186
48.2502747.121848.73947
47.61845
47.205554
47.578315
43.772823
37.393305
51.698578
43.0287
54.167322

1.216381
9.95886
1.32157-0.1704
7.36602
0.05801
0.1471060.362977
6.704362-5.993535
5.303773
2.978539-2.82505

58.11019
48.17147.11798
47.07845
46.265179
46.34779
41.648564
49.51967
47.88987
52.878359
52.02903
55.37476
48.86519
46.977604
51.88809
48.05
48.05
47.56111

6.88224
7.647318.3629
18.69228
20.140614
18.70626
1.140707
4.25832
0.267317.063981
4.16426511.9693
1.79463-0.21579
5.43069
21.46667
21.46667
20.52382

961-966
800-888843-898814-877
751-814814-840888-898814-840768-814800-898751-1027
814-883843-954826-950
888-924902-924902-947887-928864-877864-877
900-911814-840814-966875-894822-855898-922814-950926-950
888-946

Coupland (2020)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Duplessy (1985)
Morrison and Grunthal (1967)
Coupland (2020)
Coupland (2014)
Coupland (2020)
Parvérie (2018)
Coupland (2014)
Coupland (2020)
Coupland (2014)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Huszár (1955)
Kovács (1989)
Coupland (2014)
Huszár (1955)
Doménech-Belda et al. (2013)
Coupland (2020)
Hucher (1845)
Morrison and Grunthal (1967)
Coupland (2020)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Coupland (2011a)
Kovács (1989)
Kovács (1989)
Kovács (1989)

Søndre BøStrasbourg/Basel
Szabadbattyán
Szabadegyháza
SzegedSzekszárd
Tarrega
Taizy
TelochéTer Apel
Ter Heijde
Terslev
ThoiryThouars
Tiel
Tiszaeszlàr
Tiszaeszlàr
Tiszanána

Appendix Table B.14: Carolingian Hoards, Part V
31

Hoard Name

Date

Reference

Location

LatitudeLongitude

TroyesTroyes II
Tuscany
TytsjerksteradielTzummarum I
Tzummarum II
Unknown
Vale of York
Valence
Vallée de la Risle
Vercelli
Verdun I
Verdun II
Vereb
VernonVicq sur Gartempe
Vire
VrignyWagenborgen
Westerklief I
Westerklief II
Wiesbaden-Biebrich
Wijk bij Duurstede I
Wijk bij Duurstede II
Wijk bij Duurstede III
Wijk bij Duurstede IV
Wijk bij Duurstede V
Wirdum
Worms
Yde
Yonne
York
YrondeZelzate
Zetel
ZillisZuidlaren814-840
843-877
888-973814-855
819-855855-865954-986898-922819-840814-877768-814875-877881-887858-024814-877814-877843-877843-877814-877814-877814-877717-814793-822752-768768-820823-840751-768814-877814-840814-877814-840751-887
843-877814-877768-793888-949875-894

Coupland (2014)
Coupland (2020)
Ciampoltrini et al. (2001)
Coupland (2020)
Haertle (1997)
Coupland (2020)
Morrison and Grunthal (1967)
Williams and Ager (2010)
Haertle (1997)
Coupland and Moesgaard (2012)
Morrison and Grunthal (1967)
Haertle (1997)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Coupland (2020)
Coupland (2020)
Morrison and Grunthal (1967)
Haertle (1997)
Haertle (1997)
Sarfatij et al. (1999)
Besteman (2006)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Van Es and Verwers (1980)
Van Es and Verwers (1980)
Dijkstra (2005)
Coupland (2020)
Coupland (2020)
Coupland (2020)
Morrison and Grunthal (1967)
Coupland (2014)
Dolley (1965)
Morrison and Grunthal (1967)
Morrison and Grunthal (1967)
Völckers (1965)
Zäch (2001)
Haertle (1997)

TroyesTroyesTuscany
BurgumTzummarum
Tzummarum
FranceVale of York
Valence
Vale of Risle
Vercelli
VerdunVerdunVereb
VernonVicq sur Gartempe
Vire-Normandie
VrignyWagenborgen
Westerklief
Westerklief
Wiesbaden-Biebrich
Wijk-Bij-Duurstede
Wijk-Bij-Duurstede
Wijk-Bij-Duurstede
Wijk-Bij-Duurstede
Wijk-Bij-Duurstede
Wirdum
Worms
Yde
Yonne
York
YrondeZelzate
Zetel
ZillisZuidlaren

48.299055
48.58345

4.077872
3.81356

53.195748
53.238297
53.238297

5.987155
5.549116
5.549116

54.20361
44.93347
49.42445.32255
49.15952
49.15952
47.31867
49.091052
46.721302
48.83919
48.08167
53.25713
52.89494
52.89494
50.050115
51.971869
51.971869
51.971869
51.971869
51.971869
53.149585
49.632241
53.11143
47.89753
53.95333
45.6133
51.19753
53.414646.6355
53.09231-1.36398
4.890808
0.725
8.418445.382316
5.382316
18.61802
1.483426
0.862012-0.89
2.243889
6.93525
4.933224.933228.237668
5.344562
5.344562
5.344562
5.344562
5.344562
5.803308
8.362216.583653.588695-1.08342
3.254813.814637.96999.44514
6.679414

Appendix Table B.15: Carolingian Hoards, Part VI

32

Signatures

Approx.
Location
Nomisma ID

AHM
AIRAN, AYLAN
AM
APL, APR
ART, TARTAT
AU, AW
AY, AYLASBBABCLA, BJRA, DS, DST
BISH, BYS, BYSH
BN, BRMKRMAN, DL, DR, GLM,
KL, KLMAN, KLMANLCN, KR,
KRAMAN H P, KRMAN, KRMANW ST, KRMAN-GY, KRMAN-NAR,
KRMAN-NAW, NAL, NAR
D’, DA, DAP
DAPGD
GU, GW

hamadhanhulwanamol
nishapurardashir_khurrah
adurbadagan
suq_al-ahwaz
al-susctesiphonctesiphonal-basrahbishapurkirmanHL
HWC
LAM, RAMLD, RD
LYW, RIU
MA
MB, MY, PL
ML, MR
NH, NIHJ, NYHC, WH, WYHC

harat
jundi_sabur
ramhurmuz
rayy
rev-ardashirmasabadhanmaysanmarw
ctesiphon*

NHR
NIH, WYH
NIHJ
NY, NYH
SHI
SK
ST
SY
TPWRSTAN
YZ, ZR, GZ

nahr_tirabihqubadh_af-asfal
arrajanantiocheia_persis
shirazzaranj, sijistan
istakhrfars_shiraztabaristan
yazd

darabjird
fasa
jayy
gorganNotes

Eran-asankar-Kavad
Amol, Khorasan
TART: Tawwaj as dependency of Ardashir Khurra
AU is used by Al-Ush, we interpret it as ”AW”, Hormizd-Ardashir
Eran-khvarrah-Shapur. AYL: British Museum says “possibly referring to Susa.”
Following the coding in FLAME.
Court mint, probably at Ctesiphon (Gyselen)
Mallon-McCorgray interprets BCLA as al-Basra. Accoring to Schindel (2005) BJRA is al-Basra.
Multiple mints that are in Kirman province.

We follow Schindel (2005) in attributing GW to Gorgan (after Yazdegerd I). Gyselen (1977) attributes GU to
Gorgan.

Bivar (1970) associates RIU with LYW, and confirms Nö’s interpretation as Rev-Ardashir

NH, WH: Veh-Ardashir. On WYHC, Album (2011): ”A mint in northern Iraq, ostensibly the treasury mint near
Ktesiphon prior to the AH50s, and thereafter, for a series dated AH67-73, Arrajan”. We follow Album (2011),
Schindel (2005), and others in attributing it to Ctesiphon before AH50, then Arrajan.

Almost certainly the same as WYHC.
NY: Nihawand. For NYH, Schindel (2005) suggests Nihawand.

Unlocated mint, probably in Fars province (or Kirman, as has sometimes been suggested).

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45

C Mapping the Data to the Model
C.1

Constructing regions

To map our data to the model, we need to define which hoard and find locations correspond to which model
locations. We define spatial aggregates based on historical political boundaries, geography, and computational
feasibility, noting that political boundaries change over time, while our location definition must not. Moreover,administrative boundaries are not available for the same time period for all regions. We proceed in two steps; wefirst construct region boundaries based on the regions in al-Thurayya (Romanov and Seydi, 2022), covering the
maximum extent of the Umayyad caliphate, and then define regions for the remainder of our area of interest.

Constructing regions for the Arab world .We use the region tag associated with each Althurayyalocations to establish a historical provincial partition of the Arab world.
1. We manually delineate a rough approximation of the Arab world’s boundary.
2. We apply spherical voronoi tessellation (see section C.4) to all al-Thurayya locations within this boundary.
3. The resulting polygons are categorized based on the region information associated with each polygon’scorresponding location, aligning them with the 22 regions presented in table C.2 (excluding NoRegion).
4. We then take the union of the polygons under the same region tag.
5. We intersect the resulting (multi-)polygon with the coastline to remove the portions of the polygon that
extended into the sea.
6. The final (multi-)polygon represents the corresponding Arab region.
We also corrected some erroneously labelled region labels8 .

Constructing regions for the western world .We construct regions in the western world roughly
based on administrative boundaries that we retrieve from the Digital Atlas of the Roman and Medieval Civilizations(DARMC) hosted at Harvard Unversity, specifically the AD 200 and 303-325 Roman provincial boundaries (which
are based on the Barrington Atlas, Talbert, 2000) and the Medieval kingdom boundaries around AD 814, which
are an original contribution of DARMC.
We define the boundaries of our region of interest in Europe by taking the union of the AD 200 Roman provincial
boundaries with the 814 boundaries of the Frankish empire and the area of the West Slavs. The resulting border isroughly the modern-day German-Polish border, plus Bohemia, but follows otherwise roughly the AD 200 Roman
Empire boundary. In the areas covered by al-Thurayya, the border is delineated roughly by the convex hull of thespatial extent of administriative district boundaries.
Aggregating regions. We then merge a number of regions in order to have mints and hoards in all regions(which is important for identification). We merge the realm of Charlemagne with the Frankish lands in Germania;
the areas of Byzantine Thracia and Dacia, and combine Sardinia and Corsica. In the east, we combine theadministrative districts of the eastern Caliphate where hoard coverage is sparse, the regions of al-Iraq, al-Jibal,
Khuzestan, and Kirman; and finally the regions of the Arabian Peninsula.

C.2

Defining the coin sample for the structural analysis

We use the same sample of coins as for the reduced-form analysis in section 1, with the following exceptions:• We exclude coins where the mint date interval exceeds 150 years.
• We exclude non-hoard coin finds from excavations (because the tpq is meaningless).
8

See this link.

46

C.3

Constructing the geospatial model

We build our geospatial model by combining two geospatial models constructed by historians to model travel
distances and routes. The first one, ORBIS (Scheidel, 2015), is a geospatial model of the Roman world and spansroughly the maximum extent of Roman conquests. The second, Al-Thurayya (Romanov and Seydi, 2022) is adigitization of Cornu (1983)’s atlas of the Islamic word in the 9th and 10th century. Both geospatial models takethe form of undirected graphs; in the case of ORBIS this is augmented by measures of travel costs on each edge.
ORBIS also contains sea routes; for the Arab world we augment al-Thurayya with a number of known sea routes.
For al-Thurayya we also construct bilateral travel distances ourselves.

Vertices. The following links in this document point to the ORBIS city data and Althurayya city data we usedin our analysis. ORBIS data labels locations as either actual cities or crossroads. Actual cities are denoted by theirauthentic names, which correspond to those displayed on the ORBIS website. Crossroads are not designated witha name and are labeled using an ”x” and are not visible on the ORBIS website. Similarly, Althurayya locations
are characterized by more diverse types, including capitals, metropoles, quarters, sites, towns, villages, waters,
waystations, and xroads. Note that some of these locations do not have a name in the dataset.
Edges. The edge data for ORBIS and Althurayya is accessible via the respective links provided in this document.
We employ the Haversine formula to calculate the length of ORBIS edges, assuming a radius of 6371 km for the
Earth. The lengths of Althurayya edges are included within the raw dataset.
Merging the graphs. We merge the two graphs by primarily merging vertices shared between them. Somecities hold significance in both the early Islamic world and the Roman world. The challenge of this task is due todifferences in location names for the same city within each database. For instance, the city known as Cádiz in Spainis referred to as Gades in ORBIS and Qadis in Althurayya. To address this issue, we implement a preliminary
screening process to identify Althurayya locations within a 20km radius of each ORBIS location. We then manually
determine whether the location in ORBIS and its counterpart in Althurayya indeed represent the same city. The
collection of our refined city pairs and the decisions made can be accessed through the following link.
Apart from the two main data source of routes, we also added the Volga trade route, sea routes in the CaspianSea and sea routes in the Arab world (see below).
During the merge, the geometry of the routes remains unchanged. The only change resulting from the merge
is that common locations are treated as identical vertices in the adjacency matrix. All cities are re-indexed andprepared for the computation of least distances and fastest routes after the merge.
Constructing sea routes for the Arab world. We extend ORBIS’s algorithm for sea routes to theMediterranean Sea and the Black Sea, the Red Sea, the Arabian Sea, the Gulf of Persia, and the Gulf of Oman.
Our process begins by creating a grid with a resolution of 0.1 degrees by 0.1 degrees, covering the area of interestin the sea. Each point on the grid can move in eight directions (N, S, W, E, NW, NE, SW, SE). We then manually
select Althurayya locations that are close to the coastline as potential ports. Among these candidates, we choose
only those whose type is labelled as capitals, metropoles, sites, towns, villages, or waystations, excluding
regions (centroid of a region) and xroads. These selected ports are projected onto the nearest points in the grid,
and we calculate the shortest travel time paths along the grid for given routes. The measurement of travel duration
is defined below.

Constructing sea routes for Volga trade route Volga trade route plays an important role inconnecting northern Europe and northwestern Russia with the Caspian Sea via the Volga River. For the segments
within the Caspian Sea, we have extended the method outlined for sea routes. Specifically, we have chosen thefollowing sea routes: Abaskun-Derbent, Derbent-Sasqin, Kuhanrudh-Baku, and Baku-Derbent. To establish aconnection between the Caspian Sea and the Black Sea, we have added routes Sasqin-Tanais (via canal), SaraiSaqsin (via canal), and Sarai-Sarkel (via land). Sasqin-Tanais is represented by a segment in the Don River,which was retrieved from OpenStreetMap here, while Sarai-Saqsin is a segment in the Volga River, retrieved fromOpenStreetMap here. Sarai and Sarkel are connected directly since they are in close proximity to each other.

Determining weights and speed.

47Appendix Figure C.1: The ORBIS adjustment visualized
Roads. ORBIS categorizes the weight and speed of terrestrial edges in a categorical manner. Table C.4 showsten different types of adjustments used by ORBIS, and the locations of these edges are illustrated in Figure C.1.
The speed of an edge in ORBIS is defined by Equation C.1.
speed = weight × 30km/d,

weight =unadjusted length
adjusted length(C.1)

ORBIS does not provide explanations for the criteria used to select and categorize edges, otherwise we could
simply apply these rules to the Althurayya network. Nevertheless, we have collected various edge-related variablesfrom Stanford EarthWorks and 2019 ASTER project for both ORBIS and Althurayya edges. We run a regression
to explain the ORBIS weight defined in Equation C.1 using these variables. We then extrapolate this linear modelto the Althurayya data. To put it simply, we use ORBIS as the training set and Althurayya as the test set.
We collect the following variables for all edges and both directions:
uphill_3d_2d_ratio After properly sampling an edge (as described in Section ??), we identify all segments thatascend along the specified direction (where the height at the end is greater than the height at the beginning),
and then compute the ratio.
Ps goes uphill length_3Ds
uphill_3d_2d_ratio = P, s is a segment in the edges goes uphill length_2Ds
A large ratio indicates a steep uphill slope.
downhill_3d_2d_ratio Similar to uphill_3d_2d_ratio,P
downhill_3d_2d_ratio = P

s goes downhill length_3Dss goes downhill length_2Ds

cityrank_1 We find the intersection of the edge and the 20 km buffer area of rank 1 cities. Find the ratio of
the length of the intersection over the length of the entire edge. The rank of the cities are defined as thefollowing:48

ORBIS

Cumulative Proportion

6

25.1%

60

33.9%

70

48.2%

80
90
100

83.2%
98.2%
100%

Althurayya (w/o quaters, regions)xroadswaystations
sites
watersvillages
towns
capitalsmetropoles

Cumulative Proportion
8.6%
29.0%
31.2%
37.6%
48.7%
94.2%
99.4%
100%

Harmonized Rank
1
2
3
4
5
6

cityrank_2 Similar to cityrank_1 but with rank 2 cities.
cityrank_3 Similar to cityrank_1 but with rank 3 cities.
cityrank_4 Similar to cityrank_1 but with rank 4 cities.
cityrank_5 Similar to cityrank_1 but with rank 5 cities.
cityrank_6 Similar to cityrank_1 but with rank 6 cities.
landfeature_Desert The percentage of the edge’s length that intersects desert polygons, retrieved from Stanford
EarthWorks, is calculated. A correction has been manually applied to account for the misidentification of
the narrow passage along the Nile as desert
landfeature_Plateau Percentage of the edge’s length that intersects plateau polygons, retrieved from Stanford
EarthWorks.
landfeature_Plain Percentage of the edge’s length that intersects plain polygons, retrieved from Stanford EarthWorks.
landfeature_Range/mtn Percentage of the edge’s length that intersects mountain polygons, retrieved from Stanford EarthWorks.
near_river Percentage of the edge that intersects the 20 km buffer area of rivers.
We fit adjusted ORBIS weights using the mentioned variables (except leadfeature_Desert and near_river since
ORBIS edges does not traverse desert at all, and rivers are modeled separately from the road in ORBIS). To ensure
the plausibility of the coeﬀicients and prevent overfitting, we perform least squares regression with the following
constraints: (i) uphill_3d_2d_ratio has negative effect on the weight, (ii) The effect of cityrank_* has positiveeffect on the weight, (iii) The magnitudes of the effects for cityrank_* should follow the ranking cityrank_1 <cityrank_2 < cityrank_3 < cityrank_4 < cityrank_5 < cityrank_6, (iv) The effects of landfeature_Plateau
and landfeature_Range/mtn on the weight should be negative, while the effect of landfeature_Plain should bepositive. Figure C.2 shows a scatter plot between the fitted weight and the ORBIS weight.
We manually set the coeﬀicient for desert to -0.5 and for river to 0.5. This implies that the marching speed
is approximately 18.2 km/day for an edge that is 100% within a desert, and the marching speed is roughly 49.5
km/day for an edge near a river.9 The extrapolated weights and the edge locations are displayed in Figure C.3.
The choice of speed not only impacts the total travel time but also influences the traveler’s chosen route. For
instance, when considering the unweighted shortest route (Figure C.4) from Dimashq to Baghdad, it passes throughthe Syrian desert. However, with weighted speed considerations, the preferred route would bypass the desert andfollow the Euphrates. Similarly, when traveling from Sana to Isfahan, the preferred route (Figure C.5) runs along
the coast and includes a stop in Mecca before crossing the Arabian Peninsula.
Sea: We follow the methodology outlined in Arcenas (2015) to construct sailing speeds at sea, adopting thesame steps used by ORBIS. Our data source is the CCMP wind speed data, which provides wind direction and
9This value is considered suitable since ORBIS indicates that a civilian vessel typically travels at around 65
km/day. Although we do not distinguish between river and road in Althurayya, the speed for a mix of different
means of transportation should fall within the range of 30 km/day to 65 km/day

49

Appendix Figure C.2: Fitted ORBIS weight and actual ORBIS weight

Appendix Figure C.3: Al-thurayya edges and their weight50

Appendix Figure C.4: Route comparison from Damascus to Baghdad

Appendix Figure C.5: Route comparison from Sana’a to Isfahan

51

4.0

3.4

3.2

3.4 4.7
4.0

0.7

3.2

5.5

3.1

4.7 4.5
5.5

4.7

6.5

-0.2

3.1

Vship

2.5

Vship

3.4

1.0
Vship

2.5

5.5

4.5
6.5

Appendix Figure C.6: Speed rose in light, moderate, and heavy wind (left to right, unit: knot)
speed information every 6 hours for each cell in a 0.25-degree × 0.25-degree grid, spanning from 1993 to 2023. Toalign with ORBIS, we focused on speed data for the month of July.
We categorize the wind direction into eight main directions (“N”, “NE”, “E”, “SE”, “S”, “SW”, “W”, and“NW”). At the latitude × longitude × direction level, we calculated two key metrics (i) The mean wind speed,and (ii) The proportion of time the wind blew in each direction.
In accordance with Arcenas (2015), scalar wind speeds were classified into four categories: ”calm,” ”light,””moderate,” and ”heavy,” with corresponding Beaufort scale classifications: calm < Beaufort 2; light = Beaufort
2; moderate breeze = Beaufort 3-4; and heavy air ≥ Beaufort 5. Each category corresponds to a specific speed rose(see Figure C.6).
In Figure C.6, the figure on thin arrows denote the scalar velocity of the vessel if the wind blows down the
direction of the thin arrow. For instance, when the wind speed is “light,” the vessel’s speed is 1.0 knot when sailinginto the wind, 2.5 knots when the wind blows from the front-right, and 3.4 knots when the wind comes directlyfrom the right. For each coordinate, we calculated the weighted mean of the vessel’s speed across the eight winddirections, with the weight determined by the proportion of time the wind blew in each direction. We use the mean
vessel speed in eight directions for each coordinate.
We create a 0.1-degree × 0.1-degree mesh grid in the area of interest as described in Section C.3. The vessel
can go in eight directions at each vertex. The integration of the vessel speed is essential due to the precarity of
the wind speed in the sea. The difference of the least time sailing route under uniform speed and varying speed isshown in Figure C.7.

C.4

Technical details on operations in spherical geometry

Computing 2D distances. Our edges are essentially connecting segments that are characterized by a seriesof points. To determine the length of each segment, we calculate geodesic distances between the starting point andthe ending point. This distance calculation is based on the Haversine equation formula.
pd2D = 2R arcsin hav(∆lat) + cos(lat1 ) cos(lat2 ) hav(∆lon)
where
hav(x) = sin2

x
2

.

Computing 3D distances. Past efforts to incorporate elevation data into the ORBIS dataset, together
with the challenges, has been well-documented here by ORBIS. We follow the documented method, which involves
sampling our roads with a series of equidistant points spaced 50 meters apart along the edge. For each sampled
point, we retrieve elevation data from the 2019 ASTER project.
As illustrated in the diagram in Figure C.8, we calculate the angle α = d2D /R, with known d2D , R (assumed
to be 6371 kilometers), h1 (the altitude of the start of the line segment) and h2 (that of the end). Using Law of
cosines, we havepd3D = (R + h1 )2 + (R + h2 )2 − 2(R + h1 )(R + h2 ) cos α
One important consideration of computing the 3D distance is resolution and the sampling technique. It’sessential to note that the mesh-grid in the ASTER project does NOT consist of congruent rectangles. As the52Appendix Figure C.7: Changes of the sailing route before and after incorporating wind speed

53

h1

d3D

A
d2D

Bh2
R
α R
O

Appendix Figure C.8: The diagram for 3D distance computation. The circle shown in the graphis the great circle defined by A and B
latitude deviates from zero, the rectangles become elongated. When the latitude is close to zero, it resembles moreof a square shape. This distortion is due to the spherical shape of the earth.
It would be also unreliable to simply find all intersecting segments of the routes and the grid, and assign thesegments with the grid’s height data. Some edges are over-sampled than others. A path that goes from east to
west and is close to the pole would gain more resolution comparing to a similar path that are closer to the equator,
if segments are sampled this way. To address this issue, we interpolate our path each from start to end every 50
meters (in geodesic distance)10 , use these sampled points to find the corresponding height data.

Spherical Voronoi tessellation. Na et al. (2002) proved that the spherical Voronoi tessellation can becomputed by computing two planar Voronoi diagrams of sites under stereographic projection in the plane. Following
Na et al. (2002) and Patel (2018), our Algorithm is described as the following:
1. Choose one arbitrary location among our sites as the center of projection (point A in C.9), or otherwise
called the anchor.
2. (Drawn in red in C.9) Rotate the globe such that the anchor (A) meets the South pole (A*). In the rotation,
all other sites are also rotated (B → B*).
3. (Drawn in blue in C.9) Connect the South pole with sites that are not the anchor. Find the intersection(B’) of the extended line and the plane z = 1. We have now created a mapping (B → B’) that maps all thesites to points in the plane z = 1, except the anchor.
4. Perform Delaunay triangulation on the mapped sites. This can be easily done by the Python package
Shapely’s function triangulate().
5. Since the anchor is not projected, the triangulation is not complete. One needs to choose another arbitrary
location as the anchor and repeat step 1-4. Merge the two different results of triangulation.
6. Map the triangulation back to the unrotated sphere. Find the spherical circumcenter of all the mapped-backtriangles (The spherical circumcenter and the Euclidean circumcenter and the center of the sphere lies on
the same line. The spherical circumcenter is on the surface of the sphere while the Euclidean circumcenteris inside the sphere).
7. Connect all circumcenter pairs whose corresponding triangles touch.
10

The choice of 50 meters serves as an optimal compromise because it ensures that consecutive sampled points
are placed in different grid cells because 50 meters slightly exceeds the diagonal length of a 30m x 30m square grid
cell

54

z=1

z
B’

B*
B

x

y
A
A*
Appendix Figure C.9: Diagram for the stereographic projection
Caveat. Step 6 can potentially cause problems because there are two spherical circumcenters of a triangle.
They are antipodal point of each other. If all sites are spaced somewhat evenly around the sphere, it works simply
by choosing the spherical circumcenter that is on the same side of the Euclidean circumcenter. However, all oursites can be contained in one hemisphere and Althurayya sites are far from being “placed evenly”. Therefore, weare bound to encounter problems with some spherical circumcenter with the unadjusted algorithm. The remedy
is that we add an auxiliary site that is far from our original sites11 when triangulating, and we always choose thespherical circumcenter that is on the same side as the Euclidean circumcenter. Since the auxiliary point is far,the voronoi polygon containing it is outside of our spatial scope of analysis. For instance, in the Voronoi diagramof Althrayya sites, the polygon containing the auxiliary point includes Antarctica, southern part of Australia andSouth America.

Obtaining the arc between two points. This topic is relevant mainly in terms of visualization. We
can obtain an approximation of the arc between two point on the globe with the help of the gnomonic projection,
because the gnomonic projection projects all great circles into straight lines. For an arc which we only have its
two endpoints, we project them on the plane z = −1, fill the projected linestring with additional vertices so thatsegments divided by these additional vertices are no longer than the choice of maximum segment length. This canbe easily achieved by Python package Shapely’s segmentize(). We then map the processed linestring back to thesphere surface. Note that this method can result in uneven resolution. This method can come in handy when evenresolution is not important.

C.5

Other utilities

Since Shapely primarily handles shapes in Euclidean space, we had to develop most of our own utilities. For
instance, when dealing with the intersection of two lines on the globe, we treat it as the intersection of two great
circles. To achieve this, we transform latitude and longitude coordinates into XYZ coordinates. We then determine
the planes that pass through these two lines and find their intersection with the spherical surface.
We also developed our own interpolation function for arcs characterized by a start and an end. To achieve this,
we first find all the points on the sphere that are at a certain distance from the start point, effectively creating a
11

To decide which auxiliary site to add, we find the mean of longitudes and the mean of latitudes of our originalsites, then make the auxiliary site the antipodal of the point (mean of longitude, mean of latitude)55

Appendix Figure C.10: The voronoi of all sites in Althurayyacircle around the start. We identify the plane that passes through the arc and calculate the intersection points ofthe plane and the circle. This typically results in two points, and we choose the one inside of the arc.

56

C.6

Tables and references
Country
United Kingdom
Italy
TurkeyGreeceFranceSpain
Egypt
Algeria
Tunisia
Syria
Libya
Germany
Romania
Croatia
Austria
Bulgaria
IsraelPortugal
Albania
Switzerland

Total

City

Crossroad

200
119
88
69
50
43
36
23
18
17
16
16
15
15
11
11
9
8
8
7

32
111
86
69
49
42
36
23
18
14
16
15
11
12
11
10
9
5
6
7

168
8
2
0
1
1
0
0
0
3
0
1
4
3
0
1
0
3
2
0

Country
CyprusSerbiaHungary
LebanonUkraineBosnia & Herzegovina
MoroccoJordanSlovenia
West Bank
Gaza Strip
Macedonia
RussiaGeorgia
Montenegro
Netherlands
Iraq
Malta
Total

Total

City

Crossroad

7
7
6
6
6
5
5
5
4
2
2
2
2
2
1
1
1
1
844

7
7
4
5
6
2
5
5
3
2
2
2
2
2
1
1
1
1
640

0
0
2
1
0
3
0
0
1
0
0
0
0
0
0
0
0
0
204

Appendix Table C.1: ORBIS cities by their modern country, distinguishing city types (city or
crossroad)57

Country

Total

capitalsmetrop.

quarters

sites

towns

villageswaterswaystns

xroadsMā-warāʾ-l-nahrḪurāsān
al-Šām (Greater Syria)
Jazīra� al-ʿarab
Fārs(or Fāris)al-Maġrib
Aqūr (al-Jazīra�)Miṣr (Egypt)al-Andalus (Spain)
al-Jibālal-ʿIrāqal-Riḥāb (Caucasus)
Badiyya� al-ʿarabal-Sindal-Daylamal-Mafāza�KirmānSijistān (Sīstān)
Barqa� (Lybia)Ḫūzistān (al-Ahwāz)al-Yamanal-Ḫazar
NoRegion
Total

186
139
138
122
114
110
94
88
83
71
66
62
60
53
50
49
44
40
39
39
31
8
6
1692

17
12
7
3
7
6
4
4
0
9
5
2
0
4
3
0
3
4
1
7
5
1
0
104

1
1
1
1
1
0
0
1
1
1
1
1
0
0
1
0
1
0
0
1
0
0
0
13

0
0
0
0
0
0
1
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
3

3
0
0
3
1
0
0
3
0
1
1
0
2
0
0
0
0
0
1
0
0
0
0
15

93
70
57
29
45
78
45
44
62
16
37
34
4
36
20
3
26
19
8
14
14
4
0
758

21
16
23
16
15
5
9
23
3
4
7
10
1
2
6
8
6
6
4
2
2
0
0
189

0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1

31
30
27
62
42
1
25
7
0
31
11
4
48
1
18
33
5
6
24
13
4
0
0
423

20
9
23
8
3
20
10
5
17
9
3
11
5
10
2
5
3
5
1
2
6
3
6
186

Appendix Table C.2: Althurayya cities by region, distinguishing city types (capitals, metropoles,
quarters, sites, towns, villages, waters, waystations, xroads)

ORBIS
Althurayya

count

mean

std

min

10%

25%

50%

75%

90%

99%

max

1215
2053

144489.055530.4

158012.564952.5

3827.21687.031606.2
13851.8

58006.7
23842.0102353.1
38387.0

168695.0
62771.0300377.0108031.6

737576.2
332197.52142105.6
861693.0

Appendix Table C.3: Summary statistics of the lengths of the edges in graphs

Type ID

source → target

target → source

0
1
2
3
1r
2r
3r
1b
2b
3b

No adjustment
Add 18 km to the length
Add 36 km to the length
Add 54 km to the length
No adjustment
No adjustment
No adjustment
Add 18 km to the length
Add 36 km to the length
Add 54 km to the length

No adjustment
No adjustment
No adjustment
No adjustment
Add 18 km to the length
Add 36 km to the length
Add 54 km to the length
Add 18 km to the length
Add 36 km to the length
Add 54 km to the length

Appendix Table C.4: Adjustment by ORBIS

58

References
Arcenas, S. (2015): “ORBIS and the sea: A model for maritime transportation under the Roman Empire,”
Retrieved February, 22, 2021.
Cornu, G. (1983): Atlas du monde Arabo-Islamique à l’époque classique: IX.-X. siècles, Brill.
Na, H.-S., C.-N. Lee, and O. Cheong (2002): “Voronoi diagrams on the sphere,” Computational Geometry,23, 183–194.
Patel, A. (2018): “Delaunay+Voronoi on a sphere,” .
Romanov, M. and M. Seydi (2022): “al-Thurayyā,” https://althurayya.github.io/ [Accessed: (1 July2023)].
Scheidel, W. (2015): “ORBIS: The Stanford geospatial network model of the Roman world,” Tech. rep.
Talbert, R. J. (2000): Barrington Atlas of the Greek and Roman World: Map-by-map Directory, vol. 1, Princeton
University Press.

59