Trade, Merchants and Lost Cities of the Bronze Age∗

Gojko Barjamovica, Thomas Chaneyb, A. Kerem Cosarc, and Ali Hortacsud

aHarvard University, NELCbToulouse School of Economics, and CEPR

cStockholm School of Economics, and CEPRdUniversity of Chicago, Department of Economics, and NBER

January 24, 2016

(Preliminary and Incomplete, Comments Welcome)

Abstract

We analyze a dataset of business documents inscribed in the cuneiform script andpreserved on clay tablets informing us about the long-distance caravan trading systemthat connected northern Iraq, northern Syria and central Turkey in 19th century BCE.The standard gravity specification fits this ancient trade data remarkably well. Usingdata on modern-day trade of Turkish cities with Iraq and Syria, we find persistentpatterns across four millennia. An inverse-gravity exercise suggests a methodology forestimating the location of lost cities. The size distribution of merchant coalitions isapproximately Pareto.

1 Introduction

This paper analyzes a vast collection of commercial records from the earliest documented

and market-oriented long-distance trade in world history: the old Assyrian trade network

connecting northern Iraq, northern Syria and central Turkey in 19th century BCE. The

clay tablets on which the merchants inscribed their shipment consignments, expenses, and

contracts—excavated, translated and published by archeologists and historians for more than

a century—paint a rich picture of an exchange economy.

∗This research is supported by the University of Chicago Neubauer Collegium for Culture and Society.Correspondence: barjamovic@fas.harvard.edu, thomas.chaney@gmail.com, kerem.cosar@gmail.com,hortacsu@uchicago.edu.

Using a digitized sub-sample, we investigate spatial patterns of trade and the structure of

merchants’ networks. The results reveal remarkable similarities to the patterns documented

from modern datasets. A gravity equation on a measure of trade relationships between

ancient cities yields a distance elasticity of trade close to negative unity. Second, the size

distribution of merchant coalitions (precursor of firms) is approximately Pareto.

While some ancient cities have been discovered and excavated, some have been lost to

humanity. Analyzing the texts for descriptions of trade routes connecting the cities and

the landscapes surrounding them, historians have developed qualitative conjectures about

potential locations of several lost cities. We demonstrate an alternative, quantitative method-

ology based on maximizing the fit of the gravity equation. In the first stage, we estimate

the distance elasticity of trade between a sub-sample of known cities. This yields the above-

mentioned estimate that is close to negative unity. In the second stage, we maximize the fit

of the gravity equation, parameterized with this coefficient, to the entire sample of known

and unknown cities. This yields predictions for the coordinates of lost cities. In this paper,

we apply the methodology to Purushaddum, a seemingly prominent yet lost Anatolian city,

and compare the qualitative and quantitative conjectures for its location.

2 Historical Background and Data

Our data comes from a collection of around 23,500 texts excavated at the archaelogical site

of Kultepe, ancient Kanes, located in Turkey’s central Anatolian province of Kayseri. These

texts were inscribed on clay tablets in the Akkadian language in cuneiform script by ancient

Assyrian merchants, their families and business partners. The texts date to a period roughly

between 1930 and 1718 BCE, with around 90% of the sample belonging to just one generation

of traders, c. 1895 - 1865 BCE.

Originating from the city of Assur in modern-day northern Iraq, a few hundred Assyrian

merchants settled in Kanes on a permanent or temporary basis, and maintained smaller

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expatriate trading settlements in some 30 urban centres on the central Turkish plateau. In

total, an estimated 1000 Assyrian agents were settled in Anatolia at any given point during

the period in question. Kanes was the regional hub of the overland commodity trade involving

the import of luxury fabrics and tin from Assur to copper rich Anatolia in exchange for silver

and gold. Assyrian merchants were also involved in supplying tin to other Anatolian city

states from Kanes. Map 1 shows major cities and the trade routes of the period.

Most of the texts under consideration are commercial: business letters, shipment doc-

uments, accounting records and contracts. Fittingly, the tablets they were inscribed on

were found in merchants’ houses and archives. In a typical shipment document or expense

account, a merchant would inform its partner about the cargo and related expenses:

In accordance with your message about the 300 kg of copper, we hired some

Kanesites here and they will bring it to you in a wagon...Pay in all 21 shekels

of silver to the Kanesite transporters. 3 bags of copper are under your

seal...Here, Puzur-Assur spent 5 minas of copper for their food. We paid 5 2/3

minas of copper for the wagon.

Kt 92/k 313 (lines 4-8,14-22)

Occasional business letters facilitated information exchange about market and transport

conditions:

Since there is a transporter and the roads are dangerous, I have not led the

shipment to Hutka. When the road is free and the first caravan arrived safely

here, I will send Hutka with silver.

POAT 28 (lines 3-7)

While the actual cuneiform tablets are scattered all around the world in collections and

museums, around 10,000 texts have been transliterated into Latin alphabet, published in

various volumes and recently digitized by historians. In this draft, we use quantitative

information about cities and merchants mentioned in a sub-sample of 5,464 digitized texts

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available to us. The texts also contain information about prices, financial contracts and

resolution of legal disputes, which we hope to analyze in future work.

The version of the data used in this draft (tabulated by Barjamovic 2011) contains 51

unique cities mentioned in texts, with 34 having some information on their locations. Only

a small number of these 34 cities, however, have been precisely located. 14 cities have either

been excavated or strong clues exist for associating them with a current place. Another 12

cities have been lost, but the texts contain clues about their locations. From analyses of

such textual evidence and the geography of the landscape, historians developed competing

theories about potential sites. Finally, the locations of 8 cities are mostly speculative. In our

empirical investigation, we label these three types of cities having location certainty from 1

to 3, ranked in terms of increasing certainty.

To construct a measure of bilateral commercial interactions between cities, we use dyadic

counts of joint attestations to city names in tablets. For instance, the number of texts in

which any two cities i and j are mentioned in a direct relationship, such as “I am traveling

from i to j,” is Xdirij . Some texts mention city pairs without a direct relationship such as “I

arrived from i yesterday and the caravan will leave for j tomorrow.” While these cases suggest

some interaction between cities, they are plausibly less informative about trade relationships.

These counts of co-occurrences are denoted X indirij . In our empirical analysis, we estimate a

gravity equation on both measures using bilateral distances, and propose a methodology to

estimate uncertain city locations.

To finance the fixed costs of trading and overcome transaction costs, old Assyrian mer-

chants organized their trade as partnerships and coalitions within a tightly-knit ethnic net-

work.1 A sub-sample of texts contain references to the merchants involved. In the absence

of surnames, it is challenging to disambiguate the names, i.e., making sure that Mr. Puzur-

Assur mentioned in different tablets is the same person. Historians meticulously analyze

1These relationships resemble the case of 11th century Maghribi traders documented by Greif (1989,1993). It is plausible that organizing trade in this fashion helped to alleviate agency costs through similarmechanisms of repeated interaction and informal enforcement.

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the texts using the information about the time periods, patronymic lineages (Puzur-Assur,

son of...) and other familial ties to identify the individuals.2 Once disambiguated, counts of

meaningful linkages between merchants (owning a joint caravan or letters addressed to each

other) give us information about the teams or coalitions. While not necessarily indicating

joint-stock companies, these coalitions resemble firms. In our empirical analysis, we analyze

the size distribution of merchant coalitions.

3 Empirical Analysis

3.1 Gravity Equation

We start by estimating a gravity equation on our data:

ln(Xij) = zi + zj + δ ln(distij) + εij, (1)

where distij is the geodesic distance between cities i, j andXij is the count of direct or indirect

co-occurrences, depending on the specification. We only use cities with location certainty 1

or 2, which reduces the sample to 26 cities. We first estimate (1) with OLS, using positive

observations. Out of 325 possible dyadic combinations, 62 and 71 pairs have Xdirij > 0 and

X indirij > 0, respectively. 54 pairs have both measures positive. Given this prevalence of

zeros, we also estimate a PPML specification by replacing the dependent variable with Xij

(Silva and Tenreyro, 2006).

Table 1 presents the results. Validating the qualitative nature of the data, the distance

elasticity in OLS estimation is significant for direct relationships only (columns 1 and 2).

PPML regression yields a significant coefficient for both measures (columns 3 and 4). To our

knowledge, this is the oldest evidence for the gravity except the work of Bossuyt et al. (2001)

who also estimate a gravity-like specification using citations from Babylonian tablets dating

2We are thankful to Adam Anderson for sharing with us the current version of the dataset from his thesiswork at Harvard University.

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back to the 21 century BCE. Our estimates are remarkably close to distances elasticities

estimated from modern-day trade data. This confirms the puzzling persistence of the distance

effect documented by Disdier and Head (2008) across four millennia.

3.2 Persistence of Economic Significance

To inquire whether the economic significance as captured by the fixed effect estimates zi show

persistence over time, we first map the ancient cities to modern day provinces in Turkey.

Figure 2 plots the fixed effects against the (log) population of these provinces, which shows

a positive correlation. We then use the fixed effects estimated from the 2003-2012 trade of

these provinces with Iraq and Syria (Cosar and Demir, 2016). Figure 3 plots the ancient and

modern fixed effects. The correlation is 0.36, which is preliminary evidence for persistent

economic significance of locations over four millennia as it pertains to comparable trade

relationships.3

3.3 Inverse Gravity

Treating bilateral distances as an independent variable, the gravity equation helps to estimate

the elasticity of trade to distance. We now take the reverse approach: using the distance

elasticity estimated from trade between cities with high location certainty, we estimate the

geographic coordinates of a major city, Purushaddum, which has location certainty equal

to 2 in our sample. In our gravity estimation above, we used the location suggested by

Barjamovic (2011), who locates it around 300 km to the west of Forlanini (2008) in central

Anatolia (figure 4).

In the gravity setting, each city is identified by its size and coordinates (lati, longi).

Repeating the estimation presented in column 1 of table 1 by excluding Purushaddum, we

obtain a statistically significant distance elasticity of -0.748 and city fixed effects zi. The

3We also note that the city of Kanes is 20 km away from the province of Kayseri, an important regionalcommercial hub in Turkey. Without a doubt, its fertile land, central location and being the end of passagescoming from the Middle East imply strong location advantages.

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fixed effect zp and coordinates (latp, longp) of Purushaddum are unknown. We do, however,

observe its trade Xip > 0 with 11 of these 25 cities. If these relationships obey gravity with

some error εip, one can write 11 equations

ln(Xip) = zi + zp − 0.748 ln(distip) + εip, (2)

where the distance between i and Purushaddum is given by the Haversine formula:

distip = 2r · arcsin

(√sin2

(lati − latp

2

)+ cos(lati) cos(latp) sin2

(longi − longp

2

) ).

r is the radius of the Earth at the poles (6356 km) or at the equator (6378 km).

We estimate Purushaddum’s coordinates by maximizing the fit of the gravity equation

to its trade relations. In particular, we solve

(zp, ˆlatp, ˆlongp) = argmin(zp,latp,longp)

11∑i=1

ε2ip,

subject to equation (2) and the constraint that Purushaddum should be located to the west

of Kanes.4 The gravity-implied location plotted in figure 4 is closer to the area proposed by

Barjamovic (2011) based on qualitative textual evidence.

3.4 Size Distributions of Merchants, Teams and Coalitions

We now turn to a preliminary analysis of the distributional properties of the sizes of mer-

chants, of teams and of coalitions.

Figure 5 plots the counter-cumulative distribution of merchant sizes in a log-log scale.

The distribution of merchant sizes appears to be approximately log-normal, although we

have not applied an explicit statistical test of that hypothesis for the moment.

4The methodology resembles the trilateration of locations by global positioning systems. An earlyapplication is by Tobler and Wineburg (1971) who imposed a quadratic distance elasticity.

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Figure 6 plots the counter-cumulative distribution of team sizes in a log-log scale. The

distribution of team sizes appears to converge to a Pareto distribution (a straight line in

a log-log scale) for teams above size 6. We formally estimate the shape parameter of this

Pareto distribution below.

Figure 7 plots the counter-cumulative distribution of coalition sizes in a log-log-scale.

Again, the distribution appears to converge to a Pareto distribution for coalitions above size

6.

We now turn to a formal statistical test whether those size distributions can be approxi-

mated by a Pareto distribution, defined as,

Pr [Sizei ≥ S] ∝ S−β

where β is the shape parameter of the Pareto distribution. Formally, we estimate by OLS

the following rank-size relation, using the procedure in Gabaix and Ibragimov (2011),

ln

(Ranki −

1

2

)= α− β ln (Sizei) + εi. (3)

The results are presented in table 2. When data on all sizes are used, surprisingly, the

distribution of merchant, team and coalition sizes seems to be close to Zipf’s law. This result

however is misleading, as the above figure show. When only data on the upper tail of those

distributions are used, the distribution of team and coalition sizes are precisely approximated

by a Pareto distribution with shape parameter -3. This tail index is stable to truncating

the distribution anywhere above size 6. For the distribution of merchant sizes, the tail index

is not stable, and keeps increasing (in absolute terms) as the distribution is truncated at a

higher point.

The robust finding of a Pareto distribution with tail index -3 for team and coalition sizes

suggests that a strong empirical regularity is present.

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References

Barjamovic, G. (2011). A Historical Geography of Anatolia in the Old Assyrian Colony Period.Museum Tusculanum Press.

Bossuyt, A., L. Broze, and V. Ginsburgh (2001). On invisible trade relations between mesopotamiancities during the third millennium bc. The Professional Geographer 53 (3), 374–383.

Cosar, A. K. and B. Demir (2016). Domestic road infrastructure and international trade: Evidencefrom turkey. Journal of Development Economics 118, 232 – 244.

Disdier, A.-C. and K. Head (2008). The puzzling persistence of the distance effect on bilateraltrade. The Review of Economics and statistics 90 (1), 37–48.

Forlanini, M. (2008). The central provinces of hatti. an updating. In K. Strobel (Ed.), NewPerspectives on the Historical Geography and Topography of Anatolia in the II and I MillenniumBC, Number 1, pp. 145–188. LoGisma Editore.

Gabaix, X. and R. Ibragimov (2011). Rank- 1/2: a simple way to improve the ols estimation oftail exponents. Journal of Business & Economic Statistics 29 (1), 24–39.

Greif, A. (1989). Reputation and coalitions in medieval trade: evidence on the maghribi traders.The journal of economic history 49 (04), 857–882.

Greif, A. (1993). Contract enforceability and economic institutions in early trade: The maghribitraders’ coalition. The American economic review , 525–548.

Silva, J. S. and S. Tenreyro (2006). The log of gravity. The Review of Economics and statistics 88 (4),641–658.

Tobler, W. and S. Wineburg (1971). A cappadocian speculation. Nature 231, 39–41.

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Appendix: Figures and Tables

Figure 1: Cities, Routes and Trade Patterns

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Figure 2: Ancient Scale of Trade and Modern Population across Cities

Figure 3: Ancient and Modern Scales of Trade across Cities

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Figure 4: Location of Purushaddum

P_Barjamovic

P_Gravity

P_Forlanini

Kanes

Figure 5: The Distribution of Merchant Sizes

Notes: All 2,748 merchants who have at least one trading partner. A merchant’s “size” is defined as thenumber of unique trading partners of that merchant. The graph plots in a log-log scale the counter-cumulativedistribution of merchant sizes. Data sources are Old Assyrian period tablets from various archives.

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Figure 6: The Distribution of Team Sizes

Notes: All 5,778 teams of merchants. A team is defined as a group of merchants mentioned in the sametext and who are in an explicit trading relationship. Team size is defined as the number of merchants in theteam. The graph plots in a log-log scale the counter-cumulative distribution of team sizes. Data sources areOld Assyrian period tablets from various archives.

Figure 7: The Distribution of Coalition Sizes

Notes: All 4,061 coalitions of merchants. A coalition is defined as the union of teams, where two teams belongto the same coalition if and only if at least one unique merchant belongs to both. Coalition size is defined asthe number of unique merchants in the coalition. The graph plots in a log-log scale the counter-cumulativedistribution of coalition sizes. Data sources are Old Assyrian period tablets from various archives.

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Table 1: Gravity Estimates

(1) (2) (3) (4)ln(Xdir) ln(X indir) Xdir X indir

ln(dist) -0.775∗∗∗ -0.180 -1.303∗∗∗ -0.569∗∗∗

(0.170) (0.119) (0.173) (0.127)Method OLS OLS PPML PPML

Relationship Direct Indirect Direct IndirectN. Obs. 62 71 195 191R2 0.704 0.732 0.746 0.847

Notes: Standard errors in parentheses. ∗∗∗ p < 0.01. Relationship denotes whether the dependent vari-able X count meaningful commercial relationships betweens cities (direct), or any co-occurrence of theirnames in the same document (indirect).

Table 2: Testing Rank-Size Relationship

All Size 6 and aboveMerchant Team Coalition Merchant Team Coalition

β 1.20∗∗∗ 1.12∗∗∗ 1.24∗∗∗ 1.98∗∗∗ 3.11∗∗∗ 3.19∗∗∗

(0.0038) (0.0079) (0.012) (0.0032) (0.0054) (0.0043)N. Obs. 20,748 5,778 4,061 6,810 916 857R2 0.830 0.776 0.722 0.982 0.997 0.998

Notes: Standard errors in parentheses. ∗∗∗ p < 0.01. This table estimates equation 3 usingdata on merchant, team and coalition sizes.

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