They had the ability to take inventions that had been developed elsewhere and apply them on a much bigger scale. This way they could mass-produce goods such as textiles and pottery that they could then trade with other people. As Kramer writes, there was something in the Sumerian identity that drove them to dream big and think ingeniously. Here are some of the areas where the Sumerians left their mark.
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Bowl from the ancient civilizations of Mesopotamia. Other ancient people made pottery by hand, but the Sumerians were the first to develop the turning wheel, a device which allowed them to mass-produce it, according to Reed Goodman , a doctoral candidate in the art and archaeology of the Mediterranean at the University of Pennsylvania. An early writing sample from Mesopotamia using p ictographs to create a record of food supplies. They did that with a system of pictographs , which essentially were drawings of various objects. Eventually, though, they began to combine pictographs to express ideas and actions.
9 Ancient Sumerian Inventions That Changed the World - HISTORY
The pictographs evolved into symbols that stood for words and sounds. Scribes used sharpened reeds to scratch the symbols into wet clay, which dried to form tablets. The system of writing became known as cuneiform, and as Kramer noted, it was borrowed by subsequent civilizations and used across the Middle East for 2, years. A Mesopotamian relief showing the agricultural importance of the rivers. The Sumerians figured out how to collect and channel the overflow of the Tigris and Euphrates rivers—and the rich silt that it contained—and then use it to water and fertilize their farm fields.
An earlier contribution Tobler and Wineburg uses a similar data set as ours to locate Assyrian cities in Bronze Age Anatolia. Our method differs from and improves on multidimensional scaling in that we use an explicit structural economic model. This allows us to infer not only the location of lost cities but also the distance elasticity of trade, the size of cities a theory-guided counterfactual measure , formal estimates of standard errors, and two-dimensional confidence regions. Furthermore, compared to Tobler and Wineburg , we use a much larger data set that has become available for study in the meantime, systematically clean our data to identify meaningful economic exchanges, formally account for trade zeros, and compare our estimates to historical and contemporaneous evidence.
We also show that our structural estimates yield more plausible estimates than multidimensional scaling, even using the same data. Finally, we provide novel evidence on the very long-run determinants of the city size distribution. An important line of theoretical and empirical inquiry in economic geography involves attempts at explaining the distribution of economic and demographic size of cities over time. Locational fundamentals as dictated by geography are potentially an important factor Davis and Weinstein Agglomeration of economic activity for nongeographic reasons may magnify size differentials even across seemingly homogeneous locations Krugman Path dependence through lock-in effects could lead to the persistence of past factors—related to the fundamentals that may have been important once Bleakley and Lin ; Michaels and Rauch Our results and historical setting suggest that centrality in the transportation network, shaped by the topography of the land, is an important geographic factor explaining the hierarchy of city sizes.
The remainder of the article is organized as follows.
Explore Ancient Mesopotamia
Section II describes our data. Section III derives our model and estimation strategy. Section IV discusses estimates for the distance elasticity of trade and the location of lost cities. Section V presents our estimates for city sizes and explores the determinants of the distribution of ancient city sizes. Section VI compares the structural gravity model to estimates from a naive gravity model.
These texts were inscribed on clay tablets in the Old Assyrian dialect of the Akkadian language in cuneiform script by ancient Assyrian merchants, their families, and business partners. Figure I shows a picture of a well-preserved clay tablet. To some degree, this alleviates any geographical bias of the sources and the commercial geography that they reflect. The closest comparable corpora of ancient trade data are almost 3, years later, coming, for example, from the medieval Italian merchant archives and the Cairo Genizah. In a typical shipment document or expense account, a merchant would inform partners about the cargo and related expenses: I paid 6.
I paid 2 shekels of silver and 2 shekels of tin for the hire of a donkey from Timelkiya to Hurama. In accordance with your message about the kg of copper, we hired some Kaneshites here and they will bring it to you in a wagon Pay in all 21 shekels of silver to the Kaneshite transporters. Occasional business letters contain information 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.
Tablet POAT 28, lines 3—7. Concerning the purchase of Akkadian textiles which you have written about, since you left the Akkadians have not entered the City; their land is in revolt, but should they arrive before winter, and if it is possible to make purchases profitable for you, we shall buy some for you. While the actual cuneiform tablets are scattered all around the world in collections and museums, many of the texts have been transliterated into the Latin alphabet, translated into modern language, published in various volumes, and recently digitized.
We use qualitative and quantitative information about cities and merchants mentioned in a sample of 9, digitized texts available to us and approximately 2, additional nondigitized texts. To construct this measure, we proceed in several steps. First, we automatically parse through our 12, texts to identify any tablet that mentions at least two cities. To do so, we systematically isolate strings of characters corresponding to all possible spellings of city names.
A typical business document will describe one or several itineraries. In the rare cases where an itinerary loops back, we do not count the return trip. Of the 25 cities in our sample, 15 are known and 10 are lost. Known cities are either cities for which a place name has been unambiguously associated with an archaeological site, or cities for which a strong consensus among historians exists, such that different historians agree on a likely set of locations that are very close to one another.
Lost cities, on the other hand, are identified in the corpus of texts, but their location remains uncertain, with no definitive answer from archaeological evidence. From the analysis of textual evidence, archaeology, and the topography of the region, historians have developed competing hypotheses for the potential location of some of the lost cities. We propose using data on bilateral trades between known and lost cities and a structural gravity model to inform the search for those lost cities. Table I provides summary statistics. The mean number of travels across all city pairs is 0.
As with modern international trade data, many city pairs do not trade: of all the potential export-import relationships directed ij and ji pairs out of 25 cities , only have a positive flow. The ancient data come from a textual analysis of clay tablets inscribed in the cuneiform script, written by Assyrian merchants in the second millennium BCE. Most texts are digitized and will be available as tagged and searchable files through the OARE-project, currently being built as part of the Neubauer Project. Figure II plots all cities on a map, including a preview of the estimated locations of lost cities, and the bilateral trade flows between them.
As discussed, it was also the operational center of Assyrian merchants in central Anatolia. The Eaton and Kortum model is particularly well suited for two reasons.
Second, it makes an explicit prediction about the count of shipments, which we observe, rather than the value of shipments, about which we have almost no information. When bringing the model to the data, we depart from Eaton, Kortum, and Sotelo and other modern gravity estimates such as Silva and Tenreyro : unlike with modern trade data, we do not know the location of some cities. Instead we use our model to estimate those locations. In other words, we treat some distances as unknowns instead of data.
Merchants arbitrage price differentials between cities, subject to bilateral transaction costs. Equations 2 , 3 , and 4 form the basis of our estimation.
Mesopotamia Trade: Merchants and Traders
It is important to note that the Eaton and Kortum model makes explicit predictions about the probability of a shipment occurring, equation 2. The empirical counterpart to this probability can be formed using only data on shipment counts and does not require knowledge of the value of shipments.
This property is crucial to us, as our data set contains information on shipment counts but not on the value of shipments. Among modern trade models, this feature is unique to the Eaton and Kortum model and is one of our main motivations for using it. Note that this model also predicts that trade shares in value are equal to trade shares in counts. Our estimation proceeds in three steps. First, we parameterize trade costs as a function of distance only. Using data on shipments among known cities only, we can estimate the distance elasticity of trade.
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Second, imposing the estimated trade cost function, we jointly estimate city sizes for all cities and the geographic location of lost cities. Estimating unknown locations for lost cities, and therefore distances involving lost cities, is novel compared with conventional estimates of the gravity equation in trade.
Third, we combine our estimates to compute a measure of the fundamental size of cities, solving a full general equilibrium version of our model. A simple triangulation-type technique can then recover the location of lost cities. Our three-step procedure formally estimates parameters such that the gravity model fits the data as closely as possible and provides estimates of standard errors and confidence regions around our point estimates.
There are two reasons for this choice. First, when estimating our gravity model, we need to solve a complex nonlinear minimization program—see equation 8 below. With an explicit Euclidean formula for distance, we can take the first-order conditions of this program with respect to the latitudes and longitudes of lost cities.
Had we used least-effort distances instead, we would have had to compute all possible least-effort distances for pairs of points on a discrete grid and solved our minimization program by brute force. This is computationally infeasible.
B and V. Not bringing topographical data into our estimation gives credence to those validity checks. Because our model is based on Eaton et al. In particular, they can be 0, as often happens in the data, if the lowest realized cost to deliver a good from i to j is higher than the lowest realized cost from all other origins. Beyond this finite sample randomness, we can easily add a multiplicative disturbance term to the trade cost function 5 , without altering our estimation strategy.
Note that our NLLS estimator 8 uses data contained in trade zeros explicitly. For instance, consider a case where the observed trade share from i to j 1 is 0, but the trade share from i to j 2 is positive. To visually gauge the precision of estimates for the location of lost cities, we draw maps with confidence regions around our point estimates. They account not only for the precision of the latitude and longitude of city l but also for the covariance of those geocoordinates.
We present our results for the distance elasticity of trade and the estimated location of lost cities, and we confront our results with historical evidence in Section IV. To further gauge the plausibility of our estimates, we suggest a quantitative method to systematically use the qualitative information contained in our ancient texts to construct admissible regions for the lost cities in Section IV. Finally, we propose to use our gravity model to evaluate the validity of potential unnamed archaeological sites in Section IV.
Table II presents the estimated geo-coordinates of lost cities, along with robust standard errors. This suggests that the impact of distance on trade around BCE was surprisingly similar to what it is today, with modern elasticity estimates typically near unity Disdier and Head ; Chaney , and estimates for shipments transported by road above unity —see Cosar and Demir for a distance elasticity around 2 based on overland transit of exports from Turkish cities, nearly equal to our ancient estimate.
This table presents the estimated geocoordinates, latitudes and longitudes, from solving our structural gravity model 8. All latitudes are north, and all longitudes are east. Robust White standard errors are in parentheses. The last column gives the estimated correlation between latitude and longitude, used to compute confidence regions. Precision, measured in km, is defined in equation Figure III shows maps with our point estimates and confidence regions for each lost city separately.
A closed circle depicts the estimated location from our structural estimation 8 , surrounded by contours representing the confidence regions for that city 50th, 75th, 90th, and 99th percentiles. This can be compared to the average distance of km between known cities. We add to those maps two other locations.