Journal of Interdisciplinary History, L:1 (Summer, 2019), 31–58.

The 50th Year: Special Essay 2

Peter Temin

Words and Numbers: A New Approach to Writing
Ancient History Interdisciplinary history can enrich the ap-
proach of disciplinary histories. According to Beard, the author of a
recent deﬁnitive history of the Roman Republic, “What is missing
[from ancient history] is the perspective of those outside this ex-
clusive group [of well-known authors]: the view of the ordinary
soldier or voter, of the women or . . . the slaves.” Thinking about
the economy in ancient Rome provides a window into the ordi-
nary lives of ancient Romans. It expands our view of history and
helps to integrate ancient history with the economic history of
more modern times.1

But interdisciplinary history is difﬁcult to produce. Scholars can
receive training in economic history or ancient history but not in in-
terdisciplinary history per se. Instead, historians aspiring to think
about economic activities start from different disciplines according
to their tastes and training to infer “the view of the ordinary soldier”
or farmer. The question is how diverse scholars can communicate to
provide an interdisciplinary history of the Roman economy and our
economy today when their epistemologies are so divergent. As
Beard stated, most knowledge of ancient history comes from words;
ancient historians are trained primarily to parse words. Economic
historians, however, are interested in numbers. Interdisciplinary
workshops and talks routinely expose the different emphases of these
disciplinary biases. The two ways of interpreting the scant evidence
lead to different approaches. Ancient historians focus on narratives;
economic historians focus on probabilities.

We all like stories to which we can relate emotionally. Think-
ing about the stories passed down in ancient history, however,

Peter Temin is the Elisha Gray II Professor Emeritus of Economics, Massachusetts Institute of
Technology. He is the author of The Vanishing Middle Class: Prejudice and Power in a Dual
Economy (Cambridge, Mass., 2017); “The Labor Market of the Early Roman Empire,” Journal
of Interdisciplinary History, XXXIV (2004), 513–538.

© 2019 by the Massachusetts Institute of Technology and The Journal of Interdisciplinary
History, Inc., https://doi.org/10.1162/jinh_a_01375

1 Mary Beard, SPQR: A History of Ancient Rome (New York, 2015), 350.

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32 | P E T E R TE MI N

poses questions that arise from contemporary issues, but these
questions are obscured by the small amount of hard evidence
about life in ancient Rome that has managed to survive for
2,000 years. Words alone, copious as they are, cannot answer
them. The task for modern historians is how to generalize from
the little that we know about ordinary people in the past. We
can tell stories about individuals, but how are we to know how
characteristic any individual is? Can we translate one person’s
experience into a description of a society as a whole?

Consider a noneconomic example. King Agrippa II stood on
the roof of his palace in 66 A.D. to deliver a speech warning his
subjects about the revolt against Rome looming ominously in
Judaea, which eventually ended with the destruction of the Jewish
Temple in 70. Agrippa II appeared to be acting as a spokesman for
some part of the Jewish population. But how many people do we
think heard his stirring speech? We know about the speech only
because the ancient historian Josephus recorded, or fabricated, it.
Ancient authors commonly placed opinions into the mouths of
their contemporaries. Thanks to this practice, modern authors
have acquired the opportunity to personalize and dramatize an-
cient ideas, but we still do not know how many people heard
and believed Agrippa II. Was he speaking to a large collection
of people who had the power to arrest the progress of the growing
revolt, or was he speaking to a few of his friends as the revolt
gathered strength?2

Goodman, who authored a book quoting Agrippa, used the
speech to introduce his attempt to understand the growth of the
doomed revolt against Rome. Goodman described Jerusalem as a
prosperous city to which Roman tourists ﬂocked to see sights that
were ancient even in classical times. To what extent were Roman
tourists like tourists today? Did they have groups or leaders to
show them the sights? Did they arrive on private ships that trans-
ported them from, say, Italy to Jerusalem? Was it easier for rich
Roman tourists to book ship passages and accommodations in
Jerusalem than it was for poor tourists? In short, did Judaea, a small
area on the fringe of the Roman Empire, have a market economy?3

2 Martin Goodman, Rome and Jerusalem: The Clash of Ancient Civilizations (New York, 2007),
63–65.
Ibid.
3

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| 33

I tried to answer this question by examining prices, because
prices are integral to market economies, and in order to determine
whether the available prices were market prices, as opposed to
administrative or ceremonial prices, I used a simple statistical test
to see how they behaved. A simple regression on a few prices can
provide answers to these questions, though not detailed answers or
irrefutable answers. The aim is to generalize about the Roman
economy, to choose between administered or market prices or
between prices and barter, not to summarize all the details about
commerce in a simple regression. The results come only as a series
of probabilities that offer the choice of accepting the generaliza-
tions or not. Paying attention to simple regressions on a few num-
bers can aid in understanding Beard’s question and suggest
tentative answers. The point of the statistics is not to supplant
the written sources but to amplify their importance by providing
descriptions of the world in which the written sources emerged.
Such was the method of my book, The Roman Market
Economy (Princeton, 2013), which opened with a small regression—
hypotheses tests that draw implications from a basic correlation—
and followed with a succession of chapters that explain in detail
how the various parts of a market economy operated in Roman
times. The goal is hardly to preempt narrative but to provide con-
text for individual narratives. An analysis of the disagreements
about my regression with the aid of statistical theory is instructive,
leading to reﬂections about what this debate portends for inter-
disciplinary history.4

In his book, The
UNCERTAINTY IN HISTORY: WHEAT PRICES IN ROME
Corn Supply of Ancient Rome, Rickman noted that the six wheat
prices that he was able to ﬁnd were all reasonably close to each
other in value: “Curiously enough . . . what evidence we have
about corn prices had rather greater unanimity than we might have
expected.” What could have caused this unanimity of prices around
the Mediterranean? Given so few observations, two explanations
are possible—(1) chance or (2) an inﬂuence that forced prices into
the same order of magnitude. Do these six prices that Rickman
managed to collect hint at a pattern? At ﬁrst glance, chaos seems
to be more likely than order. Rickman provided several reasons

4 Temin, The Roman Market Economy (Princeton, 2013).

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34 | P E T E R TE MI N
why a pattern might be difﬁcult to detect: “There would seem to
be as many different prices for wheat as there were different places
and different periods in the history of the ancient world.”5

Although Romans talked incessantly about money, only a
few prices are available after 2,000 years to be quoted in modern
books. In addition, only a few of the surviving prices pertained to a
uniform commodity like wheat and only a few conformed to a
uniform currency that permits comparisons. Given these circum-
stances, what connections around the large geographical area that
comprised the Roman world could cause such unexpected unifor-
mity in the seemingly disparate prices that Rickman discovered?
What kind of pattern, if any, is behind it? Can these scattered
prices help to answer Beard’s question about ordinary people?

The Importance of Statistical Theory We can have conﬁdence
in the patterns that statistical analyses are able to distill from ran-
dom bits of information. Ancient historians are not taught statistics,
but economists rely on them. Because ancient and economic his-
torians have different skill sets, their attitudes toward ancient data,
and their epistemologies, are often at odds. Ancient historians
focus on the accuracy of individual observations, ascertaining
whether they are typical of their time and place as recorded and
translated. Economic historians study groups of observations,
called samples. Although accuracy is important to them as well,
they are more interested in testing hypotheses about the general-
izations and patterns that might lurk within their observations than
they are in the individual observations themselves.

An introduction to certain statistical concepts can clarify these
differences and facilitate interdisciplinary history. The central limit
theorem of probability states that the sum of random variables with
varied probability distributions is a random variable with a normal
distribution. The normal distribution also is known as the bell curve
because of its shape. The normal distribution is so called because it
is so common. For example, consider the ancient prices recorded
by Rickman. Some of them were recorded as actual sales, and some
of them were guesses made at the time. Other records of prices
disappeared for unknown reasons in the intervening two millennia.
Values observed today, which are the result of the various proba-
bility distributions involved in the recording and preserving of

5 Geoffrey Rickman, The Corn Supply of Ancient Rome (New York, 1980), 145.

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| 35

these prices for 2,000 years, are described by a normal distribution.
To identify the normal distribution that applies to these prices, we
need only ﬁnd its mean and variance—that is, the location and
thickness of the bell curve.

The application of statistics to the collection of prices in
Rickman’s book entails the observation that these prices were
not the only Roman prices. In other words, they are a sample of
Roman prices. As we will see, other Roman prices that have
survived can be combined into other samples. In addition, these
prices are a random sample of Roman prices, meaning that
Rickman’s prices had the same probability of being selected as
any other relevant Roman prices. Although we do not know
Rickman’s motives, we know that the chance of any individual
price surviving for 2,000 years is separate and small and that our
history of this period in antiquity is based on a small fraction of
the records actually composed at that time. Hence, we can invoke
the central limit theorem of probability, which states that variables
drawn from many separate distributions converge to a bell curve,
also known as a normal distribution.6

These descriptions are simple and even obvious, but they
differ from the assumptions made by ancient historians, who pre-
dominantly study the people who collected Roman prices, not
bell curves. They think of prices individually, not as samples of a
larger population, although they often extrapolate casually from
single observations. Both ancient and economic historians want
to generalize—to create a picture of the ancient economy—but
they approach this goal with different tools. What is important
to note, however, is that the two kinds of tools are complemen-
tary; they should be used together.

Subject to the simple descriptions just presented, regressions
can help us to choose between order and disorder. A regression
determines the best line that is closest to a set of points in a normal
distribution—like a collection of Roman prices—and generates a
measure that enables us to decide whether these prices follow a
pattern or are unrelated to each other. This measure, known as
the t-statistic, summarizes calculations that can assess the accuracy
of, say, Rickman’s conclusion about the chaos of pricing. To
use tables of t-statistics, we need to know only the number of

Stephen Greenblatt, The Swerve: How the World Became Modern (New York, 2011).

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36 | P E T E R TE MI N

degrees of freedom in a regression. Degrees of freedom are found by
subtracting the number of necessary relations among observations
from the total number of observations. Rickman listed six prices.
The simplest regression speciﬁes the best linear relation between
the observations. A straight line has a slope and an intercept, that
is, two relations. That makes four degrees of freedom. One of
Rickman’s observations is problematical in this test; omitting it
leaves us with only three degrees of freedom.

The regression of Rickman’s prices on the estimated distance
from Rome conﬁrms with 95 percent probability that transport
costs were proportional to distance and that the effects of distance
were larger than the idiosyncratic inﬂuences of particular markets
and places. This ﬁnding implies a uniﬁed wheat market extending
from one end of the Mediterranean Sea to the other. The proba-
bility found in this instance is the same percentage of likelihood that
the Federal Drug Administration employs today to declare a med-
ical drug safe and effective. Yet, several eminent ancient historians
reject regression on the grounds that it is too simple to be useful—
an objection that exposes the epistemological gap that tends to con-
found interdisciplinary history. The t-statistics show the results to
be highly signiﬁcant despite the few degrees of freedom—in
fact, nearly the fewest degrees of freedom necessary to calculate
t-statistics. Only a simple relation can be tested with so few obser-
vations. Simple models are not as appealing to ancient historians as
they are to economists, but they are common in ancient history
nonetheless, under the rubric of generalizations. Simple models, or
generalizations, can always be elaborated into more subtle stories.7
Rathbone, who collected many Roman prices, objected,
“The thesis of Kessler and Temin (2008) just does not ﬁt the
Roman data as a whole . . . at least in the simplistic form in which
it is presented.” In Scheidel’s words, “Moses Finley’s famous ob-
servation that ‘ancient society did not have an economic system
which was an enormous conglomeration of interdependent mar-
kets,’ [has been] countered by Peter Temin’s repeated claim that
the Roman Mediterranean did indeed form a single integrated
market for goods and labor. . . . Given the paucity and uneven
quality of the available local price data, it is easy to ﬁnd fault both

7 For the likelihood of drugs being safe and effective, see Temin, Taking Your Medicine: Drug
Regulation in the United States (Cambridge, Mass., 1980).

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| 37

with the underlying premise and the practical execution of
Temin’s analysis. After carefully exposing these problems in great
detail, Gilles Bransbourg has repeated this exercise with a revised
and expanded sample of local grain prices from twelve different
sites.” Similarly, Bang dramatically stated, “Peter Temin argued
that Finley was quite simply wrong. This is an extraordinary claim.
One might conceivably imagine that some markets had begun to
be linked by middle- and long-distance trade. But to see the entire
economy, spanning several continents, as organized by a set of
interlinked markets is quite another matter.”8

The main market in any set of interlinked Roman wheat
markets would have been in the city of Rome, the center of im-
perial administration, where the largest number of potential con-
sumers lived and the largest supply and demand for wheat would
have existed. The price of wheat would have varied over time as
harvests ﬂuctuated across the Roman world, and government
actions altered the value of the currency. Normal variations in
supply and demand elsewhere in the empire would have affected
the price, although most ﬂuctuations would have been small
relative to total production and consumption at Rome. Most
places outside Rome would have had an excess supply of wheat,
the price of which would have been set in Rome, where the
excess supply and demand would have met.

Yet, even though under normal circumstances, wheat outside
Rome would have taken its value from the price in Rome, the
status quo did not always hold. Certain isolated areas outside
Rome could have had an excess local demand as well as an excess
local supply, because of famines and gluts. For example, the usual
price of wheat in Palermo, Sicily—which was the price in Rome
minus the cost of transporting wheat there from Palermo—would
temporarily fall below the level normally set by Rome if, say, a
storm prevented shipment. Furthermore, if a harvest failure in

8 David Kessler and Peter Temin, “Money and Prices in the Early Roman Empire,” in
William V. Harris (ed.), The Monetary Systems of the Greeks and Romans (New York, 2008),
137–159; Dominic Rathbone and Sitta von Reden, “Mediterranean Grain Prices in Classical
Antiquity,” in Robartus J. van der Spek, Jan Luiten van Zanden, and Bas van Leeuwen (eds.),
A History of Market Performance: From Ancient Babylonia to the Modern World (New York, 2015),
149–235, 188; Walter Scheidel, “The Shape of the Roman World,” Journal of Roman Archae-
ology, XXVII (2014), 7–32; Peter Bang, The Roman Bazaar: A Comparative Study of Trade and
Markets in a Tributary Empire (New York, 2006), 31.

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38 | P E T E R TE MI N

Sicily created a local famine, the price of wheat in Sicily would
have risen above the level indicated by the Roman price until
new wheat supplies were available. In the absence of such extreme
events, however, a uniﬁed market would have kept Sicilian prices
near the Roman price less the transportation cost.

More concretely, given a uniﬁed market, competition would
have determined Sicilian prices. If the Sicilian price of wheat rose
above the Roman level minus transportation costs, it would not
have made sense for merchants to buy wheat in Sicily to sell in
Rome. The amount of wheat demanded in Sicily would have
fallen, thus dropping the price. If the Sicilian price of wheat
dropped below the Roman level minus transportation costs,
merchants would have bought more wheat in Sicily since they
could have made an unusually high proﬁt by taking it to Rome
and selling it there. Merchants would have bid against each other,
raising the Sicilian price.

Wheat at Lusitania in Spain would have been worth less than
wheat at Palermo because it was further from Rome. Because the
cost of transporting wheat from Spain to Rome was higher than
the cost of bringing it from Sicily, the price of wheat in Spain
would have been lower. The reasoning is exactly like that for
Sicily; only the transport cost is different. But although each price
was established on the basis of that in Rome, the price in Spain
would have been lower than that in Sicily in a uniﬁed market.
We do not know the transport costs around the Mediterranean
in any detail, but we are reasonably sure that the price of wheat
would have decreased with distance from Rome, given a uniﬁed
wheat market. As Smith stated, “The corn which grows within a
mile of the town, sells there for the same price with that which
comes from twenty miles distance.”9

In the absence of a uniﬁed market, prices in the independent
local markets would not have had any relationship to Roman
prices, as Rickman and Bang suggested; prices would have been
determined only by local conditions. The prices would have
moved together at times—if storms across the Mediterranean
caused simultaneous harvest failures, or currency debasements

9 Adam Smith, An Inquiry into the Nature and Causes of the Wealth of Nations (London, 1776),
307–310 (Book III, Chapter 1), available from the EE-T Portal at https://eet.pixel-online.
org/ﬁles/etranslation/original/ The%20Wealth%20of%20Nations.pdf.

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| 39

A N E W A P PR O A C H T O W R I T I NG AN CI E N T H IS T O R Y
caused prices to rise—but they would not have been related to
each other as a rule; any single identity of prices would have been
a coincidence. If we ﬁnd wheat prices in different places, however,
we can test whether any pattern that we notice is due to coinci-
dence or an underlying process.

The question is not whether an efﬁcient market existed or
whether particular forces brought separate local markets together
but whether the historical facts lie closer to one end of the con-
tinuum than the other. Many interventions into Roman markets
and local actions elsewhere around the Mediterranean are well
known. Local grain shortages and famines must have occurred
from time to time. Was the normal state of affairs based on inter-
connected markets in which prices typically were related or on
separate, independent markets in which no systematic relationship
between location and grain price obtained?

The simple model supplies a clear representation of Roman
trade across the Mediterranean within which wheat prices in out-
lying provinces were related to those in the city of Rome. Given
the extremely limited data about Roman prices, no more complex
model could be tested. The simple model treated herein is com-
patible with a complex pattern of actual Roman trade. Wheat
must have been shipped to provinces around the Mediterranean,
rather than to Rome, when local scarcities from famine, bad
weather, or war occurred, but Rickman’s small set of Roman
wheat prices suggests strongly that these special cases were unusual.
All of the diverse trade during the late republic and early empire
went ﬁrst and foremost through the city of Rome.

THE PROS AND CONS OF A SMALL REGRESSION The Rickman sample
of price pairs is not an overwhelming amount of evidence, but it is
enough to test whether the patterns in the data are random. In
each case, the Roman price was subtracted from the price at a dis-
tant location to yield a price differential. According to Rickman
and Duncan-Jones, wheat prices at Rome were subject to slow
inﬂation. Ancient historians characterize this period as having
stable prices elsewhere, with an allowance for slow and gradual
price changes to be described below.10

10 Rickman, Corn Supply; Richard Duncan-Jones, The Economy of the Roman Empire: Quan-
titative Studies (New York, 1982; orig. pub. 1974); Temin, Roman Market Economy.

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40 | P E T E R TE MI N

The distances of the price observations from Rome are calcu-
lated as straight-line distances on a map, which represent only an
approximation of the actual distances that wheat traveled. This
added randomness reduces the possibility of ﬁnding evidence of
an integrated market. Added randomness contributes to a judg-
ment that the various prices are unrelated.

Epistemology is important in the treatments of these samples.
Roman historians look closely at each individual observation to
ascertain who collected it and under what conditions. Economic
historians view individual errors in the underlying data merely as
obstacles to climb in their quest to discern overriding patterns. If
individual observations have differences between them—say, in
the time of year when they were observed—they are no more
than extraneous noise in the testing of a hypothesis unrelated to
the season. Formally, we refer to “ideal” observations, as distin-
guished from the added errors, or noise, that reduce the possibility
of ﬁnding a pattern by introducing something irrelevant.

The Prices in the Regression The price closest to Rome, which
was from Sicily, derives from an accusation in Cicero’s Verrine
Orations that Verres did not transact business at the market price,
even though he acknowledged its level in a letter (Cicero, 2 Verr.
3. 189). Cicero’s observation, like most other reports, gives the
prevailing local price in round numbers. Since it is not the record
of any actual transaction, it is likely to be an approximation. This
casual quality militates against ﬁnding any systematic relationship
between prices, as just noted. It introduces more noise into any
relationship between prices because of the unknown difference
between the reported averages and actual prices. These implica-
tions about the difﬁculty of drawing conclusions all depend on
the randomness of the observations. Whenever systematic biases
enter into observations, something more than general rules is
necessary.

The second price came from Polybius (34.8.7) in his discus-
sion of conditions in Lusitania; it, too, is a general statement about
a prevailing price. Although having a genuine average price can be
helpful, the casual quality of the averaging process adds noise into
any comparison of prices in different places.

The third price, which derives from the Po Valley in Italy,
also comes from Polybius (2.15.1). The Po Valley is closer to
Rome than are Sicily and Lusitania, but although its connection

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to Rome was more by river than by sea, a bulk commodity like
wheat may well have gone by sea, anyway. Fortunately, the dis-
tance that wheat would have traveled along both routes was sim-
ilar. The calculation of the prices there is based, ﬁrst, on
Diocletian’s Price Edict, which ﬁxed river transport prices at ﬁve
times the level of sea transport prices. Although this evidence dates
from more than a century later than any of the other prices,
Greene maintains that the ratio of sea and river transport costs re-
mained constant over time. Hence, we include the Po Valley in
the price data by multiplying the river distance from Rome by
ﬁve. Calculating the sea distance along two straight lines to get
around the heel of Italy arrives at an observation slightly different
from other results, even if measured by sea. As it turns out, how-
ever, the distance by sea from the Po Valley to Rome roughly
equals the distance calculated from the Diocletian Edict. Despite
the small sample, the data are sufﬁcient to test whether this unusual
attention to distance affects the statistical result.11

The fourth price comes from an ofﬁcial intervention in the
local market, an inscription recording that the wheat price in
Pisidian Antioch was high in a time of scarcity. The normal price
was eight or nine asses per modius (a Roman weight); the accept-
able limit price was one denarius per modius (AÉ1925, no. 126b).
This inscription reveals several important aspects of the Mediterra-
nean wheat market in addition to reporting the normal price. The
need to reduce famine prices indicates that local markets were
subject to local scarcities; these markets were not so well linked
that wheat from elsewhere would be available instantly to alleviate
a local shortage. The apparent success of such interventions, in
this case limiting the price to double its normal range, indicates
that many famines were not severe.

With regard to Egypt, our model preserves the spirit of
Rickman’s enterprise but improves his data; Rathbone reworked
the sale prices that Rickman took from Duncan-Jones. Our price
for Egypt—seven drachmae per artaba (a unit of dry capacity)—is an

11 Harris, “Trade and the River Po: A Problem in the Economic History of the Roman
Empire,” in Jean-François Bergier (ed.), Montagnes, ﬂeuves, forêts dans l’histoire (St. Katharinen,
1989), repr. in idem, Rome’s Imperial Economy: Twelve Essays (New York, 2011), 188–197; Kevin
Greene, The Archaeology of the Roman Economy (Berkeley, 1986), 40.

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42 | P E T E R TE MI N

average of seven Egyptian prices from agricultural areas during the
“famine” of 45 to 47 C.E., not from a Mediterranean port. Divid-
ing these prices by 4.5, as Duncan-Jones speciﬁed, converts them
from Egyptian currency and units to HS (sesterces) per modius.12
Distant Palestine provides the sixth price to be compared with
roughly contemporaneous prices at Rome. Taken from Frank’s
Economic Survey, it, like Egypt’s, is also an average of a few actual
transactions.13

Rickman argued that the price of wheat in Rome was be-
tween three and four HS per modius in the late Republic, rising
to ﬁve to six HS in the early empire. Duncan-Jones conﬁrmed
the general price level, and Rathbone conﬁrmed the smallness
of inﬂation, at least for Egypt where the data are more abundant.
The order of observations is almost chronological, even though
the order of exposition is by distance. Six prices in almost two cen-
turies do not constitute an overwhelming amount of evidence, but
they are enough to test for patterns in the data. In each case, sub-
tracting the Roman price from the price at the distant location
gives a price differential. More prices come to light all the time,
but this small sample provides a way to answer our question, at
least provisionally. The prices and the differences between the
prices in Rome and the local prices are shown in Figure 1. The
differences are all negative, consistent with general observations
that agricultural prices were lower outside Rome. Wheat prices
clearly were lower outside Rome.14

The graph of the price differentials against the distance from
Rome in Figure 1 is striking. The further from Rome a place was,
the lower was its price for wheat, and the price differentials appear
to have been proportional to distance. These prices come from all
over the Mediterranean and from various times in the late republic
and early empire. Without a uniﬁed grain market, we would have
no reason to expect a pattern in these prices. Even with a uniﬁed
market, our inability to ﬁnd more prices or more accurate trans-
portation costs might obscure any true relationship among the

12 Rathbone, “Prices and Price Formation in Roman Egypt,” in Jean Andreau (ed.), Économie
antique : Prix et formation des prix dans les économies antiques (Saint-Bertrand-de-Comminges, 1997),
183–244; Duncan-Jones, Structure and Scale in the Roman Economy (Cambridge, 1990), 372.
13 Fritz M. Heichelheim, “Roman Syria,” in Tenney Frank (ed.), Economic Survey of Ancient
Rome (Baltimore, 1938), IV, 181–83.
14 Peter Garnsey, Cities, Peasants and Food in Classical Antiquity (New York, 1998), 241.

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Fig. 1 Plot of Price Discounts by Distance from Rome

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prices. Yet Figure 1 reveals a clear pattern, even though we should
treat it as only a suggestion—a slim thread of evidence on which to
hang a grand story of market integration. However, regression
analysis can evaluate how likely a picture like Figure 1 could arise
by chance. We can test the probability that the separate areas of
the early Roman Empire were isolated economically from Rome.
Their prices would have been determined by local conditions,
including perhaps the degree of monetization. The price levels
would have had no connection with distance from Rome.

We start by drawing a line that relates the price difference
between local price and Roman price to distance from Rome.
We then adjust the line to make it the best description of the data,
in the sense that it minimizes the squared distance of the individual
observations from the line. This process of regression analysis is
known as the method of “least squares,” and the resulting
least-squares line is the regression line (see Figure 2).15

15 We use the square of the distance to minimize the distance from points both above and
below the line and to simplify the mathematics.

44 | P E T E R TE MI N

Fig. 2 Relationship between Distance from Rome and Discount

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One of the beneﬁts of regression analysis is that it generates
tests of a hypothesis being tested. We can ask if an apparent rela-
tionship between the price discount and the distance from Rome
is an illusion, a result of observing only a few prices rather than the
result of a systematic process. In order to draw this line, we as-
sumed a relationship between the distance from Rome and the
price discount. Regression analysis tests the validity of such an
association by revealing how unlikely it is to ﬁnd a line like the
one shown in Figure 2 by chance. Assume that the prices gathered
from Rickman were randomly drawn from an underlying distri-
bution of price observations. In another world, different prices
could have survived from this same distribution. Taking account
of the random quality of the observations at hand, how unlikely
is it to ﬁnd the line in Figure 2 by chance?

Regression analysis acknowledges that the slope of the line in
Figure 2 is not known with certainty. It is the best line that can be
drawn with the current data, but it is subject to errors deriving
from the incomplete sampling of the underlying distribution. In
the jargon of regression analysis, the slope of the line has a standard
error. If all the points in Figures 1 and 2 were in a straight line, the
slope of the regression line would be clear, and the standard error

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| 45

of the slope would be close to zero. If the points are spread out as
they are in these ﬁgures, the line is not as clear, and it might have
no slope at all; that is, it might not indicate any relationship
between the distance from Rome and the price difference.

The test is to compare the size of the slope, the coefﬁcient in
the regression, with the size of its standard error. If the coefﬁcient
is large relative to the standard error, it is unlikely that the line was
a random ﬁnding without support in the price data. But if the
coefﬁcient is small relative to its standard error, it is possible that
even though the regression line has a slope, price and distance have
no underlying relationship. Statisticians call this ratio a t-statistic,
and tables can translate t-statistics into probabilities that a line is
observed by chance. These tables take account of degrees of
freedom—the number of observations minus the number of co-
efﬁcients. It takes two variables to deﬁne a line, its slope, and its
position (its height in the ﬁgures). Six observations and two
variables offer four degrees of freedom. Omitting the observation
with river transport reduces the number of observations by one
and the degrees of freedom to three. The t-statistic must be larger
with such few degrees of freedom than it is with more degrees of
freedom to show that a given regression line is unlikely to be the
result of chance.

Statistics, Signals, and Noise Our data—composed of only a
few scattered values—might seem insufﬁcient for statistical analy-
sis. Statistics, however, offer the best way to distinguish signals
from noise; they are particularly useful when the noise in the sys-
tem is substantial. They give us a precise sense of how unlikely it is
that any putative pattern would have been generated by random
processes and actually is just noise. Statistics allow us to test the
formal hypothesis that wheat prices around the Mediterranean
Sea were related to those at Rome in a simple way. We can also
derive an explicit probability that this hypothesis is true, given our
observations. The key is randomness. Even a few observations
randomly drawn from a population can provide information about
that population as a whole. The literature that analyzes the difﬁ-
culties attending this procedure is voluminous. Errors in the tran-
scription or treatment of data militate against ﬁnding stable results
capable of fostering generalization because they increase the ran-
domness of the observations. In other words, ﬁnding a pattern in
these few data points would be remarkable.

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46 | P E T E R TE MI N

Errors in variables are a common problem in regressions. We
often suspect a relationship between two variables—like the price
in Rome and the price in Egypt—but cannot observe one or the
other of these values precisely. We must resort to a proxy, such as
the occasional price that happens to be mentioned in a surviving
document. The errors introduced by such a procedure are well
known. The extra uncertainty introduced by using imperfect
proxies reduces the explanatory power of regressions, resulting
in coefﬁcients near zero; the addition of noise through imperfect
observations makes the results look more like noise. The well-
known scarcity of Roman prices therefore makes discovering a
pattern in them difﬁcult. Any such discovery, however, indicates
both a strong relationship between the prices and a set of observa-
tions that is reasonably representative.

Several conclusions emerge from these results. The regression
explains three-quarters of the variance of the price differentials. It
is very unlikely that the correlation between distance and price is
due to chance. Using the price differentials themselves, the regres-
sions explain three-quarters of the price variance. Using logarithms
of the differentials, the regressions explain even more. The discov-
ery that the prices were part of a pattern rather than a random
collection conﬁrms the impression in Figure 1 that distance from
Rome was a powerful explanatory factor in determining wheat
prices around the Roman Mediterranean.

The t-statistics indicate whether the relationship between
price differentials and distance was the result of chance. They
measure the probability that each coefﬁcient is different from
zero, taking account of the number of observations used to derive
it as well as their variation. If a t-statistic is greater than three, the
observed relationship between distance and price differentials has
less than one chance in twenty of being due to chance. In the
more precise language normally used for regressions, the prob-
ability of observing the coefﬁcients in the table if the price of
wheat and the distance from Rome were unrelated is less than
5 percent in three out of four regressions and close to that prob-
ability in the fourth. The 5-percent value of the t-statistic for four
degrees of freedom (six observations) is 2.8; it is 3.2 for three
degrees of freedom (ﬁve observations). Higher t-statistics indicate
lower probabilities that the observed relationship is the result of
chance.

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| 47

The constant terms are negative in the regressions for price
discounts and positive in the regressions for the logarithms.
Because they were not estimated as precisely as the relationship
between distance and the price differentials, they could be the
result of chance. There appear to have been other costs as well,
albeit smaller and less well observed. These other costs were partly
physical—the costs of trans-shipping wheat to and from sea-going
ships—and partly administrative—port charges and taxes. Their
presence does not detract from the effect of distance or the
evidence in favor of a uniﬁed wheat market.

Finally, the inclusion or exclusion of the Po Valley makes no
difference. Removing this observation reduced our comparisons
to ﬁve, but it did not affect the proportion of the variance
explained or the evidence that the relationship of distance to price
differentials was not random. The t-statistics take account of the
reduction in the number of observations to calculate the probabil-
ity that the observed correlation was due to chance. The logic
behind this ﬁnding can be seen in Figure 2. The observation for
Bologna lies close to the regression line; removing it changes
neither the line nor the message from this regression.

Scheidel’s criticism of this regression is based on data collected
by Bransbourg that did not start out as supportive of these results.
The ﬁrst graph in Bransbourg’s article shows the effect of moving
the measured distance for one of the observations, which, in his
words, makes my regression “very weak to a point of near irrele-
vance.” This claim is unfounded. Different data produce different
answers. Changing the data in order to change the results does not
make a sample random or allow a test of hypotheses. If you do not
believe in your data, you can reach any conclusion that you desire.
If you feel free to change the facts, then you leave the domains of
history and economics.

The key, again, is randomness. As explained above, the
Rickman sample was random, in that it was created for reasons
that were totally independent of the hypothesis to be tested.
The simple theory presented at the beginning of this article is
designed to highlight the centrality of random samples in the test-
ing of hypotheses. Random samples are the keys that unlock all
the tools that statistics can offer.

New Samples and New Regressions After stating that “the
[original] equation as formulated cannot be statistically upheld,”

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48 | P E T E R TE MI N

Bransbourg added more prices to those reported in Rickman, and
he recalculated Rickman’s data to provide what he considered
more accurate distances and sometimes new values. Despite the
overlap with Rickman’s sample, the new data set provides a
new sample from this ancient price distribution that can determine
whether another sample yields the same result as the ﬁrst sample.
The beneﬁts of new data are legion. They can improve the prob-
ability that the hypotheses tested are in fact sound. They also can
allow new degrees of freedom to make the regression more
complex. Regressions are always a simpliﬁcation of reality, but
they can become more sophisticated as the data increase. Not-
withstanding its contentiousness, this discussion has generated
more observations of ancient prices.16

Despite Bransbourg’s effort to discredit the earlier results and
to stack the deck against them, his new regression obtained the
same results—a distinctly negative association between price and
distance. Yet, even though Bransbourg reproduced the signiﬁcant
effect of distance on price with his full data set of a dozen obser-
vations, he concluded that this effect explained far less of the var-
iation in Roman prices than the original regression had claimed.
Suggesting that a market may have been more of a factor for
coastal cities than for inland cities, he ran tests exclusively on
coastal cities. Reducing his sample size to the familiar half-dozen,
he found that not only was the effect of distance clearer but that
distance from Rome also explained 86 percent of the variation of
prices around the Mediterranean.

If Bransbourg’s reasoning is correct, a regression of the other
six observations—the ones from inland cities—should have shown
that distance from Rome did not have much of an effect. But
exactly the opposite is true. The coefﬁcient of distance was esti-
mated precisely, and the regression line explained 87 percent of
the price variation. This result suggested that distance was impor-
tant, and similar, for coastal and inland cities.

A further regression on all twelve of Bransbourg’s observations
and an additional variable—a dummy for inland cities—reproduced
the results of the original regressions. The effect of distance on

16 Temin, “Statistics in Ancient History: Prices and Trade in the Pax Romana,” in Giuseppe
Dari-Mattiacci (ed.), Roman Law and Economics (New York, forthcoming), available at http://
papers.ssrn.com/sol3/papers.cfm?abstract_id=2217011.

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| 49

wheat prices is clearly estimated: The regression explains three-
quarters of the variation in Roman wheat prices around the
Mediterranean, after making an allowance for the difference
between coastal and inland cities. The effect of the extra cost of
transportation is not clearly estimated, but it appears that the dis-
counts from the price in Rome in coastal cities were about one
sestertius per modius smaller than those of inland cities at the same
distance from Rome.

Small as the new sample is, ranging from six price pairs to a
dozen, it does not detract from the test of this hypothesis. As noted
above, the standard errors and t-statistics are corrected for degrees
of freedom. Having few observations makes it easier to reject hy-
potheses, but it does not affect the validity of the test. Bransbourg’s
larger number of observations conﬁrmed the importance of
distance from Rome in establishing provincial wheat prices.
Nonetheless, Scheidel rejected this conclusion on the basis of
Bransbourg’s analysis, though his graph of Bransbourg’s data, at
the end of a complex study offering new ways to calculate distance
in the Roman world, produced a ﬁgure that looks amazingly like
Figure 1. Instead of accepting the clear impression that wheat
prices were lower the further they were from Rome, Scheidel
attempted to rearrange the observations.

Bransbourg had divided his data into two halves, correspond-
ing to locations close to water and locations far from water. When
asked whether the cost of transporting wheat to the Mediterranean
coast affected the local price, his slightly more complex model
delivered an afﬁrmative answer, just as the original one did. But
Scheidel divided Bransbourg’s sample by looking at his version
of Figure 1 rather than by attending to an independent charac-
teristic of the observations. In his words, “For those eight sites
whose transport cost to Rome was less than half the highest of
the twelve values, relative transport costs to Rome account for
merely 5 percent of variance in local grain prices.” But this statistic
is no longer grounded in a random sample that can be used to test
hypotheses. Throwing away four observations because they do not
accord with the presumption of no relation between transport
costs and price makes the sample biased toward no effect. This
strategy is parallel to Bransbourg’s statement that the movement
of one or more of Rickman’s observations would destroy the cor-
relation. Scheidel’s dismissal of inconvenient data has the same

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50 | P E T E R TE MI N
outcome—destruction of the randomness of the sample and of the
ability to test hypotheses.17

More testing was performed on a newer data set from
Rathbone and von Reden, which, at ﬁrst glance, appeared to be
more promising than Bransbourg’s—twenty-three observations
ostensibly permitting more power to evaluate hypotheses. The
added data, however, served more to clarify the previous results
than to make a fresh start. The eight observations in this data set
that list prices at Rome could safely be ignored, since they are
irrelevant to the question of transport costs, and a time variable
was added to account for the slight inﬂation visible in Rickman’s
data. The result of this subtraction of eight variables and addition
of one was a decrease in the degrees of freedom by nine. Averages
were taken for price ranges, and prices with uncertain dates or
amounts, as well as prices attributed to “extreme shortages,” were
discarded, as was the observation for Judaea, which was too vague
and probably irrelevant. The period of this analysis, the long
second century, is after the Judaean revolt mentioned above.
The turmoil after the destruction of the Judean temple most likely
caused trade to be disrupted. In fact, the Talmud prohibited wheat
exports. Although the date and effectiveness of this prohibition are
unknown, the kind of price arbitrage discussed above in setting up
the regressions was probably not operative after the revolt.18

Regressions on this new data set of eight observations repro-
duce the coefﬁcients on distance in the original regression: The
coefﬁcients are the same size, and they are known with the same
precision. The regressions as a whole, however, do not have the
same explanatory power as those from Rickman’s data. Despite
the overlap between the two data sets, this one contains more un-
explained variation. The constant is larger than before because it
includes an implied price at Rome in addition to any taxes or
transport costs to the city. Oddly, the estimated inﬂation rate is
large and not estimated precisely. There may be better ways of

Scheidel, “Shape,” 29.

17
18 Rathbone and von Reden, “Mediterranean Grain Prices,” 189; Heichelheim, “Roman
Syria,” 182. I did not inquire into the timing of my Judaean observation when using the
Rickman data, but removing the Judaean price did not affect my results, although it decreased
the degrees of freedom.

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| 51

utilizing the Roman prices than replacing them with an estimated
rate of inﬂation.19

Rathbone’s data conﬁrm the effect of distance on price found
in Rickman’s data, albeit with caveats that should be acknowl-
edged. The exclusion of certain evidence from Rathbone’s data
for this new regression, though reasonable on the surface, cannot
guarantee that the remaining data constituted another random
sample; the included data could have been selected to produce
the desired pattern, even if only unconsciously. To test the ran-
domness of the Rickman sample, it would be better to have a data
set constructed by someone like Bransbourg or Rathbone who is
not invested in the original results.

As noted above, Rathbone’s data set includes observations
from periods of severe shortages. These few added observations
provide no information about the frequency of these shortages,
but they remind us that the Mediterranean wheat market was sub-
ject to events that increased the difﬁculty and cost of shipping
wheat by sea. The market worked in general, but storage was
not sufﬁcient to alleviate the difﬁculties that arose from time to
time.

Rathbone disparaged the simple model presented in this arti-
cle, even though he agreed with its conclusions: “In conclusion,
various factors made the Roman world and economy of the ﬁrst
to third centuries AD different from ancient Babylonia on the
one hand and early modern Europe on the other. The market
for wheat in the Roman world was essentially a free market, which
in the imperial period comprised and was inﬂuenced by the admin-
istered market of the imperial Annona and civic intervention.”20
Bang’s negation of the original ﬁnding was not supported by
the new data. He argued that Roman markets were like bazaars
with no ﬁxed prices at all. In other words, Rickman’s prices were
the products of random exchanges in isolated markets, manifesting
no patterns in their location. Because Bang failed to see any
pattern in Figure 1, he did not follow my statistical analysis
showing an overwhelming probability of a geographical pattern
in Roman prices. Bang’s view cannot explain how enough wheat

19 For more information about these regressions, including R2 and t-statistics, see Temin,
“Statistics in Ancient History.”
20 Rathbone and von Reden, “Mediterranean Grain Prices,” 189.

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52 | P E T E R TE MI N

was transported to Rome to feed a million or so consumers. Nor
can it explain how the Roman economy was able to spread
prosperity around the Mediterranean when its central bureaucracy
was so small. His epistemology is too strong to be shaken by the
evidence described herein.21

The evidence of Rathbones’ “free market” is clear in a series
of uniﬁed grain markets that stretched from one end of the
Mediterranean to the other in the late Roman republic and early
empire. The extent of the Roman market has been debated ex-
haustively, but previous evidence was restricted to local markets.
The presence of localized market activity has ceased to be contro-
versial, but the question of market integration is still alive. The
evidence produced in this article demonstrates the existence of
something approaching a uniﬁed grain market in the Roman
Mediterranean.

Government interventions in wheat markets demonstrate that
the market could not prevent shortages even in Rome. The gov-
ernment tampered with local wheat markets from time to time to
lower prices and alleviate shortages, particularly under Augustus.
The partial list available shows that these interventions were inter-
mittent. As Rathbone concluded, the market for wheat otherwise
worked on its own. Moreover, if traders expected the government
to interfere when famine loomed, they might have been discour-
aged from trying to corner the market in adversity. Hence, gov-
ernment intervention may have dampened speculation, thereby
making the underlying pattern of prices easier to see.22

All areas were not always connected to the market in Rome,
such as those undergoing local famines. Rathbone recorded exam-
ples of isolated markets—with prices that do not ﬁt this regression
line—showing prices lying outside the regular market. The regres-
sions demonstrate many ties between far-ﬂung Roman grain mar-
kets; more data will be able to offer a better idea of how often
outlying markets were connected to the major consuming market
in Rome.

This discussion parallels questions about the reach of Roman
law into the provinces. Laws do not appear directly in the statistics,

21 Bang, Roman Bazaar.
22 Garnsey, Food and Society in Classical Antiquity (New York, 1999).

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| 53

but they provided the context in which observed behavior took
place. Contemporary observers recorded explicit governmental in-
terventions; underlying legal frameworks need to be extrapolated
from the various records that they have left for us.23

This article illustrates the usefulness of regression analysis in
seeking testable hypotheses and arguable propositions regarding
ancient history. It transforms existing information into a format
that suggests the existence of a uniﬁed market, as shown in
Figure 1. It also presents a test of whether the observed pattern
could have arisen by chance. Given the small number of observa-
tions, a pattern could simply be a coincidence. Regression analysis
allows that possibility to be quantiﬁed. The probability that the
line in Figure 2 was due to chance is about 5 percent, or one in
twenty, indicating a far more precise estimate of the probability of
an actual relationship than has been available previously. Given the
scarcity of data and the prevalence of shortages, regressions can
help only to interpret existing data, not to provide additional
information that can result in deﬁnitive answers to all questions.

HURDLES AND BENEFITS Ancient historians may not have appreci-
ated the simple model reproduced herein, but economists who
saw it liked it well enough. The attraction for economists was a
defect for ancient historians—a sample size small enough for all
the calculations in the regression to be done by hand rather than
by computers. With even a little knowledge of statistics, ancient
historians could see this small model as a helpful step in the
description of Roman trade in basic commodities like wheat.

Rathbone’s additional wheat prices, many of which were in-
tended to show how unusual famines were, were excluded from
the new regressions herein in favor of other prices that ﬁt the re-
gression better, although all the prices could have been included in
a regression with a dummy variable for famines. The original pub-
lication of this regression was accompanied by a table of govern-
mental famine alleviations that demonstrated the fallibility of the
Roman market. As the result of market failures, famines showed
where the uniﬁed market broke down. An alternative treatment
would be to combine the original list of market failures with the

23 Dennis P. Kehoe, Law and the Rural Economy in the Roman Empire (Ann Arbor, 2007).

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54 | P E T E R TE MI N

famine prices that Rathbone collected to see how often the mar-
ket failed. Additional observations will reward further insights.24

FUTURE PROSPECTS FOR AN INTERDISCIPLINARY ANCIENT HISTORY
The epistemological problem is that although ancient historians
are fond of collecting and even rearranging numbers, they appar-
ently see no value in testing hypotheses. Economic historians start
from the other end, collecting more data only as warranted to test
a hypothesis. The discussions of regressions have exposed the ef-
fects of this epistemological difference. Ancient historians always
want to know where the observations originated and the extent
to which they are reliable. Economic historians accept the statisti-
cal argument that errors in the observations make patterns more
difﬁcult to identify and therefore make results more convincing.
Ancient historians look for linguistic evidence, whereas economic
historians rely on the theory of errors in variables to preserve the
essential randomness of their samples. This epistemological differ-
ence complicates attempts to establish interdisciplinary history.

But there are signs that give us hope for the future. Michael
McCormick’s Initiative for the Science of the Human Past at
Harvard presents interdisciplinary research from a variety of disci-
plines. Harper’s recent book, The Fate of Rome, which argues that
pandemics doomed the Roman Empire, follows in McCormick’s
footsteps. It also includes the graph reproduced herein as Figure 3.
The lines in the book are not identiﬁed explicitly as regression
lines, although they most certainly are; they are described as
regressions in a journal article that preceded the book. They show
that wages rose in the period before the Antonine plague while the
price of wheat was stable. The lines in Figure 3 correspond to the
line in Figure 2. Furthermore, as stated already, this action assumes
that the prices being observed are market prices.25

This article is based on markets all around the Mediterranean;
Harper concentrated on Egyptian markets that continued for the
ﬁrst two centuries of the Roman Empire. Unlike this article,

24 Kessler and Temin, “Money and Prices”; Temin, Roman Market Economy, 29–52.
25
Information about the Initiative for the Science of the Human Past is available at https://
sohp.fas.harvard.edu/. Kyle Harper, The Fate of Rome: Climate, Disease, and the End of an Empire
(Princeton, 2017), 34 (Figure 2.1); idem, “People, Plagues, and Prices in the Roman World:
The Evidence from Egypt,” Journal of Economic History, LXXVI (2016), 803–839.

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| 55

Fig. 3 Price Trends in the Roman Empire before the Antonine

Plague

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Kyle Harper, The Fate of Rome: Climate, Disease, and the End of an Empire (Princeton,

SOURCE
2017), 34 (Figure 2.1).

which looked only at the wheat market, Harper examined the
price of wheat, wages, land, and the rental rates for land. In other
words, wheat was not an exception; various markets and market
prices were abundant in ancient Rome. We can look at the behav-
ior of markets across space and time.

Harper focused on real wages in Figure 3. From his observa-
tions of Egyptian wages, he inferred a labor market in which at
least some residents of Roman Egypt sold their services for Roman
money. In Harper’s regression, the slope of the line is signiﬁcantly
different from zero, but he explained less of the variation of prices
than the regression in Figure 2 did. In other words, he found a
pattern that was less obvious in the surviving data than in the
sample of wheat prices in Figure 2. Harper’s regression of wheat
prices over time showed the price of wheat to have risen only
slowly, if at all, before the Antonine Plague. Harper’s regression

56 | P E T E R TE MI N

of wheat prices on time did not appear to produce a particularly
good result; the slope of the curve for wheat is not signiﬁcantly
different from zero. But Harper did not conclude that Roman
Egypt had no market for wheat. Instead, he argued for the absence
of inﬂation (he provided evidence for the absence of inﬂation that
I inferred from my regressions). Harper inferred markets for wheat
as well as participants in that market who could observe move-
ments in market prices.

Harper interpreted the pattern in Figure 3 as showing an in-
crease in real wages in Roman Egypt—in other words, a rise in the
standard of living. Harper apparently did not need to make explicit
the preliminary steps of going from individual observations to
patterns and then to institutions. He did not even think it worth-
while to mention that he was describing a labor market. His
thought process shows how rapidly ancient history is progressing.
The Roman Market Economy was ridiculed in the Times Literary
Supplement for using the term, labor market.26

Harper argued that the early Roman Empire was not subject
to any Malthusian pressure. Although population was expanding
in the late republic and early empire, living standards did not de-
cline, contra Malthus’ theory. Instead, real wages rose at the same
time as population totals rose. Figure 3 illustrates that important
ﬁnding by showing wages rising faster than the price of wheat.
The ratio of wages to the price of wheat—a good measure of
the real wage in ancient times—was on the rise. But how can
we explain this development? One way is through progress in ag-
ricultural technology, given the predominantly agricultural nature
of the Roman economy. The other way is through trade; the
ability to specialize, based on the low cost of exports and imports,
improves living standards as the result of comparative advantage,
the effect of an international division of labor. Both processes were
probably in operation, but the exploitation of comparative advan-
tage is the most relevant in this context. The extent of the wheat
market shown in Figures 1 and 2 suggests that wheat from Sicily,
Spain, and Egypt was coming to Rome. Hence, comparative

26 Peter Thonemann, “‘Who Built the Amphora Mountain?’ Review of Scheidel (ed.),
The Cambridge Companion to the Roman Economy and Temin, The Roman Market Economy,”
Times Literary Supplement, 9, Aug. 2013, 10–11.

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| 57

advantage, the result of increasing Mediterranean trade, was likely
the cause of the increase in real wages shown in Figure 3.27

Harper’s ﬁnding that the Egyptian price of wheat was stable
before the Antonine Plague supports the lack of attention to inﬂa-
tion in the original regression. Harper goes further in his book to
argue that this plague was the ﬁrst of three plagues that cumula-
tively destroyed the Roman Empire. The Roman Market Economy
argued that the Antonine Plague initiated the inﬂation that
plagued Rome during its long decline, possibly causing political
instability or vice versa. Both conditions began at the time of
the Antonine Plague. The plague started both the inﬂation and the
political instability in the Roman Empire that continued for the next
few centuries.28

Rathbone is correct: My model is too simple to be the whole story.
Solow remarked, “Oversimpliﬁcation would be the bane of econ-
omists if it were not their job.” Reiterating his message, Tirole re-
cently asserted, “We adopt a simple, even a simplistic, hypothesis to
get a sense of what is going on.” A simple model or hypothesis is a
step on the way to interdisciplinary history. The test of a simple
model is whether it provides insights for more complex investiga-
tions; this article is a step in that direction. The complications intro-
duced by Bransbourg and Rathbone expanded the message of the
original simple model.29

We need more contributions of both numbers and of words
to understand Roman markets. For example, throughout the late
republic and early empire, grain merchants sent sealed pots or
pouches containing a sample of their grain cargo on trading ships.
When the cargo arrived at its destination, recipients could open
the sealed containers to test the grain held in them against the grain
in the ship’s main hold; any difference suggested that the bulk of
the grain had been doctored in some way. These seals were signed
by a granary ofﬁcial, a merchant, and a witness. This practice was

27 For comparative advantage, see Temin, Roman Market Economy, 1–26. Scheidel, “In
Search of Roman Economic Growth,” Journal of Roman Archaeology, XXII (2009), 46–70.
Scheidel’s article bears the inﬂuence of a paper that I wrote to explain Malthusian theory
to ancient historians. The two papers were submitted together to the Journal of Roman Archae-
ology, but mine was not published, perhaps because of its different epistemology.
28 Harper, Fall of Rome; Temin, Roman Market Economy, 70–94.
29 Robert M. Solow, “How Did Economics Get That Way and What Way Did It Get?” Daedalus,
CXXVI (1997), 39–58; Jean Tirole, Economics for the Common Good (Princeton, 2017), 84.

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58 | P E T E R TE MI N

continued or, more likely, resuscitated by the early tellers of the
Bank of England around 1700. The bank mandated, “That every
Teller receiving money shall immediately weigh the same, and put
a Ticket on the Mouth of the Bag importing the weight and con-
tents thereof, and the like Ticket also within the Bag.” Procedures
installed by the Romans to make sure that shipped commodities
were not adulterated seem to have been useful almost two millen-
nia later.30

Despite their initial hostility, ancient historians, who are gen-
erally not enthusiastic about regressions, have begun to accept the
evidence for the existence of a Mediterranean market for wheat in
the Pax Romana. Comparative advantage cannot improve real
wages without free trade. This development is promising for inter-
disciplinary history, suggesting that the epistemological divide may
be decreasing. The regression lines in Figure 3 begin to answer the
question posed by Beard at the beginning of this article by show-
ing that the growth of Mediterranean trade beneﬁted ordinary
people by increasing their real wages. The trade represented by
the regression line of Figure 2 provides the framework for the
description of this trade in the early Roman Empire.31

30 For the sealed containers, see Rickman, Corn Supply, 122; Kessler and Temin, “The Orga-
nization of the Grain Trade in the Early Roman Empire,” Economic History Review, LX (2007),
313–333; Temin, Roman Market Economy, 97–113. Anne L. Murphy, “Learning the Business of
Banking: The Management of the Bank of England’s First Tellers,” Business History, LII (2012),
150–168.
Interdisciplinary history enriches other areas of study as well. Beard, SPQR, inferred that
31
Rome had a banking system from Cicero’s apparent lack of enough cash in his pocket to purchase
an expensive Roman house: “The whole transaction points … to some system of paper ﬁnance or
bonds, and so to a relatively sophisticated banking and credit system underpinning the Roman
economy, for which only ﬂeeting evidence now survives” (325). However, the interdisciplinary
evidence for an impressive banking system operating in and around Rome is abundant. See
Temin, “Financial Intermediation in the Early Roman Empire,” Journal of Economic History, LXIV
(2004), 705–733; Rathbone and idem, “Financial Intermediation in 1st-Century AD Rome and
18th-Century England,” in Koen Verboven et al. (eds.), Bankers, Loans and Archives in the Ancient
World (Leuven, 2008), 371–419; Temin, Roman Market Economy, 157–192.

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