Tariffs, Trains, and Trade:
The Role of Institutions versus Technology in the Expansion of Markets1
Wolfgang Keller
Carol H. Shiue
University of Colorado, CEPR, and
University of Colorado, CEPR, and
NBER2
NBER3
Preliminary
October 2006
Abstract
This paper studies the emergence of the increasingly unified commodity market in Central
Europe in the mid-19th century. Two major changes characterize this era: first, the reduction or
abolition of tariffs among members of the German customs union called Zollverein, and second,
the arrival of railroad networks that revolutionized the transportation of key commodities. Both
of these changes were implemented gradually and selectively. This setting thus provides insights
into the relative impact of institutions--the states’ tariff policies--and new technologies—the
railroad networks—on the emergence of a pan-European goods market. We use grain prices
across markets in 15 German states, Austria-Hungary, Belgium, France, and Switzerland, for
gauging market integration. In the 19th century, grain was extensively traded in Continental
Europe, it was frequently subjected to trade barriers between states, and it was one of the first
major commodities for which rail transport was important. Our study employs detailed
information on the timing of the completion of rail networks in Central Europe and on the timing
of the abolition of tariff barriers, which lets us identify the effects of railroads and tariff policies,
respectively. The analysis emphasizes throughout important geographic features of the 19th
century European landscape, including but not limited to distance. Preliminary results suggest
that the effect of trains on 19th century price convergence was larger than that of customs union
formation.
1
We thank Michael Kopsidis for providing us with data, and Joerg Baten for suggestions.
Department of Economics, University of Colorado, Boulder, CO 80309; [email protected]
3
Department of Economics, University of Colorado, Boulder, CO 80309; [email protected]
2
Introduction
The technological, political, and economic processes through which European economies
in the 19th century took off on a path of intensive growth and industrialization forms the basis of
material living standards in societies today. It was at that point that the economies of Europe
were transformed in measurable ways, and economic growth started to increase in an
unprecedented manner. There is little agreement, however, on the sources of that growth or on
the factors that contributed most to European industrialization.
This paper establishes new econometric and historical evidence for the role of railroad
technology, and compares this source to evidence for other explanations for European integration
and expansion. Our results demonstrate that the effects of this new transport technology to
market integration was overriding in quantitative importance relative to institutional choices
related to trade policy, such as in particular, whether or not to pursue a more open trade stance.
Both the Zollverein and railroads have been credited for promoting Germany’s industrial
expansion and subsequent growth through their effect through trade (Tilly 1966; Milward and
Saul 1977; Pollard, 1981; Bazillion 1990).4 But how important were they? We address the
comparison by quantitatively comparing the effects of railroads and the Zollverein on market
integration in Central Europe.
Railways reduced transport costs for inputs used in manufacturing and also extended the size of
the market that final goods could reach. The Zollverein was a customs union that contributed to
trade expansion through reducing the economic impact of borders and helped to achieve greater
economic integration. The decision to join the Zollverein reflects a state’s national institutions,
4
See Pollard (1981, 159) and Bazillion (1990, 192) for arguments on how the Zollverein was important for German
development, political unification, industrialization, and greater market access. Lee (1988) and Dumke (1977, 1991)
focusing more narrowly on the revenue sharing function of the Zollverein, concludes that economic union was not
critical to regional long-run development.
1
not least because trade policies are affected by special interest group activities. This setting thus
provides insights on the relative impact of institutions—the states’ tariff policies—and new
technologies—the railroad networks—on the emergence of a pan-European goods market.
We study market integration in Central and Western Europe in terms of the spatial
dispersion of grain prices in more than 100 markets in Central Europe. These markets are
located in five different countries and fifteen different German states. As a benchmark we
employ the Law of One Price, which is a useful benchmark for these relatively homogeneous
goods. Grain trade was strongly affected by improvements in transportation technology and
trade barriers in the 19th century, both inside as well as outside of Europe (e.g., Shiue 2005,
O’Rourke 1997).5 Soon after their introduction, trains were an important means of transporting
grain in Central Europe, especially in regions where waterways were not available.6 Our
analysis exploits the fact that railroad building and Zollverein accession were gradual processes.
This means that we can compare how price gaps between markets change as they become
members of the customs union with what happens as a train connection is established between
the markets. The findings suggest that the impact of railways on market expansion is stronger
than that of joining a customs union.
Our paper is related to recent work that studies the importance of trade for the onset of
modern economic growth (Shiue and Keller 2004, Acemoglu, Johnson, Robinson 2005). By
showing that trade is not sufficient for entering the era of sustained per-capita income growth,
5
Shiue (2005) analyzes the detrimental effects of customs borders in Central Europe, while O’Rourke (1997) studies
the impact of changes in tariffs and transport costs on Atlantic Trade.
6
Seuffert (1857), e.g., shows that the great majority of all grain exported from Bavaria to Switzerland in the early
1850s was transported on railways (Chapters 5 and 6). Fremdling and Hohorst note that the full opening of the
Köln-Mindener railway in the year 1847 was crucial for transporting the relatively cheap Prussian grain to the
emerging industrial areas of the Rhine-Ruhr area (1979, 64).
2
the focus of Shiue and Keller (2004) is on the effects that trade can have. Here, we study the
reasons that underlie the ease of trade.
The importance of railways for industrialization and American growth has been
examined by the pioneering studies of Fogel (1964) and Fishlow (1965) using the social savings
approach.7 By emphasizing the cost savings associated with moving from ship and road to
railroad transport, this work’s focus is on modes of transportation. In contrast, while we do not
link trains directly to economic growth, our analysis is more comprehensive in comparing the
effects of railroads with that of the political choice of adopting a customs union.
This paper contributes to the recent literature on the effects of institutions on economic
performance (e.g., Acemoglu, Johnson, Robinson 2001, Rodrik 2002, Glaeser et al. 2004). A
country’s position towards (free) trade implied by its customs union choice gives information
about institutions, namely whether they allow discriminating against foreign-produced goods at
the cost of lower economic efficiency. For present purposes, it does not matter whether this is
due, for example, to special-interest lobbying or the autocratic choice of some potentate.8 If a
sovereign state joins a customs union, the state has ‘good institutions’ in the sense that they
allow that gains from trade are materialized, and vice versa.9 Our analysis examines how
important these institutions are for the expansion of markets.10
7
See Fremdling (1977) for a study of the case of Germany, and also O’Brien (1983) for studies on other countries.
In a number of papers, Kopsidis (2002, 1996) has revisited the importance of railroads for agricultural market
development in the Rhineland-Westphalia region.
8
See Grossman and Helpman (1995) for an analysis of lobbying for protection.
9
The approach of inferring the quality of institutions from economic outcomes has recently been advocated by
Shiue and Keller (2004) and Nunn and Trefler (2006).
10
There are two caveats: first, there are a few instances when free trade does not raise economic efficiency.
However, the chances that trade policy in practice is guided by those are close to zero. Second, joining a customs
union is not the same as adopting a free trade policy, because there may be trade diversion effects. In the case of the
Zollverein accessions, it is plausible to assume that trade creation effects far outweighed any trade diversion effects
that might have occurred.
3
Our paper also contributes to the recent literature on the major determinants of market
integration (Glick and Taylor 2005, Jacks 2005, and Ejrnaes and Persson 2000). Among the
findings of these studies is that war is the most important reason for trade disruptions in the 19th
century. By comparing only trains and customs union effects, this paper has a more narrow focus,
allowing us to study this particular set of factors in greater detail. It is thus complementing the
existing literature. We also add to the literature on price convergence in history, in terms of the
Law of One Price or Purchasing Power Parity (Shiue 2002; Taylor 2002; Findlay and O’Rourke
2003) by providing new evidence on a number of European markets during the 19th century. This
helps to complete the emerging picture on global price history.
Finally, although the Zollverein was an economic agreement that maintained the
sovereignty of member states, it may nevertheless have also contributed to the political
unification of the scattered German states if the unification of policies in trade in turn eased the
way for the establishment of common political institutions. Political unification may be a goal in
and of itself, as it is today to some in Europe, but it may also be an ‘input’ to greater economic
growth.11 Our analysis sheds new light on whether economic unification and political unification
necessarily go hand in hand, and whether one causes the other.
The remainder of the paper is as follows. The next section provides some background on
the setting of the Zollverein formation, followed by a brief discussion of the introduction of
steam railways in Europe, and, specifically, Germany. The following section 3 describes the data
that will be used. The empirical analysis is in part 4, and section 5 provides some concluding
remarks.
11
Henderson (1939, 10), e.g. suggests that “the lack of effective political unity in Germany” contributed to its
relatively backward economic position in the 19th century.
4
2.1 The Zollverein
The main economic impact of the Zollverein treaties was the abolishment of tariff
barriers among member states, and the implementation of a single tariff on consumption goods
for non-members. As of 1815, Prussia’s territories were divided into two, an eastern portion
consisting of seven provinces, and a western portion that included Westfalen, the Rhineland
provinces, and the Rhine-Ruhr area. Germany’s political structure was divided into the thirtynine states of the German Confederation (Deutscher Bund). The confederation consisted of
sovereign states in which joint action depended upon unanimity. Austria was the most powerful
of the German states, followed by Prussia. Individual states tended to be highly protectionist and
the tariffs that were imposed were complicated. There is no reliable information on enforcement,
but it was likely that it was costly for the many small states to each monitor its own borders.
In the aftermath of debts from a decade of war, and new tariffs raised by Britain, Russia,
Austria, France, and the Netherlands, Prussia sought to negotiate treaties with her neighbors
while reforming the structure of internal tariffs. The Prussian Customs Union was formed in the
year 1818. With few exceptions, internal dues were abolished. Foreign raw material were
admitted free of duty and by 1821, only a single tariff for the entire Kingdom was levied on
consumption goods. In addition, transit dues on goods passing through Prussia were reduced. A
close version of this tariff was adopted by the Zollverein in 1834.
Enclaves within Prussia were the first to develop agreements with Prussia on how its
payment of duties were to be treated—with Prussia deciding to treat the enclaves as her own
territory rather than as foreign states required to pay import duties. These treaties were based on
the principle that states that adopted the Prussian system of tariff received a share of the joint
5
revenue as calculated by the ratio of their population to that of the eastern provinces of Prussia.
Their rights as sovereign states were maintained.
The Anhalt duchies and Hesse-Darmstadt were both early members of the customs union.
The move of Hesse-Darmstadt to join in 1828 had ramifications for other states that were not yet
part of the union since it threatened to surround other states with Prussian’s already dominating
presence. In the same year, Bavaria and Württemberg formed the South German Customs Union,
while a number of central German states and cities formed the Middle German Commercial
Union.12 The latter was not a customs union, but a defensive agreement among members to
commit to not joining either. The strategy was unsuccessful and the union lasted only five years.
Hesse-Cassel became the first to join the Prussian Customs Union in 1831.13 In the year 1834,
both the Thuringian states and the Kingdom of Saxony, together with the augmented Prussian
Customs Union, became the German Zollverein on January 1st, 1834. At that point the Zollverein
had an area of about 163,000 square miles and a population of about 23.5 million people.
By stages, other states entered. Three other German states joined the German Zollverein
between mid-1835 and early 1836: Baden, Nassau, and the city of Frankfurt. The entry of Baden
was significant because it meant that all the areas of Bavaria was joined without the barrier of a
customs border. The entry of Frankfurt meant that trade in manufacturing goods from Frankfurt
up the Main River to Northern Bavaria in exchange for grain without paying customs duties.
Later on, members included Braunschweig (1841), Hanover (1854), Oldenburg (1852),
Schleswig-Holstein (1866), and Mecklenburg-Schwerin, Mecklenburg-Strelitz, as well as
12
The states were Hanover, Saxony, Hesse-Cassel, Nassau, Brunswick, Oldenburg, Frankfurt, Bremen, the Saxon
duchies, and a couple of smaller ones. Henderson (1939, 67).
13
This was significant because it meant that the East and West Prussian provinces were joined without a customs
border for the first time. It also meant that British goods could not reach Frankfurt and Germany’s south anymore
without crossing the Prussian external tariff border.
6
Lübeck (1868). The Free Cities of Hamburg and Bremen were among the last territories to join
the Zollverein.
2.2. The Introduction of Railways
European economic growth from the 19th century on coincided with a series of
innovations in transportation.14 These innovations included paved roads, improvements in
waterways, railways, in materials such as iron and steel, and later on, steam power, but the rapid
increase of railway construction were particularly important for transporting commodities that
had a low value to weight ratio, such as coal, construction materials, grain, and metal goods
(O’Brien 1983, 1-2).
In the 1840’s British suppliers of locomotives dominated the market, and railway iron
exports were an important iron export for Britain. Gradually, countries on the continent started to
produce their own railway inputs. In Germany, for instance, domestic locomotives began to be
produced and substituted for British locomotives, and then iron processing plants using British
technology were established. By the 1850’s German iron industries were supplying rolled rails,
and eventually began exporting also rails to foreign countries. The effects of these innovations
appeared as price differentials between regions (and sectors) in the European economy, and
contributed to regional specialization and trade.
The first German railway was opened in December 1835. The first three lines were short
suburban lines built and run by private companies. The first line was in Bavaria, between
Nürnberg to Fürth, and only 4 miles long. The first longer route (70 miles) between Dresden and
Leipzig was built in 1839, some 5 years after the Zollverein treaties with Bavaria came into
14
See survey of the literature in O’Brien (1983), and Milward and Saul (1977). On the debate concerning the
contribution of railways, see Fogel (1964), Fishlow (1965), Williamson (1980).
7
effect. Thereafter, additional miles of rail were laid down swiftly. By 1847, there were over
2000 miles of rail in Germany (Henderson 1939, 147). Almost all of the main railway lines were
completed by 1877 (Milward and Saul 1977, 42).
Government participation in railroads differed (Fremdling 1977). In some states,
railroads were owned and run as a public enterprise. In Prussia and Saxony, railways were
primarily privately owned, and the government had a dominant shareholder role or guarantor of
minimal returns.
In France, the state auctioned the right to operate a given line. Contractors—typically
large Parisian banking houses—formed a syndicate and share the capital of the company.
Railway construction in France began as early as 1828 with 23 kilometers of track opened, but
expanded at a more rapid rate only in the 1840’s and 1850’s when over 8000 kilometers of tracks
were opened. By 1870, over 17,000 kilometers of tracks were opened. The rate of railway
construction slowed down in the period after the 1890s (Caron 1983).
The Belgium railways, by contrast, were designed as a means of international transport
from the beginning. This meant that negotiations among different states were necessary. In 1834,
the Belgium Parliament planned for a network that allowed connections to Prussia, France,
England, and the sea at Anvers, and later, an extension to Holland (Laffut 1983). In the initial
stage, from 1835 to 1843, only the state built the lines, with private companies entering later to
lay down the track. Eventually, however, the state acquired most of the lines, and by the 20th
century, 4100 kilometers of Belgium’s total 4600 kilometers of railway were operated by the
government.
3. Data
8
The data employed in this study is along four dimensions, relating to (1) market
performance, (2) institutions, (3), technology, and (4) geography. In the empirical analysis below,
we assess the performance of markets in Central and Western Europe in terms of the spatial
variation of grain prices, where the benchmark the Law of One Price serves as the benchmark.
To this end, we have assembled data on the market price of wheat and rye for 75 different
locations in Central Europe in the 19th century. These locations cover major areas in Western and
Central Europe during the 19th century; see Table 1 for a list of the wheat markets and Table 2
for the list of rye markets.
The tables list the number of observations for each market, as well as the year of the
earliest data on price. There are 68 wheat price series, for markets in five non-German countries
(Austria-Hungary, Belgium, France, the Netherlands, and Switzerland) and fifteen German states
and city states.15 These are average annual prices, and the overall sample period for wheat is
1800 to 1899. For rye, there are 45 markets, and the overall sample period is 1800 to 1875. The
great majority of the rye markets are located in German territories. Since some of the markets are
located in today’s Poland, for example Stettin, it is easily seen that the geographic area spanned
by these markets is nevertheless fairly large; specifically, the maximum distance between any of
the rye markets is about 1,040 kilometers. The highest number of rye markets in our dataset is
located in Prussia, the largest of the German territories, with 15 markets.
In the following analysis, we denote a market with subscript I, i = 1,.., I, and variables
that refer to market pairs are denoted with subscript ij, where i = 1,…, I and j= 1,…, J. We have I
15
The German territories are (1) The Grand Duchy of Baden, (2) The Kingdom of Bavaria, (3) Duchy of Brunswick,
(4) the Free City of Bremen, (5) the Free City of Frankfurt/Main, (6) the Free City of Hamburg, (7) the Free City of
Lübeck, (8) the Kingdom of Hannover, (9) the Electorate of Hesse-Cassel, (10) the Grand Duchy of HesseDarmstadt, (11), the Duchy of Hesse-Nassau, (12) the Grand Duchy of Mecklenburg-Schwerin, (13) the Kingdom of
Prussia, (14) the Kingdom of Saxony, and (15) the Kingdom of Württemberg. Some of these territories changed
their name during the 19th century, for instance the Kingdom of Hannover, which was an Electorate until 1814.
Many of these territories became part in the German Reich after the year 1871.
9
= J = 68 for wheat, and I = J = 45 for rye. We compute the bilateral price difference pdifijt as the
absolute percentage price difference between markets i and j in year t:
(1)
pdif ijt = abs (ln( pit ) − ln( p jt ) ) ,
where pit and pjt denote the prices in market i and j in year t, respectively. The variable pdifijt is
the dependent variable. The annual prices yield an unbalanced sample of observations on the
bilateral price difference between any two markets. This is important to keep in mind for the
estimations.
All prices are quoted in terms of Bavarian Gulden per one Bavarian Scheffel (about 223
liter of wheat). There were many different quantity and monetary units in use, this required
numerous conversions. We have used information given in Seuffert (1857) as well as in the
original sources to make these computations. Major data sources are the works by Seuffert
(1857), Gerhard and Kaufhold (1990), Fremdling and Hohorst (1979), Hanauer (1878), as well
as various references given in Shiue and Keller (2004).16 The Data Appendix gives additional
details on the sources and the construction of these series.
The second dimension in our dataset provides information on institutions in these
territories, specifically whether a specific territory decided to dismantle trade barriers by
adopting a customs union with other territories. The most important trade agreement in 19th
century Europe was the German Zollverein, which emerged during that time under the leadership
of Prussia. For each territory, we have therefore recorded the year in which it joined the
Zollverein, and this year is listed in Tables 1 and 2 as well.17
16
We also thank Michael Kopsidis for providing us with some of his price data used in Kopsidis (2002, 1996).
There have been other trade agreements, for example the customs union created between Bavaria and
Württemberg in the year 1828. However, most of these were short-lived—the Bavaria-Württemberg one lasted for
five years before it dissolved in the Zollverein--, and other agreements fell well short of being customs unions to
begin with (for example, the Middle German Commercial Union involving Saxony, Thuringia and other territories
17
10
As noted above, important Zollverein accession dates are 1834 and 1836, but also the
years 1841 (Braunschweig), 1854 (Hannover), and 1867 (Mecklenburg and Lübeck). Generally,
joining the Zollverein meant that tariff barriers for grain trade between any two of its markets
would be equal to zero. Unfortunately, there is no comprehensive information on the levels of
tariffs on grain that existed between markets before they joined the customs union. Therefore, we
cannot exploit the size of the change in trade barriers, and focus on the timing of the move
towards zero trade barriers (i.e., Zollverein membership). Based on the year of the Zollverein
accession of the particular markets i and j, we construct a bilateral customs union indicator,
denoted by cuijt . For any market i and j, cuijt is equal to 1 if the two markets belong to the same
customs union in year t, and 0 otherwise.
Since the goal of this study is to compare the impact of institutional choice--in the form
of the creation of a customs union--on market expansion with the effect of trains, we require
comparable data on the evolution of railroad networks. To analyze the impact of the railways,
ideally one would like detailed information on which goods were transported between cities by
rail, as well as the volume and the frequency.18 Since such detailed data is generally unavailable,
as an alternative, we begin by letting rijt denote the rail indicator for markets i and j in year t, and
tˆij the year in which the train connection between markets i and j was established. For a given
market pair ij, rijt is zero for all years in which no train connection existed, and one for all later
sample years:
from 1828 until 1834 did not reduce tariffs to zero between these countries). Clearly, the Zollverein was the major
development.
18
Note that in principle, the volume of trade does not influence the degree of market integration—instead, price
differences are a sufficient statistic for that. At the same time, information on the extent to which grain is traded on
railroads, versus other goods, is certainly relevant for our analysis.
11
(2)
1 if t > tˆij

,
rijt = 
0 if t ≤ tˆ
ij

for each ij and t. The approach of examining from which year on a direct railroad
connection existed between any two markets is analogous to the customs union indicator cuijt
discussed above.
~
The last column in Tables 1 and 2 list ti , the year in which the particular market i had its
earliest rail connection in our sample:
(3)
~
ti = min (tˆij ), ∀i
The figures reflect the well-known facts that in Continental Europe, Belgium was among
the states in which railroads were developed fastest, while Austria-Hungary and Switzerland, and
to a somewhat lesser degree France and the Netherlands built railroads more slowly. Among the
German territories, early railroad developments took place in Saxony and Prussia (especially the
Rhineland-Westfalen and Saxony provinces), but also in Bavaria as well as in Hannover and
~
Braunschweig. Note that the bilateral train connection years tˆij underlying the values of ti in the
tables are not necessarily those when it was at all possible to reach market i from market j by
train. This is because a highly indirect route would likely have been irrelevant since it would
have been dominated by land or ship transport.19 The dates tˆij that we use give the year from
when on a direct and non-circuitous train connection existed.
19
The leading example for this is the French railways system, which is centered on Paris. To reach Bordeaux from
Toulouse during the early railway days in France, one had to ship via Paris. Given the increase in distance, it is
12
Even though railway lines, as investment projects, were built first among major cities and
centers of trade, it is key to realize that the lines differed strongly in terms of their importance for
freight traffic. When the Köln-Mindener line was started in 1847, it had about 46 freight cars for
every ten kilometers of track length.20 In contrast, the Leipzig-Dresden line had only about one
fourth as many freight cars, 11 per every ten kilometers of track initially. Moreover, there is
evidence that freight traffic experienced very different rates of growth on the different railway
lines. For example, during the first 10 years of its operation, the number of freight cars per
kilometer of track length quadrupeled for the Berlin-Hamburg line, whereas it grew only by 50%
for the Magdeburg-Halle line. Just as important, not all railways experienced continuous growth
of freight traffic over the 19th century. Sometimes, the impact of a particular rail line would
actually decline due to the construction of competing lines.
To account for these differences, we analyze the freight intensity of the railway lines. For
thirty major railway lines in the German states, we know the ton-kilometers of freight that were
transported in any given year (Fremdling et al. 1995). To arrive at a freight intensity measure, we
divide this by the kilometers of track length squared (since longer lines generate more tonkilometers even for a given freight intensity). Figure 1 indicates these values for four major rail
lines around Berlin, Hamburg, and in the Kingdom of Saxony. The figure shows that the freight
intensity on the Leipzig-Dresden line peaked over the period of 1857-65, whereas freight traffic
on the other three lines continued to grow steadily throughout the 1860s. Clearly, imposing the
same trend would lead to a severe error. There is also a blip in the freight intensity for all lines
after the year 1871, due to increased economic activity associated with the German-French war
of 1871. Figure 2 shows the dynamics of freight intensity on the networks of Brunswick,
likely that the rail connection Toulouse-Bordeaux, over Paris, was not that important for arbitrage between the two
Southern cities.
20
Source: Fremdling et al. (1995).
13
Hannover, Hesse-Cassel, and the Bavarian State railways. Also here, it is apparent that the
importance of freight traffic evolved differently in these territories. For example, freight traffic
developments in Hannover and Hesse-Cassel appear to be much more closely related to each
other than between Hannover and Brunswick, even though Hesse-Cassel and Brunswick’s
distance to Hannover are similar. The difference is that Hesse-Cassel is located to the South of
Hannover, whereas Brunswick is towards the East. This suggests that Hannover rail traffic
dynamics have more influence on the North-South trade than the East-West trade. Moreover,
whereas the freight intensity for Hannover climbs more or less monotonically from 1843 to 1879,
it peaked for the Bavarian railways in the year 1863. Again, this highlights that the importance
of train lines for freight traffic underwent major changes over time.
To obtain a freight intensity measure for each bilateral market, we proceed as follows.
First, we match each of I markets to that of the 30 rail lines or rail networks which is most
important for that market, given its particular geographic location.21 This indicator of freight
traffic for market i at time t is denoted by fit, i = 1,…I. From these, we estimate the bilateral
freight indicators, denoted by fijt. We form fijt simply as the average of the two market-specific
freight intensities:
(4)
f ijt =
( fit + f jt ) ,
2
for all i = 1,..,I, j = 1,…, J, and t = 1,..,T.
21
We do not have freight data for the lines outside of the German territories at this point, so we employ the data for
the most closely related German rail line. For example, in the two French cities of Strassburg and Mulhouse are
most closely related to the railways of Baden along the Rhine river.
14
Finally, the fourth element of our empirical approach is a focus on incorporating
important features of geography into the analysis. To begin with, there is the well-known effect
that arbitrage declines with geographic distance, which is at least in part due to distance-related
trade costs. We compute the bilateral geographic distance for each market pair, and employ it in
our empirical analysis to control for trade cost and other factors that impact on arbitrage
possibilities between markets.
A more significant aspect of our work is the extent to which it incorporates geographic
features up and beyond the mere distance effect. To recognize these features, we have employed
historical maps that shed light on major geographic issues in particular circumstances (IEG
2006). For example, the Prussian provinces of Rhineland and Westphalia were economically
disjoint from the Eastern Prussian areas until the year of 1828, when Hesse-Cassel joined the
Prussian customs union. Thus, even though Prussia had abolished internal tariffs after the year
1818, from the perspective of a trader in Magdeburg (or anywhere in Prussia’s Eastern part), it
was impossible to ship grain to Cologne (or anywhere in the Western part of Prussia) before
1828 without paying customs duties.22
Just as geographic conditions can modify the customs union effect, so can it impact on
the effect of railways. First of all, the question is how direct a train connection is, since this will
have a major effect on the extent to which trains improve arbitrage possibilities. As noted above,
we have extensively used maps of the actual train networks to pin down the year tˆij , or the year
from when on two markets may in fact benefit from grain arbitrage through rail traffic. Another
22
Geography effects affecting trade can be more subtle than that. A case in point is Bavaria, which also consisted of
disjoint parts in the 19th century: the core area around Nuernberg and Munich, and the Bavaria Palatinate area
around Zweibruecken. Between these two parts of Bavaria, the territories of Wurttemberg and Baden were located,
the latter of which joined the Zollverein only in 1836. Even before that date, it was possible to reach Zweibruecken
without paying customs in Baden via a direct trade route from Northern Bavarian cities, such as Wuerzburg, while
this was not possible for Bavarian cities in the center and south of the core area. Our analysis takes these and other
geographic features into account.
15
aspect of geography that has a major influence is the location of rivers, which matters for
arbitrage by rail because of the lag with which rail bridges were built across these rivers. For
example, the railway line between Cologne and Aachen was an early one in Europe, completed
in the year 1841, and as early as 1843 this line provided an international connection to the
Belgian cities Brussels and Brugge. Grain from the relatively low-price areas of Prussia could be
shipped via Hannover to the emerging industrial areas of Cologne by the year 1847 via the KölnMindener line. But that was only the Cologne-Deutz part of Cologne, located on the east side of
the Rhine—the railway bridge across the Rhine was completed only in the year 1859, and until
then, Aachen as well as the Belgian markets could effectively not be supplied by rail with the
relatively cheap Eastern European grain. These and other important geographic features are
incorporated in our analysis.
We now turn to the empirical analysis.
4. Empirical analysis
To understand whether institutions reflected in zero-tariff agreements or technology in
form of railways had a greater impact on market development in 19th century Europe, it is useful
as a first step to compare the price differences between markets before and after customs unions
were formed, and before and after the arrival of trains. See Table 3 for the results.
For the rye sample, we have a total of 17,251 bilateral observations on the absolute
percentage price differences, pdif. For the wheat sample, there are 46,652 observations. Table 3
indicates that the overall average of this in the rye sample is 18.2% while it is 18.9% in the wheat
sample. Since customs borders fell only away over the course of the 19th century, and only
between markets in the German territories, the fraction of the rye sample for which there is a
16
customs union is less than half, specifically 28.3% (wheat: 22.7%). Similarly, since railways
arrived gradually and mostly after 1845, the fraction of rye market pairs between which there
was a train connection is considerably below 0.5, at 12.2% (wheat: 4.5%).
The average value of the absolute price differences for rye (wheat) markets in the
presence of customs borders is 20.0% (20.8%), while the average value of pdif is 13.9% (12.5%)
during customs union times. The corresponding values for rye (wheat) before and after the
arrival of trains are 19.3% (19.4%) and 10.2% (9.9%), respectively. This is consistent with
institutions as well as trains leading to a substantial improvement in market integration. At the
same time, the drop of the average price difference could also be explained by improvements in
market performance over time in other factors. There is also reason to believe that the
construction of railway lines has not been fully independent of the formation of the Zollverein,
an issue to which we will return in section 5. Here, we note that the existence of rail connections
and customs unions is positively correlated (at 0.15 for rye, and at 0.06 for wheat). Conditional
on no customs union, the arrival of the railways is associated with a drop of the average price
difference from 20.8% to 11.1%, while conditional on no train connection, the formation of a
customs union is associated with a decline of the average price gap from 20.8% to 15.1%.23 With
a customs union in place, building a train connection brings the average price gap down from
15.1% to 9.1%, while given an existing train connection, when a customs union is added, the
price gap falls from 11.1% to 9.1%.24
It may appear from this that the effect from trains is larger than from customs unions.
However, these differences in how the price difference changes could be due entirely to the fact
that on average, customs unions were established earlier than when train connections were built,
23
For wheat, Table 3 shows the corresponding numbers to be 21.2% to 12.0%, and 21.2% and 13.0%.
Adding a train connection when there is a customs union for wheat brings the average pdif down from 13.0% to
6.0%, and adding a customs union after the train connection lowers pdif from 12.0% to 6.0%.
24
17
so that price differences may be lower once trains start to operate, but the reason is spurious
correlation.25 In the next section, we use regression analysis to examine this possibility. In any
case, note that from the price gaps and their changes in Table 3, there does not seem to be a large
difference between rye and wheat in how the market development was affected by institutional
and technological innovations.
In the lower part of Table 3, the cross correlations for some of the key variables are
shown. As expected, price differences are positively correlated with distance, while customs
unions as well as train connections are associated with lower price differences. The train freight
intensity variable is negatively correlated with price differences as well. Moreover, the customs
union variable is strongly negatively correlated with distance—customs unions are formed first
with relatively near-by partners--whereas this effect is much weaker for the train variables.
We now turn to estimating OLS regressions of the following general form:
(5)
pdif ijt = α 0 + β1cuijt + β 2 f ijt + X ' γ + uijt .
Recall that cuijt is the customs union indicator, which is equal to 1 if there is a customs
union between market i and j in year t, and zero otherwise, fijt is the bilateral railway freight
intensity measure, X is a vector of other control variables, and uijt is an error term. As noted
above, the number of price differences that can be computed varies strongly across market pairs.
We want to place more weight on market pairs that are observed for many years, especially
25
It must be kept in mind that for any two markets within the same contiguous state, the customs union indicator is
equal to 1 right from the first year in the sample, which captures the fact that customs barriers within a German state
were generally negligible compared to external barriers at this time.
18
before and after the creation of rail lines and customs unions. Therefore, all regressions are
weighted by the number of observations in the sample for a given bilateral pair.
In Table 4, the first four specifications for rye and wheat indicate that overall, both
customs union accession and trains are associated with lower price gaps. Including distance
reduces the customs union parameter substantially, as one would expect from the correlation
analysis in Table 3 (R2, W2). In contrast, the train variable is not much affected. Price gaps fall
over time at a rate of 0.2 to 0.3 percent per year (R3, W3). The possibility of attributing lower
price gaps spuriously to trains is highlighted by the result that with a generalized time trend, the
arrival of trains does not lower price gaps anymore (R4, W4). However, this is due to the
heterogeneity of the trains and customs union effects. The last specification in Table 4 includes
interactions with time for the cuijt, fijt, and (log) distance variables, which leads to a negative sign
on the train coefficient (R5, W5). The coefficient on the train*year interaction is positive. This
suggests that early freight train networks are associated with a larger effect on lowering price
gaps than later train lines. It is plausible since the marginal contribution of a given train line to
the network will be lower. Also, the interactions of customs unions and trains with distance are
negative, consistent with the idea that customs unions and trains matter more for longer distances.
The results of Table 4 are preliminary since there could be important additional
heterogeneity in the sample that may be driving the results. This could be at the level of countries
or German states, the level of a given market, or even at the level of a given bilateral relation.
We therefore move now to panel estimation with the bilateral pair as the group variable. This
means that identification comes only from the change over time in the price differences for any
given market pair. The approach controls for any unobserved heterogeneity at the bilateral pair
19
level that is time-invariant, and it more general than fixed effects at the country-, German state,
or market level. There are 834 rye market pairs, and 2038 wheat market pairs.
We adopt the following error-component (GLS) model:
(6)
~
pdif ijt = α 0 + β1cuijt + β 2 f ijt + X ' γ + ϕ ij + uijt ,
where φij is the bilateral pair-specific component that is treated as stochastic. Standard Hausman
tests indicate that this hypothesis cannot be rejected, and using GLS has the additional advantage
that time-invariant variables that are specific to each pair, in particular distance, can be included
~
in the vector X on the right hand side. Table 5 shows the results.
When customs union and the railway variables are the only regressors, both are estimated
to be associated with lower price gaps (R1, W1), and as before, the inclusion of distance lowers
the customs union effect by more than the trains effect (R2, W2). Further, there is evidence that
the trains effect is stronger in the early period rather than later (R4, W4), although the
coefficients are relatively small. In specification 5, there is mixed evidence for a customs union
effect: a customs union is associated with higher price gaps in the rye sample, and with lower
price gaps in the wheat sample. In contrast, the trains effect is unambiguous: a higher freight
train intensity between markets i and j is associated with lower price gaps. While for rye markets,
the direct trains effect is dominant, for wheat markets there is relatively stronger evidence than
for rye that trains have a stronger price-lowering effect for greater distances. In specifications
(R6) and (W6), we have added quadratic terms in the freight intensity variable fijt, to see whether
20
there is evidence for non-linear effects.26 There is some evidence of that, in particular for wheat
markets.
The marginal effects for the specifications 5 and 6 are computed on the bottom of Table 5.
The marginal railroad effect is between -2.2 and -4.9 percent. How important are these effects
economically? In the wheat sample, the average value of pdif is equal to 19.4% when there is no
train connection, while it is 9.9% if there is. The mean value of the freight intensity variable is 0
when there is no train connection, and it is 1.06 when there is a connection. With a marginal
effect of -0.025 in (W5), one gets -0.025*1.06 = -0.0265 as the trains effect, while the drop in the
average price gap is 0.095 (0.194-0.099). Thus, the trains effect is 0.0265/0.095, or 28% of the
total change in price differences. For the rye markets, the corresponding value due to trains is
about 40%. These should be seen as a medium run effect since the mean value of the freight
intensity are not reached in the early years of train operation (see Figures 1 and 2). Nevertheless,
these effects are certainly substantial in economic terms.
We note that these effects are estimated with the full sample, where for many market
pairs there has been no change in terms of customs unions or train connections. This means that
the identification of the customs union and trains effects may be relatively difficult. In the
following, we focus on market pairs that were undergoing ‘changes’ and look at short- to
medium effects around these changes. The results are presented in Table 6.
For both the rye and the wheat sample, the first column repeats the results from Table 5,
(R5, W5). In the second column, we restrict the sample to observations within a window of 9
years before and 9 years after the creation of the customs union or the introduction of a train
connection. As can be seen at the bottom of Table 6, this reduces the number of observations in
26
The additional regressors are (fijt)2, (fijt)2*year, and (fijt)2*log(distij); coefficients are suppressed in Table 5.
21
the samples to less than a third. Looking at the estimated marginal effects at the bottom of Table
6, one notes that the trains effect in the rye sample is somewhat larger than before; however, the
same is not true for the wheat sample. The customs union effect is largely unchanged and
remains close to zero. Columns (R3) and (W3) of Table 6 further restrict the sample by
excluding all market pairs that had always a customs union, never a customs union, and never a
train connection in this sample. This doubles the size of the marginal railroads effect in the wheat
sample, though not for the rye sample. Finally, the last column in Table 6 indicates that there
appear to be important non-linear effects associated with the intensity of freight train traffic.
Overall, these preliminary results suggest that railways in 19th century Central Europe
had a statistically and also economically significant positive influence on market integration. In
contrast, customs unions do not seem to be as important for the expansion of markets according
to our results.
5. Concluding Discussion
Railroads as a part of the transportation revolution as well as the process of economic
unification through customs unions have long been seen as a fundamental cause for greater
commodity market integration and growth in 19th century Europe. At the same time, there is not
much quantitative evidence that assesses the contribution of individual factors, and compares
their relative size. This paper fills that gap. Our preliminary results suggest that trains had a
positive effect on the expansion of markets. Moreover, we estimate that the trains were
economically important, explaining perhaps around one third of the overall decline in price gaps
during the 19th century. In comparison, the customs union effect of the Zollverein was much less
pronounced.
22
An important direction for our research will be to ascertain that we are estimating causal
effects. It has often been noted that the decision to join a customs union may be endogenous, and
so can be the construction of railroad lines. A particularly interesting possibility for our research
is that customs union formation may affect railroad construction, for instance because train
connections within the customs union yielded a greater marginal benefit than outside of the
customs union. We plan to address these issues in the future.
23
Data Appendix
To be included.
24
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28
Table 1: Wheat price series
Overall sample period: 1800 - 1899
No
City
State/Country
Obs
Mean
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Prague
Salzburg
Venice
Vienna
Baden
Augsburg
Bamberg
Bayreuth
Erding
Kempten
Landshut
Lindau
Memmingen
Munich
Noerdlingen
Nurnberg
Regensburg
Straubing
Wuerzburg
Zweibruecken
Brugge
Brussels
Braunschweig
Bar-le-Duc
Chalons sure Marne
Luneville
Mulhouse
Strassburg
Toulouse
Bremen
Frankfurt/Main
Hamburg
Luebeck
Austria-Hungary
Austria-Hungary
Austria-Hungary
Austria-Hungary
Baden
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Bavaria
Belgium
Belgium
Brunswick
France
France
France
France
France
France
Free City
Free City
Free City
Free City
8
4
7
86
28
41
41
41
41
41
41
41
41
56
41
45
41
41
41
38
100
91
50
30
30
30
76
76
100
11
14
54
9
19.47
29.02
15.57
20.57
16.29
16.92
16.32
16.82
16.33
18.81
15.58
19.14
18.00
18.69
16.14
16.42
15.09
14.65
16.41
16.57
20.62
22.45
16.50
18.08
18.55
19.03
22.41
21.63
21.40
20.53
22.57
18.48
17.58
Year of
Earliest
Obs.
Year
1836
1849
1836
1820
1818
1815
1815
1815
1815
1815
1815
1815
1815
1800
1815
1811
1815
1815
1815
1818
1800
1800
1800
1825
1825
1825
1800
1800
1800
1837
1816
1800
1837
Year of
Year of
Zollverein
Earliest
Accession Rail Connection*
Year
Year
1845
1860
1856
1845
1836
1846
1834
1840
1834
1844
1834
1853
1834
1859
1834
1852
1834
1854
1834
1852
1834
1858
1834
1840
1834
1849
1834
1844
1834
1859
1834
1858
1834
1854
1834
1857
1838
1838
1841
1844
1851
1851
1851
1841
1841
1859
1888
1847
1836
1840
1888
1846
1867
1851
Table 1, cont'd
No
City
State/Country
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
Goettingen
Hannover
Kassel
Bingen
Giessen
Mainz
Worms
Wiesbaden
Grabow
Boizenburg
Parchim
Rostock
Schwerin
Wismar
Nijmegen
Utrecht
Aachen
Berlin
Cologne
Hamm
Herdecke
Minden
Muenster
Saarlouis
Soest
Wetzlar
Xanten
Dresden
Leipzig
Zwickau
Basel
Lucerne
Rorschach
Stuttgart
Ulm
Hannover
Hannover
Hesse-Cassel
Hesse-Darmstadt
Hesse-Darmstadt
Hesse-Darmstadt
Hesse-Darmstadt
Hesse-Nassau
Mecklenburg
Mecklenburg
Mecklenburg
Mecklenburg
Mecklenburg
Mecklenburg
Netherlands
Netherlands
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Saxony
Saxony
Saxony
Switzerland
Switzerland
Switzerland
Wurttemberg
Wurttemberg
Obs
Mean
Year of
Earliest
Obs.
68
50
27
1
1
3
1
1
71
71
71
71
71
57
93
15
61
61
61
20
20
13
64
20
20
20
20
21
21
21
10
9
14
5
6
17.12
17.81
14.22
20.34
19.12
23.68
20.68
18.13
18.45
18.30
17.43
17.57
17.67
16.65
21.46
30.66
18.88
18.14
17.15
20.86
23.23
21.49
18.91
17.70
17.71
19.27
18.48
16.78
16.74
18.44
24.75
23.94
20.79
23.68
22.81
1800
1801
1822
1840
1840
1840
1840
1840
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1832
1832
1832
1845
1845
1824
1850
1850
Prices in Bavarian Gulden, per Bavarian Scheffel (about 223 liter)
* Rail connection in this sample
Year of
Year of
Zollverein
Earliest
Accession Rail Connection*
1854
1854
1831
1828
1828
1828
1828
1836
1867
1867
1867
1867
1867
1867
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1854
1844
1849
1858
1850
1853
1853
1840
1846
1846
1880
1850
1847
1848
1856
1856
1841
1841
1841
1847
1848
1847
1848
1858
1850
1862
1880
1839
1839
1845
1844
1856
1856
1850
1850
Table 2: Rye Price Series
Overall Sample Period: 1800-1875
No
City
State/Country
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
Baden
Braunschweig
Mulhouse
Strassburg
Bremen
Frankfurt/Main
Hamburg
Luebeck
Goettingen
Hannover
Kassel
Bingen
Giessen
Mainz
Worms
Boizenburg
Grabow
Parchim
Rostock
Schwerin
Wismar
Wiesbaden
Aachen
Berlin
Cologne
Halberstadt
Hamm
Herdecke
Minden
Muenster
Saarlouis
Schweidnitz
Soest
Stettin
Stralsund
Wetzlar
Xanten
Dresden
Frankfurt/Oder
Leipzig
Magdeburg
Zwickau
Jena
Stuttgart
Ulm
Baden
Brunswick
France
France
Free City
Free City
Free City
Free City
Hannover
Hannover
Hesse-Cassel
Hessen-Darmstadt
Hessen-Darmstadt
Hessen-Darmstadt
Hessen-Darmstadt
Mecklenburg
Mecklenburg
Mecklenburg
Mecklenburg
Mecklenburg
Mecklenburg
Nassau
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Prussia
Saxony
Saxony
Saxony
Saxony
Saxony
Thuringia
Wurttemberg
Wurttemberg
Obs
Mean
Std. Dev.
Year of
Earliest
Obs.
29
50
76
76
11
14
38
9
68
50
27
1
1
3
1
69
71
71
69
70
56
1
66
66
66
31
20
20
13
66
20
30
20
31
31
20
20
21
31
21
31
21
41
2
6
10.55
12.71
13.74
14.27
13.67
17.13
11.28
12.11
13.88
13.02
10.62
15.95
12.99
18.45
15.37
13.10
12.80
11.96
12.00
12.51
11.73
12.93
14.74
12.85
13.38
12.32
16.02
17.31
16.14
14.06
12.67
10.43
12.27
11.06
10.17
13.87
13.62
11.81
10.55
11.96
12.09
13.64
12.12
12.72
17.39
1.76
5.03
3.87
4.73
2.60
7.69
3.72
1.52
4.57
4.71
3.30
n/a
n/a
5.53
n/a
4.94
4.53
4.40
4.29
4.53
4.22
n/a
4.77
4.28
4.49
3.24
4.75
5.02
4.14
4.66
5.34
2.70
4.05
3.08
2.97
3.12
3.66
3.73
2.90
4.00
3.18
3.86
4.99
5.70
5.91
1818
1800
1800
1800
1837
1816
1816
1837
1800
1801
1822
1840
1840
1840
1840
1800
1800
1800
1800
1800
1815
1840
1800
1800
1800
1821
1800
1800
1800
1800
1800
1821
1800
1821
1821
1800
1800
1832
1821
1832
1821
1832
1815
1850
1850
Prices in Bavarian Gulden, per Bavarian Scheffel (about 223 liter)
* Rail connection in this sample
Year of
Zollverein
Accession
Year of
Earliest
Rail Connection*
1836
1841
1846
1842
1841
1841
1847
1840
1846
1851
1854
1844
1849
1858
1850
1853
1853
1846
1846
1880
1850
1847
1848
1840
1841
1841
1841
1842
1847
1848
1847
1848
1858
1846
1850
1843
1863
1862
1880
1839
1842
1839
1840
1845
1858
1850
1850
1888
1836
1888
1867
1854
1854
1831
1828
1828
1828
1828
1867
1867
1867
1867
1867
1867
1836
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
1834
Table 3: Summary Statistics
mean
pdif
rye
wheat
rye
wheat
fraction of sample with
cu=1
train_i=1
0.182
0.189
0.283
0.227
mean pdif if
cu=0
cu=1
0.200
0.139
0.208
0.125
rye
wheat
rye
mean pdif if
train_i=0
train_i=1
0.122
0.045
mean pdif if
train_i=0 trains=1
0.193
0.102
0.194
0.099
wheat
mean pdif if
cu=0
0.208
0.111
cu=1
0.151
0.091
pdif
1
-0.23
-0.13
0.27
-0.11
cu
train_i=0
train_i=1
cu=0
0.212
0.12
ldist
train
cu=1
0.13
0.06
Correlations
rye
pdif
cu
train_i
ldist
train
wheat
1
0.06
-0.52
0.05
train_i
1
-0.04
0.83
1
-0.05
1
pdif
cu
train_i
ldist
train
pdif: absolute percentage price differences
cu : Customs union indicator, equal to 1 if no customs barriers, zero otherwise
train_i: Train connection indicator, equal to 1 if connection exists, zero otherwise
ldist: log distance
train: freight train intensity
pdif
1
-0.18
-0.19
0.29
-0.11
cu
train_i
ldist
train
1
0.16
-0.33
0.1
1
-0.03
0.81
1
-0.04
1
Table 4
Rye Markets
Wheat Markets
(R1)
(R2)
(R3)
(R4)
(R5)
(W1)
(W2)
(W3)
(W4)
(W5)
customs union
-0.081
[-189.44]
-0.028
[-60.83]
-0.01
[-22.55]
-0.006
[-15.57]
0.044
[41.05]
-0.089
[-339.01]
-0.039
[-126.16]
-0.021
[-69.62]
-0.02
[-69.59]
-0.03
[-37.35]
train freight
-0.046
[-119.42]
-0.046
[-126.10]
0.005
[13.35]
0.004
[9.36]
-0.063
[-11.85]
-0.056
[-166.34]
-0.055
[-165.42]
-0.012
[-34.07]
0.0003
[0.98]
-0.024
[-8.17]
0.068
[246.73]
0.076
[289.87]
0.078
[316.48]
0.144
[236.40]
0.051
[290.67]
0.06
[348.41]
0.061
[366.42]
0.076
[197.20]
distance
year
-0.003
[-263.52]
-0.002
[-312.13]
cu*year
-0.001
[-29.87]
0.001
[47.98]
train*year
0.001
[12.68]
0.001
[10.35]
distance*year
-0.002
[-93.50]
-0.0001
[-9.58]
cu*distance
-0.018
[-34.56]
-0.03
[-84.65]
train*distance
-0.008
[-18.09]
-0.013
[-32.82]
Time Fixed Effects
yes
yes
yes
yes
F-stat
[Prob>F]
28527
[0]
41077
[0]
51434
[0]
4451
[0]
4554
[0]
74922
[0]
80410
[0]
87865
[0]
5490
[0]
5415
[0]
Rbar-sq
0.08
0.158
0.239
0.346
0.366
0.076
0.116
0.161
0.234
0.24
t-statistics in hard brackets
Table 4: Panel Regressions
Rye
Wheat
(R1)
(R2)
(R3)
(R4)
(R5)
(R6)
(W1)
(W2)
(W3)
(W4)
(W5)
(W6)
customs union
-0.057
[-75.32]
-0.041
[-56.42]
0.005
[6.83]
0.031
[22.78]
0.02
[13.20]
0.021
[13.38]
-0.054
[-110.63]
-0.04
[-80.28]
0.007
[13.77]
-0.056
[-58.52]
-0.036
[-28.08]
-0.035
[-27.42]
train freight
-0.046
-0.047
[-121.96] [-124.19]
0.008
[17.32]
-0.065
[-12.51]
-0.065
[-12.54]
-0.052
[-4.56]
-0.054
-0.053
[-159.49] [-159.29]
0.004
[11.07]
-0.015
[-5.41]
-0.033
[-11.85]
-0.057
[-7.36]
0.074
[128.73]
0.141
[170.22]
0.134
[149.44]
0.134
[148.20]
0.047
[101.47]
0.065
[138.07]
0.068
[115.85]
0.071
[107.20]
0.069
[104.56]
cu*year
-0.0004
[-14.88]
-0.001
[-18.96]
-0.001
[-19.36]
0.002
[76.50]
0.002
[75.74]
0.002
[75.09]
train*year
0.001
[12.93]
0.001
[13.92]
0.001
[6.38]
0.0003
[5.53]
0.001
[16.58
0.002
[12.54]
-0.0002
[-16.90]
0
[0.32]
0.00001
[6.46]
distance
0.058
[92.24]
distance*year
-0.002
-0.002
-0.002
[-125.94] [-108.11] [-104.46]
cu*distance
0.015
[15.39]
0.015
[15.44]
-0.016
[-19.45]
-0.016
[-19.59]
train*distance
-0.006
[-13.22]
-0.009
[-7.98]
-0.016
[-38.88]
-0.038
[-39.62]
yes
yes
yes
yes
Time Fixed Effects
yes
yes
trains squared terms
LRchi2
[Prob>chi2]
yes
yes
yes
22174
[0]
Average Marginal effects
trains
customs union
t-statistics in hard brackets
28761
[0]
202760
[0]
225499
[0]
225913
[0]
226038
[0]
-0.031
0.016
-0.022
0.017
yes
37721
[0]
47067
[0]
360739
[0]
373558
[0]
375459
[0]
376179
[0]
-0.025
-0.003
-0.049
-0.003
Table 5: Panel Regressions
Rye Markets
Wheat Markets
(R1)
(R2)
(R3)
(R4)
(R5)
(R6)
(W1)
(W2)
(W3)
(W4)
(W5)
(W6)
customs union
-0.057
[-75.32]
-0.041
[-56.42]
0.005
[6.83]
0.031
[22.78]
0.02
[13.20]
0.021
[13.38]
-0.054
[-110.63]
-0.04
[-80.28]
0.007
[13.77]
-0.056
[-58.52]
-0.036
[-28.08]
-0.035
[-27.42]
train
-0.046
-0.047
[-121.96] [-124.19]
0.008
[17.32]
-0.065
[-12.51]
-0.065
[-12.54]
-0.052
[-4.56]
-0.054
-0.053
[-159.49] [-159.29]
0.004
[11.07]
-0.015
[-5.41]
-0.033
[-11.85]
-0.057
[-7.36]
0.074
[128.73]
0.141
[170.22]
0.134
[149.44]
0.134
[148.20]
0.047
[101.47]
0.065
[138.07]
0.068
[115.85]
0.071
[107.20]
0.069
[104.56]
cu*year
-0.0004
[-14.88]
-0.001
[-18.96]
-0.001
[-19.36]
0.002
[76.50]
0.002
[75.74]
0.002
[75.09]
train*year
0.001
[12.93]
0.001
[13.92]
0.001
[6.38]
0.0003
[5.53]
0.001
[16.58
0.002
[12.54]
-0.0002
[-16.90]
0
[0.32]
0.00001
[6.46]
distance
0.058
[92.24]
distance*year
-0.002
-0.002
-0.002
[-125.94] [-108.11] [-104.46]
cu*distance
0.015
[15.39]
0.015
[15.44]
-0.016
[-19.45]
-0.016
[-19.59]
train*distance
-0.006
[-13.22]
-0.009
[-7.98]
-0.016
[-38.88]
-0.038
[-39.62]
yes
yes
yes
yes
Time Fixed Effects
yes
yes
train squared terms
LRchi2
[Prob>chi2]
yes
yes
yes
22174
[0]
Average Marginal effects
train
customs union
t-statistics in hard brackets
28761
[0]
202760
[0]
225499
[0]
225913
[0]
226038
[0]
-0.031
0.016
-0.022
0.017
yes
37721
[0]
47067
[0]
360739
[0]
373558
[0]
375459
[0]
376179
[0]
-0.025
-0.003
-0.049
-0.003
Table 6: Broad and Narrow Sample
Rye Markets
(R1)
(R2)
All data
Table 5
(R5)
customs union
Wheat Markets
(R4)
(W1)
(W2)
Symmet.
10-yr
Window
(R3)
Symmet.
10-yr
Window &
Change
Symmet.
10-yr
Window
(W3)
Symmet.
10-yr
Window &
Change
add
train^2
All data
Table 5
(W5)
0.02
[13.20]
-0.011
[-3.47]
-0.061
[-6.79]
-0.062
[-6.85]
train
-0.065
[-12.54]
-0.07
[-13.64]
-0.125
[-13.77]
distance
0.134
[149.44]
0.08
[43.88]
cu*year
-0.001
[-18.96]
train*year
add
train^2
-0.036
[-28.08]
0.001
[2.02]
0.019
[2.26]
0.013
[1.55]
-0.348
[-13.75]
-0.033
[-11.85]
-0.049
[-16.59]
-0.092
[0.007]
-0.215
[-10.11]
0.107
[38.04]
0.102
[35.62]
0.071
[107.20]
0.056
[44.56]
0.068
[31.13]
0.069
[30.99]
-0.0002
[-4.24]
0.002
[8.18]
0.002
[8.44]
0.002
[75.74]
-0.0001
[-2.91]
0.001
[4.26]
0.001
[4.99]
0.001
[13.92]
0.001
[14.70]
0.003
[14.64]
0.007
[14.66]
0.001
[16.58]
0.001
[13.24]
0.002
[13.32]
0.004
[9.08]
-0.002
[-108.11]
-0.001
[-26.80]
-0.001
[-13.54]
-0.001
[-10.54]
0
[0.32]
-0.004
[-18.72]
0
[0.02]
0
[1.00]
cu*distance
0.015
[15.39]
0.021
[21.31]
0
[-0.05]
0
[-0.89]
-0.016
[-19.45]
0.005
[5.75]
-0.035
[-16.30]
-0.035
[-16.21]
train*distance
-0.006
[-13.22]
-0.009
[-14.77]
-0.008
[-5.86]
-0.031
[-10.30]
-0.016
[-38.88]
-0.005
[-10.79]
-0.032
[-24.38]
-0.021
[-4.87]
distance*year
train^2 terms
yes
(W4)
yes
LRchi2
[Prob>chi2]
225913
[0]
39700
[0]
17458
[0]
17585
[0]
375459
[0]
73618
[0]
17608
[0]
17787
[0]
No obs
17,251
6,513
1,671
1,671
46,652
12,407
1,709
1,709
Av marg effect
trains
customs union
-0.031
0.016
-0.037
0
-0.027
0.009
-0.095
0.011
-0.025
-0.003
-0.022
0.004
-0.053
0.012
-0.085
-0.003
t-statistics in hard brackets
Time fixed effects included
Figure 1
Railway Freight Transport in the 19th Century I: Berlin & Prussia, Hamburg, Saxony
Year 1870 = 1
2.000
1.800
1.400
1.200
1.000
0.800
0.600
0.400
0.200
Year
Berlin-Anhalter (Southwest)
Berlin-Hamburg
Halle-Leipzig
Leipzig-Dresden
18
81
18
83
18
77
18
79
18
71
18
73
18
75
18
67
18
69
18
61
18
63
18
65
18
57
18
59
18
51
18
53
18
55
18
47
18
49
18
41
18
43
18
45
0.000
18
39
Ton-Kilometer/Kilometer_squared
1.600
Figure 2
Railway Freight Transport in the 19th Century II: Brunswick, Hannover, Kassel, and Bavaria
Year 1870 = 1
1.600
1.200
1.000
0.800
0.600
0.400
0.200
Year
Brunswick Railways
Hannover Railways
Hesse-Cassel Railways
Bavarian State Railways
18
83
18
81
18
79
18
77
18
75
18
73
18
71
18
69
18
67
18
65
18
63
18
61
18
59
18
57
18
55
18
53
18
51
18
49
18
47
18
45
0.000
18
43
Ton-Kilometer/Kilometer_squared
1.400