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TRANSCRIPT
The Link Between Fundamentals and Proximate Factors in
Development�
Wolfgang Keller
Stanford and Coloradoy
Carol H. Shiue
Stanford and Coloradoz
September 29, 2013
Abstract
We examine the relationship between institutions, trade, and city growth in the 19th century.
There are two �ndings. First, both institutional change and the introduction of steam trains im-
proved trade, with the former quantitatively more important than the latter. Second, institutional
change had a major e¤ect on city growth, and this institutions e¤ect operated mainly through
trade. The paper introduces a general framework for studying the hierarchy of growth factors,
from deep to more immediate, that can easily be adapted to other settings. We also discuss the
generality of the �nding that institutions a¤ect growth mostly through trade.
�We thank Daron Acemoglu, Beata Javorcik and seminar participants at Oregon, Oxford and the World TradeOrganization for useful comments.Thanks go as well to Michael Kopsidis for providing us with data. David Silver andAustin Smith provided excellent research assistance. NSF support under grant numbers SES 0453040 and SES 1124426is gratefully acknowledged.
yHoover Institution, 435 Galvez, Stanford, CA 94305; [email protected] Institution, 435 Galvez, Stanford, CA 94305; [email protected]
1
1 Introduction
In the area of growth the focus has recently shifted to its deeper, or fundamental, causes, in contrast
to the more immediate signs of growth such as capital accumulation. In particular, there is evidence
that institutions are a fundamental driver of growth. At the same time, fundamentals must a¤ect
certain economic activities, or proximate factors, in order to impact growth. Do fundamentals a¤ect
the incentives to accumulate capital, to engage in trade, or to adopt new technologies? At this point
we do not know much about how fundamentals exert their in�uence on growth, nor which of the
channels are the most important.
This paper presents a formal assessment of the role of trade in the institutions e¤ect on growth.
The setting is 19th century Europe. A number of German states experienced drastic improvements
of their economic institutions as the result of French occupation. As happened elsewhere, nineteenth
century Germany witnessed a major improvement in trade through the introduction of steam railways.
We ask whether institutional change helped bring about these improvements in trade, and how
important trade was in the overall impact of institutions on growth.
The paper �rst shows that trade improved signi�cantly once two markets became linked by
steam train.1 Institutional change is found to have had an even larger impact on improving trade
than steams trains. The main result of the paper is our second �nding: trade accounts for much
of the institutions e¤ect on growth. While in the baseline estimation institutions a¤ect growth only
through trade more generally we �nd that trade in 19th century Central Europe trade was important
in translating institutional improvements into higher growth. The paper thus quanti�es the link from
1Our measure of trade is the size of spatial, city-to-city price gaps. Prices give key information on trade and itse¤ects (Stolper and Samuelson 1941). Both price and quantity measures have been employed in the literature (O�Rourke and Williamson 1999, Frankel and Romer 1999, respectively). While in historical settings the availability ofprice data often surpasses that information on quantities, we expect that either would lead to similar results for thequestions at hand.
2
institutions to trade and growth.
Other researchers before us have examined interactions among di¤erent growth factors. Most
closely related is the �nding that the growth gains triggered by the onset of Atlantic trade depended
on the quality of the initial institutions (Acemoglu, Johnson, and Robinson 2005a). Further, the
concern with multiple growth factors might suggest that our goal is to run a �horserace�in order to
�nd the key cause of growth, as for example in Rodrik et al. (2004). While other factors play a role,
the focus here is on only one deep growth factor, institutions, and our interest lies in how institutions
work by formally testing the link between institutions, trade, and growth.
Our paper builds on research establishing that institutions a¤ect economic performance. Most of
the initial work including Acemoglu, Johnson, and Robinson (2001) was interested in capturing the
impact of a broad cluster of institutions, whereas later work aimed at pinpointing a speci�c subset of
institutions that are of key importance (such property rights versus contracting institutions, Djankov
et al. 2003, Acemoglu and Johnson 2005). One way of thinking about our paper is that trade (or
trade policy) is an element of an economy�s institutions. Instead, we do not think of trade so much
as an aspect of institutions but rather we are establishing econometrically that institutions can have
a positive impact through trade on growth.
This paper relates to research on the persistent e¤ects of historic shocks that investigate the
mechanisms of persistence. Speci�cally, Dell (2010) shows that a particular colonial labor institution
in Latin America was associated with relatively low educational attainment, which in turn might
help to explain low living standards in these areas today. Our paper extends this research agenda
by providing estimates of the causal e¤ects of institutions on the intervening factor (here: trade),
and from that on growth. We are cognizant of the di¢ culty of isolating a single intervening factor
because economic change often occurs along several dimensions.
3
Major changes in an economy often occur for non-random reasons. In the spirit of Du�o and
Pande (2007), to address the potential endogeneity of steam train adoption we employ an instrumen-
tal variable estimation that relies on the di¢ culty of the terrain, as this crucially a¤ected railway
building costs. Moreover, given the timing of invention, steam railways were not available in Ger-
many before the year 1835. This yields an placebo check in that the costs of building railway lines
should matter more in the later 19th century than early on. Also institutional change may be en-
dogenous. In this regard we rely on the plausibly exogenous shock to German institutions during
French occupation between the early 1790s and 1815, which was �rst noted by Acemoglu, Cantoni,
Johnson, and Robinson (2011; ACJR for short). There are reasons why our exclusion restrictions
might not be holding, and the paper discusses several of them below.
Our approach of tracing out the link between fundamental and proximate causes of growth can be
applied to other potential causes of growth, including cultural beliefs (Greif 1994), religion (Weber
1930), and geography (Sachs 2001). While in the context of 19th century Germany institutional
di¤erences were arguably more important than other di¤erences, nevertheless geographic and religious
factors will be incorporated in the analysis below.
The paper is also part of rapidly growing literature that relies on using sub-national data to study
comparative development. As Nunn (2009) notes, this has the advantage that richer identi�cation
strategies can be applied and �ner mechanisms can be studied than with country-level data. A
concern with sub-national level data is whether the �ndings generalize to the macro level. In our
case there is evidence that the measure of sub-national development, city population growth, exhibits
the same broad patterns as GDP per capita at the country level (Acemoglu, Johnson, and Robinson
2005b).
Finally, we note that the result of a sizable impact from railways in the 19th century Germany
4
�ts well with recent studies on the impact of infrastructure projects (Michaels 2006, Duranton and
Turner 2012), especially railways in history (Donaldson 2010, Donaldson and Hornbeck 2012). Our
paper shares with Donaldson (2010) the use of rich bilateral railroad data, which in our case is
connected to the analysis of city growth using a two-sample instrumental variable approach similar
to Angrist and Krueger (1992). A key di¤erence in comparison with other infrastructure papers is
that in this paper the impact of infrastructure is linked to institutions.
The paper is structured as follows. The next section gives the background important for the
interpretation of the results. We highlight the role of steam trains for changing the way trade was
conducted, and discuss the sources and nature of institutional change in 19th century Germany.
Section 3 introduces the data, giving the major original sources, our methods to prepare the data,
and their key characteristics for the question at hand. All empirical results are presented section 4.
We begin by estimating the relative importance of institutions and steam trains in improving trade
in 19th century Germany. We then assess the extent to which institutions a¤ect growth through
trade versus non-trade factors. In the concluding section 5 we discss the generality of our results and
raise a number of open questions.
2 Institutions, Railroads and Commodity Market Integration in
19th Century Germany
During much of the 19th century Germany consisted of several independent nation states. The
following �gure shows the territorial boundaries in the year 1848.
5
We see a mix of states that di¤er substantially in size, from larger kingdoms such as Prussia
and Bavaria over medium-sized polities such as the Kingdom of Saxony to smaller entities, including
the Grandduchy of Hesse as well as a number of city states such as Hamburg and Frankfurt. More
important than the mere size di¤erences for our purposes is that until the formation of the German
Reich in 1871 these states were making independent political and economic decisions. True, the
German Reich under the leadership of Prussia was looming, but over the course of the 19th century
these states had shifting alliances and fought wars on opposing sides. Their institutions and economic
policies di¤ered over our sample period of 1820 to 1880, at times within the level of the state (such
as for the Western and Eastern parts of Prussia). Sometimes the reason for di¤erent choices was
6
external force, sometimes not, but inter-state competition played always a key role. In short, we are
a¤orded a rich historical laboratory where a variety of institutional forms potentially led to di¤erent
economic outcomes.
Out of the many elements of change in Germany during the 19th century, the focus in this
paper is going to be on the impact of economic institutions and the adoption of steam railways.
For one, earlier research has argued that during the 19th century, transportation improvements
and institutional change have been of great importance, not only in Germany but also elsewhere
in the world.2 Previous research has shown that the introduction of steam trains can improve
trade, and both the adoption of steam trains and more trade can be seen as proximate causes of
growth. In contrast, economic institutions are a fundamental factor, and most importantly for our
purposes, institutions may impact economic growth both by a¤ecting trade and non-trade channels.
More e¢ ciency-oriented institutions may for example increase the pace at which new technologies
such as steam trains are introduced, or they may reduce trade costs by improving the contracting
environment. At the same time, better institutions may also a¤ect growth by raising the return to
capital accumulation, a non-trade channel. As a consequence, this historical setting is well-suited for
our purposes. We focus on the central sixty years of the 19th century, 1820 to 1880, in order to limit
the in�uence of wars at the beginning, and the political uni�cation of Germany towards the end of
the 19th century.
We now turn to steam railways before discussing institutional change in the following section.
2On Germany in particular, see Fremdling (1975), ACJR (2011).
7
2.1 Steam railways in 19th Century Germany
European economic growth during the 19th century was accompanied by a series of innovations in
transportation.3 These innovations included paved roads, improvements in waterways, and wide-
spread railway construction which was fueled by the invention of the steam train. In the 1830s
and 1840s British suppliers of locomotives dominated the market, which were bought by states and
countries on the continent before they started to produce their own steam trains. The �rst German
railway was opened in December 1835. With only 4 miles of tracks it was a short suburban line lo-
cated in Bavaria, between Nurnberg and Fürth. The �rst longer route (70 miles) was built in Saxony
in 1839. Thereafter, additional miles of rail were laid down swiftly. By 1847, there were over 2,000
miles of rail in Germany (Henderson 1959, 147), and almost all main railway lines were completed
by 1877 (Milward and Saul 1977, 42).
In this paper we will focus on price di¤erences across cities for the major grain in these areas,
namely wheat. How important were railways as a means of transportation for wheat? Generally,
railways were important for low value-to-weight ratio good such as coal, construction materials,
metal goods, and also grain (O�Brien 1983, 1-2). At the same time, the importance of railroads for
transporting grain varied greatly. While it was cheaper to transport grain by railroads than by other
means of land transport, trains could not compete with transport by ship. In the late 19th century,
for example, sending grain from Posen (in East Prussia) to Cologne by train was at least three times
as expensive as transporting it by ship to Rotterdam and then up the Rhine river (Köttgen 1890,
64).
Consequently, long distance grain trade in the southeast direction, parallel to the major rivers
(Elbe, Rhine, and Danube), was hardly ever done by rail. At the same time, transportation of grain
3A good survey is O�Brien (1983).
8
on railways was of utmost importance when it connected the drainage areas of the main rivers.4
Grain transportation on railways was also of major signi�cance whenever sea or river transport, even
if indirect, was not an option. For example, the great majority of all grain exported from Bavaria
to the South in the early 1850s was transported on railways (Seu¤ert 1857, Chapters 5, 6). This
suggests that the analysis needs to hold constant city-(pair)-speci�c attributes related to geography
and alternative modes of transport in estimating the impact of steam trains on trade.
The pace of railway building may also have di¤ered across German states due to various levels
of support. In some areas, such as Saxony, sentiments were generally pro-railroad while in others,
such as Wurttemberg, this was not the case. Initially, railways were opened and operated line by
line, and there were often multiple railway companies operating lines in a given polity. Prussia, for
example, had a number of initially independent railway lines, such as the Koeln-Mindener and the
Berlin-Brandenburgische line. These companies laid down new track at di¤erent speeds and operated
trains in di¤erent ways (including freight charges), which implies that the relevant variation is not
only across states but also across cities. The detailed accounts of individual railway companies in
Fremdling et al (1995) are used to inform our analysis.
Perhaps the most important determinant of the potential impact steam trains might have had,
and in any case an aspect that is observable in a straightforward manner, is whether a railway
was predominantly operated by the state or by private business groups. What were the relative
contributions of private business groups versus the state in this railway building? Most early com-
mentators considered Germany around the year 1830 as so underdeveloped compared to England
that it was taken as a given that there would have been insu¢ cient demand for railways to justify
private, pro�t-seeking investments. Consequently it was up to the governments to either establish
4For example, the completion of the Köln-Mindener railway in the year 1847 was crucial for transporting therelatively cheap Prussian grain to the emerging industrial areas of the Rhine-Ruhr (Fremdling and Hohorst 1979, 64).The availability of paved roads and canals also in�uenced how important steam railroads were.
9
state railways or heavily subsidize private railways. In fact, the states�early involvement in railways
varied strongly, with Hanover, Baden, and, Brunswick, and Wurttemberg building state railways,
while Prussia and Saxony initially only had privately run railways. In the analysis below we em-
ploy information on whether a particular city was primarily served by privately-run or by state-run
railways in the empirical analysis (source: Fremdling et al. 1975).
The conventional wisdom is that the state early on actually not facilitate railway building. In
particular, often the state governments slowed down issuing railway concessions, and perhaps most
importantly governments heavily regulated in which locations the tracks were eventually built. Each
state�s government was primarily concerned with its own territory, and connections between major
cities in di¤erent states rarely existed. This is most clearly seen by comparing the proposal by
economist Friedrich List for a national railway system in Germany from the year 1833 (Figure 3a)
with the railway track actually existing in the year 1850 (see Figure 3b).5
5List�s proposal can be found in Krause (1887), pages 24-25.
10
While List had planned to connect the larger German points of trade with each other, the actual
train system that emerged looked quite di¤erent. For example, the major Southern cities of Munich,
Stuttgart, and Karlsruhe were still not connected by the late 1840s, most importantly because they
were located in three di¤erent states (Bavaria, Wurttemberg, and Baden, respectively).6 Also, the
train connection going south from Kiel (in the state of Holstein) for a long time ended in the city of
Altona (Holstein), just short of the major town of Hamburg, because Hamburg was an independent
6Eventually, Karlsruhe was connected to Stuttgart by the end of 1853, while Stuttgart was connected to Munich bythe end of 1854.
11
state (Free city). The former Hanse town of Luebeck was not yet connected to other cities by rail as
of 1850, in part because it was an independent state.
Thus the early railway policies tended to be focused on the states�own territory. Private proposals
to construct inter-state railways already in the 1830s were often rejected, typically for fear of losing
trade that originally went through the state�s own territory to other states. While in the early
phase the state governments slowed down railway building, in a later phase the states engaged in
an almost race-like competition to build railway connections. The tipping point was usually reached
when a neighboring state had eventually decided to build a railway (or to provide licenses for private
operators), because then it was thought that the suspected detrimental, trade-diverting e¤ect could
only be prevented by speeding up railway building in the own territories. This second phase has been
12
credited for the fact that railway building in Germany overall has been relatively fast compared to
some of its European neighbors, for example the more developed France (Fremdling et al. 1995).
Overall, this suggests that railway building was driven by a mix of motives at several, both
at private and state levels, where some probably responded to economic e¢ ciency considerations
while others did not. Whoever would be in charge of railways, however, the costs of building a
particular railway line would always be a¤ected by the di¢ culty of the terrain. Speci�cally, railway
building across rivers and over mountains is more expensive than railway building over �at land. Our
instrumental variable to deal with potential endogeneity of railway building is based on a ArcGIS-
based estimate of the railway building costs for a given city pair. Because comprehensive data on grain
shipments by rail between the sample cities is unavailable, the main information on railway building
employed in this study is the time at which two cities became connected by a train connection. We
have coded this using maps that show the completion of the train network in Germany on a year-by-
year basis (IEG 2013). Figure 3b shows, as an example, the train network in Germany for the year
1850.
In that year, according to Figure 3b there existed a rail connection between Berlin and Leipzig, but
not between Berlin and Munich (München in the map). That had to wait until the segment between
Leipzig and Nurnberg had been completed. While this bilateral approach ignores certain e¤ects, for
example that the existence of the Berlin-Munich connection implied that Berlin was also connected
to Nurnberg (because the Berlin-Munich connection went actually over the city of Nurnberg), it has
the advantage of a repeated event-study nature at the same time when it is plausibly capturing the
�rst-order e¤ects.
We now turn to the causes and nature of institutional change in Germany during the 19th century.
13
2.2 Institutional change in Germany
Many of the most signi�cant institutional changes in the late 18th and 19th century were undertaken
during the invasion of the French revolutionary armies.7 In the Rhineland, for example, between 1795
and 1798 the seigneurial regime and the guilds were abolished. Moreover, the institutional changes
did not end with the rise of Napoleon to power, because to some extent he continued to implement
the reforms initiated by the revolutionary armies. While at times Napoleon was more inclined to
compromise with local elites, in most places there was a signi�cant attempt to continue and deepen
the reforms brought by the French Revolution. In particular, Napoleon saw the imposition of the
civil code (Code Napoléon) in areas that he controlled as his most important reform.
After the defeat of Napoleon in the year 1815, the institutional reforms in some German states
were kept in place while in others they were rolled back. The key determinant of whether there
was a reform rollback or not was the presence and strength of new elites that had bene�ted from
the reforms. Information on the nature and the timing of some of these institutional reforms, in
particular (1) the establishment of a written civil code that guaranteed equality before the law and
(2) the abolition of trade guilds is taken from ACJR.
The French civil code (Code Napoléon) guaranteed equality before the law, but so did the Saxon
civil code of 1863. While equality before the law is bound to reduce discretion and favoritism in
business relationships, in itself it may not have an economy-wide e¤ect, and for this reason we see it
as a general sign of an e¢ ciency-oriented economic institution more than anything else. Moreover,
in late 18th century Germany, guilds tended to control entry to all major occupations and also at
times restricted the adoption of new technologies (Ogilvie 2004). While guilds in Europe have not
been uniformally opposed to e¢ ciency considerations (Epstein 1998), on balance we hypothesize that
7More details are given in Acemoglu, Cantoni, Johnson, and Robinson (2011), on which this section draws.
14
the abolition of guilds is another sign of improvement of the economic institutions in a state. The
empirical results reported below are consistent with this hypothesis.
We now turn to a description of the data.
3 Data
The sample consists of forty cities in fourteen German states. A list of the cities and states is given
in Table 1, and Figure 1 shows the sample cities on a map. There are small cities, such as Parchim
in the Northern state of Mecklenburg, as well as some of the biggest cities in Europe at the time,
in particular Berlin. In terms of size, Berlin was followed by Hamburg, Munich, and Dresden. The
number of cities per state varies, as noted in Table 1.
The sample period is from 1820 to 1880. In order to reduce issues of serial correlation the
analysis will rely on data every �ve years, that is, for the year 1820, year 1825, and so on. This
gives a maximum of thirteen data points for each of the cities, however the sample is unbalanced and
with generally fewer observations in the last two decades. The reason for the unbalanced sample is
primarily missing information on the price of wheat.8 Our analysis will emphasize results from the
sample of cities that have eight or more observations during the sample period (28 cities, listed in
Table 1B). We will also address sampling issues by providing results weighted by city size and state
size. Table 1 gives the average population growth for each city both for the entire sample period
1820 to 1880 and for years for which each particular city has all data. The correlation between these
two series is high, at 0.75.
In central Europe at the time the price of wheat tended to be relatively low towards the south and
east in our sample area. Table 1 gives summary statistics on how the price of wheat di¤ered between
8Population �gures are typically benchmarked with the years 1800 and 1850 and estimated using district populationdata; see the appendix.
15
cities. On average, the price gaps for the Mecklenburg city of Rostock were 15%, for example, placing
it roughly at the mean of the sample. Further, we know that intrastate trade arbitrage was more
e¤ective than arbitrage across di¤erent states at this time, even if formal customs borders had been
eliminated. Therefore we exclude from the sample observations where both cities belong to the same
state.9
We hypothesize that institutional change in the German states is related to the length of French
occupation during revolutionary and Napoleonic times. Table 1 lists the number of years of French
occupation that each city in the sample experienced. About a quarter of the sample experienced
French occupation, and it was ranging from a minimum of 3 to a maximum of 19 years. French
occupation is not a state e¤ect: even though both Cologne and Berlin, for example, were Prussian
cities, the former was French occupied while the latter was not. Table 1 lists the dates at which
equality before the law and the abolition of guilds was e¤ected in each city. Note that in some cities
these changes had been implemented under French in�uence between 1808 and 1816, only to be
reversed afterwards. The computation of the institutions indicator below takes these reversals into
account.
Finally, we employ a railroad construction manual together with GIS methods to estimate the
costs of a railway connection between any two cities in the sample. As noted above, a key determinant
was the grade of the terrain, and our approach has some similarity with those in Du�o and Pande
(2007) and Nunn and Puga (2012). We employ information on how the performance of a locomotive
to haul freight at the time deteriorated as the grade of the terrain increased from Nicolls (1878), see
more details in the appendix. Based on this we have constructed a cost function, fed it into a gradient
map of the area with 90 x 90 meter cells, and applied the ArcGIS least-cost module to compute the
9This issue is discussed as the �border e¤ect�; see Shiue (2005) for evidence for 19th century Germany.
16
least-cost paths as well as the cost of operating along those paths between each city pair. Our railway
cost variable is this cost of railway operation per unit of direct distance, as the crow �ies. While for
the most part the sample does not include highly mountainous areas, there is substantial variation
in our bilateral railway cost estimates. For example, the maximum railway costs for connections of
Munich is more than 20 times the minimum railway cost (last column), and on average across forty
cities this max/min ratio is about �ve.
We conclude the data overview by presenting some initial evidence. In Figure 4, we show the
price gaps for two types of city pairs, those that established early and those that established late
train connections between each other, using the sample median as the cuto¤. The �gure shows that
both sets of city pairs started out in the 1820s with price gaps of around 18%, and slightly lower in
the city pairs that would end up building train connections relatively early. Price gaps came down
for both sets of city pairs in the 1830s, before in the 1840s and 1850s price gaps fell faster for the city
pairs that established relatively early train connections. This di¤erence in timing is what one would
expect if trains helped to bring down price gaps. By the end of the sample period, the di¤erence in
price gaps between the two sets of city-pairs had evaporated, which is related to the fact that in the
years 1875 and 1880 there were train connections between all city-pairs in the sample.
In Figure 5 we show analogously city population developments for the group of city-pairs that
implemented institutional change early, versus late. As the measure of institutional change we focus
here on the abolition of guilds.
According to the �gure, cities that abolished guilds relatively early on had a lead in the 1820
of about 50% over cities that kept guilds longer (about 41,000 to 27,000). By the 1840, this had
grown to a lead of 70%, and after 1860 the population advantage of early abolishers of guilds was
more than 100%. These �gures are consistent with a positive impact of institutional change on city
17
18
population.10 At the same time, this interpretation of Figures 4 and 5 takes the building of train
connections and the timing of institutional change as exogenously given, an assumption that will be
evaluated in the following section. We now move to the empirical results.
4 Empirical Results
We begin by examining the role of institutional change and steam train adoption for improving trade
before asking to what extent institutional change had an impact on city growth through trade versus
non-trade channels. Because endogeneity is a concern for the �rst and a virtual certainty for the
second question, our approach in either case will be to employ instrumental variable (IV) estimation.
In doing so particular attention is given to reduced form results, both because they are informative on
the validity of the exclusion restrictions and because they can shed light on alternative mechanisms
that may be present. Moreover, because trade is observed at the city-pair level while growth occurs
at the level of each city, the analysis involves mapping city-pair to city characteristics, which will be
done along the lines of the two-sample IV estimation approach of Angrist and Krueger (1992).
4.1 Do Steam Trains and Institutions A¤ect Trade?
We are interested in the impact of institutional change and the introduction of steam trains on
the trade between two cities, j and k: The paper employs a price-based measure, de�ned as the
absolute value of the percentage di¤erence in the price of wheat between two cities. This will be
referred to as the price gap for short. The hypothesis is that the introduction of steam trains might
have a¤ected trade through improving spatial arbitrage, leading to lower price gaps between cities.
Better institutions might have a¤ected trade through better contract enforcement or by reducing
10 In both Figures 4 and 5 we focus on city pairs with �ve or more observations in the sample, to keep the samplerelatively balanced.
19
entry barriers. They might also have a¤ected trade indirectly: better institutions may generate a
business-minded lobby that has a stake in a more e¢ cient economy.11
The goal is to estimate the causal e¤ect of institutions and steam trains on trade in the following
equation
Tradejkt = �jk + �t + �1Instjkt + �2Trainjkt + "jkt; (1)
where the dependent variable Tradejkt is de�ned as the absolute value of the percentage gap between
the price of wheat in cities j and k in year t; T rainjkt is equal to one for years during which there
was a direct train connection between cities j and k in year t; and zero otherwise. The institutions
variable Instjkt captures the average quality of the cities� institutions in year t, computed from a
number of dichotomous variables. In our baseline de�nition, Instjkt is based on whether in each city
in a particular year (1) trade guilds were abolished and (2) equality before the law prevailed. If so,
we take this as signs of good in the sense of e¢ ciency-oriented institutions. Using an indicator for
each of the two aspects of institutions and for both cities in a given pair means that Instjkt ranges
between 0 (guilds present and no equality before the law in both cities) and 4 (guilds were abolished
and equality before the law was established in both cities), which we scale so that it ranges between
0 and 1.12
As instruments for institutional change we employ the length of French occupation. As discussed
above, the extent to which instutional change triggered by French revolutionary and Napoleonic
armies stuck was generally increasing in the length of French occupation. The instrumental variable
for train connections is the above mentioned GIS-based railway cost measure per unit of bilateral
11Note that the price gap is not necessarily equal to the transactions costs between two cities; rather, it is the upperbound of the transactions costs. Both cities might import wheat from a third source, for example, such as the UnitedStates; we will consider such possibilities in section 4.1.12Out of ACJR�s four institutions indicators, abolition of guilds and equality before the law are central for us; adding
the other two does not change the main results however.
20
distance, capturing di¤erences in the terrain across the city-pairs (see Appendix for more details).
Before pursuing this IV strategy, the following takes a close look at the reduced form relationship
between French occupation and railway costs on one, and price gaps on the other hand.
4.1.1 Reduced form relationships
For the IV strategy to work a longer French occupation in the reduced form should be associated
with lower price gaps, because it leads to institutional improvements. Also, higher costs of railway
building should be associated with higher price gaps because it tends to delay new construction of
lines. To examine this systematically, consider the following reduced form regression
Tradejkt = jk+ t+3Xs=1
s1Ist log (fr_occjk)+3Xs=1
s2Ist(rail_costjk)+3Xs=1
�s3Istdistjk+ujkt; (2)
where fr_occjk is the length of French occupation in years plus one, rail_costjk is the log of the
per-unit distance cost of building a railway between cities j and k, distjk is the bilateral distance
between j and k; and Ist is an indicator variable for each of three twenty-year windows in the sample
period (1820-35, 1840-60, and 1860-80).13 The speci�cation exploits only variation within city-pairs
over time by including city-pair �xed e¤ects ( jk), and there are year �xed e¤ects that control for
common shocks ( t).
Note that the coe¢ cients on the time-invariant variables fr_occ and rail_cost can be estimated
in conjunction with city-pair �xed e¤ects because we allow for time-varying e¤ects by including the
three twenty-year windows. This is particularly relevant for the impact of railway costs, which one
would expect to be present only once steam locomotives had made their way to Germany, which is
after the year 1835. The �rst twenty-year window can thus serve as a placebo period. Speci�cally, we
13 In the regression the period 1860-80 is the excluded category.
21
look for a higher railway cost coe¢ cient for 1840-60 than estimated for the 1820-35 period. Finally,
we include bilateral distance in all speci�cations because any type of trade cost that is rising in
distance will tend to imply higher price gaps for relatively distant city pairs.
The results from estimating (2) with OLS are shown in Table 2. For the baseline sample with
28 cities, the coe¢ cients on French occupation are signi�cantly negative in column (1), indicating
that a long period of French occupation is associated with a lower price gap. This is what one would
expect if French occupation leads to institutional improvements that bene�t trade. The estimates
for the cost of railway variable are essentially zero for the period of 1820 to 1835, and positive for the
period of 1840-60. This is consistent with our argument: the costs of railway building do not matter
during a time when steam locomotives are not yet widely available, while when they are, a high cost
of railway building is associated with relatatively high price gaps.
Given that the rail_cost variable is derived from data on the di¢ culty of the terrain between
two cities, one might be concerned that it is correlated with other determinants of the price gaps.
For example, a mountain range between cities j and k does not only make it di¢ cult to establish a
railroad connection, but even before the invention of trains traveling from j to k; by foot or horse,
has always been relatively di¢ cult. However, the walking or riding transportation technologies did
not substantially change during the sample period, and consequently these e¤ects are captured by
the city-pair �xed e¤ects.
The remaining speci�cations in Table 2 explore the robustness of these reduced form results.
Because the length of French occupation takes on only a limited number of di¤erent values (see
Table 1), we also employ a simpler approach that asks whether the cities had French occupation of
any length; for a given city-pair, this variable ranges from 0 (neither city j nor city k was occupied)
over 0.5 (one city occupied) to 1 (both cities occupied). As seen from column (2), the results with
22
this alternative approach are qualitatively similar.
Sampling issues are the focus in the following two speci�cations. The �rst employs weighted least
squares using the (log of the) average city populations over the sample period as weights, while the
second employs similarly the (log of the) average of the states�populations in which cities j and k
are located as weights. By giving greater weight to more populous areas, these speci�cations shed
light on the representativeness of the results for Germany as a whole. The results of columns (3) and
(4) are similar to the baseline in column (1), suggesting that the results do not unduly hinge on the
particular set of sample cities, some of which are relatively small.
Table 1 also shows results from a regression that downweighs the in�uence of outliers, which
leads to generally similar results (column 5).14 Lastly, we present results for the full sample of forty
cities in column (6). While in the case of railway costs the point estimates di¤er, the change in the
coe¢ cients from the period of 1820-35 to 1840-60 is positive and around 0.04, quite similar to the
baseline speci�cation of column (1). We conclude that these reduced form results are robust.
The trade exclusion restrictions The suitability of French occupation and railway costs as
instrumental variables depends on the assumption that neither has an e¤ect on price gaps through
channels other than institutions and steam railways, and that conditional on the controls, French
occupation and railway costs are not strongly correlated with other determinants of price gaps. As
usual the exclusion restrictions cannot directly be tested, however by augmenting the reduced form
with other factors we can obtain information on the plausibility of the exclusion restrictions holding.
To this end we augment the reduced form with additional factors and ask whether this a¤ects the
coe¢ cients on fr_occ and rail_cost in a major way. Consider the following modi�cation of equation
14This employs STATA�s basic robust regression routine called rreg.
23
(2):
Tradejkt = �+3Xs=1
s1Ist log (fr_occjk) +3Xs=1
s2Ist(rail_costjk) +3Xs=1
sxIstXjk + ujkt; (3)
where Xjk is another potential determinant of price gaps, and � = jk + t+P3s=1 �s3Istdistjk: The
results are shown in Table 3.
As the �rst variable we consider the importance of being placed on the national railroad plan
proposed by economist Friedrich List in the year 1833 (recall Figure 3a). The plan was never im-
plemented due to the state- as opposed to nation-minded interests of the leaders of German states.
However, it arguably captures what a well-intentioned social planner would have done in order to
maximize the national bene�t of German railways, and as such it might be correlated with the extent
to which price gaps came down across regions. From column (2), however, there is little evidence that
the List railroad plan variables should be included in the regression.15 The reduced form coe¢ cients
on French occupation and cost of railway building are largely unchanged.16
What about other determinants of trade? We �rst explore the in�uence of geographic factors. It
could be that cities in the North-South or in the East-West dimension are a¤ected di¤erently by 19th
century growth trends in a way that interferes with the proposed IV approach. In columns 3 and 4 we
include the average latitude and longitude of each city-pair, respectively. geographic factors. While
the coe¢ cient estimates on French occupation and railway cost in the case of latitude are quite similar
to the baseline of column 1, the inclusion of longitude increases the price-gap lowering e¤ect of French
occupation (column 4). Longitude itself enters with negative point estimates which are insigni�cant
at standard levels. Taken together, while the East-West dimension matters to some extent it does
15Given the city-pair structure of the analysis, we de�ne the List railroad plan variable as the average of whethercity j and city k were placed on the List plan.16Similar results are obtained for another national railway plan that was never implemented, by Hans Grote in 1835.
24
not threaten identi�cation of the institutions e¤ect with the French occupation variable. The railway
cost coe¢ cient for the 1820-35 period is largely unchanged by the inclusion of geography variables.
Another concern is whether the distance to France�s capital, Paris, mattered for the length of
French occupation. One might expect that proximity favors a long period of occupation, and if this
relationship would be strictly proportional, the e¤ect of French occupation on Trade could not be
separated from the impact of proximity to Paris. The inclusion of Distance to Paris leads generally
to a strengthening of French occupation and railway cost e¤ects on Trade (column 5). In particular,
conditional on the Distance to Paris, a given length of French occupation is associated with lower
price gaps than not holding constant the distance to Paris.
Next we turn to cultural factors in the speci�c form of Protestantism (Weber 1930 and others).
The correlation of Protestantism with Trade is fairly weak according to Table 3, column 6, and
the reduced form coe¢ cients for French occupation and railway cost do not change much. We also
examine whether di¤erential improvements in price gaps over the 19th century might be related
to di¤erences in the rates of human capital accumulation. As a proxy available to us at the city
level, we employ information on the year in which the �rst gymnasium (a place for tertiary schooling
education) was established. The results in Table 3 suggest that di¤erences in schooling across cities
do not threaten the proposed IV strategy (column 7).
In addition, we have explored the in�uence of improved international trade opportunities during
the 19th century, where England (as a source of equipment goods) and the United States (as a source
of wheat) could be important. These improvements in international trade should have been most
strongly felt in coastal regions, and we introduce an indicator variable for location near the coast
to assess this issue. Coastal location turns out not to be a signi�cant predictor of Trade, and the
reduced form coe¢ cients for French occupation and railway cost change little (column 8).
25
Because Germany witnessed a range of infrastructure improvements during the 19th century, our
results might be picking non-railroad transportation e¤ects. In particular, technological change in
river transport might explain part of the di¤erences in the speed of how price gaps came down. In
column 9 we employ the per-capita riverbank length of each state as a proxy for the importance
of shipping on rivers across Germany. River shipping is associated with relatively high price gaps,
especially in the 1840-60 period, and the inclusion of the river shipping variable lowers the 1840-60
railway cost coe¢ cient somewhat. This may capture the substitution between di¤erent transport
modes and speci�cally the possibility that regions in which river shipping was important bene�ted
relatively less from the invention of steam trains. Given the relatively small change in the railway
cost coe¢ cient, however, it is unlikely to pose a threat to identi�cation.
Overall, we �nd upon inclusion of a range of factors that the reduced form results do not drastically
change. In the following the second stage is estimated.
4.1.2 The e¤ects of trains and institutions on trade
Here we use French occupation and railway cost as instruments in an IV estimation. The structural
equation is given by
Tradejkt = �jk + �t + �1Instjkt + �2Trainjkt +3Xs=1
�s3Ist log (Distjk) + ejkt; (4)
where institutions (Inst) and steam train connection (Train) are instrumented by French occu-
pation and railway cost variables. The main results of this estimation are given in Table 4, while the
appendix Table A4.1 shows the full set of coe¢ cients of the �rst-stage regressions.
According to the IV estimation results presented in column 1 of Table 4, the coe¢ cients on both
institutions and steam trains are negative, consistent with an improvement in trade (i.e., reducing
26
price gaps). The coe¢ cients are around �0:34 for institutions and �0:10 for steam trains.17 Given
that both variables are normalized to vary between 0 and 1, this means that while both trains and
institutions improve trade, the latter e¤ect is considerably larger. The �rst stage for institutions
has a F statistic of around 6 while the trains �rst-stage F is about 29; these are highly signi�cant
at standard levels of signi�cance with robust standard errors.18 We employ errors clustered at the
city-pair level, which allows for arbitrary correlation over time between observations for the same
bilateral relationship.
Looking at the �rst-stage results in Table A4.1, we note that the length of French occupation is
positively correlated with institutional improvement while the cost of railway construction does not
matter for institutional change. Moreover, during the period 1840-60 high railway construction cost
lowers the probability that a train connection is established. Note that the same is not true during
the placebo period of 1820-35. Also interesting are the results for French occupation in the trains
�rst stage. For the period 1840-60, a longer French occupation tends to lower the probability of
there being a train connection between any two cities. There is thus little evidence that institutional
reform has a¤ected trade through speeding up railway building. Overall, the �rst stage results are
what one would expect from a successful IV strategy.
Going back to Table 4, the OLS coe¢ cients for institutions and trains are both �0:043; and
signi�cant only for trains. The smaller impact estimated with OLS suggests that OLS is upward
biased in this case through attenuation bias. From the fact that states primarily focused on their
own respective territories it could also be that cities that received train connections relatively early
17Throughout the paper we employ Hansen et al.�s (1996) version of limited information maximum likelihood esti-mation (LIML), which has the advantage that the estimation chooses endogenously the optimal variance-covarianceweighting matrix. It is well-known that LIML estimation tends to have smaller bias than two-stage least squares (TSLS),especially when the instruments may be weak (Davidson and MacKinnon 1993). In our case TSLS and standard LIMLresults are similar to what is shown in the tables.18To avoid weak instrument issues, Staiger and Stock (1997) recommend a threshold of 10 for this F-statistic. For an
update on this question, see Angrist and Pischke (2009). In our case, also the relatively large di¤erence between OLSand IV estimates indicates that the instruments are not weak; see the discussion below.
27
tended to be those for which price gaps fell relatively little (heterogeneous treatment e¤ect). This is
reinforced by the fact that the IV estimates of �0:34 and �0:10 are large compared to the average
reduction in bilateral price gaps shown in Figure 4. The large di¤erence between OLS and IV
indicates that the instruments are strong, because weak instruments would push the IV towards the
OLS estimates (Bound, Jaeger, and Baker 1995). The F-statistic for the overall strength of the �rst
stages due to Kleibergen and Paap (2006) is equal to 21.8. This is much larger than the frequently
used critical values of Stock and Yogo (2005).19 All in all, these results suggest that the equation is
identi�ed.
Table 4 also shows results from a number of alternative speci�cations. We �rst employ the simpler
measure that ignores the particular length of French occupation and focuses on whether there was
any occupation. The results are broadly similar, although the size of the institutions coe¢ cient falls
somewhat (column 2). The following two speci�cations consider weighting by city size (column 3)
and state size (column 4), and in neither case are the results very di¤erent from the baseline in
column 1.
In the last column of Table 4 results for the full sample of 40 cities are shown. While the pattern
of estimates is the same, both the institutions and the trains impact are larger in absolute value
than for the baseline sample. This is consistent with heterogeneous treatment e¤ects, where some of
the newly included city-pairs experience relatively large e¤ects. Changing sample composition might
play a role as well, which is the main reason why we emphasize results from the baseline sample.
Summarizing the �ndings thus far, both institutional change and the introduction of steam trains
had a positive impact on market integration in 19th century Germany.20 Quantatatively, our esti-
19For a 10% tolerable bias, the smallest bias Stock and Yogo (2005) consider, they report a LIML critical value withtwo endogenous variables of 4.72. Note that Stock and Yogo�s results are for i.i.d. errors; critical values for non-i.i.d.errors are not available to the best of our knowledge.20These �ndings do not change if we introduce alternative channels directly as independent variables in the IV
28
mates indicate that the institutions e¤ect was larger than that of trains, and the way institutions
appear to a¤ect trade appears to be more by improving the general conditions for engaging in spatial
arbitrage than by speeding up the introduction of steam trains.21 In the following section, we exam-
ine the extent to which the growth impact of institutions operates through trade versus non-trade
channels.
4.2 The link between institutions, trade, and growth
We are interested in estimating the causal e¤ect of institutions and trade on the size of cities. The
goal is to link the growth e¤ect of fundamental factor institutions to the more proximate factor,
trade. To �x ideas, suppose that the relationship between city size, institutions, and trade is given
by Population = �1Inst + �2Trade + u; while the relationship between institutions and trade can
be written as Trade = �1Inst + Z0� + e; where Z are other determinants of Trade: Substituting
the latter equation into the former yields �1 as the direct e¤ect of institutions, while �2 � �1 is the
indirect e¤ect of institutions on growth. We are interested in the size of these direct and indirect
institutions e¤ects.
Key to the analysis is that while institutional change may be endogenous, trade almost certainly is
endogenous and a¤ected by institutional change as just shown in the previous section. For this reason
we adopt an IV strategy, building on the previous analysis with French occupation and railway costs
as instrumental variables. Moreover, because our observations on trade are at the level of city-pairs
while (population) growth is observed at the level of the city, we adopt the two-sample IV estimation
approach introduced by Angrist and Krueger (1992). In the following we �rst present reduced form
estimation. Among the noteworthy results are that controlling for longitude lowers the institutions e¤ect while raisingthat of trains, and controlling for river shipping leads to a less precisely estimated trains e¤ect; see Table A4.2 in theappendix.21 In ongoing research we �nd signs that privately operated steam trains reduced price gaps by much more than
state-operated railway lines; these �ndings will be included in the future.
29
results before moving to the IV estimation.
4.3 Reduced form relationships
In the reduced form the goal is to estimate the impact of French occupation and railway cost on city
population. Consider the following equation
log(popkt) = �+
3Xs=1
s1Ist log (fr_occk) +3Xs=1
s2Ist(rail_costk) + ukt: (5)
Here the dependent variable is the log of the population of city k in year t; fr_occk is the length of
French occupation of city k plus one, and rail_costk is the average of the bilateral railway building
costs of city k across all cities j for which we observe price gaps in the sample. Note that the
sample varies now in city and time dimension (K � T ), as opposed to city-pair and time dimension
(JK � T ): In the baseline sample this brings down the number of observations to 268 observations,
from 2,166 observations in the city-pair analysis. The average railway cost variable, rail_costk, is
the city-level analog of the bilateral railway costs rail_costjk, and the term � includes city- and
year �xed e¤ects, as well as the average bilateral distance between city k and its trade partners,
� = k + t +P3s=1 s3Ist(distk):
We are interested in the impact of French occupation and railway cost on city size. If the occu-
pation triggered institutional improvements that led to city growth, the French occupation variable
should enter equation (5) with positive coe¢ cients. Moreover, if high railway costs stood in the
way of trade because they delayed the use of steam trains, there should be a negative coe¢ cient on
railway costs for the later period of the 19th century (when steam locomotives were available). Table
5 shows results from estimating variants of the reduced form equation (5).
Turning to the estimation results, for the baseline sample the coe¢ cients on the length of French
30
occupation are positive and signi�cant at standard levels, whereas the estimate on railway costs
for the period 1840-60 is negative though not quite signi�cant (Table 5, column 1). Nevertheless,
there is a stark shift in the railway cost estimate from 0:11 in the placebo period 1820-35 to �0:13 for
1840-60, which is consistent with the hypothesis that cities with relatively high railway building costs
experienced slower population growth in 1840-60 in part because fewer steam trains were operated,
reducing trade.
The remaining results of Table 5 examine whether these reduced-form results hold up for the
same speci�cation changes that we considered above in the case of the price gap as the dependent
variable. It turns out that the results are similar to or stronger than in the baseline speci�cation.
Speci�cally, when we reduce the in�uence of outliers on the results (column 5) or employ size weights
(columns 3 and 4), the railway cost coe¢ cient for the 1840-60 period becomes signi�cantly negative,
while the institutions coe¢ cients tend to become larger and more precisely estimated. Overall, the
evidence from these reduced form results supports our IV strategy.
The population growth exclusion restrictions Railway cost is a valid instrument if it does
not a¤ect city size through a channel other than trade, and is not strongly correlated with other
determinants of city size. To examine how likely this is we estimate equation (5) augmented with
other potential determinants of population and see whether the estimated s2 substantially change.
The exclusion restriction for French occupation is that it a¤ects city size only through institutional
change, and it is not strongly correlated with other determinants of city size. In the previous section
we have seen that French occupation a¤ects trade, so if trade a¤ects city population clearly there is
room for French occupation to a¤ect population through another channel, namely trade. Does this
violate the IV strategy? No, because a better trade environment is in part caused by institutional
change, and this is exactly the indirect e¤ect of institutions that we are interested in. The key
31
question is whether French occupation is correlated with trade�or any other determinant of city
size�through non-institutions channels that are not controlled for in equation (5).22 To �nd out we
augment the population reduced form with other potential determinants, as we have done in the
analysis of the price gap reduced form in section 4.1 above.
The results from this analysis are shown in Table 6. Several �ndings stand out. First, high
railway costs in the railway period 1840-60 have always a dampening impact on city size, no matter
what other potential determinant of city size is added on the right hand side. In some cases, high
railway costs are associated with lower city population also for the 1820-35 placebo period, such as
with Protestantism (see column 5), however the size of the detrimental impact in the later period is
invariably larger than in the earlier period. This is in line with the proposed estimation strategy.
The results for French occupation are a bit more varied. In the baseline estimation without
additional potential city size determinants, the French occupation coe¢ cients are around 0:11 and
signi�cant. By including other factors such as geography, and religion, these coe¢ cients can range
from a low of around 0:065 (with Latitude) to a high of around 0:400 (with Distance from Paris), all
quite precisely estimated. The higher the city�s latitude, the larger was city size (column 2), which
is consistent with the idea that more northern German cities bene�ted to some extent from their
geographic location independently of institutional change. An important �nding is that controlling
for distance to Paris raises, not lowers the estimated impact of French occupation on city size. Cities
far away from Paris tended to see more rapid population growth than the average city (column
4). If the length of French occupation were merely proportional to the distance from Paris but did
not a¤ect city size, controlling for distance to Paris would eliminate the correlation between French
occupation and city size. We �nd this to be not the case. Overall, these reduced form results support
22Recall that railway connections tended to be built at a slower pace where French occupation was long (see TableA4.1); at the same time, equation (5) controls for this by including the railway cost variable.
32
our approach. In the following section, we turn to the IV estimation.
4.3.1 The impact of trade and institutions on population growth
In this section we estimate the growth impact of institutions through trade and non-trade channels.
Before getting to the results the necessary details on the two-sample IV (TSIV) approach will be
provided.23 In the present case, it amounts to constructing a city-level average price gap variable from
the predicted bilateral price gaps of the �rst stage, and employing this together with the institutions
variable in the city-level population regression.
Let the trade �rst stage regression be given by
Tradejkt = X0� +
3Xs=1
�s1Ist log (fr_occjk) +3Xs=1
�s2Ist(rail_costjk) + !jkt: (6)
where Tradejkt, fr_occjk; and rail_costjk are, as before, de�ned as the bilateral price gap in year
t; the average length of French occupation in years of cities j and k plus 1, and the bilateral railway
cost between j and k; respectively. The term X includes the bilateral distance terms between j and
k as well as city-pair and year �xed e¤ects. Using OLS we obtain the predicted value of equation
(6), \Tradejkt; and then form the average predicted price gap for each city k and year t :
\Tradekt =1
Nkt
NktXj 6=k
\Tradejkt , 8k; t: (7)
This variable \Tradekt is at the level of the city and thus suitable for the city-level analysis. The
estimation equation is given by
log(popkt) = �k + �t + �1Instkt + �2 \Tradekt + ~X0� + ejk; (8)
23See Angrist and Krueger (1992), as well as Angrist and Pischke (2009, Ch. 4.3), Inoue and Solon (2010).
33
where ~X includes city- and year �xed e¤ects, as well as the average bilateral distance of city k,
interacted with the time period indicators for 1820-35, 1840-60, and 1865-80.
The potentially endogenous institutions variable Instkt is instrumented by the length of French
occupation in city k; interacted with the time period indicators (P3s=1 �s1Ist log (fr_occk)). Equation
(8) is a standard IV estimation problem with one endogenous variable (Inst) and the trade variable
\Tradekt taken as exogenously given, except that statistical packages will typically not account for
the uncertainty associated with \Tradekt arising from the fact that the trade �rst stage is estimated.
Following Bjorklund and Jantti (1997) we obtain correct standard errors through bootstrapping over
the entire two-sample IV procedure. In line of the city-level estimation equation (8), the errors are
clustered at the level of the city, and the number of bootstrap replications is 200. Results are shown
in Table 7.
In the baseline sample, the coe¢ cient on trade is estimated at about �4:3 and signi�cant at
standard levels. Recall that because the trade variable is the predicted price gap, the interpretation
is that lower price gaps lead to larger city size. The institutions point estimate is positive at about
0:2 but not signi�cant. The �rst stage regressions are shown in Table A7.1 of the appendix. The
coe¢ cients come in as expected: the length of French occupation is positively related to the insti-
tutions variable, while the �rst stage for trade is identical to that in section 4.1 above. Moreover,
the �rst stage F-statistics, together with the di¤erence between IV and OLS results provide evidence
that the instruments are strong.
Are these �ndings for trade and institutions robust to other speci�cations? The remaining results
of Table 7 provide some answers to this question. Abstracting from information on the speci�c
length of the French occupation yields a signi�cant estimate of trade around �4 while the institutions
coe¢ cient is around 0:25 and closer to signi�cance (p-value of 0:16). Applying size weights at the
34
level of the city gives roughly similar coe¢ cients (column 3), while with state population weights the
trade coe¢ cient changes to about �5:3 while the institutions estimate is larger at around 0:4 though
still insigni�cant. When we move to the full sample of 40 cities the results change somewhat, with the
trade coe¢ cient falling to about �2:5 while the institutions estimate is around 0:36 and borderline
signi�cant (p-value of 0:13). Overall, these results suggest that the indirect e¤ect of institutions that
works through trade is quite important, whereas the non-trade e¤ects of institutions appear to be
smaller and possibly zero.
Quantitatively, the impact of trade, which is driven to a large extent by institutional change as
shown in the previous section, is quite large. To see this, take the coe¢ cient on trade of �4:3 of
Table 7, column 1, and observe that from Figure 4 the price gap drops by about 0:10 on average over
the sample period. The mean change in log population in the sample between 1820 and 1880 is 0:94.
Consequently, the indirect institutions e¤ect through trade accounts for (�4:3 � �0:1)=0:94; or for
about 46% of the city population growth during the sample period.24
Our �nding of an e¤ect of institutions through trade which dominates the direct institutions e¤ect
may not be that surprising to some readers. After all, trade is the more proximate source of growth
compared to institutions, and one might expect that generally the more proximate explanation of
growth will be the more powerful one in a statistical sense. The signi�cance of our �ndings lies rather
in establishing the link between fundamental and proximate factors, at the same time when their
e¤ects on growth is demonstrated in a uni�ed framework.
We now turn to some concluding remarks.
24Also here we have explored the results further by including measures of other potential determinants of city size(such as geography, capital accumulation) on the right hand side of the estimating equation (8), see Table A7.2 in theappendix. We note the following. First, the institutions coe¢ cient tends to remain small and never reaches standardlevels of signi�cance. Second, while the trade e¤ect becomes less precisely estimated, it remains signi�cant at standardlevels in the majority of cases. And third, the additional determinants are never signi�cant themselves and theirinclusion would be rejected by a speci�cation test. Overall, these results provide additional support for the �ndings inthe text.
35
5 Conclusions
In the setting of 19th century central Europe, we have examined the link between institutional change
as a fundamental driver of development and more proximate factors, speci�cally steam train adoption
and trade. Institutions have a strong e¤ect on city size, and institutions have also a large impact on
improving trade. Institutional improvements do not necessarily increase the speed at which steam
trains are adopted, but they make it more likely that trains are privately operated, and that leads
to relatively large improvements in trade. Finally, the paper has shown that once the institutions
e¤ect on trade is accounted for, institutional change has no independent impact on city size. Put
di¤erently, institutions a¤ect growth predominantly through trade.
Does our result that institutions exert their in�uence on growth primarily through trade carry
over to settings? This is not necessarily the case, for example there might be di¤erence between
domestic and international trade, and in any case the e¢ ciency enhancing e¤ects of trade might also
strengthen a feudal (and hence �bad�) government. What this papers provides is a framework for
thinking about and empirically verifying the way how deep growth factors eventually a¤ect income
and welfare.
While the �ndings are consistent and robust to a range of other factors, at this point we cannot
claim that they would carry over quite generally. Further, the analysis was limited to one fundamental
and two proximate factors. Some of the reduced form results suggest that going further might produce
additional insights, although to estimate causal e¤ects can be challenging enough already with one
variable, and in practice there are limits to this approach. At the same time, the paper has suggested
a way to include more structure and testable implications that we hope will prove useful in studies
of comparative development.
36
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A Details on Data Sources and Construction
Institutional Change The data on institutional change in Germany during the 18th and 19th
century comes from Acemoglu, Cantoni, Johnson, and Robinson (2011). Summary statistics for our
sample are shown in Table 1. Note that while for the most part the measures of institutional change�
abolition of guilds and equality before the law�vary at the state level, for some of the larger states
such as Prussia and Bavaria there is also variation at the level of the city.
Wheat Prices The two most important sources for information on wheat prices are Shiue and
Keller (2007) and Seu¤ert (1857). The former covers markets in Bavaria and Mecklenburg, while
the latter provides information on markets in Baden, Brunswick, Hesse-Darmstadt, Hesse-Cassel,
Hesse-Nassau, Saxony, and Wurttemberg. The wheat prices for Prussian markets were provided by
Michael Kopsidis, see Kopsidis (2002). Additional sources to include the coverage are Fremdling and
Hohorst (1979), Gerhard and Kaufhold (1990) for Prussia, and Vierteljahrshefte (1935) for Berlin,
Cologne, Hamburg, Leipzig, and Munich.
Since neither quantity nor monetary units were standardized in the German states during the
19th century, conversion rates are required for our analysis of absolute price di¤erences, and all prices
are converted into Bavarian Gulden per Bavarian Schae¤el. The conversion factors are taken from
the original sources (see Shiue and Keller 2007) as well as from Seu¤ert (1857). Speci�cally, from
the latter we have (page 351):
44
State Quantity unit
Conversion
factor
into Bav.
Schae¤el
Monetary unit
Conversion
factor
into Bav.
Gulden
Baden Malter 0.67 Gulden 1.00
Brunswick Himten 0.14 Thaler 1.75
Frankfurt Malter 0.51 Gulden 1.00
Hamburg Fass 0.24 Mark Banco 0.88
Hanover Himten 0.14 Thaler 1.75
Hesse-Darmstadt Malter 0.57 Gulden 1.00
Hesse-Cassel Schae¤el 0.36 Gulden 1.00
Hesse-Nassau Malter 0.49 Gulden 1.00
Prussia Schae¤el 0.24 Thaler 1.75
Saxony Schae¤el 0.46 Thaler 1.75
Wurttemberg Schae¤el 0.80 Gulden 1.00
City population Information on the city population size comes mainly from eKompendium-
hgisg.de (Kunz 2012). This is combined with data from Bairoch, Batou, and Chevre (1988) and
de Vries (1984), used to benchmark in the years 1800 and 1850, as well as the population histories
of individual cities and towns (accessed through www.wikipedia.com). The city size data for non-
benchmark years (1816, 1850) especially for the smaller towns in the sample are our own estimates.
They are based on the population developments at the Regierungsbezirk level (roughly, county level),
from Kunz (2012). For example, in the case of Prussian cities such as Madgeburg and Aachen, our
estimates are based on the o¢ cial population �gures for the Regierungsbezirke of Madgeburg and
45
Aachen that were collected during our sample period every three years (in 1822, 1825, etc.). We have
also compared our estimates with other sources whenever possible, and there are no di¤erences that
would lead to a major change in the estimation results.
Railway data
Connections The main source of information on the timing of railway connections, as described
in section 2.1, are the digital historical maps provided at IEG�s web site at the University of Mainz,
http://www.ieg-maps.uni-mainz.de/. At times a train connection existed but it was highly circuitous
and thus hardly the least-cost route. In our coding we assume that in that case the train connection
in fact did not exist. Our results are robust to alternative assumptions in this respect within a
reasonable range.
Railway cost Our measure is based on how the capacity of a 19th century steam locomotive
to haul freight changes as a function of the gradient of the terrain, from Nicolls (1878). Speci�cally,
Nicolls gives the following data, p.82:
Gradient
(feet to the mile)
5 10 20 30 40 50 60 70 80 90
Hauling
capacity (tons)
1,150 939 686 536 437 367 315 275 242 216
Gradient
(feet to the mile)
100 110 120 130 140 150 160 170 180
Hauling
capacity (tons)
194 175 159 146 134 123 113 105 98
46
Five feet to the mile is a gradient of about 0.095%, and 180 feet to the mile is a gradient of about
3.4%. The data is for a "good" locomotive weighing 27 tons and for going at a speed of 8 to 12
miles per hour. This information is for going uphill. We do not know of comparable data for going
downhill, and it is assumed that the freight capacity of locomotives varied for downhill trips (due to
strains on the brakes, etc.) in the same way as it did for uphill trips.
To convert this into a cost measure, we assume that on �at terrain the locomotive can haul 1,200
tons at the same speed of 8 to 12 miles per hour. Then, the cost in terms of foregone freight hauling
capacity of a gradient of �ve feet to the mile is 50 tons (1; 200 � 1; 150), the cost of a gradient of
tent feet to the mile is 261 tons, and so on. We �t a logarithmic function through this data to be
able to work with any terrain gradient (R-squared of 0.98). With this cost function in hand we use
a 90 meter x 90 meter GIS map of the relevant area in central Europe and the ArcGIS least-cost
distance module to compute the least-cost routes, as well as the associated costs of those routes, from
each city to all other cities in the sample. Bodies of water (lakes) are blocked out, but not rivers.
Because these railway costs are positively related to the bilateral distance between cities j and k; we
divide by the bilateral geographic distance (using the Haversine formula) to arrive at rail_costjk;
the average gradient cost of terrain between j and k in terms of foregone railway freight capacity.
Summary statistics for this data is given in Table 1.
De�nitions and Sources of Other Data [To come]
47
Table 1 French Abolition EqualityCity State Data Range Population Population Population Wheat Price Gap Occupation of Guilds before law Railway Cost Railway Cost
(Mean '000) (Av growth 1820‐80) (Av growth sample (Mean abs perc diff) (Years) (Year, last time) (Year, last time) (Mean) (Max/Min)Aachen Prussia 1820‐1860 47.8 0.809 0.860 0.178 19 1795 1804 790329 4.85Augsburg Bavaria 1820‐1855 33.5 0.341 0.278 0.161 0 1868 1900 879575 1.90Karlsruhe Baden 1820‐1840 19.7 1.823 1.886 0.261 0 1862 1810 931409 2.79Bamberg Bavaria 1820‐1855 17.6 0.287 0.140 0.154 0 1868 1900 918738 1.45Bayreuth Bavaria 1820‐1855 13.2 0.501 0.507 0.154 0 1868 1900 925457 1.59Berlin Prussia 1820‐1860 369.1 2.853 2.193 0.119 0 1810 1900 749848 2.87Boizenburg Mecklenburg 1820‐1870 3.2 0.636 0.683 0.147 0 1869 1900 739046 8.15Braunschweig Brunswick 1820‐1850 36.1 1.473 0.851 0.128 6 1864 1900 859684 6.79Bremen Free City 1840‐1845 48.3 1.946 1.250 0.179 0 1861 1900 752743 17.85Dresden Saxony 1835‐1850 84.2 2.350 2.230 0.134 0 1862 1865 854026 2.25Erding Bavaria 1820‐1855 4.0 1.129 1.245 0.162 0 1868 1900 868282 1.66Frankfurt Free City 1840‐1845 49.0 2.214 0.318 0.106 6 1866 1900 946409 1.57Goettingen Hanover 1820‐1865 10.9 0.480 0.264 0.120 3 1809 1900 930056 5.31Grabow Mecklenburg 1820‐1870 3.4 0.634 0.701 0.133 0 1869 1900 745067 5.96Hamburg Free City 1820‐1880 203.4 1.841 1.841 0.141 0 1862 1900 703684 10.73Hannover Hanover 1820‐1850 22.7 3.279 1.776 0.128 3 1809 1900 876265 12.01Kassel Hesse‐Kassel 1825‐1845 33.1 0.594 0.970 0.175 6 1869 1900 973468 5.85Kempten Bavaria 1820‐1855 7.0 0.985 1.381 0.147 0 1868 1900 872375 2.37Cologne Prussia 1820‐1880 91.3 1.231 1.231 0.120 19 1795 1804 806150 5.11Landshut Bavaria 1820‐1855 9.4 0.593 0.600 0.185 0 1868 1900 876861 1.66Leipzig Saxony 1835‐1880 68.2 2.525 1.153 0.130 0 1862 1865 785432 2.59Lindau Bavaria 1820‐1855 6.0 1.200 1.750 0.148 0 1868 1900 881423 2.99Luebeck Free City 1840‐1845 41.7 0.884 0.456 0.144 0 1869 1900 763064 7.05Mainz Hesse‐Darmstadt 1840‐1845 41.6 0.801 0.847 0.224 6 1798 1900 916428 1.67Memmingen Bavaria 1820‐1855 6.8 0.223 0.076 0.151 0 1868 1900 865485 2.42Munich Bavaria 1820‐1880 103.2 2.398 2.398 0.124 0 1868 1900 461065 21.15Muenster Prussia 1820‐1860 21.1 0.425 0.472 0.146 6 1809 1900 1172194 8.36Noerdlingen Bavaria 1820‐1855 6.7 0.341 0.278 0.193 0 1868 1900 914334 1.79Nurnberg Bavaria 1820‐1855 50.1 1.518 1.061 0.169 0 1868 1900 915761 1.50Parchim Mecklenburg 1820‐1870 2.5 0.600 0.660 0.141 0 1869 1900 747159 5.39Regensburg Bavaria 1820‐1855 21.7 0.178 0.025 0.218 0 1868 1900 888453 1.82Rostock Mecklenburg 1820‐1870 20.9 0.634 0.701 0.150 0 1869 1900 759509 4.46Schwerin Mecklenburg 1820‐1870 18.1 0.634 0.701 0.160 0 1869 1900 745803 6.01Straubing Bavaria 1820‐1855 8.2 0.772 0.907 0.243 0 1868 1900 870977 1.92Stuttgart Wurttemberg 1850‐1855 49.7 2.732 0.908 0.095 0 1862 1900 976190 4.48Ulm Wurttemberg 1850‐1855 19.9 0.536 ‐0.329 0.088 0 1862 1900 926946 7.58Wismar Mecklenburg 1820‐1870 10.9 0.637 0.721 0.151 0 1869 1900 757602 5.53Wuerzburg Bavaria 1820‐1855 24.2 0.235 0.316 0.134 0 1868 1900 959884 1.47Zweibruecken Bavaria 1820‐1855 6.7 0.634 0.681 0.158 19 1795 1804 978455 2.84Zwickau Saxony 1835‐1850 11.6 1.802 1.546 0.213 0 1862 1865 898095 2.24
Mean 41.2 1.143 0.913 0.155 2.325 1854 1888 854593 4.90Standard Dev. 64.9 0.842 0.649 0.037 5.23 26.25 29.35 112524 4.34
Table 1B: Cities in the Baseline Sample
Number City State1 Aachen Prussia2 Augsburg Bavaria3 Bamberg Bavaria4 Bayreuth Bavaria5 Berlin Prussia6 Boizenburg Mecklenburg7 Erding Bavaria8 Goettingen Hannover9 Grabow Mecklenburg10 Hamburg Hamburg11 Cologne Prussia12 Kempten Bavaria13 Landshut Bavaria14 Leipzig Saxony15 Lindau Bavaria16 Muenster Prussia17 Memmingen Bavaria18 Munich Bavaria19 Noerdlingen Bavaria20 Nurnberg Bavaria21 Parchim Mecklenburg22 Regensburg Bavaria23 Rostock Mecklenburg24 Schwerin Mecklenburg25 Straubing Bavaria26 Wismar Mecklenburg27 Wuerzburg Bavaria28 Zweibruecken Bavaria
Table 2: The impact of railway costs and French occupation on trade ‐‐ reduced‐form results
Full SampleFrench Occup'n
Baseline Yes/No City pop'n weight State pop'n weight Robust RegressionVariables (1) (2) (3) (4) (5) (6)
[1820‐1835] x ‐0.026** ‐0.068* ‐0.026** ‐0.025** ‐0.020+ ‐0.026**French Occupation (0.000) (0.014) (0.000) (0.003) (0.084) (0.000)[1840‐1860] x ‐0.028** ‐0.075* ‐0.026** ‐0.027** ‐0.030* ‐0.022**French Occupation (0.000) (0.012) (0.000) (0.001) (0.011) (0.000)[1820‐1835] x 0.008 0.007 0.004 0.012 0.007 ‐0.028*Railway Cost (0.494) (0.556) (0.732) (0.404) (0.701) (0.018)[1840‐1860] x 0.044** 0.043* 0.032* 0.051* 0.041** 0.015Railway Cost (0.007) (0.011) (0.033) (0.021) (0.007) (0.268)[1820‐1835] x 0.046** 0.038** 0.041** 0.038* 0.033 0.029**Bilateral Distance (0.000) (0.000) (0.000) (0.047) (0.124) (0.002)[1840‐1860] x 0.067** 0.058** 0.066** 0.050** 0.072** 0.060**Bilateral Distance (0.000) (0.000) (0.000) (0.008) (0.001) (0.000)
Observations 2,166 2,166 2,166 2,166 2,166 3,570Number of Clusters 252 252 252 252 252 642Notes: Dep Variable: absolute value of percentage price gap for city‐pair; robust pval in parentheses, based on clustering at city‐pair level. French Occupation is log( mean years french occup in the city pair +1), except in column (2) where it is the mean of binary variables for whether the cities were ever occupied. Railway Cost as defined in the text. Bilateral Distance is the bilateral distance between the two cities using the Haversine formula.
Baseline Sample
** p<0.01, * p<0.05, + p<0.1
Table 3: The impact of French occupation and railway cost on price gaps -- alternative mechanisms
(1) (2) (3) (4) (5) (6) (7) (8) (9)Baseline List RR Plan Latitude Longitude Distance to Paris Protestant Schooling Coast River Shipping
[1820-1835] x -0.020+ -0.020+ -0.019 -0.047* -0.058* -0.020 -0.019 -0.020+ -0.016French Occupation (0.084) (0.084) (0.107) (0.044) (0.031) (0.131) (0.118) (0.096) (0.191)[1840-1860] x -0.029* -0.030* -0.028* -0.044+ -0.058* -0.036** -0.029* -0.030* -0.026*French Occupation (0.011) (0.011) (0.016) (0.053) (0.027) (0.010) (0.019) (0.010) (0.028)[1820-1835] x 0.007 0.007 0.008 0.010 0.011 0.008 0.007 0.007 0.009Railway Cost (0.701) (0.701) (0.631) (0.582) (0.547) (0.674) (0.687) (0.711) (0.635)[1840-1860] x 0.041** 0.042** 0.043** 0.046** 0.048** 0.045** 0.042** 0.039* 0.034*Railway Cost (0.007) (0.008) (0.006) (0.004) (0.002) (0.004) (0.008) (0.015) (0.030)[1820-1835] x 0.033 0.012 0.038+ 0.024 0.032 0.031 0.030 0.014Bilateral Distance (0.123) (0.696) (0.080) (0.295) (0.257) (0.167) (0.293) (0.534)[1840-1860] x 0.072** 0.052+ 0.077** 0.065** 0.064* 0.070** 0.063* 0.053*Bilateral Distance (0.001) (0.087) (0.000) (0.004) (0.023) (0.002) (0.024) (0.022)[1820-1835] x -0.002List RR Plan (0.931)[1840-1860] x -0.002List RR Plan (0.927)[1820-1835] x -0.015Latitude (0.321)[1840-1860] x -0.015Latitude (0.328)[1820-1835] x -0.024Longitude (0.185)[1840-1860] x -0.013Longitude (0.462)[1820-1835] x -0.001Distance to Paris (0.125)[1840-1860] x -0.001Distance to Paris (0.221)[1820-1835] x -0.003Protestant (0.967)[1840-1860] x -0.045Protestant (0.552)[1820-1835] x -0.008Schooling (0.742)[1840-1860] x -0.006Schooling (0.796)[1820-1835] x -0.003Coast (0.934)[1840-1860] x -0.020Coast (0.590)[1820-1835] x 2.310River Shipping (0.149)[1840-1860] x 3.728*River Shipping (0.018)
Observations 2,166 2,166 2,166 2,166 2,166 2,166 2,166 2,166 2,166R-squared 0.303 0.303 0.304 0.305 0.307 0.305 0.303 0.304 0.311Notes: Dependent var is absolute value of percentage price gap between j and k; results robust to outliers; pval in parentheses, French Occupation is log(mean years french occup in the city pair +1)Railway Cost as defined in the text. Bilateral distance is the bilateral distance between the two cities using the Haversine formula. List RR Plan is whether the city pair was on List's Train Map.Latitude and Longitude are the mean latitude and longitude of the city pair. Distance to Paris is the mean distance to Paris of the city pair. Protestant is the mean protestant share in the city pair. Schooling is whether both cities had a gymnasium by 1565. Coast is a dummy for both cities being within the first quartile of distance to the coast. River Shipping is the average of the stateslength of riverbank divided by the states 1816 population.** p<0.01, * p<0.05, + p<0.1
Table 4: The impact of institutions and trains on trade
Baseline French Occ Yes/No City Pop'n weighted State Pop'n weighted Full Sample(1) (2) (3) (4) (5)
Institutions ‐0.343** ‐0.266* ‐0.332** ‐0.411** ‐0.467**(0.002) (0.011) (0.002) (0.006) (0.003)
Trains ‐0.096* ‐0.101* ‐0.093* ‐0.084+ ‐0.184**(0.026) (0.019) (0.043) (0.073) (0.001)
[1820‐1835] x 0.039* 0.033* 0.036* 0.021 0.038+Bilateral Distance (0.020) (0.039) (0.034) (0.400) (0.081)[1840‐1860] x 0.051** 0.044** 0.051** 0.033 0.044*Bilateral Distance (0.001) (0.004) (0.002) (0.165) (0.046)First StagesInstitution F‐stat 5.65 5.03 3.97 3.60 5.26
(0.001) (.002) (0.009) (.014) (0.001)Train F‐stat 29.17 22.50 22.26 23.55 24.51
(.000) (.000) (.000) (.000) (.000)OLSInstitutions ‐0.043 ‐0.051 ‐0.070* ‐0.046
(0.211) (0.130) (0.029) (0.188)Trains ‐0.043** ‐0.037** ‐0.042** ‐0.041**
(0.000) (0.000) (0.000) (0.000)Observations 2,166 2166 2,166 2,166 3,570Number of Clusters 252 252 252 252 642Notes: Dep. Var is absolute value of percentage price gap; estimation method is Hansen et al.'s (1996) limited information maximum likelihoodF‐statistics are from the Angrist‐Pischke multivariate F test of excluded instruments. "City Pop'n weight" weights each city at its meanlog population over the sample timeframe. "State Pop'n weight" weights each observation as the square root of 1816 population of its state.
** p<0.01, * p<0.05, + p<0.1 based on robust standard errors clustered at the city‐pair level
Table 5: The impact of French occupation and railway costs on city population
Baseline French Occ Yes/No City pop'n weights State pop'n weights Robust Regression Full Sample(1) (2) (3) (4) (5) (6)
[1820-1835] x 0.073+ 0.105* 0.175** 0.112** 0.077+Years French Occupation (0.095) (0.028) (0.000) (0.000) (0.070)[1840-1860] x 0.069* 0.091** 0.156** 0.119** 0.071*Years French Occupation (0.020) (0.002) (0.000) (0.000) (0.019)[1820-1835] x 0.111** 0.108** 0.071 -0.047 0.0403 0.117**Railway Cost (0.009) (0.009) (0.314) (0.591) (0.181) (0.004)[1840-1860] x -0.131 -0.129 -0.209* -0.384** -0.114** -0.144+Railway Cost (0.117) (0.110) (0.039) (0.001) (0.000) (0.075)[1820-1835] x 0.153 0.230 0.218 0.020 -0.0628 0.240Bilateral Distance (0.620) (0.459) (0.549) (0.940) (0.109) (0.399)[1840-1860] x 0.309+ 0.358* 0.224 -0.066 0.112** 0.289Bilateral Distance (0.095) (0.045) (0.247) (0.683) (0.001) (0.127)[1820-1835] x 0.256+French Occupation Y/N (0.054)[1840-1860] x 0.223*French Occupation Y/N (0.011)
Observations 268 268 268 268 268 312Number of clusters 28 28 28 28 28 40Notes: Dependent variable is log city population; robust pval based on city-level clustering in parentheses (except (5)). "City Pop'n weight" weights each city at its mean log population over the sample timeframe. "State Pop'n weight" weights each observation as the 1816 population of its state. ** p<0.01, * p<0.05, + p<0.1
Table 6: The impact of French occupation and railway cost on city population -- alternative mechanisms
(1) (2) (3) (4) (5) (6) (7) (8)Baseline Latitude Longitude Distance to Paris Protestant Schooling Coast River Shipping
[1820-1835] x 0.04 -0.135** -0.075** -0.059+ -0.095** 0.032 -0.010 -0.122**Railway Cost (0.181) (0.000) (0.005) (0.069) (0.000) (0.315) (0.709) (0.000)[1840-1860] x -0.114** -0.269** -0.241** -0.247** -0.242** -0.125** -0.371** -0.265**Railway Cost (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)[1820-1835] x 0.112** 0.0588** 0.080** 0.386** 0.222** 0.112** 0.135** 0.113**French Occupation (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)[1840-1860] x 0.119** 0.0708** 0.102** 0.406** 0.235** 0.120** 0.145** 0.125**French Occupation (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)[1820-1835] x -0.063 0.328** 0.191** 0.335** 0.290** -0.063 0.031 0.215**Bilateral Distance (0.109) (0.000) (0.000) (0.000) (0.000) (0.127) (0.380) (0.000)[1840-1860] x 0.112** 0.474** 0.332** 0.458** 0.391** 0.110** 0.176** 0.361**Bilateral Distance (0.001) (0.000) (0.000) (0.000) (0.000) (0.003) (0.000) (0.000)[1820-1835] x 0.150**Latitude (0.000)[1840-1860] x 0.153**Latitude (0.000)[1820-1835] x -0.023**Longitude (0.001)[1840-1860] x -0.016*Longitude (0.024)[1820-1835] x 0.004**Distance to Paris (0.000)[1840-1860] x 0.004**Distance to Paris (0.000)[1820-1835] x 0.0103**Protestant (0.000)[1840-1860] x 0.0106**Protestant (0.000)[1820-1835] x 0.008Schooling (0.651)[1840-1860] x 0.016Schooling (0.380)[1820-1835] x 0.020Coast (0.166)[1840-1860] x -0.027**Coast (0.004)[1820-1835] x -0.317**River Shipping (0.000)[1840-1860] x -0.325**River Shipping (0.000)
Observations 268 268 268 268 268 268 268 268R-squared 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000Dependent variable is log city population; results are from robust regression; robust pval in parentheses. Railway Cost as defined in the text. French Occupation is log(years french occup +1).Bilateral distance is the bilateral distance between the two cities using the Haversine formula. Protestant is the mean protestant share within the state that city resides.Schooling is whether the city had a gymnasium by 1565. Coast is a dummy for if the city is in the first quartile of distance to the coast and the US price of wheat is low. River Shipping is the length of a states riverbank divided by the states 1816 population.** p<0.01, * p<0.05, + p<0.1
Table 7: The impact of trade and institutions on city population
Baseline French Occ'n Y/N City Pop'n weight State Pop'n weight Full Sample(1) (2) (3) (4) (5)
Trade ‐4.336 ‐4.068 ‐4.118 ‐5.250 ‐2.496 (.010) (0.021) (.041) (.088) (.051)
Institutions 0.194 0.247 0.293 0.386 0.363(.207) (.161) (.202) (.238) (.133)
First StagesTrade F‐Stat 5.99 8.93
(.000) (.000)Institutions F‐Stat 25.86 10.38 19.34 17.94 21.08
(.000) (.001) (.000) (.000) (.000)OLSTrains 0.072 0.074 0.084 0.010
(0.273) (0.308) (0.276) (0.881)Institutions ‐0.068 ‐0.123 ‐0.108 ‐0.062
(0.812) (0.670) (0.736) (0.830)Number of obs 268 268 268 268 312Number of clusters 28 28 28 28 40Notes: Results from two‐sample IV estimation as described in the text, using Hansen et al.'s (1996) limited information maximum likelihoodThe first stage F‐stat for Institutions is from a regression at the city level, taking the predicted price gaps as deterministic. The first stage F‐statfor Trade comes from the city‐pair level. The first stage for columns (1)‐(4) are identical for trade. Weighting occurs once aggregated to city level."City Pop'n weight" weights each city at its mean log population over the sample timeframe. "State Pop'n weight" weights each observation asthe square root of the 1816 population of its state. Bootstrapped p‐values from one‐sided t‐tests with clustering at the city level in parentheses (200 replications).
Table A4.1: First‐stage coefficients for the impact of institutions and trains on trade
Institutions Trade Institutions Trade Institutions Trade Institutions Trade Institutions TradeBaseline Baseline French Occ'n 0/1 French Occ'n 0/1 City Pop'n weight City Pop'n weight State Pop'n weight State Pop'n weight Full Sample Full Sample
(1) (1) (2) (2) (3) (3) (4) (4) (5) (5)[1820‐1835] x 0.075 ‐0.006 0.307 ‐0.088 0.072 0.003 0.058 0.006 0.075 ‐0.017French Occupation (.000) (.886) (.000) (.545) (.002) (.941) (.010) (.872) (.000) (.661)[1840‐1860] x 0.076 ‐0.074 0.301 ‐0.363 0.074 ‐0.068 0.059 ‐0.062 0.076 ‐0.071French Occupation (.000) (.070) (.000) (.012) (.002) (.074) (.010) (.111) (.000) (.076)[1820‐1835] x 0.001 0.017 ‐0.001 0.020 0.003 0.019 0.002 0.015 0.001 0.022Railway Cost (.890) (.124) (.897) (.052) (.789) (.126) (.813) (.131) (.893) (.017)[1840‐1860] x 0.004 ‐0.111 0.001 ‐0.092 0.005 ‐0.095 0.005 ‐0.106 0.003 ‐0.096Railway Cost (.605) (.000) (.869) (.002) (.585) (.004) (.574) (.003) (.617) (.000)[1820‐1835] x 0.021 ‐0.042 0.029 ‐0.024 0.019 ‐0.042 0.001 ‐0.078 0.025 ‐0.035Bilateral Distance (.392) (.575) (.207) (.750) (.479) (.573) (.987) (.360) (.300) (.617)[1840‐1860] x 0.025 ‐0.167 0.034 ‐0.169 0.024 ‐0.176 0.007 ‐0.212 0.027 ‐0.156Bilateral Distance (.297) (.007) (.143) (.006) (.387) (.003) (.865) (.002) (.258) (.012)
Observations 2166 2166 2166 2166 2166 2166 2166 2166 3570 3570R‐squared 0.214 0.062 0.217 0.065 0.195 0.068 0.154 0.059 0.212 0.043Notes: Robust P‐vals in parentheses; based on robust standard errors clustered at the city‐pair level. "City Pop'n weight" weights each city at its mean log population over the sample timeframe. "State Pop'n weight" weights each observation as the square root of the 1816 population of its state. "French Occ'n 0/1" indicates that the french occupation variable is now0 if neither cities ever were french occupied, 1 if both cities ever had french occupation and .5 if one of the cities was ever occupied.
Table A4.2: The impact of steam trains and institions on trade -- additional results
(1) (2) (3) (4) (5) (6) (7) (8) (9)Baseline List RR Plan Latitude Longitude Distance to Paris Protestant Schooling Coast River Shipping
Trains -0.096* -0.100+ -0.123* -0.211** -0.210** -0.081+ -0.091* -0.091* -0.079(0.026) (0.053) (0.025) (0.001) (0.005) (0.085) (0.037) (0.036) (0.109)
Institutions -0.343** -0.360** -0.353** -0.197+ -0.192+ -0.425+ -0.371** -0.318** -0.336**(0.002) (0.002) (0.003) (0.085) (0.078) (0.080) (0.002) (0.002) (0.002)
[1820-1835] x 0.039* 0.041* 0.042+ 0.031 0.040+ 0.015 0.043** 0.037* 0.038*Bilateral Distance (0.020) (0.012) (0.097) (0.144) (0.051) (0.684) (0.006) (0.020) (0.016)[1840-1860] x 0.051** 0.053** 0.056* 0.029 0.040* 0.027 0.057** 0.044** 0.054**Bilateral Distance (0.001) (0.001) (0.022) (0.151) (0.035) (0.452) (0.000) (0.005) (0.001)[1820-1835] x -0.032+City Pair on List Map (0.099)[1840-1860] x -0.020City Pair on List Map (0.340)[1820-1835] x 0.002Average Latitude (0.875)[1840-1860] x 0.006Average Latitude (0.638)[1820-1835] x 0.009Average Longitude (0.335)[1840-1860] x 0.022*Average Longitude (0.033)[1820-1835] x 0.000Average Distance to Paris (0.197)[1840-1860] x 0.000*Average Distance to Paris (0.026)[1820-1835] x -0.097Average Protestant Share (city) (0.503)[1840-1860] x -0.113Average Protestant Share (city) (0.433)[1820-1835] x 0.011Both Early First Gymnasium (0.675)[1840-1860] x 0.018Both Early First Gymnasium (0.483)y2_both_rnear_us_pricelow -0.016
(0.134)[1820-1835] x -0.016Both Riverbank/Border is high (0.256)[1840-1860] x -0.001Both Riverbank/Border is high (0.967)
Observations 2,166 2,166 2,166 2,166 2,166 2,166 2,166 2,166 2,166Number of clusters 252 252 252 252 252 252 252 252 252
Table A7.1: First‐stage coefficients for the impact of trade and institutions on city population
Institutions Trade Institutions Trade Institutions Trade Institutions Trade Institutions TradeBaseline Baseline French Occ'n Y/N French Occ'n Y/N City Pop'n weight City Pop'n weight State Pop'n weight State Pop'n weight Full Sample Full Sample
(1) (1) (2) (2) (3) (3) (4) (4) (5) (5)[1820‐1835] x 0.139 ‐0.026 0.388 0.135 0.010 0.138 ‐0.026French Occupation (.003) (.000) (.007) (.009) (.073) (.005) (.000)[1840‐1860] x 0.136 ‐0.028 0.374 0.134 0.106 0.134 ‐0.022French Occupation (.001) (.000) (.003) (.002) (.027) (.040) (.001)[1820‐1835] x ‐0.008 ‐0.028Railway Cost (.497) (.018)[1840‐1860] x 0.044 0.015Railway Cost (.008) (.270)[1820‐1835] x 0.253 0.046 0.314 0.216 0.000 0.259 0.029Bilateral Distance (.403) (.000) (.331) (.544) (.999) (0.360) (.002)[1840‐1860] x 0.264 0.067 0.310 0.240 0.034 0.262 0.060Bilateral Distance (0.373) (.000) (311) (.472) (.926) (0.340) (.000)Predicted Price Gap 1.026 1.287 1.269 2.270 0.668
(.216) (.109) (.246) (.011) (.276)
Observations 268 2166 268 268 268 312 3570R‐squared 0.247 0.076 0.243 0.221 0.200 0.245 0.064The first stage for Institutions is from a regression at the city level, taking the predicted price gap from the Trade first stage as deterministic. The first stage for Trade comes from the city‐pair level and is identical for columns (1)‐(4). Weighting occurs after aggregation to the city level. "City Pop'n weight" weights each city at its mean log population overthe sample timeframe. "State Pop'n weight" weights each observation as the square root of the 1816 population of its state. Robust p‐vals in parenthesis.
Table A7.2: The impact of trade and institutions on city population ‐‐ additional results
Baseline Latitude Longitude Distance to Paris Protestant Schooling Coast River Shipping(1) (2) (3) (4) (5) (6) (7) (8)
Trade ‐4.336 ‐3.361 ‐1.817 ‐2.361 ‐3.758 ‐2.440 ‐4.515 ‐2.828(.010) (.120) (.230) (.150) (.090) (.090) (.070) (.070)
Institutions 0.194 0.544 ‐0.339 0.343 1.651 0.330 0.831 0.375(.224) (.210) (.320) (.340) (.250) (.140) (.210) (.150)
[1820‐1835] x 0.020 0.190 0.399 0.212 0.153 0.152 ‐0.115 0.140Bilateral Distance (0.480) (.300) (.240) (.300) (.390) (.300) (.400) (.420)[1840‐1860] x 0.349 0.354 0.561 0.359 0.298 0.329 0.088 0.306Bilateral Distance (0.173) (.210) (.080) (.200) (.240) (.210) (.400) (.260)[1820‐1835] x 0.068Latitude (.240)[1840‐1860] x 0.068Latitude (.240)[1820‐1835] x ‐0.060Longitude (.320)[1840‐1860] x ‐0.056Longitude (.340)[1820‐1835] x ‐0.0004Distance to Paris (.460)[1840‐1860] x ‐0.0001Distance to Paris (.470)[1820‐1835] x 0.836Protestant Share (0.200)[1840‐1860] x 0.832Protestant Share (0.200)[1820‐1835] x 0.018Early First Gymnasium (.530)[1840‐1860] x 0.047Early First Gymnasium (.520)[1820‐1835] x 0.437Coastal (.160)[1840‐1860] x 0.374Coastal (0.170)[1820‐1835] x ‐4.865Riverbank/1816 State Pop (.260)[1840‐1860] x ‐5.133Riverbank/1816 State Pop (.270)Notes: Results from two‐sample IV estimation as described in the text, using Hansen et al.'s (1996) limited information maximum likelihood estimatorBootstrapped p‐values from one‐sided t‐tests with clustering at the city level in parentheses (100 replications); number of observations: 268; number of clusters: 28
Figure 5: The Zollverein in the year 1836
Source: IEG (2013)
Aachen.
..Hamburg
Bremen
.Berlin
.Munich
.Cologne
.Augsburg
.Karlsruhe
.Bamberg
. Bayreuth
.Boizenberg
.Braunschweig
.Dresden
.Erding
.
.Goettingen
.Grawbow
.Hannover
.Kassel
.Kempten
.Landshut
.Leipzig
. Lindau
.Luebeck
.Mainz
.Memmingen
.Muenster
.Noerdlingen
.Nurnberg
.Parchim
.Regensburg
. Rostock
.Schwerin
.Staubing.Stuttgart
.Ulm
.Wismar
.Wuerzburg
.Zweibruecken
.Zwickau
Figure 1: Location of the Sample Cities