On the Right Track? Estimating the Effects of
Transportation Infrastructure on Economic Integration
and Welfare∗
Sarah Walker
March 2015
Abstract
Does access to transportation infrastructure promote economic integration? If so, what
are the resulting effects on regional economic welfare? I answer these questions by exploiting a natural historical experiment in central Europe: Austro-Hungarian railroad
construction from 1830-1907. I address the problem of endogenous placement of rail
networks by implementing an instrumental variable to predict railroad construction:
the interaction of a region’s distance from London with the global price of iron in each
year. I find that railroad development promotes economic integration by reducing relative prices for a set of agricultural commodities. Moreover, increasing track density
increases real wages within regions. However, there is evidence of inter-regional disparities. For every 100 km of track built between two regions, relative real wages increase
by 20 percent. Economic integration raises income levels, but may have disproportionate effects on relative economic welfare.
∗
Comments and suggestions are welcome. Please do not cite or distribute without permission. I would
like to thank Jennifer Alix-Garcia, Laura Schechter, Jeffrey Williamson, and Volker Radeloff, as well as
participants at the 2012 AAEA Annual meeting and 2013 Midwest International Economic Development
Conference (MWIEDC), for helpful comments. I am grateful to Volker Radeloff and the NASA 200 Years
Carpathians Project for funding, as well as Jessica Clayton for translation assistance.
1
Introduction
Many policy-makers consider transportation infrastructure crucial for economic devel-
opment, yet little is known about the causal effects of such investments. Nonetheless,
countries−both developed and underdeveloped−have historically spent and continue to allocate billions of dollars to large transportation projects. For instance, the construction of the
Transcontinental Railroad in 19th century America is estimated to have cost $100 million
in 1860 (roughly $2.9 billion in current dollars). In the Habsburg Empire, approximately
300 million Kronen−$1.7billion today−of foreign investment had been directed to transportation bonds as of 1901 (Komlos, 1983). Today, transportation infrastructure remains a
high-priority policy-initiative in developing countries, with the World Bank committing $8.8
billion to general projects and $7.5 billion to railways, specifically, as of 2013.
As policy-makers consider the potential economic benefits of infrastructure investment,
the economic impacts are not always clear. This study contributes to a growing literature
seeking to identify the causal effects of transportation developments on economic outcomes
(Baum-Snow et al., 2015; Donaldson and Hornbeck, 2015; Donaldson, 2014; Keller and Shiue,
2014; Storeygard, 2013; Banerjee et al., 2004; Rothenberg, 2012; Keller and Shiue, 2008). My
analysis explores whether or not transport cost reductions increase economic integration and
regional economic welfare using an historical natural experiment from 19th century AustriaHungary (figure 1). In particular, I am concerned with causally identifying whether or not
railroad construction promoted economic integration in the Habsburg economy through decreased price dispersion and if this increased or decreased regional wage disparities over
time.
As figures 2-3 illustrate, Habsburg railroad construction increased steadily from 1830
to 1910, with a clear boom in construction around 1870. Over the same period, summary
statistics suggest that regional production specialization emerged within the economy (figure
4), while economic integration increased through decreasing price dispersion (figures 5-6).
Moreover, real wages increased throughout the century (figure 8). To what extent are the
2
observed trends driven by the development of transportation infrastructure throughout the
Empire? Are there clear winners and losers from such investments, or do all regions gain
equally?
To provide theoretical intuition for these observations, I adopt the Eaton and Kortum
(2002) model of a Ricardian model of trade with many regions and a continuum of goods
in the presence of transportation costs in order to generate predictions for regional specialization patterns, prices, and wages as these cost fall. The theoretical framework suggests
that as transport costs decline: 1) regions become specialized in the goods for which they
have comparative advantage, 2) prices converge between regions, and 3) real wages increase
in all regions; 4) relative real wages may converge or disperse, depending on technological
differences between regions. If one region has abosolute advantage in the production of all
goods, then relative real wages will diverge. I then empirically test these predictions using a
comprehensive and unique dataset that I have constructed from Habsburg statistical records.
In order to causally identify the effect of transport cost reductions on prices and wages, I
implement an instrumental variable to predict railroad construction in each year from 18461907: each province’s distance to London interacted with the world price of iron in each year.
Using both an extensive margin measure of binary connectivity between provinces, as well
as intensive measure of the minimum distance between provinces on the railroad network, I
examine the effect of railroad construction on relative prices and wages over roughly 60 years.
The results suggest that a binary connection between provinces reduces relative agricultural
prices by 17 to 56 percent on average, depending on the agricultural commodity. Moreover,
for every 100 km of track built between provinces, relative prices fall by 45 to 73 percent,
implying increased economic integration from infrastructure development. When I examine
the effect on wages, I find that increasing railroad track density within provinces increases
real wages by 11 percent. However, there is evidence of wage dispersion, as some regions
experience faster wage growth than others. That is, for every 100 km of track built between
provinces, relative real wages increase by 20 percent. A rising tide may lift all boats, but
3
some boats are rising faster than others.
This study contributes to the existing literature in a number ways. First, there is currently no clear consensus on the effects of transportation infrastructure on economic development. While most studies show that transportation development increases economic
integration through diminishing price dispersion in China (Baum-Snow et al., 2015), imperial India (Donaldson, 2014), and 19th century Germany (Keller and Shiue, 2014, 2008),
there is some disagreement on the welfare effects of such investments. Baum-Snow et al.
(2015); Donaldson and Hornbeck (2015); Storeygard (2013); Banerjee et al. (2004) all find
that transport cost reductions increase income levels in Chinese cities, 19th century US rural
counties, Sub-Saharan Africa, and China, respectively, while Faber (2014) finds conflicting
evidence in rural China. Famously, Fogel (1964) argued that railroad construction had limited effects on American economic growth in the 19th and early 20th centuries. Similarly,
Banerjee et al. (2004) find that proximity to railroad networks in China have a moderate
effect on per capita GDP levels and no effect on per capita GDP growth, and may even
increase inequality. This study lends more evidence to the discussion, focusing on an unexplored region−19th century Austria-Hungary−with particular focus real wage levels, as well
as relative welfare.
In addition, this study contributes to the wealth of literature seeking to empirically evaluate the gains from trade (see Costinot and Rodriguez-Clare (2014) for a review). While
some of the existing work on transportation infrastructure evaluates effects on income levels
and growth, few specifically examine inequality (Banerjee et al., 2004). In this paper, I
empirically identify the effects of transport cost reductions on relative wages and find that
while economic integration may lift income levels, it may have regionally disporprtionate
effects, contributing to overall inequality on a national level.
Lastly, while this work is historical in nature, it tells us something about the development patterns of Europe−patterns that are often espoused as the appropriate model for the
developing world today. In 1842, 74% of Austrian laborers were employed in the agricul-
4
tural sector, an agricultural labor share that is common in many developing countries today
(Good, 1984). Not to mention, it was only 70 years ago that Rosenstein-Rodan considered
the successor states of the Habsburg Empire (i.e., Eastern and Southeastern Europe) to be
“international depressed areas”. Understanding the economic trajectory of this region in its
early states of development allows researchers and policy makers to examine whether or not
the European models of development are relevant for today’s developing countries.
The paper is structured as follows. Section 2 provides the historical context for the
study, with a brief description of Habsburg economic development. In section 3 I sketch the
theoretical model that motivates the empirical strategy. Section 4 describes the data and
discusses the identification strategy used to measure causal effects, while section 5 presents
the main results. Section 6 discusses the larger implications of the findings and concludes.
2
Historical Context
The Habsburg Empire comprised much of what is Central and Eastern Europe today (fig-
ure 1), with two major regions divided between Austrian and Hungarian lands.1 The extent
to which these two regions were politically united varied throughout history. Nonetheless,
trade remained virtually free among all Habsburg lands throughout the latter half of the
19th century, with the establishment of an Austro-Hungarian customs union in 1850. Prior
to 1848, the Hungarian Kingdom was considered to be relatively autonomous, with representation of their own diet in Parliament. In addition, while trade was free within both Austria
and Hungary, separately, strict tariffs endured between the two regions for much of the 18th
and early 19th centuries. After defeat in the revolutions of 1848, however, all Hungarian
lands were fully incorporated under Austrian rule. Because the Austrian Constitution prohibited internal trade restrictions, the existing tariff wall between the two regions dissolved
1
The Austrian region included 14 provinces: Lower Austria, Upper Austria, Salzburg, Styria, Carinthia,
Carniola, Tyrol and Vorarlberg, Kustenland, Bohemia, Moravia, Silesia, Galicia, Bukovina, and Dalmatia.
The Hungarian Kingdom was comprised of 22 provinces, which were grouped into 7 statistical regions: Right
Bank Danube, Left Bank Danube, Right Bank Tisza, Left Bank Tisza, Danube Tisza Basin, Tisza Maros
Basin, and Transylvania.
5
(Eddie, 1977). Effectively, a customs union formed. 1850 marks the official creation of the
union, but the years just prior to 1850 and the following two decades (i.e., roughly, 1848 to
1873) are commonly cited as a period of political and economic liberalization throughout the
Empire (Eddie, 1977; Komlos, 1983; Good, 1984). The Great Compromise of 1867 granted
Hungary full political autonomy and established the Dual Monarchy, yet the customs union
remained in tact until the collapse of the Empire following World War I. As such, I consider
the Habsburg Empire to represent an open regional economy.2 .
The 19th century was one of rapid development throughout Europe and North America,
and Austria-Hungary was not excluded from this process. While Habsburg industrialization
was slightly delayed relative to its Western counterparts, commencing approximately fifty
years after the West, the latter half of the 19th century marked a significant turning point
for the imperial economy. Figures 2 and 3 display the evolution of the Habsburg railroad
network over time. While there are obvious regional heterogeneities, with construction beginning in the West and moving East, there is a clear boom in imperial railroad construction
around 1870. By 1872, 83 percent of provinces were connected by at least one railroad link.
Moreover, the minimum distance between regions connected on the railroad network consistently decreased over time.
Economic history is not able to quantify the exact role railroad construction played in
Habsburg economic development. However, numerous authors cite it as a potentially significant driver of growth through its linkages to other industries, namely iron (Eddie, 1977);
as a force of economic integration (Good, 1984); and as a solution to inefficient overland
routes between markets that previously lacked access to the Danube (Rosegger and Jensen,
1996). The goal of the present study is not to identify the railroad’s role in driving growth,
but rather to examine its position in shaping production patterns and determining economic
welfare through its effects on regional price and wage disparities.
2
While external tariffs did exist, the Empire enacted a number of trade liberalization measures in the
second half of the 19th century, most notably the tariff reductions of 1851, 1853, and 1865, the 1865 treaty
with England, and the “Supplementary Convention” of 1869 which limited Habsburg specific duties to agreed
maximum ad valorem levels (Eddie, 1977)
6
3
Theoretical Framework
The theoretical framework below provides intuition for understanding the mechanisms
through which the development of transportation infrastructure drives specialization and
affects economic welfare. The framework is based on Eaton and Kortum (2002), with some
slight changes to notation, and presents a Ricardian general equilibrium trade model with
many regions and goods in the presence of transportation costs. Examining the equilibrium
conditions under transport cost reductions delivers predictions that inform the identification
strategy in section 4.
3.1
Environment
Consider a perfectly competitive economy of N regions, where i = 1, ..., N . Each region produces j goods indexed on a continuum normalized to j ∈ [0, 1]. Labor, Li , is the
only factor used in production, which is mobile between sectors within a given region, but
immobile between regions. This assumption is appropriate for a setting like 19th century
Austria-Hungary, where ethnic and linguistic barriers between regions likely prohibited migration. Consequently, it holds that there is a common market wage within regions, but
between regions factor price differences may emerge.
Goods produced in i and shipped to n are subject to “iceberg” transportation costs,
dni ≥ 1, where dii = 1. That is, for each good shipped, a portion melts away in transit,
such that shipping one unit of good j to region n requires producing dni additional units
in i.3 Note that I follow Eaton and Kortum (2002) in assuming that cross-border arbitrage
opportunities obey the triangle inequality, such that for any three countries i, k, and n,
dni ≤ dnk , dki . In my empirical strategy I proxy for dni with railroad construction throughout the Habsburg Empire. As more railroad connections between provinces are built, dni
should decrease, resulting in clear implications for prices and wages that I will explore more
3
von Thünen provides an intuitive explanation for this concept in regard to the transport of grain: one
must feed the horse grain in order for it to pull the wagon to ship the grain. This concept was developed by
Samuelson (1954), and is a common assumption in models of these sorts.
7
thoroughly in the sections below.
3.2
Consumers
Each region contains a mass (normalized to one) of identical consumers with constant
elasticity of substitution (CES) preferences and Li units of labor. Consumers maximize
utility over consumption, subject to a budget constraint derived from their income wi Li ,
such that the consumer problem is defined as:
max U =
Z
1
Q(j)
(σ−1)
σ
dij
0
(1)
s.t.
w i Li =
σ
(σ−1)
Z
1
pi (j)Q(j)dj
0
where: Q(j) is consumption, σ is the elasticity of substitution between j goods, wi is the
wage, and pi (j) are good-specific prices. Maximizing utility results in the standard CES
1
hR
i (1−σ)
1
, which I use to solve for each region’s price index over all
price index, pi = 0 pi1−σ dj
prices in the economy.
3.3
Producers
Goods are produced using constant returns to scale technology and labor, such that zi (j)
represents the amount of good j that can be produced using one unit of labor. In the absence
of transportation costs, profit maximization results in the standard definition of prices as a
function of factor costs and technology: pi (j) = wi zi (j).
In the presence of transportation costs, however, recall that producers of exports in region
i must make an additional dni units of good j in order to ship it to region n. Consequently,
transportation costs drive a wedge such that the price of goods produced in i and shipped
8
to n is:
pni (j) =
wi
zi (j)
dni = pii (j)dni
(2)
Because of perfect competition, consumers in region n will search the economy for the lowest
price, such that the price consumers in n pay for good j will be the lowest amongst all
regions:
pn = min{pni (j); i = 1, ..., N }
3.4
(3)
Technology and the Price Index
I follow Eaton and Kortum (2002) in assuming that technology is the realization of a
random variable Zi (j) drawn from the Fréchet distribution4 , such that:
Fi (z) = P r [Zi ≤ z] = e−Ai z
−θ
(4)
where an exogenous Ai > 0 represents absolute advantage, and exogenous θ > 1 captures
comparative advantage. That is, Ai affects technology through the mean, while θ operates
through the variation around the mean. A higher Ai increases the probability of drawing a
higher efficiency technology, whereas a lower θ represents more heterogeneity across goods
in relative efficiencies. Note that Zi (j) is independent across j.
From equation (2) technology is a function of prices, Zi =
wi dni
,
Pni
such that prices can be
recovered from the Fréchet distribution, as well:
P r [Pni ≤ p] = 1 − Fi
wi dni
p
= Gni (p) = 1 − e[Ai (wi dni )
−θ ]pθ
(5)
Equation (5) represents the distribution of prices that consumers in n face for goods
produced in region i. Recall, however, that since consumers search for the lowest prices
4
The Fréchet distribution is derived from a class of Type II extreme value distributions. Eaton and
Kortum (2002) argue that this particular type of distribution is appropriate for modeling technologies that
are the results of inventions over time in which workers use the most efficient technology, such that output
per worker follows an extreme value distribution.
9
amongst all regions, the price distribution for goods consumers in n actually purchase is:
Gn (p) = 1 −
N
Y
[1 − Gni (p)]
i=1
= 1 − e(−
PN
i=1
Ai (wi dni )−θ )pθ
Taking the first moment of this distribution, and considering the CES price index, the price
index in region n is represented by:
E[pn (j)] = pn = γ
"
N
X
i=1
Ai (wi dni )−θ
#− θ1
(6)
1
) (1−σ) , for σ < 1 + θ and Γ is the Gamma distribution.5 As Eaton and
where γ = Γ( θ+1−σ
θ
Kortum (2002) show, equation 6 implies that the price of goods sent from i to n is exactly
the same for goods sent from any region to n. As a result, the probability that i ships to n
must be equal to n’s share of total expenditures on goods from region i:
Xni
= πni = P r[i ships to n] = γ −θ Ai (wi dni )−θ pθn
Xn
(7)
where Xni is n’s consumption of goods produced in i, Xn is n’s total consumption, and πni
represents probability.6
3.5
Solving the Model
Notice from equation 7 that trade flows are a function of the endogenous wage. To solve
the for the equilibrium wage, I assume that trade is balanced such that total income in region
5
Eaton
R ∞ and Kortum (2002) show that the moment generating function for the Gamma distribution is:
Γ(z) = 0 tz−1 e−1 dt.
6
Eaton and Kortum (2002) show that πni is derived by: πni = P r[Pni (j) ≤ min{Pnk (j); k 6= i}] =
R∞Q
A (w d )−θ
PN i i ni
. Rearranging terms in (6) in combination with this result
k6=i (1 − Gnk (p))dGni (p) =
0
A (w d )−θ
i=1
i
i ni
yields the expression in (7).
10
i is equal to the total sales of i goods to the rest of the world:
w i Li =
X
πni wn Ln
(8)
n6=i
Note that each region in the economy will have a trade balance equation like equation (8).
3.6
Predictions
The equilibrium conditions, above, provide predictions for my outcomes of interest: (i)
prices and (ii) real wages. I also explore predictions on specialization, since it is a natural
feature of Ricardian models. I derive these predictions, below.
3.6.1
Price Convergence
From (2) it can be shown that as transportation costs fall, prices between regions will converge.
Hypothesis 1. As railroad connections between any two regions increase, prices between
these regions will converge.
Proof.
pni =
wi
zi (j)
dni = pii (j)dni ⇒
dni → 1 ⇒ pni → pii
3.6.2
Real Wages
It is possible to derive predictions for real wages from equation (8), although there is no
analytic solution. I, therefore, restrict the economy to 2 regions, x and y in order to derive intuition. Note that this simplification reduces the environment to a Dornbusch et al.
11
(1977)−or DFS (1977)−type model with a continuum of goods and two regions. In this
setting, recall from equation (6) that:
− 1
px = py = γ Ax (wx dxy )−θ + Ay (wy dxy )−θ θ
and from equation 8:
Ly
Lx
Lx
wx = γ −θ Ay (wy dxy )−θ pθx wx
Ly
wx = γ −θ Ax (wx dxy )−θ pθy wy
Let real wages be represented by
thus defined as:
wx
px
and
wy
.
py
Using the fact that px = py , real wages are
1 −1
wx
Ly 1+θ 1+θ
−θ
−θ
py
= γ Ax (dxy )wy
px
Lx
1 −1
wy
Lx 1+θ 1+θ
−θ
−θ
py
= γ Ay (dxy )wx
py
Ly
(9)
Taking the partial derivatives of these expressions with respect to dxy yields the second
hypothesis.
Hypothesis 2. As railroad connections between two regions increase, real wages will increase
everywhere.
Proof.
∂ wpxx
∂dxy
∂ wpyy
∂dxy
=−
=−
θ
θ
py (γdxy )θ
L
x
A x L y wy
1
− 1+θ
<0
(10)
1
− 1+θ
<0
(11)
(θ + 1)dxy
py (γdxy )θ
xw
Ay L
x
L
y
(θ + 1)dxy
Recall that increasing dxy means higher transportation costs, such that increasing railroad
12
connections implies a lower dxy .
Will there ever be a case in which reductions in transport costs cause wages to diverge
between regions? In order to explore the conditions under which this would occur, I compare
2 1
θ+1
Ax θ+1
Lx
x
,I
the case in which (10) > (11). Using the fact that px = py and w
=
wy
Ly
Ay
find that this condition is satisfied by:
Ax >
Lx
Ly
1
θ+1
Ay
(12)
That is, we should observe regional wage dispersion in the event that one region has absolute
advantage in the production of all goods, conditional on relative labor shares. Equation (12)
characterizes the third hypothesis:
Hypothesis 3. As railroad connections between regions increase, real wages will diverge if
one region has absolute advantage in the production of all goods, conditional on relative labor
shares.
In the following sections, I empirically examine hypotheses (1)-(3) to understand the
effect of railroad development on relative prices and real wages.
3.6.3
Specialization
A fundamental prediction of Ricardian trade models is that under free trade, regions
specialize in the production of goods for which they have comparative advantage. Therefore, in the case of decreasing transportation costs, one should observe specialization across
regions.7 To show this in the current framework, let each region’s production of good j be
ordered on the continuum j ∈ [0, 1] in decreasing order of regions x’s comparative advantage,
zx (j)
zy(j)
≡ Z(j), where Z(j) represents a continuous technology schedule, with a slight abuse
7
While I would like to test this hypothesis empirically, there is insufficient production data available for
the Empire. Instead, I explore time trends in the Herfindahl Index from 1872 to 1910 in figure 4 and find
that these are increasing over time for both agricultural and industrial production. The extent to which this
is correlated with railroad construction is not clear, since I only have 34 years of production data, which is
insufficient for econometric analysis.
13
of notation (i.e., this term does not represent the random variable from which technology
parameters are drawn). That is,
zx1 (j)
zy1 (j)
>
zx2 (j)
zy2 (j)
> ... >
zxn (j)
,
zyn (j)
where the superscript denotes
the index of good j on the continuum.
Firms in region x will produce if the unit input costs are lower than region y, such that:
wx
wy
<
dxy ⇒
zx (j)
zy (j)
wx
< Z(j)dxy
ω≡
wy
(13)
where ω represents relative wages. Similarly, firms in y will produce if:
wy
wx
<
dxy ⇒
zy (j)
zx (j)
wx
> Z(j)dxy
ω≡
wy
(14)
Putting (13) and (14) together yields a range of non-traded goods:
Z(j)dxy < ω < Z(j)dxy
(15)
Figure 9 illustrates the non-traded goods phenomenon. Note from (15) that as transportation
costs decrease (i.e., dxy goes to one), the region of non-traded goods disappears and each
region produces as specialized set of goods. This process is characterized in the fourth
hypothesis:
Hypothesis 4. As railroad connections increase, the regional economy will become more
specialized.
14
4
Identification Strategy
4.1
Data and Summary Statistics
Transportation cost data comes from the combination of imperial railroad yearbooks
(Austria, 1907) and a 1913 railroad map (Wagner and Debes, 1913). From 1832 to 1907, the
railroad yearbooks identify pairs of cities between which a railroad line was built, as well as
the year of the connection. Using GIS, I then mapped Euclidean connections between all
city-pairs in each year in order to reconstruct the Austro-Hungarian railroad network, crossreferencing the 1913 railroad map for accuracy. The evolution of this network is illustrated
in figure 2. A clear west-to-east development pattern is visible, with early construction
beginning around Vienna, Linz, and Prague, expanding eastward over time.
In order to extract railroad data to the provincial level (i.e., the unit of analysis), I overlaid
province boundaries to the Euclidean railroad network and calculated two variables for each
province-pair by year: 1) a binary variable indicating whether or not a railroad connection
existed, and 2) the minimum distance (km) between each province on the Euclidean railroad
network, conditional on an established connection.
Agricultural commodity price and wage data were generously provided by Tomas Cvcrek,
which were compiled from a set of annual imperial statistical yearbooks (Austria, 1865,
1881, 1914; Országos Magyar Kir, 1913). This database comprises observations for various
agricultural commodity prices and daily agricultural wages at the provincial level from 1829
to 1912 for the fourteen Austrian provinces, and from 1872 to 1912 for the seven Hungarian
statistical regions. I also incorporate the consumer price index (CPI) from Cvcrek (2013) in
order to construct real wages. Note, however, that this data is only provincially disaggregated
for Austrian provinces.
Data on British iron export prices, which is used in the construction of the instrumental
variable, comes from Blattman et al. (2004). The series is an index of iron prices from 18461910, with the year 1900=100. Socioeconomic data, such as population, steam engine usage,
15
and production output−for the calculation of Herfindahl Indexes−was obtained from the
annual statistical yearbooks. Lastly, soil data used in the calculation of absolute advantage
comes from the European Soil Database.
Figures 4-8 display trends in production specialization, as well as relative prices and real
wages. The Herfindahl Indexes in figure 4 for wheat, barley, brown coal, and processed iron
suggest that imperial agricultural and industrial production became more specialized over
time (i.e., a Herfindahl Index = 1 suggests complete specialization), which is consistent with
a trade integration story. Over the same period, relative prices for wheat, rye, barley, and
oats declined, suggesting price convergence (figures 5 and 6). The trends for real wages,
however, are more ambiguous. Figure 7 indicates a slight downward trend in relative real
wages over time, but the magnitude appears to be small. Nonetheless, real wages withinprovince increased steadily over time, as figure 8 shows.
The descriptive trends in figures 4-8 are consistent with the transportation costs story
outlined in the theoretical model in section 3. That is, as railroad infrastructure developed
throughout the 19th century, transportation costs between provinces decreased, such that
the imperial economy became more specialized. Trade improved between regions, decreasing
price dispersion and increasing relative wage disparities. The formal empirical analysis in
the following sections tests these observations in a causal fashion.
4.2
Estimation Strategy
A major identification issue with any study of transportation infrastructure is that the
placement of such projects is likely endogenously correlated with economic outcomes. In
order to account for this potential bias, I implement an instrumental variable to predict
railroad connections:
Railijt = α + βIVijt + γP opijt + µij + τt + εijt
16
(16)
where Railijt represents two measures of railroad construction: 1) a binary variable for
whether or not a railroad connection exists between provinces i and j in year t, and 2) the
minimum railroad distance (km) between provinces i and j in year t. I also control for
P opijt , the geometric average of distance-weighted populations in province i and j, as well
as province-pair fixed effects, µij , and a quadratic time trend, τt .8 The error term, εijt , is
clustered at the province-pair level.
The instrumental variable, IVijt , is the geometric average of the interaction between
each province’s Euclidean distance from its centroid to London and the price of British iron
exports in each year: DistLondi ∗ Iront .9 I take DistLondi as exogenously given. In order
for DistLondi ∗ Iront to meet the exclusion restriction, Iront must not be correlated with
the error term in the second stage regression, equation 17 below. That is, E[IVijt uijt ] = 0.
If Austro-Hungarian demand for British iron determined iron export prices, however, this
could be a violation of the exclusion restriction. Figure 10 displays total British iron exports
from 1857-1912 and the percent exported to Austria-Hungary versus the rest of the world.
Exports to Austria-Hungary ranged from 0.1% to 1.4% of total British iron exports in a
given year, thereby making it unlikely that Habsburg demand influenced British prices.
I then use IVijt as an instrumental variable in the main identifying equation for the effect
of transportation costs on relative prices and wages:
d ijt + γP opijt + µij + τt + uijt
Pijt = α + β Rail
Wijt
d ijt + δAbsAdvtgijt + γP opijt + µij + τt + uijt
= α + β Rail
(17)
where P opijt , µijt , and τt are the same as in equation 16, and uijt are clustered standard
d ijt are the two instrumented measures of railroad conerrors at the province-pair level. Rail
nectivity: 1) binary connection and 2) minimum distance.
8
I have run alternative specifications with simple year fixed effects, but many years are highly collinear
and drop out of the estimation. In addition, I have explored linear time trends, which do not significantly
change the results.
9
I use the British price of iron as a proxy for world prices, since the British Empire was the world’s largest
producer of iron during this time period.
17
The dependent variables of interest are Pijt and Wijt −relative prices and real wages,
respectively−which I measure as the absolute value difference in the natural log of prices/wages
(i.e., |ln(pit ) − ln(pjt )|). Note that in the specification for relative real wages, I have included
the term AbsAdvtgijt , which stems from the predictions of the theoretical model in section
3 and is a proxy for whether or not one province has absolute advantage in the production
of both agricultural and industrial goods. I calculate this as a binary variable equal to one
in the event that one province in the pair has better soil quality and more steam engines in
use in a given year. Summary statistics for all variables used in the analysis are displayed
in table 1.
Note that 83 percent of all province pairs had a binary railroad connection by 1872 (see
figure 3).10 In addition, Hungarian price and wage data, necessary for the second stage, only
exist after 1871. Therefore, in order to capture the long-run binary connection process, all
regressions that examine binary connections−the “extensive margin”−cover the years 1846
to 1907 for Austrian province-pairs only. In addition, since the minimum distance measure is
conditional on a binary connection existing−the “intensive margin”−all specifications that
examine the intensive margin use years 1872 and later, and incorporate both Austrian and
Hungarian province-pairs. I also perform robustness checks on the “intensive margin” results,
using only the Austrian provinces from 1846-1907.
5
Results
The first stage results are presented in table 2. Columns (1) and (2) suggest that the
instrument is a good predictor of railroad connections, since it is significantly correlated with
both binary connections, as well as minimum distance. Specifically, increasing the average
DistLondi ∗ Iront by 10 percent increases the probability of a railroad connection between
two provinces by roughly 2 percent and decreases the minimum railroad distance by approx10
Only three province pairs established connections after 1872: Bukovina (Austrian) and Rechtes Theissufer (Hungarian), and Bukovina (Austrian) and Linkes Theissufer (Hungarian) in 1874; and Transylvania
and Bukovina (both Austrian) in 1902.
18
imately 2 kilometers. In addition, I present the first stage F-statistics in all second stage
regressions that use IVijt and find that they are almost all sufficiently large.
The extensive margin effects of railroads on relative prices are presented in table 3. The
results suggest that on average, the existence of a railroad connection between two provinces
causes a 17 to 56 percent reduction in relative prices, depending on the agricultural commodity. In particular, the results for wheat are consistent with related studies that examine
the effect of railroad connectivity on price dispersion in Germany during this time period
(Keller and Shiue, 2014, 2008). Note that the largest effects are for oats and potatoes, which
have the biggest relative prices in the sample. This may suggest possible decreasing marginal
effects of railroad construction on price convergence at the extensive margin.
Tables 4 and 5 show the results for the intensive margin effects of minimum railroad
distance on relative prices. I find that for every 100 km of track built between two provinces,
relative agricultural commodity prices decrease by 45 to 73 percent. The effects are large
and significant for all agricultural commodities when I examine both Austrian and Hungarian province-pairs. When I restrict the analysis to Austrian province-pairs only, however,
I find that the results are only consistent for wheat−the most heavily traded agricultural
commodity. Although, the magnitude of these effects are much lower. For every 100 km
of track built between Austrian province-pairs, conditional on a railroad connection exiting,
relative wheat prices decrease by 5 percent.
In regard to wages, recall that the theoretical hypotheses in section 3 predict that as
transportation costs decrease, wages in each region should increase. Wage dispersion, however, may arise when one region has absolute advantage in the production of all goods.
Tables 6 and 7 test these predictions. The results suggest that as railroad network density
increases within provinces, real wages increase by 11 percent, although the first stage F-stat
on these estimates are relatively low. Restated, as economic integration increases through
reductions in transportation costs, real wages rise within regions.
In spite of wages increases, however, table 7 reveals that welfare increase more rapidly
19
in some places than others. For every 100 km of railroad track built between two provinces,
relative real wages increase by approximately 20 percent, indicating wage dispersion. While
this is consistent with the theoretical predictions in section 3, note that the coefficient on the
interaction between minimum distance and absolute advantage is not statistically significant,
nor does it move in the intuitive direction. Nonetheless, the results suggest that declining
transportation costs leads to significant wage dispersion.
6
Discussion and Conclusion
20
References
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Konigreiche und Lander.” Vienna, 1882-1914.
Austria, K.K. Eisenbahnministerium. 1907. “Österreichisch Eisenbahnstatistik.” Vienna: Hof und Staatsdruckerel.
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Banerjee, Abhijit, Esther Duflo, and Nancy Qian. 2004. “On the Road: Access to
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Geographie.
22
7
7.1
Figures and Tables
Figures
Figure 1: Austro-Hungarian Empire as of 1900
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Figure 2: Imperial Railroad Construction: 1845-1905
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Min Dist (km) between Province Pairs
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1820
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lpoly smooth: dummy_connect
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Figure 3: Railroad Connections Between Province-Pairs
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.6
.5
.4
.3
.2
.2
.3
.4
.5
.6
.7
(b) Barley
.7
(a) Wheat
1890
lpoly smoothing grid
1875
1880
1885
1890
lpoly smoothing grid
95% CI
1895
1900
1875
lpoly smooth: (mean) herf_browncoal
1880
95% CI
(c) Brown Coal
1885
1890
lpoly smoothing grid
lpoly smooth: (mean) herf_pourediron
(d) Iron
Figure 4: Herfindahl Indexes (Austrian Provinces)
25
1895
1900
1.5
1
1.1
1.2
1.3
1.4
1.5
1.4
1.3
1.2
1.1
1
1820
1840
95% CI
1860
1880
lpoly smoothing grid
1900
1920
1820
lpoly smooth: Relative Price: Wheat
1840
95% CI
1900
1920
lpoly smooth: Relative Price: Rye
1.4
1.3
1.2
1.1
1
1
1.1
1.2
1.3
1.4
1.5
(b) Rye
1.5
(a) Wheat
1860
1880
lpoly smoothing grid
1820
1840
95% CI
1860
1880
lpoly smoothing grid
1900
1920
1820
lpoly smooth: Relative Price: Barley
1840
95% CI
(c) Barley
1860
1880
lpoly smoothing grid
lpoly smooth: Relative Price: Oats
(d) Oats
Figure 5: Relative Prices (Austrian Provinces)
26
1900
1920
5
1
2
3
4
5
4
3
2
1
1870
1880
1890
lpoly smoothing grid
95% CI
1900
1910
1870
lpoly smooth: Relative Price: Wheat
1880
1890
lpoly smoothing grid
95% CI
1910
lpoly smooth: Relative Price: Rye
4
3
2
1
1
2
3
4
5
(b) Rye
5
(a) Wheat
1900
1870
1880
1890
lpoly smoothing grid
95% CI
1900
1910
1870
lpoly smooth: Relative Price: Barley
1880
1890
lpoly smoothing grid
95% CI
(c) Barley
1900
1910
lpoly smooth: Relative Price: Oats
(d) Oats
1.5
1.4
1.3
1.2
1.1
1
1
1.1
1.2
1.3
1.4
1.5
Figure 6: Relative Prices (Austria and Hungary)
1820
1840
1860
1880
lpoly smoothing grid
95% CI
1900
1920
1870
lpoly smooth: Relative Real Wages
1880
1890
lpoly smoothing grid
95% CI
(a) 1829-1910
1900
lpoly smooth: Relative Real Wages
(b) 1872-1910
Figure 7: Relative Real Wages (Austrian Provinces)
27
1910
.6
.7
.8
.9
Figure 8: Real Wages (Austrian Provinces)
1820
1840
1860
1880
lpoly smoothing grid
95% CI
1900
1920
lpoly smooth: Real Wages
Figure 9: Specialization
(Region x)
=(j) dxy
(Region y)
=(j) / dxy
"
0
jx
jy
Non-traded Goods
28
1
Figure 10: British Iron Exports
5500
0.5%
5000
0.3%
0.3%
4500
0.1%
1,000 of Tons
4000
0.4%
0.1%
3500
AustriaHungary
3000
0.3%
0.5%
2500
1.4%
0.8%
1500
1000
500
0
Source: House of Commons Parlaimentary Papers
United
States
Rest of
World
0.4%
2000
0.1%
Year
Note: Austro-Hungarian 1902 figures represent Pig Iron exports only.
29
7.2
Tables
30
Table 1: Summary statistics of variables used in analysis
31
Variable
Description
N
Mean
SD
Min
Max
Province-Level Variables
Real Wages
Population
Steam Engines
No Ag Limitations
Internal Rail Density
Distance to London
British Price of Iron
Dist to London x Iron Price
Market Potential
Nominal daily agricultural wage (Kronen) divided by CPI
Total provincial population
Total number of steam engines recorded in use
Percent of soil that has no agricultural limitations
Meters of railroad track per km2 of provincial surface area
Euclidean distance (km) from province centroid to London
Price index of British iron exports (1900=100)
Euclidean distance (km) to London x British price of iron
Market potential
1148
1276
588
1475
1515
1515
1245
1245
1305
0.71
1754916.03
182.56
0.56
5.82
1366.86
80.23
110.43
87.73
0.19
1657332.98
333.15
0.16
6.75
286.74
20.37
36.77
31.45
0.31
145435
0.00
0.27
0.00
995.12
58.00
57.72
25.47
1.38
8206116
2216.00
0.93
35.45
1907.67
170.00
324.30
158.38
Province-Pair Variables
Relative Price: Wheat
Relative Price: Rye
Relative Price: Oats
Relative Price: Barley
Relative Price: Potatoes
Relative Real Wages
Minimum Rail Distance
Rail Connection
Avg Dist to London x Iron Price
Dist Between Province Centroids
Avg Distance-Weighted Population
Relative Steam Engines
Absolute Advantage
Kronen per 100kg with higher price in numerator
Kronen per 100kg with higher price in numerator
Kronen per 100kg with higher price in numerator
Kronen per 100kg with higher price in numerator
Kronen per 100kg with higher price in numerator
Kronen with higher real wage in numerator
Shortest Euclidean rail distance (km) between provinces
(0/1) for whether a rail connection exists
Geometric average in 1000s of units
Euclidean distance (km) between province centroids
Geometric average of distance-weighted populations
Relative steam engines with higher value in numerator
(0/1) if one province has more steam engines and better soil
11627
11517
11513
10937
11503
9139
7142
17175
11155
17175
11242
3011
3011
1.53
1.58
1.94
1.73
1.79
1.28
411.67
0.49
109.33
466.39
4221.54
23.94
0.65
0.84
0.93
1.89
1.23
1.07
0.32
306.61
0.50
32.82
224.41
3721.44
71.50
0.48
1.00
1.00
1.00
1.00
1.00
1.00
4.39
0.00
59.57
86.93
249.94
1.01
0.00
7.71
9.01
16.92
11.35
12.20
4.09
1419.26
1.00
323.32
1093.97
23983.88
1407.00
1.00
Table 2: First Stage Estimates
(1)
Rail
Connections
(2)
Minimum
Distance
Avg Dist to London x Iron Price
0.002***
(0.000)
0.216***
(0.056)
Avg Distance-Weighted Population
7.28e− 06
(0.00002)
-0.016**
(0.006)
Constant
-0.993***
(0.141)
1024.165***
(147.893)
yes
yes
4128
57.08
yes
yes
4163
52.87
Province-Pair Fixed Effects
Quadratic Year Trend
Observations
Joint Significance F-Stat
Unit of observation is the province-pair. Standard errors are in parentheses and
are clustered at province-pair level. * p< 0.10, ** p<0.05, *** p < 0.01.
Table 3: Relative Prices−Extensive Margin (1846-1907 for Austrian Provinces)
Rail Connection
Mean Relative Price:
Province-Pair Fixed Effects
Quadratic Year Trend
Observations
First Stage F-Stat
(1)
Wheat
(2)
Rye
(3)
Oats
(4)
Barley
(5)
Potatoes
-0.167***
(0.041)
0.007
(0.056)
-0.541***
(0.104)
-0.340***
(0.078)
-0.561***
(0.100)
1.19
yes
yes
4062
69.076
1.24
yes
yes
4062
69.076
1.32
yes
yes
4062
69.076
1.31
yes
yes
4062
69.076
1.61
yes
yes
4062
69.076
Unit of observation is the province-pair for Austrian Provinces only. Standard errors are in
parentheses and are clustered at province-pair level. Additional controls include average distanceweighted population. * p< 0.10, ** p<0.05, *** p < 0.01.
32
Table 4: Relative Prices−Intensive Margin (1872 and Years After)
Min Rail Distance
Mean Relative Price
Province-Pair Fixed Effects
Quadratic Year Trend
Observations
First Stage F-Stat
(1)
Wheat
(2)
Rye
(3)
Oats
(4)
Barley
(5)
Potatoes
0.0055***
(0.0014)
0.0042***
(0.0012)
0.0071***
(0.0021)
0.0073***
(0.0019)
0.0045***
(0.0014)
1.83
yes
yes
4163
17.01
1.90
yes
yes
4163
17.01
2.55
yes
yes
4163
17.01
2.10
yes
yes
4163
17.01
2.05
yes
yes
4163
17.01
Unit of observation is the province-pair for 1872 and years after. Standard errors are in
parentheses and are clustered at province-pair level. Additional controls include average distanceweighted population. * p< 0.10, ** p<0.05, *** p < 0.01.
Table 5: Relative Prices−Intensive Margin (1846-1907 for Austrian Provinces)
Min Rail Distance
Mean Relative Price:
Province-Pair Fixed Effects
Quadratic Year Trend
Observations
First Stage F-Stat
(1)
Wheat
(2)
Rye
(3)
Oats
(4)
Barley
(5)
Potatoes
-0.0006**
(0.0003)
0.0006*
(0.0004)
-0.0018**
(0.0008)
-0.0012**
(0.0005)
-0.0022**
(0.0009)
1.19
yes
yes
1916
7.3211
1.24
yes
yes
1916
7.3211
1.32
yes
yes
1916
7.3211
1.31
yes
yes
1916
7.3211
1.61
yes
yes
1916
7.3211
Unit of observation is the province-pair for 1846-1907 for Austrian Provinces only.
Standard errors are in parentheses and are clustered at province-pair level. Additional controls include
distance-weighted population. * p< 0.10, ** p<0.05, *** p < 0.01.
33
Table 6: Real Wages within Province
(1)
Real Wage
(2)
Real Wage
(3)
Real Wage
Internal Rail Density
0.1155**
(0.0525)
0.1116**
(0.0483)
0.1104**
(0.0466)
Population
-0.0212
(0.0141)
-0.0056
(0.0047)
-0.0205
(0.0133)
No Ag. Limits x Population
0.0242
(0.0209)
Steam Engines
Province Fixed Effects
Quadratic Year Trend
Observations
First Stage F-Stat
yes
yes
546
2.89
0.0234
(0.0200)
0.0000
(0.0001)
0.0000
(0.0001)
yes
yes
546
3.08
yes
yes
546
3.24
Unit of observation is the Province for Austria only (steam engine data and
CPI not available for Hungarian provinces). Standard errors are in parentheses
and are clustered at the province level. * p< 0.10, ** p<0.05, *** p < 0.01.
34
Table 7: Relative Real Wages
Min Rail Distance
Absolute Advantage
(1)
Relative Wage
(2)
Relative Wage
-0.00211***
(0.00056)
-0.00186***
(0.00053)
-0.01337
(0.02238)
-0.29283***
(0.09247)
Min Dist x Absolute Advantage
0.00126***
(0.00042)
Avg Distance-Weighted Population
Mean Relative Wage
Province-Pair Fixed Effects
Quadratic Year Trend
Observations
First Stage F-Stat
0.00001
(0.00003)
-0.00001
(0.00002)
1.25
yes
yes
2152
19.64
1.25
yes
yes
2152
14.80
Unit of observation is the province-pair for Austrian province pairs only.
Standard errors are in parentheses and are clustered at province-pair level.
* p< 0.10, ** p<0.05, *** p < 0.01.
35