Railroads and Urbanization during the Latin
American Industrial Revolution: Chile 1860–1920∗
Andrés Forero
Francisco A. Gallego
Felipe González
This Version: May 15, 2012
Abstract
This paper examines whether the construction of a transport infrastructure,
such as railroads, have a causal impact on urban and total population. For this
purpose, we look at the Latin American Industrial Revolution and construct
a panel data set using historical sources to document a sixty-year process of
railroad construction. We exploit different sources of exogenous variation, in
an instrumental variables framework, to identify the causal effect railroads
had on population composition. Our results indicate that railroads caused
significant changes in both urban and total population.
1
Introduction
In many ways, infrastructure and transportation technologies are at the core of economic development. Infrastructure, understood as the capital in a classical production function, is a necessary (although not sufficient) condition for growth. Transportation technologies, on the other hand, have been emphasized to be one of the
main causes behind the persistent decrease in transportation costs, and lower costs
are one of the main drivers of the increasing trade in the last two hundred years
(Hummels, 2007). Hence, understanding the impact infrastructure and transportation technologies have on the economy is also of prime importance to understand
trade, a main feature of economic development. Trade, after all, is the quintessence
∗
Department of Economics and Economic History and Cliometrics Lab (EH Clio Lab), Pontificia
Universidad Católica de Chile. We would like to thank the Millennium Nuclei Research in Social
Sciences for financial support.
of development without accumulation of human or physical capital, as it entails exploitation of comparative advantages and potential economies of scale. Moreover, the
World Bank (2007) report shows an impressive amount of financial support going
to transportation infrastructure, and countries tend to spend a considerable amount
of their GDP in this item (Donaldson 2010, Faber 2012). Thus, politicians should
have enough evidence to learn whether this is an efficient way to allocate resources,
and to know which are the effects these investments have on the economy (Banerjee
et al., 2012). This is by no means an easy task, as there are many econometric and
conceptual problems arising when we try to estimate this causal effect.
The main econometric problem with estimating this causal effect is endogeneity, particularly reverse causality. Fishlow (1965) puts it simply: “A key issue [on
whether railroads benefit economic development], however, is whether such railroad
influence was primarily exogenous or endogenous, whether railroads first set in motion the forces culminating in the economic development of the decade, or whether
arising in response to profitable situations, they played a more passive role”. Conceptual problems are equally harder to surpass: there is substantial difference in trying
to measure the impact railroads (or highways) have in the short and the long-run. In
the short-run there are important convergence effects, which tend to increase trade
by lowering transportation costs. However, in the long run other variables could
be important, such as labor and capital mobility, better access to services such as
education or health, and the diffusion of ideas and other technologies. All of these
long-run effects may increase or decrease the benefits from interregional trade.1
This paper tries to deal with both econometric and conceptual problems and
estimates the causal impact railroads have on urbanization.2 In trying to accomplish
this task we look at the Latin American industrial revolution. We have constructed
a panel dataset spanning a sixty-year period of industrialization in Chile, the first
Latin American country in adopting railroads, to analyze the impact the access to
this infrastructure/technology had on urbanization rates. Hence, we also make a
contribution to the economic history of Latin America because, to the best of our
knowledge, there is no research trying to econometrically measure the causal impact
1
See Gallego et al. (2012) for an empirical attempt to analyze differences in the short and longrun impacts of transportation policies. Faber (2012) actually find negative effects for peripheral
cities after the construction of China’s Highway System.
2
Urbanization rates can be considered a proxy for economic development (e.g. Acemoglu et al.
2002 and Acemoglu et al. 2011) or a proxy for human capital mobility towards cities.
2
railroads had on the Latin American economy.3 Moreover, we also contribute to other
three different literatures. First, to the long-standing literature on railroads and
economic development, which started with Fogel and Fischlow’s seminal works, and
continues until now with no clear answer about the direction of causality. Second, to
the literature on interregional trade and transport costs, which systematically founds
that lower transport costs increase trade patterns.4 And finally, to the literature on
estimating the economic effects of large infrastructure projects.5
As previously mentioned, railroads can be understood as a new transportation
technology which decreases transportation costs. In this sense, there have been
other revolutionary cost-reduction technologies studied in economic history. North
(1958) studied the economic impact of a large decreased in ocean transportation costs
due to the steamship in the western world between 1750 and 1913, and Kujovich
(1970) examined the refrigerator car, which permitted some industries to depend
less on railroads to transport their products, thus decreasing transport costs. More
recently, Michaels (2008) analyzes the effect the U.S. Interstate Highway System had
on trade barriers and, therefore, on labor market outcomes; Donaldson (2010) finds
that India’s railroad network decreased trade costs, increased interregional trade and
real income; Rua (2011) shows the positive effect the container had on international
trade; and Faber (2012) exploits the China’s Highway System and finds support for
Krugman (1980)’s market size effect.
However, there have been few studies emphasizing different effects of transportation infrastructure. In this line, Banerjee et al. (2012) study the effect infrastructure
had on GDP per capita in China, but focusing on the long-run effect, after the regional convergence took place.6 Atack et al. (2010) stands (implicitly) in the same
page than Banerjee et al. (2012) by estimating the effects railroads had on urban3
Miller (1976), Coatsworth (1979), Ramı́rez (2001), Summerhill (2005), Herranz-Loncán (2011),
and Zegarra (2011) are exceptions, but they measure social savings, the reduction in transport costs
and travel times, and do not estimate the causal effect railroads had on the economy.
4
There are a lot of ways of decreasing trade costs: lower tariffs, fewer quotas and to low other
non-tariff barriers, and better institutional arrangements, for example. See Cuesta et al. (2012) for
the economic effects of a trade liberalization which lowered tariffs in Chile and Topalova (2010) for
the case of India.
5
See Aschauer (1989), Duflo and Pande (2007), Michaels (2008), and Dinkelman (2010) for
examples.
6
Li and Li (2009) also studies the effect railroads had on China, but focusing on the causal impact this had on inventory management. Transportation infrastructure is associated with reducing
inventory and a more efficient inventory management.
3
ization and population density. Their results revived the historical discussion by
partially contradicting Fogel and Fishlow’s conclusions.7 Atack et al. (2010) measured railroad’s impact on population density and urban population in the mid-west
of the United States between 1850 and 1860 using a differences in differences approach. They concluded that railroads had a causal impact on urbanization, but
not significant impact on population density. According to their argument, the main
channels behind this relationship are: (i) lower transport costs increase interregional
trade, with urban areas as the trade center, (ii) more trade causes higher real wages,
which increases the demand for goods and services in urban areas, (iii) the possibility
of railroad branches integrating new urban centers causes an anticipated migration
process. Our work is more related to Atack et al. (2010) and Banerjee et al. (2012),
in the sense that we are interested in estimating the effects transport infrastructure
had on urbanization and labor markets and we assume this is due to mechanisms
associated to interregional trade and factor mobility. Nevertheless, we also present
the cost-reduction effects this new technology had.8
Our empirical strategy to estimate the causal effect of railroads on urbanization
and labor markets outcomes builds on the existing literature and uses the exogenous variation typically called “straight lines”. This strategy was first proposed by
Banerjee et al. (2012) and exploits the fact that certain geographic zones receive lines
just because these were located between two connecting cities.9 In addition to this
strategy, we propose a new source of exogenous variation associated to the timing of
railroad construction, particularly appealling for a panel dataset. The main rationality behind our strategy is that, conditional on a existing technology of construction
and a starting date, we should know exactly when the process of construction is
finished and how many kilometers of lines a certain zone should have each year. Any
deviation from these estimates should be due to delays or unexpected events. Therefore, we construct a variable measuring the theoretical lines each department should
have at different time periods. We believe this to be a valid source of exogenous
7
Rostow (1967) argued that railroads were crucial for the industrialization process, but Fogel
(1962, 1964) and Fishlow (1965) argued that railroads followed economic growth and helped to
sustain it, and they were not the main cause behind it.
8
There is a fairly large literature relating trade costs and trade flows. See Anderon and van
Wincoop (2004) for a detailed survey and Donaldson (2010) for a structural approach to understand
cost-reduction technologies and increasing interregional trade.
9
Thus, their identification assumption is that, excluding big cities, geographic location is assumed to be determined by factors not related to economic growth.
4
variation conditional on department and time fixed effects.
We use both sources of exogenous variation to identify the causal impact railroads had on urban and total population in departments connected to the railway
network. The context of Chile during the Latin American Industrial Revolution,
and the fact that we look at a long period of 60 years of railroad construction, is a
perfect scenario to apply both empirical strategies. We found that a 41 kilometer
construction (a 100% increase) causes: (i) a migration of 1,540 people (a 5.8% increase) to urban areas of connected departments, and (ii) an increase of 5,000 people
in the overall population of these departments (a 7.8% increase). An analysis of
different econometric tests suggests that both set of instruments, i.e. “straight lines”
and our theoretical lines, add statistical power for the identification. Moreover, all
over-identification tests we apply suggest that the instruments are valid. There are
several theoretical explanations consistent with these results, and we discuss a few
of these in the last section.
The next section briefly reviews the railroad’s history of construction in Chile, and
present historical evidence showing the reduction in transportation costs. Section
3 presents the construction of our dataset and descriptive statistics for our main
variables. Section 4 discusses our empirical strategy, in the context of the existing
literature, and our empirical results. Finally, section 5 concludes.
2
Railroad Construction and Transport Costs
This section briefly reviews the railroad construction in Chile between 1853 and 1910.
We have divided this section according to three different periods of construction, each
characterized by a geographic pattern and a time period. In the first period railroads
appeared in the north to fulfill the demand of the mining industry. In the second
period the two greatest cities at the time were connected through railroads. And
finally, in the third period, railroads reach the more extreme cities in the south of
the country.
We also present the first graphical attempt in Chile to map the timing and the
geographical distribution of railroads during this period. Finally, in the last subsection, we discuss how railroads lowered transportation costs and enable to travel
large distances in a much easier way. This decrease in transportation costs is particularly high in the context of Chile, which adds an interesting feature to analyze the
problem.
5
2.1
2.1.1
Railroad Construction
Mining and the First Railway
The first railroad constructed in Chile, and one of the first in Latin America, was
the one integrating the mining town of Copiapo with the port of Caldera. Since the
discovery of the Chañarcillo mine in 1832, Copiapo transformed itself into an important economic center, and by the 1840s old transportation mechanisms (i.e. carts
and mules) became inefficient. In 1845, Juan N. Mouat proposed the construction of
a railroad, and in 1848 obtained a permission from the President Manuel Bulnes to
build it. After a couple of years of financial problems he decided to sell his permissions to William Wheelwright, and american entrepreneur that founded the Pacific
Steam Navigation Company. Under Wheelwright’s leadership, a couple of mining
entrepreneurs decided to found the Railways Company of Copiapo, which stock was
distributed in 1,600 shares.10 The construction of this 81 kilometers railroad started
at the beginning of 1850 and ended by december of 1851. In the following twenty
years the new mining needs translated into an expansion of this railroad through
the north. By 1871 approximately 243 kilometers were constructed (Alliende, 2006,
p.14-19).
The success of this company contributed to the construction of new railroads
through the country. All of the north was suddenly integrated into a railroad network called North Longitudinal Network, which connected cities almost 2,000 kilometers apart. Moreover, the Railways Company of Copiapo also encouraged two
new ventures: the railway connecting Chile’s most important port (Vaparaiso) with
the capital (Santiago), and the railway in the south region.
2.1.2
Connecting the Two Biggest Cities
By 1842 the idea of constructing a railroad integrating Valparaiso and Santiago
was already thought by Wheelwright. However, it was not until the public road
between these two cities became obsolete, and several interest groups pressed the
government to build a railroad, that Wheelwright was able to obtain permissions
for its construction.11 However, after Wheelwright’s failure in finding investors and
10
The Railways Company of Copiapo was owned by Chileans and foreign residents until 1875,
but mostly by Europeans from then on. This company was nationalized in 1910.
11
See Oppenheimer (1976) for details about the construction of the railroads between Valparaiso
and Santiago and the one in the south.
6
the pressure of farmers on the government because of private interests due to the
wheat boom in California, the central government ended up constructing this railroad
together with private entrepreneurs. The construction began in 1852 and, although
it was supposed to finish after five years, ended eleven years later in 1863. The
capital and the port were integrated by a 187 kilometers railroad. Then in 1870 a
branch of 49 kilometers extended this railroad to the central valley.
2.1.3
Railroads Everywhere
On the other hand, the construction of railroads in the south of the country began in
1855. The process was divided into different stages, finally reaching the southernmost
city in 1913. Just like the company constructing railroads between Santiago and
Valparaiso, this venture was composed both by private and public owners. Also in
the same way, public owners ended up contributing more resources and the company
was nationalized in 1873.12
First, this line integrated Rancagua and Santiago with an 87 kilometers railroad
in 1859. Then, a 52 kilometers line to the south was constructed three years later.
Its construction continued and reached Concepcion, one of the largest city, in 1870
with a 588 kilometers railroad. Between 1876 and 1887 the construction took a
pause due to the Pacific War between Chile, Bolivia and Peru (1879–1883) and the
economic depression on the 1870s. The railroad reached the native’s zone (Arauco)
in 1894 and several other branches were constructed under Balmaceda’s government
(1886–1891).
Santiago was finally connected with Chile’s extreme zones through a 1,198 kilometers railroad, and numerous branches integrated the main line with cities close to
the mountains and the Pacific ocean.13
2.2
Railroads’ Map
The evolution of the railroad network is detailed in Figure 1. We have constructed
the timing of these maps in direct connection with the censuses date. In panels (a),
(b) and (c) we can see the construction of the mining railroad, starting in Copiapo
12
The State Railway Company was founded in 1884 and took over all railroads except those in
the north of the country (Oppenheimer, 1976, p.229-290)
13
See Alliende (2006, p.38-72), Thompson and Angerstein (1997, p.76-80), Gross (1998, p.2-9)
and Humud (1974) for a general look at railroads in Chile.
7
and then with a new start 340 kilometers to the south. In panels (b), (c), (d) and (e)
we show the railroad construction in the center and south of the country. Panel (f)
shows the last piece of railroad constructed at the south during this period of time.
We can also see in this figure the paralyzation of construction due to the Pacific War
at the of the 1870s and beginning of the 1880s in panels (c) and (d). Finally, Figure
2 shows the entire railroad map in the 1920s. Railroads are represented with two
different colors in this map because the width of the rails differ between them and
it is not straightforward to go by train from gray railways to black railways.
This map is the first empirical attempt to recreate the timing of the railroad
construction in Chile during the 19th century, and it shows the level of heterogeneity
we found in each department level of integration through railways. This department
heterogeneity, together with the timing of construction, is our main source of variation to be used in the following empirical sections. However, this source of variation
is clearly not exogenous and, thus, we will need to have an empirical strategy to
identify the effect railroads had on economic development.
2.3
Transportation Costs
In a place where carts and mules were the most usual transportation, railroads
brought significant advantages, specially regarding costs and times.14 This new transportation technology decreased transportation costs because of many features that
made the country special:
1. Chile’s inability to transport goods through rivers, because of its geographical
peculiarity: a thin strip of land that goes from north to south, while most rivers
go from east to west. This is a stark difference with the case of the United
States, because of the impossibility to substitute railroads for rivers. This
makes Fogel (1964)’s argument irrelevant, because rivers were not associated
with lower transportation costs.15
2. The cold winter and flooding from rivers made transportation difficult, particularly in the south. Railroads are basically immunes to bad weather.
14
There could be other things changing that affected transportation costs, such as institutions.
However, Keller and Shiue (2008) shows that institutional arrangements have a relatively little
effect on costs when compared to transportation technologies.
15
See Summerhill (2005) and Coatsworth (1979) for a similar argument in the case of Brazil and
Mexico. See McGreevey (1989) for Colombia, a Latin American case more close to the one in the
United States.
8
3. The poor quality of roads, even between the most traveled areas in the country.
4. The possibility of being victimized by many bandits who swarmed the roads
(Verniory, 2001).
Therefore, conditions and travel times improved considerably with the arrival of
railroads. Besides being safer and less sensitive to seasonal obstructions, it was
cheaper and more efficient than the alternatives.
Before the arrival of railroads, freight between Valparaiso and Santiago was done
by thirty to forty ox carts, with a capacity of forty to fifty quintals, during a period
of six days in the summer and twelve days in the winter. The cost of transporting
1 quintal fluctuated between $1 and $1.75. On the other hand, to travel the same
distance took only 8 hours by railway, with a cost that fluctuated between $0.44 and
$0.55. Passenger traffic suffered a very similar situation: travel time decreased from
14-20 to 6 hours and travel costs decreased from $10-$20 to $2.50-$5.16 In addition,
railroads enhanced communication systems, reflected by the fact that railways stations typically had modern telegraph systems and a post office. Therefore, it is fair
straightforward to say that railroads brought lower transportation costs and travel
times. which could potentially affect several aspects of the economy.
3
Data and Descriptive Statistics
3.1
Data Construction
We combined information from two different historical sources to document the railroad construction during the Latin American Industrial Revolution. The first one
corresponds to department level information available in the historical censuses.17
From this source we were able to construct a panel dataset of 45 departments for the
years 1865, 1875, 1885, 1895, 1907 and 1920 containing information on: (i) urban and
rural population, and (ii) the amount of people working at different labor markets
16
See Oppenheimer (1976, p. 67-71) and Alliende (2006, p. 20, 21, 33 and 38) for details. All
prices in Chilean pesos.
17
All censuses are available at the National Statistics Bureau (Instituto Nacional de Estadı́sticas,
INE, web page www.ine.cl). We take department level data because it is the smallest administrative unit we can construct in a panel data setting for this period. See Table A.1. in the appendix
for details about the construction.
9
(e.g. agriculture, manufacture, etc.).18 Thus, our panel dataset spans over almost
sixty years of the Industrial Revolution in Chile, and contains new demographic and
labor market information within the country.
We built upon this panel dataset structure and add department level information
containing cumulative kilometers of railroads constructed at each census date. We
could have constructed a year-to-year measure, but historical census contain only 10year information. The main sources we used are Espinoza (1897) for the the period
1854–1897 and Vasallo and Matus (1943) for the years 1897–1920. We complemented
and checked this information with Thompson and Angerstein (1997), Alliende (2006),
and several different web sources.
In all, we have information for 45 departments at 6 different periods, which makes
a total of 270 potential observations in our main database. However, we only use
35 departments in our main regressions, mainly because 10 departments are located
in the north of the country, and these railroads are part of a completely different
historical process. In fact, even the railways’ gauge was different and, therefore,
these could not have been merged with other railways.19 In addition, we dropped
the 1865 cross section because we use the lagged of our dependent variable to capture
convergence effects. We also lost two other observations because of missing data.20
In sum, our main regressions rely on information about 35 departments observed at
5 different periods of time, which gives us a total of 173 observations (two missing).
3.2
Descriptive Statistics
Table 1 presents descriptive statistics for the main variables to be used in our empirical analysis. We present this information including and excluding departments in
the north. This is only to document the expansion of railroads in the north, because
it is not a problem if variables are statistically different in these departments. In
fact, we expect departments in the north to differ substantially from the rest, mainly
for three different reasons:
18
This is not straightforward to do since we had to group several different occupations in order
to make reliable comparisons over time. Table A.2 presents information on the 15 occupations we
have constructed.
19
This is why Figure 1 and 2 show gray and black railroads. The main reason why these were
different is that the construction of railroads in the north were a response to the increase production
in mining and, therefore, its main purpose was to serve that industry.
20
Results are robust to omit the lagged dependent variable and, thus, to add the 1865 cross
section. Missing data is about two department’s urbanization rates, our dependent variable.
10
1. These departments were (and still are) very influenced by mining activities,
which directly affects the composition of labor force, population and urbanization rates.
2. These departments were heavily influenced by the Inca culture (Collier and
Sater, 2004) and, thus, are culturally substantially different from the rest of
the country.
3. The geography and climate of these departments is substantially different from
the rest of the country.
All these differences are reflected in the fact that almost 27,000 people lived in the
average department in the north, while in the rest of the departments lived around
44,000 people. The average urbanization rate in 1865 was 32% in the north and 28%
in the rest, but this difference increased strinkingly to 33% in the north and 50%
in the rest by the year 1920. The north was a mining geographic area, dedicated
(almost monopolically) to the production of nitre after the victory of Chile in the
Pacific War (Cariola and Sunkel, 1990). All departments located in the north part
of the country are different because they experienced a different historical process.
This is also reflected in the average construction of railways. The port city of
Copiapó was the first to introduce railways in 1854, in order to connect with the
mining city of Caldera (Figure 1, panel A). Valparaiso was the first one to receive
railways in the rest of the country, in order to connect with the capital Santiago
(Figure 1, panel B). However, by 1865 the average department in the north had 3
times more railways than the average department elsewhere (27 kilometers versus 9
kilometers). Railways not located in the north were just starting to be constructed,
while in the north they had a 10-year advantage. This pattern is maintained during
the whole period, the average north-department always had more railways than the
rest. It is also straightforward to notice the clear sudden stop in construction during
the Pacific War (1879-1883).
In sum, although 13,586 kilometers of railroads were constructed during this period according to our database, we will only analyze the 8,681 kilometers constructed
from Valparaiso to the south. This is a mid-size railroad network when compared
to other networks in Latin America: 4,508 kilometers were constructed in Perú between the 1850s and 1930 (Zegarra, 2011), and more than 24,000 kilometers were
constructed in Brazil between th 1860s and 1913 (Summerhill, 2005).
11
3.3
Basic Correlations
In order to have an empirical image of our dataset and the main statistical correlations there in, Table 2 presents regressions of the following form:
log udt = αd + λt + β log rdt + εdt
(1)
Where udt is the total amount of people living in urban areas in department d at
year t, rdt is kilometers of railroads constructed until year t, and εdt is a random
shock. Column 2 shows a strong positive and statistically significant correlation
between the logarithm of railways constructed and the logarithm of the number of
people living in urban areas, controlling for department fixed effects αi . Because
variables are measured in logarithms, the estimated coefficients are interpreted as
elasticities. An estimated coefficient of 0.24 implies that a 100% increase in railways
(a 41 kilometer construction) is associated with a 6,400 people migration to urban
areas (a 24% increase).21 This coefficient decreases significantly to 0.10 when we
add year fixed effects in column 2. This means that 41 more kilometers of railways
is associated with a 2,660 people migration. Column 3 aims to check the degree of
a potential spurious correlation by adding to the regression the lead and the lag of
railways constructed. The absence of a significant correlation between these variables
and urban population is a good statistical check for our dataset and presumption
about a potential causal relation between railways and urbanization. The last three
columns estimate the same regressions, but only using 10 departments in the north
of the country. Although there is little statistical power to estimate robust and
statistically significant correlations, we can see from the estimated coefficients that
the process seems to be quite different.
For a further understanding of this correlation we show several estimates controlling for variables that could be correlated both with railways and urbanization.
Table 3 presents these exercises. Column 1 replicates the column2 in Table 2 to facilitate comparison. Column 2 excludes big cities in order to check if the correlation
of interest is driven by some specific departments. We exclude from the regressions
three departments (Angol, Valparaiso, and Santiago), which roughly represent the
three biggest departments during these years.22 Column 3 includes a dummy that
21
The average department during the entire period had 26,618 people living in urban areas. The
estimated coefficient of 0.24 implies an increase of 0.24×25,618=6,388.
22
We also exclude big departments using different definitions, e.g. departments with more than
12
equals 1 if a department goes from having railways to also be the beggining of a new
branch of railways.23 Adding this variable, and an interaction term to allow for heterogeneity, suggest that, although having railways increases urbanization, it does not
for those departments which are also the beggining of a new branch. Nevertheless,
this statistical significance is not robust and does not add many insights to our main
argument.
Column 3 in Table 3 excludes the 1885 cross section and the correlation is basically
unchanged. It is important to do this for two different reasons: (i) this year does not
add many statistical information because railroad construction was stopped due to
the Pacific War, and (ii) this is the only yer for which we do not have information on
occupations, so it is useful to present the correlation for a later comparison. Columns
5 and 6 add a series of variables measuring the number of telegraphs, an important
communication technology of the time, and professors, public servers and public
force, which try to measure the level of public intervention in each department.
Including these control variables reduces the estimated coefficient to 0.03, which
means that 100% increase in railways is associated with almost 800 people migrating
to urban areas. However, the coefficient is now statistically insignificant. Finally,
column 7 controls for the lag of urban population to capture potential convergence
effects. We take estimates in column 7 as our OLS benchmark.
4
Railroads and Population
This section aims to estimate the causal effect from railroads to urbanization using
two different instrumental variables approach. First, we briefly summarized and
present the most usual empirical strategy used in the literature to estimate this effect:
using straight lines between two connecting cities. We do this mostly for comparison,
because the theoretical argument behind this strategy is not straightforward to apply
en the Chilean case. Then, we present our own empirical strategy, which relies
on a different source of exogenous variation. The first strategy exploits relatively
more the exogenous department variation in railroad construction, while our strategy
exploits the plausibly exogenous time variation in railroad construction. We present
both results for comparison and use both empirical strategies to apply an over100,000 inhabitants. Results are also robust to this and other similar exercises.
23
A branch is defined as a railway that connects a department which already has railways, with
one that does not have.
13
identification test, which we consider in itself a considerable contribution to the
literature.
We basically estimate equation (1) using an instrumental variables approach, first
using the most common instrument in the literature, then using our instruments, and
then using both instruments to apply and over-identification test. All regressions
include department and year fixed effects and, because the variables of interest are
measured in logs, the parameter β represents an elasticity.
4.1
Straight Lines as Source of Exogenous Variation
The most common identification strategy was first proposed by Banerjee et al. (2012)
in the first draft of their paper and involves using plausibly exogenous department
variation. The main idea behind their identification strategy involves the fact that
railroads had a clear objective in China: to connect the biggest cities in the country.
Therefore, if a certain place i happens to be in the middle of two big cities, when
we draw a theoretical straight railroad line between the two big cities, this place is
going to have railroad lines, although it was not the main purpose to do that.24 The
authors then omit the biggest cities from their database and estimate two-stage least
squares using the straight line as instrument for effective railroad construction.
This idea has been used later on, although with somewhat different motivations,
by Atack et al. (2010) and Faber (2012). Atack et al. (2010) uses straight lines
between the start and end counties, identified in historical sources, as an instrument
for railroads in the northwest of United States. The authors again using the implicit
idea that a straight line is the shortest way to connect two points. Their IV estimates
are larger than their differences-in-differences estimates.
4.2
Our Empirical Strategy: Theoretical Construction
We attempt to exploit a different source of plausibly exogenous variation in railroad construction. We do this for two different reasons. First, because Chile has a
particular geographic form (a long narrow strip of land), and big cities are located
naturally, although not univocally, from north to south. Moreover, mountains also
go from north to south, and this leaves little space for cities. This means that if we
24
Also implicit in their argument is the fact that, if we want to connect two cities, we should do
it in the most cost-effective way, which in this case involves to construct railroads the closest to a
straight line between these places.
14
attempt to connect two biggest cities using a straight line, almost all other places
are going to be close to railroads.25 In the second place, even though this source of
department variation is indeed exogenous, we could econometrically add value if we
find a second source of exogenous variation. This is mainly because we could apply
an over-identification test to analyze the instruments’ exogeneity.
The exogeneity we proposed relies on the fact that, once it has been decided to
construct a certain railroad between two any cities, we should expect the railroad to
be constructed in a certain amount of years. This is, given a construction technology,
the railroad should be finished in a known period of time. However, due to exogenous
shocks the construction could be delayed and, thus, the railroad arrives at a different
period of time than expected. This time-variation in the railroad’s arrival is plausibly
exogenous, especially considering that we present regressions with department and
year fixed effects.
The information in Oppenheimer (1976) suggests that the available technology in
the railroad industry allowed it to build approximately 36 kilometers annually. This
means that we should expect a construction of 360 kilometers in a 10-year period.
For clarity, lets consider an example. In 1868 the authority decided to construct
265 kilometers of railroad in department i. At the available construction technology,
the department should have all 265 kilometers constructed by 1875 (approximately
36 kilometers per year × 7 years). However, when we look at the effective amount
constructed by 1875 we only see 35 kilometers constructed. We claim that the fact
department i has 35 kilometers of railroad constructed, and not more or less, is
exogenous to the urbanization process of that place.26 Then, the key identification
assumption is that, although the construction of railroads in department i and not
department j is indeed endogenous, once we control for fixed effects the amount
of railroads constructed in each department is plausibly exogenous and, therefore,
affects urbanization only through effective construction.
25
This is the same that saying we have many “treated” departments, but not enough “control”
departments with this strategy.
26
This is the actual case of a department called Vallenar. There are other similar strategies we
could have used, such as a linearly or exponentially increasing construction technology. This means
that technology allowed 36 kilometers of railroad to be constructed in 1870, for example, but 72
kilometers in 1907. Results are robust to different trends of technology construction.
15
4.3
Results
Tables 4 and 5 show our empirical results using old and new instruments to estimate
the causal effect of railroads on urban population (Table 4) and total population
(Table 5). We present both the second and first stage estimates, together with the
F-test of excluded instruments to analyze the statistical power of our instruments. It
could be the case that our instruments capture the same kind variation that old instruments and, thus, we do not add new information to the estimates. Nevertheless,
we have a good theoretical background to believe our instruments capture exogenous
variation more related to the timing of construction (between), while the other instruments capture the exogenous variation associated to the cross section (within).
Adding new instruments to the IV strategy has another powerful advantage: we
can compute and over-identification test to analyze the validity of the instruments.
This is exactly what we do, and present a Kleibergen-Paap test when we use robust
standard error and a Sargan test using standard errors for comparison. The first one
is an under-identification test, and the second one a, over-identification test, which
means that the null hypothesises are the opposite.
We use two versions of straight lines to estimate the causal effect of interest.
Column 1 uses a first version, and column 2 uses a second one. The first version
identifies departments which received railroads because they are located between two
populated departments. This is the case of Casablanca, located between Valparaiso
and Santiago, for example. We also identified those departments which after receiving railroads exogenously, constructed a railway branch to a different place. The
implicit identification assumption is that, conditional on having received railways,
the construction of the branch is possible, but without initial railways the construction of the construction of the same line would not have been possible. However, we
acknolewdge this is not exactly the same instrument than in Banerjee et al. (2012),
so we construct a second version of the instrument, which only identifies those departments that received railways for being between two big cities, and ignores those
departments that constructed branches after the initial construction. Thus, the first
instrument uses 30 shocks of railways to identify the parameter of interest, and the
second instrument uses only 16 exogenous shocks.
Column 1 in Table 4 uses the first version of straight lines to estimate the causal
effect railroads had on urban population. An estimted coefficient of 0.085 means that
a 100% increase in railways, i.e. a 41 kilometer construction, causes a 8.5% increase in
16
urban population, which represents a 2,250 people increase in urban areas. Column
2 presents estimates with our second version of straight lines, and founds that the
coefficient is somehow bigger, and implies a 3,250 people increase in urban areas.
However, standard errors are now a little bit higher, consistent with the fact that
we rely on less shocks to estimate the parameter of interest and, consequently, the
difference between both estimates is not statistically significant. The first stage for
both instruments, shown in Panel B, reveales a highly significant power, with F-tests
of excluded instruments of 46.04 and 21.84.
Column 3 shows estimates using only our proposed source of exogenous variation
in the first stage. The instrument has a highly significant impact over the endogenous
variable, with a F-test of excluded instrument of 958. Although free from weak
instruments problem, we still need the theoretical lines to affect urbanization only
through railways actually constructed. We believe this to be an accurate assumption
conditional on the existence of department fixed effects. The estimated second stage
coefficient is slighly smaller, more precisely estimated, and implies that a 70 kilometer
construction causes an increase of 5.8% in urban population, approximately 1,540
more people in urban areas. Finally, column 4 present estimates using both sources
of exogenous variation. The first stage in Panel B reveals that both set of instruments
help in the identification of the parameter of interest: the F-test for straight lines is
9.38 and 462 for predicted construction. Moreover, both identification tests suggest
that instruments are indeed valid.
Table 5 presents exactly the same IV regressions but using the logarithm of total
population as dependent variable. The first stage results are basically the same,
and the estimated second stage coefficients imply that a 70 kilometer construction
causes a 7.8% increase in total population, which represents approximately 5,000
more people in departments connected by a railway network.
4.4
Discussion
Both results taken together suggest there are potentially two different effects: a
“composition effect”, and a “scale effect”. The first one relates to a migration process
within departments connected by the railroad network. Urbanization can increase
in these departments if rural people is migrating to urban areas within these departments. This means that departments outside the network need not to be changing
their total population and/or their urban/rural composition for us to find the ur-
17
banization effect in connected departments. The “scale effect”, on the other hand,
relates to the fact that we do find positive effects in the overall population of connected departments. This means that urbanization could also have been increasing
without the composition effect. This could be the case if people are migrating from
urban areas in departments outside the railroad network, to urban areas in connected
departments. In this case, we find that 70 kilometers of railways causes an increase
of 5,000 people in connected departments and 1,540 people in urban areas. This
means that there is a “scale effect”, but we do not know if this migration is from
urban to urban, from urban to rural, from rural to rural, or from rural to urban
areas. Therefore, we can not rule out the possibility that urbanization in connected
departments is only due to a within department migration.
There are several other interpretations that can explain these results. For example, following Acemoglu et al. (2002) and Acemoglu et al. (2011) we could interpret
urbanization rates as a proxy for economic development. Then, an increase in urbanization rates after the railways is constructed is a natural result, because a decrease
in transportation costs should increase trade between these departments. This result
follows directly from the exploitation of comparative advantages, just like in a Ricardian model of trade. This should translate in significant changes in labor market
composition. Moreover, we could also say that railways increases the supply of public
goods, because people in departments without schools or hospitals can now go to a
different department where this supply is abundant. We could even interpret the increase in economic development in connected departments as a natural consequence
of the difussion of ideas (Glaeser, 2011), or due to scale effects that enables exploitation of increasing returns to scale (Acemoglu, 2008). Then, one interpretation for
the overall increase in population in these places is that people move there in search
of better opportunities, better work conditions, and more access to public goods.
5
Concluding Remarks
There is a new growing literature analyzing different economic effects of transportation infrastructure using exogenous variation in an IV framework assignment. This
paper is a contribution to this literature. We examined whether the construction
of a transport infrastructure, such as railroads, have a causal impact on urban and
total population. We also complement this literature with a new source of exogenous variation that let us apply over-identification tests to analyze the validity of
18
the instruments. This exogenous variation relates to the fact that theoretical construction of infrastructure, due to the existing construction technology, enable us to
predict the timing of infrastructure availability. Deviations from this prediction are
endogenous, but we overcome this endogeneity by instrument effective with predicted
construction.
For this purpose, we look at the Latin American Industrial Revolution in Chile
and constructed a panel data set using historical sources to document a sixty-year
process of railroad construction. Our results indicate that railroads caused significant
changes in both urban and total population. However, further research is necessary
to unbundle the potentially different mechanisms behind the causal relationships
that we found.
References
Acemoglu, Daron: Introduction to Modern Economic Growth. Princeton University Press, 2008.
Acemoglu, Daron; Cantoni, Davide; Johnson, Simon and Robinson,
James:
The Consequences of Radical Reform: The French Revolution. Amer-
ican Economic Review , 2011.
Acemoglu, Daron; Johnson, Simon and Robinson, James:
Reversal of For-
tune in the Making of the Modern World Income Distribution. Quarterly Journal
of Economics, 2002.
Alliende, Marı́a Piedad: Historia del Ferrocarril en Chile. Goethe InstitutPehuén Editores, 2006.
Anderon, James E. and van Wincoop, Eric:
Trade Costs. Journal of
Economic Literature, 2004.
Aschauer, D. A.:
Is Public Expenditure Productive? Journal of Monetary
Economics, 1989.
Atack, Jeremy; Bateman, Fred and Margo, Robert:
Did Railroads In-
duce or Follow Economic Growth? Urbanization and Population Growth in the
American Midwest, 1850-60. Social Science History, 2010.
19
Banerjee, Abhijit; Duflo, Esther and Qian, Nancy:
On the Road: Access
to Transportation Infrastructure and Economic Growth in China, 2012.
Cariola, Carmen and Sunkel, Osvaldo: Un Siglo de Historia Económica de
Chile: 1830-1930. Santiago de Chile, Universitaria, 1990.
Coatsworth, John Henry:
Indispensable Railroads in a Backward Economy:
The Case of Mexico. Journal of Economic History, 1979.
Collier, Simon and Sater, William: A History of Chile, 1808-2002. Cambridge
University Press, 2004.
Cuesta, José Ignacio; Gallego, Francisco and González, Felipe:
Local
Impacts of Economic Liberalization: Evidence from the Chilean Agricultural
Sector. PUC Working Paper , 2012.
Dinkelman, T.:
The Effects of Rural Electrification on Employment: New Evi-
dence from South Africa, 2010.
Donaldson, Dave:
Railroads of the Raj: Estimating the Impact of Transporta-
tion Infrastructure, 2010.
Duflo, Esther and Pande, R.:
Dams. Quarterly Journal of Economics, 2007.
Espinoza, Enrique: Jeografı́a Descriptiva de la República de Chile. Imprenta y
Encuadernación Barcelona, 1897.
Faber, Benjamin: Trade Integration, Market Size, and Industrialization: Evidence
from China’s National Trunk Highway System. Ph.D. thesis, London School of
Economics, 2012.
Fishlow, Albert: American Railroads and the Transformation of the Antebellum
Economy. Harvard University Press, 1965.
Fogel, Robert:
A Quantitative Approach to the Study of Railroads in American
Economic Growth: A Report of Some Preliminary Findings. Journal of Economic
History, 1962.
——: Railroads and American Economic Growth: Essays in Econometric History.
The John Hopkins Press, 1964.
20
Gallego, Francisco; Montero, Juan Pablo and Salas, Christian:
The
Effect of Transport Policies on Car Use: Theory and Evidence from Latin American
Cities. PUC Working Paper , 2012.
Glaeser, Edward L.: Triumph of the City: How Our Greatest Invention Makes
Us Richer, Smarter, Greener, Healthier, and Happier. The Penguin Press, 2011.
Gross, Patricio:
Desarrollo Urbano y Ferrocarril del Sur: 1860–1960. Impacto
en Ciudades y Pueblos de la Red. Instituto de Estudios Urbanos, Pontificia
Universidad Católica de Chile, 1998.
Herranz-Loncán, Alfonso:
The Contribution of Railways to Economic Growth
in Latin America before 1914: A Growth Accounting Approach. Universitat de
Barcelona Working Paper , 2011.
Hummels, David:
Transportation Costs and International Trade in the Second
Era of Globalization. Journal of Economic Perspectives, 2007.
Humud, Carlos:
Polı́tica Económica Chile desde 1830 a 1930. Estudios de
Economı́a, 1974.
Keller, Wolfgang and Shiue, Carol:
Institutions, Technology, and Trade.
NBER Working Paper , 2008.
Kleiberger, Frank and Paap, Richard:
Generalized reduced rank tests using
the singular value decomposition. Journal of Econometrics, 2006.
Krugman, Paul:
Scale Economies, Product Differentiation, and the Pattern of
Trade. American Economic Review , 1980.
Kujovich, Mary Yeager:
The Refrigerator Car and the Growth of the American
Dressed Beef Industry. The Business History Review , 1970.
Li, Han and Li, Zhigang:
Road Investment and Inventory Reduction: Evidence
from a Large Developing Economy. Working Paper , 2009.
McGreevey, William Paul: Historia Económica de Colombia, 1845-1930. Bogotá, Tercer Mundo Editores, 1989.
Michaels, Guy:
The Effect of Trade on Demand for Skill: Evidence from the
Interstate Highway System. Review of Economics and Statistics, 2008.
21
Miller, Rory: Social and Economic Change in Modern Peru. chapter Railways
and Economic Development in Central Peru, 1890-1930. Center for Latin American
Studies, The University of Liverpool, 1976.
North, Douglass:
Ocean Freight Rates and Economic Development 1750-1913.
Journal of Economic History, 1958.
Oppenheimer, Robert: Chilean Transportatio Development: The Railroad and
Socio-Economic Change in the Central Valley, 1840-1885. Ph.D. thesis, University
of California, Los Angeles, 1976.
Ramı́rez, Marı́a Teresa:
Los Ferrocarriles y Su Impacto Sobre la Economı́a
Colombiana. Journal of Iberian and Latin American Economic History, 2001.
Rostow, Walt Whitman: Las Etapas del Crecimiento Económico. F.C.E., 1967.
Rua, Gisela:
Fixed Costs, Network Effects, and the International Difussion of
Containerization. Ph.D. thesis, University of California, Berkeley, 2011.
Summerhill, William R.:
Big Social Savings in a Small Laggard Economy:
Railroad-Led Growth in Brazil. Journal of Economic History, 2005.
Thompson, Ian and Angerstein, Dietrich: Historia del Ferrocarril en Chile.
Dirección de Bibliotecas, Archivos y Museos, 1997.
Topalova, Petia:
Factor Immobility and Regional Impacts of Trade Liberal-
ization: Evidence on Poverty from India. American Economic Journal: Applied
Economics, American Economic Association, 2010.
Vasallo, Emilio and Matus, Carlos: Historia de los Ferrocarriles en Chile.
Editorial Rumbo, 1943.
Verniory, Gustave: Diez años en Araucanı́a, 1889-1899. Pehuén Editores, 2001.
World Bank:
Evaluation of World Bank Support to Transportation
Infrastructure. Washington DC, World Bank Publications, 2007.
Zegarra, Luis Felipe:
Railroads in Peru: How Important Were They. Revista
Desarrollo y Sociedad , 2011.
22
(a) 1854
(d) 1885
(b) 1865
(e) 1897
Figure 1: Railroad construction, 1854 – 1907
(c) 1875
(f) 1907
Figure 2: Railroad Construction until 1920
56,614
(67,224)
Rest
26,886
(15,567)
65,107
(73,632)
26,984
44,268
(15,652) (35,474)
27,473
51,464
(16,230) (42,218)
28,107
60,927
(16,993) (56,625)
25,500
65,509
(15,399) (69,285)
28,459
77,331
(17,107) (88,400)
24,791
91,144
(15,809) (114,829)
North
50
(79)
13
(31)
32
(51)
35
(52)
50
(75)
62
(78)
108
(122)
All
80
(103)
27
(51)
56
(90)
65
(92)
68
(93)
90
(86)
177
(140)
North
41
(69)
9
(22)
25
(31)
27
(31)
44
(70)
54
(75)
89
(111)
Rest
Railways (in Km.)
Sources: Own construction from historical censuses. Averages for all variables. Standard deviations in parenthesis.
26,618
(55,194)
11,161
(10,058)
1920
1907
1895
1885
1875
23,157
(49,253)
All
Total
Rest
8,609
12,416
40,427
(8,340) (22,174) (32,792)
13,066
16,995
46,133
(9,665) (29,983) (39,152)
15,276
23,628
53,634
(11,899) (37,007) (52,222)
13,316
27,807
56,618
(10,581) (50,425) (63,568)
8,478
34,072
66,470
(9,650) (64,665) (80,751)
8,221
45,891
76,399
(10,032) (94,086) (104,123)
North
11,570
(19,918)
16,122
(26,767)
21,772
(33,160)
24,587
(44,998)
28,384
(58,017)
37,131
(83,818)
All
Population
1865
Year:
Sample:
Urban Population
Summary Statistics for Variable:
Table 1:
Descriptive statistics for main variables
Table 2:
Timing of railroad’s construction
Dependent variable: Log Urban Population
Without North
Log Railways
(1)
(2)
0.241***
(0.028)
0.098***
(0.032)
Yes
No
208
0.850
Yes
Yes
208
0.893
Log Railways t + 1
Log Railways t − 1
Department fixed effects
Year fixed effects
Observations
R2
Only North
(3)
(4)
0.102*** -0.073*
(0.035) (0.043)
0.024
(0.045)
-0.055
(0.037)
Yes
Yes
175
0.911
Yes
No
60
0.767
(5)
(6)
0.037
(0.043)
0.087
(0.052)
-0.011
(0.046)
0.083*
(0.048)
Yes
Yes
60
0.895
Yes
Yes
50
0.915
Notes: Robust standard errors in parentheses. Statistical significance: *** p<0.01, ** p<0.05,
* p<0.1.
Yes
Yes
208
0.893
Yes
Yes
190
0.872
0.098***
(0.036)
0.098***
(0.032)
Yes
Yes
208
0.894
0.091***
(0.033)
-0.668**
(0.268)
0.149**
(0.065)
All
(3)
Yes
Yes
173
0.909
0.094***
(0.032)
-0.825**
(0.383)
0.168*
(0.085)
Without
1885
(4)
Yes
Yes
173
0.910
0.081**
(0.033)
-0.815**
(0.354)
0.161**
(0.078)
0.083
(0.066)
(5)
Yes
Yes
173
0.935
0.031
(0.030)
-0.510
(0.355)
0.096
(0.078)
-0.001
(0.056)
0.410***
(0.092)
0.179**
(0.087)
0.040
(0.064)
All
(6)
Yes
Yes
173
0.930
0.490***
(0.115)
0.076**
(0.030)
-0.046
(0.331)
0.031
(0.078)
(7)
Notes: Regressions do not include deparments in the north, only 35 departments in the center and south of the country. Big cities
include the following departments: Angol, Valparaiso and Santiago. We exclude year 1885 in column (4) for comparison with the
following columns because there is not information about occupations in this census. Fixed effects OLS estimates using the full
panel from 1865 to 1920, at least otherwise specified. Robust standard errors in parentheses. Statistical significance: *** p<0.01,
** p<0.05, * p<0.1.
Department fixed effects
Year fixed effects
Observations
R2
Log Urban Population t − 1
Log Public Force
Log Public Servers
Log Professors
Log Telegraphs
Log Railways × D.Branch
Dummy Branch
Log railways
Without
Big Cities
(2)
All
(1)
Dependent variable is: Log Urban Population
Table 3: Branch, Public Intervention, and Convergence (Robustness Exercises)
Table 4: Using Old and New Instruments to Estimate the Causal Effect of Railroads
(1)
(2)
(3)
(4)
Panel A: Second Stage
(Dependent variable: Log Urban Population)
Log Railways
Dummy Branch
Log Urban Pop. t − 1
0.085**
(0.040)
0.089
(0.082)
0.490***
(0.110)
0.122*
(0.070)
0.085
(0.087)
0.480***
(0.111)
0.058**
(0.029)
0.092
(0.080)
0.498***
(0.110)
0.060**
(0.029)
0.092
(0.080)
0.498***
(0.110)
Panel B: First Stage
(Endogenous variable: Log Railways)
Log Straight Lines #1
0.342***
(0.071)
0.373***
(0.094)
Log Straight Lines #2
Log Predicted Railways
Kleibergen-Paap test (p-value)
Sargan test (p-value)
F-test First Stage (Old)
F-test First Stage (New)
F-test First Stage (All)
Second stage covariates
Department fixed effects
Year fixed effects
Observations
Departments
0.857***
(0.034)
0.077***
(0.022)
-0.023
(0.029)
0.803***
(0.042)
–
–
46.04
–
–
–
–
21.84
–
–
–
–
–
–
958.1
0.00
0.72
9.38
462.6
455.8
Yes
Yes
Yes
173
35
Yes
Yes
Yes
173
35
Yes
Yes
Yes
173
35
Yes
Yes
Yes
173
35
Notes: Regressions do not include deparments in the north, only 35 departments in the center
and south of the country. We exclude year 1865 in all columns because we take the lag of urban
population. Fixed effects IV estimates using the full panel from 1875 to 1920. Robust standard
errors in parentheses (except to calculate Sargan test in column (4) where we use standard errors).
Statistical significance: *** p<0.01, ** p<0.05, * p<0.1.
Table 5: Using Old and New Instruments to Estimate the Causal Effect of Railroads
(1)
(2)
(3)
(4)
Panel A: Second Stage
(Dependent variable: Log Population)
Log Railways
Dummy Branch
Log Population t − 1
0.091**
(0.044)
-0.002
(0.099)
0.062
(0.468)
0.171**
(0.077)
-0.017
(0.103)
0.039
(0.490)
0.078***
(0.030)
0.000
(0.095)
0.065
(0.458)
0.078***
(0.029)
0.000
(0.095)
0.065
(0.458)
Panel B: First Stage
(Endogenous variable: Log Railways)
Log Straight Lines #1
0.345***
(0.051)
0.376***
(0.086)
Log Straight Lines #2
Log Predicted Railways
Kleibergen-Paap test (p-value)
Sargan test (p-value)
F-test First Stage (Old)
F-test First Stage (New)
F-test First Stage (All)
Second stage covariates
Department fixed effects
Year fixed effects
Observations
Number of id
0.860***
(0.025)
0.077***
(0.019)
-0.030
(0.025)
0.807***
(0.033)
–
–
44.91
–
–
–
–
21.84
–
–
–
–
–
–
958.1
0.00
0.46
8.94
509.7
455.8
Yes
Yes
Yes
175
35
Yes
Yes
Yes
175
35
Yes
Yes
Yes
175
35
Yes
Yes
Yes
175
35
Notes: Regressions do not include deparments in the north, only 35 departments in the center
and south of the country. We exclude year 1865 in all columns because we take the lag of urban
population. Fixed effects IV estimates using the full panel from 1875 to 1920. Robust standard
errors in parentheses (except to calculate Sargan test in column (4) where we use standard errors).
Statistical significance: *** p<0.01, ** p<0.05, * p<0.1.
Table Appendix A.1: Artificial equivalent departments over time (CHECK!!!)
Artificial Department Departments included
Ancud
San Carlos, Chacao, Dalcahue
Arauco
Angol, Traiguén, Mariluan, Collipulli, Nacimiento, Mulchen, Lautaro, Temuco, Llaima, Lebu, Imperial, Cañete
Casablanca
–
Castro
Lemuy, Chonchi
Caupolicán
–
Coelemu
–
Combarbalá
–
Concepción
Quirihue
Constitución
Chanco, Cauquenes
Copiapó
Caldera
Coquimbo
La Serena
Curicó
Vichuquén, Santa Cruz
Elqui
–
Freirina
–
Illapel
–
Itata
–
La Ligua
–
Laja
–
Linares
Loncomilla
Llanquihue
Calbuco, Carelmapu
Los Andes
–
Magallanes
–
Melipilla
San Antonio, Cachapoal
Osorno
–
Ovalle
–
Parral
–
Petorca
–
Puchacay
–
Putaendo
–
Quillota
Limache
Quinchao
Quenac
Rancagua
Maipo
Rere
–
San Carlos
–
San Felipe
–
San Fernando
–
Santiago
Colina, Renca, Ñuñoa, Lampa
Talca
Lontue, Curepto
Talcahuano
–
Unión
Rio Bueno
Valdivia
Villarrica
Vallenar
–
Valparaı́so
–
Victoria
–
Yungay
Bulnes, Chillán
Source: Own construction based on National Censuses
6. Commerce
7. Liberal
8. Medical
9. Arts
10. Teaching
11. Cult
12. Public servants
13. Public force
14. Domestic service
15. Other
5. Transport
3. Mining
4. Industry
1. Hunting and Fishing
2. Agriculture
Militars.
Divers, fishermen
Farmers, amansadores, beekeepers, laborers, horticulturists, gardeners, loggers,
pruners, cheese makers, vintners.
Canteros, miners.
Sharpeners, watermen, masons, almidoneros, shipowners, gunsmiths, millers, embroiderers, bronzesmiths, woodpeckers, coal workers, caulkers, basket weavers, coppersmiths, caleros, brewers, cedaceros, chocolate makers, seamstresses, mattress
makers, leaves cutter, firework makers, shipbuilders, tanners, rope makers, distillers, gilders, confectioners, carpenters, paperhangers, bookbinders, spur makers,
estereros, broom makers, estucadores, piano makers, noodle makers, gunpowder
manufacturers, textile manufacturers, manufacturers of rigging, florists, stokers,
smelters, recorders, guitar makers, farriers, weavers, tinsmiths, bakers, soap makers,
jewelry, laundresses, lamp makers, potters, mechanics, millers, tailors, confectioners, seamstresses, umbrella stand makers, pelloneros, painters, goldsmiths, plumbers,
watchmakers, rein makers, chair makers, hat makers, saddlers, carvers, upholsterers, tile makers, dyers, printers, tinajero, coopers, braiders, sailboat makers, glaziers,
shoemakers.
Carriers, careneros, teamsters, coachmen, boaters, sailors, marines, machinist, telegraphers.
Table Appendix A.2: Occupations